University of Ghana http://ugspace.ug.edu.gh SCHOOL OF PUBLIC HEALTH, COLLEGE OF HEALTH SCIENCES UNIVERSITY OF GHANA, LEGON THE ROLE OF GENETIC AND EPIGENETIC FACTORS IN ENDOTHELIAL DAMAGE AND REPAIR AMONG GHANAIAN CHILDREN WITH CEREBRAL MALARIA BY DANIEL AMOAKO-SAKYI (10174124) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEG ON IN PARTIAL FULFILLMENT FOR THE REQUIREMENT FOR THE AWARD OF DOCTOR OF PHILOSOPHY (PHD) DEGREE IN PUBLIC HEALTH. MARCH 2019 University of Ghana http://ugspace.ug.edu.gh DECLARATION I hereby declare that this thesis is the result of my own original research, except for areas where specific references have been made and duly acknowledged. I also affirm that the studies reported in this docwnent were carried out by me under the supervision of my team of academic supervisors. Lastly, I declare that this work has not been submitted, either in part or in whole, to any other institution for an award of a degree. Prof. Isabella A. Quakyi (PhD) (Academic Supervisor) Prof.&(PbDl (Academic Supervisor) IJ'V-'~I" Prof. Julius Fobil (PhD) Date (Academic Supervisor) Dr Kwadwo Asamoah Kusi (PhD) (Academic Supervisor) \q \ \\J \ (...cJ\,\ Dr John Arko-Mensah (PhD) Date (Academic Supervisor) University of Ghana http://ugspace.ug.edu.gh DEDICATION To Reggie, Eno and Kobby ii University of Ghana http://ugspace.ug.edu.gh ACKNOWLEDGEMENTS I am eternally grateful to God for the gift of life, strength and wisdom for this pursuit. I also thank. my employers, the University of Cape Coast for offering me a career development opportunity that enabled me to enrol in this prestigious doctoral program. My time with the Department of Biological, Environmental, and Occupational Health Sciences (BEOHS), School of Public, Health (SPH) has been worthwhile and I cherish the stimulating academic environment at BEOHS. I thank the Chair of BEOH, Prof. Julius Fobil and his team for this opportunity. Majority of the work described in this thesis was conducted in the laboratories of the Immunology Department, Noguchi Memorial Institute for Medical Research (NMIMR) and I am thus, indebted the Chair of the Department for granting me access to the facility and resources. To my academic supervisors, Prof. Isabella Quakyi, Prof. Ben Gyan. Prof. Julius Fobil, Dr Asamoah Kusi and Dr John Arko-Mensah, I say a big thank you. Your expert advice, critiques, frank confrontations and love will continue to shape my life and career. I thank all students and staff on the EPCmal Study for taking time to train and equip me with the various skills and techniques I needed for this work. My special appreciation goes to Thomas Addison for being there whenever I needed him. I thank the staff of the health facilities that partnered us in this study, your time and efforts are appreciated. Finally, to all the children and parents who participated in this study, I say thank you. iii University of Ghana http://ugspace.ug.edu.gh TABLE OF CONTENTS DECLARATION ............................................................................................................ i DEDICATION ................................... .. .................................................................... ii ACKNOWLEDGEMENTS ......................................................................................... iii TABLE OF CONTENTS .............................................................................................. iv LIST OF FIGURES ...................................................................................................... ix LIST OF TABLES ........................................................................................................ xi LIST OF ABBREVIATIONS .................................................................................... xiii DEFINITION OF TERMS ........................................................................................ xvii ABSTRACT .............................................................................................................. xxvi CHAPTER ONE ............................................................................................................ 1 1.0 INTRODUCTION ................................................................................................... 1 1.1. Malaria in a global health perspective ................................................................. I 1.2. The malaria pathophysiology nexus .................................................................... 2 1.3. An emerging pathophysiologic model for CM ................................................... 5 1.4. Host genetic and epigenetic factors in the pathogenesis of CM ......................... 7 1.5. Problem statement ............................................................................................... 9 1.6. Conceptual Framework ..................................................................................... 10 1.7 Justification of study .......................................................................................... 14 1.8 General objective ................................................................................................ 15 1.9 Specific objectives .............................................................................................. 15 1.10 Hypothesis ........................................................................................................ 16 CHAPTER TWO ......................................................................................................... 17 2.0 LITERATURE REVIEW ...................................................................................... 17 2.1. Malaria as a global health problem ................................................................... 17 2.1.1 Malaria in Ghana: epidemiology ................................................................. 23 2.1.2 Malaria in Ghana: the socioeconomic burden ............................................. 25 iv University of Ghana http://ugspace.ug.edu.gh 2.2. Malaria: the parasite, vector, and disease ..................................... · .. · .. · ... ········ ... 27 2.2.1. The parasite: exploring Plasmodium parasite biology through the lifecycle .............................................................................................................................. 29 2.2.2. The disease: pathogenic mechanisms and determinates of severe malaria 35 2.2.2.1 Pathogenesis of cerebral malaria .......................................................... 36 2.2.2.2 New paradigms in the pathogenesis of cerebral malaria ..................... .42 2.3. Markers of endothelial damage and repair ........................................................ 43 2.3.1 Marker of endothelial dysfunction .............................................................. 44 2.3.2 Markers of endothelial repair ...................................................................... 45 2.4 Malaria immunology .......................................................................................... 47 2.4.1 Immune responses during pre-erythrocytic stages of Plasmodium life cycle .............................................................................................................................. 47 2.4.2 Immune responses during erythrocytic stages of Plasmodium lifecycle .... .48 2.4.2.1 Innate immune responses ...................................................................... 49 2.4.2.2 Adaptive immune responses: humoral immunity ................................. 50 2.4.2.3 Adaptive immune responses: cell-mediated immunity ......................... 52 2.5 Host genetics and epigenetics in malaria pathogenesis ofCM .......................... 53 2.5.1 Genetic disorders of erythrocytes and susceptibility to malaria .................. 54 2.5.2 Malaria immunogenetics ............................................................................. 55 2.5.3 Malaria host epigenetics .............................................................................. 58 CHAPTER THREE ..................................................................................................... 59 3.0 METHODS ............................................................................................................ 59 3.1 Study Site ........................................................................................................... 59 3.2. Study design and sample size estimations ......................................................... 60 3.3. Ethical Considerations ....................................................................................... 61 3.4 Inclusion criteria ................................................................................................. 61 3.4.1 Specific inclusion Criteria ........................................................................... 61 v University of Ghana http://ugspace.ug.edu.gh 3.4.2 Exclusion criteria ......................................................... ·· .... · .. ·· .. •. ................. 63 3.5 Blood S81llple collection ................................................ ·.· .......•.......................... 63 3.6. Sample processing and downstream analysis ..................................... ·· .. ·· .. ·· .... ·64 3.7 Measurement of angiogenic factors ................................................................... 66 3.8 SNP Genotyping ............................................................. · .. · ................................ 66 3.8.1 DNA isolation from whole blood ............................................. ··· ........ · ...... ·67 3.8.2 Pre-PeR: DNA and oligo pool preparation ................................................. 68 3.8.3 peR amplification of target loci .................................................................. 68 3.8.4 peR product clean-up with Shrimp alkaline phosphatase (SAP) protocol.69 3.8.5 iPLEX reaction ............................................................................................ 70 3.9. Other laboratory evaluations ............................................................................. 71 3.9.1 Haematological analysis .............................................................................. 72 3.9.2 Parasitological evaluation ............................................................................ 72 3.9.3 Bacteraemia evaluation ................................................................................ 72 3.10. The use of Gaussian mixture model ................................................................ 72 3.11. Statistical Analysis .......................................................................................... 73 3.12. Dealing with missing data ............................................................................... 75 CHAPTER FOUR ........................................................................................................ 76 4.0 RESULTS .............................................................................................................. 76 4.1. Demographic characteristics of study participants ............................................ 76 4.2 Haematological indices among study participants ............................................. 78 4.3. Parasitological indices among study participants .............................................. 80 4.4 Immunological indices among study population ............................................... 81 4.5 Angiogenic indices among clinical malaria phenotypes .................................... 82 4.6 Endothelial integrity, malaria phenotypes, and angiogenic factors ................... 82 4.7 Association of endothelial integrity with key haematological, parasitological. immunological and angiogenic variables ................................................................. 86 vi University of Ghana http://ugspace.ug.edu.gh 4.8. Genotyping results ............................................................................................. 87 4.9 Association ofmg SNPs with endothelial integrity and malaria. ....................... 87 4.9.1. Tag SNPs on chromosome 1 ............................................................•......•.. 89 4.9.2. Tag SNPs on chromosome 2 ...................................................................... 92 4.9.3. Tag SNPs on chromosome 4 ...................................................................... 92 4.9.4. Tag SNPs on chromosome 6 ...................................................................... 95 4.9.5. Tag SNPs on chromosome 7 ...................................................................... 97 4.9.6. Tag SNPs on chromosome 9 .................................................................... 100 4.9.7. Tag SNPs on chromosome 10 .................................................................. 104 4.9.8. Tag SNPs on chromosome 16 .................................................................. 107 4.9.9. Tag SNPs on chromosome 20 .................................................................. 109 4.10 Global association plots ................................................................................. 112 4.11 In silico analysis ............................................................................................. 116 4.11.1 Linkage disequilibrium analysis .............................................................. 116 4.11.2. Epigenetic contexts ofSNPs. .................................................................. 127 4.11.3. Potential effects of SNPs on Transcription Factors (TF) ....................... 127 4.11.4. Effects of trait- and malaria-associated SNPs on microRNA ................. 137 4.12 Prospecting biomarkers for cerebral malaria. ................................................ 140 4.13 Association of SNPs with angiogenic factors ................................................ 145 CHAPTER FIVE ....................................................................................................... 146 5.0 DISCUSSION ...................................................................................................... 146 5.1 Association SNPs with malaria and endothelial integrity: ............................... 146 5.2. SNPs and epigenetic mechanisms: insights from in silico analysis ................ 149 5.3 Endothelial integrity and malaria phenotype ................................................... 150 5.4 Malaria in context: parasitological, immunological and haematological indices . ................................................................................................................................ 152 5.4.1 Parasitological indices ............................................................................... 152 vii University of Ghana http://ugspace.ug.edu.gh 5.4.2 Immunological indices .............................................................................. 152 5.4.3 Angiogenic factors ..................................................................................... 153 CHAPTER SIX .......................................................................................................... 156 6.0 CONCLUSION AND RECOMMENDATIONS ................................................ 156 6.1 Conclusion ........................................................................................................ 156 6.2 Recommendations ............................................................................................ 157 REFERENCES .......................................................................................................... 159 APPENDICES ........................................................................................................... 197 Appendix I: Ethical Approval ................................................................................ 197 Appendix II: Scientific and Technical Committee ApprovaL .............................. 198 Appendix III: Inform Consent ................................................................................ 199 Appendix IV: Primer for iPLEX Reaction ............................................................. 208 Appendix V: iPLEX protocol. ................................................................................ 21 0 viii University of Ghana http://ugspace.ug.edu.gh LIST OF FIGURES Page Figure Figure 1. 1: Conceptual framework 13 Figure 2. 1: Malaria cases by WHO regions ..........................•..................................... 19 Figure 2. 2: Access to ITN in Sub-Saharan Africa ...................................................... 22 Figure 2. 3: Confirmed malaria cases in Ghana 2005 - 2016 ...................................... 23 Figure 2. 4: Malaria admissions in Ghana (2005 - 2016) ............................................ 24 Figure 2.5: Source of funding for malaria interventions ............................................. 25 Figure 2. 6: Ghana government expenditure on malaria .............................................. 25 Figure 2. 7: Lifecycle of the malarial parasites ............................................................ 28 Figure 2. 8: Pre-erythrocytic stage life cycle ............................................................... 32 Figure 2. 9: Pathological differences in CMl and CM2 .............................................. 39 Figure 2. 10: Adaptive immunity at the pre-erythrocytic stage ................................... 48 Figure 3. I: Study site .................................................................................................. 59 Figure 3. 2: From field to data: a schematic flowchart of sample processing ............. 65 Figure 3. 3: iPLEX reaction ......................................................................................... 70 Figure 4. I: Age and malaria phenotypes .................................................................... 77 Figure 4.2: Haematological indices in study participants ........................................... 79 Figure 4.3: Parasitological indices among malaria phenotypes .................................. 80 Figure 4. 4: Histogram of EPCs in the study population ............................................. 83 Figure 4.5: Malaria phenotypes and endothelial integrity .......................................... 84 Figure 4. 6: Endothelial integrity and malaria phenotypes .......................................... 85 Figure 4. 7: Association ofSNPs with CM in a CM vs UM comparison ................. 113 Figure 4.8: Association ofSNPs with malaria in a malaria vs HC comparison ....... 114 ix University of Ghana http://ugspace.ug.edu.gh Figure 4. 9: Association of SNPs with ProDamage in a ProDamage vs ProRepair comparison .......................................................................................................... 115 Figure 4. 10: A composite linkage disequilibrium plot for rsl0489181 .................... 117 Figure 4. 11: A composite linkage disequilibrium plot for rs2070744 ...................... 118 Figure 4. 12: A composite linkage disequilibrium plot for rs3918211 ...................... 119 Figure 4. 13: A composite linkage disequilibrium plot for rs3917419 ...................... 120 Figure 4. 14: A composite linkage disequilibrium plot for rs59055740 .................... 121 Figure 4. 15: A composite linkage disequilibrium plot for rs684951 ........................ 122 Figure 4. 16: A composite linkage disequilibrium plot for rs73422262 .................... 123 Figure 4. 17: Linkage disequilibrium for rs3818256 ................................................. 124 Figure 4. 18: A composite linkage disequilibrium plot for rs943082 ........................ 125 Figure 4. 19: A composite linkage disequilibrium for rs2304527 ............................ 126 Figure 4.20: SNPs and their Chromatin States ......................................................... 129 Figure 4. 21: Performance of angiogenic factors as a biomarker for CM ................. 141 Figure 4.22: Performance of angiogenic factors as a biomarker for endothelial integrity ............................................................................................................... 142 Figure 4.23: Performance of parasitological indices as a biomarker for CM ........... 143 Figure 4.24: Comparison of3 parasitological variables as biomarkers for CM ....... 144 Figure 4.25: Comparison ofMMP9leveis among rs3918256 in a recessive model 145 x University of Ghana http://ugspace.ug.edu.gh LIST OF TABLES Page Table Table 3. 1: PCR cycling program for target amplification 69 Table 3. 2: PCR cycling program for iPLEX reaction 71 Table 3.3: Regression models for genetic analysis 7S Table 4. 1: Association of angiogenic factors with malaria phenotypes 82 Table 4.2: Association of key variables with endothelial integrity 86 Table 4.3: Summary ofgenotyped SNPs 88 Table 4. 4: Chromosome 1: Association of SNPs with CM in CM vs UM comparison 90 Table 4.5: - Chromosome 1: Association ofSNPs with malaria in malaria vs HC comparison 91 Table 4. 6: Chromosome 2: Association ofSNPs with malaria in malaria vs HC comparison. 93 Table 4. 7: Chromosome 4: Association of SNPs with malaria in a malaria vs HC ~ari~ M Table 4. 8: Chromosome 6: SNPs with endothelial integrity 96 Table 4.9: Chromosome 7: Association ofSNPs with endothelial integrity 98 Table 4. 10: Chromosome 7: Association SNPs with malaria in a malaria vs HC comparison 99 Table 4. 11: Chromosome 9: Association ofSNPs with endothelial integrity 101 Table 4.12: Chromosome 9: Association ofSNPs with CM in a CM versus UM comparison 102 Table 4. 13: Chromosome 9: Association ofSNPs with malaria in a malaria versus HC comparison 103 xi University of Ghana http://ugspace.ug.edu.gh Table 4. 14: Chromosome 10: Association ofSNPs with CM in a CM versus UM comparison lOS Table 4. 15: Chromosome 10: Association ofSNPs with malaria in malaria vs HC comparison 106 Table 4. 16: Chromosome 16: Association ofSNPs with endothelial integrity 108 Table 4. 17: Chromosome 20: Association ofSNPs with endothelial integrity 110 Table 4. 18 Chromosome 20: Association ofSNPs with malaria in malaria vs HC comparison III Table 4. 19: Effect of SNPs on the binding affinity of transcription factors (TF) 130 Table 4.20: Top 5 transcription factors (TF) impacted by rs2304S27 _T/O 131 Table 4.21: Top 5 transcription factors (TF) impacted by rs39I 82S6_0/A 132 Table 4.22: Top 5 transcription factors (TF) impacted by rs3917419 _O/A 133 Table 4. 23: Top 5 transcription factors (TF) impacted by rs684951_T/O 134 Table 4.24: Top 5 transcription factors (TF) impacted by rs2070744_T/O 135 Table 4.25: Top 5 transcription factors (TF) impacted by rs59055740_T/O 136 rable 4. 26: microRNA-binding sites influenced by rs3918211 [O/A] variants 138 Table 4.27: MicroRNA-binding sites influenced by rs3918211 [O/A] variants 139 xii University of Ghana http://ugspace.ug.edu.gh LIST OF ABBREVIATIONS AIC Akaike Infonnation Criterion AMA-I Apical Membrane Antigen-l Angl Angiopoietin-l Ang2 Angiopoietin-l ANOVA Analysis Of Variance BCS Blantyre Coma Scale CAl Carbonic Anhydrase 1 CBC Complete Blood Count CD Cluster Of Differentiation CelTOS Cell cEPC Circulatory Endothelial Progenitor Cells CHMI Controlled Human Malaria Infection CM Cerebral Malaria CpG S' -C-Phosphate-G-3 CS Cerebrospinal Fluid CSP Circumsporozoite Protein CXCLIO C-X-C MotifChemokine Ligand DAMP Damage Associated Molecular Pattern DARC Duffy AntigenlChemokine Receptor DCs Dendritic Cells DM Diabetes Miletus DNA Deoxyribonucleic Acid dNTP Deoxy Nucleotide Triphosphate EDTA Ethylenediarninetetraacetic Acid EDV Electron Dense Vesicle ELISA Enzyme-Linked Immunosorbent Assay eNOS Endothelial Nitric Oxide Synthase xiii University of Ghana http://ugspace.ug.edu.gh EPCR Endothelial Protein C Receptor EPCs Endothelial Progenitor Cells EphA2 Ephrioe Type-A Receptor 2 G6PD Glucose 6-Phosphate Dehydrogenase Deficiency gDNA Genomic DNA GES Ghana Education Service GEST Gamete Egress And Sporozoite Traversal Protein GHS Ghana Health Services GLURP Glutamine Rich Protein G WA S Genome-Wide Association Studies Hb Haemoglobin HC Healthy Control HIV Human Immunodeficiency Virus HRP2 Histidine-Rich Protein-2 HUVEC Human Umblical Vein Endothelial Cell ICAM-l Intercellular Adhesion Molecule 1 IE Infected Erthryocyte IFN--, Interferon Gamma IL Interleukins IL-I~ Interleukin-l Beta IRB Institutional Review Board ITNs Insecticide-Treated Nets let-7 Lethal-7 MAHRP2 Membrane Associated Histidine-Rich Protein-2 MgCl2 Magnesium Chloride MI Multiple Imputation miRNA Microma MMP-9 Matrix Metalloproteinase-9 mRNA Messenger RNA xiv University of Ghana http://ugspace.ug.edu.gh MSPI Merozoite Surface Protein MSP-119 Merozoite Surface Protein-I 19 MSP-3 Merozoite Surface Protein 3 NORl Neuregulin I Noguchi Memorial Institute For Medical NMIMR Research NOS3 Nitric Oxide Synthase3 OPD Out-Patient Department PAMP Pathogen Associated Molecular Pattern pbP Peripheral Blood Parasitaemia PCR Polymerase Chain Reaction PECAM Platelet Endothelial Cell Adhesion Molecule PjEMPI P. Jalciparum Erythrocyte Membrane Protein-! PjHRPl P. Jalciparum Erythrocyte Membrane Protein-l PL Phospholipids PLPI Perfo rin-Like Protein 1 pRBC Parasitized Red Blood Cells qPCR Real-Time Polymerase Chain Reaction RBCs Red Blood Cells RIFIN Repetitive Interspersed Family Protein RNA Ribonucleic Acid ROC Receiver Operating Characteristics RON Rhoptry Neck Protein RT-PCR Reverse Transcription Polymerase Chain Reaction SIOOB S 10 0 Calcium-Binding Protein B SAO Southeast Asian Ovalocytosis SAP Shrimp Alkaline Phosphatase SBE Single Base Extension xv University of Ghana http://ugspace.ug.edu.gh SDF-I Stromal Cell Derived Growth Factor 1 SMA Severe Malarial Anaemia SNP Single Nucleotide Polymorphism Sporozoite Microneme Protein Essential For SPECT Traversal spp Species SR-BI Scavenger Receptor B 1 Signal Transducers And Activator Of STAT6 Transcription Subtelomeric Variable Open Reading Frame STEVOR Proteins Surface Associated Interspersed Gene Family SURFIN Protein TF Transcription Factor TFBS Transcription Factor Binding Sites Tie-2 Tyrosine-Protein Kinase Receptor TLP Trap-Like Proteins TLR-2 Toll-Like Receptor-2 TLR-4 Toll-Like Receptor-4 TLR-9 ToU-Like Receptor-9 TNF Tumour Necrosis Factors TRAP Thrombospondin Related Anonymous Protein TREMl Triggering Receptor Expressed On Myeloid Cells I TSR Type-l Throbospondin Repeat UM Uncomplicated Malaria UTR Untranslated Region VEGFR2 Vascular Endothelial Growth Factor Receptor 2 VSA Variant Surface Antigen WBC White Blood Cells WHO World Health Organisation xvi University of Ghana http://ugspace.ug.edu.gh DEFINITION OF TERMS Allele: One of the different forms of a gene or DNA sequence that can exist at a single locus. Apoptosis: Programmed cell death (peD); a process in which cellular DNA is degraded and the nucleus condensed; then cell is then devoured by neighbouring cells or phagocytes. Artemisinin: A class ofd rugs used for the treatment (not prevention) of malaria usually as a part ofa combination therapy, derived from the sweet wormwood or Qinghao plant (Artemisia annua). Atovaquone: A drug used against malaria. It is found in the combination atovaquone- proguanil which can be used for both prevention and treatment. Carrier: In human genetics, an individual heterozygous for a mutant allele that generally causes disease only in the homozygous state. More generally, an individual who possesses a mutant allele but does not express it in the phenotype because of a dominant allelic partner; thus, an individual of genotype Aa is a carrier of a if there is complete dominance of A over a. Cerebral malaria: A severe malaria syndrome in which infected red blood cells obstruct blood circulation in the small blood vessels in the brain andlor release cytokines that disrupt normal brain function. xvii University of Ghana http://ugspace.ug.edu.gh Chi-square (2) test: A statistical test to determine the probability that an observed deviation from the expected event or outcome occurs solely by chance. Chromatid: One of the two side-by-side replicas produced by chromosome duplication. Chromosomes: Self-replicating structures of cells that carry in their nucleotide sequences the linear array of genes. Coma: A decreased state of consciousness from which a person cannot be roused. Complementarity: The chemical affInity between specifIc nitrogenous bases as a result of their hydrogen bonding properties. The property of two nucleic acid chains having bast! sequences such that an antiparallel duplex can form where the adenines and thymines (or uracils) are opposed to each other, and the guanines and cytosines are opposed to each other. Complex disease: A disorder in which the cause is considered to be a combination of genetic effects and environmental influences. Denaturation: The separation of the two strands ofa DNA double helix, or the severe disruption of the structure of any complex molecule without breaking the major bonds of its chains. xviii University of Ghana http://ugspace.ug.edu.gh Dominance: The expression of a trait in the heterozygous condition. Downstream Sequences proceeding farther in the direction of transcription, for example, the coding region is downstream of the promoter. Elimination: In the context of malaria. reducing all local transmission down to zero cases within a defined geographic location. Endonuclease: An enzyme that cleaves the phosphodiester bond within a nucleotide chain Epigenetics: Heritable changes to DNA structure that do not alter the underlying DNA sequence, e.g., DNA methylation. Epigenomics: The application of epigenetics to the whole genome. Eradication: In the context of malaria, reducing the number of malaria parasites that circulate in the natural world to zero. Exoerythrocytic stage: A stage in the life cycle of the malaria parasite found in liver cells (hepatocytes). Exoerythrocytic stage parasites do not cause symptoms. Exoo: Any segment of an interrupted gene that is represented in the mature RNA product. The protein-coding sequences of a gene. xix University of Ghana http://ugspace.ug.edu.gh G6PD deficiency: An inherited abnormality that causes the loss of a red blood cell enzyme. People who are G6PD deficient should not take the antimalarial drug primaquine. Gene: The basic unit of inheritance. A gene is a segment of DNA that specifies the structure of a protein or an RNA molecule. Genetic association: The non-random occurrence of a genetic marker (usually a particular allele of a polymorphism) with a trait, which suggests an association between the genetic marker (or marker close to it) and disease pathogenesis. Genetic heterogeneity: A similar phenotype being caused by different mutations. Most commonly used for a similar phenotype being caused by mutations in different genes. Allelic heterogeneity refers to different mutations in the same gene. Genome: The total genetic material of an organism, i.e. an organism's complete set of DNA sequences. Genome-w ide association study (GWA S): A test for the association between genetic polymorphisms spread evenly over the entire genome, and a disease. Usually at least 300 000 markers are required to adequately cover the genome. Genotype: The genetic constitution with respect to the alleles at one or more pairs of genetic loci under observation. The genotype of an individual is the sum total of the xx University of Ghana http://ugspace.ug.edu.gh genetic m· fonnatl.O n contam. ed on the ch rom osomes, as distinguished from the individual's phenotype (idiotype). Haploid: A single genome or set of chromosomes (e.g., in human) Haplotype: A combination of alleles at closely linked gene loci that are inherited together. Heterogeneous trait: see Genetic Heterogeneity Heterozygous: Having different alleles for one or more genes in homologous chromosome segments, as opposed to being homozygous with identical alleles at these loci. Homozygote: An individual possessing a pair of identical alleles at a given locus on a pair of homologous chromosomes. Hybridization: The process of joining two complementary strands of DNA or one each of DNA and RNA to fonn a double-stranded molecule. Hypnozoite: Donnant fonn of malaria parasites found in liver cells. Hypnozoites occur only with Plasmodium vivax and P. ovale. After sporozoites (inoculated by the mosquito) invade liver cells, some sporozoites develop into dormant forms (the hypnozoites). which do not cause any symptoms. Hypnozoites can become activated months or years after the initial infection, producing a relapse. xxi University of Ghana http://ugspace.ug.edu.gh Incubation period: The interval of time between infection by a microorganism and the onset of the illness or the ftrst symptoms of the illness. In malaria, the incubation is between the mosquito bite and the ftrst symptoms. Incubation periods range from 7 to 40 days, depending on species. Indoor residual spraying (IRS): Treatment of houses where people spend night-time hours, by spraying insecticides that have residual efficacy (i.e., that continue to affect mosquitoes for several months). Residual insecticide spraying aims to kills mosquitoes when they come to rest on the walls. usually after a blood meal. Infection: The invasion of an organism by a pathogen such as bacteria, viruses, or parasites. Some, but not all. infections lead to disease. Introns: The DNA base sequences interrupting the protein-coding sequences ofa gene. These sequences are transcribed into RNA but are cut out of the message before it is translated into protein. Linkage disequilibrium (LD): Alleles at different loci that are inherited together more Linkage: Genetic linkage refers to the observation that two or more genes located on the same chromosome are inherited together. The ratio of being transmitted together versus being separated al10ws an estimate of their distance from each other (recombination fraction). Locus: A specific location on a chromosome. xxii University of Ghana http://ugspace.ug.edu.gh Mutant allele: An allele differing from the allele found in the standard, or wild type. Null hypothesis: The prediction that an observed difference is due to chance alone and not due to a systematic cause; this hypothesis is tested by statistical analysis and accepted or rejected. Oligonucleotides: Small single-stranded segments of DNA typically 20-30 nucleotide bases in size which are synthesized in vitro. Parasitaemia: The presence of parasites in the blood. The tenn can also be used to express the quantity of parasites in the blood (e.g., "a parasitaemia of 2%"). Phenocopy: A nonhereditary, phenotypic modification (caused by special environmental conditions) that mimics a similar phenotype caused by a gene mutation. Phenotype: Observable characteristics of an organism. Pleiotropy: Genes or mutations that result in the production of multiple, apparently unrelated, effects at the phenotypic level. For example, patients with phenylketonuria, caused by mutations in the PAH (phenylalanine hydroxylase) gene, have reduced hair and skin pigmentation in addition to mental retardation, resulting from toxic levels of phenylalanine. xxiii University of Ghana http://ugspace.ug.edu.gh Polymorphism (genetic): A chromosome or DNA variant that is observed in natural populations. A gene locus is defined as polymorphic if a rare allele has a frequency of 0.01 (1%) or more. Presumptive treatment: Treatment of clinically suspected cases without, or prior to, results from confirmatory laboratory tests. Primer: Short, pre-existing oligonucleotide or polynucleotide chain to which new DNA can be added by DNA polymerase. Promoter: A region of DNA involved in binding of RNA polymerase to initiate transcription. Restriction enzymes: Proteins that recognize specific, short nucleotide sequences in DNA and catalyse cutting at those sites. Silent mutation: Mutation in which the function of the protein product of the gene is unaltered. Single nucleotide polymorphism (SNP): Heritable polymorphism resulting from a single base Structural variant: Structural genomic variation includes any genetic variant that alters chromosomal Structure, including inversions, translocations, duplications and deletions. xxiv University of Ghana http://ugspace.ug.edu.gh Synonymous nucleotide change/non-synonymous nucleotide change: A change in the DNA sequence which does not result in the change in the amino acid sequence, e.g., GTf>QTC both code for Valine (Valor V). A nonsynonymous change results in the coding of a different amino acid (e.g .• GTT>GAT results in Val>Asp). Trait: Any detectable phenotypic variation of a particular inherited character. Transcription unit: The distance between sites of initiation and termination by RNA polymerase; may include more than one gene. Vector (genetic): In cloning. the plasmid, phage, or yeast chromosomal sequences used to propagate a cloned DNA segment. Vector (infection transmission): An organism (e.g., Anopheles mosquitoes) that transmits an infectious agent (e.g. malaria parasites) from one host to the other (e.g., humans). Vector competence: The ability ofa vector (e.g., Anopheles mosquitoes) to transmit a disease (e.g., malaria). Wild type: The genotype or phenotype that is found most commonly in nature or in the standard laboratory stock for a given organism. Zoonosis: A disease that naturally occurs in animals that can also occur in humans. xxv University of Ghana http://ugspace.ug.edu.gh ABSTRACT The declining malaria burden in endemic regions is predicted to increase the proportion of malaria infections that progress to cerebral malaria (CM). This epidemiologic scenario appears ominous against the backdrop of a poor understanding of CM pathogenesis, lack of effective adjunctive therapies, and poor prognosis after onset. Thus, the need to better understand the pathogenesis of CM has become more apparent. To better understand the pathogenesis of CM, this study explored both genetic and epigenetic aspects of the emerging malaria pathophysiologic paradigm, which pivots on imbalances in endothelial damage and repair in cerebral microvasculature during P. falciparum infections. The Sequenom MassARRA Y platform (iPLEX) was used to genotype a focused panel of 27 single nucleotide polymorphisms (SNPs) in a cross-sectional study involving 221 children. In silico techniques were used to characterize the epigenetic context of SNPs and assess their potential effect on microRNAs and transcription factors. Immune cells and angiogenic factors were measured with Human Magnetic Luminex Assay and flow cytometry, respectively. A striking find of this study was the association of a CDH5 SNP (rs2304527) and an MMP9 SNP (rs3918256) with CM and endothelial integrity respectively. CDH5 SNP (rs2304527) offered protection from CM under the over-dominant inheritance model assumption and children with the heterozygote T/G genotype were approximately three times less likely to have CM relative to their colleagues with the IT- GG genotype. On the other hand, MMP9 SNP (rs3918256) was a risk factor for endothelial damage. Relative to the reference genotype (GG), children with the AA genotype ofrs3918256 xxvi University of Ghana http://ugspace.ug.edu.gh were approximately 4 times more likely to be classified as ProDamage under the recessive inheritance model. These two SNPs were subsequently found to disrupt the binding sites of several transcription factors involved in the angiopoietin and tie signalling pathway. Several other SNPs were found to influence the binding affinity of transcription factors but only two (rs3918211 and rs20544) affected micro RNA target sites. Receiver operating characteristic (ROC) analysis to test the ability of angiogenic factors to discriminate between malaria and endothelial integrity phenotypes gave middling results. The best performing angiogenic factor for discriminating eM from UM was NGRI which had only a 66% chance of accurately discriminating eM from UM. Similarly, all angiogenic factors performed poorly in discriminating endothelial integrity phenotypes. This is the first study to implicate rs2304527 and rs3918256 in the pathogenesis of eM. Although in silico analysis suggests some epigenetic roles for these SNPs, future studies may want to further explore their functional roles. Unfortunately, the prospects of using angiogenic factors considered in this study to discriminate between malaria and endothelial integrity phenotypes appear dim. Taken together, this study provides valuable insights on the genetic and epigenetic aspects of endothelial damage and repair during a P. Jalciparum malaria in Ghanaian children. xxvii University of Ghana http://ugspace.ug.edu.gh CHAPTER ONE 1.0 INTRODUCTION 1.1. Malaria in a global health perspective Malaria is a global health threat that has existed since antiquity. Its persistence from prehistoric periods until now attests to the resilience of the etiologic agent, the efficiency of its transmission and the subtleness of its pathogenic mechanisms. The Plasmodia parasites responsible for human malaria have exploited anthropoid lifestyles and survived successive malaria eradication attempts in the past (Carter and Mendis, 2002.2002; Kriefet aI., 2010; Liu et al., 2010; Rich et a1., 2009; White, 2004; World Health Organization. 2016a). Today, over 200 million people in 91 different countries are infected with malaria and although this is grim statistics, it actually represents a drastic reduction in disease burden, especially, in the last two decades (World Health Organization. 2016a). Ironically, insights from the mathematical modelling of malaria epidemiology suggests that the current decline in malaria transmission may result in new epidemiologic scenarios: (i) a shift in malaria burden from younger to older children (Carneiro et at., 2010), (ii) an increase in the incidence of cerebral malaria (CM) (O'Meara et at., 2008) and (iii) a change in the population at risk (Bouyou-Akotet et aI., 2014). These predictions present malaria control stakeholders with novel challenges that will require a paradigm shift to overcome. Thus, although the decline in malaria transmission is desirable and should be pursued in earnest, its unintended consequences should not escape malaria control managers. University of Ghana http://ugspace.ug.edu.gh Planning malaria control strategies is encumbered by the far-reaching and sometimes obscured consequences of the disease burden. Apart from its potentially fatal consequence, malaria affects the economy, education, child development, maternal health and the generalliveliboods of endemic communities (Cormier, 2016; Gallup and Sachs, 2001; Nonvignon et al., 2016; Tang et al., 2017). Thus, control efforts that do not take cognizance of these complexities and harness expertise from across disciplines risk failing (Hemingway et aI., 2016). In this regard, the coincidence of a drastic decline in malaria transmission with the synergistic use of mUltiple malaria control tools/interventions in the last two decades may be instructive. The cross-disciplinary nature of malaria control notwithstanding, the role of biomedical research remains conspicuous and vital. Several effective malaria control tools and interventions in the past were made possible by breakthroughs in biomedical research and the success of future strategies still hinges delicately on advances in the field (Baird, 2015; Hemingway et al., 2016). This thesis focuses on a biomedical question within the broader malaria problem. Empirical findings from this study may be relevant to current malaria control efforts and future strategies. 1.2. The malaria pathopbysiology nexus Malaria is characterised by a wide range of clinical syndromes and disease burdens that can be partly explained in terms of the pathogenesis of the disease. Whereas some children infected with Plasmodium Jalciparum remain asymptomatic, others develop clinical malaria with varying degrees of severity. Clinical malaria may manifest as uncomplicated malaria (UM) characterised by nonspecific symptoms akin to those seen in minor systemic viral conditions (headache, lassitude, fatigue, abdominal discomfort and muscle and joint aches, usually followed by fever, chills, perspiration, anorexia, 2 University of Ghana http://ugspace.ug.edu.gh vomiting and worsening malaise) (World Health Organisation, 2015). A minority of children with UM, however, develop severe fonns of the disease which are often characterised by one or more of the following: coma (cerebral malaria), metabolic acidosis, severe anaemia, hypo glycaemia, acute renal failure or acute pulmonary oedema (World Health Organisation, 2015). The progression from UM to severe malaria (SM) is usually occasioned by poor management of the disease at the initial stages or a delay in the commencement of treatment (World Health Organisation, 2015). Host and parasite factors may also contribute to the onset and outcome of SM, but the actual mechanisms involved have remained elusive. The pathophysiology of severe malaria (SM), especially the CM phenotype, has captured the attention of scientists for decades. This fixation is justified because, after onset, CM has an unacceptably high case-fatality and significant functional deficits (Wahlgren et al., 20 17a; Wassmer et al.. 2015; World Health Organization, 20 16b). Its dire consequences notwithstanding, decades of research on CM is yet to culminate in a complete understanding of the pathophysiology mechanisms involved. The aetiology of CM is most likely multifactorial and the current body of evidence identifies four key hallmarks: (a) sequestration of IE in the microvasculature, (b) endothelial activation, (c) a pro-inflammatory immune response and (d) disruption of BBB. These hallmarks have led malariologists to posit two main hypotheses for the pathogenesis ofCM: vascular occlusion and inflammatory hypothesis. Both hypotheses invoke endothelial activation in their respective mechanisms but differ on the cause thereof. Whereas proponents of the inflammatory hypothesis point to systemic inflammation akin that seen in sepsis as the main cause of the endothelial activation seen CM, proponents of vascular occlusion blame sequestration of infected 3 University of Ghana http://ugspace.ug.edu.gh erythrocytes (IE) for endothelial activation (Stonn and Craig, 2014). A synthesis of the pathophysiologic events during a P. Jalciparum infection suggests that the two hypotheses may not be mutually exclusive. During a P. Jalciparum infection parasitized red blood cells (pRBC) try to avoid splenic clearance by adhering to endothelial cells in the microvasculature with the help of various adhesion molecules such as Intercellular Adhesion Molecule I (ICAM-I), CD36 and Endothelial protein C receptor (EPCR)(Miller, Baruch, Marsh, & Doumbo, 2002). This sequestration is believed to stimulate the adverse responses that characterise CM i.e. inflammation, endothelial activation leading to vascular occlusion, disruption of the blood-brain barrier and apoptosis of microvascular endothelium (Boehme, Werle, Kommerell, & Raeth, 1994; Miller et aI., 2002; N'Dilimabaka et al., 2014). Although these pieces of evidence seemingly lean towards vascular occlusion, studies showing endothelial activation in the absence of microvascular sequestration (Manning et al., 2012; Yeo et aI., 2010) support the inflammatory hypothesis. The reality of apparently healthy individuals with high parasitaemia (Clark and Alleva, 2009) and the association of pro- inflammatory cytokines with CM are the other lines of evidence that raises valid objections about the role of sequestration. Although previous studies have unravelled several pieces of the CM pathophysiology puzzle, substantial knowledge gaps persist. Addressing these persistent knowledge gaps is undoubtedly imperative but doing so without recourse to various theoretical frameworks may obscure important findings and hinder progress. Thus, whereas the deciphering of the minute mechanistic process in CM pathogenesis is necessary, a paradigm shifts in how we conceptualise CM pathogenesis may offer fresh insights into the CM pathophysiology nexus. 4 University of Ghana http://ugspace.ug.edu.gh 1.3. An emerging pathophysiologic model for eM An emerging pathophysiologic model for CM explains the development and recovery from CM in the light of disequilibrium in cerebral microvascular damage and repair (Gyan et aI., 2009). According to this model, a child's odds of progressing to CM or recovering from it may be partly dependent on her ability to repair damaged endothelial tissue in time to restore equilibrium. Although some studies lend credence to the endothelial damage/repair equilibrium model, it is essentially an untested construct and remains a hypothesis (Dickinson-Copeland et al., 2015; Gyan et al., 2009; Tetteh, 2014). This hypothesis is however interesting because it refocuses attention on the details of malaria-induced endothelial damaged (endothelial dysfunction) and the host- mediated mechanisms for repairing damaged microvasculature (vasculogenesis). It further pushes the frontiers of knowledge and asks several questions pertaining to endothelial dysfunction and post-natal angiogenesis. It asks if there are yet uncharacterised mediators of postnatal angiogenesis; whether children with CM have dysfunctional or insufficient mediators; and whether there is a genetic or epigenetic explanation for microvascular endothelium dysfunction. The latter of these questions is particularly interesting because it provides the opportunity to interrogate the issue at various levels of the genotype-phenotype continuum. To answer these questions, one will have to first decipher how the global process of endothelial dysfunction and postnatal angiogenesis play out in the specific context of malaria Subsequently, a decryption of the roles of growth factors, receptors, adhesion molecules, proteases, inhibitors. matrix proteins and cytokines will be necessary to gain deeper insights into how these factors interact at the genomic, epigenomic and physiologic levels to influence malaria pathogenesis. 5 University of Ghana http://ugspace.ug.edu.gh This pathophysiologic model hinges on two main factors: endothelial damage and repair. Whereas studies abound on the endothelial damage (dysfunction) arm of the model (Desruisseaux, Machado, Weiss, Tanowitz, & Golightly, 2010; Gyan et aI., 2009; Swanson et al., 2016), very little is known about postnatal angiogenesis or vascular repair in the context of malaria. Besides the established roles of pre.existing vascular wall endothelial cells in the repair of damaged endothelium, recent studies have highlighted the role ofo ther factors such as circulatory endothelial progenitor cells (cEPC) in the repair of damaged microvasculature (Asahara et aI., 1999). It is now known that cEPes are incorporated into the sites of microvasculature damage with the help of stromal cell·derived growth factor 1 (SDF·I) and the matrix metalloproteinase· 9 (MMP·9) during the repair process (Asahara et aI., 1997; Hristov et aI., 2003; Rafii, 2000; Urbich and Dimmeler, 2004). Previous work on eM pathophysiology in Ghanaian children aligns with the seminal work by Asahara et al on the role of cEPes in the repair of damaged endothelium (Asahara et aI., 1999; Gyan et aI., 2009). The study by Gyan et al found that compared to those with uncomplicated malaria, asymptomatic parasitaemia, or healthy controls, eM patients had lowered cEPe levels and increased SDF-I levels (Gyan et al., 2009). These findings have given impetus to the hypothesis that eM develops due to insufficient or dysfunctional cEPe response to malaria·induced microvascular damage. These studies do not, however, address the genetic and epigenetic underpinnings of biological dysfunctions and insufficiencies. Thus, it has become imperative to investigate and identify the genetic and epigenetic factors that may influence endothelial dysfunction and repair in eM pathogenesis. 6 University of Ghana http://ugspace.ug.edu.gh 1.4. Host genetic and epigenetic factors in the pathogenesis of eM. There is a wide body of evidence on the role(s) that host genetic factors play in malaria susceptibility and severity. Majority of these studies report on haemoglobinopathies with the best-known being sickle cell trait, a-thalassemia, Glucose-6-phosphate dehydrogenase (G6PD) deficiency, and Duffy antigen receptor negativity (Aidoo et al., 2002; Ayi et aI., 2008; Baird, 2015; Cholera et aI., 2008; Maier et al., 2003; May et aI., 2007; Ruwende et aI., 1995; Taylor et aI., 2012; Williams et aI .• 2005). In addition to haemoglobinopathies, several studies have investigated the role of host immunogenetic factors and implicated some immunogens in the pathogenesis of CM (Amoako-Sakyi et aI., 2016; Crompton et aI., 2014; Cserti-Gazdewich et aI., 2011; Hill, 1999; Mazier et aI., 2000). Although immunogenetics and haemoglobinopathies appear to be the main drivers of host genetics and malaria susceptibility, candidate gene studies and genome- wide association studies (GWAS) have reported on association between malaria susceptibility and other host genes that are neither immunogens nor haemoglobinopathies related (Manjurano et aI., 2015; Ravenhall et aI., 2018). Howbeit, there is a dearth of knowledge on how host genetic factors may influence endothelial dysfunction in the context of CM. Its importance notwithstanding, host genetic factors do not tell the whole story. This is because gene expression is partly regulated by changes in the DNA sequence at the genetic level and partly by epigenetic mechanisms including DNA methylation, chromatin and RNA modifications (Gupta et aI., 2017; Robertson, 2005; Shames, Minna, & Gazdar, 2007). Modifications to chromatin architecture are classified as either a non-pennissive (compact chromatin architecture that silence genes) or permissive (relaxed chromatin architecture that enhances transcription). These 7 University of Ghana http://ugspace.ug.edu.gh chromatin states play prominent roles in gene expression and can, therefore, affect the pathophysiology of many disease models (Berger, Kouzarides, Shiekhattar, & Shilatifard, 2009). Furthermore, aberrations to nucleosomes are possible, and when they occur, they create epigenetic marks that specifically relates to pennissive or non- permissive chromatin. Conceivably, epigenetic mechanisms collaborate with genetic mechanisms to co- regulate gene function in several disease models including malaria (Berger et aJ., 2009) and this is perhaps a more interesting aspect of epigenetic research. For instance, the occurrence of single nucleotide polymorphisms (SNPs) within epigenetic marks can affect chromatin structure at specific genomic locations by modifying methylation patterns or histone type recruitment (Zaina et aI., 2010). Similarly, SNPs can affect microRNA and transcription factors by influencing the target sites and binding affinities respectively (Hu and Bruno, 2011; Moszytiska et aI., 2017; Wang etal., 2013). Perhaps, the interactions between genetics and epigenetics may offer better explanations for scenarios where disease-associated genetic variants lie outside promoters or coding regions (Zaina, Perez-Luque, & Lund, 2010). So far, a considerable number of studies have implicated host epigenetic mechanisms in the pathophysiology of some human disease but very few have focused on malaria (Bell et aI., 2011; Dayeh et a1., 2013a; Gupta et aI., 2017; Wagner et aI., 2014) and even fewer looked at the interaction of SNPs and epigenetic marks in malaria pathogenesis. Chromatin marks and/or architecture allows for the segmentation of the genome into different chromatin states including enhancer, insulator, transcribed, repressed, inactive and even CpG islands (Blackledge and Klose, 2011). Thus, this study characterised the 8 University of Ghana http://ugspace.ug.edu.gh epigenetic contexts (chromatin states) of SNPs and explored its relationships with endothelial integrity, angiogenic, immunological and haematological factors in Ghanaian children with different phenotypes of P. falciparum malaria. 1.5. Problem statement Several pathogenic mechanisms have been proposed for CM but none have been conclusively established (Riggle et al., 2017). The rare nature of the disease and the inherent limitations of human studies makes it difficult to investigate the pathophysiology of CM. That said, animal models and in vitro studies offer some insights into the hallmarks of CM. Existing knowledge suggests that the pathogenesis of CM begins with the binding of pRBC to brain endothelium, which in tum activates the endothelium and initiates parasite antigen cross-presentation to cytotoxic T lymphocytes (CD8+ T cells). The CD8+ T cells are then recruited to sites of the binding where they employ perfo rin-dependent mechanisms to damage brain endothelium and the blood-brain barrier (BBB) leading to swelling, micro-haemorrhaging and death (Riggle et aI., 2017; Swanson et aI., 2016). Just as these mechanisms suggest a prominent role for Cytotoxic T lymphocyte (CD8+ T cells), they also posit roles for endothelial cell surface receptors and other factors that aid in the pRBC-endothelium interaction (Hansen et aI., 2007; Chen et aI., 2000; Schumak et aI., 2015; Nitcheu et aI., 2003). Surviving a P. Jalciparum infection heavily hinges on a child's ability to repair damaged endothelial cells in a timely manner to restore equilibrium and maintain endothelial integrity (Gyan et al., 2009). Generally, damaged endothelial cells are repaired through the replication of existing endothelial cells at the site of injury or by 9 University of Ghana http://ugspace.ug.edu.gh bone marrow-derived circulating endothelial progenitor cells (Asahara et a1., 1997). Thus, factors that affect the mobilization, release and eventual integration of EPCs into sites of endothelial damage are important for CM and other diseases that involve vasculopathology. So far only a few angiogenic factors have been studied in the context of endothelial damage/repair in CM pathogenesis (Adukpo et al., 2016; Gyan et a1., 2009). Furthermore, these studies have often failed to explore how host genetic and epigenetic factors influence the production of these molecules and endothelial integrity. This study investigates how dozens of factors grouped either as angiogenic, haematological or immunological influence endothelial integrity during a P. Ja/ciparum infection. The study further explores how SNPs and their epigenetic contexts influence the production of these factors and subsequently, endothelial integrity. This study overcomes the problem of in vivo assessment of endothelial integrity by fitting EPC data to a Gaussian mixture model to create a binary endothelial integrity variable with pro-damage and pro-repair as the possible outcomes. The study also used the concepts of tagged SNPs to glean information from genomic locations in linkage disequilibrium with genotyped variants. Thus, beyond studies that just seek to associate EPCs, and angiogenic factors with malaria phenotypes, this study generates a wealth of data that can be analysed at several points in the genotype-phenotype continuum. The fmdings of this study could have implications for CM pathogenesis especially in the adjunctive therapy efforts and the search for prognostic biomarkers for CM. 1.6. Conceptual Framework The theoretical framework undergirding this study conceptualises CM as cerebrovascular pathology and explores the notion that disequilibrium in damage/repair 10 University of Ghana http://ugspace.ug.edu.gh of brain endothelium during P. falciparum malaria infection partly governs the pathogenesis of CM. The discourse on this pathophysiologic model in the preceding sections of the chapter unveils dozens of key mediators that are used as variables in this conceptual framework. These variables loosely fall under four groups (angiogenic, haematological, immunological or parasitological factors) and the framework explores relationships between these factors, endothelial integrity and subsequently malaria phenotypes. Endothelial integrity is at the heart of this study as a bivariate outcome (pro-damage and pro-repair). This outcome variable is obtained by fitting empirically measured EPCs to a Gaussian mixture model to allow for dichotomization. The use of EPCs in this regard is reasonable because EPCs have been shown to be good markers of endothelial dysfunction (Gyan et al., 2009; Taguchi et ai., 2008; Venna et al., 2017; Werner et al., 2005). While a number of studies report on associations of angiogenic, immunological, and haematological factors with CM pathophysiology (Adukpo et aI., 2016; Boufenzer et al.. 2012; Gyan et al., 2009; Machado et al., 2006), the genetic and epigenetic mechanisms that regulate the productions of these factors are yet to be explored. In this conceptual framework, genetic factors (SNPs) with specific epigenetic contexts are hypothesized to influence microvascular endothelial integrity via influencing the production of the biomolecules they encode (i.e. angiogenic factors, haematological factors, and immunological factors). Thin blue lines in Figure 1 map these hypothesized relationships. Red lines represent relationships that are plausible but not explored in this study. The study also envisages the possibility of a direct association of genetic factors with endothelial integrity and such associations are mapped with medium 11 University of Ghana http://ugspace.ug.edu.gh weight blue lines. Associations between endothelial integrity and malaria phenotypes are mapped with a thick blue line. Malaria phenotype spectrum is an important outcome variable in this conceptual framework. Study participants are categorized into four (4) groups - uncomplicated malaria. severe malarial anaemia, cerebral malaria, and healthy controls. This allows for case-case and case-control comparisons. Although the framework explores the relationships malaria phenotypes may have with other variables, it does that with caution when it comes to the association of SNPs with malaria phenotype because of sample size constraints. 12 University of Ghana http://ugspace.ug.edu.gh + TEl +fNOI Malaria Phenotype Spectrum Figure 1. I: Conceptual framework University of Ghana http://ugspace.ug.edu.gh Taken together, explaining CM in the light of microvascular damage and repair is an emerging pathophysiological model that needs further clarification. Using EPCs as markers of endothelial dysfunction and a focused panel of SNPs with a well- characterised regulatory and epigenetic context, this study explored relationships between SNPs, some key mediators of endothelial cell damage/repair, endothelial integrity and malaria phenotypes in Ghanaian children with CM. 1.7 Justification ofstudy Majority of children infected with P. Jalciparum malaria remain asymptomatic or develop UM. However, a minority (about 2%) may progress to severe malaria which sometimes manifests as SMA or CM (Greenwood et ai., 1991). Emerging empiric evidence and mathematical modelling suggest that the decline in malaria transmission in hitherto endemic regions will present fresh epidemiological scenarios that could alter the status quo in malaria epidemiology and control (Nkumama et aI., 2017). One such scenario instanced by the decline in malaria transmission is the change in malaria's clinical epidemiology to favour higher incidence ofCM (Ceesay et aI., 2008; Flirnert et aI., 2014; O'Meara et aI., 2008). This predicted increase in the proportion of CM is ominous because, after onset, mortality from CM is high (15 - 25%) regardless of treatment with anti-malarial drugs (World Health Organization, 2016b). Worse still, 25% of infected children who recover from CM may suffer deficits in cognition, hearing, vision or develop epilepsy (Gupta et aI., 2017; Wahlgren et al., 20 17; Wassmer et ai., 2015). The changing clinical epidemiology of malaria and its consequences notwithstanding, reliable prognostic markers and effective adjunctive therapy for CM are still unavailable. This situation brings to the fore the need for a better understanding of CM pathogenesis. 14 University of Ghana http://ugspace.ug.edu.gh I.S General objective General objective This aim of the study is to genotype a focused panel of 27 SNPs, characterized their epigenetic context and explore their relationships with endothelial integrity, clinical malaria phenotypes, angiogenic, and immunological factors. 1.9 Specific objectives The objectives of this study are to: 1. Genotype and epigenetically characterize a focused panel of 27 SNPs in the study popUlation. 2. Explore the relationships between the focused panel of 27 SNPs, endothelial integrity and malaria phenotypes. 3. Use in silico tools to determine the potential influence of trait-associated SNPs on the binding sites of microRNAs and transcription factors. 4. Determine the association of trait-associated SNPs with serum levels of the angiogenic factors they encode. 5. Explore the relationship between malaria phenotypes, endothelial integrity, and key clinical, haematological, immunological, parasitological and angiogenic factors. IS University of Ghana http://ugspace.ug.edu.gh 1.10 Hypothesis This study hypothesises that mutant variants of a focused panel of27 SNPs compromise brain endothelial integrity and subsequently increase the risk of eM in Ghana children with P. Jalciparum malaria. 16 University of Ghana http://ugspace.ug.edu.gh CHAPTER TWO 2.0 LITERATURE REVIEW 2.1. Malaria as a global health problem. The term "global health" arguably suffers an identity crisis. Academics and practitioners have contented its definition and struggled to distinguish it from allied disciplines such as public health, international health, planetary health and the likes (Bettcher and Lee. 2002; Hoffman and Cole, 2018; Lerner and Berg, 2017). In spite of a contentious definition and blurry boundaries, there seem to be the consensus that a global health issue should be one that is transnational and require a collaborative research and health promotion efforts to curb (Beaglehole and Bonita, 2010; Koplan et al.. 2009). Thus, a review that seeks to establish the status of any disease as a global health problem should proffer insights into its transnational distribution and the need for collaborative research efforts in curbing the disease. In a bid to justify the status of malaria as a global health problem, section 2.1 of this thesis reviews the global distribution of malaria, outlines international partnerships in malaria control efforts, and highlights the threats to malaria elimination aspirations. One of the most palpable features that characterise malaria as a global health problem is its distribution. Latest malaria burden estimates show the persistence of the disease in 91 countries with 219 million cases and 4510000 deaths (World Health Organisation, 2018). Compared with estimates from 2010 through to 2015, the 2017 estimates represent a decline in the burden of malaria. This decline notwithstanding, an annual disease burden of 219 cases in over 90 countries is enough to earn malaria the tag of 17 University of Ghana http://ugspace.ug.edu.gh global health problem. More troubling, however, is the evidence suggesting that the much-touted decline in malaria burden is actually beginning to stall. For instance, there were about 2 million more cases of malaria in 2017 relative to 2016 (World Health Organisation, 2018; World Health Organization and Global Malaria Programme, 2017). The global status of malaria is self-evident and somewhat a mundane find; what is striking, however, is the disproportionate global distribution of the disease. Currently, 90% of all malaria cases and deaths occur in the WHO African region and even within this region. Sub Saharan Africa bears about 80% of the burden (World Health Organization and Global Malaria Programme. 2017) (Fig 2.1 ). Ironically, the latest data on malaria suggests that the largest gains in malaria decline did not occur in the WHO African regions with the highest burden (World Health Organization and Global Malaria Programme, 2017). This observation subtly suggests a possible misalignment of efforts and endemicity. Another feature that marks malaria as a global health problem is the nature and source of investments made towards curbing the disease. Traditionally, the bulk of the funding for malaria control programs activities in endemic regions has come from international partners. In 2016 for instance, only 31 % of expenditure on malaria control activities came from endemic countries (World Health Organization and Global Malaria Programme, 2017). 18 University of Ghana http://ugspace.ug.edu.gh University of Ghana http://ugspace.ug.edu.gh Although funding for malaria has remained stable since 2010, the US$ 2.7 billion invested in 2016 is less than half (41%) of the funding required for meeting Global technical strategy for malaria 2016-2030 (GTS) targets. Worse still, the funding available per person at risk has reduced to below US$ 2 in 34 out of the 41 high-burden countries (World Health Organization and Global Malaria Programme, 2017). Investment in proven interventions, tools, and strategies is the best way to ensure that GTS is on track and thus, it worrying to see investments in malaria control dwindle. Although individual malaria-endemic nations may face unique challenges in their efforts to eliminate malaria, some of the challenges are common to all. These challenges may include lack of sustainable funding, the emergence of drug and insecticide resistance, climate change, and political instability. Although all these are formidable threats, the generation and spread of drug- and insect resistant parasites and vectors pose the most threat of rendering current effective control measures redundant. In this regard, the increasing prevalence of histidine-rich protein-2 gene (HRP2) deletions in parasites are potentially deleterious to the rapid diagnosis of malaria (Koita et aI., 2012). Dealing with the aforementioned threats often requires a collaboration between endemic countries and international partners which is another testament to the global status of malaria. The transnational nature of malaria control efforts is another aspect of malariology that reflects the global health status of malaria The malaria control toolset has interventions, tools and strategies that could prove instructive in the fight against malaria if deployed effectively in endemic regions (Hemingway et aI., 2016). However, the deployment of these interventions be they diagnostics, medicines, insecticides, or surveillance systems 20 University of Ghana http://ugspace.ug.edu.gh is often suboptimal. For instance, insecticide-treated mosquito nets (ITNs) which form the foundation of malaria prevention in Sub Saharan Africa reached only 54% of the population at risk in 2016 (Fig 2.2). Although this shows ITN cover only about half of the population at risk, it is noteworthy that this represents an increase from 30% in 2010 (Bhatt and Gething, 2014; World Health Organization and Global Malaria Programme, 2017). On the other hand, the proportion of the population protected by indoor residual spraying (IRS), the only other vector control measure, shrunk by about 2.6% between 2010 and 2016 (pluess et al., 2010; World Health Organization and Global Malaria Programme. 2017). Variations in country-level commitment and capacity to implement malaria control strategies affect coverage of control strategies in endemic areas. The resultant mosaic nature of malaria control efforts in these sub-regions eventually waters down the efforts of compliant nations. Several other factors that are not discussed here, such as, access to health facility and medications, accurate diagnosis, robust surveillance systems and political will contributes to making malaria global health issue (Eisele et al., 2010; World Health Organization and Global Malaria Programme, 2017). This section has reviewed malaria as a global health problem and in so doing touched on malaria epidemiology and control strategies. Taken together, the review reveals that the burden of malaria may have reduced drastically in the last decade, but warns of the risk of a possible reversal of gains if investments in malaria control are not increased. In addition, the variable disease distribution and uptake of interventions suggest that endemic countries must play lead roles in malaria elimination efforts to bolster the odds of success. 21 University of Ghana http://ugspace.ug.edu.gh ~ N '" N ;S < I: ~r.. .s~: ~ rJ:J .s: '= .5" z ~'~ t: .s ~ QI CJ 1 case per 1000 population) with almost all malaria cases caused by P. jaiciparllm and transmitted either by Anopheles gambiae. Anopheles fllnesflls. or Anopheles arahiensis. Although malaria cases and deaths reported at health facilities in Ghana in 2016 stood at 4,535,167 and 1.264 respectively, the number is estimated to be 8,060.000 [5.300.000-11,950,000] and 12.880 [11.510- 14.2501 respectively (World Health Organization and Global Malaria Programme, 2017). fhere appears to be a steady rise in malaria cases in Ghana since 2005 with the highest recorded in 2016 (Fig 2.3) but it is unclear whether the increase represents an actual rise in the number of cases or a result of improved surveillance systems. Whereas malaria admission in Ghanaian health facilities has increased in the last 12 years. mortality for the same lime period seems to have decreased (Fig 2.4) . • • Figure 2. 3: Confirmed malaria cases in Ghana 2005 - 2016 23 University of Ghana http://ugspace.ug.edu.gh The count!") aligns its malaria control activities to WHO recommendation and thus, distributes ITNslLLINs free of charge to all age groups. Ghana adopted IRS in 2005 but the us~ of DDT is prohibited for IRS purposes. The malaria treatment policy in Ghana broadly foIlows WHO recommendation with the use of artemisinin-based combination therapy (ACT) (World Health Organisation, 2015). Additional and specific recommendation for managing various clinical scenarios of malaria is exhaustively outlined in the guidelines for malaria treatment published by the World Health Organisation (World Health Organisation, 2015). lr1alartft Bdlfl'''I''' )'1'" and d.ath. (Pel 1000001 25 ________ 20 2016 • .... ,., Figure 2. 4: Malaria admissions in Ghana (2005 - 2016) 2-+ University of Ghana http://ugspace.ug.edu.gh -,. I •• •I I t J j II I ."1', .'01: 201 \ JII.t 201~ 2016 ':1.'.17 2tK>" t:il!1 ' j"ll:' liMn",,' • lMiOlNCEF • Olhers Source' world report 2017 Figure 2. 5: Source of funding for malaria inten'entions Government financing of malaria control activities seemed to have increased from 2005 through to 2010 and stalled aftel"\\ards. Interestingly, all of the government's imestment in malaria control activities goes in the purchase of antimalarial and remuneration statT (Fig 2.6). Ghana government expenditure by Intervention ·• .• i.I ' ' . Figure 2. 6: Ghana go\'ernment expenditure on malaria 2.1.2 \talaria in Ghana: the socioeconomic burden Besides its clinical burden. malaria imposes a myriad of direct and indirect socio- economic burden in endemic countries. At the household level, it is estimated to cost Ghanaian households about US$ 14 to treat an episode of malaria (Asante and Asenso- Okyere, 2003: Dalaba et aI., 2014; Nonvignon et aI., 2016: fawiah et aI., 2016). On the 25 University of Ghana http://ugspace.ug.edu.gh cooperate front, businesses in Ghana lost an estimated US$6.58 million to malaria in 2014 (Dalaba et al., 2014; Nonvignon et al., 2016; Tawiah et aI., 2016). Around the same period, one of the foremost mining companies in Ghana reported that it incurs US$S5,000 per month in the treatment of malaria in their employees and dependants (Anglogold Ashanti, 2004). At the national level, a seminal work by Asenso-Okyere and colleagues in the early 2000s posited that a percentage rise in the incidence of malaria reduces productivity by 0.41% (Asante and Asenso-Okyere, 2003). Although these studies offer some insights into the socioeconomic burden of malaria in Ghana, the studies are old and heavily lopsided in favour of economics. Epidemiologist and health economics often describe the socioeconomic burden of disease in quantitative terms only and ignore the social and cultural dimension of disease burden. On the contrary, "burden" is a sociocultural contrast and thus, estimations of socioeconomic burdens that do not incorporate anthropological perspectives could be narrow and misleading (Jones and Williams, 2004). From this perspective, "burden" is not just a quantity; it has meaning too. The meaning of malaria burden will differ among sociocultural contexts but in most African communities, malaria phenotypes are perceived as different disease entities and not a continuum. Variations in the sick roles for different malaria phenotypes and the associated social vulnerabilities may differentially influence malaria interventions and control efforts in different communities (Jones and Williams, 2004). This notwithstanding, there is a dearth of knowledge in sociocultural dimensions of malaria burden in Ghana). 26 University of Ghana http://ugspace.ug.edu.gh 2.2. Malaria: tbe parasite, vector, and disease Malaria is a vector-borne infectious disease transmitted by the bite of the female anopheles mosquito. The human host, the insect vector, and the protozoan parasite are pivotal factors in the transmission of malaria and a better understanding of the interaction between these factors and the environment is instructive in the dash for elimination. Although the schematics of how malaria is transmitted is now common knowledge, the research that incrementally unravelled the lifecycle of malaria spanned almost a century (Cox, 2010; Guillemin, 2002; King, 1883; Krotoski et al., 1982; Lawrie, 1898; Manson, 1898; Shortt and Garnham, 1948). On the surface, the lifecycle of malaria appears straightforward and well describe (Fig 2.7), but advances in molecular biology, cell biology, genomics, and epigenomics has opened up a Pandora's box of new knowledge, some of which could be instructive in the search for novel therapies, vaccines and management of severe complications of malaria. Using the schematic lifecycle as a guidepost, this section reviews the literature on parasite biology, vector biology, and disease phenotypes in an integrated manner. 27 University of Ghana http://ugspace.ug.edu.gh r"'-;"--;"equlto blood n10.' pr. Ory'hr;;cvtl!hU;"a:~-;"'~';CtlOn ~- .~:.j r 'nl< , 'I""" t I ...... ,- VII.'..... I .t I •• :~ ~11: .... .,'".". ... , .,. F Oocy.' ... ~~$: ~ ... "It_ ~ ~ ~ ". "-~ ... ~ pv_ • '\ I .h. .......... "" •. · , JIm. \1 : .. a q ...: { " . .,. • ..- C Asexual ery'hrOCY~'C a'.ge ~ "..,Idgut .1 ,. '.1. "'"~1. '" VII...... ..1..'8' ' ·IM '1(.""5 .. ~ .. "'n,'~H _ i.~ L _____ ~1__~ __ ~ 0- fItJl rI n'I~3I-6 .. ylh..-O(_V1lc galTtetocyt_ devetopn"tent I M_:~, r-- , ;.': .':.: ~'~ ':"·"L .. .'J:, ~•.. u_.v .... ..~•• •- E .. ..- II "'.-~ t ,;.." (" v--:' .. 't": , .' r' .. ~~~ ~1,l ....... I "'~t-i~J-.... .:J- -... .,... --- ---...-..... (J\) Malaria infe( lion h initidltrd with the Injection of "'POI ol'oltes (!>opz,,) Into the del rni!\. by a fe~din& female anopheline mosquito. (B) The spzs enter the vascul.aturL' ... nd are transported to the liver. wht..~, e they eXit the' !>lnu!tOld5 throul" Kupffer or endothelial cell$ and enter a hepatocyte. Active Invasion IS. prt!t:\. . ded by <.ellular lr.jver~dlllntil a s.uitable hepatocyte is. found. They form a PVM and under80 schlzoeonv until tens. af thousands of d .. u8hter nl(~r()~(1It(~5 are releas.ed In pdckets. of merosorne'!> Into the vasculature. (C) tIl( it' th.-v enc.ountel ~rythrocvtes dnd bp£ln .1 chronic cycte of as.exual schizogony in the bloodstream, (D) A pr L.portion of asexually rrproduc.tng merozoltes. are reproarammed to undergo gametocytoe.enesis. ([) Witliln a 15 ct.-Jy period. eametoc.ytes sequester and develop within the bone marrow and. once mature. enter the peripheral circulation for ingestion by a mosquito where thpv e-mprge as. extracE"lIular malE" and female gametes In the mideut. (f) Mato)&; Oleurs by fusion of nliero- clnd macrogamele to for-m a zYaote transforming over 24 hr into a ookinete that migrates throueh th~ mosquito m6dKut epithelium and encysts to become an oocyst where asexual sporogenic replication occurs. Motile s.por-ozoltes are released into the hemocoel by oocyst rupture and pass Into salivary glands where thry can be injected into the next human host. Cowtnan" Healer, Marapana" & Marsh" 2016 Figure 2. 7: Lifecycle of the malarial parasites 28 University of Ghana http://ugspace.ug.edu.gh 2.2.1. The parasite: exploring Plasmodium parasite biology through the lifecycle Human malaria is caused by protozoan parasites belonging the genus of Plasmodium. There are over 100 species of Plasmodium but only five infect humans. Four of the five Plasmodium spp. (P. ovale. P. malariae. P. Jalciparum. and P. vivax) are natural pathogens of humans whilst the fifth (P. knowles;) is a natural pathogen to macaques but can cause zoonotic malaria. Almost all malaria cases in African are caused by P. Jalciparum but a significant fraction of malaria cases in the Americas, South East Asia, Eastern Mediterranean Regions, and the Western Pacific Regions of are caused by P. vivax. The other Plasmodium spp. have limited epidemiologic significance. Pre-erythrocytic parasite/orms During a blood meal. the female anopheles mosquito injects sporozoites into the dermis of the skin of a human host. A minority of these sporozoites glide through the dermis of the skin to locate and penetrate blood vessels but the fate of the majority of sporozoites that remain in the dermis is not fully known (Prudencio et at., 2006). Plausibly, inflammatory responses involving polymorphonuclear neutrophils and inflammatory monocytes in the skin destroy these sporozoites and drain them through the lymphatics (Mac-Daniel et at., 2014). The sporozoites that make it to the bloodstream locate blood vessels through homing mechanisms involving factors such as Trap-like proteins (TLP). Trap-like proteins (TLP) are believed to play significant roles in this homing mechanism because TLP-mutant sporozoites are unable to enter the bloodstream even though they have similar motility rates and patterns as TLP-wild type sporozoites (Cowman et at., 2016). Once in the bloodstream, sporozoites do not invade red blood cells but quickly move to the liver and traverse it. The process of migrating through the sinusoidal barrier of the liver which comprises fenestrated 29 University of Ghana http://ugspace.ug.edu.gh endothelial cells and kupffer cells is referred to as traversal (Tavares et ai., 2013). Several proteins are required for successful traversals but the best-known are SPECT (sporozoite microneme protein essential for traversal), phospholipase (PL), PLPI (perforin-Iike protein I), CelTOS (cell traversal protein for ookinetes and sporozoites) and GEST (gamete egress and sporozoite traversal protein) (Bhanot et al., 2005; Ishino et aI., 2004; Risco-Castillo et al., 2015). Besides clues that PLPI uses a membrane attack complex to punch holes in membranes, the specific roles of these proteins in traversal are yet to be fully clarified (Cowman et ai., 2016). Some scientists have argued that traversal somehow prepares sporozoites for hepatocyte invasion and as much as that is plausible. the primary function of traversal, which is the crossing of the sinusoidal barrier, cannot be downplayed. In P. jaiciparum, traversal is achieved through transient vacuoles and regress is via pH and PLPI sensing (Risco-Castillo et ai., 2015; Tavares et aI., 2013). In the skin, sporozoites are in the "migratory mode" and have to be converted into "invasion mode" before they can invade hepatocytes. The switching from migratory to invasive modes is regulated by several signals but the roles of highly-sulfated HSPGs and a calcium-dependent protein kinase are critical. Whiles the former activates sporozoites for invasion, the latter is critical for the switch to an invasive phenotype (Coppi et aI., 2007; Cowman et ai., 2016). The circumsporozoite protein (CSP), TYPE I thrombospondin repeat (TSR), thrombospondin-related anonymous protein (TRAP) and apical membrane antigen-l (AMA-l) are other key proteins involved in hepatocyte invasion (Cowman et aI., 2016; Herrera et aI., 2015). Important receptors on hepatocyte required for invasion include the tetraspanin CDS 1 and scavenger receptor B 1 (SR-B I) but not the famed EphA2 which appears to have no role I'n teh lO·va·sl on process b ut 30 University of Ghana http://ugspace.ug.edu.gh crucial for intra-hepatocytic development (Kaushansky et al .• 2015). After establishing liver infection. sporozoites transform into liver-stage parasites or what is referred to as exo-erythrocytic form. and finally. into merozoites that are budded off in vesicles called merosomes into the hepatic portal system (Burda et al.. 2017; Graewe et aI.. 2011). Taken together. it should be noteworthy that the transition from skin sporozoites to merosome merozoites involves complicated mechanisms. signalling. gene expressions we do not as yet fully understand. Traditionally. research has focused on blood-stage infections because of its association with clinical symptoms but recent insights suggest that liver-stage infections may hold the key to novel therapeutic and vaccinology approaches (Derbyshire et al.. 2012). 31 University of Ghana http://ugspace.ug.edu.gh A .... _.. ..................... ". .... ... '~, 1...") •• ", J' ,..". :~o~ ~, £) A Pla>rtlod,un. sporowlles (g"'"n) are depos,ted under the skIn of the human host through the bite of an infected fE-male ilnopheles mosquIto After InjectIOn mto the skin, the sporololtes move through the dermis until they ( contact blood vessels (red) and move into th" Circulatory system, which allows them to travel to the Itver, Majoritv ,.: of sporozoites remain In the dermIS and a small proportion of sporoloites C) in the promoter region of the same antigen are associated with a lower risk of vivax malaria (King et aI., 2011; Maestre et aI., 2010). Polymorphisms in other erythrocyte surface receptors use by P. !a[ciparum such as glycophorin, protein band 3 and others have been shown confer resistance in Papua New Guinea and Brazil (Tarazona-Santos et aI., 2011). A recent study reported a novel malaria resistance locus close to glycophorin 54 University of Ghana http://ugspace.ug.edu.gh gene and a haplotype at this locus provided 33% protection against severe malaria (Malaria Genomic Epidemiology Network, 2015). One of the commonest erythrocyte genetic disorder with implication for malaria pathogenesis affects the metabolic enzyme G6PD. The G6PD deficiency which affects _ 400 million people in tropics and sub-tropics is thought to reduce intracellular parasite growth, probably through enhanced phagocytosis by monocytes (Ayi et aI., 2008; Cappadoro et aI., 1998). Erythrocyte genetic disorder may also involve haemoglobin altemtions generally referred to as haemoglobinopathies. Haemoglobinopathies may involve structural alterations leading to variants ofHb, such as HbS, HbC, and HbE; or a defect in the synthesis of the globin chain in Hb (alpha- and beta-thalassemia). An SNP (rs334) in the beta-globin gene (HBB) is responsible for the famed HbS variant whose heterozygosity is consistently associated with protection from severe fonns of malaria including CM (reviewed in (Mendon~a et aI., 2012). The other Hb variants (HbC and HbE) have been shown to protect against severe malaria phenotypes (Agarwal et aI., 2000; Mendon~a et aI., 2012; Nagel et ai., 1981). Although variants of thalassaemia and HHB have been associated with malaria severity in different parts of the world, the patterns of resistance are mosaic in nature and difficult to generalise (Arese et aI., 2015; Daou et aI., 2015; Mendon~a et ai., 2012; Para et al., 2018). 2.5.2 Malaria immunogenetics Surviving a P. /alciparum infection requires a well-coordinated and finely tuned immune responses involving several immune cells, antibodies, cytokines, chemokines, receptors, transcription factors etc. Genetic and/or epigenetic modifications of any of these factors can derail the response and lead to an inappropriate response. Studies over 55 University of Ghana http://ugspace.ug.edu.gh the years have identified several polymorphisms in these factors that can potentially influence the immune response and affect the pathogenesis of malaria. Toll-like receptors play important roles in innate immune response and TLR2, TLR4 and TLR9 have been found to particularly important in malaria (Coban et al., 2005; Krishnegowda et al.. 2005; Parroche et aI., 2007). Majority of functional SNPs describe in TLRs affects ligand recognition and intracellular signalling and thus, it is unsurprising that these TLR SNPs are implicated in parasitic disease (SchrOder and Schumann, 2005). Similarly, several polymorphisms in TLR2, TLR4 and TLR9 have been associated with malaria but the most interesting is a 22-base pair deletion in the UTR in TLR2 which is associated with protection from CM (Greene et al., 2012). Polymorphisms in other TLRs are inconsistently associated with malaria in Ghana, Brazil and Malawi (Omar et aI .• 2012; Zakeri et aI., 2011). However, a meta-analysis of all TLR SNPs found the association of TLR9T -123 7C with severe malaria to be the most robust (Dhangadamajhi et al.. 2017). Cytokines play an important role in the pathogenesis of malaria and an imbalance in pro-inflammatory. anti-inflammatory and regulatory cytokines is believed to contribute to the development of severe forms of malaria including CM. Tumour Necrosis Factor Q is a key cytokine in malaria immunity and immunopathology and several polymorphisms have been described in the TNF -Q gene that can influence the pathogenesis of malaria. A study in Gambian children associated rs 1800628 and rs361525, two of the best known TNF-Q SNPs, with CM and SMA respectively (Clark et aI., 2009; McGuire et aI .• 1994). These same SNPs were associated with protection from mild (rs361525) malaria and the frequency of malaria infection (rs1800629) (Clark et aI., 2009; Meyer and Astor, 2002). Other studies on these SNPs and others 56 University of Ghana http://ugspace.ug.edu.gh (rs 1799724 and rs 1799964) have been associated with severe malaria phenotypes in different malaria-endemic regions (Wattavidanage et al., 2001). Several other studies have finds SNPs in TNF-a to be associated with eM but these studies are froth with heterogeneous study designs and mixed results (Mendon~a et al., 2012). Other cytokines and transcription factor gene polymorphisms, as well as some chromosomal regions (5q31-33), have been shown to influence malaria in different settings (Furini et aI., 2016; Naka et aI., 2009). Of note is the role of an intronic SNP and the STAT6 gene that was shown to protect against CM in Ghanaian children (Amoako-Sakyi et aI., 2016). Polymorphisms in receptors for the Fc fragment of IgG (FcyRs) which serve as a link between humoral and cellular immune responses have also been shown to influence malaria pathogenesis. Besides the famed FcyRIIA H 131 R polymorphism, several studies have reported on SNPs that could influence malaria pathogenesis (Adu et al., 2012; Braga et al" 2005; Mendon~a et aI., 2012; Munde et aI., 2017, 2012). A recent study among the Fulani who are naturally resistant from severe fOnDS malaria suggests that the mechanism of protection may be FcyRs-mediated (Cherifet aI., 2016) Polymorphism in cells, molecules and receptor that are not exactly immune-related but involved in the pathogenic mechanisms can also influence the severity of malaria. Here again, polymorphisms in ICAM-l (rs5491, rs5498), C036 (rs321 1938 and GI439C), PECAM-I (rs668, rsl2953 and rsl131012) and CRI (rs9429942) have been associated with malaria severity (Adukpo et aI., 2013b; Kikuchi et aI., 2001). Taken together, the role of host genetics on malaria pathogenesis cannot be discounted, however, 57 University of Ghana http://ugspace.ug.edu.gh methodological heterogeneity, regional differences in malaria transmission. variance in parasite virulence and other factor has led to a mosaic result that is difficult to interpret. 2.5.3 Malaria bost epigenetics Apart from heterogeneity in study designs, regional differences in malaria transmission and variable parasite virulence, host epigenetics can also cofound results of genetic susceptibility studies. The regulation of gene expression is not entirely genetic and epigenetic modifications such as DNA methylation, histone modifications and RNA- base modification can influence the pathogenesis of several diseases including malaria (Wagner et aI., 2014; Zeng et aI., 2014). Interestingly, there is evidence to suggest that epigenetic mechanisms collaborate with genetic mechanisms to co-regulate gene function in several disease models (Berger et aI., 2009). This possibility is exemplified in the occurrence of SNPs within epigenetic marks. Such occurrences affect chromatin structure at specific genomic locations by modifying methylation patterns or histone type recruitment (Dayeh et aI., 20 13b). Some researchers posit that interactions between genetics and epigenetics may offer better explanations for scenarios where disease- associated genetic variants lie outside promoters or coding regions (Zaina, Perez- Luque, and Lund 2010). The literature on the effects of epigenetic mechanisms on the pathophysiology of human disease are growing but few have focused on malaria or looked at the interaction ofSNPs and epigenetic marks in malaria pathogenesis (Bell et aI., 2011; Dayeh et aI., 2013a; Gupta et aI., 2017). However, the advancements in bioinformatics and the availability of new tools have opened the prospects of analysing such interactions in silico. S8 University of Ghana http://ugspace.ug.edu.gh CHAPTER THREE 3.0 METHODS 3.1 Study Site This study recruited volunteers from five partner health facilities (Figure 3.1) within Accra and It:ma metropolitan areas in Ghana. The partner hospitals were referral facilities. and thus. they served the communities in which they are situated as well as attend to cases referred from smaller satellite facilities such as Health Centres and CHiPS zones. Thc~c facilities were selected based on their sentinel nature and proximit~ to Noguchi Memorial Institute for Medical Research where laboratory analysis was conducted. a .\ .... 1 •. •• Locations of 5 stUdy hOSPI;~~ In relation to NMIMA Figure 3. 1: Study site University of Ghana http://ugspace.ug.edu.gh 3.2. Study design and sample size estimations Participants for this cross-sectional study comprised three different malaria phenotypes (CM. UM and SMA) collectively referred to as cases and a control group are referred to as healthy controls (HC). The cases were recruited from partner hospitals whilst healthy controls were recruited from basic school within the vicinity of the partner hospitals. All partner hospitals were within a reasonable distance from NMIMR to ensure that samples got to the laboratory on time for analysis. Recruitment of study participants was done between 2013 to 2016. This study used G*Power 3.9.1.2 for all sample size estimations. This study had multiple objectives with each requiring different sample size considerations. For instance. in comparing malaria phenotypes and controls (i.e. 4 groups), G*Power 3.9.1.2 estimated that a total sample size of 280 will be enough to detect a moderate effect size 0.25, at a power of 0.95 and a - level of 0.05. However, in comparisons among malaria phenotypes only (3 groups), a total sample size of 240 was thOUght to be enough to give the same effect size and power at an a-level of 0.05. In comparing 2 groups under the same conditions, a total of 21 0 was deemed to be enough. This study ended up using a total of 221 study participants, thus, whilst the study was slightly underpowered (0.85) in four-group comparisons, it was adequately powered and slightly overpowered in detecting differences in three-group and two-group comparisons respectively. This study prioritised detecting the difference between CM and other non-eM groups and in all these two-group comparisons. the study was adequately powered (> 0.95) in detecting an effect size 0.1. 60 University of Ghana http://ugspace.ug.edu.gh Hong & Park, 2012 have shown that sample size for candidate gene studies is dependent on the inheritance model assumed (Hong and Park, 2012). This study's sample size of 221 is deemed adequate for all inheritance models except for the recessive inheritance model. Correcting for multiple testing for the 27 SNPs. a Bonferroni approach suggested a conservative significance threshold for this study (p < 0.002). Thus, genetic testing was deemed significant p < 0.002. 3.3. Ethical Considerations This study secured ethical clearance from the Institutional Review Board (lRB) of the Noguchi Memorial Institute for Medical Research (NMIMR). Both the Ghana Health Service (GHS) and the Ghana Education Service (GES) gave approval for study participants to be recruited from their respective facilities. Parents and legal guardians consented to the study before their wards were enrolled in the study. Parents had the right to discontinue the study anytime during the study. 3.4 Inclusion criteria Children reporting at the GPO or emergency departments of partner hospitals with suspected cases of malaria. Parental consent and a child's accent were considered as an inclusion criterion. 3.4.1 Specific inclusion Criteria Study participants were categorized as either cerebral malaria (CM), uncomplicated malaria (UM), severe malarial anaemia (SMA) or healthy controls (HC) based on specific and stringent clinical case definitions: 61 University of Ghana http://ugspace.ug.edu.gh Uncomplicated malaria (UM): .:. History of fever within the last 48 hours of fever (axillary temperature ~ 37.5°C). •: . Five or more parasite per HPF (approx. 2S00/~I) .:. No other obvious cause for fever. Cerebral malaria (CM): .:. Inclusion criteria for UM . •: . Unconsciousness with coma score of < 3 for the duration of> 60 minutes on the Blantyre coma scale . •: . No record of recent severe head trauma and other cause of coma or neurological diseases . •: . Haemoglobin of> SgldL Severe malaria anaemia (SMA): .:. Inclusion criteria for UM . •: . Haemoglobin of < SgldL, .:. Fully conscious, .:. No cases of severe bleeding reported or observed .:. No convulsion. Healthy controls: .:. No fever .:. No malaria parasitaemia. 62 University of Ghana http://ugspace.ug.edu.gh 3.4.2 Exclusion criteria Potential study participants with concomitant infections such as bacteraemia and meningitis/encephalitis (ascertained from CSF testing conducted by a trained technician) at the time of recruitment were excluded from the study. Children with HIV infections and other medical conditions that can potentially influence EPC levels or microvascular damage were also excluded from the study. Other conditions and medical procedures that excluded potential study participants from the study included cardiovascular diseases, diabetes mellitus, hypercholesterolemia, surgery in the last one month, bone fracture in the last three months, major trauma in the last three month (e.g., road traffic accident (RTA » and blood transfusion in the last three months. Study clinicians in charge of recruitment used information in the folder of study participants and clinical judgement to exclude a participant from the study. 3.5 Blood sample collection Each study participant provided 2ml of venous blood in EDTA tubes for a complete blood count (CBC), blood culture, sickling test, and blood smear for malaria parasite estimation. Additional blood samples were collected into heparin tubes (lml), EDTA tubes (lml) and blood culture bootless (1 ml) for downstream laboratory analysis, which included immunoassays, flow cytometry, and genetic analysis. Healthy controls provided venous blood in heparin (lml) and EDT A (2ml) tubes. All blood samples were collected by a trained phlebotomist. 63 University of Ghana http://ugspace.ug.edu.gh 3.6. Sample processing and downstream analysis Blood samples for routine clinical procedures were taken immediately to the hospital laboratories for evaluation, whiles those for the research study were transported in cold ice chests to the laboratories of the Immunology Department, NMIMR. Cerebrospinal fluid samples were transported to either the Korle Bu Teaching Hospital or the Lancet laboratory for analysis. At NMIMR, a 400ul aliquot of EDTA treated blood were used for flow cytometry and the rest were separated into RBCs and plasma for storage at - 30°C. Heparinized blood was also processed by centrifugation and separated into RBCs and platelet-free plasma This was done by initial centrifugation at lOOO x g for 15 minutes and separated plasma at 10000 x g for 10 minutes. Both RBCs and platelet free plasma were stored at -30oe for further analysis. 64 University of Ghana http://ugspace.ug.edu.gh Figure 3.2: From field to data: a schematic flowchart "f sample processing University of Ghana http://ugspace.ug.edu.gh 3.7 Measurement of angiogenic factors Serum levels ofSDF.I, NGRI, TREMl, ANGI, ANG2, CAl, TEK. SIB and MMp·9 were multiplexed and measured with a Human Magnetic Luminex Assay (Luminex Cooperation, Texas) according to manufacturer's protocol. Briefly, calibrator diluent RD6.52 (provided by the manufacturer) was used to dilute plasma samples and standard cocktail 10·fold and 3-fold respectively whilst microparticles cocktail concentrate was diluted to I X by adding 5ml of RD2-1 diluent. The I X microparticles cocktail was then added to wells of the microplate at 501l1/well before the addition of 50 III of sample and standards following the predesign ELISA plate template. The microplate was then incubated for two 2 hours on a horizontal orbital microplate shaker at 800rpm at room temperature. After incubation, the microplate attached to a magnet and washed thrice. The attachment of the plate to a magnet ensured that microparticles were not accidentally washed away. Fifty microliters (50 Ill) IX Biotin-Ab cocktail was then added to each well of the microplate and incubated for an hour on a microplate shaker at 800rpm before washing thrice again. Streptavidin-PE diluted 24-folds was then added to the microplate at 50 Ill/well and incubated on a shaker at room temperature for 30 minutes after which the previous wash step was repeated thrice. A hundred microliters (100 Ill) of wash buffer was then added to each well of the microplate and incubated on a shaker for 2 minutes before reading with a Luminex 200 analyser not later than 90 minutes after the last incubation step. 3.8 SNP Genotyping All 27 SNPs were genotyped using the commercially available Sequenom MassARRA Y iPLEX platform. This assay comprises several different experiments or operations starting with DNA extraction from whole blood followed by an initial locus- 66 University of Ghana http://ugspace.ug.edu.gh specific PCR reaction, a single base extension, and finally mass spectrometry that identifies the SNP allele. Assay design and preparation of DNA samples were done at Noguchi Memorial Institute for Medical Research, University of Ohana. Robotics of the final iPLEX reaction and base calling was however performed on the commercial Sequenom MassARRA Y platform at Inqaba Biotec, South Africa. 3.8.1 DNA isolation from whole blood Genomic DNA was extracted from whole blood samples using Quick-gDNATM MidiPrep from Zymo Research Corp. DNA isolation was done following manufacturers protocol with minor modification (Zymo Research Corporation, 2014). Exactly 0.6 ml of genomic lysis butTer provided by manufacturer was added - 150 ul of whole blood and vortex for 4 - 6 second to mix completely. The mixture was allowed to stand for 5 minutes at room temperature and before transferring it to the Zymo- Spin™ V-E ColumniZymo-Midi Filter™ provided by the manufacturer. The Zymo- Spin™ V-E ColumnlZymo-Midi Filter™ assemblage with its content was then centrifuged at 1,000 x g for 5 minutes. The Zymo-SpinTM V-E Column was disconnected from the assemblage and transferred to a collection tube and spun at 10,000 x g for 1 minute to remove any residue from the column. Exactly 0.3 mlofDNA Pre-Wash ButTer provided by the manufacturer was added to the column and spun at 10,000 x g for 1 minute and the flow-through discarded. The previous step was repeated twice with 0.4 ml of g-DNA Wash ButTer before transferring the column to a 1.5 ml centrifuge tube. DNA Elution ButTer was then added directly to the column matrix and allowed to stand for 2 minutes. The column matrix with its content was spun at 10,000 x g for I minute elute the DNA. The concentration of eluted was measured with 67 University of Ghana http://ugspace.ug.edu.gh NanoDropTM 8000 Spectrophotometer (Thermo Fisher Scientific) and then store at - SO°C. 3.8.2 Pre-PCR: DNA and oligo pool preparation The concentration of gDNA isolated from whole blood was diluted to 2.5 nglut using TE buffer and then divided into two aliquots of 2 J.ll/well in a 384-well PCR reaction plate from (Marsh Biomedical). PCR plates with DNA a1iquots were kept at 4°C whilst awaiting downstream reactions. Unmodified and standard purified Oligonucleotides (oligos) (Integrated DNA Technologies) for PCR and iPLEX reaction were ordered for al127 SNPs. Oligos for PCR were ordered at final equimolar concentrations of240 J.lM (vendor ensured) in 96-well plates, however, they were used at a working concentration of 1 IlM (table 3.1). Probes for iPLEX extension were ordered unmixed in a 96-well deep plate at 250 to 450 J.lM (vendor ensured). To allow for multiplexing, oligo pooling for PCR and extension was performed following the pre-designed assay pool plex and oligo plate map. The oligos were divided into 3 groups based on their masses with the highest mass group (Le. group I) diluted to 15 J.lM, medium mass group (i.e. group 2) diluted to 10 11M. and the low mass group (Le. group 3) diluted to 5 J.lM. These concentration adjustments were done to ensure that the peak. intensity of extension primers is uniform. Oligos diluted to a working concentration were stored at 40C. 3.8.3 peR amplification of target loci Exactly 2 III of prepared gDNA was dispensed into each well of a 384-well PCR plate before applying the assay pool. Thus, each well received a different sample but the same assay pool. Four microliters (4 J.ll) of PCR master mix was then added to each of the wells to make a total reaction volume of 6 J.lUwell. The PCR reaction was then 68 University of Ghana http://ugspace.ug.edu.gh perfonned with a PC-controlled thennal cycler (Thermo Scientific Hybaid 384-well blocks) following the cycling program described in table 3.2. Denaturation Annealing 30 sec Extension 1 min Final extension 1 cycle 3 min Hold Final step indefinite 3.8.4 PCR product clean-up with Shrimp alkaline phosphatase (SAP) protocol The resultant PCR product was the "cleaned up" to remove all unincorporated dNTPs using the SAP protocol. The SAP cocktail solution (akin to a master mix in a PCR reaction) was prepared following manufactures protocol and added to the PCR plates containing the PCR products at 2 Ill/well. The PCR reaction plate with its content was then placed on a carrier and then mounted on the stackers in the post-PCR Multimek SpectroPREP machine. The SAP program was then implemented in StakNet. The plate was removed after the SAP operation, vortexed and then centrifuged at 425 x Ig at room temperature to bring the solution to the bottom. The SAP reaction was then incubated using the following program in a thennal cycler: 1 cycle: 40 min 37°C; 1 cycle: 10 min 85°C; final step: indefinite 4°C. The mixture was centrifuged at 425 x Ig for I minute and then stored away at 4°C until needed for the downstream process. 69 University of Ghana http://ugspace.ug.edu.gh amplificatIOn 10·mertog --..!0rwan: PCR prtmer 5~ .. (C/G] _ -- 3' 3' [GtC) ___ S' ganomlCDNA _ reverse PeR primei~mer log PCR producl [C/G) (GlC) I SAP trealmenllo neulmlize SAPlr8lllment unlcorpol'llted dNTPs iPLEX reneltOn ~ primer extension imo SNP alte,....... ________, allele 1 ~~~~~ C ~ IPLEX Gold cocktail containing - Gg extension Into SNP aite primer. enzyme, buller. and a1. ... 2 p~n~m~er~~~~ ~ m;l8s.modlfled nucleotldes sample conditioning. dispensing. and MALOf· TOF MS spectrum ~ MALOI-TOF malll spectrometry analytl. f~~1 24·plax spectrum Figure 3. 3: iPLEX reaction 3.8.5 iPLEX reaction Primer extension cocktail was prepared following the manufacturer's protocol and added (2 Ill/per well) to the cleaned peR product in the 384-well plate using the StakNet Program. The plate was then removed from the stackers, vortexed and centrifuged at 425-x g at room temperature before proceeding to thermal cycling. 70 University of Ghana http://ugspace.ug.edu.gh Thennal cycling for the primer extension reaction was done in Hybaid 384-well block (Thenno Scientific) with the following cycling program (Table 3.3) ili'R. Iii" 'lim for iPLEX re.ction initial denaturation 1 cycle sec denaturation 40 cycles 5 sec 940 C annealing 5 cycles 5 sec 52·C extension 5 sec 800 C final extension I cycle 3 min 720 C hold indefinitely 40 C The primer extension product was cleaned up using SpectroCLEAN (Sequenom) to remove salts such as Na+, K+, and Mg2+ ions and optimise for mass spectrometry analysis. Resin slurry was prepared following the manufacturer's protocol and added (16 Ill/well) to the post-PCR plate containing the product of primer extension with the help of the SpectroPREP Multimek. The plate was removed after resin addition and rotated in plate rotator before centrifuging at 425 x g for 3 minutes at room temperature. The primer extension product was then spotted on SpectroCHIPs (Sequenom) and primer extension detection was done with Compact mass spectrometer (Sequenom). The resulting spectra are analysed by SpectroTyper software© 3.9. Other laboratory evaluations Routine laboratory analysis and diagnostic tests were done in the laboratories of the partner hospitals and results were made available to the research team. However, some of these tests were repeated to meet the a priori stringent laboratory analysis of this study. 71 University of Ghana http://ugspace.ug.edu.gh 3.9.1 Haematological analysis: Complete blood count (CaC) was done for all study participants on successive visits using a 5part - Sysmex haematological analyser (Sysmex Corporation. Japan). This operation outputs many haematological indices but those of interest to this study included haemoglobin levels (Hb), total white blood cell (WBC), platelets. 3.9.2 Parasitological evaluation: Blood films for malaria diagnosis was done in the hospital laboratories following established protocols. Parasite density estimations were done at NMIMR following the WHO protocol ("Malaria diagnosis," 1988; World Health Organization. 2010). Total parasite biomass (Ptot) was estimated using a formula proposed by Dondord and colleagues Ptot = 7.3·PtHRP2 ·(I-Hct (%»·body weight (Kg)·1013, with PtHRP2 in giL (Dondorp etal., 2005; Hendriksen et aI., 2012). On the other hand. peripheral blood parasitaemia (PbP) was estimated using the formula: parasites/ilL· 10 6·blood volume, with blood volume defined as 0.08· body weight [kg] (Dondorp et al., 2005; Hendriksen et aI., 2012). The difference between Ptot and pbP makes up the sequestered parasites (Pseq). 3.9.3 Bacteraemia evaluation: bacteraemia was assessed in children recruited in the CM group using a protocol described by Cheesbrough (Cheesbrough, 1984). 3.10. The use of Gaussian mixture model Classification of endothelial integrity was based on secondary data from an earlier study on the same samples which measured serum levels ofcEPC (Oduro, 2015). The need to convert continuous biomarker data into discrete variables is common and although some studies have used the mean- or median-split approach, that approach has come 72 University of Ghana http://ugspace.ug.edu.gh under heavy criticism in recent times (DeCoster et al., n.d.; Iacobucci et aI., 2015; Rucker et a\., 2015). Mixture models are preferred because unlike the mean- or median- split. it estimates cut-off points using the distribution of the variable or by optimizing the correlation with outcomes (Budczies et al., 2012; Trang et aI., 20 IS). This study used a Gaussian mixture model to covert secondary cEPC data on the same sample into a binary variable called endothelial integrity with ProRepair (PR) and ProDamage (PO) being the two possible states. This operation was implemented in Cutoff Finder (Budczies et aI., 2012) 3.11. Statistical Analysis Data were first tested for normality. skewness and kurtosis to determine whether they were best suited for parametric or non-parametric statistical approaches. Analysis of variance (ANOV A) was used in the comparing means across groups in situations where the data were normally distributed and its non-parametric equivalents such as Mann- Whitney or Kruskal-Wallis was used in situations where the data was not normally distributed. Receiver operating characteristic (ROC) curve was used in prospecting biomarkers that may discriminate between CM and non-CM phenotypes. Association ofSNP with trait was done using SNPstats software (Sole et aI., 2006). The SNPstats analysis rubrics starts with the determination of allele and genotypic frequencies followed by a test for Hardy-Weinberg equilibrium. Logistic regression models were used to assess the association between SNPs and trait or disease because they allowed interaction between SNPs and other factors. The estimation of the OR (odds ratio) for each genotype was done with respect to a reference genotype. The general logistic regression model used was: 73 University of Ghana http://ugspace.ug.edu.gh 109(.2..) = a + fJG + yZ 1-p P = probability; G = SNP turned into a categorical variable and codified based on the inheritance model assumed: Z = variables to adjust the model. This equation, however, changed depending on the inheritance model assumed. Table 3.4 shows the logistic regression model used in each of the five inheritance models assumed in this study. The Akaike information criterion (AIC) was used to detennine the best inheritance model: the lower the score, the better. In silico analysis to characterise the epigenetic context of genotyped SNPs was done using the ChroMoS (Chromatin Modified SNPs), sTRAP, and MicroSNiPer web tools (Barenboim et aI., 2010; Barenboim and Manke, 2013; Manke et aI., 2010). ChroMoS was used to predict the chromatin state of the SNPs whilst sTRAP was used to predict the effect of these SNPs on the binding affinity of transcription factors. MicroSNiPer was used to predict the effect of the SNPs microRNA target sites. 74 University of Ghana http://ugspace.ug.edu.gh Comparison This is the most general model. It TIC vs TIT Co- dominant allows every genotype model to give a different and CIC vs TIT non-additive risk. A single copy of C is enough to modify the Dominant IOg( L risk. Thus, )=a+, 8He+ r Z model 1-p heterozygous and T/C-CC vs homozygous genotypes TIT have the same risk. A single copy of the allele is enough to Recessive L modify the risk. Thus, gmodel: to ( )=a+ {JDo+ yZ 1-p heterozygous and CIC vs TIT- homozygous genotypes TIC have the same risk. Heterozygous is Over- compared to a pool of TIC vs TIT- dominant tog( l )=a+.BRe+yZ 1-p both allele CIC model: homozygous Each copy of the Additive g L recessive allele model: to ( )=a+PAd+YZ 1-p modifies the risk in an additive manner. 3.12. Dealing with missing data Like most studies, this study was beset with missing data problems that needed to be addressed. Listwise deletion is the traditional way of dealing with missing data but this approach has been criticised lately for two main reasons: the introduction of bias into the data and the loss of statistical power. Fortunately, current advances in theoretical and computational statistics have led to statistically sound techniques, such as mUltiple imputations (MI), for handling missing data such (Deng et aI., 2016). This study implemented a multiple imputation (MI) algorithm in SPSS. Downstream data analysis highlighted differences in instances where MI datasets produced results that differed from original data. 7S University of Ghana http://ugspace.ug.edu.gh CHAPTER FOUR 4.0 RESULTS 4.1. Demographic characteristics of study participants This study involved 221 children (68 healthy controls, 60 uncomplicated malaria, 25 severe malarial anaemia and 68 cerebral malaria) aged between 11 months and 12 years with a mean age of 5 .126 years. The mean age of study participants was highest in the healthy controls (HC) and lowest in the severe malarial anaemia (SMA) groups (Table 4.1). Whereas the mean age ofHC was significantly higher in all pairwise comparisons, that of UM was only higher than SMA (p = 0.012) but not CM (p = 1). Figure 4.1 summarises age differences among malaria phenotypes and healthy controls with a post hoc pairwise comparison adjusted for multiple testing. Sex ratio in this study population expressed as the number of males per 100 females was found to be 135.106. Although the perfect sex ratio of 100 was only seen in healthy controls, the distribution of males and females was not significantly different among the different malaria phenotypes and healthy controls (p = 0.192). 76 University of Ghana http://ugspace.ug.edu.gh Age distribution among malarial Phenotypes and post hoc comparisons CM ~SMA ~ o c CII f UM .!! :; I ~ He l 6 10 12 Age (yrs) P value Comparison Unadjusted Adjusted SMAvsCM 0.002 0.015 SMA'IUM 0.002 0.012 SMA 'IS He < 0.0001 < 0.001 CM 'IS UM 0.873 1 eM 'IS HC 0.001 0.005 UM vs He 0.002 0.010 /-jo/in plot.1 representing age ,hstrihlll/,,'; among malaria phenotypes. The black bars represent tht' first and third quartile ullil rhe white dots represent the median. The table bel/mth tile \/Olin plots shows mliltip/e PI' ,lIle comparison among malaria phenotypes. Each roll' /1/ Ih" ,uhlc lest Ihe nl/It Inpolhe.I/\· Ih,,: rhe age is the same in the malarial phenotypes being wlI1pared. Significance Ine! is {J.05. Fi:!ufl' ... I: Age and malaria phenotypes University of Ghana http://ugspace.ug.edu.gh 4.2 Haematological indices among study participants Haematological indices provide valuable insight into the mechanisms of disease and are therefore central to malaria pathogenesis studies. This study reports on the association of haemoglobin (Hb) levels, platelets, and white blood cell counts (WBC) with malaria phenotypes. Since Hb levels were invoked in the categorization of SMA, a lower Hb level in this group was an insipid finding that only served as a confirmatory test for appropriate disease categorization. A more interesting observation was the finding of lower Hb levels in the CM group relative to HC (p = 0.004) and the lack of significant differences between Hb levels in UM relative to CM (p = 0.280) and UM relative HC (P = 1.000). The comparison of white blood cell (WBC) counts among malaria phenotypes was done using a non-parametric statistic (Kruskal-Wallis H test) and thus mean ranks are reported. WBC counts in the SMA group was ranked highest whiles WBC counts in HC was ranked the least with mean ranks of 149.86 and 87.33 respectively. Pairwise comparison revealed a significantly lower WBC count in HC relative to CM (p = 0.013) and SMA (p < 0.0001). Further pairwise comparisons revealed a significantly higher WBC counts in the SMA group relative to UM (p < 0.0001). Mean platelet levels were consistently higher in HC in all case-control companions at a significance level ofp < 0.0001. 78 University of Ghana http://ugspace.ug.edu.gh Figure 4.2 summarises the differences in haematological indices among malaria phenotypes and healthy controls with a post hoc pairwise comparison adjusted for multiple testing . • • Ii f v I woe, .... , <00001 0001 0.157 1.000 i', 3b.l% Repair Frequency (%) ~ • SMA 'Di:O Pro- ! .CM Damage c .!! Qj .c Pro- +o' Repair "C C -'----L-...--L..-..L----'--'--.L.---'---'--':--:t~:x, Frequency (%) L&.I _NON-CI\ Pro- _CM Damage Jf = 10.317, dl =1, p = 0.001 Pro- Repair /I 1 lIS ~% Frequency (%) Frequency (%) Figure .... 6: Endothelial integrity and malaria pbenotypes 85 University of Ghana http://ugspace.ug.edu.gh 4.7 Association of endothelial integrity with ke~ haematological, parasitological, immunological and angiogenic variables. Although endothelial integrity was neither associated with haematological nor parasitological variables, it was found to be associated with CD4+ levels and a few angiogenic factors (Table 4.2) Table .'-2: Association of key variables with endothelial integrity Mann-Whitney Ke~ variable Mean Rank P-value U Test 1"~,ji!j.lIijj~M~iiM!lIi; !lDlIllIllIllr:~~""""""""" Hb (g/dL) mean 94 102.59 4944.5 0.295 PLT(Xlo-') 103.62 88.59 3839 0.067 WBe (X 10") 101.17 92.16 4121 0.273 IQi""iMi!igll· P. den\ity (x 103) 93.98 95.23 4347 0.876 P. burden (1110") 81.92 69.37 2332 0.087 P. CIR (x IOJ ) 74.69 80.58 3005 0.422 HRP2 85.71 80.15 3079 0.466 I mmunulogll'al L~mphocyte 90.71 102.53 4892 0.146 ""culrophils 102.13 89.60 3918.3 0.126 CD4+ T cells 88.10 109.85 5518 0.008 CD8+ T cells 94.55 100.54 4782 0.464 lflNi!liMM"" ~DF-I hlrprn;JI \.ell~dl!n\C'J tal..:tor I) 78.05 86.93 3460 0.240 NGRI ,""",'.',tion I) 88.86 69.68 3894 0.010 TRE MI I fn~'!-, _ !ljo: ~,..~ptOf c ..p rc·ucd m\clPld ~(1I~ I! 81.72 78.72 2972 0.688 Ang-I,angillpmeunil 90.15 69 2626 0.023 Ang-2 (angloJl<1I (80.4%) 43 (64.2%) 0.44 (8.23-0.13) 0.012 268.1 R GIG 36 (23.5%) 15 (22.4%) 0.94 (0.47-1.86) 0.85 274.4 OD C/G 87 (56.9%) 28 (41.8%) 0.54 (0.30-0.97) 0.039 270.2 LA 0.69 ~0.45-1.06} 0.085 271.5 ·Inheritance M-;;del (IA/) Codominant (CD); Dominant (D); Recessive (R); Overdominant (OD); Log-additive (LA) Assuming biallelic SNP .I comprising X and r alleles, the reference genotypesjiJr the CD, D. R and OD inheritance models are XX, xx. X}"-)'Y and XX- YY respectil'e~\·. Red highlight: Sust't!plibiliry SNP, Green highlight: protective SNP 91 University of Ghana http://ugspace.ug.edu.gh 4.9.2. Tag SNPs on chromosome 2 Two of the SNPs considered in this study occurred in the locus encoding zinc finger RANBP2-type containing 3 (ZRANB3). None of the variants of rs16831532 and rs7604879 in ZRANB3 were associate with endothelial integrity or eM. However, rs7604879 was found to be associated with an increased risk of malaria in the case- control comparison (Table 4.6). Although this SNP was associated with malaria in two inheritance models. an Ale score of 266 indicates that the over dominant inheritance model is the most probable (4.12). 4.9.3. Tag SNPs on chromosome 4 The locus encoding Kinase Insert Domain Receptor (KDR) harboured two of the SNPs (rs2071559 and rs56233 104) assayed in this study. These SNPs were not associated with any of the malaria phenotypes in case-case comparisons. However, a variant of rs2071559 was associated with protection from clinical malaria in general in the recessive inheritance model (OR = 0.27,95% eI = 0.10 - 0.75, P = 0.012, Table 4.7) 92 University of Ghana http://ugspace.ug.edu.gh Table 4. 6: Chromosome 2: Association of SNPs with malaria in malaria, \s H(' comparison. ,_ _____, Loci SNP IM* Malaria Phcnotypc _ OR (95% el) Gcnot~'Jlc P-\alue Ale He ~M'!.'a~18~r:.!:ia~ ______ ~ C'j) CIG 1 ~ I 1'1 -to oj 37 (24,20 .) 1.32 (0.65-~,69) Ill; 273.8 I) R OD LA CD Air 21 (31.3%) 80(52.3%) ~ ,26 ( 1.20-427 J 0.012 267.7 CC II (16.4%) 14(9.2%) 0.76 (0.31-1,85) AfC-ClC 32 (47.80 0) 94 (6],4%) 1.74 (0.98-3.11) 0.06 270.9 ("IC 11 (16.4%) 14(9.2%) 0,51 (0.22-1.20) 0.13 272.1 A'C 21 (31.3%) 80(52.3%) 2.40 (U I--UO) 0.0037 266 1.16 (0.75-1. 1 274 : Dominant (0): Reces,~ive (R): Overdominanl (OD): Log-additive (LA), Assuming bial/elic SNPs comprising .r LInd r alleles, the reference genotype.\jor the CD, D, Rand OD inheritance models are n, XX, XY-YY and AX- rr respective(l', Red highlight: SUJceptihility SNP, Green highlight: protective SNP 93 University of Ghana http://ugspace.ug.edu.gh Table 4.7: Chromosome 4: Association ofSNPs "ith malaria in a malari~'-~. .l i}IC compar:~'..o.!'.. .... Malaria Phenotype Loci SNP 1M" Genot,pe OR (95% ('I) P-\alul' Ale fI( Malaria CD (jIA 29 (43.~%) 84 (54.90 0) 1.31 (0.71-2.42) 0.029 269 D G/A-A/A 39 (~1!.2%) 91 (59.5%) 1.05 (0.59-1.89) 0.86 274 R A'A 10(1 ..1 .'1%) 7 (4.60 '0) 0.27 (o.l~.75) 0.012 268 ()[) (j,'A 29(43.3%) 114 (54.9"0) 1.60 (0.89-2.85) 0.11 272 LA 0.79 (0.49-1.25) 0.31 273 .atIII&I.IDl! CD Te 10 (14.9°0) 19(12.4%) 0.81 (0.35-1.85) 0.62 274 D R OD Model (1M) Codominant (CD): Dominant W); ReL'essive (R). Owrdominant (aD): Log-additive (LA). Assuming biallelic SNPs comprising X and r al/e1es. the re{l!rt'ncc 1!.en(}~ype.'for the CD. D, R and aD inheritance models are XX, XX. XY-YY and XX- YY respectil·ely. Red highlight: Susceptibility SNP. Green highlight: pmleclh'e SNP 94 University of Ghana http://ugspace.ug.edu.gh 4.9.4. Tag SNPs on chromosome 6 Six of the 27 tag SNPs considered in this study were located on chromosome 6. Three of these SNPs were in pseudogenes (rs684951) or noncoding regions (rs4236084 and rs943082) of the human genome. The other SNPs: rs2524054, rs59055740, and rs6066303 were in the loci encoding Major Histocompatibility Complex, Class I, B (HLA-B), Zinc Finger MYND-Type (ZMYND8), and Long Intergenic Non-Protein Coding RNA 1754 (LINCOI754) respectively. Apart from rs684951 which afforded some protection from endothelial damage in the recessive inheritance model (OR = 0.45,95% CI = 0.24 - 0.87, p = 0.014, Table 4.8), none of the SNPs on chromosome 6 were associated with the outcomes of interest. 95 University of Ghana http://ugspace.ug.edu.gh Table 4. 8: Chromollome 6: SN Ps ~ ith endothelial inh'gri~ ----_. . - 1M· Endothelial intrgritl Loci SNP Genotype OR (95%CI) P-valuc AIC ProJ}_arnage ProRe(!air ~Cf) CiA 31 (2".5%) 29 (32.6~.) 1.64 (0.90-2.99) 0.18 300.6 AlA 3(2.3%) 4 (4.5'10) 2.33 (0.50-10.80) f) CIA-AlA 34 (25.8°'0) 33 (37.10 0) 1.70 (0.95-3.04) 0.074 298.8 R AlA 3 (2.3%) 4 (4.5~0) 2.02 (0.44-9.27) 0.36 .10 I.! Of) CIA 31 (23.5°0) 29 (32.6%) 1.57 (U.87-2.87) 0.14 299.7 LA 1.60 (0.97-2.63) 0.066 298.6 nElDA CD T/G 34 (25.8%) 36 (40.5%) 1.57 (0.84-2.95) 0.018 295.9 Trr 43 (32.6%) 16 (18%) 0.55 (0.27-1.12) D T/G-TT 77 (58.3%) 52 (58.4%) 1.00 (0.58-1.73) 0.99 302 R TiT 43(32.6%) 16(18%) 0.45(0.24-0.17) 0.014 296 Of) I'(j 34(25.8'10) 36(40.5%) 1.96(1.10-3.48) 0.022 296.7 LA 0.80 0.19 300.2 *lnherilal1ct: Model (1M) Codominant (CD): Doml1lanl (D): Recessive (R): Overdominant (aD): Log-additive (LA). Assuming biallelic SNPs comprising .r and Yalleles. the reference genotypes for the CD. D. R and aD inheritance models are n. XX; XY-YY and X\'- YY respectil·ely. Red highlight: Susceptibility SNP. Green highlight: protective SNP 96 University of Ghana http://ugspace.ug.edu.gh 4.9.5. Tag SNPs on chromosome 7 The SNPs on chromosome 7 were all located in the gene encoding Nitric Oxide Synthase 3 (NOS3) and comprised rs1800783, rs3918211, and rs2070744. Although variants of rs 1800783 were associated with an increased risk of endothelial damage (OR = 2.38,95% CI = 1.17 - 4.85, P = 0.054, Table 4.15), none of the SNPs were associated with CM (Table 4.9). A summary of the association of variants ofrs3918211 and rs2070744 with clinical malaria is shown in Table 4.10. 97 University of Ghana http://ugspace.ug.edu.gh Table 4. 9: Chromosome 7: Association ofSNPs with endothelial int~ri~' Loci SNP F:ndothelial Inte~rit~ IM* Genot~pe - OR (95%('1) P-value Ale _---.!!(IDall.!.a~ ProRe~air rs180078~ CD r'A 70(53%) 40(44.~0) 0.97 (0.53-1.80) 0.054 298.1 AlA 16(12.1%) 22 (24.7%) 2.34 (1.0~-5.22) 0 T/A-A/A 86 (65.2%) 62 (69.7°0) 1.23 (0.69-2.19) 0.48 .101.) R AlA 16(12.1%) 22 (24.7'lo) 2.311 (1.17-4."<;) 0.016 296.1 00 TIA 70 (53°.) 40 (44.9°;0) 0.72 (0.42-1.24) 0.24 300.6 LA 1.43 (0.97-2.13) 0.071 298.7 rs3918211 CO TIC 52 (39.4%) 43 (48.3%) 1.60 (0.89-2.88) 0.26 301.3 CIC 18(13.6%) 14 (15.7%) 1.51 (0.66-3.42) TIC-CiC 70 (53%) 57 (64%) 1.58 (0.91-2.74) 0.1 299.3 R CIC 18 (13.6°'0) 14 (15.7%) 1.18 (0.55-2.52) 0.67 301.8 00 TIC 52 (39.4%) 43 (48.3%) 1.44 (0.84-2.48) 0.19 300.2 LA 1.30 (0.89-1.92) 0.17 )00.1 rs2070744 CD TIC 46 (34.9°0) 40(44.9%) 1.62 (0.92-2.84) 0.19 300.6 CIC 6(4.5%) 6(6.7%) 1.86 (0.57-6.12) 0 T/C-C/C 52 (39.4%) 46(51.7%) 1.65 (0.96-2.83) 0.071 298.7 R C'C 6 (4.5%) 6(6.7%) 1.52 (0.47-4.87) 0.48 301.5 00 TIC 46 (34.9%) 40(44.9%) 1.53 (0.88-2.65) 0.13 299.7 LA 1.49 ~0.95-2.34~ 0.079 298.9 el (1M) Codominanl (CD). Dominant (D): Recessil'e (R): Overdominanl (OD): Log-additive (LA). A.uuming biallelic SNPs comprising X and r alleles. the reference genotypes for the CD. D, Rand OD inheritance mndels are XX, Xx. XY-YY and XX-YY respectively. Red highlight: Susceptihility SNP. Green highlight: protectiw SNP 98 University of Ghana http://ugspace.ug.edu.gh Table 4. )0: Chromosome 7: Association SNJ', "ith malarht ill a malaria " II<' comparison Malaria Phenotype Loci SNP I~" Genol~pe OR(9!'%CI) P-"alue AI( --------- Malaria HC ---~--- rs1H0078.1 III I \ i7(50.~Il,d "l~lj 20/0) 1.11 (0.5R-2 151 0.62 275.5 AlA 24 (15.7001 14PII'l"iJ) 1.52 (0.M-~5\11 f) r:A-A-A 101 (660'0) 47 (711.2%) 1.2\ (0.65·2.251 (l.5S 274.\ R AlA 24 (15.7°01 14 (20.(1''<» 1.42 (0.68·2.95) n.35 2736 Of) riA 77 (SOYo) 33 (·t respectively. Red highlight. SlIsceptibility SNP. Green highlight; protective SNP 106 University of Ghana http://ugspace.ug.edu.gh 4.9.8. Tag SNPs on chromosome 16 This study assayed two SNPs located in the gene encoding Cadherin 5 (CDH5) on chromosome 16. Although none of these SNPs were associated with endothelial integrity, both SNPs were associated with protection from CM in a CM vs UM comparison. Tables 4.16 summarises the association ofc hromosome 16 SNPs with CM. Unlike other SNPs assayed in this study, neither rsl077318 nor rs2304527 was associated with malaria in the case-control comparison. 107 University of Ghana http://ugspace.ug.edu.gh Table 4. 16: Chromosome 16: Association ofSNPs with endothelial integrit) Loci SNP 1'\1* Genotype Malaria Pheno~'~e OR (95% ('I) P-\'Blue AI(' eM l1M --'- ~CD A/C 48 (70.6%) 29 (48.3°/0) 0.30 (0.12-0.76) 0.024 175.5 ClC II (\6.2%) 13 (21.7%) 0.59 (0.19-1.84) D AlC-ClC 59 (S6.S% ) 42 (70%) 0.36 (0.1 ~-O.87) 0.02 175.5 R ClC 11 (16.2%) 13 (21.7%) 1.43 (0.59-3.49) 0.43 IS0.3 OD A/C 48 (70.6%) 29 (4S.3%) 0.39 (0.19-0.81) 0.01 174.3 LA 0.75 (0.43-1.31) 0.31 179.9 ~(,D T/G 46 (67.7%) 24 (40%) 0.32 (0.J~-0.71) 0.0069 173 GIG 6 (8.8%) 10 (16.7%) 1.03 (0.31-3.37) D T/G-G/G 52 (76.5%) 34 (56.7%) 0.40 (0.19-0.86) 0.017 175.2 R Glti 6(8.8%) 10 (16.7%) 2.07 (0.70-6.08) O.IS 179.1 OD T/G 46 (67.7%) 24 (40%) 0_'2 (0.15-0.66) 0.0016 171 LA 0.75 (0.43-1.29) 0.29 179.8 (1M) Codominanl (CD); Dommall{ (D); Recessive (R); Overdominant (OD); Log-additive (LA), Assuming biaflelic SNPs comprisinK X and r alleles. {he reference genotypes for the CD. D, R and OD inheritance models are X%', AX AT-IT and X\,- rr respectively. Red highlight: Susceptibility SNP. Green highlight: protective SNP 108 University of Ghana http://ugspace.ug.edu.gh 4.9.9. Tag SNPs on chromosome 20 The loci encoding Matrix Metallopeptidase 9 (MMP9) harboured 3 of the SNPs genotyped in this study. A variant of rs2236416 was associated with a reduced risk from endothelial damage (OR = 0.22, 95% CI = 0.08-0.59, p = 0.002, Table 4.17) and rs3918256 was associated with increased risk of endothelial damage (OR = 3.93, 95% CI = 1.63-9.50, P = 0.0015, Table 4.17). Association of other MMP9 SNPs with clinical malaria is summarised in Table 4.17. 109 University of Ghana http://ugspace.ug.edu.gh Table". 17: Chromosome 20: Asso('iation ofS~I', with t'lIllothdial integri~ ~. . -- -------~"-.- Loci SNI' IM* Genotype Endotheliallntegritv . OR (95% C1) P-\'alue AI(' ProDama~ ProRepalr ____. __ rs2236·H6 CD AlG 15 (22.4%) 54 (35.3%) 1.57 (0.79-3.10) 0,0036 265,2 GIG 12(17.9%) 7(4.6%) 0.25 (0.09-0.69) D AlG-G/G 27 (40.3%) 61 (39.9%) 0.98 (0.55-1.76) 0.95 274.4 R GIG 12 (17.9%) 7(4.6%) 0.22 (0.08-0.59) 0'()o2 264.9 OD A/G 15 (22.4%) 54 (35.3%) 1.89 (0.97-3.67) 0.053 270.7 LA 0.73 (0.47-1.12) 0.15 272.4 rs3918256 CD G/A 45 (34.1%) 25 (28.1%) 0.95 (0.52-1. 75) 0.0063 293.8 AlA 8(6.1%) 18 (20.2%) 3.86 (1.56-9.59) D G/A-AiA 53 (40.1%) 43 (48.3%) 1.39 (0.81-2.40) 0.23 300.5 R AlA 8(6.1%) 18 (20.2%) 3.93 (1.63-9.50) 0.0015 291.8 OD GIA 45 (34.1%) 25 (28.l%) 0.76 (0.42-1.36) 0.35 301.1 LA 1.58 (1.07-2.34) 0.02 296.5 *Inheritance Model (lAO Codominant (CD); Dominant (D); Recessive (R); Overdominant (OD); Log-additive (LA). Assuming biallelic SNPs comprising X and Yalleles, the reference genotypes for the CD, D, Rand OD inheritance models are xx, xx, XY- YY and XX- YY respective~l'_ Red highlight: Susceptibility SSP, (Jreen highlight: protective SNP 110 University of Ghana http://ugspace.ug.edu.gh Tab'e". J8 Chromosoml' 20: Association of SN "s "ilh malaria in malaria" lie comfl~tri~on - .. --.~- - Malaria Phmot~ P" Loci Hils SNf' 1M· Grnol)'pc OR (95% -'" ~x~ ~ ~. _I}lMl _'I~ CI-'!!1 ___ ._ . -_---.-._----"_-_"- -.. -.. -....-...-. -_-.----.... -I- -_ .. .. _ -- -- Sem",el ~NP Proxy SNP Chi Pm') Po> lOr' LDD LD Pro,> Minor MAF ~s;- D Allele .. 10489181 "1091'(!21i 169.704 934 ·7663 092 023 099 C 0.399 .. 10489181 f-s21Wt'C) I 16 169.706.030 -6 ~67 093 013 (' 0.398 n104K91~1 ,,10489182 169.710 669 -1.928 089 0.13 G 0.408 ,,1(1489181 nl2044082 Ih9.71168~ .912 099 o 2 ~ T 0.384 "111-189181 ,,104891SI 169.712,5')7 I I C 0.383 nl0489181 n16862663 169.'1.1'80 78.3 u ~,l C 0384 ..1 11489181 n6042~295 169.716.281 3.b84 09.l 02.1 O.9~ C 0398 "10489181 ro82701bl 169.716.313 3716 093 o:n 099 A 0398 "10489181 ,,10800470 Ib9 717 272 4675 088 022 0.99 G 0.41 "10411'1181 ,,10800471 169 718.948 6351 087 022 099 G 0.412 ,,1"4""181 ,,10800472 169719.01 7 6420 087 021 1 G OA14 ,,111489181 1168113309 1697:!2.881 10284 087 022 C 0.414 "10489181 no-I6S671 I 169723.575 10978 I) 8" 021 099 T 0.412 "111-189181 007819761 169723662 11.065 081 0.22 099 T 0.429 ..1 0489181 ,,~Sb71712 Ib9 7 24.610 12.013 () 8' 022 099 T 0.412 "10489181 f .. ';IJX II ~I)~ Ih9,7B.h85 IJOS8 08' 022 I A 0.414 ,,10489181 r·.tlhfll'l~Ci Ih9715.978 1>381 087 0.22 T 0.414 ,,10489181 r~6()MI"1 IlN,72l1,5b5 13968 0.87 021 0414 1'11l41l'i181 r47 IR,O:liO () 87 Il ~~ (III) nl0489.1 112272818 169.'" 345 20 -lH 'j ~7 '122 \1114 ,,1114"'111 rs34990593 I 16').733.1167 21.270 ',s7 1)!2 I (' (I II ~ Figure 4. 10: A composite linkage disequilihrium plot for rsl0489J81 117 University of Ghana http://ugspace.ug.edu.gh lInup dl. .......r lum plot ~2070744 """"0,'4..) * i , • 40 f i '".1:1. L A • <.. ;\t~l' i "'i!!'" "'!P ,"!'" H!5!! o8_£T~lr l ~ ,¥t Q!,.f~ ..J!!!!!!... 1!:.~ Mlf!'11 .-------o.iM"~p N[M'7'18 ACe1 4Q!!!I Rt"lIt~'I' .....!!!L ~&. SMA.!'CCh_ ~ciNAP!. ~1'OA--- .... t~ •. 'o W" 't!!!.. .....~I.' .505 .507 .1108 Chromoeome 7 iMb 1 _____. .. ·_,,_ .__-__ " "' ___ .n~ .......... .....~._- -.... I-\,-'---........_...... 'n-.,y_ 1,_ -~- - ......,.......... ,- ..~.. .. ...-.-.-... ...... ....... "'-~_~I.Jl.tf. ....... ~ ~ .... .. ...,.. . ... ~_(".v ........... .......,~ ___ ",-""""<>,,,-...,~ ~ ___ cA .... 5NP'n..pIof""'"*"'oINdo __ ~"'"~ __t IO'I 1 Sentinel Proxy ('hr. Proxy Pos. Distance (bp) LD r LD D LD D' Proxy Allele B MAF rs2070744 rs1800779 7 150.689,943 -136 0.98 0.12 0.99 G 0.139 rs2070744 rs2070744 7 150,690,079 0 1 I I C 0.138 Figure 4. 11: A composite linkage disequilibrium plot for rs2070744 118 University of Ghana http://ugspace.ug.edu.gh Unkage disequilibrium plot for rs~918211 • ...... 8NP ( noe .. LD L LOI2'02 ~118211 • LOr' , •• • lO" '01 • 60 ~ J ~ 40 5 I • ,~ ~ . q,w.,{:j \0 0 c 0 ~)., .\.~ 20 J ! , j '. ~ I ~. , TREK. TREMl TREMLSP - 1NIe·e43P TAUA2 RP1.i2.aoe· NFYA ... TREMl 2 RPI ~ .• ... --- - AOCYIOPI • TREML3P 'AP1-22IKmS NCII2 RP1·I."". FOXr4-AIII __- ,,=OA~RO,,-,I_ .._ -- TR~"'" -- RNA5!iP207 - RP1~1 - API '4;8'"'' • LlQlaN ~ 41.1 41_3 41.5 CIvorno8ome II 1Mb) l'n ....f 'dl1eQuthbrtumplot'!howlht .. ~Cl-"""~.;~)<",.."or. I --- __ ""~ ......... rogulolory_' 1-----lho ..-7 fl. ....J !r5. bflwff!l.SPn1inel.."...endlb ... . :h.'XI,'II' ... , .. '\t~~kW".· •• ,· _ &I~ipI .....,. .... :ton ...... 0 ~enec:tl r~_raV ........ Ion).fwIdIGnII5HP~lOns.,. Ihow5n.CIIIfNIMtoncoe'fflo""('II.thfo'd"'~_ ......,.lIIIact dl_rogoAokIry_ 0 ......0 01_..... J _ .. ·_r c:~ ...._ of HCh SHtt nw Put "f'"bH at .. ~CII ..... ~·"n' ~1tItuncaan.a1flnCllMton Figure 4.15: A composite linkage disequilibrium plot for rs684951 122 University of Ghana http://ugspace.ug.edu.gh Link .... dt ••q ulllbrium plot for r,,7"141/]' • ~:- LD~ ~O~ • &.O~ .' • lOr' -0. ~ ,.\ i 'i! ~ i ~ ~-:> ,.r,ti'~~,: i ....... ....... ....... . "-. - .'..... ,3· ~P2 ""-NAt. "!'f ....... .....,.... I"~·· _~~I_H-O_- -__ 1ItI'~,., ~tP 2'.5 2.7 ~.jMb, .~. .....- ---... - ... -- 1- .=-. ._. -- __ ._ -............... -. -__- "---. -.. ,.-..........,.. ............ ... .a.c. 1...., ............. -...-.. 1.01'1.1 ...... ·.. .._ ,,,_ ...._.....· - -_r~ ~INI' _ ~:;-;-:-,,~~~..:::.. ~~~ ........cI...... tauM ... • .......r LDD LDO' Proxy Sentinel Prox~ Chr. Proxy Po~. Distance (bp) LD r' MAF Minor Allele rs73422262 rs73422248 9 21,448.353 -4,690 0.8 0.16 I A 0.244 rs73422262 rs73422252 9 21,448.671 -4.372 0.93 0.16 1 A 0.217 rs73422262 rs59635965 9 21,449,978 -3,065 0.95 0.16 0.99 G 0.209 rs73422262 rs73422254 9 21,452,409 -634 1 0.16 1 T 0.205 rs73422262 rs73422262 9 21,453.043 0 1 1 1 A 0.205 rs73422262 rs73422.:!65 9 21.455.410 2,367 0.94 0.16 0.99 T 0.211 rs73422262 rs79988 146 9 21.461,746 8,703 0.94 0.16 0.99 C 0.211 rs73422262 rs57341614 9 21,462,996 9.953 0.9 0.16 0.99 G 0.22 rs73422262 rs6093 1924 9 21.465,734 12,691 0.84 0.15 0.97 A 0.226 Figure 4. 16: A composite linkage disequilibrium plot for rs73422262 123 University of Ghana http://ugspace.ug.edu.gh linkage disequilibrium plot for rs3818256 • ......... SNI' ".. .. lO C lO,J "04' f'. J9182S6 • lO,J 'O~ • loi ~ 60 i ~ J A ~ A i 40 I t" ! A • 5 ~ A A· t:.&tf, l:.~ ~~rJ v .•~ ~ 0~ m i . o I lSWlM. UB~ ~. ~ ThINCII NEURL2 ONTT\P. -- ACCTa SPAT42S ~!g ~ WfDC3 -- 1Aotaz. . SRP ·ClSA Pelf! FTlP, SlC.2A5 ~ RP5-fI!'HU _!A~ ~,' RP3-~ ... - ZNf33S . RPI11!!L~ RI'L!Jf'2 alHI2 44.4 44.11 44.8 Chrom_ 20 (Mb) ""..,.d .... _ ..... _ .... _01_ 1 ,,,,,om_oon"', p dl·.rqt. . mbrtum plot for rs2Ja.Sl1 ••• • i t·· j ~ I 1 ! ~~ t _. I - .t-~\ .• ". 20 1 Y-r!iA CUlW 8UH1., ... ---QG.f a,rn.h- ~u =:'':;''7.8 ..' 1-;6 CI!!!M ! 0 CTD.~3 PUt.'....,.,A7P.l. l~P1'.t ~_ e412 11114 _.11 ,_ ..... -_._ ... _"'- I -_ .... ChromOSOl'lle 1. CUb) .......... ___ n..."l., _.,.,..,.. ... c,..I.N.,.,d...n....d.. ....... .......".. .,...,....,...........,........o.r yef1.c1 I~·. .......... e.. _ECAMER_Q2 /);0178768 431J6n81 -1.3115805 153933239 _NC VSSMAD_Q6 0.0540426 0.547271 -1.00;4663 ",7341614_NG VSALX401 0.0043144 0.045378 -10219218 YSClATA2 02 U.OOl692 I 0.169841 -2.00161 VSGAT \.1-"2 0.0033785 0.152721 -165518 v~c. .· \r.\_Q6 00032992 0.1272174 -1.S861349 \ ~I ~1U2COM_()2 0.0174508 0.3838585 -1.3423568 "WI 8447_AIG \ ~(,.\I" \1 06 0.0101607 0.1'109058 ·12738972 V$l •. \ I A 1-(14 00145472 0.2623143 -12560411 VSGAIAI-02 0035281 0.4147284 -10702223 VSOATA C 0.0197873 0.2188523 -1.0437649 r ... flllfltdlJ.l :\:, V$OCTI~Qb 00050282 0.05403 -10312217 V$!>TATl 02 00159563 071~ -1.6543035 "6125005_ -\:(i VSSTAT6:02 0.0803294 0814'1153 -100623& 156203725u AfT VSE2 01 00105583 0.1623362 -1.1868227 n614951_TK.; vroC'TI 114 0.()O(';607 0.11087117 -1.191661 VSAfFI_Q6 0.0100743 0.1318196 -l.lIb7667 "73422265_CfI VS~MAD ~6 00460644 0.5461202 -10739232 130 University of Ghana http://ugspace.ug.edu.gh Table 4. 20: Top 5 transcription factors (TF) impacted by rs2304527 T/G . . SNP Impact Rank Transcription Impact on Tf Motif Scan p-"alue Factor (Tf) POUJfJ_' fIIdr ..... ,.,...,. ,-A-;;CGfAT POU2f2 Loss of ~44+~:r"4-T' 0.0005 '""''''''4 " cAAiT"f ':'!'I~ SOlI' ~A~~CGtT, STAT Gain of function 0.0017 .Acr::~n=fA. 3 EP300 ·~L#T~+T~ function 0.0070 .I'" ~LuiLrrr Acy':'c;ATTA, ..~ - .A •. t;'GG-i:A·TA'GTC~ RORA Gain of ' 1\ I \I I I', I\i \ l I function , ii-''jii- It \ 0.0127 d l I - .A. T-r.';T.·A TA.llGTC~ TFAP'_I"'" 8 ... ,. ra2J01U7 Cain Of~" .--:CAGCT~. 5 TFAP4 .\.} l'~ y <; T( :LM-, functiun 0.0153 S\P ImpUd p-"alue,\ were used in ranking the top 5 TF impacted by'\'\I\ I S\ /' 11/01 lead to U Rain ur loss inJunction which was (!,I'aluated with the atSNP webtool, atSNP uses SNP infrJrmation and position weight mutricl!s (PWM) 10 predicllhe e{fe,·t ofSNP on TF. First-order Markov model and ImportanCI! .\(Jl1Ipling methods were used 10 detl!rl1lil1l! lhl! statistical !liRnifjcance q{ the best match betwl!('l1the PWM and Ihe subsequence overlapping the SNP position with both the reference and the S\P alleles. Finally. the impact ofSNPs un PWM and subsequence matche,\' werl! tested using the same statistical approach 131 University of Ghana http://ugspace.ug.edu.gh Table 4.21: Top 5 transcription factors (TF) impacted by rs3918256 G/A . SNP Impact Rank TransactIOn Impact on TF Motif Scan p-value Factor (TF) NR2C2 Loss of <0.0001 function ............. .tIa CC<.JTACCA ~CCC~j.. 2 TBXI5 Loss of functiun 0.0011 l TBRI Loss of ~ function 0.0020 ZNF345C I ,fJ" Il' funl"lion 0.0021 ___ ..." . . nw...,. ..... T~CC~(, ~Act~;. Ref I" TEADS Gain of function 0.0033 S,\'" impact p-vallies were u\ed in ranking the lOp 5 Tf IIIlpacfed by SNPs, A SNP may lead to a Ka/n or I()~\ injunction which WQ.\' evaluated with the atSNP webtool, atSNP use., SNP information and pmilUJII weiKht matrices (PWA1) to predict the effect o/SNP on TF Fint-order Markov model and Impurtan,'e sampling methods were used to determine the statistical significance 0/ the best match h.:fllwn the PWM and the subsequence's overlapping the SNP position with both the reference and the SNP alleles, Finally, the impact o/SNPs on PWM and subsequence matches were tested using the ,\,Ime stallstieal approach. 132 University of Ghana http://ugspace.ug.edu.gh Table 4. 22: Top 5 transcription factors (TF) impacted by rs3917419 s~~~mpact Rank Transaction Impact on TF Motif Scan p-value Factor (TF! loss of 0.0004 ELK4 function ~ ..,'. . ' ..... lkMtfOf ....." '. M~:;;,.GMA!.~ . Gain of . ~ \l;~~ rr,~4-~~· 0.0011 2 IRFI function • .r. A ,\.'l1fT(;A.UA( ~,;QAAk': . Gain of 3 API 0,0025 function I~~~ NFATC2,.111oW ac. hw raUt'"'' ...c. if:r;;- l.1l'~ of NFATC2 ~r;rr+i 0.0059 IUllclion .......,"wRfI .......... ACITTTG $NP .~CCtTT~ . .A C'A;;G'G;C. Lo,~ of \(. :~~'T(;',I" \ HNF4 0.0100 fUllction \\f' Inl!'", 'Ii.values were used in ranking the top 5 TF impackti by S\P, A SVP may feud to a xam "r /"" in {unction which was evaluated with the atSNP webtool. atSNP uses SNP information and {,{).Iition weight matrices (PWM) to predict the effect ()/,SNP on TF. First·order Markov model and importance sampling methods were used to determme the statistical significance of the best match hetween the PWM and the subsequence overlapping the SNP position with hoth the reference and the SNP ullele.\'. Finally, the impact ofSNPs on PWM and subsequence matche.\' were tested using the ,Iume ,Iulwica! approach. 133 University of Ghana http://ugspace.ug.edu.gh Table 4. 23: Top S transcription factors (TI) impacted by rs684951 T/G SNP Impact Rank Transaction Impact on TF Motif Scan p-value Factor (IF) _ MEF2A Loss of 0.00106196 function • oat._ ..... -., ~ ':-!O' .....• fGuDain. ~"i";iinr~. CtlXI di. *r·~T.rAT·~T~:l 0.00185737 ·~\Wf.rfH\·T • P', ~ ~~~. ..,.~' ...............1 .~ C.r.Ai.";~AlAcr• . I MEF2B Lo~s of LU.~I·\P~ function 0.00535357 lAu;.\'($.\ I.~ ....( ;IA,,~.IA.G..:. . _.. _ ---, .. TAAACA .. -:;::-f.:xAC, FOXGI LII'~ of II ,,·1 I !'I .. r..1~T'1 function 0.00561902 II .,\ I .1~\j\1 .. 1 '.1 I.-} "TAAACA~ TGT",uCIi 1If&f1_. .......... ~ ... -a';iA~, MYEF2 Lo\\ of I 1 II\, \' I \ LH At. q function 0.01587156 ( r I T tt\-:-\..\f"n \1.r \ I .... T~ ~AAA TA ACi ~ .. S.\!' ""r,lL I {'-I'alue.~ were used in ranking Ihe lOp 5 TF impacted by SNPs. A SNP may lead to again or lo.u in Jimction which was L'\'aluated with the atSNP webtool. atSNP uses SNP information and po,ition weif{ht matrices (PWM) to predict the effect ofSNP on TF. First-order Markov model and importann! sampling methods were used to determine the statistical significance of the best match hefH'een the PWM and the suhsequence overlapping the !iNP position with both the rqerence and the SNP allele.1 Fina/~l'. the impacl ofSNPs on PWM and suhsequence matches were tested using the same stali.l'/ical approach. 134 University of Ghana http://ugspace.ug.edu.gh Table 4024: Top 5 transcription factors (TF) impacted by rs2070744 T/G . SNP Impact Rank Transaction Impact on TF Motif Scan -value Factor (TF) p NR3CI L.U\' of 0.00159594 fUllclioll .. Atohl 1.1"~ of IlIlIdioll 0.00296101 ~ 3 NFY (.ain of function 0.00342305 -' ~ CEBPB ,::'1 (.;1111 "f ~". fllndiOIl 0.00547063 b.",' ~ ........ r_ ....... _... .. .t'CTGlIAT-.-.:rr;"?-·T - ------'i!""'""--_----E ....._ •• k~_ ~ ••• lit 3 C ,J) ,/ e•n 04 ! ,., 04 1 ! 0*114 . "ot.{'l, Il,."IM iI < CAl "11m OO)tn 71 / _~I 0 . ..,.,. ~~'" 0: Ar 102 ~ Performance of MMP9. NGR1. Tie-2. CAl and K c= ' ---.-J angiopoietin 1 as biomarkers for CM. None of the OO 02 0.4 06 O. HI markers performs satisfactorily as a biomarker for CM. A satisfactory marker is expected to have an Area Under 1 • Specificity the Curve (AUe) > 8. Figure 4. 21: Performance of angiogenic factors as a biomarker for eM 141 University of Ghana http://ugspace.ug.edu.gh .•D erived from 1 - Specificity Source of the 0.0 02 0.4 06 08 10 Curve "O~[---. -.~'0 mmp_9 NGRI i ',,"./' -Tie·] 0.8 1 . 10.8 0 CAt ct Anglop01lO1 :1 - - Reference Lme I ~ ~ 0.6 i 10.6 ~ ~ \ 0 • 3 mmp_9 0.46 c 0 ~ 0.4 (. 04 ! NGRl 0.83 001 (0.54,072) •a: TEK 062 001 (0.53.0.70) I., ~ CAl 081 002 (0.52.0.70) ., .I-' % _A~opotinl 058 008 (0.49.0.69) Performance of MMP9. NGR 1. TIe-2. CAl and angiopoietln 1 --------~OO as biomarkers for eM. None of the rnalters performs 0:2 0.4 0.6 0.8 '.0 satisfactorily as a biomarker for endotheHal integrity. A satisfactory marker is expected to have an Area Under the 1 • Specificity Curve IAUC) > 8. Figure 4. 22: Performance of angiogenic factors as a biomarker for endothdial integri~ 142 University of Ghana http://ugspace.ug.edu.gh Oerlved from Sensitivity o ~ 04 o ._~ 08 • 0 ' JlI 0 r" -pPala5.1. Oenlrty HRP_: Plot 0.' .5 O' li' Referenc. lJnt ::l. •< a. E~ { 06 a' I -U•u "', ' 3 pParaslte d4?'f"lS11V 0 546 0372 (0445,0648) a. fI HRP 2 0.253 < 00001 (0 lea, 0,338) fI) 04 '04 • D10t 0.2511 < 0.0001 (0173. 0 345) n 'r 3n i 02 ~ oo-~- Performance of parasite density, HRP 2, total parasite ----- - ~o.o 0.0 02 04 06 08 10 biomass in predicting CM. A satisfactory marker is expected to have an Area Under the Curve (AUe) > 8. Sensitivity Figure 4. 23: Performance of parasitological indices as a biomarker for eM 143 University of Ghana http://ugspace.ug.edu.gh 0.2 0.4 0'" 06 10 ;.l10 r! pPafJSlte density J HRP~ -J'/- -Ptot 0.81 ;1""/' 08 0 Refetenc. line _. J' •::2. j- I •< 0,61 10.6 ~ 0 pParlllllllt denSIty 0.434 O.ln (034.0.53) 3 HRP_2 0.535 0.471 (044.0.63) !.'". Plot 0.582 0.087 (049. 068) 04 tt < 0'"." (.J1: •. 10.2 ~ I I Performance of parasite density, HRP 2, total parasite ~- biomass in predicting endothelial damage. A satisfactory marker is expected to have an Area 0.0-- .--.. - '-------' 0.0 00 0:' 04 06 0.8 10 Under the Curve IAUC) > 8. Figure 4. 24: Comparison of 3 parasitological variables as biomarker for eM 144 University of Ghana http://ugspace.ug.edu.gh 4.13 Association of SNPs with angiogenic factors Although SNPs tested in this study were not selected based on overt functional properties, those that were associated with malaria and/or endothelial integrity were further tested to determine their association with the angiogenic factor they encode. Comporiion 0/ MMP9 ,.... among r53918256 in a fffflU/1If! inh.,,"mc. mod,,', Mo......whitMy _.-Jolla • u_ :., _nl'llnllfllollM} ,aii Moon rank fOOl 74.0 P - v.lue .0046 200 400 600 800 1000 levels of MMP9 111IImL) Figure 4. 25: Comparison of MMP9 levels among rs3918256 in a recessive model The rs3918256 variant associated with almost a 4-fold risk of ProDamage was tested to determine its association with the levels of MMP9 in the study population. Since rs3918256 was associated with endothelial damage in the recessive inheritance model, levels of MMP9 were compared between GAJAA and AA variants. With a mean rank of 74.0 and 55.6. level in MMP9 in GG was significantly higher than GAJAA respectively (p = 0.046. Fig X). Other SNPs in the coding regions of NGRI (rs2466104). TEK (rs7023443 and rs7874391) and CD4 (rs2886398) were neither associated with malaria, endothelial integrity nor their respective proteins. 145 University of Ghana http://ugspace.ug.edu.gh CHAPTER FIVE 5.0 DISCUSSION This study genotyped 27 SNPs, characterised their epigenetic context In silico, and explored their interrelationships with endothelial integrity and malaria phenotypes. It also explored the potential influence of SNPs on transcription factor and microRNA binding sites. The study father explored the diagnostic value of various angiogenic and parasitological variables. This section discusses the findings from this study in integrated and thematic prose. 5.1 Association SNP! with malaria and endothelial integrity: Discussions of SNP-disease/trait association analysis excludes associations that fell within 0.002 < p > 0.05 because of the conservative significance threshold (p < 0.002) the study adopts. The only SNP (rs2304527) found to be associated with CM in CM versus UM comparisons was an intronic SNP located in the CDHS gene which encodes cadherin 5 or VE-cadherin, a vital protein for cell-cell adhesion and intracellular junction integrity. This SNP offered children with the heterozygous (T/G) genotype protection that made them about 3 times less likely to have CM relative to their colleagues with the IT- GG genotype. Regardless of the massive protection rs2304527(T/G) affords, there is very little literature on this SNP (Schubert et aI., 2014). Intronic SNPs can sometimes serve as tagged SNPs, and thus. their association with diseases and/or traits could be due to their proximity to other SNPs in coding regions. However, the four SNPs in linkage disequilibrium with rs2304527 do not occur in the coding regions ofCDH5. Rather, they occur in introns in strong enhancer (rs620372S0 146 University of Ghana http://ugspace.ug.edu.gh and rs58044782) and transcriptional elongation (rs3785286 and rs2880989) regions of the genome. Once regarded as useless. introns are now known to play important roles in gene expression. They harbour multiple functional elements such as intron splice enhancer and silencers that control alternative slicing. trans splicing and other pre- and post-transcriptional elements (Cooper. 2010). Mutations in these regions of the genome can, therefore. lead to a cascade of downstream aberrations that culminates in modifying clinical phenotypes. Taken together, although rs2304527 and it linked SNPs are yet to be associated with disease or a clinical phenotype in literature, their location and epigenetic context do not preclude them from playing a role in the pathogenesis of CM. Another SNP that breaks through this study's significance threshold is the splice region variant (rs3918256) in MMP9 which was found to be associated with endothelial integrity. In the recessive inheritance model, children with the homozygous recessive genotype (AA) were almost 4 times more likely to be ProDamage. This SNP has been evaluated for association with inflammatory diseases and angiogenesis in several studies but no associations were found except in a study among non-Hispanics whites women where it was associated with protection from pelvic prolapse (Amankwah et aI., 2012; Haq et al., 2010; Nakashima et ai., 2006; Wu et aI., 2012). This is the first study to implicate rs3918256 in endothelial integrity in the context of malaria pathogenesis. Larger studies in this population and elsewhere will be required to shed more light on the role of this SNP. The fact that rs3918256 was associated with endothelial integrity and not CM in this study popUlation is noteworthy. The association between endothelial integrity and CM has been hypothesised for decades but study design challenges and the rare nature of CM has limited studies from directly evaluating the empirical 147 University of Ghana http://ugspace.ug.edu.gh relationship between eM and endothelial integrity. (Bernabeu and Smith. 2017; Graham et al., 2016; Souza et aI., 2015). Although the case-control comparisons were not a priority in this study, it revealed several interesting results. Four SNPs located inlor close to the SELE (rs3917419), pseudogene (rs684951), ZMYND8 (rs590S5740) and NOS3 (rs3918211) loci were associated with malaria at GWAS significance thresholds in malaria versus healthy control comparisons. For instance, the intronic rs3917419 variant which has no previous malaria-related mention in literature was found to increase the risk of malaria by nearly 5 folds. Only two studies make reference to this variant associating it with elevated levels of serum E-selectin and MMP9 (Montasser et al., 2010; Santos et al., 2018). E-selectin is implicated in eM pathophysiology and thus, its association with rs3917419 suggests that the SNP may have a role in eM pathogenesis (Renia et al., 2012). Although Santos et al reported an association between rs3917419 and MMP9 levels, this study did not see a compelling biological reason to pursue that analysis (Santos et a1.. 2018). All SNP in linkage disequilibrium with rs3917419 were intronic or upstream gene variants except for two (rs7531675 and rs7543618) that were in a regulatory sequence. There are a total of9 studies that make references to SNPs linked to rs3917419 but none is in connection with malaria (Edwards et aI., 2011; Faruque et aI., 2011; Hsieh et al., 2017; Montasser et al., 2010; Mullins et aI., 2011; Nasibullin et aI., 2016; Solus et aI., 2015; Timasheva et aI., 2015; S. Wu et aI., 2012). Another of the four SNPs associated with malaria at GWAS significance threshold is the intergenic variant (rs68495I ) located in a pseudogene on chromosome 6. This SNP and the SNPs in linkage equilibrium with it have not been associated with any clinical 148 University of Ghana http://ugspace.ug.edu.gh phenotype yet. However. the synonymous variant (rs3918211) of NOS3 that was also associated with malaria at GWAS significance threshold has some malaria-unrelated mentions in literature (Blanton et al., 20 II; MacClellan et al., 2009; Shaw et al., 2009). 5.2. SNPs and epigenetic mechanisms: insights from in sillc o analysis In silico analysis found several SNPs that influenced transcription factor binding sites (TFBS) by quantitatively influencing the binding affinity of transcription factors. Interestingly, some SNPs associated with susceptibility to CM were predicted to modulate the binding affinity of transcription factors. For instance, rs3917419, rs3918256 and rs68495I were found to lower the binding affinities of some transfac matrices. This study further evaluated which TFs gained or lost binding sites through the impact of SNPs and found interesting results. For instance. rs3917419 in SELE (gene encoding E-selectin) was found to influence two different families of transcription factors (NFAT. and API). Whereas the rs3917419 mutation leads to the loss of binding site for NFATC2, it created same for API transcription factors. The NF AT family of transcription factors are known to enhance endothelial cell survival via VEGF-mediated effects, (Hamik et aI., 2006a) and thus, the rs39I74I9-mediated loss ofNFATC2 binding site could have implications for vasculopathies such as CM. In the same breath, rs39I74I9 could lead to the creation of a binding site for API, a transcription factor that regulates a wide variety of biological process including cell differentiation. proliferation, apoptosis and angiogenic related processes (Hamik et al., 2006a). Thus, although rs3917419 is an intronic SNP with yet an unknown function, it can plausibly influence CM pathogenesis via altering angiogenic-related processes during malaria. 149 University of Ghana http://ugspace.ug.edu.gh Further analysis of the effects of the CDH5 (gene encoding Cadherin 5) SNP rs2304527 revealed that the SNP influences the STAT family of transcription factors. This mutation creates a binding site for STAT, a transcription factor that promotes angiogenesis by activating VEGF. Similarly, the rs3918256 SNP in the gene encoding MMP9 disrupts the binding site for the ZnF family of transcription factors which are among the most abundant DNA binding proteins (Hamik et al., 2006b; Miyashita et al., 2004). Taken together, although there is a paucity of literature on these SNPs, In sllico analysis provides a window for further insight into these SNPs and a basis for developing future research hypotheses. The finding that SNPs associated with CM and endothelial integrity disrupt or creates microRNA target sites is interesting. There is an increasing body of evidence on the importance of SNPs in miRNA and their biological consequences. In this study, two SNPs (rs20544 and rs3918211) where found to disrupt target sites for 19 different microRNAs, some of which, play pivotal roles in the post-transcriptional modifications in various disease models Deciphering the role of miRNA in malaria is still in its infancy (Chamnanchanunt et aI., 2017; Mantel et aI., 2016; van Loon et aI., 2019) and it will be interesting to further explore the biological role ofrs20544 and rs3918211. 5.3 Endothelial integrity and malaria phenotype Downstream analysis using the endothelial integrity variable produced results consistent with conventional knowledge on CM pathogenesis (Postels and Birbeck, 2013). This finding is very instructive and constitutes a de facto validation of the endothelial integrity variable used in this study. Although scientists have long known that insults to cerebral microvasculature and concomitant endothelial damage could be 150 University of Ghana http://ugspace.ug.edu.gh central to the pathogenesis of eM, limitations in the in vivo assessment of endothelial integrity during a P. Jalciparum have hampered research efforts (Widmer and Lennan, 2014). Leveraging EPCs attributes as a good correlate of endothelial function (Dzau et al., 2005; Venna et aI., 2017), this study converted EPC data into a binary variable using a Gaussian mixture model. Traditionally, mean and median splits have been used to convert continuous biomarker data into discrete variables but this approach has come under heavy criticism in recent times (Iacobucci et al., 2015; Rucker et aI., 2015). Mixture models are preferred because unlike the mean- or median-split, it estimates cut-off points using the distribution of the variable or by optimizing the correlation with outcomes (Budczies et al., 2012; Trang et al., 2015). Gaussian Mixture Models are not without limitations but its major drawback of decreasing perfonnance with increasing dimensionality was not apparent in this study (Bouveyron et ai., 2007). This study was interested in creating and binary and thus fit the data to only two mixture models. This user-defined operation can prevent the detection of other clusters that may exist in the data (Leisch, 2004). Besides showing that children classified as ProDamage were more likely to belong to the CM phenotype, this study provides evidence for the anticipated but rarely demonstrated fact that individuals with non-CM phenotypes have better endothelial integrity. In the pathophysiologic model underpinning this study, CM is conceptualised to result from a disequilibrium between damage and repair and thus, the ability to show here that non-CM phenotypes are Pro Repair is instructive. 151 University of Ghana http://ugspace.ug.edu.gh 5.4 Malaria in context: parasitological, immunological and baematological indices. 5.4.1 Parasitological indices Parasitaemia is an important but tricky malariometric index. The sequestration of infected erythrocytes in the microvasculature of vital organs makes parasitaemia assessment based on peripheral blood (PbP) unreliable (Berendt et al., 1994; Clark and Alleva, 2009; Cunnington et al., 2013; World Health Organization, 2016c). This study accessed three parasite estimation methods and found total parasite biomass (PlOt) and HRP2 to better discriminate between UM and CM (fig 4.23). Scientists have debated for decades on whether parasitaemia is a good predictor of CM but data from this study and that from others cautions that the debate may be wrongly premised if peripheral parasite density is used in estimating parasitaemia (Addison, 2017; Giha et aI., 2005; Tangpukdee et al., 2012; Wilairatana et al., 2013). On the contrary, there appeared to be very good concordance between Plot and HRP2 predictive abilities, and thus, the prospects of developing HRP2-based quantitative diagnostic kits is promising. All the parasitological indices considered in this study were poor in discriminating ProOarnage from ProRepair (Fig 4.24). Thus, in spite of decades of research, the relationship between the triad of parasitaemia, CM and endothelial integrity remains blurred. Future studies may benefit from more robust study designs that aptly combine animal models, in vitro cell culture, and human subjects to properly triangulate research questions (Ghazanfari et aI., 2018). 5.4.2 Immunological indices With the exceptions of C04+ T cells which was higher in the ProRepair group, none of the immunological variables measured in this study differed between UM and CM. 152 University of Ghana http://ugspace.ug.edu.gh Many aspects of the role ofT cells in malaria protection are still unclear but the role of CD4+ T cells in stimulating B cells and promoting phagocytosis is well established (Motaet al., 1998; Perez-Mazliah and Langhorne, 201S; Podobaand Stevenson, 1991). Generally. CD4+ T cells are responsible for the production of IFN -y which plays an important role in malaria immunity. especially, in controlling parasitaemia in acute human infections (Wykes et al., 2017). However, in the specific case of endothelial damage during P. /alciparum infections, CD8+ T cells rather than CD4+ T is thought to playa leading role in the immune-mediated damage of the endothelium (Renia et al.. 2012; Wykes et al.. 2017). Thus, the finding of comparable levels ofCD8+ T cells in the ProRepair and Pro Damage groups was unexpected. Perhaps, this is due to a discordance in CD8+ T cells levels circulating in peripheral blood and those sequestered in brain endothelium (Dunst et aI., 2017). The higher levels of CD4+ T cells seen in the ProRepair group may reflect a generally better immune response in the ProRepair group. Taken together. endothelial damage during a P./a/ciparum is a multi- step process involving several immune players and the examinations of immune signatures rather than individual immune correlates may provide better insights (Valletta and Recker, 2017). Even though immunological parameters considered in this study differed among malaria phenotypes, the pairwise comparisons between CM and UM revealed no significant differences - a further testament for the need to consider immune signatures in future studies. 5.4.3 Angiogenic factors Angiogenic factors associated with endothelial integrity in this study included the angiopoietin receptor Tie-2, neuregulin I (NRGI), carbonic anhydrase I(CAI), and angiopoietin 1( ANG I). Higher levels ofTie-2 in the Pro Damage portrays a more potent IS3 University of Ghana http://ugspace.ug.edu.gh endothelial activation in the Pro Damage group relative to their ProRepair counterparts. These findings become interesting in the broader context of literature on Aug-Tie-2 signalling and CM pathogenesis, some of which, have proposed plasma concentrations of Ang-2 and the Aug-2/Ang-1 ratio to be an independent predictors of death in different populations (Conroy et al., 2012, 2010; Jain et al., 2011; Lovegrove et al., 2009; Prapansilp et al., 2013; Yeo et al., 2008). Both systemic factors and local vasoactive substances can induce endothelial activation in CM and further studies will be required to decipher the actual role of Aug-Tie-2 signalling in CM pathogenesis. Relative to the ProRepair group, the ProDamage group had higher levels ofNRGI and CAl. These two angiogenic factors have not been extensively studied in the context of malaria. however, considering their normal biological roles, it is conceivable that they influence the pathophysiology ofCM and endothelial dysfunction. For instance, besides CAl's role is the pathological remodelling in ischemic cardiomyopathy, it has been shown to influence endothelial cell permeability and endothelial cell apoptosis in vitro (ToreUa et al., 2014). On the other hand, NRG 1, which is a cell adhesion molecule has been implicated in the pathogenesis of experimental CM (Liu et aI., 2018; Solomon et aI., 2014). Interestingly, whereas higher levels ofNRGI was found among CM patients relative to UM, levels of CA I was not significantly different between UM and CM. These factors have not been traditional candidates in malaria pathogenesis but findings from this study suggest that they could play important roles. Despite the significant associations of some angiogenic factors with endothelial damage, ROC analysis to prospect for CM biomarkers produced middling results. This could reflect the fact that the development of CM is a multistep process with endothelial activation representing only a snapshot of that spectrum. 154 University of Ghana http://ugspace.ug.edu.gh An incidental finding from the data on general characteristics of study participants broaches the changing maIaria epidemiology and its implications. The decline in malaria burden may have several unintended consequences, one of which, is the predicted shift in the age-specific malaria burden from younger to older children. (Carneiro et aI., 2010; Pemberton-Ross et aI., 2015). With median ages that are comparable to similar but older studies conducted in sympatric populations, evidence for the predicted age shift in malaria burden was not apparent in this study (Dodoo et al., 1999; Gyan et aI., 2009; Gyan et aI., 2004; Kusi et aI., 2008; Riley et aI., 2000). Although data from this study is ill-suited for inferences on this subject, the observation is nonetheless noteworthy. This study was generally well planned and executed. That notwithstanding, it had some limitation worth mentioning. First. the problem of missing data that often-beset complex studies was a challenge for this study as well. Traditionally, researchers attempt to address missing data with Listwise deletion but this is hardly a remedy for missing data (Deng et aI., 2016; Goeij et aI., 2013). The use of multiple imputation strategies are now accepted as the best approach in dealing with missing data (Deng et aI., 2016; Goeij et aI., 2013; Pedersen et aI., 2017) and this was employed in this study and implement in SPSS. Secondly, the lack of enough genetic material for additional in vivo epigenetics analysis robs the study of additional empirical data to corroborate in silico analysis. 155 University of Ghana http://ugspace.ug.edu.gh CHAPTER SIX 6.0 CONCLUSION AND RECOMMENDATIONS 6.1 Conclusion The major findings of this study distil into three main thematic conclusions. First, Intronic CDH5 (rs2304527) and MMP9 (rs391S256) SNPs are associated with CM and endothelial integrity respectively. This is the first study to implicate these SNPs in malaria pathogenesis and endothelial damage, and thus, the dearth of knowledge on the SNPs are not surprising. In silica analysis from this study suggests that some of the SNPs may influence endothelial integrity and malaria pathogenesis via the modulation of post-transcriptional or epigenetics mechanisms. This conclusion is undergirded by the finding that the SNPs associated with malaria and endothelial integrity disrupts and creates binding sites for important microRNAs and transcription factors. In the particular case ofCDH5 (rs2304527) and MMP9 (rs3918256), the SNPs modified the binding sites for transcription factors involved in various angiogenic processes. Second, the prospects of using angiogenic factors considered in this study as a biomarker for CM or endothelial integrity are slim. Whereas the binary endothelial integrity variable created with a Gaussian mixture model behaved as expected, ROC results on the performance of angiogenic factors in discriminating clinical malaria phenotypes and endothelial integrity phenotypes were disappointing. On the other hand, ROC results indicate that HRP 2 and PIO! estimates parasitaemia better than conventional parasite density. Parasite density contributes significantly to clinical decision-making and hence its inability discriminate between clinical malaria phenotypes is worrying. All the parasite estimates measures considered in this study 156 University of Ghana http://ugspace.ug.edu.gh performed poorly in discriminating endothelial integrity. This leaves the relationship between the triad of eM, parasitaemia and endothelial integrity as blurry as ever. Finally, immunological. parasitological, haematological and other malariometric indices measured in this study provided the backdrop and context for the discussion of findings of this study. Decades of malaria pathogenesis research have relied on such indices for background and context. Thus. although the immunological. haematological and parasitological indices measured in this study provided no fresh insights, it triangulated the more interesting and novel findings of this study. Accordingly, immunological. parasitological and haematological indices can be regarded as abiding imperatives of malaria pathogenesis studies. 6.2 Recommendations The recommendations put forth in this section ensues from the empirical findings of this study and the reflections of the author on the design of this study and others in the field. Firstly, the author recommends further studies in the following areas: The specific recommendations are: a) SNPs identified in this study should be further tested in G WAS to ascertain their robustness. Previous studies with genomic data available may want to evaluate these SNPs in their study popUlation. Such retrospective research endeavours could be more meaningfully if SNPs are explored in a genomic and epigenomic context. Functional studies of the biological roles of these SNPs will also be crucial. The role of epigenetic mechanisms such as histone modifications and DNA methylation must be explored in future studies. 157 University of Ghana http://ugspace.ug.edu.gh b) Further exploration of Immunohaematologic and maIariometric signatures in a community based longitudinal studies could provide valuable insights on the development and resolution of eM. Secondly, a critical appraisal of the findings from this study and the literature on eM pathogenesis brings persistent loopholes in our conceptualisation of eM pathogenesis to the fore. Despite the complex and interdisciplinary nature of eM, pathogenesis study designs are often overly experimental and unamenable to transdisciplinary knowledge. This sometimes leads to the exclusion of subpopulations that are thought of by researchers as confounders, but, are integral to the population they try to study and help. For instance, the exclusion ofindividual with haemoglobinopathies that protects against malaria and those with bacteraemia and malaria coinfections can potentially rob researchers of insights that may be more representative of the populations they study. 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BioI. 13, e1005812. van Loon, W., Gai, P.P., Hamann, L.. Bedu-Addo, G., Mockenhaupt, F.P., 2019. MiRNA-146a polymorphism increases the odds of malaria in pregnancy. Malar. J.18. Verma, I., Syngle, A., Krishan, P., Garg, N., 2017. Endothelial Progenitor Cells as a Marker of Endothelial Dysfunction and Atherosclerosis in Ankylosing Spondylitis: A Cross-Sectional Study. Int. J. Angiol. Off. Publ. Int. Coli. Angiol. Inc 26, 3&-42. Wagner, J.R., Busche, S., Ge, B., Kwan, T., Pastinen, T., Blanchette, M., 2014. The relationship between DNA methylation, genetic and expression inter-individual variation in untransfonned human fibroblasts. Genome BioI. IS. R37. 192 University of Ghana http://ugspace.ug.edu.gh Wahlgren, M., Goel, S., Akhouri, R.R, 2017a. Variant surface antigens of Plasmodium falciparum and their roles in severe malaria. Nat. Rev. Microbiol. 15, 479-491. Wahlgren, M., Goel, S., Akhouri, R.R, 2017b. 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Wattavidanage, J., Carter, R., Perera, K.L.RL., Munasingha, A., Bandara, S., Mcguinness, D., Wickramasinghe, A.R, Alles, H.K., Mendis, K.N., Premawansa, S., 2001. TNFa·2 marks high risk of severe disease during Plasmodium falciparum malaria and other infections in Sri Lankans. Clin. Exp. Immunol. 115,350-355. Weiss, G.E., Gilson, P.R., Taechalertpaisarn, T., Tham, W.-H., de Jong, N.W.M., Harvey, K.L., Fowkes, F.J.l., Barlow, P.N., Rayner, J.C., Wright, G.J., Cowman, A.F., Crabb, B.S., 2015. Revealing the sequence and resulting cellular morphology of receptor-ligand interactions during Plasmodium falciparum invasion of erythrocytes. PLoS Pathog. II, e 10 04670. Werner, N., Kosiol, S., Schiegl, T., Ahlers, P., Walenta, K., Link, A., Bijhm, M., Nickenig, G., 2005. Circulating endothelial progenitor cells and cardiovascular outcomes. N. Eng!. J. Med. 353, 999-1007. White, N.J., 2004. Antimalarial drug resistance. J. Clin. Invest. 113, 1084-1092. White, N.J., Turner, G.D.H., Day, N.P.J., Dondorp, A.M., 2013. Lethal malaria: Marchiafava and Bignami were right. J. Infect. Dis. 208, 192-198. 193 University of Ghana http://ugspace.ug.edu.gh WHO, 2000. Severe falciparum malaria. World Health Organization, Communicable Diseases Cluster. Trans. R. Soc. Trap. Med. Hyg. 94 Suppl I, S 1-90. Widmer, R.J., Lerman, A., 2014. Endothelial dysfunction and cardiovascular disease. Glob. Cardiol. Sci. Pract. 2014, 291-308. Wilairatana, P., Tangpukdee, N., Krudsood, S., 2013. Definition of hyperparasitemia in severe falciparum malaria should be updated. Asian Pac. J. Trap. Biomed. 3, 586. Williams, T.N., Mwangi, T.W., Wambua, S., Alexander, N.D., Kortok, M., Snow, R.W., Marsh, K., 2005. Sickle Cell Trait and the Risk of Plasmodium falciparum Malaria and Other Childhood Diseases. J. Infect. Dis. 192, 178-186. Wilson. N.O., Jain, V., Roberts, C.E., Lucchi, N., Joel, P.K., Singh, M.P., Nagpal, A.C., Dash, AP., Udhayakumar, V., Singh, N., Stiles, lK., 2011. CXCL4 and CXCLI 0 predict risk offatal cerebral malaria. Dis. Markers 30, 39-49. World Health Organisation, 2015. Guidelines for the Treatment of Malaria. Third Edition. World Health Organization. World Health Organisation, 2018. World malaria report 2018. World Health Organization (Ed.), 2010. Basic malaria microscopy, 2nd ed. ed. WHO. Geneva. World Health Organization, 2016a. World malaria report 2016. Geneva WHO Embargoed Until 13. World Health Organization, 2016b. World Malaria Report 2015. World Health Organization. World Health Organization (Ed.), 2016c. Malaria microscopy quality assurance manual, Version 2. ed. World Health Organization, Geneva. World Health Organization, Global Malaria Programme, 2017. World malaria report 2017. Wu, J.M., Visco, AG., Grass, E.A., Craig, D.M., Fulton, R.G., Haynes, C., Weidner, A.C., Shah, S.H., 2012. Matrix Metalloproteinase-9 Genetic Polymorphisms and the Risk for Advanced Pelvic Organ Prolapse. Obstet. Gynecol. 120,587- 593. Wu, S., Hsu, L.-A, Teng, M.-S., Lin, J.-F., Chang, H.-H., Sun, Y.-C., Chen, H.-P., Ko, Y.-L., 2012. Association ofSELE genotypeslhaplotypes with sE-selectin levels 194 University of Ghana http://ugspace.ug.edu.gh in Taiwanese individuals: interactive effect ofMMP91evel. BMC Med. Genet. 13, 115. Wu, X., Gowda, N.M., Gowda, D.C., 2015. Phagosomal Acidification Prevents Macrophage Inflammatory Cytokine Production to Malaria, and Dendritic Cells Are the Major Source at the Early Stages of Infection: IMPLICATION FOR MALARIA PROTECTIVE IMMUNITY DEVELOPMENT. J. Bioi. Chern. 290,23135-23]47. Wykes. M.N., Stephens. R., Cockburn, I.A., 20] 7. Adaptive Immunity to Plasmodium Blood Stages. In: Malaria. Springer, Cham, pp. 47-66. Wykes, M.N., Zhou, Y.-H .• Liu. X.Q., Good, M.F., 2005. Plasmodium yoelii can ablate vaccine-induced long-term protection in mice. J. Immunol. Baltim. Md 1950 ]75.2510-2516. Yeo. T.W., Lampah, D.A., Gitawati, R., Tjitra, E., Kenangalem, E., McNeil, Y.R., Darcy, C.J., Granger. D.L., Weinberg, J.B., Lopansri, B.K., Price. R.N., Duffull. S.B.. Celermajer, D.S., Anstey, N.M., 2007. Impaired nitric oxide bioavailability and L-arginine reversible endothelial dysfunction in adults with falciparum malaria. J. Exp. Med. 204, 2693-2704. Yeo. T.W., Lampah. D.A., Gitawati, R., Tjitra, E., Kenangalem, E., Piera, K., Price, R.N., Duffull. S.B .• Celermajer, D.S .• Anstey, N.M., 2008. Angiopoietin-2 is associated with decreased endothelial nitric oxide and poor clinical outcome in severe falciparum malaria. Proc. Natl. Acad. Sci. U. S. A. 105,17097-17]02. Yeo, T.W., Lampah. D.A., Tjitra, E .• Piera, K .• Gitawati, R., Kenangalem, E .• Price. R.N., Anstey, N.M., 2010. Greater endothelial activation, Weibel-Palade body release and host inflammatory response to Plasmodium vivax, compared with Plasmodium falciparum: a prospective study in Papua, Indonesia. J. Infect. Dis. 202, 109-112. Zaina, S., Perez-Luque, E.L., Lund, G., 2010. Genetics Talks to Epigenetics? The Interplay Between Sequence Variants and Chromatin Structure. Curro Genomics 11,359-367. lakeri, S., Pirahrnadi, S., Mehrizi, A.A., Djadid, N.D., 20] 1. Genetic variation ofTLR- 4, TLR-9 and TIRAP genes in Iranian malaria patients. Malar. J. 10, 77. Zeng, L., Cui, J., Wu, H., Lu, Q., 2014. The emerging role ofcircuiating rnicroRNAs as biomarkers in autoimmune diseases. Autoimmunity 1-11. 195 University of Ghana http://ugspace.ug.edu.gh Zhu, J. • Krishnegowda, G., Li, G., Gowda, D.C., 2011. Proinflammatory responses by glycosylphosphatidylinositols (GPls) of Plasmodium falciparum are mainly mediated through the recognition ofTLR2IfLRl. Exp. Parasitol. 128, 205-211. Zbu, J., Wu, X., Goel, S., Gowda, N.M., Kumar, S., Krishnegowda, G., Mishra, G., Weinberg, R., Li, G., Gaestel, M., Muta. T., Gowda, D.C., 2009. MAPK- activated protein kinase 2 differentially regulates plasmodium falciparum glycosylphospbatidylinositol-induced production of tumor necrosis factor- {alpha} and interleukin-12 in macrophages. J. BioI. Chem. 284, 15750-15761. Zymo Research Corporation, 2014. Quick-gDNATM MidiPrep. 196 University of Ghana http://ugspace.ug.edu.gh APPENDICES Appendix I: Ethical Approval NOGUCHI MEMORIAL INsnroTE FOR MEDICAL RESEARCH EsUlbllsh«l , 919 A CoItsr/rutnr of rile CoIIc/IC of HeIItrh Sdcnccs University of Chana Ph_ +233-302418431 (Ohc:t) +233-218-622574 NMlMR. ... Fix: +233-21-6021821513202 P. O. Box LO 511 E-mail: ~."*..com.orv LMaD,Accn 0IIInI My Refe'enc:e: OF 22 DaoieI Amoako-Sakyi, PbD Ua.iVVlity of Cape COlIC, Dept ofMicrobiololY IIId Imm\lllololY R£: OUr Study 1032/14-15 At NOGUCHI MEMORIAl INSTITUTE FOR MEDICAl RESEARCH-IRS Our DIllie! Amouo-Sakyi, PhD: ....u ng 01. .: 11M014 At: NOGUCHI MEMORIAL INSTITUTE FOR MEDICAL RESEARCH-IRB ProIoc:oI TIUe: Genelc and Epigenetic baSil 01 EndoIIeUII Damage and Repair In cerebral Malllla In 0hanafM dllldren ThIs lito aClvile you IIIat !lie IIbovI ref8renc:ecI study has been presenled 10 l1li InsUlullonal Review BoarcI, and tile following ICIIon lakin IUIIjtct 10 the condlUons and txptanaUon provldld below. In ..m all: New AppI Expiration Date: 111412015 On Agenda For: InIIIaI SubmlUion Reason 1: Reason 2: OIacrlpllon: Dale RtceIvtcI- 1012112014 IRS ACTION: Con8ngtnt AIJprovaI Condition 1: Action ExpIanaUOR: The protOCOl WIll epprovtd IUIIjed 10 ICICIrestMIg tile comment below. AI the aIIbreYta1ions used In Ihelludy should be expllined upfIont Yours SIncerely. NMIMR-IRB IRB Adn*IIstrator 197 University of Ghana http://ugspace.ug.edu.gh Appendix II: Scientific and Technical Committee Approval NOGUCHI MEMORIAL INSTITUTE FOR MEDICAL RESEARCH Administration (MeMO) FROM STC COORDINATOR TO MR. DANIEL AMOAKO-SAKYI cc DIRECTOR, PROF. BEN aYAN SUBJECT STCOUTCOME DATE 13'" OCTOBER, 2014 ", Its meeting held on 2nd October, 2014, the Scientific and TechnIc:aI Committee (STC) reYleWed your protoalI entitled "Genetic and epigenetic basis d endothelial damage and repair inc:erebnll malana In Ghanaian childrenw (STC] Paper 1(2)2014-15 and reconvnended that you make the following amendments to your proposal: • The school c:hiIdren should be IIIected from sdIooIs dose to the hospiIaIs within the catchment area. • The sample sue calwlaticn should be provided. • The study is a PhD work. Therefore, "Co-PIs- should be replaced with .. student supervisorsw. • A consent fann should be provided. I&rJJkm The proposal was approved subject to addressing the above concerns. The revIIa:I version should be forwarded to the SfC Coordinator. 198 University of Ghana http://ugspace.ug.edu.gh Appendix III: Inform Consent Title: CircubtiaaJ eadolbelW celli :as die padIogeneSIS ot m.aIana PriDap!llnvrstJgatoc Baa Gym, PlIO Addreta: Dqw1maIt of ImmllDOlogy. NMIMR. BOll LG 511. Lqoo 1ll1o. ...d D.: (1'0 be read or 1raDsbted to paJl'IllJ/ljUaJ'diDI ill tbeiJ own mother 1UDp) o.ar Yotvnt.r, lbJJ ~ fIxm coaWn. iD1bmWi0ll about the reSClJ"Ch cntrtled Cl1'cuialllt8 .1!doIM/Jal c.1b tDtd Illtl ptllIJog.1IIIS1S olmaJiula III order to be 11ft that you are mformed about beang III Ibis rese.arcb. "'" are ulciq you to read (or ha\'e rud to you) IhiI Coraent Form. You W1Il allO be ukecI to AID it (or make your mart ia Jtout of. wtlJlen). We will give you. copy oflhil form. Tlu. cOllSelll fIxm IIIiIbt COIItliD. some WOI'dI that _ un1'.uDiliar to you. PIeue ask \IS to expbm anythmg you may DOt UIIdentaDd. Why Ihls .ReIy Is pluned Your child II bema .asked to participate iIIlbe above study ill order to ftad out fatton ill die blood !bat may be of risk 10 wvere ~.an.a.. Mmria II QUJed by • germ that it pUled hID ODe periOD 10 lilt olber by die bite of a mosquito !bat cames the maJ.aria germ. Mabna II • very senoUI heUh problem ill GIIaria, as it is illlIWIY At1can COUDtria. We do DOt IaJow wily some c:biIdtea become 1e1'eRly ill tom ~ari.a or wtay lOme of diose cbildrCII die hID malan.a. To 1IIIlkntmd this problem we Deed to study childrm who come 10 tile IloIpital with Ie\'ere malaria IIId compare !hem to c:hildrcn who have leu wvere ma1aria, IIId to other cJuldren who are feeliDl well. The purpoae of Ibe study iI to tiDd out wII3I !acton they already 199 University of Ghana http://ugspace.ug.edu.gh llave ill dIeir blood IIW may auIre III8a ~ &idt WIIaIIbcy IIIw IN\atIi. If_ caD II1II die IIIIWU ID dIis qucstlOQ, 11ft: bope 10 be IbIe 10 IUQCIt _ lQ)'I 01 controUiD& NCta KWR lidIDeua ill malaria. c...aJ liii0. ....1 10. ud )'lIIlf put .. die oily for a d1i1c11o quaW'y 10 be pan ofllus stud)' dial cluJd sbotIld be belwealdle ..0 I11IId 12 yean. If your cluldl\vard qrea 10 be ill die 1tIIdy, we "''ill collect veooUI blood .-pIe tbr !aboralOf)' c1iapoaillll4 2 all [teatpOOII1\II) for our r_:IIth • the um. of admiIIIOII, 1 daya ....s 14 dIyI after rKOvay. 1f}'IIU a ..a i acbecIuJed follow-up viais (7 dIyI aad 14 days aftu recovay), 1ft..., CIIIIW:t you aI bam. by pilon" or ill penoa 10 sc:Mdu.le moIber VIlA oIIId 10 tee If you IliII WIIIt 10 tab part ill the resean:h. WbeD lII1a COIIUct 11 made ),OU wjJJ DOt be iMaIified as belDl illlIus rae.udl 1'bcre are 110 cbrect beDdlb 10 yout child &om tbia 1IUdy. However, billber p:amclp3!JOG may IIrIp UI develop better malana trntment. He;she will DOt be paid for parucipabon m litis ItIIdy but you will be relDlbuoed willi m amOUlll of fifteen Gbma cedis for your rune IIId nvel dwiDa die follow up \lIIa. 'onllllllllsks 'lbll1IIIICIIIIII of blood coUeckd II 1wmIeu, altboup tIIa"e miY be allipt paiD IIId 1mIiIIDI , • die blcedmg lite. All IUbJCct! will receive approprim IftabDcIIl as DCCessuy. SIaiJe ! IleduliqIlClIIId diJpolable, siap-ule equipllleDt WIll be uaed II all lim .. L_ 200 University of Ghana http://ugspace.ug.edu.gh Whlldnwat froa ...,. We would like 10 _ dlallIuI study .. IInctIy ¥IIIuatary. Should tit cIIiId decide ... ID PII1iciP*: it will 111,. DO Coaoequmces1br1lilMler. Sbauldlllno!unte. ... lfaaypoialdtlliq tile 1IDdy. decldc dial betsb. do !lOt willi 10 J*1icJpue lDy ftIo1ber. )'ClOI ate ltft 10 IaII1iDMt die panicipatioa. dll!ctive iawcdulely. Ally __ decuioo will be RIpICted wiIbout l1li)' ftIo1ber ~ y_ dcdIiaD wiIJ DOl aJIKt tbc bcaltb care you would DmIIIIIJy RCCi\'e. Visits If lilt cbiId _ • scbeduJed YIIiI. ... may CIIIIIact you II ~ by pIIaac. or ill ~ 10 IdIeduIe lIlotbcr Wit aDd 10 tee ilyou IIiII WaD! to tile. part illlIIe martb. Wbcn IIIiI COIIIII:\ is made you wilIlIOl be KIaIblied u beiDa illlIIiI martb. CoddeadaJlly AU iIII'IlnuIIiclG pIbcftd would be lruted iD IIrict caafidcaIiaIity. We wllJ protect UlformalJoo about yoar clIiI4l1ki11a pan ill !lui racarcb 10 IIIc belt of our ability. The cbiId will DOt be lIIIDed iD l1li)' reporta. line,. .... tile lid' of (lilt a111l0llpl dial may ICCm lilt rese.cb records) may IOIIIeIimes look at bJs,ber retCilfcb ruordl. If you bve my qu<.b .... please feel fttc 10 Ilk die pllyslcilll .. dIaq. Someooc 6'om die IllB or Edu~ CommiIIee miIbt W1IIIIO ask you qllClUOUS about beiDllD IIIc rescarcb, but you do IlOl have 10 __ them. A _ 0(0 (ould order medical records sbowD 10 OIlIer people. but lllal islllllllcely. COAIaca: Ify ou ever ba\'e l1li)' qucstiaal about lilt raan:b INdy or lIIIdyoftiaed problllllS, you may CODtaClDI'. M2ame Yu Nyllko at PriDe. Mme l.ouiK HotplIaJ (Tel: 0244 OIlSS.) or Dr.Bm Gym of !be Nopcbi Memorial Institute for MedicallW. ...c II (0244 726016) II 201 University of Ghana http://ugspace.ug.edu.gh aylilllC. for quatiou about the edIicaI upectI oflbis INdy or your nIhIIu a \'Oluzuecr, you l1l.I)' COIDCt Dr. SaDtl A~ CIwnuaa, IuciIuliaaaJ IteYIew Bon, NMIMR. Uniwnity of Gbaaa (021 50117119) or CIIalnIw! of die GIIua HaI1b s.w:c EIIUcal Coau!udee (Tel 021 611109) V,ar rtph as a parddpul 1bit research bas beaI re"eMd aad apprO\'" by die NMIMR IRB aad Gballa Heal1ll Service Etbical CommiIIee. AIIIRB or Etbical Committee is a committee Ibm reviews RUarch ItUdieI ia order to help protect patIicipaII. If you have .y quatiou aboul yoar riPlI II I research paniapallt you may CODtact [Dr. Samuel Ayete-Nyampoaa, Tel 21·501·1781179 or CIIainDaD oldie Gbana HaI1b Service E1IIiI:al CaauJUIIee (Tel 021 681109) VOLUNTEER AGRIDlENT The abovt documrnl delcnbllli die baIefits, riW aDd procedura for the research tit1e CImtkltIlflIlldotMlI4I ellis fWI 1M ptJIltopl/lS/s ofWIQIar/Q lw beaI read aad explaiaed to me. I have been P\'COIII oppclIIUDIty to have lIlY queItiou about the resean:b "''''Creel to II)' salisfactioo. lqree my child/ware! to particiJllle as a vo1uateer. Date II volaa .....' . Pll'lllrlGaardlu ClllDot rtad the 10,. th_seh'n, a wilaUI mast lip ben: I was present while the beadits, rUb II1II pruc;edum were read to Ibc \'OIuateer. All questioIu were IIISwered md Ibc VOluotcer'l Guardtm'Pareat bas qreed to take part ill Ibc research. 202 University of Ghana http://ugspace.ug.edu.gh I ~ lballbe IIII1n ., PIII)lOIe. die poccatIaJ beae1Its, IIId poaiblc nib ulOdated willi panicipItIq Ia dtiI.-.:h IIIw beai apWaed to the above iDdIviduli (TdIdI, 2014) Siptw't PetIOlI who obtained COOlellt 203 University of Ghana http://ugspace.ug.edu.gh uIIs IIIId tltI pathor-b~""" Ia Older til be IIIR .. )'011 are iDIbnDed abouI beiq ill Ibu raardI, we are IIkiDI yoa til read (or llave read til yov) dIiI ~ F-. Y0II1riII aIIo be liked tlllIlD it (or mae your IUrIi: ill ht of a willlen). We \diM }'VII a copy oflllil fbrm. nw cCllJCDt tbnn might COOtailllOlllC wordI that are ~ to you. PIMIe alii: us til explailllll)1llilll you may DOC UDdentaad. WIt,. dIU HIldy b pluald Your child II beiDa asked to panicipete m Ibe Ibovc ltUdy ill order til find out factDn ill Ibe bIIIod dial l1li)' be otrilk tIIlCwre IDllari&. Mmria II CIIIICd by a Jam_ il pwcd hili CIlIa paIOIItII Ibe other by tbe bite of. lIlOIquito that carrie. tile IIIIIIria ImIL MaJana II • \'eIY smOUl beaIIh problem ill GIwII, as it is ill _y Alhcaa cOUDlriet. We do DOt bow wily lome cIIIIdrcu become severely ill li'om malaria or wtIy lome or those c:Iu1dm1 die rtom malaria. To IIIIdcntaDd tIIis problem we Deed to study cIIildreo who come to tile bOlpital With severe malaria and compare them to t:IIiIdreD who bave las severe mabria, and to other cbildreD who are t'eeIiDg well The purpose ottlle study Is to ftDd out wbat tmon they alre.,. ba\'e ill !lleir blood thai may make them severe1y lick \\1Iea they have ma1aria. IIwe CID ftDd !lie __ to !Ius questioo, we hope to be able to SlIgest Dew ways ofCODInI11iDa such m"ere liclaltua ill malaria CfttnilDlormalloD ud Y08r pan ill die mad)' For a child to ~ to be part oftllis ltUdy thai child IbouI4 be bctweeo the aaes or I and 12 )'arI. II r- c:IliIcIIwwd asrees to be ill the study. we will colla:l VerJO\ll blood Nlllple tor laboratory diagnOSIS aod :I all (taspooDfW) for our NMarth irIitiaIJy and 7 day. and 14 days later. II you mias a scbeduJed follow-up visitI (7 days 1114 14 days) ill your 1Cboo1. we may COlltact}'VII at lIome by pboaa. or m paIOII to Icbedule lDother VIIi! 1114 to _ ity ou JtilI_t 204 University of Ghana http://ugspace.ug.edu.gh to take part ill 1M researdL WIleD thai coatact il made you will IlOl be 1deaIified &I bciq In dlilmardL Possible Beadh 'I'1In IRlIII direct baIdiII to y_cbiJ4 a- tbu 1tUdy. Howna'. ..... pllltJclpatlOa auy bclp UI develop bcner JDaIaN treatmenl Helshe WIll nOl be paid tar partlcJpatiOll ill thai ltudy but you WIll be reimbuned wiIIIm lIII0II1II of fifteen Gbaaa cedis tar your lime II1II IrInl clurmg die follow up VW!l Possible lUaks The amollDt ot blood c:oIIec&ed is banDIcA. aJIbousb dIcrc may be a IIiIbt paIIl OIIId bruiliq at the bleedirll lite. AU lUbJecti will recein appropnate trutment &I aeceuary. StmIe tecbDJques mel dlspoqble.liDgIe-UK equlpmeDI will be used at all blDcs. "'ltbdrl.11 fro. ,"dy We would like to ... lllat dIiI IIUdy .. strictly vaIuaIary. Sbouid the cbild decide !lilt to pirtle 'p*; it WIll !law DO consequcm:ea Col JWn,1u:r. Should 1be voluateer. at my poiat cIunI:Ii dae 11Udy. decide 1ba helshe do not wish to participate III)' 1IIr1ba. you 1ft he to tCl1l1llllle the partJClpatson, dfedive imIIIediardy. All.y such decision WIll be rapectm without III}' ftJnbu diKusaiOll. Your deci.ioa WIll DOl a1I'ect the beaJth care you would normally receive. \'blll 20S University of Ghana http://ugspace.ug.edu.gh (fllle dulclllUSlt'S a sdIeduIed V1IiI. WI..." COIIIICt yw .lIome II)' pboDc. or ill pcncIIIlD ICbeduIe IIIIIIber viIilllld to I« ify ou IIiIlwaal.,,,, part ID IIIe re-aa. ..... IblICOIIIICt IS made you "ill DO( be idmtified u beiaa ill 1bia-.dL CealldeatbllJly AU illformatioa pdIemI woaId \Ie lUatN ID IMrict COIIIIdaatiaIit· We wiD pI'GCIct iafbnDatioa about r-- child '*iDa pelt ill duJ rnadllD IIIe \lett of our ability. TIle child wiD DO( \Ie DDe_d mtl IlYs re pIIItI. Howevcr,Ibe lWI'ot[lllt Illlf011p1tbat may _e.. die mcarchftCClfCbj may loot IlIIiI/IIIr ...-cIa ncords. (fyou bave lIlY queatioa.s. pleue Cede-to uk Ibe physlciIII ill cbarp Someone tam IIIe 1RB or Ethical COIIUDJIIee IIIiPt WlDt to uk you questlClIII about bciq mdle racardI, bile you do DOl bave to IIIIMr tb_. A court otlaw could order medial _cis sbowa to otber people, but !bat II uaIikeIy. Coautts: ltyou C\'a" ba\"e lIlY qucstioaa about Ibe researell study or 1II!dy«lated problema, you may ccaact Dr. M-. Yu Nyllto. PriDce Marie Louiac HOlpiIaJ (Tel: 0244 018888) or DrBea GYID oflbe Nop:lu MaaoriaJ lDstJtUte for Medial Jtesearch (0244 720016) at Illy tuDe. For quutioas about die etJucal upccu of tb.iJ study or your nabtt u a volualeer, you may CODtact Dr. Samuel Ayete-Ny1lJlPOlll, CIwnDaa. IJllIiluIioGa1 Review BoaId. NMIMJt.. Umvlllily of Gbma (021 50117119) or CbainDaa oldie GbaIIa Healtb SeMce Etlucal C4lnllllJtt~ (Tel. 021 681109) V. ..r ....... as I partldpu. Tbia _cb bu bcea reviewed mel approved II)' die NMIMJt 1RB IIId Gbaaa HRIIh Service EdIicaI Commil!ft. Au IRB or EtbicaI Committee II a commillee !bat r• .,.\eWI reautcb .tudies in order 10 bcIp protect partlapaDII.ltyou bavc any questiCIIII abollt r-- rilbll u a resean:ll 206 University of Ghana http://ugspace.ug.edu.gh ~_you..,cOIIIaCI[Dr.s-JA~Tdll.S01·1711179orCllaJnJuo GIlle CiII.aoa HeaJtlI Sen"ittElllit:alc-illel (Tel 021611109) VOLUNTEER ACR!:DlDO" Tbe above documeaI duaibiDc !be beaefiI.I, rIIU aDd proce4ura I'« die reMIIdI title CIn:ulDlIIIr I1ItItItJwIItII alb fftI 1M pt1IJtGptwts ~".."", .... been IUd IIId cap1aiDed 10 me. I have been SWeD an oppor\Ufti1y to .\'C DY quntiaDI abouI!be reulld!lM\\'end 10 my satut.cta0lL I apee lIlY cbildIwIrd 10 pIIticipUe U I \·olwlleer. U ftJurMr'1 P. ....l ICu.rdJaD ....e r nad tIM r.. . $ .......... wtnaaa .ut lip 11. ..: 1_p maII wbiIe die beaeftta, rilb ami procedum were read 10 !be \'OIlIIIteer AU questlODJ MR aaswae4111d !be VOIullllMr'I GuudlmlP:armtlw qreed 10 take JII'lID die rese¥Cb. I ~ tbat die DaIUre aDd IJUI'POK. die poIaItlII baellb, aDd possible rIIU UIOCiaIed mill PIltlClpalllll in tbiI raean:II have bem expIaiDed III die above iadividuaJ (TeIIeb, 2014) 207 University of Ghana http://ugspace.ug.edu.gh Appendix IV: Primer for iPLEX Reaction Forwn Primer ExIaIded Pr1I1ICI 10 Forw.d Primer Sequcnc;e Rcvcnc Primer ID Reverse Primer Sequence ID ElltcDdcd PriJDcr Scqucncc 1 .u7209I_WI_F ACGlTGGATGAOOATAGTCCTAGACA(JCAC n37209I_W I_R ACGlTGGATGGACCAA TGAGCAA TGGCT AC rs37209I_ WI E GTCCCAGCAGCAT 2 n2ai416_WI_' ACGlTGGAroccACA1T1'CCACCACTATCC n2236416_ WUt ACGTTGGATGGACACCAGACCAAGGAAGAG n2236416_ WI_E MAGGCCGAACCT 3 ISIOI9994o_WI_F ACGlTGGATGTC'1OOro1'CACTGC 1S10000000_WI_R ACGTTGGATGTGCAGTCCAGCACAGMAAG ..1 0199940_ WI_E GAGCCTGGGAGTCT • 1S2304S27 _W I1 ACGTTGGATGGGAAGACCCTCTTT ACCTTG B23CMS27 _ W1 _ R ACGlTGGATGATAGACMGGCGATroccTC n2J04S27 _W I_E 1TGCCTCI1TCAGCT , n943012_WI_F ACGlTGGATGGGTGACAACACTGAGG'CTG n943OI2_WI_R ACGTTGGATGCCTGAAATTTCTCAGCCAGC n'M3OII2 _WI _E GTGGCAGAGTGAGCC 6 1II1'm22_WI_F ACGTTGGATGAGAGACCTCMTGTCCACAG 111176722_ WI_R ACGTTGGATGCACCACCATTGGGTTAACTG "'176722_ WI_E TGTCCACAGTCACTCG 7 ",UI900'85_WI_F ACGTTGGA1'G11CAACTCTGCTATACAC ISI000000S_WI_R ACGTTGGATGATTGCACTCCAGCCTGCCT rsI09OO585_WI_E MGGAGTCTCACTCTT a n207IS59_WI_F ACGTTGGATGATCAGAAAACGCACTTGCCC n207IS59_WI_R ACGTTGGATGCTAGGCAGGTCACTTCAAAC rs2071S59 _W I_E GGAAATAGCGGGMTG 9 n3911211_WI1 ACGTTGGATGAGACCTAOOroCAGGACA TC n3911211_WI_R ACGTTGGATGGTCTGCAGGACGTTGGTTG .u911211_W I_E aTTGTCTGCGGCGATGT 10 1I2l747S'_W I_F ACGTTGGATGTTCGACGATGACGAGTTGTG rs2274US_W I_R ACGTTGGATGGGGAGAGMTGMGGGMT C 1S22747SS_ WI_E TGGGCAAGOOCGTCGGT 11 nQ524054_WI] ACGTTGGATGMCACAACCTGACTTTTACG nQ524054_WUt ACGTTGGATGTATGGGMGCTCT ACCACAC rs2524054 WI_E TGACTTTT ACGATCATCA 12 ",'T.M22262_WI1 ACGTTGGATGGACTGAGGT MAAGGACTGC 1573422262_W I_R ACGTTGGATGATTGCATGCCTM TAGGAAC rs73422262_WI_E GCCTMTAGGMCCAT CCA Il n.591155740_W I_F ACGTTGGATGMCATGGTGAAACCCCCTCT n.59IISS740_WI_R ACGTTGGATGTATGATCTTGGCTCACTGCG rsS90SS740 _W I_ E IIIjIGATT ACAGGCCACCA 14 ",16I31S32_ WI_F ACGTTGGATGGGTCATAGTTAAAGAGACCG nl683IS32_WI_R ACGTTGGATGCTTTCAGATAGACAAAGTG rsl6831S32_WI_E GACAAAGTGAAAACAAAA T IS rsllOO783_WI_F ACGTTGGATGAGTCATCCTTGGTCATGCAC nl800783 WI_R ACGTTGGATGATCCAGCCCCTACTTTTCAG nl800783 _W I_E ggcgGGAGGAGACAACAGA 16 rs56213 104_ WI_F ACGTTGGATGTCCAGAGTGGGCTCCTT AC rs5623JI04_WI_R ACGTTGGATGTCCCGAGTTCTGGGCA TTTC rsS62J3104_WI_E cas-AGCACCTTGCTCTGCAT 17 nQ070744_WI1 ACGTTGGATGACCAGGGCA TCMGCTCTTC rs2070744 _W I_R ACGTTGGATGCTGTCA TTCAGTGACGCACG rsl070744 _W I E ctnCTGAGGCAGGGTCAGCC 18 rs606630J_ WIJ ACGTTGGATGCAAGGGCAGCACT AT CTTGA rs6066303 _ WI _R ACGTTGGATGAAACCGGAGGCTATTTGTG rs606630J_ WI_E GGCTATTTGTGTAAATGT AM 19 nI331l099_WI_F ACGTTGGATGT AGMCGAT AAGGAGGGTGC nl3JI 3099_WI_R ACGTTGGATGGACTCGT AACTTAC ACCCTC 1513313099_ WI_E aGATTTT AGA TCACCTTAACTC 20 1'110489181 WI F ACGTTGGATGCATGMTGCMGGGTTTGTC nICNI9IBI_WI_R ACGTTGGATGAAGATAAATGCCAAGGGGAC nl0489181_WI_E AAAGTACAGGAGATA TT AT GGG 21 ..7 604879_WI_F ACGTTlrtiATGTGC AGC AAACGGAAACT MC ..7 604179 WI .R ACGTTGGATGCTCTGTGTACCTCAGA TTGG ",7604879_ WI_E AA TT AT TTGTTCTCAT MGTTTG 22 n42J6Ol!.1 WI_F ACGTTGGATGGGAGGTTTGAAAGTTGCCAG 1'14236014 WI R ACGTTGGATGTGCTGCTTTCACTCTCACCC 1'14236084. WI.E agallTCACTCTCACCCCCGCCCC B n3918256_WI_F ACGTTGGATGTTGACAGCGACMGMGTGG nJ918256_ WI_R ACGTTGGATGACAAACTGT ATCCTGGAGGG n3918256_WI_E cce<:GCCCCGGAGCCGCGGGACCA .~ _L_ University of Ghana http://ugspace.ug.edu.gh 24 rs3917419_WI_' ACCil'TOOATOCiC'TCATCTC'TCACCTTTAAC rs3917419_WI_R ACGTTOGATGAAAOOGGTAGAATTACAGTC rs3917419_WI_E ...T TTAACNJATAAGAACACTG 2' ral07'73II_III_F AC'GTTOOATGAQATCGATCTGATGACAAa: ..1 077311_WI_R ACGTTGGATGTTCACACTATCa:CAGGATG ..1 077311_ WI_E -.croTGAAAT GGGAAT GATA :Ii6 ..1 466S41_WI_F ACGTTGGATGAAGAGT AAGGGGT AGGGAAC ..1 466S48_WI_R ACGTTGGATGTCCAGTAATATCAATGGAOG ..1 466548 WI_E apaGGAGGATATAT GTGTGCA T CTTAAGTTCTGCTTTTAAAAATAT 27 11614951 WI F ACGTTGGATGTOGTTGAGCTTAAGTTCTGC n6lI49S1 WI R ACGTTOGATGGTAAGTGACCTAAACCTTGC n6U9S1 WI E A University of Ghana http://ugspace.ug.edu.gh Appendix V: iPLEX protocol Table I: Multiplexed peR cocktail, without DNA (Same multiplexed assays, different DNA) .'" Reagent Concentration In 5 J&l volume (1 rxi!1 . Water (HPlC grade) NA 1.8 JIl 10x PeR Butrer with 20 mM MgCI2 1x (2 mM MgClz) 0.5 JIl MgCI2 (25 mM) .. 2mM OA JIl dNTP mix (25 mM each) ••• 500 pM 0.1 III Primer mix (500 nM each) 100nM 1.0 III peR Enzyme (5 UlIll) 1.0 Ulrxn 0.2 III Total VOlume: 4.0"l The SAP mix should be prepared as below and can be scaled up to encompass more samples. Table2: SAP enzyme solution '" ... '." , Reagent Volume (1rxn)· Water (HPlC grade) 1.53 J&l SAP Buffer (1Ox) O.17~ SAP enzyme (1.7 U/JlL) O.30~ Total Volume 2.00 J&l RNgII'It Cone. In 9 III Volume (1rxn,' WIII1It (HPLC grade) NA 0.G19 III FlEX BuIll Plus (10x) 0222)( 0.200 III IPlEX Tennmlion mix 1x 0.200 III Pm. mil: (' pM: 10 pM: 15 pM)" 0.52 pM:1.04 I'M: 1.57 I'M 0.940 III IPlEX 8ftQme 1x 0.041 III Total VoIl.IIle: 2.000 III Add 2 ul of iPlex-SBE master to each well of the SAP treated plate (total vol. 9 ul). Vortex and centrifuge 210