Ecological, epidemiological, and molecular drivers of cross- species pathogen transmission among humans and non-human primates: from malaria to rhinovirus

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Citation Scully, Erik John. 2018. Ecological, epidemiological, and molecular drivers of cross-species pathogen transmission among humans and non-human primates: from malaria to rhinovirus. Doctoral dissertation, Harvard University, Graduate School of Arts & Sciences.

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Ecological, epidemiological, and molecular drivers of cross-species pathogen transmission among humans and non-human primates: from malaria to rhinovirus

A dissertation presented

by

Erik John Scully

to

The Department of Human Evolutionary Biology

in partial fulfillment of the requirements

for the degree of

Doctor of Philosophy

in the subject of

Human Evolutionary Biology

Harvard University

Cambridge, Massachusetts

May 2018

© 2018 – Erik John Scully All rights reserved.

Dissertation Advisor: Richard W. Wrangham Erik John Scully

Ecological, epidemiological, and molecular drivers of cross-species pathogen transmission among humans and non-human primates: from malaria to rhinovirus

Abstract

Malaria constitutes a major source of human mortality and morbidity worldwide. Although the bulk of this public health burden is caused by four human-adapted parasite species, these represent less than 1% of the malaria parasite diversity found in the natural world. The expansion of human populations into biodiversity hotspots has elevated exposure to novel malaria parasites, and evidence of primate-to-human transmission has begun to accumulate. There now exists a tangible public health imperative to critically evaluate the ecological and molecular mechanisms governing the host-specificity of malaria parasites in order to identify which species pose a material threat to human populations.

The purpose of this dissertation is to elucidate the ecological, epidemiological, and molecular variables that govern the transmission of infectious diseases among humans and non-human primates. In Chapter 1,

I provide a conceptual framework for zoonotic disease emergence and discuss the ecological and molecular barriers to cross-species transmission of malaria parasites. In Chapter 2, we quantify the ecological drivers of malaria transmission among wild chimpanzee hosts in equatorial Africa and map spatiotemporal variation in the risk that these parasites pose to local human populations. In Chapters 3 and 4, we review the molecular mechanisms governing the host-specificity of non-human primate malaria parasites and develop a method for the generation of immortalized erythroid progenitor cell lines from small volumes of peripheral blood, offering a genetically tractable model system for studies of malaria host-specificity. In

Chapter 5, we identify human rhinovirus C as the cause of a lethal respiratory outbreak in a community of wild chimpanzees in western Uganda and demonstrate a species-wide genetic susceptibility to this virus, underscoring the profound conservation implications of human-to-ape virus transmission. In Chapter 6, I conclude the dissertation by discussing non-human primate malaria parasite diversity in the context of global health and review historical and contemporary evidence of malaria cross-species transmission. It is

iii my hope that the studies presented in this dissertation will catalyze cooperation among stakeholders in the public health and scientific research communities by highlighting malaria zoonosis as a topic that warrants further consideration in discussions of malaria eradication.

iv Acknowledgments

This dissertation would not have been possible without the assistance and generosity of numerous people to whom I am very grateful.

First, I want to thank Richard Wrangham, my doctoral dissertation advisor, whose passion for primatology and the natural world kindled my interest in chimpanzee ecology and behavior before I had even enrolled in the program. I will be eternally grateful to Richard for encouraging me to pursue a doctoral degree at Harvard and for facilitating the numerous collaborations that came to characterize my doctoral research. Not only is Richard a brilliant and rigorous scientist, but he is also an extremely generous and compassionate person.

Next, I want to express my sincere gratitude to Manoj Duraisingh, who catalyzed my interest in the molecular biology of malaria parasites. Manoj’s boundless enthusiasm for malaria research is unmatched by anyone I have encountered, and his capacity to see the larger picture is an enviable quality. Manoj welcomed me into his lab just as my interest in malaria zoonoses began to consume my dissertation research. My foray into functional genetics would have likely been unsuccessful without the tools, resources, expertise, and summer barbecues that Manoj offered.

I am also keen to thank the Kibale Chimpanzee Project and the Makerere University Biological Field

Station, which offered a home in western Uganda during my dissertation fieldwork. Emily Otali’s wit and expertise were critical in navigating my research permits and permissions within Uganda, and Zarin

Machanda, Melissa Emery Thompson, and Martin Muller helped me to coordinate issues ranging from the collection of fecal samples to the allocation of field assistant bonuses. Zarin also provided extensive feedback on multiple projects related to chimpanzee behavioral ecology. Finally, I want to thank each of my Ugandan field assistants for offering both companionship and expertise during fieldwork in Kibale.

Without the longitudinal dataset cultivated by these field assistants, Chapter 2 of this dissertation would not have been possible.

I would be remiss if I did not thank all of the professors who facilitated my dissertation research and shaped my academic interests. Before I enrolled at Harvard, my Master’s thesis advisor, Robert Curry,

v played an instrumental role in cultivating my decision to pursue a career in the biological sciences. I am also very grateful to Beatrice Hahn for inviting me to screen my chimpanzee samples in her University of

Pennsylvania laboratory. I want to thank Harvard’s Department of Human Evolutionary Biology, especially

Terence Capellini and Maryellen Ruvolo for providing feedback on my developing research projects at multiple points during my dissertation. Charlie Nunn and Bill Hanage catalyzed my interest in the evolutionary dynamics of infectious disease. Dan Neafsey facilitated my Broadnext10 grant and was always willing to discuss elements of my research. Caroline Buckee encouraged me to adopt a mathematical approach to infectious disease transmission and Flaminia Catteruccia highlighted the critical role that the mosquito plays in malaria transmission. Tony Goldberg provided valuable insight into topics related to zoonotic disease and his feedback greatly improved this dissertation. I would also like to thank Charlotte

Rasmussen and the World Health Organization for taking an interest in my dissertation research and for cultivating its translational applications.

Numerous graduate students and post-doctoral researchers played a critical role in the development of my dissertation by offering both academic feedback and personal friendship over the course of my tenure in the program. In the Wrangham Lab, Andy Cunningham, Luke Glowacki, Manvir Singh, Collin McCabe, and Stephanie Meredith provided feedback on projects related to the ecology of non-human primate malaria parasites. In the Duraisingh Lab, Estela Shabani, Usheer Kanjee, Christof Gruring, Mudit Chaand, Martha

Clark, Gabe Rangel, Jon Goldberg, Selasi Dankwa, Natasha Archer, Christina Moreira, Caroline Keroack,

Brendan Elsworth, Deepali Ravel, Aditya Paul, Rebecca Lee, Katy Shaw Saliba, Kristen Skillman, Caeul

Lim, Markus Ganter, and Venee Tubman helped to mold ideas concerning the epidemiology and molecular biology of malaria zoonoses. Drew Enigk provided much needed companionship during multiple summers in western Uganda.

This dissertation would not have been possible without grants and fellowships from the National

Science Foundation, the Broad Institute, the National Geographic Society, the National Institutes of Health, the International Primatological Society, Harvard University, and the Cora DuBois Charitable Trust.

Finally, and most importantly, I wish to express my most sincere gratitude to my family for offering

vi unrelenting support and encouragement throughout my academic pursuits. I first want to thank my mother,

Margaret, and my father, Erik V., for imploring me to follow my dreams and for indulging late night conversations about esoteric biological topics across the campfire in western Pennsylvania. I am grateful to my brother, Ryan, and my sister, Meghan, for helping me to see the bigger picture and for providing comic relief.

Above all, I want to thank my wife, Claire, who has made everything possible. From studying vegetarian spiders on the Yucatan Peninsula in Mexico to tracking zoonotic diseases in western Uganda,

Claire has always supported my unconventional interests and has encouraged me to pursue truth. Claire’s intellectual fortitude is inspirational and has, in many ways, formed the bedrock on which this dissertation lies. It has been a long process, and I want to thank you for believing in me. I cannot wait to see what the future holds.

vii Dedication

To my wife, Claire.

viii Table of Contents

Abstract...... iii

Acknowledgments ...... v

Dedication ...... viii

Table of Contents ...... ix

List of Tables and Figures ...... xii

Chapter 1 – Introduction ...... 1

1.1 Summary ...... 1

1.2 Zoonotic disease emergence and the barriers to cross-species transmission of non-human primate malaria parasites ...... 3

1.2.1 The zoonotic spectrum ...... 3

1.3 Barriers to cross-species transmission: from the bush to the bench ...... 5

1.3.1 Ecological barriers to cross-species transmission ...... 5

1.3.2 Molecular barriers to cross-species transmission ...... 7

1.4 Summary of Aims ...... 9

1.5 Author Contributions ...... 11

1.6 References...... 11

Chapter 2 – The ecology and epidemiology of malaria parasitism in wild chimpanzee reservoirs 17

2.1 Abstract ...... 17

2.2 Introduction ...... 18

2.3 Results ...... 22

2.4 Discussion ...... 36

2.5 Materials and Methods ...... 40

2.6 Author Contributions ...... 46

2.7 References ...... 47

ix Chapter 3 – Molecular interactions governing host-specificity of blood stage malaria parasites 54

3.1 Abstract ...... 54

3.2 Introduction ...... 54

3.3 The cellular biology of erythrocyte invasion by the malaria parasite ...... 59

3.4 Plasmodium falciparum and the Laverania: a highly host restricted clade of malaria parasites ...... 62

3.5 Monkey in the middle: the emerging public health threat posed by P. knowlesi zoonosis ...... 64

3.6 Plasmodium vivax: a long neglected pandemic ...... 66

3.7 Conclusions and future directions ...... 68

3.8 Author Contributions ...... 69

3.9 References ...... 69

Chapter 4 – Generation of an immortalized erythroid progenitor cell-line from peripheral blood: a model system for the functional analysis of Plasmodium spp. invasion ...... 76

4.1 Abstract ...... 76

4.2 Introduction ...... 77

4.3 Results ...... 80

4.4 Discussion ...... 92

4.5 Methods ...... 95

4.6 Author Contributions ...... 100

4.7 References ...... 101

Chapter 5 – Lethal Respiratory Disease Associated with Human Rhinovirus C in Wild Chimpanzees, Uganda, 2013 ...... 106

5.1 Abstract ...... 106

5.2 Introduction ...... 106

5.3 Methods ...... 108

5.4 Results ...... 111

5.5 Discussion ...... 116

x

5.6 Author Contributions ...... 121

5.7 References ...... 121

Chapter 6 – Concluding Remarks ...... 126

6.1 Summary ...... 126

6.2 The zoonotic potential of non-human primate malaria parasites ...... 127

6.3 Malaria parasites of apes ...... 128

6.3.1 Plasmodium falciparum and the Laverania: a highly host-restricted clade of malaria parasites ...... 129

6.3.2 Plasmodium vivax: a long neglected pandemic ...... 131

6.3.3 Other malaria parasites of African great apes ...... 133

6.4 Malaria parasites of Old World monkeys ...... 134

6.4.1 Monkey in the middle: the emerging public health threat posed by P. knowlesi ...... 135 zoonosis

6.4.2 Other parasites of old world monkeys ...... 137

6.5 Malaria parasites of New World Monkeys ...... 137

6.5.1 P. simium and P. brasilianum: simian parasites with underestimated zoonotic potential ...... 138

6.6 Areas for future research ...... 139

6.7 Conclusion ...... 142

6.8 Author Contributions ...... 143

6.9 References ...... 143

xi List of Figures and Tables

Chapter 2

Figure 2.1 Geographic location of wild chimpanzee sampling sites...... 21

Table 2.1 Chimpanzee fecal samples analyzed in this study...... 21

Figure 2.2 Richness of malaria parasites isolated from the Kanyawara chimpanzee community ..... 24

Figure 2.3 Phylogeny of Hepatocystis parasites isolated from chimpanzee and human hosts ...... 27

Table 2.2 Longitudinal analysis of malaria parasitism among the Kanyawara chimpanzees ...... 30

Figure 2.4 Longitudinal analysis of malaria parasitism in the Kanyawara cohort of wild chimpanzees ...... 31

Table 2.3 Ecological modeling of malaria parasitism among chimpanzee hosts ...... 34

Figure 2.5 Ecological niche modeling of malaria parasitism in wild chimpanzee reservoirs across equatorial Africa ...... 35

Chapter 3

Box 3.1 Cross-species transmission of primate malaria parasites ...... 56

Figure 3.1 Evolutionary relationships of primate malaria parasites ...... 58

Figure 3.2 Malaria parasite invasion into red blood cells ...... 61

Chapter 4

Figure 4.1 Generation of an immortalized erythroid progenitor cell line from peripheral blood ...... 81

Figure 4.2 Differentiation of the ejRBC cell line mirrors erythropoiesis of primary HSCs ...... 85

Figure 4.3 Terminally differentiated ejRBCs constitute a representative model system for studies of malaria invasion ...... 88

Figure 4.4 CRISPR/Cas9-mediated disruption of the DARC gene in ejRBCs restricts invasion of P. vivax and P. knowlesi ...... 91

Figure 4.5 Expression of human, chimpanzee, and gorilla DARC alleles in the EJΔDARC ...... 92 cell line

Chapter 5

Figure 5.1 Epidemic curve of respiratory illness in the Kanyawara chimpanzee community,

xii Uganda, 2013 ...... 113

Figure 5.2 Recombination between viral genotypes rhinovirus C45 and C11 leading to RV-C45- cpz1-2013, the strain identified in the Kanyawara chimpanzee community, Uganda, 2013 ...... 114

Figure 5.3 Phylogenetic tree of rhinovirus C variants ...... 115

Table 5.1 RV-C sequences used in phylogenetic analysis ...... 120

Appendices

Appendix 2.1 Summary of ecological and demographic parameters analyzed in the Kanyawara GLMM ...... 151

Appendix 2.2 Summary of ecological parameters analyzed in the Pan-African GLMM...... 153

Appendix 4.1 Terminally differentiated ejRBCs enucleate at a rate of ~5% ...... 155

Appendix 4.2 Variable loadings of Principal Components Analysis (PCA) ...... 156

Appendix 4.3 Expression of erythropoiesis markers during differentiation of EJ cells and primary HSCs ...... 157

Appendix 4.4 Evolutionary conservation of DARC protein deleted in ΔDARC EJ cell line ...... 159

Appendix 4.5 Results of Protein Variation Effect Analyzer (PROVEAN) analysis of CRISPR/Cas9- mediated disruption of the DARC gene, resulting in a 12 amino acid deletion ...... 160

Appendix 5.1 Epidemiologic transmission model of the 2013 chimpanzee respiratory disease outbreak ...... 161

Appendix 5.2 Maximum-likelihood phylogenetic tree of adenoviruses from fecal samples of wild chimpanzees collected during the respiratory disease epidemic, Uganda, 2013 ...... 162

xiii Chapter 1

Introduction

Erik J. Scully

Parts of this chapter were adapted from a technical report written for The World Health Organization

Strategic Advisory Group on Malaria Eradication, titled “Non-human primate reservoirs of zoonotic malaria infection: an underappreciated barrier to malaria eradication?” (Scully, 2017).

1.1 Summary

Despite the success of control efforts during recent decades, malaria remains a potent source of global mortality and morbidity, responsible for more than 200 million cases and 400,000 deaths annually (World

Malaria Report, 2015). The vast majority of human malaria cases are caused by four parasites—

Plasmodium falciparum, P. vivax, P. malariae, and P. ovale (White et al., 2014)—but these species constitute less than 1% of the malaria parasite diversity found in the natural world. Over 500 malaria parasites have been isolated from avian, reptilian, and mammalian hosts (Borner et al., 2016), many of which are incapable of replicating in humans. However, the expansion of human populations into biodiversity hotspots has elevated exposure to such novel malaria parasites, a subset of which do possess the capacity to circumvent barriers to human infection. This paradigm is underscored by the growing health burden posed by P. knowlesi, a parasite of macaque monkeys, which now accounts for over 70% of human malaria infections in parts of Southeast Asia (Cox-Singh et al., 2008; Singh & Daneshvar, 2013).

Recent advances in molecular diagnostics and expanded sampling of wild primate populations have revealed that P. knowlesi is not the only malaria parasite to transcend species boundaries. The ancient origins of both P. falciparum (Liu et al., 2010; Prugnolle et al., 2010) and P. vivax (Liu et al., 2014;

Prugnolle et al., 2013)—pandemic malaria parasites, which underlie the majority of human malaria infections globally (White et al., 2014; World Malaria Report, 2015)—have been traced to African great apes (i.e., chimpanzees and gorillas). Plasmodium malariae and P. ovale have also been isolated from

1 African great ape hosts, though the directionality of transmission remains unclear (Duval et al., 2010; Kaiser et al., 2010; Liu et al., 2010; Mapua et al., 2015), and primate-to-human transmission of other malaria parasites continues to this day (Fig. 3.1). Given the taxonomic diversity of malaria hosts, the observation that most (if not all) human malaria parasites originated in our closest evolutionary relatives is striking and has compelled the scientific and public health communities to ask whether non-human primate reservoirs could impede malaria control efforts by acting as a source of recurrent human infection (Délicat-Loembet et al., 2015; Keeling & Rayner, 2014; Ramasamy, 2014; Rayner et al., 2011; Sundararaman et al., 2013;

William et al., 2014).

Although these findings demonstrate that non-human primate malaria parasites have the capacity to emerge in human populations on evolutionary timescales, the threat that these pathogens pose to contemporary public health is less clear. Primate-to-human transmission is generally rare, and most malaria parasites must overcome substantial barriers to infect human hosts (Box 3.1). However, recent studies have begun to highlight important exceptions to this rule. Advances in molecular diagnostics have revealed that at least three simian malaria parasites—P. knowlesi (Cox-Singh et al., 2008; Singh & Daneshvar, 2013), P. simium (Brasil et al., 2017; Deane et al., 1966), and P. brasilianum (Contacos et al., 1963; Lalremruata et al., 2015)—appear to transcend human barriers promiscuously. Other simian parasites—P. cynomolgi (Ta et al., 2014) and P. vivax-like parasites of African great apes (Prugnolle et al., 2013)—have also been isolated from human hosts. Finally, more work will be necessary to evaluate whether P. malariae and P. ovale infections of African great apes are capable of zoonotic transmission. This body of evidence establishes a public health imperative to critically evaluate the drivers of variation in host restriction among malaria parasites in order to identify which species, if any, pose a material threat to human populations.

The purpose of this dissertation chapter is to provide background information pertaining to zoonotic disease emergence and the barriers to cross-species transmission. I first offer a conceptual description of zoonotic disease emergence in the context of malaria cross-species transmission. I then discuss the interplay between ecological and molecular barriers to human infection by these zoonotic parasites. Finally, I conclude by contextualizing the studies presented in the subsequent chapters of the dissertation.

2

1.2 Zoonotic disease emergence and the barriers to cross-species transmission of non-human primate malaria parasites

A zoonotic disease (or zoonosis) is an infectious disease transmitted from an animal population (i.e., a

“zoonotic reservoir”) to a human host (Lloyd-Smith et al., 2009; Morse et al., 2012; Wolfe et al., 2007).

Researchers have estimated that nearly two-thirds of contemporary human pathogens—including HIV, influenza, and many other prominent human pandemics—have zoonotic origins (Daszak et al., 2000; Morse et al., 2012; Sharp & Hahn, 2011; Taylor et al., 2001). While twentieth century public health campaigns have successfully controlled the proliferation of chronic human pathogens ranging from smallpox to polio, the rate of zoonotic disease emergence has increased over this period (Jones et al., 2008; Lloyd-Smith et al., 2009; Morse et al., 2012). During recent decades, the expansion of human populations into biodiversity hotspots has precipitated the emergence of SARS, MERS, Ebola, Nipah virus, and numerous other zoonotic outbreaks of public health relevance (Azhar et al., 2014; Gatherer, 2009; Jones et al., 2008; Li et al., 2005;

Luby et al., 2009; Pigott et al., 2014). As malaria control efforts approach regional elimination, the public health community must critically evaluate whether zoonotic malaria parasites pose a similar risk of emergence. In this section, I introduce the zoonotic spectrum as a conceptual framework to structure the discussion of the zoonotic potential of non-human primate malaria parasites.

1.2.1 The zoonotic spectrum

The process of zoonotic disease emergence is typically conceptualized as an adaptive continuum, whereby parasites are classified into one of three stages: parasites transmissible within the animal reservoir

(Stage 1), parasites transmissible from animal to human (Stage 2), and parasites transmissible among humans (Stage 3) (Lloyd-Smith et al., 2009; Morse et al., 2012; Wolfe et al., 2007). The natural diversity of primate malaria parasites spans the zoonotic spectrum (Box 3.1). Four malaria parasites (i.e., P. falciparum, P. vivax, P. malariae, P. ovale) have adapted to the human population and are transmissible among human hosts (i.e., Stage 3) (White et al., 2014). Most human malaria cases are attributable to these

3 parasites. By contrast, three non-human primate malaria parasites species (i.e., P. knowlesi (Cox-Singh et al., 2008; Singh & Daneshvar, 2013), P. simium (Brasil et al., 2017; Deane et al., 1966), and P. brasilianum

(Contacos et al., 1963; Lalremruata et al., 2015) have the capacity to infect human hosts but appear to be either incapable of human-to-human transmission or produce stuttering transmission chains in the human population (i.e., Stage 2). While several other species (e.g., P. cynomolgi (Ta et al., 2014) and African great ape P. vivax lineages (Prugnolle et al., 2013)) have also been isolated from human hosts, these events appear to be fairly rare. Emergence of these Stage 2 parasites may require further adaptation to the human host.

Finally, most primate malaria parasites (e.g., the ape Laverania (Liu et al., 2010)) appear to be either incapable of crossing species boundaries or do so inefficiently (i.e., Stage 1).

A pathogen’s position along the zoonotic spectrum is determined by its basic reproductive number (i.e.,

R0) in the human host (Lloyd-Smith et al., 2009). Here, R0 is defined as the average number of secondary cases that arise from an initial index case in a wholly susceptible population (Dietz, 1993). More simply, the R0 value describes a pathogen’s capacity to persist within the host population and is a representation of the rate of transmission relative to the rate of recovery. If R0 is less than one, the pathogen is incapable of sustained transmission within the human population and will eventually go extinct therein. By contrast, if

R0 is greater than one, the pathogen will emerge and proliferate in the human population, yielding a sustained epidemic. While many zoonotic spillover events are either incapable of forward transmission

(R0=0, or Stage 1) or produce stuttering transmission chains that eventually burn out (R0<1, or Stage 2), subsequent adaptation may permit efficient replication and transmission within the novel host population, yielding an evolutionary host shift (R0>1, or Stage 3).

The basic reproductive number of an infectious disease is ultimately dictated by characteristics of both the pathogen and the host population. For pathogens that are transmitted directly from host-to-host (e.g.,

Human rhinovirus C, influenza, ebolavirus), dynamics at the host-pathogen interface (e.g., pathogen virulence and transmissibility; host adaptive immunity), as well as interactions among hosts (e.g., social structure; population density) largely dictate R0. Because malaria is a vector-borne disease transmitted by

Anopheles mosquitos, however, its basic reproductive number is influenced by interactions between (1)

4 host and parasite (e.g., parasite virulence and human-to-mosquito infectiousness), (2) host and vector (e.g., host biting rate; ratio of mosquitoes to human hosts), and (3) vector and parasite (e.g., mosquito lifespan; duration of parasite development within the mosquito; mosquito-to-human infectiousness) (Reiner et al.,

2013; Smith et al., 2012, 2014). Each of these parameters may form a barrier to cross-species transmission by influencing R0, thus determining a parasite’s zoonotic potential.

1.3 Barriers to cross-species transmission: from the bush to the bench

In order to reproduce, the Plasmodium parasite must navigate a complex lifecycle that spans both vertebrate and invertebrate hosts (White et al., 2014). Following sexual replication in the midgut of an

Anopheles mosquito, the asexual form of the Plasmodium parasite (i.e., the sporozoite) is transmitted to the vertebrate host via the bite of this insect vector. After an initial cycle of replication in the liver, the parasite emerges into the cardiovascular system in its blood-stage form, the merozoite. Repeated cycles of invasion, growth, and egress among erythrocytes enable the parasite to proliferate within the host and, ultimately, produce the clinical symptoms associated with infection. A subset of blood-stage parasites mature into sexual stage gametocytes, which complete the Plasmodium lifecycle upon ingestion by an Anopheles mosquito vector.

Although parasite-host incompatibility could theoretically result from restriction at any stage of the

Plasmodium lifecycle, a parasite’s zoonotic potential (i.e., the capacity of a parasite to transition from Stage

1 to Stage 2) is ultimately a function of two parameters: (1) the magnitude of human exposure to the novel parasite, and (2) the probability of human infection per exposure event. By extension, factors related to either the ecology of the parasite or the degree of molecular compatibility between host and parasite (or between vector and parasite) may constitute barriers to cross-species transmission. Accordingly, discrimination between these two classes of barriers is a critical step in the evaluation of the risk that a parasite poses to the human population.

1.3.1 Ecological barriers to cross-species transmission

5 The probability of cross-species transmission depends, in part, upon the magnitude of human exposure to the zoonotic parasite. Thus, environmental factors that mediate spatiotemporal variation in human exposure will tend to influence where and when a given parasite is likely to emerge. This information can be operationalized to identify hypothetical zoonotic transmission hotspots and design emerging infectious disease surveillance efforts (Jones et al., 2008; Morse et al., 2012). This section highlights three parameters that structure variation in exposure to zoonotic malaria parasites: (1) incidence of infection in the primate reservoir, (2) vector biting preferences, and (3) spatial overlap between human and reservoir hosts.

The magnitude of transmission in the animal reservoir shapes the probability of cross-species transmission by influencing the potential for ‘spillover’ exposure (i.e., incidental exposure of an alternative host species to a novel parasite) (Wolfe et al., 2007). Ecological variables play a key role in structuring malaria transmission by influencing epidemiological parameters at the parasite-mosquito interface. For example, ambient temperature influences both the population dynamics of the mosquito vector and the rate of parasite development within the vector (Craig et al., 1999; Gething et al., 2011; Martens et al., 1997;

Mordecai et al., 2013; Parham & Michael, 2010; Weiss et al., 2014). Similarly, precipitation influences transmission probability by mediating the availability of breeding habitat for aquatic larval mosquitoes

(e.g., transitory oviposition sites) (Craig et al., 1999; Gemperli et al., 2006; Parham & Michael, 2010).

Spatial variation in these ecological variables defines the suitability of a particular locale for malaria transmission, and temporal variation in this parameter accentuates the seasonality of infection. All else being equal, human exposure is most likely to occur in hotspots that are particularly conducive to transmission within the animal reservoir.

While the magnitude of transmission within the animal reservoir is one factor that structures human exposure risk, this parameter must be considered in tandem with the biting preferences of the mosquito vector. Logically, parasites transmitted by vectors that promiscuously feed upon both humans and non- human primates will pose a greater zoonotic exposure risk than will those transmitted by mosquitoes that have strict host preferences. Unfortunately, the extent to which many Anopheline vectors discriminate among humans and non-human primates largely remains unclear. A number of studies have classified

6 particular vectors as either anthrophilic (i.e., human-biting preference) and zoophilic (i.e., animal-biting preference), based largely upon the proportion of blood meals taken from humans and livestock, respectively (Githeko et al., 1994; Zimmerman et al., 2006). However, vectors may not discriminate between the chemical cues of humans and our closest evolutionary relatives to the same degree. For example, Gabonese vectors of ape malaria parasites (i.e., An. vinckei, An. moucheti, and An. marshallii) do bite humans when given the opportunity (Makanga et al., 2016) even though An. vinckei had previously been classified as strictly zoophilic (Gillies & De Meillon, 1968). The nuanced biting preferences of An. vinckei underscores the relevance of ecological context in evaluating zoonotic potential.

Finally, spatial overlap between human and reservoir populations is a critical pre-requisite for zoonotic exposure. In many tropical regions, humans and non-human primates live in close proximity, offering myriad opportunities for vector-borne transmission (Mossoun et al., 2015; Paige et al., 2014). While many infectious zoonotic pathogens require direct contact for transmission (e.g., simian retroviruses and bushmeat consumption (Paige et al., 2014; Wolfe et al., 2004; Wolfe et al., 2005; Wolfe et al., 2005a)), exposure to a zoonotic malaria parasite only requires that the human host live close enough to the primate reservoir to be within the flight range of the vector. For example, a history of forest contact is a critical risk factor for human exposure to the zoonotic macaque parasite, P. knowlesi (Fornace et al., 2016; Grigg et al.,

2017). By contrast, humans that do not interface with these forests are less susceptible to infection.

In summary, zoonotic exposure is most likely to occur where humans live in close proximity to primates that harbor a high incidence of malaria parasites that are transmitted by Anopheline vectors that exhibit promiscuous biting preferences. The identification of these zoonotic exposure hotspots will require careful consideration of the broader ecological context in which these interactions occur.

1.3.2 Molecular barriers to cross-species transmission

Given a sufficient magnitude of exposure, molecular factors may present additional barriers to emergence by influencing the efficiency of parasite replication and transmission among human hosts.

Although all stages of the Plasmodium lifecycle offer opportunities for molecular incompatibility, most

7 remain chronically understudied in the context of host-specificity. In particular, species barriers to liver stage infection and molecular incompatibilities between parasite and mosquito vector constitute important avenues for future research.

Most research concerning the host-specificity of primate malaria parasites has focused upon host- parasite interactions occurring in the context of the red blood cell. The process of erythrocyte invasion is comprised of an ordered sequence of molecular interactions between parasite ligands expressed upon the surface of the invasive merozoite and host receptors expressed upon the surface of the erythrocyte membrane. Here, I highlight two parasite protein families that play a prominent role in host tropism: (1) the erythrocyte binding-like (EBL) proteins and (2) the reticulocyte binding-like (RBL) proteins (Cowman et al., 2012; Paul et al., 2015; Tham et al., 2012). Each EBL and RBL protein binds specifically to a receptor expressed upon the surface of the erythrocyte, irreversibly committing the parasite to invasion (Cowman et al., 2012). Because all malaria pathogenesis is attributable to complications of blood stage infection (White et al., 2014), evolutionary pressures have selected for red blood cell heterogeneities that likely contribute to host restriction.

Given the magnitude of the selective pressure imposed by malaria pathogenesis during recent human evolution, it is perhaps unsurprising that proteins associated with the red blood cell constitute primary targets of parasite-induced selection (Carter & Mendis, 2002; Kwiatkowski, 2005; Leffler et al., 2017;

Williams, 2006). Quintessential examples of molecular evolution in humans (e.g., sickle-cell anemia,

Duffy-negativity, G6PD deficiency) are attributable to selection for polymorphisms that protect against severe malaria (Carter & Mendis, 2002; Rockett et al., 2014; Williams, 2006). As a result of these selective pressures, many receptors of the EBL and RBL ligands exhibit considerable genetic diversity both within and among host species (Demogines et al., 2012; Leffler et al., 2017; Wang et al., 2003).

A growing body of evidence suggests that malaria parasites have adapted to this host cell heterogeneity via an evolutionary expansion of the EBL and RBL gene families. All Plasmodium species for which genomes have been sequenced encode multiple EBL and RBL proteins. While genetic variation in the EBL and RBL protein families has been implicated in immune evasion (Persson et al., 2013), these gene

8 expansions also offer a degree of plasticity in host cell invasion by enabling the parasite to use multiple alternative ligand-receptor interactions—known as invasion pathways—for host cell entry. Not only does this flexibility enable malaria parasites to circumvent protective polymorphisms in their native hosts, but it may also enable some parasites to cross species barriers more promiscuously.

1.4 Summary of Aims

The purpose of this dissertation is to elucidate the ecological, epidemiological, and molecular variables governing the transmission of infectious diseases among humans and our closest evolutionary relatives, the non-human primates. Drawing upon a diverse set of techniques germane to the fields of ecology, epidemiology, molecular biology, and functional genetics, the subsequent chapters of this dissertation offer an interdisciplinary approach to questions of cross-species pathogen exchange.

The probability of cross-species transmission depends, in part, upon the frequency of human exposure to the zoonotic pathogen. All else equal, factors that mediate the magnitude of transmission in the animal reservoir will tend to influence the risk of spillover exposure. In Chapter 2, we characterize the ecology and epidemiology of malaria parasites in wild chimpanzee reservoirs in order to identify potential transmission hotspots. In this study, we used molecular assays to screen chimpanzee fecal specimens collected from across equatorial Africa for malaria mitochondrial DNA and found that mean ambient temperature and forest cover drive spatiotemporal variation in infection probability. In addition to Plasmodium parasites, we isolated Hepatocystis—a malaria parasite of monkeys and bats—from a subset of chimpanzee samples, and phylogenetic analyses revealed that this parasite appears to cross species barriers promiscuously.

Finally, we mapped spatiotemporal variation in the suitability of chimpanzee habitat for malaria transmission by extrapolating the ecological relationships identified in this study across equatorial Africa.

The risk map generated in this chapter offers a baseline indicator of human exposure risk to chimpanzee malaria parasites.

Although exposure to a pathogen is a necessary prerequisite for cross-species transmission, this variable in isolation is insufficient to predict zoonotic potential. Cross-species transmission also requires a degree

9 of molecular compatibility between host and parasite. In Chapter 3, we review recent evidence concerning the molecular mechanisms that underlie the host-specificity of primate malaria parasites and the adaptations that these parasites use to overcome these restrictions. In particular, we highlight mechanisms of host restriction in the context of red blood cell invasion, the font of all malaria pathogenesis. In addition to human-adapted parasites P. falciparum and P. vivax, we focus on P. knowlesi, an emerging zoonotic malaria parasite of macaque monkeys in Southeast Asia. To catalyze future studies of the molecular drivers of malaria parasite host-specificity, in Chapter 4, we describe a method for the generation of immortalized erythroid progenitor cell lines from small volumes (~100 microliters) of peripheral blood. Differentiation of these immortalized erythroid cells mirrored erythropoiesis of primary hematopoietic stem cells, and terminally differentiated cells supported invasion by both P. vivax and P. falciparum. To demonstrate the genetic tractability of this system, we used a CRISPR/Cas9-mediated approach to disrupt the Duffy

Antigen/Receptor for Chemokines (DARC) gene, which encodes the canonical receptor of P. vivax and P. knowlesi in humans. Invasion of P. vivax and P. knowlesi into this genetically modified cell line was strongly inhibited, whereas P. falciparum was unaffected, providing direct genetic evidence that P. vivax and P. knowlesi require DARC for red blood cell invasion. Finally, we expressed transgenic human, chimpanzee, and gorilla DARC alleles in this cell line, highlighting the utility of this model system for functional in vitro studies of malaria host-specificity.

Although the process of cross-species transmission is often conceptualized from a human-specific perspective, the encroachment of human populations into biodiversity hotspots also produces negative externalities for wildlife, including the potential for cross-species pathogen exchange. African great apes are particularly susceptible to human pathogens by virtue of the high degree of molecular homology that we share, and anthroponotic respiratory viruses constitute the primary cause of mortality in many wild chimpanzee communities (Köndgen et al., 2008, 2010; Williams et al., 2008). In Chapter 5, we describe a lethal respiratory outbreak in a community of wild chimpanzees living in western Uganda. Molecular analyses of post-mortem samples implicated human rhinovirus C, an anthroponotic RNA virus responsible for the common cold, as the cause of mortality. Molecular analyses of the CDHR3 gene, a receptor of this

10 virus, revealed that chimpanzees exhibit a species-wide genetic susceptibility to human rhinovirus C. The profound conservation implications of these results for endangered great apes highlight the need for a better understanding of pathogen transmission dynamics at the interface of humans and wildlife.

Finally, I conclude the dissertation in Chapter 6 by reviewing the risk that non-human primate malaria parasites pose to global health and highlighting several potentially productive avenues for future inquiry.

Taken together, this dissertation offers a holistic view of cross-species pathogen transmission, and the studies presented herein underscore the necessity of considering both ecological and molecular barriers to emergence when designing of zoonotic disease interventions.

1.5 Author Contributions

This chapter was written by Erik J. Scully.

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16 Chapter 2

The ecology and epidemiology of malaria parasitism in wild chimpanzee reservoirs

Erik J. Scully, Weimin Liu, Jean-Bosco N. Ndjango, Martine Peeters, Anne E. Pusey, Elizabeth V. Lonsdorf, Crickette M. Sanz, David B. Morgan, Alex K. Piel, Fiona A. Stewart, Mary K. Gonder, Nicole Simmons, Caroline Asiimwe, Klaus Zuberbühler, Kathelijne Koops, Colin A. Chapman, Manoj T. Duraisingh, Beatrice H. Hahn, and Richard W. Wrangham

2.1 Abstract

Chimpanzees harbor rich assemblages of malaria parasites, including three species closely related to P. falciparum (sub-genus Laverania), the most malignant malaria parasite of humans. Although Laverania infections are highly prevalent in wild chimpanzee populations, no study has elucidated the ecological factors that structure spatiotemporal variation in the prevalence of these parasites. Here we characterize the ecology and epidemiology of malaria infection in wild chimpanzee reservoirs and test the hypothesis that ambient temperature constitutes a critical driver of spatiotemporal variation in infection probability. We used molecular diagnostic assays to screen chimpanzee fecal specimens—collected via both longitudinal and cross-sectional sampling strategies—for malaria mitochondrial DNA. We found that chimpanzee malaria parasitism has an early age of onset and varies seasonally in prevalence. In addition to Plasmodium parasites, we isolate Hepatocystis—a malaria parasite of monkeys and bats—from a subset of samples. In contrast to the strong host-specificity of the Laverania, phylogenetic analyses reveal that Hepatocystis appears to cross species barriers promiscuously. Longitudinal and cross-sectional sampling strategies each independently support the hypothesis that mean ambient temperature drives spatiotemporal variation in chimpanzee Laverania infection. Infection probability peaked at ~24.5°C, consistent with the empirical transmission optimum of P. falciparum in humans. Forest cover was also positively correlated with spatial variation in Laverania prevalence, consistent with the observation that forest-dwelling Anophelines constitute the primary vectors of these parasites. By extrapolating these relationships across equatorial

Africa, we map spatiotemporal variation in the suitability of chimpanzee habitat for Laverania transmission, offering a hypothetical baseline indicator of human exposure risk.

17 2.2 Introduction

African great apes harbor a wide diversity of malaria parasites, including seven species closely related to Plasmodium falciparum (sub-genus Laverania), the most prevalent and malignant malaria parasite of humans (Liu et al., 2010, 2014, 2016, 2017, Prugnolle et al., 2010, 2013). Although the emergence of P. falciparum in the human population has been traced to an ancient gorilla-to-human transmission event

(>10,000 years ago) (Liu et al., 2010; Otto et al., 2016), Laverania species appear to exhibit considerable host-specificity on contemporary timescales (Boundenga et al., 2015; Délicat-Loembet et al., 2015;

Sundararaman et al., 2013). Chimpanzees (Pan troglodytes)—the most abundant and widely distributed great ape species (Junker et al., 2012)—host three Laverania parasites (P. reichenowi, P. gaboni, and P. billcollinsi) (Boundenga et al., 2015; De Nys et al., 2013, 2014; Kaiser et al., 2010; Liu et al., 2010, 2016;

Prugnolle et al., 2010), which are primarily transmitted by forest-dwelling Anopheles mosquitoes that feed promiscuously upon both humans and wild apes (Makanga et al., 2016; Paupy et al., 2013). Although humans that interface with chimpanzee habitat are therefore likely to be exposed to these zoonotic parasites, three surveys of rural human populations in West Central Africa have failed to document cross-species transmission (Délicat-Loembet et al., 2015; Loy et al., 2018; Sundararaman et al., 2013), suggesting that strong molecular barriers (e.g., binding affinity of parasite invasion ligand Rh5 to host receptor basigin) govern the host-specificity of the Laverania (Martin et al., 2005; Scully et al., 2017; Sundararaman et al.,

2016; Wanaguru et al., 2013). Cross-species transmission of the Laverania has, however, recently been documented among non-human primates living in confined environments (Ngoubangoye et al., 2016), suggesting that a high magnitude of exposure can overcome molecular barriers to infection.

The capacity to identify potential exposure hotspots is currently limited, however, because the ecological conditions that mediate Plasmodium transmission among chimpanzee hosts remain largely unexplored.

Previous studies employing non-invasive sampling and molecular diagnostics have shown that the prevalence of chimpanzee Laverania infection varies regionally within equatorial Africa (32-48% among chimpanzee sub-species) (Liu et al., 2010). Infections are also temporally dynamic, manifesting seasonal and inter-annual trends in prevalence (Wu et al., 2018). However, no study has quantified the ecological

18 drivers of this variation in infection probability. Doing so would offer a more mechanistic explanation of these spatiotemporal dynamics and could pave the way for follow-up studies that explicitly quantify the magnitude of human exposure in ecologically permissive settings.

Although the ecological drivers of chimpanzee Laverania infection remain understudied, both artificial experiments and field studies have revealed that ambient temperature is a particularly strong determinant of Plasmodium transmission dynamics in human populations (Craig et al., 1999; Mordecai et al., 2013;

Paaijmans et al., 2010; Parham & Michael, 2010). Ambient temperature influences both the population dynamics of the Anopheles mosquito vector and the rate of parasite development therein (i.e., the duration of sporogony) (Craig et al., 1999; LaPointe et al., 2010; Vanderberg & Yoeli, 1966). While higher temperatures accelerate the rate of parasite development within the vector, mosquito survival tends to decline above a particular temperature threshold (Craig et al., 1999; Macdonald, 1957). Taken together, empirical studies indicate that transmission of P. falciparum peaks at a mean temperature of ~25°C in human populations (Mordecai et al., 2013; Ryan et al., 2015). In essence, this optimal temperature represents the value at which the maximal percentage of mosquito vectors survive long enough to harbor the development of infectious parasites, and deviation from this value is associated with a reduction in the magnitude of transmission. Thus, spatial variation in ambient temperature defines the suitability of a particular locale for transmission, and temporal variation in this parameter underlies the seasonality of infection (Gemperli et al., 2006; Gething et al., 2011; Mordecai et al., 2013; Weiss et al., 2014).

Additional ecological variables may also influence transmission dynamics. Intra-day fluctuations in ambient temperature may amplify or dampen transmission relative to that which would be expected at a particular mean temperature (Lyons et al., 2013; Paaijmans et al., 2010). Precipitation is generally predicted to be positively correlated with transmission—an effect partially attributable to variation in the availability of breeding habitat for aquatic mosquito larvae (e.g., transitory oviposition sites) (Craig et al., 1999; Parham

& Michael, 2010)—though high levels of precipitation may flush these breeding sites, resulting in larval mortality (Paaijmans et al., 2007). Finally, given that chimpanzee malaria parasites are transmitted by forest-dwelling Anophelines (Makanga et al., 2016; Paupy et al., 2013), the availability of forested habitat

19 is also likely to be a necessary determinant of infection (Faust & Dobson, 2015), though the relationship between deforestation and human malaria parasitism has proven to be complex (Lima et al., 2017).

In this study, we characterize the ecology and epidemiology of malaria infection in wild chimpanzee reservoirs and test the hypothesis that ambient temperature constitutes a critical driver of spatiotemporal variation in chimpanzee Laverania prevalence. To achieve this goal, we used molecular diagnostic assays to screen chimpanzee fecal specimens—collected via both longitudinal and cross-sectional sampling strategies—for malaria mitochondrial DNA. These analyses revealed that the epidemiology of chimpanzee malaria parasitism is characterized by early age of onset and seasonality of infection. In addition to

Plasmodium parasites, we isolate Hepatocystis (a malaria parasite of monkeys and bats (Borner et al.,

2016)) from a subset of samples. While Laverania parasites exhibit considerable host-specificity, phylogenetic analyses revealed that Hepatocystis appears to cross species barriers promiscuously. Next, we analyzed the relationship between environmental variables and fecal parasite rates to define the ecological niche of the chimpanzee Laverania. Longitudinal and cross-sectional sampling strategies each independently supported the hypothesis that mean ambient temperature drives spatiotemporal variation in

Laverania infection probability among chimpanzees. Infection probability peaked ~24.5°C among chimpanzee populations, consistent with the empirical transmission optimum of P. falciparum. Forest cover was also positively correlated with spatial variation in Laverania prevalence, consistent with the observation that forest-dwelling Anophelines constitute the primary vectors of these parasites. Finally, we extrapolated these relationships across equatorial Africa to articulate spatiotemporal variation in the suitability of chimpanzee habitat for Laverania transmission produce, offering a hypothetical baseline indicator of human exposure risk.

20 100% IUCN chimpanzee population distribution ^_

50% Sampling sites Forest coverForest Kanyawara chimpanzee 0% ^_ community Figure 2.1 | Geographic location of wild chimpanzee sampling sites. Study sites are shown in relation to the geographic range of chimpanzees (Pan troglodytes). A total of 3314 chimpanzee fecal samples were analyzed in this study. 878 fecal samples were collected longitudinally from 54 members of the Kanyawara chimpanzee community in Kibale National Park, Uganda (yellow star). 2436 additional fecal samples were collected from 55 sampling sites across equatorial Africa (yellow circles). The coloration of the map corresponds to spatial variation in the percentage of forest cover (derived from Hansen et al. (2013)).

Table 2.1 | Chimpanzee fecal samples analyzed in this study Field Field sites Samples Field sites tested Samples References sites positive positive

Longitudinal analysis Kanyawara (KCP) 1 1 878 273 This study

Pan-African analysis BO, GI, GM, KB, KY, MH, NB, NY, 9 1 500 6 This study UG

AM, AN, AZ, BA, BB, BD, BF, BG, 46 31 1936 390 (Liu et al. BI, BL, BQ, CP, DG, DP, EB, EK, EN, 2010, EP, GO, GT, IS, KA, KO, KS, LB, LH, 2014, LU, MB, MD, MF, MK, MP, MT, MU, 2016) ON, OP, PA, PO, SL, UB, VM, WA, WB, WE, WL, YW

Total 55 32 2436 396

Combined dataset 56 33 3314 669 Key: Amunyala (AM), Ango (AN), Azunu (AZ), Babingi (BI), Bafwaboli (BA), Bafwasende (BF), Belgique (BQ), Bondo-Bili (BD), Bongbola (BL), Bossou (BO), Boumba Bek (BB), Budongo (BG), Campo Ma'an (CP), Diang (DG), Doumo Pierre (DP), E'kom (EK), Ebo (EB), Engali (EN), Epulu (EP), Gishwati (GI), Goalougo Triangle (GT), Gombari (GO), Gombe (GM), Isiro (IS), Kabuka (KA), Kagwene (YW), Kibale

21 (Table 2.1 continued) Kanyawara (KCP), Kibale Ngogo (KB), Kisangani (KS), Kotakoli (KO), Kyambura Gorge (KY), Liabelem Highlands (LH), Lobéké (LB), Lubutu (LU), Mahale (MH), Makombe (MK), Mamfé (MF), Manbele (MB), Mbam et Djerem (MD), Metep (MP), Minta (MT), Munbgere (MU), Nimba (NB), Nyungwe (NY), Onga (ON), Opienge (OP), Parisi (PA), Poko (PO), Somalomo (SL), Ubangi (UB), Ugalla (UG), Vome (VM), Walengola (WL), Wamba (WB), Wanie-Rukula (WA), Wassa Emtse (WE)

2.3 Results

Characterization of malaria infection within the Kanyawara chimpanzee community

Several recent studies have demonstrated that malaria prevalence varies both spatially and temporally among wild chimpanzee populations, but no analysis has elucidated the ecological underpinnings of these dynamics. To investigate the epidemiology of Laverania infection in wild chimpanzee hosts, we first employed a high-density, longitudinal sampling strategy. Between June 2013 and August 2016, we collected 878 fecal samples from the Kanyawara chimpanzee community living in Kibale National Park, western Uganda (N=1-29 samples per individual; median=18 samples; Appendix 2.1). This community of wild chimpanzees—which has been under continuous observation since 1987 (Muller & Wrangham,

2014)—is habituated to human observation, and all inhabitants are identifiable by both morphology and by microsatellite genotype (Langergraber et al., 2007). During the study period, samples were collected from

32 female (N=496 samples) and 22 male (N=382 samples) chimpanzees and stored in RNAlater at -20ºC until DNA extraction. At the time of sampling, subjects ranged in age between 0.3 and an estimated 55.8 years of age (median age at sampling: 16.6 years; Appendix 2.1). Fecal samples were collected only when defecation was directly observed and only when a positive identification was achievable. To evaluate identification reliability, DNA extracted from a subset of fecal samples (N=44) was genotyped at 19 nuclear microsatellite loci, as described previously (Arandjelovic et al., 2009). Microsatellite genotyping was successful in 97.7% of these samples (43/44), suggesting that fecal DNA was of sufficient quality for subsequent molecular analyses. Of the successfully genotyped samples, we observed a misidentification rate of 2.3% (1/43), indicating that identification fidelity was sufficient for analysis of demographic predictors of infection.

To quantify variation in Plasmodium infection within and among the Kanyawara chimpanzees, DNA

22 was extracted from fecal samples and was screened for malaria parasites using a single genome amplification (SGA) strategy via nested PCR, which targeted a 956-bp segment of the apicomplexan cytochrome B (cytB) mitochondrial gene as described previously (Liu et al., 2010, 2010a). To increase the sensitivity of this approach, we adopted an intensified PCR protocol, whereby all samples were screened in eight independent PCR reactions, as previously described (Liu et al., 2017). All positive reactions were confirmed via direct sequencing without interim cloning. Of these 878 samples, 31.1% tested positive for one or more malaria parasite species in at least one replicate. Because the sensitivity of this assay to detect parasite DNA has been shown to be greater in blood than in matched fecal samples (albeit in human hosts), this value likely underestimates the frequency of blood-stage infection (Loy et al., 2018). However, Loy et al. (2018) also revealed that human fecal samples that tested positive by this approach exhibited a 26-fold increase in parasite DNA copy number in the blood relative to fecal samples that tested negative. Thus, it is likely that the positive fecal samples identified in this study reflect chimpanzees that harbor elevated parasitemia, which is a correlate of both morbidity (Herbert et al., 2015; Torres, 2003; Trampuz et al., 2003) and transmission potential (Akim et al., 2000; Mackinnon & Read, 1999).

To diagnose the parasite species responsible for each infection, we aligned these newly generated sequences to a set of previously published cytB reference sequences and constructed a Bayesian phylogenetic tree using MrBayes (version 3.2.6) (Huelsenbeck & Ronquist, 2001) (Fig. 2.2A). Parasite species identity was confirmed via NCBI nucleotide BLAST. As expected, the majority of positive samples were attributable to chimpanzee Laverania parasites (23.6% P. gaboni; 8.0% P. reichenowi; 4.9% P. billcollinsi; Fig. 2.2B). Two additional members of the primate malaria clade were amplified in a minority of samples (1.2% P. vivax; 0.1% P. malariae; Fig. 2.2B), and 6.0% of samples were found to be co-infected with multiple parasite species. These results demonstrate that the Kanyawara chimpanzees harbor an assemblage of malaria parasites that is largely representative of the parasite diversity in chimpanzees across equatorial Africa (Boundenga et al., 2015; Liu et al., 2010, 2016; Prugnolle et al., 2010).

23 KP025675.1 (Plasmodium gallinaceum) HM235019.1 (Plasmodium ovale-like) 1 GU723538.1 (Plasmodium ovale wallikeri) A 1 1 KP050432.1 (Plasmodium ovale curtisi) KC262859.1 (Hepatocystis sp.) 1 KCPpts_291_cytb 1 KCPpts_036_cytb KCPpts_154_cytb 1 KY790535.1 (Plasmodium malariae-like) 1 AF069624.1 (Plasmodium malariae) 1 KCPpts_203_cytb EU400407.1 (Plasmodium coatneyi) 0.86 0.92 EU400402.1 (Plasmodium inui) 0.54 JQ345514.1 (Plasmodium knowlesi) GU815519.1 (Plasmodium vivax) 1 HM235073.2 (Plasmodium vivax-like) KCPpts_054_cytb KCPpts_084_cytb 0.82 KCPpts_103_cytb KCPpts_186_cytb KCPpts_224_cytb KCPpts_225_cytb HM234998.1 (Plasmodium blacklocki) HM235032.1 (Plasmodium billcollinsi) KCPpts_233_cytb 1 KCPpts_262_cytb KCPpts_265_cytb 0.99 0.63 KCPpts_286_cytb KCPpts_023_cytb 1 KCPpts_249_cytb KCPpts_254_cytb 1 KCPpts_067_cytb 0.99 KCPpts_091_cytb KCPpts_236_cytb AF069605.1 (Plasmodium falciparum) 1 HM235288.1 (Plasmodium praefalciparum) KCPpts_063_cytb KCPpts_110_cytb 1 KCPpts_137_cytb KCPpts_215_cytb KCPpts_218_cytb 1 KCPpts_277_cytb KCPpts_282_cytb HM235048.1 (Plasmodium reichenowi) KCPpts_002_cytb KCPpts_003_cytb KCPpts_005_cytb KCPpts_010_cytb KCPpts_012_cytb KCPpts_033_cytb 1 KCPpts_041_cytb KCPpts_042_cytb 0.98 KCPpts_056_cytb KCPpts_080_cytb KCPpts_088_cytb KCPpts_098_cytb KCPpts_151_cytb KCPpts_165_cytb KCPpts_189_cytb KCPpts_223_cytb KCPpts_018_cytb 0.95 KCPpts_087_cytb KCPpts_026_cytb 0.94 KCPpts_274_cytb HM235014.1 (Plasmodium adleri) KCPpts_007_cytb KCPpts_027_cytb KCPpts_039_cytb KCPpts_045_cytb KCPpts_053_cytb KCPpts_058_cytb KCPpts_071_cytb KCPpts_075_cytb KCPpts_083_cytb 1 KCPpts_105_cytb KCPpts_111_cytb KCPpts_118_cytb KCPpts_122_cytb KCPpts_127_cytb KCPpts_131_cytb KCPpts_133_cytb KCPpts_141_cytb KCPpts_155_cytb B KCPpts_158_cytb 1 KCPpts_160_cytb KCPpts_170_cytb 0.3 KCPpts_173_cytb KCPpts_177_cytb KCPpts_180_cytb Species KCPpts_181_cytb 0.236 P. gaboni KCPpts_191_cytb KCPpts_197_cytb P. reichenowi KCPpts_207_cytb P. billcollinsi KCPpts_211_cytb 0.2 P. vivax KCPpts_226_cytb KCPpts_229_cytb Hepatocystis spp. KCPpts_238_cytb P. malariae KCPpts_245_cytb KCPpts_260_cytb KCPpts_276_cytb HM235076.1 (Plasmodium gaboni) 0.61 0.1 KCPpts_143_cytb 0.08 KCPpts_052_cytb

Proportion samples positive 0.91 KCPpts_283_cytb KCPpts_267_cytb 0.049 0.91 KCPpts_288_cytb KCPpts_029_cytb 0.012 KCPpts_094_cytb 0.004 0.001 0.94 KCPpts_114_cytb 0.0 KCPpts_169_cytb P. gaboni P. reichenowi P. billcollinsi P. vivax Hepatocystis spp. P. malariae KCPpts_280_cytb

0.02 Figure 2.2 | Richness of malaria parasites isolated from the Kanyawara chimpanzee community. (A)

24 (Figure 2.2 continued) Bayesian phylogeny generated from a representative subset of the SGA-derived cytB sequences generated in this study. Sequences were derived from 878 fecal samples, collected from the Kanyawara chimpanzee community in Kibale National Park, western Uganda, between 2013 and 2016. Color corresponds to malaria parasite species, inferred from the phylogenetic relationships of sequences to previously published reference sequences (green: P. gaboni, red: P. reichenowi, blue: P. billcollinsi, violet: P. vivax, orange: Hepatocystis spp., magenta: P. malariae). (B) Distribution of parasite species isolated from the Kanyawara chimpanzees. Malaria parasites were amplified in 31.1% of samples (dotted line). A majority of parasites amplified were members of the subgenus Laverania (i.e., relatives of P. falciparum): P. gaboni (23.6%), P. reichenowi (8.0%), and P. billcollinsi (4.9%). Members of the primate malaria clade were amplified in a minority of samples: P. vivax (1.2%) and P. malariae (0.1%). Additionally, Hepatocystis was isolated from 0.4% of samples.

Evolutionary relationships of chimpanzee Hepatocystis isolates

In addition to the Plasmodium species highlighted above, three samples (0.4%) harbored infections attributable to Hepatocystis spp., a clade of mammalian malaria parasites primarily isolated from Old World monkeys and bats (Ayouba et al., 2012; Borner et al., 2016; Liu et al., 2014). Despite its divergent nomenclature, the Hepatocystis clade is more closely related to the primate malaria clade (e.g., P. vivax, P. malariae, P. ovale) than either is to Laverania (Borner et al., 2016; Martinsen et al., 2008). Previous studies have similarly identified Hepatocystis in chimpanzee fecal samples (De Nys et al., 2014). However, no study has evaluated the evolutionary relationships of these parasites relative to the large diversity of previously described Hepatocystis lineages to evaluate whether these species constitute (A) a novel

Hepatocystis clade endemic to chimpanzee hosts or (B) the product of multiple independent cross-species transmission events originating in other mammalian hosts.

To discriminate between these possibilities, we analyzed the phylogenetic relationships of 20 great ape

Hepatocystis sequences (i.e., three Kanyawara-derived sequences and 17 unpublished sequences isolated during previous studies of ape malaria parasites (Liu et al., 2010, 2014, 2016)) relative to 56 previously published Hepatocystis cytB sequences isolated from a wide range of hosts, including African monkeys,

Asian monkeys, bats, and other mammals (Fig. 2.3). To ensure that the samples analyzed were, indeed, of great ape origin, we sequenced the mitochondrial D loop fragment and discarded sequences from which this gene did not amplify. Of these 17 sequences, 16 were of chimpanzee origin, while one sample was derived from a human host. The human-derived sample was collected from the Democratic Republic of the

25 Congo in January of 2007 at the Baego, Bafwabula sampling site. To our knowledge, this is the first report of a Hepatocystis infection isolated from a human host.

These phylogenetic analyses revealed that all ape-derived Hepatocystis isolates clustered within a clade of Hepatocystis lineages derived from African monkey hosts, and no isolate clustered with sequences from

Asian monkeys, bats, or other mammals (Fig. 2.3). These ape lineages were phylogenetically interspersed among African monkey isolates. This pattern contrasts starkly with the topology of the Laverania phylogeny, which consists of largely host-specific clades despite extensive sympatry of hosts. Taken together, these analyses support the hypothesis that great ape-derived Hepatocystis lineages are the result of multiple independent cross-species transmission events, suggesting that these zoonotic parasites may cross species barriers promiscuously (Thurber et al., 2013).

26

Figure 2.3 | Phylogeny of Hepatocystis parasites isolated from chimpanzee and human hosts. Bayesian

27 (Figure 2.3 continued) phylogeny generated from an alignment of 19 SGA-derived Hepatocystis cytB sequences produced during this study and 56 previously published Hepatocystis sequences from African Old World monkeys, Asian Old World monkeys, and other mammals. Chimpanzee lineages cluster with a wide range of African Old World monkeys, highlighting the capacity of this these parasites to cross species boundaries. Sequences are annotated with respect to mammalian host species. Tip labels corresponding to Hepatocystis lineages isolated from Kanyawara fecal samples are colored red, and the human isolate is colored violet. The remaining tip labels are colored with respect to chimpanzee sub-species (blue: Pan troglodytes schweinfurthii; orange: Pan troglodytes troglodytes; green: Pan troglodytes ellioti).

Ecological determinants of Laverania infection among the Kanyawara chimpanzees

Given the prevalence of Laverania infection harbored by the Kanyawara chimpanzees, we next evaluated the extent to which the incidence of these parasites varied temporally among chimpanzee hosts.

Between November and May, we found that the proportion of positive samples was greater than the dataset mean (31.1%) in 9 out of 11 sampling months, and that samples were more likely to test positive for malaria parasites during this part of the year (N=404 samples; positive samples expected=125.6±9.3; positive samples observed=163; binomial test, p<0.0001; Fig. 2.4A). By contrast, the proportion of samples that tested positive was greater than the dataset mean in only 3 out of 13 sampling months between June and

October, and samples were generally less likely to test positive for malaria parasites during these months

(N=474 samples; positive samples expected=147.4±10.1; positive samples observed=110; binomial test, p<0.0001; Fig. 2.4A). Taken together, these results highlight a seasonal pattern of infection.

To evaluate the ecological drivers of infection probability, we used local weather-monitoring stations installed at the Kanyawara field site to record daily measurements of ambient temperature (minimum, maximum) and rainfall during the study period. We defined mean ambient temperature as the average of minimum and maximum air temperature estimates recorded during the 30 days prior to sample collection and defined intra-day temperature variation as the average difference between minimum and maximum temperature estimates during the same period. Mean ambient temperature during sample collection ranged from 20.0-23.0°C (median: 20.7°C) and intra-day temperature variation ranged from 7.9-15.6°C (median:

11.0°C). Mean daily rainfall during the 30 days prior to sample collection ranged from 0-14.5 mm per day

(median: 3.0 mm).

To discern the ecological drivers of this seasonal pattern of infection, we used a generalized linear

28 mixed model (GLMM) (Baayen, 2008) with binomial error structure and logit link function to evaluate the relationship between climatic variation and the probability of Plasmodium infection among the Kanyawara chimpanzees. For each sample, we specified malaria infection (binary) as the outcome variable, mean ambient temperature (quadratic) and rainfall as fixed effects, chimpanzee individual as a random effect, and age (quadratic), sex, and intra-day temperature variation as covariates (Table 2.2). A full model that included all predictor variables and covariates provided a more parsimonious fit to the data than did a null model containing only random effects (likelihood ratio test: χ2=64.5, d.f.=6, p<0.0001; ΔAIC=52.5). As predicted, ambient temperature was positively correlated with the probability of infection, though the quadratic term had no effect and was omitted from the best fit model (Fig. 2.4C; Table 2.2). Unexpectedly, we found that rainfall was negatively correlated with infection probability (Table 2.2). These results highlight ambient temperature as an important climatic predictor of seasonal variation in chimpanzee

Plasmodium infection probability at this field site.

Demographic variables also influenced infection probability. As reported previously (De Nys et al.,

2013), fecal samples of younger chimpanzees (including the youngest chimpanzee in the dataset, 3.7 months old at time of sampling) were much more likely to test positive for malaria parasites than were those of older individuals (Fig. 2.4B,D; Table 2.2). This finding is consistent with the epidemiology of P. falciparum in humans, in which children under five years of age experience the bulk of morbidity and mortality before gradually developing immunity to the parasite upon repeated exposure (Stanisic et al.,

2015; Taylor et al., 1998; World Malaria Report, 2015). Interestingly, however, while most chimpanzees adhered to this pattern, some notable outliers were evident. For example, the fourth youngest chimpanzee in the dataset (NT; mean age: 1.28 years; Fig. 2.4B) tested positive for Plasmodium infection in 0/12 samples (model prediction: 7.6±2.8 positive samples; p<0.0001). Although the father of this chimpanzee

(OG) exhibited an unremarkable pattern of infection (positive in 6/15 samples), her mother (NP) tested positive for Plasmodium in 1/29 samples, and her maternal grandfather (LK) was positive in 0/25 samples

(Fig. 2.4B). However, it remains unclear whether such striking outliers are genuinely refractory to infection or are capable of controlling parasitemia such that it is below the limit of detection in feces. By extension,

29 future studies will be necessary to evaluate whether this pattern is attributable to genetic (e.g., erythrocyte receptor polymorphism) or environmental (e.g., reduced susceptibility resulting from energy balance; physiological stress) factors.

Taken together, these results demonstrate that the epidemiology of chimpanzee Laverania infection is characterized by high prevalence, early age of onset, and seasonality of infection, consistent with an elevated magnitude of ongoing transmission (as opposed to rare, exclusively chronic infection). Given the promiscuous biting preferences of ape malaria vectors (Makanga et al., 2016), these results provide complementary, albeit indirect, support for the assertion that humans living on the borders of chimpanzee habitat are likely to be exposed to these zoonotic parasites.

Table 2.2 | Longitudinal analysis of malaria parasitism among the Kanyawara chimpanzees Parametera Estimate Std Err P-value Intercept -13.2 3.17 <0.0001 Ecological Mean ambient temperature 0.834 0.174 <0.0001 (Mean ambient temperature)2 NSb NSb NSb Intra-day temperature variation -0.466 0.085 <0.0001 Precipitation -0.096 0.039 0.006 Demographic Age -26.5 4.63 <0.0001 (Age)2 15.4 4.45 0.0005 Sex (male) 0.072 0.319 0.819 aOutput of longitudinal GLMM corresponding to the probability ape malaria parasite infection relative to ecological and demographic predictor variables of interest. Model based upon 878 fecal samples collected longitudinally from 54 individual wild chimpanzees of the Kanyawara cohort in western Uganda. Note that predictor variables were scaled to augment model convergence. All samples in the longitudinal analysis of the Kanyawara cohort were screened eight times for malaria parasites using an intensified SGA methodology, as described previously (Liu et al., 2010, 2010a, 2017). See Methods for model specification details. bMean ambient temperature2 did not improve the fit of the longitudinal GLMM and was, therefore, omitted from the final, most parsimonious, model.

30 A B Dec (0.58) 0.8 Sample Size 5 Nov (0.38) Jan (0.37) 10 0.6 15 20 Feb (0.40) 25 Oct (0.07)

0.4

Sep (0.05) Mar (0.36) OG

0.2 Aug (0.20) Proportion samples positive Apr (0.35)

Proportion samples positive NP Jul (0.28) NT LK Jun (0.30) 0.0 May (0.47) 0 10 20 30 40 50

Mean age of chimpanzee during study period C D 1.0 1.0

Sex Sex 0.8 Female 0.8 Female Male Male

0.6 0.6

0.4 0.4

Predicted infection probability Predicted infection 0.2 probability Predicted infection 0.2

0.0 0.0

20.0 20.5 21.0 21.5 22.0 22.5 23.0 0 10 20 30 40 50

Mean ambient temperature (degrees Celsius) Chimpanzee age at time of sample collection Figure 2.4 | Longitudinal analysis of malaria parasitism in the Kanyawara cohort of wild chimpanzees. (A) The proportion of chimpanzee fecal samples that tested positive for malaria parasites varied by month of sampling (dashed line corresponds to dataset mean). Bar length corresponds to proportion of samples that tested positive during a given month of sampling (monthly mean is listed in parentheses). Samples collected between November and May tended to be more likely to test positive for malaria parasites, while samples collected between June and October tended to be less likely. (B) Demographic variables also influenced infection probability. Samples collected from younger chimpanzees were generally more likely to test positive for malaria parasites than were samples collected form older chimpanzees. This result highlights the early age of infection onset (including the youngest sample in the dataset, collected from a 3 month old female), indicative of a high magnitude of ongoing transmission. Despite this observation, a subset of chimpanzees (e.g., NT) deviated from this trend, potentially indicative of resistance to infection. (C) Predicted infection probabilities, derived from the Kanyawara GLMM, demonstrate that seasonality of infection is partially driven by variation in mean ambient temperature. Mean ambient temperature (measured directly via weather monitoring stations) was positively correlated with infection probability across the range of temperature values observed at this sampling site (20.0-23.0°C). Raw data (binary) are plotted as dots and stratified vertically for visualization. (D) Infection probabilities predicted by the Kanyawara GLMM also demonstrated that the youngest study subjects tended to be the most likely to test positive for malaria parasites. Raw data (binary) are plotted as dots and stratified vertically for visualization.

31 Ecological analysis of spatiotemporal variation in malaria among chimpanzees across equatorial Africa

To evaluate the generalizability of the results of our longitudinal analysis of the Kanyawara chimpanzee community, we independently analyzed 2436 additional fecal samples collected between November 2000 and May 2016 from 55 sampling sites distributed across wild chimpanzee habitat in equatorial Africa (n=1-

258 samples per site; median=29; Fig. 2.1), including both previously published (N=1936) and unpublished

(N=500) samples (Table 2.1). As above, samples were screened for parasite mtDNA using a SGA strategy targeting a 956-bp cytB amplicon, as described previously (Liu et al., 2010a).

Although direct measurements of climatic variables were not available from all field sites, satellite remote sensing technology offers an alternative avenue for the estimation of climatic predictors of infection.

Despite the limitations of remote approaches (e.g., the resolution-dependent capacity to detect microclimate-associated effects), these publicly available datasets offer unparalleled spatial and temporal resolution for the estimation of climatic variables in remote areas of equatorial Africa for which the sparse distribution of meteorological stations inflates the inaccuracy of spatial interpolation (Shaman, 2014). To estimate ambient temperature at each sampling site, we extracted daily Land Surface Temperature (LST) estimates from the MODerate Resolution Imaging Spectroradiometer (MODIS) thermal sensor on board the NASA-Terra satellite at 1 km2 (downsampled to 10 km2) spatial and 1-day temporal resolution

(MOD11A1v005; http://modis.gsfc.nasa.gov/) (Tatem et al., 2004). We derived minimum and maximum air temperature estimates by applying the transformation outlined in Weiss et al. (2014). As above, mean ambient temperature was defined as the average of minimum and maximum air temperature estimates recorded during the 30 days prior to sample collection and intra-day temperature variation as the average difference between minimum and maximum temperature estimates during the same period. Rainfall estimates were derived from the Global Precipitation Climatology Project (GPCP V2.3; https://www.esrl.noaa.gov/psd/) (Adler et al., 2003) at one-day temporal resolution and one-degree spatial resolution. Forest cover estimates were derived from high-resolution global maps of tree canopy cover (30 meter, downsampled to 10 km), published by Hansen et al. (2013). The resulting dataset of 2436 samples included only those data points for which all predictor variables were available. In this dataset, mean

32 ambient temperature during the 30 days prior to sample collection ranged from 9.8-27.3°C (median:

23.8°C). Mean daily rainfall during the 30 days prior to sample collection ranged from 0-14.8 mm per day

(median: 4.0 mm). Intra-day temperature variation during the 30 days prior to sample collection ranged from 6.0-13.4°C (median: 8.8°C). Forest cover at sampling sites ranged from 16-100% (median: 90%).

As above, we used a GLMM with binomial error structure and logit link function to evaluate the relationship between ecological variation and the probability of Laverania infection among wild chimpanzees across equatorial African sampling sites. We coded Plasmodium infection (binary) as the outcome variable, mean ambient temperature (quadratic), rainfall, and percent forest cover as fixed effects, sampling site as a random effect, and daily temperature variation and number of replicates (i.e., 1 or 8-10, depending upon whether samples were screened using the conventional SGA method (Liu et al., 2010a) or the intensified method (Liu et al., 2017), respectively) as covariates (Appendix 2.1). A full model that included all predictor variables and covariates provided a more parsimonious fit to the data than did a null model containing only random effects (likelihood ratio test: χ2=37.9, d.f.=5, p<0.0001; ΔAIC=27.9). As predicted, ambient temperature was positively correlated with infection probability at relatively low temperature values and negatively correlated with infection at relatively high temperature values (Fig. 2.5A;

Table 2.3). Infection probability peaked at ~24.5°C, consistent with the ~25°C transmission optimum of P. falciparum (Mordecai et al., 2013; Ryan et al., 2015). Additionally, forest cover was positively correlated with infection probability (Fig. 2.5A; Table 2.3), while rainfall did not influence infection and was omitted from the best fit model (Table 2.3). As expected, the sensitivity of the assay was enhanced by usage of the intensive PCR protocol (i.e., by screening samples in 8-10 replicates versus 1 replicate; Table 2.3).

Finally, we extrapolated these relationships between ecological variables and the probability of chimpanzee Laverania infection across equatorial Africa to identify hypothetical exposure hotspots. To characterize ecological variation across equatorial Africa, we derived composite rasters from mean ambient temperature and intra-day temperature variation measurements recorded between March 2000 and February

2017 across the African continent at 1-degree spatial and 1-month temporal resolution. Using these composite temperature rasters and the Hansen et al. (2013) forest cover dataset, we projected the predicted

33 probabilities of chimpanzee Laverania infection (derived from the pan-African model) across the spatial extent of chimpanzee habitat (as defined by the International Union for the Conservation of Nature, IUCN;

Fig. 2.5C). The resulting risk map articulates spatial variation in the suitability of chimpanzee habitat for

Laverania transmission across all seasons during an average year. Stratification of these results by month of sampling highlights temporal variation in infection probability and predicts elevated transmission between January and May, as well as a low transmission season between June and September, for much of the region (Fig. 2.5B,D). This risk map also highlights spatial variation in the seasonality of transmission

(e.g., high levels of sustained transmission in Central Africa, contrasting a more sharply seasonal pattern of infection in habitat toward the edges of the chimpanzee habitat range). Although inter-annual variation would be expected to cause predictable deviations from these estimates, this risk map offers a general baseline prediction for follow-up studies of exposure and transmission.

Table 2.3 | Ecological modeling of malaria parasitism among chimpanzee hosts Parametera Estimate Std Err P-value Intercept -2.54 0.338 <0.0001 Ecological Mean ambient temperature 0.355 0.127 0.005 (Mean ambient temperature)2 -0.164 0.063 0.009 Intra-day temperature variation 0.208 0.099 0.036 Precipitation NSb NSb NSb Percent forest cover 0.032 0.010 0.001 Technical Number of replicates (8)c 1.164 0.500 0.020 aOutput of pan-African GLMM corresponding to the probability ape malaria parasite infection relative to ecological predictor variables of interest. Model was based upon 2436 fecal samples collected at 55 sampling sites in wild chimpanzee habitat across equatorial Africa. Note that predictor variables were scaled to augment model convergence. See Methods for model specification details. bPrecipitation did not improve the fit of the longitudinal GLMM and was, therefore, omitted from the final, most parsimonious, model. cSamples in the pan-African analysis were screened either in either one or eight replicates.

34 A B 1.0 A B 0.30 Percent Forest Cover 54% (25th percentile) 0.8 90% (50th percentile) 0.25 99% (75th percentile)

0.20 0.6

0.15 0.4

0.10

Predicted infection probability Predicted infection 0.2

Mean predicted infection probability Mean predicted infection 0.05 (across full extent of chimpanzee habitat) of chimpanzee (across full extent

0.0 0.00 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Mean ambient temperature (degrees Celsius) Month of sample collection

C

D

Figure 2.5 | Ecological niche modeling of malaria parasitism in wild chimpanzee reservoirs across equatorial Africa. (A) Analysis of 2436 wild chimpanzee fecal samples collected from 55 sampling sites

35 (Figure 2.5 continued) across equatorial Africa demonstrate that mean ambient temperature (inferred from MODIS remote sensing datasets; see Methods) and forest cover (Hansen et al., 2013) are critical determinants of chimpanzee Laverania epidemiology. The pan-African GLMM demonstrates that (1) infection probability peaks ~24.5°C (dotted vertical line), consistent with the empirical transmission optimum of P. falciparum (Mordecai et al., 2013), and (2) forest cover is positively correlated with infection probability, consistent with the observation that forest-dwelling Anophelines constitute the primary vectors of these parasites (Makanga et al., 2016; Paupy et al., 2013). Color corresponds to three categories of forest cover: 54% (25th percentile), 90% (50th percentile), and 99% (75th percentile). Raw data (binary) are plotted as dots and stratified vertically for visualization. (B-C) Given the ecological relationships identified in this study, we extrapolated infection probabilities across chimpanzee habitat in equatorial Africa. We derived composite rasters from mean ambient temperature and intra-day temperature variation measurements recorded between 2000 and 2017 across the African continent. Using these composite temperature rasters and the Hansen et al. (2013) forest cover dataset, we projected the predicted probabilities of chimpanzee Laverania infection (derived from the pan-African model) across the spatial extent of chimpanzee habitat. The monthly mean pixel value of each raster is plotted, and error bars correspond to standard deviation of pixel values on each raster. (D) Stratification by month highlights the seasonality of these infections. Across chimpanzee habitat, infection probability peaks between the months of January and May, and infection probability declines between June and September. Because mosquito vectors of ape malaria parasites readily bite humans (Makanga et al., 2016), these maps can serve as a baseline proxy for spatiotemporal variation in the risk of human exposure in areas where humans and apes overlap.

2.4 Discussion

Although the prevalence of wild chimpanzee Laverania infection is well-documented, the ecological drivers of spatiotemporal variation in infection remain largely unexplored. The analyses presented in this study define the ecological niche of chimpanzee Laverania parasites and support the hypothesis that variation in mean ambient temperature structures these dynamics. Extrapolation of these relationships across equatorial Africa offers a framework for the estimation of transmission intensity and could inform efforts to quantify the risk of human exposure to these parasites.

Ambient temperature influences numerous elements of the Plasmodium life cycle, including the population dynamics of the Anopheline vector and the rate of parasite development therein (Craig et al.,

1999; LaPointe et al., 2010; Mordecai et al., 2013; Paaijmans et al., 2010; Parham & Michael, 2010;

Vanderberg & Yoeli, 1966). Both longitudinal and cross-sectional sampling strategies independently support the conclusion that mean ambient temperature is correlated with the probability of Laverania infection (Fig. 2.4C, Fig. 2.5A, Table 2.2), which peaks at ~24.5°C in wild chimpanzee populations, consistent with the ~25.0°C empirical transmission optimum of P. falciparum in humans (Mordecai et al.,

2013; Ryan et al., 2015). The consistency of this result is underscored by fact that these two approaches use

36 non-overlapping datasets and alternative methods of temperature measurement (Kanyawara: direct measurement; pan-African: remote sensing). In addition to mean ambient temperature, intra-day temperature variation—which has also been shown to influence the epidemiological rate processes at the parasite-mosquito interface (Lyons et al., 2013; Paaijmans et al., 2010)—was correlated with infection probability in both datasets (Table 2.2-2.3). However, the direction of this correlation differed in these analyses, which suggests a more complicated relationship with malaria infection (e.g., countervailing effects of temperature fluctuation when observed at mean ambient temperature extremes (Paaijmans et al.,

2010) or vector-specific effects (Lyons et al., 2013)). Taken together, these results indicate that the temperature-sensitive epidemiology of the chimpanzee Laverania closely resembles that of P. falciparum, suggesting that conserved mechanisms govern the epidemiology of these taxa.

Forest cover at sampling sites was also positively correlated with infection probability (Fig. 2.5A, Table

2.3). This result is consistent with the recent finding that forest-dwelling Anopheline species (An. vinckei,

An. moucheti) constitute the primary vectors of these parasites (Makanga et al., 2016; Paupy et al., 2013) and supports a recent analysis of malaria parasitism across non-human primates (Faust & Dobson, 2015).

Although these signatures of forest-dependent transmission suggest that human encroachment into undisturbed habitat may pose a greater exposure risk than does habitat fragmentation, these relationships are likely to be complex (Lima et al., 2017) and warrant further investigation at fine-grained spatial scales.

Extrapolation of these relationships across equatorial Africa highlights spatial and temporal variation in the ecological suitability of chimpanzee habitat for Laverania transmission (Fig. 2.5B-D). Although we found two distinct transmission seasons: (1) a “high season” between January and May, and (2) a “low season” between June and September, the amplitude and character of this seasonal pattern is predicted to vary regionally. For example, northern Democratic Republic of the Congo and northern Republic of the

Congo are predicted to support high levels of transmission across seasons. Cameroon and Gabon also support highly suitable habitat, though infection probability drops between June and August in these countries. Still other countries, such as Rwanda and Tanzania, are predicted to harbor very low transmission intensity, a projection supported by the absence of infection in samples collected from these countries.

37 Although these patterns are predicted to vary between years as a function of local climatic anomalies, this map offers a baseline set of predictions for longitudinal follow-up studies of Laverania epidemiology at particular field sites.

In addition to highlighting variation in Laverania infection among wild chimpanzees, these maps offer a rough proxy for the estimation of human exposure risk. Our analyses suggest that the epidemiology of chimpanzee Laverania infection is defined by (1) high prevalence (Fig. 2.2), (2) early age of onset (Fig.

2.4B,D), and (3) seasonality of infection (Fig. 2.4A,C, Fig. 2.5A-D). These results are consistent with a high magnitude of ongoing transmission among chimpanzee hosts. Additionally, human landing capture experiments conducted in Gabon have demonstrated that the Anopheline vectors of ape malaria parasites readily bite humans when given the opportunity (Makanga et al., 2016). Given that fecal parasite rates are indicative of blood stage parasitemia (Loy et al., 2018) (a correlate of transmission potential (Akim et al.,

2000; Mackinnon & Read, 1999)), these observations suggest that the per capita probability of human exposure is likely to be greatest in areas where the magnitude of transmission among chimpanzees is similarly elevated. Accordingly, these habitat suitability maps highlight predicted human exposure hotspots, which can inform the design of zoonotic surveillance efforts that quantify exposure directly (e.g., via the measurement of entomological inoculation rates and serological exposure). These future studies should prioritize rural human populations living near forests containing apes in the Republic of Congo and the Democratic Republic of the Congo, as well as parts of southern Cameroon and eastern Gabon, between the months of January and May. Future efforts that fine-tune and extend this risk map (e.g., via the inclusion of human population density, ape population density, and range overlap data layers) will be necessary to better operationalize these predictions.

Although it is likely that human exposure to chimpanzee malaria parasites occurs with regularity in parts of equatorial Africa, chimpanzee-to-human Laverania transmission appears to be exceedingly rare.

Strong molecular barriers to cross-species Laverania transmission have been identified (Martin et al., 2005;

Scully et al., 2017; Wanaguru et al., 2013), and two surveys of rural human populations in Gabon and

Cameroon have failed to reveal evidence of zoonotic transmission (Délicat-Loembet et al., 2015;

38 Sundararaman et al., 2013). The strong host-specificity of the Laverania is underscored by the pattern of cross-species Hepatocystis transmission revealed in this study. Though Laverania and Hepatocystis parasites each achieve high prevalence in hosts that occupy sympatric distributions in equatorial Africa, ape-to-monkey transmission of the former is infrequent (Ayouba et al., 2012; Ngoubangoye et al., 2016;

Prugnolle et al., 2011). By contrast, Hepatocystis crosses species barriers more promiscuously (Thurber et al., 2013), as indicated by the multiple Hepatocystis lineages that we isolated from chimpanzee samples

(Fig. 2.3). While our evidence of human Hepatocystis infection is the first known, the incidental nature of this finding suggests that zoonotic transmission of Hepatocystis has been underestimated. Future studies that screen human populations for this parasite using Hepatocystis-specific primers will be necessary to test this hypothesis.

The analyses presented here may also catalyze future studies of host-parasite coevolution in wild chimpanzee hosts. Although malaria pathogenesis ranks among the strongest selective pressures to confront human populations during the past 50,000 years (Carter & Mendis, 2002; Kwiatkowski, 2005), little is known about the pathogenicity of the Laverania in chimpanzees. However, case studies have documented several characteristic symptoms—such as fever, anemia, elevated parasitemia, and possibly mortality—in captive chimpanzees upon initial exposure to these parasites (Herbert et al., 2015; Tarello, 2005), though other studies have produced equivocal results (Taylor et al., 1985). Our analyses revealed that the fecal samples of younger chimpanzees were more likely to test positive for malaria parasites, suggesting that these individuals harbored relatively elevated blood-stage parasitemia (Loy et al., 2018). However, particular individuals (e.g., NT) deviated from this pattern (Fig. 2.4B). If the Laverania do produce pathogenesis in wild chimpanzees, it is conceivable that natural selection has favored the evolution of polymorphisms that confer a degree of genetic resistance in a subset of individuals. Given the age-biased pattern of infection highlighted in this study, it will be critical that future efforts to validate candidate polymorphisms in wild chimpanzee populations control for the age of study subjects. It is also conceivable that chimpanzees have evolved a series of behavioral responses (e.g., medicinal plant use (Krief et al.,

2004)) to mitigate malaria pathogenesis. Future studies that synthesize molecular and behavior approaches

39 at long-term study sites, such as Kanyawara, will facilitate investigation of these hypotheses.

Although efforts to control malaria have achieved considerable success during recent decades, the emergence of non-human primate malaria parasites in Southeast Asia (P. knowlesi (Cox-Singh et al., 2008;

Singh & Daneshvar, 2013)) and South America (P. simium (Brasil et al., 2017)) have raised concerns that zoonoses could rollback these hard-fought gains. While the ape Laverania appear to exhibit considerable host-specificity on contemporary timescales, there is nevertheless a strong impetus to critically evaluate the zoonotic potential of these parasites. Chimpanzees live in close proximity to humans in parts of equatorial

Africa, two pandemic malaria parasites (P. falciparum, P. vivax) have emerged from African great ape reservoirs in the past (Liu et al., 2010, 2014), and evidence of contemporary human-to-ape transmission of

P. falciparum has been documented (Krief et al., 2010; Ngoubangoye et al., 2016; Pacheco et al., 2013;

Prugnolle et al., 2011). Additionally, chimpanzee Laverania parasites have transcended primate species barriers in captivity, and vectors of ape malaria parasites exhibit promiscuous biting preferences (Makanga et al., 2016). Accordingly, it is very likely that human exposure occurs with regularity in parts of equatorial

Africa. Given finite resources, it is critical that any effort to evaluate the zoonotic potential of the chimpanzee Laverania adopt a targeted approach. The results of this study offer an ecological framework that will facilitate the design of surveillance efforts by highlighting where and when human exposure is most likely to occur.

2.5 Materials and Methods

Chimpanzee samples

In this study, we used a combination of longitudinal and cross-sectional sampling strategies to collect

3314 fecal samples from wild chimpanzees for molecular analyses of malaria parasitism. For longitudinal analyses, 878 fecal samples were collected from 54 chimpanzees inhabiting the Kanyawara chimpanzee community, located in Kibale National Park, western Uganda (Fig. 2.1; Appendix 2.1). This cohort of wild chimpanzees was habituated to human observation by RWW and has been under continuous direct observation since 1987. All Kanyawara study subjects are identifiable by both morphological appearance

40 and microsatellite genotype. At this field site, researchers and field assistants conduct focal follows of individual chimpanzees on a daily basis and record individual-level behavior, party-level behavior, social affiliation data, and biological samples, among other parameters. Fecal samples—collected only upon direct observation of defecation and only when a positive identification was certain—were preserved in RNAlater

(1:1 vol/vol) and stored at -20°C until exportation and DNA extraction. For cross-sectional analyses, 2436 chimpanzee fecal samples were collected from 55 chimpanzee field sites, distributed across equatorial

Africa (Fig. 2.1; Appendix 2.2). This dataset included 1936 wild chimpanzee samples, previously collected for molecular studies of simian retroviruses (Etienne et al., 2012; Keele et al., 2006, 2009; Li et al., 2012;

Neel et al., 2010; Rudicell et al., 2010) or malaria parasites (Liu et al., 2010, 2014), and 500 samples that were newly collected for these analyses (Table 2.1). Samples were only included in this dataset if collection dates were recorded and corresponding ecological variables (see below) were available. This cross-sectional dataset comprised a combination of samples that were either collected from habituated chimpanzees occupying long-term research sites or opportunistically from non-habituated chimpanzees during ape and biodiversity surveys. Samples were collected in RNAlater (1:1 vol/vol), transported at ambient temperature, and stored at -80°C. For both longitudinal and cross-sectional surveys, DNA was extracted using the

QIAamp Stool DNA minikit (Qiagen, Valencia, CA). Confirmation of host species was obtained either by direct observation of defecation (longitudinal analyses) or via amplification an sequencing of host mitochondrial DNA (Etienne et al., 2012; Keele et al., 2006, 2009; Li et al., 2012; Neel et al., 2010; Rudicell et al., 2010) (cross-sectional analyses).

Microsatellite analyses

To evaluate the accuracy of Kanyawara chimpanzee identification, we used a semi-nested multiplex

PCR protocol, as described previously (Arandjelovic et al., 2009; Langergraber et al., 2007) (with slight modifications), to genotype 44 fecal samples at 19 polymorphic microsatellite loci. Briefly, all microsatellite loci were first amplified in tandem via an initial multiplexing step. For each sample, 5 µL of extracted DNA was added to a 20 µL multiplex PCR reaction containing all 19 primer pairs at 0.15 mM

41 concentration, 1X Expand Long Template Buffer without MgCl2, 1.75 mM MgCl2, 110 µM of each dNTP,

16 µg bovine serum albumin (BSA), and 0.5 U of Expand Long Template Taq DNA polymerase.

Thermocycling was performed with an initial denaturation step for 5 minutes at 95°C, 30 cycles of 30 seconds at 95°C, 90 seconds at 58°C, and 30 seconds at 72°C, followed by a final extension at 72°C for 30 minutes. Next, a singleplex PCR was performed for each locus in 10 µL volume by mixing 5 µL of the multiplex reaction (diluted 1:100), 0.25 mM of the corresponding fluorescently labelled (FAM, HEX, or

NED) forward primer, 0.25 mM nested reverse primer, 1X Expand Long Template Buffer without MgCl2,

0.875 mM MgCl2, 110 uM of each dNTP, 8 µg BSA, and 0.25 U of Expand Long Template Taq DNA polymerase. Thermocycling was performed with an initial denaturation step for 5 minutes at 95°C, 30 cycles of 30 seconds at 95°C, 90 seconds at primer-specific annealing temperatures, and 30 seconds at 72°C, followed by a final extension at 72°C for 30 minutes. Amplification products were electrophoresed on an

ABI PRISM 3100 Genetic Analyzer and sized relative to an HD400 (ROX) size standard using

GeneMapper 5.0 (Applied Biosystems). This procedure was repeated up to six times for loci that failed to amplify. The microsatellite genotype derived from each of 44 samples was then compared to those of the

Kanyawara chimpanzees, and incorrectly attributed samples were recorded as such.

Conventional PCR and single genome amplification

To quantify variation in malaria infection, fecal DNA was screened for malaria parasites using a conventional nested PCR, targeting a 956-bp segment of the apicomplexan mitochondrial cytB gene, using external primers DW2 (5′-TAATGCCTAGACGTATTCCTGATTATCCAG-3′) and DW4 (5′-TGTTTGC

TTGGGAGCTGTAATCATAATGTG-3′) in the first-round PCR, and internal primers Pfcytb1 (5′-CTCTA

TTAATTTAGTTAAAGCACA-3′) and PLAS2a (5′-GTGGTAATTGACATCCWATCC-3′) in the second round, as described previously (Liu et al., 2010, 2010a). To increase the sensitivity of this approach, a subset of fecal samples were subjected to an intensified PCR protocol, whereby samples were screened in

8-10 independent PCR reactions, as previously described (Liu et al., 2017). All Kanyawara fecal samples

42 and a subset of pan-African samples (Appendix 2.2) were subjected to this intensified PCR protocol. To minimize PCR-induced errors, we applied a single genome amplification (SGA) approach to each fecal sample found to be positive by conventional or intensive PCR, as described previously (Liu et al., 2010,

2010a, 2014). Briefly, fecal DNA from positive samples was end point diluted in 96-well plates until <30% of wells tested positive. Given a Poisson distribution, this dilution will yield a single amplifiable template per positive reaction >80% of the time. All reactions that were identified to be positive using this SGA methodology were sequenced directly without interim cloning, yielding sequences that were derived from a single template.

Phylogenetic analyses

For analysis of malaria parasitism within the Kanyawara chimpanzee community, we trimmed sequences to 863 bps and used Geneious aligner (version 10) to align sequences to a set of phylogenetically informative reference sequences, corresponding to a representative diversity of simian and human malaria parasites. Sequences shorter than 863 bps were omitted from phylogenetic analyses. Alignments were then visually inspected and sequences containing ambiguities were removed from subsequent analyses. We used jModelTest (version 2.0) (Darriba et al., 2012) to identify the best-fit nucleotide substitution model and

MrBayes (version 3.2.6) (Huelsenbeck & Ronquist, 2001) to generate Bayesian posterior probabilities, using a chain length of 5.5 million and 10% burn-in. Convergence was achieved when the average deviation of split frequencies was <0.01. Parasite species identity was inferred via NCBI nucleotide BLAST and confirmed using the phylogenetic relationships generated above. To analyze the evolutionary relationships of chimpanzee Hepatocystis isolates, we used the methodology outlined above, except sequences were trimmed to 815 bps to allow for comparison to a greater diversity of Hepatocystis reference sequences. In this case, chimpanzee and human Hepatocystis isolates were aligned to a representative diversity of previously published Hepatocystis isolates, spanning parasites of African Old World monkeys, Asian Old

World Monkeys, and other mammals.

Estimation of ecological parameters

43 We estimated ecological variables using two alternative methodologies: direct measurement and remote sensing. For longitudinal analyses of the Kanyawara chimpanzee community, CAC recorded measurements of ambient air temperature (minimum, maximum) and rainfall twice daily during the study period via local weather-monitoring stations installed at the field site. Mean ambient temperature was calculated as the average of minimum and maximum ambient air temperature measurements and intra-day temperature variation was calculated as the difference between these values. For each sample, we defined mean ambient temperature, intra-day temperature variation, and rainfall as the average of measurements of the corresponding variable during the 30 days prior to sample collection. For cross-sectional analyses, ecological variables were estimated using remote sensing datasets. For each sample, we downloaded daytime and nighttime Land Surface Temperature (LST) measurements from the MODerate Resolution

Imaging Spectroradiometer (MODIS) thermal sensor on board the NASA-Terra satellite at 1 km2 spatial resolution and 1-day temporal resolution (MOD11A1v005; http://modis.gsfc.nasa.gov/) (Tatem et al.,

2004) at the geospatial point of sample collection. We subsequently performed quality control to remove low quality or missing data (e.g., resulting from cloud cover) and down-sampled spatial resolution to 10 km2 to increase applicability of variables across the sampling site. We then converted these daytime and nighttime LST estimates to minimum and maximum ambient temperature using the equations presented in

Weiss et al. (2014) (with corrected typographical error; Daniel Weiss, pers. comm.):

(1) !"#$ = 0.209 + 0.971 × ./!$#012

(2) !"34 = 18.149 + 0.949 × ./!738 + −0.541 × ;./!738 − ./!$#012< + −0.866 × >?@AB$021

where Tmin is minimum ambient temperature, Tmax is maximum ambient temperature, LSTnight is the nighttime LST estimate, LSTday is the daytime LST estimate, and Daylength is the number of daylight hours on the day of sampling. Given these values, we calculated mean ambient temperature as the average of minimum and maximum ambient air temperature measurements and intra-day temperature variation was calculated as the difference between these values, as above. Rainfall estimates were derived from the Global

44 Precipitation Climatology Project (GPCP V2.3) (Adler et al., 2003) at one-day temporal resolution and one- degree spatial resolution (https://www.esrl.noaa.gov/psd/). Again, for each sample, we defined mean ambient temperature, intra-day temperature variation, and rainfall as the average of measurements of the corresponding variable during the 30 days prior to sample collection. Forest cover estimates were derived from high-resolution global maps of tree canopy cover (30 meter, downsampled to 10 km), published by

Hansen et al. (2013). The resulting dataset of 2436 samples included only those data points for which all predictor variables were available.

Statistical analysis

We developed two separate generalized linear mixed models to identify the ecological and demographic determinants of Laverania prevalence in wild chimpanzee hosts. For longitudinal analyses (i.e., the

Kanyawara GLMM), we specified a generalized linear mixed model (GLMM) (Baayen, 2008) with binomial error structure and logit link function to evaluate the relationship between climatic variation and the probability of Plasmodium infection among the Kanyawara chimpanzees. For each sample, we specified malaria infection (binary) as the outcome variable, mean ambient temperature (quadratic) and rainfall as fixed effects, chimpanzee individual as a random effect, and age (quadratic), sex, and intra-day temperature variation as covariates (Table 2.2). Similarly, for cross-sectional analyses (i.e., the pan-African GLMM), we specified a GLMM with binomial error structure and logit link function to evaluate the relationship between climatic variation and the probability of Plasmodium infection among wild chimpanzees inhabiting sampling sites across equatorial Africa. In this case, we coded Plasmodium infection (binary) as the outcome variable, mean ambient temperature (quadratic), rainfall, and percent forest cover as fixed effects, sampling site as a random effect, and daily temperature variation and number of replicates (i.e., 1 or 8-10, depending upon whether samples were screened using the conventional SGA method (Liu et al., 2010) or the intensified method (Liu et al., 2017), respectively) as covariates (Table 2.3). Variables in the Pan-

African analysis were mean-centered to improve model stability. In each case, we used likelihood ratio tests to compare the fit of the full GLMM (containing all predictor variables, covariates, random effect, and

45 intercept) to that of the null model (containing only random effect and intercept). Predictor variables that did not improve the fit of each model were subsequently excluded from the final models. These statistical analyses were performed in R (version 3.4.3) using the lme4 package (Bates & Maechler, 2010).

Geospatial mapping

Given the ecological relationships identified in the Pan-African GLMM, outlined above, we extrapolated infection probabilities across wild chimpanzee habitat in equatorial Africa. To characterize ecological variation across equatorial Africa, we derived composite rasters from mean ambient temperature and intra-day temperature variation measurements recorded between March 2000 and February 2017 across the African continent at 1-degree spatial and 1-month temporal resolution. Using these composite temperature rasters and the Hansen et al. (Hansen et al., 2013) forest cover dataset, we projected the predicted probabilities of chimpanzee Laverania infection (derived from the pan-African model) across the

IUCN-designated spatial extent of chimpanzee habitat, according to the following equation:

^ (3) STUVWXYUV ST[\?\W]WY@ = ^_B`(bcdefgehdij.kll×mn`j.opq×mnrij.jkr×stij.rju×nv) where intercept is −2.538 for samples screened using conventional PCR (i.e., one replicate) and −1.374 for those screened using intensive PCR (i.e., 8–10 replicates), and AT, TV, and FC are each corrected by subtracting the means of the chimpanzee data set (23.4 for AT, 9.1 for TV and 77.2 for FC). All geospatial analyses were performed in ArcMap (v10.6 Environmental Systems Research Institute Inc.).

2.6 Author Contributions

Erik J. Scully wrote this chapter, generated all figures and tables, collected chimpanzee fecal samples from Kibale National Park, Budongo Forest, and Kyambura Gorge, extracted DNA from fecal samples, performed molecular diagnostic assays, performed chimpanzee microsatellite genotyping analyses, performed phylogenetic analyses, performed all statistical analyses, extracted all remote sensing climate data, extracted all forest cover data, and performed all geospatial mapping. W.L. and B.H.H. performed

46 molecular diagnostic assays for all other field sites. J.N.N., M.P., A.E.P., E.V.L., C.M.S., D.B.M., A.K.P.,

F.A.S., M.K.G., N.S., C.A., K.Z., K.K., and R.W.W. facilitated chimpanzee fecal sample collection from field sites across equatorial Africa. C.A.C. facilitated the collection of climate data within Kibale National

Park. R.W.W. and M.T.D. offered revisions of the manuscript. All authors contributed to the intellectual content of this article.

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53 Chapter 3

Molecular interactions governing host-specificity of blood stage malaria parasites

Erik J. Scully, Usheer Kanjee, and Manoj T. Duraisingh

This chapter was published in Current Opinion in Microbiology as “Molecular interactions governing host-specificity of blood stage malaria parasites” (Scully et al., 2017).

3.1 Abstract

Non-human primates harbor diverse species of malaria parasites, including the progenitors of P. falciparum and P. vivax. Cross-species transmission of some malaria parasites—most notably the macaque parasite, P. knowlesi—continues to this day, compelling the scientific community to ask whether these zoonoses could impede malaria control efforts by acting as a source of recurrent human infection. Host- restriction varies considerably among parasite species and is governed by both ecological and molecular variables. In particular, the efficiency of red blood cell invasion constitutes a prominent barrier to zoonotic emergence. Although proteins expressed upon the erythrocyte surface exhibit considerable diversity both within and among hosts, malaria parasites have adapted to this heterogeneity via the expansion of protein families associated with invasion, offering redundant mechanisms of host cell entry. This molecular toolkit may enable some parasites to circumvent host barriers, potentially yielding host shifts upon subsequent adaptation. Recent studies have begun to elucidate the molecular determinants of host-specificity, as well as the mechanisms that malaria parasites use to overcome these restrictions. We review recent studies concerning host tropism in the context of erythrocyte invasion by focusing on three malaria parasites that span the zoonotic spectrum: P. falciparum, P. knowlesi, and P. vivax.

3.2 Introduction

Malaria is an infectious disease caused by a diverse clade of vector-borne Apicomplexan parasites that replicate asexually in the red blood cells of vertebrate hosts. Although over 500 malaria parasites have been

54 isolated from avian, reptilian, and mammalian hosts (Borner et al., 2016), only five species—Plasmodium falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi—commonly produce human infections (Fig.

3.1) (White et al., 2014). While cross-species transmission of most mammalian Plasmodium species is rarely observed in nature, the expansion of human populations into biodiversity hotspots has presented opportunities for exposure to novel zoonotic parasites. This paradigm is underscored by the growing health burden posed by P. knowlesi, a parasite of macaque monkeys, which now accounts for over 70% of human malaria infections in parts of Southeast Asia (Cox-Singh et al., 2008; Singh & Daneshvar, 2013).

However, recent advances in molecular diagnostics and the expanded sampling of wild primates have revealed that P. knowlesi is not the only malaria parasite to transcend species boundaries. The pre- civilization origins of both P. falciparum (Liu et al., 2010; Prugnolle et al., 2010) and P. vivax (Liu et al.,

2014; Prugnolle et al., 2013)—pandemic malaria parasites, which underlie the majority of human malaria infections globally (White et al., 2014; World Malaria Report, 2015)—have recently been traced to African great apes, and cross-species transmission of other malaria parasites continues to this day (Fig. 3.1). Indeed, the observation that most, if not all, contemporary human malaria parasites originated in non-human primate hosts has compelled the scientific community to ask whether these zoonotic reservoirs could impede malaria control efforts by acting as a source of recurrent human infection (Keeling & Rayner, 2014;

Ramasamy, 2014; Rayner et al., 2011).

While it is true that the primate origins of the human malaria parasites suggest that we are predisposed to these host shifts on evolutionary timescales, the low frequency of contemporary cross-species transmission indicates that most malaria parasites must nevertheless overcome substantial ecological and molecular barriers to cross species boundaries (Box 3.1). Ecological factors may present barriers to cross- species transmission by influencing the probability of human exposure to the zoonotic parasite. For example, the incidence of infection in the primate reservoir, the host biting preferences of the mosquito vector, and the degree of spatial overlap between human and reservoir hosts are all ecological factors that mediate cross-species transmission potential. Given a sufficient magnitude of exposure, molecular factors may present additional barriers to emergence by influencing the efficiency of parasite replication and

55 transmission among human hosts. Although all stages of the complex Plasmodium lifecycle (reviewed elsewhere (White et al., 2014)) offer opportunities for molecular incompatibility, most remain chronically understudied in the context of host-specificity. In particular, species barriers to liver stage infection and molecular incompatibilities between parasite and mosquito vector constitute important avenues for future research.

Most research concerning the host-specificity of primate malaria parasites has focused upon the process of red blood cell invasion. Indeed, all malaria pathogenesis is attributable to complications of blood stage infection, and the resulting evolutionary pressures have selected for red blood cell heterogeneities that likely contribute to host restriction. In this review, we highlight recent research that has begun to elucidate the molecular mechanisms that govern variation in the host-specificity of primate malaria parasites with a particular emphasis on red blood cell invasion—the font of all malaria pathogenesis.

Stage 1: Reservoir transmission cycle R =0 0 Ape Laverania P. inui P. coatneyi P. fragile & many others

Stage 2: Cross-species transmission R0<1 P. knowlesi P. cynomolgi P. simium P. brasilianum Ape P. vivax

Stage 3: Human adaptation R0>1 P. falciparum P. vivax P. malariae P. ovale

Box 3.1 | Cross-species transmission of primate malaria parasites. The process of zoonotic emergence

56 (Box 3.1 continued) is typically conceptualized as an adaptive continuum, whereby parasites are classified into one of three stages: (1) parasites transmissible within the animal reservoir, (2) parasites transmissible from animal to human (or vice versa), and (3) parasites transmissible among humans (Lloyd-Smith et al., 2009). The zoonotic potential of a parasite (i.e., its capacity to transition from Stage 1 to Stage 2), is a function of both the magnitude of exposure and the probability of infection per exposure. Accordingly, barriers to cross-species transmission may relate to the epidemiology of the parasite in the reservoir host (e.g., incidence in the reservoir, vector biting preferences, geographic overlap of hosts) or to the degree of molecular compatibility at the host-parasite interface. The basic reproductive number, R0 (i.e., the average number of secondary cases that arise from an initial primary case in a wholly susceptible population), of a parasite in the novel host defines its zoonotic potential. While many zoonotic spillover events are either incapable of forward transmission (R0=0) or produce stuttering transmission chains that eventually burn out (R0<1), subsequent adaptation may permit efficient replication and transmission within the novel host population, yielding an evolutionary host shift (i.e., Stage 3; R0>1). The natural diversity of primate malaria parasites spans this zoonotic spectrum. Four malaria parasites (i.e., P. falciparum, P. vivax, P. malariae, P. ovale) have adapted to the human population and are transmissible among human hosts (White et al., 2014). Cross-species transmission of three primate species (i.e., P. knowlesi (Cox-Singh et al., 2008; Singh & Daneshvar, 2013), P. simium (Brasil et al., 2017), P. brasilianum (Lalremruata et al., 2015)) have been documented with increasing frequency during recent years. While it appears that these species are either incapable of human-to-human transmission or produce stuttering transmission chains in the human population, subsequent adaption may facilitate emergence. Although several other species (i.e., P. cynomolgi (Ta et al., 2014), ape P. vivax lineages (Prugnolle et al., 2013)) have been isolated from human hosts, these appear to be particularly rare events. Finally, most primate species (e.g., the ape Laverania (Liu et al., 2010)) appear to be either incapable of crossing species boundaries or do so inefficiently. Although P. malariae and P. ovale parasites have also been isolated from African great apes (Kaiser et al., 2010; Liu et al., 2010; Mapua et al., 2015), the directionality of transmission (i.e., ape-to-human or human-to-ape) currently remains unclear.

57 Host Human Infection P. simium Yes P. vivax ENDEMIC P. vivax (ape) Rare P. cynomolgi Rare P. fieldi No P. simiovale No P. hylobati No P. sp. VM82 No P. inui No P. coatneyi No P. knowlesi Yes P. fragile No P. gonderi No Primate P. brasilianum Yes malaria clade P. malariae ENDEMIC P. ovale ENDEMIC P. sp. A-E No P. adleri No Laverania P. gaboni subgenus No P. blacklocki No P. billcollinsi No P. reichenowi No P. praefalciparum No P. falciparum ENDEMIC

P. gallinaceum No

Gibbons

Gorillas Orangutans New world monkeys Chimpanzees Old world monkeys Humans (global) Lemurs

Figure 3.1 | Evolutionary relationships of primate malaria parasites. Neighbor-joining tree was derived

58 (Figure 3.1 continued) from a 3486-bp gapped mtDNA alignment encompassing the cytochrome oxidase subunit 3, cytochrome oxidase subunit 1-like, and cytochrome b genes of 24 previously published non- human primate malaria parasite sequences and outgroup P. gallinaceum (GenBank accession numbers: AB354571-AB354575, AB434918, AB434919, AB444136, AB489194, AJ251941, AY791692, AY800109, AY800110, EU880499, FJ895307, GQ355477, GQ355484, HM235302, HM235369, HM235379, HQ712054, JF923761, JQ308531, and NC_008288). Lineages are colored corresponding to geographic origin of host species (blue=South America; green=Africa; orange=Asia). Human-adapted lineages, which are globally distributed, are colored red. Lineages are labeled according to host species and degree of specificity for the human host in natural settings.

3.3 The cellular biology of erythrocyte invasion by the malaria parasite

The process of erythrocyte invasion is comprised of an ordered sequence of molecular interactions between parasite ligands expressed upon the surface of the invasive merozoite and host receptors expressed upon the surface of the erythrocyte membrane (Fig. 3.2A). Two parasite protein families play a prominent role in host tropism: (1) the erythrocyte binding-like (EBL) proteins and (2) the reticulocyte binding like

(RBL) proteins (Cowman et al., 2012; Tham et al., 2012). Each EBL and RBL protein binds specifically to a receptor expressed upon the surface of the erythrocyte, irreversibly committing the parasite to invasion

(Cowman et al., 2012). Despite their functional relevance, the receptors of most EBL and RBL ligands have yet to be elucidated (Fig. 3.2B-E).

Given the magnitude of the selective pressure imposed by malaria pathogenesis during recent human evolution, it is perhaps unsurprising that proteins associated with the red blood cell constitute primary targets of parasite-induced selection (Carter & Mendis, 2002; Kwiatkowski, 2005). Quintessential examples of molecular evolution in humans (e.g., sickle-cell anemia, Duffy-negativity, G6PD deficiency) are attributable to selection for polymorphisms that reduce susceptibility to severe malaria (Carter & Mendis,

2002; Rockett et al., 2014). As a result of these selective pressures, many receptors of the EBL and RBL ligands exhibit considerable genetic diversity both within and among host species (Demogines et al., 2012;

Wang et al., 2003).

A growing body of evidence suggests that malaria parasites have adapted to this host cell heterogeneity via an evolutionary expansion of the EBL and RBL gene families. All Plasmodium species for which genomes have been sequenced encode multiple EBL and RBL proteins (Fig. 3.2B-E). While genetic and

59 expression-level variation in the EBL and RBL protein families has been implicated in immune evasion

(Iyer et al., 2007; Persson et al., 2013), these gene expansions also offer a degree of plasticity in host cell invasion by enabling the parasite to use multiple alternative ligand-receptor interactions—known as invasion pathways—for host cell entry. In the following sections, we explore the role that this adaptive mechanism plays in the host-specificity of three parasite species: (1) P. falciparum, (2) P. knowlesi, and (3)

P. vivax.

60

Cellular determinants of malaria host-specificity Scully, Kanjee and Duraisingh 25

Figure 2

(a) (i) Primary attachment (ii) Specific attachment (iii) Host cell entry

rhoptry microneme

merozotie invasion ligand

host receptor RBC

(b) Reticulocyte Binding-like Proteins

Rh1 Rh2 Rh3 Rh4 Rh5 Rh6 Rh7 P. falciparum Rh1 Rh2b Rh2a Rh3 Rh4 Rh5 Rh6 _ CR1 BSG P. reichenowi Rh1 Rh2b Rh3 Rh4 Rh5 _ Rh7 P. gaboni Rh1 Rh2b1 Rh2b2 Rh3 _ Rh5 Rh6 Rh7

(c) Reticulocyte Binding Proteins

RBP1 RBP2 RBP3

P. vivax RBP1a RBP1b RBP2a RBP2b RBP2c RBP2b RBP2e RBP2f RBP2p1 RBP2p2 RBP3 P. cynomolgi RBP1a RBP1b RBP2a RBP2b RBP2c RBP2d RBP2e RBP2f RBP3 P. inui RBP1a RBP1b1 RBP1b2 RBP2e1 RBP2e2 RBP3 P. coatneyi RBP1a RBP2e RBP3 _ P. knowlesi NBPXb NBPXb P. fragile RBP1a RBP1b1 RBP1b2 RBP2e _ P. malariae RBP1a RBP1b RBP2a RBP2b RBP3 P. ovale curtsii RBP1a RBP1b RBP2a RBP2b RBP2c RBP2d RBP2e RBP2f RBP2g RBP2h RBP2i RBP2j RBP2k RBP3a RBP3b

(d) Erythrocyte Binding Proteins - Dual DBP Domains EBA175 EBA181 EBA140 EBA165 EBL-1 P. falciparum EBA175 EBA181 EBA140 EBA165 EBL1 GypA GypC GypB

P. reichenowi EBA175 EBA181 EBA140 EBA165 _ P. gaboni EBA175 EBA181 EBA140 EBA165 EBL-1 (e) Erythrocyte Binding Proteins - Single DBP Domains DBP EBP P. vivax DBP1a DBP1b EBP DARC

P. cynomolgi DBP1 DBP2 _ P. inui DBP _ P. coatneyi DBPα DBPβ DBPγ _ P. knowlesi DBPα DBPβ DBPγ _ DARC gene invasion ligand _ P. fragile DBP1 DBP2 _ gene pseudogene P. malariae DBP1 DBP2 _ gene host receptor P. ovale curtsii DBP1 DBP2 Current Opinion in Microbiology

Figure 3.2 | Malaria parasite invasion into red blood cells. (A) Free merozoites undergo a series of events

Malariaduring parasite the processinvasion into of redinvasionblood cells. beginning(a) Free merozoites with (iundergo) primarya series attachment,of events during followedthe process byof invasion(ii) specificbeginning attachmentwith (i)

primary attachment, followed by (ii) specific attachment to the RBC surface, and finally (iii) host cell entry [14,80,81]. Specific attachment is

mediatedto the RBCby interactions surfacebetween and finallyparasite-expressed (iii) hostinvasion cell ligandsentry (secreted(Cowmanfrom µnemes Crabb,and 2006;rhoptries) Thamand cognateet al., host2012;receptors Weisson et

al., 2015). Specific attachment is mediated by interactions between parasite-expressed invasion ligands

www.sciencedirect.com(secreted from micronemes and rhoptries) and cognate host receptors onCurrent the Opinionsurfacein ofMicrobiology the erythrocyte.2017, 40 :21–31 (B- D) Diversity of parasite invasion ligands. The two major invasion ligand families involved in specific

61 (Figure 3.2 continued) attachment, the reticulocyte binding-like proteins (RBLs) (B, C) and erythrocyte binding-like proteins (EBLs) (D, E) are shown. The invasion ligands families are further sub-divided based on the relatedness of the parasite species into either Laveranian parasites (B, D) or monkey-malaria parasites (C, E). Major subfamilies of invasion ligands (e.g. RBP1, RBP2, RBP3) are highlighted with different colors and expansions within subfamilies in each parasite species are shown in the same row as the parasite name. Invasion ligand identities were obtained from PlasmoDB. Pseudogenes, where present, are shown in grey boxes with yellow text. Where host receptors have been identified, these are indicated by blue boxes beneath the respective invasion ligand and are highlighted by a red box.

3.4 Plasmodium falciparum and the Laverania: a highly host restricted clade of malaria parasites

Plasmodium falciparum is the most prevalent and malignant human malaria parasite, responsible for

200 million cases and 400,000 deaths annually, mostly in sub-Saharan Africa (Bhatt et al., 2015; World

Malaria Report, 2015). Recently, non-invasive sampling of non-human primate fecal samples from across equatorial Africa has revealed that the origin of P. falciparum is traceable to a sub-genus of African great ape malaria parasites, known as the Laverania (Liu et al., 2010; Prugnolle et al., 2010) (Fig. 3.1). Global P. falciparum isolates form a monophyletic clade embedded within the diversity of P. praefalciparum, strongly suggesting that this pandemic resulted from a single gorilla-to-human cross-species transmission event (Liu et al., 2010), which occurred ~50,000 years ago (Otto et al., 2016), coinciding with the expansion of humans out of Africa.

Despite the zoonotic origins of P. falciparum, a defining feature of the Laverania is the high degree of contemporary host-specificity that these parasites exhibit. The Laverania cluster into host species-specific clades with little or no evidence of cross-species transmission, despite geographical overlap in host distributions (Délicat-Loembet et al., 2015; Liu et al., 2010; Sundararaman et al., 2013). Mosquito vectors of the ape Laverania readily bite humans, indicating that human exposure to these parasites occurs with regularity in parts of equatorial Africa (Makanga et al., 2016). This observation suggests that barriers to cross-species transmission are primarily molecular, rather than ecological. However, some human-to-ape and primate-to-primate transmission has been documented in captive environments (Ngoubangoye et al.,

2016; Pacheco et al., 2013), demonstrating that these barriers may be porous under particular epidemiological scenarios.

Plasmodium falciparum expresses an extensive repertoire of EBL and RBL invasion ligands, offering

62 a high degree of functional redundancy during human erythrocyte invasion (Fig. 3.2B-E). Although it was traditionally believed that no single ligand was necessary for erythrocyte invasion by all P. falciparum strains, functional experiments have since identified Rh5 as an essential determinant of erythrocyte invasion

(Baum et al., 2009; Hayton et al., 2008). Hayton et al. (2008) demonstrated that polymorphism in the Rh5 ligand was associated with binding to Aotus monkey erythrocytes, suggesting that this ligand may also play an important role in host tropism. The Rh5 receptor was subsequently identified to be the Ok blood group protein, Basigin (BSG) (Crosnier et al., 2011).

Given these findings, Wanaguru et al. (2013) used a series of protein binding assays to test the hypothesis that variation in PfRh5 affinity for non-human primate BSG orthologs underlies the strong tropism of P. falciparum for human red blood cells. Indeed, PfRh5 bound to human BSG with ten-fold greater affinity relative to the chimpanzee ortholog and did not bind to gorilla BSG. Site-directed mutagenesis revealed that several amino acid substitutions in BSG accounted for this pattern of species- specific binding. A subsequent population genetic/phylogenetic analysis conducted by Forni et al. (2015) found that several of these residues have undergone positive selection in great apes, while a PfRh5 residue that stabilizes the interaction with BSG has also been positively selected.

Although these findings suggest that a co-evolutionary arms race between Rh5 and BSG structures the host tropism of P. falciparum, future studies will be necessary to determine whether this mechanism reciprocally governs the host tropism of the ape Laverania. Interestingly, it was recently found that the contemporary PfRh5 allele was horizontally transferred to the P. falciparum/P. praefalciparum ancestor from a more distantly related Laveranian parasite (i.e., P. adleri), which may have enhanced its zoonotic potential (Sundararaman et al., 2016). While striking, this event cannot explain why P. praefalciparum is apparently incapable of infecting humans.

Moving forward, functional erythrocyte invasion assays will be necessary to confirm this host-specific phenotype in vitro, particularly in the context of multiple ligand-receptor interactions. For example, it is conceivable that while Rh5-BSG forms the foundation for Laveranian host tropism, invasion is enhanced by other receptor-ligand interactions. The recently sequenced genomes of the ape Laverania (Otto et al.,

63 2014, 2016) are likely to offer additional insights. In particular, the hypothesis that pseudogenization of the

PfRh3 and PfEBA165 ligands and/or duplication of PfRh2 were necessary prerequisites for expression of alternative, human-permissive invasion pathways warrants further investigation.

3.5 Monkey in the middle: the emerging public health threat posed by P. knowlesi zoonosis

In contrast to the human-adapted P. falciparum parasite, P. knowlesi is a zoonotic malaria parasite of macaque monkeys, which is responsible for a growing number of human cases in Southeast Asia (Cox-

Singh et al., 2008; Singh & Daneshvar, 2013). Long mistaken for P. malariae due to its morphological similarity, molecular methods have since implicated zoonotic P. knowlesi in up to 70% of infections in rural

Malaysia (Cox-Singh et al., 2008; Singh & Daneshvar, 2013). Although two reservoir hosts—long-tailed macaques (Macaca fascicularis) and pig-tailed macaques (M. nemestrina)—each harbor a high prevalence of P. knowlesi infection (Lee et al., 2011), parasites of each reservoir species appear to be reproductively isolated (Divis et al., 2015). Although this parasite population structure appears to stem from geographic isolation, it remains unclear whether molecular restriction also plays a role.

During recent years, efforts to eliminate P. falciparum and P. vivax from Southeast Asia have coincided with an increase in the incidence of P. knowlesi, a finding that warrants additional investigation (William et al., 2014). Human P. knowlesi cases form three distinct clusters—originating in long-tailed macaques in

Malaysian Borneo, pig-tailed macaques in Malaysian Borneo, and long-tailed macaques in peninsular

Malaysia, respectively—with no evidence of contemporary gene flow among them (Divis et al., 2017).

Although human-to-human P. knowlesi transmission has not been conclusively demonstrated in natural settings, this parasite produces transmission stages in human blood, suggesting that larger outbreaks with human-to-human transmission are not inconceivable (Singh et al., 2004). In this section, we highlight two mechanisms that may facilitate the emergence of P. knowlesi in human hosts.

While post-translational modification of host receptors do not appear to influence erythrocyte binding of most RBL ligands, many EBL ligands rely upon the presence of sialic acids—acidic sugars that terminate glycan chains on vertebrate proteins (Varki & Varki, 2007)—for host cell binding. Two primary sialic acid

64 variants are expressed on mammalian cells: N-acetylneuraminic acid (Neu5Ac) and N-glycolylneuraminic acid (Neu5Gc) (Varki & Varki, 2007). Neu5Ac is converted to Neu5Gc by the cytidine monophosphate

Neu5Ac hydroxylase (CMAH) gene, which has been pseudogenized in the human lineage (Varki & Varki,

2007). Thus, Neu5Ac is the major sialic acid variant found on human proteins, while Neu5Gc is the principal sialic acid of apes and Old World monkeys.

To evaluate the role that sialic acid plays in P. knowlesi zoonosis, Dankwa et al. (2016) expressed the functional chimpanzee CMAH gene in human hematopoietic stem cells. Comparison of human cells expressing Neu5Ac to those expressing Neu5Gc revealed that the presence of the latter strongly enhances

P. knowlesi invasion. In particular, the authors found that two EBL ligands—PkDBPβ and PkDBPγ—bind to erythrocytes expressing Neu5Gc, but not those expressing Neu5Ac. In contrast, PkDBPα bound to erythrocytes in a sialic acid-independent manner.

These results suggest that the usage of alternative invasion assays underlies the affinity of zoonotic P. knowlesi for human erythrocytes. While sialic acid-dependent pathways (i.e., PkDBPβ and PkDBPγ) appear to enhance P. knowlesi invasion efficiency in its natural simian host, the indifference of the PkDBPα pathway to this post-translational modification serves to bridge the fitness valley associated with the foreign cellular surface of the human host red blood cell. Nevertheless, Dankwa et al. (2016) also suggest that P. knowlesi invasion and replication is less efficient in human cells relative to macaque cells, indicating that further adaptation may be necessary for widespread emergence.

Lim et al. (2013) posited that expansion of P. knowlesi’s cellular niche in the human host may constitute one such adaptation. Although most human malaria parasites tend to exploit either younger reticulocytes

(P. vivax (Kitchen, 1938), P. ovale (Collins & Jeffery, 2005)) or older normocytes (P. malariae (Kitchen,

1939)), P. falciparum achieves elevated parasitemia via relaxation of this restriction (Chotivanich et al.,

2000). Lim et al. (2013) demonstrated that while P. knowlesi is indifferent to the age of macaque erythrocytes, invasion is limited to reticulocytes in humans. However, long-term in vitro adaptation of P. knowlesi to human blood was associated with enhanced invasion of older erythrocytes, producing a marked increase in the rate of parasite proliferation.

65 Interestingly, genetic changes in P. knowlesi invasion ligands during long-term adaptation experiments appear to facilitate invasion into human erythrocytes. Dankwa et al. (2016) demonstrated that amplification of the PkDBPα gene accompanied adaptation of P. knowlesi to human erythrocytes. Increased reliance upon this invasion ligand was functionally demonstrated by inhibition with the specific cytokine MGSA (Dankwa et al., 2016). Moon et al. (2016) have since demonstrated that although deletion of the RBL ligand

PkNBPXa facilitated long-term culture of P. knowlesi in cynomologous macaque blood, doing so restricted replication in human cells. This observation suggests that PkNBPXa may be an essential determinant of human erythrocyte invasion by P. knowlesi.

These studies demonstrate that while multiple axes of host cell variation constitute potential barriers to emergence, the expression of redundant invasion pathways has endowed P. knowlesi with the functional toolkit necessary to circumvent this host restriction. As the incidence of P. knowlesi infection increases, additional research will be necessary to elucidate the compensatory ligand adaptations that underlie relaxation of reticulocyte restriction. Experimental comparison with P. cynomolgi will likely prove to be a productive avenue for future research. Plasmodium cynomolgi is a closely related zoonotic macaque parasite that has recently been isolated from human hosts (Ta et al., 2014). Like P. knowlesi, P. cynomolgi invades macaque erythrocytes indiscriminately, but is reticulocyte-restricted in human blood, potentially limiting its zoonotic potential (Kosaisavee et al., 2017). Kosaisavee et al. (2017) revealed that this phenotype is attributable to the reliance of this parasite upon transferrin receptor 1 and the Duffy antigen/receptor for chemokines (DARC) during human erythrocyte invasion. Long-term in vitro culture of P. cynomolgi in human blood and comparison with P. knowlesi may reveal convergent mechanisms of adaptation to the human host and offer novel insight into process of zoonotic emergence.

3.6 Plasmodium vivax: a long neglected pandemic

Plasmodium vivax accounts for ~50% of all human malaria cases outside of Africa (World Malaria

Report, 2015). Once believed to have emerged from macaque monkeys (Escalante et al., 2005; Mu et al.,

2005), improved sampling of non-human primates has revealed that this pandemic originated in African

66 great apes (Liu et al., 2014). Indeed, human P. vivax isolates form a monophyletic clade embedded within the diversity of closely related ape P. vivax parasites. Though this pattern is superficially reminiscent of P. falciparum’s relationship to the ape Laverania, P. vivax appears to transcend species boundaries more promiscuously. First, P. vivax is readily transmissible between chimpanzees and gorillas (Liu et al., 2014), while the ape Laverania form host-specific clades (Liu et al., 2010). Second, though rare, ape-to-human P. vivax transmission has been reported in Central Africa (Prugnolle et al., 2013). Finally, P. vivax has been secondarily transmitted from humans to New World monkeys multiple times, yielding a post-Columbian host shift (deemed P. simium) less than 500 years ago (Camargos Costa et al., 2015; Ramasamy, 2014; Tazi

& Ayala, 2011). These observations suggest that further research into the host promiscuity of P. vivax is warranted.

The interaction between PvDBP (an EBL ligand) and DARC has long been thought to be an essential determinant of P. vivax invasion (Fig. 3.2E) (Horuk et al., 1993; Miller et al., 1977). Indeed, P. vivax pathogenicity has favored the proliferation of a mutation in the DARC promoter, which abrogates expression of this protein on human erythrocytes (Horuk et al., 1993; Miller et al., 1976). This allele, which arose less than 33,000 years ago (Hedrick, 2012), can now be found at >95% frequency in parts of sub-

Saharan Africa and underlies the near complete absence of P. vivax in human populations on the African continent (Howes et al., 2011).

However, evidence of DARC-negative P. vivax invasion has begun to accumulate in recent years

(Cavasini et al., 2007; Ménard et al., 2010; Mendes et al., 2011; Ryan et al., 2006), the mechanistic basis of which is a source of current debate. Gunalan et al. (2016) recently analyzed P. vivax isolates from two

DARC-negative Ethiopians and described a novel copy number expansion of the PvDBP gene. The authors proposed that this expansion may facilitate binding to a low-affinity receptor, though experimental support through the identification of this receptor is currently lacking. To the contrary, Hostetler et al. (2016) recently demonstrated that P. vivax isolates with this PvDBP duplication are globally widespread and are unlikely to be associated with the African DARC-negative phenotype. Rather, these authors proposed that upregulation of an alternative invasion pathway—perhaps mediated by the recently described PvEBP ligand

67 (Hester et al., 2013) or the greatly expanded PvRBP family (Carlton et al., 2008; Hietanen et al., 2016)— may facilitate invasion of DARC-negative erythrocytes. Given the important public health implications of these findings for the spread of P. vivax in Africa, evaluation of these hypotheses will be a productive avenue for future inquiry.

Further, the functional determinants of P. vivax host-specificity remain largely unexplored. While P. vivax infection of Old World monkeys has not been documented, New World monkeys appear to be highly susceptible (Araújo et al., 2013; de Castro Duarte et al., 2008). Interestingly, the CMAH gene was independently pseudogenized in the ancestor of New World monkeys (Springer et al., 2014), potentially offering a permissive, human-like cellular environment for human-adapted parasites that require the expression of Neu5Ac for invasion. If confirmed, New World monkeys could represent an ongoing source of human P. vivax/P. simium infection (Brasil et al., 2017; de Castro Duarte et al., 2008), potentially forming a major hurdle to malaria control efforts in South America. However, functional assays will be necessary to confirm this hypothesis, and progress may be slow until a continuous in vitro P. vivax culture system is developed (Golenda et al., 1997).

3.7 Conclusions and future directions

Efforts to eliminate malaria from the human population have achieved considerable success in recent years, including a 90% reduction in the incidence of P. falciparum in Africa during the last decade (Bhatt et al., 2015; World Malaria Report, 2015). During this period, P. knowlesi zoonosis has increased in

Southeast Asia (William et al., 2014), and a growing body of evidence suggests that monkey-to-human transmission of P. simium has produced malaria outbreaks in areas of Brazil where elimination had been achieved (Brasil et al., 2017). While the public health community has traditionally ignored animal reservoirs in the context of malaria eradication, it is our position that the impetus to systematically evaluate the risk of malaria zoonosis has never been greater.

Moving forward, it is critical that we adopt a holistic view of malaria zoonosis that simultaneously considers the ecological and molecular barriers to emergence. Field studies will be necessary to quantify

68 the ecological drivers of zoonosis. In particular, longitudinal sampling of high-risk human populations, primate reservoirs, and mosquito vectors will offer measures of zoonotic incidence and exposure. These epidemiological approaches will inform and contextualize laboratory studies of host-specificity at the cellular scale. Functional approaches will benefit from the recent generation of immortalized erythroid progenitor cell lines (Kurita et al., 2013; Trakarnsanga et al., 2017), which offer genetically tractable in vitro culture systems for the study of host-specificity. Finally, mathematical models of malaria zoonosis will be necessary to link transmission dynamics across these disparate scales of analysis. Without this integrated approach, efforts to elucidate the zoonotic potential of this diverse parasite clade are unlikely to be successful.

3.8 Author Contributions

Erik J. Scully wrote this chapter and generated Figure 3.1 and Box 3.1. U.K. generated Figure 3.2. All authors contributed to the intellectual content of this article and offered revisions of the manuscript.

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75 Chapter 4

Generation of an immortalized erythroid progenitor cell-line from peripheral blood: a model system for the functional analysis of Plasmodium spp. invasion

Erik J. Scully, Estela Shabani, Gabriel W. Rangel, Martha A. Clark, Mudit Chaand, Christof Grüring, Usheer Kanjee, Ryo Kurita, Yukio Nakamura, Carlo Brugnara, and Manoj T. Duraisingh

4.1 Abstract

Malaria pathogenesis is caused by the replication of Plasmodium parasites within the red blood cells

(RBCs) of the vertebrate host. This selective pressure has favored the evolution of protective polymorphisms in erythrocyte proteins, a subset of which serve as cognate receptors for parasite invasion ligands. Efforts to elucidate the functional determinants of host susceptibility in vitro have traditionally relied upon naturally occurring polymorphic RBCs as the substrate for parasite invasion assays. However, these cells are often rare, of variable quality, and are inherently non-isogenic, complicating efforts to isolate the phenotypic effects of genetically-encoded polymorphisms. Recently, the generation of RBCs from immortalized hematopoietic stem cell (HSC) lines has offered a more tractable system for genetic manipulation and long-term in vitro culture. To this end, we generated an immortalized erythroid progenitor cell line from as few as 100,000 peripheral blood mononuclear cells (PBMCs), offering a robust method for the creation of customized model systems from small volumes of peripheral blood. PBMCs were enriched from peripheral blood via density gradient and immortalized using an inducible HPV16-E6/E7 expression system. Limiting dilution cloning yielded immortalized erythroid progenitor cell lines, which resembled HSC-derived basophilic erythroblasts in both morphology and cell surface markers.

Differentiation of these immortalized cells mirrored erythropoiesis of primary HSCs, yielding terminally- differentiated orthochromatic erythroblasts and enucleated RBCs after eight days. Terminally differentiated cells supported invasion by both P. vivax and P. falciparum. To demonstrate the genetic tractability of this system, we used a CRISPR/Cas9-mediated approach to disrupt the Duffy Antigen/Receptor for

Chemokines (DARC) gene, which encodes the canonical receptor of P. vivax and P. knowlesi in humans.

Invasion of P. vivax and P. knowlesi into this DARCDISRUPTANT cell line was strongly inhibited, whereas P.

76 falciparum was unaffected, providing direct genetic evidence that P. vivax and P. knowlesi require DARC for RBC invasion. Taken together, the peripheral blood immortalization method presented in this study offers the capacity to generate biologically representative model systems for studies of blood-stage malaria invasion. Application of this method to samples from donors harboring unique genetic backgrounds or rare polymorphisms will catalyze functional studies of the molecular determinants of malaria susceptibility.

4.2 Introduction

Malaria is responsible for more than 200 million cases and 400,000 deaths annually (World Malaria

Report, 2015), ranking among the strongest selective pressures to confront the human species during recent human evolution (Carter & Mendis, 2002; Kwiatkowski, 2005). Plasmodium parasites are introduced into the blood stream via the bite of an Anopheles mosquito vector, migrate to the liver for one cycle of replication, and emerge to undergo repeated cycles of invasion, growth, and egress within the red blood cells (RBCs) of the vertebrate host (White et al., 2014). All malaria pathogenesis is attributable to complications of blood stage infection (Cowman et al., 2012), and proteins expressed upon the erythrocyte surface—which serve as receptors for parasite invasion ligands (Cowman & Crabb, 2006; Crosnier et al.,

2011; Paul et al., 2015)—constitute considerable targets of parasite-induced selection (Carter & Mendis,

2002; Kwiatkowski, 2005; Leffler et al., 2017; Williams, 2006). The capacity to identify and elucidate these ligand-receptor pairings holds important implications for the development of blood-stage malaria vaccines and anti-malarial therapies that target these interactions (Grimberg et al., 2007; Ord et al., 2014; Reiling et al., 2012).

Efforts to identify the host determinants of malaria susceptibility have traditionally relied upon functional studies involving primary cells. Since the 1940s, epidemiological and population genetic analyses have associated naturally-occurring polymorphisms, such as sickle cell trait, glucose-6-phosphate dehydrogenase deficiency, thalassemia, and Duffy-negativity, with reduced susceptibility to P. falciparum and/or P. vivax (Allison, 1961; Haldane, 1949; Miller et al., 1976). Analysis of blood samples from donors harboring these RBC disorders have enabled researchers to elucidate the functional basis for these traits in

77 vitro, and additional invasion pathways have been identified through biochemical manipulation of mature erythrocytes (Crosnier et al., 2011, 2013; Wanaguru et al., 2013; Williams, 2006). Finally, although the enucleated nature of the erythrocyte impedes genetic perturbation, targeted knockdown of erythrocyte membrane proteins has been achieved in nucleated CD34+ hematopoietic stem cells (HSCs) (Bei et al.,

2010; Giarratana et al., 2005; Giarratana et al., 2011). Terminal differentiation of these HSCs has facilitated the identification of novel P. falciparum receptors, such as CD44 and CD55 (Egan et al., 2015).

Although approaches based upon primary cells offer a degree of biological authenticity, their experimental utility is limited by several factors. First, the capacity to infer the invasion phenotype of a naturally-occurring RBC mutant requires experimental comparison to wildtype control RBCs. Such analyses could be confounded if the genetic background of mutant and wildtype blood is mismatched (e.g., other polymorphisms of relevance to parasite invasion) or if samples are handled differently (e.g., sample age, cryopreservation method) (Bei & Duraisingh, 2012). Additionally, due to the senescent nature of primary cells, analyses of natural polymorphisms require a continuous source of erythrocytes harboring genotypes of interest. While blood from donors with common disorders may be more readily available, sources harboring rare polymorphisms (e.g., the CD55-null Inab phenotype) or unique genetic backgrounds are much more difficult to acquire. An additional suite of logistical issues complicates HSC-based approaches including inter-donor genetic variation, the short window of genetic tractability, inefficiencies in the generation of genetic knockdowns, the long (~2.5-week) duration of terminal differentiation, and the need to repeat genetic perturbations for each assay.

In response to these challenges, Kanjee et al. (2017) developed a genetically tractable experimental system from a naturally-derived erythroleukemia cell line. This erythroid cancer cell line (deemed JK-1) can be expanded indefinitely as proerythroblasts and basophilic erythroblasts, can be experimentally induced to differentiate into polychromatic erythroblasts in 10-14 days, and supports invasion by P. falciparum (Kanjee et al., 2017) and P. vivax (Gruszczyk et al., 2018). The generation of CRISPR/Cas9- mediated genetic knockouts using this system has allowed for the identification of novel Plasmodium receptors (e.g., the P. vivax receptor, CD71) (Gruszczyk et al., 2018). Despite their great utility to studies

78 of Plasmodium invasion, erythroleukemia cell lines are rare and exhibit considerable variation in cellular properties, limiting the capacity of researchers to investigate natural polymorphisms or multiple genetic backgrounds (Kanjee et al., 2017). Also, ~98% of terminally differentiated JK-1 cells arrest at the polychromatic erythroblast stage of erythropoiesis, raising questions as to whether cell lines that differentiate further along the erythroid lineage might more accurately reflect parasite invasion into more mature red blood cells.

Recently, several studies have demonstrated the capacity to immortalize novel erythroid progenitor cell lines via expression of the Human Papilloma Virus (HPV16) E6/E7 fusion protein in HSCs derived from induced pluripotent stem cells, umbilical cord blood, and bone marrow (Kurita et al., 2013; Trakarnsanga et al., 2017). By using a tet-inducible expression system, these immortalized lines can be induced to differentiate into orthochromatic erythroblasts and enucleated RBCs (Kurita et al., 2013; Trakarnsanga et al., 2017). Other groups have achieved similar results via the ectopic expression of genetic factors in embryonic stem cells (Hirose et al., 2013) and umbilical cord blood mononuclear cells (Huang et al., 2014).

However, no study has evaluated the utility of these experimentally immortalized erythroid cell lines for studies of Plasmodium invasion. Also, these immortalized cell lines have only been generated from a limited number of genetic backgrounds using material that can be difficult to source. Accordingly, replication of these approaches may be untenable for a variety of uncommon genotypes and globally- relevant genetic backgrounds of interest.

In this study, we generated an immortalized erythroid progenitor cell line from as few as 100,000 peripheral blood mononuclear cells (PBMCs), offering a robust method for the creation of customized model systems from small volumes of peripheral blood. This method yielded immortalized cell lines, which resembled basophilic erythroblasts in both morphology and cell surface markers. Differentiation of these immortalized cells mirrored erythropoiesis of HSCs, yielding terminally-differentiated orthochromatic erythroblasts and enucleated RBCs after eight days. These terminally differentiated cells supported invasion by both P. vivax and P. falciparum. To demonstrate the genetic tractability of this system, we used a

CRISPR/Cas9-mediated approach to disrupt the Duffy Antigen/Receptor for Chemokines (DARC)—the

79 canonical RBC receptor of both P. vivax and P. knowlesi (Chitnis et al., 1996; Singh et al., 2006). Invasion of P. vivax and P. knowlesi into this DARCDISRUPTANT cell line was strongly inhibited, whereas P. falciparum was unaffected. These results provide direct genetic evidence confirming DARC as a critical receptor of P. vivax and P. knowlesi and highlight the utility of this cell line as a tractable genetic model for studies of the host determinants of blood-stage malaria infection. This peripheral blood immortalization method offers the capacity to generate biologically representative model systems from small volumes of accessible starting material and will catalyze efforts to generate cell lines from a wider diversity of patients.

4.3 Results

Generation of an immortalized erythroid cell line from peripheral blood

Previous efforts to generate erythroid progenitor cell lines have relied upon the immortalization of

HSCs derived from induced pluripotent stem cells, embryonic stem cells, umbilical cord blood, or bone marrow (Hirose et al., 2013; Huang et al., 2014; Kurita et al., 2013; Trakarnsanga et al., 2017). Because these materials may be difficult to source from a wide variety of patients harboring particular genotypes of interest, we sought to adapt this method to immortalize peripheral blood-derived HSCs. To do so, we used a Ficoll Paque density gradient to isolate PBMCs from 50 mL of human peripheral blood obtained from a hemochromatosis patient (Fig. 4.1A). After culturing cells for 24 hours in maintenance media without doxycycline (Table 1), 5.73x107 PBMCs were harvested. The concentration of PBMCs harvested from this patient is comparable to that which would be expected in healthy donors. PBMCs were split into three experimental conditions, comprising a total of 1.0x105 cells, 2.0x105 cells, and 5.7x107 cells, respectively.

For each condition, we expressed the HPV16-E6/E7 gene in PBMCs under a tet-inducible promoter via lentiviral transduction and cultured cells in maintenance media with doxycycline, changing media every 3-

4 days. After 15-46 days in culture, senescent cells had lysed and immortalized cells (deemed the “EJ cell line”) expanded in all three experimental conditions (Fig. 4.1B). Cells were then cloned via limiting dilution, yielding isogenic immortalized erythroid progenitor cell lines. Although we generated multiple clonal lines, in this study we characterized the EJ-E3 clone—derived from the 1.0x105 PBMC condition

80 (corresponding to ~100 microliters of peripheral blood).

During expansion, we maintained EJ cells at a concentration of 50,000-750,000 cells/mL. At this concentration, EJ cells exhibited a doubling rate of ~14 hours, and cells exhibited morphological characteristics characteristic of the early basophilic erythroblast stage of erythropoiesis (Fig. 4.1C).

However, if the concentration of cells in culture eclipsed 1,000,000 cells/mL, we observed an increase in the age heterogeneity of the cell line due to an elevated proportion of polychromatic cells. In these circumstances, we performed a 40% (vol/vol) Percoll-PBS gradient and retained the interface to eliminate these spontaneously differentiating cells.

A

1.0.E+09

PBMCs 1.0.E+08

1.0.E+07

1.0.E+06

1.0.E+05

1.0.E+04 0 10 20 30 40 50 60 70

Collect Harvest Transduce PBMCs Culture cells in Peripheral PBMCs via with maintenance blood Ficoll gradient HPV-E6/E7 construct media

B

C EJ cells EJ cells (maintenance)

1.0x105 2.0x105 5.7x107

No. PBMCs transduced

Figure 4.1 | Generation of an immortalized erythroid progenitor cell line from peripheral blood. (A)

81 (Figure 4.1 continued) To generate immortalized erythroid progenitor cell lines, PBMCs were harvested from 50 mL of peripheral blood via Ficoll Paque density gradient. After culturing PBMCs for 24 hours in maintenance media, the HPV16-E6/E7 fusion protein was expressed in PBMCs under a tet-inducible promoter via lentiviral transduction. Transduced cells were cultured in maintenance media with doxycycline for up to 65 days, changing media every 3-4 days. Cells were then cloned via limiting dilution to derive immortalized erythroid progenitor cell lines. (B) Following transduction with the HPV16-E6/E7 transgene, senescent cells lysed and immortalized cells proliferated, resulting in volatile population dynamics early in culture. However, after ~15-46 days in culture, cells in all three experimental conditions exhibited a stable rate of exponential expansion, indicative of successful immortalization. Cells derived from the 1.0x105 PBMC (red) and 5.7x107 PBMC (green) transduction conditions exhibited a similar rate of exponential expansion after 35 and 45 days, respectively. However, cells in the 2.0x105 PBMC condition (blue) exhibited a considerably lower rate of expansion, attributable to a more heterogenous cell population prior to cloning. (C) Cytospin images of cell populations after 60 days revealed that the 1.0x105 PBMC and 5.7x107 PBMC transduction conditions exhibited more homogenous populations of basophilic erythroblasts prior to cloning. By contrast, the 2.0x105 condition was more heterogenous after 60 days, consisting of a mixture of basophilic, polychromatic, and orthochromatic erythroblasts. This heterogeneity likely accounted for the lower rate of exponential expansion in the bulk population prior to cloning.

Differentiation of the EJ cell line to produce terminally differentiated ejRBCs mirrors erythropoiesis of primary HSCs

Erythropoiesis is the process by which nucleated erythroid progenitor cells divide and differentiate into enucleated reticulocytes. During the terminal phase of erythropoiesis, each successive division of the proerythroblast yields a morphologically distinguishable erythroid stage: (1) basophilic erythroblast, (2) polychromatic erythroblast, (3) orthochromatic erythroblast, and (4) enucleated reticulocyte (Chen et al.,

2009; Dzierzak & Philipsen, 2013; Hu et al., 2013). Each of these stages is characterized by a progressive decrease in cell size, an increase in chromatin condensation and hemoglobinization, and dynamic changes in proteins expressed upon the erythrocyte surface (Fig. 4.2A) (Dzierzak & Philipsen, 2013; Hu et al.,

2013).

By expressing the HPV16-E6/E7 transgene under a tet-inducible promoter, the EJ cell line can be induced to differentiate into orthochromatic erythroblasts and reticulocytes (deemed “ejRBCs”) via the removal of doxycycline from the culture media. The kinetics of cell growth during differentiation exhibited three general phases: (1) expansion from day 0-3 (mean: 6.5-fold expansion; range: 5.5-8.7, n=5 experiments), (2) plateau from day 3-5 (mean: 1.3-fold; range: 0.8-1.8; n=5 experiments), and (3) senescence from day 5-8 (mean: 0.7-fold; range: 0.5-1.1; n=5 experiments; Fig. 4.2B), yielding a mean net

82 expansion of 6.7-fold (range: 1.6-14.1; n=5 experiments) during this procedure (Fig. 4.2B). Although we continued differentiation beyond day 8 in a subset of experiments, we observed a decrease in cell counts of

~50% between days 8 and 9. Given this tradeoff between terminal differentiation and cell senescence, we deemed the optimal point of cell harvest to be eight days after inducing erythropoiesis, and all experiments involving terminally differentiated ejRBCs presented hereafter correspond to cells harvested at this time point.

Throughout differentiation, the morphology of EJ cells recapitulated the stages of erythropoiesis as modeled by primary CD34+ HSCs. Basophilic erythroblasts comprised over 96% of the culture after three days of differentiation, while 89.6% of cells resembled polychromatic erythroblasts by day 5 (Fig. 4.2E).

After 8 days of differentiation, 63.3% of cells were orthochromatic erythroblasts by morphology (Fig.

4.2E). This composition of cells in culture resembled primary HSCs after 10-12 days of differentiation (Fig.

4.2E). The morphology of EJ cells at each of these stages was indistinguishable from primary cells (Fig.

4.2D). We harvested terminally differentiated day 8 ejRBCs via a 40% Percoll-PBS gradient, in which the pellet (which contained terminally differentiated cells) was retained and the interface (which contained dead and undifferentiated cells) discarded. On day 8, ejRBCs exhibited an average rate of enucleation of 3.1%

(range: 2.2-3.8%; n=5 experiments), which increased to 5% after nine days in culture (Appendix 4.1).

Filtration of these terminally differentiated cells yielded a pure population of enucleated reticulocyte-like cells (Fig. 4.2D).

Next, we used a panel of antibodies targeting stage-diagnostic markers CD34, CD45, thrombospondin receptor (CD36), α-4 integrin (CD49d), transferrin receptor (CD71), and glycophorin A (GPA, CD235a) to characterize the dynamic changes in the expression of cell surface proteins in EJ cells during erythropoiesis. The proerythroblast—the first committed erythroblast stage—is characterized by weak

CD45 expression and the initiation of GPA expression. Proerythroblasts are also positive for CD71, α-4 integrin, and CD36 (Dzierzak & Philipsen, 2013; Fajtova et al., 2013; Hu et al., 2013; Van Lochem et al.,

2004; Wood, 2004). During erythropoiesis, the proerythroblast divides and differentiates, in the process

83 losing CD45 expression, followed successively by CD36, CD49d, and CD71, while GPA expression increases (Dzierzak & Philipsen, 2013; Fajtova et al., 2013; Hu et al., 2013; Van Lochem et al., 2004;

Wood, 2004). This analysis revealed that undifferentiated EJ cells are characterized by a CD34-

CD235a+CD45-CD36+CD49d+CD71+ expression profile (Appendix 4.3). The morphology and expression profile of the EJ cell line in maintenance culture is consistent with early basophilic erythroblasts derived from primary HSCs. Principal component analysis (PCA) of surface protein expression data revealed that these undifferentiated EJ cells cluster with primary HSCs after 10-12 days of differentiation (Fig. 4.2C;

Appendix 4.2). After five days of differentiation, the expression profile of EJ cells resembled day 15 HSCs, consistent with their orthochromatic morphology (Fig. 4.2C). By day 8, cells were primarily poly- and orthochromatic erythroblasts, which exhibited a CD49lowCD71lowGPAhigh expression profile that strongly resembled day 18 HSCs (Fig. 4.2C; Appendix 4.3). Taken together, these results demonstrate that the eight- day ejRBC differentiation protocol adequately replicates terminal erythropoiesis of primary HSCs.

84 1

0

A BFU/CFU Proerythroblast Basophilic Polychromatic−1 Orthochromatic Reticulocyte Erythrocyte CD34 CD45 CD36 CD49d CD71 −2 PC2 (18.9% of variation) Expression lost −3 Expression gained

−3 −2 −1 0 1 2 GPA 3 EJ cell line PC1 (59.2% of variation) B C EJ Day 0 EJ Day 03 EJ Day 05 ejRBC Day 08 ejRBC Day 09 HSC Day 07 HSC Day 10 HSC Day 12 HSC Day 15 HSC Day 18 1 1 EJ cells Day 0-3 0 0

−1 −1 Terminally differentiated −2 −2 cells PC2 (18.9% of variation)

PC2 (18.9% of variation) HSCs −3 −3 Day 7

−3 −2 −1 0 1 2 3 −3 −2 −1 0 1 2 3 PC1 (59.2% of variation) PC1 (59.2% of variation) EJ Day 0 EJ Day 03 EJ Day 05 ejRBC Day 08 ejRBC Day 09 EJHSC Day Day 0 07 EJHSC Day Day 03 10 EJHSC Day Day 05 12 ejRBCHSC Day Day 15 08 HSCejRBC Day Day 18 09 HSC Day 07 HSC Day 10 HSC Day 12 HSC Day 15 HSC Day 18 D E EJ cells 1.00

0.75

0.50 Proportion EJ EJ cells 0.25

Day 0 Day 3 Day 5 Day 8 Filtered 0.00 Day 0 Day 3 Day 5 Day 7 Day 8

DayPrimary of differentiation HSCs

Basophilic Polychromatic Orthochromatic Reticulocyte HSCs Primary

Day 7 Day 10 Day 12 Day 15 Day 18

Figure 4.2 | Differentiation of the ejRBC cell line mirrors erythropoiesis of primary HSCs. (A) During erythropoiesis, HSCs undergo a series of mitotic divisions, yielding progressively more differentiated erythroid stages. Each erythroid stage is distinguishable by morphology and by the expression of characteristic surface proteins (Chen et al., 2009; Dzierzak & Philipsen, 2013; Hu et al., 2013). As a general rule of thumb, each successive erythroid stage can by identified via the loss of one or more surface proteins. For example, in the figure above, loss of CD34 and CD45 reflect progression to the BFU/CFU and basophilic erythroblast stages, respectively. The terminally differentiated erythrocyte is characterized by the loss CD34, CD45, CD117, CD36, CD49d, and CD71 and elevated expression of GPA. The morphology and expression profile of undifferentiated ejRBCs closely resemble the basophilic erythroblast stage. (B) Terminal differentiation of ejRBCs was achieved via an 8-day step-wise differentiation protocol. Each data point in the figure above corresponds to the fold increase in cell number relative to the previous time point (log10 scale). Average and standard error or mean (SEM) from 5 experiments are presented. (C) Principal components analysis (PCA) of erythropoiesis marker median fluorescent intensity (MFI) data (adjusted for the background of stained controls). This analysis included CD34, CD45, CD36, CD49d, CD71, and GPA protein expression data, derived from two primary HSC differentiation experiments (circles) and 3-7 EJ cell differentiation experiments (triangles). Principal components 1 and 2 (PC1 and PC2, respectively), which together explained 78.1% of variation, are plotted. Dotted arrows outline trajectory of erythropoiesis.

85 (Figure 4.2 continued) In maintenance culture (black) and after three days of differentiation (gray), the expression profile of EJ cells resembled primary HSCs after 10-12 days of differentiation (yellow). After five days of differentiation (light green), EJ cells resembled day 15 HSCs (dark green). By day 8 (light blue) and 9 (light violet), ejRBCs clustered closely with day 18 HSCs (dark blue). Thus, terminally differentiated ejRBCs reliably replicate the expression of stage diagnostic erythropoiesis markers in primary HSCs. PCA variable loadings are presented in Appendix 4.2. Representative flow cytometry plots of all erythropoiesis markers are presented in Appendix 4.3. (D-E) The morphology of EJ cells recapitulated erythropoiesis of primary HSCs. Prior to differentiation, EJ cells were primarily basophilic erythroblasts by morphology, and most retained this trait after three days of differentiation. By day 5, most cells were polychromatic erythroblasts, while nearly two-thirds of cells harvested on day 8 resembled orthochromatic erythroblasts, a subset of which successfully enucleated (Appendix. 4.1). This pattern generally mirrored terminal differentiation of primary HSCs between days 10 and 18. Filtration of terminally differentiated ejRBCs harvested on day 8 yielded a pure population of enucleated reticulocyte-like cells.

ejRBCs support invasion of P. falciparum and P. vivax

Efforts to elucidate the host determinants of malaria susceptibility have traditionally relied upon either primary cells or erythroleukemia cell lines as the substrate for parasite invasion assays. While the former grants a degree of biological authenticity, the latter offers a more tractable system for genetic manipulation and long-term in vitro culture. To evaluate whether ejRBCs constitute a representative model system for studies of the host determinants of malaria invasion, we next sought to compare the expression of previously identified Plasmodium receptors among terminally differentiated ejRBCs, reticulocytes, and fully mature normocytes. To do so, we used a panel of antibodies targeting receptors of both P. falciparum (GPA,

Glycophorin B, Glycophorin C, Basigin, CD44, and CD55) and P. vivax (DARC and CD71). Expression of these Plasmodium receptors in ejRBCs was largely representative of reticulocytes (Fig. 4.3A). Although

CD71 (an essential P. vivax receptor (Gruszczyk et al., 2018)) and Basigin (a strain-transcendent P. falciparum receptor (Crosnier et al., 2011)) were expressed more highly in ejRBCs than in reticulocytes, this observation is consistent with the fact that most terminally differentiated ejRBCs are orthochromatic erythroblasts and have arrested before progressing to the enucleated reticulocyte stage (Hu et al., 2013).

Nevertheless, these results demonstrate the ejRBCs express the full complement of known Plasmodium receptors.

Although ejRBCs exhibit low rates of enucleation (Appendix 4.1), previous studies have demonstrated that nucleated RBCs, particularly orthochromatic erythroblasts, support Plasmodium invasion (Bei et al.,

86 2010; Egan et al., 2015; Gruszczyk et al., 2018; Kanjee et al., 2017; Tamez et al., 2009). To elucidate the utility of this system for functional studies of malaria invasion in vitro, we compared invasion of P. falciparum and P. vivax into ejRBCs, reticulocytes, and normocytes. Both parasites invaded primary cells more efficiently (P. falciparum strain 3D7, nested ANOVA: F2,27=7.60, p=0.0024; P. falciparum strain

Dd2, nested ANOVA: F2,18=4.34, p=0.0289; P. vivax, nested ANOVA: F2,10=21.83, p<0.0001; Fig. 4.3B).

Nevertheless, these results demonstrated that terminally differentiated ejRBCs support invasion of both sialic acid-dependent (Dd2, 68% invasion efficiency relative to reticulocytes) and sialic acid-independent

(3D7, 71% invasion efficiency relative to reticulocytes) P. falciparum laboratory strains, as well as clinical

P. vivax isolates (31.5% invasion efficiency relative to reticulocytes; Fig. 4.3B). By demonstrating that ejRBCs express the full complement of known Plasmodium receptors and support Plasmodium invasion, these results indicate that ejRBCs constitute an adequate in vitro model system for the investigation of research questions related to P. falciparum and P. vivax invasion.

87 A

B

*** NS NS NS NS *** *** * *

C

P. falciparum (3D7) P. falciparum (Dd2) P. vivax Figure 4.3 | Terminally differentiated ejRBCs constitute a representative model system for studies of malaria invasion. (A) Comparison of protein expression of previously identified Plasmodium receptors in ejRBCs (nucleated: pink; enucleated: red), reticulocytes (black), and normocytes (gray). Median fluorescence intensity (MFI) for each sample was adjusted for background MFI by subtracting the MFI of the stained control from the MFI of the sample. MFI for each receptor was normalized to reticulocytes. The mean and SEM of data derived from three experiments are presented. (B) Comparison of invasion efficiency of P. falciparum (strains 3D7 and Dd2) and P. vivax into ejRBCs, reticulocytes, and normocytes. In vitro invasion assays were set up at 0.1-0.25% hematocrit and 0.5-2% initial parasitemia. Parasitized erythrocyte multiplication rate (PEMR) was estimated by dividing percent ring parasitemia at 16-24 hours by percent initial schizontemia. PEMR of normocytes and ejRBCs were normalized to PEMR of reticulocytes. Data represents mean and SEM from three independent experiments (9-15 technical replicates). P-values were estimated using a nested ANOVA and Tukey’s HSD adjusted for multiple comparisons (***: p<0.001; **: p<0.01; *: p<0.05; NS: not significant). (C) Representative images of P. falciparum (strain 3D7), P. falciparum (strain Dd2), and P. vivax invasion into ejRBCs. Ring-stage parasites are highlighted with white arrows.

88 CRISPR/Cas9-mediated disruption of DARC limits P. vivax and P. knowlesi invasion into ejRBCs

Although the ancient origin of human P. vivax lineages has been traced to African great ape reservoirs

(i.e., chimpanzees and gorillas) (Liu et al., 2014), this parasite has been largely excluded from human populations in sub-Saharan Africa by a widespread polymorphism in the promoter of the DARC gene, which abrogates expression on erythrocytes (Hamblin & Di Rienzo, 2000; Howes et al., 2011). Researchers have used naturally occurring DARCNULL erythrocytes to confirm the functional role of the DARC receptor during P. vivax invasion (Barnwell et al., 1989; Grimberg et al., 2007). However, a genetically tractable system is necessary to elucidate synergistic effects of other P. vivax receptors (e.g., CD71 (Gruszczyk et al., 2018)), which may help to explain recent observations of DARCNULL P. vivax invasion (Cavasini et al.,

2007; Ménard et al., 2010; Mendes et al., 2011; Ryan et al., 2006). Thus, we next sought to evaluate whether the ejRBC model system is conducive to genetic perturbation.

As a proof-of-concept, we generated a CRISPR/Cas9-mediated disruption of the DARC gene in EJ cells. To do so, we transduced EJ cells with the lentiCRISPR v2 expression system (Sanjana et al., 2014), enabling co-expression of the Cas9 enzyme and a single-guide RNA (sgRNA) targeting the second exon of the DARC gene (Fig. 4.4A). After one week of selection on puromycin, cells expressing the transgene were cloned via limiting dilution, and clones were screened for indels in DARC. In one clone, Sanger sequencing and Tracking of Indels by Decomposition (TIDE) (Brinkman et al., 2014) revealed two deletions: (1) a 1- bp deletion yielding a frameshift, and (2) a 36-bp in-frame deletion (Fig. 4.4A-C). While the former yielded a premature stop codon and truncated protein, the latter deleted 12 amino acids spanning part of the second intracellular and fourth transmembrane domains of the DARC protein (Fig. 4.4D) (Horuk, 1994). This deletion occurs in a region of the gene that is invariant in humans and 8/12 sites are identical among 35 primate species (Appendix 4.4) (Demogines et al., 2012). Further analysis via Protein Variation Effect

Analyzer (PROVEAN) (Choi, 2012; Choi et al., 2012) confirmed that the protein resulting from this deletion is not likely to be viable (Appendix 4.5). Although comparison of DARC protein expression in wildtype and disrupted ejRBCs via flow cytometry was consistent with a DARC knockout, low levels of expression in wildtype ejRBCs limited a conclusive diagnosis of a genuine knockout (Fig. 4.4E). To be

89 conservative, we hereafter refer to this genetically modified line as “DARC disruptant” (ΔDARC).

To provide further evidence of DARC disruption, we compared invasion of P. falciparum, P. vivax, and P. knowlesi (a zoonotic malaria parasite of Southeast Asian macaque monkeys (Divis et al., 2017; B.

Singh & Daneshvar, 2013)) into ΔDARC and wildtype ejRBCs. Much previous work has demonstrated that

DARC constitutes a critical receptor of P. vivax and P. knowlesi in human RBCs, while P. falciparum uses other receptors for invasion (Chitnis et al., 1996; Cowman et al., 2012; Scully et al., 2017; Singh et al.,

2006). Our analyses demonstrated that ΔDARC cells are largely refractory to P. vivax and P. knowlesi invasion (P. vivax nested ANOVA: F1,6=312.4, p<0.0001; P. knowlesi nested ANOVA: F1,9=82.2, p<0.0001), consistent with successful disruption of the DARC gene. By contrast, P. falciparum showed no difference in invasion efficiency between wildtype and ΔDARC conditions (nested ANOVA: F1,5=0.013, p=0.913). Taken together, these results indicate that the specific disruption of the DARC gene underlies the differential susceptibility to P. vivax and P. knowlesi invasion, highlighting the dependence of these parasites on the DARC receptor.

Finally, to highlight the utility of this system for studies of malaria host-specificity, we expressed human, chimpanzee, and gorilla DARC alleles in ΔDARC EJ cells via lentiviral transduction. Transgenes were cloned into the pLVX-FADD-DD vector (Valiente et al., 2014) and expressed under a minimal beta globin promoter to elevate expression in erythroid cells. After two weeks of selection on Hygromycin B,

ΔDARC cells expressed transgenic DARCHUMAN, DARCCHIMP, and DARCGORILLA alleles (Fig. 4.5). By expressing these alleles in an isogenic background, these cell lines will enable researchers to test the hypothesis that genetic variation in DARC underlies differential susceptibility to P. vivax among humans and apes. This result demonstrates the capacity of the EJ cell line to express transgenic protein and highlights its utility to future studies of the host-specificity of malaria parasites.

90 SpCas9 cut site A B Wildtype Wildtype

sgRNA PAM DARC Δ

Deletion 1 Deletion 2

C 450 540 Wildtype TGCCTCCCTGGGCCACAGACTGGGTGCAGGCCAGGTCCCAGGCCTCACCCTGGGGCTCACTGTGGGAATTTGGGGAGTGGCTGCCCTACTG ΔDARC Allele 1 TGCCTCCCTGGGCCACAGACTGGGTGCAGG------AATTTGGGGAGTGGCTGCCCTACTG ΔDARC Allele 2 TGCCTCCCTGGGCCACAGACTGGGTGCAGGCCAGGTCCCAGGCCTCACCCTGGGGCTCACTGTGGG-ATTTGGGGAGTGGCTGCCCTACTG

D 1 68 Key Wildtype MGNCLHRAELSPSTENSSQLDFEDVWNSSYGVNDSFPDGDYGANLEAAAPCHSCNLLDDSALPFFILT Extracellular ΔDARC Allele 1 MGNCLHRAELSPSTENSSQLDFEDVWNSSYGVNDSFPDGDYGANLEAAAPCHSCNLLDDSALPFFILT Transmembrane ΔDARC Allele 2 MGNCLHRAELSPSTENSSQLDFEDVWNSSYGVNDSFPDGDYGANLEAAAPCHSCNLLDDSALPFFILT Intracellular 69 136 DARC nanobody epitope Wildtype SVLGILASSTVLFMLFRPLFRWQLCPGWPVLAQLAVGSALFSIVVPVLAPGLGSTRSSALCSLGYCVW CRISPR/Cas9 perturbation ΔDARC Allele 1 SVLGILASSTVLFMLFRPLFRWQLCPGWPVLAQLAVGSALFSIVVPVLAPGLGSTRSSALCSLGYCVW *: stop codon ΔDARC Allele 2 SVLGILASSTVLFMLFRPLFRWQLCPGWPVLAQLAVGSALFSIVVPVLAPGLGSTRSSALCSLGYCVW -: gap 137 204 Wildtype YGSAFAQALLLGCHASLGHRLGAGQVPGLTLGLTVGIWGVAALLTLPVTLASGASGGLCTLIYSTELK ΔDARC Allele 1 YGSAFAQALLLGCHASLGHRLGA------GIWGVAALLTLPVTLASGASGGLCTLIYSTELK ΔDARC Allele 2 YGSAFAQALLLGCHASLGHRLGAGQVPGLTLGLTVFGEWLPY*------205 272 Wildtype ALQATHTVACLAIFVLLPLGLFGAKGLKKALGMGPGPWMNILWAWFIFWWPHGVVLGLDFLVRSKLLL ΔDARC Allele 1 ALQATHTVACLAIFVLLPLGLFGAKGLKKALGMGPGPWMNILWAWFIFWWPHGVVLGLDFLVRSKLLL ΔDARC Allele 2 ------273 336 Wildtype LSTCLAQQALDLLLNLAEALAILHCVATPLLLALFCHQATRTLLPSLPLPEGWSSHLDTLGSKS* ΔDARC Allele 1 LSTCLAQQALDLLLNLAEALAILHCVATPLLLALFCHQATRTLLPSLPLPEGWSSHLDTLGSKS* ΔDARC Allele 2 ------

E F

EJ wildtype NS EJΔDARC Secondary control Unstained

*** ***

Figure 4.4 | CRISPR/Cas9-mediated disruption of the DARC gene in ejRBCs restricts invasion of P. vivax and P. knowlesi. (A) Sanger sequencing chromatogram of DARC gene in wildtype and ΔDARC EJ cells. The SpCas9 cut site is depicted as a black dashed vertical line. Black horizontal lines depict the sgRNA binding site and PAM sequence. Solid red horizontal lines underline the two CRISPR/Cas9- mediated deletions observed in the ΔDARC line. (B) Tracking of Indels by Decomposition (TIDE) (Brinkman et al., 2014) analysis revealed that the ΔDARC was heterozygous for two deletions: an in-frame 36-nucleotide deletion, and 1-nucleotide deletion (producing a frameshift and premature stop codon). (C) Partial sequence of DARC gene spanning experimentally-induced deletions. Wildtype sequence is presented, along with each allele of the ΔDARC line. Cas9-mediated deletions are highlighted in gray. (D) Full protein sequence of DARC gene in wildtype and ΔDARC EJ cells. Extracellular (blue), transmembrane

91 (Figure 4.4 continued) (black), and intracellular (red) domains are represented. Cas9-mediated perturbations in the ΔDARC line (highlighted in gray) resulted in (1) a 12 amino acid deletion in a highly conserved region of the protein, and (2) a frameshift mutation resulting in a premature stop codon and truncated protein. The epitope of the DARC nanobody used for flow cytometry is highlighted in yellow. (E) Flow cytometry plot of DARC expression in wildtype (green line) and ΔDARC (orange) ejRBCs demonstrates successful disruption of the DARC protein in terminally differentiated ejRBCs. Unstained EJ cells represented in red, and secondary antibody controls are presented in blue. Due to low levels of DARC expression in wildtype cells, we conservatively refer to this cell line as “DARC disruptant.” (F) Invasion of both P. vivax (black) and P. knowlesi (dark gray) into ΔDARC ejRBCs was strongly inhibited relative to wildtype, while invasion of P. falciparum (light gray) was largely unaffected. This result provides direct genetic evidence for the critical role of the DARC receptor in P. vivax and P. knowlesi invasion, and confirms previous research (Chitnis et al., 1996; Cowman et al., 2012; Scully et al., 2017; Singh et al., 2006). Invasion efficiency of each parasite relative to the wildtype is presented. P-values were estimated using a nested ANOVA and Tukey’s HSD adjusted for multiple comparisons (***: p<0.001; **: p<0.01; *: p<0.05; NS: not significant).

Human Chimpanzee Gorilla Day 0 Day 0 Day 0

Human Chimpanzee Gorilla Day 8 Day 8 Day 8

Unstained EJΔDARC (stained) Secondary control EJΔDARC (stained) TRANSGENIC Figure 4.5 | Expression of human, chimpanzee, and gorilla DARC alleles in the EJΔDARC cell line. Flow cytometry plots of terminally differentiated ΔDARC EJ cells expressing human (left), chimpanzee (middle), and gorilla (right) DARC alleles on day 0 (top row) and day 8 (bottom row) of differentiation. Expression of the transgene under the minimal beta globin promoter enhances DARC expression. Thus, expression in these transgenic lines (orange) eclipses that of the ΔDARC line (red) and wildtype EJ cells (Fig. 4.4E) and may facilitate efforts to develop a long-term P. vivax in vitro culture system. Note that the gorilla DARC allele contains a Valine to Alanine amino acid substitution in the DARC nanobody epitope, potentially resulting in reduced binding affinity and underestimation of transgene expression.

4.4 Discussion

Although HSCs derived from induced pluripotent stem cells, embryonic stem cells, umbilical cord

92 blood, and bone marrow have been experimentally immortalized in the past (Hirose et al., 2013; Huang et al., 2014; Kurita et al., 2013; Trakarnsanga et al., 2017), these materials are often difficult to acquire from a diverse set of patients with underrepresented genetic backgrounds or unique genotypes. This study presents a robust method for the generation of immortalized erythroid progenitor cell lines from as little as

100 microliters of peripheral blood. The wide accessibility of these inputs stands to catalyze studies of the functional determinants of malaria susceptibility in a greater diversity of genetic backgrounds than had previously been available.

In this study, we used a tet-inducible expression system to generate an immortalized basophilic erythroblast line that is largely representative of primary HSCs (Fig. 4.1A). We successfully generated multiple stably proliferating cell lines by enriching PBMCs from small volumes of peripheral blood, ranging from ~100 microliters (1.0x105 PBMCs) to ~50 mL (5.7x107 PBMCs; Fig. 4.1B-C). Differentiation of the EJ cell line via the step-wise removal of doxycycline (Table 1) yielded orthochromatic erythroblasts after eight days (Fig. 4.2D-E). Throughout erythropoiesis, EJ cells mirrored primary HSCs with respect to both morphology and the expression of stage-diagnostic cell surface proteins (Fig. 4.2C-E).

While several erythroid lines produced by other groups exhibit terminal differentiation defects and very little enucleation (Hirose et al., 2013; Kurita et al., 2013), ejRBCs enucleate at a rate of ~3-5% (Appendix

4.1). Although this enucleation rate is eclipsed by erythroid lines generated by Huang et al. (2014) (~30%) and Trakarnsanga et al. (2017) (“up to 30%”), these cell lines were immortalized at an earlier stage of erythropoiesis and require 16 and 18 days for terminal differentiation, respectively. By contrast, terminal differentiation of ejRBCs is accomplished in eight days of culture, reducing the effort and resources necessary to derive orthochromatic erythroblasts. Thus, this shortened differentiation time offers a practical advantage over other cell lines in applications for which enucleation is not critical (e.g., studies of malaria invasion; forward genetic screens). For applications that prioritize enucleated cells, the EJ cell culture can be expanded, differentiated, and filtered to produce a pure population of enucleated cells as appropriate.

Nevertheless, efforts to further optimize the enucleation rate of the EJ cell line are underway.

Given that malaria parasites can successfully invade orthochromatic erythroblasts, the EJ line

93 constitutes a useful model system for studies of malaria invasion. This study demonstrates that terminally differentiated ejRBCs express all known Plasmodium receptors at comparable levels to reticulocytes (Fig.

4.3A). Terminally differentiated ejRBCs also supports invasion of both P. falciparum and P. vivax, though invasion efficiency of these parasites is higher in normocytes and reticulocytes, respectively (Fig. 4.3B).

Nevertheless, the efficiency of invasion into terminally differentiated ejRBCs (P. falciparum: ~60% efficiency in ejRBCs relative normocytes; P. vivax: ~30% invasion efficiency in ejRBCs relative to reticulocytes) is sufficient to detect an invasion phenotype in genetic perturbation experiments. Specifically, generation of a CRISPR/Cas9-mediated disruption of the DARC gene in this cell line strongly inhibited P. vivax and P. knowlesi invasion (Fig. 4.4F), highlighting the dependence of these parasites on the DARC receptor, supporting previous studies (Barnwell et al., 1989; Grimberg et al., 2007).

Further, transgenic expression of human, chimpanzee, and gorilla DARC alleles in this DARC disruptant line (Fig. 4.5) highlights the utility of EJ cells for studies of malaria host-specificity. For example, the origin of P. vivax in the human population has been traced to an ancient cross-species transmission event originating in African great apes (Liu et al., 2014). Human P. vivax isolates form a monophyletic clade embedded within the diversity of P. vivax-like parasites of chimpanzees and gorillas (Liu et al., 2014), suggesting that molecular factors (e.g., divergence in DARC proteins) may restrict cross-species transmission of these parasites on contemporary timescales. However, ape-to-human P. vivax transmission has recently been documented in a European (i.e., DARC+) traveler (Prugnolle et al., 2013), suggesting that this phylogenetic restriction may rather be attributable to the high rates of DARC negativity in the African populations that are most frequently exposed to these parasites. By serving as the substrate for P. vivax invasion assays, ejRBCs expressing African great ape DARC proteins will allow for discrimination between these hypotheses.

While no previous study has characterized the suitability of experimentally immortalized erythroid lines for studies of malaria invasion, the JK-1 erythroleukemia line has been employed to identify and confirm

P. falciparum and P. vivax receptors (Gruszczyk et al., 2018; Kanjee et al., 2017). Although the EJ and JK-

1 lines each have benefits and demerits, the former offers several advances over the latter. First, the EJ cell

94 line differentiates more rapidly than does JK-1, which requires 10-14 days for differentiation. As discussed above, a more rapid differentiation time offers a series of practical benefits with regard to researcher effort and financial burden. Second, JK-1 exhibits a terminal differentiation defect. During differentiation, ~98% of JK-1 cells arrest as polychromatic erythroblasts, whereas more than 60% of EJ cells reach the orthochromatic stage. Thus, the more advanced stage of erythropoiesis of ejRBCs replicates terminally differentiated primary cells with higher fidelity. Third, the method used to generate the EJ line can be replicated to create novel cell lines across a variety of genetic backgrounds, whereas the discovery of novel erythroleukemia cell lines depends upon serendipity. Nevertheless, the JK-1 line does appear to be superior to the EJ line in other regards. In particular, JK-1 supports a level of invasion by P. falciparum strains 3D7 and Dd2 that is more comparable to primary normocytes, whereas the efficiency of parasite invasion into ejRBCs is reduced by comparison. However, future efforts to manipulate the EJ cell line via genetic engineering (e.g., overexpression of particular Plasmodium receptors) may further enhance the utility of this model system for studies of malaria invasion.

Malaria pathogenesis ranks among the strongest selective pressures to confront the human species during the last 50,000 years (Carter & Mendis, 2002; Hedrick, 2012; Williams, 2006), and proteins expressed upon the RBC membrane have been particular targets of parasite-induced selection. The capacity to generate immortalized erythroid progenitor cell lines from small volumes of peripheral blood will allow for the creation of customized model systems from a wider diversity of patients. The EJ cell line produced via this method expands indefinitely, differentiates rapidly, is representative of primary HSCs, supports

Plasmodium invasion, and is conducive to genetic manipulation. Application of this peripheral blood immortalization method to human populations living in malaria endemic regions will grant researchers access to a renewable source of erythroid cells harboring natural polymorphisms of interest. Accordingly, by offering to expand the diversity of cell lines available to researchers, this study stands to catalyze future studies of the host determinants of malaria susceptibility.

4.5 Methods

95 Generation of the EJ cell line

Anonymized discarded human blood samples used in this study were obtained under Boston Children’s

Hospital IRB protocol # 04-02-017R. We centrifuged 50 mL of peripheral whole blood at 3000 RPM for 5 min, collected the serum, and washed packed blood twice in RPMI media. Packed blood was then resuspended in 2 mM EDTA-PBS at 50% hematocrit. To enrich PBMCs, we gently layered 8 mL of this packed blood-EDTA-PBS suspension on 4 mL of Ficoll Paque (GE Healthcare) in 15 mL conical tubes

(BD Falcon) and repeated this procedure for the remainder of the sample. Samples were centrifuged at 1200 x g for 25 minutes at room temperature using low acceleration/deceleration. Upon completion, we harvested the buffy coat and washed PBMCs twice in maintenance media (see below) without doxycycline. Cells were counted using a hemocytometer and light microscope, resuspended in T-flasks (BD Falcon) at a

o concentration of 500,000 cells/mL, and incubated at 37 C in a humidifier chamber with 5% (vol/vol) CO2.

After 24 hours, PBMCs were washed in pIMDM media (see below) without octaplasma, divided into three experimental conditions (i.e., 1.0x105 PBMCs, 2.0x105 PBMCs , and 5.7x107 PBMCs) and transduced with the lentiviral vector CSIV-TRE-HPV16-E6/E7-UbC-KT (Kurita et al., 2013). Lentivirus was generated via transfection of 293T cells (a kind gift of Robert Doms) and concentrated 27:1 via ultracentrifugation.

Following transduction, cells were cultured in pIMDM media without doxycycline at a concentration of

500,000 cells/mL and incubated as above. After four days, 1 µg/mL doxycycline and 1 µM dexamethasone were added to the culture media, and cells were monitored for 65 days post-transduction. During this period, we changed culture media every 3-4 days and monitored cell concentration to ensure that cells did not exceed 1,000,000 cells/mL. Once cultures exhibited a stable rate of expansion and senescent cells had lysed, cells were cloned in 96-well plates via limiting dilution, yielding immortalized erythroid progenitor cell lines.

Maintenance and differentiation of EJ cells

During both maintenance and differentiation, EJ cells were cultured in Iscove’s modified Dulbecco’s medium (IMDM; Biochrom) supplemented with 4 mM L-glutamine (Sigma-Aldrich), 330 µg/mL iron-

96 saturated human holo-transferrin (BBI solutions), 10 µg/mL recombinant human insulin (Sigma-Aldrich),

2 IU/mL heparin sodium salt (Affymetrix), and 0.5% Pen/Strep (Life Technologies). Hereafter, we refer to this primary media as “pIMDM.” Maintenance media was comprised of pIMDM supplemented with 5% solvent/detergent virus-inactivated plasma (octaplasma; Octapharma), 3 IU/mL erythropoietin (EPO;

Roche), 50 ng/mL stem cell factor (SCF; R&D systems), 1 µg/mL doxycycline (Sigma-Aldrich), and 1 µM dexamethasone (Sigma-Aldrich). We maintained EJ cells at a concentration of 50,000-750,000 cells/mL, changing media every 3-4 days. Every 2-3 weeks, or if cell concentration eclipsed 1,000,000 cells/mL, the culture was enriched for basophilic erythroblasts via a 40% (vol/vol) Percoll-PBS density gradient. To do so, cells were resuspended in 4 mL of pIMDM media supplemented with 0.1% bovine serum albumin

(BSA), layered on 4 mL of 40% Percoll-PBS, and centrifuged at 1000 x g for 10 minutes with low acceleration/deceleration. Upon completion, we harvested and washed the interface three times in pIMDM before resuspending in culture media.

To induce differentiation, EJ cells were seeded at 200,000 cells/mL and cultured for 3 days in pIMDM supplemented with 5% octaplasma, 3 IU/mL EPO, 10 ng/mL SCF and 0.5 µg/mL doxycycline. On day 3, cells were counted and seeded at 500,000 cells/mL in pIMDM containing 5% octaplasma and 3 IU/mL

EPO. Cells were maintained in this media for the remainder of differentiation. We changed media on day

5 and reseeded cells at 500,000 cells/mL. Terminally differentiated cells were harvested on either day 8 or

9 of differentiation via 40% Percoll-PBS gradient. In this case, we retained the pellet, which contained terminally differentiated cells and discarded the interface. To monitor EJ cell differentiation, cytospins were taken routinely, fixed in methanol, and stained with May-Grünwald (Sigma-Aldrich) followed by Giemsa

(Sigma-Aldrich) according to the manufacturer’s instructions as described previously (Egan et al., 2015).

We used cytospins taken during differentiation to quantify the progression of EJ cells and primary HSCs along the erythroid lineage (basophilic erythroblast, polychromatic erythroblast, orthochromatic erythroblast, reticulocyte) using established morphological diagnostic criteria (Kaushansky et al., 2015;

Rozenberg, 2011).

97 Flow cytometry

Cells were washed twice in 1x PBS and pelleted in a 96-well plate. For conjugated antibodies, cells were stained in 100 µl of flow buffer (0.1% BSA in 1x PBS) for 20 minutes at 4oC in the dark. Cells were washed three times in flow buffer and resuspended in 200 µl of flow buffer prior to reading. We used the following antibodies at the indicated dilutions: anti-CD34 FITC (1:11; Miltenyi Biotec), anti-CD45 Violet

Blue (1:11; (Miltenyi Biotec), anti-CD117 APC (1:50; Clone REA996; Miltenyi Biotec), anti-CD49d PE-

Violet 770 (1:11; Miltenyi Biotec), anti-CD36 Violet Blue (1:11; Miltenyi Biotec), anti-CD71 APC (1:11;

Miltenyi Biotec), anti-CD235a FITC (1:5; Clone 2B7; Stem Cell Technologies), anti-basigin FITC (1:10;

Invitrogen), anti-CD44 APC (1:11; Miltenyi Biotec), anti-CD55 APC (1:11; Miltenyi Biotec), anti- glycophorin C FITC (BRIC10; 1 µg per million cells; Santa Cruz), and anti-glycophorin A/B (1:8000; clone E3; Sigma-Aldrich). To measure GPB expression, cells were trypsinized with 50 µl of 1 mg/mL trypsin (Sigma-Aldrich) in incomplete RPMI for 1 hour at 37oC to remove GPA. Trypsin was deactivated with complete RPMI and cells were stained with the primary antibody for 30 minutes at 4oC followed by

Alexa Fluor 488 goat anti-mouse secondary antibody (1:1000; Invitrogen) for 30 minutes at 4oC. To measure DARC, we stained cells with an HA-tagged nanobody (a kind gift from the Chris King lab) at

1:100 dilution for 1 hour at room temperature. Samples were then washed 3 times in flow buffer and stained with a rat anti-HA antibody (1:1000; clone 3F10; Roche). Lastly, samples were washed 3 times in flow buffer and stained with goat anti-rat Alexa Fluor 488 conjugated antibody (1:500; Invitrogen). Prior to running cells on a flow cytometer, cells were resuspended in 200 µl staining buffer containing propidium iodide (PI, for the erythropoiesis markers) or in 200 µl of PI and Vybrant Dicycle Violet (1:10,000;

Invitrogen for the receptors) and incubated in the dark for 20 minutes at 37oC. Cells were run on a

MACSQuant flow cytometer (Miltenyi Biotec) and data were analyzed using FlowJo (version 10.4).

Enucleation rate of ejRBCs was estimated via flow cytometry using Vybrant Dicycle Violet DNA dye.

After gating out dead cells, the DNA negative population (i.e., enucleated ejRBCs) was determined using an unstained control.

98 CRISPR/Cas9-mediated perturbation of the DARC gene

CRISPR/Cas9-mediated genetic perturbations of the EJ cell line were generated via adaptation of previously published protocols (Sanjana et al., 2014; Shalem et al., 2015). We used the lentiCRISPR v2 system (Sanjana et al., 2014) to co-express the Cas9 enzyme and a single-guide RNA targeting the second exon of the DARC gene in EJ cells via lentiviral transduction. Transduced cells were selected on 2 µg/mL puromycin (Sigma-Aldrich) for one week. Resistant cells were then cloned via limiting dilution in 96-well plates and monitored for up to two weeks, changing media ever 7 days. Clones that expanded in culture were screened for indels in DARC via Sanger sequencing and Tracking of Indels by Decomposition (TIDE)

(Brinkman et al., 2014) using the DARCseqgRNA3_Fwd (5’-TGCTGGATGACTC TGCACTG-3’) and

DARCseqgRNA3_Rev (5’-GTTCAGCAGCAGGTCCAGAG-3’) primer set. The chromatograms of clones that exhibited evidence of bi-allelic indels via TIDE analysis were manually inspected to identify the location of indels within the sequence. Sequences were then translated to identify the effect of these genetic perturbations on the sequence of the protein. The functional effect of in-frame indels was evaluated bioinformatically using Protein Variation Effect Analyzer (PROVEAN). Finally, we compared DARC expression in wildtype and DARC knockout lines via flow cytometry and confirmed the phenotype via

Plasmodium invasion assays (see below) involving DARC-dependent parasites P. vivax and P. knowlesi.

To express human, chimpanzee, and gorilla DARC alleles in the ΔDARC cell line, the respective sequences were cloned into the pLVX-FADD-DD vector (Valiente et al., 2014) (a kind gift from Joan

Massague; Addgene plasmid #58263). We included synonymous mutations in the sgRNA binding site of these DARC sequences to prevent the constitutively-expressed Cas9 from cutting these transgenes. We also substituted the CMV promoter for a minimal beta globin promoter to increase expression of the transgene in erythroid cells. We then expressed the transgenic DARC alleles in the ΔDARC cell line via lentiviral transduction. Cells were selected on 200 µg/mL Hygromycin B (Sigma-Aldrich) for two weeks, and expression was confirmed via flow cytometry.

99 Invasion assays

Plasmodium falciparum and P. knowlesi cultures were maintained in human O-positive erythrocytes

(Interstate Blood bank) at 2% hematocrit in complete RPMI media with 0.5% albumax and 0.2% sodium

o bicarbonate at 37 C with 5% CO2 and 1% O2 mixed gas. Plasmodium vivax isolates were obtained from

Brazil, and were maintained and enriched as previously described (Rangel et al., 2018).

Prior to performing invasion assays, ejRBCs were passed through a 40% Percoll-PBS gradient to remove dead cells and undifferentiated erythroblasts as described above. The pellet was retained and washed in complete RPMI media. Invasion assays were performed in the respective parasite maintenance media in 2-3 technical replicates at 0.1-0.25% hematocrit and 0.5-2% initial parasitemia. We took cytospins immediately after initiation of the invasion assay, as well as 16-24 hours post-invasion. Parasitemia was quantified using a reticle and light microscopy and presented either as PEMR (parasitized erythrocyte multiplication rate) or invasion efficiency (parasitemia at 16-24 hours normalized to the corresponding control as indicated).

Statistical analyses

Principal components analysis (PCA) of erythropoiesis markers CD34, CD45, CD36, CD49d, CD71, and GPA was performed in R (version 3.4.3) using the ‘pcaMethods’ package of Bioconductor

(https://www.bioconductor.org/packages/release/bioc/html/pcaMethods.html). MFI input data for PCA were derived from flow cytometry of EJ cells and primary HSCs at multiple points during differentiation

(EJ cells: day 0, day 3, day 5, day 8, and day 9; primary HSCs: day 7, day 10, day 12, and day 18). All other statistical tests were also performed in R (version 3.4.3).

4.6 Author Contributions

Erik J. Scully wrote this chapter, generated all figures and tables, generated the immortalized cell lines, enriched PBMCs from peripheral blood via density gradient, generated lentivirus, performed lentiviral transductions, cultured EJ cells, cultured primary HSCs, enriched reticulocytes from peripheral blood, took

100 cytospins, cloned cells, differentiated EJ cells, staged EJ cells, performed a subset of P. falciparum invasion assays, quantified parasitemia and invasion efficiency, generated CRISPR/Cas9-mediated disruptions of the DARC gene, cloned transgenic DARC alleles into expression vectors, expressed transgenic human, chimpanzee, and gorilla DARC alleles in the EJ cell line via lentiviral transduction, contributed to flow cytometry, performed PCA analysis, performed PROVEAN analysis, and performed all statistical analyses.

E.S. cultured EJ cells, cultured primary HSCs, performed flow cytometry, and performed P. falciparum invasion assays. G.W.R. and M.A.C. performed P. vivax invasion assays. M.C. cultured EJ cells and performed P. falciparum invasion assays. C.G. and U.K. cultured EJ cells and provided feedback concerning the expression of transgenic DARC alleles in EJ cells. R.K. and Y.N. provided the HPV 16-

E6/E7 vector. C.B. collected the peripheral blood sample from a deanonymized hemochromatosis patient.

M.T.D. and E.S. offered revisions of the manuscript. All authors contributed to the intellectual content of this article.

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105 Chapter 5

Lethal Respiratory Disease Associated with Human Rhinovirus C in Wild Chimpanzees, Uganda, 2013

Erik J. Scully, Sarmi Basnet, Richard W. Wrangham, Martin N. Muller, Emily Otali, David Hyeroba, Kristine A. Grindle, Tressa E. Pappas, Melissa Emery Thompson, Zarin Machanda, Kelly E. Watters, Ann C. Palmenberg, James E. Gern, Tony L. Goldberg

This chapter was published in Emerging Infectious Diseases as “Lethal Respiratory Disease Associated with Human Rhinovirus C in Wild Chimpanzees, Uganda, 2013” (Scully et al., 2018).

5.1 Abstract

We describe a lethal respiratory outbreak among wild chimpanzees in Uganda in 2013 for which molecular and epidemiologic analyses implicate human rhinovirus C as the cause. Postmortem samples from an infant chimpanzee yielded near-complete genome sequences throughout the respiratory tract; other pathogens were absent. Epidemiologic modeling estimated the basic reproductive number (R0) for the epidemic as 1.83, consistent with the common cold in humans. Genotyping of 41 chimpanzees and examination of 24 published chimpanzee genomes from subspecies across Africa showed universal homozygosity for the cadherin-related family member 3 CDHR3-Y529 allele, which increases risk for rhinovirus C infection and asthma in human children. These results indicate that chimpanzees exhibit a species-wide genetic susceptibility to rhinovirus C and that this virus, heretofore considered a uniquely human pathogen, can cross primate species barriers and threatens wild apes. We advocate engineering interventions and prevention strategies for rhinovirus infections for both humans and wild apes.

5.2 Introduction

Rhinoviruses are antigenically diverse members of the family Picornaviridae, genus Enterovirus, that cause the common cold (Jacobs et al., 2013). Rhinovirus C causes an estimated 50% of all human upper respiratory tract infections (Jacobs et al., 2013) and most acute exacerbations of asthma in children (Steinke

& Borish, 2016). More than 160 distinct rhinovirus genotypes have been identified in human populations

106 globally, and extensive antigenic heterogeneity limits cross-protective immunity (Palmenberg & Gern,

2015). Accordingly, persons typically acquire several rhinovirus infections annually throughout childhood and continue to acquire new infections throughout adulthood (Saraya et al., 2014).

Although many rhinovirus infections are only mildly symptomatic, rhinovirus C has been associated with influenza-like respiratory symptoms and acute exacerbations of asthma in children (Bizzintino et al.,

2011). The elevated virulence of rhinovirus C derives from its unique reliance on the cadherin-related family member 3 (CDHR3) receptor for host cell binding (Liu et al., 2016). A single-nucleotide polymorphism in CDHR3 (rs6967330, C529Y) is associated with a 10-fold increase of RV-C binding and progeny yield and is a major risk factor for rhinovirus C infection and asthma (Bochkov et al., 2015). The protective CDHR3-C529 nonrisk allele has been detected exclusively in modern humans, whereas archaic

Neanderthal and Denisovan humans each expressed the ancestral risk allele (Mathieson et al., 2015). The origin of rhinovirus C and the spread of the protective allele among modern human populations is thought to have occurred ~8,000 years ago (Palmenberg, 2017).

Humans and chimpanzees (Pan troglodytes) are closely related species that share many physiologic similarities, including a general predisposition for pathogen exchange, which, in some cases, might lead to human pandemics (Sharp et al., 2013). Anthroponotic (i.e., of human origin) respiratory viruses have caused notable mortality rates in wild chimpanzee populations (Grützmacher et al., 2016; Köndgen et al., 2008,

2010; Palacios et al., 2011), and molecular testing of both postmortem and noninvasive (fecal) samples from wild apes during respiratory disease outbreaks have implicated human paramyxoviruses (family

Paramyxoviridae) as anthroponotic causes (Grützmacher et al., 2016; Köndgen et al., 2008, 2010; Palacios et al., 2011). However, because of the limitations of field-based diagnostics, direct detection of pathogens from lesions of the respiratory tract has proven difficult (Grützmacher et al., 2016; Köndgen et al., 2010).

In this study, we were able to identify rhinovirus C, a pathogen not previously known to infect species other than humans, as the causative agent of an epidemic of respiratory disease in chimpanzees by directly sampling the lesions of a dead animal and by subsequent noninvasive sampling (from feces) and epidemiological modeling.

107 5.3 Methods

Ethics Statement

This study involved only observational, noninvasive research with wild chimpanzees. All animal protocols were approved by the Harvard University Institutional Animal Care and Use Committee

(Cambridge, MA, USA; protocol 96-03) and the University of New Mexico Office of Animal Care

Compliance (Albuquerque, NM, USA; protocol 11-100726-MCC). These protocols adhered to guidelines and laws set forth by the Weatherall Report on the use of nonhuman primates in research

(https://royalsociety.org/policy/publications/2006/weatherall-report/), as well as the Guide for the Care and

Use of Laboratory Animals of the National Institute of Health, Office of Animal Welfare, US Department of Agriculture Animal Welfare Act, Institute for Laboratory Animal Research Guide for the Care and Use of Laboratory Animals, US Public Health Service, US National Academies of Sciences National Research

Council, and US Centers for Disease Control and Prevention.

Sample Collection and Sequence Analysis

Starting approximately in February 2013 and continuing through August, the Kanyawara community of chimpanzees in Kibale National Park, western Uganda, experienced an outbreak of severe respiratory disease. At the onset of the epidemic, the community consisted of 56 chimpanzees <1 week‒56.9 years of age. All Kanyawara chimpanzees are individually identifiable and habituated to human observers, such that trained field assistants collected direct data on behavior and respiratory signs daily. We compiled these data into weekly measures of clinical signs (coughing or sneezing, further classified as mild or severe) and deaths. During June‒August 2013, we collected fecal samples from 41 chimpanzees. Immediately after observing defecation by a chimpanzee, we placed 5 mL of the fecal sample into an equal volume of

RNAlater buffer (Thermo Fisher Scientific, Waltham, MA, USA), homogenized the mixture, and stored the sample at -20°C for up to 3 months, until export to the United States.

One chimpanzee showing clinical signs (Betty, a 2.2-year-old female) died during the outbreak; her body was recovered immediately after death. An experienced veterinarian (D.H.) performed the

108 postmortem examination and collected samples from her oropharynx, trachea, and lung using swabs (sterile, plastic shaft with Dacron tip) and stored the samples separately in 0.25 mL of RNAlater buffer at -20°C.

We homogenized the swab tips, extracted sample RNA, and converted the RNA to double-stranded cDNA in the field. We shipped the cDNA to the United States for unbiased sequencing and pathogen discovery on an Illumina MiSeq instrument (Illumina, San Diego, CA, USA) using 300-bp paired-end read chemistry as previously described (Toohey-Kurth et al., 2017).

We analyzed sequence data using CLC Genomics Workbench version 8.5 (CLC bio, Aarhus,

Denmark). In brief, we trimmed low-quality bases (phred quality score <30), discarded short reads (<75 bp), and subjected the remaining reads to de novo assembly. We then analyzed raw sequence reads and assembled contiguous sequences (contigs), which we examined for similarity to known viruses in GenBank databases.

From these analyses, we inferred the presence of a rhinovirus, which we subsequently identified as a member of the rhinovirus C species. We performed pairwise sequence comparisons in MEGA7 (Kumar et al., 2016) to assess the genetic similarity of this virus to its relatives, and we evaluated recombination with other rhinovirus C strains by using RDP4 (Martin et al., 2015). We performed phylogenetic analyses on this virus and all complete rhinovirus polyprotein genes available in the GenBank database. We first aligned genes by codon using the MAFFT algorithm (Katoh et al., 2002) implemented in Translator X (Abascal et al., 2010) and removed poorly aligned regions using the Gblocks algorithm (Talavera & Castresana, 2007).

We then used jModeltest (Santorum et al., 2014) to estimate the model of molecular evolution from the data and PhyML (Guindon et al., 2010) for phylogenetic inference.

We tested chimpanzee fecal samples for a suite of respiratory pathogens by multiplex Luminex assay using the NxTAG Respiratory Pathogen Panel (Luminex Corporation, Austin, TX, USA) (Krunic et al.,

2011) and confirmed positive results by singleplex PCR. The NxTAG Respiratory Pathogen Panel includes influenza virus A (multiple subtypes), human respiratory syncytial viruses A and B, coronaviruses (multiple subtypes), human metapneumovirus, rhinovirus/enterovirus, adenovirus, parainfluenza viruses 1–4, bocavirus, and the bacterial pathogens Chlamydophila pneumoniae and Mycoplasma pneumoniae. In brief,

109 we added up to 50 µL of fecal suspension to 350 µL of saline; homogenized the mixture; and clarified the mixture by centrifugation at 15,000 rpm for 3 minutes, after which we added Luminex internal control MS2 before nucleic acid extraction with the NucliSENS EasyMag kit (bioMérieux, Marcy-l’Étoile, France). We then used 10 µL of eluate in the Luminex assay according to the manufacturer’s specifications. We applied this same assay to swab samples and assessed the viral load of rhinovirus C‒positive swab samples using a published real-time quantitative PCR (qPCR) (Bochkov & Gern, 2012).

CDHR3 Genotyping

Given the influence of CDHR3 allelic variants on rhinovirus C pathogenesis in humans, we genotyped the CDHR3 allele of 41 chimpanzees from the Kanyawara community using their fecal specimens. In brief, we extracted DNA from fecal samples using the MagMAX Kit (Thermo Fisher Scientific), and we then genotyped the extracted DNA using a qPCR-based allelic discrimination assay (CDHR3 TaqMan SNP

Genotyping Assay; Thermo Fisher Scientific). We assayed serial 2-fold dilutions of eluted DNA to identify the dilution yielding the strongest fluorescent signal; then, we tested 2 additional replicates of each sample at that optimal dilution. We also examined the CDHR3 locus of 24 additional chimpanzees from across

Africa (including 4 recognized subspecies) using whole genome sequences previously published as part of the Great Ape Genome Project (Prado-Martinez et al., 2013).

Epidemiologic Modeling

To infer epidemiologic parameters related to transmission, we constructed an SIR (susceptible- infectious-removed) mathematical model. Following Althaus et al. (2014), we fit the SIR model to cumulative incidence data and produced maximum-likelihood estimates of epidemiologic parameters (e.g., transmission rate, recovery rate) using the Nelder and Mead optimization algorithm in the optim package in R version 3.3.2 (https://www.r-project.org/). Because the epidemic was triphasic, we fit each of the three

2013 respiratory episodes separately. We assumed a well-mixed population of 50 chimpanzees and did not

110 explore heterogeneity of social interactions to minimize model complexity and provide baseline estimates of epidemiologic parameters (Appendix 5.1).

5.4 Results

The epidemic occurred in 3 phases (Fig. 5.1): an early phase (21 days in February‒March 2013), during which 24 animals became ill and 1 infant died; a middle phase (22 days in May‒June 2013), during which

40 animals became ill and 4 adults died; and a late phase (19 days in August‒September 2013), during which 31 animals became ill and none died. Of the ~56 chimpanzees in the community at the beginning of

2013, five died during the outbreak (1 infant 2.2 years of age and 4 adults 24.0‒57.9 years of age), for an overall mortality rate of 8.9%.

A postmortem analysis was conducted on the infant that died in March (peak of the early phase); other animals that died were not recovered in time for such analyses. The postmortem analysis revealed ecchymotic hemorrhages on the lung surfaces, consolidation of the parenchyma of both lungs

(approximately two thirds of both lungs affected), mucoid exudate in the bronchi, hepatomegaly, and hepatic congestion. On the basis of these findings, the cause of death was concluded to be severe acute pneumonia. Deep sequencing of swab samples from this chimpanzee’s oropharynx, trachea, and lung yielded 10,722,035 sequence reads, of which 8,918 had high similarity to rhinovirus C. No other pathogens were detected. Subsequent qPCR confirmed infection with RV-C, with viral loads of 7.41 × 106 copies/swab in the oropharynx, 1.05 × 107 copies/swab in the trachea, and 1.74 × 106 copies/swab in the lung; these values are comparable to the average viral load (7.76 × 106 copies/mL) found in rhinovirus C‒infected children with acute wheezing illness treated in an emergency department (Sikazwe et al., 2016). Further sequencing yielded a near-complete rhinovirus C genome consisting of a complete polyprotein gene of

6,450 bases, a complete 3′-untranslated region (UTR) of 35 bases excluding the poly(A) tail, and a partial

5′-UTR of 480 bases. The viral polyprotein sequence was 94.59% similar at the nucleic acid level and

99.99% similar at the amino acid level to its closest known relative, a rhinovirus C45 sequence (GenBank accession no. JN837686) from a 2-month-old male patient who had a history of mild nasal congestion and

111 discharge in the United States in 2000. RDP4 analyses showed the chimpanzee-derived sequence to be a rhinovirus C45-C11 recombinant, with a breakpoint in the 5′-UTR (Fig. 5.2). In phylogenetic analyses, the virus clustered within known rhinovirus C genotypes, appearing as a sister taxon to the reference rhinovirus

C45 isolate (Fig. 5.3). The chimpanzee-derived strain was designated RV-C45-cpz1-2013 to indicate its presumptive serotype and discovery in a chimpanzee in 2013 (GenBank accession no. KY624849).

We confirmed that all swab samples from the deceased infant chimpanzee were positive for rhinovirus

C by Luminex assay. Two of the 41 fecal samples collected during the epidemic were positive for enteroviruses, but subsequent molecular typing using published methods (Bochkov et al., 2014) demonstrated these to be non-rhinovirus enteroviruses, which occur in wild apes without known clinical significance (Grützmacher et al., 2016; Harvala et al., 2012; Sadeuh-Mba et al., 2014). Fifteen samples were also positive for adenoviruses by Luminex assay. Subsequent typing of these viruses by PCR and direct sequencing of a portion of the hexon gene according to published methods (Lu & Erdman, 2006) was successful for 11 samples. The resulting sequences (GenBank accession nos. KY624838‒KY624848) were identical or closely related to adenoviruses found in fecal samples from apparently healthy wild chimpanzees and other nonhuman primates (Hoppe et al., 2015; Nkogue et al., 2016; Wevers et al., 2011)

(Appendix 5.2).

Genotyping of chimpanzee DNA from fecal samples showed all 41 animals to be homozygous for the

CDHR3-Y529 allele, which is associated with increased susceptibility to rhinovirus C and wheezing illness in humans (Bochkov et al., 2015). Similarly, all 24 chimpanzee genomes from the Great Ape Genome

Project (Prado-Martinez et al., 2013) were homozygous for the CDHR3-Y529 allele.

Epidemiologic modeling of the 2013 chimpanzee respiratory epidemic yielded daily transmission rate estimates of 0.85, 0.44, and 0.62 (average 0.68) and duration of infection estimates of 1.6 days, 5.8 days, and 2.1 days (average 3.2 days) for the 3 phases of the epidemic. Together, these parameters equate to basic reproductive numbers (R0 values) of 1.38, 2.56, and 1.56 for the 3 phases of the epidemic, with an overall

R0 estimate of 1.83 (Appendix 5.1).

112

Figure 5.1 | Epidemic curve of respiratory illness in the Kanyawara chimpanzee community, Uganda, 2013. Observational data on clinical severity (mild or severe) of respiratory signs (coughing and sneezing) were obtained and compiled into weekly measurements. The proportions of animals showing signs of respiratory illness are displayed by severity. Dashed line indicates 2013 mean rate of respiratory signs, and dotted line indicates 2 SD above that mean. Asterisks above bars indicate the timing of individual animal deaths.

113

Figure 5.2 | Recombination between viral genotypes rhinovirus C45 and C11 leading to RV-C45- cpz1-2013, the strain identified in the Kanyawara chimpanzee community, Uganda, 2013. Analyses were performed in RDP4 (Martin et al., 2015) on aligned rhinovirus C genome sequences of 36 known genotypes. Each alignment entry encoded the full or nearly full polyprotein gene sequence, but some sequences were missing fragments (<400 bp) of their respective 5′-UTRs (Δ seq, yellow box at left). The 3′ poly(A) tail was not included. A recombination event between the 2 viruses shown (GenBank nos. JN837686 and EU840952) is the most likely event among all full alignment comparisons (window size 20 bps) according to 6 of the 9 RDP4 algorithms. The average p values were RDP 2.8 × 10-81, GENECONV 3.0 × 10-70, MaxChi 1.4 × 10-18, Chimaera 2.1 × 10-21, SiScan 3.6 × 10-34, and 3Seq 1.5 × 10-27. BootScan, PhylPro, and LARD made no call for these particular parents. Purple box in the 5′-UTR denotes the 99% breakpoint confidence level (combined). Dashed box indicates the position of the most likely swapped fragment. The (Monte Carlo corrected) probability for this event is 2.8 × 10-81. The virus map is scaled to the alignment. Conf, confidence level; RV-C, rhinovirus C; Δ seq, missing sequence; UTR, untranslated region.

114

Figure 5.3 | Phylogenetic tree of rhinovirus C variants. The tree was constructed from a codon-based alignment (6,234 positions) of the new chimpanzee-derived sequence identified in the Kanyawara chimpanzee community, Uganda, 2013 (indicated by the asterisk and chimpanzee silhouette), and all human-derived RV-C complete polyprotein gene sequences available in GenBank as of December 18, 2016, with rhinoviruses A and B from the RefSeq database included as outgroups. We created alignments using the MAFFT algorithm (Katoh et al., 2002) implemented in the computer program Translator X (Abascal et al., 2010), with the Gblocks algorithm (Talavera & Castresana, 2007) applied to remove poorly aligned regions. We constructed trees using the maximum-likelihood method implemented in PhyML (Guindon et al., 2010), with best-fit models of molecular evolution estimated from the data by using jModeltest (Santorum et al., 2014). Circles on nodes indicate statistical confidence on the basis of 1,000 bootstrap replicates of the data (closed circles 100%; open circles >75%). Scale bar indicates nucleotide substitutions per site. GenBank accession numbers and other details of the RV-C sequences included in the analysis are in Table 5.1.

115 5.5 Discussion

Rhinovirus C is unusual among rhinoviruses because of its clinical severity, unique biochemistry of receptor attachment, and role in modern human evolution (Palmenberg, 2017). The analyses we present show that rhinovirus C is also distinguished by its ability to cross primate species barriers and cause severe disease. Although experiments conducted in the 1960s showed rhinoviruses A and B to be capable of infecting chimpanzees in captivity, infections were mild and self-limiting (Dick, & Dick, 1968; Dick, 1968).

Among other enteroviruses, only poliovirus has been implicated as a cause of death in wild chimpanzees

(Goodall, 1986). By showing that rhinovirus C is highly pathogenic in wild chimpanzees, our findings expand the known host range and clinical consequences of one of the most common causes of human respiratory disease.

Variant RV-C45-cpz1-2013 occupies an unremarkable phylogenetic position among rhinovirus C genomic variants from humans around the world (Fig. 5.3). Although this virus is a recombinant between

2 genotypes, C11 and C45, it is unlikely that this property is related to its origin in a chimpanzee host.

Indeed, rhinoviruses often recombine; the parent strains of RV-C45-cpz1-2013 and the genomic location of the recombination event are similar to those described previously (Palmenberg & Gern, 2015). RV-C45- cpz1-2013 also shares with other rhinovirus C variants the capacity to infect the lower respiratory tract and cause severe disease. Viral load values from this animal’s oropharynx (7.41 × 106 copies/swab), trachea

(1.05 × 107 copies/swab), and lung (1.74 × 106 copies/swab) are comparable to the average viral load (7.76

× 106 copies/mL) found in rhinovirus C‒infected children with acute wheezing illness treated in an emergency department (Sikazwe et al., 2016). Although published estimates of rhinovirus epidemiologic transmission parameters are sparse, the values estimated for the 2013 chimpanzee outbreak (transmission rate 0.68/d; duration of infection 3.2 days) are close to those of common cold viruses in humans

(transmission rate 0.74/d; duration of infection 3.0 days) (Eggo et al., 2016). Despite this similarity, these values are likely similar for many acute respiratory viruses.

Epidemics of respiratory disease have been recorded in ape populations across Africa, but the causes have often remained undiagnosed. For example, during 47 years of wild chimpanzee observation in Gombe,

116 Tanzania, respiratory disease accounted for 48% of illness-related deaths and was the predominant cause of lethal illness in this population (Williams et al., 2008). When noninvasive diagnoses were attempted in other populations, human paramyxoviruses were sometimes implicated (Grützmacher et al., 2016; Köndgen et al., 2008, 2010; Nunes et al., 2014). However, rhinovirus C could have been missed in outbreaks for which the cause was never identified. Standard molecular diagnostics for rhinoviruses might have failed to detect rhinovirus C, which is genetically divergent from rhinoviruses A and B (Bochkov et al., 2014).

Furthermore, rhinoviruses are adapted to the respiratory tract and are acid sensitive, such that virions dissociate below pH 6.0 (Griggs et al., 2015). Because of this property, rhinoviruses are unlikely to survive gastrointestinal transit, and their nucleic acids are unlikely to be found in feces, except during acute and fulminant infections (Honkanen et al., 2013). Noninvasive diagnostics of ape respiratory disease outbreaks might, therefore, be inherently limited in detecting rhinoviruses, which is unfortunate given that the remote locations in which these endangered apes live often preclude invasive diagnostics (Köndgen et al., 2010).

In humans, the CDHR3-Y529 allele is associated with increased susceptibility to rhinovirus C infection

(Bochkov et al., 2015). This elevated risk results from increased cell surface expression of CDHR3, enhanced viral binding to the cellular receptor and increased progeny yields (Bochkov et al., 2015; Liu et al., 2016). GenBank searches of all published nonhuman primate genome sequences suggest that modern humans are the only primate species in which the CDHR3-C529 nonrisk allele occurs (A.C. Palmenberg, unpub. data). In agreement with this finding, all chimpanzees genotyped from the Kanyawara community and across Africa were homozygous for the CDHR3-Y529 risk allele. The fact that only modern humans

(and not Neanderthals or Denisovans) possess the nonrisk allele implies that resistance to rhinovirus C infection was selected for only in modern humans (Palmenberg, 2017). These results highlight a species- wide susceptibility to rhinovirus C infection among chimpanzees.

Our inference about rhinovirus C as the likely cause of the epidemic is based on clinical samples from a single animal early in the outbreak. We consider it unlikely, however, that rhinovirus C was an incidental finding. The virus was found in this animal’s lung, which was a site of severe pathology, at clinically relevant titers, and paramyxoviruses and other respiratory pathogens were not identified in these samples

117 or in fecal samples from the 41 other animals. All adenoviruses sequenced from fecal samples were similar or identical to adenoviruses that have been documented in apparently healthy wild chimpanzees and other primates (Hoppe et al., 2015; Nkogue et al., 2016; Roy et al., 2009; Wevers et al., 2011). Finally, clinical signs during the chimpanzee outbreak bore a striking resemblance to those caused by rhinovirus C in susceptible humans, and epidemiologic transmission parameter estimates were consistent with the common cold in human populations (Eggo et al., 2016). Nevertheless, we cannot rule out adenoviruses or other undetected co-infecting agents as contributing factors because such infections can enhance the clinical severity of rhinovirus C (Jacobs et al., 2013). In this regard, we note that simian immunodeficiency virus, which can cause immunodeficiency in wild chimpanzees (Keele et al., 2009), was not detected and is thought to be absent from this chimpanzee population (Santiago et al., 2003).

Rhinovirus C is genetically diverse and common among the human populations of sub-Saharan Africa

(Palmenberg, 2017). Kibale National Park, wherein the home range of the Kanyawara community lies, is frequented by researchers, tourists, and persons living at the periphery of the park, and chimpanzees sometimes leave the park to raid crops in local villages. Accordingly, myriad pathways exist by which the

Kanyawara chimpanzees could have been exposed to rhinovirus C from humans. Current guidelines for visiting wild apes in Uganda and other countries are based in part on generalized risks for respiratory disease transmission from humans (e.g., quarantine periods for arriving travelers are mandated at Kanyawara)

(Gilardi et al., 2015). We suggest that specific consideration of rhinovirus C and its particular biologic attributes might improve such guidelines. For example, rhinovirus virions are nonenveloped and might therefore persist in the environment for extended periods of time, even maintaining infectivity after desiccation (Reagan et al., 1981).

Long-term records from Kanyawara indicate that respiratory disease outbreaks of varying severity have occurred approximately 2 times per year for at least the past decade, with fatalities occurring every ~2 years, typically among young animals <5 years of age and adults >35 years of age (R.W. Wrangham, unpub. data).

Observations also suggest that respiratory disease outbreaks have occurred in other chimpanzee communities in Kibale at the same times as outbreaks in the Kanyawara community. Kibale contains ~1,500

118 chimpanzees in ~10–20 interconnected communities; that such a population could sustain rhinovirus C or other similar infectious agents in cycles of within-group and between-group transmission is possible, at least for limited time periods (Eggo et al., 2016). If respiratory viruses of human origin can circulate independently in wild chimpanzee populations of sufficient size, this fact would be troublesome not only for chimpanzee conservation but also for human health, in that chimpanzees could also serve as a reservoir for human infections.

Although the mandate that researchers and tourists visiting wild apes wear facemasks is controversial, this practice has successfully reduced the transmission of rhinoviruses and other co-infecting agents that exacerbate rhinovirus clinical severity in hospital settings (Bischoff et al., 2007). Similarly, proper hygiene and the use of hand sanitizer effectively reduce the amounts of rhinovirus on human fingers (Turner et al.,

2010). Decades ago, captive chimpanzees were used as study subjects in biomedical research intended to evaluate putative rhinovirus prevention and treatment options (Dick, & Dick, 1968; Dick, 1968; Huguenel et al., 1997). Our results suggest that these investigations could ultimately benefit the wild relatives of the chimpanzees that were the original subjects of these laboratory experimentations. We advocate building upon existing data to engineer novel interventions and prevention strategies for rhinovirus infections in both humans and wild apes.

119 Table 5.1 | RV-C sequences used in phylogenetic analysis* Accession Virus Isolate NC_001617 RV-A89 RV-A89 NC_001490 RV-B14 RV-B14 NC_009996 RV-C04 24 KY624849 RV-C45† RV-C45-cpz1–2013 JN837686 RV-C45 RV-C45-p1084-s3920–2000 DQ875932 RV-C07 NY-074 EF077279 RV-C01 NAT001 EF077280 RV-C02 NAT045 EF186077 RV-C03 RV-QPM EF582386 RV-C05 25 EF582387 RV-C06 26 EU840952 RV-C11 CL-170085 GQ223227 RV-C08 N4 GQ223228 RV-C09 N10 GQ323774 RV-C10 QCE GU219984 RV-C15 W10 HQ123440 RV-C35 CU072 HQ123443 RV-C01 CU184 JF317013 RV-C25 LZ269 JF317014 RV-C15 LZY79 JF317015 RV-C51 LZ508 JF317016 RV-C06 LZ651 JF317017 RV-C12 LZY101 JF907574 RV-C49 RV-C49-p1102-sR889–2008 JN205461 RV-C39 WA823M02 JN798567 RV-C03 RV-C03-p1280-s6359–1999 JN837688 RV-C15 RV-C15-p1259-s2935–1999 JN990702 RV-C06 RV-C06-p1031-sR2724–2009 JQ245968 RV-C02 RV-C02-p1264-s3775–1999 JX074056 RV-C43 RV-C43-p1154-sR1124–2009 JX291115 RV-C51 JAL-1 KF734978 RV-C06 1515-MY-10 KF958310 RV-C02 6331 KF958311 RV-C41 2536 KJ675505 RV-C42 570-MY-10 KJ675506 RV-C23 8713-MY-10 KJ675507 RV-C22 3430-MY-10 KM486097 RV-C34 Mex14 KP282614 RV-C54 C54-D3490 KP890662 RV-C01 7383-MY-10 KP890663 RV-C12 3805-MY-10 KP890664 RV-C26 8097-MY-11 KX348031 RV-C35 18455–35 *RV-C, rhinovirus C. †Isolate from current manuscript.

120 5.6 Author Contributions

Erik J. Scully wrote this chapter, generated Figure 5.1 and Appendix 5.1, conducted fieldwork in Kibale

National Park during this respiratory outbreak, analyzed CDHR3 alleles derived from Great Ape Genome

Project data, and performed all epidemiological modelling. T.L.G. co-wrote this article, generated Figures

5.2 and 5.3, Appendix 5.2, and Table 5.1, performed viral sequencing and assembly, and performed phylogenetic analyses. R.W.W., M.N.M, E.O., M.E.M., and Z.M. facilitated fieldwork in Kibale National

Park during this respiratory disease outbreak. D.H. performed veterinary post-mortem examinations of chimpanzees that died during this respiratory disease outbreak. S.B., K.A.G., T.E.P., K.E.W., A.C.P., and

J.E.G. performed Luminex assays and performed TaqMan SNP genotyping of CDHR3. All authors contributed to the intellectual content of this article and offered revisions of the manuscript.

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125 Chapter 6

Concluding remarks

Erik J. Scully

Parts of this chapter were adapted from a technical report written for The World Health Organization

Strategic Advisory Group on Malaria Eradication, titled “Non-human primate reservoirs of zoonotic malaria infection: an underappreciated barrier to malaria eradication?” (Scully, 2017).

6.1 Summary

Malaria control efforts have achieved considerable success during recent decades (Bhatt et al., 2015;

World Malaria Report, 2015), but several concerning trends have been documented during this period. In

Southeast Asia, the incidence of zoonotic P. knowlesi has increased and now accounts for over 70% of malaria cases in parts of rural Malaysia (Cox-Singh et al., 2008; Singh & Daneshvar, 2013; William et al.,

2013, 2014). In South America, zoonotic P. simium outbreaks have recently been documented in areas of

Brazil where regional elimination had been achieved over 50 years ago (Brasil et al., 2017). Globally, evidence of primate-to-human transmission of other malaria parasites (i.e., P. cynomolgi of macaques; P. vivax-like parasites of African great apes; P. brasilianum of New World monkeys) has also begun to accumulate (Lalremruata et al., 2015; Prugnolle et al., 2013; Ta et al., 2014). Although advances in the sensitivity of molecular diagnostics may explain this widespread trend, these phenomena point to the unfortunate possibility that the efforts to eliminate human malaria parasites may have inadvertently facilitated the emergence of zoonotic parasites that compete to fill the vacated human erythrocyte niche. If this possibility is confirmed, the incidence of these zoonoses will likely continue to increase moving forward. It is, therefore, critical that we identify and elucidate the barriers to cross-species transmission of non-human primate malaria parasites in order to quantify the threat that they pose to malaria eradication.

The analyses presented in this dissertation offer insight into the ecological, epidemiological, and molecular variables governing the transmission of pathogens among humans and non-human primates. In

126 Chapter 2, we quantify the ecological drivers of malaria parasite transmission among wild chimpanzee hosts in equatorial Africa and map spatiotemporal variation in the risk that these parasites pose to local human populations. In Chapters 3 and 4, we review the molecular mechanisms governing the host-specificity of non-human primate malaria parasites and develop a method for the generation of immortalized erythroid progenitor cell lines from small volumes of peripheral blood, offering a genetically tractable model system for studies of malaria host-specificity. In Chapter 5, we identify human rhinovirus C as the cause of a lethal respiratory outbreak in a community of wild chimpanzees in western Uganda and demonstrate a species- wide genetic susceptibility to this virus, highlighting the profound conservation implications of anthroponotic virus transmission. Taken together, these studies underscore the value of utilizing an interdisciplinary approach to investigate questions related to pathogen cross-species transmission and host- specificity.

In this chapter, I conclude the dissertation by discussing non-human primate malaria parasite diversity in the context of zoonotic disease emergence and global health. I first review historical and contemporary evidence of malaria cross-species transmission by focusing on three high-risk reservoir host clades—apes,

Old World monkeys, and New World monkeys. I then conclude by highlighting key gaps in our understanding of zoonotic disease emergence and briefly propose several productive avenues for future inquiry.

6.2 The zoonotic potential of non-human primate malaria parasites

The primate order is composed of over 500 species in ~70 genera, which shared a common ancestor between 76 and 99 million years ago (Perelman et al., 2011). Although non-human primates are distributed throughout tropical and sub-tropical regions of Africa, Asia, South America, and Central America, over

60% of these species currently face the threat of extinction (Estrada et al., 2017). This threatened status is largely attributable to the expansion of human populations, which has occurred at the expense of primate habitat. As human encroachment intensifies, there is little doubt that novel zoonotic pathogens will continue to emerge. This section tentatively seeks to identify the non-human primate malaria parasites that pose the

127 greatest threat to human populations during ensuing decades.

The primate order can be sub-divided into two major groups: (1) prosimian primates (lemurs, lorises, and tarsiers), and (2) anthropoid primates (apes, Old World monkeys, and New World monkeys). Although prosimian malaria parasites have been described previously (Pacheco et al., 2011), this section focuses exclusively upon parasites of anthropoid primates. Indeed, although over 500 avian, reptilian, and mammalian malaria parasites have been described (Borner et al., 2016), only anthropoid primate reservoirs are known to harbor malaria parasites capable of infecting humans. In the following sections, I describe malaria parasites of three anthropoid primate clades—apes, Old World monkeys, and New World monkeys—and discuss the extent to which these pathogens pose a threat to malaria eradication.

6.3 Malaria parasites of apes

As our closest evolutionary relatives, apes (superfamily: Hominoidea) share a high degree of molecular and cellular homology with humans, which has engendered a general predisposition to cross-species pathogen exchange on contemporary timescales (Calvignac-Spencer et al., 2012; Sharp et al., 2013;

Streicker et al., 2010). Taxonomically, the apes are comprised of the Hylobatids (i.e., gibbons and siamangs, endemic to Southeast Asia), orangutans (endemic to Borneo and Sumatra), and African great apes (i.e., chimpanzees, gorillas, bonobos, and humans). Although malaria parasites of Hylobatids and orangutans have been described previously (Collins et al., 1972; Hayakawa et al., 2008; Kilbourn et al., 2003; Pacheco et al., 2012), very little is known about their ecology, epidemiology, and molecular biology. Additionally, malaria parasitism of wild bonobos has only recently been described (Liu et al., 2017). Future work will be necessary to evaluate whether these primate species constitute relevant zoonotic reservoirs.

In this section, I focus upon malaria parasites of chimpanzees and gorillas. Humans diverged from chimpanzees less than seven million years ago and from gorillas less than nine million years ago (Perelman et al., 2011). Although both chimpanzees and gorillas are endemic to equatorial Africa, chimpanzees occupy a significantly wider habitat range (Junker et al., 2012). During the last decade, non-invasive surveys of African great ape fecal samples have revealed that chimpanzees and gorillas harbor diverse

128 assemblages of malaria parasites, which vary considerably in their zoonotic potential. In the following sections, I focus upon three groups of African great ape malaria parasites: (1) relatives of P. falciparum

(i.e., the Laverania sub-genus), (2) relatives of P. vivax, and (3) other African great ape malaria parasites

(i.e., relatives of P. malariae and P. ovale).

6.3.1 Plasmodium falciparum and the Laverania: a highly host-restricted clade of malaria parasites

Plasmodium falciparum is the most prevalent and malignant human malaria parasite, responsible for

200 million cases and 400,000 deaths annually, primarily in sub-Saharan Africa (Bhatt et al., 2015; World

Malaria Report, 2015). Non-invasive sampling of wild primates has revealed that the origin of P. falciparum is traceable to a sub-genus of African great ape malaria parasites, known as the Laverania (Liu et al., 2010; Prugnolle et al., 2010) (Fig. 3.1). Plasmodium falciparum isolates collected from across the globe form a single monophyletic clade embedded within the diversity of a gorilla parasite, which has been termed P. praefalciparum (Liu et al., 2010). This pattern strongly suggests that the human P. falciparum pandemic emerged from a single gorilla-to-human cross-species transmission event.

Despite the zoonotic origin of P. falciparum, a defining feature of the Laverania is the high degree of contemporary host-specificity that these parasites exhibit. The Laverania cluster into host species-specific clades with little evidence of cross-species transmission, despite geographical overlap in host distributions

(Délicat-Loembet et al., 2015; Liu et al., 2010; Sundararaman et al., 2013). Given that the Laverania are highly prevalent in African great ape populations (Liu et al., 2010) and mosquito vectors of these parasites readily bite humans (Makanga et al., 2016), it is likely that human exposure occurs with regularity in parts of equatorial Africa. These observations suggest that barriers to cross-species transmission of the Laverania are primarily molecular rather than ecological. However, some human-to-ape and primate-to-primate transmission has been documented in captive environments (Duval et al., 2010; Krief et al., 2010;

Ngoubangoye et al., 2016; Pacheco et al., 2013), which demonstrates that these barriers may be porous under particular epidemiological scenarios.

The extensive repertoire of P. falciparum EBL and RBL invasion ligands offers a high degree of

129 functional redundancy during human erythrocyte invasion. Although it was traditionally believed that no single ligand was necessary for erythrocyte invasion by all P. falciparum strains, functional experiments have since identified Rh5 as an essential determinant of erythrocyte invasion (Baum et al., 2009; Cowman

& Crabb, 2006; Hayton et al., 2008). Polymorphism in the Rh5 ligand underlies variation in binding affinity to the erythrocytes of Aotus monkeys, suggesting that this ligand may also be an important determinant of host preferences (Hayton et al., 2008). The Rh5 receptor was subsequently identified to be the Ok blood group protein, Basigin (BSG) (Crosnier et al., 2011).

These findings have spurred researchers to test the hypothesis that variation in Rh5 affinity for non- human primate BSG proteins underlies the strong affinity of P. falciparum for human red blood cells.

Indeed, the P. falciparum Rh5 ligand binds to human BSG with ten-fold greater affinity than to the chimpanzee protein and does not bind to gorilla BSG, an effect attributable to several amino acid substitutions in non-human primate BSG proteins (Wanaguru et al., 2013). These findings suggest that a co-evolutionary arms race between Rh5 and BSG structures the host preferences of P. falciparum. Future studies must determine whether this mechanism reciprocally governs the host-specificity of its zoonotic relatives.

Moving forward, erythrocyte invasion assays will be necessary to identify other molecular barriers to cross-species transmission. For example, while Rh5-BSG forms the foundation for Laverania host tropism, it is conceivable that invasion is enhanced by other receptor-ligand interactions. The recently sequenced genomes of the ape Laverania (Otto et al., 2014, 2016) are likely to offer novel insights. In particular, the hypothesis that pseudogenization of the PfRh3 and PfEBA165 ligands and/or duplication of PfRh2 were necessary prerequisites for expression of alternative, human-permissive invasion pathways warrants further investigation.

Additionally, although three previous surveys of humans living in Gabon and Cameroon have failed to identify zoonotic Laverania infections (Délicat-Loembet et al., 2015; Loy et al., 2018; Sundararaman et al.,

2013), more intensive sampling of human populations living in exposure hotspots will be necessary to confidently rule out low levels of cross-species transmission. Indeed, while there is no evidence of human-

130 to-ape transmission of P. falciparum in the wild, the high levels of P. falciparum exposure that apes experience in captivity is sufficient for human-to-ape transmission (Duval et al., 2010; Krief et al., 2010;

Ngoubangoye et al., 2016; Pacheco et al., 2013). It remains unclear whether the converse (i.e., ape-to- human transmission in the context of elevated exposure) also occurs in parts of equatorial Africa. The ecological drivers of spatiotemporal variation in transmission within great ape reservoirs must be quantified in order to identify hypothetical exposure hotspots (Fig. 2.5). Finally, next-generation sequencing methods that more intensively sample the within-host diversity of human infections will be necessary to eliminate the possibility that zoonotic transmission has been masked by P. falciparum co-infection.

While there is much work to be done, the current body of evidence suggests that ape Laverania parasites pose little or no risk to human populations given the high degree of host-specificity that these species exhibit.

6.3.2 Plasmodium vivax: a long neglected pandemic

Plasmodium vivax accounts for ~50% of all human malaria cases outside of Africa (World Malaria

Report, 2015). Once believed to have emerged from macaque monkeys (Escalante et al., 2005; Mu et al.,

2005), improved sampling of non-human primates has revealed that this pandemic originated in African great apes (Liu et al., 2014). Indeed, human P. vivax isolates form a monophyletic clade embedded within the diversity of closely related ape parasites. Though this pattern is superficially reminiscent of P. falciparum’s relationship to the ape Laverania, P. vivax appears to transcend species boundaries more promiscuously. First, P. vivax is readily transmissible between chimpanzees and gorillas (Liu et al., 2014), while the ape Laverania form host-specific clades (Liu et al., 2010). Second, though rare, ape-to-human P. vivax transmission has recently been reported in Central Africa (Prugnolle et al., 2013).

The interaction between the P. vivax invasion ligand, PvDBP, and its host receptor, the Duffy antigen/receptor for chemokines (DARC), has long been thought to be an essential determinant of P. vivax invasion (Horuk et al., 1993; Miller et al., 1977). Indeed, P. vivax pathogenicity has favored the proliferation of a mutation in the DARC promoter, which abrogates expression of this protein on human erythrocytes

131 (Horuk et al., 1993; Miller et al., 1976). This DARC-null allele, which arose less than 33,000 years ago

(Hedrick, 2012), can now be found at >95% frequency in parts of sub-Saharan Africa and is thought to underlie the near complete absence of P. vivax in human populations in central Africa (Howes et al., 2011).

However, evidence of DARC-null P. vivax invasion has begun to accumulate in recent years (Cavasini et al., 2007; Ménard et al., 2010; Mendes et al., 2011; Ngassa Mbenda & Das, 2014; Ryan et al., 2006;

Woldearegai et al., 2013), the mechanistic basis of which is a source of current debate (Gunalan et al., 2016;

Hostetler et al., 2016). Given the important public health implications of these findings for the spread of P. vivax in Africa, evaluation of these hypotheses will be a productive avenue for future inquiry. However, functional assays will be necessary to test these hypotheses, and progress may be slow until a continuous in vitro P. vivax culture system is developed (Golenda et al., 1997).

Because the ape origin of P. vivax has only been described recently (Liu et al., 2014; Prugnolle et al.,

2013), the functional determinants of ape P. vivax host-specificity remain largely unexplored. Nevertheless, it has been proposed that the DARC-null phenotype restricts ape-to-human P. vivax transmission within equatorial Africa (Liu et al., 2014). If ape P. vivax does, indeed, require expression of the DARC allele for human erythrocyte invasion, then these parasites likely pose a low risk to human populations overall. In this scenario, only travelers and DARC-positive Africans would face recurrent infection risks. Indeed, the only previous report of ape-to-human P. vivax transmission involved a Caucasian (i.e., DARC-positive) traveler, who spent 18 days working in a forest in the Central African Republic (Prugnolle et al., 2013).

The great ape reservoir still warrants careful consideration in light of the emerging body of evidence concerning DARC-null P. vivax invasion. Reports of P. vivax outbreaks in equatorial Africa are striking

(Culleton et al., 2009), and several recent studies conducted in countries harboring African great apes have isolated P. vivax from Duffy-negative individuals (Mendes et al., 2011; Ngassa Mbenda & Das, 2014;

Russo et al., 2017). While it is possible that we have underestimated the magnitude of human-to-human P. vivax transmission within Africa, these observations also raise the possibility that ape reservoirs are continuously seeding infections to the human population. Additional phylogenetic analyses that encompass

132 a greater diversity of ape and African human isolates will be necessary to discriminate between these two hypotheses.

6.3.3 Other malaria parasites of African great apes

Non-invasive sampling of wild African great apes has demonstrated that chimpanzees and gorillas also harbor malaria parasites very closely related to P. malariae and P. ovale (Duval et al., 2010; Kaiser et al.,

2010; Liu et al., 2010; Mapua et al., 2015). Is it possible that the origins of all four human-adapted malaria parasites are traceable to our closest evolutionary relatives? Although this hypothesis is compelling

(Ramasamy, 2014), such an assertion is premature and further analysis will be necessary to reach a formal conclusion.

Three distinct hypotheses could explain the isolation of P. malariae and P. ovale from both human and

African great ape hosts. First, it is possible that human infections are, indeed, the result of ancient and/or contemporary zoonoses of ape origin. In this scenario, malaria control efforts should carefully consider the ape reservoir as a potential source of ongoing human P. malariae and P. ovale infection. Second, it is conceivable that these ape infections are anthroponotic (i.e., transmitted from human to ape). Given that anthroponotic pathogens constitute a major source of mortality among wild apes (Grützmacher et al., 2016;

Köndgen et al., 2008, 2010; Scully et al., 2018), this finding would hold significant relevance to the conservation of these endangered primates. Third, it is possible that some P. malariae and P. ovale strains hold the capacity for promiscuous transmission among humans and apes. This possibility represents a

‘worst-case scenario,’ and would warrant careful consideration by both the public health and wildlife conservation communities.

Several courses of action will facilitate discrimination among these possibilities. Because few ape P. ovale and P. malariae isolates have been sequenced, more intensive sampling of a wider geographic distribution of humans and apes in Africa must precede further analysis. Subsequent analysis of these isolates will be necessary to evaluate the degree to which human and ape strains are phylogenetically inter- mixed (an indicator of cross-species transmission). Finally, comparison of the genetic diversity of human

133 and ape isolates will enable researchers to infer whether apes constitute a reservoir of human infection or vice versa.

The public health community has largely overlooked P. ovale and P. malariae due to the relative obscurity of these parasites. However, as efforts to control P. falciparum and P. vivax inch closer to regional elimination, these neglected tropical diseases may begin to factor more prominently into policy discussions.

The recent sequencing of the P. ovale and P. malariae genomes constitutes an important first step in understanding the biology of these parasites (Rutledge et al., 2017). Inference of the magnitude of ape-to- human transmission will be necessary to further elucidate their epidemiological dynamics and zoonotic potential.

6.4 Malaria parasites of Old World monkeys

The Old World monkeys (OWMs) populate a diverse clade of over 130 species, which are distributed throughout the African and Asian tropics (Fig. 3.1). OWMs diverged from the apes (including humans) between 20 and 27 million years ago (Perelman et al., 2011). Because OWMs are more evolutionarily distant than the apes, malaria parasites that have adapted to the OWM cellular environment may be generally less capable of efficient replication in humans. Indeed, none of the human-adapted malaria parasites appear to have originated in OWM reservoirs, suggesting that molecular barriers may reduce human susceptibility to most OWM malaria parasites.

However, it is also likely that the magnitude of human exposure to OWM malaria parasites exceeds that of ape parasites. OWMs are more widely distributed than apes, and some OWM species live in very close proximity to humans in both urban and rural environments. This high degree of close contact has facilitated the cross-species transmission of a number of OWM pathogens (e.g., Herpes B virus (Chomel et al., 2007)). Indeed, two OWM species—long-tailed and pig-tailed macaques (M. fascicularis and M. nemestrina, respectively)—harbor P. knowlesi, the most important contemporary zoonotic malaria parasite.

In this section, I discuss P. knowlesi zoonosis, and briefly highlight evidence for cross-species transmission of another macaque parasite, P. cynomolgi.

134 6.4.1 Monkey in the middle: the emerging public health threat posed by P. knowlesi zoonosis

Plasmodium knowlesi is a zoonotic malaria parasite of macaque monkeys which is responsible for a growing number of human cases in Southeast Asia (Cox-Singh et al., 2008; Singh & Daneshvar, 2013).

Long mistaken for P. malariae due to its morphological similarity, molecular methods have since implicated zoonotic P. knowlesi in up to 70% of malaria infections in rural Malaysia (Cox-Singh et al.,

2008; Singh & Daneshvar, 2013). Interestingly, efforts to eliminate P. falciparum and P. vivax in Southeast

Asia have coincided with an increase in the incidence of P. knowlesi, an effect that cannot be explained by elevated surveillance or improved molecular diagnosis methodology (William et al., 2013, 2014).

Accordingly, P. knowlesi zoonosis constitutes a major hurdle to malaria eradication efforts in Southeast

Asia and may represent a bellwether of future challenges in other geographic locations as zoonotic parasites compete to fill the host niche left by P. falciparum and P. vivax.

Although long-tailed macaques and pig-tailed macaques each harbor a high prevalence of P. knowlesi infection (Lee et al., 2011), parasites of each reservoir species appear to be reproductively isolated (Divis et al., 2015). While it remains unclear whether this parasite population structure stems from geographic isolation or molecular restriction, both clades are capable of infecting human hosts (Divis et al., 2015).

Although human-to-human P. knowlesi transmission has not been conclusively demonstrated in natural settings, this parasite produces gametocytes (i.e., transmission stages) in human blood, suggesting that larger outbreaks with human-to-human transmission are conceivable (Singh et al., 2004). Nevertheless, the current body of evidence indicates that monkey-to-human transmission is the sole route of human P. knowlesi infection and that outbreaks will likely be confined to the geographic distribution of the macaque reservoir. On finer-grained spatial scales, changes in patterns of land-use, such as deforestation, have been implicated as risk factors for P. knowlesi zoonosis (Fornace et al., 2016). Adult males working in agricultural areas appear to be particularly susceptible to infection, suggesting that behavioral factors also play an important role in exposure (Grigg et al., 2017).

In addition to ecological barriers, P. knowlesi must also overcome a number of molecular barriers to infect human hosts. Sialic acids represent one such barrier to emergence. Sialic acids are acidic sugars that

135 terminate glycan chains on vertebrate proteins (Varki, 2009; Varki & Varki, 2007). More simply, if the backbone of a protein can be likened to the trunk of a tree, sialic acids are analogous to its leaves. Two primary sialic acid variants are expressed on mammalian cells: N-acetylneuraminic acid (Neu5Ac) and N- glycolylneuraminic acid (Neu5Gc) (Varki, 2009; Varki & Varki, 2007). Neu5Ac is converted to Neu5Gc by the CMAH gene, which has been pseudogenized in the human lineage (Varki, 2009; Varki & Varki,

2007). Thus, Neu5Ac is the sole sialic acid variant found on human proteins, while Neu5Gc is the principal sialic acid of apes and Old World monkeys.

Because EBL ligands of many malaria parasites rely upon sialic acids for host cell binding, parasites adapted to hosts expressing Neu5Gc may invade cells expressing Neu5Ac less efficiently. Indeed, P. knowlesi preferentially invades cells expressing the OWM sialic acid, Neu5Gc. This pattern is driven by two EBL ligands—PkDBPβ and PkDBPγ—which bind to erythrocytes expressing Neu5Gc, but not those expressing Neu5Ac (Dankwa et al., 2016). In contrast, a third ligand—PkDBPα—does not require sialic acids for host cell binding (Dankwa et al., 2016).

These results suggest that the usage of alternative invasion pathways underlies the affinity of zoonotic

P. knowlesi for human erythrocytes. While sialic acid-dependent pathways (i.e., PkDBPβ and PkDBPγ) appear to enhance P. knowlesi invasion efficiency in its natural simian host, the indifference of the PkDBPα pathway to sialic acid variant serves to bridge the fitness valley associated with the foreign cellular surface of the human red blood cell. Nevertheless, P. knowlesi invasion and replication is less efficient in human cells relative to macaque cells, indicating that further adaptation may be necessary for widespread emergence.

Expansion of P. knowlesi’s cellular niche in the human host may constitute one such adaptation (Lim et al., 2013). Although invasion of most human malaria parasites is largely restricted to either younger reticulocytes (P. vivax (Kitchen, 1938), P. ovale (Collins & Jeffery, 2005)) or older normocytes (P. malariae (Kitchen, 1939)), P. falciparum achieves elevated parasitemia via relaxation of this restriction

(Chotivanich et al., 2000). In vitro assays have demonstrated that while P. knowlesi is indifferent to the age of erythrocytes within the macaque host, invasion is limited to reticulocytes in humans (Lim et al., 2013).

136 However, in vitro adaptation of P. knowlesi has revealed that enhanced invasion of older erythrocytes via amplification of PkDBPα enables this parasite to achieve a marked increase in the rate of parasite proliferation in human blood (Dankwa et al., 2016; Lim et al., 2013).

These studies demonstrate that while multiple axes of host cell variation constitute potential barriers to emergence, the expression of redundant invasion pathways has endowed P. knowlesi with the functional toolkit necessary to circumvent this host restriction. As the incidence of P. knowlesi infection increases, additional research will be necessary to elucidate the compensatory ligand adaptations that underlie relaxation of reticulocyte restriction.

6.4.2 Other parasites of Old World monkeys

Although a number of other malaria parasites of OWMs have been identified (Fig. 3.1), few appear to be capable of infecting human hosts in natural settings. However, P. cynomolgi constitutes an interesting exception. Plasmodium cynomolgi is a macaque parasite closely related to P. knowlesi, which has recently been isolated from human hosts (Ta et al., 2014). Like P. knowlesi, P. cynomolgi invades macaque erythrocytes of all ages indiscriminately, but is reticulocyte-restricted in human blood, potentially limiting its zoonotic potential (Kosaisavee et al., 2017). Long-term in vitro culture of P. cynomolgi in human blood and comparison with P. knowlesi may reveal convergent mechanisms of adaptation to the human host and offer novel insight into the process of zoonotic emergence. For the time being, however, P. cynomolgi is unlikely to pose a major risk of recurrent human infection.

6.5 Malaria parasites of New World Monkeys

The New World monkeys (NWMs) occupy a clade of over 130 primate species, which are confined to the South and Central American tropics (Fig. 3.1). NWMs diverged from the ancestor of OWMs and apes between 36 and 50 million years ago (Perelman et al., 2011). Despite the greater phylogenetic distance, emerging evidence suggests that we should not assume that malaria parasites of NWMs pose a lesser threat to human populations. Of particular note, the CMAH gene was independently pseudogenized in the ancestor

137 of both NWMs and modern humans (Springer et al., 2014; Varki, 2009). Accordingly, while apes and

OWMs express the Neu5Gc sialic acid variant upon cell surface proteins, humans and NWMs each express the Neu5Ac variant. Given that a number of erythrocyte invasion pathways depend upon sialic acids for host cell entry (discussed above), it is conceivable that this trait endows humans and NWMs with a similar cellular environment that is mutually permissive of cross-species pathogen exchange.

6.5.1 P. simium and P. brasilianum: simian parasites with underestimated zoonotic potential

Relative to OWMs and apes, the species richness of NWM malaria parasites is greatly reduced. In fact, only two species—P. simium and P. brasilianum—have heretofore been isolated from NWMs. While howler monkeys (Alouatta spp.) are a commonly cited reservoir of P. simium and P. brasilianum, these parasites have now been isolated from a wide range of wild NWM species, suggesting that these parasites can cross species barriers promiscuously (Araújo et al., 2013; de Castro Duarte et al., 2008). Future work will be necessary to comprehensively quantify the prevalence, distribution, and phylogenetic partitioning of these parasites within NWM reservoirs.

Phylogenetic analyses have revealed that P. simium is very closely related to P. vivax, while P. brasilianum is a close relative of P. malariae (Fig. 3.1). Interestingly, patterns of phylogenetic clustering and genetic diversity suggest that each of these parasites likely emerged in NWM populations less than 500 years ago as the result of human-to-NWM cross-species transmission events (Camargos Costa et al., 2015;

Ramasamy, 2014; Tazi & Ayala, 2011). Now that these parasite species have adapted to NWMs, a key question concerns whether they have retained the capacity for human infection.

Despite limited sampling, both P. simium and P. brasilianum have now been isolated from human hosts

(Brasil et al., 2017; Deane et al., 1966; Lalremruata et al., 2015). Because P. simium and P. brasilianum are morphologically indistinguishable from their human-adapted relatives, it is likely that the magnitude of monkey-to-human transmission of these parasites has been underestimated. Indeed, Brasil et al. (2017) recently used a molecular assay to demonstrate that outbreaks of (presumed) P. vivax in Rio de Janeiro state, Brazil—where elimination had been achieved over 50 years ago—are actually attributable to P.

138 simium. If these zoonotic outbreaks are generalizable to other regions of South America (Brasil et al., 2017; de Castro Duarte et al., 2008; Deane et al., 1966), surveillance of NWM reservoirs will be an integral component of malaria control efforts in these regions. It will, therefore, be of central importance to invest in molecular epidemiological studies that quantify rates of zoonotic exposure and transmission in these regions to thoroughly quantify the magnitude of this underappreciated threat.

6.6 Areas for future research

In this section, I highlight several avenues for future research that may prove to be particularly productive in ensuing years.

In Chapter 2, we identify a set of ecological factors—namely ambient temperature and forest cover— that structure spatiotemporal variation in Laverania prevalence within and among wild chimpanzee populations. While these results support a growing body of evidence indicating that humans living near wild African great apes are likely to be exposed to these zoonotic parasites, it will be critical to elucidate the extent to which transmission in the animal reservoir translates into human exposure. The capacity to study this question will require dedicated efforts to sample all components of the zoonotic system (from human to mosquito to animal reservoir) in predicted exposure hotspots. In particular, studies of entomological inoculation rates (via sampling and analysis of parasite rates in Anopheles mosquito vectors) and serological measures of exposure (via longitudinal sampling of human populations) in multiple transmission settings will offer direct estimates of zoonotic exposure.

Further, efforts to evaluate the magnitude of cross-species transmission depend upon the development and application of tools that assay the complexity of infection within hosts more comprehensively. The elevated parasitemia of human-adapted malaria parasites, such as P. falciparum, may mask the signal of simian parasites that inefficiently invade human cells. While the low parasitemia of the latter may minimize pathogenicity, these subclinical infections could present opportunities for adaptation of the zoonotic parasite to the human host. Accordingly, it will be critical that surveys seeking to uncover evidence of primate-to-human transmission use next generation sequencing methods that more comprehensively sample

139 the richness of parasite species within a host. It will also be important to minimize other sources of sampling bias (e.g., by conducting surveys of asymptomatic hosts, as well as clinical cases).

Zoonotic disease surveillance efforts should also seek to form mutually beneficial partnerships with long-term biological field sites, many of which—like the Kibale Chimpanzee Project (Chapter 2)—have been operational for decades. Wild primate populations can act as sentinels for emerging infectious diseases

(Calvignac-Spencer et al., 2012; Ramasamy, 2014), and the capacity to sample primate populations longitudinally offers the opportunity to identify trends in pathogen incidence relative to factors such as climatic variation or land-use change. By offering supplemental funding to these field sites in exchange for cooperation in disease surveillance programs, public health organizations can leverage the expertise and infrastructure that these studies have cultivated over decades of biological field research. In return, biological field research programs stand to mitigate uncertainties associated with the funding climate.

Even if such partnerships never materialize, primatologists would be well-served to implement infectious disease surveillance programs at long-term field sites due to the notable conservation implications that infectious diseases hold for endangered populations of wild primates (Bermejo et al.,

2006; Köndgen et al., 2008, 2010; Leroy et al., 2004; Scully et al., 2018; Williams et al., 2008). In Chapter

5, we add Rhinovirus-C to the growing list of human pathogens that have caused lethal outbreaks in wild chimpanzee populations. Although we elucidated the epidemiology of this virus at the group level, there remain many open questions pertaining to individual-level susceptibility to this and other respiratory diseases of human origin. For example, it is interesting that the mortality imposed by this Rhinovirus-C outbreak was particularly concentrated upon adult males. If genuine, such a pattern could either result from differential exposure or differential susceptibility of this class of individuals to this virus. If chimpanzees that occupy more central positions within the social network are more likely to exhibit symptoms early in the outbreak, one might find support for the former. Alternatively, the latter might predict that immunocompromised or malnourished individuals will be more likely to manifest symptoms.

Discrimination between these hypotheses could enable more accurate projections of the manner by which anthroponotic respiratory pathogens spread through great ape populations and inform the design of

140 intervention strategies. Comparison of these relationships with other anthroponotic respiratory viruses might yield additional interesting insights.

Infectious disease research conducted in the context of long-term field sites also offers a myriad of enriching biological questions relevant to the fields of biological anthropology, evolutionary biology, and veterinary medicine. For example, while the study presented in Chapter 2 adopted a population-level perspective, analysis of the age-biased pattern of infection among the Kanyawara chimpanzees revealed considerable variation among individuals in the probability of testing positive for malaria. One of the youngest individuals in the dataset (NT) appears to be resistant to infection, a finding that strongly deviated from the group-level pattern (Fig. 2.4B). The Laverania are widespread in chimpanzee populations and are closely related to the highly pathogenic human malaria parasite, P. falciparum. The latter ranks among the strongest selective pressures to confront the human species during the last 50,000 years (Carter & Mendis,

2002; Williams, 2006), and pathogenicity of the ape Laverania has been documented in chimpanzee hosts

(Herbert et al., 2015). Although environmental variables—such as dietary quality or physiological stress— could partially explain these observations, it is also plausible that natural selection has favored the evolution of protective red blood cell polymorphisms that confer a degree of genetic resistance to these parasites in chimpanzee populations. Collection of non-invasive samples from these resistant individuals and sequencing of candidate loci stands to catalyze studies of the molecular basis for these traits.

Efforts to validate these candidate polymorphisms in vitro will benefit from the generation of immortalized erythroid progenitor cell lines—such as the EJ line described in Chapter 4—which offer genetically tractable model systems. Disruption of the endogenous DARC receptor and expression of

African great ape DARC alleles in the EJ cell line highlights the utility of this model system for studies of the host determinants of red blood cell invasion. Future studies should also venture beyond the red blood cell in search of additional barriers to cross-species transmission. Species barriers to liver stage infection and molecular incompatibilities between parasite and mosquito vector will constitute particularly important avenues for future research.

A critical next step will be to adapt this methodology for the immortalization of non-human primate

141 hematopoietic stem cells. The capacity to culture blood-stage malaria parasites in vitro requires a reliable source of terminally differentiated red blood cells (Bei et al., 2010, 2010a). While sources of human erythrocytes are abundant, studies of simian malaria parasites have traditionally relied upon blood samples collected invasively from captive primates housed in biomedical research facilities. Ethical opposition to the use of primates in biomedical research (de Waal, 2012; Wendler, 2014) has greatly reduced the availability of these samples, and recent U.S. legislation banning the use of great ape subjects in invasive biomedical research (Bennett, 2015; Grimm, 2015) has rendered African great ape blood samples to be particularly inaccessible. Demand for these samples has rapidly outpaced supply, producing a fundamental bottleneck in the output of studies that rely upon African great ape red blood cells. The inducible production of non-human primate red blood cells from immortalized hematopoietic stem cells would alleviate this bottleneck and could facilitate efforts to adapt primate malaria parasites to long-term in vitro culture

(Grüring et al., 2014; Lim et al., 2013). Accordingly, the generation of a panel of immortalized non-human primate hematopoietic stem cell lines would be a truly transformative advance that would catalyze future studies of malaria host-specificity.

6.7 Conclusion

Efforts to reduce the incidence of malaria in human populations have achieved considerable success in the last decade (Bhatt et al., 2015; World Malaria Report, 2015). However, during this period, the incidence of P. knowlesi zoonosis has increased in Southeast Asia (William et al., 2014), and a growing body of evidence suggests that monkey-to-human transmission of P. simium has produced malaria outbreaks in areas of Brazil where elimination had been achieved (Brasil et al., 2017). While the public health community has traditionally ignored animal reservoirs in the context of malaria eradication, it appears that the impetus to systematically evaluate the risk of malaria zoonosis has never been greater. Moving forward, it is critical that we adopt a holistic view of malaria zoonosis that simultaneously considers the ecological and molecular barriers to emergence. Without this integrated approach, efforts to elucidate the zoonotic potential of this diverse parasite clade are unlikely to be successful. Thus, it is my hope that this dissertation

142 will catalyze cooperation among stakeholders in the public health and scientific research communities by highlighting malaria zoonosis as a topic worthy of further consideration in the context of malaria eradication.

6.8 Author Contributions

This chapter was written by Erik J. Scully.

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150 Appendix 2.1 | Summary of ecological and demographic parameters analyzed in the Kanyawara GLMM. Average parameter values are presented with standard deviation listed in parentheses. ID N Pos %Pos Sex Age Date range MATa ITVb RFc AE 16 13 81.3% F 1.8 (0.5) 06/18/2014 - 08/06/2016 20.9 (0.6) 9.9 (1.1) 5.4 (3.8) AJ 3 0 0.0% M 39.6 (0.6) 06/18/2013 - 06/11/2014 20.6 (0.3) 10.1 (1.9) 4.9 (2.4) AL 17 0 0.0% F 32.9 (0.7) 06/24/2013 - 04/28/2016 20.9 (0.7) 10.4 (1.7) 4.5 (3.1) AN 25 17 68.0% F 6.0 (0.8) 06/24/2013 - 08/04/2016 21.1 (0.8) 11.2 (2.1) 3.4 (2.9) AT 25 3 12.0% M 15.4 (1.0) 06/08/2013 - 08/04/2016 21.0 (0.7) 11.1 (1.8) 3.1 (2.9) AZ 23 7 30.4% M 10.4 (0.9) 06/18/2013 - 08/04/2016 20.9 (0.7) 10.8 (1.6) 3.7 (3.3) BB 14 1 7.1% M 48.8 (1.2) 06/17/2013 - 07/19/2016 21.1 (0.9) 11.4 (1.3) 3.6 (3.9) BL 18 3 16.7% F 54.8 (0.9) 06/18/2013 - 04/08/2016 20.9 (0.8) 10.8 (1.7) 4.3 (3.0) BO 20 10 50.0% M 11.5 (1.0) 07/08/2013 - 08/01/2016 20.8 (0.7) 10.7 (1.7) 3.8 (3.4) BT 20 12 60.0% M 5.3 (0.7) 07/28/2013 - 08/15/2016 21.0 (0.8) 10.8 (1.5) 4.3 (3.6) DL 4 0 0.0% F 13.8 (0.3) 01/04/2016 - 08/03/2016 21.0 (0.1) 11.4 (0.2) 1.3 (0.5) ES 25 5* 20.0% M 20.7 (1.0) 06/05/2013 - 07/25/2016 20.9 (0.8) 11.1 (2.0) 3.6 (3.2) GG 14 8 57.1% F 13.1 (0.4) 02/14/2015 - 07/07/2016 20.8 (0.8) 9.9 (2.0) 5.0 (4.0) LK 25 0 0.0% M 32.9 (0.9) 06/08/2013 - 08/05/2016 21.0 (0.7) 11.1 (1.9) 3.4 (3.2) LL 11 9 81.8% F 2.8 (0.6) 07/17/2014 - 07/22/2016 20.8 (0.5) 10.6 (1.9) 4.1 (3.2) LN 18 2 11.1% F 17.7 (1.0) 06/08/2013 - 07/22/2016 20.8 (0.6) 10.7 (1.5) 4.3 (3.3) ML 18 3 16.7% F 17.6 (1.0) 08/03/2013 - 07/23/2016 20.6 (0.5) 10.6 (1.6) 4.3 (3.4) MM 22 15 68.2% F 3.8 (0.5) 06/11/2014 - 08/08/2016 21.2 (0.9) 11.2 (1.7) 3.6 (3.5) MN 14 11 78.6% M 6.7 (0.5) 07/12/2014 - 04/06/2016 21.1 (0.8) 10.6 (1.7) 4.3 (3.8) MU 2 0 0.0% F 43.0 (0.1) 06/06/2013 - 08/17/2013 20.6 (0.6) 11.4 (0.2) 1.8 (2.4) MX 15 2 13.3% M 17.5 (0.7) 08/17/2013 - 04/06/2016 20.9 (0.9) 10.8 (1.9) 3.9 (3.2) NP 29 2* 6.9% F 15.3 (0.9) 06/04/2013 - 08/01/2016 21.1 (0.8) 11.1 (2.0) 3.3 (2.9) NT 12 0 0.0% F 1.3 (0.4) 06/02/2015 - 07/27/2016 21.2 (0.9) 11.5 (1.8) 3.8 (3.6) OB 20 12 60.0% M 3.7 (0.4) 01/30/2015 - 08/05/2016 21.1 (0.7) 11.1 (2.0) 3.6 (3.7) OG 21 6 28.6% M 14.1 (1.0) 06/11/2013 - 08/03/2016 20.8 (0.7) 10.7 (1.8) 3.8 (3.1) OL 20 9 45.0% F 6.0 (0.9) 06/13/2013 - 03/18/2016 20.8 (0.6) 10.5 (1.7) 3.9 (3.0) OM 20 11 55.0% F 9.9 (0.9) 06/18/2013 - 08/04/2016 20.9 (0.8) 11.0 (2.2) 3.9 (3.4) OP 11 8 72.7% F 1.7 (0.3) 03/26/2015 - 03/27/2016 21.2 (0.9) 11.3 (1.4) 3.5 (2.0) OT 22 1 4.5% F 17.3 (0.9) 06/13/2013 - 08/04/2016 21.0 (0.7) 11.0 (2.1) 4.2 (3.9) OU 17 1 5.9% F 35.8 (0.9) 06/19/2013 - 04/30/2016 21.0 (0.8) 10.9 (2.1) 3.6 (2.6) PB 18 0 0.0% M 20.1 (1.0) 06/05/2013 - 08/05/2016 20.7 (0.5) 10.7 (1.9) 4.3 (3.5) PL 3 1 33.3% F 1.4 (0.4) 09/16/2015 - 07/20/2016 21.1 (0.5) 11.8 (0.1) 2.1 (2.7) PO 25 4 16.0% F 15.7 (1.0) 06/04/2013 - 08/11/2016 21.0 (0.7) 11.1 (1.8) 3.5 (3.2) QK 3 1 33.3% F 1.2 (0.5) 08/18/2015 - 07/17/2016 20.4 (0.4) 10.0 (1.5) 5.9 (6.4) QT 15 0 0.0% F 22.6 (1.2) 06/04/2013 - 08/09/2016 20.8 (0.6) 10.6 (1.3) 4.1 (3.4) QV 24 11 45.8% M 6.8 (0.9) 06/19/2013 - 08/09/2016 20.9 (0.7) 10.8 (1.8) 4.0 (3.2) RD 18 0 0.0% F 19.0 (0.8) 07/01/2013 - 08/02/2016 21.0 (0.8) 11.2 (1.8) 3.9 (3.4) RS 1 0 0.0% M 1.1 (NA) 12/05/2015 - 12/05/2015 20.1 (NA) 8.3 (NA) 8.0 (NA)

151 Appendix 2.1 (continued) TG 22 2 9.1% F 34.8 (1.0) 06/21/2013 - 08/11/2016 20.9 (0.7) 11.2 (1.8) 3.8 (3.2) TJ 20 3 15.0% M 19.6 (1.0) 06/04/2013 - 08/10/2016 20.9 (0.7) 11.0 (1.9) 3.0 (3.1) TR 19 14 73.7% F 4.0 (0.7) 07/31/2013 - 08/11/2016 21.1 (0.7) 10.9 (1.8) 3.8 (3.8) TS 16 1 6.3% F 10.1 (1.0) 06/21/2013 - 04/05/2016 20.9 (0.8) 10.8 (2.0) 3.8 (3.5) TT 23 3 13.0% M 14.5 (1.0) 06/21/2013 - 08/06/2016 20.9 (0.7) 10.9 (2.0) 3.5 (3.1) TU 4 1 25.0% M 53.0 (0.1) 06/17/2013 - 08/12/2013 20.5 (0.3) 11.7 (0.6) 2.7 (0.9) UK 3 1 33.3% F 2.3 (0.0) 07/29/2013 - 07/31/2013 20.2 (0.0) 11.2 (0.1) 2.7 (0.0) UM 13 5 38.5% F 33.6 (1.2) 07/09/2013 - 07/12/2016 20.9 (0.9) 11.0 (1.8) 4.4 (4.3) UN 14 8 57.1% M 10.5 (1.1) 06/08/2013 - 07/19/2016 21.0 (0.8) 11.4 (1.9) 3.5 (4.1) WA 16 4 25.0% F 23.7 (0.8) 06/06/2013 - 03/07/2016 21.1 (0.8) 11.5 (2.4) 3.9 (3.9) WC 22 16 72.7% M 6.8 (0.9) 07/12/2013 - 07/14/2016 20.8 (0.7) 10.2 (1.5) 4.3 (3.2) WE 17 10 58.8% F 8.2 (0.5) 06/06/2014 - 06/21/2016 21.0 (0.8) 10.7 (2.1) 4.7 (4.1) WL 20 7 35.0% F 22.8 (1.0) 06/08/2013 - 07/14/2016 20.8 (0.7) 10.6 (1.7) 4.0 (3.4) WO 10 6 60.0% F 1.1 (0.3) 11/16/2015 - 07/23/2016 21.0 (0.9) 10.9 (2.2) 3.4 (4.0) WZ 11 3* 27.3% M 3.3 (0.5) 06/11/2014 - 06/21/2016 20.7 (0.5) 10.4 (1.6) 6.3 (3.9) YB 20 1 5.0% M 41.7 (1.0) 06/27/2013 - 07/19/2016 20.9 (0.5) 10.7 (1.7) 3.9 (3.2) Total 878 273 31.1% 16.6 (12.6) 06/04/2013 - 08/15/2016 20.9 (0.7) 10.9 (1.8) 3.9 (3.3) aMAT: mean ambient temperature (average of estimates recorded 30 days prior to sample collection; measured directly) bITV: intra-day temperature variation (average of estimates recorded 30 days prior to sample collection; measured directly) cRF: rainfall (average of estimates recorded 30 days prior to sample collection; measured directly)

152 Appendix 2.2 | Summary of ecological parameters analyzed in the Pan-African GLMM. Average parameter values are presented with standard deviation listed in parentheses. Site N (int*) Pos %Pos Date range MATa ITVb FCc RFd AM 37 (23) 0 0.0% 01/15/2005 - 08/04/2005 24.7 (0.8) 8.5 (0.2) 90 4.3 (1.4) AN 15 (0) 0 0.0% 03/16/2007 - 03/26/2007 23.7 (0.1) 11.5 (0.2) 47 2.9 (0.3) AZ 32 (31) 0 0.0% 09/05/2007 - 05/20/2016 26.0 (0.7) 8.0 (0.5) 70 5.5 (1.0) BA 258 (0) 61 23.6% 12/23/2005 - 11/08/2009 23.9 (0.6) 8.7 (0.8) 99 3.9 (2.1) BB 33 (0) 8 24.2% 04/10/2003 - 06/08/2003 24.3 (0.6) 8.3 (0.3) 90 5.2 (0.2) BD 6 (0) 0 0.0% 01/25/2001 - 02/10/2002 21.2 (0.4) 11.5 (0.4) 81 0.3 (0.1) BF 31 (0) 20 64.5% 12/04/2006 - 07/04/2007 24.8 (0.6) 9.1 (0.6) 90 3.3 (2.0) BG 42 (0) 7 16.7% 05/19/2014 - 05/31/2014 23.0 (0.0) 9.5 (0.1) 100 3.5 (0.3) BI 93 (87) 10 10.8% 03/15/2003 - 12/03/2007 24.2 (0.4) 8.6 (0.3) 90 5.9 (2.3) BL 7 (0) 0 0.0% 03/22/2007 - 05/03/2007 23.8 (0.5) 9.4 (0.2) 100 6.1 (1.3) BO 29 (0) 0 0.0% 01/15/2012 - 03/10/2012 21.8 (0.8) 11.7 (0.4) 54 0.3 (0.7) BQ 67 (0) 13 19.4% 02/05/2003 - 08/08/2004 22.8 (0.3) 8.4 (0.4) 95 4.3 (2.1) CP 17 (0) 0 0.0% 03/24/2004 - 03/28/2010 24.5 (0.9) 8.5 (0.7) 90 3.9 (1.0) DG 44 (0) 6 13.6% 05/20/2004 - 10/14/2004 23.2 (0.4) 8.4 (0.6) 82 6.0 (0.8) DP 157 (0) 31 19.7% 02/15/2003 - 03/27/2004 22.9 (0.4) 8.8 (0.6) 90 3.6 (2.2) EB 25 (0) 1 4.0% 03/26/2007 - 04/17/2008 22.1 (0.3) 8.6 (0.6) 87 3.0 (2.3) EK 18 (0) 1 5.6% 07/08/2004 - 08/18/2004 22.7 (0.1) 8.4 (0.2) 90 5.0 (1.0) EN 26 (26) 1 3.8% 02/29/2016 - 04/29/2016 26.9 (0.3) 8.8 (0.6) 100 4.2 (1.8) EP 10 (0) 1 10.0% 01/30/2006 - 09/05/2006 24.6 (0.6) 8.7 (0.2) 100 2.6 (1.4) GI 42 (0) 0 0.0% 04/14/2009 - 09/15/2009 13.3 (2.0) 11.6 (1.4) 37 1.2 (0.6) GM 169 (0) 0 0.0% 11/06/2000 - 05/24/2005 24.5 (0.9) 8.1 (0.9) 20 2.3 (2.2) GO 2 (0) 0 0.0% 12/15/2005 - 12/29/2005 21.5 (0.7) 11.0 (0.4) 77 0.4 (0.1) GT 168 (0) 56 33.3% 08/30/2003 - 06/22/2005 24.9 (0.5) 8.5 (0.5) 100 4.7 (1.8) IS 3 (0) 0 0.0% 12/05/2005 - 12/15/2005 22.1 (0.3) 10.5 (0.1) 100 1.3 (0.3) KA 104 (0) 32 30.8% 12/12/2005 - 10/03/2007 23.7 (0.8) 8.9 (0.5) 99 3.8 (1.6) KB 46 (0) 6 13.0% 03/09/2003 - 04/19/2003 22.1 (0.1) 10.4 (0.2) 89 2.3 (0.8) KO 45 (0) 1 2.2% 02/24/2008 - 07/25/2009 25.0 (0.8) 9.6 (1.2) 29 3.0 (1.1) KS 11 (11) 1 9.1% 06/10/2004 - 01/18/2006 25.6 (0.6) 11.2 (1.1) 16 4.1 (1.8) KY 34 (0) 0 0.0% 07/02/2014 - 07/08/2014 22.6 (0.1) 11.7 (0.1) 16 2.6 (0.1) LB 13 (0) 3 23.1% 03/04/2003 - 01/10/2004 24.8 (0.5) 8.2 (0.2) 100 2.8 (0.4) LH 3 (0) 0 0.0% 06/20/2007 - 12/07/2007 24.3 (0.8) 8.1 (0.1) 76 6.0 (3.1) LU 120 (98) 12 10.0% 03/31/2007 - 06/13/2008 23.7 (0.6) 9.6 (1.1) 16 5.9 (1.6) MB 16 (0) 5 31.3% 03/10/2003 - 12/15/2003 23.6 (0.7) 8.3 (0.5) 90 4.3 (1.6) MD 4 (0) 0 0.0% 04/04/2008 - 04/09/2008 23.4 (0.0) 9.4 (0.2) 79 2.8 (0.0) MF 13 (0) 0 0.0% 08/21/2005 - 08/25/2005 24.3 (0.0) 7.5 (0.0) 56 6.7 (0.2) MH 26 (0) 0 0.0% 02/10/2002 - 11/25/2003 20.1 (0.3) 11.6 (0.7) 47 2.5 (3.0) MK 4 (0) 2 50.0% 04/05/2008 - 05/08/2008 21.1 (1.3) 9.8 (0.7) 90 5.4 (1.0) MP 1 (0) 0 0.0% 12/02/2005 - 12/02/2005 23.6 (NA) 10.1 (NA) 68 1.1 (NA)

153 Appendix 2.2 (continued) MT 56 (0) 7 12.5% 05/25/2003 - 05/29/2004 22.2 (0.6) 10.2 (1.2) 59 2.4 (2.9) MU 34 (0) 15 44.1% 11/27/2005 - 03/12/2007 24.2 (0.4) 9.6 (0.7) 100 5.6 (2.8) NB 62 (0) 0 0.0% 12/14/2011 - 03/06/2012 22.8 (0.4) 9.5 (0.2) 95 0.3 (0.4) NY 27 (0) 0 0.0% 08/14/2002 - 02/11/2004 13.5 (1.3) 11.2 (0.6) 51 2.3 (1.5) ON 40 (0) 16 40.0% 01/14/2007 - 02/20/2007 23.2 (0.2) 9.1 (0.1) 100 1.6 (0.6) OP 4 (0) 0 0.0% 06/20/2007 - 08/20/2007 22.9 (0.9) 9.1 (0.6) 98 4.9 (1.9) PA 86 (80) 28 32.6% 05/27/2003 - 12/13/2010 25.0 (0.4) 8.8 (0.4) 100 5.3 (1.2) PO 7 (0) 0 0.0% 08/28/2004 - 12/08/2005 23.6 (0.1) 8.9 (0.4) 55 5.5 (2.5) SL 1 (0) 0 0.0% 06/28/2005 - 06/28/2005 23.8 (NA) 7.0 (NA) 49 3.2 (NA) UB 94 (0) 4 4.3% 12/27/2005 - 07/16/2009 24.4 (0.7) 8.6 (0.5) 100 3.4 (2.0) UG 65 (0) 0 0.0% 11/13/2009 - 03/05/2010 20.7 (0.9) 10.2 (0.8) 51 4.4 (1.9) VM 6 (0) 2 33.3% 04/25/2008 - 04/25/2008 21.6 (0.0) 10.3 (0.0) 74 4.3 (0.0) WA 115 (106) 26 22.6% 02/22/2003 - 04/28/2006 23.6 (0.8) 8.6 (0.7) 93 5.6 (1.7) WB 1 (0) 0 0.0% 11/30/2007 - 11/30/2007 22.6 (NA) 10.6 (NA) 37 6.7 (NA) WE 26 (0) 7 26.9% 05/19/2004 - 05/28/2004 23.9 (0.3) 7.6 (0.1) 20 5.4 (0.5) WL 38 (37) 11 28.9% 02/26/2004 - 04/16/2005 23.8 (0.2) 8.9 (0.3) 100 7.0 (1.9) YW 3 (0) 2 66.7% 01/22/2008 - 01/22/2008 24.4 (0.0) 8.5 (0.0) 85 0.9 (0.0) 23.4 9.1 3.9 Total 2436 (499) 396 16.3% 11/06/2000 - 05/20/2016 77.1 (30.0) (2.1) (1.1) (2.3) *Int: Number of replicates to which an intensive PCR protocol, in which samples were screened in 8-10 replicates, was applied. aMAT: mean ambient temperature (average of estimates recorded 30 days prior to sample collection; estimated from remote-sensing MODIS LST (Tatem et al., 2004) dataset) bITV: intra-day temperature variation (average of estimates recorded 30 days prior to sample collection; estimated from remote-sensing MODIS LST (Tatem et al., 2004) dataset) cFC: forest cover (derived from Hansen et al. (2013) dataset) dRF: rainfall (average of estimates recorded 30 days prior to sample collection; estimated from remote-sensing GPCP (Adler et al., 2003) dataset)

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154 6

4

2 % enucleation

0 0 3 5 8 9

Days from induction Appendix 4.1 | Terminally differentiated ejRBCs enucleate at a rate of ~5%. The percentage of enucleated ejRBCs increased during differentiation. After 8 days of differentiation, ~3% of ejRBCs successfully enucleated. Although we generally harvested ejRBCs at this time, a 5% rate of enucleation was observed after 9 days of differentiation. Enucleation rate was estimated via flow cytometry using Vybrant Dicycle Violet DNA dye. After gating out dead cells, the DNA negative population (i.e., enucleated ejRBCs) was determined using an unstained control.

155

Appendix 4.2 | Variable loadings of Principal Components Analysis (PCA). Height of bar articulates the contribution of corresponding variable to a given principal component. Values close to zero represent very little contribution to the principal component, while values that deviate from zero to a greater degree reflect a greater contribution. Principal component 1 (PC1) through Principal Component 5 (PC5) are represented.

156 CD49d CD45 GPA CD71 CD36 CD34 CD117 Day 7 Day Day 10 Day PrimaryHSCs Day 12 Day Day 15 Day Day 18 Day

CD49d CD45 GPA CD71 CD36 CD34 CD117 Day 0 Day EJ EJ cells Day 3 Day Day 5 Day Day 8 Day

Unstained cells Stained with respective antibody

Appendix 4.3 | Expression of erythropoiesis markers during differentiation of EJ cells and primary HSCs. Representative flow cytometry plots of erythropoiesis markers CD49d, CD45, GPA, CD71, CD36, CD34, and CD117. Unstained cells are represented in red, while cells stained with corresponding antibody

157 (Appendix 4.3 continued) are represented in blue. Median fluorescent intensity (MFI) data derived from these and other experiments were used in the principal components analysis (PCA) presented in Figure 2C.

158 139 190 Consensus SAFAQALLLGCHASLGPKLGAGQVPGLTLGLSVGLWGVAALLTLPITLASGA Homo sapiens ...... HR...... T..I...... V...... Allenopithecus nigroviridis ...... Aotus sp ...... T.....A...... S..G Ateles geoffroyi ...... T.....A...... Callithrix jacchus ...... T...... D. Cercocebus agilis ...... Cercocebus galeritus ...... Cercocebus torquatus ...... G...... Cercopithecus mitis ...... Cercopithecus mona ...... Chiropotes satanas ...... TM....A...... Gorilla gorilla ...... HR...... T..I...... V...... Hylobates sp ...... H...... T..I...... V..T... Lophocebus aterrimus ...... Macaca fascicularis ...... D...... Macaca fuscata ...... H...... Macaca mulatta ...... Macaca nemestrina ...... Macaca nigra ...... Macaca thibetana ...... Mandrillus leucophaeus ...... Mandrillus sphinx ...... Pan troglodytes ...... HR...... T..I...... V...... Papio anubis ...... Papio hamadryas ...... D...... V...... Pithecia pithecia ...... T.....A...... Rhinopithecus roxellana ...... T...... Saguinus imperator ...... A...... D. Saguinus midas ...... A...... D. Saimiri boliviensis ...... P.....AT...... Saimiri sciureus ...... T.....A...... Saimiri ustus ...... T.....A...... Sapajus apella ...... T.....A.....V...... Symphalangus syndactylus ...... H...... T..I...... V...... Theropithecus gelada ...... Key Deleted in ΔDARC Allele 1 Intact in ΔDARC Allele 1 ‘.’ residue matches consensus

Appendix 4.4 | Evolutionary conservation of DARC protein deleted in ΔDARC EJ cell line. Protein sequence alignment of a partial segment of the DARC gene of 35 primate species. This segment spans the 12 amino acid deletion observed in one allele of the ΔDARC EJ cell line (highlighted in red). Of the 12 amino acids affected by this deletion, eight are invariant among primates and all 12 are invariant among humans. Nucleotide sequences were downloaded from NCBI Genbank and translated to generate these protein sequences.

159 Appendix 4.5 | Results of Protein Variation Effect Analyzer (PROVEAN) analysis of CRISPR/Cas9- mediated disruption of the DARC gene, resulting in a 12 amino acid deletion. Reference Ref. position Ref. residue Alt. residue Score Prediction* No. sequences No. cluster NP_002027.2 160 G . -5.90 Deleterious 215 30 NP_002027.2 161 Q . -7.39 Deleterious 215 30 NP_002027.2 162 V . -6.84 Deleterious 215 30 NP_002027.2 163 P . -6.98 Deleterious 215 30 NP_002027.2 164 G . -5.91 Deleterious 215 30 NP_002027.2 165 L . -6.47 Deleterious 215 30 NP_002027.2 166 T . -8.10 Deleterious 215 30 NP_002027.2 167 L . -7.85 Deleterious 215 30 NP_002027.2 168 G . -6.65 Deleterious 215 30 NP_002027.2 169 L . -6.54 Deleterious 215 30 NP_002027.2 170 T . -6.23 Deleterious 215 30 NP_002027.2 171 V . -6.66 Deleterious 215 30 *Cutoff for prediction=-2.5 (Choi et al., 2012)

Choi, Y., Sims, G. E., Murphy, S., Miller, J. R., & Chan, A. P. (2012). Predicting the Functional Effect of Amino Acid Substitutions and Indels. PLoS ONE, 7(10).

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Appendix 5.1 | Epidemiologic transmission model of the 2013 chimpanzee respiratory disease outbreak. We constructed an SIR (susceptible-infectious-removed) model using the following set of ordinary differential equations:

!" −&"' = !# (

!' &"' = − *' !# (

!+ = *' !# where S is the number of susceptible chimpanzees, I the number of infectious chimpanzees, and R the number of recovered chimpanzees; & is the daily transmission probability; * is the recovery rate; and N is the total number of chimpanzees in the population (i.e., N = S + I + R). We estimated & and * and associated CIs by fitting the SIR model to observed cumulative incidence data (Althaus, 2014) for each phase of the outbreak, and we assumed a total population size of 50 chimpanzees. Here, the basic reproductive number, - R0, is calculated as + = . Graphs show model predictions superimposed on data from observations of , . clinical signs for the 3 phases of the epidemic. The table shows estimated epidemiologic parameters.

Althaus, C. (2014). Estimating the reproduction number of Ebola Virus (EBOV) during the 2014 Ooutbreak in West Africa. PLoS Currents Outbreaks, 1–9.

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Appendix 5.2 | Maximum-likelihood phylogenetic tree of adenoviruses from fecal samples of wild chimpanzees collected during the respiratory disease epidemic, Uganda, 2013. We constructed the tree from a codon-based alignment (528 positions) of partial adenovirus hexon gene sequences using the same methods as for the tree in Fig 5.3. Included are sequences generated during this study (bold) and their closest relatives (GenBank accession nos. in parentheses). Numbers above branches indicate statistical confidence on the basis of 1,000 bootstrap replicates (only bootstrap values >50% are shown); scale bar indicates nucleotide substitutions per site.

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