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Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

GENETICS – RESEARCH AND ISSUES SERIES

VIRAL GENE EXPRESSION REGULATION

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

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GENETICS – RESEARCH AND ISSUES SERIES Bacterial DNA, DNA Polymerase and DNA Helicases Walter D. Knudsen and Sam S. Bruns (Editors) 2009. ISBN: 978-1-60741-094-2 Genetic Diversity Conner L. Mahoney and Douglas A. Springer (Editors) 2009. ISBN: 978-1-60741-176-5 Genetic Diversity Conner L. Mahoney and Douglas A. Springer (Editors) 2009. ISBN: 978-1-60876-541-6 (Online Book) Sex Chromosomes: Genetics, Abnormalities, and Disorders Cynthia N. Weingarten and Sally E. Jefferson 2009. ISBN: 978-1-60741-304-2

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The Human Genome: Features, Variations and Genetic Disorders Akio Matsumoto and Mai Nakano (Editors) 2009. ISBN: 978-1-60741-695-1 Genotoxicity: Evaluation, Testing and Prediction Andor Kocsis and Hajna Molnar (Editors) 2009. ISBN: 978-1-60741-714-9 Cystic Fibrosis: Etiology, Diagnosis and Treatments Paul N. Leatte (Editor) 2009. ISBN: 978-1-60741-833-7

Viral Gene Expression Regulation Eli B. Galos (Editor) 2010. ISBN: 978-1-60741-224-3

Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

GENETICS – RESEARCH AND ISSUES SERIES

VIRAL GENE EXPRESSION REGULATION

ELI B. GALOS

Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved.

EDITOR

Nova Biomedical Books New York

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Copyright © 2010 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers‘ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Viral gene expression regulation / Eli B. Galos, editor. p. ; cm. Includes bibliographical references and index. ISBN 978-1-61324-202-5 (eBook) 1. Viral genetics. 2. Genetic regulation. I. Galos, Eli B., 1968[DNLM: 1. Gene Expression Regulation, Viral--physiology. 2. Virus Diseases--genetics. QW 160 V4147 2009] QR456.V557 2009 572.8'65--dc22 2009030337

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CONTENTS Preface Chapter 1

Viral Gene Expression and Host Cell Immunity Jia-Hai Lee and Fredric Abramson

Chapter 2

Retroviral Gene Expression Regulation María Rosa López-Huertas and Mayte Coiras

51

Chapter 3

Modulation of Cellular Signaling and Gene Expression by Vitamin E Jean-Marc Zingg and Angelo Azzi

97

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vii

Vitamin E Activity in Immune Response A Possible Immunohenancing Role in Chronic Viral Infections Sirio Fiorino, Florian Bihl, Annagiulia Gramenzi, Stefania Lorenzini, Carmela Cursaro, Elisabetta Loggi, Cinzia Fortini, Mauro Bernardi and Pietro Andreone

1

143

Regulation of Geminivirus Gene Expression: Potential Applications in Biotechnology Kathleen L. Hefferon

163

Chapter 6

Gene Expression Regulation in the Developing Brain Ching-Lin Tsai and Li-Hsueh Wang

183

Chapter 7

A Bioinformatical Approach to the Analysis of Viral and Cellular Internal Ribosome Entry Sites Martin Mokrejš, Václav Vopálenský, Tomáš Mašek and Martin Pospíšek

201

Analysis of Gene Family Expression in African Endemic- and AIDS-Related Kaposi‘s Sarcoma Antoinette C. van der Kuyl, Remco van den Burg, Fokla Zorgdrager, Celeste Lebbé and Marion Cornelissen

237

Chapter 5

Chapter 8

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Contents

Chapter 9

Diagnostic Classification using Gene Expression Profiling in AML K. I. Mills and A. F. Gilkes

267

Chapter 10

Regulation of Baculovirus-Mediated Gene Expression Wen-Hsin Lo and Yu-Chen Hu

279

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Index

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299

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PREFACE Chapter 1 - Infectious viruses need to escape from the surveillance of human immune systems before they can successfully infect host cells to initiate the life cycle of viral replication. In addition, it is also well known that the internalized cellular viruses first have to disable intracellular host defense proteins followed by highjacking useful cellular proteins for viral replication, resulting in the dynamic changes of intracellular signal transduction pathways. Vaccines are developed to strengthen human immune systems against infectious pathogens and are currently the most popular strategies to prevent infectious diseases. Selected cellular proteins have been used for drug targets of cancer therapies, suggesting that diseases can be treated by targeting, inside host cells, specific gene expression and gene products associated with disease syndromes. However, the same philosophy has not been well accepted to treat or even prevent infectious diseases caused by viruses. In this review article, we will describe how understanding the mechanisms of viral infection and replication open the prospect of working with the body‘s non-immune defenses against viral infection. The dynamics of viral infection and replication indicates that certain types of viruses may be blocked from replication by selective targeting of host genes and/or host gene related products. This approach adds to the well-established viral disease control strategies of vaccines and anti-viral drugs. One potential advantage of this approach is the proposition that all recombinant strains of a specific virus such as influenza may require the same host genes or proteins to drive their life cycles in host cells. To the extent that the virus can be prevented from using those needed genes or proteins, it is possible to develop a more general approach to prevent or treat such viral infections that operates regardless of the strain. Certainly, a chemical intervention that targets the host genes or gene products that are essential for viral replication will also reduce the rate of creating drug-resistant pathogens. Identifying the host genes in response to viral gene expression may be an alternative strategy to prevent infectious diseases in the post genomic era. Chapter 2 - Retroviruses are RNA viruses that infect birds and mammals. They can be divided into two categories: simple, which contain three main reading frames (gag, pol, env), and complex, which also code for regulatory and accessory proteins essential in viral replication, e.g., Tax and Rex in human T-cell leukemia virus (HTLV), or Tat and Rev in human immunodeficiency virus (HIV). Two terminal non-coding sequences at both ends of

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Eli B. Galos

the genome, which contain consensus sites specific to cellular and viral transcription factors, act as promoter regions. Retroviruses, as other viruses, lack an independent metabolism and are unable to replicate outside living cells. As a result, their gene expression is regulated by both viral and cellular factors. However, the key feature that differentiates retroviruses from other viruses is that they encode the enzyme reverse transcriptase, which synthesizes a double-stranded DNA copy of the viral genome. Viral DNA is integrated into the host genome as a provirus and can induce an active viral production or a post-integration latency. The provirus acts as a host gene and can be transmitted to the progeny cells. The interplay of the viral genome with the host metabolic machinery involves modifications in both gene expression and regulation. In fact, retroviruses have adapted themselves to use this machinery while maintaining cell integrity, which is essential to preserve their survival. Consequently, there can be variable host pathogenicity associated with several diseases—such as malignancies, immunodeficiencies, and neurological disorders—due to the down- or up-regulation of different cellular genes. For example, the HTLV-1 protein Tax modifies cell proliferation by activating the expression of interleukin receptors and cytokines. Moreover, retroviruses also isolate the infected cell by modifying the expression of surface receptor or molecules essential for cell communication. For example, the HIV-1 protein Nef down-regulates the expression of CD4 receptors in T cells, and therefore contributes to the overall immunodeficiency and the onset of superinfections caused by opportunistic pathogens. Retroviruses are also able to modulate gene expression through direct regulation of the transcriptional machinery. For example, in human acute promyelocytic leukemia (APL), chromosomal translocations and mutations in nuclear hormone receptors yield oncoproteins that alter chromatic structure and deregulate transcription. A better understanding of retroviral gene expression regulation is essential to develop prevention and therapeutic strategies. However, the variability of the viral targets and the strong dependence of viral replication on the host factors are major concerns. Chapter 3 - In recent years, the specific cellular effects of vitamin E that are the consequence of modulating signal transduction and gene expression have been described. The natural (α-, β-, γ-, and δ-tocopherol and α-, β-, γ-, and δ-tocotrienol) and synthetic vitamin E analogues affect the cellular behavior differentially, suggesting that they do not act on a single molecular target. Furthermore, these effects are often not explainable by a general antioxidant action and thus most likely reflect specific interactions of vitamin E with enzymes, structural proteins, lipids and transcription factors. At the cellular level, the different vitamin E analogues can modulate cell proliferation, apoptosis, platelet aggregation, monocyte adhesion and the differentiation of hippocampus neurons. At the enzyme level, the tocopherols inhibit protein kinase C (PKC), protein kinase B (PKB), tyrosine kinases, 5lipoxygenase and phospholipase A2, and activate protein phosphatase 2A and diacylglycerol kinase. At the transcriptional level, the expression of a growing number of genes is modulated by the tocopherols. Further research is required to define which of these activities render tocopherol (and, in particular, α-tocopherol), an essential nutrient—a vitamin—in humans. Chapter 4 - In recent years, many roles of Vitamin E have been demonstrated and include not only antioxidant functions, but also cell signaling and immunemodulatory functions. In

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Preface

ix

this paper the experimental and clinical evidence of vitamin E immunomodulatory properties as well as the vitamin E biological activities able to stimulate and enhance immune activities are briefly reviewed. Several lines of evidence suggest that vitamin E might be useful to improve immune unresponsiveness induced by several pathological conditions such as chronic viral infections and make its use in this setting promising. Further studies investigating the effects of Vitamin E on are of interest as they might help to identify its therapeutic utility in chronic infections. Chapter 5 - Geminiviruses are plant viruses with small single-stranded DNA genomes. Replication takes place via a rolling circle mechanism and is initiated from a nonanucelotide motif common among all geminiviruses. This motif is located on a hairpin structure within a long intergenic region (LIR). All members of the geminiviridae possess an LIR; this contains the cis-acting elements required both for virus replication and regulation of gene expression. As a result, virus replication is intrinsically connected to the regulation of gene expression. The following chapter examines in detail the relationship between geminivirus replication and gene expression. Virus and host factors that have been identified as components of both replicational and transcriptional machinery are included, and the significance of this information with respect to the development of expression vectors based upon geminiviruses is examined. Future directions for research pertaining to the regulation of gene expression of geminiviruses and potential applications in biotechnology are described. Chapter 6 - The developing central neural circuits are genetically controlled and initiated by developmental signals. Recent progress in molecular and cellular developmental biology provides evidence of how the brain is feminized or masculinized during the critical developmental period. Research into the development of brain architecture requires experimenting with animals, specifically, interfering with normal development and with environmental conditions. Drosophilae, sea urchins, and metazoans are simple invertebrates used for standard research models. Recently, the teleosts, bony fish with biological and genomic complexity found in the higher vertebrates, have become important models for developmental and molecular neurobiology studies. As in mammals, sexual dimorphic genetic expression is found in the developing brain of teleosts. The cellular and synaptic organization of brain architecture is determined by the genomic program and triggered by environmental cues, such as the photoperiod and temperature. This review highlights some of the methodological issues related to current findings about the gene expression regulation involved in the complex process of neural development, particularly in brain-sex differentiation. Chapter 7 - The internal ribosome entry site (IRES) is a part of the mRNA sequence which is able to attract the eukaryotic ribosomal initiation complex and to directly promote the initiation of protein synthesis independently of the presence of 5'-terminal 7mG cap. RNA structures bearing the IRES activity were first discovered in certain eukaryotic viruses where they very often play a pivotal role in viral strategies, allowing the viral invader to overcome the overall decrease of the host protein synthesis caused either by viral proteins or by the cellular antiviral defense system. Although the IRES segments and thus the cap-independent translation initiation were first described in viruses, extensive evidence has appeared in the past few years that a similar principle of the translation initiation is utilized also by some cellular mRNAs. Demonstration of IRES activity of a particular RNA region is not a simple

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task. A proper design of the experiment and a careful selection of the controls – excluding artificial signals generated by leaky scanning, ribosome hopping and undesirable cryptic transcription, splicing or physical breakage at the hot-spots – are very important. A number of false positives described in the literature as well as difficulties in designing appropriate controls have become the major stimuli for creating IRESite – the publicly available manually annotated database of experimentally verified IRES structures. This chapter presents the current status of the IRESite database (http://www.iresite.org), the complete list of known viral and cellular IRESs as well as novel results obtained from the comparative analyses of IRES segments accumulated to date. The article also presents a brief description and comparison of other available databases containing IRES and 5' untranslated region (5'UTR) related information. Chapter 8 - Kaposi‘s sarcoma (KS) is subdivided into four epidemiological variants, all of which have in common a similar histopathology and expression of human herpes virus 8 (HHV-8) in the lesions. Two forms are associated with immune suppression, post-transplant KS and AIDS-related KS, while the two others, classic KS and African endemic KS are not. HHV-8 infections are normally benign, with the incidence of HHV-8 infection being much higher than the incidence of KS. The cellular deregulation that leads to the formation of KS lesions is poorly understood, as is the infected cell type. It is also unknown whether gene expression patterns are similar between the epidemiological forms, especially as two forms are not associated with immune dysfunction. To gain insight into the genes expressed in KS lesions, we have generated Serial Analysis of Gene Expression (SAGE) libraries from both AIDS-KS and African endemic KS tissue to analyze mRNA levels in KS. The SAGE libraries were compared with each other and with libraries in the SAGEmap database. Systematic analysis of the level of gene expression of twelve specific gene families or related genes, including HHV-8 genes, was performed for both endemic KS and AIDS-KS tissue. The results suggested that endemic KS and AIDS-KS have a similar pattern of gene expression, in line with their comparable histopathology. High or very high expression was found in KS compared with other libraries for psoriasin, HLA-C, complement component 1, keratin 16, galectin 9, plexin D1, CD51, CD31, CCL18, CCL19, CCL21, and many other genes. In general, not the tag counts but rather the tag count ratio of KS versus other libraries gave information about the level of gene expression. Interestingly, a few genes were overexpressed in AIDS-KS compared with endemic KS (e.g. Von Willebrand Factor, D component of complement), while others were overexpressed in endemic KS (e.g. HLA-F, MMP-12, CD74, calgranulin-A). It has been suggested that iron is an important factor in the establishment of KS. Analysis of iron-related gene expression in KS, however, suggested no clear abnormalities. Relatively high expression was only found for the light polypeptide of ferritin, and heme oxygenase-1, and intermediate expression was seen for the hemoglobin scavenger receptor CD163, compatible with macrophage-related iron-uptake as erythrocytes leak from abnormal vessels in the KS lesions. In conclusion, the comparison of gene family expression in KS SAGE libraries has provided new insights into the molecular mechanism involved in the pathogenesis of KS. Chapter 9 - Gene expression profiling initially showed that it could be used to distinguish between the major variants of acute leukaemia. Several studies have now been published that

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have illustrated the potential of micro array analysis in the diagnosis of haematological malignancy. For acute myeloid leukaemia (AML), in particular, expression profiling has also provided novel insights into disease pathogenesis and shown it could lead to refinements in risk-stratification, prognostic markers, and eventually a more rational and personalised approach to therapy. These gene expression studies will be compared and contrasted in terms of results; with a view to speculating whether arrays can complement current laboratory methodologies for leukaemia diagnosis. Chapter 10 - Baculoviruses are a diverse group of insect viruses, among which Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is the best characterized. AcMNPV has been widely utilized for recombinant protein production in insect cells, and has captured growing interest as a vector for gene delivery into mammalian cells. This chapter primarily reviews the regulation of AcMNPV gene expression in insect cells and approaches to modulating transgene expression in both insect and mammalian cells. Recent progress and efforts directed towards enhancing the expression levels and extending the expression duration are particularly emphasized.

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In: Viral Gene Expression Regulation Editor: Eli B. Galos

ISBN 978-1-60741-224-3 © 2010 Nova Science Publishers, Inc.

Chapter 1

VIRAL GENE EXPRESSION AND HOST CELL IMMUNITY Jia-Hai Lee1, and Fredric Abramson2, 1

World Patent & Trademark Law Group, 257 Congrsssional Lane, suite 209, Rockville, MD 20852, USA; 2 AlphaGenics, Inc., 21155 Woodfield Road, Gaithersburg, MD 20882, USA.

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ABSTRACT Infectious viruses need to escape from the surveillance of human immune systems before they can successfully infect host cells to initiate the life cycle of viral replication. In addition, it is also well known that the internalized cellular viruses first have to disable intracellular host defense proteins followed by highjacking useful cellular proteins for viral replication, resulting in the dynamic changes of intracellular signal transduction pathways. Vaccines are developed to strengthen human immune systems against infectious pathogens and are currently the most popular strategies to prevent infectious diseases. Selected cellular proteins have been used for drug targets of cancer therapies, suggesting that diseases can be treated by targeting, inside host cells, specific gene expression and gene products associated with disease syndromes. However, the same philosophy has not been well accepted to treat or even prevent infectious diseases caused by viruses. In this review article, we will describe how understanding the mechanisms of viral infection and replication open the prospect of working with the body‘s non-immune defenses against viral infection. The dynamics of viral infection and replication indicates that certain types of viruses may be blocked from replication by selective targeting of host genes and/or host gene related products. This approach adds to the well-established viral disease control strategies of vaccines and anti-viral drugs. One potential advantage of this approach is the proposition that all recombinant strains of a specific virus such as influenza may require the same host genes or proteins to drive their life cycles in host Correspondence concerning this article should be addressed to: Jia-Hai Lee, E mail: [email protected]. Correspondence concerning this article should be addressed to: Fredric Abramson, E-mail: [email protected]. Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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Jia-Hai Lee and Fredric Abramson cells. To the extent that the virus can be prevented from using those needed genes or proteins, it is possible to develop a more general approach to prevent or treat such viral infections that operates regardless of the strain. Certainly, a chemical intervention that targets the host genes or gene products that are essential for viral replication will also reduce the rate of creating drug-resistant pathogens. Identifying the host genes in response to viral gene expression may be an alternative strategy to prevent infectious diseases in the post genomic era.

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INTRODUCTION According to the global burden of disease reported by the World Health Organization in 2004, infectious and parasitic diseases are the number two cause of death, next to cardiovascular diseases, regardless of gender. In the 2004 report, HIV/AIDS ranked the sixth leading cause of death and tuberculosis was the seventh. These two infectious diseases caused 3.5 million deaths in total; each caused death more than any cancer. In low- income countries, defined by gross national income per capita of $825 or less, HIV/AIDS advanced its new rank to the fourth leading cause of death. To further characterize HIV/AIDS by geographic region, it is the number one leading burden of disease in Africa. With the advanced public transportation system worldwide, infectious diseases are communicable without national boundaries. Thus, it is important to control the spreading of infectious diseases when vaccines and drugs are insufficient to prevent or eliminate the diseases. Viruses and other parasites in host cells can take advantage of cellular milieu for mass production of viral progeny and descendants. Most times, the immune system in the human body is able to eliminate viral or bacterial pathogens to restore health from disease syndromes. Therefore, vaccines are designed to enhance host immunity by inducing specific memory cells and antibodies to recognize immunogens that are used to mimic naturally occurring pathogen infection. Unlike other viral vaccines for hepatitis virus B, polio and smallpox, HIV-1 does not have effective vaccines to prevent viral infection [1-4]. Although vaccines for influenza viruses are available, influenza viruses can transform themselves into new strains through gene mutations or even, in rare cases, acquire new genomic RNA across host species [5-7]. The frequent changes of the viral genome in influenza viruses force new influenza vaccines to be redeveloped every year, and the rare frequency of transformation of human influenza viruses from avian influenza viruses can even cause disasters in human history [7-10]. Although it is difficult to have permanent influenza viral vaccines [5-6], the vaccine development for HIV-1 is even more challenging, since the human body does not successfully develop broadly neutralizing antibodies in response to designed HIV-1 vaccines [1,3]. In the recent failure of Merck‘s HIV-1 vaccine trial, people received adenovirus based HIV-1 vaccines were more vulnerable to HIV infection. In addition, retroviruses, such as HIV-1, can insert their own reverse-transcribed proviral genomic DNA into the host cell‘s chromosomes to create ―built-in‖ long-term threats [11-13], such as simultaneous viral genome replication together with cell cycle progression of infected host cells. The reasons for failure in HIV-1 vaccine trials are complex; thus, alternative strategies may be needed to control or slow down AIDS disease progression as well as to prolong patient‘s lifespan during the ―waiting periods‖ of developing effective HIV vaccines. One potential alternative

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Viral Gene Expression and Host Cell Immunity

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strategy is to disrupt viral-host cell interaction at the intracellular level to suppress viral replication, which may be applied to prevent or to delay AIDS disease progression [14-17]. In this chapter, we would like to use human immunodeficiency virus HIV-1 (Retroviridae) as an example to discuss the mechanisms of viral gene expression and the interaction between viral proteins and host cell proteins. In addition, we would also like to discuss the conundrum of the peak mortality rate for the age group 25–40 in the 1918–19 influenza pandemic, which was caused by influenza A virus (Orthomyxoviridae). In contrast to traditional knowledge, the age group is known to have the strongest immunity to defend against pathogens, but the survey showed that a stronger immune system did not necessarily provide better protection for people infected by the pandemic influenza virus [9]. Moreover, the secondary opportunistic pneumonia infection can weigh heavily on the primary infection, leading to the death of patients [8-9]. With the advancement of biotechnology, pharmaceutical companies can take advantage of recombinant DNA technology in cell culture to produce recombinant viral vaccines or virus like particle (VLP) with reduced virulence [20-21]. However, it is still constantly required for new flu vaccine development by monitoring circulated strains of seasonal influenza viruses in the environment to identify the predominant influenza viruses as vaccine targets. Also, the frequent antigenic changes of influenza viruses significantly decrease the efficacy of influenza vaccines [5-6]. On the other hand, viruses require host cells to replicate themselves regardless of their species or strains even if they constantly change their viral antigens [22-24]. Therefore, we hypothesize that destruction of virus-host interaction may be an alternative approach to prevent infectious diseases. Indeed, it has been shown that viruses have numerous strategies to influence host gene expression at both transcription and translation levels to create the environment in favor of viral proliferation [25-30]. In fact, new biotechnology, such as small interfering RNA (siRNA) and microRNA (miRNA), can be utilized to screen host candidate genes to identify genes required for viral replication but not essential for cell survival [31-34]. A drug targeted to nonessential host cell gene product(s) should have the advantages of less chance to create drug-resistant strains of pathogens under selection pressure and less cytotoxicity to host cells. The concept of preventing infectious diseases by intervention of host gene expression can be useful, in particular, when effective vaccines are not available, as well as in AIDS patients who have already lost their adaptive immunity.

IMMUNITY The human‘s immune system consists of innate (also called natural or naïve) immunity and acquired immunity. The innate immune system is the first host defense in response to pathogen infection. Damage of the epithelium caused by pathogens can trigger local inflammation to attract neutrophil leukocytes to the inflamed areas; within hours, other nonspecific immune cells, including monocytes, natural killer (NK) cells, or even lymphocytes are mobilized in the area. The innate immune reaction, naïve immunity, can be activated within minutes to hours to protect the host from immediate threats of pathogen invasion, much faster than the primary immune response usually activated in several days after

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infection [35]. In addition to destroying pathogen infected host cells, certain types of innate immune cells, such as NK cells, have been reported to kill cancer cells as well [36-38]. Many molecules, such as double-stranded RNA, lipopolysaccharides, or glycoproteins, can serve as warning signs to trigger inflammatory response and to activate the innate immunity [39-43]. One of the biological functions of inflammation is to stimulate cytokine production against viral infection as well as bacterial invasion. Cytokines are like polypeptide hormones able to activate target cells through their specific membrane receptors [44-46]. A cytokine-stimulated cell can be the same cytokine-producing cell (autocrine), a nearby recipient cell (paracrine), or a distal cell (endocrine). Cytokines can act on many different types of cells and even crossreact with one another [47-48]. New gene transcription and protein synthesis are the common effects of target cells in response to cytokine stimulation. Specific cytokines are also produced at different stages of inflammation. The four major types of cytokines that mediate the innate immunity are type 1 interferons (INFs), tumor necrosis factor (TNF), interleukin, and chemokines.

INNATE IMMUNITY AND CYTOKINES

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Type I Interferon Type I INFs contain two groups of proteins, interferon- and interferon- . Mononuclear phagocytes are the major interferon- producers. Many cells can make both interferon- and interferon- . Interferon- is also called fibroblast interferon since the cultured fibroblasts are frequently used as the resource of interferon- producer. INFs have been shown to activate receptor-associated tyrosine kinases [49-51], resulting in activation of downstream genes whose promoters contain interferon sequence response elements [52-54]. The four principal biological functions of type I interferons are 1) antiviral infection through paracrine effect for triggering defensive protein systhesis (2′-5′ oligoadenylate synthetase), 2) inhibition of cell proliferation, resulting from altering metabolisms such as essential amino acid Tryptophan synthesis, 3) activation of NK cell activities, and 4) down-regulation of MHC class II but upregulation of HMC class I molecules to enhance cytotoxic T lymphocyte (CTL) activities [55-58].

Tumor Necrosis Factor (TNF) TNF is a special membrane protein with its amino terminus in the intracellular domain and its carboxyl terminus as the extracellular domain. TNF can turn on downstream gene transcription by way of activation of transcription activators NF B [59] or AP-1 [60]. TNF is mainly secreted by mononuclear phagocytes after the immune cells exposed to lipopolysaccharides (LPS) or bacterial endotoxins shed from bacteria. In fact, the concentration of LPS or other stimuli can have different degrees of impact on TNF‘s biological function. Low concentrations of TNF can induce vascular endothelial cells to express adhesion molecules on their plasma membrane surface to recruit monocytes and

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lymphocytes at the inflammatory sites [61-62]. However, high concentration of TNF shows negative effects leading to tissue damage [63-66].

Interleukin Interleukins (IL) are cytokines largely produced by leukocytes. They have different functions in regulation of cell proliferation and differentiation. Each IL can act on a specific group of cells containing the appropriate receptor in response to a particular IL ligand. In the 1970s, a popular hypothesis was that cytokines were mainly produced by leukocytes and acted on other leukocytes, so cytokines were named Interleukins at that time. For instance, IL-1 was initially identified as a factor causing fever in response to bacterial infection but was considered a co-stimulator of thymocyte proliferation by immunologists [67-69]. As the the advancement of biotechnology, many more cytokines were discovered to be involved in diverse biological functions, including transcriptional regulation (IL-1 ), apoptosis (IL-1 ), T-cell growth (IL-2), producing antibody by B cells (IL-4), and down-regulation of INF-γ (IL-10).

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Chemokines Chemokines were first thought to be interleukins with the capabilities to activate receptors containing seven transmembrane domains, a G protein coupled receptor family [7073]. Chemokines are a group of small proteins secreted by phagocytes and dendritic cells to serve chemoattractants for recruiting effector cells such as neutrophils and monocytes in the blood to the inflammed locations [74]. On the other hand, chemokines are utilized by cancer cells for angiogenesis and metastasis [75-76]. The co-receptors for HIV entry of target cells, CCR5 and CXCR4, are also chemokine receptors [77-79]. A new generation of small molecule compound from Pfizer and an anti-CCR5 monoclonal antibody from Human Genome Sciences are created to block HIV-1 entry by competing HIV-1 binding to the CCR5 co-receptor. Although Pfizer‘s compound showed great success in clinical trials, it is not clear whether new selection pressure from the drug could create a situation to enrich the CXCR4-tropic HIV strain or even to create new HIV strains able to infect people through alternative receptors [80-81]. Specific cytokines can be produced by CD4+ or CD8+ cells after antigen activation, depending on the stages of infection. In general, the inflammatory effect does not usually cause tissue damage. However, exacerbated inflammation response in the 1918-19 pandemic influenza outbreak and prolonged activation of inflammation in HIV patients showed negative effects in individual patients. Ironically, more patients actually died from pneumonia as a second infection in both the 1918–19 pandemic influenza and HIV infection, rather than died from the original viral infection [8-9]. Interestingly, Wise reported that a new tuberculosis vaccine could reduce the incidence of the disease by 37% in HIV-positive people, according to phase III study data presented at the 39th Union World Conference on Lung Health, Paris, France (Oct 16–20, 2008) [82]. The clinical data substantiate the

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argument that long-term survival of HIV-1 patients can be further improved by suppression of the opportunistic bacterial infection in addition to taking antiviral drugs.

Other Intracellular Innate Immunity Intracellular immunity can be activated after viral infection by way of pattern recognition receptors [review in 83], including Toll-like receptors, RNA helicases RIG-1 and MDA5 [8486], double stranded RNA-dependent protein kinase (PKR)[87], and the DNA receptor (DAI) [88]. When the receptors encounter viral proteins or nucleic acids, they can trigger the cascades of cell signaling pathways to activate transcription factors, and to secret type I interferons and pro-inflammatory cytokines. The response serves as alarms in warning of pathogen invasion inside the infected cells and their neighbors as well as alarming immune cells about threats. PKR is the major cytoplasmic effector protein in response to double-stranded RNA (dsRNA), presumably due to dsRNA as a consequence of viral infection or intermediates of viral gene replication. DsRNA can recruit PKR monomers to contact each other, resulting in dimerizaton and autophosphorylation of PKR [89]. After activation, PKR dissociates from the dsRNA and then phosphorylates the eukaryotic initiation factor 2 alpha-subunit at serine 51, resulting in inhibition of global protein synthesis [90].

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Small Interfering RNA (siRNA) Another mechanism of host defense against viral infection is called RNA interference (RNAi) triggered by a double-stranded small interfering RNA (siRNA). RNAi is a posttranscriptional gene silencing mechanism conserved in both animals and plants [91-93]. The RNAi effect was first reported in the species of nematode worm Caenorhabditis elegant to show sequence-specific gene silencing [94]. Fire et al. further demonstrated that dsRNA mixture had more than 10-fold potency of gene silencing activities compared to using either sense RNA or antisense RNA alone. Another phenomenon of RNAi is its ability of amplification and spreading of the gene silencing effects [95-99] and a similar result was also reported in plants [100-103]. Moreover, genomic methylation of siRNA homogenous sequences was induced as a consequence of the silencing effects both in lower level organisms [96, 99] and in plants [100, 102-103]. One potential drawback of siRNA is that their effects in mammalian cells are transient because there is no known mechanism to amplify siRNA in mammalian cells. However, a short hairpin RNA (shRNA) can be expressed by a vector [104-108] and then siRNA can be produced through the microRNA maturation mechanism. The dsRNA is processed by a protein called dicer [109], an RNA III endonuclease, to become short RNA fragments at the size of 20–25 nucleotide duplexes called siRNA. During the process of siRNA incorporation into the RNA-induced silencing complex (RISC), the duplex of small interfering RNA (siRNA) is converted into a single-stranded guide RNA by degradation of the sense strand RNA [110-112]. Rand et al. (2005) modified siRNA with 2′-

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O-methyl modifications to block the cleavage of the sense (passenger) RNA strand in the duplex and showed a reduction in RISC activity, suggesting the removal of passenger RNA is critical for RNAi activities [113]. The single-stranded guide RNA can recognize its complementary sequence within the targeted messenger RNA (mRNA), resulting in cleavage of the targeted mRNA at Argonaute-2 dependent manner [114]. The cleaved mRNA is further degraded by other cellular RNases [115]. In a GFP reporter assay to illustrate the processing of siRNA within the RISK complex, Chiu and Rana discovered that the hydroxyl groups on the 5′ phosphate of the antisense strand of the siRNA duplex are required for RNAi but not for the hydroxyl groups at the 3′ end [116]. In a nutshell, the activation of RNAi consists of three steps in the order of digesting long dsRNA by dicer, separating passenger RNA from guide RNA in the RISC complex, and cleaving the targeted RNA containing the complementary sequences in multiple runs illustrated in Figure 1. Pri-miRNA

DsRNA or viral RNA nuclear export

Dicer

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siRNA

Pre-miRNA

RISC complex

Activation

mRNA cleavage perfect base-pairing

Translational suppression mismatched base-pairing

P body AAAAA AAAAA

Figure 1. Biosynthesis of siRNA and miRNA. Exogenous dsRNA or replicated viral dsRNA can be processed by dicer to create siRNA in the cytoplasm. The guide RNA derived from siRNA has perfect base-pairing to the complementary sequence within the targeted RNA. The siRNA targeted mRNA is subject to degradation, resulting in loss of protein synthesis. MiRNA is generated from its precursor Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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miRNA (pri-miRNA) in nucleus and exported to the cytoplasm where it is processed by dicer. In contrast, miRNA contains mismatches in forming the gene silencing complex for translational suppression.

MicroRNAs are small single-stranded RNA produced from non-coding regions of Pol-II transcripts. Primary miRNAs (pri-miRNA) are first created from non-coding transcripts in the nucleus at the size about 70 nucleotides to become pre-miRNAs [117]. The pre-miRNAs are exported to the cytoplasm and further processed by dicer to the size at about 22 nucleotides. After integration into the RISK complex, the passenger sequence of a pre-miRNA will be removed from the RNA duplex to generate the matured miRNA. The main biological function of miRNAs has been shown to regulate translation of messenger RNAs (mRNAs), resulting in either degradation of mRNAs or translational interruption of protein synthesis [118]. The fate of a targeted mRNA depends on the degrees of base-paired complementary sequences involved between miRNA and mRNA interactions. In addition to mammals, miRNAs have also been discovered in plants [119] and viruses [120-121], suggesting that miRNA is also a conserved mechanism for regulation of global protein expression in other organisms. Various viruses have utilized miRNAs encoded in viral genome to alter host cell gene expression [122-124]. Furthermore, miRNAs are downregulated in a variety of tumors and restoring the expression level of the miRNAs shows anti-cancer effects [125-128]. Recently, it has been reported that HIV-1 may encodes an interfering RNA to counter-react the suppressor of RNA silencing [129], HIV Tat protein interacts with dicer [130], and HIV TAR RNA sequesters TAR RNA-binding protein (TRBP) [131]. In addition, the NS1 protein of Influenza A viruses can bind small interfering RNAs to suppress RNA silencing in plants [132-134]. All evidence suggests that the dynamic host-virus interaction occurs at different levels in the pathogenesis of infectious diseases.

Activity of Immunity

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MicroRNAs (miRNAs)

NK cell

3

Specific CTL

7 Days after Viral Infection

Figure 2. Activation of immunity after viral infection. The natural killer cell (NK) can be activated by interferons (INF- and INF-β) or cytokines (TNF- and IL-12). NK cell activity can be enhanced up to 100-fold after activation. NK plays an essential role in suppressing viral proliferation by killing virusViral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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infected cells at the early stage of viral infection until specific cytotoxic T lymphocytes (CTL) are developed form adaptive immunity to eliminate the virus.

ADAPTIVE IMMUNITY Although adaptive immunity takes longer time to be established after invasion by pathogens, shown in Figure 2 [135], it creates many advantages for the host to defend for the pathogen infection. The adaptive immune response is highly antigen-specific as a result of its capacity able to be stimulated by diversified antigen resources. After stimulation by antigens and cytokines, B lymphocytes will be activated to produce specific antibodies in humoral immunity. Cell-mediated immunity, including help T cell, cytolytic T lymphocyte, and cytotoxic T lymphocytes, is also mobilized through clonal expansion to kill infected host cells, which are not able to be destroyed by circulated antibodies [136-138]. A subset of lymphocytes can maintain their ―memory‖ from adaptive immunity as a result of repeated exposure to the antigen resources. Vaccination is mimicking the naturally occurring human immunity developing strategies by introducing antigens or pathogens with reduced virulence to stimulate production of antibodies and specific ―memory‖ lymphocytes circulating in the host body in response to sudden attack by pathogens containing similar antigens [review in 139]. A flow chart of the development of immunity from bone marrow stem cells is in figure 3. Blood

Tissue Monocyte

Macrophage

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Monoblast

Antigen Activation Bone Marrow Stem Cell

Cytotoxic T cell T Helper-1

Thymus

Precursor T - cell

T Helper-2 Memory T cell

Antigen Activation

Lymphoblast

Plasma cells B - cells

Memory B cell

Figure 3. Development of immunity from bone marrow stem cells. Bone marrow stem cells differentiate into monoblasts, which become macrophages at targeted tissues from blood-circulated monocytes. Macrophages can serve as antigen presenting cells (APC) to further stimulate precursor T cells (resting T cells) to become effector cells, including CTL, T Helper-1, T Helper-2, and memory T cells. Lymphoblasts, derived from bone marrow stem cells, can differentiate into B cells and become plasma cells (antibody producing cells) and memory cells after antigen stimulation. Immature T cells,

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prothymocytes, migrate into thymus and become precursor T cells after education for tolerance of selfantigens. Precursor T cells can also be stimulated by antigens and cytokines to differentiate into various types of effector T-cells.

Humoral Immunity The adaptive immunity consists of humoral immunity and cell-mediated immunity. When antigen concentrations are low, specific B cells become the essential antigen presenting cells (APC). B cells serving as APC can physically contact helper T cells and are stimulated by Tcell secreted cytokines to proliferate and differentiate [140-142]. Humoral immunity is based upon antigen specific antibodies containing heavy chains and light chains. There are two types of light chain, lambda ( ) and kappa ( ), but five main classes of heavy chain including immunoglobulin M (IgM), immunoglobulin D (IgD), immunoglobulin G (IgG), immunoglobulin A (IgA), and immunoglobulin E (IgE). The differentiation of B cell through heavy chain isotype switching can allow the higher affinity of antigen recognition domain in the variable regions of an antibody to be reused by other antibody isotypes with different constant regions at various situations after exposure to regulatory cytokines.

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Cell-Mediated Immunity T cells recognize antigens presented by self-MHC genes. CD4+ helper T lymphocytes interact with class II HMC APC [143-144], whereas CD8+ cells recognize antigens under class I MHC restricted recognition [145-146]. Antigens presented by the class II MHC complexes are internalized foreign proteins or proteins derived from intracellular pathogens processed within cytoplasmic acidic vesicles. Some of the processed antigens can bind to class II MHC molecules to form immunogenic protein complexes on the surface of APC to interact CD4+ help T cells. The antigens recognized by CD8+ CTL are foreign proteins, such as viral antigens synthesized or processed inside APC [147-148]. The endogenously synthesized foreign antigens interact with class I MHC molecules in ER to form immunogenic protein complexes presented on the surface of APC. HIV-1 proteins gp120 specifically recognize CD4 receptors for viral entry, resulting in mass loss of CD4+ T cells at the acute phase of HIV-1 infection [149-152]. Thus, APC associated with the class II MHC immunogenic protein complexes cannot be activated efficiently due to loss of CD4 + help T cells including a particular subset of memory CD4+ T cells. Loss of CD4+ cells, in particular T helper 1 cells, results in failure to activate macrophages to kill the internally living pathogens, creating a loophole in host defense to nurture the opportunistic pathogens, leading to the death of AIDS. It is particularly interesting that the victims of highest death rate are at the age group of 25-40 with the strongest immunity [8-9]. The same phenomenon was also observed in severe acute respiratory syndrom (SARS) [153]. It appears that immunity to defend an infectious disease may not always be the best solution since the acute infection of viruses can cause aberrant host immune responses to infections resulting in cytokine storms [9]. In fact, inflammatory reactions have been reported in many other human diseases [154-155]. Thus,

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we hope new drug targets or novel therapeutic approaches can be developed by intervention of host cell gene expression as a preventive strategy to avoid infectious diseases. With a human genomic sequence map available, the advanced biotechnology, including siRNA or miRNA, can be powerful methods to discover relevant host cell genes or viral genes involved in infectious diseases [156-159]. A new concept of prevention of infectious diseases proposed here is to reprogram host cell gene expression in an unfavorable environment for pathogen survival or proliferation in host cells.

HIV-1 INFECTION

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Host Cell Receptor and HIV-1 Entry CD4 is a 55 kDa type I transmembrane glycoprotein transcribed from a single copy human CD4 gene located at chromosome 12 [160]. CD4 is expressed mainly on the surface of T helper cells and it is the main receptor utilized by HIV for entry of T cells [161-165]. However, CD4 cells including memory and regulatory T lymphocytes, macrophages, and dendritic cells are also targeted by HIV-1. HIV-1 has been reported to down-regulate CD4 receptor expression in infected cells through direct viral proteins (Vpu and Nef) interactions with the cytoplasmic tail of CD4, leading to the degradation of CD4. In general, HIV-1 can replicate vigorously in activated CD4 T-cells, but remain in latent phase in resting CD4 cells before stimulation. However, it has been shown that active HIV gene expression can also occur in resting CD4 lymphocytes of HIV-positive individuals as well [166-167]. Moreover, cytokines IL-2, IL-4, IL-7 and IL-15 are able to bind to the γc-cytokine receptor family members to further support HIV-1 infection [168-169]. Upon cytokines bound to their receptors, a protein tyrosine kinase JAK3 of the Janus family kinase can be activated to phosphorylate STAT5 recruited from cytoplasm to plasma membrane. The phosphorylated STAT5 can further travel into the nucleus to activate the HIV LTR [170]. A model of HIV-1 life cycle is shown in figure 4. It is well known that an increase of viral load correlates with a decrease of CD4 counts as well as AIDS disease progression. Ho et al. demonstrated CD4 cell depletion in AIDS is primary due to destruction of CD4 cells by HIV-1 but not a lack of production of CD4 cells [152]. In his studies of 18 HIV-1 patients, the minimum number of destroyed CD4 cells in the blood was characterized to be between 4.3 x 106 and 108 x 106, consistent with other‘s report [149, 151]. He also estimated that the overall CD4 lymphocyte turnover in the patient group was from 0.2 x 109 to 5.4 x 109 cells per day. Chun et al. further showed that highly active anti-viral therapy (HAART) in early primary HIV-1 infection did not provide statistically significant difference in inhibition of proviral DNA integration into host cell chromosomes [11]. The data suggest that the best approach for HIV-1 therapy is to block HIV-1 entry, or at least before the proviral DNA integration into its host chromosomes. Certainly, the data provide a strong reason for development of HIV vaccines to induce specific neutralization antibodies to prevent HIV entry of CD4 cells. Chemokine receptors are also required for HIV-1 entry [77-78]. Cocchi et al. discovered β chemokines (RANTES, MIP-1 α, and MIP-1 β) produced by CD8+ T cells were able to

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suppress HIV-1 [167]. The findings illustrated how the immune system is combating with HIV-1 infection in an alternative mechanism. Chemokine‘s specific inhibitory effects on HIV-1 entry make them very attractive as drug targets for HIV therapy. CCR5/CXCR4 Step 1

CD4

Step 2

Step 3

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Step 4

Step 5

Step 6

Figure 4. HIV-1 life cycle. Step 1. HIV binding to CD4 receptor and CCR5/CXCR4 co-receptor followed by membrane fusion during viral entry. Step 2. Reverse transcription and viral DNA synthesis to convert HIV single-stranded RNA to double-stranded proviral DNA. Step 3. Integration of HIV proviral DNA into host cell chromosomes. Step 4. HIV gene transcription and translation. Step 5. Assembling of 2 copies of HIV genomic RNA and other accessory proteins. Step 6. Budding of the HIV virion from host cell plasma membrane.

CXCR4 was the first discovered chemokine receptor required for the entry and fusion of T cell tropic strains of HIV-1 [73,171]. Afterwards, another chemokine receptor CCR-5 was also identified for the entry and fusion of macrophage-tropic strains of HIV-1 [172-175]. Not surprisingly, HIV-1 with the capability to infect both co-receptors were also discovered to exist as duel-tropic viruses, able to infect cells containing either type of the chemokine receptors. The third hypervariable (V3) loop within the HIV-1 envelope glycoprotein gp120

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plays an essential role for the selectivity of co-receptors utilized for viral entry of host cells [176]. After the interactions between CD4 and gp120, G protein (CCR5 or CXCR4) and tyrosine kinases can be activated and then turn on downstream signal transduction pathways. Studies in human genetics showed that deletion of 32 nucleotides in the homozygous alleles of CCR5 (Δ32) results in strong resistance to HIV-1 infection in patients [177-178]. People with heterozygous alleles also showed slower rate of AIDS disease progression [179-181]. The evidence of naturally occurring human genetic variations also substantiates the argument that a better way for anti-HIV therapy is to block the HIV-1 at the initial entry step.

HIV-1 Proteins HIV-1 proteins are made from different transcripts and further processed by viral protease to produce functional proteins shown in figure 5. The viral proteins can be classified in three different categories, including structure proteins (Gag, Pol, and Env), regulatory proteins (Tat and Rev), and other accessory proteins (Vpu, Vpr, Vif, and Nef). A viral virion structure is also shown in figure 6. The biological function of individual HIV-1 proteins is briefly described in the following. (A) HIV protein tat

vif

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gag LT R

nef

vpu vp r

pol

env

LT R

rev

(B) Gag precursor protein (55 kilodalton) MA

CA

NC

P6

(C) Gag-Pol precursor (160 kilodalton) MA

CA

NC

PR

RT/RNaseH

IN

Figure 5. HIV-1 viral proteins. (A) HIV proteins can be made from unspliced viral RNA, partially spliced RNA, or fully spliced RNA. HIV proteins can also be characterized to three different categories consisting of structure proteins (Gag, Pol, and Env), regulatory proteins (Tat and Rev), and other accessory proteins (Vpu, Vpr, Vif, and Nef). (B) The p55 gag protein can be further processed by HIV protease (PR) to create four smaller proteins, including matrix (MA), capsid (CA), nucleocapsid (NC),

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and p6. (C) The Gag-Pol fusion protein is essential for retroviruses to make reverse transcriptase (RT) intergrase (IN), and protease.

gp 120

Lipid bilayer

gp160

gp 41

Matrix

CA Protease Integrase HIV RNA genome

RT

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Capsid protein Figure 6. Structure of HIV-1 virion. Two copies of single-stranded HIV-1 RNA genome are encapsulated by HIV capsid proteins. The envelope protein gp 160 consists of gp 120 and gp 41. One viral spike contains 3 gp120 molecules and 1 gp 41. Gp120 can directly binds to the CD4+ receptor on hoes cell surface to initiate viral entry. A co-receptor CCR5/CXCR4, G protein coupled receptor, is also involved in receptor binding and viral entry.

Gag The gag protein is translated from the unspliced HIV RNA at a molecular weight about 55 kilodalton (KD). The N terminus of gag proteins is myristoylated for membrane translocation [181-182]. The membrane-translocated gag proteins can further recruit two copies of full-length HIV genomic RNA as well as other viral and cellular proteins to trigger the budding of HIV virions from HIV producing cells [183-184]. The p55 gag protein can be processed by a viral protease derived from the pol transctipt into four smaller proteins matrix protein (p17), capsid protein (p24), nucleocapsid protein (p9), and p6 [185].

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Matrix Protein (MA) A small portion of MA protein is the N-terminal region of the p55 gag protein processed by the HIV-1 protease. The majority of MA protein is associated with viral lipid bilayer to support the shape of virion. MA can be found inside the virion associated with viral genome. It has been reported that the karyophilic signal on MA can interact with cellular nuclear import machinery to enhance nuclear translocation of HIV genome [186]. This can be a reason why HIV can infect non-dividing cells effectively [187]. Recently, Avolio et. al. reported that HIV-1 matrix protein p17 is able to prevent loss of CD28 expression during IL2-induced maturation of naïve CD8(+) T cells [188], suggesting that the p17 protein can interfere with host gene regulation in favor of HIV-1 survival from host immunity.

Capsid Protein (CA) The CA protein is the major building block in the core of HIV virion. This CA region of p55 gag protein is essential for the gag protein to interact with cellular factor Cyclophilin A. Destruction of interaction between gag and Cyclophilin A can severely suppress HIV replication [189-190].

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Nucleocapsid Protein (NC) The NC domain of gag protein can recognize the HIV genomic packaging signal, consisting of a four-stem loop folding structure at the 5‘ end of HIV RNA for viral genome dimerization [191-192]. The NC protein can binds to viral RNA through its two zinc-finger motifs. In addition, NC has also been reported to assist reverse transcription [193].

P6 The P6 domain of gag protein interacts with the HIV accessory protein vpr [194]. Furthermore, a domain within the p6 protein, called late domain, is required for the effective release of budding virions from the HIV producing cells [195-197]. Hemonnot et al. demonstrated that phosphorylation of a unique site of the p6gag domain by ERK-2 plays a critical role in the late stage of the HIV-1 life cycle, by contributing to the regulation of virion assembling and releasing from cell membrane [198].

Gag-Pol Fusion Protein Several viral proteins, protease, intergase, RNase H, and reverse transcriptase are processed from the Gag-Pol fusion protein, which results from ribosomal frameshift during translation [199]. This occurrence of frameshift is about 5%, resulting in a 1:20 ratio of Gag-

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Pol and gag in HIV virions. In the process of viral maturation, the viral encoded protease protein separates gag from the Gag-Pol fusion protein and then the Pol protein is further digested to protease (p10), RT (p50), RNase H (p15), and integrase (p31). Since the enzymatic reaction is not very effectively, a significant amount of RNase H is still associated with reverse transcriptase [200].

Protease (PR) The HIV protease protein is an aspartyl protease [201] and functions as a dimer [202]. Its main function is to release individual viral proteins encoded in gag and Gag-Pol fusion proteins. The structure of HIV protease has been studied [203-204]; therefore, small molecule compounds are designed to block protease activities for the treatment of HIV infection [205].

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Reverse Transcriptase (RT) Reverse transcriptase is transcribed in the Pol gene. The protein has both RNAdependent and DNA-dependent polymerase activities. RT is responsible for the conversion of HIV RNA to double-stranded proviral DNA in a complex process [206]. The first step of reverse transcription is to create a single-stranded commentary DNA using HIV RNA genome as the template. Many cis-acting elements of HIV RNA are also involved in the single-stranded commentary DNA synthesis. For instance, a small stem loop RNA structure, called TAR, at 5´ end of HIV RNA attracts HIV Tat protein to initiate the reverse transcription [207]. Many AZT-like drugs are designed to block the reverse transcription of HIV life cycle in infected cells. RNase H functions in digestion of the viral RNA template to release the single-stranded DNA. The single-stranded DNA serves as the template used by the heterodimer consisting of p50 (RT) and p66 (RT/RNase H) [208] to produce doublestranded HIV proviral DNA.

Integrase (IN) HIV integrase is essential for the insertion of the reverse transcribed HIV proviral DNA into genomic DNA of host cells [209]. The accessibility of chromosomal DNA rather than specific DNA sequences determines the sites of proviral DNA integration [210-211]. The complicated integration process requires to break host genomic DNA by exonuclease activities and to repair the gap of integrated proviral DNA by ligation [reviewed in 212]. Both Merck and Gilead biopharmaceutical companies are developing compounds targeted to the HIV integrase for treatment of HIV-1 infection.

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Envelope (ENV) HIV envelope protein (gp160) is synthesized in endoplasmic reticulum and modified at asparagine residues by glycosylation of N-linked carbohydrate side chains in Golgi apparatus. The glycosylation of gp160 is also required for HIV infectivity [213]. Gp160 can be cleaved by different cellular enzymes and becomes gp41 and gp120 [214-215], but the two glycoproteins still matain noncovalent interaction. Gp41 contains the transmembrane domain of ENV and gp120 stays on the surface of infected host cells and virions interacting with gp41. Env forms a trimeric protein complex on the surface of infected cells and virions [216217].

Tat HIV tat protein is a strong transcription activator and essential for HIV-1 replication [218]. Tat is also an RNA binding protein and it activates HIV gene transcription by binding to a short-stem loop RNA structure, transactivation response element (TAR), located at the 5' terminus of HIV RNAs [219]. The tat-TAR interaction can further recruit cellular proteins, cyclin T and CDK9 to stimulate the elongation step processed by RNA Polymerase II [220221]. Tat can also activate other cellular transcription activators to further enhance HIV-1 gene transcription, such as c-Fos [222] from the long terminal repeat (LTR) and more detailed discussion of Tat is described at HIV-LTR.

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Rev Rev is a sequence-specific RNA binding protein and plays a role in the transition of HIV gene expression from early phase to late phase [223-224]. Rev can bind to an introncontaining RNA structure called Rev response element (RRE) and export incompletely spliced viral RNA to cytoplasm through protein-protein interaction with CRM1 [225-227]. Rev is also essential for HIV-1 replication as it is required for viral late gene expression [228229].

Nef Nef is a myristoylated protein and it is a viral protein that can be detected early in host cells after HIV-1 infection. Nef has shown multiple functions to facilitate HIV replication and all primate lentiviruses (HIV-1, HIV-2, SIV) encode this protein. Nef down-regulates the CD4 receptor expression by increasing endocytosis of CD4 for lysosomal degradation [230231]. Nef also decreases Class I MHC [232] to reduce the efficiency of cytotoxic T cells targeted to HIV infected cells. Nef also stimulates the infectivity of HIV virions [233].

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Vpr The vpr protein is integrated into HIV virion through protein-protein interaction with the p6 domain of gag proteins [194]. Vpr causes cell cycle arrest at G2 phase by inhibiting the p34cdc2/cyclin B complex [234-235]. Moreover, vpr facilitates nuclear localization of the preintegration complex of HIV [236] to infect nondividing cells.

Vpu Vpu has two major functions with respect to HIV-1 pathogenesis, down-regulation of CD4 receptor expression through the ubiquitin-mediated protein degradation mechanism [237-238] and enhancement of virion released from infected host cells [239]. Vpu is translated from the messenger RNA (mRNA) encoding the HIV-1 envelope protein, but it is expressed at a much lower level than the envelope protein as a consequence of its poor translation initiation codon within the shared mRNA transcript [240-241].

Vif

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HIV-1 vif protein is required for the viral infectivity in peripheral blood lymphocytes, macrophages, and certain non-permissive cell lines [242] by triggering the degradation of host defense proteins of APOBEC 3 (apolipopretein BmRNA editing catalytic peptide) gene family, in particular APOBEC 3F and APOBEC 3G [243-244]. More detailed discussion of vif will be described in protein-protein interaction between HIV-1 and host cells.

HIV Long Terminal Repeat (LTR) HIV-1 long terminal repeat (LTR) promoter contains many motifs to recruit various transcription factors, including NF-κB, AP-1, Sp1, steroid hormone receptors, glucocorticoid receptor, and NFAT to regulate HIV LTR promoter activities [245-249]. In addition, the chromatin structure can also affect HIV LTR expression [250-251]. The unique sequence of HIV-1 LTR promoter contributes to its high replication rate of HIV-1 in response to a variety of stimuli. In addition to the primary sequence of the LTR promoter, HIV-1 Tat protein in association with a trans-activation response RNA element (TAR) can further enhance HIV gene expression by 100 fold [252-253]. Centlivre et al. also reported that the HIV-LTR promoters in different clades could strongly influence spatial and temporal dynamics of viral replication in vivo [254].

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NF-κB NF-κB exists as a protein family containing NF- κB1 (p50 and p105), NF- κB2 (p52 and p 100), RelA (p65), RelB and c-Rel [review in 255]. It has been reported that NF-κB is able to activate more than 150 genes, comprising proinflammatory cytokines, growth factors, and apoptotic regulatory proteins. NF-κB is associated with inhibitory proteins (I-κB) in cytoplasm, including I-kBa, I-kBb, I-kBe, p105, p100, and bcl-3. Upon stimulation by cytokines, such as TNFα or IL-1, I-κB is phosphorylated by I-κB kinase (IKK) to trigger the degradation of I-κB through proteasome-mediate protein degradation pathway [256-257]. The released NF-κB will enter the nucleus to activate NF-κB responsive genes [258].

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AP-1 Activator protein-1 (AP-1) is able to regulate cell cycle and cell death [259]. The mammalian AP-1 exists as either a homodimer or heterodimer containing leucine zipper DNA binding motifs. AP-1 can be formed from several protein family, including Jun (c-Jun, JunB and JunD), Fos (c-Fos, FosB, Fra-1, and Fra-2), Jun dimerization partners, and other related activating transcription activators (ATF2, LRF1/ATF3 and B-ATF) [260-261]. It has been reported that posttranslational modification, such as phosphorylation of AP-1, is an alternative regulatory mechanism to modulate AP-1 activities [262]. AP-1 can be activated by many growth factors and cytokines [263-265]. In addition, members of AP-1, c-Fos and c-Jun are classified as protooncogenes. The evidence suggests that AP-1 can be involved in growth regulation and transformation. AP-1 can also be activated by proinflamatory cytokines, TNF and IL-1, indicating AP-1‘s roles in inflammation and innate immune response. A possible explanation of AP-1‘s role in apoptosis is that its capability to activates Fas-ligane gene directly [266-268]. However, the caveat is why certain AP-1 members can activate production of FasL, but some cannot. Another apoptotic mechanism regulated by AP-1 is the inhibition of p21cip1/waf1 by Jun in UV irradiated cells, resulting in activation of p53-mediated apoptosis [269]. The various regulatory mechanisms of AP-1 also have profound influence on HIV LTR activation.

Sp1 Sp1 is one of the Sp (specificity protein) transcription factor family proteins [270] recognizing the cis-acting DNA GC box sequence, GGGGCGGGG, in the promoter/enhancer regions to activate gene expression [271-273]. On the other hand, the zinc finger (Cys2-His2) containing DNA binding protein Sp1 is also able to suppress gene expression [274-275]. Several cell cycle regulatory proteins, such as CDK4, SKP2, Rad51, BRCA2 and p21, can activate Sp1 gene expression [276-277]. Moreover, Sp1 phosphorylation regulates apoptosis of smooth muscle cells by activating the FasL promoter [278]. Recently, Curry et al. discovered that Macrophage-Colony Stimulating Factor triggers nuclear translocation of Sp1 in human monocytes to activate VEGF expression and to induce angiogenesis in vivo [279].

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Furthermore, the research group demonstrated that the VGEF production is due to MAP kinase rather than PI3 kinase/Akt pathway. The results suggest that Sp1 may be a drug target to inhibit angiogenesis, in addition to members in the MAP kinase pathway. Since three Sp1 binding motifs are located in the HIV LTR, the MAP kinase may also influence HIV LTR activation. Opijnen et al. demonstrated HIV subtypes with diverse motifs in their LTR promoters could influence the proliferation rate of HIV subspecies in host cells [280]. Another mechanism to enhance HIV-1 gene expression is the interaction between trans-activator of transcription (Tat) protein and trans-activation response RNA element (TAR), in addition to the compositions of transcriptional activator‘s binding motifs within the HIV-1 LTR promoter (Figure 7). HIV Tat protein is a 14 Kd small protein capable of activating the HIV-1 LTR promoter by 100-fold at an RNA element dependent manner [252-253]. The trans-activation response RNA element (TAR) is located at the R region of the HIV-1 LTR promoter. TAR RNA forms a unique stem loop structure containing bulges to attract the HIV-1 Tat protein for HIV-1 gene transcription [281-283] and replication [284-285]. Tat can associate with HAT (TAH) is in the activation of chromatinized HIV-1 LTRs, possibly through the acetylation of histones [286]. In addition, Tat also interacts with other cellular factors to enhance HIV-1 gene expression at transcriptional initiation and elongation steps [287]. Thus, the HIV Tat protein is absolutely essential for mass production of HIV. U3

R TAR

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HIV Tat Steroid hormone response element

AP 1

C/EBP

myb A

Sp1 LEF-1

E-Box

glucocorticoid receptor

U5

Enhancer

TATA

NF-κB family NFAT family ETS family

Figure 7. Binding motifs of transcription factor in the HIV-1 LTR promoter. The HIV-1 long terminal repeat (LTR) promoter contains various binding motifs of different transcription factors. Nuclear translocation of NF-κB can be activated by cytokines in many inflammatory related pathways, resulting in activation of the HIV-1 LTR promoter. Other motifs, such as AP 1 and Sp 1 are also playing important roles in regulation of the HIV-1 LTR promoter in response to different stimuli. HIV TAR is the initial transcribed RNA at the R5 region. The unique stem loop structure of TAR is recognized by HIV-1 Tat protein. Tat can further enhance HIV-1 gene transcription at transcriptional initiation and elongation steps through interacting with other cellular proteins, including the C-terminal domain of RNA polymerase II transcriptsome, resulting in increase of HIV-1 gene expression by 100-fold.

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8(A) HIV

HIV infection

G GG

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A

GG

AG

G

U

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+ strand DNA – strand DNA hypermutation

Loss of function

8(B)

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E3

E3

APOBEC3G

Vif

APOBEC3G

Vif

polyubiquitin E3

proteasome A

P

O

B

E

C

3

G

Vif

Ψ Ψ Ψ ΨΨ APOBEC3G

E3

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Figure 8. Host defense proteins and HIV vif. (A) Host defense proteins APOBEC 3F and 3G can be incorporated into HIV virion in the absence of HIV Vif. After entry of a target cell, the host defense proteins can introduce hypermutation of C to U into the reverse transcribed negative strand HIV DNA. The positive strand HIV DNA will accumulate G to A hypermutation in the viral genome as a result of insufficient DNA repair of HIV DNA. (B) Vif can serve as an adaptor protein to bring APOBEC 3G close to the Cul5-E3 ubiquitin ligase complex. After poly ubiquitination of APOBEC 3G, the proteins will be decomposed through the proteasome-mediated protein degradation pathway.

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22

HIV-1 Vif and Host Defense Proteins APOBEC3F and 3G

3.5 1.0

Mortality rate

3.0

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Influenza/Pneumornia Reltaed Mortality (%)

Age group and Pneumornia rate (%)

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HIV Vif protein is a 23 KD accessory protein and plays a critical role in viral infectivity because it is essential for the degradation of innate cellular anti-viral protein family of APOBEC (apolipopretein BmRNA editing catalytic peptide), in particular the proteins APOBEC3F (A3F) and APOBEC3G (A3G). Sheehy et al. first identified a host cell factor called CEM15, but was renamed as A3G [288]. In addition, Chiu et al. further demonstrated that A3G plays a key role in protection of resting human CD4+ T cells from HIV infection because A3G forms high-molecular-mass ribonucleoprotein complex after activation resulting in loss of its enzymatic activities [289]. Both Human A3F and A3G have cytidine deaminase activities by introducing hypermutation of cytidine to uracile into HIV negative strand DNA during reverse transcription of the HIV RNA genome [290-293]. A model of A3G-mediated anti-HIV mechanism is shown in figure 8(A). In addition to suppression of HIV replication, A3G can also inhibit retrotransponsons, such as Alu elements, in human genome [294-297]. Other cellular function about APOBEC3 family proteins had also been reported [298-300]. A3G-mediated antiviral mechanism restricts many other viruses including, human T cell leukemia virus type-I (HTLV-1) hepatitis B virus (HBV), and foamy virus, suggesting that A3G is general host defense protein not just only for restriction of HIV replication [301-303]. The pathway of vif-mediated A3G degradation has been studied extensively in Dr. Strebel‘s research group at National Institute of Allergy and infections Disease [304]. A model of vif-mediated A3G degradation mechanism is shown in figure 8(B). However, it is also reported by Strebel‘s group that vif can also prevent encapisadation of A3F and A3G by way of a degradation-independent pathway [305].

≥75

AGE

Figure 9. Age groups and influenza/pneumornia-related mortality of US people in the 1918–19 influenza pandemic. People in the age range from 25 to 40 showed the highest mortality rate of influenza-related death. However, the strength of immunity of this age group is expected to be strongest among all groups. A similar pattern is also observed in the pneumonia infection. The data demonstrate that the deaths of people are correlated to pneumornia infection.

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DISCUSSION It is known that the spread of a virus in humans is not uniform, where different population groups and age groups demonstrate different incidence rates and susceptibilities. We acknowledge that a major part of the difference in susceptibility and severity has a lot to do with the host‘s immune system. But immune system variation by itself cannot explain differences in mortality outcomes, as illustrated by the data in figure 9 [9]. Other mechanisms appear to be at work. For example, deregulation of immune response of releasing cytokines has been suspected to damage host tissues and make the host more vulnerable for second infection of bacterial pneumonia in the 1918-19 influenza pandemic. Tuberculoid leprosy is one example where tissue damages are caused by the immune response [306-308]. Another example is the wheezy broncheolitis caused by respiratory syncytial virus (RSV) infection. Broncheolitis caused by RSV resulted in as many as 90,000 admissions to the hospital and 4,500 deaths along in US. It was observed that infants received vaccines made by alumprecipitated killed viruses undergoing a worse disease than others without vaccination [309311]. The reason was because the vaccine failed to induce neutralization antibodies but activated T helper 2 cells. After infection, T helper 2 cells releases cytokines IL-3, IL-4, and IL-5, which result in bronchospasm, increased secretion of mucus, and tissue eosinophilia [review in 312]. More research needs to be done to determine whether a similar mechanism can explain the failure in HIV vaccine trials. The cytokine expression pattern could be a clue for further improvement of HIV or other vaccine trial design [313]. It has been shown that the death from tuberculosis is linked to HIV/AIDS in Africa. It is also understandable that influenza viruses cause short-term aberrant immune response of cytokine storms resulting in tissue damages. However, it is far more complex in the pathogenesis of HIV-1 infection due to the length of AIDS disease progression. In addition, record keeping is more difficult in the long-term observation of HIV patients. The slow progress of HIV vaccine development reflects insufficient knowledge about interactions between host cells and HIV-1 [314]. By definition, viruses cannot replicate themselves alone outside host cells. Pfizer‘s anti-HIV drug Maraviroc is a small molecular compound targeted to the chemokine receptor CCR5, a successful example to prevent infectious disease by using drugs targeted to a host cell gene product. In our opinions, a targeted cellular gene is not necessarily limited to membrane receptors as long as the disruption of host-viral interaction to intervene viral replication in host cells is effective. Other HIV-1 genes such as HIV integrase, Tat, and vif are also well characterized in their roles of HIV‘s life cycle. Therefore, their counterparts of cellular proteins can be ideal targets for intervention of host-viral interactions. In light of this concept, other infectious diseases, such as influenza virus infection, may also be preventable by reprograming host gene expression to the levels or conditions to be unfavorable for viral replication. In theory, the strategy may to be applied to different influenza viral strains, so the threat from potential pandemic influenza H5N1 or recently reported H1N1 may be reduced to the same level as seasonal flu viruses. Ideally, it would be possible to manipulate a person‘s gene expression pattern in key target genes needed by the virus to replicate by using a diet calibrated to impact the target genes in order to suppress flu virus replication in host cells. This mechanism would provide an alternative defensive

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Jia-Hai Lee and Fredric Abramson

method that is parallel to their natural immunity. The success of this strategy could then be transferred to use in people who lost adaptive immunity. AIDS patients could benefit from it to defend for secondary infection caused by opportunistic pathogens. HIV-1 is a unique viral species in human‘s history because the pathogenesis of HIV-1 is at the cost of losing CD4+ lymphocytes, resulting in loss of adaptive immune capabilities in the human body. CD4 containing lymphocytes have two major biological functions in activation of other cells. The T-helper 1 cells, inflammatory T cells, activate macrophages to kill harbored pathogens, whereas the T-helper 2 cells stimulate B cells to produce pathogenspecific antibodies. Therefore, the mass lost of CD4+ lymphocytes at the acute phase of HIV1 infection mixed with prolong activation of patients‘ immunity by cytokine storms makes it arguable to answer the fundamental question ―What causes AIDS?‖ In another aspect of disease control and treatment, three out of the four Bill Gates Foundation‘s African targeted diseases, HIV, Leishmania, and tuberculosis are using macrophages as shelters to escape from antibodies created from humoral immunity. Therefore, there is a possibility to treat all the three different infectious diseases simultaneously by targeting to macrophage genes essential for pathogen survival or replication. The model presented in figure 10 illustrates a strategy to defend infectious diseases by regulating host gene expression. Many advanced technologies needed to study the mechanisms of hostpathogen interaction are only available in the 21th century. Thus, there is high expectation that new therapeutic approaches and cost-effective medicines will be developed to solve cancer and infectious diseases in human population. We can postulate that human immunity is less effective in killing HIV-1 infected cells. For HIV-1 to survive in host cells, the pathogen must be able to 1) escape from both innate and adaptive immunity and 2) replicate in host cells. Indeed, HIV-1 has ―figured out‖ a way to destroy CD4+ T helper-1 cells to avoid immunity against HIV-1 harbored macrophages and to inhibit antibody production by B lymphocytes through killing CD4+ T helper-2 cells [315]. This is just a part of natural selection process that HIV-1 disables the host defense system of human immunity for survival. In addition, a synergistic strategy for HIV-1 to survive in host cells is to integrate its proviral DNA into host cell chromosomes, so HIV-1 can duplicate itself together with host cell proliferation. Unlike, ALU repeats, SINE, and other retroposons inherited in human chromosomal DNA, HIV-1 also needs to defeat intracellular host defense proteins, such as dsRNA RNA-dependent protein kinase (PKR)[87] and APOBEC3 gene family [294-297], which successfully suppress the mobilization of ancestral retroposons. HIV-1 vif protein can effectively remove the host defense proteins, APOBEC3F and APOBEC3G, through the proteasome-mediated protein degradation pathway to avoid introduction of hypermutation during reverse transcription of HIV-1 RNA genome in newly infected host cells. The case of HIV pathogenesis is by no means just a co-incident event since the virus has to overcome selection pressure from both internal and external environment of the host cells. An alternative model of HIV-1 infection may indicate that human genetics may be at the cross road of an evolution to either acquire new genomic sequences from HIV-1 or to reject the exogenous genetic materials. A patient may successfully adopt HIV-1‘s sequence like Vpr F72L HIV long-term non-progressors; however, the carrier‘s HIV can still transmit AIDS to others [316]. Nevertheless, it is possible to ―inactivate‖ HIV-1 proviral gene

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expression if the two key proteins, Tat and Vif, can be neutralized in infected host cells. On the other hand, a rejection of HIV infection is more obvious by the fact that human population reserves the CCR5 delta32 mutation from ancestors. Pfizer‘s small molecular compound Maraviroc has been developed targeted to the chemokine receptor CCR5. Although the compound demonstrated its effects in clinical trials, it is not known whether the selection pressure will create selection pressure in favor of CXCR4-tropic or dual HIV strains. Delta32 of CCR5 is one of many examples to show a way to defend the threats of infectious viruses by human genetic variations.

I Tuberculosis

I, III

III HIV-1

I, II, III II, III

I, II

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II Leishmania

Figure 10. A model of host gene expression in response to multiple pathogen infections. In this hypothesis, pathogens will use common gene products for their replication or survival in the host cell, shown by the overlapped zone of gene expression files I (Tuberculosis infection), II (Leishmania infection), and III (HIV-1 infection). By using advanced gene screening technologies, such as siRNA or miRNA, specific host genes can be identified as drug targets. In this model, all three different species of pathogens are using macrophage as the host. Therefore, all three infectious diseases may be treated simultaneously if a drug can be developed to intervene pathogen-host gene interactions in the overlapped zone of I, II, and III.

Mitochondria have also been hypothesized to be intruders but were later adapted by host cells [review in 317]. According to evolution theories, living creatures either accept or assimilate ―foreign genetic materials‖, such as drug resistant genes or an organelle, to make organisms more adaptive in their living environment. Delta32 of CCR5 homozygous mutations reject the R5 strain of HIV‘s entry and people become resistant to HIV-1 infection, whereas the Vpr F72L patients adapted the HIV proviral sequences as parts of human genome with the risk of spreading HIV to others. The solution of evolution is a non-stop dynamic selection process of either rejection or assimilation of foreign materials. Although ALU repeats, SINE and other retroposons have been reported in human genetic diseases [318], there are not as severe as the infectious disease caused by HIV-1. In instead of killing HIV-1, an alternative hypothesis based upon the evolution theory is to ―accelerate‖ human‘s

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Jia-Hai Lee and Fredric Abramson

adoption of HIV as part of the evolution path by suppression of the CD4+ cell activation or manipulating cytokines to restrict HIV-1 replication. If HIV patients die from secondary infection due to loss of adaptive immunity, it would be reasonable to reserve the ―potency‖ of adaptive immunity by ―silencing‖ the proviral DNA within human chromosomes. There is also the prospect of identifying natural anti-HIV compounds. Dr. James Duke in personal correspondence stated ―We didn't even know AIDS four decades ago, but there are many promising anti-HIV leads in our Latin American forests. Abrus contains glycyrrhizin, Acacia contains gallic-acid and quercetin, Alexa contains castanospermine, Annona contains procyanidins, Artocarpus contains betulinic-acid, Capsicum contains caffeic-acid, Chenopodium contains ascaridole and oleanolic acid; even Cinchona contains epicatechin, Curcuma contains curcumin, Drimys contains lignin, Erythroxylum contains geraniin, Euphorbia contains betulin and tannic-acid, Gossypium contains gossypol, Haematoxylum contains myricetin, Ilex contains ursolic-acid, Lycopersicon contains naringenin, Momordica contains momordicin, Opuntia contains luteolin, Phytolacca contains phytolaccin, Plumeria contains fulvoplumierin, Psidium contains ellagic-acid, Pteridium contains chicoric-acid and tilirioside, Punica contains ellagitannin, Ricinus contains ricin, and Tagetes contains quercetagetin, to name a few anti-HIV phytochemicals occurring in native or introduced Latin American species.‖ It appears that many naturally occurring plants already contain active components for anti-HIV therapy. So, it is possible for one to control or influence the host gene expression unfavorable for HIV replication by using non-antiviral drug approaches; namely, it is possible for patients to reach the equivalent health conditions without suffering from side effects of anti-viral drug therapy. These alternative approaches may be unlikely considered a way to ―cure‖ AIDS by removing the integrated proviral DNA from host chromosomes, but it is highly likely to inhibit or delay AIDS disease progression by a substitute of antiviral drugs with less side-effect. The idea ―Prevent infectious diseases by regulating host cell gene expression‖ can be materialized by understanding pathogen-host cell interaction using advanced biotechnology siRNA or miRNA to identify the host cell genes essential for specific pathogen replication. In the model proposed in figure 10, each pathogen has its own molecular signature to interact with different subsets of host cell genes. Thus, either a small molecule compound or its substitute strategy to disrupt the viral-host gene interaction may be used to cure or prevent diseases.

CONCLUSION Scientists don‘t know when effective vaccines to protect from HIV-1 infection will be developed. In the meantime, the economic costs of the anti-HIV battle are tremendously high, and include much more than just research grants and expenses of developing antiviral drugs. In the journey toward the successful development of effective HIV vaccines, people infected by HIV-1 in poor countries continue to die from secondary bacterial pneumonia, hunger, and other factors. It appears that most scientists are focused on finding effective vaccines to prevent HIV infection. The challenge is that this focus may mean that we are putting all of the eggs in one basket. Therefore, we suggest that there is real value to seek alternative

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approaches. One strategy that we view as having strong potential is to manipulate human gene expression to prevent or suppress replication of HIV-1 or other pathogens, with the effect of reducing morbidity and mortality. In addition, to the extent this strategy uses compounds from our natural environment, the costs may be substantially lower and the ability to disseminate the solution substantially greater.

ACKNOWLEDGMENTS We appreciate Dr. Yung-Nien Chang at Synaptic Research Inc., Dr. Jim Duke (former USDA scientist), Dr. Mei-Ling Yang at Yale University School of Medicine, and Ms. ShingFen Kao at National Institutes of Health for their opinions and support of the written manuscript.

REFERENCES

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[1]

Johnston, M. I. and A. S. Fauci. 2008. An HIV Vaccine—Challenges and Prospects. N. Engl. J. Med. 359(9), 888-890. [2] Johnston, M. I. and A. S. Fauci. 2007. An HIV vaccine—evolving concepts. N Engl J Med. 356(20), 2073-2081. [3] Burton, D. R., R. C. Desrosiers, R. W. Doms, W. C. Koff, P. D. Kwong, J. P. Moore, G. J. Nabel, J. Sodroski, I. A. Wilson, and R. T. Wyatt. 2004. HIV vaccine design and the neutralizing antibody problem. Nat Immunol. 5, 233-236. [4] Fauci, A. S., M. I. Johnston, C. W. Dieffenbach, D. R. Burton, S. M. Hammer, J. A. Hoxie, M. Martin, J. Overbaugh, D. I. Watkins, A. Mahmoud, and W. C. Greene. 2008. HIV Vaccine Research: The Way Forward. Science 321, 530-532. [5] Scholtissek, C. 1994. Source of influenza pandemics. European Journal of Epidemiology 10, 455-458. [6] Ghendon Y. 1994. Influenza vaccines: A main problem in control of pandemics. European Journal of Epidemiology 10, 485-486. [7] Skeik, N. and F. I. Jabr. 2008. Influenza viruses and the evolution of avian influenza virus H5N1. Int J Infect Dis. 12(3), 233-238. [8] Brundag, J. F. and G. D. Shanks. 2007. What Really Happened during the 1918 Influenza Pandemic? The Importance of Bacterial Secondary Infections. J Infect Dis. 196, 1717-1718. [9] Brundag, J. F. and G. D. Shanks. 2008. Deaths from Bacterial Pneumonia during 1918– 19. Influenza Pandemic. Emerging Infectious Diseases. 14(8), 1193-1199. [10] Taubenberger, K. J., A. H. Reid, T. A. Janczewski and T. G. Fanning. 2001. Integrating historical, clinical and molecular genetic data in order to explain the origin and virulence of the 1918 Spanish influenza virus. Phil. Trans. R. Soc. Lond. B 356, 18291839.

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[24] Resa-Infante, P., N. Jorba, N. Zamarreño, Y. Fernández, S. Juárez, and J. Ortín. 2008. The Host-Dependent Interaction of alpha-Importins with Influenza PB2 Polymerase Subunit Is Required for Virus RNA Replication. PLoS ONE. 3(12): e3904. [25] Cameron, J. E., Q. Yin, C. Fewell, M. Lacey, J. McBride, X. Wang, Z. Lin, B. C. Schaefer, and E. K. Flemington. 2008. Epstein-Barr virus latent membrane protein 1 induces cellular MicroRNA miR-146a, a modulator of lymphocyte signaling pathways. J Virol. 82(4), 1946-1958. [26] Haasnoot, J., V.W. de, E. J. Geutjes, M. Prins, H.P. de, and B. Berkhout. 2007. The Ebola virus VP35 protein is a suppressor of RNA silencing, PLoS Pathog. 3: e86. [27] Hussain, M, R. J. Taft, and S. Asgari. 2008. An insect virus-encoded microRNA regulates viral replication. J Virol. 82(18), 9164-9170. [28] Sullivan, C.S., A. T. Grundhoff, S. Tevethia, J. M. Pipas, and D. Ganem. 2005. SV40encoded microRNAs regulate viral gene expression and reduce susceptibility to cytotoxic T cells. Nature 435, 682-686. [29] Skalsky, R. L., M. A. Samols, K. B. Plaisance, I. W. Boss, A. Riva, M. C. Lopez, H. V. Baker, and R. Renne. 2007. Kaposi's sarcoma-associated herpesvirus encodes an ortholog of miR-155. J Virol. 81, 12836-12845. [30] Zhang, J. and G. –H. Fan. 2008. Plectin regulates the signaling and trafficking of the HIV-1 co-receptor CXCR4 and plays a role in HIV-1 infection. Experimental Cell Research 314, 590-602. [31] Paddison, P.J., J. M. Silva, D. S. Conklin, M. Schlabach, M. Li, S. Aruleba, V. Balija, A. O‘Shaughnessy, L. Gnoj, K. Scobie, K.Chang, T. Westbrook, M. Cleary, R. Sachidanandam, W. R. McCombie, S. J. Elledge, and G. J. Hannon. 2004. A resource for large-scale RNAi based screens in mammals. Nature 428, 427-431. [32] Sui, G., C. Soohoo, El. B. Affar, F. Gay, Y. Shi, W. C. Forrester, and Y. Shi. 2002. A DNA vector-based RNAi technology to suppress gene expression in mammalian cells. Proc Natl Acad Sci USA 99 (8), 5515-5520. [33] He, M. L., B. Zheng, Y. Peng, J. S. Peiris, L. L. Poon, K. Y. Yuen, M. C. Lin, H. F. Kung, and Y. Guan. 2003. Inhibition of SARS-associated coronavirus infection and replication by RNA interference. JAMA 290 (20), 2665-2666. [34] Li, T., Y. Zhang, L. Fu, C. Yu, X. Li, Y. Li, X. Zhang, Z. Rong, Y. Wang, H. Ning, R. Liang, W. Chen, L. A. Babiuk, and Z. Chang. 2005. siRNA targeting the leader sequence of SARS-CoV inhibits virus replication. Gene Ther. 12 (9), 751-761. [35] Okado, K., N. Shinzawa, H. Aonuma, B. Nelson, S. Fukumoto, K. Fujisaki, S. I. Kawazu, and H. Kanuka. Rapid recruitment of innate immunity regulates variation of intracellular pathogen resistance in Drosophila. Biochem Biophys Res Commun. 2008 Dec 2. [Epub ahead of print] [36] Terabe, M. and J. A. Berzofsky. 2008. The role of NKT cells in tumor immunity. Adv Cancer Res. 101, 277-348. [37] Zucchini, N., K. Crozat, T. Baranek, S. H. Robbins, M. Altfeld, and M. Dalod. 2008. Natural killer cells in immunodefense against infective agents. Expert Rev Anti Infect Ther. 6(6), 867-885.

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[235] He, J., S. Choe, R. Walker, P. Di Marzio, D. O. Morgan, and N. R. Landau. 1995. Human immunodeficiency virus type 1 viral protein R (Vpr) arrests cells in the G2 phase of the cell cycle by inhibiting p34cdc2 activity. J Virol. 69, 6705-6711. [236] Depienne, C., P. Roques, C. Créminon, L. Fritsch, R. Casseron, D. Dormont, C. Dargemont, and S. Benichou. 2000. Cellular distribution and karyophilic properties of matrix, integrase, and Vpr proteins from the human and simian immunodeficiency viruses. Exp Cell Res. 260(2), 387-395. [237] Levesque, K., Y. S. Zhao, and E. A. Cohen EA. 2003. Vpu exerts a positive effect on HIV-1 infectivity by down-modulating CD4 receptor molecules at the surface of HIV1-producing cells. J Biol Chem. 278(30), 28346-28353. [238] Willey, R. L., F. Maldarelli, M. A. Martin, and K. Strebel. 1992. Human immunodeficiency virus type 1 Vpu protein induces rapid degradation of CD4. J Virol. 66(12), 7193-7200. [239] Neil, S. J., S. W. Eastman, N. Jouvenet, and P.D. Bieniasz. 2006. HIV-1 Vpu promotes release and prevents endocytosis of nascent retrovirus particles from the plasma membrane. PLoS Pathog. 2(5): e39. [240] Schwartz, S., B. K. Felber, E. M. Fenyö, and G. N. Pavlakis. Env and Vpu proteins of human immunodeficiency virus type 1 are produced from multiple bicistronic mRNAs. J Virol. 64(11), 5448-56. [241] Strebel, K., T. Klimkait, and M. A. Martin. 1988. A novel gene of HIV-1, vpu, and its 16-kilodalton product. Science 241, 1221-1223. [242] von Schwedler, U., J. Song, C. Aiken C, and D. Trono. 1993. Vif is crucial for human immunodeficiency virus type 1 proviral DNA synthesis in infected cells. J Virol. 67, 4945-4955. [243] Rose, K. M., M. Marin, S. L. Kozak, and D. Kabat. 2004. Transcriptional regulation of APOBEC3G, a cytidine deaminase that hypermutates human immunodeficiency virus. J. Biol. Chem. 279, 41744-41779. [244] Peng, G., K. J. Lei, W. Jin, T. Greenwell-Wild, and S. M. Wahl. 2006. Induction of APOBEC3 family proteins, a defensive maneuver underlying interferon-induced antiHIV-1 activity. J. Exp. Med. 203, 41-46. [245] Gómez-Gonzalo, M., M. Carretero, J. Rullas, E. Lara-Pezzi, J. Aramburu, B. Berkhout, J. Alcamí, and M. López-Cabrera. 2001. The hepatitis B virus X protein induces HIV-1 replication and transcription in synergy with T-cell activation signals: functional roles of NF-kappaB/NF-AT and SP1-binding sites in the HIV-1 long terminal repeat promoter. J Biol Chem. 276(38), 35435-35443. [246] Perkins, N. D., N. L. Edwards, C. S. Duckett, A. B. Agranoff, R. M. Schmid, and G. J. Nabel. 1993. A cooperative interaction between NF-kappa B and Sp1 is required for HIV-1 enhancer activation. EMBO J. 12(9), 3551–3558. [247] Rabbi, M. F., M. Saifuddin, D. S. Gu, M. F. Kagnoff, and K. A. Roebuck. 1997. U5 region of the human immunodeficiency virus type 1 long terminal repeat contains TRElike cAMP-responsive elements that bind both AP-1 and CREB/ATF proteins. Virology. 233(1), 235-245.

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[248] Wilson, M. E., K. F. Allred, A. J. Bisotti, A. Bruce-Keller, A. Chuahan, and A. Nath. 2006. Estradiol negatively regulates HIV-LTR promoter activity in glial cells. AIDS Res Hum Retroviruses. 22(4), 350-356. [249] Copeland, K. F. 2005. Modulation of HIV-1 transcription by cytokines and chemokines. Mini Rev Med Chem. 5(12), 1093-1101. [250] Marzio, G, M. Tyagi, M. I. Gutierrez, and M. Giacca. 1998. HIV-1 tat transactivator recruits p300 and CREB-binding protein histone acetyltransferases to the viral promoter. Proc Natl Acad Sci USA 95(23), 13519-13524. [251] Benkirane, M., R. F. Chun, H. Xiao, V. V. Ogryzko, B. H. Howard, Y. Nakatani, and K. T. Jeang. 1998. Activation of integrated provirus requires histone acetyltransferase. p300 and P/CAF are coactivators for HIV-1 Tat. J Biol Chem. 273(38), 24898-24905. [252] Lee, J. H., V. R. Yedavalli, K. T. Jeang. 2007. Activation of HIV-1 expression and replication by cGMP dependent protein kinase type 1-beta (PKG1beta). Retrovirology, 4:91. [253] Feinberg, M. B., D. Baltimore, and A. D. Frankel. 1991. The role of Tat in the human immunodeficiency virus life cycle indicates a primary effect on transcriptional elongation. Proc Natl Acad Sci USA 88(9), 4045-4049. [254] Centlivre, M., P. Sommer, M. Michel, T. F. R. Ho, S. Gofflo, J. Valladeau, N. Schmitt, F. Thierry, B. Hurtrel, S. Wain-Hobson, and M. Sala. 2005. HIV-1 clade promoters strongly influence spatial and temporal dynamics of viral replication in vivo. J Clin Invest. 115(2), 348-58. [255] Verma, I. M., J. K. Stevenson, E. M. Schwartz, D. Van Antwerp, and S. Miyamoto. 1995. Rel/NF-kB/IkB family: Intimate tales of association and dissociation. Genes Dev. 9, 2723-2735. [256] Scheidereit, C. 2006. IκB kinase complexes: gateways to NF ‑ κB activation and transcription. Oncogene 25, 6685-6705. [257] Li, X. H. and R. B. Gaynor. 2000. Mechanisms of NF-kB Activation by the HTLV Type 1 Tax Protein. AIDS Res Hum Retroviruses 16 (16), 1583-1590. [258] Natoli, G, S. Saccani, D. Bosisio, and I. Marazzi. 2005. Interactions of NF-kappaB with chromatin: the art of being at the right place at the right time. Nat Immunol. 6(5), 439445. [259] Shaulian, E. and M. Karin. 2001. AP-1 in cell proliferation and survival. Oncogene 20, 2390-2400. [260] Zenz, R., R. Eferl, C. Scheinecker, K. Redlich, J. Smolen, H. B Schonthaler, L. Kenner, E. Tschachler, and E. F. Wagner. 2008. Activator protein 1 (Fos/Jun) functions in inflammatory bone and skin disease. Arthritis Res Ther. 10(1): 201. [261] Abate, C. and T. Curran. 1990. Encounters with Fos and Jun on the road to AP-1. Semin Cancer Biol. 1(1), 19-26. [262] Ozolins, T. R. S. and B. F. Hales. 1999. Post-Translational Regulation of AP-1 Transcription Factor DNA-Binding Activity in the Rat Conceptus. Mol Pharmacol. 56(3), 537-544. [263] Claret, F. X., M. Hibi, S. Dhut, T. Toda, and M. Karin. 1996. A new group of conserved coactivators that increase the specificity of AP-1 transcription factors. Nature 383, 453-457.

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[278] Kavurma, M. M., F. S. Santiago, E. Bonfoco, and L. M. Khachigian. 2001. Sp1 phosphorylation regulates apoptosis via extracellular FasL-Fas engagement. J Biol Chem. 276(7), 4964-4971. [279] Curry, J. M., T. D. Eubank, R. D. Roberts, Y. Wang, N. Pore, A. Maity, and C. B. Marsh. 2008. M-CSF signals through the MAPK/ERK pathway via Sp1 to induce VEGF production and induces angiogenesis in vivo. PLoS ONE. 3(10): e3405. [280] van Opijnen, T., R. E. Jeeninga, M. C. Boerlijst, G. P. Pollakis, V. Zetterberg, M. Salminen, and Berkhout B. 2004. Human immunodeficiency virus type 1 subtypes have a distinct long terminal repeat that determines the replication rate in a host-cell-specific manner. J Virol. 78(7), 3675-3683. [281] Berkhout, B., and K. T. Jeang. 1991. Detailed analysis of TAR RNA: Critical spacing between the bulge and loop recognition domains. Nucleic Acids Res. 19, 6169-6176. [282] Berkhout, B., and K. T. Jeang. 1989. Trans-activation of human immunodeficiency virus type1 is sequence specific for both the single-stranded bulge and loop of the trans-acting- responsive hairpin: A quantitative analysis. J. Virol. 63, 5501-5504. [283] Delling, U., S. Roy, M. Sumner-Smith, R. Barnett, L. Reid, C. A. Rosen, and N. Sonenberg. 1991. The number of positively charged amino acids in the basic domain of Tat is critical for trans-activation and complex formation with TAR RNA. Proc. Natl. Acad. Sci. USA 88, 6234-6238. [284] Klaver, B., and B. Berkhout. 1994. Evolution of a disrupted TAR RNA hairpin structure in the HIV-1 virus. EMBO J. 13, 2650-2659. [285] Rounseville, M. P., H. C. Lin, E. Agbottah, R. R. Shukla, A. B. Rabson, and A. Kumar. 1996. Inhibition of HIV-1 replication in viral mutants with altered TAR RNA stem structures. Virology 216, 411-417. [286] Benkirane, M., R. F. Chun, H. Xiao, V. V. Ogryzko, B. H. Howard, Y. Nakatani, and K. T. Jeang. (1998). Activation of integrated provirus requires histone acetyltransferase: p300 and P/CAF are coactivators for HIV-1 Tat. J. Biol. Chem. 273, 24898-24905. [287] Barry, P. A., E. Pratt-Lowe, R. E. Unger, and P. A. Luciw. 1991. Cellular factors regulate transactivation of human immunodeficiency virus type 1. J Virol. 65, 13921399. [288] Sheehy, A. M., N. C. Gaddis, J. D. Choi, and M. H. Malim. 2002. Isolation of a human gene that inhibits HIV-1 infection and is suppressed by the viral Vif protein. Nature. 418, 646-650. [289] Chiu, Y. L., V. B. Soros, J. F. Kreisberg, K. Stopak, W. Yonemoto, and W. C. Greene. 2005. Cellular APOBEC3G restricts HIV-1 infection in resting CD4+ T cells. Nature. 435, 108-114. [290] Suspène, R., P. Sommer, M. Henry, S. Ferris, D. Guétard, S. Pochet, A. Chester, N. Navaratnam, S. Wain-Hobson, and J. P. Vartanian. 2004. APOBEC3G is a singlestranded DNA cytidine deaminase and functions independently of HIV reverse transcriptase. Nucleic Acids Res. 32(8), 2421-2429. [291] Mangeat, B., P. Turelli, G. Caron, M. Friedli, L. Perrin, and D. Trono. 2003. Broad antiretroviral defence by human APOBEC3G through lethal editing of nascent reverse transcripts. Nature 424, 99-103.

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[292] Rose, K. M., M. Marin, S. L. Kozak, and D. Kabat.Transcriptional regulation of APOBEC3G, a cytidine deaminase that hypermutates human immunodeficiency virus. J Biol Chem. 279(40), 41744-41749. [293] Wiegand, H. L., B. P. Doehle, H. P. Bogerd, and B. R. Cullen. 2004. A second human antiretroviral factor, APOBEC3F, is suppressed by the HIV-1 and HIV-2 Vif proteins. EMBO J. 23(12), 2451-2458. [294] Esnault, C., O. Heidmann, F. Delebecque, M. Dewannieux, D. Ribet, A. J. Hance, T. Heidmann, and O. Schwartz. 2005. APOBEC3G cytidine deaminase inhibits retrotransposition of endogenous retroviruses. Nature 433, 430-433. [295] Schumacher, A. J., D. V. Nissley, and R. S. Harris. 2005. APOBEC3G hypermutates genomic DNA and inhibits Ty1 retrotransposition in yeast. Proc. Natl. Acad. Sci. USA 102, 9854-9859. [296] Bach, D., S. Peddi, B. Mangeat, A. Lakkaraju, K. Strub, and D. Trono. 2008. Characterization of APOBEC3G binding to 7SL RNA. Retrovirology. 5:54. [297] Dutko, J. A., A. Schafer, A. E. Kenny, B. R. Cullen, and M. J. Curcio. 2005. Inhibition of a yeast LTR retrotransposon by human APOBEC3 cytidine deaminases. Curr. Biol. 15, 661-666. [298] Bogerd, H. P., H. L. Wiegand, B. P. Doehle, K. K. Lueders, and B. R. Cullen. 2006. APOBEC3A and APOBEC3B are potent inhibitors of LTR-retrotransposon function in human cells. Nucleic Acids Res. 34, 89-95. [299] Peng, G., K. J. Lei, W. Jin, T. Greenwell-Wild, and S. M. Wahl. 2006. Induction of APOBEC3 family proteins, a defensive maneuver underlying interferon-induced antiHIV-1 activity. J. Exp. Med. 203, 41-46. [300] Bishop, K. N., M. Verma, E. Y. Kim, S. M. Wolinsky, and M. H. Malim. 2008. APOBEC3G inhibits elongation of HIV-1 reverse transcripts. PLoS Pathog. 4(12): e1000231. [301] Sasada, A., A. Takaori-Kondo, K. Shirakawa, M. Kobayashi, A. Abudu, M. Hishizawa, K. Imada, Y. Tanaka, and T. Uchiyama. 2005. APOBEC3G targets human T-cell leukemia virus type 1. Retrovirology. 2:32. [302] Turelli, P., B. Mangeat, S. Jost, S. Vianin, and D. Trono. 2004. Inhibition of hepatitis B virus replication by APOBEC3G. Science 303, 1829. [303] Delebecque, F., R. Suspene, S. Calattini, N. Casartelli, A. Saib, A. Froment, S. WainHobson, A. Gessain, J. P. Vartanian, and O. Schwartz. 2006. Restriction of foamy viruses by APOBEC cytidine deaminases. J Virol. 80, 605-614. [304] Goila-Gaur, R. and K. Strebel. 2008. HIV-1 Vif, APOBEC, and intrinsic immunity. Retrovirology. 5:51. [305] Opi, S., S. Kao, R. Goila-Gaur, M. K. Khan, E. Miyagi, H. Takeuchi, and K. Strebel. 2007. Human immunodeficiency virus type 1 Vif inhibits packaging and antiviral activity of a degradation-resistant APOBEC3G variant. J Virol. 81(15), 8236-8246. [306] Belgaumkar, V. A., N. R. Gokhale, P. M. Mahajan, R. Bharadwaj, D. P. Pandit, and S. Deshpande. 2007. Circulating cytokine profiles in leprosy patients. Lepr Rev. 78(3), 223-230.

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[307] Fafutis-Morris, M., C. M. Guillen-Vargas, S. Navarro-Fierro, R. Morales-Ortiz, and J. Armendariz-Borunda. 1999. Serum IL-1ra is elevated in lepromatous leprosy patients. Int J Lepr Other Mycobact Dis. 67(3), 287-291. [308] Soilleux, E. J., E. N. Sarno, M. O. Hernandez, E. Moseley, J. Horsley, U. G. Lopes, M. J. Goddard, S. L. Vowler, N. Coleman, R. J. Shattock, and E. P. Sampaio. 2006. DCSIGN association with the Th2 environment of lepromatous lesions: cause or effect? J Pathol. 209(2), 182-189. [309] Martinez, F. D. 2003. Respiratory syncytial virus bronchiolitis and the pathogenesis of childhood asthma. Pediatr Infect Dis J. 22(2 Suppl), S76-82. [310] Graham, B. S., G. S. Henderson, Y. W. Tang, X. Lu, K. M. Neuzil, and D. G. Colley. 1993. Priming immunization determines T helper cytokine mRNA expression patterns in lungs of mice challenged with respiratory syncytial virus. J Immunol. 151(4), 20322040. [311] Janeway, C. A., P. Travers, M. Walport, and M. Shlomchik. Immunobiology: the immune system in health and disease (5th ed.). New York: Garland Publishing; 2001. [312] Tang, Y. W. 2004. Cytokine pattern is solely influenced by priming vaccine but immunity and disease by both priming and boosting vaccines in mice challenged with respiratory syncytial virus. Virus Res. 99(1), 81-87. [313] Nikolaeva, L. G., T. V. Maystat, V. S. Pylypchuk, Y. L. Volyanskii, V. M. Frolov, and Kutsyna GA. 2008. Cytokine profiles of HIV patients with pulmonary tuberculosis resulting from adjunct immunotherapy with herbal phytoconcentrates Dzherelo and Anemin. Cytokine. 44(3), 392-396. [314] Baker, B., B. Block, A. Rothchild, and B. Walker. 2009. Elite control of HIV infection: implications for vaccine design. Expert Opin Biol Ther. 9(1), 55-69. [315] Kabeya, H., M. Sase, M. Yamashita, and S. Maruyama. 2006. Predominant T helper 2 immune responses against Bartonella henselae in naturally infected cats. Microbiol Immunol. 50(3), 171-178. [316] Caly, L., N. K. Saksena, S. C. Piller, and D. A. Jans. 2008. Impaired nuclear import and viral incorporation of Vpr derived from a HIV long-term non-progressor. Retrovirology. 5:67. [317] Gray, M. W., G. Burger, and B. F. Lang. 2001. The origin and early evolution of mitochondria. Genome Biol. 2(6): REVIEWS1018. [318] van der Klift, H., J. Wijnen, A. Wagner, P. Verkuilen, C. Tops, R. Otway, M. Kohonen-Corish, H. Vasen, C. Oliani, D. Barana, P. Moller, C. Delozier-Blanchet, P. Hutter, W. Foulkes, H. Lynch, J. BurnJ, G. Möslein, and R. Fodde. 2005. Molecular characterization of the spectrum of genomic deletions in the mismatch repair genes MSH2, MLH1, MSH6, and PMS2 responsible for hereditarynonpolyposis colorectal cancer (HNPCC). Genes Chromosomes Cancer. 44(2), 123-138.

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Chapter 2

RETROVIRAL GENE EXPRESSION REGULATION María Rosa López-Huertas and Mayte Coiras AIDS Immunopathology Unit, National Center of Microbiology, Instituto de Salud Carlos III, Majadahonda, Madrid, Spain.

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ABSTRACT Retroviruses are RNA viruses that infect birds and mammals. They can be divided into two categories: simple, which contain three main reading frames (gag, pol, env), and complex, which also code for regulatory and accessory proteins essential in viral replication, e.g., Tax and Rex in human T-cell leukemia virus (HTLV), or Tat and Rev in human immunodeficiency virus (HIV). Two terminal non-coding sequences at both ends of the genome, which contain consensus sites specific to cellular and viral transcription factors, act as promoter regions. Retroviruses, as other viruses, lack an independent metabolism and are unable to replicate outside living cells. As a result, their gene expression is regulated by both viral and cellular factors. However, the key feature that differentiates retroviruses from other viruses is that they encode the enzyme reverse transcriptase, which synthesizes a doublestranded DNA copy of the viral genome. Viral DNA is integrated into the host genome as a provirus and can induce an active viral production or a post-integration latency. The provirus acts as a host gene and can be transmitted to the progeny cells. The interplay of the viral genome with the host metabolic machinery involves modifications in both gene expression and regulation. In fact, retroviruses have adapted themselves to use this machinery while maintaining cell integrity, which is essential to preserve their survival. Consequently, there can be variable host pathogenicity associated with several diseases—such as malignancies, immunodeficiencies, and neurological disorders—due to the down- or up-regulation of different cellular genes. For example, the HTLV-1 protein Tax modifies cell proliferation by activating the expression of interleukin receptors and cytokines. Moreover, retroviruses also isolate the infected cell by modifying the expression of surface receptor or molecules essential for cell communication. For example, the HIV-1 protein Nef down-regulates the expression of CD4 receptors in T cells, and therefore contributes to the overall immunodeficiency and the onset of

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María Rosa López-Huertas and Mayte Coiras superinfections caused by opportunistic pathogens. Retroviruses are also able to modulate gene expression through direct regulation of the transcriptional machinery. For example, in human acute promyelocytic leukemia (APL), chromosomal translocations and mutations in nuclear hormone receptors yield oncoproteins that alter chromatic structure and deregulate transcription. A better understanding of retroviral gene expression regulation is essential to develop prevention and therapeutic strategies. However, the variability of the viral targets and the strong dependence of viral replication on the host factors are major concerns.

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1. INTRODUCTION The family of retroviruses (Retroviridae) comprises a large and diverse group of RNA viruses that infect birds and mammals causing different pathogenic features. Although retroviruses were among the first known viruses, they were not found in humans until the 1980s [1]. As other viruses, retroviruses lack an independent metabolism and, consequently, they are unable to replicate outside living host cells. Therefore, their gene expression is not only regulated by viral factors but also by cellular factors. Viral proteins orchestrate and modify host cell gene expression so that retroviruses accommodate the cellular environment in order to increase both replication and pathogenesis. The key feature that differentiates retroviruses from other viruses is that they encode an enzyme, reverse transcriptase (RT). In fact, the viral particles contain an RNA genome that should be retrotranscribed to DNA to be integrated in the host cell genome. This RT enzyme makes possible the synthesis of a double-stranded DNA (dsDNA) copy of the viral genome, which is subsequently integrated into the host genome as a provirus. A provirus is an integrated form of viral DNA that serves as a template for the synthesis of viral RNAs and proteins that assemble to form progeny virions. When the viral DNA is integrated into the host cell genome, it determines the complex interplay that occurs between retroviruses and its principal target: e.g., human immunodeficiency virus (HIV) and human CD4+ T lymphocytes. The provirus can behave like a cellular gene and be transmitted vertically to the progeny cells or instead it can induce a post-integration latent state, thereby causing an active infection but without producing progeny virions. Under these circumstances, the infected cells are invisible to the immune system and cannot be eliminated. Only when the cell conditions are favorable, i.e., upon cellular activation, both viral and cellular factors are able to induce an active viral replication.

1.1. Structure and Genome Organization The retroviral genome is functionally diploid, constituted by a homodimer of linear, positive-sense, single-stranded RNA (ssRNA) linked by hydrogen bonds [2]. The RNA genome of the virion is flanked by two terminal non-coding sequences that are at both sides of the genome and act as enhancer and promoter regions. These regions contain at its termini one R region composed by short terminal repeats (18–250nt), so called because it is present

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twice in the RNA and therefore redundant (Figure 1A). Downstream of the 5′ R is another sequence, termed U5 for unique 5′ sequence, which include sites required for proviral integration. And upstream of the 3′ R is the 3' unique sequence (U3), a non-coding region of 200–1,200nt, which contains consensus sites specific for binding several key cis-acting elements essential for the retroviral gene expression such as the families of transactings factors NF- B or CREB/ATF. The U5 region is followed by the primer-binding site (pbs) formed by 18nt complementary to the 3' end of a specific cellular tRNA primer used by the virus to begin reverse transcription. Consequently, here lies the site of initiation of minusstrand DNA synthesis [3]. In the genome of all retroviruses, the region downstream from the pbs contain a relatively long (90–500nt) non-translated leader sequence and three or four large genes or open reading frames (ORFs) called gag (group-specific antigen); pol (polymerase); and env (envelope) [2]. The pro gene is located between gag and pol genes. Sometimes pro is fused in frame onto the 3′ end of gag (e.g., avian leukemia virus, ALV), sometimes it is fused to the 5′ end of pol (e.g., HIV), and sometimes it is present as a separate reading frame (e.g., mouse mammary tumor virus, MMTV). Gene order 5′-gag-pol-env-3′ is invariant in all retroviruses and their present specific functions are the following: the gag gene encodes the internal virion proteins that form the capsid and nucleoprotein structures; pol gene contains genomic information of reverse transcriptase and integrase; env gene directs the synthesis of viral envelope proteins; and pro gene encodes the synthesis of virion protease. Downstream of gag, pro, pol and env genes is a short polypurine tract (ppt) [4] that is a short run of approximately ten A/G residues responsible for initiating the plus-strand DNA synthesis during reverse transcription. The ppt is followed by the downstream sequence U3 before the 3′ R, as well as one att site required for DNA integration [5]. Other att site lies in the upstream U5 region. When the retroviral genome is integrated into the host cell genome it becomes a provirus. The dsDNA form of the retrovirus genome is larger than the RNA form because it has an extra sequence duplicated at each end during the retrotranscription process: the U3 region has been duplicated and lies at the opposite end of the DNA strand and the U5 region has also been duplicated and lies at the other end of the DNA strand (Figure 1B). The group of sequence regions U3, R and U5, positioned at both ends of the proviral genome, constitutes the long terminal repeat (LTR), which acts as the retroviral promoter [6]. Consequently, the integrated provirus has two flanking LTRs. The LTR includes all of the requisite signals for gene expression such as enhancer, promoter, transcription initiation (capping), transcription terminator and polyadenylation signal. Because the U3 region forms the 5′ end of the provirus after reverse transcription and contains cis-acting regulatory sequences essential for retroviral transcription, here lies the promoter elements responsible for initiation of transcription of the provirus.

1.2. Retrovirus Classification Based on their genome organization, retroviruses are divided into two categories: simple, which contains the three main reading frames gag, pol, and env; and complex, which also code for regulatory and accessory proteins essential in viral replication: e.g., Tax and Rex in

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human T-cell leukemia virus (HTLV), or Tat and Rev in HIV. The regulatory proteins encoded by the most representative examples of complex retroviruses, HIV and HTLV, are summarized in Table 1. Table 1. Regulatory proteins encoded by HIV-1 and HTLV-1

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Genes

Regulator y Protein

Function

tat

Tat

rev

Rev

nef

Nef

vif

Vif

vpr

Vpr

vpu

Vpu

tax rex

Tax p27 (Rex) p21 p12I

HIV-1 Transcriptional activator Viral mRNA elongation mRNA transport from nucleus to cytoplasm mRNA processing CD4 and MHC-I down-regulation Infectivity enhancement through increasing retrotranscription Viral infectivity increase APOBEC (cellular enzyme with antiviral activity) degradation Pre-integration complex transport Transcriptional activator G2 cell cycle arrest Virion releasing increase CD4 degradation in endoplasmic reticulum HTLV-1 Transcriptional activator Control of HTLV-1 gene expression Regulation of subcellular distribution of rex gene products G2 cell cycle arrest

p30II p13II

G2 cell cycle arrest Essential in early phase of virus infection

pX Region ORF I pX Region ORF II

Retroviruses are classified in seven groups or genera according to these categories [2]. Simple retroviruses are gathered in three genera: alpha-retroviruses (e.g., Rous sarcoma virus, RSV), beta-retroviruses (e.g., MMTV) and gamma-retroviruses (e.g., Moloney murine leukemia virus, MoMuLV). Complex retroviruses comprise the genera delta-retroviruses (e.g., HTLV), epsilon-retroviruses (e.g., Walleye dermal sarcoma virus, WDSV), lentiviruses (e.g., HIV), and spumaviruses (e.g., human foamy virus, HFV). Representative examples of each retrovirus genera are summarized in Table 2. Although most of the retroviruses currently known infect vertebrates, there have been also identified some retro-transcribing viruses in other organisms as fungi and invertebrates [7] (Table 3).

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Table 2. Representative examples of simple and complex retroviruses that infect vertebrate organisms Category Simple Retroviruses

Genera Alpha-retroviruses

Beta-retroviruses Gammaretroviruses Complex Retroviruses

Delta-retroviruses Epsilonretroviruses Lentiviruses

Spumaviruses

Species Rous sarcoma virus (RSV) Avian sarcoma virus (ASV) Avian leukemia virus (ALV) Mason-Pfizer monkey virus (MPMV) Mouse mammary tumor virus (MMTV) Moloney murine leukemia virus (MoMuLV) Feline leukemia virus (FLV) Gibbon ape leukemia virus (GALV) Human T- cell lymphotropic virus (HTLV) Bovine leukemia virus (BLV) Walleye dermal sarcoma virus (WDSV) Human immunodeficiency virus (HIV) Simian immunodeficiency virus (SIV) Feline immunodeficiency virus (FIV) Equine immunodeficiency virus (EIV) Human foamy virus (HFV) Simian foamy virus (SFV)

Table 3. Retro-transcribing viruses that infect invertebrate organisms

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Family Metaviridae Pseudoviridae

Genera Metavirus Errantivirus Pseudovirus Hemivirus

Species Saccharomyces cerevisiae Ty3 virus Drosophila melanogaster gypsy virus Saccharomyces cerevisiae Ty1 virus Drosophila melanogaster copia virus

Host Fungi Invertebrates Fungi Invertebrates

1.3. Structure and Organization of the Retroviral Promoter LTR 1.3.1. Structure of the LTR The ability of convert the genomic ssRNA into a dsDNA by the RT enzyme [8] is an essential and unique characteristic of the retroviruses. The retrovirus reverse transcription is a highly complex and ordered step of the retroviral life cycle. It involves the DNA synthesis at prearranged sites and translocations of DNA intermediates that conclude in the duplication of sequence blocks in the final product [9]: duplication of U5 region during minus-strand strong-stop DNA translocation and duplication of U3 region during plus-strand strong-stop DNA translocation. For example, in HIV-1, extension of a tRNALys primer from the pbs to the 5'-end of the genomic RNA generates a 199nt minus strand strong-stop DNA (-sssDNA). In this retrovirus, the 97-nt sequence R present at both ends of the genome facilitates the translocation of -sssDNA to the 3'-end of genomic RNA, in a process referred to as minus strand strong-stop transfer [10]. The RT RNase H is then required for the degradation of 5'-

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copied genomic RNA (donor RNA template), allowing the subsequent minus strand transfer to the 3'-end of the RNA genome (acceptor RNA template) [11]. The resulting proviral DNA contains two blocks of LTR that are assembled during reverse transcription. Here lies the control center for the retroviral gene expression. Each LTR consists of the sequence regions U3, R and U5, positioned in the same order at both ends of the retroviral genome (Figure 1). These regions are repeated sequences that have an important role during DNA integration into the host cell genome and to produce the progeny RNA genome. The sequences that control transcription of the provirus are located at the U3 region. The 3' LTR is not normally functional as a promoter, although it has exactly the same sequence arrangement as the 5' LTR. In fact, transcription is initiated at the U3-R limit of the 5' LTR, whereas the transcripts are processed and polyadenylated at the R-U5 limit of the 3' LTR [12], recreating the exact structure of the input RNA, which is packaged in the newly formed virion particles. However, although the 5' LTR has dominant control as a promoter, when its integrity is disrupted, the 3' LTR can act as a promoter [13]. Moreover, although the promoter regions are mainly located in the U3 region of the LTR, enhancer sequences have also been mapped to the gag (e.g., gag enhancer (nucleotides 813 to 872) of RSV and other avian retroviruses) [14] and gag-pol (e.g., SIV, HIV) [15] regions of some viruses that may play a significant role in some phase of viral replication although their exact meaning in the virus life cycle has not well established. The major function of the retroviral LTR is the regulation of the viral RNA synthesis. Expression directed by LTR signals is carried out entirely by cellular transcriptional machinery, using host cell enzymes such as the RNA polymerase II, without any participation by a virus-encoded polymerase [16] (Figure 3). Consequently, because the RNA genome is generated by normal host transcriptional machinery, it exhibits many features of eukaryotic mRNA. Both transcriptional promoter and adjacent regulatory elements, involved in recruitment the RNA polymerase II to the start site of viral RNA synthesis, are located within the U3 region at the 5´LTR [6]. The transcript starts at the beginning of R region, which is capped, and proceeds through U5 and the rest of the provirus, usually terminating by the addition of a poly A tract just after the R sequence in the 3' LTR. Host transcriptional factors that promote the RNA polymerase II binding to the TATA box (e.g., TFIID and other transcription factors), or act as enhancers on LTR sequences (e.g., Sp1, NF- B) [17], are tissue- and cell-specific in their function. For example, in retroviruses like MoMuLV, embryonal tissues and cells do not induce viral gene expression whereas differentiated or adult tissues and cells express high levels of viral RNA [18].

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A) Cap

R

U5

pol

gag

pbs Leader

env

ppt

7000

8000

U3

R

AAAAAA...

B) 0

1000

2000

3000

4000

5000

6000

9000

10000

LTR

LTR U3 R U5

U3 R U5

ALV gag

pro pol env LTR

LTR U3 R U5

U3

R U5

MMTV gag

orf pol pro

env

LTR U3 R U5

LTR U3 R U5

Mo-MLV gag

pro

pol env LTR

LTR U3 R U5

U3 R U5

HTLV gag

pol

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pro

env tat rex LTR

LTR U3 R U5

U3 R U5

HIV gag

env vif pro

pol

vpr

tat rev

nef

vpu

Figure 1. Retroviral genome organization. A) General organization of the retroviral genome. B) Organization of the genome of proviruses from the most remarkable retroviruses. Genes encoding for regulatory and accessory proteins contained in complex retroviruses are marked in light green.

1.3.2. Transcriptional Factors that Bind to the LTR There is an intense interaction between retroviral proteins and host cell proteins, essential for the viral life cycle. Accordingly, the retroviral promoter regions contain some typical eukaryotic enhancer sequences for the interaction of host cell ubiquitous transcription factors (e.g., Sp1, NF- B, CREB/ATF) or specialized cellular molecules (e.g., hormones). For example, the LTR of HIV contains three binding sites for the constitutively expressed Sp1 transcription factor arranged in tandem that are located upstream of the canonical TATA box (Figure 2). Both Sp1 and TATA box constitute the HIV-1 core promoter and are indispensable for LTR-directed RNA synthesis. Adjacent to the Sp1 binding sites, there are

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two tandem recognition sites for the inducible nuclear factor-kappa B (NF- B)/Rel family of transcription factors, which act as enhancer for HIV-1 LTR-directed expression [19]. The cis-acting transcription control elements of retroviruses are not limited solely to the LTR, but some regulatory elements outside of the LTR have been identified in the genomes of avian retroviruses, MoMuLV, HIV-1, HTLV-1, MMTV, and BLV [14,15,20]. In addition to the structural and enzymatic proteins, the complex retroviruses also contain unique regulatory and accessory proteins (Table 1). The small regulatory proteins can transactivate transcription from the viral LTR. The most important retroviral regulators include the HTLV Tax and Rex proteins [21] and the HIV Tat and Rev proteins [22]. In the case of HTLV LTR, both proteins are essential for viral replication and cellular transformation. Three imperfect 21-nucleotide repeats, termed Tax response element 1 (TRE-1), are necessary for trans-acting transcription activation by Tax [23] (Figure 2). The tax genes present in HTLV-1 and -2 encoded two phosphoproteins Tax-1 and Tax-2 that share amino acid homology but are not identical. Tax is a transcription activator that contains an activation domain and a nuclear localization signal (NLS). It increases the rate of transcription initiation from the promoter in the 5‘ LTR of the provirus genome. As a consequence, the presence of Tax in HTLV-infected cells greatly enhances the transcription of the HTLV LTR [24]. On the other hand, protein Rex is also involved in the control of HTLV gene expression and consequently, is essential for viral replication. However, unlike Tax, Rex acts at a posttranscriptional level to regulate viral gene expression [25]. Rex accomplishes a mechanism by which the genome-length unspliced mRNA as well as the partially spliced mRNAs is exported from the nucleus instead of being subjected to splicing or degradation. This is a critical step in the life cycle of complex retroviruses and it is carried out through the recognition of a specific response element on the incompletely spliced mRNAs. Rex stabilizes them, inhibits their splicing, and utilizes the importin/chromosome region-maintenance protein-1 (CRM1)-dependent cellular pathway for transporting them from the nucleus to the cytoplasm, thereby permitting the expression of Gag, Pol and Env proteins. Consequently, in HTLV-infected cells, Rex regulates the levels of expression of genes encoding components for production of infectious virions [26]. HTLV Tax and Rex have homologue proteins in HIV as Tat and Rev. Tat is a nuclear protein that acts as a potent essential activator of the viral transcription from the LTR [27]. This viral protein is a highly unusual transactivator because it does not directly bind to gene promoters but to a 59-nucleotide stem-loop structure that lies in the 5′ end of the nascent viral transcript of RNA termed Tat trans-activation response element (TAR) [28]. Binding of Tat to the TAR element is the first critical step for producing full-length HIV RNA because the Tat-TAR interaction is able to recruit to the RNA the host positive transcriptional elongation factor (P-TEFb), which is a complex formed by the proteins cyclin T1 and the cyclindependent kinase 9 (CDK9) [29]. These proteins enhance the ability of the RNA polymerase II to elongate beyond the LTR and down the genome with high processivity by phosphorylation of the C-terminal repeat domain (CTD) of the polymerase [30]. On the other hand, Rev acts similarly to HTLV Rex by mediating the export of unspliced and singly spliced viral mRNAs from the nucleus through the interaction with the importin CRM1 [31]. In conclusion, both HTLV Tax and HIV Tat proteins are able to get a strong positive feedback loop that results in high levels of viral RNA transcripts [22,32].

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EF II EF III

59

EF I EF III CCAAT

TATA

AAUAAA R

U3

U5 ALV

GRE

NRE

NRE

GRE

GRE

Histone octamers

ISBP

NF-1 TATA

AAUAAA R

U3 MCREF-1

GRE

U5 MMTV

LVa GRE CBFEts NF-1Ets NF-1

MCREF-1 CCAAT

TATA

AAUAAA R

U3

U5 MoMuLV

SP1 SP1 SP1 NFAT-1 NFAT-1 GRE Ets NRF NF- B NF- B

TAR LBP-1 LBP-1

AP-1 AP-1

AAUAAA

TATA

R

U3

U5 HIV-1

CREB/ATF Myb

CREB/ATF

Myb

CREB/ATF

Myb NRF Myb

Myb AAUAAA

Myb

TATA

U3

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21 bp TREs

R

U5 HTLV-1

Figure 2. Retroviral LTR organization. The LTR of the retroviruses is organized in three sequential regions U3, R and U5. The position of the TATA box from the R sequences differs in distance for each retrovirus. The presence and position of specific sequence signals used for transcription and binding sites for known or suspected transcription sites are shown. These sequences include: CCAAT box, which binds the enhancer binding protein (C\EBP) and the poly A addition signal (AAUAAA). The transcription factors binding to regulatory sequences indicated are the following: AP-1, activation protein-1; CBF, core binding factor; CREB/AFT, cAMP response element binding/activating transcription factor-2; EF, enhancer factor; NRE, core-negative regulatory element; Ets, erythroblast transformation specific transcription factor family; GRE, glucocorticoid-responsive element; ISBP, initiation site binding protein; LBP-1, transcription factor leader binding protein 1; Lva, leukemia virus factors a; MCREF-1, Mammalian type C retrovirus enhancer factor 1; NF- B, Nuclear factor - B; NF1, nuclear factor-1; NF-AT, nuclear factor of activated T cells; NRF, NF- B repressing factor; TAR, Tat trans-activation response element; TRE, Tax response element.

In Spumaviruses such as HFV and the simian foamy virus (SFV), there is also a protein that acts as a transcription transactivator from the viral LTR, termed Tas (transactivator of spumavirus) [20]. Protein Tas binds to sequences near 5′ end of the genome in a mechanism of action similar to HIV Tat. Accordingly, Tas governs the levels of viral transcripts initiated by both Tas-responsive target elements (TRE) located in the LTR promoter (TRELTR) and in the internal promoter (IP) within the env gene (TREIP) of these viruses. TRELTR and TREIP are located 5' of the TATA box in both viral promoters and function as orientation- and position-independent enhancers [33]. There is also a strong Tas-responsive element TREGP, near the 3' end of the gag gene and preceding the pol gene of SFV-1. Other multiple viral and cellular transcription factors bind to different regions of the LTR in a complex pattern to regulate the retroviral transcription and replication (Figure 2). Within those factors, the family of CREB/ATF (cyclic AMP (cAMP)-response element-binding protein transactivation factors) controls the most important functional interaction for HTLV

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replication. In fact, although the transcription activity of Tax does not involve direct binding to the LTR [34], Tax positively regulates HTLV gene expression through activation of other factor as CREB/ATF by binding to three cAMP response elements in the viral LTR [35]. Tax and Tat also regulate other host transcription factors such as NF-AT (nuclear factor of activated T-cells) [36] or those that belong to the NF- B family [19,37]. The last one is a family of transacting factors that are sequestered in the cytoplasm as inactive dimmers through the high affinity binding to the family of inhibitors I B [38]. The mechanism of activation of NF- B by Tax or Tat involves site-specific phosphorylation of the main inhibitors I B and I B , followed by their ubiquitination and degradation in the proteasome, thereby causing the nuclear translocation of the active NF- B dimmers, where bind their cognate sequences in several gene promoters.

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1.4. Diseases caused by Retroviruses Retroviruses first attracted attention as the etiological agents of tumors in birds, cats, rodents and primates [39]. However, retroviruses are also known to cause immunodepression and inflammatory diseases and a vast majority appear to be non-pathogenic [40]. Diseases induced by retroviral infection can be acute, episodic, or chronic, appearing soon after infection or after a long period of latency. Although most of the provirus integrations are benign, mutagenesis induced after retrovirus insertion is common mechanisms to induce cancer. In fact, retroviruses can induce chromosomal translocations in cellular or viral protooncogenes, resulting in mutations, deletions and fusions that alter their cognate protein functions and therefore cause aberrant gene regulation and the subsequent induction of cancer [41]. The retrovirus-induced tumors comprise above all T- and B-cell leukemia/lymphoma, chronic myelogenous leukemia and mammary carcinoma [42]. Moreover, retroviruses not only cause T cells to growth abnormally but also severe immunodeficiencies. Within the complex retroviruses, HIV-1 and HTLV-1 are the widest spread pathogens, both target primary T cells, and they show an important incidence in the population. HTLV-1 was the first retrovirus shown to be associated with a human disease [43]. This retrovirus has been associated with two distinct types of disease: the adult T-cell leukemia/lymphoma (ATLL)—an aggressive lymphoproliferative disorder—and a range of chronic inflammatory conditions including the central nervous system disease HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) [44,45]. On the other hand, HIV-1 is the causative agent of the acquired immunodeficiency syndrome (AIDS) [46], a disease characterized by the massive mucosal CD4+ T cell depletion and the progressive reduction of circulating CD4+ T cells [47]. About 20 million people are estimated to be infected with HTLV-1 [48] and more than 30 million people live currently with HIV-1/AIDS through the world [49]. Whereas HTLV-1 is endemic in southwest Japan, the Caribbean islands, countries surrounding the Caribbean basin and parts of Central Africa, HIV-1 is worldwide spread although highest incidence is found in South Africa. Furthermore, 90% of HIV-1 infected people will develop deadly AIDS within ten years of infection [50] if highly active antiretroviral therapy (HAART) is not established, meanwhile only 1% to 2% of HTLV-1 infected people develop either HAM/TSP [51] or T cell leukemia [52].

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2. EFFECTS OF THE RETROVIRAL INFECTION ON THE HOST GENE EXPRESSION REGULATION The retroviral infection can modify the expression of many host genes, and some of these genes may have critical roles in the viral replication cycle. Therefore, retroviral infection exerts general effects on cell morphology, cell cycle or cellular metabolism. Although these profound changes are associated with significant modifications in the host cell gene expression pattern, the global influence of viral infection on the host cell metabolism and its influence on the development of retroviral diseases remain to be completely elucidated. Emergence of high-density DNA arrays—microarrays or oligonucleotide chips—has revolutionized gene expression studies. Microarrays consist of ordered sets of cDNAs, each corresponding to a single gene, immobilized on a solid substrate. The level of expression of each gene is assessed by hybridization with mRNA isolated from cells of interest. Therefore, differences in mRNA levels can be simultaneously measured in thousand different genes, giving a global idea of the cell host transcriptional modifications induced by pathogens [53]. Gene expression profiling determined by microarrays analysis is extremely useful to study cellular functions impaired by viral infection and host-pathogen interactions over the course of the infection. Data related to host cell gene expression modulated by simple retroviruses have been traditionally assessed by only conventional methods such as RT-PCR assays. Nevertheless, several studies based on the genome-wide mRNA profiling analysis have described changes in host gene expression due to complex retroviruses such as HIV-1 [54,55,56] or HTLV-1 infections [57,58], helping to identify specific cellular processes implicated in viral infection, including cellular activation, subcellular trafficking, metabolism pathways, immune regulation or apoptosis. Apparently, these genes differentially modulated would play a role in facilitating viral life cycle. Unfortunately, RNA and protein expressions do not always correlate. Differences in mRNA levels and protein expression are directed by transcriptional control and/or by molecular half-life [59]. As a consequence, the confirmation of similar patterns in protein expression is necessary to find out the functional importance of the gene expression profile observed by microarray assays. This validation can be achieved by gene reporter assays, immunoblotting assays or by using more accurate proteomics techniques such as differential in gel electrophoresis (DIGE) combined with mass spectrometry (MS) including matrixassisted time-of-flight (MALDI-TOF). The infection by complex retroviruses impacts coordinately on the expression of the cellular genes involved in different processes and pathways. In the following sections, changes produced on the gene expression for each cellular process will be reviewed.

2.1. Modification of the Expression of Host Genes Associated with the Immune cell Function 2.1.1. Major Histocompatibility Complex-related Genes Retroviruses causing lymphomas persist in immunocompetent infected animals, thereby suggesting strategies for immune evasion such as the impairment of the immune system. In

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this context, major histocompatibility complex (MHC) molecules are essential for the presentation of antigenic peptides and involve partly the cellular defense against viral infections. Because the escape from immune detection is important for the aggressive nature of cancers [60], alterations in MHC protein expression on tumor cells clearly correlate with the tumorigenicity and metastatic potential of those cells. Class I genes of the H-2 gene complex in mouse, and MHC class I (MHC-I) (HLA-A, B and C) genes in humans encode protein products which are expressed in association with 2-microglobulin, conforming MHC-I complexes on the surfaces of nucleated cells [61]. For example, MoMuLV increase progressively the expression of both H-2 class-I and 2-microglobulin mRNAs in infected fibroblasts [62], and this effect occurs in parallel to an increasing in specific gene transcription. However, most retroviral strategies focus on blocking the synthesis of MHC-I molecules or their display on the surface of infected cells so that viral peptides presentation by MHC-I molecules is impaired in these cells [63]. This is the case of HIV-infected cells, where Nef and Tat proteins induce down-regulation of MHC-I surface expression [64,65]. However, the mechanisms underlying this down-regulation are not related to direct gene expression modulation but to the process of internalization of receptors [66]. On the other hand, indirect gene expression modulation has also been related to MHC cell surface expression. For example, the constitutive expression of the HTLV-1 accessory protein p12 enhances the expression of the gene encoding the MHC-I-associated calcium-dependent ADP-ribosylation factor (ARF) 6 [67], which is involved in increasing receptor-mediated endocytosis and indirectly, in decreasing MHC-I cell surface expression [68]. Moreover, the expression MHC-II-associated genes such as HLA-DMB, HLA-DRB5, and HLA-DRB1 was decreased in p12I-expressing Jurkat T cells [67], according to the pattern observed in transition from acute to chronic crisis in patients with ATLL [58]. 2.1.2. T cell Receptor-related Genes HTLV infection has been shown to block the expression of most T cell receptor (TCR)associated signaling pathways [69]. Moreover, HTLV-1 Tax protein induces the downregulation of genes encoding factors involved in the TCR rearrangement such as the TCRchain, the recombination activating gene 1 (RAG-1), the DNA-dependent protein kinase (DNA-PK), and the hydroxymethylglutaryl-coenzyme A reductase gene of the isoenzyme 2 (HMG-2) [70,71]. Besides, TCR- chain and CD8- mRNAs, involved in recognition of antigen via interaction with MHC molecules, were up-regulated after three days of HIV-1 infection [54]. It could partially account for the increase in CD8+ T cell counts observed in some HIV-infected patients [72]. HTLV-1 is the etiological agent of ATLL as well as of the tropical spastic paraparesis (TSP), a chronic neurological disease. HTLV-1 can infect several types of cells in vitro, but it transforms only human T lymphocytes in vivo. This observation suggests that T-cell-specific events induced by HTLV-1 infection may trigger the lymphoproliferative process. T lymphocytes can be activated by the stimulation of the TCR/CD3 complex with processed antigen in association with MHC proteins, and one of the earliest detectable consequences of receptor ligation is the tyrosine phosphorylation of multiple cellular substrates [73]. In this context, the tyrosine phosphorylation events are regulated sequentially by two classes of protein tyrosine kinases (PTKs), the Src family and the Syk/Zap-70 family. Accordingly, the

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inhibition of PTKs from the Src and Syk families plays a central role in TCR-mediated transmembrane signal transduction [74] and is required for the antiproliferative effects of interferon alpha (IFN- ) [75]. Several gene expression assays show that Lck and Hck, important members of the Src PTK family, were clearly down-regulated in HTLV-1immortalized T cells [76,77]. Moreover, based on microarrays results, Akl et al. [78] proposed that HTLV-1 infection induces a progressive decrease in the expression of CD3 genes, which eventually abrogates CD3 expression and therefore impaired immune responses. Indeed, the loss of TCR/CD3 complex expression could contribute to HTLVinfected T cells escaping from normal growth control [69]. Furthermore, loss of CD3 is known to perturb calcium transport, thereby exerting a role on regulating apoptosis or cell death. Altogether, these gene expression data suggest that inhibition of TCR signaling pathway appears to be a hallmark of HTLV-mediated T-cell transformation and may favor survival and proliferation of infected cells. In fact, ligation of the TCR complex by antigens is known to trigger apoptotic cell death of activated T cells [79] and is also required for anti-viral immune responses mediated by cytotoxic T cells [80]. Therefore, down-regulation of TCR would allow the malignant HTLV-infected T cells to escape from apoptosis as well as from cytotoxic T-cell recognition and the subsequent death. In case of HIV-infected cells, Nef protein may trigger T-cell activation, thereby inducing the activated phenotype of lymphocytes, which is a main hallmark of HIV-1 infection [81]. 2.1.3. Chemokine and Cytokine Gene Expression Deregulation of the chemokines, cytokines and their receptors in retrovirus-infected cells may influence the clinical manifestation of the pathologies and implies the complete disruption of the cellular cytokine network. For example, the expression of genes encoding the chemokine (C-C motif) ligands 2 (CCL2) and 8 (CCL8), the monocyte chemotactic protein-1 (MCP-1), the chemokine (C-C motif) receptor 5 (CCR5), CD16, the tumor necrosis factor alpha (TNF- ), and the interleukins 6 (IL-6) and 15 (IL-15), is up-regulated in HIVinfected macrophages [82], hypothetically to enhance virus propagation by inducing the recruitment of target macrophages to the sites of infection [83]. Actually, HIV-1 Tat protein induces the expression of genes encoding IL-8 [84], IL-10 [85], TNF- [86], the transforming growth factor beta (TGF- ) [87] and IFN- [88]. Moreover, a RNase protection assay revealed that the expression of other chemokines including CCL2, CCL3, the macrophage inflammatory proteins 1 alpha (MIP-1 ) and beta (MIP-1 ), CCL5/RANTES (acronym for Regulated on Activation, Normal T Expressed and Secreted), the chemokine (C-X-C motif) ligands 2 (CXCL2) and 10 (CXCL10), and the chemokine (C motif) ligand 1 (XCL1), are induced by HIV-1 Tat protein in different cell types [89], and this expression induces T lymphocyte infiltration that influences on the central nervous system (CNS) damage. Some of these chemokines such as MIP-1 , MIP-1 , TGF- , and IL-6 receptor subunit are also up-regulated by Nef expression in HIV-infected macrophages [81,90]. The differential expression of chemokines receptors might determine the migration of cells infected with retrovirus. For example, HIV Tat protein can induce the expression of the chemokine (C-X-C motif) receptor 4 (CXCR4), the major receptor for the stromal-derived cell factor-1 alpha (SDF-1 )/CXCL12 [91], thereby enhancing T-cell migration. Gene

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expression profiles of several cytokines and their receptors, including IL-1, IL-15, IL-15 receptor alpha (IL-15R ), IL-6, IL-6R, IL-7, and IL-7R are also deregulated in HTLVinfected T cells [92]. In fact, HTLV Tax protein enhances the production of cytokines such as TNF [93] or IL-15 [94], which may damage the CNS or cause eosinophilia. Moreover, both Tax and Tat increase the transcription of the gene encoding the subunit of the IL-2 receptor [95,96,97], suggesting that these retroviral proteins might contribute to T-cell hyperactivation. Gene expression of the interleukins IL-2, IL-6, IL-10 and IL-12 can be also altered by the infection of other complex retroviruses such as BLV. For example, IL-10 mRNA is overexpressed in cows with persistent lymphocytosis [98,99], and it may potentially inhibit the expression of cyclooxygenase-2 (COX-2), macrophage-derived COX-2, and prostaglandin E2, as well as the antigen-specific cell proliferation [100]. On the other hand, transcripts encoding IL-6 are strongly and rapidly up-regulated after culturing B cells from cows with persistent lymphocytosis [101]. Because IL-6 strongly suppresses viral expression, this suggests that IL-6 could play a role in viral latency [102], a hallmark of the retroviral infection.

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2.2. Modification of the Expression of Host Genes Associated with Transacting Factors Because the LTR of complex retroviruses contain consensus recognition motifs for several host cell transcription factors (Figure 2), cell activation appears to be necessary for an efficient replication and the establishment of productive infection. This is a main characteristic in the case of complex retroviruses such as HTLV and HIV, where host cell transcription factors as CREB/ATF or NF- B are indispensable [103,104]. Consequently, since retroviruses require host transcription factors for their own replication, they have developed mechanisms to functionally enhance some transcriptional activator pathways. Activation of these factors can occur at either transcriptional or post-translational level. 2.2.1. Transcription Factors involved in LTR Transactivation 2.2.1.1. Transcription Factors involved in HTLV LTR Transactivation HTLV modifies the host cell gene expression through the expression of different viral proteins in the infected host cell. For example, the viral protein p21I plays an essential role in HTLV-mediated T-cell activation by increasing mRNA expression of the E1A binding protein p300 and consequently enhancing p300-dependent transcription [67]. Protein p300 is a coactivator with histone acetyltransferase (HAT) activity and pleiotropic functions in chromatin modulation and transcriptional regulation of multiple genes [105]. Protein p300 interacts with transcription factors such as NF-AT, AP-1, and NF- B, which are essential for T-cell activation and proliferation [106,107] as well as for the LTR transactivation of complex retroviruses and viral replication [108]. HTLV Tax protein also activates the expression of several transcription factors including NF- B [109,110]. Actually, activation of NF- B has been linked to the transforming events in a number of HTLV-related malignancies including ATLL [111,112], and enhanced transcription of c-Rel, a Tax target protein

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belonging to the NF- B family, has been detected in HTLV-transformed cell lines [76,113]. In acute ATLL, the proteasome subunit HsN3 gene was up-regulated [58] and therefore, it could enhance I B degradation through enhancing the binding of I B to HsN3 [114]. Moreover, Tax induces over-expression of the genes encoding the mixed lineage kinase-3 (MLK-3) and Bcl-3 [70]. MLK-3 is a member of the SH3-domain-containing proline-rich kinase (SPRK)/mixed-lineage kinase family that can activate NF- B when is over-expressed [115]; whereas Bcl-3 is a member of the I B family with predominantly nuclear localization [116] that can act as a transcriptional activator or form an inhibitory complex depending on its phosphorylation status [117]. Interestingly, Bcl-3 not only is able to interact functionally with the NF- B subunits p50 and p52 [116,118], or with the AP-1 transcription complex, but also with members of the general transcription machinery such as the tumor necrosis factor binding protein (TBP), TFIIA and TFIIB, or even with the CREB-binding protein (CBP) and p300 [119,120]. In fact, Tax is also known to associate with CBP/p300 [121-123] and it has been speculated that the Tax-CBP/p300-Bcl3 complex may exist, providing a novel means of modulating NF- B activation [70]. All these mechanisms further contribute to the persistent NF- B activation detected in retroviruses infected patients. 2.2.1.2. Transcription Factors involved in HIV LTR Transactivation HIV-1 Tat directly binds to canonical enhancer sequences of NF- B [124], Sp1 [125] and NF-AT [126], thereby regulating both LTR transactivation and cellular gene expression. Moreover, Jurkat cells expressing stably HIV-1 Nef protein show significant induction of mRNAs encoding many transcription factors that positively enhance the viral LTR transactivation, including NF-AT, NF- B (subunits p52 and p100), IFN regulatory factors 1 (IRF-1) and 2 (IRF-2), c-Fos, and Jun-D protooncogene [81]. The cytokines TGF- and IL-4, which promote LTR transactivation in an autocrine and/or paracrine manner, were also elicited by Nef expression [81]. However, not only the retroviral regulatory proteins Tax and Tat can modify the host gene expression, but also the structural proteins such as the HIV envelope protein gp120, which can induce the up-regulation of the expression of genes of the NF- B family, NF-AT, the Signal Transducer and Activator of Transcription/Janus Activated kinases (STAT/JAKs), Jun and AP-1 in stimulated macrophages [127]. These data indicate the potential for envelope interactions to induce cellular activation independently of the infection process [82]. Furthermore, it seems that the expression of HIV accessory proteins can also modulate the host cell gene expression. In fact, in correlation with the activation of the NF- B pathway and with de novo synthesis of proteins that is induced after HIV-1 macrophage infection, several - B dependent genes are triggered in a Vpr-dependent manner, such as gene encoding IL-8 [128], or in a Nef-dependent manner, such as genes encoding IL1 and IL-6 [129]. Both IL-6 and IL-8 genes are also triggered by Tat protein expression in HIV-infected cells. On the other hand, HIV-1 accessory protein Vpu is an integral membrane protein with tremendous affinity for the -transducin repeat containing protein ( -TrCP), a key member of the ubiquitin ligase complex involved in the controlled degradation of cellular proteins, including the family of NF- B inhibitors, I B [130,131]. Interestingly, Vpu is resistant to the TrCP-mediated degradation and competitively inhibits TrCP-dependent degradation of I B, thus suppressing NF- B activity [132]. Moreover, interaction of Vpu with TrCp contributes

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to the induction of apoptosis in the HIV-1 infected cells. Actually, Vpu reduces the expression of NF- B-dependent genes that encode for antiapoptotic factors such as Bcl-xL, the Bcl 2-related protein A1/Bfl-1, and the TNF receptor-associated factor 1 (TRAF1). At the same time, Vpu induces the activation of caspase-3, a major cell-death effector [133]. Moreover, HIV-1 infection of macrophages is associated with up-regulation of the gene encoding a secretory leukocyte protease inhibitor (SLPI), which may have antiviral properties through the inhibition of the NF- B pathway as well as the proteasome-proteolytic activity [134]. These contradictory effects could account for the multifactorial nature of the retroviral proteins. 2.2.2. Transcription Factors involved in mRNA Transcript Elongation 2.2.2.1. Transcription Factors involved in HTLV mRNA Transcript Elongation Apart from specific enhancer binding protein, retroviruses like HTLV also induce the expression of general transcription factor components, such as the RNA Polymerase II Elongation Factor SIII, p15 subunit (TCEB1), and the TATA-binding-protein-associated factor TAFII31 in HTLV-1 transformed cells [76]. The CREB/ATF family of transcription factors plays an important role in HTLV-1 LTR transcription and thus in viral expression and replication. It has been reported the up-regulation of mRNA from several members of the CREB group including the activating transcription factors 3 (ATF3) and 6 (ATF6), and the cAMP-responsive element modulator (CREM) in HTLV-infected cells [92]. In fact, Tax protein directly activates the expression of CREB/ATF [135]. Other member of the CREB group that is modulated by retroviral infection is CREB-2, a bZIP-containing transcription factor involved in Tax-mediated activation of the LTR [136]. CREB-2 is up-regulated in HTLV-1 transformed cells [76] and interestingly, it participates in the activation of the IL-2 gene expression [137]. Moreover, CREB-2 is able to inhibit the apoptosis-inducing serine/threonine ZIP kinase by direct interaction [138]. The Y box binding transcription factor 1 (YB-1) is also significantly up-regulated in HTLV-1 immortalized T cells [76]. YB-1 can in turn activate the multidrug resistance 1 gene (MDR-1) [139], thus playing a role in protecting cells from the cytotoxic effects of agents that induce DNA cross- linking damage [140]. Other example of modification in host cell gene expression is constituted by the upregulation of mRNA for both activators IRF-4 and IRF-5 in HTLV infected cells, which bind to the IFN-stimulated response element (ISRE) presented in the immunoglobulin light chain enhancer cells [92,141]. On the contrary, there are other members of the IRF family that are down-regulated in HTLV-infected T cells, thereby suggesting that HTLV-1-infection may impact only a specific subset of IRF-regulated genes [92]. 2.2.2.2. Transcription Factors involved in HIV mRNA Transcript Elongation Other complex retroviruses present a similar behavior, maybe for aiding the viral spread and disease progression. For example, several genes are differentially expressed during HIV1 infection depending on the expression of the viral protein Nef. Those genes encode some transcription elongator factors such as the HIV Tat-specific factor 1 (Tat-SF1), IRF-2, the U1 small nuclear ribonucleoprotein particle (snRNP), and CDK9 [81]. In fact, the interaction between P-TEFb—composed of CDK9 and cyclin T1—and HIV-1 Tat in the elongation complex that is formed with the TAR loop is required for efficient transcriptional elongation

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of the retroviral genome (Figure 3) [142,143]. Other genes required for improving processivity of transcription were also up-regulated in a Nef-dependent manner, including the TAR-binding protein (TRBP) and the RNA polymerase II [81].

2.3. Modification of the Expression of Host Genes Associated with Apoptosis and Cell Survival

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The genetic program called apoptosis largely controlled the life span of normal T cells [144]. Apoptosis not only is essential for maintaining a constant lymphocyte population size [145,146] but also is required during immune response against foreign antigens to eliminate most activated antigen-specific T cells, preventing autoimmunity [147]. Besides, apoptosis is a mechanism of cellular defense against viral infection as far as the overall gene expression pattern regarding to apoptosis is disrupted, both at transcriptional and postraductional levels, in order to improve viral replication [148] and the latently infected cells that act as reservoirs present an unnatural long half-life. However, specific mechanisms by which HTLV-1 and HIV-1 gene products are modifying the T-cell survival is not yet fully understood. 2.3.1. Modification of Apoptosis-related Genes during HTLV Infection Emerging evidence suggest that HTLV-1 not only induces cell proliferation but also protects the infected cells from apoptosis. Therefore, HTLV-1 interference with normal T-cell apoptosis is considered a mechanism of tumorigenicity [149]. In fact, retroviruses causing leukemia induce over-expression of genes encoding anti-apoptotic factors so that cell survival is promoted. For example, members of the inhibitor of apoptosis (IAP) family of proteins such as the human cIAP-2/HIAP-1 and the apoptosis inhibitor 1 (API1) are overexpressed in HTLV-infected T cells [76,92]. cIAP2 is an anti-apoptotic molecule induced by NF- B that inhibits caspase-8, a cysteine protease acting in the initiation of the apoptotic proteolytic pathway responsible for activating the pathway that ends in the processing of caspase-3 [150]. It has been proven that stably expression of Tax induces cIAP2 gene expression [70]. Both cIAP-2 and API1 potently repress apoptosis in mammalian cells by inhibiting caspases [151]. Besides, HTLV-infected cells show an increase expression of other anti-apoptotic factors including Bcl-xL [76,92], the anti-apoptotic chemokine I-309 (CCR8 ligand) [76], the defender against cell death 1 (Dad1), and the heat shock protein 27kD (HSP27) [76], which inhibits apoptosis induced by Fas and staurosporine [152,153]. Interestingly, apoptosis is also controlled by inhibition of genes encoding apoptosis inducers. For instance, the expression of caspase-8 is repressed in the HTLV-1 infected cells [76,92]. Similarly, caspase-4 and caspase-6 mRNA levels are down-regulated in the HTLVinfected cells [92,154]. The expression of the apoptosis accelerator Bax, which suppresses the anti-apoptotic activity of Bcl-2 [155], was dramatically decreased in HTLV-transformed T cells in comparison to normal activated T cells [76] and the viral protein Tax is involved in this down-regulation [156].

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68 A)

CTD SP1

NFAT-1 NF- B

RNA pol II TATA Proviral dsDNA

Pre-initiation complex formation

B) P CTD RNA pol II TATA

TAR NELF

DISF

Promoter clearance and RNApol II pause

Fb P- TE Cyclin Tat CDK9 T1

C)

P P CTD RNA pol II TATA

TAR

P NELF

P DISF RNApol II-mediated elongation

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D)

Fb P-TE Cyclin Tat P CDK9 T1 P3 CA F 00 P /C P BP CTD TAR RNA pol II TATA

RNApol II-mediated elongation

Figure 3. Scheme of the steps involved in HIV-1 transcription. a) Binding of cellular transcription factors to the HIV-1 LTR induced arrangement of the pre-initiation complex, including the recruitment of the RNA polymerase II to the promoter. b) The negative transcriptional elongation factors DSIF (DRB sensitivity-inducing factor) and NELF (negative elongation factor) are then recruitment and concurrently loop TAR is formed at the 5′ end of the viral transcript. C) HIV-1 Tat interacts with TAR and this Tat-TAR interaction leads HIV-1 RNA transcript elongation through the recruitment of cellular P-TEFb to the HIV-1 promoter. P-TEFb is composed of cyclin T1 and CDK9, which phosphorylates the RNA polymerase II in the C-terminal repeat domain (CTD) and thereby increases RNA polymerase II procreativity rate. P-TEFb also phosphorylates DSIF and NELF, which in turn dissociate from TAR. D) Tat also enhances RNA polymerase II-dependent elongation through the recruitment of the histone acetil-transferases p300/CBP and PCAF (P300/CBP-associated factor).

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altered along with HTLV-mediated T cell immortalization [76]. For example, the surface receptors TNF-R1 and TNF-R2 that mediate TNF- -induced apoptosis are markedly downregulated in HTLV-immortalized T cells. 2.3.2. Alteration of Apoptosis Related Genes during HIV Infection The depletion of CD4+ T cells during HIV-1 infection has been attributed, among other mechanisms, to apoptosis [158]. Actually, different data support that HIV-1 protects infected cells from apoptosis and induces it in non-infected but bystander cells [159]. More precisely, Tat can be secreted by infected cells and mediates its effects in non-infected bystander cells (extracellular Tat) [160], thereby acting as a potent apoptosis inductor whereas endogenous Tat is a protector of infected cells [161]. T cells stably expressing Tat show the downregulation of the expression of genes involved in induction of apoptosis such as caspase-10, and the over-expression of genes involved in cell proliferation and survival, thereby confirming that intracellular Tat protects HIV-infected cells against apoptosis [162]. This viral strategy may be involved in developing long-lived viral producer cells or reservoirs of the retroviral infection [163]. However, Tat expression up-regulates the production of TNFand the tumor necrosis factor-related apoptosis inducing ligand (TRAIL) in infected macrophages [164], and Nef expression up-regulates the Fas ligand (FasL) in SIV-infected macaques [165]. These proteins are potentially able to induce apoptosis in bystander cells. In this context and based on experimental data, Zhang et al. [166] suggest a model in which HIV-1 infected cells produce extracellular Tat protein that in turn up-regulates the expression of TRAIL mRNA and protein in macrophages, therefore inducing apoptosis in the bystander T cells. Furthermore, different data prove that apoptotic pathway is modified in CD4+ T cells so that apoptosis is favored [82]. Both up-regulation of pro-apoptotic p53 gene and p53dependent apoptotic genes such as PDCD5, PUMA, Bax and Bak have been shown in HIVinfected cell lines and confirmed in primary cells [167-169]. Further microarray analysis reveals other genes differentially modulated in HIV-infected CD4+ T cells including first, the increased expression of the apoptotic proteins HSP90- , the death domain-associated protein (DAXX), the death-associated protein (DAP), Fas and FasL; and second, down-regulation of anti-apoptotic factors such as Bcl-2, Bcl-xL, and HSP105 [54,55,167]. Protection against apoptosis in HIV-infected cells can also be observed in macrophages. These cells can support high levels of viral replication despite being non-dividing differentiated cells. However, HIV-infected macrophages display up-regulation of antiapoptotic and anti-stress genes conversely to CD4+ T cells, in which genes mediating apoptosis are up-regulated [167]. Up-regulation of anti-apoptotic Bcl-2 has been specifically associated with the presence of Tat in macrophages [170] and with the presence of Nef in T cells [81]. On the other hand, up-regulation of Bcl-xL and STAT3 together with downregulation of protein Bad and the apoptosis signal-regulating kinase (ASK) have been associated with the expression of Nef in the infected cells [81,171,172]. Other HIV accessory proteins can be involved in the onset of apoptosis. It was described above that the protein Vpu blocks I B proteasome degradation and therefore reduces NF- B activation. Subsequently, the transcription of NF- B-dependent anti-apoptotic genes such as A1/Bfl-1, TRAF and Bcl-xL is also impaired [133].

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2.4. Modification of the Expression of Host Genes Associated with Cell Cycle 2.4.1. Modification of Cell Cycle-related Genes during HTLV Infection A hallmark of most cancers is the uncontrolled cellular proliferation, an event that under normal circumstances is controlled by cell cycle checkpoint proteins such as p21/waf1—a member of the cyclin-dependent kinases inhibitors‘ family (CIP/KIP) that inhibits the late G1/S checkpoint kinases—or the pro-apoptotic protein p53. Consequently, apoptotic events can be triggered by the aberrant induction of cell cycle progression. In the case of HTLVinfected cells, the disruption of the cell cycle regulatory points is achieved in order to favor cell survival. In fact, changes in the factors controlling G2/M progression have been reported during HTLV-1 infection [173]. For example, mRNA expression of the Cdc25C tyrosine phosphatase, which functions as a dose-dependent inducer of mitotic control and is modulated by hyperphosphorylation, was increased in HTLV-transformed cells [92]. At the appropriate time in the cell cycle, hypo- or un-phosphorylated Cdc25C dephosphorylates the cyclin-dependent kinase-1 (CDK1), thereby activating its kinase activity. It has been described that Jurkat cells stably expressing p30II or p12I HTLV proteins show a downregulation of the expression of genes encoding cyclin B1 [173], growth arrest and DNA damage-inducible protein (GADD45A)—which plays an important role in the G2/M checkpoint—as well as the mitogen-activated protein kinase kinase 6 (MAP2K6), an inducer of G2 arrest [67]. These data indicate a deregulation of G2/M cell cycle control and suggest that the viral proteins p30II and p12I are directly involved in the G2 cycle arrest. On the other hand, HTLV-infected cells express high levels of cyclin D2 mRNA, in contrast to uninfected cells, which predominantly expressed cyclin D3 [174]. In agreement, cyclin D3 is downregulated in HTLV-1 immortalized cells [76] and interestingly, inhibition of cyclin D3 expression largely blocks Myc-dependent apoptosis [175]. Moreover, G1 arrest is also altered in HTLV-infected cells since the stably expression of p12I in Jurkat cells induces down-expression of genes encoding the FK506-binding protein 12–rapamycin-associated protein 1 (FRAP1)—a phosphatidylinositol kinase-related kinase critical for G1 phase progression—and the cell cycle progression 2 protein (CPR2), which helps to overcome G1 arrest [67]. HTLV-infected cells also show abnormal high levels of p21/waf1 expression, in a Tax dependent manner [70,176]. The high levels of p21/waf1 in HTLV-infected cells may be related to the ability of p21/waf1 to inhibit apoptosis [177]. On the other hand, Jurkat T cells expressing p12I from HTLV-1 show also increased expression of cell division cycle-2 like-1 (CDC2L1) and -5 (CDC2L5) proteins, as well as reduced expression of CDK9 and CDC45 cell division cycle-45 like protein (CDC45L) [67]. Furthermore, HTLV-1 Tax protein represses the expression of cellular genes encoding enzymes involved in DNA repair such as -polymerase, DNA polymerase [178], Ras21, Rad54, ERCC5 (acronym for Excision Repair Cross-Complementing rodent repair deficiency, complementation group 5 protein), XRCC5 (acronym for X-Ray Repair Complementing defective repair in Chinese hamster cells 5), and DNA-dependent protein kinase [70].

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2.4.2. Modification of Cell Cycle-related Genes during HIV Infection A strategy to increase HIV expression in the infected cells is the arrest of the cell cycle at the G2/M checkpoint [179]. Therefore, genes involved in promoting cell cycle transition from G1/S to G2/M as well as in arresting cell cycle in the G2/M checkpoint—such as BRCA1 (breast cancer 1 protein), PP2A (protein phosphatase 2 alpha), GADD45A, and YWHAE (tyrosine 3-monooxygenase/tryptophan 5- monooxygenase activation protein, epsilon polypeptide)—have been identified as mediators of cell cycle modulation required for supporting HIV-1 replication in infected macrophages [180]. Instead, the CD4+ T cells infected with HIV-1 also show cell cycle alterations, although favoring cell death. This effect occurs via down-regulation of specific genes involved in DNA repair such as DNA-PK or genes involved in the mitochondrial mediated apoptosis pathway such as the cytochrome c (Cyt-c), enoyl-CoA hydratase, and the voltage-dependent anion channel (VDAC) [167]. Similarly to HTLV infection, the classical G1-phase cell-cycle inhibitor p21/waf1 gene is up-regulated in HIV-infected macrophages not only immediately after infection but also during the emergence of viral replication [134,181]. Furthermore, the accessory protein Vpr induces cell-cycle arrest by p21/waf1 up-regulation in T lymphoid and myeloid cells [182]. Interestingly, p21/waf1 also plays a role in survival and differentiation of U937 cells [177]. In conclusion, the control of the expression of specific genes such as those mentioned above might contribute to the cell-cycle arrest, not only facilitating chromatin modification, DNA repair, but also protecting HIV-infected cells from apoptosis and permitting viral replication. In some cases, the modification of gene expression can produce disequilibrium between the pro-apoptotic and anti-apoptotic proteins present in the infected cell in a precise moment of the cell cycle, thereby causing either the cell death or its survival. 2.4.3. Modification of Cell growth- and Survival-related Genes Antioxidants constitute important cell survival factors, since excess in reactive oxygen species production causes either necrosis or apoptosis [183]. For example, HTLV-1 immortalized cells show up-regulation of the expression of genes encoding antioxidant factors such as the Cu/Zn-superoxide dismutase (SOD), the natural killer cell enhancing factor (NKEF), and the proliferation association gene (pag) [76]. In the case of HIV infection, stably expression of Tat in CD4+ T cells up-regulates the expression of genes related to cell survival during stress oxidative responses such as the glutathione S-transferase gene-1 (GST-1) and -12 (GST-12), SOD-1 and TDPX-1 [97]. Moreover, Tat expression is bound to over-expression of the proto-oncogene c-myc [97], a factor strictly involved in cell proliferation [184] and essential for the HIV effective infection of activated T cell [185]. On the other hand, the T-cell growth factor IL-2 induces G1/S transition in normal T cells [186]. This cytokine is essential for T-cell proliferation and survival. In this context, HTLV Tax protein induces the transactivation of genes encoding IL-2 [187,188] and the subunit of its high affinity receptor complex (IL-2R ) [95,96,188], together with other NFB dependent genes involved in cell proliferation such as IL-15 [94], IL-6 [189] and the Granulocyte-macrophage colony-stimulating factor (GM-CSF) [190]. However, although IL2-dependent activation might be involved in the growth of HTLV-infected cells, the proliferation of HTLV transformed cells is not mediated by the autocrine IL-2 secretion [191]. Accordingly, the HTLV-1 transformed T cells show a down-regulation of mRNA IL-2

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level. Moreover, it has been proved that IL-2R is dispensable for growing in vitro HTLV-1 transformed T cell lines [191]. In the same way, T cells isolated from lymph nodes and peripheral blood of BLV-infected cattle express increased levels of IL-2 mRNA [192]. However, the amounts of IL-2 mRNA are significantly reduced in CD4+ T cells from cows with persistent lymphocytosis [102]. There are other genes with potential activity for enhancing cell survival that can be overexpressed in retrovirus-infected cells. This is the case of CD27, which acts in concert with the TCR to enhance and support cell expansion and to promote survival of activated T cells through the interaction with its ligand CD27L [193,194]. Interestingly, CD27L gene expression was markedly induced in T cell immortalized with HTLV-1 [76], thereby supporting the fact that retroviruses prolong the activation and survival of the infected cells in order to enhance their own existence and perpetuation.

2.5. Modification of the Expression of Host Genes Associated with Tumorigenesis

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The mechanisms by which oncoproteins deregulate transcription of specific target genes during oncogenic transformation are still poorly understood. Mutations, deletions and fusions resulting from chromosomal translocations in cellular or viral proto-oncogenes may modify their cognate protein functions [195]. Probably these chromosomal alterations provide a selective growth advantage to the tumor cells [102]. 2.5.1. Genes Deregulated in Lymphomas caused by HTLV Infection Many proto-oncogenes were firstly identified as common sites for retrovirus proviral integration, including gfi1, myc and pim gene families [196]. Gene pim-1, which encodes for a serine/threonin kinase involved in the control of cell growth, differentiation and apoptosis, was originally discovered as a preferential site for proviral integration of the simple retrovirus MoMuLV [197]. Up-regulation of pim-1 is generally associated with cell survival and downregulation of pro-apoptotic genes [198]. Pim-1 and c-myc genes cooperate to promote oncogenesis in T and B lymphocytes as well as to rescue defects in STAT3 proliferative signaling whereas either pim-1 or c-myc alone are not sufficient [196,199]. Gene c-myc is also involved in the regulation of cellular proliferation, apoptosis and differentiation, processes altered in leukemia and lymphoma in several species, including humans [200,201]. Consequently, retroviruses inducing leukemia and lymphoma can activate c-myc oncogene expression by at least three different ways. First the use of viral proteins to directly activate cmyc transcription, such as the high levels of Tax expression that are necessary to get levels of c-Myc required for lymphomagenesis in HTLV-infected cells [202]. Second, c-myc oncogene can also be activated through the transduction and modification of the c-myc gene to generate a virally encoded form of the gene (known as v-myc). Retroviruses package a processed intron-negative copy of c-myc RNA into virions, and using reverse transcription, they incorporate the oncogene into the viral genomic RNA [203]. Those virions that incorporate oncogenes into their viral genome can quickly transform the infected cells into tumorigenic. In retrovirus-infected cells, v-Myc protein shows higher concentration and longer half-live

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than c-Myc protein, and besides v-myc gene is not regulated by the normal mechanisms ordered by the c-myc promoters [203]. The third and most common mechanism is the proviral integration in or near the sequence corresponding to the c-myc gene [203]. In fact, in 80% of B-cell lymphomas induced by ALV, the integration of the provirus leads to a chimera transcript that contains a full-length c-myc RNA highly expressed from the ALV promoter [204]. However, synthesis of a c-myc fusion RNA is not common in other retroviruses such as MoMuLV [197,205] or mink cell focus-forming murine leukemia viruses (MCF MLV) [206], although it has been reported that insertion of MoMuLV genome up to 300kb away from the c-myc promoters is still able to induce c-myc over-expression [207]. In these cases, high levels of c-myc transcripts are generated by DNA proviral insertion at some distance from the c-myc promoter [203]. It has been suggested that retroviruses can also activate cmyc by disruption of negative regulatory elements [208] or by alteration of the chromatin structure [209]. Because it has not been described any truncation of the c-myc gene in any retroviral insertions reported, Dudley et al. [203] proposed that intact c-Myc protein is required for induction of leukemia and lymphoma during insertional mutagenesis caused by retroviral infection. The v-myb oncogene of AMV causes monoblastic leukemia in chickens and transforms only myelomonocytic cells [210]. This could be due to the fact that the v-myb gene is a structurally altered form of the chicken c-myb gene [211], which plays a fundamental role in the development of the hematopoietic system. AMV v-Myb protein localizes in the nucleus where it binds to specific DNA sequences and can activate the expression of reporter genes [212]. Moreover, v-Myb oncoprotein not only activates gene expression but also represses physiologically important genes such as ets-2, a DNA-binding transcription factor, which expression contributes to highly virulent leukemogenic phenotype [213]. Other cellular genes directly regulated by v-Myb and cMyb in myelomonocytic cells include mim-1 gene [213], which encodes a mitochondrial outer membrane protein required for mitochondrial protein import [214]. Accordingly, previous data showed that mim-1 is highly expressed in myelomonocytic cells transformed by v-Myb [215,216]. However, it has been shown that vMyb does not activate the expression of mim-1 gene directly but in cooperation with members of the C/EBP transcription factor family [216]. HTLV-infected T cells also show altered expression of protooncogenes such as c-Jun [76] or the tumor repressor p53. Protein c-Jun is a major member of the AP-1 family of enhancer binding factors that forms the AP-1 early response transcription factor in combination with c-Fos [217]. It has been described that HTLV-encoded Tax elevates AP-1 activity through the induction of some AP-1 family member gene expression, including cJun, JunD, c-Fos, and Fra-1 [218]. On the other hand, in HTLV-1 transformed T cells, Tax inactivates p53 expression by two mechanisms: the repression of p53 gene transcription [219]; and the inhibition of the p53-dependent transcription through the interference with its transactivation domain [220]. The lack of fully functional activity of p53 may contribute to HTLV-1 induced tumorigenesis. Mutations in p53 gene are contained in half of the solid tumors induced by BLV in cattle [102]. These mutations alter crucial p53 functions involved in gene transactivation and cell growth suppression [221]. Moreover, the coactivator p300, up-regulated in T cells stably expressing HTLV-1 p21I protein [67], is recruited in p53dependent signaling pathways [222]. In fact, certain cases of acute myeloid leukemia have

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been linked to recurrent chromosomal translocations that result in frame fusions of p300 with the monocytic leukemia zinc finger protein and the myeloid/lymphoid leukemia gene products [223-225]. 2.5.2. Genes Deregulated in lymphomas caused by HIV Infection Although HIV-1 is not a tumorigenic retrovirus itself, HIV-infected patients are prompted to develop different lymphoid tumors such as polyclonal B cell lymphoproliferative disorders, aggressive B cell non-Hodgkin lymphomas or Kaposi‘s sarcoma (KS) [226-228]. KS is a highly vascularised skin lesion characterized by marked endothelial proliferation and migration, resulting in the formation of new capillaries, and its incidence has dramatically decreased since the introduction of HAART [229]. Development of AIDS-related aggressive B-cell lymphomas involved several processes: the uncontrolled Epstain-Barr virus infection and transformation of B cells, the c-myc gene deregulation, and the Bcl-6 overexpression, which overall lead to HIV-associated B cell hyperactivation [226,227]. It may be related to the fact that HIV Tat protein increases c-myc gene expression [230] and also acts as a cytokine in human endothelial cells, which become tumorigenic during KS [231]. As a consequence, it has been proposed that Tat plays a role in the pathogenesis of KS [232]. In this context, the His-domain protein tyrosine phosphatase (HD-PTP), a relatively unknown member of the PTP superfamily that encodes a 185-kDa protein, is expressed in human endothelial from the umbilical cord and in human Kaposi-spindle cells [233]. Tat induces HD-PTP mRNA expression in human cell lines derived from different tumors including adenocarcinoma, leiomyosarcoma—a type of sarcoma that is a neoplasm of smooth muscle— and cutaneous Kaposi-like lesions [234]. Moreover, Tat-dependent expression of the matrix metallopeptidase 9 (MMP-9)—a proteolytic enzyme involved in the breakdown of extracellular matrix in normal physiological processes, such as embryonic development, as well as in disease processes, such as metastasis—is mediated by activation of PTPs in human monocytes [235]. Tat has been also proposed to contribute to the pathogenesis of AIDSrelated KS through the induction of mRNA and protein expression of proliferative and proinflammatory cellular genes such as the vascular cell adhesion molecule 1 (VCAM-1), the intercellular adhesion molecule 1 (ICAM-1), and the cytokines MCP-1 and IL-6 [236]. Accordingly, extracellular Tat protein also increases the metastatic potential of human breast cancer cells by inducing mRNA and protein expression of IL-6 and IL-8, thus generating a proinflammatory tumor microenvironment and enhancing leukocyte recruitment [237].

2.6. Modification of the Expression of Host Genes Associated with Neurodegeneration HTLV is able to infect the CNS, causing neurological diseases shown as motor and behavioral abnormalities and neuronal loss [238-240]. Lentivirus such as FIV and HIV can exert neuronal damage without productive infection of neurons. Vascular abnormalities and disturbed blood-brain barrier (BBB) are common alterations responsible for the development of dementia during the course of the complex retroviruses infection [241]. The mechanisms involved in this process remain unclear but may include the inherent toxicity of viral proteins

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and the release of cytokines or excitotoxic amino acids by infected and activated brain macrophages [242] in concert with the BBB disruption. As was commented above, the MMPs are proteolytic enzymes involved in the extracellular matrix degradation [243]. Increased expression of MMPs has been described in the CNS after retroviral infection [244-246]. In fact, HTLV-1 Tax expression induces the upregulation of MMP-2 and MMP-3 [70]. The MMP-induced neuropathogenesis may be related to the capacity to breakdown the BBB integrity, thus facilitating the entry of the HIV-1 into the CNS as well as the flow of cytokines to the brain [247]. In fact, analysis of MMP mRNA levels in brain samples from HIV-infected persons or FIV-infected cats showed significantly increased MMP-2 and MMP-9 mRNA expression in lentivirus-infected brains [248]. Elevated MMP expression was accompanied by significant up-regulation of STAT-1 mRNA, a transcriptional factor involved in regulating MMP gene expression [249]. Besides, there is a significant correlation between increased number of macrophages and HIV-associated dementia in HIV-infected patients [250-252]. Activated macrophages represent the most likely source of the increased MMP expression detected in infected brains during SIV infection [244]. Furthermore, it has been proved that the interaction of the HIV-1 protein gp120 with the human mannose receptor (hMR) induces MMP-2 expression in astrocytes [253]. Altogether these data show that evolutionarily different retroviruses share conserved mechanisms to induce neurodegeneration in different cell types. Macrophage lineage cells are the primary source of the elevated levels of TNF- mRNA observed in CNS during HIV-1 infection [254], providing further evidence that macrophage activation is a key element in the pathogenesis of HIV-associated neurological disease. This statement is supported by the fact that circulating monocytes display increased expression of CD16, CCR5, CCL2, and sialoadhesion genes in chronically infected patients with high viral replication in vivo. It was suggested that these individuals might be more susceptible to HIVassociated dementia because the macrophages show enhanced capacity of invasion [74,255]. HIV Tat protein itself is able to enhance the chemotaxis and hence the invasive behavior of monocytes in vitro [256]. In fact, levels of the chemotactic protein MCP-1, which induces quimiotaxis and transmigration of lymphocytes, monocytes and granulocytes [257] as well as up-regulation of adhesion molecules and cytokines [258], are markedly elevated in brains and cerebrospinal fluid of patients with HIV-1 associated dementia [259]. Furthermore, injection of Tat exon 1 in mouse brain tissue and vascular endothelium resulted in a significant upregulation of MCP-1 mRNA [260].

2.7. Modification of the Expression of Host Genes Associated with the Cellular Metabolism Several studies have highlighted the importance of cholesterol and lipid rafts in HIV-1 life cycle, facilitating viral entry, virions assembly and budding from the membrane [261,262]. It has been suggested that Nef protein increases synthesis and transport of cholesterol to lipids rafts [263]. The cholesterol biosynthesis pathway consists of more than 20 enzymes which expression is regulated by the sterol-responsive element binding factor 2 (SREBF-2) [264]. The rate-limiting step of this pathway in controlled by the 3-hydroxy-3-

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methylglutaryl coenzyme A reductase (HMGCR). The expression of low-density lipoprotein receptor (LDLR), involved in uptaking extracellular LDL, is also controlled by SREBF-2. In an elegant microarray study, van‗t Wout et al. [55] proved that LDLR and most cholesterol biosynthesis-related enzymes—including rate-limiting HMGCR—were up-regulated 24 hours post-infections in HIV-infected T cell lines and primary CD4+ T cells. Moreover, upregulation of gene expression is dependent on the expression of functional HIV Nef protein. On the other hand, the receptor-interacting protein 140 (RIP140) is a transcriptional regulator that has been found to be up-regulated at 3 days post-HIV-1 infection in T cells [54]. RIP140 negatively regulates the transcription of the peroxisome proliferator-activated receptor (PPAR), which in turn modulates the expression of different genes involved in lipid metabolism [265]. Therefore, it has been proposed that the lipid metabolism could be perturbed in HIV-infected cells, accordingly to the dorsocervical fat pad enlargement observed in HIV-infected patients, especially those non-exposed to protease inhibitor therapy [266].

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2.8. Modification of the Expression of Host Genes Associated with the Cytoskeleton Activity The ability to migrate is inherent in T lymphocytes. The quimioattraction of leukocytes to tissues is an essential step in the inflammatory process as well as in the host response against infection [267]. Other type of cells such as neutrophils and monocytes also transmigrate to tissues during inflammatory responses. Since cellular migration involves attachment, extravasation and invasion, changes in the expression of genes related to cytoskeletal rearrangements, quimiotaxis, cellular adhesion, and extracellular matrix remodelation has been studied in most common retroviruses. HTLV-associated diseases are often characterized by lymphoid infiltration of secondary lymphoid organs and various tissues. Actually, it has been reported the increased expression of genes encoding molecules involved in cellular adhesion such as selectin, galectins and the integrin-associated protein CD47 [58], the platelet/endothelial cell adhesion molecule-1 (PECAM-1/CD31), the secreted protein acidic and rich-in-cystein SPARC/osteonectin [70], and CD58 [268], during HTLV-1 infection. For example, SPARC and PECAM-1/CD31 play a role in increasing tumor cell adhesion and invasiveness or transendothelial migration [269,270]. In accordance, Jurkat cells stably expressing p12I has also increased levels of two calcium-dependent cell adhesion molecules named cadherin-2 type-1 (CDH2) and protocadherin 9 (PCDH9), two lectin family of proteins that promote cell adhesion—known as galectin 8 and sialoadhesin (CD169)—as well as the selectin P ligand (CD162) [173]. Specific function of these proteins related to cytoskeletal mobility may explain how HTLV enhances invasiveness of infected cells. For example, CD47 facilitates leukocyte migration [271,272] and is required to prevent elimination of lymphohematopoietic cells [273]. Different studies have been focused on the production of chemotactic factors during HIV-1 and SIV-1 infection [274,275]. Up-regulation of cytoskeletal reorganization genes such as syntaxins and flotillins and their role in enhancing HIV-1 fusion to host membranes were reported [276]. Moreover, Jurkat cells stably expressing HIV protein Tat show down-

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regulation of the expression of a wide variety of proteins related to the cytoskeleton—actin, -tubulin, annexin II, gelsolin, cofilin, Rac/Rho-GDI complex [277]—that should impair the ability of T cell to migrate and respond to quimiotactic stimuli. Modification in the expression of proteins related to the cytoskeleton can also be observed in other retroviruses, such as MoMuLV. The stably or transient introduction of MoMuLV virions or the MoMuLV LTR alone in HeLa cells induces an increase in the transactivation of the MCP-1 gene [278].

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3. CONCLUSION Retroviruses are special viruses that have an RNA genome that must be retrotranscribed to DNA in order to be integrated as a provirus in the host cell DNA genome. This process is catalyzed by the enzyme reverse transcriptase that synthesizes dsDNA from RNA. This constitutes a reversal of the usual cellular processes of transcription of DNA into RNA, thereby proving that genetic information is able to flow from RNA to DNA in exceptional cases. Once integrated, the provirus acts as a host gene and can be transmitted to the progeny cells, thereby perpetuating the infection. However, retroviruses can also lie quiescent in the cellular genome, without producing infectious progeny and being undetectable by the immune system but able to reactivate viral replication upon cell activation. This mechanism of latency is utilized for some viruses to maintain the infection in the host organism between episodes of viral replication. During the course of retroviral infection, several changes are induced in the host cell gene expression. In fact, an efficient viral replication relies not only on viral but also on cellular factors. In this regard, retroviruses take advantage of the entire cellular machinery with the objective of modifying the host cell gene expression in a manner that facilitates viral replication. In this process, the retroviruses induce an intense reorganization not only of the cellular gene expression but also of the cellular functions, which are altered in order to fulfill retroviruses requirements. Increasing knowledge about specific gene expression alteration will be helpful in elucidating how retroviral pathogenesis is caused and therefore in designing actions against these viruses. New genomic assays that permit identifying thousands of genes differentially expressed—such as high-density DNA/RNA/protein arrays or genome-wide mRNA profiling analysis—have proved that virtually any cellular process can be targeted by the retroviruses, causing alteration in essential functions such as immune system control, cell cycle and apoptosis, and status of transcriptional activation. Different types of retroviruses can alter similar functions in different cell types, although induction or repression of the gene expression strictly depends on each virus, and therefore defines specific features of the pathogenesis caused by different retroviruses. Moreover, different proteins from each retrovirus can have a specific effect on cellular gene expression or function, demonstrating the multifactorial nature of most retroviral proteins. The control of the expression of specific cellular genes might contribute to the protection of the infected cells from apoptosis in order to prolong the activation and survival of the infected cells, thereby permitting a sustained viral replication to maintain their own existence. In some cases, these modifications can produce disequilibrium in the apoptotic mechanisms

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that can finally cause tumor generation in the host organism. The mechanisms by which oncoproteins deregulate transcription of specific target genes during oncogenic transformation are still poorly understood. Consequently, more studies of the complex interplay between the retroviruses and their host will be necessary to identify specific targets for antiretroviral therapy.

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through hMR-mediated intracellular signaling in astrocytes. Biochim Biophys Acta, 2005 1741, 55-64. [254] Wesselingh, SL; Takahashi, K; Glass, JD; McArthur, JC; Griffin, JW; Griffin, DE. Cellular localization of tumor necrosis factor mRNA in neurological tissue from HIVinfected patients by combined reverse transcriptase/polymerase chain reaction in situ hybridization and immunohistochemistry. J Neuroimmunol, 1997 74, 1-8. [255] Pulliam, L; Sun, B; Rempel, H. Invasive chronic inflammatory monocyte phenotype in subjects with high HIV-1 viral load. J Neuroimmunol, 2004 157, 93-98. [256] Lafrenie, RM; Wahl, LM; Epstein, JS; Hewlett, IK; Yamada, KM; Dhawan, S. HIV-1Tat protein promotes chemotaxis and invasive behavior by monocytes. J Immunol, 1996 157, 974-977. [257] Mukaida, N; Harada, A; Matsushima, K. Interleukin-8 (IL-8) and monocyte chemotactic and activating factor (MCAF/MCP-1), chemokines essentially involved in inflammatory and immune reactions. Cytokine Growth Factor Rev, 1998 9, 9-23. [258] Jiang, Y; Beller, DI; Frendl, G; Graves, DT. Monocyte chemoattractant protein-1 regulates adhesion molecule expression and cytokine production in human monocytes. J Immunol, 1992 148, 2423–2428. [259] Cinque, P; Vago, L; Mengozzi, M; Torri, V; Ceresa, D; Vicenzi, E; Transidico, P; Vagani, A; Sozzani, S; Mantovani, A; Lazzarin, A; Poli, G. Elevated cerebrospinal fluid levels of monocyte chemotactic protein-1 correlate with HIV-1 encephalitis and local viral replication. AIDS, 1997 12, 1327–1332. [260] Toborek, M; Lee, YW; Pu, H; Malecki, A; Flora, G; Garrido, R; Hennig, B; Bauer, HC; Nath, A. HIV-Tat protein induces oxidative and inflammatory pathways in brain endothelium. J Neurochem, 2003 84, 169-179. [261] Chazal, N; Gerlier, D. Virus entry, assembly, budding, and membrane rafts. Microbiol Mol Biol Rev, 2003 67, 226-237. [262] Mañes, S; del Real, G; Lacalle, RA; Lucas, P; Gómez-Moutón, C; Sánchez-Palomino, S; Delgado, R; Alcamí, J; Mira, E; Martínez, AC. Membrane raft microdomains mediate lateral assemblies required for HIV-1 infection. EMBO Rep, 2000 1, 190-196. [263] Zheng, Y; Plemenitas, HA; Fielding, CJ; Peterlin, BM. Nef increases the synthesis of and transports cholesterol to lipid rafts and HIV-1 progeny virions. Proc Natl Acad Sci U S A, 2003 100, 8460-8465. [264] Horton, JD; Goldstein, JL; Brown, MS. SREBPs, activators of the complete program of cholesterol and fatty acid synthesis in the liver. J Clin Investig, 2002 109, 1125-1131. [265] Miyata, KS; McCaw, SE; Meertens, LM; Patel, HV; Rachubinski, RA; Capone, JP. Receptor-interacting protein 140 interacts with and inhibits transactivation by peroxisome proliferator-activated receptor alpha and liver-X-receptor alpha. Mol Cell Endocrinol, 1998 146, 69-76. [266] Lo, JC; Mulligan, K; Tai, VW; Algren, H; Schambelan, M. Buffalo hump in men with HIV-1 infection. Lancet, 1998 351, 867-870. [267] Sánchez-Madrid, F; del Pozo, MA. Leukocyte polarization in cell migration and immune interactions. EMBO J, 1999 18, 501-511.

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[268] Imai, T; Tanaka, Y; Fukudome, K; Takagi, S; Araki, K; Yoshie, O. Enhanced expression of LFA-3 on human T-cell lines and leukemic cells carrying human T-cellleukemia virus type I. Int J Cancer, 1993 55, 811-816. [269] Ledda, MF; Adris, S; Bravo, AI; Kairiyama, C; Bover, L; Chernajovsky, Y; Mordoh, J; Podhajcer, OL. Suppression of SPARC expression by antisense RNA abrogates the tumorigenicity of human melanoma cells. Nat Med, 1997 3, 171-176. [270] Muller, C; Coffey, TJ; Koss, M; Teifke, JP; Langhans, W; Werling, D. Lack of TNF alpha supports persistence of a plasmid encoding the bovine leukaemia virus in TNF(-/) mice. Vet Immunol Immunopathol 2003 92, 15-22. [271] Imhof, BA; Weerasinghe, D; Brown, EJ; Lindberg, FP; Hammel, P; Piali, L; Dessing, M; Gisler, R. Cross talk between alpha(v)beta3 and alpha4beta1 integrins regulates lymphocyte migration on vascular cell adhesion molecule 1. Eur J Immunol, 1997 27, 3242-3252. [272] Ticchioni, M; Deckert, M; Mary, F; Bernard, G; Brown, EJ; Bernard, A. Integrinassociated protein (CD47) is a comitogenic molecule on CD3-activated human T cells. J Immunol, 1997 158, 677-684. [273] Blazar, BR; Lindberg, FP; Ingulli, E; Panoskaltsis-Mortari, A; Oldenborg, PA; Iizuka, K; Yokoyama, WM; Taylor PA. CD47 (integrin-associated protein) engagement of dendritic cell and macrophage counterreceptors is required to prevent the clearance of donor lymphohematopoietic cells. J Exp Med, 2001 194, 541-549. [274] Gallo, P; Frei, K; Rordorf, C; Lazdins, J; Tavolato, B; Fontana, A. Human immunodeficiency virus type 1 (HIV-1) infection of the central nervous system, an evaluation of cytokines in cerebrospinal fluid. J Neuroimmunol, 1989 23, 109-116. [275] Sasseville, VG; Smith, MM; Mackay, CR; Pauley, DR; Mansfield, KG; Ringler, DJ; Lackner, AA. Chemokine expression in simian immunodeficiency virus-induced AIDS encephalitis. Am J Pathol, 1996 149, 1459-1467. [276] Cicala, C; Arthos, J; Selig, SM; Dennis, G Jr; Hosack, DA; Van Ryk, D; Spangler, ML; Steenbeke, TD; Khazanie, P; Gupta, N; Yang, J; Daucher, M; Lempicki, RA; Fauci; AS. HIV envelope induces a cascade of cell signals in non-proliferating target cells that favor virus replication. Proc Natl Acad Sci U S A, 2002 99, 9380-9385 [277] Coiras, M; Camafeita, E; Ureña, T; López, JA; Caballero, F; Fernández, B; LópezHuertas, MR; Pérez-Olmeda, M; Alcamí, J. Modifications in the human T cell proteome induced by intracellular HIV-1 Tat protein expression. Proteomics, 2006 Suppl S63-73. [278] Weng, H; Choi, SY; Faller, DV. The Moloney leukemia retroviral long terminal repeat trans-activates AP-1-inducible genes and AP-1 transcription factor binding. J Biol Chem, 1995 270, 13637-13644.

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Chapter 3

MODULATION OF CELLULAR SIGNALING AND GENE EXPRESSION BY VITAMIN E† Jean-Marc Zingg and Angelo Azzi University of Bern, Bern, Switzerland.

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ABSTRACT In recent years, the specific cellular effects of vitamin E that are the consequence of modulating signal transduction and gene expression have been described. The natural (α, β-, γ-, and δ-tocopherol and α-, β-, γ-, and δ-tocotrienol) and synthetic vitamin E analogues affect the cellular behavior differentially, suggesting that they do not act on a single molecular target. Furthermore, these effects are often not explainable by a general antioxidant action and thus most likely reflect specific interactions of vitamin E with enzymes, structural proteins, lipids and transcription factors. At the cellular level, the different vitamin E analogues can modulate cell proliferation, apoptosis, platelet aggregation, monocyte adhesion and the differentiation of hippocampus neurons. At the enzyme level, the tocopherols inhibit protein kinase C (PKC), protein kinase B (PKB), tyrosine kinases, 5-lipoxygenase and phospholipase A2, and activate protein phosphatase 2A and diacylglycerol kinase. At the transcriptional level, the expression of a growing number of genes is modulated by the tocopherols. Further research is required to define which of these activities render tocopherol (and, in particular, α-tocopherol), an essential nutrient—a vitamin—in humans.

Keywords: Vitamin E (tocopherols, tocotrienols), tocopherol analogues, α-tocopheryl phosphate, tocopherol binding proteins, signal transduction, gene regulation, nonantioxidant effects, transport, metabolism, proteasome, NADPH-oxidase, hypoxia. †

A version of this chapter was also published in New Topics in Vitamin E Research, edited by Oliver H. Bellock published by Nova Science Publishers, Inc. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. Correspondence concerning this article should be addressed to: Jean-Marc Zingg, E-mail: [email protected].

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INTRODUCTION Several studies have described preventive effects of vitamin E supplementation against atherosclerosis, as well as a number of other diseases, including cancer, and neurodegenerative diseases, such as Alzheimer‘s and Parkinson‘s diseases (reviewed in [1-9]). Most of these studies are based on the finding that vitamin E plasma levels can be increased by extra dietary supplementation, implying that pathways involved in vitamin E uptake and body distribution are often not saturated. Plasma vitamin E concentrations could vary in different individuals as a consequence of consumption by excessive production of oxidants or of a deficient uptake and transport into plasma and tissues. The uptake of dietary hydrophobic antioxidants (tocopherols, carotenoids, and flavonoids) and transport by chylomicrons from the intestine to the liver are impaired in abetalipoproteinemia and a number of lipid malabsorption syndromes such as cholestatic liver disease, short bowel syndrome and cystic fibrosis [10]. In these diseases, the transport of α-tocopherol is impaired either in the liver or in the intestine, leading to extremely low plasma α-tocopherol levels that ultimately lead to vitamin E deficiency syndromes in peripheral tissues [11]. Moreover, polymorphisms of tocopherol binding proteins involved in uptake, tissue distribution and metabolism of vitamin E and their cellular expression levels could be the reason for the individual vitamin E uptake and response, and explain the differential susceptibility to disorders such as atherosclerosis, certain cancers and neurodegenerative diseases [12]. In a number of studies, vitamin E has been postulated to play a central role in preventing lipid peroxidation. However, in addition to the possible antioxidant effects, vitamin E can also exert non-antioxidant activities suggesting alternative molecular pathways for disease prevention. Vitamin E modulates signal transduction and gene expression and these events most likely reflect specific interactions of vitamin E with enzymes, structural proteins, lipids and transcription factors. This review summarizes the molecular activities of vitamin E.

THE NATURAL TOCOPHEROL ANALOGUES HAVE ESSENTIALLY EQUAL ANTIOXIDANT POTENCY Natural vitamin E comprises eight different forms: α-, β-, γ-, and δ-tocopherol and α-, β-, γ-, and δ-tocotrienol. These eight tocopherol analogues are synthesized only in plants and their relative amounts depend on the plant itself and on the plant tissue. Tocotrienols have an unsaturated side chain, whereas tocopherols contain a phytyl tail with three chiral centres that naturally occur in the RRR configuration (Figure 1). In nature, the eight analogous compounds are widely distributed; the richest sources are latex lipids (~80 mg/g of latex), followed by edible plant oils (Figure 2). Sunflower seeds contain almost exclusively α-tocopherol (59.5 mg/g of oil); oil from soybeans contains the γ-, δ-, and α-tocopherol (62.4, 20.4, and 11.0 mg/g oil); while palm oil contains high concentrations of tocotrienols (17.2 mg/g oil) and α-tocopherol (18.3 mg/g oil) [13]. More than 50% of vitamin E that is taken up comes from dietary oil. Since the relative amount of each tocopherol analogue depends on the oil source and since countries differ in their dietary

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oil preferences, the plasma and tissue levels of certain tocopherols can be different. In the United States, the intake of γ-tocopherol is generally higher than that of α-tocopherol because of the high intake of corn oil; in Europe, the intake of α-tocopherol is higher than that of γtocopherol because of the high intake of sunflower and olive oil; whereas the higher intake of oil from soybeans in Asia leads to a higher intake of δ-tocopherol and γ-tocopherol. R1 HO

CH3 4'R

2R O

R2 CH3

CH3 8'R

CH3 CH3

CH3

tocopherol

R1

R2

CH3

CH3 CH3 H H

CH3 H CH3 H

R1 HO

CH3

CH3

2R O

R2 CH3

CH3

CH3

tocotrienol

Figure 1. Structure of the four natural tocopherols and tocotrienols. Four tocopherols and four tocotrienols, here referred to as α-, β-, γ-, δ-tocopherol/tocotrienol, all with a side-chain in the natural RRR configuration, are synthesized in plant tissues. The relative concentrations of the tocopherols and tocotrienols depend on the plant species and on the plant tissue.

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4% 20%

3%

Olive

T

73%

T

93% 7%

T T

Sunflower

4% 23%

1%

25%

4%

Corn

Soybean 69%

67%

Figure 2. Relative amounts of the four natural tocopherols in dietary oils. The four commonly used dietary oils contain different relative amounts of the four tocopherols [8]. Since more than 50% of dietary tocopherols originate from these oils, the predominant tocopherol analogue taken up depends on dietary oil preferences.

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100

Peripheral tissues Intestine

Diet

Cm CmR

Bile Tocopherol metabolites

Chylomicron tocopherol cycle

Kidney

6% 1% T T 26% T T

-TTP

67%

P450 Liver -TTP

Relative affinity of liver -TTP for tocopherols vLDL

-Tocopherol salvage pathway

HDL LDL

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Peripheral tissues

Figure 3. The α-tocopherol salvage pathway. The four tocopherols are taken up with equal efficiency (average 30%) via the intestine and distributed to peripheral tissues by chylomicrons (Cm). In the course of chylomicron lipolysis, a part of the vitamin E is distributed to peripheral tissues, and the liver with the chylomicron remnants captures the other part. Chylomicron remnants (CmR) are taken up by the liver; the α-tocopherol is preferentially recognized, sorted and secreted with VLDL. The remaining tocopherols and excess α-tocopherol is metabolized (reviewed in [14]). The metabolites are water soluble and are cleared by the kidney. Part of the liver tocopherols (up to 14%) is also secreted with bile, up to 60% of biliary α-tocopherol is reabsorbed, thus possibly undergoing a second chylomicron cycle [15, 16]. In the liver the α-tocopherol transfer protein (α-TTP) preferentially recognizes αtocopherol and incorporates it into VLDL. During circulation in the blood, VLDL converts to LDL and HDL and delivers its content including the α-tocopherol to the peripheral tissues. Excess tocopherols are transported back in LDL and HDL to the liver, where they undergo the next round in this cycle. Since the presence of α-tocopherol in chylomicrons after dietary uptake is transient, the α-tocopherol salvage pathway may allow maintaining a continuously increased level (~20-30 fold enrichment) of αtocopherol in plasma and tissues [8].

Although the tocopherols have essentially the same overall antioxidant activity, clear chemical and physical effects can be distinguished at the molecular level. RRR-α-Tocopherol is the most abundant form in plasma, whereas the plasma γ-tocopherol level is only about 10% of that of α-tocopherol despite that a higher amount of γ-tocopherol is often present in the diet. This specificity is the consequence of a selective enrichment of RRR-α-tocopherol in the body by means of the liver α-tocopherol transfer protein (α-TTP), whereas the other tocopherols are selectively metabolized and later eliminated (Figure 3). The biological potency of the tocopherols can be summarized with the order of α >> γ > δ > β, which can be explained by the selective enrichment of α-tocopherol in the plasma by the liver (20 to 30 fold). In normal subjects the average plasma α-tocopherol concentration is

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about 22 μM, a concentration below 11 μM is considered to be deficient, whereas severe vitamin E deficiency symptoms occur below 2.2 μM (Table 1) [17]. The plasma concentration of the β-, γ-, δ- tocopherols is normally between 0.3 and 3 μM. The free radical scavenging reactivity has been measured as being in the order of α > β > γ > δ. The chemical reactivity of the four tocopherols with singlet molecular oxygen (1O2) has been found to be very low, with α > γ > δ > β. The physical quenching ability of 1O2 has been measured as being in the order of α ≥ β > γ > δ [18]. The rather complex physical and chemical properties of tocopherols have been extensively reviewed [19]. Table 1. α-Tocopherol concentrations in plasma and cell culture Cplasma, average

*

22 μM 9.5 μg/ml

Cmax, plasma, supplemented*

46 20

Cdeficient plasma

Ccell culture+

< 2.2 < 0.95

50 21.5

RRR-α-tocopherol (300 mg, 447 IU) for 8 days [20]. +commonly used in cell culture experiments.

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Despite Having Essentially Equal Antioxidant Activity, the Natural Tocopherol Analogues Often Have Different Biological Effects What are the reasons for the selective retention of α-tocopherol? Possibly, α-tocopherol has some specific characteristics; e. g the fully methylated chromanol-head group may be required for optimal interactions with enzymes and/or putative ―α-tocopherol receptors‖. On the other hand, the β-, γ-, and δ- tocopherols and the tocotrienols may have biological effects that interfere at higher concentrations with normal cellular processes, so that they need to be specifically recognized, metabolized by the liver and later eliminated. Competition by β-, γand δ- tocopherols in reactions that require specifically α-tocopherol may be another reason for the selective α-tocopherol retention. α-Tocopherol also undergoes specific interactions with membrane phospholipids, but the cellular consequences of such structural alterations are not clear [21, 22]. In several in vitro experiments, differences between the tocopherols were detected that are supporting this model. Purified α-tocopherol, but not β-, γ-, or δ-tocopherol, induces macrophage fusion. This is not observed with similar antioxidants such as probucol or Trolox, suggesting that the α-tocopherol effects are independent of its antioxidant activity. This study indicates a novel, specific role unique to α-tocopherol, being a highly potent macrophage fusion factor, with possible beneficial effects during chronic inflammation [23]. The four tocopherols inhibited HMC-1 mast cells proliferation with different potency (δ > α = γ > β), and δ-tocopherol even led to apoptosis at higher concentrations [24]. Similar to that, induction of apoptosis by γ- and δ-tocopherol, but not by α-tocopherol was shown with prostate cancer cells [25], with mouse activated macrophages [26] and with mammary epithelial cells [27]. In prostate cancer cells it was suggested that the inhibition of dihydroceramide desaturase by γ-tocopherol is involved in the induction of apoptosis [25]. A nascent body of epidemiological data suggests that γ-tocopherol is a better negative risk

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factor for certain types of cancer and myocardial infarction than is α-tocopherol [28]. Taken together, these findings can be explained by activities of the tocopherols and their metabolites that do not overlap directly with their chemical antioxidant behavior, but rather reflect antiinflammatory, antineoplastic, and other cellular functions possibly mediated through specific binding interactions with proteins. A unique feature of α-tocopherol is the location of the reactive –OH group between two methyl groups; after reacting with a lipid peroxide the unpaired electron can delocalize over the fully substituted chromanol ring what is known to increase its stability and chemical reactivity [19, 22]. As a consequence, α-tocopherol and α-tocotrienol, but not the other forms of tocopherol, can reduce in vitro Cu(II) to give Cu(I) together with α-tocopheryl and αtocotrienyl quinones, respectively, and they can exert pro-oxidant effects in the oxidation of methyl linoleate in SDS micelles [29]. However, albeit α-tocotrienol has higher antioxidant activity than α-tocopherol in membranes because of its unsaturated side chain [30-32], only α-tocopherol is retained; this suggests that the liver α-TTP protein evolved to recognize and retain α-tocotrienol inefficiently, since the presence of continuously increased levels of αtocotrienol in serum possibly would interfere with normal cellular reactions. In addition to that, since α-tocotrienol is taken up into certain cells in culture with higher efficiency than αtocopherol, selective plasma enrichment may not be required since biological effects may be exerted already at lower effective concentrations. In line with this model, it was shown that α-tocotrienol was more potent in many cellular reactions than α-tocopherol, such as in reducing cholesterol levels by inhibition of HMGCoA reductase activity and its mRNA translation, by decreasing the secretion of apoB and increasing its proteasomal degradation [33-37], or by blocking glutamate-induced cell death [38]. Several isoprenoids, including γ-tocotrienol, inhibit HMG-CoA reductase synthesis and accelerate reductase degradation, leading to suppression of cell proliferation and induction of apoptosis in cancer cells [39, 40]. During ischemia/reperfusion, tocotrienol restored both 20S- and 26S- proteasome activities and significantly inhibited the phosphorylation of c-Src [41]. Among the vitamin E analogs examined, α-tocotrienol exhibited the most potent neuroprotective actions in rat striatal cultures [42]. Furthermore, the efficacy of tocotrienol for reduction of VCAM-1 expression and adhesion of THP-1 cells and monocytes to HUVECs was 10-fold higher than that of α-tocopherol, and this was explained by a higher cellular uptake of α-tocotrienol in these cells [43-45]. The tocotrienols were more potent than the tocopherols in inhibiting proliferation and inducing apoptosis in the estrogen-responsive MCF7 and estrogen-non-responsive MDA-MB-435 human breast cancer cell lines in culture [46], as well as in preneoplastic and neoplastic mouse mammary epithelial cells [27, 47, 48]. The α- and γ-tocotrienols were effective against transplantable murine tumors (sarcoma 180, Ehrlich carcinoma, and IMC carcinoma), whereas α-tocopherol had only a slight effect [49]. Recent studies showed that tocotrienol-induced apoptosis results from the activation of specific intracellular cysteine proteases (caspases) associated with death receptor activation and signal transduction (reviewed in [50]). Oral supplementation of α-tocotrienol to spontaneously hypertensive rats led to increased α-tocotrienol levels in the brain leading to a higher protection against stroke-induced injury compared with matched controls. Such protection was associated with lower c-Src activation and 12-LOX phosphorylation at the stroke site, what can be explained by blocking glutamate-induced death by suppressing early

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activation of c-Src kinase and 12-lipoxygenase in neuronal cells, at nmol/L concentrations of α-tocotrienol, but not α-tocopherol [51]. Using human Tenon's capsule fibroblast cultures as a model for a fibrotic response that can occur after glaucoma filtration surgery, the antiproliferative and cytotoxic effects of the different vitamin E forms (α-tocopherol, αtocopheryl acetate, α-tocopheryl succinate and α-tocotrienol) were compared with those of mitomycin C; only α-tocotrienol significantly inhibited growth at non-toxic concentrations [52].

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THE DIFFERENT FORMS OF VITAMIN E ARE METABOLIZED DIFFERENTLY The selectivity of higher organisms for α-tocopherol has been impressively demonstrated in recent years by analysing the metabolism of vitamin E. Excess α-tocopherol and the other tocopherol analogues are extensively metabolized before excretion; thus, the correct vitamin E level is maintained by selective retention of α-tocopherol, and by specific metabolism of all the other tocopherols and of the excess α-tocopherol. Initially, two major metabolites of αtocopherol, the so-called Simon metabolites (tocopheronic acid and tocopheronolactone) were described [53, 54], which are excreted in the urine as glucuronides or sulfates. These metabolites have a shortened side chain and an opened chroman structure and thus are often quoted to demonstrate the antioxidant function of α-tocopherol in vivo. The level of these metabolites increases markedly in the urine of healthy volunteers after a daily intake of 2-3 g all rac-α-tocopherol. However, a novel pathway of tocopherol metabolism suggests that oxidation of tocopherol is not required for its degradation and the resulting metabolites were proven to modulate cellular reactions (reviewed in [14]). Instead of Simon-metabolites, a water-soluble compound with a shortened side chain but an intact chroman structure, α-carboxyethyl hydroxychroman (α-CEHC), was identified after supplementation with RRR-α-tocopherol [55]. This metabolite is analogous to that of δ-tocopherol found previously in rats [56] and that of γ-tocopherol identified in human urine and proposed as a natriuretic factor [57]. The intact chromanol structure of CEHCs suggests that they are derived from tocopherols which have not reacted as antioxidants. The proposed pathway of side-chain degradation proceeds first via - and then β-oxidation [56]. The initial step, the -hydroxylation of the side chain is catalyzed by the action of cytochrome P450 (CYP)-dependent hydroxylases. Inhibitors of the CYP3A family, like sesamin and ketoconazole, inhibit the formation of γ-CEHC, and dietary intervention with sesame oils in humans leads to increased serum γ-tocopherol levels [58, 59]. In HepG2 cells, induction of CYP3A by rifampicin results in an increase of the αtocopherol metabolites [60]. α-CEHC excretion was increased with increasing vitamin E intake after a threshold of plasma α-tocopherol had been exceeded [55]. CEHC accumulation may mediate anti-inflammatory and antioxidative effects or have other regulatory properties [28, 61]. The metabolite of γ-tocopherol, γ-CEHC has natriuretic activity, and thus acts as a "natriuretic hormone‖. γ-CEHC acts by inhibition of the 70 pS potassium channel of the thick ascending limb of the loop of Henle without inhibiting the Na+/K+-ATPase. The analogous α-tocopherol metabolite (α-CEHC) showed no inhibition

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[62]. Both γ-tocopherol and γ-CEHC inhibit cyclooxygenase-2 (COX-2) activity and thus inhibit the synthesis of prostaglandin E2 (PGE2) in activated macrophages and epithelial cells [63, 64]. In carrageenan-induced inflammation in male Wistar rats, administration of γtocopherol or γ-CEHC, but not α-tocopherol, reduces prostaglandin E2 (PGE2) synthesis at the site of inflammation, and inhibits leukotriene B4 formation, a potent chemotactic agent synthesized by the 5-lipoxygenase of neutrophils [65]. Interestingly, it was also found that vitamin E activates the human Pregnane X Receptor (PXR) in a tocopherol specific manner: α-tocopherol activates weakly, whereas β-, γ-, and δtocopherol and the tocotrienols lead to stronger induction, whereas the tocopherol metabolic products do not activate. PXR is involved in the drug hydroxylation and elimination pathways, it activates genes such as cytochromes P450 (CYP), e.g. CYP3A and some ABC Transporters [66]. A physiological reason for the selective retention of α-tocopherol and the elimination of all the others tocopherol analogues could thus be due to the absence of strong PXR activation by α-tocopherol and the consequent absence of induction of enzymes involved in its metabolism. In addition to that, α-tocopherol may be specifically sorted by α-TTP into vesicles destined for incorporation into VLDL. Only when the level of α-tocopherol exceeds the capacity of α-TTP, transport to the metabolic enzymes may occur. The other tocopherols are not retained by α-TTP, activate PXR and then become metabolized and eliminated by CYP3A. It is furthermore possible that the eliminated tocopherols have undesired effects at the concentration reached normally by α-tocopherol (Table 1), or have homology to compounds that need to be eliminated. δ-Tocopheryl quinone and γ-tocopheryl quinone, but not αtocopheryl quinone, are cytotoxic in smooth muscle cell culture, acute lymphoblastic leukaemia (ALL) cells and AS52 cells [67, 68]. Furthermore, γ-tocopheryl quinone is highly mutagenic in AS52 cells whereas α-tocopheryl quinone is not, possibly giving an evolutionary advantage to organisms limiting γ-tocopherol, the precursor of γ-tocopheryl quinone [68]. In vivo, dietary α-tocopherol decreased genetic instability in the mouse mutatect tumor model, whereas γ-tocopherol had no effects, suggesting advantages for the genomic integrity as a consequence of α-tocopherol retention [69]. Alternatively, the eliminated tocopherols could bind and modulate a similar class of receptors like PXR when present at high concentrations, and affect the expression of genes in a non-physiological manner. A pharmacophore that represents key features of ligands to the PXR receptor suggests that some receptors can be activated by a number of molecules with similar structure [70].

NON-ANTIOXIDANT CELLULAR EFFECTS OF THE NATURAL TOCOPHEROL ANALOGUES The mechanism by which vitamin E produces cellular events could in principle be related to the known radical chain breaking properties of the molecule. This would imply that regulation of certain cellular functions is controlled by the production and elimination of lipid soluble free radicals and that vitamin E serves as a radical scavenger. The biological

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difficulty of controlling the propagation of radical chain reactions makes this mechanism improbable, and it is unlikely that specific cellular effects seen with different tocopherols can be explained by the general reduction of oxidative stress. Furthermore, if this were the mechanisms of action of the tocopherols, other similar radical chain braking molecules, and in particular the eight natural tocopherol analogues, would act analogously: this is however often not the case. Thus, it can be assumed that α-tocopherol modulates cellular behavior by specific interactions with enzymes, structural proteins, lipids and transcription factors. Similarly, troglitazone, an antidiabetic drug of the thiazolidinedione class, acts as an insulin sensitizer and diminishes hyperglycemia. Structurally, it contains a chromanol moiety similar to vitamin E and has been shown to have antioxidant properties in vitro; nevertheless, the main therapeutic effect occurs via binding to the peroxisome proliferator activated receptor gamma (PPARγ) [71]. Given the non-antioxidant regulatory function of vitamin E, it would be inefficient to consume it as a radical scavenger. Rather, it would be important to protect vitamin E through a network of cellular antioxidant defenses, such as catalases, superoxide dismutases, Lascorbic acid, glutathione, α-lipoic acid etc., similarly to what occurs with proteins, nucleic acids, hormones and lipids. In the following we will focus on the non-antioxidant cellular properties of vitamin E, the antioxidant actions of vitamin E have been extensively reviewed [4, 72-78].

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Modulation of Enzymatic Activity by Vitamin E Over the last decade, vitamin E has been shown to have specific effects on cellular signaling and gene regulation [2, 4, 8, 79]. In the following the main effects of vitamin E on the enzymes that are involved in signal transduction and gene expression are summarized. In many situations, only α-tocopherol has been checked, and it is unclear whether other tocopherols or tocotrienols work equally well. In other experiments, the effects of vitamin E have been only tested in the test tube, and need to be confirmed in vivo.

Inhibition of Protein Kinase C (PKC) In a first study, vitamin E (dl-α-tocopherol) was found to inhibit in vitro brain protein kinase C (PKC) at a concentration that can be found in cells. It thus appeared that vitamin E, in addition to its antioxidant function, plays a role in regulating the activity of PKC [80]. In 1991 inhibition of PKC activity was found to be at the basis of the vascular smooth muscle cell growth arrest induced by α-tocopherol [81, 82]. A number of reports have subsequently confirmed the involvement of PKC in the effects of α-tocopherol on different cell types, including monocytes, macrophages, neutrophils, fibroblasts and mesangial cells [83-86]. αTocopherol, but not β-tocopherol, inhibits thrombin-induced PKC activation and endothelin secretion in endothelial cells [87]. α-Tocopherol, and not β-tocopherol or trolox, inhibits the activity of PKC from monocytes, followed by inhibition of phosphorylation and translocation

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106

30

active

-tocopherol

40

-tocopherol

50

-tocopherol

PKC inhibition,%

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60

-tocopherol

of the cytosolic factor p47(phox), what leads to an impaired assembly of the NADPH-oxidase and lowers superoxide production [88]. Inhibition of PKC by α-tocopherol in vascular smooth muscle cells is observed to occur at concentrations of α-tocopherol close to those measured in healthy adults [89]. βTocopherol per se is ineffective but prevents the inhibitory effect of α-tocopherol. The mechanism involved is not related to the radical scavenging properties of these two molecules, which are essentially equal [90]. In vitro studies with recombinant PKC have shown that inhibition by α-tocopherol is not caused by a tocopherol-protein interaction and also not by affecting PKC expression. Inhibition of PKC activity by α-tocopherol occurs at a cellular level by producing dephosphorylation of the enzyme, whereby β-tocopherol is much less potent (Figure 4) [91, 92]. Dephosphorylation of PKC occurs via the protein phosphatase 2A (PP2A), that is activated by the treatment with α-tocopherol [91-95]. In normal mammary epithelial cells, tocopherols and tocotrienols inhibit activation of PKCα by epidermal growth factor (EGF) via reduction of PKCα translocation to the membrane [48]. PMA-induced phosphorylation of extracellular signal-regulated kinase (ERK) is inhibited by α-tocopherol in bovine pulmonary artery smooth muscle cells but not in HL-1 human cardiac muscles cells [96].

20 10

-P

PKC

+ PP2A

-tocopherol

PKC inactive

0 Figure 4. Inhibition of protein kinase C (PKC) by α-tocopherol. Protein kinase C (PKC) is best inhibited by α-tocopherol, whereas β-, γ- and δ-tocopherol have weaker effects, suggesting a nonantioxidant mechanism. α-Tocopherol activates protein phosphatase 2A (PP2A) which in turn dephosphorylates and inactivates PKC [91-94].

It remains to be clarified why in the first experiment [80] dl-α-tocopherol was found to inhibit in vitro brain PKC while in later experiments the effect on PKC was shown to be mediated by the activation of the phosphatase PP2A. The answer to this question comes from the nature of the ―PKC‖ used by Mahoney and Azzi [80], not a pure protein but a crude PKC enriched fraction. It is probable that this fraction contained also PP2A and that this was the target of the action of α-tocopherol (Azzi, unpublished).

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In diabetic rats prevention of glomerular dysfunction can be achieved by treatment with α-tocopherol [97]. Such a protection occurs through inhibition of PKC, which is activated by high glucose levels. In this case, however, α-tocopherol would act on the diacylglycerol pathway, by activating the enzyme diacylglycerol kinase with consequent diminution of diacylglycerol and weaker PKC activation (Figure 5). In these studies, high glucose was responsible for increased diacylglycerol synthesis, which was counteracted, in the presence of α-tocopherol, by the activation of diacylglycerol kinase. Thus, the stimulation of diacylglycerol (DAG) kinase activity by vitamin E, and the consequent suppression of DAG by conversion to phosphatitic acid, may also contribute to the inhibition of PKC, at least in some experimental systems (Figure 5) [98].

active PKC

+ DAG

DAGK

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PKC

+

-tocopherol

PA

inactive Figure 5. Stimulation of Diacylglycerol Kinase by α-tocopherol leads to inhibition of PKC. Phosphorylation of the PKC-activator diacylglycerol (DAG) by diacylglycerol kinase (DAGK) leads to phosphatitic acid. Lower levels of DAG result in weaker activation of protein kinase C (PKC).

Inhibition of Protein Kinase B (PKB) A growing number of data supports the importance of PKB signaling in many processes, including proliferation of cancer cells, cellular migration, apoptosis, survival, and secretion [99, 100]. PKB or Akt has a wide range of cellular targets and its increased activity can be found during atherosclerosis and tumorigenesis [101]. Activation of PKB involves a membrane translocation step, followed by phosphorylation of two key regulatory sites, Ser473 and Thr308. The PH domain (Pleckstrin Homology Domain) present in the PKB molecule binds phosphatidylinositol trisphosphate, produced by activated phosphatidylinositol 3-kinase (PI3K) at the plasma membrane. By the same mechanisms phosphoinositide-dependent kinase 1 (PDK-1), a kinase phosphorylating Thr308 in PKB, becomes active. Phosphorylation of Thr308 leads, however, only to partial activation of PKB.

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Only after phosphorylation at the second site (Ser473) by a yet unidentified kinase (―PDK-2‖, such as ATM [102], DNA-dependent protein kinase [103], ILK [104], PKCα [105], or PKCβ [106]), the enzyme becomes fully active [107, 108]. The tocopherols were described to interfere with PKB (Ser473) phosphorylation, leading to reduced proliferation of HMC-1 mast cells [24]. In THP-1 monocytes, oxLDL-induced PKB phosphorylation was antagonized by α-tocopherol, what may lead to reduced CD36 scavenger receptor expression [109]. In breast cancer cells, PKB phosphorylation is inhibited by tocotrienols after stimulation by EGF [110], and also by the two tocopherol derivatives, αtocopheryl succinate and α-tocopheryl oxybutyric acid [111]. Further studies showed that γtocotrienol induced a large decrease in the relative intracellular levels of the phosphorylated forms of PDK-1, PKB, and glycogen synthase kinase 3 (GSK-α/β [50, 112]. Reduction of PKB phosphorylation by γ-tocotrienol was independent of activation of phosphatases like PP2A and PTEN, and led to reduction of NF-κB activity [113].

Receptor Tyrosine Kinase T PDK1 PDK2 (ILK, PKC , , ) ?

PI3K p85

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TAP

PKB

T

T

GSK /

Inhibition of constitutive cell proliferation by tocopherols Figure 6. Modulation of the PI3K/PDK/PKB-signal transduction pathway by α-tocopherol and TAP. The tocopherols inhibit PKB (Ser473) phosphorylation [24] by modulating signal transduction from a membrane receptor via phosphatidylinositol 3-kinase (PI3K) to protein kinase B (PKB).

The tocopherols and tocotrienols may inhibit PKB (Ser473) phosphorylation either directly, or they may act on enzymes upstream of PKB such as the c-kit tyrosine kinase or other receptor tyrosine kinases, PI3K, a kinase phosphorylating PKB (PDK1/2) or a phosphatase dephosphorylating it, such as protein phosphatase PP2A, or PTEN, a lipid phosphatase, which hydrolyses the products of PI3K (Figure 6) [114].

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The tocopherols may also modulate PI3K activity via binding to tocopherol binding proteins such as TAPs. The tocopherol associated proteins (TAP1, TAP2, TAP3, also known as the sec14-like proteins 2, 3 and 4, respectively), bound tocopherols, associated with PI3Kγ and inhibited PI3Kγ activity in vitro, suggesting that the tocopherols may be able to modulate PI3K and subsequent PKB activity via these proteins [115]. In support of a role of these proteins in PI3K regulation, the mouse TAP1 protein was recently shown to compete with p85 for PI3Kα binding, with consequent inhibition of PKB activity and reduced formation of prostate tumors (Figure 6) [116]. However, in this in vivo model, the inhibition of PI3K and PKB activity by TAP was not further modulated by tocopherol.

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Inhibition of Protein Tyrosine Kinases Some evidence for the involvement of tyrosine phosphorylation in the above effects on PKB has been described, since α-tocopherol was recently shown to inhibit Tyk2 tyrosine kinase activity in oxLDL-stimulated macrophages [117], and tyrosine phosphorylation of JAK2, STAT1 and STAT3 is decreased by α-tocopherol in oxLDL-stimulated MRC5 fibroblasts [118]. Related to that, in HT4 hippocampal neuronal cells, glutamate stimulated pp60c-Src tyrosine kinase activity is normalized by α-tocotrienol, but not by α-tocopherol [119]. In vascular smooth muscle cells, angiotensin II-induced tyrosine phosphorylation of two major proteins (p120, p70), and ERK activation were markedly reduced by α-tocopherol, whereas ERK activation by epidermal growth factor was unaffected [120]. Tyrosine phosphorylation is also decreased by α-tocopheryl succinate in human neutrophils via activation of a tyrosine phosphatase [121]. Since class I and II PI3K are regulated by tyrosine phosphorylation, it can be speculated that inhibition of tyrosine kinase activity by tocopherols may reduce PI3K activity and ultimately lead to reduced PKB membrane translocation and phosphorylation [100].

Inhibition of Phospholipase A2 One of the most important functions of phospholipase A2 is the release of arachidonic acid from membrane phospholipids for the synthesis of biologically active eicosanoids. Tocol inhibits the enzyme to a greater extent than either d- or dl-α-tocopherol, while there is little or no effect from dl-α-tocopheryl acetate. These results emphasize the importance of the hydroxyl moiety of the chromanol of the vitamin E molecule for its inhibitory action, compared to that of the methyl groups which are absent in tocol. This inhibitory action of vitamin E on platelet phospholipase A2 suggests a crucial function for vitamin E in regulating arachidonate release from the membrane phospholipids and its subsequent metabolism [122, 123]. Phospholipase A2 activity towards lamellar fluid membranes was best inhibited by α-tocopherol, whereas β-, γ- and δ-tocopherol inhibited to a lower extent [124]. Co-crystallization of α-tocopherol and phospholipase A2 showed direct binding of αtocopherol to the enzyme [125].

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α-Tocopherol also enhanced the release of prostacyclin from human endothelial cells via stimulation of phospholipase A2 [126]. This observation is in contrast to other observations in which tocopherol inhibited platelet and cardiac phospholipase A2 activity in rats, and reduced thrombin-stimulated thromboxane release in rat platelets [122, 127].

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Inhibition of Cyclooxygenase and 5-Lipoxygenase Cyclooxygenase-2 (COX-2)-catalyzed synthesis of prostaglandin E2 (PGE2) plays a key role in inflammation and its associated diseases, such as cancer and vascular heart disease. Both γ-tocopherol and γ-CEHC, but not α-tocopherol, inhibit cyclooxygenase activity and, thus, possess anti-inflammatory properties. COX-2 activity is directly inhibited by γtocopherol and not the result of changes of protein expression or substrate availability, and appears to be independent of its antioxidant activity [63-65]. α-Tocopherol activates mouse BV-2 microglial PP2A activity and thereby silences a LPS-activated PKC/ERK/NF-κB signaling cascade resulting in significant attenuation of COX-2 synthesis. These in vitro results suggest that α-tocopherol could slow down pathways that are associated with acute or chronic inflammatory conditions in the central nervous system [128]. Vitamin E also plays a role in the posttranslational events related to the age associated enhancement of COX-2 activity [129, 130]. Thus, vitamin E reverses the age-associated increase in macrophage PGE2 production and COX activity. Vitamin E exerts its effect post-translationally, by inhibiting COX activity. In activated human monocytes, α-tocopherol inhibits the release of the proinflammatory cytokine, IL-1β, via inhibition of the 5-lipoxygenase pathway [131]. β-Tocopherol has no effect on IL-1β release, although it has similar antioxidant activity as α-tocopherol. The protein kinase C inhibitor, bisindolylmaleimide, does not inhibit IL-1β release from activated monocytes, in spite of α-tocopherol decreasing protein kinase C activity, suggesting additional pathways affected by vitamin E. α-Tocopherol has no effect on IL-1β mRNA levels or stability, suggesting a posttranscriptional effect [131].

Inhibition of Glutathione S-Transferase Isoforms The glutathione S-transferases (GSTs) perform important cellular detoxification functions since they conjugate various electrophiles with glutathione. The GSTs are a diverse superfamily, and in mammals the cytosolic GSTs have been grouped into the A, Mu, Pi, Sigma, Theta and Zeta classes [132]. The Pi class is the most abundant isozyme in many tissues and is mostly expressed in tumor tissues, where it may contribute to resistance against cytostatic drugs. Therefore, compounds that inhibit GST activity in cancer cells could be used as adjuvant in cancer therapy. α-Tocopherol inhibits the GST P1-1 most efficiently, probably by binding to a lipophilic pit-like structure in the enzyme; other isoforms (A1-1, M1a-1a, A22) are less efficiently inhibited [133]. α- and γ-Tocotrienols, which accumulate specifically in skin (up to 13%), can also inhibit GST P1-1 [134].

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Inhibition of NADPH-Oxidase The NADPH-oxidase system in phagocytic cells is an electron transport system that catalyzes the reduction of O2 to O2-., a process not only thought to be part of the antibacterial defenses but to contribute also to chronic inflammatory processes, including scleroderma, liver fibrosis and neurodegeneration. Activation of NADPH-oxidase requires the assembly of a multiprotein complex at the plasma-membrane. The assembly of NADPH-oxidase is inhibited by tocopherol via inhibition of PKC, suggesting that tocopherol may reduce scleroderma and liver fibrosis by reducing the production of superoxide by NADPH-oxidase (Figure 7) [88]. Monocyte superoxide, in high glucose media, is released by the NADPH-oxidase but not by the mitochondrial respiratory chain, and α-tocopherol inhibits superoxide release via inhibition of PKCα. PKCα inhibition attenuates p47(phox) membrane translocation and phosphorylation [88, 135], two events necessary for NADPH-oxidase assembly and activation. Related to that, vitamin E restores normal endothelial functions in heart failure by attenuation of vascular superoxide formation and increases the expression of the soluble guanylyl cyclase in rats with heart failure [136]. In microglia cells, α-tocopherol inactivates PKC via a phosphatase-mediated pathway (PP1 or PP2A) and, as a consequence, blocks the phosphorylation-dependent translocation of p67(phox) to the plasma membrane. As a result the production of O2- by the microglial NADPH-oxidase system is substantially inhibited, offering a partial explanation for the beneficial effect of α-tocopherol on a variety of neurodegenerative diseases [128].

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DPI

NADP+ + H+ + O2

PIP345

.-

Tocopherol

cytb558

p47 p67 p40

NADPH + O2 PKC

Tocopherol

p47 p40 p67

PMA

Figure 7. Inhibition of NADPH-oxidase assembly by α-tocopherol. Activation of protein kinase C (PKC) is required for the assembly of the subunits of NADPH-oxidase at the plasma membrane. The NADPH-oxidase subunits bind membrane phosphatidylinositols, such as PI3P, PI34P and PI345P. αTocopherol reduces oxidative stress by scavenging superoxide and by inhibiting the assembly of an active NADPH-oxidase [88]. DPI: Diphenyleneiodonium sulfate, an inhibitor of NADPH-oxidase.

Similarly, in alcoholic liver disease, NADPH-oxidase-derived free radicals are key oxidants [137], and inhibition of NADPH-oxidase by diphenyleneiodonium sulfate (DPI) prevents early alcohol-induced liver injury in the rat [138]. These results suggest that αtocopherol could have beneficial effects in liver fibrosis by reducing NADPH-oxidase activity. Interestingly, the assembly of NADPH-oxidase is mediated by activation of

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phosphatidylinositol-3-kinase (PI3K), which is involved in the recruitment of the subunits to the plasma membrane [139]. At least in vitro, α-tocopherol modulates via hTAP proteins PI3K activity [140], suggesting that α-tocopherols could also be involved in the early activation steps of the PI3K/NADPH-oxidase pathway.

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MODULATION OF PROTEASOME ACTIVITY BY TOCOPHEROLS The 20S proteasome plays a central role for the degradation of oxidized proteins in an ATP independent manner [141, 142], whereas the 26S proteasome recognizes mainly ubiquitinated proteins. Both proteasomes are inhibited by oxidative stress suggesting that compounds with antioxidant action may be able to normalize proteasome activity after oxidative stress [143]. Direct oxidative modification was found to inhibit the proteasome after coronary occlusion/reperfusion [144]. On the other hand, oxidative stress was increased by inhibition of the proteasome by several proteasome inhibitors (Bortezomid, MG-132, PSI (Z-IE(OtBu)AL-CHO), lactacystin, epoxomicin), ultimately leading to apoptosis [145-147]. Bortezomib-induced oxidative stress and subsequent DNA damage was reduced by the free radical scavengers L-N-acetylcysteine and α-tocopherol [148]. Altogether, these results suggest that oxidative stress plays a central role in target recognition by the proteasome, as well as in modulating its proteolytic activity. Several compounds with antioxidant activity can modulate proteasome activity, but the detailed molecular mechanisms are still unclear. The polyhydroxyl compound Tiron, an antioxidant agent, blocked the bortezomid-induced production of oxidative stress and prevented mitochondrial cytochrome c release and cell death [147]. However, later studies suggested that Tiron functions as a competitive inhibitor of bortezomib and not as scavenger of reactive oxygen species produced as a consequence of bortezomib action [149]. The inhibitory effect of Tiron against bortezomib was selective, since it was not shared by other antioxidants, such as vitamin E, MnTBAP, L-N-acetyl-cysteine, and FK-506. The heat shock protein 90 protected the 20S proteasome from oxidative inactivation [150]. Glutathione ameliorated proteasome activity after inhibition by the lipid peroxidation product, 4hydroxynonenal, in aging spinal cord [151]. The two antioxidant compounds, BO-653 and probucol, inhibited the expression of three α-type proteasome subunits, PMSA2, PMSA3 and PMSA4 in human umbilical vein endothelial cells leading to inhibition of proteasome activity possibly independent of their antioxidant function [152]. Genistein, a soy isoflavone, inhibited the proteasomal chymotrypsin-like activity in vitro and in vivo. In prostate cancer (LNCaP) and breast cancer (MCF-7) cells, proteasome inhibition by genistein is associated with accumulation of ubiquitinated proteins and three known proteasome target proteins, the cyclin-dependent kinase inhibitor p27(Kip1), inhibitor of nuclear factor-kappa B (I kappa Balpha), and the pro-apoptotic protein Bax, and was accompanied by induction of apoptosis in these solid tumor cells [153]. The ester bond-containing tea polyphenols, such as epigallocatechin-3-gallate (EGCG), and the dietary polyphenol known as tannic acid (TA), which contains multiple similar gallate moieties linked by ester bonds, inhibit the proteasome activity in vitro and in vivo at the concentrations found in the serum of green tea drinkers [154, 155]. Troglitazone, a selective ligand for PPARγ containing a chromanol moiety similar

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to α-tocopherol, inhibited the proteasome [156, 157]. Resveratrol, a naturally occurring polyphenol, increased β-amyloid clearance in a proteasome-dependent manner [158]. Resveratrol inhibited human ovarian cancer progression and angiogenesis by induction of HIF-1α protein degradation through the proteasome pathway leading to inhibition of HIF-1α and VEGF expression [159]. Several lines of evidences suggest that tocopherols can influence proteasome activity; however, a direct modulation of proteasomal activity by vitamin E has not yet been demonstrated. Tocotrienol enhanced the degradation of apolipoprotein B, possibly by diverting more apoB into a cytosolic proteasome-dependent degradation pathway [35]. During myocardial ischemia/reperfusion proteasome activity is inhibited [144], and γtocotrienol can restore both 20S- and 26S- proteasome activities and inhibits the phosphorylation of c-Src [41]. In rat hepatoma cells, serum deprivation leads to activation of the proteasome and higher oxidative stress, as well as to a reduction of glutathione levels and induction of apoptosis, and these events are prevented by melatonin or vitamin E [160]. The inhibition of proteasome activity by ritonavir is normalized by α-tocopherol but the mechanism for this is not yet clear. Ritonavir is a relatively weak and reversible inhibitor of the chymotrypsin-like activity of the 20S proteasome by binding to the human proteasome subunits MB-1(X) and LMP7 [161]. It has a similar potency as the covalent and strongly acting proteasome inhibitor, ALLN [162]. In yeast, ritonavir and ALLN both were suggested to bind to the active center of the PRE2 subunit (which is the Saccaromyces cerevisiae homologue of human subunit MB-1(X)) [161]. Thus, α-tocopherol may directly interfere with ritonavir binding, whereas the covalent binding of ALLN may not be reversible. Alternatively, the proteasome activity may be normalized by α-tocopherol, much like it was shown with fatty acids and phospholipids [163-165], sodium dodecylsulphate (SDS) [163, 166], or cardiolipin [164]. It is also possible that ritonavir and α-tocopherol compete during the transport to the proteasome, or that they modulate proteasome activity indirectly (e. g. by changing the phosphorylation, the expression or the assembly of the regulatory/catalytic subunits). Interestingly, GeneChip analysis of vitamin E deficient mice showed that indeed the expression of some proteasome subunits is modulated by α-tocopherol [167]. Since the proteasome becomes inactivated by oxidative stress [143], and ritonavir is known to elevate superoxide production via NADPH-oxidase [168], α-tocopherol could also affect proteasome activity by reduction of superoxide production resulting from inhibiting the assembly of NADPH-oxidase [88].

MODULATION OF GENE EXPRESSION BY VITAMIN E Regulation of gene expression requires transcription factors and the specific cellular effects of the tocopherols analogues require proteins capable of distinguishing the different tocopherols from each other, from tocotrienols and from synthetic analogues. Several genes have been described as being modulated by tocopherol (reviewed in [8, 79]). However, the mechanism by which tocopherol can modulate gene expression is not yet clearly resolved, and indeed it may involve several different molecular mechanisms. To explain all the effects seen at the level of gene expression several regulatory pathways have to be considered:

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Jean-Marc Zingg and Angelo Azzi (1) α-Tocopherol could act as an antioxidant, and therefore could change the activity of redox-sensitive transcription factors and signal transduction pathways modulated by pro- and anti-oxidants conditions [169]. Moreover, α-tocopherol modulates specifically the activity of some enzymes, such as protein kinase C alpha (PKCα) [2], protein kinase B (PKB) [24], phospholipase A2 [123], 5-lipoxygenase [131], and cyclooxygenase 2 [170] and these events can influence gene expression indirectly, via the modulation of signal transduction. (2) α-Tocopherol may also influence gene expression by modulating the activity of specific transcription factors in a non-antioxidant fashion, for example via the pregnane X receptor [66], nuclear receptors such as the peroxisome proliferators activated receptors (PPARs), other orphan nuclear receptors, or possibly via one of three human tocopherol associated proteins, hTAPs [115]. Indeed, hTAP1 was recently reported to modulate gene expression [115, 171, 172]. Moreover, the tocopherols have been shown to induce PPARγ gene expression in a dosedependent manner in stimulated and non-stimulated mouse splenocytes and in human keratinocytes [173, 174]. (3) α-Tocopherol may also influence gene expression by binding to proteins like hTAP that may act as ―molecular chaperones‖ for the water-insoluble tocopherols, thus generating specificity to the action of the different tocopherols. These proteins may regulate tocopherol access to specific enzymes and transcription factors or control the level of ―free‖ tocopherol. The hTAPs modulate in vitro the activity of recombinant phosphatidylinositol-3-kinase (PI3K) and α-tocopherol modulates kinase activity in an hTAP-dependent manner, possibly by competition with phosphatidylinositol. Thus, by modulating the intracellular targeting of the ligands to enzymes and organelles, the hTAPs may influence the activity of lipid dependent enzymes [115]. The TAP1 protein increases the cellular concentration of αtocopherol, possibly leading to modulation of enzymes requiring a higher concentration of α-tocopherol [116]. (4) Tocopherols may be metabolized to other bioactive compounds, which can bind to transcription factors and enzymes and modulate their activity. The metabolite of γtocopherol, γ-CEHC has natriuretic activity by inhibition of the 70 pS potassium channel of the thick ascending limb of the loop of Henle without inhibiting the Na+/K+-ATPase. The analogous α-tocopherol metabolite showed no inhibition [62]. γ-CEHC inhibits also cyclooxygenase-2 (COX-2) and prostaglandin E2 (PGE2) synthesis in activated macrophages and epithelial cells, events that could change the cellular behavior and affect gene expression [63, 64]. A metabolite of vitamin E, 2,2,5,7,8-pentamethyl-6-chromanol (PMCol), inhibits growth of androgen-sensitive prostate carcinoma cells, which is due to the potent anti-androgenic activity of this compound [175]. Recently, a novel form of tocopherol, α-tocopheryl phosphate, was found to be present in the diet as well as in human tissues [176]. α-Tocopheryl phosphate is more potent in inhibiting cell proliferation and CD36 scavenger receptor expression [177], suggesting a potential role in mediating the effects of vitamin E.

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MODULATION OF GENE EXPRESSION BY NATURAL TOCOPHEROLS Since α-tocopherol modulates enzymes involved in signal transduction such as PKC, and PKC ultimately regulates the phosphorylation of several transcription factors, it was postulated that several genes may be regulated by tocopherols (reviewed in [2, 4, 8, 79]. Indeed, the expression of the aryl hydrocarbon hydroxylase gene, a P450 oxygenase (P1-450) involved in the detoxification of polynuclear aromatic hydrocarbons and in the disposition of certain drugs, was affected by vitamin E [178]. Early studies have shown that α-tocopherol can modulate several PKC-regulated genes. α-Tocopherol, but not β-tocopherol, can regulate AP-1-mediated gene expression [179, 180], in particular after activation of PKC by 12-O-Tetradecanoylphorbol 13-acetate (TPA). αTocopherol also increased de novo synthesis of protein kinase C molecules [181]. In human skin fibroblasts, PKCα protein expression increases during in vivo aging as a function of the donor's age [182]. Concomitant with the increase in PKCα, also collagenase (MMP-1) gene transcription and protein expression increases with age. α-Tocopherol is able to diminish collagenase gene transcription without altering the level of its natural inhibitor, tissue inhibitor of metalloproteinase, TIMP-1 [182]. The regulation of α-tropomyosin gene transcription and protein expression was detected by using a differential display technique. Northern and Western blot analyses revealed a time-dependent transient up-regulation of the amount of α-tropomyosin mRNA (with a peak between 2 and 3 h) and protein (with a peak at 5 h) in α-tocopherol-treated cells. No effect was observed in cells treated with β-tocopherol [181, 183]. Glycoprotein IIb is the α-subunit of the platelet membrane protein glycoprotein IIb/IIIa, which functions as a specific receptor for platelet aggregation. Transient transfection of the glycoprotein IIb promoter-reporter plasmid into cells in which PKC was stimulated with TPA shows that α-tocopherol inhibits glycoprotein IIb promoter activity. This event may result in a reduction of glycoprotein IIb protein expression by α-tocopherol and thus contribute to anti-platelet aggregation [184]. The cytoplasmic retinoic acid binding protein II (CRABP-II) was up-regulated by the tocopherols by influencing PKC leading to phosphorylation/dephosphorylation of RXRα [185]. Later on, genes were found to be modulated by tocopherols independent of PKC, and specific regulatory elements were found in their promoters, but a specific transcription factor responsive only to tocopherol or a ―tocopherol nuclear receptor‖ has so far not been identified. In primary cultures of quiescent stellate cells, inhibition of collagen α1(I) transactivation by α-tocopherol requires only -0.44 kb of the 5' regulatory region. Transfection of stellate cells with a collagen-luciferase chimeric reporter construct allowed localization of an "antioxidant"-responsive element (ARE) [186, 187]. Long- and short-term supplementation with α-tocopherol of mice selectively decreases liver collagen mRNA by approximately 70% [187]. Similarly, chronic treatment of rats with carbon tetrachloride increases TGF-β1 gene expression and α-tocopherol inhibits both TGF-β and α2(I) procollagen mRNA expression [188]. α-Tocopherol induces a 2-3 fold increase of connective tissue growth factor (CTGF) expression in human vascular smooth muscle cells by a nonradical chain braking mechanism, and a TGF-β-response element which mediates the effect of α-tocopherol has been identified [189].

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A series of studies have pointed to the effects of α-tocopherol on monocytes/macrophages and smooth muscle cells with possible relationships with atherosclerosis and inflammatory events. The CD36 scavenger receptor (a specific receptor for oxidized LDL, oxLDL) is expressed in monocytes/macrophages and cultured human aortic smooth muscle cells. Studies indicate that CD36 transports oxLDL into the cytosol of these cells and that α-tocopherol inhibits oxLDL uptake by a mechanism involving downregulation of CD36 mRNA and protein expression. Therefore, the beneficial effect of αtocopherol against atherosclerosis can be explained, at least in part, by lowering the uptake of oxidized lipoproteins, with consequent reduction of foam cell formation [190, 191]. Interestingly, in THP-1 monocytes, α-tocopherol could antagonize oxLDL-induced CD36 expression, and inhibition of oxLDL-induced PKB phosphorylation was centrally involved in this event [192]. A reduction of the scavenger receptor SR-A expression and activity in the presence of α-tocopherol was also observed [193]. The role of α-tocopherol in diminishing scavenger receptor activity (CD36 and SR-B1) has been confirmed in vivo [194, 195]. Correspondingly, rats depleted of vitamin E show an increased expression of the scavenger receptor SR-B1 as well as CD36 [192, 196]. Isermann et al have shown that α-tocopherol induces leptin expression in vitro and also in healthy individuals [197].

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Vitamin E Modulation of Drug Metabolizing Genes Several cytochrome P(450) (CYP) genes, coding for enzymes usually involved in the metabolism of xenobiotics, are up-regulated by vitamin E (reviewed in [198, 199]). The expression of the CYP1A1, CYP1A2, CYP2B1, CYP2C genes increased by α-tocopherol treatment of rats [200, 201]. The CYP3A11, the murine homolog to human CYP3A4, is upregulated by α-tocopherol treatment of mice, whereas γ-tocotrienol has no effect [202]. In HepG2 cells, the endogenous CYP3A4 and CYP3A5 mRNA was up-regulated by γtocotrienol via the pregnane X receptor (PXR), a nuclear receptor regulating a variety of drug metabolizing enzymes, with the same efficacy as with rifampicin [66]. The glutathione Stransferase isozymes (GSTs) are involved in phase II metabolism; the tocopherols and tocotrienols inhibited the GST P, GST P1-1, GST M and GST A isozymes at the enzymatic level [133, 134, 203].

Vitamin E Modulation of Inflammatory Genes Several events that are associated with inflammation appear to be regulated by tocopherol (reviewed in [204, 205]). Treatment of cultured microglial cells with α-tocopherol and L-ascorbic acid induced ramified microglial morphology after 48 h in vitro. Ramification of microglia was accompanied by downregulated expression of adhesion molecules leukocyte function antigen-1, very late antigen-4, and intercellular adhesion molecule-1, as assessed by FACS analysis and immunocytochemistry [206]. The cytokine interleukin-1β (IL-1β) is decreased by α-tocopherol by a mechanism involving down-regulation of IL-1β mRNA expression. Combined α-tocopherol and selenium deficiency is characterized by alterations in

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the expression level of genes encoding for proteins involved in inflammation (multispecific organic anion exporter, SPI-3 serine protease inhibitor) and acute phase response (α-1 acid glycoprotein, metallothionein 1). Down regulation of matrix metalloproteinase-19 by αtocopherol has also been described [207]. The adhesion-dependent expression of matrix metalloproteinase is down-regulated or even abrogated by blockade of adhesion or interfering with adhesion-controlling signaling using α-tocopherol. Additionally, reduction of integrins expression by α- but not β-tocopherol has been observed, possibly reducing monocyte cell adhesion, an important event both in inflammation and atherosclerosis [208]. In an in vitro screening assay for anti-inflammatory agents, lipopolysaccharides (LPS) induced TNFαrelease from THP-1 cells was prevented by α-tocopherol, N-acetylcysteine, catechin, epigallocatechin gallate, as well as several pharmacological agents [209]. Similar to that, the inflammatory response to LPS in vitamin deficient α-TTP(-/-)-knockout mice was altered when compared to normal mice; in the vitamin E deficient mice, increased levels of IL-10 and lung lavage TNFα and less pronounced induction of heme oxygenase-1 and iNOS were observed [210].

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Vitamin E Modulation of Genes Involved in Cancer A protective role of tocopherols against a number of tumors has been described. Tumors development in animals exposed to 7,12 dimethylbenz(a)anthracene (DMBA) is significantly reduced after vitamin E supplementation which is possibly the result of a notable increased expression of the p53 tumor suppressor gene [211]. γ-Tocopherol inhibits human cancer cell cycle progression and prostate cell proliferation by down-regulating cyclins D1 and E [212]. γ-Tocopherol more significantly inhibits cell proliferation and DNA synthesis than αtocopherol, suggesting that a non-antioxidant mechanism is at the basis of these effects.

Hypoxia and Vitamin E A rat clonal pheochromocytoma cell line (PC12), cultured under normoxic (21% O2) and hyperoxic (50% O2) conditions, underwent apoptotic cell death when cultured in charcoalstripped medium in a high-oxygen atmosphere. α-Tocopherol was more effective than γ- and δ-tocopherol in preventing hyperoxia-induced cell death [213]. Vitamin E has also been shown to have protective effects against cerebral ischemia. In rats, orally administered vitamin E significantly reduced not only the brain infarct volume but also space navigation disability after permanent middle cerebral artery (MCA) occlusion. Vitamin E induced the expression of the alpha subunit of hypoxia-inducible factor-1 (HIF-1α) and its target genes, including vascular endothelial growth factor (VEGF), heme oxygenase-1, p21 and Glut3. The hypoxia response element on the VEGF promoter was responsible for this vitamin E-induced transcriptional activation of the VEGF gene. Cerebral infarction increased the permeability of vitamin E across the blood-brain barrier, and the resulting increased vitamin E level in brain tissue elicited neuroprotective effects not only through scavenging oxidants, but also by

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transactivating HIF-1-dependent genes, which results in protection of brains from ischemic insults [214]. Native LDL isolated from hypercholesterolemic mini-pigs induced endothelial VEGF and VEGFR-2 overexpression by increasing oxidative stress (superoxide anion, O2-), and HIF-1α is one of the signaling mechanisms involved. Contrary to the above effects, the addition of vitamin C and α- or β-tocopherol to the culture medium prevented the induction of VEGF and VEGFR-2 expression by LDL from hypercholesterolemic mini-pigs, both at mRNA and protein levels. Prevention of VEGF and VEGFR-2 upregulation could help explain the beneficial effects of vitamins C and E in hypercholesterolemia-induced experimental atherosclerosis [215]. Hypoxia in tumors is generally associated with chemoresistance and radioresistance. With increasing size of DU-145 prostate multicellular tumor spheroids the pericellular oxygen pressure and the generation of reactive oxygen species decreased, whereas the alphasubunit of HIF-1 (HIF-1α) and the multidrug resistance (MDR) transporter P-glycoprotein (Pgp) were up-regulated. The pro-oxidants H2O2 and buthionine sulfoximine down-regulated HIF-1α and P-glycoprotein, whereas up-regulation was achieved with the radical scavengers dehydroascorbate, N-acetylcysteine, and vitamin E [216]. Similar to that, in Nox-1overexpressing DU-145 prostate tumor spheroids (DU-145Nox1) generation of ROS as well as expression of Nox-1 was significantly increased as compared to DU-145 tumor spheroids. In DU-145Nox1 tumor spheroids, expression of HIF-1α as well as P-glycoprotein was significantly decreased as compared to DU-145 spheroids, which resulted in an increased retention of the anticancer agent doxorubicin. Pretreatment with the free radical scavengers vitamin E and vitamin C increased the expression of P-glycoprotein as well as HIF-1α in DU145Nox1 cells, whereas no effect of free radical scavengers was observed on multidrug resistance (MDR) transporter-1 mRNA expression [217].

Modulation of Gene Expression by Tocotrienols Tocotrienols possess powerful neuroprotective, anti-cancer and cholesterol lowering properties that are often not exhibited by tocopherols (reviewed in [218]). Potential antiproliferative effects of tocotrienols, the major vitamin E component in palm oil, were investigated with both estrogen-responsive and estrogen-unresponsive human breast cancer cells. Complete suppression of growth is achieved at 8 µg/ml (in the estrogen-responsive) and at 20 µg/ml tocotrienol (in the estrogen unresponsive cells), in both the presence and absence of estradiol. The γ- and δ-tocotrienols are the most potent inhibitory forms [219]. αTocotrienol is the most effective vitamin E for reducing endothelial expression of adhesion molecules and consecutive adhesion of monocytes [44]. Supplementation with α-tocotrienol improves bone calcium content in vitamin E deficient rats, but supplementation with αtocopherol does not, suggesting that tocotrienols play an important role in bone calcification [220]. When compared to tocopherols, tocotrienols show additional effects in mammalian cells mainly by influencing the mevalonate pathway. Tocotrienols inhibit the 3-hydroxy-3methylglutaryl-coenzyme A reductase (HMG-CoA reductase) at the posttranscriptional level

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by specifically modulating the intracellular mechanism for its controlled degradation. γTocotrienol inhibits the rate of [14C]-acetate but not [3H]-mevalonate incorporation into cholesterol in a concentration- and time-dependent manner, with 50% inhibition observed at approximately 2 µM. Maximum inhibition (80%) was observed in HepG2 cells within 6 h. HMG-CoA-reductase total activity and protein levels are reduced concomitantly with the decrease in cholesterol synthesis [34]. Tocotrienols were recently reported to increase transcription of IKAP mRNA in patients with familial dysautonomia, a neurodegenerative genetic disorder that is caused by mutations in the IKBKAP gene which encodes the IkappaB kinase complex-associated protein (IKAP). These findings suggest that in vivo supplementation with tocotrienols may elevate IKBKAP gene expression [221].

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MODULATION OF GENE EXPRESSION AS ANALYZED BY GENE ARRAY EXPERIMENTS In recent years gene expression arrays have allowed to screen for genes modulated by vitamin E. Cell culture experiments allow to isolate responsive genes that are immediately regulated by vitamin E. These studies may give insight into the regulatory mechanisms modulated by vitamin E, such as specific enzymes involved in signal transduction and possible transcription factors involved in gene expression. In a first study using gene arrays, several genes were found to be consistently regulated by vitamin E in human smooth muscle cells, some of these genes were confirmed by other methods (like the CTGF gene and the prostacyclin stimulating factor) [189]. Recently, in another cell culture study using gene array experiments and HepG2 hepatoma cells, the natural RRR-α-tocopherol was found to have similar effects on gene expression as the synthetic all-rac-α-tocopherol [222]. However, the evidence of a regulatory function by α-tocopherol for most of these genes still needs confirmation by other methods. Animal studies allow screening for genes that ultimately may be the causes of diseases associated with vitamin E deficiency. However, since the vitamin E deficient state needs long time to be reached, these studies may also show genes that are secondary or tertiary modulated by vitamin E. A myriad of genes were found to be regulated in rats by combined selenium and vitamin E deficiency [223]. Vitamin E increases the expression level of genes important in the inhibition of apoptosis (defender against cell death 1 protein, Bcl2-L1), in cell cycle progression (G1/S-specific cyclin D1) and in antioxidant defense (γglutamylcysteine synthetase catalytic subunit) [223]. In another study, analysis of the expression pattern of over 7000 genes by comparing normal and vitamin E deficient rats revealed, that vitamin E supplementation down-regulated scavenger receptor CD36, coagulation factor IX and 5-alpha-steroid reductase type 1 mRNA levels, while hepatic gamma glutamyl-cysteinyl synthetase was significantly up-regulated [224]. Several genes were found to be modulated by tocotrienols in developing rat fetal brain using high density oligonucleotide microarray analysis, what is possible involved in the neuroprotective properties of the tocotrienols [225]. Using GeneChip analysis and the rat Dunning prostate cancer model it was found that vitamin E and lycopene suppressed several genes involved in steroid metabolism and signalling [226]. In rat hippocampus, α-tocopherol modulated the

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expression of a set of genes with possible involvement in the clearance of β-amyloid, what is consistent with a protective effect of vitamin E on alzheimer‘s disease [227]. Vitamin E deficiency induced several genes related to testosterone synthesis and cell cycle progression in rat testes (7-dehydrocholesterol reductase and GATA binding protein 4, cyclin D3 and wilms tumor 1) [228] Vitamin E and selenium supplementation was also studied in rats fed a high fat diet and several genes in rat skeletal muscle were modulated by supplementation [229]. In rats fed vitamin E deficient diet many genes show an altered expression level, and vitamin E (αtocopherol and α-tocotrienol) supplementation regulates some of them [225]. The modulation of some of these genes were confirmed by RT-PCR; heme oxygenase 3 (HO-3), cyclin D1, high mobility group protein 1 (HMG1), and nuclear phosphoprotein p140 (NOPP140) are upregulated and the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is down-regulated. Although it is mechanistically interesting that vitamin E can modulate gene expression in vivo, these changes, in order to be relevant for the prevention of disease, should be detectable at the protein level. Many of the above described vitamin E regulated genes show also alterations at the protein level, others have not been tested. The proteomics experimental approach was taken in one study using human cytokine antibody arrays to show that vitamin E can affect many genes at the protein level in healthy human individuals [230]. Several cytokines, like the monocyte chemoattractant protein 1 (MCP-1), ENA-78, IL-1α, RANTES, MIG and TNF-β were significantly down-regulated by supplementation with vitamin E [230]. Some of these chemokines, like RANTES, MCP-1 and MIP-1α, were also regulated by vitamin E in other studies [231].

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NOVEL TOCOPHEROL BINDING PROTEINS Several proteins have been shown to play a role in the uptake of distribution of vitamin E. Uptake of vitamin E is mediated by the scavenger receptor SR-BI in enterocytes as well has in brain capillary endothelial cells at the blood-brain barrier [196, 232]. Moreover, the plasma phospholipid transfer protein (PLTP) enhances the vitamin E exchange between lipoproteins and between lipoproteins and cells [233]. PLTP-deficient mice show reduced vitamin E content in brain [234] and testes [235], and elevated lipofuscin, cholesterol oxides and cellular peroxides in brain. These mice had increased levels of vitamin E in circulating apoB-lipoprotein containing lipoproteins at the expense of the vascular wall [236]. The effects of vitamin E on signal transduction and gene expression could be the result of direct binding to enzymes and transcription factors, or they could be mediated by tocopherol binding proteins, which either regulate the cellular vitamin E uptake and distribution or interact specifically with cellular targets. Several cellular tocopherol binding proteins have been studied [237-241], but so far only α-TTP and TAPs have been cloned and shown in vitro to bind tocopherol with reasonable affinity [242]. Initially, the novel α-TocopherolAssociated Protein (TAP) has been isolated from bovine and human liver [243]; later it was found that the TAP protein is identical to the previously described microsomal supernatant protein factor (SPF), which stimulates squalene epoxidation required for cholesterol synthesis, either by directly stimulating squalene transport, by modulation of HMG-CoA

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reductase, or possibly by increasing the transport of vesicles carrying squalene [244, 245]. Sequence analysis has established that three TAP genes exist in the human genome, and that the TAPs structural motifs have similarity with a family of hydrophobic ligand-binding proteins (RALBP, CRALBP, α-TTP, SEC14, PTN 9, RSEC45). The TAP proteins may be involved in the regulation of cellular α-tocopherol concentration, tocopherol transport and αtocopherol-mediated signaling [246]. In line with this, the expression of TAP1 increased the level of α-tocopherol and α-tocopheryl succinate, suggesting that intracellular vitamin E transport is one of the functions of the TAP proteins [116]. However, this protein is also known to bind phospholipids, such as phosphatidylglycerol, phosphatidylinositol, phosphatidylserine, and phosphatidic acid [247]. Whether these ligands bind to the TAP proteins within cells depends on their affinity and concentration; in vitro KD measurements of several ligands suggest that only a few of them may occur at high enough concentrations to bind within cells (Table 2); nevertheless, it has to be considered that locally, e.g., in membranes or organelles, a ligand concentration may be reached that allows binding. Several human TAPs (hTAP1, hTAP2 and hTAP3; also named sec14-like 2, sec14-like 3, and sec14-like 4, respectively) have been cloned recently and their localization, ligand binding ability and cellular functions are presently under investigation. The hTAPs can bind phosphatidylinositol, phosphatidylcholine, and the tocopherols can compete with binding, suggesting that the tocopherols may modulate via hTAP phospholipid-dependent signaling pathways [115, 246]. The hTAP proteins could be involved in tocopherol transport to the Golgi apparatus or to the mitochondria, since they carry a carboxy-terminal GOLD domain which in other proteins (GCP60, PAP7) is known to serve as adaptor for binding to Golgi giantin or to the mitochondrial peripheral benzodiazepine receptor [115, 251]. By construction of a fusion between hTAP1 and the green fluorescent protein (GFP), it was observed that TAP1 translocates from cytosol to nuclei in an α-tocopherol-dependent manner [171]. As described above, vitamin E activates the human pregnane X receptor (PXR) in HepG2 cells [66]. The role of TAPs and similar proteins may be that of conferring specificity to the action of the different tocopherols, through recognition and selective transport to enzymes, transcription factors, nuclear receptors such as PXR, or organelles (Figure 8). Indeed, the hTAP1 protein recognizes the different natural tocopherols with different specificity (Table 2) [242]. The main function of the α-tocopherol transfer protein (α-TTP) in the liver is to incorporate α-tocopherol into VLDL; the low level or absence of α-TTP expression in most tissues suggests that α-TTP does not participate in the intra-cellular distribution in other tissues than liver. Since in human endothelial cells in culture, α-tocopherol and γ-tocopherol are differently taken up, it seems probable that the cellular transport in peripheral cells is also specific and requires tocopherol specific transporters and receptors [252]. In particular, the relatively high level of γ-tocopherol in skin (Figure 9) could be explained by selective enrichment of γ-tocopherol by proteins such as TAP, which has a relatively high affinity to γtocopherol (Table 2). In this context it is interesting to mention that vitamin E (α- and γtocopherol) delivery to facial skin by sebaceous glands has been correlated with the secretion of squalene, which can also bind to TAP proteins and thus may be co-secreted with tocopherols [253].

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122

Table 2. Dissociation constants KD (μM) of hTAP1 and α-TTP Compound

α-Tocopherol β-Tocopherol γ-Tocopherol δ-Tocopherol αTocopherylquinone Squalene Phosphatidylinosito l Phosphatidylcholine

hTAP1 KD (μM) [242] 0.615 0.393 0.268 0.731 0.441 0.879 0.216

Plasma concentration (μM) [248-250]

α-TTP KD (μM) [242] 0.025 0.124 0.266 0.586 0.814

20-30* 0.3 0.5-1* 0.3 0.4

1.415

0.6 55*

1.186

1764*

*Plasma concentration higher than KD of hTAP1

Enrichment of -tocopherol in organelles (Golgi, mitochondria)

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Intracellular distribution by tocopherol transport proteins (e. g. TBP, TAPs)

Modulation of signal transduction

Modulation of gene expression

PLTP LDLR LDL

Tocopherol export and secretion

SR-BI

HDL

LDLR, LRP

Metabolism of tocopherols and tocopherylquinones

ABCA1

Chylomicrons and chylomicron remnants HDL Chylomicrons

Tocopherol metabolites (CEHCs)

Figure 8. Intracellular distribution of tocopherol by tocopherol binding proteins in a peripheral cell. The tocopherols are taken up from lipoproteins (LDL, HDL, chylomicrons) via their receptors (LDLR, LDL receptor; SR-BI, scavenger receptor BI; LRP, LDL receptor related protein) and the plasma phospholipid transfer protein (PLTP). Specific tocopherol transport proteins may distribute the tocopherols to organelles, enzymes involved in signal transduction and possibly to nuclear receptors modulating gene expression. Similar to α-TTP which in the liver incorporates selectively α-tocopherol into VLDL (Figure 3), these proteins may also be involved in the incorporation of tocopherols into HDL or chylomicrons, as well as in their metabolic degradation [7].

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The hTAP proteins are candidates for the intracellular distribution of vitamin E, since they are widely expressed, can bind natural tocopherol analogues with reasonable affinity, and carry a carboxy-terminal GOLD domain which can serve as targeting domain for organelles such as Golgi and mitochondria [115, 251]. Overexpression of mouse TAP1 increased the cellular uptake of tocopherol, suggesting that TAP play a role in intracellular distribution of tocopherol in the cell [116]. Very recently it has been found that vitamin E is enriched at the soma-neurite junction and plays a direct role in regulation of adult hippocampal neurogenesis, a process possibly requiring specific intracellular transporter protein [254]. Moreover, in analogy with α-TTP, the TAP proteins could be involved in the incorporation of tocopherols into chylomicrons during their assembly in the intestine [255]. It remains to be determined whether the tocopherol specific transport seen in a number of cell systems is the result of proteins such as TAP or possibly other proteins. A further 14.2 kDa tocopherol binding protein (TBP) has been described to enhance up to 10 fold the transport of α-tocopherol to the mitochondria, but the identity of this protein is still unresolved [238]. 13%

T

2%

2%

T T

Plasma

T 9%

83% 31% 38% 47% 53%

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Epidermis

Adipose

69%

Skin

Muscle

62%

91%

Figure 9. Relative distribution of tocopherols in human plasma and tissues. Most tissues and plasma contain mainly α-tocopherol as a result of selective retention and enrichment by the liver α-tocopherol salvage pathway (Figure 3) [8]. Relatively high levels of γ-tocopherol in certain tissues like skin and muscle can be explained by the usually high level of γ-tocopherol in the diet (Figure 2), the postprandial absorption from chylomicrons (Figure 3), and possibly by selective enrichment of γ-tocopherol by specific transport proteins (Figure 8).

To date no diseases linked with the hTAPs proteins have been described. Since there are three relatively closely related TAP genes in the human genome, a genetic disease may only show up if the three genes perform non-redundant functions. However, mutations in related proteins with similar function can lead to hereditary disease, clearly showing the importance of a correct protein-mediated distribution of hydrophobic ligands. The Drosophila melanogaster RdgB protein prevents retinal degeneration [256]. The vibrator knockout mouse (vb-/vb-), which is deficient in the phosphatidylinositol-transfer-protein alpha (PITPα), shows degeneration of neurons of the spinal chord, brain stem and dorsal root ganglions, leading to symptoms similar to progressive neurodegenerative disorders [257]. CRALBP deficient humans show autosomal recessive retinitis pigmentosa, caused by a

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deficient transport of 11-cis-retinol and 11-cis-retinaldehyde [258]. Patients with ataxia with vitamin E deficiency (AVED), caused by α-TTP gene mutations, are affected by ataxia, loss of neurons, retinal atrophy, massive accumulation of lipofuscin in neurons and retinitis pigmentosa [259]. The Meg2 phosphotyrosine phosphatase (PTP-MEG2), which has a sec14like domain similar to TAP, is activated in polycythemia vera, a clonal myeloproliferative disorder with increased production of red cells, granulocytes and platelets [260].

CONCLUSION The term vitamin E was originally used to summarize the essential function of this group of compounds in preventing fetal resorption in rats. Later on, vitamin E was shown in vitro and in vivo to act as an antioxidant in several experimental systems, but this may not explain the essentiality of vitamin E. In fact, the predominant neurodegenerative symptoms of vitamin E deficiency in humans, as well as many results from animal studies, suggest that vitamin E is essential for the optimal function, in particular of the nervous system, whereas other organs are less affected. Moreover, the results reviewed in this chapter suggest that the tocopherols and tocotrienols show a number of cellular activities, such as the modulation of signal transduction and gene expression, that do not necessarily result from an antioxidant action. Further research is required to define which of these activities render tocopherol (and in particular α-tocopherol) an essential nutrient—a vitamin—in humans.

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[37] Black, T. M., Wang, P., Maeda, N. and Coleman, R. A. (2000) Palm tocotrienols protect ApoE +/- mice from diet-induced atheroma formation, J Nutr. 130, 2420-6. [38] Khanna, S., Roy, S., Ryu, H., Bahadduri, P., Swaan, P. W., Ratan, R. R. and Sen, C. K. (2003) Molecular basis of vitamin E action. Tocotrienol modulates 12-lipoxygenase, a key mediator of glutamate-induced neurodegeneration, J. Biol. Chem. [39] Mo, H. and Elson, C. E. (2004) Studies of the isoprenoid-mediated inhibition of mevalonate synthesis applied to cancer chemotherapy and chemoprevention, Exp Biol Med (Maywood). 229, 567-85. [40] Mo, H. and Elson, C. E. (1999) Apoptosis and cell-cycle arrest in human and murine tumor cells are initiated by isoprenoids, J Nutr. 129, 804-13. [41] Das, S., Powell, S. R., Wang, P., Divald, A., Nesaretnam, K., Tosaki, A., Cordis, G. A., Maulik, N. and Das, D. K. (2005) Cardioprotection with Palm Tocotrienol: Antioxidant Activity of Tocotrienol Is Linked with Its Ability to Stabilize Proteasomes, Am J Physiol Heart Circ Physiol. [42] Osakada, F., Hashino, A., Kume, T., Katsuki, H., Kaneko, S. and Akaike, A. (2004) Alpha-tocotrienol provides the most potent neuroprotection among vitamin E analogs on cultured striatal neurons, Neuropharmacology. 47, 904-15. [43] Theriault, A., Chao, J. T., Wang, Q., Gapor, A. and Adeli, K. (1999) Tocotrienol: a review of its therapeutic potential, Clin Biochem. 32, 309-19. [44] Theriault, A., Chao, J. T. and Gapor, A. (2002) Tocotrienol is the most effective vitamin E for reducing endothelial expression of adhesion molecules and adhesion to monocytes, Atherosclerosis. 160, 21-30. [45] Noguchi, N., Hanyu, R., Nonaka, A., Okimoto, Y. and Kodama, T. (2003) Inhibition of THP-1 cell adhesion to endothelial cells by alpha-tocopherol and alpha-tocotrienol is dependent on intracellular concentration of the antioxidants, Free Radic. Biol. Med. 34, 1614-20. [46] Yu, W., Simmons-Menchaca, M., Gapor, A., Sanders, B. G. and Kline, K. (1999) Induction of apoptosis in human breast cancer cells by tocopherols and tocotrienols, Nutr Cancer. 33, 26-32. [47] McIntyre, B. S., Briski, K. P., Tirmenstein, M. A., Fariss, M. W., Gapor, A. and Sylvester, P. W. (2000) Antiproliferative and apoptotic effects of tocopherols and tocotrienols on normal mouse mammary epithelial cells, Lipids. 35, 171-80. [48] Sylvester, P. W., McIntyre, B. S., Gapor, A. and Briski, K. P. (2001) Vitamin E inhibition of normal mammary epithelial cell growth is associated with a reduction in protein kinase C(alpha) activation, Cell. Prolif. 34, 347-57. [49] Komiyama, K., Iizuka, K., Yamaoka, M., Watanabe, H., Tsuchiya, N. and Umezawa, I. (1989) Studies on the biological activity of tocotrienols, Chem Pharm Bull (Tokyo). 37, 1369-71. [50] Sylvester, P. W. and Shah, S. J. (2005) Mechanisms mediating the antiproliferative and apoptotic effects of vitamin E in mammary cancer cells, Front Biosci. 10, 699-709. [51] Khanna, S., Roy, S., Slivka, A., Craft, T. K., Chaki, S., Rink, C., Notestine, M. A., Devries, A. C., Parinandi, N. L. and Sen, C. K. (2005) Neuroprotective Properties of the Natural Vitamin E {alpha}-Tocotrienol, Stroke.

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[182] Ricciarelli, R., Maroni, P., Ozer, N., Zingg, J. M. and Azzi, A. (1999) Age-dependent increase of collagenase expression can be reduced by alpha-tocopherol via protein kinase C inhibition, Free Radic. Biol. Med. 27, 729-37. [183] Aratri, E., Spycher, S. E., Breyer, I. and Azzi, A. (1999) Modulation of alphatropomyosin expression by alpha-tocopherol in rat vascular smooth muscle cells, FEBS Lett. 447, 91-4. [184] Chang, S. J., Lin, J. S. and Chen, H. H. (2000) Alpha-tocopherol downregulates the expression of GPIIb promoter in HEL cells, Free Radic. Biol. Med. 28, 202-7. [185] Gimeno, A., Zaragoza, R., Vina, J. R. and Miralles, V. J. (2004) Vitamin E activates CRABP-II gene expression in cultured human fibroblasts, role of protein kinase C, FEBS Lett. 569, 240-4. [186] Houglum, K., Venkataramani, A., Lyche, K. and Chojkier, M. (1997) A pilot study of the effects of d-alpha-tocopherol on hepatic stellate cell activation in chronic hepatitis C, Gastroenterology. 113, 1069-73. [187] Chojkier, M., Houglum, K., Lee, K. S. and Buck, M. (1998) Long- and short-term Dalpha-tocopherol supplementation inhibits liver collagen alpha1(I) gene expression, Am. J. Physiol. 275, G1480-5. [188] Parola, M., Muraca, R., Dianzani, I., Barrera, G., Leonarduzzi, G., Bendinelli, P., Piccoletti, R. and Poli, G. (1992) Vitamin E dietary supplementation inhibits transforming growth factor beta 1 gene expression in the rat liver, FEBS Lett. 308, 26770. [189] Villacorta, L., Graca-Souza, A. V., Ricciarelli, R., Zingg, J. M. and Azzi, A. (2003) Alpha-Tocopherol Induces Expression of Connective Tissue Growth Factor and Antagonizes Tumor Necrosis Factor-Alpha–Mediated Downregulation in Human Smooth Muscle Cells, Circ. Res. 92, 104-110. [190] Ricciarelli, R., Zingg, J. M. and Azzi, A. (2000) Vitamin E reduces the uptake of oxidized LDL by inhibiting CD36 scavenger receptor expression in cultured human aortic smooth muscle cells, Circulation. 102, 82-87. [191] Devaraj, S., Hugou, I. and Jialal, I. (2001) Alpha-tocopherol decreases CD36 expression in human monocyte-derived macrophages, J. Lipid. Res. 42, 521-7. [192] Munteanu, A., Zingg, J. M., Ricciarelli, R. and Azzi, A. (2005) CD36 overexpression in ritonavir-treated THP-1 cells is reversed by alpha-tocopherol, Free Radic Biol Med. 38, 1047-56. [193] Teupser, D., Thiery, J. and Seidel, D. (1999) Alpha-tocopherol down-regulates scavenger receptor activity in macrophages, Atherosclerosis. 144, 109-15. [194] Fuhrman, B., Volkova, N. and Aviram, M. (2002) Oxidative stress increases the expression of the CD36 scavenger receptor and the cellular uptake of oxidized lowdensity lipoprotein in macrophages from atherosclerotic mice: protective role of antioxidants and of paraoxonase, Atherosclerosis. 161, 307-16. [195] Witt, W., Kolleck, I., Fechner, H., Sinha, P. and Rustow, B. (2000) Regulation by vitamin E of the scavenger receptor BI in rat liver and HepG2 cells, J. Lipid Res. 41, 2009-16.

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Chapter 4

VITAMIN E ACTIVITY IN IMMUNE RESPONSE A POSSIBLE IMMUNOHENANCING ROLE IN CHRONIC VIRAL INFECTIONS† Sirio Fiorino1, Florian Bihl2, Annagiulia Gramenzi1, Stefania Lorenzini1, Carmela Cursaro1, Elisabetta Loggi1, Cinzia Fortini1, Mauro Bernardi1 and Pietro Andreone1, 1

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2

University of Bologna‘ Bologna., Italy; Partners AIDS Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA;

ABSTRACT In recent years, many roles of Vitamin E have been demonstrated and include not only antioxidant functions, but also cell signaling and immunemodulatory functions. In this paper the experimental and clinical evidence of vitamin E immunomodulatory properties as well as the vitamin E biological activities able to stimulate and enhance immune activities are briefly reviewed. Several lines of evidence suggest that vitamin E might be useful to improve immune unresponsiveness induced by several pathological conditions such as chronic viral infections and make its use in this setting promising. Further studies investigating the effects of Vitamin E on are of interest as they might help to identify its therapeutic utility in chronic infections.

Keywords: Vitamin E, immune response, interleukins, aging, viral infections. †

A version of this chapter was also published in New Topics in Vitamin E Research, edited by Oliver H. Bellock published by Nova Science Publishers, Inc. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. Correspondence concerning this article should be addressed to: Pietro Andreone, MD. Dipartimento di Medicina Interna, Cardioangiologia, Epatologia -Università di Bologna, Policlinico S.Orsola-Malpighi, via Massarenti 9, 40138 Bologna (ITALY). e-mail: [email protected]; phone: +39.051.6363618; fax: +39.051.345806.

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NORMAL IMMUNITARY SYSTEM FUNCTION A specific and efficacious immune response against intracellular pathogens such as viruses requires a fine and tightly regulated interaction and cooperation in the context of a dynamic network among antigens, responding cells, cytokines and accessory molecules and includes several key steps [2]: antigen-presenting cells (APCs) capture and process antigens by endocytosis and proteolysis and exhibit protein fragments on their cellular membrane to Tlymphocytes [3]. While B-cells may be directly stimulated by viral determinants, through specific interaction with their receptors on cellular membrane [4] naïve T cells may be activated only by ―professional‖ APCs and once they recognize their specific antigens, they enter cell-cycle, proliferate and differentiate into cytotoxic and/or cytokine secreting cells [5]; cooperation between APCs and T helper cells results in B and CD8 T cells activation by production and releasing of a broad cytokine pattern [6]; finally, naive B lymphocytes may differentiate in secreting B plasma cells [7] and CD8 T lymphocytes perform their cytotoxic function against target infected cells.

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Specific interaction of CD4 T helper cell receptor with a processed viral epitope is restricted by an appropriate HLA class II molecule on APCs, in association with costimulatory molecules [8], while CD8 T cytotoxic cells recognise antigenic determinants displayed in the groove of APC-bound HLA I molecule [9]. Any obstacle in the sequence of T lymphocytes activation leads T cells to apoptosis or anergy.

TH1 AND TH2 EFFECTOR FUNCTION On the basis of the pattern of cytochine production, CD4 T-cell population can be differentiated into Th1 and Th2 cells, the former releasing IL-2, TNF- and interferon- , the latter IL-4, IL-5, IL-6 and IL-13 [10]. However, in the early stage of lymphocyte activation, T-cell clones show a mixed pattern of cytochines, producing heterogeneous combinations of IL-2, IL-4, IL-5 and interferon- . These cellular subsets are defined Th0 [11]. Th1 and Th2 populations are able to control naive T cells activity, cross-regulating each other‘s growth and differentiation not only by means of their interleukins, but also with the interaction with cytokines produced by other cellular types deeply involved in the immune response. Particularly, whereas interferon- and IL-12 (favour amplification of Th1 clones and down-regulate Th2 cells activation, IL-4 stimulates Th2 and inhibits Th1 response [12,13]. IL-12 is produced by dendritic cells (DCs) and macrophages, IL-4 derives from basophils, mast-cells and CD4+-Natural Killer (NK) T cells [14]. Each cytochine, binding a specific receptor located on T and NK cells surface, activates a complex endocellular pathway, involving non receptor protein Janus-kinases. These kinases phosphorylate and activate a

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family of seven endocitoplasmatic proteins known as STAT (signal transducers and activators of transcription) [15]. Following phosphorylation, these transcription factors, perform their function by entering the nucleus and binding to the promoters of specific target genes [16]. IL-12, through its cognate receptor composed of IL12R 1 and IL12R 2 subunits [17,18], stimulates three STAT proteins, STAT1, STAT3 and STAT4, inducing Th1 priming [19]. IL-2, IL-7 and IL-15 promote up-regulation of IL-12R 1 expression and IL-12 binding to its specific receptor [20], while IL-12R 1 down-regulation is promoted by TransformingGrowth-Factor (TGF- ), IL-4 and IL-10 [21]. In humans IL-12R 2 is induced by IFN[22]. Activation of STAT6 by IL-4 elicits Th2 differentiation [23]. Additional biological mediators carry out a pivotal immunoregulatory role; among them, prostaglandin E2 (PGE2), the major compound of arachidonic acid metabolism, produced by several immune cells such as antigen-presenting-cells (APCs) in response to pathogens and inflammatory processes [24]. PGE2 shows a selective, direct and dose-dependent suppressive activity on early stage of naïve T cells proliferation and differentiation, by decreasing IL-2, IFN- and IL-12 release and down-regulating expression of their cognate receptors [25,26,27]. PGE2-primed naïve T cells release in a dose-dependent fashion high levels of anti-inflammatory cytokines, such as IL-4, IL-10 and IL-13 [28], whereas they directly lose the ability to produce IL-2 and IFN-γ. IL-2 itself is a key cytokine during the early stage of immune response. Through the up-regulation of IL-2 receptors on activated T cells, IL-2 generates a positive feed-back loop and increases responsiveness of T lymphocytes by means of an autocrine and paracrine mechanism, favouring their proliferation and differentiation. IL-2 also increases cytolytic activity of NK cells, promotes development of lymphokineactivated killer-cells and elicits clonal expansion and immunoglobulin secretion by B lymphocytes. The final outcome of this intricate plot is inhibition of Th1 differentiation pathway and promotion of Th2 response [29]. In order to inhibit inappropriate or excessive immune activation, causing potential deleterious effects to host, lymphocyte populations have to be tightly regulated. Recently, a subset of specialized CD4-CD25 positive T cells Treg cells or Th3) with constitutive regulatory immunosuppressive properties has been described. Treg cells express, in addition to CD25 receptor for IL-2), several markers on cellular surface such as OX40, 4-1BB, CD62L and cytotoxic T lymphocyte-associated antigen 4 and play a key role in host homeostasis, contributing to maintain self-tolerance in the periphery (30,31,32). CD4-CD25 T cells, blocking the effector function of CD4, CD8 and NK cells seem to prevent autoimmune disorders. On the other hand Treg cells, limiting the intensity of effector immune response, may induce a failure in adequate control of infection diseases or tumors [33,34]. In addition to CD4-CD25 T cells, other lymphocyte populations, such as CD8 and NK cells, have been reported to exert immunomosuppressive activities, but their role in immune response control is poorly understood [35,36].

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DENDRITIC CELLS REGULATION O TH1-TH2 PRIMING In humans, two distinct DCs subclasses are detectable, the monocyte-derived ones elicite a Th1 polarization whilethe plasmocytoid-derived promote a Th2 differentiation [40]. During the initial phase of the immune response, the cytokine microenvironmental milieu may modulate the function and differentiation of DCs subsets [41]. In vitro studies show that an efficacious regulatory loop may be postulated: IL-10 elicits CD8 +DCs (a subclass of DCs) apoptosis and consequently a prevalent CD8 -DCs activity and a Th2-like phenotype [42], whereas IFN- , decreasing CD8 -DCs and promoting CD8 +DCs, induces a Th1 priming [43]. Depending on distinct T helper or DCs subsets and cytokine release at site of antigenic recognition, the same antigenic epitope may induce a very different pattern of immune response. In vitro models of DCs, isolated from peripheral blood precursors and cultured in presence of PGE2, promote generation of IL-12 deficient APCs and subsequently favour a cytokine Th2-like pattern commitment of maturing naïve T cells [44,45]. A recent study also showed that glutathione (GSH) levels in APCs may affect cytokine secretion pattern: particularly, glutathione depletion induces both a Th2 cytokines production and decreases Th1 cytokines release. These results strongly suggest that conditions characterized by an impairment in GSH concentrations, such as cancer or viral infections, may modulate the final immune response pattern [46]. However, the exact mechanisms and cytokines modulating induction of Treg cells(CD4-CD25 positive T cells) and role of DCs in this process are still poorly understood. Recent data appear to indicate that two different subsets of Treg cells may be identified natural T reg cellsthat are thymus-derived and T reg cells induced in periphery [47]. It has been suggested that natural T reg cells, in association to IL-2 and TGF- are able to induce conventional peripheral CD4 positive-CD25 negative cells to acquire a CD25 positive regulatory phenotype with suppressive activities and to release IL10, TGF- [48,49]. These interleukins in an appropriate microenvironment requiring unidentified set of immature DCs, are able to educate other conventional peripheral CD4 positive-CD25 negative cells to become Treg cells, via a cytokine-dependent self-maintaining loop [50]. In addition these cells are able to block the maturation of DCs, decreasing T-cell immunohenancing activities [51]. Anyway, available data appear to indicate that a direct link exists between T-cell subsets and disease susceptibility [52]. Whereas Th1 are showed to mainly influence cell-mediated immunity, preventing cells from infection by intracellular pathogens such as viruses, Th2 are involved in humoral immune response, but they are unable to eliminate endocellular microorganisms [53,54]. In mouse models and in humans studies, interlukins secreted by Th lymphocytes are proved to exert a strong influence in isotype class of antibodies. In fact, IL-4 Th2-type cytokine stimulates IgE and IgG1 responses, whereas IFN-γ, a Th1-type cytokine, promotes IgG2a and IgG2b profile [55]. Several reports are showing more and more in detail factors involved in immune response regulation, responsible for commitment toward Th1 or Th2 subsets, such as dose and concentration of antigen, type of antigen-presenting cells and costimulatory molecules, cytochine environment and antigenic specificity [56,57]. Several studies have been just performed in order to manipulate Th1 [58] and Th2 [59] subsets either favouring protective or blocking deleterious immune responses [60,61,62].However, a more complete knowledge

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of mechanisms involved in these processes will allow development of new strategies for treatment of disease related to Th1/Th2 imbalance.

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VITAMIN E ABSORPTION, TRANSPORT AND METABOLISM Vitamin E represents a family of essential and widespread available fat soluble compounds, including two subclasses, the tocopherols and tocotrienols, each with the four , , and isomers with similar well-known antioxidant properties, but distinct biological action at molecular levels [63]. These compounds are more abundant in latex lipids and edible plant oils at different concentrations. Particularly, d- -tocopherol isomer is the most available form of vitamin E in diet [64]. Vitamin E (d- -tocopherol) is the major lipid soluble chain-breaking antioxidant in cellular membranes. It is able to control free-radicals generation [65] and prevent mitochondrial oxidative damage, a known factor involved in several human disorders [66] entrapping peroxyl-radicals and oxygen species, released during peroxidative reactions. Vitamin E uptake is a very complex and tightly regulated process and only few data are currently available about its cellular and intracellular distribution [67]. Owing to its hydrophobicity, dietary vitamin E absorption occurs in combination with lipids and bile in duodenum. Then tocopherols, collected into chilomicrons together with triglycerides, cholesterol, phospholipids and apolipoproteins, are carried into lymphatyc circulation and transported to tissue. The chilomicrons renmants, escaping lipolysis, are removed by the liver. In hepatic tissue vitamin E is assembled into very low density lipoproteins (VLDL) and distributed to peripheral cells. Incorporation of vitamin E into lipoproteins depends on a specific cytosolic protein, identified as the 32 KDa- -tocopherol transfer protein ( -TTP) [68]. Normal -TTP function is essential to maintain an adequate amount of -tocopherol levels in plasma and extrahepatic tissues. This protein is detectable not only in hepatic cells, but also in brain [69], retina (70), fibroblasts and, more notable, in lymphocytes [71]. About 90% of the total serum vitamin E in humans is transported by the low density lipoproteins (LDL) and high density lipoproteins (HDL) [72]. A lot of α-tocopherol is stored unmodified in skeletal muscle and adipose tissue cells. Tocopherols are metabolized by citochrome p450 enzymes (CYPs), particularly CYP4F2 and CYP3A4, via -oxidation and subsequently via -oxidation. Then their metabolites carboxyethyl-hydroxychromanes (CEHCs) are glucuronidated or sulfated and excreted in urine [73].

IMMUNOSTIMULATING ACTIVITY OF VITAMIN E IN ELDERLY HEALTHY HUMANS AND ANIMALS Available data show that vitamin E status plays a key role in regulating normal function of immune system [74], not only for its anti-oxidant properties but also for its immunostimulating activity.

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Free radicals and oxygen species, generated during oxidative reactions, are known to be associated with an alteration in both humoral and cell-mediated immune responses [75]. In elderly healthy humans and animals [76], it has been demonstrated that age immune response disregulation appears associated with an increased production of free radicals such as hydrogen-peroxide [77] or arachidonic acid metabolites such as PGE2 [78,79] or leukotriens [80], if compared with immune response of younger healthy persons or animals. The most important age-dependent defects is related to T cells responsiveness [81] and, in particular, to an impaired T lymphocyte activity. Age-related changes in T-cell function include a decline in T lymphocytes proliferative responses to mitogens, a reduction in delayed-typehypersensitivity [82,83] and IL-2 release and an impaired antibody production in response to stimulation with T cell-dependent antigens [84] as well as an increased ratio between antigenexperienced memory T lymphocytes and naive T cells [85]. Naive and memory T lymphocytes show different functional profiles in elderly, the former require a greater stimulating antigen dose, elicit a more slow response and are less resistant to oxidative stress than memory T cells [86,87], while the latter, at least in aged transgenic mice, show a decreased proliferative ability and an impaired IL-2 production. On the contrary, IL-4 and IL6 release appears to be increased with aging. All these data are consistent with the skewing from a proinflammatory Th1 phenotype to an antiinflammatory Th2-like response, observed in the elderly [88]. Experimental studies have provided evidences for a role of vitamin E in protecting the immune system of elderly subjects.Studies in vitro and in aged-mice have documented that vitamin E causes a decrease in free radicals and PGE2 release by activated macrophages [90]. This action is due to the inhibition of phospholipase A2 [91] to the reduction in cyclooxigenase (COX) and lipoxygenase activities [92,93], key enzymes in arachidonic acid metabolism. On the other hand, macrophage-PGE2 production in young mice is not affected by vitamin E intake. Furthermore, dietary vitamin E supplementation has been proved to be a very useful approach to enhance immune responsiveness in aged-healthy subjects (94). Several clinical trials, heterogeneous with regard to both vitamin E dose supplementation and treatment period, have showed a positive stimulatory effect on different immune system activities of elderly subjects, including an increase in antibody titre to hepatitis B and tetanus vaccine, in delayed-type hypersensitivity response, in T cell proliferation and in IL-2 release in response to mitogens [95]. Recent data indicate that vitamin E may improve T cells activity in aged-mice via different mechanisms: a) a decrease in suppressive PGE2 production mediated by macrophages, b) a direct stimulation of their naïve T cells to produce and release IL-2, enhancing their proliferating capacity [89], c) an increase in thymic lymphocyte proliferation through enhanced T cell differentiation in the thymus [37]. In addition, in a murine cytotoxic T-cell line, vitamin E has been able to stimulate IL-2 dependent cellular growth, glycosylation and expression of certain glycoprotein [96]. According to its numerous effects vitamin E increase CD4/CD8 ratio and acts probably promoting a Th1 like pattern immune responses and improving T cell mediated-immunity, whereas no significant changes in other phenotypic markers, such as CD62L, HLA-DR and CD25 have been reported. Therefore activities of Treg CD4-CD25 positive cells are not affected by vitamin E [97]. The age-related alterations in IL-2 production by T cell have been

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associated to an increase in reactive oxygen species ROS) generation with oxidative stress, as described in old mice [98]. This process causes a dysregulation in intracellular signal transduction and an impairment in activity of cellular factors involved in IL-2 transcription [99,100]. Similar alterations in T cell function occur in several different pathological conditions such as inflammatory diseases and chronic infections, characterized by an increased free radical damage, associated to an enhanced ROS burden [101,102]. Consequently, information from studies conducted in aged animals and/or in elderly people can be very useful in the context of the immune response observed in chronic viral infections.

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VITAMIN E ACTIVITY IN CHRONIC VIRAL INFECTIONS IN HUMANS AND ANIMALS An efficacious defence against viral infections requires a tightly regulated cooperation and activity of CD4 and CD8 virus specific T cells [103,104]. CD8 cytotoxyc T lymphocytes CTLs) have been reported to exert a key role in control of persisting virus infections, such as Cytomegalovirus CMV) [105], Epstein-Barr virus EBV) [106], Human Immunodeficiency virus HIV) [107], Hepatitis B virus HBV) [108] and Hepatitis C virus HCV) [109]. Impaired functions of these cellular subset, favouring viral persistence, have been extensively described and reviewed [104]. Several essential mechanisms in chronic infections have been shown to induce a defective immune response a) dysregulation in interleukin production and release, b) epitope escape, c) negative lymphocyte selection or deletion, d) peripheral lymphocytic anergy, with unresponsiveness of virus specific CD8 subsets with effector properties against host infected cells [110,111]. Several studies have been performed in mice with persistent lymphocytic choriomeningitis virus [112] infection or in humans with CMV, EBV, HCV, HBV and HIV chronic infections [113], evaluating and analyzing human and animal T lymphocyte response.. Furthermore, in the last years, technological advances have made possible to identify and study directly virus-specific CD8 T cells, by means of HLA peptidic tetrameric complexes technology approach [114,115]. Available data suggest that, in persistently infected host, antigen-virus specific CD8 T cells are activated but are unable to perform an effective antiviral immune response [112]. High viral load, DCs dysfunction and decrease in peripheral CD4 help might explain in part the described CD8 T cell sustained impairment (104). In addition, an immunosuppressive modulation of CD4 and CD8 T cells specific antiviral response by CD4-CD25 Treg cells have been reported in patients with HBV, HCV and HIV chronic infections [116,117,118]. In subjects with HBV or HCV related chronic hepatitis the proportion of these Treg cells in peripheral blood has been shown to be higher in comparison to healthy or to spontaneously recovered individuals. Treg cells appear to contribute to virus tolerance and chronicity with still unclearmechanisms, even if suppression of CD4-CD25 T regulatory cells functions has been found to improve specific anti HBV- or anti HCV-CD8 T cell response [116,117]. If viral persistence in chronic viral infections is the result of T cell hyporesponsiveness, therapeutic strategies able to restore CD4 and CD8 activities might be a useful and interesting option for their management and treatment. The immunostimulatory nature of vitamin E could theorically provide a basis for its use in chronic viral infections control, via modulating

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immune responses. Some studies performed in animals and humans have evaluated vitamin E role with this purpose. Influenza virus infected aged-mice, fed with high dose (500 parts per million) of vitamin E, showed both significantly lower pulmonary viral titres and PGE2 production and higher IL-2 and IFN- levels than old animals supplemented with control diet (30 parts per million of vitamin E). Moreover, splenocytes isolated from young and aged-mice, fed with a diet rich in vitamin E, produced lower IL-1 and TNF amounts than non-supplemented young mice [119]. According to these data, a double blind controlled trial, has been performed in elderly nursing home residents, who received for a year 200 IU/day vitamin E versus placebo. In vitamin E supplemented subjects the incidence rate of common colds and the number of cold affected patients were lower in comparison to controls [120]. Remarkable observations by Wang and coworkers have been obtained in LP-BM5 leukemia retrovirus-infected murine models. This virus causes murine AIDS, with a clinical course characterized by several defects in immune system function linked to B and T cells dysregulation or suppression, such as splenomegaly, lymphadenopathy, hypergammaglobulinemia, decreased disease resistance and cytokine imbalance [121]. In this same murine models, high dose of vitamin E supplementation has also been proved to normalize nutritional status and immune response by reversing increased IL-4, IL-5, IL-6 and TNF levels and by restoring IL-2, IFN- and NK function induced by retrovirus infection [122]. In patients with HIV infection, increased serum levels of TNF- and IL-6 have been also observed in association to hypergammaglobulinemia and lymphocyte B dysfunction [123]. Therefore, a treatment including -tocopherol in association to antiretroviral drugs has been evaluated in HIV infected subjects, demostrating that the vitamin E supplemented regimen was more effective in decreasing viral load, in enhancing percentage of viable lymphocytes and in reducing proportion of apoptotic lymphocytes than the non-supplemented one [124,125]. Furthermore, it has also been observed that vitamin E is able to prevent oxidative alteration in lymphocytic DNA of patients with HIV [126,127]. In a further in vivo study, high serum -tocopherol levels have been observed to slow HIV disease progression [128]. Although the very small number of patients enrolled in these trials, all these clinical data suggest a positive -tocopherol role in HIV infection. However, vitamin E intake has been demonstrated to increase the expression of the CCR5 coreceptor in HIV carriers [129], a receptor that has been shown to mediate HIV entry into CD4 cells. Vitamin E has been also used in the treatment of patients with HBV persistent infection, with promising results. In a small pilot trial, 32 subjects with HBV chronic hepatitis were randomised to receive -tocopherol at the dose of 300 mg twice daily for three months versus no therapy. At the end of the study period, in vitamin E supplemented group undetectable levels of HBV-DNA and normal ALT were observed in 7/15 subjects in comparison to none of the controls [130]. Interestingly, this complete response gradually increased over a followup of 15 months, suggesting that vitamin E may have a delayed immunostimulating effect, as observed for other immunomodulatory drugs such as Thymosin- 1 [131]. On the other hand, conflicting results have been obtained when Vitamin E, alone or in combination with IFN, has been tempted in patients with HCV chronic hepatitis [132,133,134,135]. Nevertheless, these trials have provided no data concerning a possible immunoenhancing effect of vitamin

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E in subjects with HCV persistent infection. No studies have evaluated vitamin E supplementation in immune response in CMV and EBV chronic carriers so far. It should be pointed out that a recent meta-analysis reported that a supplemental intake of vitamin E, at dosages exceeding 150 UI/d, progressively enhances all-cause mortality [136]. Whereas some conclusions in this meta-analysis are questionable [141,142,143,144,145], it is clear that vitamin E at high dose should be considered a drug, with beneficial and detrimental effects. It is remarkable to note that, until recently, vitamin E supplementation has been widely utilized at high dosage in the prevention of several pathological conditions with health benefit and no potential risk [140,146,147,148]. However, some currently available studies [138,139], emphasize an increased mortality and morbidity [137] in subjects taking vitamin E especially at high dose, but there are still insufficient evidence to answer definitively the question of whether the long-term effects of vitamin E supplementation are favourable, unfavourable or neither. As far as dose of vitamin E is concerned, in an experimental study, high dose of vitamin E has the opposite effect on the survival. In particular, low dose of vitamin E improved survival, whereas high dose of vitamin E decreased the survival of autoimmune prone mice [38]. Furthermore, high concentration of vitamin E seemed to inhibit IL-2 secretion and stimulate IL-4 and IL-10 production [39]. Therefore, the dose of vitamin E may exert different effects and needs to be further clarified.

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CONCLUSIONS In conclusion the available data on vitamin E seem to suggest that it might contribute to improve immune unresponsiveness induced by chronic viral infections, restoring APCs, CD4 and CD8 T cells normal function. Vitamin E might begin part of immunotherapeutic strategies to induce a coordinate activation of components involved in adaptive immune response in chronic viral infections. Although these results are promising, low number of patients enrolled, short follow-up and heterogeneity in design make these studies inconclusive. Furthermore the results of the recent meta-analysis reporting a potential harmful effects of high dose of vitamin E administration vitamin E should be taken into account [136,137]. Based on these evidence, further well-designed clinical studies with an appropriate number of patients are warranted to test usefulness, safety, optimal dosage, duration and type of vitamin E, alone or in combination with specific antiviral treatment.

ACKNOWLEDGMENTS This work was supported in part by A.R.M.E (Associazione Ricerca Malattie Epatiche; via Massarenti 9, 40138 Bologna, Italy).

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selegiline, alpha-tocopherol, or both as treatment for Alzheimer's disease. The Alzheimer's Disease Cooperative Study. N Engl J Med. 1997;336:1216-1222. [141] Blatt DH, Pryor WA High-dosage vitamin E supplementation and all-cause mortality. Ann Intern Med. 2005;143:150-151; author reply 156-158. [142] Hemila H. High-dosage vitamin E supplementation and all-cause mortality. Ann Intern Med. 2005;143:151-152; author reply 156-158. [143] Krishnan K, Campbell S, Stone WL. Ann Intern Med. 2005;143:151; author reply 156158. High-dosage vitamin E supplementation and all-cause mortality. [144] Marras C, Lang AE, Oakes D, McDermott MP, Kieburtz K, Shoulson I, Tanner CM, Fahn S. High-dosage vitamin E supplementation and all-cause mortality. Ann Intern Med. 2005; 143:152-153; author reply 156-158. [145] Meydani SN, Lau J, Dallal GE, Meydani M. High-dosage vitamin E supplementation and all-cause mortality. Ann Intern Med. 2005;143: 153; author reply 156-158. [146] GISSI-Prevenzione Investigators. Dietary supplementation with n-3 polyunsaturated fatty acids and vitamin E after myocardial infarction: results of the GISSI-Prevenzione trial. Gruppo Italiano per lo Studio della Sopravvivenza nell'Infarto miocardico. Lancet. 1999;354:447-455. Erratum in: Lancet 2001;357:642. [147] Lonn E, Yusuf S, Hoogwerf B, Pogue J, Yi Q, Zinman B, Bosch J, Dagenais G, Mann JF, Gerstein HC; HOPE Study; MICRO-HOPE Study Effects of vitamin E on cardiovascular and microvascular outcomes in high-risk patients with diabetes: results of the HOPE study and MICRO-HOPE substudy. Diabetes Care. 2002;25:1919-1927. [148] Yusuf S, Dagenais G, Pogue J, Bosch J, Sleight P Vitamin E supplementation and cardiovascular events in high-risk patients. The Heart Outcomes Prevention Evaluation Study Investigators. N Engl J Med. 2000;342:154-160.

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Copyright © 2010. Nova Science Publishers, Incorporated. All rights reserved. Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

In: Viral Gene Expression Regulation Editor: Eli B. Galos

ISBN 978-1-60741-224-3 © 2010 Nova Science Publishers, Inc.

Chapter 5

REGULATION OF GEMINIVIRUS GENE EXPRESSION: POTENTIAL APPLICATIONS IN BIOTECHNOLOGY

Kathleen L. Hefferon Cornell Research Foundation, Cornell University, NY 14850, USA.

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ABSTRACT Geminiviruses are plant viruses with small single-stranded DNA genomes. Replication takes place via a rolling circle mechanism and is initiated from a nonanucelotide motif common among all geminiviruses. This motif is located on a hairpin structure within a long intergenic region (LIR). All members of the geminiviridae possess an LIR; this contains the cis-acting elements required both for virus replication and regulation of gene expression. As a result, virus replication is intrinsically connected to the regulation of gene expression. The following chapter examines in detail the relationship between geminivirus replication and gene expression. Virus and host factors that have been identified as components of both replicational and transcriptional machinery are included, and the significance of this information with respect to the development of expression vectors based upon geminiviruses is examined. Future directions for research pertaining to the regulation of gene expression of geminiviruses and potential applications in biotechnology are described.

INTRODUCTION TO GEMINIVIRUSES Geminiviruses, characterized by small particles with twinned icosahedral morphology, are small, single-stranded DNA plant viruses. Geminiviridae are organized into four different

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genera based on genome organization, host range, and insect vectors (Figure 1) [1]. Mastreviruses (the type member is maize streak virus) have a monopartite genome, are transmitted by leafhoppers and infect monocotyledonous plants (with the exception of tobacco yellow dwarf virus and bean yellow dwarf virus, which infect dicotyledons). Curtoviruses (the type member is beet curly top virus), like mastreviruses, are characterized by a monopartite genome but infect dicotyledonous plants and have a genomic organization which differs from that of mastreviruses. Topocuviruses (the type member is tomato pseudocurly top virus) have only recently been recognized as a distinct genus and have a monopartite genome. Begomoviruses (the type member is bean golden mosaic virus) have a bipartite genome [2-5]. Begomoviruses are whitefly-transmitted viruses that infect dicotyledenous plants [1,6-8]. This chapter will cover geminivirus genome structure, replication and transcription in general, followed by a more detailed description of what is currently known regarding promoter control for individual geminiviruses. The chapter will end with a description of how this knowledge has been utilized to generate expression vectors, both for applications in gene silencing as well as for foreign protein accumulation.

Mastrevirus

Curtovirus

MP Rep

Topocuvirus

MP

V2

IR

LIR

Rep

CR

Rep C4

V2

RepA intron

SIR

CP

CP

REn

CP

C2

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C2

C3

Begomovirus Rep

CR

CR C4

DNA A

TrAP

CP

BC1

DNA B

BV1

REn

Figure 1. Genomic organization of the four genera of geminiviruses. Open reading frames (ORFs) are depicted by arrows. An intron is located within the C1 ORF of the mastrevirus genome. LIR and SIR; long intergenic and short intergenic regions, respectively. IR; intergenic region, CR; common region. ORFs are described in more detail in Figure 1.

GEMINIVIRUS GENOME STRUCTURE The genome of mastreviruses consists of a small, single-stranded (ss) circular DNA and contains four open reading frames (ORFs). ORF C1 encodes replication associated protein A (RepA). C2 fuses with C1 to produce a C1:C2 fused protein (Rep) which is essential for viral

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DNA replication. The ORFs V1 and V2 encode the movement protein (MP) and coat protein (CP), respectively. The coding regions are divided bidirectionally by long and short intergenic regions (LIR and SIR). Replication follows a rolling circle replication (RCR) system via double-stranded (ds) DNA intermediates in infected cells [7,9]. In contrast, curtoviruses and topocuviruses have, in addition to MP and CP, a V2 protein on the virionsense strand, and four open reading frames encoding Rep, C2, REn (replication enhancer) and C4 on the complementary-sense strand. Begomoviruses have A and B components of their bipartite genomes; the A component encodes the CP on the virion-sense strand and Rep, TrAP (a transcriptional activator), REn and C4 on the complementary-sense strand, whereas the B-component encodes the movement proteins BC1 and BV1 on the complementary-sense and virion-sense strands, respectively. The function and nomenclature of the geminiviridae open reading frames are briefly described in Table 1 [5]. Table 1. Gene Products of Geminiviridae Genus

ORF

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Mastreviridae C1:C2 C1 V1 V2

Strand

c c v v

Gene Product Rep RepA MP CP

Curtoviridae and Topocuviridae C1 c C1 C2 c C2 C3 c REn C4 c C4 v MP v CP V2 v V2 Begomoviridae DNA A AC1 c Rep AC2 c TrAP AC3 c REn AC4 c AC4 DNA B BV1 v NSP BC2 c MP

Function

replication associated protein RBR-binding, transactivation of late gene expression virus movement coat protein, encapsidation, movement replication unknown replication enhancer protein unknown movement protein coat protein unknown replication associated protein transcription activator protein replication enhancer protein no function known nuclear shuttle protein movement protein

ROLES OF LIR AND SIR IN VIRUS REPLICATION AND REGULATION OF GENE EXPRESSION Expression of geminivirus genes is controlled by bidirectional promoters which are located in the large intergenic region (LIR) of the circular DNA genomes and are specifically regulated by virus encoded proteins. The LIR of mastreviruses contains promoter elements

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which possess homology to RNA polymerase II type promoter sequences. The LIR contains sequences responsible for transcription of genes in both genome senses as well as an inverted repeat sequence which forms the hair-loop structure required for replication. A conserved nonanucleotide sequence (TAATATT AC) located within the loop of the hairpin structure contains the origin of replication. Cis-acting elements responsible for both complementary and virion-sense gene expression are also located within the LIR. The Rep protein binds to a tandem repeat (TGGAGGCA), next to the TATA box in complementary-sense [12-14] (Figure 2). This enables Rep to mediate repression of its own promoter by interfering with initiation of transcription of the Rep gene. RepA of WDV, a related mastrevirus, has been shown to bind to the LIR in addition to Rep, and may play a role in regulating both complementary and virion-sense gene expression. The SIR is located at opposite sides of the viral genome from the LIR. It contains the origin of complementary-sense strand synthesis including an ~80 nucleotide long DNA primer, already present within the virus particle. It also contains the transcription termination signal [15]. 2

Complementary-sense strand synthesis 3

1 hairpin

4 primer

dsDNA intermediate

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Release of nascent virion-sense strand, ligation

Rep

Rep-binding to LIR, nicking of virion-sense strand

5 Strand displacement

7

6

Virion-sense strand synthesis Adapted from Vanitharani et al. (2005).

Figure 2. Rolling-circle model of geminivirus replication. Replication stages are listed from 1 to 7. 1: release of single-stranded virion-sense DNA after uncoating. 2 and 3: complementary-sense strand synthesis initiates from a primer located within the SIR to form a double-stranded DNA intermediate. 4: Rep binds and nicks to a hairpin structure located within the LIR and initiates virion-sense strand DNA synthesis. 5 and 6: as replication proceeds, the virion-sense strand is displaced and eventually is released by nicking. 7: the nascent DNA strand is recircularized by ligation via Rep and is now ready to undergo a new cycle of replication. Long and short rectangular boxes represent LIR and SIR, respectively.

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OLIGOMERIZATION PROPERTIES OF REP AND REPA Rep is the only viral protein of the mastreviruses that is absolutely required for viral DNA replication. ORFs C1 and C2 slightly overlap, and the 86 nucleotide intron is spliced out to produce genomic Rep (C1:C2) protein (41 kilo dalton, kDa), while the unspliced RNA from ORF C1 is translated through the intron to produce RepA (33.4 kDa) (Figure 2). The Rep protein itself possesses a number of functional domains, including an NTP binding domain, a retinoblastoma (RBR) binding domain, and a DNA binding domain [16,17]. In mastreviruses, Rep can interact with another Rep protein and/or RepA to form homodimeric or heterodimeric complexes [18-20]. Protein complex formation is important for the initiation of DNA replication, as well as origin recognition and binding in prokaryotes and eukaryotes. For instance, large T-antigen, which is the only viral protein required for viral DNA replication, forms a double hexamer in the replication origin of simian virus 40 (SV40) [2124]. Also, human papillomavirus type 16 (HPV-16) E1 forms a hexamer at its replication origin [25]. In begomoviruses, tomato golden mosaic virus (TGMV) and bean golden mosaic virus (BGMV) both encode AL1, which is analogous to Rep of mastreviruses and catalyzes the initiation of DNA replication. AL3 is a second replication protein of BGMV which enhances viral DNA accumulation. AL1 and AL3 form homo-oligomersbut also interact with each other to form hetero-oligomers. AL1 forms large multimeric complexes, and different AL1 complexes exhibit various effects on replication. AL3 may increase affinity of AL1 binding to the origin or may direct Rep to its cleavage site in the origin during replication [26,27]. In the case of the mastrevirus WDV, only monomers of RepA and Rep can bind with high efficiency to DNA, and their binding sites within the LIR differ slightly. Their oligomeric status is dramatically affected by pH in solution; at pH 6.2~7.0 an octameric form is dominant, while a monomer predominates with a slight increase to pH 7.4~7.8, which is very close to physiological conditions (19). It is possible that the monomer binds to DNA first, and protein-protein interactions form oligomers for stabilization in WDV similar to the SV40 T-antigen [21-23,28]. A core element essential for replication has been identified within the LIR of WDV, and DNA-protein complexes have been visualized [29]. A model of Rep assembly for WDV replication is illustrated in Figure 3 [1].

Interaction between Rep open Reading Frames and Cellular Proteins Germinivirus Rep proteins have been shown to interact with a number of cellular proteins. A number of them are listed here.

RBR RepA (in mastreviruses) and Rep (in begomoviruses) each possess a domain which is able to interact with human retinoblastoma (RBR) protein. RBR is thought to control the cell cycle by sequestering transcription factors such as E2F, which is necessary for transition into the S-phase. RepA can interact with RBR-like proteins in plant cells to modify the cellular

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environment to render it more permissive for viral DNA replication [5,34,35]. Other cellular proteins which interact with RepA were identified and characterized for geminivirus-host interaction. The GRAB1 and GRAB2 (for Geminivirus RepA-Binding) proteins, for example, were isolated from wheat. GRAB proteins are highly conserved at the N-terminus but contain unique C-termini. The N-terminus exhibits high homology (to the NAC-domain) which is involved in plant development and senescence [36-38]. The RBR-binding motif (LXCXE) is analogous to oncoproteins of animal DNA tumor viruses such as SV40 large T-antigen, adenovirus E1A, and HPV-16 E7 protein, and is also highly conserved among geminiviruses. Both RepA and Rep in BeYDV have the same LXCXE motif encoded within their gene products. Nonetheless, only RepA can bind to RBR protein in a yeast two-hybrid system [36]. It has been suggested that exclusive RepA binding to RBR may be due to differences in predicted secondary structure within this area of the two proteins (1). Modification of the BeYDV RepA RBR binding motif to IXCXE or LXSXE reduced RBR binding ability of RepA by 50%, although replacement with LXCXQ reduced RBR binding by 95%. Therefore, an intact RBR binding motif (LXCXE) is required for efficient viral DNA replication [35,39]. AL1 of the begomovirus TGMV does not have the LXCXE motif, but can still bind to RBR through another site known as the helix 4 motif (located in close proximity to the original RBR-binding domain). Clearly there is an alternative pathway for Rep-RBR protein interaction in geminiviruses in lieu of the LXCXE motif. Most likely, the AL1/C1 helix 4 motif of curtoviruses and topoviruses, which also lack the LXCXE motif, also bind to RBR [40]. Rep binding, oligomerization

Rep binding to hairpin structure

Initiation of Replication

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Rep 3’OH

LIR RBS C-complex formation

O-complex formation

Adapted from Gutierrez et al. (2004).

Figure 3. Schemmatic diagram of role of Rep binding and oligomerization in initiation of mastrevirus replicaton. Evidence suggests that Rep binds as a monomer to a Rep binding site upstream to the hairpin structure within the LIR. Other Rep monomers oligomerize at this site to form a C-complex. Bending of DNA within the LIR enables the Rep oligomeric complex to bind to the hairpin structure (O-complex) in such a way that the nonanucleotide motif TAATATTAC can be reached and initiation of replication can proceed.

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PCNA Proliferative cell nuclear antigen (PCNA), a conserved plant protein as well as a processivity factor for DNA polymerase delta, has been shown to become induced in response to geminivirus infection in the resting cells of the phloem tissues [41]. Recently, the role of PCNA in rolling circle replication (RCR) of the geminivirus Indian mung bean yellow mosaic virus (IMYMV) was examined by Bagewadi et al. (2004) [42]. Pea nuclear PCNA was shown to bind to Rep, and the site-specific nicking-closing activity and ATPase function of IMYMV Rep were found to be impaired by PCNA. The authors speculated that since PCNA appeared to be an inhibitor of RCR, PCNA may play a dual role with an end resulting in control of viral DNA copy number within infected host plants. In this model, at the initial phase of replication, a Rep-PCNA-RF-C complex may form, which may disassemble to allow for primer formation and elongation steps of RCR. During later phases of infection, the level of PCNA increases drammatically and thus acts to limit DNA copy number by inhibiting initiation of RCR by Rep. .

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GRIK Geminivirus Rep-interacting kinase 1 (GRIK1) and GRIK2 constitute a small protein kinase family found in Arabidopsis plants. Both full-length GRIK1 and GRIK2 have been shown to interact with geminivirus replication protein AL1 in yeast two-hybrid studies and both Arabidopsis kinases were found to become elevated in infected leaves [43]. However, unlike the protein patterns, GRIK1 and GRIK2 transcript levels exhibit only a small increase during infection and do not change significantly during development. Further investigation indicated that the accumulation observed is modulated by posttranscriptional processes. Complementation studies using a yeast system demonstrated that GRIK proteins are able to replace the corresponding yeast kinase analogues PAK1, TOS3, and ELM1, suggesting that the GRIKs mediate similar processes.

S1NAC1 Geminivirus replication enhancer (REn) proteins are capable of dramatically increasing the accumulation of viral DNA species. SlNAC1, a new member of the NAC domain protein family from tomato, has recently been identified using a two-hybrid yeast system as playing a role in geminivirus tomato leaf curl virus (TLCV) REn function [43]. TLCV induces SlNAC1 expression specifically in infected cells, and overexpression of SlNAC1 results in a substantial increase in viral DNA accumulation, implying that SlNAC1 plays an important role in the process by which REn enhances TLCV replication.

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170 Other Host Proteins

Tu and Sunter (2007), have identified a nine base pair sequence in Tomato golden mosaic virus (TGMV) that is required for binding of nuclear proteins from tobacco and Arabidopsis to viral DNA [45]. Mutation of these sequences resulted in a reduction of viral DNA levels. This binding site was found to be conserved within both begomo- and curtoviruses, suggesting a common mechanism involved in regulating expression of the viral gene involved in TGMV replication. Furthermore, Kong and Hanley-Bowdoin (2002) used a yeast two-hybrid library to show that geminivirus AL1 interacts with host cell Ser/Thr kinase, kinesin, and histone H3, all host proteins involved in plant cell division and development [46]. Possible functions of these host factors in healthy and geminivirus-infected plants have been considered by the authors. Interactions between Rep and histone H3 are most likely involved in the disassembly of nucleosomes for the initiation of virus replication. Kinesin is located in the spindle and is involved in microtubule dynamics. AL1 thus may be involved in the remodeling of the minichromosome structure during virus infection. The Ser/Thr kinase has no known function to date, but is most likely involved in many different cellular processes. It is possible that the role of AL1 binding to this kinase has significance with respect to the establishment and maintenance of a cellular environment that is favorable for geminivirus infection.

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ROLE OF REP IN ROLLING-CIRCLE REPLICATION Virus replication is believed to take place via a rolling-circle mechanism involving a double-stranded replication intermediate, and resembles the rolling circle replication mechanism of single-stranded DNA coliphages ( X174) and some plasmids (pT181 and pC194) [47-49]. Figure 2 illustrates the general RCR system of single-stranded DNA replication for mastreviruses. Introduction of a nick by Rep at a hairpin structure located within the LIR followed by DNA synthesis and strand-displacement enables multiple singlestranded circular copies of the virion-sense strand to be made. The nascent DNA strand is then ligated to produce a single-stranded circular molecule. The highly conserved nine-nucleotide (TAATATT AC) at the hairpin structure in the LIR resembles the origin of viral DNA replication of X174 (TG ATATTAT) and pC194 (TG ATAATAT). The specific site ( ) is nicked by Rep protein of geminiviruses and pT181 or protein A of X174. This site-specific cleavage initiates plus-strand DNA synthesis [12,50]. Unit-length viral genomes are then released from tandem repeats by RCR [13].

Role of Rep in Regulation of Gene Expression Geminivirus replication takes place in the nucleus via a double-stranded intermediate. This intermediate also serves as a template for bidirectional transcription by RNA polymerase II. All geminiviruses follow a bidirectional transcription strategy, with promoters responsible for virion-sense and complementary-sense replication located within the LIR.

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Complementary-sense Replication

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In order to further elucidate the roles of Rep and RepA in mastrevirus BeYDV replication and regulation of gene expression, Hefferon and Dugdale (2003) separated Rep and RepA activities by individually placing them under constitutive promoter control. Rep constructs were cobombarded independently of one another along with replication incompetent BeYDV-based constructs containing the viral elements required for both virion and complementary-sense gene expression. Both Rep A and Rep wewre found to act as an inhibitor of complementary-sense gene expression [20]. Hong and Stanley (1995) have also demonstrated that African cassava mosaic virus (ACMV) AC1 gene expression is negatively regulated by its own protein product. AC1 also suppressed AC4 gene expression. Nucleotide sequences responsible for this suppression were mapped to a 92 bp fragment located immediately upstream of the AC1 initiation codon encompassing the consensus TATA box and transcription start point [50]. It has long been thought that geminivirus DNA replication is coupled to the cell-cycle regulatory complex of the plant cell and that the virus-early (complementary or C sense) gene products Rep and RepA may be able to manipulate the regulation of the cycle. Furthermore, complementary-sense regulation of expression likely also takes place in a tissue and developmentally-specific manner. Nikovics et al. (2001) examined expression from the Csense promoter of Maize streak virus (MSV) in transgenic maize to determine whether it exhibited cell-cycle specificity. Histochemical staining of plant roots containing C-sense promoter sequences upstream of the GUS (beta-glucuronidase) reporter gene showed that promoter activity was restricted to the meristematic region of the roots [51].

Virion-sense Gene Expression A number of studies have indicated that in mastreviridae, the virion-sense promoter is transactivated by Rep gene products. Hofer et al. (1992) showed that no activity was detectable from the virion-sense promoter of wheat dwarf virus (WDV) in the absence of Rep expression [52]. Furthermore, a replication-deficient mutant which still produced Rep was able to transactivate virion-sense gene expression. Similarly, Zhan et al. (1993) found that Rep could enhance virion-sense gene expression of chloris striate mosaic virus (CSMV) [53]. Further studies in which constructs containing a frameshift mutation in ORF C2 have lost their ability to activate virion-sense expression would suggest that Rep and not RepA (C1) is the only transactivator. Conversely, Collin et al. (1996) showed that a cDNA form of Rep, which lacks the intron and thus could not produce RepA, was unable to promote virion-sense gene expression from a replicating WDV construct, whereas the full-length Rep gene, with the intron intact, produced high levels, suggesting that RepA (C1) alone is required for virion-sense expression [30]. More recently, using maize, WDV RepA was shown to activate virion-sense gene expression in MSV and WDV, with the RBR-binding domain of RepA being essential for activation in MSV but nonessential in WDV [54]. Using RepA RBRbinding mutants, the authors of this study suggested that the interferance of RepA with an

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RBR-dependent cellular pathway for gene expression in one virus but not the other indicates that two alternative means of activating virion-sense gene expression may exist. In our studies conducted using the mastrevirus BeYDV, Rep and RepA were expressed independently of each other to determine their relative effects on regulation of virion-sense gene expression [39]. The authors found that while Rep A acts as a potent transactivator, Rep had a much weaker effect on virion-sense gene expression. Further studies using RBR mutant RepA constructs indicated that RepA transactivation could still take place, albeit to a lesser degree, in the absence of an intact RBR-binding domain. Using a different approach, Frey et al. (2001) simultaneously examined the regulation of both orientations of DNA A and DNA B promoters of African cassava mosaic virus (ACMV) using two different luciferase genes with the firefly luciferase gene in complementary-sense and the Renilla luciferase gene in virion-sense orientation. The regulation of the ACMV promoters by transactivator (TrAP) or replication-associated (Rep) proteins was assessed in tobacco and cassava protoplasts using dual luciferase assays. The authors found that TrAP activates virion-sense expression strongly both in cassava and tobacco protoplasts, but not in transgenic tobacco plants. In contrast to this, DNA A encoded proteins activate virion-sense expression both in protoplasts and in transgenic plants while at the same time reducing the expression of the complementary-sense Rep gene on DNA A [55]. Promoters and transcripts of mungbean yellow mosaic virus-Vigna (MYMV), a bipartite geminivirus in the genus begomovirus, have been mapped by Shivaprasad et al. (2005). Both DNA-B leftward and rightward promoters share the transcription activator AC2-responsive region, but do not overlap the common region. The authors found that AC1 negatively regulates its own leftward promoter on DNA-A, and in cooperation with AC2, synergistically transactivates the rightward promoter. Furthermore, AC2 and the replication enhancer AC3 are expressed from one dicistronic transcript driven by a strong promoter mapped within the upstream AC1 gene [56]. Hur et al. (2007) used promoter::reporter (beta-glucuronidase) gene fusions in transgenic Arabidopsis to identify the putative promoter region of ORF C1 of the curtovirus Beet curly top virus (BCTV) genome. C1 is known to play an important role during initiation of viral DNA replication. Unlike other geminiviruses, the intergenic region of BCTV was not sufficient to promote C1 expression in transgenic plants. When sequences extending into the coding region of C1 were tested, strong expression of the reporter protein was observed in vascular tissues of transgenic plants. This expression was not dependent on the presence of the intergenic regions or proximal 5' portions of the C1 coding region. The authors concluded that important transcriptional activator elements for C1 expression may reside within the 3' portion of C1 coding area itself. It appears therefore, that unlike other geminiviruses, C1 protein does not auto-regulate its own expression [57].

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ROLE OF MOVEMENT PROTEIN (MP) AND COAT PROTEIN (CP) IN VIRUS LIFE CYCLE The function of MP and CP have been well characterized in maize streak virus (MSV), a member of the mastreviruses. The lack of CP causes a loss of infectivity of monopartite viruses, but has no effect on DNA accumulation of bipartite viruses in plants. Thus, the MSV MP (10.9 KDa) and CP (27 KDa) are required for systemic infection and symptom development in infected plants [4,36,37]. The MSV CP can bind to single-stranded and double-stranded DNA. Deletion of 20 amino acids from the N-terminus of the CP rendered MSV CP unable to bind to either single-stranded or double-stranded DNA. When the MSV CP was injected into tobacco and maize epidermal cells with viral single-stranded or doublestranded DNA, the majority of DNA was imported rapidly into the nucleus in both plant cells. This indicates that MSV CP is a nuclear-targeting protein and facilitates nuclear transport of viral DNA [8,58]. MSV MP::GFP fusion proteins were found to extend to adjacent cells as well as bombarded cells, indicating that MSV MP moves from cell- to- cell for systemic infection. Unlike other plant DNA virus MP, MSV MP alone cannot bind to viral DNA, but can interact with the CP to form a MSV MP-CP complex. MSV MP diminished the nuclear accumulation of the CP-DNA complex when introduced in maize and tobacco cells by microinjection. Therefore, MSV MP most likely interacts with the CP-DNA complex to move from cell-to-cell through the plasmodesmata [59-61].

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Geminivirus suppressors of gene silencing Gene silencing is a plant defense system which may occur either in the cytoplasm or in the nucleus [62]. Transcriptional silencing (TGS) may involve DNA-DNA pairing, DNA methylation, or heterochromatinization within nuclei. In the cytoplasm, post-transcriptional gene silencing (PTGS) includes either silencing of endogenous RNAs by cytoplasmic short interfering RNA (siRNA) or by microRNAs (miRNAs). In all of the above cases, degradation of dsRNA by a double-stranded ribonulease is required [62-68]. More recently, a number of geminivirus proteins have been found to act as suppressors of gene silencing in plants. To counteract the plant antiviral response, many viruses produce suppressor proteins that block host RNA silencing by targeting different steps of the silencing pathway. The mechanisms by which suppression of silencing takes place are widely variable and follow different pathways depending on the specific geminivirus in question. In many cases, it is the transcriptional activator, or Rep protein, which acts to suppress silencing. This information could have enormous potential for the design and application of geminivirus expression vectors for multiple purposes. Further information regarding geminiviruedses and suppression of gene silencing can be found in a review by Bisaro, 2006 [69].

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GEMINIVIRUSES AS EXPRESSION VECTORS

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The use of geminiviruses for Virus-induced Gene Silencing (VIGS) Plant virus-based vectors have been used for either over-expression of genes or suppression of gene expression (VIGS) in plants [69-72]. Many examples of VIGS of either transgene or endogenous genes have been reported [69,70]. When chalcone synthase A gene (ChsA) was cloned into a geminivirus-based episomal vector (TYDV of mastreviruses) driven by the 35S CaMV promoter, random white-spotted flowers were produced with high copy numbers in petunia [69]. The loss of pigmentation (from purple to white) in the petunia flowers is one example of PTGS, which has been termed ―co-suppression‖ [71]. In this mastrevirus-based vector, replication of episomes required Rep expression under its native promoter. Rep expression may be affected by position and chromatin structure related to TDNA integration site. This random white-spotted phenotype resulted from PTGS of both the endogenous and episomal ChsA gene. Higher copy number causes more gene silencing, but a relatively small increase in episome copy number (less than three-fold) appears sufficient to trigger the gene silenced phenotype [74,75]. Due to the different levels of Rep expression, transgene replication levels will differ even though the copy number is the same, and this explains the random white-spotted phenotype. When the GUS gene was cloned into TYDV driven by 35S CaMV promoter in tobacco, a speckled GUS expression was also observed, further implicating the differential expression of Rep in different cells [74]. Virus-induced gene silencing can therefore be initiated by viral vectors carrying portions of host genes. Gene expression of transcripts homologous to these host genes are degraded and the endogenous gene inactivated. Such a strategy involving geminivirus vectors is currently being used to study functional genomics in plants [66]. Another potential means of overcoming silencing effects was examined by Hefferon and Dugdale (2003). In the expression system described in this paper, the authors demonstrated that replication of a BeYDV-based vector and high levels of gene expression could be observed when Rep was placed under independent promoter control [39]. These results open the possibility that by being placed under inducible promoter control, Rep could be turned on or off at will, and in this way, the expression vector could escape cellular defense mechanisms of gene silencing. As an alternative to using a geminivirus expression vector to avoid the effects of gene silencing, Morilla et al. (2006) developed a geminivirus-based system which can easily identify host factors required for geminivirus replication by employing virus-induced gene silencing in their techniques. An expression cassette was developed which contains the replication origins of the monopartite begomovirus Tomato yellow leaf curl Sardinia virus (TYLCSV), along with the reporter gene GFP. Transgenic plants containing one copy of this cassette stably integrated into the genome could, upon superinfection with TYLCSV, release and replicate the cassette as an episomal replicon in a Rep-dependent fashion. This expression system is able to monitor the replication status of TYLCSV in plants, as induction of GFP expression is only produced in those tissues where Rep is present. As a proof of concept, the authors then used the system to transiently silence the host plant‘s proliferating cellular nuclear antigen (PCNA), known to directly interact with Rep, resulting in a reduction

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of viral gene synthesis and GFP expression. By silencing the expression of individual host genes and examining their effect on replicon release and replication, this expression system can be used to determine the role of specific host proteins in geminivirus infection [77].

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GEMINIVIRUSES AS FOREIGN GENE EXPRESSION VECTORS The production of heterologous plant proteins in quantities sufficient for experimental use has proven to be a constant challenge for molecular biologists. A number of different expression systems have been developed over the last few years, each with its own individual advantages and limitations, depending on the specificities of the gene of interest and its corresponding protein product. While eukaryotic proteins can often be expressed quickly and at enhanced levels in bacterial expression systems, such systems are incapable of performing the critical post-translational modifications which occur in many eukaryotes, and the protein of interest produced is less likely to be biologically active. Plant molecular biologists often turn to insect cell expression systems for protein production; such systems can generate proteins with post-translational modifications which more closely resemble those found in other eukaryotes and can thus produce functionally active proteins. However, such systems often prove to be time consuming, involving the completion of baculovirus purification titering and production steps. Similarly, enhanced expression of plant proteins in yeast or mammalian expression systems may not produce the protein of interest with the desired posttranslational modifications, can also be time consuming and may require additional equipment and materials not readily available to the plant molecular biologist. Plants also offer enormous possibilities to become one of the most cost-effective and safe systems for large-scale production of proteins for industrial, pharmaceutical, veterinary and agricultural uses [78]. Among these are vaccine proteins which can be extracted from plants via simple purification procedures or else directly ingested by animals or humans. Such plantderived vaccines have been expressed in transgenic plants or by plant viral vectors [79-84]. It is often difficult to generate transgenic plants which express proteins at sufficient levels to evoke an immune response. In addition, high expression of foreign proteins may be toxic to plants. While viral vectors also have been used to produce vaccine proteins, expression is often short-lived and there may be limitations as to the length of peptides which can be expressed [84]. The geminivirus expression system features attributes of both systems by expressing proteins at high levels in a controlled manner, and eliminating the restrictions in protein size [86-88]. Plant viruses with DNA genomes, such as the caulimovirus cauliflower mosaic virus (CaMV) and geminiviruses, have also been investigated as potential protein expression systems. The polycistronic genome, RNA-mediated mode of replication, and restricted host range makes CaMV difficult to work with [85]. Geminiviruses, on the other hand, with their simple genomic organization and broad host range, have been viewed as far more attractive candidates to develop as vectors for foreign gene expression. They are capable of accumulating to extremely high copy numbers in inoculated cells, resulting in greatly elevated levels of gene expression [86]. Initial studies involving the replacement of the geminivirus CP gene with a foreign gene have established increases of viral DNA as great as

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Kathleen L. Hefferon

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300,000 copies per cell, suggesting that foreign protein expression can be enhanced enormously [87]. A BeYDV-based expression sytstem has been developed which expresses reporter genes at high levels. The system was designed further to rapidly produce correct post-translationally modified, biologically functional proteins such as mitogen-activated protein kinase (MAPK) in quantities large enough to enable easy purification (Figure 4) [84]. In addition, geminivirus expression systems can be used for the production of vaccine proteins in plants. For example, a synthetic, plant-optimized version of Staphylococcus Endotoxin B (SEB) was inserted into a BeYDV reporter cassette and high levels of SEB were detected by ELISA [88]. High levels of expression of vaccine proteins using geminivirus expression vectors will serve as attractive alternatives for a means of generating fast, cost-effective and safe vaccines for developing countries.

P

pIB210MAPK

N

S

MAPK

35S

Tvsp

6HIS

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(b)

pBYMAPK

Figure 4. Schematic diagram of BeYDV expression vector. (a) genomic organization of pSKBYD1.4; P, PstI; Xb, XbaI; S, SacI; B, BamHI; E, EcoRI; C, ClaI; Bg, BglII; C1, C2, V1 and V2 represent complementary and virion-sense open reading frames. Bar represents 500 bp. (b). Construction of geminivirus expression vector pIB210MAPK and pBYMAPK. Line refers to PstI-PstI fragment of BeYDV corresponding to (a). Dotted lines indicate site of insertion of BeYDV sequences into pIB210MAPK. P, PstI; N, NcoI; S, SacI.

CONCLUDING REMARKS AND FUTURE DIRECTIONS This review has focused on what is known about geminivirus replication, regulation of gene expresssion and its application in the design of viral expression vectors, both for RNA silencing purposes as well as for production of foreign proteins in plants. Further research

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focusing on the identification of cellular factors involved in geminivirus replication and their respective roles in the cell cycle and other cellular pathways are currently underway in a number of geminivirus laboratories. In addition to this, research groups such as Velton et al. (2005) have ―mined‖ a large set of viral intergenic regions for transcriptional enhancers and have identified a previously described ―conserved late element‖ (CLE) that possesses intrinsic enhancer activity in the absence of viral gene products, as well as a number of other active elements [89]. Uncovering elements such as these will assist basic researchers who are interested in investigating promoter function as well as biotechnologists who are instead focused on creating a more refined expression vector system based on geminiviruses. A more detailed picture of geminivirus replication, for example, with respect to the host factors that geminivirus Rep proteins interact with, will lead in turn to the construction of more sophisticated expression vectors and host plant systems. The uses for such vectors range from the generation and purification of large quantities of proteins in plants for pharmaceutical purposes to the study of functional genomics through RNA silencing; applications that both harbor enormous potential.

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REFERENCES [1] Gutierrez, C., Ramirez-Parra, E., Castellano, M.M., Sanz-Burgos, A.P., Luque, A. and Missich, R. (2004). Geminivirus DNA replication and cell cycle interactions. Veterinary Microbiology 98; 111-119. [2] Fauquet, C. M., Maxwell, D. P., Gronenborn, B. and Stanley, J. (2000). Revised proposal for naming geminiviruses. Arch. Virol. 145(8): 1743-1761. [3] Gronenborn, B., Bendahmane, M., David, C., Desbiez, C., Heyraud, F., Jupin, I., Kheyr, P. A., Laufs, J., Schumacher, S. and Wartig, L. (1995). Geminiviruses: Genome organization and protein functions. Agronomie 15(7-8): 496-497. [4] Lazarowitz, S. G. (1989). Maize Streak Virus Genes Essential for Systemic Spread and Symptom Development. EMBO J. 8: 1023-1032. [5] Hanley-Bowdoin, L. (1999). Geminiviruses: Model for Plant DNA Replication, Transcription, and Cell Cycle Regulation. Crit. Rev. Plant Sci. 18(1): 71-106. [6] Petty, I. T. D., Carter, S. C., Morra, M. R., Jeffrey, J. L. and Olivey, H. E. (2000). Bipartite geminivirus host adaptation determined cooperatively by coding and noncoding sequences of the genome. Virology 277(2): 429-438. [7] Palmer, K. E., Rybicki, E. P., Maramorosch, K., Murphy, F. A. and Shatkin, A. J. (1998). The Molecular Biology of Mastreviruses. Adv. Virus Res. 50: 183-234. [8] Liu, H., Boulton, M. I. and Davies, J. W. (1997). Maize streak virus coat protein binds single- and double-stranded DNA in vitro. J. Gen. Virol. 78(6): 1265-1270. [9] Liu, L., Van, T. T., Pietersen, G., Davies, J. W. and Stanley, J. (1997). Molecular characterization of a subgroup I geminivirus from a legume in South Africa. J. Gen. Virol. 78(8): 2113-2117. [10] Gutierrez, C., Suarez, L. P., Ramirez, P. E., Sanz, B. A., Poenninger, J. and Xie, Q. (1995). DNA bending as a potential regulatory cis-acting element of the geminivirus intergenic region. Agronomie 15(7-8): 415-420.

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[77] Kjemtrup, S., Sampson, K. S., Peele, C. G., Nguyen, L. V., Conkling, M. A., Thompson, W. F. and Robertson, D. (1998). Gene silencing from plant DNA carried by a geminivirus. Plant J. 14(1): 91-100. [78] Hefferon, KL. 6.58.4.6. Transgenic Plants. United Nations Encyclopedia of Life Sciences, 2001. [79] Morilla G, Castillo AG, Preiss W, Jeske H, Bejarano ER. A versatile transreplicationbased system to identify cellular proteins involved in geminivirus replication. J Virol. 2006 Apr;80(7):3624-33. [80] Richter, LJ, Thanavala, Y, Arntzen, CJ, Mason, HS. Production of hepatitis B surface antigen in transgenic plants for oral immunization. Nature Biotech. 2000; 18:1167-1171. [81] Biemelt, S, Sonnewald, U, Galmbacher, P, Willmitzer, L, Muller, M. Production of human papillomavirus type 16 virus-like particles in transgenic plants. J Virol 2003; 77: 9211-20. [82] Khandelwal, A, Sita, GL and Shaila, MS. Expression of hemagglutinin protein of riderpest virus in transgenic tobacco and immunogenicity of plant-derived protein in a mouse model. Virology 2003; 308(2): 207-215. [83] Yusibov, V, Hooper, DC, Spitsin, SV, Fleysh, N, Kean, RB, Mikheevat, T, Deka, D, Karasev, A, Cox, S, Randall, J and Koprowski, H. Expression in plants and immunogenicioty of plant virus-based experimental rabies vaccine. Vaccine 2002; 20(2526): 3155-64. [84] 85. Awram, P, Gardner, RC, Forster, RL, and Bellamy, AR. The potential of plant viral vectors and transgenic plants for subunit vaccine production. Adv Vir Res 2002; 58: 81124. [85] Hefferon, K.L., Kipp, P. and Moon, Y.S. (2004). Expression and purification of heterologous proteins in plant tissue using a geminivirus expression system. J. Mol. Micro. Biotechnol. 7; 109-114. [86] Porta C and Lomonosoff, GP: Use of viral replicons for the expression of genes in plants. Mol Biotechnol 1996; 5(3): 209-21. [87] Timmermans, MCP, Das, OP, and Messing, J: Geminiviruses and their uses as extrachromosomal replicons. Ann Rev Plant Phy Plant Mol Biol 1994; 45: 79-112. [88] Hefferon, K.L. and Fan, Y. (2004). Expression of a vaccine protein in a cell line using a geminivirus-based replicon system. Vaccine 23; 404-410. [89] Velten J, Morey KJ, Cazzonelli CI. Plant viral intergenic DNA sequence repeats with transcription enhancing activity. Virol J. 2005 Feb 24;2:16.

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In: Viral Gene Expression Regulation Editor: Eli B. Galos

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Chapter 6

GENE EXPRESSION REGULATION IN THE DEVELOPING BRAIN Ching-Lin Tsai1 and Li-Hsueh Wang2 1

2

National Sun Yat-sen University, Kaohsiung 804, Taiwan; National Museum of Marine Biology and Aquarium, Pingtung 944, Taiwan.

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ABSTRACT The developing central neural circuits are genetically controlled and initiated by developmental signals. Recent progress in molecular and cellular developmental biology provides evidence of how the brain is feminized or masculinized during the critical developmental period. Research into the development of brain architecture requires experimenting with animals, specifically, interfering with normal development and with environmental conditions. Drosophilae, sea urchins, and metazoans are simple invertebrates used for standard research models. Recently, the teleosts, bony fish with biological and genomic complexity found in the higher vertebrates, have become important models for developmental and molecular neurobiology studies. As in mammals, sexual dimorphic genetic expression is found in the developing brain of teleosts. The cellular and synaptic organization of brain architecture is determined by the genomic program and triggered by environmental cues, such as the photoperiod and temperature. This review highlights some of the methodological issues related to current findings about the gene expression regulation involved in the complex process of neural development, particularly in brain-sex differentiation.

INTRODUCTION The development of neural architecture in the brain is crucial to the physiological functions and behaviors of an animal. Sexual dimorphism in brain structure has been recognized since the pioneering studies by Raisman and Field (1973). The different brain

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neural circuits are initiated by sex-biased gene expression and are hormone-controlled (Davies and Wilkinson, 2006). The cellular and synaptic organization of the central nervous system is determined not only by genetic regulation, but also by extrinsic, environmental, and epigenetic influences that operate during development (Carrer and Cambiasso, 2002). The generation of neurons and glial cells in the developing brain is mediated by estrogen and neurotransmitters (Carrer and Cambiasso, 2002; Nguyen et al., 2001). The rodent brain has been a prominent model, and non-mammalian species have become leading models for studies of development and genetics. We devote a major discussion to gene expression regulation in the developing brain, to investigate brain-sex differentiation and highlights in a non-mammalian model.

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SEXUAL DIMORPHISM OF THE BRAIN Brain architecture and functions are developed with sex-specific patterns. The most widely known animal model of neural sexual dimorphism is found in the rat's medial preoptic area (mPOA). Within the mPOA of rats, the region of the sexually dimorphic nucleus of the mPOA (SDN-POA) shows between 2.5 and 5.0 times more neurons in males than in females (Morris et al., 2004). The function of sexual dimorphism in the SDN-POA is not clear. Sexual dimorphism of the SDN-POA in humans also shows more neurons in males than in females (Swaab et al., 2004a,b; Swaab, 2007). In another famous animal model, the vocal control area of the songbird brain, the vocal control area in zebra finches is about 6 times larger in males than in females (Gurney and Konishi, 1980). The sexually dimorphic distribution of neurotransmitters in the brain is widely investigated in mammals. In tilapia, teleosts, the central neurotransmitters such as serotonin (5-HT), norepinephrine (NE) in different brain areas (telencephalon, optic lobe, and hypothalamus) show sexually dimorphic distribution (Tsai et al., 1995). Furthermore, in response to different environmental cues (higher- or lower-than-normal water temperatures) or to chemical pollutants (mercury), tilapia showed a sex-difference response (Tsai et al., 1995; Tsai and Wang, 1997a). The 5-HT concentration, measured using a high-performance liquid chromatography system with an electrochemical detector, was significantly different between each region: the hypothalamus had a higher concentration than did the telencephalon and optic lobe. The 5-HT concentration in the female hypothalamus was significantly lower than in males. However, 5-HT concentration in the telencephalon and optic lobe was not different between males and females (Tsai et al., 1995). After they had been exposed to mercuric chloride (HgCl2) for 6 months, male fish showed a significantly dose-dependent decrease in 5-HT concentration in the hypothalamus, but not in other regions of the brain. These data provide evidence that the influence of HgCl2 on the central serotonergic system is sex-specific and brain-regionspecific (Tsai et al., 1995). In sexually mature males and females exposed to elevated water temperatures, 26°C, 29°C, or 32°C, for 3 weeks, hypothalamic 5-HT concentrations decreased. Similar results were found in the hypothalamic NE system. In the optic lobe, acclimation to elevated temperature resulted in higher 5-HT concentrations in both males and females; however, NE concentrations increased in females but were not altered in males. In the telencephalon, elevated temperature had no affect on 5-HT concentrations in males or

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females, but they did result in lower NE concentrations in both. These findings show that neurotransmitter activity is influenced by thermal acclimation in a sexually and regionally dependent pattern. Sexually and regionally specific responses of central neurotransmitter systems were also found in tilapia that had been exposed to lower-than-normal temperatures (Tsai and Wang, 1997a). Changes in 5-HT and NE concentrations in the central nervous system may be involved in the physiological and biochemical responses that occur during thermal acclimation. This segment of brain architecture seems to have developed to produce the necessary neural circuitry for integrating information from the external environment with the internal physiological states of a male or female animal. The sex-based difference in brain circuits is based on the sexual dimorphism of neurogenesis. The type of cells, the number of cells, neurogenesis, and the organization of the neural connections are crucial to the development of brain architecture.

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BRAIN-SEX DIFFERENTIATION IN THE DEVELOPMENT OF BRAIN NEUROTRANSMITTER SYSTEMS In addition to the volumetric difference, synaptic connectivity patterns are an important indicator for the structural sexual dimorphism of the brain. Brain neural circuits are genetically initiated and regulated by estrogen and neurotransmitters (Toran-Allerand et al., 1999; Schwarz et al., 2008). While the sexual differences in the architecture and function of central neurotransmitter systems is widely known, the development of central neurotransmitter systems has scarcely been studied in vivo in mammals. Brain-sex differences in the development of neurotransmitter systems have been well studied in tilapia, Oreochromis mossambicus, by interfering with their normal development or with their environmental conditions. Zero-day-old (the hatching day) tilapia were kept at four different temperatures: 20 (lower), 24 (control), and 28 and 32°C (elevated), respectively. On the 5th day, brain 5-HT, NE, γ-aminobutyric acid (GABA), and glutamate (Glu) contents were quantified. Similar experiments on days 5, 10, 15, 20, and 25 posthatching showed that before day 30 posthatching (day 33 postfertilization) is a developing period of brain neurotransmitter systems in tilapia. During this period, the neurotransmitter content consistently increased with age. Subsequently, the influence of both lower and elevated temperatures on the neurotransmitter content differed according to the stage of development. This is evidence that the development of central neurotransmitter systems is differentially influenced by aquatic temperature, according to the stage of development, during its specific effective period (Wang and Tsai, 2000b). Combined with an understanding of the critical period of brain-sex differentiation, whether a neurotransmitter system is involved in brain-sex differentiation can be predicted. Being exposed to lower temperature before day 10 posthatching induced a high proportion of females, whereas being exposed to elevated temperature after day 10 posthatching induced a high proportion of males (Wang and Tsai, 2000a). Before day 10 posthatching, a critical period for low temperature to induce a female tilapia, both elevated and lower temperatures downregulated brain 5-HT and brain NE, but only lower temperature downregulated GABA and Glu. It is possible that the suppression of brain Glu and GABA, but neither 5-HT nor the

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NE system, is involved in low-temperature-induced brain feminization, because neural excitation by GABA and Glu during the developmental period is thought to be a potential mechanism that mediates the masculinization of brain neural circuits in mammals (McCarthy et al., 1997; Todd et al., 2007). The influence of photoperiod (light/dark cycle) on the development of central neurotransmitter systems has also been investigated. During the developing period of central neurotransmitter systems, brain neurotransmitter content is consistently increased with age. Zero-day-old tilapia were raised in three different photoperiods: 12/12, 24/0, and 0/24 h, respectively. On the 5th day, brain 5-HT, NE, GABA, and Glu contents were quantified. Similar experiments on days 5, 10, 15, 20, and 25 posthatching showed that, before day 10 posthatching, the photoperiod altered both brain NE and GABA content. Brain 5-HT content was differentially influenced, either up- or downregulated, according to the developing stage, but brain Glu content was not altered by being exposed to different photoperiods (Huang et al., 2004; Wang and Tsai, 2004). A serial study showed that development of the central 5HT, NE, and GABA systems was regulated by both environmental temperature and photoperiod, but that the development of the Glu system was modified by the environmental temperature and not by the photoperiod. These facts show that the development of central neurotransmitter systems is age-specifically and neurotransmitter-system-specifically influenced by environmental cues (Wang and Tsai, 2000b; Huang et al., 2004; Wang and Tsai, 2004). The effects of sex steroids, viz. estrogen and androgen, on the development of brain neurotransmitter systems in the early developing tilapia brain have been well investigated (Tsai et al., 2001a; Tsai and Wang, 1997b; Tsai and Wang, 1998; Tsai and Wang, 1999; Wang and Tsai, 1999; Huang et al., 2004). Before day 30 posthatching, the brain's NE, 5-HT, GABA, and GLU content significantly increased with age, which showed that before 30 days old is a developing period for the NE, 5-HT, GABA, and GLU systems in the tilapia brain. During this period, both in vivo treatment of 17 -estradiol (E2) and methyltestosterone (MT) upregulate the GABA and Glu systems during a restricted effective period: before day 20 posthatching. Treatment with E2 in vivo upregulates, but does not inhibit, the development of the brain NE system during a specific period. These serial in vivo studies provide evidence that the development of brain neurotransmitter systems are differentially altered, both developmental-stage- and neurotransmitter-system-specifically, by being exposed to the sex steroids androgen and estrogen during the restricted developing period. 5-HT is an important signal for neural development. In tilapia, before day 10 posthatching, in vivo E2 treatment induces a significantly higher proportion of females, as happens when the development of the 5-HT system is inhibited. These effects can be mimicked by treating tilapia with para-chlorophenyalanine, a 5-HT synthesis blocker (Tsai et al., 2000). Suppression of the central 5-HT system, therefore, may be an indicator of in vivo E2-treatment-induced brain-sex differentiation, viz. the formation of female brain neural circuits. During the critical period for in vivo E2-treatment-induced brain feminization, being exposed to low temperature also induces a higher proportion of females (Wang and Tsai, 2000a). However, being exposed to both low and elevated temperatures during this period significantly decreased the development of the brain 5-HT system (Wang and Tsai, 2000b). The effect of in vivo E2 treatment on sexual differentiation may be mediated by the 5-HT

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system, which is consistent with what occurs in mammals. The development of brain 5-HT is influenced by water temperature in tilapia; however, the alteration of both 5-HT content and the gene expression of 5-HT receptors (5-HT1A and 5-HT1D) does not coincide with the brainsex differentiation in tilapia. Serial studies indicate that low-temperature-induced feminization is mediated neither by 5-HT1A or 5-HT1D receptors nor by altering brain 5-HT content. Therefore, neither brain neurotransmitter 5-HT content nor the gene expression of brain 5-HT receptors (5-HT1A and 5-HT1D) is critical for temperature-induced sexual differentiation (Wang and Tsai, 2006). In summary, the mechanism of sex steroid-induced brain-sex differentiation is not consistent with temperature-induced brain-sex differentiation. One thing that should be mentioned is that in the in vivo study of tilapia, the estrogen concentration in the brain is lower in the E2-treated group than that in the untreated group during the critical period of feminization (unpublished data). This result is consistent with the notion that brain estrogen acts on neurons to feminize brain neural circuits. How to define the brain feminization/masculinization process is still an open question not easily resolved by the mammalian model.

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BRAIN-SEX-BIASED GENE EXPRESSION IN THE SEX-DIFFERENTIATING BRAIN Brain-sex differentiation is regulated by estrogen, a product of androgen converted by aromatase, and neurotransmitters. Serotonin, a differentiation signal in neural development, induces a dimorphic sexual structure (Azmitia, 2001). The brain 5-HT concentration in perinatal rats is correlated with the process of brain-sex differentiation (Hardin, 1973). It is not clear whether the 5-HT receptor is activated when the brain is feminized. Tilapia were used to resolve this question. cDNA sequences of 5-HT 1A and 1D receptors were cloned from the brain of the tilapia, O. mossambicus. Quantitative real-time polymerase chain reaction (PCR) showed that the ontogenetic expression of neither 5-HT1A nor 5-HT1D was altered by in vivo E2 treatment during the critical period of brain-sex differentiation. No correlation between the ontogenetic expression of the 5-HT receptors 5-HT1A and 5-HT1D and temperature-induced brain feminization/masculinization has been found in in vivo studies. Neither 5-HT1A nor 5-HT1D gene expression was associated with either temperature-induced or sex-steroid-induced brain-sex differentiation (Wang and Tsai, 2006). Brain aromatase and brain estrogen receptors (ERs) are thought to be involved in brain differentiation in teleosts as they are in mammals. In the latter, estrogen-forming (aromatase) and estrogen-sensitive (ER-containing) networks of neurons developing peri- and postnatally are crucial in brain differentiation (Naftolin, 1994; Beyer, 1999). Aromatase, a key enzyme for converting androgen to estrogen (Naftolin et al., 1975; Balthazart et al., 1998), is involved in neural differentiation and maturation in the brain (Hutchison et al., 1997; Horvath et al., 1999). There is a remarkable sex difference in both aromatase (Hutchison et al., 1997; Lauder et al., 1997; Jeyasuria et al., 1998; Karolczak et al., 1998) and ER expression (Kuhnemann et al., 1994; Karolczak et al., 1998; Kuppers and Beyer, 1999; Ivanova and Beyer, 2000) in the developing brain. There is greater aromatase activity and gene expression in neurons

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developing in the embryonic male brain than in the female brain (Hutchison et al., 1997; Lauder et al., 1997; Jeyasuria et al., 1998; Karolczak et al., 1998). The estrogen-forming capacity of the male hypothalamus may affect brain differentiation at specific sex-steroidsensitive stages in the ontogeny of mammals (Hutchison et al., 1997). In zebra fish, the timely and appropriate expression of aromatase should be important in development, and the expression of aromatase b (the 'extragonadal' form) may be associated with sexual differentiation if not sexual determination (Trant et al., 2001). In turn, many of the effects of estrogens during development are mediated through neuronal ERs that produce changes in the expression of estrogen-responsive genes, which ultimately influence important developmental processes, such as neuronal proliferation, migration, synapse formation, and apoptosis (Beyer, 1999). Estrogen-receptor concentration is an important component of the mechanism of brain-sex differentiation (Beyer, 1999; MacLusky et al, 1997; Simerly et al., 1997). Numerous recent studies (Toran-Allerand et al., 1999; Kuhnemann et al., 1994; Kuppers and Beyer, 1999; Ivanova and Beyer, 2000; Simerly et al., 1997) have shown that the simultaneous expression of ERs and aromatase during pre- and postnatal ontogeny are potentially involved in mammalian brain differentiation. As in mammals, the ontogeny of brain aromatase and brain ER expression are involved in the process of temperature-induced sex differentiation in tilapia. The feminizing thermosensitive period, before day 10 posthatching, is the same as the estrogen-sensitive period in tilapia (Tsai et al., 2000; Wang and Tsai, 2000a). Similar to the low-temperature-induced effects, exogenous estrogen has a feminizing effect on the gonad before day 10 posthatching when the expression of brain aromatase and brain ER is downregulated (Tsai et al., 2001a, 2003). It seems that the downregulation of brain ER and aromatase gene expression is the common pathway for the development of sex-specific brain neural circuits. Similar to in vivo E2 treatment, water temperature regulates neural development in the tilapia brain. In an in vivo study on tilapia (Tsai et al., 2000; Wang and Tsai, 2000a; Tsai et al., 2001a, 2003), exogenous estrogen and low temperature during the critical period separately induced brain feminization, while brain 5-HT content was decreased in the E2treated group but not altered in the low-temperature-treated group. However, both ER and aromatase mRNA expression were downregulated by both exogenous estrogen and low temperature. The gene expression of both aromatase-containing and ER-containing systems in the developing brain might be an indicator of both estrogen- and temperature-induced brain-sex differentiation in tilapia. Research on tilapia provides evidence that the gene expression of brain-sex differentiation-related genes is controlled by sex-steroids and temperature, and subsequently determines whether brain neural circuits are feminized or masculinized. In mammals, the ontogenetic expression of both brain aromatase and ERs during the peri- and postnatal periods is crucial in brain differentiation (Toran-Allerand et al., 1999; Kuhnemann et al., 1994; Kuppers and Beyer, 1999; Ivanova and Beyer, 2000; Simerly et al., 1997). Aromatase expression is higher in neurons developing in the embryonic male brain than in the female brain (Hutchison et al., 1999). Both ER and ER are expressed in the developing brain of mammals. There are significant sex differences in ER mRNA expression in the developing brain (Kuhnemann et al., 1994; Karolczak and Beyer, 1998; Kuppers and Beyer, 1999; Ivanova and Beyer, 2000). Estrogen treatment during either the peri- or the postnatal period decreases ER expression. ER mRNA downregulation may be an

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important estrogen-regulated event in the process of brain-sex differentiation (Kuhnemann et al., 1994; Karolczak and Beyer, 1998; Kuppers and Beyer, 1999; DonCarlos et al., 1995), and changes in ER concentrations may be one of the hallmarks of this process (Kuhnemann et al., 1994). In songbirds, brain aromatase activity and mRNA expression may serve a sexually dimorphic function (Saldanha et al., 2000). Estrogen plays a major role in masculinizing the song function (Ramachandran et al., 1999). The ontogenetic investigation of tilapia during the critical period of brain-sex differentiation provides evidence that the sex-biased gene expression of brain aromatase and ER is one of the hallmarks of brain-sex differentiation processing. Estrogen acts directly on neurons to mediate the sex differentiation of brain neural circuits. The generation of neurons and glial cells mediated by estrogen in the developing brain during this specific period is expected to lead to broad changes in the structure and functions of the brain. A serial review in mammals (Carrer and Cambiasso, 2002) reported that male-type brain circuitry results from exposure to androgens during a "critical period" of brain development, whereas female-type brain circuitry develops in the absence of testicular secretion, irrespective of chromosomal sex. This is consistent with the finding in tilapia that in vivo E2 treatment during the restricted developing period induced brain feminization. Brain alpha-fetoprotein (AFP)-binding estrogen is thought to be critical in the developing brain. The inhibiting estrogen rescues the brain masculinization found in female mice lacking this gene, which suggests that -fetoprotein inhibits estrogen activity in females. In females, the AFP binding of estrogens blocks them from the brain and keeps them circulating long enough to be metabolized into inactive steroids. Another hypothesis is that AFP-escorted estrogen may result in very selective and specific estrogen delivery to particular sets of neurons (Bakker et al., 2006), which may contribute to feminine brain development (Puts et al., 2006). The masculinizing actions of androgen are mediated by estrogen, the product of aromatization by aromatase. Serial studies in tilapia provide evidence that the downregulation of brain ER and brain aromatase gene expression during the brain-sex differentiation period is an indicator of the processing of sex-steroid- and temperature-induced brain feminization. Based on this understanding of the critical period for exogenous sex-steroid- and temperature-induced brain-sex differentiation, the genetic and brain-sexual differentiation in tilapia may become a powerful animal model providing insights into questions that have remained unanswered by studies on other vertebrate systems.

THE DEVELOPMENT OF BRAIN NEURAL CIRCUITRY For the past 50 years, sexually differentiated development has been thought to be the effect of estrogen on neurons in vertebrates. However, growing evidence suggests that there are probably direct genetic effects that induce sex-specific neural circuits in the brain. A different pattern of synaptic connections in the mPOA of male and female rats was reported 35 years ago (Raisman and Field, 1973). Histological studies have revealed the sex-specific brain neural circuits in mammals and non-mammals. Recently, microarray analysis of gene expression has become a powerful tool for discovering the sex-specific expression of sexbiased genes involved in the development of brain neural circuitry. A comprehensive

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microarray analysis of gene expression in mouse brains (Yang et al., 2006) reported 355 female-biased genes and 257 male-biased genes. The expression of several X escapee genes is indeed higher in female than in male brain tissue. Some of the sex differences are found only in the adult brain but not in other types of tissue (Yang et al., 2006). The sex-linked genes, which are asymmetrically inherited between males and females, may directly influence sexually dimorphic neurobiology (Arnold et al., 2004; Davies and Wilkinson, 2006). However, because the brain is comprised of highly heterogeneous tissue, sex differences in gene expression within individual regions of the brain may be masked when the whole brain is studied, owing the limited sensitivity of microarrays for detecting genes expressed at low levels (Isensee and Ruiz Noppinger, 2007). The transitional gene-by-gene approach is necessary to study brain-sex-differentiation-related genes. Many sexual dimorphisms in brain neural circuitry and gene expression have been proved to be due to sex steroids and metabolites that act in the developing brain and permanently write to the brain in a sexspecific brain architecture (McCarthy and Konkle, 2005; Becker et al., 2005; Morris et al., 2004). Genetic and physiological studies in teleosts with the biological and genomic complexity found in the higher vertebrate have been especially important in providing insights into the answers to questions that remained unanswered by studying other vertebrates. We are fortunate that tilapia are an almost ideal animal model for investigating the progressing alteration at the molecular, genetic, cellular, and circuitry levels in the brain in parallel with the critical period of brain-sex differentiation. In vertebrates, the accumulated evidence suggests that genetic mechanisms controlling gender-specific neural characteristics precede or are concomitant with hormonal effects (Carrer and Cambiasso, 2002). Sexual dimorphism of gene expression in the mouse brain, for example, occurs before gonadal differentiation (Dewing et al., 2003). As in mammals, sexual dimorphic gene expression in the teleost brain precedes gonadal differentiation. As with the feminization effect of estrogen treatment in vivo, being exposed to lower temperature during the critical period of feminization in tilapia (O. mossambicus) before posthatching day 10 induces a high proportion of females when the mRNA expression of brain aromatase and brain estrogen receptor (ER ) are downregulated (Tsai et al., 2000; Wang and Tsai, 2000a; Tsai et al., 2001a, 2003). The genetic expression of the developing brain, therefore, is critical for brain-sex differentiation. However, the molecular mechanism for this differentiation is not clear. Expressed sequence tags (ESTs) vary with species, tissue, age, sex, and physiological conditions. Adult brain ESTs have been derived from many species, mammalian and nonmammalian. EST cataloging and profiling provides a basis for functional genomic research. Developing brain ESTs will be useful for studying the cellular and molecular mechanisms of the development of the brain. A few ESTs have been cloned from the developing animal, particularly from the brain. The analysis of ESTs provides significant additional functional structure and evolutionary information (Quackenbush et al., 2000). However, there is no transcriptome analysis concentrated specifically on the developing brain during the critical period of brain-sex differentiation. Identifying genes expressed in the cells of the developing brain, particularly during the critical period of brain-sex differentiation, is important for studying both the molecular mechanism of sexual differentiation in brain neural circuits and the physiological functions during the developmental stages. A list of transcripts expressed in

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the developing tilapia brain using ESTs has been derived from the developing brain during the critical period for the formation of sex-specific brain neural circuits (Tsai et al., 2007). Based on available protein domain information and GO annotation, 14 genes have been classified as neural-development-related genes: discs large homolog 5, dishevelled-1 isoform (DVL-1), endothelial differentiation-related factor 1, inhibitor of differentiation protein 2 (Id2), midkine-related growth factor 2 (Mdk2), mitogen-activated protein kinase 14b (mitogen-activated protein kinase p38b), myelin expression factor 2, nuclear protein NAP, beta-catenin-like isoform 1, odd Oz/ten-m homolog 1 (tenascin M), p53 tumor suppressor protein, plasticity-related protein 2 (PRG-2), pleiotropic factor b (heparin-binding neuritepromoting factor), tsc2 gene product, and ubiquitin-activating enzyme E1. All of these neural development-related genes are expressed in the early developing brain. Their ontogenetic expression of the remaining neural development-related genes varies with the stage of development. These neural development-related genes have been classified into four types based on real-time-quantification-reverse transcriptase PCR analysis of their responses to different temperatures. − − −

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−

Type 1: The ontogenetic expression was not altered by different temperatures. Type 2: The ontogenetic expression was particularly influenced by elevated temperature (32 C). Type 3: The ontogenetic expression was particularly influenced by lower temperature (20 C). Type 4: The ontogenetic expression was differentially influenced by elevated and lower temperatures according to the stage of development.

Discs large homolog 5, myelin expression factor 2, plasticity-related protein-2, tsc2 geneproduct-related genes, and an inhibitor of differentiation protein 2 (Id2) were differentially temperature-influenced according to their developmental stages. Endothelial-differentiationrelated factor 1, midkine-related growth factor b, and mitogen-activated protein kinase 14b are specifically influenced by elevated temperature, and beta-catenin-like isoform 1 by lower temperature. Neural-development-related genes, expressed in the sex-differentiating brain, are related to the development of sex-specific brain neural circuits. These neural development genes with thermosensitive ontogenetic expression should be involved in temperatureinduced brain-sex differentiation. In order to screen the brain-sexual-differentiation-inducing gene, a gene-by-gene approach in neural/glial culture is a powerful tool. Indicators of brainfeminized neural circuits and brain-masculinized neural circuits are required for the research. The completion of the genome sequence of Drosophila melanogaster (Adams and Sekelsky, 2000; Rubin, 2000; Yoshihara et al., 2001) provides an important foundation for an important animal model that will allow us to apply genetic screens to identify mutants that interrupt specific neural functions. The annotation of approximately 14,000 genes contained within the 120-megabase euchromatic genome allows the fly-research community to make homology-based comparisons to comprehensively identify gene families and homologs of known prokaryotic and eukaryotic proteins. Neurobiology in Drosophila has covered a wide range of experimental questions ranging from the specification of the nervous system during early development to the molecules involved in learning and memory. Behavioral mutants

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initiated a wave of genetic studies into the function of the nervous system and led to the characterization of mutants such as Shibire (Ikeda et al., 1976) and Shaker (Kaplan and Trout 1969). These studies provided the foundation for a new generation of fly neurobiologists that employed systematic genetic screens for specific neurological phenotypes. The fru gene of Drosophila functions at the head of one of the branches of the sex-determination pathway, and acts specifically in the central nervous system (CNS) to govern sexual orientation and male courtship behavior (Ryner et al., 1996; Taylor et al., 1994; Goodwin, 1999). The synaptic formation-related gene and protein are found at Drosophila neuromuscular junctions. Using microarrays has allowed us to identify the sex-enriched transcripts expressed during three different stages of the development of C. elegans larvae. The TRA-1 Zinc finger protein is a sex-determination controller in C. elegans, a Ci/GLI homolog that determines the fate of female cells throughout the body. The sex specific neuron and, consequently, the underlying sex-specific neural circuitry are important for producing sex-specific behaviors (Goodwin, 1999; Thoemke et al., 2005). Though sex dimorphism in the neural circuitry is ubiquitous, the expression and regulation of the gene to induce brain-architecture sexdifferentiation remain poorly understood.

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(a)

(b)

Figure 1. Cell cultures derived from the adult hypothalamus of tilapia, Oreochromis mossambicus. Approximately 48 h after plating, the cells started to differentiate. (a) Neurons were stained with MAP2 (2a+2b) (red) and nuclei were stained with Hoechst 33342 (blue). (b) 5-HT-containing neurons (black).

Estrogen acts directly on neurons to mediate the sex differentiation of brain neural circuits. The generation of neurons and glial cells mediated by estrogen in the developing brain is expected to lead to broad change in the structure and functions of brain. A serial review in mammals (Carrer and Cambiasso, 2002) reported that male-type brain circuitry is the result of the brain's having been exposed to androgens during a "critical period" of brain development, whereas female-type brain circuitry is the result of the absence of testicular secretion, irrespective of the animal's chromosomal sex. In our in vivo study of tilapia (unpublished data), the estrogen concentration in the brain was lower in the E2-treated group than in the untreated group during the critical period of brain feminization. Alpha-fetoprotein (AFP) in the developing brain is thought to inhibit estrogen activity in females. Selective estrogen delivery to a specific neural circuit may contribute to the development of a female brain (Puts, 2006). Estrogen directs the formation of sexually dimorphic circuits by influencing axonal guidance and synaptogenesis. In vivo and in vitro studies show that the

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development of glial cells and neurons in the brain is regulated by estrogen, which controls the formation of sex-specific brain neural circuits. Estrogen is thought to be involved in the formation of the sexually dimorphic distribution of central serotonergic innervation via the ER-containing 5-HT cells in rats (Lu et al., 2004). However, little is known about the mechanism that induces the development of sex-specific brain neural circuits at the genetic level. The genetic and epigenetic mechanisms involved in determining the development of brain neural circuits have been explored in cultures of neurons, cultures of glial cells, and cocultures. The genomic determinants expressed in the brain, those that shape the sex-specific characteristics of neurons and glial cells, include these sex-, region-, and time-specific responses to estrogen. The effects of estrogen and neurotransmitters on the proliferation of brain cells have been investigated in the primary neuronal culture of the tilapia brain (Figure 1). In an in vitro study (Tsai et al., 2001b), E2 significantly increased the proliferation of neurons in the primary neural culture. In vitro E2 treatment significantly and dosedependently increased the serotonergic cells of the primary brain neural cell (Figure 2). 5-HT treatment also induced the 5-HT-induced proliferation of neurons in the neural culture. The antagonist of 5-HT1A receptor, WAY-100635, inhibited this proliferation (Figure 3). These results showed that E2 increased the number of neural cells, including 5-HT-containing neurons, which is evidence that estrogen acts on neurons to induce a masculinized brain neural circuit, in part, by increasing the number of 5-HT-containing neurons associated with the 5-HT1A receptor. This is consistent with the in vivo E2-treatment-induced brain feminization while brain 5-HT content is decreased. An in vivo study (Tsai et al., 2001a, 2003) showed that the gene expression of neither brain 5-HT1A nor 5-HT1D is associated with the formation of sex-specific brain neural circuits, which is induced by in vivo sex-steroid treatment or temperature-induced brain-sex differentiation. In vitro studies, however, show that 5-HT1A receptor deals with the sex-steroid-induced mechanism. 5-HT may be mediated in part by both 5-HT1A and 5-HT1D receptors to induce the proliferation of neurons in mammals (Barnes and Sharp, 1999). 5-HT1A receptor may play a neurotrophic role in both the developing brain and the adult brain. The cell-specific role for the formation of sexspecific brain architecture may be a reason for the different results between the in vivo and in vitro studies. Cell-specific gene expression will be marked when the whole brain is studied. Therefore, cell-by-cell-specific gene expression should be investigated, using cell culture, for the formation of sex-specific neural circuits. Combined with the gene-by-gene approach, the knockdown and silencing of genes in neural and glial cell cultures is a powerful tool for studying the regulation of the expression of feminization-/masculinization-related genes in brain neural circuits. Finally, in vitro results should be confirmed by in vivo studies.

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1000

E2: 0 M (control)

d

5-HT containing neuron number

-8

E2: 10 M 800

E2: 10-7M

c

-6

E2: 10 M b

600

400

a

200

0 Figure 2. Effects of E2 on the 5-HT-containing proliferated neurons cultured from the tilapia hypothalamus. Statistical data are means SD. Different letters indicate significant differences between groups at the same stage (one-way ANOVA, and then Duncan's multiple-range test).

1000

5-HT: 0 M (control) 5-HT: 10-6M -6 5-HT: 10 M+ WAY100635 10-8M 5-HT: 10-6M+ -6 WAY100635 10 M

5-HT containing neuron number

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b 800 a 600 c 400

d

200

0 Figure 3. Effects of 5-HT on the 5-HT-containing proliferated neurons cultured from the tilapia hypothalamus. Statistical data are means SD. Different letters indicate significant differences between groups at the same stage (one-way ANOVA, and then Duncan's multiple-range test).

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CONCLUSION Sex-specific genes control the formation of brain architecture. Environmental cues regulate sex-biased gene expression in the brain. The regulation of the expression of feminization-/masculinization-related genes in brain neural circuits is an open question. The mechanisms for sex-steroid- and temperature-induced brain-sex differentiation as well as for the development of central neurotransmitter systems have been well investigated in tilapia, O. mossambicus. Their critical periods for forming male and female brain neural circuits are well defined. The development of the central neurotransmitter system and the up- and downregulation of the number of males and females caused by in vivo sex-steroid treatment during the critical period of brain feminization/masculinization, respectively, may be an important indicator for the progression of brain-sex differentiation. Based on this information, the regulation of environmental cues affecting sex-biased gene expression during a critical period of brain-sex differentiation can be studied. Gene expression of brain aromatase and brain ER is associated with brain-sex differentiation in both temperatureinduced and sex-steroid-induced brain-sex differentiation. It will be interesting to determine the role of the brain's 5-HT system in both. In vivo studies on genetic and brain sexual differentiation induced by sex steroids and temperature in tilapia have been especially important in providing insights into questions that remain unanswered from other vertebrate models. The in vivo genomic approach using tilapia appears to be a good model for this investigation. Downregulation of the gene expression of brain ER and brain aromatase induced by estrogen or low-temperature treatments in in vivo studies during the critical developing period indicates the feminization of brain neural circuits. Transcriptome analysis is useful for functional genomic research on development, comparative genomic studies, and genomic evolution. The functional analyses of the ESTs derived from the developing brain cDNA library of tilapia will provide more information for the molecular mechanism of the physiological functions as well as the cellular and synaptic organization of the brain during its developmental stages. The genes identified as neural-development-related, and cloned from the developing tilapia brain, should play a role in the cellular and synaptic organization of the central neural circuits. Based on this information, knockdown-gene expression, using a gene-by-gene approach in vitro combined with microarray analysis, will be an animal model for resolving the open questions about the regulation of gene-expression regulation in sexdifferentiation brain neural circuits at the cellular level. The participation of glial cells and neurons in the formation of brain-sex-specific neural circuits is an emerging field of great interest. Regulation of sex-specific gene expression at the level of glial cells and neurons requires more study, and all in vitro findings should be confirmed by in vivo studies.

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In: Viral Gene Expression Regulation Editor: Eli B. Galos

ISBN 978-1-60741-224-3 © 2010 Nova Science Publishers, Inc.

Chapter 7

A BIOINFORMATICAL APPROACH TO THE ANALYSIS OF VIRAL AND CELLULAR INTERNAL RIBOSOME ENTRY SITES Martin Mokrejš1,*, Václav Vopálenský1, Tomáš Mašek1 and Martin Pospíšek1,2,* 1

2

Charles University, 128 43 Prague, Czech Republic; Institute of Computer Science AS CR, 182 07 Prague 8, Czech Republic.

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ABSTRACT The internal ribosome entry site (IRES) is a part of the mRNA sequence which is able to attract the eukaryotic ribosomal initiation complex and to directly promote the initiation of protein synthesis independently of the presence of 5'-terminal 7mG cap. RNA structures bearing the IRES activity were first discovered in certain eukaryotic viruses where they very often play a pivotal role in viral strategies, allowing the viral invader to overcome the overall decrease of the host protein synthesis caused either by viral proteins or by the cellular antiviral defense system. Although the IRES segments and thus the cap-independent translation initiation were first described in viruses, extensive evidence has appeared in the past few years that a similar principle of the translation initiation is utilized also by some cellular mRNAs. Demonstration of IRES activity of a particular RNA region is not a simple task. A proper design of the experiment and a careful selection of the controls – excluding artificial signals generated by leaky scanning, ribosome hopping and undesirable cryptic transcription, splicing or physical breakage at the hot-spots – are very important. A number of false positives described in the literature as well as difficulties in designing appropriate controls have become the major stimuli for creating IRESite – the publicly available manually A version of this chapter was also published in New Messenger RNA Research Communications, edited by Lee B. Kwang published by Nova Science Publishers, Inc. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. * Corresponding authors: M. Mokrejš [email protected], M. Pospíšek [email protected]. Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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Martin Mokrejš, Václav Vopálenský, Tomáš Mašek et al. annotated database of experimentally verified IRES structures. This chapter presents the current status of the IRESite database (http://www.iresite.org), the complete list of known viral and cellular IRESs as well as novel results obtained from the comparative analyses of IRES segments accumulated to date. The article also presents a brief description and comparison of other available databases containing IRES and 5' untranslated region (5'-UTR) related information.

ABBREVIATIONS 7mG – 7-methylguanosine; IRES – internal ribosome entry site; ITAF – IRES trans-acting factor; TE – translational enhancer; UTR – untranslated region.

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INTERNAL RIBOSOME ENTRY SITE (IRES) SEGMENTS FACILITATE INITIATION OF TRANSLATION IN EUKARYOTES Eukaryotic protein synthesis is a highly regulated cellular process which consumes about 5% of total human caloric uptake (Mathews et al., 2000). Kinetic studies of translation revealed that the rate-limiting step of protein synthesis is the initiation, during which the necessary translation factors have to build physical connection between the 5'-end of mRNA molecule and the 40S ribosome subunit to assemble a functional initiation complex. The whole process consists of a complicated series of interactions among the proteins, RNAs and low molecular weight molecules having a specific order and timing. Eukaryotic cells utilize a 7mG cap moiety to protect the 5'-end of the mRNA molecules against nuclease attack as well as to facilitate their translation by attachment of cap-binding translation initiation factors (Hershey and Merrick, 2000). The mechanism of translation initiation of certain viral RNAs was discovered 20 years ago in the labs of E. Wimmer and N. Sonenberg (Dorner et al., 1984; Jang et al., 1988; Pelletier and Sonenberg, 1988). It appeared that eukaryotic ribosomes can – besides the canonical cap-dependent pathway – enter the mRNA transcript also independently of the 5'cap structure in a region called Internal Ribosome Entry Site (IRES). Some of the eukaryotic viruses use this uncommon mechanism of translation initiation as part of their life strategy allowing them to completely usurp the cellular translational machinery for preferential synthesis of viral proteins. However, the growing list of experimental data accumulated during the past decade provided evidence that the cap-independent translation initiation is not limited only to the particular viruses but can also be utilized by certain cellular mRNAs at least in cases when the cap-dependent translation initiation is compromised, as happens often during stress, hypoxia, viral infection, etc. When thinking about the IRESs it must be stated first that the internal ribosome entry site is defined by functional assays on principle. Attempts to find some common sequence or structural criteria for the IRES have failed so far. Viral IRESs are usually much better

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characterized than cellular IRESs and some of them – including the prominent hepatitis C virus IRES – have been studied so intensively that the results contributed significantly to the current view of translation initiation. Viral IRESs are often highly structured and even if stronger similarities can be found only in closely related viruses, they can be classified by the operational criteria (Jackson, 2000). In the field of cellular IRESs the situation is currently less transparent. It is now becoming accepted that a remarkable part of the currently reported IRESs encoded by nuclear genes has been published without proper experimental controls. These findings gave rise to doubts about the existence of many reported cellular IRESs and prompted scientists to discuss extensively an introduction of new strict evaluation criteria for IRES determination and classification (Jackson, 2000; Kozak, 2005). Several reviews and articles shedding light on IRESs from different perspectives have recently been published (Jackson, 2000; Kean, 2003; Merrick, 2004; Stoneley and Willis, 2004; Holcik and Sonenberg, 2005; Komar and Hatzoglou, 2005; Spriggs et al., 2005). Table 1. List of animal and human viruses containing IRES (51 in total) Virus abbreviation ALPV

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BEV BVDV CSFV CVB3 CrPV DCV E6 E11 E25 EAV EBV EMCV ERAV ERBV EV71 FMDV F-MLV

GBV-A GBV-B GBV-C GLV Gypsy HAV

Full name of the virus Aphid lethal paralysis virus Bovine enterovirus Bovine viral diarrhea virus Classical swine fewer virus Coxsackie virus B3 Cricket paralysis virus Drosophila C virus Echovirus 6 Echovirus 11 Echovirus 25 Equine arteritis virus Epstein-Barr virus Encephalomyocarditis virus Equine rhinitis A virus Equine rhinitis B virus Enterovirus 71 Foot-and-mouth disease virus Friend murine leukemia virus (mouse retrotransposon) Hepatitis G virus A Hepatitis G virus B Hepatitis G virus C Giardia lamblia virus Drosophila retrotransposon Hepatitis A virus

Family Dicistroviridae

References (van Munster et al., 2002)

Picornaviridae Flaviviridae Flaviviridae

(Zell et al., 1999) (Poole et al., 1995) (Rijnbrand et al., 1997)

Picornaviridae Dicistroviridae Dicistroviridae Picornaviridae Picornaviridae Picornaviridae Arteriviridae Herpesviridae Picornaviridae

(Yang et al., 1997) (Wilson et al., 2000) (Cherry et al., 2005) (Beaulieux et al., 2005) (Gharbi et al., 2006) (Bailly et al., 1996) (Tijms et al., 2001) (Isaksson et al., 2003) (Jang et al., 1988)

Picornaviridae Picornaviridae Picornaviridae Picornaviridae Retroviridae

(Hinton et al., 2000) (Hinton and Crabb, 2001) (Lee et al., 2005) (Belsham and Brangwyn, 1990; Kuhn et al., 1990) (Berlioz and Darlix, 1995)

Flaviviridae Flaviviridae Flaviviridae Totiviridae Retroviridae Picornaviridae

(Simons et al., 1996) (Grace et al., 1999) (Simons et al., 1996) (Garlapati and Wang, 2004) (Ronfort et al., 2004) (Brown et al., 1994)

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Martin Mokrejš, Václav Vopálenský, Tomáš Mašek et al. Table 1. (Continued)

Virus abbreviation HCV HHV8 / KSHV HIV-1

HIV-2

HoCV-1 HRV HSV Idefix LINE-1 MHV-68 MMLV / Mo-MuLV PEV-8 PSIV

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PTV-1 PV REV-A

RhPV RSV SIV SV2 SV40 SVDV TMEV TrV TSV VL30m WSSV

Full name of the virus Hepatitis C virus Kaposi‘s sarcomaassociated herpesvirus Human immunodeficiency virus type 1 Human immunodeficiency virus type 2 Homalodisca coagulata virus 1 Human rhinovirus Herpes simplex virus Drosophila retrotransposon Mouse retrotransposon Murine gammaherpesvirus 68 Moloney murine leukemia virus Porcine enterovirus 8 Plautia stali intestinalis virus Porcine teschovirus 1 Poliovirus

Family Flaviviridae Herpesviridae

References (Tsukiyama-Kohara et al., 1992) (Bieleski and Talbot, 2001)

Retroviridae

(Buck et al., 2001)

Retroviridae

(Herbreteau et al., 2005)

Dicistroviridae

(Hunnicutt et al., 2006)

Picornaviridae Herpesviridae Retroviridae

(Borman and Jackson, 1992) (Griffiths and Coen, 2005) (Meignin et al., 2003)

Retroviridae Herpesviridae

(Li et al., 2006) (Coleman et al., 2003)

Retroviridae

(Vagner et al., 1995b)

Picornaviridae Dicistroviridae

(Chard et al., 2006) (Sasaki and Nakashima, 1999)

Picornaviridae Picornaviridae

Avian reticuloendotheliosis virus type A Rhopalosiphum pali virus Rous sarcoma virus Simian immunodeficiency virus Simian virus 2 Simian virus 40 Swine vesicular disease virus Theiler‘s murine encephalomyelitis virus Triatoma virus Taura syndrome virus Mouse retrotransposon Shrimp white spot syndrome virus

Retroviridae

(Kaku et al., 2002) (Dorner et al., 1984; Pelletier and Sonenberg, 1988) (Lopez-Lastra et al., 1997)

Dicistroviridae Retroviridae Retroviridae

(Domier et al., 2000) (Deffaud and Darlix, 2000) (Ohlmann et al., 2000)

Picornaviridae Polyomaviridae Picornaviridae

(Chard et al., 2006) (Yu and Alwine, 2006) (Sakoda et al., 2001)

Picornaviridae

(Pilipenko et al., 2000)

Dicistroviridae Dicistroviridae Retroviridae Nimaviridae

(Czibener et al., 2005) (Hatakeyama et al., 2004) (Lopez-Lastra et al., 1999) (Han and Zhang, 2006)

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Table 2. List of plant viruses utilizing a cap-independent translation. IRES containin g virus (abbrev.) BYDV

Family / Genus

7mG cap / poly(A)

Barley yellow dwarf virus

Luteoviridae / Luteovirus

-/-

Crucifer infecting tobamovirus Hibiscus chlorotic ringspot virus Potato leafroll polerovirus Red clover necrotic mosaic virus Satellite tobacco necrosis virus

Tobamovirus

TBSV

Mechanism

References

(Wang et al., 1997; Wang et al., 1999; Guo et al., 2001)

+/-

TE in 3‘-UTR, 18S rRNA complementarity, capindependent scanning from 5‘-end, kissing loops 5‘ IRES

Tombusviridae

-/-

5‘ IRES

(Koh et al., 2003)

Luteoviridae / Polerovirus Tombusviridae

VPg / -

5‘ IRES

(Jaag et al., 2003)

-/-

TE in 3‘-UTR

Tombusviridae

-/-

Tomato bushy stunt virus

Tombusviridae

-/-

TCV 1.45kb sgRNA TEV

Turnip crinkle virus

Tombusviridae

-/-

TE in 3‘-UTR, eIF4F and eIFiso4F recruitment TE in 3‘-UTR, capindependent scanning from 5‘-end, kissing loops TE in 5‘-UTR and 3‘UTR, cap-independent

(Mizumoto et al., 2003) (Gazo et al., 2004)

Tobacco etch potyvirus

Potyviridae

VPg / +

TMV

Tobacco mosaic virus

Tobamovirus

+/-

TNV-A sgRNA2

Tobacco necrosis virus A

Tombusviridae

-/-

TNV-D

Tobacco necrosis virus D Turnip mosaic potyvirus

Tombusviridae

-/-

Potyviridae

VPg / +

crTMV HCRSV PLRV RCNMV RNA1 STNV

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Full name of the virus

TuMV

TE in 5‘-UTR, 18S rRNA complementarity, VPg recruits eIF4E and eIFiso4E, IRES TE in 5‘-UTR, eIF4F and eIFiso4F recruitment TE in 3‘-UTR, 18S rRNA complementarity, kissing loops TE in 3‘-UTR, kissing loops 5‘ IRES

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(Ivanov et al., 1997)

(Fabian and White, 2004, , 2006)

(Qu and Morris, 2000)

(Niepel and Gallie, 1999; Schaad et al., 2000; Zeenko and Gallie, 2005) (Gallie, 2002)

(Meulewaeter et al., 2004)

(Shen and Miller, 2004) (Basso et al., 1994)

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Table 3. List of eukaryotic IRESs reported in 5’ UTRs of cellular mRNAs (82 in total) IRES containing gene α-CaM kinase II AML1 / RUNX1 Antennapedia APAF-1 APC ARC AT1R BAG-1 BCL-2 Bcl-xL BiP CAT-1 CBFA1 / RUNX2 Cx26 Cx32 Cx43 Cyclin D1 CYR61 c-JUN c-MYC DAP5 / p97 / NAT1 dendrin eIF4G E2F6 ELH FGF1 FGF2 FMR1 GLI1 GRP58

Donor organism Rattus norvegicus Homo sapiens Drosophila melanogaster Homo sapiens Homo sapiens Rattus norvegicus Homo sapiens Homo sapiens Homo sapiens Mus musculus Drosophila melanogaster Rattus norvegicus Mus musculus Homo sapiens Mus musculus Rattus norvegicus Homo sapiens Homo sapiens Gallus gallus Homo sapiens Homo sapiens Rattus norvegicus Homo sapiens Mus musculus Aplysia californica Homo sapiens Homo sapiens Homo sapiens Mus musculus Homo sapiens

GluR2 Gtx Hairless HAP4 HIAP2 / c-IAP1 / MIHB / BIRC2 Hif-1α HSP70 HSP70 HSP90 HSP101 IGF-IR IGF-II KCNA4 / Kv1.4 La1 LEF-1 L-MYC MAP2

Rattus norvegicus Mus musculus Drosophila melanogaster Saccharomyces cerevisiae Homo sapiens Mus musculus Drosophila melanogaster Homo sapiens Drosophila melanogaster Zea mays Rattus norvegicus Homo sapiens Mus musculus Homo sapiens Homo sapiens Homo sapiens Rattus norvegicus

References (Pinkstaff et al., 2001) (Pozner et al., 2000) (Oh et al., 1992; Ye et al., 1997) (Coldwell et al., 2000) (Heppner Goss et al., 2002) (Pinkstaff et al., 2001) (Martin et al., 2003) (Packham et al., 1997; Mitchell et al., 2001) (van Eden et al., 2004a) (Yoon et al., 2006) (Macejak and Sarnow, 1991) (Fernandez et al., 2001) (Xiao et al., 2003) (Lahlou et al., 2005) (Hudder and Werner, 2000) (Schiavi et al., 1999) (Shi et al., 2005) (Johannes et al., 1999) (Sehgal et al., 2000) (Nanbru et al., 1997) (Henis-Korenblit et al., 2000) (Pinkstaff et al., 2001) (Gan and Rhoads, 1996; van Eden et al., 2004a) (Dahme et al., 2002) (Dyer et al., 2003) (Martineau et al., 2004) (Vagner et al., 1995a) (Chiang et al., 2001) (Wang and Rothnagel, 2001) (Ferguson et al., 1998; Qin and Sarnow, 2004; Scheu et al., 2006) (Myers et al., 2004) (Chappell et al., 2000) (Chartrand et al., 2002) (Iizuka et al., 1994; Hecht et al., 2002; Seino et al., 2005) (Warnakulasuriyarachchi et al., 2003; van Eden et al., 2004a; Warnakulasuriyarachchi et al., 2004) (Lang et al., 2002) (Rubtsova et al., 2003; Hernandez et al., 2004) (Ahmed and Duncan, 2004) (Dinkova et al., 2005) (Fronzes et al., 2003) (Pedersen et al., 2002) (Negulescu et al., 1998; Kim et al., 2004) (Carter and Sarnow, 2000) (Jimenez et al., 2005) (Jopling et al., 2004) (Pinkstaff et al., 2001)

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Table 3. (Continued) IRES containing gene MNT MS MYT2 NAP1L1 NDST1 – NDST4 NRF / NF-κB NPM1 Nkx6.1 Notch2 n-MYC ODC1 PDGF2 / c-SIS p58PITSLRE PKCδ PIM-1 p27kip p53 RBM3 RPS18C RC3 rgr Rpr SCAMPER SMAD5 SNM1 spliced leader RNA (SL RNA) TIF4631

Donor organism Homo sapiens Homo sapiens Homo sapiens Homo sapiens Mus musculus Homo sapiens Homo sapiens Mus musculus Felix sp. Homo sapiens Homo sapiens Homo sapiens Homo sapiens Rattus norvegicus Homo sapiens Homo sapiens Homo sapiens Mus musculus Arabidopsis thaliana Rattus norvegicus Oryctolagus cuniculus Drosophila melanogaster Canis familiaris Homo sapiens Homo sapiens Leishmania tarentolae

References (Le Quesne et al., 2001) (Oltean and Banerjee, 2005) (Kim et al., 1998) (Qin and Sarnow, 2004) (Grobe and Esko, 2002) (Oumard et al., 2000) (Qin and Sarnow, 2004) (Watada et al., 2000) (Lauring and Overbaugh, 2000) (Jopling and Willis, 2001) (Pyronnet et al., 2000) (Bernstein et al., 1997; Han et al., 2003) (Cornelis et al., 2000) (Morrish and Rumsby, 2002) (Johannes et al., 1999; Wang et al., 2005) (Miskimins et al., 2001; Liu et al., 2005) (Ray et al., 2006) (Chappell et al., 2001) (Vanderhaeghen et al., 2006) (Pinkstaff et al., 2001) (Hernandez-Munoz et al., 2003) (Hernandez et al., 2004) (De Pietri Tonelli et al., 2003) (Shiroki et al., 2002) (Zhang et al., 2002) (Zeiner et al., 2003)

Saccharomyces cerevisiae

TrkB Ubx UNR URE2 UtrophinA V1bR VEGF XIAP YAP1

Homo sapiens Drosophila melanogaster Homo sapiens Saccharomyces cerevisiae Mus musculus Rattus norvegicus Homo sapiens Homo sapiens and Mus musculus Saccharomyces cerevisiae

(Zhou et al., 2001; Altmann et al., 2004; Mauro et al., 2004; Verge et al., 2004) (Dobson et al., 2005) (Ye et al., 1997) (Zuidmeer et al., 2005) (Komar et al., 2003) (Giersing et al., 2005) (Rabadan-Diehl et al., 2003) (Huez et al., 1998) (Schneider et al., 2001; van Eden et al., 2004a) (Zhou et al., 2001)

We present herein an up-to-date compilation of viral, cellular and artificially constructed IRESs. To date, 56 viruses (Table 1 and 2) as well as 82 cellular genes (Table 3) have been reported to contain IRES in their 5‘-untranslated regions. Several attempts to select IRES segments from the pool of random sequences led only to short sequences showing a high degree of complementarity to rRNA (Table 4). Many of the IRESs listed in Tables 1 to 4 have already been included in the IRESite database and became the source for the analyses presented in this study (Table 5).

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IRES name ARC-1

Functional in this host Oryza sativa

ICS1-23b

Rattus norvegicus

ICS2-17.2

Rattus norvegicus

PS3

Homo sapiens

PS4

Homo sapiens

14 IRESs

Saccharomyces cerevisiae

Mechanism rRNA complementarity rRNA complementarity rRNA complementarity rRNA complementarity rRNA complementarity rRNA complementarity

References (Akbergenov et al., 2004) (Owens et al., 2001) (Owens et al., 2001) (Venkatesan and Dasgupta, 2001) (Venkatesan and Dasgupta, 2001) (Zhou et al., 2003)

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Table 5. Cellular and viral IRES segments covered by IRESite by September 2006 IRESite_Id 57 71 71 71 110 103 108 48 35 117 122 204 204 204 204 66 116 83 82 84 85 1 58 67 118 124 89 220 51

gene name AML1/RUNX1 Antp Antp Antp Apaf-1 BCL2 c-IAP1 c-jun c-myc DAP5 FMR1 Gtx Gtx Gtx Gtx Hairless hap4 hAT1R-A hAT1R-B hAT1R-C hAT1R-D Hif1a hSNM1 Hsp70Aa HSPA1A Kcna4 L-myc LEF1 MNT

mRNA length 7288 3490 3490 3490 7042 5086 3753 1717 1958 3820 4397 1244 1244 1244 1244 5621 2937 2263 2347 2321 2405 3973 4468 2630 2404 4780 3258 3612 4865

IRES name AML1/RUNX1 Antp-CDE Antp-D Antp-DE Apaf-1 BCL2 c-IAP1 c-jun c-myc DAP5 FMR1 Gtx-1-120 Gtx-1-166 Gtx-1-196 Gtx-133-141 hairless Sc_HAP4 AT1R_var2 AT1R_var1 AT1R_var3 AT1R_var4 HIF1a hSNM1 hsp70 hsp70 Kcna4 L-myc LEF1 MNT

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IRES length 1561 1730 252 408 233 1137 1159 301 395 306 252 120 166 196 9 435 272 272 356 330 414 257 918 503 195 1197 52 1167 193

IRES origin cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular

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Table 5. (Continued) IRESite_Id 49 55 107 138 8 62 111 65 65 81 115 109 37 37 40 40 39 148 225 140 29 26 54 38 41 69 69 69 42 222 73 139 68 59 77

gene name MYT2 n-myc odc1 PIM1 Rbm3 Rpr Scamper Ubx Ubx UNR Ure2 XIAP BVDV1 BVDV1 CrPV CrPV crTMV CSFV CVB3 EMCV ERAV_1 ERBV_1 GBV-B GBV_A GBV_C Gypsy Gypsy Gypsy HAV HCV_type_1b HIV-1 HRV Idefix PSIV TSV

mRNA length 2308 2613 2035 2717 1430 851 1243 4729 4729 4115 1069 8751 12573 12573 9185 9185 6298 12311 7399 7835 7697 8828 9399 9653 9378 7468 7468 7468 7478 9416 9181 7108 6919 8797 10205

IRES name MYT2 n-myc ODC1 Pim-1 Rbm3 reaper Scamper ultrabithorax ultrabithorax UNR Sc_Ure2 XIAP BVDV1_1-385 BVDV1_29-391 CrPV_5NCR CrPV_IGR crTMV_IRES_CP CSFV CVB3 EMCV ERAV_1 ERBV_1 GBV_B GBV_A GBV_C gypsy1 gypsy2 gypsyD1 HAV_HM175 HCV_type_1b HIV-1 HRV-2 idefix PSIV_IGR TSV_IGR

IRES length 156 320 302 396 22 168 365 968 594 429 165 460 385 363 708 192 148 373 750 576 714 718 424 693 630 330 261 517 584 341 233 614 521 188 250

IRES origin cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular cellular viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral viral

As seen from Table 1, since the time of the first discovery of viral cap-independent mode of translation initiation the number of viruses known to utilize internal ribosome entry site has grown substantially. It is obvious that the use of any kind of internal ribosome entry site should be especially advantageous for single stranded +RNA viruses lacking the cap structure. Indeed the vast majority of the animal and human viruses containing IRES are RNA viruses or retroviruses including retrotransposons, and most of them belong only to the three families of +ssRNA viruses (Picornaviridae, Dicistroviridae and Flaviviridae). A mere

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six DNA viruses have so far been reported to contain IRES, four of them belonging to the Herpesviridae family (Table 1). The translation initiation strategy of many plant viruses relies on translational enhancers (TE) at their 3‘-UTR or 5‘-UTR, which are often based on complementarity to 18S rRNA, or on 5‘ to 3‘ ends crosstalk facilitated by the kissing stem loop structures (Table 2). Translational control in plant +RNA viruses has been reviewed recently (Dreher and Miller, 2006; Kneller et al., 2006). Table 3 presents the list of known IRESs found in 5‘-UTRs of cellular mRNAs. The table does not contain any IRESs or translational enhancers located in 3‘-UTR. It should be noted that only few IRESs listed in Table 3 have been studied more extensively and some of them have been claimed afterwards not to be IRES at all. The latter are notably TIF4631 (Zhou et al., 2001; Altmann et al., 2004; Mauro et al., 2004; Verge et al., 2004), HAP4, TFIID, YAP1 (Hecht et al., 2002; Seino et al., 2005), eIF4G (Han and Zhang, 2002), c-SIS/PDGF2 (Han et al., 2003), p27kip (Liu et al., 2005) and Pim-1 (Wang et al., 2005) which have been reported to contain a promoter in their cDNA copy. The Gtx, RBM3 and RPS18C genes and the spliced leader (SL) RNA from Leishmania contain a region complementary to ribosomal RNA of the small ribosomal subunit. All reported IRES segments selected from the pool of random sequences in vitro also fall into the same group of IRESs (Table 4).

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EXPERIMENTAL DIFFICULTIES MAKE PROOF OF IRES A CHALLENGE The existence of any IRES segment is usually evidenced experimentally by transfer of the putative IRES region from the donor cellular mRNA or viral RNA to reporter RNA. This is very often followed by in vivo analyses of the translation levels of the two reporter genes lying in tandem on bicistronic RNA and surrounding inserted putative IRES from both its sides. In most of these experiments the transcription of bicistronic RNA is driven by polymerase II promoters in the cell nucleus and thus the translation of the first cistron is capdependent and usually very efficient. Translation of the second cistron is expected to be capindependent and – depending on the activity of the particular IRES – is sometimes quite low. The protein product of the first (5‘-proximal) reporter gene is used both for determining the transfection efficiency and for normalizing of the signal derived from translation of the second cistron. Thus, the translation of the second cistron relies on the putative IRES activity of the sequence inserted between the two reporter genes. This fairly simple assay has many pitfalls. When the difference between the measured translational activities of the first and second cistron is high, the weak activity of the putative IRES can be concealed or mimicked by the accidental occurrence of shorter, functionally monocistronic mRNAs containing solely the second cistron. This situation can happen due to the presence of cryptic splicing sites, breakage hot-spots within the mRNA molecule or due to cryptic transcription initiation. Such transcripts could be capped and thus have a very good chance to be translated as efficiently as the first reporter gene in the master bicistronic mRNA. Therefore, the experiments evaluating the IRES activity are very often prone to produce artificial data and must be accompanied by a number of precisely designed controls. On the other hand, it should be noted that the

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presence of cryptic promoters, cryptic splicing sites, etc. within the IRES segment tested does not necessarily mean failure in proving the IRES activity. Such an IRES segment can pass other assays based for example on direct RNA transfection into the cells, on the use of 7mG cap analogs or on depletion or modification of cap-binding translation initiation factors. The methodology used to determine IRES segments has been extensively discussed during the past few years (Jackson, 2000; Kozak, 2001; Schneider et al., 2001; van Eden et al., 2004a; Kozak, 2005) and some of the former conclusions have been re-evaluated. Besides the already mentioned IRESs containing cryptic promoters, care must be taken in selecting the proper plasmid backbone. Several internal ribosome entry sites (Bcl2 (Sherrill et al., 2004), c-IAP (van Eden et al., 2004b), Kv1.4 (Jang et al., 2004), L-myc (Jopling et al., 2004), CVB3 (Jang et al., 2004) have been reported to reveal a cryptic splicing site in the context of the chimeric intron containing pRF vector and its derivatives (Stoneley et al., 1998; Jang et al., 2004). In spite of the unknown nature of the function of most of the reported cellular IRESs it is possible to classify them into the following four simple groups: 1. The IRES region shows an apparent complementarity to rRNA which is expected to confer the IRES activity observed (Chappell et al., 2000). Such IRES functionally resembles the prokaryotic Shine-Dalgarno sequence. 2. The IRES activity is mediated by protein-RNA or protein-protein interactions. Either direct ribosome binding or the action of various trans-acting protein factors (ITAFs) is responsible for the IRES dependent translation initiation. Currently, the research in this field is in the stage of accumulating new descriptive data about the existence of novel ITAFs and their specificities. 3. The mechanism of IRES action has not been found or even studied. Most of the cellular IRESs belong to this group. 4. Those putative IRES segments whose characterization has been reported without providing proper controls or have already been claimed as experimental artifacts.

IRESITE PRESENTS BOTH STRUCTURAL AND EXPERIMENTAL DATA The IRESite database is dedicated to presenting exhaustive information about the particular internal ribosome entry sites including their structural characteristics, interacting partners as well as experimental evidence of IRES function (Mokrejš et al., 2006). In principle it contains two different groups of records covering either the natural properties of the RNA molecule containing a particular IRES or describing the experiments, which were performed to obtain an evidence or further data of the IRES function. The first group of the database entries is denominated as natural, the latter is referred to as engineered. IRESite contains a powerful search engine and, where applicable, the individual entries are linked to PubMed, GenBank and other publicly available resources. The aim of the natural entries is to summarize available data about IRESs in their natural sequence context including the annotated full-length sequences of mRNAs or viral RNAs,

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IRES lengths and positions, IRES secondary structures when available and the reported interacting proteins. In contrast, the engineered records, while reflecting variants in the experimental setup, store information about the results of IRES translation experiments carried both in vitro and in vivo. These records accommodate data about the utilized plasmids, the regulatory sequences, the reporter genes, observed IRES activity and much more. Basically, the engineered records may also present sequences of, and experimental data about various mutated forms of particular IRES, which are frequently employed for detailed analyses of IRES function. A more detailed description of the IRESite database has been published recently elsewhere (Mokrejš et al., 2006). One of the most important points, which make the IRESite database unique, is the declared aim to provide and annotate the full-length RNA sequences both for natural and engineered records whenever possible. However, this is not a simple task. In most cases such sequences are not available in public sequence repositories or have never been even published. Therefore either the authentic sequence data have to be obtained from original authors or the sequences have to be reconstituted in silico from the most similar sequences available in GenBank, printed publications and other resources. Frequently, new DNA vectors are updated for the particular research from preexisting ones, which were originally created for other purposes. Sometimes, the precise sequence analysis, which is necessary for detailed annotation of IRESite entries and could not be accomplished without the kind help of the original authors, reveals hidden properties of the provided respective plasmids. This situation can be exemplified by the pRSTF-CBV3 plasmid containing human coxsackie virus IRES (Jang et al., 2004) (kindly provided by J. Jimenez). We have found that the second reporter gene coding for the firefly luciferase is fused in frame at its N-terminus with the 2A peptide encoding the sequence from the footand-mouth disease virus (Figure 1). This ―protease‖ peptide does not cleave the fusion protein but rather acts as a modulator of the ribosomal activity and causes release of the Nterminal peptide from the ribosome, while the ribosome continues in the translation of the rest of the mRNA template, starting the following protein with upcoming prolyl-tRNA. However, this ―cleavage‖ action of 2A peptide is not hundred percent efficient and a part of the ribosomes stop further translation (Donnelly et al., 2001a; Donnelly et al., 2001b). Although the yield of the second reporter product and thus the measured activity of CBV3 IRES could be perhaps underscored, in this case we expect no important influence on the quality of the obtained results. In summary, IRESite can provide useful information not only about a particular IRES itself but also about the surrounding sequences both in the coding and non-coding RNA regions. The growing number of engineered records in IRESite facilitates a comparison of IRES translation efficiencies as well as an evaluation of the different experimental setups employed for IRES characterization. By September 2006 IRESite covered 56 unique natural RNAs of cellular or viral origin (Table 5). In some cases multiple, either non-contiguous or overlapping sequence regions of the same transcript have been shown to possess the IRES activity and therefore such IRESs span multiple lines in the table. In addition the database contains 101 engineered records with

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annotated key experimental parameters and 25 records containing sequences of various control plasmids.

Figure 1. Part of the pRSTF-CBV3 vector sequence. The coding regions are depicted in uppercase letters, the Renilla and firefly luciferase genes are coloured in green, IRES from the human coxsackie virus B3 is shown in blue, stem-loop structure used to block ribosomal reading-through from the first cistron is in yellow and the FMDV 2A peptide coding sequence is coloured in magenta.

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WHAT CAN WE GATHER FROM THE SIZE AND THE GC CONTENT OF IRES SEGMENTS? We analyzed and correlated the sizes, the number of AUG codons and the GC content of the IRES segments and corresponding full-length RNA transcripts and viral RNAs, which are currently included in the IRESite database (Table 5). Figure 2 and Figure 3 show the analyses of the IRES GC contents plotted against the mRNAs GC contents and the IRES lengths, respectively. Both analyses demonstrate that the group of cellular IRESs is more heterogeneous than the group of viral IRESs. The GC content of viral IRES segments is generally only a little bit higher than the GC content of the corresponding viral +RNAs. Contrary to the common opinion, the selection pressure for increased GC content and thus for increased occurrence of secondary and higher order IRES structures will not be probably much different from the other parts of the viral genomic +RNA. Figure 2 shows that while viral IRESs create a homogeneous group, cellular IRES segments appear to separate into the three distinct clusters. One group consists of short or medium sized IRESs with very high GC content, the second involves IRESs of the same size as the first group but with low or intermediate GC content and the third group contains extremely long IRES segments. The biological relevance of this finding is unclear, it can reflect both the biological reality as well as the biased ―selection by scientist‖ driven, instead of the power of natural selection, by the opinion that the internal ribosome entry sites of cellular mRNAs more frequently occur in long or highly structured 5‘ UTRs (Gan and

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Rhoads, 1996; Huez et al., 1998; Negulescu et al., 1998; Oumard et al., 2000; Pozner et al., 2000; Watada et al., 2000; Grobe and Esko, 2002; De Pietri Tonelli et al., 2003; Sherrill et al., 2004; van Eden et al., 2004b; Jimenez et al., 2005).

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Figure 2. Comparison of the GC content within IRES and the respective mRNA or viral RNA. 3‘-UTRs including possible poly(A) sequences were excluded from the analysis. Retrotransposonal IRESs are distinguished from viral ones by open squares. Cellular IRESs containing promoter in their cDNA are marked with circles.

Figure 3. Correlation of the IRES GC content and its length. Retrotransposonal IRESs are distinguished from viral ones by open squares. Cellular IRESs containing a promoter in their cDNA are marked with circles. Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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Analysis of the number of the AUG codons in the untranslated part of the internal ribosome entry sites (Figure 4 and 5) again clearly shows the apparent uniformity of viral IRESs, which generally have the number of the AUG codons roughly corresponding to the expected frequency of the AUG codon in random nucleotide sequences. Figure 5 presents the number of the AUG codons per hundred nucleotides of the IRES plotted against the corresponding IRES GC content. With one exception, this analysis revealed that only the ATrich cellular IRES segments appear above the line of the theoretically expected AUG frequency in the random nucleotide sequence. Additionaly, most of the cellular IRESs with higher GC content contain fewer than predicted or no AUG codons in their untranslated regions. On the contrary, the majority of viral IRESs is grouped around the line of the theoretically expected AUG frequency regardless of their GC content.

Figure 4. The number of AUG codons within the IRES sequences correlated to the IRES lengths. Retrotransposonal IRESs are distinguished from viral ones by open squares. Cellular IRESs containing a promoter in their cDNA are marked with circles.

Figure 2 and Figure 3 show the analyses of the IRES GC contents plotted against the mRNAs GC contents and the IRES lengths, respectively. Both analyses demonstrate that the group of cellular IRESs is more heterogeneous than the group of viral IRESs. The GC content of viral IRES segments is generally only a little bit higher than the GC content of the corresponding viral +RNAs. Contrary to the common opinion, the selection pressure for increased GC content and thus for increased occurrence of secondary and higher order IRES structures will not be probably much different from the other parts of the viral genomic +RNA. Figure 2 shows that while viral IRESs create a homogeneous group, cellular IRES segments appear to separate into the three distinct clusters. One group consists of short or medium sized IRESs with very high GC content, the second involves IRESs of the same size as the first group but with low or intermediate GC content and the third group contains extremely long IRES segments. The biological relevance of this finding is unclear, it can reflect both the biological reality as well as the biased ―selection by scientist‖ driven, instead

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of the power of natural selection, by the opinion that the internal ribosome entry sites of cellular mRNAs more frequently occur in long or highly structured 5‘ UTRs (Gan and Rhoads, 1996; Huez et al., 1998; Negulescu et al., 1998; Oumard et al., 2000; Pozner et al., 2000; Watada et al., 2000; Grobe and Esko, 2002; De Pietri Tonelli et al., 2003; Sherrill et al., 2004; van Eden et al., 2004b; Jimenez et al., 2005).

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Figure 5. The frequency of the AUG codons within the IRES sequences versus IRES GC content. Retrotransposonal IRESs are distinguished from viral ones by open squares. Cellular IRESs contaning promoter in their cDNA are marked with circles.

The fruit fly retrotransposonal IRESs form a distinct group in all analyses. We cannot explain now if the reason for such a difference consists in special features of the retrotransposons themselves or if it is rather caused by the long period of their evolution within the fruit fly genomic environment. Interestingly, the IRES from the HIV retrovirus contains no AUG and thus resembles retrotransposons in this respect. Five cellular IRESs involved in the present study have been reported to contain promoter in their cDNA. These IRESs are marked with open circle and apparently do not form a distinguishable group in any of the graphs. If we suppose that, according to the cap-dependent scanning model of translation initiation, the translatability of particular mRNA is lowered by secondary structures (high GC content) or multiple AUG containing 5‘ UTRs (Kozak, 1989), then we can hypothesize that the presented results confirm that the 5‘ UTRs containing cellular IRESs but no viral IRESs are at least on some occasions also translated by the scanning mechanism.

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IRESITE IN THE CONTEXT OF OTHER RNA-ORIENTED SEQUENCE DATABASES A number of databases cover various EST and cDNA sequences, which are frequently incompletely sequenced mRNAs and the chances to analyze complete 5'-UTR regions containing IRESs are therefore limited. Besides IRESite, there are two other databases (UTRdb and IRESdb) dealing to some extent with internal ribosome entry sites.

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UTRDB CONTAINS IRESS AUTOMATICALLY PREDICTED BY SEQUENCE PATTERN MATCHES The UTRdb database (Pesole et al., 1998) (http://www.ba.itb.cnr.it/UTR/) is automatically generated by a UTRdb_gen program which parses input EMBL database records and extracts from them the 5'-UTR sequences. Not all EMBL records have tagged the 5'-UTR regions but the UTRdb_gen program uses its own heuristics to find 5' and 3'-UTR based on the position of coding sequences, exons, introns and other feature keys although the details have never been disclosed. Database flat files are available for download from the FTP server directory at ftp://bighost.ba.itb.cnr.it/pub/Embnet/Database/UTR/data and are regularly updated with newer EMBL releases. A pattern-matching algorithm is used to find known sequence motifs in UTRdb records. To our concern, the prediction of IRES segments in UTRdb is based on the presence of a predicted Y-type shaped stem-loop structure followed by a stem-loop just upstream of the AUG initiation codon (Le and Maizel, 1997). The model is based on analysis of mRNAs coding for the human immunoglobulin heavy chain binding protein (BiP), fruit fly Antennapedia (Antp) and the human fibroblast growth factor 2 (FGF2). Figure 6 shows a full text definition of the IRES element in the UTRSite database including the search pattern (ID U0015). The UTRSite database contains currently 52 such records (in the downloadable file) whereas only 40 of them are accessible through the UTRSite Sequence Retrieval System (SRS). Users of the UTRSite web server can submit their own sequence and the server searches through the query for the recognized sequence pattern of 52 (or 40?) motifs. Users can also search UTRdb for sequences similar to their query by the BLAST algorithm and SRS tools (text search, feature search, etc.). The SRS search for the feature key 'IRES' returns 30 168 current entries from the non-redundant UTR database. Interestingly, the search against the redundant database which should contain more entries returns only 14 491 entries. It seems that the redundant and non-redundant files have swapped names. This suspicion was confirmed by the downloaded flat file data from the FTP server. In this text we will refer to the files as redundant or non-redundant based on their actual contents without regards to the actual file names (redundant files have mistakenly _nr.dat in their filename whereas the non-redundant files are labeled _r.dat). UTRdb is not an absolutely nonredundant database because certain overlaps between sequences as well as pairwise sequence similarity up to 95% (Pesole et al., 1998) are allowed between any two records. However, the

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large number of IRES hits in both datasets still cannot be easily explained and was subjected to our further studies.

Figure 6. Annotation of the IRES segment (U0015) including its searching pattern in the UTRSite database.

A detailed examination of the flat files of the UTRdb release 22 (derived from the EMBL release 86 and from the UTRef release 7 data derived from the REFSEQ release 17) confirmed a high number of the retrieved IRESs from UTRdb website (Table 6). To evaluate capability of the Le and Maizel pattern used in UTRdb for prediction of IRES segments (Le and Maizel, 1997) we compared a list of genes predicted to contain IRES segment with the list of already published IRESs. While inspecting the actual records with predicted IRESs we found that at least two types of redundancies increase the obtained counts. First, several IRESs can stem from multiple mRNA transcripts derived from a single EMBL gene record. Such transcripts can differ either due to alternative transcription starts or splicing events (while the IRES is predicted in a common exon). The second type of redundancy comes from multiple allelic variants of the same gene/mRNA in EMBL database, which somehow leaked through the similarity filter. To avoid these multiplicated instances of the same gene we decided to pick up every gene name only once. We also eliminated records containing putative chromosomal coding

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regions, where no gene names have been annotated or no experimental evidence for the corresponding transcript was supposed. Cellular IRESs are mostly studied in mammals. When we eliminated by gene name IRES containing records common to both human-related files, 1266 entries remained as unique to the non-redundant file (5UTR.Hum_r.dat) whereas the redundant file (5UTR.Hum_nr.dat) had 2493 IRES containing genes (which do not have a match in the non-redundant file 5UTR.Hum_r.dat). Thus, the non-redundant and redundant files still do differ significantly and we explain this difference by alternatively used gene names (aliases) for the de facto same records and by generally inconsistent annotation used in the projects which deposited their data into public sequence databanks. The appearance of gene names like ―p53 related protein‖ or ―GLYCOGEN SYNTHASE KINASE 3 BETA‖ demonstrates that the abbreviated gene consists of multiple words. Such records have also been eliminated. For the purpose of the presented comparative analysis we also eliminated records corresponding to the putative and hypothetical genes by matching certain strings of characters in them because such genes are unlikely to be included among the published IRESs. Examples of the eliminated records are DKFZP566I1024 (see UTRdb:BB251405 in 5UTR.Hum_r.dat), LOC285888, TMP_LOCUS_28, C9ORF125, etc. More thorough analysis by all-against-all BLAST search between all records of each dataset would have to be done to identify all the duplicates. However, even the presented results raised some important issues. We argue it would be more convenient for the user if UTRdb would avoid the problem with gene name aliases, redundancy and hopefully even inconsistent annotation of gene names altogether and predict the recognized 52 (or 40?) features in data from non-redundant, gene-oriented databases like UniGene (Wheeler et al., 2004). Further, although UTRdb is a collection of all 5'-UTRs we see no reason for predicting IRES segments in chloroplast mRNA (UTRdb:BB407559 derived from EMBL:AB087484 in file 5UTR.Pln_nr.dat). We have also found that dsRNA from a chloroplast (UTRdb:BB407233 derived from EMBL::AB070653 in the same file) is misplaced with plant genes in this file while we prefer to include it in the file with viral sequences (although this is probably an annotation issue of the upstream databases). Finally, although the UTRdb_gen program correctly parsed EMBL records and determined the 5'-UTR region the provided resulting flat files do not comply with the definition of EMBL file format. At least 5 types of errors in the format were uncovered when we have parsed UTRdb flat file data. A fix for some of the supposedly more common while non-critical errors in the format has been committed to the source code of bioperl in Sept 2006 (Stajich et al., 2002). The fixed EMBL and GenBank formatted flat files used throughout this analysis together with the resulting list of predicted IRES-containing genes are available from http://www.iresite.org/UTRdb. Results of our examination of UTRdb can be exemplified on its fungal dataset. This analysis showed that the number of fungal entries with the annotated IRES feature in the redundant 5UTR.Fun_nr.dat file reaches 343 whereas only 79 records can be found in the non-redundant 5UTR.Fun_r.dat file. The number of records decreased substantially to 134 and 37 respectively after the cleanup procedure described above.

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Table 6. The number of records predicted in UTRdb to contain IRES is unexpectedly large filename 5UTR.Fun_nr.dat 5UTR.Fun_r.dat 5UTR.Hum_nr.dat 5UTR.Hum_r.dat 5UTR.Inv_nr.dat 5UTR.Inv_r.dat 5UTR.Mam_nr.dat 5UTR.Mam_r.dat 5UTR.Mus_nr.dat 5UTR.Mus_r.dat 5UTR.Patent_nr.dat 5UTR.Patent_r.dat 5UTR.Pln_nr.dat 5UTR.Pln_r.dat 5UTR.Rod_nr.dat 5UTR.Rod_r.dat 5UTR.Vrl_nr.dat 5UTR.Vrl_r.dat 5UTR.Vrt_nr.dat 5UTR.Vrt_r.dat SUM of IRESs in EMBL 5UTRef.Inv.dat 5UTRef.Mam.dat 5UTRef.Pln.dat 5UTRef.Pri.dat 5UTRef.Rod.dat 5UTRef.Vrt.dat SUM of IRESs in REFSEQ

# of IRES containing records 343 79 7 546 5 176 3 048 441 1 983 242 4 011 1 606 80 26 5 540 2 081 1 696 342 903 3 802 5 018 696 44 659 4 971 6 617 3 862 8 053 8 740 4 932 37 175

# of IRESs 134 37 3 477 2 297 1 900 266 568 97 2 782 977 4 1 3 617 1 308 1 031 172 125 260 2 987 395 22 435 1 777 673 1 279 3 804 5 313 2 367 15 213

The files derived from the EMBL (filenames starting with 5UTR are in the left half of the table) and from the REFSEQ (filenames starting with 5UTRef are in the right half of the table) databases include hypothetical, conserved hypothetical and putative genes (having yet no gene names assigned). The filenames in 1st and 4th column are the names of files available on the UTRdb ftp server and each of them contains a subset of the taxonomically distinct records. The numbers in the 2nd and 5th column represent all predicted IRES containing genes in the respective taxonomic group. The numbers in the 3rd and 6th column represent all predicted IRES containing genes in the respective taxonomic group purified of the hypothetical and the putative genes. Please note that the ―non-redundant‖ and ―redundant files‖ are probably swapped (see the main text for more information). The underlined numbers of IRESs predicted for the human and the mouse indicate the predicted number of IRES containing genes per organism. However, some genes may appear under multiple aliases, making thus the number overrepresented to unknown extent. Files with the following substrings within their filenames contain the nucleotide sequences as indicated: Fun – fungal/yeast sequences, Hum – human sequences, Inv – invertebrate sequences, Mam – mammalian sequences except the human and the mouse, Mus – mouse sequences, Patent – various patented sequences, Pln – plant sequences, Rod – rodents, Vrl – viral sequences and Vrt – vertebrates except the human and the mouse.

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IRESs Predicted in 5UTR.Fun_nr.dat After Elimination of Duplicates and Hypothetical and Putative Genes At least 25 IRESs were predicted in Saccharomyces cerevisiae while none of them has been reported to contain IRES yet: ARV1, CKI1, DBM1, HAP2, HEM1, HIS7, LEU3, MSS51, NCE2, NHP6A, OPY1, PDR5, POX1, RAD3, RPL37A, SIN3, SPE1, SPO1, STE14, TPK1, URA6, UTR2, VAS1, WSC3, YAP2. Similarly, at least 29 IRESs were predicted in Schizosaccharomyces pombe while again none of them has been reported to contain IRES: ABC1SP, CDS1, CSH3, CUT17, DFR1, DMF1, FRP1, GHT2, HIS5, HSP70, KRP1, PAS1, PDS5, PHP3, PKA1, PLA1, PTC1, PUB1, RAD9, RES1, RHP55, RPB4, RPB7, SPAPN1, SPC1, SPJ1, STM1, TAP1, UVDE.

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IRESs Predicted in 5UTR.Fun_r.dat after Elimination of Duplicates and Hypothetical and Putative Genes Only 10 IRESs came out from the previously mentioned 25 genes in the case of Saccharomyces cerevisiae: HAP2, HEM1, LEU3, MSS51, PDR5, POX1, SIN3, SPE1, SPO1, VAS1. We speculate that the genes missing in the list disappeared during the automated procedure used to create the non-redundant dataset. Into the resulting set it possibly randomly picked up another record similar in sequence but either lacking abbreviated gene name or containing the abbreviated gene name consisted of multiple words or although the record was correctly annotated the IRES feature was not predicted by pattern match. Similarly, only 20 IRESs came out from the list of 29 genes listed above for Schizosaccharomyces pombe: CSH3, DFR1, DMF1, KRP1, PAS1, PDS5, PHP3, PKA1, PLA1, PTC1, PUB1, RES1, RHP55, RPB7, SCT1, SPAPN1, SPC1, SPJ1, STM1, TAP1. Similar analysis of human redundant dataset (5UTR.Hum_nr.dat) revealed 3477 genes predicted to contain IRES after elimination of duplicates and hypothetical and putative genes. However, only 12 of them have been reported to contain IRES so far in either human, mouse, rat, dog or cat genomes (Table 3): AML1, ARC, CYR61, E2F6, FGF1, FGF2, HSP70, NAT1, NDST3, NPM1, ODC1, SMAD5. 65 of the currently reported mammalian IRESs have not been recognized showing thus that the prediction quality of the pattern search (Le and Maizel, 1997) employed by UTRdb is only 12%. We did not attempt to evaluate whether the predicted IRES regions actually overlap with the published IRES thus the predictions might still include some false positives. In summary, if the intent of any UTRdb user is to get rather complete albeit redundant list of any genes one should preferably use the redundant files with _nr.dat in their names (unless UTRdb curators swap the filenames) which contain many highly similar entries and some genes possibly appear under alternative names several times. The files with _r.dat in their names contain the non-redundant set of sequences and one should be aware that the employed automated procedure probably eliminates well annotated genes containing putative IRESs (or any other UTRSite feature as well) in some cases while retaining records with poor annotation with a merely same sequence or with alternative gene name annotated.

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I.R.E.S. DATABASE (IRESDB) COMPILES REFERENCES TO GENBANK RECORDS AND LITERATURE IRESdb database (Bonnal et al., 2003) is a manually curated database (http://www.rangueil.inserm.fr/IRESdatabase/) which contains a list of IRES segments of cellular (51 records) and viral (31 records) origin. The website presents IRES segments sorted according to their origin (viral or cellular), according to IRES trans-acting factors (ITAFs) interacting with them and according to the function of the proteins encoded by the IRES harboring mRNAs. Thus, it is possible to reach the same IRES through multiple menu items. IRESdb sequence entries consist either both from the annotated 5‘-UTRs and the corresponding GenBank link or from the link to the GenBank only. The 5‘-UTR sequences are mostly routinely extracted from the respective GenBank entries and are presented just until the initiation AUG codon. A search tool allows the search for text strings through the website by employing probably some external search engine (like Google site search). IRESdb and IRESite databases both present valuable data and well complement each other. The IRESdb currently presents more data about the ITAFs and enables one to browse the cellular IRESs according to the function of the corresponding mRNAs. In contrast, the IRESite database is focused primarily on annotation of the original full-length RNA sequences usually obtained directly from their authors or after their manual and exigent reconstruction from various electronic and printed resources. IRESite presents the IRES regions conveniently within the full length RNAs. The 5‘ and 3‘ borders of the respective IRES can thus be easily described even in the cases when IRES overlaps with the downstream coding region or the particular RNA contains both multiple or modular IRESs. In addition, IRESite annotates the experimental procedures which were used to characterize the particular IRES segment and can even record the IRES secondary structures if available. Both the IRES oriented databases provide links to relevant PubMed literature.

ACKNOWLEDGMENT We would like to thank all the colleagues who helped us and cooperated with us in building the IRESite database and who kindly provided us with the sequences and their experimental data. We also would like to thank K. Sigler for his language assistance. This work was supported by Czech Grant Agency (Grants No. 204/03/1487 and 301/07/0607), by the Grant Agency of the Charles University (Grant No. 251/2004/B-BIO/PrF) and by the Ministry of Education (Grants No. MSM 0021620813; 1M06014 and LC06066).

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Warnakulasuriyarachchi D, Ungureanu NH, Holcik M. 2003. The translation of an antiapoptotic protein HIAP2 is regulated by an upstream open reading frame. Cell Death Differ 10:899-904. Watada H, Mirmira RG, Leung J, German MS. 2000. Transcriptional and translational regulation of beta-cell differentiation factor Nkx6.1. J Biol Chem 275:34224-34230. Wheeler DL, Church DM, Edgar R, Federhen S, Helmberg W, Madden TL, Pontius JU, Schuler GD, Schriml LM, Sequeira E, Suzek TO, Tatusova TA, Wagner L. 2004. Database resources of the National Center for Biotechnology Information: update. Nucleic Acids Res 32:D35-40. Wilson JE, Powell MJ, Hoover SE, Sarnow P. 2000. Naturally occurring dicistronic cricket paralysis virus RNA is regulated by two internal ribosome entry sites. Mol Cell Biol 20:4990-4999. Xiao ZS, Simpson LG, Quarles LD. 2003. IRES-dependent translational control of Cbfa1/Runx2 expression. J Cell Biochem 88:493-505. Yang D, Wilson JE, Anderson DR, Bohunek L, Cordeiro C, Kandolf R, McManus BM. 1997. In vitro mutational and inhibitory analysis of the cis-acting translational elements within the 5' untranslated region of coxsackievirus B3: potential targets for antiviral action of antisense oligomers. Virology 228:63-73. Ye X, Fong P, Iizuka N, Choate D, Cavener DR. 1997. Ultrabithorax and Antennapedia 5' untranslated regions promote developmentally regulated internal translation initiation. Mol Cell Biol 17:1714-1721. Yoon A, Peng G, Brandenburg Y, Zollo O, Xu W, Rego E, Ruggero D. 2006. Impaired control of IRES-mediated translation in X-linked dyskeratosis congenita. Science 312:902-906. Yu Y, Alwine JC. 2006. 19S late mRNAs of simian virus 40 have an internal ribosome entry site upstream of the virion structural protein 3 coding sequence. J Virol 80:6553-6558. Zeenko V, Gallie DR. 2005. Cap-independent translation of tobacco etch virus is conferred by an RNA pseudoknot in the 5'-leader. J Biol Chem 280:26813-26824. Zeiner GM, Sturm NR, Campbell DA. 2003. The Leishmania tarentolae spliced leader contains determinants for association with polysomes. J Biol Chem 278:38269-38275. Zell R, Sidigi K, Henke A, Schmidt-Brauns J, Hoey E, Martin S, Stelzner A. 1999. Functional features of the bovine enterovirus 5'-non-translated region. J Gen Virol 80 (Pt 9):2299-2309. Zhang X, Richie C, Legerski RJ. 2002. Translation of hSNM1 is mediated by an internal ribosome entry site that upregulates expression during mitosis. DNA Repair (Amst) 1:379-390. Zhou W, Edelman GM, Mauro VP. 2001. Transcript leader regions of two Saccharomyces cerevisiae mRNAs contain internal ribosome entry sites that function in living cells. Proc Natl Acad Sci U S A 98:1531-1536. Zhou W, Edelman GM, Mauro VP. 2003. Isolation and identification of short nucleotide sequences that affect translation initiation in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 100:4457-4462. Zuidmeer L, van Leeuwen WA, Budde IK, Cornelissen J, Bulder I, Rafalska I, Besoli NT, Akkerdaas JH, Asero R, Rivas MF, Mancebo EG, van Ree R. 2005. Lipid transfer

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proteins from fruit: cloning, expression and quantification. Int Arch Allergy Immunol 137:273-281.

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In: Viral Gene Expression Regulation Editor: Eli B. Galos

ISBN 978-1-60741-224-3 © 2010 Nova Science Publishers, Inc.

Chapter 8

ANALYSIS OF GENE FAMILY EXPRESSION IN AFRICAN ENDEMIC† AND AIDS-RELATED KAPOSI’S SARCOMA Antoinette C. van der Kuyl1, , Remco van den Burg1, Fokla Zorgdrager1, Celeste Lebbé2 and Marion Cornelissen1 1

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Academic Medical Centre of the University of Amsterdam, Amsterdam, The Netherlands; 2 Centre Hospitalier Universitaire Saint-Louis, Paris, France.

ABSTRACT Kaposi‘s sarcoma (KS) is subdivided into four epidemiological variants, all of which have in common a similar histopathology and expression of human herpes virus 8 (HHV8) in the lesions. Two forms are associated with immune suppression, post-transplant KS and AIDS-related KS, while the two others, classic KS and African endemic KS are not. HHV-8 infections are normally benign, with the incidence of HHV-8 infection being much higher than the incidence of KS. The cellular deregulation that leads to the formation of KS lesions is poorly understood, as is the infected cell type. It is also unknown whether gene expression patterns are similar between the epidemiological forms, especially as two forms are not associated with immune dysfunction. To gain insight into the genes expressed in KS lesions, we have generated Serial Analysis of Gene Expression (SAGE) libraries from both AIDS-KS and African endemic KS tissue to analyze mRNA levels in KS. The SAGE libraries were compared with each other and †

A version of this chapter was also published in New Topics in Cancer Research, Horizons in Cancer Research, Volume 34, edited by Fredrick G. Drabell published by Nova Science Publishers, Inc. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. Correspondence concerning this article should be addressed to: A.C. van der Kuyl, Laboratory of Experimental Virology, Department of Medical Microbiology, Centre of Infection and Immunity Amsterdam (CINIMA), Academic Medical Centre of the University of Amsterdam, Meibergdreef 15, 1105 AZ Amsterdam, The Netherlands.

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Antoinette C. van der Kuyl, Remco van den Burg, Fokla Zorgdrager et al. with libraries in the SAGEmap database. Systematic analysis of the level of gene expression of twelve specific gene families or related genes, including HHV-8 genes, was performed for both endemic KS and AIDS-KS tissue. The results suggested that endemic KS and AIDS-KS have a similar pattern of gene expression, in line with their comparable histopathology. High or very high expression was found in KS compared with other libraries for psoriasin, HLA-C, complement component 1, keratin 16, galectin 9, plexin D1, CD51, CD31, CCL18, CCL19, CCL21, and many other genes. In general, not the tag counts but rather the tag count ratio of KS versus other libraries gave information about the level of gene expression. Interestingly, a few genes were overexpressed in AIDS-KS compared with endemic KS (e.g. Von Willebrand Factor, D component of complement), while others were overexpressed in endemic KS (e.g. HLA-F, MMP-12, CD74, calgranulin-A). It has been suggested that iron is an important factor in the establishment of KS. Analysis of iron-related gene expression in KS, however, suggested no clear abnormalities. Relatively high expression was only found for the light polypeptide of ferritin, and heme oxygenase-1, and intermediate expression was seen for the hemoglobin scavenger receptor CD163, compatible with macrophage-related iron-uptake as erythrocytes leak from abnormal vessels in the KS lesions. In conclusion, the comparison of gene family expression in KS SAGE libraries has provided new insights into the molecular mechanism involved in the pathogenesis of KS.

Keywords: Kaposi‘sarcoma, endemic, AIDS, HHV-8, SAGE, gene expression.

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INTRODUCTION Even though Kaposi‘s sarcoma (KS) was already described more than a century ago, the disease process at the cellular level is still not completely clear, e.g. whether it is predominantly a hyperplastic or a neoplastic process. Massive neoangiogenesis, extravasation of red blood cells, hemosiderin deposits, whorls of spindle-shaped cells surrounding slit-like spaces, and the presence of a substantial inflammatory infiltrate characterize KS lesions. Progression from patch-, plaque-, to nodular stages is frequent in KS, as is clinical progression from mucocutaneous involvement to visceral disease. The increased incidence of KS in AIDS patients has revived the interest in this otherwise rare disease, and led to the discovery of human herpes virus-8 (HHV-8) replication in the lesions [1]. However, the exact nature of the lesions, as well as the cell type (s) involved, are still the subject of much research and debate (see e.g. [2]). To begin with, the name sarcoma in Kaposi‘s sarcoma suggests that KS is a tumour. But KS spindle cells do not fulfill criteria for neoplastic cells: aneuploidy is rare, as are chromosomal rearrangements, cells do not grow permissively in culture, and clonality is infrequently observed [3,4,5], and probably confined to the later stages of the disease. Also, lesions may regress spontaneously. Altogether, a picture emerges of KS as an inflammatory systemic disease, where the multiple lesions occurring simultaneously are not metastases, but seeding of progenitor cells, features already noticed by Moritz Kaposi [6]. That KS progenitor cells are present in otherwise healthy, HHV-8+ persons, that can establish lesions in immunosuppressed patients, became clear after analysis of KS cells in transplant-patients [7].

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The origin of KS spindle cells, which express both endothelial and macrophage antigens [8] and are the main HHV-8 infected cell in KS lesions, is another matter of debate. The expression profile of HHV-8- infected endothelial cells of different origin have been examined with gene arrays [9,10,11], and correlated with protein expression in KS lesions [10,11]. The results suggest that endothelial cells in lesions resemble lymphatic endothelial cells better than blood vascular endothelial cells [11], but HHV-8 can reprogram blood vascular endothelium towards lymphatic development [10]. Furthermore, the infection of dermal endothelial cells with HHV-8 induces the expression of macrophage-related genes [9]. Possibly, HHV-8 in vivo infects haemangioblasts (a progenitor of both haematopoietic and endothelial cells), and deregulates their normal maturation, accounting for the aberrant coexpression of macrophage, lymphatic- and blood vascular endothelial markers in KS spindle cells. Yet, spindle cells are not the only cells that contribute to the pathology of KS. Infiltrating macrophages and lymphocytes are also implicated in the process, and are likely involved by paracrine mechanisms. KS is divided into four epidemiological subtypes: classical KS, African endemic KS, post-transplant KS, and AIDS-related KS that are indistinguishable by histopathology. In all forms, lytic replication of HHV-8 can be detected in the lesions. To investigate total gene expression in KS tissue of different epidemiological origin, the SAGE (Serial Analysis of Gene Expression) method was used to obtain an expression profile from pooled nodal KS tissue of four African patients without HIV infection (African endemic KS), and compare it with SAGE libraries from AIDS-related KS [12], and with all other libraries present in the SAGEmap database (www.ncbi.nlm.nih.gov/SAGE/). Totally, twelve gene families or functionally related genes were analyzed for their expression in KS, including HHV-8 genes, and the results were interpreted in relation to the (uncertainties in the) disease process.

METHODS Patient Material Biopsies were obtained from nodal KS lesions from four HIV-1 negative male African endemic KS patients: two patients were from Zaire, one patient was from Mali, and the fourth patient originated from Cameroon. The presence of HHV-8 DNA in the endemic KS lesions was tested with a nested HHV-8 PCR before generation of the SAGE library [13]. All KS lesions were HHV-8 DNA positive (data not shown). In addition, frozen sections were cut with a microtome, fixed with acetone and stained with antibodies to HHV-8 LANA (ORF73, Advanced Biotechnologies, MD, USA). Spindle cells in all four lesions showed the typical nuclear dot-like LANA staining pattern (not shown). HIV-1 RT PCR confirmed the absence of HIV-1 RNA in the tissues (data not shown).

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Serial Analysis of Gene Expression (SAGE) A SAGE library was constructed from frozen skin biopsies taken from lesions from four African endemic KS patients. The biopsies were cut with a microtome in 10-14 m sections for RNA isolation. The SAGE library was constructed from the four pooled tissues using the I-SAGE kit™ from Invitrogen (Carlsbad, CA, USA). Sequencing was done with the Bigdye Terminator Cycle Sequencing kit (ABI, Foster City, CA, USA) and an ABI 377 automated sequencer (ABI, Foster City, CA, USA), using M13 forward and M13 reverse primers on colony inserts.

SAGE Library Analysis

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Primary analysis of the SAGE library was performed with USAGE version2 [14]. The program performs initial analyses on raw sequence data, e.g. ditag and tag extraction, tag counting, and tag identification. Tags were identified with the SAGEmap database available through NCBI (www.ncbi.nlm.nih.gov/SAGE/), to which USAGE is directly linked. Viral tags were identified with a USAGE viral database created by extracting relevant SAGE tags from all viral cDNA sequences present in the NCBI database. The program USAGE was also used to compare taglists and to do statistical analyses on tag count differences. Additionally, the endemic KS library (referred to as library KSc) was compared with SAGE libraries generated earlier from two AIDS-KS tissues, referred to as libraries KSa and KSb (GEO accession numbers GSM3240 and GSM3241), respectively [12], and with the complete SAGEmap database that contains over 200 SAGE libraries from multiple tissue types, both normal and diseased.

RESULTS Histological Staining of African Endemic-KS Tissue All tissues were collected, snap frozen, and stored at -80ºC. Histological staining was performed on the frozen tissue. All four African endemic-KS skin biopsies showed a similar histopathological picture of nodular advanced-stage Kaposi‘s sarcoma with irregular, dense areas of spindle cell proliferations that were HHV8-LANA positive (not shown). ZiehlNeelsen, grotcott and PAS staining demonstrated the KS tissues to be negative for mycobacteria and fungi (data not shown).

Description of the SAGE Library A total of 48,497 tags were analysed in endemic-KS SAGE library: 1453 tags occurred five times or more, 3491 were seen between two and four times, and 11,482 tags occurred only once. The frequency distribution fits with the overall pattern of gene expression in

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mammalian cells, wherein only a few percent of mRNA species reach high copy numbers whereas most transcripts display low levels [15].

Tags Differentially Expressed between African Endemic KS and AIDSRelated KS Analysis of the three KS libraries from endemic- and AIDS-related KS did not reveal substantial differences in tags or tag numbers expressed. In total, 76 tags were significantly higher expressed in endemic KS than in AIDS-related KS, while 54 tags were significantly higher expressed in both AIDS-KS libraries than in endemic KS. Tags higher expressed in endemic KS included many tags for HLA proteins, including the invariant chains, and immunoglobulins, suggestive of extensive immune dysfunction in AIDS-related KS. African endemic KS is not associated with immunodeficiency [16]. Tags overexpressed in AIDS-KS included those for the interleukin-1 receptor and for Von Willebrand factor (VWF). HIV-1 infection is known to increase IL-1 expression with negative effects on AIDS-related KS [17]. AIDS-KS cells express very high levels of IL-1 [18] and also express the IL-1 receptor [19]. IL-1R antagonists inhibit spindle-cell formation [19]. IL-1 and IL-1R expression in other forms of KS has not been tested, but as no HIV-1 infection is involved there, IL-1 may be more important in the pathology of AIDS-KS. Table 1. HHV-8 tag counts in AIDS-related- and endemic-KS SAGE libraries For comparison tag numbers for each group were normalized to 50,000.

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Tag sequence TCTCCTGTCG TTCAAAAACA TTATACTTTT TTGGGTCCAC CGCGGCGGTG ATAAAAAAAA

AIDSKSa 8

AIDSKSb 0

EndemicKSc 4

5 5

1 1

1 6

3 3 0

1 0 0

0 2 1

HHV-8 ID ORF59 and ORF58 mRNA (DNA replication): secondary lytic gene T0.7 transcript: primary lytic gene T1.1 transcript (NUT-1/PAN): primary lytic gene ORF K4.2, or IE-3: secondary lytic gene ORF 18: secondary lytic gene ORF K8.1 mRNA

GAGCTTCTGA

ORF K2 (vIL-6 gene): primary lytic gene

0

0

3

GTAGGTGAGG

ORF 74 (vGCR): primary lytic gene

0

0

2

GCCACTTACG

ORF72 (v-Cyclin)

0

0

1

AIDS-KS patients demonstrate higher VWF plasma levels than classical KS patients do, and stain more intense for VWF in cutaneous lesional biopsies [20]. Our results suggest that VWF levels in endemic KS could also be lower than in AIDS-KS. HHV-8 derived tags were identified with a SAGE tag database created by extracting SAGE tags from all viral cDNA sequences present in the NCBI database, and are compiled in

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Table 1. Some HHV-8 encoded mRNAs do not contain an NlaIII site, e.g. v-MIP II (ORF K4), v-MIP I (ORF K6), and v-FLIP (ORF 71), so they go undetected by our SAGE method. An expanded set of HHV-8 derived transcripts was detected in endemic KS compared with AIDS-related KS, including tags for v-IL6 and v-GCR. Interestingly, type III lytic transcripts, involved in virion formation and structure, were not found in any KS library.

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S100 Protein Expression in KS S100 proteins, the largest group of calcium-binding proteins, have been implicated in both inflammation and cancer. Most of them are located in a cluster on chromosome 1q21, suggestive of a common regulatory mechanism, but their individual expression is tissue- and cell-type related. Earlier analysis has shown that some S100 proteins are expressed at high levels in AIDS-related KS [12]. We have analysed the complete S100 mRNA expression in both AIDS-related KS and endemic KS by extracting tags for all known human S100 mRNA‘s from the UniGene database and determining their counts in our KS SAGE libraries (Table 2). Overall, tags for ten out of 18 known S100 proteins were consistently detected in the KS SAGE libraries. From these ten, S100A7, S100A8, and S100A9 were highly expressed compared with other SAGEmap libraries, while moderate expression was found for S100A2, S100A4, and S100A14. Low-level expression was seen for S100A6, S100A10, S100A11, and S100A16. High expression of S100A7 is seen in many skin diseases and in invasive breast cancer (for a review, see: [21]). It has been linked to the abnormal epidermal differentiation, inflammation and the cellular infiltration seen in those conditions. Both AIDS-related KS and endemic KS skin lesions show high expression of S100A7 mRNA (Table 2), supporting its role in inflammatory skin disease. The S100 proteins A8, A9 and A12 are phagocytic proinflammatory molecules [22]. High expression of S100A8/A9, as observed in our KS SAGE libraries (Table 2), has been implicated in uncontrolled and harmful inflammation [23]. Unfortunately, S100A12 mRNA cannot be measured by our SAGE analysis, as it lacks an NlaIII restriction site. Several S100 proteins have been implicated in cancer or tumour progression. High expression of S100A4, S100A6 and S100B was associated with specific types of cancer, including melanoma, while significant S100A2 expression was seen in lymphoma [24]. Of these, only S100A6 mRNA was detected at intermediate levels in KS, while expression of the other mRNA‘s was either low or absent (Table 2). Also, upregulation of S100A16 expression, of which moderate levels are expressed ubiquitously, has been associated with malignant transformation [25]. No such upregulation is detectable in KS (Table 2).

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Table 2. S100 protein tag counts in AIDS-related- and endemic-KS SAGE libraries For comparison tag numbers for each group were normalized to 50,000.

AIDSKSa

AIDSKSb

EndemicKSc

Gene

Tag

S100A4 S100A6 S100A7 (Psoriasin 1) S100A8 (Calgranulin A) S100A9 (Calgranulin B) S100A10 (Calpactin 1) S100A11 (Calgizzarin) S100A12 (Calgranulin C)

GATCTTTTGG GATCTCTTGG ATGTGTAACG CCCCCTGGAT GAGCAGCGCC TACCTGCAGA GTGGCCACGG AGCAGATCAG CAGGCCCCAC No tag, mRNA lacks NlaIII restriction-site

0 4 3 10 182 63 149 32 23 -

0 1 6 37 19 22 43 58 33 -

0 29 5 37 126 525 126 38 45 -

TGGGGAGAGG TTCCCTTATA GAGCGGCGCC AGCAGGAGCA

9 0 1 5

7 0 0 12

8 0 0 20

S100A2

S100A14 S100A15 S100A16

Expression level compared with other SAGEmap libraries Low Low Intermediate High (KSa, KSc) High (KSc) High Intermediate Intermediate Expression cannot be measured by SAGE Low Intermediate-low

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No tags were detected in the KS SAGE libraries for: S100A1, S100A3, S100A5, S100A13, S100B (neural), and S100P.

MHC-Related Gene Expression in KS To escape from cytotoxic T-cell (CTL) surveillance, HHV-8 encodes two proteins, K3 and K5, that downregulate MHC class I expression from infected cell surfaces by targeting the MHC proteins for degradation. In addition, HIV-1 vpu- and nef-proteins have similar functions, albeit by different mechanisms (for a review, see: [26]). To investigate a possible effect upon the mRNA levels of MHC-related proteins in KS, we have tabulated all MHCrelated genes for which SAGE tags were available (Table 3). Overall, MHC class I genes are highly expressed in both AIDS-related KS and endemic KS, and their tag counts, including those for the invariant chain beta-2-microglobulin, belong among the highest measured in the SAGEmap database. Tags for HLA-G, which is normally limited to the placental trophoblast, are absent. Tag counts for the B-cell specific HLA-F are very high in endemic KS, but are only moderate in AIDS-related KS. The absence of tags for HLA-C in library AIDS-KSa is possibly due to an undetermined genetic variation (the tissue from which it was generated originated from a patient of Asian descent). MHC class II expression is much lower in KS (and in the complete SAGEmap database) than is class I expression, and is less consistent among the three libraries (Table 3). Invariant chain (CD74) expression is approximately 10 times higher in endemic KS than in AIDS-

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related KS. The variable HLA-DQB1 and HLA-DPA1 genes are also overexpressed in endemic KS compared to AIDS-related KS.

Complement Component Expression in KS The complement system, consisting of the classical, alternative and lectin-binding activation pathways, is a major element of the innate immune response. The system is normally tightly regulated, as uncontrolled complement activation causes severe inflammatory damage to the host. HHV-8 encodes a viral homologue of a complement regulator (KCP/ORF 4) that is involved in establishing viral latency by enabling escape from complement-mediated virus clearance [27,28]. This gene is only expressed in infected B-cells [28], suggesting that complement in possibly not downregulated in KS lesions. In line with this hypothesis high expression of complement component 1 q subcomponent was detected earlier in AIDS-related KS [12]. Table 3. Tag counts of MHC- related genes in AIDS-related- and endemic-KS SAGE libraries

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Gene

MHC class

AIDSKSa

AIDSKSb

Endemic -KSc

AGAAAAAAAA GTGCACTGAG

4 114

2 130

75 144

AGAACCTTCC CTGACCTGTG GTGCGCTGAG

64 57 0

16 127 186

111 157 32

AGGGGCTACC ACCCTTTAAC TGCAGCACGA GTTGTGGTTA

0 13 1 262

0 7 10 266

1 20 79 688

GTGTGTCTGA ACAGCGCTGA TCCAGTAACA CCTGGGGTAA

0 0 0 4

0 0 0 0

2 3 0 9

Tag

HLA-A

I

HLA-B HLA-C

I I

HLA-E

I

HLA-F Beta-2microglobulin HLA-DRB3

I I

HLA-DQA1

II

HLA-DQA2

II

GGCATTGTGG

0

0

0

HLA-DQB1

II

GAAGCAATAA ATCCTGAGTT

1 7

2 4

4 57

II

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Expression level compared with other SAGEmap libraries Tag has multiple ID‘s High (tag could also be HLA-C) High High Very high (KSb), high (KSc) High (KSc) High (KSa/KSc) Very high (KSc) Very high (KSc), moderate (KSa, KSb) Moderate (KSc) Moderate-low (KSc) Generally: very low expression Moderate (KSa/KSc) Generally: very low expression Low, unreliable tag Moderate (KSa/KSb), high (KSc)

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Table 3. (Continued)

Gene

MHC class

Tag

AIDSKSa

AIDSKSb

Endemic -KSc

HLA-DQB2 HLA-DPA1

II II

ACAAACAAAA TGAAAACTAC

0 0

1 1

0 19

HLA-DMA HLA-DMB CD74

II II II

CTGACAGTGA CCTCTCCAAC GTTCACATTA

1 2 51

4 0 48

3 4 469

Expression level compared with other SAGEmap libraries Low Low (KSa/KSb), moderate (KSc) Moderate High (KSa/KSc) Very high (KSc), moderate (KSa, KSb)

For comparison tag numbers for each group were normalized to 50,000. No tags were detected in the KS SAGE libraries for: HLA-G (class I, very low expression in SAGEmap), HLA-DPB1 (class II, tag TTTTATCATT is unreliable), HLA-DOA (class II), HLA-DOB (class II, low expression in SAGEmap), and HLA-DRA (class II, low expression in SAGEmap).

We have analysed complement expression in KS by tabulating all available tags for complement components and their counts in the three KS libraries (Table 4). Indeed, high tag counts for classical pathway complement component 1 subcomponents were seen in both AIDS-related and endemic KS. Of the alternative pathway, for which counts are generally low in the SAGEmap database, only relatively high expression of the D component was seen in AIDS-related KS. Expression of regulators of complement was either low or absent in KS (lower nine tags in Table 4).

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Table 4. Tag counts of complement components in AIDS-related- and endemic-KS SAGE libraries

Gene Complement component 1, q subcomponent, alpha polypeptide Complement component 1, q subcomponent, beta polypeptide Complement component 1 q subcomponent, gamma-polypeptide Complement component 1, s subcomponent Complement component 1, r subcomponent Complement component 1, q subcomponent binding protein Complement component 1, q subcomponent, receptor 1 Complement component 3

AIDS-KSa

AIDS -KSb

Endemi c-KSc

CTCTAAGAAG

64

16

26

GAGGGTGCCA

21

22

7

AAATCAATAC

6

11

8

Expression level compared with other SAGEmap libraries Very high (KSa), high (KSb, KSc) High (KSa, KSb), Moderate (KSc) Moderate

ACTGAAAGA A TTCTGTGCTG

4

5

2

Moderate

19

50

2

ATAGACATAA

0

1

0

Very high (KSb), high (KSa) Low

CTGATTAGGA

1

0

0

High (KSa)

GTTGTCTTTG

19

24

7

Moderate (KSa, KSb)

Tag

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Antoinette C. van der Kuyl, Remco van den Burg, Fokla Zorgdrager et al. Table 4. (Continued)

AIDS-KSa

Endemi c-KSc

AACACAGCCT ACTTTAATGA

6 1

5 0

1 4

AGTGACTGTG CCTGTGCCAA (unreliable tag) GAGGTGGGTG

1 0

1 0

0 1

1

3

0

TCTACACGTG GCCGGGCCCT GCAGGCCAA G CTCTCCAAAC

0 0 1

0 1 4

1 0 0

Very high (KSa, KSb) Moderate (KSc) Low (KSb) Low (KSa, KSb)

2

3

1

Moderate

ACAATGGAG G TATGGAGAAT CTTTTCAAGA GGCTTGCTGA

0

0

1

Moderate (KSc)

0 0 2

1 2 1

0 2 1

Low Low (KSb, KSc) Low

Gene

Tag

Complement component 4A Complement component 5, receptor 1 Complement component 7 Complement component 8, beta polypeptide D component of complement (adipsin) Properdin P factor, complement Complement S protein (vitronectin) B factor, properdin (complement)

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Complement component 1 inhibitor (SERPING 1) Complement component 4, binding protein, beta H factor 1 (complement) Membrane cofactor protein (CD46) DAF (CD55)

Expression level compared with other SAGEmap libraries Low Moderate (KSc), low (KSa) High (KSc)

AIDS -KSb

No tags were detected in the KS SAGE libraries for: complement component 1 r subcomponent-like, complement component 2, complement component 3A receptor 1, complement component receptor 2 (CD21), complement component receptor 3 alpha (CD11b), complement component 5, complement component 6, complement component 8 alpha polypeptide, complement component 8 gamma polypeptide, complement component 9 (only expression in liver), complement component 4 binding protein alpha, I factor (complement), complement component 3B/4B, receptor 1 (CD35), and CD59.

Table 5. Iron-related genes expressed in KS SAGE libraries

Gene SLC40A1: ferroportin FTH1: ferritin, heavy polypeptide ACO1: aconitase 1, soluble SLC39A1: solute carrier family 39 (zinc transporter), member 1 UQCRF: ubiquinolcytochrome c reductase, Rieske iron-sulfur protein TF: transferrin

GAGACTGCAA TTGGGGTTTC

6 106

7 132

13 234

Expression level compared with other SAGEmap libraries Intermediate Intermediate

GATAGGTCGG CCCAGATGAT

5 4

2 1

2 0

Low Intermediate

GGTCACACTA

0

1

5

Low

TGTGCTGAAC TATTAGTTAT

0 0

1 0

0 0

Very low

Tag

AIDSKSa

AIDSKSb

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EndemicKSc

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Table 5. (Continued)

Tag

AIDSKSa

AIDSKSb

EndemicKSc

Expression level compared with other SAGEmap libraries

AGCTGCTGGC

0

1

0

Low

TTTTAGACAG GGCCGTGGAG CCCTGGGTTC

0 0 164

2 1 237

0 0 210

Low Low High

CTGGCCAGGC

0

3

3

Moderate (KSb, KSc)

AAATAATTGT

0

0

1

Low

TAGATTTCAA

0

0

1

Low

HBA2: hemoglobin, alpha 2

CTTCTTGCCC CCCAACGCGC

0 0

30 123

5 5

Low-intermediate (KSb) High (KSb), low (KSc)

LCN2: lipocalin 2, oncogene 24p3

TGCCCTCAGG

0

0

2

Low

HBB: hemoglobin, beta

GCAAGAAAGT

0

49

7

LTF: lactoferrin

GCAAAACAAC

1

0

1

MYC: v-myc CYP17A1: cytochrome P450, family 17, subfamily A, polypeptide 1 EGLN2 (Egl nine homolog 2 (C. elegans) MFI2: antigen p97, melanotransferrin precursor HMOX1: heme oxygenase (decycling) 1 HMOX2: heme oxygenase (decycling) 2 BLVRA: biliverdin reductase A BLVRB: biliverdin reductase B RHCE: rhesus blood group, CcEe antigens PRKWNK4: protein kinase, lysine deficient 4 FTHL17: ferritin, heavy polypeptide-like 17

ATCAAATGCA

0

1

0

Low-intermediate (KSb) Very low, unreliable tag Very low

GCCTGGCTGC

1

0

0

High (KSa)

GGTGTGGAAG

4

1

10

Intermediate

GCCAAGACCT ACCATACAAA

0 0

1 0

0 0

High (KSb)

CGTGGGTGGG

276

66

203

High

Gene

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FRDA: Friedreich ataxia, frataxin HEPH: hephaestin, ferroxidase TFR2: transferrin receptor 2 FTL: ferritin, light polypeptide SLC11A1: solute carrier family 11 (proton-coupled divalent metal ion transporters), member 1 SLC11A2: solute carrier family 11 (proton-coupled divalent metal ion transporters), member 2 HIF1A: hypoxia-inducible factor 1, alpha subunit

GGTGACTATC

0

1

0

Intermediate (KSb)

TTGGGGAAAC

3

1

6

AGGAGCAAAG

11

3

2

ACAGCAAAGT ACAGCAAAGC GAGCTTGTGT

0 0 0

1 0 0

0 0 1

High (KSc), moderate (KSb), low (KSb) Moderate (KSsa), low (KSb, KSc) High (KSb)

TTGCCCTTTC

0

0

1

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248

Antoinette C. van der Kuyl, Remco van den Burg, Fokla Zorgdrager et al. Table 5. (Continued) AIDSKSa

AIDSKSb

EndemicKSc

Expression level compared with other SAGEmap libraries High (KSb)

Gene

Tag

RHCE: rhesus blood group, CcEe antigens PRKWNK4: protein kinase, lysine deficient 4 FTHL17: ferritin, heavy polypeptide-like 17 CYP2A6: cytochrome P450, family 2, subfamily A, polypeptide 6 CD163: hemoglobin scavenger receptor

ACAGCAAAGT ACAGCAAAGC GAGCTTGTGT

0 0 0

1 0 0

0 0 1

TTGCCCTTTC

0

0

1

Intermediate (KSc)

AGCTTCCTGC

0

0

3

Intermediate (KSc)

AGTCAGCTGA

1

5

2

Intermediate-high (KSb)

Intermediate (KSc)

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For comparison tag numbers for each group were normalized to 50,000. No tags were detected in the KS SAGE libraries for: HFE (hemochromatosis), TFRC (transferrin receptor, CD71), ANK1 (ankyrin 1, erythrocyte), ABCB7 (ATP-binding cassette, subfamily B (MDR/TAP), member 7), NFE2 (nuclear factor erythroid-derived 2, 45kDa), HAO1 (hydroxyacid oxidase 1, glycolate oxidase), PRKWNK1 (protein kinase, lysine deficient 1), ABCB6 (ATP-binding cassette, subfamily B (MDR/TAP), member 6), EGLN1 (Egl nine homolog 1 (C. elegans)), PX (hemopexin), HBD (hemoglobin, delta), HBG (hemoglobin, gamma), CA4 (carbonic anhydrase IV), FDX1 (ferredoxin 1), FECH (ferrochelatase), and ALAS2 (aminolevulinate, delta-, synthase 2).

Together these results are suggestive of a dysregulated expression of components of complement of at least the classical pathway in KS, with especially high expression of the complement component 1 subcomponents and a low presence of regulators of complement activation. This dysregulation could well contribute to the disease process in KS.

KS and Iron Metabolism KS lesions are characterized by massive neoangiogenesis of leaky vessels from which erythrocytes extravasate into the surrounding tissue. Large numbers of haemosiderin-laden macrophages and ferritin granules are present in the KS lesions. Also, heme oxygenase-1, the rate-limiting enzyme in the degradation of heme, is highly expressed in KS lesions [12,29], and is induced upon HHV-8 infection of endothelial cells [29]. It has been suggested that iron is a possible co-factor in the pathogenesis of KS [30,31,32], although treatment of KS patients with the iron chelator desferrioxamine resulted in enhancement of symptoms [33,34]. To analyse the role of iron-related gene expression in KS, we have searched the NCBI databases OMIM and UniGene for ―iron‖, which resulted in 45 gene hits other than enzymes containing catalytic iron. The extracted tags and their counts in the KS SAGE libraries are shown in Table 5. A total of 15 of these genes were not expressed in KS at all, while for the other 30 genes expression was generally low compared with other SAGE libraries. Relative high expression was detected for the light (L) polypeptide of ferritin, and for heme oxygenase-1. Consistent (in all three libraries) moderate till high expressed was the heavy (H)

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polypeptide of ferritin, and the hemoglobin scavenger receptor CD163. The higher expression of the L-polypeptide of ferritin could indicate that ferritin in KS lesions is L-rich, as in ironstoring organs (liver and spleen). L-rich ferritin can bind more iron than H-rich ferritin, which might be beneficial in KS. The hemoglobin scavenger receptor CD163 mediates the endocytosis of hemoglobin:haptoglobin complexes, is expressed by anti-inflammatory macrophages, and induces both IL-10 and heme oxygenase-1 [35]. The latter enzyme is also highly expressed in KS lesions.

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Keratins and Keratin-Binding Proteins in KS Some keratin genes, including constitutive epidermal keratins and keratins involved in hyperproliferation, are highly expressed in AIDS-related KS tissue [12]. To complete the list of keratins expressed in KS, we have analysed the tag counts of keratins and keratin-binding proteins in both AIDS-related KS and endemic KS (Table 6). Expression of some cytokeratins, e.g. keratins 5, 10, 14, and 18, was consistently found in all three libraries. As in the earlier analysis [12], keratins 6A and 16 were also detected at moderate to high levels in AIDS-KS and endemic KS (Table 6). These keratin genes are rapidly induced in the epidermis upon wounding or inflammation. Cytokeratin 17, expressed by basal ―stem‖ cells in complex epithelia, was also consistently detected in the three KS libraries. Of the proteins interacting with keratin, desmoplakin was highly expressed in all three libraries, while periplakin, vimentin and desmin were only expressed in two out of three libraries. High expression for vimentin was seen in endemic KS with moderate expression in AIDS-KSb, while desmin, which is mainly expressed in muscle, was highly expressed in AIDS-KSb. Vimentin, an intermediate filament protein which was formerly thought to only participate in stabilization of the cytoplasmic architecture, is also abundantly secreted by activated macrophages [36], suggesting it has a function in the immune response. In addition, binding of the heavy chain of IgG to cytoskeletal vimentin can activate the classical pathway of the complement cascade in vitro [37].

Galectin Expression in KS Of the 10 known human galectins and galectin-like proteins for which a reliable tag is available, galectins 1, 3, 7, and 9 are expressed in all three KS SAGE libraries, while low expression of galectin 2 is seen in endemic KS (Table 7). Galectins display a vast array of functions, both intra- and extra-cellular (for a review see: [38]). Amongst others, galectins are involved in inflammation (for a review see: [39]), regulation of immune responses (for a review see: [40]), and fulfil yet unclear roles in cancer processes (for a review see: [41]). Especially galectins 1 and 3 are speculated to play an important role in inflammatory-induced tissue destruction. Both are mainly expressed by epithelium, endothelium and activated tissue macrophages, but have generally opposite effects in inflammation. Galectin 1 is a negative regulator of the immune response, whilst galectin 3 is a pro-inflammatory molecule, which enhances cell adhesion and prevents T-cell apoptosis. Galectins 7, expressed only in stratified

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epithelia, and galectin 9 also have multiple regulatory roles in inflammation and immune processes [42]. Galectin 9 is expressed in many tissues, and in endothelial cells after stimulation with interferon-gamma [42]. So, the final effects of galectin expression in KS are unclear, but it is likely that changing levels of galectins exert a profound influence upon the disease process.

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Table 6. Keratins and keratin-binding proteins expressed in KS SAGE libraries For comparison tag numbers for each group were normalized to 50,000

*

Gene*

Tag

Keratin 2A Keratin 5 Keratin 6A Keratin 6B Keratin 6E Keratin 7 Keratin 8 Keratin 10 Keratin 14 Keratin 15 Keratin 16 Keratin 17 Keratin 18 Keratin 19 Keratin 23 Envoplakin Desmoplakin Bullous phemphigoid antigen 1, 230/240kDa Periplakin Plectin Vimentin Desmin

CCTCTTTGCA GCCCCTGCTG AAGCACAAG CGAATGTCCT TCAAGAAGCA CCTGGTCCCA CCTCCAGCTA GAAAACAAAG GATGTGCACG TAATAAAGAA CAGCTGTCCC CTTCCTTGCC CAAACCATCC GACATCAAGT AGACCAATGA GTGATGGGCT ACAGCGGCAA TTCTGGTATT TGAGGTTTTC TGAGAATAAT GTAAATATGG AAAATAAACC TTCCACTAAC TCCAAATCGA CCCCGGCCAC

AIDSKSa 0 62 17 0 0 2 1 7 35 0 12 22 2 0 0 1 26 0 1 0 0 1 3 0 1

AIDSKSb 1 102 20 4 0 5 1 20 50 0 12 20 6 1 1 2 31 1 0 0 0 0 3 12 9

EndemicKSc 2 132 57 34 1 0 0 26 28 4 28 97 4 0 0 1 28 1 0 0 1 2 2 129 0

Expression level compared with other SAGEmap libraries Moderate (KSb, KSc) Moderate Moderate (Ksa, KSb), high (KSc) Moderate (KSc), low (KSb) Moderate (KSc) Low Low Moderate (KSa), high (KSb, KSc) Moderately high Low, tag has second reliable ID High Moderate (KSa, KSb), high (KSc) Low Low Moderate (KSb) Moderate High Low Low Low Moderate (KSa, KSc) Low Moderate (KSb), high (KSc) Moderate (KSa), high (KSb)

Hair keratins have not been included. No tags were detected in the KS SAGE libraries for: keratin 1, keratin 1B, keratin 3, keratin 4, keratin 5B, keratin 6L, keratin 9, keratin 12 (only expressed in eye), keratin 13, keratin 20 (expressed in intestinal epithelium), keratin 24, keratin 25A, keratin 25C, keratin 25D, nor for any keratin-associated protein, or for any UniGene entry with the description ―similar to keratin x‖.

Semaphorin, Plexin and Neuropilin Expression in KS Semaphorins are a family of highly conserved signalling molecules with diverse functions, including cell migration, immune response, angiogenesis, and tumour growth and

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metastasis (for a review see: [43]). Two types of semaphorin receptor have been identified: the plexins and the neuropilins. Currently, the presence of any of these molecules in KS has not been investigated. Therefore, we have tabulated all known tags for semaphorins, plexins and neuropilins, and their counts in the three KS SAGE libraries (Table 8). As can be seen from this table, at least 5 semaphorin tags are consistently expressed in KS. However, three of these tags do not have a unique ID, a severe drawback of the SAGE method, so that their expression cannot unequivocally be attributed to a specific gene. Leaving out these three tags, we can conclude that at least Sema3C and Sema6B are expressed in KS, while Sema3D is expressed in endemic KS, and Sema3F in AIDS-related KS. SEMA3 proteins are involved in endothelial cell migration and angiogenesis through the regulation of integrin function [44]. Unfortunately, the tag derived from SEMA3A mRNA, the best studied of these proteins, is absent from the KS libraries, and is not expressed in the complete SAGEmap database. Possibly, the mRNA for Sema3A deposited in the database is not correct, or otherwise the mRNA could be expressed at very limited occasions. Tags for the semaphorin receptors are either low or absent, or have a double ID (as for neuropilin 2). Surprisingly, the only one of these tags highly expressed in both AIDS-related and endemic KS is the one for the atypical plexin D1, which is known to be involved in embryonic angiogenesis in mice, and is expressed by endothelial cells in vascular endothelium [45]. Our results suggest that plexin D1 is also involved in the development of blood vessels in Kaposi‘s sarcoma. It has been speculated that as plexins of the B and C subfamilies bind semaphorin classes 4 and 7, respectively, and the neuropilins are the receptors for class 3 semaphorins, plexins of the A and D subfamilies could be the receptor for the other semaphorin subclasses [46]. In mice, however, SEMA3E was shown to be the ligand for plexin D1 [47]. Table 7. Galectin expression in KS SAGE libraries For comparison tag numbers for each group were normalized to 50,000

Gene*

*

Tag

AIDSKSa

AIDSKSb

EndemicKSc

Galectin 1

TTTTTTGTAA GTACCCGTAC GCCCCCAATA

0 0 65

0 0 75

1 0 157

Galectin 2 Galectin 3 Galectin 7 Galectin 9

TCCTCTTTCA TTCACTGTGA TAAACCTGCT CTCAGTCCCC

0 16 28 3

0 31 77 1

2 27 20 1

Expression level compared with other SAGEmap libraries Low Moderate (KSa, KSb) high (KSc) Moderate (KSc) Moderate Moderate High

Galectin 5 is only found in rat, galectin 6 is only found in mouse, OvGal11 is only found in sheep/cow. No tags were detected in the KS SAGE libraries for: galectin 4, galectin 8, galectin 12, galectin 13, and HSPC159.

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Adhesion Molecule Expression in KS SAGE Libraries Massive leukocyte infiltrates are a hallmark of inflammation and of (early) KS. Several molecules have been implicated in leukocyte adhesion, e.g. selectins, integrins, and members of the immunoglobulin superfamily (e.g. ICAM, VCAM). To study mRNA expression of these adhesion-related molecules in KS, all tags available for entries of these families in the UniGene database were retrieved from SAGEmap (Table 9). Table 8. Semaphorin, plexin and neuropilin expression in KS SAGE libraries For comparison tag numbers for each group were normalized to 50,000

Semaphorin 3C Semaphorin 3D Semaphorin 3E Semaphorin 3F Semaphorin 4A Semaphorin 4C

TTGAATTCCC ACTGTTCTAT AGGTCAGGAG GATGCGAGGA CAGGGATCTG TGTTTGTGTG

AIDSKSa 1 0 22 3 1 4

Semaphorin 4D

ATAGGATTTG

0

0

1

Semaphorin 4F Semaphorin 6B Semaphorin 6C Semaphorin 6D

GCTCTAGGCT CCACGTGGCT GTGACTGCCA TAAAGTCAAA CACATACATA ATTGTGCTAG TTCGACTTCC TGGGAGACGG GAATAAAATA GTGAGGGCTA CCCTAGGTTG GGGGCTGGAG CATTTTTATC

0 10 9 1 0 1 0 0 0 0 0 29 1

1 5 13 0 0 0 0 0 0 1 1 27 0

0 2 19 0 0 0 0 0 3 0 0 37 0

Expression level compared with other SAGEmap libraries Low High (KSc) Tag has at least 15 reliable ID‘s High (KSa, KSb) Moderate Moderate-high, tag has a second reliable ID Moderate (KSc), tag has a second reliable ID Moderate (KSb) High (KSa), moderate (KSb, KSc) Tag has a second reliable ID Low Low, tag has a second reliable ID Low Low Very high Moderate (KSa)

TTTTGTACCA TGTGTGTGTG CTGGGGATGC AATGACAAGA TACTGTAGTC

0 6 0 0 0

1 3 0 0 0

0 1 0 0 1

Low Tag has 7 other reliable ID‘s Low

Gene

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Plexin A1 Plexin A4* Plexin B1 Plexin B2 Plexin D1 Plexin domain containing 1 (TEM7) Neuropilin 1 Neuropilin 2

*

ESDN, endothelial and smooth muscle cell-derived neuropilin-like protein

Tag

AIDSKSb 1 0 24 2 0 1

EndemicKSc 5 3 10 0 1 6

No mRNA is known for plexin A3. No tags were detected in the KS SAGE libraries for: semaphorin 3A (note: tag AGTAACTTTC has never been seen in any SAGEmap database), semaphorin 3B, semaphorin 4B, semaphorin 4G, semaphorin 5A, semaphorin 5B, semaphorin 6A, semaphorin 7A, LOC56920 (semaphorin sem 2), plexin A2, plexin B3, plexin C1, plexin domain containing 2, likely ortholog of mouse plexin 3, NETO1 (neuropilin and tolloid-like 1), and NETO2 (neuropilin and tolloid-like 2).

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Table 9. Adhesion molecules expressed in KS SAGE libraries

L-selectin P-selectin (CD62) Integrin, alpha 3 Integrin, alpha 5

CAATTTTGCA TCACCGTGGA GTACTGTAGC ATGGCAACAG

AIDSKSa 0 3 2 13

Integrin, alpha 6 Integrin, alpha 7 Integrin, alpha D Integrin, alpha E (CD103) Integrin, alpha L (CD11a) Integrin, alpha V (CD51) Integrin, alpha X (CD11c) Integrin, beta 1 (CD29)

ATTAGAAATT GGGTAGGGTG CCACTGCACT GAGCTGTTGG TATTTATCCA TAACTTGTGA CACAAAAAGA TGAAGTTATA CAGCCCAGAA ACAGAAGGGA GAGACTTGAG AAGGGGGCAA CCAGGCTGCG GTTCACTGCA GCCACCACCA TTAGGGAGGA GTACGGAGAT CTGAAGGCTG GGAACCAGGT TCTGAATTAT AAATATGTGT ACTGGACAGC TTGCCCAGCA AAAGGGTCAC GGGATTAAAG TGACTGTATT

1 0 418 6 2 20 0 3 0 1 0 0 5 0 3 2 5 10 1 1 0 2 5 1 0 1

0 3 350 4 0 7 0 3 0 2 2 7 10 3 3 0 0 11 4 0 2 1 20 0 0 3

0 0 89 2 1 35 4 6 0 1 1 0 2 0 3 0 2 14 2 0 0 0 2 0 7 1

Expression level compared with other SAGEmap libraries High (KSc) High (KSa, KSc) Low Moderate (KSa, KSb), tag has second reliable ID Low Moderate High, tag has multiple reliable IDs High (Ksa, KSb), moderate (KSc) Moderate High High (KSc) Low Low Low Moderate (KSb, KSc) Moderate (KSb) High (KSb), moderate (KSa, KSc) Moderate (KSb) Moderately high Moderate (KSa) High (Ksa), moderate (KSc) Very high High (KSb), moderate (KSa, KSc) Low Moderate (KSb) Moderate (KSa, KSb) High (KSb), moderate (KSa, KSc) Low High (KSc) High (KSb), moderate (KSa, KSc)

AGCCTGGACT GTGCCAGGCA AAGATTGGGG AATGAACAAT TGTGGGTGCT CACACACACA TAAGAAAATG TACGGTGGCG ACAAGTACTG CAAAAAAAAA GCCGGGCCCT GTGTCAGATA ATCTTGTTAC TTCTGCTCTT

10 0 0 3 4 3 0 0 21 1 0 0 8 21

7 3 1 1 2 2 0 0 8 3 1 1 18 28

1 1 3 7 1 1 2 2 14 33 0 0 17 6

High (KSa, KSb), low (KSc) Moderate (KSb), low (KSc) Low Moderate (KSc), low (KSa, KSb) Low Moderate Moderate (KSc) Moderate (KSc) High Tag has many reliable IDs Low (KSb) Low (KSb) Moderate High (KSa, KSb), moderate (KSc)

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Gene

Integrin, beta 2 (CD18) Integrin, beta 4 Integrin, beta 5 ICAM 1 (CD54) ICAM 2 ICAM 3 VCAM 1 PECAM 1 (CD31) JAM 3 CEACAM 1 ESAM DSCAM CEECAM 1 NRCAM MCAM (CD146) Vascular adhesion protein 1 (AOC3) ADRM1 ASAM CD44 Ninjurin 1 E-cadherin H-cadherin OB-cadherin P-cadherin VE-cadherin M-cadherin Vitronectin Fibronectin VWF

Tag

AIDS -KSb 0 0 1 16

Endemi c-KSc 3 2 1 1

For comparison tag numbers for each group were normalized to 50,000.

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No tags were detected in the KS SAGE libraries for: selectin E, integrin alpha 1, integrin alpha 2, integrin alpha 2b, integrin alpha 4, integrin alpha 8, integrin alpha 9, integrin alpha 10, integrin alpha 11, integrin alpha M (CD11b), integrin beta 6, integrin beta 7, integrin beta 8, ICAM 4, ICAM 5, NCAM 1, NCAM 2, L1CAM, ALCAM, DSCAML1, AMICA, JAM 1, JAM 2, SPAM 1, MADCAM 1, PUNC, CEACAM 3, CEACAM 4, CEACAM 5, CEACAM 6, CEACAM 7, CEACAM 8, OPCML, neurofascin, CD226, N-cadherin, KSP-cadherin, R-cadherin, and all type 2 cadherins.

Selectins (L-, P, and E-selectin) were not consistently expressed in the three KS SAGE libraries. However, tag counts for selectins are low in the SAGEmap database, suggesting that expression of these genes cannot accurately be measured using SAGE. Integrins that are consistently expressed in KS are integrin 3, integrin V, integrin 1, and integrin 5. The highest expression was measured for integrin V and integrin 5. Immunohistochemistry has shown that integrins V, 5, and 1 are expressed by both spindle cells and by the endothelium in KS lesions, while integrins 3 and 3 were only expressed by the endothelium [48]. Integrin 5 was not studied. Tags derived from integrin 5 and D mRNA‘s do not have an unique ID, rendering them useless for this analysis. Integrin V, in vivo part of the vitronectin receptors (integrin V 3 and integrin V 5), has been described as a negative regulator of angiogenesis, although it is often highly expressed on angiogenic blood vessels [49]. Antibodies directed against integrin V 3 were found to target KS tumour cells and inhibit tumour growth in a nude mouse model of KS [50]. Surprisingly, proangiogenic integrins, e.g. integrin 5 1 and its ligand fibronectin, are expressed at much lower levels in KS than the angiogenesis inhibiting integrin V 5. Integrin 3 1, which is expressed in KS at low levels, can function as a cellular receptor for HHV-8 [51]. Adhesion molecules of the immunoglobulin superfamily that were always expressed in the KS SAGE libraries were ICAM2 (which is constitutively expressed by endothelial cells), PECAM 1 (CD31, a well-known marker for KS lesions), JAM3 (junctional adhesion molecule 3, involved in leukocyte transmigration through the endothelial barrier), and CEECAM1 (cerebral endothelial cell adhesion molecule 1). Other adhesion molecules consistently expressed are AOC3, also named vascular adhesion protein 1, an endothelial sialoglycoprotein whose cell surface expression is induced under inflammatory conditions, and ADRM1, a gamma-interferon-inducible gene. Of the cadherins, calcium-binding proteins that mediate cell-cell interactions, only VE-cadherin is highly expressed in KS, while Hcadherin and E-cadherin are moderately or lowly expressed, respectively. Downregulation of E-cadherin has been associated with cancer progression (for a review see: [52]). Expression of E-cadherin in KS is lower than in normal skin (not shown), suggestive of downregulation. In most cancer types, however, loss of E-cadherin expression is accompanied by a gain in mesenchymal cadherin expression (N-cadherin and OB-cadherin, known as the ―cadherinswitch‖), which is not evident in KS (Table 9). In skin tumours, downregulation of Hcadherin is associated with increased invasiveness as well. Table 9 also lists a few ligands for adhesion molecules, of which especially Von Willebrand factor (VWF) is highly expressed in AIDS-KS, and somewhat lower in endemic KS. VWF is mainly synthesized by endothelial cells, can bind to the V 3 and V 5 integrins, and has been reported to be elevated in plasma from HIV-1+ patients and patients with AIDS-related KS (for a review, see: [53]), and to a lower extent in patients with classic

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KS [20]. Generally, increased expression of VWF is an indication for endothelial damage and activation. Table 10. Matricellular and basement membrane protein mRNA expression in KS SAGE libraries

Osteopontin Osteonectin Thrombospondin 1 Thrombospondin 2 Thrombospondin 3 COMP

AATAGAAATT ATGTGAAGAG AGGTCTTCAA AAAAAAAAAA CGAGGGGGGC CGGGGTGGCC

AIDSKSa 1 112 0 6 3 2

Hevin Tenascin C Tenascin XB CYR61 (CCN1) CTGF (CCN2) NOV WISP-2

TGCACTTCAA AAGCTGTATA AGCTACCACA AGTGTCTGTG TTTGCACCTT TATCTGGTTT CACACGGGCG

25 1 0 4 26 0 1

65 3 3 9 14 1 13

13 0 1 2 14 0 3

Collagen, type IV, alpha 1 Collagen, type IV, alpha 2 Laminin, alpha 1 Laminin, alpha 2 Laminin, alpha 4 Laminin, alpha 5 Laminin, beta 1 Laminin, beta 2 Laminin, beta 3 Laminin, gamma 2

GACCGCAGGA

56

179

137

Expression level compared with other SAGEmap libraries Low (KSa) Very high (KSc), high (KSa, KSb) Low (KSb, KSc) Tag has multiple reliable IDs High Very high (KSb), moderate (KSa, KSc) High (KSb), moderate (KSa, KSb) Moderate (KSb), low (KSa) Moderate (KSb, KSc) Moderate (KSa, KSb), low (KSc) Moderate Moderate (KSb) High (KSb), moderate (KSc), low (KSa) Very high (KSb, KSc), high (KSa)

TTCTCCCAAA

6

3

6

High (KSa, KSc), moderate (KSb)

TGCAACAAAT TACATAGAAT ACAGAGCACA ACTCGCTCTG CTTGTAACAG TCTGCCTATG CCATTGAAAC GTGAAACCCT GGGGCACTTG CATAAACGGG CCACCCTCAC ACCTCACCCC TGATCAATAT CAGCTGGCCA ACAGAATGCC TACATTATAT AGCTACCGGG CTATGTTCTG

0 0 9 2 6 6 0 20 0 0 1 1 0 6 6 0 1 0

1 4 17 5 11 1 1 59 0 1 0 0 2 19 12 3 4 3

0 3 15 2 7 0 1 50 0 3 1 1 1 6 0 0 0 1

Moderate (KSb) High (KSb, KSc) Very high (KSb, KSc), high (KSa) Moderate (KSb), low (KSa, KSc) Very high (KSb), high (KSa, KSc) High (KSa), low (KSb) Low Tag has many reliable IDs

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Gene

Laminin, gamma 3 Perlecan Nidogen 1 Nidogen 2 Fibulin 1C Fibulin 1D Fibulin 2 Fibulin 4 Fibulin 5

Tag

AIDSKSb 0 130 4 5 4 54

EndemicKSc 0 365 1 110 2 4

High (KSc), moderate (KSb) Low High (KSa, KSc) High (KSb, KSc) High (KSb), moderate (KSa, KSc) High (KSb), moderate (KSa) High (KSb) Moderate (KSb), low (KSa) High (KSb), moderate (KSc)

For comparison tag numbers for each group were normalized to 50,000. No tags were detected in the KS SAGE libraries for: thrombospondin 4, tenascin N, tenascin R, WISP1, WISP-3, collagen type IV alpha 3, collagen type IV alpha 4, collagen type IV alpha 5, collagen type IV alpha 6, agrin, laminin alpha 3, laminin beta 2, laminin gamma 1, fibulin 1A, fibulin 1B, fibulin 3, and fibulin 6.

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Matricellular and Basement Membrane Protein Expression in KS SAGE Libraries Members of the matricellular protein family are functionally related, minor constituents of the extracellular matrix (ECM) that appear only shortly upon specific developmental or pathological events. They include thrombospondins, tenascins, CCN family members, osteopontin, and osteonectin (for a review, see: [54]). Matricellular proteins are involved in T-cell maturation and ECM remodelling. In all three KS libraries highly expressed are osteonectin and thrombospondin 3 (Table 10). Lower expression was found for hevin, CCN1, CCN2, and WISP-2. Cartilage oligomeric matrix protein (COMP) is highly expressed in a single AIDS-KS library, but is found at only a moderate level in the other two libraries. Inconsistent expression was found for osteopontin, thrombospondin 1 and 2, tenascin C, tenascin XB, and NOV. Surprisingly, tenascin C that is highly induced early in inflammatory processes, and at the invasion borders of malignant tumours [55], is barely detectable in our KS libraries. Of the CCN family of matricellular proteins, CCN1 (CYR61), and CCN2 (connective tissue growth factor), both produced by endothelial cells, are important regulators of angiogenesis [56], and both are expressed in KS. So, matricellular protein family members are widely present in KS, in line with the extensive ECM remodelling observed in KS lesions. Of the basement membrane proteins, high-level mRNA expression in all three KS libraries was detected for type IV collagens alpha 1 and alpha 2, and laminins alpha 4 and beta 1 (Table 10). Consistent, but lower expression was seen for alpha 5 laminin (Table 10). Laminin alpha 4 has been implicated in angiogenesis, as it is necessary for the development of microvessels [57]. Fibulins are notably expressed in blood vessels, but are also found as part of basement membranes (for reviews, see: [58,59]). Fibulin 1C is expressed in all three KS libraries, while fibulins 1D, 2, and 5 are found in only one or two libraries. Fibulin 5 has been shown to promote endothelial cell adhesion, and fulfil a role in vascular development and remodelling [60].

MMP and TIMP Expression in KS SAGE Libraries Matrix metalloproteinases and their inhibitors play an important role in angiogenesis and degradation of the extracellular matrix, and are involved in tissue remodelling and invasion. MMP-2, MMP-9, MMP-12, and MMP-19, and sometimes MMP-1, are expressed in KS lesions [61]. MMP-12, exclusively produced by CD68+ macrophages, was mainly detected in advanced nodular KS lesions, which was also true for MMP-9 [61]. Clinical trials in KS patients with an MMP inhibitor were promising [62], confirming the importance of MMP expression in disease progression. Table 11 shows tags for MMP‘s and their inhibitors (TIMP‘s: tissue inhibitors of metalloproteinases) detected in KS. Consistent expression was seen for MMP-1, MMP-2, MMP-15, MMP-25, TIMP-1, and TIMP-3. MMP-18 (=MMP-19) and MMP-12 expression was only seen in endemic KS. MMP-28, which is increased in basal keratinocytes during

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tissue repair [63], was detected in two out of three KS libraries. Besides MMPs, another extracellular matrix degrading enzyme, cathepsin B, is also highly expressed in KS (91 tags in KSa, 66 tags in KSb, and 35 tags in KSc). TIMP‘s have multiple activities besides inhibition of MMP‘s (for a review, see: [64]). The high expression of TIMP-1, an inducible TIMP, has not been described before in KS. TIMP-1 has been shown to have cell-growth promoting and anti-apoptotic activities. However, in cancer, overexpression of TIMP-1 and TIMP-2 is beneficial, while downregulation is associated with increased invasiveness [64]. Table 11. MMP and TIMP expression in KS SAGE libraries

MMP-1

TGCAGTCACT

AIDSKSa 9

MMP-2

GGAAATGTCA

14

38

50

MMP-7 MMP-9 MMP-11 MMP-12 MMP-14 MMP-15 MMP-17 MMP-18 (=MMP19) MMP-25

AACTTGGCCA TAAATCCCCA CAGGAGACCC CTCTGTAAGT GTACCGGGGA GCCCTGAGCG ACCTCTCCCT TTCATAAAAA GAAGACAGTG

0 1 0 0 0 1 2 0 0

1 0 1 0 1 1 0 0 0

0 1 3 11 1 1 6 2 0

MMP-28 TIMP-1

TATGAGGAGG AGTTTGGCCA AGGACAGAAG GATGGGGATG GAGAGTGTCT

1 0 1 1 14

1 0 0 0 48

1 0 2 1 10

TIMP-2 TIMP-3

AATAAAACAC TTATTTATGA

1 1

0 1

0 1

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Gene

Tag

AIDSKSb 3

EndemicKSc 10

Expression level compared with other SAGEmap libraries High (KSa, KSc), moderate (KSb) High (KSb, KSc), moderate (KSa) Unreliable tag Moderate (KSa, KSc) Moderate (KSb, KSc) Very high (KSc) High (KSb, KSc) Moderate High (KSc), moderate (KSa) Moderate (KSc) Moderate Low (Ksa), moderate (KSc) Moderate (KSa, KSc) Very high (KSb), moderate (KSa, KSc) Tag has second reliable ID Low

For comparison tag numbers for each group were normalized to 50,000. No tags were detected in the KS SAGE libraries for: MMP-3, MMP-8 (=MMP-27), MMP-10, MMP13, MMP-16, MMP-20, MMP-21, MMP-23B, MMP-24, MMP-26, and TIMP-4.

Chemokine Expression in KS SAGE Libraries Chemokines (for a review, see: [65]) play an important role in the establishment of Kaposi‘s sarcoma, as many different cell types are attracted to the lesions. Both IL-8 (CXCL8) and GRO-alpha (CXCL1) are known growth factors for KS, and are involved in KS angiogenesis [66,67]. To survey the possible expression of other chemokine genes in KS, we have collected tags for all classes of human chemokines, and tabulated their counts in the

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three KS SAGE libraries (Table 12). Several chemokines were inconsistently expressed between the three libraries, including CXCL8 (IL-8), which was virtually absent from the AIDS-related KS libraries, but was moderately expressed in endemic KS (Table 12). Highly expressed in all three libraries were tags for CCL18, CCL19, CCL21, CXCL14, and CXCL16. In addition, CCL27 was highly expressed in AIDS-related KS. Table 12. Chemokine expression in KS SAGE libraries For comparison tag numbers for each group were normalized to 50,000 Gene

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CCL2* CCL3 CCL4 CCL5 (Rantes) CCL8 CCL13 CCL17 CCL18 CCL19 CCL21 CCL22 CCL26 CCL27 XCL2 CXCL2** CXCL3 CXCL6 CXCL8 (IL-8) CXCL9 CXCL12

*

CXCL14 CXCL16 SCYE1

GTACTAGTGT GCACCAAAGC GATAACACAT AAAAATCGGC

AIDSKSa 1 1 0 0

AIDSKSb 0 0 0 1

Endemic -KSc 3 12 6 2

Expression level compared with other SAGEmap libraries Low Low Moderate (KSc) Low

GATCATCAAG AAGGGATGCT GGCACAAAGG GATCAATCAG GCCCTGCTAC AGAGGAGGCA GACAAGGATG AACGGGGCCC GACCCGCTGG GGACTCTGCC AATAAAATTA TTGAAGCTTT ATAATAAAAG TTGGTTTTTG TGGAAGCACT

0 1 0 5 1 6 0 0 1 1 0 0 0 0 1

0 1 0 23 2 5 0 0 0 5 0 1 0 0 0

1 0 1 13 1 8 0 1 0 0 1 0 5 3 22

Moderate (KSc) High, rare tag Moderate (KSc) Very high (KSb), high (KSa, KSc) Very high (KSb), high (KSa, KSc) Very high Moderate (KSc) Moderate (KSa) High (KSb), rare tag Tag has a second reliable ID Low Low Moderate (KSc) Moderate (KSc), low (KSa)

CATACACTCT TAGAAAATTT GTATAAACGT CAGGTTTCAT CCTGGCCCTA AGCAACAGTG

2 0 2 14 2 1

2 0 0 24 1 6

4 1 0 26 2 1

Moderate Moderate (KSc) Moderate (KSa) High High High (KSb), low (KSa, KSc)

Tag

CCL = chemokine (C-C motif) ligand; **CXCL= chemokine (C-X-C motif) ligand No tags were detected in the KS SAGE libraries for: CCL1, CCL7, CCL11, CCL15, CCL16, CCL20, CCL23, CCL24, CCL25, CCL28, XCL1, CXCL1, CXCL4, CXCL5, CXCL7, CXCL10, CXCL11, CXCL13.

CC chemokines are subdivided into four groups plus the structurally unique CCL27. Only members of the pro-inflammatory (CCL3, CCL4, CCL18) and homeostatic subgroups (CCL19, CCL21), but none of the allergenic and developmental subgroups are found in KS. Some consider CCL19 and CCL20 to be pro-inflammatory chemokines [68]. CCL18 is

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expressed at a low level by dendritic cells, and at a high level by alternatively activated macrophages [69]. This subset of macrophages is mainly found in healing wounds and in chronic inflammation, and is highly angiogenic [70]. CCL19 and CCL21 are two chemokines using CCR7 as a receptor; both are downregulated by IL-10, and both play a role in T-cell adhesion. CCL21 is overexpressed in endothelial cells of T-cell mediated skin diseases, while CCL19 expression in disease is less clear, and was only seen in a single case of atopic dermatitis [71]. CCL27 is constitutively expressed by basal keratinocytes and also attracts T-cells, but was not overexpressed in several human autoimmune skin diseases [71]. Of the CXC chemokines, members of the ELR-motif (+) group (CXCL3, CXCL6, and CXCL8 are detected in KS) are much lower expressed than members of the ELR-motif negative group (CXCL9, CXCL14 and CXCL16 are found in KS), which are chemotactic for monocytes (CXCL9, CXCL14) and T-cells (CXCL9, CXCL16). CXC chemokines are involved in the mediation of inflammation and the angiogenesis balance (for a review, see: [72]). Interestingly, CXC chemokines with angiogenic properties (CXCL2, CXCL3, CXCL6, CXCL8, and CXCL12) are, albeit weakly, more often expressed in KS than angiostatic chemokines (CXCL9, Table 12), suggesting that an imbalance of CXC chemokine mediated angionesis, which finally leads to transformation and tumour progression, contributes significantly to the disease process in KS. Tags for chemokine receptors were investigated, but repeatedly found to be negative. Only two single tags for CCR5 and CCR10, respectively, were detected (result not shown). In general, tags representing receptor mRNA‘s are difficult to detect using SAGE, suggesting that these mRNA‘s are present in lower concentrations than mRNA‘s coding for other types of proteins.

CONCLUSION The novel approach of analyzing gene family expression in nodal Kaposi‘s sarcoma lesions as measured by the SAGE method contributes to the common perception of the disease as an uncontrolled inflammatory, rather than a primary neoplastic process. Of the S100 protein mRNA‘s, the pro-inflammatory subset (S100A7, S100A8, S100A9) involved in unrestrained and harmful inflammation was highly expressed, rather than the subset implicated in cancer or tumour progression (e.g. S100A2, S100A4, S100A6, S100B). In addition, high levels of pro-inflammatory chemokine mRNA‘s were found in the tissues, together with high and aberrant expression of complement components, another cause of severe inflammatory damage. Keratin 6A and 16 mRNA, both induced by wounding and inflammation, were prominently present in KS tissue, as was TIMP-1 mRNA, which is believed to be beneficial and is associated with reduced invasiveness in cancer. Analysis of iron-related gene expression in KS was not suggestive of any pecularity despite the suggested role of iron in the disease. Significant expression was detected only for the light polypeptide of ferritin, heme oxygenase-1, and the hemoglobin scavenger receptor CD163, compatible with macrophage-related iron-uptake as erythrocytes leak from the abnormal vessels found in KS.

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As expected, genes involved in ECM remodelling and angiogenesis were also prominently expressed in KS, as were adhesion molecules. In further disagreement with a neoplastic process is the absence of the cadherin switch (loss of E-cadherin expression accompanied by a gain in N-cadherin or OB-cadherin expression) in KS lesions. Plexin D1 mRNA, originally implicated in embryonic angiogenesis, is highly expressed in KS, suggestive of a function in the development of blood vessels in KS. Probably, it also plays a role in neoangiogenesis in other non-embryonic tissues. Furthermore, the results suggested that endemic KS and AIDS-related KS have a similar pattern of gene expression, in line with their comparable histopathology. Very few genes, mainly MHC-related, but also Von Willebrand factor (VWF), D component of complement, MMP-12, and calgranulin-A, are differentially expressed between the two forms of KS.

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[55] Chiquet-Ehrismann, R. and Chiquet, M. (2003). Tenascins: regulation and putative functions during pathological stress. J Pathol., 200, 488-499. [56] Brigstock, D. R. (2002). Regulation of angiogenesis and endothelial cell function by connective tissue growth factor (CTGF) and cysteine-rich 61 (CYR61). Angiogenesis., 5, 153-165. [57] Thyboll, J., Kortesmaa, J., Cao, R., Soininen, R., Wang, L., Iivanainen, A., Sorokin, L., Risling, M., Cao, Y., and Tryggvason, K. (2002). Deletion of the laminin alpha4 chain leads to impaired microvessel maturation. Mol Cell Biol, 22, 1194-1202. [58] Argraves, W. S., Greene, L. M., Cooley, M. A., and Gallagher, W. M. (2003). Fibulins: physiological and disease perspectives. EMBO Rep., 4, 1127-1131. [59] Timpl, R., Sasaki, T., Kostka, G., and Chu, M. L. (2003). Fibulins: a versatile family of extracellular matrix proteins. Nat. Rev. Mol Cell Biol, 4, 479-489. [60] Nakamura, T., Ruiz-Lozano, P., Lindner, V., Yabe, D., Taniwaki, M., Furukawa, Y., Kobuke, K., Tashiro, K., Lu, Z., Andon, N. L., Schaub, R., Matsumori, A., Sasayama, S., Chien, K. R., and Honjo, T. (1999). DANCE, a novel secreted RGD protein expressed in developing, atherosclerotic, and balloon-injured arteries. J Biol Chem, 274, 2247622483. [61] Impola, U., Cuccuru, M. A., Masala, M. V., Jeskanen, L., Cottoni, F., and SaarialhoKere, U. (2003). Preliminary communication: matrix metalloproteinases in Kaposi's sarcoma. Br. J Dermatol., 149, 905-907. [62] Cianfrocca, M., Cooley, T. P., Lee, J. Y., Rudek, M. A., Scadden, D. T., Ratner, L., Pluda, J. M., Figg, W. D., Krown, S. E., and Dezube, B. J. (2002). Matrix metalloproteinase inhibitor COL-3 in the treatment of AIDS-related Kaposi's sarcoma: a phase I AIDS malignancy consortium study. J Clin Oncol., 20, 153-159. [63] Lohi, J., Wilson, C. L., Roby, J. D., and Parks, W. C. (2001). Epilysin, a novel human matrix metalloproteinase (MMP-28) expressed in testis and keratinocytes and in response to injury. J Biol Chem, 276, 10134-10144. [64] Lambert, E., Dasse, E., Haye, B., and Petitfrere, E. (2004). TIMPs as multifacial proteins. Crit Rev. Oncol. Hematol., 49, 187-198. [65] Laing, K. J. and Secombes, C. J. (2004). Chemokines. Dev. Comp Immunol, 28, 443-460. [66] Masood, R., Cai, J., Tulpule, A., Zheng, T., Hamilton, A., Sharma, S., Espina, B. M., Smith, D. L., and Gill, P. S. (2001). Interleukin 8 is an autocrine growth factor and a surrogate marker for Kaposi's sarcoma. Clin Cancer Res, 7, 2693-2702. [67] Lane, B. R., Liu, J., Bock, P. J., Schols, D., Coffey, M. J., Strieter, R. M., Polverini, P. J., and Markovitz, D. M. (2002). Interleukin-8 and growth-regulated oncogene alpha mediate angiogenesis in Kaposi's sarcoma. J Virol, 76, 11570-11583. [68] Rossi, D. L., Vicari, A. P., Franz-Bacon, K., McClanahan, T. K., and Zlotnik, A. (1997). Identification through bioinformatics of two new macrophage proinflammatory human chemokines: MIP-3alpha and MIP-3beta. J Immunol, 158, 1033-1036. [69] Kodelja, V., Muller, C., Politz, O., Hakij, N., Orfanos, C. E., and Goerdt, S. (1998). Alternative macrophage activation-associated CC-chemokine-1, a novel structural homologue of macrophage inflammatory protein-1 alpha with a Th2-associated expression pattern. J Immunol, 160, 1411-1418.

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[70] Kodelja, V., Muller, C., Tenorio, S., Schebesch, C., Orfanos, C. E., and Goerdt, S. (1997). Differences in angiogenic potential of classically vs alternatively activated macrophages. Immunobiology, 197, 478-493. [71] Christopherson, K. W., Hood, A. F., Travers, J. B., Ramsey, H., and Hromas, R. A. (2003). Endothelial induction of the T-cell chemokine CCL21 in T-cell autoimmune diseases. Blood, 101, 801-806. [72] Romagnani, P., Lasagni, L., Annunziato, F., Serio, M., and Romagnani, S. (2004). CXC chemokines: the regulatory link between inflammation and angiogenesis. Trends Immunol, 25, 201-209.

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Chapter 9

DIAGNOSTIC CLASSIFICATION USING GENE EXPRESSION PROFILING IN AML† K. I. Mills and A. F. Gilkes Cardiff University, Cardiff, Wales, UK

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ABSTRACT Gene expression profiling initially showed that it could be used to distinguish between the major variants of acute leukaemia. Several studies have now been published that have illustrated the potential of micro array analysis in the diagnosis of haematological malignancy. For acute myeloid leukaemia (AML), in particular, expression profiling has also provided novel insights into disease pathogenesis and shown it could lead to refinements in risk-stratification, prognostic markers, and eventually a more rational and personalised approach to therapy. These gene expression studies will be compared and contrasted in terms of results; with a view to speculating whether arrays can complement current laboratory methodologies for leukaemia diagnosis.

Golub et al. (1999) described the first approach to disease classification based on microarrays. Using an unsupervised, class discovery approach they distinguished between AML and ALL without previous knowledge of the disease of each sample identifying previously unrecognized disease subtypes. The data produced four sub-groups representing AML, T-lineage ALL, and B-lineage ALL (two sub-groups). This demonstrated that a gene †

A version of this chapter was also published in Progress in Tumor Marker Research, edited by Lee I. Swenaon published by Nova Science Publishers, Inc. It was submitted for appropriate modifications in an effort to encourage wider dissemination of research. Correspondence concerning this article should be addressed to: K.I. Mills, Department of Haematology, School of Medicine, Cardiff University, Heath Park, Cardiff, CF14 4XN. Wales UK. Tel: +44 29 2074 4522; Fax: +44 29 2074 4523; e-mail: [email protected].

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expression based cancer classification could discover, and predict, cancer classes without previous biological knowledge and led to the speculation that further sub-classifications could be identified in other haematological or solid tumour diseases. AML is generally classified by the FAB (French-American-British) classification (Bennett et al. 1976) in which morphology is used to form groupings based on the differentiation status of the immature cells. The WHO (Vardiman, Harris, & Brunning 2002) disease classification of AML takes into account both morphology and the recurrent cytogenetic translocations t(8;21), t(15;17) and inv(16), and 11q23 abnormalities to form distinct diagnostic sub-groups. Patients without these chromosomal abnormalities are then grouped on the basis of morphology. Additional genetic mutations are also observed in AML distributed across the cytogenetic or morphology groupings. Several studies have been done on small numbers of AML patients (less than 60 patients) using the cytogenetically defined groups; these groupings included t(8;21); t(15;17), inv(16), 11q23 abnormalities or normal cytogenetics (Debernardi et al. 2003;Gutierrez et al. 2005;Haferlach et al. 2005c;Morikawa et al. 2003;Schoch et al. 2002;Vey et al. 2004;Virtaneva et al. 2001). Schoch et al. (2002) examined 37 samples from three distinct cytogenetic subtypes of AML: t(8;21), inv(16), and t(15;17) specifically associated with the four distinct morphological subgroups, AML M2, AML M4Eo, AML M3, and AML M3v. Their analyses, including Significance Analysis of Microarrays (SAM) (Tusher, Tibshirani, & Chu 2001), identified 36 genes whose expression could accurately discriminate between the cytogenetic classes. However, a set of only 13 genes were needed to separate the samples into these distinctive cytogenetic sub-groups. A similar study (Debernardi et al. 2003) used hierarchical clustering and statistical group comparison to group the major cytogenetic classes and identified 145 genes that could correctly distinguish the chromosomal sub-groups of: t(8;21), t(15;17), inv(16), 11q23, and normal karyotype. Members of the class I homeobox A and B gene families were shown to have a distinct up-regulation within the normal karyotype group. Patients with newly diagnosed AML with high blast counts could be separated into three patient subgroups using un-supervised hierarchical cluster analysis. In general, there was no relationship between the clusters and the FAB classification (Oyan et al. 2006). AML patients with normal cytogenetics constitute the single largest group in AML although trisomy 8 is the most common numerical aberration observed as either a sole abnormality or part of more complex cytogenetics. Patients with normal karyotype are classified in an intermediate-risk group with an heterogeneous clinical outcome (Grimwade et al. 2001). A comparison of patients with normal cytogenetics versus patients with trisomy 8 as the only cytogenetics abnormality (Virtaneva et al. 2001) showed that the two groups of patients were genetically similar populations. Gene dosage effects were seen however not all of the 29 genes most significantly up-regulated in trisomy 8 samples were mapped to chromosome 8. The most significant differences in expression level between the two groups involved apoptosis genes. A further study (Radmacher et al. 2006) showed that patients with a normal karyotype could be divided into two prognostically relevant subgroups and developed a classification rule to predict outcome for individual patients. A strong association of the outcome classifier with the FLT3 ITD mutation may explain the prognostic

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significance of the signature. These two studies confirmed that gene expression profiling can be applied to outcome prediction in cytogenetically normal AML. A microarray study focusing on patients with acute promyelocytic leukaemia (APL, also classified as AML M3) with the t(15;17) translocation and patients with AML FAB group M1 (AML without maturation characterized by morphologically and phenotypically immature AML blasts and no recurrent chromosomal abnormalities) (Morikawa et al. 2003), and using a multivariate sigma-classifier algorithm, identified 33 genes that could distinguish FAB-M3 with t(15;17) from the AML M1 sub-group and a further 24 that classify FAB-M1. Unsurprisingly, this showed that two distinct morphologically-defined sub groups (FAB-M1 and FAB-M3) have distinct gene expression signatures. Over-expression of one of these genes, myeloperoxidase, in APL is consistent with the conventional cytochemical staining pattern. Haferlach et al. (2005c) compared patients with variants of APL: AML-M3 or M3v. In contrast to M3, (abnormal pro-myelocytes with heavy granulation and bundles of Auer rods), M3v has a non- or hypogranular cytoplasm and a bi-lobed nucleus. An unsupervised analysis separated these APL samples could be separated from other AMLs with defined cytogenetic abnormalities and those with a normal karyotype. In this well designed study, supervised pair-wise comparison showed discrimination between M3 and M3v, based on gene signatures, with a median classification accuracy of 90%. This discrimination was also identified and expanded in a comparative study of parameters indicative of T-cell lymphoid specification between M3 and M3v (Chapiro et al. 2006). This study suggests a different cellular origin for the two types of disease and subsequent transformation of progenitor populations. Two studies on larger relatively unselected patient populations were reported in 2004. The first determined expression levels in samples from 116 adults with AML (including 45 with a normal karyotype). They found that 6283 genes had variable expression across most samples and used this gene set to perform an unsupervised two-way, hierarchical cluster analysis (Bullinger et al. 2004). In common with previous studies, they reported that patients with t(15;17) had a highly specific pattern of expression. Even within this diverse sample population, gene signatures could be obtained for patients with t(8;21) or inv(16). Those with normal cytogenetics were further sub-divided into two distinct groups, although each group was similar with respect to sex, age and white-cell count, it was noted that FAB M1 or M2 was significantly more common in group I than in group II, whereas FAB subtype M4 or M5 was more common in group II. An unequal distribution of samples with mutations of the FLT3 gene was also observed with statistically more mutations observed in group I. The authors took the opportunity to combine unsupervised and supervised analysis methods by using a training data set to identify a gene list of 133 genes which was then used on a separate test data set to predict clinical outcome. The subgroup of samples predicted to have a poor outcome correlated with significantly shorter survival than those samples predicted to have a good outcome. However, it should be noted that when a similar analysis using prediction analysis of microarrays (PAM) (Tibshirani et al. 2002) was used for samples with normal cytogenetics only, in contrast to the whole population, no difference in outcome could be determined. The authors suggest that this was due to either a relatively small sample or an inherently poorer performance of patients with a normal cytogenetic. Nevertheless, this study

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showed that potential for gene expression profiling at diagnosis to obtain a risk classification for individual patients. In a second study on 285 AML patients (Valk et al. 2004), fully characterised for the presence of mutations within the FLT3 ITD or TKD region, NRAS, KRAS or CEBPα and the over-expression of EVI1, data were analysed by SAM (Tusher, Tibshirani, & Chu 2001) and PAM (Tibshirani et al. 2002) in addition to using the Pearson correlation algorithms within the OmniViz software package. The AML samples were subjected to un-supervised ordering and the authors reported that optimal clustering was obtained using 2856 probe sets representing 2008 annotated genes and 146 expressed-sequence tags (ESTs). This analysis produced sixteen distinct groups of patients on the basis of strong similarities in geneexpression profiles. Distinct clusters of t(8;21), inv(16), and t(15;17) were readily identified with 1692 probe sets which emphasised the strong effect of these distinctive and recurrent translocations on gene expression. Separate clusters (using 2856 probe sets) were identified containing samples with monosomy 7, mutations of FLT3, or over-expression of EVI1. In addition clusters were defined for 11q23 abnormalities or CEPBα mutations. Patients with t(15;17) also clustered into one main group although two sub-groups could be identified separating patients with high or low white blood cell count which also correlated with the presence of FLT3 mutations. Several other clusters had distinct gene expression profiles but were not associated with any defined morphological, cytogenetic or mutation status. In addition overall survival data was analysed with the best survival rates being determined for those clusters containing the recurrent translocations (t(8;21), t(15;17) and inv(16)). This is perhaps not too surprising as these cytogenetic abnormalities have previously been associated with a favourable prognosis (Grimwade et al. 1998). Interestingly, the molecular signatures associated with FLT3 ITD were not distinctive; and the authors suggested that this type of mutation which occurred across the clusters reflect the heterogeneity of AML. Serial analysis of gene expression (SAGE) was reported on a small series of 22 AML patients with the most common translocations, (t(8;21), t(15;17), t(9;11), and inv(16)). Around 1100 transcripts were identified which showed that each translocation had a unique expression profile for different functional categories highlighting specific pathways for targeted therapy (Lee et al. 2006). A more recent study also confirmed that gene expression profiling could identify signatures for novel prognostic and therapeutically relevant groups of AML with a potential impact on therapy (Wilson et al. 2006). Unsupervised clustering algorithms partitioned the AML patients into six distinct and stable groups based on strong similarities in among the 9463 gene expression levels. This study also found significant outcome differences between the clusters for levels of resistant disease and complete remission rates after induction therapy. In order to expand the molecular characterization of different acute leukaemia‘s, 90 adult patients with fully characterised acute lymphoblastic leukaemia (ALL) or AML were analysed (Kohlmann et al. 2003) using oligonucleotide microarrays (Affymetrix U95Av2/U133A). Only a small set of differentially expressed genes was needed to accurately discriminate eight clinically relevant acute leukaemia subgroups including those with t(8;21), t(15;17), t(11q23)/MLL, or inv(16) as well as precursor B-ALL with t(9;22), t(8;14), or t(11q23)/MLL and precursor T-ALL. This approach was expanded in a study (Haferlach et

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al. 2005b) of 892 patients mainly with AML or ALL but which also included CLL, CML and some non-leukaemia samples. The authors used a combination of un-supervised and supervised statistical methods to identify gene lists that reproduced 12 predefined cytogenetic or morphological leukaemia classification groupings, with 95.1% accuracy, distinct from each other and the non-leukaemia samples. Within AML, specificities of 100% were obtained for t(15;17), t(8;21) and inv(16); 97.7% for patients with 11q23 abnormalities and those with complex cytogenetic abnormalities; and 93.7% for the larger and diverse sub-group of ―normal cytogenetics and other abnormalities‖. The most common genetic abnormality in AML is a mutation within the FLT3 gene. Two types of FLT3 mutations are seen: internal tandem duplications (ITD) in the transmembrane region occur in around 15 – 25% of AML samples; and point mutations in the tyrosine kinase domain (TKD) occur in ~5-10% of cases (Kottaridis et al. 2001;Mills et al. 2005;Moreno et al. 2003;Steudel et al. 2003). FLT3 ITD mutations may have an influence on prognosis (Kottaridis et al. 2001). Other mutations that frequently occur are partial tandem duplications (PTD) within the MLL gene (~5%) (Steudel et al. 2003), point mutations of the CEPBα gene in ~5-10% of patients with normal cytogenetics (Frohling et al. 2004;Snaddon et al. 2003) and the more recently identified point mutations of the NPM gene again in 25-53% of patients with normal cytogenetics (Boissel et al. 2005;Cazzaniga et al. 2005;Schnittger et al. 2005). RAS mutations seen in around 4 – 10% of AML patients are clustered into specific cytogenetic sub-groups and do not influence prognosis (Bowen et al. 2005). These mutations that occur in AML are usually observed across FAB, cytogenetic or WHO sub-groups. Indeed it is probable the heterogeneity of AML masks the clear identification of a FLT3 specific gene expression profile in the global population studies (Bullinger et al. 2004;Valk et al. 2004). However, if the heterogeneity is removed, such as in the recent study (Gale et al. 2005) which focused only on patients with APL, AML M3 containing the t(15;17) translocation, then those patients with FLT3 mutations, whether ITD or TKD, clearly clustered differently from patients with wild type FLT3. Interestingly, the TKD mutations formed a separate branch of the mutant cluster, showing an expression profile intermediate between ITD mutants and wild-type FLT3 samples. This study reinforces the scenario, that FLT3 is a secondary, but important mutation, whose genetic effects may be ―over-shadowed‖ when examined in a heterogeneous AML population. The larger studies showed that patients could be clustered into several groupings mainly based on distinct cytogenetic abnormalities, however gene signatures for patients with FLT3, and in particular MLL and RAS, mutations were more difficult to identify (Bullinger et al. 2004;Schoch et al. 2002;Valk et al. 2004). In a study of patients with normal cytogenetics, a 10-gene signature was identified allowed the correct classification of FLT3-ITD, FLT3-TKD, NRAS-PM, MLL-PTD, and wild-type samples with an accuracy of 83.7%. Their data showed that FLT3-ITD and FLT3-TKD mutations underlie a clinical phenotype distinct from wt specimens (Neben et al. 2005). The MLL gene is also involved in a wide range of chromosomal translocations involving chromosome 11q23. Often MLL PTD mutations co-occur with FLT3 mutations making the identification of specific gene expression profiles more difficult (Olesen et al. 2005). A study of paediatric AML samples failed to show a distinct expression signature for AML with a

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MLL-PTD despite clearly identifying the other major cytogenetic types including a subgroup containing patients with rearrangements of the MLL gene (Ross et al. 2004). About 1/3rd of AMLs have a mutation of the NPM gene within the putative nucleolar localization signal domain. These mutations are frequently associated with normal cytogenetics. Seventy-eight de novo AML samples, characterized for the sub-cellular localization and mutation status of NPM were expression profiled and subjected to unsupervised clustering which clearly distinguished NPM mutants from NPM wild-type AML, with the expression profile dominated by a stem cell molecular signature (Alcalay et al. 2005). NPM mutations dominated in one cluster (78%) and were enhanced in another cluster (61%) in the study by Wilson et al. (2006). Most of the studies are diverse in terms of sample size, patient characteristics, cytogenetic background or mutational status and each has produced a different molecular classification although certain features are common to each, especially the dominance of the recurrent cytogenetic translocations to form specific subgroups. The potential of gene expression arrays for diagnosis and prognostication was suggested to be supportive of the information obtained by cytogenetic and molecular studies (Dunphy 2006). The larger studies identified gene signatures or lists that could cluster groups into those containing the recurrent translocations but also into several other groupings with a broad, but not exact, relationship to either morphological (i.e. FAB groupings) or mutational status (Bullinger et al. 2004;Valk et al. 2004;Wilson et al. 2006) [Figure 1]. However it is often difficult to directly compare gene lists obtained from one study with those from a different study as these studies tend to use different platforms, with samples shipped and stored for differing lengths of time and extracted by different methods. However, recently, the gene expression signatures for clustering AML patients with normal karyotypes, obtained using cDNA arrays (Bullinger et al. 2004), into two relevant subgroups was successfully applied to a similar, but independent, group of patient profiles generated with oligonucleotide arrays (Radmacher et al. 2006). This cross-platform analysis using independent patient cohorts confirmed the prognostic significance of the signature. A small study of 65 diagnostic bone marrow specimens of adult patients with AML, ALL, CML or CLL identified a group of leukaemia-specific genes giving expression profiles distinctly different from normal bone marrow samples. In addition, gene subsets were identified that distinguish between the AML, ALL, CML and CLL patient groups. The results indicated that expression profiling may provide a novel classification of leukaemia patients allowing for more effective treatment (Song et al. 2006). This global approach to leukaemia classification was further developed in a large multi-centre comparison assessing microarray diagnosis with standard diagnostic techniques (Haferlach et al. 2005b). A major step in molecular diagnostic classification was the launch of an international multi-centre clinical research program (MILE: Microarray Innovations in LEukaemia study). The MILE study assesses the application of a microarray test for its potential use in the diagnosis and subclassification of haematologic malignancies (Haferlach et al. 2005a). The MILE research program was launched in 11 centres: 7 European centres in association with the European Leukaemia Network (ELN) (http://www.leukemia-net.org/index.htm), 3 centres from the USA, and one in Singapore. This study will include 4,400 patients (with all leukaemia types included) and will compare the clinical accuracy of gene expression profiles for 16 acute and

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chronic leukaemia subclasses with current gold-standard routine diagnostic work-up. The advantage of this study is that each participating centre will use an identical microarray protocol, identical reagents, laboratory equipment, and statistical interpretation. This type of standardisation was recommended by the European Standardisation Working Group for the implementation and analysis of gene expression data (Staal et al. 2006).

Haferlach

Valk

329

476 71

7

9

10

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88

Rachmacher Figure 1. Comparison of genes differentiating between major subgroups in AML between studies. Comparison of the gene lists from three large gene expression studies of AML. (Red: genes identified from the supplementary material for the 6 AML groupings from Haferlach et al. (2005b). Green: genes from the similar supplementary data used for producing 16 clusters from Valk et al. (2004). Blue: 106 genes represented by 149 cDNAs that were used for clustering AML with normal karyotypes from Bullinger et al. (2004) and validated by Radmacher et al. (2006).) The genes were translated to only those included on the Affymentrix U133A gene chip. Each study showed that patients could be grouped in several different clusters however the resulting gene lists show very little overlap.

CONCLUSION The use of microarrays for disease classification has probably been examined more extensively in AML than other types of leukaemia and cancers. The inherent heterogeneity of the disease means that clear clinical benefits have yet to become available, but studies have confirmed that cytogenetic sub-groups have a strong and over-riding influence on gene

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expression profiles. These studies have also shown that attempts to force current morphological classifications onto a molecular classification may not be successful. The future for gene expression profiling assays, using a defined gene list, is to complement current gold standard diagnostic methods (immunophenotyping, PCR, cytogenetics and FISH). However, if this to be accepted as part of the routine diagnostic workup, co-ordinated, standardised, international and multi-centre protocols need to be developed and tested. Studies, such as the MILE study, suggest that microarray developments are rapidly progressing towards improvements in disease diagnosis and monitoring, and the determination of individualised outcome prediction and personalised treatment regimens.

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REFERENCES Alcalay, M., Tiacci, E., Bergomas, R., Bigerna, B., Venturini, E., Minardi, S. P., Meani, N., Diverio, D., Bernard, L., Tizzoni, L., Volorio, S., Luzi, L., Colombo, E., Lo, C. F., Mecucci, C., Falini, B., & Pelicci, P. G. Acute myeloid leukemia bearing cytoplasmic nucleophosmin (NPMc+AML) shows a distinct gene expression profile characterized by up-regulation of genes involved in stem cell maintenance. Blood 106, 899-902. 14-42005. Bennett, J. M., Catovsky, D., Daniel, M. T., Flandrin, G., Galton, D. A., Gralnick, H. R., & Sultan, C. Proposals for the classification of the acute leukaemias. French-AmericanBritish (FAB) co-operative group. Br.J.Haematol. 33[4], 451-458. 1976. Boissel, N., Renneville, A., Biggio, V., Philippe, N., Thomas, X., Cayuela, J. M., Terre, C., Tigaud, I., Castaigne, S., Raffoux, E., de, B. S., Fenaux, P., Dombret, H., & Preudomme, C. Prevalence, clinical profile and prognosis of NPM mutations in AML with normal karyotype. Blood. 26-7-2005. Bowen, D. T., Frew, M. E., Hills, R., Gale, R. E., Wheatley, K., Groves, M. J., Langabeer, S. E., Kottaridis, P. D., Moorman, A. V., Burnett, A. K., & Linch, D. C. RAS mutation in acute myeloid leukemia is associated with distinct cytogenetic subgroups but does not influence outcome in patients < 60 yrs. Blood. 16-6-2005. Bullinger, L., Dohner, K., Bair, E., Frohling, S., Schlenk, R. F., Tibshirani, R., Dohner, H., & Pollack, J. R. Use of gene-expression profiling to identify prognostic subclasses in adult acute myeloid leukemia. N.Engl.J.Med. 350[16], 1605-1616. 15-4-2004. Cazzaniga, G., Dell'Oro, M. G., Mecucci, C., Giarin, E., Masetti, R., Rossi, V., Locatelli, F., Martelli, M. F., Basso, G., Pession, A., Biondi, A., & Falini, B. Nucleophosmin mutations in childhood acute myelogenous leukemia with normal karyotype. Blood 106[4], 1419-1422. 15-8-2005. Chapiro, E., Delabesse, E., Asnafi, V., Millien, C., Davi, F., Nugent, E., Beldjord, K., Haferlach, T., Grimwade, D., & Macintyre, E. A. Expression of T-lineage affiliated transcripts and TCR rearrangements in acute promyelocytic leukemia: implications for the cellular target of the t(15;17). Blood. 20-7-2006. Debernardi, S., Lillington, D. M., Chaplin, T., Tomlinson, S., Amess, J., Rohatiner, A., Lister, T. A., & Young, B. D. Genome-wide analysis of acute myeloid leukemia with normal karyotype reveals a unique pattern of homeobox gene expression distinct from

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those with translocation-mediated fusion events. Genes Chromosomes.Cancer 37[2], 149-158. 2003. Dunphy, C. H. Gene expression profiling data in lymphoma and leukemia: review of the literature and extrapolation of pertinent clinical applications. Arch.Pathol.Lab Med. 130[4], 483-520. 2006. Frohling, S., Schlenk, R. F., Stolze, I., Bihlmayr, J., Benner, A., Kreitmeier, S., Tobis, K., Dohner, H., & Dohner, K. CEBPA mutations in younger adults with acute myeloid leukemia and normal cytogenetics: prognostic relevance and analysis of cooperating mutations. J.Clin.Oncol. 22[4], 624-633. 15-2-2004. Gale, R. E., Hills, R., Pizzey, A. R., Kottaridis, P. D., Swirsky, D., Gilkes, A. F., Nugent, E., Mills, K. I., Wheatley, K., Solomon, E., Burnett, A. K., Linch, D. C., & Grimwade, D. The relationship between FLT3 mutation status, biological characteristics and response to targeted therapy in acute promyelocytic leukemia. Blood Online August 16. 16-8-2005. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., & Lander, E. S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286[5439], 531-537. 15-10-1999. Grimwade, D., Walker, H., Harrison, G., Oliver, F., Chatters, S., Harrison, C. J., Wheatley, K., Burnett, A. K., & Goldstone, A. H. The predictive value of hierarchical cytogenetic classification in older adults with acute myeloid leukemia (AML): analysis of 1065 patients entered into the United Kingdom Medical Research Council AML11 trial. Blood 98[5], 1312-1320. 1-9-2001. Grimwade, D., Walker, H., Oliver, F., Wheatley, K., Harrison, C., Harrison, G., Rees, J., Hann, I., Stevens, R., Burnett, A., & Goldstone, A. The importance of diagnostic cytogenetics on outcome in AML: analysis of 1,612 patients entered into the MRC AML 10 trial. The Medical Research Council Adult and Children's Leukaemia Working Parties. Blood 92[7], 2322-2333. 1-10-1998. Gutierrez, N. C., Lopez-Perez, R., Hernandez, J. M., Isidro, I., Gonzalez, B., Delgado, M., Ferminan, E., Garcia, J. L., Vazquez, L., Gonzalez, M., & San Miguel, J. F. Gene expression profile reveals deregulation of genes with relevant functions in the different subclasses of acute myeloid leukemia. Leukemia 3, 402-409. 20-1-2005. Haferlach, T., Kohlmann, A., Basso, G., Bene, M. C., Downing, J. R., Hernandez, J. M., Hofmann, W. K., Kipps, T. J., te Kronni, T., Liu, W. M., Ro, S., Macintyre, E. A., Mills, K. I., Preudhomme, C., Rassenti, L., de Vos, K., Williams, M., Wieczorek, L., & Foa, R. A Multi-Center and Multi-National Program to Assess the Clinical Accuracy of the Molecular Subclassification of Leukemia by Gene Expression Profiling. Blood 106[11], #757. 2005a. Haferlach, T., Kohlmann, A., Schnittger, S., Dugas, M., Hiddemann, W., Kern, W., & Schoch, C. A global approach to the diagnosis of leukemia using gene expression profiling. Blood 106, 1189-1198. 5-5-2005b. Haferlach, T., Kohlmann, A., Schnittger, S., Dugas, M., Hiddemann, W., Kern, W., & Schoch, C. AML M3 and AML M3 variant each have a distinct gene expression signature but also share patterns different from other genetically defined AML subtypes. Genes Chromosomes. Cancer 43[2], 113-127. 2005c.

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Kohlmann, A., Schoch, C., Schnittger, S., Dugas, M., Hiddemann, W., Kern, W., & Haferlach, T. Molecular characterization of acute leukemias by use of microarray technology. Genes Chromosomes. Cancer 37[4], 396-405. 2003. Kottaridis, P. D., Gale, R. E., Frew, M. E., Harrison, G., Langabeer, S. E., Belton, A. A., Walker, H., Wheatley, K., Bowen, D. T., Burnett, A. K., Goldstone, A. H., & Linch, D. C. The presence of a FLT3 internal tandem duplication in patients with acute myeloid leukemia (AML) adds important prognostic information to cytogenetic risk group and response to the first cycle of chemotherapy: analysis of 854 patients from the United Kingdom Medical Research Council AML 10 and 12 trials. Blood 98[6], 1752-1759. 159-2001. Lee, S., Chen, J., Zhou, G., Shi, R. Z., Bouffard, G. G., Kocherginsky, M., Ge, X., Sun, M., Jayathilaka, N., Kim, Y. C., Emmanuel, N., Bohlander, S. K., Minden, M., Kline, J., Ozer, O., Larson, R. A., LeBeau, M. M., Green, E. D., Trent, J., Karrison, T., Liu, P. P., Wang, S. M., & Rowley, J. D. Gene expression profiles in acute myeloid leukemia with common translocations using SAGE. Proc.Natl.Acad.Sci.U.S.A 103[4], 1030-1035. 241-2006. Mills, K. I., Gilkes, A. F., Walsh, V., Sweeney, M., & Gale, R. Rapid and sensitive detection of internal tandem duplication and activating loop mutations of FLT3. Br.J.Haematol. 130[2], 203-208. 2005. Moreno, I., Martin, G., Bolufer, P., Barragan, E., Rueda, E., Roman, J., Fernandez, P., Leon, P., Mena, A., Cervera, J., Torres, A., & Sanz, M. A. Incidence and prognostic value of FLT3 internal tandem duplication and D835 mutations in acute myeloid leukemia. Haematologica 88[1], 19-24. 2003. Morikawa, J., Li, H., Kim, S., Nishi, K., Ueno, S., Suh, E., Dougherty, E., Shmulevich, I., Shiku, H., Zhang, W., & Kobayashi, T. Identification of signature genes by microarray for acute myeloid leukemia without maturation and acute promyelocytic leukemia with t(15;17)(q22;q12)(PML/RARalpha). Int.J.Oncol. 23[3], 617-625. 2003. Neben, K., Schnittger, S., Brors, B., Tews, B., Kokocinski, F., Haferlach, T., Muller, J., Hahn, M., Hiddemann, W., Eils, R., Lichter, P., & Schoch, C. Distinct gene expression patterns associated with FLT3- and NRAS-activating mutations in acute myeloid leukemia with normal karyotype. Oncogene 24[9], 1580-1588. 24-2-2005. Olesen, L. H., Nyvold, C. G., Aggerholm, A., Norgaard, J. M., Guldberg, P., & Hokland, P. Delineation and molecular characterization of acute myeloid leukemia patients with coduplication of FLT3 and MLL. Eur.J.Haematol. 75[3], 185-192. 2005. Oyan, A. M., Bo, T. H., Jonassen, I., Gjertsen, B. T., Bruserud, O., & Kalland, K. H. cDNA microarray analysis of non-selected cases of acute myeloid leukemia demonstrates distinct clustering independent of cytogenetic aberrations and consistent with morphological signs of differentiation. Int J Oncol. 28[5], 1065-1080. 2006. Radmacher, M. D., Marcucci, G., Ruppert, A. S., Mrozek, K., Whitman, S. P., Vardiman, J. W., Paschka, P., Vukosavljevic, T., Baldus, C. D., Kolitz, J. E., Caligiuri, M. A., Larson, R. A., & Bloomfield, C. D. Independent confirmation of a prognostic gene-expression signature in adult acute myeloid leukemia with a normal karyotype: A cancer and leukemia group B study. Blood. 2-5-2006.

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Ross, M. E., Mahfouz, R., Onciu, M., Liu, H. C., Zhou, X., Song, G., Shurtleff, S. A., Pounds, S., Cheng, C., Ma, J., Ribeiro, R. C., Rubnitz, J. E., Girtman, K., Williams, W. K., Raimondi, S. C., Liang, D. C., Shih, L. Y., Pui, C. H., & Downing, J. R. Gene expression profiling of pediatric acute myelogenous leukemia. Blood 104[12], 36793687. 1-12-2004. Schnittger, S., Schoch, C., Kern, W., Mecucci, C., Tschulik, C., Martelli, M. F., Haferlach, T., Hiddemann, W., & Falini, B. Nucleophosmin gene mutations are predictors of favourable prognosis in acute myelogenous leukemia with a normal kayotype. Blood. 28-2005. Schoch, C., Kohlmann, A., Schnittger, S., Brors, B., Dugas, M., Mergenthaler, S., Kern, W., Hiddemann, W., Eils, R., & Haferlach, T. Acute myeloid leukemias with reciprocal rearrangements can be distinguished by specific gene expression profiles. Proc.Natl.Acad.Sci.U.S.A 99[15], 10008-10013. 23-7-2002. Snaddon, J., Smith, M. L., Neat, M., Cambal-Parrales, M., Dixon-McIver, A., Arch, R., Amess, J. A., Rohatiner, A. Z., Lister, T. A., & Fitzgibbon, J. Mutations of CEBPA in acute myeloid leukemia FAB types M1 and M2. Genes Chromosomes. Cancer 37[1], 7278. 2003. Song, J. H., Kim, H. J., Lee, C. H., Kim, S. J., Hwang, S. Y., & Kim, T. S. Identification of gene expression signatures for molecular classification in human leukemia cells. Int J Oncol. 29[1], 57-64. 2006. Staal, F. J., Cario, G., Cazzaniga, G., Haferlach, T., Heuser, M., Hofmann, W. K., Mills, K., Schrappe, M., Stanulla, M., Wingen, L. U., van Dongen, J. J., & Schlegelberger, B. Consensus guidelines for microarray gene expression analyses in leukemia from three European leukemia networks. Leukemia. 8-6-2006. Steudel, C., Wermke, M., Schaich, M., Schakel, U., Illmer, T., Ehninger, G., & Thiede, C. Comparative analysis of MLL partial tandem duplication and FLT3 internal tandem duplication mutations in 956 adult patients with acute myeloid leukemia. Genes Chromosomes. Cancer 37[3], 237-251. 2003. Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G. Diagnosis of multiple cancer types by shrunken centroids of gene expression. Proc.Natl.Acad.Sci.U.S.A 99[10], 6567-6572. 145-2002. Tusher, V. G., Tibshirani, R., & Chu, G. Significance analysis of microarrays applied to the ionizing radiation response. Proc.Natl.Acad.Sci.U.S.A 98[9], 5116-5121. 24-4-2001. Valk, P. J., Verhaak, R. G., Beijen, M. A., Erpelinck, C. A., Barjesteh van Waalwijk van Doorn-Khosrovani, Boer, J. M., Beverloo, H. B., Moorhouse, M. J., van der Spek, P. J., Lowenberg, B., & Delwel, R. Prognostically useful gene-expression profiles in acute myeloid leukemia. N.Engl.J.Med. 350[16], 1617-1628. 15-4-2004. Vardiman, J. W., Harris, N. L., & Brunning, R. D. The World Health Organization (WHO) classification of the myeloid neoplasms. Blood 100[7], 2292-2302. 1-10-2002. Vey, N., Mozziconacci, M. J., Groulet-Martinec, A., Debono, S., Finetti, P., Carbuccia, N., Beillard, E., Devilard, E., Arnoulet, C., Coso, D., Sainty, D., Xerri, L., Stoppa, A. M., Lafage-Pochitaloff, M., Nguyen, C., Houlgatte, R., Blaise, D., Maraninchi, D., Birg, F., Birnbaum, D., & Bertucci, F. Identification of new classes among acute myelogenous

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leukaemias with normal karyotype using gene expression profiling. Oncogene 23, 93819391. 15-11-2004. Virtaneva, K., Wright, F. A., Tanner, S. M., Yuan, B., Lemon, W. J., Caligiuri, M. A., Bloomfield, C. D., de la, C. A., & Krahe, R. Expression profiling reveals fundamental biological differences in acute myeloid leukemia with isolated trisomy 8 and normal cytogenetics. Proc.Natl.Acad.Sci.U.S.A 98[3], 1124-1129. 30-1-2001. Wilson, C. S., Davidson, G. S., Martin, S. B., Andries, E., Potter, J., Harvey, R., Ar, K., Xu, Y., Kopecky, K. J., Ankerst, D. P., Gundacker, H., Slovak, M. L., Mosquera-Caro, M., Chen, I. M., Stirewalt, D. L., Murphy, M., Schultz, F. A., Kang, H., Wang, X., Radich, J. P., Appelbaum, F. R., Atlas, S. R., Godwin, J., & Willman, C. L. Gene expression profiling of adult acute myeloid leukemia identifies novel biologic clusters for risk classification and outcome prediction. Blood. 4-4-2006.

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In: Viral Gene Expression Regulation Editor: Eli B. Galos

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Chapter 10

REGULATION OF BACULOVIRUS-MEDIATED GENE EXPRESSION Wen-Hsin Lo and Yu-Chen Hu National Tsing Hua University, Hsinchu, Taiwan.

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ABSTRACT Baculoviruses are a diverse group of insect viruses, among which Autographa californica multiple nucleopolyhedrovirus (AcMNPV) is the best characterized. AcMNPV has been widely utilized for recombinant protein production in insect cells, and has captured growing interest as a vector for gene delivery into mammalian cells. This chapter primarily reviews the regulation of AcMNPV gene expression in insect cells and approaches to modulating transgene expression in both insect and mammalian cells. Recent progress and efforts directed towards enhancing the expression levels and extending the expression duration are particularly emphasized.

1. INTRODUCTION Baculoviruses are a diverse group of DNA viruses capable of infecting more than 600 insect species, and several baculoviruses have been registered for use as biological pesticides. One notably successful example is A. gemmatalis nucleopolyhedrovirus (AgMNPV), which is used to control the velvet bean caterpillar in soybeans [1]. Among the numerous baculoviruses, the best characterized and most extensively employed is Autographa californica multiple nucleopolyhedrovirus (AcMNPV). AcMNPV contains a circular doublestranded DNA genome that is condensed with a protamine-like protein into the core and packed into the nucleocapsids (typically 30–60 nm in diameter and 250–300 nm in length). Correspondence concerning this article should be addressed to: Yu-Chen Hu, Phone: (886)3-571-8245; FAX: (886)3-571-5408; Email: [email protected].

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AcMNPV infection occurs in a bi-phasic fashion. In the first phase, the progeny virus buds in the form of a budded virion (BV) from the infected cells to spread the infection within the insect or among the cultured insect cells. In the second phase the nucleocapsids are retained in the nuclei of infected cells and become occluded in a matrix mainly composed of polyhedrin and p10 proteins, forming the polyhedral occlusion-derived virus (ODV). The BV is responsible for transmission between infected cells whereas the ODV is responsible for disease transmission to other susceptible insects. To occlude the nucleocapsids, the virus-encoded polyhedrin and p10 proteins are abundantly synthesized, indicating the strong transcriptional activities of these promoters. However, polyhedrin and p10 proteins are nonessential for virus replication [2], thus the polyhedrin (polh) and/or p10 gene can be replaced with the heterologous gene to construct the budded recombinant baculovirus. Infection of insect cells with the budded form of recombinant baculovirus results in a high level of expression of the heterologous gene, thereby leading to the development of a baculovirus/insect cell expression system [3]. Currently, Sf-9 derived from the ovarian tissue of Spodoptera frugiperda and BTI-TN-5B1-4 (trade name High-FiveTM, Invitrogen) derived from Trichoplusia ni are the most popular host cells. Such a baculovirus/insect cell expression system was first exploited for the expression of human interferon- (IFN- ) in 1983 [3] and has been utilized for the production of numerous recombinant proteins because: (1) the foreign gene expression level is generally high thanks to the strong promoter activities of polh and p10, and (2) insect cells are capable of various post-translational modifications essential for biologically active proteins (for review see [4,5]). To date, a human cervical cancer vaccine based on papilloma virus-like particles produced from the baculovirus/insect cell expression system, CervarixTM (GlaxoSmithKline), has been approved in more than 80 countries. A number of other baculovirus-produced products, such as a trivalent influenza vaccine based on recombinant hemagglutinin (FluBlØkTM, Protein Sciences), are already in various phases of clinical trials. Aside from insect cells, baculovirus (AcMNPV) is able to enter mammalian cells and mediate transgene expression provided that the transgene is driven by a promoter that is active in mammalian cells [6,7]. Baculovirus is capable of efficiently transducing a wide variety of dividing and non-dividing cells from mammals (e.g. human, porcine, bovine, rodent, rabbit) (for review see [8-11]), fish [12] and avian species [13]. Baculovirus is also able to transduce highly primitive cells such as mesenchymal stem cells [14], amniotic fluidderived stem cells [15] and embryonic stem cells [16]. These discoveries have led to the attempts to develop baculovirus vectors carrying mammalian expression cassettes for in vitro and in vivo gene therapy studies [17-19], development of cell-based assays [20], surface display of eucaryotic proteins [21], delivery of vaccine immunogens [22-24], cancer therapy [25], production of virus vectors [26,27] and tissue engineering [28,29].

2. REGULATION OF BACULOVIRAL GENES IN INSECT CELLS AND IMPROVEMENT OF FOREIGN GENE EXPRESSION The most well-characterized baculovirus AcMNPV has a genome of 133.9 kb, which encodes 154 open reading frames (ORFs) with protein coding potential [30]. After

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infection, baculovirus gene transcription takes place in three phases (early, late and very late) in a temporarily regulated cascade [31]. Early gene expression by definition precedes DNA replication and peaks between 6 and 12 h post-infection (hpi) [32]. Late and very late genes are transcribed by the virus-encoded RNA polymerase during or after DNA replication [33]. Table 1 summarizes some of the well-characterized genes. Table 1. A partial list of AcMNPV genes (adapted from [30,32,35,36]) Phase Early

Gene ie-1

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ie-2 pe38

Late

Very late

dnapol helicase lef-1 ie-2 lef-4 lef-5 lef-7 lef-8 lef-9 lef-11 p35 p47 p143 bv/odv-c42 fp25K gp41 gp64 odv-ec56 pif-1 pif-2 pif-3 pp31/39K p6.9 p74 vp39 vp91 vlf-1 ie-0 polh p10

Proposed functions Early transcription factor, major viral transactivator, hr-binding protein/enhancer Early transcription coactivator, cell-cycle control Transactivates p143 gene promoter, augments apoptosis in the presence of IE-1 DNA polymerase Helicase DNA primase Primase-binding protein RNA polymerase subunit Transcription initiation factor Single-stranded DNA binding protein RNA polymerase subunit RNA polymerase subunit Auxiliary DNA replication protein Apoptosis suppressor/ Caspase inhibitor RNA polymerase subunit ATPase, helicase Nucleocapsid protein Intracellular trafficking of structural proteins O-linked glycosylated ODV protein Envelope glycoprotein essential for virus entry Structural protein Mediate binding in midgut cells Mediate binding in midgut cells Essential for oral infection Inhibits RNA polymerase in vitro, increases levels of most viral transcripts DNA condensation Mediate binding in midgut cells Major nucleocapsid protein Capsid-associated protein Stimulates hyperexpression of very late genes, genome packaging Transactivator Major occlusion body protein Synthesis of occlusion body

The early genes can be subdivided into immediate-early and delayed-early genes depending on whether they require de novo viral protein synthesis [31]. The immediate-early Viral Gene Expression Regulation, Nova Science Publishers, Incorporated, 2010. ProQuest Ebook Central,

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genes ie-1 and ie-2 are highly expressed in transient assays in the absence of other viral proteins or enhancer elements, indicating that they only require host transcription factors. The expression product IE-1 is the major viral transactivator and is essential for downstream viral replication. IE-2 also transactivates early gene expression but is not essential for viral replication. Delayed-early genes include lef-1 (late expression factor 1), lef-2, dnapol and helicase which are responsible for viral DNA replication, and their expression begins at 6-8 hpi and continues exponentially until 24 hpi [34]. Other delayed-early genes such as lef-4, -8, -9 and p47 are required for viral gene transcription [31]. Although the putative functions of about half of the late genes remain poorly characterized [31], the products of some late genes are known to function for packaging and assembly (e.g. p.6.9, vp39, vlf-1, vp91, gp64, 38K), cell cycle arrest (e.g. odv-ec27) and oral infectivity (e.g. pif-1, pif-2 and p74). For instance, gp64 is the envelope glycoprotein essential for virus entry, vp39 is the major nucleocapsid protein, and 38K is also for nucleocapsid assembly [35]. The very late genes include polyhedrin (polh) and p10 whose products are responsible for the synthesis of occlusion bodies. The polh transcripts can account for up to 25% of the mRNA in the infected cells [36], but the hyperexpression of very late genes requires a late gene product, very late factor-1 (VLF-1). Although baculovirus vectors harboring foreign gene(s) under the transcriptional control of strong very late polh (or p10) promoter generally results in high expression levels in infected insect cells, cells are lysed at the very late stage of virus infection, during which the integrity of the cells is deteriorating. Therefore, the recombinant protein quality may be compromised due to incomplete post-translational modifications and extensive protein degradations [37,38]. In this regard, choice of early promoters or cellular promoters that enable earlier expression might potentially produce recombinant proteins of better posttranslational modifications at lower costs. However, all other baculoviral and cellular promoters are relatively weak compared to the very late promoters. As such, the improvement of recombinant protein expression level and quality may require further vector engineering, which can be attempted via the following approaches.

2.1. Insertion of Transcriptional Enhancers A common feature of baculovirus genome is the presence of homologous regions (hrs) that are composed of direct repeats and long imperfect palindromes [34]. AcMNPV contains 9 hrs (hr1, hr1a, hr2, hr2a, hr3, hr4a, hr4b, hr4c and hr5) which are interspersed throughout the genome [39] and function as enhancers of gene expression as well as origins of replication [34]. These hrs have been shown to upregulate several early baculoviral promoters including those of ie-2, 39K, p143 and p35 [40,41]. Promoter enhancement by hrs occurs in a position and orientation manner [42] and is further augmented by IE-1 that binds to the hrs sequences [41]. The enhancer function of hrs has been exploited by inserting an additional copy of hr1 into the AcMNPV genome, which elevates the transcription of foreign genes driven by the homologous polh promoter and the heterologous Drosophila heat shock protein (hsp70) promoter in insect cells [39]. The foreign gene expression levels are

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enhanced 40- to 90-fold depending on the promoter used. The additional hr1 also helps maintain the genetic stability of the bacmid-derived baculovirus [43]. Besides the AcMNPV hrs, the hr3 derived from another baculovirus, Bombyx mori nucleopolyhedrovirus (BmNPV), has been placed downstream of the reporter gene driven by either homologous BmNPV ie-1 promoter or heterologous AcMNPV ie-1 promoter [44]. This arrangement dramatically stimulates the transcription level, indicating the super-enhancer function of hr3. In the baculovirus-infected larvae, this promoter-enhancer combination gives rise to a recombinant protein expression level that is comparable to that driven by the polh promoter [44]. Considering the finding that cis-linked hr3 of BmNPV together with transactivator IE-1 can potently increase the foreign gene expression driven by the actin promoter in stably transformed insect cells [44], this combination might be suited for developing transgenic insects and the non-lytic expression system [44].

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2.2. Insertion of Other Regulatory Elements Significant increases in expression levels can be attained by including other regulatory elements to baculovirus. A DNA sequence upstream of the AcMNPV polh gene (pu), containing 3 ORFs (orf4, orf5 and lef2), can strongly activate the reporter gene expression under the control of full or minimal promoters derived from AcMNPV and heterologous sources [45]. These include the minimal CMV (CMVm) promoter from human cytomegalovirus, the full hsp70 promoter from Drosophila, and the minimal p35 promoter. Pu and hr can also function synergistically, giving rise to as much as 18,000-fold promoter activation. Importantly, when a modified CMVm promoter containing pu and/or hr is inserted into the baculovirus genome to drive the luciferase expression, the expression occurs much earlier. Although it expresses somewhat less than does the p10 promoter, the CMVm promoter results in greater luciferase activity [45]. Additionally, pu can function in concert with ie1, ie2 and pe38 genes to activate target promoters [37]. These 3 viral factors can substitute for the entire virus for the synergistic promoter activation mediated by pu and hrs. Moreover, a 21-bp element derived from the 5 -untranslated leader sequence of the lobster tropomyosin cDNA (L21) has been introduced into the baculovirus vector [46]. This 21-bp element contains the Kozak sequence and the A-rich sequence, and hence promotes the expression level of tropomyosin and luciferase 20- and 7-fold, respectively. With the addition of hrs, this 21-bp element may further stimulate recombinant protein production, yet the universal application of this approach entails more investigation. Another element incorporated into the context of baculovirus is a 15-bp element derived from the 5'-end partial sequence of the polh gene [47]. This element consists of the non-coding sequence ATAAAT and the coding sequence ATGCCGAAT and is inserted to the 5'-end of the CTB (choleratoxin B subunit)-INS (insulin) fusion gene [47]. Appending this 15-bp element to the fusion gene in the baculovirus setting enhances the expression levels 4-fold in both insect cells and larvae without impairing the biological activity of the fusion protein, demonstrating its potential as a constitutive element immediately downstream of the polh promoter to enhance the expression levels [47].

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2.3. Use of Transactivators The baculovirus immediate-early gene product, IE-1, transactivates the expression of many downstream genes such as p35, p143, and 39k [48]. Structurally, IE-1 protein is composed of a transcription-regulating N-terminus and a DNA-binding C-terminus. The AcMNPV IE-1 can stimulate the Drosophila hsp70 promoter in Sf-21 and TN368 cells when the promoter is cis-linked to the enhancer [49]. In Sf-21 cells, IE-1 also acts cooperatively with the p35 gene product to activate baculovirus orf21 and orf25 promoters and a heterologous HindIII-I-1 (hhi1) promoter derived from HzNV-1 virus (Heliothis zea Nudivirus-1) [50]. When both IE1 and P35 are present, the activity of the early promoter hhi1 is upregulated over 700-fold in the AcMNPV-infected Sf-21 cells. These data demonstrate that the cooperation of ie1 and p35 genes can upregulate the expression of a specific set of AcMNPV and HzNV-1 early promoters [50]. Besides, IE-2 stimulates the gene expression in the presence of IE1 and cis-linked enhancer elements in Sf-21 cells [49]. The immediateearly gene pe-38 also acts as an early transactivator. The p143 gene promoter is transactivated by PE-38, and this transactivation is augmented by IE-2. Given these findings, overexpression of ie1 and p35 (or other transactivator) genes could potentially elevate the expression levels of foreign genes under the control of an early promoter. This strategy may alleviate the problem of impaired protein processing as a result of the use of very late polh or p10 promoter.

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2.4. Development of Bicistronic Vectors One of the most appealing features of baculovirus vectors is the huge DNA genome, which allows for the accommodation of large inserts or multiple genes. In the case of expressing multi-subunit proteins composed of more than one polypeptide (e.g. immunoglobulin composed of heavy and light chains), it is necessary to express multiple genes at a time. Although co-expression can be attained by adding viruses each expressing the individual gene, the stringent requirement for simultaneous co-infection of the same cell tends to reduce the yield. Co-expression vectors which allow the insertion of genes under the control of different promoters (e.g. polh and p10) into separate multiple cloning sites offer an attractive alternative and have been commercially available (e.g. pFastBac DUAL, Invitrogen). However, in some cases promoter interference may occur and it is difficult to precisely control the expression levels of both genes. Another approach for co-expression is the use of bicistronic baculovirus vectors, in which two different genes are placed under the transcriptional control of one single promoter, but are separated by an internal ribosomal entry site (IRES). The initial attempt employs the IRES element of the encephalomyocarditis virus (EMCV) [51]. However, this element does not promote efficient internal translation of the second cistron in various insect cell lines originating from different species. Recently, the IRES from the 5‘-UTR of the Rhopalosiphum padi virus (RhPV) genome was used. A recombinant baculovirus encoding the red and green fluorescent proteins flanking the RhPV IRES can produce dual fluorescence in infected Sf-21 cells [52]. Quantification of the fluorescent proteins by

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fluorescence spectrophotometry indicates that the translational efficiency of the RhPV IRES is about 3-fold weaker than cap-dependent translation. Nonetheless, this vector can be used for simultaneous expression of enhanced green fluorescent protein (EGFP) and human interferon- (IFN- ). Interestingly, this RhPV IRES has cryptic promoter activity in baculovirus-infected Sf-21 cells and is also functional in mammalian cells, which allows it to act as a shuttle IRES between insect cells and mammalian cells [53]. This bicistronic vector system is also employed to generate stabilized baculovirus vector expressing a heterologous gene and gp64 from a single mRNA [54]. The bicistronic expression cassette contains the gfp gene in the first cistron and gp64 gene in the second cistron, flanking the RhPV IRES. The translation of gp64 is mediated by the IRES while the native gp64 gene is deleted. In this way, a dominant selection pressure is placed on the entire bicistronic mRNA and hence the maintenance of the foreign gene. The bicistronic vector is superior to the control baculovirus vector in that GFP expression remains at much higher levels upon continued virus passage, and the versatility of this stabilized vector is demonstrated by its ability to propagate in a number of cell lines including Sf-21, Sf-9 and High FiveTM cells. Another IRES element that possesses internal translation activity in baculovirus-infected Sf21 cells originates from the 5‘ UTR (473 nucleotides) of the Perina nuda virus (PnV) [55]. Incorporating the first 22 codons of the predicted PnV ORFs enhances the internal translation activity by approximately 18 times, implicating the potential of this IRES for the development of bicistronic baculovirus vectors.

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2.5. Enhanced Protein Expression Using Engineered SUMO Fusions A fusion tag that enhances expression and solubility would greatly facilitate both structure/function studies and therapeutic protein production. Small ubiquitin-related modifier (SUMO) can enhance functional protein production significantly by improving folding, solubility and stability when used as a fusion partner in the Escherichia coli system [56]. In eukaryotic expression systems, however, the SUMO tag could be cleaved by endogenous desumoylase [57]. To overcome this limitation, Liu et al have developed an alternative SUMO-derived tag, which is designated SUMOstar and is not processed by native SUMO proteases [58]. In the baculovirus/insect cell system, the fusion of SUMOstar tag to several proteins, including mouse UBP43, human tryptase beta II, USP4, USP15 and GFP, promotes the expression levels at least 4-fold compared to either the native or histidinetagged proteins. The SUMOstar system may make a significant impact on difficult-to-express proteins.

3. REGULATION OF BACULOVIRAL-MEDIATED GENES IN MAMMALIAN CELLS Baculovirus has emerged as a promising gene delivery vector. In comparison with other common viral gene therapy vectors such as lentivirus, adenovirus and adeno-associated virus (AAV), baculovirus possesses a number of advantages: (1) baculovirus neither replicates nor

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is toxic inside the transduced cells [59] and baculoviral DNA degrades in the cells over time [14,60], hence reducing the possible side effects. (2) Baculovirus is an insect virus in nature, therefore humans do not appear to possess pre-existing antibody and T-cells specifically against baculovirus [23], which circumvents the possible vector neutralization after administration. (3) The large baculovirus genome enables a huge cloning capacity of at least 38 kb [61], thus allowing the inclusion of regulatory elements with the transgene. (4) The construction, propagation and handling of baculovirus can be performed readily in Biosafety Level 1 facilities without the need of specialized equipments [9,62]. Similar to the insect cell system, AcMNPV is the predominantly employed baculovirus for gene delivery into mammalian cells. Therefore the following primarily reviews the factors that regulate the transgene expression mediated by AcMNPV.

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3.1. Promoters and Post-Transcriptional Regulatory Element Since baculovirus genes are either silenced or only weakly expressed in mammalian cells, heterologous promoters that are active in mammalian cells are required to drive the transgene expression, and the promoter strength profoundly dictates the transgene expression level. To date, cytomegalovirus immediate-early (CMV-IE) promoter, Rous sarcoma virus (RSV) promoter, and a hybrid CAG promoter comprised of the CMV-IE enhancer, minimal chicken β-actin promoter and rabbit -globin polyadenylation signal are most widely used (for review, see [59,62]). In general, CAG promoter is stronger than CMV-IE promoter [63]. Conversely, a study that systematically compares the transgene expression driven by SV40, CMV-IE and RSV promoters in CHO, COS-1 and HEK293 cells reveals that CMV-IE and RSV promoters are more active, while SV40 promoter is the weakest in the tested cell lines [64]. Besides these viral promoters, the baculovirus-borne transgene is also driven by other mammalian cellular promoters including U6 [65], Pol III H1 [66], and EF-1 [16,67]. Targeted gene expression mediated by a tissue-specific promoters is desired for gene therapy. It has been shown that a baculovirus vector bearing a reporter gene under control of the hepatocyte-specific α-fetoprotein (AFP) promoter/enhancer results in hepatocyte-specific gene expression in AFP-producing cells, but not in AFP-negative cell lines [68]. Other tissuespecific promoters that have been employed in the baculovirus setting include astrocytespecific GFAP (glial fibrillary acidic protein) [69], neuron-specific platelet-derived growth factor (PDGF) [70] and neuron-specific synapsin-1 (SYN) [70] promoters. However, these cellular promoters are generally transcriptionally weaker than viral promoters. To enhance the GFAP-driven expression, the expression cassette is appended with an upstream CMV enhancer and flanked by AAV inverted terminal repeats (ITRs) [25]. Conversely, the weak PDGF-mediated expression can be augmented by co-expressing a chimeric transactivator consisting of a part of the transcriptional activation domain of NF- B p65 protein fused to the DNA binding domain of yeast GAL4 protein [70]. This approach results in up to a 100-fold increase in reporter gene expression in cultured neurons and 20-fold improvement in the rat brain in vivo. Aside from the promoters derived from mammalian species, insect virus promoters such as baculovirus early-to-late (ETL) promoter [71] and white spot syndrome virus (WSSV) ie1

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promoter [72] have been shown to drive transgene expression in the baculovirus-transduced mammalian cells. Intriguingly, the WSSV ie1 promoter activity in mammalian cells not only is stronger than the baculovirus-dependent ETL promoter, but also is independent of baculovirus gene expression [72]. Woodchuck hepatitis virus post-transcriptional regulatory element (WPRE) is a cisacting RNA element present in the viral 3‘-UTR and improves the gene expression at a posttranscriptional level by modifying RNA polyadenylation, export and/or translation [73]. It has been shown that appending the WPRE element to the 3‘-UTR of the transgene ameliorates baculovirus-mediated gene expression in mammalian cells [74]. In HepG2 cells, WPRE-mediated enhancement is comparable to the enhancing effect of sodium butyrate on baculovirus-mediated gene expression [74]. The WPRE is also used to promote the EGFP expression driven by human EF-1 promoter in human embryonic stem (hES) cell clumps, which greatly enhances the transduction efficiency from 25%-40% to 80% [16]. Such WPRE-containing baculoviral vectors also enable highly efficient transduction and prolonged EGFP expression in hES-derived neurons (personal communication).

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3.2. Effects of Cell Type and Cellular Physiology Baculovirus can efficiently transduce mesenchymal stem cells (MSCs) derived from human [14] and rabbit (unpublished data) bone marrow. Notably, baculovirus transduction hinges on cellular differentiation states as the transduction efficiency ( 21-90%), transgene expression level and duration (7-41 days) vary widely with the differentiation lineages and stages of the adipogenic, osteogenic and chondrogenic progenitors originating from human MSCs [75]. The varied expression arises from neither the discrepancies in virus entry efficiency nor the disparities in nuclear transport efficiency. Instead, the varied expression levels are attributed to the rapidly altering cellular transcription machinery in the course of differentiation progression [67]. Besides mesenchymal cells, myogenic cells such as C2C12, Sol 8 and primary myoblasts can express the transgene for a remarkably prolonged period exceeding 63 days when they differentiate into myotubes [76]. The preferred long-term expression in the myogenic cells echoes the findings that baculovirus-mediated expression of erythropoietin (EPO) persists up to 178 days if baculovirus harboring the EPO cDNA is injected into the quadriceps of DBA/2J mice [77]. These results attest that baculovirusmediated transgene expression is preferably sustained under certain differentiation statuses, but the exact mechanisms contributing to the sustained expression remain to be elucidated. As to rat articular chondrocytes, baculovirus transduction is dependent on the cell cycle [78]. The chondrocytes predominantly in G2/M phase are approximately twice as efficient in the transgene expression as the cycling cells, while the cells in S and G1 phases express the transgene as efficiently as the cycling cells. However, the chondrocyte population rich in quiescent cells results in poorer transgene expression due to less effective nuclear transport of baculoviral DNA and higher degree of methylation [78]. Moreover, we unravel that baculovirus transduction of rat articular chondrocytes triggers immediate yet transient expression of IFN- and IFN- [79], which agrees with the induction of IFN- and IFN- in baculovirus-transduced human and murine cells [18,80,81]. Notably, these antiviral cytokines

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suppress the transgene expression in a dose-dependent manner [79]. The attenuation is observed for transgene expression driven by different promoters and arises from neither internalization nor nuclear import of baculovirus. These interferon responses and the ensuing transgene attenuation may hinder the attempt to prolong the transgene expression by repeated transduction.

3.3. Effects of Histone Deacetylase (HDAC) Inhibitors Histone deacetylase (HDAC) inhibitor induces histone hyperacetylation, thereby remodeling the chromatin structure [82]. One HDAC inhibitor, valproic acid (VPA), has even been exploited for the generation of induced pluripotent stem cells (iPS) [83]. Three HDAC inhibitors, sodium butyrate [84], trichostatin A [84] and VPA [64,85] have been used to enhance the baculovirus-mediated gene expression in mammalian cells, suggesting the importance of the condensation state of the baculovirus genome for transgene expression. Notably, sodium butyrate has been used to elevate the production yield of hepatitis delta virus-like particles [60] and recombinant adeno-associated viral vector [27] in baculovirustransduced cells. It should be noted, however, that high doses of sodium butyrate (e.g. 10 mM) induce cytotoxicity.

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3.4. Upregulation of Baculoviral and Host Cellular Genes by Baculovirus Transduction Although baculovirus transduction of mammalian cells has been regarded safe, recently at least 12 baculoviral genes (e.g. orf149, ie0, ie1, ie2, p35, he65, pe38 and gp64) are found to be expressed in AcMNPV-transduced HeLa and BHK cells [86]. In Vero E6 cells baculovirus-mediated IE1 overexpression strongly activates gp64 and pe38 to levels that gp64 protein expression can be detected by Western blot [87]. The IE1 overexpression also up-regulates ie2, he65, pcna, orf16, orf17 and orf25 while IE2 overexpression only activates two genes pe38 and orf17. Strikingly, overexpression of IE1 and IE2 via plasmid transfection synergistically activates 59 out of the 155 genes placed on the microarray [87]. These data suggest that the baculoviral genes are induced by these immediate-early transactivators, whose transcription relies on host factors. Aside from the endogenous baculoviral genes, IE1 transactivates the CMVm promoter, resulting in the reporter gene expression in Vero cells. This IE1-mediated transactivation of CMVm promoter could be further augmented by baculovirus hrs either in trans or in cis [88]. Therefore, insertion of an additional copy of hr1 in the AcMNPV genome represents an attractive approach to promoting overexpression of foreign proteins in mammalian cells as has been demonstrated in insect cells [39].

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4. SUSTAINED BACULOVIRUS-MEDIATED EXPRESSION IN MAMMALIAN CELLS The baculovirus genome does not replicate in mammalian cells, instead it is degraded over culture time [89] and undergoes dilution upon cell proliferation. As a result, the transgene expression is transient, which may exclude its applications for the treatment of inherited and acquired diseases necessitating stable expression. To address this issue, hybrid baculovirus vectors have been designed to enable persistence of the transgene in the cells in the integrated or episomal form.

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4.1. Integrating Vectors It is found that transduction of CHO cells with a baculovirus expressing neomycin phosphotransferase followed by G418 selection can give rise to CHO cell clones stably expressing GFP over a 5-month period, as a result of random integration of discrete baculovirus fragments (5 to 18 kb in size) into the chromosome [84,90]. Using a similar approach, Martyn et al. have constructed a stable cell line (HL-1) that constitutively expresses hepatitis C virus (HCV) E1/E2 proteins to study HCV replication [91]. A more common strategy utilizes a hybrid baculovirus-AAV vector containing a gene cassette flanked by the AAV inverted terminal repeats (ITRs) and another baculovirus vector expressing the AAV rep gene which is necessary for the integration of ITRs-flanking the AAV genome into the host genome in a site-specific manner [92]. Co-transduction of 293 cells with these two vectors results in specific integration of the ITR-flanked transgene into the AAVS1 site of chromosome 19 and prolongs the transgene expression. A similar baculovirus-AAV hybrid vector incorporating ITR-flanking luciferase gene under a neuronspecific promoter also provides transgene expression in the rat brains for at least 90 days even without the help of rep gene expression [93]. Strikingly, stable gene expression in hES cells can also be mediated by a hybrid baculoviral vector accommodating the AAV rep 78/68 genes and AAV ITR-flanking egfp gene [16]. The hybrid vector yields stable transgene expression during the undifferentiated proliferation of hES cells and after differentiation. Baculovirus transduction does not affect the normal growth, phenotype and pluripotency of hES cells, thus offering an attractive option for genetic manipulation of hES cells [16].

4.2. Episomal Vector In contrast to gene integration, transgene expression can be prolonged by extrachromosomal replicating vectors which should be more resistant to gene silencing or insertional mutagenesis [94]. To exploit the episomal maintenance, a baculoviral vector harboring the Epstein-Barr virus (EBV)-derived origin of replication (oriP) and the gene encoding the nuclear antigen (EBNA1) is developed [95]. Such a design enables the persistence of baculoviral genome in a significant proportion of proliferating HEK293, Vero and Cos-7 cells without any selective pressure [95]. The key to the stable maintenance of the

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viral genome is that EBNA1 interacts with oriP, facilitating the replication of viral DNA and subsequent segregation to daughter cells during cell mitosis. In addition, we have developed a dual binary hybrid baculoviral vector system that exploits FLP/Frt-mediated recombination for circular episome formation and oriP/EBNA1 for the retention of episomes [96]. The first baculovirus expresses the yeast-derived FLP recombinase while the second baculovirus harbors a transgene cassette that encompasses EBV-derived oriP/EBNA1 and is flanked by Frt sites. After co-transduction the expressed FLP cleaves the Frt-flanking transgene cassette off the baculovirus genome and catalyzes the intracellular episome formation via the recombined Frt sites, while oriP/EBNA1 aids in the episomal self-replication. The recombination efficiency is considerably elevated by sodium butyrate, reaching 75% in HEK293 cells, 85% in BHK cells, and 77% in primary chondrocytes. In HEK293 cells this system substantially prolongs the transgene expression to 48 days without selection and >63 days with selection thanks to the maintenance of replicon and transgene transcription. In contrast to the baculovirus system that does not encompass FLP/Frt-mediated recombination as described by Shan et al. [95], the baculovirus genome carrying the oriP/EBNA1 alone is rapidly degraded, thereby easing the concerns about the residual viral DNA in the cells. The underlying mechanism that results in the discrepancy between these two systems is unclear.

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5. FUTURE DEVELOPMENT AND PROSPECTS The baculovirus/insect cell expression system has gained wide and increasing popularity for recombinant protein production since its inception in 1983. The strong polh and p10 promoters in general confer satisfactorily high level production of proteins with appropriate post-translational modification. Therefore, this technology has gradually entered a mature phase, as reflected by the declining numbers of research papers in this field over the last few years. Recent studies, consequently, have attempted to improve protein quality that is impaired owing to cell lysis and deteriorating post-translational modification machinery. The most common approach employs early promoters or other cellular promoters in lieu of the very late promoter, which however suffers from low expression levels. Future studies to identify new enhancers, transactivators or early promoters, as well as efforts to explore appropriate combinations are required to develop baculovirus vectors that can confer satisfactory expression levels and quality. A promising trend in baculovirus research is the exploitation of baculovirus as a vector for in vitro and in vivo gene delivery, vaccine delivery, cancer therapy and tissue regeneration (for review see [36,62,97]. However, systemic administration of baculovirus is hampered due to virus inactivation in vivo by the serum complement proteins [98]. Consequently, strategies to effectively stabilize baculovirus are required in order to expand its in vivo applications. Another hurdle is the transient nature of baculovirus-mediated transgene expression, which excludes its applications to those requiring stable expression. Despite the recent development of different hybrid vectors for prolonged expression, these studies have only demonstrated expression duration for 90–120 days at most. Persistent expression for a longer period is required to prove the feasibility of these vectors. Finally, although baculovirus genome

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degrades, rather than replicates, in mammalian cells, the probability of baculovirus gene integration should be intimately examined. Baculovirus transduction of mammalian cells in vitro and in vivo also triggers antiviral effects and innate immunity. These new findings raise safety concerns regarding the in vivo administration of baculovirus, thus entailing in-depth investigations to elucidate the molecular pathways and evaluate how the host cells are disturbed by baculovirus transduction. Nonetheless, the induction of innate immunity by baculovirus lends itself as an ideal adjuvant that can protect animals from virus infections, thus leading to the rapidly growing number of studies to develop baculovirus as a vaccine delivery/expression vector [22,24,99-103].

ACKNOWLEDGMENT The authors acknowledge the support from the National Tsing Hua University Booster Program (97N2511E1), VTY Joint Research Program, Tsou's Foundation (VGHUST98-P517), National Tsing Hua University-Chang Gung Memorial Hospital Joint Research Program (96N2425E1 and CMRPG361041) and National Science Council (NSC 97-2627-B-007-014, NSC 97-2622-E-007-009-CC3), Taiwan.

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INDEX

4 4G, 252 4-hydroxynonenal, 112

5 5-hydroxytryptophan, 196

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A AAA, 255 AAV, 285, 286, 289 abnormalities, x, 75, 94, 125, 238, 268, 269, 270, 271 absorption, 123, 128, 147 accelerator, 68 acceptor, 56 access, 114 accessibility, 16 accidental, 210 accommodation, 284 accounting, 239 accumulation, 103, 112, 124, 129, 135, 140 accuracy, 269, 271, 272 acetate, 103, 109, 115, 119, 130 acetone, 239 acetylation, 20 Ach, 179 acid, 4, 26, 58, 88, 95, 103, 105, 107, 108, 109, 112, 115, 117, 121, 131, 132, 133, 135, 145, 148, 157, 185, 225, 288 acidic, 10, 76, 286

acquired immunity, 3 acquired immunodeficiency syndrome, 60, 93 actin, 16, 19, 46, 58, 77, 283, 286, 296 activated receptors, 114 activation, 4, 5, 6, 7, 8, 18, 19, 20, 22, 24, 26, 30, 31, 32, 33, 36, 43, 44, 45, 46, 47, 52, 58, 59, 60, 61, 63, 64, 65, 66, 69, 70, 71, 72, 74, 75, 77, 78, 81, 82, 83, 84, 85, 86, 88, 90, 91, 92, 93, 94, 102, 104, 105, 106, 107, 108, 109, 111, 113, 115, 117, 127, 130, 131, 132, 135, 137, 138, 144, 145, 151, 152, 159, 160, 171, 180, 226, 244, 248, 255, 264, 283, 286, 293, 294 activators, 4, 17, 19, 66, 92, 95, 145, 296 acute, viii, x, 24, 32, 37, 38, 52, 60, 62, 65, 74, 93, 104, 110, 117, 152, 158, 267, 269, 270, 272, 274, 275, 276, 277, 278 acute infection, 10 acute kidney injury, 38 acute leukemia, 276 acute myelogenous leukemia, 274, 277 acute myeloid leukemia, 74, 93, 267, 274, 275, 276, 277, 278 acute promyelocytic leukemia, viii, 52, 274, 275, 276 adaptation, 177 Adeno-associated virus, 297 adenocarcinoma, 74 adenosine, 156 adenovirus, 2, 168, 285, 295 adhesion, viii, 4, 31, 74, 75, 76, 88, 95, 96, 97, 102, 116, 118, 127, 129, 138, 249, 252, 253, 254, 256, 259, 260, 263 adipogenic, 287

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300 adipose tissue, 147 administration, 104, 126, 151, 286, 290, 292 ADP, 62 adult, 56, 60, 81, 85, 123, 142, 190, 192, 193, 197, 270, 272, 274, 276, 277, 278 adult T-cell, 60, 81, 85 adult tissues, 56 adults, 106, 157, 269, 275 affect, viii, 97, 104, 113, 114, 120, 146, 234 Africa, 2, 23, 60, 177 age, 3, 10, 22, 23, 110, 115, 148, 156, 185, 186, 190, 269 agent, 60, 62, 104, 112, 118 agents, 29, 60, 66, 87, 117, 138 aggregation, viii, 97, 115, 130 aging, 112, 115, 134, 143, 148, 154, 155, 156, 157 agricultural, 175 aiding, 66 AIDS, x, 2, 3, 10, 11, 13, 23, 24, 26, 38, 39, 45, 51, 60, 74, 81, 82, 86, 87, 88, 93, 94, 95, 96, 143, 150, 159, 160, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 260, 261, 262, 264 airway hyperresponsiveness, 155 AKT, 33, 131, 232 alcohol, 111, 133 alcoholic liver disease, 111 algorithm, 217, 269 ALL, 104, 267, 270, 272 allele, 39 alleles, 13, 40, 226 allergy, 32 alpha, 1, 6, 29, 31, 32, 54, 63, 71, 83, 84, 85, 89, 91, 96, 112, 114, 117, 118, 119, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 152, 154, 155, 156, 159, 161, 189, 196, 197, 245, 246, 247, 253, 254, 255, 256, 257, 263, 264 alpha activity, 130 alpha-fetoprotein, 189 alpha-tocopherol, 124, 125, 126, 127, 128, 129, 130, 132, 133, 135, 136, 137, 138, 139, 140, 141, 142, 154, 155, 156, 159, 161 ALT, 150 altered peptide ligand, 30 alternative, vii, 2, 3, 5, 12, 19, 23, 24, 25, 26, 98, 168, 172, 174, 196, 218, 221, 224, 230, 233, 244, 245, 284, 285

Index alternative hypothesis, 25 alternatives, 176 alters, 28, 89 Alzheimer's disease, 135, 161 amino acid, 4, 47, 58, 75, 88, 173, 225 amino acids, 47, 75, 173 AML, vi, xi, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276 AMLs, 269, 272 amniotic, 280, 292 amniotic fluid, 280, 292 AMPA, 198 Amsterdam, 198, 237, 261 amyloid, 32, 46, 113, 120, 135 androgen, 114, 186, 187, 189, 197 androgens, 189, 192, 197 aneuploidy, 238 angiogenesis, 5, 19, 47, 113, 250, 254, 256, 257, 259, 260, 263, 264, 265 angiogenic, 254, 259, 265 angiotensin II, 109, 132, 229 anhydrase, 248 animal studies, 124 animals, ix, 6, 61, 117, 147, 148, 149, 150, 175, 183, 291 annexin I, 77 annotation, 191, 198, 212, 219, 221, 222 ANOVA, 194 antagonist, 36, 40, 136, 155, 193, 261 antagonists, 241 anthracene, 117 antiapoptotic, 66, 86, 234 anti-apoptotic, 66, 67, 68, 69, 70, 71, 257 antibacterial, 111 antibodies, 254 antibody, 5, 9, 10, 24, 27, 92, 120, 140, 148, 160, 198, 263, 286 anticancer, 8, 118, 134, 139 anticancer activity, 134 antidiabetic, 105 antigen, 5, 9, 10, 31, 36, 37, 38, 53, 62, 64, 67, 82, 116, 135, 144, 145, 146, 148, 149, 152, 153, 154, 155, 156, 157, 158, 160, 167, 168, 169, 174, 182, 225, 247, 250, 289 antigen presenting cells, 9, 10 antigen-presenting cell, 31, 144, 146, 152, 154 antigens, 10 anti-HIV, 13, 22, 23, 26, 44, 48 anti-inflammatory agents, 117 antineoplastic, 102

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Index antioxidant, viii, 71, 97, 98, 100, 101, 102, 103, 105, 106, 110, 112, 114, 115, 117, 119, 124, 125, 126, 129, 130, 134, 136, 140, 141, 143, 147, 155, 160 anti-platelet, 115 antisense, 6, 7, 35, 78, 96, 234 antisense oligonucleotides, 78 antisense RNA, 6, 96 antiviral, ix, 4, 6, 22, 26, 33, 48, 54, 66, 149, 151, 173, 201, 234, 287, 291, 296, 297 antiviral drugs, 6, 26 anxiety, 140 APC, 9, 10, 144, 206, 226 APCs, 144, 145, 146, 151 APL, viii, 52, 269, 271 apoptosis, viii, 5, 19, 31, 32, 34, 47, 61, 63, 66, 67, 68, 69, 70, 71, 72, 77, 78, 83, 86, 87, 88, 89, 90, 91, 97, 101, 102, 107, 112, 113, 119, 127, 130, 131, 134, 135, 139, 144, 146, 188, 196, 226, 230, 232, 233, 249, 268, 281 apoptotic, 19, 63, 67, 68, 69, 70, 71, 72, 78, 89, 112, 117, 126, 127, 132, 134, 136, 150, 154, 226, 257 apoptotic cells, 226 apoptotic effect, 126, 127 apoptotic mechanisms, 78 apoptotic pathway, 69, 136, 154 application, 141, 173, 176, 272, 283 Arabidopsis thaliana, 207 arachidonic acid, 109, 132, 133, 145, 148, 157 ARC, 206, 208, 221, 223 ARF, 62 aromatic hydrocarbons, 115 arrest, 18, 31, 54, 70, 71, 105, 127, 135, 282 arteries, 136, 264 arteritis, 203 artery, 106, 117, 292 ascorbic, 105, 116 ascorbic acid, 105, 116 aspirin, 136 assimilation, 25 association, 144, 146, 150, 234 asthma, 49 astrocyte, 286, 295 astrocytes, 75, 84, 95 astrocytoma, 34 ataxia, 124, 142, 156, 247 ATF, 19, 44, 53, 57, 59, 64, 66, 87 atherogenesis, 30

301 atherosclerosis, 98, 107, 116, 117, 118, 124, 129, 136 ATM, 108, 131 atmosphere, 117 atopic dermatitis, 259 ATP, 112, 225, 248 ATPase, 169, 178, 281 atrophy, 124 attachment, 43, 76, 202 attention, 128 autocrine, 4, 65, 72, 90, 140, 145, 261, 264 autoimmune, 140, 145, 151, 155, 259, 265 autoimmune disease, 155, 265 autoimmune diseases, 265 autoimmune disorders, 145 autoimmunity, 67, 81, 153 autosomal recessive, 123, 142 availability, 110, 225 avian influenza, 2, 27, 292 axonal, 192

B B cell, 5, 9, 10, 24, 28, 37, 60, 64, 73, 74, 91, 93, 144, 152, 243, 244 B cells, 5, 9, 10, 24, 37, 64, 74, 91, 144, 152, 244 B lymphocytes, 9, 24, 72, 93, 144, 145 back, 100, 153 bacteria, 4 bacterial, 2, 4, 5, 6, 23, 26, 30, 175 bacterial infection, 5, 6 barley, 232 barrier, 42, 75, 117, 120, 254 base pair, 43, 170 basement membrane, 255, 256 basic research, 124, 177 basophils, 144 Bax, 68, 69, 88, 112, 135 BBB, 75 B-cell lymphoma, 73, 74 bcl-2, 91, 92 Bcl-2, 68, 69, 88, 89 Bcl-xL, 66, 67, 69, 70, 206 behavior, viii, 66, 75, 95, 97, 102, 105, 114, 192, 196, 197, 198 bending, 177 beneficial effect, 32, 101, 111, 116, 118 benefits, 273 benign, x, 60, 237 benzodiazepine, 121

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302 beta interferon, 291 bile, 100, 147 biliverdin, 247 biliverdin reductase, 247 bioactive compounds, 114 biogenesis, 35 bioinformatics, 264 biological activity, 127, 283 biological media, 145 biopsies, 240, 241 bioreactor, 293 bioreactors, 294 biosynthesis, 76, 141 biotechnology, ix, 3, 5, 11, 26, 163 birds, vii, 51, 52, 60 blocks, 41, 55, 56, 70, 111, 189 blood, 5, 9, 11, 18, 32, 72, 75, 80, 83, 88, 100, 117, 120, 141, 146, 149, 153, 154, 238, 239, 247, 248, 251, 254, 256, 260, 270, 297 blood group, 247, 248 blood monocytes, 32, 83 blood plasma, 141 blood vessels, 251, 254, 256, 260 blood-brain barrier, 75, 117, 120 blot, 115, 288 body, 98, 100, 101 bonds, 52, 112 bone marrow, 9, 272, 287 Bortezomib, 112, 134 bovine, 84, 91, 92, 96, 106, 120, 234, 280 bowel, 38, 98 brain, ix, 75, 94, 95, 102, 105, 106, 117, 119, 120, 123, 139, 140, 147, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 195, 196, 197, 198, 199, 286, 295, 297 brain development, 189, 192 brain stem, 123 brain structure, 183 breakdown, 74, 75 breast cancer, 71, 74, 87, 94, 102, 108, 112, 118, 127, 131, 139, 242 bronchiolitis, 49 bronchitis, 297 bronchospasm, 23 budding, 14, 15, 40, 76, 95 Burkitt lymphoma, 92 bypass, 262 bystander cells, 69

Index

C cadherin, 76, 253, 254, 260 cadherins, 254, 263 Caenorhabditis elegans, 34 calcification, 118, 139 calcium, 62, 63, 76, 82, 89, 118, 242, 254 caloric restriction, 158 cAMP, 44, 59, 66, 80, 86 cancer, vii, 1, 2, 4, 5, 8, 24, 31, 33, 36, 46, 49, 60, 71, 74, 80, 85, 89, 91, 92, 94, 98, 101, 102, 107, 108, 110, 112, 117, 118, 119, 126, 127, 129, 130, 131, 132, 134, 135, 139, 140, 146, 157, 160, 228, 242, 249, 254, 257, 259, 263, 268, 275, 276, 277, 280, 290 cancer cells, 4, 5, 31, 36, 74, 89, 94, 101, 102, 107, 108, 110, 118, 126, 127, 129, 134, 135, 139, 228 cancer progression, 113, 254 candida, 34 candidates, 123, 175 capillary, 94, 120, 140 capsule, 103 carbohydrate, 17 carbon, 115 carbon tetrachloride, 115 carboxyl, 4 carcinoma, 60, 79, 102, 114, 135, 136, 141 cardiac muscle, 106, 130 cardiopulmonary, 262 cardiopulmonary bypass, 262 cardiovascular disease, 2, 129, 130, 160 cardiovascular physiology, 131 cargo, 93 carotenoids, 98 carrier, 24, 246, 247 caspase, 66, 67, 68, 69, 87, 88, 132, 226 caspases, 67, 102 cassettes, 280 CAT, 206 catabolism, 84 catalytic activity, 41 cathepsin B, 257 C-C, 63, 258, 295 CCL19, x, 238, 258, 259 CCL21, x, 238, 258, 259, 265 CCR, 12, 40 CD163, x, 238, 248, 249, 259, 262 CD8+, 5, 10, 11, 32, 33, 37, 38, 62, 153, 158, 159

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Index CDC, 70 CDK4, 19 CDK9, 17, 58, 66, 68, 70, 80, 87 cDNA, 32, 81, 171, 179, 187, 195, 210, 214, 215, 216, 217, 227, 240, 241, 272, 276, 283, 287, 294 cell adhesion, 74, 76, 96, 117, 127, 138, 249, 254, 256, 259 cell culture, 3, 83, 101, 104, 119, 136, 141, 193 cell cycle, 2, 18, 19, 31, 40, 43, 44, 46, 54, 61, 70, 71, 77, 117, 119, 139, 167, 177, 179, 224, 231, 282, 287, 296 cell death, 19, 46, 63, 67, 71, 88, 102, 112, 117, 119, 126, 134, 135, 233 cell differentiation, 148, 154, 157, 232, 234 cell division, 70, 157, 170 cell growth, 5, 35, 71, 72, 74, 81, 90, 91, 105, 127, 132, 294 cell line, 18, 37, 39, 65, 69, 72, 74, 76, 78, 81, 82, 84, 85, 87, 89, 91, 96, 102, 117, 136, 148, 157, 182, 284, 286, 289, 294, 297 cell lines, 18, 37, 65, 69, 72, 74, 76, 78, 81, 82, 85, 89, 91, 96, 102, 136, 284, 286, 294, 297 cell metabolism, 61 cell signaling, viii, 6, 143 cell surface, 14, 38, 62, 82, 138, 243, 254 cellular adhesion, 76 cellular immunity, 154, 156 central nervous system, 60, 63, 96, 110, 184, 185, 192, 197, 199, 228, 263 cerebral aneurysm, 32 cerebral cortex, 94 cerebral ischemia, 117 cerebrospinal fluid, 75, 95, 96 cervical cancer, 280 c-Fos, 17, 19, 43, 65, 73 chaperones, 114 charcoal, 117 chemical properties, 101 chemical reactivity, 101, 102 chemoattractant, 95, 120 chemokine, 5, 12, 23, 25, 39, 40, 63, 67, 83, 140, 257, 258, 259, 264, 265 chemokine receptor, 5, 12, 23, 25, 39, 40, 259 chemokines, 4, 5, 11, 45, 63, 95, 120, 257, 258, 259, 264, 265 chemoprevention, 127 chemoresistance, 118 chemotaxis, 75, 84, 95 chemotherapy, 127, 276

303 chimera, 73 chiral, 98, 139 chloride, 184 chloroplast, 219 CHO cells, 289 cholestatic liver disease, 98 cholesterol, 76, 95, 102, 118, 119, 120, 126, 136, 141, 147 chondrocyte, 287 chondrocytes, 287, 290, 293, 296 chondrogenic, 287 choriomeningitis, 149, 159 chromatin, 18, 20, 28, 45, 64, 71, 73, 83, 92, 174, 288 chromatography, 184 chromosomal abnormalities, 268, 269 chromosomal alterations, 72 chromosome, 11, 58, 242, 261, 268, 271, 289 chromosomes, 2, 11, 12, 24, 26, 49, 195, 275, 276, 277 chronic myelogenous, 60 chronic viral infections, ix, 143, 149, 151, 158 chymotrypsin, 112, 113 circulation, 100, 147 cis, ix, 16, 19, 46, 53, 58, 79, 124, 163, 177, 179, 233, 234, 283, 284, 287, 288 cisplatin, 87 cistron, 210, 213, 284 c-jun, 208 classes, 10, 62, 110, 141, 251, 257, 261, 268, 277 classical, 71, 231, 239, 241, 244, 245, 248, 249, 297 classification, vi, 53, 203, 267, 268, 269, 270, 271, 272, 273, 274, 275, 277, 278 cleanup, 219 cleavage, 7, 35, 40, 167, 170, 178, 212, 224, 226 clinical trial, 5, 25, 148, 280 clinical trials, 5, 25, 148, 280 clonality, 92, 238, 260 clone, 152 cloning, 32, 141, 142, 152, 235, 284, 286 cluster analysis, 268, 269 clustering, 268, 270, 272, 273, 276 clusters, 213, 215, 268, 270, 273, 278 CML, 271, 272 CMV, 30, 149, 151, 283, 286 c-myc, 71, 72, 74, 87, 91, 92, 93, 208, 228, 230, 232 c-Myc, 72, 89, 90, 91 C-N, 296

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304 CNS, 63, 64, 75, 192 Co, 38, 46, 109, 260, 284, 289, 294 coagulation factor, 119 coding, vii, 8, 51, 52, 79, 116, 165, 172, 177, 180, 212, 213, 217, 218, 222, 228, 234, 259, 280, 283 codon, 18, 171, 215, 217, 222, 224, 225, 226, 228, 231 codons, 213, 215, 216, 285 coenzyme, 62, 76, 118, 126 cofactors, 28, 39 cofilin, 77 colds, 150 colitis, 37 collagen, 115, 137, 230, 255 colon, 31 colon cancer, 31 colony-stimulating factor, 72 colorectal cancer, 49, 89, 157 combination therapy, 160 commitment, 146 communication, viii, 51, 264, 287 community, 191 competition, 114 compilation, 207 complement, x, xi, 222, 238, 244, 245, 246, 248, 249, 259, 260, 262, 267, 274, 290, 297 complement components, 245, 259 complement system, 244, 297 complementarity, 205, 207, 208, 210, 211, 233 complete remission, 270 complexity, ix, 183, 190 components, ix, 26, 38, 58, 66, 83, 90, 141, 151, 163, 165, 245, 248, 259 compounds, 16, 26, 27, 40, 98, 104, 110, 112, 114, 124, 139, 147, 296 concentration, 4, 73, 100, 104, 105, 114, 119, 121, 122, 127, 146, 151, 184, 187, 188, 192, 198 condensation, 281, 288 configuration, 98, 99 connective tissue, 115, 256, 264 connectivity, 185 connectivity patterns, 185 consensus, viii, 51, 53, 64, 171, 179 construction, 121, 177, 286 consumption, 98 contaminant, 78 control, vii, 1, 2, 24, 26, 27, 49, 56, 58, 61, 63, 70, 71, 72, 77, 78, 80, 83, 86, 87, 114, 136,

Index 144, 145, 147, 149, 150, 152, 153, 154, 155, 164, 167, 169, 171, 174, 181, 184, 185, 195, 210, 213, 223, 224, 225, 226, 227, 229, 231, 233, 234, 263, 279, 281, 282, 283, 284, 286 conversion, 16, 107, 141 corepressor, 80 coronavirus, 29, 297 correlation, 65, 75, 187, 270 correlations, 94 cortex, 94 cost-effective, 24, 175, 176 costimulatory molecules, 144, 146 costs, 26, 282 couples, 35, 233 covalent, 113 covering, 211 cows, 64, 72 COX-2, 64, 104, 110, 114 coxsackievirus, 234 CREB, 44, 45, 53, 57, 59, 64, 65, 66, 85, 86, 93 critical period, 185, 186, 187, 188, 189, 190, 192, 195, 199 crosstalk, 210 crystal structure, 132, 294 crystallization, 109 CSF, 47, 72, 91 CTD, 58, 68, 80 C-terminal, 20, 41, 58, 68 C-terminus, 284 cues, ix, 183, 184, 186, 195 culture, 3, 89, 101, 102, 104, 118, 119, 121, 141, 191, 193, 238, 289, 293 curcumin, 26 cutaneous T-cell lymphoma, 80 CXC, 259, 265 CXC chemokines, 259, 265 cycles, vii, 1 cyclic AMP, 59 cyclin D1, 119, 120 cyclin-dependent kinase inhibitor, 46, 112, 229 cycling, 157, 287 cyclins, 117, 139 cyclooxygenase, 64, 84, 104, 110, 114, 128, 133, 136 cyclooxygenase-2, 64, 104, 114, 136 cyclosporine, 40 cysteine, 67, 102, 112, 117, 118, 264 cysteine proteases, 102 cystic fibrosis, 98

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Index cytochrome, 71, 103, 112, 116, 138, 198, 246, 247, 248 cytogenetics, 268, 269, 271, 272, 274, 275, 278 cytokeratins, 249 cytokine, 4, 10, 11, 23, 24, 31, 38, 39, 48, 49, 63, 71, 74, 83, 88, 95, 110, 116, 120, 144, 145, 146, 150, 152, 153, 154, 155, 157, 159 cytokine receptor, 11, 152 cytokines, viii, 4, 5, 6, 8, 9, 10, 11, 19, 20, 23, 26, 45, 51, 63, 64, 65, 74, 75, 84, 87, 95, 96, 120, 140, 144, 145, 146, 153, 155, 261, 287 cytomegalovirus, 158, 283, 286, 295 cytoplasm, 7, 8, 11, 17, 19, 54, 58, 60, 140, 173, 269 cytoplasmic tail, 11 cytoskeleton, 76, 77 cytosol, 116, 121, 141 cytosolic, 106, 110, 113, 141, 147 cytostatic drugs, 110 cytotoxic, 4, 9, 17, 29, 37, 63, 66, 82, 103, 104, 129, 144, 145, 148, 153, 157, 158, 159, 243 cytotoxicity, 3, 126, 288

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D damage, 126, 128, 149, 244, 255, 259 data set, 269 database, x, 31, 202, 207, 211, 212, 213, 217, 218, 222, 223, 230, 231, 238, 239, 240, 241, 242, 243, 245, 251, 252, 254 daughter cells, 290 de novo, 34, 65, 115, 208, 272, 281 death, 2, 3, 10, 19, 22, 23, 46, 63, 66, 67, 69, 71, 87, 88, 89, 102, 112, 117, 119, 126, 132, 134, 135, 139, 233 death rate, 10 deaths, 2, 22, 23 defects, 72, 148, 150, 293 defence, 47, 149 defense, vii, ix, 1, 3, 6, 10, 18, 21, 22, 24, 33, 37, 62, 67, 119, 173, 174, 201 defense mechanisms, 174 defenses, vii, 1, 33, 105, 111 deficiency, 46, 71, 81, 98, 101, 116, 119, 124, 125, 139, 140, 156 deficits, 94 definition, 23, 42, 217, 219, 281 degenerative disease, 155 degradation, 6, 7, 8, 11, 17, 18, 19, 21, 22, 24, 34, 44, 48, 54, 56, 58, 60, 65, 70, 75, 86, 102,

305 103, 112, 113, 119, 122, 126, 134, 158, 173, 181, 243, 248, 256 degradation mechanism, 18, 22 degradation pathway, 19, 21, 24, 86, 113 degrading, 257 dehydrogenase, 120 delivery, xi, 121, 141, 189, 192, 279, 280, 285, 286, 290, 291, 292, 293, 295, 297 dementia, 28, 75, 94 dendritic cell, 5, 11, 36, 37, 83, 96, 144, 152, 153, 154, 155, 259 dendritic spines, 198 dengue, 32 density, 61, 76, 77, 119, 132, 137, 140, 147, 228 dephosphorylating, 108 dephosphorylation, 106, 115, 224 deposits, 238 deprivation, 113, 135 deregulation, x, 23, 70, 74, 237, 275 derivatives, 108, 138, 211 dermatitis, 259 destruction, 3, 11, 249 detection, 62, 276 detergents, 135 detoxification, 110, 115 developing brain, ix, 183, 184, 187, 188, 189, 190, 192, 193, 195, 197 developing countries, 176 developmental process, 188 deviation, 30, 155 diabetes, 161 diacylglycerol, viii, 97, 107, 131 diarrhea, 203 diet, 23, 100, 114, 120, 123, 127, 128, 140, 147, 150 dietary, 98, 99, 100, 103, 104, 112, 129, 137, 138, 140, 147, 148, 155, 157 dietary supplementation, 98, 137, 157 differentiated cells, 69 differentiation, viii, ix, 5, 10, 71, 72, 88, 90, 92, 97, 144, 145, 146, 148, 154, 157, 159, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 195, 196, 197, 198, 199, 223, 232, 234, 242, 268, 276, 287, 289, 292, 296 digestion, 16 dimer, 16 dimerization, 15, 19, 33, 41 dimorphism, 183, 184, 185, 190, 192, 197 diploid, 52 direct repeats, 282

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Index

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306 disability, 117 discrimination, 269 discs, 191 disease progression, 150, 160, 256 disease progression, 2, 11, 13, 23, 26, 66, 84 diseases, vii, 1, 2, 3, 10, 24, 25, 26, 60, 61, 75, 76, 98, 111, 119, 123, 145, 149, 242, 259, 289 disequilibrium, 71, 78 disorder, 60, 119, 124, 261 displacement, 41, 42, 170 disposition, 115 disseminate, 27 dissociation, 45 distribution, 44, 54, 78, 98, 120, 121, 122, 123, 125, 147, 184, 193, 240, 269 diversity, 82, 94, 155 division, 70, 157, 170 DNA damage, 70, 112 DNA polymerase, 70, 78, 169, 281 DNA repair, 21, 28, 70, 71 domain, 107, 121, 123, 124, 132, 141, 142, 228, 252, 271, 272 dominance, 272 donor, 41, 56, 96, 115, 210, 260 dopaminergic, 198 dopaminergic neurons, 198 dosage, 151, 160, 161, 268 download, 217 down-regulation, 4, 5, 18, 43, 54, 62, 63, 69, 70, 71, 72, 77, 82, 116, 130, 139, 145 Drosophila, 29, 34, 55, 123, 142, 191, 195, 196, 197, 198, 199, 203, 204, 206, 207, 226, 229, 231, 282, 283, 284, 294 drug discovery, 292 drug metabolism, 125 drug targets, vii, 1, 11, 12, 25 drug therapy, 26 drug-resistant, vii, 2, 3 drugs, vii, 1, 2, 6, 16, 23, 26, 110, 115, 150 duodenum, 147 duplication, 55, 276, 277 duration, xi, 151, 279, 287, 290 dysregulated, 248 dysregulation, 35, 149, 150, 248

E Ebola, 29 E-cadherin, 253, 254, 260 ECM, 256, 260

eicosanoids, 109, 128 elderly, 148, 149, 150, 156, 157, 159 election, 42 electron, 102, 111 electrophoresis, 61 ELISA, 176 elongation, 17, 20, 43, 45, 48, 54, 58, 66, 68, 84, 87, 169 embryo, 199 embryogenesis, 263 embryonic development, 74 embryonic stem, 88, 280, 287, 292 embryonic stem cells, 88, 280, 292 embryos, 226 ENA-78, 120 encapsulated, 14 encephalitis, 95, 96 encephalomyelitis, 204 encephalopathy, 95 encoding, 18, 57, 58, 62, 63, 64, 65, 66, 67, 68, 70, 71, 76, 96, 117, 142, 165, 179, 212, 284, 289 endocrine, 4 endocytosis, 17, 37, 43, 44, 62, 82, 144, 249 endonuclease, 6 endoplasmic reticulum, 17, 54, 233 endoreduplication, 179 endothelial cell, 4, 30, 31, 32, 74, 76, 93, 94, 105, 110, 112, 120, 121, 127, 130, 131, 133, 134, 140, 141, 239, 248, 250, 251, 254, 256, 259, 260, 262, 264 endothelial cells, 4, 30, 32, 74, 93, 94, 105, 110, 112, 120, 121, 127, 130, 131, 133, 134, 140, 141, 239, 248, 250, 251, 254, 256, 259, 262 endothelial dysfunction, 133 endothelium, 75, 95, 129, 136, 239, 249, 251, 254, 260, 263 endotoxins, 4 end-stage renal disease, 128 engagement, 47, 96 enlargement, 76 enterovirus, 203, 204, 229, 234 environment, 3, 11, 24, 25, 27, 49, 52, 146, 168, 170, 185, 216 environmental conditions, ix, 183, 185 enzymatic, 16, 22, 58, 116 enzymes, viii, 17, 35, 56, 70, 75, 76, 97, 98, 101, 104, 105, 108, 114, 115, 116, 119, 120, 121, 122, 138, 147, 148, 248 eosinophilia, 23, 64

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Index epidemic, 81, 263 epidemiology, 81, 124, 130 epidermal cells, 173 epidermal growth factor, 36, 106, 109 epidermal growth factor receptor, 36 epidermis, 249 epigallocatechin gallate, 117 epigenetic, 184, 193 epigenetic mechanism, 193 epithelia, 249, 250 epithelial cell, 37, 101, 102, 104, 106, 114, 126, 127, 128, 131, 132 epithelial cells, 37, 101, 102, 104, 106, 114, 126, 127, 128, 131, 132 epithelium, 3, 249, 250 epitope, 144, 146, 149 Epstein-Barr virus, 29, 93, 149, 158, 203, 227, 289 equipment, 273 erythrocytes, x, 238, 248, 259 erythroid, 84, 142, 248 erythroid cells, 84, 142 erythropoietin, 287 Escherichia coli, 285 EST, 190, 217 ester, 112 ester bonds, 112 estradiol, 118, 126, 186, 198, 199 estrogen, 102, 118, 139, 184, 185, 186, 187, 188, 189, 190, 192, 193, 195, 196, 197, 198, 199 estrogen receptors, 187, 197 estrogens, 188, 189, 196, 197 eukaryotes, 167, 175, 228, 232 evidence, ix, 109, 119, 132, 143, 151, 201, 202, 211, 219, 232 evoked potential, 94 evolution, 24, 25, 27, 49, 195, 216 excitation, 186 excitotoxic, 75 excretion, 103, 125, 128 exons, 217 exonuclease, 16 exploitation, 290 exporter, 117 exposure, 9, 10, 158, 189, 197 expressed sequence tag, 199 external environment, 24, 185 extracellular matrix, 74, 75, 76, 256, 257, 263, 264 extrachromosomal, 182, 289, 297

307 extraction, 240 extrapolation, 275 extravasation, 76, 238 eye, 250

F failure, 2, 10, 23, 111, 133, 145, 211 false positive, x, 201, 221 familial, 119, 139 familial dysautonomia, 119, 139 family, x, 5, 11, 18, 19, 22, 24, 42, 44, 45, 46, 48, 52, 58, 59, 62, 65, 66, 67, 69, 70, 73, 77, 85, 103, 121, 141, 145, 147, 152, 169, 179, 210, 227, 233, 237, 238, 246, 247, 248, 250, 256, 259, 262, 263, 264 family members, 11, 85, 256 Fas, 19, 46, 47, 67, 69, 83, 87, 88, 89 FasL, 19, 47, 69, 83 fat, 76, 120, 140, 147, 156 fat soluble, 147 fatty acid, 95, 113, 161 fatty acids, 113, 161 fax, 143 F-box, 86 feedback, 58, 145 feeding, 296 females, 184, 185, 186, 189, 190, 192, 195 ferritin, x, 238, 246, 247, 248, 259 fertility, 140 fetal, 119, 124, 140 fetal brain, 119, 140 fever, 5, 231, 297 FGF2, 206, 221 FGF-2, 217 fibroblast, 4, 103, 217, 229, 233 fibroblast growth factor, 217, 229, 233 fibroblasts, 4, 62, 105, 109, 115, 128, 137, 147 fibronectin, 254 fibrosis, 98, 111 filament, 249 filtration, 103 fish, ix, 183, 184, 188, 280, 292 FISH, 274 flavonoids, 98 flexibility, 43 flight, 61 flow, 9, 75, 77 fluid, 75, 94, 95, 96, 109, 280, 292 fluorescence, 233, 284

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Index

308 focusing, 177, 269 folding, 15, 285 food, 125 Ford, 32 forests, 26 frameshift mutation, 171 free radical, 101, 104, 111, 112, 118, 126, 133, 148, 149, 158 free radical scavenger, 112, 118 free radicals, 104, 111, 133, 148, 158 free-radical, 147 frequency distribution, 240 Friedreich ataxia, 247 functional analysis, 34 fungal, 219, 220 fungi, 54, 240 fusion, 12, 14, 15, 16, 28, 32, 38, 39, 42, 73, 77, 101, 121, 173, 212, 275, 283, 285, 294, 295 fusion proteins, 16, 173

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G G protein, 5, 13, 14 GABA, 185, 186, 198, 199 Galectins, 249, 262 Gamma, 55, 132, 139 gamma-tocopherol, 126, 128, 129, 141 gastric, 135 GCC, 247 G-CSF, 91 GE, 87, 161, 291 GenBank, 211, 212, 219, 222 gender, 2, 190 gene arrays, 119, 239 gene promoter, 58, 60, 80, 82, 281, 284, 293 gene silencing, 6, 8, 34, 35, 164, 173, 174, 181, 289 gene therapy, 280, 285, 286, 291 gene transfer, 291, 292, 293, 295, 296, 297 generation, 5, 78, 91, 118, 134, 146, 147, 149, 154, 157, 158, 177, 184, 189, 192, 239, 288 genetic abnormalities, 125 genetic defect, 224 genetic disease, 25, 123 genetic information, 77 genetic instability, 104, 129 genetic mutations, 268 genetics, 13, 24, 155, 184, 195 genistein, 112, 134

genome, viii, 2, 8, 14, 15, 16, 21, 22, 24, 25, 51, 52, 53, 56, 57, 58, 59, 61, 67, 73, 77, 79, 121, 123, 164, 166, 172, 174, 175, 177, 191, 198, 199, 223, 227, 232, 233, 279, 280, 281, 282, 283, 284, 286, 288, 289, 290, 291, 293 genomes, ix, 58, 163, 165, 170, 175, 221 genomic, vii, ix, 2, 6, 11, 12, 14, 15, 16, 24, 34, 48, 49, 53, 55, 73, 77, 104, 164, 167, 175, 176, 183, 190, 193, 195, 213, 215, 216, 224, 226, 229, 230, 231, 233 genomics, 38, 174, 177, 293 GFAP, 286 GFP, 7, 121, 173, 174, 285, 289 gland, 141 glaucoma, 103 GlaxoSmithKline, 280 glial, 45, 94, 95, 184, 189, 191, 192, 193, 195, 223, 286 glial cells, 45, 94, 95, 184, 189, 192, 193, 195, 223 glial fibrillary acidic protein, 286 glioma, 36, 292 glucocorticoid receptor, 18 glucose, 107, 111, 130 glutamate, 84, 102, 109, 127, 132, 185, 198, 199 glutathione, 71, 105, 110, 113, 116, 133, 138, 146 glycation, 90 glycogen, 108, 131 glycogen synthase kinase, 108, 131 glycoprotein, 11, 12, 38, 39, 42, 115, 117, 118, 125, 139, 148, 157, 281, 282, 295, 297 glycoproteins, 4, 17, 297 glycosylated, 281 glycosylation, 17, 148, 157 GM-CSF, 72, 91 gonad, 188 gossypol, 26 government, iv GPI, 263 G-protein, 32 grafting, 34 grants, 26 granules, 248 green fluorescent protein, 121, 181, 284 green tea, 112 groups, 4, 7, 22, 23, 54, 102, 109, 177, 194, 211, 258, 267, 268, 269, 270, 271, 272, 273 growth, 5, 19, 28, 31, 32, 35, 36, 46, 60, 63, 70, 71, 72, 74, 81, 88, 90, 91, 103, 105, 106, 109,

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Index 114, 115, 117, 118, 127, 132, 135, 137, 139, 142, 144, 148, 157, 191, 197, 217, 225, 226, 229, 230, 233, 250, 254, 256, 257, 261, 262, 264, 286, 289, 294 growth arrest, 135 growth factor, 19, 28, 31, 36, 46, 63, 71, 81, 88, 106, 109, 115, 117, 135, 137, 191, 217, 225, 226, 229, 230, 233, 256, 257, 264, 286 growth factors, 19, 28, 31, 46, 257 GST, 71, 110, 116 guidance, 192 guidelines, 277 Guinea, 131 gut, 37

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H H1, 286, 295 H1N1, 23 H2, 31 H5N1, 23, 27, 292, 297 HA, 86, 91, 92, 95, 292 HAART, 11, 60, 74, 263 half-life, 61, 67, 125 handling, 286 haptoglobin, 249 harmful effects, 151 HBV, 22, 149, 150 HDL, 100, 122, 138, 147 head and neck cancer, 30 healing, 259 health, 2, 26, 49, 151 heart, 32, 110, 111, 127, 133, 141, 161 heart disease, 110 heart failure, 111, 133 heat, 67, 112, 134, 222, 226, 282, 294 heat shock protein, 67, 112, 226, 282 helix, 168, 294 helper cells, 11, 144, 154 hemagglutinin, 182, 280, 292 hematopoietic, 73 hematopoietic system, 73 heme, x, 117, 120, 238, 247, 248, 259, 262 heme oxygenase, x, 117, 120, 238, 247, 248, 259, 262 hemiptera, 226 hemochromatosis, 248 hemoglobin, x, 238, 247, 248, 249, 259, 262

309 hepatitis, 2, 22, 28, 36, 44, 48, 137, 148, 149, 150, 152, 158, 159, 160, 182, 203, 223, 225, 228, 233, 287, 288, 289, 295, 296 hepatitis A, 203 hepatitis B, 22, 28, 44, 48, 148, 158, 159, 160, 182 hepatitis B, 149 hepatitis C, 36, 137, 158, 159, 160, 203, 225, 228, 233, 289 hepatitis C virus, 149, 204, 227 hepatitis d, 288, 295 hepatocellular, 135 hepatocellular carcinoma, 135 hepatocyte, 286 hepatocytes, 291, 297 hepatoma, 113, 119, 135 herbal, 49 herpes, x, 225, 237, 238 herpes simplex, 225 herpes virus, x, 237, 238, 262 hES, 287, 289 heterochromatinization, 173 heterodimer, 16, 19 heterogeneity, 151, 270, 271, 273 heterogeneous, 144, 148, 190, 213, 215, 268, 271 HHV-8, x, 237, 238, 239, 241, 243, 244, 248, 254, 263 high density lipoprotein, 147 high fat, 120 high-fat, 140 high-frequency, 92 high-level, 256 high-performance liquid chromatography, 184 high-risk, 161 Hilbert, 296 hippocampal, 109, 123, 142 hippocampus, viii, 97, 119, 140, 196 histidine, 285 histone, 45, 47, 64, 68, 93, 170, 288 histone deacetylase, 288, 296 histone deacetylase (HDAC), 288 histopathology, x, 237, 239, 260 HIV infection, 2, 5, 16, 22, 25, 26, 33, 49, 69, 71, 74, 86, 94, 150, 159, 239, 263 HIV/AIDS, 2, 23, 81 HIV-1, viii, 2, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23, 24, 25, 26, 28, 29, 32, 36, 37, 39, 40, 41, 42, 43, 44, 45, 47, 48, 51, 54, 55, 57, 58, 60, 61, 62, 63, 65, 66, 67, 68, 69, 71, 74, 75, 76, 77, 79, 80, 81, 82, 83, 84, 86,

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310 88, 89, 94, 95, 96, 135, 159, 160, 204, 209, 239, 241, 243, 254 HIV-1 proteins, 10, 13 HIV-2, 17, 48, 204, 226 HLA, x, 62, 144, 148, 149, 156, 238, 241, 243, 244, 245 HLA-B, 244 Hoechst, 192 homeostasis, 82, 87, 125, 145 homogenous, 6 homolog, 88, 116, 191, 192, 247, 248 homology, 58, 104, 132, 152, 166, 168, 191, 294 hormone, viii, 18, 52, 103, 184, 196 hormones, 4, 57, 105, 196, 261 hospital, 23 host, vii, viii, ix, 1, 2, 3, 6, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 26, 28, 47, 51, 52, 53, 56, 57, 58, 60, 61, 64, 65, 66, 67, 70, 72, 75, 76, 77, 78, 80, 81, 89, 145, 149, 163, 164, 168, 169, 170, 173, 174, 175, 177, 201, 208, 227, 244, 280, 282, 288, 289, 291, 296 host tissue, 23 HPV, 167, 168 HRV, 204, 209 HSP27, 67 HSP90, 69, 206 HTLV, vii, viii, 22, 45, 51, 54, 55, 58, 59, 60, 61, 62, 63, 64, 66, 67, 68, 69, 70, 71, 72, 73, 75, 76, 79, 80, 81, 82, 83, 84, 85, 87, 88, 89, 90, 91, 92, 94 human brain, 94 human embryonic stem cells, 292 human genome, 22, 25, 121, 123 human immunodeficiency virus, vii, 3, 28, 32, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 51, 52, 78, 79, 80, 81, 83, 84, 86, 87, 88, 90, 93, 94, 223 human leukemia cells, 277 human mesenchymal stem cells, 292 human neutrophils, 109, 132 human papillomavirus, 167, 182 humans, viii, 23, 52, 62, 72, 97, 103, 123, 124, 125, 145, 146, 147, 148, 149, 150, 158, 175, 184, 286 humoral immunity, 9, 10, 24 hybrid, 168, 169, 170, 232, 286, 289, 290, 297 hybridization, 61, 95 hydro, 115 hydrocarbon, 115, 136

Index hydrocarbons, 115 hydrogen, 52, 136, 148 hydrogen bonds, 52 hydrogen peroxide, 136 hydrolysis, 132 hydrophobic, 42, 98, 121, 123, 131 hydrophobicity, 147 hydroxyl, 7, 109 hydroxyl groups, 7 hydroxylation, 103, 104 hypercholesterolemia, 118 hypergammaglobulinemia, 150 hyperglycemia, 105 hyperphosphorylation, 70 hyperproliferation, 249 hypersensitivity, 148, 156 hypertensive, 102 hypothalamic, 184, 198 hypothalamus, 184, 188, 192, 194, 197, 198 hypothesis, 5, 25, 130, 189, 244 hypoxia, 31, 97, 117, 135, 139, 202, 228, 247 Hypoxia, 90, 117, 118, 228 hypoxia-inducible factor, 117, 135, 139, 247

I IAP, 67, 88, 211 ICAM, 74, 252, 253, 254 icosahedral, 163 id, 11 identification, 177, 234, 240, 271 identity, 123 IFN, 30, 63, 65, 66, 145, 146, 150, 153, 154, 280, 285, 287, 296 IgE, 10, 146 IGF, 206 IGF-I, 206 IGF-IR, 206 IgG, 10, 249, 262 IL-1, 5, 8, 11, 19, 32, 49, 63, 64, 65, 72, 84, 110, 116, 120, 144, 145, 146, 150, 151, 152, 154, 155, 241, 249, 259 IL-10, 5, 63, 64, 117, 145, 146, 151, 154, 249, 259 IL-13, 144, 145 IL-15, 11, 63, 64, 72, 84, 145 IL-2, 5, 11, 15, 30, 40, 64, 66, 71, 80, 90, 91, 136, 144, 145, 146, 148, 150, 151, 153, 154, 157, 158

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Index IL-4, 5, 11, 23, 65, 144, 145, 146, 148, 150, 151, 153, 154, 155, 157 IL-6, 30, 63, 64, 65, 72, 74, 144, 148, 150 IL-8, 63, 65, 74, 83, 86, 95, 257, 258 imaging, 94 immature cell, 268 immune activation, 145 immune cells, 3, 4, 6, 83, 145 immune function, 156, 157, 159 immune reaction, 3, 95 immune regulation, 61 immune response, 3, 9, 10, 19, 23, 49, 63, 67, 80, 83, 143, 144, 145, 146, 148, 149, 150, 151, 154, 156, 158, 159, 175, 225, 244, 249, 250, 263, 292, 296, 297 immune system, vii, 1, 2, 3, 12, 23, 49, 52, 61, 77, 92, 147, 148, 150, 152, 157 immunity, 2, 3, 4, 6, 8, 9, 10, 15, 22, 24, 26, 28, 29, 30, 33, 34, 48, 49, 87, 94, 146, 148, 152, 153, 154, 155, 156, 158, 291, 292 immunization, 49, 182 immunocytochemistry, 116 immunodeficiency, vii, viii, 3, 28, 32, 38, 39, 40, 41, 42, 43, 44, 45, 47, 48, 51, 52, 55, 60, 78, 79, 80, 81, 83, 84, 86, 87, 88, 90, 93, 94, 96, 159, 204, 223, 230, 241, 261 immunodeficient, 158 immunogenicity, 182, 297 immunoglobulin, 10, 66, 145, 155, 217, 252, 254, 284 immunoglobulin G, 10 immunoglobulin superfamily, 252, 254 immunoglobulins, 241 immunohistochemical, 94 immunohistochemistry, 95 immunological, 156 immunomodulation, 159 immunomodulatory, ix, 143, 150, 159, 263 immunostimulatory, 149 immunosuppressive, 145, 149 immunotherapy, 49 implementation, 273 in situ, 95 in situ hybridization, 95 in transition, 62 in vitro, 34, 37, 42, 43, 62, 72, 75, 79, 88, 91, 101, 102, 105, 106, 109, 110, 112, 114, 116, 120, 124, 128, 129, 133, 135, 138, 141, 148, 155, 158, 177, 192, 193, 195, 210, 212, 227, 231, 233, 249, 262, 263, 280, 281, 290

311 in vivo, 18, 19, 32, 37, 41, 45, 47, 62, 75, 84, 86, 88, 91, 103, 105, 109, 112, 115, 116, 119, 120, 124, 135, 150, 152, 155, 157, 185, 186, 187, 188, 189, 190, 192, 193, 195, 210, 212, 232, 239, 254, 262, 280, 286, 290, 292 inactivation, 112, 134, 158, 290 inactive, 60, 189 incidence, x, 5, 23, 60, 74, 150, 237, 238 inclusion, 286 income, 2 Indian, 169, 179 indication, 255 indices, 197 inducer, 70 induction, 30, 31, 33, 60, 65, 66, 69, 70, 73, 74, 78, 79, 80, 82, 88, 90, 93, 101, 102, 103, 104, 112, 113, 117, 118, 135, 139, 146, 158, 174, 233, 265, 270, 287, 291, 292 inductor, 69 industrial, 175 infants, 23 infarction, 102, 117, 161 infections, vii, ix, x, 2, 10, 22, 25, 28, 61, 62, 76, 143, 146, 149, 151, 152, 158, 159, 237, 261, 291 infectious, vii, 1, 2, 3, 8, 10, 23, 24, 25, 26, 58, 77, 153, 297 infectious disease, vii, 1, 2, 3, 8, 10, 23, 24, 25, 26, 153 infectious diseases, vii, 1, 2, 3, 8, 11, 23, 24, 25, 26 inflammation, 3, 5, 19, 32, 81, 101, 104, 110, 116, 128, 132, 136, 138, 153, 155, 242, 249, 252, 259, 265 inflammatory, 4, 5, 6, 10, 20, 24, 45, 60, 63, 76, 86, 94, 95, 102, 103, 110, 111, 116, 117, 138, 145, 149, 153, 155, 156, 238, 242, 244, 249, 254, 256, 258, 259, 262, 264 inflammatory disease, 60, 149 inflammatory mediators, 262 inflammatory response, 4, 76, 94, 117, 138 inflammatory responses, 76, 138 influence, 113, 114, 146, 156, 250 influenza, vii, 1, 2, 3, 5, 22, 23, 27, 28, 36, 159, 280, 292, 297 influenza a, 5, 36 influenza vaccine, 2, 3, 280, 297 inherited, 24, 190, 289 inhibition, 4, 6, 11, 19, 31, 40, 63, 66, 68, 70, 73, 101, 102, 103, 105, 106, 107, 109, 110, 111,

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312 112, 113, 114, 115, 116, 119, 127, 128, 129, 132, 133, 134, 135, 136, 137, 138, 145, 148, 227, 257, 297 inhibitor, 41, 42, 46, 66, 67, 71, 76, 87, 88, 94, 110, 111, 112, 113, 115, 117, 133, 134, 135, 136, 153, 169, 171, 191, 229, 233, 246, 256, 264, 281, 288 inhibitors, 28, 31, 48, 60, 65, 70, 112, 132, 134, 256, 288 inhibitory, 12, 19, 65, 106, 109, 112, 118, 134, 234 inhibitory effect, 12, 106, 112 initiation, ix, 6, 18, 20, 35, 53, 58, 59, 67, 68, 79, 166, 167, 168, 169, 170, 171, 172, 178, 201, 202, 203, 209, 210, 211, 216, 217, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 281 injection, 75 injections, 262 injury, 32, 38, 102, 111, 133, 155, 264 innate immunity, 4, 29, 30, 34, 291 innervation, 193 iNOS, 117 inositol, 89 input, 217 INS, 283 insects, 280, 283 insertion, 16, 60, 73, 79, 92, 176, 284, 288 insight, x, 119, 237 instability, 104, 129 insulin, 105, 131, 230, 283 insulin-like growth factor I, 230 integration, viii, 11, 16, 28, 40, 42, 51, 52, 53, 54, 56, 72, 78, 92, 174, 289, 291, 297 integrin, 76, 88, 96, 131, 251, 254, 263 integrins, 96, 117, 138, 252, 254, 263 integrity, viii, 51, 56, 75, 104, 282 intensity, 145 intent, 221 interaction, 3, 8, 15, 17, 18, 20, 23, 24, 26, 35, 36, 40, 44, 57, 58, 59, 62, 65, 66, 68, 72, 75, 81, 88, 89, 91, 95, 106, 144, 168, 178, 179, 224, 225, 228, 232 interactions, viii, 11, 13, 23, 25, 41, 42, 46, 61, 65, 81, 96, 97, 98, 101, 102, 105, 167, 177, 202, 211, 224, 254, 294 intercellular adhesion molecule, 74, 116 interest, ix, 143, 238 interface, 43

Index interference, 6, 28, 29, 34, 36, 38, 67, 73, 79, 82, 284, 295 interferon, 4, 30, 31, 33, 44, 48, 63, 83, 87, 144, 160, 250, 254, 280, 285, 288, 291, 296 interferons, 4, 6, 8 interleukin, viii, 4, 30, 32, 39, 51, 83, 84, 87, 90, 91, 94, 116, 129, 133, 149, 153, 154, 155, 156, 157, 241, 261, 262 interleukin-1, 83, 84, 116, 133, 153, 154, 155, 241, 261, 262 Interleukin-1, 30 interleukin-2, 84, 87, 90, 91, 157 interleukin-6, 30, 94 interleukin-8, 94 interleukins, 5, 63, 64, 143, 144, 146 internal ribosome entry site (IRES), ix, 201, 202, 203, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 284, 285, 294 internalization, 62, 288 interpretation, 273 intervention, vii, 2, 3, 11, 23, 103 intestine, 98, 100, 123 intracellular cysteine proteases, 102 intracellular signaling, 95 intrinsic, 48, 87, 177 intron, 17, 73, 164, 167, 171, 211 introns, 217 invasive, 46, 75, 95, 242 invertebrates, ix, 54, 183 ion transport, 247 ionizing radiation, 131, 277 iron, x, 238, 246, 248, 259, 262 irritable bowel syndrome, 38 ischemia, 102, 113, 117, 139 ischemic, 118 isoenzymes, 133 isoforms, 110, 230, 231 isolation, 80, 81, 240 isomerization, 41 isomers, 132, 147 isoprenoid, 127 isozyme, 110 isozymes, 116, 225

J JAK2, 109, 132 JAMA, 29, 156, 160

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Index Japan, 60 Jefferson, 232 JNK, 31, 46 joining, 178 Jordan, 39, 197 Jun, 19, 31, 45, 46, 65, 73, 92, 156, 231 Jung, 128, 261

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K K+, 103, 114 kainate receptor, 198 Kaposi sarcoma, 88, 260, 261, 262 kappa, 10, 31, 44, 58, 79, 80, 85, 87, 90, 112, 295 kappa B, 31, 44, 58, 79, 80, 85, 87, 90, 112, 295 karyotype, 268, 269, 274, 276, 278 karyotypes, 272, 273 keratin, x, 238, 249, 250 keratinocytes, 33, 114, 136, 256, 259, 264 kidney, 38, 100 killer cells, 29 killing, 8, 24, 25, 82 kinase, viii, 6, 11, 19, 20, 24, 31, 33, 34, 41, 45, 46, 58, 62, 65, 66, 69, 70, 71, 72, 82, 83, 85, 87, 97, 103, 105, 106, 107, 108, 109, 110, 111, 112, 114, 115, 119, 125, 126, 127, 129, 130, 131, 132, 133, 136, 137, 169, 170, 176, 180, 191, 206, 225, 229, 230, 247, 248, 271 kinase activity, 70, 107, 109, 114, 131 kinases, viii, 4, 13, 62, 65, 70, 80, 82, 85, 97, 108, 109, 144, 169, 179 Kirchhoff, 46 knowledge, 124, 146, 267 KSP, 254

L L1, 119 laboratory method, xi, 267 lactoferrin, 247 lambda, 10 lamellar, 109 lamina, 37 laminin, 255, 256, 264 Laminin, 255, 256 LANA, 239, 240 language, 222 large-scale, 29, 38, 175 larvae, 192, 283

313 larval, 198 latency, viii, 28, 51, 60, 64, 77, 244 latex, 98, 147 Latin America, 26 LDL, 76, 100, 116, 118, 122, 129, 130, 137, 139, 147 lead, 98, 104, 108, 109, 123 learning, 191 lectin, 77, 244 legume, 177 Lentiviruses, 55 leprosy, 23, 48, 49 leptin, 116, 138 lesions, x, 49, 74, 93, 237, 238, 239, 240, 242, 244, 248, 254, 256, 257, 259, 260 leucine, 19 leukaemia, x, 81, 93, 96, 104, 267, 269, 270, 272, 273 leukemia, vii, viii, 22, 28, 48, 51, 52, 53, 54, 55, 59, 60, 67, 72, 73, 74, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 90, 91, 92, 93, 94, 96, 150, 203, 204, 223, 233, 272, 274, 275, 276, 277, 278 leukemia cells, 85, 277 leukemias, 91, 92, 276, 277 leukemic, 81, 90, 96 leukemic cells, 96 leukocyte, 66, 74, 77, 96, 116, 252, 254 leukocyte function antigen, 116 leukocytes, 3, 5, 76 LFA, 96 LH, 225 life cycle, vii, 1, 11, 12, 15, 16, 23, 45, 55, 56, 57, 58, 61, 76, 173 life sciences, 232 life span, 67 lifespan, 2 ligand, 5, 30, 37, 63, 67, 69, 72, 77, 88, 89, 112, 121, 132, 136, 251, 254, 258 ligands, 30, 63, 104, 114, 121, 123, 129, 134, 254 lignin, 26 limitation, 285 limitations, 175 linear, 52 links, 46, 222, 261 lipid, 15, 31, 37, 76, 95, 98, 102, 104, 108, 112, 114, 125, 128, 129, 137, 141, 147, 234 lipid metabolism, 76 lipid oxidation, 129 lipid peroxidation, 98, 112 lipid rafts, 76, 95

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314 lipids, viii, 76, 97, 98, 105, 126, 135, 140, 147 lipofuscin, 120, 124 lipolysis, 100, 147 lipophilic, 110 lipopolysaccharide, 30 lipopolysaccharides, 4, 117 lipoprotein, 76, 120, 125, 132, 137, 140, 142 Lipoprotein, 131 lipoproteins, 116, 120, 122, 140, 147, 156 lipoxygenase, viii, 97, 103, 104, 110, 114, 127, 133, 148 liquid chromatography, 184 liver, 32, 95, 96, 98, 100, 101, 102, 111, 115, 120, 121, 122, 123, 126, 133, 135, 137, 138, 139, 140, 141, 147, 155, 246, 249 liver disease, 98, 111, 133 living environment, 25 localization, 18, 40, 58, 65, 87, 92, 95, 115, 121, 132, 227, 272 location, 102, 125 locus, 92 long period, 60, 216 longevity, 87 low molecular weight, 202 low-density, 76, 132, 137 low-density lipoprotein, 76, 132, 137 low-density lipoprotein receptor, 76 low-temperature, 186, 187, 188, 195 LPS, 4, 31, 110, 117 luciferase, 115, 172, 180, 212, 213, 283, 289 lung, 33, 117, 134, 157 lung cancer, 33, 134 lungs, 49 lupus, 31 lycopene, 119 lying, 210 lymph, 72, 150 lymph node, 72 lymphadenopathy, 150 lymphatic, 239, 261 lymphocyte, 4, 9, 11, 29, 63, 67, 82, 84, 90, 96, 144, 145, 148, 149, 150, 152, 153, 156, 158 lymphocytes, 3, 5, 9, 10, 11, 18, 24, 32, 37, 39, 40, 52, 62, 63, 72, 75, 76, 80, 82, 84, 87, 88, 89, 93, 144, 145, 146, 147, 148, 149, 150, 152, 153, 155, 157, 158, 159, 160, 239, 263 lymphocytosis, 64, 72, 84, 91 lymphoid, 43, 71, 74, 76, 83, 87, 227, 269 lymphoid organs, 76 lymphokine-activated killer, 145

Index lymphoma, 60, 72, 80, 81, 90, 91, 92, 93, 242, 275 lymphomagenesis, 72, 91 lymphomas, 61, 72, 73, 74, 91, 92 lysine, 247, 248 lysis, 290

M M1, 269, 277 machinery, viii, ix, 15, 51, 56, 65, 77, 79, 163, 202, 287, 290 macrophage, x, 12, 24, 25, 28, 39, 43, 63, 64, 65, 72, 75, 90, 96, 101, 110, 133, 148, 238, 239, 259, 260, 262, 264 macrophage inflammatory protein, 63, 264 macrophages, 9, 10, 11, 18, 24, 38, 40, 63, 65, 66, 69, 71, 75, 83, 84, 86, 89, 90, 95, 101, 104, 105, 109, 114, 116, 128, 132, 133, 137, 144, 148, 156, 157, 239, 248, 249, 256, 259, 262, 265 macular degeneration, 30 magnetic resonance imaging, 94 maintenance, 58, 158, 170, 274, 285, 289, 290, 297 maize, 164, 171, 173, 178, 179, 181, 224 major histocompatibility complex, 37, 43, 62, 82, 159 malabsorption, 98, 156 malaria, 225 males, 140, 184, 185, 190, 195 malignancy, xi, 264, 267 malignant, 63, 93, 242, 256, 292 mammalian, 33, 59, 230, 285, 289 mammalian brain, 188, 196 mammalian cell, xi, 6, 29, 34, 35, 43, 67, 118, 126, 231, 241, 279, 280, 285, 286, 287, 288, 289, 291, 293, 294, 295, 296, 297 mammalian cells, xi, 6, 29, 34, 35, 43, 67, 118, 126, 231, 241, 279, 280, 285, 286, 287, 288, 289, 291, 293, 294, 295, 296, 297 mammals, vii, ix, 8, 29, 51, 52, 110, 183, 184, 185, 186, 187, 188, 189, 190, 192, 193, 219, 280 management, 149, 262 manganese, 46 manipulation, 196, 289 MAPK, 30, 47, 176, 232 MAPKs, 31 mapping, 223

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Index marrow, 9, 272, 287 mass loss, 10 mass spectrometry, 61 mast cell, 101, 108, 126 mast cells, 101, 108 matrix, 13, 14, 15, 40, 44, 61, 74, 75, 76, 93, 94, 95, 117, 256, 257, 263, 264, 280 matrix metalloproteinase, 93, 94, 95, 117, 264 matrix protein, 14, 15, 40, 256, 263, 264 maturation, 6, 15, 16, 40, 41, 146, 154, 187, 196, 239, 256, 264, 269, 276 MCA, 117 MCP, 30, 63, 74, 75, 77, 95, 120, 182 MCP-1, 30, 63, 74, 75, 77, 95, 120 MDA, 102 MDR, 66, 118, 248 media, 111 median, 269 mediated gene delivery, 293 mediation, 259 mediators, 71, 80, 83, 145, 262 medicine, 126 melanogenesis, 38 melanoma, 96, 242 melatonin, 113, 135 membranes, 77, 102, 109, 121, 125, 147, 256 memory, 2, 9, 10, 11, 37, 39, 148, 152, 157, 158, 191 men, 96 Merck, 2, 16 mercury, 184, 198 mesangial cells, 105, 130 mesenchymal stem cell, 31, 280, 287, 292, 295, 296 mesenchymal stem cells, 31, 280, 287, 292 messenger RNA, 7, 8, 18, 261 meta-analysis, 151 metabolic, viii, 51, 104, 122 metabolism, viii, 51, 52, 61, 76, 90, 91, 97, 98, 103, 104, 109, 116, 119, 125, 128, 131, 132, 138, 145, 148, 157, 199, 224, 248, 261 metabolite, 103, 114, 128 metabolites, 100, 102, 103, 126, 128, 147, 148, 190 metabolizing, 116, 138 metalloproteinase, 93, 94, 95, 115, 117, 138, 264 metalloproteinases, 94, 256, 264 metastases, 238 metastasis, 5, 74, 251 metastatic, 62, 74

315 metazoans, ix, 183 methionine, 230 methodology, 211 methyl groups, 102, 109 methylation, 6, 34, 173, 287 methyltestosterone, 186 MHC, 4, 10, 17, 31, 37, 54, 62, 82, 94, 154, 243, 244, 245, 260 MHC class II molecules, 37 mice, 30, 38, 49, 91, 96, 113, 115, 116, 117, 120, 125, 127, 135, 137, 138, 140, 148, 149, 150, 151, 152, 154, 155, 157, 159, 189, 199, 251, 287, 292, 297 micelles, 102 microarray, 61, 69, 76, 119, 140, 189, 195, 227, 269, 272, 274, 276, 277, 288 microarray technology, 276 microarrays, 61, 268 microbes, 31, 36, 83, 159 microenvironment, 74, 146 microglia, 111, 116, 130 microglial, 110, 111, 116, 133, 138 microglial cells, 116, 138 microinjection, 173 micro-organisms, 146 microRNAs (miRNAs), 8, 29, 35, 36, 173 microsomes, 157 microtome, 239, 240 microtubule, 170 microvascular, 32, 161 migration, 33, 63, 74, 76, 96, 107, 153, 188, 250, 263 mimicking, 9 Ministry of Education, 222 MIP, 11, 39, 63, 120, 242, 264 mitochondria, 49, 92, 121, 123 mitochondrial, 71, 73, 111, 112, 121, 134, 140, 147 mitogen, 70, 176, 191 mitogen-activated protein kinase, 70, 176, 191 mitosis, 234, 290 mitotic, 70, 231 MLL, 93, 270, 271, 276, 277 MMP, x, 74, 75, 94, 95, 115, 238, 256, 257, 260, 264, 297 MMP-2, 75, 94, 95, 256, 257, 264 MMP-3, 75, 257 MMP-9, 74, 75, 94, 256, 257 MMPs, 75, 94, 257 MMTV, 53, 54, 55, 58

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316 mobility, 77, 120, 134 mode, 209 models, ix, 91, 146, 150, 156, 183, 184, 195 modulation, 34, 62, 64, 71, 83, 85, 93, 113, 114, 120, 124, 136, 149, 153, 154 modules, 232 moieties, 112 molecular mass, 140 molecular mechanisms, 112, 113, 139, 190 molecular oxygen, 101, 125 molecular weight, 14, 202 molecules, viii, 4, 10, 14, 31, 37, 43, 44, 51, 57, 62, 75, 76, 82, 104, 105, 106, 115, 116, 118, 127, 138, 144, 146, 154, 158, 191, 202, 242, 250, 252, 253, 254, 260, 261, 263 monitoring, 274, 275 monkeys, 159 monoclonal, 5 monoclonal antibody, 5 monocyte, viii, 63, 86, 95, 97, 117, 120, 129, 137, 146, 156, 262 monocyte chemoattractant protein, 120 monocyte chemotactic protein, 63, 95 monocytes, 3, 4, 5, 9, 19, 31, 32, 74, 75, 76, 83, 93, 95, 102, 105, 108, 110, 116, 118, 127, 130, 133, 259 monomer, 167, 168 monomers, 6, 167, 168 mononuclear cell, 153 mononuclear cells, 153 monosaccharide, 31 monosomy, 270 morbidity, 27, 151 morphogenesis, 40, 93, 263 morphological, 268, 270, 271, 272, 274, 276 morphology, 61, 116, 163, 268 mortality, 3, 22, 23, 27, 151, 160, 161 mortality rate, 3, 22 mosaic, 164, 167, 169, 170, 171, 172, 175, 179, 180, 205, 223, 225, 230 mouse, 31, 32, 53, 62, 75, 91, 101, 102, 104, 109, 110, 114, 123, 126, 127, 129, 140, 146, 152, 155, 182, 190, 196, 197, 198, 203, 220, 221, 229, 251, 252, 254, 261, 263, 285, 292, 296, 297 mouse model, 146, 182, 254, 297 mouth, 203, 212, 223, 224, 226, 228 movement, 165, 181 MSCs, 287 mucus, 23

Index multidrug resistance, 66, 118, 139 multiple sclerosis, 30 multivariate, 269 murine cell, 287 murine model, 82, 150 murine models, 150 muscle, 19, 46, 74, 104, 105, 106, 109, 115, 116, 119, 120, 123, 129, 130, 136, 137, 140, 147, 249, 252, 296 muscle cells, 19, 106, 109, 115, 116, 119, 130, 136, 137 muscles, 106 mutagenesis, 60, 73, 289 mutagenic, 104 mutant, 40, 80, 171, 172, 271 mutants, 47, 171, 191, 197, 224, 271, 272 mutation, 25, 142, 156, 171, 196, 224, 226, 231, 268, 270, 271, 272, 274, 275 mutations, viii, 2, 25, 28, 41, 42, 52, 60, 74, 119, 123, 179, 223, 268, 269, 270, 271, 272, 274, 275, 276, 277, 291 MYC, 206, 207, 247 mycobacteria, 240 myelin, 30, 191 myelin antigens, 30 myeloid, xi, 71, 74, 89, 92, 93, 138, 267, 274, 275, 276, 277, 278 myeloid cells, 71, 89, 138 myeloperoxidase, 269 myoblasts, 287 myocardial infarction, 102, 161 myocardial ischemia, 113 myricetin, 26

N Na+, 103, 114 N-acety, 112, 117, 118, 225 naming, 177 National Academy of Sciences, 261 national income, 2 National Institutes of Health, 27 natural, viii, 3, 8, 24, 26, 27, 37, 71, 93, 94, 97, 99, 105, 115, 119, 121, 123, 125, 126, 130, 146, 211, 212, 213, 216, 225 natural environment, 27 natural killer, 3, 8, 71 natural killer cell, 8, 71 natural selection, 24, 213, 216 neck, 30

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Index neck cancer, 30 necrosis, 4, 63, 65, 69, 71, 84, 89, 95, 205, 225, 232 needs, 119, 151 negative regulatory, 46, 59, 73 nematode, 6 neoangiogenesis, 238, 248, 260 neonatal, 196, 197, 297 neoplasm, 74, 260 neoplasms, 277 neoplastic, 102, 126, 132, 238, 259, 260 neoplastic cells, 238 nervous system, 60, 63, 94, 95, 96, 110, 124, 184, 185, 191, 197, 199, 228, 263 network, 63, 105, 144 neural connection, 185 neural development, ix, 183, 186, 187, 188, 191, 199 neural function, 191 neurobiological, 198 neurobiology, ix, 183, 190 neurodegeneration, 75, 111, 127, 135, 142 neurodegenerative, 94, 98, 111, 119, 123, 124, 156 neurodegenerative diseases, 98, 111 neurodegenerative disorders, 123, 156 neurodegenerative processes, 94 neuroendocrine, 197 neurogenesis, 123, 142, 185 neurological disease, 62, 75 neurological disorder, viii, 51 neuronal cells, 103, 109, 132, 227 neuronal death, 139 neuronal loss, 75 neurons, viii, 75, 97, 123, 127, 184, 187, 188, 189, 192, 193, 194, 195, 198, 286, 287 neuroprotection, 127 neuroprotective, 102, 117, 118, 119, 140 neurotoxins, 94 neurotransmission, 197 neurotransmitter, 185, 186, 187, 195, 196 neurotransmitters, 184, 185, 187, 193, 199 neurotrophic, 193, 196 neurotrophic factors, 196 neutralization, 11, 23, 42, 286 neutrophil, 3 neutrophils, 5, 76, 104, 105, 109, 132 Newton, 36 NF-kB, 45, 91 NF-κB, 18, 19, 20, 46, 108, 110, 207

317 Ni, 94, 132, 197, 263, 276, 292 Nielsen, 94, 224, 230 nitric oxide, 85, 134 nitric oxide synthase, 85 NK cells, 4, 144, 145 NMDA, 30 NMR, 141 nodes, 72 non-Hodgkin lymphoma, 74 non-small cell lung cancer, 134 norepinephrine, 184, 199 normal, ix, 56, 63, 67, 69, 70, 71, 73, 74, 81, 100, 101, 102, 106, 111, 117, 119, 127, 129, 139, 147, 150, 151, 183, 184, 185, 239, 240, 254, 268, 269, 271, 272, 273, 274, 275, 276, 277, 278, 289 normal development, ix, 183, 185 N-ras, 230 NS, 85, 91, 157, 223, 233 NSC, 291 N-terminal, 15, 180, 212 nuclear, viii, 15, 18, 19, 35, 40, 43, 49, 52, 58, 59, 60, 65, 66, 86, 93, 112, 114, 115, 116, 120, 121, 122, 132, 138, 158, 165, 169, 170, 173, 174, 181, 191, 203, 228, 232, 239, 248, 287, 289, 291, 293 nuclear factor, 59, 85 nuclear receptors, 114, 121, 122, 138 nuclease, 202 nuclei, 121, 173, 192, 280 nucleic acid, 6, 105 nucleocapsids, 279, 280 nucleoprotein, 53 nucleosome, 28, 42 nucleosomes, 170 nucleotide sequence, 78, 215, 220, 234 nucleotides, 8, 13, 56, 215, 285 nucleus, 8, 11, 19, 54, 58, 73, 145, 170, 173, 184, 210, 269 nursing home, 150 nutrient, viii, 97, 124

O observations, 110, 150 occlusion, 112, 117, 134, 280, 281, 282 oil, 98, 99, 118 oils, 98, 99, 103, 128, 147 older adults, 157, 275 oligomeric, 167, 168, 256

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Index

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318 oligomerization, 43, 168, 178 oligomers, 167, 234 oligonucleotide arrays, 272 oligonucleotides, 78, 230 olive, 99 olive oil, 99 oncogene, 36, 45, 46, 71, 72, 73, 80, 81, 82, 83, 85, 88, 89, 91, 92, 223, 226, 227, 230, 231, 233, 247, 264, 276, 278 oncogenes, 60, 72, 73, 91, 92 oncogenesis, 72 oncology, 80 oncoproteins, viii, 52, 72, 78, 168 oral, 182, 281, 282 organelle, 25 organelles, 114, 121, 122, 123 organic, 117 organism, 77, 78, 206, 207, 220 organization, 133, 232, 233 orientation, 59, 172, 192, 198, 282 osteonectin, 76, 256 osteopontin, 256 osteosarcoma, 82 ovarian cancer, 113, 135 oxidants, 98, 111, 114, 117, 118, 129, 133 oxidation, 102, 103, 128, 129, 130, 140, 147 oxidative, 71, 95, 105, 111, 112, 113, 118, 132, 134, 138, 147, 148, 149, 150, 156, 157, 158, 159, 160 oxidative damage, 134, 147, 157 oxidative reaction, 148 oxidative stress, 105, 111, 112, 113, 118, 132, 134, 138, 148, 149, 156, 158, 159 oxide, 85, 134 oxides, 120 oxygen, 71, 87, 101, 112, 117, 118, 125, 132, 134, 139, 147, 148, 149, 156, 158

P P300, 68 p38, 232 p53, 19, 38, 46, 69, 70, 73, 89, 92, 93, 117, 139, 191, 207, 219, 231 packaging, 15, 41, 48, 281, 282 pairing, 7, 173, 225 palm oil, 98, 118 PAN, 241 pandemic, 3, 5, 22, 23 Papillomavirus, 178

paracrine, 4, 65, 140, 145, 239, 261 paralysis, 203, 233, 234 paraoxonase, 137 parasites, 2, 155 parasitic diseases, 2 Parkinson‘s disease, 98 particles, 44, 52, 56, 80, 163, 182, 280, 288 PAS stain, 240 passenger, 7, 8 pathogenesis, x, xi, 8, 18, 23, 24, 49, 52, 74, 75, 77, 90, 180, 238, 248, 261, 262, 267 pathogenic, 52, 60, 83 pathogens, vii, viii, 1, 2, 3, 9, 10, 24, 25, 27, 52, 60, 61, 81, 144, 145, 146, 152 pathology, 239, 241 pathways, vii, 1, 6, 13, 20, 28, 29, 33, 38, 46, 61, 62, 64, 74, 81, 83, 89, 93, 95, 98, 104, 110, 113, 114, 121, 130, 152, 156, 173, 177, 244, 270, 291 patients, 3, 5, 11, 13, 23, 24, 25, 26, 30, 32, 33, 48, 49, 62, 65, 74, 75, 76, 86, 88, 90, 95, 119, 128, 141, 149, 150, 151, 156, 157, 159, 160, 161, 238, 239, 240, 241, 248, 254, 256, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277 pattern recognition, 6, 30 PC12 cells, 139 PCR, 61, 120, 187, 191, 239, 274 PDGF, 286 PDK, 107, 108 pediatric, 277 peptidase, 135 peptide, 18, 22, 30, 159, 212, 213 peptides, 62, 135, 175 per capita, 2 perception, 259 perinatal, 187 peripheral blood, 18, 32, 72, 83, 146, 149, 153, 154 peripheral blood lymphocytes, 18 peripheral blood mononuclear cell, 153 permeability, 117 permit, 77, 83 peroxidation, 98, 112 peroxide, 102, 136, 148, 156 personal communication, 287 perspective, 124 pesticides, 279, 291 PG, 80, 223 P-glycoprotein, 118, 125, 139 pH, 167

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Index phagocytic, 111, 156, 242 pharmaceutical, 3, 175, 177 pharmaceutical companies, 3 pharmacologic agents, 138 pharmacological, 117 phenotype, 63, 73, 95, 146, 148, 153, 155, 157, 159, 174, 195, 271, 289 phenotypes, 192, 225 phenotypic, 148 pheochromocytoma, 117 philosophy, vii, 1 phloem, 169 phorbol 12- myristate 13-acetate, 130 phosphatases, 80, 108, 132 phosphate, 7, 30, 97, 114, 120, 30, 136 phosphates, 136 phosphatidic acid, 121 phosphatidylcholine, 121 phosphatidylserine, 121 phosphodiesterase, 90 phosphoinositides, 134 phospholipids, 101, 109, 113, 121, 133, 141, 147 phosphoprotein, 120 phosphorylates, 6, 68 phosphorylation, 15, 19, 30,41, 46, 47, 58, 60, 62, 65, 85, 86, 102, 105, 106, 107, 108, 109, 111, 113, 115, 116, 130, 131, 132, 145, 152 photoperiod, ix, 183, 186, 199 photoreceptor, 142 physiological, 74, 104, 129, 167, 183, 185, 190, 195, 263, 264 physiology, 131 phytochemicals, 26 PI3K, 33, 107, 108, 109, 112, 114 pigs, 118 pilot study, 137, 160 PKC, 105, 106, 107, 111, 115, 130 placebo, 150, 160 placental, 243 plants, 6, 8, 26, 34, 36, 98, 164, 169, 170, 172, 173, 174, 175, 176, 177, 181, 182, 223 plaque, 238 plasma, 4, 9, 11, 12, 37, 40, 44, 98, 99, 100, 101, 102, 103, 107, 111, 112, 120, 122, 123, 125, 140, 141, 144, 147, 156, 241, 254, 261 plasma cells, 9, 144 plasma levels, 98, 241 plasma membrane, 4, 11, 12, 40, 44, 107, 111, 112 plasmid, 96, 115, 211, 212, 288, 295

319 plasmids, 170, 212, 213 Plasmodium falciparum, 225, 292 plasticity, 191 platelet, viii, 76, 97, 109, 110, 115, 132, 157, 225, 286 platelet aggregation, viii, 97, 115 platelets, 110, 124, 130 platforms, 272 play, ix, 56, 61, 64, 76, 98, 118, 120, 123, 145, 166, 169, 172, 193, 195, 201, 249, 256, 257, 259 pleiotropic factor, 191 pluripotency, 289 PMA, 106 pneumonia, 3, 5, 22, 23, 26 point mutation, 271 polarization, 96, 146 polio, 2 poliovirus, 224, 230 pollutants, 184 polycythemia, 124, 142 polycythemia vera, 124, 142 polymerase, 16, 20, 34, 53, 56, 58, 67, 68, 70, 78, 80, 90, 95, 166, 169, 170, 187, 210, 281, 293 polymerase chain reaction, 95, 187 polymorphisms, 98, 125 polypeptide, x, 4, 71, 238, 245, 246, 247, 248, 259, 284 polyphenols, 112, 135 polysaccharide, 37 polyunsaturated fat, 161 polyunsaturated fatty acid, 161 polyunsaturated fatty acids, 161 population, 23, 24, 25, 60, 67, 144, 269, 271, 287 population group, 23 population size, 67 positive feedback, 58 post-translational, 64, 110, 175, 176, 280, 282, 290, 293 post-translational modifications, 175, 280, 282 potassium, 103, 114, 227, 230 potato, 227 power, 213, 216 PP2A, 71, 106, 108, 110, 111 PPARγ, 105, 112, 114 pRb, 46 pRB, 179 prediction, 90, 217, 218, 221, 269, 274, 275, 278 predictors, 277 press, 136, 154, 296

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320 pressure, 3, 5, 24, 118, 213, 215, 285, 289 prevention, viii, 11, 52, 98, 107, 120, 129, 151, 160 preventive, 11, 98, 126 primary cells, 69, 82, 292, 295 primate, 17 primates, 60 priming, 49, 145, 146, 153 principle, ix, 104, 201, 202, 211 pro-apoptotic protein, 70, 112 probability, 291 probe, 270 procyanidins, 26 producers, 4 production, viii, xi, 2, 4, 9, 11, 19, 20, 24, 30, 31, 32, 39, 47, 51, 58, 64, 69, 71, 77, 83, 89, 95, 98, 104, 106, 110, 111, 112, 113, 124, 126, 133, 134, 144, 146, 148, 149, 150, 151, 152, 153, 154, 157, 159, 175, 176, 182, 279, 280, 283, 285, 288, 290, 293, 294, 295 progenitor cells, 142, 238, 296 progenitors, 260, 287, 295 progeny, viii, 2, 51, 52, 56, 77, 95, 280 prognosis, 270, 271, 274, 277 prognostic marker, xi, 267 prognostic value, 276 program, ix, 67, 83, 95, 183, 217, 219, 240, 272 progressive neurodegenerative disorder, 123 proinflammatory, 19, 74, 110, 128, 148, 242, 261, 264 pro-inflammatory, 6, 249, 258, 259 prokaryotes, 167 prokaryotic, 191, 211 Proliferating Cell Nuclear Antigen, 179 proliferation, viii, 3, 4, 5, 8, 11, 20, 24, 32, 36, 45, 51, 63, 64, 67, 69, 70, 71, 72, 74, 85, 90, 91, 97, 101, 102, 107, 108, 114, 117, 126, 128, 129, 132, 136, 139, 145, 148, 156, 188, 193, 196, 289 promoter, viii, 18, 19, 20, 44, 45, 46, 51, 52, 53, 55, 56, 57, 58, 59, 68, 73, 79, 80, 82, 84, 86, 87, 90, 115, 117, 137, 164, 165, 171, 172, 174, 177, 180, 198, 210, 214, 215, 216, 225, 226, 229, 233, 280, 281, 282, 283, 284, 286, 287, 288, 289, 290, 293, 294, 295 promoter region, viii, 51, 52, 56, 57, 84, 172 promyelocytic, viii, 52, 269, 274, 275, 276 pro-oxidant, 102, 118 propagation, 39, 63, 105, 228, 286 prophylactic, 38

Index proposition, vii, 1 prostaglandin, 64, 104, 110, 114, 145, 154 prostaglandins, 156 prostanoids, 157 prostate, 101, 109, 112, 114, 117, 118, 119, 126, 132, 136, 139, 140 prostate cancer, 101, 112, 119, 126, 132, 140 prostate carcinoma, 114, 136 proteases, 88, 102, 285 proteasome, 112, 134 protection, 3, 22, 28, 63, 78, 102, 107, 118, 126, 132, 181, 292, 297 protective role, 117, 124, 137, 155 protein arrays, 77 protein binding, 92 protein family, 19, 22, 141, 169, 179, 256 protein function, 60, 72, 177, 178 protein kinase C, viii, 83, 97, 105, 106, 107, 110, 111, 114, 115, 127, 129, 130, 131, 132, 133, 136, 137, 230 protein kinase C (PKC), viii, 97, 105, 106, 107, 111 protein kinases, 179 protein synthesis, ix, 4, 6, 7, 8, 201, 202, 223, 226, 227, 228, 231, 281 proteinase, 135 protein-protein interactions, 167, 211 proteins, 13, 22, 41, 120, 179, 181 proteolysis, 144 proteolytic enzyme, 74, 75 proteome, 96 proteomics, 61, 120 protocol, 273 protocols, 274 proto-oncogene, 60, 65, 71, 72, 85, 92, 230 protoplasts, 172 PSI, 112 PTPs, 74 public, 2, 212, 219 PUMA, 69, 89 purification, 38, 141, 175, 176, 177, 182

Q quadriceps, 287 quercetin, 26 query, 217 quinone, 104, 141 quinones, 102, 129

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R RAC, 132 race, 130 radiation, 131, 277 radioresistance, 118 RAGE, 261 random, 174, 207, 210, 215, 230, 289 range, 22, 60, 107, 125, 164, 175, 177, 191, 194, 271 RANTES, 11, 39, 63, 120 rapamycin, 70, 232 ras, 230 RAS, 271, 274 rat, 92, 102, 110, 111, 113, 117, 119, 120, 126, 133, 135, 137, 138, 139, 140, 141, 184, 196, 197, 221, 251, 286, 287, 289, 295, 296, 297 rats, 28, 102, 103, 104, 107, 110, 111, 115, 116, 117, 118, 119, 120, 124, 125, 128, 130, 139, 157, 158, 184, 187, 189, 193, 196 reactive oxygen species (ROS), 71, 87, 112, 118, 132, 134, 139, 149 reactivity, 101, 102 reading, vii, 51, 53, 164, 167, 176, 213, 223, 234, 280 reagents, 273 reality, 125, 213, 215 receptors, viii, 4, 5, 6, 10, 11, 12, 18, 23, 30, 39, 51, 62, 63, 69, 88, 101, 104, 114, 121, 122, 138, 144, 145, 152, 187, 193, 196, 197, 198, 199, 251, 254, 259, 263 recognition, 6, 10, 30, 37, 43, 47, 58, 62, 63, 64, 112, 121, 146, 152, 167 recombinant DNA, 3 recombination, 62, 82, 290 reconstruction, 197, 222 record keeping, 23 recovery, 142 recruiting, 5 red blood cells, 238 redox, 114, 136 reduction, 102, 105, 106, 108, 111, 113, 115, 116, 117, 127, 148 redundancy, 218, 219 regeneration, 290 regulators, 58, 245, 248, 256, 263 Reimann, 38 rejection, 25 relationship, ix, 42, 163, 268, 272, 275 relationships, 116

321 relevance, 136, 213, 215, 275 remission, 270 remodeling, 83, 170, 288 remodelling, 256, 260 renal, 128, 141 renal cell carcinoma, 141 renal disease, 128 reoxygenation, 90 repair, 16, 21, 28, 49, 71, 257, 293 reperfusion, 32, 102, 112, 113, 134 repression, 46, 73, 78, 92, 166 repressor, 33, 73, 81, 90 reptiles, 197 reserves, 25 reservoirs, 67, 69 residues, 17, 41, 53 resistance, 13, 29, 40, 42, 66, 110, 118, 134, 139, 150 resolution, 133 resources, 9, 211, 212, 222, 234 respiratory, 10, 23, 38, 49, 111, 130, 159 respiratory syncytial virus, 23, 49 responsiveness, 145, 148, 153, 156 Resveratrol, 113, 135 retention, 101, 103, 104, 118, 123, 290 reticulum, 17, 54, 233 retina, 147 retinitis, 123, 142, 156 retinitis pigmentosa, 123, 142, 156 retinoblastoma, 167, 179, 180 retinoic acid, 115 retinol, 124 retroviral, 42, 51, 57, 59, 61, 91 retrovirus, 44, 53, 54, 55, 59, 60, 63, 72, 74, 78, 79, 80, 81, 150, 216 retroviruses, viii, 2, 14, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 64, 66, 67, 72, 75, 76, 77, 78, 79, 80, 81, 91, 209 returns, 217 reverse transcriptase, viii, 14, 15, 41, 42, 47, 51, 52, 53, 77, 95, 191 Reynolds, 180 rhinitis, 203, 226 Rho, 77 ribosomal, ix, 15, 201, 210, 212, 213, 223, 224, 226, 227, 228, 229, 233, 284 ribosomal RNA, 210 ribosome, ix, 201, 202, 209, 211, 212, 213, 215, 216, 217, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 294

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Index

322 ribosomes, 202, 212, 227, 231, 233 RISC, 6, 35 risk, xi, 25, 81, 101, 151, 161, 267, 268, 270, 276, 278 RNAi, 6, 7, 29, 34, 35, 38, 227 rodent, 71, 184, 280 rodents, 60, 220 rods, 269 rolling, ix, 163, 165, 169, 170 RSV infection, 28

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S Saccharomyces cerevisiae, 55, 206, 207, 208, 221, 225, 227, 231, 233, 234 safety, 151, 157, 291 sample, 267, 269, 272 SARS, 10, 29, 38, 297 SARS-CoV, 29, 38 satellite, 225 scaffold, 31 scalable, 293 scavenger, x, 104, 105, 108, 112, 114, 116, 119, 120, 122, 132, 137, 238, 248, 249, 259, 262 scleroderma, 111 sclerosis, 30 SDF-1, 63 SDS, 102, 113 sea urchin, ix, 183 search, 125, 211, 217, 219, 221, 222, 226 search engine, 211, 222 searches, 217 searching, 218 sebaceous glands, 121 secret, 6 secrete, 152 secretion, 23, 72, 102, 105, 107, 121, 125, 129, 130, 141, 145, 146, 151, 153, 189, 192, 293 seeding, 238, 260 seeds, 98 segregation, 290 selecting, 211 selectivity, 13, 103 selenium, 116, 119, 120, 139 self, 145, 146 self-antigens, 10 self-renewal, 36 senescence, 87, 168 sensitivity, 42, 68, 87, 190, 197, 227 sensory systems, 196

series, 116, 202, 270 serine, 6, 66, 72, 87, 117 serotonergic, 184, 193, 197, 199 serotonin, 31, 184, 187, 196, 198, 199 serum, 46, 86, 102, 103, 112, 113, 126, 128, 132, 135, 147, 150, 160, 290, 297 services, iv sesame, 103, 128 severe acute respiratory syndrome, 38 severity, 23 sex, ix, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 195, 196, 197, 198, 199, 269 sex differences, 185, 188, 190, 196, 197, 198 sex steroid, 186, 187, 190, 195 sexual behavior, 198 sexual dimorphism, 184, 185, 190 sexual orientation, 192, 198 SH, 78, 88, 90, 91, 153, 155, 228 sheep, 251 shock, 67, 112, 134, 222, 226, 282, 294 short run, 53 short-term, 23, 84, 115, 137, 157 shrimp, 225 side effects, 26, 286 signal transduction, vii, viii, 1, 13, 30, 33, 63, 97, 98, 102, 105, 108, 114, 115, 119, 120, 122, 124, 130, 149, 158 signaling, viii, 6, 28, 29, 31, 32, 33, 36, 62, 63, 72, 74, 89, 95, 105, 107, 110, 117, 118, 121, 132, 136, 143, 152, 158 signaling pathway, 6, 28, 29, 31, 62, 63, 74, 89, 121 signaling pathways, 6, 28, 29, 62, 74, 89, 121 signalling, 43, 83, 119, 129, 250, 263 signals, ix, x, 39, 40, 44, 47, 53, 56, 59, 85, 86, 96, 153, 183, 197, 201 signs, 4, 276 similarity, 121, 217, 218, 225 single nucleotide polymorphism, 125 SIR, 164, 165, 166 siRNA, 3, 6, 7, 11, 25, 26, 29, 35, 36, 38, 173 SIS, 207, 210 sites, viii, 5, 16, 40, 42, 44, 51, 53, 55, 57, 59, 63, 72, 79, 107, 167, 210, 211, 213, 215, 216, 217, 223, 224, 225, 226, 227, 228, 230, 231, 233, 234, 284, 290 skeletal muscle, 120, 147, 296 skin, 32, 45, 46, 74, 110, 115, 121, 123, 141, 240, 242, 254, 259, 260, 262 skin cancer, 46

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Index skin diseases, 242, 259 SLPI, 66 smallpox, 2 smooth muscle, 19, 46, 74, 104, 105, 106, 109, 115, 116, 119, 129, 130, 136, 137, 252 smooth muscle cells, 19, 106, 109, 115, 116, 119, 130, 136, 137 SOD, 71 sodium, 113, 287, 288, 290 sodium butyrate, 287, 288, 290 software, 270 solid tumors, 74 solubility, 285 songbirds, 189 sorting, 93, 227 South Africa, 60, 177 soy, 112 soybeans, 98, 279 Spanish influenza, 27 spastic, 60, 62, 81 spatial, 18, 45 species, 2, 3, 6, 24, 25, 26, 71, 72, 87, 99, 112, 118, 132, 134, 139, 147, 148, 149, 169, 184, 190, 241, 279, 280, 284, 286 specificity, 19, 45, 100, 114, 121, 133, 141, 146, 158, 171 spectrophotometry, 285 spectroscopy, 141 spectrum, 49 speculation, 268 sperm, 140 SPF, 120 S-phase, 167, 179 spinal cord, 112, 134 spindle, 74, 170, 238, 239, 240, 241, 254 spines, 198 spleen, 249 splenomegaly, 150 SRS, 217 stability, 92, 102, 110, 283, 285, 294 stabilization, 81, 167, 249 stabilize, 290 stages, 4, 5, 166, 188, 190, 191, 192, 195, 238, 287 Staphylococcus, 176 STAT5, 11, 39 staurosporine, 67 stellate, 115, 137, 160 stellate cells, 115

323 stem cells, 9, 31, 88, 280, 287, 288, 292, 293, 296 steroid, 18, 119, 187, 188, 189, 193, 195, 196, 197 steroid hormone, 18 steroids, 186, 188, 189, 190, 195, 199 stimulant, 32 stimulus, 131 stomatitis, 295 storms, 10, 23, 24 strain, vii, 2, 5, 25, 90 strains, vii, 1, 2, 3, 5, 12, 23, 25, 32 strategies, vii, viii, ix, 1, 2, 3, 9, 52, 61, 81, 147, 149, 151, 201, 290 strategy use, 27 stratification, xi, 267 strength, 22, 286 stress, 69, 71, 90, 105, 111, 112, 113, 118, 124, 129, 132, 134, 135, 137, 138, 148, 149, 156, 158, 159, 202, 226, 232, 233, 264 striatum, 197 stroke, 102 stromal, 63 structural characteristics, 211 structural gene, 39 structural protein, viii, 65, 97, 98, 105, 158, 234, 281 sub-cellular, 92, 272 subgroups, 258, 268, 270, 272, 273, 274 substrates, 62, 134 suffering, 26, 160 sulfate, 111, 133, 225 sulfur, 246 sunflower, 99 supernatant, 120, 141 superoxide, 46, 71, 90, 105, 106, 111, 113, 118, 133, 136 superoxide dismutase, 46, 71, 90, 105 supplemental, 151 supplements, 138, 157 supply, 128 suppression, x, 6, 8, 22, 26, 35, 74, 88, 102, 107, 118, 126, 142, 149, 150, 153, 156, 159, 171, 173, 174, 181, 185, 197, 237 suppressor, 8, 29, 35, 36, 117, 153, 154, 173, 191, 281 suppressor cells, 153 suppressors, 36, 42, 173 surgery, 103, 262 surveillance, vii, 1, 243

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324 survival, viii, 3, 6, 11, 15, 24, 25, 45, 51, 63, 67, 69, 70, 71, 72, 78, 83, 88, 89, 90, 91, 107, 142, 151, 154, 269, 270 survival rate, 270 susceptibility, 23, 29, 98, 146 SV40, 29, 167, 168, 204, 286 switching, 10, 79 symptom, 173 symptoms, 94, 101, 123, 124, 248 synapse, 188 synaptogenesis, 192 syndrome, 38, 60, 81, 93, 98, 204, 225, 286 synergistic, 24, 283 synthesis, ix, 4, 6, 7, 8, 12, 16, 41, 42, 44, 52, 53, 55, 56, 57, 62, 65, 73, 76, 95, 102, 104, 107, 109, 110, 114, 115, 117, 119, 120, 126, 127, 130, 133, 136, 156, 157, 166, 170, 175, 178, 186, 196, 199, 201, 202, 223, 226, 227, 228, 231, 233, 262, 281, 282 systems, vii, 1, 107, 123, 124, 175, 176, 177, 185, 186, 188, 189, 195, 196, 229, 285, 290, 291, 297

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T T and C, 17 T cell receptors, 152, 158 T lymphocyte, 4, 9, 10, 11, 32, 39, 52, 62, 63, 76, 80, 82, 84, 87, 89, 144, 145, 148, 149, 152, 153, 155, 158, 159, 263 T lymphocytes, 9, 10, 11, 32, 39, 52, 62, 76, 80, 82, 84, 87, 89, 144, 145, 148, 149, 152, 155, 158, 159, 263 T regulatory cells, 149, 154 tandem repeats, 170 tannic acid, 135 TAR, 8, 16, 17, 18, 20, 36, 42, 43, 47, 58, 59, 66, 68, 80, 87 targets, vii, viii, 2, 3, 11, 12, 23, 25, 48, 52, 78, 107, 120, 234 taxonomic, 220 TBP, 65, 123 TCR, 62, 63, 72, 158, 274 tea, 112, 135 technology, 3, 29, 35, 149, 276, 290, 292, 297 telencephalon, 184 temperature, ix, 183, 184, 185, 186, 187, 188, 189, 190, 191, 193, 195, 197, 199 temporal, 18, 45 tenascin, 191, 255, 256

test data, 269 test procedure, 233 testes, 120, 140 testis, 264 testosterone, 120, 199 tetanus, 148, 156 TGF, 63, 65, 83, 115, 145, 146, 154, 264 Th1 polarization, 146 therapeutic approaches, 11, 24 therapy, xi, 12, 13, 26, 28, 37, 60, 76, 78, 90, 93, 110, 131, 139, 150, 155, 159, 160, 263, 267, 270, 275, 280, 285, 286, 290, 291, 292 theta, 133 thinking, 202 threat, 23 threats, 2, 3, 6, 25 threonine, 66, 87 threshold, 103 thrombin, 105, 110, 130, 131 thromboxane, 110 thymidine, 225 thymocytes, 32, 37 thymus, 10, 146, 148 tilapia, 187 TIMP-1, 115, 256, 257, 259 tissue, x, 5, 23, 32, 56, 75, 95, 98, 99, 115, 117, 125, 147, 171, 182, 190, 196, 237, 239, 240, 242, 243, 248, 249, 256, 259, 261, 264, 280, 286, 290, 293 tissue engineering, 280, 293 tissue remodelling, 256 TLR, 30 TLR3, 33 TLR4, 30 T-lymphocytes, 144, 157 TMP, 219 TNF, 4, 8, 19, 31, 32, 63, 64, 66, 69, 75, 83, 88, 89, 96, 117, 120, 144, 150 TNF-alpha, 31, 32, 83, 89 tobacco, 164, 170, 172, 173, 174, 182, 205, 225, 230, 232, 234 tocopherols, viii, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 136, 137, 138, 139, 147, 156 tocotrienols, 97, 98, 99, 101, 102, 104, 105, 106, 108, 113, 116, 118, 119, 124, 125, 126, 127, 131, 147 tolerance, 10, 145, 149, 153

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Index tomato, 164, 167, 169, 180, 224 toxic, 103, 175, 286 toxicity, 75 toxin, 292 TPA, 115 trade, 280 training, 269 transcript, 18, 56, 58, 66, 68, 73, 169, 172, 202, 212, 219, 241, 294 transcriptase, viii, 14, 15, 16, 41, 42, 47, 51, 52, 53, 77, 78, 79, 95, 191 transcription factor, viii, 6, 18, 19, 20, 39, 45, 46, 51, 56, 57, 59, 64, 65, 66, 68, 73, 79, 80, 86, 87, 91, 93, 96, 97, 98, 105, 113, 114, 115, 119, 120, 121, 136, 145, 158, 167, 281, 282 transcription factors, viii, 6, 18, 20, 45, 46, 51, 56, 57, 59, 64, 65, 66, 68, 80, 97, 98, 105, 113, 114, 115, 119, 120, 121, 145, 167, 282 transcriptional, viii, ix, 5, 20, 34, 43, 45, 46, 52, 56, 58, 61, 64, 66, 67, 68, 75, 76, 77, 79, 83, 84, 85, 97, 117, 126, 136, 152, 163, 165, 172, 173, 177, 181, 233, 280, 282, 284, 286, 287, 296 transcripts, 8, 13, 47, 48, 56, 58, 59, 64, 73, 140, 156, 172, 174, 180, 190, 192, 210, 213, 218, 241, 242, 270, 274, 281, 282 transducer, 152 transducin, 65 transduction, vii, viii, 1, 13, 30, 33, 63, 72, 97, 98, 102, 105, 108, 114, 115, 119, 120, 122, 124, 130, 149, 158, 287, 288, 289, 290, 291, 296 transfection, 115, 210, 288 transfer, 56, 100, 120, 121, 122, 123, 135, 138, 140, 141, 142, 147, 155, 156, 158, 210, 234, 291, 292, 293, 294, 295, 296, 297 transferrin, 246, 247, 248 transformation, 2, 19, 58, 59, 63, 72, 74, 78, 93, 242, 259, 269 transforming growth factor, 63, 137 transgene, xi, 34, 174, 279, 280, 286, 287, 288, 289, 290, 292, 295, 296, 297 transgenic, 38, 91, 135, 148, 155, 171, 172, 174, 175, 181, 182, 283 transgenic mice, 38, 91, 135, 148 transgenic plants, 172, 175, 181, 182 transglutaminase, 136 transition, 17, 62, 71, 167 translation, ix, 3, 8, 12, 15, 18, 102, 201, 202, 203, 205, 209, 210, 211, 212, 216, 222, 223,

325 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 284, 285, 287, 294 translational, 8, 35, 64, 175, 202, 210, 223, 224, 226, 229, 230, 231, 234, 280, 282, 285, 290, 293 translocation, 14, 15, 19, 20, 55, 60, 93, 105, 106, 107, 109, 111, 126, 130, 133, 141, 269, 270, 271, 275 translocations, viii, 52, 55, 60, 72, 74, 92, 268, 270, 271, 272, 276 transmembrane, 5, 11, 17, 32, 38, 63, 158, 263, 271 transmembrane glycoprotein, 11 transmembrane region, 271 transmission, 39, 196, 280 transparent, 203 transplant, x, 237, 238, 239, 260 transplantation, 153, 155 transport, 54, 63, 76, 80, 97, 98, 104, 111, 113, 120, 121, 122, 123, 124, 125, 156, 173, 181, 287 transportation, 2 travel, 11 trial, 2, 23, 124, 150, 160, 161, 275 trichostatin, 288, 295 trichostatin A, 288, 295 triggers, 19, 83, 233, 287, 291 triglycerides, 147 trisomy, 268, 278 troglitazone, 112, 135 trophoblast, 243 tryptophan, 4, 71 tuberculosis, 2, 5, 23, 24,25, 33, 49 tumor, 4, 29, 35, 46, 53, 55, 62, 63, 65, 69, 72, 73, 74, 76, 78, 81, 84, 89, 95, 104, 110, 112, 117, 118, 120, 127, 134, 135, 139, 153, 168, 191, 262 tumor cells, 62, 72, 89, 112, 127 tumor necrosis factor, 4, 63, 65, 69, 84, 89, 95 tumorigenesis, 72, 74, 107 tumorigenic, 73, 74 tumors, 8, 37, 60, 74, 91, 102, 109, 117, 118, 129, 145, 261, 262 tumour, 78, 238, 242, 250, 254, 259, 268 tumour growth, 250, 254 tumours, 91, 254, 256 turnover, 11, 37, 46, 199 two-way, 269 tyrosine, viii, 4, 11, 13, 30, 62, 70, 71, 74, 82, 93, 97, 108, 109, 132, 152, 271

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Index

326

U ubiquitin, 18, 21, 43, 65, 191, 285 ulcerative colitis, 37 ultraviolet (UV), 19, 87 ultraviolet light, 87 umbilical cord, 74 uniform, 23 untranslated regions, 207, 215, 225, 231, 233, 234 urinary, 128 urine, 103, 147 USDA, 27 UTRs, 206, 210, 213, 214, 216, 219, 222

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V vaccination, 9, 23, 292, 297 vaccine, 2, 3, 5, 23, 27, 28, 33, 49, 148, 175, 176, 182, 225, 280, 290, 292, 297 validation, 61, 141 valproic acid, 288 values, 261 variability, viii, 52 variable, 244, 269 variation, 23, 29, 243, 296 vascular cell adhesion molecule, 74, 96 vascular endothelial growth factor (VEGF), 19, 47, 113, 117, 118, 135, 139, 207, 226 vascular wall, 120 vasopressin, 231 VCAM, 74, 102, 252, 253 vector, xi, 6, 29, 35, 43, 174, 176, 177, 211, 213, 279, 282, 283, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297 VEGF expression, 19, 113 vein, 112 velvet, 279 versatility, 285 vertebrates, ix, 54, 183, 189, 190, 220, 263 very low density lipoprotein, 147 vesicles, 10, 104, 121 vessels, x, 238, 248, 251, 254, 256, 259, 260 victims, 10 vimentin, 249 viral envelope, 53 viral hepatitis, 152, 160 viral infection, vii, ix, 1, 2, 4, 5, 6, 8, 33, 34, 36, 61, 62, 67, 143, 146, 149, 151, 158, 202

viral promoter, 45, 59, 286 viral vectors, 174, 175, 182 virulence, 3, 9, 27, 83 virus infection, 23, 28, 36, 40, 54, 74, 94, 149, 158, 159, 170, 282, 291, 292 virus replication, ix, 23, 28, 29, 48, 96, 163, 165, 170, 178, 179, 180, 280 viruses, vii, viii, ix, xi, 1, 2, 3, 8, 10, 12, 22, 23, 25, 27, 33, 36, 41, 44, 48, 51, 52, 54, 55, 56, 59, 73, 77, 78, 81, 93, 144, 146, 152, 163, 168, 173, 175, 201, 202, 203, 205, 207, 209, 210, 224, 227, 228, 232, 262, 279, 284 visualization, 158, 179 vitamin A, 160 vitamin C, 118 vitamin E, viii, ix, 97, 98, 100, 101, 102, 103, 104, 105, 107, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 123, 124, 125, 126, 127, 128, 129, 132, 133, 136, 137, 138, 139, 140, 141, 143, 147, 148, 149, 150, 151, 154, 155, 156, 157, 159, 160, 161 vitamins, 118, 124, 129, 160 Vitronectin, 253 VLDL, 100, 104, 121, 122, 147 Von Willebrand factor, 241, 254, 260

W water, 100, 103, 114, 184, 187, 188 water-soluble, 103 web, 217, 261 web-based, 261 wheat, 168, 171, 178, 179, 180 white blood cell count, 270 wild type, 271 Wistar rats, 104 women, 128 work, 105, 151 World Health Organization (WHO), 2, 268, 271, 277 worm, 6

X xenobiotic, 138 xenobiotics, 116 xenograft, 262 X-linked, 88, 234

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Y yeast, 48, 113, 168, 169, 170, 175, 179, 220, 225, 226, 227, 228, 232, 286, 290, 294 yield, viii, 52, 212, 284, 288

Z ZAP-70, 30 Zea mays, 206 zebrafish, 198 zinc, 15, 19, 46, 74, 192, 233, 246, 261 Zn, 71, 90

Β

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β-amyloid, 113, 120

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327