Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011 [1 ed.] 9781614991847, 9781614991830

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SPECTROSCOPY OF BIOLOGICAL MOLECULES

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

Advances in Biomedical Spectroscopy Spectroscopic methods play an increasingly important role in studying the molecular details of complex biological systems in health and disease. However, no single spectroscopic method can provide all the desired information on aspects of molecular structure and function in a biological system. Choice of technique will depend on circumstance; some techniques can be carried out both in vivo and in vitro, others not, some have timescales of seconds and others of picoseconds, whilst some require use of a perturbing probe molecule while others do not. Each volume in this series will provide a state of the art account of an individual spectroscopic technique in detail. Theoretical and practical aspects of each technique, as applied to the characterisation of biological and biomedical systems, will be comprehensively covered so as to highlight advantages, disadvantages, practical limitations and future potential. The volumes will be intended for use by research workers in both academic and in applied research, and by graduate students working on biological or biomedical problems. Series Editor: Dr. Parvez I. Haris De Montfort University, Leicester, United Kingdom

Volume 7 Recently published in this series

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Vol. 6. Vol. 5. Vol. 4. Vol. 3. Vol. 2. Vol. 1.

F. Severcan and P.I. Haris (Eds.), Vibrational Spectroscopy in Diagnosis and Screening M. Ghomi (Ed.), Applications of Raman Spectroscopy to Biology – From Basic Studies to Disease Diagnosis A.B. Dahlin, Plasmonic Biosensors – An Integrated View of Refractometric Detection A.J. Dingley and S.M. Pascal (Eds.), Biomolecular NMR Spectroscopy A. Barth and P.I. Haris (Eds.), Biological and Biomedical Infrared Spectroscopy B.A. Wallace and R.W. Janes (Eds.), Modern Techniques for Circular Dichroism and Synchrotron Radiation Circular Dichroism Spectroscopy

ISSN 1875-0656 (print) ISSN 1879-811X (online)

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

Spectroscopy of Biological Molecules Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011

Edited by

Maria Paula Marques University of Coimbra, Portugal

Luís A.E. Batista de Carvalho University of Coimbra, Portugal

and

Parvez I. Haris

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Faculty of Health & Life Sciences, De Montfort University, UK

Amsterdam • Berlin • Tokyo • Washington, DC

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

©2013 The authors and IOS Press. 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, without prior written permission from the publisher. ISBN 978-1-61499-183-0 (print) ISBN 978-1-61499-184-7 (online) Library of Congress Control Number: 2013951688 Cover image: Raman images, reconstructed via hierarchical cluster analysis from raw hyperspectral datasets of (A) a cultured HeLa cell, (B) an exfoliated oral mucosa cell, and (C) a squamous cell from the distal urethra. See Diem et al. for details (pages 1–29). Publisher IOS Press BV Nieuwe Hemweg 6B 1013 BG Amsterdam Netherlands fax: +31 20 687 0019 e-mail: [email protected]

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Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

Spectroscopy of Biological Molecules M.P. Marques et al. (Eds.) IOS Press, 2013 © 2013 The authors and IOS Press. All rights reserved.

v

Preface

Proceedings of 14th European Conference on the Spectroscopy of Biological Molecules Maria Paula Marques a , Luis A.E. Batista de Carvalho a and Parvez I. Haris b a

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Molecular Physical-Chemistry Group, Department of Life Sciences, Faculty of Science and Technology, University of Coimbra, Coimbra, Portugal b Faculty of Health and Life Sciences, De Montfort University, Leicester, UK

The 14th European Conference on the Spectroscopy of Biological Molecules was held in the campus of the University of Coimbra, Portugal, from 29th August to 3rd September 2011. The local organisers of the conference were Maria Paula Marques and Luis A.E. Batista de Carvalho. The University of Coimbra, founded in 1270, is one of the oldest in the world. It provided an excellent venue for spectroscopists from Europe and other parts of the world to meet, present their latest research work and engage in a fruitful exchange of ideas. Some of the topics discussed in the conference are presented in this book. This special volume, containing the Proceedings of the 14th European Conference on Spectroscopy of Biological Molecules (28 August–3 September 2011, Coimbra, Portugal), is dedicated to Professor Juana Bellanato, who is an inspiration to us all. We all had the pleasure of meeting her in Coimbra. One of us (PIH) previously communicated with Prof. Bellanato to obtain photograph of her, next to an early commercial infrared spectrometer, for inclusion in a book (see [1]). Prof. Bellanato is one of the early pioneers in the use of vibrational spectroscopy for the study of biological molecules and her work has been highlighted by Barth and Haris [1]. Maria Paula Marques (MPM), one of the organisers of the conference, had a conversation with Prof. Bellanato (JB) in Coimbra and a summary of this is presented below. Prof. Juana Bellanato obtained her BSc in Chemistry, in 1950, and her PhD in Chemistry in 1954 (both from the Complutense University of Madrid, Spain). She started her academic career in the Spanish National Research Council (CSIC, Madrid) as a research assistant at the “Patronato Alfonso X el Sabio”, in 1955, and as a scientific collaborator of the “Patronato Juan de la Cierva”, in 1956. She obtained postdoctoral fellowships for the Institute of Physical-Chemistry of the University of Freiburg (Germany), between 1956 and 1957, and for the Physical-Chemistry Laboratory of the University of Oxford (UK), from 1959 to 1960. Correspondence: M.P. Marques, e-mail: [email protected]. L.A.E. Batista de Carvalho, e-mail: [email protected]. Parvez I. Haris, Faculty of Health and Life Sciences, De Montfort University, Leicester, LE1 9BH, UK, e-mail: [email protected].

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M.P. Marques et al. / Preface

In 1967, she became a research scientist at the “Patronato Juan de la Cierva”, and from 1971 to 1990 she was a research professor at the Institute of Optics of CSIC (Madrid). Between 1990 and 1994 she was Doctor “ad honorem” at the Institute of Optics. Since 1994, she is a Doctor “ad honorem” at the Institute of Structure of Matter of CSIC. She is the author of more than 200 scientific papers, 1 book, and several chapters in books. She has also collaborated with numerous international groups, with short stays in research centers from Montreal, Florence and Bologna Universities. She was appointed member of numerous scientific boards: 1975–1979 Head of the Molecular Spectra Section and of the Infrared Spectroscopy Laboratory of the Institute of Optics of CSIC. 1979–1990 Head of the Molecular Spectroscopy Department of the Institute of Optics of CSIC. 1985–1988 President of the Spanish Spectroscopy Committee and Vice-president of the Spanish Spectroscopy Group. 1990–1994 President of the Spanish Spectroscopy Group. Since 1987 Member of the International Organizing Committee of the European Congress on Molecular Spectroscopy (EUCMOS). She was awarded several prizes: Perkin Elmer Prize to the best work on Absorption Spectroscopy (with A. Hidalgo). Silver Medal of the Spanish Spectroscopy Committee. Honor Fellow and Silver Medal of the Spanish Optical Society. Medal of the Spanish Royal Society of Chemistry. Prize “Jesús Morcillo Rubio” of the Iberic Spectroscopy Conference (CIE) (sponsored by Bruker). 2006 Institution Award of CSIC. 2007 Gold and Brilliant Insignia of the Chemists’ Association of Madrid.

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1968 1996 2002 2003 2006

A short talk with Prof. Juana Bellanato in Coimbra: MPM: What do you think should be the main driving force of a researcher (in Science)? JB: It is a question of vocation. MPM: When did you start your research activity on Spectroscopy? What was that first project? JB: I started in 1950 with the aim to work as a scientist and to obtain a PhD. MPM: Was there another research field that interested you in those days? JB: No. I was happy with my spectroscopic work. Everything was new for me. I had just finished a course on Atomic Spectroscopy at the University, with Prof. Miguel A. Catalán, which I liked very much. Later, fortuitously, Prof. José Barceló, a former Chemistry teacher of mine from grammar school who was working at the Spectroscopy Department of the Institute of Optics of Madrid, offered me to work with him in the field of vibrational spectroscopy, aiming at a PhD degree. I agreed to this, and that was how I started my research in this area. MPM: How did it feel being one of the few women in Science (as a researcher)? JB: There were several women in my Institute working for their PhD degree. Therefore, I did not feel like a special woman. When I studied Chemistry at the University of Madrid women were a third of the total number of students. Later, in the Spanish National Research Council (CSIC) where I worked, there were also several

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

M.P. Marques et al. / Preface

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Fig. 1. 1956 – Juana Bellanato at the Institute of Physical-Chemistry of the University of Freiburg (Germany). Sir C.V. Raman, Nobel laureate in Physics in 1930, was visiting the Institute at the time (sitting, the third one from the left).

women in research. Therefore, I could not feel as a “special woman”. People from outside the academic community, however, did not quite understand what my work was. MPM: What would you say was the most important event in your scientific life? JB: To obtain my PhD degree, and later to be recognized as a scientific woman, i.e., to realize that my work was recognized by several people. Besides, I was happy to help young people in their scientific research. MPM: How would you best describe the time you spent studying/working in Oxford? JB: Before Oxford I spent more than one year studying in Freiburg/Br (Germany). In both cases I discovered a New World, learning new things, knowing great and experienced persons and widening my view of the world. MPM: Coming back to Spain, which were the main differences that you found regarding research activity in general, and your own in particular? JB: At that time (1956–1961) in both Germany and England scientists had better instrumentation and more research means than in my laboratory, in Spain. The differences, however, disappeared gradually over time. MPM: Which were the most relevant scientific contributions from your research team?

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M.P. Marques et al. / Preface

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Fig. 2. Juana Bellanato, at present.

JB: At that time there were few people devoted to the Vibrational Spectroscopy field. With scarce financial media, our scientific contributions were modest. However, in Atomic Spectroscopy, Prof. Miguel Catalán (the head of our laboratory) and his team achieved relevant and world-recognised results. MPM: Which do you consider to be the main events/developments in Science since your early days as a researcher? JB: As everyone knows, spatial conquest, advances in biology (e.g., knowledge of the human genome), biotechnology, new drugs, computer science, etc. MPM: How did you feel the impact of such a marking event as the development of the atomic bomb in the scientific community and the research activity? JB: First, I was young. Later I realized that nuclear energy has light and shade, depending on its uses. MPM: Which would you choose as the subject, in the spectroscopy field, that surprised you most in the last few years? JB: The many applications of lasers; the continued advances in instrumentation, namely in photon detectors, among others. MPM: Do you foresee a Nobel prize award in the (vibrational) spectroscopy field, in a near future? JB: I hope so. We have “antecedents” – I am thinking of Prof. G. Herzberg.† However, it is only a hope. †

Nobel prize in Chemistry 1971, “for his contributions to the knowledge of electronic structure and geometry of molecules, particularly free radicals”.

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MPM: What’s the thrill in spectroscopy studies for a young student today? Did it change much from the time you started in this research area? JB: It changed a lot. There are more media, more researchers, better instrumentation, various uses of computers, international cooperation, etc. MPM: What is the driving force that keeps you “active and kicking” in Science till today? JB: I suppose my scientific vocation. It appeared when I was very young and I was encouraged by my parents (specially my father) and teachers, although those times were difficult in Spain (mid 1940s) and families preferred to promote boys to a higher education. In my case we were only girls... Later, my interests extended to other subjects, such as languages, humanities, etc. MPM: What would you say to a young XXI century spectroscopist? JB: The choice is worthwhile. Reference

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[1] A. Barth and P. Haris, Infrared spectroscopy – Past and present, in: Biological and Biomedical Infrared Spectroscopy, A. Barth and P.I. Haris, eds, IOS Press, Amsterdam, 2009, pp. 1–52.

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CONTENTS M.P. Marques, L.A.E. Batista de Carvalho and P.I. Haris Preface: Proceedings of 14th European Conference on the Spectroscopy of Biological Molecules

v

M. Diem, M. Miljkovi´c, B. Bird, T. Chernenko, J. Schubert, E. Marcsisin, A. Mazur, E. Kingston, E. Zuser, K. Papamarkakis and N. Laver Applications of infrared and Raman micro-spectroscopy of cells and tissue in medical diagnostics: Present status and future promises

1

I.M. Torcato, M.A.R.B. Castanho and S.T. Henriques The application of biophysical techniques to study antimicrobial peptides

31

R.H. Bisby, S.W. Botchway, A.G. Crisostomo, A.W. Parker and K.M. Scherer Fluorescence lifetime imaging of propranolol uptake in living glial C6 cells

39

A. Salman, I. Lapidot, A. Pomerantz, L. Tsror, Z. Hammody, R. Moreh, M. Huleihel and S. Mordechai Detection of Fusarium oxysporum fungal isolates using ATR spectroscopy

47

R. Hielscher and P. Hellwig Specific far infrared spectroscopic properties of phospholipids

53

A. Popp, L. Wu, T.A. Keiderling and K. Hauser Impact of β-turn sequence on β-hairpin dynamics studied with infrared detected temperature jump

61

O.F. Nielsen, M. Bilde and M. Frosch Water activity

69

L. Bednárová, J. Palacký, V. Bauerová, O. Hrušková-Heidingsfeldová, I. Pichová and P. Mojzeš Raman microspectroscopy of the yeast vacuoles

73

ˇ V. Kopecký Jr., L. Monincová, M. Pazderková, E. Koˇcišová, T. Pazderka, P. Malon, ˇ rovský and L. Bednárová V. Ceˇ Antimicrobial peptide from the eusocial bee Halictus sexcinctus interacting with model membranes

79

D. Cvijanovi´c, V. Damjanovi´c, I. Picek and B. Foreti´c Spectroscopic studies of methimazole reactivity toward the aquapentacyanoferrate(II) ion in aqueous solutions

85

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

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xii

Contents

G. Graça, S.O. Diaz, J. Pinto, A.S. Barros, I.F. Duarte, B.J. Goodfellow, E. Galhano, C. Pita, M. do Céu Almeida, I.M. Carreira and A.M. Gil Can biofluids metabolic profiling help to improve healthcare during pregnancy?

91

E. Koˇcišová, A. Vodáková and M. Procházka DCDR spectroscopy as efficient tool for liposome studies: Aspect of preparation procedure parameters

99

T.T. Nguyen, C. Gobinet, J. Feru, S. Brassart-Pasco, M. Manfait and O. Piot Characterization of type I and IV collagens by Raman microspectroscopy: Identification of spectral markers of the dermo-epidermal junction

105

T.M. Pereira, M.L.Z. Dagli, G. Mennecier and D.M. Zezell Influence of fixation products used in the histological processing in the FTIR spectra of lung cells

111

D. Russo, A. Orecchini, A. De Francesco, F. Formisano, A. Laloni, C. Petrillo and F. Sacchetti Brillouin neutron spectroscopy as a probe to investigate collective density fluctuations in bio-molecules hydration water

115

R.P. Lopes, M.P.M. Marques, R. Valero, J. Tomkinson and L.A.E. Batista de Carvalho Guanine – A combined study using vibrational spectroscopy and theoretical methods

127

A.L.M. Batista de Carvalho, S.M. Fiuza, J. Tomkinson, L.A.E. Batista de Carvalho and M.P.M. Marques Pt(II) complexes with linear diamines. I – Vibrational study of Pt-diaminopropane

147

T. Cardoso, C.I.C. Galhano, M.F. Ferreira Marques and A. Moreira da Silva Thymoquinone β-cyclodextrin nanoparticles system: A preliminary study

157

M. Malferrari, G. Venturoli, F. Francia and A. Mezzetti A new method for D2 O/H2 O exchange in infrared spectroscopy of proteins

165

P. Šimáková, M. Procházka and E. Koˇcišová SERS microspectroscopy of biomolecules on dried Ag colloidal drops

171

E. Chikhirzhina, T. Starkova, E. Kostyleva and A. Polyanichko Spectroscopic study of the interaction of DNA with the linker histone H1 from starfish sperm reveals mechanisms of the formation of super condensed sperm chromatin

177

A. Polyanichko and E. Chikhirzhina Supramolecular organization of the complexes of DNA with chromosomal proteins HMGB1 and H1

185

ˇ P. Praus, E. Koˇcišová, P. Mojzeš, J. Štepánek and F. Sureau Study of cellular uptake of modified oligonucleotides by using time-resolved microspectrofluorimetry and florescence imaging

191

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

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Contents

xiii

T. Duˇci´c, J. Thieme and A. Polle Phosphorus compartmentalization on the cellular level of Douglas fir root as affected by Mn toxicity: A synchrotron based FTIR approach

197

J.F.M. Almarashi, N. Kapel, T.S. Wilkinson and H.H. Telle Raman spectroscopy of bacterial species and strains cultivated under reproducible conditions

205

A.A. Deeg, T.E. Schrader, H. Strzalka, J. Pfizer, L. Moroder and W. Zinth Amyloid-like structures formed by azobenzene-peptides: Light-triggered disassembly

209

M. Polakovs, N. Mironova-Ulmane, A. Pavlenko, E. Reinholds, M. Gavare and M. Grube EPR and FTIR spectroscopies study of human blood after irradiation

215

V. Parfejevs, M. Gavare, L. Cappiello, M. Grube, R. Muceniece and U. Riekstina Evaluation of biochemical changes in skin-derived mesenchymal stem cells during in vitro neurodifferentiation by FT-IR analysis

221

´ E. Regulska, M. Samsonowicz, R. Swisłocka and W. Lewandowski Theoretical and experimental studies on alkali metal phenoxyacetates

227

M. Samsonowicz, E. Regulska and W. Lewandowski Spectroscopic (FT-IR, Raman, NMR) and DFT quantum chemical studies on phenoxyacetic acid and its sodium salt

235

N. Kourkoumelis, A. Polymeros and M. Tzaphlidou Background estimation of biomedical Raman spectra using a geometric approach

243

O.V. Stepanenko, O.V. Stepanenko, I.M. Kuznetsova, V.V. Verkhusha and K.K. Turoverov Structural perturbation of super-folder GFP in the presence of guanidine thiocyanate

249

O.V. Stepanenko, O.V. Stepanenko, A.V. Fonin, V.V. Verkhusha, I.M. Kuznetsova and K.K. Turoverov Protein–ligand interactions of the D-galactose/D-glucose-binding protein as a potential sensing probe of glucose biosensors

255

S. Ramos, J.J.G. Moura and M. Aureliano A comparison between vanadyl, vanadate and decavanadate effects in actin structure and function: Combination of several spectroscopic studies

261

C.S.H. Jesus, D.C. Vaz, M.J.M. Saraiva and R.M.M. Brito The V30M amyloidogenic mutation decreases the rate of refolding kinetics of the tetrameric protein transthyretin

267

N.N. Brandt, A.Yu. Chikishev, A.A. Mankova, M.M. Nazarov, I.K. Sakodynskaya and A.P. Shkurinov THz and IR spectroscopy of molecular systems that simulate function-related structural changes of proteins

273

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

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Contents

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E. Bulard, M.-P. Fontaine-Aupart, H. Dubost, W. Zheng, J.-M. Herry, M.-N. Bellon-Fontaine, R. Briandet and B. Bourguignon The effect of bacterial adhesion on grafted chains revealed by the non-invasive sum frequency generation (SFG) spectroscopy

283

M. Grube, R. Rutkis, M. Gavare, Z. Lasa, I. Strazdina, N. Galinina and U. Kalnenieks Application of FT-IR spectroscopy for fingerprinting of Zymomonas mobilis respiratory mutants

291

Subject Index

297

Author Index

299

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ˇ ˇ B. Rezᡠcová, Y.-M. Coïc, C. Zentz, P.-Y. Turpin and J. Štepánek Spectroscopic determination of pKa constants of MADS box segments

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

Spectroscopy of Biological Molecules M.P. Marques et al. (Eds.) IOS Press, 2013 © 2013 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-61499-184-7-001

1

Applications of infrared and Raman micro-spectroscopy of cells and tissue in medical diagnostics: Present status and future promises Max Diem ∗ , Miloš Miljkovi´c a , Benjamin Bird a , Tatyana Chernenko a , Jen Schubert a , Ellen Marcsisin a , Antonella Mazur a , Erin Kingston a , Evgenia Zuser a , Kostas Papamarkakis a and Nora Laver b a

Laboratory for Spectral Diagnosis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA b Department of Pathology, Tufts Medical Center, Boston, MA, USA Abstract. This review summarizes the progress achieved over the past fifteen years in applying vibrational (Raman and IR) spectroscopy to problems of medical diagnostics and cellular biology. During this time, a number of research groups has verified the enormous information content of vibrational spectra; in fact, genomic, proteomic and metabolomic information can be deduced by decoding the observed vibrational spectra. This decoding process is aided enormously by the availability of high power computer workstations, and advanced algorithms for data analysis. Furthermore, commercial instrumentation for the fast collection of both Raman and infrared microspectral data has rendered practical the collection of images based solely on spectral data. The progress in the field has been manifested by a steady increase in the number and quality of publications submitted by established and new research groups in vibrational biological and biomedical arenas. Keywords: Infrared spectroscopy, Raman spectroscopy, medical diagnosis, cytopathology, histopathology, cellular imaging, multivariate methods

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1. Introduction The concept of using vibrational spectroscopic method as adjunct medical diagnostic tools dates back over half a century to a time when infrared spectroscopy was itself in its infancy [13,104]; yet even then, forward-looking spectroscopists thought of the possibility of using the biochemical information obtainable by spectroscopic methods, rather than the morphological information commonly used in classical cytopathology and histopathology, for medical diagnoses. However, it really took until the first decade of the 21st century that the promise for spectral cytopathology (SCP, spectral diagnosis of cells) and spectral histopathology (SHP, spectral diagnosis of tissue) became practical. Notwithstanding a flurry of *

Corresponding author: Max Diem, Laboratory for Spectral Diagnosis (LSpD), Department of Chemistry and Chemical Biology, 316 Hurtig Hall, Northeastern University, 360 Huntington Ave, Boston, MA 02115, USA. E-mail: [email protected].

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M. Diem et al. / Applications of infrared and Raman micro-spectroscopy of cells and tissue

review articles of a decade earlier which proclaimed spectral diagnostic successes, it took over a dozen of years of intense efforts to understand even the basic effects that confound infrared spectroscopy of cells and tissues [5,64], to develop the computational methods to detect the often minute changes in the spectra of cells and tissues with disease [85], and develop medically acceptable methods for the comparison between spectral and classical diagnostic results. Advances in the spectroscopic efforts were enormously aided by concomitant improvement in measurement technology in the early 2000s, and an explosive growth of computational power available to spectroscopists. Interestingly, in the eyes of the authors, the increased computational power, along with the development of some fundamental theoretical underpinnings, were the most important developments to propel SCP and SHP toward the commercial realm. The somewhat sobering consequence of this last statement is the fact that spectral changes between states of disease, or other cell biological event, that are visible to the naked eye are most likely not due to the anticipated effects, but due to the aforementioned confounding variations of spectral features based mostly on morphological changes within the tissue or the cells studied. Thus, one universally applicable and highly important result of the spectroscopic studies of cells and tissues is the realization that infrared (micro)spectra are highly dependent on sample morphology: if the sample is not a homogeneous film, but consists of discrete particles, and if the particle size is approximately the same as the wavelength of the infrared light, scattering effects will confound the observed infrared spectra. This scattering and other physical phenomena (vide infra) may cause the mixing of absorptive and dispersive line shapes in infrared spectroscopy, which was first documented by researchers in the field of biomedical applications of infrared spectroscopy [4,5,36,87]. A typical example of cell morphology dependent effects, from the authors’ own laboratory, was our first attempt to distinguish normal and cancerous cervical cells. To this end, normal exfoliated cells from the human cervix were to be compared with cultured cervical cancer (HeLa) cells. However, the enormous change in morphology of these cells made this either a trivial or impossible task [25]: the cultured cells have large, relatively thick nuclei which gave good infrared absorption spectra and exhibit strong protein, DNA and RNA features. Their cytoplasm, on the other hand, is thin and spread-out with pronounced pseudo-pod features that are common for cultured cells. The cytoplasm of such cell gives very scant spectra dominated by protein features and strong band distortions (see below) at the edges of the cells. In exfoliated cervical cells, on the other hand, the spectrum of the cytoplasm is stronger and often exhibits pronounced glycogen features. Their pyknotic nuclei exhibit virtually no DNA/RNA features [53]. Thus, the spectral distinction of “cancerous” cervical HeLa cells from normal cervical cells is trivial but medically totally irrelevant. When we attempted to improve the situation by removing the cultured cells from their substrate by trypsination, and mix them with the exfoliated cells, we learned (the hard way) that trypsination, although a commonly used procedure in cell biology, changes cell morphology (and possibly the biochemical composition of the cell) drastically; these changes revert when trypsinized cells are subsequently re-plated in culture flasks and allowed to grow. Upon trypsination, cells typically go from a spread-out morphology, in which a cell can measure up to 25 μm (or larger) in size to nearspherical shape of about 10 μm in size; the accompanying changes in light scattering properties of the can confound their infrared spectra, and produce large shifts in some spectral bands. The resulting spectral changes again are so strong that a comparison between exfoliated (normal) and trypsinized cultured (cancer) cells is totally trivial [25]. Furthermore, spectra of cells or even adjacent tissue pixels do exhibit a natural variance, due to a number of factors (metabolic activity, stage in the cell cycle, tissue architecture, etc). Thus, any changes

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

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in spectral characteristics should be based on a large number of spectra (a spectral “dataset”) of cells or tissue pixels acquired microscopically. In addition, visible images of the cells or tissue pixels must be available to allow correlation of the spectral changes to either confounding morphological causes, contamination or the actual desired changes in cellular events (disease). Finally, multivariate methods of data analysis should be carried out on the datasets to help differentiate uncorrelated changes (noise) with changes correlated to the desired (or suspected) cause of change. Thus, this review article “Applications of infrared and Raman micro-spectroscopy in medical diagnostics” differs in the scientific philosophy from that normally seen in the field of “Spectroscopy of Biological Molecules” (the subject of the ECSBM conference series) in that the spectra of cells and tissue pixels are more complicated (they are superpositions of component spectra of unknown abundance), and they are no longer static, but subject to changes that are normally ignored in spectroscopy (morphology, metabolic activity, disease). Thus, those spectroscopists who are accustomed to spectral reproducibility and constancy are forewarned herewith that the remainder of the discussion below will challenge these concepts, but will demonstrate that vibrational spectroscopy of biological systems such as cells and tissue can be interpreted, and valuable diagnostic information can be deduced from spectral results. This review article will concentrate mostly on recent results in SCP from the authors’ laboratory, the Laboratory for Spectral Diagnosis (LSpD) at Northeastern University in Boston. The reason for concentrating this review on SCP is that the LSpD has contributed to this field more than to the other fields in spectral diagnostics. Also, the size of the datasets in terms of patient numbers (>250 for oral and cervical cytology) exceeds by far any other datasets investigated by other groups. Spectral histopathology is being pursued actively at the LSpD as well, with large independent training and test sets available to date for a number of malignancies. These data are not included in this review, since the work is being carried out under a licensing agreement, and cannot yet be divulged. Rather, SHP is introduced from a methodological point of view in which the general procedures and pitfalls are discussed. Raman imaging aspects are treated briefly toward the end of this review.

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2. Methods Instrumental aspects. All infrared spectroscopic results presented in this article were acquired microscopically via one of three imaging infrared micro-spectrometers (Spectrum One/Spotlight 400, PerkinElmer Corporation, Shelton, CT, USA) at the LSpD, henceforth referred to as the PE400’s. Infrared spectra of cells or tissues were collected in transflection (reflection-absorption) mode from samples mounted on “low-e” (also known as MirrIR) slides (Kevley Technologies, Chesterland, OH, USA) at a spectral resolution of 4 cm−1 . All IR data represented in this review (both for SCP and SHP) were collected in imaging mode at 6.25 μm pixel size. The spatial resolution of the PE400 was established using the military resolution targets, and was about twice the diffraction limit at 1600 cm−1 , ca. 12 μm. For simplicity’s sake, we may assume that the voxel size interrogated by the instrument is ca. 10×10×5 μm3 (in x, y and z direction, respectively) in the mid-IR, where the z-direction is not so much determined by the diffraction limit, but the maximal thickness of the sample before detector non-linearity is observed. The pixel size used in these studies allows the detection of spectral differences of items as small as a cellular nucleus. Since the goals of both SCP and SHP are the detection and diagnosis of individual cancer cells, it is advantageous to operate the spectrometers at a pixel resolution of about the size of a cellular nucleus. In the past, between 4 and 8 interferograms were co-added for each pixel; however, after the implementation of the noise-adjusted principal component analysis (see below), only one or two

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interferograms are co-added. Under these conditions, acquisition of a complete (ca. 700–4000 cm−1 ) FTIR spectrum for one pixel requires between 5 and 10 ms using the PE400. Spectra were collected at 4 cm−1 spectral resolution, and are stored as 800 point intensity vectors with 2 cm−1 data spacing from 800 to 4000 cm−1 in native PE 400 imaging format (.fsm files). All Raman data were acquired using a confocal Raman microscope (Model CRM 2000, WITec, Inc., Ulm, Germany). In Raman microspectroscopy using mid-visible lasers (ca. 500 nm) for excitation, the diffraction limited voxel size is about 0.3 × 0.3 × 1 μm3 [74] and proportionally larger for longer wavelength excitation. Laser power at the sample was typically about 10 mW; under these conditions, a Raman spectrum (300–3300 cm−1 ) can be acquired in 250–300 ms. The raw Raman data are stored as 1024 intensity points with non-linear wavenumber spacing between data points. The spacing depends on the exciting laser wavelength and grating used, and the wavenumber range studied. A 1024 point vector of wavenumber values corresponding to each intensity data point is output with the Raman spectral dataset. Before multivariate analysis of Raman data, all spectra are interpolated to linear wavenumber spacing, and corrected for cosmic rays. Cell cultures. Most cells grown in the authors’ laboratory were purchased from (ATCC, Manassas, VA, USA) and cultured in 75 cm3 culture flasks (Corning, Lowell, MA, USA) using minimum essential Eagle’s medium (ATCC, Manassas, VA, USA) supplemented with 10%, by volume, fetal bovine serum (FBS, ATCC). Cultured flasks were incubated at 37◦ C and kept in an atmosphere of 5% CO2 . Cells were cultured until confluent, and removed from the flasks using trypsin-EDTA (ATCC). Cells were then reseeded onto the windows of choice, “low-e” slides (see below) for infrared measurements or CaF2 disks for Raman spectroscopy, immersed in fresh culture medium supplemented with 10% FBS, and placed back into incubation for approximately 12 h. Cells were fixed with 4% buffered, aqueous formalin for Raman measurements, and in Surepath® solution (see below) prior to infrared data acquisition. Exfoliated cells. Oral cells exfoliated by LSpD personnel as part of an oral cancer screening program at Northeastern University (under a local IRB), as well as cells collected from clinical patients at Tufts Medical Center (TMC) in Boston, were treated exactly the same way. The cells were exfoliated via cytobrushes which were immersed into Surepath® fixative immediately after exfoliation. This fixation medium consists of 24% aqueous solution of ethanol, and 1% each of methanol and isopropanol. We have shown that this fixative changes the cellular spectra minimally, even after prolonged exposure of the cells (1 month) to the fixative, and that spectral changes due to disease are much larger than those produced by fixation protocols and exposure to fixatives (see below) [61]. The Surepath® fixation protocol was adapted at the LSpD since it was the method of choice at TMC, from where all clinical samples were derived. A comparison of formalin- and Surepath® -fixed cells showed minimal differences [61]. After repeated wash and centrifugation cycles, cells were spin-deposited via cyto-centrifugation onto “low-e” slides (see above). Sparse, uniform samples of cells, which adhere to the substrate very strongly, could be produced this way. Tissue sections. Tissue sections were cut, using a microtome, to a thickness of 5 μm from formalinfixed, paraffin embedded tissue blocks from the archives of the Pathology Department at TMC. The sections were mounted on low-e slides, and de-paraffinized using standard protocols [7]. Some spectra were also obtained from the tissue pixels while still embedded in paraffin. After infrared data acquisition, tissue sections were stained with hematoxylin–eosin (H&E) to permit correlation of visual and infrared spectral images. Computational methods. All data manipulation and analysis was carried out, using software developed in house using the MATLAB (Mathworks, Natick, MA, USA) platform. The analyses start with the raw (Raman or infrared) instrument-based data files. Most of the data analysis routines are contained in a

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software package referred to as “ViChe” (Vibrational Chemometrics), which includes all of the preprocessing and multivariate imaging reconstruction algorithms, for example principal component and hierarchical cluster analysis (HCA) imaging. The latter have recently been discussed in detail [63]. Among the pre-processing routines, noise-adjusted principal component analysis (NA-PCA), was taken from the literature [34,82], whereas the correction routines for band shape distortion, to be discussed in the next section, were developed in-house [6,12,27]. The algorithm to construct spectra of individual cells from imaging datasets has been reported [93] and submitted for IP protection. Following earlier arguments [48,85] all data analysis was carried out on second derivative spectra.

3. Results and discussion This paper follows the presentation by the author at ECSBM14 both in subject matter as well as in order. Thus, the first subject to be discussed will be methods for the correction of dispersive band shape distortions that are frequently encountered in infrared spectroscopy of human cells and tissues. This particular problem, the sample morphology-dependent spectral distortion,1 has plagued this research area since its inception more than 60 years ago, and is not restricted to microscopic data acquisition in transflection mode. The difficulties arising from the dispersive band shapes are so severe that there was wide-spread pessimism about the future of infrared micro-spectroscopy as a possible medical diagnostic tool. Only after the SPEC2010 Conference in Manchester, UK, where a number of research groups presented their views and approaches to overcome this problem, did the mood swing drastically. At the time of writing of this review, there are three methods of correcting the dispersive band shapes in the literature or submitted for patent protection. 3.1. Correction of dispersive band shapes In two pioneering papers, the research group around Peter Gardner at the University of Manchester, UK, described [4,5] how reflection processes and Mie scattering can mediate the mixing of dispersive and absorptive band shapes. However, similar mixing phenomena were well known to occur, for example in specular reflection [16], in absorption measurements from metal surfaces [38,39] as well as in ATR spectroscopy. In all these modalities of IR spectroscopy, the absorption spectra are not measured directly, but via methods that depend on the complex refractive index η, which is given by

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η = n − inκ = n − iελ/4π.

(1)

In (1), n is the real part of the refractive index, κ the absorptivity, ε the molar extinction coefficient, and λ the wavelength of light. Whenever κ or ε have a maximum (i.e., whenever one observes a peak in the absorption spectrum), n undergoes anomalous dispersion, shown in Fig. 1. The real and imaginary parts of the refractive index are related to each other by the Kramers–Kronig transform: 2 n(νi ) − n(∞) = π

 ∞ νκ(ν) 0

ν 2 − νi2

dν,

1

(2)

In the very early work on infrared spectroscopy of tissue, Woernley [13] suggested to put a drop of oil on the tissue sections to match the refractive index of sample and surroundings.

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in which the νi are the frequencies of the spectral peaks, and n(∞) is the refractive index at infinitely long wavelength where no transitions occur. Thus, the dispersion of the refractive index can be calculated from the absorptivities, and vice versa. A comparison between an absorption spectrum, and the corresponding dispersion curve, is shown in Fig. 1. These phenomena are well known in classical optics, and in chiroptical spectroscopy: circular dichroism and optical rotatory dispersion are typical examples of two effects related by the Kramers–Kronig transform. However, in both classical optics, and in spectroscopy, one normally shies away from conditions where the two effects, absorption and dispersion, interact: most textbooks of optics treat the refractive index as a quantity that changes very mildly with wavelength, because most optical materials are chosen such that they have no absorption in the spectral range of interest; i.e., they are colorless (clear) in the visible spectrum. In absorption spectroscopy, on the other hand, one assumes that the reflection losses at the sample, caused by the refractive index, are small and will not distort absorption spectra noticeably. However, under certain condition this simplistic situation breaks down, and one observes extensive mixing of reflective and absorptive band profiles. This was first formulated for the case of Mie scattering by Bassan et al. [5], and can be visualized as follows. Mie scattering is not a molecular, but rather a macroscopic effect in which spherical or near spherical metallic or dielectric particles scatter incoming radiation to produce broad, undulating background patterns. This effect predominates if the particle size and the wavelength of light, typically between 5 and 12 μm for mid-IR measurements, are approximately equal. Consequently, small human cells, or the nuclei of cells, can exhibit strong Mie scattering. The classical physical equations for Mie scattering are quite complicated [90]; however, the Mie scattering cross section but can be approximated relatively accurately [100,103] for a transparent sphere by (3): Qsca = 2 − (4/ρ)(sin ρ) + (4/ρ2 )(1 − cos ρ)

(3)

with the scattering factor ρ given by ρ = 4πr(n12 − 1)/λ,

(4)

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where r is the radius of the scattering sphere, λ the wavelength of the light, and n12 the ratio of the refractive index of the scattering sphere and the surroundings. However, if the scattering material exhibits absorptions, and therefore, a wavelength-dependent refractive index, n12 needs to be replaced by the

Fig. 1. Observed absorption coefficient and computed dispersion of the refractive index for a tissue pixel or cell.

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Fig. 2. Classical Mie, resonance Mie (RMie) and pure absorption spectra of tissue. The olive-shaded area denotes the amide I manifold, the gray the amide II region, and the blue and pink areas the regions of antisymmetric and symmetry phosphodiester stretching vibrations.

dispersion curve shown in Fig. 1. The resulting Mie scattering, referred to as resonance Mie (RMie) by Gardner’s group [5], is shown in Fig. 2 for a region in which classical Mie scattering would exhibit a very gently varying profile. Similar mixing of reflective and absorptive band profiles may be observed, at times, in pure reflection spectroscopy, but also appeared in studies where surface enhanced infrared absorption (SEIRA) was investigated. In these studies, coagulated (coalesced) gold or silver surfaces were prepared by vapor deposition of the respective metals, and used as substrates for neat liquids. The “absorption” spectra observed for a number of these liquids showed purely reflective band profile. This can be understood in terms of the near constant refractive index of the metal particles undergoing Mie scattering, in contact with the neat liquids, whose refractive indices undergo anomalous dispersion [38,39]. As mentioned before, the distortions observed in infrared spectra of spherical cells (for example lymphocytes) can be so severe that interpretation of the spectra is impossible. Also, the use of multivariate methods of data analysis, such as Principal Component Analysis (PCA), was severely confounded by the intensity distortions and frequency shifts caused by reflective and RMie scattering contributions. Therefore, Bassan et al. [6] proposed a method to correct distorted spectra by fitting a refractive index (“interference”) spectrum obtained via Kramers–Kronig transform of the absorption spectrum to minimize the distortion. A similar approach, requiring much less computation time but more than one “interference” spectrum, was published by the LSpD group shortly thereafter [11,12].

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However, both of these approaches required “uncontaminated” spectra as reference spectra, and their Kramers–Kronig transforms as dispersive interference spectra, and results obtained using these two approaches either reduced [6] or amplified [12] the real variance in the spectra. Thus, we introduced [27] another method based on the well-documented “phase correction” (PC) approach that is widespread in standard FTIR spectroscopy. The phase correction approach is based on the concept that the complex Fourier transform separates the real and imaginary parts of spectra or interferograms by varying the phase angle between them. In classical FTIR spectroscopy, the collected interferogram is generally asymmetric about the zero path difference (ZPD) peak; such a “chirped” interferogram gives, upon forward FFT, a spectrum that contains a mixture of reflective and absorptive band shapes [35]. Nearly all commercial FTIR instruments use the Mertz phase correction method [62] for which the instrumental phase angle is determined experimentally, and is used to correct the spectra. A PC-based approach was attempted by us [87] earlier but worked only intermittently due to some computational and theoretical problems. Recently, a revised phase correction algorithm was implemented for fast, reliable and elegant correction of reflective band contributions. In short, the distorted spectra, expanded to the desired frequency range and de-noised by NA-PCA (see above) are reverse Fourier transformed back into interferogram space. The resulting real (Re) and imaginary (Im) interferograms are zero-filled, and phase shifted by a trial phase according to 

Re Im





=

cos θ − sin θ

sin θ cos θ



Re Im



.

(5)

Phase corrected spectra are computed by complex forward FFT of (Re + iIm ). The “best” phase is assumed to be the one that produces the highest intensity corrected, since addition of a reflective component will always reduce the peak intensity [35]. In principle, a wavenumber-dependent phase angle can also be computed, as in the Mertz algorithm, from a low resolution interferogram obtained by truncating the FFT to fewer data points.

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3.2. Spectral cytopathology 3.2.1. General remarks One of the early goals of researchers involved in SCP was the developments of methods to aid in the diagnosis of cervical cell smears [21,22,105,106] used for screening for cervical cancer (the so-called Papanicolaou test (or “Pap” test for short) [77,78]. The reason for selecting this subject was the welldocumented high rate of false positive and false negative readings of these samples by cytologists and cyto-technicians. Interestingly, one should not take the low sensitivity and specificity2 of the Pap test as a failure of the method; quite contrary, no single test has reduced the incidence of, and morbidity from a given cancer as much as the Pap test. Yet, for a single reading of a classical “smear”, the overall accuracy (average of sensitivity and specificity) was less than 70%. Improvements were achieved by producing better samples: rather than smearing the exfoliated cells on a microscope slide, followed by staining, liquid-based methods were devised [101] that produced sparse monolayers of cells, which, after staining, provided a much clearer picture of individual cells. A small section of such a stained sample is shown in Fig. 3. 2 The terms sensitivity and specificity are used in this paper in the clinical sense, where sensitivity refers to the ratio of true diagnoses divided by the total number of true plus false negative diagnoses, and specificity refers to the ratio of true negative diagnoses, divided by the true negative plus false positive of diagnoses.

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Fig. 3. Stained cytopathological sample of cervical epithelial cells.

This figure shows about 50 cervical squamous cells from a small section of a liquid-based sample which stain either pale blue or pale pink; in addition, this sample contains bacteria (1), polymorphonuclear leukocytes (PMNs, 2), cellular debris such as naked nuclei, and one “abnormal” cell (3), indicated by an enlarged nucleus. The difficulty in classical (visual) cytopathology is the detection of as few as a few percent of abnormal cells in a sample that may contain 1000–10,000 cells. Furthermore, an enlarged nucleus per se (a larger nucleus/cytoplasm (N/C) ratio) is also observed for cells from the lower layers of the cervical epithelium; thus further criteria, such as the morphology of the nuclear membrane, need to be invoked for a reliable discrimination of normal from abnormal cells. The level of abnormality also needs to be graded; for cervical cytology, the grades (in order of increasing severity include reactive, low-grade dysplasia (low grade squamous intra-epithelial lesion, LSIL), high-grade dysplasia (high grade squamous intra-epithelial lesion, HSIL), carcinoma-in-site (CIS) and invasive cancer. Given the complexity of the problem, it was no surprise that cervical cancer screening was selected as a target for early spectral diagnostics. When first attempts at this goal were made in the early 1990s, infrared micro-spectroscopy had not progressed to a level that permitted acquisition of spectra of individual cells in reasonable times; thus, cell pellets of unknown composition in terms of the cell types contained in the pellet were used as samples, and the measurements were carried out macroscopically. Results from these early efforts are exemplified by the PCA scores plot [21,23] shown in Fig. 4. In this plot, each symbol represents one spectrum collected macroscopically from a cell pellet; the confirmatory diagnosis was by classical pathology. In retrospect, it is amazing that these early cell pellet results gave encouraging results, and it took quite a while to understand why these crude measurements showed any kind of discrimination. To allow direct assessment of individual cells, and to convince cytologists of the value of the spectral method, the author’s laboratory switched to single cell-based spectral cytopathology in 2002. Although much more time consuming than the pellet-based approach, the single cell method permits a direct comparison of spectral results with visual inspection of a cell, and thus, is of much higher inherent value to a cytologist. To this end, the sample is stained and cover-slipped after infrared data acquisition, and cells can be relocated on the substrate by their stored positions. Early results showed promise: the distinction between squamous cells from the distal urethra from urothelial (bladder) cells by spectral methods proved to be straightforward [8,26], and the classification of superficial and intermediate cells

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Fig. 4. PCA scores plot for macroscopically acquired cervical cell samples.

from cervical epithelium, and effects of hormonal influences could be established quite readily [26]. The hormonal effects were originally sought to help classify samples from pre-menopausal and menopausal women, but needed to be expanded to include menstrual status since the level of hormones influences the maturation of cervical cells. This was first established for canine cervical cells [84] but was found to hold for human cervical cells as well. In order to facilitate the detection and diagnosis of cervical disease, to be discussed below, only women using oral contraceptives were used, since they keep the hormonal level constant, and thereby eliminate one variable in the process of establishing SCP as a possible diagnostic method. Results of the cervical work will be presented later in this section. Concurrent with the efforts on cervical cells, development of a screening test for oral cancer was initiated at the LSpD. Oral mucosa, like cervical mucosa, consists of stratified squamous epithelium, but due to digestive enzymes in the saliva, does not exhibit large spectral contributions due to glycogen, which mask a large part of the low frequency spectrum (1000–1200 cm−1 ). The original results for the oral mucosa were extremely intriguing, and largely form the basis of our present understanding of spectral cytology. Before presenting these results, a short introduction of stratified squamous tissue will be presented, followed by cursor interpretation of a typical vibrational spectrum of a cell. Stratified squamous epithelial tissue is a frequently found epithelium in the human body (nasopharyngeal and oral cavities, esophagus, urethra, vagina, cervix and others). It is a multilayered structure (see Fig. 5) consisting of a layer of actively dividing basal cells anchored to the basement membrane, beneath which one finds connective tissue (stroma). The daughter cells created by division of the basal cells form the parabasal layer and mature and migrate to the surface layer. In this process, their morphology and chemical composition change drastically. Whereas the basal cells are roughly cuboidal in shape, about 15 μm on edge, with a large nucleus and very little cytoplasm, the mature stratified (flat) cells may measure up to 60 μm on edge and exhibit very small, pyknotic nuclei. They also accumulate glycogen for energy storage (except for the oral mucosal cells, see above); since they are fully differentiated, their chemical composition reflects a reduction of certain compounds in the cytoplasm. Using high spatial resolution synchrotron radiation FTIR imaging and enzymatic digestion studies, we have shown

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Fig. 5. Schematic drawing (top) and actual image (bottom) of stained stratified squamous tissue (from Wikepedia).

that RNA signatures, for example, are nearly absent in the cytoplasm of mature squamous cells [24,58]. Furthermore, we have shown that the nuclear DNA is virtually unobservable in pyknotic cells [64], but contributes to the observed spectra in rapidly dividing cells, for example, cancer cells and lymphocytes. Finally, a very cursory interpretation of a typical spectrum of a cell (or tissue pixel) will be presented. The 1500–1700 cm−1 region of the spectrum of a cell or tissue pixel is dominated by the protein amide I and amide II bands, shaded olive and gray in Fig. 2; both these bands split into sub-bands in the second derivative spectra, and are known to be due to exciton-like coupled states of mainly the C=O and O=C–N stretching coordinates, respectively [15]. Certain proteins, in particular the proteins of connective tissue (collagen), have sufficiently different infrared spectra to allow detection of their spectral signatures with the naked eye. A high frequency band, at about 1740 cm−1 , is due to the ester linking of phospholipids [58,85]. The antisymmetric and symmetric phosphodiester stretching vibrations of DNA, RNA and phospholipids are observed at ca. 1235 and 1090 cm−1 . The intensity of these bands varies enormously in disease. The C=O stretching bands of non-hydrogen-bonded nucleotides are observed mostly as high frequency shoulders of the amide I peak. The amide III vibration occurs superimposed on the antisymmetric phosphodiester stretching vibration. Carbohydrates show strong peaks due to C–O–H deformation and C–O stretching coordinates between 1000 and 1200 cm−1 . One of the most abundant cellular carbohydrate is glycogen, which shows three strong bands at 1151, 1078 and 1028 cm−1 . Carbohydrate bands are also observed for glycoproteins, particularly in mucus. 3.2.2. Spectral cytopathology of the oral mucosa We start the discussion of SCP by presenting the results of a pre-clinical trial presently ongoing on the campus of Northeastern University, in collaboration with the Department of Pathology at Tufts Medical Center (TMC) in Boston. Oral cytology was selected as an initial large scale target because of the ease of

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Fig. 6. PCA scores plot of fixed normal and precancerous oral mucosa cells.

sample collection, the prevalence of viral diseases in the oral cavity (human papillomavirus, herpes simplex, Epstein–Barr), and the prevalence of oral and nasopharyngeal cancers in the far eastern population, who represent a large percentage of oral cancer incidence seen at TMC. Cytological samples were harvested as described above, and immediately inserted into a vial filled with Surepath® fixative. A frequent criticism, by referees and grant reviewers, of the authors’ efforts to use SCP as a diagnostic tool, has been regarding the effect of fixation on cells and tissues. We have – hopefully once and forever – answered these issues in a recently published paper which demonstrates that even prolonged exposure to fixative, and fixation by different methods (drying, formalin and Surepath® fixation) cause changes in spectral features that are significantly less than those caused by disease. This is shown via the PCA plot depicted in Fig. 6. In the past, enormous spectral changes of cells and tissues upon fixation had been reported [42], which we believe were mostly due to morphological changes upon fixation. In tissue, the changes upon formalin-fixation/paraffin embedding are larger, but they do not interfere with vibrational spectral diagnostics if all tissue sections that are compared are treated the same way. This was demonstrated nicely by back-to-back papers published on rat brain gliomas in 2006 [1,2] which were either flash frozen, or formalin-fixed and paraffin embedded, and subsequently de-paraffinized (see below). In Fig. 7, we present first results on the work on oral mucosa cells [76], which initially were somewhat surprising but followed a finding that had been reported before for cervical cells [21,22]. The results in this earlier work implied that morphologically normal cells from abnormal samples exhibit abnormal spectra, that represented a progression from normal to cancerous cells. However, the sample set reported then was too small to reach any detailed conclusions. All the cell spectra shown in Fig. 7 were from cells harvested from the tongue, since we had demonstrated earlier that there exist small, but reproducible changes in the spectra of cells harvested from different anatomical regions of the oral cavity. In the scores plot of Fig. 7, the normal cells (from six volunteers) shown as blue symbols form a tight cluster. The cells harvested directly from a cancerous lesion of the tongue, shown in red, form a less homogeneous cluster which is well separated from the normal cells. Most interesting is the diffuse cluster represented by the green symbols. These spectra were from cells, which exhibited normal morphology, but were

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Fig. 7. PCA Scores Plot of oral SCP. The cell images represent a normal cell (left), a morphologically normal cell from an abnormal sample (middle) and a clump of cancerous cells. All cells were harvested from the tongue.

collected from cancer patients from areas quite remote from the cancerous lesions, and from patients diagnosed with pre-cancerous disease. This observation, namely that the majority of exfoliated cells that still exhibit normal morphology but exhibit abnormal spectra, can be explained by the fact that most of the area surrounding the cancerous lesion is already affected by a biochemical change or mutation that pathologists refer to as “malignance induced changes” or “field cancerization”. Although the definition of these terms is somewhat vague, it is well known that – particularly in the case of oral cancer – the rate of reoccurrence after treatment of a first cancer incidence is 20-fold higher than for healthy patients. This implies that there are pre-cancerous changes in the cells that do not manifest themselves morphologically. Completely analogous results were obtained by in-vivo Raman spectral measurements by the Vanderbilt research group [83] for the ecto-cervix. Another interpretation of these results will be presented at the end of the SCP section. In addition, we demonstrated that infection by the herpes simplex virus could be detected by SPC, and that the cells collected from different anatomical regions of the oral cavity (cheeks and gums, tongue and mouth floor under the tongue) can be distinguished by SCP. Furthermore, the spectral patterns of degradation products of pain killer medication (ibuprofen) could be found in cell spectra, as could be the byproducts of nicotine use [76]. Although the changes produced by these degradation products were too small to be perceived by visual inspection of cells, PCA could easily classify uncontaminated from the contaminated cell spectra. In particular, the PCA loading vectors along which the spectral classes were differentiated often gave a good indication of what the spectral changes detected by PCA were. This will be discussed further in the next section.

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Fig. 8. PCA scores plot of spectra of cervical squamous cells from normal subjects (blue), and from LSIL (red) and history of HSIL (yellow) patients.

3.2.3. Spectral cytopathology of cervical mucosa In cervical cytology, which was the original goal of the research describe here (see above), completely analogous results were observed, namely that the majority of cells from abnormal samples showed spectral abnormality, although they exhibited normal morphology. These results mirror the earlier findings by Cohenford [21,22], but took into account additional confounding factors, as indicated above: the cells are subject to hormone-mediated maturation processes that include, for example, the accumulation of glycogen in the final stage of maturation. Since the glycogen absorption bands mask a large part of the low frequency (1000–1200 cm−1 ) spectrum, valuable information in the nucleic acid phosphate stretching region (ca. 1090 and 1235 cm−1 ) is rendered inaccessible. Thus, the studies reported below were from subjects using oral contraceptives which prevent complete maturation of the squamous cervical epithelium, and therewith, reduce the glycogen abundance. The changes in maturation patterns of cervical cells in response to menopause could be demonstrated nicely using SCP [26]. Figure 8 shows the results of PCA analysis of cells exfoliated from normal patients, and patients diagnosed with LSIL/HSIL (see Section 2.2.1) [94]. Here, the results of the oral cytology are repeated in that most of the cells from patients with dysplasia exhibit spectral abnormalities, although the cell morphology is normal (see cell images in Fig. 8). Even more surprising is the fact that the cells from a patient with a prior diagnosis of HSIL, and subsequent treatment, still exhibit abnormal spectral patterns and cluster with the abnormal spectra. The implications of this observation are quite far reaching in that detection of abnormality is not restricted to the few cells in a cervical exfoliate that exhibit abnormal morphology. Rather, SCP detects abnormal spectral signatures that are exhibited by most of cells, even if they still have normal morphology. The fact that the abnormality persist after treatment led us to explore the possibility that the spectral changes (and, therewith the “malignancy associated changes” [71] or “field cancerization” [102] mentioned earlier) are actually due to viral infection. In part, this thought was provoked by results from a patient with an acute h. simplex infection of the oral cavity. In this case, most of the cells showed spec-

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Fig. 9. PCA scores plot of SCP results of hrHPV positive and negative samples (from [92]).

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tral abnormality, not only those that were so grotesquely deformed by the viral infection that they could be visually diagnosed by a cytologist [76]. Since statistically over 95% of all cervical dysplasia occur along with (and likely are caused by) infection with the human papillomavirus (HPV), the possibility exists that SPC detects the infection by HPV in cervical cytology [108]. Similarly, oral dysplasia could be caused by the Epstein–Barr or HPV as well. These observations may help explain the positive results reported for cell pellet studies (Fig. 4): although the composition of the cell pellet in terms of contributions from superficial, intermediate and parabasal cells, as well as PMN’s and bacteria, was not known for these samples, the abundance of virally infected cells may have been responsible for the distinction of disease states. Efforts to answer the possibility of the involvement of viral infection toward the observed spectral changes will be pursued in the next section. 3.2.4. Viral effects In order to explore the sensitivity of SCP toward viral infections, a study was undertaken in which 48 samples were tested for high risk HPV infection (hrHPV) via the Digene Hybrid Capture test (Qiagen, Valencia, CA, USA). The spectral results were analyzed by SIMCA [92].The result of a 10 sample training subset, shown in Fig. 9, looked extremely promising, with good spectral separation of HPV positive and HPV negative samples. When applied to the remaining set of samples, a sensitivity of 88% was achieved, yet the specificity was only 43%. This implies that SCP was quite good at detection hrHPV strain when it was present (as determined by the Digene test), but not accurate when no hrHPV infection was present. We attributed the low specificity to the fact that low risk HPV (lrHPV) infection is epidemic in the population of women between 20–25 year of age, with infection rates of about 30%, or about the same as the infection rate with hrHPV [81]. Thus, it is quite likely that the samples which tested negative for hrHPV by the Digene test had low risk HPV infection, which the spectral methods were not (yet) able to differentiate. Inspection of the PCA loading vector (not shown) indicated that the spectral changes, along which PCA and SIMCA distinguish the HPV-infected from normal cells, occurred in the protein spectral region

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by a distinct shift of the amide I band and the appearance of small shoulders. This leads to the conclusion that it is not a change in the viral DNA which is detected, but rather, different proteins expressed by the virus. Given the size of the viral genome (ca. 5000 base pairs, bp), and the number of copies of the viral genome in a cervical cancer cell (maximally ca. 600 in the CaSki cell line, fewer in HeLa cells), one arrives at a number of about 3 million base pairs added to the human genome in the case of HPV infection. The human genome consists of 3 billion bp’s; thus, it is impossible to detect this change with present methodology. If affected cells produce proteins different from the normal proteome of cervical cells, such changes can be amplified and detected spectroscopically. Efforts are underway to shed light onto the reasons for the observed spectral changes [73,92]. 3.2.5. Future potential of SCP Aside from the fixation studies, which were carried out for both exfoliated and cultured cells, and the viral load studies, which were carried out on cultured cells only, the majority of the work presented in the sections above has dealt with exfoliated cell; i.e., it reported a truly new form of cytology, namely SCP. To the best of our knowledge, work on exfoliated cells at the cell-by-cell level is now being carried out exclusively at the LSpD, and the size of the datasets at the LSpD far exceeds all previously collected datasets combined [76,92]. At the time of writing of this review (Summer, 2011), it appears that the SCP has matured to a level which allows for to detection of cellular abnormalities, such as dysplasia, cancer and viral infection in exfoliated cells, and thus, is poised to be applied to areas where classical cytology has very poor performance, in many cases below 50% accuracy. The reason that the progress in SCP has been somewhat slower than that in other areas of spectral diagnostics is the fact that the correlation between classical cytopathology and SCP is difficult. In SCP, one has to rely on luck that within an ensemble of cells scrutinized by SCP there is a diagnosable, abnormal cell. After a few thousands of cells from dysplastic patients, however, it will become very likely that some cells are found that display abnormal spectra and can, indeed, be diagnosed. Such a case is shown in Fig. 10 which shows a clearly dysplastic cell whose spectrum clustered with other abnormal spectra [92]. For cultured cells, the efforts and research directions are more diversified, and represent a number of other research groups as well as the LSpD. These efforts have demonstrated that infrared microspectroscopy can detect the stages of a cells division cycle [14], the effect of drug treatment on cells [30,32,56], the degree of aggressiveness [33], μRNA expression [75], cancer activation of fibroblasts [40] and a few others. In general, the results of these studies demonstrate that carefully carried out FTIR studies can reveal an extraordinary amount of information on the complex biochemical changes that occur when cells undergo natural or induced processes. A few general rules seem to apply for carefully planned and executed studies. The raw spectra, whether monitoring drug treatment or any other of the changes listed above, exhibit no or barely visible spectral changes and multivariate methods of analysis need to be employed to visualize spectral variations. A typical example is the study by the Brussels group [32], which demonstrated elegantly that the spectra of untreated cells, and those treated with drugs are virtually identical to the naked eye, but that statistical (or, in this case, 2-D) analysis of these datasets reveals changes that can be interpreted biochemically. Some of the spectral changes, for example due to drug interactions, are smaller than the changes due to cancerous disease; thus, the authors believe that the spectroscopy of cells can reveal much more information and can be used to reveal very subtle details. For bacterial cells, for example, infrared spectroscopy, coupled to analysis by neural networks, could predict the mode of action of newly discovered drugs [91].

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Fig. 10. (A) PCA plot of cervical cells from patients diagnosed with low grade dysplasia. Red circles are mostly from cells with normal morphology (shown in (B), whereas the green squares are from “diagnostic” cells with abnormal cytology, shown in (C)) (from [92]).

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3.3. Spectral histopathology (SHP) 3.3.1. General remarks Although classical histopathology is the gold standard of primary medical diagnostics (nearly every cancer diagnosis is initially based on histopathology), and has high sensitivity and specificity in detecting cancers, the method is somewhat more ambiguous when it comes to grading disease. It also is an inherently subjective approach to diagnostics, and lacks reproducibility and cannot easily be carried out via a quantitative and reproducible measurement. Furthermore, the detection of specific subtypes, for example the over-expression of cancer genes, requires immuno-histochemical stains and subsequent pathological analysis. SHP has the promise to enhance many of these aspects, and combine morphological aspects and biochemical compositional information into a novel approach. SHP has progressed at a faster pace than SCP, mostly for the reason that correlation with classical methods, i.e., standard histopathology, is more straightforward, and parallel images from histopathology and SHP can readily be compared (see Figs 11–13). In these figures, even a layman can perceive that the tissue morphology and architectural information available from classical histopathology translates directly into tissue structures revealed by SHP. Thus, it becomes obvious that the different biochemical composition indicated by tissue morphological variations is what is also detected in SHP. The similarity of SHP and H&E images allows a detailed “annotation”, that is, the correlation with tissue and cell morphological feature with corresponding spectral features, which, in turn, permits the training of diagnostic algorithms. The course to be taken for successful SHP studies was first outlined in a series of pioneering papers by the group at the Robert Koch Institut, Berlin [48–51], and involves the following key steps: acquisition of very high S/N spectral data (the spatial resolution in the original studies were restricted by instrument performance), pre-processing including computation of 1st or 2nd derivatives and normalization to minimize instrumental and background artifacts, data pre-segmentation by unsupervised methods such

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Fig. 11. (A) Photomicrograph of an H&E-stained tissue section from a sample with cervical adenocarcinoma. (B) Overlay of H&E image and infrared pseudo-color map from hierarchical cluster analysis (HCA). See text for details.

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Fig. 12. Superposition of HCA-based detection of breast cancer micro-metastases on H&E-stained tissue sections of sentinel lymph nodes.

as hierarchical cluster analysis (HCA), very careful annotation of diseased areas by a pathologist, and sufficiently large training datasets to construct a robust diagnostic algorithm. The diagnostic algorithm used in these initial studies was an artificial neural network (ANN) trained on thousands of spectra [51]. This work laid the ground rules in SHP, and demonstrated that the patient-to-patient variations of the observed spectra were smaller than those due to disease classification or tissue type [48]. Over the past decade, tissue sections from bladder, bone, brain, breast, cartilage, cervix, colon, esophagus, kidney, liver, skin, spleen, teeth, thyroid and a few others have been studied, mostly by SHP but more recently also by Raman spectral imaging. For a summary of all these studies, the reader is referred to some recent reviews [80,86,89]. Unfortunately, many of these studies were carried only up to the presegmentation (HCA) stage, since a sufficient number of samples from different patients with the same disease diagnosis often was not available. This aspect has changed drastically since the introduction of commercial tissue microarrays (TMAs). A TMA consists of between 50 and 120 individual tissue cores, each about 1–1.5 mm in diameter, which have been punched out of paraffin-embedded tissue blocks and

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Fig. 13. (A) Comparison of 2nd derivative spectra of two different breast cancer micrometastases. (B) Comparison of 2nd derivative spectra of different breast cancer micrometastases while still paraffin-embedded.

may be seen as proto-typical examples of a given cancer type. These cores themselves are embedded in paraffin, and sectioned to standard thickness. Thus, one can purchase a TMA which contain samples from dozens of patients or disease stages. The use of TMAs was pioneered by the group around Levin at the NIH [29] and has been adopted by several groups [80], including the LSpD. Since these archived tissue sections are available with detailed disease diagnoses and often with disease outcome, the author believes that the future of SHP will be tied to TMA methodology for some time to come. As in the case of SCP, fixation issues have been the source of many questions and criticism of SHP. Early studies [55,70] have reported large spectral changes upon fixation, which could not be reproduced by other groups. However, there doubtlessly exist spectral changes caused by treatment with some of the harsher fixation protocols; here, only the two most commonly tissue treatment methods will be discussed. The least damaging way of tissue preparation is, of course, flash-freezing and cryo-sectioning the tissue section, and performing spectral analysis immediately after thawing and drying the tissue section [48]. The other method involved formalin-fixation and embedding the tissue section in paraffin, sectioning the tissue block, and subsequent de-paraffination. These procedures, which are commonly used in standard histopathology laboratories, will certainly change protein structure; on the other hand, these changes are sufficiently small that immuno-histochemical agents still recognize specific protein structures and binding sites. It is, of course, impossible to directly compare frozen and formalin-fixed and paraffin-embedded tissue sections, but if studies are carried out which do not mix the tissue preparation procedures, both methods yield comparable results. The equivalence of the two approaches was demonstrated when the author of this review was a guest editor of a special journal issue, and coincidentally, two virtually identical infrared imaging studies on a rat model of glioblastoma multiform were submitted for publication [1,2]. One study used frozen tissue section, the other formalin fixed and paraffin-embedded sections. Although there were, of course, spectral differences between the two tissue preparations, both studies arrived at images that were quite comparable, and reached similar conclusions. In SHP, different tissue types are frequently found in one section, such as white and gray brain matter, stroma, epithelial layers, inflammatory cells, and of course, diseased tissue types. In general, infrared imaging techniques, combined with unsupervised multivariate methods, can detect the different tissue types, and allow a biochemical interpretation of the spectral changes between tissue types. A typical

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example is the spectral detection of the maturation of squamous epithelial tissue via the accumulation of glycogen, which is polymer of glucose found as glycogen granules in the cytoplasm of mature squamous cells. Another example is the detection of different protein classes: the stroma and some other tissues contain collagen, which has a very characteristic infrared spectrum and can be realized in the spectra even by visual inspection. Keratin, a structural protein, is often detected in keratinizing squamous cell carcinomas, and the resulting “keratin pearls” were first described by Schultz and Mantsch [95]. Similarly, parakeratosis (the deposition of keratin) in squamous epithelium was described by Wood [107]. 3.3.2. Cervical adenocarcinoma Infection of cervical tissue by the HPV virus is thought to start at the squamous – columnar junction (SCJ), and to proceed within the basal layer of squamous tissue and eventually lead to squamous cell carcinoma of the cervix. Thus, efforts at the LSpD are aimed at following the pathways of the virus in the cervical epithelium. Several papers have reported the normal spectral changes within the layers of squamous tissue, and the distinction of the underlying stroma from the squamous tissue [19,97,107]. Spectral detection of cervical dysplasia and squamous carcinoma was reported by Steller et al. [97], but spectral characterization of cervical adenocarcinoma has not yet been recorded in the literature, partially because of the rare occurrence of this disease. Here, we present selected results from a large section of tissue (ca. 12×2 mm2 ) that contains normal squamous tissue, the SCJ, areas of normal columnar (glandular) epithelium, and large areas of cervical adenocarcinoma. In particular, we wish to focus on the abundance of inflammatory cells in the vicinity of the adenocarcinoma. In both squamous and glandular cancer of epithelium (carcinomas and adenocarcinomas, respectively), inflammatory cells are frequently observed. Steller et al. [97] reported spectral changes due to these cells in the stroma underlying a squamous cell carcinoma, but the infiltration of inflammatory cells was relatively mild. Here, we report results on a tissue section that is heavily inflamed; in fact, bands of inflammatory cells can be detected visually in Fig. 11A. These inflammatory cells, shown in light blue and red hues in Fig. 11B, are easily separated by hierarchical cluster analysis from the surrounding stroma. Normal, un-inflamed stroma is shown as the transparent regions on the right side of Fig. 11B, and the adenocarcinoma is shown in green. The purple layer denotes the body of the glandular cells, excluding the layer of nuclei closest to the basement membrane. The cell nuclei (green) underlying the purple layer cluster with the cancerous cells, indicating that these cells are abnormal. Like all images based on hierarchical cluster analysis (HCA), no reference data set is utilized in this image reconstruction process; rather, the image is based entirely on spectral similarities. Spectral classes obtained from HCA images, and pathological diagnoses of the cluster-based regions, have been used to train diagnostic algorithms for the automatic diagnosis of tissue sections. The tissue sections from this sample of cervical adenocarcinoma have presented significant difficulties for the interpretation of the spectral results, the unsupervised cluster analysis, and the training of diagnostic algorithms, due to the abundance of inflammatory cells. However, the separation of stroma and both squamous and glandular epithelial tissue is trivial by SHP, as is the distinction of the different layers of the squamous tissue. Here, the spectral changes are so reproducible that a diagnostic network such as an ANN, can be trained to separate these tissue types. However, the regions of inflammation do present some difficulties. First, inflammatory cells are small and nearly spherical in shape, and present strong RMie scattering. Once corrected, it appears that the spectral characteristics of these immune cells change with the proximity to the cancer. In some areas of this tissue section, the spectra of the inflammatory cells are nearly indistinguishable from the spectra of the adenocarcinoma cells, such as in the red

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areas of Fig. 11, while in other (light blue areas in Fig. 11), the inflammatory cells separate quite nicely from the cancerous regions. It is possible that the two classes of inflammatory cells are biochemically different: we have shown before that the activation of B-lymphocytes can be detected by SHP; furthermore, we have indications that the spectra of certain immune cells (specifically phagocytic histiocytes) change upon interacting with, and destroying cancer cells. Thus, the similarity of the adenocarcinoma cells and the histiocytes may have biochemical origins. The only way to address these problems is via immunohistochemical staining to further identify the cell types in the sample. 3.3.3. Breast cancer micrometastases in lymph nodes In this section, we shall present results of infrared imaging studies, combined with hierarchical cluster analysis (HCA), of lymph node tissue sections infiltrated by breast cancer micro-metastases [7,9–12]. This is a significant medical problem since treatment depends on the presence or absence of cancerous cells in the sentinel lymph nodes, where they form metastatic tumors. Metastases less than 2 mm in size are referred to as micrometastases, which tend to form in the sub-capsular sinuses of the lymph nodes. In this study, over 50 1 mm × 1 mm spectral images were collected, each consisting of 25,600 spectra. Here, we were particularly interested whether or not HCA can reliably segment the raw datasets into spectra of capsule, lymphocytes, metastatic cancer, etc. In particular, we wished to establish that the spectra of the metastatic cancers were sufficiently similar to permit their detection by a trained, diagnostic algorithm. Figure 12 shows three typical 1 × 1 mm2 images of H&E-stained lymph node tissue sections. In each of them, the capsule of the lymph node, composed of fibro-connective tissue, is shown in pink, whereas the lymphocytes within the body of the lymph node appear dark violet. Depending on the scarcity of the tissue section, the lymphocytes may exhibit strong RMie-scattering. The dark read regions in the three panels of Fig. 12 are superpositions of the infrared spectral regions indicative of metastatic cancer cells. These regions co-register exactly with the regions of the lymph nodes that show morphological abnormalities consistent with invasive breast cancer cells. Thus, it appeared that the automatic detection and diagnosis of breast cancer micro-metastases by infrared spectral imaging methods should be possible using a trained diagnostic algorithm. However, in spite of the excellent discrimination of the cancerous regions from the surrounding lymphocytes, the original studies showed poor similarity between the metastatic cancer spectra from different samples [9]. This is shown in Fig. 13A which depicts large spectral differences in the amide I region of the breast cancer regions. Below ca. 1480 cm−1 , the spectra are nearly identical, aside from an intensity scale factor. This spectral difference was soon realized to be an artifact due to interference with dispersive line shapes. When spectra from five different micro-metastases were collected from tissue sections still embedded in paraffin [10], nearly identical spectra were observed, see Fig. 13B.3 In this case, there is much better matching of the refractive index of areas occupied by tissue and areas devoid of sample; index matching enormously reduces the incidence of scattering effects that mix absorptive and reflective line shapes as discussed before (see Eqs (2) and (3)). The dispersive line shapes predominantly affect the high wavenumber side of the amide I band, and cause enormous changes in the 2nd derivative spectra, see Fig. 13A. After correction for these effects, the spectra of micro-metastases were found to be sufficiently similar that diagnoses with trained algorithms is possible. These results explained why in the past, best SHP images were obtained if the amide I spectral region was excluded [88]. 3

Spectral maps of tissue section, still embedded in paraffin, were first reported by the Reims group around Manfait [54]. They showed that very similar maps could be obtained if the few strong vibrations of paraffin are properly accounted for.

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Since the seminal papers on the origin of dispersive band contaminations were published [5,6], these difficulties are understood and are being corrected routinely. This has opened the possibilities for a wide application of SHP for diagnostic purposes, since the major variance of the spectral data has been eliminated. Several research groups have used tissue microarrays to increase the size of datasets, and have found that the spectral signatures for similar disease states are remarkably reproducible. In dense and cohesive tissues sections, for example from the colon [52] or liver [20], scattering effects were much smaller and thus, allowed early studies to be carried out without interference from dispersive band shapes. On the other hand, very sparse tissue, such as normal lung tissue, consists of very few “filaments” of aligned cells which exhibit enormous band shape distortion; in these, no reliable data can be obtained without scatter correction. 3.3.4. Diagnostic algorithms and future prospects of SHP The inherent sensitivity of vibrational spectral fingerprints toward changes in biochemical composition of tissue pixels makes SHP an ideal candidate for medical diagnostic imaging. With recent advances in data pre-processing, the increasing number of research groups involved in the field, and the generally good agreement between spectral and pathological results, it appears that SHP is poised to enter the mainstream diagnostic arena. The major obstacle to a broad application of SHP is, in the eyes of the authors, the difficulty in obtaining sufficient and reliable annotations to train diagnostic algorithms. The severity of this problem was first indicated in a review chapter authored by Stone [98], who has been on the forefront of Raman spectral histopathology and in vivo Raman diagnostics. He reported that a consensus diagnosis by a group of three pathologists was obtained in only about 30% of all cases presented to them. Anecdotal evidence from collaborators of the authors has indicated that the same tissue section can produce different pathological diagnoses at different times. The approach that appears most successful in obtaining accurate and reproducible annotation involves the use of high resolution digital images of the H&E-stained tissue sections onto which HCA images can be superimposed. This step requires that the digital H&E and the HCA images are exactly registered, and can be zoomed together. This approach permits the pathologist to annotated the spectral images based on single cell features; i.e., the pathologist can select the most typical features in a tissue section and correlate it to spectral features at the level of one or a few cells. Spectra from the pathologist – annotated tissue areas are subsequently extracted from the datasets and used to train diagnostic algorithms. As indicated above, it is imperative that tissue samples from different patients are used in this training phase, because small but systematic differences may exist between the extracted spectra from different patients. At present, it is not known whether or not these differences can be later correlated to special aspects of disease type and progression. These questions can only be answered by parallel spectral and immunohistochemical studies. At the LSpD, the diagnostic methodology of choice has been artificial neural nets (ANNs) in various implementations. ANNs are self-learning methods modeled after the neural interactions in the human brain. They can be used as binary (two-class) classifiers, or to differentiate more than two input classes. They can be “stacked” to operate as hierarchical networks, for example, as several consecutive binary classifiers. Recent studies comparing them to other multivariate classifiers have established that they perform at a similar level of predictive accuracy as, for example, the “random forest” algorithm. The authors’ use of ANNs has been frequently criticized by reviewers of proposals and publications emerging from the LSpD, and “overtraining” and the well-publicized failures of early applications of ANNs are repeatedly cited. These two points will be addressed briefly. First, there exist well-established rules in bio-informatics on the size of training and validation sets required to produce reliable algorithms; failure to adhere to these rules certainly will produce algorithms that can be hopelessly overtrained. Yet, any

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discriminatory algorithm, including the operation of the human brain, suffers from this shortcoming. In the latter case, the subjectivity of classical histopathology is certainly a manifestation of insufficient training. Secondly, the well-published failures of discriminate algorithms of any kind can be attributed to insufficient training, such as omitting entire classes of possible inputs or conditions. Also, many tasks to which discriminate algorithms were applied, i.e., the morphological discrimination of abnormal from normal exfoliated cells, or facial recognition, require the translation of certain features, be it the nuclearto-cytoplasm ratio of a cell, or the height vs. width ratio of a face, to be collected in metrics which are subsequently analyzed by the ANN. It is conceivable that these metrics lacked specificity for the task at hand, and that the discrimination failed, not because of shortcomings of the discriminatory method, but a shortcoming of the input data. In SCP and SHP, the form of spectral results – one-dimensional vectors of intensity data at given wavenumber point – is ideal for an ANN or “random forest”, and does not involve the constructing of metrics. 3.4. Raman spectral images of squamous cells

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The review of Raman data in this paper is somewhat biased toward cellular imaging, rather than diagnostic applications. All the diagnostic work carried out at the LSpD utilizes infrared micro-spectroscopy which offers much higher speed and a spatial resolution of about the size of a cell. Raman imaging, on the other hand, offers much higher spatial resolution, and is therefore highly sensitive to detect biological changes at a much smaller (subcellular) level. Thus, this review shall concentrate on cell imaging applications of Raman micro-spectroscopy; however, other groups have used Raman micro-spectroscopy for diagnostic purposes as well by defocusing the laser beam to larger spot size (2–5 μm in diameter) and sacrificing spatial information. Here, we wish to report information that supplements the cytological efforts at the LSpD by developing label-free methods to visualize cellular organization. Figure 14 shows Raman images of three different squamous epithelial cells obtained by raster scanning the laser beam, focused to a spot of about 300 nm diameter, over the cell and collecting an entire Raman spectrum from each spot. Subsequently, unsupervised hierarchical cluster analysis was used to convert the hyperspectral dataset into a pseudo-color image. Figure 14A shows a Raman image of a cultured cervical cancer (HeLa) cell [57]. It was reconstructed from a dataset containing ca. 10,000 spectra via HCA. The segmentation of spectra into different classes

Fig. 14. Raman images, reconstructed via hierarchical cluster analysis from raw hyperspectral datasets of (A) a cultured HeLa cell, (B) an exfoliated oral mucosa cell and (C) a squamous cell from the distal urethra. See text for details.

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is based on their similarity; i.e., pixels shown in the same color result from very similar spectra. The cellular details available in this image are astounding: the large nucleus (typical for an actively growing cell, see Introduction), shown in dark green, is easily distinguished from the cytoplasm. Furthermore, two nucleoli shown in dark blue are detected within the nucleus. The spectra of the nucleus (mostly protein and DNA) and nucleolus (mostly protein and RNA) differ very minutely; in a single spectrum, such differences would not be significant. However, HCA detects correlated spectral differences which have physical significance; thus, the mean cluster spectra between nuclei and nucleoli show distinct spectral changes which could be interpreted biochemically [57], and were reproducible between different cells imaged. The cytoplasm also shows small, but significant spectral differences. Using a mitochondria-specific stain, we were able to assign the yellowish-green and salmon-colored clusters in the perinuclear region to be due to high abundance of mitochondria. This was accomplished by adding the stain to the cell in aqueous environment, and without re-registering the sample, re-scanning the cell using the Raman microscope as a confocal fluorescence microscope. Such experiments can be carried out staining for other specific cell organelles [45]. The image in Fig. 14B is from a mature oral mucosa cell with a pyknotic nucleus [58]. Spectra from the pyknotic nucleus separate readily from those of the cytoplasm. Within the cytoplasm, an interesting feature is observed, represented by the occurrence of the purple spots. The mean cluster spectra of these spots indicates a superposition of cytoplasmic protein and phospholipids, indicated by strong aliphatic CH2 deformation and stretching modes at ca. 1445 and 2950 cm−1 . Natural phospholipid spots could be due to intracellular lipid droplets, or due to structures such as the Golgi apparatus, vacuoles or multilamellar vesicles. When exposing a cell to deuterated phospholipids [59] (e.g., liposomes produced entirely from deuterated phospholipids) we found that the deuterated lipids equilibrate with the naturally occurring lipids within the cytoplasm. Thus, Raman imaging can be used to study transport and exchange phenomena which are difficult to perform by other imaging methods. Finally, Fig. 14C depicts a squamous cell from the distal urethra. These cells constitute the majority of cells found in urine cytology. Like most stratified squamous cells, they accumulate glycogen upon maturation. The glycogen is not distributed uniformly within the cytoplasm, but forms granules which can be visualized by Raman spectral imaging. In Fig. 14C, the areas shown in red exhibit the signatures of cytoplasmic protein and glycogen, which can be identified by comparison with reference spectra. The purple areas, as in Fig. 14B, are due to phospholipids. Raman spectral imaging bears the advantage over other cellular imaging methods in that no specific label or dye needs to be added to the cells, but that the image is based on an inherent vibrational spectroscopic fingerprint pattern that can be detected with a spatial resolution similar to that of confocal fluorescence microscopy. Sample preparation is trivial for confocal Raman microscopy: a live or fixed cell is grown or placed on a CaF2 substrate, immersed in buffer solution which is brought in contact with a water immersion objective. This method produces information as closely as possible to an “undisturbed”, non-invasive approach. In particular, the possibility of Raman imaging methods to be carried out in aqueous surroundings on live cells opens the possibility to monitor cells for later medical use, for example, stem cells. Indeed, the early differentiation steps of stem cell colonies [17,68,69,79,96, 109] and embryonic bodies have been detected. Recently, Notingher reported Raman spectra of beating cardiomyocytes [67]. In addition, a number of studies have appeared in the literature that used Raman micro-spectroscopy (at lower spatial resolution, as pointed out above) for Raman spectral cytopathology. Here, the ability to observe live cells in their native environment, and the high selectivity of vibrational spectroscopy bear

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enormous promise for the application of Raman spectroscopy for identifying cell types in blood, and to use this technique to cell sorting applications, for example for the detection and isolation of circulating tumor cells. Efforts in this direction have been spearheaded by the Jena (Germany) group around Popp, who has reported the identification of different cell types in blood, even under flow conditions [28,65,66]. In addition, a study to differentiate cells with different viral infections has been published [73]. Also, the spectral differences due to over-expression of an oncogene were recently reported [37]. At the LSpD, the uptake of drug-loaded targeted and non-targeted nanoparticles, and the subsequent release of the drug inside the cell, has been studied [18,60]. These studies are aimed at a readership in pharmaceutical science, and will not be discussed here any further. Raman spectroscopy has been used for in-vivo and ex-vivo diagnostic applications. In the former category, the efforts for in-vivo diagnosis of cervical [83,99] cancer, and the work on in-vivo detection of esophageal and bladder cancer [3,44] have defined the enormous potential of fiber optic-based Raman diagnostics. Stone’s group at the Gloucestershire Hospital (UK) was instrumental in these in-vivo efforts; they also have explored the possibility of intra-operative assessment of tissue using Raman spectroscopy [41,72]. Ex-vivo Raman images of tissue have been collected, using mostly near-IR excitation, for liver [47] and lung [46] and have revealed information similar to images described in the section on Spectral Histopathology above, albeit with higher spatial resolution. At this point, it appears that both infrared and Raman micro-spectroscopy have comparable diagnostic sensitivity, and are poised to enter the medical diagnostic fields. The two techniques have somewhat different strengths (speed, ability to measure in aqueous environment) and may complement each other in the same way classical Raman and infrared spectroscopy are complimentary. The next frontier in this field promise to be non-linear techniques, which have shown to increase data acquisition by orders of magnitude [31,43].

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4. Conclusions In this review, aspects of infrared and Raman spectral imaging and medical diagnostics have been presented. Although there are major differences to classical spectroscopy of biomolecules, due to the size and complexity of the systems reported in this review, it is important to point out that this work is based on, and relies on decades of research in biospectroscopy. The aspects most different from “classical” biospectroscopy is the heavy reliance of this new research endeavor on mathematical methods for data analysis, partially necessitated by the fact that the amount of data collected often measures in the gigabyte regime, and visual interpretation of such an amount of data is impossible. Furthermore, the multivariate methods of analysis are highly suitable for extracting small, correlated spectral differences that are often smaller than, and are buried in uncorrelated noise level. Thus, the authors hope that this research not only advances the field of medical diagnostics by spectral methods, but also helps to usher in new ways to look and process spectral data.

Acknowledgements Partial support of this research by Grants CA 090346, CA 111330 and CA 153148 from the National Institutes of Health during the past five years is gratefully acknowledged.

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The application of biophysical techniques to study antimicrobial peptides Inês M. Torcato, Miguel A.R.B. Castanho and Sónia T. Henriques ∗ Instituto de Medicina Molecular, Faculdade de Medicina da Universidade de Lisboa, Lisboa, Portugal Abstract. The increasing bacteria resistance to conventional antibiotics has led to the need for alternative therapies. Being part of the human innate defence system and with a broad spectrum of activity against bacteria, viruses, protozoa and cancer cells, antimicrobial peptides (AMPs) are a very promising alternative. The mechanism of action of AMPs seems to broadly correlate with their ability to target the bacterial cell membrane. To understand and improve their effect, it is of major importance to unravel their mechanism of action and in particular, to understand the peptide–membrane binding. Several biophysical techniques such as fluorescence spectroscopy, circular dichroism, zeta potential determination and atomic force microscopy can be used to achieve this goal. Characteristics of AMPs interactions and the use of these biophysical techniques will be discussed. Keywords: Antimicrobial peptides, bacteria, model membranes, biophysical techniques

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1. Antimicrobial peptides (AMPs) The effectiveness of antibiotics has become limited due to an increase in bacterial resistance; thus, the development of new antimicrobial agents that increase the effectiveness and reduce the side effects, in comparison to traditional treatments, is of major relevance. With a broad action spectrum, and less susceptible to the development of bacterial resistance, AMPs have been regarded as an exciting alternative to classical antibiotics [25]. They exist in nearly all organisms as part of their innate immune system and have activity against bacteria, viruses, protozoa and cancer cells [1,19]. By selecting the bacterial cell membrane over the host cell, AMPs induce bacterial cell lysis and death without being toxic to the host cells. Generally, AMPs are short (less than 50 amino acid residues), cationic and have an amphipathic character [18]. Nevertheless, these peptides exhibit great sequence variability and can be divided in four major classes classified based on their secondary structure: α-helical, β-sheet, looped and extended [18, 19,27]. AMPs with α-helical structure commonly exist as unstructured monomers in solution but acquire helical conformation upon contact with phospholipid bilayers [27]. The interaction with bacterial cell membrane has been shown to be crucial for the activity of AMPs. Therefore, understanding the interaction of AMPs with the bacterial membrane and identifying the membrane properties/components responsible for the activity is of major importance to improve efficiency and decrease toxicity against the host. *

Corresponding author: Dr. Sónia T. Henriques. Current address: Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia. Tel.: +61 7 33462023; Fax: +61 7 3346 2101; E-mail: [email protected].

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2. Interaction of AMPs with cellular membranes The plasma membrane is responsible for the compartmentalization of the cell and works as a selective barrier to control the gradients indispensable to life. It is mainly constituted by phospholipids and proteins, but each organism have its own characteristic membrane composition [26]. For example, eukaryotic membranes are mainly constituted by neutral glicerophospholipids (such as phospholipids containing phosphatidylcholine or phosphatidylethanolamine headgroups), whereas bacterial membranes possess a large percentage of anionic lipids such as cardiolipin and phospholipids containing phosphatidylglycerol [26,27]. Furthermore, bacteria have a more electronegative transmembrane potential than eukaryotic cells and possess negatively charged constituents on their surface (e.g., lipopolysaccharide and lipoteichoic acids in Gram-negative and Gram-positive bacteria, respectively) [27]. With a net positive charge, AMPs have selectivity towards the negatively-charged surface of bacterial membranes over neutral eukaryotic membranes [27]. Nevertheless, AMPs may have distinct modes of action and can be broadly divided into two main groups: AMPs that cause the disruption of the bacterial membrane or AMPs that cross the membrane and attack an intracellular target without disrupting the membrane [19]. The first interaction between peptides and membranes seems to be driven by electrostatic interactions between the phospholipids phosphate groups and the peptide hydrophilic amino acids. After this first interaction, the peptide can insert in the membrane and either perturb the integrity of the plasma membrane, or translocate into the cytoplasm of the cell in a non-disruptive way [1,19,27]. The action of disruptive AMPs can be explained by the formation of pores in the membrane (“barrel stave” and “toroidal pore” mechanisms) or by accumulation of peptide molecules on the membrane surface (“carpet mechanism”) [19,27]. On the other hand, non-disruptive peptides are able to cross the membrane and attack cytoplasmatic targets, such as nucleic acids and/or cellular proteins, or might have an inhibitory action on the synthesis of these compounds [27]. For instance, pyrrhocoricin, a proline-rich insect peptide, has been shown to bind the heat shock protein DnaK inhibiting chaperone-assisted protein folding [19]. The time necessary to detect loss of viability differs for disruptive and non-disruptive AMPs [19]. The disruptive peptides cause cell death in few minutes, whereas non-disruptive peptides act in a slower manner [19].

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3. Membrane models As above referred, peptide–membrane interactions are important for the mechanism of action of AMPs. Biological membranes are complex and heterogeneous structures composed by hundreds of lipids and proteins [7,26] and with domains of distinct composition [7]. For that reason, simple model membranes are commonly used to distinguish the role of the different cell components on the mode of action of AMPs. The lipid charge, membrane viscosity and the absence/presence of sterols are some properties that can be modulated [20]. In addition, model membranes are easily prepared with synthetic lipids or with lipids extracted from a certain type of cell [20]. The most commonly used membrane models are phospholipid vesicles that are classified based on their size and on the number of lamellae that constitutes them [23]. Unilamellar vesicles with a diameter equal or superior to 100 nm are referred to as large unilamellar vesicles (LUVs). Due to their non-constrained curvature at molecular scale, LUVs have a large stability and possess identical lipid packing as biological membranes and are preferred as a model [14]. These vesicles can be employed in

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the study of peptide–membrane interactions using biophysical techniques; in particular, the intrinsic fluorescence of peptides can be used to follow peptide–membranes interactions by means of fluorescence methodologies. SUVs, small unilamellar vesicles, are also extensively used, but their small size (20–50 nm) and therefore their high surface curvature can lead to distorted lipid packaging and consequently to anomalous peptide packing and metastability [14]. Although SUVs are less prone to cause artifacts due to light scattering, the disadvantages above mentioned and the fact that the scattering artifacts associated with LUVs can be easily corrected, makes LUVs the preferred model [14]. It is also worth to mention GUVs, giant unilamellar vesicles, with a diameter larger than 1 μm, they can reach eukaryotic cells size and therefore are very useful for microscopy studies [23]. Human (e.g., erythrocytes) and bacterial cells can also be used to study peptide–membrane interactions and are important to validate the results obtained with simple model membranes. Nevertheless, the application of biophysical techniques with cells is limited; for instance, the intrinsic fluorescence of the peptide cannot be distinguished from the large cellular background fluorescence [10]. This limitation requires AMPs to be derivatized with extrinsic fluorophores such as rhodamine and nitrobenzoxadiazole; besides an increase in experimental and monetary requirements, derivatization of peptides can alter their properties [10]. Theoretical models can also be used and in general are a good complement to experimental data. However, the small size of the virtual membrane patches, the limited number of peptide molecules and the short time-scales that can be sampled restricts the extrapolation that can be made by these studies [13]. 4. Relevant biophysical studies for AMP–membrane interactions There are various biophysical techniques that can be employed to study peptide–membrane interactions. In this section some of these techniques will be briefly described. 4.1. Fluorescence spectroscopy The intrinsic fluorescence of peptides is exceptionally useful to study AMPs–membrane interactions as it provides structural and dynamic information in a non-invasive way [21]. Several fluorescence methodologies can be employed and some will be addressed bellow.

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4.1.1. Partition constant, Kp As above referred, AMPs have to interact with plasma membranes to exert their antimicrobial activity, therefore their extension of partition into the membranes is particularly relevant. Partition can be quantified by the determination of the partition constant (Kp ) defined as the ratio of the peptide molecules in the lipid (L) over the peptide in the aqueous phase (W ) [20,24]: Kp =

nS ,L /VL nS ,W /VW

(1)

where nS ,i is the moles of solute present in the aqueous (i = W ) and lipid (i = L) phases and Vi is the water (i = W ) and (i = L) lipid volume. When peptides partitionate into membranes an increase in the fluorescence quantum yield and a spectral shift to more energetic wavelengths is usually observed

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Fig. 1. The use of spectroscopic methodologies to evaluate the interaction of AMPs with model membranes. (A) Partition of the AMP LEAP-2 into lipid vesicles followed by Trp fluorescence. An increase in the LEAP-2 fluorescence intensity upon titration with lipid vesicles is evident. The Kp value can be determined applying Eq. (2). (B) Normalized fluorescence emission spectra of LEAP-2 illustrate a blue-shift upon titration with lipid vesicles. (C) Circular dichroism spectra of pardaxin 4 in the absence (full line) or presence (dashed line) of lipopolysaccharide. Panels A and B were adapted from [11] and panel C was adapted from [3].

(as exemplified with the AMP LEAP-2 in Fig. 1A and B). Therefore, the response is a combination of the signal emitted by free (IW ) and bound (IL ) peptides and Eq. (2) can be used to quantify the Kp (Fig. 1A) [24]: I=

IW + Kp γL [L]IL 1 + Kp γL [L]

(2)

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The peptide partition constant obtained for different lipid systems can be compared to evaluate the peptide lipid preference. A greater affinity towards negatively charged membrane models (mimetic of bacteria) is expected for AMPs. 4.1.2. Differential quenching Quenching of the intrinsic fluorescence of peptides can be used to study their in-depth localization in the lipid membrane [8]. This method applies simple diffusional quenching concepts to the restricted dimensions of a bilayer [17]. The bilayer is treated as a slab in which fluorophores and quenchers exist with a determined in-depth distribution. The statistical distributions of the quenchers were previously determined by single-molecule Brownian dynamics simulations whereas the distribution of the fluorophores can be estimated using pairs of quenchers that are in general brominated or doxyl-derivatized acyl chains (in different positions of the acyl chain) in fatty acids or phospholipids [17]. One example is the pair 5 and 16-NS that are fatty acids derivatized with a doxyl group on the positions 5 and 16, respectively. 5-NS locates in the aqueous environment–membrane interface, whereas the 16-NS localizes in the hydrophobic core [8]. The relative degree of quenching between quenchers and fluorophores depends of the proximity between these molecules and therefore, fluorescence intensity will give information about the prevailing localization of the peptides in the membrane [8]. Additionally, a water soluble quencher such as acrylamide can be used to determine if the fluorophore is accessible to the aqueous environment and thus, complement this analysis. 4.1.3. Membrane permeabilization As previously mentioned, certain AMPs can cause the disruption of the plasma membrane by different proposed mechanisms. The ability of a specific peptide to induce the permeation/lysis of a certain model

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membrane can be quantified by the percentage of leakage induced by these peptides [2]. One frequently used method is to follow carboxyfluorescein fluorescence intensity [2]. This probe is incorporated inside LUVs, being highly concentrated and thus in self-quenching. However, when AMPs induce membrane leakage, carboxyfluorescein is released and diluted, which leads to an increase of fluorescence intensity proportional to the percentage of leakage [4]. Applying Eq. (3), it is possible to calculate the leakage percentage induced by the peptide on a membrane with the composition studied. %Leakage =

I − I0 Ipositive control − I0

(3)

I is the fluorescence intensity in the presence of the peptide, I0 is the fluorescence intensity in the absence of peptide and Ipositive control is the fluorescence intensity in the presence of the positive control; usually Triton X-100 is used [4]. 4.2. Circular dichroism Circular dichroism is a spectroscopic technique that can be used to estimate the overall secondary structure of peptides [12]. Peptide bonds are the most absorbing components in the far UV region (240– 180 nm) and every secondary structure element has a characteristic spectra [12]. The secondary structure of peptides can change upon insertion in lipid vesicles. Such alteration can be followed by circular dichroism and is relevant to understand the mechanism of action of AMPs. Figure 1C compares the secondary structure of pardaxin 4 in the absence and presence of a model of the outer membrane of Gram-negative bacteria [27]. A change from random coil to α-helix structure upon interaction with the model membrane is evident. Other studies, such as the orientation of peptides are possible using spectroscopic techniques resorting to linear dichroism. They are out of the scope of this manuscript due to their specificity. The reader is referred to references [15,16] for this subject. 4.3. Atomic force microscopy

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Atomic force microscopy (AFM) can be used to obtain three-dimensional images of the microbial cell surface and of the lipid membrane. AFM images can be acquired in real time, under physiological conditions, with minimal sample preparation [6] and can be used to examine model membranes [9] or bacterial cells [1] treated with AMPs (as exemplified with the AMP BP100 in Fig. 2A and B). Bacterial cell surface can exhibit changes in the shape and rugosity, whereas in model membranes the presence of holes, and changes in the phase are possible effects. In addition, force measurements can be employed to study intra- and intermolecular forces [22] and measure mechanical properties of the bacterial cells after treatment with AMPs [6]. With this technique is possible to directly compare the effects on model and cell membranes. 4.4. Zeta potential The superficial negative charge of bacterial membranes can be neutralized upon titration with cationic AMPs and this phenomenon can be followed by zeta potential (ζ-potential) [5]. In solution, polyelectrolyte particles have a layer of ions at their surface that moves with the particles. Above this layer there are ions that do not move with the molecule and electric potential existent in this boundary is referred to

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Fig. 2. The use of bacterial cells in biophysical studies. Three-dimensional orthogonal projection of Escherichia coli cell (A) non-treated or (B) treated with the AMP BP100 obtained with AFM imaging. (C) Titration of Escherichia coli cells with BP100 induces an increase in the zeta potential and a decrease in the bacterial viability, revealing that antibacterial activity correlates with the surface membrane charge neutralization. Panels A–C adapted from [1].

as ζ-potential [5]. It has been previously observed that ζ-potential of anionic lipid vesicles and bacterial cells become less negative upon increase of the peptide/lipid ratio. Such effect reflects the interaction between the peptide and the membrane as exemplified in Fig. 2C, with Escherichia coli cells titrated with the AMP BP100 [5]. The possibility of doing these studies with lipid vesicles and bacterial cells is a great advantage because it enables a direct comparison between the two systems. The ζ-potential is measured using the Henry’s relation: UE =

2εzf (κα) , 3η

(4)

where f (κα) is the Henry’s function, z is the ζ-potential, UE the electrophoretic mobility, ε is the dielectric constant and η is the viscosity of the solution. UE is calculated by laser Doppler velocimetry in which the particle velocity is related to the frequency measured by variations of the intensity of the scattered light. For particles in aqueous solution the Henry’s function takes the value 1.5, following the Smoluchowski approximation. When the particles are suspended in nonaqueous solution the value of the function is 1, according to Huckel approximation [5].

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5. Conclusion With a large spectrum of activity, high efficiency and low toxicity, AMPs are a potential alternative to conventional therapies. Their mode of action is dependent on peptide–membrane interaction; therefore for a broad application of those peptides, to improve their activity and to reduce eventual toxicity it is important to understand their mode of action. Biophysical techniques are useful tools to understand peptide–membrane interactions. Using lipid vesicles and fluorescence spectroscopy methodologies it is possible to obtain information on peptide–membrane affinity, peptide in-depth location and membrane stability upon interaction of AMPs with model membranes. With circular dichroism spectroscopy the secondary structure motifs of the peptides can be identified and alterations on the secondary structure upon contact with model membranes can be evaluated. On the other hand, ζ-potential and AFM can be employed with either model membranes or biological cells. ζ-potential can give details on superficial charge neutralization and with AFM, AMPs actions on model membranes and biological cells can be

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directly observed. Altogether is possible to conclude that biophysical techniques are valuable tools to study the mechanism of action of AMPs and other membrane-active peptides, as insights on the peptide– membrane interactions can be obtained. Acknowledgements STH is a Marie Curie International Outgoing Fellow within the 7th European Community Framework Program (PIOF-GA-2008-220318). Work in our laboratory on antimicrobial peptides is supported by a grant from the Fundação para a Ciência e Tecnologia, Portugal (PTDC/SAU-BEB/099142/2008).

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References [1] C.S. Alves, M.N. Melo, H.G. Franquelim, R. Ferre, M. Planas, L. Feliu, E. Bardaji, W. Kowalczyk, D. Andreu, N.C. Santos, M.X. Fernandes and M.A. Castanho, Escherichia coli cell surface perturbation and disruption induced by antimicrobial peptides BP100 and pepR, J. Biol. Chem. 285 (2010), 27536–27544. [2] E.E. Ambroggio, F. Separovic, J.H. Bowie, G.D. Fidelio and L.A. Bagatolli, Direct visualization of membrane leakage induced by the antibiotic peptides: maculatin, citropin, and aurein, Biophys. J. 89 (2005), 1874–1881. [3] A. Bhunia, P.N. Domadia, J. Torres, K.J. Hallock, A. Ramamoorthy and S. Bhattacharjya, NMR structure of pardaxin, a pore-forming antimicrobial peptide, in lipopolysaccharide micelles: mechanism of outer membrane permeabilization, J. Biol. Chem. 285 (2010), 3883–3895. [4] M.M. Domingues, M.A. Castanho and N.C. Santos, rBPI(21) promotes lipopolysaccharide aggregation and exerts its antimicrobial effects by (hemi)fusion of PG-containing membranes, PLoS One 4 (2009), e8385. [5] M.M. Domingues, P.S. Santiago, M.A. Castanho and N.C. Santos, What can light scattering spectroscopy do for membrane-active peptide studies?, J. Pept. Sci. 14 (2008), 394–400. [6] Y.F. Dufrene, Atomic force microscopy, a powerful tool in microbiology, J. Bacteriol. 184 (2002), 5205–5213. [7] G.W. Feigenson, Phase boundaries and biological membranes, Annu. Rev. Biophys. Biomol. Struct. 36 (2007), 63–77. [8] M.X. Fernandes, J. Garcia de la Torre and M.A. Castanho, Joint determination by Brownian dynamics and fluorescence quenching of the in-depth location profile of biomolecules in membranes, Anal. Biochem. 307 (2002), 1–12. [9] H.G. Franquelim, S. Chiantia, A.S. Veiga, N.C. Santos, P. Schwille and M.A. Castanho, Anti-HIV-1 antibodies 2F5 and 4E10 interact differently with lipids to bind their epitopes, AIDS 25 (2011), 419–428. [10] S.T. Henriques, M.N. Melo and M.A. Castanho, How to address CPP and AMP translocation? Methods to detect and quantify peptide internalization in vitro and in vivo (Review), Mol. Membr. Biol. 24 (2007), 173–184. [11] S.T. Henriques, C.C. Tan, D.J. Craik and R.J. Clark, Structural and functional analysis of human liver-expressed antimicrobial peptide 2, Chembiochem. 11 (2010), 2148–2157. [12] S.M. Kelly and N.C. Price, The use of circular dichroism in the investigation of protein structure and function, Curr. Protein Pept. Sci. 1 (2000), 349–384. [13] H. Khandelia, J.H. Ipsen and O.G. Mouritsen, The impact of peptides on lipid membranes, Biochim. Biophys. Acta 1778 (2008), 1528–1536. [14] A.S. Ladokhin, S. Jayasinghe and S.H. White, How to measure and analyze tryptophan fluorescence in membranes properly, and why bother?, Anal. Biochem. 285 (2000), 235–245. [15] S.C. Lopes and M.A.R.B. Castanho, Overview of common spectroscopic methods to determine the orientation/alignment of membrane probes and drugs in lipidic bilayers, Curr. Organic Chem. 9 (2005), 889–898. [16] S.C. Lopes, E. Goormaghtigh, B.J. Cabral and M.A. Castanho, Filipin orientation revealed by linear dichroism. Implication for a model of action, J. Am. Chem. Soc. 126 (2004), 5396–5402. [17] P.M. Matos, H.G. Franquelim, M.A. Castanho and N.C. Santos, Quantitative assessment of peptide–lipid interactions. Ubiquitous fluorescence methodologies, Biochim. Biophys. Acta 1798 (2010), 1999–2012. [18] M.N. Melo, R. Ferre and M.A. Castanho, Antimicrobial peptides: linking partition, activity and high membrane-bound concentrations, Nat. Rev. Microbiol. 7 (2009), 245–250. [19] J.P. Powers and R.E. Hancock, The relationship between peptide structure and antibacterial activity, Peptides 24 (2003), 1681–1691. [20] M.M. Ribeiro, M.N. Melo, I.D. Serrano, N.C. Santos and M.A. Castanho, Drug–lipid interaction evaluation: why a 19th century solution?, Trends Pharmacol. Sci. 31 (2010), 449–454.

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[21] N.C. Santos and M.A. Castanho, Fluorescence spectroscopy methodologies on the study of proteins and peptides. On the 150th anniversary of protein fluorescence, Trends Appl. Spectrosc. 4 (2002), 113–125. [22] N.C. Santos and M.A. Castanho, An overview of the biophysical applications of atomic force microscopy, Biophys. Chem. 107 (2004), 133–149. [23] N.C. Santos and M.A.R.B. Castanho, Liposomes: has the magic bullet hit the target?, Química Nova 25 (2002), 1181– 1185. [24] N.C. Santos, M. Prieto and M.A. Castanho, Quantifying molecular partition into model systems of biomembranes: an emphasis on optical spectroscopic methods, Biochim. Biophys. Acta 1612 (2003), 123–135. [25] S. Thomas, S. Karnik, R.S. Barai, V.K. Jayaraman and S. Idicula-Thomas, CAMP: a useful resource for research on antimicrobial peptides, Nucleic Acids Res. 38 (2010), D774–D780. [26] G. van Meer, D.R. Voelker and G.W. Feigenson, Membrane lipids: where they are and how they behave, Nat. Rev. Mol. Cell Biol. 9 (2008), 112–124. [27] M.R. Yeaman and N.Y. Yount, Mechanisms of antimicrobial peptide action and resistance, Pharmacol. Rev. 55 (2003), 27–55.

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Spectroscopy of Biological Molecules M.P. Marques et al. (Eds.) IOS Press, 2013 © 2013 The authors and IOS Press. All rights reserved. doi:10.3233/978-1-61499-184-7-039

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Fluorescence lifetime imaging of propranolol uptake in living glial C6 cells Roger H. Bisby a,∗ , Stanley W. Botchway b , Ana G. Crisostomo a , Anthony W. Parker b and Kathrin M. Scherer a a

Biomedical Research Centre, University of Salford, Salford, UK Central Laser Facility, Research Complex at Harwell, Science and Technologies Facility Council, Rutherford Appleton Laboratory, Chilton, Harwell Oxford, Oxon, UK

b

Abstract. Uptake of the β-blocker drug propranolol by living glial C6 cells has been observed using fluorescence lifetime imaging with two-photon excitation at 630 nm. Both uptake and release of propranolol occur within minutes and are temperature dependent, being about 5 times faster at 37◦ C than at 20◦ C. The intracellular fluorescence lifetime of propranolol is generally shorter than the value of 9.8 ns determined in dilute neutral aqueous solution and the difference is ascribed to concentration quenching. Within the cells propranolol is accumulated within intracellular acidic vesicles and the cytoplasm, but is excluded from the cell nucleus. On incubation of cells in medium containing 100 μM propranolol, the drug is accumulated to reach intracellular concentrations up to 10 mM in a process that is believed to be driven by protonation within acidic cellular compartments. Keywords: Propranolol, fluorescence, lifetime, imaging, multiphoton, living cell

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1. Introduction Propranolol is a non-selective β-blocker drug which prevents binding of epinephrine and noradrenaline to β1- and β2-andrenergic receptors [4] as well as inhibiting cellular uptake of both serotonin and dopamine [13]. Propranolol is a weakly basic lipophilic compound known to interact and modify the behaviour of bilayer lipid membranes [6,22,25]. Studies have shown propranolol to be taken up into a range of cells including epithelial cells [8] and hepatocytes [10]. In the latter case it has recently been reported that there are two intracellular sites, one of high affinity and low capacity, and the other a low affinity and non-saturable site proposed to be the cellular membranes [10]. It is known that propranolol exhibits intrinsic fluorescence in the ultraviolet region with a solvent dependent maximum between 320 and 360 nm [1,12,20,29]. This has been used for a number of purposes, including quantification of propranolol in drug assays, detection during microchip electrophoresis and monitoring binding to imprinted polymers. However the wavelengths required for one-photon fluorescence excitation in the ultraviolet (260–320 nm) are damaging to cellular systems, producing potentially toxic intermediates [2], and are poorly transmitted by normal microscope optics [14]. These constraints *

Corresponding author: Prof. Roger H. Bisby, Biomedical Research Centre, University of Salford, Salford, M5 4WT, UK. Tel.: +44 161 295 4912; Fax: +44 161 295 5210; E-mail: [email protected].

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R.H. Bisby et al. / Fluorescence lifetime imaging of propranolol uptake in living glial C6 cells

therefore make difficult direct excitation and imaging of propranolol fluorescence in cellular systems with UV excitation. In contrast two-photon microscopy using red and near infrared wavelengths with the benefit of confining the region of excitation to the focal volume of a femtosecond laser beam focussed to the diffraction limit, which further allows pseudo confocal imaging [24,28] with reduced overall phototoxicity. Multiphoton microscopy has applications in pharmaceutical sciences [21] effectively permitting imaging of UV intrinsic fluorescence from biochemical chromophores such as serotonin [5, 26] and dehydroergosterol [27]. 2. Materials and methods 2.1. Reagents All chemicals were obtained from Sigma-Aldrich and used as received. The rat glioma line, designated as C6 glial cells, was purchased from LGC Promochem (ATCC Number CCL-107). They were grown as monolayer cultures in F-12K medium supplemented with 2.5% (v/v) FCS at 37◦ C with humidified 5% CO2 . For imaging, cells were treated with 0.25% trypsin and seeded into dishes with a No. 1 coverslip base (MaTek Corporation) at a concentration of ∼2.5 × 105 cells ml−1 and grown for over 50 h. The microscope system for fluorescence lifetime imaging has been previously described [5]. Briefly it is based on a Ti:sapphire laser (Coherent Mira) pumping an optical parametric oscillator (APE) coupled to an inverted microscope (Nikon TE2000U) with a water-immersion ultraviolet corrected objective (Nikon VC ×60, NA 1.2). For cell work, power at the sample was limited to KPA. But the significant increase is noticed for O7–C8, C8–C9, O10–M and O11–M bond length in the same series. As they might have been expected, the differences between O10–M and O11–M significantly decrease. In the case of angles, the increase in the order LiPA < NaPA < KPA was observed for C1–C2–C3, C2–C3–C4, C5–C6–O1 angles in the aromatic ring, but the decrease in this order was noticed for C3–C4–C5 and C6–C1–C2. The angles between carbon, oxygen and metal atoms in alkil chain of molecule increase with exception of C8–C9–O10 and C8–C9–O11 angles.

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Table 1 The bond lengths, angles, aromatic indices, values of dipole moment and energy calculated (B3LYP/6-311++G∗∗ ) for alkali metal phenoxyacetates (MPA) Atoms

LiPA

NaPA

KPA

1.3992 1.3977 1.3901 1.3984 1.3873 1.4024 1.3617 1.4162 1.5290 1.2559 1.2689 2.2199 2.2169

1.3996 1.3976 1.3902 1.3985 1.3873 1.4029 1.3599 1.4183 1.5330 1.2544 1.2675 2.5286 2.5288

a

Bond lengths (Å) C1–C2 C2–C3 C3–C4 C4–C5 C5–C6 C6–C1 C1–O7 O7–C8 C8–C9 C9–O10 C9–O11 O10–M O11–M

1.3981 1.3978 1.3899 1.3982 1.3875 1.4013 1.3649 1.4121 1.5222 1.2601 1.2727 1.8691 1.8647 Angles (◦ )

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C1–C2–C3 C2–C3–C4 C3–C4–C5 C4–C5–C6 C5–C6–C1 C6–C1–C2 C6–C1–O7 C2–C1–O7 C1–O7–C8 O7–C8–C9 C8–C9–O10 C8–C9–O11 C9–O11–M O10–C9–O11 M–O10–C9 HOMAb AJ c BACd I6 e NICSf Dipole moment (D) Energy (hartree)g

119.48 120.97 119.13 120.62 120.03 119.77 115.45 124.78 118.63 111.03 121.99 115.83 82.55 122.18 72.85 Aromaticity indices 0.979262 0.99714 0.92688 95.81867 −9.1055 4.30 −542.55

119.55 121.01 119.07 120.63 120.14 119.59 115.55 124.87 118.64 111.45 120.84 114.37 87.23 124.79 87.41 0.976809 0.99672 0.92338 95.52024 −8.8957 6.99 −697.26

119.58 121.04 119.05 120.63 120.20 119.50 115.61 124.89 118.70 111.67 120.52 113.94 90.75 125.54 91.07 0.975763 0.99656 0.92207 95.41015 −8.9725 8.67 −1134.90

1 Å = 10−10 m; b abbreviation from harmonic oscillator model of aromaticity; c normalized function of variance of bond lengths; d bond alternation coefficient; e Bird’s index [4]; f nuclear independent chemical shifts; g 1 hartree = 2625.5 kJ/mol. a

Geometric and magnetic aromaticity indices [4,7,8], dipole moments and energies were calculated and also shown in Table 1. Almost all aromaticity indices decrease in the following order: LiPA > NaPA > KPA. Values of energy also decrease in the same series. It might indicate the increase of stability of

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salt molecules in this order. Mulliken, APT, NPA, MK and ChelpG atomic charges on the atoms of phenoxyacetic acid molecule and its alkali metal salts are gathered in Table 2. The increase of total charge of carboxylate group (negative values) is observed in the order LiPA < NaPA < KPA, when Mulliken and APT methods were used for calculation. In NPA method there are no changes whereas the decrease is observed for MK and ChelpG methods. The total charge in aromatic ring is not changed in above series irrespective of used method. The theoretical wavenumbers of IR and Raman spectra as well as chemical shifts in NMR spectra were obtained and compared with experimental spectra. 3.2. FT-IR, FT-Raman and UV spectra The vibrational (in KBr) and electronic spectra of synthesized alkali metal phenoxyacetates were recorded and presented in Fig. 2. In the UV spectra there were no changes in the wavelengths. The influence of studied metal cations on the vibrational structure of phenoxyacetates expresses in the shift of selected bands along the metal series. The wavenumbers of asymmetric stretching vibration bands of carboxylate group in IR spectra decrease in the series LiPA → CsPA, however for rubidium phenoxyacetate the insignificant increase is noted in comparison to potassium salt. In Raman spectra there are no changes in this series. For symmetric stretching vibration bands in Raman spectra the increase of the wavenumbers is noticed from lithium to cesium phenoxyaetate, but in IR spectra the wavenumbers increase to potassium phenoxyacetate and from KPA to CsPA the decrease is observed. The magnitudes of separation between wavenumbers due to asymmetric and symmetric stretching vibration bands Δνas−s = νas (COO)–νs (COO) in Raman spectra decrease in the series LiPA > NaPA = KPA > RbPA > CsPA. The corresponding values for lithium, sodium, potassium, rubidium and cesium phenoxyacetates in IR spectra are equal 382, 361, 353, 357 and 361, respectively. The wavenumbers of asymmetric in plane deformations βas (COO) decreased in the following series LiPA > NaPA > KPA > RbPA > CsPA, whereas the magnitudes of separation between wavenumbers due to asymmetric and symmetric in plane deformations of COO− group increase in this order. The values of Δβ = βs (COO)–βas (COO) are equal 518, 524, 528, 530 and 536 cm−1 . The wavenumbers as well as intensity of the aromatic ring bands did not significantly change. The correlation between chosen bands and some metal parameters, such as electronegativity, ionization energy, atomic and ionic radius, atomic mass and inverse of atomic mass [2] have been studied. The best correlation was obtained for ionic radius and ionization energy for band of ν(C–O–C) etheric group vibration (R2 = 0.978).

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3.3. NMR spectra Values of the experimental as well as calculated (B3LYP/6-311++G∗∗ ) chemical shifts in 1 H and 13 C NMR spectra of lithium, sodium, potassium, rubidium and cesium phenoxyacetates are presented in Table 3. Data for the last two salts are only experimentally obtained. Experimental chemical shifts show decreasing tendency in the series Li > Na > K > Rb > Cs salts for H8a and H8b protons, but for protons connected with the aromatic ring the decrease was observed only for lithium, sodium and potassium phenoxyacetates, for rubidium and cesium salts these values increase in comparison to potassium one. The changes in 13 C NMR spectra indicated decreasing tendency from lithium to potassium phenoxyacetate, then the increase from potassium to rubidium and finally the insignificant decrease for cesium salt was observed for almost all carbon atoms in the aromatic ring (except of C1 and C4) and

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

C1 C2 H2a C3 H3a C4 H4a C5 H5a C6 H6a O7 C8 H8a H8b C9 O10 O11 M Ring COO

Mulliken LiPA −0.20 0.34 0.16 −0.25 0.17 −0.31 0.14 −0.11 0.16 −0.20 0.19 −0.07 0.35 0.19 0.19 −0.30 −0.30 −0.32 0.17 −0.73 −0.92

NaPA −0.19 0.32 0.16 −0.27 0.17 −0.31 0.14 −0.10 0.16 −0.19 0.19 −0.07 0.44 0.19 0.19 −0.32 −0.43 −0.45 0.38 −0.74 −1.20

APT KPA −0.28 0.42 0.16 −0.27 0.17 −0.26 0.14 −0.17 0.16 −0.17 0.19 −0.07 −0.90 0.18 0.18 −0.37 −0.48 −0.49 1.09 −0.73 −1.34

LiPA 0.67 −0.19 0.06 0.06 0.03 −0.15 0.03 0.06 0.03 −0.17 0.06 −0.93 0.45 −0.03 −0.03 1.23 −0.95 −1.03 0.82 0.28 −0.75

NaPA 0.68 −0.20 0.06 0.06 0.03 −0.16 0.03 0.07 0.03 −0.18 0.06 −0.95 0.47 −0.04 −0.04 1.21 −0.95 −1.03 0.85 0.27 −0.77

NPA KPA 0.69 −0.20 0.06 0.07 0.02 −0.16 0.03 0.07 0.02 −0.18 0.06 −0.96 0.46 −0.04 −0.04 1.25 −0.99 −1.07 0.93 0.29 −0.81

LiPA 0.32 −0.29 0.21 −0.18 0.20 −0.24 0.20 −0.19 0.20 −0.24 0.22 −0.53 −0.12 0.20 0.20 0.76 −0.78 −0.83 0.89 −0.82 −0.85

NaPA 0.33 −0.30 0.21 −0.19 0.20 −0.24 0.20 −0.19 0.20 −0.24 0.22 −0.53 −0.12 0.19 0.19 0.76 −0.78 −0.83 0.92 −0.83 −0.85

MK KPA 0.33 −0.30 0.21 −0.19 0.20 −0.24 0.20 −0.19 0.20 −0.24 0.22 −0.53 −0.13 0.19 0.19 0.77 −0.79 −0.83 0.94 −0.83 −0.85

LiPA 0.51 −0.31 0.12 −0.08 0.12 −0.23 0.13 −0.04 0.11 −0.33 0.17 −0.51 0.38 0.02 0.02 0.77 −0.70 −0.98 0.90 −0.48 −0.91

ChelpG NaPA 0.55 −0.33 0.13 −0.07 0.12 −0.24 0.13 −0.03 0.11 −0.36 0.17 −0.46 0.19 0.03 0.03 0.78 −0.77 −0.84 0.87 −0.48 −0.83

LiPA 0.43 −0.27 0.11 −0.04 0.09 −0.15 0.09 −0.06 0.09 −0.23 0.13 −0.49 0.40 −0.03 −0.03 0.71 −0.77 −0.83 0.84 −0.32 −0.89

NaPA 0.47 −0.29 0.12 −0.02 0.08 −0.20 0.10 −0.03 0.08 −0.28 0.14 −0.49 0.34 −0.02 −0.02 0.77 −0.78 −0.84 0.86 −0.35 −0.85

231

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Atom

E. Regulska et al. / Theoretical and experimental studies on alkali metal phenoxyacetates

Table 2 Calculated (B3LYP/6-311++G∗∗ ) Mulliken, APT, NPA, MK and ChelpG atomic charges (e = 1.6021892 × 10−19 C) for lithium, sodium and potassium phenoxyacetates

entral, http://ebookcentral.proquest.com/lib/multco/detail.action?docID=1588993.

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Fig. 2. Experimental FT-IR spectra registered in KBr pellet (a) and UV spectra (b) for: phenoxyacetic acid (PAA) and its salts (LiPA – lithium, NaPA – sodium, KPA – potassium, RbPA – rubidium and CsPA – cesium phenoxyacetates). Table 3 Calculated (B3LYP/6-311++G ) as well as experimental chemical shifts (1 H and 13 C NMR) of alkali metal phenoxyacetates (ppm) ∗∗

Atom

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H2a H3a H4a H5a H6a H8a H8b C1 C2 C3 C4 C5 C6 C8 C9

LiPA Exp. 6.848 7.211 6.835 7.211 6.848 4.210 4.210 158.76 129.06 119.80 114.50 119.80 129.06 67.48 172.75

NaPA Calc. 6.640 7.283 6.889 7.293 7.274 4.029 4.093 166.94 113.06 133.33 122.86 134.07 123.04 68.15 178.09

Exp. 6.834 7.204 6.818 7.204 6.834 4.110 4.110 159.01 128.93 119.50 114.50 119.50 128.93 67.83 170.85

KPA Calc. 6.650 7.297 6.923 7.321 7.209 4.237 4.236 166.11 112.4 133.58 123.02 133.6 122.74 67.58 184.50

Exp. 6.781 7.185 6.760 7.185 6.781 4.010 4.010 159.30 128.88 119.15 114.41 119.15 128.88 68.11 169.25

Calc. 6.660 7.269 6.880 7.301 7.209 4.121 4.121 166.48 112.26 133.55 122.53 133.39 122.78 67.72 184.05

RbPA

CsPA

Exp. 6.828 7.194 6.786 7.194 6.828 4.080 4.080 159.08 128.96 119.40 114.40 119.40 128.96 67.61 169.62

Exp. 6.825 7.196 6.787 7.196 6.825 4.060 4.060 159.18 128.92 119.27 114.39 119.27 128.92 67.96 169.53

C9 atom. For C1 and C8 carbon atoms the opposite tendency was observed, whereas an insignificant decreasing tendency was observed for C4 carbon atom in the series LiPA = NaPA > KPA > RbPA > CsPA.

4. Conclusions Some regular changes were observed in the calculated values of studied alkali metal phenoxyacetates. Almost all bond lengths decreases in the series LiPA > NaPA > KPA. All geometric aromaticity indices

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decreased in the same order. Values of energy also decreased in studied series. On the other hand the shift of selected bands in IR, Raman and NMR spectra along the phenoxyacetate series was also observed. The calculated parameters were compared to experimental characteristic of studied compounds. Good correlation between experimental and theoretical IR and Raman spectra was noted. The correlation coefficients (R2 ) for IR spectra for lithium, sodium and potassium phenoxyacetates amount to 0.9986, 0.9990, 0.9984, respectively. The corresponding values for Raman spectra are 0.9970, 0.9975 and 0.9973. The linear correlation between calculated and experimental data of NMR spectra was also observed. Correlation coefficients for 13 C NMR are in the range 0.9002–0.9063 and for 1 H NMR the range is 0.9845–0.9878. It was interesting to note the linear correlation between wavenumbers of νs (COO) and νas (COO) bands and distances between C9–O11 and O10–M. The corresponding correlation coefficients for νs (COO) wavenumbers amounts to 0.9951 and 0.9767 and for νas (COO) 0.9999 and 0.9469. Good correlation between chemical shifts in 13 C NMR spectra and Mulliken atomic charges on C1 and C6 atoms was also found (R2 = 0.9718 and 0.9932, respectively). Acknowledgement ´ Presented work was supported by Białystok University of Technology (theme No. S/WBiIS/1/2012). References

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[1] T. Cserhati and E. Forgacs, J. Chromatogr. B 717 (1998), 157–178. [2] J. Emsley, The Elements, 2nd edn, Clarendon Press, Oxford, 1991. [3] M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery Jr., J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, N.J. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev, A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, Ö. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, D.J. Fox, Gaussian’09 (Revision A.1), Gaussian, Inc., Wallingford, CT, 2009. [4] T.M. Krygowski and M.T. Cyra´nski, Tetrahedron 52 (1996), 1713–1722. [5] W. Lewandowski, M. Kalinowska and H. Lewandowska, Inorg. Chim. Acta 358 (2005), 2155–2166. ´ [6] M. Samsonowicz, R. Swisłocka, E. Regulska and W. Lewandowski, J. Mol. Struct. 887 (2008), 209–215. [7] P.R. Schleyer, M. Manoharan, Z.X. Wang, B. Kiran, H. Jiao, R. Puchta and N.J.R.E. Hommes, Org. Lett. 3 (2001), 2465– 2468. ´ [8] R. Swisłocka, E. Regulska, M. Samsonowicz and W. Lewandowski, Polyhedron 28 (2009), 3556–3564.

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Spectroscopic (FT-IR, Raman, NMR) and DFT quantum chemical studies on phenoxyacetic acid and its sodium salt M. Samsonowicz, E. Regulska ∗ and W. Lewandowski Division of Chemistry, Bialystok University of Technology, Bialystok, Poland Abstract. FT-IR, Raman and NMR spectra of phenoxyacetic acid and its sodium salt were recorded and analyzed. Optimized geometrical structures of studied compounds were calculated by B3LYP/6-311++G∗∗ method. The atomic charges were calculated by Mulliken, NPA (Natural Population Analysis), APT (Atomic Polar Tensor), MK (Merz–Singh–Kollman method) and ChelpG (Charges from electrostatic potentials using Grid based method) methods. Geometric as well as magnetic aromaticity indices, dipole moments and energies were also calculated. The theoretical wavenumbers and intensities of IR spectra as well as chemical shifts in 1 H and 13 C NMR spectra were obtained. The calculated parameters were compared with experimental characteristics of these molecules. Keywords: Phenoxyacetic acid, sodium phenoxyacetate, FT-IR, Raman, NMR, DFT, molecular structure

1. Introduction Phenoxyacetic acid has been investigated by various researches because of its biological activities. It is useful in the treatment of insulin resistance and hyperglycemia [6]. Derivatives of phenoxyacetic acid are widely used in herbicide and pesticide formulations. The molecular basis of their mode of action is not fully understood. The estimation of the electronic charge distribution in metal complexes and salts allows to predict what kind of deformation of the electronic system of ligand would undergo during complexation [3]. It also permits to make more precise interpretation of mechanism by which metals affect the biochemical properties of ligands. In this paper the influence of sodium cation on the electronic system of phenoxyacetic acid was studied.

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2. Experimental section Sodium phenoxyacetate was prepared by dissolving the powder of phenoxyacetic acid in the water solution of the appropriate sodium hydroxide in a stoichiometric ratio (1:1). Both reagents were obtained from Aldrich Chemical Company. The solution was left at the room temperature for 24 h until the sample crystallized in the solid-state. Precipitants were filtered, washed by water and drying under reduced *

Corresponding author: E. Regulska, Division of Chemistry, Bialystok University of Technology, Zamenhofa 29, 15435 Bialystok, Poland. Tel.: +48 085 746 97 90; E-mail: [email protected].

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pressure at 110◦ C. Obtained complex was anhydrous – in the IR spectra of solid state sample the lack of bands characterized for crystallizing water was observed. The IR spectra were recorded with the Equinox 55, Bruker FT-IR spectrometer within the range 4000– 400 cm−1 . Samples in the solid state were measured in KBr pellets. The resolution of spectrometer was 1 cm−1 . Raman spectra of solid samples in capillary tubes were recorded in the range of 4000– 400 cm−1 with a FT-Raman accessory of the Perkin Elmer System 2000. The resolution of spectrometer was 1 cm−1 . The NMR spectra of DMSO solution were recorded with the NMR AC 200 F, Bruker unit. TMS was used as an internal reference. The density functional (DFT) hybrid method B3LYP/6-311++G∗∗ was used to calculate optimized geometrical structures of studied compounds (Fig. 1). All theoretical calculation were performed using the Gaussian’09 [1] of programs running on a PC computer. 3. Results and discussion

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3.1. Vibrational spectra Experimental and theoretical bands together with their relative intensities and band assignments for phenoxyacetic acid and its sodium salt in FT-IR and Raman spectra were obtained. Complete assignments of all bands require application of both IR and Raman methods supported by theoretical calculations and literature data [7]. The calculated wavenumbers were obtained by B3LYP method and 6-311++G∗∗ basis set. The correlation between calculated and experimentally obtained wavenumbers in IR and Raman spectra of phenoxyacetic acid and sodium phenoxyacetate was studied and good agreement was found. The correlation coefficient R2 for phenoxyacetic acid spectra is amount 0.9972 and for its sodium salt R2 = 0.9990. The corresponding values for Raman spectra amount to 0.9969 and 0.9975. IR and Raman spectra for phenoxyacetic acid and its sodium salt are presented in Fig. 1. Comparing results obtained for sodium phenoxyacetate to the respectively values of phenoxyacetic acid, certain changes of intensities and wavenumbers of the bands of aromatic system and carboxylic group can be noticed. The changes of intensities and wavenumbers of the bands of aromatic system and carboxylate group in the case of sodium salt were discussed comparing to the free ligand. The characteristic bands occurring in the IR spectra of sodium phenoxyacetate, which do not exist in the spectra of free acid, for example: symmetric or asymmetric stretching vibrations ν(COO), in plane β(COO) and out of plane γ(COO) deformations of carboxylic group were noticed. On the other hand the lack of bands, which are characteristic for phenoxyacetic acid (the C=O band, 1736 and 1703 cm−1 ; β(OH) band, 1300 cm−1 , broad band of ν(OH), for example) were observed in the sodium salt spectra. The wavenumbers of aromatic bands numbered as 20b, 9b, 17a and 5 in IR as well as in Raman spectra increase in comparison to free acid. For 2, 3 and 17b bands the increase of wavenumbers was noticed only in IR spectra. In the case of 8a and 6a bands the decrease in comparison to phenoxyacetic acid was observed in IR spectra, whereas for 2, 3, 17b and 19a bands in Raman the decrease of wavenumbers was noticed. There are also some changes of alkil chain part of studied molecules. The wavenumbers of νas (CH2 ) and ν(O–CH2 ) bands shift to higher values in IR spectra of sodium phenoxyacetate, while νs (CH2 ) band shifts to lower values. 3.2. NMR spectra Theoretically as well as experimentally obtained 1 H NMR and 13 C NMR chemical shifts of phenoxyacetic acid and its sodium salt are presented in Fig. 2. The linear correlation between proton as well

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(a)

(b) Fig. 1. Raman (a) and IR (b) spectra for phenoxyacetic acid (PAA) and its sodium salt (NaPA).

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Fig. 2. Calculated (B3LYP/6-311++G∗∗ ) as well as experimental chemical shifts: (a) 1 H NMR, (b) 13 C NMR of phenoxyacetic acid (PAA) and its sodium salt (NaPA).

as carbon NMR shieldings of studied compounds and experimental data is observed. The correlation coefficient (R2 ) for 1 H NMR spectra are amount to 0.9739 for phenoxyacetic acid (PAA) and 0.9868 for sodium phenoxyacetate (NaPA). For 13 C NMR spectra corresponding values are 0.8945 and 0.9035. All protons in sodium phenoxyacetate are shifted diamagnetically in comparison to PAA. This tendency suggests that introduction of sodium atom causes the decrease in ring current intensity. Some changes in 13 C NMR spectra were also observed. The chemical shifts of almost all carbon atoms in sodium phenoxyacetate molecule, except of C1, C8 and C9 atoms, are lower than those in free acid.

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3.3. Calculated molecular structure Optimized geometrical structures of phenoxyacetic acid as well as of sodium phenoxyacetate molecule were obtained using B3LYP/6-311++G∗∗ method. The bond lengths and the angles between bonds in sodium salt molecule in comparison to free acid were presented in Table 1. The increase of almost all bond lengths in aromatic ring except of C2–C3 and C5–C6 bonds in sodium salt molecule in comparison to acid was observed. The increase of O7–C8, C8–C9, C9–O10 and O10–11a bond lengths was also noticed, whereas bond lengths of C1–O7, C9–O11 and O11–11a decreased in NaPA in comparison to PAA molecule. The differences between C9–O10 and C9–O11 as well as O10–11a and O11–11a bond lengths almost disappeared. In the case of angles, the increase was observed for almost all angles in the aromatic ring, except of C3–C4–C5 and C6–C1–C2 angles in NPA molecule. For C2–C1–O7 and O7– C8–C9 angles the increase was observed, but decrease was noticed only for C8–C9–010, C9–O11–11a and C9–O10–11a angles. Geometric and magnetic aromaticity indices [2,4,5], dipole moments and energies were calculated and also shown in Table 1. All geometric aromaticity indices calculated for sodium phenoxyacetate in comparison to acid molecule decreased. It indicates that aromaticity of salt molecule decreased in comparison to free acid. This conclusion was confirmed by values of magnetic aromaticity indices NICS. Mulliken, NPA, APT, MK and ChelpG methods were used to calculate atomic charges on the atoms of phenoxyacetic acid molecule and its sodium salt. One of them exemplary are presented in Fig. 3. The highest changes irrespective of used method are observed for carboxylate group. Total charges calculated for COO significantly decrease in comparison to free acid, for example, the values calculated by Mulliken method for PAA amount to −0.766, but for NaPA molecule −1.194. In the case of other

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Table 1 The bond lengths, angles, aromatic indices, values of dipole moment and energy calculated (B3LYP/6-311++G∗∗ ) for alkali metal phenoxyacetates Atoms

PAA Bond lengths (Å)a 1.3963 1.3981 1.3897 1.3980 1.3877 1.3995 1.3706 1.4038 1.5174 1.1977 1.3576 0.9689 2.3148

C1–C2 C2–C3 C3–C4 C4–C5 C5–C6 C6–C1 C1–O7 O7–C8 C8–C9 C9–O10 C9–O11 O10–H(Na)11a O11–H(Na)11a C1–C2–C3 C2–C3–C4 C3–C4–C5 C4–C5–C6 C5–C6–C1 C6–C1–C2 C6–C1–O7 C2–C1–O7 C1–O7–C8 O7–C8–C9 C8–C9–O10 C8–C9–O11 C9–O11–H(Na)11a O10–C9–O11 H(Na)11a–O10–C9

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HOMAb AJ c BACd I6 e

Angles (◦ ) 119.34 120.89 119.24 120.61 119.84 120.10 115.28 124.62 118.49 108.85 127.23 109.00 107.43 123.78 107.43

Geometric aromaticity indices 0.982557 0.99765 0.93095 96.21196

NICSf Dipole moment (D) Energy (hartree)g

Magnetic aromaticity indices −11.0192 2.48 −535.5

NaPA

Atomic numbers of PAA and NaPA molecules

1.3992 1.3977 1.3901 1.3984 1.3873 1.4024 1.3617 1.4162 1.5290 1.2559 1.2689 2.2169 2.2199 119.55 121.01 119.07 120.63 120.14 119.59 115.55 124.87 118.64 111.45 120.84 114.37 87.23 124.79 87.41 0.976809 0.99672 0.92338 95.52024 −8.8957 6.99 −542.55

1 Å = 10−10 m; b abbreviation from Harmonic Oscillator Model of Aromaticity; c normalized function of variance of bond lengths; d bond alternation coefficient; e Bird’s index [2]; f nuclear independent chemical shifts; g 1 hartree = 2625.5 kJ/mol. a

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(a)

(b)

Fig. 3. Electronic charge distribution (NPA method) calculated for molecules of phenoxyacetic acid (a) and its sodium salt (b).

methods corresponding values are: −0.473 and −0.852 (NPA); −0.292 and −0.763 (APT); −0.473 and 0.835 (MK) and −0.341 and −0.851 (ChelpG method). 4. Conclusions Replacement of hydrogen by sodium in molecule causes significant changes in geometrical structure of studied molecules. The highest changes are noticed for carboxylate group, as may be expective. However almost all bond lengths in aromatic ring insignificantly increase, the decrease of aromaticity of studied molecules are observed. The displacements of bands in FT-IR, Raman as well as in NMR spectra are also noticed. In IR and Raman spectra different changes of bands are observed, some of them shift to higher, other to lower wavenumbers. In 1 H and 13 C NMR spectra almost all bands shift to lower values in sodium phenoxyacetate spectra in comparison to free acid. It is characteristic tendency for molecules, in which the decrease of aromaticity is noticed. Acknowledgement

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Presented work was supported by Białystok University of Technology (theme No. N N312 427639). References [1] M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, G. Scalmani, V. Barone, B. Mennucci, G.A. Petersson, H. Nakatsuji, M. Caricato, X. Li, H.P. Hratchian, A.F. Izmaylov, J. Bloino, G. Zheng, J.L. Sonnenberg, M. Hada, M. Ehara, K. Toyota, R. Fukuda, J. Hasegawa, M. Ishida, T. Nakajima, Y. Honda, O. Kitao, H. Nakai, T. Vreven, J.A. Montgomery Jr., J.E. Peralta, F. Ogliaro, M. Bearpark, J.J. Heyd, E. Brothers, K.N. Kudin, V.N. Staroverov, R. Kobayashi, J. Normand, K. Raghavachari, A. Rendell, J.C. Burant, S.S. Iyengar, J. Tomasi, M. Cossi, N. Rega, N.J. Millam, M. Klene, J.E. Knox, J.B. Cross, V. Bakken, C. Adamo, J. Jaramillo, R. Gomperts, R.E. Stratmann, O. Yazyev,

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M. Samsonowicz et al. / Spectroscopic (FT-IR, Raman, NMR) and DFT quantum chemical studies

[2] [3] [4] [5]

A.J. Austin, R. Cammi, C. Pomelli, J.W. Ochterski, R.L. Martin, K. Morokuma, V.G. Zakrzewski, G.A. Voth, P. Salvador, J.J. Dannenberg, S. Dapprich, A.D. Daniels, Ö. Farkas, J.B. Foresman, J.V. Ortiz, J. Cioslowski, D.J. Fox, Gaussian’09 (Revision A.1), Gaussian, Inc., Wallingford, CT, 2009. T.M. Krygowski and M.T. Cyra´nski, Tetrahedron 52 (1996), 1713–1722. W. Lewandowski, M. Kalinowska and H. Lewandowska, J. Inorg. Biochem. 99 (2005), 1407–1423. ´ E. Regulska, M. Samsonowicz, R. Swisłocka and W. Lewandowski, J. Phys. Org. Chem. 20 (2007), 93–108. P.R. Schleyer, M. Manoharan, Z.X. Wang, B. Kiran, H. Jiao, R. Puchta and N.J.R.E. Hommes, Org. Lett. 3 (2001), 2465– 2468. N. Sundaraganesan, C. Meganathan, B. Anand and C. Lapouge, Spectrochim. Acta A 66 (2007), 773–780. G. Varsányi, Assignments for Vibrational Spectra of 700 Benzene Derivatives, Akademiai Kiado, Budapest, 1973.

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[6] [7]

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Background estimation of biomedical Raman spectra using a geometric approach Nikolaos Kourkoumelis a,∗ , Alexandros Polymeros b and Margaret Tzaphlidou a a b

Department of Medical Physics, Medical School, University of Ioannina, Ioannina, Greece Department of Physics, University of Ioannina, Ioannina, Greece

Abstract. Raman spectroscopy grows into an essential tool for biomedical applications. Nevertheless, the weak Raman signal associated mainly with biological samples, is often obscured by a broad background signal due to the intrinsic fluorescence of the organic molecules present, making further analysis unfeasible. A novel computational geometry method based on the definition of convex hull is described to estimate the background from Raman spectra of samples with biological interest. The method is semi-automated requiring sample-dependent user intervention. It does not rely however on curve fitting, requires no information about background distribution or source and keeps the original spectral data intact. Keywords: Raman spectroscopy, background estimation, convex, computational geometry

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1. Introduction Raman spectroscopy has been extensively applied in recent years in a variety of biological research ranging from the in situ tissue diagnosis to the analysis of subcellular components. Being a vibrational spectroscopic technique based on inelastic scattering, Raman spectroscopy provides rich molecular information about the chemical composition of samples and exhibits high sensitivity to minute biochemical changes. Furthermore, it is attractive for biomedical studies since it is intrinsically nonintrusive and does not require external labels. The positions and relative intensities of the Raman bands are the basic spectral characteristics for exploring the structure and the function of several biological molecules. This interpretation however, is often hindered by the broad background signal mostly due to fluorescence from organic molecules and contaminants. The intensity of fluorescent is usually much higher than the weak Raman signal in biological samples and therefore the subtraction of background is an essential process to extract reliable analytical information from biomedical Raman data. Apart from instrumental specific design approaches, a number of computational methods have been proposed for background removal from Raman spectra. These methods include polynomial fitting [1, 7,8,10,11,15,21], first and second-order differentiation [13,19], wavelet transformation [2,3,6,9,14,20], frequency-domain filtering [12] and Principal Component Analysis (PCA) [5]. All of the above methods have certain strengths and drawbacks depending on the problem they are trying to deal with. For example, low-order polynomial fitting is suitable for spectra with broad background but it is not effective *

Corresponding author: Nikolaos Kourkoumelis, Department of Medical Physics, University of Ioannina, Ioannina 45110, Greece. Tel.: +302651007594; Fax: +302651007854; E-mail: [email protected].

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for biological samples which feature Raman spectra with several adjacent, not readily obvious, peaks. Higher-order polynomials may be susceptible to data over fitting [1]. Differentiation may also distort peak shapes and therefore creates an inconsistent spectrum compared to the pre-processed one [10]. Wavelets analysis, which is the Fourier transform analog for localized functions, is a promising solution although the transformation of the signal into predetermined frequency bands may cause distortion in some part of the spectra [20]. In the present study, we describe a novel semi-automated background removal method which is based on the geometric definition of convex hull [16]. The effectiveness of the method is demonstrated through theoretical and experimental biomedical Raman spectra.

2. Theoretical background

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The signal, S, is assumed as a composition of a low-frequency component (B(x), background) and the true information, P (x), so that: S = P (x)  B(x). The background is the part of the signal that propagates very slowly and independently of the true information and resides in the vicinity of the lowest frequencies range. With the application of low-pass filtering, we extract from the composite signal a rough estimate of the true background component. The first step works by applying a Fourier transform to the signal, i.e. decreasing the high-frequency components and inverse transforming the result. In this way, we have managed to break up the signal into a superposition of infinitely many sinusoids. Each sinusoid can be manipulated individually and then recombined to obtain an approximation to the original periodic function [18]. The second step is to decompose the signal to parts which have the characteristics to be convex sets. This is accomplished by taking regions from peaks to valleys, of the previously filtered signal, via a simple pattern search of a table consisting of 0 and 1 referring to the slope of the signal. A convex hulling minimization routine supplies the single optimal solution for all sets [16] and is able to extract the true background part of the region by introducing a new parameter “median”. The latter is a line segment calculated form the statistical dispersion of data and by definition is constructed to divide the convex region into two parts. All points with values higher than the median is part of the upper part of convex hull and represent the peaks while the remaining points represent the true background. The only remaining problem is the continuity of one convex region in respect with the previous or the next one. Although, the best procedure is to use the “overlap-add method” [4], the simplest approach is by defining user variable (joins) which controls the number of linking points of the lower part of the convex region which must be included in the final background array. The outcome captures every essential feature of the background component through a purely geometric semi-automated procedure. Due to its high point of reduction degree, the signal is suitable for subsequent polynomial interpolation, smoothing, etc.

3. Materials and methods The algorithm was implemented in Mathematica software package (Wolfram Research). For signals sampled at discrete intervals, as in our case, Mathematica uses the discrete Fourier transform [17]. Raman spectra were chosen from literature for comparison purposes. Simulated data is identical to that from [20] while experimental data was acquired with permission from [20] and the hyperSpec project (hyperspec.r-forge.r-project.org).

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4. Results and discussion Simulated spectrum consisting of three Gaussian peaks with curved background and random noise is shown in Fig. 1. As previously discussed, the first step, (a), is the low-pass filtering, the second step, (b), is finding and optimizing the convex sets and the last one, (c), is joining the convex sets in a continuous manner. In the case of simulated data, the performance of the algorithm is flawless. Figures 2–4 depict the experimental Raman spectra of paracetamol, prednisone acetate tablets (PAT) and chondrocytes in cartilage, respectively. It is evident that the more complicated the signal is the more Fourier components are needed to approximate the experimental baseline curve. A rough approximation however is adequate even for complicated spectra with several bands (Fig. 2). In all cases the background is clearly defined and the signal which does not belong to peak areas is efficiently diminished. Since the Fourier transformation is not applied

Fig. 1. Simulated spectrum with curved background and random noise. (a) Fourier filtering, (b) convex optimization and (c) background estimation.

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Fig. 2. Raman spectrum of paracetamol. (a) Fourier filtering, (b) convex optimization and (c) background estimation.

Fig. 3. Raman spectrum of PAT. (a) Fourier filtering, (b) convex optimization and (c) background estimation.

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Fig. 4. Raman spectrum of chondrocytes in cartilage. (a) Fourier filtering, (b) convex optimization and (c) background estimation.

for smoothing but for extracting the geometric characteristics, the signal retains all its original features avoiding distortions. Nevertheless, in some spectra with low S/N ratio this may result in negative peaks in the background estimation procedure (circle in Fig. 3(c)) due to the calculation methodology of the “median” which does not take into consideration the local slope of the signal. A fitting procedure of the data within each convex region will immediately remove such artifacts. However, we did not introduce this computationally intensive improvement because (i) negative peaks appeared only once in our test cases and (ii) we tried to keep the method simple and purely geometric. 5. Conclusions A novel computational geometry method for the estimation of the Raman background signal of highly fluorescent samples has been described in this study. The results obtained from simulated and experimental spectra achieved comparable success with other methods reported in the literature although we are currently working to further confirm its validity via PCA methods [20]. The proposed algorithm is semi-automated and requires user input for two variables which define the degree of the Fourier series approximation and the continuity of the convex sets. The method is valid for all signals which are convex, i.e., one-directional and as such it can be also applied to other spectroscopic techniques and X-ray powder diffractograms. Acknowledgements The authors thank Dr. Zhi-Min Zhang and Dr. Claudia Beleites for providing the raw data considered in this manuscript.

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References [1] B.D. Beier and A.J. Berger, Method for automated background subtraction from Raman spectra containing known contaminants, Analyst 134 (2009), 1198–1202. [2] T.T. Cai, D.M. Zhang and D. Ben-Amotz, Enhanced chemical classification of Raman images using multiresolution wavelet transformation, Appl. Spectrosc. 55 (2001), 1124–1130. [3] C. Camerlingo, F. Zenone, G.M. Gaeta, R. Riccio and M. Lepore, Wavelet data processing of micro-Raman spectra of biological samples, Meas. Sci. Technol. 17 (2006), 298–303. [4] D.W. Griffin and J.S. Lim, Signal estimation from modified short-time Fourier transform, IEEE Trans. Acoust. Speech Signal Process. 32 (1984), 236–243.

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[5] T. Hasegawa, J. Nishijo and J. Umemura, Separation of Raman spectra from fluorescence emission background by principal component analysis, Chem. Phys. Lett. 317 (2000), 642–646. [6] Y.G. Hu, T. Jiang, A.G. Shen, W. Li, X.P. Wang and J.M. Hu, A background elimination method based on wavelet transform for Raman spectra, Chemometr. Intell. Lab. 85 (2007), 94–101. [7] A. Jirasek, G. Schulze, M.M.L. Yu, M.W. Blades and R.F.B. Turner, Accuracy and precision of manual baseline determination, Appl. Spectrosc. 58 (2004), 1488–1499. [8] M.N. Leger and A.G. Ryder, Comparison of derivative preprocessing and automated polynomial baseline correction method for classification and quantification of narcotics in solid mixtures, Appl. Spectrosc. 60 (2006), 182–193. [9] J.H. Li, L.P. Choo-Smith, Z.L. Tang and M.G. Sowa, Background removal from polarized Raman spectra of tooth enamel using the wavelet transform, J. Raman Spectrosc. 42 (2011), 580–585. [10] C.A. Lieber and A. Mahadevan-Jansen, Automated method for subtraction of fluorescence from biological Raman spectra, Appl. Spectrosc. 57 (2003), 1363–1367. [11] V. Mazet, C. Carteret, D. Brie, J. Idier and B. Humbert, Background removal from spectra by designing and minimising a non-quadratic cost function, Chemometr. Intell. Lab. 76 (2005), 121–133. [12] P.A. Mosier-Boss, S.H. Lieberman and R. Newbery, Fluorescence rejection in Raman-spectroscopy by shifted-spectra, edge-detection, and FFT filtering techniques, Appl. Spectrosc. 49 (1995), 630–638. [13] A. O’Grady, A.C. Dennis, D. Denvir, J.J. McGarvey and S.E.J. Bell, Quantitative Raman spectroscopy of highly fluorescent samples using pseudosecond derivatives and multivariate analysis, Anal. Chem. 73 (2001), 2058–2065. [14] P.M. Ramos and I. Ruisanchez, Noise and background removal in Raman spectra of ancient pigments using wavelet transform, J. Raman Spectrosc. 36 (2005), 848–856. [15] T.J. Vickers, R.E. Wambles and C.K. Mann, Curve fitting and linearity: data processing in Raman spectroscopy, Appl. Spectrosc. 55 (2001), 389–393. [16] E.W. Weisstein, Convex hull, from MathWorld – A Wolfram web resource, available at: http://mathworld.wolfram.com/ ConvexHull.html. [17] E.W. Weisstein, Discrete Fourier transform, from MathWorld – A Wolfram web resource, available at: http://mathworld. wolfram.com/DiscreteFourierTransform.html. [18] E.W. Weisstein, Fourier series, from MathWorld – A Wolfram web resource, available at: http://mathworld.wolfram.com/ FourierSeries.html. [19] D.M. Zhang and D. Ben-Amotz, Enhanced chemical classification of Raman images in the presence of strong fluorescence interference, Appl. Spectrosc. 54 (2000), 1379–1383. [20] Z.M. Zhang, S. Chen, Y.Z. Liang, Z.X. Liu, Q.M. Zhang, L.X. Ding, F. Ye and H. Zhou, An intelligent backgroundcorrection algorithm for highly fluorescent samples in Raman spectroscopy, J. Raman Spectrosc. 41 (2010), 659–669. [21] J. Zhao, H. Lui, D.I. McLean and H. Zeng, Automated autofluorescence background subtraction algorithm for biomedical Raman spectroscopy, Appl. Spectrosc. 61 (2007), 1225–1232.

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Structural perturbation of super-folder GFP in the presence of guanidine thiocyanate Olesya V. Stepanenko a,∗ , Olga V. Stepanenko a , Irina M. Kuznetsova a , Vladislav V. Verkhusha b and Konstantin K. Turoverov a a b

Institute of Cytology of the Russian Academy of Sciences, Saint-Petersburg, Russia Albert Einstein College of Medicine, New York, NY, USA

Abstract. The guanidine thiocyanate induced denaturation-renaturation of sfGFP was studied. It was shown that the disruption of sfGFP native structure occurs in the range of guanidine thiocyanate concentrations from 0.5 to 2.5 M. This process was accompanied by simultaneous changes of all recorded parameters. It was found that the small guanidine thiocyanate concentrations (less then 0.1–0.2 M) triggered local structural disturbances in protein which result in significant decrease of chromophore and tryptophan fluorescence intensity and change of protein visible absorption spectrum. Keywords: Fluorescent proteins, super-folder GFP, folding of proteins with beta-barrel topology, small guanidine thiocyanate concentrations

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1. Introduction A broad variety of fluorescent proteins (FPs) and their engineered analogs are currently used as fluorescent tags of different color in bioimaging [8,13,21,24,25]. The application of FPs has facilitated the routine monitoring of gene activation and the selective labeling of single proteins, cellular organelles or the whole cells [6,7]. A number of FP-based biosensors have been designed to sense the properties of the cell’s environment such as pH, ion flux, redox potential [4,5,9,12,18]. Genetically encoded FP-containing biosensors based on FRET (fluorescence resonance energy transfer) have been widely adopted for the investigation of protein–protein interactions and other biologically-relevant events occurring in the living cell [10,24]. FPs with light-modulated spectral properties, collectively termed photoactivatable fluorescent proteins (PAFPs), allow to break diffraction limit and obtain the super-resolution imaging in fixed and live cells [25]. Recent development of FPs emitting in far-red region and FPs possessing large Stokes shifts have enabled visualization of cellular processes in living animals [15,17]. The key feature of all FPs which attracts an enormous interest to them is ability to self-generate the intrinsic chromophore from three amino acids in the position 65–67 without engaging any cofactors or enzymatic components [16]. Chromophore is encapsulated in the rigid β-barrel-like shell of protein matrix which functions are to protect the chromophore from the environment and restrict its flexibility thus *

Corresponding author: Dr. Olesya V. Stepanenko, Institute of Cytology of the Russian Academy of Science, Tikhoretsky av. 4, 194064 St. Petersburg, Russia. Tel.: +7 812 247 19 57; Fax: +7 812 247 03 41; E-mail: [email protected].

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preventing from non-radiative deactivation. Moreover, correct protein folding results in the proper orientation of the catalytic amino acids near the chromophore-forming tripeptide and induces chromophore maturation. Thus, the study of FP’s folding and structural dynamics is of high interest. FPs are prone to aggregate leading to the irreversibility of denaturation and impeding the investigation of FPs folding–unfolding. Super-folder, or sfGFP, is the only FP free of this drawback [14]. In this work we studied the denaturant-induced structural changes of sfGFP using spectroscopic techniques (absorbance, fluorescence, circular dichroism) and size-exclusion chromatography.

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2. Materials and methods The plasmid pET-28a (+)-sfGFP encoding super-folder GFP [14] with polyhistidine tags was constructed as described previously [23] and was transformed into an Escherichia coli BL21(DE3) host (Invitrogen). The sfGFP expression was induced by an incubation of the cells with 0.5 mM IPTG (Fluka, Switzerland) during 24 h at 25◦ C, and protein was purified with Ni-NTA agarose (GE Healthcare, Sweden). The purity of the recombinant proteins was not less than 95%, as indicated by SDS-PAGE. Guanidine thiocyanate (Fluka, Switzerland) was used without further purification. Measurements were performed in 50 mM TrisCl buffer, pH 8.0. Protein concentration was 0.15 mg/ml. Absorption spectra were recorded using a EPS-3T (Hitachi, Japan) spectrophotometer. The experiments were performed in microcells 101.016-QS 5 mm × 5 mm (Hellma, Germany) at room temperature. Fluorescence experiments were carried out using a Cary Eclipse spectrofluorimeter (Varian, Australia) and a homemade spectrofluorimeter for registration of fluorescence polarization [22]. Protein intrinsic fluorescence was excited at the long-wave absorption spectrum edge (297 nm), where the contribution of tyrosine residues in the bulk protein fluorescence is negligible. Specific “green” fluorescence of sfGFP was excited at 365 and 470 nm, and emission was detected at 510 nm. The position and form of the fluorescence spectra were characterized by the parameter A = I320 /I365 , where I320 and I365 are fluorescence intensities at λem = 320 and 365 nm, respectively [11]. The values of parameter A and of fluorescence spectrum were corrected by the instrument sensitivity. The anisotropy of tryptophan fluorescence was calculated by the equation r = (IVV − GIHV )/(IVV + 2GIHV ), where IVV and IHV are the vertical and horizontal components of fluorescence intensity excited by vertically polarized light and G is the relation of vertical and horizontal components of fluorescence intensity excited by horizontally polarized light (G = IVH /IHH ), λem = 365 nm (19). All kinetic experiments were performed in microcells FLR 10 mm × 10 mm (Varian, Australia). Unfolding of the protein was initiated by manual mixing of protein solution (50 mkl) with buffer containing desired GTC concentrations (450 mkl). The dead time was determined from the control experiments to be about 4 s [11]. The dependences of different fluorescent characteristics of sfGFP on GTC concentration were recorded after protein incubation in the solution of appropriate concentration at 23◦ C during 2, 24, 45, 69, 95 h. The spectrofluorimeter was equipped with thermostat that held a constant temperature of 23◦ C. The gel filtration experiments of sfGFP unfolded in GTC were performed on a Superdex75 PC 3.2/30 column (GE Healthcare, Sweden) using an AKTA purifier system (GE Healthcare, Sweden). Solutions of sfGFP were preincubated at 23◦ C for 24 h in the presence of the desired GTC concentration. Then 10 μl of this solution were loaded on a column equilibrated with the same GTC concentration. The change of hydrodynamic dimensions of sfGFP was evaluated as a change of elution volume of sfGFP. In the transition region the averaged elution volume of sfGFP was calculated by the equation V = fc Vc + fd Vd ,

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where Vc and Vd are the elution peaks of compact and denatured molecules, fc and fd are the portion of compact and denatured molecules estimated as area of the corresponding peak.

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3. Results and discussion Figure 1 shows denaturation curves of different parameters of tryptophan fluorescence (fluorescence intensity, parameter A and fluorescence anisotropy; excitation at 297 nm) and sfGFP chromophore fluorescence intensity excited at two wavelength (365 and 470 nm; registration at 510 nm) as a function of final GTC concentration recorded after 1–94 h of equilibration of the protein in the presence of desired denaturant concentration. It was shown that GTC induced unfolding of sfGFP reaches quasi-equilibrium after 24 h in contrast to several days in the case of GdnHCl induced denaturation [19,20]. Further incubation of sfGFP in the presence of GTC, at least up to 4 days, does not result in measurable changes of recorded characteristics. Apparently, the unfolding of protein structure takes place in the range of GTC concentrations from 0.5 to 2.5 M. This process was accompanied by simultaneous changes of all recorded parameters. In the subrange of small GTC concentrations (less then 0.1–0.2 M) we have observed no noticeable changes of tertiary protein structure as indicated by fluorescence anisotropy and parameter A (Fig. 1c and d). Nevertheless gel filtration experiments showed a slight increase of hydrodynamic dimensions of sfGFP (Fig. 1j). At the same time small amount of GTC induces significant decrease of chromophore (Fig. 1e and f) and tryptophan fluorescence intensity (Fig. 1a and b) and change of protein visible absorption spectrum (Fig. 1i). Visible absorption spectra of sfGFP demonstrated pronounced drop of absorption band corresponding to the anionic form of chromophore with concomitant rise of absorption band corresponding to neutral chromophore. This data indicate that the small addition of GTC changes the balance between sfGFP molecules bearing neutral and anionic chromophores. Denaturation curves of chromophore fluorescence intensity were corrected to take into account the change of absorption of anionic and neutral chromophore in the presence of GTC (Fig. 1g and h). The response of thus corrected chromophore fluorescence excited at two wavelengths is completely different. Excitation at 365 nm results in significant fluorescence drop (up to 13% of native protein signal), while excitation at 470 nm leads to a 20% increase of fluorescence with respect to native sfGFP. Significant decrease of fluorescence quantum yield under excitation at absorption band of protonated chromophore could be concerned with inhibition of proton transfer and thus chromophore ionization in the presence of small GTC concentrations. We suppose that all observed changes induced by pre-denaturing GTC concentrations is a result of local structural perturbations of sfGFP rather than global reorganization of protein structure. Renaturation of sfGFP was induced by dilution of pre-denatured protein in 2.2 M GTC to the various final denaturant concentrations (Fig. 1). Refolding reaches equilibrium after 24 h of protein incubation. It was shown that under strongly refolding conditions (final concentration of GTC is 0.22 M) the recovery of all recorded characteristics of sfGFP takes place indicating the reversibility of protein unfolding. The unfolding and refolding curves in the transition area do not coincide showing apparent hysteresis. Hysteretic behavior observed during folding of sfGFP is supposed to be connected with the presence of the chromophore inside the barrel which has to be locked in the correct active form in the protein core on the latest folding step [3]. Indeed the mutant variants of green FP which unable to form chromophore show no hysteresis of the unfolding and refolding curves [3]. The refolding curves of parameter A and fluorescence anisotropy for sfGFP exhibit the presence of plateau in the range of GTC concentration from 0.5 to 0.8 M (Fig. 1c and d), while fluorescence intensity of sfGFP chromophore measured during denaturation and renaturation experiments coincide in

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Fig. 1. GTC-induced folding–unfolding of sfGFP recorded by the changes of fluorescence intensity at 320 nm (a) and at 365 nm (b), parameter A (c), fluorescence anisotropy at 365 nm (d), chromophore fluorescence intensity excited at 365 nm (e) and 470 nm (f). Unfolding and refolding are presented by circles and squares, respectively. Measurements were performed after 1 h (closed circles) and 24 h (open circles and squares), 45 h (triangles), 69 h (reversed triangles) and 94 h (gray crosses) incubation of native or denatured protein in the presence of corresponding denaturant concentration. Data presented in panels e and f were corrected to take into account the change of optical density at the wavelength of excitation (g and h). The conformational changes of sfGFP are further characterized by the changes of absorbance at 390 (squares, i) and 490 nm (circles, i). Insert to panel i: the visible fluorescence spectra of sfGFP in the presence of increasing GTC concentration (shown by arrows). The change of sfGFP hydrodynamic dimensions during GTC-induced denaturation was studied by gel filtration method (j). The position of elution peaks of compact and denatured molecules (gray circles and squares) and the change of averaged elution volume of sfGFP (black triangles) are shown.

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this range of GTC concentrations (Fig. 1e and f). In the recent works by molecular dynamics simulations it was shown that initial fast formation of sfGFP β-barrel is followed by a slow search through chromophore isomerization and structural fluctuations toward the locked active native fold [1]. During second folding step, the incorporating the final strands into the barrel and structural rearrangement at the lid of the barrel occur [1,2]. It should be pointed out that Trp57 is near to those β-strands. Probably sfGFP refolding is accompanied by accumulation of intermediate state in which a core around chromophore is organized while structure around Trp57 is not formed.

Acknowledgements This work was in part supported by Ministry of Education and Science (Contracts 02.740.11.5141) and Program MCB RAS.

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[18] E.A. Souslova, V.V. Belousov, J.G. Lock, S. Stromblad, S. Kasparov, A.P. Bolshakov, V.G. Pinelis, Y.A. Labas, S. Lukyanov, L.M. Mayr and D.M. Chudakov, Single fluorescent protein-based Ca2+ sensors with increased dynamic range, BMC Biotechnol. 7 (2007), 37. [19] O.V. Stepanenko, V.V. Verkhusha, V.I. Kazakov, M.M. Shavlovsky, I.M. Kuznetsova, V.N. Uversky and K.K. Turoverov, Comparative studies on the structure and stability of fluorescent proteins EGFP, zFP506, mRFP1, “dimer2” and DsRed, Biochemistry 43 (2004), 14913–14923. [20] O.V. Stepanenko, I.M. Kuznetsova, V.V. Verkhusha, M. Staiano, S. D’Auria and K.K. Turoverov, Denaturation of proteins with beta-barrel topology induced by guanidine hydrochloride, Spectroscopy 24 (2010), 367–373. [21] O.V. Stepanenko, V.V. Verkhusha, I.M. Kuznetsova, V.N. Uversky and S.S. Turoverov, Fluorescent proteins as biomarkers and biosensors: throwing color lights on molecular and cellular processes, Curr. Protein Pept. Sci. 9 (2008), 338–369. [22] K.K. Turoverov, A.G. Biktashev, A.V. Dorofeiuk and I.M. Kuznetsova, A complex of apparatus and programs for the measurement of spectral, polarization and kinetic characteristics of fluorescence in solution, Tsitologiia 40 (1998), 806– 817. [23] V.V. Verkhusha, H. Otsuna, T. Awasaki, H. Oda, S. Tsukita and K. Ito, An enhanced mutant of red fluorescent protein DsRed for double labeling and developmental timer of neural fiber bundle formation, J. Biol. Chem. 276 (2001), 29621– 29624. [24] Y. Wang, J.Y. Shyy, and S. Chien, Fluorescence proteins, live-cell imaging, and mechanobiology: seeing is believing, Annu. Rev. Biomed. Eng. 10 (2008), 1–38. [25] B. Wu, K.D. Piatkevich, T. Lionnet, R.H. Singer and V.V. Verkhusha, Modern fluorescent proteins and imaging technologies to study gene expression, nuclear localization, and dynamics, Curr. Opin. Cell. Biol. 23 (2011), 310–317.

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Protein–ligand interactions of the D-galactose/D-glucose-binding protein as a potential sensing probe of glucose biosensors Olga V. Stepanenko a,∗ , Olesya V. Stepanenko a , Alexander V. Fonin a , Vladislav V. Verkhusha b , Irina M. Kuznetsova a and Konstantin K. Turoverov a a b

Institute of Cytology of the Russian Academy of Sciences, St. Petersburg, Russia Albert Einstein College of Medicine, New York, NY, USA

Abstract. In this work we have studied peculiarities of protein–ligand interaction under different conditions. We have shown that guanidine hydrochloride (GdnHCl) unfolding–refolding of GGBP in the presence of glucose (Glc) is reversible, but the equilibrium curves of complex refolding-unfolding have been attained only after 10 days incubation of GGBP/Glc in the presence of GdnHCl. This effect has not been revealed at heat-induced GGBP/Glc denaturation. Slow equilibration between the native protein in GGBP/Glc complex and the unfolded state of protein in the GdnHCl presence is connected with increased viscosity of solution at moderate and high GdnHCl concentrations which interferes with diffusion of glucose molecules. Thus, the limiting step of the unfolding–refolding process of the complex GGBP/Glc is the disruption/tuning of the configuration fit between the protein in the native state and the ligand. Keywords: D-galactose/D-glucose-binding protein, protein stability, intrinsic fluorescence of proteins, biosensor system, viscosity

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1. Introduction Construction of biosensor system for non-invasive permanent monitoring of glucose level in the human blood is of high importance for diabetic patients [5,8]. One of the most promising directions for persistent glucose monitoring is the design and development of biosensor systems in which glucose specifically binds to proteins acting as the sensitive element [10]. D-galactose/D-glucose-binding protein (GGBP) can be used as a sensing element of such biosensor system as the interaction between GGBP and glucose results in a significant conformational change of the protein structure [9]. GGBP has a low dissociation constant of glucose binding (1 μM) meaning that it can be used as a sensitive element of biosensor systems in which sampling of blood or interstitial liquid is associated with dilution. *

Corresponding author: Dr. Olga V. Stepanenko, Institute of Cytology of the Russian Academy of Science, Tikhoretsky av., 4, 194064 St. Petersburg, Russia. Tel.: +7 812 297 19 57; Fax: +7 812 297 03 41; E-mail: [email protected].

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In particular, reverse iontophoresis decreases the glucose concentration in samples by a thousand fold [7,11]. An important and desirable feature of any biosensor system is the stability of its sensitive element under different denaturing conditions. Thus this paper is focused on the study of stability of GGBP and its complex with glucose (GGBP/Glc) to denaturing action of guanidine hydrochloride (GdnHCl) and heating. An essential influence of viscosity of solution on protein–ligand interaction has been observed.

2. Materials and methods 2.1. Materials GGBP from Escherichia coli was obtained and purified as described follows. E. coli BL21(DE3) cells transformed with pET-11d plasmids encoding for GGBP from E. coli was used. The protein expression was induced by adding 0.5 mM isopropyl-beta-D-1-thiogalactopyranoside (IPTG; Nacalai Tesque, Japan). Bacterial cells were cultured for 24 h at 37◦ C. Recombinant protein was purified using Ni+ agarose packed in His-GraviTrap columns (GE Healthcare, USA). Protein purification was controlled using denaturing SDS-electrophoresis in 15% polyacrylamide gel [3]. Measurements were performed in a 20 mM Na-phosphate buffer at pH 8.0. The concentration of protein was 0.2–0.7 mg/ml. The samples of D-glucose (Sigma, USA) and GdnHCl (Nacalai Tesque, Japan) were used without purification. GdnHCl concentration in solution was determined by Abbe refractometer (LOMO, Russia). D-glucose concentration was 10 mM in all experiments with GGBP/Glc complex. For Ca2+ removal EDTA (Fluka, Switzerland) was added to its final concentration of 0.18 mM. 2.2. Fluorescence measurements

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Fluorescence experiments were carried out using a Cary Eclipse spectrofluorimeter (Varian, Australia) with micro-cells (10 × 10 mm; Varian, Australia). Fluorescence was excited at 297 and 280 nm. The values of parameter A = I320 /I365 characterizing the fluorescence spectra position (I320 and I365 are fluorescence intensities at λem = 320 and 365 nm, respectively [12]), and of fluorescence spectrum were corrected by the instrument sensitivity. The equilibrium dependences of different fluorescent characteristics of GGBP on GdnHCl concentration were recorded during several days after protein incubation in the solution of appropriate concentration at 4◦ C. For a more detailed analysis of the protein unfolding process and in order to determine the number of intermediate states appeared on the pathway from native to unfolded protein we used the method of phase diagrams [2]. The evaluation of the protein free energy differences in native and unfolded states ΔGN−U (0) was performed according to the standard scheme [6]. 2.3. Circular dichroism measurements CD spectra were obtained with spectrophotometer Jasco-810 (Jasco, Japan). Far UV CD spectra were recorded in a 1 mm path length cell from 260 to 190 nm. For all spectra, an average of 3–5 scans was obtained. The protein CD spectra were calculated taking into account the CD signal of the appropriate buffer solutions.

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2.4. DSC measurements Differential scanning calorimetry (DSC) experiments were performed using a DASM-4 differential scanning microcalorimeter (“Biopribor”, Pushchino, Russia) as described earlier [4]. Protein samples were heated at a constant rate of 1 K/min and a constant pressure of 2.4 atm. The reversibility of the thermal transitions was assessed by reheating the sample immediately after the cooling step from the previous scan. The thermal transition curves were baseline corrected by subtracting a scan of the buffer only in both cells. The thermal stability of the proteins was described by the temperature of the maximum of thermal transition (Tm ). Thermal denaturation of GGBP was also studied using protein fluorescence. The thermal dependencies of tryptophan fluorescence intensity of proteins were recorded at constant rate of 1◦ C/min. 3. Results and discussion GGBP consists of two globular domains of practically identical topology connected by three mobile regions. Sugar-binding site is located in a deep cleft between two domains [13]. The central part of both domains consists of six β-sheets, surrounded by α-helixes on both sides: two on one side and three on the other one. Ca2+ ion is localized in the loop of C-terminal domain (134–142 residues), forming coordination bonds with oxygen atoms of every second residue of this loop and with Glu 205 residue. Structure of Ca-binding center resembles “EF-hand” motive, typical for intracellular Ca-binding proteins [1,13]. To characterize the stability of GGBP, GdnHCl-induced unfolding–refolding experiments have been carried out. Different structural probes (fluorescence intensity at a fixed registration wavelength, parameter A, anisotropy and ellipticity at 222 nm) were used to evaluate the equilibrium dependences on GdnHCl concentration for GGBP and it complex GGBP/Glc, as well as their calcium-depleted forms (GGBP-Ca and GGBP-Ca/Glc, respectively). Stationary curves of GGBP unfolding–refolding processes after protein incubation in the GdnHCl solutions of appropriate concentrations for 24 h coincide and are characterized by sigmoid shape with midpoint being equal to 0.36 ± 0.10 M GdnHCl (Fig. 1a). In reality the equilibration is reached even faster. It suggests that the GGBP unfolding process is reversible and follows the next kinetic scheme: k1

k2

k−1

k−2

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(GGBP-Ca)U + Ca  (GGBP-Ca)N + Ca  (GGBP)N .

(1)

This means that Ca binding with (GGBP-Ca)N is a fast process. Apparently, the limiting stage of GGBP folding is the formation of protein native state. The curve of GGBP/Glc unfolding measured after 24 h of samples incubation in the GdnHCl solutions of appropriate concentrations is shifted to the larger concentrations of GdnHCl in comparison with GGBP equilibrium unfolding–refolding curve (Fig. 1b). As for GGBP the glucose binding constant is very large (about 1.0 μM−1 [9]), we supposed that GGBP/Glc complex formation from GGBP and Glc would be fast process. Nonetheless, the curve of GGBP/Glc renaturation recorded after 24 h incubation in the solutions of appropriate concentrations of GdnHCl does not coincide with GGBP/Glc denaturing curve, but is much closer to the equilibrium curve of GGBP unfolding–refolding. This result is rather unexpected, because it can be so only if the process of GGBP complex formation with Glc is a limiting stage in GGBP/Glc formation from (GGBP)U in the presence of the excess of Glc and Ca. This means that the GGBP/Glc denaturation–renaturation curves recorded after 24 h of protein incubation in

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(a)

(b)

(c)

(d)

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Fig. 1. GdnHCl and heat-induced denaturation of GGBP in the presence and in the absence of its ligands – calcium and glucose. (a) Equilibrium dependencies of fluorescence intensity at 320 nm of GGBP and GGBP-Ca (black and grey circles, respectively) and GGBP/Glc and GGBP-Ca/Glc (black and gray squares, respectively). Open symbols – unfolding, closed – refolding, λex = 297 nm. (b) The change of parameter A = I320 /I365 at protein unfolding and refolding. Unfolding curves were measured for GGBP after incubation in solutions of an appropriate denaturant concentration at 4◦ C during 24 h (gray solid line and gray open circles) and for GGBP/Glc after incubation during 24 h (black dashed line and black open squares) and 10 days (black solid line and black open circles). Data characterizing protein renaturation from unfolded state were measured after incubation in solutions of an appropriate denaturant concentration at 4◦ C during 24 h for GGBP (gray closed circles) and during 24 h (black closed squares) and 10 days for GGBP/Glc (black closed circles), λex = 297 nm. (c) Temperature dependencies of the excess heat capacity of GGBP (gray) and GGBP/Glc (black). (d) Heat-induced denaturation of GGBP (gray) and GGBP/Glc (black) as recorded by fluorescence experiments. Two sequential scans (solid and dashed lines, respectively) are shown to characterize the reversibility of the thermal transitions, λex = 297 nm.

the appropriate concentrations of GdnHCl are not equilibrium curves. We have shown that equilibrium curves of GGBP/Glc unfolding and refolding coinciding to each other can be obtained after 10 day of incubation in the GdnHCl of appropriate concentration (Fig. 1a and b). Thus, the process of GGBP/Glc unfolding–refolding is determined by the following kinetic scheme: k1

k2

k3

k−1

k−2

k−3

(GGBP-Ca)U + Glc, Ca  (GGBP-Ca)N + Glc, Ca  (GGBP)N + Glc  (GGBP/Glc)N . (2) The equilibrium curves of unfolding–refolding of GGBP/Glc presenting sigmoid shape are shifted to the large GdnHCl concentration with respect to that of GGBP. The midpoint of GGBP/Glc unfolding

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(0.93±0.03 M GdnHCl) occurs at higher GdnHCl concentrations compared to GGBP unfolding (Fig. 1a and b). The values of the fluorescence characteristics and ellipticity at 222 nm are dramatically different between GGBP and GGBP-Ca even at low denaturant concentrations (Fig. 1a). However, the equilibrium dependencies of different structural probes of the GGBP-Ca/Glc practically coincide to those of GGBP/Glc (Fig. 1a). The curve of the GGBP/Glc-Ca unfolding reaches equilibrium after 10 days of incubation. At the same time renaturation of GGBP/Glc-Ca takes even larger time (data not shown). Being parametrically represented dependences of fluorescence intensities recorded at 320 and 365 nm of GGBP and GGBP-Ca both in the absence and in the presence of glucose are well described by a straight line. Alone with sigmoid shape of equilibrium unfolding–refolding curves of studied proteins, these data support a two-state unfolding for GGBP. The equilibrium curves of unfolding–refolding of GGBP and GGBP-Ca alone and in complexes with glucose have been used for estimation of difference of protein free energy between native and unfolded state ΔGN−U (0). The value of ΔGN−U (0) of GGBP is almost half as great as that of GGBP/Glc (8.04 ± 3.77 and 14.11 ± 4.48 kJ/mol, respectively). The ΔGN−U (0) value of GGBP-Ca cannot be defined accurately because it is impossible to estimate the fluorescence intensity of the native state, while the ΔGN−U (0) value of GGBP-Ca/Glc is practically unchanged (13.31 ± 4.40 kJ/mol). It is obvious that calcium-depleted GGBP form is very unstable. All these data reflect the stabilizing effect of glucose of GGBP structure, while in the absence of bound glucose GGBP is stabilized by calcium ion. The thermal stability of GGBP in the presence and in the absence of ligands has been investigated by differential scanning calorimetry (DSC) and by UV-fluorescence (Fig. 1c and d). The calorimetric traces of GGBP and GGBP/Glc have a maximum at temperature 51.3 and 64.7◦ C (Fig. 1c). The dependences of fluorescence intensity on temperature of GGBP and GGBP/Glc are S-shaped with melting temperatures corresponding to Tm values obtained by DSC (Fig. 1d). Calcium depletion results in significant shifts of calorimetric trace of GGBP-Ca (Tm = 42.7◦ C). At the same time, the maximum of calorimetric trace of GGBP-Ca/Glc (Tm = 61.5◦ C) is close to that of GGBP/Glc. The heat-induced unfolding of all GGBP is reversible both in the presence and in the absence of the ligand as indicated by the almost complete reproducibility of the calorimetric traces assessed a second time by reheating the sample immediately after the cooling step. This is also supported by the coincidence of two sequential scans of fluorescence intensity recorded in the thermal denaturation range of these proteins (Fig. 1d). Minor deviations of repeated scans can be attributed to protein aggregation occurring at high temperature. In conclusion, slow equilibration of GdnHCl-induced unfolding–refolding curves of GGBP/Glc has not been observed at heat-induced denaturation of GGBP in presence of glucose. Slow equilibrium acquisition between the native protein in GGBP/Glc complex and the unfolded state of protein in the GdnHCl presence is connected with increased viscosity of solution at moderate and high GdnHCl concentrations which interferes with diffusion of glucose molecules. Before equilibrium is established for a long time there is the excess concentration (in comparison with equilibrium) of complex (GGBP/Glc)N on the pathway of unfolding, or unfolded protein (GGBP)U on the pathway of renaturation. It is so because the activation barrier must be overcome in both cases. On the pathway of unfolding the elementary act of complex dissociation does not lead to the disturbance of configuration fit of interacting molecules of GGBP and Glc and consequently the probability of the inverse reaction is high. Contrary, on the pathway of refolding it so because for complex formation not only the formation of native molecule (GGBP)N but also appearance of configuration fit of (GGBP)N molecule and Glc is needed. Thus, the limiting step of the unfolding–refolding process of the complex GGBP/Glc is the disruption/tuning of the configuration fit between the protein in the native state and the ligand.

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Acknowledgement This work was in part supported by Ministry of Education and Science (Contracts 02.740.11.5141 and 16.512.11.2114), Program MCB RAS. References

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[1] M.J. Borrok, L.L. Kiessling and K.T. Forest, Conformational changes of d-glucose/D-galactose binding protein illuminated by apo and ultrahigh resolution ligand-bound structures, Prot. Sci. 16 (2007), 1032–1041. [2] I.M. Kuznetsova, K.K. Turoverov and V.N. Uversky, Use of the phase diagram method to analyze the protein unfolding– refolding reactions: fishing out the “invisible” intermediates, J. Proteome Res. 3 (2004), 485–494. [3] U.K. Laemmli, Cleavage of structural proteins during the assembly of the head of bacteriophage T4, Nature 227 (1970), 680–685. [4] D.I. Levitsky, E.V. Rostkova, V.N. Orlov, O.P. Nikolaeva, L.N. Moiseeva, M.V. Teplova and N.B. Gusev, Complexes of smooth muscle tropomyosin with F-actin studied by differential scanning calorimetry, Eur. J. Biochem. 267 (2000), 1869–1877. [5] E.A. Moschou, B.V. Sharma, S.K. Deo and S. Daunert, Fluorescence glucose detection: advances toward the ideal in vivo biosensor, J. Fluoresc. 14 (2004), 535–547. [6] B. Nolting, Protein Folding Kinetics: Biophysical Methods, Springer, Berlin, 1999. [7] N.S. Oliver, C. Toumazou, A.E. Cass and D.G. Johnston, Glucose sensors: a review of current and emerging technology, Diabet. Med. 26 (2009), 197–210. [8] A. Ramachandran, A. Moses, S. Shetty, C.J. Thirupurasundari, A.C. Seeli, C. Snehalatha, S. Singvi and J.P. Deslypere, A new non-invasive technology to screen for dysglycaemia including diabetes, Diab. Res. Clin. Pract. 88 (2010), 302–306. [9] B.H. Shilton, M.M. Flocco, M. Nilsson and S.L. Mowbray, Conformational changes of three periplasmic receptors for bacterial chemotaxis and transport: the maltose-, glucose/galactose- and ribose-binding proteins, JMB 264 (1996), 350– 363. [10] L. Tolosa, On the design of low-cost fluorescent protein biosensors, Adv. Biochem. Eng. Biotechnol. 116 (2009), 143–157. [11] A. Tura, A. Maran and G. Pacini, Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria, Diab. Res. Clin. Pract. 77 (2007), 16–40. [12] K.K. Turoverov and I.M. Kuznetsova, Intrinsic fluorescence of actin, J. Fluorescence 13 (2003), 41–57. [13] N.K. Vyas, M.N. Vyas and F.A. Quiocho, Sugar and signal-transducer binding sites of the Escherichia coli galactose chemoreceptor protein, Science 242 (1988), 1290–1295.

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A comparison between vanadyl, vanadate and decavanadate effects in actin structure and function: Combination of several spectroscopic studies S. Ramos a,b , J.J.G. Moura a and M. Aureliano b,∗ a

REQUIMTE/CQFB, Departamento Química, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Caparica, Portugal b CCMar, DCBB, Faculdade de Ciências e Tecnologia, Universidade do Algarve, Faro, Portugal Abstract. The studies about the interaction of actin with vanadium are seldom. In the present report the effects of vanadyl, vanadate and decavanadate in the actin structure and function were compared. Decavanadate clearly interacts with actin, as shown by 51 V-NMR spectroscopy. Decavanadate interaction with actin induces protein cysteine oxidation and vanadyl formation, being both prevented by the natural ligand of the protein, ATP. Monomeric actin (G-actin) titration with vanadyl, as analysed by EPR spectroscopy, indicates a 1:1 binding stoichiometry and a kd of 7.5 μM. Both decavanadate and vanadyl inhibited G-actin polymerization into actin filaments (F-actin), with a IC50 of 68 and 300 μM, respectively, as analysed by light scattering assays. However, only vanadyl induces G-actin intrinsic fluorescence quenching, which suggest the presence of vanadyl high affinity actin binding sites. Decavanadate increases (2.6-fold) actin hydrophobic surface, evaluated using the ANSA probe, whereas vanadyl decreases it (15%). Finally, both vanadium species increased ε-ATP exchange rate (k = 6.5 × 10−3 and 4.47 × 10−3 s−1 for decavanadate and vanadyl, respectively). Putting it all together, it is suggested that actin, which is involved in many cellular processes, might be a potential target not only for decavanadate but above all for vanadyl. Keywords: Actin, vanadyl, decavanadate, vanadate, cisteine oxidation, intrinsic fluorescence, ANSA probe, NMR, EPR

Abbreviations

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ANSA: 8-anilino-1-naphthalene sulfonic acid; G-actin: monomeric actin; F-actin: filamentous polymerized actin. 1. Introduction Vanadium impact in biology, pharmacology and medicine is well known, mainly after the discovery that the “muscle inhibitor factor”, present in ATP obtained from horse muscle and responsible for Na+ , *

Corresponding author: M. Aureliano, CCMar, DCBB, Faculdade de Ciências e Tecnologia University of Algarve, 8005139 Faro, Portugal. Tel.: +351 289 800905; Fax: +351 289 800066; E-mail: [email protected].

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K+ -ATPase inhibition was, in fact, vanadate, which acts as a transition state analogue for phosphoenzyme hydrolysis, blocking the enzyme catalysis [4]. The most outstanding vanadium and vanadium compounds effect is perhaps the specific inhibition of protein tyrosine phosphatases (PTP), promoting an increasing of glucose uptake in several types of cells and mimicking insulin effects [14]. Additionally, the usage of vanadium as a tool to understand several biochemical processes is well recognized [3]. Actin is one of the most abundant proteins in cells, being involved in many cellular processes, such as muscle contraction, in muscular cells, and cytoskeleton structure and dynamics, in non-muscle cells. Very few studies concerning the interaction of vanadium with actin have been so far described [1,5,6]. These previous studies demonstrated that vanadate (vanadium(V)), upon binding to F-actin-ADP subunits, increases the strength of actin–actin interactions, stabilizing the F-actin filament [5], and vanadyl interaction with the monomeric G-actin reveal one strong binding site, among others [1]. More recently, it has been demonstrated that vanadate oligomers, such as tetrameric and decameric vanadate (decavanadate, V10 ), prevent G-actin polymerization, whereas no effects were observed for vanadate up to 2 mM [11]. It was also demonstrated that G-actin stabilizes decavanadate species, increasing its half-life time from 5 to 27 h [11]. On the other hand, cellular studies have shown that vanadium compounds induce changes in actin cytoskeleton, which are responsible for morphological and cell proliferation alterations [13,15]. These effects are probably induced through the inhibition of protein tyrosine phosphatases, as referred above, or eventually through reactive oxygen species generation, once it is well known that transition elements, such as vanadium, promote Fenton-like reactions. These actions could explain, at least in part, the antitumor effects of vanadium [7]. In the present report, we compare the effects of vanadyl, vanadate and decavanadate in actin structure and function, combining several spectroscopic and kinetic studies.

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2. Decavanadate, vanadate and vanadyl interactions with actin: NMR and EPR studies It was verified that decavanadate 51 V-NMR signals broadened upon actin titration, whereas no changes were observed for the other vanadate oligomers signals [10]. Decavanadate interactions with actin are of particular interest once it was observed that only V10 species are able to promote protein cysteine oxidation [10,12]. ATP prevents decavanadate reduction and protein cysteine oxidation [10,12], suggesting interaction blockadge, eventually due to the same protein binding site. In fact, upon decavanadate interaction with actin, a concomitant reduction to vanadyl (V(IV)) was detected, as analysed by EPR spectroscopy (Fig. 1). Typical EPR vanadium(IV) signals can be detected upon decavanadate incubation with actin (Fig. 1B), as well as with vanadyl incubation with the protein (Fig. 1D). The presence of ATP into the medium, prevents decavanadate reduction to vanadyl (Fig. 1C), as well as the interaction of vanadyl with actin (Fig. 1E). Recently reviewed decavanadate effects in biological systems have pointed out that decavanadate is more or less efficient than the corresponding simple oxovanadates in targeting specific proteins, particularly at the nucleotide binding site [2,3]. However, while the putative occurrence of decavanadate in cells remains to be completely elucidate, vanadate usually occurs inside the cells in its reduced form, vanadyl, being this cation recognized to bind to several proteins. We have shown recently, using EPR spectroscopy, that vanadyl interacts with G-actin with a dissociation constant (kd ) of 7.48 ± 1.11 μM and a 1:1 vanadyl–actin stoichiometry [10]. It was suggested that vanadyl binds to actin at the ATP binding site, since the presence of this nucleotide in the medium assay prevents the interaction between the metal and the protein (Fig. 1), as it was also observed for decavanadate [9,10,12].

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Fig. 1. Frozen aqueous solution X-band EPR spectra. (A) 2 mM Tris (pH 7.5), 0.2 mM CaCl2 and 25 mM decavanadate with (B) 100 μM G-actin, (C) 50 μM G-actin plus 0.2 mM ATP; (D) 2 mM Tris (pH 7.5), 0.2 mM CaCl2 and 250 μM VOSO4 with 50 μM G-actin, (E) same as (D) plus 0.2 mM ATP. Vanadium(IV) EPR typical signals, were observed upon both decavanadate (B) or VOSO4 (D) incubation with G-actin. No EPR signals were detected when ATP was present in the medium (C and E).

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3. Decavanadate, vanadate and vanadyl interactions with actin: Fluorescence studies As described above, decavanadate revealed a particular interaction with actin. As analysed by light scattering spectroscopy, it was observed that decavanadate prevents the extension of G-actin polymerization into F-actin filaments, being more effective than vanadyl [9,11]. At the same experimental conditions, the observed IC50 for the inhibition of polymerization reaction was lower for decavanadate, by comparison with vanadyl (68 and 300 μM, respectively) whereas no effects were observed up to 2 mM vanadate [9,11]. Further studies, using fluorescence spectroscopy, were performed to compare the interaction between decavanadate, vanadate and vanadyl with actin. It was verified that vanadyl induces a total quenching of actin intrinsic fluorescence (Fig. 2), whereas decavanadate increases its fluorescence [9,12]. It should be noted that decavanadate concentration used in these studies is 10 times less than that of vanadyl. This means that 500 μM decavanadate (in total vanadate) corresponds to 50 μM of decameric vanadate species. Regarding protein intrinsic fluorescence, care must be taken to evaluate the interaction of decavanadate or vanadate with proteins, due to inner filter effects [12]. However, many biochemical studies using vanadium do not take in consideration those effects, which decreases the fluorescence measurements accuracy. For a decavanadate solution with a concentration as lower as 0.05 mM (meaning 0.5 mM total vanadium), the absorbances values at excitation and emission wavelengths used in assays (295 and 340 nm, respectively) attain to 1.72 OD (1.17 + 0.55), which is more than vanadate (1.02; 0.85 + 0.17) or vanadyl (0.17; 0.13 + 0.04), for the same vanadium concentrations. Correction of the fluorescence intensities due to these inner filter effects are desirable and can be done using proper equations [8]. Besides the effects on intrinsic fluorescence, it was also observed that actin hydrophobic surface, as determined using the ANSA probe, increases upon decavanadate exposure (2.6-fold), whereas vanadyl promotes its decreasing by 15%, suggesting that the changes caused by the former are clearly different from the ones induced by vanadyl, favouring a protein hydrophobic environment [9,12]. However, both decavanadate and vanadyl (up to 200 μM total vanadium) increased ε-ATP exchange rate (k = 6.5×10−3 and 4.47× 10−3 s−1 , respectively, in comparison with the control: k = 3.0× 10−3 s−1 ), and both species

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Fig. 2. G-actin intrinsic fluorescence. G-actin (5 μM) was incubated with VOSO4 (Q) or decavanadate () for 20 min, at 25◦ C, in 2 mM Tris–HCl (pH 7.5), 0.2 mM CaCl2 . The maximum of intrinsic fluorescence spectra (λex = 295 nm) were plotted against vanadyl or decavanadate (total) concentrations, considering 1.00 the value for native actin. The results shown are the average of, at least, triplicated measurements.

decreased ATP exchange the half-life time, denoting a more available cleft [9,12]. Actin conformational changes induced by vanadyl were also verified by 1 H-NMR [9].

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4. Concluding remarks In order to evaluate and to compare the effects in actin structure and function, a major and revelant cellular protein, induced by several vanadium species, namely vanadyl, vanadate and decavanadate, a combination of EPR, 51 V-NMR and fluorescence spectroscopic studies along with kinetic ones were used. Taken together, the studies reveal the presence of G-actin high affinity binding sites for vanadyl, with a 1:1 actin–vanadium(IV) stoichiometry. Also, a specific decavanadate interaction with the protein was observed, leading to cysteine oxidation and vanadyl formation. Both vanadium species interactions with actin were prevented by ATP. Putting it all together, it is proposed that the biological effects of vanadium, which major biological role stills to be clarified, may be explained, at least in part, by its capacity to interact with actin and to affect several biological processes where actin may be involved. It is concluded in this paper: (i) decavanadate and vanadyl inhibits actin polymerization, at μM concentrations; (ii) only decavanadate interaction with actin induces cysteine oxidation and vanadate reduction, being these effects prevented by ATP; (iii) decavanadate and vanadyl induce actin conformational changes affecting protein ATP binding site; (iv) the presence of actin high affinity binding sites for vanadyl. It is suggested that actin, a protein involved in many cellular processes, is a plausible protein target for decavanadate and above all for vanadyl.

Acknowledgements This work was supported by CCMAR funding. S.R. would like to thank to the Portuguese Foundation “Fundação para a Ciência e Tecnologia (FCT)” for the PhD Grant SFRH/BD/29712/2006.

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References

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[1] F. An, B.Y. Zhang, B.W. Chen and K. Wang, The interaction of vanadyl ions with G-actin, Chem. J. Chinese Univ. 17 (1996), 667–671. [2] M. Aureliano, Decavanadate: a journey in a search of a role, Dalton Trans. (2009), 9093–9100. [3] M. Aureliano and D. C. Crans, Decavanadate (V10 O28 6− ) and oxovanadates: oxometalates with many biological activities, J. Inorg. Biochem. 103 (2009), 536–546. [4] L.C. Cantley Jr., L. Josephson, R. Warner, M. Yanagisawa, C. Lechene and G. Guidotti, Vanadate is a potent (Na, K)ATPase inhibitor found in ATP derived from muscle, J. Biol. Chem. 252 (1977), 7421–7423. [5] C. Combeau and M.-F. Carlier, Probing the mechanism of ATP hydrolysis on F-actin using vanadate and the structural analogs of phosphate BeF; and AlFi, J. Biol. Chem. 263 (1988), 17429–17436. [6] S.C. El-Saleh and P. Johnson, Non-covalent binding of phosphate ions by striated muscle actin, Int. J. Biol. Macromol. 4 (1982), 430–432. [7] A.M. Evangelou, Vanadium in cancer treatment, Crit. Rev. Oncol./Hematol. 42 (2002), 249–265. [8] J.R. Lackowicz, Principles of Fluorescence Spectroscopy, Plenum Press, New York, 1983. [9] S. Ramos, R.M. Almeida, J.J.G. Moura and M. Aureliano, Implications of oxidovanadium(IV) binding to actin, J. Inorg. Biochem. 105 (2011), 777–783. [10] S. Ramos, R.O. Duarte, J.J.G. Moura and M. Aureliano, Decavanadate interactions with actin: cysteine oxidation and vanadyl formation, Dalton Trans. (2009), 7985–7994. [11] S. Ramos, M. Manuel, T. Tiago, R.O. Duarte, J. Martins, C. Gutiérrez-Merino, J.J.G. Moura and M. Aureliano, Decavanadate interactions with actin: inhibition of G-actin polymerization and stabilization of decameric vanadate, J. Inorg. Biochem. 100 (2006), 1734–1743. [12] S. Ramos, J.J.G. Moura and M. Aureliano, Actin as a potential target for decavanadate, J. Inorg. Biochem. 104 (2010), 1234–1239. [13] J. Rivadeneira, D. A. Barrio, G. Arrambide, D. Gambino, L. Bruzzone and S.B. Etcheverry, Biological effects of a complex of vanadium(V) with salicylaldehyde semicarbazone in osteoblasts in culture: mechanism of action, J. Inorg. Biochem. 103 (2009), 633–642. [14] E. Tsiani, E. Bogdanovic, A. Sorisky, L. Nagy and I.G. Fantus, Tyrosine phosphatase inhibitors, vanadate and pervanadate, stimulate glucose transport and GLUT translocation in muscle cells by a mechanism independent of phosphatidylinositol 3-kinase and protein kinase C, Diabetes 47 (1998), 1676–1686. [15] X.-G. Yang, X.-D. Yang, L. Yuan, K. Wang and D.C. Crans, The permeability and cytotoxicity of insulin-mimetic vanadium compounds, Pharm. Res. 21 (2004), 1026–1033.

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The V30M amyloidogenic mutation decreases the rate of refolding kinetics of the tetrameric protein transthyretin Catarina S.H. Jesus a,b , Daniela C. Vaz b , Maria J.M. Saraiva c and Rui M.M. Brito a,b,∗ a

Department of Chemistry, University of Coimbra, Coimbra, Portugal Center for Neuroscience and Cell Biology, University of Coimbra, Coimbra, Portugal c Institute of Molecular and Cell Biology, University of Porto, Porto, Portugal b

Abstract. Transthyretin (TTR) is a homotetrameric protein implicated in several amyloid diseases. The mechanism by which TTR is converted into elongated fibrillar assemblies has been extensively investigated, and numerous studies showed that dissociation of the native tetrameric structure into partially unfolded monomeric species precedes amyloid formation. The small differences observed in the crystal structures of different TTR variants, as well as the thermodynamics and kinetics of tetramer dissociation, do not seem to completely justify the amyloidogenic potential of different variants. With this in mind, we have studied the refolding kinetics of WT-TTR and its most common amyloidogenic variant V30M-TTR, monitoring changes in intrinsic tryptophan fluorescence at different urea and protein concentrations. Our results demonstrate that the in vitro refolding mechanisms of WT- and V30M-TTR are similar, involving a dimeric intermediate. However, there are large differences in the refolding rate constants for the two variants, specially at nearly native conditions. Interestingly, tetramer formation occurs at a much slower rate in the amyloidogenic variant V30M-TTR than in WT-TTR, which in the in vivo setting may promote the accumulation of monomeric species in the extracellular environment, resulting in higher susceptibility for aggregation and amyloid formation instead of spontaneous refolding. Keywords: Amyloidosis, FAP, folding kinetics, transthyretin, TTR

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1. Introduction Insoluble amyloid fibrils are found in patients with diseases such as systemic amyloidosis, spongiform encephalopathies and Alzheimer’s, among many others [8]. Pathologies such as senile systemic amyloidosis (SSA), familial amyloid polyneuropathy (FAP) and familial amyloid cardiomyopathy (FAC) are characterized by extracellular fibril deposits of transthyretin [3]. In certain forms of FAP these deposits are mostly constituted by variants of the protein transthyretin (TTR), while in senile systemic amyloidosis the fibrils consist essentially of wild type TTR. FAP is an autosomal dominant lethal disease affecting individuals from their twenties in many countries, particularly Portugal, Japan, Sweden and the USA. The most common type of FAP is associated with the variant V30M-TTR. *

Corresponding author: Rui M.M. Brito, Departamento de Química, Faculdade de Ciências e Tecnologia, Universidade de Coimbra, 3004-535 Coimbra, Portugal. Fax: +351 239 827 703; E-mail: [email protected].

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Transthyretin is a homotetrameric plasma protein that transports thyroxine and retinol, the later in association with the retinol binding protein [3,14]. The concentration of TTR in serum ranges from 170 to 420 μg/ml, and in the cerebrospinal fluid varies between 5 and 20 μg/ml [16]. The small structural differences observed in the crystal structures of TTR variants do not seem to justify their varying amyloidogenic potential [5,15], and a significant effort has been devoted to search for thermodynamic and kinetic factors that may play a critical role on TTR stability, in order to fully understand the molecular mechanism of amyloid formation by TTR. According to calorimetric studies, amyloidogenic and non-amyloidogenic TTR variants are highly stable to thermal unfolding [13]. Moreover, although suggested as rate-limiting for amyloid formation [2,7], tetramer dissociation kinetics do not seem to completely justify the observed spectrum of amyloidogenic potential among TTR variants [3]. Interestingly, other studies revealed that the more amyloidogenic TTR variants produce large amounts of partially unfolded monomeric species as a consequence of the marginal conformational stability of the non-native monomers resulting from tetramer dissociation, even in solution conditions close to physiological [10, 11]. Thus, a combination of tetramer dissociation kinetics, monomer conformational stability, and aggregation kinetics may play a deciding role in amyloid formation by TTR [3]. In the present work, we investigate the potential role of refolding kinetics on amyloid formation by TTR. Physiologically relevant TTR concentrations were used and the refolding mechanism for V30Mand WT-TTR is shown to involve the presence of a dimeric intermediate. The refolding kinetics data also show that, in the absence of denaturant, the refolding process from unfolded monomers to the corresponding native homotetramer is significantly more favourable for WT-TTR than for its amyloidogenic variant V30M-TTR. 2. Experimental procedures 2.1. Materials Recombinant WT- and V30M-TTR were expressed in an Escherichia coli [4] and purified as described previously [1]. All chemicals were of the highest commercially available purity and were purchased from Sigma Chemical Company. Fluorescence spectra were performed on a Varian Eclipse spectrofluorometer, with continuous stirring, at 25◦ C. 2.2. TTR denaturation

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TTR concentrations were determined spectrophotometricaly at 280 nm [12]. Protein samples were incubated in 2 M guanidinium thiocyanate (GdmSCN) for 12 h, followed by dialysis against 6 M urea during 10 h. Denaturant solutions were prepared in 20 mM sodium phosphate buffer, 150 mM sodium chloride, at pH 7.0. The concentration of stock solutions of GdmSCN and urea were checked by their refractive index. 2.3. Refolding experiments Protein refolding experiments were carried out at several urea and protein concentrations. WT- and V30M-TTR, denatured as described above, were refolded to the desired urea and protein concentrations, by dilution into 20 mM sodium phosphate buffer, 150 mM sodium chloride, pH 7.0, at 25◦ C. The refolding reaction was allowed to proceed for 12 h and was monitored by fluorescence emission

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at 380 nm, upon excitation at 290 nm. In the case of WT-TTR, the urea concentration ranged between 1.8 and 2.6 M, while for V30M-TTR the refolding experiments were performed between 0.4 and 1.2 M urea. The refolding assays were repeated several times and found to be reproducible within experimental errors. 3. Results and discussion 3.1. Transthyretin denaturation and refolding TTR unfolding was chemically-induced in two steps as reported in the Experimental Procedures, because tetrameric TTR is highly stable and difficult to denature even in high urea concentrations [6]. The intrinsic fluorescence spectra of TTR (graph inset in Fig. 1(a)) show a red-shift in the emission maximum upon TTR unfolding, as a consequence of a more polar environment around the tryptophan residues due to higher solvent exposure in the denatured state [10]. Moreover, the fluorescence spectra of denatured TTR in 2 M GdmSCN and after dialysis against 6 M urea overlap, which demonstrates that urea is able to maintain the GdmSCN-induced denatured state of TTR. The fluorescence spectra (graph inset in Fig. 1(a)) also show that the refolded species have emission spectra very similar to those of the native tetrameric TTR, with emission maxima of approximately 340 nm, indicating that protein refolding was achieved upon urea dilution. The refolded species of WT- and V30M-TTR were also characterized by size exclusion chromatography and thyroxine binding assays (data not shown), demonstrating that both TTR variants refold to their native tetrameric forms with a very high yield. 3.2. Transthyretin refolding mechanism

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The refolding kinetic mechanisms of WT- and V30M-TTR were investigated following the changes in intrinsic tryptophan fluorescence at 380 nm, and using different urea and protein concentrations.

(a)

(b)

Fig. 1. (a) Example of a refolding kinetics experiment for WT-TTR monitored by the variation over time of the intrinsic fluorescence emission at 380 nm, pH 7.0 and 25◦ C. The curve (solid line) is the fit to the experimental data points using the simplest, best-fitting refolding model. The graph inset shows the spectral differences reflecting the conformational changes of TTR upon unfolding and refolding. (b) Schematic view of the TTR refolding mechanism.

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Figure 1(a) shows an example of the fluorescence intensity changes accompanying TTR refolding as a function of time. Several kinetic models were tested and the simplest refolding mechanism that better describes the folding and assembly of the TTR tetramers involves three states and one dimeric folding intermediate (Fig. 1(b)). The dimeric nature of this folding intermediate may be postulated due to the observed dependence of both rate constants (k1 and k2 ) on protein concentration, which indicates the presence of two oligomerization steps. Thus, the simplest best-fitting mechanism to our kinetic data is a folding and assembly mechanism of the type Monomer–Dimer–Tetramer (MDT), and the rate equations associated with this mechanism are listed below: d[M ] = −2k1 [M ]2 , dt d[D] = k1 [M ]2 − 2k2 [D]2 , dt d[T ] = k2 [D]2 . dt Kelly and collaborators performed refolding studies with WT-TTR, monitored by a small molecule binding assay, using protein concentrations in the range of 0.72–36 μM (in monomers), and in order to globally fit the data for such a wide range of protein concentrations, the authors proposed an atypical Monomer (M)–Dimer (D)–Trimer (R)–Tetramer (T) assembly (MDRT) mechanism [17]. However, they suggested that, at lower protein concentrations, a simpler mechanism like the conventional MDT is bound to occur. Our kinetic data, at lower TTR concentrations and using a more direct monitoring method, fits best the MDT mechanism, in accordance to what has been postulated for various homotetrameric proteins [9]. 3.3. Refolding rates of the amyloidogenic mutant protein V30M-TTR

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The refolding mechanism of WT- and V30M-TTR, at low protein concentrations (1–0.1 μM), involves a single oligomeric intermediate, dimeric in nature. Both TTR variants refold through this MDT mechanism with a high yield, however in the case of V30M-TTR the folding and assembly process is much slower. Table 1 shows the experimentally determined apparent kinetic constants k1 and k2 , at low protein concentration and in the absence of denaturant. Comparison of the extrapolated individual kinetic constants for V30M-TTR with those obtained for WT-TTR reveals a noteworthy difference, with the lower rate constants corresponding to the amyloidogenic V30M-TTR. These lower rate constants, in particular k1 , may reflect the reduced conformational stability of the V30M-TTR monomers compared to WT-TTR [11]. Table 1 Apparent refolding kinetic constants of WT- and V30M-TTR at a protein concentration of 1 μM in the absence of denaturant, obtained by extrapolation of the refolding rates at different urea concentrations, using the equation: ln k = ln k 0 + m[urea] WT-TTR V30M-TTR

k10 (M−1 s−1 )

k20 (M−1 s−1 )

2.2 × 106 1.8 × 103

8.1 × 103 1.9 × 102

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4. Conclusions In conclusion, we showed that the amyloidogenic variant V30M-TTR has much longer refolding times than WT-TTR, and this might be of critical importance to explain the increased amyloidogenicity of this variant. In vivo, the slower rate at which refolding and assembly of the native tetrameric form of the amyloidogenic variant V30M-TTR occurs, may facilitate the accumulation of monomers of this variant in the extracellular environment, which could result in a higher susceptibility to aggregation and consequently amyloid formation, instead of refolding to the native tetramer. Acknowledgements The authors acknowledge the support of the “Fundação para a Ciência e Tecnologia”, Portugal, through grant PTDC/BIA-PRO/72838/2006 (to RMMB) and doctoral fellowship SFRH/BD/43896/2008 (to CSHJ). The authors also thank Elsa S. Henriques for critically reading the manuscript and Cândida G. Silva for advice on integration of differential equations. References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15]

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[16] [17]

M.R. Almeida, A.M. Damas, M.C. Lans, A. Brouwer and M.J. Saraiva, Endocrine 6 (1997), 309. A.R.H. Babbes, E.T. Powers and J.W. Kelly, Biochemistry 47 (2008), 6969. R.M.M. Brito, A.M. Damas and M.J.M. Saraiva, Curr. Med. Chem. Immun. Endoc. Metab. Agents 3 (2003), 349. H. Furuya, M.J. Saraiva, M.A. Gawinowicz, I.L. Alves, P.P. Costa, H. Sasaki, I. Goto and Y. Sakaki, Biochemistry 30 (1991), 2415. J.A. Hamilton, L.K. Steinrauf, B.C. Braden, J. Liepnickes, M.D. Benson, G. Holmgren, O. Sandgren and L. Steen, J. Biol. Chem. 268 (1993), 2416. P. Hammarström, X. Jiang, S. Deechongkit and J.W.Kelly, Biochemistry 40 (2001), 11453. P. Hammarström, X. Jiang, A.R. Hurshman, E.T. Powers and J.W. Kelly, Proc. Natl. Acad. Sci. USA 99(Suppl. 4) (2002), 16427. S. Ohnishi and K. Takano, Cell. Mol. Life Sci. 61 (2004), 511. E.T. Powers and D.L. Powers, Biophys. J. 85 (2003), 3587. A. Quintas, M.J.M. Saraiva and R.M.M. Brito, J. Biol. Chem. 274 (1999), 32943. A. Quintas, D.C. Vaz, I. Cardoso, M.J.M. Saraiva and R.M.M. Brito, J. Biol. Chem. 276 (2001), 27207. A. Raz and D.S. Goodman, J. Biol. Chem. 244 (1969), 3230. V.L. Shnyrov, E. Villar, G.G. Zhadan, J.M. Sanchez-Ruiz, A. Quintas, M.J.M. Saraiva and R.M.M. Brito, Biophys. Chem. 88 (2000), 61. D.R. Soprano, J. Herbert, K.J. Soprano, E.A. Schon and D.S. Goodman, J. Biol. Chem. 260 (1985), 11793. C.J. Terry, A.M. Damas, P. Oliveira, M.J.M. Saraiva, I.L. Alves, P.P. Costa, P.M. Matias, Y. Sakaki and C.C. Blake, EMBO J. 12 (1993), 735. G.T. Vatassery, H.T. Quach, W.E. Smith, B.A. Benson and J.H. Eckfeldt, Clin. Chim. Acta 197 (1991), 19. R.L. Wiseman, E.T. Powers and J.W. Kelly, Biochemistry 44 (2005), 16612.

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THz and IR spectroscopy of molecular systems that simulate function-related structural changes of proteins N.N. Brandt a , A.Yu. Chikishev a , A.A. Mankova a,∗ , M.M. Nazarov a , I.K. Sakodynskaya b and A.P. Shkurinov a a b

Faculty of Physics and International Laser Center, Moscow State University, Moscow, Russia Faculty of Chemistry, Moscow State University, Moscow, Russia

Abstract. The activity of enzymes in organic solvents substantially increases in the presence of crown ethers. Tris(hydroxymethyl)aminomethane (tris) is chosen as a model compound to simulate the interaction of surface amino groups of protein with crown ether. The methods of FTIR and time-domain THz spectroscopy are used to study the interaction of tris with 18-crown-6. The THz spectra of the complexes are measured for the first time. Keywords: THz spectroscopy, FTIR spectroscopy, tris, protein amino groups

1. Introduction

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The analysis of the protein structure is an important problem in the study of biological molecules, since the functional activity of protein is related to structural changes. It is known that the enzymatic activity in organic solvents (e.g., of chymotrypsin) sharply increases in the presence of crown ethers [2,5]. One of the possible reasons is the interaction of crown-ether molecules with the surface amino groups of protein. Note that the vibrational bands of the amino groups are strongly overlapped with the protein amide bands and the methods of IR and Raman spectroscopy cannot be directly employed in the corresponding study. However, the spectroscopic methods can be used to investigate the interaction of amino groups with crown ethers in model chemical systems. One of the model compounds is tris(hydroxymethyl)aminomethane (tris) (HOCH2 )3 C–NH2 [3], and 18-crown-6 (C12 H24 O6 ) is the crown ether in our experiments. The purpose of this work is the detection of the spectral changes in the IR and THz spectra due to the interaction of amino group with crown ether. 2. Experimental We employ tris and 18-crown-6 from REAHIM. Tris and its complexes with 18-crown-6 at several relative concentrations are prepared as powders using the 20-h-long lyophilization of solutions with different pH. Hydrochloric acid is used for the adjustment of pH (pH 10, pH 8.3, pH 7.8, pH 3). The *

Corresponding author: A.A. Mankova, Faculty of Physics and International Laser Center, Moscow State University, Moscow, 119991, Russia. E-mail: [email protected].

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samples represent pressed tablets with a diameter of 5 mm and a thickness of about 300 μm. For the measurement of the IR spectra, we employ a Nicolet 6700 FTIR spectrometer interfaced with a Smart Orbit ATR unit. The THz setup is described in [4].

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3. Results and discussion In the wavenumber interval 1500–1700 cm−1 , the IR spectrum of tris at pH 10 exhibits a single band peaked at about 1586 cm−1 , whereas the spectrum of tris at pH 3 exhibits two bands peaked at 1550 and 1628 cm−1 . The three bands are detected in the spectra of tris at intermediate pH (pH 7.8 and pH 8.3). The IR spectrum of the complex of tris with 18-crown-6 at a relative molar concentration of 1:1 and pH 10 can be well fitted using the spectra of components and polynomial background [1]. The measured spectrum of the complex and the fitting curve are very close to each other except for minor spectral differences in the interval 1050–1200 cm−1 . However, the spectrum of the same complex at pH 3 significantly differs from the sum of the spectra of components in the fingerprint range. Note that the two bands of the protonated tris (pH 3) peaked at 1550 and 1628 cm−1 are changed by a single band peaked at 1587 cm−1 with a shoulder at 1600 cm−1 . These results indicate that the protonated tris is involved in significantly stronger interaction with 18-crown-6. Figure 1(a) demonstrates the THz spectra of tris at several pH. It is seen that the spectrum of the protonated tris (pH 3) substantially differs from the remaining spectra, since it does not exhibit developed bands peaked at 1.51, 1.73, 2.11 and 2.30 THz. Figure 1(b) shows the spectra of tris at pH 10, 18-crown-6 and the complexes at relative molar concentrations (tris/crown) of 1:1 and 1:10 and pH 10. Note that the spectra of the complexes at relative molar concentrations of 1:2 and 1:5 (not shown) slightly differ from the spectrum of the complex at 1:10. The complex formation leads to significantly lower intensities of the bands peaked at 1.52, 1.75 and 2.30 THz. When the relative molar concentration of 18-crown-6 increases to 10, the above bands of the pure tris almost vanish and the broad band at 1.65 THz can be assigned to the free 18-crown-6. Nevertheless, the band peaked at 2.11 THz survives in the spectra of complexes. Figure 1(c) demonstrates the spectra of tris at pH 3, 18-crown-6, and the complexes at relative molar concentrations of 1:1 and 1:10. As distinct from the results at pH 10, the spectra of the complexes at intermediate relative concentrations of 1:2 and 1:5 (not shown) differ from the spectra at 1:1 and 1:10. Note also significant spectral changes due to the interaction with the crown ether. The strongest spectral changes correspond to the transition from the spectrum of the protonated tris to the spectrum of the equimolar complex. The latter exhibits three developed bands. The first (broadest) band with a width of about 0.4 THz is peaked at about 1.30 THz. Two remaining (narrower) bands are peaked at 2.10 and 2.31 THz. The results indicate structural changes of the protonated tris caused by the interaction with 18-crown-6. It is seen that an increase in the relative concentration of the crown ether leads to a decrease in the intensity of the band peaked at 1.30 THz. Apparently, the band is not assigned to 18-crown-6. We assume that the spectra of complexes with high relative concentrations of the crown ether can be represented as the sums of the spectra of the crown ether and the tris complex with the crown ether at a relative concentration of 1:1. Based on such an assumption, we assume that the broad band can be assigned to the protonated tris that is structurally modified owing to the interaction with the crown ether. Thus, the spectral data indicate two transformations of the protonated tris related to (i) the transition from the unbound molecule to the equimolar complex and (ii) an increase in the relative concentration of the crown ether. Note that the spectra of the complexes of protonated tris at relative concentrations of 1:5 and 1:10 are similar to the spectrum of the equimolar complex of tris at pH 10. This circumstance can be an

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Fig. 1. THz spectra of (a) tris at (solid line) pH 10, (triangles) pH 7.8 and (squares) pH 3; (b) tris-18-crown-6 complexes at pH 10 and relative molar concentrations of (circles) 1:0, (squares) 1:1, (triangles) 1:10, and (solid line) 0:1; and (c) tris-18-crown-6 complexes at pH 3 and relative molar concentrations of (circles) 1:0, (squares) 1:1, (triangles) 1:10 and (solid line) 0:1.

indication of the deprotonation of the protonated tris in the complexes with high relative concentrations of the crown ether.

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References [1] N.N. Brandt, O.O. Brovko, A.Yu. Chikishev and O.D. Paraschuk, Optimization of the rolling-circle filter, Appl. Spectrosc. 60 (2006), 288–293. [2] J. Broos, I.K. Sakodinskaya, J.F.J. Engberson, W. Verboom and D.N. Reinhoudt, J. Chem. Soc. Chem. Commun. No. 2 (1995), 255–256. [3] K. Griebenow and A.M. Klibanov, Biotechnol. Bioeng. 53 (1997), 351–362. [4] M.M. Nazarov, A.P. Shkurinov and V.V. Tuchin, Tooth study by terahertz time-domain spectroscopy, Proc. SPIE Int. Soc. Opt. Eng. 6791 (2008), 679109-1–679109-9. [5] D.J. Van Unen, J.F.J. Engberson and D.N. Reinhoudt, Biotechnol. Bioeng. 59 (1998), 553–556.

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Spectroscopic determination of pKa constants of MADS box segments ˇ cová a,b,∗ , Yves-Marie Coïc c , Christian Zentz b , Pierre-Yves Turpin b and Barbora RezᡠJosef Štˇepánek a a

Institute of Physics, Faculty of Mathematics and Physics, Charles University in Prague, Prague, Czech Republic b Laboratoire Acides Nucléiques et Biophotonique, Université Pierre et Marie Curie, Paris, France c Unité de Chimie des Biomolécules, Institut Pasteur, Paris, France Abstract. We have introduced a new promising approach for the determination of pKa constants of oligopeptide intrinsic fluorophores and spectral components referring to their differently charged states. The method is based on the factor analysis of multiwavelength spectroscopic pH titration data. As an illustration we present its application on the study of short segments of the MADS box, which is a highly conserved sequence of a so-called family of transcription factors, by techniques of UV absorption and fluorescence spectroscopies. Investigated oligopeptides contain no tryptophan but one tyrosine serving as an intrinsic fluorophore and absorber. The results indicate both good sensitivity and spectroscopic selectivity of our method, which thus may be considered as a complementary technique to conventional electrochemical methods. Keywords: Factor analysis, dissociation constant, fluorescence spectroscopy, MADS box, tyrosine

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1. Introduction Proteins are biological molecules that usually contain amino acids with both acidic and basic functional groups. These amino acids can be divided into several categories according to their residue charge, polarity and hydrophobicity. Titratable amino acids of the protein may be completely exposed to solution or buried inside the 3D structure of the protein. Their pKa constants are thus influenced by protein folding and may be sensitive to protein oligomerization, binding of ligand, denaturation, etc. Determination of pKa constants of proteins is usually done by electrochemical techniques (e.g., polarimetric, potentiometric and voltammetric titration). Direct techniques such as NMR can be used on small peptides to monitor their ionization state. There are also several methods of computational biology, molecular modelling and structural bioinformatics, which can be used to estimate roughly the pKa values of titratable amino acids within proteins. *

ˇ cová, Institute of Physics, Faculty of Mathematics and Physics, Charles University in Corresponding author: Barbora RezᡠPrague, Ke Karlovu 5, CZ-12116 Prague 2, Czech Republic and Laboratoire Acides Nucléiques et Biophotonique, FRE 3207, Université Pierre et Marie Curie, place Jussieu 4, 75252 Paris Cedex 5, France. Tel.: +420 221 911 346; Fax: +420 224 922 797; E-mail: [email protected].

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In this work we present a highly sensitive method of pKa determination (complementary to the methods mentioned previously), based on a factor analysis of spectroscopic data, obtained namely from UVvisible absorption and fluorescence measurements. Analogous methodology can though be applied to any other kind of spectroscopic data. It should be mentioned that factor analysis has already been used to determine pKa values of small organic molecules [7,15], however, it has not been used to study peptides so far (to our current knowledge). The MADS box family of transcription factors counts over 200 members playing a crucial role in the gene regulation of higher organisms. The MADS box acronym is derived from initials of four of the originally identified members of the family: MCM1, AG, DEFA and SRF [13]. These transcription factors share a highly conserved DNA binding motif showing a wide sequence homology of 56 amino acids. Still little is known about how targeted DNAs are recognized by these transcription factors. Available structural data reveal that MADS box protein–DNA complexes have a high structure homology, but with differences in the DNA binding process [2,3,8,9,11,16]. Core SRF contains its own intrinsic fluorophores: three tyrosines with different amino-acidic environments. In order to distinguish between these tyrosines and to see the effect of diverse environments, the pH dependency measurements of two small segments of SRF, each containing one tyrosine, were performed by using UV absorption and fluorescence spectroscopies: 154 KLLRYTTFS162 and 168 IMKKAYEL175 . 2. Materials and methods 2.1. Materials Synthesis of octamer oligopeptide of sequence Ac-IMKKAYEL-amid, average Mw = 1036.30, was carried out according to the Fmoc/tBu solid-phase strategy [1] on a 433A peptide synthesizer (Applied Biosystems, Forster City, CA, USA), using a Fmoc Amide Resin (Applied Biosystems). Mass characterization of the purified peptide (above 98% by RP-HPLC) was consistent with the expected value. HPLC 98% purified oligopeptide Ac-KLRRYTTFS-amid, Mw = 1212.43 was purchased from Apigenex (Prague, Czech Republic). Purity and mass weight were tested by HPLC and MS. Spectroscopically pure KOH and HCl were obtained from Normapur and MERCK.

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2.2. Optical absorbance and fluorescence measurements Optical absorbance spectra were recorded on a Cary 3E UV-visible spectrophotometer. Fluorescence measurements were performed on a SLM Aminco-Bowman series 2 luminescence spectrometer. The excitation and emission spectral bandwidths used were 4 nm. The optical path length was 1 cm. Upon excitation at 276 nm, both tyrosine and tyrosinate contribute to the fluorescence emission. All fluorescence spectra were corrected for a minor inner filter effect (optical density being bellow 0.1) [4] and contributions of Rayleigh and Raman light scattering were subtracted. Spectra were also corrected (slight intensity renormalization) for the small changes of concentration of oligopeptides resulting from the addition of HCl and/or KOH. Fluorescence and absorption spectra of oligopeptides were measured in the pH range from 3 to 11 at room temperature (20◦ C). Concentration of the oligopeptide was 8 × 10−5 M. Addition of HCl or KOH caused no precipitation. Measurements of pH were made on a IQ Scientific Instruments pH meter IQ170G equipped with a stainless steel pH probe.

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2.3. Spectra analysis To find out the individual components forming the resulting fluorescence or absorption spectra, we submitted the set of spectra Fi (λ) obtained at various pH conditions to factor analysis, i.e. singular value decomposition (SVD) algorithm decomposing a set of N spectra into a set of orthogonal normalized spectral profiles Sj (λ), j = 1, 2, . . . , N , Fi (λ) =

M

Vij Wj Sj (λ),

(1)

j=1

where the normalized coefficients Vij quote the relative portions of the jth spectral profile Sj (λ) in the original spectra Fi (λ) [6]. The singular numbers Wj stand for the statistical weights of spectral profiles. As the spectral components are ordered to reach a descending succession of singular values, usually a few first terms at the right side are sufficient to approximate the original spectral set within an experimental error. This value of M is referred to as factor dimension. It represents the minimal number of independent components resolved in the analyzed spectral set. In case of M equal to two (which is actually the case in the present paper), the spectral components attributable to the two different components A and B in the spectra (from either absorption or fluorescence) can be extracted, so that Fi (λ) = F A (λ) ∗ CiA + F B (λ) ∗ CiB ,

(2)

where F A (λ) and F B (λ) are the spectral components forming the original spectra proportionally to their current concentrations CiA and CiB . These are given by the current pH value as CiA =

1 , 1 + 10pH−pKa

CiB = CiA ∗ 10pH−pKa ,

(3)

where pKa is the dissociation constant of the chromophore (here tyrosine). Thanks to the factor dimension of two, both F A (λ) and F B (λ) components can be well approximated as linear combinations (with coefficients rjA and rjB ) of the first and the second SVD spectral profiles F A (λ) =

2

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j=1

rjA ∗ Sj (λ),

F B (λ) =

2

j=1

rjB ∗ Sj (λ).

(4)

Considering the above mentioned basic SVD formula for Fi (λ) and orthogonality of SVD spectral components we obtained a final set of 2×N equations 



1 1 Wj Vij = rj ∗ + rjB ∗ 1 − , pH − pK a 1 + 10 1 + 10pH−pKa A

i = 1, 2, . . . , N , j = 1, 2.

(5)

The five unknown parameters (pKa , rjA , rjB ; j = 1, 2) can be solved by means of a simultaneous nonlinear least-square fit of the right sides in Eq. (5) to the left ones.

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3. Results and discussion 3.1. Absorption spectra In the measured region 220–330 nm both oligopeptides show a characteristic peak at 275 nm typical for tyrosine. Noticeable changes were observed by increasing the pH: the absorption band moved to longer wavelength at 294 nm, with a 1.5-fold increase of intensity (data not shown). 3.2. Fluorescence spectra

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In the measured region 290–390 nm both oligopeptides have a characteristic peak at 303 nm typical for tyrosine. With an increase of the pH, the emission significantly decreases thanks to deprotonation of tyrosine to tyrosinate (see Fig. 1A), which has a much lower fluorescence quantum yield [10] and its emission is shifted to higher wavelengths at about 345 nm [14].

Fig. 1. Fluorescence pH dependency of the MADS box segment Ac-IMKKAYEL-amid (A), determined fractions (B), normalized emission spectra of the two components – normalization factor NA = 0.003, NB = 0.201 (C) and factor analysis results (D).

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3.3. Analysis of pH dependent fluorescence spectra Results of factor analysis applied on fluorescence spectra of oligopeptide Ac-IMKKAYEL-amid are shown in Fig. 1D. We obtained a factor dimension of 2 (the same as for absorption spectra). The first spectral profile S1 represents an average of measured spectra and the 1st coefficients V1 thus describe the decrease of the overall spectral intensity as a function of pH. The second spectral profile S2 , which is about 100 times less significant than the first one, accounts for the main spectral changes. Contribution of this profile is about zero for spectra below pH 7, while above this pH its value increases, as can be seen from the behaviour of the 2nd coefficients V2 . By using our method, pKa constants were determined for both fluorescence and absorption data with a good agreement for both oligopeptides. From the oligopeptide Ac-IMKKAYEL-amid absorption we obtained pKa = 9.45 ± 0.05, from its fluorescence spectra pKa = 9.43 ± 0.06. For oligopeptide AcKLRRYTTFS-amid absorption we obtained pKa = 9.01 ± 0.05 and using fluorescence spectra, we calculated pKa = 9.03 ± 0.06. This is in good agreement with previous determinations of tyrosine pKa in various oligopeptides [12,17]. In correspondence with our assumptions we observed that the positively charged amino acids (R, Arginines; K, Lysines) do shift the dissociation constant of the free state tyrosine to lower values (the dissociation constant of free tyrosine is 10.07 [5,12]). Moreover, using this method we were able to distinguish the two emission components (see Fig. 1C) and their respective fractions (Fig. 1B). Component A represents the tyrosine emission spectrum (maximum at 303 nm) whereas component B represents the emission of tyrosinate (maximum near 345 nm), which is about 60 times weaker. 4. Conclusions Using the factor analysis on absorption and fluorescence monitored titration we were able to determine the pKa constants of two tyrosine containing oligopeptides with a high precision as well as absorption and fluorescence spectral profiles corresponding to the differently charged forms. Furthermore, we obtained quantitative information about the effect of a specific environment on pKa of tyrosine, which will help us to better distinguish between the different tyrosines in MADSSRF . The presented methodology is applicable to any oligopeptide bearing at least one pH dependent photosensitive amino acid or probe, and in general can be applied to a vast number of spectroscopic techniques reflecting protonation changes in peptides. Further study on larger oligopeptide segments of SRF will be performed.

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Acknowledgements This work is supported by the Czech Science Foundation (project 202/09/0193) and Grant Agency of Charles University (project 402111). B.R. gratefully acknowledges the French Government support for her stay in Laboratoire Acides Nucléiques et Biophotonique. References [1] W.C. Chan and P.D. White, Fmoc Solid Phase Peptide Synthesis: A Practical Approach, Oxford Univ. Press, London, 2000. [2] M. Hassler and T.J. Richmond, EMBO J. 20 (2001), 3018–3028.

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[3] K. Huang, J.M. Louis, L. Donaldson, F.L. Lim, A.D. Sharrocks and G.M. Clore, EMBO J. 19 (2000), 2615–2628. [4] J.R. Lakowicz, Principles of Fluorescence Spectroscopy, 3rd edn, Springer, Singapore, 2006. [5] A.L. Lehninger, D.L. Nelson and M.M. Cox, Lehninger Principles of Biochemistry, 4th edn, W.H. Freeman, New York, 2004. [6] E.R. Malinowski, Factor Analysis in Chemistry, 2nd edn, Wiley, New York, 1991. [7] M. Meloun, Z. Ferencikova and A. Vrana, Cent. Eur. J. Chem. 8 (2010), 494–507. [8] Y. Mo, W. Ho, K. Johnston and R. Marmorstein, J. Mol. Biol. 314 (2001), 495–506. [9] L. Pellegrini, S. Tan and T.J. Richmond, Nature 376 (1995), 490–498. [10] J.B.A. Ross, W.R. Laws, K.W. Rousslang and H.R. Wyssbrod, in: Biochemical Applications, J.R. Lakowicz, ed., Topics in Fluorescence Spectroscopy, Vol. 3, Kluwer Academic, New York, 2002, pp. 1–63. [11] E. Santelli and T.J. Richmond, J. Mol. Biol. 297 (2000), 437–449. [12] M. Sela and E. Katchalski, J. Am. Chem. Soc. 78 (1956), 3986–3989. [13] P. Shore and A.D. Sharrocks, Eur. J. Biochem. 229 (1995), 1–13. [14] A.G. Szabo, K.R. Lynn, D.T. Krajcarski and D.M. Rayner, FEBS Lett. 94 (1978), 249–252. [15] K.Y. Tam and K. Takacs-Novak, Pharm. Res. 16 (1999), 374–381. [16] S. Tan and T.J. Richmond, Nature 391 (1998), 660–666. [17] R.L. Thurlkill, G.R. Grimsley, J.M. Scholtz and C.N. Pace, Protein Sci. 15 (2006), 1214–1218.

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The effect of bacterial adhesion on grafted chains revealed by the non-invasive sum frequency generation (SFG) spectroscopy Emilie Bulard a,∗ , Marie-Pierre Fontaine-Aupart a , Henri Dubost a , Wanquan Zheng a , Jean-Marie Herry b , Marie-Noëlle Bellon-Fontaine b , Romain Briandet b and Bernard Bourguignon a a b

Institut des Sciences Moléculaires d’Orsay, ISMO-CNRS, Université Paris Sud 11, Orsay, France INRA AgroParisTech, UMR, Micalis, Massy, France

Abstract. In biomedical and food industry, surface colonization by bacteria is harmful: it leads to biofilm formation, a microbial consortia more resistant to antibiotics than planktonic bacteria. In order to design materials able to limit the biofilm formation, the effect of bacteria on materials has to be well characterized. In this work, a well-defined surface composed of a self-assembled monolayer (SAM) of OctaDecaneThiol (ODT) onto a gold surface is probed in situ. The SAM conformation is obtained using the femtosecond vibrational sum frequency generation (SFG) spectroscopy. This technique provides selectively the molecular vibrational signature of the interface. The behaviour of the ODT SAM is studied in different environments: in air, in water and upon exposure to hydrophilic or hydrophobic Lactococcus lactis bacteria. Modelling the experimental SFG spectra reveals a measurable change of the SAM conformation which depends on the environment, especially on the hydrophilic-hydrophobic character. Keywords: Bacterial adhesion, SFG spectroscopy, surface, self-assembled monolayers, molecular conformation

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1. Introduction All surfaces are exposed to living entities such as bacteria in natural environments. The initial colonization of the surface by pathogenic bacteria is of concern in many fields such as biomedical or food industry [12,18]. Indeed it evolves into the formation of a biofilm, a three dimensional bacterial organization where bacteria are more resistant to antibiotics and biocides than planktonic counterparts, leading to nosocomial or food born infections. Therefore, the scientific community is mobilized to understand the mechanisms involved in the bioadhesive process in order to inhibit and/or control it and find new preventive routes for biofilms control. Bacteria adhere to an inert surface through non-covalent molecular interactions such as van der Waals, electrostatic, Lewis acid/base interactions, depending on the characteristics of the bacteria (peptidoglycan nature, cell wall proteins and polysaccharides, extracellular appendices as pili, etc.) and the physicochemical properties of the surface [13,15]. Experiments have been carried out to understand *

Corresponding author: Emilie Bulard, Institut des Sciences Moléculaires d’Orsay, Université Paris Sud 11, France. Tel.: +331 69 15 82 62; Fax: +331 69 15 75 30; E-mail: [email protected].

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bacterial adhesion on functionalized polymers or grafted chains, by changing the topography, the hydrophobic/hydrophilic character of the surface and so forth [2,17]. However, divergences appear between predictive models and experimental results [7,14], likely due to the lack of non-invasive surface characterization at the molecular level when bacteria are on the surface. The aim of the present study is to characterize the initial event of the bacterial colonization with a method able to provide information on the surface at the molecular level and in situ. The vibrational sum frequency generation (SFG) spectroscopy is employed here for the first time to probe a specific surface: a self-assembled monolayer (SAM) of OctaDecaneThiol (ODT) onto a gold surface colonized by bacteria. This technique based on second order non-linear optics, consists in overlapping in time and in space an infrared (tuned in this case to C–H band wavelengths) and a visible beam. The main interest of this spectroscopy is that the SFG response originates only from non-centro-symmetric regions (in general, the bulk of solids does not contribute to the SFG signal) allowing to acquire specifically the molecular spectral signature of the surface which can be related to its molecular conformation. SFG spectroscopy appears as a non-destructive method, well adapted to study selectively interfaces of biomolecular systems [10,11,16] due to its high sensitivity. Previous SFG studies have described the conformation of ODT SAM exposed to air [3]. In this work, the conformation of ODT SAM is determined when this surface is exposed to pure water and to an aqueous solution of hydrophilic or hydrophobic Lactococcus lactis cells (non-pathogenic models for Streptococcus agalactie responsible of harsh neonatal infections). It is shown that the spectral signature of the SAM in contact with bacteria can be detected using femtosecond IR-visible sum frequency generation spectroscopy. Moreover, modelling the experimental SFG spectra reveals a measurable change of the SAM conformation depending on the hydrophobic– hydrophilic character of the environment. These results show that bacteria are able to modify their support at the molecular level [4]. Therefore, this result should be taken into account for the design of new biomaterials. 2. Materials and methods

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2.1. Material Substrates consisted of borosilicate glass coated with a polycrystalline gold film of 250 nm thickness and annealed in an oven at 600◦ C during 30 s. A self-assembled monolayer of octadecanethiol was created on the gold-coated substrate employing the following procedure: the coated surface was dipped in a 1 mM ODT solution in absolute ethanol during 3 h, rinsed in absolute ethanol and dried under nitrogen flow. As described previously [3], we obtained in these conditions an ODT SAM thickness of ∼2 nm. Surface topography of these substrates was characterized by atomic force microscopy (AFM) in contact mode [4]. The gold surface topography presents plantens of 1–2 μm2 delimitated by grooves of 20–30 nm depth. Moreover, water contact angle measurements were performed: the ODT SAM presents a contact angle of 109 ± 2◦ on all its surface so a strong hydrophobic character. 2.2. Lactococcus lactis bacteria Bacteria used for the adhesion on ODT SAMs were L. lactis ssp. cremoris strains MG1363 and its derivative mutant PRTP+ expressing the PrtP protease [6,9]. Bacteria were stored at −20◦ C in M17 broth (Difco) containing 0.5% (w, vol) of glucose and 50% (vol/vol) of glycerol. They were subcultured

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twice in M17-glucose broth at 30◦ C, until stationary phase was reached. Finally, they were cultivated overnight (working cultures) at 30◦ C. 90 ml of bacteria from final working cultures were harvested by centrifugation (10 min, 7000g, 4◦ C), washed twice and resuspended in 25 ml of distilled water at a final cell density of approximately 4.109 cells/ml. The microbial adhesion to solvents (MATS) method was employed for the evaluation of the hydrophobic/hydrophilic character of the cell surface of the two strains. The wild type L. lactis strain MG1363 presented a weak affinity (7 ± 5%) to both hydrophobic decane and hexadecane indicating a hydrophilic cell wall, whereas cells from the strains MG1363 PRTP+ presented a strong affinity of (90 ± 5%) indicating their strong hydrophobic character. 2.3. Bacterial adhesion To investigate the ODT SAM response to bacterial adhesion, a volume of ∼200 μl of the bacterial suspension at 109 cells/ml in distilled water was deposited over the ODT SAM. The solution was allowed to incubate for 90 min and then washed with the suspending fluid to remove the non-adherent bacteria. For SFG measurements, a CaF2 plate was added to ensure a uniform thin water layer above the surface sample. Its thickness was ∼5 μm, which is sufficiently thin to allow infrared radiation to reach the sample. To control in situ the bacterial surface coverage on the ODT SAM, Scanning Electron Microscopy (SEM) (described previously [4]) and epifluorescence microscopy measurements were performed. For epifluorescence images, adherent bacteria were stained with the nucleic acid dye acridin orange (0.01% in water) for 15 min in the dark. The dye solution was washed and replaced by pure water before mounting the sample under a Leica DM2 microscope equipped with an Olympus Camedia C5060WZ digital camera. Both microscopies show that our procedure ensures a homogeneous bacterial deposit. The bacterial surface coverage was estimated to (50 ± 20%) of the ODT SAM surface, corresponding to 2200 ODT molecules across the diameter of each bacterium. Morphology of adherent bacteria on ODT SAM was also analyzed by SEM. Lactococcus lactis cells maintain their characteristic ovococcoid morphology after adhesion on the functionalized gold surface. There is no evidence in our collection of SEM images that bacteria are preferentially localized on the grooves of the film. Moreover, no extracellular matrix is present on the surface demonstrating that experimental conditions allow us to stay in the first bacterial colonization step.

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2.4. SFG technique Details about our broad band SFG setup can be found in our previously published work [3]. Tunable IR pulses (4 μJ, 145 fs and 150 cm−1 bandwidth) and “visible” pulses (800 nm, 2 μJ, adjustable duration and bandwidth 1–6 ps ≡ 15–2.5 cm−1 ) are superimposed on the sample in a collinear co-propagating configuration at the incident angle of ∼66◦ in p polarization. In this experimental geometry, the laser spot size on the sample surface is ∼100 μm. The generated SFG signals are collected during 100– 300 s to obtain an acceptable signal to noise ratio and analyzed by a high-resolution detection system (a spectrometer of resolution 0.4 cm−1 at 650 nm, equipped with a cooled CCD camera). The vibrational bands are superimposed to a so-called non-resonant background which has the spectral profile of the IR laser and arises from the broad resonant response of the gold surface electronic states. In order to

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deconvolute the vibrational bands from this non-resonant background, experimental spectra are fitted to the standard formula:  2

  Aν iϕ   , I(ωSFG ) ∝ g(ωIR )χNR · e +  ω − ω + iΓ IR ν ν

(1)

where g(ωIR ) is the IR laser spectral profile recorded on a GaAs reference sample which provides only a non-resonant SFG signal. The term χNR · eiϕ is the Au constant non-resonant response with phase ϕ and Aν , ων and Γ are the Lorentzian amplitude, frequency and half width of mode ν, respectively. Γ is related to the decay of vibrational energy to the surface and it is supposed to be the same for all CH modes. The reference GaAs spectrum is measured in air. Therefore the IR laser profile g(ωIR ) must be corrected from absorption by the water layer that surrounds the bacteria. In our fitting procedure, the well-known CH stretch frequencies are fixed and the width of each stretching mode is the same for all bands. The value of the phase in the different media is also fixed and corresponds to an average obtained over ∼50 experiments. This limitation of the number of fitted parameters ensures that we cannot obtain multiple solutions [5]. We only admit a phase change from air to water, because the first water layers may affect electrostatically the Au substrate.

3. Results 3.1. ODT SAM in air The SFG spectrum in the wavenumber range 2800–3050 cm−1 of the ODT SAM has been reported several times in the literature (spectrum not shown). We have analysed it in details [4] and extracted the two ODT molecular conformations involved in the SAM. The spectrum consists mainly of the three strong bands of the methyl group: the methyl symmetric stretch band at 2875 cm−1 , the Fermi resonance of the CH3 symmetric mode with two quantas of the CH3 bending mode at 2936 cm−1 and the methyl asymmetric stretch band at 2964 cm−1 . The regular CH2 band intensities are 2850 cm−1 (symmetric stretching mode) and 2918 cm−1 (asymmetric one). The –CH2 –S-band frequencies are shifted to higher frequencies (2905 and 2973 cm−1 ) and very weak.

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3.2. ODT SAM in distilled water The SFG spectrum of ODT SAM in distilled water is displayed in Fig. 1A. Figure 1B shows the deconvoluted vibrational bands of the CH3 and CH2 groups according to Eq. (1). The methyl band intensities are different in water and in air: the relative intensity of the CH3 asymmetric mode is smaller in water than in air. In order to quantify the intensity changes of the ODT SAM methyl bands, we have defined the phenomenological parameter R equal to the intensity ratio between the CH3 symmetric and the CH3 asymmetric vibrations: R = (Isym + IFermi )/Iasym where the intensity of vibration i is equal to Ii = (Ai /Γ)2 . The parameter R measured for ODT in air is R = 3.7 ± 0.5 as compared to R = 5.8 ± 0.5 when the ODT SAM is exposed to water.

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Fig. 1. (A) Experimental (dots) and fitted (bright thick lines) SFG spectrum of an ODT SAM in distilled water. The SFG reference spectrum (dashed line) is also presented. (B) Deconvoluted vibrational bands of an ODT SAM in distilled water (lines), in the presence of hydrophobic L. lactis bacteria PRTP+ (dashed lines) and in the presence of hydrophilic L. lactis bacteria (bright thick lines) in distilled water after 90 minutes of adhesion.

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3.3. ODT SAM in contact with hydrophilic and hydrophobic bacteria We have investigated the effect of bacterial adhesion onto the ODT SAM. For hydrophobic and hydrophilic bacteria, the pattern of the ODT SAM SFG spectra changes compared to that in water (spectra not shown). For both strains, the experimental spectra are fitted to our model as a function of bacterial coverage ranging from 0 to 100%. The best fits are obtained for a bacterial coverage of ∼60% which is in good agreement with coverage estimates derived from fluorescence and SEM images. The relative intensities of vibrational modes and thus the R values differ significantly from those found in water (Fig. 1B). Moreover, the R value depends on the hydrophilic (R = 8.8 ± 0.5)/hydrophobic (R = 3.8 ± 0.5) character of the bacteria. 4. Discussion The conformational changes of ODT molecules in the SAM can be extracted from the experimental relative intensities. Owing the fact that interactions between the SAM, water and bacteria are weak,

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we assume that molecules only adjust by rotation around the axis of their carbon chain. Such a rotation changes the orientation of the methyl groups, which is easily detected by SFG. The intensity of a vibrational mode depends on the orientation of its dipole moment with respect to the surface normal. On metals only the projection of the transition moment along the surface normal contributes significantly to SFG. Therefore, the CH3 symmetric stretch is strong when the methyl group symmetry axis is perpendicular to the surface. By contrast, the transition moments of the doubly degenerate asymmetric stretching modes are perpendicular to the CH3 symmetry axis and are therefore minimal when this axis is perpendicular to the surface. It results that the value of R increases when the methyl terminal part of the ODT SAM raises. To evaluate quantitatively the methyl orientation of ODT, SFG spectrum modelling of the ODT chains has been performed according to the model described by Bourguignon et al. [3]. The model can calculate the SFG spectrum of adsorbed alkanethiol molecules for any conformation of the alkyl chain. It is based on ab initio calculations of the molecular hyperpolarizability tensors of the modes of individual methyl and methylene groups, and it takes into account the molecular conformation, the geometrical and optical parameters such as incidence angles, polarization of the beams and optical indexes. Moreover, it was established that the ODT SAM structure involves two types of molecules, subscripted A and B, which differ by the rotation of their C plane about their average molecular axis. Taking into account the data in the SFG spectrum modelling, the methyl axis tilt angle of the ODT SAM is obtained (Fig. 2). When exposed to air, the methyl axis tilt angle is 55.1/15.8◦ for molecules A and B, respectively (Fig. 2A). Methyl SFG intensities happen to be extremely sensitive to the methyl axis tilt angle: a change of 5◦ results in the doubling of the R parameter. We expect therefore that SFG will allow probing conformational changes of the SAM even in the case where they are due to weak interactions. When exposed to distilled water, the tilt of the CH3 axis decreases slightly to the value of 1.7 and 2.5◦ for molecules A and B with respect to air, respectively: the aqueous environment has a “brush effect” on ODT (Fig. 2B).

Fig. 2. Schematic ODT SAM conformations (the effect of ODT environment is strongly exaggerated for clarity). A: in air. B: in distilled water. C and D show the two extreme possibilities of methyl orientation adjustment allowed by the rotation of the C backbone planes about the average molecular axis. C and D correspond ideally to hydrophilic and hydrophobic interactions, respectively. The directions of the dipole moments of CH3 (symmetric μs3 , asymmetric in plane μas3ip and out of plane μas3op ) are also indicated. Values of methyl axis tilt angle of the ODT SAM in different environments are added below.

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In the case of bacterial adhesion, the spectral changes and so the R value changes could result from a change of the ODT SAM conformation, and/or from an additional signal from the bacteria themselves. We have recorded the SFG spectrum after bacterial adhesion on a bare gold surface (spectrum not shown): only a non-resonant signal is obtained, indicating that bacteria do not contribute to the SFG spectrum and that the observed relative intensity changes result only from a change of the ODT SAM conformation. In the case of hydrophilic bacterial adhesion onto the substrate, the “brush effect” is enhanced with respect to water (Fig. 2C). The methyl tilt angles decreases accordingly by 3.6◦ and 4.4◦ for A and B molecules in comparison to the ODT SAM conformation in water. On the contrary, the effect of hydrophobic bacteria is to decrease the R value and so to flatten the ODT SAM: the methyl tilt angle increases of 3.3◦ and 4.9◦ for A and B molecules with respect to water (Fig. 2D). These different behaviours can be rationalized by considerations of the hydrophobic/hydrophilic and hydrophobic/hydrophobic interactions [8]. The ODT SAM, composed of CH2 and CH3 groups, is a hydrophobic surface. When the ODT SAM is in contact with water molecules which are polar and hydrophilic, the system tends to remain separated in two phases, which is best achieved when the methyl terminal groups of the surface point up. This minimizes the interaction between the two phases, which maximizes the hydrogen bonding network inside the solvent and leads to a formation of on ordered water layer on the top of the ODT SAM. In the case of hydrophobic Lactococcus lactis adhesion, only van der Waals interactions are present and there is no phase separation. Interactions are maximized when the ODT SAM is flattened because methylene group near the methyl terminal part can also be in interaction with bacteria and contributes to the attractive interactions. For hydrophilic bacteria adhesion, we expect a behaviour similar to water: it is indeed the case. Interestingly, the brush effect is larger for bacteria than for water. However, the complexity of the bacterial membrane is such that it is not possible to propose a simple explanation to this observation.

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5. Conclusion The present results demonstrate that the effect of bacterial adhesion can be observed by SFG spectroscopy and that bacteria alter the conformation of the surface, here an ODT SAM on a gold surface. We note the high sensitivity of SFG to molecular conformation: a small variation of the methyl tilt angle produces a measurable change of the SFG spectrum. The effect of water or bacteria consists of only a small adjustment of angles inside the SAM, which is found to depend on the hydrophobic or hydrophilic character of the medium interacting with the SAM. This demonstrates that the effect of bacterial adhesion depends primarily in the case of ODT SAM and Lactococcus bacteria, on bacterial physico-chemical properties. It also implies that engineering a functionalized polymer or other surface layer to prevent the surface from being colonized [7,14] must take into account the fact that bacteria might change the conformation of the protective layer. This phenomenon could explain the differences observed between predictive models and experimental adhesion measurements. In conclusion, this study shows that a general understanding of the interaction between bacteria and the surface at the molecular level is required to design new materials effective against bacterial contamination. The presence of other organic molecules such as proteins in the surrounding medium or in biofilms might also affect the conformation of the surface and it must be taken into account [1]. This is now under investigation.

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Acknowledgements The authors wish to thank the MIMA2 platform (INRA, Massy, France) for allowing us to use the scanning electron microscope and the CPBM platform of LUMAT (Orsay, France) for the use of their biological infrastructures. References

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[1] Y.H. An and R.J. Friedman, Concise review of mechanisms of bacterial adhesion to biomaterial surfaces, J. Biomed. Mater. Res. (Appl. Biomater.) 43 (1998), 338–348. [2] C.J.P. Boonaert, Y.F. Dufrêne, S.R. Derclaye and P.G. Rouxhet, Adhesion of Lactococcus lactis to model substrata, direct study of the interface. Colloids Surf. B: Biointerf. 22 (2001), 171–182. [3] B. Bourguignon, W. Zheng, S. Carrez, A. Ouvrard, F. Fournier and H. Dubost, Deriving the complete molecular conformation of self-assembled alkanethiol molecules from sum-frequency generation vibrational spectra, Phys. Rev. B 79 (2009), 125433. [4] E. Bulard, Z. Guo, W. Zheng, H. Dubost, M.-P. Fontaine-Aupart, M.-N. Bellon-Fontaine, J.-M. Herry, R. Briandet and B. Bourguignon, Noninvasive vibrational SFG spectroscopy reveals that bacterial adhesion can alter the conformation of grafted “brush” chains on SAM, Langmuir 27 (2011), 4928–4935. [5] B. Busson and A. Tadjeddine, Non-uniqueness of parameters extracted from resonant second-order nonlinear optical spectroscopies, J. Phys. Chem. C 113 (2009), 21895–21902. [6] M.P. Chapot-Chartier, E. Vinogradov, I. Sadovskaya, G. Andre, M.Y. Mistou, P. Trieu-Cuot, S. Furlan, E. Bidnenko, P. Courtin, C. Péchoux, P. Hols, Y.F. Dufrêne and S. Kulakauskas, Cell surface of Lactococcus lactis is covered by a protective polysaccharide pellicle, J. Biol. Chem. 285(14) (2010), 10464–10471. [7] G. Cheng, Z. Zhang, S. Chen, J.D. Bryers and S. Jiang, Inhibition of bacterial adhesion and biofilm formation on zwitterionic surfaces, Biomaterials 28 (2007), 4192–4199. [8] J.-C. Chottard, J.C. Depezay and J.P. Leroux, Chimie fondamentale, études biologiques et médicales, II. Structure moléculaire, Paris, Hermann, 1995. [9] O. Habimana, C. Le Goff, V. Juillard, M.-N. Bellon-Fontaine, G. Buist, S. Kulakauskas and R. Briandet, Positive role of cell wall anchored proteinase PrtP in adhesion of Lactococci, BMC Microbiol. 7 (2007), 36. [10] C. Howell, M.-O. Diesner, M. Grunze and P. Koelsch, Probing the extracellular matrix with the sum-frequency-generation spectroscopy, Langmuir 24 (2008), 13819–13821. [11] T.S. Koffas, J. Kim, C.C. Lawrence and G.A. Somorjai, Detection of immobilized protein on latex microspheres by IRvisible sum frequency generation and scanning force microscopy, Langmuir 19 (2003), 3563–3566. [12] D. Lindsay and A. von Holy, Bacterial biofilms within the clinical setting, what healthcare professionals should know, J. Hosp. Infect. 64 (2006), 313–325. [13] M.R. Nejadnik, H. van der Mei, W. Norde and H.J. Busscher, Bacterial adhesion and growth on a polymer brush-coating, Biomaterials 29 (2008), 4117–4121. [14] P.M. Sivakumar, G. Iyer, L. Natesan and M. Doble, 3 -Hydroxy-4-methoxychalcone as a potential antibacterial coating on polymeric biomaterials, Appl. Surf. Sci. 256 (2010), 6018–6024. [15] G. Speranza, C. Pederzolli, L. Lunelli, R. Canteri, L. Pasquardini, E. Carli, A. Lui, D. Maniglio, M. Brugnara and M. Anderle, Role of chemical interactions in bacterial adhesion to polymer surfaces, Biomaterials 25 (2004), 2029–2037. [16] J. Wang, S.M. Buck and Z.J. Chen, Sum frequency generation vibrational spectroscopy studies on protein adsorption, J. Phys. Chem. B 106 (2002), 11666–11672. [17] K.M. Wiencek and M. Fletcher, Bacteria adhesion to hydroxyl- and methyl-terminated alkanethiol self-assembled monolayers, J. Bacteriol. 177 (1995), 1959–1966. [18] A.C.L. Wong, Biofilms in food processing environments, J. Dairy Sci. 81 (1998), 2765–2770.

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Application of FT-IR spectroscopy for fingerprinting of Zymomonas mobilis respiratory mutants M. Grube, R. Rutkis, M. Gavare ∗ , Z. Lasa, I. Strazdina, N. Galinina and U. Kalnenieks Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia Abstract. Z. mobilis ATCC 29191 and its respiratory knock-out mutants – kat-, ndh-, cytB- and cydB-, were grown under anaerobic and aerobic conditions. FT-IR spectroscopy was used to study the variations of the cell macromolecular composition. Quantitative analysis showed that the concentration ratios – nucleic acids to lipids, for Z. mobilis parent strain, kat-, ndh-, cytBand cydB- strains, clearly distinguished Z. mobilis parent strain from its mutant derivatives, and corresponded fairly well to the expected degree of biochemical similarity between the strains. Two different FT-IR-spectra hierarchical cluster analysis (HCA) methods were created to differentiate Z. mobilis parent strain and respiratory knock-out mutant strains. HCA based on discriminative spectra ranges of carbohydrates, nucleic acids and lipids allowed to evaluate the influence of growth environment (aeration, growth phase) on the macromolecular composition of cells and differentiate the strains. HCA based on IR spectra of inoculums, in a diagnostic region including the characteristic nucleic acid vibration modes, clearly discriminated the strains under study. Thus it was shown that FT-IR spectroscopy can distinguish various alterations of Z. mobilis respiratory metabolism by HCA of biomass spectra. Keywords: FT-IR spectroscopy, Zymomonas mobilis, respiratory mutants, HCA, oxidative stress

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1. Introduction Discrimination between different strains of microorganisms can be based on the whole organism biochemical fingerprinting. For that purpose, Fourier-transform infrared (FT-IR) spectroscopy is one of the methods of choice, proven to be efficient for quantitative analysis of the cell macromolecular composition. FT-IR spectroscopy is a time- and chemicals-saving biophysical method that enables characterization, screening, discrimination and identification of intact microbial cells or cell components. Main advantages of this whole-organism fingerprinting method are small sample amounts, and simple, time-saving sample preparation without chemical pre-treatment, allowing to obtain real-time information about the macromolecular composition of cells. It has been shown that FT-IR spectroscopy has a sufficient resolution power to distinguish between single-gene knock-out mutants in yeast [11], Bacillus subtilis and its gerD and gerA mutants [4], welldefined discrimination of different phenotypes of Staphylococcus aureus in liquid media for diagnostic and research purposes [2]. FT-IR microspectroscopy of leaves was used to develop a rapid method for *

Corresponding author: Marita Gavare, Institute of Microbiology and Biotechnology, University of Latvia, Kronvalda blvd. 4, Riga LV-1010, Latvia. Tel.: +371 28631510; Fax: +371 67034885; E-mail: [email protected].

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screening of mutant plants for a broad range of cell wall phenotypes [3] and to identify different classes of Arabidopsis mutants [9]. Lately FT-IR spectroscopy was used to study the bacterium Enterobacter cloacae and several of its biofilm mutants [5]. For certain reasons, respiratory mutants of the Gram-negative bacterium Zymomonas mobilis might represent special interest for FT-IR analysis and strain fingerprinting. Zymomonas mobilis is a facultatively anaerobic, obligately fermentative bacterium with a highly active ethanol fermentation pathway. At the same time, it possesses aerobic respiratory chain, supporting high oxygen uptake rates. Due to still unknown mechanisms, respiration in this bacterium is poorly coupled to ATP synthesis [7]. Recently, we have constructed a type-II NADH dehydrogenase knock-out strain (ndh-) [8], a knock-out of the cytochrome b subunit of the bc1 complex (cytB-), a knock-out of the subunit II of bd terminal oxidase (cydB-), as well as catalase knock-out (kat-) strain [12]. All these mutant strains are able to grow under aerobic conditions, but have distinct alterations of respiratory metabolism to various degrees. All the mutant strains show clear indications of oxidative stress: relative to the parent type they have 3–4 times upregulated transcription of superoxide dismutase, as measured by quantitative RT-PCR [12]. Apparently, the mechanism of oxidative stress should be different for each strain, as far as the alterations of respiratory metabolism differ. Macromolecular components, analyzed by FT-IR spectroscopy, like cell membrane lipids, proteins and nucleic acids, are the primary molecular targets of reactive oxygen species during oxidative stress [1]. Hence, our aim was to investigate the macromolecular composition of these respiratory mutants by FT-IR spectroscopy, to see if it is possible to discriminate various alterations of respiratory metabolism by hierarchical cluster analysis of biomass spectra.

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2. Materials and methods Z. mobilis ATCC 29191, the type-II NADH dehydrogenase knock-out strain (ndh-) [8], the knockout of the cytochrome b subunit of cytochrome bc1 complex (cytB-), the knock-out of the subunit II of bd terminal oxidase (cydB-), as well as catalase knock-out (kat-) strain [12] were constructed, by disruption of the respective genes with the chloramphenicol-resistance determinant, using homologous recombination [8,12]. Inoculum biomass was grown under microaerophilic conditions in liquid medium with glucose for 24 h. The batch cultures were grown under anaerobic and aerobic conditions in plastic 20 ml test-tubes and in 50 ml glass flasks, respectively, and aerobic cultures were stirred at 200 rpm. Growth medium contained 20 g/l glucose, 5 g/l yeast extract and mineral salts; pH 6 and temperature +30◦ C. Biomass samples were collected at exponential (7 h) and stationary (24 h) growth phases. Cells were washed twice with distilled water and centrifuged. FT-IR analysis was performed using 5– 15 μl of washed cell water suspension poured out by drops on a silicon plate and dried at T < 50◦ C. Absorption spectra were recorded on a HTS-XT microplate reader (Bruker, Germany) over the range 4000–400 cm−1 , with a resolution of 4 cm−1 . Quantitative analysis of carbohydrates, nucleic acids, proteins and lipids in biomass was carried out as in [6]. Data were processed with OPUS 6.5 software. Hierarchical cluster analysis (HCA) was used to create dendrograms from Z. mobilis and its knock-out mutant IS absorption spectra using Ward’s algorithm. 3. Results and discussion FT-IR spectra of Z. mobilis ATCC 29191 parent type and its mutant biomass grown under aerobic or anaerobic conditions were analyzed by quantitative analysis and HCA.

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Comparison of recorded absorption spectra showed changes of band profiles in spectral regions 1140– 1110 and 1665–1645 cm−1 , thus indicating differences in the macromolecular composition of parent and mutant cells. As discriminative bands were shown those at 1183 (P=O; C–O), 1095 (P=O; carbohydrates), 1108, 1515 (protein, NH3 + of α-amino groups, tyrosine), 1521 (NH3 + of side-chain amino groups), 1660 and 1657 cm−1 (Amide I, β-sheet) by changing the shape and/or intensities [10]. The shift between 1515 and 1521 cm−1 specifies the changes in cell protein composition depending on the growth conditions – anaerobic/aerobic, exponential/stationary phase. Quantitative analysis of all samples was done to gain data on the carbohydrate, nucleic acid, protein and lipid concentrations in biomass (data not shown). Variations of concentrations were not wide (2–8% depending on a component) yet well expressed. Analysis of these data showed that the cell macromolecular composition depends on the growth conditions. In all strains lipid concentrations were higher under aerobic growth conditions than anaerobic growth. For example the content of lipids in Z. mobilis parent strain cells was 4% dry weight (DW) and 2% DW under aerobic and anaerobic growth conditions correspondingly. It is known that the increase of lipid content is one of cells responses to stress. The content of total carbohydrates was higher in mutant strain cells and was influenced by growth conditions (+/− oxygen, 7 or 24 h). For example, the carbohydrate concentration in cydB- mutant at exponential phase (after 7 h) under aerobic environment was 21% DW but under anaerobic conditions 17% DW. Data analysis of quantitative results showed that discrimination of parent and mutant strains can be based on nucleic acid and lipid concentrations in inoculum’s cells. As the concentrations of nucleic acids and lipids in parent and mutant inoculums biomasses were in various proportions, their ratio was used for the strain differentiation. Nucleic acid to lipid concentration ratios for Z. mobilis parent strain, kat-, ndh-, cytBand cydB- strains were 6.95, 4.42, 5.33, 5.16 and 5.13 (±0.3) correspondingly. Notably, the values of this ratio clearly distinguished Z. mobilis parent strain from its mutant derivatives, and corresponded fairly well to the expected degree of biochemical similarity between the strains. Thus, the catalase knock-out strain differed from all the respiratory chain mutants. There was more similarity found between cytBand cydB- strains, each with partially disrupted electron transport, than between either of them and ndh-, having near-zero respiration rate. Next step was to create HCA method for differentiation of Z. mobilis parent and knock-out mutant strains. It was established that vector normalized, 2nd-derivative spectra in three spectral ranges 1185– 950, 1483–1360 and 3022–2832 cm−1 , was functional for HCA of all samples at various growth conditions and phases. This dendrogram clearly showed two distinct clusters of aerobically and anaerobically grown strains. All inoculum samples of parent and knock-out mutant strains were clearly discriminated and formed one sub-cluster. These results showed that the biochemical composition of cells is influenced and can be changed by choosing appropriate fermentation conditions. Since the dendrogram of the above mentioned HCA and quantitative analysis data discriminated Z. mobilis parent and knock-out mutant strains even using the spectra of inoculums, another HCA method was created. The second derivative inoculums spectra were analyzed in several regions – 1665– 1645, 1544–1510, 1301–1086, 1220–1174 and 1120–1086 cm−1 to choose the diagnostic peaks or regions for HCA. As characteristic were chosen two regions – 1120–1086 and 1301–1086 cm−1 , and as diagnostic region was selected 1301–1086 cm−1 including the characteristic nucleic acid vibration modes. HCA dendrogram (Fig. 1) clearly shows discrimination between Z. mobilis parent and knockout mutant strains, difference between kat- and respiratory mutants, differences between respiratory mutants and the similarity of cydB- and cytB- mutants. These results are in agreement with quantitative analysis data and estimated from the mutant construction.

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Fig. 1. HCA of Z. mobilis ATCC 29191 and its knock-out mutant inoculums spectra of 3 folds experiment (Ward’s algorithm, vector normalization, 2nd derivative, region 1301–1086 cm−1 ).

4. Conclusions FT-IR quantitative analysis showed variations of the cell macromolecular composition depending on the strain peculiarities and growth conditions. Discrimination of Z. mobilis ATCC 29191 parent strain and its knock-out mutant strains can be based on the ratio of nucleic acid to lipid concentrations. HCA showed to be effective for Z. mobilis ATCC 29191 parent strain and respiratory knock-out mutant strain discrimination on the basis of inoculum IR-spectra and indicated the influence of growth environment (aeration, growth phase) on the macromolecular composition of cells.

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Acknowledgements This work was supported by the project Nr. 2009/0207/1DP/1.1.1.2.0/09/APIA/VIAA/128 Establishment of Latvian interdisciplinary interuniversity scientific group of systems biology, http://www. sysbio.lv. References [1] S.V. Avery, Molecular targets of oxidative stress, Biochem. J. 434 (2011), 201–210. [2] K. Becker, N. Al Laham, W. Fegeler, R.A. Proctor, G. Peters and C. von Eiff, Fourier transform infrared spectroscopic analysis is a powerful tool for studying the dynamic changes in Staphylococcus aureus small-colony variants, J. Clin. Microbiol. 44 (2006), 3274–3278.

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[3] L. Chen, N.C. Carpita, W.D. Reiter, R.H. Wilson, C. Jeffries and M.C. McCann, A rapid method to screen for cell-wall mutants using discriminant analysis of Fourier transform infrared spectra, Plant J. 16 (1998), 385–392. [4] H.Y. Cheung, J. Cui and S.Q. Sun, Real time monitoring of Bacillus subtilis endospore components by attenuate total reflection Fourier-transform infrared spectroscopy during germination, Microbiology 145 (1999), 1043–1048. [5] R.J. Delle-Bovi, A. Smits and H.M. Pylypiw, Rapid method for the determination of total monosaccharide in Enterobacter cloacae strains using Fourier transform infrared spectroscopy, Amer. J. Anal. Chem. 2 (2011), 212–216. [6] M. Grube, M. Bekers, D. Upite and E. Kaminska, IR-spectroscopic study of Zymomonas mobilis levan fermentation, Vibr. Spectrosc. 28 (2002), 277–285. [7] U. Kalnenieks, Physiology of Zymomonas mobilis: some unanswered questions, Adv. Microbial Physiol. 51 (2006), 73– 117. [8] U. Kalnenieks, N. Galinina, I. Strazdina, Z. Kravale, J.L. Pickford, R. Rutkis and R.K. Poole, NADH dehydrogenase deficiency results in low respiratory rate and improved aerobic growth of Zymomonas mobilis, Microbiology 154 (2008), 989–994. [9] G. Mouille, S. Robin, M. Lecomte, S. Pagant and H. Hofte, Classification and identification of Arabidopsis cell wall mutants using Fourier transform infrared (FT-IR) microspectroscopy, Plant J. 35 (2003), 292–404. [10] D. Naumann, in: Encyclopedia of Analytical Chemistry, R.A. Meyers, ed., Wiley, Chichester, UK, 2000, pp. 102–131. [11] S.G. Oliver, M.K. Winson, D.B. Kell and F. Baganz, Systematic functional analysis of the yeast genome, Trends in Biotechnology 16 (1998), 373–378. [12] I. Strazdina, Z. Kravale, N. Galinina, R. Rutkis, R.K. Poole and U. Kalnenieks, Electron transport and oxidative stress in Zymomonas mobilis respiratory mutants, Arch. Microbiol. 194 (2012), 461–471.

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Subject Index β-cyclodextrin β-turn 1,3-diaminopropane 2-mercapto-1-methylimidazole

157 61 147 85

actin Ag colloidal nanoparticles alkali metal phenoxyacetates amniotic fluid amyloid-disassembly amyloidosis ANSA probe antibacterial peptides anticancer antimicrobial peptides asolectin azobenzene

261 171 227 91 209 267 261 79 147 31 99 209

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background estimation bacteria bacterial adhesion bacterial identification biophysical techniques biosensor system blood bonds bound water Candida albicans cell model cellular imaging chemical composition chromatin circular dichroism cisteine oxidation collective dynamics computational geometry conformational analysis convex cytopathology D-galactose/D-glucose-binding protein decavanadate DFT diabetes diagnosis

243 31 283 205 31 255 215 53 53, 69 73 111 1 73 177, 185 79, 177, 185 261 115 243 127 243 1 255 261 127, 227, 235 91 91

dissociation constant DNA DNA melting drop-coating deposition Raman DSC D2 O/H2 O exchange

277 177, 185 177 99 157 165

epidermis/dermis interface epithelial EPR EPR (Electron paramagnetic resonance)

105 111 261 215

factor analysis 277 FAP 267 far infrared 53 fluorescence 39, 79 fluorescence imaging 191 fluorescence lifetime 191 fluorescence spectroscopy 277 fluorescent proteins 249 folding kinetics 267 folding of proteins with beta-barrel topology 249 free water 69 FTIR (Fourier transform infrared) 157, 215, 221, 227, 235 FTIR spectroscopy 111, 165, 273, 291 FTIR-ATR 47 fungal detection 47 Fusarium 47 guanine

127

hairpin peptide halictine HCA hemoglobin histone H1 histopathology HMGB1 hydration water hydrogen

61 79 291 215 177 1 185 115 53

imaging inclusion compounds infrared difference spectroscopy infrared spectroscopy INS spectroscopy

39 157 165 1, 61, 79, 127, 147 127, 147

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

298

Subject Index

intrinsic fluorescence intrinsic fluorescence of proteins isopiestic method

261 255 165

kinetics LDA lifetime liposome living cell lung MADS box manganese toxicity medical diagnosis metabolomics metabonomics micelle model membranes molecular conformation molecular structure multiphoton multivariate analysis multivariate methods nanostructures neurodifferentiation neutrons NMR

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oxidative stress PALS PCA pentacyanoferrate(II) complexes peptides phenoxyacetic acid phosphatidylcholine phospholipid phosphorus plane-wave calculations plant tissue polyphosphate porphyrin pregnancy prenatal health preterm principal component analysis propranolol protein amino groups protein hydration protein stability

61 47 39 79, 99 39, 73 111 277 197 1 91 91 79 31 283 227, 235 39 91 1 209 221 115 91, 227, 235, 261 291 157 47 85 209 235 99, 171 53, 79 197 127 197 73 171, 191 91 91 91 205 39 273 165 255

protons Pt(II) complexes R(ν)-representation radioisotope Tc99m diagnosis Raman Raman microspectroscopy Raman spectroscopy reaction centre respiratory mutants

115 147 69 215 235 73, 105 1, 69, 127, 147, 205, 243 165 291

self organization self-assembled monolayers SERS SFG spectroscopy skin skin mesenchymal stem cells small guanidine thiocyanate concentrations sodium phenoxyacetate spectroscopy super-folder GFP surface synchrotron based FTIR

53 283 171 283 105 221 249 235 85, 215 249 283 197

temperature jump thymoquinone THz spectroscopy time-resolved microspectrofluorimetry time-resolved-vibrational spectroscopy transthyretin tris trisomy tryptophan tryptophan zipper TTR type I collagen type IV collagen tyrosine

61 157 273 191 209 267 273 91 61, 171 61 267 105 105 277

urine blood UV

91 227

vacuole vanadate vanadyl vapour pressure viscosity

73 261 261 69 255

water activity

69

yeast

73

Zymomonas mobilis

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

291

Spectroscopy of Biological Molecules M.P. Marques et al. (Eds.) IOS Press, 2013 © 2013 The authors and IOS Press. All rights reserved.

299

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Author Index Almarashi, J.F.M. Aureliano, M. Barros, A.S. Batista de Carvalho, A.L.M. Batista de Carvalho, L.A.E. Bauerová, V. Bednárová, L. Bellon-Fontaine, M.-N. Bilde, M. Bird, B. Bisby, R.H. Botchway, S.W. Bourguignon, B. Brandt, N.N. Brassart-Pasco, S. Briandet, R. Brito, R.M.M. Bulard, E. Cappiello, L. Cardoso, T. Carreira, I.M. Castanho, M.A.R.B. ˇ rovský, V. Ceˇ Chernenko, T. Chikhirzhina, E. Chikishev, A.Yu. Coïc, Y.-M. Crisostomo, A.G. Cvijanovi´c, D. Dagli, M.L.Z. Damjanovi´c, V. De Francesco, A. Deeg, A.A. Diaz, S.O. Diem, M. do Céu Almeida, M. Duˇci´c, T. Duarte, I.F.

205 261 91 147 v, 127, 147 73 73, 79 283 69 1 39 39 283 273 105 283 267 283 221 157 91 31 79 1 177, 185 273 277 39 85 111 85 115 209 91 1 91 197 91

Dubost, H. Ferreira Marques, M.F. Feru, J. Fiuza, S.M. Fonin, A.V. Fontaine-Aupart, M.-P. Foreti´c, B. Formisano, F. Francia, F. Frosch, M. Galhano, C.I.C. Galhano, E. Galinina, N. Gavare, M. Gil, A.M. Gobinet, C. Goodfellow, B.J. Graça, G. Grube, M. Hammody, Z. Haris, P.I. Hauser, K. Hellwig, P. Henriques, S.T. Herry, J.-M. Hielscher, R. Hrušková-Heidingsfeldová, O. Huleihel, M. Jesus, C.S.H. Kalnenieks, U. Kapel, N. Keiderling, T.A. Kingston, E. Koˇcišová, E. Kopecký Jr., V. Kostyleva, E. Kourkoumelis, N. Kuznetsova, I.M.

283 157 105 147 255 283 85 115 165 69 157 91 291 215, 221, 291 91 105 91 91 215, 221, 291 47 v 61 53 31 283 53 73 47 267 291 205 61 1 79, 99, 171, 191 79 177 243 249, 255

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

Copyright © 2013. IOS Press, Incorporated. All rights reserved.

300

Laloni, A. Lapidot, I. Lasa, Z. Laver, N. Lewandowski, W. Lopes, R.P. Malferrari, M. Maloˇn, P. Manfait, M. Mankova, A.A. Marcsisin, E. Marques, M.P.M. Mazur, A. Mennecier, G. Mezzetti, A. Miljkovi´c, M. Mironova-Ulmane, N. Mojzeš, P. Monincová, L. Mordechai, S. Moreh, R. Moreira da Silva, A. Moroder, L. Moura, J.J.G. Muceniece, R. Nazarov, M.M. Nguyen, T.T. Nielsen, O.F. Orecchini, A. Palacký, J. Papamarkakis, K. Parfejevs, V. Parker, A.W. Pavlenko, A. Pazderka, T. Pazderková, M. Pereira, T.M. Petrillo, C. Pfizer, J. Picek, I. Pichová, I. Pinto, J. Piot, O. Pita, C. Polakovs, M. Polle, A. Polyanichko, A. Polymeros, A. Pomerantz, A.

Author Index

115 47 291 1 227, 235 127 165 79 105 273 1 v, 127, 147 1 111 165 1 215 73, 191 79 47 47 157 209 261 221 273 105 69 115 73 1 221 39 215 79 79 111 115 209 85 73 91 105 91 215 197 177, 185 243 47

Popp, A. Praus, P. Procházka, M. Ramos, S. Regulska, E. Reinholds, E. ˇ Rezᡠcová, B. Riekstina, U. Russo, D. Rutkis, R. Sacchetti, F. Sakodynskaya, I.K. Salman, A. Samsonowicz, M. Saraiva, M.J.M. Scherer, K.M. Schrader, T.E. Schubert, J. Shkurinov, A.P. Šimáková, P. Štˇepánek, J. Starkova, T. Stepanenko, O.V. Stepanenko, O.V. Strazdina, I. Strzalka, H. Sureau, F. ´ Swisłocka, R. Telle, H.H. Thieme, J. Tomkinson, J. Torcato, I.M. Tsror, L. Turoverov, K.K. Turpin, P.-Y. Tzaphlidou, M. Valero, R. Vaz, D.C. Venturoli, G. Verkhusha, V.V. Vodáková, A. Wilkinson, T.S. Wu, L. Zentz, C. Zezell, D.M. Zheng, W. Zinth, W. Zuser, E.

Spectroscopy of Biological Molecules : Proceedings from the 14th European Conference on the Spectroscopy of Biological Molecules 2011, edited by M. P. Marques, et al., IOS

61 191 99, 171 261 227, 235 215 277 221 115 291 115 273 47 227, 235 267 39 209 1 273 171 191, 277 177 249, 255 249, 255 291 209 191 227 205 197 127, 147 31 47 249, 255 277 243 127 267 165 249, 255 99 205 61 277 111 283 209 1