[1st Edition] 9780128115596, 9780128115589

Table of contents :
Content:
Advances in ParasitologyPage i
Series EditorPage ii
Front MatterPage iii
CopyrightPage iv
ContributorsPages ix-x
Chapter One - Chagas Disease Diagnostic Applications: Present Knowledge and Future StepsOriginal Research ArticlePages 1-45V. Balouz, F. Agüero, C.A. Buscaglia
Chapter Two - Host–Parasite Relationships and Life Histories of Trypanosomes in AustraliaOriginal Research ArticlePages 47-109C. Cooper, P.L. Clode, C. Peacock, R.C.A. Thompson
Chapter Three - The Compatibility Between Biomphalaria glabrata Snails and Schistosoma mansoni: An Increasingly Complex PuzzleOriginal Research ArticlePages 111-145G. Mitta, B. Gourbal, C. Grunau, M. Knight, J.M. Bridger, A. Théron
Chapter Four - Targeting the Parasite to Suppress Malaria TransmissionOriginal Research ArticlePages 147-185R.E. Sinden
Chapter Five - The Role of Spatial Statistics in the Control and Elimination of Neglected Tropical Diseases in Sub-Saharan Africa: A Focus on Human African Trypanosomiasis, Schistosomiasis and Lymphatic FilariasisOriginal Research ArticlePages 187-241M.C. Stanton
Chapter Six - Is Predominant Clonal Evolution a Common Evolutionary Adaptation to Parasitism in Pathogenic Parasitic Protozoa, Fungi, Bacteria, and Viruses?Original Research ArticlePages 243-325M. Tibayrenc, F.J. Ayala

Citation preview

VOLUME NINETY SEVEN

ADVANCES IN PARASITOLOGY

SERIES EDITOR D. ROLLINSON Life Sciences Department The Natural History Museum, London, UK [email protected]

J. R. STOTHARD Department of Parasitology Liverpool School of Tropical Medicine Liverpool, UK [email protected]

EDITORIAL BOARD T. J. C. ANDERSON Department of Genetics, Texas Biomedical Research Institute, San Antonio, TX, USA
NEZ ~ M. G. BASA Professor of Neglected Tropical Diseases, Department of Infectious Disease Epidemiology, Faculty of Medicine (St Mary’s Campus), Imperial College London, London, UK S. BROOKER Wellcome Trust Research Fellow and Professor, London School of Hygiene and Tropical Medicine, Faculty of Infectious and Tropical, Diseases, London, UK R. B. GASSER Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, Victoria, Australia N. HALL School of Biological Sciences, Biosciences Building, University of Liverpool, Liverpool, UK J. KEISER Head, Helminth Drug Development Unit, Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland

R. C. OLIVEIRA Centro de Pesquisas Rene Rachou/ CPqRR - A FIOCRUZ em Minas Gerais, Rene Rachou Research Center/CPqRR - The Oswaldo Cruz Foundation in the State of Minas Gerais-Brazil, Brazil R. E. SINDEN Immunology and Infection Section, Department of Biological Sciences, Sir Alexander Fleming Building, Imperial College of Science, Technology and Medicine, London, UK D. L. SMITH Johns Hopkins Malaria Research Institute & Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA R. C. A. THOMPSON Head, WHO Collaborating Centre for the Molecular Epidemiology of Parasitic Infections, Principal Investigator, Environmental Biotechnology CRC (EBCRC), School of Veterinary and Biomedical Sciences, Murdoch University, Murdoch, WA, Australia X.-N. ZHOU Professor, Director, National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention, Shanghai, People’s Republic of China

VOLUME NINETY SEVEN

ADVANCES IN PARASITOLOGY Edited by

D. ROLLINSON Life Sciences Department The Natural History Museum London, United Kingdom

J.R. STOTHARD Department of Parasitology Liverpool School of Tropical Medicine Liverpool, United Kingdom

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States 525 B Street, Suite 1800, San Diego, CA 92101-4495, United States First edition 2017 Copyright © 2017 Elsevier Ltd. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/ permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein).

Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-12-811558-9 ISSN: 0065-308X For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Zoe Kruze Acquisition Editor: Alex White Editorial Project Manager: Helene Kabes Production Project Manager: Magesh Kumar Mahalingam Designer: Matthew Limbert Typeset by TNQ Books and Journals

CONTRIBUTORS F. Ag€ uero Instituto de Investigaciones Biotecnol ogicas – Instituto Tecnol ogico de Chascom us (IIB-INTECH), Universidad Nacional de San Martín (UNSAM) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina F.J. Ayala University of California at Irvine, United States V. Balouz Instituto de Investigaciones Biotecnol ogicas – Instituto Tecnol ogico de Chascom us (IIB-INTECH), Universidad Nacional de San Martín (UNSAM) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina J.M. Bridger Brunel University London, Uxbridge, United Kingdom C.A. Buscaglia Instituto de Investigaciones Biotecnol ogicas – Instituto Tecnol ogico de Chascom us (IIB-INTECH), Universidad Nacional de San Martín (UNSAM) – Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina P.L. Clode The University of Western Australia, Crawley, WA, Australia C. Cooper The University of Western Australia, Crawley, WA, Australia B. Gourbal University of Perpignan, Perpignan, France C. Grunau University of Perpignan, Perpignan, France M. Knight The George Washington University, Washington, DC, United States; University of the District of Columbia, Washington, DC, United States G. Mitta University of Perpignan, Perpignan, France C. Peacock The University of Western Australia, Crawley, WA, Australia; Telethon Kids Institute, Subiaco, WA, Australia

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x R.E. Sinden The Jenner Institute, Oxford, United Kingdom M.C. Stanton Liverpool School of Tropical Medicine, Liverpool, United Kingdom A. Théron University of Perpignan, Perpignan, France R.C.A. Thompson Murdoch University, Murdoch, WA, Australia M. Tibayrenc Institut de Recherche pour le Développement, Montpellier, France

Contributors

CHAPTER ONE

Chagas Disease Diagnostic Applications: Present Knowledge and Future Steps € ero, C.A. Buscaglia1 V. Balouz, F. Agu

Instituto de Investigaciones Biotecnol ogicas e Instituto Tecnol ogico de Chascom us (IIB-INTECH), Universidad Nacional de San Martín (UNSAM) e Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina 1 Corresponding author: E-mail: [email protected]

Contents 1. Introduction 2. Trypanosoma cruzi, an ‘all-wheel drive’ parasite 2.1 Epidemiological features 2.2 Genetic and phenotypic variability 3. Diagnostic Applications for Chagas disease: Present Knowledge 3.1 Parasitological and clinical methods 3.2 Serological methods 3.3 Molecular methods 4. Diagnostic Applications for Chagas Disease: Pending Issues 4.1 Early diagnosis of congenital transmission 4.2 Rapid assessment of therapy efficacy 4.3 Indication/prediction of Chagas disease progression 4.4 Typing of parasite strains 4.5 Point-of-care diagnosis 5. Diagnostic Applications for Chagas Disease: The Road Ahead 6. Concluding Remarks Acknowledgement References

2 4 4 6 8 8 12 14 16 16 18 19 21 22 24 26 27 27

Abstract Chagas disease, caused by the protozoan Trypanosoma cruzi, is a lifelong and debilitating illness of major significance throughout Latin America and an emergent threat to global public health. Being a neglected disease, the vast majority of Chagasic patients have limited access to proper diagnosis and treatment, and there is only a marginal investment into R&D for drug and vaccine development. In this context, identification of novel biomarkers able to transcend the current limits of diagnostic methods surfaces as a main priority in Chagas disease applied research. The expectation Advances in Parasitology, Volume 97 ISSN 0065-308X http://dx.doi.org/10.1016/bs.apar.2016.10.001

© 2017 Elsevier Ltd. All rights reserved.

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is that these novel biomarkers will provide reliable, reproducible and accurate results irrespective of the genetic background, infecting parasite strain, stage of disease, and clinical-associated features of Chagasic populations. In addition, they should be able to address other still unmet diagnostic needs, including early detection of congenital T. cruzi transmission, rapid assessment of treatment efficiency or failure, indication/ prediction of disease progression and direct parasite typification in clinical samples. The lack of access of poor and neglected populations to essential diagnostics also stresses the necessity of developing new methods operational in point-ofcare settings. In summary, emergent diagnostic tests integrating these novel and tailored tools should provide a significant impact on the effectiveness of current intervention schemes and on the clinical management of Chagasic patients. In this chapter, we discuss the present knowledge and possible future steps in Chagas disease diagnostic applications, as well as the opportunity provided by recent advances in high-throughput methods for biomarker discovery.

1. INTRODUCTION Chagas disease or American Trypanosomiasis, caused by the parasitic protozoan Trypanosoma cruzi (Kinetoplastida, Trypanosomatidae), is a lifelong, neglected tropical disease and leading cause of cardiomyopathy in endemic areas (Rassi et al., 2010). With 8e10 million people already infected and up to 120 million individuals at risk of infection, Chagas disease constitutes the most important parasitic disease in Latin America and one of the most common globally (Stanaway and Roth, 2015). Its exact burden is, however, difficult to assess due to several factors including the widespread geographic distribution of T. cruzi vectorborne transmission, the decadeslong lag between infection and appearance of symptoms, certain pitfalls of current diagnostic methods, biased prevalence data and incomplete recognition of Chagas disease-attributable symptoms (Stanaway and Roth, 2015). The most recent estimates indicate that Chagas disease is responsible for w550,000 disability-adjusted life years (DALYs), a measure that captures both premature mortality (w12,000 deaths per year) and nonfatal health losses (Stanaway and Roth, 2015). Despite this enormous toll, only two trypanocydal drugs, benznidazole and nifurtimox, are currently available for chemotherapy. Both are nitroheterocyclic, oral compounds that require prolonged administration, may display severe adverse effects, cannot be used to treat pregnant women due to their uncertain teratogenic risks and, most importantly, show high efficacy solely if administered at the onset of infection (Carlier and Truyens, 2015; Rassi et al., 2010; Viotti et al., 2006). The prospects for the development of an effective vaccine for

Diagnostic Applications for Chagas Disease

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prophylactic and/or therapeutic purposes, on the other hand, are still clouded by substantial scientific and socioeconomic challenges (Beaumier et al., 2016; Bustamante and Tarleton, 2015). T. cruzi transmission primarily occurs when humans are exposed to the contaminated feces of infected, haematophagous triatomine vectors. Largescale intervention schemes launched in different regions of Latin America in the 1990s have successfully shrunk the geographic limits and prevalence of vectorborne parasite transmission and led to an overall w40% reduction of disease prevalence (Schofield et al., 2006). However, different ecological and demographic issues converged in the last decades to shift the epidemiological landscape for this disease. For instance, recent outbreaks of acute cases in certain regions from Brazil and Venezuela were not strictly vectorborne but rather due to accidental ingestion of T. cruzi-tainted food and fluids (Alarcon de Noya et al., 2010; Segovia et al., 2013). This ‘foodborne’ transmission mode likely constitutes an ancient epidemiological trait, very important to the zoonotic spreading of the parasite (Gurtler and Cardinal, 2015), and appears to be associated with increased virulence and a higher case-fatality rate in humans (Alarcon de Noya et al., 2010; Segovia et al., 2013). In addition, migratory trends of infected populations from rural areas to urban centres and/or to nonendemic regions along with changes in the ecogeographical distribution of vector populations have led to the gradual urbanization and globalization of Chagas disease, which is now recognized as an emerging worldwide threat to public health (Eisenstein, 2016). Indeed, the risk of acquiring Chagas disease through infected blood transfusion and organ transplantation is becoming a major problem even in areas of nonendemicity, such as the United States, Australia and Europe (Requena-Mendez et al., 2015; Schmunis and Yadon, 2010). Moreover, the congenital route of infection, which constitutes the main transmission mode of T. cruzi in nonendemic areas, is now estimated to be responsible for 22% of new annual infections in endemic countries with active programs for home vector infestations control (Carlier and Truyens, 2015). In this scenario, a strong and global partnership aimed to coordinate actions to control parasite transmission is urgently needed. In particular, we need to redouble our efforts to control home vector infestation, to screen blood supplies and to identify and subsequently treat T. cruzi-infected people who are still in the early stages of the disease to avoid sequelae, morbidity and economic losses. As a major step towards these goals, we ought to develop novel biomarkers able to overcome the limitations of current diagnostic applications. In this chapter, we critically appraise what has so

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far been achieved in this area. We also discuss possible ways to proceed to address major and still unmet diagnostic demands and the opportunity provided by recent advances in high-throughput methods (i.e., peptide synthesis technology, genomics and proteomics) in Chagas disease biomarker discovery.

2. TRYPANOSOMA CRUZI, AN ‘ALL-WHEEL DRIVE’ PARASITE T. cruzi is a promiscuous parasite that traverses a complex life cycle involving extracellular proliferation and differentiation inside haematophagous insect vectors from different genera and intracellular proliferation and differentiation in a variety of vertebrate hosts (De Souza, 2002). Host switching, immune pressure as well as constant transition from intracellular to extracellular niches (and vice versa) pose significant adaptation challenges and are concomitantly accompanied by extensive remodelling of different aspects of T. cruzi such as intracellular transport, primary metabolism, gene expression profiling and overall cellular architecture (De Souza, 2002). This striking plasticity can be also readily recognized in the diverse genetic, phenotypic and epidemiological features displayed by different strains and field isolates comprised within the T. cruzi taxon (Zingales et al., 2012). In this first section, we outline some aspects that underlie the biological flexibility of this ‘all-wheel drive’ parasite and that may be relevant in terms of biomarker discovery for Chagas disease diagnostic purposes.

2.1 Epidemiological features Potential T. cruzi vectors present a broad geographic distribution (from central Argentina and Chile to southern USA) and include more than 140 species of ‘kissing bugs’ from the subfamily Triatominae (Hemiptera, Reduviidae). Of these, only a few (i.e., Triatoma infestans, Triatoma dimidiata, Triatoma brasiliensis, Rhodnius prolixus and Pastrongylus megistus) have adapted to live in domiciliary setting and to blood-feed on humans and/or domestic animals and thus define the ‘domestic/peridomestic’ cycle of Chagas disease (Gurtler and Cardinal, 2015; Zingales et al., 2012). The ‘sylvatic’ cycle of T. cruzi, on the other hand, is actually an array of poorly understood cycles with different ecoepidemiological properties, each one involving multiple sylvatic and/or synanthropic triatomine species, which in turn feed on a variable range of animals. The latter include a variety of rodents, primates, carnivores, bats, marsupials (i.e., opossums) and xenarthrans (i.e., armadillos, sloths,

Diagnostic Applications for Chagas Disease

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anteaters) (Fig. 1) (Fernandes et al., 1999; Noireau et al., 2009; Zingales et al., 2012). In general terms, and although not yet fully established, all mammals are considered susceptible, whereas birds and reptiles are considered refractory to T. cruzi. From an epidemiological standpoint, these nonhuman hosts may play key roles as parasite reservoirs and/or as determinant factors affecting T. cruzi transmission dynamics in endemic areas (Gurtler and Cardinal, 2015; Noireau et al., 2009). Importantly, they may also work as complex selective systems leading to the emergence of novel parasite traits (Noireau et al., 2009). Interestingly, distinct though partially overlapping sets of strains circulate in the ‘domestic/peridomestic’ and the ‘sylvatic’ cycles of T. cruzi (Gurtler

Figure 1 Schematic diagram showing the Trypanosoma cruzi life cycle and different biological features that contribute to ensure its transmission and the establishment of multiple interactions with insect vectors and infected humans. Those features for which there is direct or indirect experimental evidence suggesting interstrain variability are denoted in italics.

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and Cardinal, 2015; Noireau et al., 2009; Zingales et al., 2012). In the last decades, environmental alterations and demographic issues converged in favouring the intermingling of the two cycles. This translates into a steady increase of emergent transmission patterns involving ‘exotic’ T. cruzi genotypes, with the possible occurrence of atypical disease physiopathologies (Coura et al., 2002; Zingales et al., 2012).

2.2 Genetic and phenotypic variability As revealed for several pathogenic protozoa and fungi, T. cruzi displays a basically clonal reproduction mode, with occasional events of genetic exchange leading to the emergence of hybrid genotypes (Messenger and Miles, 2015). These features led to a complex population structure, made up of multiple ‘clonal’ strains showing remarkable genetic diversity (Tibayrenc and Ayala, 2015). Interstrain variations may be grasped at the nucleotide level (Ackermann et al., 2012) but also structurally, in terms of dosage/diversification of antigenic gene families (Campo et al., 2004; Cerqueira et al., 2008; Llewellyn et al., 2015; Urban et al., 2011), DNA content and overall genome architecture (Lewis et al., 2009a; Minning et al., 2011; Reis-Cunha et al., 2015; Souza et al., 2011). Importantly, biochemical and genetic typing schemes developed throughout the last decades converged in the delineation of six major T. cruzi evolutionary lineages or discrete typing units (DTUs) termed TcI to TcVI, with multiple strains and even cryptic sublineages within each DTU (Tibayrenc and Ayala, 2015; Zingales et al., 2012). A potential seventh lineage, termed TcBat, has been recently identified in South and Central American bats (Marcili et al., 2009; Pinto et al., 2012). So far, and although all six (or seven, including TcBat) T. cruzi DTUs are capable of infecting humans, certain DTUs such as TcI, TcII, TcV, and TcVI are most frequently isolated from clinical samples (Ramirez et al., 2014; Zingales et al., 2012). The reasons for this skewed distribution are unclear, although current evidence suggest that parasite strains detected in patients reflect the principal DTUs circulating among ‘domestic/ peridomestic’ cycles in that geographical area (Messenger et al., 2015). T. cruzi genotypic heterogeneity could also be grasped at the phenotypic level when different biological parameters are studied. These include, for instance, the rate of epimastigote proliferation in the vector midgut (Castro et al., 2012; de Lana et al., 1998; Vieira et al., 2016); and the extent of epimastigote differentiation into metacyclic trypomastigotes, the developmental form that bring the infection into vertebrates (Fig. 1) (da Silveira Pinto et al., 2000; de Lana et al., 1998). The molecular basis for these

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differences is not yet understood, but it might be related to the dissimilar resistance capacity of parasite strains to antimicrobial peptides or haemolytic factors and/or to their differential interaction with receptor(s) inside the crop of triatomines (Castro et al., 2012; Gonzalez et al., 2013; Vieira et al., 2016). Importantly, these biological traits define both T. cruzi infectivity towards insect vectors and its potential transmissibility to vertebrate hosts (Fig. 1). These, together with differential eco-geographical distribution and certain preference of triatomids for their blood source, are in turn major determinants of Chagas disease epidemiology (Gurtler and Cardinal, 2015; Noireau et al., 2009; Zingales et al., 2012). Parasite genotypic heterogeneity also seems to modulate key aspects of its interaction with vertebrate hosts, including humans. For instance, the capacity of metacyclic trypomastigotes to resist the harsh conditions of the gastric milieu and to invade gastric epithelium following oral infection is largely dependent on the strain-specific glycoprotein composition of their surface coat (Camandaroba et al., 2002; Hoft et al., 1996; Maeda et al., 2016). On the same lines, interstrain genetic variations underpin a variety of biological traits involved in parasite infectivity and long-term persistence such as antigenic profile, subversion of the immune system, host cell invasion capacity, intracellular growth rate and survival of amastigotes, and sensitivity to anti-Chagasic drugs (Fig. 1) (Magalhaes et al., 2015; Martin et al., 2006; Moraes et al., 2014; Mortara et al., 2008; Nagajyothi et al., 2012; Ruiz et al., 1998; Toledo et al., 2003). In contrast, no clear association between a particular T. cruzi genotype and an eventual tropism for congenital transmission could be yet established. Even though distinct strains may display subtle differences in their ability to invade trophoblasts or chorionic villi explants in vitro (Castillo et al., 2013), genetic profiling experiments have conclusively shown that (1) the same set of strains circulate in the bloodstream of transmitting and nontransmitting mothers and (2) nearly identical T. cruzi genetic signatures are recovered from infected infants born to Chagasic mothers coursing concurrent, multistrain infections (Fig. 1) (Burgos et al., 2007; del Puerto et al., 2010; Virreira et al., 2006a). Overall, the actual consensus is that maternal parasite load and human polymorphisms constitute the main risk factors for T. cruzi congenital transmission (Bua et al., 2012; Fabbro et al., 2014; Juiz et al., 2016; Kaplinski et al., 2015; Rendell et al., 2015). Interestingly, certain (though not all) epidemiological studies have shown a partial correlation between the prevalence of particular clinical manifestations of Chagas disease and the genotype of the infecting strain (Andrade et al., 1983; D’Avila et al., 2009; Luquetti et al., 1986; Macedo and Pena,

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1998; Virreira et al., 2006b; Zafra et al., 2011; Zingales et al., 2012). This may be attributed in part to the genetic aspects and immune competence of local human populations (Ayo et al., 2013; Deng et al., 2013; Frade et al., 2013; Luz et al., 2016; Nogueira et al., 2012) and/or to parasite genetic heterogeneities. The latter hypothesis finds support in animal studies (that do not strictly recapitulate Chagas disease-associated physiopathologies), which revealed interstrain variations in complex phenotypes such as parasitaemia, virulence, tissue tropism/distribution and pathogenicity (Fig. 1) (Andrade et al., 1999; Andrade, 1990; Camandaroba et al., 2002; de Souza et al., 1996; Laurent et al., 1997; Monteiro et al., 2013; Revollo et al., 1998; Roellig et al., 2010). However, generalized conclusions are difficult to derive, particularly because these epidemiological studies might have been skewed by a number of intrinsic shortcomings. Briefly, (1) they often lacked detailed genetic/clinical information on the studied populations; (2) the infecting genotype has been in some cases inferred based on the prevailing parasite genotypes circulating in the area and not typed directly from patients; (3) patients might have been coinfected with other coendemic pathogens that impact on the clinical presentation of Chagas disease (Salvador et al., 2016); (4) patients might have been infected with multiple parasite strains, which is usually the case in endemic areas (Perez et al., 2014) and (5) these studies might have been biased due to parasite typing pitfalls [i.e., samples were collected only from peripheral blood, which may not be representative of the situation within affected organs (Manoel-Caetano Fda et al., 2008; Vago et al., 2000)] and/or associations between local parasites and disease, making it difficult to determine whether the absence of a specific strain/ DTU in patients with a given disease phenotype is due to parasite factors or to lack of patient exposure to this DTU. Overall, and although this issue may have major implications for Chagas disease diagnosis and treatment, the existence of particular associations between T. cruzi genotype and susceptibility to different clinical presentations on Chagasic patients remains to be addressed (Messenger et al., 2015).

3. DIAGNOSTIC APPLICATIONS FOR CHAGAS DISEASE: PRESENT KNOWLEDGE 3.1 Parasitological and clinical methods Upon T. cruzi infection, patients undergo the acute phase of Chagas disease, which extends for 40e60 days. Symptoms, if indeed occur, are

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usually very mild and atypical, thus often misleading its clinical recognition (Rassi et al., 2010). In rare cases of vectorborne transmission, a skin nodule (called ‘chagoma’) or painless prolonged eyelid oedema (called the ‘Romanha’s sign’) may indicate the site of parasite inoculation. Due to the patent parasitaemia verified at this initial phase, conventional microscopy (i.e., visualization of circulating trypomastigotes in peripheral blood films or buffy coat smears) remains the gold standard for diagnosis, both in acute cases and in newborns that were infected congenitally (Freilij and Altcheh, 1995; Gomes et al., 2009). Either direct tests or concentration tests (i.e., microhematocrit or Strout test) are routinely used for this purpose. These techniques, however, present certain limitations in terms of sensitivity (w80e90%) and commonly require highly trained personnel (Table 1) (Freilij and Altcheh, 1995; Gomes et al., 2009). Following the initial, acute phase, if untreated, patients enter the indeterminate form of the chronic phase that may last for several years or persist indefinitely (Rassi et al., 2010). This phase is characterized by the absence of relevant clinical symptoms and very low and intermittent or null parasitaemia. During this phase, parasite replication is maintained in check by the elicitation of a strong and parasite-specific B cell- and T cell-mediated immunity (Tarleton, 2015), being the latter the most important in terms of controlling the infection. However, elaborate pathogen immune evasion systems (Albareda et al., 2009; Giraldo et al., 2013; Padilla et al., 2009; Paiva et al., 2012; Vasconcelos et al., 2012) and their ability to quickly invade host cells (Mortara et al., 2008; Nagajyothi et al., 2012) turn this immune response only partially effective, and most patients maintain a subpatent infection for life. T. cruzi reactivation in immunocompromised Chagasic patients provides solid support to this hypothesis (Tarleton, 2015). Direct T. cruzi detection during the chronic phase requires biological amplification methods, such as hemoculture and xenodiagnosis (Brener, 1962), which are also difficult, expensive, time-consuming and require special laboratory biosecurity conditions. In the case of xenodiagnosis, in addition, it is not applicable to certain patient populations. Most importantly, these methods yield positive results in only a proportion of serologically positive patients, thus limiting their usefulness in diagnosis and/or in monitoring drug efficacy (Table 1). Up to 20 years after the infection, w35% of patients develop pathological signs characteristic of Chagas disease such as cardiomyopathy, peripheral nervous system damage or dysfunction of the digestive tract often leading to megaesophagus and/or megacolon (Rassi et al., 2010). These pathological

Table 1 Overview of performance and features of available diagnostic applications for Chagas disease 10

Diagnostic Performancea

Hemoculture

Drug efficacy

N.A.

Xenodiagnosis

Parasite fraction Recombinant Antigens

Serological Methods

Congenital cases

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.

IFAc

N.A.

N.A.

ELISAc

N.A.

N.A.

N.A.

N.A.

N.A.

N.A.

?

N.A.

Parental Repertoire c,e

N.A.

N.A.

SAPA

?

?

?

?

TESA

N.A.

N.A.

N.A.

Parasite typing

IHAc

CoML

N.A.

Disease prognosis

N.A.

d

F2/3

?

Ag13/TcD

?

F29/FCaBP/Tc24/Tc-28/1F8

?

?

?

Ag1/JL7/ FRA/H49 JL5

?

TSSA

?

?

?

PCR, qPCR Genotypification Markers f Biochemical Markers g

? N.A. N.A.

N.A.

N.A.

N.A.

?

?

N.A.

Remarks Microscopy-based methods, gold standard for diagnosis of acute and congenital cases Improved sensitivity due to parasite amplification, time-consuming, expensive Improved sensitivity due to parasite amplification, time-consuming, expensive, not well tolerated Moderate specificity, trained personnel required Moderate specificity, trained personnel and infrastructure required Method of choice for diagnosis of most clinical situations, cost-effective, easy to perform Measures antibodies that are cleared in parasitological cured hosts early after drug treatment Secreted/Excreted Antigens from trypomastigotes Highly O-glycosylated trypomastigote mucins, expensive, low purification yields High specificity but impaired sensitivity as compared to whole parasite-based methods. Repetitive, surface antigen associated to enzymatically active trans-sialidases Repetitive, surface antigen associated to enzymatically inactive trans-sialidases Flagellar Calcium Binding Protein Repetitive, internal antigen Ribosomal antigen that elicits 'auto-antibodies' able to cross-recognize endogenous receptors Mucin-like, surface antigen showing polymorphisms among strains High specificity, trained personnel required

N.A. N.A.

PoC settingb

N.A. ?

High specificity, trained personnel required, manual technique , expensive Low specificity and sensitivity, in most cases only 'indicative/predictive' of cardiomyopathy

a Performance is arbitrarily indicated as appropriate (green boxes), nonappropriate (vermillion boxes) or intermediate/needs to be improved (yellow boxes) according to available data. N.A. and question marks (?) stand for nonapplicable or not enough experimental data available to assess the performance, respectively.

V. Balouz et al.

Molecular Methods

Acute cases

Direct blood examination tests

Whole Parasitebased

Parasitological Methods

Technique/Tool

Chronic cases

Adaptability to be deployed in PoC settings, which depends on specific features of the tool/technique and which is arbitrarily indicated as above. Commercially available from different vendors. d TESA assays include diverse techniques that measure anti-TESA antibodies in serum samples (i.e., TESA-ELISA, TESA-dot blot, TESA-blot) and techniques that capture TESA antigens directly in urine samples (i.e., Chunap assays). e The term ‘parental’ repertoire refers to Trypanosoma cruzi antigens identified in large-scale initiatives carried out in the 1980s and includes Ag1/JL7/FRA/H49; Ag2/B13/TCR39/PEP-2; Ag10; Ag13/TcD; Ag19; Ag26; Ag30/CRA/JL8/TCR27; Ag36/JL9/MAP; Ag54; JL1; JL5; JL9; SAPA; B12; TcE; A13; F29/FCaBP/Tc-24/Tc-28/1F8; Tc-40; HSP70; HSP78 and FL-160, according to current nomenclature. These antigens were assayed for conventional diagnosis in different combinations using a variety of technological platforms (ELISA, LFIA, Western blot, dot blot), some of which are commercially available from different vendors. The individual performance of some of these antigens showing particular features is indicated below. f Include RFLP, PCR-RFLP, RAPD, Southern blot and other DNA hybridization methods, karyotyping methods and sequence-based methods either using a single locus or MLST. g Include TNF-a, ACE2, BNP, ANP, ET-1 and other biochemical markers indicated in text. ACE2, angiotensin-converting enzyme 2; ANP, atrial natriuretic peptide; BNP, brain natriuretic peptide; CoML, complement-mediated lysis; ELISA, enzyme-linked immunosorbent assay; ET-1, endothelin-1; F2/3, purified highly O-glycosylated and antigenic trypomastigote mucins; HIA, haemagglutination inhibition assay; IFA, immunofluorescence assay; LFIA, lateral-flow immunochromatographic assays; MLST, multilocus sequence typing assay; PCR, polymerase chain reaction; PCR-RFLP, PCR-based restriction fragment length polymorphism assay; PoC, point-of-care; RAPD, random amplification of polymorphic DNA; SAPA, shed acute-phase antigen; TESA, trypomastigote excreted/secreted antigens; TNF-a, tumour necrosis factor a; TSSA, trypomastigote small surface antigen. c

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changes can be revealed by electrocardiogram clinical diagnosis, X-rays and ultrasound. The nature and relative contribution of the multiple factors involved in this quite broad pathogenic range has been the subject of intense debate (Bonney and Engman, 2015; Machado et al., 2012; Teixeira et al., 2011). The current consensus states that a failure to downregulate the inflammatory response, which is maintained by parasite persistence in tissues, appears to play a predominant role. Other contributory factors include sex, age and genotypic features of the patient, route of infection, sustained vector exposure, autoimmune responses and the existence of coinfections (Ayo et al., 2013; Bonney and Engman, 2015; Deng et al., 2013; Frade et al., 2013; Luz et al., 2016; Machado et al., 2012; Nogueira et al., 2012; Tarleton, 2015; Teixeira et al., 2011). As mentioned, a possible role of parasite genotype on Chagas disease progression/outcome has been proposed, although further research supported by novel and robust epidemiologic and diagnostic tools, and appropriate animal models are needed to address this issue.

3.2 Serological methods Considering that patients seroconvert early upon infection, detection of antiT. cruzi antibodies remains the most effective method for demonstrating direct exposure to the parasite (Gomes et al., 2009). At present, the most widely used serologic methods are indirect haemagglutination assay (IHA), indirect immunofluorescence assay (IFA), and enzyme-linked immunosorbent assay (ELISA) (Table 1). First-generation ELISA tests were originally developed using total parasite homogenates or, later on, using biochemically purified, parasite antigenic fractions. Among the latter, TESA (trypomastigote excreted/secreted antigens) and F2/3 (highly O-glycosylated trypomastigote mucins obtained by sequential solvent partitions) demonstrated the highest diagnostic potential (Table 1) (Almeida et al., 1997; Umezawa et al., 1996b). In addition to ELISA, multiple techniques including dot blot and Western blot were developed for the evaluation of these reagents. In the late 1980s, the advent of recombinant DNA technology allowed the generation of parasite DNA or cDNA expression libraries, which were then coupled to high-throughput screenings using serum samples from Chagasic patients or experimentally infected animals. These approaches provided the first glimpses into the genomic make-up of this parasite and, most importantly, led to a ‘golden era’ of T. cruzi antigen discovery. Indeed, by using these methods, several of the immunodominant T. cruzi antigens that are still in use were identified and characterized (Burns et al., 1992;

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Cotrim et al., 1990; Hoft et al., 1989; Houghton et al., 1999; Ibanez et al., 1987, 1988; Lafaille et al., 1989; Levin et al., 1989). For a complete list of these antigens (henceforth the ‘parental repertoire’), see Table 1. The fact that many of them emerged from independent screenings carried out in different laboratories further supported their diagnostic potential but, unfortunately also led to a confusing nomenclature that still persist (Table 1) (reviewed in da Silveira et al., 2001; Frasch et al., 1991). Some of the antigens included in the T. cruzi ‘parental repertoire’ were extensively validated, in certain cases by way of multicentre trials (Caballero et al., 2007; Reithinger et al., 2010), and set the stage for the development of second-generation Chagas disease diagnostic methods. A variety of antigen expression procedures (i.e., using full-length or partially deleted recombinant, fusion proteins or synthetic peptides functionalized in different ways) and antigen display strategies (i.e., using mono- or multiepitopic molecules) were evaluated before them being incorporated either individually or in defined mixtures into in-house and/or commercial kits (Godsel et al., 1995; Hernandez et al., 2010; Houghton et al., 2000; Krieger et al., 1992; Longhi et al., 2012; Oelemann et al., 1999; Pastini et al., 1994). Some of these tests have shown very good performances and were thus extensively used in basic research and/or as confirmatory tests in clinical practice up to this day. The major advantage of recombinant antigens-based tests is that they minimize the extent of specificity problems. As previously shown, sera from individuals with other coendemic infections such as leishmaniasis and/or afflicted of certain autoimmune disorders cross-react with crude preparations of T. cruzi antigens (Araujo, 1986; Gomes et al., 2009; Schnaidman et al., 1986). On the down side, recombinant antigen-based tests present lower sensitivity than whole parasite-based techniques. In addition, it should be said that the proven success of this ‘parental repertoire’, along with a shift in R&D priorities, converged in curbing the enthusiasm for subsequent large-scale antigen discovery efforts for Chagas disease. Indeed, most of the additional antigens/biomarkers with moderate-to-excellent diagnostic performance that emerged in the last decades were identified either as by-products of basic hypothesis-driven research (Buchovsky et al., 2001; Di Noia et al., 2002; Martinez et al., 1991; Saborio et al., 1990) or using low-to-medium throughput antigen expression/synthesis approaches (see later discussion). Most importantly, just a few of these novel antigens have been incorporated into the already existing diagnostic application platforms.

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New generation tests displaying potentially improved accuracy such as the chemiluminescent microparticle immunoassay (CMIA) and its improved version, Architect Chagas (both from Abbott Laboratories, Wiesbaden, Germany) (Abras et al., 2016; Praast et al., 2011), or the Multi-cruzi test (InfYnity Biomarkers, Lyon, France) (Granjon et al., 2016) have been recently developed. Confirming the above-mentioned trend, both use a large panel of T. cruzi antigens belonging to the ‘parental repertoire’ e those discovered in the 1980s e which in the case of the Multi-cruzi test is supplemented with TSSA (trypomastigote small surface antigen (Di Noia et al., 2002)). Despite the virtual lack of antigen innovation, these tests incorporate a large degree of automation and highly sensitive detection systems (Abras et al., 2016) or major technical improvements, such as a multiplexed printing method inside ELISA microplates (Granjon et al., 2016). Overall, currently available serodiagnostic tests are simple, affordable and display quite good results in terms of large-scale population diagnosis. However, they all present certain minor concerns with regards to their reproducibility, reliability, specificity (those using whole parasites) and sensitivity (those using parasite fractions and/or recombinant antigens) which in turn affect their overall performance. In addition, and likely due to their biased antigenic composition, they also show suboptimal performance with particular Chagasic populations (i.e., acute and/or congenital infections) (Gomes et al., 2009) and often discordant results between assays depending on the geographic origin of the patients (Caballero et al., 2007; Guzman-Gomez et al., 2015). The latter may be attributed to variations in the local human immune responses and/or, as stated earlier, to differences in the antigenic constitution of T. cruzi DTUs that prevail in different endemic areas. Importantly, even after successive improvements, none of the available tests emerged as the ‘gold standard’, i.e., able to show w100% specificity and sensitivity. In this context, current guidelines developed by the World Health Organization advise the use of at least two serological tests based on different principles for reaching a ‘conclusive’ diagnosis. In the case of ambiguous or discordant results, a third technique should be conducted. These guidelines thereby increase the cost of diagnosis and risk of patient ‘loss’, particularly in endemic areas. Most importantly, these diagnostic limitations delay the initiation of chemotherapy, thus limiting its efficacy.

3.3 Molecular methods In the late 1980s, DNA-based molecular methods emerged as an appealing alternative for the diagnosis of Chagas disease, mainly to overcome the low

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sensitivity of direct parasitological approaches (Moser et al., 1989; Sturm et al., 1989). The introduction of the recently developed polymerase chain reaction (PCR) assay, in particular, held promises of high sensitivity, specificity and high-throughput potential. The first targets for PCR amplification were focused on the major molecular component of the mitochondrial DNA (also known as kinetoplast DNA or kDNA). In T. cruzi and other kinetoplastid parasites, this component is present in the form of circular DNA of short length (minicircles), which add up to several thousand copies per cell (Simpson, 1986). Later on, a large repertoire of kDNA or nuclear DNA targets including single- or multicopy genes and satellite sequences, as well as different multitarget strategies, has been explored (Schijman et al., 2011). More recently, a parasite concentration approach based on short and stable RNA aptamers was developed to facilitate PCR-based detection methods and proposed as a potential alternative tool in monitoring parasite load in Chagasic patients (Nagarkatti et al., 2012, 2014). Major challenges for the clinical implementation of PCR-based techniques derive from a number of technical factors such as source (i.e., cord blood, umbilical tissue, placental tissue), volume, conservation and transportation of the samples, and underlying molecular biology protocols (i.e., DNA isolation, purification, pretreatment, thermocycling conditions, etc.) (Schijman et al., 2011). In addition, the reproducibility and overall performance of PCR-based methods are significantly affected by the fluctuations in parasitaemia that characterize the chronic phase of Chagas disease and by inter-DTU variations in dosage and/or sequence of the targets of amplification (Lewis et al., 2009b). Despite these issues, several PCR-based and, more recently, quantitative real-time PCR (qPCR)-based procedures have been developed. These allowed detection and quantification of parasite DNA from clinical samples with variable levels of reliability, complexity, selectivity and analytical sensitivity (Duffy et al., 2009, 2013; Piron et al., 2007; Qvarnstrom et al., 2012; Valadares et al., 2008). In a recent work, a multicentre trial optimized and evaluated in parallel two last generation qPCR methods targeting either satellite or kDNA targets (Ramirez et al., 2015). These methods were tested using a large and heterogeneous panel of blood samples from acute and chronic patients either asymptomatic or showing different clinical manifestations and infected through different transmission modes (Ramirez et al., 2015). Though highly specific and reproducible, both methods showed clinical sensitivities of w80%, which is still not good enough for their application as confirmatory

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testing of blood donors or in clinical settings. In addition, it is worth noting that DNA-based molecular methods are expensive and difficult to be implemented in endemic areas, e.g., in point-of-care (PoC) sites with limited infrastructure (Table 1).

4. DIAGNOSTIC APPLICATIONS FOR CHAGAS DISEASE: PENDING ISSUES As described earlier, current diagnostic methods and particularly those based on serology are highly accurate in detecting most of T. cruzi infections in humans. However, there are some clinical and/or epidemiological situations, discussed in this section, in which their performance is significantly hampered by methodological and/or biological issues.

4.1 Early diagnosis of congenital transmission According to epidemiological data, maternalefoetal transmission occurs in an average of 5% of infected mothers in endemic areas, thus leading to w15,000 congenital cases per year (Carlier and Truyens, 2015). Diagnosis in newborns is commonly based on the microscopic observation of trypomastigotes in peripheral and/or umbilical cord blood, which is more effective by the microhematocrit concentration technique (Freilij and Altcheh, 1995). Considering the usually high parasitaemia at the initial stage, PCR-based analyses provide a valuable supporting tool to detect infection and to evaluate treatment outcome in these patients (Table 1) (Duffy et al., 2009; Russomando et al., 1998; Schijman et al., 2003). Standard serodiagnostic methods, though routinely used, have low positive predictive value until 8e9 months after birth due to passive transfer of maternal IgG antibodies (Freilij and Altcheh, 1995). In this context, identification of novel antigens recognized by foetal IgM or IgA appeared as an appealing approach. However, and despite initial promising results (Antas et al., 2000; Betonico et al., 1999; Corral et al., 1998; Lorca et al., 1995), these efforts have been discontinued due to the high number of false negatives (Blanco et al., 2000; Freilij and Altcheh, 1995; Gomes et al., 2009). It should be noted though, that a serious, high-content and unbiased screening for congenital IgM specificities have not been yet undertaken. As an alternative approach to discover surrogate markers of congenital infection, the group of Dr. Frasch in Argentina undertook the identification of T. cruzi antigens exclusively and/or preferentially recognized by foetal IgGs. This approach relied on the parallel evaluation of paired

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mother/newborn serum samples against a multiplexed antigen panel followed by the comparison between signatures of recognition. Using this strategy, they identified SAPA (shed acute-phase antigen) and, to a lesser extent, Ag13/TcD as suitable markers to detect congenital transmission (Table 1) (Reyes et al., 1990). Both antigens belonged to the abovementioned T. cruzi ‘parental repertoire’ and, interestingly, both (particularly SAPA) had been previously found to display a biased, though not exclusive recognition towards acute infection sera (Affranchino et al., 1989). This is consistent with the assumption that congenital T. cruzi infection constitutes in fact an acute infection in newborns (Carlier and Truyens, 2015). Further research disclosed that SAPA is a repetitive sequence displaying a complex antigenic structure (Alvarez et al., 2001) and involved in improving the pharmacokinetics of trans-sialidase, a major T. cruzi virulence factor (Buscaglia et al., 1999; Dc-Rubin and Schenkman, 2012; Frasch, 2000). Up to this day, SAPA remains the only available serological marker of early infection and has been widely used to diagnose recently acquired vectorborne infections and congenitally transmitted cases (Mallimaci et al., 2010; Russomando et al., 2010; Volta et al., 2015). More sophisticated, laborious and difficult-to-interpret methods were also developed. These include testing of the newborn sample by immunoblot against the TESA fraction (Table 1) (Umezawa et al., 1996a) and, more recently, the Chunap (Chagas urine nanoparticle) test (Castro-Sesquen et al., 2014). In the latter method, instead of T. cruzispecific antibodies, active infection is indirectly assessed by the capture and concentration of T. cruzi TESA antigens from patient’s urine, which are then revealed using a monoclonal antibody directed to a parasite lipophosphopeptideglycan, included in the test (Castro-Sesquen et al., 2014). When evaluated on children living in endemic areas and tested at the peak of parasitaemia (w1-month old), Chunap was able to accurately diagnose congenital infections (Castro-Sesquen et al., 2014) and is now being evaluated for other diagnostic applications (Castro-Sesquen et al., 2016). On a final note, it should be stressed that given the high efficacy of trypanocidal drugs in infected newborns, their rapid diagnosis and subsequent treatment is essential. Intensive screening for distinctive immune responses (i.e., those based on IgM and/or IgA antibodies) and/or for specific molecular signatures that may translate into surrogate biomarkers for early detection of congenital T. cruzi transmission should be thus considered a top-ranking priority in Chagas disease applied research.

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4.2 Rapid assessment of therapy efficacy Overall, the best chemotherapy results have been achieved in acute or early chronic infections. However, even in children the cure rate is up to 62% at 2 years of follow-up, although this figure may vary according to population and geographical location (Ribeiro et al., 2009). This variability may be attributed in part to differences in the prevalence of human-associated genotypes across endemic areas. Indeed, substantial interstrain variations in drug resistance have been ascertained both in in vitro systems and in animal infection models (Fig. 1) (Moraes et al., 2014; Toledo et al., 2003). During the chronic stage, the efficacy of drug treatment is lower and variable and is also difficult to assess because most studies used different treatment regimens and outcome evaluation methods (variable assays, frequency and duration of follow-up) (for a recent review, see Pinazo et al., 2014). Even in successful treatments, the gold standard for evaluating drug efficacy, which relies on consistent negative results using conventional parasitological and whole parasite-based serological tests, may take years to decades to assess, thus stressing the necessity of novel and improved therapeutic response markers. Several therapeutic studies support the usefulness of PCR-based strategies to evaluate treatment outcome in congenital or acute cases of Chagas disease. Moreover, PCR-based methods may also assist, though with suboptimal performance due to lower parasitemias, in evaluating chemotherapy in chronic cases of Chagas disease (Britto et al., 1995; Galvao et al., 2003; Munoz et al., 2013; Russomando et al., 1998; Schijman et al., 2003). In particular, PCR-based methods seem to be a rapid indicator of the parasite’s susceptibility to drugs, allowing early therapy modification in cases of resistance or reactivation of Chagasic infection (Lages-Silva et al., 2002; Pinazo et al., 2014; Schijman et al., 2000). In 1982, Drs. Krettli and Brener in Brazil demonstrated the existence of a special type of antibodies, which they termed ‘antibodies against living blood forms’, that were absent in parasitological cured hosts early after drug treatment (Krettli and Brener, 1982). These antibodies were shown to participate in resistance against T. cruzi and were solely detectable by complementmediated lysis (CoML) assays (Krettli and Brener, 1982). Upon these findings, the authors proposed the inclusion of the CoML technique in the context of cure criteria. Indeed, the CoML technique offered an important contribution for posttherapeutic monitoring of Chagas disease in clinical trials, mostly by significantly reducing the required follow-up periods (Table 1) (Galvao et al., 1993). Numerous improvements in terms

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of the sensitivity and operational safety of the CoML method have been later on introduced (Alessio et al., 2014; Martins-Filho et al., 2002; Vitelli-Avelar et al., 2007). Most importantly, this concept of ‘nonconventional’ serological responses sets the basis for multiple screenings looking for parasite molecules that could be the target of antibody responses showing different qualitative, quantitative and/or kinetic properties. Several potential biomarkers have been thereby identified and evaluated for their posttherapeutic application (Andrade et al., 2004; Fernandez-Villegas et al., 2011; Gazzinelli et al., 1993; Laucella et al., 2009; Machado-de-Assis et al., 2012; Sanchez Negrette et al., 2008; Silva et al., 2002). They included different parasite fractions (i.e., TESA, F2/3), individual recombinant antigens belonging to the ‘parental repertoire’ such as F29/FCaBP/Tc24/Tc-28/1F8 (Sosa Estani et al., 1998) and other molecules such as TSSA (our unpublished results) (Table 1). Overall, the best results were obtained with the F2/3 fraction (Table 1) (Andrade et al., 2004; de Andrade et al., 1996). Interestingly, antibodies directed to a-galactosyl-containing epitopes (i.e.. the main antigenic determinant in the F2/3 fraction) were later on shown to induce trypomastigote lysis independently of complement (Pereira-Chioccola et al., 2000). More recently, the group of Dr. Tarleton explored the use of a multipronged approach based on the simultaneous evaluation of T. cruzi-specific B cell and T cell responses as an alternative way of measuring treatment efficacy (Alvarez et al., 2016; Laucella et al., 2009). By doing so, a decline in the frequency of IFNg-producing T cells and in antibody titres measured by a previously developed recombinant multiplex serological assay (Cooley et al., 2008) were observed shortly after benznidazole treatment and were thus proposed as surrogate markers for refining the posttherapeutic cure criterion (Albareda and Laucella, 2015; Alvarez et al., 2016; Laucella et al., 2009). Identification of novel biomarkers for early evaluation of antitrypanocidal drug efficacy will fasten and improve the assessment of current chemotherapy treatments in clinical trials and, importantly, will be instrumental for the development of novel and improved treatments.

4.3 Indication/prediction of Chagas disease progression As mentioned, Chagas disease evolves into a wide range of pathological symptoms, ranging from subclinical to potentially fatal megasyndromes (Rassi et al., 2010). Identification of diagnostic and/or predictive biomarkers of disease progression would therefore represent a major achievement

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towards improving the clinical management of Chagasic patients. Some authors proposed serological alternatives to tackle this issue, such as quantifying the antibody titres to Ag1/JL7/FRA/H49 to distinguish between Chagas disease-associated cardiac pathology and other nonChagasic cardiological dysfunctions (Table 1) (Abraham and Derk, 2015; Bhattacharyya et al., 2014; Kaplan et al., 1997; Levin and Hoebeke, 2008; Thomas et al., 2012). On the other hand, antibodies to another ‘parental repertoire’ antigen, termed JL5, were shown to cross-recognize endogenous b1-adrenergic and M2 muscarinic receptors. Such ‘autoantibodies’ were shown to be associated with arrhythmogenic anomalies, which may contribute to cardiac alterations in Chagasic patients (Kaplan et al., 1997), and were thus proposed to be included in the context of disease prognosis (Table 1). More recently, a certain correlation between serological responses to TSSA and electrocardiogram abnormalities was also noted (Table 1) (Bhattacharyya et al., 2014). Alternatively, several groups attempted to identify biochemical markers of cardiac damage and/or inflammation such as TNF-a (Talvani et al., 2004a), angiotensin-converting enzyme 2 (ACE2) (Wang et al., 2010), brain and atrial natriuretic peptides (BNP and ANP, respectively) (Garcia-Alvarez et al., 2010; Heringer-Walther et al., 2005; Ribeiro et al., 2002) as candidates for disease prognosis (Table 1). In particular, the concentrations of BNP and ANP in serum were systematically studied as markers of heart damage in Chagasic patients (Garcia-Alvarez et al., 2010; Heringer-Walther et al., 2005; Ribeiro et al., 2002) since they had been previously related with cardiovascular diseases (Wang et al., 2006; Wondergem et al., 2001). Increased concentrations of BNP and ANP strongly correlated with the severity of Chagas-associated cardiac damage, being BNP more sensitive than ANP (Fernandes et al., 2007; Heringer-Walther et al., 2005; Talvani et al., 2004b). Upon these findings, the authors proposed that BNP could be measured periodically in asymptomatic patients as screening test to detect incipient ventricular dysfunction (Heringer-Walther et al., 2005). On the same lines, higher levels of plasma ACE2, which catalyses the conversion from angiotensin II to angiotensin 1e7 (Keidar et al., 2007), were shown to correlate with clinical severity and worsening echocardiographic parameters in Chagasis patients (Wang et al., 2010). More importantly, given that the combined determination of BNP concentration and ACE2 activity had better positive predicted value than when separately analysed, authors encouraged the use of both markers to predict fatal outcomes (Wang et al., 2010).

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On the same lines, the role of endothelin-1 (ET-1) as a prognostic marker for T. cruzi-induced pathogenesis has also been extensively studied, though in animal models. ET-1 is a vasoactive peptide synthesized by many cell types including cardiac myocytes and cardiac fibroblasts associated with vasospasm, vascular damage, cardiovascular remodelling and inflammation (Kedzierski and Yanagisawa, 2001). Mice infected with T. cruzi exhibit increased levels of ET-1 and endothelin-converting enzyme (ECE), the enzyme responsible for the conversion of the precursor to ET-1, in plasma, in the vasculature and in T. cruzi-infected myocardial cells (Huang et al., 2000; Petkova et al., 2000). Interestingly, phosphoramidon, an inhibitor of ECE, ameliorated the pathology and reduced the extent of cardiac remodelling in these animals (Jelicks et al., 2002). Moreover, ET-1 KO mice showed certain protection against chronic Chagasic cardiomyopathy (Tanowitz et al., 2005). Overall, and despite multiple and disparate attempts, none of the serological and/or biochemical markers that have been explored so far translated into a reliable, easy to assay and interpret marker to assess cardiac damage and/or disease prognosis (recently reviewed in Pinazo et al., 2015). The complex and likely multifactorial nature of Chagas disease pathogenesis together with intrinsic difficulties in establishing appropriate experimental/ epidemiological models converge in turning this area as the most challenging in terms of T. cruzi biomarker discovery.

4.4 Typing of parasite strains Pioneer studies aimed at fingerprinting the infecting parasite strain(s) directly in clinical samples were based on biochemical markers (i.e., MLEE, multilocus enzyme electrophoresis) (Tibayrenc and Ayala, 2015). Recent advances in typing schemes based on RFLP (restriction fragment length polymorphism), PCR-RFLP, RAPD (random amplification of polymorphic DNA), DNA hybridization, karyotyping and, particularly, sequencebased markers either using a single locus or multiple loci have greatly improved parasite genotypic resolution in vitro (Table 1) (Tibayrenc and Ayala, 2015). However, in vivo, these genotyping methods are timeconsuming and costly and require parasite isolation and amplification or a high quantity of DNA, therefore necessitating invasive sampling with medical risks. Moreover, the fact that concurrent infections with multiple T. cruzi strains appear to be the norm rather than the anomaly (Perez et al., 2014) and the discovery that the most prevalent T. cruzi genotype present in the bloodstream can differ from the strain(s) found sequestered

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within organs (Manoel-Caetano Fda et al., 2008; Vago et al., 2000) further complicate this task. In this context, serotyping methods emerge as an appealing alternative to overcome these limitations, as demonstrated in other human infectious diseases (Dunbar et al., 2015; Maksimov et al., 2012). Serotyping is based on the use of specific antigens with qualitative and/or quantitative differences among parasite strains/DTUs to detect strainspecific antibody signatures. TSSA (Di Noia et al., 2002) is a parasite adhesin displaying significant sequence homology to TcMUC, a huge family of polymorphic genes that code for the polypeptide backbones of the trypomastigote mucin molecules (Buscaglia et al., 2004; Campo et al., 2006), some of which are terminally decorated with a-galactosyl residues and constitute the F2/3 antigenic fraction. Interestingly, detailed genetic characterization of the TSSA locus disclosed minor sequence variations between TSSA variants expressed by different parasite DTUs (Bhattacharyya et al., 2010; Di Noia et al., 2002). Some of these variations were shown to have major impact on TSSA biological function and antigenicity, thereby leading to differential antibody profiles between variants (Balouz et al., 2015; Bhattacharyya et al., 2014; Canepa et al., 2012; De Marchi et al., 2011; Di Noia et al., 2002). So far, TSSA remains the only polymorphic antigen that has been successfully used for the development of DTU-specific serology methods for T. cruzi infections of humans (Table 1) (Bhattacharyya et al., 2014; Bisio et al., 2011; Burgos et al., 2010; Longhi et al., 2014; Risso et al., 2011). Despite this, the resolution and specificity of TSSA-based serotyping assays need to be improved. Discrimination between certain DTUs remains challenging as they possess identical or almost identical TSSA alleles or they are poorly immunogenic (Bhattacharyya et al., 2014; Canepa et al., 2012). Serotyping could be a rapid, sensitive, cost-effective and relatively noninvasive alternative to stringent T. cruzi genotyping in humans and may also be used in animal reservoirs for epidemiological studies (Bhattacharyya et al., 2015; Cimino et al., 2011). Most importantly, development of novel serotyping tools will facilitate the unravelling of possible relationships between parasite genetic variability and clinical features, a major issue in Chagas disease applied research.

4.5 Point-of-care diagnosis People living in Chagas disease-endemic areas have restricted access to laboratory facilities and/or to appropriate health centres. This, together with the associated costs and expertise needed for conventional diagnostic

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methods, points to a number of technological and economic barriers that further stress the need to deploy PoC tests for T. cruzi infection diagnosis. PoC devices, in addition, allow the patients to see the results for themselves, which contributes to a better working relationship between local communities and people carrying out the testing (e.g., during field surveys). Moreover, the need for follow-up visits to surveyed individuals and therefore the operational costs and risks of possible attrition bias are reduced. From an epidemiological point of view, a reliable PoC test would allow intervention strategies to be implemented in situ, such as for serologic surveillance, vaccine or clinical trials; as well as for rapid initiation of treatment of infected individuals during outbreaks of acute cases, such as those recently reported (Alarcon de Noya et al., 2010; Segovia et al., 2013). Several rapid diagnostic and PoC tests to detect infection based on immunochromatography, particle agglutination, immunofiltration, immunodot, lateral-flow immunochromatographic assays (LFIA) or DNA detection are available for a variety of infectious diseases (Nair et al., 2016; Natoli et al., 2014; Teles and Fonseca, 2015). These may be either qualitative or semiquantitative and are characterized by the delivery of quick results, in most cases without the need for electrical equipment. In the case of Chagas disease, several LFIAs based on antigenic fractions or recombinant antigens and a single PoC test aimed at the capture and concentration of T. cruzi TESA antigens in urine samples (Chunap, see earlier discussion) were developed (Table 1) (Castro-Sesquen et al., 2014; Houghton et al., 2009; Reithinger et al., 2010). Serological tests, however, show substantial variations in their sensitivity in different geographical areas (Verani et al., 2009) and display suboptimal performances (Sanchez-Camargo et al., 2014). Unfortunately, and despite being widely used in different parasitic diseases (Mondal et al., 2016; Sriworarat et al., 2015), PCR-based methods applicable in PoC settings such as those based on simple colorimetric loopmediated isothermal amplification or recombinase polymerase amplification (RPA) techniques are not yet available for T. cruzi detection. As a first step towards the development of alternative PoC devices for diagnosis, a new diagnostic platform based on superparamagnetic microbeads coated with recombinant antigens and fluorescent (FMBIA) or electrochemical (EMBIA) detection was recently developed (Cortina et al., 2016). The EMBIA platform, including antigen-functionalized magnetic microbeads, disposable electrochemical cells-electrode cartridges and a portable potentiostat, was evaluated for serodiagnosis of human and cattle infectious diseases. In the particular case of Chagas disease, a more

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extensive validation was performed showing that the EMBIA platform displayed an excellent diagnostic performance almost indistinguishable from the well-established ELISA methods (Cortina et al., 2016).

5. DIAGNOSTIC APPLICATIONS FOR CHAGAS DISEASE: THE ROAD AHEAD As discussed throughout this chapter, parasite-specific immune signatures provided a prime source of biomarker candidates for development of Chagas disease diagnostic applications (see Table 1). However, they may now be reexplored using modern and powerful ‘omics’-based fingerprint approaches. Indeed, the availability of complete T. cruzi genomes from several strains together with a variety of recent genome mining exercises was performed to identify potential serodiagnostic reagents and vaccine candidates (Bhatia et al., 2004; Carmona et al., 2012; Cooley et al., 2008; Goto et al., 2008; Reis-Cunha et al., 2014). Importantly, most of the antigens that emerged from these in silico-guided screenings were not included in the ‘parental repertoire’ (Table 1) and could thereby entail novel diagnostic capabilities, such as early assessment of drug efficacy (Alvarez et al., 2016; Laucella et al., 2009). Genome-wide approaches could be also used as a starting point to identify novel DNA-based DTU resolution markers (Cosentino and Aguero, 2012) and/or novel serotyping reagents. As previously shown, genetic variation among T. cruzi DTUs translates into differential proteomes (Telleria et al., 2010) and therefore also likely in distinct epitope collections (‘epitomes’) recognized during human infections. By carrying out genome-wide B cell epitope prediction on proteins derived from allelic pairs of the hybrid T. cruzi CL Brener genome, the group of Dr. Bartholomeu in Brazil was able to identify three novel polymorphic epitopes potentially able to discriminate between parasite DTUs (Mendes et al., 2013), although one of them turned out not to be DTU specific (Bhattacharyya et al., 2014). Unfortunately, this study and those mentioned earlier were somehow limited by the use of low-tomedium throughput antigen expression/synthesis approaches and/or by the use of nonhuman sources of serum samples. Recent advances in computerized photolithography and photochemistry, however, have allowed the development of a novel peptide chip technology where up to 1 million individual peptides are synthesized in situ on a glass slide (Buus et al., 2012). This in situ synthesis makes the production of the chip highly cost-effective and allows, for the first time,

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to interrogate complete proteomes. Recent work in our laboratories demonstrated both the high technical reproducibility and epitope mapping consistency of this platform when compared with earlier technologies (Balouz et al., 2015; Carmona et al., 2015). Most importantly, by screening the complete length of 457 parasite proteins (w7% of the T. cruzi deduced proteome) we were able to identify 2031 Chagas disease-specific peptides and 97 novel parasite antigens (Carmona et al., 2015). Together with above-mentioned studies, and besides emphasizing the huge potential of genomic/proteomic methods as major driving forces in antigen discovery, these findings indicate that a vast majority of the T. cruzi antigenic repertoire remains uncharacterized. We aim now at interrogating the entire T. cruzi deduced proteome, including interstrain polymorphisms revealed by previously developed genetic diversity maps (Ackermann et al., 2012; Panunzi and Aguero, 2014). The use of samples collected from particular patients populations such as infected with different parasite DTUs or showing distinct Chagas disease-associated pathologies followed by a final integration of the emerging data will put us in position to discriminate between the common linear B cell ‘epitome’ of T. cruzi and sets of epitopes showing differential recognition among distinct Chagas disease populations. Considering the huge functional and diagnostic significance of carbohydrates on T. cruzi biology (de Lederkremer and Agusti, 2009), it could be hypothesized that similar high-throughput approaches carried out on glycan and/or lectin microarrays (Fernandez-Tejada et al., 2015) will have a major impact on Chagas disease biomarker discovery. These methodologies have been successfully explored in different parasitic infections (Anish et al., 2013; Aranzamendi et al., 2011; Martin et al., 2013). From a wider perspective, high-throughput strategies may be also pursued to develop novel biomarkers based on the detection of T. cruziderived molecules (Castro-Sesquen et al., 2014; de Titto and Araujo, 1988) and/or parasite-induced modifications on host molecules and/or cells (Li et al., 2016; Mucci et al., 2002; Muia et al., 2010; Risso et al., 2007; Trocoli Torrecilhas et al., 2009). Theoretically, these strategies would have the additional advantage to discriminate between active from past infections and/or to assist in monitoring disease progression, as validated in other human diseases (Karsdal et al., 2010; Leeansyah et al., 2013; Tritten et al., 2014). In this context, it is important to keep in mind (1) that the extent of variability due to host genetic background on setting these putative biochemical markers and (2) that T. cruzi-derived molecules, and particularly during the chronic phase of Chagas disease, are present in biological

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fluids at extremely low concentrations and likely aggregated in membranecoated vesicles (Bayer-Santos et al., 2013; Fernandez-Calero et al., 2015; Lantos et al., 2016; Trocoli Torrecilhas et al., 2009). Nevertheless, modern ‘-omic’ technologies are offering alternative strategies for such a difficult system biology exploration (Cantacessi et al., 2015; Hockl et al., 2016; Preidis and Hotez, 2015). In particular, mass spectrometry (MS) methods provide a robust, versatile and sensitive analytical technology allowing high-throughput detection with mass accuracy, precise quantitation and verification of protein variants, splice isoforms, metabolites and disease-specific posttranslational modifications (Crutchfield et al., 2016). Indeed, using MS-based approaches, global changes in metabolic profiles in Chagasic patients showing acute myocarditis (Girones et al., 2014) and serological biomarkers showing differences between Chagasic and healthy subjects (Santamaria et al., 2014) were recently identified. In the latter case, biomarker peaks with the best discriminatory power were further characterized by a range of proteomic and immunological techniques, indicating that specific fragments derived from proteolysis of apolipoprotein AeI and one fragment of fibronectin are specifically upregulated in Chagasic patients (Santamaria et al., 2014). Interestingly, these biomarkers returned to normal values more rapidly than whole parasite-based serological tests in patients treated with nifurtimox, thus supporting their potential use for evaluation of therapeutic efficacy (Table 1) (Santamaria et al., 2014). In addition to the obvious impact in the diagnostic application field, it is expected that the utilization of these and other high-throughput, ‘-omic’ techniques will provide a vast amount of putative biomarkers to be explored in other Chagas disease research areas such as molecular epidemiology and prioritization of targets for vaccine development. Most importantly, together with appropriate animal models and robust bioinformatics, molecular and cellular tools (Aguero et al., 2008; Crowther et al., 2010; Katsuno et al., 2015; Lewis et al., 2015; Magarinos et al., 2012; Moraes et al., 2014), these biomarkers will be instrumental to screen, prioritize and evaluate safety and efficacy of novel drugs/treatments.

6. CONCLUDING REMARKS Over 100 years after its discovery, and despite its huge medical, economic and social burden, Chagas disease remains a major threat in several

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countries of Latin America and an emergent global health problem. Great efforts have been made and are still being made in Latin America and other developed countries to halt T. cruzi transmission. However, one of the key issues concerning Chagas disease control remains that of diagnosis. Without accessible and effective diagnostics tools and methods, infected individuals cannot be timely identified and hence treated. Even if treated, the success of treatment cannot be efficiently assessed. As discussed here, current diagnostic methods are highly accurate in detecting most of T. cruzi infections in humans. However, there are still some clinical and/or epidemiological situations in which their performance is severely impaired. In addition, the complex epidemiological features of Chagas disease and the remarkable phenotypic diversity displayed by distinct T. cruzi strains, which may be associated to certain aspects of disease progression/outcome, also stress the necessity of developing new methods able to be deployed in PoC settings and capable of typing the infecting parasite(s) directly in clinical samples. The implementation of modern ‘-omic’, highthroughput and aggressive strategies constitutes an appealing alternative to move on towards filling current diagnostic gaps. Emergent diagnostic tests integrating these novel and tailored tools will provide a significant impact on the effectiveness of current intervention schemes and, most importantly, will improve the clinical management of Chagasic patients by providing the intervening physician with an accurate and integrated diagnosis.

ACKNOWLEDGEMENT We express our gratitude to Dr. Carlos Frasch (IIB-INTECh) for critical reading of the manuscript and to Dr. Javier Di Noia (IRCM, Canada) for the enthusiasm and superb graphic art. We apologize to people whose work was not referenced due to limited space. Research carried out in our labs is supported by grants and contracts from the Agencia Nacional de Promoci on Científica y Tecnol ogica (ANPCyT, Argentina) (to FA and CAB), the National Institutes of Health (NIAID/NIH, USA) (to FA), and Fundaci on Bunge y Born (Argentina) (to CAB). VB holds a PhD fellowship, and FA and CAB are career investigators from the National Research Council of Argentina (CONICET).

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Telleria, J., Biron, D.G., Brizard, J.P., Demettre, E., Seveno, M., Barnabe, C., Ayala, F.J., Tibayrenc, M., 2010. Phylogenetic character mapping of proteomic diversity shows high correlation with subspecific phylogenetic diversity in Trypanosoma cruzi. Proc. Natl. Acad. Sci. U.S.A. 107, 20411e20416. Thomas, M.C., Fernandez-Villegas, A., Carrilero, B., Maranon, C., Saura, D., Noya, O., Segovia, M., Alarcon de Noya, B., Alonso, C., Lopez, M.C., 2012. Characterization of an immunodominant antigenic epitope from Trypanosoma cruzi as a biomarker of chronic Chagas’ disease pathology. Clin. Vaccine Immunol. 19, 167e173. Tibayrenc, M., Ayala, F.J., 2015. The population genetics of Trypanosoma cruzi revisited in the light of the predominant clonal evolution model. Acta Trop. 151, 156e165. Toledo, M.J., Bahia, M.T., Carneiro, C.M., Martins-Filho, O.A., Tibayrenc, M., Barnabe, C., Tafuri, W.L., de Lana, M., 2003. Chemotherapy with benznidazole and itraconazole for mice infected with different Trypanosoma cruzi clonal genotypes. Antimicrob. Agents Chemother. 47, 223e230. Tritten, L., Burkman, E., Moorhead, A., Satti, M., Geary, J., Mackenzie, C., Geary, T., 2014. Detection of circulating parasite-derived microRNAs in filarial infections. PLoS Negl. Trop. Dis. 8, e2971. Trocoli Torrecilhas, A.C., Tonelli, R.R., Pavanelli, W.R., da Silva, J.S., Schumacher, R.I., de Souza, W., NC, E.S., de Almeida Abrahamsohn, I., Colli, W., Manso Alves, M.J., 2009. Trypanosoma cruzi: parasite shed vesicles increase heart parasitism and generate an intense inflammatory response. Microbes Infect 11, 29e39. Umezawa, E.S., Nascimento, M.S., Kesper Jr., N., Coura, J.R., Borges-Pereira, J., Junqueira, A.C., Camargo, M.E., 1996a. Immunoblot assay using excreted-secreted antigens of Trypanosoma cruzi in serodiagnosis of congenital, acute, and chronic Chagas’ disease. J. Clin. Microbiol. 34, 2143e2147. Umezawa, E.S., Shikanai-Yasuda, M.A., Stolf, A.M., 1996b. Changes in isotype composition and antigen recognition of anti-Trypanosoma cruzi antibodies from acute to chronic Chagas disease. J. Clin. Lab. Anal 10, 407e413. Urban, I., Santurio, L.B., Chidichimo, A., Yu, H., Chen, X., Mucci, J., Aguero, F., Buscaglia, C.A., 2011. Molecular diversity of the Trypanosoma cruzi TcSMUG family of mucin genes and proteins. Biochem. J. 438, 303e313. Vago, A.R., Andrade, L.O., Leite, A.A., d’Avila Reis, D., Macedo, A.M., Adad, S.J., Tostes Jr., S., Moreira, M.C., Filho, G.B., Pena, S.D., 2000. Genetic characterization of Trypanosoma cruzi directly from tissues of patients with chronic Chagas disease: differential distribution of genetic types into diverse organs. Am. J. Pathol. 156, 1805e1809. Valadares, H.M., Pimenta, J.R., de Freitas, J.M., Duffy, T., Bartholomeu, D.C., Oliveira Rde, P., Chiari, E., Moreira Mda, C., Filho, G.B., Schijman, A.G., Franco, G.R., Machado, C.R., Pena, S.D., Macedo, A.M., 2008. Genetic profiling of Trypanosoma cruzi directly in infected tissues using nested PCR of polymorphic microsatellites. Int. J. Parasitol. 38, 839e850. Vasconcelos, J.R., Bruna-Romero, O., Araujo, A.F., Dominguez, M.R., Ersching, J., de Alencar, B.C., Machado, A.V., Gazzinelli, R.T., Bortoluci, K.R., AmaranteMendes, G.P., Lopes, M.F., Rodrigues, M.M., 2012. Pathogen-induced proapoptotic phenotype and high CD95 (Fas) expression accompany a suboptimal CD8þ T-cell response: reversal by adenoviral vaccine. PLoS Pathog. 8, e1002699. Verani, J.R., Seitz, A., Gilman, R.H., LaFuente, C., Galdos-Cardenas, G., Kawai, V., de LaFuente, E., Ferrufino, L., Bowman, N.M., Pinedo-Cancino, V., Levy, M.Z., Steurer, F., Todd, C.W., Kirchhoff, L.V., Cabrera, L., Verastegui, M., Bern, C., 2009. Geographic variation in the sensitivity of recombinant antigen-based rapid tests for chronic Trypanosoma cruzi infection. Am. J. Trop. Med. Hyg. 80, 410e415.

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Vieira, C.S., Waniek, P.J., Castro, D.P., Mattos, D.P., Moreira, O.C., Azambuja, P., 2016. Impact of Trypanosoma cruzi on antimicrobial peptide gene expression and activity in the fat body and midgut of Rhodnius prolixus. Parasit. Vectors 9, 119. Viotti, R., Vigliano, C., Lococo, B., Bertocchi, G., Petti, M., Alvarez, M.G., Postan, M., Armenti, A., 2006. Long-term cardiac outcomes of treating chronic Chagas disease with benznidazole versus no treatment: a nonrandomized trial. Ann. Intern. Med. 144, 724e734. Virreira, M., Alonso-Vega, C., Solano, M., Jijena, J., Brutus, L., Bustamante, Z., Truyens, C., Schneider, D., Torrico, F., Carlier, Y., Svoboda, M., 2006a. Congenital Chagas disease in Bolivia is not associated with DNA polymorphism of Trypanosoma cruzi. Am. J. Trop. Med. Hyg. 75, 871e879. Virreira, M., Serrano, G., Maldonado, L., Svoboda, M., 2006b. Trypanosoma cruzi: typing of genotype (sub)lineages in megacolon samples from bolivian patients. Acta Trop. 100, 252e255. Vitelli-Avelar, D.M., Sathler-Avelar, R., Wendling, A.P., Rocha, R.D., TeixeiraCarvalho, A., Martins, N.E., Dias, J.C., Rassi, A., Luquetti, A.O., Eloi-Santos, S.M., Martins-Filho, O.A., 2007. Non-conventional flow cytometry approaches to detect anti-Trypanosoma cruzi immunoglobulin G in the clinical laboratory. J. Immunol. Methods 318, 102e112. Volta, B.J., Russomando, G., Bustos, P.L., Scollo, K., De Rissio, A.M., Sanchez, Z., Cardoni, R.L., Bua, J., 2015. Diagnosis of congenital Trypanosoma cruzi infection: a serologic test using Shed Acute Phase Antigen (SAPA) in mother-child binomial samples. Acta Trop. 147, 31e37. Wang, T.J., Gona, P., Larson, M.G., Tofler, G.H., Levy, D., Newton-Cheh, C., Jacques, P.F., Rifai, N., Selhub, J., Robins, S.J., Benjamin, E.J., D’Agostino, R.B., Vasan, R.S., 2006. Multiple biomarkers for the prediction of first major cardiovascular events and death. N. Engl. J. Med. 355, 2631e2639. Wang, Y., Moreira Mda, C., Heringer-Walther, S., Ebermann, L., Schultheiss, H.P., Wessel, N., Siems, W.E., Walther, T., 2010. Plasma ACE2 activity is an independent prognostic marker in Chagas’ disease and equally potent as BNP. J. Card. Fail 16, 157e163. Wondergem, J., Strootman, E.G., Frolich, M., Leer, J.W., Noordijk, E.M., 2001. Circulating atrial natriuretic peptide plasma levels as a marker for cardiac damage after radiotherapy. Radiother. Oncol. 58, 295e301. Zafra, G., Mantilla, J.C., Jacome, J., Macedo, A.M., Gonzalez, C.I., 2011. Direct analysis of genetic variability in Trypanosoma cruzi populations from tissues of Colombian Chagasic patients. Hum. Pathol. 42, 1159e1168. Zingales, B., Miles, M.A., Campbell, D.A., Tibayrenc, M., Macedo, A.M., Teixeira, M.M., Schijman, A.G., Llewellyn, M.S., Lages-Silva, E., Machado, C.R., Andrade, S.G., Sturm, N.R., 2012. The revised Trypanosoma cruzi subspecific nomenclature: rationale, epidemiological relevance and research applications. Infect. Genet. Evol. 12, 240e253.

CHAPTER TWO

HosteParasite Relationships and Life Histories of Trypanosomes in Australia C. Cooper*, 1, P.L. Clode*, C. Peacock*, {, R.C.A. Thompsonjj *The University of Western Australia, Crawley, WA, Australia { Telethon Kids Institute, Subiaco, WA, Australia jj Murdoch University, Murdoch, WA, Australia 1 Corresponding author: E-mail: [email protected]

Contents 1. Parasite Diversity and Community Relationships 1.1 The importance of parasites in the community 1.2 The importance of trypanosomes

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1.2.1 Causative agents of American trypanosomiasis 1.2.2 Causative agents of African trypanosomiasis 1.2.3 Causative agents of animal trypanosomiasis

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1.3 Trypanosomes in Australia 2. The History of Trypanosomes in Australia 2.1 Australian trypanosomes found in reptiles, birds, amphibians and fish 2.1.1 2.1.2 2.1.3 2.1.4

Reptile trypanosomes Bird trypanosomes Amphibian trypanosomes Fish trypanosomes

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2.2 Australian trypanosomes found in mammals 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.2.6 2.2.7 2.2.8 2.2.9

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Trypanosoma sp. H25 Trypanosoma copemani Trypanosoma vegrandis Trypanosoma irwini Trypanosoma gilletti Trypanosoma binneyi Trypanosoma sp. ABF Trypanosoma thylacis Trypanosomes in bats

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3. Evolutionary Relationships of Australian Trypanosomes 3.1 The southern supercontinent theory 3.2 Host-fitting and shared environments 3.3 The bat-seeding hypothesis 3.4 The trouble with bird trypanosomes Advances in Parasitology, Volume 97 ISSN 0065-308X http://dx.doi.org/10.1016/bs.apar.2016.06.001

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4. Trypanosome HosteParasite Interactions in Australia 4.1 Implication of disease in Australia From trypanosomes 4.1.1 4.1.2 4.1.3 4.1.4

Trypanosoma cruzi and experimental infection of Australian marsupials Trypanosoma lewisi and the rats of Christmas Island Poor health in koalas and quokkas and the potential zoonotic significance Trypanosoma copemani and the woylie of Southwest Australia

4.2 A brief history of intracellular behaviour in trypanosomes 4.2.1 4.2.2 4.2.3 4.2.4

The processes involved in Trypanosoma cruzi cell invasion Intracellular trypanosomes from America Intracellular trypanosomes outside America Intracellular trypanosomes in Australia

5. Future Research Acknowledgements References

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Abstract Trypanosomes constitute a group of flagellate protozoan parasites responsible for a number of important, yet neglected, diseases in both humans and livestock. The most significantly studied include the causative agents of African sleeping sickness (Trypanosoma brucei) and Chagas disease (Trypanosoma cruzi) in humans. Much of our knowledge about trypanosome hosteparasite relationships and life histories has come from these two human pathogens. Recent investigations into the diversity and life histories of wildlife trypanosomes in Australia highlight that there exists a great degree of biological and behavioural variation within and between trypanosomes. In addition, the genetic relationships between some Australian trypanosomes show that they are unexpectedly more closely related to species outside Australia than within it. These findings have led to a growing focus on the importance of understanding parasites occurring naturally in wildlife to (1) better document parasite biodiversity, (2) determine evolutionary relationships and degree of host specificity, (3) understand hosteparasite interactions and the role of parasites in the natural ecosystem and (4) identify biosecurity issues of emerging disease in both wildlife and human populations. Here we review what is known about the diversity, life histories, hosteparasite interactions and evolutionary relationships of trypanosomes in Australian wildlife. In this context, we focus upon the genetic proximity of key Australian species to the pathogenic T. cruzi and discuss similarities in their biology and behaviour that present a potential risk of human disease transmission by Australian vectors and wildlife.

1. PARASITE DIVERSITY AND COMMUNITY RELATIONSHIPS 1.1 The importance of parasites in the community Parasites constitute a considerable proportion of global biodiversity and are responsible for numerous socioeconomically important diseases. While

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the majority of research is focussed on the health of the vertebrate host, concerns need to extend to the wider community they inhabit because naturally occurring parasites can be expected to play an important role as part of the microbiota of the species they inhabit and consequently their environment (Dobson et al., 2008). The ‘One Health’ triad proposes that wildlife, livestock and human health are interconnected, and the health of each group is reliant on the others (Thompson, 2013). For example, naturally occurring parasites in wildlife represent unique genetic lineages important in preserving biodiversity and hosteparasite relationships within their communities. A decline in parasite richness from depletion or local extinction may lead to an increase in the proportion of viral and bacterial infection in wildlife, which has important implications for community health (Harris and Dunn, 2013). Spillover of disease can occur in any direction among humans, wildlife, and domesticated animals, leading to possibly devastating effects. There are concerns that wildlife may act as reservoirs or amplifiers for known livestock and human disease if these were to become prevalent in a population (Kruse et al., 2004). A greater understanding of the life histories of wildlife parasites may provide insight into identifying and controlling parasite infections, and developing more effective management plans for disease threatening community health (Thompson et al., 2009, 2010; Thompson, 2013). In Australia, this is of particular importance as little is known of the life histories of many wildlife parasites that exist in vulnerable wildlife species, such as trypanosomes.

1.2 The importance of trypanosomes Trypanosomes are flagellate protozoan parasites that occur in almost every animal taxon. They are referred to as kinetoplastids due to an extranuclear mass of DNA in a unique mitochondrial-like structure called the kinetoplast. Trypanosomes include parasitic organisms responsible for a number of human diseases including leishmaniasis caused by parasites from the genus Leishmania and trypanosomiasis caused by parasites from the genus Trypanosoma. Diseases caused by trypanosomes are referred to as neglected tropical diseases due to their high prevalence in poor communities. Here we are concerned primarily with the genus Trypanosoma, which generally have a diphasic life cycle occurring between a vertebrate host and invertebrate vector. Trypanosoma is traditionally separated into two biological groups, the Stercoraria and the Salivaria. Stercorarian trypanosomes reside in the hindgut of the invertebrate vector and are transmitted via the faecal route. Trypanosoma cruzi, the causative agent of American trypanosomiasis in humans, is an example of a Stercorarian trypanosome (Hoare, 1972). In the Salivaria,

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Figure 1 Representative life cycles of Trypanosoma cruzi and Trypanosoma brucei. (A) T. cruzi life cycle. (i) Metacyclic trypomastigotes, which possess a kinetoplast posterior to the nucleus, are the morphological form transmitted by the invertebrate vector that infects the vertebrate host. (ii) Metacyclic trypomastigotes invade vertebrate cells. (iii) Following entry, parasites differentiate into amastigotes (nonflagellated) and divide inside the mammalian cell, amastigotes differentiate into sphaeromastigotes before forming flagellated trypomastigotes. (iv) Parasites eventually rupture the cell as bloodstream trypomastigotes. (v) Bloodstream trypomastigotes can reenter mammalian cells in T. cruzi or infect the invertebrate vector. (vi) Epimastigotes, which have a kinetoplast anterior to the nucleus, are the dividing morphological form in the invertebrate host gut in T. cruzi. (vii) Dividing epimastigotes differentiate into the infective metacyclic trypomastigote ready to infect another vertebrate host via invertebrate faeces. (B) T. brucei typical life cycle. (i) T. brucei ‘stumpy’ metacyclic trypomastigotes infect the vertebrate host. (ii) Once in the bloodstream, trypanosomes differentiate into ‘slender’ or ‘intermediate’ bloodstream trypomastigotes. (iii) Replication by binary fission occurs extracellularly, and parasites are transported around the body entering lymph or spinal fluid. (iv) The ‘slender’ forms differentiate into ‘stumpy’ nonreplicating trypomastigotes (v), which the tsetse fly ingests during a blood meal. (vi) Trypomastigotes differentiate into procyclic trypomastigotes. (vii) Procyclics differentiate into mesocyclics within the ectoperitrophic space of the insect gut. (viii) Mesocyclics develop into long trypomastigotes that differentiate into asymmetric dividing trypomastigotes in the proventricus at the base of the tsetse mouthparts. (ix) The parasites differentiate into long and short epimastigotes. Epimastigotes migrate to the anterior of the tsetse alimentary canal and attach to the epithelial cells. Short and long epimastigotes differentiate into metacyclic trypomastigotes (i), which are transmitted via the mouthparts into a new mammalian host. Parasite nuclei are represented as larger pink circles and kinetoplasts as purple circles in the different morphological forms.

trypanosomes reside in the mouthparts and transmission is inoculative (via saliva). Trypanosoma brucei, the causative agent of African trypanosomiasis in humans, belongs to the Salivaria (Hoare, 1972). T. cruzi and T. brucei are the most significantly studied trypanosomes due to their role in causing disease, yet somewhat surprisingly, their life histories are vastly different (Hoare, 1972) (Fig. 1). These two causative agents of trypanosomiasis utilize vastly different mechanisms for immune evasion, have chronic versus

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neurological pathogenesis, exhibit pathogenic intracellular versus extracellular mammalian life stages, possess different (bug versus fly) vectors, and even have very different means of sexual recombination. Ultimately, their various life histories lead to damaged organ tissue, interference in brain function, anaemia, and poor health in numerous animal species including humans (Hoare, 1972; Masocha and Kristensson, 2012; Murray and Dexter, 1988). Such differences reflect the extent of variability one could therefore expect across the trypanosomatid group, yet comparisons within and between Trypanosoma are typically referenced or expected to be similar to the characteristics of one of these two species. It is most likely that other Trypanosoma species will exhibit and possess considerable variation in their life history, vector, cellular metabolism and behaviour, pathogenesis, and host specificity. However, to date, many Trypanosoma species, which are perceived to have little or no impact on their host or community, have been understudied, and thus little information about them, and their potential for impact, is actually known. 1.2.1 Causative agents of American trypanosomiasis T. cruzi is responsible for American trypanosomiasis, or Chagas disease, an acute or chronic infection that affects six to seven million people and kills around 15,000 each year (Clayton, 2010; WHO, 2015). Less than 5% of young children that develop an acute infection will die of myocarditis or meningoencephalitis (WHO, 2015). Fatalities associated with chronic infection can occur sometimes 10e30 years after infection either from tissue damage and enlargement of heart ventricles resulting in heart failure in 20e30% of patients or from gastrointestinal and neurological complications in up to 10% of patients (WHO, 2015). The life history of T. cruzi begins with an invertebrate host, a hematophagous bug from the family Reduviidae that deposits faeces containing trypanosomes onto the skin of a mammalian host while feeding. The infective trypanosomes, which are called metacyclic trypomastigotes, are recognizable by their undulating membrane and their elongated nucleus that is positioned anterior to the kinetoplast (Fig. 1A(i)). Metacyclic trypomastigotes enter the host via a mucosal membrane or a break in the skin, and once in the bloodstream, they can migrate to, and invade, a number of mammalian cell types (see Section 4.2) (Fig. 1A(ii)). Inside cells, metacyclic trypomastigotes differentiate into spherical amastigotes, which lack the long free flagella, and replicate until they completely fill the cell (Fig. 1A(iii)). Sphaeromastigotes represent the intermediate morphological form of the differentiating amastigotes that are

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Figure 2 Representative internal structure of a Trypanosoma spp. epimastigote showing organelles.

becoming trypomastigotes inside the cell (Carvalho et al., 1981). The amastigotes divide by binary fission, differentiate into sphaeromastigotes to then form trypomastigotes, which leads to the eventual rupture of the cell releasing the trypomastigotes back into the bloodstream (Fig. 1A(iv)). The invertebrate vector becomes infected during a blood meal by ingesting trypomastigotes from the bloodstream (Fig. 1A(v)). Upon reaching the midgut, bloodstream trypomastigotes differentiate into epimastigotes (Fig. 1A(vi)) and multiply (Fig. 1A(vii)). Epimastigotes are stouter than trypomastigotes and recognizable by their round nucleus, which is slightly posterior to the kinetoplast, and their free flagella (Fig. 2). Epimastigotes differentiate into metacyclic trypomastigotes in the intestine of the invertebrate ready to pass into another mammalian host, thereby completing the life cycle (Hoare, 1972). Transmission generally ceases after the acute phase of infection as during the chronic phase trypanosomes reside within the tissue of the host. 1.2.2 Causative agents of African trypanosomiasis The parasites that cause human African trypanosomiasis are part of the T. brucei complex. T. brucei is responsible for African sleeping sickness

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with an estimated 10,000 human cases reported each year in Africa (CDC, 2012). T. brucei gambiense (human reservoir) causes West African sleeping sickness, T. brucei rhodesiense (animal reservoir) causes East African sleeping sickness, and T. brucei brucei, under normal conditions, does not infect humans (Hoare, 1972). T. brucei infection presents particular problems for treatment due to an ability to evade the host immune system by changing the antigens expressed on their surface. This subsequently allows them to cross the bloodebrain barrier leading to mental deterioration and organ failure (Masocha and Kristensson, 2012). Death from African sleeping sickness can occur within months of infection with T. b. rhodesiense and 1e 3 years with T. b. gambiense, although not all infections are fatal. T. brucei is transmitted by the tsetse fly, which infects a mammalian host with ‘stumpy’ metacyclic trypomastigotes during a blood meal, inoculatively (Vickerman, 1962; Hoare, 1972; Walshe et al., 2009) (Fig. 1B(i)). Once in the bloodstream, trypanosomes differentiate into ‘slender’ bloodstream trypomastigotes (Fig. 1B(ii)) that replicate by binary fission extracellularly and are transported around the body entering lymph or spinal fluid (Vickerman, 1962) (Fig. 1B(iii)). The ‘slender’ or ‘intermediate’ forms (Fig. 1B(iv)) differentiate into ‘stumpy’ nonreplicating trypomastigotes, which the tsetse fly ingests during a blood meal (Fig. 1B(v)). Differentiation in the invertebrate differs depending on the Trypanosoma spp. in question (Walshe et al., 2009). In the tsetse fly, T. b. brucei ‘stumpy’ trypomastigotes differentiate into procyclic trypomastigotes (Fig. 1B (vi)). Procyclics differentiate into mesocyclics within the ectoperitrophic space of the insect gut (Fig. 1B (vii)). Mesocyclics develop into long trypomastigotes that differentiate into asymmetric dividing trypomastigotes in the proventriculus at the base of the tsetse mouthparts, which differentiate into long and short epimastigotes (Fig. 1B (viii)). Epimastigotes migrate to the anterior of the tsetse alimentary canal and attach to the epithelial cells. Short and long epimastigotes differentiate into metacyclic trypomastigotes, which are transmitted via the mouthparts into a new mammalian host (Van den Abbeele et al., 1999). 1.2.3 Causative agents of animal trypanosomiasis Animal trypanosomiasis causes economically important diseases in agriculture. Nagana is a disease caused by T. b. brucei, T. b. rhodesiense, Trypanosoma congolense and Trypanosoma vivax in cattle, and Trypanosoma simiae in pigs (Loker and Hofkin, 2015). Trypanosoma evansi causes surra, a disease in

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camels, horses, cattle and buffaloes (Reid, 2002). The trypanosomes that cause nagana and surra are more closely related to T. brucei than those to T. cruzi. Animal trypanosomiasis generally involves fever and lethargy leading to anaemia, which can be fatal if left untreated (Reid et al., 2001). Anaemia following trypanosome infection is typically diagnosed by the presence of a low ‘packed cell volume’ of erythrocytes. The mechanisms of anaemia are thought to arise due to symptoms from the infection, induced by the innate immune response, leading to haemolysis, or from haemolysins released by trypanosomes (Murray and Dexter, 1988; Noyes et al., 2009). The onset of anaemia occurs in the early stages of infection when parasitaemia is at its height. In nagana, anaemia accompanies a chronic infection regardless of whether parasites are still detectable in blood smears, but the anaemia is cured when infected animals are treated using trypanocidal drugs. A number of novel aspects of trypanosome biology are seen during animal trypanosomiasis. For example, while the majority of trypanosomes are transmitted via developmental transmission where the trypanosomes establish an infection and differentiate in the invertebrate gut, T. evansi can only be transmitted mechanically. T. evansi passes through the insect gut to infect another host, as it has lost the ability to differentiate in the invertebrate vector (Hoare, 1972). Trypanosoma equiperdum is another derivative of T. bruceieinfecting horses. It is unusual because it is transmitted sexually and does not involve an invertebrate vector (Oriel and Hayward, 1974). The majority of naturally occurring trypanosomes differ to the abovementioned pathogens in that they are not usually credited with invading tissues, entering cells, crossing the bloodebrain barrier, or causing anaemia or disease. The majority of wildlife trypanosomes are historically considered benign in the vertebrate host and to occur at low parasitaemia (Hoare, 1972). For example, bats (Molyneux, 1991), marsupials (Travi et al., 1952) and African wildlife (Boltzer and Brown, 2014) are considered reservoirs for trypanosome pathogens but generally exhibit resistance to trypanosomes that cause disease in humans or domestic animals. In addition, experimental infection with local trypanosome species isolated from wildlife in vivo is generally unsuccessful (Mackerras, 1959, 1960; Hoare, 1972; Noyes et al., 1999). Whether this is true or merely indicative of the limited data available regarding wildlife trypanosomes remains to be determined. Few trypanosomes isolated from wildlife that lack clinical importance to humans have been studied beyond their initial characterization.

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1.3 Trypanosomes in Australia Information available on the life histories and hosteparasite interactions of trypanosomes isolated from Australian wildlife is limited. Early research on the trypanosomes of Australian wildlife focussed mainly on the identification of trypanosomes in blood smears from various hosts (Mackerras and Mackerras, 1959, 1960; Mackerras, 1959, 1960). Recent investigations into Australian wildlife trypanosomes demonstrated the occurrence of high genetic diversity (Noyes et al., 1999; Hamilton et al., 2004; Botero et al., 2013), low species specificity (Averis et al., 2009; Botero et al., 2013), high morphological diversity (Noyes et al., 1999; Thompson et al., 2013), mixed infections with multiple trypanosome species (Paparini et al., 2011) and no geographic boundaries within Australia (Averis et al., 2009). The discovery of novel trypanosome species is often accompanied by concerns they could become emerging infectious diseases in either livestock or humans (Thompson et al., 2009, 2010). In Australia, there have been a number of cases of emerging infectious diseased30 zoonotic diseases have been identified since 1973 (McFarlane et al., 2013). Information on life histories is necessary in order to assess any risks to wildlife, livestock or human populations (Thompson et al., 2009, 2010). This is especially important considering the continual pressures placed on wildlife communities in Australia from climate change, reduction of habitat, expansion of the human population and the prevalence of introduced predators. These pressures may threaten small populations of vulnerable marsupials by making them susceptible to opportunistic infections from naturally occurring trypanosomes in the event that animals are compromised due to health status, stress, limited food or concurrent infections (McInnes et al., 2011a). There are concerns that trypanosomes are able to cause disease or population decline in Australian wildlife. Exotic trypanosomes in Australia have been experimentally implicated in causing disease in native Australian wildlife. For example, T. cruzi was experimentally pathogenic to common brush-tailed possums (Trichosurus vulpecula) (Backhouse and Bolliger, 1951), T. evansi to agile wallabies (Macropus agilis) (Reid et al., 2001; Reid, 2002), and Trypanosoma lewisi (a common rodent trypanosome) has been implicated in the extinction of native rats on Christmas Island (Wyatt et al., 2008) (see Section 4). At least one trypanosomedTrypanosoma sp. H25disolated from marsupials in Australia is closely related to T. cruzi at two different genetic loci glyceraldehyde 3-phosphate dehydrogenase (gGAPDH) and the small subunit18S ribosomal DNA (18S rDNA)

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(Noyes et al., 1999; Botero et al., 2013) leading to concerns that T. cruzi could be transmitted by the invertebrate vector that transmits T. sp. H25 in Australia (see Section 4.2). In addition, native trypanosomes isolated from Australian wildlife have been implicated in disease and population decline of native marsupials. For example, Trypanosoma irwini, Trypanosoma gilletti, and Trypanosoma copemani have all been implicated in the poor health of koalas (Phascolarctos cinereus) (McInnes et al., 2009, 2011a) and T. copemani in the decline of the now critically endangered woylie, Bettongia penicillata (Botero et al., 2013). In addition to being associated with the decline of the woylie, T. copemani was observed to invade mammalian cells in vitro, in a manner similar to the pathogenic T. cruzi (Botero et al., 2013) (see Section 4). When all the above are considered, a number of key questions about trypanosomes in Australian wildlife emerge: • What can explain the genetic diversity of Australian wildlife trypanosomes? • How significant is the genetic proximity of T. sp. H25 to T. cruzi? • Are the morphology and life histories of T. sp. H25 and T. cruzi similar? • Are T. sp. H25 and T. cruzi similar at additional genetic loci? • Could the vector of T. sp. H25 transmit T. cruzi, and what are the vectors? • What constitutes a vector and is mechanical transmission just as important as cyclical/developmental transmission in the spread of disease? • How many species of Australian trypanosomes exhibit intracellular behaviour? • What factors cause cell invasion to occur? • Is the entry of trypanosomes into tissues linked to immunosuppression in the host, or is it an in vitro phenomenon? • Are the various genotypes reported for Australian wildlife trypanosomes different species? • What is the potential of Australian trypanosomes to cause pathogenicity in their marsupial hosts? Here, we review what is known about the biodiversity, life histories, evolutionary relationships and hosteparasite interactions of trypanosomes in Australian wildlife. We highlight gaps in our understanding of the ultrastructure and life history of Australian trypanosomes, focussing on their apparent ability to invade mammalian cells in vitro, and their relationship(s) to the pathogenic T. cruzi clade of trypanosomes.

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2. THE HISTORY OF TRYPANOSOMES IN AUSTRALIA The list of described trypanosomes naturally occurring in Australian wildlife is continually growing (Table 1). Of the 66 mammals so far screened for trypanosomes in Australia, 28 mammal species have exhibited infection and eight species of trypanosomes have been described (reviewed by Thompson et al., 2014). Naturally occurring trypanosomes have also been Table 1 Trypanosome species identified from Australian vertebrate wildlife Trypanosoma spp. Vertebrate host References MD GD

Trypanosoma sp. AAI Trypanosoma sp. AAP Trypanosoma sp. AAT Trypanosoma sp. ABF Trypanosoma sp. ABI Trypanosoma anellobiae Trypanosoma anguillicola Trypanosoma aulopi Trypanosoma bancrofti Trypanosoma binneyi Trypanosoma carchariasi Trypanosoma chelodina

Wombat (Vombatus ursinus)

Hamilton et al. (2005) Wombat Hamilton et al. (2005) Currawong (Strepera sp.) Hamilton et al. (2005) Brush-tailed rock wallaby (Petrogale Hamilton et al. penicillata) (2005) Wombat Hamilton et al. (2005) Little wattlebird (Anthochaera Cleland and chrysoptera) Johnston (1910) Speckled longfin eel (Anguilla Johnston and reinhardtii) Cleland (1910) Sergeant baker (Latropiscis Mackerras (1925) purpurissata) Eel-tailed catfish (Tandanus Johnston and tandanus) Cleland (1910) Platypus (Ornithorhynchus anatinus) Mackerras (1959) Noyes et al. (1999) Shark (Carcharias sp.) Burreson (1989)

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

Y

N

Y

N

Y

N

Y Y

N Y

Y

N

Johnston (1907) Y Murray River turtle (Emydura macquarii), short-necked tortoise (Emydura krefftii), white-throated snapping turtle (Elseya dentata) Saw-shelled tortoise (Elseya Jakes et al. (2001) Y latisternum), eastern snake-necked tortoise (Chelodina longicollis), Brisbane river tortoise (Emydura signata)

N

N

(Continued)

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Table 1 Trypanosome species identified from Australian vertebrate wildlifedcont'd Trypanosoma spp. Vertebrate host References MD GD

Trypanosoma chlamydoderae Trypanosoma clelandi

Trypanosoma copemani

Trypanosoma egerniae Trypanosoma eudyptulae Trypanosoma gilletti Trypanosoma sp. H25

Trypanosoma hipposideri Trypanosoma irwini Trypanosoma mackerasi

Great bowerbird (Chlamydera nuchalis) Ornate burrowing frog (Limnodynastes ornatus), spotted grass frog (Limnodynastes tasmaniensis) Wombat

Breinl (1913)

Y

N

Johnston and Y Cleland (1910)

N

Noyes et al. (1999) Austen et al. (2009)

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

Y

N

N

Y

Y

Y

N

Y

N

Y

Y

N

Y

Y

Y

N

Quokkas (Setonix brachyurus), Gilberts potoroo (Potorous gilbertii) Koala (Phascolarctos cinereus), McInnes et al. wombat (2011b) Quenda (Isoodon obesulus fusciventer), Botero et al. (2013) western quoll (Dasyurus), common brush-tailed possum (Trichosurus vulpecula), woylie (Bettongia penicillata) Cunningham’s skink (Egernia Mackerras (1960) cunninghami) tree skink (Egernia striolata) Little penguin (Eudyptula minor) Jones and Woehler (1989) Koala McInnes et al. (2011b) Eastern grey kangaroo (Macropus Noyes et al. giganteus) (1999) Botero et al. Woylie, banded hare wallaby (2013) (Lagostrophus fasciatus), boodie (Bettongia lesueur) Common brush-tailed possum Paparini et al. (2011) Dusky horseshoe bat (Hipposideros Mackerras (1959) bicolor albosinensis) Koala McInnes et al. (2009) Epaulette shark (Hemiscyllium Burreson (1989) ocellatum), ocellated angelshark (Squatina tergocellatoides)

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Table 1 Trypanosome species identified from Australian vertebrate wildlifedcont'd Trypanosoma spp. Vertebrate host References MD GD

Trypanosoma myzanthae

Noisy minor bird (Manorina melanocephala)

Trypanosoma notophoyxis

White-faced heron (Ardea novaehollandiae), Pacific reef heron (Egretta sacra) Southern leaf-tailed gecko (Phyllurus platurus) Black flying fox (Pteropus gouldii)

Y

N

Y

N

Mackerras (1960) Y

N

Mackerras (1959) Y

N

Sea perch (Hypoplectrodes semicincta), Mackerras (1925) Y white-ear (Parma microlepis) Southern brown bandicoot (Isoodon Mackerras (1959) Y obesulus), northern brown bandicoots (Isoodon macrourus) Trypanosoma Chuditch (Dasyurus geoffroii) Smith et al. N vegrandis (2008) Woylie Paparini et al. N (2011) Western grey kangaroo (Macropus Thompson et al. Y fuliginosus), western quoll, (2013) southern brown bandicoot, tammar wallaby (Macropus eugenii) Quenda Botero et al. N (2013) Y Gould’s wattled bat (Chalinolobus Austen et al. (2015b) gouldii), lesser long-eared bat (Nyctophilus geoffroyi), little red flying fox (Pteropus scapulatus), black flying fox (Pteropus Alecto) Koala Barbosa et al. Y (2016) Trypanosoma spp. Southern brown bandicoots, Bettiol et al. N eastern barred bandicoots (1998) (Perameles gunnii) Shark bay mouse (Pseudomys fieldi), Averis et al. N dibbler (Parantechinus apicalis), (2009) common planigale (Planigale maculata), golden bandicoot (Isoodon auratus)

N

Trypanosoma phylluri Trypanosoma pteroid Trypanosoma pulchra Trypanosoma thylacis

Mackerras and Mackerras (1959) Breinl (1913)

N

Y Y Y

Y Y

Y Y

Y

(Continued)

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Table 1 Trypanosome species identified from Australian vertebrate wildlifedcont'd Trypanosoma spp. Vertebrate host References MD GD

62 Anura spp. in five families Skink (Sphenomorphus taeniolatus) Brown snake (Diemenia textilis), rocket frog (Litoria nasuta), Lesueur’s frog (Litoria lesueurii) Grey falcon (Falco hypoleocus), brahminy kite (Haliastur indus), morepork (Ninox novaeseelandiae) Yellow-faced honeyeater (Caligavis chrysops), fuscous honeyeater (Meliphaga fusca), blue-faced honeyeater (Entomyzon cyanotis), jacky winter (Microeca fascinans), olive-backed oriole (Oriolus sagittatus), black-headed pardalote (Pardalotus melanocephalus) Scarlet myzomela (Myzomela sanguinolenta) Mangrove golden whistler (Pachycephala melanura) Australian magpie (Cracticus tibice)

Delvinquier and Y Freeland (1989) Johnston and Y Cleland (1912) Johnston and Y Cleland (1910)

N

Breinl (1913)

Y

N

Cleland and Johnston (1911)

Y

N

Cleland (1915)

Y

N

Cleland (1922)

Y

N

Mackerras and Mackerras (1959)

Y

N

N N

Trypanosome isolate (Trypanosoma spp.), vertebrate host, and reference are included. The collection of morphological data (MD) and genetic data (GD) is indicated as yes (Y) or no (N).

described in reptiles (Mackerras, 1960; Jakes et al., 2001), birds (Mackerras and Mackerras, 1959; Hamilton et al., 2004), fish (Mackerras and Mackerras, 1960; Lester and Sewell, 1989) and amphibians (Johnston and Cleland, 1909; Mackerras and Mackerras, 1960; Delvinquier and Freeland, 1989). Exotic trypanosomes have also been identified, including T. lewisi in rats (Rattus rattus), Trypanosoma melophagium in sheep (Ovis aries), Trypanosoma theileri in cows (Bos Taurus) (Mackerras and Mackerras, 1959), and Trypanosoma nabiasi in rabbits (Oryctolagus cuniculus) (Mackerras, 1959). Many wildlife trypanosomes, which have been observed in blood smears or characterized using molecular techniques, have not yet been described to species level

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(Mackerras, 1959, 1960; Delvinquier and Freeland, 1989; Noyes et al., 1999; Hamilton et al., 2004; Averis et al., 2009; Paparini et al., 2011) (Table 1). Information collected on trypanosomes in Australian wildlife has been sourced mainly from opportunistic sampling conducted during wildlife surveys resulting in insufficient data on their life histories (Boxes 1 and 2).

Box 1 Investigating and understanding Trypanosoma life histories Investigating the life cycle and metacyclogenesis There are two aspects in the life of trypanosomes that are of particular interest when understanding life histories. The cyclical transmission occurs between vertebrate host and invertebrate vector, and the identification of the morphological life stages and processes of differentiation occur within this cycle. Early identification of trypanosomes relied on measuring the length and breadth of the cells, and the distance between kinetoplast and nucleus in bloodstream trypomastigotes isolated in blood smears taken directly from the vertebrate host (Mackerras and Mackerras, 1959, 1960; Mackerras 1959, 1960). The progression from investigating simple blood smears using microscopy to the development of improved in vivo and in vitro techniques greatly enhanced the capacity to investigate interactions with both the vertebrate hosts and invertebrate vectors in controlled environments, leading to the development of detailed schematics of trypanosome life cycles (Fig. 1) (Hoare, 1972). Studies looking at trypanosomes in vivo are an on-going and important area of research that contributes to understanding life histories in both invertebrate vectors (Guevara et al., 2005) and vertebrate hosts (Goyard et al., 2014). The introduction and design of molecular techniques to better identify and categorize trypanosomes has enabled extensive sampling of wildlife and more efficient confirmation of the prevalence of parasites. Two gene regions are commonly used in the identification of trypanosomesd18S rDNA and gGAPDHddue to their ability to differentiate between Trypanosoma species when used together (Hamilton et al., 2004). Molecular characterization of trypanosomes in Australian wildlife has resulted in the identification of many novel isolates, while phylogenetic investigations have begun to reveal interesting evolutionary relationships (Noyes et al., 1999; Hamilton et al., 2004; Austen et al., 2009; McInnes et al., 2011b; Thompson et al., 2013). The detection of trypanosomes in a vertebrate indicates that they are a host. The presence of trypanosomes in an invertebrate does not determine they are a vector, as the trypanosomes may have been ingested in a blood meal and will not survive passage through the insect to infect another host. Invertebrate vectors may act as a host in which cyclical development occurs followed by transmission to a vertebrate host or facilitate mechanical transmission of parasites (Continued)

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Box 1 Investigating and understanding Trypanosoma life histories (cont'd) to a vertebrate host. Cyclical transmission occurs when the trypanosomes establish a chronic infection in the hindgut and differentiate inside the invertebrate vector into a number of morphological forms before infecting a new vertebrate host (Hoare, 1972) (Fig. 1). Mechanical transmission occurs when invertebrates containing live trypanosomes from a previous blood meal can transmit them to additional hosts. For example, in Trypanosoma evansi, parasites are believed to be transmitted mechanically by tabanid flies (Hoare, 1972; Reid, 2002), and in Trypanosoma culicavium, hosts are infected by directly ingesting the invertebrate vectors (Votýpka et al., 2012). It is important to demonstrate that any potential vector can cause infection in a new host. Without infecting naïve-endangered marsupials, there are ways to study vectors in Australia to provide conclusive evidence of vectorial capability. Things that should be considered are the prevalence, distribution and life cycle of the vector. Keeping potential vectors alive for a week or so after removal from the hosts may indicate vector capability. Methods used to observe this phenomenon include microscopical examination of the vectorial candidate gut to observe the differentiation of trypanosomes or by maintaining potential invertebrate candidates in controlled environments and feeding them blood inoculated with parasites to determine their ability to establish an infection or survive passage through the digestive tract (Hoare, 1972). Specimens can additionally be processed and serially sectioned in order to observe internally differentiating parasites (Votýpka et al., 2012). In addition, protein markers could be used to identify metacyclic parasites in invertebrate candidates. The development of in vitro cell culture methods has helped to decipher key aspects of Trypanosoma development, contributing significantly to our knowledge of trypanosome life histories. Carrel (1912) first grew tissue in culture establishing an immortal chicken heart cell line with early media including chick embryo extract, blood serum and saline. Today, there are numerous cell lines and media commercially available for the cultivation of a large number of single-celled organisms for research. Despite the disadvantages of culturing protozoan parasites in vitro (see Box 2), it is essential in elucidating hosteparasite interactions in detail. The main advantage of in vitro cultivation of organisms includes the opportunity of conducting important baseline studies when little is known about an organism, or where it is not possible to conduct experiments in vivo (Freshney, 2011). It is possible to investigate a number of research questions that cannot be answered by other means due to the ability to dictate and control environmental and physiological conditions, both spatially and temporally. There are fewer legal, moral and ethical questions surrounding in vitro studies because the need for in vivo animal experiments is reduced. A significant amount of progress can be made in testing a hypothesis using in vitro

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Box 1 Investigating and understanding Trypanosoma life histories (cont'd) techniques before the research moves to using live animal models or conducting clinical trials. In vitro methods are especially important in studying metacyclogenesis or the differentiation of the trypanosome life stages. Metacyclogenesis is the actual duplication and segregation of the organelles in dividing morphological forms and involves a number of different triggers in nondividing forms. It was suggested that endocytic storage may contribute to differentiation of the parasites (Figueueiredo et al., 2000) and that trypanosome life cycles are subject to the generation time of the parasite (Elias et al., 2007; Lacomble et al., 2010). The morphological form most commonly seen in vitro is the epimastigote, which divides by binary fission when grown at 28 C and is maintained in a blood agar medium with a liquid overlay (Fig. 2). This is the morphological form expected to exist in the gut of the invertebrate. Trypanosomes are maintained at this temperature because epimastigotes proliferate so well. In order to transform parasites from epimastigotes into their various other morphological forms a variety of methods can be used including cultivation at differnt temperatures, pH levels, amino acid concentrations, or allowing their nutrients to be depleted (Castellani et al., 1967). For example, in Trypanosoma cruzi, infectivity is increased when epimastigotes are under stress and grown in media with reduced foetal calf serum to transform them into metacyclic trypomastigotes (Garcia Silva et al., 2014). In T. dionisii, metacyclic trypomastigote production is increased when parasites are grown without serum (Oliviera et al., 2009) or grown initially in liver infusion tryptose medium before being transferred to Grace’s medium to differentiate (Meada et al., 2012a). The shape of the cell, the position of the kinetoplast and nucleus and the region where the flagellum emerges from the flagellar pocket are all characteristics that define the stage of differentiation (De Souza, 1984; Elias et al., 2007; De Souza et al., 2010). In order to investigate the transformation of parasite life stages, an understanding of their internal structure is required, which is easily facilitated when large numbers of parasites can be cultivated and differentiated in vitro. Information on the ultrastructure of the multiple forms of trypanosomes contributes to define and categorize parasites as well as detailing their subcellular structure in order to understand cellular mechanisms and development (Elias et al., 2007). Modern day cell culture techniques combined with correlative microscopy techniques have made it possible to investigate trypanosome biology at a detailed level resulting in a number of discoveries.

Ultrastructural characteristics Trypanosomes were one of the first biological organisms viewed in an electron microscope. Today, electron microscopy remains a powerful tool for investigating cellular structures and interactions between organelles, and has (Continued)

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Box 1 Investigating and understanding Trypanosoma life histories (cont'd) underpinned a number of advances in protozoan biology (reviewed in De Souza, 2008). Opportunities to extend the acquisition of such information to three dimensions offer an exciting and unparallelled view into cellular mechanisms (Galbraith and Galbraith, 2011). The size and shape of the parasite itself, along with the cell membrane structure, cytoskeleton, nucleus position, size and location of the kinetoplastemitochondrion complex, structure of the flagella region, and the presence and content of reservosomes and glycosomes, are key areas of interest in trypanosome cellular biology (Fig. 2) (Martins et al., 2012). The size and shape of the trypanosome generally allows identification of the morphological stage of the parasite because the cytoskeleton is remodelled as the trypanosome differentiates into different life stages (Field and Carrington, 2009). The cell membrane of trypanosomes can be visualized using microscopy and immunoblots, which are used to compare surface profiles or identify particular components of interest. This has been applied in the past to explore and compare the differences between species, such as T. cruzi and Trypanosoma dionisii, which are the two intracellular trypanosomes (Maeda et al., 2012a; Oliveira et al., 2013) (Fig. 2). The flagella region is comprised of a flagellar pocket, the flagellar sheath, paraflagellar rod and basal body (Fig. 2) (Lacomble et al., 2009; Martins et al., 2012). The flagellum is found in a 9 þ 2 arrangement of the microtubules in trypanosomes. This is found inside the cytoplasmic membrane attached to the cell body through an invagination of the cell wall called the flagellar pocket (Field and Carrington, 2009). The flagellar pocket is at the base of the flagellum, and it is responsible for all endocytic and exocytic processes in the cell cycle in Trypanosoma brucei including cellular polarity, division, trafficking and immune evasion (Portman and Gull, 2010). In T. cruzi epimastigotes, it is the cytostome, which is an invagination of the plasma membrane penetrating into the cell body that is involved in endocytosis (Alcantara et al., 2014). The paraflagellar rod, which is inside the flagellum, varies in size between species. The basal body is a continuation of the flagellum and is closely associated with the kinetoplast as they are connected via protein filaments (Fig. 2). Trypanosomes typically only have one mitochondrion that has a double membrane, which is connected to the kinetoplast to make the kinetoplastemitochondrion complex (Fig. 2) (De Souza, 2002). The kinetoplast contains extranuclear DNA called kDNA, which is unique to kinetoplastids. The kDNA is believed to have a species-specific structure made up of mainly minicircles, with some maxicircles, and the size of the structure is determined by the minicircles (Luke
s et al., 1999). Kinetoplastids have a unique form of RNA editing of kDNA, which involves guide RNAs encoded by the minicircles that insert or delete uridine residues and posttranscriptionally edit the encrypted maxicircles (Luke
s and Yurchenko, 2000; Hajduk and Ochsenreiter, 2010).

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Box 1 Investigating and understanding Trypanosoma life histories (cont'd) Minicircle size and kinetoplast thickness have even been used in the past to differentiate between trypanosome strains (Votýpka et al., 2004) using TEM. The kDNA of Trypanosoma avium was found to have larger minicircles compared to other trypanosomatids (Luke
s and Yurchenko, 2000). The kinetoplast has been investigated in a number of trypanosomes including T. cruzi, T. brucei, Trypanosoma corvi and T. avium (Luke
s et al., 1999; Votýpka et al., 2004). Acidocalcisomes are membrane-bound organelles that contain calcium, phosphorus, sodium, potassium and zinc, and store the majority of the calcium content in different forms of T. cruzi (Miranda et al., 2000). The acidocalcisomes are thought to arise in response to different environmental conditions in the host (Fig. 2). Due to the importance of calcium in the cellular invasion process, acidocalcisomes are of particular interest in T. cruzi. Acidocalcisomes have been found to have a species-specific composition in a number of different trypanosomes (Miranda et al., 2004), which has been determined using electron microscopy techniques (Lu et al., 1998), immunocytochemistry (Scott et al., 1998) and X-ray microanalysis (Scott et al., 1997; Miranda et al., 2004). Reservosomes, which are described as lysosome-related organelles, are found in the posterior region of epimastigotes; these have been studied extensively in T. cruzi (Fig. 2) (De Souza, 1999; De Souza, 2002; Sant’Anna et al., 2008). They act as a form of storage in trypanosomes, containing proteases and accumulating proteins utilized in endocytosis, which is important during the process of differentiation and growth. Lysosomal proteases in T. cruzi that are found in reservosomes include cruzipain and serine carboxypeptidase, which are also important in the cell invasion process (Cunha-e-Silva et al., 2002; Sant’Anna et al., 2008). Reservosomes lack lysosomal markers and have an acidic nature, but no reservosome molecular marker has been identified. Interestingly, T. brucei and Leishmania spp. exhibit high levels of endocytic activity but lack reservosomes. Glycosomes are another type of organelle present in trypanosomes, which contain the majority of glycolysis enzymes similar to lysosomes, or vesicles likened to peroxisomes (Fig. 2). In addition, the golgi complex is involved in protein glycosylation and membrane trafficking in T. cruzi (Fig. 2) (Martins et al., 2012). With much of the morphological data that have been acquired, there are a number of difficulties associated with interpreting and interpolating two-dimensional images and relating these back to the overall structure of a living, threedimensional organism. This is particularly true for TEM where prepared sections, which are w100-nm thick, allow for high resolution ultrastructural cell analysis but represent only a very small segment of a cell or organism. However, the very recent advent of automated, high-throughput imaging systems capable of three-dimensional imaging at subcellular resolution has led to the beginnings (Continued)

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Box 1 Investigating and understanding Trypanosoma life histories (cont'd) of a revolution in parasite biology, particularly in regard to cellular ultrastructure and hosteparasite interactions. With this, the 3D cellular architecture of T. brucei, T. cruzi and T. dionisii has been documented to some degree for a variety of morphological forms (Lacomble et al., 2009; Ramos et al., 2011; Girard-Dias et al., 2012; Oliveira et al., 2013). Examples include electron tomography, which has been used to investigate the structure of the flagellar pocket and cytoskeleton in T. brucei (Lacomble et al., 2009) and demonstrated the movement of the basal body aids in cell division (Lacomble et al., 2010). Of most interest to parasitologists, the world over will be the development and application of superresolution techniques (e.g., structured illumination microscopy (SIM), stochastic optical reconstruction microscopy (STORM)) (Galbraith and Galbraith, 2011), focused ion beam scanning electron microscopy (SEM; De Winter et al., 2009; Alcantara et al., 2014), block-face serial imaging by SEM (Denk and Horstmann, 2004) and automated tape collection ultramicrotomy and serial imaging by SEM (Schalek et al., 2011).

Box 2 Looking into the past: difficulties with early records There are significant challenges associated with trying to fully understand, interpret and compare available data on trypanosomes that have been isolated from Australian wildlife. In many cases, isolates have not been reported again following the original description and morphological forms may have been misrepresented where the species has been named from only a few samples (Mackerras, 1959, 1960; Mackerras and Mackerras, 1959, 1960). These issues stem from the typical opportunistic nature of sampling and reporting on trypanosomes in wildlife, which has been comprised mainly of incidental observation while investigating the host’s health in general and not looking specifically for Trypanosoma spp. The molecular techniques used routinely in modern times to isolate and investigate DNA sequences from various gene regions did not exist when early records began. Early records were restricted to describe trypanosomes based on the assumptions that they were host species specific and that dimensions of the parasite in blood smears were sufficient to characterize different species. In many of the early accounts of trypanosomes described from Australia, they were simply compared to trypanosomes from a similar host organism that originated from Europe or Asia (Mackerras, 1960; Hoare, 1972). These assumptions are unfounded and the characterization of trypanosome life histories, and the description of trypanosome species is far more complex (Votýpka et al., 2015). Recent evidence shows that Australian trypanosomes

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Box 2 Looking into the past: difficulties with early records (cont'd) rarely exhibit species specificity (Thompson et al., 2014) and can exhibit several different morphological forms in the natural host (Austen et al., 2009; Thompson et al., 2013). In addition, due to the impossibility in confidently describing trypanosomes to species level based on a few individuals seen in a blood smear, many trypanosomes have only been reported as Trypanosoma spp (Table 1). The development of molecular tools to complement morphological analysis has allowed advances in this space, but such tools still do not hold all the answers when it comes to classify and define trypanosome species. For example, the two gene regions (18S rDNA and gGAPDH) commonly used to characterize trypanosomes are excellent tools when used together; however, it is important to be careful with interpretation of 18S rDNA phylogenies alone, due to the large evolutionary shift that occurred early in this gene region, which makes it unsuitable to differentiate between taxa (Hamilton and Stevens, 2011; Lymbery et al., 2011). Further, the parameters that separate species based on molecular data are unclear and can be difficult to decipher. There is no consensus on the number of mutations that define the boundaries between species (Votýpka et al., 2015). This has resulted in the description of numerous genotypes of both described species and unnamed species of trypanosomes isolated from Australian wildlife (Table 1) (Averis et al., 2009; Paparini et al., 2011). In some cases, trypanosomes have not been given a species name when only molecular and morphological data from an in vitro system were available (Noyes et al., 1999; Hamilton et al., 2004). This may be due to previous studies on T. cruzi and T. brucei, which demonstrated that in vitro forms of trypanosomes showed high levels of morphological plasticity in their life-cycle stages (Contreras et al., 1985; Rondinelli et al., 1988; Tyler and Engman, 2001). The current understanding of wildlife trypanosomes is further confused by the discovery of mixed infections of trypanosome species in the same host (Paparini et al., 2011; Thompson et al., 2013; Botero et al., 2013). This unexpected finding was made while screening woylie blood samples for trypanosome DNA. The prevalence of such multiple species infections in host populations is essentially unknown. It is likely that previous reports indicating the presence of different morphological forms of trypanosomes in blood smears could actually reflect the existence of multiple species infections, but this is difficult to determine without complementary molecular approaches (Paparini et al., 2011; Botero et al., 2013). In the past, many animals have been considered free from trypanosomes due to the absence of these organisms in blood smears. Recent research suggests that even if trypanosomes are not observed in blood smears, trypanosome DNA can be isolated from the blood, leading to potentially misleading results regarding real parasite prevalence (Botero et al., 2013; Thompson et al., 2013). (Continued)

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Box 2 Looking into the past: difficulties with early records (cont'd) Many of the problems with understanding genetic relationships between Australian trypanosomes arise from the lack of genetic data available from other associated taxonomic groups within Australia and from the fact that essentially no data are available from samples that were collected before molecular techniques were developed. There are a number of Australian trypanosomes for which there are no available molecular data. In particular, trypanosomes isolated from reptiles, amphibians and fish taxa are highly underrepresented. However, it is evident that genetic data cannot characterize unknown specimens of trypanosomes without complementary morphological information to offer additional insight into behaviour, life histories and hosteparasite relationships. When one tries to go beyond this lack of comparative morphological and genetic data available for Australian trypanosomes, the fundamental problem remains that there is little opportunity to adequately study trypanosomes in Australian wildlife in vivo. This is often because (1) many marsupial and other native species are considered vulnerable or endangered and cannot be biopsied or killed for research; (2) appropriate in vivo models for these animals are not established; (3) wildlife populations are often in remote areas, and trapping/sampling is not straightforward; and (4) opportunistic sampling of blood from wildlife does not go far enough in allowing us to understand the nature of trypanosome infections and in vivo behaviour. Conversely, the main disadvantages of experiments conducted in vitro stem from the acknowledgement that the environment is unnatural (i.e., altered nutrient availability, pH and temperature) and the biological system has been simplified (i.e., single cell types only, low levels of antibodies), so data describing morphological appearances and stages must be interpreted carefully (Wilson, 2005). Given the complexity of trypanosome life histories, which can exhibit multiple morphological stages and lack host specificity, resulting in exceptional morphological plasticity in their natural environments (Castellani et al., 1967), it can be assumed that in vitro methods do not sufficiently cater for the complexity of parasite life cycles. When in vitro, organisms are not responding under normal environmental conditions but are being selected based on the particular physiological conditions they are exposed to (Freshney, 2011), and the exact components of cell media and growth conditions cannot always be controlled. Wildlife parasites are not often cultivated; therefore their preferred growing requirements are not generally well defined, unlike for example, T. cruzi, which has been grown in culture for decades. Many of the descriptions of Australian trypanosome stages in vitro are not commonly seen in vivo and are based on few observations. It is difficult to decipher the morphological stages that actually occur in the host or the vector and the stages that only exist in the carefully controlled (manipulated)

Australian Trypanosome Life Histories

Box 2 Looking into the past: difficulties with early records (cont'd) laboratory environment. This stems from the lack of in vivo investigations in the natural host and vector. The trypanosomes isolated from wildlife in Australia are isolated from the host but grown as the invertebrate vector morphological form at 28 C because this is the most viable method used to cultivate trypanosomes in vitro (Castellani et al., 1967; Rondinelli et al., 1988; Tyler and Engman, 2001). The problem with this is the trypanosomes that have not been observed in the invertebrate vector because the invertebrate vectors of Australian trypanosomes are not even identified. In addition, other factors that may influence whether trypanosome infection is a factor in host health decline such as parasite load, stress, immune responses or concurrent infections, simply cannot be observed or monitored in vitro. Despite these limitations, it remains unfortunate that few trypanosomes have been isolated in vitro from Australian wildlife. There is no doubt that studies utilizing in vitro populations facilitate our understanding of cell differentiation and the development of key, morphological stages, allow for detailed studies into characterization of cell architecture and composition across a range of length scales (e.g., kinetoplast, acidocalcisome structure) (Luke
s et al., 1999; Miranda et al., 2004; Lacomble et al., 2010; Girard-Dias et al., 2012), and provide opportunities to study trypanosome interactions with each other and with host cells, in real time (see Box 1). A number of potential vectors have been suggested for various Australian trypanosomes based on their presence in various invertebrate candidates (Mackerras and Mackerras, 1959; Austin et al., 2009; Paparini et al., 2014). The vectors of trypanosomes in Australia have been difficult to identify as most vectorial candidates are hematophagous insects, and any insect recently feeding on a mammal infected with trypanosomes would most likely contain parasites. The presence of trypanosomes in an insect does not make it a vector, although mechanical transmission cannot be ruled out (see Box 1). The trypanosomes may not be differentiating in the gut of the invertebrate but can survive in their digestive tract to be passed on to another host. Vectors will be important in understanding the vast genetic divergence of Australian trypanosomes (Hamilton et al., 2004). Therefore it is unfortunate that no conclusive evidence exists with regard to invertebrate vector(s) of trypanosomes in Australian wildlife. Overall, the very likely possibility that many of the existing reported species of trypanosomes in Australian wildlife belong to the same species or belong to another, already identified, species, cannot be ignored (Table 1). In short, the entire area needs to be taxonomically overhauled and reclassified, but when you consider the issues identified previously, including the paucity of available samples, such a task will be difficult, if not impossible.

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2.1 Australian trypanosomes found in reptiles, birds, amphibians and fish 2.1.1 Reptile trypanosomes The first record of a trypanosome isolated from an Australian reptile was Trypanosoma chelodina described from an eastern snake-necked tortoise (Chelodina longicollis) in South Australia (SA), and this species was subsequently found in New South Wales (NSW) and Queensland (QLD) (Johnston, 1907; Mackerras, 1960). The host range was later widened to include the Murray River turtle (Emydura macquarii), short-necked tortoise (Emydura krefftii), saw-shelled tortoise (Elseya latisternum), white-throated snapping turtle (Elseya dentate) (Mackerras, 1960), and the Brisbane River tortoise (Emydura signata) (Jakes et al., 2001). Bloodstream trypomastigotes observed from blood smears were 39.5e43 mm in length and T. chelodina was compared to Trypanosoma vittatae from a Ceylon tortoise (Johnston, 1907). A phylogenetic tree utilizing the 18S rDNA region shows that this trypanosome is closely related to other trypanosomes isolated from aquatic environments outside Australia that are usually transmitted by water-borne vectors such as leeches (Jakes et al., 2001). Trypanosoma egerniae was the second trypanosome described from a reptile and was observed in blood smears from the Cunningham’s skink (Egernia cunninghami) and tree skink (Egernia striolata) in Sydney, NSW, and Eidsvold, QLD (Mackerras, 1960). The average length of the trypanosomes from the tree skink was 22e28 mm, measured from only one blood smear. A single trypanosome found in a blood smear from the Cunningham’s skink was reported as larger, but otherwise similar, resulting in a diagnosis of T. egerniae. Considering the difference in size between these two accounts, it is difficult to be sure they are the same species. Trypanosoma phylluri was the third trypanosome isolated from a reptile in Sydney, NSW, in blood smears from leaf-tailed geckos (Phyllurus platurus) (Mackerras, 1960). Of the 69 geckos tested, 11 were positive for trypanosomes 38e48 mm in length. T. phylluri was described as very delicate, containing a granular cytoplasm, close together nucleus and kinetoplast, and a short flagellum. The authors remarked that it was similar in appearance to a number of trypanosomes from geckos in Ceylon (Trypanosoma pertenue) and Tunisia (Trypanosoma platydactyli) (Mackerras, 1960). Leaf-tailed geckos were experimentally infected using citrated heart blood from one gecko with a known infection, but no trypanosomes were seen in blood smears either 3 weeks or 3 months after infection. Nothing else is known about T. phylluri life history

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or development. There are currently no molecular data available on T. egerniae or T. phylluri, and neither species has been reported after the original description. 2.1.2 Bird trypanosomes The first record of a trypanosome isolated from a bird in Australia was Trypanosoma anellobiae (Cleland and Johnston, 1910), observed in a little wattlebird (Anthochaera chrysoptera) in QLD. Bloodstream trypomastigotes were 30-mm long with a well-developed undulating membrane (Mackerras and Mackerras, 1959). Trypanosoma chlamydoderae (Breinl, 1913) was described from one of five great bower birds (Chlamydera nuchalis) examined, and ‘stout’ and ‘broad’ morphological forms were described (Mackerras and Mackerras, 1959). Trypanosoma notophoyxis was isolated from one of six white-faced herons (Ardea novaehollandiae) examined in QLD (Breinl, 1913), and two morphological forms of different sizes were observed. A similar trypanosome found in the Pacific reef heron (Demigretta sacra) was described as being possibly homologous to T. notophoyxis despite being smaller in size (Mackerras and Mackerras, 1959). Trypanosoma myzanthae was described as 19e24 mm in length and observed in three of four noisy minor birds (Manorina melanocephala). T. myzanthae was differentiated from other trypanosomes by a membrane that extended around the cell body and differed from T. notophoyxis in dimensions only (Mackerras and Mackerras, 1959). Oddly, the two trypanosomes from the heron were given the same species name despite differences in size, while the trypanosome from the minor bird was described as a novel species due to a difference in size. Trypanosoma eudyptulae in Tasmania was the first trypanosome isolated from a penguin, the little penguin (Eudyptula minor) (Jones and Woehler, 1989). Blood smears were collected from 57 animals, and nine animals were infected. T. eudyptulae was 36e52 mm in length based on measurements of only six individual trypanosomes and considered a novel species based on the close proximity of the nucleus and kinetoplast. Trypanosoma sp. AAT remains the only trypanosome isolated from an Australian bird (currawong, Strepera sp.) and grown in vitro (Hamilton et al., 2005). T. sp. AAT is a large trypanosome, and epimastigotes grown in vitro exhibit a free flagellum up to 42 mm in length. Subsequent genetic investigations have placed T. sp. AAT with members of the species Trypanosoma corvi, a common bird trypanosome found in Europe, America, and Asia (Zídkova et al., 2012) (see Section 3).

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2.1.3 Amphibian trypanosomes The only trypanosome named to species level isolated from an Australian amphibian was originally considered a specimen of Trypanosoma rotatorium (Cleland and Johnston, 1910), a trypanosome of European frogs. It was subsequently given the species name Trypanosoma clelandi 6 years later on reexamination (Johnston, 1916). It was first described in the ornate burrowing frog (Limnodynastes ornatus) and then the spotted grass frog (Limnodynastes tasmaniensis) found in QLD and SA (Johnston, 1916). The nucleus and kinetoplast were described as being close together, and the total length was 22.6e 37.7 mm. Infections were found in 4 of 17 ornate burrowing frogs in a subsequent study, which observed a ‘smaller’ and ‘larger’ morphological form in the blood smears (Mackerras and Mackerras, 1960). No recent identification of this parasite has been recorded, and there are no molecular data available for this species. The only trypanosome from an Australian frog that was characterized using molecular methods was isolated from Fleay’s barred frog (Mixophyes fleayi) (Hamilton et al., 2005). The frog trypanosome was highly similar to T. cyclops from a Malaysian primate and Trypanosoma sp. ABF isolated from a swamp wallaby (Wallabia bicolor). 2.1.4 Fish trypanosomes Trypanosoma bancrofti was the first trypanosome isolated from an Australian fish and was described from a freshwater catfish (Tandanus tandanus) in QLD (Johnston and Cleland, 1910). It was pleomorphic ranging from 27 to 50 mm in length in blood smears. T. bancrofti was reported many years later in a heavily infected catfish in Cairns, but the trypanosomes observed in blood smears were conserved in size with no dividing forms 21.5e25 mm in length (Mackerras and Mackerras, 1960). It can only be assumed that the trypanosome specimens were considered homologous due to their presence in the same host species. The large difference observed in their size may indicate the presence of more than one species. Trypanosoma anguillicola was described from a longfin eel (Anguilla reinhardtii) in QLD and was 35.5e 40 mm in length including the free flagella (Johnston and Cleland, 1910). It was different to Trypanosoma granulosum (a trypanosome found in eels in Europe) in size because it lacked granules in the cytoplasm. Trypanosoma pulchra (Mackerras and Mackerras, 1925) was found in a sea perch (Hypoplectrodes semicincta) and a white-ear (Parma microlepis) from Sydney harbour and was described as a long slender trypanosome 40.8e51.7 mm in length. It remains unknown why these two trypanosomes from the sea perch and whiteear were considered homologous. Trypanosoma aulopi (Mackerras and

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Mackerras, 1925) was found in a single sergeant baker (Latropiscis purpurissata) from Sydney Harbour out of eight specimens examined. T. aulopi was separated from T. pulchra based purely on the observation that taxonomically the host species were far apart. These unrelated host species living in Sydney harbour may share ectoparasites, and both ‘small’ and ‘large’ morphological forms were present in the same host, indicating that they were possibly the same species (Mackerras and Mackerras, 1925). Trypanosoma carchariasi was described as a long narrow trypanosome 60e70 mm in length including the free flagellum isolated from an unidentified shark (Carcharias sp.) (Mackerras and Mackerras, 1960). Trypanosoma mackerasi is listed in the Atlas of living Australia and in memoirs of the QLD museum under Burreson (1989), found in an epaulette shark (Hemiscyllium ocellatum) and an ocellated angelshark (Squatina tergocellatoides) (O’Donoghue and Adlard, 2000). Currently, no molecular data are available on trypanosomes isolated from Australian fish, making confirmation of species descriptions and differentiation difficult.

2.2 Australian trypanosomes found in mammals 2.2.1 Trypanosoma sp. H25 The Australian trypanosome T. sp. H25 was initially isolated from an eastern grey kangaroo (Macropus giganteus) (Stevens et al., 1999; Noyes et al., 1999). Following initial isolation, additional genotypes of T. sp. H25 have been identified in the blood of a number of different marsupials in Western Australia (WA) including; Trypanosoma sp. D15/D17/D64 isolated from the common brush tail possum (T. vulpecula), and Trypanosoma sp. H25 (G8) isolated from the banded hare-wallaby (Lagostrophus fasciatus), boodie (Bettongia lesueur), and woylie (Noyes et al., 1999; Paparini et al., 2011; Botero et al., 2013). T. sp. H25 bloodstream trypomastigotes have not been identified in blood smears of infected animals, indicating that they exhibit low parasitaemia or they exist in the tissue cells when the host immunity suppresses them in the bloodstream (Noyes et al., 1999; Paparini et al., 2011; Thompson et al., 2013). T. sp. H25 isolated from the kangaroo was isolated in vitro, and the dominant morphological forms observed were slow-moving ‘promastigotes’ 17 mm in length, with extended flagella sheaths that aid in the formation of rosettes by the parasites, attached by hemidesmosomes (Noyes et al., 1999). The fusion of hemidesmosomes was observed in a transmission electron microscopy (TEM) micrograph of sectioned ‘promastigotes.’ While the description itself is appropriate, it should be noted that the term promastigotes actually refers to the morphological form of Leishmania parasites found in the

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invertebrate host. The term is not usually used in reference to species of Trypanosoma. The reason for the use of the term by Noyes is unclear but should be discouraged when referring to Trypanosoma. Based on the information available, these promastigotes described by Noyes et al. (1999) are most likely epimastigotes. In fewer numbers, fast-moving trypomastigotes 5.5 mm in length were observed that would inhabit the invertebrate gut along with the epimastigotes. Morphological forms similar to these small trypomastigotes have been observed in other trypanosomes in vitro, such as T. lewisi isolated from flea rectums (Hoare, 1972; Noyes et al., 1999). In T. lewisi, the small trypomastigotes invade the cells of the invertebrate gut and are not present in the vertebrate host (Hoare, 1972). However, the function of the small metacyclic trypomastigotes in T. sp. H25 remains unknown. Two additional morphological forms were reported in T. sp. H25 in very low numbers that were referred to as ‘nectomonads’ (a morphological form observed only in Leishmania spp.) and ‘epimastigotes’ (Noyes et al., 1999). In the light micrographs, these ‘nectomonads’ and ‘epimastigotes’ appear the same as the ‘promastigote’ except they have an emergent flagellum. T. sp. H25 was not infective to rhesus monkey kidney epithelial cells (LLCMK2) in vitro, or mice in vivo (Noyes et al., 1999). However, there is no report of the presence of large numbers of metacyclic trypomastigotes (the infective morphological form in the vertebrate host); therefore it must be assumed that slow-moving epimastigotes were used to infect the cells and mice. Infection would not be expected from morphological forms other than metacyclic trypomastigotes, or possibly amastigotes (Hoare, 1972). T. sp. H25 DNA has been isolated from biting flies including tabanid flies, mosquitos and sand-flies identifying them as potential vectors (Thompson, 2014). Invertebrate samples were screened in pooled data sets after collection using both Marris Style Malaise and Nzi traps rather than collecting insects directly from animals in order to reduce the possibility of false positives. Unfortunately the presence of trypanosomes is not sufficient to implicate biting flies as a vector. The morphology and life history of T. sp. H25 are of particular importance due to its close genetic proximity to T. cruzi (see Section 3). It is the intracellular life cycle of T. cruzi that is pathogenic; therefore it is important to determine if T. sp. H25 can cause symptoms of chronic trypanosomiasis in the vertebrate host. 2.2.2 Trypanosoma copemani T. copemani has been identified in a number of marsupials under a range of different synonyms, due to its high intraspecific diversity

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(Austen et al., 2009; Hamilton et al., 2005; Botero et al., 2013; Austen et al., 2015a). T. copemani was initially identified in wombats (Vombatus ursinus) as T. sp. H26 (Noyes et al., 1999), and subsequently Trypanosoma sp. AAP and Trypanosoma sp. AAI in NSW (Hamilton et al., 2005). T. copemani was designated a species name after isolation of four genotypes from the Gilbert’s potoroo (Potorous gilbertii) and the quokka (Setonix brachyurus) in WA (Austen et al., 2009). Two additional strains were described from the woylie-designated genotype 1 (G1) and genotype 2 (G2) in WA (Botero et al., 2013). The host range was further extended through subsequent studies to include the koala (Phascolarctos cinereus) (McInnes et al., 2011b), quenda (Isoodon obesulus fusciventer), western quoll (Dasyurus spp.), and common brush-tailed possum (Botero et al., 2013). Together, these genotypes comprise the T. copemani clade. Interestingly, the trypanosomes within the T. copemani clade are closely related to two other Australian trypanosome species, Trypanosoma gilletti and Trypanosoma vegrandis (see Section 3.2). The kinetoplast DNA (kDNA) of T. copemani G1 and G2 also appear to be different from each other (Botero et al., 2013). Genetic sequences of the kDNA demonstrated conserved and divergent regions between G1 and G2 when compared to T. cruzi, signifying that the two strains of T. copemani isolated from the woylie could actually be different species. However, further genomic, proteomic, and morphological data are required to confirm whether the genotypes within the T. copemani clade are all the same or potentially different species (Austen et al., 2009; Botero et al., 2013). Various morphological forms of T. copemani have been described in samples from the bloodstream. Both a ‘slender’ and a ‘broad’ bloodstream trypomastigotes were observed in T. copemani isolated from the Gilbert’s potoroo, which were on average 37 mm in length. A similar ‘slender’ bloodstream trypomastigote was observed in the quokka (38 mm in length) (Austen et al., 2009). Bloodstream trypomastigotes of T. copemani from the woylie also exhibited ‘slender’ and ‘broad’ morphological forms (Thompson et al., 2013). While samples in the bloodstream are readily recognized, a number of additional morphological forms of T. copemani have been reported from in vitro systems, leading to conflicting ideas regarding what stages exist (Austen et al., 2009, 2015a; Hamilton et al., 2005; Botero et al., 2013). It is important to remember that trypanosomes grown at 28 C and maintained in culture are expected to take the form of the epimastigote, which would typically be seen in the invertebrate vector gut (Castellani et al., 1967; Hoare, 1972). The first reported images of

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T. copemani in vitro were dividing trypomastigotes of T. sp. H26 and T. sp. AAP (Noyes et al., 1999; Hamilton et al., 2005). Promastigotes and sphaeromastigotes were described as dividing in vitro in T. copemani isolated from the Gilbert’s potoroo (Austen et al., 2009). The appearance of ‘promastigotes’ most likely reflects changing conditions in the environment because these morphological forms are not observed in Trypanosoma spp. in vivo. The morphological form observed here is most likely an epimastigote (Fig. 2). The extracellular sphaeromastigotes in vitro occur when amastigotes differentiate into epimastigotes in vitro at 28 C due to varying glucose levels (Tyler and Engman, 2001) and are not the morphological form seen when T. cruzi amastigotes transform into trypomastigotes inside the mammalian cell at 37 C (Fig. 1). Sphaeromastigotes, amastigotes, promastigotes, and an elongated trypanosome were reported in vitro of T. copemani isolated from the quokka, and from blood smears confirmed using fluorescence in situ hybridisation (FISH) (Austen et al., 2015a). The presence of extracellular amastigotes in blood smears and in in vitro culture was reported, but no nucleus or kinetoplast can be seen, and the trypanosomes do not exhibit the smooth round shape typical of an amastigote. In addition, amastigotes are not usually reported from the bloodstream because they are known to be the intracellular stage in both Leishmania spp. and T. cruzi in the vertebrate host. Images of ‘promastigotes’ in T. copemani are shown with two flagella and a tapered end, suggesting that they are actually dividing epimastigotes, which appear to be swelling in the midarea due to the division of the kinetoplast and nucleus. A scanning electron micrograph of this life stage was presented, but neither the flagella nor the flagellar pocket can be seen to confirm the morphological form. Two life stages that were reported, including a ‘thin’ and ‘tiny’ morphological form, do not appear to be trypanosomes due to the lack of any kinetoplast or nuclear structures. Of particular interest, surrounding T. copemani stages was the initial report of intracellular amastigotes by Botero et al. (2013). Epimastigotes, sphaeromastigotes, trypomastigotes, and intracellular amastigotes were all reported in in vitro cultures of T. copemani isolated from the woylie (Botero et al., 2013). However, the significance of observing amastigotes inside mammalian cells relates to the understanding that this behaviour only exists in a small number of trypanosome species and that this intracellular stage is linked to trypanosome pathogenicity. A number of questions about T. copemani’s intracellular behaviour and its potential to cause disease remain unanswered (see Section 4.1.4).

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Together, the plethora of different morphologically discreet forms described in culture most likely reflect the pleomorphism of trypanosomes in vitro, which is highly dependent on the physiological conditions they are grown in, rather than true natural metacyclogenesis of the morphological forms. These data highlight the difficulty in understanding and comparing the morphology of parasites grown in vitro, with those that have been detected by in vivo sampling (see Box 2). It is likely T. copemani divides as either epimastigotes or trypomastigotes when in vitro and that this is dependent on the nutrients in the media. However, further investigation is required in order to truly understand the life history and behaviour of T. copemani in both in vivo and in vitro systems. Austen et al. (2011) identified Ixodes australiensis as a potential vector for T. copemani after tick haemolymph and midgut tested positive for T. copemani both 49 and 117 days after collection. Intact trypomastigotes were observed from gut and haemolymph samples suggesting that they may have survived in the invertebrate for the duration of storage (30 days). T. copemani DNA was identified in the midgut but not the faeces of the ticks, yet some trypanosomes were identified in dried tick faeces left in the collection tube. Strangely, these do not resemble the trypanosomes found in quokka blood and instead resemble the in vitro forms of T. copemani. In relation to this report, it is important to recognize that the presence of trypanosomes in an invertebrate is not sufficient to implicate transmission, whether cyclical, mechanical, or via ingestion. In this case, ticks were collected directly from animals making it possible that the trypanosomes were simply acquired by the tick during a blood meal prior to collection. Nevertheless, ticks are a possible vector for T. copemani because they prey on all known hosts of T. copemani and inhabit all regions throughout Australia. As woylies in wildlife sanctuaries have been infected with T. copemani continuously for a number of years, it is assumed that the vector is not seasonal and is relatively common (Thompson et al., 2013). However, the continual presence of trypanosome DNA in woylie blood could also indicate that they are suffering from a chronic infection and not be related to the vector or transmission. As such, it is clear that more research is required before the vector of T. copemani is identified. 2.2.3 Trypanosoma vegrandis T. vegrandis was recently identified as the smallest trypanosome described to date in blood smears isolated from the woylie in WA (6.9e10.5 mm), and two morphological forms were observed (Thompson et al., 2013). However, due to their very small size, further studies are required to confirm that there

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are two morphological forms. T. vegrandis is prevalent in the southwest region of WA and occurs in mixed infections with T. copemani (Thompson et al., 2013). Before this species was formally identified, T. vegrandis was reported as a number of different genotypes under the synonyms; TRY1, TRY2, WYA1, CHA1, WYA2 (Smith et al., 2008), D4, D27, D28 (Paparini et al., 2011), and clade B (G3, G4, G5, G6, G7) (Botero et al., 2013). Together, these studies extended the host range to include the woylie, western grey kangaroo (Macropus fuliginosus), quenda and tammar wallaby (Macropus eugenii). Subsequently, T. vegrandis has been identified in bats (Austen et al., 2015b) and koalas (Barbosa et al., 2016). It was the continual isolation of T. vegrandis DNA in blood samples that led researchers to investigate other means to visualize the parasite. A specific FISH probe was applied to tag the parasites in blood smears (Thompson et al., 2013). The discovery of T. vegrandis raises the question of whether other species have not been identified due to the difficultly of adequately resolving them in an optical microscope. In phylogenetic studies, T. vegrandis is closely related to Australian trypanosomes T. gilletti and T. copemani (Smith et al., 2008; Paparini et al., 2011; Botero et al., 2013). It is not clear if the different genotypes that exist in the T. vegrandis clade represent different species as the clade exhibits high levels of intraspecific diversity. 2.2.4 Trypanosoma irwini T. irwini was isolated and described in QLD from koalas in both NSW and QLD (McInnes et al., 2009). Bloodstream forms of the parasite were 32e 38 mm in length, and attempts to culture the parasite in vitro were unsuccessful. T. irwini occurs in mixed infections with T. gilletti (McInnes et al., 2011a). The most closely related trypanosome to T. irwini was T. bennetti, an avian trypanosome isolated from an American kestrel in phylogenetic studies (McInnes et al., 2009). 2.2.5 Trypanosoma gilletti T. gilletti was described in koalas from 18S rDNA molecular data only (McInnes et al., 2011b). The parasite was not isolated in vitro or identified in blood smears. Therefore no morphology is available for comparison of T. gilletti to other trypanosomes, and it is not possible to determine if this trypanosome has previously been described. Two distinct morphological forms of trypanosomes were observed in blood smears from animals infected with both T. gilletti and T. copemani (McInnes et al., 2011a). However, bloodstream trypomastigotes of T. copemani are pleomorphic in blood smears

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making them difficult to identify by morphology alone (Thompson et al., 2013). Therefore it is not possible to confidently differentiate between the two different morphological forms seen in blood, since they may have both been T. copemani, or one T. copemani and one T. gilletti. Koalas are often infected with ticks, so they were suggested as possible invertebrate vector candidates (McInnes et al., 2011a). T. gilletti is closely related to T. copemani and T. vegrandis at two genetic loci, 18S rDNA and gGAPDH (Paparini et al., 2011; Botero et al., 2013; Thompson et al., 2013). 2.2.6 Trypanosoma binneyi T. binneyi was described by Mackerras (1959) after the collection of blood from a platypus (Ornithorhynchus anatinus) in 1950. A large (47e67 mm) broad trypanosome with a darkly staining nucleus was observed in Giemsa-stained smears. It was the second trypanosome described from a monotreme, the first being a vague description of a long slender trypanosome 75-mm long found in two animals collected in 1933 by a Dr Owen, with no distinguishing features about the specimen recorded (Mackerras, 1959; Hoare, 1972). It was noted that T. binneyi was similar morphologically to trypanosomes from a crocodile and a water bird (Mackerras, 1959). The first molecular data for this parasite were provided by Noyes et al. (1999). T. binneyi remains the only trypanosome described morphologically and subsequently characterized genetically, except T. chelodina. Phylogenetic studies found that T. binneyi in the platypus was closely related to T. chelodina isolated from turtles in Australia, T. granulosum from European eels, and T. boissoni isolated from fish (Jakes et al., 2001; Hamilton et al., 2005; Paparini et al., 2014). Leeches and water-borne ectoparasites were considered as potential vectors for T. binneyi in the platypus (Hamilton et al., 2005). Their potential status as vectors arose not just from the presence of trypanosomes but from the large amount of DNA present in samples when parasitaemia was generally low in the vertebrate host. In addition, the insects were not taken from the hosts, but collected separately indicating the potential vectors had not fed for some time (Hamilton et al., 2005). The aquatic leech was proposed as the most likely candidate for the vector by a later study, which isolated T. binneyi using molecular techniques (Paparini et al., 2014). 2.2.7 Trypanosoma sp. ABF Hamilton et al. (2005) described the trypanosome T. sp. ABF, which was isolated from a swamp wallaby in NSW. This research was concerned with the genetic relationship between the organisms isolated in the study, and

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consequently morphological notations on the strains were brief and restricted to optical microscopy images of in vitro forms. T. sp. ABF appears stout with a darkly staining cytoplasm after Giemsa staining. Molecular investigations positioned T. sp. ABF within the T. theileri clade alongside Trypanosoma cyclops, a trypanosome isolated from a Malaysian primate. Haemadipsid leeches collected in QLD and Victoria tested positive for T. sp. ABF along with a frog trypanosome isolated in Australia (Hamilton et al., 2005). 2.2.8 Trypanosoma thylacis T. thylacis was isolated from the northern brown bandicoot (Isoodon macrourus) and southern brown bandicoot (I. obesulus) (Mackerras, 1959). Bandicoots were sampled in Brisbane, QLD, and 12 of 82 bandicoots were found to be infected. One bandicoot infected with T. thylacis was sampled 25 times during 7 weeks, and parasites were always present, but in low densities. Trypanosomes from blood smears were described as broad, the cytoplasm either clear or granular after Giemsa staining and 22e32 mm in length. T. thylacis was also found in subcutaneous tissue, serous fluid and the lymph gland. Trypanosomes from the lymph gland were described as being slender with the kinetoplast being just posterior to the nucleus at 19e22 mm in length. Attempts to infect laboratory animals with T. thylacis were unsuccessful. Parasites were grown in vitro in Novy-MacNeal-Nicolle (NNN) media at 28 C, and epimastigotes and intermediate stages were described as crithidial (epimastigote) and leishmanial (amastigote) forms. Epimastigote and amastigote morphological forms are noninfective in the vertebrate host, which would contribute to no infection being observed. No distinct relationship was described between the different forms of the parasite in vitro, although it was suspected that they were the same organism. Slender trypanosomes similar to T. thylacis were observed in tick nymphs (Ixodes holocyclus) collected from bandicoots during the study (Mackerras, 1959). Despite the prevalence of T. thylacis in the bandicoots, this species has not been identified since. A subsequent study identified trypanosomes in southern brown bandicoots and eastern barred bandicoots (Perameles gunnii), but the authors concluded that the morphology of these was different to T. thylacis (Bettiol et al., 1998). Bandicoots have since been screened for trypanosomes (Botero et al., 2013), but only blood samples were collected for genetic screening, which would not have identified T. thylacis as there are no molecular data available on this species. However, considering the prevalence of T. thylacis in the study describing it, it is likely that DNA for the parasite has been isolated and was not recognized. There remains a possibility

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that T. thylacis is homologous to T. copemani, T. sp. ABF, or another trypanosome recently characterized using molecular techniques. 2.2.9 Trypanosomes in bats In 1913, Trypanosoma pteropi was the first trypanosome described in an Australian mammal from a single flying fox (Pteropus gouldii) in QLD (Breinl, 1913). Subsequent observations noted that they appeared similar to Trypanosoma vespertilionis, a trypanosome infecting bats in Europe and a member of the T. cruzi clade (Mackerras, 1959). However, T. pteropi was not isolated in vitro in either of these studies, and this observation was not investigated further. The comparison to trypanosomes from the T. cruzi clade indicates that T. pteropi could be similar to T. sp. H25. Trypanosoma hipposideri was a relatively small (10.5e13 mm) trypanosome identified from the blood smear of a dusky horseshoe bat (Hipposideros bicolor albosinensis) in QLD (Mackerras, 1959). No subsequent identifications of this species have been made, and there are no genetic data available on any trypanosome isolated from an Australian bat from this period. T. vegrandis was subsequently isolated in a number of bat species including the Gould’s wattled bat (Chalinolobus gouldii), lesser long-eared bat (Nyctophilus geoffroyi), little red flying fox (Pteropus scapulatus), and black flying fox (Pteropus alecto) (Austen et al., 2015b). The prevalence in the bats was high with over 80% infected. However, the flying foxes had been clinically infected with Australian bat lyssavirus, which may have increased the incidence of infection. The small size of T. hipposideri could indicate that it is homologous to T. vegrandis, but there is no way to confirm this.

3. EVOLUTIONARY RELATIONSHIPS OF AUSTRALIAN TRYPANOSOMES 3.1 The southern supercontinent theory Australian trypanosomes exhibit vast genetic diversity and unexpected relationships even though trypanosomes are considered monophyletic (Stevens et al., 1999; Hamilton et al., 2007). A number of theories contribute to explaining this diversity, including the southern supercontinent theory, host-fitting, and the bat-seeding hypothesis (Stevens et al., 1999; Hamilton et al., 2004, 2007, 2012). Trypanosomes isolated from the kangaroo (T. sp. H25), wombat (T. sp. H26dT. copemani) and platypus (T. binneyi) (Noyes et al., 1999) were the first trypanosomes from Australia included in a phylogenetic study (Stevens et al., 1999). The study

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investigated the origins of T. brucei (salivaria) and T. cruzi (stercoraria) using the 18S rDNA gene region (Stevens et al., 1999). It found that the salivarian and stercorarian trypanosomes have different lineages and different patterns of evolution. This research indicated that the T. brucei clade trypanosomes are evolving much faster than the T. cruzi clade (Stevens et al., 1999). An interesting result from the study was the vast genetic distance between the three different trypanosomes isolated from Australia. T. sp. H25 from the kangaroo was included in the T. cruzi clade, while T. copemani from the wombat was closely related to Trypanosoma pestanai from the European badger. T. binneyi isolated from the platypus was related to trypanosomes in an aquatic clade, containing isolates from aquatic leeches and fish from outside Australia (Fig. 3). The surprising similarity between T. sp. H25 and T. cruzi led to the southern supercontinent theory of trypanosome evolution, which suggests that T. cruzi clade trypanosomes separated from other trypanosomes over 40 million years ago when South America was still connected to Australia (Stevens et al., 1999; Noyes et al., 1999). This would indicate that the T. cruzi clade trypanosomes could have evolved from a marsupial host in Australia based on the presence of trypanosomes in Australian and South American marsupials, and the absence of native placental mammals in Australia at the time.

3.2 Host-fitting and shared environments Some Australian trypanosome isolates were included in a phylogenetic study investigating the evolutionary diversion of the three trypanosomatid pathogens Leishmania major, T. brucei and T. cruzi (Hamilton et al., 2004). These included T. sp. AAT isolated from the currawong, T. sp. ABF isolated from the swamp wallaby, an unnamed isolate from Fleay’s barred frog, and T. sp. ABI (T. copemani) and T. sp. AAP (T. copemani) isolated from the wombat (Hamilton et al., 2004, 2005) (Table 1). Separate phylogenies were produced for the 18S rDNA and gGAPDH loci, which demonstrated that these two gene regions result in a more robust tree topology supporting the early division of the trypanosomatid pathogens. The tree topologies generated were consistent with Stevens et al. (1999) as T. sp. H25 was found to be closely associated with the T. cruzi clade, while T. binneyi was closely linked to freshwater and aquatic clades of trypanosomes at both loci (Hamilton et al., 2005). Interestingly, T. copemani (T. sp. H26) was found to be closely associated with T. pestanai at the 18S rDNA region but not in the gGAPDH region. In the latter region, T. copemani (T. sp. AAP) was actually closer to the periphery of the T. cruzi clade. This was the

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Figure 3 Representative diagram of the relative positions of Australian wildlife trypanosomes in phylogenetic studies based on consensus in 18S rDNA and genetic loci glyceraldehyde 3-phosphate dehydrogenase (gGAPDH) gene regions. Branches do not indicate actual genetic distances. Australian trypanosomes are featured in bold, and host organisms are in brackets. Relationships inferred from Stevens, J.R., Noyes, H.A., Dover, G.A., Gibson, W.C., 1999. The ancient and divergent origins of the human pathogenic trypanosomes, Trypanosoma brucei and T. cruzi. Parasitology 118, 107e116, Hamilton, P.B., Stevens, J.R., Gaunt, M.W., Gidley, J., Gibson, W.C., 2004. Trypanosomes are monophyletic: evidence from genes for glyceraldehyde phosphate dehydrogenase and small subunit ribosomal RNA. Int. J. Parasitol. 34, 1393e1404, Paparini, A., Irwin, P.J., Warren, K., McInnes, L.M., De Tores, P., Ryan, U.M., 2011. Identification of novel trypanosome genotypes in native Australian marsupials. Vet. Parasitol. 18, 21e30, and Botero, A., Thompson, C.K., Peacock, C., Clode, P.L., Nicholls, P.K., Wayne, A.F., Lymbery, A.J., Thompson, R.C.A., 2013. Trypanosomes genetic diversity, polyparasitism and the population decline of the critically endangered Australian marsupial, the brush tailed bettong or woylie (Bettongia penicillata). Int. J. Parasitol. Parasites Wildl. 2, 77e89.

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only instance where the 18S rDNA and gGAPDH trees differed on phylogenetic arrangements of Australian trypanosomes. This difference was not seen in subsequent, larger studies (Hamilton et al., 2007) that supported the 18S rDNA observation, highlighting the importance of using more than one gene region to interpret phylogenetic relationships. T. sp. AAT was associated with other trypanosomes isolated from birds. At the 18S rDNA locus, T. sp. AAT was almost identical to a trypanosome isolated from a hippoboscid fly, which was identified as T. corvida common bird trypanosome with a worldwide distribution (Votýpka et al., 2004) (Fig. 3). In the gGAPDH gene tree, T. sp. AAT was closely related to Trypanosoma avium, which is another common bird trypanosome worldwide (see Section 3.4). T. sp. ABF was similar to the trypanosome isolated from Fleay’s barred frog, T. cyclops isolated from a Malaysian primate and T. theileri from cattle. A subsequent phylogenetic study that included a combined tree utilizing both the 18S rDNA and gGAPDH loci (Hamilton et al., 2007) confirmed again that T. sp. H25 was within the T. cruzi clade, T. binneyi, and T. chelodina from a tortoise in the aquatic clade; T. sp. ABF in the T. theileri clade; and T. sp. AAT in the avian clade. With the discovery of additional new isolates from Australian wildlife, some patterns begin to emerge in their relationships, which assist in understanding their vast genetic diversity. The Australian trypanosomes tend to demonstrate relationships with others that share similar environments, not solely geographic regions (suggested in the southern supercontinent hypothesis), and this appears to be influencing trypanosome evolution (referred to as ‘host-fitting’) (Hamilton et al., 2007). In host-fitting scenarios, trypanosomes are able to adapt to organisms that are similar to their established host or vector, and this often accompanies subsequent adaptation into novel environments. T. binneyi was isolated from the platypus, a monotreme that spends most of its time in freshwater, and it is closely related to other trypanosomes from freshwater and aquatic environments, such as turtles, fish and frogs, supporting the theory that these organisms share a common invertebrate vector, which is allowing the trypanosomes to switch hosts. The molecular characterization of T. irwini isolated from the koala demonstrated that it was similar to Trypanosoma bennetti from a falcon and not closely related to any other Australian trypanosome (McInnes et al., 2009). Trypanosomes from arboreal animals, including koalas and birds, are closely related to trypanosomes isolated from birds in other regions, supporting the host-fitting hypothesis of shared environments and shared vectors (Fig. 3). The

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connection between T. sp. ABF isolates, T. cyclops, and T. theileri could be haemadipsid leeches, which are likely vector candidates for these species (Hamilton et al., 2005). A more distinct clade of Australian trypanosomes emerged after the characterization of a number of novel isolates from Australian marsupials, including T. gilletti isolated from the koala (McInnes et al., 2011b) and T. vegrandis isolated from the woylie (Thompson et al., 2013). It was found that T. copemani, T. gilletti and T. vegrandis clade trypanosomes were close together in a number of tree topologies, ultimately distinguishing an Australian clade of trypanosomes and separating T. pestanai from T. copemani (Paparini et al., 2011; Botero et al., 2013) (Fig. 3). There are high levels of intraspecific diversity observed between T. copemani and T. vegrandis with a large number of characterized strains emerging, which could be indicative of genetic exchange (Noyes et al., 1999; Austen et al., 2009; Botero et al., 2013; Thompson et al., 2013). Curiously, T. copemani and T. vegrandis occur on both the east and west coasts of Australia in a number of marsupials, while T. gilletti has only been isolated on the east coast of Australia. Whether this represents truly geographically isolated, but related, populations or simply reflects a lack of available data on the actual distribution of these species within Australia is unknown. Similarly, whether these different trypanosome species share ectoparasites that could be aiding in host-fitting is unknown, as there are essentially no conclusive data available regarding vectors of Australian trypanosomes.

3.3 The bat-seeding hypothesis The development of the bat-seeding hypothesis assisted in the explanation of the emerging relationships seen between trypanosomes from the T. cruzi clade, offering an alternative explanation to the southern supercontinent theory (Hamilton et al., 2012). The T. cruzi clade contains trypanosomes that infect bats in America, Africa and Eurasia (Hamilton et al., 2012; Lima et al., 2012, 2013), and terrestrial land mammals including marsupials in South America and Australia (Stevens et al., 1999). Unnamed trypanosome species isolated from a civet and a monkey in Africa and T. conorhini isolated from a rat found throughout the tropics are also part of the T. cruzi clade (Hamilton et al., 2012). The bat-seeding hypothesis proposed that T. cruzi clade relationships can be explained by the high mobility of bats that allowed bat trypanosomes to switch hosts to terrestrial mammals, throughout different regions of the world. Host-fitting was implicated as the mechanism facilitating host-switching, which in the bat-seeding

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hypothesis would follow the principal that parasites adapt to new hosts that are closely related to their primary host. There are less host-switching evolutionary events involved in this scenario increasing the probability of this evolutionary relationship influencing trypanosome evolution. This theory proposes that T. cruzi evolved more recently than previous studies had indicated explaining the appearance of the African bat trypanosomes and T. sp. H25 in the T. cruzi clade. T. sp. H25 appears to be at the periphery of the T. cruzi clade, close to the T. lewisi clade (Lima et al., 2012, 2013) (Fig. 3). A recently characterized bat trypanosome T. wauwau is the most closely related trypanosome to T. sp. H25 (Lima et al., 2015). The bat-seeding hypothesis can explain the relationship between T. cruzi and other members of the clade that infect bats. However, inconsistent with this theory is the fact that T. sp. H25 is yet to be isolated from bats. This may simply result from the fact that trypanosome populations in Australian bats have not been investigated extensively and that a wider sample of trypanosome isolates from wildlife is necessary to clarify the extent relationships have been influenced from host-fitting by the host or vector.

3.4 The trouble with bird trypanosomes Initially, trypanosomes from birds around the world were named to species level based on the assumption that bird trypanosomes were species specific. It is now established that despite the reported identification of almost 100 bird trypanosomes worldwide, the majority actually belong to one of three key speciesdT. corvi, T. avium or T. bennettidwith the actual total number of trypanosome species occurring in birds being closer to 12 (Nandi and Bennett, 1994; Zídkova et al., 2012). The knowledge that bird trypanosomes show less diversity than previously reported and that they are not species specific indicates that the multiple specimens described from Australian birds are unlikely all to be different species (see Section 2.1.2). T. sp. AAT appears to be part of the T. corvi clade based on genetic evidence, despite being isolated from a native Australian bird (Zídkova et al., 2012; Votýpka et al., 2012). The vertebrate host, the currawong, is an Australian bird belonging to the genus Strepera and the family Artamidae, which includes magpies, wood swallows and butcher birds that are all native to Australia. While birds from Artamidae resemble ravens or crows from the family Corvidae, they are only distantly related, belonging to the superfamily Malaconotoidea, which originated in Australia.

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Malaconotoidea includes old world carnivorous songbirds from Australia and Africa, which separated from other corvids almost 45 million years ago during the late-Eocene (Fuchs et al., 2012). The family Corvidae subsequently underwent an initial radiation in Southeast Asia (Ericson et al., 2005). The trypanosome T. corvi was originally described from a crow in India (Corvus splendens) and then in a number of birds in the United Kingdom including ravens, blackbirds and jackdaws, and was successfully, experimentally transmitted to canaries (Nandi and Bennett, 1994). Consequently, this lead to the conclusion that all large trypanosomes that had been previously described and been found in members of the family Corvidae were actually conspecific to T. corvi (Nandi and Bennett, 1994). The vectors of T. corvi are believed to be flies from the family Hippoboscidae. Commonly known as louse flies, they are obligate parasites of birds and mammals, and transmission of the parasite is believed to be mechanical arising from the ingestion of flies whilst grooming (Votýpka et al., 2012). The invertebrate vector that transmits the Australian trypanosome T. sp. AAT is unknown. Hippoboscid flies are present in Australia, although there are a number of other candidates for T. corvi vectors including tabanid flies and mosquitos (Votýpka et al., 2012). Interestingly, Trypanosoma sp. BDA4 isolated from an Australian mammal, the boodie (B. lesueur) on Barrow Island situated on the WA coast, was found to be genetically very similar to T. sp. AAT, showing 99% similarity (Averis et al., 2009). However, these researchers failed to comment on the similarity and only supplied partial 18S rDNA sequences of the isolate from the boodie. Analysis of the complete gene region may have shown increased diversity between these two genotypes. While bird trypanosomes have lower levels of diversity than previously thought, the presence of T. corvi infection in both avian and marsupial hosts would be unique. The prevalence and geographical range of T. sp. AAT in Australia remains unknown, and there is a need for more extensive sampling of trypanosomes in the birds of Australia. Considering the ancient divergence between the avian hosts of T. corvi and T. sp. AAT, it is worth investigating the level of genetic and morphological diversity observed between T. corvi and T. sp. AAT. The highly pleomorphic state of bird trypanosomes led to suggestions that in vitro investigations may be more accurate when trying to characterize their structure and understand the differences between species as in vitro studies provide a more stable, controlled environment (Zídkova et al., 2012).

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4. TRYPANOSOME HOSTePARASITE INTERACTIONS IN AUSTRALIA 4.1 Implication of disease in Australia From trypanosomes 4.1.1 Trypanosoma cruzi and experimental infection of Australian marsupials Although the majority of people infected with T. cruzi are from Central or South America, there are concerns. Chagas disease will become a worldwide health problem driven by international migration, the movement of people from rural to urban areas over time, deforestation, poor detection and response to drug treatment, and insecticide resistance in vectors (Schmunis and Yadon, 2006; Munoz-Saravia et al., 2010). T. cruzi was estimated to infect 3000 Australian immigrants from Latin America by 2006 with further increases in the population of immigrants causing this number to rise (Schmunis and Yadon, 2006; Guscon et al., 2010). As such, growing numbers of T. cruzi cases in Australia may represent a biosecurity risk to native wildlife as well as to humans. For example, if bugs from the family Reduviidae, which are vectors for T. cruzi, were to become established pests in Australia, they may be able to transmit T. cruzi to local marsupial species and humans. While reduviid bugs are not expected to become pests in Australia, there are a number of local candidates including one reduviid bugdTriatoma leopoldi, which was found in Cape York Peninsula in QLD (Monteith, 1974). Due to their genetic similarity, there is a possibility that the vectors that transmit T. sp. H25 in Australian ecosystems could also spread T. cruzi. If T. sp. H25 vectors cannot transmit T. cruzi via cyclical transmission they may be able to do so through mechanical transmission. This is possible, especially considering the recent discovery that bedbugs can transmit T. cruzi mechanically when previously only reduviid bugs had been implicated (Salazar et al., 2015). Outside of the natural vectors, Chagas disease can be spread congenitally (Schenone et al., 2001), through breast milk (Norman and Lopez-Velez, 2013), contaminated food (Signori Pereira et al., 2009), blood transfusions and organ donation (Kransdorf et al., 2014) and laboratory accidents. A single study in Australia demonstrated that T. cruzi could cause disease in native possums and short-beaked echidna (Tachyglossus aculeatus). A 60% mortality rate in possums experimentally infected with T. cruzi was observed (Backhouse and Bolliger, 1951). There is a difference in virulence between different isolates of T. cruzi, and the isolate used in this study was ‘sent from

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London’ but not otherwise identified. Therefore the expected virulence of the T. cruzi isolate was not known. Notably, these animals were in captivity and under considerable stress, which may have contributed to the high mortality rate. Although, the introduction of T. cruzi to any naive small population of vulnerable marsupial species as incidental hosts could have devastating effects. Marsupials in the wild may not suffer high mortality from a natural infection but may become reservoirs. Marsupials in South America are reservoirs of T. cruzi and can amplify the parasite by increasing the number of infected animals and vectors creating spillover into human populations (Travi et al., 1952). It is unknown whether local marsupial species in Australia would become reservoirs or incidental hosts, although both scenarios pose a biosecurity risk. 4.1.2 Trypanosoma lewisi and the rats of Christmas Island T. lewisi is a trypanosome naturally infective to the common rat (R. rattus), which is transmitted by fleas (Hoare, 1972). The trypanosome was an invasive species introduced to Christmas Island, which was implicated in the decline of two native Christmas Island rats (Rattus macleari and Rattus nativitatis) when DNA of T. lewisi was extracted from museum specimens of both rat species from the time of extinction (Wyatt et al., 2008). It was noted that symptoms of trypanosomiasis were seen in the native rats in the period before their extinction (Wyatt et al., 2008). T. lewisi DNA was absent from endemic rat samples collected before the introduction of the common rat indicating the trypanosomiasis was caused by T. lewisi. However, the presence of other diseases in the rat populations cannot be ruled out. The small population size of the native rats before extinction and the small size of the island may have been mitigating factors in the emergence of trypanosomiasis within the Christmas Island populations. Similarly, T. lewisi has been identified in species of introduced rats in QLD, NSW and WA in densely populated areas (Mackerras, 1959). Considering this identification was based solely on morphology alone, these trypanosomes could have belonged to a local species of Trypanosoma that was not recognized. There are currently no records of T. lewisi infecting native mammals in mainland Australia and T. lewisi has not been recorded since 1959, although a number of novel isolates from West Australian marsupials were phylogenetically similar to T. lewisi (Averis et al., 2009). These included Trypanosoma sp. DBA1 from the dibbler, BRA1/BRA2/BRA3 from the bush rat, BDA from the boodie and AMA1 from the ash grey

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mouse. This study only included partial sequences of 18S rDNA, which makes tree topologies less robust when isolates are compared to different organisms, and the tree branches have low confidence values indicating little support for these arrangements (Averis et al., 2009). All other tree topologies to date positioned T. lewisi with Trypanosoma microti next to the T. cruzi clade (Noyes et al., 1999; Stevens et al., 1999; Hamilton et al., 2004; Smith et al., 2008). It is possible that T. lewisi could have been involved in some marsupial declines when initially introduced into mainland Australia, and since this time, there has been some host/parasite adaptation. Investigating the presence of T. lewisi in densely populated areas within Australia may assist in answering these questions. 4.1.3 Poor health in koalas and quokkas and the potential zoonotic significance Trypanosomes have been implicated in causing poor health and anaemia in Australian koalas that were infected with T. copemani, T. irwini, or T. gilletti. These koalas displayed symptoms consistent with trypanosomiasis, such as skin lesions, extravascular haemolysis and anaemia (McInnes et al., 2011b). However, multiple infections and symptoms unrelated to trypanosomiasis were reported; the presence of koala retrovirus, bone marrow disease and chlamydiosis were common in koalas in the region. T. irwini was also found in healthy koalas suggesting that a healthy koala may not manifest trypanosomiasis from an infection but that immunocompromised koalas could imply a condition-dependent pathogenicity (McInnes et al., 2011a,b). Considering the high incidence of koala retrovirus and chlamydia in the region sampled, it is likely that the koalas were stressed and immunocompromised, subsequently rendering them more susceptible to secondary infections with trypanosomes, rather than direct immunosuppression induced by trypanosome infection. T. copemani was also implicated in causing anaemia in wild quokkas due to the observation of trypanosomes attached to erythrocytes in blood smears (Austen et al., 2015a). This was not confirmed with electron microscopy and may have been a misinterpretation of the image. The blood smears were collected from wildlife and not laboratory animals indicating that there could have been a number of causes for the presence of anaemia because the history of the individual host remains unknown. In neither of these cases, trypanosomes can be confirmed as the primary cause of anaemia or ill health. However, if trypanosomes commonly manifest as secondary infections in wildlife, their potential to contribute to the clinical conditions caused by other infectious agents warrants further investigation.

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There have been claims that T. copemani is resistant to human serum in vitro (Austen et al., 2015c). The blood incubation infectivity test (BIIT) was used to test whether T. copemani could grow in vitro as epimastigotes after the incubation of bloodstream trypomastigotes in nonimmune human serum for 5 h (Austen et al., 2015c). The BIIT is normally used to differentiate between strains of the infective T. brucei rhodesiense and noninfective T. b. brucei because preincubation with human serum prevents infection of T. b. brucei metacyclic trypomastigotes in mice in vivo (Rickman and Robson, 1970). The BIIT has subsequently been adapted to be used in vitro where different strains of T. brucei trypomastigotes are differentiated by using a fluorescence assay to observe which strain has perished in the serum (Turner et al., 2004). The validity of the test has not been investigated in species other than T. brucei, and the survival of the parasites is linked to the SRA gene, which has only been identified in T. brucei. This gene is not known to exist in any other trypanosome. Similar tests have investigated the ability of T. cruzi and Trypanosoma rangeli metacyclic trypomastigotes to resist human complement in vitro. It was found that ineffective removal of metacyclic trypomastigotes by the complement pathway in some T. cruzi strains allows some metacyclic trypomastigotes to invade cells establishing an infection (Cestari and Ramírez, 2010). T. cruzi bloodstream trypomastigotes and amastigotes are resistant to human complement. Usually in vitroe grown metacyclic trypomastigotes are grown in large numbers (5  105) and suspended in buffer to inoculate into the BIIT (Turner et al., 2004; Cestari and Ramírez, 2010). However, for T. copemani, trypanosomes were inoculated in quokka blood containing low numbers of bloodstream trypomastigotes in human serum (estimated five parasites per sample). Importantly, metacyclic trypomastigotes were not used, which would have been more relevant due to their infectivity. No explanation was provided for why they were inoculated in this manner and why doses were not more representative of previous BIIT investigations. As T. copemani was inoculated in biphasic blood media, it is not clear if this test is intended to investigate survival in the vertebrate or invertebrate host because it is not known if the parasites were maintained as epimastigotes or trypomastigotes after inoculation (Austen et al., 2015c). As T. copemani is neither in the T. cruzi nor in the T. brucei clade of trypanosomes, the relevance of these tests and their implications of their findings cannot be fully evaluated. It remains an interesting phenomenon that T. copemani can survive in human serum in such low numbers. The authors note that it is unlikely that T. copemani would be pathogenic in humans even if it was confirmed as resistant to human

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serum because passage from the unknown vector of the parasite to a human would need to be demonstrated (Austen et al., 2015c). This is especially important as the life cycle and vector(s) of T. copemani are unknown. 4.1.4 Trypanosoma copemani and the woylie of Southwest Australia The woylie is an endemic Australian marsupial that represents the plight of many Australian endemic species existing in small populations that may be vulnerable to disease. Before the arrival of European settlers, the geographic distribution of the woylie was believed to be extensive across Australia. Due to reduction of suitable habitat after European settlement and the introduction of predators such as the red fox (Vulpes vulpes), the woylie suffered a major population reduction and received an endangered status (Wayne et al., 2013). Following programs to reduce the number of introduced predators, the woylie increased in abundance and were removed from the threatened species list in 1996. However, from 1999, there was an unexplained population decline that reduced the population by 90% over 7 years (Wayne et al., 2013). Woylies are now listed as critically endangered (Wayne et al., 2008). Woylies are considered an important species due to their value as endemic Australian marsupials in the biodiversity hotspot of Southwest Australia (Myers and Mettimier, 2000) and their importance in seed dispersal in the area (Wayne et al., 2013). The current action plan for the woylie lists disease as a threat and is an important area of research in the decline of the species (Yeatman and Groom, 2012). There are three natural habitats left for the woylie, which are all in Southwest Australia: the Upper Warren Region, the Dryandra Woodlands and the Tutanning Nature Reserve (Wayne et al., 2013). The Upper Warren region has two genetically distinct populations that exhibit the majority of genetic diversity, although the population is declining (Pacioni et al., 2010). In addition to natural populations, the woylie has been subjected to a number of translocations, more than any other Australian mammal (Yeatman and Groom, 2012). Karakamia wildlife sanctuary has a stable translocated population and was used to compare the prevalence of trypanosomes with the declining populations (Botero et al., 2013; Thompson et al., 2013). Mixed infections with T. copemani and T. vegrandis were common in the declining population, which had a high percentage of T. copemani infection. The stable population had a higher infection rate with T. vegrandis, although T. copemani was also present at a lower rate.

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Woylies infected with T. copemani commonly showed signs of inflammation in various organs, and T. copemani DNA was isolated from a number of different woylie tissues. Interestingly, structures suggestive of intracellular amastigotes were observed in histological sections of woylie heart tissue infected with T. copemani G2 (Botero et al., 2013; see Section 4.2.4). The identity of the intracellular amastigotes could not be confirmed using immunohistochemistry because antibodies against T. copemani could not be generated. The concern with finding intracellular life stages of T. copemani in woylie tissues is that such intracellular amastigotes typically exist only in pathogenic species. This raises the question: is T. copemani pathogenic and causing disease in the woylie (Botero et al., 2013; Thompson et al., 2013). Due to their vulnerable status as critically endangered marsupials, it is an area of research that requires priority.

4.2 A brief history of intracellular behaviour in trypanosomes It is the intracellular aspect of the T. cruzi life cycle that is responsible for its pathogenesis, and the presence of this behaviour in trypanosomes may indicate that they can also cause disease. Few species of trypanosomes have been observed entering cells, T. cruzieinfecting humans and Trypanosoma dionisiieinfecting bats are the most widely studied, and both originate from South America (Baker and Sheldon, 1978). Trypanosoma erneyi (Lima et al., 2012), a bat trypanosome from Africa, and T. theileri (Lee et al., 2012), a stercorarian cattle parasite, were observed inside cells in vitro, while there is some controversy regarding the ability of T. rangeli from South America, to enter and multiply within cells (Tanoura et al., 1999; Eger-Mangrich et al., 2001; Zu~ niga et al., 1997). T. copemani was the first parasite observed invading cells in vitro and in wildlife tissues that is outside the T. cruzi clade, except for T. theileri (Botero et al., 2013). T. cruzi is the only intracellular trypanosome confirmed to infect mice, in vivo (Lima et al., 2012). 4.2.1 The processes involved in Trypanosoma cruzi cell invasion The processes involved in cell invasion are complicated, and despite the phenomenon being studied for over 100 years, the mechanisms are not fully understood (Clayton, 2010). It has been established that there are a number of stages involved in the invasion process. These include adhesion and recognition, signalling, cell entry, formation of the parasitophorous vacuole (PV), and escape from the PV (Epting et al., 2010; De Souza et al., 2010; Maeda et al., 2012b). A series of endocytic pathways are used by parasites to gain

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entry into cells, and there appears to be considerable variation in the molecules involved (De Souza et al., 2010; Barrias et al., 2013), which are dependent on the strain of parasite and the host cell in question (Yoshida and Cortez, 2008; Epting et al., 2010). T. cruzi was first observed inside cells in vitro in heart muscle cells from chicken embryos where the process of cell invasion was observed and the intracellular life cycle documented (Kofoid et al., 1935; Meyer and Xavier de Oliveira, 1948). This early work focussed on the life stages involved and the time taken for T. cruzi to invade and replicate before being released from the cell. Over time, it was discovered that T. cruzi has the ability to invade a number of different cell types including phagocytic and nonphagocytic cells. Active penetration was first thought to be the mode of T. cruzi entry into cells, which is dependent on the host-cell actin cytoskeleton (Kipnis et al., 1979). However, it was found that inhibiting actin polymerization, which is essential in nonprofessional phagocytic cells, did not stop the parasites from invading (Schenkman et al., 1991). This was further confirmed by the discovery that cell entry was increased if the host cell cytoskeleton was disrupted (Tardieux et al., 1992; Rodriguez et al., 1999). There is a gradual change in the literature of referring to active penetration of parasites and the move towards referring to induced endocytosis and the development of a membrane-bound PV (De Souza and de Carvalho, 2013). Surface-initiated signalling causes a rise in calcium (Ca2þ) (Tardieux et al., 1994; Rodriguez et al., 1999), which triggers trafficking resulting in the fusion and invagination of membranes at the site of parasite attachment (Sibley and Andrews, 2000). These processes can be dependent on dynamin (clathrin or caveolin mediated), lipid raftemediated endocytocis, or micropinocytosis. A large number of host cell receptors and the parasite’s surface glycoproteins (gp82/83, gp80, gp35/50, gp85) and proteases are involved in the invasion process (De Souza et al., 2010; Epting et al., 2010; Barrias et al., 2013). Parasite molecules implicated in cell invasion processes include many mucins (sugar residues that interact with mammalian host cells), which are the major surface glycoproteins in T. cruzi that act as ligands, transialidases that form glycophosphatidylinositol-linked proteins (Flannery et al., 2010), cathepsin Lelike cysteine protease (CatL), and cruzipain peptides that can promote invasion through inducing Ca2þ signalling (Rodrigues et al., 2010). The host cell receptors that recognize these molecules vary, which is expected given the large number of molecules involved and include for example toll like receptors, kinases, sialic acid, and cholesterol the major component of membrane rafts (Woolsey et al., 2003; Barrias et al., 2007;

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Fernandes et al., 2007; De Souza et al., 2010; Epting et al., 2010; Rodrigues et al., 2010; Fernandes et al., 2011; Barrias et al., 2013). It was ascertained that lysosomes were involved in cell entry, which fuse to the phagosome after internalization of the parasite because disruption to the lysosomes reduced the ability of T. cruzi to invade cells (Kress et al., 1975; Rodriguez et al., 1999; Milder and Kloetzel, 2004). Early and late endosomes were subsequently implicated in the internalization process (Barrias et al., 2013). Following PV formation, it ruptures after roughly an hour and the parasites escape into the cytoplasm. The breaking of the PV requires a low pH thought to be mediated by an acid-active pore-forming haemolysin secreted by T. cruzi (Ley et al., 1990; Andrews and Whitlow, 1989). Following escape from the PV, the parasite differentiates into an amastigote and divides by binary fission until they fill the cell. The amastigotes differentiate into sphaeromastigotes, transform to trypomastigotes and finally lyse the cell expelling the parasites.

4.2.2 Intracellular trypanosomes from America The majority of trypanosomes that invade cells are found in the T. cruzi clade. However, not all trypanosomes in this clade invade tissue, including Trypanosoma marinkellei and Trypanosoma livingstonei. T. dionisii is a trypanosome that infects bats in South America that is similar to T. cruzi. It is in the T. cruzi clade, and the vectors for this parasite are also Triatoma bugs (Hoare, 1972). Intracellular behaviour in T. dionisii was first established in 1971, and subsequent studies developed a picture of the life cycle in vitro (Baker et al., 1971, 1972; Baker and Green, 1973; Baker and Seldon, 1978; Glauert et al., 1982). Until recently, the mechanisms involved in the infection had not been investigated. T. dionisii cell invasion is similar to T. cruzi and exhibits the use of lysosome mobilization and exocytosis in cell invasion, and it was revealed that T. dionisii displays lower levels of trans-sialidase activity (Oliveira et al., 2009) and a distinctly different surface profile (Maeda et al., 2012a). T. dionisii is unique due to the discovery of epimastigotes and trypomastigotes inside host cell nuclei in heavily infected cells (Oliveira et al., 2009). T. cruzi does not normally enter the nuclei of cells, and it is metacyclic trypomastigotes and amastigotes that invade cells in T. cruzi. It has been concluded in past studies that T. dionisii may have developed slightly different mechanisms of evading the immune system resulting in it being less pathogenic than T. cruzi (Oliveira et al., 2009; Maeda et al., 2012a). Higher levels of trans-sialidase were proposed as the evolutionary

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difference between immune evasion processes that makes T. cruzi more virulent (Oliveira et al., 2009). There are other trypanosomes similar to T. cruzi that also share this behaviour. T. rangeli is a parasite that shares a similar geographic distribution with T. cruzi, although it is morphologically different. The vectors of the parasite are Triatoma bugs, and it infects a variety of mammals including humans but is not known to cause disease. There is some controversy around the ability of T. rangeli to infect cells. In vitro trypomastigotes were shown to cause 70% mortality in experimentally infected mice, while trypomastigotes obtained from mouse blood did not (Zuniga et al., 1997). Another study determined that metacyclic trypomastigotes harvested from humans grown with mouse fibroblasts in vitro increased cell invasion but demonstrated that intracellular trypanosomes decreased over time and that a strain of T. rangeli from culture (which had been maintained over a long period) was less virulent (Tanoura et al., 1999). T. rangeli was also investigated in vitro in a number of mammalian cell lines (Eger-Mangrich et al., 2001). While this study observed parasites inside cells, infectivity remained below 15% and decreased with time (1 h after infection with the rate highest in macrophages) possibly due to the continual division of the cells, or the eventual absorption and digestion of the parasite by the cell. Intracellular multiplication of parasites was not observed in any of the cell lines tested (Koerich et al., 2002). 4.2.3 Intracellular trypanosomes outside America T. theileri is not in the T. cruzi clade and is a stercorarian trypanosome of cattle. It is transmitted via the faecal origin route by tabanid flies and is associated with long-lasting infection and anaemia (Ward et al., 1984). T. theileri has not been observed invading host tissues. Initially, T. theileri amastigotes were found following incubation with cells after 18 days (Moulton and Krauss, 1972) and then observed in cerebral spinal fluid (Braun et al., 2002). Intracellular life stages of T. theileri in vitro were observed in four different cell lines (Lee et al., 2012). Interestingly, unless T. theileri was regularly grown with cells in vitro, it would perish, suggesting that it requires entry into host cells in order to complete its life cycle in vitro. After T. theileri invasion of mammalian cells in vitro, new metacyclic trypomastigotes escaping from cells were observed 5e7 days after incubation, which is consistent with observations of T. cruzi. Molecules implicated in cell invasion included membrane rafts enriched in GM1 and CATL, similar to T. cruzi (Rodriguez et al., 2010; Lee et al., 2012). No signs of infection were seen in in vivo attempts to develop rat models (Lee et al., 2012).

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A recently discovered intracellular trypanosome from the T. cruzi clade is T. erneyi, a bat trypanosome from Africa genetically similar to T. cruzi and T. marinkellei (Lima et al., 2012). Like T. dionisii and T theileri, T. erneyi could infect mammalian cells in vitro but failed to infect mice in vivo. The authors mention that parasites were susceptible to human complement-mediated lysis, but these results were not shown in the study. Subsequent research has not been conducted on this parasite, and the mechanisms of infection and life cycle are unknown. The research conducted so far supports the hypothesis that mechanisms of mammalian host cell invasion in trypanosomes is a conserved trait, as all known intracellular trypanosomes are from the T. cruzi clade, except for T. theileri and T. copemani. T. erneyi and T. theileri were the first examples of intracellular species of Trypanosoma outside the Americas. 4.2.4 Intracellular trypanosomes in Australia T. copemani is the only trypanosome from Australia believed to have an intracellular life-cycle stage (Botero et al., 2013) (see Section 4.1.4). Structures suggestive of amastigotes were observed in woylie heart sections, but as it was not possible to generate genotype-specific antibodies without an animal model for T. copemani, no immunochemistry could be undertaken on the sections to determine with any certainty what species or genotype these amastigotes were. In addition to the structures suggestive of amastigotes observed in woylie heart sections, T. copemani G2 was reported to have intracellular stages in vitro in different mammalian cell lines including VERO, L6, HCT8 and THP1, although it was not observed multiplying in cells (Botero et al., 2016). Due to the occurrence of mixed infections with T. copemani G1 and G2 in the woylie, further investigation is required to confirm if it is only G2 invading cells. The most divergent genotype in phylogenetic studies in the T. copemani clade is G2, which could indicate that it is actually a separate species. The highest infection rate was observed in VERO cells with an estimated 70% of cells infected at 48 h after infection, while in the other cell lines, it was between 7% and 15% (Botero et al., 2016). While light microscopy images stained with Giemsa show structures resembling amastigotes inside the cells, it is unclear if they are dividing like T. cruzi amastigotes. Structures suggestive of trypanosomes were observed inside TEM sections of VERO cells infected with T. copemani G2 (Botero et al., 2013). T. copemani is the only intracellular trypanosome that has not been seen dividing in cells in vitro, except for T. rangeli. In addition, the mechanisms of cellular invasion in T. copemani remain unknown.

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The discovery of other intracellular trypanosomes besides T. cruzi challenges preconceived ideas about trypanosome life histories. T. cruzi invades cells to replicate inside them and in doing so evades the immune system. Other intracellular trypanosomes have not been observed replicating in cells in vivo. Still, why would a parasite enter a cell if not to avoid the host immune system and to divide and transform into the infective form for the invertebrate host? One answer could be that the ability to invade cells occurred early in the lineage and some trypanosome species lost the ability to enter cells as it did not confer an advantage in their new environment. A host-switching event could have provided a host with a different immune system that no longer required entry into cells to replicate, for some trypanosomes. An alternative to this is that these intracellular trypanosomes are disposed of by the cells in their natural host, which could provide another explanation for why some are not pathogenic in vivo. Considering either of these options, the mechanisms utilized by intracellular trypanosomes should be the same because the process of invading cells is likely a conserved trait. Trypanosomes enter cells via a number of different endocytic pathways, and it seems unusual that this would have evolved in a number of different trypanosome lineages separately. Intracellular trypanosomes excluding T. cruzi may still possess the ability to invade cells in vivo, but these events could be rare. Immunocompromised hosts may still be susceptible, which could affect vulnerable wildlife, especially those that exist in small populations. Exactly how many other trypanosomes exhibit this intracellular behaviour has not been investigated. It is important to investigate how trypanosome species invade mammalian cells for the development of disease treatments, to understand and identify biosecurity risks and to understand the evolution of trypanosomes/protozoans and the evolution of this distinct behaviour.

5. FUTURE RESEARCH Unfortunately, the majority of information regarding trypanosomes in Australia at present is sourced from trypanosome morphology in blood smears, which is an important preliminary step in their description, but contributes little to answering questions regarding their life history, developmental biology and diversity. It is evident that a combination of both morphological and genetic data is needed to define and characterize trypanosomes to species level. As such, the priority in better understanding Australian trypanosomes now lies in investigating detailed cyclical life

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histories and evaluating genetic diversity. This will need to include extensive sampling of wildlife and relevant invertebrate communities from across Australia, in order to generate the necessary level of information to determine biodiversity and find vectorial candidates, which remain essentially unknown at this time. From this, isolating uncharacterized trypanosome species or currently described species in vitro will be essential in understanding their complex life histories and pleomorphic life stages. The investigation of different trypanosome morphological forms in vitro using live-cell and high resolution microscopy techniques will lead to a much better understanding of the ultrastructure of trypanosomes isolated from Australian wildlife. This is particularly important for understanding intracellular behaviour and the potential for pathogenicity (especially with comparisons to the highly pathogenic T. cruzi). This may also provide morphological insight into differences between the genotypes (or separate species?) of T. copemani, T. vegrandis, and T. sp. H25, which are found in various Australian marsupials (Austen et al., 2009; McInnes et al., 2011b; Botero et al., 2013). Due to similarities in the morphology of many trypanosomes, the introduction of molecular techniques has become crucial in fully characterizing trypanosome diversity and revealing evolutionary relationships. However, simply reporting the prevalence of parasites and characterizing new genotypes using molecular techniques cannot offer insight into life histories, hosteparasite interactions, or the recognition of invertebrate vectors. The introduction of additional genetic loci to 18S rDNA and gGAPDH, and the applications of proteomic approaches to understanding molecular differences present between trypanosomes in Australia would better clarify relationships, particularly between those that appear to be closely related. With this, it is clear that correlative approaches to investigate biodiversity, evolutionary relationships, and hosteparasite interactions are necessary to provide the missing gaps in information around whether Australian trypanosomes have the potential to impact on wildlife, domestic animals or human health. The key focus in this area is the relationship(s) and similarities between T. sp. H25 and T. cruzi (due to their close genetic similarity), and T. copemani and T. cruzi (due to the presence of intracellular life stages that are usually linked to pathogenicity in the host). In addition, it is essential that we look beyond the trypanosomes themselves and identify the vectors involved in their transmission. For example, could the vectors of T. sp. H25 transmit T. cruzi? This can only be resolved by extensively sampling potential candidates to explore their vectorial capacity using in vivo, in vitro,

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genetic, and ultrastructural analyses, to confirm that vectors can transmit viable parasites to new hosts. The possibility that mixed infections (polyparasitism) may exacerbate the consequences of infection in vulnerable marsupials like the woylie cannot be ignored (McInnes et al., 2011a; Thompson et al., 2013). More extensive and improved sampling of wildlife from across Australia may assist in discovering the prevalence of multiple infections, and the effects this can have on the host. Despite the difficulties in studying Australian wildlife trypanosomes in the past, the constant advancement in molecular and microscopy techniques will contribute to bridging the extensive knowledge gap regarding hosteparasite relationships and life histories of this important group.

ACKNOWLEDGEMENTS The authors thank Mark Preston, Graphic Designer at Murdoch Design, for preparing all of the schematic diagrams presented here. Thanks are also extended to the University of Western Australia (Centre for Microscopy, Characterisation, and Analysis, and the School of Pathology and Laboratory Medicine), Murdoch University (Parasitology), the Australian Research Council and Department of Parks and Wildlife.

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CHAPTER THREE

The Compatibility Between Biomphalaria glabrata Snails and Schistosoma mansoni: An Increasingly Complex Puzzle G. Mitta*, 1, B. Gourbal*, C. Grunau*, M. Knightx, {, J.M. Bridgerjj, A. Théron* *University of Perpignan, Perpignan, France x The George Washington University, Washington, DC, United States { University of the District of Columbia, Washington, DC, United States jj Brunel University London, Uxbridge, United Kingdom 1 Corresponding author: E-mail: [email protected]

Contents 1. Introduction 2. The Genetic Determinism of the Compatibility/Incompatibility of Biomphalaria glabrata and Schistosoma mansoni 3. Crosses and Genetic Approaches for Identifying Compatibility/IncompatibilityLinked Loci 4. Use of Molecular Comparative Approaches on Compatible and Incompatible Strains of Biomphalaria glabrata to Identify Candidate Genes 5. Use of Molecular Comparative Approaches on Strains of Schistosoma mansoni and the Discovery of Schistosoma mansoni Polymorphic Mucins 6. Other Putative Effector and/or Antieffector Systems Could Play Roles in Compatibility 7. The Compatibility Polymorphism Can Be Explained by a Combination of Matching Phenotype Status and Virulence/Resistance Processes 8. A Snail’s History of Interaction With a Schistosome Can Influence a Subsequent Infection 9. Epigenetics Appear to Make the System Even More Complex 9.1 Snail Epigenetics: The Stress Pathway and Plasticity in Susceptibility of Biomphalaria glabrata to Infection With Schistosoma mansoni 9.2 Schistosome Epigenetics 10. Conclusion Acknowledgements References

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Abstract This review reexamines the results obtained in recent decades regarding the compatibility polymorphism between the snail, Biomphalaria glabrata, and the pathogen, Schistosoma mansoni, which is one of the agents responsible for human schistosomiasis. Some results point to the snail’s resistance as explaining the incompatibility, while others support a “matching hypothesis” between the snail’s immune receptors and the schistosome’s antigens. We propose here that the two hypotheses are not exclusive, and that the compatible/incompatible status of a particular host/parasite couple probably reflects the balance of multiple molecular determinants that support one hypothesis or the other. Because these genes are involved in a coevolutionary arms race, we also propose that the underlying mechanisms can vary. Finally, some recent results show that environmental factors could influence compatibility. Together, these results make the compatibility between B. glabrata and S. mansoni an increasingly complex puzzle. We need to develop more integrative approaches in order to find targets that could potentially be manipulated to control the transmission of schistosomiasis.

1. INTRODUCTION Schistosomes are the causative agents of schistosomiasis, one of the most important neglected human tropical diseases in the world (WHO, 2016; http://www.who.int/mediacentre/factsheets/fs115/en/ accessed2015/01/07). Schistosomes infect over 200 million people worldwide, causing both acute and chronic debilitating diseases (King, 2010; King et al., 2005). There is no effective vaccine against schistosomes, and treatment still relies on a single drug: praziquantel (Doenhoff et al., 2009). As praziquantel resistance can be easily selected in the laboratory (Fallon and Doenhoff, 1994) and mass treatment chemotherapy used now in the field show evidence of Schistosome reduced drug susceptibility (Melman et al., 2009), alternate control strategies are needed. Some such strategies seek to block the transmission of the disease at the level of the snail that acts as the intermediate host. We must understand the mechanisms through which snails and schistosomes interact, as they could offer valuable clues for developing new strategies aimed at disrupting the transmission of schistosomiasis. A community of investigators has concentrated on understanding the mechanisms of snail-trematode compatibility. The interaction between Biomphalaria glabrata and Schistosoma mansoni was chosen as a model system and has received concentrated research efforts over the past four decades. Indeed, most of the existing studies concerning snail-trematode compatibility were developed using these species. The genome of S. mansoni has been

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sequenced and annotated (Berriman et al., 2009), and the sequencing and annotation of the B. glabrata genome was recently submitted for publication (Adema C, personal communication). It is largely accepted that the success or failure of the infection of B. glabrata by S. mansoni reflects a complex interplay between the host’s defence mechanisms and the parasite’s infective strategies. Moreover, within the interacting populations of snails and schistosomes, it appears that some S. mansoni parasites succeed in infecting Biomphalaria snails while others fail. To explain this phenomenon (called “compatibility polymorphism”), two alternative hypotheses have been proposed. The first is that an unsuccessful infection reflects the existence and success of snail resistance processes, whereas a successful infection reveals the susceptible status of the snail host (Webster and Davies, 2001). The second hypothesis is that the success or failure of an infection does not depend on the snail’s susceptibility/resistance status, but rather on the matched or mismatched status of the host and parasite phenotypes (Theron and Coustau, 2005). In the latter scheme, all snails are potentially susceptible if they are exposed to a schistosome with a matching phenotype (Theron et al., 2008). The present review will reexamine the studies performed in recent decades to test the compatibility between B. glabrata and S. mansoni. As described in the following sections, some results support the “resistance hypothesis” while others support the “matching hypothesis.” We propose here that these two hypotheses are not exclusive, and that the compatible/incompatible status of a specific B. glabrata/S. mansoni interaction probably reflects a balance among multiple molecular determinants that fall into two different categories. The first category corresponds to the effector/antieffector systems of the host and the parasite, and the involved molecular determinants tend to induce resistance processes. The second category corresponds to immune receptors and antigens, whose intraindividual diversifications and polymorphisms could favour the matched or mismatched status of host and parasite phenotypes. We propose that the compatibility between individual snails and schistosomes reflects the sum of these different determinants, which are variable between and within populations. Finally, the situation will be made more complex by environmental factors known to influence the compatibility between B. glabrata and S. mansoni. Indeed, two of these environmental factors, the water temperature or successive exposures of B. glabrata to schistosomes can change the phenotype of both partners, thereby altering their compatibility. This will be presented in the last two sections of this review.

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2. THE GENETIC DETERMINISM OF THE COMPATIBILITY/INCOMPATIBILITY OF BIOMPHALARIA GLABRATA AND SCHISTOSOMA MANSONI The low prevalence of snails with patent schistosome infection, which is usually observed in transmission foci (Anderson and May 1979; Sire et al., 1999), was first believed to be explained by the low probability of an encounter between the partners. This hypothesis cannot be excluded in the interaction between B. glabrata and S. mansoni. However, a molecular screening approach yielded interesting results regarding the interaction between Schistosoma haematobium and Bulinus globosus snails. This work showed that although patency was often very low (less than 4% shed cercariae), more than 40% of the snails were exposed to the parasite in the field (Allan et al., 2013). Thus, snails experienced high levels of parasitic exposure, but only a small proportion of infected snails reached the stage of cercarial shedding. Moreover, even if snails were penetrated by miracidia, the infections often failed to develop to patency (Allan et al., 2013). These results suggest that nonsusceptibility or incompatibility of a particular host and parasite combination is a major factor in the low prevalence observed in the field. This nonsusceptibility/incompatibility can also reflect a biochemically unfavourable intramolluscan environment for the parasite, which is termed “unsuitability” (Lie and Heyneman, 1977; Sullivan and Richards, 1981). However, the most frequently involved mechanism consists of the recognition, encapsulation and killing of the parasite by immune cells (haemocytes) of the snail (Fig. 1 A). Numerous infection-based experiments have been performed between different homopatric and heteropatric isolates and lines of B. glabrata and S. mansoni, and the results have revealed high degrees of compatibility polymorphism within and between the populations (Richards and Shade, 1987; Theron et al., 2014). Using inbred lines of B. glabrata, researchers showed that compatibility has a genetic basis, with genes of both the snail and parasite affecting the outcome of an infection. For instance, the infection resistance seen in resistant adult snail stocks (e.g., BS-90, 13-16-R1, and 10-R2) is a dominant single-gene trait that is inherited in a simple Mendelian fashion (Richards et al., 1992; Spada et al., 2002). In contrast, resistance in juvenile snails is determined by five or six genes that each have multiple alleles (Ittiprasert et al., 2010; Richards and Merritt, 1972). Several studies have also shown that multigenic factors of the parasite influence compatibility (Kassim and Richards, 1979; Combes, 1985; Jourdane, 1982; Richards, 1975).

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Figure 1 Schistosoma mansoni that show incompatibility (A) or compatibility (B) with Biomphalaria glabrata. (A) In an incompatible combination, the parasite is recognized as nonself after penetration. It is then encapsulated and killed by haemocytes, and a multilayer haemocyte capsule surrounding a dead sporocyst can be observed. (B) In a compatible combination, the parasite is not recognized and develops normally in snail tissues. The primary sporocyst (Sp1) is seen growing in the head-foot of the snail after penetration.

3. CROSSES AND GENETIC APPROACHES FOR IDENTIFYING COMPATIBILITY/INCOMPATIBILITYLINKED LOCI Crosses between snail lines compatible (M-line or NMRI) and incompatible (BS-90) towards the same schistosome strain (PR1) have been used to investigate the genetic loci that govern the compatibility trait. Various DNA genotyping tools have been used to identify heritable markers related to the parasite-incompatible phenotype of adult snails (Knight et al., 1999). However, these sequences are repetitive in the snail genome, and thus further attempts to characterize the associated genes were not successful. A reverse genetic approach using linkage analysis of polymorphic expressed sequence tags (ESTs, particularly expressed simple sequence repeats, or eSSRs) and previously identified bi-allelic microsatellite markers (genomic SSRs or gSSRs) led to the identification of putative genomic locations for incompatibility gene loci (Ittiprasert et al., 2013). Moreover, studies in a different snail strain (13-16-R1) revealed that snail incompatibility could be associated with allelic variation in a linked cluster of redox genes that includes sod1, which encodes a cytosolic copper/zinc superoxide dismutase, a

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cytosolic enzyme that catalyses the conversion of superoxide anion to H2O2 (Blouin et al., 2013; Bonner et al., 2012; Goodall et al., 2006). Finally, a Restriction site associated DNA (RAD) genotyping approach was developed using experimentally evolved lines of Guadeloupean B. glabrata selected for their incompatibility (Tennessen et al., 2015). This work revealed association of the incompatibility phenotype with a 1000 salivary gland sporozoites; green- 100e1000 sporozoites; purple- 10e100; and orange- 1e10. Solid lines-untreated control mouse recipients, broken lines-mice passively immunized with anti- circumsporozoite protein (CSP) antibody (Mab 3D11) at a concentration estimated to reduce the overall probability of mouse infection by 50% (Churcher et al., 2016).

differentiating into trophozoites and schizonts, which in turn release merozoites that infect new red blood cells (RBCs). At each subsequent cycle the number of infected erythrocytes can rise between 3- and 20-fold. Whilst erythrocytic schizogony can result in life-threatening disease, except in the case of the death of the host it is of little direct consequence to transmission. When considering transmission, the key biology is that at each round of erythrocytic schizogony a small percentage of the asexual parasites commit to the sexual developmental pathway, resulting in the ‘terminal’ differentiation of intraerythrocytic male and female gametocytes. Gametocytes are uniquely responsible for the infection of the bloodfeeding female anopheline. The proportion of gametocytes developing per cycle is variable. Following extended periods of asexual replication this population falls progressively due to the accumulation of mutations and chromosomal deletions in the parasite genome (Birago et al., 1982, 1994). Even within the brief span of a primary infection, not all mature gametocytes produced are equally infectious. One driver of this variability is the hosts’ elevated cytokine response to the rupturing schizonts (Schofield et al., 1996) which can temporarily suppress gametocyte infectivity in heavily infected hosts (Motard et al., 1990;

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Targett et al., 1994). Additionally, in semiimmune hosts, naturally acquired transmission-blocking antibodies can reduce the probability of infection of the vector (Graves et al., 1988). As with many important factors describing transmission it is fair to state we have a great deal more to learn about the global relationships between gametocyte number in the host and the probability of infecting the mosquito (Churcher et al., 2013; Roeffen et al., 1994). Within the bloodmeal of the mosquito the ingested gametocytes transform, in minutes, into fertile gametes. Fertilization normally ensues within an hour of feeding. Fertilization efficiency is variable, between 0% and 80%; female gametes may be fertilized and form ookinetes. Typical losses in vivo are of the order of 90% (see below). Key environmental regulators of gamete formation are: elevated levels of (mosquito-derived) xanthurenic acid; a fall in temperature of >5 C; a bloodmeal pH in the range of 7.4e7.8, and a bicarbonate level above 20 mM (Sinden et al., 1996). In the next 24e 36 h the zygotes mature into motile invasive ookinetes which burst through the midgut epithelium and come to rest against the basal lamina. Here they differentiate into oocysts. In the wild often just five unicellular parasites survive, each containing the four haploid meiotic genomes from the zygote. It is this oocyst population that presents the smallest genetic reservoir that might be targeted by an intervention, but unfortunately it is not readily accessible to controlled attack. Each oocyst will undergo about 11 rounds of endomitosis, regulated by CYC3, to produce w3000 sporozoites (Pringle, 1965; Roques et al., 2015; Rosenberg et al., 1990; Ponnudurai et al., 1989) some 10e20 days after infection. Of these it is commonly stated that 10% are carried to the salivary glands, but more correctly stated, the efficiency of transfer is variable and declines as oocyst numbers increase (Dawes et al., 2009b; Sinden et al., 2008). This saturating relationship may be driven by the nonspecific immune responses generated in the vector both by the parasite and the expansion in the midgut microbiome following the uptake of the bloodmeal (Cirimotich et al., 2011; Ngwa and Pradel, 2015)eparameters that may also contribute to the reduced survival of infected vectors (Dawes et al., 2009a).

3. IMPORTANT LESSONS LEARNT FROM PREVIOUS CONTROL EFFORTS To block transmission obviously we must place barriers (physical, spatial, temporal or molecular) between mosquitoes and susceptible hosts. Nowhere have the basic ‘rules of combat’ in this war been more clearly

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revealed than in the experience gained in extensive previous efforts targeting the mosquito vectors. These lessons include, but are not confined to: 1. Attack simultaneously as many different aspects of biology as possible e.g., population size (insecticide fogging; sterile technologies); host-finding (house design; bednets; decoy bait); adult survival (IRS; biological control); larval survival (larviciding; biological control); and susceptibility to infection (GM-technologies). At a molecular level this may be equated to hitting multiple target genes/gene products simultaneously to avoid escape mutants. 2. For each intervention, recognize the balance required between targetspecificity and wider (e.g., cross-species) utility (consider for instance the prolonged debate over the use of DDT). 3. Identify and focus upon areas of unique vulnerability in the target population to permit more effective targeting of broad spectrum interventions such as insecticides e.g., larval breeding sites (Fig. 4); or human blood sources (by using ITNs). 4. A strategy of particular relevance to eradication campaigns, and one that has perhaps remained underutilized, is that of attacking when the target is numerically weak, e.g., hitting vectors in the dry season (Fig. 5). 5. Delivery must be practical and sustainable under conditions prevailing in the field, even when case incidence has fallen almost to the point of extinction, when political-will might falter. This facet of intervention

Figure 4 Illustration of how targeting mosquito breeding sites [pink shaded area] minimizes the area that could be treated without compromising impact upon the neighbouring human population. Red/green pie charts indicate the proportion of the population infected/uninfected.

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Figure 5 Showing the correlation between the mosquito number and the seasonal change in rainfall. If identifiable, the tiny population in the dry season (here December to April) represents the most vulnerable target for elimination. Reproduced from KerahHinzoumbé, C., Péka, M., Antonio-Nkondjio, C., Donan-Gouni, I., Awono-Ambene, P., SameEkobo, A., Simard, F., 2009. Malaria vectors and transmission dynamics in Goulmoun, a rural city in south-western Chad. BMC Infect. Dis. 9, 71.

delivery has been powerfully discussed, highlighting the difference between efficacy (in the lab) and effectiveness (in the field) (Galactionova et al., 2015). 6. Understand not only the immediate and anticipated impact of the intervention, but also whether there are unforeseen downstream consequences e.g., DDT and the ‘silent-Spring’ effect. Ask whether target species resistant to your intervention (e.g., insecticide or drug resistant mosquitoes and parasites) transmit more efficiently than the wild-type?

3.1 Targeting parasite biology Whilst outwardly appearing complex (Fig. 2), at the coarsest level of cellular understanding the malarial parasite life cycle is simple, involving just 3 major cellular/molecular strategies. Strategy 1 is motility/invasion; it is used in three stages of the parasite life cycle (merozoite, sporozoite and ookinete). These extracellular ‘zoites’ carry on their surfaces an extensive battery of molecules which individually or in combination provide defence against host attack; identify host cells for invasion, and trigger the programmed release of molecules to facilitate entry into, or traversal of the host barriers. These different extracellular ‘coats’ provide the most common class of target for antiparasitic vaccines (see below).

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Invasion of host tissues is additionally dependent upon the parasites’ conserved glideosome motor. Whilst the organization of the motor is based on standard eukaryotic mechanisms of both energy and force generation (actin/myosin) it nonetheless offers unique intracellular targets for motility-inhibiting drugs (Opitz and Soldati, 2002; Baum et al., 2006). Strategy 2, again used three times in the life cycle is replication/cell division. The liver or preerythrocytic schizont, the asexual bloodstage schizont (ABS) and the oocyst share molecular strategies and cell structures, but each is specifically tailored to their unique and contrasting environments. The preerythrocytic schizont lies within the metabolically hyperactive hepatocyte, the ABS inside a ‘near-dead’ erythrocyte, and the oocyst lies in an extracellular location under the basal lamina of the mosquito midgut epithelium where it is bathed in mosquito haemocoelomic fluid. Recognizing first that the rapid replication/metabolism of these schizonts will offer a wide variety of drug targets, and second that the rupturing ABS schizonts are responsible for morbidity and mortality of the infected human host, it is unsurprising that the ABS has been the near exclusive focus of past drug development campaigns. However when considering elimination/eradication as the target, it is the nonreplicating, metabolically quiescent mature gametocytes; and the latent hypnozoite stages of the relapsing parasites (e.g., Plasmodium vivax; Plasmodium cynomolgi), the latter residing in the infected hepatocyte, that proffer the more difficult, but potentially the most important drug targets for suppressing transmission. Strategy 3 is sex, which the parasite undergoes whilst making the precarious jump from the human host into the mosquito. At each round of blood schizogony (and in some species in the liver schizont) a small percentage of the population commit to the sexual development pathway. This on current evidence is founded on the initial expression of a transcription factor AP2-G which controls a second essential regulator AP2-G2 that in turn controls the expression of some 1500 downstream genes (Sinha et al., 2014; Kafsack et al., 2014; Yuda et al., 2015). In the case of P. falciparum the metabolism of the immature gametocyte for the first w60% of their development is largely similar to that of the asexual trophozoite (Lamour et al., 2014; Sinden and Smalley, 1979) and consequently similarly drug sensitive (Smalley, 1977). But during the final w40% of the development (Fig. 6), the gametocytes become progressively less metabolically active and therefore increasingly insensitive to schizonticides (Ruecker et al., 2014; White et al., 2014; Delves et al., 2013; Adjalley et al., 2011; Johnston et al., 2014; Lucantoni et al., 2013; Plouffe et al., 2015). This pattern of metabolism is entirely

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consistent with the transmission strategy of the gametocyte, i.e., when mature it lingers for very extended periods (e.g., 20þ days) in the peripheral blood (Fig. 7) awaiting the improbable opportunity of being ingested by female anopheline. Metabolic ‘shut-down’ of mature gametocytes is exemplified morphologically by the mature male (microgametocyte) which has virtually dispensed with its entire protein synthetic machinery (ribosomes) (Sinden et al., 1978). Nonetheless unique and vulnerable processes still occur, most notably translation repression. In the mature female gametocyte, which retains a very prominent ribosome population, the translation regulators DOZI and CITH (Mair et al., 2010; Guerreiro et al., 2014), form structural complexes of repressed messengers (Thompson and Sinden, 1994). It is unsurprising therefore that while the mature gametocytes of Plasmodium are insensitive to many schizonticides, they remain sensitive to inhibitors of energy metabolism (e.g., primaquine and other 8-aminoquinolines) (Lelievre et al., 2012; Graves et al., 2015). The vast majority of gametocytes are destined to die within the human host, and thus all gametocyte antigens, irrespective of their location will be presented to the hosts’ immune system. Such responses will boost

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vaccine-induced immunity targeted to some gametocyte antigens (e.g., P230; P48/45), but vaccines targeting ookinete-specific immunogens (e.g., P25 & P28) would not share this benefit (Ranawaka et al., 1988). By contrast to the prolonged maturation of the infectious gametocytes, development in the mosquito vector is positively explosive! e driven by the need to escape digestion by the mosquito. Critically there are both constitutive-, and bloodmeal-induced populations of proteolytic enzymes in the mosquito gut (Briegel and Lea, 1975; Muller et al., 1995). The parasite is sensitive to these enzymes (Gass and Yeates, 1979), which reach peak activities around 24 h after blood ingestion (Billingsley and Hecker, 1991). It is somewhat bizarre that it is at this moment the haploid parasite pursues the essential but risky strategy of gamete formation, fertilization and meiotic division of the zygote genome. This stretches the evolutionary concept of ‘Sex in Adversity’ to its conceptual limit! Within minutes of their ingestion into the bloodmeal all mature gametocytes respond to the change in environment via a combination of calcium-, temperature-, and xanthurenic acid-mediated pathways (Billker et al., 2004, 1997). Phenotypically both male and female increase in volume (becoming spherical-irrespective of

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previous morphology) and secrete proteolytic enzymes including PPLP2 from cytoplasmic vesicles (osmiophilic bodies) (Wirth et al., 2014). These events rupture the host erythrocyte and the parasites now lie free in the bloodmeal where they are able to fertilize, but they are now also vulnerable to attack! Cellular organization of the female gamete is dominated by the derepression of the DOZI/CITH mRNA complexes permitting synthesis of the macromolecules required for the formation/function/survival of the zygote and thereafter the motile ookinete. As examples; synthesis of the major zygote/ookinete surface-protective proteins [e.g., P25 and P28 (Tomas et al., 2001) begins within 30 min, and their surface location is achieved within 90 min (Winger et al., 1988)]. Some 731 mRNA transcripts (Guerreiro et al., 2014; Ngwa et al., 2013; Ukegbu et al., 2015; Akinosoglou et al., 2014) and 615 proteins (Talman et al., 2014) are reportedly expressed at this time. Conceptually this outburst of novel cellular activity must offer new and unique opportunities for attack by drugs, and the newly expressed surface proteins for attack by antibodies delivered into the bloodmeal from vaccinated hosts. Whereas one might not anticipate ‘low molecular weight’ drugs to be deactivated by exposure to the proteolytic enzymes of the mosquito bloodmeal, the same cannot be said for immune effector mechanisms received from the infected host. Nonetheless, there is incontrovertible evidence that complement (Margos et al., 2001; Grotendorst and Carter, 1987); antibody (Vermeulen et al., 1985; Tirawanchai et al., 1991); cytokine (Naotunne et al., 1993) and cellular immune effector mechanisms remain functional in the gut of the vector, and that they modulate malaria infection. Fortunately for us, and in stark contrast to the brief survival of complement and cellular effectors (Grotendorst and Carter, 1987; Sinden and Smalley, 1976; Margos et al., 2001), antibodies survive at effective concentrations throughout the 24e36 h sojourn of the parasites in the gut and can even enter the haemocoele (do Rosario et al., 1989). It is on this foundation that laboratory development of very effective transmission-blocking vaccines has been pursued for the past 3 decades (Wu et al., 2015). Whereas the female gametocyte produces one, egg-like, female gamete, the previously haploid male exuberantly engages in the production of eight microgametes (sperm). This is achieved in a staggeringly quick 10e20 min. Needless to say the three rounds of mitosis and synchronized production of 8 14e22 mm long axonemes requires both phenomenal cellular coordination but potentially unique cellular strategies of which we are still absurdly ignorant, one such example would be the intracytoplasmic assembly of the

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axonemes and their redistribution whilst linked to the newly synthesized haploid gamete genomes -which lie on the ‘other’ side of a persistent nuclear envelope (Marques et al., 2014; Guttery et al., 2012; Sinden et al., 1976). Recognizing that the first events of these wondrous processes begin within a minute of entry into the mosquito it will be possible to target these unique events with both antibodies and drugs (see below). The protracted (24e36 h) development of the gametes into an invasive ookinete potentially offers both vaccine and drug targets, the former exemplified by P25; P230 and P48/45, and the latter by cytochrome B in the upregulated mitochondrion-which can be inhibited by atovaquone-a drug developed as a schizonticide, but to which the ookinete is most sensitive (Fowler et al., 1995). Whilst the unusual metabolic profile of these stages suggests many potential drug targets might yet be found, there are counter indications as to their potential utility based upon current lack of mechanisms for controlled drug delivery to the ookinete in the gut of the mosquito many hours after feeding. Invasion of the midgut by the ookinete is a multistep process, the parasite must cross: (1) the peritrophic matrix, (2) a tubular network associated with the brush border and (3) the epithelial cell monolayer. Molecular participants in these interactions reportedly include enolase-binding protein (Vega-Rodriguez et al., 2015); carboxypeptidase (Lavazec et al., 2007); croquemort (Gonzalez-Lazaro et al., 2009); APN1 (Dinglasan et al., 2007b); annexin-like protein (Kotsyfakis et al., 2005); O-linked glycans (Dinglasan et al., 2003); sulfated proteoglycans (Dinglasan et al., 2007a); and secreted glycoconjugates (Mathias et al., 2014; Basseri et al., 2016). Early work showed that disruption of the peritrophic matrix was dependent upon the secretion of micronemal chitinase (Shahabuddin and Kaslow, 1994) which could be inhibited by administration of allosamidin in the bloodmeal (Shahabuddin et al., 2000). A bizarre range of molecules have since been described that, when added to a bloodmeal, inhibit ookinete-to-oocyst development including: scorpine (Conde et al., 2000); bee venom (Moreira et al., 2002); PLA2 (Zieler et al., 2001); SHIVA (Rodriguez et al., 1995); C-type lectins (Yoshida et al., 2007); and complex immunotoxins (Yoshida et al., 2001; Fang et al., 2011). It is clear we remain woefully ignorant of the mechanisms of infection of the mosquito vector at the molecular level, and this currently precludes the rational development of appropriate drugs or vaccines. Nonetheless despite unpromising early studies, the development of the first candidate ‘anti-vector’ vaccine of this type (anti-APN1) is showing considerable potential, notably in that it can be used to inhibit

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transmission of both P. falciparum and P. vivax to anophelines (Armistead et al., 2013; Atkinson et al., 2015). Following invasion/disruption of the midgut wall the ookinete comes to rest under the basal lamina of the epithelium, where it ceases movement and, presumably after sensing some external stimulus, switches to a schizogonic strategy forming the extracellular oocyst. Isn’t it fascinating that the parasite has an essentially extracellular life in the mosquito host which lacks cellmediated immunity, and an intracellular one in the host that can attack its infected cells? Whilst the tiny oocyst population represents numerically the ideal target for intervention, and it has long been known that sporogony can be susceptible to certain schizonticides e.g., the sulfa drugs (Shute and Maryon, 1954; Coleman et al., 1988, 1994, 1992; Coleman, 1990) and lumefantrine (Delves et al., 2012), and new drug-sensitive enzyme targets have been identified (Roques et al., 2015; Boysen and Matuschewski, 2011; Boysen and Matuschewski, 2013; Aly and Matuschewski, 2005), it has to be questioned whether deploying these hard-won compounds when there is little, if any, control over the quantity of drug delivered to the target cell, represents a plausible strategy for deployment.

4. TRANSMISSION-BLOCKING ANTIPARASITIC DRUGS In the new search for TBDs an early question has been ‘what molecular libraries do we screen, and how’? Currently there is great interest in rescreening compound libraries that have already demonstrated schizonticidal activity, on the premise that in a new drug combination, a partner compound that not only kills the asexual bloodstage parasites (ABS) but an additional stage of the life cycle (termed a dual-active drug) would have enhanced efficacy. If we are to minimize the risk of selecting resistant mutations, this advantage has to be balanced with being able to ensure that the dual active drug is delivered in a controlled manner to all (both) target populations. Clearly providing a second dual active drug with a complementary mode of action, irrespective of whether it acts upon the preerythrocytic parasite [Causal prophylactic or hypnozoiticide (CPH)] or the sexual/sporogonic stages (TBD) will reduce the probability of selecting resistant mutants to either drug. But recognizing the drug combination will be given to infected and sick persons, it will be the large schizont population that will select for resistance, including to the dual active compound. Conversely, and most importantly, were a schizonticide to be complemented by a

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CPH/TBD drug that exclusively targets a bottleneck population we gain on two fronts: we retain the utility of the combination to reduce selection of single-resistance mutants, but additionally we are hitting a much smaller population with the CPH/TBD (Fig. 8). Irrespective of whether the resistant mutant was part of the original and potentially clonal sporozoite inoculum, or originated during the expansion of the ABS infection (Pongtavornpinyo et al., 2009; Hastings, 2004), this must delay the selection of resistant mutants to either compound and increase the useful lifespan of the combination. It is therefore essential that we screen new libraries that will secure the discovery of novel and specific transmission blocking drugs. Whilst the following ‘numbers-game’ is purely theoretical, it might illustrate this important concept clearly. An acutely infected adult seeking treatment may have 1011 asexual blood stage parasites in their bloodstream (Miller, 1977). Past experience suggests resistant mutations will be selected, irrespective of their origin, in this population often within a very few years. The same person would have w1089 mature gametocytes, of which 103 are ingested by each mosquito, of these 102 produce ookinetes and thereafter five oocysts. Thus if we target gamete formation we have reduced the target population 1089-fold. Even allowing for a person to be bitten by 103 infected mosquitoes in a season this still results in the target population being

Figure 8 Plot illustrating the relative abundance of parasites at each stage of the life cycle [based upon the estimated burden asexual parasite burden in a clinically sick infected adult (Miller, 1977)]. Note the logarithmic scale on the Y axis and the bottlenecks as the parasite passes into and out of the mosquito vector. Modified from Sinden, R.E., 2010. A biologist’s perspective on malaria vaccine development. Hum. vaccin. 6, 3e11.

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reduced by 1056 fold. Thus if the time to establish resistance to a single schizonticide in the field might be say 2e3 years, a combination of two such drugs would be 4e9 years, but that of a schizonticide and a TBe only drug (of similar parasite-killing efficacy) 4e9  1056 years!. Whilst the foundations of this argument are simplistic (Pongtavornpinyo et al., 2009; Hastings, 2004) the implications are profound, even to extend the useful life of a combination 10-fold would revolutionize both the attractiveness of drug development to commercial companies; and the practicalities of delivery in the field. Hopefully having established the logic of combining schizonticides with TB-only drugs, a question then arises as to how such a combination could be delivered. Here, considering only TBD, the understanding of the parasite biology and human treatment-seeking behaviour is critical. It is assumed in the following discussion that the drugs will most commonly be dispensed to febrile persons attending clinic due to significant current asexual infection (see Fig. 7). The purpose of treating this patient will be (1) to reduce the probability of severe or fatal sequelae, and (2) to reduce transmission. Schizonticide treatment alone will prevent disease in the patient but will it prevent onward transmission through the population? Currently, with the exception of the 8-aminoquinolines and the newly described DDD107498 (Baragana et al., 2015) and KAF 156 (Kuhen et al., 2014), schizonticides rarely kill the mature gametocytes, which then persist as a selected pure infectious population. In the case of P. falciparum this selected population will predominate 5e10 days post treatment (see Figs 7 and 9) (Hogh et al., 1998; Field and Shute, 1955). Whilst this phenomenon has understandably been interpreted by some as suggesting drug-induced gametocytogenesis, the unequivocal distinction between selection and induction mechanisms requires careful modelling of the timing and abundance of gametocytes in both control and treated groups. Recognizing the importance of preventing onward transmission, it is critical that we find mechanisms to attack this ‘late’ population. This requires not only the identification of gametocyte specific drugs, but an understanding of how to deliver the drug effectively to parasites potentially appearing so long after initial treatment. There has been significant effort in recent years to develop screens for gametocytocidal drugs. Recognizing the unique pathways that control gametocyte induction and early development, some assays have been designed to discover drugs killing the metabolically active young gametocytes (stages 1e3) (Lucantoni et al., 2013; Buchholz et al., 2011;

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D’Alessandro et al., 2013; Adjalley et al., 2011; Ruecker et al., 2014; Delves et al., 2012). Whilst in drug combinations such compounds will unquestionably and usefully reduce the emergence of resistance phenotypes, they may however fail to kill the mature gametocytes (stages 4 & 5) already in the circulation of 80% of individuals attending the clinic (Sinden et al., 2012), and thus when combined with a schizonticide will have no direct impact upon onward transmission through the population (see Fig. 9 plots s and s&i). Conversely were the transmission-blocking drug to target the mature (and metabolically suppressed) gametocyte, onward transmission could be totally abrogated e the schizonticide will kill all the asexual parasites and the young gametocytes and the TB-drug the remaining mature gametocytes (Fig. 9 s/ s&m). Clearly this is the strategy that has to be pursued. Should such a partner TBD be less than 100% effective there is obvious advantage were it to kill gametocytes/gametes of both sexes. For all malaria species in the genus Plasmodium where gametocyte and asexual parasite development are concurrent, questions of drug delivery and physico-chemical properties of a schizonticide/gametocide combination are straightforward, but for the parasites of the genus Laverania e.g., P. falciparum and Plasmodium reichenowi the 10 day separation between peak ABS and gametocyte populations might confound effective delivery to both (Fig. 7). Many understandably consider it would be essential to administer the gametocide 5e10 days after the schizonticide. This conclusion is founded on the premise that the drug must be delivered at the time of bloodfeeding to the mosquito e which, if true, does present new difficulties, but is this inevitable? Recently one of the screens to discover drugs preventing onward development of mature gametocytes has produced fascinating data on the modes of action of transmission-suppressing compounds (Ruecker et al., 2014). The assay exposes mature (stage 4 & 5) gametocytes to compounds, and then tests whether the treated mixed-sex gametocytes are separately capable of making functional male and female gametes. The findings can be summarized as follows: male gametocytes are susceptible to 7 times as many compounds as females; whilst male-specific compounds are frequently found, few compounds have been found that inactivate the female exclusively; and importantly active compounds in any class can have reversible or irreversible modes of action. Two consequences of these important observations are: first we must appreciate that the ultrahigh throughput assays screening unfractionated gametocytes (where the M/F ratio is 1/5) will be less likely to detect the largest class of active compounds i.e., those that block the

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Figure 9 Plot to show the relative impact of drugs that kill (with absolute efficacy) -asexual parasites, immature or mature gametocytes, hypothetically given to a malaria infected person attending clinic, on the size and duration of the ensuing populations of asexual parasites [green], immature gametocytes [blue] and mature gametocytes - the infectious reservoir [red]. i, immature gametocytocide; m, mature gametocytocide/gametocide; S, schizonticide.

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maturation of the male gametocyte; and second that perhaps the most useful area to develop transmission blocking drugs will be to look for compounds that bind irreversibly to the mature (stage 4 & 5) gametocytes blocking onward formation of gametes of both sexes. The irreversible mode of action may permit the compound to be delivered concurrently with the schizonticide. The gametocytes, which at the time of treatment may still be sequestered in the bone marrow, will be sterilized and incapable of transmission when they emerge into the peripheral blood some days later. An alternative approach to the development of transmission blocking drugs has exploited the observation that the development and circulation time of the crescentic gametocytes of P. falciparum may be reduced by compounds that stiffen the elaborate parasite cytoskeleton. Compounds of this type [e.g., Viagra, calyculin (Tiburcio et al., 2012, 2015; Duez et al., 2015; Tiburcio et al., 2012)] would require either delivery over the entire gametocyte lifespan or an irreversible mode of action. Whether ‘spherical’ gametocytes of other species of parasite form in the extravascular space, and whether such compounds would act similarly has yet to be determined. Whereas it is probable that transmission can be prevented by combinations of schizonticides and gametocytocidal drugs, it is unlikely with current technologies that we can design drugs whose properties and delivery in the field can target with any reliability any parasite stage later than gamete formation in the mosquito vector. Whilst mass drug administration (MDA) is openly discussed for the distribution of causal prophylactic antimalarials (Newby et al., 2015; Graves et al., 2015), little attention has been paid to possible MDA of transmission blocking drugs. What is patently clear is that drugs to kill the hypnozoite in infected persons must remain a priority area for drug research and development, for it is this stage of transmission that will present the greatest challenge to global eradication of all human malarias. Notwithstanding the huge differences in the biology of the mature gametocytes and the hypnozoite, they share a common basic organization. It should not be surprising therefore that compounds such as the 8-aminoquinolines (which kill both these cell types) may hit some of the conserved ‘minimal essential pathways for life’. Thus any newly discovered compound that inactivates female gametocytes should be screened in the expectation that it may have causal prophylactic (hypnozoiticidal) activity. The closest we have come yet to such a pan-specific compound is DDD107498, interestingly, a compound that inhibits the translation elongation factor eEF2 (Baragana et al., 2015).

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5. TRANSMISSION-BLOCKING ANTIPARASITIC VACCINES To embrace the diverse modes of action of transmission blocking vaccines the term ‘Vaccines that Interrupt Malaria Transmission’ (VIMT) has been proposed (Alonso et al., 2011). In the context of this discussion, only Vaccines preventing PreErythrocytic development (PEV) and those preventing mosquito infection, the Sexual Sporogonic and MosquitoVIMT, SSM-VIMT (for which I retain the use of the traditional term TBV) will be considered. Considering the mechanisms of PEV i.e., acting in the human host before merozoite emergence from the hepatocyte, antibodies to surface/secreted molecules on the sporozoite may ‘simply’ identify the parasite for phagocytic attack (opsonization) or complement mediated lysis; or perhaps block the function of a critical parasite molecule. Alternatively cytotoxic T cells may attack the infected MHC-bearing hepatocyte (Hill, 2011). In marked contrast the parasites targeted by TBVs are extracellular in the mosquito bloodmeal therefore all killing mechanisms are (1) antibody mediated, and (2) potentially constrained by the lifetimes of effector mechanisms in the mosquito bloodmeal. Progress in TBV development in both the laboratory and in early field trials has recently been reviewed comprehensively (Nikolaeva et al., 2015) and will not be redescribed here. VIMT have the potential to be the most useful of all transmission blocking interventions in the field. The key advantages they bring to the table are relative long term efficacy (at present, months); comparative genetic conservation of some TBV target molecules, and relative ease in the identification, and assessment of, the required target human population. The pioneering vaccine RTS,S, based on the circumsporozoite surface protein was conceived in the 1980’s as a protective intervention, analysis of vaccine impact has therefore focussed on the reduction in disease in a population (Penny et al., 2015a,b, 2008). The vaccine may be anticipated to have transmission blocking potential, indeed it has been shown that significant (30e87%) protection from infection can be achieved (Stoute et al., 1997; Polhemus et al., 2009; Bojang et al., 2009). If this impact persists over many cycles of transmission any reductions achieved, may be expected to rise over time (Blagborough et al., 2013a) (see below). Clearly work to expand the antigenic repertoire of CSP-based vaccines (Neafsey et al., 2015) or provide additional targetable sporozoite and preerythrocytic antigens could capitalize on the pioneering work already achieved.

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The development of vaccines targeting the parasite in man, notably the bloodstage antigens, but also including CSP on the sporozoite, can be blighted by mechanisms that have evolved to permit the parasite to escape immune attack by the human host (Brown et al., 1968). In the case of TBVs one such parasite strategy (translation-suppression) has ‘backfired’ and potentially enhanced our ability to discover TBV’s of long-lived utility. The translation-suppression mechanisms employed in the mature gametocyte prevent expression in the human host of many (but not all) proteins that will be accessible to the human immune system in the mosquito bloodmeal. Where the parasite has been exceptionally cunning is that, had the protein been expressed in the human host, and even if not exported onto the parasite or RBC surface, it would nonetheless generate an immune response - because the overwhelming majority of gametocytes die in the human host, thus presenting all antigens, irrespective of their cellular location (e.g., P230 and P45/48). We can therefore deduce that the molecules repressed at the level of the mRNA must include ones that are extremely sensitive to immune pressure should naturally occurring antibodies to be transferred into the bloodmeal. Thus in the gametocytes, the repertoire of translationally repressed, gamete/zygote/ookinete surface proteins must include key candidate transmission blocking targets. Among these are the related proteins P25 and P28 - the major constituents of the ookinete surface proteome (Stanway, 2007). A correlate, or perhaps evolutionary consequence, of the proteins not being exposed to the adaptive immune system of the human host is that they are comparatively invariant. In 329 isolates, just one polymorphism in the antibody binding sites has been described in P25, as opposed to 38 haplotypes (Feng et al., 2015), and 12 mutations in P45/48 (Juliano et al., 2015). The former vaccine may consequently have wider geographic utility. By comparison with the ABS, and PEV vaccines the delayed benefit conferred by TBVs upon the vaccinee has generated much discussion as to the appropriate clinical development path. The satisfactory resolution of this controversy has been summarized comprehensively and concisely (Nunes et al., 2014) and is therefore not pursued here. Considerable debate has emerged as to the immune effector mechanisms of TBVs. Before reviewing the contrasting data, it is essential to understand how the different effector arms actually survive the onslaught of digestion in the mosquito midgut (see above). The complement cascade (C3b), remains active for only 3e5 h (Grotendorst and Carter, 1987; Margos et al., 2001), the cellular arm (as phagocytes) for 12 h (Sinden and Smalley, 1976), and antibody, albeit at ever-reducing titre for 24e36 h (do Rosario et al.,

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1989). It has long fascinated the author that the gametes co-opt complement regulator factor H from the plasma and are not therefore lysed by the ingested complement (Simon et al., 2013), but the ookinete, which forms after complement has been inactivated, is susceptible to lysis. It is unsurprising that P230 which is expressed early, on the gametes, is very effectively targeted by both agglutinating (Aikawa et al., 1981) and complement-dependent antibody lysis mechanisms (Healer et al., 1997). In contrast the effector mechanisms against the late-proteins (e.g., P25/28 expressed on the female gamete/zygote/ookinete) must be, and are, largely antibody-only (Fab-mediated/complement independent), but still include minor cell-mediated mechanisms (Ranawaka et al., 1994a,b, 1993), thus both immune-identification, and more importantly immune-blockade of P25/P28 function may be operating. Knowing that exflagellation is inhibited by elevated cytokine levels (Naotunne et al., 1991, 1993), it would be interesting to know whether the cell-based inhibition seen when monoclonal antibodies are added are cytokine mediated. Key to vaccine development is that vaccines targeting transmission to the vector are entirely antibody dependent, this ‘simplifies’ technical development. Knowing that the impact of TBVs titrates out with antibody concentration (Miura et al., 2007), it is therefore critical to understand the relationship between the duration and effective titre of the antibody response and the transmission season. Population studies (Blagborough et al., 2013a) have already emphasized the significant enhancement of impact gained by suppressing successive rounds of transmission (by whatever mechanism). One of the advantages of vaccines over drugs is that if a complex immunogen is used it will generate responses to many epitopes on a single antigen. Nonetheless there remains the clear potential to combine multiple, complex VIMTs e.g., of immediate interest would be to combine P25 and CSP and simultaneously hit the two population bottlenecks in the parasite life cycle. Early studies looking at this combination (Mizutani et al., 2014) suggest that at least additivity of impacts may occur. Recognizing that antigenic competition might compromise the integration of multiple immunogens, perhaps we should now also be evaluating whether and how immune and nonimmune interventions can be combined and delivered effectively (Nunes et al., 2014).

5.1 Targeting the parasite indirectly through the mosquito An intriguing possibility suggested by recent observations would be to coadminister an insecticide (ivermectin) together with and antimalarial schizonticide (Ouedraogo et al., 2015; Chaccour et al., 2013).

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Alternatively recognition that the mosquito innate immune system, notably the expression of the complement-like protein TEP1 (Blandin et al., 2004) can influence transmission of Plasmodium through the vector, has opened new potential avenues for intervention, both by inclusion of immune activators in the bloodmeal, or by construction of novel refractory GM vectors. The complexity of design of such interventions is highlighted by the recent observation that P. falciparum has evolved multiple haplotypes of the protein Pfs47 which protect the female gamete against geographical variants of the TEP mechanism (Molina-Cruz et al., 2013).

6. GENETICALLY MODIFIED MOSQUITOES Reduction or replacement of natural vector species by genetically modified (GM)-attenuated lines has attracted the attention of many enterprising laboratories. The successes of population reduction efforts are entirely dependent upon (1) the proportion of the total vector population represented by the targeted species, and (2) whether other vector species occupy the vacated ecological niche. Concepts being explored for population replacement include the generation of vectors lacking key receptors required for successful infection (e.g., ligands for midgut or salivary gland invasion) (Ghosh and Jacobs-Lorena, 2009; Molina-Cruz et al., 2015), or those where the established parasite is subject to enhanced and lethal immune attack. Todate analyses of the TEP1 (Blandin et al., 2004); insulin-like peptide (Pietri et al., 2015); NOS (Luckhart and Li, 2001; Molina-Cruz et al., 2008); and serpin (Williams et al., 2013; An et al., 2012) systems have introduced interesting lines of enquiry. For the reasons stated above it would be advantageous if the target acted prior to sporozoite formation simply to ensure the target population is as small as possible (Sinden, 2010). Even recognizing that malaria infection carries some negative selection pressure (Dawes et al., 2009a), the key problem to be overcome with this technology is the same as for any other GM technology, ‘How does one drive the necessary genes through reproductively isolated populations of the multiple species responsible for transmission?’ Encouraging laboratory successes are now being published (Gantz et al., 2015; Nolan et al., 2011). It has been demonstrated in the laboratory that expression of monoclonal short-chain antibody by GM vectors can effectively inhibit parasite development in both the bloodmeal [anti ePfs25 (Yoshida et al., 1999; Hammond et al., 2016)] and haemocoele [anti-CSP (Isaacs et al., 2012)] and reduce parasite transmission. Whilst the elegance of underlying strategy cannot be

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denied, noting the antibody concentration-dependence of the equivalent vaccines, it would be necessary to undertake extensive studies to ensure the expression of the antibody would remain undiminished as the GM construct spreads throughout the field populations. This concern pales to insignificance when we recognize that the mode of action of a short-chain antibody, like that of a drug is to attack a single small molecular structure (epitope), on past evidence this would inevitably and perhaps rapidly select resistance mutants.

7. PARATRANSGENIC DELIVERY SYSTEMS The effort required to engineer a single strain of GM vector expressing a single infection-supressing molecule is considerable, and becomes daunting when considering the production of multivalent approaches. An alternative strategy, paratransgenesis of the mosquito overcomes many of these issues. Prokaryotic symbionts are both (comparatively) simple to engineer, and have the unique advantage that multiple targets in multiple vector species can be addressed by simple mixture of the bacterial/fungal GM strains prior to introduction to the insects (Bisi and Lampe, 2011; Dinparast Djadid et al., 2011; Wang and Jacobs-Lorena, 2013). Whilst acknowledging the excellent underlying research that has been done and the ingenuity of the concept to attack the parasites using designeremix combinations of recombinant symbionts in their myriad host vectors this author does not consider paratransgenesis a sustainable core mechanism for the delivery of transmission blocking reagents. The greatest weakness of the concept again lies in the inability to control the effector concentration of the intervention at the point of delivery, irrespective of whether it is a physically inherited symbiont (e.g., Wolbachia) or an environmental contaminant (e.g., Serratia). Without such control it is inevitable that suboptimal exposure will occur and parasite escape mechanisms will be selected. These technologies might however be appropriate for short-lived elimination campaigns.

8. HOW DO WE ANALYSE THE IMPACT OF TRANSMISSION BLOCKING INTERVENTIONS? Until now individual blocking interventions have commonly been assayed using ‘internal’ metrics tailored to the specific study. These metrics whilst useful in comparing, within a single controlled experiment, the

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impact of candidate interventions at the chosen endpoint, may have little predictive value as to the impact upon infection of the human population. As examples: TBVs have been measured by the antibody titre induced (Miura et al., 2007), or more commonly by the reduction in the intensity or prevalence of mosquito infection (Gwadz, 1976; Huff et al., 1958; Churcher et al., 2012), and in the case of PEVs by the reduction of infection (in the lab) and disease (in the field) (Penny et al., 2015a,b). TBDs are variously measured by the reduction in gametocyte (Lelievre et al., 2012), or gamete production or mosquito infection (Ruecker et al., 2014; Adjalley et al., 2011). GM immune-vectors are evaluated by mosquito infection (Christophides, 2005); GM-survival vectors by the abundance of mosquitoes (Catteruccia et al., 2003); but finally it is only for the assessment of bednets and IRS that the key index is commonly used, namely the change in prevalence of host/human infection. Whilst the merits of the above, and other indirect outputs (e.g., multiplicity of infection, Force of Infection etc.) have been discussed at length (Tusting et al., 2014), until the relationships between each of the internal/indirect metrics and the key output (host-case prevalence) are carefully described, the wider utility of the indirect data generated is severely compromised. As an example: at present we still have to ask ‘What predictable impact will reducing the gametocyte, gamete or indeed oocyst number by x% have on the human infection prevalence’, and ‘How do we prioritise between two interventions: one blocking gametocyte number by 50% and another blocking oocyst production by 50%’? We need a common framework for measuring the impact of TBIs. It is now clear that the efficiency of Plasmodium’s passage through its developmental cascade in the vector is density dependent, declining substantially with increasing parasite number (Sinden et al., 2008; Vaughan, 2006). It has also been shown that increasing parasite challenge (number) significantly depresses measured efficacy of TBIs when calculated by changes in oocyst prevalence (Churcher et al., 2012). This ‘context-sensitive’ impact of interventions (see also Fig. 3) makes the comparison of published data generated from different experiments/labs difficult. A standardized SMFA protocol is still required. One solution to the problems described above is to measure the ‘effectsize’ of an intervention (Smith et al., 2007). A key property of this measure is that it applies to all transmission settings, and has direct applicability in suggesting the geographical areas where an intervention has any probability of achieving elimination (Fig. 10).

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Figure 10 Use of a TBI [which reduces oocyst density/prevalence by 57/32% respectively at a mean control challenge of 50 oocysts, measured using no less than 50 mosquitoes (observations) per group] over successive transmission cycles results in an effect size of 20.4% (95% CI 15.2e24.9%) i.e., it would eliminate malaria where Rc < 1.255 [yellow] but not where Rc (Basic reproductive number in the challenged population) is >1.255 [blue] (Blagborough et al., 2013a).

A useful early stage laboratory approach to understanding the fundamental principles of transmission blockade by differing interventions has been developed in which effect-size is used to measure impact of TBDs, TBVs and PEVs (Blagborough et al., 2013a,b). The model examines cyclical populations of transmission of P. berghei through laboratory populations of Anopheles stephensi vectors and mouse hosts. Whilst recognizing that the parasite of greatest medical importance P. falciparum is a notorious outlier of Plasmodium biology, it is indisputable that this rodent model will identify key properties/relationships that will determine whether and how combinations of interventions could underpin eradication/elimination campaigns against any of the six parasite species naturally infecting man. Key initial observations arising from the first reports of this population approach were: 1. Application of a TBI over repeated cycles highlights the utility of interventions that are less than 100% efficient in a single cycle/developmental step (e.g., an intervention reducing gametocyte-oocyst development by only 57/32% intensity/prevalence could eradicate the parasite in as little as three generations). 2. There is no threshold value for TB reduction that defines utility. [Prevailing thinking has assumed an 85% reduction in oocyst prevalence/intensity is required (Saul, 1993; Nunes et al., 2014)].

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3. The underlying intensity of transmission in the population has a critical impact as to whether a reduction in transmission will either achieve elimination, or can be measured at all. The intervention described above (1) eradicated when the mouse received T Þ, where T is a threshold of interest. Secondly, models of this form not only provide information on which of the considered covariates are associated with the disease outcome, but further [assuming parsimony was taken into consideration when undertaking model selection (Lunn et al., 2000; Spiegelhalter et al., 2002; Austin and Tu, 2004; Hoeting et al., 2006)] provide information on the nature of those

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associations. Further, the estimated surface b S ðxÞ indicates which geographical areas have a higher or lower disease risk after accounting for measured risk factors, which may aid further investigations to identify other unmeasured elements that may be affecting disease risk. Finally, these models are not restricted to only account for environmental variables as risk factors, and other influential measures, e.g., demographic, socioeconomic can be incorporated, with the caveat being that maps of the resulting risk can only be produced if the risk factor is available at each location where predictions of risk are to be made. Fig. 2 presents the predicted mean schistosomiasis prevalence in Namibia, obtained by fitting a binomial geostatistical model using the R-INLA package. The model included the average maximum normalized difference vegetation index (NDVI) over a 3-year period obtained at 250 m resolution (NASA, 2016a), average annual rainfall at 100 m resolution (WorldClim, 2016) and topographical wetness index (TWI) derived from 90-m resolution elevation data (Sørensen et al., 2006; NASA, 2016b) as covariates. As with the map produced using IDW, the predictions were made on a 2.5  2.5 km grid. In comparison to the IDW map, the predicted prevalence is much more spatially variable as the relationship between the covariates and the prevalence survey data allows predictions to be made at locations far from those surveyed. There are plenty of additional examples of model-based geostatistics in the schistosomiasis literature including large-scale schistosomiasis prevalence mapping (Schur et al., 2013; Lai et al., 2015), and intensity of infection in East Africa (Clements et al., 2006), plus the distribution of freshwater snails in Lake Victoria (Standley et al., 2012). Model-based geostatistical application to LF for sub-Saharan Africa are less common, with just a small number of papers using this approach to map LF prevalence at the large geographical scale (Slater and Michael, 2013; Moraga et al., 2015), whereas (Kelly-Hope et al., 2006; Stensgaard et al., 2011) use model-based geostatistics to explore the spatial association between LF and malaria. Similarly, there are currently few applications of model-based geostatistics to HAT transmission data, with publications predominantly focussing on Rhodesian HAT (Batchelor et al., 2009; Wardrop et al., 2010). As model-based geostatistics becomes more accessible through the development of more user-friendly software, it is possible that the popularity of this method will increase.

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3.2.3 Common spatially implicit methods There are numerous other disease risk-mapping approaches that do not explicitly incorporate spatial dependence, and instead assume that all of the spatially structured variability can be explained by measurable risk factors, commonly environmental variables. These methods often fall under the category of species distribution models or ecological/environmental niche models. Here we briefly describe several approaches that have been applied in the tropical disease literature, predominantly to map the spatial variability in disease vectors, i.e., MaxEnt, discriminant analysis and boosted regression trees. These methods acknowledge that the hosteparasite relationship is very complex and nonlinear, hence intricate interactions need to be taken into consideration when trying to map disease risk. Although in theory it is possible to incorporate this complexity into spatial regression models such as geostatistical models, this would be at the expense of parsimony, and by extension, interpretability. These approaches generally make use of presence only or presence/absence data with the aim of predicting the probability that the species or disease of focus is present at a given location. MaxEnt is a popular species distribution model as it only requires information on presence data to be able to elucidate the relationship between environmental variables and the species of interest (Phillips et al., 2006; Elith et al., 2006; Phillips and Dudík, 2008). This approach is commonly applied in ecology to determine the spatial patterns of the species using records of species sightings (i.e., presence only data), although it is also an alternative to logistic regression when there is doubt over the accuracy of absence data (Dicko et al., 2014). MaxEnt is a machine-learning technique, which in this context refers to automatic approaches which find the best combination (including nonlinear, complex combinations) of risk factors that describe the variability in the data whilst controlling for overfitting (Elith and Leathwick, 2009). This process is referred to as regularization. The MaxEnt software enables users to fit models of this form to their presence only data either using the software interface itself (https://www. cs.princeton.edu/wschapire/maxent/), or by using functions within the R package dismo (Merow et al., 2013; Hijmans et al., 2016). MaxEnt, and its accompanying software, is however a good demonstration of the disconnect between researcher working in different disciplines as it has since been demonstrated that MaxEnt and Poisson regression are in fact equivalent (Renner and Warton, 2013). By using regularization techniques such

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as lasso or ridge regression (Hoerl and Kennard, 1970; Tibshirani, 2011), users can fit the equivalent of MaxEnt models to their presence only data using R packages such as glmnet (Friedman et al., 2015), without restricting themselves to the limitations of the MaxEnt software. With regards to the three NTDs of focus, MaxEnt has been used both in an ecological and an epidemiological context. For example, it has been used to provide species distribution maps for guiding tsetse control strategies (Matawa et al., 2013; Dicko et al., 2014), and for establishing the distribution of snails (Stensgaard et al., 2013; Pedersen et al., 2014), and has further been used to explore the large-scale spatial variability in LF (Slater and Michael, 2012; Mwase et al., 2014) using disease presence data at the regional and national level, respectively. Popular binary classification methods that are frequently used to explore the spatial distribution of species, and perhaps less commonly, diseases, using both presence and absence data include discriminant analysis and decision trees. As with MaxEnt, the goal of these classification methods is to determine which combination of covariates best explains the data without focussing on the interpretability or parsimony. Nonlinear discriminant analysis focuses on identifying a combination of continuous covariates to discriminate locations where the outcome of interest is likely to be present or absent, and it has been used to map the distribution of disease vectors including mosquitoes, sandfly and tsetse fly (Robinson, 2000; Rogers, 2000, 2006). As the relationship between the outcome and the environment may be inconsistent across the full range of the environmental covariates under consideration, in many applications the environmental is stratified into areas of similar conditions. Nonlinear discriminant analysis is then undertaken within each of the strata separately, such that within each strata there is a nonlinear axis discriminating between the two groups in multivariate space (Rogers, 2000). Nonlinear discriminant analysis is equivalent to maximum likelihood classification, which is frequently implemented in GIS software to classify remotely sensed (RS) data; however, machine learningebased techniques such as decision trees (e.g., random forests) and support vector machines have been shown to consistently outperform this approach (Lu and Weng, 2007; Otukei and Blaschke, 2010; Cianci et al., 2015). The boosted regression trees approach is an increasingly popular method being used in species distribution modelling and is a combination of two methods: regression trees and boosting (De’ath 2007; Elith et al., 2008; Stevens and Pfeiffer, 2011). Regression trees are a method of

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classification which aims to predict a continuous outcome. There are two stages to the process. Firstly the data are partitioned into small groups using a recursive partitioning approach. In brief, covariate-based binary decision rules are used to successively partition the data into smaller groups that are relatively homogeneous with regards to their relationship to the outcome. Once these groups have been established, simple regression models are then fitted to the data in each group independently (De’ath & Fabricius, 2000). Boosting is a method of improving the accuracy of a model and is based on the concept that it is easier to build multiple models based on a less strictly accurate decision rules and take an average that it is to build a single model based on a highly accurate ones. Boosting is therefore a sequential process where models such as regression trees are fitted iteratively to the data, and at each iteration the emphasis shifts to improve those outcomes that were poorly predicted in the previous iteration (Schapire, 2003; Elith et al., 2008). The boosted regression tree approach has been implemented in a number of recent prominent tropical disease epidemiology papers including an assessment of the global burden of dengue (Bhatt et al., 2013), and the global map of dominant malaria vectors (Sinka et al., 2010; Sinka et al., 2012), the global distribution of LF (Cano et al., 2014). Each of these examples utilizes presence and absence data rather than prevalence or abundance, with model-based geostatistics being the method of choice when suitable prevalence or abundance data are available (Hay et al., 2013b; Bhatt et al., 2015; Moraga et al., 2015; Grimes and Templeton, 2015).

4. COMMON ISSUES IN SPATIAL ANALYSIS Spatial analyses of point-level data are sensitive to a number of factors which need to be accounted for when collating, analysis and interpreting the data. In this section we highlight several of the most common issues affecting tropical disease applications, i.e., the spatial scale at which the data are recorded, the sampling methods used to collect the data and the problems associated with combining data from different sources.

4.1 Spatial scale In this context, when we refer to spatial scale we are predominantly concerned with the scale at which we would like to make our spatial predictions. This scale may be influenced by the objectives of the analysis, the characteristics of the disease under consideration, and the spatial scale

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of the available covariates. For example, the objective of the study may be to map the spatial distribution of disease at the large spatial scale (national, regional level) for descriptive purposes or to provide estimates of global burden, or maps may be required at a more detailed spatial scale to guide more targeted control interventions. The spatial scale of the former is therefore likely to be coarser than that of the latter. Consider for example a highly spatially heterogeneous disease such as HAT (see Section 1 for more details). Whilst predictions at a relatively coarse spatial scale are useful in providing a general sense of the distribution of disease (Simarro et al., 2010), due to the highly focal nature of the disease, important spatial variability in disease distribution will be masked at this scale and hence may produce inaccurate estimates of disease burden, or insufficient information to guide targeted intervention strategies (Sciarretta et al., 2005; Hackett et al., 2014). However, if disease risk is relatively spatially homogeneous within some geographical limit, for example, soil-transmitted helminths, it may be a waste of resources to produce maps at the finer spatial level (Sturrock et al., 2010). Covariates used in the spatial analyses of tropical disease data are often derived from RS data (see Section 5). When incorporating these covariates, it is important to recognize that these data are already a spatial aggregation of a spatially continuous phenomenon, with the initial size and positioning of the boundaries of each RS cell (referred to in the statistical literature as the support) being defined by the instrument being used and the organization from which the data were sourced. Further, when undertaking the spatial analysis, these cells may be aggregated or disaggregated to ensure a consistent (and scientifically relevant) spatial resolution and alignment across all of the RS data being included in the analysis. The method by which the RS data are processed to be incorporated into the analysis can have a significant impact on the form of the relationship between RS data and the diseaserelated point-level data, and as such needs to be taken into consideration when evaluating the output (Atkinson and Graham, 2006; Raj et al., 2013; Hamm et al., 2015). This is a known problem in spatial statistics, referred to as the modifiable areal unit problem (MAUP) (Jelinski and Wu, 1996; Cressie, 1996; Gotway and Young, 2002). As a consequence, researchers may find that they draw different and sometimes opposing conclusions about the influence of a spatially aggregated covariate and the disease-related outcome of interest. Whilst the MAUP may cause inconsistencies in the relationship between covariates and the disease-related outcome, it is often the case

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that the relative influence of covariates naturally differs at different spatial scales. For example, in epidemiology studies the dominant disease drivers at the large geographical scale tend to be associated with the climate and environment e.g., rainfall, temperature, elevation whereas at the smaller scale, the sociodemographic characteristics tend to become more influential (Simoonga et al., 2009; Hamm et al., 2015). Similarly, in ecological studies, e.g., when exploring the spatial distribution of disease vectors, the dominant drivers at the large-scale drivers are similar to those found in epidemiological studies, whereas the environmental nuances, e.g., fragmentation of habitat, river flow speed and steepness of the landscape need to be taken into consideration at the smaller spatial scale (Guerrini et al., 2008; Jacob et al., 2013; Hardy et al., 2015; Mweempwa et al., 2015).

4.2 Spatial bias 4.2.1 Sources of spatial bias It is often the case that spatially referenced data that are used to explore the spatial variability in disease risk have been collected for a different purpose, with the spatial analysis being an opportunistic addition. As a result, when undertaking a post hoc spatial analysis, it is essential that the analyst is aware of any potential sources of spatial bias and adapt their models accordingly (Wardrop et al., 2014). For example, when conducting a survey, the location of samples is often influenced by the accessibility of an area such that vector sampling sites may not include densely forested areas, or epidemiological surveys may not be conducted in the more remote, impoverished communities. If collating historical data, differing diagnostic tools with nonequivalent sensitivity and specificity may be used in different geographical areas, or only data considered to be of epidemiological/ ecological interest might be reported, excluding those geographical areas where disease prevalence or vector abundance is found to be low. Further, areas may be preferentially sampled, i.e., targeted specifically because they are suspected to have high or low disease risk. If such biases are identified, it’s important that these are accounted for in the subsequent spatial analysis (Phillips et al., 2009; Diggle et al., 2010; Fourcade et al., 2014; Grimes and Templeton, 2015; Giorgi et al., 2015; Diggle and Giorgi, 2015). 4.2.2 Spatial sampling design When designing studies to explicitly explore spatial variability, the spatial sampling design needs to be carefully considered, which includes the consideration of where to sample, how many locations to sample and

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how much data to collect at each sampled location, e.g., how many schoolchildren to survey per sampled school. Common sampling strategies employed in epidemiological surveys generally do not explicitly consider the spatial variation in the disease distribution and instead are designed to provide population-level summaries. These are referred to as design-based strategies. For example, at the small geographical scale, commonly employed design-based approaches include simple random sampling (where a suitable sampling frame is available) or systematic sampling using techniques such as the ‘spin the bottle’ approach (Bostoen and Chalabi, 2006). At the larger scale, to make the data collection more logistically feasible, cluster sampling is frequently employed where firstly the clusters (commonly villages or schools) are selected, following which individuals or households within the selected clusters are sampled. Sample size calculations for these types of survey are based on obtaining a sufficiently precise estimate of an arealevel summary measure, e.g., prevalence. Whilst these approaches provide unbiased estimates of overall prevalence, they are not optimal for determining the spatial variability in disease risk within the study area. For highly spatially heterogeneous diseases, this may result in the important geographical areas of disease transmission being missed. A simple approach to addressing this is to use stratified sampling where the strata are geographically contiguous areas, with random or cluster sampling being used within the strata. Often, these strata are selected using environmental features, and whilst this may result in reduced within-strata spatial heterogeneity, this approach may not enable spatial variability to be explored in continuous space. Further, neither random, cluster, nor stratified sampling strategies account for spatial dependency when determining sample size, as in a scenario where there is high spatial dependency, sampling two locations that are geographically close together may not provide much more information than if one point were sampled in that area. Thus, the effective sample size in areas with high spatial dependency is reduced (Griffith, 2005). Model-based sampling approaches are those that aim to learn about the spatial correlation structure of the data and make predictions at unsampled locations, as opposed to determining a single value to represent an entire area such as a mean. Within the spatial statistical literature, the location of the sampled points, as opposed to the quantity, is generally more of a consideration when determining an optimum sampling design (Diggle and Lophaven, 2006; Wang et al., 2012; Evangelou and Zhu, 2012). A

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commonly applied model-based sampling strategy for the purpose of spatial prediction is based on sampling at regular intervals in one dimension (transect) or two dimensions (lattice) (Diggle and Ribeiro, 2007; Miller et al., 2013; Buckland et al., 2015). Numerous examples of this can be found in the ecological literature, whereas spatial sampling has been less of a focus in tropical disease epidemiology, with examples of its consideration being relatively sparse (Gyapong et al., 2002; Zouré et al., 2011). If the focus of the research is to gain a better understanding of the spatial correlation structure of the data, the lattice design should be supplemented to also include close pairs of points within the lattice to estimate the spatial dependency over short distances (Diggle and Ribeiro, 2007). In practice, spatial sampling strategies for diseases in resource-poor settings are likely to be influenced by pragmatism, with the number of sampling locations, plus the number sampled at each individual location, being limited by time and resources as well as scientific rigour (Magalh~aes et al., 2011). As disease landscapes change, as a result of a change in environment, behaviour as well as due to the influence of control, more consideration needs to be given to sampling strategies that promote the production of dynamic as opposed to static disease risk maps, e.g., adaptive spatial sampling (Peyrard et al., 2013; Siegfried and Siegfried, 2014) is an approach by which sampling is undertaken sequentially such that results of the previous samples are used to guide the selection of the next.

4.3 Combining different resources Due to the lack of large-scale, spatially dense data from a single survey, disease maps at the national or regional level are often produced using historical data from multiple sources. Combining data from historical surveys can be very challenging, as the data under consideration often includes that collected using a variety of survey designs, e.g., different sampling strategies, diagnostic tests, target populations etc. In failing to incorporate these data heterogeneities into the model, the resulting predicted risk surfaces may be misrepresentative of the true underlying risk (Grimes and Templeton, 2015; Giorgi et al., 2015; Diggle and Giorgi, 2015). For example, it may be more appropriate to split the data into smaller, more homogeneous datasets before undertaking the analysis, to derive additional covariates which attempt to measure these differences, or to extend the model to include a temporal component.

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5. SOURCES OF SPATIALLY REFERENCED DATA Spatially referenced data is available in two categories, i.e., vector (points, lines, polygons) and raster. Due to the prolific increase in geospatial technology and the drive for open access data, there is now an abundance of spatially referenced data that is relevant to disease control in developing countries both publically and commercially available, plus we have the tools available to easily generate data ourselves. In the following sections we describe sources of data applicable to tropical disease control, with a focus on the three NTDs of interest (see Table 2).

5.1 Disease transmission data With the growing realization that open access to survey data can have wide public health benefits, many leading scientific journals and research funders now require researchers to make their data available publically and numerous online global disease databases are being developed (Flueckiger et al., 2015). These databases often collate, and if necessary georeference, available disease data obtained from sources such as the published literature or directly from the researchers involved in collecting the data. 5.1.1 Global disease databases There are a growing number of initiatives which aim to collate all historical spatially referenced prevalence/individual case data at the global scale for a specific disease. Example of this include the Global Atlas of Helminth Infections (www.thiswormyworld.org) which includes historical and contemporary data for LF, schistosomiasis and soil-transmitted helminths (Brooker et al., 2010; Cano et al., 2014; Sime et al., 2014), the Global NTD database which focuses on schistosomiasis, and the Atlas of HAT (Simarro et al., 2010; Lumbala et al., 2015). Not all data presented on these sites are available to download; however, some data may be shared on request. NTD Mapper (www.ntdmap.org) is an NTD-specific Website, currently focused on trachoma, Loa loa, LF, onchocerciasis, schistosomiasis and STH. Data presented come from a variety of sources, including prevalence surveys and literature searches with varying geographical scales. A large proportion of the data presented are available for download. Contemporary and historical disease data may also be accessible from unpublished sources. For example, Healthmap (www.healthmap.org), founded in 2006, is an online resource for informal disease surveillance data, collating information from sources such as ProMed Mail, WHO and

Disease case data

Global Atlas of Point prevalence survey data Helminth Infections for schistosomiasis, LF and soil-transmitted helminths Global NTD database Point prevalence survey data for schistosomiasis Atlas of human African Number of HAT cases trypanosomiasis since 2000 by village

All point prevalence maps, plus the raw data for a subset of countries are available to download All point prevalence data available. Registration required All case maps are available, and case data is available on request

NTD Mapper

Schistosomiasis, LF, soil-transmitted helminths and trachoma prevalence data are available to download

Healthmap

Point prevalence survey data for schistosomiasis, LF, soil-transmitted helminths, onchocerciasis, Loa loa and trachoma Various diseases

Locations of reported cases, and the source of the report are available to visualize online

http://www.thiswormyworld. org/ (Cano et al., 2014) http://www.gntd.org/ (H€ urlimann et al., 2011) http://www.who.int/ trypanosomiasis_african/ country/foci_AFRO/ (Simarro et al., 2010) http://www.ntdmap.org/ (Flueckiger et al., 2015)

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Table 2 Resources for spatially referenced data relating to the risk of human African trypanosomiasis, schistosomiasis, lymphatic filariasis. Note that this list is not designed to be exhaustive, and other resources are available Resource Description Data availability References

http://www.healthmap.org/

Vector data

Atlas of tsetse and African animal trypanosomiasis Malaria Atlas Project

Point-level tsetse count data

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Predicted probability of occurrence of Anopheles mosquitoes as a 5  5 km resolution

Maps and raw data currently Cecchi et al. (2014) unavailable, although preliminary maps found in Cecchi et al., (2015) Maps plus raster data are available http://www.map.ox.ac.uk/ to download (Sinka et al., 2010)

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Table 2 Resources for spatially referenced data relating to the risk of human African trypanosomiasis, schistosomiasis, lymphatic filariasis. Note that this list is not designed to be exhaustive, and other resources are availabledcont'd Resource Description Data availability References

Remotely sensed data Landsat

MODIS

AVHRR

Landsat data is available at 30 m The raw data are available to resolution and includes 11 download at 30 m resolution spectral bands, which include using the USGS Earth Explorer. the visible spectrum, infrared, Landsat data has also been used to near infrared, short-wave create a number of land cover infrared and thermal infrared. products including GlobeLand30 The data collection period is 1972epresent. MODIS (Moderate Resolution The raw data are available at their Imaging Spectroradiometer) respective resolutions from is available at 250 m LAADS Web. Real-time data can resolution (2 bands), 500 m also be viewed using World View. (5 bands) and 1 km (29 bands). Numerous products derived The data collection period from MODIS data are also is 1999epresent. available to download, including MODIS land cover and NDVI.

https://ladsweb.nascom.nasa. gov/data (download raw data) https://worldview.earthdata. nasa.gov/ (view real-time data) http://modis.gsfc.nasa.gov/ data/dataprod/ (list of products) http://earthexplorer.usgs.gov/ (download raw data and NDVI products) M.C. Stanton

AVHRR (Advanced Very The raw data, plus products derived High Resolution Radiometer) from the raw data such as NDVI data is available at 1.1 km are available from Earth Explorer resolution and currently includes 5 spectral bands in the red, near infrared and thermal infrared portions of the electromagnetic spectrum. The data collection period is 1979epresent.

http://landsat.usgs.gov/ http://earthexplorer.usgs.gov/ (download raw data) http://www.globallandcover. com/ (Globeland30)

SPOT

RapidEye

Open Aerial Map

https://asterweb.jpl.nasa.gov/ http://reverb.echo.nasa.gov/ reverb (download data)

https://earth.esa.int/web/ guest/data-access

https://earth.esa.int/web/ guest/data-access

openaerialmap.org

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ASTER (Advanced Spaceborne The raw data are currently available Thermal Emission and to download from multiple Reflection Radiometer) data sources including NASA Reverb is available at 15e90 m resolution and includes 14 spectral bands in the visible, near infrared, shortwave infrared and thermal infrared portions of the electromagnetic spectrum. The data collection period is February 2000epresent SPOT (Satellite Pour l’Observation A small subset of data is available to de la Terre) is a commercial download for free from the satellite with a resolution European Space Agency. of 6 m, measuring 4 spectral Registration is required. bands (visible spectrum and near infrared) RapidEye is a commercial A small subset of data is available to satellite cluster with a download for free from the resolution of 5 m, measuring 5 European Space Agency. spectral bands (visible spectrum, Registration is required. red edge and near infrared) Open Aerial Map is a set of tools Data availability is currently limited, for sharing and using openly with low geographical coverage. licenced satellite and unmanned aerial vehicle imagery.

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ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer)

Google Earth

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Table 2 Resources for spatially referenced data relating to the risk of human African trypanosomiasis, schistosomiasis, lymphatic filariasis. Note that this list is not designed to be exhaustive, and other resources are availabledcont'd Resource Description Data availability References

Google Earth displays imagery Imagery accessed using Google Earth https://www.google.com/ collated from a number of can only be used for visualization earth/ sources, particularly commercial purposes. satellite vendors. The majority of the globe is covered in at least a spatial resolution of 15 m, with some areas having a resolution of 0.15 m.

Other data

OpenStreetMap

WorldPop

http://www.openstreetmap. org/ http://planet.osm.org/ (download entire dataset)

http://www.worldpop.org.uk/ M.C. Stanton

OpenStreetMap (OSM) is a All data within OSM is available to community-driven mapping download using a number of project, which provides open ways, including the OSM data on landscape features such Website download area. as transport routes (e.g., roads, Planet.osm can be used to footpaths, railways), water download the whole dataset features (e.g., rivers, lakes), and is updated once a week. buildings, amenities, land use categories and administrative boundaries. WorldPop provides 100 m Estimated population data are resolution data on human available at 1 km for Africa, Asia population distributions, and South and Central America, modelled from census, survey, whereas 100 m data is available satellite and GIS datasets, for a subset of countries within including OSM. these continents.

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Google News to produce maps of reported cases of a variety of diseases. This resource cannot be considered to be a complete record of cases, but may allow geographical areas of interest to be identified. 5.1.2 Vector/intermediate-host databases There is a wealth of vector distribution data in the published literature, and as with disease data, there are initiatives in place to collate this information and make it more easily accessible. There are however many gaps remaining in data relating to the three NTDs of focus. For example, the Food and Agriculture Organization of the United Nations (FAO) are developing an Atlas of tsetse, which in combination with the disease cases atlas (Atlas of HAT) is intended to provide comprehensive information on disease transmission risk to national control programmes (Cecchi et al., 2014, 2015). A similar project has been undertaken by the malaria control community to create maps of malaria parasite prevalence (Guerra et al., 2007). This tsetse data does not however appear to be publically available. Due to its linkages with malaria, there is an extensive amount of information available on the distribution of Anopheles which may be of interest to the LF elimination community, notably that produced by the Malaria Atlas Project, although this focuses on mapping presence of mosquitoes at the large geographical scale and may therefore be of limited operational use. Less has been done to develop resources relating to the distribution of Culex mosquitoes, despite being transmitters of LF and West Nile virus in sub-Saharan Africa. To our knowledge, there are no resources relating to the spatial distribution of schistosomiasis-transmitting snails available. It is also worth noting that it is rare to find spatial information relating to the infection status of the vectors/intermediate hosts for any of the diseases under consideration. 5.1.3 Spatial data collection As well as using previously collected data, researchers may also want to collect their own spatially referenced data to analyse. The capacity for the public to record precise geographical location data has been available since May 2000, when the US government switched off the pseudorandom errors in the publically available GPS signal, known as ‘selective availability’. Since this date it has become increasingly easy to record location information. Initially, dedicated GPS receivers were required to record coordinates during field activities, which were often prohibitively expensive. However, since the proliferation of the GPS-enabled smartphone and tablet

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computers, this is no longer the case. Further, unlike many dedicated GPS receivers, smartphones/tablets have many additional features that can be taken advantage of when collecting disease data in the field. For example, they could be used to collect survey responses which can be automatically uploaded to a database once a data connection is available, thus circumventing the need for manual data entry. There are an increasing number of products and services available to develop these data collection platforms, some of which are designed to be accessible to researchers who do not have any programming skills. One popular example of such a product is the open source, Android-based OpenDataKit (ODK, www.opendatakit. org) (Anokwa et al., 2009; King et al., 2013). ODK is a suite of tools that enable users to develop their own data collection forms that can incorporate text and numeric data, GPS coordinates, photographs, videos and barcodes, which can be used to collect field data without being connected to either the mobile network provider or the Internet. An Internet connection is only required to upload the data, with the data being uploaded to online servers that are either hosted by Google’s App Engine, or hosted locally. ODK has been successfully used to collect tropical disease data (King et al., 2013; Sime et al., 2014; Tom-Aba et al., 2015; F€ahnrich et al., 2015), the largest scale of which has been in relation to trachoma mapping (Pavluck et al., 2014), such that between 2012 and 2015 the Global Trachoma Mapping Project (GTMP, http://www.sightsavers.org/gtmp/) collected data from 2.6 million people across 29 countries using ODK. Whilst large-scale mapping projects tend to be led by dedicated survey teams, as smartphones become more ubiquitous in developing countries, there is the growing potential to collect spatially referenced health data directly from the affected communities (Stanton et al., 2016b). Community members can either be requested to submit data related to specific field activities, or data can be obtained on a more voluntary or passive basis. Participatory data collection approaches have the potential to provide vast amounts of real-time spatially referenced data that could be of relevance to disease control (Hay et al., 2013a). The most successful examples of the use of crowd-sourced spatial data to improve health outcomes in the developing world primarily relates to improving geographical maps to aid disaster response (Zook et al., 2010; Médecins Sans Fronti
eres, 2014), and detecting epidemics (Broniatowski et al., 2014; Milinovich et al., 2015) although there are also examples in the literature of participatory mapping of malaria vector exposure (Fuller et al., 2014; Mwangungulu et al., 2016).

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5.2 Population movement data A limitation of spatial methods is that they make the implicit assumption that the subject of interest is associated with a single geographical location, e.g., survey participants are usually assigned the coordinates of their homes, schools or villages when undertaking prevalence surveys, when it is of course recognized that exposure to disease risk may not occur at that location. In a purely spatial model this limitation is often accounted for by considering environmental risk factors within a fixed sensible buffer of the assigned location, or asking participants questions that might identify activities that expose them to risk outside of their homes/schools/villages. These approaches cannot, however, capture all of the intricacies of the participant’s movements that may be linked to disease risk and could weaken the spatial signal in the outcome of interest. There is therefore a growing interest in improving measures of human mobility both in terms of large-scale patterns such as migration and small scale day-to-day movement (Kraemer et al., 2015). One research area of growing interest is the harnessing of mobility information collected by mobile phone operators via anonymized call logs. These human mobility data have been shown to be complementary to travel survey data and provide a deeper understanding of the spatial dynamics of infectious diseases in developing countries over time (Buckee et al., 2013; Wesolowski et al., 2014, 2015; Flowminder Foundation, 2017). On a smaller geographical scale, GPS-enabled wearable technology, which in its most basic form consists of a small GPS data logger which records coordinates at regular time intervals, can provide detailed information on human movement that might be of relevance to disease control. For example, GPS data loggers have been used to measure differences in infected and uninfected mother and children’s spatial movement patterns in relation to schistosomiasis (Stothard et al., 2011), and assess the impact of LF-related morbidity on mobility (Stanton et al., 2016a).

5.3 Geographical disease risk factor data Here we restrict ourselves to consider physical measures of the natural and man-made environment as opposed to other risk factors that can be spatially referenced, e.g., socioeconomic measures associated with a particular location. 5.3.1 Natural geographical features RS data refers to data acquired from a distance, with the term being most commonly associated with data obtained from satellites. In areas where in

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situ environmental data is sparse, such as sub-Saharan Africa, satellite-derived RS data are often the only environmental data available at a spatially and temporally consistent scale, hence are an exceptionally valuable resource. Whilst the exact number of operational satellites is unknown, approximately 940 spacecraft have been launched since October 1957 for the purpose of earth science (Belward and Skøien, 2014; NASA, 2016c). These satellites tend to operate at a relatively low altitude (w700e800 km) and provide information at a range of spatial resolutions. As the value of these data continues to be realized, RS datasets that were once only available at considerable financial cost are being released to researchers or the general public for free. The Landsat and ASTER programmes are good examples of this. NASA’s Landsat programme has been providing data on a global scale since its launch in 1972, and in 2008 decided to release all data to the public (Woodcock and Allen, 2008). Landsat 1 was equipped with a multispectral scanner and initially provided data in four spectral bands (red, green and two infrared) at a spatial resolution of 80 m, with data for each geographical location being provided every 18 days. Most recently, Landsat 8 was launched in 2013 and is equipped with two instruments, namely the Operational Land Imager and the Thermal Infrared Sensor which provide data on the visible spectrum (red, green, blue), plus infrared, near infrared and short-wave infrared at 30 m resolution, thermal infrared at 100 m resolution and a grayscale/panchromatic image is produced at a resolution of 15 m. Data are provided every 16 days and are available to download using the USGS Earth Explorer (http://earthexplorer.usgs.gov/) shortly after collection. ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) is a Japanese owned sensor on board NASA’s Terra satellite. It was launched in 1999 and is still providing data today, despite its initial 5-year design life. All ASTER imagery was made available to the public in April 2016, prior to which only digital topographical maps were available. ASTER data is comprised of 14 spectral bands: four bands in the visible and near infrared ranges (15 m resolution), six bands in the shortwave infrared ranges (30 m resolution) and five bands in the thermal infrared ranges (90 m) resolution (Abrams et al., 2015). As with Landsat, data are provided every 16 days and are available to download using the USGS Earth Explorer. Products such as Landsat require the user to be well versed in remote sensing and/or GIS to extract information that may be of relevance to their

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disease of focus, e.g., Landsat data can be used to derive measures of vegetation such as the NDVI, or classification algorithms can be applied to identify land cover type. In acknowledgement of this limitation, there are an increasing number of preprocessed resources available to non-RS/ GIS experts. With regards to land cover, for example, whilst users continue to have the opportunity to use RS data such as AVHRR (Advanced Very High Resolution Radiometer, w1 km resolution), MODIS (Moderate Resolution Imaging Spectroradiometer, 250 m/km resolution) and Landsat to use image classification techniques to derive land cover categories of interest (Townshend et al., 1991; Friedl and Brodley, 1997), there are also a number of global-scale land cover products freely available. The disadvantage of using these products is that they provide land cover information for only one time point, which may not match that of the disease-related data under consideration. Examples of global land cover products include MODIS Land Cover (250 m resolution, annual data 2001e12; Friedl et al., 2002, 2010), GLC-SHARE (1 km, data collated between 1998 and 2012), GlobCover [300 m resolution, 2009 RS data (Congalton et al., 2014);] and GlobeLand30 (30 m resolution, 2010). These data have been used extensively in the development of predictive disease models and maps, including the NTDs of focus, e.g., tsetse mapping (Kitron et al., 1996; Odiit et al., 2006; Guerrini et al., 2008; DeVisser and Messina, 2009). Elevation is an important factor when considering the distribution of many tropical diseases, as it often impacts transmission, e.g., through influencing vector habitat suitability. The highest resolution global, publically available elevation data is currently 30 m and was collected by the Shuttle Radar Topography Mission (SRTM) in February 2000. This data was made available to the public at its finest scale in 2015 and can be accessed using the USGS Earth Explorer (http://earthexplorer.usgs.gov). As well as elevation, this data can be used to derive information on river networks using hydrological modelling. For example, the HydroSHEDS (HYDROlogical data based on Shuttle Elevation Derivatives at multiple Scales) has produced river network and drainage basin datasets using 90 m resolution SRTM data (http://hydrosheds.cr.usgs.gov/). Data from civilian earth observation satellite sensors provide a vast amount of contemporary information on the global environment; however, given that the multispectral spatial resolution of these sensors rarely exceeds 30 m, important small-scale details associated with disease risk may be masked (see Section 4). When considering disease risk at a

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reasonable large geographical scale, this loss of information may not be of importance. However, as interest in more targeted disease control efforts, more detailed environmental information may be required. Commercial earth observation satellites include RapidEye (6.5 m multispectral resolution), SPOT (6 m multispectral resolution), IKONOS (3.2 m multispectral resolution), QuickBird (2.62 m, decommissioned in 2015), Pleiades (2 m multispectral resolution), GeoEye (1.84 m multispectral resolution) and WorldView (1.24 multispectral resolution), and archived data can be purchased from the satellites operators (DigitialGlobe, GeoEye, Blackbridge etc.) or from various value added resellers. The price of these products is very variable, depending on the resolution of the image, the size of the area required and the time period it is required for, and as such it has not been extensively applied to NTD research (Hamm et al., 2015), although the potential of this resource is becoming increasingly recognized as mapping disease risk at the small (micro) spatial scale becomes increasingly important, particularly for diseases where targeted control is possible (Bousema et al., 2012; Jacob et al., 2013; Walz et al., 2015; Shaw et al., 2015; Rayaisse et al., 2015). There are a number of initiatives to release archived very high resolution data for free, however. For example, the European Space Agency (ESA, https:// earth.esa.int) has made their own archives of SPOT and RapidEye data available to the public following a brief registration process, whereas other datasets (e.g., QuickBird, GeoEye, WorldView, PLEIADES) are available to researchers on the submission and acceptance of a research proposal. The ESA does not however store all data produced by these commercial satellites in their archives hence there are still gaps in both geographical space and time. A small amount of very high resolution data can also be obtained from Open Aerial Map (https://openaerialmap. org), which is a platform for sharing open-licenced RS imagery, although this resource is in its early stages of development. Google Earth is another valuable resource for researchers as it enables the user to visualize high resolution products such as those listed earlier and to create simple vector features (points, lines, polygons). Whilst this is an excellent resource, it is limited in use as users only have access to data from a small subset of time points, the resolution at which data are available are not geographically consistent, and users are not able to access the raw data and subsequently any of the additional spectral bands (Lozano-Fuentes et al., 2008; Jacobson et al., 2015). Finally, unmanned

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aerial vehicles (UAVs), commonly known as drones, are also now recognized as an accessible method of generating high resolution RS data that is applicable to public health (Fornace et al., 2014). 5.3.2 Man-made geographical features Whilst historically there is a disparity in the availability of accurate, digitized, georeferenced data on the man-made landscape (roads, settlements, health facilities etc.) between the developed and developing world, advances in geospatial technologies are reducing this knowledge gap. We focus on two types of data resources in this paper, namely voluntary geographic information (VGI) resources and RS resources. VGI resources are geographical information resources that are compiled from geographically referenced data provided by volunteers. Other terms used for this are participatory mapping or crowdsourced mapping (Goodchild, 2007; Sui et al., 2012). An excellent example of a VGI resource on a global scale is OpenStreetMaps (OSM, www. openstreetmap.org) (Budhathoki and Haythornthwaite, 2012; Neis and Zielstra, 2014). OSM was founded in 2004 with the goal of creating a geographic database that was both free to use and contribute to, thus harnessing the power of local knowledge and the enthusiasm of experienced and amateur mappers. Data contributed to OSM can be that collected directly in the field using GPS-enabled devices or digitized from sources such as aerial images or locally generated paperbased maps. Vector data created by users are publically available via numerous resources such as Planet OSM (http://planet.openstreetmap. org/) or using the OSM import functionality within QGIS (http://wiki. openstreetmap.org/wiki/QGIS). See http://wiki.osmfoundation.org/ wiki/How_To_Get_OpenStreetMap_Data a more comprehensive list of sources. The level of detail available within a geographical area of interest, and the quality of the data, is however dependent on the contributions made by the OSM community and hence is not globally consistent (Haklay, 2010; Neis et al., 2011; Arsanjani et al., 2013; Barron et al., 2014). In recognition of the power of maps for humanitarian aid and economic development in the developing world, the Humanitarian OpenStreetMap Team (HOT) was established (www.hotosm.org). Through the medium of the HOT Task Manager, OSM users are asked to assist in improving the quality of maps in particular areas of interest with regards to humanitarian

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response. This was first implemented in response to the 2010 earthquake in Haiti, and more recently the HOT has been involved in mapping areas in West Africa affected by Ebola (Teng et al., 2014; Soden and Palen, 2014; Médecins Sans Fronti
eres, 2014; Palen and Soden, 2015; Koch, 2015). Not only do these maps provide valuable information to those directly responding to these crises, but they leave behind a legacy of freely available geographical information that can be used to benefit these population further (Eckle and Albuquerque, 2015; Stevens and Pfeiffer, 2015). In addition to providing maps for immediate humanitarian response, HOT also supports community development. Current projects supported by HOT include mapping all residential areas, all building plus water and sanitation facilities southern parts of Malawi affected by extensive flooding in January 2015 to improve flood risk management in area and similarly mapping infrastructure relating to flood risk in Dar es Salaam, Tanzania following a series of flooding events over the last five years. These community development initiatives are part of the Missing Maps Project (www.missingmaps.org), which supports HOT through promoting the mapping of vulnerable populations preemptively rather than responsively. Fig. 3 presents a comparison between Google Maps and OSM of the historic downtown of Monrovia, Liberia, highlighting the level of detail available in OSM as a result of the Ebola crisis. As these spatial data increase in accuracy, we speculate that older resources for man-made features will be replaced by more contemporary OSM data, which have the capacity to improve our knowledge on the spatial distribution of disease risk. For example, the population-mapping project WorldPop (http://www.worldpop.org.uk/) has started to integrate OSM data into their maps to improve the precision of their 100 m spatial resolution population density maps (Linard et al., 2014; Sorichetta et al., 2015). These population data can subsequently be used to obtain more accurate estimate of disease burden. An improved knowledge of infrastructure, and subsequently the spatial distribution of the human population, can also aid in understanding the distribution of disease risk at finer spatial scales through the derivation of relevant risk indices. For example, detailed land use and transport network data can assist in determining the accessibility of an area, or the proximity between an at-risk population and geographical risk factors. More practically, resources such as OSM can be used to better plan the distribution of interventions and treatments.

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Figure 3 A comparison of the level of detail provided by Google Maps (left) and OpenStreetMap (right) for Monrovia, Liberia.

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6. CONCLUSIONS It is encouraging to see the quantity of spatially referenced data that are being made publically available, particularly in terms of disease prevalence and RS data. Not only is the spatial resolution of the available data becoming increasingly fine, some data, particularly RS data such as Landsat and ASTER are available at a continuous temporal resolution in near-real time. Thus detailed contemporary spatio-temporal models and maps are increasingly achievable. This trend needs to continue to allow researchers and control programme implementers to maximize on their potential to benefit disadvantaged populations. There are however still gaps in the data that need to be addressed to understand the changing epidemiology of the diseases of interest and increase the likelihood of sustained control and elimination. For example, more regularly collected geographically referenced disease monitoring and surveillance data is required at the spatial scale of interest to the respective control programmes. Further, and crucially, to avoid premature cessation of control efforts, more sensitive diagnostics, plus sustained vector/intermediate-host surveillance are required for all three diseases (Hawkins et al., 2016; Hotez et al., 2016; Stothard et al., 2017). This chapter has demonstrated the utility of spatial methods in tropical epidemiology, showing that a great deal has been learnt about the spatial patterns of diseases targeted for control and elimination in recent years as a result. The focus should now be on putting the information acquired from spatial statistical methods into operational use (Bergquist et al., 2015). There have been some examples of this within the context of HAT control, a disease that is close to elimination and highly spatially heterogeneous, and hence engenders a more targeted approach (Sciarretta et al., 2005, 2010). Current control strategies for the other two diseases of focus treat the selected at-risk population with preventive chemotherapy indiscriminately at present. However, as prevalence decreases as a result of control a more targeted approach may be necessary, particularly if the decrease is not spatially uniform. It is therefore essential to consider how statistical models and spatially referenced data can be fully utilized to make the end stages of these programmes as resource efficient as possible. It is however worth bearing in mind that in situations where indiscriminate control strategies are much more operationally feasible than the costs associated with developing a targeted approach, that the former may be a more valid elimination strategy than the latter for some diseases

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(Stothard et al., 2014). In developing risk maps to inform a public health intervention, it is always necessary to be mindful on the fine balance between producing risk maps that are incredibly detailed and accurate and producing maps that are operationally useful. Thus, the lines of communication between the data providers, the statistical modellers and the disease control implementers should always be open to maximize the potential impact of the end product and ultimately reduce the burden of disease.

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CHAPTER SIX

Is Predominant Clonal Evolution a Common Evolutionary Adaptation to Parasitism in Pathogenic Parasitic Protozoa, Fungi, Bacteria, and Viruses? M. Tibayrenc*, 1, F.J. Ayalax *Institut de Recherche pour le Développement, Montpellier, France x University of California at Irvine, United States 1 Corresponding author: E-mail: [email protected]

Contents 1. Introduction 2. The Model of Predominant Clonal Evolution and Its Last Developments 2.1 Strong (statistically significant) linkage disequilibrium 2.2 Strong phylogenetic signal evidencing the occurrence of stable, discrete genetic subdivisions (‘near-clades’) 2.3 Repeated multilocus genotypes that are overrepresented under panmictic expectations 2.4 Propagation of stable multilocus genotypes over vast spans of time and space 2.5 The ‘Russian doll’ model 2.6 The ‘starving sex’ model 2.7 Biases towards recombination or clonality 3. Evidence for Predominant Clonal Evolution Features in Various Kinds of Micropathogens 3.1 Parasitic protozoa 3.2 Fungi and yeasts 3.3 Bacteria 3.4 Viruses 4. The ‘Starving Sex’ Hypothesis 5. A debate in the debate: unisex/selfing/inbreeding versus ‘strict’ clonality 6. How can clones survive without recombination? 7. Meiosis genes and experimental evolution: what do they tell us about predominant clonal evolution? 8. Is predominant clonal evolution an ancestral or convergent character? 9. Can predominant clonal evolution features be explained by natural selection? Inbuilt mechanisms favouring clonality Advances in Parasitology, Volume 97 ISSN 0065-308X http://dx.doi.org/10.1016/bs.apar.2016.08.007

© 2017 Elsevier Ltd. All rights reserved.

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10. Identical multilocus genotypes are a relative notion: implications for the semiclonal/epidemic clonality model 11. Genomics and the predominant clonal evolution model 12. Relevance of the predominant clonal evolution model for taxonomy and applied research 13. Conclusion Acknowledgements References

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Abstract We propose that predominant clonal evolution (PCE) in microbial pathogens be defined as restrained recombination on an evolutionary scale, with genetic exchange scarce enough to not break the prevalent pattern of clonal population structure. The main features of PCE are (1) strong linkage disequilibrium, (2) the widespread occurrence of stable genetic clusters blurred by occasional bouts of genetic exchange (‘near-clades’), (3) the existence of a “clonality threshold”, beyond which recombination is efficiently countered by PCE, and near-clades irreversibly diverge. We hypothesize that the PCE features are not mainly due to natural selection but also chiefly originate from in-built genetic properties of pathogens. We show that the PCE model obtains even in microbes that have been considered as ‘highly recombining’, such as Neisseria meningitidis, and that some clonality features are observed even in Plasmodium, which has been long described as panmictic. Lastly, we provide evidence that PCE features are also observed in viruses, taking into account their extremely fast genetic turnover. The PCE model provides a convenient population genetic framework for any kind of micropathogen. It makes it possible to describe convenient units of analysis (clones and near-clades) for all applied studies. Due to PCE features, these units of analysis are stable in space and time, and clearly delimited. The PCE model opens up the possibility of revisiting the problem of species definition in these organisms. We hypothesize that PCE constitutes a major evolutionary strategy for protozoa, fungi, bacteria, and viruses to adapt to parasitism.

List of Abbreviations AFLP CNV LD MCI MLEE MLG MLST PCE PFGE RAPD RD

Amplified fragment length polymorphism Copy number variation Linkage disequilibrium Multiclonal infection Multilocus enzyme electrophoresis Multilocus genotype Multilocus sequence typing Predominant clonal evolution Pulse field gel electrophoresis Random amplified polymorphic DNA Russian doll

Predominant Clonal Evolution in Micropathogens

RFLP SNP ST WGS

245

Restriction fragment length polymorphism Single nucleotide polymorphism Sequence type (MLST genotype) Whole genome sequencing

1. INTRODUCTION The population genetics of microbial pathogens (parasitic protozoa, fungi, bacteria and viruses) has strong implications in applied science, human and veterinary medicine, and agronomy. Moreover, it provides fascinating models to the evolutionists through specific features such as short generation times, huge population sizes, adaptation to parasitism, hostepathogen coevolution, among others. It is regrettable that this field has suffered from a considerable compartmentalization among specialists. This has occurred even between specialists working on closely related organisms, such as Trypanosoma and Leishmania, or even African and American trypanosomes. The result is that common features have been occluded by this tendency of specialists working on different pathogens failing to make relevant comparisons with other models. We have used the opposite strategy and have performed, for the first time to our knowledge, in-depth comparisons through the thorough analysis of more than 450 articles (not all listed here) among a large number of species of parasitic protozoa (25 species), fungi (9 species), bacteria (32 species), and viruses (23 species and categories) (Table 1). This approach is justified for three reasons. First, pathogens pose similar problems and challenges when applied research is concerned: the needs are the same for defining units of analysis, delimitating strains and species, and tracking epidemiological spreads; second, although pathogenic bacteria and viruses are not traditionally classified as parasites, they are from an evolutionary point of view, and one can expect that adaptation to parasitism selects adaptive traits similar to those of eukaryotic microbes; third, only a comparative approach makes it possible to draw general patterns, and, at the same time, to evidence the specificities of each model. This survey has uncovered striking similarities of population structure and evolutionary patterns among these micropathogen categories, although they are separated from each other by vast evolutionary differences. These similarities could be either ancestral or due to convergent evolution. They might represent common adaptive strategies to parasitism. Shapiro (2016)

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Table 1 List of the species under study Bacteria Fungi

Bacillus anthracis Bacillus cereus Bartonella bacilliformis Bartonella henselae Bartonella quintana Borrelia burgdorferi Burckholderia pseudomallei Campylobacter coli Enterococcus feacium Escherichia coli Helicobacter pylori Legionella pneumophila Listeria monocytogenes Mycobacterium bovis Mycobacterium tuberculosis Neisseria gonorrhoeae Neisseria lactamica Neisseria. meningitidis Pseudomonas aeruginosa Pseudomonas syringae Salmonella enterica Salmonella typhi

Parasitic protozoa

Aspergillus fumigatus Candida albicans

Cryptosporidium andersoni Cryptosporidium hominis Candida Cryptosporidium Dubliniensis muris Candida glabrata Cryptosporidium parvum Cryptococcus gattii Giardia intestinalis Cryptococcus Neoformans Fusarium oxysporum Penicillium marneffei Pneumocystis jirovecii

Viruses

Adenovirus Chikungunya DENV Ebola EchovirusEnterovirus HAV

Leishmania braziliensis Leishmania infantum HBV complex Leishmania HCV guyanensis Leishmania killicki HEV Leishmania lainsoni Leishmania major Leishmania mexicana Leishmania peruviana Leishmania tropica Plasmodium falciparum Plasmodium floridense Plasmodium vivax Toxoplasma gondii Trypanosoma brucei Trypanosoma brucei gambiense Trypanosoma brucei rhodesiense Trypanosoma congolense

HIV Influenza Maize streak virus Measles virus Picornavirus Poxvirus RABV ScoV (SARS) SIV SLCov VARV VZV WNV

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Table 1 List of the species under studydcont'd Bacteria Fungi Parasitic protozoa

Staphylococcus aureus Streptococcus mitis Streptococcus oralis Streptococcus pneumoniae Streptococcus pseudopneumoniae Streptococcus pyogenes Vibrio cholerae Vibrio parahaemolyticus Vibrio vulnificus Xanthomonas campestris

Viruses

Trypanosoma cruzi Trypanosoma evansi Trypanosoma vivax

DENV, dengue virus; HAV, hepatitis virus; HBV, hepatitis B virus; HCV, hepatitis C virus; HEV, hepatitis E virus; HIV, human immunodeficiency virus; RABV, rabies virus; SARS, severe acute respiratory syndrom virus; VARV, Variola virus; VZV, varicella-zoster virus; WNV, West Nile virus.

has noted that ‘pathogens are more likely than free-living bacteria to undergo clonal expansions, due in part to their ecology and transmission dynamics’.

2. THE MODEL OF PREDOMINANT CLONAL EVOLUTION AND ITS LAST DEVELOPMENTS The predominant clonal evolution (PCE) model proposed by us (Tibayrenc et al., 1990; Tibayrenc and Ayala, 2012, 2013, 2014a; b) includes several specific assumptions that deserve to be recalled, because they are frequently misunderstood or misquoted. In this model, clonality is understood with a clear and simple definition, that is to say: strongly restrained genetic recombination. This definition is widely accepted by many authors working on general evolution as well as on population genetics and evolution of micropathogens, including protozoa, fungi, bacteria and viruses. Our claim that many authors accept this definition, and accept also the view that selfing/inbreeding is a particular case of clonality (see A Debate in the Debate: Unisex/Selfing/ Inbreeding versus ‘Strict’ Clonality) is not based mainly on self-citations (Ramírez and Llewellyn, 2015; Rougeron et al., 2015), but rather on the thorough analysis of a large number of articles (Table 2). Many times, authors consider scarcity of recombination, clonality and asexuality as

Arnaud-Haond et al. Anderson et al. (2007) a (2000) b,c,e c Avise (2015) Andersson (2012)

Bacteria

Viruses b

a,b

Awadalla (2003) e Annan et al. (2007) b,c de Meeus et al. Barnabé et al. (2011) d a (2007b) Maynard Smith et al. Barnabé et al. (2013) b (1993) b Prugnolle and de Beck et al. (2009) b,c,e a,b Meeus (2008) Schurko et al. Birky (2009) c e (2008) Branch et al. (2011)

e

Buscaglia et al. (2015) c

Chenet et al. (2012)

e

Cooper et al. (2007)

b

de Waele et al. (2013)

e

Holmes (2009 , 2013) a,b Morel et al. (2011) Perales et al. (2015) Simon-Loriere and Holmes (2011) b

b

Buscaglia et al. (2015) a Xu (2004) b

b

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Chargui et al. (2009)

c

Badoc et al. Baker et al. (2010) (2002) a,d Bovers et al. Balloux (2010) b b (2008) Calo et al. (2013) d Bessen (2010) a Campbell and Bobay et al. (2015) d e Carter (2006) Campbell et al. Budroni et al. (2005) b,c (2011) b Carriconde et al. Ch’ng et al. (2011) b a,b,e (2011) Chaturvedi and Chaudhuri and Chaturvedi Henderson (2011) a (2012) b Chowdhary et al. Clermont et al. (2011) e (2011) b Feretzaki and Coscoll
a et al. Heitman (2013) d (2011) b Fraser et al. (2005) a Dagerhamm et al. (2008) e Giraud et al. Dale et al. (2011) b d (2008) Heitman (2010) c Denamur et al. (2010) b Henk et al. Didelot and Falush (2012) a,b (2007) b

All pathogens b

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Table 2 Definitions of clonality used by various authors General evolution Parasitic protozoa Fungi

Downing et al. (2011)

Falk et al. (2015)

Khayhan et al. Didelot (2010) b b,e (2013) Lin and Heitman Dos Vultos et al. (2006) b (2008) b McManus and Edwards et al. (2008) e b Coleman (2014) Ngamskulrungroj Fargier et al. (2011) b,e et al. (2009) b,e Ni et al. (2013) b,c Feil (2010) b

c,e

c,e

Feretzaki and Heitman (2013) c Flores-L
opez and Machado (2011) b Gatei et al. (2007) b,e Gelanew et al. (2010) d Griffing et al. (2011)

b,c

Grigg and Sundar (2009) Heitman (2006)

Taylor (2015) Xu (2006) b,e

c

b,c

Herges et al. (2012)

e

Iwagami et al. (2012)

e

Karunaweera et al. (2008) e Khan et al. (2011) b,e Kuhls et al. (2008)

c,e

b,c

Predominant Clonal Evolution in Micropathogens

Duffy et al. (2013)

c

Fraser et al. (2007) b Gomez-Valero et al. (2009) b Guttman and Stavrinides (2010) b,e Hanage et al. (2006) a,b Henriques-Normark et al. (2008) b,e Kurtenbach et al. (2010) b Maiden (2006 a, 2008) b Martin et al. (2010) e Pérez-Losada et al. (2006), (2013) b Pirnay et al. (2009) a,b

249

(Continued)

Leblois et al. (2011)

b

Lehmann et al. (2004)

c,e

Llewellyn et al. (2009a d, 2009b, 2011 b) Lymbery and Thompson (2012) c Minning et al. (2011) a,b Miotto et al. (2013) c Morrison et al. (2008a b,e, 2008b b, 2009 c) Mu et al. (2005) b,c,e Mzilahowa et al. (2007) c,e

Viruses

All pathogens

Prasad Narra & Ochman (2006) a,b Robinson et al. (2011) b Sarkar and Guttman (2004) b,e Sheppard et al. (2010) b Smyth and Robinson (2010) b,e Supply et al. (2003) e Tenaillon et al. (2010) b Vogel et al. (2010) b,e Wiehlmann et al. (2007) a,b,e

M. Tibayrenc and F.J. Ayala

Nkhoma et al. (2013) b,c,e Orjuela-S
anchez et al. (2010) e Rajendran et al. (2012) b,e Ramírez et al. (2012) e Razakandrainibe et al. (2005) b,c Rezende et al. (2010) e Rogers et al. (2014) a,e

Bacteria

250

Table 2 Definitions of clonality used by various authorsdcont'd General evolution Parasitic protozoa Fungi

Predominant Clonal Evolution in Micropathogens

Rougeron et al. (2009, 2010, 2014, 2015 d) Sibley and Ajioka (2008) b,c Smith (2009) b Su et al. (2003) a,b Su et al. (2006 e, 2010 b, 2012 c) Takumi et al. (2012) b,e Tanriverdi et al. (2008) b,e Thompson et al (2011) b,c Tomasini et al. (2014) e Volkman et al. (2007 e, 2012a e, 2012b b,c,e) Wang et al. (2012) b,e Weedall and Hall (2014) c,e Weir et al. (2016) d,e Wendte et al. (2010 c, 2011 b) a

Clonality asexual reproduction. Clonality amounts to restrained recombination. c Selfing/inbreeding are particular forms of clonality and are not distinct from it. d Clonality is restrained to mitotic clonality and should be distinguished from selfing/inbreeding. e Linkage disequilibrium is used for detecting restrained recombination. b

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synonymous terms (Hanage et al., 2006; Holmes, 2013). Including selfing/ inbreeding in the general frame of clonality or distinguishing ‘true’ clonality (mitotic reproduction) from selfing/inbreeding is a matter of definition. We do not state that the second view should be definitely rejected. We only assert that the first view permits to enlighten highly relevant, common predictive properties and raises fewer problems of interpretation. An important feature of the model, recalled many times by us, is that it does not state that recombination is totally lacking or that its evolutionary and epidemiological consequences are negligible (as wrongly understood by some authors: Calo et al., 2013; Messenger and Miles, 2015; Miles et al., 2009; Ramírez et al., 2012; Ramírez and Llewellyn, 2014), but only that it is not frequent enough to break up the predominance of clonal evolution (see further for a sharp definition of ‘predominance/ predominant’). Knowing whether populations of pathogens recombine freely or scarcely (PCE) has considerable implications for our knowledge of the basic biology of these organisms and for applied research (dynamics of genes of interest, strain typing, clinical research, vaccine and drug design). In case of abundant recombination, multilocus genotypes (MLGs) are ephemeral, since they are constantly disrupted by frequent genetic exchange. The evolutionary unit is not the MLG, but rather, the individual gene. When the organism undergoes PCE, MLGs are stable in space and time. They are the relevant evolutionary units. In sexual (recombining) organisms, there are no properly said ‘strains’ (stable MLGs). Unambiguously, the PCE concept of clonality refers to genetic clonality, and not to any specific type of cytological mechanism. It includes, not only mitotic clonality but also all cases where recombination is rare or absent (Tibayrenc et al., 1990), such as several cases of parthenogenesis, gynogenesis and hybridogenesis (Avise, 2004, 2008, 2015), as well as self-fertilization in homozygous states, strong inbreeding and ‘unisexuality’ (Feretzaki and Heitman, 2013). As we have shown (Tibayrenc and Ayala, 2012, 2013, 2014a and b), this definition is widely used by researchers working on population genetics of all kinds of micropathogens (Table 2). It is also accepted by scientists working on unisexual vertebrates (Avise, 2004, 2008, 2015). Nevertheless, this definition is sometimes misunderstood, or not accepted by some researchers, who recommend distinguishing selfing/inbreeding from ‘strict’ clonality (see ‘A debate in the debate: unisex/selfing/inbreeding versus “strict” clonality’).

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Based on this definition, the three main features generated by PCE are (1) strong (statistically significant) linkage disequilibrium (LD), not generated by physical obstacles (time and/or space isolation: the Wahlund effect); (2) strong phylogenetic signal evidencing the occurrence of stable, discrete genetic subdivisions, for which we have coined the term ‘near-clade’ (Tibayrenc and Ayala, 2012), the reason for which will be stated later; and (3) ‘clonality threshold’, beyond which recombination is efficiently countered by PCE, and the near-clades irreversibly diverge. Two additional properties of the PCE model are: (4) the repetition of MLGs that are overrepresented under panmictic expectations and (5) the propagation of stable MLGs over vast spans of time and space. Obviously, these different properties of the PCE model are linked to each other. Another specific proposal of the PCE model is that restrained recombination is not ‘passive’, that is, due to lack of opportunity for mating (the so-called starving sex hypothesis; Tibayrenc and Ayala, 2014a,b ). In the PCE model, restrained recombination is also not caused chiefly by natural selection acting on an otherwise recombining species, as it has been proposed for Neisseria meningitidis (Buckee et al., 2008). The PCE model hypothesizes that restrained recombination is a specific evolutionary strategy of micropathogens and is governed by their specific in-built properties (Tibayrenc and Ayala, 2002). PCE can be considered as the total set of reproductive strategies used by micropathogens to escape the ‘recombinational load’ (disrupting favourable multilocus associations; Agrawal, 2006; Beck and Agrawal, 2012; Butlin, 2012; Feretzaki and Heitman, 2013; Michod et al., 2008), probably as a major adaptation to parasitism. Lastly, the PCE approach focuses more on those parts of the genome that will better evidence the overall phylogeny of the species, and its ‘clonal backbone’ (Sarkar and Guttman, 2004). In the case of eukaryotic microbes, it focuses on the nuclear genome rather than on the mitochondrial genome, and for bacteria, on the chromosomal DNA rather than on extrachromosomal elements, and, within the chromosomal DNA, on the core genome rather than on the dispensable genome. Other genomic parts are placed onto this overall phylogeny/clonal backbone in a phylogenetic character mapping approach (Avise, 2004). As a matter of fact, these other genomic elements follow different evolutionary patterns, selective pressures and modes of inheritance. They are bound to yield phylogenies that are not congruent with the overall phylogenies of the DNA sequences targeted by the PCE approach, even in the absence of recombination. We will now consider in more detail the main PCE features.

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2.1 Strong (statistically significant) linkage disequilibrium LD is the nonrandom association of genotypes occurring at different loci. It is the logical consequence of lack or scarcity of recombination. Its strength can be measured by various statistics, the most classical one being the Ia association index (Maynard Smith et al., 1993). We have proposed several other tests for evaluating LD (Tibayrenc et al., 1990). A specially telling case of LD is when the genetic distances measured with radically different molecular markers prove to be highly correlated (the ‘g’ test: Tibayrenc et al., 1990). Sometimes, when such a congruence between different genetic markers is strong, it can be visualized on gels (Fig. 1) or on phylogenetic trees (Fig. 2).

Figure 1 A striking case of linkage disequilibrium in Trypanosoma cruzi. Multilocus enzyme electrophoresis (top) and random amplified polymorphic DNA (bottom) are totally linked to each other. Cross-genotypes (for example: A1 with D10) have never been observed among more than 500 strains. After Tibayrenc, M., Kjellberg, F., Ayala, F.J., 1990. A clonal theory of parasitic protozoa: the population structure of Entamoeba, Giardia, Leishmania, Naegleria, Plasmodium, Trichomonas and Trypanosoma, and its medical and taxonomical consequences. Proc. Natl. Acad. Sci. U.S.A. 87, 2414e2418.

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Figure 2 Double tree showing the close parity between random amplified polymorphic DNA (RAPD) (left) and multilocus enzyme electrophoresis (MLEE) (right) in Trypanosoma cruzi. The two kinds of markers uncover the same six near-clades or ‘discrete typing units’ (DTUs). Near-clade labelling has been changed (I to VI; Zingales et al., 2012). After Brisse, S., Barnabé, C., Tibayrenc, M., 2000. Identification of six Trypanosoma cruzi phylogenetic lineages by random amplified polymorphic DNA and multilocus enzyme electrophoresis. Int. J. Parasitol. 30, 35e44.

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LD has been considered unreliable for exploring the reproductive strategy of pathogens for its supposed lack of resolution, as exposed in the case of Leishmania (Rougeron et al., 2010). However, (1) it is widely used by authors who explore clonality in parasitic protozoa, fungi, bacteria and viruses (Tibayrenc and Ayala, 2012; see Table 2); (2) it is the specific statistic designed for exploring lack or scarcity of recombination, the very definition of clonality used by us and by many others; and (3) when a sufficient number of variable loci is used, its resolution power is considerable. Our early proposals on clonal evolution in various eukaryotic microbes (Table 3) were mainly based on LD. Since many of these hypotheses have been further confirmed, this suggests that LD is not an unreliable statistics to explore the population structure of pathogens. Actually, LD and phylogenetic analysis can be considered as two complementary ways to explore the same phenomenon, that is to say: genetic isolation among evolutionary lines. Biases such as insufficient samplings and Wahlund effects are not limited to LD analysis, and also affect phylogenetic approaches.

2.2 Strong phylogenetic signal evidencing the occurrence of stable, discrete genetic subdivisions (‘near-clades’) As we have insisted on, the PCE model does not imply that recombination is absent, but only that it is insufficient to erase the manifestations of PCE. Actually, it can be suspected that pathogens that are 100% clonal are very rare. The fact that some recombination most times goes on at various doses has two implications: (1) Pathogens’ genetic subdivisions should not be named by the term ‘clade’, which, strictly speaking, designates evolutionary lines that are totally separated from each other. However, this term, which is most times improper in the context of micropathogen evolution, is widely used in the literature, in parasitic protozoa (Ramírez et al., 2012; Su et al., 2012), fungi (McManus and Coleman, 2014; Voelz et al., 2013), bacteria (Chaudhuri and Henderson, 2012; Croucher et al., 2011) and viruses (Liu et al., 2011; Raghwani et al., 2011). This is why we have coined the term ‘near-clade’ to designate genetic clusters of pathogens that are somewhat blurred by occasional recombination (Tibayrenc and Ayala, 2012). (2) As a linked proposal to (1), the occurrence of occasional bouts of recombination implies that a strict cladistic approach is not suitable to characterize the phylogenetic signal of the near-clades, even when it is strong. We have proposed (Tibayrenc and Ayala, 2012) to rather use a flexible phylogenetic approach based on a congruence criterion inspired from the principle of genealogical concordance between independent genes proposed for the

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Table 3 Species analysed in Tibayrenc and Ayala (1991a, Table 2): rank of evidence for clonality Criteria supporting Organism Rank clonality Fungi

Candida albicans Candida tropicalis/Candida paratropicalis C. neoformans B þ C serotypes C. neoformans A þ D serotypes C. neoformans all serotypes Saccharomyces cerevisiae

i ii ii ii ii ii

None d1, d2, e, f e, f f d1, d2, e, f f

iii iii iv iv iv iv iv iii ii ii ii ii ii ii ii

d1, d2, e, f d1, d2, e, f, g d1, d2, e, f d1, d2, e, f a, d1, d2, e, f d1, d2, e, f d1, d2, e, f g a, d a a d1, d2, e, f, d d1, d2, f d d

iv iv ii iv iv iv iv iv iv ii iii iv iv

d1, d2, e, f d1, d2, e, f e, f d1, d2, e, f d1, d2, e, f d1, d2, e, f d1, d2, e, f d1, e, f d1, f a, d1, d2, e, f a, d1, d2, e, f a, b, c, d, f, g d1, d2, e, f

Protozoa

Entamoeba histolytica Giardia sp. Leishmania braziliensis guyanensis Leishmania infantum Leishmania tropica Leishmania major Leishmania Old World as a whole Leishmania sp. Naegleria australiensis Naegleria flowleri Naegleria gruberi Plasmodium falciparum Toxoplasma gondii Trichomonas foetus Trichomonas vaginalis Trypanosoma brucei s.l. West Africa East Africa East Africa (wild) Liberia Busoga, Uganda Lambwe Valley, Kenya Lambwe Valley (nonhuman stocks) Ivory Coast Ivory Coast (nonhuman stocks) Trypanosoma brucei rhodesiense Trypanosoma congolense Trypanosoma cruzi Trypanosoma vivax

i, the available data do not evidence clonality; ii, clonality is only a working hypothesis because the supporting evidence comes from small samples; iii, there is evidence for clonality but the limited number of markers prevents equating the strains with actual clones; iv, clonal population structure is well ascertained. Criteria supporting clonality are based on population genetics tests proposed by Tibayrenc et al. (1990). aec, segregation tests (within loci), relying on HardyeWeinberg equilibrium; deg, recombination tests (between loci), relying on linkage disequilibrium analysis.

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recognition of biological taxa (Avise and Ball, 1990; Avise, 2004). Sharp genealogical concordance between any two independent genes is too strict a criterion, since (1) within the PCE model, some recombination may occur and somewhat cloud the phylogenetic signal and (2) discrepancies between independent genes may occur for several reasons other than recombination, even in the case of different species (see further, ‘Biases Towards Recombination or Clonality’). We rather recommend using the congruence criterion as follows. If the phylogenetic signal weakens with additional adequate data, this is an indication that recombination plays a major role in the micropathogen’s population structure and efficiently counters the structuring of the species considered. This is the pattern described by the ‘semiclonal model’ (Maiden, 2006) and the ‘epidemic clonality model’ (Maynard Smith et al., 1993, Fig. 3). If on the contrary, additional relevant data reveal a growing phylogenetic signal, the hypothesis of PCE is supported. This is what we have called the ‘clonality threshold’, beyond which clonal evolution becomes preponderant, that is, efficiently counters the impact of recombination and causes this growing phylogenetic signal. This clonality threshold concept is therefore neither ‘pseudoquantitative’ nor ‘vague’, and ‘predominant’ is not open to ‘wide interpretation’ either (Ramírez and Llewellyn, 2015). The clonality threshold relies on the observation of a growing phylogenetic signal, which is easy to verify with appropriate data (see later). When bacteria are concerned, the similar concept of ‘clonal/sexual threshold’ states that beyond a given rate of recombination, divergence is inhibited, and clusters no longer diverge but are constantly reabsorbed into the parent population by the cohesive force of recombination (Fraser et al., 2007). This would correspond to the semiclonal model (Maiden, 2006) and the ‘epidemic clonality model’ (Maynard Smith et al., 1993). Below this threshold, or beyond the clonality threshold, divergence counters homogenization and PCE begins (Tibayrenc and Ayala, 2012). Clonal species are assumed to exhibit ‘tree-like’ phylogenies (Maiden, 2006). In contrast to the clonality threshold proposed by us, the clonal/sexual threshold model of bacteria (Fraser et al., 2007) is not based on the observation of a growing phylogenetic signal, but rather, on the recombination/ mutation (r/m) ratio, which is subject to clashing data. As an example, r/m was estimated as 7.2 in a study dealing with Streptococcus pneumoniae whole genome sequencing (WGS) (Croucher et al., 2011), whereas Multilocus Sequence Typing (MLST) analysis (Maiden, 2006) had given a ratio of 66 (Feil et al., 2000). Vos and Didelot (2009), with a homogeneous

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Figure 3 Models of pathogen population structures proposed by Maynard-Smith et al. (1993). Top, clonal evolution; middle, there is no genetic exchange between the two major branches, but recombination is frequent within each of them, leading to a network structure rather than to a tree-like structure; bottom, epidemic clonality.

methodology (MLST) analysed with the ClonalFrame method, have found r/m rates that are poorly compatible with the known biology of the species surveyed by them (see Table 1 in their article). Salmonella enterica, assumed to be highly clonal (Maynard Smith et al., 1993), has an r/m of 27.4e48.2; Helicobacter pylori, considered panmictic (Suerbaum et al., 1998) or ‘almost nonclonal’ (Henriques-Normark et al., 2008), has an r/m of 12.2e15.4; and N. meningitidis, presented as ‘highly recombining’ (‘epidemic population structure’; Maynard Smith et al., 1993), has an r/m of 5.1e9.5. WGS in Escherichia coli shows that the rate of recombination is almost equal to the

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rate of mutation. However, this high rate of recombination ‘does not destroy the clonal status of E. coli’ (Bobay et al., 2015). These results on recombination versus mutation rates suggest that they have a poor predictive power regarding the actual population structure of the species considered. It might be illusory to rely on such fallaciously sharp mathematical indices to explore the population structure of micropathogens. This is all the more true, because (1) near-clades should not be defined by precise levels of phylogenetic divergence, as it is the case for the genospecies concept in bacteria (Grimont, 1988), but only by their discreteness and stability in space and time and (2) pathogenic microbes are separated by vast evolutionary distances. They exhibit considerably diverse genomic structures and evolutionary patterns (Hupalo et al., 2015). Thus a common denominator in terms of mathematical estimation of the impact of recombination is probably unattainable. On the contrary, the clonality threshold and PCE features above exposed can constitute such a common denominator. Sets of data relying on the congruence criterion for identifying nearclades and evidencing a growing phylogenetic signal could be, for example, based on our g test (Tibayrenc et al., 1990). If different kinds of genetic markers give congruent phylogenies, it is a clear indication of PCE. Data could be also based on different phylogenetic methods relying on different working hypotheses and giving congruent phylogenies, or the comparison between phylogenetic and non-phylogenetic methods (for example, STRUCTURE; Pritchard et al., 2000). If they show comparable clustering patterns, this is an evidence that phylogenies are robust, since they persist through various approaches based on different assumptions and evolutionary models. Other possible lines of evidence are adding more loci when multilocus enzyme electrophoresis (MLEE) is concerned (Breniere et al., 2003), or the phylogenies from different genes when MLST is used: phylogenies of individual genes could show discrepancies among them. However, the concatenated phylogeny based on the complete set of genes is strong and becomes stronger as other genes are considered (Diosque et al., 2014). Lastly, stability of the near-clades in time and space can be conveniently ascertained by retrospective studies, analysis of ancient collections and phylogeography/ phylodynamics (Avise, 2000; Holmes, 2008; Holmes and Grenfell, 2009; Kenefic et al., 2010). This makes it possible to ascertain that near-clades are not the mere product of a Wahlund effect (isolation by distance and/ or time; see further, ‘Biases Towards Recombination or Clonality’) Near-clades are a remarkable consequence of strongly restrained recombination acting at an evolutionary scale. They have important implications in

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terms of molecular epidemiology, taxonomy and applied research (see further, ‘Relevance of the Predominant Clonal Evolution Model for Taxonomy and Applied Research’). In the examples given later, near-clades cannot be explained by mere geographical separation, even if this factor may contribute. As we will see later, near-clades have received many different names in the literature, leading to an extraordinarily confusing terminology (Table 4). It has been argued that ‘genetic subdivisions (i.e., near-clades) act as reproductive barriers’, and therefore, that ‘it makes little sense to address each parasite species (or genus) as a whole’ (Ramírez and Llewellyn, 2014). For genera, we agree. For species, the PCE approach definitely proposes that the presently described species should be the starting point of analysis for exploring the pathogen’s population structure. The existence of the near-clades within species is one of the major components of the PCE model. Establishing that near-clades are the results of reproductive barriers is the core of the PCE model, and one of its most important outcomes. ‘A highly structured (e.g., clonal) population indicates that the main mode of reproduction for such a species lacks genetic exchange (is primarily asexual) or sex occurs only rarely’ (Buscaglia et al., 2015). Considering that such a pattern was ‘self-evident’ (Ramírez and Llevellyn, 2015) is easy when arriving long after the battle and amounts to saying that the results of any phylogenetic analysis are self-evident. With such a view, the discovery of the newly described Trypanosoma cruzi near-clades Tc-Bat (Lima et al., 2015; Marcili et al., 2009; Pinto et al., 2012, 2016) was self-evident, as was the description of the lesser near-clades within the T. cruzi near-clade TCI (Guhl and Ramírez, 2011). Evidencing near-clades is all the less self-evident, since even the widely accepted number of near-clades within the thoroughly studied species T. cruzi (Brisse et al., 2000; Zingales et al., 2012) still is under debate (Barnabé et al., 2016). Lastly, it should be recalled that obstacles to gene flow and clonal evolution in various major species of eukaryotic microorganisms, including T. cruzi, Trypanosoma brucei, Leishmania sp., Giardia intestinalis, and Toxoplasma gondii, and the unexpected result of clonal propagation in Plasmodium falciparum have been proposed long before any within-species phylogenetic data were available for these species (Tibayrenc et al., 1981; 1986, 1990, 1991a; see Table 3). Such hypotheses were far from being self-evident. In a second time, convenient phylogenetic analyses were able to show that in T. cruzi, for example, obstacles to gene flow led to the individualization of six near-clades (Brisse et al., 2000). Unexpectedly, Ramírez and Llewellyn (2014) did not raise the problem of this ‘artefactual

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Table 4 The many different terms used in the micropathogen literature to designate pathogens’ genetic clusters Viruses Bacteria Parasitic protozoa Fungi

Clades

Clades

Clusters Genogroups Genotypes Groups Lineages Major genotypes

Clonal complexes Clonal lineages Clonal subgroups Clusters Family strains Genetic groups

Major lineages Phylogenetic groups Phylogroups Subclades Subgenotypes Subgenotype clusters Subgroups

Genoclouds Genogroups

Amplified fragment length polymorphism groups Clades Clades Clonal haplogroups Clonal groups Clonal haplotypes Clonal lineages Clonal lineages Clusters Clonal types Clonal groups Clones Genetically distinct subgroups Clonotypes Genotypes Clusters Genotypic groups

Genome groups Genospecies Groups Lineages

Core subgroups Groups Discrete typing units Lineages Divergent entities Major clades Genetic groups Minor clades

Major branches

Genetic types

Sublineages Substrains Subtypes Subvariants Types Variants

Assemblages

Molecular genotypes Molecular types Phylogenetic species Subclusters

Major clusters Genotypes Main/major lineages Groups Major phylogenetic Haplogroups groups Phylogenetic clades Haplotypes Subgenotypes Phylogenetic Lesser subgroups Subgroups groupings Populations Lineages Subpopulations Primary clusters Main haplogroups Varieties Principal genetic Major clades groups Secondary clusters Major clonal lineages Semi discrete Major groups lineages Subclones Major monophyletic groups Subclusters Phylogenetic lineages Subgroups Populations

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263

Table 4 The many different terms used in the micropathogen literature to designate pathogens’ genetic clustersdcont'd Viruses Bacteria Parasitic protozoa Fungi

Sublineages Subpopulations Subspecies Subspecies groups Subtypes

Subassemblages Subclades Subclusters Subgroups Sublineages Subpopulations Subgenotypes Subgroups Subspecies Subtypes Subtype groups Types

This shows that the conceptual bases for describing them are quite uncertain, although they probably correspond to the same evolutionary entity (‘near-clade’).

approach’ in the case of T. gondii, for which they accept the hypothesis of PCE based on data that consider the whole species (Sibley et al., 2009). We are not claiming that we have been the only ones to describe clustering patterns within microbial species. On the contrary, the past and present developments of the PCE model are based on the analysis of a great deal of data published by other authors. However, describing clusters is one thing, whereas evidencing that they correspond to the precise evolutionary definition of near-clades is another thing and can be done only by confronting various sets of data, as we have done. Most times, the evolutionary status of the clusters described within microbial species is unclear or wrong, for example, when calling them ‘clades’. This uncertainty is traduced by the extremely confusing terminology that these clusters have received (Table 4). This clearly shows that the conceptual bases for describing genetic clusters in micropathogens are disparate and fluctuating. The unifying concept of ‘near-clade’ would be welcome to get rid of this semantic Babel tower. The ambition of the PCE model is not to flatly corroborate the existence of within-species clusters in microbes, but rather to give a common conceptual framework and precise definitions, to many disparate data and approaches. The literature dealing with population genetics of micropathogens is also made confusing by the widespread use of vague, subjective expressions for evaluating the respective impact of clonality and recombination, such as

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‘far from being a clonal species’ (Coscoll
a et al., 2011), ‘extensive genetic exchange’ (Caugant and Maiden, 2009), ‘gross’ incongruences (between phylogenetic trees) (Messenger et al., 2012), ‘widespread genetic exchange’ (Ramírez et al., 2012; Ramírez and Lllewellyn, 2014), ‘intense lateral exchange of genetic information’ (Ramírez and Llewellyn, 2015), ‘genetic exchange is a fairly common phenomenon’ (Lima et al., 2014), ‘genetic exchange playing a major role in T. cruzi population structure’ (Minning et al., 2011), and others. It is impossible to totally discard such expressions in the scientific literature. However, they are misleading and poorly informative. The criteria above exposed, that is strong (statistically significant) LD and congruent data leading to a growing phylogenetic signal and a clonality threshold (near-clade pattern), make it possible to replace these imprecise expressions with a precise definition based on a clear-cut cursor marking the PCE threshold. It could be proposed that expressions such as ‘widespread genetic recombination’ be restrained to those situations where PCE can be clearly rejected (panmixia, or semiclonality/epidemic clonality). Two other informative PCE features, tightly linked to each other, are the following.

2.3 Repeated multilocus genotypes that are overrepresented under panmictic expectations It is a classical manifestation of clonality and a logical consequence of LD. However, if the marker considered has a fast molecular clock, and generates much diversity in a short time, this feature may not be observed e all individuals would have a different MLG even in the case of PCE (ArnaudHaond et al., 2005; Seridi et al., 2008). As we will see further (‘Identical Multilocus Genotypes is a Relative Notion’), this notion of repeated genotypes strongly depends on the mutation rate of the marker considered.

2.4 Propagation of stable multilocus genotypes over vast spans of time and space Provided that the warning about the marker’s mutation rate just exposed is considered, this is a strongly telling manifestation of PCE, with considerable epidemiological implications. Some pathogen MLGs behave like ‘superspreaders’, and are encountered, not only locally but also over continents and for tens of years. We have advanced additional considerations that complete the PCE model, namely, the ‘Russian doll (RD) model’, and the ‘starving sex model’.

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2.5 The ‘Russian doll’ model (Tibayrenc and Ayala, 2013) We have forged this concept for the case when one or the two main PCE features (LD and near-clading) are obtained, not only at the level of the whole species but also within the near-clades that subdivide it (Fig. 4). This is evidence that the populations within these near-clades undergo PCE too. They are beyond the clonality threshold, where recombination is insufficient to counter PCE. Some authors indeed have proposed that apparent clonality in a given species could be due to the fact that recombination is inhibited between the clusters that subdivide this species, but not or very little within them (Campbell et al., 2005). The population structure within the clusters would be network-like rather than tree-like, and there would be no LD within them (Maynard-Smith et al., 1993, Fig. 3). The clusters would be similar to biological species, and clustering/LD at the level of the whole species would be due to this presence of these unknown recombining subdivisions within it. This is the very alternative (null

Figure 4 ‘Russian doll’ model. When population genetic tests are practiced with adequate markers (of suitable resolution) within each of the near-clades that subdivide the species under study (large tree, left part of the figure), they evidence a miniature picture of the whole species, with the two main predominant clonal evolution features, namely, linkage disequilibrium and lesser near-clades (small tree, right part of the figure). This is evidence that the near-clades are not cryptic recombining entities (see Fig. 3, middle), and that they also undergo predominant clonal evolution.

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hypothesis) to the RD model, which is definitely not falsified by occasional recombination occurring without erasing PCE features within the nearclades (Ramírez and Llewellyn, 2015). As we have insisted (Tibayrenc and Ayala, 2013), when exploring PCE within each near-clade, it is important to adapt the resolution of the markers and to carefully consider population sizes. As a matter of fact, if one changes evolutionary scales, apparent recombination could be due to a statistical type II error because the marker has too low a resolution power or because the sample size is insufficient, or both. Many examples aimed at challenging the RD model either rely on improper samples or denote a miscomprehension of the model (Ramírez and Llelwellyn, 2014). This model indeed has been considered as largely relying on insufficient samples and being an ‘unnecessary oversimplification’ (Ramírez and Llewellyn, 2015). However, all models are based on simplifications of diversified situations in which a common denominator can be evidenced. So are the RD and the PCE models, which rely on sharply defined, highly falsifiable, working hypotheses. The RD model is convenient for exploring within-near-clade population structure. Abundant examples with convenient sampling sizes supporting the model, in parasites, fungi, bacteria and viruses, will be exposed later.

2.6 The ‘starving sex’ model It has been proposed that signs of clonality in P. falciparum, a parasite that had been long considered as panmictic (Walliker, 1991), were explained, not by intrinsic biological properties of the parasite, but rather, by the scarcity of multiclonal infections (MCIs) in low transmission cycles, leading to a lack of opportunity for different MLGs to mate and therefore, forcing the parasite to inbreed (Anderson et al., 2000). We have called this hypothesis the ‘starving sex’ model (Tibayrenc and Ayala, 2014a).

2.7 Biases towards recombination or clonality Some biases can generate artefactual negative LD results, and may also yield clashing phylogenetic reconstructions. These are (1) homoplasy (‘loci mutationally saturated’; Pearson et al., 2009b), which can be very important for microsatellites (Lehmann et al., 2004; Messenger et al., 2012; Sch€ onian et al., 2010; Sibley and Ajioka, 2008), although Koffi et al. (2015) consider its impact as ‘insignificant’; (2) different selective pressures; (3) different molecular clocks; and (4) different modes of genetic inheritance (mitochondrial versus nuclear genome).

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On the contrary, the Wahlund effect (separation by physical obstacles: space and/or time) may restrain recombination in separate populations mistakenly pooled together and mimic the effects of PCE, a bias thoroughly exposed by Koffi et al. (2015) and Rougeron et al. (2014, 2015). The Wahlund effect may operate at microgeographic scales (Rougeron et al. (2014, 2015) and has certainly to be carefully taken into account in population genetic surveys. At macroscales of space and time, the ones that are considered by the PCE model, the Wahlund effect can be evidenced by showing that (1) overrepresented MLGs tend to be restricted to different geographical areas and/or different times (Tibayrenc et al., 1991a) and (2) near-clades tend to be linked to space and/or time, which can be evidenced by correlation between genetic and geographical distances, or a link between genetic distance and time. If on the contrary, MLGs and near-clades are persistent and widespread over vast geographic areas and spans of time, this supports the hypothesis that they are caused, not by the Wahlund effect, but rather, by in-built properties of the organisms under survey. It is difficult to understand how local Wahlund effects could lead to the same pattern over many years, different countries and continents, with the same MLGs, the same near-clades and RD patterns (Tibayrenc and Ayala, 2012, 2015d). The occurrence of stable near-clades and RD patterns in close sympatry, including in the same host, with therefore ample opportunity for genetic recombination, is a strong argument against the hypothesis that they are caused by a Wahlund effect (see further the case of T. cruzi). As a counterexample, the genetic subdivisions evidenced within T. brucei gambiense I by Koffi et al. (2015) are obviously linked to geographical distance, and could be equated to near-clades only if they are both observed together in sympatry or are recorded each over different countries in further studies. The PCE model has been quoted ‘artefactual’, ‘conceptually outdated’, ‘unnecessarily complicated’ (Ramírez and Llewellyn, 2014, 2015), and ‘challenged’ (Messenger et al., 2012; Messenger and Miles, 2015; Oca~ na-Mayorga et al., 2010; Rougeron et al., 2010). However, a model can be considered outdated or challenged only when it has been clearly rejected, not the way it is misleadingly enunciated by its opponents, but rather, as it is presented in the very terms (as mentioned earlier) of its authors. We argue that it is not the case for the PCE model, which is easy to understand, as we have been able to verify it with many non-specialists. It relies on the direct observation of abundant data that can be easily verified by anybody, the main ones being presented later (see also Tibayrenc and Ayala, 2012, 2013, 2014a,b, 2015aed).

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3. EVIDENCE FOR PREDOMINANT CLONAL EVOLUTION FEATURES IN VARIOUS KINDS OF MICROPATHOGENS 3.1 Parasitic protozoa Examples selected in this part will focus on T. cruzi, the agent of Chagas disease; G. intestinalis; T. gondii and P. falciparum. A few other models will be more briefly considered. T. cruzi is a paradigmatic case of PCE. Population genetic interpretation of MLEE variability made it possible to show that the ‘zymodemes’ (MLEE MLGs) evidenced by Miles’ pioneering studies (1978) could be equated to genetic clones (Tibayrenc et al., 1981, 1986). In this species, there is LD: (1) among MLEE loci (Tibayrenc et al., 1981, 1986) and (2) between different kinds of markers: nuclear and mitochondrial polymorphisms (de Freitas et al., 2006; Machado and Ayala, 2001; Ramírez et al., 2011; Spotorno et al., 2008), random amplified polymorphic DNA (RAPD) and MLEE (Tibayrenc et al., 1993; Brisse et al., 2000), microsatellite polymorphism and DNA content (Lewis et al., 2009), neutral markers and strongly selected antigen genes (Lima et al., 2012; Rozas et al., 2007), as well as neutral genetic markers and the protein polymorphism revealed by proteomic analysis (Telleria et al., 2010). Although Minning et al. (2011) considered that their data suggested ‘genetic exchange playing a major role in T. cruzi population structure’, in their study, there is an obvious LD between CNV polymorphism and near-clade classification: all near-clade TCI strains clearly group together, as do all non-TCI strains (see their Fig. 3). This pattern is compatible with some genetic exchange going on, although other explanations are quite plausible (see ‘Biases Towards Recombination or Clonality’). However, the overall tendency is that CNV polymorphism is linked to near-clade structuring. The same pattern of linkage between CNV polymorphism and near-clading has been observed by Reis-Cunha et al. (2015). Ubiquitous MLGs are very frequent in T. cruzi (Tibayrenc et al., 1986; Zingalez et al., 2012; Tibayrenc and Ayala, 2012, 2013). T. cruzi presents one of the most demonstrative cases of near-clading in trypanosomatidae, as well as one of the most clear among pathogens in general. The species is subdivided into six near-clades (known as ‘discrete typing units’ or DTUs) (Brisse et al., 2000; Zingales et al., 2012) (see Fig. 2). These near-clades are believed to have undergone hybridization processes, the history of which is still under dispute (Westenberger et al., 2005; de Freitas et al., 2006). It is widely accepted (Zingalez et al., 2012) that the near-clades TC V and VI have a

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hybrid origin. They propagate themselves clonally, and are strongly associated to ‘domestic cycles’. Hybridization in T. cruzi might be linked to adaptation to human environments and the conquest of new ecological niches, a phenomenon considered as widespread in parasites (King et al., 2015). All T. cruzi near-clades are very stable in space (from the United States to Argentina and Brazil), and time. They have been corroborated by many genetic markers, present some ecological and epidemiological specificities and exhibit differential protein expression patterns (Machin et al., 2014; Telleria et al., 2010). Interestingly, the six near-clades can be also discriminated by antigen genes (Rozas et al., 2007), although these genes undergo a strong selective pressure. This observation illustrates the strength of PCE in T. cruzi, which is reflected in all genes of this parasite, including strongly selected ones. An additional near-clade, referred to as Tc-Bat (because it has been isolated from bats only), has been recorded in Brazil, Colombia, Ecuador and Panama years apart, in different species of bats (Lima et al., 2015; Marcili et al., 2009; Pinto et al., 2012, 2016). This is a striking illustration of the permanency of the near-clades. The partitioning into seven near-clades has been questioned by Barnabé et al. (2016) on a vast sample of strains, but with a limited set of one nuclear gene and two mitochondrial genes. This proposal has obviously to be tested with a broader set of genetic markers. However, it shows that even in the extremely well-studied species T. cruzi, evidencing near-clades is far from being ‘self-evident’ (Ramírez and Llewellyn, 2015). It has been suggested that genetic isolation among T. cruzi near-clades could be due to ecological separation rather than due to in-built biological properties of the parasite (Messenger and Miles, 2015). Ecological separation might play a role. However, there are many instances of MCIs with two or more near-clades within the same chagasic patient and the same insect vector, providing ample opportunity for mating (Tibayrenc and Ayala, 1988). Within the near-clade TCI, illustrative RD patterns can be observed (Tibayrenc and Ayala, 2013). As a matter of fact, additional near-clades within TCI, which are stable, widespread and sometimes sympatric, have been labelled a to e (Guhl and Ramírez, 2011). They have been corroborated by various markers (Cura et al., 2010; Ramírez et al., 2011). These lesser near-clades present some pathogenicity specificities (Llewellyn et al., 2009b). They are statistically linked to geographical distance (Llewellyn et al., 2009b); however, they cannot be explained by a mere Wahlund effect, since, they have been recorded over vast geographical distances (Guhl and Ramírez, 2011). Additionally, strong LD and widespread clonality have been recorded in TCI (Llewellyn et al., 2009b, 2011). TCI

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substructuring (RD pattern) in Northern Argentina (‘Chaco 1-4’) has been corroborated by analysis of the miniexon gene and MLST (Tomasini et al., 2014). A few studies have challenged an RD pattern within T. cruzi near-clades and are consistent with the hypothesis that recombination is frequent within TCI (Barnabé et al., 2013; Messenger et al., 2012; Oca~ naMayorga et al., 2010) and TCII (de Paula Baptista et al., 2014). However, studies by Barnabé et al. (2013) and Oca~ na et al. (2010) were based on limited samples (79 strains partitioned into six populations in the first one, 16 strains in the second one), and have to be confirmed by broader sets of data to eliminate a possible statistical type II error. In the study by Messenger et al. (2012), evidence for recombination was based on discrepancies between trees designed from mitochondrial and nuclear genotypes. The two categories have different modes of inheritance, and mix neutral (miniexon, microsatellites) and selected genes (coding mitochondrial genes). The data are compatible with mitochondrial introgression and occasional hybridization, although other explanations could be explored (see ‘Biases Towards Recombination or Clonality’). They definitely do not reject the hypothesis of a PCE within TCI. In the study dealing with TCII, there is a contradiction between the apparent lack of LD and the evidence for a strong structuration of the populations, as shown by the STRUCTURE test by the authors themselves (de Paula Baptista et al., 2014). Moreover, constant positive Fis values are at odds with the HardyeWeinberg equilibrium inferred by the authors. One study inferring that recombination is frequent in TCI Colombian populations (Ramírez et al., 2013) is based on a strong misinterpretation. As a matter of fact, the authors claim that the populations exhibit linkage equilibrium, whereas the p values for linkage analysis are 4  104 (LD test) and 0.037 (Ia index of association), hence quite significant. Moreover, the hypothesis of linkage equilibrium in this population is at odds with the presence of two clearly delimited near-clades (RD pattern) supported by significant bootstrap values (Ramírez et al., 2013). This bootstrap result illustrates the usefulness of the flexible phylogenetic approach based on the congruence criterion used in the framework of the near-clade model. The authors have identified incongruences among trees of individual genes. However, in the final tree based on all genes, the near-clade pattern is quite clear. We are not arguing that RD patterns are always verified within T. cruzi near-clades. However, they have been observed in several studies based on reliable samples and diversified genetic markers. Within-nearclade population genetics is just at its start in T. cruzi as well as in other

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pathogens. The RD model gives a convenient working hypothesis to explore it. Data are also very strong and complete in G. intestinalis, for which we have proposed that clonal evolution was observed (Tibayrenc et al., 1990, 1991a). Evidence for strong LD between different kinds of markers is abundant in this species (Feng and Xiao, 2011; Monis et al., 2009), with ‘assemblages’, that are perfectly equatable to near-clades (Tibayrenc and Ayala, 2014b). Giardia assemblages (labelled A to G) exhibit a neat, but not strict, host specificity and some phenotypic differences, and are corroborated by MLEE and gene sequences (Cacci o and Ryan, 2008; Feng and Xiao, 2011; Lasek-Nesselquist et al., 2009; Lebbad et al., 2011; Monis et al., 2009; Ortega-Pierres et al., 2009; Plutzer et al., 2010; Takumi et al., 2012; Xu et al., 2012). Additional subdivisions (‘subassemblages’) can be identified within the assemblages. These subassemblages (RD patterns) have been corroborated by many authors on various populations (Cacci o and Ryan, 2008; Feng and Xiao, 2011; Monis et al., 2009; Ortega-Pierres et al., 2009; Wielinga et al., 2011). They have been labelled A1 and A2 within assemblage A, and B3 and B4 within assemblage B. There are indications for subdivisions in the other assemblages (RD pattern) (Cacci o and Ryan, 2008; Feng and Xiao, 2011; Monis et al., 2009; Ortega-Pierres et al., 2009; Plutzer et al., 2010; Wielinga et al., 2011). The only example cited by Ramírez and Llewellyn (2015) to challenge the RD model in Giardia (Cooper et al., 2007) actually concerns not a whole assemblage (near-clade) but rather the subassemblage A2, whose existence is itself an evidence for an RD pattern. Moreover, it deals with the discrepancy of the phylogenies of only three genes and six strains. Although the data are compatible with recombination, (1) they say nothing about the frequency of recombination and (2) other explanations are equally possible (see ‘Biases Towards Recombination or Clonality’). With very comparable data (discrepancies between sequences inferred from different genes in Giardia), Wielinga and Thompson (2007), rather than attributing them to recombination, invoked homoplasy and differences of molecular clock. T. gondii, an apicomplexa parasite, besides strong LD (Khan et al., 2011; Su et al., 2012), shows typical near-clading patterns (Tibayrenc and Ayala, 2012, 2014a). This parasite, due to the classical notion of an ‘obligatory’ sexual cycle, like P. falciparum, has long been considered as panmictic (as recalled by Grigg and Sundar, 2009). However, we have proposed that clonality is present in T. gondii, and perhaps predominant (Tibayrenc et al., 1991a). This has been corroborated by further studies (Sibley and

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Boothroyd, 1992). The three main clonal lineages recorded by various authors (Beck et al., 2009; Boothroyd, 2009; Rajendran et al., 2012; Sibley and Ajioka, 2008; Sibley and Boothroyd, 1992; Smith, 2009; Su et al., 2010), which predominate in European and North American domestic cycles, correspond to typical near-clades (Tibayrenc and Ayala, 2014a), corroborated by various markers and software. Clustering of antigen genes parallels that of introns (Khan et al., 2011). Remarkably, the phylogeny of the ROP 18 gene, which undergoes strong natural selection, is identical to the overall phylogeny of the species (Khan et al., 2009). The three clonal lineages exhibit phenotypic specificity (virulence) (Boothroyd, 2009; Sibley and Boothroyd, 1992). Additional lineages assimilable to near-clades have been recorded in other cycles and regions of the world (Dubey et al., 2011; Khan et al., 2011; Mercier et al., 2010, 2011). A classical notion has emerged, namely, that T. gondii undergoes more recombination in South America than in North America and Europe (Lehmann et al., 2004, 2006; Su et al., 2010). However, this view needs being nuanced. Indeed, highresolution typing of no less than 956 strains collected worldwide based on PCRerestriction fragment length polymorphism (RFLP), intron sequences of housekeeping genes and microsatellites reveals that this parasite’s overall population structure (including South America) exhibits deep phylogenies and is composed of 6 ‘major clades’ (near-clades) (Su et al., 2012). Among the six clades described by Su et al. (2012), three of them (clades A, B and F) exhibit RD patterns, with LD and additional near-clades within each of them (Tibayrenc and Ayala, 2014a). An RD pattern is also observed within the near-clade ‘haplogroup 12’, with strong LD and overrepresented MLGs (Khan et al., 2011). Lastly, ubiquitous MLGs are very frequent in T. gondii (see for review Tibayrenc and Ayala, 2014a). P. falciparum, the agent of the most malignant form of malaria, obviously constitutes a specific case in our survey. It has long been considered as panmictic (Walliker, 1991). However, we have proposed (Tibayrenc et al., 1990, 1991a; Ben Abderrazak et al., 1999; Urdaneta et al., 2001) that this parasite could undergo some kind of uniparental propagation. Later, the hypothesis of panmixia has been considered ‘oversimplified’ (Heitman, 2006). Indeed, P. falciparum shows strong cases of LD (Tibayrenc et al., 1990; Anderson et al., 2000; Ben Abderrazak et al., 1999; Urdaneta et al., 2001; Tibayrenc and Ayala, 2012, 2014a). P. falciparum shows many examples of overrepresented, ubiquitous MLGs (Tibayrenc and Ayala, 2014a). Identical P. falciparum MLGs have been recorded in different patients in various countries. Persistent MLGs have been recorded for up to 8 years

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(Nkhoma et al., 2013). The same MLG has been found in Bolivia in 1994 and in Brazil in 1997e98 (Anderson et al., 2000). P. falciparum also shows indications of structuration and tendency for near-clading (Tibayrenc and Ayala, 2012, 2014a). However, these genetic subdivisions definitely cannot be equated to typical near-clades, since they are unstable and change over time (Griffing et al., 2011), although they can persist for up to 5 years (Branch et al., 2011). Despite the fact that they are more labile than typical near-clades, these clusters introduce a major stratification feature in P. falciparum natural populations. This should be taken into account in all studies dealing with the analysis of this parasite’s relevant characters. Within these labile nearclades, LD is observed (Griffing et al., 2011), suggesting a nascent, probably unstable RD pattern. In West Cambodia, P. falciparum has been considered as ‘essentially clonal’ (Miotto et al., 2013). In this region, three clearly differentiated, sympatric subpopulations have been stable in four different sites for 5 years (Miotto et al., 2013). In conclusion, although P. falciparum does appear to be capable of clonal propagation, as proposed earlier (Tibayrenc et al., 1990, 1991a), leading to a clear genetic structuration of many populations of this parasite, it does not fit the main features of the PCE model. We will now briefly consider other examples in parasitic protozoa. Among apicomplexa parasites, Plasmodium vivax exhibits a very similar pattern to that of P. falciparum. It also shows clear cases of LD (see for review Tibayrenc and Ayala, 2014a). As in P. falciparum, labile near-clades are observed in P. vivax. In South Korea, two dominant groups persist in spite of high variability and a high rate of MCIs (Iwagami et al., 2012), which is at odds with the starving sex hypothesis (see further). In India, 3 clusters, not linked to geographical distance, are corroborated by Neighbour Joining (NJ), STRUCTURE, and PCA (Gupta et al., 2012). P. vivax population structuring is also recorded in Brazil (Orjuela-S
anchez et al., 2010; Rezende et al., 2010). Lability of genetic subdivisions in P. falciparum and P. vivax is a manifestation of the neat impact of genetic recombination in these parasites. However, the presence of this structuring pattern is one of the clear indications for clonal propagation in them and permits to definitely falsify the ‘panmictic prejudice’ (Tibayrenc et al., 1990, 1991a; Tibayrenc and Ayala, 2002, 2014a). In the lizard parasite Plasmodium floridense, Falk et al. (2015) have attributed to PCE the existence of 11 genetic subdivisions (near-clades) tentatively equated to new species by the authors. In Cryptosporidium andersoni, one MLG has been sampled in the United States, Canada and the Czech Republic (Feng et al., 2011). Another one

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has been sampled in several Chinese provinces (Wang et al., 2012). In the species pertaining to the genus Cryptosporidium, some indications for nearclading are present. However, the data are not sufficient to strongly ascertain it (Tibayrenc and Ayala, 2014b). In Cryptosporidium hominis, four ‘subtype groups’ have been described (Gatei et al., 2007). Subtypes/clusters in Cryptosporidium muris and C. andersoni are supported by microsatellites, minisatellites and protein coding genes (Feng et al., 2011). They can be widespread and observed in different host species (Wang et al., 2012). Substructuring has also been observed in Cryptosporidium parvum (Ortega-Pierres et al., 2009). Population genetic studies should be completed in Cryptosporidium parasites. However, available data, although they suggest that clonal propagation is present in some populations, seem to not fit the PCE criteria (Tibayrenc and Ayala, 2014b). Concerning kinetoplastid parasites, we have proposed (Tibayrenc et al., 1990, 1991a) that several species of the genus Leishmania undergo clonal evolution. A lot of recently obtained data support this hypothesis. The Leishmania braziliensis/Leishmania peruviana complex exhibits nearclades that are corroborated by (1) the splitstree and STRUCTURE software and (2) MLEE, MLST and pulse field gel electrophoresis (PFGE) (Odiwuor et al., 2012). In Leishmania donovani (included in the Leishmania infantum complex), there is a strong LD between single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) (Downing et al., 2011; Minning et al., 2011).). In the same species, high-resolution typing by WGS/SNP of 204 clinical isolates from Nepal and India (Imamura et al., 2016) reveals a remarkable case of RD pattern. The strains are split into three distinct genetic lines. The main one, the ‘core group’, is composed of 191 strains. Remarkably, this group exhibits no polymorphism by microsatellite typing. However, SNPs, in this group, which represents a very small subsample of the whole L. donovani species, reveal a highly structured diversity at the microevolutionary scale. Six congruent monophyletic groups, supported by high bootstrap values, can be distinguished, supported by both maximum likelihood and model-based clustering (near-clades). One of these groups (ISC5) exhibits a lesser subgroup. Apart from these six monophyletic groups, eight hybrid lineages are observed. However, these hybrid lines give no indication for recombination within this core group. LD is recorded within each of the six monophyletic groups, which exhibit a strong stability in space and time: most of the monophyletic groups were recorded from 2002 to 2011, and many of them were sampled both in Nepal and India. All these

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genotypes have a recent origin: the most common recent common ancestor of the core group originates from the middle of the 19th century, whereas the ISC2 and ISC 4e6 are as recent as the year 1960. This definitely shows a clear RD pattern with lesser near-clades and LD at this microevolutionary level. This shows also that the ‘measurably evolving pathogen’ approach (Biek et al., 2015) can now be applied to eukaryotic micropathogens: the epidemiology of these organisms can be followed in recent times through WGS and time-serial samplings. LD between different genetic markers in the L. infantum complex is clear (Gouzelou et al., 2012; Kuhls et al., 2008; Mauricio et al., 2006). Some microsatellite MLGs of this species are sampled in both the Ancient and the New World (Kuhls et al., 2011). The L. infantum MLEE MLG ‘MON 1’ is another typical overrepresented, widespread genotype (Tibayrenc et al., 1990). Near-clades are also observed in the L. infantum complex. In Turkey and Cyprus, they show concordance between (1) STRUCTURE, NJ trees and factorial correspondence analysis and (2) between K26-PCR, MLEE and microsatellites (Gouzelou et al., 2012). Near-clading agreement between Internal Transcribed Spacer, MLEE, MLST, miniexon typing and gp63 intergenic RFLP has also been observed (Mauricio et al., 2006). In Turkey, a monophyletic near-clade has been delimitated by WGS and PCR-MLST, with concordance with MLEE (Rogers et al., 2014). In Latin America, near-clading agreement is obtained between: (1) STRUCTURE and NJ and (2) MLEE and microsatellites (Kuhls et al., 2011). Geographical separation apparently interferes with the near-clading pattern observed in the L. infantum complex, but can explain it only partially. The clonal MLEE MLG MON 1 exhibits an RD pattern. As a matter of fact, microsatellite analysis reveals the existence of three near-clades within this MLEE MLG, which is itself a tiny subdivision of L. infantum. Within-MON 1 near-clades, which are corroborated by STRUCTURE, factorial component analysis and distance methods (congruence principle), are widespread in Brazil and Paraguay. Of a total sample of 173 strains, the ubiquitous microsatellite MLG 10 has been sampled 52 times in 14 Brazilian states and Paraguay (Ferreira et al., 2012). High microsatellite diversity within this group of parasites, which appeared monomorphic with MLEE, illustrates the fact that ‘identical genotypes’ is a relative notion, that depends highly on the resolution power and mutation rate of the concerned marker (see ‘Identical Multilocus Genotypes is a Relative Notion’). The use of powerful markers (WGS and PCR-MLST) has made it possible to discriminate a monophyletic group (near-clade) in Turkish L. infantum strains

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(Rogers et al., 2014). This near-clade corresponds to a unique MLEE MLG, again another case where a powerful marker (PCR-MLST) reveals additional genetic variability within a given MLG. Contrary to what has been claimed (Ramírez and Llewellyn, 2014, 2015), this case does not challenge the RD model. On the contrary, it is a fine illustration of it. As a matter of fact, within this near-clade, reproduction is ‘primarily clonal’; ‘intraspecific linkage patterns show low levels of recent recombination’, which ‘may have been an infrequent event’; and one observes ‘mainly clonal reproduction in the parasite population’, according to the very terms of the authors (Rogers et al., 2014). The species Leishmania killicki appears to be a phylogenetic subdivision (near-clade) of Leishmania tropica (Chaara et al., 2015a,b). For this reason, the authors have proposed to rename it ‘L. killicki syn. Tropica’. Within L. killicki as well as non-killicki strains of L. tropica, various genetic subdivisions (RD patterns) are apparent (Chaara et al., 2015a,b). In Asia, the population structure of L. tropica has remained stable for 55 years (Schwenkenbecher et al., 2006). The question of PCE in African trypanosomes responsible for human African trypanosomiasis (HAT) and cattle diseases (the T. brucei complex) is not simple. After the seminal article by Tait (1980) and successful mating experiments (Jenni et al., 1986), T. brucei has been considered as a sexual species (‘panmictic prejudice’). However, we have proposed that it could undergo clonal evolution (Tibayrenc et al., 1990). Near-clading in the T. brucei complex is not obvious. T. brucei is traditionally composed of three subspecies, namely, T. brucei gambiense (West African HAT), T. brucei rhodesiense (East African HAT) and T. brucei brucei (cattle disease). These three subspecies have different geographical distributions and different pathogenicities, although these specificities are not clear-cut. The three subspecies do not correspond to clear genetic subdivisions that could be equated to nearclades. However, the so-called T. brucei gambiense group I (within the subspecies T. brucei gambiense) does correspond to a monophyletic near-clade (Balmer et al., 2011; Koffi et al., 2009; Mathieu-Daudé et al., 1994; Weir et al., 2016). The so-called T. brucei brucei ‘Kiboko B’ (Balmer et al., 2011) appears to be a near-clade too. LD is recorded in T. brucei gambiense (Morrison et al., 2008b) and T. brucei rhodesiense (Duffy et al., 2013). T. brucei gambiense I seems to be strictly clonal (Koffi et al., 2009, 2015), a hypothesis that is strongly supported by WGS analysis (Weir et al., 2016). Near-clades can be observed in Trypanosoma congolense (the subgroups Savannah, Forest and Kilifi) (Holzmuller et al., 2010). It has been postulated

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that mating was frequent in the Savannah group (Morrison et al., 2009), a proposal that has to be confirmed by more data and has been questioned in 2015 (Koffi et al., 2015). Strong LD is observed within this group (Morrison et al., 2009). LD and near-clades are present in T. evansi (McInnes et al., 2012) and T. rangeli (Hamilton et al., 2011). Trypanosoma vivax, for which we have hypothesized clonal evolution (Tibayrenc et al., 1991a), exhibits strong LD (Duffy et al., 2009). However, Koffi et al. (2015) consider that the population structure of this species still has to be clarified. In summary, when parasitic protozoa are considered, the PCE pattern is quite clear in T. cruzi, many Leishmania species, G. intestinalis and T. gondii. P. falciparum and P. vivax do exhibit clear indications of clonal population structure in some populations, but definitely do not fit the typical PCE pattern. Other species call for further studies.

3.2 Fungi and yeasts According to Taylor (2015), in fungi, clonality, which the author equates to restrained recombination, can have extrinsic and intrinsic cause. Extrensic causes include dispersal (bottleneck/founder effects leading to a deficiency of mating types and adaptation to new hosts) and hybridization between very divergent parental genotypes, leading to the impossibility of meiosis, a mechanism invoked also by Avise (2015) to explain asexuality in clonal vertebrates. Intrinsic causes include mitotic clonality, selfing and intratetrad mating. In Candida albicans, the genetic distances calculated from ca3 markers, MLEE, MLST and microsatellites are highly correlated (McManus and Coleman, 2014). This meets the criteria of our g test of LD (Tibayrenc et al., 1990). This species shows a very clear near-clading pattern (Tibayrenc and Ayala, 2012), corroborated by several genetic markers, with strong links with phenotypes (drug resistance, pathogenicity) (Ch
avez-Galarza et al., 2010; McManus and Coleman, 2014; Tavanti et al., 2005). Worldwidedistributed major clades are subdivided into various minor clades (RD pattern) (McManus and Coleman, 2014; Tavanti et al., 2005). Recombination is rare among and within clades (McManus and Coleman, 2014). LD and ubiquitous genotypes are observed in Candida dubliniensis (Badoc et al., 2002). We have proposed (Tibayrenc et al., 1991a) that Cryptococcus neoformans and its serotype subdivisions could undergo clonal evolution. There is strong LD revealed by amplified fragment length polymorphism (AFLP), MLST,

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PCR fingerprint, RAPD, RFLP and gene sequences in the two sibling species Cryptococcus gattii and C. neoformans (Bovers et al., 2008; Lin and Heitman, 2006; Ngamskulrungroj et al., 2009). In these two species, LD is observed between genetic types and serotypes (Campbell and Carter, 2006). In C. gattii, the clonal genotype responsible for the Vancouver epidemics has also been sampled in San Francisco, and is identical to the NIH 444 strain isolated in 1970 (Carriconde et al., 2011; Chaturvedi and Chaturvedi, 2011). It is supposed to have originated from Australia by ‘same sex mating’ (Fraser et al., 2005). Ubiquitous clones are uncovered in C. neoformans var. grubii. One MLG has been sampled from 1996 to 2007 in Africa and Asia. Another one has been recorded from 1983 to 2009 in North and South America, in Europe and in Asia (Khayhan et al., 2013). C. gattii and C. neoformans also exhibit very typical near-clades (Tibayrenc and Ayala, 2014b), corroborated by various molecular markers, including AFLP, MLST, PCR fingerprinting, RAPD and RFLP, and strongly linked to serotypes (Bovers et al., 2008; Campbell et al., 2005; Campbell and Carter, 2006; Carriconde et al., 2011; Chaturvedi and Chaturvedi, 2011; Fraser et al., 2005; Khayhan et al., 2013; Lin and Heitman, 2006; Litvintseva and Mitchell, 2012; Ngamskulrungroj et al., 2009; Voelz et al., 2013; Xu, 2006a). In C. gattii, there are 4 ‘molecular types’ (near-clades) I to IV. In molecular type II, there are three clonal groups a, b and c (RD pattern) (Chaturvedi and Chaturvedi, 2011; Ngamskulrungroj et al., 2009; Voelz et al., 2013). In the cluster (near-clade) VGI of this species, four subdivisions (C1e4) are observed (RD pattern) (Campbell et al., 2005). Fusarium oxysporum exhibits LD between various markers. Near-clades in this species are corroborated by AFLP, multi-gene phylogenies, PFGE, RAPD and RFLP (Fourie et al., 2011). Lastly, in Pneumocystis jirovecii, identical MLGs have been sampled in 10 European hospitals from different countries for 9 years, and in particular patients for 8 weeks (Matos and Esteves, 2010). However, the evidence for PCE in this species still is weaker than for the species listed earlier.

3.3 Bacteria Three paradigmatic examples will be reviewed for bacteria, namely, E. coli, N. meningitidis and Mycobacterium tuberculosis. Other species will be more briefly treated. E. coli could be considered as a kind of ‘bacterial twin’ for eukaryotic pathogens that perfectly fit the PCE model, such as T. cruzi, G. intestinalis,

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T. gondii, C. albicans and C. neoformans/C. gattii. This is so to the point that through a blind lecture, genetic data dealing with E. coli and these eukaryotic species could be confounded. In E. coli, LD is strong between various markers (MLEE, RAPD, RFLP, MLST, WGS) (Chaudhuri and Henderson, 2012; Tenaillon et al., 2010) and between genetic markers and phenotypes (Miller and Hartl, 1986). This species counts among the most demonstrative cases of near-clading. The MLEE A, B1, B2, D groups identified in the historical ECOR collection of strains by pioneer studies (Ochman and Selander, 1984; Whittam et al., 1983) have been fully corroborated, and their permanency as well, by many studies relying on various genetic markers (Chaudhuri and Henderson, 2012; Clermont et al., 2011; Denamur et al., 2010; Walk et al., 2009; Wirth et al., 2006). The fact that the rate of recombination in E. coli almost equals that of mutation (Bobay et al., 2015) has not prevented the long-term stability of a strong structuring into typical near-clades. WGS makes it possible to refine this picture and to uncover new near-clades, especially in environmental strains (Chaudhuri and Henderson, 2012; Luo et al., 2011). Lastly, E. coli near-clades seem to exhibit phenotypic differences (Chaudhuri and Henderson, 2012; Miller and Hartl, 1986). N. meningitidis is described as a paradigmatic case of the ‘semiclonal model’ (Maiden, 2006) or ‘epidemic clonality model’ (Maynard Smith et al., 1993), that is, occurrence of occasional bouts of clonality in an otherwise highly recombining species (Fig. 3). However, LD is considerable throughout the range of the species, with ‘distinct cocirculating lineages that constitute a small subset of the possible allele combinations’ (i.e., LD) (Buckee et al., 2008). LD is confirmed by MLEE, MLST (with loci different from the MLEE loci) and RAPD (Bart et al., 2001). Remarkably, LD includes a strong association between MLST genotypes [sequence types (STs)] on the one hand and phenotypes (capsular serogroups a, d: ‘finetypes’ {antigen sequence typing}) on the other hand (Joseph et al., 2011; Vogel et al., 2010). We have shown that many data are at odds with the semiclonal model in N. meningitidis and have proposed that this bacterium fits better the PCE model (Bart et al., 2001; Tibayrenc and Ayala, 2012, 2015c). N. meningitidis shows abundant examples of widespread, persistent MLGs (Bennett et al., 2007; Caugant and Maiden, 2009; Maiden, 2008; Vogel, 2010). This is the case also for Neisseria gonorroeae and Neisseria lactamica (Bennett et al., 2007). Lastly, N. meningitidis exhibits typical near-clades, whose stability in space and time is corroborated by several genetic markers (Bart et al., 2001; Achtman, 2004; Falush, 2009). These near-clades are correlated

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with gene content (Joseph et al., 2011) and serotypes (Buckee et al., 2008; Caugant, 2008; Caugant and Maiden, 2009; Joseph et al., 2011). WGS deep phylogeny (Budroni et al., 2011) confirms the stability of the nearclades (‘phylogenetic clades’) at an evolutionary scale, which challenges the ‘semiclonal’ model (Maiden, 2006). Contrary to what Ramírez and Llewellyn (2015) state, the phylogenetic clades show a typical RD pattern. As a matter of fact, several subclusters, which correspond to ‘clonal complexes’ (CCs), are visible within each of them (Fig. 5). These CCs are widespread and persist for many years in spite of recombination (Budroni et al., 2011). Establishing deep phylogeny was impossible with MLST, even with 20 loci (Didelot et al., 2009b), which shows that this result is due, not to abundant recombination, but rather to an unadapted resolution power of MLST at this level of phylogenetic divergence in N. meningitidis. The manifestation of a stronger near-clade pattern (Budroni et al., 2011) by the use of highresolution markers (WGS) is a clear illustration of the efficacy of the congruence criterion. PCE features are observed in pathogenic strains of N. meningitidis. Commensal strains seem to exhibit a different pattern, with more genetic diversity (Caugant, 2008). It is possible that PCE corresponds to a specific evolutionary strategy of the species to adapt to a specific environment and a parasitic life. In M. tuberculosis, considered as highly clonal species (HenriquesNormark et al., 2008), LD has been observed between various markers, including SNPs and large sequence polymorphisms (Comas and Gagneux, 2009), and between MIRU microsatellites and IS 6110 markers (Supply et al., 2003) (g test; Tibayrenc et al., 1990). The W strain has spread from the United States to France; and the Beijing family strains have a global distribution (Bifani et al., 2002). This species shows a clear near-clading pattern with 6 ‘phylogenetic groupings’ with differences in their geographical distribution, host specificity (ethnicity) and pathogenicity (Achtman, 2008). It is remarkable that this near-clading pattern was not apparent with less discriminating markers, which seemed to show that this species was almost monomorphic. The use of more powerful markers (SNPs, WGS) has revealed these hidden subdivisions (Achtman, 2008; Comas and Gagneux, 2009; Smith, 2012). It is relevant to note that the phylogeny based on the ‘3R genes’ (DNA repair, recombination, replication), which are under intense selective pressure, parallels the overall M. tuberculosis phylogeny (dos Vultos et al., 2008). Other cases of bacterial models are reviewed below.

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Figure 5 Deep phylogenies and Russian doll patterns in Neisseria meningitides. Within the ‘phylogenetic clades’ (near-clades), additional subdivisions are clearly visible. Each phylogenetic clade is composed of several clonal complexes that are stable in space and time (‘Russian Doll pattern’). After Budroni, S., Siena, E., Dunning Hotopp, J.C., Seib, K.L., Serruto, D., Nofroni, C., Comanducci, M., Riley, D.R., Daugherty, S.C., Angiuoli, S.V., Covacci, A., Pizza, M., Rappuoli, R.E. Moxon, E.R., Tettelin, H., Medini, D., 2011. Neisseria meningitidis is structured in clades associated with restriction modification systems that modulate homologous recombination. Proc. Natl. Acad. Sci. U.S.A. 108, 4494e4499.

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The Bacillus cereus group is subdivided into 3 ‘clades’ (near-clades) that are corroborated by both unweighted pair group method with arithmetic mean (UPGMA) analysis and the ClonalFrame software. The near-clades exhibit a pathogenic specificity, since all strains of Bacillus anthracis are in clade 1, whereas clade 3 shows no pathogenic genotypes (Didelot et al., 2009a). B. anthracis, which is a subdivision of B. cereus, is itself divided into nearclades, although its overall genetic diversity is very limited, to the point that it has been considered a monoclonal species (Achtman, 2004). However, the use of markers that have an adequate resolution make it possible to unravel the presence of 12 stable, ubiquitous subgroups (near-clades) (Kenefic et al., 2010). This presence of near-clades within a near-clade is a typical RD pattern. Bartonella bacilliformis exhibits strong LD, corroborated by MLST, AFLP and Infrequent Restriction Site PCR (Chaloner et al., 2011). In this species, the MLG ST 1 has been sampled repeatedly at various places in Peru from 1960 to 2007. ST8 has been sampled twice 150 km and 9 years apart, in spite of an inferred ‘strong influence of recombination’ (Chaloner et al., 2011). Bartonella henselae has strong LD (Mietze et al., 2011). This species shows three major clusters (near-clades) corroborated by six different genetic markers (Mietze et al., 2011). In Bartonella quintana, the same MLG has been sampled over 60 years and three continents (Arvand et al., 2010). Borrelia burgdorferi in North America is subdivided into discrete clusters (near-clades) revealed by phylogeography (Kurtenbach et al., 2010). Burckholderia pseudomallei, in spite of a supposedly high recombination rate, exhibits a strong phylogenetic signal and reveals a deep phylogeny by the use of WGS/SNP, ‘as individual lateral gene transfer events do not involve a large enough portion of the genome to disrupt the core phylogenetic patterns’. It is interesting to note that MLST was inappropriate to reveal this deep phylogeny (Pearson et al., 2009a). Campylobacter coli is subdivided into three clearly differentiated clades (near-clades) (Sheppard et al., 2010). The Enterococcus faecium hospital cluster represents a ‘genogroup’ (nearclade) corroborated by MLST, AFLP and variability of the dispensable genome (Willems, 2010; Willems et al., 2011). In Legionella pneumophila, the clonal strain ‘Paris’ has a worldwide distribution (Gomez-Valero et al., 2009). L. pneumophila exhibits also strong LD (Edwards et al., 2008). MLST analysis reveals the presence of ‘sequence clusters’ (near-clades) in this species (Edwards et al., 2008).

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Listeria monocytogenes exhibits LD and near-clades, corroborated by both MLST and ribotyping (den Bakker et al., 2008, 2010). Mycobacterium bovis exhibits LD and near-clades corroborated by various markers: deletions, variable numbers tandem repeats (VNTRs), spoligotyping (Smith, 2012). Pseudomonas aeruginosa is another species considered as frequently recombining (Pirnay et al., 2009). However, LD is strong: (1) between different markers: PFGE with microsatellites, multiple locus VNTR and MLST (van Mansfeld et al., 2010) and (2) within the core and accessory genomes and between them (Wiehlmann et al., 2007). P. aeruginosa shows examples of worldwide clones (Pirnay et al., 2009; van Mansfeld et al., 2010; Wiehlmann et al., 2007). Moreover, clear indications for near-clading are confirmed by several genetic markers (van Mansfeld et al., 2010). Nearclades are also delimited by microarrays relying on 58 markers (Wiehlmann et al., 2007) and by strongly selected genes (Pirnay et al., 2009). However, WGS deep phylogenies are not available. In Pseudomonas syringae, the same MLG has been sampled in the United States in 1965 and in Japan in 1979 (Sarkar and Guttman, 2004). P. syringae also shows LD, and clear near-clades, although they have been identified only by MLST (Sarkar and Guttman, 2004). S. enterica is subdivided into five lineages (near-clades) corroborated by both ClonalFrame and STRUCTURE, as evidenced by sequencing 10% of the core genome. These near-clades are highly linked to the serovars, and to pathogenicity (Didelot et al., 2011). In S. typhi, WGS shows that the H58 clade (near-clade) is widespread worldwide. It exhibits a clear microclustering (RD pattern), although the most recent common ancestor is estimated to be dated to the year 1959 only (Wong et al., 2015). This shows that ‘measurably evolving pathogens’ (Biek et al., 2015), that is, pathogens whose epidemiology can be followed up through WGS and time-serial samplings, are not only limited to fastevolving viruses but also concern bacteria (Wong et al., 2015) and even parasitic protozoa (Imamura et al., 2016). Staphylococcus aureus shows strong LD (Smyth and Robinson, 2010). This species’ MLEE MLGs often have intercontinental distributions (Musser et al., 1990). S. aureus exhibits two near-clades. This near-clading pattern uncovered by modern markers (Feil et al., 2003; Smyth and Robinson, 2010) parallels the one revealed by MLEE 25 years ago (Musser et al., 1990). The ‘highly recombining’ species, S. pneumoniae (Pérez Losada et al., 2006) exhibits LD between MLST (core genome), microarrays and

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accessory genes (Dagerhamn et al., 2008; Henriques-Normark et al., 2008). This species has been analysed by WGS and SNPs. Again, these highly resolutive markers reveal the deep phylogenies of clear near-clades with significant correlation between core and accessory genome phylogenies, and between phylogenies and antibiotic resistance (Chewapreecha et al., 2014; Croucher et al., 2011; Muzzi and Donati, 2011). WGS/SNP analysis reveals the presence of 33 primary clusters subdivided into 183 secondary clusters (RD pattern) (Chewapreecha et al., 2014). Streptococcus pyogenes also shows clear indications of near-clading, corroborated by different markers and WGS. Ten ‘major subclones’ are each composed of various CCs (RD pattern) (Beres et al., 2010). In Vibrio cholerae, Vibrio parahaemolyticus and Vibrio vulnificus, successful clonal lineages persist for decades (Bisharat, 2010). V. vulnificus, presented as a ‘model of clonal evolution’, exhibits 2 clusters (near-clades) corroborated by both MLEE and MLST. One contains mainly environmental strains, whereas the other one contains mainly clinical strains. The second cluster is itself clustered (RD pattern) (Bisharat, 2010). In Xanthomonas campestris, some MLGs pertaining to the same ‘pathovar’ have persisted worldwide from 1949 to 1981(Fargier et al., 2011). X. campestris exhibits several clusters corroborated by both NJ tree and Splitstree, which could correspond to near-clades, although the evidence is based on only one marker (MLST). The clusters correspond to the ‘pathovars’ (Fargier et al., 2011). In summary: E. coli, M. tuberculosis, S. aureus and S. typhi perfectly fit the PCE model. This seems to be also the case for N. meningitidis, which is unexpected, since this species was considered as highly recombining. Other species show clear indications of PCE patterns. However, they deserve more detailed analysis.

3.4 Viruses Many viral species are recognized as being mainly clonal (with low rates of recombination) (Holmes, 2009, 2013). It has been proposed that clonality (defined as restrained recombination) is the standard evolutionary mode for viruses because recombination is ‘largely inconsequential’ (Perales et al., 2015). As a consequence, viral lineages (near-clades) can be defined (Perales et al., 2015), as we have proposed it for viruses as well as for other pathogens (Tibayrenc and Ayala, 2012). Recombination rate seems to (1) vary between virus categories and (2) be rather specific of a given category (Pérez-Losada et al., 2015).

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Many virus species do show structures similar to near-clades and RD. Of course, the rapid turnover of viral genomes entails that the oldness of these viral near-clades cannot be compared with that of other microbes’ nearclades. Still the fact remains that they show discreteness, as well as permanency in space and time, including in some highly recombining species. Many cases are illustrative of a PCE pattern in viral species. Maybe the most demonstrative one is the dengue virus (DENV). However, it is difficult to hierarchize the level of evidence in the many species cited below. DENV shows four phylogenetic subtypes (DENV 1e4: near-clades) that are linked to serotypes (Holmes, 2008; Weaver and Vasilakis, 2009). It has been inferred that the linkage between subtypes and serotypes was less strict than what was classically thought. However, it remains statistically very strong (Katzelnick et al., 2015). The four subtypes are stable since at least 1943, and circulate sympatrically, especially in Latin America and Asia, which provides ample opportunity for mating (Messina et al., 2014). The phylogenetic subtypes are subdivided into ‘genotypes’ (Weaver and Vasilakis, 2009) or ‘clades’ (Raghwani et al., 2011) (RD pattern). The highly recombining Human Immunodeficiency virus I (HIV I) exhibits stable groups M, N, O and P linked to phenotypes (epidemiology, transmissibility, infectivity for different cellular types), in spite of the existence of circulating recombinant forms (Etienne et al., 2011; Lam et al., 2010; Ndung’u and Weiss, 2012). Within the M group nine subgroups can be discriminated (Pérez-Losada et al., 2015) (RD pattern). Chikungunya virus exhibits three major widespread ‘phylogroups’ I, II and III (near-clades), which are clearly linked to geographical separation (Cui et al., 2011). However, within the main phylogroups, some genotypes are widespread and stable in time. The phylogroup II has been identified in India, Indonesia, Malaysia, the Philippines, Thailand and the United States. The phylogroup III is subdivided into ‘subgroups’ III a, b and c (RD pattern). The ‘subgroup’ IIIa includes strains isolated from 1952 to 1986 in Tanzania, Senegal, Congo and India. The ‘subgroup’ IIIc has been discovered in Bangladesh, California, China, India, Italy, Malaysia, Singapore, Sri Lanka and Thailand. Recent importations can account for some of these results dealing with ubiquitous genotypes. However, it would be tentative to explain this overall pattern by only a trivial Wahlund effect. The Ebola virus shows three ‘groups’ A, B and R (near-clades), with some indices of recombination (Wittmann et al., 2007). The Hepatitis A virus (HAV) exhibits three main ‘genotypes’ I, II and III with a worldwide distribution (Vaughan et al., 2014). Within these main

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‘genotypes’ (near-clades), various subtypes (IA, IB, IIA, IIB, IIIA, IIIB) can be identified and occur sympatrically (RD pattern). There are three major clusters within the subgroup IA (RD pattern). The Hepatitis B virus has eight genotypes A to H and several subgenotypes A1e6, B1e8, C1e7, D1e7, and F1e4 (RD pattern). One observes specificities for pathogenicity and geographical distribution. Recombinant forms are recorded (Araujo et al., 2011). The Hepatitis C virus has six to seven main genotypes divided into up to 67 ‘subtypes’ (near-clade þ RD pattern) (Fishman and Branch, 2009; Jackowiak et al., 2014; Morel et al., 2011; Simmonds et al., 2005). The Hepatitis E virus has four ‘genotypes’ and many ‘subtypes’ (near-clade þ RD pattern) (Purdy and Khudyakov, 2011). The measles virus is subdivided into several ‘clades’ or ‘genotypes’ (WHO, 2003; Mankertz et al., 2011). However, these data have not been analysed in terms of multilocus population genetics and phylogenetics. This should be done before these ‘clades/genotypes’ can be equated to near-clades. The rabies virus (RABV) exhibits two distinct lineages in West Africa that shows partial allopatry, but can be found sympatrically (Hayman et al., 2011). At the level of the whole species, three ‘clades’ (near-clades) can be identified (I, South East Asia; II, worldwide; III, only bats and raccoons in North and South America). Two of these clades are present in China (Zhang et al., 2009). Various subdivisions can be observed within these clades (Zhang et al., 2009) (RD pattern). Intra- and interclade recombination events can be detected. However, their frequency seems to be very low (Liu et al., 2011). The varicella-zoster virus according to a new common nomenclature, has five major ‘clades’, partially linked to geography, and several subclades (near-clades þ RD pattern) (Schmidt-Chanasit and Sauerbrei, 2011). The West Nile virus has two major ‘lineages’ (near-clades): I and II, and four others (III to VI), the existence of the latter being questionable. Lineages seem to be associated with specific pathogenic properties (Pesko and Ebel, 2012; Zehender et al., 2011). Lineage I Is subdivided into 1a, 1b and 1c. The sublineage 1a is itself subdivided into A and B (Zehender et al., 2011) (RD pattern). These different subdivisions have specific geographical distributions. They are not explainable by a Wahlund effect only. Several of them are widespread. The numerous examples cited earlier show that in many micropathogen species, not only parasitic protozoa but also fungi, bacteria and

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viruses, the marks of PCE are clear. This manifests that these pathogens, although phylogenetically quite diverse, share this important common evolutionary trait. This common pattern is so strong in some cases that blindly read genetic data from T. cruzi, G. intestinalis, T. gondii, C. albicans, C. neoformans/C. gattii, E. coli, as examples, could make it difficult to identify which case is under survey. The existence of such a common evolutionary trait has nothing unlikely and probably is a mark of the adaptation to parasitism that is shared by all these pathogens. Another important evolutionary trait, namely, reduction of genome size, is common to many organisms having a parasitic lifestyle (Buscaglia et al., 2015). Of course, the level of evidence is not the same among these species. This is due to the fact that progress among the different fields of research concerned is not the same from one species to another. Moreover, evolutionary specificities of the different pathogens condition this progress. For example, the tiny size of viral genomes makes them much more accessible to routine WGS. Also, technical specificities bring strong limitations to evolutionary analyses. As examples, Plasmodium parasites are much more difficult to culture than trypanosomes and Leishmania, and Pneumocystis cannot be cultivated. Lastly, although many species appear to undergo PCE, this is not the case for P. falciparum and P. vivax for example. These parasites, although they are capable of clonal propagation, do not meet the criteria of the PCE model (near-clades that are stable in space and time, as a result of the clonality threshold). We will now expose more in detail some relevant aspects of the PCE model.

4. THE ‘STARVING SEX’ HYPOTHESIS Clonality in P. falciparum and P. vivax at first view clashes with the classical notion of an obligatory sexual cycle in these parasites. Clonality in these species is generally explained by the fact that MCIs are rare in low transmission areas. Thus, mating between partners having different genotypes (outcrossing) is impossible. The parasite therefore undergoes selfing, which leads to clonality (Anderson et al., 2000). Many authors explain clonality in Plasmodium by this hypothesis (see, for example, Branch et al., 2011; Conway, 2007; Falk et al., 2015; Iwagami et al., 2012; Miotto et al., 2013; Neafsey et al., 2008; Weedall and Hall, 2014). We have called this situation the ‘starving sex hypothesis’, or ‘passive clonality’ (Tibayrenc

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and Ayala, 2014a). We have shown that in both P. falciparum and P. vivax, a large amount of data are at odds with starving, which suggests that these parasites may also undergo clonality by in-built mechanisms. We have presented many examples of such cases (Tibayrenc and Ayala, 2014a). Some of them will be cited now. A highly significant LD is observed in P. falciparum populations of Papua New Guinea and Zimbabwe, although transmission is strong in New Guinea and MCIs are frequent in Zimbabwe (Anderson et al., 2000). LD is stronger in Zimbabwe than in Brazil, even though transmission is higher in Zimbabwe (Anderson et al., 2000). Clonality has been evidenced in P. falciparum despite high transmission in Kenya and Cameroon (Annan et al., 2007; Razakandrainibe et al., 2005). WGS and typing by 86,158 SNPs has shown that inbreeding was very strong in Papua New Guinea in spite of high transmission (Manske et al., 2012). Inbreeding has been shown in P. vivax, in spite of frequent MICs, a result considered ‘puzzling’ by the authors (Ferreira et al., 2007). Starving sex probably occurs in Plasmodium and in other microparasites. However, the many examples we have cited (see also Tibayrenc and Ayala, 2014a) lead to not rule out the hypothesis of in-built clonality, the more so because the two hypotheses are not mutually exclusive. We have proposed to move the debate in Plasmodium from sexuality versus clonality to starving sex versus in-built clonality (Tibayrenc and Ayala, 2014a). If it is verified that the starving sex hypothesis is unsatisfactory in P. falciparum, an important epidemiological implication will be that LD cannot be used as a reliable indicator of the efficacy of control measures (Volkmann et al., 2012b), since clonality and transmission rate will not be correlated. Starving sex by scarcity of MCIs has been proposed for bacteria: it would lead to selfing and ‘invisible sex’ (Balloux, 2010). However, there is no experimental evidence for it, and it can be suspected that MCIs in bacteria are grossly underestimated. In viruses, lack of recombinants in HAV has been explained by starving sex (Vaughan et al., 2014). However, MCIs are recorded for this virus in Korea and Japan. Starving sex has been inferred to explain higher recombination rates of HIV-1 in Africa, where MCIs are more frequent (Pérez-Losada et al., 2015). Superinfection exclusion could favour starving sex in viruses (Jackowiak et al., 2014; Perales et al., 2015). Starving sex actually amounts to a Wahlund effect, by physical separation of putative recombinant partners. We propose to extend it to all cases where recombination is not restrained by in-built properties of the organisms, but rather, by lack of physical opportunity for mating.

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5. A DEBATE IN THE DEBATE: UNISEX/SELFING/ INBREEDING VERSUS ‘STRICT’ CLONALITY Our definition of PCE clearly states that it is defined by strongly restrained recombination only. This concept definitely includes selfing/ strong inbreeding and different forms of parthenogenesis as well as mitotic propagation (Tibayrenc and Ayala, 1991, 2002, 2012). As we have recalled it, this definition is shared by many authors working on micropathogen population genetics and general evolution (Table 2). However, some authors prefer to distinguish between ‘strict’ clonality (i.e., mitotic propagation) and other cases where recombination is restricted too. This is the case for ‘unisexual reproduction’ (Feretzaki and Heitman, 2013), selfing and inbreeding (Bobay et al., 2015; Rougeron et al., 2009, 2010). This is a matter of definition. It is informative to refine our views on these evolutionary phenomena. However, we have insisted on the fact that in our view, the most relevant common trait is restrained recombination, since it appears to be the major adaptive strategy that pathogens use to escape the recombinational load (Agrawal, 2006; Becks and Agrawal, 2012), to spread successfully welladapted MLGs, and therefore to adapt to a parasitic lifestyle. Mitotic propagation and all kinds of unisex/selfing/inbreeding are the various means used by pathogens towards reaching this goal. Selfing/inbreeding is certainly very frequent in bacteria (Maiden, 2008; Caugant and Maiden, 2009) and viruses (Jackowiak et al., 2014; Perales et al., 2015). However, in general, the authors do not distinguish it from clonality. Nevertheless, Bobay et al. (2015) consider that selfing in bacteria should be distinguished from mitotic clonality even if it do not make a difference on the apparent structure of the population. With respect to applied research, restrained recombination is the most important parameter to consider for strain typing (molecular epidemiology) and tracing of genes of interest. When haploid pathogens are considered, it is impossible to distinguish selfing/inbreeding from mitotic clonality by population genetic tests. The means used for eukaryotic microorganisms rely on tests based on the hypothesis of diploidy (de Mee^ us et al., 2007a). It is asserted that mitotic clonality should generate an excess of heterozygotes, till fixed heterozygosity, whereas selfing/inbreeding should produce the opposite. This very statement is disputable even if the hypothesis of diploidy is retained. In the strictly clonal cladocera Daphnia pulex, long-term experiments have shown that the tendency is actually loss of heterozygosity through mitotic

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recombination, which is 1000 times more frequent than increasing heterozygosity by accumulation of divergent mutations (Omilian et al., 2006). In Leishmania mexicana and L. braziliensis (Rogers et al., 2011) and T. cruzi (Yeo et al., 2011), microsatellites show a deficit of heterozygotes, whereas SNP polymorphism shows the contrary, although the markers should show convergent results if selfing was verified. In G. intestinalis, heterozygote excess has been considered as evidence for a recent sexual event/hybridization, whereas heterozygote deficit has been rather regarded as an indication of ancient clonal evolution by purifying selection/gene conversion (Andersson, 2012). Caution is needed for the interpretation of heterozygosity statistics of parasite microsatellite data, since strong evidence for LD is found with both positive and negative values for Fis (Ramírez et al., 2012). Genome-wide mitotic gene conversion rather than selfing has been considered a parsimonious explanation for heterozygote deficit in T. cruzi (Llewellyn et al., 2009a). In T. brucei gambiense I, which is considered strictly clonal (Weir et al., 2016), long runs of homozygosity have been accounted for by gene conversion and mitotic recombination rather than by selfing. Even more troublesome, the hypothesis of diploidy has been challenged by many studies, suggesting that parasites and fungi may undergo widespread aneuploidy. The results are especially convincing for Leishmania (Boité et al., 2012; Downing et al., 2011; Inbar et al., 2013; Lachaud et al., 2014; Mannaert et al., 2012; Rogers et al., 2011, 2014; Sterkers et al., 2011, 2012, 2014), but are also available for T. cruzi (Buscaglia et al., 2015; Minning et al., 2011; Reis-Cunha et al., 2015; Souza et al., 2011). Aneuploidy is also frequent in C. albicans and C. neoformans (Ene and Bennet, 2014; McManus and Coleman, 2014; Ni et al., 2013). Microsatellite data could seem to clash with these results, since they do not suggest aneuploidy (Rougeron et al., 2015). However, ‘the conclusion that microsatellite data support diploidy rather than aneuploidy is not justified. Indeed, PCR amplification of a given locus reveals either one or two bands that correspond to a phenotype and not a genotype. This, for example when applied to microsatellites, does not allow determining the number of allele (hence chromosome copies) present in the genome.’ ‘Over-replication of one homologue generates two identical alleles and those will be undistinguishable by PCR amplification.’ (Lachaud et al., 2014). If widespread aneuploidy occurs, this renders population genetics tests based on the hypothesis of diploidy invalid (Reis-Cunha et al., 2015; Tibayrenc and Ayala, 2012, 2013). Moreover, aneuploidy leads to rapid elimination of heterozygosity

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by frequent passage through haploidy (Sterkers et al., 2012). It could therefore be an explanation for the apparent heterozygote deficit frequently observed in Leishmania and T. cruzi. It has been proposed that aneuploidy in Leishmania could be transitory (Rougeron et al., 2015). This is not supported by genomic analyses that deals with natural isolates and not experimental populations (Downing et al., 2011; Imamura et al., 2016). Moreover, even if aneuploidy were transitory, heterozygosity purging at each haploid cycle (Sterkers et al., 2012) should remain. Lastly, widespread aneuploidy should render impossible any Mendelian mechanism, including meiosis (Lachaud et al., 2014) and hence, endogamy/self-fertilization through meiosis. On the contrary, PCE is perfectly compatible with widespread aneuploidy, and even more, aneuploidy suggests that asexual reproduction (Reis-Cunha et al., 2015) and PCE occur. We do not argue that population genetic tests relying on the hypothesis of diploidy should be definitely discarded. However, we recommend to use them very cautiously. We have long-privileged LD analysis, which can be performed whatever be the ploidy of the species under study.

6. HOW CAN CLONES SURVIVE WITHOUT RECOMBINATION? A classical view is that clonal organisms are an evolutionary dead end, due to the so-called ‘Muller’s ratchet’ (inability to purge deleterious mutations through recombination). For this reason, ancient clonal organisms such as bdelloid rotifers have been called ‘ancient asexual scandals’ (Birky, 2010). Traditionally, natural selection is considered to be favourable, for it leads to the rapid generation of new MLGs able to face evolutionary challenges (Weismann, 1889). When micropathogens are concerned, this view is widely accepted (Campbell and Carter, 2006; Monis et al., 2009; Prasad Narra and Ochman, 2006 as examples). However, this conventional view has been considered ‘glibly’ (Charlesworth, 2006). Recombination in viruses has been interpreted as a by-product of mechanical constraints in genome structure (Holmes, 2013), not as an opportunity to generate new MLGs. Other authors consider that the first role of recombination is DNA repair (de Mee^ us and Prugnolle, 2011). According to Gorelick and Heng (2010), recombination’s most important functions are (1) DNA repair, (2) epigenetic reset at each meiosis, and (3) maintenance of species integrity and ploidy. Moreover, within the concept of recombinational load

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(Agrarwal, 2006), the generation of new MLGs is not considered to be always favourable, and could be rather detrimental, especially for species that undergo parasitism. It would be therefore simplistic to consider restrained recombination as a selective drawback. This is all the more true because, as we have already noted, organisms that are 100% clonal are probably quite exceptional. T. brucei gambiense I would be one exception (Weir et al., 2016). There is most times some recombination/hybridization going on (Tibayrenc et al., 1990; Tibayrenc and Ayala, 2012). Occasional bouts of recombination make it possible to benefit from virtually all the advantages of regular recombination (Birky, 2010; Schurko and Logsdon, 2008). In the case of sexual parasitism in vertebrates (gynogenesis, hybridogenesis), although populations are mainly clonal (Avise, 2015), occasional introgression (‘genetic leakage’) occurs (Lehtonen et al., 2013). Moreover, organisms that restrain recombination are able to repair their DNA through selfing. Another possible way to escape Muller’s ratchet is by the population size of micropathogens, which is always considerable (Balloux, 2010; Perales et al., 2015). Lastly, even in the framework of PCE, clonal organisms are able to generate some genetic variability. At the same time, they avoid the recombinational load, Muller’s ratchet, the costs for sex and search for partners (Ni et al., 2013). Widespread aneuploidy is one of the major means for generating variability without recombination between different genotypes. It permits rapid adaptation through gene dose effect, and modulates gene expression (Reis-Cunha et al., 2015). Aneuploidy is considered favourable for clonal populations (cancer, microparasites) (Mannaert et al., 2012). It allows the organisms concerned to explore a large phenotypic landscape, and is associated to drug resistance in C. albicans and C. neoformans. In a typical yeast population, aneuploidy makes up a large proportion of the genetic variants (Chen et al., 2012). ‘Same sex mating’ (considered by us to be included in PCE) promotes adaptation through aneuploidy in C. albicans and C. neoformans (Ene and Bennett, 2014). Gene conversion and mitotic recombination are other means to generate additional genetic variability within clones (Buscaglia et al., 2015; Calo et al., 2013; Flot et al., 2013; Omilian et al., 2006). Ackermann (2015) has proposed that phenotypic heterogeneity in genetically identical microorganisms was maintained through (1) stochastic gene expression, (2) periodic oscillations in cellular functions, (3) differential

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cellular ageing, (4) cellular interactions by metabolites or physical contacts, and (5) epigenetics. For the reasons exposed above, PCE should definitely not be considered an evolutionary dead end. It appears to be a sustainable, long-term evolutionary strategy that parasitic protozoa, fungi, bacteria and viruses have used to adapt to the specific features of parasitic life. On the contrary, in vertebrates, clonality seems to be a short-term evolutionary dead end (Avise, 2015).

7. MEIOSIS GENES AND EXPERIMENTAL EVOLUTION: WHAT DO THEY TELL US ABOUT PREDOMINANT CLONAL EVOLUTION? Genes homologous to meiosis genes [homologues of meiosis-specific genes (HMGs); Poxleitner et al., 2008) are frequently observed in micropathogens. The ‘meiosis toolkit’ is a set of eight genes present in animals, plants, fungi, and protists. If they are all present in a given organism, it would be an indication that meiosis occurs (Schurko and Logsdon, 2008). However, the ‘meiosis’ genes may have other functions in addition to meiosis: ‘Evolution is constantly re-using old genes for new purposes’ (Birky, 2009). Meiosis genes could serve for DNA repair or mitotic recombination (Birky, 2010) or diplomixis in Giardia (Poxleitner et al., 2008). They could also code for unusual, primitive forms of meiosis (Birky, 2005). At best, the meiosis toolkit could be an indication that meiosis occurs, but it says nothing about its frequency. As a matter of fact, it is remarkable that meiosis genes have been identified in organisms that exhibit a typical PCE pattern. They are present in Giardia (Birky, 2005; Cacci o and Sprong, 2010; Heitman, 2006; Lasek-Nesselquist et al., 2009; Ortega-Pierres et al., 2009; Monis et al., 2009; Schurko et al., 2008), Leishmania major (Birky, 2005; Heitman, 2006), L. donovani (Birky, 2005), T. cruzi (Heitman, 2006) and T. vivax (Duffy et al., 2009), among others. In Giardia, there is no apparent correlation between the presence or absence of meiotic genes and the observation of meiotic life cycles (Andersson, 2012). HMGs are present in Aspergillus fumigatus, C. albicans and the C. neoformans complex. However, these fungi ‘retain their sexuality’ to not disrupt fit MLGs (Ene and Bennett, 2014; Heitman, 2006). All meiosis genes are present in Penicillium marneffei, although this fungus is highly clonal (Henk et al., 2012). It is remarkable that meiosis genes are expressed in T. brucei gambiense I, which is considered strictly clonal (Weir et al., 2016). Besides, in Drosophila melanogaster, seven major meiosis genes are absent (Weedall and Hall, 2014). In D. pulex,

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meiosis genes are activated even in parthenogenetic lines (Schurko et al., 2009). The authors propose that this cladoceran crustacean has a double machinery parthenogenesis/sexuality. We have also proposed that micropathogens could have a ‘sexuality/machinery kit’ that would allow them to restrain their recombination to face certain evolutionary challenges (Tibayrenc and Ayala, 2012). In conclusion, the widespread presence of HMGs is perfectly compatible with PCE and should not be used as evidence against it. The same is true for experimental genetic exchange in pathogens. Successful laboratory mating has been obtained for L. major (Akopyants et al., 2009), T. brucei (Jenni et al., 1986) and T. cruzi (Gaunt et al., 2003). These experiments show only that the potentiality for genetic exchange exists in these parasites. They say nothing about the frequency of these genetic exchanges and their impact on population structure. They should therefore not be taken as evidence against PCE.

8. IS PREDOMINANT CLONAL EVOLUTION AN ANCESTRAL OR CONVERGENT CHARACTER? PCE appears to be a common character of many pathogens, a very specific evolutionary strategy used by these organisms, probably to adapt to parasitism. Is it ancestral, or the result of convergence? We have proposed that PCE is commanded by a ‘sexuality/machinery kit’, acting like a biallelic system. Could such a system have an ancestral origin? Several indices suggest that the mechanisms involved in recombination (and hence, possibly, also in restrained recombination) are very ancient, with maybe a direct filiation from prokaryotes to eukaryotes. Although clonality is widespread in viruses, most of them retain an active recombination machinery (Perales et al., 2015). Many of the enzymes involved in prokaryotic recombination are homologous to those of eukaryotes (Charlesworth, 2006). In Giardia, the meiosis machinery is similar to that of higher eukaryotes, and homologous to bacterial genes involved in DNA repair (Michod et al., 2008). The ‘pre-LUCA (last universal common ancestor)’ hypothesis (Holmes, 2013) proposes that many characters of cellular organisms may have been inherited from ancient viruses. The viral eukaryogenesis hypothesis (Holmes, 2013) proposes that ancient viruses may be at the origin of some traits of cellular organisms. The eukaryotic nucleus could originate from the viral envelope. There is still a ‘continuous rain of viral genes

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into cellular genomes’, which could be at the origin of many properties of cellular organisms (Forterre, 2006). To test the hypothesis of a sexuality/clonality machinery and of its possible ancient origin, it would be necessary to identify and characterize it in organisms that undergo PCE with occasional bouts of recombination/ hybridization. T. cruzi would be an adequate model for it. If this sexual/clonal kit is characterized, it will be possible to look for homologous genes in bacteria and other micropathogens.

9. CAN PREDOMINANT CLONAL EVOLUTION FEATURES BE EXPLAINED BY NATURAL SELECTION? IN-BUILT MECHANISMS FAVOURING CLONALITY Many authors defend the view that apparent clonality in micropathogens is mainly explained by natural selection, which would eliminate most possible genetic variants, thus leading to LD and clustering (near-clading). This has been asserted for N. meningitidis (Buckee et al., 2008; Caugant, 2008; Caugant and Maiden, 2008; Maiden, 2008). ‘Meningococci display signs of a highly recombinogenic population with purifying events and consecutive clonal expansion of fit variants. Their population structure has therefore been categorized as “epidemic”’ (Maynard Smith et al., 1993). In viruses, selection and non-selective epidemiological processes have been inferred to account for RNA viruses’ population structure (Grenfell et al., 2004). Purging selection is considered a main factor driving HAV population structure (Vaughan et al., 2014). Similarly, natural selection has been inferred as a major parameter for Toxoplasma population structure (Su et al., 2003). The PCE model proposed by us (Tibayrenc and Ayala, 2002, 2012) considers that natural selection (downstream elimination of recombinants) alone cannot account for pathogen population structure. We have rather defended the concept of an upstream inhibition of recombination by in-built properties of the organisms under study. Concerning N. meningitidis, it is hard to imagine that natural selection would be the main explanation for stable ubiquitous clonal genotypes and near-clades, which face many diverse ecological environments. Moreover, the existence of deep phylogenies revealed by WGS (Budroni et al., 2011) is hardly compatible with the natural selection model, since it shows that N. meningitidis near-clades are stable at an evolutionary scale. A main challenge to the natural selection hypothesis is that it would amount to eliminating in each generation most possible MLGs to maintain

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LD and near-clading, which amounts to a considerable genetic load (Tibayrenc, 1995). This view is shared by scientists working on Toxoplasma (Lehmann et al., 2004) and C. dubliniensis (Badoc et al., 2002). The natural selection hypothesis in N. meningitidis has been challenged by Didelot et al. (2009b). The long-term maintenance of N. meningitidis ‘lineages’ is considered to be compatible with neutral evolution (Budroni et al., 2011). Natural selection, both positive and purging, certainly has a strong impact on micropathogens’ population structure. However, we propose that it is not the main factor responsible for PCE patterns, which are better explained by in-built properties of microbes. Several in-built traits restraining recombination have been identified in bacteria. In many species, the probability of genetic exchange decreases exponentially with the genetic distance between donor and recipient due to the DNA mismatch repair system (Didelot et al., 2011). This is one of the reasons inferred to explain that, in S. enterica, recombination within clades (near-clades) is more frequent than recombination between clades (Didelot et al., 2011). In N. meningitidis, the restriction modification systems appear to promote genetic exchange among closely related genotypes and to lower genetic exchange among strains that belong to different CCs or related species (Caugant and Maiden, 2009), leading to ‘unobservable recombination’. The phylogenetic clades (near-clades) revealed by WGS in this species are associated with specific restriction modification systems that modulate homologous recombination (Budroni et al., 2011). In S. pneumoniae, some worldwide CCs are not transformable, and therefore do not have access to the DNA from other genotypes (Henriques-Normark et al., 2008). In viruses, Holmes (2009, 2013) and Simon-Loriere and Holmes (2011) consider that recombination is linked to genomic architecture, and that obstacles to recombination are mechanistic and not only due to purging selection. The in-built mechanisms that restrain recombination hypothesized in the PCE model therefore do exist in bacteria and probably in viruses. In fungi, it has been proposed that, although the sexual machinery was present in fungi, they ‘retain sex’ and their populations are often clonal (Heitman, 2006). Taylor (2015) defends the view that intrinsic obstacles to recombination exist in fungi. The hypothesis of a ‘clonality/sexuality machinery’ in parasitic protozoa and in other pathogenic microorgansism (Tibayrenc and Ayala, 2012) still

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has to be explored. However, we consider unlikely that natural selection be the only, or even, the main mechanism explaining the PCE features.

10. IDENTICAL MULTILOCUS GENOTYPES ARE A RELATIVE NOTION: IMPLICATIONS FOR THE SEMICLONAL/EPIDEMIC CLONALITY MODEL We have long called attention to the fact that MLG monomorphism depends highly on the level of resolution and molecular clock of the marker concerned. Clonal genotypes should be better considered to be families of closely related clones. We have proposed the concept of ‘clonet’ (Tibayrenc et al., 1991b) to refer to those sets of stocks that appear to be identical with a given set of genetic markers in a basically clonal species. Using markers with a higher resolution is bound to reveal additional variability within each clonet. An illustrative example of clonet is the L. infantum MLEE MLG MON1, which proves to be genetically heterogeneous by the use of microsatellites (Ferreira et al., 2012). A more recent, striking case is the so-called L. donovani core group in India and Nepal, which is monomorphic with microsatellites, while it exhibits a strong genetic structuration into six congruent monophyletic groups with SNPs (Imamura et al., 2016). The clonet feature does not invalidate population genetics tests based on MLEE and microsatellite polymorphisms. As a matter of fact, considering as a clonal genotype what is actually a family of closely related clones leads only to a lack of resolution of the tests, but does not bias them. However, it specifically questions the approach proposed by Maynard Smith et al. (1993) for exploring clonality in micropathogens, namely, the so-called ‘epidemic clonality’ model, which is analogous to the ‘semiclonal model’ (Maiden, 2006). It proposes that LD in certain bacterial populations is explained by the occurrence of occasional bursts of ‘epidemic’, ephemeral clonal genotypes in an otherwise recombining species (Fig. 3). It is an exact mirror image of the ‘fireworks’ model proposed by Avise (2015) for clonal vertebrates (occasional ‘fireworks’ of ephemeral outcrossing events in a clonal species). In the approach proposed by Maynard Smith et al. (1993), repeated MLGs are considered to result from such recent clonal expansions. When counting each MLG only once, one should discard this bias and see whether the species is clonal (LD persists) or ‘epidemic’ (LD disappears). There is a bias in the approach, in that discarding many individuals many times reduces the sample size, which leads to an increased risk of statistical type II error. More concerning, the clonet concept shows that ‘identical’ MLGs can be genetically

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very diverse; in other words, their common ancestor could be ancient. It can be therefore arbitrary to consider them as recent clones. We have seen earlier that the seimiclonal/epidemic clonality model in N. meningitidis is questioned by the PCE approach. The bias suggested by the clonet concept could be an explanation of it.

11. GENOMICS AND THE PREDOMINANT CLONAL EVOLUTION MODEL Ramírez and Llewellyn (2015) urged us to wait for the forthcoming wealth of high-resolution data before considering whether it is appropriate to refine or reiterate our PCE hypothesis. We will certainly try and refine it, as we have done, especially in the recent years. However, in the light of the already available abundant genomic data, we do reiterate the PCE model. As a matter of fact, nothing in WGS and massive use of SNPs challenges the model. On the contrary, a large amount of data reinforce it. They have already been exposed, and only some of them will be briefly recalled in the following discussion. WGS has made it possible to reveal hidden deep phylogenies and to confirm near-clade patterns in N. meningitides (Budroni et al., 2011), S. pneumoniae (Chewapreecha et al., 2014; Croucher et al., 2011; Muzzi and Donati, 2011) and S. pyogenes (Beres et al., 2010). It has shown a fine RD pattern in the ubiquitous near-clade H58 of S. typhi (Wong et al., 2011). In E. coli, WGS has confirmed the long-lasting near-clades evidenced years ago by MLEE (Chaudhuri and Henderson, 2012; Tenaillon et al., 2010) and has shown that the high rate of recombination in this species did not destroy its clonal population structure (Bobay et al., 2015). WGS in M. tuberculosis has evidenced 6 ‘phylogenetic groupings’ (near-clades) that were not visible with classical markers (Achtman, 2008; Comas and Gagneux, 2009; Smith, 2012). When parasites are concerned, genomic data have shown that T. brucei gambiense I was strictly clonal (Weir et al., 2016). WGS makes it possible to distinguish strict asexuality from PCE with occasional bouts of genetic exchange, which is difficult or impossible with classical markers and population genetic analysis (Weir et al., 2016). This makes it possible to refine the PCE model, not to refute it. Genomic data have evidenced a monophyletic near-clade in Turkish populations of L. infantum and have shown that the population structure within this near-clade was ‘primarily clonal’ (Rogers et al., 2014), which shows that ‘multiple’ later genetic exchange events (Ramírez and Llewellyn, 2015) do not prevent clonality and an RD pattern.

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Genomics has revealed a fine RD pattern in the L. donovani ‘core group’, which is monomorphic with microsatellites (Imamura et al., 2016). In P. falciparum, WGS has shown that Papua New Guinea populations of the parasite were highly inbreeding in spite of abundant transmission (Manske et al., 2012) and that West Cambodian populations were ‘essentially clonal’ (Miotto et al., 2013). Lastly, WGS has made it possible to support the hypothesis of widespread aneuploidy in Leishmania (Downing et al., 2011) and T. cruzi (Reis-Cunha et al., 2015), which is highly relevant to the PCE model and supports it.

12. RELEVANCE OF THE PREDOMINANT CLONAL EVOLUTION MODEL FOR TAXONOMY AND APPLIED RESEARCH The near-clade concept makes it possible to revisit the species concept in micropathogens. Either already described species and subspecies can be equated to near-clades from an evolutionary point of view or presently identified near-clades could constitute the starting point for describing new species or subspecies. The flexible phylogenetic approach based on the congruence criterion relaxes the demands of a strict cladistic analysis, which is indispensable, since some recombination occurs in almost all micropathogen populations. The near-clade model therefore gives a convenient and flexible framework for describing new species and subspecies, based on the phylogenetic species concept (Cracraft, 1983). The use of the mixiologic species concept (Mayr, 1940) has been tentatively proposed for some micropathogen species on the basis of occasional genetic exchange (Cacci o and Sprong, 2010; Dykhuizen and Green, 1991; Ngamskulrungroj et al., 2009; Voelz et al., 2013). However, genetic exchange in most micropathogens does not play the same evolutionary role as in metazoa, and cannot be retained as a valid criterion for species definition in their case. In eukaryotic microbes, the sibling species C. neoformans and C. gattii can be equated to near-clades (Tibayrenc and Ayala, 2014b), since hybridization is possible between them (Bovers et al., 2006, 2008). It is also most probably the case for several, if not most, Leishmania species, since hybridization is widespread in this group of parasites (Odiwuor et al., 2011; Ravel et al., 2006). From an evolutionary point of view, many presently described Leishmania species are equivalent to the T. cruzi near-clades. The case of L. killicki and Leishmania tropica gives the opportunity to discuss the use of the near-clade concept to describe new subspecies. L. killicki appears

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to be a genetic subdivision (near-clade) of L. tropica (Chaara et al., 2015a,b), which has led the authors to consider that this species was not valid. They have proposed to rename it ‘L. killicki syn. Tropica’. As a matter of fact, if one taxon is included in another one, one cannot give to both an equal taxonomic level. However, the fact that L. killicki corresponds to a clearly identified near-clade could give a ground to describe it as a subspecies, L. tropica killicki, since its epidemiological specificities could justify it. We have proposed that new entities described in the genus Plasmodium may correspond to the evolutionary definition of near-clades and have warned against the tendency to equate them to new species without further evidence. Even their status as near-clades has to be ascertained, since their description most times is based on limited population and genetic samples (Tibayrenc and Ayala, 2014a). In the genus Pneumocystis, several species have replaced the former species Pneumocystis carinii. Description of these species is mainly based on the criteria of host specificity and phylogenetic divergence. However, this specificity is not strict. The Pneumocystis ‘species’ might well be equated to nearclades, although data are far less abundant than for C. albicans and C. gattii/ C. neoformans (Tibayrenc and Ayala, 2014b). In G. intestinalis, the ‘assemblages’ that subdivide the species, which are perfectly equivalent to near-clades (Tibayrenc and Ayala, 2014b), have not been given a species status until now, although this has been proposed (Xu et al., 2012). Once never-clades have been identified and delimited (which is the case for the ‘assemblages’ in Giardia), their description as new species is a matter of convenience, if the concerned specialists (such as the Giardia scientific community) find it desirable and informative (Tibayrenc and Ayala, 2014b). The B. cereus group is traditionally divided into six species, namely, B. anthracis, B. cereus, Bacillus mycoides, Bacillus thuringiensis and Bacillus weihenstephanensis. MLST analysis of 667 strains shows that the B. cereus group is actually composed of three major clades, which show no strict concordance with the six species, although all B. anthracis strains are in clade 1, most of the B. thuringiensis are in clade 2, and clade 3 is composed of all the B. mycoides and B. weihenstephanensis strains (Didelot et al., 2009a). Since there is recombination among the three clades (Didelot and Falush, 2007), they perfectly fit the definition of near-clade. These three near-clades therefore do not show a strict concordance with the six species described in the B. cereus group. The three species N. meningitidis, N. gonorrhoeae and N. lactamica, which are discriminated by MLST, but among which some recombination goes on

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(Achtman and Wagner, 2008; Bennett et al., 2007; Hanage et al., 2005), can be equated to near-clades. The case of the mitis group of Streptococcus (the species S. pneumoniae, pseudopneumoniae, Streptococcus mitis and Streptococcus oralis) is quite comparable. The four species exhibit phenotypic specificities, and correspond to clear MLST clusters that can be equated to near-clades, since recombination is not totally absent among them (Fraser et al., 2007). In addition to revisiting the problem of species and subspecies in micropathogens, the PCE model makes it possible to identify relevant units of analysis: clonets and near-clades, for molecular epidemiology (strain typing, epidemiological follow-up, tracing of genes of interest), clinical studies as well as vaccine and drug design. As an example, it has been recommended to take the T. cruzi near-clades (‘DTUs’) as a basis for all studies dealing with drug research concerning this parasite (Zingales et al., 2014). These units of analysis, as ascertained by the PCE approach, display the characteristics that make them relevant for applied studies (discreteness, stability in space and time). The clonets (clonal MLGs) and near-clades can be characterized by one or a few genes, because of LD (indirect typing). Lastly, clonets and near-clades can be conveniently used for experimental evolution studies. We have used T. cruzi near-clades to explore their specificity for gene expression (Machin et al., 2014; Telleria et al., 2010).

13. CONCLUSION To our knowledge, this is the first time that informative comparisons have been made concerning evolutionary traits of all kinds of micropathogens (parasitic protozoa, fungi, bacteria and viruses). Until now, with few exceptions (Xu, 2004, 2006b), this field of research has remained strongly compartmentalized. Our broad survey has made it possible to show that micropathogens’ population structures display striking similarities that appear to be major evolutionary strategies of these organisms, probably towards adapting to the parasitic way of life. As noted by Shapiro (2016), pathogens are more likely than free-living bacteria to undergo clonal expansions. Moreover, clonality appears to be a stable trait in given populations. ‘Clonal populations tend to stay clonal’ (Shapiro, 2016). It remains to be determined whether these features are ancestral or rather, constitute a remarkable case of convergent evolution. The evidence for PCE is not the same among all micropathogens we have surveyed. Several species show a very typical PCE pattern, based on

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strong and diversified evidence. This is the case for C. albicans, the C. neoformans complex, E. coli, G. intestinalis, L. infantum, M. tuberculosis, S. aureus, T. gondii, and T. cruzi. This is to the point that, through blind lectures, population genetic data from, let us say, G. intestinalis, T. cruzi, C. neoformans and E. coli could be confounded. The PCE approach has made it possible to confirm that P. falciparum and P. vivax are capable of clonal propagation. However, these parasites undoubtedly undergo frequent recombination, which implies that PCE features (LD and near-clades) are unstable in them. Yet, it is the case that clonality in them introduces a major populational stratification feature that should be taken into account in all studies dealing with these parasites’ genetic variability. It remains to be determined whether clonality in Plasmodium is due to ‘starving sex’ (lack of opportunity for mating) or to ‘in-built clonality’, or a combination of both. It is remarkable that species previously considered highly recombining (N. meningitidis, S. pneumoniae) display typical PCE features. The advent of powerful technologies (WGS) has made it possible to evidence in these species the existence of deep phylogenies, which confirms the stability of the near-clades at an evolutionary scale. Viruses constitute a special case in our study. Although several species show typical PCE features (near-clading and RD patterns), the evolution rate of these pathogens is considerably faster than that of cellular organisms, which implies that their genome undergoes a very rapid turnover. Still the fact remains that viral near-clades can persist for many years, as evidenced by retrospective studies or analyses of ancient collections of strains. For many other species surveyed here (Table 1), evidence for PCE remains partial and has to be confirmed by more detailed studies. Lastly, PCE criteria make it possible to settle the PCE ‘boundary’ at the clonality/recombination threshold, beyond which clonality definitely counters the effects of recombination, and the near-clades are bound to diverge more and more. It is probable that beyond this threshold, micropathogens do not exhibit a homogeneous strength of PCE. For example, N. meningitidis and S. pneumoniae probably undergo more recombination than E. coli and S. aureus. Our point is that they seem to have crossed the clonality threshold beyond which the near-clades will never get erased by recombination. Apart from allowing the spread of successful MLGs and avoiding the recombinational load, a major feature of the PCE model is the generation of near-clades. Through this mechanism, which can be seen as an incipient speciation, or incomplete speciation, micropathogens could explore new

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ecological niches through phenotypic specialization of the near-clades, including within a given host. It is also possible that mixed infections with two or more near-clades play a role in the pathogenicity of some microbial species. All these hypotheses are highly falsifiable. PCE provides highly falsifiable assumptions that can be easily tested since our approach relies on a close analysis of a large amount of rough data with as few working hypotheses and models as possible. The PCE model establishes clearly defined predictive properties (relevant units of analysis that are stable in space and time) of high interest for evolutionary and applied studies dealing with pathogens.

ACKNOWLEDGEMENTS We thank Jenny Telleria (IRD, Montpellier, France) for designing Fig. 4.

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