University of Ghana SCHOOL OF ...

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)

Prof. Julius Fobil (PhD) (Academic Supervisor)

Dr Kwadwo Asamoah Kusi (PhD) (Academic Supervisor)

Dr John Arko-Mensah (PhD) (Academic Supervisor)

IJ'V-'~I" Date

\q \ \\J \ (...cJ\,\ Date


DEDICATION

To Reggie, Eno and Kobby

ii


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


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


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


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


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




4.9.6. Tag SNPs on chromosome 9 .................................................................... 100

4.9.7. Tag SNPs on chromosome 10 .................................................................. 104



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


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


LIST OF FIGURES

Figure

Figure 1. 1: Conceptual framework

Page

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


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. 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


LIST OF TABLES

Table

Table 3. 1: PCR cycling program for target amplification

Table 3. 2: PCR cycling program for iPLEX reaction

Table 3.3: Regression models for genetic analysis

Table 4. 1: Association of angiogenic factors with malaria phenotypes

Table 4.2: Association of key variables with endothelial integrity

Table 4.3: Summary ofgenotyped SNPs

Page

69

71

7S

82

86

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


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. 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


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


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 W AS 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


MSPI

MSP-119

MSP-3

NORl

NMIMR

NOS3

OPD

PAMP

pbP

PCR

PECAM

PjEMPI

PjHRPl

PL

PLPI

pRBC

qPCR

RBCs

RIFIN

RNA

ROC

RON

RT-PCR

SIOOB

SAO

SAP

SBE

Merozoite Surface Protein

Merozoite Surface Protein-I 19

Merozoite Surface Protein 3

Neuregulin I

Noguchi Memorial Institute For Medical Research

Nitric Oxide Synthase3

Out-Patient Department

Pathogen Associated Molecular Pattern

Peripheral Blood Parasitaemia

Polymerase Chain Reaction

Platelet Endothelial Cell Adhesion Molecule

P. Jalciparum Erythrocyte Membrane Protein-!

P. Jalciparum Erythrocyte Membrane Protein-l

Phospholipids

Perf orin-Like Protein 1

Parasitized Red Blood Cells

Real-Time Polymerase Chain Reaction

Red Blood Cells

Repetitive Interspersed Family Protein

Ribonucleic Acid

Receiver Operating Characteristics

Rhoptry Neck Protein

Reverse Transcription Polymerase Chain Reaction

S 1 00 Calcium-Binding Protein B

Southeast Asian Ovalocytosis

Shrimp Alkaline Phosphatase

Single Base Extension

xv


SDF-I

SMA

SNP

SPECT

spp

SR-BI

STAT6

STEVOR

SURFIN

TF

TFBS

Tie-2

TLP

TLR-2

TLR-4

TLR-9

TNF

TRAP

TREMl

TSR

UM

UTR

VEGFR2

VSA

WBC

WHO

Stromal Cell Derived Growth Factor 1

Severe Malarial Anaemia

Single Nucleotide Polymorphism

Sporozoite Microneme Protein Essential For Traversal

Species

Scavenger Receptor B 1

Signal Transducers And Activator Of Transcription

Subtelomeric Variable Open Reading Frame Proteins

Surface Associated Interspersed Gene Family Protein

Transcription Factor

Transcription Factor Binding Sites

Tyrosine-Protein Kinase Receptor

Trap-Like Proteins

Toll-Like Receptor-2

Toll-Like Receptor-4

ToU-Like Receptor-9

Tumour Necrosis Factors

Thrombospondin Related Anonymous Protein

Triggering Receptor Expressed On Myeloid Cells I

Type-l Throbospondin Repeat

Uncomplicated Malaria

Untranslated Region

Vascular Endothelial Growth Factor Receptor 2

Variant Surface Antigen

White Blood Cells

World Health Organisation

xvi


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 of drugs 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


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


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


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 (GW AS): 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


· . . th hro as distinguished from the genetic mfonnatlOn contamed on e c mosomes,

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


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


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


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


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


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


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


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.


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


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


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


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


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 of other 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


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


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


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 perf orin-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


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


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


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


+ TEl +fNOI

Malaria Phenotype Spectrum

Figure 1. I: Conceptual framework


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


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


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


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


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


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


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


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2.1.1 Malaria in Ghana: epidemiolo~

Located along the Gulf of Guinea between latitudes 4° and 12°N and longitudes 4°W

and l°E. Ghana has a malaria epidemiological profile typical of the West African Sub-

region. It is a high malaria transmission area (> 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


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-+


I • ,. • -':1.'.17 2tK>"

I I t J j I I I • ."1', .'01: 201 \ JII.t 201~ 2016

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


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


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


.~:.j :~

~~$: F Oocy.'

r"'-;"--;"equlto blood n10.'

~pr. Ory'hr;;cvtl!hU;"a:~-;"'~';CtlOn ~-r 'nl< , 'I""" t I ...... ,- VII.'..... I .t I ••

11: .... .,'"." .... , .,.

~ ... "It_ ... ~ ~

~ ". "-~ ... ~ pv_

.h ........... "" •. · , iL\1 • '\ I

JIm. : .. a q ... :{ " . .,. • ..-

~ "..,Idgut .1 .. ~ ..

_ .~ _____ ~1__~ __ ~

C Asexual ery'hrOCY~'C a'.ge ,. '.1. "'"~1.1''' ·IM '1(.""5

'" VII...... ....8 "'n,'~H

0- fItJl M_:~,

"'.-~ ....... ~~~ ~1,l

r In'I~3I-6 .. ylh..-O(_V1lc galTtetocyt_ devetopn"tent I r-- , ;.': .':.: ~'~ ':"·"L .. .'J:, ~ •.. u_.v .... ..~ ••• - E .. ..- II

t ,;.." (" v--:' .. 't": , .' r' .. "'~ti~J-.... :J- - . .,... --- ........ I - ... ----

(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


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


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 th·· b e lOvaslon process ut

30


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


A

);010 i I

:)

( , ,.:

...... ]Ia C) I )

.... _ ...................... .". .... ... '~, 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 <an enter the Iymphahc ,ystem (vellow),

Il Once th .. 'I'orOlolles (green) ... ach the lover "nusold" Ihey glid .. over the endotheloum and Interacl WIth heparan ,ulphate proteoglycans (HSPGs) from hepatocyte and strllate cells TheV then cross the sinusoidal laver, posslblV through Kupffer cells as shown

C Once the sporoloite (green) has crossed the sinusoidal layer and entered a hepatocyte, it subsequently traverses several hepatocytes until it becomes established in one, In which a parasitophorous vacuole is formed. Each Invading sporololte develop, and Inultlplies in, ide a hepatocyte. forming the ,chllont, which Is made up of thousands of merOlolt",

D. The final ,.II!p involve, til£' release of meroloites (green) Into the bloodstream. The signal(s, that trigger the release remain unknown, Plasmodium meroloites are released bV the formation of merozoite·filled vesicles (merosomes), which bud offfrom the infected hepatocytes into the sinusoidal lumen

- (Prudlnclo, RodrlfIW~, .. Mora, ZOO6J

Figure 2. 8: Pre-Cl),throcytie stage life cycle

32


Erythrocytic parasite forms

In broad strokes, the cardinal events in the erythrocytic phase of the lifecycle of

Plasmodium parasites are invasion of erythrocytes, schizogony, egress, and the

transitions from merozoites into gametocytes. Once in hepatic circulation, merozoites

invasion of erythrocytes is a fast and vigorous process. Although the invasion is a multi

step process, it takes under 2 minutes to complete. The first step in this process is the

pre-invasion interaction which is thought to involve the merozoites surface protein I

(MSPI) (Holder, 1994). However. studies showing that merozoites lacking MSPI can

also invade erythrocytes suggests that MSPI may have other functions that may even

tum to be more fundamental than erythrocyte invasion (Das et aI., 20 IS). The details of

pre-invasion interaction. the signalling and proteins involved are thoroughly reviewed

in Tham et al., 2012 but a synthesis of the literature portrays to fierce merozoites -

erythrocyte interaction that leads to the deformation of host cells (Gao et ai., 2013;

Singh et al.. 2010; Tham et al.. 2012; Weiss et aI., 2015). Merozoites reorient

themselves after the deformation and get ready for the active invasion phase which

involves an irreversible attachment of merozoites to erythrocytes through the formation

of tight junctions between AMA I and RON complex. After the active invasion phase,

the fusion of the posterior end of merozoites with erythrocyte membranes seals off the

parasite within parasitophorous vacuole within the now infected erythrocyte (Riglar et

al.. 20 II). After invasion, merozoites undergo schizogony leading to the egress of

between 16 - 32 merozoites after 48 hrs. Egression destroys infected erythrocytes and

the free merozoites look for another erythrocyte to repeat the process.

As schizogony continues in the erythrocytic phase of the life cycle a proportion of

merozoites make a developmental switch and become committed to gametogenesis.

33


The molecular events governing the switch to gametogenesis is not fully understood

but environmental stimuli such as parasitaemia and presence of anti-malarial have been

shown to increase gametocyte production. The role of epigenetic regulation of sexual

differentiation is also gradually coming to the fore and this has generated a lot of interest

in the possible role of transcription factor with AP2 domain (AP2-G) as a master

regulator of gametocytogenesis. (Kafsack et al., 2014; Waters, 2016). Although sexual

development from merozoites to gametes is crucial for the transmission of malaria from

humans to the insect vector, details of this transition for transmission are lacking.

Increased research in this area may offer clues for transmission-blocking vaccines or

therapies (Cowman et aI., 2016).

Akin to renovating a new apartment to suit your lifestyle, merozoites remodels host

cells invasions and equip it with organelles and structure that aids optimal development

and protect them from host immune responses (Boddey and Cowman, 2013). The

parasite does this by sorting exporting several proteins to specific subcellular locations

via a trafficking network characterised by Maurer's clefts, MAHRP2 (membrane

associated histidine-rich protein-2), EDV (electron dense vesicles), J-dots and other

membranous structures(Boddey and Cowman, 2013). The P. falciparum erythrocyte

membrane protein 1 (PfEMPI) family is one of the best study surface proteins on

infected erythrocytes. Although PfEMP 1 elicit strong humoral immune responses from

the host, the parasite can evade host immune responses and establish chronic infections.

This is made possible by the ability of the parasite to express antigenically distinct

isoforms of PfEMPl(Hviid and Jensen, 2015)

34


Parasite forms in the vector

The development of Plasmodium spp in the mosquito vector is intriguing and have

captured the attention of researchers since time immemorial. However, the push for a

better understanding of the developmental events in the mosquito has become urgent in

the era of heightened efforts towards transmission-blocking vaccine research.

Generally, development in the vector starts with the ingestion ofan infected blood meal

containing gametocytes. Gametocytes egress from blood cells and differentiate into

gametes; differentiation of the male gametocytes is most dramatic with the

transformation known as exflagellation. Microgametes searches and fertilise female

gametes giving rise to a diploid zygote that further differentiates to form a motile

ookinite. The ookinite then comes out of the blood bolus and invade the midgut

epithelium of the insect. The ookinete traverses the epithelium to the basal lamina and

differentiates into a round oocyst that grows and produce thousands of sporozoites

through sporogony. Matured oocysts release sporozoites into the haemolymph that

eventually invade the salivary gland and are deposited alongside with saliva in the

dermis of a human host on the next feeding. Reviews of the current understanding of

these processes are unequivocal in pointing out the need to go beyond schematics and

unravel the genetic and epigenetic regulation of the process (Smith et al., 2014)

2.2.2. The disease: pathogenic mechanisms and determinates of severe malaria

Malaria is a complex disease with a wide clinical spectrum. Infection with p.

/alciparum may result in asymptomatic parasitaemia or malaria with varying degrees

of severity. Clinical malaria may manifest as uncomplicated malaria or take the form

of life-threating syndromes such as severe malarial anaemia, cerebral malaria,

metabolic acidosis or even a multiorgan system failure (Wassmer et al., 2015).

35


Symptoms of uncomplicated malaria include fever, chills, and headaches and they

appear 10-15 days after an infective bite. Most of the symptoms in uncomplicated

malaria are due to erythrocytic schizogony and correlates with the rapture and release

factors and toxins from infected erythrocytes. If prompt treatment is delayed,

uncomplicated malaria may rapidly turn into severe malaria with dire consequences

(World Health Organisation, 2015).

Although only a minority (1 - 2 %) of P. Jalciparum infection ends up as severe

malaria(Marsh, 1992), mortality associated with severe malaria is unacceptably high

and has remained unchanged for decades (Wassmer and Grau, 2017). For instance, after

the onset of CM, mortality is between 15 - 25 % irrespective of antimalarial treatment

following standard protocols (Gupta et al., 2017; Idro et al., 2010; Wahlgren et aI.,

2017; Wassmer et aI.. 2015). Majority of stakeholders blame the current situation on

our poor understanding of the pathogenic mechanisms of CM, which severely hinders

efforts to find good prognostic markers and develop effective therapies. Considering

the imminent decline in malaria and the predicted concomitant rise in CM, scientists

are obliged to better understand the pathogenesis, find accurate prognostic markers and

developed effective therapies for CM. Adopting a conscious bias towards cerebral

malaria, this section reviews the current knowledge on malaria pathogenesis and

highlights the crucial gaps in our understanding of severe malaria.

2.2.2.1 Pathogenesis of cerebral malaria

The World Health Organization defines CM as a clinical syndrome characterized by

coma (at least 60 minutes after cessation of seizure and correction of hypoglycaemia),

presence of asexual P. Jaiciparum parasites in peripheral blood, and no other possible

36


cause coma (WHO. 2000). This definition is non-specific and can be confusing in

malaria-endemic regions where virial- and bacterial-encephalitis are common (Idro et

al .• 20 I 0). It might also lead to misdiagnosis in situations where malaria is incidental to

previously undiagnosed neurological conditions. This is epitomized in the landmark

Malawian post-mortem study where over 24% of children who were thought to have

died from CM. actually died of courses other than CM (Taylor et al., 2004). The

prognosis ofCM is often poor, especially when coma is accompanied by brain swelling,

intracranial hypertension. retinal changes, and other complications such as metabolic

acidosis. hypoglycaemia and shock (Idro et al., 20 10; Newton et al., 2000). Researchers

do not yet fully understand the pathogenic mechanism of CM but two hypotheses

(vascular occlusion and inflammatory), which might not be mutually exclusive, have

been proposed. Both hypotheses invoke endothelial activation in their respective

mechanisms. the contention, however, bothers on whether the endothelial activation is

a result of systemic inflammation akin to sepsis or endothelial sequestration of infected

erythrocytes (IE) in cerebral microvasculature (Storm and Craig, 2014).

Proponents of the vascular occlusion hypothesis posits that IE sequestration in the

microvasculature via PI EMP I binding to endothelial surface proteins (ICAM-I,

VCAM-l. CD36 or EPCR) leads to obstruction in microvascular blood flow. ischemia,

tissue hypoxia and the coma seen in CM (Chen et aI., 2000; Dunst et aI., 2017; Rowe

et aI., 2009). Rosettes formation and clumps may further worsen microvascular

obstruction and heighten cerebral pathology (Dunst et al., 2017; Rowe et aI., 2009). On

the other hand. proponents of the inflammatory posit that imbalance in pro- and anti

inflammatory immune responses trigger immune-induced pathology that might lead to

CM (Clark et aI., 2006; Clark. and Alleva, 2009). Although both pathogenic

37


mechanisms haves some loose ends, each is backed by evidence from several good

quality studies and reviews.

The most convincing evidence in support for IE sequestration (and for that matter

vascular occlusion) as the pathogenic mechanism for eM has come autopsy studies.

Despite the inherent limitation in autopsy studies, histological analysis of eM autopsy

samples has provided invaluable insights into the pathogenesis of eM. First, they

clearly demonstrated that sequestration of IE is a hallmark of eM (Dorovini-Zis et aI.,

2011; MacPherson et al.. 1985; Maneerat et aI., 1999; Taylor et aI., 2004). Secondly,

they provided evidence that IE sequestration in brain microvasculature causes

microvascular obstruction, which in turn leads to microvascular pathology, coma and

ultimately death (Dorovini-Zis eta1., 2011; Idro et aI., 2005; Postels and Birbeck, 2013).

Finally. they give a strong indication of the possible role of monocytes and fibrin

platelet thrombi in IE sequestration and microvascular congestion (Ponsford et aI.,

2012). Perhaps. the most important study in this arena is one that compared the

histopathology of Malawian children dying with clinically defined eM to the

histopathology in malaria-infected children with non-eM causes of death. Apart from

finding that approximately 25% of children clinically diagnosed as eM had died from

non-eM causes. the study more profoundly found true eM (i.e. CM patients with

evidence of IE sequestration) to have two distinct pathological patterns: CMI and CM2.

Whereas a one-third of the true eM patients had histologic evidence of IE sequestration

only (CM I), the remaining two-thirds had intra- and perivasCUlar pathology in addition

to IE sequestration (fig) (Taylor et aI., 2004).

38


,; ... ,

eM?

"~~.-• -J A ., .:--

~--'~-. "'-,-. -,;

Sourr. Tiylorft aI~lOO4

Figure 2. 9: Pathological differences in CM 1 and CM2

e

~ l.-

I

39

SchematIc lepresentahon of thp pathologIcal dIfferences between cerebral malaria CM 1 and CM2 Autopsy studies In chIldren have dIvided CM cases Into two groups based on histologIcal features. eM 1 cases have Infected erythrocyte sequestratIon In the cerebral mIcrovasculature and no associated vascular pathology CM2 cases are defined by cerebral sequestration plus Intra- and penvascular pathology. Including nng hemorrhages flbrtn-platelet thrombi. and Intravascular monocytes In tlH' CM2 group Infected erythrocyte (IE') sequestratIon IS frequently assocIated wIth f,brtn-platelet thrombi in both capIllaries and postcapillary venules Insets provIde examples of pathologIcal features observed In CM2. (A) shows a small branching capIllary In whIch the upstream region IS filled with sequestered IEs and one of the branches i8 occluded by a thrombus This fOvent IS associated with a rtng henlorrhage In which the mlcrove:,sL"1 I~ partially denuded of endothelial cC'lIs and is surrounded by a zone of neClO:'15 and a ring of unmtected red blood cells In the white matter, Inset (B) shows a small vessel packed with sequestered IEs and surrounded by extravasated fltxinogen Indicating i"creased permeability of the blood-brain barrier Inset (C) shOWS a micovessel filled wIth monocytes containing phagocytosed hemozoln piglTlent Intravascular pigmented monocytes are found adherent to the microvessel wall. but do not transverse across the blood-bulln barrier.


Thus. although it appears the body of evidence supports a CM pathogenic mechanism

that involves IE sequestration leading microvascular obstruction, data from Taylor et

at caution that this pathogenic mechanism is not a histologically uniform syndrome

(Taylor et al., 2004).

The data discussed so far. particularly fmdings of Taylor et ai., stokes the age-old

debate of whether sequestration, and therefore microvascular congestion, is the main

cause of coma in CM (Berendt et al., 1994; Cunnington et aI., 2013; Hanson et al.,

2013; Ponsford et al., 2012; White et al.. 2013). The arguments of the proponents of

sequestration are well known: (a) correlation of sequestration and disease severity, (b)

correlations between obstruction induced lactate production and severity of disease, (c)

association of abnormal distribution or decreased expression of PfEMP 1 with disease

severity and (d) poor performance of adjunctive therapies based the alternative CM

pathogenic mechanisms (Storm and Craig, 2014). On the other side of the aisle,

proponents of the inflammatory (cytokine) hypothesis argue on the following points:

(a) activation of endothelium in the absence microvascular sequestration (Manning et

aI., 2012; Yeo et a1., 2010), (b) the reality of apparently healthy individuals with high

parasitaemia (Clark and Alleva, 2009) and (c) association of pro-inflammatory

cytokines with CM.

Whereas the preceding paragraphs give an audience to the sequestration side of the

debate, very little has been adduced in favour of an inflammatory hypothesis. Results

of studies on the role of cytokine in CM pathogenesis has been difficult to intemperate

for a number of reasons. Firstly, the lack of consistency in the choice of control groups

poses huge interpretability challenges. Control groups for these studies could be drawn

40


from patients categorised as non-CM, severe malarial anaemia (SMA), uncomplicated

malaria (UM), encephalitis, febrile illnesses or even healthy individuals (Storm and

Craig, 2014). Secondly, the prevalence of co-infections means that whatever cytokine

profile described among CM is probably not due to malarial alone. These limitations

notwithstanding, the role of cytokines in the pathogenesis is patent and extensively

reviewed by Dunst and colleagues (Dunst et aI., 2017). The dominant idea emerging

from a synthesis of the role of cytokines in CM pathogenesis suggests that

disequilibrium in pro- and anti-inflammatory immune responses initiate immune

induced pathology that might play a leading role in CM pathogenesis (Clark and

Rockett, 1994; Dunst et al., 2017). Early studies in this arena focused on the roles of

individual cytokines but it quickly became clear that a complex interplay of several

cytokines is more conceivable. Thus, the focus now is to determine common

cytokine/chemokine signatures for CM and although unambiguous cytokine signature

is yet to be identified, the body of evidence suggests roles for cytokines in endothelial

activation, blood-brain barrier integrity and neuroinflammation (Bakmiwewa et al.,

2016; Combes et aI., 2010; Dunst et aI., 2017; Obermeier et aI., 2013; Renia et aI.,

2012).

Regardless of the equivocations in the literature on a pathogenic mechanism for CM,

certain cardinal features of the syndrome are undisputed: (a) sequestration of IE in the

microvasculature, (b) endothelial activation, (c) a pro-inflammatory immune response

and (d) disruption of BBB. The dispute appears to be on the lines of causality is

endothelial activation caused by IE sequestration or pro-inflammatory cytokines; do

pro-inflammatory cytokines aid IE sequestration; can cytokines on their own induce

coma without IE sequestration; etc. Some of these questions will be difficult to answer

41


within the limitations of autopsy studies. Although some imaging technologies are on

the horizon, the application of these technologies in CM pathogenesis studies is in its

infancy (Dunst et al., 2017; Wassmer and Grau, 2017). The potential of robust

biomarkers for endothelial activation becomes apparent in the backdrop of the

limitation of autopsy study studies.

2.2.2.2 New paradigms in the pathogenesis of cerebral malaria

Despite the methodological challenges in studying the pathogenesis of CM, this

research arena is active and bourgeoning with new knowledge. Recent studies shed

light on several aspects of CM pathogenesis including parasite-brain microvascular

specificity, new causes and repercussions of endothelial dysfunction, and the

consequences of rosette formation and clumping (Riggle et al., 2017; Wassmer et al.,

2015). Other areas where further insights have emerged include BBB integrity, novel

parasites pathogenic factors, new methodological approaches and adjunct therapies

(Wassmer and Grall, 2017). But perhaps, the most interesting twist is this bourgeoning

research arena is the novel pathogenic mechanism emerging from the work of Gyan

and colleagues which propose a pathophysiological model that does not only look at

microvascular damage but considers hosts capacity to repair damaged endothelium

(Gyan et ai., 2009). This pathophysiological model is intriguing because it balances the

details of malaria-induced endothelial damage with host-mediated mechanisms for

repairing damage microvasculature (Desruisseaux et al., 2010; Dickinson-Copeland et

al., 2015; Gyan et aI., 2009; Tetteh, 2014). Thus, unlike the two main

pathophysiological models for eM that only focuses on IE sequestration in endothelial

microvasculature, endothelial activation, inflammation and microvascular pathology,

42


this model additionally focuses on factors that influence repairs such as post-natal

angiogenesis and its regulation during malaria infections.

In this pathogenic model, the prognosis of eM is hypothesized to partly hinge on a

child's ability to promptly repair damaged microvasculature and restore equilibrium.

Although this appears to be an elegant hypothesis, evidence in support of this

pathophysiological model is scanty. Repair of damaged endothelium was thought to

occur through replication of cells adjacent to the site of injury but a seminal work

highlighted the role of other factors such as circulatory endothelial progenitor cells

(cEPC) in the repair of damaged microvasculature almost a decade ago (Asahara et aI.,

1999). The role of cEPes in repairing endothelial damage has since become clearer and

scientists agree that the incorporation of cEPes into sites of microvasculature damage

with the help of stromal cell-derived growth factor 1 (SDF-l) and the matrix

metalloproteinase-9 (MMP-9) is crucial (Asahara et aI., 1997; Hristov et aI., 2003;

Rafii, 2000; Urbich and Dimmeler, 2004). Interesting, a study in Ghana found

significantly lower cEPe levels and higher SDF-l in eM patient relative to healthy

controls and non-eM malaria phenotypes (Gyan et aI., 2009). These findings suggest

that the repair of endothelial damage could play significant roles in eM pathogenesis.

These finding notwithstanding, this eM pathophysiologic model will require further

research.

2.3. Markers of endothelial damage and repair

There are structural and functional differences in the endothelia of the vascular tree and

the endothelium lining the microvasculature is distinguishable by the absence of

fenestrations, extensive tight junctions, and sparse pinocytotic vesicular transport

43


(Poggesi et aI., 2016a). When its integrity is intact, the cerebral endothelium serves as

a barrier that strictly controls the exchange of solutes and circulating cells between the

plasma and the interstitial space (poggesi et al., 2016b). However, the integrity of this

barrier is breached in certain pathological situations leading to a cascade in

microvascular pathology. In P. falciparum infection, this breach may be due to the

sequestration of IE and/or inflammation - be it localised or systemic. An accurate

assessment of cerebral endothelial damage or dysfunction is not always possible during

malaria infection and thus, the availability of reliable biomarkers of microvascular

endothelial health would be invaluable. This section reviews the biomarkers of

endothelial damage and repair with the aim of identifying those with the greatest

potential of becoming proxy markers. Although subtle differences discriminate

between tenns endothelial damage, endothelial activation and endothelial dysfunction,

they are used interchangeably in this thesis.

2.3.1 Marker of endothelial dysfunction

Generally, markers that consistently characterise endothelial dysfunction include von

WilIebrand factor (Hollestelle et aI., 2006), angiopoietin-l and -2 (Lovegrove et aI.,

2009). and soluble endothelial receptors (Turner et aI., 1998). Physiologically.

angiopoietin-I functions to stabilise the endothelium whiles angiopoietin-2, its

antagonist, is released in response to inflammatory stimuli to regulate vascular

penneability and the expression of endothelial receptors (Stonn and Craig, 2014). In

the context ofCM pathogenesis, studies found increasing Ang-2/Ang-l ratio, which in

reality, represents either a decrease in Ang-l or an increased in Ang-2, to be associated

CM (Conroy et aI., 2010, 2009; Jain et aI., 2011; Lovegrove et aI., 2009; Prapansilp et

al., 2013). Other studies have found low bio-availability of nitric oxide to be associated

44


with malaria severity; but this is unsurprising considering the fact that nitric oxide

regulates the actions of Ang-lIAng-2, causes vasorelaxation and downregulates the

expression of endothelial adhesive molecules (Anstey et aI., 1996; Lopansri et al., 2003;

Yeo et al., 2008, 2007). In addition to the angiopoietins, von Willebrand factor, and

nitric oxide, studies have implicated other related molecules as vasoactive substances

but their role in malaria appears insignificant relative to Ang-l and Ang-2 (Bergmark

et aI., 2012; Canavese and Spaccapelo, 2014; Kim et aI., 2013; Starke et al., 2011).

Soluble endothelial receptors such as ICAM-l, V-CAM, E-selectin and EPCR play

vital roles in the characterisation of endothelial dysfunction, however, heterogeneous

study designs tend to obscure their impact in the context of malaria. For instance,

soluble ICAM-l which did not appear to correlate with CM in Ghanaian children was

associated with SM when compared among UM and other SM cases (Adukpo et aI.,

2013a; Cserti-Gazdewich et aI., 2010; Erdman et aI., 2011). This situation is further

complicated by discordant findings in children and adults (Combes et aI., 2010; Sahu

et aI., 2013). Apart from ICAM-I, EPCR, another receptor for PtEMPI has been

associated with 8M including CM. Interestingly, seven newly described receptors for

PfEMP I expressed in the brain failed to show any association with malaria severity

(Esser et al., 2014; Turner et aI., 2013). Taken together, angiopoietin-l, angiopoietin-2

and EPCR appear to be a promising candidate as biomarkers for endothelial

dysfunction.

2.3.2 Markers of endothelial repair

Sequestration of IE in cerebral microvasculature and/or inflammation, be it localised in

the brain or systemic, can lead to endothelial activation and damage. The extent of the

45


injury is detemrined by the degree of IE sequestration and inflammation, however, the

role of biologic capacity to repair damage endothelium cannot be discounted (Idro et

ai., 2010; Turner et ai., 1998). Thus, a review of the repair mechanisms of endothelial

offers great insights into potential biomarkers of endothelial repair.

Two main repair mechanisms have been described. The first mechanism assumes an

endothelium with terminally differentiated cells, low proliferative turnover and an

inherently weak self-repair capacity. In this scenario, the repair of damaged

endothelium is through the replication of mature endothelial cells surrounding the

injured site (Avogaro et ai., 2011; Dzau et aI., 2005). However, it is now known that

circulating EPCs recruited from the bone marrow and other tissues can embed

themselves at the injured site and differentiate into mature endothelial cells (Asahara et

aI., 1999; Ribatti, 2007). This establishes the importance of cEPCs for the maintenance

and repair of endothelial cells.

The precise characterisation of EPCs is complicated by the heterogeneous nature and

shared surface markers among that group of cells (Asahara et aI., 20 11; Resch et aI.,

2012; Ribatti. 2007). Currently, cells positive for both a hematopoietic stem cell marker

such as CD34 and an endothelial marker protein such as VEGFR2 are considered as

EPCs (Resch et aI., 2012). Several factors including nitric oxide, VEGF, SDF-I,

erythropoietin, and oestrogens are crucial for the mobilisation, migration and homing

of EPCs into sites of endothelial injury. Thus, quantitative or qualitative deficits in any

of these factors may influence the repair of damaged endothelium. Accordingly, recent

evidence shows overexpression ofVEGF in brain tissues ofCM patients (Canavese and

Spaccapelo, 2014). Similarly, SDF-l, nitric oxide and EPCs have been implicated in

46


the pathogenesis CM (Anstey et al., 1996; Gyan et aI., 2009). Taken together, VEGF,

SDF-l and EPCs are "front-liners" in the search of biomarkers for endothelial repair.

The role of EPCs as a biomarker of endothelial repair is further bolstered by studies

that show higher EPCs levels to be associated with a better endothelial function

(assessed by brachial artery flow-mediated dilatation (FMO) (Verma et al., 2017).

2.4 Malaria immunology

2.4.1 Immune responses during pre-erythrocytic stages of Plasmodium life cycle

Pre-erythrocytic stage infections with P. jalciparum are clinically silent and thus, host

immune response against parasite forms at this stage remains largely unexplored.

However, it is has been suggested that interaction of the immune system with pre-

erythrocytic phase parasites primes the innate immune system (Offeddu et aI., 2012).

Most of our understanding of the immune responses at this stage come from murine

studies and the data suggests an interaction between innate immune cells and liver

sporozoites of P. berghei (Liehl et al., 2014). These studies suggest a type I interferon

(lFN) response and the subsequent recruitment of a number of cytokines and

chemokines (Crompton et al., 2014; Liehl et aI., 2014; Liehl and Mota, 2012; Miller et

al..2014). In human studies, however, the evidence for potent innate immune responses

in the skin and liver is lacking, and unsurprisingly, antibody, C04+ and CD8+ T cell

responses to the infecting sporozoites are also lacking (Offeddu et aI., 2012). The

clinical silence and relatively weak innate and adaptive immunity to pre-erythrocytic

stages of the Plasmodium are poorly understood. Perhaps, this reflects the low inoculum

dose of sporozoites per infective bite (10 - 100 sporozoites) andlor the parasite's

exploitation of the inherent immune-regulatory environment of the skin and liver

(Crispe, 2009; Honda et al., 2011). Thus, although parasites interact with the immune

47


I'ti 1-' mune responses against pre-. h erythrocytic stages of the 1 e cyc c, lffi system In t e pre-

erythrocytic parasites are not potent enough to limit the further development of the

parasite and the transition to the erythrocytic stage (Kordes et aL 2011).

I(::zo-._J' • • e. 'f .~. _.

Adaptive Immune responses to preerythrocytic antigens.

-. -. -- ---e---'l~ ~~~j ~.'~~I •

B

A.;.., to.

Figure 2. 10: AdaptiH immunit~ at the pre-erythrocytic -;tage

2.4.2 Immune responses during erythrocytic stages of Plasmodium lifecycle

Almost all the clinical symptoms associated with malaria can be attributed to the

erythrocytic phase of Plasmodium's Iifecyclc which stans with the release of

merozoites into the bloodstream. Merozoites releast: inll \ the bloodstream are only

brietly (- 60 S) exposed to the immune system before they infect erythrocytes. The

characteristic recurrent fever seen in malaria coincide~ with synchronized rupture of IE

which introduces parasite- and host-deri\ ed molecules (PAMP and DAMPs) capable

of inducing T"F (Evans et aI., 2015: Kv.iatkowski ct al.. 1990, 1989). Studies found

levels of T!\F to coincide with fever suggesting that fever is induced by the repeated

exposure to PAMPs and DAMPs (Kwiatk'l\\ ski et al .. 1990. 1989; Oakley et aI., 20] I).

48


Although generally associated with the resolution of infection, uncontrolled fever can

lead to a dysregulation of the immune system and ultimately severe malaria (Oakley et

aI., 2011). These findings are consistent with the textbook understanding offever as a

physiologic innate response that precedes the involvement cells of the innate immune

system.

2.4.2.1 Innate immune responses

Cells of the innate immune system are thought to playa crucial role in providing the

first line of defence against malaria. Thus, macrophages, dendritic cells (DC) and other

non-professional immune cells interact with merozoites and mount strong pro

inflammatory responses during the erythrocytic phase. For instance, toll-like receptor

2 (TLR2) interacts with PAMPs such as GPI to produce a strong TNF response from

murine macrophages (Krishnegowda et al., 2005; Schofield and Hackett, 1993;

Tachado et aI., 1996; Zhu et aI., 2011, 2009). The importance of GPI was however

questioned when studies failed to show that TLR deficiency impaired immune

responses in mice (Lepenies et aI., 2008; Togbe et al., 2007). Hemozoin, which is

released during erythrocyte rapture is another potential malaria PAMP that also induce

the expression of TNF and IL-I ~ from monocytes, human monocyte-derived DCs and

murine macrophages, probably, via binding to TLR9 (Bujila et aI., 2016; Coban et aI.,

2005; Olivier et al., 2014). Interestingly, the responsiveness of macro phages and DCs

to TLR-mediated signalling has been shown to be impaired by phagosomal

acidification upon phagocytosis (Wu et aI., 2015). Thus, regardless of phagocytosis

being an important feature in innate immune response that helps in the uptake ofIE and

increases serum levels of pro-inflammatory cytokine, it counterbalances PRR-mediated

signalling. Another contributory factor to the pro-inflammatory environment created

49


during malaria infection is host-derived DAMPs such as urate crystals, heme, and

macro vesicles release upon rapturing of host cells. In line with this thought, studies

have found higher levels plasma microparticles in severe malaria cases relative to

uncomplicated malaria (Campos et aI., 2010; Nantakomol et aI., 2011; Sahu et aI.,

2013). It is important to reiterate here again that the inflammatory environment created

during infection also influences the severity of malaria and several cytokines and

chemokines, including CXCLlO and lNF have been associated with CM (Kwiatkowski

et aI., 1990; Wilson et aI., 20 II). Levels oflNF are thought to be particularly important

in influencing malaria severity and even though more recent studies failed to find an

association between cerebrospinal fluid (CSF) lNF levels and CM, among CM cases,

it was associated with longer-term neurologic deficits (Shabani et aI., 2017).

2.4.2.2 Adaptive immune responses: humoral immunity

The role B cells and humoral immunity is most pronounced during the erythrocytic

phase but almost six decades after showing the central role of antibodies in immunity

against blood-stage parasites, we still do not fully understand the parasite proteins

targeted, the actual mechanisms of protection, and acquired immunity albeit nonsterile

(Cohen et aI., 1961; Gardner et aI., 2002a). By showing that IgG from immune adults

could reduce parasitaemia in subjects with clinical malaria, the passive transfer studies

fundamentally demonstrated the importance of antibodies in malaria immunity.

Additionally, these studies demonstrated antibody cross-reactivity, the role of B cells

in immune protections against malaria and the induction of memory in B cells

(Hirunpetcharat et aI., 1999; Meding and Langhorne, 1991; Ndungu et aI., 2012, 2009;

Sabchareon et aI., 1991; Wykes et aI., 2005). These studies and the many that followed

have however failed to identify the targets of protective immunity using the immune

50


correlates of protection approach and/or growth inhibition assays. Although yet to be

fully clarified, variant surface antigens expressed on IE and merozoites surface antigens

used for invasion have emerged as the most likely target of protective immunity (Wykes

et al .• 2017). Four classes ofVSA have been identified in P.falciparum malaria namely:

P. jalciparum erythrocyte membrane protein 1 (PfEMP1), repetitive interspersed

family proteins (RIFIN), subtelomeric variable open reading frame proteins

(STEVOR), and surface-associated interspersed gene family proteins (SURFIN). Of

these 4 groups, studies have found that antibodies targeting PfEMP 1 and RIFIN show

the strongest association with protection (Abdel-Latif et aI., 2003; Gardner et al.,

2002b; Kraemer and Smith, 2003; Tan et al., 2016). Antibodies targeting some

merozoites surface proteins involved in erythrocyte invasion have also been shown to

be associated with protection. The best-studied are the apical membrane antigen 1

(AMA1), glutamine-rich protein (GLURP), merozoites surface protein (MSP)-3, and

MSP-119 (Fowkes et aI., 2010). Selection of antigens for candidate vaccine studies

have been based on these studies but findings of a recent study in Mali that found 49

potential vaccine candidates not been considered as possible vaccine antigens in

ongoing vaccine candidate studies points to gapping gaps in knowledge or lack of

methodological rigour (Davies et aI., 2015). For instance, most GIA studies using heat

inactivated purified sera would have inadvertently excluded the effects of compliment,

lymphocytes and monocytes all of which work in concert in natural situations

(Bouharoun-Tayoun et aI., 1990; Boyle et aI., 2015b; Kennedy et aI., 2016; Rosa et aI.,

2016).

51


2.4.2.3 Adaptive immune responses: cell-mediated immunity

Malaria T cell immunity is usually under-researched, nonetheless, it appears to play an

important role in malaria immunity. Most of what we know about T cell immunity

comes from murine studies and these studies confirm the crucial role of CD4+ T cells.

Besides aiding B cells and promoting phagocytosis, CD4+ T cells help control

parasitaemia through the induction oflFN-y and TNF -a both of which, contributes to

a vast and intricate network of protective immunity and immunopathology (Langhorne

et aI., 1989; Mota et al., 1998; Podoba and Stevenson, 1991). In a recent controlled

human malaria infections (CHMO study that was not confounded by the prolonged

effects of atovaquone in vivo, multiple immunization with live sporozoites under drug

cover induced sporozoites and blood-stage-specific T cells responses that were

polyfunctional (Edstein et al., 2005; Pombo et aI., 2002; Roestenberg et aI., 2009).

Subsequently. it was found that although the immunised individual was protected

against sporozoite challenge, they were no such protection when they were challenged

with blood stage parasites (Roestenberg et aI., 2009).

Early IFN-y responses to P. Jalciparum is a good correlate of better anti-parasitic

immunity but this has been difficult to measure in many studies. However, a

longitudinal study in Uganda found malaria-specific CD4+ T cells responses in almost

all children with majority producing of the CD4+ T producing IFN-y and interleukin-

10 (IL-IO) in response to IE (Jagannathan et aI., 2014). Interestingly, IFN-r responses

to MSP-I antigens were commonly associated recent exposure to malaria but offered

no protection from subsequent malaria challenge and inversely, IFN-y responses to pre

erythrocytic antigens were uncommon but protected against subsequent infections

(Boyle et aI., 2015a). Other studies have reported differences between adults and

52


children in high and low transmission regions (Boyle et aJ., 2015a, 2015b). The

contribution of CD8+ T cell-mediated immunity against blood-stage malaria parasites

has been difficult to decipher because research on this has been limited to their effect

on liver-stage infection, the pathogenesis of cerebral malaria, and damage to splenic

architecture (Beattie et aJ., 2006; Hafalla et aI., 2006). However, several studies point

to the importance of CD8+ T lymphocytes in the immunity against blood-stage

parasites. For instance, it has been shown in animal experimental studies that the

depletion of CD8+ T lymphocytes during blood-stage P. chabaudi infections delayed

the clearance of infection (McCall and Sauerwein, 2010; Suss et aI., 1988). In another

experimental study, naIve mice transfused with CD8+ T lymphocytes derived from

infected mice survived lethal blood-stage challenge (Imai et aI., 2010). Besides the

possibility that immature erythrocytes can activate CD8+ T lymphocytes, there is

evidence to suggest that severe malarial anaemia is mediated by CD8+ T lymphocytes

dependant parasite clearance (Imai et aI., 2013; Safeukui et aI., 2015).

2.5 Host genetics and epigenetics in malaria pathogenesis of eM

The interplay between host genetics and susceptibility to malaria has been known for

almost a century (Allison, 1954; Bryceson et al., 1976; Fleming et aI., 1979). In fact,

malaria is believed to have acted as an evolutionary force to keep otherwise deleterious

genetic mutations in the population (Allison, 1954; Hill et aI., 1997). This fact is

epitomised by the prevalence and distribution of genetic diseases like sickle cell trait,

a-thalassemia, Glucose-6-phosphate dehydrogenase (G6PD) deficiency, and Duffy

antigen receptor negativity (Hill et al., 1997). Similarly, the fact of interethnic

differences in malaria susceptibility among the Fulani and their sympatric neighbours

further confirms the importance of host genetic factors in the epidemiology of malaria

53


(Arama et ai., 2015; Cherif et aI., 2016; Fleming et aI., 1979). The mechanisms of

genetic susceptibility are not fully understood, however, deciphering these mechanisms

could further elucidate pathogenic mechanisms and lead to therapeutic breakthroughs

(Handel and Horuk, 2010; Kidson et aI., 1981). This section of the thesis reviews the

role of host genetics and epigenetic factors in the pathogenesis of malaria with a

conscious bias on CM.

2.5.1 Genetic disorders of erythrocytes and susceptibility to malaria

The clinical symptoms of malaria are associated with the erythrocytic stage of the life

cycle and thus, genetic disorders of erythrocytes (be it membranes-, enzymes-, or

haemoglobin-related) can potentially influence the pathogenesis of malaria. For

instance. studies have shown membrane-related genetic disorders such as

spherocytosis, ovalocytosis, elliptocytosis, pyropoikilocytosis, and acanthocytosis to be

either associated with lower parasitaemia or resistance to invasion (Hadley and Miller,

1988; Schulman et al.. 1990). The protection conferred by a particular type of Southeast

Asian ovalocytosis (SAO) is of interest because heterozygous individuals do not show

clinical symptoms or have haemolysis upon infection (Kidson et aI., 1981). Similarly,

a SNP (rs2814778) that causes a glycine to asparagine change in the Duffy

antigenlchemokine receptor (DARC) and another polymorphism (-33T>C) in the

promoter region of the same antigen are associated with a lower risk of viva x 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


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


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


(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


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


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


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


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


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


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 (RT A» 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 EDT A 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


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 EDT A 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


Figure 3.2: From field to data: a schematic flowchart "f sample processing


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


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


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


perfonned with a PC-controlled thennal cycler (Thermo Scientific Hybaid 384-well

blocks) following the cycling program described in table 3.2.

Denaturation Annealing Extension Final extension Hold

1 cycle Final step

30 sec 1 min 3 min 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


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 SAPlr8lllment

iPLEX reneltOn ~

SAP trealmenllo neulmlize unlcorpol'llted dNTPs

primer extension imo SNP alte,....... ________ ,

~~~~~ C ~ IPLEX Gold cocktail containing allele 1

a1 .... 2 - G extension Into SNP aite primer. enzyme, buller. and p~n~m~er~~~~ g ~ m;l8s.modlfled nucleotldes

spectrum

sample conditioning. dispensing. and MALOf· TOF MS

~

f~~1 24·plax spectrum

Figure 3. 3: iPLEX reaction

3.8.5 iPLEX reaction

MALOI-TOF malll spectrometry analytl.

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


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


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· 1 06·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


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


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


Codominant model

Dominant model

Recessive model:

Over-dominant model:

Additive model:

IOg( L )=a+ ,8He+ r Z 1-p

tog( L )=a+ {JDo+ yZ 1-p

tog( l )=a+.BRe+yZ 1-p

tog( L )=a+PAd+YZ 1-p

3.12. Dealing with missing data

This is the most general model. It allows every genotype to give a different and non-additive risk.

A single copy of C is enough to modify the risk. Thus, heterozygous and homozygous genotypes have the same risk. A single copy of the allele is enough to modify the risk. Thus, heterozygous and homozygous genotypes have the same risk. Heterozygous is compared to a pool of both allele homozygous Each copy of the recessive allele modifies the risk in an additive manner.

Comparison

TIC vs TIT

CIC vs TIT

T/C-CC vs TIT

CIC vs TITTIC

TIC vs TITCIC

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


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


Age distribution among malarial Phenotypes and post hoc comparisons

CM

~SMA ~ o c CII

f UM .!!

:; I ~ He l

Comparison

SMAvsCM

SMA'IUM

SMA 'IS He CM 'IS UM

eM 'IS HC UM vs He

6 10 12

Age (yrs)

P value

Unadjusted Adjusted

0.002 0.015 0.002 0.012

< 0.0001 < 0.001 0.873 1 0.001 0.005 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


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


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 .

•

I i v

o

I .. J

<00001 0001

<O.CllOl cO...,1 ,,00001 <00001

0017 0280 0001 0004

0171 1

(..100'))

-00001

00001 I

•

f I

SM,. y~ ~H_ c;,... ... ~ UM CMv',tH

UMv\HC

woe, .... ,

0.157 .~

<0.0001 0.011 0.002 0.226

1.000

~ <o.oocu

O.OiII D.OU 1.000

Violin plot.~ representing the distribution of Hb(A). WBe (C). and platelets (e) data among malaria phenotypes.

The black bars represent the first and third quartile and the white dots represent the median. The table belleath the violin plots shows multiple post hoc comparison of Hh. WBe and platelets among malaria phenotypes.

Each row in the table test the null hypothesis that the distrihution of Hh. WBe or platelets is the .\·ame in the malarial phenotypes being compared.

Significance level is 0.05

Figure 4.2: Haematological indices in study participants

79


4.3. Parasitoiogiclil indices among study participants

Parasitaemia and parasite sequestration during malarial infections is thought to have

profound implications for clinical outcomes and thus, several approaches to parasitaemia

estimation have been described. This study evaluated the association of three

parasitological indices: total parasite biomass (PUll). peripheral blood parasitaemia (PbP)

and histidine-rich protein 2 (HRP2) with clinical malaria phenotypes .

• •

- -

•

-

~ 00001

-00001

Comparison

UMvsSMA

UMvsCM SMAvsCM

0001

·00001 , UMVISMA UM'IICM

SMAV10CM

II

UNdlus,od

00~9

0.007 0.2~~

pval\.if:'

----/~------lOCI 110" IOC

Unadjusted

0.014 < 0.0001

0.291

P value

Adjusted

0.041 <0.0001

0.873

Adlusted

0.178

0.021 0.766

l/fl/", pltll,~ Il'pn.·,,:nttng th~ ,1J.lilnhulllln orHRP'(A) lolul puru~ I h !~~'.nunp.., Th,. hiade ban "'1',.. .. '"0/ Ih,. jirsl ,,:d I;'inl quartii: :1I;(I,~a.".:B). ;nd peripheral h/o"d paru.tlle., (e) dala uMong malaria I'''''''. ,h" .. , mull/p,' •• po." hot: wmpomon "rA, B Gild C Each row i I ,f! \I III' ols I"PpI"Fsenllhr melltun. The table beneallr lire ",olin 1P1ul•I,.,., " lire 'am, rn Ih" malaria/ plrenol\'~' ""lnO wm"" d S n .Ir,~ ,able lesllh" null "rpolhes,., Ihallhe distrlbullOn of Hb WSC ar

• . r" ~ r-I"F 'lint 'cum'e lel-e/,.' O.[}S. .

Figure 4. 3: Parasitological indices among malaria phenotypes

80


PlOIranked highest in CM (93.57) and least in UM (54.58). Pairwise comparison revealed

PlOt to be significantly higher in CM relative to UM (p < 0.0001) but not SMA (p = 0.873).

Further pairwise comparison revealed Plol to be higher in SMA relative UM (p = 0.041).

Comparison of HRP2 and pbP revealed similar trends albeit at higher a-levels (Fig 4.3).

4.4 Immunological indices among study population

Immunology plays an important role in malaria pathogenesis and several studies have

explored the roles immunological parameters play in the pathogenesis of malaria. This

study explored the relationships between four cellular immunity parameters (neutrophils,

lymphocytes, CD4 and CD8), malaria phenotypes, and endothelial damage. A Kruskal

Wallis H test found lower neutrophil levels in HC relative to UM (P < 0.0001), CM (P <

0.0001), and SMA (P = 0.017). Neutrophils levels were also higher in UM relative to

SMA but comparable with levels in CM (p =1). Lymphocytes levels were highest in HC

(46.5) and least in UM (25.70). All pairwise comparisons of lymphocytes levels across

malaria phenotypes revealed significant differences at p < 0.0001 except in the

comparison between CM and UM where lymphocytes levels in CM were significantly

higher at a borderline p-value (p = 0.043). Similarly, CD4+ T cells were highest in the

HC (14.30) and least among the UM (4.6), here again. all pairwise comparisons were

significant at a p < 0.0001 except for the CM and SMA comparison which differed at p

= 0.022. Levels of CD8+ T cells followed similar trends save that the CM and UM

comparison which did not reveal significant differences p = 0.078.

81


4.5 Angiogenic indices among clinical malaria phenotypes.

Table 4. 1: Association of angiogenic factors with malaria phenotypes

Angiogenic Factors

SDF -I hlrnmal ccll-deri,ed laclor I)

NG R I (nclireguhn 1 ) TREM I \nl!ll'nn~ ''"1''''' cxprnscd myelOId ,dl, II

Ang-I (anglOpmellnl)

Ang-2 (anglopmelin21

Ang-2/Ang-1 (ang-2,ang-1 mho)

CAl ,,:arbon I, dIlhydrase I)

Tie2Ianglop",ctinrcceplOr) SI 008 1<'100 ,.iclum-b,ndons protem HI

M M P9r rrtatr" mctallopepl,dasc 9)

Mean Rank Endothelial Integrity • 62.28 74.39 59.85 71.01 61.94 58.57 71.18 65.06 67.41 64.53

67.02 53.29 69.77 57.12 64.40 71.22 56.93 63.87 61.20 64.47

P-value

0.471 0.001 0.131 0.034 0.41 0.47

0.030 0.856 0.344 0.992

Angiogenic factors as used in this study refers to a loose collection of receptors, cells.

molecules, enzymes and proteins that are directly or indirectly involved in the process of

angiogenesis. They comprise S 1 00 calcium-binding protein B (S 1 OOB), Stromal cell-

derived factor 1 (SDF-l), Neuregulin 1 (NRGl). Triggering receptor expressed on

myeloid cells 1 (TREM-l), Angiopoietin-l (Ang-l), Angiopoietin-2 (Ang-2), Carbonic

anhydrase (C1\ 1 ), Tyrosine-protein kinase receptor (Tie-2) and Matrix

metalloproteinase-9 (MMP9). The levels of these factor among malaria phenotypes are

presented in Tables 4.1. The comparison was limited to excluded SMA because of the

potential of anaemia to influence angiogenic factors to obscure the findings of this study

(Dunst et al.. 1999).

4.6 Endothelial integrity, malaria phenotypes, and angiogenic factors.

Secondary cEPe data on the samples used in this study was fitted to a Gaussian mixture

model to dichotomise endothelial integrity into Pro-Repair and Pro-Damage. To this

end, a histogram of cEPC data was plotted and then fitted with a mixture model of two

Gaussian distributions. This was implemented in "Cutoff Finder", a web tool that uses

82


the tlemix function from the R package flemix (Budczies et al.. 2012; Leisch, 2004).

An optimal cut-off value of 0.07234 was returned after implementation and study

participants who recorded cEPe levels less than 0.07234 were classified as Pro

Damage and those with cEPe level above the optimal cut-off were classified as Pro

Repair. After this classification. 41 % of the study participants were classified as Pro

Repair and 59% classified as Pro-Damage.

Post categorization and to address objective five of this study, a series of amenable

statistical tests were conducted. To this end. a chi-square test revealed a significant

association between endothelial integrity and malaria phenotypes (X2

= 13.331, df = 3,

p = 0.004, Fig 4.5) with over 70% of the eM cases occurring among the Pro-Damage

phenotype.

Figure 4.4: Histogram of EPCs in tbe study popUlation

83


Relationship of malarial phenotypes with endothelial integrity

20.0% 400%

I I HC

.... ~ . .... '. .. t, 52296

UM 50.0%

SMA 52.0%

CM 250%

Pro-Damage Pro-Repair

.1 bll!Terf/, , lIor' comparlllg 'he distribuNollo!lIIatnria phellotypes alllong e"dolhe/iat 111'"~''''' <:' UIII'I Resu/'s of tlte clll sqllare statistics!or assoc/aholl /s shaw 011 the right.

figure 4. 5: Malaria phenotypes and endothelial integrity

60.0%

I

Relative to UM <x: = 8.574, df= 3, p = 0.003), SMA (x: = 6.098, df= 3, p = 0.026) and

Non-eM Li = 10.377. df I. P = 0.0(1) malaria phenotypes, the proportion of eM

patients in Pro-Damage group were significantly higher than the proportion in the Pro-

Repair group (Figure 4.6). Endothelial integrity was however neither associated with

age (p=0.479) nor sex (p = 0.094).

84


~

ProDamage

ProRepair

'i: DO Pro-! Damage c

.!! Qj .c Pro+' o Repair "C

Jf = 8.574, d/ =1, P = 0.003

0; >i', 3b.l%

.UM .CM

Frequency (%)

• SMA .CM

C L&.I

-'----L-...--L..-..L----'--'--.L.---'---'--':--:t~:x, Frequency (%)

ProDamage

ProRepair

Jf = 10.317, dl =1, p = 0.001

/I 1 lIS ~%

Frequency (%)

Figure .... 6: Endothelial integrity and malaria pbenotypes

85

_NON-CI\ _CM

Frequency (%)


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

Ke~ variable Mean Rank Mann-Whitney

U Test P-value

1~!I~~!I!lDlIllIllIllr:~~""""""""" ",jij.lijjMiiMli; Hb (g/dL) mean 94 0.295 102.59 4944.5 PLT(Xlo-') 103.62 0.067 88.59 3839

WBe (X 10")

IQi""iMi!igll· P. den\ity (x 103) P. burden (1110") P. CIR (x IOJ

)

HRP2

I mmunulogll'al L~mphocyte

""culrophils CD4+ T cells CD8+ T cells

lflNi!liMM"" ~DF-I hlrprn;JI \.ell~dl!n\C'J tal..:tor I) NGRI ,""",'.',tion I)

TRE M II fn~'!-, _ !ljo: ~,..~ptOf c .. prc·ucd m\clPld ~(1I~ I!

Ang-I,angillpmeunil Ang-2 (angloJl<1I<lon21

Ang-2/Ang-1 l.mg-2iang-1 ratio,

C A I I,arb"m, anh) drase I ,

Tie2 (angillpolctm receptor)

SIOOB ''''''tI calCium bIRding rrtlkJlI HI

101.17

93.98 81.92 74.69 85.71

90.71 102.13 88.10 94.55

78.05 88.86

81.72

90.15 83.36 109.10 90.35 91.94

83.96

92.16

95.23 69.37 80.58 80.15

102.53 89.60 109.85 100.54

86.93 69.68

78.72

69 75.02 113.67 71.42 70.57

79.19

4121

4347 2332 3005 3079

4892 3918.3 5518 4782

3460 3894 2972

2626 2721

6179.5 2510 2454 3028

0.273

0.876 0.087 0.422 0.466

0.146 0.126 0.008 0.464

0.240 0.010

0.688

0.023 0.361 0.600 0.012 O.OOS

0.526 M MP9(maim metaliopcPllda.,c Y, 85.06 91.13 3894 0.436

Children with the Pro-Damage endothelial integrity phenotype had significantly lower

median CDot.,.. relative to the Pro-Repair phenotype (p = 0.008). As shown in Table 4.2,

all angiogenic factors associated with endothelial integrity were higher in the Pro-

Damage phenotype (Tie-2: p = 0.005; NGRl: p = 0.010; CAl: p = 0.012; ANGI: p =

0.023).

86


4.8. Genotyping results.

To achieve objectives 2, this study genotyped a focused panel of 27 SNPs (tag SNPs)

in 16 different loci on 10 different chromosomes. Conformity to Hardy-Weinberg

equilibrium (HWE) is used as an indicator of proper sampling and genotyping in most

genetic studies, however. a deviation from HWE can also be an indicator of the

existence ofa gene-disease association (Lee. 2003; Salanti et al., 2005). Twelve of the

SNPs genotyped in this study deviated from HWE at a significance threshold of 0.05,

however, only 8 SNPs deviated from HWE after correcting for multiple testing (Table

4.3).

4.9 Association of tag SNPs with endothelial integrity and malaria.

Detail results of tag SNPs and their association with endothelial integrity and malaria

phenotypes are shown in Tables 4.4 -18. These tables are organised by chromosomes

and for each chromosome, several case-case and case-control comparisons were done

to determine the association of tag SNPs with malaria phenotypes and endothelial

integrity. Data analysis for this section generated a lot of results, some of which are

presented in the appendix as supplementary data. The tables presented here are a

summary of the most interesting findings. This study prioritised the discrimination of

eM from UM and thus associations of SNPs in CM versus UM and ProDamage versus

Pro Repair comparisons are prioritised.

87


Table 4. 3: Summarv ofgenotyped SNPs "WE

Chr Gene Loci SNP MAF Test

rsl0489181 0.4 0.49 SELE

rs3917419 0.38 0.0f)(l1 ~ (Selectin E)

ATP2B" rs I 0900585 0.49 (1(11 1

(ATPas~ Plasma Membrane Ca2'" Transporting 4)

ERMAP rs I 466548 0.49 0.59 El)throblast Membrant: Associated

Protein (Scianna Blood Group) • ZRANB3 rsl6831532 0.11 0.085

zinc rmger RANBP2-typt: containing ~ rs7604879 0.35 0.1);"

• rs2071559 0.34 KDR Kinase Insert Domain Receptor rs56233 104 0.07 0.61

HLA-B Major Histocompatibility Complex. Class I. rs2524054 0.11 0.63

B

rs4236084 0.35

II (JOOI

ZMYND8 rs59055740 0.34 f).OOOI

Zinc Finger MYND-T)'pe Containing 8

LlNCOl754 rs6066303 0.07 1I.IJOOI

rs68495I 0.43 0.0001

pseudogene rs943082 0.45 0.5

I NOS3 rs I 800783 0.42 0.78

Nitric Oxide Synthase, rs3918211 0.25 0.72

rs2070744 0.36 0.31

I IFNWP19 rs73422262 0.62

Interferon Omega I Pseudogene 19 0.28

ABO rs8176722

0.24 0.061 Hislo-Blood Group A Transferase

I L1NCOO840 rsl0899940

0.23 L.ung Intergenic Non-Protein

rsl3313099 Coding RNA 840 0.4

• HBEI. HBG2 rs372091 Haemoglobin Subunit Epsilon I 0.18 i) (lfilli

• CDH5 rs1077318 0.48 0.0012 Cadherin 5 rs2304527 0.4 0.017

rs2236416 0.24 ().O(j2~ MMP9

Matrix Metallopeptidase 9 rs2274755 0.09 0.38

0.OO~4 rs39 I 8256 0.28

88


4.9.1. Tag SNPs on chromosome I

Four of the SNPs assayed in this study were located on chromosome 1. Two of these

SNPs, rs10489181 and rs3917419, were in the gene encoding E-selectin (SELE) whiles

the third (rs10900585) and a fourth (rs1466548) were in the genes encoding ATPase

Plasma Membrane Ca2+ Transporting 4 (A TP2B4) and Erythroblast Membrane

Associated Protein (ERMAP) respectively. Although none of the SNPs on chromosome

I were associated with endothelial integrity, variants ofrs3917419 were associated with

an increased risk of CM in the CM versus UM comparison (Table 4.4). In the less

prioritised case-control comparisons, all SNPs on chromosome I were associated with

reduced risk of malaria except a variant of rs3917419, which was associated with

increased risk of malaria in the recessive inheritance model (Table 4.5). However, AIC

score suggested that the over-dominant inheritance model, rather than the recessive

model, was most probable.

89


Table 4.4: Chromosome J: Association ofS!,/Ps l\'i~(:l\lin _('M n lll\1 eumpa"hull

Loci SNP IM* Genotype Malaria Phenotype

OR (95% CI) P-value Ale liM eM

II'I.'IIIH:UII:J_ co 1 ( 34 (56.700' 35 (51.5%) 0.66 (0.30-1.44) 0.42 11l1.2 CC 10 (16.70'0) 8(11.8%) 0.51 (0.17-1.57)

0 TIC-CC 44 (73.3%) 43 (63.2%) 0.63 (0.29-1.33) 0.22 179.4 R CIC 10(16.7%) 8(11.8%) 0.67 (0.24-1.82) 0.43 ISO.3

00 TIC' 34 (56.7~0) 35 (51.5%) O.SI (0.40-1.63) 0.56 180.6 LA 0.70 (0.41-1.20) 0.19 179.3

CO GIA 43 (71.7%) 53 (77.9%) 1.90 (II XI-~ -<'I) O.u2:' 175.6 AlA o (0° .. ) 4(5.9%) NA

0 G/A-AIA 43 (71.7%) 57 (S3.8%) 2.05 (0.8704.S2) 0.096 178.2 R AlA 0(0%) 4(5.9%) NA 175.8

00 G'A 43 (71.7%) 53 (77.9%) 1.40 (0.63-3.12) 0.41 ISO.3 LA 2.39 ( I.OS-5 .28) 0.027 176

II'I!II!~ CD CIG 33 (55%) 40(58.8%) O.SI (0.32-2.03) 0.42 ISI.2 GIG 17 (2S.3%) \3 (19.1%) 0.51 (0.17-1.50)

0 C/G-G/G 50 (S3.3%) 53 (77.9%) 0.71 (0.29-1.72) 0.44 180.4 R GIG 17(28.3%) 13 (19.1%) 0.60 (0.26-1.36) 0.22 179.4

00 CG 33 (55%) 40 (58.8%) 1.17 (0.58-2.36) 0.66 180.S

• LA 0.71 ~0.41-\.21) 0.21 179.3 Model (1M) Codominal1l (CD): Dominant (D); Recessil'e (R): Overdominant (OD); Log-additive (LA).

AssuminK bial/elic SNPs comprising X and Yalleles. the reference genotypes/or the CD. D, Rand OD inheritance models are XX. xx. XY-YY and XX-rr respectively. Red highlight: Susceptibility SNP. Green highlight: protective SNP

90


Table 4. 5: - Chromosome I: As,ul'iati_'.!n of SN~, l\ HI1 malaria in malaria n 11(' comparison .----- ------~ .. Loci SNP IM* (;enotype

Malaria Pht'notlpe - OR(9S%CI) P-value AI('

HC Malaria --.~-

CD I.l 81 (56.9°'0) 23 (1.t.3°,u) 0.40 (0.21-0.76) 0.0083 266.Q ClC 19 (12.4°0) 13 (19,4° 0 ) 1.04 (0.45-2.40)

D T/C-ClC 106 (69.3%) 36(53.1%) O.~I (0.29-0.93) 0.028 269.6 R CIC 19 (12,4°'0) 13 (19.4%) 1.70 (0.18-3.68) 0.\9 212.7

OD T,C 87 (56.9°0) 23 (34.3%) 0.40 (8.22-0.72) 0.002 264.9 LA 0.S.1 (0.54-1.27) 0.39 273.7

~ CO GA 109(71.2°0) 21 (31.3%) 0.21 (0.11-0.40) AA 6(3.9%) 11 (16.4%) 1.99 (0.61-5.95) <0.0001 244.1

D G/A-NA 115 (75.2%) 32 (47.8%) 0.30(0.17-0.~) 0.0001 259.1 R AlA 6 (3.90.) II (16.4%) ·I.S I (1.70- 13.63) 0.0024 265.2

OD G/A 109 (71.2%) 21 (31.3%) 0.11 (0.10-0.34) <0.0001 243.7 LA 0.64 (0.39-1.06) 0.081 271.4

..... l!rII&1!~ CD C/G 87 (56.9%) 28 (4U!%) 0.40 (8.20-0.88) 0.033 269.6 GIG 36 (23.5%) 15(22.4%) 0.52 (0.23-1.17)

0 C/G-G/G 12:> (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 SN P.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


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).


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


Table 4. 6: Chromosome 2: Association of SNPs with malaria in malaria, \s H(' comparison. , ______ ,

Loci SNP IM* Gcnot~'Jlc

~ C'j) CIG I)

R OD LA

CD Air CC AfC-ClC ("IC

A'C

Malaria Phcnotypc _ OR (95% el) He ~M'!.'a~18~r:.!:ia~ _____ _

1 ~ I 1'1 -to oj

21 (31.3%) II (16.4%) 32 (47.80 0)

11 (16.4%) 21 (31.3%)

37 (24,20 .)

80(52.3%) 14(9.2%) 94 (6],4%) 14(9.2%) 80(52.3%)

1.32 (0.65-~,69)

~ ,26 ( 1.20-427 J

0.76 (0.31-1,85) 1.74 (0.98-3.11) 0,51 (0.22-1.20) 2.40 (U I--UO) 1.16 (0.75-1.

: Dominant (0): Reces,~ive (R): Overdominanl (OD): Log-additive (LA),

P-\alue

Ill;

0.012

0.06 0.13

0.0037 1

Ale

273.8

267.7

270.9 272.1 266 274

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 AXrr respective(l', Red highlight: SUJceptihility SNP, Green highlight: protective SNP

93


Table 4.7: Chromosome 4: Association ofSNPs "ith malaria in a malari~'-~ .. li}IC compar:~'..o.!'.. ....

Loci SNP 1M" Genot,pe Malaria Phenotype

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 XXYY respectil·ely. Red highlight: Susceptibility SNP. Green highlight: pmleclh'e SNP

94



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


Table 4. 8: Chromollome 6: SN Ps ~ ith endothelial inh'gri~

Loci SNP 1M· Genotype Endothelial intrgritl OR (95%CI)

ProJ}_arnage ProRe(!air

~Cf) CiA 31 (2".5%) 29 (32.6~.) 1.64 (0.90-2.99) 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) R AlA 3 (2.3%) 4 (4.5~0) 2.02 (0.44-9.27) Of) CIA 31 (23.5°0) 29 (32.6%) 1.57 (U.87-2.87) LA 1.60 (0.97-2.63)

nElDA CD T/G 34 (25.8%) 36 (40.5%) 1.57 (0.84-2.95) 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) R TiT 43(32.6%) 16(18%) 0.45(0.24-0.17) Of) I'(j 34(25.8'10) 36(40.5%) 1.96(1.10-3.48)

LA 0.80 *lnherilal1ct: Model (1M) Codominant (CD): Doml1lanl (D): Recessive (R): Overdominant (aD): Log-additive (LA).

----_ .. -P-valuc

0.18

0.074 0.36 0.14 0.066

0.018

0.99 0.014 0.022 0.19

AIC

300.6

298.8 .10 I.! 299.7 298.6

295.9

302 296 296.7 300.2

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



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


Table 4. 9: Chromosome 7: Association ofSNPs with endothelial int~ri~'

Loci SNP IM* Genot~pe F:ndothelial Inte~rit~

- 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


Table 4. )0: Chromosome 7: Association SNJ', "ith malarht ill a malaria " II<' comparison

Loci SNP

rs1H0078.1

rs3918211

rs2070744

I~" Genol~pe Malaria Phenotype

OR(9!'%CI) Malaria HC ---------

III

f)

R Of)

IA

CD

D R

on LA

CD

()

R

I \ i7(50.~Il,d "l~lj 20/0) 1.11 (0.5R-2 151

AlA 24 (15.7001 14PII'l"iJ) 1.52 (0.M-~5\11 r:A-A-A 101 (660'0) 47 (711.2%) 1.2\ (0.65·2.251 AlA 24 (15.7°01 14 (20.(1''<» 1.42 (0.68·2.95) riA 77 (SOYo) 33 (·t<l2%) \I % (0.54-1.70)

\ .22 (0.80-1.84)

)'II.' \0(14.9%) 84 (54.9%) 5.45 (2.51-11.84) OC 20 (29.9~0) 12 (7.X%) 0.39 (0.17-0.19) T/C-Ot' 30 (44.8°0) 96(62.8%) 2.OS (1.16-3.72) CIC :!0(29.9%) 12(7.8%) 0.20 (0.09-0.44) \'1(' 10(14.9%) 84 (54.9%) 6.94 (J.JO-14.60)

0.92 (0.61-1.38)

TIC 84(54.9%) 10(\4.9%) 0.18(0.08 ... .40) CIC 12 (7.8%) 20 (29.9%) 2.57 (1.12.!'.87) T/C-CiC 96 (62.8°0) 30 (448%) 0.48 (0.27-0.86) CIC 12 (7.8%) 20 (29.9%) 5.00 (2.27-11.00) TIC 84 (54.9%

) 10 (14.9%) 0.14 (O.87 ... .l8)

A I. -Inheritance Model (1M) Codominant (CD); Dominant (D): Recessive (R): Overdominant (OD); Log-additive (LA).

P-"alue

0.62

(l.5S n.35 0.88 0.36

<0.0001

0.013 <0.000\ <0.0001 0.7

<0.0001

0.013 <0.0001 <0.0001 0.7

AI(

---~---

275.5

274.\ 2736 2744 273.6

238.1

268.3 2S7.x 241.2 274.3

238,1

26!!.3 257.8 241.2 274.3

Assuming bial/elic ,\'NPs comprising X and r alleles. the reference genotypesfor the CD. D. R andOD inheritance models are XX. XX, XY-YYandXX-Yr respectively. Red highlight: Susceptibili~I' S:\'p, Green highlight. protective SNP

99



Two SNPs located in the genomic regions encoding Interferon Omega 1 Pseudogene

19 (lFNWPI9) and Histo-Blood Group A Transferase (ABO) were assayed on

chromosome 9. The Histo-Blood Group A Transferase (ABO) SNP (rs8176722) was

associated with an increased risk ofCM (OR = 2.19, 95% CI = 1.08-4.45, P = 0.029,

Table 4.11) and endothelial damage (OR = 2.14, 95% CI = 1.24-3.71, P = 0.006, Table

4.) 1) in the dominant inheritance model. However, in the case-control comparison, it

was associated with clinical malaria in the codominant model (Table 4.12). The

IFNWPI9 SNP (rs73422262) was associated with reduced risk of clinical malaria in

the case-control comparison (Table 4.13).

100


Table 4. J J: Chromosome 9: Assodatiun uf SNPs with l'ndothcJial integrity

Loci SNP IM* Genotype ~dothelial Integrity OR (95% ('I) P-value AI(' ProDan~ ProRepair -------.

rs73422262 CD (j A 52 (3Q.4%) 35 (39.3%) 0.97 (0.55-1.72) 0.95 303.8 AlA 12(9,1%) 7 (7.9%) 0.84 (0.31-2.30)

D G/A-A/A 64 (48.5%) 42 (47.2%) 0.95 (0.55-1.63) 0.85 301.9 R AlA 12(9.1%) 7 (7.9%) 0.85 (0.32-2.26) 0.75 301.X

00 G/A 52 (39.4%) 35 (39.3%) 1.00 (0.58-1.73) 0.99 302 LA 0.94 (0.62-1.43) 0.78 301.9

rs8J76722 CD CIA 44 (33.3%) 46 (51.7%) 2.20 (1.26-3.84) 0.021 296.2 AlA 4(J%) 3 (3.4%) 1.57 (0.34-7.37)

D CIA-AlA 48 (J6.4%) 49 (55.1%) 2.14 (1.24-3.71) 0.006 294.4 R AlA 4(3%) 3 (3.4%) 1.12 (0.24-5.11) 0.89 301.9

00 CIA 44 (33.3%) 46 (51.7%) 2.14 (1.23-3.71) 0.0065 294.5 LA 1.84 {1.13-3.00! 0.013 295.8

-Inheritance Model (1M) Codominunt (CD): Dominant (D); Recessive (R): Overdominunt (OD); Log-additive (LA) Assuming biallelicSNPs comprising X and Yalleles. the reference genotypes/or the CD. D. Rand OD inheritance models are xx. X¥. XY-YY andXX-l'l respel·/ively. Red highlight: Susceptibility SNP. Green highlight: protective SNP

101


T.b'e 4. 12: Chromosome 9: AssociatioJl of SNPs with ('1\1 in a ('M \"Crsus t '1\1 comparison

Loci SNP 11\1' Genol~pc Malaria Phenot}pe

OR (95% CI) P-\alue AIC UM CM

~------.-.

rs73422262 CO G/A 26 (43.3%) 27 (39.70'0) 0.85 (0.42-1.74) 0.9 182.7 AlA 20.3%) 2 (2.9°0) 0.82 (0.11-6.15)

0 G/A-A/A 28 (46.70,.0) 29 (42.6°0) (j.85 (0.42-1.71) 0.65 180.7 R A'A 20.3%) :! (2.9";0) 0.88 (0.12-6.44) 0.9 180.9

00 GA 26 (43.3%) 27 (39.7%) 0.86 (0.43-1.74) 0.68 180.8 LA 0.87 (0.47-1.62) 0.66 180.7

rs8176722 CD CIA 21 (35%) 36(52.9%) 2.17 (1.06-4.46) 0.092 178.2 AlA 1(1.7%) 2 (2.9"/0) 2.53 (022-29.29)

0 CIA-AlA 22 (36.7%) 38 (55.9"/0) 2.19(1.08-4.45) 0.029 176.2 R AlA 1(1.7%) 2 (2.9"/0) 1.79 (0.16-20.23) 0.63 180.7

01) CIA 21 (35%) 36 (52.9"/0) 2.0'1 (1.02-4.26) 0.041 176.8 LA 2.04 (1.05-3.95\ 0.032 I

(1M) Codominalll (CD); Dominant (0); Recessive (R); Ol'erdominant (OD); Log-additive (LA). Assuming biallelicSNPs comprising X and Yalleles. the reference genotypes for the CD. D. R andOD inheritance modelll are AX. xr. XY-YY andXX-YY respectively. Red highlight: Susceptibility SNP. Green highlight. protectivl' s.1\'P

102


Table 4.13: Chromosome 9: Associatiun ofSNPs with malaria in a malaria \l'r'u~ U(, l'Itn11)arison

Loci SNP 1M· Genot~pe Malaria Phenotype __

OR (95% CI) P-vslue AIC ~-- Malaria

rs73422262 CD G!A 20 (29.9° 0) 67 (43.8°0) 1.41 (0.74-2.67) 0.0004 260.7 AlA 13(1'1.4%) 5 {3.3%) 0.16 (0.05-41.49)

D GIA-AIA 33 (49.2°0) 72 (47.1%) 0.92 (0.52-1.63) 0.76 274.4 R A'A 13 (1'I.4°u) 5(3.3%) U.I"(O.O~."I) 0.0004 259.8

00 G/A 20 (29.9%) 67 (43.8%) 1.83 (0<)'1·3.38) 0.049 270.6 LA 0.65 (0.42·1 .0 I) 0.053 270.7

rs8176722 CD CIA 19 (28,4%) 71 (46.4%) 2.08 (1.11-3.89) 0.021 268.8 A/A 4(6%) 3 (2%) 0.42 (0.09·1.95)

D CIA-AlA 23 (34.3%) 74 (48.4%) 1.79 (0.99-3.25) 0.052 270.7 R A'A 4(6%) 3(2%) 0.32 (0.07-1.45) 0.14 272.2

OD CIA 19 (28.4%) 71 (46.4%) 2.11) (1.18-4.06) 0.011 268 LA 1.39 (0.82-2.36) 0.22 272.9

·Inheritance Model (1M) Codominant (CD): Dominant (D): Recessive (R); Overdominant (OD); Log-additive (LA). Assuming biallelic SNPs comprisinK X and Yalleles. the reference genotypes for the CD. D. Rand OD inheritance models are XX. xr. XY -YY and XX- YY respect ive~)' Red highlighJ: Susceptibility SNP. <ireen highlight: protective SNP

103



The SNPs assayed on chromosome 10 were in the loci encoding Long Intergenic Non

Protein Coding RNA 840 (LINCOO840). Case - case comparisons found variants of

rsl0899940 and rs13313099 to be associated with increased risk ofCM (Table 4.14)

and clinical malaria (Table 4.15). None of the SNPs was associated with endothelial

damage. Tables 4.14-15 summarises the association of chromosome 10 SNPs with

malaria phenotypes and endothelial damage.

104


Table 4.14: Chromosome 10: Association ofSNPs ni.h eM in a (,M \'erSliS liM comparison

Loc SNP IM* (;l'no~pe Malaria Pbenoo'~e

OR (95% ('I) P-\'alue AI(' 11M eM -----.

C'D ('IG 15 (25%) 31 (45.6%) 2.78 (1.29-~.98) 0.017 174.8 GIG 2 (3.3%) 5 (7.3%) 3.36 (0.61-18.44)

D ('rG-G/G 17 (28.3%) 36 (52.9%) 2.85 ( 1 36-5, tJ4) 0.0045 172.9 R GIG 2 (3.3%) 5 (7.3%) 2.30 (0.43-12.33) 0.31 179.9 OD ('IG 15(25%) 31 (45.6%) 2.51 (1.1 8-5 .. '4) 0.015 175 LA 2.35 ( 1.24-4.4") 0.006 173.4

rs 133 13099 CD TIC' 26 (43.3%) 38 (55.9%) 2.28 (1.02-5.09) 0.088 178.1 ('Ie 9 (15%) 14 (20.6%) 2.43 (0.85-6.92)

D pc-e/C' 35 (58.3%) 52 (76.5%) 2.32 (1.09-4.96) 0.028 176.1 R ell' 9 (15%) 14 (20.6%) 1.47 (0.59-3.69) 0.41 180.3 OD TIC' 26 (43.3%) 38 (55.9%) 1.66 (0.82-3.34) 0.16 178.9 LA 1.66 (0.99-2.78) 0.052 177.2

Model (1M) Codominant (CD); Duminant (0); Recessive (R); Overdominant (OD); Log-additn'e (LA). Assuming hialleli,· SYPs cumprisillfo:.r and Yalleles. the reference genotypes far the CD. D, Rand OD inheritance models are XX, X\' .. \T-YY and X\,-Yr I'l'spl'clively. Red highlight: Susceptihility SNP. Green highlight: protective SNP

105


T.b'e 4. J5: Chromosome 10: Association of SNP, "ith malaria in malaria \"S HC cnmparison ----"-- ~---"--- ----------

Loci SNP IM* Geno~'pe Malari~l Phenot)'pe

OR (95% ('I) P-\'alue AI(' He Malaria

rs]0899940 CD (. (j 15 (22.4%) 62 (40.5%) 2.44 (1.25-4.75) 0.024 269 GIG 3 (4.5%) 8 (5.2%) 1.57 (0.40-6.21)

D C/n-G/G 18 (26.9%) 70 (45.8%) 2.W ().B-4.~0) 0.0075 267.3 R G/(j 3 (4.5%) 8 (5.2%) I . I g (0.30-4.58) 0.81 274.4 OD C/G 15 (22.4%) 62 (40.5%) 2.3(. (1.22-·t56) 0.0079 267.4

LA 1.86 ( 1.08-3.] 9) 0.019 269

rs13313099 CD llC 24 (35.8%) 80 (52.3%) 2.18 (1.15-4.]2) 0.054 270.6 CIC II (16.4%) 24 (15.7%) 1.42 (0.61-3.30)

D T/C-C/C 35 (52.2%) 104 (68%) 1.94 (1.08-3.49) 0.027 269.6 R ('IC' II (16.4%) 24 (15.7%) 0.95 (0.43-2.07) 0.89 274.4 OD TIC 24 (35.8%) 80 (52.3%) 1.96 (1.09-3.55) 0.024 269.3 LA 1.37 (0.90-2.10) 0.14 272.3

(1M) Codominant (CD); Dominant (D); Recessive (R); Owrdominant (OD): Log-additive (LA). Assllming biallelicSNPs comprisinx X and Yalleles. the reference genotypesfor the CD. D. Rand OD inheritance models are XX, Xr. XY-YY andXX-Y> respectively. Red highlight. SlIsceptibility SNP. Green highlight; protective SNP

106



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 of chromosome 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


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



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


Table". 17: Chromosome 20: Asso('iation ofS~I', with t'lIllothdial integri~ ~ .. -- -------~"-.-

Loci SNI' IM* Genotype Endotheliallntegritv . OR (95% C1) ProDama~ ProRepalr ____ . __

P-\'alue AI('

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


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% <I) P-"Hlu" Ale

Malaria HC

rs2236416 CO A/G 54 (35,3 0 0) 15 (22.4%) 0,64 (0.32- 1.26) 0,0036 265.2 GG 7 (4,6°'0) 12 (17,1)° 0 ) -; 'I,j (I 45-10 7~)

0 A/G-G/G 61 (39,9%) 27 (40,3%) ),02 (0,57- 1,83) 0,95 274.4 R GIG 7 (4,60",) I:? ()7,1)°0) 4,55 (1,70-12.15) 0.002 2649

00 A/G 54 (35.3%) 15 (22.4%) 0.53 (0,27-1.03) 0.053 270.7 LA 1.37 (0.89-2.11) 0.15 272.4

rs2274755 GI 20(13.1%) 17 (25.4%) ~:::h (I 10-4,66) 0 R

00 LA

rs59055740 CD NG 113 (73.9"AI) 28(41.8%) 0.36 (0. 1 8-0.73) <0.0001 252.3 AlA II (7.2%) 19(28.4%) 2.50 (0.98-6.39)

0 A!G-A/A 124(81%) 47 (70.2%) 0.55 (0.28-1.06) I.OOE-04 258.2 R AlA II (7.2%) 19 (28.4%) 5.11 (2.27-11.50) <0.0001 254.1

OD NG 113 p3.9"AI) 28 ~41.8%~ US {O.I4-0.471 0.24 273.1 (1M) lodominuflI (CD): Dominant (D): Recessive (R); Overdominant (aD); Log-additive (LA).

Assuming biaJlelic SNPs comprising X and r "fides, the reference genotypesfor the CD, D. R and aD inheritance models are AX, xx. xy-yy andXX-YY respective(v. Red highlight: Sliscepfibilifr S,.,P, Green highlight protective SNP

III


4.10 Global association plots

To have a global picture of the association of SNPs with endothelial integrity and

malaria phenotypes, a pseudo-GWAS approach was used to construct Manhattan plots

from LocusZoom's regional association plots (Pruim et al., 2010). The significance

threshold was adjusted to p < 0.002 to correct for multiple testing in this analysis.

A CDH5 SNP (rs2304527) emerged as the only SNP associated with eM in a UM vs

CM comparison under this conservative significance threshold. Under the over

dominant inheritance model assumption, children with the heterozygote riG genotype

were protected from CM relative to their colleagues with the TT -GG genotype (OR =

0.32,95% CI = (0.15-0.66), p=O.OOJ6. Fig 4.7). Several SNPs were associated with

malaria in the comparison between malaria and healthy controls (Fig 4.8) but the most

striking of these SNPs were Ts3917419, rs684951, rs2070744 and rs59055740 which

were associated with malaria at significance threshold comparable to that employed in

GWAS studies (i.e. p < 0.5 x 10-8) Fig 4.8. The MMP9 SNP (rs3918256) was the only

SNP associated with endothelial integrity at this studies significance threshold. Relative

to the reference genotype (GG), individuals with the AA genotype ofrs3918256 were

almost 4 times more likely to be classified as ProDamage under the recessive

inheritance model (OR = 3.93, 95% CI =1.63 - 9.50,p=O.OOJ5. Fig 4.9).

112


Association of tac SNPs with eM relative to UM

GWAS p-value threshold: < 5XIO·a - .. -.. - ---------------------------------------

~ ~ ~ i i~~~7

," ... n·fll.ISoO"'~

__ ~~~~~!..:~~~~~_~~:~~~!~ __ ::_..:_~~~~~__ ~ . -~~~---1 -------.. ------------~--------------=~=.:.!:::.----• • It. p< 0.05

· i • • ..

t i i J I • I .! • • ... • ... .."..... L....,......~ -..... Genehltj·

ci!1 J r MJ [5h;4- Jr~~r6j [c!:il M J ~1!~1 [Ch!t6...- ChrZO~"

Lit ... I i ( Chrom~som~ nu';'be~ In:rrn annotation key

~nt •

Dominant • Log-addiIi\Ie • <Nerdominant .. Recessive •

\ \t.nha"_n plol ,ho"ing ~,p, ."orialrd .. ilh C\I 81 differenl signifiesnrr Ihre.hold. \'.IIow, grHn and red lincs denole p.ulue Ihruhold at <0.05, < 0.002 and < 5.10""respeetlvdy

Figure ... 7: Association ofSNPs with eM in a eM vs UM comparison

113


~ c!. '0

~ 4

Association of tag SNPs with malaria relative to healthy controls

rs3917419 • r.;684951 • .r.;2070744

r.;S90SS740

c.WA\pnletJveeheld: s .1~

------------.---------------

• ___ ~-----------------------------!--------------------_______________________ ~~.;.~!!.a::_

• •••• • I • - • • • • J -, • • Ii Ie I

I • .

• ..

u..... --I.-\ Chromosome number I" • i. I •

___ 1.._ • • •

- ----~.-~

I

.... ~. Ch~_~.

amoIaIJon My

A Manhattan plot showing SNPs associated with malaria at different significance threshold. Yelhm. green and red lines denote p-value threshold at < 0.05, < 0.002 and < 5.10-8 respectively.

Figure 4. 8: Association of SNPs with malaria in a malaria vs HC comparison

114


~ ~

1'.)

1 I 241

Association of tag SNPs with endothelial damap

393 (1.63·9 SO)

.................... "'., .. -----------------_. -

rsJ918256 OR = 3.93, 95% CI ~ (1."9.501. p ... HI5

---------l

---- ~ -. ----------------------- ---------------------------------------9--------=--:::::.':.::.----t • • •

• • . •

I • a a • i

! I • J , i I •

U-...........i.1{ Chromosome number J-1L~

. ..... I - Genehll.

J annoIatlOfl key

Codonwlant • Dominant • ~ • • RecessIVe •

A Manhattan plot showing SNPs associated with endothelial integrity at different significance threshold. Yellow, green and red lines denote p-value threshold at < 0.05, < 0.002 and < 5.10-8 respectivel~.

Figure 4. 9: Association of SNPs with ProDamage in a ProDamage vs ProRepair comparison

115


4.11 In silko analysis

The functional significance of trait- or disease-associated SNPs are sometimes not

decipherable on their genetic attributes alone. To better appreciate the plausible roles

of disease-associated SNPs identified in this study, a number of in silico analysis were

conducted to: (a) find proxy SNPs for trait- and disease-SNPs identified in this study;

(b) characterised the epigenetic context of these SNPs (both tag and proxy SNPs); (c)

predict SNPs that may enhance or inhibit the binding affmity of transcription factors;

and (d) to identify SNPs that disrupts microRNA target sites.

4.11.1 Linkage disequilibrium analysis.

In line with the concept of tag or sentinel SNPs, this study did not only focus on SNPs

associated with malaria and endothelial integrity but also on the SNPs in linkage

disequilibrium with ''test'' SNPs. Using SNPs with significant associations as tag or

sentinel SNPs, linkage disequilibrium (LO) plots were generated using SNiPA - a tool

for functional annotations and linkage disequilibrium analysis for bi-allelic genomic

variants (Arnold et ai., 2015). LO plots were generated using the African population

database in Ensembl87 - grch37/1kgpp3vS with an LO threshold of 0.8. The list of

SNPs in linkage disequilibrium was incorporated in LO plots to create composite

figures (Figs 4.10 - 19).

116


. -:;. . 11>-'"

~x~ .. ~ --.-----.--- I -_ .. _._-_ . ..-.- .... - ------""---- --.. _- ...... _ .. _--

Sem",el ~NP Proxy SNP Chi Pm') Po>

.. 10489181 "1091'(!21i 169.704 934

.. 10489181 f-s21Wt'C) I 16 169.706.030 n104K91~1 ,,10489182 169.710 669 ,,1(1489181 nl2044082 Ih9.71168~

"111-189181 ,,104891SI 169.712,5')7 nl0489181 n16862663 169.'1.1'80 .. 111489181 n6042~295 169.716.281

"10489181 ro82701bl 169.716.313

"10489181 ,,10800470 Ib9 717 272

"10411'1181 ,,10800471 169 718.948

,,1"4""181 ,,10800472 169719.01 7

,,111489181 1168113309 1697:!2.881

"10489181 no-I6S671 I 169723.575

"111-189181 007819761 169723662 .. 10489181 ,,~Sb71712 Ib9 7 24.610

"10489181 f .. ';IJX II ~I)~ Ih9,7B.h85 ,,10489181 r·.tlhfll'l~Ci Ih9715.978 ,,10489181 r~6()MI"1 IlN,72l1,5b5

1'11l41l'i181 r<o.-Jh~t"·J2 169,716815

"10489181 r~(''''bI9~ 169727175

"10489181 r .. "54.~248 Ib9 '27 403 nl0489111 r~4265482 16')."2'" kR2 nl0489181 r"lo~",:?n50"i Il,fj.-28,1'"' nt_181 r~ "'S I ~O64 169 i,(l "/0((,

"10489111 n7S45453 169 73u 1>47 nl0489.1 112272818 169.'" 345 ,,1114"'111 rs34990593 I 16').733.1167

~. _I}lMl _'I~ CI-'!!1 ___ ._ .

~s;- lOr'

·7663 092

-6 ~67 093 -1.928 089 .912 099

I 78.3

3.b84 09.l

3716 093 4675 088

6351 087 6420 087

10284 087 10978 I) 8"

11.065 081 12.013 () 8' IJOS8 08'

1>381 087

13968 0.87 14218 0.87 14578 0.87 14806 087 15285 OHI 15540 U8: J7 989 088 IR,O:liO () 87 20 -lH 'j ~7

21.270 ',s7

I-

LDD LD Pro,> Minor MAF D Allele

023 099 C 0.399 013 (' 0.398 0.13 G 0.408 o 2 ~ T 0.384 I C 0.383 u ~,l C 0384 02.1 O.9~ C 0398 o:n 099 A 0398 022 0.99 G 0.41

022 099 G 0.412 021 1 G OA14 022 C 0.414 021 099 T 0.412 0.22 099 T 0.429 022 099 T 0.412 022 I A 0.414 0.22 T 0.414 021 0414 () 2~ A 0.412 0.22 C 0414 0.:!2 0414 f) .:' ~ 043 IJ"; 0414 I) ~~ I) ()I) 0411 Il ~~ (III)

'122 \1114 1)!2 I (' (I II ~

Figure 4. 10: A composite linkage disequilihrium plot for rsl0489J81

117


Sentinel rs2070744 rs2070744

i i

8£Tlrl o_~

.-------o.iM"~p N[M'7'18 ACe1 ~ciNAP!. ~1'OA---

.505

lInup dl ........ rlum plot ~2070744

""""0,'4..)

*

• , '".1:1. L A • <..

;\t~l' "'i!!'"

"'!P ,"!'" H!5!! ~ ,¥t Q!,.f~

..J!!!!!!... 1!:.~ Mlf!'11 4Q!!!I Rt"lIt~'I' .....!!!L ~&. SMA.!'CCh_

.... t~ •. 'o W" 't!!!.. .....~I.'

.507 .1108

Chromoeome 7 iMb 1

"' ___ .n~ .......... ......, ....

40 f

i

____ .~_ .... I\,-'---...._. n.,y_ ,_ ~ .-.".-----......... '- 1 --- ........ -~ ........ ..... ~_(".v ........... .......,~ __ _

1

_____ ... ·_,,_ .... "'-~_~I.Jl.tf ........ ~ ~ .... ...,....., ..... ---... ... .... ",-""""<>,,,-...,~ ~ ___ cA .... 5NP'n..pIof""'"*"'oINdo __

~"'"~ __ tIO'I

Proxy ('hr. Proxy Pos. Distance (bp) LD r LD D LD D' Proxy Allele B rs1800779 7 150.689,943 -136 0.98 0.12 0.99 G rs2070744 7 150,690,079 0 1 I I C

Figure 4. 11: A composite linkage disequilibrium plot for rs2070744

118

MAF 0.139 0.138


~

J ~ 5

I

• ...... 8NP ( noe .. LD L LOI2'02

• LOr' , •• • lO" '01

TM~718 ~_

TM~lleA

150.5

Unkage disequilibrium plot for rs~918211

~118211

•

• ~ . q,w.,{:j \0 0 c 0 ~)., .\.~

COK5

-.P FASTl<

A8CII8 -na.e. I\IOS3 -- 9Lcw/.

--ATGIB ~,.12

<h CO

~ RPiI,.:,"""o • ~

150.7

at_e7(Mb)

CHI'F2

MiRe7' A8CFI &.w.cm

,:!!!!o --;u. .. ~ ... GBX1 ~L Rf'S.1~2

150.9

60

40

~ , 20 J

unuc·d,wq"",_ .... "_ .... _01_ Ii --. ~"wntlnll .... "t.l'Idtts~..,.,..ntsn."a.lt'\ _ tI'WIIICripI showt.llwocorrttt.noncoeffla.nrlrl).the .. -...,tI'ICM'I "'*'town ..... __ oI_h9<.""' .... _oI__ --------,nckatftlt, ....... 'ONf ....... tOn

_011 ..... ,~ ., ..-.noguIaIory ...... 11 ____ ..... .". ....... 71'........,.

_n~~';;:':;:-: ::==- I =:-=.,...-,,-_ ........ , ......


119


] I

API -.ota. ~ • ~C'Ma

Link .... dlsequtllbrium plot for n:J917419

.. • 6.Ml

6. • 6. 6....,;., ;-.Ce' ~ 4. ·&~'ir.l.f.~'

t.7

III:TTI. ••

RPI "'''''$ .. <: ..... , •

etvorno.om. 11Mb)

~"""'''"-tt. . L

~.!.':.s_ -

I

I~,'h,"~

I .----=:-:.. ..J

i ,f

!

==:7'..:==--:::.=.:';:;~~-:::. no. ="ec1 :.c'.If_..;::.;"'::'---: =---=... =.~OOftI ~--------u ............. ~_ ......... I ............. oI--""... _____ ~ ...... ........,. .. .- , ....... ___ ........ -...,.. ...... ,,,........,, ..

::::::--..:::::; of..m", n. ............ .,. _ ....

Sentinel Proxy Chr. Proxy Pos. Distance (bp) LD r' LD 0 LD D' Proxy Allele B MAF rs3917419 rs2076059 1 169.698.921 -898 0.99 0.19 I T 0.247 rs3917419 rs3917419 169.699.819 0 1 1 1 A 0249 rs3917419 rs753 1675 169,711,306 11,487 0.89 0.18 0.97 A 026 rs3917419 rs7543618 169.711.3~6 11,507 0.89 0.18 0.97 C 0261 rs3917419 r..12-l10806 169.718.236 18,417 0.93 0.18 0.97 A 0.248 rs3917419 rsl~n8631 169.725,635 25,816 0.88 0.18 0.96 A 0.256 rs3917419 rs7526645 169.728.916 29,097 0.88 0.18 0.96 T 0.256 rs3917419 rs7549412 169,729,250 29,431 0.88 0.18 0.96 G 0256 rs3917419 rsl2063022 169,731,865 32,046 0.92 0.18 0.97 G 0.247


120


Sentinel

rs59055740 rs59055740 rs59055740 rs59055740 rs59055740 rs59055740 B59055740 rs59055740 rs5<)055740 rs5'Xl55740

Un,,- .u..quIUbr,uR"I plot for ... 59055740

. '

I t .1

""~'In ~

. ,-...,-_ ...... __ .. - I -_, ... _. __ ...." •• ~--..n n....... ____ =_=~:::J: ... <+:.:.~ _ ;._.~~ .....

Pro~) ('hr. Proxy Pos.

rs6018446 20 46.007.753 rs6018447 20 46.011.511 ,."t.09-!o81 20 46.013,864 rs6125U05 20 46.014.346 rs59055740 20 46.014.727 ,,606629R 20 46.016.583 rs6066303 20 46.019.0·B rs6094689 20 40.033.012 rs1535172 20 46.033.609 rshl25012 20 4h.034.35I

.. . . • ' .

• ..... \.,

~.2'0(.., •

40 ('

'<:;. · i --- 1IIPs,..,..., •• ... ~p

=:-.. ~~: =-~ ... _ ......... _r ..... ___ • .---.. ............... _ ---- --- 1------_· __ · Oistanee LD LO LO

PrOlty Allele B !bl!l r' D 0' -6.974 0.98 0.25 0.99 C -3.216 0.95 0.24 0.98 G -863 0.98 0.25 0.99 T -381 0.96 0.24 0.99 G 0 1 I I G 1.856 0.88 0.23 I G 4,316 0,85 0.23 0.98 T 18.285 0.83 0.23 0.97 C 18.882 0.83 0.23 0.98 C 19.624 0,82 0.23 0.97 G

Figure 4.14: A composite linkage disequilibrium plot for rs59055740

121

MAF

0.492 0.498 0.492 0.488 0.494 0.461 0.463 0.459 0.457 0.457


~

i ~

J .c t 3

__ SNP

noftnLD '- LO?_-02 • LOr' ... 05

I ,- lOr' -08

Unkage disequilibrium plot for ~684951

r5684'.:151

...

... ,

~.

, '. ,

TREK. TREMl TREMLSP - 1NIe·e43P

TAUA2 RP1.i2.aoe· NFYA ... TREMl 2 RPI ~ .•

... --- - AOCYIOPI • TREML3P 'AP1-22IKmS NCII2 RP1·I."". __ -,,=OA~RO,,-,I_ .. _-- TR~"'" -- RNA5!iP207 - RP1~1 - API '4;8'"'' •

41.1 41_3

CIvorno8ome II 1Mb)

60

40 I I>

! j

~ I FOXr4-AIII

LlQlaN ~

41.5

l'n .... f'dl1eQuthbrtumplot'!howlht .. ~Cl-"""~.;~)<",.."or. I ---bflwff!l.SPn1inel.."...endlb ... .:h.'XI,'II' ... , .. '\t~~kW".· •• ,· _ &I~ipI

Ihow5n.CIIIfNIMtoncoe'fflo""('II.thfo'd"'~_ ......,.lIIIact c:~ .... _ of HCh SHtt nw Put "f'"bH at .. ~CII ..... ~·"n' ~1tItuncaan.a1flnCllMton

.....,. ... .:ton ...... 0 ~enec:tl r~_raV ........ Ion).fwIdIGnII5HP~lOns.,. __ ""~ ......... rogulolory_' 1-----lho . .-7fl ..... J!r5.

dl_rogoAokIry_ 0 ...... 001_..... J _ .. ·_r


122


Sentinel

rs73422262 rs73422262 rs73422262 rs73422262 rs73422262 rs73422262 rs73422262 rs73422262 rs73422262

Link .... dt •• qulllbrium plot for r,,7"141/]' •

~:- LD~ ~O~

• &.O~ .' • lOr' -0.

....... ""-NAt.

.....,....

....... ...... ,3· -' I"~··

.~ ...... ----... - ... -- 1 .-._-~:;-;-:-,,~~~..:::.. -= .. -Prox~ Chr. Proxy Po~.

rs73422248 9 21,448.353 rs73422252 9 21,448.671 rs59635965 9 21,449,978 rs73422254 9 21,452,409 rs73422262 9 21,453.043 rs73422.:!65 9 21.455.410 rs79988 146 9 21.461,746 rs57341614 9 21,462,996 rs6093 1924 9 21.465,734

~ \ ,. ~

~-:> ,.r,ti'~~,:

....... . "-. ~P2

"!'f .......

---_~~I_HO __ _

2'.5

~.jMb,

1ItI'~,., ~tP

2.7

i 'i! i ~ i

- . .... -----. ,......,. .......... .a.c. 1...., .......... --.. ...... · .... ,,, ......... · ........... __ "--. ........... ....-...-_r~ ........ 101'11 ~INI' ___ _ __ ._ ~~~ . -........cI...... tauM ... • ....... r

Distance (bp) LD r' LDD LDO' Proxy Minor Allele

-4,690 0.8 0.16 I A -4.372 0.93 0.16 1 A -3,065 0.95 0.16 0.99 G -634 1 0.16 1 T 0 1 1 1 A 2,367 0.94 0.16 0.99 T 8,703 0.94 0.16 0.99 C 9.953 0.9 0.16 0.99 G 12,691 0.84 0.15 0.97 A


123

MAF

0.244 0.217 0.209 0.205 0.205 0.211 0.211 0.22 0.226


i ~ ~

i " t 5

o I

• ......... SNI' ".. .. lO

C lO,J "04'

• lO,J 'O~ • loi

linkage disequilibrium plot for rs3818256

f'. J9182S6

~ J

A

• A

A

A A·

t:.&tf, l:.~ ~~rJ . ~ 0~ v .• ~

lSWlM.

UB~ ~. ~ ThINCII NEURL2

ONTT\P. -- ACCTa SPAT42S

WfDC3 -- 1Aotaz .. SRP ·ClSA

_!A~ ~,' RP3-~ ...

44.4

~!g

Pelf! FTlP, SlC.2A5 - ZNf33S . RPI11!!L~

44.11

Chrom_ 20 (Mb)

~ ~

RI'L!Jf'2

60

40 I ! ~

m i RP5-fI!'HU

alHI2

44.8

""..,.d .... _ ..... _ .... _01_ 1 ,,,,,om_oon"', bft...en.MftfIrw4 ... ,..... .... ttt."',.rouncfIl1C\lQftanb- .......... '" -......,.,.

"""" ... COI'TWIetIaft~{,.l}. ...... I'1ohawI unknownelt8d -........_ .. _"' ......... ..- .. --"'IftIts "-ct ........... 1Oft

<Inc',Hee'"""""",,, ...-~- '1 _____ . .... ....... 71JtupN" puIIIIve_ ... ,_ 0 ....... _ . __ .. r .... ~ ___ _ ..... ..-., ... ect 0 ___ c~ .. ·_

Figure 4. ) 7: Linkage disequilibrium for rs3818256

124


Sentinel

rs2304527 rs2304527 rs2304527 rs2304527 rs2304527

llnk .. gf> dl·.rqt .. mbrtum plot for rs2Ja.Sl1

i I

e412

,_ ..... -_._ ... _"'- I -_ .... ~r- .......... ___ eftIIII .. '---. __ .. n..."l., _.,.,..,.. ~1 .. d"..t.aon~I,lll................. ~.n.d

~ru:==n..P'cR.,.....-"vatt.m ~

Pro~) Chr. Proxy Pos.

rs62037250 16 66.416,399 rs58044782 16 66,416.482 rs2304527 16 66,424,235 rs3785286 16 66,424,827 rs2880989 16 66,434.280

• • • • t··

1 ~~ t

- .t-~\ .• ".

11114

ChromOSOl'lle 1. CUb)

_. Y-r!iA

CUlW 8UH1., ... ---QG.f a,rn.h-

j ~ ! I

20 1 ~u =:'':;''7.8 .. '1-;6 CI!!!M ! 0

CTD.~3 APll~P1'.t

~ PUt.'....,.,7.. ~_

_.11

,....,.,..n.d .... ...".,...,.,........ · ........... e_' .... ~~SIItP......-.. ... cINd............... ..........,.....oryef1.c1 I ~ .. <IfIn!I' ..... ~ •. ~.oot •• ...-!'~"""'-....n/1 ......... ... ,..,..".a.cr 0 ............... »-...... -.... .....,.

Distance (bp) LO LO LO Pn)\~ rl 0 0' Minor Allele

-7.836 0.82 0.22 0.92 T -7,753 0.9 0.23 0.97 A 0 I I I G 592 I -0.24 -I C 10,045 0.82 0.22 0.96 C


125

MAF

0.407 0.384 0.396 0.396 0.423


Sentinel

rs3918256 rs3918256 rs3918256 rs3918256 rs3918256 rs3918256 rs3918256

i t

lInkaae d.seqvtUbrtum plot for "94~2

... ,.. ... . ,- •

,

~~~

-!!!!:!L_~~ ~..,.,...~'.,....'. .. _. API ".I=I;a.I~. ~.r!'

"P'l~"

......... ~---- ... .,. .............. ..... _1·41·1'-· .. ••• 1_- - ... - ::::'7':":::'::--: ..... --=~

..• _SNP'-_ .. ~.

Proxy Chr. Proxy Pos. Distance LD LD ~b~) ... D

rs1169680~ 20 44,628.380 -12.,579 0.8 0.13 rs3933239 20 44,628.668 -12,291 0.8 0.13 rsl1698788 20 44,632,032 -8,927 0.85 0.13 rs3918253 20 44,639,511 -1,448 I 0.13 rs39 I 8256 20 44,640.959 0 I 1 rs20544 20 H645,010 4,051 0.95 0.\3 rs35567443 20 44.648,361 7.402 0.92 0.13

Figure 4. 19: A composite linkage disequilibrium for rs2304527

126

I

i ~ta. ~.~-----------i

1------· .. ·-·,-· ........... r __ ............ '·---__ ---LD Proxy Minor Allele MAF D' 0.98 T 0.\85 0.98 C 0.185 I T 0.181 I T 0.159 I A 0.159 0.98 T 0.157 0.97 T 0.157


4.11.2. Epigenetic contexts of SNPs

The epigenetic context of SNPs significantly associated with endothelial damage.

malaria phenotype and angiogenic factors were detennined using ChroMoS which is a

web tool that combines genetic and epigenetic data to classify and prioritise SNPs.

Using data based on Human Umbilical Vein Endothelial Cells (HUVEC), ChroMoS

found SNPs and their proxies to occur in 6 different human chromatin states. Figure

4.20 shows the specific chromatin state each SNP belonged to and their proportions.

As expected, the majority of the SNPs (50.7%) occurred in the low signal

heterochromatin. Just over 5% of SNPs occurred in strong enhancers and

transcriptional elongation chromatin state. Only 2.8% of SNPs occurred in insulators.

Sixteen per cent of SNPs occurred in weak transcribe chromatin states.

4.11.3. Potential effects ofSNPs on Transcription Factors (TF)

Based on the fact that noncoding SNPs in specific chromatin states affect various pre

and post-transcriptional mechanisms including the activity transcriptional factors, this

study assessed the potential effect ofSNPs on transcription factors (TF). The study used

the sTRAP function in ChroMoS to assess the possible effects of these SNPs on the

binding affinity of transcription factors. Thus, 71 SNPs comprising nine trait- and

malaria-associated SNPs and 62 proxies were inputted into ChroMoS. Using all 9-cell

types available in ChroMoS, 22 SNPs that lay in specific chromatin state in at least 4

cell types were selected for sTRAP analysis (Figure 4.21). At a threshold of 1 (which

returns only significant results), 16 SNPs that potentially influence 49 different

transcription factors were identified (Table 4.19). Table 4.19 depicts transfac matrixes

grouped by SNPs: -transfac matrices with a lowered binding affinity have a negative

ratio and those with increased binding have positive ratios (Table 4.19). Furthennore,

127


and perhaps more importantly. the direct effects ofSNPs associated with either malaria

or endothelial integrity were evaluated for their impact on transcription factors using

atSNP. atSNP is a web tool for predicting which TF binding sites created on eliminated

by SNPs. Tables x-x reports the top 5 TFs impacted by the 6 SNPs.

128


Polycomb .. pressed _

18·3%

Hetrochrom_low Signal __ _

50·7%

Figure 4. 20: SNPs and their Chromatin States

Weak Transcrrbed

16·9% Trans.cnptlonal Elongation

5·6% Strong Enhancer

5·6%

--'---2~8%

129

rs4G5619S rs4656711 rs465671a

rs55671712 rs57341614 rs58270161 rs59635965 r559811562 rs60425295 rs6661955 r56665171

rs67819761 rs6S113309 rs73422259 r573422262 r57512064 r57526645 rs7543268 rs7545453 .,.7549412

.,.79988146


Table 4. 19: Effect of SNPs on the binding amnit)' of transcription factors (TF)

SNP Id Matrix name Ref. seq Alt seq Diff P - value P - value log(p)

1520544 crr \'S\TAfJ 02 06061423 0.0337574 1.2q~()~8

n.' .. OjU .. ·· VSQ,\TA(..03 0.731451 (1010&761 I 0136852

1535567443 crr V$CMYB 01 0.2206408 o IJ()H4334 14176806

1539 I 74 I 9)la V!lftF1_OI 0.0911315 0.0083572 1.0376075

153918253 l T V$HMGIY_Q6 05560821 0.0551234 1.0038031 VSXVENTI_OI 0.0232621 0.002028 1.0595758

n60I 8446_T"l' vSNfn_c ~31118 0:"0067255 ,1.0774719

,,1J1I18447 \/(J vSAR_Q6 0,4810886 0.0038588 2.0957685

rs6I 2500(AIG VSGLI_Q2 11.2743208 0.0230467 1'015M94

,,"~".1'250_AfT VSTAII_V6 u.0476729 0.0041181 1.0635773 VSMYOGENIN_Q6 0.1470118 0.0115685 1.1040762

,,:.142ll65_crr V$FRI!AC4_11 0.2138665 0.0193179 1.0641825

V$I BX5 O~ 0.03'1876'1 0.6037171 -I 1801125 VSfXR_Q3 0.0033952 0.047'1123 -I 1495793

"llo'loKfI4_cn vSTBX501 0.0277218 03011918 -1.0360213 V$DllLTAEFI_(JI 0.052597 0.5538026 -1.0223937 VSARJn 0.0018993 001939i3 -1.0()'1U II 2 VSIK2 01 0.0154923 0.5231781 -1.5285343 VSNFAT_Q6 0.0105491 03416377 -1.5103506

,,20701lW \ "I VSCETSI P54_02 0.UI97449 0.5708857 -14610939 VSRIXI 02 0.0207801 0.2650878 -1.1057425 VSNFAT~Q4_01 0.0325844 03713534 ·1.0567778 VSGAr,uJ" 001-16743 0.9018547 -1.7885199

r.3918253_crr VSGATAI 01 00352089 0.9617289 ·1.4364002 V5HSfl_oi 00491717 0.5588443 -105557SS

".N I 8256_ GI A VSZIC2 01 0.0157337 0.2536809 -I 2074561 VSPADS C 0.0192206 0.278160b -11614649

153933239 _NC VSP3'lJ>ECAMER_Q2 /);0178768 431J6n81 -1.3115805 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

"WI 8447_AIG \ ~I ~1U2COM_()2 0.0174508 0.3838585 -1.3423568 \ ~(,.\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

"6125005_ -\:(i V$!>TATl 02 00159563 071~ -1.6543035 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

"73422265_CfI VSAfFI_Q6 0.0100743 0.1318196 -l.lIb7667

VS~MAD ~6 00460644 0.5461202 -10739232

130


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)

POU2f2

STAT

3 EP300

RORA

5 TFAP4

POUJfJ_' fIIdr ..... ,.,...,.

Loss of

'""''''''4 " ':'!'I~ SOlI'

Gain of function

function

.I'"

,-A-;;CGfAT ~44+~:r"4-T' cAAiT"f ~A~~CGtT,

.Acr::~n=fA. ·~L#T~+T~

~LuiLrrr Acy':'c;ATTA,

.. ~ - .A •. t;'GG-i:A·TA'GTC~ Gain of ' 1\ I \I I I', I\i \ l I

function , d ii-''jii- It \ l I

Cain Of~" functiun

- .A. T-r.';T.·A TA.llGTC~

TFAP'_I"'" 8 ... ,. ra2J01U7

.--:CAGCT~.

.\.} l'~ y <; T (:LM-,

0.0005

0.0017

0.0070

0.0127

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


Table 4.21: Top 5 transcription factors (TF) impacted by rs3918256 G/A

Rank

2

l

. SNP Impact TransactIOn Impact on TF Motif Scan p-value Factor (TF)

NR2C2

TBXI5

TBRI

ZNF345C

TEADS

Loss of function

Loss of functiun

Loss of function

I ,fJ" Il' funl"lion

Gain of function

~

<0.0001 ............. .tIa

CC<.JTACCA ~CCC~j..

0.0011

0.0020

0.0021 ___ ... " .. nw...,. .....

T~CC~(,

~Act~;.

Ref

I"

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


Table 4. 22: Top 5 transcription factors (TF) impacted by rs3917419 s~~~mpact Rank Transaction Impact on TF Motif Scan p-value

Factor (TF!

ELK4

2 IRFI

3 API

NFATC2

HNF4

loss of function

Gain of function

Gain of function

l.1l'~ of IUllclion

Lo,~ of fUllction

~

• I~~~

..,' .. ' ..... lkMtfOf ..... "'.

M~:;;,.GMA!.~ . . ~ \l;~~ rr,~4-~~·

.r. A ,\.'l1fT(;A.UA( ~,;QAAk': .

NFATC2,.111oW ac. hw raUt'"'' .... cif:r;;-~r;rr+i

.......,"wRfI .......... ACITTTG

$NP .~CCtTT~ .

. AC'A;;G'G;C. \(. :~~'T(;',I" \

0.0004

0.0011

0,0025

0.0059

0.0100

\\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


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 function

0.00106196

• •

':-!O' .....• ~

P', ~

CtlXI Gain. fuDdi.

oat._ ..... -., ~"i";iinr~ . *r·~T.rAT·~T~:l ·~\Wf.rfH\·T ~~~.

0.00185737

I MEF2B

FOXGI

MYEF2

Lo~s of function

LII'~ of function

Lo\\ of function

..,.~' ............... 1

. ~C.r.Ai.";~AlAcr •. LU.~I·\P~

lAu;.\'($.\ I.~ .... (;IA,,~.IA.G..: ..

_ .. _---, .. TAAACA .. -:;::-f.:xAC, II ,,·1 I !'I .. r..1~T'1

II .,\ I .1~\j\1 .. 1 '.1 I.-} "TAAACA~ TGT",uCIi

1If&f1_ ........... ~

... -a';iA~, I 1 II\, \' I \ LH At. q ( r I T tt\-:-\..\f"n \1.r \ I

.... T ~~AAA T AACi ~ ..

0.00535357

0.00561902

0.01587156

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


Table 4024: Top 5 transcription factors (TF) impacted by rs2070744 T/G

Rank . SNP Impact

Transaction Impact on TF Motif Scan -value Factor (TF) p

NR3CI L.U\' of fUllclioll

0.00159594

.. Atohl 1.1"~ of

IlIlIdioll 0.00296101

~

3 NFY (.ain of function 0.00342305

-' ~

,::'1 (.;1111 "f

fllndiOIl ~". b.",'

CEBPB 0.00547063

~ ........ r_ ....... _ .....

Lo~\ of function

RORA

.t'CTGlIAT-.-.:rr;"?-·T <A.

~ll WI,1 ~ I \\ .. 0.00703981

,t. ' .~\ I j \t I~, ,-. -\

$t;.r" riA:~ - ,"CTG.GAT A· TGG-T. lAo. SNP Impact p-values were used in ranking the top 5 TF impacted by SNPs. A SNP may lead to a gam ur luss in functiun which was evaluated with the atSNP webfuol. atSNP uses SNP i"formation and position wt!ighf matri"es fPWM) to predict the effect of SNP on TF. First-order Marku\' model and importan,'e Jamplinf( methods were u.led lu determine the statistical significance of the best match between the PWM and the sub,~equen,'e overlapping the SNP position with both the reference and Ihe SNP allele-s. Finally. the impact of SNPs on PWM and subsequence matches were tested using the same statistical approach

135


Table 4. 25: Top 5 transcription factors (TF) impacted by rs59055740 T/G Transaction M 'f S SNP Impact

Rank Factor (TF) Impact 00 TF 011 can p-value

NR2C2

FOXGI

MLX J:1

TEADJ

ATF6

Gain of function

Loss of function

(.ain of

'"lIclion

Lo" of funelilm

Gain of function

~ , .-

;~

_.,_--.. ..-. -,W-uCAOh...

".1l.lt u.f \lIrit..-L . ~I,.

ll\J'c'~~h'CYvIJlI.I~ ,t;iGAC'CAAT '.

.TGAC(rfG~ ltLLy4~Y(~

o

0.00223591

0.00318962

0.00349957

0.00414664

S\ f' impu( f p-I('//lt',\ were used in ranking the top 5 TF impacted by the It!lf S,\{, , S,\P may /<!ad to "gain ur IusI' in function which was evaluated with the atSNP webtool. afSNP uses SNP information £Ind position weight matrices (PWM) for a transcription/actor to predict the effect o/SNP on TF. First-order Markov model and importunce ,wmpling n1<!thod are used to determine the statistical significance 0/ the best match hetween the PWM and the subsequen"e overlappinK the SNP pOJition "'ith both the reference and the SNP alleles. Finally, the impact o/S/vPs on PWM and subsequence matches are tested using the same statistical approach.

136


4.11.4. Effects of trait- and malaria-associated SNPs on microRNA

The MicroSNiPer tool in ChroMoS was used to check for SNPs that may affect target

sites ofmicroRNAs. Two of the SNPs, rs3918211 and rs20S44, affect microRNA target

sites: the former affected 5 microRNAs whiles the latter affected 14 (Table 4.20 -21).

The reference and alternate variants of rs20S44 [Crr] affected seven microRNA each

whilst that of the rs3918211 affected two and three microRNAs respectively.

137


Table 4. 26: microRNA-bindin~ ,ite~ influenced by rs3918211 IGIAI variants SNP microRN

microRNA Target Site \ariant A

hsa-miR- :... t60 fr:: 110 430 420 ~CATGlT.f~::: -;C7GCGATGTTACCATGGCAACCAACGTCCTGCAGA

rs39182I 409-5p AGGt1tIACCCGAGCAACUUOGCAO

lC 20

IIG/AI hsa-miR- He 'IS: HO 430 4:10

:~GJGCCACATGTTTGTCTG: ';GCGAT-GT fACCATGGCAACCAACGTC

4467 UGG"::;~':;':GGUAGUUAOGGGCUU

lC 20

hsa-miR- HO iSO HO 430 420 110 8'3'; JGCCACATGTTTGTCTG::GGCGAl'GT' ACCATGGCAACCAACGTCCTGCAGACCGT

767-5p OGCACCAOGGOOGOCOGAGCAOG 10 20

rs391821 hsa-miR- f70 He 450 110 430 420 CTCGA;C 3GGGCCACATGTTTGTCTGCGGCGATGTCACCATGGCAACCAACGTCCTGCAG

I 1(;1 \ I 2964a-5p AGAOGOCCAGCCACAAOOCOCG 10 20

h~a-miR- t'O '\10 430 420 no 118CaGGGCCAc'_: .. :-. -;::1 A .JGCGATGT(ACCATGGCAACCAACGTCCTGCAGACCG

1226-3p OCACCAGCCCOGOGOOCCCOAG 10 20

138


Table 4. 27: \licroRNA-binding sites influenced by rs3918211 IC/AI variants SNP \ ariant

rs20544 (errl

rs20S443 ICITI

microRNA microRNA Target Site

hsa-miR-6165 Tccc:rG~~ad;. - <::~~~~c= <7C

CAGC1.GGAGGUGAGGGGAG

_________ -'-:,I.,J.{ ... ;:_<~~-·. -, ;,c,~=::;:~ul hsa-miR-483-5p

hsa-miR-505-5p ~ A:;"" ;.~::Ac:~CAAA~CGGGA <: ... -

j'.Y·'·' ' ~I..oAcn......:A~QAD<il1 20

at ~ .... : ~·~(·A":TGCCAAAGCAGGACGGGAr.cC

hsa-miR-5591-5p - ________ -il:Iof.,~Ol'~.F': , UGGGAacu~""'()AUGG<;U"G

hsa-miR-920

hsa-miR-6090

hsa-miR-4300

hsa-miR-4436a

hsa-miR-468I

hsa-miR-76I

hsa-miR-184

hsa-miR-425-3p

hsa-miR-4471

hsa-miR-4422

c; ~GGAGCOG'7GGa aaa.G1D 20

30 20 '10 :iGGATTTACATGGCltCTGCCAAAGCAGGACGGGAr.cC

GGGGAGCGoAGGGGCGGGGC 10

:10 10 -;,~GATTTACATGGCACTGCCAAAGCAGGACGGGAGCC

UGGGAGCUGGACUACUOC 10

'I ~ J0 20 10 ~ ...,:- :.GGGATTT ACATGGCAC'IGCCAAAGCAOGACGGGAACC

GCAGG,.c;~"*.·GUGGAD

10 20

40 u 10 T, ,'· .... ATTTACATGGCACTGCCAAAGCAGGACGGGAA

:;::::1 AACClGGl.A.OCCA.G<JCtJGaAOetJ

10 20

1:0 30 20 to ": : CCAGTGGGGATTTACATGGCACl'GCCAAAGCAGGACGG<a.AcC

GCAGCAGG--GU~

10 20

30 20 10 ;":'-;G·JV.G.ATTrACATGGCACIGCO'AGCAGG.\CGG-GA.ACC

::::1: :::: tl~AtlAAGGGO

10 ----------

30 ao 10 .~(jGAI'TT ACATGGCAC'IGCCAAAGCAGGACGGGAACC

30 20' ~o

AOCCiGGAAOGUCGOGOCCGCCC 10

GGGATTTACJ;TaGCACTGCC1.AAGCAGGACGG<a.ACC

10 '0 ~o 10 ",. T ~ C :AGTGGGGAl'TTACAl'GGCACTGCCAAAGCAGGACGGGAIoCC

: ::1:

139

A.AAAGCA--~ACCCA 10


4.12 Prospecting biomarkers for .:erebral malaria.

This study evaluated the prospects of angiogenic and parasitological variable as

biomarkers of endothelial damage and CM. Variables that were evaluated were chosen

based on their association with endothelial integrity or malaria phenotypes. Receiver

operating characteristic (ROC) curves were used to assess how sensitive and specific

these variables were in discriminating CM from VM and ProDamage from Pro Repair

(Fig 4.21-24). The best performing angiogenic factor for discriminating CM from VM

was NORI with an AVC of 0.66 (p = 0.0013,95% CI = 0.6 - 0.76, Fig 4.21). Figure

4.22 shows the performance of angiogenic factors in discriminating ProDamage

individual from ProRepair. NORl, eAl and TEK (Tie-2) to perform comparably in

discriminating ProDamage from ProRepair, but all the factors had an AVC < 0.65.

Results on the performance of parasitological indices (total parasite biomass (Ptot),

HRP2 and parasite density) evaluated for similar discriminatory abilities is shown in

Fig 4.23. The similarities in the performance of Ptot and HRP2 in discriminating CM

from VM is noteworthy (fig4.23). However, the performance of the often parasite

density estimate was disappointing (AVe = 0.54, 95% eI = 0.45 - 0.65, P = 0.37).

Figure 4.24 shows that none of the parasitological indices was a good discriminator of

ProDamage from ProRepair (AVe = 0.434, 95% el = 0.34 - 0.53, P = 0.172); HRP2

(AUe = 0.535, 95% CI = 0.44 - 0.63, P = 0.471); Ptot (AVe = 0.582, 95% ci = 0.49-

0.68, P = 0.087).

140


Derived from 1 . Specificity 00 o~ 04 Oli o. 10

10 --~--• ,.i1' 0

08..-----t-- - --~---o~ 0

ri • :::1 <

_-1' • ~ 06

Q.

>

) (' ,06 :

E 3 lit C

104 ! • 04

,J ,/ en ! ,.,

iI / <

0: Ar 102 ~

K c= ' ---.-J OO 02 0.4 06 O. HI

1 • Specificity

Figure 4. 21: Performance of angiogenic factors as a biomarker for eM

141

mfnP_9 NGRI TI~':! CAl AngIopoltnl

- - Reference line

- ------'i!""'""--_----..... _ •• k~_ ~ •••

CAl

_~I

0*114 . "ot.{'l, Il,."IM

"11m OO)tn 71

0 . ...,.,. ~~'"

Performance of MMP9. NGR1. Tie-2. CAl and angiopoietin 1 as biomarkers for CM. None of the markers performs satisfactorily as a biomarker for CM. A satisfactory marker is expected to have an Area Under the Curve (AUe) > 8.


Derived from 1 - Specificity 0.0 02 0.4 06 08 10 .• '/

"O~[---. -.~'0 i ,,".'

0.8 1 . 10.8 0 ct :1

I ~ ~ 0.6 i 10.6 ~ ~ \ 0 • 3 c 0 ~ 0.4 (. 04 !

• a:

., .I-' % I., ~ --------~OO

0:2 0.4 0.6 0.8 '.0

1 • Specificity

Source of the Curve

mmp_9 NGRI

-Tie·] CAt Anglop01lO1

- - Reference Lme

mmp_9 0.46

NGRl 0.83 001

TEK 062 001

CAl 081 002 _A~opotinl 058 008

(0.54,072)

(0.53.0.70)

(0.52.0.70)

(0.49.0.69)

Performance of MMP9. NGR 1. TIe-2. CAl and angiopoietln 1 as biomarkers for eM. None of the rnalters performs satisfactorily as a biomarker for endotheHal integrity. A satisfactory marker is expected to have an Area Under the Curve IAUC) > 8.

Figure 4. 22: Performance of angiogenic factors as a biomarker for endothdial integri~

142


Oerlved from Sensitivity -o ~ 04 o ._~ 08 • 0

r" ' JlI 0 pPala5.1. Oenlrty

HRP_: Plot

0.' .5 O' li' Referenc. lJnt ::l. < •

~{ a.

U"', ' 06 a' E I -

u 3 pParaslte d4?'f"lS11V 0 546 0372 (0445,0648) • a. HRP 2 0.253 < 00001 (0 lea, 0,338) fI) 04 '04 fI

• D10t 0.2511 < 0.0001 (0173. 0 345) n

'r 3i n 02 ~

oo-~- Performance of parasite density, HRP 2, total parasite ----- - ~o.o

biomass in predicting CM. A satisfactory marker is 0.0 02 04 06 08 10

Sensitivity expected to have an Area Under the Curve (AUe) > 8.

Figure 4. 23: Performance of parasitological indices as a biomarker for eM

143


0.2 0.4 0'" 06 10

;.l10 r! pPafJSlte density

-J'/- J HRP~

-Ptot 0.81 ;1""/' 08 0 Refetenc. line

_. J' • ::2. <

I • 0,61 j-

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)

'" 1:

04 !

0." (.J •. .. tt <

10.2 ~ Performance of parasite density, HRP 2, total parasite I

I ~- biomass in predicting endothelial damage. A

0.0-- .--.. - '-------' 0.0 satisfactory marker is expected to have an Area

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


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


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


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


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


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 sill co 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


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


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


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


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


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


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


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


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


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.

This study, therefore. recommends that malaria researchers explore other innovative

study designs that harness current advances in data sciences and statistics to enable

researchers to simultaneously focus on specific hypotheses without losing sight of the

big picture.

158


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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

Fix: +233-21-6021821513202 E-mail: ~."*..com.orv

My Refe'enc:e: OF 22

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NMlMR .... P. O. Box LO 511 LMaD,Accn 0IIInI

R£: OUr Study 1032/14-15 RESEARCH-IRS

At NOGUCHI MEMORIAl INSTITUTE FOR MEDICAl

Our DIllie! Amouo-Sakyi, PhD:

.... ung 01 .. : 11M014 RESEARCH-IRB ProIoc:oI TIUe:

At: NOGUCHI MEMORIAL INSTITUTE FOR MEDICAL

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 .. mall: 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


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


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 .... dD.: (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


,

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 ..... 110. ud )'lIIlf put .. die oily

for a d1i1c11o quaW'y 10 be pan ofllus stud)' dial cluJd sbotIld be belwealdle .. 0I11IId 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


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: If you 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 .... cII (0244 726016) II

201


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


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


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 _ it you JtilI_t

204


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


(fllle dulclllUSlt'S a sdIeduIed V1IiI. WI..." COIIIICt yw .lIome II)' pboDc. or ill pcncIIIlD

ICbeduIe IIIIIIber viIilllld to I« if you 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

DDed m lIlY repIIItI. Howevcr,Ibe lWI'ot[lllt Illlf011p1tbat may _e.. die mcarchftCClfCbj

may _ts 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


~_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 ..... lICu.rdJaD .... er nad tIM r. .. $ .......... wtnaaa .ut lip

11 ... :

1_ pmaII 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


Appendix IV: Primer for iPLEX Reaction

ExIaIded Pr1I1ICI Forwn Primer 10 Forw.d Primer Sequcnc;e Rcvcnc Primer ID Reverse Primer Sequence ID ElltcDdcd PriJDcr Scqucncc

1 .u7209I_WI_F

2 n2ai416_WI_'

3 ISIOI9994o_WI_F

• 1S2304S27 _ WI1

, n943012_WI_F

6 1II1'm22_WI_F

7 ",UI900'85_WI_F

a n207IS59_WI_F

9 n3911211_WI1

10 1I2l747S'_ WI_F

11 nQ524054_WI]

12 ",'T.M22262_WI1

Il n.591155740_ WI_F

14 ",16I31S32_ WI_F

IS rsllOO783_WI_F

16 rs56213 104_ WI_F

17 nQ070744_WI1

18 rs606630J_ WIJ

19 nI331l099_WI_F

20 1'110489181 WI F

21 .. 7604879_WI_F

22 n42J6Ol!.1 WI_F

B n3918256_WI_F

ACGlTGGATGAOOATAGTCCTAGACA(JCAC n37209I_ WI_R

ACGlTGGAroccACA1T1'CCACCACTATCC n2236416_ WUt

ACGlTGGATGTC'1OOro1'CACTGC 1S10000000_WI_R

ACGTTGGATGGGAAGACCCTCTTT ACCTTG B23CMS27 _ W 1_ R

ACGlTGGATGGGTGACAACACTGAGG'CTG n943OI2_WI_R

ACGTTGGATGAGAGACCTCMTGTCCACAG 111176722_ WI_R

ACGTTGGA1'G11CAACTCTGCTATACAC ISI000000S_WI_R

ACGTTGGATGATCAGAAAACGCACTTGCCC n207IS59_WI_R

ACGTTGGATGAGACCTAOOroCAGGACA TC n3911211_WI_R

ACGTTGGATGTTCGACGATGACGAGTTGTG rs2274US_ WI_R

ACGTTGGATGMCACAACCTGACTTTTACG nQ524054_WUt

ACGTTGGATGGACTGAGGT MAAGGACTGC 1573422262_ WI_R

ACGTTGGATGMCATGGTGAAACCCCCTCT n.59IISS740_WI_R

ACGTTGGATGGGTCATAGTTAAAGAGACCG nl683IS32_WI_R

ACGTTGGATGAGTCATCCTTGGTCATGCAC nl800783 WI_R

ACGTTGGATGTCCAGAGTGGGCTCCTT AC rs5623JI04_WI_R

ACGTTGGATGACCAGGGCA TCMGCTCTTC rs2070744 _ WI_ R

ACGTTGGATGCAAGGGCAGCACT A TCTTGA rs6066303 _ W I_R

ACGTTGGATGT AGMCGAT AAGGAGGGTGC nl3JI 3099_WI_R

ACGTTGGATGCATGMTGCMGGGTTTGTC nICNI9IBI_WI_R

ACGTTlrtiATGTGC AGC AAACGGAAACT MC .. 7604179 WI .R

ACGTTGGATGGGAGGTTTGAAAGTTGCCAG 1'14236014 WI R

ACGTTGGATGTTGACAGCGACMGMGTGG nJ918256_ WI_R

ACGlTGGATGGACCAA TGAGCAA TGGCT AC rs37209I_ WI E GTCCCAGCAGCAT

ACGTTGGATGGACACCAGACCAAGGAAGAG n2236416_ WI_E MAGGCCGAACCT

ACGTTGGATGTGCAGTCCAGCACAGMAAG .. 10199940_ WI_E GAGCCTGGGAGTCT

ACGlTGGATGATAGACMGGCGATroccTC n2J04S27 _ WI_E 1TGCCTCI1TCAGCT

ACGTTGGATGCCTGAAATTTCTCAGCCAGC n'M3OII2 _WI _ E GTGGCAGAGTGAGCC

ACGTTGGATGCACCACCATTGGGTTAACTG "'176722_ WI_E TGTCCACAGTCACTCG

ACGTTGGATGATTGCACTCCAGCCTGCCT rsI09OO585_WI_E MGGAGTCTCACTCTT

ACGTTGGATGCTAGGCAGGTCACTTCAAAC rs2071S59 _ WI_E GGAAATAGCGGGMTG

ACGTTGGATGGTCTGCAGGACGTTGGTTG .u911211_ WI_E aTTGTCTGCGGCGATGT

ACGTTGGATGGGGAGAGMTGMGGGM TC 1S22747SS_ WI_E TGGGCAAGOOCGTCGGT

ACGTTGGATGTATGGGMGCTCT ACCACAC rs2524054 WI_E TGACTTTT ACGATCATCA

ACGTTGGATGATTGCATGCCTM TAGGAAC rs73422262_WI_E GCCTMTAGGMCCA TCCA

ACGTTGGATGTATGATCTTGGCTCACTGCG rsS90SS740 _ WI_ E IIIjIGATT ACAGGCCACCA

ACGTTGGATGCTTTCAGATAGACAAAGTG rsl6831S32_WI_E GACAAAGTGAAAACAAAA T

ACGTTGGATGATCCAGCCCCTACTTTTCAG nl800783 _ WI_E ggcgGGAGGAGACAACAGA

ACGTTGGATGTCCCGAGTTCTGGGCA TTTC rsS62J3104_WI_E cas-AGCACCTTGCTCTGCAT

ACGTTGGATGCTGTCA TTCAGTGACGCACG rsl070744 _ WI E ctnCTGAGGCAGGGTCAGCC

ACGTTGGATGAAACCGGAGGCTATTTGTG rs606630J_ WI_E GGCTATTTGTGTAAATGT AM

ACGTTGGATGGACTCGT AACTTAC ACCCTC 1513313099_ WI_E aGATTTT AGA TCACCTTAACTC

ACGTTGGATGAAGATAAATGCCAAGGGGAC nl0489181_WI_E AAAGTACAGGAGATA TT A TGGG

ACGTTGGATGCTCTGTGTACCTCAGA TTGG ",7604879_ WI_E AA TT A TTTGTTCTCAT MGTTTG

ACGTTGGATGTGCTGCTTTCACTCTCACCC 1'14236084. WI.E agallTCACTCTCACCCCCGCCCC

ACGTTGGATGACAAACTGT ATCCTGGAGGG n3918256_WI_E cce<:GCCCCGGAGCCGCGGGACCA

.~ _L_


24 rs3917419_WI_' ACCil'TOOATOCiC'TCATCTC'TCACCTTTAAC rs3917419_WI_R ACGTTOGATGAAAOOGGTAGAATTACAGTC rs3917419_WI_E ... TTTAACNJATAAGAACACTG

2' ral07'73II_III_F AC'GTTOOATGAQATCGATCTGATGACAAa: .. 1077311_WI_R ACGTTGGATGTTCACACTATCa:CAGGATG .. 1077311_ WI_E -.croTGAAA TGGGAA TGATA

:Ii6 .. 1466S41_WI_F ACGTTGGATGAAGAGT AAGGGGT AGGGAAC .. 1466S48_WI_R ACGTTGGATGTCCAGTAATATCAATGGAOG .. 1466548 WI_E apaGGAGGATATA TGTGTGCA T CTTAAGTTCTGCTTTTAAAAATAT

27 11614951 WI F ACGTTGGATGTOGTTGAGCTTAAGTTCTGC n6lI49S1 WI R ACGTTOGATGGTAAGTGACCTAAACCTTGC n6U9S1 WI E A


Appendix V: iPLEX protocol

Table I: Multiplexed peR cocktail, without DNA (Same multiplexed assays, different

DNA)

.'" volume Reagent Concentration In 5 J&l (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


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