Molecular Epidemiology of Gastrointestinal Parasites in Farmers and Their Ruminant Livestock from Ghana

Molecular epidemiology of gastrointestinal parasites in farmers and their ruminant livestock from Ghana

Sylvia Afriyie Squire BSc. Zoology, MPhil. Zoology (Applied Parasitology)

A thesis submitted to Murdoch University to fulfil the requirements for the degree of Doctor of Philosophy in the discipline of Veterinary Studies

School of Veterinary and Life Sciences

2019

Author’s declaration

I declare that this thesis is my own account of my research and contains as its main content, work that has not previously been submitted for a degree at any tertiary education institution.

Sylvia Afriyie Squire PhD Student 22nd February 2019

Statement of contributors

Chapter 1 (section 1.6) of the literature review presented in this thesis includes is a slight modification of a published peer reviewed article co-published by Sylvia Afriyie Squire and Una

Ryan. The experimental chapters, 3 and 4 have been published as peer reviewed articles with multiple co-authors and chapter 5 has been submitted to a peer reviewed journal. Sylvia Afriyie

Squire, the first author of all publications, was primarily involved in conceiving ideas and project design, fieldwork, laboratory work, data analysis, preparation and submission of manuscripts. All publication co-authors have consented to their work being included in this thesis and have accepted the statement of contribution presented in the tables below.

CHAPTER CONTRIBUTION

Cryptosporidium and Giardia in Africa – current Chapter 1.6 Title of Paper and future challenges Publication Status Published Squire, S. A. and Ryan U. (2017). Review: Publication Details Cryptosporidium and Giardia in Africa – current and future challenges. Parasites and Vectors, 10, 195. Principal Author

Name of Principal Sylvia Afriyie Squire Author (Candidate) Searched, analysed and reviewed data from a variety of literature sources including Google Scholar, Contribution to the PubMed, Science Direct, Murdoch University Library Paper (online), Google search and international organization’s websites. Overall percentage 70%

Signature Date: 15th Feb 2019

Co-Author Contribution By signing the statement of Authorship, each author certifies that: i. the candidate-s stated contribution to the publication is accurate (as detailed above)

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ii. permission is granted for the candidate to include publication in the thesis; and iii. the sum of all co-author contribution is equal to 100% less the candidate’s stated contribution

Name of Co-Author Una Ryan Contributed to the search and review of data and Contribution to the supervised the writing, correction and publication of Paper manuscripts

Signature Date: 18th Feb 2019

Molecular characterization of Cryptosporidium and Giardia in farmers and their ruminant Chapter 3 Title of Paper livestock from the Coastal Savannah zone of Ghana Publication Status Published Squire, S. A., Yang, R., Robertson, I., Ayi, I., Ryan, U. (2017). Molecular characterisation of Cryptosporidium and Giardia in farmers and their Publication Details ruminant livestock from the Coastal Savannah zone of Ghana. Infection, Genetics and Evolution, 55, 236- 243. Principal Author

Name of Principal Sylvia Afriyie Squire Author (Candidate) Was the principal investigator and contributed to the Contribution to the design of experiment, field and laboratory work, data Paper collection and analysis, and writing of manuscript Overall percentage 80%

Signature Date: 15th Feb 2019

Co-Author Contribution By signing the statement of Authorship, each author certifies that: i. the candidate-s stated contribution to the publication is accurate (as detailed above) ii. permission is granted for the candidate to include publication in the thesis; and iii. the sum of all co-author contribution is equal to 100% less the candidate’s stated contribution

Name of Co-Author Una Ryan Contributed to the design of experiment, field and Contribution to the laboratory work, data analysis and writing of Paper manuscript. Signature Date: 18th Feb 2019

Name of Co-Author Rongchang Yang Contribution to the Contributed to the design of study, laboratory work

Paper and writing of manuscript Signature ate: 18 Feb 2019

Name of Co-Author Ian Robertson Contribution to the Contributed to the design of experiment, data analysis

Paper and writing of manuscript Signature Date: 21st Feb 2019

Name of Co-Author Irene Ayi Contribution to the Contributed to the design of experiment, fieldwork

Paper and writing of manuscript

Signature Date: 15/02/2019

Gastrointestinal helminths in farmers and their Chapter 4 Title of Paper ruminant livestock from the Coastal Savannah zone of Ghana

Publication Status Published Squire, S. A., Yang, R., Robertson, I., Ayi, I., Squire, D. S. and Ryan, U. (2018). Gastrointestinal helminths Publication Details in farmers and their ruminant livestock from the Coastal Savannah zone of Ghana. Parasitology Research, 117(10), 3183-3194. Principal Author Name of Principal Sylvia Afriyie Squire Author (Candidate) Was the principal investigator and contributed to the Contribution to the design of experiment, field and laboratory work, data Paper collection and analysis, and writing of manuscript Overall percentage 80%

Signature Date: 15th Feb 2019

Co-Author Contribution By signing the statement of Authorship, each author certifies that: i. the candidate-s stated contribution to the publication is accurate (as

detailed above) ii. permission is granted for the candidate to include publication in the thesis; and v

iii. the sum of all co-author contribution is equal to 100% less the candidate’s stated contribution

Name of Co-Author Una Ryan Contributed to the design of experiment, field and Contribution to the laboratory work, data analysis and writing of Paper manuscript.

Signature Date: 18th Feb 2019

Name of Co-Author Rongchang Yang Contribution to the Contributed to the design of study, laboratory work

Paper and writing of manuscript Signature Date: 18 Feb 2019

Name of Co-Author Ian Robertson Contribution to the Contributed to the design of experiment, data analysis

Paper and writing of manuscript Signature Date: 21st Feb 2019

Name of Co-Author Daniel Sai Squire Contribution to the Contributed to field and laboratory work, data

Paper analysis and writing of manuscript Signature Date: 18/02/2019

Name of Co-Author Dr. Irene Ayi Contribution to the Contributed to the design of experiment, fieldwork

Paper and writing of manuscript

Signature Date: 15/02/2019

Prevalence and risk factors for gastrointestinal Chapter 5 Title of Paper parasites in ruminant livestock in the Coastal Savannah zone of Ghana

Publication Status Submitted Squire, S. A., Yang, R., Robertson, I., Ayi, I., Squire, D. S. and Ryan, U. (Submitted). Prevalence and risk Publication Details factors for gastrointestinal parasites in ruminant livestock in the Coastal Savannah zone of Ghana. Acta Tropica Principal Author Name of Principal Sylvia Afriyie Squire Author (Candidate)

Was the principal investigator and contributed to the Contribution to the design of experiment, field and laboratory work, data Paper collection and analysis, and writing of manuscript

Overall percentage 85%

Signature Date: 15th Feb 2019

Name of Co-Author Una Ryan Contributed to the design of experiment, field and Contribution to the laboratory work, data analysis and writing of Paper manuscript. Signature Date: 18th Feb 2019

Name of Co-Author Rongchang Yang Contribution to the Contributed to the design of study, laboratory work

Paper and writing of manuscript Signature Date: 18 Feb 2019

Name of Co-Author Ian Robertson Contribution to the Contributed to the design of experiment, data analysis

Paper and writing of manuscript Signature Date: 21st Feb 2019

Name of Co-Author Irene Ayi Contribution to the Contributed to the design of experiment, fieldwork

Paper and writing of manuscript

Signature Date: 15/02/2019

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Abstract

Gastrointestinal (GIT) parasites can cause economic losses in livestock production. Among the GIT parasites infecting ruminant livestock are zoonotic species including

Cryptosporidium, Giardia duodenalis and Trichostrongylus sp. that can cause severe diarrhoea and other symptoms in humans. However, nothing is known about the species and subtypes of these zoonotic parasites in farmers and their ruminant livestock in Ghana.

Therefore, the aim of this thesis was to determine the prevalence and species of these parasites in these two groups.

A total of 925 faecal samples were collected from humans (n=95), cattle (n=328), sheep (n= 217) and goats (n=285) from the Coastal Savannah agroecological zone of Ghana.

Faecal samples were examined by microscopy and/or molecular techniques to determine the gastrointestinal parasites present and genetically characterise the species of

Cryptosporidium, Giardia duodenalis and Trichostrongylus present. The farmers were interviewed using a structured questionnaire on farm management systems and practices to determine the risk factors associated with parasites detected in the livestock. Faecal consistency and body condition scores of the animals were also recorded to determine their association with specific parasite infections.

This thesis describes the first molecular study of Cryptosporidium and Giardia in livestock in Ghana and the identification of zoonotic species. The prevalence of

Cryptosporidium and Giardia by quantitative PCR (qPCR) at the 18S rRNA and glutamate dehydrogenase (gdh) loci was 8.4% and 10.5% in humans, 26.5% and 8.5% in cattle, 34.1% and 12.9% in sheep, and 33.3% and 12.3% in goat faecal samples, respectively. Molecular typing of G. duodenalis at the triose phosphate isomerase (tpi), beta-giardin (bg) and gdh loci detected assemblages A and B in humans and assemblage E in livestock.

Cryptosporidium parvum was the only species identified in humans; C. andersoni, C. bovis, ix

C. ryanae and C. ubiquitum were identified in cattle; C. xiaoi, C. ubiquitum and C. bovis in sheep; and C. xiaoi, C. baileyi and C. parvum in goats by typing at 18S rRNA locus. The C. parvum subtype IIcA5G3q, which has previously been identified in children in Ghana was identified in livestock by typing at gp60 locus.

This thesis also describes the identification of GIT helminths in farmers and their livestock, the first molecular characterisation of Trichostrongylus in humans and animals in

Ghana and the identification of zoonotic as well as potentially novel species. Overall 21 farmers tested positive for at least one GIT helminth by formal ether concentration microscopy and/or PCR at the ITS-2 locus, 80.9% of which were single infections and 19.0% were co-infections. The parasites identified in the farmers consisted of hookworms (n=13)

(9 were Necator americanus and the other 4 could not be amplified by PCR),

Trichostrongylus spp. (n=9), Schistosoma mansoni (n=1), Schistosoma haematobium (n=1) and Diphyllobothrium latum (n=1). In livestock, Eimeria was dominant (78.4%) followed by strongylid nematodes (56.6%), Paramphistomum spp. (16.9%), Dicrocoelium spp.

(7.1%), Thysaniezia spp. (5.8%), Trichuris spp. (3.3%), Moniezia spp. (3.1%), Fasciola spp.

(2.8%), Toxocara spp. (1.1%) and Schistosoma spp. (0.2%) using microscopy. Genotyping of Trichostrongylus spp. in the farmer’s stools identified T. colubriformis (n=6), similar to

T. colubriformis detected in cattle, sheep and goats in the present study. In addition, two

Trichostrongylus spp. with 98.3% and 99.2% genetic similarity to T. probolurus respectively and one Trichostrongylus. spp. which showed 96.6% similarity to both T. probolurus and T. rugatus were identified and T. axei was identified in cattle, sheep and goats.

The distribution and risk factors associated with GIT parasite infection is also described in this thesis. Overall, 90.8% of the total livestock were infected by at least one of the ten different parasites (Eimeria, Strongylids, Toxocara, Trichuris, Schistosoma,

Dicrocoelium, Paramphistomum, Fasciola, Moniezia and Thysaniezia). There was significant association between the prevalence of specific parasites and the animals’ age x

group, region, farming system, and housing floor type. The level of infection was generally light, however 99.0% of the 142 herds were infected. The risk factors significantly associated with specific GIT parasite infections in the livestock included region, type of housing floor, flock size, the number of ruminant species kept, infection with other GIT parasites and education.

In summary, the prevalence and species of GIT parasites in farmers and their ruminant livestock in the Coastal Savannah zone of Ghana is reported in this thesis. The zoonotic potential of the parasites detected are discussed and recommendations for effective control and further studies involving larger numbers of farmers and their household members have been made. The outcome of the present study is relevant for effective management and control of GIT parasites for both veterinary and public health in Ghana. Dedicated and co- ordinated commitments from African governments involving “One Health” initiatives with multidisciplinary teams of veterinarians, medical workers, relevant government authorities, animal health and public health specialists working together is necessary for effective control and prevention of the burden of disease caused by these GIT parasites.

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Acknowledgements

I am most grateful to God for the gift of life, health and ability to complete this research.

Apart from acquiring academic and professional knowledge, I have learnt many valuable lessons about life and people during the period of this journey. God also sent into my life many good people who help and encouraged me every step of the way.

I am very grateful to my amazing supervisors at Murdoch University, Professor Una

Ryan, Prof Ian Robertson and Dr Rongchang Yang for all their support throughout this PhD research. Prof. Una Ryan has not just been a supervisor but also a great mentor and a form of encourager from the beginning and I am very grateful. I also appreciate the support of Dr

Irene Ayi at University of Ghana for being my field supervisor and Dr. Ravi Tiwari for his support as my advisory committee chair.

I thank the members of the Vector and Water-Borne Pathogen Research Group at

Murdoch University for all their encouragement and support throughout this research. The technical support of the staff of the Western Australian State Agricultural Biotechnology

Centre (SABC), Murdoch University is also appreciated.

I acknowledge the logistic contribution of the former and current directors (Dr. N.

Karbo and Dr. E. K. Adu) of the Council for Scientific and Industrial Research - Animal

Research Institute (CSIR-ARI), Accra, Ghana, the technical support of Mr. Hayford.

Reindolf, Mr. Julius. Beyuo, Mr. Humfery Amafu-Dey, Mr. Emmanuel. Pappoe and Mrs.

Antoinette Keleve of the CSIR-ARI parasitology laboratory; Mr. Frederick Sarpong-

Kumankumah, Mr. Daniel Tetteh and Mr. Patrick Kofi Bonney also of CSIR-ARI; Mr.

Daniel Squire and Mr. Nashiru Mutaru of Usher Polyclinic, Accra, Ghana during the fieldwork. We also appreciate Mr. Bashara Ahmed of the Centre for Remote Sensing and

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Geographic Information Services (CERSGIS), University of Ghana, Legon who helped to draw the location map for this study area.

I thank the directors and staff of the Central Regional, Awutu Senya, Komenda Edina

Eguafo Abirem, Kpong Katamanso, Shai Osudoku, Central Tongu and North Tongu District offices of the Ministry of Food and Agriculture, Ghana, including Mr Ramzi Nuru Adams, for their technical support during the fieldwork.

A special thanks to my mum (Ms. Naomi A. Tagoe), dad (Mr. Ebenzer K. Edu), husband (Mr. Daniel S. Squire), sisters (Mrs. Josephine Amanfo and Mrs. Abigail

McCarthy), the Mustard Seed Chapel International family and all my good friends for always being there and encouraging me to press on.

Finally, I thank all the farmers who participated in this research and the Australian

Government, Department of Foreign Affairs and Trade for granting me the scholarship

(Scholarship ID AAS1416640) and financial support to do this research.

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Publications arising from this thesis

Journal publications

1. Squire, S. A., Yang, R., Robertson, I., Ayi, I., Squire, D. S. and Ryan, U. (Submitted).

Prevalence and risk factors for gastrointestinal parasites in ruminant livestock in the

Coastal Savannah zone of Ghana. Acta Tropica.

2. Squire, S. A., Yang, R., Robertson, I., Ayi, I., Squire, D. S. and Ryan, U. (2018).

Gastrointestinal helminths in farmers and their ruminant livestock from the Coastal

Savannah zone of Ghana. Parasitology Research, 117(10), 3183-3194.

3. Squire, S. A., Yang, R., Robertson, I., Ayi, I., Ryan, U. (2017). Molecular

characterisation of Cryptosporidium and Giardia in farmers and their ruminant livestock

from the Coastal Savannah zone of Ghana. Infection, Genetics and Evolution, 55, 236-

243.

4. Squire, S. A. and Ryan U. (2017). Review: Cryptosporidium and Giardia in Africa –

current and future challenges. Parasites and Vectors, 10, 195.

Conference oral presentations

1. Squire, S. A., Yang, R., Robertson I., Ayi, I. and Ryan, U. Prevalence and molecular

characterisation of intestinal helminths in livestock farmers from the coastal savannah

agroecological zone in Ghana. Australia Society for Parasitology (ASP) Annual

Conference, 26 – 29 June 2017, Leura - Blue Mountains, NSW, Australia.

2. Squire, S. A., Yang, R., Robertson I., Ayi, I. and Ryan, U. Molecular characterisation

of Giardia duodenalis in farmers and their ruminant livestock from the coastal savannah

agroecological zone in Ghana. Australia Society for Parasitology (ASP) Annual

Conference, 26 – 29 June 2017, Leura - Blue Mountains, NSW, Australia.

3. Squire, S. A., Robertson I., Yang, R., Ayi, I. and Ryan, U. Molecular characterisation

of Cryptosporidium in ruminant livestock from the Coastal Savannah zone of Ghana. xv

International Conference for Tropical Medicine and Malaria (ICTMM), 18-22 Sept

2016, Brisbane, Qld, Australia.

Conference poster presentations

1. Squire, S. A., Yang, R., Robertson I., Ayi, I. and Ryan, U. Molecular characterisation

of Cryptosporidium in ruminant livestock from the Coastal Savannah zone of Ghana.

Combined Biological Science Meeting (CBSM), 26 Aug 2016, University of Western

Australia, WA, Australia.

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A note of thesis layout

This thesis consists of chapters that have been prepared as stand-alone manuscripts for publication in journals. To maintain contents and formatting consistency throughout the thesis, the chapters here presented differ slightly from the corresponding published manuscripts.

Chapter 1, section 1.6 (Cryptosporidium and Giardia in Africa – current and future challenges) of the literature review is a slightly modified version of a review article published in a peer-reviewed journals. Chapters 3 and 4 have been published as original research articles in peer-reviewed journals, while Chapter 5 has recently been submitted to a peer- reviewed international journal for publication.

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Table of Contents

Author’s declaration ...... i Statement of contributors ...... iii Abstract ...... ix Acknowledgements ...... xiii Publications arising from this thesis ...... xv A note of thesis layout ...... xvii Table of Contents ...... xix List of Figures ...... xxv List of Tables ...... xxvii Abbreviations ...... xxix Symbols ...... xxx

Chapter 1. Literature Review ...... 1 Preface ...... 1 Introduction ...... 2 Cattle, sheep and goat production in Ghana ...... 4 Cryptosporidium species ...... 7 1.4.1 Introduction ...... 7 1.4.2 Transmission and life cycle ...... 8 Giardia duodenalis ...... 10 1.5.1 Introduction ...... 10 1.5.2 Transmission and life cycle ...... 10 Cryptosporidium and Giardia in Africa – current and future challenges ... 12 1.6.1 Introduction ...... 12 1.6.2 Diagnosis and prevalence of Cryptosporidium and Giardia in Africa .. 15 1.6.3 Molecular detection and characterisation of Cryptosporidium and Giardia ...... 16 1.6.4 Epidemiology ad risk factors associated with Cryptosporidium and Giardia duodenalis ...... 17 1.6.5 Cryptosporidium and Giardia species reported in humans in Africa .... 30 1.6.6 Cryptosporidium and Giardia in domesticated animals in Africa ...... 50 1.6.7 Cryptosporidium and Giardia in African wildlife ...... 52 1.6.8 Waterborne and foodborne cryptosporidiosis and Giardiasis in Africa 63 1.6.9 Treatment of cryptosporidiosis and giardiasis in Africa ...... 65 1.6.10 The impact of climate change and HIV status on cryptosporidiosis and giardiasis in Africa ...... 67 1.6.11 Conclusions and future recommendations ...... 69 Gastrointestinal helminths ...... 70 1.7.1 Introduction ...... 70 xix

1.7.2 Gastrointestinal Nematodes ...... 71 1.7.2.1 Introduction ...... 71 1.7.2.2 Transmission and life cycle ...... 72 1.7.3 Gastrointestinal trematodes ...... 73 1.7.3.1 Introduction ...... 73 1.7.3.2 Transmission and life cycle ...... 74 1.7.4 Gastrointestinal Cestodes ...... 75 1.7.4.1 Introduction ...... 75 1.7.4.2 Transmission and life cycle ...... 76 1.7.5 Diagnosis of gastrointestinal helminths ...... 77 1.7.6 The impact of gastrointestinal helminths infections ...... 78 1.7.7 Zoonotic importance of gastrointestinal helminths ...... 79 1.7.8 Treatment of gastrointestinal helminths ...... 82 Thesis aims and objectives ...... 83

Chapter 2. General Materials and Methods ...... 85 Preface ...... 85 Study Design ...... 86 Study Area ...... 86 Study Population ...... 87 Ethics Statement ...... 89 Questionnaire Interview ...... 89 Sample Collection ...... 89 Microscopic Analysis ...... 90 Feedback Visits ...... 91 Molecular Detection and Characterisation of Gastrointestinal Parasites ... 91 2.10.1 DNA isolation ...... 91 2.10.2 Detection of Cryptosporidium spp.by qPCR ...... 92 2.10.3 PCR amplification of Cryptosporidium at the 18S rRNA gene ...... 92 2.10.4 Genotyping of Cryptosporidium at the 60 kDa glycoprotein (gp60) gene ...... 93 2.10.5 Detection of Giardia duodenalis by qPCR ...... 93 2.10.6 PCR amplification of Eimeria at the cytochrome c oxidase subunit I (COI) gene ...... 93 2.10.7 PCR amplification of Giardia duodenalis at the gdh, bg and tpi genes 94 2.10.8 PCR inhibition test for Cryptosporidium and Giardia duodenalis ...... 95 2.10.9 PCR amplification of gastrointestinal nematodes at the ITS-2 locus .... 95 2.10.10 Agarose gel electrophoresis and gel excision ...... 99 2.10.11 Gel purification of PCR products ...... 99 2.10.12 Sequencing ...... 99 2.10.13 Sequence and phylogenetic analyses ...... 100 Statistical Analyses ...... 101

Chapter 3. Molecular Characterisation of Cryptosporidium and Giardia in Farmers and their Ruminant Livestock from the Coastal Savannah Zone of Ghana ...... 103 Preface ...... 103 Abstract ...... 104 Introduction ...... 104 Materials and Methods ...... 106 3.4.1 Ethics statement ...... 106 3.4.2 Study area, design and sample collection ...... 106 3.4.3 Molecular analysis ...... 106 3.4.4 Statistical analyses ...... 106 Results ...... 107 3.5.1 Prevalence of Cryptosporidium and Giardia in livestock farmers ...... 107 3.5.2 Prevalence of Cryptosporidium and Giardia in livestock ...... 109 3.5.3 Prevalence of Cryptosporidium and Giardia in different age groups of livestock ...... 113 3.5.4 Distribution of Cryptosporidium spp. and Giardia assemblages in farmers, livestock and different locations ...... 115 Discussion ...... 118 Conclusions ...... 122

Chapter 4. Gastrointestinal Helminths in Farmers and their Ruminant Livestock from the Coastal Savannah Zone of Ghana ...... 123 Preface ...... 123 Abstract ...... 124 Introduction ...... 124 Materials and Methods ...... 127 4.4.1 Study area, design and sample collection ...... 127 4.4.2 Microscopic analysis and feedback visits ...... 127 4.4.3 DNA isolation, PCR amplification, genotyping and sequencing of selected gastrointestinal nematodes ...... 127 4.4.4 Statistical analyses ...... 128 Results ...... 128 4.5.1 Prevalence of gastrointestinal helminths in farmers and their livestock by microscopy ...... 128 4.5.2 Molecular detection and characterisation of gastrointestinal nematodes in farmers and their livestock ...... 131 Discussion ...... 137 Conclusions ...... 143

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Chapter 5. Prevalence and risk factors for gastrointestinal parasites in ruminant livestock in the Coastal Savannah zone of Ghana ..... 145 Preface ...... 145 Abstract ...... 146 Introduction ...... 147 Materials and Methods ...... 148 5.4.1 Study design, area and population ...... 148 5.4.2 Questionnaire interview ...... 148 5.4.3 Microscopic analysis ...... 149 5.4.4 Statistical analyses ...... 149 Results ...... 149 5.5.1 Husbandry characteristics ...... 149 5.5.2 Prevalence and intensity of gastrointestinal parasites in livestock ...... 150 5.5.3 Distribution of gastrointestinal (GIT) parasites amongst different age groups, locations (regions), farming systems and animal housing floors ...... 152 5.5.4 Risk factors associated with gastrointestinal parasite infection in livestock ...... 155 Discussion ...... 156 Conclusions ...... 162

Chapter 6. General Discussion ...... 163 Introduction ...... 164 Public health implications of gastrointestinal parasites identified in Ghana ...... 165 Veterinary implications of gastrointestinal parasites to Ghanaian small holder livestock industry ...... 168 Zoonotic transmission of gastrointestinal parasites in farmers and their ruminant livestock ...... 170 Limitations ...... 172 Future directions ...... 174 6.7 Conclusions ...... 176

References Cited ...... 179

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APPENDICES 283

Appendix A Cryptosporidium and Giardia in Africa – current and future challenges ...... 284

Appendix B Molecular characterization of Cryptosporidium and Giardia in farmers and their ruminant livestock from the Coastal Savannah zone of Ghana ...... 285

Appendix C Gastrointestinal helminths in farmers and their ruminant livestock from the Coastal Savannah zone of Ghana ...... 286

Appendix D Questionnaire on farm management practices ...... 287

Appendix E Questionnaire on farmers knowledge, attitude and practices in relation to gastrointestinal parasites infection and zoonoses .... 293

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List of Figures

Figure 1.1 Map of Africa and Ghana showing the location in Africa and the location of the ten administrative regions in Ghana and the capital, Accra (image modified from www.mapsopensource.com)...... 4 Figure 1.2 Diagram showing the life cycle of Cryptosporidium (Barta & Thompson, 2006)...... 9 Figure 1.3 Diagram showing the life cycle of Giardia duodenalis (Ankarklev et al., 2010)...... 11 Figure 1.4 General life cycle of GIT nematodes (order Strongylida) (adapted from Demeler, 2005; Roeber et al., 2013a)...... 73 Figure 1.5 Life cycle of trematodes, represented by Fasciola hepatica, (available at http://parasite.org.au/para-site/fasciola/fasciola-clinical.html. Accessed 3 Sept 2018)...... 75 Figure 1.6 Life cycle of cestodes represented by Taenia sp. (adapted from (Levinson, 2016; available at www.accessmedicine.com)...... 77 Figure 2.1 Location map of Ghana showing study area...... 87 Figure 4.1 Phylogenetic relationship of Trichostrongylus spp. constructed based on ribosomal Internal Transcribed Spacer 2 sequences using the Maximum Likelihood method based on the Tamura 3-parameter model (Tamura, 1992). Bootstrap values (>50%) based on 1000 replicates from NJ, ML and MP analysis, respectively, are indicated at the left of the supporting node (dash [−] = value was ≤ 50%). Sequences from this study are indicated bold letters and the countries Australia (AUS), Brazil (BRA), Ghana (GHA), Iran (IRN), Laos (LAO), Malaysia (MYS), New Zealand (NZL), Russia (RUS), Thailand (THA), Togo (TGO), United States of America (USA) except Scotland (SCT) are represented with country codes (ISO 3166-1 alpha-3 codes)...... 133

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List of Tables

Table 1.1 Prevalence of Cryptosporidium and Giardia in African countries (2010- 2016)...... 20 Table 1.2 Cryptosporidium species and subtypes reported in humans from Africa ...... 34 Table 1.3 Giardia duodenalis assemblages and sub-assemblages reported in humans from Africa ...... 46 Table 1.4 Prevalence and Cryptosporidium species and subtypes reported from domesticated animals and wildlife in Africa ...... 54 Table 1.5 Prevalence and Giardia duodenalis assemblages and subtypes reported in domesticated animals and wildlife from Africa ...... 60 Table 1.6 GIT helminths of zoonotic significance in ruminant livestock (cattle, sheep and/or goats)...... 80 Table 2.1 Primers used in the amplification and characterisation of gastrointestinal parasites ...... 96 Table 3.1 Prevalence of Cryptosporidium and Giardia in faecal samples from livestock farmers by qPCR at the 18S and gdh loci respectively...... 108 Table 3.2 Prevalence of Cryptosporidium and Giardia in livestock by qPCR (at the 18S and gdh loci respectively), as categorised by livestock species, district, management type, faecal consistency score (FCS) and body condition score (BCS)...... 111 Table 3.3 Prevalence of Cryptosporidium and Giardia in different age groups of livestock by qPCR at the 18S and gdh loci respectively...... 114 Table 3.4 Distribution of Cryptosporidium species and G. duodenalis assemblages in farmers, livestock and different locations in coastal savannah zone in Ghana ...... 117 Table 4.1 Prevalence of gastrointestinal (GIT) helminths in farmers and their livestock (cattle, sheep and goats) by microscopy from the coastal savannah agroecological zone of Ghana...... 130 Table 4.2 Single or mixed GIT helminths among livestock farmers that tested positive by at least one test method (microscopy and/or PCR) (n=21)...... 131 Table 4.3 Species of GIT nematodes identified in farmers and their livestock from the Coastal Savannah zone of Ghana...... 134 Table 4.4 Genetic distances (%) between Trichostrongylus species from farmers and their livestock and other Trichostrongylus species (from GenBank) at the ITS-2 locus...... 136 Table 5.1 Prevalence of GIT parasite infections in livestock from October 2014 - May 2015 ...... 151

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Table 5.2 Distribution of GIT parasites amongst different age groups, locations (regions), farming systems and animal housing floors...... 154 Table 5.3 Binary logistic regression analysis to determine risk factors associated with parasite infections in livestock...... 156

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Abbreviations

AIDS Acquired Immunodeficiency Virus Syndrome ARI Animal Research Institute ART Anti-Retroviral Therapy AUS Australia BCS Body Condition Score Bg Beta-giardin gene BRA Brazil CAHW Community Animal Health Worker CDC Centers for Disease Control and Prevention CI Confidence interval COI Cytochrome c oxidase COWP Cryptosporidium oocyst wall protein gene CSIR Council for Scientific and Industrial Research Institute Ef1-α Elongation factor 1-alpha gene ELISA Enzyme Linked ImmunoSorbent Assay FAO Food and Agriculture Organisation FCS Faecal Consistency Score Gdh Glutamate dehydrogenase gene GIT Gastrointestinal GEMS Global Enteric Multicentre Study GHA Ghana Gp60 Glycoprotein 60 gene GSS Ghana Statistical Service HIV Human Immunodeficiency Virus HMG-CoA Human 3-hydroxy-3-Methyl-Glutaryl-Coenzyme A HSP70 Heat Shock Protein 70 IRN Iran ITS Internal Transcribed Spacer LAMP Loop-mediated isothermal amplification LAO Laos MYS Malaysia Min Minutes

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MoFA Ministry of Food and Agriculture NZL New Zealand No. Number OR Odds ratio PCR Polymerase Chain Reaction qPCR Quantitative Polymerase Chain Reaction rDNA Ribosomal Deoxyribonucleic Acid rRNA Ribosomal Ribonucleic Acid RFLP Restriction Fragment Length Polymorphism RUS Russia SCT Scotland Sec Second SRID Statistics, Research and Information Directorate SSU Small-subunit THA Thailand TGO Togo Tpi Triose Phosphate Isomerase gene TRAP Thrombospondin-Related Adhesive Protein USA United States of America WFP World Food Program WHO World Health Organisation

Symbols

~ Approximately > Greater than < Less than ≤ Less than or equal to ≥ Greater than or equal to % Percent - To α Alpha β Beta

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Chapter 1. Literature Review

Preface

This chapter is a review of ruminant livestock production in Ghana and gastrointestinal parasites of veterinary, public health and zoonotic importance. Emphasis has been placed on the zoonotic parasites Cryptosporidium and Giardia (protozoa), Trichostrongylus

(nematode), Fasciola (trematode) and Taenia (cestode) species.

Section 1.6, “Cryptosporidium and Giardia in Africa – current and future challenges” of this chapter is a slightly modified version of a review article published in a peer review journal

(Appendix A).

Squire, S. A. and Ryan U. (2017). Review: Cryptosporidium and Giardia in Africa – current and future challenges. Parasites and Vectors, 10, 195. DOI 10.1186/s13071-017-2111-y.

Introduction

Livestock farming has been an important aspect of human existence for many years. The domestication of sheep (Ovis aries) and goats (Capra aegagrus or Capra hircus or Capra hircus aegagrus) began between 10,000 and 7,000 years ago on the Taurus–Zagros

Mountains (Gade, 2000a, 2000b; Zeder & Hesse, 2000), and around the same period for the

European cattle (Bos Taurus) and zebu cattle (Bos indicus) in southwestern Asia and Africa respectively (Gade, 2000c; Zeder et al., 2006; Stock & Gifford-Gonzalez, 2013). Over the years, the increasing demand for livestock produce has led to advancements in farm management systems and improvement of livestock breeds for different purposes across the world (Steinfeld et al., 2006; Flint & Woolliams, 2008). However, livestock husbandry systems remain undeveloped in many developing regions including sub-Saharan Africa

(Thornton, 2010; Herrero et al., 2014).

In these regions, livestock farming involves a wide range of production systems from pastoral/grassland-based systems (which occupy most of the land area and have low human population densities), through mixed crop-livestock systems (usually in areas suitable both for arable farming and livestock production and where the bulk of rural human population live), intensive systems (usually in peri-urban/urban areas) and landless systems (often found in urban areas) (Herrero et al., 2013). Approximately 62% of the world’s poor livestock keepers live in Sub-Saharan Africa and South Asia (Thornton et al., 2003), and many of the farmers depend on livestock farming for their livelihood (Grace et al., 2017).

In Ghana, livestock rearing forms an integral part of the social and cultural lives of most people particularly in rural areas and cattle, sheep and goats are the main ruminant species kept (MoFA, 2016). In 2015, the estimated population of cattle, sheep and goats in

Ghana was 1.734 million, 4.522 million and 6.352 million, respectively (MoFA, 2016) which was unable to meet the demands of the people. Therefore, in that same year, a total of

80,338 tonnes of frozen meat and milk products, and 17,968, 15,768 and 20,004 live cattle, sheep and goats respectively were imported into the country (MoFA, 2016).

The Ghanaian livestock industry experiences several constraints including structural limitations, divestment in pasture production, lack of grassland policies, lack of improved breeding stock and capital, inadequate stock water, poor nutrition and marketing, and diseases (Oppong - Anane, 2006; Amankwah et al., 2012; Squire et al., 2013a; Squire et al.,

2013b; Blackie, 2014; Banson et al., 2016). Gastrointestinal (GIT) parasites are among the major causes of disease (Blackie, 2014) and globally, parasitic diseases can lead to significant economic losses and tens of billions of dollars are expended on anti-parasitic compounds annually (Fitzpatrick, 2013; Roeber et al., 2013a).

Apart from the impact of these parasites on livestock, zoonotic GIT species, including Cryptosporidium hominis, C. parvum, Giardia duodenalis, Trichostrongylus orientalis and T. colubriformis can cause diarrhoea and other diseases in humans (Xiao,

2010; Ryan & Cacciò, 2013; Ashrafi et al., 2015), but little is known about the species of these parasites in farmers and livestock in Ghana. Meanwhile, livestock rearing in Ghana is conducted mainly by small holders who keep their livestock within or near their households and have close interactions with their animals (Oppong - Anane, 2006), hence the increased risk of zoonotic transmission of parasites between farmers and their livestock. Other GIT parasites of zoonotic significance in humans and ruminant livestock include some species of helminths including Bunostomum, Fasciola, Dicrocoelium, Schistosoma, Monieza and

Taenia (McCarthy & Moore, 2000; el-Shazly et al., 2004; CDC, 2017).

This chapter describes the production systems used by the cattle, sheep and goat industry in Ghana. An overview of the life cycle, diagnoses, impact, zoonotic significance and treatment of GIT parasites in humans and ruminant livestock will be presented, along

with a detailed review on zoonotic parasites such as Cryptosporidium spp. and Giardia duodenalis.

Cattle, sheep and goat production in Ghana

Ghana is a developing country in West Africa, located within the Sub-Saharan African region and spans across an area of 238,540 km2 (Figure 1.1). The country is divided into 10 administrative regions (sub divided into districts), with Accra, the capital located in the

Greater Accra Region. The population of the country is over 24 million people (5,467,136 households) and agriculture is a major part of the livelihood of many people (45.8% of total households) (GSS, 2012). About 40.5% (1,013,244 households) of all households engage in livestock keeping only or in combination with other agriculture activities (GSS, 2012).

Figure 1.1 Map of Africa and Ghana showing the location in Africa and the location of the ten administrative regions in Ghana and the capital, Accra (image modified from www.mapsopensource.com).

There is no defined pastoral or nomadism system in the country and farmers practice mainly extensive or free-range cattle rearing and sheep and goat production, with the exception of a few commercial farmers operating mainly in the Coastal Savannah zone

(Oppong - Anane, 2006). Cattle are the main livestock animals milked (Okantah et al., 1999) and the daily care of animals is either by younger members of the household or hired herdsmen known as the Fulanis (Oppong - Anane, 2006). Small ruminant farming (sheep and goats) is mostly small-scale with an average flock size of 6 to 10 sheep or goats (Baah et al., 2012). The most common husbandry system practiced by small ruminant farmers is backyard farming. In this system, sheep and goats are often kept in simple pens within or close to the owner’s homestead and usually under the care of younger family members

(Asafu-Adjei & Dantankwa, 2001; Oppong - Anane, 2006).

Feeding of ruminants is basically agropastoral (Okantah et al., 1999), and three main systems of production; intensive, semi-intensive and extensive systems (Adzitey, 2013). In the semi-intensive system, animals are kept in a form of housing and are released to graze on naturally grown pastures during the day and may be provided some form of food supplementation. This system is frequently practiced by commercial farmers (mostly state or institutional) who may provide some sown pastures in addition to the natural pastures

(Oppong - Anane, 2006). Small holders provide various feed stuffs including farm residues

(root vegetable (cassava) peelings, potatoes and plantains, groundnut haulms, maize bran, rice and sorghum, etc.) and cut and carry forages, with natural pastures. In the intensive system, animals are fed farm residues, cut and carry forages and/or commercial/home-made feed, and are not allowed to graze, but this is less common. Farmers who practice the extensive (free range) system usually do not house their animals nor provide other feed apart from the natural pastures.

It is a common practice for animals from different herds to share common grazing fields and where available, to drink from shared open waters. In less developed communities 5

where there is no portable drinking water, humans share a common source of drinking water with the animals, which has serious public health implications. It is therefore important to know the infection status of farm animals and the species that can be transmitted to humans as well as transmitted between different flocks.

Diseases are major constraints in livestock production in Ghana (Turkson &

Naandam, 2003; Baah et al., 2012). Several ruminant livestock diseases are endemic in

Ghana, including endo- and ecto-parasites, contagious bovine pleuropneumonia, anthrax, blackleg, trypanosomiasis, peste des petits ruminants, pneumonias, heartwater, mange, foot rot and gastroenteritis (Turkson, 2011). In addition, a study on the cause of death of 464 sheep over a 6-year period, identified tapeworms and parasitic gastroenteritis as the major cause of death in young lambs, which was second only to pneumonia (Oppong, 1973). Thus, the limited studies conducted to date indicated that GIT parasites are a major health problem in ruminant livestock in Ghana.

Veterinary services in Ghana suffers from inadequate financing, inadequate veterinary service management, poor attitude of farmers to animal care, inferior farming systems and the inability of farmers to afford veterinary services (Turkson, 2003; Turkson,

2009; Kwadwo et al., 2014). To enhance accessibility to animal health services in the country, the Community Animal Health Workers (CAHWs) system, which involves training individuals within local communities to provide animal health services to complement the efforts of the government veterinarians and para-veterinarians was introduced and has been very useful in several developing nations including Ghana (Leyland & Catley, 2002; Peeling

& Holden, 2004). However, a study conducted in some communities in northern Ghana showed that farmers mostly preferred the government para-veterinarians to the CAHWs due to high costs and low performance resulting from limited training of the CAHWs (Mockshell et al., 2014). The problem of providing veterinary services to farmers therefore persists and farmers either treat animals themselves or sell sick animals for consumption, which has 6

public health implications. In some areas, farmers resort to the use of ethnoveterinary medicine (Turkson & Naandam, 2002) or alternative medicine to manage the health of their animals.

Cryptosporidium species

1.4.1 Introduction

Cryptosporidium species are apicomplexan parasites that infect the microvillus border of the gastrointestinal epithelium of animals and humans and cause diarrhoea (Xiao et al., 2004;

Xiao, 2010). Cryptosporidium was first discovered in 1907 by Ernest E. Tyzzer in the gastric glands of laboratory mice and was named C. muris (Tyzzer, 1907). Although not associated with diarrhoea then, Cryptosporidium was recognised to have some effect on the nutritional status of the laboratory mice (Tyzzer, 1910). Later, Tyzzer reported a new species, C. parvum, which was smaller than C. muris, in the small intestine of mice instead of the stomach (Tyzzer, 1912). Another species, C. meleagridis was found 30 years later, in the small intestines of turkeys and was suspected to be a potential cause of enteritis (Slavin,

1955). However, Cryptosporidium was only confirmed as a diarrhoea-causing pathogen in the 1970s, when it was associated with diarrhoea in an 8-month old female calf (Panciera et al., 1971).

The first report in humans was in 1976, when Cryptosporidium was associated with diarrhoea in an immuno-suppressed adult (Meisel et al., 1976) and a 3-year old child (Nime et al., 1976). Cryptosporidium has since been associated with scouring and gastritis symptoms in a wide variety of vertebrate animals including snakes (Brownstein et al., 1977), macaques (Wilson et al., 1984), foals (Snyder et al., 1978), cats (Poonacha & Pippin, 1982), domesticated production animals including cattle (Pohlenz et al., 1978), sheep (Lihua et al.,

1993) and goats (Mason et al., 1981). Currently 39 Cryptosporidium species are considered valid (Li et al., 2015; Costa et al., 2016; Holubová et al., 2016; Jezkova et al., 2016; Kváč et

al., 2016; Zahedi et al., 2016; Zahedi et al., 2017a; Čondlová et al., 2018; Horčičková et al.,

2018; Kváč et al., 2018). Among these, more than 20 Cryptosporidium spp. and genotypes have been reported in humans, although C. parvum and C. hominis remain the most common

(Xiao, 2010; Ryan & Xiao, 2014; Zahedi et al., 2016).

1.4.2 Transmission and life cycle

The oocyst, the transmission stage of the parasite is small in size (~5 µm) (and contains four sporozoites), which is transmitted by ingestion via the faecal-oral route or rarely through respiratory secretions (Fayer et al., 2000; Sponseller et al., 2014). Excystation of ingested oocysts occurs inside the host, leading to the release of sporozoites in epithelial cells of the gastrointestinal tract or tissues in the respiratory tract which then mature into trophozoites

(Xiao & Fayer, 2008; Sponseller et al., 2014). Trophozoites undergo asexual (schizogony or merogony) and then sexual (gametogony) division into both thick-walled and thin-walled oocysts, which are immediately infectious (Hijjawi et al., 2004; Barta & Thompson, 2006)

(Figure 1.2).

The thin-walled oocysts remain within the host and are responsible for autoinfection

(Bouzid et al., 2013). The thick-walled oocyst has a tough protective wall that makes it environmentally robust and able to penetrate most water filter systems and is resistant to most disinfectants used in water treatment plants including chlorine (Hsu et al., 2001;

Duhain et al., 2012). Due to these characteristics, Cryptosporidium has been responsible for several waterborne disease outbreaks including the massive Milwaukie outbreak in 1993 involving 403,000 people (Mac Kenzie et al., 1994). Apart from being a waterborne pathogen, Cryptosporidium can contaminate food at any point of the production chain from

“farm-to-fork” (Budu-Amoako et al., 2011). It has been transmitted through contaminated foods such as raw fruits and vegetables (Robertson & Gjerde, 2001; El Said Said, 2012),

meat (Yoshida et al., 2007; Rai et al., 2008), fresh-pressed apple cider (Millard et al., 1994) and unpasteurised milk (Cowell et al., 2002).

Contact with animals (livestock) has also been linked with human cryptosporidiosis.

Calves for example have been implicated as the cause of many small cryptosporidiosis outbreaks including outbreaks in students working with calves (Preiser et al., 2003; Kiang et al., 2006; Gait et al., 2008). Dairy farmers in contact with cattle have been reported to be at greater risk of Cryptosporidium infection than those with no such contact (Lengerich et al., 1993). Also, visitors of petting farms have been associated with a cryptosporidiosis outbreak (Smith et al., 2004; Gormley et al., 2011; Utsi et al., 2016). In domestic ruminants,

Cryptosporidium is frequently involved in neonatal diarrhoeal outbreaks (Diaz et al., 2015).

Although oocysts are shed in large quantities in humans (Jokipii & Jokipii, 1986; Chappell et al., 1996) as well as animals (Atwill et al., 2003; Atwill et al., 2006; Yang et al., 2014a), only10-30 oocysts are required for infection (DuPont et al., 1995; Chappell et al., 1996;

Okhuysen et al., 1999).

Figure 1.2 Diagram showing the life cycle of Cryptosporidium (Barta & Thompson, 2006). 9

Giardia duodenalis

1.5.1 Introduction

Giardia is an intestinal flagellate that causes diarrhoea in humans and animals. The motile trophozoite of the protozoan has four pairs of flagella, a characteristic attachment organelle

(ventral sucking disc) and is bilaterally symmetrical (Thompson, 2004). Presently, eight valid species of Giardia have been identified and recognised; Giardia agilis in amphibians,

Giardia ardeae and Giardia psittaci in birds, Giardia microti, Giardia muris and G. cricetidarum in rodents, Giardia duodenalis in mammals and G. peramelis in Australian bandicoots (Isoodon obesulus) (Cacciò et al., 2005; Ryan & Cacciò, 2013; Hillman et al.,

2016; Lyu et al., 2018). Giardia duodenalis (syn. Giardia intestinalis and Giardia lamblia) is the only species found in humans and several other mammals including livestock (Feng

& Xiao, 2011). Giardia duodenalis consists of eight assemblages (A to H) with different host specificities; Assemblage A in humans, livestock and other mammals; B in humans, primates and some other mammals, C and D mainly in dogs and other canids; E mainly in hoofed animals including cattle, sheep and goats and more recently in humans; F in cats and humans; G in rats and more recently in humans; and H in marine mammals (Cacciò et al.,

2005; Ryan & Cacciò, 2013).

1.5.2 Transmission and life cycle

Giardia cysts, which are the transmission stage of the parasite are shed in large quantities in faeces, are environmentally stable and can survive harsh conditions (Hsu et al., 2001; Smith et al., 2007). Excystation of ingested cysts occurs in the small intestine to release trophozoites which divide by binary fission and eventually undergo encystation to form cysts which are shed in the faeces (Gardner & Hill, 2001; Ankarklev et al., 2010; Ryan & Cacciò,

2013) (Figure 1.3).

Cysts are immediately infective when excreted in faeces and can be transmitted by person-to-person or animal-to-animal contact (Thompson, 2004). They have a low infective dose of about 10 to 100 cysts (Rendtorff, 1954) and have been responsible for numerous waterborne and foodborne outbreaks (Osterholm et al., 1981; Porter et al., 1990; Robertson et al., 2007; Daly et al., 2010; Bedard et al., 2016; Efstratiou et al., 2017). Giardia and

Eimeria coinfection have also been linked to a gastroenteritis outbreak in lambs on a farm in Brazil, resulting in the death of some of the lambs (Bastiani et al., 2012).

Figure 1.3 Diagram showing the life cycle of Giardia duodenalis (Ankarklev et al., 2010).

Cryptosporidium and Giardia in Africa – current and future challenges

1.6.1 Introduction

Infectious diarrhoea is a major cause of death in children under 5 years old in Africa (Walker et al., 2012). Unsafe water supplies, and inadequate levels of sanitation and hygiene increase the transmission of diarrhoeal diseases and despite ongoing efforts to enhance disease surveillance and response, many African countries face challenges in accurately identifying, diagnosing and reporting infectious diseases due to the remoteness of communities, lack of transport and communication infrastructures, and a shortage of skilled health care workers and laboratory facilities to ensure accurate (WHO, 2015).

The enteric protozoan parasites Cryptosporidium and Giardia are important causes of diarrhoeal disease (Eckmann, 2003; Chalmers & Davies, 2010; Xiao, 2010; Feng & Xiao,

2011), with Cryptosporidium the most common diarrhoea-causing protozoan parasite worldwide (UNICEF/WHO, 2009). The Global Enteric Multicenter Study (GEMS) and other studies to identify the aetiology and population-based burden of paediatric diarrhoeal disease in sub-Saharan Africa, revealed that Cryptosporidium is second only to rotavirus as a contributor to moderate-to-severe diarrhoeal disease during the first 5 years of life (Kotloff et al., 2013). It has been estimated that 2.9 million Cryptosporidium-attributable cases occur annually in children aged <24 months in sub-Saharan Africa (Sow et al., 2016) and infection is associated with a greater than two-fold increase in mortality in children aged 12 to 23 months (Kotloff et al., 2013).

Giardia duodenalis is the species infecting mammals, including humans, and is estimated to cause 2.8 × 108 cases of intestinal diseases per annum globally (Lane & Lloyd,

2002; Thompson, 2004), with a higher prevalence in developing countries including Africa

(Feng & Xiao, 2011). Most infections are self-limited but recurrences are common in

endemic areas. Chronic infection can lead to weight loss and malabsorption (Thomas et al.,

2014) and is associated with stunting (low height for age), wasting (low weight for height) and cognitive impairment in children in developing countries (Berkman et al., 2002;

Nematian et al., 2008; Al-Mekhlafi et al., 2013). Furthermore, acute giardiasis may disable patients for extended periods and can elicit protracted post-infectious syndromes, including irritable bowel syndrome and chronic fatigue (Hanevik et al., 2014).

In Africa, GEMS reported that Giardia was not significantly positively associated with moderate-to-severe diarrhoea (Kotloff et al., 2013). However, experimental challenge studies unequivocally document that some strains of G. duodenalis can cause diarrhoea in healthy adult volunteers (Rendtorff & Holt, 1954; Nash et al., 1987), and a recent systematic review and metanalysis of endemic paediatric giardiasis concluded that while infection may provide protection from acute diarrhoea, it is also associated with an increased risk of persistent diarrhoea (Muhsen & Levine, 2012).

In addition to diarrhoea, both protozoans have been associated with abdominal distension, vomiting, fever and weight loss in mostly children and HIV/AIDS individuals

(Tumwine et al., 2003; Abdel-Messih et al., 2005; Ignatius et al., 2012; Adamu et al., 2014;

Ignatius et al., 2014; Wanyiri et al., 2014; Helmy et al., 2015; Mengist et al., 2015; Wumba et al., 2015; Breurec et al., 2016). Malnutrition, which impairs cellular immunity, is an important risk factor for cryptosporidiosis (Gendrel et al., 2003) and Cryptosporidium infection in children is associated with malnutrition, persistent growth retardation, impaired immune response and cognitive deficits (Mølbak et al., 1997; Guerrant et al., 1999; Mondal et al., 2009). The mechanism by which Cryptosporidium affects child growth seems to be associated with inflammatory damage to the small intestine (Kirkpatrick et al., 2002).

Undernutrition (particularly in children) is both a sequela of, and a risk factor for, cryptosporidiosis (MacFarlane & Horner‐Bryce, 1987; Sallon et al., 1988; Checkley et al.,

1997; Bushen et al., 2007; Mondal et al., 2009; Mondal et al., 2012; Quihui-Cota et al., 13

2015). For both parasites, breast-feeding is associated with protection against clinical cryptosporidiosis and giardiasis (Creek et al., 2010; Muhsen & Levine, 2012; Abdel-Hafeez et al., 2013; Pedersen et al., 2014a), even though it does not generally prevent acquisition of

Giardia infection or chronic carriage (Muhsen & Levine, 2012).

Infection may be acquired through direct contact with infected persons (person-to- person transmission) or animals (zoonotic transmission) and ingestion of contaminated food

(foodborne transmission) and water (waterborne transmission) (Thompson, 2004; Xiao,

2010; Burnet et al., 2014). Numerous studies have demonstrated that respiratory cryptosporidiosis may occur commonly in both immunocompromised and immunocompetent individuals and that Cryptosporidium may also be transmitted via respiratory secretions (Sponseller et al., 2014). Several studies also suggest that flies may play an important role in the mechanical transmission of Cryptosporidium and Giardia including human infectious species (Graczyk et al., 2000; Graczyk et al., 2004;

Szostakowska et al., 2004; Graczyk et al., 2005; Conn et al., 2007; Getachew et al., 2007;

Fetene & Worku, 2009; Fetene et al., 2011; El-Sherbini & Gneidy, 2012; Adenusi &

Adewoga, 2013; Zhao et al., 2014; Zifang et al., 2014).

Relatively little is known about the epidemiology of cryptosporidiosis and giardiasis in African countries (Mor & Tzipori, 2008; Aldeyarbi et al., 2016), although a recent review of Cryptosporidium in Africa focussed on the epidemiology and transmission dynamics

(Aldeyarbi et al., 2016). In that review, there was little information on the diversity of

Cryptosporidium species and subtypes in animals, foodborne cryptosporidiosis and the prevalences and diversity of Cryptosporidum in individual African countires. The purpose of this review is to compare the prevalence and molecular epidemiology of both

Cryptosporidium and Giardia in Africa, with a focus on current and future challenges and to develop recommendations for better control of these important parasites.

1.6.2 Diagnosis and prevalence of Cryptosporidium and Giardia in Africa

Morphological identification of Giardia and Cryptosporidium (oo)cysts in faecal samples by microscopy either directly, or after the application of stains, including Acid Fast, Lugol’s iodine and immunofluorescent antibody staining are the most widely used methods for diagnosis of these parasites in Africa due to their relatively low cost (Table 1.1). In-house and commercial immunoassays including copro-antigen tests kits, Crypto-Giardia immunochromatographic dipstick kits, faecal antigen ELISA kits ImmunoCard STAT and

CoproStrip™ Cryptosporidium are also widely used either alone or in combination with other techniques for research purposes (Table 1.1). Studies on Cryptosporidium and Giardia have mostly been in children aged 0–16 years, at primary schools, with or without gastrointestinal symptoms or community-based studies, while others have involved different groups of individuals including both HIV/ AIDS-positive and negative patients (Table 1.1).

There are also studies on food handlers or vendors and high-risk individuals in close contact with animals such as national park staff, people living close to national parks, and farmers and their households as well as solid waste workers (Eassa et al., 2016). As a result of the different diagnostic methods utilised, the prevalence of Cryptosporidium and Giardia in different African studies varies widely (Table 1.1), with a prevalence of 0.1% in patients with digestive disorders from Angola to >72% in diarrhoeic patients from Egypt reported for Cryptosporidium and 0.3% in HIV-positive and negative patients from Cameroon to

>62% in primary school children from Tanzania for Giardia duodenalis (Table 1.1).

The immune status of the host, both innate and adaptive immunity, has a major impact on the severity of cryptosporidiosis and giardiasis and their prognosis (Eckmann,

2003; Cacciò et al., 2005; Ryan & Cacciò, 2013; Ryan et al., 2016). With both parasites, immunocompetent individuals typically experience self-limiting diarrhoea and transient gastroenteritis lasting up to 2 weeks and recover without treatment, suggesting efficient host

anti-Cryptosporidium/Giardia immune responses (Eckmann, 2003; Ryan et al., 2016). With cryptosporidiosis, immunocompromised individuals including HIV/AIDS patients (not treated with antiretroviral therapy) often suffer from intractable diarrhoea, which can be fatal

(Current & Garcia, 1991). HIV status is an important host risk factor particularly for cryptosporidiosis and although Cryptosporidium is an important pathogen regardless of

HIV-prevalence (Kotloff et al., 2013), HIV-positive children are between three and eighteen times more likely to have a Cryptosporidium infection than those who are HIV-negative

(Tumwine et al., 2005; Mbae et al., 2013; Tellevik et al., 2015). The unfolding HIV/AIDS epidemic in African countries, with > 25 million adults and children infected with HIV/

AIDS in 2015 (WHO, 2015), is a major contributor to the increased prevalence of cryptosporidiosis and giardiasis in Africa, which makes this review very relevant.

The impact of malnutrition usually falls mainly on children under 5 years of age

(WFP, 2000) and malnutrition is an important risk factor for both diarrhoea and prolonged diarrhoea caused by Cryptosporidium and Giardia (Muhsen & Levine, 2012), with significantly higher rates of Cryptosporidium infection in malnourished children controlling for HIV status (Amadi et al., 2001; Mondal et al., 2009; Moore et al., 2010; Mondal et al.,

2012).

1.6.3 Molecular detection and characterisation of Cryptosporidium and Giardia

Molecular tools for the detection and characterisation of these parasites are increasingly being used, particularly for research purposes due to increased specificity and sensitivity and the ability to identify species (Verweij et al., 2004; Samie et al., 2006; Moshira et al., 2009;

Elsafi et al., 2013; Helmy et al., 2014a; Breurec et al., 2016; Easton et al., 2016). The most commonly used genotyping tools for Cryptosporidium in Africa are PCR and restriction fragment length polymorphism (RFLP) and/or sequence analysis of the 18S rRNA gene

(Adamu et al., 2010; Akinbo et al., 2010; Amer et al., 2010a; Amer et al., 2010b; Ayinmode 16

et al., 2010; Maikai et al., 2011; Molloy et al., 2011; Ayinmode et al., 2012; Nyamwange et al., 2012; Baroudi et al., 2013; Helmy et al., 2013; Laatamna et al., 2013; Samra et al., 2013a;

Samra et al., 2013b; Adamu et al., 2014; Lobo et al., 2014; Wanyiri et al., 2014; Bodager et al., 2015; Eibach et al., 2015; Flecha et al., 2015; Laatamna et al., 2015; Parsons et al., 2015;

Tellevik et al., 2015; Wumba et al., 2015; de Lucio et al., 2016; Ojuromi et al., 2016) (Table

1.1), although some studies have relied on the Cryptosporidium oocyst wall protein (COWP) gene (Graczyk et al., 2001; Adamu et al., 2010; Amer et al., 2010b; Abd El Kader et al.,

2012; Berrilli et al., 2012; Salyer et al., 2012; Helmy et al., 2015; Laatamna et al., 2015;

Ibrahim et al., 2016a), which is not as reliable as the 18S locus at identifying and differentiating Cryptosporidium species (Jiang & Xiao, 2003). Subtyping of

Cryptosporidium has been conducted mainly at the glycoprotein 60 (gp60) gene locus

(Adamu et al., 2010; Amer et al., 2010c; Amer et al., 2013a; Amer et al., 2013b; Baroudi et al., 2013; Helmy et al., 2013; Laatamna et al., 2013; Adamu et al., 2014; Mahfouz et al.,

2014; Rahmouni et al., 2014; Eibach et al., 2015; Helmy et al., 2015; Laatamna et al., 2015;

Mbae et al., 2015; Parsons et al., 2015; Ibrahim et al., 2016a) (Table 1.2) while others targeted the heat shock protein 70 (hsp70) gene (Geurden et al., 2006; Goma et al., 2007;

Siwila et al., 2007; Amer et al., 2010a; Amer et al., 2010b; Laatamna et al., 2015).

Genotyping of Giardia in Africa, has mainly been conducted using the triose-phosphate isomerase (tpi) gene, beta-giardin (bg) and glutamate dehydrogenase (gdh) genes, either alone or using a combination of two or three loci (Lalle et al., 2009; Johnston et al., 2010;

Soliman et al., 2011; Sak et al., 2013; Di Cristanziano et al., 2014; Helmy et al., 2014a;

Hogan et al., 2014; Flecha et al., 2015; de Lucio et al., 2016) (see Tables 1.1, 1.3 and 1.4).

1.6.4 Epidemiology ad risk factors associated with Cryptosporidium and Giardia duodenalis

Risk factor analysis have associated a higher prevalence of Cryptosporidium and/or Giardia with various factors including contact with animals and manure (Wegayehu et al., 2013;

Wanyiri et al., 2014; Helmy et al., 2015), the location of an individual such as living in villages versus cities, drinking underground or tap water (Helmy et al., 2014a; Wanyiri et al., 2014; Helmy et al., 2015), living in a household with another Cryptosporidium-positive person (Parsons et al., 2015), and eating unwashed/raw fruit (Mengist et al., 2015).

Precipitation is thought to be a strong seasonal driver for cryptosporidiosis in tropical countries (Jagai et al., 2009; Lal et al., 2012). Many studies in Africa have reported a higher prevalence of Cryptosporidium during high rainfall seasons. For example, studies in Ghana

(West Africa), Guinea Bissau (West Africa), Tanzania (East Africa), Kenya (East Africa) and Zambia (southern Africa) have reported a higher prevalence of Cryptosporidium just before, or at the onset of the rainy season and a higher prevalence of Giardia in cool seasons in Tanzania (Tumwine et al., 2003; Siwila et al., 2011; Tellevik et al., 2015). However, other studies from Rwanda, Malawi, Kenya and South Africa have reported a higher prevalence of cryptosporidiosis at the end of rainy seasons and beginning of the drier months (Gatei et al., 2006; Kang'ethe et al., 2012a). Studies in Egypt (North Africa) reported a peak prevalence for both Cryptosporidium and Giardia during summer (drier months) with a second peak in winter for Giardia (El-Badry et al., 2015; Ismail et al., 2016). It is possible that the apparent seasonality of human disease, is reflective of different transmission pathways, hosts, and/or Cryptosporidium and Giardia species in different locations. As climate change occurs, transmission patterns of many waterborne diseases may shift, and studies in African locations with unusual seasonality patterns will help inform our understanding of what climate change may bring.

Cryptosporidium and Giardia co-infections and coinfections with other pathogens have been observed in numerous studies in Africa (Abdel-Messih et al., 2005; Gatei et al.,

2006; Nazeer et al., 2013; Banisch et al., 2015; Krumkamp et al., 2015; Wasike et al., 2015a).

In Kenya, polyparasitism was more common in patients with diarrhoea than those with single infections of intestinal parasites (Wanyiri et al., 2013). Multiple infections could impact on 18

the host’s response to infection, as synergistic interaction between co-infecting pathogens has been shown to enhance diarrhoeal pathogenesis. For example, in Ecuador, South

America, simultaneous infection with rotavirus and Giardia resulted in a greater risk of having diarrhoea than would be expected if the co-infecting organisms acted independently of one another (Bhavnani et al., 2012).

Table 1.1 Prevalence of Cryptosporidium and Giardia in African countries (2010-2016).

Study Age group Percentage Percentage Diagnostic technique Genotyping Reference Country population positive for positive for (Target Giardia (%) Cryptosporidium gene) (%)

Algeria Patients with ≤ 80 years 3.6 % (38/1042) 0.1% (1/1042) Direct wet mount, Not (Benouis et al., 2013) digestive formal ether genotyped disorders concentration, Iodine solution and Acid-fast staining Algeria Sporadic cases ≤ 75 years 41.7% (25/542) - Iodine staining Not (Hamaidi-Chergui et genotyped al., 2013)

Angola Children (with ≤ 12 years 0.1% (44/328), 30.0% (101/337) Iodine and acid-fast Not (Oliveira et al., 2015; and without 21.6% (73/338) staining, and genotyped Gasparinho et al., diarrhoea) immunoassay 2016)

Botswana Children with < 5 years 60.0% (45/75) Not stated Not (Creek et al., 2010) diarrhoea genotyped

Burkina Faso Patients with All ages 26.5 %, (77/291) Acid fast staining Not (Sangaré et al., 2015) diarrhoea genotyped Cameroon Individuals Not specified 7.1% (14/197) 1.5% (3/194) Immunoassay Not (Richardson et al., from Nloh and genotyped 2011) Bawa villages

Cameroon HIV/AIDS 15 to 70 years 0.3% (1/396) 2.5% (10/396), Direct microscopy, Not (Nkenfou et al., 2013; positive and 6.o% (12/200), formal ether genotyped Bissong et al., 2015; negative 14.4% (46/320) concentration, iodine Ngum et al., 2015) individuals and acid-fast staining

Central Employees at Not specified 2.1% (1/48) PCR tpi gene (Sak et al., 2013) Africa Dzanga- Republic Sangha Park

Central Children (with Not stated, < 5 15.6% (27/173), 10.4% (18/173), Merthiolate iodine Not (Bouyou-Akotet et African and without years 4.4% (29/666) 7.7% (51/666) formaldehyde genotyped al., 2016; Breurec et Republic diarrhoea) concentration, acid fast al., 2016) staining, immunoassay and multiplex PCR

Chad Individuals ≤ 76 years 3.5% (16/462) Merthiolate-iodine- Not (Hamit et al., 2008) from different formaldehyde genotyped regions concentration

Chad European 21 to 51 years 22.3% (55/247) Direct microscopy and Not (Korzeniewski, 2012) military UN iodine staining genotyped peace keepers Côte d'Ivoire Individuals ≥ 1 year 19.9% (61/307), 6.2% (19/307), Direct microscopy, bg gene, 18S (Berrilli et al., 2012; with intestinal 29% (39/136) 8.8% (12/136) iodine staining, formol- rRNA gene, Becker et al., 2015) disorders, ethyl acetate COWP gene persistent Concentration, diarrhoea and Formalin–ether controls concentration, Immunoassay and PCR

Côte d'Ivoire Children ≤ 19 years 21.6 % (66/306), Direct microscopy, Not (Berrilli et al., 2014; 20.7% (25/121) iodine staining, formol- genotyped Coulibaly et al., ethyl acetate 2016) concentration, ether- concentration technique

Democratic HIV/AIDS 15 to 73 years 1.7% (3/175) 9.7% (17/175), Modified Ritchie 18S rRNA (Wumba et al., 2010; Republic of positive 5.4% (13/242) formalin-ether gene Wumba et al., 2015) Congo individuals concentration, acid fast staining and PCR assay Egypt Individuals ≤ 60 years 19.0% (55/290) Acid fast staining COWP gene (Ibrahim et al., with livestock 2016a) in their household

Egypt Individuals All ages 12.5% (15/150) to 5.9% (23/391) to Direct microscopy, COWP, tpi, (Soliman et al., 2011; with and 27.3% (75/330) 72.2% (52/92 formalin-ethyl acetate gdh, bg gene Abd El Kader et al., without sedimentation, acid fast, 2012; El-Badry et al., diarrhoea iodine and trichrome 2015; Mohammad & staining, immunoassay Mohammad, 2015; and PCR Ibrahim et al., 2016b)

Egypt Children (with < 18 years 18.9 % 2.1% (15/707) to Direct microscopy, 18S rRNA, (Helmy et al., 2013; and without (224/1187) to 49.1% (81/165) modified Ritchie’s TRAP-C2, Sadek et al., 2013; diarrhoea) 29.2% (47/161) biphasic concentration, COWP, tpi, El-Shabrawi et al., iodine and acid-fast gdh, bg gene 2014; Eraky et al., staining, immunoassay 2014; Fathy et al., and PCR 2014; Helmy et al., 2014a; Helmy et al., 2014b; Helmy et al., 2015; Ismail et al., 2016)

Egypt Mentally All ages 8.5% (17/200) 23.5% (47/200) Trichrome and acid-fast Not (Shehata & handicapped staining genotyped Hassanein, 2014) individuals

Egypt Children with ≤15 years 18.2% (10/55) Acid fast staining and Not (Hassanein et al., acute serum immunoassay genotyped 2012) lymphoblastic leukaemia and controls Egypt Municipality 21 to 59 years 3.8% (13/346) 23.4% (81/346) formol–ether Not (Eassa et al., 2016) solid-waste concentration and acid- genotyped workers fast staining Equatorial HIV positive ≤ 76 years 14.2% (44/310), 2.7% (9/333) to Direct microscopy, COWP gene (Roka et al., 2012; Guinea and negative 4.2% (14/333) 18.1% (31/171) formol-ether Roka et al., 2013; individuals concentration, iodine Blanco et al., 2014) and acid-fast staining, Immunoassay and PCR

Ethiopia HIV positive ≤ 86 years 4.0% (15/378) to 8.5% (32/378) to Direct microscopy, 18S rRNA (Getaneh et al., 2010; and negative 7.9% (39/491) 26.9% (140/520) formol-ether gene Adamu et al., 2013; individuals concentration, iodine Adamu et al., 2014; and acid-fast staining Shimelis & Tadesse, and PCR 2014; Kiros et al., 2015; Shimelis et al., 2016)

Ethiopia Children ≤15 years 4.6% (16/350) to 4.6% (18/393) to Direct microscopy, 18S rRNA, (Wegayehu et al., 55.0% (216/ 7.3% (28/348) formol-ether tpi, gdh, bg 2013; Shimelis & 393) concentration, iodine gene Tadesse, 2014; de and acid-fast staining, Lucio et al., 2016; immunoassay and PCR Wegayehu et al., 2016a)

Ethiopia Individuals ≤ 80 years 10.9% (10/92) 1.1% (1/92), Direct microscopy, acid 18S rRNA, (Adamu et al., 2010; with diarrhoea 7.6% (79/1034) fast staining, COWP, gdh, Flecha et al., 2015) or Immunoassay and PCR bg gene gastrointestinal symptoms Ghana Children (with ≤ 17 years 1.0% (1/101) to 4.9% (59/1199) to Direct microscopy, 18S rDNA, (Walana et al., 2014; and without 37.9% (455/1199) 8.5% (204/2400) formol-ether gdh gene Dankwa et al., 2015; diarrhoea/gastr concentration, iodine Duedu et al., 2015a; ointestinal and acid-fast staining, Eibach et al., 2015; symptoms) immunoassay and PCR Krumkamp et al., 2015; Anim-Baidoo et al., 2016)

Ghana HIV positive All ages 12.6% (101/800) 8.2% (34/413), Direct microscopy, Not (Acquah et al., 2012; and negative 9.0% (72/800) formol-ether genotyped Boaitey et al., 2012) individuals concentration, iodine and acid-fast staining

Ghana Food vendors 10 to 70 years 10.7% (15/140) Direct microscopy, Not (Adams & Lawson, iodine staining and genotyped 2016) formalin-ether concentration

Guinea- Children from ≤ 7.5 years 56.0 % (28/50) Ritchie concentration ssu-rRNA (Ferreira et al., 2012) Bissau villages method and PCR and bg gene

Guinea Children (with < 5 years 33.9% (37/109) Direct microscopy Not (Centeno-Lima et al., Bissau and without genotyped 2013) malnutrition)

Kenya Certified food- ≥ 18 years 1.3% (4/312) Iodine staining Not (Kamau et al., 2012) handlers genotyped

Kenya HIV/AIDS ≥ 18 years 34.1% (56/164) Immunoassay and PCR 18S rRNA (Wanyiri et al., 2014) positive gene patients

Kenya Individuals in ≤ 81 years 41.3% (329/796) <1% Multi-parallel qPCR Not (Easton et al., 2016) villages genotyped

Kenya Children (with ≤ 15 years 4.6% (98/2112) to 3.7% (36/981) to Direct microscopy, 18S rDNA, (Nyamwange et al., and without 11.1% (109/981) 9.8% (31/317) formal-ether tpi, gdh, bg 2012; Mbae et al., diarrhoea) concentration, acid fast gene 2013; Pavlinac et al., staining, Immunoassay 2014; Mbae et al., and PCR 2015; Wasike et al., 2015a; Mbae et al., 2016) Libya Children with ≤ 5 years 1.3% (3/239) 2.1% (5/239) Immunoassays Not (Rahouma et al., diarrhoea genotyped 2011)

Libya Individuals Not specified 1.3% (4/305) 2.3% (7/305) Direct microscopy, Not (Mergani et al., 2014) with diarrhoea iodine, genotyped Eosin and acid-fast staining, concentration methods Madagascar Children (with < 5 years 11.7% (314/2692) Direct microscopy and Not (Randremanana et al., and without iodine staining genotyped 2012) diarrhoea)

Madagascar Individuals Not specified 0.8% (1/120) PCR-RFLP 18S rRNA (Bodager et al., 2015) from gene rural south- eastern Madagascar

Mali European Not specified 3.8% (2/53) 5.7% (3/53) Multiplex Real time Not (Frickmann et al., soldiers PCR genotyped 2015)

Morocco Children 5 to 15 years 12.5% (84/673) Iodine, acid fast and 18S rRNA, (El Fatni et al., 2014) (urban and Giemsa staining and gdh genes rural) Faust’s and Ritchie’s concentration methods

Mozambique HIV Positive < 5 years 6.5% (6/93) Direct microscopy and Not (Fonseca et al., 2014) and negative Ritchie’s concentration genotyped individuals method

Nigeria Children (with ≤ 15 years 37.2% (74/199) 1.0% (2/199) to Direct microscopy, acid 18S rRNA (Molloy et al., 2010; and without 46.8% (88/188) fast staining, gene Molloy et al., 2011; diarrhoea or Immunoassay and qPCR Ayinmode et al., gastroenteritis) (18S rRNA gene) 2012; Aniesona & Bamaiyi, 2014; Efunshile et al., 2015; Gambo et al., 2015; Nassar et al., 2017)

Nigeria HIV/AIDS ≤ 80 years 1.9% (9/476) to 1.9% (3/157) to Direct microscopy, acid 18S rRNA (Akinbo et al., 2010; positive and 3.2% (5/157) 35.9% (171/476) fast staining, PCR- tpi, ITS Maikai et al., 2012; negative RFLP rDNA gene Ojuromi et al., 2015; individuals Obateru et al., 2017; Ojuromi et al., 2016)

Rwanda Children ≤ 18 years 35.7% (222/622), Direct microscopy, tpi gene (Ignatius et al., 2012; 59.7% (366/583) ether-based Ignatius et al., 2014; concentration, Real-time Heimer et al., 2015) PCR assays and multiplex qPCR

São Tomé Children (with ≤ 10 years 8.3% (29/348), 5.5% (19/348) Direct microscopy, 18S rRNA, (Lobo et al., 2014; and Príncipe and without 41.7% (185/ 444) modified water-ether tpi gene Ferreira et al., 2015) diarrhoea) sedimentation, formol- ether concentration, iodine and acid-fast staining and PCR-RFLP

Senegal Children (with < 15 years 6.13% (23/375) Acid fast stain and Not (Faye et al., 2013) and without immunoassay genotyped diarrhoea)

South Africa Children (with ≤ 11 years 9.9% (16/162) 5.6% (8/143), Acid fast staining and 18S rRNA (Nxasana et al., 2013; and without 12.2% (54/442) PCR gene Samra et al., 2013b; diarrhoea) Samra et al., 2016)

South Africa HIV positive All ages 11.9% (18/151) 65.5% (165/252) Acid fast stain, Not (Bartelt et al., 2013; individuals 26.5% (40/151) Immunoassay, qPCR, genotyped Samie et al., 2014) PCR (18S rDNA) and loop-mediated isothermal amplification (LAMP)

Sudan Inhabitants of All ages 13.3% (115/866) Acid fast staining Not (Sim et al., 2015) rural areas genotyped

Sudan Children (with ≤ 13 years and 10.1% (91/900) to Direct microscopy and Not (Abdel-Aziz et al., and without primary school 33.4% (167/500) concentration technique. genotyped 2010; Mohamed et diarrhoea) aged al., 2012; Gabbad & Elawad, 2014; Saeed et al., 2015)

Sudan Food handlers 4 to 57 years 20.5% (16/259) Direct microscopy Not (Saeed & Hamid, genotyped 2010)

Sudan Individuals 1 to 80 years 22.0% (22/100) Direct microscopy, Not (Ahmed, 2016) with diarrhoea formal ether genotyped concentration technique

Tanzania Individuals All ages 6.9% (218/3152), 1.1% (2/174) Formol- ssu-rRNA (Mazigo et al., 2010; with and 53.4 % (93/174) Ether concentration gene Forsell et al., 2016) without technique, Iodine and diarrhoea acid-fast staining, qPCR and conventional PCR Tanzania Children (with < 5 years, 1.9% (5/270) to 10.4% (131/1259), Multiplex real-time PCR 18S rRNA, (Moyo et al., 2011; and without school going 62.2% (28/45) 18.9% (51/270) and Immunoassays gdh genes Di Cristanziano et al., diarrhoea) age 2014; Tellevik et al., 2015)

Tanzania Individuals Not stated 4.3% (8/185) PCR-RFLP ssu-rRNA (Parsons et al., 2015) living in and gene around Gombe National Park

Tanzania HIV positive 30-43 years 9.6% (8/83) Direct microscopy and Not (Ringo et al., 2015) individuals iodine staining genotyped

Tunisia Children (with < 5 years 1.7% (7/403) Acid fast staining 18S rRNA (Rahmouni et al., and without gene 2014) diarrhoea) Uganda Individuals ≤ 75 years 40.7% (44/108) 32.4% (35/108) Real-time PCR, nested COWP, ef1- (Johnston et al., 2010; from villages PCR α, gdh, ssu- Salyer et al., 2012) near Kibale rDNA, tpi National gene

Uganda HIV positive 10-69 years 3.6% (4/111) Acid fast staining Not (Echoru et al., 2015) patients with genotyped Diarrhoeal

Uganda Children (non- ≤ 12 years 20.1% (86/427) 12.5% (116/926) Direct microscopy, acid 18S rRNA, (Mor et al., 2010; symptomatic fast staining and PCR bg, gdh, tpi Ankarklev et al., and HIV gene 2012) seronegative)

Zambia Preschool ≤ 7 years 29.0% (117/403) 28.0% (113/403) to Immunofluorescence Not (Siwila et al., 2010, children to 30.7% (241/786) stain genotyped 2011; Siwila & 29.0% (228/786) Olsen, 2015)

Zimbabwe Individuals in All ages 2.7% (8/300) 6.3% (19/300) Acid fast staining Not (Masungo et al., urban areas genotyped 2010)

Abbreviations: bg, beta-giardin; COWP, Cryptosporidium oocyst wall protein gene; ef1-α, elongation factor 1-alpha; gdh, glutamate dehydrogenase; ITS, internal transcribed spacer; 18S rRNA, 18S ribosomal ribonucleic acid; tpi, triose phosphate isomerase gene; TRAP, thrombospondin-related adhesive protein

1.6.5 Cryptosporidium and Giardia species reported in humans in Africa

Genotyping of Cryptosporidium species in Africa have identified at least 13 species and genotypes in humans including C. hominis, C. parvum, C. meleagridis, C. ubiquitum, C. viatorum, C. andersoni, C. bovis, C. canis, C. cuniculus, C. felis, C. muris, C. suis and C. xiaoi (Gatei et al., 2002; Essid et al., 2008; Molloy et al., 2010; Molloy et al., 2011; Adamu et al., 2014; Wanyiri et al., 2014; Anim-Baidoo et al., 2015; Bodager et al., 2015; Helmy et al., 2015; Mbae et al., 2015; Parsons et al., 2015; Ojuromi et al., 2016) (see Table 1.2).

Cryptosporidium hominis and C. parvum are the main species infecting humans

(Xiao & Fayer, 2008; Xiao, 2010; Nazemalhosseini-Mojarad et al., 2012; Ryan et al., 2014).

Out of the 56 molecular studies in African countries analysed, C. hominis was the most prevalent Cryptosporidium species in humans in 38 of the studies (2.4–100%), followed by

C. parvum (3.0–100%) in 13 studies and C. meleagridis (75%) in one study, C. viatorum and C. hominis (40% each) in one study and a single species of C. muris, C. suis and C. viatorum in the remaining three studies (See Table 1.2).

Apart from C. hominis and C. parvum, C. meleagridis is also recognised as an important human pathogen in many African countries including Kenya, Cote d’Ivoire,

Equatorial Guinea, Ethiopia, Malawi, Nigeria, South Africa, Tunisia and Uganda (Morgan et al., 2000; Gatei et al., 2003; Tumwine et al., 2005; Akiyoshi et al., 2006; Gatei et al., 2006;

Morse et al., 2007; Essid et al., 2008; Blanco et al., 2009; Molloy et al., 2010; Ben Abda et al., 2011; Molloy et al., 2011; Berrilli et al., 2012; Samra et al., 2013b; Adamu et al., 2014;

Blanco et al., 2014; Rahmouni et al., 2014; Wanyiri et al., 2014; Wasike et al., 2015b;

Ojuromi et al., 2016; Samra et al., 2016). In immunocompromised individuals, the prevalence of C. meleagridis can reach 75% (3 out of 4 of samples typed) (Morgan et al.,

2000; Ben Abda et al., 2011; Adamu et al., 2014; Blanco et al., 2014; Wanyiri et al., 2014;

Ojuromi et al., 2016), but also 75% (9 out of 12 of samples typed) in immunocompetent individuals (Peng et al., 2001; Morse et al., 2007; Eida et al., 2009; Molloy et al., 2010;

Molloy et al., 2011; Berrilli et al., 2012; Rahmouni et al., 2014; Wasike et al., 2015b). In comparison, the prevalence of C. meleagridis in the developed world is ~1% (Aldeyarbi et al., 2016).

Other Cryptosporidium species including C. viatorum, C. canis, C. muris, C. felis,

C. suis and C. xiaoi have been detected in immunocompromised individuals (Gatei et al.,

2002; Gatei et al., 2003; Akinbo et al., 2010; Adamu et al., 2014) and C. andersoni, C. bovis,

C. viatorum, C. canis, C. muris, C. felis and C. suis in immunocompetent individuals, particularly children (Gatei et al., 2006; Morse et al., 2007; Molloy et al., 2010; Molloy et al., 2011; Helmy et al., 2013; Bodager et al., 2015; Wasike et al., 2015b; de Lucio et al.,

2016).

Subtyping studies of Cryptosporidium to date supports the dominance of anthroponotic transmission in African countries, despite close contact with farm animals.

For example, a study conducted in children in the rural Ashanti region of Ghana reported that the human-to-human transmitted C. hominis subtype families Ia, Ib, Id and Ie made up

58.0% of all Cryptosporidium isolates typed, and within C. parvum, the largely anthroponotically transmitted subtypes families IIc and IIe, were detected in 42.0% of samples typed (Eibach et al., 2015). High levels of subtype diversity are also frequently reported, which is a common finding in developing countries and is thought to reflect intensive and stable anthroponotic Cryptosporidium transmission (Molloy et al., 2010; Xiao,

2010; Samra et al., 2013b; Adamu et al., 2014; Lobo et al., 2014; Eibach et al., 2015; Mbae et al., 2015). Similarly, another study in Kenyan children identified C. hominis subtypes in the majority of positives typed (82.8%), while the C. parvum IIc subtype family was identified in 18.8% of positives (Mbae et al., 2015) (Table 1.2). To date, seven C. hominis

subtype families (Ia, Ib, Id, Ie, If and Ih) have been identified in African countries (Table

1.2).

The mainly anthroponotically transmitted C. parvum IIc subtype family is the predominant subtype in sub-Saharan Africa, including Malawi, Nigeria, South Africa, and

Uganda (Williams et al., 2000; Akiyoshi et al., 2006; Molloy et al., 2010; Ayinmode et al.,

2012; Samra et al., 2013b; Blanco et al., 2014; Eibach et al., 2015; Mbae et al., 2015).

However, it is important to note that the IIc subtype family has been detected in hedgehogs in Europe (Dyachenko et al., 2010; Krawczyk et al., 2015; Sangster et al., 2016), suggesting potential zoonotic transmission.

In addition to the C. parvum IIc and the rarer anthroponotically transmitted IIe subtype family, a range of additional C. parvum subtype families (IIa, IIb, IId, IIg, IIi, IIh and IIm) have been identified in humans (Table 1.2). The C. parvum subtype family IIm, which was discovered in Nigeria (Molloy et al., 2010), also appears to be anthroponotically transmitted, as it has not been identified in animals. High occurrences of zoonotic C. parvum subtype families (IIa and IId) have however been detected in some studies in Egypt, Ethiopia and Tunisia (Essid et al., 2008; Adamu et al., 2010; Helmy et al., 2013; Adamu et al., 2014;

Ibrahim et al., 2016a). Few subtyping studies have been conducted on C. meleagridis isolates, with C. meleagridis subtype IIIdA4 identified in humans in South Africa (Samra et al., 2013b).

Recently a gp60 subtyping assay has been developed for C. viatorum (Stensvold et al., 2015). A single subtype family, XVa, was identified containing multiple alleles

(XVaA3a-XVaA3f) (Stensvold et al., 2015). A single case of XVaA3b originating in Kenya has been identified and nine samples from Ethiopia belonged to XVaA3d; however, this subtype is not a strictly African subtype as the same subtype was also identified in a United

Kingdom patient with a history of traveling to Barbados (Stensvold et al., 2015). Currently

no animal reservoir has been identified for C. viatorum, but extensive studies of animals in the same areas where the human infections originated are required to clarify whether animal reservoirs exist.

The relative clinical impact of C. hominis and C. parvum in African communities is poorly defined. In a study in children under 15 years in Ghana, C. hominis infection was mainly associated with diarrhoea whereas C. parvum infection was associated with both diarrhoea and vomiting (Eibach et al., 2015). A study in Tanzania reported that C. hominis was the predominant species and was associated with a longer duration of symptoms, a higher rate of asymptomatic infection, and a lower CD4 cell count versus C. parvum-infected patients (P < 0.05) (Houpt et al., 2005). However, another study in Uganda reported that the vast majority of children presenting with diarrhoea lasting for 31 days or longer were HIV- positive and were infected with isolates belonging to the C. parvum subtype family IIi, followed by the C. hominis subtype Ie. The C. parvum IIc and IIg and C. hominis Ia, Ie, and

Id subtype families were found in children with diarrhoea lasting for 21 days or less

(Akiyoshi et al., 2006).

Table 1.2 Cryptosporidium species and subtypes reported in humans from Africa

Cryptosporidium species Country Patient group gp60 subtypes Reference (Prevalence - %)a

Botswana Children with diarrhoea (< C. parvum (50.0) (Creek et al., 2010) 5 years) C. hominis (41.0)

Côte d'Ivoire Individuals with intestinal C. meleagridis (75.0) (Berrilli et al., 2012) disorders C. parvum (16.7) C. hominis (8.3)

Democratic Republic of HIV-infected adults C. hominis (80.0) (Wumba et al., 2015) Congo C. parvum (20.0

Egypt Individuals with livestock C. hominis (75.0) (Ibrahim et al., in their household C. parvum (25.0) IIdA20G1 2016a)

Egypt Children with diarrhoea (≤ C. hominis (60·5) (Helmy et al., 2015) 10 years) C. parvum (38·3)

Egypt Diarrhoeal patients C. hominis (97.0) (El-Badry et al., C. parvum (3.0) 2015)

Egypt Children with diarrhoea (1 C. parvum (82.0) (Eraky et al., 2014) to 14 years) C. hominis (12.0) Mixed infections (6.0)

Egypt Children with diarrhoea C. hominis (60.5) (Helmy et al., 2013) (<10 years) C. parvum (38.3) IIaA15G1R1; IIaA15G2R1; IIdA20G1b C. parvum plus C. bovis (1.2)

Egypt Diarrhoeal patients C. hominis (60.0) (Abd El Kader et al., C. parvum (20.0) 2012) C. hominis plus C. parvum (20.0)

Egypt Gastrointestinal C. parvum (66.7) (Eida et al., 2009) symptomatic patients C. hominis (27.7) C. meleagridis (5.6)

Egypt Immunocompromised C. parvum (68.4) (El-Hamshary et al., patients C. hominis (26.3) 2008) C. parvum plus C. hominis (5.3)

Equatorial Guinea HIV-infected and C. parvum (52.9) (Blanco et al., 2009) immunocompetent patients C. hominis (44.1) C. meleagridis (2.9)

Equatorial Guinea HIV-seropositive patients C. parvum (54.8) IIcA5G3b; IIeA10G1 (Blanco et al., 2014)

C. hominis (50.0) IaA18R3 b; IaA24R3; IbA13G3; IdA15; IdA18b C. meleagridis (3.2)

Ethiopia Diarrhoeal patients (14-71 C. parvum (95.1) IIaA15G2R1b; IIaA16G2R1; (Adamu et al., 2010) years) IIaA16G1R1

C. hominis (2.4) IbA9G3 C. hominis plus C. parvum (2.4)

Ethiopia HIV/AIDS patients C. parvum (65.7) IIaA13G2R1; IIaA14G2R1; (Adamu et al., 2014) IIaA15G2R1b; IIaA16G2R1 IIaA16G3R1 IIaA17G2R1; IIaA18G2R1; IIaA19G1R; IIbA12; IIcA5G3a; IIdA17G1; IIdA19G1; IIdA22G1; IIdA24G1; IIeA12G1; If-like

C. hominis (17.9) IbA10G2; IdA20b; IdA24; IdA26; IeA11G3T3

C. viatorum (7.1) C. felis (3.6) C. meleagridis (2.1) C. canis (1.4) C. xiaoi (1.4) C. hominis plus C. parvum (0.7)

Ethiopia Patients C. hominis (100) IbA9G3 (Flecha et al., 2015)

Ethiopia Cryptosporidium positive C. viatorum (90.0) XVaA3d (Stensvold et al., (7 months to 27 years) 2015)

Ethiopia Children (6 to 15 years) C. hominis (40.0) (de Lucio et al., C. viatorum (40.0) 2016) C. parvum (20.0)

Ghana Children (≤ 5 years) C. parvum (100) (Anim-Baidoo et al., 2015)

Ghana Children with and without C. hominis (58.0) IaA15T1R3; IaA15G1R4; (Eibach et al., 2015) gastrointestinal symptoms IaA17; IaA18R3; IaA19R3; IaA21R3; IaA22R3; IaA24R3; IbA13G3b; IdA15; IeA11G3T3

C. parvum (42.1) IIcA5G3ab; IIcA5G3b; IIeA10G1; IIeA10G2

Kenya HIV patients C. hominis (66.7) (Morgan et al., 2000) C. parvum (16.7) C. meleagridis (16.7)

Kenya Not stated C. hominis (C. parvum human) (90.0) Ib; Ieb (Peng et al., 2001) C. meleagridis (10.0) Kenya An HIV-infected adult with C. muris “rock hyrax” (Gatei et al., 2002) diarrhoea Kenya HIV positive and negative C. parvum human (69.7) (Gatei et al., 2003) children and adults C. parvum bovine (21.2) C. meleagridis (3.0) C. muris (3.0)

Kenya Children (< 5 years) C. hominis (87.4) (Gatei et al., 2006) C. parvum (8.6) C. canis (1.7) C. felis (1.1) C. muris (0.6) C. meleagridis (0.6)

Kenya Children (≤ 5 years) C. hominis (82.1) (Nyamwange et al., C. parvum (17.9) 2012)

Kenya HIV/AIDS patients with C. hominis (60.7) Ia; Ibb; Id; Ie; If (Wanyiri et al., 2014) and without diarrhoea (≥18 years)

C. parvum (23.2) IIaa; IIb; IIeb; IIc C. canis (7.1) C. meleagridis (5.3) C. suis (3.6)

Kenya HIV infected and C. hominis (82.8) IaA25R5; IaA27R3; IaA30R3; (Mbae et al., 2015) uninfected children (5 IaA7R1; IbA9G3; IbA9G3R2; years) IdA22; IdA24; IdA19; IdA25; IdA21; IdA20; IdA17G1; IdA18; IdA15G1; IdA23G1; IeA11G3T3R1b; IeA11G3T3; IfA19G1; IfA14G1; IfA12G1

C. parvum (11.9) IIcA5G3R2

C. felis (2.6) C. meleagridis (1.3) C. parvum plus C. hominis (0.7)

Kenya Cryptosporidium spp. C. viatorum XVaA3d (Stensvold et al., positive female (25 years) 2015)

Kenya Children (≤ 5 years) C. hominis (38.9) (Wasike et al., C. parvum (36.1) 2015b) C. meleagridis (16.7) C. canis (5.6) Unknown genotype (2.8)

Madagascar Children with acute C. hominis (91.7) IaA22R3; IdA15G1b; (Areeshi et al., 2008) diarrhoeal disease (≤ 16 IeA11G3T3 years) C. parvum (8.3) IIcA5G3

Madagascar Humans from rural south- C. suis (100.0%) (Bodager et al., eastern Madagascar 2015)

Malawi HIV positive and negative C. hominis (63.6) (Gatei et al., 2003) children C. parvum (36.4)

Malawi Children HIV positive and C. hominis (95.3) Ia; Ib; Idb; Ie (Peng et al., 2003) seronegative (≤ 30 months)

C. parvum (4.7) IIc; IIe

Malawi Diarrhoeal children (<5 C. hominis (58.1) (Morse et al., 2007) years) C. parvum (23.3) C. parvum plus C. hominis (2.3) C. parvum/C. hominis (9.3) C. meleagridis (4.7) C. muris /C. andersoni (2.3)

Nigeria HIV-infected (majority) C. hominis (47.2) IaA14R3; IaA16R3; IaA24R3; (Akinbo et al., 2010) and non-infected patients IaA25R3; IbA13G3; IeA11T3G3b

C. parvum (44.4) IIcA5G3a; IIcA5G3h; New subtype family 1b; New subtype family 2 C. felis (5.6) C. canis (2.8)

Nigeria Children (6 months to 6 C. hominis (44.2) IaA18R2; IaA22R2; IaA24R2; (Molloy et al., 2010) years) IaA25R2; IaA28R2; IaA21R1; IbA10G2; IbA13G3b; IdA11; IdA17; IeA11G3T3b; Ih (Novel subtype); IhA14G1

C. parvum (32.5) IIaA15G2R1; IIaA16G1R1; IIcA5G3ab; IIcA5G3b; IIiA11; IImA14G1

C. parvum plus C. hominis (5.2) C. meleagridis (6.5) C. cuniculus (6.5) C. ubiquitum (3.9) C. canis (1.3)

Nigeria Children (19.5 to 72 C. hominis (37.3) (Molloy et al., 2011) months) C. parvum (35.3) C. parvum and C. hominis (7.8) C. meleagridis (7.8) C. cuniculus (3.9) C. ubiquitum (2.0) C. canis (2.0) Nigeria Diarrhoeal and non- C. hominis (60.0) IaA24R3b; IbA13G3 (Ayinmode et al., diarrhoeal children (1 to 2012) >12 months) C. parvum (40.0) IIcA5G3k

Nigeria Patients (2 months to 70- C. hominis (66.7) IaA23R3; IaA25R3 (Maikai et al., 2012) year) C. parvum (33.3) IIeA10G1

Nigeria HIV-infected patients (22 C. hominis (50.0) IeA11G3T3 (Ojuromi et al., to 65 years) C. parvum (25.0) IIcA5G3k 2016)

C. meleagridis plus C. hominis (25.0)

São Tomé and Príncipe Children (2 months to 10 C. hominis (73.7) IeA11G3T3R1; IeA11G3T3; (Lobo et al., 2014) years) IaA27R3b; IaA23R3

C. parvum (26.3) IIdA21G1a; IIdA26G1b; IIaA16G2R1; IIaA15G2R1

South Africa Children (< 5 years) C. hominis (75.0) IbA12G3R2; IbA10G2; (Samra et al., 2016) IeA11G3T3 C. meleagridis (25.0)

South Africa Children (<5 years) C. hominis (76.0) IaA20R3; IaA25G1R3; (Samra et al., 2013b) IaA17R3; IbA9G3; IbA10G1; IdA20; IdA25IdA26; IdA24; IeA11G3T3b; IfA14G1; IfA12G1

C. parvum (20.0) IIbA11; IIcA5G3bb; IIeA12G1

C. meleagridis (4.0) IIIdA4

South Africa School children and C. hominis (81.8) (Samie et al., 2006) hospital patients (≤ 88 C. parvum (18.2) years)

South Africa HIV-infected children C. hominis (75.0) Ia; Ib; Id; Ie (Leav et al., 2002)

C. parvum (25.0) IIc

Tanzania People living in and around C. hominis (100.0) IfA12G2 (Parsons et al., 2015) Gombe National Park Tanzania Diarrhoeal children and C. hominis (84.7) (Tellevik et al., 2015) controls (< 2 years old) C. parvum (7.6) C. parvum or C. hominis (7.6)

Tanzania HIV patients (18 to 65 C. hominis (71.4) (Houpt et al., 2005) years) C. parvum (28.6)

Tunisia Children (< 5 years old) C. parvum (57.1) IIaA15G2R1b; IIdA16G1 (Rahmouni et al., 2014) C. meleagridis (42.9)

Tunisia Children with primary C. hominis (40.0) (Ben Abda et al., immunodeficiencies C. parvum (20.0) 2011) C. meleagridis (20.0) C. hominis/C. meleagridis (20.0)

Tunisia Immunocompetent and C. parvum (42.1) (Essid et al., 2008) immunodeficiency children C. hominis (36.8) C. meleagridis (21.1)

Uganda Volunteers (1.9 months to C. parvum (97.1) (Salyer et al., 2012) 75 years) C. parvum or C. hominis or C. cuniculus (2.9)

Uganda Children (2 to 84 months) C. hominis (74.4) Ia; Ib; Id; Ieb (Akiyoshi et al., 2006) C. parvum (18.3) IIcb; IIg; IIh; Iii C. hominis plus C. parvum (3.7) C. meleagridis (3.7)

Uganda Children with persistent C. hominis (73.7) (Tumwine et al., diarrhoea, with and without C. parvum (18.4) 2005) HIV/AIDS (<6 months) C. parvum plus C. hominis (3.9) C. meleagridis (3.9)

Uganda People who share habitats C. parvum (100) (Nizeyi et al., 2002) with free-ranging gorillas

Zambia Dairy farm workers and C. parvum (80.0) (Siwila et al., 2007) their household members C. hominis (20.0) aPrevalence (percentage of species identified out of the total number of species genotyped per animal) bDominant gp60 subtype

Relatively few Giardia genotyping studies have been conducted in Africa, however available reports reveal that five G. duodenalis assemblages (A, B, C, E and F) have been identified in humans (Table 1.3). In Africa, Assemblage B was the most prevalent among typed samples (19.5– 100%) in 18 out of 28 studies reviewed (Table 1.3) with Assemblage

A the dominant assemblage (1.4–100%) in the remaining 10 studies (Graczyk et al., 2002;

Gelanew et al., 2007; Helmy et al., 2009; Maikai et al., 2012; Sadek et al., 2013; Sak et al.,

2013; Lobo et al., 2014) (see Table 1.3). Although many studies have reported that Giardia is not associated with severe diarrhoea (Kotloff et al., 2013), one study reported that the prevalence of G. duodenalis Assemblage A was higher among children with vomiting and abdominal pain (Ignatius et al., 2012). Assemblage C was detected in an adult immunocompromised male suffering from bladder cancer and diarrhoea in Egypt (Soliman et al., 2011) and Assemblage F was reported in six diarrhoeal and one asymptomatic individual in Ethiopia (Gelanew et al., 2007). In that study, four of the identified Assemblage

F isolates were mixed infections with Assemblage A. Assemblage E has been reported in humans in three separate studies in Egypt with a prevalence of up to 62.5% in one study population (Foronda et al., 2008; Helmy et al., 2014a; Abdel-Moein & Saeed, 2016).

Subtyping studies in Africa have identified sub-assemblages AI, AII, BIII, BIV and various novel sub-assemblages (Table 1.3).

Table 1.3 Giardia duodenalis assemblages and sub-assemblages reported in humans from Africa

Assemblage Country Patient group Subtypes Reference (Prevalence - %)a

Algeria Children (8 and 13 years) A (37.5%) AI; AIIb; Novel (Lalle et al., 2009) subtype B (56.3%) BIII; BIV; Novel subtypes A + B (6.3%)

Central Africa Republic Dzanga-Sangha Protected Areas Park employees A (100.0%) AII (Sak et al., 2013) Côte d'Ivoire Patients with intestinal disorders (2-63 years) A (19.6%) (Berrilli et al., 2012) B (76.8%) A + B (3.6%)

Côte d'Ivoire Patients with intestinal disorders (2-63 years) A (34.4%) (Berrilli et al., 2012) B (59.0%) A + B (6.6%) Egypt Diarrhoeal patients (2 -70 years) A (6.7%) AI/AII (Soliman et al., 2011) B (80.0%) BIII; B/BIII; BIV; BIII/BIVb A + B (6.7%) C (6.7%)

Egypt Diarrhoeal patients (All ages) A (18.8 %) (Ismail et al., 2016) B (81.2 %)

Egypt Children (<10 years) A (30.4%) AII (Helmy et al., 2014a)

B (52.2%) E (8.7%) A/B (4.3%) A/E (4.3%)

Egypt Children (5-12 years) A (77.1%) AII (Sadek et al., 2013) B (22.9%) BIII

Egypt Children with and without diarrhoea (1.5–12 E (100.0%) (Abdel-Moein & Saeed, years) 2016)

Egypt Diarrhoeal patients (4 to 65 years) A (75.6%) AIb; AII (Helmy et al., 2009) B (19.5%) A + B (5%)

Egypt Human A (5.0%) (Foronda et al., 2008) B (80.0%) E (15.0%)

Ethiopia Children (6 to 15 years) A (17.9%) AIIb; AII/AIII (de Lucio et al., 2016)

B (82.1%) BIII; BIVb; BIII/BIV

Ethiopia Children (≤14 years) A (22.9 %) AIIb (Wegayehu et al., 2016a) B (77.1 %) BIV; Novel subtypesb

Ethiopia Hospital patients (0.5 to 80 B (100.0%) BIII; BIVb (Flecha et al., 2015) Years) Ethiopia Humans isolates from urban and rural areas A (52.5%) (Gelanew et al., 2007) B (22.0%) A + F (11.9%) A + B (13.6%)

Ghana Children (≤ 5 years) B (100.0%) BIII (Anim-Baidoo et al., 2016)

Guinea-Bissau Children (8.3 months to A (11.5 %) AII (Ferreira et al., 2012) 7.5 years) B (84.6 %)

Kenya Diarrhoeal children (≤5 years) A (1.4%) AII (Mbae et al., 2016) B (88.9%) BIII; BIV; BIII/BIVb

A + B (9.7 %)

Morocco Urban and rural school children (5 to 15 years) A (18.1%) AII (El Fatni et al., 2014) B (81.8%) BIII; BIVb

Nigeria Hospital patients (2 months to 70 years) A (100.0%) AII (Maikai et al., 2012)

Rwanda Largely asymptomatic children (< 5 years) A (13.5%) AI; AIIb (Ignatius et al., 2012) B (85.9%) A + B (0.5%)

Rwanda Children (< 5 years) A (14.0%) (Ignatius et al., 2014)

B (86.0%)

Sao Tome Children (2 months -10 years) A (55.5%) (Lobo et al., 2014) B (45.5%)

Tanzania Diarrhoeal and non-diarrhoeal outpatients (≤71 A (6.7 %) (Forsell et al., 2016) years) B (88.8 %) A + B (4.5%) Tanzania Diarrhoeal children and controls (< 2 years) A (50.0%) (Tellevik et al., 2015)

B (50.0%)

Tanzania Primary school children A (21.4%) AII (Di Cristanziano et al., 2014) B (78.6%) BIII; BIV b Uganda Individuals living around Kibale National Park A AII (Johnston et al., 2010) B BIII; BIV

Uganda Apparently healthy children (0–12 years) A (14.7%) AII (Ankarklev et al., 2012) B (73.5%) A+B (11.8%)

Uganda Individuals sharing gorilla habitats A (100.0%) (Graczyk et al., 2002) aPrevalence (percentage of species identified out of the total number of species genotyped per animal); bDominant subtype

1.6.6 Cryptosporidium and Giardia in domesticated animals in Africa

In Africa, Cryptosporidium and Giardia have been reported in several domesticated animal species including cattle, sheep, goats, farmed buffalo, horses, poultry (chicken and turkey), pigs, cultured tilapia (fish) and dogs (Ayinmode et al., 2010; Ghoneim et al., 2012; Amer et al., 2013b; Baroudi et al., 2013; Laatamna et al., 2013; Di Cristanziano et al., 2014;

Abubakar et al., 2015; Bodager et al., 2015; Helmy et al., 2015; Syakalima et al., 2015;

Olabanji et al., 2016). However, the majority of research has been conducted on cattle.

Prevalences ranging from < 1% (Samra et al., 2016) to >86% (Soltane et al., 2007a) in calves have been reported for Cryptosporidium and < 6% (Di Cristanziano et al., 2014) to > 30%

(Sabry et al., 2009) for Giardia in adult cattle and calves respectively. As with most studies, the prevalence of Cryptosporidium was greater in young animals (1 day to 3 months) than older ones. Age, source of drinking water and diarrhoea has been associated with

Cryptosporidium prevalence in cattle (Siwila et al., 2007; Soltane et al., 2007a; Helmy et al.,

2015). For example, in a study in Egypt, calves watered with canal or underground water were at a higher risk of infection than calves watered with tap water (Helmy et al., 2015).

C. parvum, C. ryanae, C. bovis and C. andersoni are the most common species detected in cattle (Bos taurus and Bos indicus), although C. hominis, C. suis and

Cryptosporidium deer-like genotype have also been reported (Siwila et al., 2007; Soltane et al., 2007a; Szonyi et al., 2008; Amer et al., 2010c; Ayinmode et al., 2010; Amer et al., 2013a;

Helmy et al., 2013; Samra et al., 2013a; Mahfouz et al., 2014; Bodager et al., 2015; Helmy et al., 2015; Baroudi et al., 2017). Younger calves had a higher occurrence of C. bovis and

C. ryanae while C. parvum seems to be dominant in pre-weaned calves (Maikai et al., 2011;

Amer et al., 2013a).

Although little research has been done in other domesticated animals, C. ryanae, C. bovis and C. parvum have been reported in farmed buffalos (Amer et al., 2013b; Helmy et al., 2013; Mahfouz et al., 2014), C. xiaoi, C. bovis and C. suis in sheep (Goma et al., 2007; 50

Soltane et al., 2007b; Mahfouz et al., 2014; Parsons et al., 2015) and C. xiaoi and C. parvum in goats (Goma et al., 2007; Parsons et al., 2015) (Table 1.4). In addition, C. parvum and C. suis have been identified in pigs (Bodager et al., 2015), C. erinacei in horses (Laatamna et al., 2013) and C. canis in dogs (Bodager et al., 2015). Cryptosporidium meleagridis was identified in both turkeys and chickens (Soltane et al., 2007b; Baroudi et al., 2013) and C. baileyi has been identified in chickens (Baroudi et al., 2013). All the species reported in domesticated animals, with the exception of C. ryanae and C. baileyi have been identified in humans from Africa (Gatei et al., 2002; Essid et al., 2008; Molloy et al., 2010; Adamu et al., 2014; Wanyiri et al., 2014; Anim-Baidoo et al., 2015; Bodager et al., 2015; Helmy et al.,

2015; Mbae et al., 2015; Parsons et al., 2015; Ojuromi et al., 2016) (see Table 1.2), suggesting that domestic animals may act as zoonotic reservoirs for human infections.

Humans working closely with farmed animals especially calves are known to be more at risk of zoonotic infection with C. parvum and may excrete oocysts without showing clinical symptoms and act as a source of infection for household members (Siwila et al., 2007).

Subtyping of C. parvum from animals at the gp60 locus identified C. parvum subtypes IIa and IId, with IIaA15G1R1, IIaA15G2R1 and IIdA20G1 as the most common

(Amer et al., 2010c; Amer et al., 2013a; Amer et al., 2013b; Helmy et al., 2013; Mahfouz et al., 2014) (see Table 1.4). A unique subtype IIaA14G1R1r1b, was also isolated from a calf in Egypt (Amer et al., 2013a). Cryptosporidium erinacei subtype XIIIa, was found in horses from Algeria (Laatamna et al., 2013). In a study in rural Madagascar, peri-domestic rodents were infected with Cryptosporidium rat genotype III, rat genotype IV, C. meleagridis, C. suis and 2 unknown genotypes (Bodager et al., 2015).

Giardia duodenalis Assemblage E is the dominant species in ruminant livestock

(cattle, farmed buffalo and goats) from the Central African Republic, Egypt, Rwanda,

Tanzania and Uganda (Sabry et al., 2009; Johnston et al., 2010; Amer, 2013; Sak et al., 2013;

Di Cristanziano et al., 2014; Helmy et al., 2014a; Hogan et al., 2014; Abdel-Moein & Saeed,

2016). Assemblage A (subtypes AI and AII) has been reported in goats, cattle, buffalos, ducks and chickens from Cote d’Ivoire, Egypt, Tanzania and Uganda and Assemblage B

(BIV) and/or Assemblage A and B have been reported in goats, ducks and cattle from Cote d’Ivoire, and Tanzania (Sabry et al., 2009; Johnston et al., 2010; Berrilli et al., 2012; Amer,

2013; Di Cristanziano et al., 2014; Helmy et al., 2014a; Abdel-Moein & Saeed, 2016).

Assemblage A was also identified in cultured tilapia and mullet (Tilapia nilotica and Mugil cephalus, respectively) from Egypt (Ghoneim et al., 2012).

1.6.7 Cryptosporidium and Giardia in African wildlife

The majority of studies on Cryptosporidium and Giardia in African wildlife have been conducted in wildlife parks. These studies have included western lowland gorillas from the

Lope National Park in Gabon (Van Zijll Langhout et al., 2010), mountain gorillas from the

Bwindi Impenetrable National Park in Uganda and the Volcanoes National Park in Rwanda

(Nizeyi et al., 1999; Graczyk et al., 2001; Graczyk et al., 2002; Hogan et al., 2014; Sak et al., 2014), chimpanzees from Tanzania, elephants, buffalos and impalas from the Kruger

National Park, South Africa (Samra et al., 2011; Samra et al., 2013a), olive baboons from the Bwindi Impenetrable National Park, Uganda (Hope et al., 2004) and bamboo lemurs and eastern rufous mouse lemurs from the Ranomafana National Park, Madagascar

(Rasambainarivo et al., 2013; Bodager et al., 2015). In addition, Cryptosporidium oocysts and Giardia cysts together with other gastrointestinal parasites were found in dolphins

(Nasitrema attenuata, Zalophotrema spp. and Pholeter gastrophilus) in Egypt, but no genotyping was conducted (Kleinertz et al., 2014).

Cryptosporidium hominis was reported in olive baboons from Kenya and Tanzania and in lemurs from Madagascar, suggesting possible spill-back from humans.

Cryptosporidium hominis and C. suis has been reported in chimpanzees from Tanzania and

(Li et al., 2011; Bodager et al., 2015; Parsons et al., 2015) and C. parvum was reported in gorillas from Uganda (Graczyk et al., 2001). Subtyping at the gp60 locus identified C. 52

hominis subtypes IfA12G2 (the commonest), IbA9G3 and a novel subtype IiA14 in olive baboons and chimpanzees from Kenya and Tanzania, respectively (Li et al., 2011; Parsons et al., 2015). In wild ruminants, C. ubiquitum and C. bovis have been identified in forest buffalos and C. ubiquitum in Impala from South Africa (Samra et al., 2013a).

Cryptosporidium ubiquitum is considered an emerging zoonotic pathogen (Li et al., 2014) and has been reported in humans in Africa in Nigeria (Molloy et al., 2010; Molloy et al.,

2011) and increasing human encroachment into wildlife-populated areas in Africa, is likely to increase zoonotic transmission.

Giardia duodenalis Assemblage A and B (subtypes BIII and BIV) have been reported in gorillas from Uganda and Rwanda, respectively (Graczyk et al., 2002; Hogan et al., 2014) and Assemblage B in an usrine colobus monkey from Ghana (Teichroeb et al.,

2009) (Table 1.5), again suggesting spill-back. Giardia duodenalis cysts have been found in the faeces of other animals including grasscutters (Thryonomys swinderianus) (Futagbi et al., 2010) but no genotyping was conducted. Almost all the Cryptosporidium and Giardia species identified in wildlife are infectious to humans with potential for zoonosis or spillback from humans to animals. For example, a high prevalence of cryptosporidiosis was reported in park staff members (21%), who had frequent contact with gorillas versus 3% disease prevalence in the local community in Uganda (Nizeyi et al., 2002).

Table 1.4 Prevalence and Cryptosporidium species and subtypes reported from domesticated animals and wildlife in Africa

Reported a b Country Animal type Animal species prevalence (No. Species (Prevalence - %) gp60 subtypes Reference positive/No. tested)

Algeria Domesticated Turkey (Meleagris gallopavo) 44.6% (25/57) C. meleagridis (100) IIIgA18G4R1; IIIgA19G5R1; (Baroudi et al., Chickens (Gallus domesticus) IIIgA20G4R1; IIIgA21G3R1c; 2013) IIIgA24G3R1; IIIgA26G2R1 34.4% (31/90) C. meleagridis (83.9) IIIgA18G4R1; IIIgA19G5R1c; IIIgA21G3R1; IIIgA24G2R1; IIIgA26G3R1c C. baileyi (16.1)

Algeria Domesticated Horse (Equus caballus) 2.9% (4/138) Cryptosporidium hedgehog XIIIaA22R9 (Laatamna et genotype (100) al., 2013)

Algeria Domesticated Horse (Equus caballus) 2.3% (5/219) C. parvum (60.0) IIaA16G1R1c (Laatamna et al., 2015) C. hominis (20.0) IkA15G1 C. muris RN66 (20.0) IIaA16G1R1 Donkey (Equus africanus 1.6% (2/124) C. parvum (50.0) asinus) C. muris TS03 (50.0)

Central Africa Wildlife Wild western lowland gorillas 0.5% (1/201) C. bovis (100) (Sak et al., Republic (Gorilla gorilla gorilla) 2013)

African buffalo (Syncerus 5.0% (1/20) C. muris TS03 (100) caffer)

Côte d'Ivoire Domesticated Chicken (Gallus gallus) 16.1% (5/31) C. meleagridis (80.0)

C. parvum (20.0) (Berrilli et al., 2012) Egypt Domesticated Duck (Anas platyrhynchos) 39.9 % (365 / 915) C. meleagridis (100) (Kalifa et al., Incidence 2016)

Egypt Domesticated Cattle (Bos taurus) and Cattle = 31·2% C. parvum (65.7) IIaA15G1R1; IIdA19G1; (Helmy et al., Buffalo (Bubalus bubalis) (185/593) IIdA20G1c 2015) Buffalo = 35·5% (75/211) C. ryanae (11.8) C. bovis (4.1) C. parvum plus C. ryanae (11.2) C. parvum plus C. bovis (5.3)

C. parvum plus C. andersoni (1.8)

Egypt Domesticated Cattle (Bos taurus) 10.2% (49/480) C. parvum (100) IIdA20G1 (Ibrahim et al., 2016a) Buffalo (Bubalus bubalis) 12.3% (38/310) C. parvum (100) IIdA20G1

Egypt Domesticated Cattle (Bos taurus) 13.6% (269/1974) C. parvum (43.5) IIdA20G1; IIaA15G1R1c; (Amer et al., IIaA14G1R1r1b 2013a)

C. andersoni (10.1)

C. bovis (10.1) C. ryanae (18.8)

Mixed species (17.4)

Egypt Domesticated Water Buffalo calves (Bubalus 9.5% (17/179) C. parvum (41.2) IIdA20G1a; IIaA15G1R1 (Amer et al., bubalis) 2013b) C. ryanae (58.8)

Egypt Domesticated Calves (Bos taurus) 30.2% (29/96) C. parvum (92.3) IIdA20G1a; IIaA15G2R1 (Amer et al., 2010c) C. andersoni (7.7)

Egypt Buffalo (Bubalus bubalis) 1.3% (6/466) C. parvum (33.3) IIdA20G1; IIaA15G1R1 (Mahfouz et al., 2014) C. ryanae (66.7) Cattle (Bos taurus) 6.9% (31/450) C. parvum (74.2) IIdA20G1c; IIaA15G1R1 C. ryanae (16.1) C. bovis (9.7) Sheep (Ovis aries) 2.5% (3/120) C. xiaoi (100)

Egypt Domesticated Cattle (Bos taurus) 10.2% (49/480) C. parvum (100) IIdA20G1 (Ibrahim et al., Buffalo (Bubalus bubalis) 12.3% (38/310) C. parvum (100) IIdA20G1 2016a)

Ethiopia Domesticated Calves (Bos taurus) 15.8% (71/449) C. andersoni (76.1) (Wegayehu et C. bovis (19.7) al., 2016b) C. ryanae (4.2)

Kenya Domesticated Calves and cattle (Bos taurus) 7.7% (134/1734) C. parvum-like (68.0) (Kang'ethe et C. ryanae (16.0%) al., 2012b) C. andersoni (12.0%) C. hominis (4.0)

Kenya Domesticated Cattle (Bos taurus) Not reported Cryptosporidium deer-like (Szonyi et al., genotype (100) 2008)

Kenya Wildlife Olive baboon (Papio anubis) 2.6% (6/235) C. hominis (100) IbA9G3c; IIfA12G2c; IiA14 (Li et al., 2011) Madagascar Domesticated Cattle (Bos taurus) 29.0% (18/62) C. suis (94.4%) (Bodager et al., Cryptosporidium spp. 2015) (5.6%) Pigs (Sus scrofa) 23.5% (4/17) C. parvum (75.0%) C. suis (25.0%) Rodents 33.3% (16/48) Rat genotype III (31%) Rat genotype IV (6.3%) C. meleagridis (43.8%) C. suis (6.3%) Unknown (12.5%) Dog (Canis lupus) 100% (1/1) C. canis (100.0%) Wildlife Lemur 4.0% (1/25) C. hominis (100.0%)

Nigeria Domesticated Calves (Bos taurus) 52.3% (34/65) C. bovis (52.9%) (Ayinmode et C. ryanae (14.7%) al., 2010) C. bovis and C. ryanae (32.4%)

Nigeria Domesticated Calves (Bos taurus) 16.0% (31/194) C. bovis (45.2%) (Maikai et al., C. ryanae (25.8%) 2011) C. andersoni (16.1%) C. bovis plus C. ryanae (9.7%) C. bovis plus C. andersoni (3.2%)

Rwanda Wildlife Mountain Gorilla (Gorilla 4.0% (4/100) C. muris (50%) (Sak et al., beringei beringei) C. meleagridis (50.0%) 2014)

South Africa Domesticated Calves (Bos taurus) 0.6% (2/352) C. parvum (50.0%) (Samra et al., C. bovis (50.0%) 2016)

South Africa Domesticated Calves (Bos taurus) 8% (4/51) C. andersoni (50.0%) (Samra et al., C. bovis (50.0%) 2013a) Wildlife African buffalo (Syncerus 2.8% (2/71) C. ubiquitum (50.0%) caffer) C. bovis (50.0%)

Impala (Aepyceros melampus) 2.8% (2/71) C. ubiquitum (100.0%)

Tanzania Wildlife Baboon (Papio spp.) 10.6% (5/47) C. hominis (100.0%) IfA12G2 (Parsons et al., 2015) Chimpanzees (Pan spp.) 19.0% (16/84) C. hominis (68.8%) IfA12G2 C. suis (25.0%) C. suis and C. hominis (6.2%)

Domesticated Sheep (Ovis aries) 22.2% (2/9) C. xiaoi (100.0%) Goats (Capra hircus) 13.9% (5/56) C. xiaoi (100.0%)

Tunisia Domesticated Calves (Bos taurus) 21.4% (15 /70) C. parvum (100.0%) IIaA15G2R1c; IIaA16G2R1; (Rahmouni et IIaA13G2R1; IIaA20G3R1; al., 2014) IIdA16G1

Tunisia Domesticated Sheep (Ovis aries) 11.2% (10/89) C. bovis (100.0%) (Soltane et al., 2007b) Chicken (Gallus gallus 4.5% (9/200) C. meleagridis (100.0%) domesticus) Tunisia Domesticated Calves (Bos taurus) 86.7 % (26/30) C. parvum (100.0%) (Soltane et al., 2007a) Uganda Wildlife Black-and-white colobus 3.4% (1/29) C. parvum (100.0%) (Salyer et al., (Colobus guereza;) 2012)

Red colobus (Procolobus 26.7% (8/30) C. parvum (75.0%) rufomitratus) C. parvum/C. hominis/C. cuniculus (25.0%)

Domesticated Goats (Caprus hircus) 3.5% (2/57) C. parvum (100.0%)

Uganda Wildlife Free-ranging mountain gorillas 4% (4/100) C. parvum (100.0%) (Graczyk et al., (Gorilla beringei) 2001)

Zambia Domesticated Calves (Bos taurus) 34% (70/207) C. parvum (61.9%) (Siwila et al., 2007) C. bovis in (33.3%) Cryptosporidium deer-like genotype (4.8%)

Zambia Domesticated Goat kids (Caprus hircus) 4.8% (5/105) C. parvum (100.0%) (Goma et al., Lambs (Ovis aries) 12.5% (19/152) C. parvum (83.3%) 2007) C. suis (16.7%)

Zambia Domesticated Calves (Bos taurus) 19.2% (142/744) C. parvum (64.4%) (Geurden et al., C. bovis (33.3%) 2006) C. suis (2.2%) aPrevalence (percentage of number positive out of the total number of animal species tested) bPrevalence (percentage of species identified out of the total number of species genotyped per animal) cDominant gp60 subtype

Table 1.5 Prevalence and Giardia duodenalis assemblages and subtypes reported in domesticated animals and wildlife from Africa

Country Animal type Patient group Prevalencea Giardia duodenalis Sub- Reference (No. positive/ assemblage assemblage No. tested) (Prevalence - %)b

Central Africa Wildlife Wild western lowland gorillas 2.0% (4/210) A (100) AII (Sak et al., 2013) Republic (Gorilla gorilla gorilla) Domesticated Goats (Capra aegagrus hircus) 11.1% (1/9) E (100)

Côte d'Ivoire Domesticated Dogs (Canis familiaris) 54.5% (6/11) A (33.3) (Berrilli et al., A+B (16.7) 2012) C (33.3) D (33.3%) Goats (Capra hircus) 50% (1/2) A+B (100)

Ducks (Cairina moschata) 33.3% (1/3) A+B (100) Chicken (Gallus gallus) 58.1% (18/31) A (38.9) B (38.9) A+B (22.2) Egypt Domesticated Cattle (Bos taurus) 6.7% (40/593) A (15.4) AI (Helmy et al., AII 2014a) E (82.7) A/E (1.9) Buffalo (Bubalus bubalis 4.7% (10/211) A (20.0)

E (80.0) Both Cattle and Buffalo 53.2% (424/804) by RT-PCR

Egypt Domesticated Cattle (Bos taurus) 8.7 % (4/46) E (100.0%) (Abdel-Moein & Saeed, 2016) Egypt Domesticated Calves (Bos taurus) 30.8% (25/58) A (20.0) (Sabry et al., E (80.0) 2009) Egypt Wild and Fish (Tilapia nilotica 3.3% (3/92) A (100) (Ghoneim et al., cultured Mugil cephalus) 2012)

Ghana Wildlife Colobus monkeys (Colobus 69.2% (74/107) B (100) (Teichroeb et al., vellerosus) 2009)

Rwanda Wildlife Mountain Gorillas (Gorilla 8.5% (11/130) B (100) BIVc; BIII (Hogan et al., beringei beringei) 2014)

Domesticated Cattle (Bos taurus) 5.9% (8/135) E (100)

Tanzania Domesticated Zebus cattle (Bos primigenius 21.1% (4/19) A (75.0) BIV (Di Cristanziano indicus) et al., 2014) B (25.0) Goats (Capra hircus) 22.0% (9/41) E (66.7) BIV B (22.2) A (11.1)

Uganda Wildlife Gorilla (Gorilla gorilla 2.0% (2/100) A (100) (Graczyk et al., beringei) 2002) Domesticated Cattle (Bos taurus) 10.0% (5/50) A (100)

Uganda Wildlife Red colobus (Procolobus 11.1% AII (Johnston et al., badius tephrosceles) 2010) BIV E Domesticated Livestock (cattle, Bos taurus 12.4% E and B. indicus, n=25; goats, Caprus hircus, n=57; and sheep, Ovis aries, n=7) aPrevalence (percentage of number positive out of the total number of animal species tested) bPrevalence (percentage of species identified out of the total number of species genotyped per animal) cDominant subassemblage

1.6.8 Waterborne and foodborne cryptosporidiosis and Giardiasis in Africa

As Cryptosporidium and Giardia (oo)cysts are robust and resistant to environmental conditions, including disinfectants such as chlorine used in water treatment systems, numerous waterborne and foodborne outbreaks of human cryptosporidiosis and giardiasis have been reported, with Cryptosporidium and Giardia responsible for >95% of outbreaks worldwide (Smith et al., 1989; Sorvillo et al., 1992; Mac Kenzie et al., 1994; Fayer, 2004;

Smith et al., 2006; Robertson et al., 2007; Daly et al., 2010; Baldursson & Karanis, 2011;

Budu-Amoako et al., 2011; Karon et al., 2011; Bedard et al., 2016; Efstratiou et al., 2017).

Relatively little is known about the presence and prevalence of Cryptosporidium and

Giardia in food and water in Africa. Both parasites have been detected in food such as fresh fruits and vegetables in Ethiopia, Egypt, Ghana, Libya and Sudan (Abougrain et al., 2010;

El Said Said, 2012; Duedu et al., 2014; Tefera et al., 2014; Mohamed et al., 2016), and Tiger nuts (Cyperus esculentus) from Ghana (Ayeh-Kumi et al., 2014). Cryptosporidium was detected in 16.8% of reared black mussels (Mytilus galloprovincialis) in Mali (Mladineo et al., 2009). Cryptosporidium does not multiply in bivalves and is therefore not a biological host but can be an effective transmission vehicle for Cryptosporidium oocysts, especially within 24–72 h of contamination, with viable oocysts present in bivalves up to 7 days post infection (Sutthikornchai et al., 2016). Cryptosporidium and Giardia (oo)cysts were identified from 34.3% and 2.0% of coins and 28.2% and 1.9% of bank notes respectively used by food-related workers in Alexandria, Egypt (Hassan et al., 2011). As coins and banknotes are some of the objects most handled and exchanged by people, this raises the potential of parasite transmission even between countries.

In many rural African households, untreated water is used for various purposes such as bathing, cooking, drinking and swimming, often exposing them to waterborne

Cryptosporidium and Giardia (WHO, 2007; CDC, 2015). More than 300 million people in

63 sub-Saharan Africa have poor access to safe water, predisposing them to infections from waterborne pathogens, and cryptosporidial infections are known to be prevalent among communities which lack access to clean potable water supply (Tiwari & Jenkins, 2008;

Yongsi, 2010; WHO, 2014). Poverty is therefore a key limiting factor to accessing safe water. In many communities, particularly those living in rural areas where the average income of ~US$1 per person per day (Munthali, 2007), individuals have limited access to privately owned water resources that provide safe water (Sente et al., 2016). This coupled with inadequate water treatment, poor hygiene practices, drinking unboiled water and lack of education programmes, predisposes many rural African communities to cryptosporidiosis and giardiasis (Sente et al., 2016).

Cryptosporidium oocysts and Giardia cysts have been detected in a variety of

African water sources including irrigation water in Burkina Faso (Kpoda et al., 2015), a stream, well, spring and lake in Cameroon (Ajeagah et al., 2007; Ajeagah, 2013), wastewater in Côte d’Ivoire (Yapo et al., 2014), packaged drinking water in Ghana (Kwakye-Nuako et al., 2007; Osei et al., 2013; Ndur et al., 2016), tap water, drinking water treatment plants, canals, tanks and swimming pools in Egypt (Youssef et al., 1998; Ali et al., 2004; Elshazly et al., 2007; Abd El-Salam, 2012; El-Kowrany et al., 2016). They have also been detected in water sources (surface and well), treated water storage tanks and tap water in Ethiopia (Fikrie et al., 2008; Atnafu et al., 2012), the Kathita and Kiina rivers and surface water in Kenya

(Kato et al., 2003; Muchiri et al., 2009), water from wells and the Kano river in Nigeria

(Uneke & Uneke, 2007), the surface waters of the Vaal Dam system (Grundlingh & De Wet,

2004), treated and untreated effluents, sewage, drinking water and roof-harvested rainwater in South Africa (Kfir et al., 1995; Dungeni & Momba, 2010; Dobrowsky et al., 2014; Samie

& Ntekele, 2014). In Tunisia, they have been detected in watersheds, treated and raw wastewater and sludge samples (Khouja et al., 2010; Ben Ayed et al., 2012), in Uganda, in natural and communal piped tap water from the Queen Elizabeth protected area (Sente et al.,

2016), in piped water in Zambia (Kelly et al., 1997; Nchito et al., 1998) and in wells, springs, tap water and rivers in Zimbabwe (Dalu et al., 2011).

Genotyping of Cryptosporidium and Giardia from these water sources identified C. parvum from the Kathita and Kiina rivers, C. parvum and C. andersoni in Muru regional surface waters (both in Kenya) (Kato et al., 2003), C. hominis and Giardia Assemblages A and B in sewage treatment plants from South Africa (Dungeni & Momba, 2010; Samie &

Ntekele, 2014), C. hominis (subtypes IdA15G1, IaA27R3), C. parvum (subtypes IIaA21,

IIcA5G3b), C. muris, C. andersoni, Giardia Assemblage A (subtypes A1 and AII), B and a novel Giardia subtype were isolated from treated, raw wastewater and sludge samples in

Tunisia (Khouja et al., 2010). In addition, C. parvum (subtypes IIaA15G2R1, IIaA17G2R1,

IIaA18G3R1, IIaA20G2R1, IIaA21R1, IIaA21G2R1, IIcA5G3b), C. muris, C. andersoni,

C. hominis (subtypes IaA26R3, IaA27R4, IdA14), C. ubiquitum, Cryptosporidium rat genotype IV, novel Cryptosporidium genotypes, C. meleagridis, avian genotype II, Giardia

Assemblage A (subtypes AI and AII), Assemblages B and E were isolated from treated and raw wastewater plants and sludge samples, also from Tunisia (Ben Ayed et al., 2012). In the latter report, the most prevalent genotypes were Assemblage A (86.8%) and C. andersoni

(41.2%) in 99 Giardia and 114 Cryptosporidium-positive PCR products, respectively.

1.6.9 Treatment of cryptosporidiosis and giardiasis in Africa

Another contributing factor to the high prevalence and widespread distribution of

Cryptosporidium and Giardia in Africa is the lack of treatment options. Currently no effective vaccine exists for Cryptosporidium and only one drug, nitazoxanide (NTZ, Alinia;

Romark Laboratories, Tampa, Florida, USA), is available for use against Cryptosporidium.

This drug, however, is currently not recommended for use in infants < 12 months of age, exhibits only moderate clinical efficacy in malnourished children and immunocompetent people, and none in immunocompromised individuals like people with HIV (Abubakar et al., 2007; Amadi et al., 2009). In 2015, >25 million adults and children were infected with

HIV/AIDS in Africa (WHO, 2015), and the UN Food and Agriculture Organisation estimates that 233 million people in sub-Saharan Africa were malnourished in 2014 (FAO,

2015). The ineffectiveness of nitazoxanide in HIV-positive individuals and the contribution of malnourishment to impaired immunity (Gendrel et al., 2003), means that nitazoxanide is ineffective against the most important target population in Africa. In individuals co-infected with HIV, antiretroviral therapy (ART) has been successful in controlling chronic diarrhoea and wasting due to cryptosporidiosis (Carr et al., 1998; Miao et al., 2000; Mengist et al.,

2015). Currently, supportive care and ART (for HIV/AIDS patients) forms the basis for treatment of cryptosporidiosis.

As with Cryptosporidium, a human vaccine for giardiasis is not available. Several classes of antimicrobial drugs are available for the treatment of giardiasis. The most commonly utilised worldwide are members of the 5- nitroimidazole (5-NI) family such as metronidazole and tinidazole. However, this first line therapy fails in up to 20% of cases and cross-resistance between different agents can occur (Gardner & Hill, 2001), and resistance to all major anti-giardial drugs has been reported (Ansell et al., 2015). Albendazole is also effective in treating giardiasis (Gardner & Hill, 2001; Solaymani-Mohammadi et al., 2010), although its efficacy varies markedly (25–90%), depending on the dosing regimen

(Miyamoto & Eckmann, 2015). Nitazoxanide has been shown to reduce symptom duration in individuals with giardiasis (Rossignol et al., 2012) and quinacrine, an old malaria drug, reportedly has 90% efficacy against giardiasis (Requena-Méndez et al., 2014), but has potentially severe adverse effects, including a number of psychiatric and dermatological manifestations (Miyamoto & Eckmann, 2015). For Cryptosporidium, new classes of more effective drugs are a high priority and for Giardia, improvements in potency and dosing of currently available drugs, and the ability to overcome existing and prevent new forms of drug resistance, are priorities in anti-giardial drug development (Miyamoto & Eckmann,

2015).

The prohibitive cost of de novo drug development, estimated to be between $500 million and $2 billion per compound successfully brought to market (Adams & Brantner,

2006), is another major limiting factor in the development of anti-cryptosporidial and anti- giardial drugs. Treatment of Cryptosporidium and Giardia in African countries, despite having a large target population, has a small market in the developed world and pharmaceutical companies are often hesitant to invest in costly de novo campaigns to develop new therapeutics for developing countries. Therefore, the primary challenge for further drug development is the underlying economics, as both parasitic infections are considered Neglected Diseases with low funding priority and limited commercial interest

(Miyamoto & Eckmann, 2015). For this reason, there has been a movement to ‘repurpose’ existing therapeutics for off-label applications, as repurposed drugs cost around 60% less to bring to market than drugs developed de novo (Chong & Sullivan, 2007). For example, drugs such as the human 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase inhibitor, itavastatin and auranofin (Ridaura®) were initially approved for the treatment of rheumatoid arthritis and have been shown to be effective against Cryptosporidium in vitro

(Bessoff et al., 2013; Debnath et al., 2013), which holds promise for future anti- cryptosporidial drugs. In animals, halofuginone lactate is used in the treatment and prevention of cryptosporidiosis, particularly in calves (De Waele et al., 2010; Trotz-

Williams et al., 2011).

1.6.10 The impact of climate change and HIV status on cryptosporidiosis and giardiasis in Africa

Waterborne transmission is a major mode of transmission for both Cryptosporidium and

Giardia. Climate change represents a major threat for access to safe drinking water in Africa, which has more climate sensitive economies than any other continent (Bain et al., 2013).

Increasingly variable rainfall patterns are likely to affect the supply of fresh water in Africa.

Some regions in Africa have become drier during the last century (e.g. the Sahel) (Boko et

67 al., 2007) and by the 2090s, climate change is likely to widen the area affected by drought, double the frequency of extreme droughts and increase their average duration six-fold

(Arnell, 2004). Climate change will also increase levels of malnutrition in Africa, as it will lead to changes in crop yield, higher food prices and therefore lower affordability of food, reduced calorie availability, and growing childhood malnutrition in Sub-Saharan Africa

(Ringler et al., 2010). Malnutrition in turn undermines the resilience of vulnerable populations to cryptosporidial and giardial infections, decreasing their ability to cope and adapt to the consequences of climate change.

Surface water concentrations of Cryptosporidium and Giardia in Africa are also expected to increase with increased population growth. The Global Waterborne Pathogen model for human Cryptosporidium emissions, predicts that while Cryptosporidium emissions in developing countries will decrease by 24% in 2050, in Africa, emissions to surface water will increase by up to 70% (Hofstra & Vermeulen, 2016). Given the lack of treatment options, particularly for Cryptosporidium, high-level community awareness, policy formulations and regular surveillance are needed in order to limit the waterborne, zoonotic and anthroponotic transmission of Cryptosporidium and Giardia.

This cannot be achieved, however, unless there is a commitment from African governments to supply clean potable water, particularly to rural communities, improve sanitation by connecting the population to sewers and improved waste water treatment.

Community programmes must be initiated to educate the people on water safety measures, personal hygiene and water treatment processes. The achievement of these goals hinge on the elimination of malnutrition and a significant reduction in HIV levels in African populations. The introduction of ART in HIV patients which partially restores the immune function has been important in reducing the prevalence of Cryptosporidium in HIV patients

(Missaye et al., 2013; Kiros et al., 2015). Furthermore, it has been suggested that HIV protease inhibitors can act as antiparasitic drugs. For example, in experimental studies, the

68 drugs indinavir, saquinavir, and ritonavir have been reported to have anti-Cryptosporidium spp. effects both in vitro and in vivo (Mele et al., 2003). However, most African government have not invested enough funds and resources to ensuring alleviation of malnutrition and

HIV (Global HIV Prevention Working Group, 2009; Bain et al., 2013) and many HIV- prevention services still do not reach most of those in need (Global HIV Prevention Working

Group, 2009), largely due to under-staffing and the poor geographical distribution of available services for those in need.

Despite the millennium development goals target to reduce hunger by half by 2015, major failures have been recorded in Africa. Out of the > 800 million people still suffering from hunger in the world, over 204 million come from Sub-Saharan Africa. The situation is currently getting worse in this region, as it moved from 170.4 million hungry people in 1990 to 204 million in 2002 (FAO, 2002). This increase has generally been attributed to poverty, illiteracy, ignorance, big family sizes, climate change, policy and corruption (Bain et al.,

2013).

1.6.11 Conclusions and future recommendations

Cryptosporidium and Giardia are prevalent in both humans and animals in Africa with both anthroponotic and zoonotic transmission cycles. Cryptosporidium is unequivocally associated with moderate-to-severe diarrhoea in African children but further studies are required to determine if Giardia infections in early infancy are positively linked to moderate- to-severe diarrhoea, whether some paediatric hosts (e.g. more stunted) are more prone to develop persistent diarrhoea, whether Giardia decreases the risk of acute diarrhoea from other specific enteropathogens, and whether specific Giardia assemblages exhibit enhanced pathogenicity over other assemblages and sub-assemblages. Efforts in reducing HIV in

African countries should focus on earlier identification of HIV, providing earlier access to

ART and improved case management for HIV-infected individuals (particularly children) and reducing the cultural and social stigma directed at persons living with HIV/AIDS. “One

Health” initiatives involving multidisciplinary teams of veterinarians, medical workers, relevant government authorities, water and sanitation engineers, water managers and public health specialists working together are essential for the control and prevention of cryptosporidiosis and giardiasis in African countries.

Gastrointestinal helminths

1.7.1 Introduction

Parasitic helminths of the gastrointestinal (GIT) tract in humans and animals are of socioeconomic significance globally (de Silva et al., 2003; Cantacessi et al., 2012; van der Voort et al., 2013). An estimated 75,000 to 300,000 species of parasitic helminths can affect humans, animals and plants, and these can be classified into three the major phyla, nematoda

(roundworms), platyhelminthes (flatworms) consisting of the classes trematoda (flukes), cestoda (tapeworms) and Acanthocephala (thorny-headed worms) (Taylor et al., 2016). In humans, more than a billion people are at risk of infection with soil-transmitted helminths

(STH) (GIT helminths infecting humans that are transmitted through contaminated soil contaminated with faecal matter) and majority of the infected people are in sub-Saharan

Africa (George et al., 2016; Wilson et al., 2016). In livestock, GIT parasite infections can cause morbidity, leading to reduction in milk yield, growth rate, carcass weight, feed intake, carcass composition, wool growth and fertility (Fitzpatrick, 2013). Several GIT helminths of livestock are also zoonotic and can cause disease in humans (Al-Mekhlafi et al., 2013;

Taylor et al., 2016). Infection can be acquired through ingestion of infective larvae or helminth eggs on contaminated food, water, vegetation or soil, arthropod and gastropod intermediate hosts, ingestion of infective cysts in the tissue of intermediate hosts, transplacental transmission or skin penetration (Greenland et al., 2015).

1.7.2 Gastrointestinal Nematodes

1.7.2.1 Introduction

Most nematodes (round worms) are characterised by an anterior opening (buccal cavity) and an outer cuticle with several unique features that are used in identification such as sensory organs (sensory papillae), wing-like protrusions (alae), spicules and the presence or absence of a posterior bursa (Jacobs et al., 2016).

In humans, an estimated one sixth of the population is known to be infected with soil-transmitted helminths (STHs), mostly hookworms (Ancylostoma duodenale, Necator americanus, Ancylostoma ceylanicum), roundworms (Ascaris spp.) and whipworms

(Trichuris trichiura) (Brooker et al., 2006; Hotez et al., 2009; Harhay et al., 2010; Clarke et al., 2018), and Strongyloides stercoralis infects about 30 to 100 million people (Viney &

Nutman, 2017). These nematode infections in humans can cause morbidity and other symptoms including anaemia and diarrhoea in particularly children (Bethony et al., 2006;

Becker et al., 2011).

In livestock, GIT nematodes cause substantial economic losses by reducing productivity and reproductive potential, increasing the costs of anthelmintic treatment and leading to the death of animals (Newton & Meeusen, 2003; Knox et al., 2012). The order

Strongylida, is one of the most important parasitic nematode groups. Four of the superfamilies (Diaphanocephaloidea, Ancylostomatoidea, Strongyloidea and

Trichostrongyloidea) of the order Strongylida primarily inhabit the GIT tracts of vertebrates, while the fifth superfamily, Metastrongyloidea, inhabiting the lungs (Anderson, 2000;

Roeber et al., 2011; Taylor et al., 2016). The nematode species, Haemonchus contortus,

Teladorsagia circumcincta and Trichostrongylus sp., of the superfamily

Trichostrongyloidea are considered major parasitic species in small ruminants with

Haemonchus contortus the most infectious and pathogenic (Besier & Love, 2003; Roeber et

71 al., 2013b). Also, Cooperia curticei, Nematodirus spathiger, Nematodirus fillicollis,

Oesophagostomum venulosum are common species that inhabit the small and/or large intestine, while Chabertia ovina and Bunostomum trigonocephalum, are less common

(Zajac, 2006).

Among the species that infect livestock are zoonotic species which also infect humans. For example, Trichostrongylus spp. which predominantly infects ruminants have been reported in humans in different parts of the world including Ghana (Fuseini et al.,

2009), France (Lattès et al., 2011), Lao (Sato et al., 2011), Iran (Gholami et al., 2015) and

Italy (Buonfrate et al., 2017) and Bunostomum phlebotomum, a hookworm of cattle is also known to infect humans (Mayhew, 1947).

1.7.2.2 Transmission and life cycle

The life cycle of most parasitic nematodes is typically straight forward, although some variations exist based on the sex and class of nematode (Figure 1.4). For example, eggs produced by the adult females of order Strongylida, pass with the faeces of the definitive host and hatch under favourable conditions to release the rhabditiform larvae (L1), which moult twice into filariform infective larvae (L3) (Jacobs et al., 2016; CDC, 2017). Infection occurs after ingestion of the infective larvae through contaminated food, water, soil or surfaces. For parasites such as hookworms (Ancylostoma spp., Necator americanus and

Bunostomum spp.), the filariform larvae can penetrate the skin of the definitive host to cause infection (Mayhew, 1947; Murphy & Spickler, 2013). Indirect parasitic nematode life cycles usually involve an intermediate host as with nematodes of superfamily Filarioidea. In this cycle, infection may occur after ingestion of an intermediate host carrying the infective larvae or by inoculation during feeding or blood sucking by an intermediate host (Taylor,

2016).

Figure 1.4 General life cycle of GIT nematodes (order Strongylida) (adapted from Demeler, 2005; Roeber et al., 2013a).

1.7.3 Gastrointestinal trematodes

1.7.3.1 Introduction

Parasitic trematode (flukes) can be categorised into two main groups, monogeneans

(ectoparasites of fishes) and digenean trematodes (endoparasites of vertebrates) (Jacobs et al., 2016). Among the digenean trematodes of veterinary importance are Fasciola,

Dicrocoelium, Paramphistomum and Schistosoma, while the trematodes of public health importance include Fasciola, Schistosoma, Fasciolopsis, Gastrodiscoides, Watsonius,

Clonorchis, Opisthorchis and Echinostoma (Fried et al., 2004; Hall et al., 2008; Fried &

Abruzzi, 2010; Jacobs et al., 2016).

Approximately 70 species of digenetic trematodes have been reported in humans worldwide, with an estimated 750 million people at risk of infection with food-borne

73 trematodes (Keiser & Utzinger, 2009; Chai, 2014). Trematodes linked to serious diseases include Clonorchis in cholangiocarcinoma and gall stones, Opisthorchis in cholangiocarcinoma and hepatocarcinoma and Fasciola in severe clinical liver disease

(WHO, 1995; Keiser & Utzinger, 2009; Fried & Abruzzi, 2010). In livestock, trematode infections cause losses by reducing productivity and liver condemnation (Dargie, 1987;

Mungube et al., 2006; Abebe et al., 2010).

1.7.3.2 Transmission and life cycle

The life cycle of digenean trematodes generally involves a definitive host and one or two intermediate hosts. There are many variations in the life cycle of trematodes of veterinary and public health importance but basically, eggs from an adult fluke are passed in the faeces of the definitive host, which then develops into the miracidia larva (first stage larva, L1) and hatches under favourable condition including water (Figure 1.5). The miracidium swims, attaches to and penetrates a suitable molluscan (snail) intermediate host, which then transforms into a sporocyst (second stage larva, L2). The sporocyst multiplies and develops into numerous rediae (third stage larvae, L3), which develops into the cercariae larvae

(fourth stage larvae, L4) (Jacobs et al., 2016). One miracidium can produce approximately six hundred or more cercaria, that further develop into the infective metacercaria larvae which may be independent (eg. Fasciola and Fasciolopsis buski) or inside a second intermediate host such as an ant, as with the case of Dicrocoelium (Fürst et al., 2012a; Jacobs et al., 2016).

Infection generally occurs by ingesting the metacercaria in vegetation and in uncooked food or water (Fürst et al., 2012b). However, for trematodes such as Schistosoma, there is no metacercaria, but the cercariae is able to cause infection by penetrating the skin of the definitive host (CDC, 2012).

Figure 1.5 Life cycle of trematodes, represented by Fasciola hepatica, (available at http://parasite.org.au/para-site/fasciola/fasciola-clinical.html. Accessed 3 Sept 2018).

1.7.4 Gastrointestinal Cestodes

1.7.4.1 Introduction

The adult cestode (tapeworms), characterised by proglottids (independently maturing reproductive segments) and anterior hooks or suckers, inhabits the small intestine of hosts and cause relatively minor symptoms compared to the larval stage (Jacobs et al., 2016).

There are two main orders of cestodes, namely, Cylophyllidae (includes Taenia, Moniezia and Echinococcus) and Pseudophyllidae (includes Diphyllobothrium) (Jacobs et al., 2016).

Taenia solium, T. saginata and T. asiatica are among the most important cestodes infecting humans and are the tapeworms which lead to cysticercosis/taeniasis (Ale et al.,

2014; Sato et al., 2018), although, Hymenolepis and Diphyllobothrium are also common

(Hall et al., 2008). Also, cysticercosis has debilitating effects on the health and livelihoods

75 of mainly subsistence farming communities in Latin America, Asia and Africa (WHO,

2018a).

In livestock, Taenia infections reduce the market value of cattle and pigs (Assana et al., 2013; WHO, 2018a). Moniezia expansa (mostly in small ruminants) and M. benedeni

(mostly in cattle) are the most common intestinal cestodes in ruminants, however their pathogenic significance (if any) has yet to be defined (Taylor, 2016). In most regions,

Moniezia is also the only adult tapeworm found in the small intestine of farmed ruminants

(Jacobs et al., 2016).

1.7.4.2 Transmission and life cycle

The life cycle of the Cyclophyllidean cestodes involve the passage of the gravid proglottid at the posterior end of the worm through the anus of the definitive host. A single tapeworm can produce thousands of eggs in a day. For example, the adult T. saginata tapeworm can produce 1,000 to 2,000 proglottids and each may produce about 80,000 eggs with a mean daily output of approximately 720,000 eggs (Penfold, 1937; Pawlowski & Schultz, 1972;

CDC, 2013). The eggs can also remain viable for over six months in the environment (Ilsøe et al., 1990). Eggs released from the proglottids are immediately infectious and hatch to release the oncosphere (embryo) when swallowed by a suitable intermediate host. The oncosphere transforms into the metacestode larva which can develop into cysts (cysticerci) in the host tissue, known as cysticercosis (Minozzo et al., 2002; Jacobs et al., 2016). Humans are the only definitive host of Taenia and the life cycle includes the intermediate hosts cattle

(for T. saginata), humans and pigs (for T. solium) and pigs (for T. asiatica) (Figure 1.6)

(Dorny & Praet, 2007; Levinson, 2016). Infection in humans is caused by ingesting raw or undercooked meat or food contaminated with the cysticercus.

Unlike, Taenia, the life cycle of Moniezia includes an invertebrate intermediate host

(an oribatid mite), with ungulates as the definitive host (Stunkard, 1938; Taylor et al., 2016),

76 and although predominantly a livestock parasite, M. expansa has also been reported in a boy in Egypt (el-Shazly et al., 2004).

Figure 1.6 Life cycle of cestodes represented by Taenia sp. (adapted from (Levinson, 2016; available at www.accessmedicine.com).

1.7.5 Diagnosis of gastrointestinal helminths

Conventionally, GIT helminths can be diagnosed using copromicroscopic techniques such as direct wet mounts, Kato-Katz, FLOTAC and concentration techniques with solutions of various specific gravity such as sodium chloride, formol-ether and zinc sulphate (MAFF,

1986; Cheesbrough, 2009; Glinz et al., 2010; Roeber et al., 2013b). However, Kato-Katz method has low sensitivity, particularly in low-transmission settings (Clarke et al., 2018) and it is difficult to identify and distinguish between the eggs of helminths species such as hookworms and strongyles by conventional microscopy, unless larval culture is performed

(MAFF, 1986; Foreyt, 1989; de Gruijter et al., 2005; Roeber & Kahn, 2014; Ashrafi et al.,

2015). The larval culture technique is laborious, time consuming and sometimes inaccurate due to reported overlap in some morphological features (McMurtry et al., 2000; Roeber &

Kahn, 2014).

Adequate species identification and differentiation of helminths can be achieved by the use of molecular tools including qPCR, PCR and Sanger sequencing analysis of the

Internal Transcribed Spacer (ITS) 1 and 2, mitochondrial cytochrome oxidase 1 (COI), ribosomal RNA (rRNA), NADH dehydrogenase 1(ND), and nuclear protein-coding genes

(Romstad et al., 1997; de Gruijter et al., 2005; Itagaki et al., 2005; Yong et al., 2007;

Johansen et al., 2010; Sato et al., 2010; Yakhchali et al., 2014; Shoriki et al., 2016; Guo,

2017; Ng-Nguyen et al., 2017; Papaiakovou et al., 2017; Clarke et al., 2018; Hii et al.,

2018; Vaz Nery et al., 2018), and also Next Generation Sequencing (NGS) (Cantacessi et al., 2010; Roeber et al., 2013c).

1.7.6 The impact of gastrointestinal helminths infections

Of the 342 species of helminths infecting humans, approximately 200 species mainly inhabit the GIT tract and close to 20 of these species are considered to cause disease (Crompton,

1999; Horton, 2003). These helminth infections cause malnutrition due to maldigestion and/or malabsorption of carbohydrate, fat and protein (Taren et al., 1987; Crompton &

Nesheim, 2002; Lunn & Northrop-Clewes, 2007). Thus, although symptoms such as nausea, vomiting, diarrhoea, abdominal pain, dermatitis, pneumonitis and protein-losing enteropathy are common with GIT helminth infections, anaemia and iron deficiency are major outcomes, particularly with hookworm infections, Trichuris trichiura and Schistosoma (Friedman et al., 2005; Thomas et al., 2005; Ajanga et al., 2006; Baidoo et al., 2010; Gyorkos et al., 2011).

GIT helminth infections have been associated with reduced work performance (King, 2015), allergic diseases (Jõgi et al., 2018), and impaired growth, reduced cognitive ability and poor

78 school performance in children (Nokes et al., 1999; King et al., 2005a; Hall et al., 2008;

LaBeaud et al., 2015; Pabalan et al., 2018).

GIT helminths are the most important cause of livestock diseases based on their impact on poor livestock keepers (Perry et al., 2002) and can cause reductions in weight gain, fertility, milk production and carcass quality in livestock (Charlier et al., 2014), and severe anaemia in especially Haemonchus contortus infections (Abbott et al., 1988; Vatta et al.,

2001; Vatta et al., 2002). However, the majority of the impact of parasitic infections in livestock are also due to sub-clinical signs which leads to significant economic loss by increasing the cost of anthelmintic compounds and therefore an increase in the price of animal produce and also zoonotic diseases (Rushton & Bruce, 2017).

High STH burdens are associated with high morbidity, however, chemotherapy can reduce and maintain the burden worms below the level associated with morbidity (Bethony et al., 2006). Thus, current chemotherapeutic control strategies are aimed at reducing morbidity rather than eliminating infection, which also applies to livestock and drenching strategies that maximize production impact and minimize development of resistance.

1.7.7 Zoonotic importance of gastrointestinal helminths

Zoonoses accounts for 61% of human diseases (Cleaveland et al., 2001), the majority of which are emerging zoonoses (Taylor et al., 2001). Through the use of molecular tools in epidemiology, the zoonotic potential of previously unidentified pathogens are also being recognised (Traub et al., 2005) and several GIT nematodes, trematodes and cestodes which infect cattle, sheep and /or goats have also been identified in humans (see Table 1.6).

Of about 22 Schistosoma species infecting animals, only 8 have been reported in humans and four of these (S. japonicum, S. curassoni, S. bovis, S. mattheei), are also found in cattle, sheep and/ or goats (Wang et al., 2006; Standley et al., 2012; Léger et al., 2016).

Likewise, of the ̴ 30 Trichostrongylus species found in ruminants, T. colubriformis, T.

79 orientalis, T. axei, T. capricola, T. vitrines, T. probolurus, T. skrijabini and T. lerouxi, have also been reported in humans (Phosuk et al., 2013; Sharifdini et al., 2017).

Due to globalisation (food and live animal importation or exportation and movement of people), climate change (leading to vector spread and increased survival of eggs and larvae) and urbanisation (environmental modification, encroachment and spill over), the impact of zoonoses on humans is gradually increasing (Gordon et al., 2016). Thus, movement of people from endemic areas to non-endemic areas can facilitate the spread of zoonotic disease, while helminths such as soil transmitted helminths (STH), which thrive in warm conditions such as the tropics, can become more established in temperate areas due to climate change. Temperature was found to be the main variable associated with distribution of Biomphalaria pfeifferi, Bulinus globosus and Lymnaea natalensis, all of which are intermediate hosts of trematodes (Pedersen et al., 2014b) and also, the viability and development of hookworms in the environment (Weaver et al., 2010).

Table 1.6 GIT helminths of zoonotic significance in ruminant livestock (cattle, sheep and/or goats).

Infection Helminth species Mode of Reference transmission Nematodes

Hookworms Bunostomum phlebotomum Direct skin (Mayhew, 1947; penetration by Wang et al., 2012;

larvae (frequent); Murphy & ingestion of Spickler, 2013) infective larvae

Trichostrongylosis Trichostrongylus colubriformis Ingestion of (Ghadirian et al., infective larvae; 1974; Ghadirian T. orientalis direct skin & Arfaa, 1975; T. axei penetration by Ghadirian, 1977; larvae (infrequent) Ghasemikhah et T. capricola al., 2012; T. vitrines Sharifdini et al., 2017) T. probolurus T. skrijabini T. lerouxi

Trematodes

Schistosomiasis S. japonicum Direct skin (Hira et al., 1981; penetration by De Bont & S. curassoni larvae Vercruysse, 1998; S. bovis Wang et al., 2005; Wang et al., 2006; S. mattheei Standley et al., 2012; Léger et al., 2016)

Fascioliasis Fasciola hepatica Ingestion of (Amer et al., metacercaria on 2016) F. gigantica water plants, vegetables, etc. Dicrocoeliasis Dicrocoelium dendriticum Ingestion of (King, 1971; metacercariae in Wolfe, 2007; D. hospes contaminated ant Samaila et al., intermediate host 2009; Cengiz et (by livestock); al., 2010; Elelu et ingestion of al., 2016) contaminated undercooked liver from heavily infected animal (by humans)

Eurytrematosis Eurytrema pancreaticum Ingestion (Basch, 1965; metacercaria in Ishii et al., 1983; contaminated locust Zheng et al., intermediate host 2007)

Cestodes

Taeniasis Taenia saginata Ingestion of egg or (Ale et al., 2014; proglottids (by Jacobs et al.,

livestock); ingestion 2016; WHO, of cysticerci in 2018a, 2018b) contaminated meat/food (by humans)

Echinococcosis Echinococcus granulosus Ingestion of eggs by (Eckert et al., canids (livestock 2001; Frédéric et

and humans are both al., 2014) intermediate hosts)

Monieziasis Monieza expansa Ingestion of infected (el-Shazly et al., mites 2004; Bashtar et al., 2011; Taylor et al., 2016)

1.7.8 Treatment of gastrointestinal helminths

As in humans, chemotherapy remains the backbone of GIT helminthiasis treatment and control in livestock and benzimidazoles, levamisole-morantel, avermectins-milbemycins and amino-acetonitrile derivatives (AADs) such as monepantel, are the main classes of anthelmintic compounds used (Wolstenholme et al., 2004; Stuchlíková et al., 2014).

Praziquantel is mainly used in the treatment of schistosomiasis, benzimadazoles, imidazothiazoles and macrocyclic lactones and monepantel are used against nematodes, benzimadazoles such as albendazole are also used against F. hepatica in the bile duct and nitazoxanide is used against F. gigantica and netoblim against Dicrocoelium (Lateef et al.,

2008; Jacobs et al., 2016). However, some GIT helminths including Trichostrongylus, H. contortus, H. placei, Cooperia punctata, C. spatulata, C. pectinata and Ostertagia circumcincta have developed resistance against many of the anthelmintic drugs used including monepantel (Van Wyk et al., 1999; Geerts & Gryseels, 2000; Jackson & Coop,

2000; Besier & Love, 2003; Kaplan, 2004; Wolstenholme et al., 2004; Bentounsi et al., 2012;

Domke et al., 2012; Torres-Acosta et al., 2012; Geurden et al., 2014; Stuchlíková et al.,

2014; Borges et al., 2015). Consequently, non-chemical helminth control strategies such as grazing management or a combination of chemical and non-chemical control have been proposed by many researchers, as appropriate grazing management also reduces the risk of parasite reinfection in livestock (Waller, 1997; Barger, 1999; Krecek & Waller, 2006; Hoste

& Torres-Acosta, 2011; Kotze & Prichard, 2016).

Phyto or ethnoveterinary medicine including the use of plant enzymes or secondary metabolites such as cysteine proteinases, alkaloids, glycosides and tannins against gastrointestinal helminth infections is gradually becoming popular in developing countries

(Hammond et al., 1997; Paolini et al., 2003; Githiori et al., 2006). However, the antiparasitic activity of many of these compounds are yet to be scientifically validated and some plant extracts are known to have anti-nutritional effects on animals (Githiori et al., 2006;

Athanasiadou et al., 2007; Borges & Borges, 2016). The use of ethnoveterinary medicine is common among resource-poor or small-scale livestock farmers in West Africa (Okoli et al.,

2010) and in countries like Kenya (Gakuubi & Wanzala, 2012), South Africa (Sanhokwe et al., 2016) and Burkina Faso (Hilou et al., 2014).

Research and clinical trials towards the development of vaccines against GIT helminth infections in humans and livestock is ongoing. These include the human hookworm vaccine initiative by the Sabin Vaccine Institute Product Development Partnership comprising two recombinant antigens, Na-GST-1 and Na-APR-1 (Hotez et al., 2013;

Bottazzi, 2015), the Schistosoma mansoni vaccine comprising the recombinants Sm-TSP-2 and Sm-14 (Merrifield et al., 2016) and the Haemonchus contortus vaccines Barbervax® and recombinant rHc23 (González-Sánchez et al., 2018; Sheep CRC, 2019).

Thesis aims and objectives

This study seeks to determine the epidemiology of Cryptosporidium, Giardia and other GIT parasites of veterinary and public health importance to farmers and their ruminant livestock.

The research will be conducted using microscopy and molecular tools to identify parasites in faecal samples collected from animals and farmers, to the species level and where appropriate to sub-type level. The impact of the identified parasites and risk factors associated with infection will be determined and used to make recommendations towards effective preventive and control.

The main aim of the study is to estimate the prevalence of GIT parasites in farmers and different species of ruminant livestock in Ghana and to identify the zoonotic species and the factors associated with transmission of the parasites between farmers and their livestock for effective prevention and control.

Specifically, this thesis aims to:

1. Determine the species and subtypes of Cryptosporidium and Giardia in farmers and their

livestock in Ghana

2. Identify the species of GIT helminths in farmers and their ruminant livestock in Ghana

and their zoonotic potential of species identified.

3. Determine the prevalence and risk factors for GIT parasites in ruminant livestock in

Ghana.

The general hypothesis of this thesis is that molecular tools will identify and characterise the GIT parasites that infect farmers and their ruminant livestock and determine their zoonotic potential.

Specifically, it was hypothesised that:

1. Molecular tools can identify the species and subtypes/assemblages of Cryptosporidium and Giardia duodenalis infecting farmers and their ruminant livestock in Ghana and determine their zoonotic penitential.

2. The use of molecular tools in addition to conventional microscopy can identify the GIT parasites present and reveal the zoonotic potential of GIT helminth species identified in farmers and their ruminant livestock in Ghana.

3. The risk factors associated with the GIT parasites identified in livestock can be estimated using statistical tools.

Chapter 2. General Materials and Methods

Preface

This chapter provides a detailed description of the study design, area and population, and the diagnostic techniques and analysis used in this research.

Study Design

This was a cross-sectional survey, conducted from October 2014 to February 2015 on livestock farmers and their cattle, sheep and goats in Ghana. The country is stratified based on the seven major agroecological zones namely, Coastal Savannah, Wet and Moist

Evergreen Forest, Deciduous Forest, Transitional, Guinea Savanna and Sudan Savannah

(Oppong - Anane, 2006; Stanturf et al., 2011) (see Figure 2.1), with the Coastal Savannah zone selected for the present study based on available logistics. Six districts recognised for livestock activities were subsequently selected in collaboration with the Ministry of Food and Agriculture officials from the zone (Figure 2.1). Data collection consisted of three main categories; (1) a questionnaire interview to assess risk factors, (2) examination of animals for relevant health parameters and age to estimate the impact of infection, and (3) collection of faecal samples for laboratory analysis by microscopy and/or polymerase chain reaction

(PCRs) based techniques followed by sequencing and phylogenetic analysis.

Study Area

The Coastal Savannah zone lies along the coastal belt located in the southern part of the country. Similar to most parts of the country, this zone has a binomial rainfall pattern made up of a major (March to July) and a minor (September to October) rainy season (Oppong -

Anane, 2006). It is characterised by diverse vegetations including shrub lands and grasslands similar to the Sudan and Guinea Savannah zones in the northern part of the country and coastal lands. Three of the countries’ ten administrative regions, the Central, Greater Accra and Volta regions lie fully or partially within the zone which encompasses much of the

Accra, Keta and the Ho Plain noted for livestock rearing (Oppong - Anane, 2006). Each region is subdivided into districts and two districts noted for cattle, sheep and goat production from the Greater Accra region (Shai Osudoku and Kpong Katamanso Districts),

Central (Awutu Senya and Komenda Edina Eguafo Abirem Districts) and Volta (North

Tongu and Central Tongu Districts) regions were purposely selected from each region for the study with the help of relevant Officers from the Ministry of Food and Agriculture

(Regional and District) for their significant numbers of cattle and small ruminants in the zone (Figure 2.1). The ruminant industry in Ghana is composed largely of small-scale enterprises involved in the rearing of cattle, sheep, and goats (Oppong - Anane, 2006).

Livestock in this study area, except for a few cattle farms, were usually kept in or around households (within communities) and were predominantly local breeds (Sanga cattle and

West African Dwarf goats and sheep).

Figure 2.1 Location map of Ghana showing study area.

Study Population

Due to the absence of adequate records on ruminant farms in the study area and logistics and finances available for the study, a convenient two-stage sampling approach was adopted in

87 a cross-sectional survey. Farms/households were selected from a list of farmers from the six selected districts provided by relevant officials based in the community (to ensure an even distribution of farms within each district) and willingness of the farmer to participate.

Overall, 104 farms/households were selected from the list provided by the MoFA officials, which comprised approximately 5,153 animals from 142 herds (animals of the same species). The representativeness of the study population might be affected by the conditions surrounding the selection of farms for this study however, this limitation was unavoidable due the enormous logistic challenges of the field work in this study area.

The study population consisted of cattle, sheep and goats and the respective farmers from the six selected districts. Sample size calculations were conducted using Epi-Tools

(http://epitools.ausvet.com.au) at 95% confidence interval and 5% error, based on the estimated livestock population for the districts (MoFA, 2011) and existing prevalence reports of Cryptosporidium and GIT parasites in Ghana. When there was no existing report available from Ghana, reports from Nigeria, a country in West Africa with similar environmental and husbandry systems to Ghana was used. GIT protozoan

(Cryptosporidium) and helminth prevalences of 29% and 95.5% were used for cattle (Squire et al., 2013a; Squire et al., 2013b), 16% and 80.0% for sheep, and 24% and 88.3% for goats, respectively (Assoku, 1981; Pam et al., 2013). Cattle, sheep and/or goats were randomly selected from farms within the selected districts and the livestock farmers (caretakers of the selected livestock) were included. Each herd was categorised by sex (males and females) and age group (young ≤12 months and adults >12 months). The inclusion criteria for a farmer was a minimum of 5 animals per farm, based on the hypothesis that most livestock farmers in Ghana cater for an average of 6-10 animals (Asafu-Adjei & Dantankwa, 2001; Baah et al., 2012).

Ethics Statement

All participants enrolled in the present study signed written and where appropriate oral informed consent prior to their participation, with the assistance of a translator. Approval and ethics clearance for this study was obtained from Murdoch University human (permit number: 2014/135) and animal ethics (permit number R2683/14) committees and the Ghana

Council for Scientific and Industrial Research (CSIR) Institutional Review Board and

Animal Care and Use Committee (permit number PRN 002/CSIR-IACUC/2014).

Questionnaire Interview

A pilot structured oral questionnaire study was initially conducted with a limited number of farmers in the study area. Once feedback from these farmers about the questionnaire was obtained, any necessary modifications to the questionnaire was made in collaboration with the district Agricultural officials in the district, some of whom acted as interpreters.

With the help of the structured questionnaire, farmers were interviewed in English and the farmers’ local language with the help of interpreters, to obtain information about their farm management practices and their knowledge, attitude and practices that may expose their animals to gastrointestinal parasite infections including zoonotic species. Visual observations were also made to confirm questionnaire responses and access farm management practices. Questionnaire interviews were done together with the district

Agricultural officials who were familiar with the language and culture of the area.

Sample Collection

A total of 925 faecal samples were collected from 95 farmers and 830 animals including cattle (n=328), sheep (n=217) and goats (n=285) once. For human samples, coded clean stool containers with screw caps were given to the farmers (18 years and above) with specific

89 instructions on how to collect and submit their stool samples. Although children were actively involved in farming activities, they were omitted from the present study based on the assumption that usually worked under instructions from older farmers/family members, who made decisions on the farm management practices used, which is a limitation of the present study. For animals, stool samples were collected directly from the rectum of each animal into individually labelled airtight containers using sterile latex gloves to prevent cross contamination between samples. Faecal consistency scores (FCS) of animals were recorded using a scale of 1 (dry pellets) to 5 (liquid or fluid faeces) as previously described (Greeff &

Karlsson, 1997) and body condition scores (BCS) using a scale of 1 (emaciated) to 5 (very fat) (Villaquiran et al., 2005; Sutherland et al., 2010). Information on sex, age group, location and farm management system were also recorded. All samples were transported under appropriate conditions to the Council for Scientific and Industrial Research, Animal

Research Institute (CSIR-ARI) laboratory in Accra, Ghana, where 250 mg of stool was used for DNA extraction. For human stool, the remaining sample were immediately transported to the Ussher Polyclinic laboratory in Accra, Ghana for microscopic analysis by qualified medical laboratory personnel. All livestock samples were analysed at CSIR-ARI laboratories, where faecal samples were stored at 4 °C from 6 to 28 days prior to DNA extraction.

Microscopic Analysis

The consistency of each human faecal sample was recorded as formed or not formed (soft, loose or watery) taking notice of unusual appearances (e.g. mucoid or bloody). One gram of each stool sample was processed by the formol-ether concentration technique (Cheesbrough,

2009). The entire faecal pellet suspensions after centrifugation were examined under a microscope at x10 and x40 objectives and any parasites’ eggs per gram of stool (EPG)

90 recorded. Faecal samples from animals were also screened by the formol-ether concentration technique.

The formol-ether concentration technique used is able to identify a variety of parasites including oocysts (Cryptosporidium spp., Isospora spp. and Cyclospora cayetanensis), cysts (Entamoeba and Giardia cysts), helminth eggs (hookworm, Ascaris spp., T. trichiura, Taenia spp., Schistosoma spp. and Opisthorchis spp.) and larvae

(Strongyloides spp.) and because organisms are killed by the formalin solution, it reduces the risk of laboratory-acquired infection (Cheesbrough, 2009). The formol-ether concentration technique has a reported sensitivity of 63.1% to 78.3% and a specificity of

94.5%, as compared to 48.9% to 52.7% and 93.7% to 81.0% sensitivity, and 94.5% specificity each for wet mount and Kato-Katz, respectively (Endris et al., 2013; Yimer, et al.

2015).

Feedback Visits

After microscopic analysis, the farmers were given feedback on the intestinal parasites detected in their faecal samples and their livestock. As an incentive, infected farmers were given a tablet of albendazole (an over the counter drug in Ghana) and advice to see a medical practitioner when further treatment was required, and also an anti-helminthic compound (one litre of albendazole) for their animals.

Molecular Detection and Characterisation of Gastrointestinal Parasites

2.10.1 DNA isolation

Genomic DNA was extracted from 250 mg of each faecal sample using a PowerSoil DNA puriﬁcation Kit (MolBio, Carlsbad, California) with some minor modifications as described

91 by Yang et al. (2015), at the Biotechnology laboratory of CSIR-Animal Research Institute in Accra, Ghana. Briefly, faecal samples for DNA extraction were subjected to four cycles of freeze and thaw in liquid nitrogen followed by boiling water to ensure efficient lysis of parasites before being processed using the manufacturer's protocol. A blank (no faecal sample) was used as a control in each extraction group. Genomic DNA was shipped to the

State Agricultural Biotechnology Centre (SABC) at Murdoch University, Australia for molecular analysis under AQIS import permit IP14015324 for further analysis.

2.10.2 Detection of Cryptosporidium spp.by qPCR

DNA samples were screened at the 18S rRNA locus for Cryptosporidium using quantitative

PCR (qPCR) as previously described by Yang et al. (2014b), to produce a 161 base pair (bp) product (Table 2.1). Briefly, each 15 µl PCR mixture contained, 1× Go Taq PCR buffer

(KAPA Biosystems), 5 mM MgCl2, 1 mM dNTP’s, 1.0 U Kapa DNA polymerase (MolBio,

Carlsbad, CA), 0.2 µM each of forward and reverse primers (18SiF and 18SiR), 50 nM of the probe and 1 µl of sample DNA. The PCR cycling conditions consisted of a pre-melt at

95°C for 10 min and then 45 cycles of 95°C for 30 seconds (s) (melt) and 60°C for 1 min

(annealing and extension) on a Rotor Gene Q (Qiagen).

2.10.3 PCR amplification of Cryptosporidium at the 18S rRNA gene

Samples that were positive for Cryptosporidium by qPCR, were typed at the 18S rRNA locus using a nested PCR as described by Silva et al. (2013) to produce a 450 bp product (Table

2.1). The PCR reaction volume of 25 μL contained 2.5 μL of 10× Kapa PCR buffer, 2 μL of

25mM MgCl2,1 μL of 10 mM dNTP’s, 100nM of each primer, 1 unit of Kapa Taq

(Geneworks, Adelaide, SA) and 1 μL of DNA (about 50 ng). For samples that did not produce an amplification product, the PCR reaction was repeated multiple times by including

5% (DMSO), 2 μL of DNA as well as 1 µl of a 1:10 dilution of DNA. The PCR conditions at the 18S locus consisted of an initial cycle of 94°C for 3 min, 40 cycles (94°C for 45 s,

58°C for 45 s and 72°C for 1 min) and a final extension of 72°C for 7 min for the primary amplification. Similar conditions were used for the secondary reaction except the annealing temperature was 55°C and 45 instead of 40 cycles.

2.10.4 Genotyping of Cryptosporidium at the 60 kDa glycoprotein (gp60) gene

Cryptosporidium parvum and C. ubiquitum were subtyped by sequence analysis of a fragment of the 60 kDa glycoprotein (gp60) gene to produce 450 bp and 948 bp products, respectively (Zhou et al., 2003; Li et al., 2014), see Table 2.1.

2.10.5 Detection of Giardia duodenalis by qPCR

DNA samples were screened at the glutamate dehydrogenase (gdh) locus for Giardia using quantitative PCR (qPCR) as previously described (Yang et al., 2014c) to produce a 261 bp product. Each 15 µl PCR mixture contained, 1× Go Taq PCR buffer (KAPA Biosystems), 5 mM MgCl2, 1 mM dNTP’s, 1.0 U Kapa DNA polymerase (MolBio, Carlsbad, CA), 0.2 µM each of forward and reverse primers (gdh F1 and gdh R1), 50 nM of the probe and 1 µl of sample DNA. The PCR cycling conditions consisted of a pre-melt at 95°C for 3 min and then 45 cycles of 95°C for 30 s, and a combined annealing and extension step of 60°C for

45 sec.

2.10.6 PCR amplification of Eimeria at the cytochrome c oxidase subunit I (COI) gene

Samples that were positive for Eimeria by qPCR were also amplified at cytochrome c oxidase subunit I (COI) gene by nested PCR (Yang et al., 2014d ) using the primers COIF1

5'–GGTTCAGGTGTTGGTTGGAC–3 (Ogedengbe et al., 2011) and COXR1 5'–CCA AGA

GAT AAT AC (AG) AA (AG) TGG AA–3' (Dolnik et al., 2009) for the primary and COIF2

5'–TAA GTA CAT CCC TAA TGT C–3' (Yang et al., 2013a) and COXR2 5'–ATA GTA

TGT ATC ATG TA (AG)(AT)GC AA–3' (Dolnik et al., 2009) for the secondary reaction to

93 produce a 723 bp product. Each PCR mixture contained, 2.5 μL of 10 × Kapa PCR buffer, 2

μL of 25 mM MgCl2, 1.5 μL of 10 nM dNTPs, 10 pM of each primer, 1 unit of KapaTaq

(Geneworks, Adelaide, SA) and 1 μL of DNA. The PCR cycling conditions for the primary

PCR were 1 cycle of 94 °C for 3 min, followed by 35 cycles of 94 °C for 30 s, 58 °C for 30 s and 72 °C for 2 min and a final extension of 72 °C for 5 min. The conditions for the secondary PCR were the same except for 45 cycles instead of 35.

2.10.7 PCR amplification of Giardia duodenalis at the gdh, bg and tpi genes

All samples that tested positive for Giardia by qPCR were amplified by nested PCR of the beta-giardin (bg), gdh and triose-phosphate isomerase (tpi) genes using primers and protocols previously described (Cacciò et al., 2002; Sulaiman et al., 2003; Read et al., 2004;

Lalle et al., 2005; Cacciò et al., 2008; Geurden et al., 2008; Levecke et al., 2009; Hijjawi et al., 2016). Similar PCR reaction volumes as stated for Cryptosporidium at the 18S locus was used for both gdh and bg gene amplification with different PCR conditions as follows; for the gdh gene, PCR conditions for both primary and secondary reactions was performed with an initial cycle of 94°C for 3 min, 48 cycles (94°C for 30 s, 60°C for 30 s and 72°C for 1 min) and a final extension of 72°C for 7 min. The PCR condition for the bg gene, consisted of an initial cycle (94°C for 2 min, 65°C for 1 min and 72°C for 2 min), 40 cycles (94°C for

30 s, 65°C for 1 min and 72°C for1 min) and a final extension of 72°C for 7 min for the primary reaction. The PCR conditions was similar for the secondary reaction except for a lower annealing temperature of 55°C. For the tpi gene, the primary PCR reactions had an initial cycle of 94°C for 5 min, 45 cycles (94°C for 45 s, 50°C for 45 s and 72°C for1 min) and an extension of 72°C for 10 min. The secondary reactions were performed using assemblage specific primers with annealing temperatures of 64°C for assemblage A, 62°C for assemblage B and 67°C for assemblage E.

2.10.8 PCR inhibition test for Cryptosporidium and Giardia duodenalis

To test for PCR inhibitors in the DNA samples, 20% of samples that tested positive for

Cryptosporidium or Giardia by qPCR but negative by nested PCR were randomly selected and spiked with a 1µl of Cryptosporidium or Giardia positive control DNA, respectively and then re-amplified.

2.10.9 PCR amplification of gastrointestinal nematodes at the ITS-2 locus

DNA samples were amplified at the ITS-2 locus for selected intestinal nematodes using a hemi-nested PCR reaction. Briefly, all samples were amplified by nested PCR using NC1 forward (ACGTCTGGTTCAGGGTTGTT) and NC2 reverse

(TTAGTTTCTTTTCCTCCGCT) primers for the primary reaction at an initial cycle of 94°C for 5 minutes (min), 25 cycles of 94°C for 30 s, 55°C for 30 s and 72°C for 30 s and a final extension of 72°C for 5 min. (see Table 2.1) One microliter of the primary PCR product was used for the secondary PCR amplification using species specific forward primers and the NC2 reverse primer. For Trichostrongylus spp., 1 µl each of all primary PCR products was used for the secondary PCR reaction using primers described by Bott et al. (2009). One microliter each of the primary PCR product from farmers only was used for in the secondary

PCR to amplify A. duodenale, N. americanus and O. bifurcum, using primers previously described (Romstad et al., 1997; de Gruijter et al., 2005) (see Table 2.1). A 25 μL PCR reaction containing 2.5 μL of 10 × Kapa PCR buffer, 2 μL of 25 mM MgCl2, 1.25 μL of

DMSO (dimethyl sulfoxide), 1 μL of 10 mM dNTP’s, 100 nM of each primer, 1 unit of Kapa

Taq (Geneworks, Adelaide, SA) and 1 μL of DNA (about 50 ng) was used for both primary and secondary amplifications.

Table 2.1 Primers used in the amplification and characterisation of gastrointestinal parasites

Molecular Genetic Product Target organism Primer name Primer sequence References method marker (bp) Cryptosporidium qPCR 18SrRNA 18SiF AGTGACAAGAAATAACAATACAGG 298 (Morgan et 18SiR CCTGCTTTAAGCACTCTAATTTTC al., 1997; Probe FAM- AAGTCTGGTGCCAGCAGCCGC-black hole King et al., quencher 1 (BHQ1) 2005b; Yang et al., 2014b)

Nested PCR 18SrRNA SHP1 (External) ACC TAT CAG CTT TAG ACG GTA GGG TAT 450 (Silva et SHP2 (External) TTC TCA TAA GGT GCT GAA GGA GTA AGG al., 2013) SHP3 (Internal) ACA GGG AGG TAG TGA CAA GAA ATA ACA SSU-R3 (Internal) AAG GAG TAA GGA ACA ACC TCC A

C. parvum Nested PCR gp60 GP60F1 (External) ATAGTCTCGCTGTAT TC 450 (Zhou et GP60R1 (External) GCAGAGGAACCAGCATC al., 2003) GP60F2 (Internal) TCCGCTGTATTCTCAGCC GP60R2 (Internal) GAGAT ATATCTTGGTGCG

C. ubiquitum Nested PCR gp60 Ubi-18S-F1(External) TTTACCCACACATCTGTAGCGTCG 948 (Li et al., Ubi-18S-R1 (External) ACGGACGGAATGATGTATCTGA 2014) Ubi-18S-F2 (Internal) ATAGGTGATAATTAGTCAGTCTTTAAT Ubi-18S-R2 (Internal) TCCAAAAGCGGCTGAGTCAGCATC

Giardia qPCR gdh gdhF1 GGGCAAGTCCGACAACGA 261 gdhR1 GCACATCTCCTCCAGGAAGTAGAC (Yang et Probe Cy5-TGC CCG CTG CCT CAG TAG TGC (BHQ2) al., 2014c)

Nested PCR tpi AL3543 (External) AAATIATGCCTGCTCGTCG 605 (Sulaiman Al3546 (External) CAAACCTTITCCGCAAACC et al., 2003)

AF (Assemblage A, CGCCGTACACCTGTCA 332 (Geurden et Internal) al., 2008) AR (Assemblage A, AGCAATGACAACCTCCTTCC Internal)

BF (Assemblage B, CCCCTTTCTGCCGTACATTTAT 400 (Levecke et Internal) al., 2009) BR (Assemblage B, GGCTCGTAAGCAATAACGACTT Internal)

EF (Assemblage E, GTTGTTGTTGCTCCCTCCTTT 388 (Geurden et Internal) al., 2008) ER (Assemblage E, CCGGCTCATAGGCAATTACA Internal)

Nested PCR gdh GDHeF (External) TCAACGTYAATCGYGGYTTCCGT 800 (Read et GDH2 (External) ACCTCGTTCTGRGTGGCGCA al., 2004; GDHiF (Internal) CAGTACAACTCYGCTCTCGG Cacciò et GDH4 (Internal) GTGGCGCARGGCATGATGCA al., 2008)

G7 External F AAGCCCGACGACCTCACCCGCAGTGC 511 (Cacciò et G759 External R GAGGCCGCCCTGGATCTTCGAGACGAC al., 2002; Nested PCR bg G Internal F GAACGAACGAGATCGAGGTCCG Lalle et al., G Internal R CTCGACGAGCTTCGTGTT 2005)

Eimeria Nested PCR COI COIF1 (External) GGTTCAGGTGTTGGTTGGAC 723 (Dolnik et COXR1 (External) CCA AGA GAT AAT AC (AG) AA (AG) TGG AA al., 2009; COIF2 (Internal) TAA GTA CAT CCC TAA TGT C Ogedengbe COXR2 (Internal) ATA GTA TGT ATC ATG TA (AG) (AT) GC AA et al., 2011; Yang et al., 2013a)

Hookworms Hemi-nested ITS-2 NC1 (Forward, ACGTCTGGTTCAGGGTTGTT (Gasser et PCR External) al., 1993) NC2 (External and TTAGTTTCTTTTCCTCCGCT Internal) AD1 (Ancylostoma CGACTTTAGAACGTTTCGGC 130 (Gruijter et duodenale, Internal) al., 2005) NA (Necator ATGTGCACGTTATTCACT 250 (Romstad americanus, Internal) et al., 1997)

Strongylids Hemi-nested ITS-2 NC1 (Forward) ACGTCTGGTTCAGGGTTGTT (Gasser et PCR NC2 (Reverse) TTAGTTTCTTTTCCTCCGCT al., 1993) OB (Forward: TATATTGCAACAGGTATTTTGGTAC 220 (Romstad Oesophagostomum et al., 1997) bifurcum) TRI (Trichostrongylus TCGAATGGTCATTGTCAA 267–268 (Bott et al., spp.) 2009)

2.10.10 Agarose gel electrophoresis and gel excision

PCR products were separated and visualised using agarose gel electrophoresis on a 1% agarose gel in 1x TAE buffer (40 mM Tris-HCl, 20 mM acetate, 2 mM EDTA, pH adjusted to 7.9). Gels were stained prior to casting using 1x SYBR® Safe DNA Gel Stain (Invitrogen,

Victoria, Australia) and viewed using the Dark Reader blue light transilluminator (Clare

Chemical Research, Inc., Colorado, U.S.A.). PCR fragment sizes were estimated using a

100bp ladder DNA marker (Axygen Biosciences, California, U.S.A.). A new scalpel blade was used to excise each appropriate band size to avoid contamination between amplicons.

2.10.11 Gel purification of PCR products

Excised gels were then purified using an in-house filter tip method as previously described

(Yang et al., 2013b). Briefly, positive bands were cut from the gel and the gel fragment transferred to a 100 µl filter tip (with the tip cut off) (Axygen, FisherBiotech, WA), and then placed in a 1.5 ml Eppendorf tube and spun at full speed in a microfuge for 15 s. The filter tip was then discarded, and the eluent was retained and used for sequencing without any further purification.

2.10.12 Sequencing

Purified PCR products were sequenced using the Big Dye version 3.1 Dye Terminator Cycle

Sequencing Kit (Applied Biosystems, Foster City, California) with each sequencing reaction containing 1 μl dye terminator mix, 1.5 μl of 5x sequencing buffer (Applied Biosystems),

3.25 pmol/ μl of the primer used to generate the PCR product, and 30-50 ng of purified PCR product. The reaction was made up to a final volume of 10 μl with PCR grade water (Fisher

Biotech). Sequencing reactions were carried out on an Applied Biosystems Gene Amp PCR

System 2700 thermal cycler (Life Technologies, California, U.S.A.) using the following thermal cycling conditions: 96°C for 2 min, followed by 30 cycles of denaturation at 96°C

99 for 10 secs, annealing at 55°C for 5 sec and extension at 60°C for 4 mins. Sequencing reactions were then ethanol precipitated and analysed on an Applied Biosystems 3730 DNA

Analyzer (Applied Biosystems Inc.).

Ethanol precipitation of sequencing reaction products were carried out to remove salts and remaining dye terminators. For each 10 μl sequencing reaction, 1 μl of 125 mM

EDTA, 1 μl of 3M sodium acetate and 25 μl of 100% ethanol was added. The reaction plate was then tightly sealed with a piece of 3M Scotched Tape 432 or 439 adhesive backed foil to prevent leakage and vortexed for 15 secs to mix. The reaction plate was left at room temperature in the dark for at least 15 minutes and then centrifuged at 3,000 x g for 30 min and the supernatant was discarded by inverting the reaction plate onto a paper towel and spinning at 185 g for 2 sec. The resulting pellet was rinsed with 35 μl of 70% ethanol and centrifuged at 1650 x g for 15 min and the supernatant discarded.

2.10.13 Sequence and phylogenetic analyses

Nucleotide sequences were analysed using Finch TV 1.4.0, Molecular Evolutionary

Genetics Analysis software (MEGA version 6) or Geneious 8.0.4 (Kearse et al., 2012) and were compared to sequence data available from GenBank™ using the using BLAST (Basic

Local Alignment Search Tool) program (http://blast.ncbi.nlm.nih.gov/Blast.cgi).

Phylogenetic analysis was conducted for all nucleotide sequences attained using additional sequences retrieved from the GenBank using MEGA version 6 (Tamura et al.,

2013). Evolutionary history was inferred based on the Tamura 3-parameter model using

Maximum Likelihood (ML) (Tamura, 1992), Neighbour-Joining (NJ) (Saitou & Nei, 1987) and Maximum Parsimony (MP). Bootstrap analyses were conducted using 1000 replicates to assess the reliability of inferred tree topologies. Estimates of genetic divergence between sequences were generated in MEGA 6 based on the Tamura 3-parameter model, using uniform rates and a partial deletion of 95%.

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Statistical Analyses

Prevalence was estimated as a percentage of positive samples with 95% confidence intervals calculated using the exact binomial method (Ross, 2003). Odds ratios were calculated using

Woolf’s Method (Kahn & Sempos, 1989) or SPSS 21 for windows (Statistical Package for the Social Sciences) (SPSS Inc. Chicago, USA). Chi-square and Fisher’s Exact tests for independence were used to determine the associations between parasite prevalence and various categorical data (host, sex, age group, location, farm management system, FCS and

BCS) also using SPSS 21 for windows. P-values less than 0.05 were considered statistically significant.

To determine the risk factors associated with GIT parasite infections, univariate analysis of factors considered to be relevant for infection were performed and variables with a p<0.25 were entered into a binary logistic regression model and factors that were not significant (cut-off for significance=0.05) were removed in a stepwise backward regression elimination procedure. A Hosmer-Lemeshow statistic was calculated to determine the fit of the model.

The numbers of Cryptosporidium and Giardia oo/cysts per gram (OPG) of faeces in positive samples were also determined by qPCR as described (Yang et al., 2014a; Yang et al., 2014c). The intensity of parasitic infection was categorised as light (<200 O/EPG), moderate (200-800 O/EPG) and heavy infection (800+ O/EPG) for cattle, and light (<800

O/EPG), moderate (800-1200 O/EPG) and heavy infection (1200+ O/EPG) for small ruminants based on a slight modification of Hansen and Perry (1994), where O/EPG is oocyst/egg per gram of faeces.

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Chapter 3. Molecular Characterisation of Cryptosporidium and Giardia in Farmers and their Ruminant Livestock from the Coastal Savannah Zone of Ghana

Preface

The contents of this chapter was published as a journal article which can be found in

Appendix B.

Squire, S.A., Yang, R., Robertson, I., Ayi, I. and Ryan, U., 2017. Molecular characterisation of Cryptosporidium and Giardia in farmers and their ruminant livestock from the Coastal

Savannah zone of Ghana. Infection, Genetics and Evolution. 55, 236-243. https://doi.org/10.1016/j.meegid.2017.09.025.

This chapter describes the prevalence, genotypes and subtypes of Cryptosporidium and

Giardia duodenalis in farmers and their ruminant livestock in Ghana.

Highlights:

• Prevalence of Cryptosporidium and Giardia in farmers and their livestock in Ghana

• First genetic characterisation of Cryptosporidium and Giardia in livestock in Ghana

• Mainly, zoonotic species were identified

• Possible zoonotic transmission of Cryptosporidium in Ghana

• First report of C. parvum IIcA53Gq in goats

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Abstract

Cryptosporidium and Giardia are major causes of diarrhoea in developing countries including Ghana, however, nothing is known about the species and subtypes of

Cryptosporidium and Giardia in farmers and their ruminant livestock in this country. A total of 925 faecal samples from humans (n=95), cattle (n=328), sheep (n=217) and goats (n=285), were screened for Cryptosporidium and Giardia by quantitative PCR (qPCR) at the 18S rRNA and glutamate dehydrogenase (gdh) loci respectively. Cryptosporidium positives were typed by sequence analysis of 18S and 60 kDa glycoprotein (gp60) loci amplicons. Giardia positives were typed at the triose phosphate isomerase (tpi), beta-giardin (bg) and gdh loci.

The prevalence of Cryptosporidium and Giardia by qPCR was 8.4% and 10.5% in humans,

26.5% and 8.5% in cattle, 34.1% and 12.9% in sheep, and 33.3% and 12.3% in goat faecal samples, respectively. Giardia duodenalis assemblages A and B were detected in humans and assemblage E was detected in livestock. Cryptosporidium parvum was the only species identified in humans; C. andersoni, C. bovis, C. ryanae and C. ubiquitum were identified in cattle; C. xiaoi, C. ubiquitum and C. bovis in sheep; and C. xiaoi, C. baileyi and C. parvum in goats. This is the first molecular study of Cryptosporidium and Giardia in livestock in

Ghana. The identification of zoonotic species and the identification of C. parvum subtype

IIcA5G3q in livestock, which has previously been identified in children in Ghana, suggests potential zoonotic transmission. Further studies on larger numbers of human and animal samples, and on younger livestock are required to better understand the epidemiology and transmission of Cryptosporidium and Giardia in Ghana.

Introduction

Cryptosporidium and Giardia are enteric protozoan parasites known to cause diarrhoea and other clinical symptoms in many mammals including humans (Xiao, 2010; Ryan & Cacciò,

2013). Both protozoans are included in the WHO “Neglected Diseases Initiative” (Savioli et

104 al., 2006) and Cryptosporidium is considered the second greatest cause of diarrhoea and death in children in developing countries after rotavirus (Kotloff et al., 2013; Striepen, 2013).

Giardia duodenalis is estimated to cause 280 million cases of gastroenteritis per annum worldwide (Lane & Lloyd, 2002), with high infection rates in developing countries including

Africa and Asia (Feng & Xiao, 2011).

In livestock, Cryptosporidium and Giardia cause high morbidity and mortality, particularly in young animals, leading to significant economic losses (Olson et al., 2004;

Noordeen et al., 2012) Infection in humans may be acquired through direct contact with infected persons (person-to-person transmission) or animals (zoonotic transmission), or through ingestion of contaminated food (foodborne transmission) (Xiao, 2010; Ryan &

Cacciò, 2013). As both parasites shed environmentally robust oo/cysts in faeces, that are resistant to disinfectants used in water treatment, water is also a major mode of transmission

(Baldursson & Karanis, 2011; Duhain et al., 2012).

Currently, 39 Cryptosporidium species have been recognised and of these more than

20 have been identified in humans (Jezkova et al., 2016; Ryan et al., 2016). By far the most common species reported in humans worldwide are C. parvum and C. hominis (Xiao, 2010).

Giardia duodenalis is the species which infects mammals and is composed of at least eight assemblages (A to H), with assemblage A in humans, livestock and other mammals; B in humans, primates and some other mammals; C and D in dogs and other canids; E mainly in ungulates including cattle, sheep and goats; F in cats; G in rats; and H in marine mammals

(Ryan & Cacciò, 2013).

Both Cryptosporidium and Giardia are recognised as important causes of diarrhoea in children and HIV/AIDs patients in Ghana (Squire & Ryan, 2017). Little however, is known about the molecular epidemiology of these diseases in humans and livestock in

Ghana, with genotyping studies to date confined to children (Anim-Baidoo et al., 2015;

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Eibach et al., 2015; Anim-Baidoo et al., 2016). Therefore, the aim of the present study was to determine the species and subtypes of Cryptosporidium and Giardia infecting farmers and their ruminant livestock in Ghana to better understand the transmission dynamics of these parasites in this country.

Materials and Methods

3.4.1 Ethics statement

All participants enrolled in this study signed written and where appropriate oral informed consent prior to their participation as described in section 2.5.

3.4.2 Study area, design and sample collection

The study was carried out in Ghana in the Coastal Savannah agroecological zone as described in sections 2.2, 2.3 and 2.7.

3.4.3 Molecular analysis

Genomic DNA Genomic DNA extraction and molecular analyses of Cryptosporidium and

Giardia was conducted as described in section 2.10.

3.4.4 Statistical analyses

Statistical analyses were conducted as described in section 2.11. Briefly, prevalence was estimated as a percentage of positive samples with 95% confidence intervals calculated using the exact binomial method (Ross, 2003). Odds ratios were calculated using Woolf’s Method

(Kahn & Sempos, 1989). Chi-square and Fisher’s Exact tests for independence were used to determine the associations between prevalence and various categorical data (host, sex, age group, location, farm management system, FCS and BCS) using SPSS 21 for windows

(Statistical Package for the Social Sciences) (SPSS Inc. Chicago, USA). P-values less than

0.05 were considered statistically significant. The numbers of Cryptosporidium and Giardia

106 oo/cysts per gram of faeces in positive samples were also determined by qPCR as previously described (Yang et al., 2014a; Yang et al., 2014c).

Results

3.5.1 Prevalence of Cryptosporidium and Giardia in livestock farmers

The prevalence of Cryptosporidium and G. duodenalis in the farmers was 8.4% (8/95, 95%

CI 3.7-15.9) and 10.5% (10/95, 95% CI 5.2-18.5) by qPCR at the 18S rRNA and gdh loci respectively (Table 3.1). A total of 17.9% (n=17, 95% CI 10.8-27.1) out of the 95 human samples were positive for at least one of these protozoa; 7.4% (7/95) for Cryptosporidium only, 9.5% (9/95) for Giardia only and 1.1% (1/95) had mixed infections. There was no significant difference between the prevalence of Cryptosporidium and Giardia among the different age groups or sexes. Infection did not cause poor faecal consistency, as

Cryptosporidium prevalence was significantly higher (24.1%, p=0.008) in farmers with formed faecal samples. Unlike the Greater Accra and Volta regions, none of the farmers from the Central region tested positive for Cryptosporidium (Table 3.1). Oo/cyst numbers per gram of faeces (OPG) for human qPCR positive samples ranged from 393 to 1.4 x105

OPG (median = 586 OPG) and 52 to 2.8 x 106 OPG (median = 244 OPG) for

Cryptosporidium and Giardia, respectively.

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Table 3.1 Prevalence of Cryptosporidium and Giardia in faecal samples from livestock farmers by qPCR at the 18S and gdh loci respectively.

Any protozoan (Cryptosporidium and/or Cryptosporidium Giardia Giardia) Total Number Number number Number P- Category examined positive 95% CI 95% CI P- OR (95% CI) P-value positive positive (%) value (%) (%) value (%) Age group (years) ≤ 30 34 (35.8) 5 (14.7) 5.0-31.1 5 (14.7) 5.0-31.1 9 (26.5) 2.16 (0.5-9.1) 31-50 40 (42.1) 2 (5.0) 0.6-16.9 0.258 3 (7.5) 1.6-20.4 0.594 5 (12.5) 0.86 (0.2-4.0) 0.262 > 50 21 (22.1) 1 (4.8) 0.1-23.8 2 (9.5) 1.2-30.4 3 (14.3) 1.00 Sex Female 24 (25.3) 2 (8.3) 1.0-27.0 2 (8.3) 1.0-27.0 4 (16.7) 1.00 Male 71 (74.7) 6 (8.5) 3.2-17.5 0.675 8 (11.3) 5.0-21.0 0.513 13 (18.3) 1.12 (0.3-2.8) 0.563 Location (Region) Central 30 (31.6) 0 0-11.6 2 (6.7) 0.8-22.1 2 (6.7) 1.00 Greater Accra 36 (37.9) 6 (16.7) 6.4-32.8 0.049 4 (11.1) 3.1-26.1 0.665 9 (25.0) 4.67 (0.9-23.6) 0.138 Volta 29 (30.5) 2 (6.9) 0.8-22.8 4 (13.8) 3.9-31.7 6 (20.7) 3.65 (0.7-19.9) Faecal consistency Watery 1 (1.1) 0 0-97.5 0 0-97.5 0 Semi-formed 58 (61.1) 1 (1.7) 0-9.2 0.010 6 (10.3) 3.9-21.2 0.936 7 (12.1) 1.00 Formed 36 (37.9) 7 (19.4) 8.2-36.0 4 (11.1) 3.9-31.7 10 (27.8) 2.80 (0.96-8.21) 0.139 Total 95 8 (8.4) 3.7-15.9 10 (10.5) 5.2-18.5 17 (17.9)

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3.5.2 Prevalence of Cryptosporidium and Giardia in livestock

Table 3.2 provides a summary of the prevalence of Cryptosporidium and Giardia in livestock as categorised by livestock species, district, management type, faecal consistency score (FCS) and body condition score (BCS). Overall, 35.4% (294/830, 95% CI 32.2-38.8) of the livestock tested positive for at least one protozoan by qPCR at the 18S and gdh loci;

24.5% (203/830) for Cryptosporidium only, 4.6% (38/830) for Giardia only and 6.4%

(53/830) for mixed infections. The prevalence of Cryptosporidium and G. duodenalis in livestock was 30.8% (256/830, 95% CI 27.7-34.1) and 11.0% (91/830, 95% CI 8.9-13.3) respectively. The prevalence of Cryptosporidium and Giardia were both associated with the location of livestock (district) and FCS (p < 0.05), but not sex, BCS or farm management systems.

The prevalence of Cryptosporidium species was higher in sheep, 34.1%, (95% CI

27.8-40.8) and goats 33.3% (95% CI 27.9-39.1), compared to cattle, 26.5% (95% CI 21.8-

31.7), but this was not statistically significant (p=0.092). Similarly, Giardia prevalence was higher in sheep, 12.9% (95% CI 8.7-18.1) and goats, 12.3% (95% CI 8.7-16.7), than in cattle,

8.5% (95% CI 5.7-12.1) but again was not statistically significant (p = 0.190). However, among the different livestock species, both goats and sheep were slightly at more risk of infection with Cryptosporidium and Giardia compared to cattle (p=0.024) (Table 3.2).

Cryptosporidium and Giardia positives were found in all six districts, however, the prevalence of Cryptosporidium was highest in the North Tongu district (Volta Region)

(53.0%, 95% CI 43.7-60.3) and Shai Osudoku (50.0%, 95% CI 41.8-58.2) and Kpong

Katamanso districts (Greater Accra Region) (26.6%, 95% CI 19.1-35.1) (Table 3.2). In addition, livestock from the Shai Osudoku and North Tongu districts were over five times more likely to be infected with Cryptosporidium and Giardia (OR 10.25, 95% CI 5.54-18.91 and OR 6.36, 95% CI 3.48-11.63, respectively) than those from the Awutu Senya district.

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Similarly, the prevalence of Giardia was highest in livestock from the North Tongu district

(29.1%, 95% CI 21.9-37.1) (Table 3.2).

Cryptosporidium prevalences seemed to increase with increasing FCS with the highest prevalence (69.6%, 95% CI 47.1-86.6) in livestock with an FCS of 5 (fluid/liquid faeces) (p<0.001). Animals with fluid/liquid faeces also had a higher prevalence of Giardia

(39.1%, 95% CI 19.7-61.5) and were more than four times at risk of being infected with both

Cryptosporidium and Giardia, than livestock with an FCS of 1 (firm balls/hard pellets), (OR

4.54, 95% CI 1.75-11.77) (Table 3.2). Cryptosporidium OPG in livestock ranged from 42 to

2.9 x 107 OPG (median: 1.3 x 104 OPG) and Giardia cyst numbers ranged from 47 to 4.6 x

107 OPG (median: 690 OPG) as determined by qPCR.

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Table 3.2 Prevalence of Cryptosporidium and Giardia in livestock by qPCR (at the 18S and gdh loci respectively), as categorised by

livestock species, district, management type, faecal consistency score (FCS) and body condition score (BCS).

Any protozoan (Cryptosporidium and/or Number Cryptosporidium species Giardia duodenalis Giardia) of samples Number Number Number Category examined P- positive 95% CI positive 95% CI P-value positive OR (95% CI) P-value (%) value (%) (%) (%) Livestock Cattle 328 (39.5) 87 (26.5) 21.8-31.7 28 (8.5) 5.7-12.1 98 (29.9) 1.00 Sheep 217 (26.1) 74 (34.1) 27.8-30.8 0.092 28 (12.9) 8.7-18.1 0.190 87 (40.1) 1.57 (1.10-2.25) 0.024 Goat 285 (34.3) 95 (33.3) 27.9-39.1 35 (12.3) 8.7-16.7 109 (38.3) 1.45 (1.04-2.03) Sex Female 626 (75.4) 195 (31.2) 27.5-34.9 70 (11.2) 8.8-13.9 221 (35.3) 1.00 Male 204 (24.6) 61 (29.9) 23.7-36.7 0.737 21 (10.3) 6.5-15.3 0.724 73 (35.8) 1.02 (0.73-1.42) 0.901 Location: District (Region) Awutu Senya 117 (14.1) 16 (13.7) 8.0-21.3 2 (1.7) 0.2-6.0 17 (14.5) 1.00 (Central) Komenda Edina Eguafo Abirem 158 (18.9) 31 (19.6) 13.7-26.7 15 (9.5) 5.4-15.2 40 (25.3) 1.99 (1.07-3.74) (Central) Kpong Katamanso 128 (15.4) 34 (26.6) 19.1-35.1 <0.001 15 (11.7) 6.7-18.6 <0.001 40 (31.3) 2.67 (1.42-5.05) <0.001 (Greater Accra) Shai Osudoku 154 (18.6) 77 (50.0) 41.8-58.2 11 (7.1) 3.6-12.4 80 (52.0) 6.36 (3.48-11.63) (Greater Accra) North Tongu 148 (17.8) 77 (52.0) 43.7-60.3 43 (29.1) 21.9-37.1 94 (63.5) 10.24 (5.54-18.91) (Volta)

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Central Tongu 125 (15.2) 21 (16.8) 10.7-24.5 5 (4.0) 1.3-9.1 23 (18.4) 1.33 (0.67-2.63) (Volta) Farm management system Extensive 50 (6.0) 10 (20.0) 10.0-33.7 2 (4.0) 0.5-13.7 11 (22.0) 1.00 Semi-Intensive 742 (89.4) 233 (31.4) 28.1-34.9 0.216 87 (11.7) 9.5-14.3 0.123 270 (36.4) 2.03 (1.02-4.04) 0.119 Intensive 38 (4.6) 13 (34.2) 19.6-51.4 2 (5.3) 0.6-17.7 13 (34.2) 1.84 (0.72-4.75) Faecal consistency score 1 (Firm balls / 385 (46.4) 131 (34.0) 29.3-39.0 41 (10.7) 7.8-14.2 148 (38.4) 1.00 hard pellets) 2 (Soft pellets) 225 (27.1) 54 (24.0) 18.6-30.1 23 (10.2) 6.6-14.9 64 (28.4) 0.64 (0.45-0.91) 3 (Firm faecal 163 (19.5) 42 (25.8) 19.2-33.2 <0.001 15 (9.2) 5.2-14.7 0.001 51 (31.3) 0.73 (0.49-1.08) <0.001 mass) 4 (Soft faecal 35 (4.2) 13 (37.1) 21.5-55.1 3 (8.6) 1.8-23.1 14 (40.0) 1.07 (0.53-2.16) mass) 5 (Fluid/liquid 23(2.8) 16 (69.6) 47.1-86.8 9 (39.1) 19.7-61.5 17 (73.9) 4.54 (1.75-11.77) faeces) Body condition score 1 (Emaciated) 6 (0.7) 1 (16.7) 0.4-64.1 0 (0) 0-45.9 1 (16.7) 1.00 2 (Thin) 55 (6.6) 26 (47.3) 33.7-61.2 5 (9.1) 3.0-20.0 26 (47.3) 4.48 (0.49-40.92) 3 (Average) 384 (46.3) 111 (28.9) 24.4-33.7 0.077 42 (10.9) 8.0-14.5 0.899 130 (33.9) 2.56 (0.30-22.13) 0.313 4 (Heavy) 328 (39.5) 99 (30.2) 25.3-35.5 37 (11.3) 8.1-15.2 116 (35.4) 2.74 (0.32-23.70) 5 (Very fat) 57 (6.9) 19 (33.3) 21.4-47.1 7 (12.3) 5.1-23.7 21 (36.8) 2.92 (0.32-26.68) Total 830 256 (30.8) 27.7-34.1 91 (11.0) 8.9-13.3 294 (35.4)

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3.5.3 Prevalence of Cryptosporidium and Giardia in different age groups of livestock

In general, the prevalence of Cryptosporidium and Giardia in particular seemed to reduce with increasing age, but this was not significant for Cryptosporidium (p=0.104 and p=0.007, respectively), (Table 3.3). The prevalence of Giardia was higher in younger livestock (0-12 months old) (14.5%, 95% CI 11.1-18.3), compared to older animals (> 24 months old) (7.0%,

95% CI 4.3-10.7), (Table 3.3). However, there was no association (p>0.05) between Giardia prevalence and different age groups of cattle and goats. In sheep, Giardia prevalence was higher (p=0.027) in lambs 0-12 months old (19.1%, 95% CI 12.0-27.9), than in 13-24 months old sheep (5.3%, 95% CI 1.1-14.6). There was no significant difference in Cryptosporidium prevalences among the various age groups of cattle, sheep and goats (Table 3.3).

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Table 3.3 Prevalence of Cryptosporidium and Giardia in different age groups of livestock by qPCR at the 18S and gdh loci respectively.

Any protozoan (Cryptosporidium Cryptosporidium spp. Giardia duodenalis and/or Giardia duodenalis) Number of Number Number Number Animals and samples P- P- P- positive 95% CI positive 95% CI positive OR (95% CI) ages (months) examined value value value (%) (%) (%) (%) Cattle N=328 0-12 140 (42.6) 38 (27.1) 20.0-35.3 17 (12.1) 7.2-18.7 44 (31.4) 1.09 (0.66-1.78) 13-24 33 (10.1) 7 (21.2) 9.0-38.9 0.767 2 (6.1) 0.7-20.2 0.131 8 (24.2) 0.76 (0.32-1.81) 0.718 >24 155 (47.3) 42 (27.1) 20.3-34.8 9 (5.8) 2.7-10.7 46 (29.7) 1.00 Sheep N=217 0-12 105 (48.4) 40 (38.1) 28.2-48.1 20 (19.1) 12.0-27.9 48 (45.7) 2.36 (1.17-4.77) 13-24 55 (25.3) 20 (36.4) 23.8-50.4 0.204 5 (9.1) 3.0-20.0 0.027 24 (43.6) 2.17 (0.98-4.80) 0.046 >24 57 (26.3) 14 (24.6) 14.1-37.8 3 (5.3) 1.1-14.6 15 (26.3) 1.00 Goats N=285 0-12 149 (52.2) 49 (32.4) 25.4-41.0 20 (13.4) 8.4-20.0 57 (38.3) 1.30 (0.69-2.47) 13-24 77 (27.0) 31 (40.3) 29.2-52.1 0.189 8 (10.4) 4.6-19.4 0.800 33 (42.9) 1.58 (0.78-3.21) 0.448 >24 59 (20.7) 15 (25.4) 15.0-38.4 7 (11.9) 4.9-22.9 19 (32.2) 1.00 Overall age group N=830 0-12 394 (47.5) 127 (32.2) 27.6-37.1 57 (14.5) 11.1-18.3 149 (37.8) 1.45 (1.04-2.02) 13-24 165 (19.9) 58 (35.2) 27.9-43.0 0.104 15 (9.1) 5.2-14.6 0.007 65 (39.4) 1.55 (1.03-2.33) 0.044 >24 271(32.7) 71 (26.2) 21.1-31.9 19 (7.0) 4.3-10.7 80 (29.5) 1.00

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3.5.4 Distribution of Cryptosporidium spp. and Giardia assemblages in farmers, livestock and different locations

Of the 264 Cryptosporidium qPCR positives, only 79 samples were successfully genotyped at the 18S locus, although a spike analysis of ~20% of negative samples indicated no evidence of inhibition. Seven Cryptosporidium species were identified; C. xiaoi (44), C. bovis (19), C. ryanae (7), C. ubiquitum (4), C. andersoni (2), C. baileyi (1) and C. parvum

(2) (Table 3.4). Cryptosporidium parvum was identified in the one of the eight positive farmers, four species (C. andersoni, C. bovis, C. ryanae and C. ubiquitum) were identified in cattle, three (C. bovis, C. ubiquitum and C. xiaoi) in sheep and three (C. baileyi, C. parvum and C. xiaoi) in goats (Table 3.4). The 18S nucleotide sequences for the Cryptosporidium species identified have been submitted to GenBank under accession numbers KY711393 -

KY711403. With the exception of C. xiaoi (KY711402) identified in sheep which showed

99.8% similarity with C. xiaoi (KY055408) from GenBank with a single nucleotide polymorphism, all the 18S nucleotide sequences (KY711393-KY71143) showed 100% similarity to corresponding species nucleotide references acquired from GenBank (C. andersoni - KX259131, C. baileyi - KY352489, C. bovis - MF074602, C. parvum -

MF353928, C. ryanae - KX668207 C. xiaoi - KY055408 and C. ubiquitum - KT027455).

All C. ubiquitum and C. andersoni positives were from livestock in the Volta region.

The remaining Cryptosporidium species, with the exception of C. parvum and C. baileyi that were identified in goats from the Greater Accra Region, were spread throughout all three regions within the study area.

Subtyping of C. parvum at the gp60 locus identified C. parvum subtype IIcA5G3q from a goat, which was 100% identical to subtype IIcA5G3q (GenBank accession numbers

KM539028, KM539034, KM539035 and KM539041), previously isolated from children in

Ghana. Cryptosporidium ubiquitum subtype XIIa was identified from one sheep and one of

115 the cattle and both sequences (KY711404 - KY711406) were 100% identical to C. ubiquitum subtype XIIa (JX412915) from GenBank. Unfortunately, subtyping of the single C. parvum from the positive farmer and the remaining two C. ubiquitum positive samples (one from cattle and one from sheep) at the gp60 locus was not successful.

For Giardia, of the 101 qPCR positives, 34 were successfully typed; 5 from farmers and 29 from livestock using a combination of three loci (gdh, bg and tpi) (Table 3.4). Again, spike analysis indicated no evidence of inhibition. All 29 positives from livestock were typed as assemblage E (tpi only = 4; gdh and bg =2; bg and tpi = 8; gdh, bg and tpi =15).

Three of the positives from farmers were typed as assemblage A (tpi only-1, gdh and tpi=1, gdh, bg and tpi=1), all of which were 100% identical to subtype AII (EF507673) and the remaining two (isolates 262 and 658) were assemblage B (gdh and tpi=1 and gdh, bg and tpi=1). At the gdh locus, they exhibited 99% similarity to both subtype BIV (EF507665) (5 and 4 single nucleotide polymorphisms, SNP’s, respectively) and subtype BIII (KX228242)

(9 and 5 SNPs). Giardia nucleotide sequences have been submitted to GenBank under accession numbers KY711407 - KY711417.

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Table 3.4 Distribution of Cryptosporidium species and G. duodenalis assemblages in farmers, livestock and different locations in coastal savannah zone in Ghana

Cryptosporidium species (n = 79) G. duodenalis assemblages (n = 34) C. C. C. C. C. bovis C. ryanae C. xiaoi Category andersoni baileyi parvum ubiquitum Total A (%) B (%) E (%) Total (%) (%) (%) (%) (%) (%) (%) Host Farmers 0 0 0 1 (100.0) 0 0 0 1 3 (60.0) 2 (40.0) 0 5 14 Cattle 2 (6.9) 0 18 (62.1) 0 7 (24.1) 2 (6.9) 0 29 0 0 14 (100.0) 12 Sheep 0 0 1 (3.7) 0 0 2 (7.4) 24 (88.9) 27 0 0 12 (100.0) Goat 0 1 (4.5) 0 1 (4.5) 0 0 20 (90.9) 22 0 0 3 (100.0) 3 Location: District (Region) Awutu Senya (Central) 0 0 2 (40.0) 0 2 (40.0) 0 1 (20.0) 5 0 0 1 (100.0) 1 Komenda Edina Eguafo 0 0 2 (18.2) 0 0 0 9 (81.6) 11 0 1 (11.1) 8 (88.9) 9 Abirem (Central) Kpong Katamanso (Greater 0 1 (9.1) 2 (18.2) 1 (9.1) 0 0 7 (63.6) 11 1 (14.3) 0 6 (85.7) 7 Accra) Shai Osudoku (Greater Accra) 0 0 7 (35.0) 1 (5.0) 1 (5.0) 0 11 (55.0) 20 0 0 3 (100.0) 3 North Tongu (VR) 2 (7.4) 0 4 (14.8) 0 3 (11.1) 2 (7.4) 16 (59.3) 27 2 (18.2) 1 (9.1) 8 (72.7) 11 Central Tongu (Volta) 0 0 2 (40.0) 0 1 (20.0) 2 (20.0) 0 5 0 0 3 (100.0) 3 Total 2 (2.5) 1 (1.3) 19 (24.1) 2 (2.5) 7 (8.9) 4 (5.1) 44 (55.7) 79 3 (8.8) 2 (5.9) 29 (85.3) 34

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Discussion

This is the first molecular study of Cryptosporidium and Giardia species in livestock and respective farmers in Ghana. The prevalence of Cryptosporidium and Giardia in the 95 farmers screened (8.4% and 10.5% respectively), was within the range of previous prevalence reports for Cryptosporidium (1.0% to ~38%) and Giardia (4.5% to ~9%) in both children and adults in Ghana cf. (Squire & Ryan, 2017). Similarly, the prevalences of

Cryptosporidium in cattle (26.5%), sheep (34.1%) and goats (33.3%), were within the range of prevalences reported previously in livestock in Africa and globally (<5-70%) (Robertson,

2009; Squire et al., 2013b; Squire & Ryan, 2017). The prevalence of Giardia in cattle (8.5%), sheep (12.9%) and goats (12.3%) was also within the range of previous studies (Robertson,

2009; Geurden et al., 2010; Squire & Ryan, 2017).

Both protozoans were more prevalent in livestock with fluid/liquid faeces (FCS of 5) and this difference was significant. Cryptosporidium and Giardia are known to cause morbidity and other clinical symptoms including diarrhoea in livestock, particularly young animals (Olson et al., 2004; Noordeen et al., 2012). This suggests that Cryptosporidium and

Giardia may have a clinical impact on infected livestock by causing diarrhoea, however confounding factors including infection with other pathogens and diet need to be examined.

Neither protozoan appeared to be associated with reduced BCS in livestock, farm management systems and sex, however, further studies using a larger number of samples is required to determine the clinical impact of Cryptosporidium and Giardia in livestock in

Ghana.

In contrast to previous studies [e.g. Li et al. (2016) and Zhang et al. (2015)], there was no significant difference in the prevalence of Cryptosporidium in different age groups of animals but overall, Giardia prevalence was significantly higher (p=0.007) in younger

118 animals (0-12 months old) and was also higher in sheep 0-12 months old, but not in cattle and goats.

In the present study, only ~30-34% of the Cryptosporidium and Giardia qPCR positives were successfully typed. Why this is so is unclear, as spike analysis indicated no evidence of inhibition but could be due to the fact that (1) qPCR is much more sensitive than conventional PCR (Hadfield et al., 2011) and (2) the size of the amplicons generated, as the qPCR amplicon sizes for Cryptosporidium and Giardia were 298 bp and 261 bp respectively, whereas the secondary amplicon size for the 18S nested PCR for Cryptosporidium was 611 bp and ranged from 332 -743 bp for Giardia. It is well known that shorter amplicons amplify much more efficiently than longer amplicons (Shagin et al., 1999). It is also possible that some of the qPCR positives were due to non-specific amplification, however both qPCR assays have been extensively validated (Yang et al., 2014b; Yang et al., 2014c). Although samples were stored at 4 ºC and analysed within 28 days of collection and spike analysis showed no evidence of inhibition, the presence of inhibitors and degradation of DNA during storage cannot be ruled out.

In the current study, C. parvum was identified from one farmer, but unfortunately could not be subtyped. Previous genotyping studies in children from the Greater Accra

Region identified C. parvum only (Anim-Baidoo et al., 2015), while both C. parvum (42.1%) and C. hominis (58.0%) were identified in Cryptosporidium-positive children from the

Ashanti Region (outside the present study area) (Eibach et al., 2015). In the latter study, C. parvum (IIc, IIe) and C. hominis (Ia, Ib, Id and Ie) gp60 subtype families were identified, with subtypes IIcA5G3q, IbA13G3 and IaA21R3, being the most frequent (Eibach et al.,

2015).

This is the first molecular study of Cryptosporidium and Giardia species in livestock in Ghana and the single C. parvum from a goat was typed as IIcA5G3q. The IIc subtype

119 family was previously thought to be anthropologically transmitted only, but subtype

IIcA5G3j has recently been detected in hedgehogs (Erinaceus europaeus) in Germany and the UK (Dyachenko et al., 2010; Sangster et al., 2016). Subtype IIcA5G3 was also reported in hedgehogs in Holland, but as sequences were not submitted to GenBank, it is not possible to to obtain more information (Krawczyk et al., 2015). The present study is the first report of the IIc subtype in livestock and the fact that identical subtypes (IIcA5G3q) were detected in livestock and in children from Ghana (KM539028, KM539034, KM539035 and

KM539041) (Eibach et al., 2015), suggests potential transmission between goats and humans in Ghana. Further studies however are required to confirm this.

Cryptosporidium bovis was the most commonly detected species in cattle (62.1%) followed by C. ryanae (24.1%), C. andersoni and C. ubiquitum (6.9% each). This is similar to results of previous studies in cattle from Nigeria (Maikai et al., 2011), with the exception of C. ubiquitum, which was not reported in cattle in that study. Cryptosporidium parvum was not detected in cattle in the present study, which may have been due to the ages of the cattle sampled, as the majority of cattle in the 0-12 month category, were approximately 12 months old (data not shown). Previous studies have shown that the prevalence of C. parvum is much higher in pre-weaned calves (0-2 months old) (Santin et al., 2008). Further studies on faecal samples from cattle of all ages, in a wider range of locations is required to determine the role that cattle may play in the zoonotic transmission of Cryptosporidium in

Ghana and West Africa.

In the present study, C. xiaoi accounted for 88.9% and 90.9% of infections in sheep and goats, respectively. Cryptosporidium bovis (3.7%) and C. ubiquitum (7.4%) were also detected in sheep and C. baileyi (4.5%) and C. parvum (4.5%) were detected in goats. In other African countries including Egypt and Tanzania, C. xiaoi was the only species detected in sheep and goats, while C. parvum was identified in sheep and goats form Zambia, C. suis in sheep from Zambia, C. bovis in lambs from Tunisia and C. ubiquitum in goats from

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Algeria (Squire & Ryan, 2017). This is the first report of C. baileyi (which commonly infects birds), in an 18 month old goat from the Kpong Katamanso district. The goat was emaciated

(BCS of 1) and recovering from a severe diarrhoea outbreak on the farm, that had previously resulted in the loss of a number of goats. Further investigation is however needed to ascertain if this was mechanical transmission from ingesting bird faeces or an actual infection.

Of the species identified in sheep and goats in the present study, C. parvum and C. ubiquitum are the most important in terms of potential zoonotic transmission, as C. parvum is the second most common species infecting humans (Xiao, 2010) and C. ubiquitum is considered an emerging human pathogen (Li et al., 2014). Subtyping of C. ubiquitum identified XIIa in one of the cattle and one sheep, which is a common subtype found in ruminants and humans worldwide and is therefore potentially zoonotic (Li et al., 2014).

Cryptosporidium xiaoi is not considered a major zoonotic species as it has only been reported once in two HIV-positive individuals in Ethiopia (Adamu et al., 2013). Similarly,

C. bovis is predominantly a parasite of livestock and has only been reported in humans on a few occasions (Khan et al., 2010; Ng et al., 2012). Cryptosporidium baileyi is not considered to be of public health significance, as it has not been identified by molecular analysis in humans (Zahedi et al., 2016).

Giardia duodenalis assemblages A and B were detected in the farmers in the present study, which are the main assemblages in humans globally (Ryan & Cacciò, 2013). A previous study identified G. duodenalis assemblage B (subtype BIII) in samples from children in Ghana (Anim-Baidoo et al., 2016). In the present study, all 29 typed Giardia isolates from livestock belonged to assemblage E, which is commonly reported in livestock in other parts of Africa (Squire & Ryan, 2017). The only other molecular study of animals in Ghana, identified assemblage B in wild Ursine colobus monkeys (Teichroeb et al., 2009).

Assemblages A and/or B have been identified in cattle from Egypt, Tanzania and Uganda

121 and goats from Côte d'Ivoire and Tanzania (Squire & Ryan, 2017). Assemblage E and F have been identified in humans in Africa (Squire & Ryan, 2017) and in a recent study in

Egypt, assemblage E was detected at a prevalence of 62.5% in human samples (Abdel-Moein

& Saeed, 2016), therefore assemblage E should be considered potentially zoonotic.

Conclusions

In conclusion, ruminant livestock in Ghana are hosts to various species of Cryptosporidium and to G. duodenalis assemblage E, all of which, with the exception of C. ryanae and C. baileyi are potentially zoonotic and have been previously identified in humans in Africa.

This is the first report of C. parvum subtype IIcA5G3q in livestock and suggests potential zoonotic transmission in Ghana. Further studies however, on larger numbers of human and animal samples and on younger livestock are required to better understand the epidemiology and transmission of Cryptosporidium and Giardia in Ghana.

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Chapter 4. Gastrointestinal Helminths in Farmers and their Ruminant Livestock from the Coastal Savannah Zone of Ghana

Preface

The contents of this chapter was published as a journal article which can be found in

Appendix C.

Squire, S. A., Yang, R., Robertson, I., Ayi, I., Squire, D. S. and Ryan, U. (2018).

Gastrointestinal helminths in in farmers and their ruminant livestock from the Coastal

Savannah zone of Ghana. Parasitology Research, https://doi.org/10.1007/s00436-018-

6017-1

This chapter describes the prevalence and types of gastrointestinal parasites identified in farmers and the ruminant livestock and the species of and zoonotic potential of

Trichostrongylus identified.

Highlights

• Prevalence of GIT helminths in farmers was 21%

• N. americanus and Trichostrongylus, most common species in farmers

• Strongylid nematodes most prevalent (56.6%) in livestock

• T. colubriformis detected in farmers and their cattle, sheep and goats

• Three potentially novel Trichostrongylus spp. identified

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Abstract

To identify the gastrointestinal helminths of veterinary, zoonotic and public health importance in farmers and their ruminant livestock in Ghana, faecal samples were collected from 95 farmers and their livestock (cattle=328, sheep=285 and goats=217) and examined by microscopy and/or molecular techniques. Overall 21 farmers tested positive for at least one gastrointestinal helminth, 80.9% of which were single infections and 19.0% co- infections. The parasites identified in the farmers consisted of hookworms (n=13) (9 were

Necator americanus and the other 4 could not be amplified by PCR), Trichostrongylus spp.

(n=9), Schistosoma mansoni (n=1), Schistosoma haematobium (n=1) and Diphyllobothrium latum (n=1). In livestock, strongylid nematodes were dominant (56.6%), followed by

Paramphistomum spp. (16.9%), Dicrocoelium spp. (7.1%), Thysaniezia spp. (5.8%),

Trichuris spp. (3.3%), Moniezia spp. (3.1%), Fasciola spp. (2.8%), Toxocara spp. (1.1%) and Schistosoma spp. (0.2%). Genotyping of Trichostrongylus spp. in the farmer’s stools identified six T. colubriformis similar to T. colubriformis detected in cattle, sheep and goats in the study, two Trichostrongylus spp. with 98.3% and 99.2% genetic similarity to T. probolurus respectively and one Trichostrongylus. spp. which showed 96.6% similarity to both T. probolurus and T. rugatus. Trichostrongylus axei was also identified in cattle, sheep and goats. This is the first molecular characterisation of Trichostrongylus spp. in Ghana and the species identified in the present study suggests zoonotic transmission from cattle, sheep and goats. Further studies involving larger numbers of farmers and their household members is essential to understand the transmission dynamics and impact of these parasites on farming communities in Ghana.

Introduction

Livestock farming forms a major part of life for many Ghanaian households and about a fifth of the total 5,467,136 households are engaged in livestock rearing (GSS, 2012). However,

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ruminant livestock in Ghana, particularly, cattle, sheep and goats are infected with many gastrointestinal (GIT) helminths including Fasciola spp., Moniezia expanza, Strongyloides pappillosus, Trichuris ovis and mostly strongylid nematodes (Agyei et al., 2005; McGarry et al., 2007) and these parasites can lead to significant economic losses (Roeber et al., 2013a;

Mavrot et al., 2015).

Also, there are occupational and health risks associated with livestock rearing including zoonoses (White & Cessna, 1989; Langley & Morrow, 2010). Reverse zoonoses

(human-to-animal transmission) or spill-back is likewise possible with some parasite species

(Messenger et al., 2014), yet little is known about the GIT helminths infecting livestock farmers in Ghana. Several GIT helminths that infect cattle, sheep and /or goats including nematodes (Bunostomum phlebotomum and Trichostrongylus spp.), trematodes (Fasciola hepatica, F. gigantica, Dicrocoelium dendriticum, D. hospes, Schistosoma japonicum, S. intercalatum, S. curassoni, S. bovis and S. mattheei) and cestodes (Monieza expansa,

Echinococcus granulosus and Taenia saginata) can also infect humans (McCarthy & Moore,

2000; el-Shazly et al., 2004; CDC, 2017). Infection can be acquired through ingestion of helminth eggs or infective larvae on contaminated food, vegetation, water or soil, infective larval forms within the tissue of intermediate hosts, arthropod and gastropod intermediate hosts, skin penetration or transplacental transmission (Greenland et al., 2015).

Trichostrongylus spp. are emerging zoonotic nematodes, which predominantly infect ruminants, but have also been reported in humans in different parts of the world including

France (Lattès et al., 2011), Italy (Buonfrate et al., 2017), Lao (Sato et al., 2011), Iran

(Gholami et al., 2015) and Ghana (Fuseini et al., 2009). At least 30 Trichostrongylus species are found in herbivores and of these T. colubriformis, T. orientalis, T. axei, T. capricola, T. vitrines, T. probolurus, T. skrijabini and T. lerouxi have also been reported in humans with

T. colubriformis and T. orientalis the most frequent species detected (Phosuk et al., 2013;

Sharifdini et al., 2017). The intestinal nematode, Oesophagostomum bifurcum infects

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monkeys and humans and has been reported in humans from northern Togo and Ghana

(Gasser et al., 2006; Ziem et al., 2006a).

More than a quarter of the world's population is at risk of infection with the soil- transmitted helminths (STHs) (Ascaris lumbricoides, hookworm - Ancylostoma duodenale and Necator americanus, Trichuris trichiura, and Strongyloides stercoralis) (George et al.,

2016). Soil-transmitted helminths cause diseases in over a billion people especially in sub-

Saharan Africa and are among the five main neglected tropical diseases (Wilson et al., 2016;

WHO, 2017a).

In Ghana, N. americanus and A. duodenale and the rarely reported O. bifurcum have been identified in separate studies in mostly school children and villagers (children and adults) from Northern Ghana, respectively (Gasser et al., 2006; Ziem et al., 2006a; van Mens et al., 2013). Other GIT helminths reported in humans in Ghana include nematodes (A. lumbricoides, T. trichiura, Strongyloides stercoralis, Enterobius vermicularis and

Trichostrongylus spp.), trematodes (Fasciola hepatica, Schistosoma mansoni, S. haematobium and Fasciolopsis buski) and cestodes (Diphyllobothrium latum, Hymenolepis nana and Taenia spp.) (Ayeh-Kumi et al., 2009; Duedu et al., 2015a; Adams & Lawson,

2016). Due to the importance of GIT parasites, particularly STHs in Ghana, there is an existing school-based deworming program for school aged children according to WHO recommendations (Ziem et al., 2006b; Humphries et al., 2013).

The diagnosis of GIT helminths can be achieved by several methods including copromicroscopic techniques such as direct wet mounts, Kato-Katz thick smear, FLOTAC, formol-ether, sodium chloride and zinc sulphate concentration (Cheesbrough, 2009; Glinz et al., 2010). However, the different species of hookworm and Oesophagostomum cannot be reliably identified by routine microscopy and molecular methods are required (Romstad et al., 1997; de Gruijter et al., 2005; Sato et al., 2010). It is also difficult to identify the eggs of

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various species of Trichostrongylus and to distinguish them from hookworm eggs leading to misdiagnoses by microscopists. They can, however, be successfully distinguished by the use of molecular techniques such as Sanger sequence analysis of the ribosomal RNA (rRNA)

Internal Transcribed Spacer (ITS) 1 and 2 regions (Yong et al., 2007; Ashrafi et al., 2015) and also Next Generation Sequencing (NGS) (Cantacessi et al., 2010).

The present study therefore aimed to identify the GIT helminths of veterinary, zoonotic, and public health importance, as well as the species of Trichostrongylus that infect farmers and their cattle, sheep and goats from the Coastal Savannah agroecological zone of

Ghana.

Materials and Methods

4.4.1 Study area, design and sample collection

The study area, design, study population and sample collection were conducted as described in sections 2.2-2.4.

4.4.2 Microscopic analysis and feedback visits

Microscopic analysis of faecal samples and feedback visits were conducted as described in sections 2.8-2.9.

4.4.3 DNA isolation, PCR amplification, genotyping and sequencing of selected gastrointestinal nematodes

Genomic DNA was extracted as described in section 2.10.1. Based on the frequency and species of parasites detected by microscopy, all DNA were screened at the ITS-2 locus for

Trichostrongylus spp. and DNA from farmers were also amplified at the ITS-2 locus to differentiate the various the hookworm species (A. duodenale and N. americanus) and O. bifurcum using primers and protocols as described in section 2.10.10 – 2.10.12. Sequencing and phylogenetic analysis was conducted as described in sections 2.10.13-2.10.14.

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4.4.4 Statistical analyses

The frequency of gastrointestinal helminth infection was determined for farmers, cattle, sheep and goats as a percentage of positive samples using SPSS 24 (Statistical Package for the Social Sciences) for windows (SPSS Inc. Chicago, USA) as described in section 2.11.

Prevalence was estimated as a percentage of positive samples, with 95% confidence intervals

(CI) using the exact binomial method (Ross, 2003).

Results

4.5.1 Prevalence of gastrointestinal helminths in farmers and their livestock by microscopy

Out of the total 925 samples (from farmers and their livestock) examined by microscopy, strongylid nematodes (56.2%) were the most frequent helminths identified. Strongylid nematodes and Schistosoma spp. (0.4%) were detected in both farmers and their livestock.

Toxocara spp. (1.0%), Trichuris spp. (2.9%), Fasciola spp. (2.5%), Paramphistomum spp.

(15.1%), Dicrocoelium spp. (6.4%), Thysaniezia spp. (5.2%) and Moniezia spp. (2.8%) were detected only in livestock, while hookworms and Diphyllobothrium latum were detected in the farmers only (Table 4.1).

A total of 155 farmers were approached and faecal samples were received from 95 of these. Based on microscopy, 10.5%, (n=10, CI =5.2 – 18.5) of the 95 livestock farmers were positive for at least one GIT helminth, consisting of the nematodes (roundworms); hookworms (n=6) and strongylid nematodes (Trichostrongylus spp., n=2), the cestode

(tapeworm); Diphyllobothrium latum (n=1) and trematodes (flukes); Schistosoma spp. (co- infection of S. mansoni, n=1 and S. haematobium, n=1) (Table 4.1). Flukes (Fasciola spp.,

Dicrocoelium spp. or Paragonimus spp.), Ascaris spp., Trichuris spp. and Strongyloides spp. were not detected in the farmers by microscopy.

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Gastrointestinal parasites were identified in a total of 64.3% (n=534, CI=61-67.6) of the 830 livestock, with strongylid nematodes the most prevalent (56.6%), followed by

Paramphistomum spp. (16.9%), Dicrocoelium spp. (7.1%,), Thysaniezia spp. (5.8%),

Trichuris spp. (3.3%), Moniezia spp. (3.1%), Fasciola spp. (2.8%), Toxocara spp. (1.1%) and Schistosoma spp. (0.2%) (Table 4.1). Strongylid nematodes were also the most prevalent helminths detected in cattle (65.9%), sheep (52.1%) and goats (49.5%). Thysaniezia spp. was identified in cattle only, Toxocara spp. was identified in cattle and sheep, and two

Schistosoma spp. in sheep only (Table 4.1). The remaining parasites (Trichuris spp., strongylid nematodes, Fasciola spp., Paramphistomum spp., Dicrocoelium spp. and

Moniezia spp. were found in all three livestock species (cattle, sheep and goats).

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Table 4.1 Prevalence of gastrointestinal (GIT) helminths in farmers and their livestock (cattle, sheep and goats) by microscopy from the coastal savannah agroecological zone of Ghana.

Cattle (n=328) Sheep (n=217) Goats (n=285) Total Livestock (n=830) Farmers (n=95) Total GIT helminths detected No. No. No. No. No. No. by microscopy positive 95% CI positive 95% CI positive 95% CI positive 95% CI positive 95% CI positive (%) (%) (%) (%) (%) (%) Nematodes (roundworms) Toxocara spp./Ascaris spp. 8 (2.4) 1.1– 4.7 1 (0.5) 0 – 2.5 0 0 – 1.3 9 (1.1) 0.5 – 2.0 0 0 – 3.8 9 (1.0) Hookworms 0 0 – 1.1 0 0 – 1.7 0 0 – 1.3 0 0 – 0.4 6 (6.3) 2.4 – 13.2 6 (0.6) Trichuris spp. 7 (2.1) 0.9 – 4.3 7 (3.2) 1.3 – 6.5 13 (4.6) 2.5 – 7.7 27 (3.3) 2.2 – 4.7 0 0 – 3.8 27 (2.9) Strongylid nematode 216 (65.9) 60.4 – 71.0 113 (52.1) 45.2 – 58.9 141 (49.5) 43.5 -55.4 470 (56.6) 53.2 – 60.0 2 (2.1) 0.3 – 7.4 472 (56.2) Sub-total 231 (70.4) 65.2 – 75.3 121 (55.8) 48.9 – 62.5 154 (54.0) 48.1 -59.9 506 (61.0) 57.6 – 64.3 8 (8.4) 3.7 – 15.9 514 (55.6) Trematode (flukes) Fasciola spp. 15 (4.6) 2.6 – 7.4 7 (3.2) 1.3 – 6.5 1 (0.4) 0 -1.9 23 (2.8) 1.8 – 4.1 0 0 – 3.8 23 (2.5) Paramphistomum spp. 121 (36.9) 31.7 – 42.4 17 (7.8) 4.6 – 12.2 2 (0.7) 0.1 -2.5 140 (16.9) 14.4 – 19.6 NA 0 – 3.8 140 (15.1) Dicrocoelium spp. 22 (6.7) 4.3 – 10.0 21 (9.7) 6.1 – 14.4 16 (5.6) 3.2 – 9.0 59 (7.1) 5.5 – 9.1 0 0 – 3.8 59 (6.4) Schistosoma spp. 0 0 – 1.1 2 (0.9) 0.1 – 3.3 0 0 – 1.3 2 (0.2%) 0 – 0.9 1 (1.1) 0.3 – 7.4 4 (0.4) Sub-total 158 (48.2) 42.6 – 53.7 47 (21.6) 16.4 – 27.7 19 (6.7) 4.1 – 10.2 224 (27.0) 24.0 – 30.1 2 (2.1) 0.3 – 7.4 226 (24.4) Cestodes (tapeworms) Thysaniezia spp. 48 (14.6) 11.0 – 18.9 0 0 – 1.7 0 0 -1.3 48 (5.8) 4.3- 7.6 0 0 – 3.8 48 (5.2) Moniezia spp. 4 (1.2) 0.3 – 3.1 6 (2.8) 1.0 – 5.9 16 (5.6) 3.2 -9.0 26 (3.1) 2.1 – 4.6 0 0 – 3.8 26 (2.8) Diphyllobothrium latum 0 0 – 1.1 0 0 – 1.7 0 0 -1.3 0 0 – 0.4 1 (1.1) 0.0 – 5.7 1 (0.1) Sub-total 52 (15.8) 12.1 – 20.3 6 (2.8) 1.0 – 5.9 16 (5.6) 3.2 – 9.0 74 (8.9) 7.1 – 11.1 1 (1.1) 0.0 – 5.7 75 (8.1)

CI: Confidence interval

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4.5.2 Molecular detection and characterisation of gastrointestinal nematodes in farmers and their livestock

Nine (9.5%, CI=4.4-17.2) of the livestock farmers tested positive for Trichostrongylus spp. by PCR amplification at the ITS-2 locus and seven of these were negative by microscopy.

Similarly, nine (9.5%, CI=4.4-17.2) samples tested positive for N. americanus by PCR amplification at the ITS-2 locus. Seven of these were negative for hookworm by microscopy but positive for N. americanus by PCR and four were microscopically positive but could not be amplified by the PCR primers used. Ancylostoma. duodenale and O. bifurcum were not identified by amplification at the ITS-2 locus.

Therefore, based on both microscopy and PCR amplification, the majority, (n=17,

80.9%) of the total 21 positive farmers had a single infection and the remaining 4 (19.0%) had co-infections of N. americanus and Trichostrongylus spp. (n=3), and S. mansoni and S. haematobium (n=1) (Table 4.2).

Table 4.2 Single or mixed GIT helminths among livestock farmers that tested positive by at least one test method (microscopy and/or PCR) (n=21).

Percentage GIT helminths No. positive positive Single infections Hookworm a 4 4.2 Necator americanus b 6 6.3 Trichostrongylus spp. 6 6.3 Diphyllobothrium latum 1 1.1 Sub-total 17 80.9 Co-infections Necator americanus and Trichostrongylus spp. 3 3.2 Schistosoma mansoni and Schistosoma 1 1.1 haematobium Sub-total 4 19.0 Total 21 100 aSamples were positive for hookworm microscopically but not identified by any of the hookworm primers used in the present study. bA hookworm species identified.

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Sequence analysis of the nine N. americanus positives showed that six samples (268,

271n, 280, 282n, 650 and 658) were 100% identical to N. americanus (LC088287), previously described in a gorilla from Gabon (Figure 4.1). The remaining three positives

(272n, 281 and 283) had 3 nucleotide deletions compared to the other six positives and were

100% identical to N. americanus from a human in Australia (KC632571).

As shown in the phylogenetic tree (Figure 4.1), genotyping of the nine

Trichostrongylus spp. identified six T. colubriformis positives (isolates 25, 258, 272t, 273,

278 and 282t) with 100% similarity to T. colubriformis in cattle, sheep and goats in the present study and T. colubriformis from a human in Thailand (KC337063). Two of the remaining Trichostrongylus species (261 and 271t) showed 98.3% and 99.2% similarity to

T. probolurus (EF427623), with 3 nucleotide polymorphisms and a single nucleotide polymorphism respectively, and the final Trichostrongylus species (142) exhibited 96.6% similarity to T. probolurus (Y14817) and T. rugatus (Y14818).

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Figure 4.1 Phylogenetic relationship of Trichostrongylus spp. constructed based on ribosomal Internal Transcribed Spacer 2 sequences using the Maximum Likelihood method based on the Tamura 3-parameter model (Tamura, 1992). Bootstrap values (>50%) based on 1000 replicates from NJ, ML and MP analysis, respectively, are indicated at the left of the supporting node (dash [−] = value was ≤ 50%). Sequences from this study are indicated bold letters and the countries Australia (AUS), Brazil (BRA), Ghana (GHA), Iran (IRN), Laos (LAO), Malaysia (MYS), New Zealand (NZL), Russia (RUS), Thailand (THA), Togo (TGO), United States of America (USA) except Scotland (SCT) are represented with country codes (ISO 3166-1 alpha-3 codes).

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A total of 644 (cattle, n=233; sheep, n=179 and goats, n=232) of the 830 livestock samples were amplified at the ITS2 region for Trichostrongylus spp. Genotyping of approximately 10% of the 644 Trichostrongylus amplicons from the livestock samples

(cattle, n=23; sheep, n=18; goats, n=25) identified T. colubriformis (n=1), T. axei (n=2),

Haemonchus contortus (n=10), H. placei (n=5), Cooperia punctata (n=2) and C. spatulata

(n=3) in cattle, T. axei (n=1), T. colubriformis (n=9), H. contortus (n=6), H. similis (n=1) and C. curticei (n=1) in sheep, and T. axei (n=4), T. colubriformis (n=12) and H. contortus

(n=9) in goats (GenBank accession numbers MH481544 - MH481609) (Table 4.3). Thus, the Trichostrongylus spp. primer also amplified Haemonchus spp. and Cooperia spp. in the livestock samples.

Table 4.3 Species of GIT nematodes identified in farmers and their livestock from the Coastal Savannah zone of Ghana.

No. of species identified GIT helminths Cattle (n=328) Sheep (n=217) Goats (n=285) Farmers (n=95) Necator americanus NA NA NA 9 Trichostrongylus spp. T. colubriformis 1 9 12 6 T. axei 2 1 4 0 T. sp. 1 0 0 0 1 T. sp. 2 0 0 0 1 T. sp. 3 0 0 0 1 Haemonchus spp. H. contortus 10 6 9 NA H. placei 5 0 0 NA H. similis 0 1 0 NA Cooperia spp. C. curticei 0 1 0 NA C. punctata 2 0 0 NA C. spatulata 3 0 0 NA

Total no. genotyped 23 a 18 b 25 c 18 d a 23 (10.1%) out of 233 Trichostrongylus spp. amplicons from cattle were genotyped b 18 (9.9%) out of 179 Trichostrongylus spp. amplicons from sheep were genotyped c 25 (10.8%) out of 232 Trichostrongylus spp. amplicons from goats were genotyped NA no amplification

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Table 4.4 shows the genetic distances between Trichostrongylus spp. sequences generated in the present study and reference sequences from GenBank. Sequences generated from the present study were submitted to the GenBank under accession numbers MG770097-

MG770112 and MH481544 - MH481572.

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Table 4.4 Genetic distances (%) between Trichostrongylus species from farmers and their livestock and other Trichostrongylus species (from

GenBank) at the ITS-2 locus.

Genetic distance (%) Trichostrongylus species from farmers and their livestock T. colubriformis T. axei T. probolurus T. rugatus T. retortaeformis T. vitrinus T. tenuis (KC337063) (KC337066) (EF427623) (Y14817) (KC521412) (KF872228) (X78067) T. colubriformis (sample 25, 258, 272t, 273, 278, 282t, 182, 197,199, 205, 213, 235, 237, 0 4.3 5.1 2.5 3.4 3.4 5.2 249, 313, 317, 318, 337, 590, 703, 750, 784, 796, 801, 802, 941 and 992) T. colubriformis (sample 619) 2.5 7.0 7.9 5.2 6.1 6.1 8.0 T. sp. 1 (sample 261) 5.2 4.3 1.7 2.5 3.4 3.4 7.0 T. sp. 2 (sample 271t) 4.3 3.4 0.8 1.7 2.5 2.5 6.1 T. sp. 3 (sample142) 5.1 5.1 5.1 3.4 4.3 4.3 7.9 T. axei (65, 104, 215, 333,586, 4.3 0 4.2 1.7 0.8 2.5 6.2 776 and 929)

Genetic distances were calculated in Mega 6 (Tamura et al., 2013) based on the Tamura 3-parameter model (Tamura, 1992).

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Discussion

A total of eleven different gastrointestinal helminths (Toxocara spp., hookworm,

Strongylids, Trichuris spp., Fasciola spp., Paramphistomum spp., Dicrocoelium spp.,

Schistosoma spp., Diphyllobothrium latum, Thysaniezia spp. and Moniezia spp.) were detected in farmers and/or their livestock (cattle, sheep and goats) from the Coastal Savannah zone of Ghana, the majority (55.6%) of which were nematodes. Although strongylid nematodes and Schistosoma spp. were the only helminths found in both farmers and their livestock, all the gastrointestinal helminths identified in the present study, with the exception of Thysaniezia spp. and Paramphistomum spp. include zoonotic species that can infect both humans and cattle, sheep and/or goats. For example, several Trichostrongylus spp.

(Sharifdini et al., 2017), the hookworm, Bunostomum phlebotomum (Wang et al., 2012), F. hepatica and F. gigantica (Garcia et al., 2007), D. dendriticum and D. hospes (Wolfe, 2007;

Cengiz et al., 2010), S. japonicum (Wang et al., 2005; Xiao et al., 2018), S. curassoni, S. bovis and S. mattheei (Kruger & Evans, 1990; Léger et al., 2016) and Monieza expansa (el-

Shazly et al., 2004) which infect livestock (cattle, sheep and or/ goats), have also been reported in humans.

In the present study, the majority of the livestock farmers (20.0%) had STH infections (including hookworms and Trichostrongylus spp.) followed by Schistosoma spp.

(2.1%) (S. mansoni and S. haematobium co-infections) and D. latum (1.1%). This is not surprising as nematodes, particularly STHs are easily transmitted in areas where sanitation is poor, and the prevalence of STH infections are higher in tropical and subtropical areas, with the greatest numbers occurring in sub-Saharan Africa including Ghana, the Americas,

China and East Asia (WHO, 2017b). Comparing the gastrointestinal helminths detected in farmers in the present study to those from food vendors in a previous study at a university in

Kumasi, Ghana, more helminths were detected, with A. lumbricoides the most dominant

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species; Ascaris lumbricoides (37.1%), hookworms (17.9%), Taenia sp. (11.4%), Fasciola hepatica (11.4%), Hymenolepis nana (6.4%), Schistosoma mansoni (6.4%), Schistosoma haematobium (2.9%), Strongyloides stercoralis (2.9%), Fasciolopsis buski (2.1%),

Diphylobothrium latum (1.4%), Trichostrongyles (1.4%) and Trichuris trichiura (0.7%)

(Adams & Lawson, 2016). That study used a combination of copromicroscopic techniques

(direct saline wet mount, iodine preparation, and formalin-ether sedimentation) as compared to only formol-ether concentration in the present study. A combination of different copromicroscopic techniques including Kato-Katz, Koga agar plate, ether-concentration, and FLOTAC have been shown to improve the diagnostic accuracy of S. mansoni and STH’s

(Glinz et al., 2010; Rinaldi et al., 2011). The use of only one method in the present study could have contributed to the low number of positives detected in the farmers, however other factors including age, hygiene practices and geographic location of the study can contribute to the variety and prevalence of parasites detected.

It is also not surprising that Oesophagostomum bifurcum and A. duodenale were not detected in the farmers, as O. bifurcum is commonly found in the Northern part of Ghana, which is outside the study area (Ziem et al., 2006a) and N. americanus was reported to be more prevalent than A. duodenale in a previous study in Ghana (de Gruijter et al., 2005).

Another study in Malawi however, reported a higher prevalence of A. duodenale than N. americanus in pre-school children (Jonker et al., 2012). Four samples that were positive for hookworms by microscopy could not be amplified by PCR, possibly due to the species- specific primers used in the present study, which targeted the ITS-2 locus for N. americanus,

O. bifurcum and A. duodenale. The unidentified hookworms could belong to genera or species which the primers in the present study, were not designed to amplify. However, seven more samples that were negative by microscopy for hookworms, were also positive by PCR, suggesting that a combination of both microscopy and molecular methods is necessary for the accurate diagnosis of hookworm eggs. Similarly, with Trichostrongylus

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spp., only two were identified microscopically, but an additional seven Trichostrongylus positives were detected by PCR. The PCR technique used increased the sensitivity of

Trichostrongylus diagnosis in humans, and for livestock, it also amplified Cooperia spp. and

Haemonchus spp. The primers pair (TRI-NC2) used in this study was previously reported to be capable of detecting four major Trichostrongylus species, including T. colubriformis, T. axei, T. vitrinus and T. rugatus, and also O. columbianum and O. venulosum (Bott et al.,

2009).

It was not surprising that Taenia was not detected in samples in the present study as

Taenia was also not found in humans (Ayeh-Kumi et al., 2009; Duedu et al., 2015a; Dankwa et al., 2015) and ruminant livestock (Agyei, 2003; Squire et al., 2013a; Owusu et al., 2016) in previous studies in Ghana. The fish tapeworm, Diphyllobothrium latum, which is acquired by eating raw or uncooked fish was found in one of the farmer faecal samples in the present study and has previously been reported in dogs and in humans in Ghana (Duedu et al., 2015b;

Johnson et al., 2015; Amissah-Reynolds et al., 2016). A co-infection with S. mansoni and S. haematobium was identified in one farmer from the Volta Region of Ghana, which is one of the endemic regions noted for both urinary and intestinal schistosomiasis, usually acquired from the Volta Lake (Kabore et al., 2013). It is estimated that at least 92% of those requiring treatment for schistosomiasis live in Africa including Ghana, and it is a disease of the poor and rural communities particularly agricultural and fishing populations (WHO, 2017c). The identification of S. haematobium in a farmer’s faecal sample in the present study may have been due contamination by infected urine during sample collection, as the infected individual confirmed having symptoms of schistosomiasis including blood in urine during the feedback visit. However, S. haematobium, which was known to be a parasite of the urinary tract has been detected in faecal samples in other studies (Cunin et al., 2003; Adams & Lawson,

2016).

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In the present study, strongylid nematodes (56.6%) were the most prevalent helminths detected in livestock, followed by Paramphistomum spp. (16.9%), Dicrocoelium spp. (7.1%), Thysaniezia spp. (5.8%), Trichuris spp. (3.3%), Moniezia spp. (3.1%), Fasciola spp. (2.8%), Toxocara spp. (1.1%) and Schistosoma spp. (0.2%). This is not surprising as strongylid nematodes were the most common species reported in previous studies in livestock (cattle, sheep and/or goats) in Ghana (Squire et al., 2013a; Owusu et al., 2016).

Haemonchus spp., Trichostrongylus spp., Cooperia spp., Oesophagostomum spp. and

Ostergia spp. are some of the strongylid nematodes previously reported, with H. contortus the most dominant species detected (Agyei, 1997; Agyei et al., 2005). In a similar study on the gastrointestinal parasites in cattle from Ethiopia, Fasciola spp., Paramphistomum spp.,

Bunostomum spp., Oesophagostomum spp., and Trichuris spp. were the helminths identified, with Fasciola spp. (26.2%) the most common species reported (Getahun et al., 2017). In

Nigeria, a similar study on trematode parasites in cattle also identified F. gigantica as the most prevalent (74.9%) out of the trematodes (Fasciola gigantica, paramphistomes,

Dicrocoelium hospes and Schistosoma bovis) detected (Elelu et al., 2016). Although most of the helminths identified in livestock in the present study are potentially zoonotic, the specific species was not identified in the majority of samples and should be considered in future studies.

The use of molecular methods is essential for species identification, as the egg stage of hookworms and Trichostrongylus spp. are difficult to differentiate into various species microscopically (Yong et al., 2007; Ashrafi et al., 2015) and although species identification by larval morphology after coproculture may be possible, it is time consuming, laborious and is frequently inaccurate (Roeber et al., 2013b). This is the first molecular characterisation of Trichostrongylus spp. in Ghana, as previous studies in both animals and humans only identified the parasite eggs or larvae microscopically without further species identification (Agyei et al., 2004; Fuseini et al., 2009; Adams & Lawson, 2016). One study

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which identified the species of adult Trichostrongylus worms after post-mortem examination of lambs detected Trichostrongylus colubriformis, Trichostrongylus axei and

Trichostrongylus vitrines (Agyei, 1997).

In the present study, the nine N. americanus identified in farmers were 100% identical to N. americanus (LC088287), previously described in a gorilla from Gabon. Like

O. bifurcum, N. americanus is zoonotic and can also be transmitted between primates and humans. Trichostrongylus colubriformis was the most common (n=6, 66.7%) of the nine

Trichostrongylus spp. identified in the livestock farmers and this species was also identified in cattle, sheep and goats. Two (samples 261 and 271t, GenBank accession numbers

MG770096 and MG770097, respectively) of the remaining three positive Trichostrongylus spp. samples were similar (98.3% and 99.2%) to T. probolurus (JF276027) and the final one

(sample 142, GenBank accession number MG770098) exhibited 96.6% similarity to T. probolurus (Y14817) and T. rugatus (Y14817) respectively. Trichostrongylus axei was identified in livestock only (cattle, sheep and goats) and was similar (100%) to T. axei previously reported in cattle, sheep and humans (KP150521, KC008725 and KC337066).

Trichostrongylus spp. are primarily species of animals and out of over 30 species occurring,

T. orientalis and T. colubriformis are the most frequently reported to infect humans, although other species, including T. vitrinus, T. axei, T. capricola, T. probolurus and T. skrijabini have also been reported (Ghadirian & Arfaa, 1975; Phosuk et al., 2013). In Iran, T. colubriformis was also the most common species (29 out of 33, 78.9%) reported in humans followed by T. axei (Gholami et al., 2015). There is however limited Trichostrongylus spp.

ITS-2 sequences available in GenBank, as most previous studies have used microscopic identification of larvae to identify species.

A previous study which conducted multiple alignments of ITS-2 sequences of

Trichostrongylus spp. reported intra-specific variations of 0–1.8% and 0–0.6% within isolates of T. colubriformis and T. axei, respectively, and higher (1.8–7.0%) inter-specific

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sequence differences among Trichostrongylus nematodes (Sharifdini et al., 2017). Other studies have reported 1.3% intra-specific variation for T. colubriformis and for T. axei respectively and genetic distances of 1.3-7.6% between species (T. colubriformis, T. axei, T. retortaeformis, T. vitrinus and T. tenuis), with T. tenuis the most distant (Hoste et al., 1995;

Heise et al., 1999). In the present study, two Trichostrongylus species (271t and 261) clustered with the T. probolurus clade and were 0.8% and 1.7% genetically distant from T. probolurus (EF427623), respectively, while Trichostrongylus species (142) exhibited 3.4% genetic distance from T. probolurus (Y14818) and T. rugatus (Y14817). Hence,

Trichostrongylus species (271t) is most likely to be T. probolurus, sample 261, a T. probolurus variant and sample 142 may represent a novel species. Further investigations are however needed to confirm this.

Transmission of diseases between animals and humans is a major challenge among livestock keepers in Africa and Asia who depend on their animals for their livelihood and sustenance (WHO, 2017d) and further research and education is necessary to reduce infection. Livestock farmers must also be encouraged to routinely deworm themselves and their families as well as animal handlers, as the current mass deworming program in Ghana is basically school-based and focuses on school aged children. It is also important for the government to consider zoonotic helminths in planning future deworming programs to include farmers and their families. A limitation of the present study is however the small sample size of farmers used (n=95). Approximately 38% of farmers initially approached in the present study declined to supply faecal samples for various reasons including cultural (it is taboo to handle human faeces), social and religious reasons and therefore future studies should involve larger numbers of faecal samples and from the farmers’ entire households including children. This is particularly important, as caring for livestock was found to be the duty of mainly household members (74.1%), rather than outsiders in the present study.

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Conclusions

Farmers and the ruminant livestock in Ghana were infected with a variety of intestinal helminths many of which are potentially zoonotic. This is the first molecular characterisation of Trichostrongylus spp. in Ghana and zoonotic species as well as potentially novel species were identified, which need to be characterised further to better understand the impact and transmission of Trichostrongylus spp. in Ghana.

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Chapter 5. Prevalence and risk factors for gastrointestinal parasites in ruminant livestock in the Coastal Savannah zone of Ghana

Preface

The contents of in this chapter is under review by an Acta Tropica.

This chapter describes the prevalence and intensity of GIT parasites and the risk factors associated with GIT parasite infections in the animals.

Highlights

• Prevalence of 90.8% for GIT parasites in Ghana livestock, with Eimeria most prevalent

(78.4%)

• Almost all (99.0%) herds were infected with at least one GIT parasite

• More species (n=10) were found animals kept in semi-intensive system and on earthen

housing floors.

• Location, floor type, other parasites’ infection, flock size and species kept were more

likely to increase the susceptibility livestock to infections.

• Livestock belonging to farmers with secondary or higher levels education were less

likely to be infected.

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Abstract

Gastrointestinal (GIT) parasite infections result in significant economic losses to ruminant livestock production. To determine the prevalence and risk factors associated with GIT parasite infections in livestock from the Coastal Savannah zone of Ghana, a cross-sectional survey was conducted in cattle and small ruminants kept under different management systems in the Coastal Savannah zone from October 2014 to February 2015. Faecal samples were collected from 328 cattle and 502 small ruminants (sheep and goats) and examined by formal ether concentration microscopy. The management systems and environmental conditions of the farm or household were observed, and a questionnaire administered to the livestock owners. The animals were predominantly local breeds (Sanga cattle and West

African Dwarf goats and sheep) of all ages. Overall, 90.8% (754/830) of livestock were infected with at least one of ten different parasites (Eimeria, Strongylid nematodes.,

Toxocara, Trichuris, Schistosoma, Dicrocoelium, Paramphistomum, Fasciola, Moniezia and Thysaniezia), with Eimeria the most prevalent (78.4%). Most (64.5%) livestock had coinfections with two to five parasites with parasite intensity mostly light and at least one parasite was found in 99.0% (140/142) of the herds. Binary logistic regression models were generated to assess the risk factors associated with infection with the three most prevalent parasites identified. Variables that yielded p-values <0.25 in the univariable analysis were entered into each binary logistic regression model. Location (Greater Accra) and with

Dicrocoelium infection increased Eimeria infections, earthen floors and infection with

Eimeria, Moniezia, Paramphistomum or Trichuris increased Strongylid nematode infection, while owners with a secondary or higher education level owned animals with a lower prevalence of Strongylid nematodes, and multiple ruminant species, flock size (> 25 animals), flocks from the Greater Accra region, presence of earthen floors in the animal pens and coinfections with Fasciola, Dicrocoelium or Strongylid nematodes increased

Paramphistomum infections. It was concluded that education of farmers on appropriate 146

animal health management and husbandry practices could reduce the spread of GIT parasites in livestock in Ghana.

Introduction

Livestock diseases result in significant economic impacts globally. Amongst livestock diseases, gastrointestinal (GIT) parasite infections in ruminant livestock result in adverse effects on feed intake, growth rate, carcass weight and composition, wool growth, fertility and milk yield (Fitzpatrick, 2013). These effects are most significant in developing countries, with GIT helminths ranked as the most important livestock disease in a global study that ranked livestock disease significance according to their impact on the poor (Perry et al.,

2002), with the majority of the world’s poor livestock keepers in Sub-Saharan Africa (Grace et al., 2017). Other GIT protozoan parasites, such as Eimeria, can also cause high morbidity and mortality, particularly in young livestock, leading to significant economic losses (Reddy et al., 2015). Unfortunately, in poor countries, even highly cost-effective and technically feasible disease control programmes may not be supported because stakeholders are not willing to devote resources to the programme (Perry & Grace, 2009). Ghana is a developing country located in the sub-Saharan African region and GIT parasites are a major cause of morbidity in ruminants and mortality in small ruminants (Agyei et al., 2004; Blackie, 2014).

In Ghana, cattle and small ruminants (sheep and goats) are the main ruminants farmed and are important for the sociocultural and economic wellbeing of most people, particularly in rural communities (MoFA, 2016). There are three main systems of ruminant livestock production: extensive; intensive; and semi-intensive systems (Adzitey, 2013).

Ruminants are kept mainly in simple pens within or close to the farmers’ homestead and under the care of younger family members or in the case of cattle by hired herdsmen known as Fulanis (Asafu-Adjei & Dantankwa, 2001; Oppong - Anane, 2006). Ruminant farming is

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basically on a smallholder basis with an average flock size of 6 to 10 sheep or goats (Baah et al., 2012), and feeding is mostly agro-pastoral (Okantah et al., 1999).

Previous studies have identified various GIT parasites in cattle and small ruminants in Ghana with prevalences as high as 95.5% in cattle and 98.2% in sheep from the Coastal

Savannah and Semi-deciduous Forest zones respectively (Squire et al., 2013; Owusu et al.,

2016). The prevalence of GIT parasites in livestock can be influenced by several factors including climate and geographical conditions, vegetation, management and livestock density (Hansen & Perry, 1994). However, little is known about the risk factors associated with GIT parasite infections in livestock in Ghana, despite the high prevalences reported in many studies (Blackie, 2014; Owusu et al., 2016; Squire et al., 2018). Knowledge on parasite species and prevalence, and risk factors associated with infection is relevant for the development of appropriate integrated parasite management programmes (Krecek et al.,

2006), especially, as most Strongylid species affecting ruminants are now resistant to common anthelminthic compounds available globally (Whittaker et al., 2017). The purpose of the present study was therefore to determine the prevalence, distribution and factors associated with GIT parasite infections in cattle and small ruminants from selected communities in the Coastal Savannah zone of Ghana.

Materials and Methods

5.4.1 Study design, area and population

The study design, study area, study population, ethics and sample collection were as described in sections 2.2-2.5 and 2.7.

5.4.2 Questionnaire interview

A structured questionnaire was administered to the livestock farmers in English and the farmers’ local language with the help of interpreters, to assess their knowledge of livestock

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health and husbandry and farm management practices. Visual observations were also made to confirm questionnaire responses and assess farming practices as discussed in section 2.6.

5.4.3 Microscopic analysis

Microscopy was conducted as described in section 2.8.

5.4.4 Statistical analyses

Statistical analyses, including risk factor analyses, were conducted as described in section

2.11.

Results

5.5.1 Husbandry characteristics

In the present study, the average number of animals per herd was 70 cattle (range=7–263 cattle), 17 goats (range=1–100 goats) and 24 sheep (range=1–125 sheep) and 32.7% of the farms/household kept multiple ruminant species. Some farmers owned or cared for animals from more than one farm at different locations. There were more female cattle (72.6%) and small ruminants (77.3%) than males among the 830 animals sampled. The livestock were mostly (89.4%) kept under a semi-intensive farming system, where animals were housed in a simple structure and released to graze during the day with or without some form of feed supplementation. Only 6.0 and 4.6% were kept under extensive (no housing) and intensive

(housed with no grazing) systems, respectively. Grazing was mainly on communal fields with or without the supervision of a herder, and only 10 (1.2%) out of the total 782 animals that grazed were tethered. Many farmers made their young animals (12 months and below) graze close to the farm/household due to their vulnerability to injury, predation and theft.

The housing floors were mainly earthen (87.1%) with some concrete/cemented (10.0%) and wooden (2.9%) floors. Ruminant farmers routinely dewormed either all their animals

(41.9%), only young animals up to 12 months of age (19.9%) or only milking cows (1.2%), 149

using albendazole (73.3%), Ivomec (ivermectin) (2.2%) or herbal remedies (0.7%). Overall

29.2% of the farmers were members of a farmers’ association, mainly for financial support.

Livestock were mainly (62.4%) watered from open waters including dams, streams and rivers, but 33.0% of the livestock were watered from tap (treated) water, 2.8% from underground water and 1.8% from a combination of tap and open or underground water.

5.5.2 Prevalence and intensity of gastrointestinal parasites in livestock

Overall 754 (90.8%, 95% CI=88.7-92.7) of the 830 livestock were infected with at least one parasite, of which 268 (35.5%) were single infections and the remaining (64.5%) were coinfections with two to five parasites. More cattle (93.3%, 95% CI=90.0-92.7) were infected than small ruminants (89.2%, 95% CI=86.2–91.8). Ten GIT parasites (Eimeria spp.,

Strongylid spp., Toxocara spp., Trichuris spp., Schistosoma spp., Dicrocoelium spp.,

Paramphistomum spp., Fasciola spp., Moniezia spp. and Thysaniezia spp.) were detected in the livestock with Eimeria the most prevalent (78.4%, 95% CI=75.5–81.2) (Table 5.1).

Eimeria was also the most prevalent (80.5% and 77.1%) parasite in cattle and small ruminants, respectively. The prevalence of Strongylid nematodes, Toxocara, Fasciola,

Paramphistomum and Thysaniezia was significantly (p<0.05) higher in cattle than in the small ruminants, while the prevalence of Moniezia was higher in the small ruminants (Table

5.1). There was no significant difference in the prevalences of Eimeria, Trichuris,

Dicrocoelium and Schistosoma between cattle and small ruminants. At least one parasite was detected in 140 (98.6%, 95% CI=95.0–99.8) of the 142 herds.

The intensity of parasite infection in the livestock was generally low, ranging from 2-3

EPG for Schistosoma to 1–756 EPG for Toxocara (Table 5.1). All the small ruminants had light parasite infections (<800 O/EPG). Likewise, all the cattle had light infections (<200

O/EPG), except for a 2 months old calf that had a moderate Toxocara infection (756 EPG)

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and two calves (3 and 5 months old) that had a moderate infection with Strongylid nematodes

(206 and 239 EPG respectively). No heavy infections (800+ and 1200+ O/EPG) were detected in the livestock in the present study.

Table 5.1 Prevalence of GIT parasite infections in livestock from October 2014 - May 2015

Range: Oocyst/egg No. concentrations in Median Host positive 95% CI positive samples (O/EPG) (%) (O/EPG) Cattle Eimeria spp. 264 (80.5) 75.8 – 84.6 1 – 105 19 Strongyle spp. 216 (65.9) 60.4 -71.0 1 – 239 25 Toxocara spp. 8 (2.4) 1.1 – 4.7 1 – 755 3 Trichuris spp. 7 (2.1) 0.9 – 4.3 2 – 8 4 Schistosoma spp. 0 0 – 1.1 0 0 Dicrocoelium spp. 20 (6.1) 3.8 – 9.3 1 – 10 2 Paramphistomum spp. 121 (36.9) 31.7 – 42.4 1 – 63 16 Fasciola spp. 15 (4.6) 2.6 – 7.4 1 – 14 3 Moniezia spp. 3 (0.9) 0.2 – 2.6 1 – 9 1 Thysaniezia 48 (14.6) 11.0 – 18.9 1 – 13 4 Small ruminants (sheep and goats) Eimeria spp. 387 (77.1) 73.2 – 80.7 1 – 263 11 Strongyle spp. 254 (50.6) 46.1 – 55.1 1 – 728 23 Toxocara spp. 1 (0.2) 0 – 1.1 15 -15 15 Trichuris spp. 20 (4.0) 2.5 – 6.1 2 – 208 19 Schistosoma spp. 2 (0.4) 0 – 1.4 2 – 3 3 Dicrocoelium spp. 36 (7.2) 5.1 – 9.8 1 -142 9 Paramphistomum spp. 19 (3.8) 2.3 – 5.8 1 – 49 10 Fasciola spp. 8 (1.6) 0.7 – 3.1 1 – 10 3 Moniezia spp. 22 (4.4) 2.8 – 6.6 1 - 206 Thysaniezia spp. 0 0 – 0.7 0 0 Overall Livestock Eimeria spp. 651 (78.4) 75.5 – 81.2 1 – 263 14 Strongyle spp. 470 (56.6) 53.2 – 60.0 1 – 728 24 Toxocara spp. 9 (1.1) 0.5 – 2.0 1 – 756 83 Trichuris spp. 27 (3.3) 2.2 4.7 2 – 208 14 Schistosoma spp. 2 (0.2) 0 – 0.9 2 – 3 2.5 Dicrocoelium spp. 56 (6.7) 5.1 – 8.7 1 – 42 6 Paramphistomum spp. 140 (16.9) 14.4 – 19.6 1 – 63 15 Fasciola spp. 23 (2.8) 1.8 – 4.1 1 – 14 3 Moniezia spp. 25 (3.0) 2.0 – 4.4 1 – 206 18 Thysaniezia spp. 48 (5.8) 4.3 – 7.6 1 – 13 4 OPG: Oo/cysts per gram of faeces; EPG: eggs per gram of faeces. P-value (Level of significance): Eimeria = 0.245, Strongyle < 0.001, Toxocara = 0.002, Trichuris = 0.142, Schistosoma = 0.252, Dicrocoelium = 0.789, Paramphistomum < 0.001, Fasciola= 0.011, Moniezia = 0.011, Thysaniezia < 0.001

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5.5.3 Distribution of gastrointestinal (GIT) parasites amongst different age groups, locations (regions), farming systems and animal housing floors

Out of 830 livestock, 52.5% were adults (>12 months), 5.7% were 0–3 months and 41.8% were 4–12 months of age. Trichuris and Moniezia were more prevalent (8.5%, 95% CI=2.4–

20.4 and 10.6%, 95% CI=3.5–23.1) in young animals (0–3 months) than adults above 12 months, while Paramphistomum and Thysaniezia were more prevalent in the adults above

12 months (22.9%, 95% CI=19.1–27.2 and 7.6%, 95% CI=5.3–10.5) than young animals 0-

3 months and 4-12 months respectively (Table 5.2). The only two Schistosoma ova identified were from two animals aged 4-12 months and no Thysaniezia was found in animals 0-3 months. There was no significant association between the prevalences of Eimeria, Strongylid nematodes, Toxocara, Schistosoma, Dicrocoelium and Fasciola and age group.

Eimeria was more prevalent (95.3%, 95% CI 92.5–97.7) in livestock from the

Central region than the Volta and Greater Accra regions, respectively (83.1% 95% CI 78.2–

87.4 and 57.4%, 95% CI=51.4–6.3). However, the prevalence of Trichuris (5.3%, 95%

CI=3.0–8.6), Dicrocoelium (11.3%, 95% CI=3.0–8.6), Paramphistomum (23.0%, 95%

CI=18.3–28.4) and Moniezia (5.3%, 95% CI=3.0–8.6) was higher in livestock from Greater

Accra and no Schistosoma was found in livestock from the Central Region (Table 5.2). There was no significant association between the prevalences of Strongylid nematodes, Toxocara,

Schistosoma, Fasciola and Thysaniezia and region.

All the ten parasites identified were present in livestock kept under the semi-intensive system, but, only three parasites (Eimeria, Strongylid nematodes and Trichuris) were found in livestock from the intensive system. For livestock kept under the extensive system, five parasites (Eimeria, Strongylid nematodes, Paramphistomum, Fasciola and Thysaniezia) were identified with higher prevalences (except for Thysaniezia) and these prevalences were significantly (p<0.05) different from that of the semi-intensive system (Table 5.2).

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Similarly, all ten parasites identified were found in livestock kept on earthen floors but no Toxocara, Schistosoma, Dicrocoelium, Fasciola, Moniezia and Thysaniezia were detected in livestock kept on wooden floors, and no Toxocara, Schistosoma, Fasciola and

Thysaniezia were found in livestock kept on concrete floors. In addition, Strongylid nematodes, Paramphistomum and Thysaniezia were more prevalent (56.6%, 95% CI=55.9–

63.2; 19.1%, 95% CI=16.3–22.1 and 6.6%, 95% CI=4.9–8.7, respectively) in livestock kept on earthen floors than animals kept on wooden and concrete floors (Table 5.2).

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Table 5.2 Distribution of GIT parasites amongst different age groups, locations (regions), farming systems and animal housing floors.

Number Number positive (%) Category examined Toxoca- Trichu- Schisto- Dicrocoe- Param- Thysanie- Eimeria Strongyle Fasciola Moniezia (%) ra ris soma lium phistomum zia Age group 0-3 47 (5.7) 32 (68.1) 28 (58.6) 2 (4.3) 4 (8.5) 0 3 (6.4) 3 (6.4) 1 (2.1) 5 (10.6) 0 4-12 347 (41.8) 277 (79.8) 196 (56.5) 2 (0.6) 17 (4.9) 2 28 (8.1) 37 (10.7) 6 (1.7) 12 (3.5) 15 (4.3) >12 436 (52.5) 342 (78.4) 246 (56.4) 5 (1.1) 6 (1.4) 0 27 (6.2) 100 (22.9) 16 (3.7) 9 (2.1) 33 (7.6) P-value 0.185 0.915 0.072 0.002 0.248 0.584 <0.001 0.249 0.005 0.033 Region Greater Accra 282 (34.0) 162 (57.4) 154 (54.6) 5 (1.8) 15 (5.3) 1 (0.4) 32 (1.3) 65 (23.0) 12 (4.3) 15 (5.3) 14 (5.0) Volta 273 (32.9) 227 (83.2) 155 (56.8) 3 (1.1) 5 (1.8) 1 (0.4) 22 (8.1) 44 (16.1) 8 (2.9) 1 (0.4) 23 (8.4) Central 275 (33.1) 263 (95.3) 161 (58.5) 1 (0.4) 7 (2.5) 0 4 (1.5) 31 (11.3) 3 (1.1) 10 (3.6) 11 (4.0) P-value <0.001 0.643 0.275 0.049 0.608 <0.001 0.001 0.074 0.003 0.066 Farming system Intensive 38 (4.6) 28 (73.7) 4 (10.5) 0 3 (7.9) 0 0 0 0 0 0 Semi-intensive 742 (89.4) 574 (77.4) 434 (58.5) 9 (1.2) 24 (3.2) 2 (0.3) 58 (7.8) 130 (17.5) 19 (2.6) 26 (3.5) 47 (6.3) Extensive 50 (6.0) 49 (98.0) 32 (64.0) 0 0 0 0 10 (20.0) 4 (8.0) 0 1 (2.0) P-value 0.002 <0.001 0.583 0.117 0.888 0.025 0.016 0.043 0.204 0.131 Floor type Earthen 723 (87.1) 567 (78.4) 431 (59.6) 9 (1.2) 21 (2.9) 2 (0.3) 53 (7.3) 138 (19.1) 23 (3.2) 24 (3.3) 48 (6.6) Wooden 24 (2.9) 20 (83.3) 9 (37.5) 0 4 (16.7) 0 0 1 (4.2) 0 0 0 Concrete 83 (10.0) 64 (77.1) 30 (36.1) 0 2 (2.4) 0 5 (6.0) 1 (1.2) 0 2 (2.4) 0 P-value 0.808 <0.001 0.510 <0.001 0.862 0.358 <0.001 0.174 0.606 0.023 Total 830 (100) 651 (78.4) 470 (56.6) 9 (1.1) 27 (3.3) 2 (0.2) 58 (7.0) 140 (16.9) 23 (2.8) 26 (3.1) 48 (5.8)

P-value: Level of significance

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5.5.4 Risk factors associated with gastrointestinal parasite infection in livestock

To assess the risk factors associated with Eimeria infection in livestock, the predictors of infection (sex, varieties of ruminant species kept, region, type of farming management system, feed supplementation, grazing, frequency of cleaning housing floors and infection with Dicrocoelium or Strongylid nematodes) that yielded p-values <0.25 in the univariable analysis (odds ratio analyses and either Pearson’s chi squared test or Fisher’s exact two- sided test for independence) were entered into a binary logistic regression model. The factors which significantly (p<0.05) influenced Eimeria infection were Greater Accra region

(OR=5.92) and Dicrocoelium infection (OR=6.54), with a Hosmer-Lemeshow value

(p=0.572), which suggested that the model adequately fitted the data (Table 5.3).

The predictor variables (livestock species, flock size, farming management system, education, grazing, housing floor, source of drinking water and infection with Eimeria,

Moniezia, Fasciola, Paramphistomum, Toxocara or Trichuris) with p-values <0.25 were entered into a model. Interestingly, there was no association (p=0.998) between deworming and Strongylid nematode infections. In the final model, five factors (earthen floors, Eimeria,

Moniezia, Paramphistomum and Trichuris infections) significantly (p<0.05) increased an animal’s susceptibility to Strongylid nematode infections but animals under the care of farmers with secondary or higher levels of education presented less (OR=0.55) Strongylid nematode infections (Table 5.3).

For the risk factors associated with Paramphistomum infection, the predictors, age group, sex, livestock species, variety of ruminant species kept (multiple or single species), flock size, location, separating young (<12 months) animals from their mother, feed supplementation, grazing, housing floor, source of drinking water and infection with

Thysaniezia, Fasciola, Dicrocoelium or Strongylid nematodes Strongylid nematodes were offered to a binary logistic regression model. The factors, keeping multiple ruminant species, flock size (> 25 animals), Greater Accra Region, earthen floors and infection with Fasciola, 155

Dicrocoelium and Strongylid nematodes significantly influenced the likelihood of infection with Paramphistomum (Table 5.3).

Table 5.3 Binary logistic regression analysis to determine risk factors associated with parasite infections in livestock.

Factors β OR (95% CI) P-value Eimeria Greater Accra Region 1.778 5.92 (4.09-8.57) <0.001 Dicrocoelium infection 1.878 6.54 (3.54-12.08) <0.001 Constant -1.379 Strongylids Secondary or higher education -0.591 0.55 (0.35-0.87) 0.010 Earthen floor 0.877 2.40 (1.40-4.13) 0.001 Eimeria infection 0.791 2.21 (1.44-3.39) <0.001 Moniezia infection 1.909 6.75 (1.43-31.83) 0.016 Paramphistomum infection 0.866 2.38 (1.41-4.01) 0.001 Trichuris infection 1.694 5.44 (1.16-25.48) 0.032 Constant -1.010 Paramphistomum Multiple ruminant species 0.432 1.54 (1.02-2.32) 0.039 Flock size over 25 1.273 3.57 (2.30-5.54) <0.001 Greater Accra Region 0.561 1.75 (1.15-2.66) 0.009 Earthen floor 2.131 8.42 (2.21-35.11) 0.003 Fasciola infection 1.463 4.32 (1.71-10.92) 0.002 Dicrocoelium infection 0.721 2.06 (1.05-4.04) 0.036 Strongyle infection 1.222 3.39 (2.12-5.43) <0.001 Constant -7.010

β: Intercept; P-value: level of significance; OR: Odds ratio; CI: 95% Confidence interval

Hosmer-Lemeshow value (p-value): Eimeria = 0.572; Strongylids = 0.677; Paramphistomum = 0.720

Discussion

Gastrointestinal parasites (GIT) were common in cattle and small ruminants from the Coastal

Savannah zone of Ghana, with at least one parasite detected in 90.8% of the livestock. This prevalence was higher than a similar study in cattle, sheep and goats from Nigeria, where

Eimeria was also the most prevalent parasite identified (Agbajelola et al., 2015). In the present study, the overall GIT prevalence of 93.8% in cattle was consistent with a previous study in cattle from this same zone (Squire et al., 2013a), but in small ruminants, the prevalence of 89.2% was lower than 98.2% previously reported in sheep from Ayeduase in 156

the semi-deciduous forest zone (Owusu et al., 2016). The Semi-deciduous Forest zone of

Ghana has relatively higher rainfall with denser vegetation than the Coastal Savannah zone

(Oppong - Annane, 2006), and climate is believed to affect the survivability of parasites

(Okulewicz, 2017). Hence, variations in seasonal or climatic conditions could contribute to the difference in prevalences. Seasonal factors such as rainfall, temperature and humidity have previously been reported to influence the level of larval pasture infectivity and prevalences of GIT parasite in the Coastal Savannah zone of Ghana (Agyei, 1997) as well as in Ethiopia and India (Haile et al., 2018; Rashid & Irshadullah, 2018).

In the present study, formol ether concentration was used despite the McMaster counting technique being widely used for faecal egg/oocyst counts (Hansen & Perry, 1994), as the McMaster technique is not suitable for some parasite eggs with higher densities, such as trematodes/flukes (Cringoli et al., 2004; Vadlejch et al., 2011). Thus, the use of the formol-ether concentration technique may have contributed to the generally light infection of parasites detected in the livestock. The previous study in cattle from the same study area that used the McMaster technique reported higher numbers of OPG/EPG for parasites such as Eimeria (0 – 24.6 x 104 OPG) and Moniezia (0 – 40.2 x 103 EPG) (Squire et al., 2013a), compared to 1 – 105 OPG for Eimeria and 1 – 9 EPG for Moniezia in cattle from the present study. Higher EPG have also been reported with the McMaster technique in other studies

(Owusu et al., 2016; Getahun et al., 2017), therefore it is important to consider the sensitivity of faecal egg counting techniques when estimating the intensity of parasites in livestock faeces. It is also necessary to consider the effect of seasonal conditions on parasite’ intensity, as the previous study in the Coastal Savannah zone was conducted in the rainy season (June to July 2010), whilst the present study was mainly conducted during the dry season (October to February 2014).

Although parasite intensity was basically light, at least one parasite was detected in almost all (99.0%) the sampled herds, which suggests that GIT parasites are widespread. To

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reduce the cost of chemotherapeutic treatments, routine treatments could be minimised during the dry seasons and when infection levels are low, as many GIT helminths of ruminants have developed resistance to the common anthelmintic drugs (Geurden et al.,

2014; Borges et al., 2015). Instead, improved grazing management and hygienic husbandry practices could be adopted to reduce the spread of infections amongst herds, particularly those sharing communal pastures with different herds. Other proposed non-chemical control measures include selection of animals for resistance, vaccination, natural plant medicine and good nutrition (Greer, 2008; Hoste & Torres-Acosta, 2011).

In the present study, Trichuris and Moniezia infections were higher in the young animals (0-3 months), while, Paramphistomum and Thysaniezia were higher in adult animals

(above 12 months). However, young animals are supposed to be more susceptible to infections with GIT parasites for several reasons including naïve immunity, hyper- responsiveness and high nutrient demands for growth which may compete with the nutrients available for the development of an immune response (Coop et al., 1995; Colditz et al.,

1996). Therefore, the higher prevalence of Paramphistomum and Thysaniezia in the adult livestock may be because many farmers limited the extent to which the young animals grazed on communal pastures due to their vulnerability. This practice may also have reduced the young animals’ exposure to parasite vectors such as the snail intermediate hosts of

Paramphistomum.

Several factors including variations in husbandry practices and environmental conditions could have contributed to the higher prevalences of Trichuris, Dicrocoelium,

Paramphistomum and Moniezia in the Greater Accra region and Eimeria in the Central region. Although all three regions sampled were in the coastal region, there may be variations in environmental factors such as vegetation index, soil type, proximity to water and the level of environmental contamination, which was not included in the present study.

To better understand this finding, further studies that include climatic parameters such as

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rainfall, humidity and temperature and environmental parameters from all three regions should be conducted. This will help provide evidence for recommendations on appropriate control and prevention in relation to farm location.

It is not surprising that, all ten parasites (Eimeria spp., Strongylid spp., Toxocara spp., Trichuris spp., Schistosoma spp., Dicrocoelium spp., Paramphistomum spp., Fasciola spp., Moniezia spp. and Thysaniezia spp.) identified were present in livestock kept under the semi-intensive system and on earthen housing floors but fewer parasite species were found in livestock raised under the intensive or extensive systems, and in farms that used concrete and wooden floors for their animals. The semi-intensive system, which was also the most common farming system, is a combination of both the extensive and intensive systems, hence, parasites from grazing fields can easily be transferred to the farms by infected animals and transmitted to susceptible animals in the herd. However, the prevalence of most parasites found in livestock kept in the extensive system was relatively higher than the other that of the other management systems, confirming that infections were mainly acquired from the grazing fields or pastures. Earthen floors, also the most common housing floor type, probably provided better conditions for the development of parasites than the concrete and wooden floors. Appropriate levels of moisture, temperature and humidity are required for the development of most parasitic oocysts and eggs to become infective (Taylor et al., 2016) and these conditions are more likely to be met on earthen floors.

Eimeria causes coccidiosis in animals and leads to economic losses in livestock production. In the present study, livestock from the Greater Accra Region and those infected with Dicrocoelium were more susceptible (OR=5.92 and OR=6.54) to Eimeria infections than livestock from the central and Volta regions than those without Dicrocoelium infections. This was different from the observations from another study in cattle from

Pakistan, where a closed housing system and non-cemented floors were associated with increased Eimeria infection and open housing systems and partially cemented floor types

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were associated with lower Eimeria infections (Rehman et al., 2011). These differences are likely associated with the associated husbandry systems and management practices adopted.

Although, early anti-coccidia treatment has been found to reduce Eimeria infection and prevent clinical disease (Mundt et al., 2003; Iqbal et al., 2013), changes in management practices were found to be effective in the control Eimeria and prevention of disease, as the use of prophylactic anti-coccidial drugs is sometimes not sustainable (Berriatua et al.,

1994). Farmers therefore require a good balance between the use of anti-coccidial treatments and improving management practices for effective control of Eimeria infections and prevention of the severity of coccidiosis.

Parasite co-infections are a common occurrence in tropical and subtropical regions and may cause more or less harm to hosts, depending on the parasites’ interactions and infection mechanisms (Supali et al., 2010; Alizon & Van Baalen, 2008). In the present study, co- infections were common in livestock, therefore, in addition to earthen floors, infections with

Eimeria, Moniezia, Paramphistomum or Trichuris significantly increased an animal’s susceptibility to Strongylid nematode infections. Farmers with secondary or higher levels of education had a lower prevalence of Strongylid nematode infection in their animals, because they may have easier access to information on farm and health management than those with relatively lower levels of education, as most resources available in the country are in the

English. Education is therefore necessary for proper animal health management and good husbandry practices, so farmers should be encouraged to improve their level of education or be trained informally through farmer associations. Previous studies have identified lack of knowledge about management practices, animal health and access to veterinary drugs, uncertified veterinary drugs on the market and the inability of herdsmen or farmers to comprehend instructions on veterinary drugs due to a lack of a formal education are some of the major constraints to livestock production and veterinary practices in Ghana (Turkson &

Naandam, 2003; Addah et al., 2009).

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Animals with other parasite infections (Fasciola, Dicrocoelium and Strongylid nematodes) were more prone to Paramphistomum infections in the present study. Also, multiple ruminant species, a flock size of over 25 animals, Greater Accra Region and earthen floors increased an animal’s exposure to Paramphistomum infection. Keeping multiple ruminant species or larger number of animals may be challenging for some farmers, as it requires more skills and resources as compared to single ruminant species and fewer animals.

This may lead to poor animal conditions which result in increased susceptibility to infections. The majority of the world’s poor livestock keepers are found in Sub-Saharan

Africa (Grace et al., 2017), which includes Ghana. In addition to factors such as climate, vegetation and geographical conditions, livestock density is also known to influence the prevalence on GIT parasites (Hansen & Perry, 1994).

Observations from the present study suggests some interactions among co-infecting species such as Eimeria and Dicrocoelium, Trichuris and strongylids, and Fasciola and

Paramphistomum. Although no report was found to directly support this present findings, it is evident that, species co-infection can increase the suceptibility of hosts to other parasites.

Forr example, Heligmosomoides polygyrus infection in wood mice was able increase susceptibility to other helminth species, particularly those associated with the intestine

(Behnke et al., 2009) and H. contortus infection could suppress the host’s immunity to T. colubriformis (Lello et al., 2018). Direct or indirect interspecies antagonism may also occur through inhibition of parasite transmission (Tunholi et al., 2017), attentuation of host symptoms (Nacher et al., 2002) or a decrease in parasitaemia (Lello et al., 2018). The finding from this study therefore iterates the need for further research on parasite interactions among co-infecting species which can lead to the development of novel preventive strategies.

To improve livestock health and production in Ghana, good sanitation, grazing management, nutrition and other non-chemical control strategies should be promoted to reduce the spread of GIT parasites in the animals, particularly during the dry season when

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parasite intensity is low, since chemotherapeutic treatments may be ineffective and costly due to the high likelihood of reinfection from communal grazing pastures and resistance of many parasites to anthelminthics.

Conclusions

Cattle and small ruminant livestock in Ghana are infected by a variety of GIT parasites, the majority of which were co-infections. Although parasite intensity was generally low, infection was widespread among individual animals and across herds. Many parasite infections were age and location related and were influenced by the type of farming system and type of housing flooring used for the livestock. Several factors, including co-infections, strongly increased the predisposition of livestock to parasites, but education seemed to be a way to mitigate the level and spread of parasite infections among livestock in the zone.

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Chapter 6. General Discussion

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Introduction

This thesis estimated the prevalence of GIT parasites, including protozoans, nematodes, trematodes and cestodes in farmers and their cattle, sheep and goats in Ghana. The species and subtypes of Cryptosporidium, Giardia and Trichostrongylus in farmers and their livestock were identified, and zoonotic species potentially circulating between them were determined. This research is the first molecular characterisation of Cryptosporidium and

Giardia duodenalis in livestock and Trichostrongylus in both humans and animals in Ghana.

The findings from this research are relevant for understanding the transmission of zoonotic parasites in Ghana and elsewhere. This thesis also contributed to existing scientific knowledge on GIT parasites, particularly from Africa. Genetic data generated from this thesis, including potentially novel species were added to the existing genetic data in

GenBank.

The importance of molecular tools (qPCR, conventional and nested PCR, Sanger sequencing and phylogenetic analysis) in epidemiological studies was demonstrated in this thesis. By genotyping Cryptosporidium, Giardia and Trichostrongylus and subtyping the species identified (Cryptosporidium and Giardia), the zoonotic potential, age distribution and regional distribution of parasite species and subtypes were determined, which provided a better understanding of the epidemiology of these parasites.

In addition, the risk factors associated with specific GIT parasite infections in livestock was determined in this thesis, which is necessary for the formulation of appropriate prevention and control measures and for more targeted and cost-effective treatment strategies.

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Public health implications of gastrointestinal parasites identified in Ghana

In the areas studied, Cryptosporidium (8.4%), G. duodenalis (10.5%), hookworms (13.7%),

Trichostrongylus (9.5%), S. mansoni (1.1%), S. haematobium (1.1%) and Diphyllobothrium latum (1.1%) were identified in the livestock farmers, all of which have significant public health implications. In Ghana, these parasites have been associated with diarrhoea and malnutrition, particularly in children (Eibach et al., 2015; Krumkamp et al., 2015; Ayeh-

Kumi et al., 2016), although these and other symptoms have been reported in other groups of people including pregnant women (Yatich et al., 2010).

As recommended by the WHO (WHO, 2017e), there is an ongoing mass deworming exercise against intestinal helminths among school aged children in Ghana. This exercise involves an annual single dose of albendazole (400 mg) and has been effective in reducing the faecal egg and larval counts of intestinal nematodes in treated individuals (Ziem et al.,

2006b; Humphries et al., 2013). The annual anthelminthic treatment exercise has also been proven to enhance the appetite and physical fitness of school children in Kenya (Crompton

& Nesheim, 2002).

Accurate and sensitive diagnostic tests are essential to accurately assess the impact of benzimidazole programs and identify emerging benzimidazole resistance. The Kato–Katz technique is still the most commonly used method for STH diagnosis, despite the low sensitivity, which requires multiple samples per individual to be examined (Nikolay et al.,

2014). Therefore, more sensitive qPCR-based assays, although more expensive, should be used, particularly for light-intensity settings approaching transmission elimination (Clarke et al., 2018).

Some studies have questioned the efficacy of single dose benzimidazole treatments and reported low efficacy against hookworm and Trichuris trichiura in school-aged children

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in Lao PDR (Soukhathammavong et al., 2012) and decreased anthelmintic efficacy of single dose albendazole (400 mg) against A. lumbricoides in Rwandan schoolchildren (Krücken et al., 2017). Another study used qPCR to assess the efficacy of albendazole against N. americanus and Ascaris spp. in Timor-Leste and reported that while albendazole had a cure rate of 91.4% against Ascaris, for N. americanus, the cure rate was only 58.3% (Vaz Nery et al., 2018).

Anthelmintic resistance is a common problem in nematodes of veterinary importance and therefore the recent increase in mass control programmes in African countries against

STH’s, relying almost exclusively on benzimidazole anthelmintics, could select for anthelmintic resistance (Vercruysse et al., 2011). It is therefore important that monitoring programs are in place so that resistance is detected as it emerges in order to implement mitigation strategies including drug combinations, to ensure that the effectiveness of the few existing anthelmintic drugs is preserved (Vercruysse et al., 2011). Praziquantel is the drug used against schistosomiasis in most endemic areas in Ghana (van der Werf et al., 2003;

Campbell et al., 2018), which is also effective against other trematode and cestode infections

(Jeri et al., 2004; Keiser & Utzinger, 2004; Fürst et al., 2012b; WHO, 2018a). However as with benzimidazoles, long term reliance on praziquantel would likely lead to resistance and new candidates including ozonides and oxamniquine are currently being trialled (Panic &

Keiser, 2018).

New drug targets for Cryptosporidium are urgently needed, as the only FDA‐ approved drug, nitazoxanide does not provide benefit for malnourished children and immunocompromised patients (Sparks et al., 2015). Also, a successful vaccine against cryptosporidiosis would be of great benefit for both public and veterinary health (Avendaño et al., 2018). Current treatments for Giardia include nitazoxanide and 5-nitroimidazole compounds such as metronidazole, tinidazole and albendazole (Miyamoto & Eckmann,

2015; Einarsson et al., 2016), however, resistance has been reported to most anti-giardial 166

drugs (Ansell et al., 2015). Currently, auranofin (Ridaura), a US Food and Drug

Administration (FDA) approved drug for the treatment of rheumatoid arthritis, is in clinical trials as an anti-parasitic drug against Giardia (and Entamoeba histolytica) and shows potential as a broad spectrum anti-parasitic drug (Capparelli et al., 2017).

Although chemotherapy has been effective in reducing the impact of schistosomiasis and soil-transmitted helminths (STHs) infections in Ghana, these parasites remain prevalent in the country. In addition to resistance, other factors including recurrent contamination of the environment with helminth eggs and therefore reinfection of treated individuals is a factor. Previous studies in Ghana identified that not using latrines, not wearing shoes or appropriate footwear, not scrubbing nails during hand washing, the source of drinking water, farming, poor nutritional status, co-infection with malaria and increased serum levels of antibodies against hookworm, were risk factors for STH infections (Humphries et al., 2011;

Adu-Gyasi et al., 2018), confirming that transmission in mainly through environmental contamination, while reduced immunity due to diseases or other infections and poor nutrition increased the susceptibility of individuals to infection.

Almost all the parasites identified in the farmers in this thesis, can be transmitted through the oral-faecal route or skin penetration, except for D. latum, which is a fish tapeworm (Mahmud et al., 2017). Also, the majority of the parasites identified in the farmers have also been detected in water or on vegetables in Ghana. These include the identification of Cryptosporidium oocysts, A. lumbricoides eggs and S. stercoralis larvae in drinking water

(Labite et al., 2010; Petersen et al., 2014; Ndur et al., 2016), STH eggs in wastewater that was commonly used for vegetable irrigation (Amoah et al., 2016; Amoah et al., 2018),

Cryptosporidium oocysts, Giardia cysts and hookworm eggs on vegetables (Duedu et al.,

2014; Petersen et al., 2014) and Cryptosporidium oocysts on nuts (Cyperus esculentus L)

(Ayeh-Kumi et al., 2014). Thus, in addition to chemotherapy, health education, improved sanitation and availability of good drinking water can effectively reduce and prevent GIT 167

parasite transmission in Ghana. However, as discussed in section 1.6, this requires the

Ghanaian government to commit adequate resources to not just improved sanitation but also alleviation of malnutrition and HIV.

Veterinary implications of gastrointestinal parasites to Ghanaian small holder livestock industry

Livestock farming in Ghana is mainly conducted by small holders with a few commercial farms. An important finding of this research was the overall high prevalence (94.3%) of GIT parasites in livestock in the Coastal Savannah zone. In total, 12 parasites; Eimeria (78.4%),

Strongylids (56.6%), Cryptosporidium (30.8%), G. duodenalis (11.0%), Paramphistomum

(16.9%), Dicrocoelium (6.7%), Thysaniezia (5.8%), Moniezia (3.0%), Trichuris (3.3%),

Fasciola (2.8%), Toxocara (1.1%), and Schistosoma (0.2%) were identified in the livestock.

Eimeria was most prevalent followed by Strongyle nematodes in cattle, sheep and goats. In

Ghana, GIT parasites remain an important cause of loss in the livestock industry, as similarly high prevalences have been reported in the past (Assoku, 1981, 1983; Squire et al., 2013a;

Squire et al., 2013b; Blackie, 2014; Owusu et al., 2016).

Despite the introduction of the Community Animal Health Workers (CAHWs) system to compensate for the shortage of veterinarians in the country, the veterinary service delivery system is unable to meet the demands of livestock keepers, who as a consequence, sell their sick animals or cater for their animals themselves (Addah et al., 2009; Turkson,

2009; Mockshell et al., 2014). Meanwhile, most farmers lack knowledge about animal health, management practices and lack access to veterinary drugs (Turkson & Naandam,

2003) and where veterinary drugs are available, some are uncertified by the Ghana Food and

Drugs Authority by law or with instructions in English, which are not comprehensible to farmers or herdsmen without a formal education (Addah et al., 2009). It is therefore

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necessary to train more animal health workers and especially livestock farmers on effective animal health management, in order to sustain the livestock industry in Ghana.

As many livestock keepers also rely on cheap and readily available but unvalidated ethnoveterinary medicine for the treatment of their animals (Turkson & Naandam, 2002;

Wanzala et al., 2005), research on the of the efficacy and safety of ethnoveterinary medicine is also important, particularly as most strongyle species are resistant to common anthelminthic compounds (Whittaker et al., 2017).

The risk factors associated with parasites identified in these livestock were also reported in this thesis. Location (Greater Accra region), earthen housing floor, keeping multiple ruminant species and infection with other GIT parasites increased the risk of GIT parasite infection, while education (secondary or higher level) decreased the likelihood of acquiring infection. It may be necessary for farmers and veterinarians to focus on specific parasites based on the location of the farms. For example, where farms are located near water bodies or in marshy areas, the animals may be more prone to trematode infections because, the snail intermediate hosts of many trematodes require water to survive and transmit parasites (Taylor et al., 2016). It is also important to note that, a major outcome of GIT parasite infection in an animal is to make the animal susceptible to other infections as observed in the present study. Parasite control measures should therefore include education of farmers on appropriate animal health and husbandry practices, targeting a broad spectrum of parasites and improving the resistance of the animal against further infections through measures such as selecting animals for resistance, breed improvement and good nutrition.

Although, providing better nourishment can be a challenge for resource poor farmers, this can be achieved by supplementing the natural grass or forage with available local food supplementation and cultivating highly nutritious pastures. For example, suckling lambs grazed on protein rich pasture supplements such as Brachiaria spp. were found to be more

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resistant to GIT helminth infections than the control group (de Melo et al., 2017). It is known that well-nourished animals better survive the effects of worm infection (Coop & Kyriazakis,

1999; Greer, 2008). Frequent removal and cleaning of housing floors and where possible rotational housing can also reduce the level of contamination and reinfection in animals.

Also, rotational grazing has been found to reduce parasite infections in ruminant livestock

(Burke et al., 2009). However, because of the communal and nomadic grazing commonly practiced in Ghana, rotational grazing can only be achieved by commercial farmers with privately owned grazing fields or through community efforts. However, animals that were kept on earthen floors showed a higher variation of GIT species as compared to those housed on concrete and wooden floors. Frequent removal and cleaning of floors could therefore be advantageous, and if possible, animals could be kept on concrete or wooden floors which seemed to offer some protection against GIT parasite infections. Farmers are also encouraged to cultivate their own pastures and reduce the interaction between their animals and those from other herds as in the semi-intensive and extensive systems, which seemed to predispose animals to GIT parasite infections compared to the intensive system. Where changes in type of housing floor or cultivation of privately-owned pastures is not achievable, due to the challenges in accessing credit and acquisition of land for agriculture, maintaining good hygienic practices and improving animal nutrition could to reduce the effect of GIT parasite infections on livestock.

Zoonotic transmission of gastrointestinal parasites in farmers and their ruminant livestock

Another important finding of this research was the identification of zoonotic species in farmers and their livestock. The zoonotic C. parvum was identified in both a farmer and a goat, and the 11cA5G3q subtype, which had previously been reported in children in Ghana, was identified in a single goat. This is the first identification of the IIc subtype previously thought to almost exclusively infect humans in a goat. Apart from C. ryanae and C. baileyi 170

that are not zoonotic, all the other Cryptosporidium species (C. ubiquitum, C. andersoni, C. bovis, and C. xiaoi) identified in livestock from this research are of zoonotic importance, as they have been also reported in humans in other countries (Feng et al., 2018). Giardia duodenalis assemblage A and B was identified in the farmers and Assemblage E was identified in livestock. Of these, assemblages A and E are both zoonotic, infecting both humans and animals (Abdel-Moein & Saeed, 2016; Zahedi et al., 2017b).

This study further detected other zoonotic parasites, including T. colubriformis in farmers and their cattle, sheep and goats, T. axei in livestock only and three potentially novel

Trichostrongylus spp./sub-species in the farmers with 96.6 to 99.2% similarity to T. probolurus found in sheep. All the Trichostrongylus species identified (except for the potentially novel species/subspecies which requires further investigation), are zoonotic and have been previously reported in humans and animals.

In addition to these findings, Fasciola spp., Dicrocoelium spp., Moniezia spp. and

Schistosoma spp. were also identified in livestock in the present study and are potentially zoonotic, as F. hepatica and F. gigantica, D. dendriticum, D. hospes, M. expansa, S. curassoni, S. bovis and S. mattheei can all be transmitted between humans and livestock (see

Chapter 1, Table 1.6). However, this research did not conclusively identify all parasites to the species level and therefore further studies are required using molecular tools in addition to microscopy to better understand the zoonotic significance of these parasites in Ghana.

The majority of the zoonotic parasites reported in farmers and livestock in this thesis have been previously reported in drinking water, wastewater and fruits in Ghana (see

Chapter 6.2) and can easily be transmitted between animals and humans. The treatment and control of such zoonotic pathogens therefore requires a “One Health” which recognises that the health of people, animals and the environment are connected, particularly with zoonotic diseases that can be transmitted from vertebrate animals to humans and vice versa (WHO,

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2017d). Thus, co-operation between animal and public health workers will maximise the limited human and capital resource available to offer better strategies towards the control of zoonotic parasites in a more be cost effective manner.

Limitations

A major limitation of this thesis was the small number of farmers sampled, as about 38% of the farmers declined to supply faecal samples due to social, religious and cultural reasons.

Thus, future studies should include larger numbers of human faecal samples including the farmer’s children, as this will provide a more meaningful analysis of the zoonotic transmission of parasites to farmers and their families. This could be accomplished by extended education of farmers prior to sampling, recruitment of participants from the various farmers associations, seeking the assistance of community leaders and also, recruiting children from schools in addition to the farms or households.

The circumstances surrounding the selection of animals for the present study including selection of animals based on the farmers’ willingness to participate and the use of inadequate records of farms and animal population in the zone could have introduced a bias to the study population. For a true representation of the population, a comprehensive census on livestock numbers, farms and their distribution within the regions must be conducted prior to selection of study participants and animals. This will however be time consuming and expensive but should be considered when planning similar studies in the future.

The formol-ether concentration microscopy used in the present study was limited in its ability to sensitively determine the faecal egg or oocyst count of parasites detected. This method was chosen because it could identify a variety of parasites larvae, eggs, cysts and oocysts and the formalin solution reduces the risk of laboratory-acquired infection by killing other organisms. In future, similar research should use a combination of faecal concentration

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and microscopy techniques as well as qPCR tools for effective detection of parasites and estimation of the intensity of infection.

Another limitation of the present study is that due to Australian quarantine regulations, only DNA samples and not faeces could be imported from Ghana. This meant that it was not possible to follow up microscopic examinations made in Ghana or extract additional DNA for further genetic analysis. As a result of this, the majority of the parasites were identified by microscopy only in Ghana (Strongylids, Trichuris, Toxocara, Fasciola,

Dicrocoelium, Schistosoma, Paramphistomum, Moniezia and Thysaniezia) and due to insufficient DNA sent to Australia, could not be conclusively identified to species level. It also meant that the morphological features of the potentially novel Trichostrongylus species could not be examined further, as the faecal samples could not be accessed. Also, attempts to characterise Eimeria species by PCR and Sanger sequencing analysis was not successful as only mixed infections were identified. DNA extraction of morphologically identical, sporulated oocysts from the faeces using micromanipulation was also not possible. Future studies require the use of a more efficient method such as the Next Generation Sequencing

(NGS). Unlike the Sanger method, at adequate sequencing depths, next generation sequencing (NGS), can resolve mixtures of amplicons, thanks to the massive parallelisation of the sequencing reaction (Paparini et al., 2017). While it is widely used for profiling bacterial communities, transition to eukaryotic parasites has been slow. So far, two NGS- based studies have been conducted on Eimeria from wildlife (Vermeulen et al., 2016) and poultry (Hinsu et al., 2018) using the Illumina MiSeq platform, based on amplicon NGS of a fragment of the 18S rRNA locus. These studies successfully identified and distinguished mixed Eimeria infections and should be used to identify the extent of mixed Eimeria infections in Ghanaian livestock. Other applications include determining levels of naturally occurring parasite population diversity and assessment of the impact of treatments such as chemoprophylaxis and vaccination.

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This thesis has however provided comprehensive evidence of the GIT parasites of veterinary and public health significance in Ghana, their epidemiology and risk factors for infection and recommendations towards appropriate control. The zoonotic significance of some parasites identified was also established with the help of molecular tools. Like other countries located in the tropics, the climatic and environmental conditions in Ghana are conducive for development and transmission of many parasites which cause diseases in humans and animals. The research protocols for this thesis, which was the first to use molecular tools to identify parasites in both humans and animals in Ghana, can be applied for other pathogens of zoonotic significance.

Future directions

The study approach in this thesis can be applied for other pathogens with zoonotic potential.

However, for future studies, larger community-based studies including not just the farmers but their family members including children and their animals from a larger study area will provide more information on the transmission of GIT parasites in Ghana. It will also be beneficial to include a longitudinal study that will provide better data on the impact of parasites on faecal consistency, body condition, growth rate, nutrition and other parameters relevant to determine the economic impact of parasites on the livestock industry.

As the three potentially novel Trichostrongylus species were not fully characterised, future research targeting other loci apart from the ribosomal ITS-2 locus used in the present study, followed by phylogenetic analysis as well as microscopic examination of larval and adult stages are required to identify the species of Trichostrongylus in humans in Ghana.

Currently, few molecular studies have been conducted on Trichostrongylus and the ITS-2 locus has been most commonly used to detect and distinguish between species at the egg stage or third stage larvae of the parasite (Hoste et al., 1995; Heise et al., 1999; Lattès et al.,

2011; Sato et al., 2011; Ghasemikhah et al., 2012; Phosuk et al., 2013; Tan et al., 2014;

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Gholami et al., 2015; Sharifdini et al., 2017), with the ITS-1 (Yong et al., 2007; Phosuk et al., 2013), the 18S and 28S loci (Langrová et al., 2008) and the β-tubulin gene (Ramünke et al., 2016) used less frequently. Previous studies have reported low levels of intra-specific sequence variation (0-1.8%) within Trichostrongylus isolates, and inter-specific variations of 1.3–7.6% between species (Hoste et al. 1995; Heise et al. 1999; Sharifdini et al., 2017).

Future studies should amplify and sequence the β-tubulin gene to better describe the new species.

Molecular characterisation of the Fasciola species identified in the present study should also be conducted. Although Fasciola adult worms in the liver and bile duct of animals in Ghana and Fasciola eggs from human and animal samples can be morphologically differentiated (Assoku, 1981; Squire et al., 2013a; Futagbi et al., 2015;

Duedu et al., 2015a), molecular tools are required to confirm the species present in the country. Molecular detection and differentiation of Fasciola larvae, adult worms and eggs

(rarely) can be achieved by PCR and sequencing analysis of the nuclear ribosomal ITS and

28S gene, and mitochondrial DNA makers such as cytochrome C oxidase subunit I (COI) and mitochondrial NADH dehydrogenase sub-unit 1 (nad1) gene (Van Herwerden et al.

2000; Marcilla et al., 2002; Itagaki et al., 2011; Le et al., 2012; Choi et al., 2015; Galavani et al., 2016). Fasciola gigantica is considered to be the major cause of fasciolosis in Africa

(Ichikawa-Seki et al., 2017), however, both F. hepatica and F. gigantica were reported in sheep from Egypt (Amer et al., 2016). Fasciola hepatica has been reported in humans in

Ghana by microscopy analysis (Duedu et al., 2015a) and the only previous molecular study in Ghana identified F. gigantica only, from the liver of five cattle from the Upper East and

West regions (Addy et al., 2018). Further molecular studies are therefore required to confirm species of Fasciola in humans and animals in Ghana.

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6.7 Conclusions

This study is crucial to understanding the potential for transmission of GIT parasites between humans and animals in Ghana. The zoonotic C. parvum and T. colubriformis were prevalent in farmers and their ruminant livestock in Ghana, suggesting that these parasites circulate between humans and animals in the country. Trichostrongylus axei, C. ubiquitum, C. xiaoi,

C. andersoni, C. bovis and G. duodenalis assemblage E, which have been reported in humans in other parts of Africa, were also identified in the ruminants and can therefore be transmitted to humans. In addition, three potentially novel Trichostrongylus species/subspecies that clustered with T. probolurus from sheep, were identified in the farmers and require additional studies to determine their zoonotic source. Furthermore, the GIT helminths,

Dicrocoelium, Fasciola, Schistosoma and Moniezia which may include zoonotic species were present in the ruminants and can likewise be potentially transmitted to humans.

Eimeria, Thysaniezia and Toxocara were also found in the ruminants, while D. latum, S. mansoni, S. haematobium and the hookworm N. americanus were found in the farmers and are known to have significant public or veterinary health implications. In conclusion, farmers and their ruminant livestock in Ghana are infected with a wide variety of GIT parasites, the majority of which are zoonotic, and by using molecular tools, a more comprehensive understanding of the zoonotic potential of some of these parasites were established.

This study also revealed a widespread but predominantly low level of parasitic infections in ruminants in the agroecological zone during the dry season when sampling was conducted. There was also a strong association between the prevalence of specific GIT parasites with age of animals, regional location of farms and type of farming system and housing floor, which must be considered in the planning of GIT parasite control measures.

Finally, the risk factors associated with of GIT parasites infection in ruminants in

Ghana was also assessed and infection with other parasites (coinfections) was more likely to increase the susceptibility of animals to infections. Also, the location of the farm (region),

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floor type, multiple ruminant species and flock size were identified as potential risk factors associated with specific GIT parasite infections in the ruminants, while farmers with secondary or higher levels of education were more likely to have fewer animals with

Strongylid infections. Hence educating farmers and training them on improved animal husbandry and health management practices can reduce the prevalence of GIT parasites in livestock and therefore the public health and veterinary impact of these parasites in farmers and their livestock in Ghana.

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APPENDICES

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Appendix A Cryptosporidium and Giardia in Africa – current and future challenges

Squire, S. A. and Ryan U. (2017). Review: Cryptosporidium and Giardia in Africa – current and future challenges. Parasites and Vectors, 10, 195.

Note: due to copyright restrictions, the appendix above can be found at: https://doi.org/10.1186/s13071-017-2111-y

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Appendix B Molecular characterization of Cryptosporidium and Giardia in farmers and their ruminant livestock from the Coastal Savannah zone of Ghana

Squire, S. A., Yang, R., Robertson, I., Ayi, I., Ryan, U. (2017). Molecular characterization of Cryptosporidium and Giardia in farmers and their ruminant livestock from the Coastal

Savannah zone of Ghana. Infection, Genetics and Evolution, 55, 236-243.

Note: due to copyright restrictions, the appendix above can be found at: https://doi.org/10.1016/j.meegid.2017.09.025

285

Appendix C Gastrointestinal helminths in farmers and their ruminant livestock from the Coastal Savannah zone of Ghana

Squire, S. A., Yang, R., Robertson, I., Ayi, I., Squire, D. S. and Ryan, U. (2018).

Gastrointestinal helminths in in farmers and their ruminant livestock from the Coastal

Savannah zone of Ghana. Parasitology Research, 117(10), 3183-3194.

Note: due to copyright restrictions, the appendix above can be found at: https://doi.org/10.1007/s00436-018-6017-1

286

Appendix D Questionnaire on farm management practices

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Questionnaire on Farm Management practices Murdoch University, Australia/CSIR-Animal Research Institute, Ghana

Farm ID: ______Date: ___/___/___ (DD/MM/YY)

Region: ______District: ______Community: ______

Herd structure 1. Which of the following farm animals do you keep? (please tick all applicable): Cattle Sheep Goat Pigs Poultry (specify)______

Other (please specify) ______2. How many animals are there in your farm/herd by age and sex? (Please fill the applicable session) Age group Sheep Goat Age group Cattle

Total Total Males <6months (kid/lamb) Male <12 months (calves) Female <6months (kid/lamb) Female <2 months (calves) Males 6-12 months Male 12-24 months Female 6-12 months Female 12-24months Males over 12 months Male over 24 months Females over 12months Female over 24 months

3. Are the males separated from the females for a period of time? Cattle: Yes No Other (please specify) ______Sheep: Yes No Other (please specify) ______

Goats: Yes No Other (please specify) ______

Kid/lamb/calf management 4. Are the young animals separated from their mother when they are born? Cattle: Yes, at age ______No Sheep: Yes, at age ______No Goats: Yes, at age ______No 5a. Do you have a special place for lambing/kidding/calving? Cattle: Yes (please specify) ______No (go to 6) Sheep: Yes (please specify) ______No (go to 6) Goats: Yes (please specify) ______No (go to 6) 5b. What is the floor of the lambing/kidding/calving place made of? Cattle: Concrete Wood shavings Wooden Earth Other______

Sheep: Concrete Wood shavings Wooden Earth Other______

Goat: Concrete Wood shavings wooden Earth Other______

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5c. Do you clean this place before lambing/kidding/calving? Cattle: Yes No Sheep: Yes No Goats: Yes No

If no, please go to Q6 5d. If yes, how do you clean this place? (Please tick all applicable) Sweep Wash with water Wash with detergent and water

Disinfect using ______Other (please specify) ______

6. Are the newborns given any medication? Cattle: Yes (please specify medication) ______No

Sheep: Yes (please specify medication) ______No

Goat: Yes (please specify medication) ______No

Farm hygiene 7a. What is the floor do you have for your livestock? Cattle: Concrete Wood shavings Wooden Earth Other______

Sheep: Concrete Wood shavings Wooden Earth Other______

Goat: Concrete Wood shavings wooden Earth Other______

7b. Do you clean this floor? Cattle: Yes No Sheep: Yes No Goats: Yes No

If no, please go to Q9 7c. If yes, how do you clean this place? (Please tick all applicable) Sweep Wash with water Wash with detergent and water

Disinfect using ______Other (please specify) ______

7d. How often do you clean the pen or kraal? Cattle: Daily 2-3 times a day Once a week Other______

Sheep: Daily 2-3 times a day Once a week Other______

Goat: Daily 2-3 times a day Once a week Other______

8a. How do you manage the waste from your farm? (Please tick all applicable) Put on field for fertilizer Pile up and used for fertilizer Pile up and sold

Treated Other (please specify) ______

Please go to Q9 if waste is not treated 8b. If you treat your waste, how do you treat it and why? ______

Feeding and watering 9a. How are the animals fed? (please tick all applicable) Cattle: Cut and carry Grazing Feed Sheep: Cut and carry Grazing Feed Goats: Cut and carry Grazing Feed If cut and carry and/grazing please go to Q10 9b. If you provide feed, how is the feed acquired? (please answer all applicable) Make or provide feed myself using ______Buy commercially made feed from ______Other (please specify) ______289

9c. Where are the animals fed or where do you place the feed for the animals to eat? On the ground Feeding trough Other please specify) ______

Please use the following to answer Q10 Intensive (Animals are enclosed and provided with food, and not allowed to graze); Semi-intensive (animals are provided with some form of housing/enclosure and/or feed but also allowed to graze); Extensive system (animals are not provided any form of housing/enclosure and depend only on grazing) 10. What system of farming do you practice (please tick all applicable) Cattle: Intensive Semi-intensive Extensive Sheep: Intensive Semi-intensive Extensive

Goats: Intensive Semi-intensive Extensive

If intensive system for all animals, please go to Q14 11. At what age are the young/new born animals allowed to graze freely? Cattle: ______Sheep: ______Goats: ______12. Do other herds share a common grazing field with your herd? Cattle: Yes (please specify animal type) ______No Not sure

Sheep: Yes (please specify animal type) ______No Not sure

Goats: Yes (please specify animal type) ______No Not sure

13a. What kind of grazing system is adopted for your herd? Cattle: Restricted within a particular area No restriction (free area)

Sheep: Restricted within a particular area No restriction (free area)

Goats: Restricted within a particular area No restriction (free area)

If no restriction for all the animals, please go to Q14 13b. What form of restriction is in place for the herd?

Cattle: Tethered Wooden fence Herder Other ______Sheep: Tethered Wooden fence Herder Other ______

Goat: Tethered Wooden fence Herder Other ______

13c. Are the animals restricted at all times or during specific seasons? Cattle: Farming seasons only All times Other (specify)______

Sheep: Farming seasons only All times Other (specify)______

Goats: Farming seasons only All times Other (specify)______

14. What is the source of drinking water for your animals (please tick all applicable)? Cattle: Tap water Underground Dugout/dam River Other____

Sheep: Tap water Underground Dugout/dam River Other____

Goat: Tap water Underground Dugout/dam River Other____

Contact with other herds/animals Please use this information to answer Q15 to 22 (Assume you are purposefully walking to the farm, where, 10mins walk is less than 1km; 10-45mins is about 1-5km; 90mins or 1.5hours walk is about 6 -10km; and over 1.5 hours walk is Over 10km) 15. Approximately how far is the nearest cattle herd from your farm? Cattle: ______Sheep: ______Goats: ______

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16. How far is your sheep herd from your farm? Cattle: ______Sheep: ______Goats: ______17. How far is the nearest goat herd your farm? Cattle: ______Sheep: ______Goats: ______18. How far is the nearest piggery your farm? Cattle: ______Sheep: ______Goats: ______19. How far is the nearest poultry from your farm? Cattle: ______Sheep: ______Goats: ______20a. Apart from cattle, sheep, goats, piggery and poultry, are there other farms or animals such as donkeys or horses kept in your community? Yes (please specify) ______No Not sure

If No or Not sure, please go to Q21 20b. If yes, how far are they from your farm? ______21. What other animals do you keep on your farm apart from the cattle, sheep and/or goats? (Please specify) ______22a. How often do you introduce new cattle/sheep/goats into your herd in a year, on the average? Cattle: ______Sheep: ______Goats: ______22b. When was the last time you introduced a new animal into your herd? Cattle: ______Sheep: ______Goats: ______

Health management 23a. Do you know what the signs will be if your animals had worm and/or intestinal infection? Yes (please specify) ______No (go to 24)

23b. If yes, are any of your animals currently showing these clinical signs? Cattle: Yes No Sheep: Yes No Goats: Yes No

24a. Are your animals given routine medications for worm and/or intestinal infections? Cattle: Yes, all animals in my herd Yes, only calves (max. age)

Other (please specify) ______No

Sheep: Yes, all animals in my herd Yes, only lambs (max. age)

Other (please specify) ______No

Goats: Yes, all animals in my herd Yes, only kids (max. age)

Other (please specify) ______No

If no to all, please go to Q26 24b. If yes, who administers the medication? Veterinarian/MoFA staff Locally trained animal health officer

Herbalist Myself Other (please specify) ______

24c. What medications do you receive? (Please answer all applicable) Traditional/herbal (please specify) ______

Orthodox (please specify) ______

Other (please specify) ______Not sure

24d. How often are your animals given medication for worms/intestinal infections on average in a year? Cattle: ______Sheep: ______Goats: ______

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25. In the last month, have any of your animals received medication for worms/intestinal infections? Cattle: Yes, (please specify medication) ______No

Sheep: Yes, (please specify medication) ______No

Goats: Yes, (please specify medication) ______No

26a. In the last month, have you observed diarrhoea in any of your animals? Cattle: Yes No Sheep: Yes No Goats: Yes No

26b. If yes, were the animals(s) treated and if so what was used? Cattle: Yes, (please specify medication) ______No

Sheep: Yes, (please specify medication) ______No

Goats: Yes, (please specify medication) ______No

Thank you for taking the time to complete this questionnaire General comments: ______

Contact Details: Sylvia Afriyie Squire (PhD Student) School of Veterinary and Life Sciences Murdoch CSIR-Animal Research Institute University, Perth, Australia P. O. Box AH 20 Phone: +61449181697 Achimota, Accra, Ghana Email: [email protected] Tel: 0244959834 Email: [email protected]

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Appendix E Questionnaire on farmers knowledge, attitude and practices in relation to gastrointestinal parasites infection and zoonoses

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Questionnaire on Farmers Knowledge, Attitude and Practices in Relation to Gastrointestinal Parasites Infection and Zoonoses Murdoch University, Australia/CSIR-Animal Research Institute, Ghana

Farmer ID: ______Date: ___/___/___ (DD/MM/YY)

Region: ______District: ______Community: ______

Demographic Features

5. Gender (please choose as appropriate): Male Female

6. Age: ______

7. Current level of education (please tick as appropriate): No formal education

Basic education Secondary/vocational education Tertiary education

4a. Is livestock rearing your main occupation? Yes (go to 5) No

4b. What is your main occupation aside livestock rearing? ______

5. How many years have you engaged in livestock (cattle, sheep and/or goats) farming? (

Cattle: ______Sheep: ______Goats: ______

6a. Do you belong to any farmers association? Yes No (go to Q7)

6b. If yes, please specify______

6c. What form of support is offered by the association?

Credit Training Credit and training Other (please specify) ______

Farmers KAP in relation to Intestinal parasites and zoonoses

Environmental factors

7a. Where is your farm located, in relation to your house?

Cattle: Within compound Backyard/around house Away from house

If within compound or backyard, please go to Q8

(Assume you are purposefully walking to the farm, where, 10mins walk is less than 1km; 10-45mins is about 1-5km; 90mins or 1.5hours walk is about 6 -10km; and over 1.5 hours walk is Over 10km) 7b. If away from house, how far is your farm from your house (in minutes-walk)?

Cattle: ______Sheep: ______Goat: ______

7c. If away from house, how far is your farm from you’re the nearest house (in minutes’ walk)?

Cattle: ______Sheep: ______Goat: ______

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8. Do you keep other animals such as donkeys, dogs, cats, etc. in your house or at your backyard?

Yes (please specify) ______No 9a. Are there other herds (cattle, sheep and/goats) or animals kept near your house?

Yes (please specify animal type) ______No Not sure

If no or not sure, please do to Q10

9b. If yes how far are they from your house? ______

10a. Are there other animals such as donkeys, dogs, cats, etc. kept near your house?

Yes (please specify animal type) ______No Not sure

10b. If yes, how far are they from your house? ______

11. Do other members of your family or household work on your farm/herd?

Yes (please specify) ______No

12. Do you have workers on your farm who are not of your household?

Yes (how many number) ______No

Farmers’ Hygiene and practices

13. Do you were protective clothes whiles working on your farm? (Please tick as appropriate)

Wellington boots Gloves Overall/coats Other (please specify) ______No protective clothes

14a. How often do you wash your hands when working on your farm/herd?

Every time I finish working Whenever I am about to eat

Only when my hands get dirty Other (please specify) ______

I don’t wash my hands (go to 15a)

14b. What do you use in washing your hands with?

Water Water and soap Other (please specify) ______15a. Do you produce milk on your farm? Yes No (go to Q16)

15b. If yes, do you pasteurize the milk? Yes No

15c. Do drink raw or unpasteurized milk? Yes No

16. Do you eat near your animals (ie. within 15 walking steps or less than 10 meters distance)?

Yes No

17. What is your source of drinking water? (please tick all applicable)

Tap water Underground Dugout/dam River Other______

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18. In the last month, have you handled an animal(s) with diarrhoea?

Yes No Not sure

Farmers’ Knowledge on parasitic infections in humans and zoonoses

19a. Which of the following are clinical signs of worm/intestinal infections in humans?

Anaemia Stomach ache loss of appetite Diarrhoea

Weakness Other (please specify) ______Not sure 19b. In the last month, have you had any of these signs? Yes No 19c. In the last month, have any member of your family or household had any of these signs?

Yes No Not sure 20. By which of the following ways may humans get worm/intestinal infections?

Eating with dirty hands Ingesting dirty food/water Walking bare-footed

Other (please specify) ______Not sure

21. Can humans be infected with worms or pathogens that cause intestinal infections from animals?

Yes, (specify)______No Not sure 22. How can worm and/or intestinal infection be prevented or controlled in humans?

Taking medicine Eating/drinking clean food/water Drinking alcohol/bitters Eating food with a lot of hot pepper Other (please specify) ______Not sure

23a. Have you ever been treated for worm and/or intestinal infections?

Yes No (go to Q24) Not sure (go to Q24)

23b. If yes, when was the last time you were treated for worm and/or intestinal infections?

Less than I month ago 6months ago 6-12months ago

Over 12 months ago Several years ago Not sure (go to 24)

23c. Where did you receive the treatment? Hospital/Clinic/Health post Herbalist On my farm Pharmacy/Chemical shop Treated by myself 23d. What treatment did you receive? Tablets/capsules Injection Herbal preparation/Bitters Other 23e. What is the name of the treatment you received? Specify______Not sure 24a. Do you know of any disease(s), not yet mentioned that can be transmitted from animals to humans?

Yes (please specify) ______No (finish) 24b. If yes, do you know how the disease is transmitted to humans?

Yes (please specify) ______No

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24c. Do you know how the disease you mentioned can be prevented and controlled?

Yes (please specify) ______No

Thank you for taking the time to complete this questionnaire

General comments: ______

Contact Details:

Sylvia Afriyie Squire (PhD Student)

School of Veterinary and Life Sciences CSIR-Animal Research Institute

Murdoch University, Perth, Australia P. O. Box AH 20

Phone: +61449181697 Achimota, Accra, Ghana

Email: [email protected] Tel: 0244959834

Email: [email protected]

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