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Author: Alzaylaee, Hind A O Title: Molecular approaches to improve understanding of the distribution of human-infecting Schistosoma species

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Your claim will be investigated and, where appropriate, the item in question will be removed from public view as soon as possible. Molecular approaches to improve understanding of the

distribution of human-infecting Schistosoma species

Hind Alzaylaee

A dissertation submitted to the University of Bristol in accordance

with the requirements of the degree of Doctor of Philosophy (PhD)

in the Faculty of Life Sciences

School of Biological Sciences, March 2020

Word Count: 36,090

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Abstract

It is estimated that over 200 million people are at risk from schistosomiasis, a Neglected Tropical

Disease caused by Schistosoma trematode parasites that has significant health and socioeconomic consequences. To enable the successful control and eradication of this disease, it is crucial to develop and employ accurate diagnostics of Schistosoma trematodes in the natural environment. The overarching aims of the work for this thesis were i) to assess the potential of environmental DNA

(eDNA)-based approaches to assess the distribution and abundance of schistosomes, and ii) to investigate the utility of eDNA to enable a broader understanding of the ecology of the freshwater communities where schistosomes are found. Environmental DNA-based tests for the human-infecting schistosomes S. mansoni and S. haematobium are reported. These are shown to exhibit a high-level of species specificity, especially the S. mansoni assay, and shown to be capable of detecting schistosomes in natural freshwater environments. A novel eDNA xenomonitoring approach to detect the schistosome infection in intermediate host snails is reported. The results correspond strongly with those obtained from direct tests of snails infection status from molecular analyses of their tissue samples, suggesting eDNA-based xenomonitoring could be a powerful surveillance tool for assessing infection status of host snail populations. A metabarcoding study of biological diversity within African freshwaters is reported. The results highlight the potential of eDNA metabarcoding for the characterization of metazoan communities from different habitats, but clearly demonstrate the need for very high levels of sequence read coverage and/or taxon-specific primers to more fully describe the biological communities present. Overall, the results presented support the concept that eDNA- based approaches are useful for monitoring schistosome presence in endemic freshwaters, potentially enabling enhanced detection of schistosomiasis infection risk, and evaluating the success of interventions to control and eliminate this disease.

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Dedication

I dedicate my thesis and PhD journey to my loving parents. I am extremely grateful for the unconditional love and support they have shown me, as well as the countless sacrifices they have made to support my development. In fact, without their compassion and encouragement, this achievement would not have been possible. Long may their love cultivate my success.

I would have been very happy if I could share this moment with my father and older brother but, tragically, they passed away during my PhD study. It was a particularly hard time for me, but I am convinced that their souls are always by my side. I am absolutely certain that I have made them both proud and I will not forget them in my prayers. I would also like to thank my family, brothers and sisters for their support, prayers and for not leaving my side.

I also dedicate my work to my dear husband and would like to specially thank him for his love, faith, understanding, acceptance, prayers and constant support to pursue my dreams. He unconditionally supports me in all my endeavours and motivates me to do better. Without his patience for the past two years, I would not have been to complete my PhD. I would further like to thank him for teaching me to believe in God, in myself, and in my dreams.

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Acknowledgments

I wish to express sincere gratitude to my research supervisor, Prof. Martin Genner, for giving me the opportunity to carry out my PhD research under his kind supervision. His constant encouragement and valuable guidance have been instrumental for carrying out this research project. He taught me the methodology I used as well as the best ways to carry out and present one's research with clarity and impact. I also express my thanks to Prof. Eric Morgan for his help, guidance and knowledge. Both of them have been crucially enabling for broadening my knowledge by attending courses, workshops and conferences.

I am grateful to my sponsors, Princess Nourah Bint Abdul Rahman University and Saudi Arabia Culture

Bureau in UK, for funding my scholarship and giving me the opportunity to study abroad. I extend a special thanks to Dr. Rupert Collins for helping me in the laboratory and providing essential advice during my research process, as well Prof Richard Wall for his support. I would like to thank Prof. Russell

Stothard, Dr. Gabriel Rinaldi, Dr. Benjamin Ngatunga, Dr Asilatu Shechonge and Zikmund Bartonicek for providing me with the samples for my study. I am thankful to my research group, especially Carlos for his help, as well as Pavrally, Girardo and Sandra. Dr. Martha Betson was kind enough to let me train in her lab at University of Surrey and taught me the necessary molecular skills for my lab work, for which I am very grateful.

I thank my friend Maryam, who was always there when I needed and made the PhD journey more enjoyable, and Mona and Shatha, with whom I have had many informative and motivating conversations with.

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Author’s Declaration

I declare that the work in this dissertation was carried out in accordance with the requirements of the

University's Regulations and Code of Practice for Research Degree Programmes and that it has not been submitted for any other academic award. Except where indicated by specific reference in the text, the work is the candidate's own work. Work done in collaboration with, or with the assistance of, others, is indicated as such. Any views expressed in the dissertation are those of the author.

SIGNED: DATE: 02.03.2020

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Table of Contents

Abstract ………………………………………………………………………………………………………………………………… ii

Dedication …………………………………………………………………………………………………………………………….. iii

Acknowledgements ………………………………………………………………………………………………………………. i v

Author’s declaration ……………………………………………………………………………………………………………… v

Table of contents ………………………………………………………………………………………………………………….. vi

List of figures ………………………………………………………………………………………………………………………… x

List of tables …………………………………………………………………………………………………………………………. xii

Abbreviations………………………………………………………………………………………………………………………… xiii

Publications …………………………………………………………………………………………………………………………… xv

Chapter 1: General Introduction………………………………………………………………………………………… 1

1.1 Schistosomiasis …………………………………………………………………………………………………………..…… 2

1.2 The biology of schistosomes and their host species ………………………………………………………… 2

1.2.1 Species and their definitive host …………………………………………………………………………………… 2

1.2.2 Schistosoma (life cycle) ………………………………………………………………………………………………… 3

1.2.3 Intermediate host – freshwater snails ………………………………………………………………………….. 5

1.3 The distribution of Schistosoma and the epidemiology of schistosomiasis ………………………. 15

1.4 Pathology and morbidity of schistosomiasis …………………………………………………………...... 18

1.4.1 Cercarial dermatitis ………………………………………………………………………………………………………. 19

1.4.2 Acute schistosomiasis (Katayama syndrome) ……………………………………………………………….. 19

1.4.3 Chronic schistosomiasis ………………………………………………………………………………………………… 20

1.5 Treatment of schistosomiasis ………………………………………………………………………………………….. 22

1.6. Control strategies …………………………………………………………………………………………………………… 24

1.7 Diagnosis of schistosome presence in the natural environment ………………………………………. 27

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1.7.1 Molecular techniques …………………………………………………………………………………………...... 28

1.7.2 Environmental DNA ……………………………………………………………………………………………………… 28

1.8 Aims ………………………………………………………………………………………………………………………………… 35

Chapter 2: Schistosoma species detection by environmental DNA assays in African 36 freshwaters ………………………………………………………………………………………………………………………

Abstract ……………………………………………………………………………………………………………………………….. 37

2.1 Introduction ……………………………………………………………………………………………………………………. 38

2.2 Materials and Methods …………………………………………………………………………………………………… 40

2.2.1 Primer and probe design ………………………………………………………………………………………………. 40

2.2.2 Specificity of S. mansoni, S. haematobium and S. japonicum assays ……………………………… 41

2.2.3 Sensitivity of S. mansoni and S. haematobium assays …………………………………………………… 42

2.2.4 Environmental DNA from aquarium water samples………………………………………………………. 44

2.2.5 Environmental DNA from the natural environment ………………………………………………………. 45

2.3 Results …………………………………………………………………………………………………………………………….. 47

2.3.1 Environmental DNA assays exhibit high levels of species specificity ……………………………… 47

2.3.2 High sensitivity of S. mansoni and S. haematobium eDNA assays …………………………………. 47

2.3.3 Environmental DNA assays detected schistosomes from aquarium water samples ………. 48

2.3.4 Environmental DNA detected the presence of schistosomes from the natural 48 environment ………………………………………………………………………………………………………………………….

2.4 Discussion ……………………………………………………………………………………………………………………….. 60

2.5 Conclusions ……………………………………………………………………………………………………………………… 65

Acknowledgments ………………………………………………………………………………………………………………… 66

Supplementary Information ………………………………………………………………………………………………….. 67

Chapter 3: Environmental DNA-based xenomonitoring for determining Schistosoma 70 presence in tropical freshwaters ………………………………………………………………………………………..

Abstract ………………………………………………………………………………………………………………………………… 71

3.1 Introduction ……………………………………………………………………………………………………………………. 72

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3.2 Materials and Methods …………………………………………………………………………………………………… 74

3.2.1 Site description …………………………………………………………………………………………………………….. 74

3.2.2 Experimental design and sample collection …………………………………………………………………… 75

3.2.3 DNA extraction from Sterivex filters ……………………………………………………………………………… 77

3.2.4 DNA extraction from molluscs ………………………………………………………………………………………. 77

3.2.5 eDNA assay of samples …………………………………………………………………………………………………. 77

3.2.6 Data analysis ………………………………………………………………………………………………………………… 79

3.3 Results …………………………………………………………………………………………………………………………….. 79

3.4 Discussion ……………………………………………………………………………………………………………………….. 87

3.5 Conclusions ……………………………………………………………………………………………………………………… 92

Acknowledgments ………………………………………………………………………………………………………………… 92

Supplementary Information …………………………………………………………………………………………………. 93

Chapter 4: Uncovering the biodiversity of African freshwaters using COI metabarcoding of 98 environmental DNA ………………………………………………………………………………………………………….

Abstract ………………………………………………………………………………………………………………………………… 99

4.1 Introduction ……………………………………………………………………………………………………………………. 100

4.2 Materials and Methods …………………………………………………………………………………………………… 102

4.2.1 Site collection ……………………………………………………………………………………………………………….. 102

4.2.2 DNA extraction from filters and metabarcoding PCR amplification ………………………………. 103

4.2.3 Illumina library preparation and High-throughput sequencing (MiSeq) ………………………… 104

4.2.4 Data processing ……………………………………………………………………………………………………………. 104

4.2.5 Taxonomic assignments ……………………………………………………………………………………………….. 105

4.2.6 Read count and taxon richness …………………………………………………………………………………….. 105

4.2.7 Spatial structure in community composition ………………………………………………………………… 105

4.3 Results …………………………………………………………………………………………………………………………….. 106

4.3.1 MiSeq sequencing and Taxonomic assignments …………………………………………………………….. 106

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4.3.2 Sampling effort and diversity patterns …………………………………………………………………………... 107

4.3.3 Metabarcoding and important taxa in different transmission …………………………………………. 108

4.4 Discussion ……………………………………………………………………………………………………………………….. 119

4.4.1 Characterisation of unseen freshwater diversity …………………………………………………………… 119

4.4.2 Assignment biases ………………………………………………………………………………………………………… 119

4.4.3 Spatial variation in freshwater communities ………………………………………………………………… 120

4.4.4 Potential biases in sampling methods …………………………………………………………………………… 120

4.4.5 Reliability of eDNA for identifying species of interest for health or conservation …………. 121

4.4.6 Prospects for eDNA-based characterisation of freshwater assemblages ………………………. 122

4.5 Conclusions ……………………………………………………………………………………………………………………… 123

Acknowledgments ………………………………………………………………………………………………………………… 123

Supplementary Information …………………………………….……………………………………………………………. 124

Chapter 5. General discussion ………………………………………………………………………………………….. 127

References ………………………………………………………………………………………………………………………… 136

Appendix I ………………………………………………………………………………………………………………………… 166

Appendix II ..……………………………………………………………………………………………………………………… 186

ix

List of Figures

Figure 1.1 The life cycle of Schistosoma species 5

Figure 1.2 Shell morphology of Biomphalaria group 10

Figure 1.3 Shell morphology of the Bulinus group 14

Figure 1.4 The global distribution of Schistosomiasis 16

Figure 1.5 Swimmer's itch 20

Figure 2.1 Maximum likelihood phylogenetic reconstruction of the mtDNA 16S sequences 56 used for the establishing the specificity of the assays designed for S. haematobium, S. mansoni and S. japonicum. Numbers above branches represent the branch support (proportion of 100 bootstrap replicates).

Figure 2.2 Agarose gel image of the amplification test using conventional PCR. The newly 57 designed primers were used in PCR reactions for: a) S. haematobium, b) S. mansoni and c) S. japonicum. Good levels of amplification were observed for the products of the expected sizes (104 bp for S. mansoni, 143bp for S. haematobium and 87 bp for S. japonicum. No cross amplification was present between three species.

Figure 2.3 Locations of eDNA sampling sites in the Mbeya Region of Tanzania in 2018. In total 58 eight sites were surveyed, labelled A-H. The key results from eDNA survey (helix symbol), host snail survey (snail symbol) and test for the presence of schistosomes in snail tissue (cercariae symbol) are given for S. haematobium (H) and S. mansoni (M) assays, with + indicating the site was positive, while a dot indicates no detection was made. Map drawn using the following open source software and data: DIVA-GIS7.5 (https://www.diva-gis.org); Africa river network (15 sec resolution), African drainage basin (15sec resolution) and African Digital Elevation Model data (30 sec resolution) from HydroSHEDS (https://www.hydrosheds.org); African Water Bodies shapefile from RCMRD GeoPortal (http://geoportal.rcmrd.org).

Figure 2.4 Probability of qPCR amplification of DNA standards of concentrations ranging from 59 1 copy/μl to 1,000,000 copies/μl. a) S. mansoni assay – probability of amplification in any one qPCR across all standard templates tested. b) S. mansoni assay – probability of amplification in any one standard template that is subject to triplicate qPCR, c) S. haematobium assay – probability of amplification in any one qPCR across all standard templates tested, d) S. haematobium assay – probability of amplification in any one standard template that is subject to triplicate qPCR. Lines represent logistic models of the form y = a / (1 + b*e(-cx)). a) a = 1.002, b = 27.303, c = 3.826. b) a = 1.000, b = 7.501, c = 4.786. c) a = 1.004, b = 1.009, c = 1.415. b) a = 1.004, b = 0.342, c = 1.399. All models r > 0.995. Grey shading indicates 95% confidence intervals.

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Figure 3.1 Overview of steps in the eDNA-based xenomonitoring assay, from sampling to 85 analysis. Validation of the assay is conducted using qPCR analysis of tissue from preserved snails.

Figure 3.2 Associations between eDNA copies and infected snails in experimental containers. 86 a Schistosoma haematobium copies and number of infected host snail individuals. b S. haematobium copies and biomass of infected host snail individuals. c S. mansoni copies and number of infected host snail individuals. d S. mansoni copies and biomass of infected host snail individuals. Lines illustrate linear models of associations between the variables, with 95% confidence intervals.

Figure 4.1 Distribution of 61 collection localities across sub-Saharan Africa of samples 109 included in the main analysis. For full details of the sample collection localities see Table 4.1.

Figure 4.2 Total number of reads and OTUs (≥70% probability) for taxonomic groupings 110 within (a,d) Superkingdoms, (b,e) Kingdoms and (c,f) Phylq. *dominated by Rhodophyta, Bacillariophyceae, Oomycetes, Phaeophyceae, Raphidophyceae, Spirotrichea ** dominated by Oomycetes, Phaeophyceae, Raphidophyceae, Spirotrichea

Figure 4.3 Total number of reads and OTUs (≥70% probability) for taxonomic groupings 111 within Class and Order.

Figure 4.4 Probability of OTUs in each Phylum being assigned to finer taxonomic levels. 112

Figure 4.5 Associations between the number of reads in a sample and the number of OTUs 113 recovered for six taxonomic groupings. For statistical significance of associations see main text.

Figure 4.6 Non-metric Multi-Dimensional Scaling ordinations for six taxonomic groups, based 114 on the presence or absence of OTUs (as identified as belonging to group with 70+% sequence identity).

Figure 4.7 Non-native species identified in Tanzanian freshwaters using a complete match to 115 reference COI sequences a) redbelly tilapia (Coptodon zillii) native to North Africa was present in Lake Sagera (Tanzania), b) largemouth bass (Micropterus salmoides) native to North America was present in a man-made dam in Vwawa, Tanzania. Habitat photos are from the locations, while fish photos are from elsewhere in their introduced range (Lake Naivasha, Kenya).

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List of Tables

Table 1.1 Classification of African Biomphalaria, updated from (Mandahl-Barth, 1957) 9

Table 1.2 Classification of Bulinus, updated from Mandahl-Barth (1957) 13

Table 2.1 Species-specific primers and probes designed for PCR amplification of the 16S 50 rRNA mitochondrial gene from three human-infecting Schistosoma species

Table 2.2 Tests of S. mansoni, S. haematobium and S. japonicum qPCR assays on the 51 genomic (gen) and synthetic (syn) DNA of Schistosoma species

Table 2.3 Ten-fold serial dilutions used in the S. mansoni and S. haematobium assays. In 52 total these reflect the results of 17 S. mansoni assays, and 12 S. haematobium assays, with mean CT efficiency and r2 range.

Table 2.4 Detection of S. mansoni DNA from aquarium water samples 53

Table 2.5 Sampling locations and environmental characteristics of eDNA from Tanzania in 54 2018. – indicates no data, abs = host snails absent

Table 2.6 Results of qPCR detection assays of both S. mansoni and S. haematobium eDNA 55 from water samples collected in Tanzania 2018

Table 3.1 Summary of the number of replicates in each treatment, and the numbers of snails 81 in used in each of the treatments A-D (that were all housed in water from Site 1)

Table 3.2 Details of species-specific assays for S. mansoni and S. haematobium 82

Table 3.3 Results of S. mansoni and S. haematobium DNA experimental analyses 83

Table 3.4 Summary linear models, predicting the number of eDNA copies/µl (log10 84 transformed)

Table 4.1 Details of sampling events, and the number of reads and OTUs assigned to each 116 sample following bioinformatic processing.

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Abbreviations

AmpR Ampicillin resistance gene BOLD Barcode of life data system COI Mitochondrial cytochrome c oxidase 1 gene COSTECH Tanzania commission for science and technology cPCR Conventional PCR Ct Cycle threshold Cq Cycle quantification DALYs Disability-adjusted life years DNA Deoxyribonucleic acid dNTPs Deoxynucleotide triphosphates EB Elution buffer eDNA Environmental DNA GIS Geographic information analysis Gen Genomic GPS Global positioning system GR Gabriel Rinaldi HIV Human immunodeficiency virus IDT Integrated DNA technologies LAMP Loop-mediated isothermal amplification LOD Limit of detection

LOQ Limit of quantification

MgCl2 Magnesium chloride MJG Martin J Genner NCBI National center for biotechnology information NIAID National institute of allergy and infectious diseases NMRI Naval Medical Research Institute NTD Neglected tropical disease OTU Operational taxonomic unit PAST PAlaeontological STatistics PCR Polymerase chain reaction PZQ Praziquantel qPCR Real-time PCR RCMRD Regional centre for mapping resource for development RNA Ribonucleic acid

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16S rRNA 16S Ribosomal ribonucleic acid SCI Schistosomiasis control initiative Syn Synthetic DNA UV light Ultraviolet light WHA World health assembly WHO World health organization WSI Wellcome Sanger Institute HKY Hasegawa Yano model

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Publications

Alzaylaee, H., Collins, R.A., Rinaldi, G., Shechonge, A., Ngatunga, B.P., Morgan, E.R. and Genner, M.J. (2020). Schistosoma species detection by environmental DNA assays in African freshwaters. PloS Neglected Tropical Diseases, 14, e0008129.

Alzaylaee, H., Collins, R.A., Shechonge, A., Ngatunga, B., Morgan, E.R. and Genner, M.J. (2020). Environmental DNA-based xenomonitoring for determining Schistosoma presence in tropical freshwaters. Parasites and Vectors, 13, 63.

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Chapter 1: General Introduction

Chapter 1

General Introduction

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Chapter 1: General Introduction

1.1 Schistosomiasis

Schistosomiasis, also known as snail fever, is a Neglected Tropical Disease (NTD) with many important medical and economic implications. The global burden of schistosomiasis is estimated to be between

1.7 and 4.5 million disability-adjusted life years (DALYs; the number of years lost due to ill-health, disability or early death) (Utzinger and Keiser, 2004; Steinmann et al., 2006). Sub-Saharan Africa is the most burdened region for schistosomiasis, with the highest prevalence and infection intensity appearing in school-age children, adolescents and young adults (Gryseels et al., 2006; Colley et al.,

2014). Very often infection of adolescents is as high as 60–80%, with steadily decreases toward adulthood (20–40% of adults) (Colley et al., 2014). However, prevalence may also be high among adults that regularly enter freshwater environments for activities such as fishing, washing clothes, bathing and washing cars. Therefore, long-term residents of locations where schistosomes are present invariably have a high risk of becoming infected with schistosomes at some stage of their lives.

1.2 The biology of schistosomes and their host species

1.2.1 Species and their definitive host

Schistosomiasis is caused by trematode blood flukes that belong to the phylum Platyhelminthes, family Schistosomatidae, and genus Schistosoma. Representatives of the genus use birds and mammals as definitive hosts, including humans (Kolarova et al., 2013). The three species that primarily infect humans are Schistosoma mansoni, Schistosoma haematobium and Schistosoma japonicum

(Gryseels et al., 2006; Colley et al., 2014). Schistosoma mansoni is widespread across African countries and is also present in South America, Middle Eastern areas and Madagascar, and causes intestinal schistosomiasis. S. haematobium is restricted to the Middle East, some African regions (Mauritius,

Madagascar, and Zanzibar) and some islands of the Indian Ocean and is associated with urinary schistosomiasis. S. japonicum is commonly found in Indonesia, the Philippines and China, and causes

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Chapter 1: General Introduction

intestinal schistosomiasis (Gryseels et al., 2006; Colley et al., 2014). Additional species that infect humans include Schistosoma intercalatum that is prevalent in Central and West Africa, and

Schistosoma mekongi that is endemic to the Mekong River basin (Figure 1.4) (Gryseels et al., 2006).

The intermediate snail hosts of these species can be found in a wide range of water bodies, including man-made water channels, small ponds, seasonal pools, streams, wetlands, rice paddies, rivers, lake margins and irrigation canals (Gryseels et al., 2006; Falade and Otarigho, 2015). The disease schistosomiasis is particularly prevalent in African countries, where 97% of all infections occur

(Steinmann et al., 2006).

1.2.2 Schistosoma (life cycle)

Schistosomes have a complex digenetic life cycle, involving asexual reproduction in the intermediate host (freshwater snails) and sexual reproduction in the definitive host (bloodstream of humans)

(Figure 1.1). Adult worms (males and females) commonly live in the veins of the definitive host where they mate and lay eggs. In the case of S. mansoni and S. japonicum, veins around the intestines are affected, whereas in S. haematobium veins around the urogenital apparatus are used, specifically those near the kidney and bladder (Colley et al., 2014; Uberos and Jerez-Calero, 2012). Adult schistosomes are white or greyish in colour, ranging in length from 7–20 mm. They are bilaterally symmetrical, have a cylindrical body, and have oral and ventral suckers. These worms usually feed on globulins and other blood proteins through anaerobic glycolysis, discarding the debris in the blood of the host. Unlike other trematodes, Schistosoma species are unisexual with the females having thinner and longer bodies than the males, which are stouter and shorter. The females reside inside the body of the males in a groove called the gynaecophorical canal (Gryseels et al., 2006).

The adult females can breed and produce thousands of eggs per day (Gryseels et al., 2006), each containing a developing embryonic larva called a miracidium. These eggs then penetrate the walls of

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Chapter 1: General Introduction

the blood vessels, and depending on the Schistosoma species, migrate into the bladder (S. haematobium), or into the lumen of the intestine (S. mansoni and S. japonicum). From there they are released into the environment through urine (S. haematobium) or faecal matter (S. mansoni and S. japonicum) and can remain viable for up to seven days. In certain cases, the eggs can get trapped in the tissues of the host, which can lead to inflammation of the host’s organs (Warren, 1982; Colley et al., 2014). The eggs that succeed in reaching freshwater sources hatch to release the ciliated, free- living miracidia larvae. They swim about actively, seeking to find and enter an appropriate snail host

(Colley et al., 2014). Locating a suitable intermediate snail host is crucial for the schistosome miracidia to advance to the next phase of their life cycle.

Inside the snail host, each miracidium develops into multicellular sporocysts (rediae), which proliferate asexually and produce cercariae (the infective stage for the human host). These are released back into the water, typically 4-6 weeks after initial infection of the snail. The cercariae can stay alive for up 72 hours in water, searching for definitive hosts (Gryseels et al., 2006; Colley et al.,

2014). Light plays a key role in stimulating cercarial shedding, as well as seeking of the next host. An individual snail can shed thousands of cercariae every day for months (Gryseels et al., 2006). On finding a suitable definitive host, the cercariae penetrate through the skin, lose their tails and develop into schistosomulae (young worms) (Gryseels et al., 2006; Colley et al., 2014). Infected hosts usually develop immediate symptoms like rashes or itchy skin after this penetration, later symptoms include coughing, chills and fever. The schistosomulae take a few days to pass into the venous system of the liver via the lungs, where they feed and develop further (Gryseels et al., 2006). The young worms mature into adults (female or male) within 4–6 weeks and these adult pairs then penetrate and inhabit the veins surrounding the urogenital system (S. haematobium) or intestines (S. mansoni, S. japonicum), where the cycle commences again (Gryseels et al., 2006; Colley et al., 2014). The average lifespan of an adult schistosome is 3–5 years (Gryseels et al., 2006).

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Chapter 1: General Introduction

Figure 1.1 The life cycle of Schistosoma species. Image is public domain, available from: https://commons.wikimedia.org/wiki/File:Schistosomiasis_Life_Cycle.png

1.2.3 Intermediate host – freshwater snails

Freshwater gastropods are the subject of considerable research in Africa, primarily because of their role as intermediate hosts during the life cycles of multiple disease-causing trematodes (Brown, 1994).

As intermediate hosts, they harbour the larval stages of digenetic trematodes, namely the rediae and cercariae (Arshad et al., 2011). The involvement of snails in the transmission of schistosomiasis, fasciolosis and other trematode-caused infections contributes to serious public health problems in people and livestock in tropical and subtropical regions of the world (Stephenson and Holland, 1987;

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Chapter 1: General Introduction

Gryseels, 1989; Tanner, 1989; Brown, 1994; King and Dangerfield-Cha, 2008; Espinoza et al., 2010).

Notably, substantive agricultural losses have been attributed to schistosomiasis and fascioliasis in livestock, as infection can lead to enhanced animal mortality and reduced production (Falade and

Otarigho, 2015).

The number of snails that release cercariae (prevalence of infection) and the number of cercariae released from each infected snail (intensity of infection) are critical factors in the transmission of trematodes (Tigga et al., 2014). The size and age of snails, as well as temperature occupancy, light conditions and depth of water are some of the factors that may affect the intensity and prevalence of trematode infections in the snails (Fingerut et al., 2003; Graham, 2003). Notably, each of the human- infecting Schistosoma species infects different gastropod lineages, with S. haematobium targeting

Bulinus species (family Planorbidae), S. mansoni targeting Biomphalaria species (family Planorbidae), and S. japonicum utilising Oncomelania hupensis (family Pomatiopsidae) (Colley et al., 2014).

Planorbidae

Snails that belong to the Planorbidae tend to be small to medium-sized (< 2 cm height), with a discoidal or lens-shaped shell with internal septa (lamella) in some species. The excretory and genital openings lie on the left side of the animal (Brown, 1994). These snails can be observed in a wide range of natural and man-made freshwater bodies, including streams, wetlands, rice paddies, small ponds, seasonal pools, rivers, lakes and irrigation canals (Woolhouse and Chandiwana, 1989; Bandoni et al., 1990;

Akufongwe et al., 1995; Sturrock et al., 2001; Ayanda, 2009; Falade and Otarigho, 2015). The temperature requirements and habitat preferences of species of Biomphalaria and Bulinus can differ markedly (Brown, 1994). This can drive large-scale differences in the distribution of the species, but also differences in local conditions such as water depth, oxygenation, shading, and the density of aquatic vegetation can lead to considerable local variation in the abundance of snails (Khallaayoune

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Chapter 1: General Introduction

et al., 1998; Boelee and Madsen, 2006). Although high rates of transmission can be found in both natural and man-made water sources, transmission of schistosomiasis is often significantly increased in man-made water bodies. This is typically because of the presence of high-density planorbid snail populations (Birley, 1991; Boelee and Madsen, 2006), which can be a result of the abundance of aquatic plants or an absence of natural gastropod predators. Efforts to control planorbid snail populations can include the removal of aquatic macrophytes, temporary drying out canals and structures, and application of pesticides. In some cases, this can be successful, but such interventions can affect snail populations in ways that are not always predictable (Boelee and Madsen, 2006).

Genus Biomphalaria

Species in the genus Biomphalaria are the intermediate hosts of intestinal schistosomiasis caused by

S. mansoni (Colley et al., 2014; Standley et al., 2012), and so the abundance of Biomphalaria present can be a key factor determining the distribution and intensity of infection S. mansoni. Twelve of 34 extant species of Biomphalaria are Old World species, restricted to a geographical range encompassing most of sub-Saharan Africa, Madagascar, northeast Africa and parts of the Middle East

(the remaining species are found in the Americas). All of the twelve Old World species are susceptible to S. mansoni infection, although it is also present in Biomphalaria species in South and Central

America (Brown, 1994; Chitsulo et al., 2000; Brooker, 2002). The African species are placed into four species groups (the B. pfeifferi, B. choanomphala, B. alexandrina and B. sudanica groups), based on morphological characteristics of the shell, radula and copulatory organ (Mandahl-Barth, 1957a). (Table

1.1; Figure 1.2). Biomphalaria species tend to be present in slightly turbid water with moderate organic content and a substrate rich in organic matter (Levitz et al., 2013). However, it is thought that each species has its own specific ecological requirements. Fully grown snails in this genus tend to be medium sized, up to 22 mm in diameter (Brown, 1994).

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Chapter 1: General Introduction

a) Biomphalaria pfeifferi group. The shell is typically up to 5.2 mm wide and 13 mm high (reaching 17 mm high). The B. pfeifferi group is distributed across northern and central Africa as far as South Africa.

However, it is notably absent from the coastal area extending from Somalia to northern Mozambique

(Brown, 1994). The group is found in streams and a variety of man-made waterbodies, including dams, irrigation channels and swimming pools (Brown, 1994; De-Kock et al., 2004), but is typically absent from large swamps and small seasonal pools. It is most important intermediate host for S. mansoni in

Africa and it is reported to be the intermediate host for S. rodhaini in the Democratic Republic of

Congo, Kenya, Rwanda, Burundi and Ethiopia (Brown, 1994).

b) Biomphalaria sudanica group. The shell is typically maximally 4.2 mm wide and 16mm high

(reaching 22 mm high). It is commonly found in Uganda, the Democratic Republic of Congo, the Lake

Tanganyika basin, Lake Edward and the Sudan northwards to Shambat. It is typically distributed along lake shores and in inland swamps and rivers that are typically rich with aquatic vegetation (Stensgaard et al., 2013). It is an intermediate host for S. mansoni in many areas across its range, and host for S. rodhaini in Uganda and the Democratic Republic of Congo (Brown, 1994).

c) Biomphalaria choanomphala group. This species group has representatives that are comparatively small with shells typically reaching 4.2 mm wide and 9.7 mm high (Brown, 1994). It is distributed in

East Africa, including Lake Victoria, the Victoria Nile, Lake Kyoga, Lake Albert and the other lakes of the Albertine Rift (Mandahl-Barth, 1957a). One species in the group (B. stanleyi) has been recorded in

Lake Chad. Species have been found on gravel and soft sedimentary rock at depths of 2–3 m offshore from sandy beaches where higher plants were absent. They are also often found offshore on mixed substrata of mud and sand, or on stones at the water’s edge. It is an intermediate host for S. mansoni around densely-populated areas of Lake Victoria, such as Mwanza, Bukoba and near Entebbe (Brown,

1994).

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Chapter 1: General Introduction

(D) Biomphalaria alexandrina. Species in this group have a shell reaching 4.8 mm wide and 14.2 mm high. This species in the group have a Nilotic distribution, being present in Egypt and Sudan (Brown,

1994; Mandahl-Barth, 1957a). They occupy slowly flowing irrigation channels and somewhat brackish lakes near outflows from freshwater drains. In Sudan, they prefer steady conditions with fairly dense aquatic vegetation. It is intermediate host for S. mansoni in Egypt and might contribute transmission in Sudan (Brown, 1994).

Table 1.1 Classification of African Biomphalaria, updated from Mandahl-Barth (1957).

B. pfeifferi group B. choanomphala group B. pfeifferi (Krauss, 1848) B.barthi (Brown, 1973) B. rhodesiensis Mandahl-Barth, 1957 B. choanomphala (Martens, 1879) B. germaini (Ranson, 1953) B. smithi (Preston, 1910) B. stanleyi (Smith, 1888)

B. alexandrina group B. sudanica group B. alexandrina (Ehrenberg, 1831) B. camerunensis (Boettger, 1941) B. angulosa (Mandahl-Barth, 1957) B. sudanica (Martens, 1870) B. salinarum (Morelet, 1868) B. tchadiensis (Germain, 1904)

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Chapter 1: General Introduction

Biomphalaria pfeifferi Biomphalaria sudanica

Biomphalaria choanomphala Biomphalaria alexandrina

Figure 1.2 Shell morphology of Biomphalaria group. Image is public domain, available from: https://commons.wikimedia.org/wiki/Main_Page and https://www.biolib.cz/en/image/id371525/

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Chapter 1: General Introduction

Genus Bulinus

The shell is sinistral, with a spire which is varied in its height and shape relative to the aperture. The whorls might be evenly curved and blunt, angular or rarely carinate. The genus Bulinus comprises four groups: B. forskalii, B. africanus, B. reticulatus and the B. truncatus/tropicus complex (Table 1.2; Figure

1.3). Thirty-seven extant species that constitute these four groups have been identified. All species are from the Old World (Africa, Madagascar and the Middle East) with representatives of the genus also present on Mediterranean islands and the Iberian Peninsula. Several species in this genus are known to act as intermediate hosts for larval stages of Schistosoma haematobium (Brown, 1994).

a) B. africanus group. Shells of these species are up to 24.5 mm high and 13.4 mm wide (Brown, 1994).

Species of the B. africanus group are confined to the Afrotropical region (Rollinson et al., 2001). It is distributed in Ethiopia, Kenya, Uganda, Tanzania, Zambia, Zaire, Mozambique, Rhodesia and south

Africa (Brown, 1994). This group present in a wide variety of waterbodies, but the highest numbers are found in rivers and streams (De-Kock and Wolmarans, 2005). This is the most medically significant group because most species are known to be intermediate hosts of S. haematobium (Mandahl-Barth,

1957b), and have high susceptibility to S. mattheei, a cause of bovine schistosomiasis (Brown, 1994).

b) The B. forskalii group. The shell is up to 17mm high and 5.4 mm wide. This group is characterised by slender shells and often high spires (Rollinson et al., 2001). The B. forskalii group is broadly common throughout Africa south of the Sahara, and is also prevalent in Egypt, Madagascar, the Arabian

Peninsula and adjacent islands. It is found in a wide range of natural and man-made waterbodies.

Species in this group are known to be intermediate hosts for several schistosome species, including S. haematobium. It is intermediate host of S. intercalatum in Cameroon and is reported to host S. bovis in East Africa (Mazigo et al., 2012).

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Chapter 1: General Introduction

c) B. reticulatus group. The shell of these snails is small, maximally 8.5 mm high x 6 mm wide. It has a patchy distribution in parts of east, central and southern Africa, such as Kenya, Ethiopia, Tanzania,

Mozambique, Zambia and Zimbabwe. The two species in this group tend to occupy highly seasonal ephemeral habitats such as small pools. B. reticulatus has been shown to be susceptible to S. haematobium and S. bovis infection in experimental conditions (Brown, 1994).

d) B. truncatus/tropicus complex. A mature shell of most forms is typically a maximum 10 mm in height. However, it can be grown as large as 20 mm in Egypt (Mandahl-Barth, 1957b). This group is distributed south of the Mediterranean countries, as far as the East African Great Lakes. It is primarily found in large lakes and rivers in tropical Africa. Interestingly, disease transmission for each species in the complex varies significantly between and within countries. For example, B. truncatus contributes to the transmission of S. haematobium to a far greater extent than B. globosus in Cameroon (Njiokou et al., 2004), but the scenario is different in Senegal, where B. globosus, B. umbilicatus and B. senegalensis are the predominant intermediate hosts for S. haematobium (Sene et al., 2004). In the

B. tropicus subgroup the shells are typically 7 to 17 mm high (Mandahl-Barth, 1957b). This subgroup is found in small lakes and streams only; it is not present in large lakes. Forms of this B. tropicus subgroup do not usually constitute an intermediate host of S. haematobium, but in Kenya it is an intermediate host of S. bovis.

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Chapter 1: General Introduction

Table 1.2 Classification of Bulinus, updated from Mandahl-Barth (1957).

Bulinus africanus group Bulinus tropicus/truncatus group B. abyssinicus (Martens, 1866) B. angolensis (Morelet, 1866) B. africanus (Krauss, 1848) B. depressus (Haas, 1936) B. bigbtoni (Brown and Wright, 1978) B. hexaploidus Burch, 1972 B. globosus (Morelet, 1866) B. liratus (Tristram, 1863) B. jousseaumei (Dautzenberg, 1890) B. natalensis (Kuster, 1841) B. nasutus (Martens, 1879) B. nyassanus (Smith, 1877) B. obtusispira (Smith, 1882) B. octoploidus (Burch, 1972) B. obtusus (Mandahl-Barth, 1973) B. permembranaceus (Preston, 1912) B. ugandae (Mandahl-Barth, 1954) B. succinoides (Smith, 1877) B. umbilicatus (Mandahl-Barth, 1973) B. transversalis (Martens, 1897) B. trigonus (Martens, 1892) B. tropicus (Krauss, 1848 B. truncatus (Audouin, 1827) B. yemenensis Paggi et al., 1978

Bulinus forskalii group Bulinus reticulatus group B. barthi Jelnes, 1979 B. reticulatus (Mandahl-Barth, 1954) B. bavayi (Dautzenberg, 1894) B. wrighti (Mandahl-Barth, 1965) B. beccarrii (Paladilhe, 1872) B. browni Jelnes, 1979 B. camerunensis (Mandahl-Barth, 1957) B. canescens (Morelet, 1868) B. cernicus (Morelet, 1867) B. crystallinus (Morelet, 1868) B. forskalii (Ehrenberg, 1831) B. scalaris (Dunker, 1845) B. senegalensis (Muller, 1781)

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Chapter 1: General Introduction

Bulinus africanus Bulinus forskalii

Bulinus reticulatus Bulinus tropicus

Figure 1.3 Shell morphology of the Bulinus group. Image is public domain, available from: https://commons.wikimedia.org/wiki/Main_Page

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Chapter 1: General Introduction

1.3 The distribution of Schistosoma and the epidemiology of schistosomiasis

Schistosomiasis continues to be considered as a leading global health problem. It has been determined to be the second largest cause of morbidity and mortality in poor communities with unsanitary conditions, and is particularly prevalent in sub-Saharan Africa (Chitsulo et al., 2000; Adenowo et al.,

2015). It is estimated that over 200 million individuals may be infected with this disease, and 779 million people are at risk globally (Steinmann et al., 2006). Although the prevalence of schistosomiasis has changed over the years, the number of people infected or at risk has not reduced (Chitsulo et al.,

2000; Engels et al., 2002).

Tanzania (a focal country for work in this thesis) has the second highest burden of schistosomiasis in the African subcontinent, after Nigeria. The primary species of Schistosoma found in the country are

S. haematobium and S. mansoni, and while S. haematobium is distributed widely across the country, the distribution of S. mansoni is more localised (Steinmann et al., 2006; Mazigo et al., 2012). Historical studies in Tanzania have showed that S. mansoni and S. haematobium have been endemic for a considerable time. The first report on intestinal bilharzia in Tanzania was published in 1895 by

Manson-Bahr. Early studies were then conducted in the Lake Victoria region during 1905, which highlighted the presence of both S. haematobium and S. mansoni (Mazigo et al., 2012).

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Chapter 1: General Introduction

Figure 1.4 The global distribution of Schistosomiasis (from Colley et al., 2014).

Attempts have recently been made to map overall distributions and high-risk regions for schistosomiasis within Tanzania, using GIS-based spatial analysis and environmental variables sourced from remote sensing technology (Brooker et al., 2001; Brooker, 2002; Clements et al., 2008; Brooker et al., 2009). Such efforts have shown S. haematobium to be highly endemic along the eastern and south-eastern of Lake Victoria and the eastern coast, inner land areas in the north-western zones, and on the Indian Ocean islands of Unguja and Pemba (McCullough, 1972; Mazigo et al., 2012). By contrast,

S. mansoni is absent from coastal areas due to the absence of a suitable intermediate host (Clements et al., 2006; Brooker et al., 2009; Mazigo et al., 2012), but it is prevalent along the shores and islands of Lake Victoria (McCullough, 1972; Mazigo et al., 2012). In a study aimed at determining the extent of infection in Tanzania in 2000, it was estimated that 15.4 million people out of a population of 29.6 million (52.0 %) were infected with both S. mansoni and S. haematobium. In 2012, it was reported that

23.2 million out of a population of 43.5 million were infected (53.3%) with schistosomiasis. Thus, the

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Chapter 1: General Introduction

population remains at high risk of infection, owing to a continued widespread distribution of schistosomes (Mazigo et al., 2012).

In Africa, schistosomiasis is now extending into new geographical regions (Lotfy and Alsaqabi, 2010).

The prevalence and intensity of infection depends on several factors, including variation in snail population size due to factors such as vegetation cover and temperature (Gryseels et al., 2006; Allan et al., 2013; Fenwick and Jourdan, 2016). Human behaviour also results in differences in infection rates, with the incidence of the disease varying among people according to their participation in agriculture, fishing, and freshwater-based domestic and leisure activities (Gryseels et al., 2006; Lotfy and Alsaqabi, 2010; Fenwick and Jourdan, 2016). In addition, infection risk is affected by human population density, quality of sanitation infrastructure, availability of piped water in local schools, proximity to health centres, distance between infection sites and human habitation, and the extent to which snail control measures have been implemented locally. Such snail control measures include vegetation clearance, canal lining, introduction of competitive snails, introduction of predators and the application of molluscides (Gryseels et al., 2006; Fenwick and Jourdan, 2016). Several studies have explored the potential implications of environmental and ecological changes on the transmission and prevalence of Schistosoma (Phiri et al., 2000; Patz et al., 2000; Van-Bocxlaer et al., 2014). Findings demonstrate that climatic factors, such as wind and wave action, temperature, rainfall and water levels in lakes can affect epidemiology of the disease by regulating transmission (Madsen et al., 2004;

Kubiriza et al., 2010; Colley et al., 2014).

Escalation in the transmission of schistosomiasis can be associated with areas where appropriate habitats for the snails exist in close contact with high-density populations (for example, near irrigation systems). Fluctuations in predator populations and snail habitats also play a pivotal role in determining transmission (Colley et al., 2014), and a lack of predators has been suggested to promote transmission

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Chapter 1: General Introduction

of schistosome parasites. In Lake Malawi, overfishing of the molluscivorous cichlid fish Trematocranus placodon has been linked to an increase in the number of Bulinus snails, subsequently creating a spike in the spread of schistosomiasis (Pongsiri et al., 2009; Stauffer et al., 2006). Thus, Stauffer et al. (2006) have suggested that ensuring an increase in the number of molluscivorous fish (natural control of schistosomes intermediate hosts) would lead to a decrease in the number of cercariae in open aquatic areas like Lake Malawi. However, introduction of molluscivorous cichlids has not been able to reduce schistosomiasis incidence in Kenya and Cameroon (Slootweg et al., 1994), perhaps because such molluscivorous fish are not the only ecological influences on snail population abundance. Research with a focus on the relationship between schistosomiasis transmission, the abundance of snail species, and the abundance of snail predators could potentially lead to novel methods of controlling transmission by regulating snail populations (Pongsiri et al., 2009).

1.4 Pathology and morbidity of schistosomiasis

Schistosomiasis is considered one of main detrimental factors for the promotion of education, health nutrition and socio-economic development in high incidence countries (Tanner, 1989; Parker, 1992;

Chitsulo et al., 2000). Related health problems include malnutrition, anaemia, and impaired childhood development (King and Dangerfield-Cha, 2008). The most prevalent form of anaemia in patients infected with schistosomiasis is anaemia of inflammation, possibly as a result of blood loss or increased burden of parasites loads, causing iron deficiency in the entire body (Friedman et al., 2005;

Leenstra et al., 2006; Bustinduy et al., 2013), although the causal pathways have not been fully elucidated (Leenstra et al., 2006). While anaemia of inflammation is one of the common features of schistosomiasis, each Schistosoma species may cause more specific pathologies and severities

(Weerakoon et al., 2015). After symptoms of an initial infection (cercarial dermatitus), the general stages of the disease can be classified as acute and chronic (Gray et al., 2011; Weerakoon et al., 2015).

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Chapter 1: General Introduction

1.4.1 Cercarial dermatitis

Cercarial dermatitis, also called swimmer's itch (Figure 1.5), occurs as an immediate reaction to cercarial antigens while the cercaria penetrate the skin. It can cause urticarial rashes which develop as a maculopapular skin eruption, especially after repeated contacts with the parasites. Although rashes may continue appearing for up to a week, tourists or migrants infected for the first time might develop a skin reaction within a few hours. It tends to be a temporary condition, which persists just for a few days (Gray et al., 2011; Gryseels et al., 2006; Kolarova et al., 2013).

1.4.2 Acute schistosomiasis (Katayama syndrome)

This condition usually occurs within 14 – 84 days after the initial infection, due to larval migration and worm maturation, early oviposition, release of egg antigens and corresponding immune responses

(Visser et al., 1995; Bottieau et al., 2006; Ross et al., 2007). The symptoms characterising this stage include headache, fever, eosinophilia, malaise, myalgia, fatigue, non-productive cough and abdominal pain. After onset the symptoms last for between 2-10 weeks, after which patients tend to recover.

However, some may develop more serious forms of the disease with diffuse abdominal pain, hepatomegaly, diarrhoea, weight loss, dyspnoea and general rash (Ross et al., 2007). Most often, such intense levels of pathology occur in immigrants or travellers if they are exposed to contaminated water in endemic regions (Colley et al., 2014; Gryseels et al., 2006). Surprisingly, residents of endemic regions present limited occurrence of this syndrome, which could be explained as a result of repeated exposure to infectious cercariae or possibly due to underdiagnosis (King et al., 1998; Ross et al., 2013).

The common factors that can determine the level of morbidity in infected people are intensity of infection (Kabatereine et al., 2004), the length and degree of exposure (Booth et al., 2004b), co- infections (Kamal et al., 2004; Booth et al., 2004c) and other host and parasite factors such as host immune reactions to the parasite (Mentink-Kane et al., 2004; Booth et al., 2004a).

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Chapter 1: General Introduction

Figure 1.5 Swimmer's itch, available from:http://www.tabletsmanual.com/wiki/read/schistosomiasis

1.4.3 Chronic schistosomiasis

This stage presents after occurrence of acute schistosomiasis and is most commonly noted in residents of endemic regions. In such settings, initial infection in a child often happens by age 2 years and the burden of infection increases in intensity during the next 10 years as new worms proliferate and colonise the child’s body. The highest incidence and intensities of the infection manifest in young adolescents, after which both incidence and intensity steadily reduce with progression into adulthood.

The chronic form of schistosomiasis occurs due to a granulomatous inflammatory reaction against the schistosome eggs, when the eggs migrate in peri-intestinal region. In this phase, eggs can get deposited and trapped within tissues or organs like the spleen, liver, lungs, or cerebrospinal system

(Mohamed et al., 1990; Gryseels et al., 2006), which ultimately lead to inflammatory conditions in the intestine like hepatosplenic inflammation and liver fibrosis (S. mansoni, S. japonicum, S. intercalatum and S. mekongi), or in the urinary system (S. haematobium) (Gryseels et al., 2006; Gray et al., 2011).

In places where the eggs secrete proteolytic enzymes, they induce eosinophilic inflammatory and granulomatous reactions. These are then gradually replaced by fibrotic deposits (Gryseels et al., 2006).

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Chapter 1: General Introduction

The severity of the symptoms is often closely linked to the intensity of infection (Gryseels et al., 2006;

Colley et al., 2014).

In intestinal schistosomiasis, eggs are deposited in the intestinal wall and liver, causing multiple- granuloma formation and tissue lesions. The symptoms of the chronic intestinal form caused by S. mansoni and S. japonicum are loss of appetite, diarrhoea, continuous abdominal pain, and rectal bleeding; and these symptoms are also related to the intensity of the infection (Mohamed et al., 1990;

Gryseels et al., 2006; Elbaz and Esmat, 2013). Granuloma formation can sometimes cause periportal fibrosis, which can then extend to advanced forms of the disease, with portal hypertension and hepatosplenomegaly. The complications at this phase can often be severe, such as variceal bleeding and ascites, which can even lead to death (Cao et al., 2010; Gray, et al., 2011; Gryseels et al., 2006).

Chronic urinary schistosomiasis occurs following egg deposition and granuloma formation in the urinary bladder wall, giving rise to abnormalities in the mucosa (Burke et al., 2009; Colley et al., 2014).

The main symptom for S. haematobium (urogenital schistosomiasis) is haematuria, which is often presented along with an increased frequency of urination and dysuria. Further conditions may include chronic fibrosis of the urinary tract, resulting in obstructive uropathy hydroureter and hydronephrosis

(Khalaf et al., 2012) and bladder malignancies (Bamgbola, 2014), which in severe cases, can cause renal failure (Burke et al., 2009). These pathologies, co-morbid with bacterial superinfection and renal dysfunction, are very frequently fatal. Moreover, squamous-cell carcinoma of the bladder is also highly related with S. haematobium infections (Schwartz, 1981; Correia-da-Costa et al., 2014). S. haematobium is classified as a class 1 carcinogen and has also been shown to increase the susceptibility of sexually transmitted infections, including HIV (Kjetland et al., 2012; Jourdan et al.,

2013). It can affect women’s reproductive health if the eggs in the vesical plexus migrate to the genital

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Chapter 1: General Introduction

tract. These can cause inflammatory lesions on the ovaries, fallopian tubes, cervix, vagina, and vulva leading to bleeding, pain and infertility (Kjetland et al., 2012).

1.5 Treatment of schistosomiasis

Schistosomiasis is a major cause of concern for the public health care system in many endemic countries (Colley et al., 2014; Chitsulo et al., 2000). Medical attention and treatment given in the early stages of infection can help to prevent a substantial part of the effects of Schistosoma infection that are mediated by the immune system (Colley et al., 2014). Praziquantel (PZQ) is currently the principal drug employed for treatment (Fenwick and Jourdan, 2016), and it is distributed to millions of people every year through mass administration programs (Cioli et al., 2014). It was first produced in the late

1970s and early 1980s, and the generic form was eventually developed around the 1990s. The World

Health Assembly adopted PZQ as the recommended global control method for schistosomiasis in 2001

(Engels et al., 2002; Bergquist et al., 2017). In 2002, the Schistosomiasis Control Initiative (SCI) was instituted at the Imperial College London, UK which provided treatment to at-risk and infected children in the sub-Saharan African countries using PZQ (Fenwick et al., 2009). Moreover, large quantities of the drug have been provided as donations to health programmes since 2003. In 2012, 42 million people were treated using single oral doses of PZQ, most of which were school age children

(Montresor et al., 2001). Although this actual number of treatments was fairly large, it still represented only 14.4% of the actual population in need of treatment.

PZQ has been deemed to be highly optimal as compared to other drug treatments that have been tested, in terms of safety, antiparasitic efficacy and cost (Cioli et al., 2014). Data suggest that PZQ may be the safest of all anthelmintic drugs, considering both immediate and later effects. Moreover, no occurrence of PZQ resistance has yet been recorded, although there is no guarantee that serious PZQ resistance will never appear in cases of heavy and frequent usage (Cioli et al., 2014). PZQ can also have

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Chapter 1: General Introduction

effects on other trematodes for example Fasciolopsis, Opisthorchis, Heterophyes, Paragonimus and

Metagonimus. It is also effective against most cestodes (Taenia, Echinococcus, Hymenolepis,

Diphyllobothrium) and, with the notable exception of Fasciola, some larval cestode infections such as sparganosis and hydatid disease (Chai, 2013). The efficiency of PZQ has been measured after four weeks since treatment with 40 mg/kg and showing to be effective in between 60-90 % of individuals

(no parasite egg excretion). This was considered an optimal result, since 100% cures can rarely be achieved (Doenhoff, 1998; Doenhoff et al., 2009; Cioli et al., 2014). PZQ acts most effectively against adult schistosome worms (Colley et al., 2014). It can also act on early migrating larval stages during the first few days after infection. After this, the susceptibility to PZQ decreases to low levels by day

28, but gradually increases to a maximum point after 6-7 weeks when the worm reaches adulthood

(Gonnert and Andrews, 1977; Sabah et al., 1986; Pica-Mattoccia and Cioli, 2004). This ontogenetic shift in susceptibility to PZQ means that if the schistosomes are still immature at the time of treatment, especially in endemic areas, the cure will not be achieved (Utzinger et al., 2000). Therefore, a dose of

PZQ is required every two weeks for effective treatment of those patients living in endemic areas that take no specific precautions to avoid reinfection (Utzinger et al., 2000; Bergquist et al., 2017).

The current cost of PZQ is approximately US$ 0.20 per treatment, and about the same again for the distribution of the drug. Initially when PZQ was first discovered as an effective treatment cost was a major obstacle to mass distribution. However, in 1983 the Korean company Shin Poong rapidly reduced the price after the advent of a new manufacturing process. Although Merck KgaA and other organizations are now providing up to 250 million tablets of PZQ annually for free, the availability of the drug still proves to be insufficient (Cioli et al., 2014). The increase in demand of PZQ has eventually led to a reduction in the supply (Fenwick et al., 2009). Moreover, despite PZQ having many positive aspects, there are still insufficient suitable substitutes to the drug and therefore an associated fear of

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Chapter 1: General Introduction

development of PZQ resistance. Thus, a search for new anti-schistosome drugs is urgent and imperative (Cioli et al., 2014).

1.6 Control strategies

In 2001 the World Health Organization (WHO) called for national governments to implement programmes dedicated towards the control of secondary diseases caused by schistosomiasis (Fenwick and Jourdan, 2016). Following that initiative, millions of PZQ doses have been distributed, but schistosomiasis is now an even more serious problem in sub-Saharan Africa compared to the period before praziquantel’s discovery. This might partly be due to reinfection after treatment, which can obstruct disease control in the long-term (Sokolow et al., 2016). Thereafter, the World Health

Assembly (WHA) endorsed the elimination of schistosomiasis as a public health priority in 2012, and advocated strategies that couple the use of anthelminthic drugs with snail control, alongside other available eliminative tools (Fenwick and Jourdan, 2016; Sokolow et al., 2016; Sokolow et al., 2018).

The major integrative schistosomiasis control measures recommended by WHO include chemotherapy, along with good sanitation, hygiene and health education, safe water supplies, avoidance of contaminated water, and control of vector snails (Fenwick and Jourdan, 2016). However, chemotherapy via the use of PZQ is still the strongest pillar for schistosomiasis control and is expected to bring about the most rapid reduction of prevalence and morbidity of the disease (Utzinger et al.,

2003; Hotez et al., 2010; Chitsulo et al., 2000). This control method is primarily focused on school-age children and other high-risk groups. However, there is still low coverage due to limited access to the drug because of the high cost for many African health ministries (Hotez et al., 2010). Despite the price of the drug being drastically reduced over the past decades, it is still expensive for many developing country health budgets (Chitsulo et al., 2000).

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Chapter 1: General Introduction

The WHO has recently released a road map for controlling global impacts of Neglected Tropical

Diseases, including schistosomiasis, and the emphasis will lie on the provision of regular treatment for at least 75% of the children in need by 2020, with the eventual aim of complete disease elimination.

The development and application of control strategies, including accuracy of diagnostic testing, demand careful studies of financial implications, available resources, and administrative and political support. Successful elimination of disease will require the application of complex interventions and accurate monitoring measures, which will further require countries to establish nationalised plans of action (Weerakoon et al., 2015).

A safe water supply and education on behavioural changes constitute the foundational factors in the control of schistosomiasis (Rollinson et al., 2013; Adenowo et al., 2015). Additionally, ecological surveys are critical for determining the risk levels of water sources, especially in non-endemic regions which can eventually turn suitable for schistosomiasis transmission by the presence of intermediate hosts (snails). Other factors such as climate change and civilisation are also expected to have a direct impact on the habitat of the snails (King et al., 2006; Stensgaard et al., 2013). Conventional snail control methods usually include the use of chemical molluscicides, and new niclosamide formulations are considered safe, effective and species specific. Habitat modification, for example wetland drainage and vegetation removal can be useful too. On the other hand, habitat manipulations such as irrigation expansion, dam construction and other water-related changes can lead to unintentional outbreaks of the disease (e.g. Sokolow et al., 2017). Implementation of biological controls, including the use of snail–feeding crustaceans, birds, and fishes may also be effective (Sokolow et al., 2018).

Schistosomiasis control was first successfully initiated in countries such as China and Japan, where the public health cost of schistosomiasis has been widely appreciated. In these countries, S. japonicum has been responsible for high morbidity and mortality, even leading to breakdown of human

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Chapter 1: General Introduction

community cohesion and consequently causing a reduction in agricultural production (Chitsulo et al.,

2000; Wang et al., 2008). Now Japan, the Caribbean islands, Mauritius and Tunisia have all succeeded in the elimination of schistosomiasis, while extremely reduced prevalence has been achieved in China, parts of South America, Cambodia, Egypt, Laos, the Philippines, Botswana, Iran, Iraq and Morocco.

These changes were brought about by integrated control and treatment programmes, as well as improved socio-economic conditions (Chitsulo et al., 2000; Fenwick and Jourdan, 2016).

There has been great international support for elimination of NTD’s in general, and schistosomiasis in particular. However, despite extensive efforts having being undertaken that have achieved significant progress in disease control over recent years, the disease burden of schistosomiasis still remains high with over 200 million people still infected globally (Chitsulo et al., 2000). This may be attributed to a number of barriers and limitations which can prevent complete elimination of schistosomiasis. i)

Governmental commitment remains inadequate towards the goal of complete elimination of schistosomiasis. ii) Financial support is still too limited to implement sustainable control programmes, even in governments which welcome the implementation of NTD elimination programmes in theory. iii) Public health services need to be further strengthened, especially within countries which are suffering from political instability, civil unrest or epidemics of other infectious diseases such as Ebola

(Fenwick et al., 2009). iv) Most programmes are exclusively aimed at school-age children, and do not reach out to children of pre-school age. Moreover, there is a gap in PZQ treatment, where infected children may have to wait up to 4 – 5 years before entering school and receiving their first treatment, leading to further transmission in the meantime (Fenwick et al., 2009; Stothard et al., 2011). v) There is a need for more practical evidence on ideal dosage, efficacy and frequency of PZQ on schistosomiasis species and also their related morbidity (Zwang and Olliaro, 2014; Ross et al.,2015a). vi) There is a need for validated, sensitive and diagnostic-friendly tools to enable monitoring and evaluation of the disease, especially in endemic countries. vii) Elimination of this parasitic infection

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Chapter 1: General Introduction

will require a substantial upscaling of the skill base of stakeholders, including health professionals and environmental scientists. The expertise for treating schistosomiasis has increased since 2000, but to achieve complete elimination, a broader base of expertise will be needed among all NTD elimination stakeholders. Due to these issues, the WHO target of “elimination as a public health problem” across much of the world by 2025 might be too ambitious, given the extensiveness of the target (Ross et al.,

2015b).

1.7 Diagnosis of schistosome presence in the natural environment

A variety of diagnostic tools have been developed for detection of schistosomiasis within human hosts over recent decades. These range from basic microscopic detection to sophisticated molecular techniques. The various clinical diagnostic strategies can be grouped into four categories (Weerakoon et al., 2015; Weerakoon et al., 2018). 1) Direct parasitological diagnosis through the presence of eggs in faeces (e.g. S. mansoni and S. japonicum), or in urine (e.g. S. haematobium). 2) Immunological diagnosis. 3) DNA and RNA detection. 4) Use of cytokines, metabolites, and other Schistosoma molecules as biomarkers. The clinal diagnosis strategies have been reviewed previously (e.g. Gray et al., 2011).

Environmental-detection tools for Schistosoma in their snail hosts and the environment can be extremely beneficial for surveillance of infection risk (McManus et al., 2018). This can be achieved by direct sampling of cercariae in the water body (Muhoho et al., 1997; Aoki et al., 2003), but more commonly the presence of schistosomes is established through xenomonitoring assays of gastropod intermediate hosts. This is usually done by exposing snails to light after collection which induced cercarial shedding (Wolmarans et al., 2002; Kariuki et al., 2004). Notably, this type of survey is very labour intensive and demands expertise and specific training. Moreover, the sensitivity of this method can be low, because despite high human infection incidence, it is possible for as few as 1-2% of snail

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Chapter 1: General Introduction

population to infected (Angelo et al., 2014; Weerakoon et al., 2018). Due to these limitations, a schistosomiasis-endemic area may in fact seem to be free of the infection, whereas transmission may continue and spread to other communities, eventually leading to a higher need for control and elimination efforts (Weerakoon et al., 2018).

1.7.1 Molecular techniques

Molecular xenomonitoring, where individual snails are tested for presence of schistosome DNA can potentially be useful in large-scale screening programs (Melo et al., 2006; Weerakoon et al., 2018).

Specifically, PCR is a highly sensitive and specific technique for detecting parasitic DNA and has the potential to become a powerful tool augmenting morphological and microscopic diagnostic approaches (Klein, 2002; Verweij et al., 2004; Espy et al., 2006; Ten-Hove et al., 2008). Schistosoma

DNA in snails has been detected using both conventional end point PCR, and real-time quantitative

PCR-based techniques (Melo et al., 2006; Kane et al., 2013; Farghaly et al., 2016; Bakuza et al., 2017).

Real-time PCR (qPCR) has several advantages over conventional PCR (cPCR) since it is can provide estimates of infection intensity. It is also more sensitive than conventional PCR and can detect lower concentrations of target DNA. It is less labour-intensive as compared to cPCR, which needs an additional electrophoresis step for visualizing the product. qPCR also offers the opportunity to use multiplex probe-based assays for detecting multiple infections within a single sample, enabling convenience in high-throughput applications. Potential drawbacks of PCR-based tests are the need for skilled expertise and appropriate equipment, and the high cost of reagents (Pontes et al., 2003; Lier et al., 2006).

1.7.2 Environmental DNA

Environmental DNA has been commonly referred to as nuclear or mitochondrial DNA that is released by aquatic and terrestrial organisms through epithelial tissue, hair, gametes, faeces, cells and mucous,

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Chapter 1: General Introduction

or obtained from carcasses (Livia et al., 2006; Nielsen et al., 2007; Merkes et al., 2014). This eDNA can be sampled and isolated from environmental samples, including snow, water, air, soil and sediment, without the need to isolate any target organisms and without having to monitor the organism itself

(Willerslev et al., 2007; Ficetola et al., 2008; Foote et al., 2012; Thomsen et al., 2012a; Yoccoz et al.,

2012; Deiner and Altermatt, 2014). There is discourse on whether the term eDNA should be restricted to residual DNA from macroorganisms in the environment, or whether DNA sampled from living microorganisms also should be included. Notably, a pragmatic parasitological definition of an eDNA study has been proposed - an investigation “starting with DNA (or RNA) being extracted from environmental or organismal matrices, in other words from the environment or the host organism”

(Bass et al., 2015). Thus, it is possible to consider DNA from both macro- and micro-organisms in an environmental matrix as environmental DNA.

Environmental DNA has primarily emerged as a promising non-invasive tool for monitoring aquatic and terrestrial biodiversity (Bohmann et al., 2011; Rees et al., 2014), potentially providing species distribution data (Yoccoz et al., 2012) in a highly cost-efficient manner (Doi et al., 2015). Arguably, the greatest benefits from eDNA methods have been from their application to understanding the ecology aquatic environments, where species may be difficult to detect using other approaches (Lawson-

Handley, 2015). In addition to biodiversity inventories and monitoring, eDNA methods can be used for informing diet analysis (Huver et al., 2015), and reconstructing past fauna and flora compositions from the soil and other sediments (Sonstebo et al., 2010; Andersen et al., 2012; Pedersen et al., 2013).

Environmental methods are frequently employed to expose invasive species (Dejean et al., 2012) or bio-monitor those that those that are at low densities in the environment (Ficetola et al., 2008;

Goldberg et al., 2011), including species at risk of extinction (Thomsen et al., 2012b). In such cases eDNA detection has been able to outperform traditional survey methods which can have high

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Chapter 1: General Introduction

personnel and material costs, limited seasons and a greater risk of harming sensitive species (Turner et al., 2014b; Lawson-Handley, 2015). Importantly, DNA-based species identification also requires no taxonomic expertise (Waits and Paetkau, 2005; Ko et al., 2013), and can therefore be especially useful when researchers for studying species genetically divergent species with similar morphological identities (Furlan et al., 2016). However, despite all the early recognised benefits of using eDNA, it still requires further intensive research to overcome future challenges (Lawson-Handley, 2015).

Analyses of eDNA can be focussed on single taxa of interest, or specific biological assemblages. Early eDNA protocols for detection of aquatic organisms used fragment analysis of conventional PCR products. Recently, probe-based quantitative PCR (qPCR) methods have been utilised for detecting single species with target amplification primers. These techniques have offered improved sensitivity, especially with low concentrations, specificity and an ability to quantify the eDNA in the sample (Pilliod et al., 2013; Wilcox et al., 2013; Amberg et al., 2015), along with estimation of the number of target

DNA copies and relative abundance estimation (Lawson-Handley, 2015). Currently, probe-based qPCR is considered the most efficient tool to detect eDNA of single or few target species. A key consideration during assay design is there is a possibility of that an as single assay can amplify multiple target species, which can lead to false positive results. This necessitates a further confirmation of positive samples, by methods such as direct Sanger sequencing of amplified product.

eDNA metabarcoding

DNA metabarcoding, the DNA barcoding of multiple species within environmental samples, is an emerging approach with the potential to revolutionise biodiversity monitoring (Taberlet et al., 2012a;

Soininen et al., 2013). It extends the barcoding concept in order to identify multiple species using a single experiment (Taberlet et al., 2012b). The concept of DNA metabarcoding was initially developed by microbiologists (Sogin et al., 2006), but has now gained popularity as a method of taxonomic

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Chapter 1: General Introduction

identification ranging from meiofauna (Chariton et al., 2010, Baldwin et al., 2013) to larger animals

(Andersen et al., 2012; Thomsen et al., 2012a) and plants (Sonstebo et al., 2010; Yoccoz et al., 2012), including fungi (Zinger et al., 2009), nematodes (Porazinska et al., 2009) and earthworms (Bienert et al., 2012). Interestingly, this method is not only being used for biodiversity-based assessments, but also for other ecological studies, such as dietary analyses (Berry et al., 2017).

Metabarcoding, like conventional DNA barcoding, involves the use of “universal” PCR primers to amplification of a standard DNA section of various organisms (Ji et al., 2013). Typically, metabarcoding has been limited to the sequencing only one specific part of any sequenced gene, such as the mitochondrial cytochrome c oxidase 1 (COI) gene. This has been largely due restrictions on the size of

DNA fragments that can be amplified by PCR and then sequenced from eDNA using high-throughput sequencing technologies (Shokralla et al., 2014; Brandon-Mong et al., 2015). After PCR and sequencing using next generation sequencing technology, identification and quantification of organisms is achieved by comparing the obtained sequences to a reference database (Valentini et al., 2009,

Soininen et al., 2013).

Persistence of eDNA

Despite the great advantages of data from eDNA analyses, their success depends on the availability of

DNA of sufficient quality. Once the DNA is released into the environment, degradation begins to occur.

The eDNA persistence depends on multiple factors including its state (encapsulated in cells, or free), as well as multiple external abiotic and biotic environmental factors (Strickler et al., 2015; Hansen et al., 2018). Generally, temperature is considered to be a major factor which influences eDNA persistence, where reducing the temperature leads to increased preservation of eDNA particles (from two days in temperate aquatic ecosystems to hundreds of thousands of years under permafrost)

(Hansen et al., 2018). It has also been suggested that exposure to solar radiation has degrading effects

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Chapter 1: General Introduction

on the persistence of eDNA particles (Barnes et al., 2014; Pilliod et al., 2014; Strickler et al., 2015).

Additionally, persistence of eDNA particles has been linked to pH, with higher mean degradation rate at pH 4 compared to pH of 7 and 10 (Strickler et al., 2015). Overall therefore, the presence of cold and alkaline environments without exposure to solar radiation can therefore be considered the most optimal aquatic conditions for highest preservation of eDNA (Pilliod et al., 2014; Strickler et al., 2015).

However, the role of biotic factors, specifically microbial communities should be considered. In aquatic ecosystems there is a wide diversity of microorganisms which can take up DNA during normal growth, a process known as natural transformation (Pietramellara et al., 2009).

Practical eDNA sampling

Perhaps one of the most common applications of eDNA methods is species detection, and therefore analyses are dependent on detection probability (MacKenzie et al., 2002; Schmidt et al., 2013; Ficetola et al., 2015). This probability is influenced by multiple stages of the eDNA assay process, including capture efficacy, extraction efficacy, the presence of PCR inhibitors and assay sensitivity (Goldberg et al., 2016). DNA capture from environmental samples such as water or soil is frequently done by filtration (up to several litres of water can be needed in some instances) (Jerde et al., 2011; Olson et al., 2012; Goldberg et al., 2013; Pilliod et al., 2013), precipitation (typical sample water volume, 15 ml)

(Ficetola et al., 2008; Thomsen et al., 2012b) and centrifugation (typical sample water volume, 50 ml)

(Klymus et al., 2015). The above methods can be used for concentrating the DNA from samples, where filtration and precipitation are the most common methods for concentrating eDNA from water samples (Goldberg et al., 2016).

The optimal sampling methods of aqueous eDNA depends in part on how samples will be processed.

Samples to be used for DNA precipitation are preserved by adding a salt (sodium acetate) and absolute ethanol followed by storage of the sample at 20 °C. Another closely related method is centrifugation,

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Chapter 1: General Introduction

where the water sample (stored on ice) is centrifuged shortly after collection and the obtained pellet is stored in 95% ethanol or other DNA preservatives. More commonly however, water samples are filtered is usually done on the field and when that is not possible, the samples are stored on ice and transported to laboratories with filtration facilities. Filters (and collected materials) are then preserved by freezing (Jerde et al., 2011) and adding a preservative (e.g. ethanol; Goldberg et al., 2011). Studies utilizing filtration capture methods have at their disposal a wide range of filter types and pore sizes, which are chosen in accordance with properties of the target organisms. It has been shown that small pore size filters hold more eDNA (Liang and Keeley, 2013; Turner et al., 2014a), ultimately allowing optimal eDNA recovery from small water volumes, as compared to filtration through a larger pore sized filter which can require several litres of water (Turner et al., 2014a). However, larger pore size

filters are still used, due to their accuracy in biomass quantification (Takahara et al., 2012). The range of materials used in eDNA sample filters vary from glass fibres (Jerde et al., 2011), polycarbonate

(Takahara et al., 2012), cellulose nitrate (Goldberg et al., 2011), nylon (Thomsen et al., 2012a), polyethersulfone (Renshaw et al., 2015) and cellulose acetate (Takahara et al., 2013), each providing material-specific benefits. Filtration generally allows higher detection than simple precipitation of the same volume (Deiner et al., 2015), however these material-specific properties (pore size, filter material, and DNA extraction methods) interact to estimate the final detection rates (Deiner et al.,

2015; Renshaw et al., 2015). Sterivex filters (enclosed filters, typically used with pore size 0.22 μm) have been proved to be as an efficient capture method for sampling eDNA. Also, since these filters are enclosed this reduces the risk of contamination (Spens et al., 2017).

To obtain accurate results from eDNA samples, protocols must be strictly followed during all processes, from water processing and filtering (if not done in the field) to PCR analyses (Goldberg et al., 2016). Since environmental DNA begins to decay immediately after shedding (Thomsen et al.,

2012a, b) samples must be quickly preserved after collection, in accordance with a standardized

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Chapter 1: General Introduction

protocol. Post-preservation, some protocols suggest filtering within 16 hours, however samples can be kept on ice up to 24 hours without harming the detection probability (Pilliod et al., 2013).

eDNA in parasitology

Environmental DNA methods have a strong potential for monitoring the risk of infectious diseases, by providing information on both the presence and quantity of infectious agents in the environment

(Huver et al., 2015). Despite the broad applicability of eDNA however, studies on parasites are still relatively rare. Nevertheless, the eDNA approach has successfully been used to detect the liver fluke,

Opisthorchis viverrini and its intermediate hosts (fish) in water bodies of southeast Asia (endemic areas) (Hashizume et al., 2017). The trematode Ribeiroia ondatrae has also been successfully detected using eDNA methods in North America (Huver et al., 2015), while the trematodes Fasciola hepatica and Calicophoron daubneyi DNA have been detected in the pasture environments in Europe (Jones et al., 2018).

With regards to schistosomiasis, two studies relating to Schistosoma have been done using eDNA. One study was carried out on S. mansoni, which successfully detected the eDNA in an endemic area in

Madagascar. The study concluded that the technique can be considered a useful detection tool for epidemiological studies and schistosomiasis surveillance (Sato et al., 2018). A second study reported successfully detected the presence of S. mansoni at known transmission sites in Kenya. Notably, it was found that results of the eDNA survey showed a 71% overlap with conventional snail surveys (snail collection and cercariae shedding). Moreover, it has also detected this target species at two additional sites where snail shedding failed, potentially indicating a higher sensitivity of eDNA sampling

(Sengupta et al., 2019). Overall, this evidence is highly supportive of the potential of eDNA methods for improved surveillance of infectious schistosome species (McManus et al., 2018). However, these published studies also indicate that there is scope to improve and adapt these methods to enable

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Chapter 1: General Introduction

large-scale surveillance of multiple schistosome species. Specifically, these two studies (Sato et al.,

2018; Sengupta et al., 2019) focussed only on S. mansoni, and did not test the specificity of their assays using in-vitro approaches. In the work presented in this thesis, I build on existing work by further developing and testing eDNA approaches to detect human Schistosoma in freshwater bodies.

1.8 Aims

The main study objective of this research has been to evaluate the potential for eDNA to be used as a diagnostic tool to understand the distribution and abundance of schistosomes, and to understand the broader ecology of freshwater communities where schistosomes are found.

The following specific aims are addressed:

• To develop and test species-specific probes for human-infecting Schistosoma species, including

comparisons with conventional methods of testing for their presence.

• To test the efficiency of a novel eDNA xenomonitoring approach for quantifying the schistosome

infections of host snails, and therefore informing stakeholders of the risk of infection.

• To investigate if metabarcoding of African freshwater has the potential to improve our

understanding of freshwater community structure, and characterise Schistosoma species

present.

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Chapter 2: Schistosoma species detection by eDNA assays

Chapter 2

Schistosoma species detection by environmental DNA assays in African freshwaters

A version of this chapter has accepted and now in press:

Alzaylaee, H., Collins, R.A., Rinaldi, G., Shechonge, A., Ngatunga, B.P., Morgan, E.R. and Genner, M.J.

(2020). Schistosoma species detection by environmental DNA assays in African freshwaters. PLOS

Neglected Tropical Diseases, 14, e0008129. (Appendix I)

Author contributions

HA and MJG designed the study, with input from RAC. GR, AS and BPN provided the sample resources.

HA and MJG collected samples, collected data, analysed the data and led the writing of the manuscript.

RAC, GR, ERM contributed critically to the drafts

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Chapter 2: Schistosoma species detection by eDNA assays

Abstract

Schistosomiasis is a neglected tropical parasitic disease associated with severe pathology, mortality and economic loss worldwide. Programs for disease control may benefit from specific and sensitive diagnostic methods to detect Schistosoma trematodes in aquatic environments. Here we report the development of novel environmental DNA (eDNA) qPCR assays for the presence of the human- infecting species Schistosoma mansoni, S. haematobium and S. japonicum. We first tested the specificity of the assays across the three species using genomic DNA preparations. These showed successful amplification of target sequences, with no cross amplification between the three main species. In addition, we evaluated the specificity of the assays using synthetic DNA of multiple

Schistosoma species, and demonstrated a high overall specificity; however, S. japonicum and S. haematobium assays showed cross-species amplification with very closely-related species. We next tested the effectiveness of the S. mansoni assay using eDNA samples from aquaria containing infected host gastropods, with the target species revealed as present in all infected aquaria. Finally, we evaluated the effectiveness of the S. mansoni and S. haematobium assays using eDNA samples from eight discrete natural freshwater sites in Tanzania, and demonstrated strong correspondence between infection status established using eDNA and conventional assays of parasite prevalence in host snails. Collectively, our results suggest that eDNA monitoring is able to detect schistosomes in freshwater bodies, but refinement of the field sampling, storage and assay methods are likely to optimise its performance. We anticipate that environmental DNA-based approaches will help to inform epidemiological studies and contribute to efforts to control and eliminate schistosomiasis in endemic areas.

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Chapter 2: Schistosoma species detection by eDNA assays

2.1 Introduction

Schistosomiasis, a neglected tropical disease caused by trematodes of the genus Schistosoma, is the second most prevalent parasitic disease in humans, after malaria (Gryseels et al., 2006; Steinmann et al., 2006). Studies have indicated that upwards of 207 million people may be infected globally, with a further 779 million individuals at risk of infection (Steinmann et al., 2006). Around 93% of infected people live in sub-Saharan Africa (Steinmann et al., 2006; Hotez and Kamath, 2009), where widespread poverty and the absence of clean water and sanitary facilities contribute to the incidence and propagation of the disease (Chitsulo et al., 2000; Adenowo et al., 2015). Schistosomiasis is associated with health problems including malnutrition, anaemia, and impaired childhood development. It is also a major impediment to education, health nutrition and socio-economic development in the infected populations (King et al., 2005; Gryseels et al., 2006; King and Dangerfield-Cha, 2008; Colley et al.,

2014).

There are three main Schistosoma species that infect humans. S. haematobium causes urogenital schistosomiasis and is present across Africa and the Middle East. S. mansoni, the agent of intestinal schistosomiasis, is prevalent across Africa, the Middle East and South America. S. japonicum causes hepatic schistosomiasis and is mainly found in the Philippines, Indonesia and China. As trematodes,

Schistosoma species have a complex life cycle that requires an intermediate freshwater snail host and a final vertebrate host (Gryseels et al., 2006; Colley et al., 2014). Snails that are responsible for schistosomiasis in humans belong to one of three genera: Biomphalaria (for S. mansoni), Bulinus (for

S. haematobium) and Oncomelania (for S. japonicum) (Colley et al., 2014). Transmission to humans occurs when the free-swimming larval form (cercaria) is shed from infected freshwater snails and penetrates the skin of the definitive host (Colley et al., 2014; Machacek et al., 2018). Once mature, the male and female schistosomes mate, reproduce, and produce eggs that are excreted via urine or

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Chapter 2: Schistosoma species detection by eDNA assays

faeces, depending on the species. Eggs excreted into freshwater environments hatch and release mobile miracidia which infect snails to continue the cycle (Colley et al., 2014).

Awareness of safe water sources by human populations in endemic regions is critical for campaigns to prevent schistosomiasis (Rollinson et al., 2013; Fenwick and Jourdan, 2016). However, assessment and therefore mitigation of the risk posed by bodies of freshwater is not straightforward. Despite large integrated control programs during the last few decades, rapid and reliable diagnostic tools to detect the schistosomes in endemic areas are still needed (Weerakoon et al., 2015). Historically the presence of schistosomes has been established by microscope-based testing of cercarial shedding of patent infections, and/or molecular screening to detect both patent and pre-patent infections within the snails (Wolmarans et al., 2002; Kariuki et al., 2004; Melo et al., 2006; Kane et al., 2013; Farghaly et al.,

2016; Bakuza et al., 2017). Although there are clear benefits of undertaking these conventional snail- focussed surveys, they can be labour intensive and demand specific training and expertise. Moreover, the sensitivity of snail-based survey methods may be limited in some locations, such as those where snail infection rates are as low as 1–2% of the population, despite a high prevalence of infected humans (Angelo et al., 2014; Weerakoon et al., 2018).

Over recent years conservation biologists and ecologists have widely adopted the use of environmental DNA (eDNA) methods to establish the presence of species that are difficult to study using other approaches (Brinkman et al., 2003; Walker et al., 2007; Ficetola et al., 2008; Jerde et al.,

2011; Goldberg et al., 2011; Thomsen et al., 2012b; Schmidt et al., 2013). These methods can be particularly useful for studying parasites because they can be challenging to locate and reliably identify. From a parasitological perspective, the term eDNA can be defined as “DNA extracted from an environmental or organismal matrix” (Bass et al., 2015). Specifically, this definition includes target

DNA from whole microscopic organisms present in the sample and is not restricted to DNA in solution

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Chapter 2: Schistosoma species detection by eDNA assays

or cellular debris (Bass et al., 2015). Successful detection of trematodes using aquatic eDNA include

Ribeiroia ondatrae in North America (Huver et al., 2015), Opisthorchis viverrini in southeast Asia

(Hashizume et al., 2017), and Fasciola hepatica and Calicophoron daubneyi in Europe (Jones et al.,

2018). Previous studies have also shown the feasibility of recovering Schistosoma mansoni DNA from both field and laboratory environments (Sato et al., 2018; Sengupta et al., 2019). They have also demonstrated that Schistosoma DNA present in laboratory water decayed below levels of detection eight days after the removal of source snails (Sengupta et al., 2019). Collectively, this evidence is highly supportive of the potential of eDNA methods for improved surveillance of all harmful schistosome species (McManus et al., 2018), but also indicates that improvements are needed to adapt these methods to large-scale surveillance, and to identify species other than S. mansoni.

In this study, we built on previous studies by developing novel eDNA methods to detect human

Schistosoma in freshwater bodies. Specifically, we report the development of primers and probes for the mitochondrial 16S rRNA region for S. mansoni, S. haematobium and S. japonicum, and undertake thorough tests of assay specificity. We then validated functionality of the S. mansoni assay by testing eDNA from water collected from aquaria holding infected host snails. Finally, we evaluated the applicability of the new assays to detect S. mansoni and S. haematobium eDNA in Tanzanian freshwaters where both species are co-endemic.

2.2 Materials and Methods

2.2.1 Primer and probe design

Species-specific sets of primers and probes were developed to target short (< 150 basepair) fragments of the mitochondrial DNA 16S rRNA gene in S. japonicum, S. mansoni and S. haematobium. The following GenBank sequences were used as templates: S. mansoni HE601612, S. haematobium

DQ157222 and S. japonicum JQ781206. Sequences were aligned to available mitochondrial 16S rRNA

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Chapter 2: Schistosoma species detection by eDNA assays

sequences of eight other Schistosoma species using ClustalW in Bioedit v.7.2.6 (Hall, 1999), namely

Schistosoma curassoni AP017708, Schistosoma mekongi AF217449, Schistosoma margrebowiei

AP017709, Schistosoma spindale DQ157223, Schistosoma bovis QMKO01004774, Schistosoma rodhaini LL973454, Schistosoma indicum EF534284 and Schistosoma incognitum EF534285. We used

PrimerBlast (Ye et al., 2012) and PrimerQuest (Integrated DNA Technologies, Coralville IA; https://eu.idtdna.com/Primerquest/Home/Index) to design primers and probes. In both tools we selected default settings, except PCR product size was restricted to between 70 to 170 bp. To avoid cross-species amplification, primers were selected that had at least two mismatches with non-target species. Probes were designed with a dual labelled 5(6)-carboxy-fluorescein (FAM) fluorescent tag at the 5’ end and with a Black Hole Quencher 1 at the 3’ end (Integrated DNA Technologies, Coralville,

IA). Final expected lengths of PCR products for S. mansoni, S. haematobium and S. japonicum were

104, 143 and 87 bp, respectively (Table 2.1).

Prior to using focussing on the 16S gene for the primer and probe design, we explored potential primers for both the mitochondrial cytochrome oxidase subunit I (COX1) gene and the mitochondrial

12S ribosomal RNA (rRNA) gene. Although primer and probe sets for both genes were successfully developed, they showed high levels of cross-species amplification.

2.2.2 Specificity of S. mansoni, S. haematobium and S. japonicum assays

Specificity of the primer/probe sets was established using quantitative PCR (qPCR) tests on both genomic and synthetic DNA of known origin. Genomic DNA from S. mansoni, S. haematobium and S. japonicum was sourced from BEI Resources Repository of the National Institute of Allergy and

Infectious Diseases (NIAID, Manassas, VA, USA), and diluted to 0.01ng/μl for qPCR tests. The synthetic

DNA was generated from GenBank published sequences for S. mansoni, S. haematobium, S. japonicum, S. curassoni, S. margrebowiei, S. spindale, S. bovis, S. rodhaini, S. indicum, S. incognitum

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Chapter 2: Schistosoma species detection by eDNA assays

and S. mekongi (Table 2.2). These species were chosen on the basis of the availability of published 16S sequences in 2018. Synthesis was carried out by Eurofins Genomics (Ebersberg, Germany) within a pEX-A128 E. coli vector, and Invitrogen by Thermo-Fisher Scientific (Waltham, MA, USA) within a pMA-

T E. coli vector or pMA-RQ (AmpR) E. coli vector. Accession numbers, fragment lengths and total synthesised DNA concentration are shown in Supplementary Information Table S2.1. To visualise the evolutionary relationships of these mitochondrial sequences, they were aligned using Clustalw in

DAMBE7 (Xia, 2018) and a maximum likelihood phylogeny was generated using the HKY+Γ model in

Mega-X (Kumar et al., 2018) with branch support estimated from 100 bootstrap replicates (Figure 2.1).

The template DNA concentration (copies of synthetic template DNA) for each qPCR reaction was calculated using a copy number and dilution calculator (Thermo-Fisher, Waltham, MA, USA; https://bit.ly/2JPZchd). Each total reaction volume of 5 µl per reaction included: 1 µl DNA template,

2.5 µl Master Mix (PrimeTime Gene Expression Master Mix; IDT), 1.25 µl molecular grade water and

0.25 µl of primer/probe mix. The primer/probe mix included 4 µl of each primer (100 µM, IDT) and 2

µl of probe (100 µM, IDT) and 40 µl of molecular grade water. Thermocycling conditions were 95°C for

3 min, followed by 45 cycles of 95°C for 0.05 seconds and 60°C for 30 seconds, as recommended by the PrimeTime Gene Expression Master Mix protocol. In addition to test samples, and positive control samples, negative no-template control samples were included for quality assurance. The reactions were run on an Eco48 real time qPCR system (PCRmax, Staffordshire, UK) in 48-well plates with ROX normalisation. DNA detection was expressed by cycle threshold (Ct) values.

2.2.3 Sensitivity of S. mansoni and S. haematobium assays

We tested the sensitivity of S. mansoni and S. haematobium assays, but not the S. japonicum assay.

To make a quantified stock solution for qPCR, the target 16S rRNA region was first amplified from genomic DNA extracts of S. mansoni and S. haematobium sourced from BEI Resources Repository of

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the NIAID using the species-specific primers described above, and a conventional end-point PCR. Each

PCR comprised 5µl polymerase buffer, 3µl MgCl2 of 25 mM, 0.2µl GoTaq DNA polymerase (5U/ml)

(Promega, Madison, WI, USA), 0.5µl dNTPs (10mM each), 12.3 µl molecular grade water, 1µl of each

10 µM primer (IDT) and 2µl template DNA (total volume of 25µl per reaction). The PCR conditions were: 95°C for 2 min of initial denaturation, followed by 40 cycles of 95°C for 30 sec, 55°C for 30 sec, and 72°C for 30 sec, and a final extension at 72°C for 5 min. A negative no-template control was included. The presence of a single PCR product of expected size was confirmed on an 1.5% agarose gel stained with gel red nucleic acid gel stain, 10,000X (Biotium, Fremont, CA) and a PCRSizer 100bp

DNA Ladder (Norgen Biotek, Thorold, Canada) (Figure 2.2).

To make the serial dilutions to measure assay sensitivity, each PCR product was then purified using the QIAquick PCR purification kit (Qiagen) following the manufacturer’s protocol. Then, the concentrations of DNA were measured using a Qubit fluorometer (Invitrogen, Waltham, MA). The number of target copies was calculated using the Thermo-Fisher DNA copy number and dilution calculator (above). Then, the stock solution was stored at -20°C for qPCR tests. Before each qPCR test, a dilution series of known concentrations ranging from 1,000,000 copies/μl decreasing ten-fold down to 1 copy/μl was prepared for each assay (Table 2.3). These serial dilutions were used within two days to limit potential effects of DNA degradation.

Three qPCR technical replicates were run for each of the seven ten-fold dilution standards, including negative no-template controls, using the qPCR protocol described above. The limit of detection within a single qPCR reaction (LODI) was defined as the lowest concentration where there is a 95% chance of amplification success in any one individual PCR reaction. We also calculated the lowest concentration with a 95% chance of amplification success in any one of three technical replicate qPCR reactions of the same sample (LODIII), which we considered a useful metric given that qPCR reactions of eDNA are

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Chapter 2: Schistosoma species detection by eDNA assays

typically conducted in triplicate. These concentration limits were derived from all standards run and calculated by fitting logistic models (Forootan et al., 2017) using CurveExpert Basic 2.1.0 (Hyams

Development). The limit of quantification (LOQ) was defined as the lowest concentration at which 90% of all standards run were able to be amplified, following protocols described in (Klymus et al., 2015).

2.2.4 Environmental DNA from aquarium water samples

To validate the accuracy of the S. mansoni qPCR assay, water samples were collected from four aquaria housing patent Biomphalaria glabrata host snails infected with S. mansoni, at the Wellcome Sanger

Institute (WSI), Cambridgeshire, UK. The complete life cycle of Schistosoma mansoni (NMRI strain) is maintained at the WSI by breeding and infecting B. glabrata snails and mice. Mouse infections are performed under the Home Office Project Licence No. P77E8A062 held by GR. We did not specifically quantify the number of infected snails in each aquaria, but the total number of snails in each aquarium was recorded (range 13 to 75). Additionally, water samples were collected from two aquaria holding non-infected B. glabrata, and a sample of sterile water was collected at the site for use as a negative control. Sampled water was filtered through a Sterivex filter with a pore size 0.22 µm and a polyethersulfone membrane (EMD Millipore corporation, UK), using a peristaltic pump unit. Volumes of water filtered in a single replicate varied between 60–500 ml, depending on the saturation of the

Sterivex filters (Table 2.4). After filtration, absolute ethanol was pushed through the filter with a sterile

50 ml syringe to preserve the samples, and each filter was kept individually in a labelled 118 ml capacity Whirl-Pak bag (Sigma-Aldrich, UK). Filters were placed in a polystyrene box or a cooler with ice during transportation to the laboratory, and kept at -20oC until being processed.

Before extraction, the laboratory bench was cleaned with 10% bleach, then with 70% ethanol, and finally UV light was used to eliminate residual DNA. All tools that were to be used in handling the filter, including tweezers, blades and scissors were wiped with 10% bleach then washed with 70% ethanol

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Chapter 2: Schistosoma species detection by eDNA assays

to avoid sample cross-contamination. Gloves were exchanged for new at each step of the extraction process. The extraction of the eDNA from the individual filters was performed using the DNeasy Power

Water Kit (Qiagen, UK). This required first breaking the top off each filter capsule, removing the filter, and cutting the membrane into strips. DNA was then extracted from the membrane strips using the manufacturer’s protocol. After extraction, the sample was eluted into 50 μl of EB buffer. The S. mansoni DNA was quantified using qPCR procedures as described above for the specificity assays.

Three PCR replicates were carried out for each sample, alongside three replicates of a negative no- template PCR control, and three replicates of each concentration in a standard curve serial dilution of control positive PCR-derived DNA of S. mansoni (ranging from 1,000,000 copies/μl to 1 copy/μl).

2.2.5 Environmental DNA from the natural environment

To test the S. mansoni and S. haematobium assays on samples from natural habitats, water samples were collected from the surface of freshwaters at eight locations in Tanzania during September 2018

(Table 2.5; Figure 2.3). These included locations where both S. mansoni and S. haematobium were plausibly co-endemic due to the presence of host snails and close proximity of human settlements. It also included locations where both S. mansoni and S. haematobium were plausibly absent due to the absence of host snails and the greater distance from settlements. At each location coordinates were obtained using a handheld etrex GPS (Garmin, Olathe, KS), and the pH, conductivity, total dissolved solids and temperature of the water were measured using a multimeter (Hach, Loveland, CO) (Table

2.5). At each location MJG conducted a 10 minute search for aquatic snails in shallow water benthic habitats including rock surfaces, soft sediments and marginal vegetation in order to obtain an estimate of snail population density. The collected snails from each site were preserved in absolute ethanol and stored in 118 ml capacity whirlpak bags. Additional snail samples were also collected at sites A and B allowing more thorough testing of the frequency of infected snails at these sites (Table 2.5). Snails were identified to the species level based on shell morphology (Brown, 1994).

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Chapter 2: Schistosoma species detection by eDNA assays

At each of the eight sites, three replicate eDNA samples were collected. Each collection involved the pumping of water through a sterile 0.22 µm Sterivex filter using a sterile 50 ml single-use syringe.

Volumes of water filtered varied between 100 to 400 ml (Table 2.6). After filtration, absolute ethanol was pushed through the filter with a sterile 50 ml syringe to preserve the samples, and filters were kept in labelled 118 ml capacity whirlpak bags. Bottled drinking water was filtered alongside the samples in the field and used as a negative sampling control. DNA was isolated from the filters using the DNeasy Power Water Kit as described above. Each qPCR was performed in triplicate on each eDNA extract, alongside the negative sampling control, the negative non-template PCR control and each of ten-fold serial dilutions of control positive DNA. Analyses of samples were done for only S. mansoni and S. haematobium, and they were conducted separately.

To quantify the infection status of individual molluscs, snails were separated, and individual snails were washed with distilled water. For most of extractions, the whole body was crushed, homogenised, a small sample of tissue (no more 20mg) was used, to ensure that both patent and non-patent infections can be detected. DNA extracted using the DNeasy Blood & Tissue Kit (Qiagen, UK) according to the manufacturer’s protocol. Analyses were conducted using the qPCR approach as described above, although only presence or absence of an amplification is reported herein. Due to limitations on available field time, it was not possible to test if individual snails were shedding cercariae, although this would have represented valuable information. A PCR-based screening approach was chosen as it allowed us the opportunity to collect the samples in the field and screen the samples at a later date accurately for the focal species, the method can also amplify individuals with prepatent infections that are not currently shedding cercariae (Farghaly et al., 2016). To confirm the identity of PCR products amplified during these tissue assays, conventional end-point PCR products from three Biomphalaria pfeifferi samples and three Bulinus globosus samples from site 1 were Sanger sequenced. PCR products were purified using DNA Clean & Concentrator-5 (Zymo Research, Irvine, CA, USA) according

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to the manufacturer’s protocol, and sequenced by Eurofins Genomics (Ebersberg, Germany) using the forward PCR primer. The identity of derived sequences was confirmed using a default BLAST search against the NCBI GenBank nucleotide database after the sequences were trimmed and base calls checked.

2.3 Results

2.3.1 Environmental DNA assays exhibit high levels of species specificity.

In the qPCR specificity experiments, no amplifications occurred from the negative controls, and we observed no cross-species amplifications between S. mansoni, S. haematobium, S. japonicum.

When the qPCR assays were tested on the species specific synthetic DNA (Figure 2.1), the results indicated some cross-species amplification of the S. haematobium and S. japonicum assays with closely-related sister species, albeit with lower levels of qPCR amplification intensity for the non-target species (Table 2.2). Specifically, the assay for S. japonicum not only amplified synthetic S. japonicum

DNA (average Ct score 20.19), but also amplified synthetic DNA of closely-related S. mekongi (average

Ct score 28.47). Likewise, the assay for S. haematobium amplified synthetic S. haematobium DNA

(average Ct score 27.93), but also amplified synthetic DNA of the closely-related S. bovis (average Ct score 35.67) and S. curassoni (average Ct score 30.59) (Table 2.2).

2.3.2 High sensitivity of S. mansoni and S. haematobium eDNA assays

Efficiencies of the S. mansoni qPCR assay across 17 dilution series ranged from 91.55 to 110.86%, while

R2 ranged from 0.97 to 0.99. The S. mansoni assay amplified all standards between 1,000,000 copies/µl and 100 copies/µl. Amplification success became inconsistent at 10 copies/μl (32/51 PCR reactions,

63%) and was rare at the lowest concentration of 1 copy/ μl (2/51 amplifications, 4%). The resolved

LOQ for the S. mansoni assay was therefore 100 copies/µl. The resolved limit of detection where there 47

Chapter 2: Schistosoma species detection by eDNA assays

was 95% probability of successful individual PCR amplification (LODI) was 41.68 copies/µl, while the

95% probability of successful amplification in at least one of the triplicate qPCRs (LODIII) was 10.84 copies/µl (Table 2.3; Figure 2.4).

Efficiencies of the S. haematobium qPCR assay across 12 dilution series ranged from 94.87 to 105.45%, while R2 ranged from 0.98 to 0.99. The S. haematobium assay amplified all standards between

1,000,000 copies/µl and 1,000 copies/µl. Amplification was mostly successful at 100 copies/µl (34/36

PCR reactions, 94%) and 10 copies/μl (29/36 PCR reactions, 81%), but was inconsistent at 1 copy/μl

(Table 2.3). The resolved LOQ for the S. haematobium assay was therefore 100 copies/µl. The resolved limit of detection where there was 95% probability of successful individual PCR amplification (LODI) was 108.39 copies/µl, while the 95% probability of successful amplification in at least one of the triplicate qPCRs (LODIII) was 19.19 copies/µl (Table 2.3; Figure 2.4).

2.3.3 Environmental DNA assays detected schistosomes from aquarium water samples

The qPCR assay detected S. mansoni DNA in all infected aquaria with mean Ct values ranging from

24.64 to 27.77 (Table 2.4). There were no positive amplifications from non-infected aquaria, or sterilised water controls. Average qPCR efficiency was 103% for the assays on these aquarium samples

(range 99.38- 106.95) with mean R2 value of 0.99. All positively amplified samples were above LOQ,

LODI and LODIII values for the species assay, and the mean number of copies of DNA detected in each sample ranged from 7,565 to 48,264 copies/μl (Table 2.4)

2.3.4 Environmental DNA detected the presence of schistosomes from the natural environment

For S. mansoni, four sites out of the eight (A, B, C and D) were positive for S. mansoni eDNA (Tables

2.5 & 2.6; Figure 2.3). At two of these (sites A and B), B. pfeifferi were found, while at sites C and D, B. pfeifferi was not found (Table 2.5). The other four locations (E, F, G, H) were eDNA negative, only one

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had B. pfeifferi present, and these snails were determined to be infected through PCR analysis of tissue

DNA (Table 2.5; Figure 2.3). Thus, the eDNA assay was congruent with the PCR tests for infected snails in 5/8 (62.5%) of locations. Notably, the estimated numbers of S. mansoni copies resolved in most samples were below the defined LOQ of 100 copies/μl, the LODI of 41.68 copies/μl and the LODIII of

10.84 copies/μl.

For S. haematobium, the target species was detected in eDNA at a single site (Site B; Table 2.6). At this site all replicates successfully amplified, and infected Bulinus snails were abundant (Table 2.5).

Infected Bulinus were only detected at one other location (Site A), and this site was resolved as negative for eDNA. Thus, the eDNA assay was congruent with the PCR tests for infected snails in 7/8

(87.5%) of locations. The estimated numbers of S. haematobium copies were below the defined LOQ of 100 copies/μl, below the LODI of 108.39 copies/μl, and mainly below the LODIII of 19.19 copies/μl.

Sequencing confirmed the source of PCR product from B. globosus tissue as S. haematobium (100%

BLAST match to GenBank accession EU567132). Similarly, the source of PCR product from B. pfeifferi tissue was confirmed as S. mansoni (100% BLAST match to GenBank accessions AF130787 and

LR214937), (Table S2.2).

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Table 2.1 Species-specific primers and probes designed for PCR amplification of the 16S rRNA mitochondrial gene from three human-infecting Schistosoma species.

Target Geographical Product Primers / Probe Nature Sequences (5’-3’) species strains length (bp)

SM-16SrRNA-F Forward CTGCTCAGTGAAGAAGTTTGTTT Schistosoma Puerto Rico SM-16SrRNA-P Probe AGCCGCGATTATTTATCGTGCTAAGGT 104 bp mansoni SM-16SrRNA-R Reverse CCTCATTGAACCATTCACAAGTC SH-16SrRNA-F Forward AATGAACATGAATGGCCGCA Schistosoma Egypt SH-16SrRNA-P Probe TGGAGACTTGTGAATGGTCGAACG 143 bp haematobium SH-16SrRNA-R Reverse ATGGGTTCCTCACCACTTAAACT SJ-16SrRNA-F Forward TATGGCCTGCCCAATGTTGT Schistosoma China SJ-16SrRNA-P Probe TGGTCGCAGTTTTACTGTGCTAAGGT 87 bp japonicum SJ-16SrRNA-R Reverse ACAAGCCACTAATTAAGAAGCGA

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Chapter 2: Schistosoma species detection by eDNA assays

Table 2.2 Tests of S. mansoni, S. haematobium and S. japonicum qPCR assays on the genomic (gen) and synthetic (syn) DNA of Schistosoma species.

Target DNA Primers S. mansoni S. haematobium S. japonicum Ct mean (range) Ct mean (range) Ct mean (range) S. mansoni (gen) 0.01 ng 27.88 (27.79-28.03) - - S. haematobium (gen) 0.01 ng - 28.71 (28.54-28.87) - S. japonicum (gen) 0.01 ng - 26.83 (26.49-27.01) S. mansoni (syn) 1000 copies/µl 29.38 (28.94-30.17) - - S. rodhaini (syn) 1000 copies/µl - - - S. japonicum (syn) 1000 copies/µl - - 28.47 (28.06-28.84) S. mekongi (syn) 1000 copies/µl - - 34.94 (34.61-35.35) S. indicum (syn) 1000 copies/µl - - - S. spindale (syn) 1000 copies/µl - - - S. incongnitum (syn) 1000 copies/µl - - - S. haematobium (syn) 1000 copies/µl - 27.93 (27.72-28.20) - S. bovis (syn) 1000 copies/µl - 35.67 (35.61-35.71) - S. curassoni (syn) 1000 copies/µl - 30.59 (30.43-30.72) - S. margrebowiei (syn) 1000 copies/µl - - -

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Chapter 2: Schistosoma species detection by eDNA assays

Table 2.3 Ten-fold serial dilutions used in the S. mansoni and S. haematobium assays. In total these reflect the results of 17 S. mansoni assays, and 12 S. haematobium assays, with mean CT efficiency and r2 range.

Assay Concentration Amplification Amplification Mean CT (Range) Efficiency r2 range (copies) success (number of success (at cycle range (%) individual PCRs) least one qPCR of three replicates) S. mansoni 1,000,000 51/51 17/17 21.01 (20.08 - 22.32) 91.6 - 110.9 0.97 - 0.99 100,000 51/51 17/17 24.44 (23.33 - 25.55) 10,000 51/51 17/17 27.40 (26.31 - 28.44) 1000 51/51 17/17 31.10 (30.02 - 32.84) 100 51/51 17/17 34.27 (32.44 - 36.67) 10 32/51 16/17 37.09 (35.38 - 42.02) 1 2/51 2/17 37.30 (37.06 - 37.55) S. haematobium 1,000,000 36/36 12/12 18.35 (16.94 - 21.86) 94.9 - 105.5 0.98 - 0.99 100,000 36/36 12/12 21.68 (20.22 – 25.21) 10,000 36/36 12/12 25.09 (23.61 – 28.46) 1000 36/36 12/12 28.37 (24.93 – 32.41) 100 34/36 12/12 31.54 (30.06 - 37.72) 10 29/36 11/12 34.24 (32.87 - 37.73) 1 18/36 9/12 36.73 (34.98 - 38.17)

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Table 2.4 Detection of S. mansoni DNA from aquarium water samples.

Status Replicate No. snails Water DNA Amplification Ct mean Mean copies per μl (individuals) filtered concentration success (cycle) (range) (ml) ng per μl Non-infected Tank 1: Rep 1 33 500 54.95 0/3 - - Tank 1: Rep 2 460 58.50 0/3 - - Tank 2: Rep 1 28 500 115.00 0/3 - - Tank 2: Rep 2 460 61.3 0/3 - - Infected Tank 3: Rep 1 19 160 84.50 3/3 26.95 9041 (8580 to 9949) Tank 3: Rep 2 90 63.65 3/3 27.77 9011 (8392 to 9958) Tank 4: Rep 1 75 200 85.90 3/3 27.19 7565 (7342 to 7737) Tank 4: Rep 2 200 114.10 3/3 26.34 14038 (13384 to 14518) Tank 5: Rep 1 13 70 88.95 3/3 25.58 24599 (22498 to 28659) Tank 5: Rep 2 60 102.20 3/3 25.39 28130 (26232 to 30259) Tank 6: Rep 1 18 73 100 3/3 25.15 33296 (33066 to 33739) Tank 6: Rep 2 78 114.30 3/3 24.64 48264 (45033 to 52181) Negative NA 0 500 - 0/3 - - control

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Table 2.5 Sampling locations and environmental characteristics of eDNA from Tanzania in 2018. – indicates no data, abs = host snails absent

Site Name Latitude Longitude Sampling Maximum Total Conductivity Temperature pH Bulinus Biomphlaria Lymnaea Infected Infected (decimal (decimal date water dissolved (μS/m) (˚C) globosus pfeifferi natalensis Bulinus Biomphalaria degrees) degrees) depth (m) solids (Individuals (Individuals (Individuals / total tested / total tested (ppm) per 10 min per 10min per 10min by PCR* by PCR* search) search) search) A Mpemba -9.24289 32.84196 16-Sep-18 1 205 300 23.3 8.15 4 133 1 29 / 48 145 / 364 River B Mpemba -9.26564 32.84167 16-Sep-18 0.5 288 430 27.5 8.67 55 36 1 49 / 52 14 / 16 River C Vwawa -9.12298 32.91646 16-Sep-18 0.5 57 86 21.8 7.82 0 0 0 abs abs River D Mlowo -9.01295 33.01048 16-Sep-18 0.5 106 114 22.4 7.56 0 0 1 abs abs River E Myovizi -8.97372 33.0663 17-Sep-18 0.5 90 135 18.6 8.40 0 0 0 abs abs River F Songwe -8.9526 33.22449 17-Sep-18 0.5 153 198 29.5 - 0 0 0 abs abs River G Nzovwe -8.89937 33.3272 17-Sep-18 0.5 374 558 24.3 - 0 0 0 abs abs River H Igowilo, -8.89683 33.55588 18-Sep-18 0.1 184 242 24.6 - 0 16 0 abs 2 / 16 Mbeya *Includes snail samples additional to those collected in the 10 minute search.

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Table 2.6 Results of qPCR detection assays of both S. mansoni and S. haematobium eDNA from water samples collected in Tanzania 2018.

S. mansoni S. haematobium

Site Name Replicate Water sampled Total DNA Amplification Ct (mean)** Copies mean Amplification Ct (mean)** Copies mean (ml) concentration success* (range)*** success* (range)*** (ng/ul) A Mpemba River 1 400 5.35 1/3 38.5 0.9 (0-2.7) 0/3 - - 2 400 2.05 0/3 - - 0/3 - - 3 400 6.85 0/3 - - 0/3 - - B Mpemba River 1 120 7.60 2/3 39.3 1.02 (0-1.8) 3/3 34.3 9.2(5.6-11.7) 2 120 7.90 3/3 36.1 19.8 (3.6-29.6) 3/3 33.0 22.7(15.5-31.8) 3 120 9.35 3/3 39.8 1.7(0.2-2.8) 3/3 34.5 7.7(5.5-11.9) C Vwawa River 1 190 2.45 0/3 - - 0/3 - - 2 190 2.10 3/3 43.01 0.12 (0.1-0.2) 0/3 - - 3 190 1.30 0/3 - - 0/3 - - D Mlowo River 1 150 2.80 0/3 - - 0/3 - - 2 150 1.50 1/3 37.48 0.7 (0-5.8) 0/3 - - 3 150 3.25 0/3 - - 0/3 - - E Myovizi River 1 100 1.70 0/3 - - 0/3 - - 2 100 1.25 0/3 - - 0/3 - - 3 100 0.10 0/3 - - 0/3 - - F Songwe River 1 150 0.70 0/3 - - 0/3 - - 2 150 1.85 0/3 - - 0/3 - - 3 150 1.05 0/3 - - 0/3 - - G Nzovwe River 1 150 2.45 0/3 - - 0/3 - - 2 150 2.90 0/3 - - 0/3 - - 3 150 0.05 0/3 - - 0/3 - - H Igowilo, Mbeya 1 160 2.0 0/3 - - 0/3 - - 2 160 3.55 0/3 - - 0/3 - - 3 160 3.80 0/3 - - 0/3 - -

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Chapter 2: Schistosoma species detection by eDNA assays

S. haematobium

0.97 S. bovis haematobium group 0.87 S. curassoni

0.66 S. margrebowiei

S. spindale 0.75 indicum group 0.90 S. indicum

S. mansoni 0.94 1.00 mansoni group S. rodhaini

S. incognitum

S. japonicum 0.94 japonicum group S. mekongi

Figure 2.1 Maximum likelihood phylogenetic reconstruction of the mtDNA 16S sequences used for the establishing the specificity of the assays designed for S. haematobium, S. mansoni and S. japonicum. Numbers above branches represent the branch support (proportion of 100 bootstrap replicates.

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Chapter 2: Schistosoma species detection by eDNA assays

(a (b (c Ladder

200 bp 100 bp

Figure 2.2 Agarose gel image of the amplification test using conventional PCR. The newly designed primers were used in PCR reactions for: a) S. haematobium, b) S. mansoni and c) S. japonicum. Good levels of amplification were observed for the products of the expected sizes (104 bp for S. mansoni, 143bp for S. haematobium and 87 bp for S. japonicum. No cross amplification was present between three species.

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Chapter 2: Schistosoma species detection by eDNA assays

Figure 2.3 Locations of eDNA sampling sites in the Mbeya Region of Tanzania in 2018. In total eight sites were surveyed, labelled A-H. The key results from eDNA survey (helix symbol), host snail survey (snail symbol) and test for the presence of schistosomes in snail tissue (cercariae symbol) are given for S. haematobium (H) and S. mansoni (M) assays, with + indicating the site was positive, while a dot indicates no detection was made. Map drawn using the following open source software and data: DIVA-GIS7.5 (https://www.diva-gis.org); Africa river network (15 sec resolution), African drainage basin (15sec resolution) and African Digital Elevation Model data (30 sec resolution) from HydroSHEDS (https://www.hydrosheds.org); African Water Bodies shapefile from RCMRD GeoPortal (http://geoportal.rcmrd.org).

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Chapter 2: Schistosoma species detection by eDNA assays

a) b)

S. mansoni S. mansoni

Probability of amplification of Probability Probability of amplification of Probability

Number of copies (log10) Number of copies (log10) c) d)

S. haematobium S. haematobium

Probability of amplification of Probability Probability of amplification of Probability

Number of copies (log10) Number of copies (log10)

Figure 2.4 Probability of qPCR amplification of DNA standards of concentrations ranging from 1 copy/μl to 1,000,000 copies/μl. a) S. mansoni assay – probability of amplification in any one qPCR across all standard templates tested. b) S. mansoni assay – probability of amplification in any one standard template that is subject to triplicate qPCR, c) S. haematobium assay – probability of amplification in any one qPCR across all standard templates tested, d) S. haematobium assay – probability of amplification in any one standard template that is subject to triplicate qPCR. Lines represent logistic models of the form y = a / (1 + b*e(-cx)). a) a = 1.002, b = 27.303, c = 3.826. b) a = 1.000, b = 7.501, c = 4.786. c) a = 1.004, b = 1.009, c = 1.415. b) a = 1.004, b = 0.342, c = 1.399. All models r > 0.995. Grey shading indicates 95% confidence intervals.

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2.4 Discussion

In this study PCR-based assays have been developed to detect the three main human-infecting

Schistosoma species from tropical freshwaters; S. mansoni, S. haematobium and S. japonicum. Probes and primer pairs were designed to amplify the 16S rRNA region of the mitochondrial DNA, with product size <150 bp which makes them well suited to amplify small fragments of degraded DNA. Our study provided newly characterised primers that reliably amplify and distinguish each of the three main species that infect humans, unlike previous investigations of Schistosoma eDNA (Sato et al.,

2018; Sengupta et al., 2019). In addition, we have exhaustively tested the sensitivity and species- specificity of the assays in-vitro. The latter is critical for the evaluation of Schistosoma eDNA assays, not only because multiple species of schistosome may be present within the same area, potentially leading to false positive results, but also because in-silico testing of primers has frequently been shown to yield inaccurate results when compared to those from in-vitro tests (Henriques et al., 2012).

In addition to testing genomic DNA from the three main human species, we chose to use synthetic

DNA in the in-vitro species specificity tests. This approach was selected in order to ensure confidence in the species that the DNA represented, without risk of using natural samples potentially contaminated during their collection and/or curation. Notably, given the sensitivity of this assay, even small amounts of cross-species tissue contamination during handling or processing, on forceps for example, would result in positive PCR results. Nevertheless, if it is possible to obtain guaranteed contamination-free schistosome tissue or DNA extractions, then these would be preferable to the use of synthetic DNA due to the possibilities of different results, perhaps because of the presence of alternative priming sites in the genome. It would be valuable to experimentally compare assay results from synthetic DNA with extracted DNA from tissue collection, such as those within SCAN (the schistosomiasis collection at the NHM; http://scan.myspecies.info/).

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We were able to test our assays against DNA from a broad variety of relevant species, natural samples of which would be difficult to obtain. Our assays consistently amplified both genomic and synthetic

DNA of the target species, but they also amplified closely-related species. Specifically, the S. haematobium assay amplified S. bovis and S. curassoni from the ‘haematobium group’, while the S. japonicum assay amplified S. mekongi from the ‘japonicum group’ (Figure 2.1). Such cross-species amplification could lead to false-positive amplifications, and in practice the impact of any cross-species amplification will depend on the geographic context where the assay is used. For example, application of the S. haematobium assay at locations in north-west Africa where it occurs with S. curassoni and S. bovis may be affected by false positives. Co-transmission may be widespread (Huyse et al., 2009). We were unable to assess the full impact of cross-transmission, because there were multiple species for which existing 16S sequence data were unavailable when the assay was being tested. These included species in the ‘haematobium group’, such as S. mattheei, S. kisumuensis, S. intercalatum, S. guineensis and S. leiperi. Ideally knowledge of cross-amplification with S. mattheei in particular would be valuable, given it is present in within the Lake Malawi catchment (Webster et al., 2019), which is in close proximity to our field sampling site in southern Tanzania.

During deployment of the assays it may be beneficial to undertake screening to establish Schistosoma species present using alternative methods. These could include morphological (cercarial shedding) analyses, and genetic methods such as molecular identification of cercariae, sequencing of individual infected snails, and sequencing of amplicons from eDNA samples. Another consideration is that there is potential for cross-species amplification where introgressive hybridization is present among closely- related species, for example between S. haematobium and S. bovis, and between S. haematobium and

S. mattheei (Huyse et al., 2009; Boissier et al., 2016; Webster et al., 2013; Leger and Webster, 2017;

Webster et al., 2019). In such cases, hybridization can lead to sharing of mitochondrial DNA between parental species, and an environmental DNA assay alone would not be able to distinguish between

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the species, or identify the presence of hybrids, so it would be useful to additionally amplify multiple nuclear loci.

We showed it was possible to reliably amplify schistosome eDNA from filters collecting material from natural water bodies and experimental aquaria. None of our negative control samples amplified, and thus we have confidence that the PCRs were amplifying only target schistosome material. It is possible that the filters contained environmental DNA in solution, in cellular debris, and also as whole individual cercariae. From a practical perspective for testing for the presence or absence of the schistosome parasites, it may be of little consequence whether the sample is obtained from partial or whole organisms. However, if the method is to be used for evaluating the relative biomass of schistosomes in the environment, then capture of whole organisms on filters may be problematic as it could dramatically elevate measure levels of DNA samples above the background levels of eDNA in the environment. It is possible that the use of pre-filtration steps may be required to exclude whole organisms, potentially providing more standardised quantitative spatial and temporal comparisons

(Sengupta et al., 2019). Alternatively, it would be possible to modify the environmental DNA methods used here to focus sampling exclusively on cercariae (Rudko et al., 2018; Rudko et al., 2019). Such

“qPCR cercariometry” requires relatively large volumes of water (e.g. 25 litres) that are filtered through a 20µm mesh zooplankton net, before residual material is collected and concentrated allowing qPCR analyses (Rudko et al., 2018; Rudko et al., 2019). This cercariometry method would allow the collection of large volumes of genetic material from the environment; however such zooplankton nets are expensive and their re-use requires decontamination between sampling events if they are to remain effective.

Our eDNA assays showed agreement with tests of direct qPCR-based tests of the infection status of snail hosts. Specifically, eDNA showed 62.5% consistency with tests for S. mansoni in host gastropods,

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and 87.5% consistency with tests for S. haematobium in host gastropods. Thus, our assays are similar in performance to those reported by Sengupta et al. (Sengupta et al., 2019) for S. mansoni in Kenya with a 71% agreement between eDNA and conventional survey methods. Importantly, both our study and Sengupta et al. (Sengupta et al., 2019). detected cases where the conventional survey failed to detect evidence of S. mansoni that was present in the eDNA assay. Such results could have been because schistosomes were present locally, but host snails were rare and therefore difficult to sample.

Alternatively, given that the sites at which we detected eDNA but no host snails had flowing water, our analyses could have detected allochthonous schistosome DNA from upstream locations. In either case, the presence of the schistosome eDNA would be indicative of a risk of infection, and perhaps eDNA surveillance could help to identify locations where risk is present but may otherwise go unnoticed. It is also possible that we sampled material from non-transmissive life stages (eggs, miracidia) derived from local contamination sources. Notably, there were also instances where eDNA assays tested negative, but host infected snails were present. In such circumstances it is possible that cercarial production is limited at those locations, or more likely that either eDNA concentrations were below detection limits, or snails were not patent, or PCR inhibitors were present. Low eDNA concentration is the most likely explanation, however, as the PowerWater extraction kit used has dedicated inhibitor removal steps, and is designed for these kinds of heterogeneous samples. It would be beneficial to add an internal control to formally test for inhibition. This control can be non-target

DNA, added to the same sample tube, and co-amplified simultaneously with the target sequence

(Radstrom et al., 2008), as used for human diagnostics (Hoorfar et al., 2004). Additionally, further simultaneous sampling of eDNA, host snails and cercarial shedding of samples is recommended to more reliably estimate the sampling effort required to have high confidence in negative results from these assays.

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Multiple studies have described molecular-based diagnostic techniques for Schistosoma species (Kato-

Hayashi et al., 2010; Webster et al., 2010; Webster et al., 2012; Fernandez-Soto et al., 2014). These methods, alongside technical improvements in the reliability of PCR, have resulted in an increasing number of research centres in low income countries having access to the real-time PCR diagnostic technology (Ten-Hove et al., 2008). Widespread uptake of eDNA methods for Schistosoma surveillance is therefore feasible in principle. However, the extent to which eDNA methods can replace existing survey protocols will depend on the reliability of the assays for detecting low concentrations of eDNA, as this study identified sites that were eDNA negative but positive by alternative sampling methods.

This is most likely because eDNA levels were below detection limits. Specifically, our tests demonstrated that extracted concentrations of schistosome eDNA were at most ~40 copies/ul (which corresponds with ~16 copies/ml of sampled water), which is below our measured limits of detection where individual PCRs becomes 95% reliable. Expectedly, the S. mansoni assay was most successful at locations A and B where the Biomphalaria colonies were large and infection prevalence was extremely high (40% and 88% of snails, respectively). Moreover, the S. haematobium assay was most successful at the location B where the Bulinus colony was large and infection prevalence was as high as 94%. At location A, where the Bulinus colony was small yet infection prevalence was 60%, the assay was negative. Thus, the success of the assay is likely to depend both on snail abundance and infection prevalence. Importantly, all the locations where we identified schistosome-positive snails had much higher rates of infection than is typically observed, which can be around 2% (Angelo et al., 2014), although we recognize that the PCR-based tests of snail infection may have revealed cases of prepatent infections in non-shedding snails (Farghaly et al., 2016). Additionally, there was potential for cross-snail contamination with schistosome material during sampling, and future studies may benefit from testing the extent of this, perhaps by testing Biomphalaria samples for S. haematobium

DNA, and testing Bulinus samples for S. mansoni DNA.

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Further work is needed to develop improved methods of collecting, preserving and extracting aqueous environmental DNA in tropical environments for schistosome surveillance, including the number and volume of field replicates, the DNA preservation reagents used, and the extraction protocols. Recently

Sengupta et al. (Sengupta et al., 2019) estimated that up to seven one litre water samples may be enough to provide 95% confidence in the presence or absence of S. mansoni. In this study we used smaller volumes, with the upper limits determined by filter clogging. It may be that more filters are needed to collect the higher volumes of material required for reliable eDNA assays in turbid environments, which are often characteristic of schistosome transmission sites. Other characteristics of the environment may also influence the persistence of eDNA and chances of detection (Jane et al.,

2015; Sato et al., 2018; Barnes et al., 2014). For example, suspended sediment can adsorb DNA (Lorenz and Wackernagel, 1987), and the presence of PCR inhibitors such as humic acids, algae and siliceous sediments can determine the success of qPCR-based eDNA assays (McKee et al., 2015; Stoeckle et al.,

2017), increasing distance from the DNA source, and extent of flow can also determine detection probability (Jane et al., 2015). Therefore, further sampling and tests will be required to optimize the eDNA detection power in light of environmental variation. In particular, tests for the presence of PCR inhibition within sampled sites would also be valuable through developing internal controls.

2.5 Conclusions

To conclude, rapid and inexpensive diagnosis and surveillance of schistosome prevalence in freshwaters will provide timely advice to stakeholders who could promote interventions to interrupt transmission of schistosomiasis in developing countries. As social and environmental change proceeds even more rapidly, strategies for control of schistosomiasis must increasingly be adapted to local context, and tools for rapid evaluation of parasite presence at fine spatial scales are therefore urgently needed (Booth and Clements, 2018). Our results, together with those of recent parallel studies (Sato et al., 2018; Sengupta et al., 2019) provide promising indications that eDNA approaches could be

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considered as a tool for standard monitoring of parasite presence. However, further research is needed to resolve a sampling design that is reliable across habitat types and robust to idiosyncrasies of tropical fieldwork. Moreover, further work is needed to address the shortcomings of the methods we used in terms of specificity and sensitivity. If such limitations could be overcome, the tool could be valuable for surveillance studies and provide direct support to control strategies for this neglected tropical disease. However, it has been highlighted that the utility of eDNA assays is dependent on the nature of the schistosome life stages present in the environment (Stothard et al., 2017). More specifically, eDNA assays that yield positive results are unable to distinguish between a site where transmission is active (production of cercariae) or simply contaminated by eggs and miracidia from urine and faeces. Hence, eDNA assays alone may be unable to reliably distinguish locations where transmission has been successfully interrupted by intervention, and in such cases, it will need to be coupled with DNA screening of collected snails (Stothard et al., 2017). Therefore, the next chapter will test the potential of combining eDNA with xenomonitoring for estimating the prevalence of

Schistosoma infections in snail populations from natural freshwater bodies.

Acknowledgments

We thank staff from the Tanzania Fisheries Research Institute for contributions to fieldwork. The

Tanzania Commission for Science and Technology (COSTECH) provided fieldwork permits. We thank

Fiona Allan for providing museum samples we used in preliminary tests of the assays. We thank BEI

Resources Repository of the National Institute of Allergy and Infectious Diseases for the provision of schistosome material. We thank Richard Wall for his supervisory support, and Michelle Thieme for permission to use HydroSHEDS data for mapping. We thank Marcello Otake Sato and Russell Stothard for valuable comments on the manuscript.

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Chapter 2

Supplementary Information

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Table S2.1 Sequences used to develop synthetic DNA for testing species specificity of probes

Geographical Fragment Synthesised DNA Species Accession number Sequences Source strain length quantity TTAGTTAATTGTTTAATAGTAAGGCCTGCTCAGTGAAGAAGTTTGTTTAAATAGCCGCGATTATTTATCGTGCTAAGGT Eurofins Genomics (pEX- S. mansoni HE601612 Puerto Rico AGCATAATATATAGTCTTTTAATTGTAGACTTGTGAATGGTTCAATGAGGTGTGATTAAGGTGATAGTCTATTATCTGA 223 bp 2.4 µg A128) ATTTAGTTTAGTGGTTAGGAACCCATTGTTACATTATTAGACGGAAAGACCCCAAGAGCTTTTTT TTAGTTAGTTAGTTAATAGTAAAACCTGCTCAGTGAAATATATATTTTAAATAGCCGCGATTATTGATCGTGCTAAGGT Eurofins Genomics (pEX- S. rodhaini LL973454 Burundi AGCATAATATATAGTCTTTTAATTGTGGACTTGTGAATGGTTCAACGAGGTGTGATTAAGGTGATAGTTTGTTTCTGAA 222 bp 5.1 µg A128) TTTAGTTTGGTGGTTAAGAACCCACTATTATAATATTAGACGGAAAGACCCCAAGAGCTTTTCT TTTGTGTAAGAGGTAAATAGTATGACCTGCCCAATGAACAGATATATGAATGGCCGCAGCTTTTCGCTGTGCTAAGGT Invitrogen by Thermo- S. spindale DQ157223 Sri Lanka AGCATAATATATAGTTTTTTAATTGGGGACTTGTGAATGGTTTAATGAAGGGTGTCTAAAATAATAATTATTTCTGAAA 220 bp 5 µg TTAATTTAGTGGTAAGGAATCCACTATTAAGATATAGGACGGAAAGACCCCAAGAGCTTTACA Fisher Scientific (pMA-T) TTTGTTAGATATAAATGATATGACCTGCTCAATGTAGAAGTGTATAAATGGCCGCGGTTAAGAGTTTCGTGCTAAGGTA Invitrogen by Thermo- Bangladesh: S. incognitum EF534285 GCATAATATATAGCTTTTTAATTGAGGGCTTGTGAATGGTTTAATGAGATTTACATAAAAGGGCGATCTTTTTTGAAAT 220 bp 5 µg Fisher Scientific (pMA- Sunamgonj TGATTTATTAGTTAAGAATCTGATATTATAATATAAGACGGAAAGACCCCGAGATCTTTACT RQ (AmpR) TCTGATGAGGTTTAGATAGTATGACCTGCTCGATGAAAATGAACATGAATGGCCGCAGCTTTAGCTGTGCTAAGGTAG Invitrogen by Thermo- S. haematobium DQ157222 Mali CATAATATATAGTTTTTTGATTGGAGACTTGTGAATGGTCGAACGAAAGGTGTCTAAAATGATAATTATGTCTGAATTT 219 bp 5 µg AGTTTAAGTGGTGAGGAACCCATTTTTTGATTATAGGACGGAAAGACCCCAAGAGTTTTACT Fisher Scientific (pMA-T) TCTGATGAGGTTTAGATAGTATGACCTGCCCAATGAAAGTAAACATGAATGGCCGCAGCTTTAGCTGTGCTAAGGTAG Eurofins Genomics (pEX- S. bovis QMKO01004774 Tanzania: Iringa CATAATATATAGTTTTTTGATTGGAGACTTGTGAATGGTTGAACGAAAGGTGTCTAAAATGATAATTATATCTGAATTT 218 bp 2.9 µg A128) AGTTTAGTGGTGAGGAATCCATTATTTAAATATAGGACGGAAAGACCCCAAGAGTTTTACT TCTGATGAGATTTAGATAGTATGACCTGCCCAATGAAAGTAAACATGAATGGCCGCAGCTTTAGCTGTGCTAAGGTAG Invitrogen by Thermo- S. curassoni AP017708 Dakar, Senegal CATAATATATAGTTTTTTGATTGGAGACTTGTGAATGGTTGAACGAAAGGTGTCTAAAATGACAATTATATCTGAATTT 218 bp 5 µg AGTTTAGTGGTGAGGAACCCATTATTTAGATATAGGACGGAAAGACCCCAAGAGTTTTACT Fisher Scientific (pMA-T) TTTTGGTAGATTTAAATAGTATGACCTGCCCAATGAAACTTAACATGAATGGCCGCGGCATTAGCCGTGCTAAGGTAG Invitrogen by Thermo- S. margrebowiei AP017709 Zambia CATAATATATAGTTTTTTAATTGGAGACTTGTGAATGGTTGAACGAAAGGTGTCTAAAATGGTAACTGTATCTGAATTT 218 bp 5 µg Fisher Scientific (pMA-T) AGTTGAGCGGTGAGGAACCCGTTGTTATAGTATAGGACGGAAAGACCCCAAGAGTTTTACT Bangladesh: TTTGGGAGAGATAAATAGTATGACCTGCCCAATGAGCTTTTAAATGAATGGCCGCGGCTTTAGTCGTGCTAAGGTAGC Invitrogen by Thermo- S. indicum EF534284 ATAATATATAGTTTTTTAATTGGAGACTTGTGAATGGTTTAATGAAAGGTGTCTAAAATGATAGCTGTTTCTGAATTTA 217 bp 5 µg Mymensingh GTACGGTGGTCAGGAATCCATTGTTGAGATATAGGACGGAAAGACCCCAAGAGCTTTACA Fisher Scientific (pMA-T) Philippines: TTTGTTGGATTTAAATAGTATGGCCTGCCCAATGTTGTAAATTAATGGTCGCAGTTTTACTGTGCTAAGGTAGCATAAT Invitrogen by Thermo- S. japonicum JQ781206 ATATCGCTTCTTAATTAGTGGCTTGTGAATGGTTTAATGAAATAGAATATTTAAATGATGACTATATCTGAAATTGATTT 216 bp 1.5 µg Asuncion AGTGGTGCGGAATCCATTGTTATAGTATAAGACGGAAAGACCCCGAGATCTTGAATT Fisher Scientific (pMA-T) TTTGTTAGGTTTAAATAGTATGGCCTGCCCACTGTTGGAATAAATGGTCGCAGATTTTCTGTGCTAAGGTAGCATAATA Invitrogen by Thermo- S. mekongi AF217449 Khong Island TATCGCTTCTTAATTAGTGGCTTGTGAATGGTTTAATGAAATAGGGTGCTTAAATGATGACTATTTCTGAAATTGGTTTA 215 bp 3 µg GTGATGAGGAACTCGCTGTGATAATATAAGACGGAAAGACCCCAAGATCTTGGATT Fisher Scientific (pMA-T)

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Table S2.2 Sequences from PCR amplicon of Biomphalaria and Bulinus tissues after they were Sanger sequenced

Geographical Fragment Species Accession number Sequences strains length S. mansoni AF130787 Unknown GATCGTGCTAAGGTAGCATAATATATAGTCTTTTAATTGTAGACTTGTGAATGGTTCAATGAGG 63 bp Puerto Rico S. mansoni LR214937 AGGTAGCATAATATATAGTCTTTTAATTGTAGACTTGTGAAT 41 bp genome assembly Puerto Rico S. mansoni LR214937 AGGTAGCATAATATATAGTCTTTTAATTGTAGACTTGTGAATGGTTCAATGAGG 53 bp genome assembly ATATATAGTTTTTTGATTGGAGACTTGTGAATGGTCGAACGAAAGGTGTCTAAAATGATAATTATGTCTGAATTTAGTT S. haematobium EU567132 Malawi-LgHap2 97 bp TAAGTGGTGAGGAACCCAT ATATATAGTTTTTTGATTGGAGACTTGTGAATGGTCGAACGAAAGGTGTCTAAAATGATAATTATGTCTGAATTTAGTT S. haematobium EU567132 Malawi-LgHap2 96 bp TAAGTGGTGAGGAACCCA TATATAGTTTTTTGATTGGAGACTTGTGAATGGTCGAACGAAAGGTGTCTAAAATGATAATTATGTCTGAATTTAGTTT S. haematobium EU567132 Malawi-LgHap2 97 bp AAGTGGTGAGGAACCCATT

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Chapter 3: eDNA-xenomonitoring for determining Schistosoma

Chapter 3

Environmental DNA-based xenomonitoring for determining

Schistosoma presence in tropical freshwaters

A version of this chapter has been published in:

Alzaylaee, H., Collins, R.A., Shechonge, A., Ngatunga, B.P., Morgan, E.R. and Genner, M.J. (2020).

Environmental DNA-based xenomonitoring for determining Schistosoma presence in tropical freshwaters. Parasites and Vectors, 13, 63. (Appendix II)

Author contributions

HA and MJG designed the study with input from RAC. AS and BPN provided the sample resources. HA and MJG collected data, analysed the data and led the writing of the manuscript. RAC and ERM contributed critically to the drafts.

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Abstract

Schistosomiasis is a neglected tropical disease that infects over 200 million people worldwide. Control measures can benefit from improved surveillance methods in freshwaters, with environmental DNA

(eDNA) surveys having the potential to offer effective and rapid detection of schistosomes. However, sampling eDNA directly from natural water bodies can lead to inaccurate estimation of infection risk if schistosome eDNA is rare in the environment. Here we report a xenomonitoring method that allows schistosome infections of host snail species to be determined from eDNA in water used to house those snails. Host snail species were collected and placed in containers of water and allowed to shed cercariae, and then water samples were filtered and tested using qPCR assays specific to the African species Schistosoma mansoni and Schistosoma haematobium. We evaluated this “eDNA-based xenomonitoring” approach by experimentally comparing the results to those obtained from direct qPCR screening of tissue sourced from the snails in the experiment. We found that our method accurately diagnosed the presence of S. mansoni-infected snails in all tests, and S. haematobium- infected snails in 92% of tests. Moreover, we found that the abundance of Schistosoma eDNA in experiments was directly dependent on the number and biomass of infected snails. These results provide a strong indication that this surveillance method combining the utility of eDNA-based monitoring with the reliability of traditional xenomonitoring approaches could be used to accurately assess the presence of Schistosoma species in natural habitats. This approach may be well-suited for epidemiological studies and monitoring in endemic areas, where it can assist schistosomiasis control by indicating infection risk from freshwaters and guiding necessary interventions to eliminate the disease.

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3.1 Introduction

Schistosomiasis, also known as snail fever or bilharzia, affects an estimated 207 million people in over

67 countries worldwide, and there are over 779 million further people at risk of infection (Steinmann et al., 2006). The disease is considered a major cause of disability impeding socioeconomic development in regions of the world where it is endemic (Colley et al., 2014). It is listed as a ‘neglected tropical disease’ and has been recognised by the World Health Assembly as a disease that should be targeted by control programmes and elimination campaigns where appropriate (Fenwick and Jourdan,

2016. The disease is caused by parasitic trematodes that as adults are present in the blood vessels surrounding the urogenital or gastrointestinal tracts of human hosts. Eggs are then released into freshwaters via urine and faeces, miracidia hatch from eggs and infect snail hosts. Infected snails later release cercariae into the water, and the disease is acquired by humans when they encounter the cercariae (Colley et al., 2014). While the disease can be treated in humans using anthelmintic medication, a key factor in elimination of the disease will be the prevention of reinfection after treatment (King et al., 2006; Fenwick and Jourdan, 2016; Sokolow et al., 2016; Sokolow et al., 2018).

This could be achieved by reducing exposure of the human population to free-swimming schistosome cercariae, either by treating or manipulating freshwater habitats to eliminate snail hosts (Kariuki et al., 2013), or by alerting local human populations to the infection risk associated with use of freshwater environments. Both strategies will require an appropriate surveillance framework for the presence of schistosomes in freshwaters (Stothard et al., 2017). Moreover, expansion of areas suitable for transmission under climate change requires pro-active monitoring of new at-risk areas (McCreesh et al., 2015).

Conventionally, environmental monitoring for Schistosoma spp. has primarily focused on snail-based surveys in which snails are collected and exposed to light to induce cercarial shedding. Microscopical examination of schistosome cercariae is then used to determine the infection status of snails (Loy and

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Haas, 2001; Wolmarans et al., 2002; Kariuki et al., 2004), and the method requires considerable time, effort and expertise in the taxonomic identification of schistosome cercariae using microscopy.

Alternatively, it is possible to test infection status of individual snails using molecular xenomonitoring tests for the presence of Schistosoma DNA in snail tissue using conventional end-point PCR (Melo et al., 2006; Farghaly et al., 2016; Lu et al., 2016; Bakuza et al., 2017; Chua et al., 2017) or quantitative

PCR (Thanchomnang et al., 2011; Kane et al., 2013). While these methods requiring testing of individual snails have been very effective, they are limited by the need to test large numbers, as often only 1–2% of a total snail population are infected (Angelo et al., 2014; Weerakoon et al., 2018). Thus, without extensive testing using conventional methods it is possible for a schistosomiasis-endemic area with a low parasite burden to appear free of the infection risk, but transmission may continue with the potential to expand in future (Weerakoon et al., 2018).

Movement towards tests that can rapidly and reliably assess infection risk from trematode parasites in natural water bodies has been achieved through “cercariometry” (the collection and molecular testing of free-swimming cercariae) (Hung and Remais, 2008; Worrell et al., 2011), or testing of environmental DNA (eDNA) sampled directly from freshwaters (Sato et al., 2018; Sengupta et al.,

2019). Methods sampling “environmental DNA” are varied, but from a parasitological perspective, the term eDNA has been defined as “DNA extracted from environmental or organismal matrices, in other words from the environment or host organism” (Bass et al., 2015). Because environmental samples are inherently heterogeneous, it is therefore appropriate from this standpoint to define any DNA from microscopic organisms present within environmental samples to be eDNA, irrespective of whether the

DNA sampled originated from whole cercariae, cellular debris, chemically bound DNA, or DNA in solution.

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For schistosome surveillance, eDNA methods that rely on screening water samples collected directly from natural environment are promising, given the relative ease of field sampling, and the absence of any firm requirements to directly sample living organisms. However, the use of eDNA from such samples still needs evaluation, particularly in cases where infected snails are rare and Schistosoma

DNA is consequently below the limits of detection. Assays could yield false negatives if the water is turbid and adequate water volumes cannot be filtered, if turbid water contains PCR inhibitors, or if water movement transports eDNA away from a sampling site. Moreover, where Schistosoma eDNA is detected in areas where no snails were found during manual searches (Sengupta et al., 2019), it can be uncertain if infected snails are present but went undetected, or if parasite material has been transported to the sampling site from elsewhere. It is also possible that the local environment contains schistosome material released as miracidia from infected mammalian hosts, but the infectious cercarial stage is absent (Sengupta et al., 2019).

Given the potential limitations of testing for the presence of Schistosoma species using eDNA collected directly from the sampling site, particularly the risk of false negatives in cases of low schistosome density, it would be beneficial to design a protocol that enables the schistosome eDNA originating from cercariae shed by snail hosts to be concentrated prior to molecular testing. In this study, we report an approach where snails are collected and housed in experimental containers to allow them to shed cercariae, before eDNA in the water - from whole cercariae, cellular debris, or DNA chemically bound or in solution - is collected and its abundance measured using quantitative PCR (Figure 3.1).

3.2 Materials and Methods

3.2.1 Site description

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Tanzania is a country with high schistosomiasis endemism, where primary intermediate hosts include the freshwater snails Biomphalaria pfeifferi for S. mansoni and Bulinus globosus for S. haematobium.

On 16th September 2018, potential schistosome host snails were collected from two proximate locations in the Mpemba River in the Lake Rukwa catchment. At the time of the sampling, Site 1

(9.242°S, 32.841°E; site A in Chapter 2 of this thesis) was slowly flowing, and shallow (1.0 m maximum depth), with a temperature of 23.3 °C, pH 8.15, and conductivity 300 μS. Site 2 (9.265°S, 32.841°E; Site

B in Chapter 2 of this thesis) was not flowing, shallow (0.5 m maximum depth) with a temperature of

27.5 °C, pH 8.67, and conductivity 430 μS. At both sites the schistosome host snails B. pfeifferi and B. globosus were present, and snails were collected by scooping along 50 m of river for approximately 1 hour. These sites were chosen as they are within an endemic area of S. haematobium and S. mansoni, and our pilot work reported in Chapter 2 of this thesis indicated that both species were present in the river. Moreover, both sites are near towns, and at the time of sampling there was clear evidence that the river was being used by the local population regularly for bathing, fishing, washing clothes, washing vehicles and collection of water for household activities.

3.2.2 Experimental design and sample collection

All field-collected snails were taken back to the laboratory and identified to the species level based on shell morphology (Brown, 1994). At the start of the experiment the infection status of individual snails was not known. Although this would have provided valuable data in the infection status of individuals, it would have considerably extended the time between collection and our main experiment. Such as delay may have potentially affected results, and would have made the experimental trial less representative of an application of the method for biosurveillance. The experiment included five different treatments (A-E) that differed in the number of snails kept in each container and used river water collected from Site 1 (Table 3.1). The design aimed to achieve the objective of a range of infected snail numbers and infected snail biomass across containers in the experiment as a whole.

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Specifically, there were six replicates of the following; Treatment A included a median of 20 B. pfeifferi and 3 B. globosus, Treatment B included a median of 20 B. pfeifferi and 6 B. globosus, Treatment C included a median of 10 B. pfeifferi and 3 B. globosus, Treatment D included a median of 10 B. pfeifferi and 6 B. globosus, and Treatment E was a negative control of river water that contained no snails

(Table 3.1). In addition, Treatment F was a negative control treatment of tap water that contained no snails.

Each of the six replicates within each of the river water treatments (A-E) included the use of one clear plastic sealable container (bottle with lid) filled with 300 ml of water collected from Site 1 at approximately 10am, 12 hours prior to the start of the experiment. Although it is possible that trace eDNA was present in this river water, it was necessary to use water from the natural environment to reduce the likelihood that snails and schistosomes would be negatively affected by substantive changes to the physio-chemical parameters of the water. Treatment F comprised two replicates each with one clear plastic sealable container (bottle with lid) filled with 300 ml of tap water.

After snails were introduced to containers, they were placed under artificial light, to induce cercarial shedding. After a period of 12 h, one 50 ml water sample was taken from each container using a sterile

50 ml syringe and filtered using a Sterivex filter with a pore size 0.22 µm and a polyethersulfone membrane (Merck, Darmstadt, Germany). To preserve the samples, absolute ethanol was passed through the filter with a sterile syringe. Post-filtering, Whirl-Pak bags (118 ml capacity; Nasco, Fort

Atkinson, USA) were used to keep each filter separate and reduce potential contamination. After filtering the water, snails were preserved in absolute ethanol; each group in one bag. All field-collected samples (eDNA and snails) were kept as cool as possible in the field, and transported to the UK where they were stored at -20 °C until DNA extraction. This eDNA-based method will potentially collect DNA from whole schistosomes, chemically bound or free DNA in solution in the environment, or DNA within

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cellular debris in the environment. In practical terms, however, the result is a measure of parasite DNA present in the immediate environment of the snail host, following augmentation by stimulation of cercarial release.

3.2.3 DNA extraction from Sterivex filters

The laboratory bench was cleaned with 10% bleach, then with 70% ethanol, and finally UV light was used to eliminate residual DNA. All tools used for cutting and handling the materials, including blades, tweezers and scissors were wiped with 10% bleach then washed with 70% ethanol to avoid sample cross-contamination. Used gloves were exchanged for new at each step of the extraction process.

The eDNA from filter samples was extracted using the DNeasy Power Water Kit (Qiagen, Venlo,

Netherlands). The filter capsule was opened, and filter membrane was cut as strips along the filter.

Each strip was then removed using tweezers and placed into the bead tube. The subsequent extraction steps followed the manufacturer’s protocol. To avoid cross-contamination, extraction of eDNA and

DNA from tissue samples was carried out in different laboratories.

3.2.4 DNA extraction from molluscs

Prior to extractions, snails were separated and individual snails were washed with distilled water. The length (mm) and wet weight (g) of each snail was then measured. For most of the snails, the soft tissue was removed, before a small mixed sample of this tissue (no more than 20 mg) was used for the extraction. This ensured that both patent and non-patent infections could be detected. DNA was extracted using the DNeasy Blood & Tissue Kits (Qiagen) according to the manufacturer’s protocol.

3.2.5 eDNA assay of samples 77

Chapter 3: eDNA-xenomonitoring for determining Schistosoma

DNA quantification of eDNA samples used a qPCR approach based on the mitochondrial 16S rRNA gene of S. mansoni and S. haematobium, reported in Chapter 2 of this thesis. Reactions were performed in a 5 µl final volume, containing 1 µl DNA template, 2.5 µl Master Mix (PrimeTime Gene

Expression Master Mix; Integrated DNA Technologies, Coralville, IA), 1.25 µl molecular grade water

(VWR International, Leicestershire, UK) and 0.25 µl of the primer/probe premix. The primer/probe premix was prepared with 4 µl of each primer (100 µM, Integrated DNA Technologies), 2 µl of probe

(100 µM, Integrated DNA Technologies), and 40 µl of molecular grade water. The species-specific forward and reverse primers and probes are shown in Table 3.2. The qPCR conditions were as follows:

3 min at 95 °C for initial denaturation, followed by 45 cycles of 95 °C for 0.05 s and 60 °C for 30 s. Each sample was run in triplicate (technical replicates) and each plate of samples quantified with a 10-fold serial dilution of a control positive DNA sample (ranging from 1,000,000 copies/μl to 1 copy/μl), and a no-template negative control. The reactions were run on a Eco48 thermal cycler machine (PCRMax,

Staffordshire, UK) in 48-well plates with ROX normalisation. DNA detection was expressed by quantification cycle threshold (Cq) values. We report the theoretical limits of detections as estimated as the number of copies at which there was a 95% probability of amplification in any one PCR of a sample (termed LODI) and the 95% probability of amplification in any one of three PCRs of a sample

(termed LODIII), using the standard dilution series, and fitting logistic models (Forootan et al., 2017) using CurveExpert Basic 2.1.0 (Hyams Development). The theoretical limit of quantification (LOQ) was estimated as the minimum dilution in which 90% of standards amplified reliably (Klymus et al., 2015).

Tests for the presence of S. mansoni and S. haematobium within the tissue samples of B. pfeifferi and

B. globosus were also conducted using the same qPCR approach as described above. However, in these tests only the presence or absence of amplification was recorded, from a single replicate per sample.

These samples were run alongside positive and no-template negative controls, but target DNA copy number was not formally quantified using a dilution series.

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3.2.6 Data analysis

Cq values, DNA concentrations and the qPCR quantification parameters r2 and percent efficiency were calculated using EcoStudy version 5.2.16 (PCRMax) using the default settings. Data for each assay (S. haematobium and Bulinus; S. mansoni and Biomphalaria) were analysed separately using linear models in R 3.6.0 (R Core Team, 2019). Each model included the eDNA quantity as the response variable (measured as the mean number of eDNA copies across the technical replicates). The predictor variables were either the number of infected host snail individuals or the total infected host snail biomass in experimental replicates, as determined from the qPCR assays of snail tissue.

3.3 Results

For S. mansoni eDNA assay, the qPCR efficiency was 103% (range 91.55–110.86%) across the four dilution series assays with a mean r2 value of 0.99 (range 0.97–0.99). The LODI was 32.36 copies/μl, the LODIII was 1.49 copies/μl, and the LOQ was 100 copies/µl. No amplifications were observed in the no-template qPCR controls, the negative control samples of water collected from the natural water body, or from the local tap water controls. The tests revealed the presence of S. mansoni eDNA in water from all 24 containers with Biomphalaria host snails, with all 72 qPCR replicates showing positive amplifications (Table S3.1). In total S. mansoni was present in 145 of 364 (40%) Biomphalaria individuals from the experiment that had their tissue tested. There was complete congruence between the presence of S. mansoni in the eDNA assay and the presence of S. mansoni in the tissue of the Biomphalaria host snails (Table 3.3).

For S. haematobium assay, the qPCR efficiency was 101% (range 91.7–102.33%) across the four

2 dilution series assays with a mean r value of 0.99 (range 0.98–0.99). The LODI was 1.33 copies/μl,

LODIII was ≤ 1 copy/μl while the LOQ was 100 copies/μl. Again, no amplifications were observed in the 79

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no-template qPCR controls, the negative control samples of water collected from the natural water body, or from local tap water controls. The results also revealed the presence of S. haematobium eDNA in water from 22 of the 24 experimental containers with Bulinus host snails, with 61 of 72 eDNA qPCR replicates showing positive amplifications (Table S3.1). In total, S. haematobium was present in

79 out of 102 (77%) Bulinus individuals that had their tissue tested. There was a strong congruence between the presence of S. haematobium in the eDNA assay and the presence of S. haematobium in the tissue of the Bulinus host snails, with only three exceptions. Two experimental containers negative for S. haematobium eDNA contained B. globosus snails positive for S. haematobium in tissue assays, and one experimental container that was positive for S. haematobium eDNA contained B. globosus found negative for S. haematobium in the tissue assay (Table 3.3).

There were strong associations between the number of infected snails and eDNA copy number; as the number of infected snails in an experiment increased, the number of eDNA copies increased (Figure

3.2; Table 3.4). We also found strong associations between the infected biomass of host snails and eDNA abundance in the experimental containers, measured using eDNA copies. As the infected snail biomass increased, eDNA copy number increased (Table 3.4).

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Table 3.1 Summary of the number of replicates in each treatment, and the numbers of snails in used in each of the treatments A-D (that were all housed in water from Site 1)

Treatment No. of replicates No. of Location source No. of Location source B. pfeifferi of B. pfeifferi B. globosus of B. globosus median (range) median (range) A 6 20 (19–21) Site 1 3 (2–3) Site 1 B 6 20 (19–21) Site 1 6 (3–6) Site 1 C 6 10 (10–11) Site 1 3 (3–3) Site 2 D 6 10 (10–11) Site 1 6 (5–6) Site 2 Ea 6 – – – – Fb 2 – – – – aTreatment E was a negative control (using water from Site 1) bTreatment F was a negative control (using tap water) Notes: There is some slight variation in the numbers of snails in each replicate, as on inspection after the experiment some snails used were found to be empty shells containing only sediment, while some small snails had tucked into shells of larger snails

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Table 3.2 Details of species-specific assays for S. mansoni and S. haematobium

Species Primer or probe Sequence (5'-3') Product length (bp) S. mansoni Forward primer CTGCTCAGTGAAGAAGTTTGTTT 104 Probe AGCCGCGATTATTTATCGTGCTAAGGT Reverse primer CCTCATTGAACCATTCACAAGTC

S. haematobium Forward primer AATGAACATGAATGGCCGCA 143 Probe TGGAGACTTGTGAATGGTCGAACG Reverse primer ATGGGTTCCTCACCACTTAAACT Notes: Probes were designed with a dual labelled 5(6)-carboxy-fluorescein (FAM) fluorescent tag at the 5' end and with a black hole quencher 1 (BHQ1) at the 3'-end

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Table 3.3 Results of S. mansoni and S. haematobium DNA experimental analyses.

S. mansoni S. haematobium Treatment Replicate eDNA eDNA Cq eDNA n/N n/N % Infected snail eDNA eDNA Cq eDNA n/N n/N % Infected snail Code amplification (mean) copies/µL biomass amplification (mean) copies/µL biomass success (mean) (total, g) success (mean) (total, g)

A 1 3/3 32.47 211.48 8/21 38 1.49 3/3 32.88 20.14 3/3 100 1.38 2 3/3 35.13 35.62 10/21 48 1.71 3/3 34.53 6.35 3/3 100 1.44 3 3/3 33.92 72.73 6/19 32 0.83 2/3 36.11 2.64 2/3 67 1.03 4 3/3 37.11 6.71 5/20 25 0.59 3/3 32.36 29.30 2/3 67 0.76 5 3/3 38.59 2.57 12/21 57 1.91 0/3 - 0 1/2 50 0.38 6 3/3 38.48 2.45 3/20 15 0.37 2/3 37.28 1.03 0/3 0 0 B 7 3/3 36.55 10.20 9/20 45 0.56 3/3 28.91 325.97 4/6 67 2.07 8 3/3 34.47 52.78 6/20 30 1.07 2/3 36.28 1.95 1/3 33 0.33 9 3/3 35.03 33.27 11/20 55 1.72 3/3 33.10 19.53 6/6 100 2.48 10 3/3 33.85 77.28 7/19 37 0.84 3/3 29.56 222.73 4/6 67 1.68 11 3/3 31.47 435.27 11/21 52 1.20 3/3 30.07 160.39 3/6 50 1.17 12 3/3 33.59 94.77 6/20 30 0.76 3/3 33.10 19.50 1/6 17 0.49 C 13 3/3 36.14 18.16 4/10 40 0.64 3/3 34.86 8.01 2/3 67 0.53 14 3/3 34.81 40.36 3/10 30 0.39 0/3 - 0 2/3 67 0.64 15 3/3 39.68 1.09 4/10 40 0.30 3/3 37.09 1.48 3/3 100 0.78 16 3/3 34.05 67.49 2/11 18 0.14 3/3 35.05 4.95 2/3 67 0.59 17 3/3 36.21 22.03 3/10 30 0.37 3/3 24.17 8445.25 3/3 100 0.48 18 3/3 36.03 20.46 5/10 50 0.54 3/3 28.77 342.91 3/3 100 1.20 D 19 3/3 35.03 43.35 4/10 40 0.66 3/3 34.29 7.61 5/5 100 1.23 20 3/3 35.89 26.35 4/10 40 1.13 3/3 29.65 180.51 6/6 100 1.44 21 3/3 37.26 12.39 7/10 70 0.97 3/3 31.49 49.80 5/5 100 1.07 22 3/3 35.63 27.94 6/11 55 1.27 3/3 27.90 611.29 6/6 100 1.79 23 3/3 37.95 5.25 4/10 40 0.58 3/3 35.36 3.49 6/6 100 1.44 24 3/3 36.04 21.65 5/10 50 0.44 3/3 31.36 53.09 6/6 100 1.34 E 25 0/3 - - - - - 0/3 - - - - - 26 0/3 - - - - - 0/3 - - - - - 27 0/3 - - - - - 0/3 - - - - - 28 0/3 - - - - - 0/3 - - - - - 29 0/3 - - - - - 0/3 - - - - - 30 0/3 - - - - - 0/3 - - - - - F 31 0/3 - - - - - 0/3 - - - - - 32 0/3 - - - - - 0/3 - - - - -

Note: The table includes the results from eDNA monitoring and the results of the qPCR tests of host snail infection Key: -, no amplification was observed Abbreviation: n, number of infected snails; N, number of all snails in the assay

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Table 3.4 Summary linear models, predicting the number of eDNA copies/µl (log10 transformed)

2 Assay Predictor variable Estimate (SE) F(1, 28) r P S. haematobium Infected host individuals 0.308 (0.073) 4.220 0.389 < 0.001 Infected host biomass 0.916 (0.230) 3.980 0.361 < 0.001 S. mansoni Infected host individuals 0.136 (0.032) 4.257 0.393 < 0.001 Infected host biomass 0.804 (0.209) 3.854 0.347 < 0.001 Abbreviations: SE, standard error

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Figure 3.1 Overview of steps in the eDNA-based xenomonitoring assay, from sampling to analysis. Validation of the assay is conducted using qPCR analysis of tissue from preserved snails.

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Figure 3.2 Associations between eDNA copies and infected snails in experimental containers. a Schistosoma haematobium copies and number of infected host snail individuals. b S. haematobium copies and biomass of infected host snail individuals. c S. mansoni copies and number of infected host snail individuals. d S. mansoni copies and biomass of infected host snail individuals. Lines illustrate linear models of associations between the variables, with 95% confidence intervals.

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3.4 Discussion

Our results confirmed a strong association between the detection of DNA in the experimental chamber and the presence of infected snails for both S. haematobium and S. mansoni assays. One experimental replicate detected S. haematobium eDNA, but no S. haematobium in the tissue. This may be explained by schistosome life-cycle stages being absent from the specific sub-sample of tissue used, or alternatively it may be linked to PCR inhibition given potential for polysaccharides in mollusc tissue to act as PCR inhibitors (Schrader et al., 2012). It is also possible that snails within that specific replicate were cross contaminated with cercariae from other snails. Additionally, two experimental replicates failed to detect S. haematobium eDNA, but did detect S. haematobium in the tissue. This may be explained by the parasite DNA concentration in water sample being below the estimated level of detection with three qPCR technical replicates, or because of a failed DNA extraction, or perhaps cercariae were not shed into the surrounding water. We did not measure the number of snails actively shedding cercariae, but they can be absent if infection is prepatent (Muhoho et al., 1997; Aoki et al.,

2003; Opisa et al., 2011). Further experiments would benefit from information on the number of individual snails actively shedding, and preferably the numbers of cercariae that are being shed. With such information it may be possible to construct more accurate model predicting the quantity of schistosome eDNA in the water. Moreover, cercariae can affected by environmental factors such as interactions with other organisms in the freshwater matrix. For example, rotifers can limit cercarial motility and infectivity, which may influence detectability (Stirewalt and Lewis, 1981; Gao et al., 2019).

Nevertheless, despite the modest inconsistencies in results of the two testing methods we used, we clearly demonstrated that both numbers and biomass of infected host snails were significantly positively related to eDNA abundance, thus demonstrating that it is possible to use eDNA abundance to predict the number of infected host snails within containers used in the assay.

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The results presented show for the first time that standardised eDNA-based xenomonitoring assays using consistent conditions can provide quantitative information on infection prevalence within snail populations sampled in the field, and therefore if coupled with snail abundance information it may be possible to quantify transmission risk. In principle, this method could be more consistent and reliable than direct eDNA sampling of water, which may depend on extrinsic factors, such as water flow, temperature and light regime in the days prior to sampling. Moreover, it would also be less labour- intensive than testing individual snails in order to quantify the prevalence of infection by conventional microscope-based identification of emerging cercariae (Loy and Haas, 2001; Wolmarans et al., 2002;

Kariuki et al., 2004). Our method also has potential to overcome potential taxonomic complications due to the sympatric coexistence of human and non-human schistosome species with morphologically similar cercariae (Hertel et al., 2002; Norton et al., 2008; Christiansen et al., 2016; Allan et al., 2017;

Leger and Webster, 2017). However, we note that our S. haematobium assay did show cross amplification with S. curassoni and S. bovis (Chapter 2 of this thesis), so there is some uncertainty about where Bulinus were exclusively shedding S. haematobium.

Our methods may have some advantages when compared to PCR-based approaches of screening snail tissue (either end-point PCR (Bakuza et al., 2017) or qPCR as in this study), and loop-mediated isothermal amplification (LAMP) on snail tissue (Gandasegui et al., 2016), given the time-consuming nature of DNA extraction from multiple individual snails, and the potential presence of polysaccharide

PCR inhibitors in mollusc tissue (Schrader et al., 2012). More specifically, this eDNA-based xenomonitoring method that requires only one eDNA extraction from the water in each experimental chamber could replace the need to extract DNA individually from potentially hundreds of snails, thus providing significant savings in cost and time. In comparison to the subsampling and homogenising of large numbers of snails prior to DNA extraction, water sampling with enclosed filters can also represent an advantage in terms of speed, cost, and reduced likelihood of laboratory contamination.

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Nevertheless, molecular techniques that diagnose infections in individual snails are accurate and sensitive and can detect prepatent schistosome infections (Hanelt et al., 1997; Jannotti-Passos et al.,

1997; Hamburger et al., 1998). Meanwhile, traditional methods (cercarial shedding) are still widely used in practice due to their field applicability, technical simplicity and ease of application in resource- poor settings (Weerakoon et al., 2015). Microscope-based tests, for example, can reliably describe seasonal patterns, diurnal variation, and spatial distributions of cercariae (Muhoho et al.,1997; Aoki et al., 2003).

A practical challenge faced during the collection of eDNA samples is rapid blocking of filters from the turbid water that is typical of Schistosoma transmission sites. Typically, it is necessary to use fine small pore sizes (0.22 μm) (Spens et al., 2017; Sengupta et al., 2019) and this can mean that it is only practical to sample 500 ml of water, or often much less, per filter. However, we note that large pore size units

(350 μm) for pre-filtering water samples have been used successfully (Sengupta et al., 2019).

Nevertheless, the xenomonitoring method we used overcame these difficulties as it allowed sediment to settle prior to sampling. It also required only smaller (50 ml) volumes of water, as schistosome eDNA would have been more concentrated in our experimental containers than it would be in the natural environment. Moreover, it overcame issues surrounding selection of appropriate water sampling volumes for field-based eDNA analysis, and whether the results have been influenced by the characteristics of the environment, such as the temperature or flow regime. In principle, it would also be possible to precipitate eDNA directly from water samples, therefore foregoing the need to use filters (Ficetola et al., 2008; Eichmiller et al., 2016).

One of the key advantages of eDNA sampling directly from the environment is that it does not require collection or analysis of snails. By contrast, eDNA-based xenomonitoring, like conventional tests of the presence of cercariae, requires both sampling of snails and housing them in controlled conditions for

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the duration of an experiment. In practice, where snail infection is low, it may be necessary to collect and test several hundred individual snails to reach detectable levels of infection, but group testing could be readily achieved within experimental containers, if sufficient numbers can be collected from the natural environment. Hence, this method may be best for situations where detection of low-levels of transmission. Further research will be required to determine the relative power of different analytical approaches for detecting and quantifying schistosome prevalence across natural densities of snails and across differing intensities of snail infection. Specifically, it would be useful to compare the detection probabilities using directly sampled eDNA, to both conventional and eDNA-based xenomonitoring methods where estimates of schistosome abundance require snail density estimates from searches and tests of snail infection status.

It might not be possible to reliably quantify snail infection rates if snails are not readily shedding cercariae (Stirewalt and Lewis, 1981; Kariuki et al., 2004), and although we succeeded in using the eDNA-based xenomonitoring method with the schistosome hosts B. pfeifferi and B. globosus, the effectiveness for other host species, perhaps with specific habitat requirements, is unknown. For example, in Lake Malawi the endemic Bulinus nyassanus, a host of S. haematobium, is found in the upper 2–3 cm of the sediment on open sandy shorelines (Madsen et al., 2004; Madsen et al., 2011;

Madsen and Stauffer, 2012). Additionally, the eDNA testing of Schistosoma species may be compromised by hybridization among species, as observed between the closely related S. mansoni and S. rodhaini (Leger and Webster, 2017). Previous work has demonstrated that hybridization can result in sharing of mitochondrial haplotypes, and distinguishing between the two species at some locations would therefore be intractable using any methods that rely exclusively on amplification of targeted fragments of the mitochondrial genome. Instead, analysis of multiple nuclear loci of individual cercariae would be essential. A final consideration is that given evidence of some cross- species amplification within assays, in some situations where closely-related schistosome species

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coexist then primers and probes may need to be considered carefully, and potentially with bespoke designs.

In 2012, the World Health Assembly resolved to continue efforts to eliminate schistosomiasis through control and surveillance measures. It is widely recognized that there are multiple required facets to an integrated control programme, focused on both treatment of existing human infections with chemotherapy in synergy with intervention techniques focused on the life stages of the intermediate gastropod host (Sokolow et al., 2018; Colley et al., 2014). Physical measures to reduce habitat for snail populations could be used more widely, for example cementing irrigation canals, draining wetlands, or aiming to eliminate snail populations by applying molluscicides (such as new niclosamide formulations) and biological control (including intentional introductions of competitor snails or snail predators). However, in practice such snail control methods may not be practical or ethical in many environmental circumstances. Regardless, the surveillance of freshwaters can enable an early warning of infection risk, and may prove increasingly critical for prevention of reinfection during the elimination phase of control programmes (Sato et al., 2018; Weerakoon et al., 2018), as well as the early detection and elimination of new foci of infection resulting from environmental change (Booth and Clements, 2018).

A key consideration for further development of the methods outlined in this study would be to evaluate their effectiveness across different host gastropod species and in different environments.

We may expect, for example, that the rate of schistosome cercarial production would vary among sites dependent upon the environmental conditions, for example the temperature and light regimes of assay containers. Therefore, to evaluate potential for effective application, the method needs to be refined enabling robust and systematically consistent results. It also requires the development of appropriate positive control methods. A key consideration is that the method would not necessarily

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be appropriate where snails are rare or hard to sample, and instead in those circumstances eDNA sampling of water collected directly from the natural environment may be more appropriate.

Additionally, consideration should be given to the need to standardise training and materials typically available for sampling and analysis in endemic areas (Xu et al., 2017; Weerakoon et al., 2018).

3.5 Conclusions

Here we provide evidence that eDNA-based assays can determine the presence of schistosomes in intermediate host snail species. We suggest that the methods may be appropriate for epidemiological studies and large-scale monitoring in some endemic areas. They could prove useful, alongside other surveillance methods, to inform schistosomiasis control programmes by highlighting freshwater bodies where there is a risk of schistosomiasis transmission. However, further comparative investigations are needed to evaluate the power and practicality of this method in the field, alongside the range of other diagnostic and surveillance methods available.

Acknowledgements

We thank staff from the Tanzania Fisheries Research Institute for contributions to fieldwork. The

Tanzania Commission for Science and Technology (COSTECH) provided fieldwork permits, while the

Ministry of Agriculture, Livestock and Fisheries permitted export of materials. We thank BEI Resources

Repository of the National Institute of Allergy and Infectious Diseases for the provision of material.

We thank Richard Wall for his support.

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Chapter 3

Supplementary Information

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Table S3.1 Results of S. mansoni and S. haematobium qPCR experiments, including Ct scores and eDNA copies for all replicates in the experiment. (*) indicates no amplification was observed.

S. mansoni S. mansoni S. mansoni S. mansoni S. haematobium S. haematobium S. haematobium S. haematobium Treatment replicate amplification Ct eDNA copies replicate amplification Ct eDNA copies A 1 a 32.51 203.787 1 a 32.92 19.547 A 1 b 32.6 191.53 1 b 32.7 22.696 A 1 c 32.3 239.133 1 c 33.02 18.168 A 2 a 35.21 27.315 2 a 34.61 5.976 A 2 b 36.15 13.525 2 b 34.67 5.725 A 2 c 34.03 66.024 2 c 34.31 7.361 A 3 a 33.85 75.46 3 a 35.09 4.264 A 3 b 34.3 53.661 3 b 37.13 1.017 A 3 c 33.62 89.078 3 c * 0 A 4 a 37.19 6.233 4 a 32.58 24.754 A 4 b 36.83 8.15 4 b 32.05 35.926 A 4 c 37.3 5.743 4 c 32.44 27.221 A 5 a 38.42 2.478 5 a * 0 A 5 b 39.65 0.994 5 b * 0 A 5 c 37.71 4.223 5 c * 0 A 6 a 38.93 1.707 6 a 37.98 0.562 A 6 b 38.19 2.954 6 b * 0 A 6 c 38.31 2.697 6 c 36.57 1.506 B 1 a 36.57 9.857 1 a 28.78 355.084 B 1 b 36.54 12.451 1 b 29.1 283.12 B 1 c 36.54 8.301 1 c 28.84 339.692 B 2 a 34.59 43.167 2 a 36.72 1.354

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B 2 b 33.65 87.042 2 b 35.83 2.54 B 2 c 35.17 28.116 2 c * 0 B 3 a 35.31 26.416 3 a 32.65 25.936 B 3 b 35.22 28.047 3 b 33.21 17.635 B 3 c 34.57 45.337 3 c 33.44 15.008 B 4 a 33.73 83.247 4 a 29.46 236.258 B 4 b 33.71 84.784 4 b 29.74 195.418 B 4 c 34.1 63.815 4 c 29.46 236.5 B 5 a 31.46 436.744 5 a 30.38 125.687 B 5 b 31.55 409.704 5 b 29.57 220.336 B 5 c 31.39 459.35 5 c 30.27 135.174 B 6 a 33.28999502 115.161 6 a 32.82 23.055 B 6 b 33.99 68.836 6 b 32.9 21.877 B 6 c 33.48 100.302 6 c 33.59 13.57 C 1 a 35.88 17.406 1 a 33.87 11.209 C 1 b 37.48 5.409 1 b 36.86 1.404 C 1 c 35.05999775 31.671 1 c 33.84 11.403 C 2 a 35.12 30.417 2 a * 0 C 2 b 34.16 61.161 2 b * 0 C 2 c 35.16 29.509 2 c * 0 C 3 a 39.85 0.96 3 a 37.46 0.927 C 3 b 39.47 1.26 3 b 37.99 0.64 C 3 c 39.71 1.06 3 c 35.83 2.871 C 4 a 33.71 85.061 4 a 35.04 4.979 C 4 b 34.11 63.124 4 b 34.85 5.629 C 4 c 34.32 54.279 4 c 35.27 4.25 C 5 a 35.86 23.054 5 a 24.23 8118.348 C 5 b 35.23 36.099 5 b 24.15 8543.7 95

Chapter 3: eDNA-xenomonitoring for determining Schistosoma

C 5 c 37.55 6.932 5 c 24.13 8673.707 C 6 a 35.91 22.302 6 a 28.39 429.959 C 6 b 36.09 19.579 6 b 29.34 220.853 C 6 c 36.1 19.494 6 c 28.58 377.916 D 1 a 34.52 59.795 1 a 33.47 12.004 D 1 b 35.06 40.606 1 b 35.24 3.457 D 1 c 35.51 29.641 1 c 34.16 7.376 D 2 a 37.1 9.586 2 a 29.61 182.627 D 2 b 35.26 35.215 2 b 29.97 142.015 D 2 c 35.3 34.251 2 c 29.37 216.899 D 3 a 36.38 15.963 3 a 31.52 47.662 D 3 b 36.12 19.167 3 b 31.85 37.72 D 3 c 39.28 2.04 3 c 31.09 64.019 D 4 a 35.21 36.52 4 a 27.83 642.186 D 4 b 35.65 26.703 4 b 28.11 525.217 D 4 c 36.02 20.583 4 c 27.77 666.481 D 5 a 37.92 5.356 5 a 35.92 2.146 D 5 b 38.13 4.614 5 b 35.65 2.583 D 5 c 37.81 5.767 5 c 34.51 5.764 D 6 a 35.85 23.235 6 a 31.41 51.413 D 6 b 36.77 12.074 6 b 31.29 55.946 D 6 c 35.5 29.652 6 c 31.39 51.896 E 1 a * 0 1 a * 0 E 1 b * 0 1 b * 0 E 1 c * 0 1 c * 0 E 2 a * 0 2 a * 0 E 2 b * 0 2 b * 0 E 2 c * 0 2 c * 0 96

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E 3 a * 0 3 a * 0 E 3 b * 0 3 b * 0 E 3 c * 0 3 c * 0 E 4 a * 0 4 a * 0 E 4 b * 0 4 b * 0 E 4 c * 0 4 c * 0 E 5 a * 0 5 a * 0 E 5 b * 0 5 b * 0 E 5 c * 0 5 c * 0 E 6 a * 0 6 a * 0 E 6 b * 0 6 b * 0 E 6 c * 0 6 c * 0 F 1 a * 0 1 a * 0 F 1 b * 0 1 b * 0 F 1 c * 0 1 c * 0 F 2 a * 0 2 a * 0 F 2 b * 0 2 b * 0 F 2 c * 0 2 c * 0

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Chapter 4

Uncovering the biodiversity of African freshwaters using COI

metabarcoding of environmental DNA

Author contributions

Hind Alzaylaee (HA) and Martin J. Genner (MJG) designed the study, with input from Rupert A. Collins

(RAC). HA, MJG, Carlos Gracida Juarez, Zikmund Bartonicek. Russell Stothard collected samples. HA and MJG collected data, analysed the data and led the writing of the manuscript. R.A.C. and Eric A.

Morgan contributed critically to the drafts

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Abstract

Freshwaters are highly diverse and complex environments, providing habitats for large number of species. Evaluating the species assemblage present, however, is often difficult or ineffective within aquatic environments. Metabarcoding of environmental DNA has the ability to detect species composition and provide information on relative species abundance. However, to date there have been few studies that have comprehensively reported on the species detected in freshwaters using this approach. Here we investigate the biodiversity and structure of communities from tropical freshwaters of sub-Saharan Africa, using metabarcoding primers designed to target the cytochrome c oxidase subunit I (COI) of metazoan taxa. We found that less than 6.3% of operational taxonomic units resolved in the metabarcode sequence data were confidently assigned to Metazoa, with the remainder being dominated by bacteria and non-metazoan eukaryotic lineages including diatoms

(Bacillariophyta) and the green algae/land plants (Viridiplantae). Metazoan diversity was dominated by annelids, arthropods, rotifers and chordates (primarily fish). Notably however, assignment precision of reads differed considerably among taxonomic groups. For example, while 78.2% of chordate OTUs were assignable to individual families, only 13.8% of arthropod OTUs could be similarly assigned, suggesting that there is considerable invertebrate diversity remaining to be catalogued on global barcoding databases. We found that across samples the number of OTUs present was highly dependent on the number of reads obtained in a sample, and there were strong indications that our read coverage was largely inadequate to capture the full diversity of freshwater biodiversity at sampled locations. Nevertheless, across samples there was a strong spatial structure among recovered OTUs, demonstrating the ability of metabarcoding to describe spatial heterogeneity in freshwater communities. Moreover, there were notably occurrence in the dataset of invasive fish species that were not previously known from collection localities. These results highlight the potential of using eDNA metabarcoding to characterise freshwater communities, but also demonstrate a need to use methods that focus read coverage on taxonomic groups of key interest to enable ecological inference.

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4.1 Introduction

Although freshwaters represent a small proportion of the world’s aquatic ecosystems by volume (~3%), they are highly diverse and complex environments that provide habitats for a large number of species

(Dudgeon et al., 2006), as well as beneficial ecosystem services including clean drinking water and renewable energy (Vorosmarty et al., 2010). Freshwaters also demonstrate extensive spatial variability in their structure and biodiversity, due both to abiotic factors such as climate, soil composition and geology, as well as to biotic factors such as the microhabitat preferences of species and evolutionary history (Huston and McBride, 2002; Woodward et al., 2010). Importantly, research characterising biodiversity tends to be limited to assessment of the abundance or diversity of specific species group of interest, such as fishes or arthropods. This means that there is scope to develop and evaluate methods for more comprehensive biodiversity surveys, and the application of these methods has potential to be informative for a broader ecosystem-level assessment of freshwaters, incorporating their value for conservation, fisheries and human health.

Gaining a detailed knowledge of the species composition of freshwater communities can be difficult due to both the requirement for deep taxonomic expertise (Hopkins and Freckleton, 2002), and the practical difficulties in reliably sampling freshwater habitats. This is in part due to the cryptic nature of many aquatic species, but also due to the structural complexity of their habitat. Conventional monitoring methods can also be disruptive to the ecosystem or to species under study (Rees et al.,

2014). In recent years, the capacity for non-destructive and repeatable biodiversity monitoring has improved with the development of techniques capture, preserve and analyse environmental DNA

(eDNA). One specific method that can be used is eDNA metabarcoding, which entails the use of universal PCR primers to amplify fragments of interest from an environmental DNA sample without any prior knowledge of the species that may be present. These fragments are then sequenced and compared to reference databases to build biodiversity inventories of the sampled habitat (Taberlet et

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al., 2012a). This approach has the potential to be an efficient tool for detecting species, for assessing biodiversity, and for the relative quantification of aquatic organisms (Thomsen et al., 2012a).

Moreover, highly sensitive taxonomic identification of complex communities can take place even in the absence of significant taxonomic expertise (Thomsen and Willerslev, 2015). For these reasons, eDNA is considered to be at the forefront of ecological approaches by ecologists and conservation scientists, especially with the continued development of high-throughput sequencing technologies (Shokralla et al., 2012).

Environmental metabarcoding has been successfully used to assess cross-phylum biodiversity and community structure of marine ecosystems (Esling et al., 2015; Guardiola et al., 2015; Lanzen et al.,

2016; Leray and Knowlton, 2015). However, despite its great potential for biodiversity monitoring, only a few studies have employed eDNA for cross-phyla investigations of freshwater communities (Lim et al., 2016; Klymus et al., 2017; Fernandez et al., 2018). Instead, most community-level metabarcoding studies using freshwater eDNA tend to have focussed on specific classes of organisms, most notably fish (e.g. Civade et al., 2016; Bylemans et al., 2018). Those studies that have attempted to study natural freshwater communities using cross-phylum eDNA metabarcoding have demonstrated the choice of primers is critical in determining the number of taxa recovered from samples. Notably there are indications that the “barcoding” gene cytochrome c oxidase subunit I (COI) has the capacity to resolve a high diversity of metazoan taxa within freshwater samples (Fernandez et al., 2018), not least because of the availability of the Barcode of Life (BOLD) reference library (Ratnaingham and Hubert, 2007; http://www.boldsystems.org/).

Alternative metabarcoding markers are available, including 28S rRNA (Hirai et al., 2014),18S rRNA

(Creer et al., 2010; Capra et al., 2016), ITS2 (Anslan and Tedersoo, 2015; Avramenko et al., 2017), 16S rRNA (Elbrecht et al., 2016; Saitoh et al., 2016) and 12S (Machida et al., 2012). The choice of marker

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relies on the scientific question, as different markers can give strikingly different results, even in the same target community (Andujar et al., 2018). It is generally though that if the aim is to get a taxonomic identification at the species level, then COI is most convenient (Elbrecht et al., 2016). However, more studies are required to investigate the capability of COI method to resolve the diversity and structure of freshwater systems using currently available databases.

Here we describe a cross-phylum eDNA metabarcoding study based on the COI gene using samples collected from African freshwaters. We use the data to describe the diversity of organisms recovered, describe the taxonomic resolution achieved for sequences from the dominant groups of metazoan, and assess capacity for studies to resolve the differences in freshwater assemblages. We also use the data to discuss the potential application for eDNA metabarcoding to determine the presence of trematode parasites harmful to human health, and invasive species that are potentially detrimental to indigenous biodiversity.

4.2 Material and Methods

4.2.1 Sample collection

To evaluate the biodiversity of the total community in freshwater bodies, water samples were obtained from four countries in Africa: Malawi (14 samples), Cameroon (14 samples), Sudan (33 samples) and

Tanzania (34 samples) (Table 4.1). In addition, we analysed four negative control samples of bottled drinking water filtered in-situ. Sampling methods varied between countries. The Tanzania, Malawi and

Sudan sampling involved filtering of water across a 0.22 µm polyethersulfone membrane Sterivex filter

(EMD Millipore Corporation, UK) until the filter was saturated. The water was collected using a 60 ml sterile syringe, and the volume of collected of water varied between 95 ml and 1185 ml. All samples were then preserved by pushing absolute ethanol through the filter with a 60 ml syringe. For Cameroon samples water was filtered across either a 5 µm nylon membrane 25mm diameter filter paper

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(Whatman, GE Healthcare, Buckingham, UK) or a 25 µm nylon membrane 25mm diameter filter paper

(Whatman, GE Healthcare, Buckingham, UK), after water was pre-filtered through a 100µm membrane. Individual filter papers was kept inside labelled 118 ml Whirl-Pak bag (Sigma-Aldrich, UK).

As soon as was practical, samples were stored in the laboratory freezer (-20 0C).

4.2.2 DNA extraction from filters and metabarcoding PCR amplification eDNA was extracted from each individual filters using the DNeasy Power Water Kit (Qiagen, UK), eluting into 50 μl of elution buffer, following the manufacturer’s protocol. To avoid sample cross- contamination all tools used during extraction, including scissors, blades and tweezers, were wiped with 10% bleach and then rinsed with 70% ethanol. Similarly, the laboratory bench surface was cleaned with 10% bleach, then with 70% ethanol and finally UV light was used to eliminate residual DNA. New gloves were used at each step of the extraction process.

A 313 bp fragment of cytochrome c oxidase subunit I (COI) was amplified using the Leray-XT primer set (Leray et al., 2013; Wangensteen et al., 2018). The forward primer used was mlCOIintF-XT (5'-

GGWACWRGWTGRACWITITAYCCYCC-3') (Leray et al., 2013), while the reverse primer was jgHCO2198

(5'-TAIACYTCIGGRTGICCRAARAAYCA-3'; Geller et al., 2013). Both forward and reverse primers each had a 8-base sample-tag, with each tag differing by at least three bases. We employed 96 different pairs, so we could multiplex up to 96 samples in one library (91 samples, 4 field negative controls, 1 no- template control). DNA amplifications were carried out to a final volume of 20 µl using 10 µl of

AmpliTaq Gold Master Mix (Applied Biosystems, California, USA), 0.16 µl of BSA (20 µg/µl) (Thermo-

Fisher Scientific, Waltham, MA, USA), 5.84 µl molecular grade water (VWR International,

Leicestershire, UK), 1 µl of each of the 5 µM forward and reverse tagged primers (including 8-bp sample tags), and 2 µl of DNA template. Amplification was performed using the following thermocycling conditions: an initial 10 min denaturation step at 95 °C, followed by 35 cycles of denaturation at 94 °C

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for 1 min, then annealing at 45 °C for 1 min, and extension at 72 °C for 1 min. There was then a final 5 min extension step at 72 °C. Amplification was run three times for each sample, including negatives.

4.2.3 Illumina library preparation and High-throughput sequencing (MiSeq)

PCR products were run in a 1% agarose gel to confirm the success of the amplification. This was done by loading 2 µl of PCR product in each well. The gel was stained with gelred nucleic acid stain (10,000X;

Biotium, Fremont, CA), and then loaded and run at 140 V for 30 minutes, then visualized under ultraviolet light. A 100 bp DNA ladder (NorgenBiotek, Thorold Canada) was used to confirm the length of the amplicon. The rest of the PCR products (18 µl per sample, including the blanks) were pooled together and later purified using MinElute columns (Qiagen, UK) following the manufacturer’s protocol, in order to remove DNA fragments below 70 bp. At the final purification step, 13 µl of elution buffer was added to every column, which were then pooled together and homogenized by vortexing.

The concentration of purified DNA was measured using a Qubit fluorometer with Qubit dsDNABR assay kit (Invitrogen, Waltham, MA). The library preparation was conducted using the NEXTflex PCR-free

DNA Sequence Kit (BIOO Scientific, USA), with NEXTflex DNA adapter (50µM), following the manufacturer’s protocol. Library quality was checked using a TapeStation (Agilent, Santa Clara, CA,

USA). The final concentration of the library was 7.24 ng/μl, with an average size of 651 bp. The library, alongside a 25% Phi-X spike, was sequenced on an Illumina MiSeq using the 2×300 bp paired-end (v3,

600 cycle) sequencing kit, according to the manufacturer’s instructions at the University of Bristol

Genomics facility.

4.2.4 Data processing

The Illumina-generated eDNA metabarcoding data was processed using OBITools 1.2.13 (Boyer et al.,

2016). This workflow consists of the following steps: merging paired-end reads (R1 and R2), discarding poorly aligned sequences, assigning sequences to their original biological sample via demultiplexing

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based on primer tags, separating and purifying reads into unique sequences, denoising PCR and sequencing errors by only keeping reads between 320-300 bp long and with at least 5 read counts. See

Supplementary Information S4.1 for code.

4.2.5 Taxonomic assignments

Taxonomic assignments for each OTU (Operation Taxonomic Unit) from the OBITools output were made using RDP classifer_2.12 (https://sourceforge.net/projects/rdp-classifier/) and the reference database COI v4_trained, which was constructed from sequences mined from GenBank in April 2019 and BOLD data releases [iBOL_phase2.0_COI.tsv to iBOL_phase_6.50_COI.tsv] (Porter and Hajibabaei,

2018; https://github.com/terrimporter/CO1Classifier/releases). The resulting assignment data for each OTU included a probability of assignment to a taxon in the following taxonomic levels;

Superkingdom, Kingdom, Phylum, Class, Order, Genus and Species. This enabled exploration of the diversity of reads in the sample across multiple taxonomic levels, and the accuracy of assignments across taxonomic groups.

4.2.6 Read count and taxon richness

Focussing on six taxonomic groups (all taxa, Eukaryota, Metazoa, Arthropoda, Chordata and Rotifera), for each sample we calculated the total reads and number of OTUs assigned with a probability of 0.70 or greater to the taxonomic group of interest. Both metrics were then log10(x+1) transformed, and associations between total reads and number of OTUs calculated using linear models in R3.6.1 (R Core

Team, 2019). Plots, including 95% confidence intervals were generated using ggplot2 (Wickham, 2016) and ggpubr (Kassambara, 2019) in R 3.6.1.

4.2.7 Spatial structure in community composition

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To assess the spatial structure and variation of communities’ compositions, and the spatial structure of OTU composition among sampling sites, we used non-metric multidimensional scaling. We focussed on the same six taxonomic groups (all taxa, Eukaryota, Metazoa, Arthropoda, Chordata and Rotifera).

We included all samples that included OTUs assigned with a probability of 0.70 or greater to the taxonomic group of interest. For each group a site x OTU matrix was generated, which was converted to a presence-absence matrix using the vegan package in R3.6.1. The matrix was then ordinated using non-metric multidimensional scaling (nMDS) in PAST 3.24 (Hammer et al., 2001), using the correlation similarity index. The significance of differences between groups of samples was tested using a one- way Permanova in PAST 3.24 with 10,000 permutations, again using the correlation similarity index.

Ordination data were plotted using ggplot and ggpubr in R 3.6.1.

4.3 Results

4.3.1 MiSeq sequencing and Taxonomic assignments

A total of 21.65 million MiSeq reads were obtained from all samples, of which 4,804,522 remained after the OBItools pipeline, assigned to 18,548 OTUs. The template-free negative had 53 reads assigned. The four negative field samples had between 786 and 5548 reads each. We retained only the

61 samples with greater than 6000 samples for further analysis. This yielded 4,749,470, assigned to

18,290 OTUs.

From these 61 samples there were 3,774,227 reads assigned to Superkingdom level with probability

0.7 or greater. Of these 3,505,686 reads were assigned to Eukaryota (11,371 OTUs), while 268,541 were assigned to Bacteria (2,665 OTUs; Figure 4.2). Within the Eukaryotes, four groups were present at the Kingdom level, namely Fungi, Metazoa, Viridiplantae (green algae and land plants) and a group dominated by algal taxa that were undefined to Kingdom level in the database. This group included

Apicomplexa (parasitic alveolates), Bacillariophyta (diatoms), Cryptophyta (unicellular photosynthetic

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flagellates), Euglendia (excavate flagellates), Rhodophyta (red algae), as well as Dinophyceae

(dinoflagellates) and Oomycetes (water moulds) (Figure 4.2).

Within the 548 OTUs (273,108 reads) assigned to metazoan phyla with a probability of 0.7 or greater,

Annelida, Arthropoda, Chordata, Cnidaria, Gastrotricha, Mollusca, Nematoda, Platyhelminthes,

Porifera, and Rotifera were all recovered (Figure 4.3). The majority of OTUs were dominated by

Arthropoda (primarily insects of order Diptera) and Chordata (primary bony fish in the orders

Cichliformes and Cypriniformes). However, within the reads, the rotifer order Ploima dominated the samples (Figure 4.3).

Although there was an expected decline in reliability of assigning OTUs with increasing taxonomic granularity from Kingdom to Species levels, there were evident biases in the taxonomic coverage of the reference database (Figure 4.4). For example, only 13.8% of Arthropoda OTUs were assigned to

Family level, in comparison to 32.3% of rotifer OTUs, and 78.2% of Chordate OTUs (Figure 4.4).

Similarly, only 12.1% of Arthropoda OTUs were assigned to Genus level, in comparison to 32.4% of rotifer OTUs, and 29.3% of Chordate OTUs (Figure 4.4).

4.3.2 Sampling effort and diversity patterns

We found a highly significant association between sampling effort (measured though sequence reads) and alpha diversity (the total number of OTUs) recovered across all samples (linear model, r2 = 0.60,

F1,59 = 89.3, p < 0.001; Figure 4.5). The strong positive association was also present for all eukaryotes

2 2 2 (r = 0.58, F1,59 = 80.7, p < 0.001), all metazoans (r = 0.79, F1,59 = 216.7, p < 0.001), arthropods (r = 0.76,

2 2 F1,59 = 190.5, p < 0.001), chordates (r = 0.83, F1,59 = 293.8, p < 0.001) and rotifers (r = 0.86, F1,59 = 364.6, p < 0.001)(Figure 4.5).

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Comparisons of the beta diversity clearly showed differences in species composition among sampling locations for all eukaryotes, all metazoans, arthropods, chordates and rotifers (Figure 4.6). There were highly significant differences among the collections all of these taxonomic groupings [1-way permanova; all taxa (F = 9.39, p < 0.001, n = 61), eukaryotes (F = 8.60, p < 0.001, n = 61), metazoans (F

= 5.16, p < 0.001, n = 57), arthropods (F = 3.48, p < 0.001, n = 52), chordates (F = 3.67, p < 0.001, n =

40) and rotifers (F = 4.62, p < 0.001, n = 29); Table 4.1]. Taxonomic diversity of the samples from

Cameroon, Sudan and Malawi was relatively homogenous among samples collected from those locations. By contrast the samples from Tanzania were more heterogenous in their species compositions (Figure 4.6).

4.3.3 Metabarcoding and important taxa in different transmission

Reliably assigned OTUs were assessed for the presence of taxa of notable importance for human health

(schistosomiasis) or ecosystem status (invasive species). We found no Schistosoma species within the recovered reads. However, we were able to identify reads assigned to the Bulinus tropics/truncatus complex, the hosts of Schistosoma haematobium, within three samples from Sudan (Figure 4.7). There were however no reads of the genus Biomphalaria, the hosts of Schistosoma mansoni. Reads assigned to the redbelly tilapia (Coptodon zillii) were recovered from one sample from Lake Sagera, part of the

Malagarasi/Lake Tanganyika catchment of Tanzania. Reads assigned to the of the largemouth bass

(Micropterus salmoides) were recovered from both of the samples collected from the dam next to the

Coffee Research Institute at Vwawa, Tanzania (Figure 4.7).

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Figure 4.1 Distribution of 61 collection localities across sub-Saharan Africa of samples included in the main analysis. For full details of the sample collection localities see Table 4.1.

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a) b) c)

**

*

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*

0 200,000 400,000 600,000 800,000 0 100,000 200,000 300,000 400,000 500,000

Figure 4.2 Total number of reads and OTUs (≥70% probability) for taxonomic groupings within (a,d) Superkingdoms, (b,e) Kingdoms and (c,f) Phylq. *dominated by Rhodophyta, Bacillariophyceae, Oomycetes, Phaeophyceae, Raphidophyceae, Spirotrichea ** dominated by Oomycetes, Phaeophyceae, Raphidophyceae, Spirotrichea

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a) b)

c) d)

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Figure 4.4 Probability of OTUs in each Phylum being assigned to finer taxonomic levels.

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a) All OTUs b) Eukaryotes

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Figure 4.7 Non-native species identified in Tanzanian freshwaters using a complete match to reference COI sequences a) redbelly tilapia (Coptodon zillii) native to North Africa was present in Lake Sagera (Tanzania), b) largemouth bass (Micropterus salmoides) native to North America was present in a man- made dam in Vwawa, Tanzania. Habitat photos are from the locations, while fish photos are from elsewhere in their introduced range (Lake Naivasha, Kenya).

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Table 4.1 Details of sampling events, and the number of reads and OTUs assigned to each sample following bioinformatic processing.

Samples Location Country Latitude Longitude Collector(s) OTUs Reads Sample_01 Rep 1, Msaginya River, Mpanda, Rukwa Catchment Tanzania -6.26506 31.24681 M. Genner, B.P. Ngatunga, A. Shechonge 1597 85940 Sample_02 Rep 1, Mpemba River, Tunduma, Rukwa Catchment Tanzania -9.24284 32.84201 M. Genner, B.P. Ngatunga, A. Shechonge 801 114890 Sample_03 Rep 1, Kidatu Dam, Kidatu, Ruaha/Rufiji Catchment Tanzania -7.63375 36.88564 M. Genner, B.P. Ngatunga, A. Shechonge 773 114275 Sample_04 Rep 1, Mfisi River, Paranawe, Rukwa Catchment Tanzania -7.30967 31.06021 M. Genner, B.P. Ngatunga, A. Shechonge 146 2657 Sample_05 Rep 1, Lake Kwela, Rukwa Catchment Tanzania -8.40282 31.97453 M. Genner, B.P. Ngatunga, A. Shechonge 543 97035 Sample_06 Rep 1, Coffee Research Institute, Vwawa, Rukwa Catchment Tanzania -9.08801 32.95695 M. Genner, B.P. Ngatunga, A. Shechonge 531 59283 Sample_07 Rep 2, Kidatu Dam, Kidatu, Ruaha/Rufiji Catchment Tanzania -7.63375 36.88564 M. Genner, B.P. Ngatunga, A. Shechonge 403 28389 Sample_08 Rep 2, Lake Kwela, Rukwa Catchment Tanzania -8.40282 31.97453 M. Genner, B.P. Ngatunga, A. Shechonge 72 1302 Sample_09 Rep 2, Coffee Research Institute, Vwawa, Rukwa Catchment Tanzania -9.08801 32.95695 M. Genner, B.P. Ngatunga, A. Shechonge 928 123922 Sample_10 Rep 2, Msaginya River, Mpanda, Rukwa Catchment Tanzania -6.26506 31.24681 M. Genner, B.P. Ngatunga, A. Shechonge 60 1570 Sample_11 Rep 2, Mpemba River, Tunduma , Rukwa Catchment Tanzania -9.24284 32.84201 M. Genner, B.P. Ngatunga, A. Shechonge 1443 260307 Sample_12 Rep 2, Mfisi River, Paranawe, Rukwa Catchment Tanzania -7.30967 31.06021 M. Genner, B.P. Ngatunga, A. Shechonge 56 3580 Sample_13 Negative (bottled water) Malawi NA NA R. Stothard 212 1354 Sample_14 Negative (bottled water) Malawi NA NA R. Stothard 37 5548 Sample_15 Rep1, Malawi Malemia Malawi -14.36527 35.17349 R. Stothard 4341 176410 Sample_16 Rep2, Malawi Malemia Malawi -14.36527 35.17349 R. Stothard 2479 66958 Sample_17 Rep3, Malawi Malemia Malawi -14.36527 35.17349 R. Stothard 5053 323640 Sample_18 Rep1, Malawi Malemia Malawi -14.36873 35.17667 R. Stothard 3966 120958 Sample_19 Rep2, Malawi Malemia Malawi -14.36873 35.17667 R. Stothard 3558 130475 Sample_20 Rep3,Malawi Malemia Malawi -14.36873 35.17667 R. Stothard 3277 106295 Sample_21 Rep1, Malawi Malemia Malawi -14.36143 35.17086 R. Stothard 3819 142313 Sample_22 Rep2, Malawi Malemia Malawi -14.36143 35.17086 R. Stothard 3371 128089 Sample_23 Rep3, Malawi Malemia Malawi -14.36143 35.17086 R. Stothard 3864 132834 Sample_24 Rep1, Malawi, Nkopola Malawi -14.27752 35.10419 R. Stothard 1844 40935 Sample_25 Rep2, Malawi, Nkopola Malawi -14.27752 35.10419 R. Stothard 3025 128319 Sample_26 Rep3, Malawi, Nkopola Malawi -14.27752 35.10419 R. Stothard 2614 106498 Sample_27 Barombi New Town, Lake Barombi Cameroon 4.4689 9.26452 Z. Bartonicek 95 26100 Sample_28 Barombi New Town, Lake Barombi Cameroon 4.4689 9.26452 Z. Bartonicek 54 3663 Sample_29 Barombi New Town, Lake Barombi Cameroon 4.47157 9.26285 Z. Bartonicek 101 26529 Sample_30 Barombi New Town, Lake Barombi Cameroon 4.47157 9.26285 Z. Bartonicek 112 85771 Sample_31 Barombi New Town, Lake Barombi Cameroon 4.46829 9.26222 Z. Bartonicek 9 57 Sample_32 Barombi New Town, Lake Barombi Cameroon 4.46829 9.26222 Z. Bartonicek 105 25649

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Table 4.1: continued

Samples Location Country Latitude Longitude Collector(s) OTUs Reads Sample_33 Barombi New Town, Lake Barombi Cameroon 4.46979 9.26182 Z. Bartonicek 111 7658 Sample_34 Barombi New Town, Lake Barombi Cameroon 4.46979 9.26182 Z. Bartonicek 97 20607 Sample_35 Mesambe, Lake Mbwandong Cameroon 4.46332 9.26322 Z. Bartonicek 37 1675 Sample_36 Mesambe, Lake Mbwandong Cameroon 4.46332 9.26322 Z. Bartonicek 84 25682 Sample_37 Mesambe, Lake Mbwandong Cameroon 4.46685 9.25847 Z. Bartonicek 78 21029 Sample_38 Mesambe, Lake Mbwandong Cameroon 4.46685 9.25847 Z. Bartonicek 85 11351 Sample_39 Negative (bottled water) Cameroon NA NA Z. Bartonicek 51 4932 Sample_40 Negative (bottled water) Cameroon NA NA Z. Bartonicek 47 786 Sample_41 Luiche River Tanzania -4.85134 29.7244 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 388 71567 Sample_42 River Kaseke Tanzania -5.211933 29.84295 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 1110 268096 Sample_43 TAFIRI Kigoma Center Tanzania -4.886414 29.62006 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 843 289722 Sample_44 River Kibirizi Tanzania -4.886414 29.62006 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 1740 264809 Sample_45 River Malagarasi Tanzania -5.112443 30.39205 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 864 94195 Sample_46 River Malagarasi Tanzania -5.112443 30.39205 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 521 42789 Sample_47 Lake Sagara Tanzania -5.186618 31.06013 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 768 224616 Sample_48 Lake Sagara Tanzania -5.186618 31.06013 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 462 61939 Sample_49 Malagarasi River Tanzania -5.112443 30.39205 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 177 4622 Sample_50 Lake Nyamagoma Tanzania -4.998381 31.0289 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 558 89151 Sample_51 Dam near Tabora Tanzania -5.01139 32.89159 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 36 352 Sample_52 Igombe Dam Tanzania -4.853004 32.74488 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 9 9 Sample_53 Igombe Dam Tanzania -4.896955 32.72666 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 1 1 Sample_54 Igombe Dam Tanzania -4.896961 32.72666 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 11 61 Sample_55 Mwamapuri Tanzania -4.356333 33.87663 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 15 35 Sample_56 Mwamapuri Acuaculture center Tanzania -4.356305 33.88938 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 10 10 Sample_57 Malimbe Lake Tanzania -2.627422 32.89968 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 25 492 Sample_58 Nyashisi River Tanzania -2.641956 32.95941 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 3 24 Sample_59 Pond Near TAFIRI Tanzania -2.584532 32.89899 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 9 117 Sample_60 Lake Victoria Makobe Island Tanzania -2.363848 32.92185 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 37 1340 Sample_61 Lake Victoria South Tanzania -2.492431 32.87561 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 2 70 Sample_62 Lake Victoria TAFIRI Mwanza gulf Tanzania -2.580378 32.89706 C. Gracida Juarez, B.P. Ngatunga, A. Shechonge 19 114 Sample_63 Sudan, Al-Lamab Sudan 15.56654 32.49723 H. Alzaylaee 300 24123 Sample_64 Sudan, Al-Lamab Sudan 15.56654 32.49723 H. Alzaylaee 209 2587

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Table 4.1: continued

Samples Location Country Latitude Longitude Collector(s) OTUs Reads Sample_65 Sudan, Al-Lamab Sudan 15.5665411 32.49723 H. Alzaylaee 24 144 Sample_66 Sudan, Southern Egyptian Irrigation Sudan 15.5383741 32.48296 H. Alzaylaee 171 9861 Sample_67 Sudan, Southern Egyptian Irrigation Sudan 15.5383741 32.48296 H. Alzaylaee 348 27866 Sample_68 Sudan, Southern Egyptian Irrigation Sudan 15.5336811 32.48252 H. Alzaylaee 403 43185 Sample_69 Sudan, Northern Egyptian Irrigation Sudan 15.545123 32.48496 H. Alzaylaee 75 1536 Sample_70 Sudan, Northern Egyptian Irrigation Sudan 15.5420586 32.48508 H. Alzaylaee 150 10532 Sample_71 Sudan, Northern Egyptian Irrigation Sudan 15.5098154 32.47919 H. Alzaylaee 175 15343 Sample_72 Sudan, kabri aldabasin Sudan 15.5098154 32.47919 H. Alzaylaee 317 11463 Sample_73 Sudan, kabri aldabasin Sudan 15.5098154 32.47919 H. Alzaylaee 148 4805 Sample_74 Sudan, kabri aldabasin Sudan 15.5098154 32.47919 H. Alzaylaee 100 3638 Sample_75 Sudan, alkalakilat alqiba Sudan 15.4402209 32.46379 H. Alzaylaee 38 1032 Sample_76 Sudan, alkalakilat alqiba Sudan 15.4402209 32.46379 H. Alzaylaee 114 3204 Sample_77 Sudan, alkalakilat alqiba Sudan 15.4402209 32.46379 H. Alzaylaee 211 12137 Sample_78 Sudan, South Salayt 15/16 Sudan 15.5421966 32.63605 H. Alzaylaee 265 20186 Sample_79 Sudan, South Salayt 15/16 Sudan 15.5421966 32.63605 H. Alzaylaee 408 21916 Sample_80 Sudan, South Salayt 15/16 Sudan 15.5905022 32.69017 H. Alzaylaee 298 6635 Sample_81 Sudan, South Salayt B2 Sudan 15.5579839 32.67212 H. Alzaylaee 284 15266 Sample_82 Sudan, South Salayt B2 Sudan 15.5579839 32.67212 H. Alzaylaee 184 10102 Sample_83 Sudan, South Salayt B2 Sudan 15.5318833 32.67193 H. Alzaylaee 171 8207 Sample_84 Sudan, South Salayt, canal B18 Sudan 15.558011 32.67208 H. Alzaylaee 84 10350 Sample_85 Sudan, South Salayt, canal B18 Sudan 15.558011 32.67208 H. Alzaylaee 200 8732 Sample_86 Sudan, South Salayt, canal B18 Sudan 15.5578437 32.6 H. Alzaylaee 160 12724 Sample_87 Al Jazirah , Junaid Canal 3 Sudan 14.88 33.31 H. Alzaylaee 33 536 Sample_88 Al Jazirah , Junaid Canal 3 Sudan 14.88 33.31 H. Alzaylaee 50 274 Sample_89 Al Jazirah , Junaid Canal 3 Sudan 14.88 33.31 H. Alzaylaee 80 2872 Sample_90 Al Jazirah , Jneid, El Hejilij canal Sudan 14.88 33.34 H. Alzaylaee 856 114652 Sample_91 Al Jazirah , Jneid, El Hejilij canal Sudan 14.88 33.34 H. Alzaylaee 606 44880 Sample_92 Al Jazirah , Jneid, El Hejilij canal Sudan 14.88 33.34 H. Alzaylaee 651 79935 Sample_93 Al Jazirah , Jneid, Elias Diem Canal Sudan 14.9 33.28 H. Alzaylaee 408 50776 Sample_94 Al Jazirah , Jneid, Elias Diem Canal Sudan 14.9 33.28 H. Alzaylaee 645 39335 Sample_95 Al Jazirah , Jneid, Elias Diem Canal Sudan 14.9 33.28 H. Alzaylaee 273 6239 Sample_96 Laboratory no template control NA NA NA NA 12 53

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4.4 Discussion

4.4.1 Characterisation of unseen freshwater diversity

Our survey of the freshwater diversities of four geographical regions performed using eDNA metabarcoding, yielded exceptional diversity of freshwater organisms, with primers mainly amplifying eukaryotic taxa. Alongside metazoans, the samples dominated by bacteria, eukaryotic algae and fungi.

There was notable invertebrate species diversity, including insects (odonates, butterflies, true bugs, true flies, mayflies and beetles), arachnids (spiders and mites), decapods (e.g. crabs), chaetonotids, gastropods, sponges and rotifers. There was also a diversity of freshwater chordate species present in reads, primarily fish. Similar patterns have been recovered in analogous studies using COI metabarcoding in freshwaters, for example Yang et al. (2017) also recovered rotifers as one of the most abundant groups, while Lim et al. (2016) also used COI and demonstrated high proportion of reads belonged to arthropods. Collectively, this demonstrates the power of eDNA-metabarcoding to identify freshwater taxa, and in principle it is possible for high-throughout sequencing to be able capture the diversity of a single freshwater community. However, it is also notable from our study that across sample sites, there was a highly significant associations between sampling intensity (read number) and the number of taxa recovered. This is suggestive of a need for very high read densities (perhaps tens of millions of reads per sample), to fully capture the diversity of specific freshwater systems.

4.4.2 Assignment biases

The success of metabarcoding relies on comprehensive and high-quality reference databases, and the

COI gene is a useful marker of choice for environmental metabarcoding because of its widespread use in specimen-based biodiversity barcoding initiatives, with sequences deposited to BOLD (Ratnaingham and Hubert, 2007) and/or Genbank (https://www.ncbi.nlm.nih.gov/genbank/). However, in relatively poorly studied systems, such as African freshwaters and their riparian habitats, then it is likely that

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there will be large gaps in sequence databases. This was plainly evident in the results of this analysis, where chordates were confidently assigned to the Family and Genus levels with a much higher frequency than arthropods, for example. This highlights the extensive core biodiversity research that is needed to both to understand the full species richness of freshwater environments, as well as to make metabarcoding an effective tool for studying these assemblages from holistic perspective.

4.4.3 Spatial variation in freshwater communities

We observed substantial differences in the frequency of OTUs among sampling regions. This is likely to reflect biogeographic differences among the locations, but also fundamental differences in the habitats sampled. For instance, the samples from Lake Malawi were collected from the shores of this deep largely oligotrophic lake. The samples from Cameroon were collected from a shallow relatively turbid lake. The samples from Sudan were largely collected from freshwater habitats modified for intensive agriculture. The Tanzanian samples were from a range of habitats, including streams, rivers, shallow lakes and the shores of deep oligotrophic Lake Tanganyika. It is possible that the elevated beta diversity present among Tanzanian sample reflects this habitat diversity. Although linking environmental variables to specific aspects of the diversity of freshwater assemblages is beyond the scope of this exploratory study, it would be interesting to investigate how habitat differences influence freshwater communities, both in terms of natural physical and water chemistry variation, but also potential influences from human activities, including land use and proximity to pollution sources

(Baldwin et al., 2014; Drake, 2014).

4.4.4 Potential biases in sampling methods

The samples from Malawi, Tanzania and Sudan were all collected, preserved and analyses using the

Sterivex filter-based methods. The samples from Cameroon were collected using standard circular filters, with larger pore sizes than the 0.22 μm Sterivex. The sampling methods are known to influence

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not just the quantity of material recovered (e.g. Spens et al., 2017), but also species diversity in resulting metabarcoding data (Li et al., 2018). Hence, the differences we observed may in part be related to different sampling methods.

4.4.5 Reliability of eDNA for identifying species of interest for health or conservation

Our study also highlighted a high diversity of species present, but we noted that there were multiple species groups that are ubiquitous in African freshwaters, but were rare or absent from the reads recovered. Two particular groups of interest are parasitic trematodes of the genus Schistosoma, and their host gastropods of the genera Bulinus and Biomphalaria. These groups are of interest given their role in causing the disease schistosomiasis. We detected Bulinus at only three sites, and found no

Biomphalaria and no Schistosoma, despite these genera being represented inside the reference database. Given that we sampled water bodies where Schistosoma-infected snails have routinely been collected (e.g. Mpemba river in Tanzania, Chapters 2 and 3), it is likely that this represents a false negative result. This may be due to insufficient depth of sequencing of the metabarcode libraries, or the efficiency of the PCR primers that we used (Andujar et al., 2018). We suggest that use of group- specific primers may be more conclusive in determining the composition of parasite communities

(Bass et al., 2015).

It was noteworthy that the eDNA barcoding revealed of the presence of fish species that were otherwise not known to be present at those sites, specifically the redbelly tilapia (C. zillii) and the largemouth bass (M. salmoides). Redbelly tilapia is naturally distributed across northern and western

Africa, but was introduced into Lake Victoria in the 1950s, and has been recorded at multiple locations in Tanzania since (Genner et al., 2018). Environmental DNA evidence of the presence of this species inside Lake Sagera represents the first record from the Malagarasi catchment. It is plausible that it was introduced from the Lake Victoria region alongside blue-spotted tilapia (Oreochomis lecuostictus), that

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is now widespread throughout the Malagarasi system (Genner et al., 2018; Shechonge et al., 2019).

Largemouth bass is naturally distributed across North America, but has been introduced to water bodies in Africa primarily for sport angling (Wely and Cowley, 2015). This environmental DNA evidence from the Coffee Research station dam in Vwawa, near Mbeya, represents the first strong evidence for the extant presence of the species in Tanzania. It is plausibly introduced from neighbouring countries such as Zambia or Kenya to support sport angling in the region.

4.4.6 Prospects for eDNA-based characterisation of freshwater assemblages

The use of COI-based metabarcoding for the study of aquatic communities is useful due to the following reasons: (i) the existing and growing database for COI gene from standard barcoding, (ii) the possibility of comparing different studies due to the widely accepted standardization of this marker,

(iii) the ability of universal primers to recover taxonomically complex communities composed of a wide range of metazoans (Andujar et al., 2018). Moreover, metabarcoding can capture eDNA derived not only from “free-living” forms, but also from gut contents, parasites, and other forms of eDNA (Leray and Knowlton, 2015). It therefore has the potential of being a powerful bioassessment tool for evaluating water quality (Lim et al., 2016).

However, on the basis of the evidence from this study we suggest that universal primers capable of capturing a broad-spectrum of eukaryotic diversity require read numbers of orders of magnitude greater than our study to accurately reflect the full diversity of freshwater communities. Alternatively, or additionally, it may be beneficial to develop primer sets that enable more focussed sequencing effort on taxonomic groups of interest.

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4.5 Conclusion

This study demonstrates the potential of using eDNA metabarcoding to characterise whole metazoan communities within freshwater habitats. This approach can provide valuable information about geographical and ecological distributions of freshwater biodiversity, which is fundamental for integrating molecular methods with environmental-related decision-making. Further research is however needed in order to establish optimal eDNA-based bioassessment tools, allowing for more comprehensive community-level investigations within and across multiple regions. It was notable from this work that eDNA metabarcoding, using COI universal primers, actually provided very little information about parasites present, their main hosts, or their vectors. Thus, use of non-specific eukaryotic-focussed metabarcoding approaches alone is unlikely to enable understanding about the distribution of parasites in the environment, or their ecology. Although there is potential for eDNA to contribute to a broader understanding of the ecology of parasite in freshwaters (Bass et al., 2015), methodological developments will be needed to efficiently access the information that eDNA has the potential to provide.

Acknowledgements

We thank staff from the Tanzania Fisheries Research Institute for contributions to fieldwork. The

Tanzania Commission for Science and Technology (COSTECH) provided fieldwork permits. We thank

Richard Wall for his support.

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Chapter 4

Supplementary Information

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S4.1. Code for metabarcoding pipelines and read processing #join the files together and merging paired-end reads illuminapairedend --score-min=40 -r HA-HACo1-1_S1_L001_R1_001.fastq HA-HACo1-1_S1_L001_R2_001.fastq > HindS1.fastq illuminapairedend --score-min=40 -r HA-HACo1-1_S2_L001_R1_001.fastq HA-HACo1-1_S2_L001_R2_001.fastq > HindS2.fastq cat HindS1.fastq HindS2.fastq > Hind_all.fastq

#run the OBItools pipeline

#Remove unaligned sequence: obigrep -p 'mode!="joined"' Hind_all.fastq > Hind_all_ali.fastq

#Primers-demultiplex: ngsfilter -t ngsfilter_COI_LerayXT_Salford.tsv -u Hind_unidentified.fastq Hind_all_ali.fastq > Hind_all_ali_assigned.fastq

#Dereplicate reads into unique sequences: obiuniq -m sample Hind_all_ali_assigned.fastq > Hind_all_ali_assigned_uniq.fasta obiannotate -k count -k merged_sample Hind_all_ali_assigned_uniq.fasta > $$ ; mv $$ Hind_all_ali_assigned_uniq_s.fasta

#Denoise the sequence dataset: obigrep -L 320 -l 300 Hind_all_ali_assigned_uniq_s.fasta > Hind_all_ali_assigned_uniq_s_l300_320.fasta obistat -c count Hind_all_ali_assigned_uniq_s_l300_320.fasta | sort -nk1 | head -20 obigrep -p 'count>=5' Hind_all_ali_assigned_uniq_s_l300_320.fasta > Hind_all_ali_assigned_uniq_s_l300_320_5plus.fasta

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obiclean -s merged_sample -r 1 -H Hind_all_ali_assigned_uniq_s_l300_320_5plus.fasta > Hind_all_ali_assigned_uniq_s_l300_320_5plus_clean1_C.fasta obitab -o Hind_all_ali_assigned_uniq_s_l300_320_5plus_clean1_C.fasta > Hind_all_ali_assigned_uniq_s_l300_320_5plus_clean1_C.tab

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Chapter 5: General Discussion

Chapter 5

General Discussion

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Schistosomiasis is considered a major global health problem. It has been determined to be the second largest cause of morbidity and mortality in poor communities with unsanitary conditions, especially in

Sub-Saharan Africa (Hotez and Kamath, 2009; Adenowo et al, 2015). In high incidence countries, it also significantly impacts education, healthy nutrition, and socio-economic development (Tanner,

1989; Parker, 1992; Chitsulo et al., 2000). Schistosomiasis constitutes an ongoing challenge as the number of people infected or at risk of infection has not shown significant signs of decrease over the last few decades (Chitsulo et al., 2000; Engels et al., 2002), and the disease has even spread to new geographical regions in recent years (Lotfy and Alsaqabi, 2010). This persistence of the disease may be caused by multiple factors, ranging from specific environmental and ecological conditions, to failure to satisfactorily address the challenges through treatment, education, water/sanitation infrastructure and direct snail control measures (Gryseels et al., 2006; Lotfy and Alsaqabi, 2010; Allan et al., 2013; Fenwick and Jourdan, 2016).

Despite the possibility to treat this disease in humans by using anthelmintic medications, the prevention reinfection is a crucial factor to address when trying to eliminate the disease. This may be achieved by reducing the exposure of human population to free-swimming schistosome cercariae (the larval form of the parasite), either by informing the local populations of the risk of infection associated to the use of freshwater environments, or by treating or manipulating freshwater habitats to eliminate snail hosts (Kariuki et al., 2013). These strategies require the setup of efficient surveillance systems for the detection of schistosomes in freshwaters (Stothard et al., 2017). There is also an urgent need for pro-active monitoring of new at-risk areas, particularly in light of the observed expansion of regions suitable for schistosomiasis transmission due to climate change (McCreesh et al., 2015). Accurate monitoring measures are therefore of paramount importance for successful elimination of this disease and also for monitoring after elimination or transmission interruption to make sure transmission does not return.

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The establishment of nationalised plans of action for the implementation of sustainable control programmes is important for the elimination of schistosomiasis, and the significance of this target has been also emphasized by the World Health Organization. The development and application of control strategies at the national level, including accurate and sensitive diagnostic tests, can enable more efficient monitoring and evaluation of the disease. The detection of parasites in freshwaters, however, presents important challenges due to their small size (less than 1 μm), low intensities in some locations, and difficulty in morphologically distinguishing between genetically-divergent species (Bass et al., 2015), this is particularly important for Schistosoma species whose cercariae cannot be readily distinguished by eye. Moreover, a substantial upscaling of the expertise of specific stakeholders, such as environmental scientists, is also crucial.

Given all of the above factors, together with the rapid social and environmental changes, in order to provide efficient strategies to control and eliminate schistosomiasis, it is essential to identify dependable tools for diagnosing the presence of parasites. In Chapter 2, I report the development of an environmental DNA (eDNA)-based approach for the detection of human-infecting Schistosoma species. This technology has the potential to provide a reliable estimate of infection-risk within natural freshwater bodies, which might be difficult to achieve by traditional monitoring methods. In addition, eDNA can be used to determine the presence of host species, thus providing novel insights into their complex interactions (Bass et al., 2015). Given its potential for supplying a rapid and inexpensive diagnosis, eDNA results may provide timely warning to stakeholders who could thus promote prompt interventions aimed at blocking the transmission of schistosomiasis in developing countries.

In agreement with recent studies (Sato et al., 2018; Sengupta et al., 2019), the results in Chapter 2 showed that eDNA-based methods can detect the presence of Schistosoma cercariae in freshwater bodies. This technology can thus be used for the monitoring of the presence of the parasite, and

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therefore inform estimates infection-risk. In particular, both the results in this thesis, and those of

Sengupta et al. (2019) showed that eDNA can successfully detect S. mansoni in cases where conventional survey methods fail. These results thus demonstrate the potential value of this approach.

More specifically, Chapter 2 describes the development of novel PCR assays capable of detecting each of the three main human-infecting Schistosoma species (i.e., S. mansoni, S. haematobium, and S. japonicum) in freshwater bodies, and reliably distinguishing the co-endemic S. mansoni and S. haematobium. Previous studies have focused on one species only (S. mansoni) in the field without testing the specificity of their assays in-vitro (Sato et al., 2018; Sengupta et al., 2019). Additionally,

Chapter 2 describes the exhaustive testing of the sensitivity and species-specificity of these PCR assays, for both S. mansoni and S. haematobium, using an in-vitro approach. This allowed investigation of the potential for false positives caused by the presence of other schistosome species. In general, the in- vitro approach can be considered more informative than in-silico tests of PCR assays, which tend to be conservative and lead to inaccurate results (Henriques et al., 2012). Importantly, the tests of the qPCR assays on the species-specific synthetic DNA showed some cross-species amplification of the S. haematobium assays with closely related sister species, suggesting further assay development may be needed to fully distinguish potentially co-endemic species.

In Chapter 3 a novel protocol is described that combines eDNA with xenomonitoring and allowed estimation of the prevalence and abundance of Schistosoma infections in snail populations from natural freshwater bodies. This is the first study to report the design and application of an eDNA-based xenomonitoring protocol for schistosome snail host species. Using this method, it was possible to show the relationship between schistosome DNA copy number and the biomass of infected host snails. This result provides evidence that the protocol could be used to estimate the extent of schistosome infection across a range of circumstances, from those locations where the frequency of infected snails are extremely high, to those where infections are very rare within a snail population. The protocol

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would be particularly useful in areas where snails are abundantly found and easily collected, but may be less useful in circumstances where snails are rarer and harder to sample.

This novel method for eDNA-based xenomonitoring overcomes a number of well-known challenges in estimating schistosome presence using other methods. The key advantage of the method over classical xenomonitoring based on cercarial emergence is that the snails from a site can be pooled into a small number of experimental containers instead of being housed individually. Avoiding work on individual snails reduces laboratory work needed, and a quantitative diagnosis of could be made from relatively few DNA extractions and qPCRs. Conventional DNA-based xenomonitoring protocols where DNA extraction and PCR tests need to conducted for each individual snail, and it could require over 100 snails to be tested per site, given that infection load may be as low as 1-2% of the total snails population

(Bakuza et al., 2017). It is also possible that such tests may be unreliable if PCR inhibitors are present in the DNA extracts from snail tissue. However, it should be recognised that molecular studies based on schistosomes present in individual snails can provide useful information, including detailed records of schistosome-snail associations, accurate knowledge of snail infection levels and estimates of the genetic diversity of schistosome populations.

Looking forward, it is possible that the eDNA-based xenomonitoring protocol may have particular value in those situations when schistosomes are rare, such as during the final stages of elimination of the disease. In such circumstances eDNA analyses of water collected in-situ are unlikely to be reliable, as the copies of schistosome DNA present are likely to be below the limits of detection of standard eDNA assays. This new protocol, however, significantly increases its detectability by enabling the concentration of schistosome eDNA derived from the cercariae shed by snail hosts. This method could additionally be used alongside other monitoring methods, for example the eDNA assessment of water samples collected in situ, and the results of these methods could be quantitatively compared to

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evaluate relative efficiency. It would be interesting to explore how each method performs at locations with turbid water, for example, where difficulties in filtering adequate water volumes. Environmental-

DNA based xenomonitoring may also prove more effective than eDNA analyses of water collected in- situ when localising snail populations infected by schistosomes. This is because schistosome eDNA may move away from a sampling site in flowing water leading to false negatives, or flowing water may bring schistosome eDNA to a sampling site where schistosomes are otherwise absent, leading to false positives.

Chapter 4 reports a study that explored the potential of metabarcoding to provide comprehensive biodiversity information from freshwater bodies within different geographical regions of Africa. The purpose of the study was to evaluate the utility of eDNA metabarcoding primers, that were developed to target Metazoa and other eukaryotic groups, to detect freshwater species of interest quantify community structure. In particular, a key goal was to assess whether this approach might allow the evaluation of the diversity of trematode parasites and their host species. It was clear from the results that eDNA metabarcoding can be used to characterise whole metazoan communities derived from different habitats. It was shown, for instance, that the species distribution and the community structure differed across sampling regions. On the other hand, the data was not sufficient for quantifying the diversity of parasite species. Nevertheless, the results do provide some indication that in future work could be used to quantify the species present in samples from complex environments

(e.g. Taberlet et al., 2012a), but it appears such studies may require both higher-quantities of sequence data, and primers that target taxonomic groups of interest with greater specificity.

Future prospects

To achieve a comprehensive understanding of the dynamics and prevalence of schistosome in endemic areas, ideally we would employ an efficient and standardised monitoring strategy across locations. This

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will, in time, provide the background data required for the full elimination of the disease. Further work is however needed to improve methods for collecting, preserving, and extracting eDNA from water samples of tropical environments. For example, work is needed to develop efficient methods that increase the volume of water filtered and DNA captured in field replicates. Additionally, research is required to optimise the reagents used for DNA preservation in the field, and the protocols for its extraction are currently labour intensive and require considerable attention to detail to limit the risk of cross-contamination among samples, and with laboratory standards. Further research is also needed to quantify how schistosome eDNA detection levels are linked to environmental variables, including the presence of PCR inhibitors, and how the environment influences the persistence of eDNA

(Sengupta et al., 2019). It is also unclear how the number of copies of DNA detected in an eDNA assay from a natural water body relate to the density of cercariae present.

There are multiple shortcomings of the assays that are reported in this thesis. There is scope to improve the in species-specificity of eDNA assays. Specifically, it would be useful if assays could be tuned to exclusively amplify the target species. This may require optimisation of PCR conditions, or the design of novel primer sets based on either mitochondrial or nuclear genomic variants. There is also scope to test the assays on whole genome (i.e. non-synthetic) DNA from multiple schistosome species, and to explore the extent that amplification success varies across the geographical ranges of the target species. Furthermore, field applications of these assays would benefit from the design and implementation of internal controls to rule out false negative results.

Although the eDNA-based xenomonitoring would not be suitable to monitor freshwater bodies where snails are rare or hard to sample, this method and especially the associated laboratory assays described in Chapter 3 should be further developed, as it has a potential to provide a considerably less

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expensive protocol than other xenomonitoring methods, both in time and consumables and it would overcome multiple challenges faced by currently used xenomonitoring methods. For these reasons, further applications across different host gastropod species and in different environments are needed to better assess the true effectiveness of this method. Also, it would be useful to have a good understanding of how effect environmental conditions (e.g. temperature and light regimes) in assay containers that hold snails influence the rate of schistosome cercarial production and schistosome eDNA abundance. Further tests of this xenomonitoring approach would benefit from knowing the status of snail infection based on cercarial shedding which would enable a better measure on quantification analysis. It may also help if cercariae from future experimental tests were individually sequenced, to provide accurate information on the species composition of schistosomes present.

Metabarcoding methods for freshwater samples should be further optimised in order to improve taxonomic identification, especially of pathogens, and to improve our understanding of local community structure, including the presence of parasite host species and their predators. More studies should be performed to determine the optimal sampling and analysis methods to achieve a reasonable detection level of specific parasites. To better understand the ecology of parasites, further investigations taking into consideration temporal variation in transmission are also desirable.

Collectively, these data would enable predictive approaches that are required for developing preventative measures. However, considerable further work is required to identify and test sets of primers that can provide information on all species groups of interest, and to determine whether the results reliably associate with those from conventional sampling methods.

To significantly reduce the rate of schistosomiasis transmission at sites that test positive, the first practical step is to encourage people to change their behaviour by educating them through awareness programmes on the risk of schistosome infection and transmission. Access to safe water supplements

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Chapter 5: General Discussion

is also key to keeping people away from transmissions sites (Rollinson et al., 2013; Adenowo et al.,

2015). For this, it is crucial to persuade governments of the need to build and maintain water infrastructure. Local government, in particular, must take administrative and political initiatives in order to promote the establishment of nationalised plans of action based on financial policies and available resources (Weerakoon et al., 2015). More generally, the combination of provision of chemotherapy, good hygiene, sanitation, safe water supplies, control of vector snails, and avoidance of contact with contaminated water should lead to lower infection levels and eventual elimination

(Adenowo et al., 2015; Fenwick and Jourdan, 2016). However, a broader base of expertise will also be needed among all Neglected Tropical Disease elimination stakeholders, including social scientists.

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