High-Throughput Sequencing As a Tool for Studying Strongylid Nematode Communities in Primates

Home , Chromadorea, Crenosoma, Rhabditida

Faculty of Science

High-throughput sequencing as a tool for studying strongylid nematode communities in primates

Diploma thesis Bc. Vladislav Ilík

Supervisor: Mgr. Barbora Pafčo, Ph.D. Department of botany and zoology Field of study: Zoology

Brno 2020

Přírodovědecká Fakulta

Velkokapacitní sekvenování jako nástroj studia strongylidních hlístic u primátů

Diplomová práce Bc. Vladislav Ilík

Vedoucí práce: Mgr. Barbora Pafčo, Ph.D. Ústav botaniky a zoologie obor Zoologie

Brno 2020

Bibliografický záznam

Autor: Bc. Vladislav Ilík Přírodovědecká fakulta Masarykova univerzita Ústav botaniky a zoologie Název práce: Velkokapacitní sekvenování jako nástroj studia strongylidních hlístic u primátů Studijní program: Zoologie Studijní obor: Zoologie Vedoucí práce: Mgr. Barbora Pafčo, Ph.D. Rok: 2020 Počet stran: 101 Klíčová slova: Nematoda; Strongylida; Společenstvo; Člověk; Gorila; Šimpanz; Přenos; Zoonóza; Velkokapacitní sekvenování; ITS-2

Bibliographic entry

Author: Bc. Vladislav Ilík Faculty of Science Masaryk University Department of Botany and Zoology Title of Thesis: High-throughput sequencing as a tool for studying strongylid nematode communities in primates Degree Programme: Zoology Field of Study: Zoology Supervisor: Mgr. Barbora Pafčo, Ph.D. Year: 2020 Number of pages: 101 Keywords: Nematoda; Strongylida; Community; Human; Gorilla; Chimpanzee; Transmission; Zoonosis; High-throughput sequencing; ITS-2

Abstrakt

Úvod

Strongylidní hlístice patří k nejdůležitějším patogenům suchozemských obratlovců, včetně lidí a primátů. Více než 400 milionů lidí celosvětově je nakaženo měchovci, kteří mají významné ekonomické a zdravotní dopady, zejména v rozvojových zemích. V posledních desetiletích zaznamenává lidská populace nebývale vysoký nárůst a rozšíření lidských sídel. Naopak, u volně žijících divokých zvířat dochází spíše k populačním poklesům a vykořisťování z jejich původně přirozeného prostředí. Těžba dřeva, lov a zemědělství jsou dnes hlavními příčinami poklesu biodiverzity. Vzrůstající lidský kontakt s divokými zvířaty však s sebou přináší riziko vzájemné výměny a sdílení některých patogenů. V Africe lidé často žijí v malých, místních komunitách a v úzkém soužití s divokými zvířaty. Jejich životní styl stále zůstává pre-industriální a zaměřený spíše na tradiční způsoby obživy, jako je lov, sběr či jednoduché zemědělství. Jelikož jsou primáti úzce fylogeneticky příbuzní lidem, předpokládá se, že mezi nimi bude docházet k vzájemné výměně patogenů. Obzvláště, když strongylidní hlístice představují tvoří dominantní složku jejich parazitofauny.

Metodika a výsledky

V této studii jsem využil jsem velkokapacitní sekvenování (HTS) jako nástroj ke studiu a popisu společenstev strongylidních hlístic u lidí a primátů, společně obývajících severní okraj přírodní rezervace DJA v Kamerunu. Výsledky přibližně odpovídají výsledkům předchozích studií, jelikož u rodů Oesophagostomum a Necator byly zaznamenány nejvyšší prevalence a primáti vykazovali větší diverzitu parazitofauny než lidé. Velkokapacitní sekvenování také zachytilo přítomnost vzácných, jinak opomíjených taxonů jako Ancylostoma, Ternidens a Trichostrongylus. Navíc odhalilo přítomnost několika zoonotických haplotypů, sdílených mezi lidmi a primáty. U primátů běžně přítomné O. stephanostomum bylo zaznamenáno i u lidí. Zároveň jsem odhalil jeden přítomnost rodu Trichostrongylus u všech studovaných druhů, avšak u lidí byl zaznamenán pouze v jednom případě. Necator americanus, původně označován jako lidský parazit, byl nalezen u lidí i u goril. Na druhou stranu, N. gorillae, původně popsán u goril nížinných, byl nalezen u všech hostitelských druhů.

Závěr Výsledky této studie rozšiřují aktuální znalosti o komplexních společenstvech strongylidních hlístic. Také podtrhují stále rostoucí trend, kdy dochází k nárůstu lidského kontaktu s divokými zvířaty a vzájemnému přenosu patogenů. Kvůli nepopiratelnému významu některých popsaných parazitů by mělo být v budoucnu vloženo úsilí do minimalizování kontaktu a přenosů, jelikož důsledky mohou být vážné na obou stranách. I když jsou paraziti primátů často ve vědeckém “hledáčku”, pouze málo je známo o komplexních společenstvech které tvoří, a proto mohou představovat vhodné organismy pro budoucí výzkum. Závěrem, velkokapacitní sekvenování (HTS), aplikované na směsné vzorky ze stolice představuje levný a rychlý způsob k nahlédnutí nejen do diverzity strongylidních hlístic. Navíc s vyšší rozlišovací schopností než za použití tradičních postupů. Aplikace této metody boří hranice klasického Sangerova sekvenování a umožňuje analýzu hostitelské specifity strongylidních hlístic ve složitých parazito- hostitelských vztazích.

Abstract

Introduction

Strongylid nematodes belong to one of the most important pathogens of terrestrial vertebrates, including humans and non-human primates. Hookworms alone infect over 400 million people wordlwide, bringing significant economic losses and public health concerns, especially in developing countries. In past decades, human population is recording unprecedented rapid growth and expansion of the human settlements. On the contrary, wild, free-living animals are going through the exact opposite trend with generally decreasing populations and forcing out of their (once natural) habitats. Logging, hunting and agriculture are the main driving forces standing behind the global biodiversity decrease. Intensified human contact with wildlife, however, brings along a risk of mutual pathogen exchange and sharing. Great apes are phylogenetically closely related to humans, therefore pathogen exchange is presumed, especially with strongylid nematodes being dominant parasites of both humans and great apes.

Methodology and principal findings

In this study, I used metabarcoding ITS-2 high-throughput sequencing (HTS) approach as a tool for evaluation of strongylid nematode communitites and the zoonotic potential between humans and great apes cohabiting the northern periphery of the DJA Faunal Reserve in Cameroon. The results were relatively consistent with previously conducted studies, as I found Oesophagostomum and Necator being the most prevalent genera. Moreover, great apes exhibiting greater diversity of parasite fauna than humans. In addition, the HTS approach captured presence of the rare, often neglected taxa, such as Ancylostoma, Ternidens and Trichostrongylus as well as closely unidentified strongylid nematodes. HTS revealed several zoonotic haplotypes being shared between humans and great apes. There was evidence of O. stephanostomum (commonly found in great apes) in humans. Necator americanus, formerly believed to be a human-specific parasite, was found in humans and gorillas. On the contrary, N. gorillae, originally described in western lowland gorilla was found to be widespread across all host species. Last, a single haplotype of closely unidentified Trichostrongylus was also found to be shared across all studied hosts, however, only one human was infected.

Conclusion

The results of this study brought additional knowledge to the understanding of complex strongylid nematode communities. They also underline the growing trend of intensified human contact and mutual pathogen exchange. Due to the indisputable importance of some described parasites, there should be put some efforts to minimise the contact and transmission events as it can have consequences on both sides. Even though primate parasites are often under scientific “finderscope”, little is known about their parasite communitites and therefore they may represent suitable organisms for future scientific studies and focus. In conclusion, high-throughput sequencing of strongylid nematodes from fecal samples represent a time- and cost-efficient way of studying helminth communitites and provides a resolution superior to traditional approaches. Its application overcomes the limitations of classical Sanger sequencing and allows for analyses of strongylid nematode host-specifity in complex parasite-host systems.

Čestné prohlášení

Prohlašuji, že jsem svoji diplomovou práci vypracoval samostatně pod vedením vedoucího práce s využitím informačních zdrojů, které jsou v práci citovány.

V Brně 19. května 2020 ...... Bc. Vladislav Ilík

Acknowledgements

Hereby, I would like to thank primarily my dearest supervisor Dr. Barbora Pafčo, especially for her constant “24/7” support and warm, human-kind attitude. She introduced me the mysteries of the “scientific world”, helped with the laboratory work and gave me valuable contacts. Finally, she helped me propose the life-changing opportunity to spend a whole year in the USA as I am a successful Fulbright scholar. Although I was pretty sceptical about possible success in the beginning. I am fully aware of the amount of the time and effort she put into me and I strongly hope that my following work (and the work during the time of our collaboration) will bring a dignifying result. I would like to also thank my co-supervisor Dr. Martin Kašný, who introduced me to the HPI-Lab team and helped me with finalising this thesis. The last person who contributed significantly to my work is Dr. Jakub Kreisinger, who prepared the basic dataset and helped me a lot with bioinformatic processing of the data. Therefore, I would like to express my gratitude to him.

I also express my gratitude to all members of the HPI-Lab Team, who accepted me into the collective, and for me, it was a pleasure to work with them (I hope for them as well). Namely: the “chieftain” of our team, prof. David Modrý, the main coordinator and the Wonder Woman in one person, Dr. Klára Petrželková, the Rwandan fellow-worker- traveller Dr. Barbora Červená, the English Patrol Bethan Mason (as she provided also the English corrections of presented thesis) and the Women squad (Anna Stryková, Rita Cameira, Tereza Prokopová and Eva Jiroušová). I am very grateful to Dr. Klára Petrželková and Dr. Barbora Červená, who organised and helped me during my trip to Rwanda. Also, my gratitude goes to Genomic Core Facility at CEITEC, Masaryk University, where I carried out the DNA quality controls and sequencing libraries preparation, especially to Dr. Filip Pardy and Tereza Deissová. I would also like to thank my whole family and my closest people for their unrelenting, endless support during my studies, and even during the harder times, this thesis not excepting.

I express my gratitude also to the Ministère de la Recherche Scientifique et de l’Innovation a Ministère des Forêts et de la Faune, Cameroon for permission to conduct the research in Cameroon; Royal Zoological Society of Antwerp (KMDA), Belgium, Project Grands Singes, Cameroon, especially to Dr. Nikki Tagg from welcoming the

13 project and logistical support in the field; and all people who helped with sample collection: Barbora Pafčo, Dagmar Jirsová, Zuzana Tehlarová, Arlette Tchankugni Nguemfo, Charmance Irene Nkombou, Klára Vlčková and all local trackers and assistants.

Contents

1. Introduction, aims of the work ...... 17 2. Current state of knowledge ...... 19 2.1 Emerging zoonotic diseases ...... 19 2.1.1 Zoonotic diseases ...... 19 2.1.2 Parasitic infections ...... 21 2.2 Parasite transmission ...... 23 2.2.1 Transmission patterns ...... 23 2.2.2 Host specificity, filtering and supershedders ...... 24 2.2.3 Human impact on transmission patterns ...... 24 2.3 Strongylid nematodes ...... 25 2.3.1 General characterization and taxonomy ...... 25 2.3.2 Life-history strategies ...... 27 2.3.3 Health and economic impacts ...... 29 2.4 Great apes and humans in Africa ...... 31 2.4.1 African Great apes...... 31 2.4.2 Local humans cohabiting the forests of Africa ...... 33 2.5 Strongylid nematodes infecting great apes and humans ...... 34 2.5.1 Oesophagostomum and Necator ...... 35 2.5.2 Other strongylid nematodes ...... 36 2.6 Transmission between humans and great apes ...... 37 2.7 Diagnostics focused on strongylid nematodes ...... 38 2.7.1 Traditional approaches based on microscopy ...... 38 2.7.2 Molecular approaches – identification and diagnostics ...... 41 2.7.3 High-throughput sequencing (HTS) ...... 43 3. Material and methods ...... 48 3.1 Study site ...... 48 3.2 Samples collection ...... 50 3.3 DNA isolation, library preparation and HTS sequencing ...... 51 3.4 Bioinformatic processing ...... 52 3.5 Statistical analysis ...... 53 4. Results ...... 54 4.1 Basic exploration of dataset...... 54 4.2 Strongylid nematode communities within the host species ...... 57

4.2.1 Description of strongylid nematode communities ...... 57 4.2.2 Zoonotic potential ...... 59 4.3 Divergence in strongylid composition according to the host species ...... 59 4.3.1 Alpha diversity ...... 59 4.3.2 Beta diversity ...... 60 5. Discussion ...... 63 5.1 Methodology evaluation ...... 63 5.2 Strongylid nematodes found in the studied hosts ...... 65 5.2.1 Humans ...... 66 5.2.2 Great apes ...... 67 5.3 Zoonotic patterns ...... 70 5.4 Conclusions ...... 71 6. Aims for my future work ...... 73 7. Reference ...... 74 8. Supplements ...... 89

1. Introduction, aims of the work Strongylid nematodes belong to the world’s most important, widespread parasites. They can be found across continents and different host species. For humans, they represent literal multidimensional concern, as they threaten the health of human populations, together with economic impacts that cannot be easily overlooked. Even though most of the strongylid nematodes do not regularly parasitise on humans and a vast majority of strongylid species are found mainly in wildlife and domestic animal populations, situation is changing apace. Rapidly growing human population brings great concerns to conservation activities, especially those for endangered or critically endangered animals. Human contact with wild free-ranging animals has significantly increased in the past few decades, bringing along increased risk of two-sided sharing and transmission of pathogens. Great apes close phylogenetic relationship with humans, together with parasites ability to infect multiple host species, can bring more than devastating effects. Moreover, increasing nematode resistance to drug treatment has been reported. Therefore, deeper understanding of complex strongylid diversity, epidemiology, transmission patterns and mechanisms standing behind infection is urgently needed. Classical morphological and basic molecular approaches are unsuitable for robust studies with focus on strongylid nematodes, which are often neglected by public, despite their importance. However, High-throughput sequencing (HTS) offers a possibility of generating large amounts of data in a short time and sufficiently high- sensitivity for detecting “rare” species, which are traditionally overlooked when using classical approaches. HTS can therefore represent a suitable platform for studying strongylid diversity and their transmissions.

During this study I focused on evaluation of strongylid diversity and their possible transmission between great apes (chimpanzees and gorillas) and humans inhabiting DJA Faunal Reserve, Cameroon. I used metabarcoding data obtained via HTS approach on Illumina MiSeq Platform. The data for my thesis were already prepared, however, I implemented some bioinformatic tools and computed all statistical analyses in the study. I evaluated and interpreted results and additionally, I also became a part of multidisciplinary research group, called HPI-lab. I participated on the project “Epidemiology and pathological effects of gastrointestinal helminthiases in critically endangered mountain gorillas”, during which I (i) conducted field work in Rwanda where

I was also an assistant during the training in molecular diagnostic of gorilla parasite for local researchers (ii) prepared amplicon HTS libraries from mountain gorilla samples for current project of HPI-lab and (iii) participated in current project focused at Borrelia canis at the Department of pathological morphology and parasitology at VFU, Brno, where I was responsible for DNA isolation from dog blood samples. Therefore, I am familiar with all steps necessary for accomplishment of presented thesis.

Particular aims of the study

o to become familiar with High-throughput sequencing (HTS) methodology o to learn how to process and statistically evaluate HTS data o to describe strongylid nematode communities and evaluate their diversity across different host species (humans, gorillas and chimps) cohabiting the DJA Faunal Reserve in Cameroon o to search for possible transmission events and zoonotic potential of strongylid nematodes found in studied hosts

2. Current state of knowledge

2.1 Emerging zoonotic diseases World Health Organization (WHO) defines a zoonotic disease as: “any disease or infection that is naturally transmissible from vertebrate animals to humans” (WHO, www.who.int). Throughout history, infectious diseases existed side by side with the growth of the civilisation and people had to deal with several health issues, such as tuberculosis, plague, malaria, anthrax, influenza and others. Origin of these diseases was often credited to domestic animals, poultry and livestock (Dobson & Carper, 1996). Onset of vaccination facilitated the management of many diseases, but only a few were successfully eradicated and most of them still persist in populations or reappear again (Choi, 2010). High mutation rates and fast adaptation mechanisms significantly contribute to the fitness of microorganisms (such as viruses, bacteria, parasites or fungi), as it was previously described in The Red Queen hypothesis by Van Valen, (1973). These abilities enable infection of multiple hosts / host species and formation of new species / types of diseases, often followed by uncontrollable infection spread and high mortality rates among hosts. Newly emerging viral respiratory syndrome outbreaks as SARS (Hsu et al., 2003), MERS (Mohd et al., 2016) or recent Sars-CoV-2 (Yang et al., 2020) are the living proofs of ongoing disease emergence.

2.1.1 Zoonotic diseases Animals pose an important role in terms of providing a wide range of benefits to people’s everyday life. Domestic animals can be a companion as pets or provide a substantial part of material resources (wool, leather, etc.) or food (livestock). Impact of domestic animals and livestock on the economy around the world is appreciable especially in developing countries (Delgado et al., 2001). Lately, human and livestock contact with wildlife is on the rise and attracts more scientific attention (Siembieda et al., 2011). Mainly for the capability of animals to carry a variety of pathogens, which can may infect humans, sometimes resulting in an uncontrollable disease spread and epidemics on a world-wide scale, with devastating effects for human population (Hsu et al., 2003).

It is estimated that 75 % of all infectious diseases have origin in animals, meaning they are zoonotic. Some of the world’s most known diseases are believed to have originated in wild animals. For example, HIV virus (human immunodeficiency virus, responsible for human AIDS) is believed to have origin in primates, where it was first described, probably as a distinct lineage of SIV (simian immunodeficiency virus) infections, commonly found in primates (Sharp & Hahn, 2010). Another serious human illness, the Ebola virus and its outbreaks, is believed to have zoonotic origin in wild populations of bats (Saéz et al., 2015), with primates as an important interlink in transmission to humans, as they are susceptible to Ebola viral infection. However, high pathogenicity was reported among non-human primates, where for example, the Zaire Ebola strain is responsible for killing nearly one third of the world’s gorilla (Gorilla gorilla Savage &Wyman, 1847) and chimpanzee (Pan troglodytes Blumenbach, 1775) population (Bermejo et al., 2006).

Spread of pathogens is not only one-sided. Pathogens originating in animals can infect humans and vice versa. Rapidly increasing human population, rising migration flow and more frequent contact with wildlife present a true challenge in infectious disease prevention and control (Wang et al., 2008). Mutual pathogen exchange can also result in serious complications in wildlife populations. Several cases of animal deaths related to transmission from humans were reported for human viral respiratory diseases. A long time ago, measles was proven to be transmitted across the human-primate interface (Meyer et al., 1962) and can even be responsible for animal deaths (Remfry, 1976). More recently Köndgen et al., (2008) found two strains of common human paramyxoviruses in dead chimpanzees, which were closely related to strains responsible for worldwide human epidemics. Moreover, there is evidence of pathogens being transmitted from humans to wild apes via tourism and research (Ferber, 2000), suggesting a high possibility of human- wildlife pathogen transmissions, especially those of low pathogenicity (Goldberg et al., 2007).

Due to the often rough consequences, major concerns and conservation efforts are pointed against the spread and transmission of infectious diseases between humans and wildlife populations (Ryan & Walsh, 2011). Currently, scientists all over the world struggle with unravelling transmission patterns and tracing the origin of pathogens infecting multiple host species (Wang & Crameri, 2014; Webster et al., 2017). Deeper understanding of epidemiology and transmission of emerging diseases became truly

20 challenging but remains a keystone for animal and public health of modern society (Woolhouse et al., 2001).

2.1.2 Parasitic infections Well-known zoonotic diseases are those caused by parasites. First of all, parasites can pose serious problems to both domestic and wild animals. As an example, former or newly emerging outbreaks of avian malaria, caused by protozoan parasites of the genus Plasmodium Marchiafava & Celli, 1885 and Haemoproteus Kruse, 1890 (e.g. Plasmodium elongatum Huff, 1930 or Plasmodium relictum Grassi et Feletti, 1891), which are transmitted between birds via mosquito vectors (e.g. Culex Linnaeus, 1758), are still being reported (Lapointe et al., 2012). In Africa, other important parasites are of the genus Trypanosoma Gruby, 1843, where animal form of trypanosomiasis (e.g. Trypanosoma congolense Broden, 1904 or Trypanosoma evansi Steel, 1855) can cause problems to many domestic animals including cattle, goats, sheep, horses, rodents, dogs or cats (Losos & Ikede, 1972).

Parasite infections also represent serious health threats to millions of people (Tab. 1) all over the world. Billions of infections and millions of human deaths are associated with parasites annually. For example, human trypanosomiasis, as an “immense veterinary and medical problem” in Africa, was first described long ago, but still poses an important health threat not merely in Africa (Roque et al., 2008).

Table 1: Well-known zoonotic parasitoses, their cause and associated morbidity. Data obtained from World Health Organization, WHO (www.who.int). Disease Pathogen Morbidity Malaria Plasmodium spp. 228 mil. people infected Leishmaniasis Lesihmania spp. 700,000-1mil. people/year Soil-transmitted helminth Helminth worms 1.5 bil. people infected infections Foodborne trematodiases Trematoda 2 mil. deaths/year Chagas disease Trypanosoma cruzi 6-7 mil. people infected

In the shadow of these life-threatening diseases, most common infections, like those caused by soil-transmitted helminths, are often disregarded. The evidence of gastrointestinal helminths such as Ascaris Linnaeus, 1758 or Enterobius Leach, 1853, in hunter-gatherer communities, together with other common parasites (ticks, louses), dates back to ancient times (Dobson & Carper, 1996). Actual data points up to 1.5 billion people infected nowadays (WHO). The main species infecting humans (Fig. 1) are hookworms (Necator americanus Stiles, 1902; Ancylostoma duodenale Dubini, 1843), the whipworm (Trichuris trichiura Linnaeus, 1771) and the roundworm (Ascaris lumbricoides Linnaeus, 1758), infecting up to 800 million people alone.

Soil-transmitted helminth infections in 2010 (in millions)

438,9 464,6 Ascaris Trichuris Hookworms 819,0

Figure 1: Numbers of soil-transmitted helminth infections in year 2010, according to Pullan et al., (2014).

Several globally important parasites are known zoonotic diseases. Gastrointestinal parasites of the genus Giardia Künstler, 1882 were described as zoonotic agens with waterborne transmission long ago. Today, it represents one of the most widely studied organism (Thompson, 2004). Other well-known zoonotic diseases include for example cryptosporidiosis, tick-borne infections and ancylostomiasis (Schantz, 1991). Moreover, life threating food-borne diseases, such as trichinellosis, caused by nematode Trichinella spiralis Owen, 1835, cysticercosis caused by larval form of the pig tapeworm Taenia solium Linnaeus, 1758 (Zhou et al., 2008) or from domestic pets (dogs) or wild animals (foxes, wolfs) acquired echinococcosis (caused by Echinococcus Rudolphi, 1801) (Robertson & Thompson, 2002).

2.2 Parasite transmission

2.2.1 Transmission patterns Transmission of parasites can be connected with direct physical contact between infected individuals, via intermediate hosts or vectors, but also with a spatial sharing of common environment by the hosts (indirect contact). In this case, many parasites produce resistant stages (cysts, eggs, larvae) contaminating the common environment. The viability of these stages maintains the potential of infection and transmission for many days or even months (Zajac, 2006). Therefore, the viability and infectiveness of free- living stages (together with reproductive potential) in the outside environment became, for those parasites, rather crucial (Anderson, 2000).

Studies conducted on the diversity and abundance of parasite communities revealed significant differences in their composition across different taxa (Arneberg et al., 1998) and listed many factors influencing the load of parasites and susceptibility of the host to infection. These factors include individual characteristics of the host and a wide variety of social and environmental factors (Parker et al., 2019). Individual characteristics are, for example, sex (Zuk & McKean, 1996), body condition (Sánchez et al., 2018) or age, when younger and older individuals tend to be more susceptible to infection than middle- aged individuals, probably due to a not developed / senescing immune system (Albery et al., 2018). Environmental factors, such as available vegetation or cleanliness of water sources can also contribute to the exposure of the host to infection (Khan et al., 2010). Moreover, dietary habits play an important role in parasite transmission. For example, folivorous species consume a higher volume of resources, have more frequent contact with the soil or vegetation (root-digging, fruit-picking, vegetation-tearing, plant-picking) and may therefore represent a better target for parasites with indirect transmission. Additionally, cases of self-medication were reported in wild animals (especially in primates), possibly reducing the parasite load (Huffman, et al., 1997; Huffman, 2003). There is also evidence that sociality may play an important role in parasite transmission, as with increasing group size, individuals located more centrally within a social network may be more exposed to parasite infections (MacIntosh et al., 2012).

2.2.2 Host specificity, filtering and supershedders Host specificity is one of the factors that influence greatly the zoonotic potential of parasites. It is expressed as a number of host species which can be infected by a particular parasite taxon. Highly host-specific parasites are restricted to one host species (Enterobius Leach, 1853), whilst less host-specific parasites (generalists) can occupy a broad host range (Giardia intestinalis Alexeieff, 1914) (Poulin, 2007). Host specificity is influenced by a range of ecological factors, for example, physiological similarity between hosts significantly increase the potential for zoonotic transmission (Hasegawa et al., 2014).

Parasites can be transmitted by vectors / intermediate hosts or through the environment. Combes’ filter concept (Combes, 1991) introduced two filters determining the zoonotic potential of a given parasite species: host-parasite compatibility (compatibility filter) and opportunities for exposure (encounter filter). The compatibility filter excludes animals in which parasites are not capable to develop and survive due to morphological, physiological or immunological reasons (i.e. host specificity). The encounter filter excludes animals, which cannot meet parasites because of ethological or ecological reasons (Combes, 2001).

The fact that sociality plays an important role in host-parasite systems and disease dynamics is supported by supershedders (superspreaders) concept (Paull et al., 2012). Almost all living organisms harbour some parasites, however, they vary in parasite burden. In groups of individuals, living together, only few individuals are commonly heavily infected, while others have relatively few parasites. This is referred as an 80-20 rule (supershedders harbour 80 % of all parasites in the community). The supershedders are responsible for most of the transmission events and their identification in populations is rather crucial (Galvani & May, 2005; Lloyd-Smith et al., 2005).

2.2.3 Human impact on transmission patterns Human population may have a great impact and can contribute significantly to the transmission of parasites and emergence of zoonoses. A major shift in infectious diseases followed the rise of agriculture, animal domestication, environmental modification and increasing population densities as permanent settlements were formed (Harper & Armelagos, 2010). Recently, as human population grows rapidly, spatial and resource

24 requirements are higher than before. People modify (once natural) habitats of animals and contact with wildlife is intensified. As it was previously described, habitat alteration stands behind biodiversity decrease, especially endangered animals, who are critically threatened by fragmentation and loss of their natural habitats (Schwitzer et al., 2019). Recently, main driving forces of the biodiversity decrease are intense logging and related deforestation (Barlow et al., 2016). In developing countries, people often live in small local communities in close proximity to free-ranging wild animals. For many of them, wild animals, or their natural environment, represent a source for everyday livelihood. Farmers, “bushmeat” hunters, forest gatherers, all significantly raise the risk of transmission of pathogens both ways (Wolfe et al., 2005). Due to lower hygiene standards and different lifestyle, distribution of pathogens between local people is rapidly fast. Last but not least, conservation activities and tourism can contribute to transmission of pathogens (Goldberg et al., 2007). Such as through habituation (familiarizing animals to the presence of humans), which leads to more intimate contact between humans and wild animals. Habituation is beneficial for conservation, research and tourism, however, it increases the risk of pathogen transmission (Doran-Sheehy et al., 2007).

2.3 Strongylid nematodes

2.3.1 General characterization and taxonomy Today, the suborder Strongylida belongs to the order Rhabditida, class Chromadorea of the phylum Nematoda (Nematoda: Chromadorea: Rhabditida: Strongylida) (Wellehan & Walden, 2019). Parasites (of vertebrates, marine mammals, invertebrates and plants) as well as free-living representatives are part of this suborder. Due to the presence of a copulatory bursa, they are sometimes referred to as bursate nematodes (Anderson, 2000). They form complex communities within their hosts, for example in ruminants, elephants, equids, non-human primates (NHPs) or humans (Mclean et al., 2012; Rothman & Bowman, 2003; Sutherland & Scott, 2009). Many species are of veterinary or medical importance (Zajac, 2006).

Formerly, according to Anderson et al., (1974), based on morphological observation, Strongylida consisted of five, well-defined superfamilies: Ancylostomatoidea, Diaphanocephaloidea, Strongyloidea, Metastrongyloidea and Trichostrongyloidea. Phylogenetic relationships were further studied and modified over the time. It was broadly accepted, that strongylid nematodes partially evolved from free-living ancestors originated in Rhabditid families (Fig. 2a), with basal taxonomic groups of Metastrongylina and Trichostrongylina (Durette-Desset et al., 1994). Onset of various molecular approaches significantly contributed to deeper understanding of complex phylogenetic relationship. Chilton et al., (2006) amplified 18S and 28S rDNA (genes associated with small and large subunit of eukaryotic cytoplasmic ribosomes) region of significant number of strongylid nematodes from various host species and verified the Rhabditid families as likely ancestral groups and proposed four, well-defined suborders with slightly different phylogenetic relationships (Fig. 2b).

As a basal group, he defined the Strongylina suborder, including, among others, important gastrointestinal (Chabertia Railliet & Henry, 1909; Oesophagostomum Molin, 1861) and pulmonary (Syngamus Siebold, 1836; Mammomonogamus Ryzhikovk, 1948) parasitic genera of animals and humans (Nosanchuk et al., 1995). According to the diagram, complex phylogenetic relationships between Strongylina and Ancylostomatina suborders remain partly unsolved, but a close relationship is obvious. The Ancylostomatina suborder contains clinically and economically important hookworms (Necator and Ancylostoma Dubini, 1843) both of animals and humans (Crompton, 2000). Sister taxa are represented by two suborders: Metastrongylina, consisting mostly of parasites of pulmonary and circulatory system (e.g. Protostrongylus Kamensky, 1905, Angiostrongylus Kamensky, 1905) (Hobmaier & Hobmaier, 1930) and Trichostrongylina, which includes parasites (e.g. Trichostrongylus Looss, 1905; Haemonchus Cobb, 1898) with remarkable global impacts on livestock industry (Tan et al., 2014).

Figure 2: Phylogenetic relationships within the suborder Strongylida: a) earlier insight based on the adult strongylids morphological observation according to Durette-Desset et al., (1994), adjusted; b) recent insight based on 18S and 28S rDNA, according to Chilton et al., (2006), adjusted.

2.3.2 Life-history strategies Strongylid nematodes are soil-transmitted helminths, known for their simple direct life cycles, in exception of some lungworms, such as rat lungworm (Angiostrongylus cantonensis Chen, 1935) or fox lungworm (Crenosoma vulpis Dujardin, 1845), where the presence of intermediate hosts (snails / slugs) is typical (Wang et al., 2012). Adult worms (Fig. 3a,b) are found in gastrointestinal or pulmonary tract, where they feed on blood or tissues (Zajac, 2006). Further, they can be found in perirenal space, circulatory or nervous system, muscles and sometimes in nasal sinuses (Wang et al., 2012).

Adult females, attached to the surface of the specific organ, produce thin-walled eggs (Fig. 3c), which are passed through the host faeces into outside environment. Under favourable outside conditions (moisture, warmth, shade), first larval stage (L1) hatches from the egg, still in the feces as it provides protection against unfavourable environmental conditions. L1 become free-living (rhabditiform larva) in feces, contaminating soil, water or vegetation. During a few days, larvae feed on bacteria, molt two times and develop into the infective third larval stage (L3) (Fig. 3d). Infective third larval stages (filariform larva) are ingested (oral infection) via contaminated environment (Fig. 4a) or penetrate the hosts’ skin (percutaneous infection) (Fig. 4b), typically through bare feet in humans. Larvae then migrate through the body, additionally molt a few times and attach in the final destination (e.g. small intestine), where they mature into adults and reproduce (Anderson, 2000).

Figure 3: Strongylid nematodes: a) male copulatory bursa, adult Ancylostoma ceylanicum; b) buccal

capsule of adult A. ceylanicum; c) strongylid egg from a western lowland gorilla; d) L3 larvae of Oesophagostomum sp. Pictures from Modrý et al., (2018), adjusted.

Figure 4: Life cycles of strongylid nematodes: a) Oesophagostomum sp. (oral infection); b) Necator sp. (percutaneous infection). Pictures from Modrý et al., (2018), adjusted.

2.3.3 Health and economic impacts Strongylid nematodes live many years within their hosts and generally do not cause mortality, however, cases of animal as well as human deaths attributed to gastrointestinal nematodes were reported in cases of severe infections (Lynsdale et al., 2017; Roeber et al., 2013). Their considerable impact on world economy and health cannot be overlooked, as they represent serious health threats to domestic animals and livestock, bringing significant economic losses every year (Charlier et al., 2014).

Gastrointestinal strongylids are commonly found among equid populations (horses, zebras etc.), where they form a parasite majority (Lichtenfels et al., 2008). They appear to be broadly dispersed among horses, as in the case of naturally infected individuals from spatially separated horse yards in Europe (Traversa et al., 2010). In animals, gastrointestinal strongylids often cause inflammatory reactions and lesions, leading to clinical signs, including weakness, weariness, intestinal colic, changeable periods of diarrhoea and constipation, severe weight loss and malnutrition (Delano et al., 2002). Moreover, cases of impeded growth and decreased reproductive output were reported (Akinyi et al., 2019). In cases of livestock production, carcass weight, wool growth, fertility and milk yield can be affected by strongylid infections (Charlier et al., 2009). Pulmonary parasites are attached to the tracheal mucosa and suck blood, consequently leading to inflammatory reactions and anemia. In heavy infections, worms can migrate through lungs, causing lung bruises, oedema and even pneumonia. Moreover, drooling of blood from mouth was reported during Syngamus trachea Montagu, 1811 infection in Emu birds (Narayanan et al., 2014).

Therapy of strongylids relies on treatment with benzimidazoles or macrocyclic lactones (McKellar & Jackson, 2004). However, there is an increasing resistance of nematodes to multiple anthelmintics. For small livestock industries or local farmers, resistance to treatment is a growing problem (Besier, 2007). Therefore, alternative methods, such as organic farming and improved animal welfare have become an important area of interest (Stear et al., 2007) and finding an efficient vaccine can be truly helpful for countries with high rates of strongylid infections.

The most important strongylid infections in humans are caused by hookworms and nodular worms (Oesophagostomum). Hookworms alone infect over 400 million people

29 worldwide. They are endemic to many regions worldwide, including Africa, Asia and Central America (Brooker et al., 2004). Typical symptoms in humans are human eosinophilic enteritis, iron deficiency and anemia (Hotez et al., 2005), but clinical signs are being rather inconspicuous (abdominal pain, diarrhoea, malnutrition) and can be easily mistaken for another gastrointestinal disease. As in animals, cases of stunted growth and development in children were reported (Hotez et al., 2013). Nodular worms cause serious clinical disease called oesophagostomiasis (Polderman & Blotkamp, 1995), with three forms previously described as (I) intestinal tumour-like lesions (nodules) along the colon wall, containing worms and larvae (II) intestinal abscesses obstruction and ulcers, usually related to nodule form and (III) rare cases of subcutaneous ecnapsuled cysts, causing femoral hernia (Haaf & Van Soest, 1964), lately described as the “Dapaong tumour”. The “Dapaong tumour” was presented as a painful, well-demarcated, smooth, spherical, 3–6 cm peri-umbilical (on the left side of the abdomen) mass, adherent to the abdominal wall and associated with fever (Storey et al., 2000). Clinical signs of nodular oesophagostomiasis are often abdominal pain, persistent diarrhoea, weight loss and palpable “tumours” in the abdomen, often noticed by the infected people themselves. Even in local communities in Africa, there is evidence of oesophagostomiasis being recognized by indigenous healers as a “Turtle in the belly” (Krepel, 1994).

Despite all the threats caused by strongylid nematodes, a new, rapidly evolving discipline called Helminth Therapy (HT) has arisen in humans using also strongylid nematodes. HT is especially suitable in strongylid-naive human populations. The frequency of autoimmune disorders, allergies and inflammatory diseases has significantly increased in modern societies (primarily in developed countries) in comparison to those of pre-industrial. Because helminth infections (including those caused by hookworms) dampen the host’s immune system (Navarro et al., 2013), identifying the immunomodulation mechanisms may represent a future hope in therapies of autoimmune disorders, such as Crohn’s disease, rheumatoid arthritis, sclerosis, allergies and even some neuropsychiatric disorders (Sobotková et al., 2019).

2.4 Great apes and humans in Africa

2.4.1 African great apes Great apes belong to the largest terrestrial mammals. They are mainly herbivorous, consuming fruits, flowers, seeds, young leaves and sometimes small mammals and invertebrates. In Africa, there exist four species of great apes: common chimpanzee (Pan troglodytes), bonobo (Pan paniscus Schwartz, 1929), western gorilla (Gorilla gorilla) and eastern gorilla (Gorilla beringei Matschie, 1903) (Caldecott et al., 2005). Four subspecies of chimpanzees are recognized: the most known central chimpanzee (Pan troglodytes troglodytes Blumenbach, 1799), the western chimpanzee (Pan troglodytes verus Schwarz, 1934), the eastern chimpanzee (Pan troglodytes schweinfurthii Giglioli, 1872) and the nigeria-cameroon chimpanzee (Pan troglodytes ellioti Matschie, 1914). All subspecies are classified as endangered by the International Union for Conservation of Nature (IUCN) (Humle et al., 2018), except the western chimpanzee, which is classified as critically endangered (Kühl et al., 2017). Gorillas are further divided into four subspecies, all classified as critically endangered by the IUCN except the mountain gorilla classified as endangered due to the population growth during the last two decades. The eastern strain involves the grauer’s gorilla (Gorilla beringei graueri Matschie, 1914) and the mountain gorilla (Gorilla beringei beringei Matschie, 1903). The western strain consists of the cross river gorilla (Gorilla gorilla dielhi Matschie, 1904) and the western lowland gorilla (Gorilla gorilla gorilla Savage, 1847) (Maisels et al., 2016). Even though a lot of resources are spent on primate conservation efforts (N’Goran et al., 2012), their numbers (especially of great apes) remain critically low (Tab. 2).

Having very cosmopolitan ecology, chimpanzees inhabit various ecosystems, ranging from sea-level to high mountain (2,790 m) elevations (Humle et al., 2016). They can be found throughout rainforests, woodlands, grasslands, savannas or secondary forests ranging from western (Senegal), across the forests of the Congo River, to eastern (Uganda, Tanzania) parts of Africa (Fig. 5) (Strindberg et al., 2018). Bonobos occur only in the Democratic Republic of the Congo (DRC) (Takemoto et al., 2015). Due to high levels of “bushmeat” poaching, deforestation, zoonoses, infectious diseases outbreaks and loss / degradation of habitat caused by expanding human activities, chimpanzees gone

31 through significant population decline and the current population trend is still decreasing. This population decline is suspected to continue over a 75 year timescale (1975–2050) and the population decline is thought to exceed 50 % (Humle et al., 2018).

The grauer’s gorilla can be found only in DRC as well (Plumpture et al., 2016). The mountain gorilla can be found only at two localities – Virunga mountain consisting of three national parks within three countries: Volcanos national Park (Rwanda), Mgahinga Gorilla National Park (Uganda) and Virunga National Park (DRC) and second the Bwindi Impenetrable National Park (Uganda) (Gray et al., 2013; Guschanski et al., 2009). Thanks to intensive management and conservation activities, their population is increasing (Gray et al., 2013). The most endangered is indisputably the cross river gorilla, with estimated population around 250 individuals (Dunn et al., 2014). On the contrary, the second western subspecies, the western lowland gorilla represents the most abundant gorilla taxon (Strindberg et al., 2018), however, the population has undergone decline (more than 80% decline over three generations). They inhabit forests across western equatorial Africa, including both swamp and terra firma lowland forests (primary, disturbed even the secondary forests) (Maisels et al., 2016).

Table 2: Great apes and their estimated numbers in the Africa. Species Population Reference (individuals) Pan troglodytes troglodytes 130,000 Strindberg et al., 2018 Pan troglodytes verss 18,000 – 65,000 Humle et al., 2018 Pan troglodytes 181,000 – 256,000 Humle et al., 2018 schweinfurthii Pan troglodytes ellioti 6,000 – 9,000 Humle et al., 2018 Pan paniscus 15,000 – 20,000 Fruth et al., 2016 Gorilla beringei graueri 3,800 Plumpture et al., 2016 Gorilla beringei beringei 1,000 Gray et al., 2013; Guschanski et al., 2009 Gorilla gorilla dielhi 250 Dunn et al., 2014 Gorilla gorilla gorilla 360,000 Strindberg et al., 2018

Figure 5: Distribution of great apes in the Western and Equatorial Africa. Picture from Sharp & Hahn, (2010).

2.4.2 Local humans cohabiting the forests of equatorial Africa Small communities of local people are found across the African continent. People live in close proximity of free-living animals (Fig. 6) and for many of them, wildlife and its natural environment represent a source for everyday livelihood. In Africa, people often live in small local communities and in close proximity of free-living wild animals (Fig. 6). For many of them, wild animals, or their natural environment, represent a source for everyday livelihood. Unfortunately, farmers, “busmeat” hunters and forest gatherers are often present in the forest environment, which leads to an increased close contact with wild animals, significantly increasing the risk of pathogen transmission both ways (Wolfe et al., 2005). Two major ethnic groups can be found cohabiting the forests with great apes. The groups of hunter-gatherers incorrectly labelled also as pygmies (e.g. BaAka, Baka, Bambenga, Bambuti or Batwa) live in a strong association with the forest environment and being dependent on forest resources (Robinson & Remis, 2014). The Bantu, heterogenous group of multiple lingual and ethnic backgrounds, are more settled in the villages, having more agriculture-orientated lifestyle (Gomez et al., 2016).

Figure 6: Lifestyle patterns of the BaAka and Bantu in the Dzanga Sector, Central African Republic: a) BaAka family in the village; b) Two staple foods in the BaAka diet (bitter manioc and Koko leaves); c) BaAka preparing food (killed blue duiker); d-f) Traditional Bantu village and markets. Pictures from Gomez et al., (2016), adjusted.

2.5 Strongylid nematodes infecting great apes and humans

In general, knowledge about the parasitic communities of great apes is very low. Obtaining representative material for identification of adult worms is possible only during animal necropsies (Durette-Desset et al., 1992) and morphological determination of strongylid taxa from fecal material is rather impossible. Moreover, molecular approaches struggle with lack of available reference data, thus, new approaches, allowing deeper insights into the broad diversity of strongylid nematodes, are still being developed (Santos et al., 2020). However, strongylid nematodes together with Strongyloides sp. Grassi, 1879 are believed to be the most prevalent parasites in wild great apes and have been reported in wildlife populations in great numbers (Landsoud-Soukate et al., 1995; Modrý et al.,

2018; Petrášová et al., 2010). The most common strongylid nematodes of great apes and humans in Africa are undoubtedly Oesophagostomum and Necator species.

2.5.1 Oesophagostomum and Necator Oesophagostomum spp. are commonly listed parasites in great apes (Huffman et al., 1997; Krief et al., 2008), with two main species identified as Oesophagostomum stephanostomum Stossich, 1974 and Oesophagostomum bifurcum Creplin, 1849 (Gasser et al., 1999). However, the exact identification and epidemiology of Oesophagostomum spp. are often discussed topics. Clinical signs in primates include lesions, corresponding to the nodular form oesophagostomiasis found in humans (Krief et al., 2008). The hypotheses is, that NHPs may serve as reservoirs of oesophagostomiasis for humans (Polderman & Blotkamp, 1995), but the evidence is questionable, and was disproved several times (e.g. Van Lieshout et al., 2005). Oesophagostomum stephanostomum is commonly found in groups of great apes (Huffman et al., 1997; Krief et al., 2008; Makouloutou et al., 2014) but in humans there exist only a single evidence of O. stephanostomum (Cibot et al., 2015). On the contrary, O. bifurcum is believed to parasitize humans regularly (Polderman & Blotkamp, 1995), with high prevalence in endemic areas of West Africa (Ziem et al., 2006). Occurrence of O. bifurcum in NHPs is common, but in great apes rather rare (Krief et al., 2010; Ota et al., 2015, Pafčo et al., 2019). Other Oesophagostomum spp. can be also found in African great apes and humans, but the occurrence is rare (Modrý et al., 2018).

Hookworms of the genus Necator are often found in both humans and great apes. Human hookworm N. americanus is one of the most important hookworms, infecting humans worldwide but with highest prevalence in sub-Saharan Africa (Hotez et al., 2005). It was believed that N. americanus is a human-specific parasite (infecting only humans), yet Orihel, (1971), found, that non-human primates (chimpanzees and patas monkeys Erythrocebus patas Schreber, 1775) are susceptible hosts for human N. americanus and suggested zoonotic potential of this species. He also described clinical signs of the disease, called necatoriasis, in NHPs. He recorded impaired digestion and severe anemia in cases of heavy infections, corresponding to human form of necatoriasis, where abdominal pain, diarrhoea and anemia are often symptoms (see paragraph about hookworm health and economic impacts in chapter 2.3.3). Besides human N. americanus,

35 other species – Necator exilidens Loos, 1912, Necator congolensis Gedoelst, 1916 (both described in chimpanzees) and Necator gorillae Noda & Yamada, 1964 (described in western lowland gorilla), were found in Africa (Hasegawa et al., 2014).

2.5.2 Other strongylid nematodes A lack of information limits the knowledge about other genera of strongylid nematodes and their epidemiology and distribution. Besides genus Necator, hookworms Ancylostoma duodenale can be found in great apes and humans in Africa (Hotez et al., 2005). Little is known about a neglected nematode Ternidens deminutus Railliet & henry, 1905, which appears to be closely related to the genus Oesophagostomum. It has been reported in great apes and humans in parts of Africa and showed significant variation among hosts and possible existence of cryptic species (Schindler et al., 2005). There also exist studies discussing its zoonotic potential and clinical impacts (Bradbury, 2019).

Evidence of pulmonary parasite Mammomonogamus loxodontis Vuylsteke, 1935 in western lowland gorillas was recorded, however, only based on the eggs due to specific location of the adult worms in the pulmonary tract of gorillas and therefore they have never been collected. Hence, clinical impact is hard to assess, there was evidence of gorillas coughing and sneezing, however, there were no indicators for Mammomonogamus affecting the health state of the gorillas (Červená et al., 2017). Nematodes of the species Murshidia Lane, 1914, were found during necropsies of mountain gorillas, confirmed also by egg cultivation and larvae identification (Ashford et al., 1996), but nothing is known about its impact on gorilla health.

Members of the trichostrongylidae family, such as Impalaia Mönnig, 1924, Paralibyiostrongylus kalinae Durette-Desset, 1992 or Hyostrongylus kigeziensis Durette- Desset, 1992 were found during necropsies of dead great apes (Ashford et al., 1996; Durette-Desset et al., 1992), There was also recorded presence of Trichostrongylus colubriformis Giles, 1892 in humans (Sato et al., 2011). The presence of Trichostrongylid haplotypes was also confirmed in NHPs by recent molecular studies (McLennan et al., 2017; Pafčo et al., 2019). In general, Trichostrongylid nematode is a numerous taxonomic group and the close identification is very complicated as well as evaluation of clinical impact on animals.

2.6 Transmission between humans and great apes

Close phylogenetic relationship between humans and NHPs (especially great apes) significantly facilitates overlap and reciprocal exchange of pathogens between the hosts (see chapter 2.2.1) and can have a devastating effect both on humans and NHP endangered species populations (Calvignac-Spencer et al., 2012; Dunay et al., 2018). Recently, several molecular-oriented and morphological studies highlighted the impact of human-great ape interaction on transmission of strongylid nematodes and suggest zoonotic potential of some strongylid nematode genera (e.g. Cibot et al., 2015; Ghai et al., 2014; Makouloutou et al., 2014).

In great apes, the main zoonotic agents are strongylid nematodes of Oesophagostomum and Necator genus. For a long time, it was believed, that Oesophagostomum species are host specific and strictly adapted to their hosts. However, recently a novel genotype (Oesophagostomum sp.), which seems to be broadly shared among great apes, other non- human primates and humans in Uganda, was discovered in Kibale National Park, Uganda (Ghai et al., 2014). The presence of multiple cryptic forms was further confirmed (Ota et al., 2015). Moreover, first record of common species in free-ranging chimpanzees - O. stephanostomum, was found infecting humans in Kibale (Cibot et al., 2015). Chimpanzees also harboured O. bifurcum, a species commonly described in humans (Verweij et al., 2000). These studies are the living proof and suggest zoonotic transmission events between humans and great apes cohabiting the same area.

Necator spp. are known to be parasites with high zoonotic potential. As coproscopic and morphological methods are unable to distinguish exact species/genotypes, so far the extent of cross-transmissions was, until recently, not well explored. Recent studies suggest the transmission of Necator species between humans and other non-human primates, as N. americanus (presumably of human origin) was found in gorillas, while species distinct from N. americanus – probably originating from great apes were found in humans in DSPA (Hasegawa et al., 2014). Additionally, based on morphological results, N. gorillae (believed to be originally a parasite of NHPs) was found in researchers working with western lowland gorillas (Kalousová et al., 2016). Results of these studies were further confirmed by Hasegawa et al., (2017), who compared Necator spp. from

37 chimpanzees living near human settlements in Bulindi, Uganda and humans and gorillas cohabiting the Moukalaba-Doudou National Park (MDNP), Gabon. Necator gorillae was broadly found across all studied hosts, confirming the primate origin and possible transmission to humans. In contrast, N. americanus was shared between humans and gorillas in MDNP, but N. americanus was not found in chimpanzees in Bulindi, despite living in close proximity to humans. Pafčo et al., (2018) found N. americanus in gorillas and mangabeys, cohabiting the Dzanga-Sangha Protected Areas (DSPA) in Central African Republic (CAR), using ITS-2 metabarcoding approach. The following study (Pafčo et al., 2019), using the same approach, extended the previous results and together with sharing N. americanus among human, gorillas, chimpanzees and mangabeys, widespread transmission of N. gorillae between humans and great apes in DSPA was discovered. The study also revealed that humans entering the forest environment more frequently and those in closer contact with wild NHPs (BaAka trackers) overlapped more in strongylid composition with NHPs, than people settled more in the villages (BaAka villagers).

2.7 Diagnostics focused on strongylid nematodes

2.7.1 Traditional approaches based on microscopy Traditionally, the helminths species are described based on the adult worm morphology. Necropsies of the host animals are therefore a valuable source of adult worms for morphological observation. Moreover, during the necropsies, exact abundance (quantification of the adults) can be evaluated. However, often the dead wild animals are not found or are found in the late stages of decomposition. In addition, conservation efforts put an emphasis on non-invasive studies (without human-animal direct contact) and even more; necropsies of the animals in the field condition are dangerous due to the risk of infectious diseases. Therefore, nowadays necropsies of wild animals are rarely performed (Durette-Desset et al., 1992).

On that account, fecal material is suitable for parasitological diagnostics as parasite stages pass to the outside environment via host feces and thus can be obtained non- invasively. Parasitological studies using traditional methods are focused on taxonomical

38 identification / determination of parasites, describing the parasitic prevalence (proportion of infected individuals in populations) and consequent evaluation of the factors affecting parasite infections and suggestion of the possible therapy (e.g. anthelmintic treatment). Correct identification of parasite stages (cysts, trophozoites, eggs, larvae, adult worms, etc.) is a keystone for parasitological diagnostics (Anderson et al., 1974). Generally, traditional parasite diagnostic methods follow several approaches, called coproscopic examination. It is a toolbox of various techniques for detection of parasite stages and their determination, using light microscopy and morphometrics. The key morphological characteristics for diagnostic of parasite stages during the microscopy are: shape, size, presence of external / internal structures or surface ornamentation

Diagnostic methods

Coproscopic procedures used for parasite detection, including strongylid nematodes, ideally combine several methods: direct macroscopic and microscopic diagnostics and concentration and larvoscopic techniques. Macroscopic observation of fresh feces (by the naked eye) is the first step of the parasite detection. Often, adult worms or stages of other parasites as cestode proglottids are expelled with feces and can be directly visible. For better parasite (especially worms / larvae) recovery, fecal washing method is an effective procedure. Even more, this method can be used for a high volume of material or for preserved samples. Feces are being washed through the set of meshes with various aperture sizes, which capture different sizes of adult worms / larval stages that are later isolated from the remaining material on the mesh, using stereomicroscope. The easiest and the fastest microscopic method for parasite diagnostic that is applicable also in the field conditions is the wet mount and its modifications (e.g. Kato-Katz procedure). It is based on direct examination of fecal samples mixed with water and detection of parasite stages under the light microscope. The parasite stages can be highlighted using, for example, malachite green during the Kato-Katz procedure focused on the egg diagnostic.

Higher sensitivity can be reached using concentration methods, which are the most commonly used during the parasite diagnostics (Watson et al., 1988). However, it demands higher equipment requirements, such as a centrifuge is often needed. Concentration methods are based either on flotation or sedimentation of parasite stages and can be applied on fresh or preserved material. Flotation techniques separate the

39 parasite stages due to their lower specific gravity – they float on the surface of the flotation solution (Cringoli et al., 2010). Compared to that, parasite stages (typically eggs) sink to form sediment, using sedimentation techniques. Combination of those two approaches can be used to detect almost the full spectrum of gastrointestinal parasites passed in the feces (Gillespie, 2006).

Last, larvoscopic techniques or coprocultures flowed by larvoscopic techniques are often used during the helminth diagnostic. Both adult worms and larvae are hydrophilic and using larvoscopic techniques help to separate them from fecal material, gastrointestinal content, vomit or coprocultures. However, eggs are mostly present in the feces. Fresh fecal material is incubated for several days or weeks (depending on the nematode species) to develop third larval stages (L3) from the eggs. The larvae migrate from the feces into the water and larval suspension is obtained and can be later examined and analyzed (Greiner & McIntosh, 2009). Unlike the strongylid eggs, developed L3 larvae carry morphological characteristics and can be identified to the genus level.

Quantification

Quantification techniques brought an improved understanding of parasite disease dynamics and infection intensity and the results has much higher values than simple parasite presence / absence approach. The quantity of parasite stages passed in the feces are actually their numbers counted during the light microscope examination. Traditionally, the quantity of parasite stages is expressed per gram of feces (EPG – eggs per gram, LPG – larvae per gram, OPG – oocysts per gram, CPG – cysts per gram, etc.). Semi-quantitative scoring is the easiest method based on subjective evaluation of the infection intensities using crosses (+/++++). However, more sophisticated flotation-based methods, such as FLOTAC, miniFLOTAC and McMaster are commonly used both in human and veterinary parasitology (Cringoli et al., 2010; Levecke et al., 2011).

Traditional approaches and its limits

Strongylid nematodes commonly occur in complex communities and their eggs are morphologically indistinguishable. Based on the eggs they can be assigned barely to a family level (Metzger, 2014), resulting in insufficient classification, usually within collective category “strongylids”. Only the genus Mammomonogamus can be identify

40 using basic coproscopy due to the unique egg surface ornamentation and presence usually of two blastomeres inside the egg. Cultured larvae can be morphologically determined usually up to genus level, which brings a higher-resolution, but in case of strongylid nematodes, larval morphology cannot be used for exact species identification and obtaining the adult worms becomes fundamental for exact determination (Kalousová et al., 2016).

The quantity of strongylid eggs may correlate with the number of mature adult females in the host, indicating the approximate parasite “load” (Nielsen et al., 2008), but several other factors should be considered when interpreting the results. For example, presence of males, discontinuous distribution of eggs in feces, prepatent period (presence of parasites without clinical signs) or viability of adult helminths can have an impact on quantity of detected eggs. Moreover, quantification of strongylid eggs based on flotation methods (FLOTAC, McMaster) on formalin preserved material is not possible as those eggs are extremely fragile and destroyed during the procedure (Pafčo, 2017; Pafčo et al., 2017).

Results from most published studies have insufficient determination of observed parasites or, conversely, assign the parasite uncritically to species level without reasonable justification. The latter situation specifically complicates any meta-analyses of published data. Primate-parasite studies are mostly based on coproscopic methods and morphology/morphometric analyses (Drakulovski et al., 2014; Ravasi et al., 2012). However, only detailed molecular parasite identification provides the data necessary for determining the infection source, possible risk for hosts (including humans) and, in some cases, pathogenicity. Therefore, none of routine coproscopic approaches, provides sufficient species determination and the identification is mostly dependent on nucleic acid amplification and sequence analyses (Cibot et al., 2015; Ghai et al., 2014; Narat et al., 2015; Ota et al., 2015)

2.7.2 Molecular approaches – identification and diagnostics PCR-based methods followed by Sanger sequencing

DNA-based diagnostics has become a routine part of modern parasitology (Gasser et al., 2008). Although strongylid nematodes are still a rather understudied group, in the past few decades, knowledge about them has significantly increased, with a great contribution

41 from application of molecular approaches (e.g. De Gruijter et al., 2006; Gasser et al., 1999). With the onset of polymerase chain reaction (PCR) (Mullis et al., 1986), several studies were conducted on strongylid diagnostics, diversity and phylogenetic relationships. Most of the studies target primarily ribosomal DNA (rDNA) markers, specifically Large (LSU) or Small (SSU) ribosomal subunits and its variable sections called internal-transcribed spacers (ITS-1, ITS-2) (Fig. 7) followed by Sanger sequencing (Sanger et al., 1977). However, Sanger sequencing requires a single-stranded DNA as single adult worm, egg or larvae, which is very problematic during evaluation of fecal or larval culture mixed infections. General primers can be used for description common, abundant species, however, the low abundance or so-called “rare” species are often overlooked, because of their low abundance in the sample and almost impossible to design specific primers for their detection (Carreno & Nadler, 2003; Chilton, 2004; Chilton & Beveridge, 1997).

Figure 7: Subunits of rDNA and internal transcribed spacers (ITS-1, ITS-2), Picture Zhang et al. (2015).

A higher-resolution and a reliable semi-quantitative analysis can be achieved using real-time PCR (qPCR) (Heid et al., 1996). This method allows quantification of amplified DNA in real time, starting DNA concentration in the sample as well. Together with species specific melting curves, possibility for evaluation of multiple samples (multiplex via specific primers, which need to be designed) in a single run, allows relatively high- throughput detection, identification and quantification (e.g. egg counts correlate with the amount of starting amount of the DNA) of parasites, as there were studies conducted on

42 strongylid nematodes (Bott et al., 2009; Nielsen et al., 2008). However, as specific primers have to be designed, rare or unexpected species are also overlooked.

Other PCR-based methods, using restriction enzymes to digest DNA, such as Amplified fragment length polymorphism (AFLP) (Vos et al., 1995) or Restriction fragment length polymorphism (RFLP) (Nadler, 1990) were also performed on strongylid nematodes, proving to be useful especially for identification and detection of hidden cryptic (morphologically nearly identical) species (De Gruijter et al., 2005; Nadler & De León, 2011). Improved identification and diagnostics allow robust study designs and often reveal hidden zoonotic potential of parasites, as recently reported in cases of non- human primates (Cibot et al., 2015; Ghai et al., 2014; Hasegawa et al., 2014, 2017; Krief et al., 2010; Ota et al., 2015; Traub et al., 2008). All these studies are proving that even a simple PCR-based genotyping methods can often reveal hidden diversity, cryptic species and zoonotic transmissions, which could not be found using morphological approaches. This also proves that PCR-based amplification methods still remain as useful and powerful tools in strongylid nematode diagnostics and identification (Gordon et al., 2011). Although, none of these studies were able to describe entire complex strongylid community.

2.7.3 High-throughput sequencing (HTS) Classical Sanger sequencing methods become more expensive, time-consuming and not sensitive to detect “rare” species in comparison to cheaper second- or next-generation sequencing (NGS) or high-throughput sequencing (HTS) technologies (Schuster, 2008). The methods use sequencing platforms such as Roche 454, SOLiD or Illumina. These platforms are able to generate a large amount of sequence data in a short time and relatively low cost in comparison to classical Sanger sequencing (Von Bubnoff, 2008). The most advanced Illumina platforms - Illumina NextSeq 2000, can generate up to 1 billion reads (300 Gb output) of 2 x 150bp fragments in a single run within 48 hours and Illumina HiSeq 2500 is able to generate up to 900 Gb – 1 Tb output of 2 x 125 bp reads in 6 days (data from www.illumina.com). Moreover, Illumina sequencing allows examination of samples containing a mixture of DNA from multiple species / genotypes and capturing even the rare taxa. Sequencing principle is based on bridge-amplification and simultaneous sequencing of multiple samples (up to 96 samples in a single library,

43 together with 8 sequencing lines, in a full single Illumina run, up to 768 different samples can be sequenced at a time) (Zhou et al., 2011).With the fast evolution of sequencing techniques recently, a more advanced generation of sequencing, known as third- generation sequencing (TGS) has come up. In contrast to HTS, the sequencing is focused more on long-reads and de novo genome assembly. Most-known technologies, such as Pacific Biosciences single molecule, real-time sequencing (PacBio SMRT) or Oxford Nanopore technologies (MinION, GridION, PromethION), are able to generate up to 1500 long reads within few hours (PacBIO 2–3h, Oxford Nanopore 24–48h) which significantly facilitates de novo genome assembly and identification (Lee et al., 2016).

Basic principle of Illumina sequencing

Illumina is a platform designed for DNA/RNA HTS sequencing (using bridge- amplification / sequencing by synthesis) from both ends of the DNA molecule. It has good assumptions for searching single nucleotide polymorphisms (SNPs) and genotyping (Zhou & Li, 2015). Illumina sequencing process consist of four basic steps (Fig. 8).

Sequencing library preparation – this step varies a lot, depending on the template for sequencing - whole-genome sequencing (WGA), transcriptome sequencing, amplicon sequencing (amplified PCR products), etc. The template is isolated DNA/RNA using various protocols or commercial kits followed by sequencing libraries preparation again mostly by using commercial kits (e.g. Nextera DNA Flex Library Prep Kit, Illumina®). The DNA/RNA has to be fragmented on a particular fragment size according to a chosen sequencing platform (e.g. Illumina MiSeq series paired-end sequencing 2 x 300bp). Illumina adaptors (short nucleotide sequences allowing the sample ligation to the Illumina platform), together with unique barcodes (short unique sequence facilitating samples identification during data analyses) are ligated to the sample DNA. Barcodes can be also a part of adaptors (f.e. Illumina indexes).

Clustering – prepared sequencing libraries are loaded into a flow cell, which is a glass slide containing eight lines, on the surface of which are complementary oligostructures (“oligos”) - DNA strands containing Illumina adaptors. After library loading, many PCR cycles are performed, using bridge-amplification. Due to repeated bridge-amplification cycles, clusters of each fragment copies (more than 1000 copies/fragment) are formed.

Sequencing – the sequencing process is called sequencing by synthesis. It runs in a flow cell from one end (“single end sequencing”) or both ends of fragments (“paired end sequencing”). During each cycle, fluorescently tagged nucleotides are added to the clustered fragment strands based on the sequence template. After the addition of each nucleotide, the clusters are excited by a light source and a fluorescence signal, which is characteristic for each base. The emission wavelength along with the signal intensity determines the base call, which is recorded. For a given cluster, all identical strands are read simultaneously – with all clusters read simultaneously in a massively parallel process.

Data analysis – demultiplexing of raw sequence data is performed using advanced bioinformatic software. Sorted fragments (based on unique barcodes) can be mapped to a reference sequence / genome or can be also analyzed de novo.

Figure 8: Principle of Illumina sequencing process. Picture from Zhou & Li, (2015).

Metabarcoding

Meta-taxonomic or metabarcoding approaches are based on use of universal primers, which are able to amplify standardized DNA region (e.g. strongylid ITS-2 rDNA). In combination with sample barcoding, demultiplexed sequences can be mapped to the reference sequences and DNA from each taxa present in the sample can be identified, resulting in a OTU (operation taxonomic units) or haplotype table (Valentini et al., 2009). Recently, more studies started to focus on high-throughput sequencing of metazoan parasites (Aivelo & Medlar, 2017; Aivelo et al., 2015; Tanaka et al., 2014). ITS-2 markers were established as the most reliable markers for HTS metagenomics (Avramenko et al., 2017; Lott et al., 2015). Due to the high sensitivity of this method, resulting data provide more complex insight into whole parasite communities and even the rare taxa are captured. Therefore it is a gold standard for studying zoonotic potential not only for strongylid nematodes, but for other parasites as well (Pafčo et al., 2018, 2019; Vlčková et al., 2018).

Whole genome sequencing Individual larvae or adult worms can undergo whole genome sequencing, as it represents the best resolution for species exact identification and allows future fast detection using for example, amplicon sequencing approaches (Avramenko et al., 2015). Whole genome sequencing allows much deeper insight into diversity, evolution and transmission and epidemiology of parasites. It can also identify mechanisms and genes for targeted treatment and help in searching for efficient vaccine. Strongylid nematodes are of significant medical and economic importance, and together with increasing resistance, deeper insight into their epidemiology and infective mechanisms are urgently needed. However, only a few strongylid nematode genomes have been sequenced. Four complete hookworm genomes were published: N. americanus, A. ceylanicum, A. duodenale and A. caninum (Coghlan et al., 2019; Schwarz et al., 2015; Tang et al., 2014); as well as a genome of the most common ruminant parasitic strongylid - Haemonchus contortus (Schwarz et al., 2013). There were five complete genomes sequenced for other strongylids: Oesophagostomum dentatum Rudolphi, 1803; Nippostrongylus brasiliensis Travassos, 1914; Cycliostephanus goldi Boulenger, 1917; Haemonchus placei Place, 1893; Heligmosomoides polygyrus Dujardin, 1845 (Coghlan

46 et al., 2019). However, many worms described above came from laboratory-cultivated animals, which had likely undergone genetic adaptations to their hosts and data from naturally infected individuals are still not available.

3. Material and methods

3.1 Study site DJA Faunal Reserve (DJA FR), located in South-East Cameroon (Fig. 9), is the largest protected area (5,260 km2) in Cameroon and belongs to the UNESCO World Heritage site. The DJA river creates a natural border on the reserve periphery.

Figure 9: DJA Faunal Reserve (DJA FR) location in south-east Cameroon. Map from Encyclopaedia Britannica (www.britannica.com), adjusted.

The reserve is a part of the semi-deciduous lowland forest (500–700 m above sea level) of Equatorial Guinea and the Congo basin (Letouzey, 1985). The climate is equatorial and humid, usually with two dry (December–January / July) and two wet seasons (minor wet peak in May / major wet peak in September). The average rainfall is approximately 1600 mm with mean temperature around 23 °C. These conditions form an ideal habitat for many plant and animal species. Rare species can be abundantly found in the reserve due to its protection (Bruce et al., 2018; Dupain et al., 2004). High densities of several

48 endangered primate species were reported in the reserve (Poulsen et al., 2001), including two subspecies of great apes: the western lowland gorilla (Gorilla gorilla gorilla) and the central chimpanzee (Pan troglodytes troglodytes).

Local people (mainly belonging to Bantu and Baka) live here in close proximity to free-ranging animals. Although population density is low, the human pressure on the reserve is remarkable, as crops (Arlet & Molleman, 2010), hunting (Muchaal & Ngandjui, 1999) and logging (Betti, 2004) remain the main sources of livelihood for the local people.

Research took place on the northern periphery of DJA Faunal Reserve. Project Grands Singes (PGS) under the Royal Zoological Society of Antwerp, Belgium (RZSA) superintends 40 km2 large area (Fig. 10a) dominated by secondary forest (after intensive selective logging 30 years ago). The area includes research camp La Belgique and three villages where humans are settled, approximately 25 km distant from the camp. Although the DJA FR is protected area, this particular area on its northern periphery still remains unprotected. Consequently, local people live together with wild animals in tight coexistence. Intense logging activities with relatively high hunting pressure are reported in this area, however, high chimpanzee and gorilla densities were also recorded (Dupain et al., 2004). Due to the intense hunting, great apes living in the area remain unhabituated (even in the close proximity of human settlements) as it would be a great risk for the animals. Therefore, PGS only monitor the primates at the study site.

Sampling was carried out at three different sites: (I) human samples from three villages – Duomo-Pierre, Malen V and Mimpala (Fig. 10b) (II) great ape samples in the close proximity of human-inhabited villages and (III) great ape samples in the forest surrounding the La Belgique research camp.

Figure 10: a) Research area on the northern periphery of the DJA FR; b) Research sites, villages Duomo- Pierre, Malen V and Mimpala, their surroundings and research area around the La Belgique research camp. Tagg & Willie, (2013), adjusted.

3.2 Samples collection Fresh fecal samples (total number: n = 186) were collected non-invasively from humans (n = 94) and non-habituated free-ranging great apes: central chimpanzees (n = 31) and western lowland gorillas (n = 61). Samples were collected during September and October 2014.

Samples were collected early in the morning from their night nests (gorillas) and from the forest floor beneath the night nests (chimpanzees). To assure the sample freshness, sampling took place within 3 hours after individuals left their nests. To prevent re- sampling of the same individuals and groups of individuals, at the same time, only groups of different size at the same locality or groups of the same size but different spatial distribution (not coexisting at the same locality) were sampled. Human sampling followed the protocol approved by the Ethics committee of biological centre of Academy of Sciences, České Budějovice, Czech Republic and was performed after obtaining informed consent of all registered volunteers.

All samples were fixed in 96% ethanol and stored at room temperature for a maximum of two weeks. Subsequently the samples were sent to Department of Pathology and

Parasitology of Veterinary and Pharmaceutical university, Brno and stored at -20°C, until the DNA isolation.

3.3 DNA isolation, library preparation and HTS sequencing Total genomic DNA was extracted from fecal samples using PowerSoil DNA isolation kit (MO BIO Laboratories, Qiagen company, USA). Isolation was followed by PCR amplification of ribosomal DNA (rDNA), specifically variable section of rDNA – internal transcribed spacer 2 (ITS-2). Sequencing libraries were prepared using two-step PCR following the Fluidigm Access Array primer design according to the protocol of Pafčo et al. (2018). Specific primers used for ITS-2 amplification (1st PCR) were forward Strongyl_ITS-2_F (ACG TCT GGT TCA GGG TTG) and reverse Strongyl_ITS-2_R (ATG CTT AAG GGG TA). Primers were optimised to amplify broad range of strongylid nematodes and contained “tails”, which served as a future primers site for the second PCR. Primers in the second PCR included sample specific barcodes and sequencing adaptors. Unique barcodes (Access Array Barcode Library for Illumina Sequencers, Fluidigm Corporation, USA) implemented in the primers facilitated significantly later identification of samples/individuals in the dataset. DNA Polymerase Kapa 2 G Robust Hot Star (Kapa Biosystems) was used during both PCR steps. Conditions for the first PCR were set to 95 °C for 3 min, (95 °C 15s, 55.5 °C 15s, 72 °C 15s) x 30 and 72 °C for 1 min. Conditions for the second PCR were set to 95 °C for 3 min., (95 °C 15s, 55 °C 30s, 72 °C 30s) x 16 and 72 °C for 3 min.

Each sample was processed in duplicate – twice, using different primer tags for each duplicate during the second PCR, to prevent or better to reveal biases and contaminations during amplification and sequencing. In addition, two negative and three positive controls were incorporated in the study: total DNA isolated from strongylid negative human feces plus water as negative controls and synthetic templates carrying combination of ITS-2 strongylid sequences (Necator sp., N. americanus, O. stephanostomum) mixed in equimolar ratios in three concentration (9, 99, 999 copies of ITS-2 sequences in PCR template) as positive controls. Sequence constructs were based on GenBank sequences and the final construct contained 4 bp tags with no match to any real ITS-2 sequences, which facilitated later identification of controls.

Complete library was cleaned up using Agencourt AmpureXP beads (Beckman Coulter Life Science) and target DNA specific size was selected using Pippin Prep (Sage Science, Inc., USA). The final library was quantified using Kapa Library Quantification Kit (Kapa Biosystems) and sequenced on Illumina MiSeq platform (Illumina MiSeq Reagent Kit v2, sequencing 500 cycles of 2 x 250 bp paired-end reads). As a sequencing output, folders containing all sequences in fastq (.fastq) format for each sample were generated. In addition, large metadata table (containing sample ID, type of the sample, collection site and host species) was created (Supplement 1).

3.4 Bioinformatic processing Demultiplexing of raw .fastq sequences was carried out using software Skewer (Jiang et al., 2014) followed by paired-end reads assembly in PEAR merger (Zhang et al., 2014). Low quality sequences (with error rate > 1) were eliminated from the dataset. ITS-2 haplotypes and sample relative abundances were estimated using software dada2 (Callahan et al., 2016). Using dada2 algorithm for chimera detection the sequences from both duplicates were compared and the sequences (haplotypes), which were not present in both duplicates were marked as chimeras and sequences consistently present in both duplicates were merged and used for downstream analyses.

Searching for corresponding sequences to our haplotypes was performed via locally installed software BLAST+ (NCBI Standalone BLAST 2.10.0+, 64-bit). To prevent unreliable alignment of sequences to a random environmental uncultured samples, a list of negative environmental records was created and excluded from BLAST database. Then, similar sequences (blast hits) were additionally filtered by following criteria: all hits with < 85% similarity and < 90% overlap with original haplotypes were excluded from the file. Taxonomy for blast hits was downloaded using taxize package (Chamberlain & Szöcs, 2013) and taxonomic ranks up to “species” level were assigned to blast hits. Fasta sequences from hits were extracted and searched for overlap positions with original haplotypes. After a successful alignment, sequences were trimmed and merged with original blast hits and taxonomy into a single reference database. Taxonomic assignation of original ITS-2 haplotypes was then executed using dada2. This package implements a Naïve Bayesian Classifier (Wang et al., 2007) algorithm for a rapid

52 assignment of sequences into taxonomy. The resulting taxonomy table was merged with the metadata table into a single phyloseq object, suitable for later data analyses (McMurdie & Holmes, 2012).

3.5 Statistical analysis All data analyses were executed in statistical software R studio (version 1.2.5033) built into a statistical software R (version 3.6.3). “Raw” dataset was filtered (“de-noised”) to remove unclassified (up to “family” level) and “non-strongylid” haplotypes (e.g. Ascaris sp., Caenorhabditis sp.). Also, samples and haplotypes with zero proportion of sequences (reads) were removed from the dataset. Basic exploration of the dataset was performed, followed by several statistical tests to evaluate if strongylid community diversity and community composition differ among host species (script available in Supplement 2).

Generalized linear model (GLM) with quasipoisson error distribution was performed in order to test differences in alpha diversity, evaluated as number of haplotypes per samples, among the studied hosts. In addition, post-hoc testing (Tukey) was employed to test the differences among levels of factorial explanatory variables. Moreover, community composition was defined as prevalence and relative representation of ITS-2 haplotypes using two different ecological distances – Jaccard (based on haplotypes presence/absence) and Bray-Curtis (working with haplotypes relative abundances). Clustered heat map and bar graph were used to visualise the composition of haplotypes’ relative abundances for each sample as well as Principal Coordinate Analysis (PCoA) on both Jaccard and Bray-Curtis dissimilarities. To test the interspecific differences in strongylid nematode community compositions among the hosts, Permutational Analysis of Variance (PERMANOVA), followed by post-hoc testing (Tukey) and Analysis of Similarity (ANOSIM), were performed. In order to prevent negative eigenvalues during computation, square root transformation of the dataset was performed.

Multivariate GLMs from the R package mvabund (Wang et al., 2012) were implemented to search for community-wide divergence and identification of significant haplotypes that varied due to the different host species effect. For a better resolution, a diagram showing proportion of reads for significant haplotypes was constructed.

4. Results

4.1 Basic exploration of dataset After dataset filtering and elimination of samples with low number of sequences, 139 samples were finally analysed (74,7 % of all sequenced samples). Forty six human and one gorilla sample were eliminated, resulting in 48 human, 60 gorilla and 31 chimpanzee samples. It is possible that those samples were negative for strongylid nematodes. In total, 2,943,087 high-quality reads were identified, with median sequencing depth per sample 푥̅ = 15,612 high-quality reads (min. = 9, max. = 375,905). Taxonomic assignment revealed 97 ITS-2 variants (haplotypes) (Tab. 3), belonging to at least 5 genera (Tab. 4) of suborder Strongylida: Oesophagostomum (20 ITS-2 haplotypes, present in 66 % of the individuals), Necator (38 ITS-2 haplotypes, present in 87 % of the individuals), Trichostrongylus (5 ITS-2 haplotypes, present in 50 % of the individuals), Ancylostoma (closest hit A. ceylanicum, 1 ITS-2 haplotype, present in one individual), Ternidens (assigned to T. deminutus, 1 ITS-2 haplotype, present in one individual). I found 32 unassigned haplotypes (present in 45 % of the individuals), additionally classified closest to Nematodirus sp. / Travassostrongylus sp., using blast searches against on-line nt/nr NCBI database, however, with low sequence identity and match score in both genera.

Table 3: List of identified strongylid nematodes found in studied hosts, sequences NCBI Accession numbers and reference. Family Genus Species NCBI Accession Reference Chabertidae Oesophagostomum Oesophagostomum KR149648.1 Cibot et al., 2015 stephanostomum type I Oesophagostomum Oesophagostomum AB821022.1 Makouloutou et al., 2014 stephanostomum type II Oesophagostomum Oesophagostomum sp. KR149658.1 Cibot et al., 2015 Ternidens Ternidens deminutus AJ888729.1 Schindler et al., 2005 Ancylostomatidae Necator Necator americanus LC088287.1, LC036563.1 Hasegawa, 2015; Hasegawa MG256601.1 et al., 2017; Jariyapong & Punsawad, 2017 Necator Necator gorillae LC088299.1 Hasegawa et al., 2017 Necator Necator sp. AB793535.1 Hasegawa et al., 2014 Ancylostoma Ancylostoma sp.* LC036567.1 Hasegawa, 2015 Trichostrongylidae Trichostrongylus Trichostrongylus sp. type I Unassigned** NA Trichostrongylus Trichostrongylus sp. type II LC185220.1 McLennan et al., 2017 Unclassified Unclassified Unclassified Unassigned*** NA *Closest hit A. ceylanicum (similarity 95.5 %). **Closest hits T. vitrinius (similarity 98.48 %), and T. colubriformis (similarity 97.34 %). ***Probably two taxa: closest hits Nematodirus sp. (similarity 84.1 %) and Travassostrongylus sp. (similarity 76.8 %).

Table 4: List of numbers of identified haplotypes, their proportion of total reads, numbers of infected hosts and haplotype prevalence among host species. Number of Total reads Number of Number of Number of Prevalence Prevalence Prevalence Parasite taxa identified proportion haplotypes haplotypes haplotypes in humans in gorillas in chimps haplotypes (%) in humans in gorillas in chimps (%) (%) (%) Oesophagostomum stephanostomum type I 16 41.1 1 12 11 27.1 81.7 96.8 Oesophagostomum stephanostomum type II 3 0.7 0 2 3 0 6.5 35.5

Oesophagostomum sp. 1 > 0.1 0 1 0 0 1.7 0

Necator americanus 16 21.7 15 5 0 66.7 31.7 0

Necator gorillae 14 20.0 2 14 4 16.7 96.7 87.1

Necator sp. 8 0.1 0 6 3 0 13.3 9.7 Trichostrongylus sp. type I 3 0.2 0 3 0 0 13.3 0 Trichostrongylus sp. type II 2 6.3 1 2 1 2.1 76.7 61.3

Ancylostoma sp. 1 > 0.1 0 0 401 0 0 3.2

Ternidens deminutus 1 > 0.1 0 1 0 0 1.7 0

Unclassified 32 10.0 4 18 17 8.3 58.3 77.4

4.2 Strongylid nematode communities within the host species

4.2.1 Description of strongylid nematode communities A bar graph that visualized relative abundances of strongylid haplotypes for all studied individuals (Fig. 11) revealed interspecific differences in composition of strongylid nematode communities according to host species.

Figure 11: Bar graph representing relative community composition of strongylid nematodes for all studied individuals. Each column represents a single individual and relative abundances of reads are depicted as colour panels.

From total 97 haplotypes, 23 haplotypes were found in humans (Tab. 4), 64 in gorillas and 40 in chimpanzees. The strongylid community of humans was dominated by genus Necator, mainly by N. americanus haplotypes (prevalence 66.7 %) and less by N. gorillae haplotypes (prevalence 16.7 %). Few individuals were also infected by haplotypes belonging to O. stephanostomum type I (prevalence 27.1 %), Trichostrongylus sp. type II (prevalence 2.1 %) and 4 unassigned haplotypes (prevalence 8.3 %).

The strongylid communities of great apes were dominated by Oesophagostomum haplotypes (prevalence 86.8 %) and N. gorillae haplotypes (93.4 %). Also, other Necator sp. were found in great apes and N. americanus haplotypes were found in gorillas (31.7 %), while there was no evidence for N. americanus in chimpanzees. High prevalence was recorded also for Trichostrongylus sp. type II (71.4 %) and unassigned haplotypes (64.8 %). Four taxa were found in low prevalence only in gorillas (Oesophagostomum sp., Trichostrongylus type I, T. deminutus) and one taxa was found only in chimpanzees (Ancylostoma sp.).

A cluster heatmap (Fig. 12) operating with relative abundances confirmed relatively clear separation of human-dominated (A) and great-ape-dominated (B) clusters, based mostly on the presence and abundance of different Necator and Oesophagostomum haplotypes. There is also a relatively clear separation of predominantly chimpanzee (B1) and gorilla (B2) clusters, based on different Necator haplotypes.

Figure 12: A cluster heatmap of relative abundances of strongylid taxa for all individuals. Clustering of rows reflects phylogenetic relationships among individual haplotypes. Columns indicate host species identity and are clustered based on significant/non-significant abundance differences of a given haplotype among sampled hosts according to mvabund analyses.

4.2.2 Zoonotic potential I found four taxa, shared between humans and great apes, suggesting zoonotic transmission (Tab. 5). One haplotype of O. stephanostomum type I was found infecting both humans and great apes. Two N. gorillae and one Trichostrongylus sp. type II haplotypes were also found in all studied host species. Four haplotypes of N. americanus were shared between humans and gorillas only.

Table 5: List of shared haplotypes and their prevalence among studied host species. Taxa Number of Prevalence in Prevalence in Prevalence in shared humans (%) gorillas (%) chimps (%) haplotypes Oesophagostomum 1 27.1 81.7 96.8 stephanostomum type I Necator americanus 4 64.6 31.7 0 Necator gorillae 2 16.7 96.7 87.1 Trichostrongylus sp. 1 2.1 76.7 61.3 type II

4.3 Divergence in strongylid composition according to the host species

4.3.1 Alpha diversity The number of strongylid haplotypes per individual = haplotype diversity (푥̅ = 7; min. = 1, max. = 17) revealed, that individuals with lower number of haplotypes were more frequent (Fig. 13). Statistically significant difference was found in haplotype diversity among the studied hosts (GLM: F(2,138) = 203.36, p < 0.0001), where human diversity was lower compared to both groups of great apes (Fig. 14a), supported by Tukey post-hoc testing (p = 0.0001 for all pair-wise combination) (Fig. 14b), while there was no evidence of statistically significant differences between gorillas and chimpanzees (p > 0.3). The diversity was additionally measured by Shannon’s and Simpson’s index and the results corresponded to those of GLM testing.

Figure 13: Histogram showing frequency of haplotypes for each individual host/sample.

Figure 14: Alpha diversity of strongylid nematode communities: a) boxplot of haplotype counts for each individual (dots) according to host species. Different letters above boxes indicate statistically significant differences according to GLM test; b) diagram of Tukey post-hoc testing (GLM) of haplotype counts.

4.3.2 Beta diversity PCoA diagrams based on both Jaccard (presence/absence) (Fig. 15a) and Bray-Curtis (relative abundances) (Fig. 15b) ecological distances showed clear difference between humans and great apes in both composition and relative abundance of strongylid haplotypes. Interspecific difference in composition of strongylid nematode communities was further tested between host species with significant results for both PERMANOVA

(Jaccard: F(2,138) = 11.655, p = 0.001; Bray-Curtis: F(2,138) = 14.644, p = 0.001) and ANOSIM (Jaccard: R = 0.4456, p = 0.001; Bray-Curtis: R = 0.4204, p = 0.001) tests. Tukey post-hoc testing revealed significant differences between humans and other great apes both for Jaccard and Bray-Curtis (p < 0.01 for all pair-wise combinations) distances.

Within great apes, there was no statistically significant result for Jaccard (p = 0.36) indicating roughly the same composition of strongylid haplotypes, however, results for Bray-Curtis indicated difference in relative abundances (proportion) of haplotypes between studied great apes (p < 0.001).

Mvabund test confirmed the interspecific differences (mvabund: ΔDF = 2, χ2 = 1002.371, p = 0.001) and identified 17 ITS-2 haplotypes with different relative abundance being the main driving force of the host species induced diversity (Fig. 16). For the differences among studied hosts were mainly responsible haplotypes of O. stephanostomum, N. gorillae, Trichostrongylus type II and unclassified strongylids being dominant in great apes, whilst Necator americanus dominated humans.

Figure 15: PCoA ordination diagrams of beta diversity of strongylid nematode communities: a) Jaccard ecological distance (presence/absence of haplotypes); b) Bray-Curtis ecological distance (relative abundances of reads).

Figure 16: Plots showing relative abundance of ITS-2 haplotypes indicated by Mvabund analyses as a driving force of differences among studied hosts.

5. Discussion

I used a high-throughput sequencing of ITS-2 locus for identification of strongylid nematodes and description of strongylid nematode communitites from fecal samples of humans and great apes (gorillas, chimpanzees), cohabiting the northern periphery of DJA faunal Reserve in Cameroon. Illumina MiSeq platform was chosen for sequencing, as it offers sufficient length of final amplicons (2 x 300 bp), which can cover the whole ITS- 2 region (Zhang et al., 2015). My main focus was to assess the diversity among host species and search for overlap of pathogens between humans and great apes, suggesting possible transmission events.

5.1 Methodology evaluation Although strongylids exact species identification relies on obtaining adult worms and their consequent morphological observation, it is very difficult to obtain such material even more from the wild protected animals (Durette-Desset et al., 1992). Therefore, strongylid nematodes, their epidemiology, diversity, transmission etc. is evaluated based on the eggs or larvae found in the feces. However, eggs obtained from feces cannot be morphologically distinguished, as the measurements often overlap between different strongylid species (Metzger, 2014). Identification and recognition of strongylids identity has much improved with onset of modern genetic methods (De Gruijter et al., 2005; Gasser et al., 2008). A growing number of reference sequences from identified strongylids in genomic databases provide an opportunity for strongylid determination (A. Coghlan et al., 2019). However, little is known about strongylid nematodes, especially of those which are not common, neglected or “rare”, despite that molecular methods such as Sanger sequencing or qPCR are often used in the strongylid nematodes studies (Bott et al., 2009; Nadler & De León, 2011). Using single specimen (adult worm, larva, egg) or species-specific primers is needed, therefore it would be extremely expensive and time- consuming even to try to use such methods for community evaluation and rare taxa would not be detected. Therefore, they are not sufficient for complex community evaluation, which strongylid nematodes form in their hosts (Valentini et al., 2009).

Use of HTS is the most suitable approach for complex communities evaluation. Of course, it brings its own limitations. For example, numbers of sequencing errors on Illumina platforms are higher than using classical Sanger sequencing, which may impact the final diversity. Aivelo & Medlar (2017) found during their extensive comparative study of metabarcoding approaches, that all sequencing platforms deal with different kinds of bias. On that account, the sequencing errors were prevented by running all samples in duplicates, and the presence of sequencing errors was easily identified and removed. Moreover, the length of the amplicons generated by Illumina MiSeq platform is too short for appropriate phylogenetic analyses, but phylogenetic distances for acquired OTUs can be computed and visualised in a phylogenetic tree as it provides higher resolution and facilitates the interpretation of the data (Pafčo et al., 2018). Most important, the length of amplicons is sufficient for their comparisons, allowing comparison between the host species and evaluation of possible overlaps, which is the goal of this study.

Both nuclear and mitochondrial genes are targeted in phylogenetic and taxonomic studies on strongylid nematodes. ITS-2 marker has proven to be reliable even for HTS evaluation. It exhibits high interspecific and low intraspecific variability as was previously described in Newton et al., (1998), where < 1% intraspecific variability and 10–40% interspecific variability was recorded. It is also a commonly used marker in other studies, therefore relatively sufficient number of comparative sequences can be found in public databases. Moreover, it was successfully used for describing strongylid haplotype community composition and representation in humans and various NHPs (Pafčo, 2017; Pafčo et al., 2018, 2019) as well as other animals (Avramenko et al., 2015; Lott et al., 2015). The more conservative genes, as for example 18S, used for HTS (Aivelo & Medlar, 2017; Hamad et al., 2014) proved to be insufficient for describing such complex parasite communities as they have limited distinctiveness. For instance, Aivelo et al. (2015) focused on intestinal nematodes of rufous mouse lemurs using 18S metabarcoding approach and identified the taxa only on level of the suborder Strongylida. As ITS-2 is a commonly well-tested and used marker, it was used for evaluation of our samples.

On the other hand, using ITS-2 marker brings a risk of increasing diversity due to the presence of paralogues in genomes. When two species are under concerted evolution and the speciation of one of them is faster than natural evolution mechanisms, then a single

64 genome can contain divergent paralogues (descendants of a duplicated ancestral gene) (Buckler et al., 1997). Consequently, some of the less / high represented haplotypes can actually belong to low- / high-copy paralogues (Stevenson et al., 1995). However, as I interpret the results using genera or “species”, I hope to reduce the effect of paralogs in my study. The solution would be to sequence using HTS single eggs/larvae/adult worms and clarify the paralogs (more haplotypes) for each species. However, the material is not available (adult worms) and isolating of single eggs, which cannot be differentiated, would provide results only for the common species.

5.2 Strongylid nematodes found in the studied hosts The HTS approach based on ITS-2 used in presented study revealed co-occurrence of more than one haplotype in more than 75 % of samples. Importantly, 65 % of individuals were coinfected by more than one species. The results correspond to other studies conducted on HTS metabarcoding approach on strongylid nematodes and show that hardly identificable „strongylid“ found in fecal samples and detected by coproscopic tools represent a complex communitites of several genera/species/haplotypes.

Overall, talking about the species composition, my results remain pretty consistent with previous studies conducted using HTS approaches focused on strongylid ITS-2 (Pafčo et al., 2018, 2019). Necator and Oesophagostomum were the most prevalent genera in presented study. The median number of haplotypes in a single sample was 7, however, the richest sample contained 17 different haplotypes. Great apes exhibited higher diversity, which also corresponds to other previously conducted studies as wild animals harbour more parasites and parasite species (Pafčo, 2017). Unlike traditional approaches, HTS also detected rare, otherwise overlooked taxa such as Ternidens deminutus, Ancylostoma duodenale and Trichostrongylus spp. present in a small number of samples and with low numbers of reads. Additionally, I found a significant portion of unassigned haplotypes, which cannot be detected using species-specific primers (as they are not expected) combined with Sanger sequencing. Unassigned taxa could not be confidently assigned to genera, as their ITS-2 haplotypes did not have close matches with any sequences in the reference databases.

5.2.1 Humans Parasites are important companions of humans through the whole history, although nowadays they remain most common in the tropical regions, which is also connected with living standards in developing countries. Strongylid nematodes form a significant part of the human parasitofauna. The dominant and therefore also the most important genera in humans are hookworms and nodular worms, as was proved in my study.

Humans were dominantly infected by Necator americanus haplotypes (prevalence almost 70 %), which corresponds to widely accepted hypothesis of N. americanus being a worldwide widespread human-originated parasite. Historically, N. americanus was associated with rural poverty and impoverished people. Hookworm infections (including N. americanus) were common throughout pre-liberation China and still, more recent studies indicate significant numbers of people being infected by hookworms in China (Hotez, 2002). South America was also an endemic area to hookworm infections a century ago and lots of efforts were put through time into the eradication and management of the infection, however, it was pretty unsuccessful, as N. americanus infections in South America are still being reported (Chammartin et al., 2013). In Africa, N. americanus is endemic and commonly found in rural areas (Hasegawa et al., 2014; Palmer & Bundy, 1995). My finding therefore supports the theory that N. americanus is still the dominant hookworm found in humans.

On the other hand, I found two haplotypes of N. gorillae, originally described in western lowland gorillas (Noda & Yamada, 1964) infecting humans in DJA, Cameroon. Both haplotypes correspond to those found in humans in Central African Republic (CAR) (Hasegawa et al., 2014). Although the prevalence was not as high (almost 17 %) as N. americanus. It was further found in humans in Gabon (Hasegawa et al., 2017), researchers participating in gorilla research in CAR (Kalousová et al., 2016) and confirmed by studies using metabarcoding HTS approaches on human samples from CAR (Pafčo, 2017; Pafčo et al., 2019). As traditional approaches were formerly not able to distinguish closely related species, the former existence of N. gorillae in humans is possible and it also could be often mistaken for N. americanus.

One haplotype of O. stephanostomum was found in humans. It corresponds to the O. stephanostomum found in Kibale National Park, Uganda (Cibot et al., 2015). Actually,

66 besides this one particular study, this is the second case of O. stephanostomum being reported in humans. People are traditionally infected by O. bifurcum (Gasser et al., 1999), especially in West Africa which, due to its high prevalence, is classified as an endemic area (Ziem et al., 2006).

Lastly, one haplotype of Trichostrongylus spp. corresponding to the haplotype found in chimpanzees living within agricultural fields of degraded forest fragments in Bulindi, Uganda (McLennan et al., 2017) was found infecting one human individual. Little is known about neglected parasites of the Trichostrongylus genus. Although several cases of trichostrongylosis were reported in Southern America (Souza et al., 2013), Asia (Phosuk et al., 2013) and formerly also in Africa (O’neal & Magath, 1947), human infection is considered rather incidental.

5.2.2 Great apes Strongylid nematodes are the dominant part of the gastrobiome of great apes. The coproscopical results revealed 100% prevalence in gorillas in CAR (Pafčo et al., 2017) and the situation is pretty similar at different sites (Cibot et al., 2015; Narat et al., 2015; Ota et al., 2015; Pafčo et al., 2017). My results highlighted that all studied animals harbour strongylid nematodes (except one gorilla excluded from the dataset, however, it could be caused by amplification or sequencing error) and except four individuals, all studied animals were infected by at least two different haplotypes. As in other studies, we found Necator, Oesophagostomum, trichostrongylids and other closely unspecified strongylids being dominant in great apes cohabiting DJA FR.

Four haplotypes of “human” N. americanus were found also in gorillas. Additionally, one N. americanus haplotype was found infecting only gorillas and not humans. These results are pretty consistent with recently published studies, where N. americanus was broadly shared between humans and gorillas (Hasegawa et al., 2014; Pafčo, 2017; Pafčo et al., 2018, 2019). We recorded no evidence of N. americanus in chimpanzees, which is also consistent to previous hypotheses, where Hasegawa et al. (2017) suggested the lesser susceptibility of chimpanzees to be infected by N. americanus. However, laboratory experiments provided evidence that chimpanzees can harbour N. americanus worms (Orihel, 1971). Using ITS-2 metabarcoding approach N. americanus was also recorded in chimpanzees in DSPA, CAR, however, in small numbers (Pafčo et al., 2019). This

67 clearly needs deeper, complex examination and also use of more advanced molecular tools, such as whole-genome sequencing of N. americanus worms from human and great ape hosts to find human/primate-specific adaptations

Necator gorillae is the most common Necator species infecting great apes and is broadly shared between them (Hasegawa et al., 2014, 2017). Our results are pretty much consistent with this hypothesis, as we found N. gorillae being the dominant Necator species within great apes. Moreover, great apes harboured few haplotypes of Necator sp., corresponding to those found by Hasegawa et al., 2014 in humans cohabiting the DSPA. However, this is not the first case of this haplotype being found in NHPs, as it was first recorded in western lowland gorillas in Gabon (Hasegawa et al., 2017) and further in NHPs in DSPA (Pafčo et al., 2019). In great apes, other Necator species such as N. congolensis and N. exilidens were described, however, the description was made at the beginning of the last century, therefore DNA is not available and on the contrary, fresh material for new study is also not available. As a result, the new species cannot be closely identified.

Great apes are often infected by O. stephanostomum, (e.g. Krief et al., 2008; Ota et al., 2015), as it is commonly reported especially in western Africa, which is an endemic area for this species (Ziem et al., 2006). I revealed presence of three haplotype Oesophagostomum groups. The majority of Oesophagostomum sequences, present in both groups of great apes, belong to the O. stephanostomum type I, which I found also in humans. The sequences corresponds to the O. stephanostomum found in humans and NHPs in Kibale National Park, Uganda (Cibot et al., 2015). The second group are haplotypes belonging to O. stephanostomum type II, which was found only in great apes in my study. It corresponds to the O. stephanostomum found in western lowland gorillas in Gabon (Makouloutou et al., 2014). The last, Oesophagostomum sp., found only in one gorilla corresponds to the one found in baboons in Kibale, Uganda (Cibot et al., 2015). However, it was originally described in humans and non-human primates at the same locality, being widely shared (Ghai et al., 2014). Although O. bifurcum is another common species, I did not find it in the studied great apes. Past studies showed that O. bifurcum is rarely found in great apes, but common in other NHPs and humans (Ota et al., 2015). Besides these two common Oesophagostomum species, others can be found infecting great apes, specifically O. blanchardi, O. dentigerum, O. polydentatum (Modrý

68 et al., 2018), but there was no evidence of any of these species in our study. Our results corresponds to other studies and as it was suggested (Ghai et al., 2014), the diversity of Oesophagostomum is still unexplored and demand further examination and use of advanced molecular tools, as was also suggested for Necator species.

Several haplotypes of Trichostrongylus spp. were found infecting great apes. First, I found haplotypes belonging to Trichostrongylus sp. type I only in great apes. Closest hits in NCBI were gastrointestinal T. vitrinius, causing severe chronic infections in sheep (Sykes et al., 1979) and T. colubriformis which besides humans parasitise also sheep (Athanasiadou et al., 2000). The second group of trichostrongylid haplotypes corresponded to Trichostrongylus sp. type II, found in great apes but also in humans. Trichostrongylids are probably the most numerous group of strongylid nematodes with very difficult morphological characteristics (Durette-Desset et al., 1992), therefore very little is known about this group of strongylids and most probably a lot of species remain undescribed. Moreover, I found many haplotypes that could not match any ITS-2 sequences in NCBI database exactly. Closest hits were, however, other trichostrongylids as Nematodirus sp. or Travassostrongylus sp. The sequence identity and match score had very low values. Even the original host and geographical distribution of Travassostrongylus (parasite of Central and South American opossums) indicates inaccuracy of the results. This is the proof of strongylid nematodes being under-explored and often neglected as other metabarcoding studies exhibited also high numbers of unclassified haplotypes (Pafčo et al., 2019). Several species were described in great apes such as Hyostrongylus kigeziensis or Paralibyostrongylus kalinae, however, sequences are missing in the databases. Adult worms of these taxa should be collected, sequenced and put into databases for better future identification, however, necropsies deal with several issues (as it was mentioned several times in this study).

In addition, I found two rare taxa, specifically Ternidens deminutus in one gorilla and Ancylostoma sp. (closest hit Ancylostoma duodenale) in one chimpanzee. Ternidens deminutus is believed to be closely related to Oesophagostomum, which was also supported by DNA phylogeny. Symptoms of the disease are also similar to oesophagostomiasis (Bradbury, 2019). Ternidens deminutus is a truly neglected parasite, sometimes being called as “The False Hookworm” for the similarity of its eggs to those of hookworms (Goldsmid, 1968). Ternidens deminutus was firstly described in a dead

69 native inhabitant in Mozambique, suggesting human origin (Amberson & Schwarz, 1952). It is often reported in non-human primates and humans, sometimes with high prevalence (Amberson & Schwarz, 1952). Ancylostoma duodenale is, besides Necator americanus, one of the most important human hookworm infections (Hotez et al., 2005). Although it seems uncommon in great apes, its presence was supported by finding of adult worms in dissected wild great apes (Myers & Kuntz, 1972) and by experimental infection of laboratory chimpanzees (Miller, 1968).

5.3 Zoonotic patterns Pathogen transmission as a result of close physical contact between non-human primates and humans is recently reported more often due to the come-up of advanced molecular approaches (Goldberg et al., 2007). Humans have always shared habitat with non-human primates, but the situation has changed dramatically in the past decade and the contact intensified. In DJA FR, although significant hunting and logging pressure is put on the reserve (Dupain et al., 2004), the animals remain unhabituated, wild and completely free-living even in the close proximity of human settlements. Combes’ filter concept (Claude Combes, 1991) can be useful in partial explanation of zoonotic transmissions; studied host species share the same infected environment (encounter filter) and are phylogenetically related (compatibility filter). Therefore, there is a high probability of sharing the same parasite taxa.

The most prevalent genera and also those shared between great apes and humans were traditionally Necator and Oesophagostomum. Found taxa with zoonotic potential, however, follow different transmission patterns. The transmission pattern for genus Necator or all hookworms is via skin penetration. This is a very simple way of transmission requiring sharing of the same habitat only. As a result, although Necator americanus was for a long time stated as a sole known species infecting humans, this study, alongside with other numerous studies (Hasegawa et al., 2014, 2017; Pafčo et al., 2019) confirmed its zoonotic potential. Necator americanus was found in gorillas in my study and vice versa, N. gorillae originally described in western lowland gorilla, commonly found in great apes, was found infecting humans. People in the DJA FR live often near the forest environment or enter the forest frequently. They are often barefoot

70 and in direct contact with the soil, which is contaminated by infected L3 larvae, that actively penetrate the host skin.

Humans often harbour O. bifurcum, although we found O. stephanostomum infecting humans, which is actually second case of O. stephanostomum being found in humans (first recorded by Cibot et al., 2015). Our results correspond to other studies (Ghai et al., 2014; Ota et al., 2015) and suggest zoonotic transmission patterns of this genera, with great apes being possible reservoirs for human oesophagostomiasis. In contrast to Necator, Oesophagostomum is transmitted by oral ingestion. Local people often have their agricultural fields near or directly in the forest environment, meaning crop-raiding by wild animals occurs (Hockings & McLennan, 2012). People can be also infected while consuming contaminated fruits, herbs and other supplies from the forest environments. As the hygiene standards are often low, the infected people can spread the pathogen between other villagers. The same pattern can be probably applied for the shared Trichostrongylus species, as trichostrongylids are orally transmitted parasites as well.

5.4 Conclusions The results of this study brought additional knowledge to the understanding of complex strongylid nematode communities. As it was presumed, great apes exhibited greater diversity of the parasite fauna, as they in general harboured more haplotypes and presence of rare taxa was detected. Oesophagostomum and Necator were the main components of all studied communities and the driving force of strongylid overlaps. Humans’ communities were dominated by Necator americanus, originally human worldwide spread species. However, N. americanus was also shared by gorillas. Necator gorillae, original NHPs parasite, was widespread across all studied host species, thus the zoonotic transmission from great apes to humans is proposed. Yet, a second case of O. stephanostomum infection in humans transmitted from great apes was reported. Additionally, Trichostrongylus sp. was found infecting humans, which is quite surprising, as human infection is rather incidental. Importantly, I revealed a number of unidentified strongylids in great apes, which testifies the limited knowledge of strongylid nematodes infecting wild animals.

The results underline the growing trend of intensified human contact and mutual pathogen exchange. Due to the indisputable importance of some described parasites, there should be put some efforts to minimise the contact and transmission events as it can have consequences on both sides. Even though primate parasites are often under scientific “finderscope”, little is known about their parasite communitites and therefore they may represent suitable organisms for future scientific studies and focus. In conclusion, high- throughput sequencing of strongylid nematodes from fecal samples represent a time- and cost-efficient way of studying helminth communitites and provides a resolution superior to traditional approaches. Its application overcomes the limitations of classical Sanger sequencing and allows for analyses of strongylid nematode host-specifity in complex parasite-host systems.

Outputs from this research should be considered in both ecological and geographical contexts. For example, comparison of strongylid composition between great apes and people with different forest environment contact (people entering forest frequently or farming in the forest versus villagers settled in the villages) is strongly recommended as well as comparison of great apes ranging in close proxy to humans versus those ranging deeper in the forest. This information can extend currently achieved knowledge about strongylid transmission patterns. However, it is clear that molecular barcoding now offers broad opportunities for future comparative studies and this insight can contribute to the understanding of strongylid nematodes, their diversity, epidemiology or zoonotic transmissions. Moreover, it may provide valuable information for conservation strategies, management of wild endangered animals or possible treatment.

6. Aims for my future work In the light of the obtained results, HTS has proven to be a suitable platform for studying strongylid nematode communities. However, additional analyses should be implemented in this study. For example, questionnaires from examined people were obtained, therefore I will search for differences in strongylid communities between people settled more in the villages with those entering the forest frequently, eventually people who farms in the forest fields. Moreover, data about the human host traits, such as age, sex, hygiene habits etc. are also available. To compare animals ranging near by the villages with these ranging around the La Belgique research camp is also necessary. Last, I will include hosts’ microbiome data. Due to enormous effort required to process even the presented data, I was not able to incorporate all proposed analyses to my diploma thesis. These analyses will take significant part in my following PhD study at Masaryk University and will complete presented data/results for preparation of a scientific paper. Also, comparison with other African localities should help extend the knowledge about strongylid nematodes and help with conservation and management of great apes.

The major part of my future PhD goals is focused on whole genome sequencing of hookworms naturally infecting humans and NHPs in Africa – human origin N. americanus and other species (N. gorillae, Necator sp., etc.) originating in non-human primates. I will focus on sequencing and assembly of HTS and third-generation sequencing data (Oxford Nanopore, PacBio). I already processed and prepared whole genome Illumina libraries of pilot worms obtained after anthelmintic treatment from local people and researchers working in Central African Republic. Moreover, the project will be supported by an acquired Fulbright internship and cooperation with laboratory of Dr. Erich Schwarz at Cornell University in the USA. Sequencing the genomes of worms from naturally infected individuals will provide a substantially more accurate insight into hookworms and their mechanisms that enable infection. I will also use bioinformatic approaches for identification of possible drug targets, necessary for development of anti- hookworm vaccines as well as for better use of strongylids during the helminth therapy. Lastly, I will focus on analysing whole genomes of worldwide distributed N. americanus and try to reconstruct the possible route and timing of migration of N. americanus from Africa to other parts of the world.

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8. Supplements

Supplement 1: Metadata table

Sample_name ID Type Host Locality Site D_CH10_Necator4 S108 feces chimp DJA-Cameroon NA D_CH11_Necator4 S109 feces chimp DJA-Cameroon NA D_CH12_Necator4 S110 feces chimp DJA-Cameroon NA D_CH13_Necator4 S111 feces chimp DJA-Cameroon NA D_CH14_Necator4 S112 feces chimp DJA-Cameroon NA D_CH15_Necator4 S113 feces chimp DJA-Cameroon NA D_CH16_Necator4 S114 feces chimp DJA-Cameroon NA D_CH17_Necator4 S115 feces chimp DJA-Cameroon NA D_CH18_Necator4 S116 feces chimp DJA-Cameroon NA D_CH19_Necator4 S117 feces chimp DJA-Cameroon NA D_CH20_Necator4 S118 feces chimp DJA-Cameroon NA D_CH21_Necator4 S119 feces chimp DJA-Cameroon NA D_CH22_Necator4 S120 feces chimp DJA-Cameroon NA D_CH23_Necator4 S121 feces chimp DJA-Cameroon NA D_CH24_Necator4 S122 feces chimp DJA-Cameroon NA D_CH25_Necator4 S123 feces chimp DJA-Cameroon NA D_CH26_Necator4 S124 feces chimp DJA-Cameroon NA D_CH27_Necator4 S125 feces chimp DJA-Cameroon NA D_CH28_Necator4 S126 feces chimp DJA-Cameroon NA D_CH29_Necator4 S127 feces chimp DJA-Cameroon NA D_CH2_Necator4 S102 feces chimp DJA-Cameroon NA D_CH30_Necator4 S128 feces chimp DJA-Cameroon NA D_CH31_Necator4 S129 feces chimp DJA-Cameroon NA D_CH32_Necator4 S130 feces chimp DJA-Cameroon NA D_CH33_Necator4 S131 feces chimp DJA-Cameroon NA D_CH34_Necator4 S132 feces chimp DJA-Cameroon NA D_CH3_Necator4 S103 feces chimp DJA-Cameroon NA D_CH5_Necator4 S104 feces chimp DJA-Cameroon NA D_CH7_Necator4 S105 feces chimp DJA-Cameroon NA D_CH8_Necator4 S106 feces chimp DJA-Cameroon NA D_CH9_Necator4 S107 feces chimp DJA-Cameroon NA D_G10_Necator4 S142 feces gorilla DJA-Cameroon NA D_G11_Necator4 S143 feces gorilla DJA-Cameroon NA D_G12_Necator4 S144 feces gorilla DJA-Cameroon NA D_G13_Necator4 S145 feces gorilla DJA-Cameroon NA D_G14_Necator4 S146 feces gorilla DJA-Cameroon NA D_G1_Necator4 S134 feces gorilla DJA-Cameroon NA D_G21_Necator4 S147 feces gorilla DJA-Cameroon NA D_G22_Necator4 S148 feces gorilla DJA-Cameroon NA D_G23_Necator4 S149 feces gorilla DJA-Cameroon NA

D_G24_Necator4 S150 feces gorilla DJA-Cameroon NA D_G25_Necator4 S151 feces gorilla DJA-Cameroon NA D_G26_Necator4 S152 feces gorilla DJA-Cameroon NA D_G27_Necator4 S153 feces gorilla DJA-Cameroon NA D_G28_Necator4 S154 feces gorilla DJA-Cameroon NA D_G29_Necator4 S155 feces gorilla DJA-Cameroon NA D_G2_Necator4 S135 feces gorilla DJA-Cameroon NA D_G30_Necator4 S156 feces gorilla DJA-Cameroon NA D_G31_Necator4 S157 feces gorilla DJA-Cameroon NA D_G32_Necator4 S158 feces gorilla DJA-Cameroon NA D_G33_Necator4 S159 feces gorilla DJA-Cameroon NA D_G34_Necator4 S160 feces gorilla DJA-Cameroon NA D_G35_Necator4 S161 feces gorilla DJA-Cameroon NA D_G36_Necator4 S162 feces gorilla DJA-Cameroon NA D_G37_Necator4 S163 feces gorilla DJA-Cameroon NA D_G38_Necator4 S164 feces gorilla DJA-Cameroon NA D_G39_Necator4 S165 feces gorilla DJA-Cameroon NA D_G3_Necator4 S136 feces gorilla DJA-Cameroon NA D_G40_Necator4 S166 feces gorilla DJA-Cameroon NA D_G41_Necator4 S167 feces gorilla DJA-Cameroon NA D_G42_Necator4 S168 feces gorilla DJA-Cameroon NA D_G43_Necator4 S169 feces gorilla DJA-Cameroon NA D_G44_Necator4 S170 feces gorilla DJA-Cameroon NA D_G45_Necator4 S171 feces gorilla DJA-Cameroon NA D_G46_Necator4 S172 feces gorilla DJA-Cameroon NA D_G47_Necator4 S173 feces gorilla DJA-Cameroon NA D_G48_Necator4 S174 feces gorilla DJA-Cameroon NA D_G49_Necator4 S175 feces gorilla DJA-Cameroon NA D_G4_Necator4 S137 feces gorilla DJA-Cameroon NA D_G50_Necator4 S176 feces gorilla DJA-Cameroon NA D_G51_Necator4 S177 feces gorilla DJA-Cameroon NA D_G52_Necator4 S178 feces gorilla DJA-Cameroon NA D_G53_Necator4 S179 feces gorilla DJA-Cameroon NA D_G54_Necator4 S180 feces gorilla DJA-Cameroon NA D_G55_Necator4 S181 feces gorilla DJA-Cameroon NA D_G56_Necator4 S182 feces gorilla DJA-Cameroon NA D_G57_Necator4 S183 feces gorilla DJA-Cameroon NA D_G58_Necator4 S184 feces gorilla DJA-Cameroon NA D_G59_Necator4 S185 feces gorilla DJA-Cameroon NA D_G5_Necator4 S138 feces gorilla DJA-Cameroon NA D_G60_Necator4 S186 feces gorilla DJA-Cameroon NA D_G61_Necator4 S187 feces gorilla DJA-Cameroon NA D_G62_Necator4 S188 feces gorilla DJA-Cameroon NA D_G63_Necator4 S189 feces gorilla DJA-Cameroon NA D_G64_Necator4 S190 feces gorilla DJA-Cameroon NA D_G65_Necator4 S191 feces gorilla DJA-Cameroon NA

D_G66_Necator4 S192 feces gorilla DJA-Cameroon NA D_G67_Necator4 S193 feces gorilla DJA-Cameroon NA D_G68_Necator4 S194 feces gorilla DJA-Cameroon NA D_G7_Necator4 S139 feces gorilla DJA-Cameroon NA D_G8_Necator4 S140 feces gorilla DJA-Cameroon NA D_G9_Necator4 S141 feces gorilla DJA-Cameroon NA D_H100_Necator4 S247 feces human DJA-Cameroon Mimpala D_H101_Necator4 S248 feces human DJA-Cameroon Mimpala D_H102_Necator4 S249 feces human DJA-Cameroon Mimpala D_H103_Necator4 S250 feces human DJA-Cameroon Mimpala D_H104_Necator4 S251 feces human DJA-Cameroon Mimpala D_H105_Necator4 S252 feces human DJA-Cameroon Mimpala D_H106_Necator4 S253 feces human DJA-Cameroon Mimpala D_H107_Necator4 S254 feces human DJA-Cameroon Mimpala D_H108_Necator4 S255 feces human DJA-Cameroon Mimpala D_H110_Necator4 S256 feces human DJA-Cameroon Mimpala D_H111_Necator4 S195 feces human DJA-Cameroon Mimpala D_H113_Necator4 S257 feces human DJA-Cameroon Mimpala D_H114_Necator4 S258 feces human DJA-Cameroon Mimpala Doumo- D_H116_Necator4 S228 feces human DJA-Cameroon pierre D_H117_Necator4 S259 feces human DJA-Cameroon Malen_V. D_H118_Necator4 S260 feces human DJA-Cameroon Malen_V. D_H119_Necator4 S261 feces human DJA-Cameroon Malen_V. D_H120_Necator4 S262 feces human DJA-Cameroon Malen_V. D_H122_Necator4 S263 feces human DJA-Cameroon Malen_V. D_H123_Necator4 S264 feces human DJA-Cameroon Malen_V. D_H124_Necator4 S265 feces human DJA-Cameroon Malen_V. D_H125_Necator4 S266 feces human DJA-Cameroon Malen_V. D_H127_Necator4 S267 feces human DJA-Cameroon Malen_V. D_H129_Necator4 S268 feces human DJA-Cameroon Malen_V. Doumo- D_H12_Necator4 S200 feces human DJA-Cameroon pierre D_H130_Necator4 S269 feces human DJA-Cameroon Malen_V. D_H131_Necator4 S270 feces human DJA-Cameroon Malen_V. D_H132_Necator4 S271 feces human DJA-Cameroon Malen_V. D_H133_Necator4 S272 feces human DJA-Cameroon Malen_V. D_H136_Necator4 S273 feces human DJA-Cameroon Malen_V. D_H137_Necator4 S274 feces human DJA-Cameroon Malen_V. D_H138_Necator4 S275 feces human DJA-Cameroon Malen_V. D_H139_Necator4 S276 feces human DJA-Cameroon Malen_V. D_H141_Necator4 S277 feces human DJA-Cameroon Malen_V. D_H143_Necator4 S278 feces human DJA-Cameroon Malen_V. D_H145_Necator4 S279 feces human DJA-Cameroon Malen_V. D_H147_Necator4 S280 feces human DJA-Cameroon Malen_V. D_H148_Necator4 S281 feces human DJA-Cameroon Malen_V.

D_H149_Necator4 S282 feces human DJA-Cameroon Malen_V. Doumo- D_H14_Necator4 S201 feces human DJA-Cameroon pierre D_H150_Necator4 S283 feces human DJA-Cameroon Malen_V. D_H151_Necator4 S284 feces human DJA-Cameroon Malen_V. D_H152_Necator4 S285 feces human DJA-Cameroon Malen_V. D_H157_Necator4 S286 feces human DJA-Cameroon Malen_V. D_H158_Necator4 S287 feces human DJA-Cameroon Malen_V. D_H159_Necator4 S288 feces human DJA-Cameroon Malen_V. Doumo- D_H17_Necator4 S202 feces human DJA-Cameroon pierre Doumo- D_H1_Necator4 S196 feces human DJA-Cameroon pierre Doumo- D_H22_Necator4 S203 feces human DJA-Cameroon pierre Doumo- D_H23_Necator4 S204 feces human DJA-Cameroon pierre Doumo- D_H27_Necator4 S205 feces human DJA-Cameroon pierre Doumo- D_H29_Necator4 S206 feces human DJA-Cameroon pierre Doumo- D_H2_Necator4 S197 feces human DJA-Cameroon pierre Doumo- D_H30_Necator4 S207 feces human DJA-Cameroon pierre Doumo- D_H31_Necator4 S208 feces human DJA-Cameroon pierre Doumo- D_H32_Necator4 S209 feces human DJA-Cameroon pierre Doumo- D_H33_Necator4 S210 feces human DJA-Cameroon pierre Doumo- D_H34_Necator4 S211 feces human DJA-Cameroon pierre Doumo- D_H36_Necator4 S212 feces human DJA-Cameroon pierre Doumo- D_H37_Necator4 S213 feces human DJA-Cameroon pierre Doumo- D_H39_Necator4 S214 feces human DJA-Cameroon pierre Doumo- D_H41_Necator4 S215 feces human DJA-Cameroon pierre Doumo- D_H44_Necator4 S216 feces human DJA-Cameroon pierre Doumo- D_H48_Necator4 S217 feces human DJA-Cameroon pierre Doumo- D_H55_Necator4 S218 feces human DJA-Cameroon pierre Doumo- D_H56_Necator4 S219 feces human DJA-Cameroon pierre

Doumo- D_H59_Necator4 S220 feces human DJA-Cameroon pierre Doumo- D_H5_Necator4 S198 feces human DJA-Cameroon pierre Doumo- D_H60_Necator4 S221 feces human DJA-Cameroon pierre Doumo- D_H62_Necator4 S222 feces human DJA-Cameroon pierre Doumo- D_H66_Necator4 S223 feces human DJA-Cameroon pierre Doumo- D_H69_Necator4 S224 feces human DJA-Cameroon pierre Doumo- D_H70_Necator4 S225 feces human DJA-Cameroon pierre Doumo- D_H73_Necator4 S226 feces human DJA-Cameroon pierre Doumo- D_H75_Necator4 S227 feces human DJA-Cameroon pierre D_H78_Necator4 S229 feces human DJA-Cameroon Mimpala D_H80_Necator4 S230 feces human DJA-Cameroon Mimpala D_H81_Necator4 S231 feces human DJA-Cameroon Mimpala D_H83_Necator4 S232 feces human DJA-Cameroon Mimpala D_H84_Necator4 S233 feces human DJA-Cameroon Mimpala D_H85_Necator4 S234 feces human DJA-Cameroon Mimpala D_H86_Necator4 S235 feces human DJA-Cameroon Mimpala D_H87_Necator4 S236 feces human DJA-Cameroon Mimpala D_H88_Necator4 S237 feces human DJA-Cameroon Mimpala D_H89_Necator4 S238 feces human DJA-Cameroon Mimpala Doumo- D_H8_Necator4 S199 feces human DJA-Cameroon pierre D_H90_Necator4 S239 feces human DJA-Cameroon Mimpala D_H91_Necator4 S240 feces human DJA-Cameroon Mimpala D_H93_Necator4 S241 feces human DJA-Cameroon Mimpala D_H95_Necator4 S242 feces human DJA-Cameroon Mimpala D_H96_Necator4 S243 feces human DJA-Cameroon Mimpala D_H97_Necator4 S244 feces human DJA-Cameroon Mimpala D_H98_Necator4 S245 feces human DJA-Cameroon Mimpala D_H99_Necator4 S246 feces human DJA-Cameroon Mimpala

Supplement 2: Reference taxonomy and analysis script

###### --- DIPLOMA HTS – STRONGYLID NEMATODES DJA --- ######

### LOADING LIBRARIES library(BiocManager) library(phyloseq) library(ggplot2) library(multcomp) library(mvabund) library(DECIPHER) library(phangorn) library(ggtree) library(NMF) library(Biostrings) library(ShortRead) library(ape) library(dplyr) library(devtools) library(pairwiseAdonis) library(vegan) library(knitr)

###### --- CREATING REFERENCE TAXONOMY DATABASE --- ######

## 1 FILTERING BLAST RESULTS ##

BLAST <- read.delim("outfile", header = F) head(BLAST) dim(BLAST)

BLAST.sub <- BLAST[BLAST$V3>85,] dim(BLAST.sub)

BLAST.sub <- BLAST.sub[BLAST.sub$V4>90,] dim(BLAST.sub) write.table(BLAST.sub, "outfile.sub", row.names = F, col.names = F, quote = F, sep = "\t")

## 2 TAXONOMY FOR BLAST HITS ##

BLAST <- read.delim("outfile.sub", header = F, stringsAsFactors = F )

TAXA <- BLAST[,12] TAXA <- unique(TAXA) CLASS <- list() options(ENTREZ_KEY = "f8cb847866e1b1aca91459de46ac53e75b08")

for(i in 1:length(TAXA)){ # print(i) # print(TAXA[i]) if(regexpr(";",TAXA[i])<1){CLASS[i]<-classification(TAXA[i],db="ncbi") Sys.sleep(1.5)} if(regexpr(";",TAXA[i])>0){SEP<-list() TAXA.sep<-unlist(strsplit(TAXA[i], ";")) for(j in 1:length(TAXA.sep)){ SEP[j]<-classification(TAXA.sep[j],db="ncbi") Sys.sleep(1) NO<-sapply(SEP,function(x) sum(regexpr(" sp.",x))) } SEP<-SEP[NO==min(NO)] SEP<-SEP[1] CLASS[i]<-SEP} }

names(CLASS)<-TAXA

RANKS <- c("superkingdom", "phylum", "class", "order", "family", "genus", "species") TAX.df <- data.frame(rank=RANKS, stringsAsFactors = F) i<-1 for(i in 1:length(CLASS)){ ACT<-CLASS[[i]] ACT.sub<-ACT[ACT$rank%in%RANKS,] ACT.sub<-ACT.sub[,1:2] colnames(ACT.sub)[1]<-names(CLASS)[i] TAX.df<-join(TAX.df,ACT.sub) }

TAX.df[,1:3] save(TAX.df, file = "D:/School/Masters/Diploma/HTS_R_First_SHit/Tax.df.R")

## 3 EXTRACTING FASTA FOR BLAST HITS

Accessions.list <- sapply(strsplit(as.character(BLAST.sub$V2), "[|]"), function(x) x[2], simplify=TRUE) Accessions.list <- unique(Accessions.list) writeLines(Accessions.list,"D:/School/Masters/Diploma/HTS_R_First_SHit/ Accessions.list")

## 4 FASTA TRIMMING

BLAST <- read.delim("outfile.sub", header = F, sep = "\t") FLANK <- 100 kk <- "D:/School/Masters/Diploma/HTS_R_First_SHit/fasta.out" LIST <- list.files("D:/School/Masters/Diploma/HTS_R_First_SHit") REMOVE <- LIST[grep(".fai",LIST)] REMOVE file.remove(REMOVE) fa <- open(FaFile(kk)) (idx <- scanFaIndex(fa)) file.remove("fasta.out.trim") for(i in 1:length(idx)){ dna <- scanFa(fa, param=idx[i]) # print(i) BLAST.sub<-BLAST[grep(names(dna),BLAST[,2]),] MIN<-min(BLAST.sub[,8]) MAX<-max(BLAST.sub[,9]) if(MIN>MAX){MAX.trim<-ifelse(MAX-FLANK<1,1,MAX-1) MIN.trim<-ifelse(MIN+FLANK2000) {print(paste(i,names(dna.narrow),width(dna.narrow)))} writeFasta(dna.narrow,"fasta.out.trim",mode="a") }

## 5 REFERENCE DATABASE PREPARATION

FASTA <- readDNAStringSet("D:/School/Masters/Diploma/HTS_R_First_SHit/fasta.out.trim") BLAST <- read.delim("outfile.sub", header = F, sep = "\t") BLAST.nonred <- BLAST[duplicated(BLAST[,2])==F,] dim(BLAST.nonred)

ORDER <- as.numeric() for(i in 1:length(FASTA)){ ORDER[i]<-grep(names(FASTA)[i],BLAST.nonred[,2]) }

BLAST.nonred<-BLAST.nonred[ORDER,] names(BLAST.nonred)[12]<-"taxid"

MERGED <- apply(TAX.df[,2:dim(TAX.df)[2]],2,function(x) paste(x, collapse = ";")) TAXONOMY <- data.frame(taxid=names(MERGED), TAXONOMY=MERGED,stringsAsFactors = F) BLAST.nonred.tax <- join(BLAST.nonred,TAXONOMY) tail(BLAST.nonred.tax)

ACCES <- sapply(strsplit(as.character(BLAST.nonred.tax[,2]), "[|]"), function(x) x[4], simplify = TRUE) BLAST.nonred.tax$TAXONOMY.ACCESS <- paste(BLAST.nonred.tax$TAXONOMY,";", ACCES,";", sep = "") BLAST.nonred.tax[BLAST.nonred.tax$TAXONOMY.ACCESS=="NA;",] names(FASTA) <- BLAST.nonred.tax$TAXONOMY.ACCESS FASTA writeFasta(FASTA, "D:/School/Masters/Diploma/HTS_R_First_SHit/REF.fasta")

## 6 CLEAN-UP REFERENCE DATABASE

FASTA.species <- FASTA[-grep(" sp.", names(FASTA))] writeFasta(FASTA.species, "D:/School/Masters/Diploma/HTS_R_First_SHit/REF.species.fasta")

## 7 TAXONOMIC ASSIGNATION

TO_CLASS <- readDNAStringSet("haplotypes_lada.fasta.txt") taxa <- assignTaxonomy(as.character(TO_CLASS), "REF.fasta", multithread = TRUE, minBoot = 80, tryRC = T) taxa.species <- assignTaxonomy(as.character(TO_CLASS), "REF.species.fasta", multithread = TRUE,minBoot = 80, tryRC=T) rownames(taxa) <- names(TO_CLASS)

## 8 LOADING PHYLOSEQ, DATABASE AND MERGING DATA load("phylosITS.RDP.nondupl.R") data_table_ITS_nondupl <- read.delim2("data_table_ITS_nondupl.csv", header = T, sep = ";") genus <- data_table_ITS_nondupl genus <- sample_data(genus) sample_names(genus) <- genus$X

NEW <- merge_phyloseq(otu_table(phylosITS.RDP.nondupl), refseq(phylosITS.RDP.nondupl), tax_table(phylosITS.RDP.nondupl), genus) phylos_brand_new <- merge_phyloseq(otu_table(NEW), refseq(NEW), tax_table(NEW), taxa) save(phylos_brand_new, file = "phylos.ITSDP.brandnew.R", envir = globalenv(), compress = FALSE)

### --- THE END --- ### ###### --- DIPLOMA HTS DATA ANALYSIS --- ######

### 1 LOADING BRAND NEW PHYLOSEQ AND MERGING IT WITH METADATA TABLE ### load("phylosITS.DP.brandnew.R")

MERGED <- read.csv2("data_table_ITS_nondupl.csv", header = T) MERGED <- sample_data(MERGED) sample_names(MERGED) <- MERGED$X phyloseq_merged<-merge_phyloseq(otu_table(phylos_brand_new), refseq(phylos_brand_new), tax_table(phylos_brand_new), MERGED)

### 2 CREATING BRAND NEW DJA-SUBSET ###

SUBSET <- subset_samples(phyloseq_merged, site=="DJA-Cameroon") tax_table(SUBSET) dim(otu_table(SUBSET))

# FILTERING THE DATASET

FILTER <- as.logical(is.na(tax_table(SUBSET)[,4])==F) summary(FILTER) SUBSET <- prune_taxa(FILTER, SUBSET) dim(otu_table(SUBSET))

SUBSET <- prune_samples(sample_sums(SUBSET)>0,SUBSET) SUBSET <- prune_taxa(taxa_sums(SUBSET)>0, SUBSET) dim(otu_table(SUBSET)) tax_table(SUBSET) dim(otu_table(SUBSET))

# EXCLUDING NOT STRONGYLID SAMPLES tax_table(SUBSET) badTaxa = c("OTU_57", "OTU_568") goodTaxa <- setdiff(taxa_names(SUBSET), badTaxa) SUBSET <- prune_taxa(goodTaxa, SUBSET) SUBSET <- prune_samples(sample_sums(SUBSET)>0, SUBSET) dim(otu_table(SUBSET)) otu_table(SUBSET) tax_table(SUBSET)

### 3 HAPLOTYPE COUNTS, READS COUNTS, FREQUENCY AND DIVERSITY ###

Haplotypes <- rowSums(otu_table(SUBSET)>0) summary(Haplotypes) hist(Haplotypes, col = "blue") otu_tab_reads <- (otu_table(SUBSET)) sum(otu_tab_reads)

Reads2 <- rowSums(otu_tab_reads) summary(Reads2)

DF <- data.frame(Haplotypes,sample_data(SUBSET)) ggplot(data = DF,aes(x=species,y=Haplotypes))+geom_boxplot()+geom_jitter()

# SETTING INTERCEPT (HUMAN) levels(as.factor(DF$species)) DF$species <- factor(DF$species, levels = c("human","gorilla","chimp")) levels(as.factor(DF$species))

# BOXPLOT (HUMAN INTERCEPT) boxplot <- ggplot(data = DF,aes(x=species,y=Haplotypes, fill = species), col = c("#ffd500","#9a02e0","#eb2865"))+geom_boxplot(fill = c("#ffd500","#9a02e0","#eb2865"))+geom_jitter() # boxplot for haplotypes boxplot

# GLM TESTING DIVERSITY (HUMAN INTERCEPT) glmodel_qas_human <- glm(Haplotypes~species,quasipoisson,data = DF) summary(glmodel_qas_human) glmodel_pos_human <- glm(Haplotypes~species,poisson,data = DF) summary(glmodel_pos_human)

# GLM POST-HOC TESTING

TUKEY_GLM <- (glht(glmodel_qas_human, mcp(species="Tukey"))) summary(TUKEY_GLM) plot(TUKEY_GLM)

# SIMPSON INDEX simp_df <- as.matrix(otu_table(SUBSET)) simp_df simp_haplo <- diversity(simp_df, "invsimpson") simp_haplo DF3000 <- data.frame(simp_df, sample_data(SUBSET)) glmodel_simp <- glm(simp_haplo~species,quasipoisson,data = DF3000) summary(glmodel_simp)

# SHANNON INDEX simp_shan <- diversity(simp_df) simp_shan DF3001 <- data.frame(simp_shan, sample_data(SUBSET)) glmodel_shan <- glm(simp_shan~species,quasipoisson,data = DF3001) summary(glmodel_shan)

### 4 ABUNDANCE DIVERSITY PLOT ### colors <- c("#1456b3","#f08a0e","#23e016","#f70cd8","#04fff7", "#efff0c")

SUBSET.trans <- transform_sample_counts(SUBSET,function(x) x/sum(x)) mdf2 <- mdf[order(mdf$sample_species),] mdf2[13:22] <- list(NULL) mdf2[1:10] <- list(NULL) mdf2[2:10] <- list(NULL) mdf2$accession <- NULL

mdf=psmelt(SUBSET.trans) mdf$genus <- factor(mdf$genus) p = ggplot(mdf, aes_string(x="Sample", y="Abundance", fill = "genus", order = "genus"))+theme_bw(base_size = 12) p = p + geom_bar(stat = "identity", position = "stack") p = p + theme(axis.text.x = element_text(angle = -90, hjust = 0,size=0),axis.text.y = element_text(hjust = 0,size=8))+theme(panel.spacing = unit(0, "lines")) p = p + facet_grid(.~sample_species, scales = "free",space="free",margins = F)+ theme(legend.position="bottom")+scale_fill_manual(values = colors, na.value="#b01a4c") p = p + theme(strip.text = element_text(size = 10)) p = p + theme(axis.title = element_text(size = 12)) p

### 5 ORDINATIONS ON JACCARD (PRESENCE/ABSENCE) AND BRAY-CURTIS (RELATIVE ABUNDANCE) DISTANCES ###

# CREATING ECOLOGICAL DISTANCES

JACC <- sqrt(vegdist(data.frame(otu_table(SUBSET)),method = "jaccard", binary = T)) JACC[1:10]

BRAY <- sqrt(vegdist(otu_table(SUBSET), method = "bray")) BRAY[1:10]

# ORDINATE PCOA

JACC_ORD_PCOA <- ordinate(SUBSET,distance = JACC, method = "PCoA") BRAY_ORD_PCOA <- ordinate(SUBSET,distance = BRAY, method = "PCoA") summary(JACC_ORD_PCOA) # PLOT PCOA colors_pcoa <- c("#9a02e0","#ffd500","#eb2865") plot_ordination(SUBSET, JACC_ORD_PCOA, color = "species")+scale_color_manual(values = colors_pcoa)+stat_ellipse(geom = "polygon", type = "norm", alpha = 0.1) plot_ordination(SUBSET, BRAY_ORD_PCOA, color = "species")+scale_color_manual(values = colors_pcoa)+stat_ellipse(geom = "polygon", type = "norm", alpha = 0.1)

### 6 PERMANOVA ANALYSIS (PERMUTATIONAL ANALYSIS OF VARIANCE) + PERMDISP + TUKEY

# JACCARD

PERMANOVA_JACC <- adonis(JACC~DF$species) PERMANOVA_JACC

# BRAY

PERMANOVA_BRAY <- adonis(BRAY~DF$species) PERMANOVA_BRAY

# PERMDISP JACC

PERMDISP_JACC <- betadisper(JACC, DF$species, type = c("median","centroid")) PERMDISP_JACC

TUKEY_JACC <- TukeyHSD(PERMDISP_JACC) TUKEY_JACC

# PERMDISP BRAY

PERMDISP_BRAY <- betadisper(BRAY, DF$species, type = c("median","centroid")) PERMDISP_BRAY

TUKEY_BRAY <- TukeyHSD(PERMDISP_BRAY) TUKEY_BRAY

## MVABUND abu <- data.frame(otu_table(SUBSET)) abu <- mvabund(abu) SD <- data.frame(sample_data(SUBSET)) mod_nb <- manyglm(abu~species, data = SD, family = 'negative.binomial') mod_nb2 <- manyglm(abu~1, data = SD, family = 'negative.binomial') mod_nb_aov <- anova(mod_nb, mod_nb2, p.uni = 'adjusted', test = 'LR') mod_nb_aov$table

DF.uni <- data.frame(t(mod_nb_aov$uni.p), t(mod_nb_aov$uni.test)) names(DF.uni) <- c("na","p","na2","dev") SIG <- DF.uni[DF.uni$p<0.05,] SUBSET.SIG <- transform_sample_counts(SUBSET, function(x) x/sum(x)) SUBSET.SIG.SUB <- prune_taxa(taxa_names(SUBSET.SIG)%in%rownames(SIG),SUBSET.SIG) sum(otu_table(SUBSET.SIG.SUB))/sum(otu_table(SUBSET.SIG)) ### 8 PROPORTION OF READS ###

Proportions<-as.numeric() OTU<-as.character() Host<-as.character() for(i in 1:dim(otu_table(SUBSET.SIG.SUB))[2]) { Proportions<-c(Proportions,as.numeric(otu_table(SUBSET.SIG.SUB)[,i])) OTU<-c(OTU,rep(as.character(taxa_names(SUBSET.SIG.SUB)[i]), dim(otu_table(SUBSET.SIG.SUB))[1])) Host<-c(Host,as.character(data.frame(sample_data(SUBSET.SIG.SUB))[,8])) }

DF2<-data.frame(Proportions,OTU,Host) DF2

Proportions <- ggplot(data=DF2, aes(y=Proportions,x=Host)) +geom_jitter(color="black",size=1.5,alpha=0.5,width=0.25)+theme_bw(base_size = 13, base_line_size = 0.5, base_rect_size = 0.1)+scale_y_continuous(name="Proportion of reads")+ facet_grid(OTU~.,scales = "free_y")+theme(strip.text = element_text(size=8)) Proportions

### 9 HEATMAP ### # BETTER ORDER (GENUS!!!)

OTU_TAB <- t(data.frame(otu_table(SUBSET.trans))) Annotation <- data.frame(as.character(sample_data(SUBSET.trans)$species)) names(Annotation) <- c("Species") Annotation.tax <- data.frame(as.character(tax_table(SUBSET.trans)[,7])) names(Annotation.tax) <- "Genus" str(Annotation.tax)

# ORDERING Annotation.tax2 <- Annotation.tax[order(Annotation.tax$Genus),] Annotation.tax2 c25 <- c("#9a02e0","#ffd500","#eb2865","#1456b3","#f08a0e","#23e016","#f70cd8","#04fff7", "#efff0c") COL=rev(heat.colors(100)) aheatmap(t(log10(otu_table(SUBSET.trans)+0.0001)),color=COL, distfun = 'canberra', annCol=Annotation,annRow = Annotation.tax2,hclust="ward",annColors =list(c25[1:9],c25[4:9]), Rowv = NA)

### --- THE END --- ###