The Association of Toll-Like Receptor Polymorphisms With

THE ASSOCIATION OF TOLL-LIKE RECEPTOR POLYMORPHISMS WITH

HUMAN IMMUNODEFICIENCY VIRUS INFECTION IN NORTH AMERICANS

BARNE WILLIE

Submitted in partial fulfillment of the requirement for

the degree of Master of Science

Dissertation Advisor: Dr. Peter A Zimmerman (PhD)

Biology

Case Western Reserve University

May 2014

Case Western Reserve University

School of Graduate Studies

We hereby approve the thesis/dissertation of

BARNE WILLIE …………………………………………………………………………………………………………………

candidate for the Master in Science degree*.

(signed) Roy Ritzmann (PhD) …………………………………………………………………………………………………………………… (Chair of the committee)

Peter A Zimmerman (PhD) ……………………………………………………………………………………………………………………

Daniel Tisch (PhD) ……………………………………………………………………………………………………………………

Christopher Cullis (PhD) ……………………………………………………………………………………………………………………

(date) 21st March 2014 …………………………………………………

* We also certify that the written approval has been obtained for any proprietary material contained therein.

Dedication

I would take this precious time that I have to dedicate this work to my

Bikpela Papa - Naiyo Niniko and Bikpela Ankol - Phillip Felix Buri. I know you twos will be proud to see me through this time of my life journey. But now, I could only wish you twos were here to witness this. Miss you twos so much, and what more can I say...?

I shall see you twos one day.

III

Table of Content

List of Tables ...... VII

List of Figures ...... VIII

Acknowledgement ...... IX

List of Abbreviations ...... X

Thesis Abstract ...... XII

1.0 Introduction ...... 1

1.1. General Immunology ...... 1

1.3. Innate Immunity ...... 3

1.4. Toll-Like Receptors ...... 6

1.5. TLR1-9 properties ...... 8

1.5.1. TLR1 (cluster of differentiation 281-CD281) ...... 8

1.5.2. TLR2 (cluster of differentiation 282-CD282) ...... 9

1.5.3. TLR3 (cluster of differentiation 283-CD283) ...... 9

1.5.4. TLR4 (cluster of differentiation 284-CD284) ...... 10

1.5.5. TLR5 (cluster of differentiation 285-CD285) ...... 10

1.5.6. TLR6 (cluster of differentiation 286-CD286) ...... 11

1.5.7. TLR7 (cluster of differentiation 287-CD287) ...... 11

1.5.8. TLR8 (cluster of differentiation 288-CD288) ...... 12

1.5.9. TLR9 (cluster of differentiation 289-CD289) ...... 12

1.5.10. TLR10 (cluster of differentiation 290-CD290) ...... 13

1.6. Human TLR Groupings ...... 13

1.7. Chemistry of ligand binding ...... 14

1.8. PAMPs Recognition by TLRs ...... 15

1.9. TLR signaling pathways ...... 17

1.10. SNPs, gene expression and functionality ...... 19

1.11. SNP analysis-Genome Wide Association Studies ...... 21

1.12. TLR genes SNPs and disease association ...... 22

1.13. Thesis Objectives ...... 27

2.0 Methods ...... 28

2.1. Selection of TLR gene SNPs ...... 28

2.2. Sample preparation for genotyping ...... 29

2.3. Genotyping assay ...... 30

2.3.1. Golden Gate Vera Code Bead Express Platform ...... 30

2.3.2. Establishing the Bead Express Genotyping Assay ...... 31

2.4. Sample ...... 32

2.5. Genotype data processing ...... 33

2.6. Statistical association analysis ...... 35

3.0 Result ...... 37

3.1. Batch Effect (Plate - Plate) ...... 37

3.2. Hardy Weinberg Equilibrium Test (HWE) ...... 37

3.3. Allele frequencies ...... 37

3.4. Statistical analysis - Logistic regression ...... 39

3.4.1. Unadjusted ...... 39

3.4.2. Adjusted: Stratified by race with HIV status as covariant ...... 40

3.4.3. Haplotype analysis ...... 41

3.5. Linkage Disequilibrium analysis ...... 42

4.0 Discussion ...... 45

4.1. Logistic regression, Linkage Disequilibrium and Haplotype analysis ...... 47

4.2. Biological importance of statistical significant SNPs in this study ...... 51

4.2.1. TLR1 rs5743618 ...... 51

4.2.2. TLR1 rs5743551 ...... 52

4.2.3. TLR4 rs10759932 ...... 53

4.2.4. TLR6 rs5743810 ...... 53

4.2.5. TLR6 rs1039559 ...... 54

4.2.6 Biological summary ...... 54

5.0 Conclusion ...... 56

Reference ...... 58

List of Tables

Table 1: Differences between Innate and Adaptive Immunity ...... 2

Table 2: Innate immunity germ line encoded receptors-the pattern recognition receptors, PAMPs and function...... 5

Table 3: TLR and associated PAMPs ...... 15

Table 4: TLRs and associated adaptor recruitment ...... 18

Table 5: Summary of studies that have been done and what was found with infection/disease...... 24

Table 6: Individual demography in the study ...... 33

Table 7: Fishers Exact t-test plate to plate variation results ...... 37

Table 8: Observed allele frequency in HIV status and within each population...... 38

Table 9: Logistic regression statistical analysis results showing only significant alleles at p < 0.05 ...... 39

Table 10: Additive and Dominant genetic model test statistical analysis results showing significant alleles at p < 0.05 in AfA population ...... 40

Table 11: Additive and Dominant genetic model test statistical analysis results showing significant alleles at p < 0.05 in CA population ...... 41

Table 12: Haplotype analysis results showing only significant alleles at p < 0.05 in CA population in CA group ...... 42

Table 13: LD results showing only significant alleles at D’ > 0.8 and r2 > 0.5 in CA and

AfA population ...... 43

VII

List of Figures

Figure 1: The main cell components of the innate immune system ...... 3

Figure 2: TLR protein structure (left pane), the zoomed-in view of the LRR section

(right pane) ...... 7

Figure 3: Genome based grouping (phylogenetic tree) of human TLR ...... 14

Figure 4: TLR pathway ...... 19

Figure 5: Principle chemistry in genotyping assay (http://www.illumina.com) ...... 32

VIII

Acknowledgement

First and foremost, I would like to say a very big thank you to my advisor, Dr. Peter A

Zimmerman for all the directions and assistance throughout the course of this thesis and academic year. I very much appreciated it – Yu nambawan (you the best).

Also, a big thankyou to Dr. Rajeev Melhotra (PhD) for the great support and advice -

Yu nambawan tu (You are the best too) and to Noemi Hall – wanpela nambawan wanwok (a great colleage).

I would like to acknowledge; the PNGIMR management under Dr. Peter Siba (PhD),

Center for Global Health and Diseases, Case Western Reserve University under the direction of Dr. Peter A Zimmerman and James Kazura (MD) to whom the Global

Infectious Disease Research Training Program, John E Fogarty International, National

Institute of Health Scholarship was made possible. Thanks Dr. Peter A Zimmerman and James Kazura. Not forgetting Dr. Claire Ryan (PhD) for shaping my path.

Word of thanks extended to friends and staff of Dr. Peter Zimmerman and Dr. Aaron

Weinberg (PhD) labs at Case Western Reserve University who had helped me in one way or the other. Also, to my thesis committee, Drs. C. Cullis (PhD), D. Tisch (PhD) and R. Ritzmann (PhD).

To the lovely Jasper Family – Taryl and Suzie, Connor, Dina – may the Lord God bless your giving hands according to His riches.

Special thankyou to the two most important people in my life to whom without them I would not be, my Dad and Mum – Laikim tumas. My happiness extended to my brother Barnak and Barnabas and only sister Dahlia and my extended family.

Thanks for all your everlasting support.

List of Abbreviations aa Amino Acid

ADD Additive Genetic Model Test

AfA African Americans

ASO Allele Specific Oligonucleotides

CA Caucasian Americans

CHR Chromosome dbSNP Database for Single Nucleotide Polymorphisms

DNA Deoxyribonucleic Acid

DOM Dominant Genetic Model Test

GVS Genome Variation Server

GWAS Genome Wide Association Study

Hap-Freq Haplotype Frequency

HapMap Human Haplotype Mapping Consortium

Hap-Score Haplotype Score

HIV Human Immunodeficiency Virus hTLR Human Toll-Like Receptor

HWE Hardy Weinberg Equilibrium

LD Linkage Disequilibrium

LP Lipoprotein

LPP Lipopeptides

LPS Lipopolysaccharides

LRR Leucine-Rich Repeats

LRR-CT Leucine-Rich Repeats C-Terminal

LRR-NT Leucine-Rich Repeats N-Terminal

LSO Locus Specific Sequence

MAF Minor Allele Frequency

MTCT Mother to Child Transmission

MyD88 Myeloid Primary Differentiation Factor Protein 88

OI Opportunistic Infection

OR Odds Ratio

PAMP/s Pattern Associated Molecular Pattern/s

PCR Polymerase Chain Reaction

PGN Peptidoglycan

PRR/s Pattern Recognition Receptor/s

SNP Single Nucleotide Polymorphisms

TIR Toll/Interleukinin-1 Receptor

TIRAP/MAL Toll/Interleukinin-1 Receptor Associated Protein

TLR/s Toll-Like Receptor/s

TRIF Toll/Interleukinin-1 Receptor inducible Factor

USA United States of America

The Association of Toll-Like Receptor Polymorphisms With

Human Immunodeficiency Virus Infection in North Americans

BARNE WILLIE

Thesis Abstract

Toll-like receptors (TLR) are transmembrane receptor proteins. They form the first line of innate immunity defense by early recognition of viruses, bacteria, fungi and protozoans. Mutations in TLRs have been shown to be associated with either protection against or susceptibility to and furthermore in disease severity and progression. Association study was performed on 41 SNPs in 8 TLR genes with HIV status in North American population.

276 samples were collected. The race distributions are; AfA (n = 150, HIV+ = 102,

HIV- = 48) and CA (n = 102, HIV+ = 54, HIV- = 48).

DNA was isolated and genotyped using GoldenGate BeadXpress genotyping assay.

Statistical analysis was performed using Genome Studio, PLINK and R Studio software. This study showed that the association might be race specific, where 5

SNPs and 4 haplotypes were found significant (p < 0.05) in the CA and only one SNP in the AfA population.

XII

1.0 Introduction

1.1. General Immunology

All organisms, humans and animals (mammals), insects and plants have some form of mechanism that blocks and eliminates invading microorganisms and substances that cause harm, disease and death. In mammals, when a foreign microorganism enters the body, it has to make its way through different lines of defense. In humans, there are three defined lines of defense. The first line of defense is the physical barrier that prevents physical entry of pathogenic microorganisms which is primarily made up of the dry epidermis (skin), ciliated epithelial cells in the respiratory tract and the vascular endothelium[1]. The second line of defense is called the chemical barrier which is made up of the mucosal surfaces with its antimicrobial secretions and other chemical secretions of the epithelial lining tissues of the nose, throat, oesophungus and the intestinal walls[1]. When pathogen gets pass the first and second line of defense, the third line of defense is activated which is solely cell mediated.

The cellular defense activates two responding immune systems, either innate or adaptive immunity. The innate immunity is normally the first line of cellular defense, which afterwards has a cross interaction priming the adaptive immunity response,

(Table 1)[2-4]. The innate immunity relies on evolutionarily ancient germ line- encoded receptors, pattern-recognition receptors (PRRs) that recognize highly conserved microbial molecular structures, known as pathogen associated molecular patterns (PAMPs)[3-5]. The innate immunity does not confer long term immunity

against pathogen but it is the dominant immune system of host defense in initial response[6].

There are four well defined innate immune receptors in human; the Toll-like

Receptors (TLRs), Nucleotide-binding Oligomerization domain (NOD) like Receptors

(NLRs), Retinoic acid-inducible gene (RIG)-like Receptors (RLRs), and C-type Lectin

(CTL) Receptors (CLR) Table 2[7-9].

These receptors recognize specific components on pathogens and act as molecular switches by either promoting or inhibiting innate immune activation. Of the four immune receptors, TLR has gained more interest and has been extensively studied[10-12] in genome wide association studies (GWAS) for disease protection/resistance, susceptibility, severity and progression.

Table 1: Differences between Innate and Adaptive Immunity

Characteristics Innate Immunity Adaptive Immunity Response is practically Response is practically against foreign against foreign Self/Non-self- microbes/substances, microbes/substances. discrimination recognized by germ-line encoded receptors. Response is within hours of Response takes 3-5 days, Response time first invasion of foreign clonal development of microbes/substances. cells. Response to variety of Highly specific. invading Specificity microbes/substances- PAMPs. Little or no memory of past Response by memory to Memory exposure. prior exposure, 90-100% effective.

1.3. Innate Immunity

Innate immunity plays a crucial defensive role during initial infection by early recognition and triggering of pro-inflammatory response towards invading pathogens and substances. The innate immune response relies on recognition of

PAMPs through PRRs[11, 12] for example TLRs (Table 2 and 3).

The innate immunity composed of mast cells, monocytes/macrophages, neutrophils, eosinophils, basophils, natural killer cells (NK) and dendritic cells (DC) (Figure 1) [13].

Innate Immunity (rapid response) Adaptive Immunity (slow response)

Figure 1: The main cell components of the innate immune system (adopted from Dranoff A, 2004[13])

Upon PAMP recognition, PRRs expressed on the cell surface or within the cells cytoplasm transmit the signal to the host cascades of immune system warning the presence of a foreign substance and triggering pro-inflammatory and antimicrobial responses by activating a multitude of intracellular signaling pathways. PRR-induced

signal transduction pathways finally result in the activation of gene expression and production of a broad range of molecules, cytokines, chemokine, cell adhesion molecules, and immune-receptors[14] that coordinates the early host response to infection and at the same time providing an important link to recruiting adaptive immunity.

The adaptive immunity is called into action after the antigen presenting cells (APCs) like macrophage and other phagocytic cells presents the invading particle to it[10-

12]. The adaptive immunity primes the production of cytokines and antibodies that opsonize or directly kill the invading microorganism[10-12] and ultimately eliminating the pathogen and generating immunological memory.

Table 2: Innate immunity germ line encoded receptors-the pattern recognition receptors, PAMPs and function.

PRR PAMPs Microbes Adaptive response recognized induced Transmembrane Microbes cell wall Gram positive Th1, antibody (Ig2), components. and negative CD8+ T cell TLR1, 2, 4, 5, 6 bacteria, responses[15, 16]. fungi, protozoa. Fungal cell wall Fungi Th17 and antibody CLR components, beta responses[17, 18]. glucan.

Cytosolic Bacteria cell wall Bacteria Th2 and antibody components, LPS, responses, PGN, pores forming potentiates Th1, 2, toxins, bacterial 17 and antibody secretion systems, responses initiated also DNA. by TLR, may factor Th17 responses [19, NLR 20] Bacteria Required for robust T cell dependent hypersensitivity, may potentiate Th2 driven antibody responses[19, 20].

RNA RNA virus Sufficient to induce CD4+ T cell and RLR CD8+ T cells responses[21] Nucleic acid Gram positive Th1, antibody (Ig2), sequence. and negative CD8+ T cell TLR3, 7, 8, 9 bacteria, responses[15, 16]. viruses, fungi, protozoa. Pattern recognition receptors (PRR), Toll-like receptor (TLR), C-type Lectin-like receptor (CLR), , nucleotide-binding oligomerization domain receptor (NLR), T helper cells (Th1, 2, 17), Immunoglobulin (Ig2), Deoxyribonucleic acid (DNA), Ribonucleic acid (RNA), lipopolysaccharides (LPS), Petidoglycan (PGN).

1.4. Toll-Like Receptors

TLRs are type 1 trans-membrane PRR proteins characterized by presence of leucine- rich repeat (LRR) motifs in the extracellular domain that mediates PAMP recognition[22, 23], and a hydrophilic LRR at the extracellular and a hydrophobic

Toll/Interleukin-1 Receptor (TIR) domain located within the cells cytoplasm[22]. The extracellular domain LRR sequence forms a horse-shoe structure with LRR-C terminal

(LRR-CT) and LRR-N terminal (LRR-NT) at each end. The LRR-CT connects the LRR domain to the transmembrane. At the extreme extracellular end is the LRR-NT which functions in PAMP recognition (Figure 2) [24, 25]. The LRR and TIR are connected by a trans-membrane protein that carries the signals from the extracellular LRR into the intracellular TIR domain. For, TLRs expressed in the cell cytoplasm on endosomal or lysosomal vacuoles, the transmembrane transmission of signal is transferred from within the endosome or lysosome vacuole internal compartment out into the cells cytoplasm.

TLRs are generally expressed on almost all the immune effector cells; macrophages, mast cells, neutrophils, DCs, basophils, eosinophils and NKs.

Figure 2: TLR protein structure - left, the zoomed-in view of the LRR section - right (adopted from http://www.bio.indiana.edu and http://www.nau.edu)

TLR was first discovered in drosophila in the early 19 century and later in mammals other than humans and later in humans in 1992[22, 23]. In 1997, TLRs was first shown to have clinical importance by recognizing and eliminating pathogens and harmful particles in humans. From this discovery, TLR has gained much interest in its involvement in medical and immunological fields where scientists were more interested in better understanding their immunological role[26, 27]. At least 13 mammalian TLR have been identified to date. Of the 13 TLR, 10 of them are found in human. Of the 10 human TLRs, TLR10 ligands and function remained yet to be determined[26, 27]. The locality of expression of the TLRs varied with TLR1, 2, 4, 5, 6 and 10 where they are expressed on the cells surface and are excited by lipids, lipoproteins (LPP), peptidoglycans (PGN) or exotoxins from extracellular bacteria and fungi walls[26, 27].

TLR3, 7, 8 and 9 detect bacterial and viral nucleic acids and are found in the cells cytoplasm on endosomal and lysosomal vacuoles[26, 27]. TLRs are strategically

located on or within the cells that expresses them to recognize their specific PAMPs.

Extracellular TLRs (expressed on cell surface) recognize exotoxins and proteins found on microbes cell walls. TLRs that recognize nucleic acids are located within the cytoplasm of the cells on the endosomal and lysosomal vacuole membrane where they scan for nucleic acid after the microbes have been phagocytized and digested exposing the nucleic acids.

TLRs have now been recognized as the primary sensory protein molecules that enable the first line of innate immune surveillance system in detecting the presence of foreign pathogens/substances and to rapidly mounting immune defense[28, 29].

1.5. TLR1-9 properties

1.5.1. TLR1 (cluster of differentiation 281-CD281)

TLR1 is expressed by macrophages and neutrophils[30]. Found to be highly expressed in spleen, ovary, peripheral blood leukocytes, thymus and small intestine[30] www.uniprot.org. Its primary response is to extracellular pathogens through recognition of PGN and triacyl LPP, organic molecules that make up the gram-positive bacterial cell wall. By heterodimer formation with TLR2, recognition of

PAMPs are enhanced[31-33]. TLR1 contains 19 LRR, 1 LRR C-terminal (LRRCT) and 1

TIR. The gene is about 786 amino acid (aa) long (2358 bp) and has a mass of 90291

Da www.uniprot.org, www.genecards.org . The gene is found on chromosome 4, cytogenetic band 4p14 with a 66141 base (38792298 to 38858438) sequence on the minus strand[34] www.uniprot.org, www.genecards.org,

http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov. It is located

33113 bases away from TLR6.

1.5.2. TLR2 (cluster of differentiation 282-CD282)

TLR2 is expressed by monocytes/macrophages, DCs, B cells, and T cells where it is more highly expressed in bone marrow, lymph node and in spleen[34] www.uniprot.org. Its primary response is to extracellular pathogens through the recognition of cell membrane protein make-up, PGN, LP and triacyl LPP found on gram-positive bacteria. Additionally, TLR2 recognize and triggers immunity in the presence of porins on gram-positive bacteria and mannan proteins found on fungi – candida and yeast. An enhanced PAMP recognition response is usually achieved by heterodimer formation with either TLR1 or TLR6.

TLR2 is about 784 aa in length. TLR2 contains 19 LRR, 1 LRRCT and 1 TIR and has a mass of 89838 Da www.uniprot.org, www.genecards.org. The gene is found on chromosome 4, cytogenetic band 4q31.3 with a 21803 base (154605441 to

154627243) sequence on the plus strand[34] www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov.

1.5.3. TLR3 (cluster of differentiation 283-CD283)

The primary respond of TLR3 is towards double stranded RNA found in viruses. TLR3 is expressed abundantly in placenta and pancreas www.uniprot.org.

TLR3 is about 904 aa long and contains 22 LRR, 1 LRRCT, 1 LRRNT and 1 TIR and has a mass of 103829 Da www.uniprot.org, www.genecards.org. The gene is found on

chromosome 4, cytogenetic band 4q35.2 with a 15950 base (186990306 to

187006255) sequence on the plus strand www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov.

1.5.4. TLR4 (cluster of differentiation 284-CD284)

TLR4 is highly expressed in placenta, spleen and peripheral blood leukocytes www.uniprot.org. TLR4 has also been detected in monocytes, macrophages, DC and several other T-cells. The primary involvement of TLR4 is in the recognition of LPS, glycoprotein and other proteins found on gram-negative bacteria including fungi. TLR4 is 839 aa in length and contains 18 LRR, 1 LRRCT and 1 TIR and has a mass of 95680 Da www.uniprot.org, www.genecards.org. The gene is found on chromosome 9, cytogenetic band 9q33.1 with a 13309 base (204666460 to

120479768) sequence on the plus strand[34] www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov.

1.5.5. TLR5 (cluster of differentiation 285-CD285)

This gene product is expressed in monocytes and granulocytes www.uniprot.org and recognizes bacterial flagellin, a principal component of bacterial flagella. It is found to be highly expressed in ovary and peripheral blood leukocytes www.uniprot.org.

TLR5 is 858 aa long and contains 15 LRR and 1 TIR domain and has a mass of 97834

Da www.uniprot.org, www.genecards.org. The gene is found on chromosome 1, cytogenetic band 1q41 with a 33877 base (223282748 to 223316624) sequence on

the minus strand www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov.

1.5.6. TLR6 (cluster of differentiation 286-CD286)

TLR6 receptor can form heterodimer with TLR2 to mediate cellular response to gram-positive bacteria and fungi by recognizing diacylated LPP. TLR6 has been detected in monocytes, and dermal micro-vessel endothelial cells www.uniprot.org.

TLR6 is 796 aa long and contains 19 LRR, 1 LRRCT and 1 TIR and has a mass of 91880

Da www.uniprot.org, www.genecards.org. The gene is found on chromosome 4, cytogenetic band 4p14 with a 33114 base (38825325 to 38858438) sequence on the minus strand[34]. It is 33113 base upstream from TLR1 www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov.

1.5.7. TLR7 (cluster of differentiation 287-CD287)

The primary response of TLR7 is the viral single stranded RNA (ssRNA) a common feature of viral genomes which are internalized by macrophages. TLR7 is found localized in endoplasmic reticulum, endosome and lysosome. It has been found to be expressed in brain, placenta, spleen, stomach, small intestine, lung and in DC www.uniprot.org. It lies in close proximity to TLR8 on chromosome X. Together with

TLR8, they response to ssRNA[35].

TLR7 is 1049 aa long and contains 27 LRR and 1 TIR domain and has a mass of 120922

Da www.uniprot.org, www.genecards.org. The gene is found on chromosome X,

cytogenetic band Xp22.2 with a 23298 base (12885202 to 12908499) sequence on the plus strand www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov. It is 16240 bases away from TLR8.

1.5.8. TLR8 (cluster of differentiation 288-CD288)

The primary response of TLR8 is viral ssRNA. This receptor is predominantly expressed in lung and peripheral blood leukocytes www.uniprot.org. TLR8 and TLR7 recognize ssRNA. TLR8 has been found to be expressed in brain, heart, lung, liver, placenta, monocytes, and DC www.uniprot.org. It is 1041 aa in length. It contains 23

LRR, 1 LRRCT domain and 1 TIR domain and has a mass of 119828 Da www.uniprot.org, www.genecards.org. The gene is found on chromosome X, cytogenetic band Xp22.2 with a 16550 base (12924739 to 12941288) sequence on the plus strand. www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov. It is 16240 bases upstream from TLR7.

1.5.9. TLR9 (cluster of differentiation 289-CD289)

TLR9 primary response is to non-methylated CpG motif on bacterial nucleic sequence[14, 36-38]. It is found to be highly expressed in spleen, lymph node, tonsil and peripheral blood leukocytes, especially on DC www.uniprot.org. It is 1032 aa in length and contains 26 LRR and 1 TIR and has a mass of 115 860 Da www.uniprot.org, www.genecards.org. The gene is found on chromosome 3,

cytogenetic band 3p21.3 with a 5084 base (52255096 to 52260179) sequence on the plus strand www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov.

1.5.10. TLR10 (cluster of differentiation 290-CD290)

TLR10 ligands are yet to be defined. It is found to be highly expressed in spleen, lymph node, thymus, and tonsil www.uniprot.org. It is 811 aa in length and contains

15 LRR, 1 LRRCT and 1 TIR and has a mass of 94 964 Da www.uniprot.org, www.genecards.org. The gene is found on chromosome 4, cytogenetic band 4p14 with a 10752 base (38773860 to 38784611) sequence on the minus strand www.uniprot.org, www.genecards.org, http://www.ncbi.nlm.nih.gov/SNP, http://hapmap.ncbi.nlm.nih.gov.

1.6. Human TLR Groupings

The locality of human TLR genes on the different chromosomes have been determined and are as follows: TLR1 and TLR6 map very close to 4p14[39-41].

TLR2 and TLR3 map to 4q32 and 4q35, respectively; TLR4 and TLR5 map to 9q32-33 and 1q33.3, respectively[39]. TLR7 and TLR8 are located in tandem in Xp22, whereas

TLR9 maps to 3p21.3[41, 42].

Genomic analysis of TLRs amino acid sequences revealed ﬁve subfamilies based on amino acid similarity and are: TLR2, TLR3, TLR4, TLR5, and TLR9. The TLR2 subfamily composed of TLR1, TLR2, TLR6, and TLR10; the TLR9 subfamily composed of TLR7,

TLR8, and TLR9; and the others are TLR3, TLR4 and TLR5 (Figure 3).

Sequencing analysis showed that there is 69.3% identity in overall amino acid sequence within each subfamily. TIR domain of all receptors is highly conserved, with over 90% aa similarities within and across subfamilies in humans. The transmembrane protein is also highly conserved within and across subfamilies. This pointed to the fact that TLRs topological domains are an inherited property of mammalians genome[40, 43, 44].

Figure 3: Genome based grouping (phylogenetic tree) of human TLR (adopted from Takeda K et al, 2003[43])

1.7. Chemistry of ligand binding

The C-terminal (Figure 2) functions in the transmembrane signal transduction into the cytoplasm. The N-terminal is the site that undergoes N-linked glycosylation which results in binding of TLR LRR to lipids, lipoproteins and other organic particles and initiate the PAMP recognition phase. The recognition of the PAMPs takes place with the biochemistry of glycosylation and phosphorylation occurring at the LRR C- terminal[45-49].

1.8. PAMPs Recognition by TLRs

TLRs like other PRR protein molecules, respond to certain conserved regions found on pathogenic microbe protein and genomic makeup. A single TLR can respond to more than one organism (Table 3)[50]. For example TLR2 can respond to Neisseria bacteria (Gram negative bacteria) as well as Candida albican (fungi).

Table 3: TLR and associated PAMPs

Location TLR PAMP Organism Origin Reference on cell Surface Triacyl LPP Bacteria and [26, 27, 51- 1 (Lipopeptides) mycobacteria 54] Surface Not determined, HCMV, HSV-1, [26, 27, 51- Hemaglutinin protein, Measles viruses, 57] Glocoinositolphospholi Trypanosome sp., pids (GPI), Fungi (candida),

Glucoronoxylomannan, Cryptococcus Zymosan, neoformans LPA, Saccromyces Porins. ceravisiae, Phospholipomannan, Mycobacteria, 2 Triacyl LPP,LTA, PGN, Neisseria sp., Candida albicans Diacyl LPP, Gram positive bacteria, Group B streptococcus,

GPI-mucin, β-Glycan, Mycoplasma sp., Envelop glycoprotein, Protozoa, Fungi, Lipoprotein. Bacteria and Mycobacteria Lysosome/ dsRNA Viruses [26, 27, 54, 3 Endosome 58, 59]

Surface Envelope glycoprotein Respiratory syncytial [26, 27, 54, (proteins), virus (RSV), Mammary 60-62] Tumor Virus (MTV), LPS, Gram negative bacteria, 4 Glucoronoxylomannan, Cryptococcus, neoformans, Mannan, Candida albicans, GPI, Trypanosome sp., HSP70 Human (Host), Surface Flagellin, Flagellated bacteria, [26, 27, 54, 5 63] Surface Diacyl LPP, Gram positive bacteria [26, 27, 51- as Mycoplasma sp., 54] 6 LTA, Streptococcus, Zymosan, Saccromyces ceravisiae, Lysosome/ ssRNA RNA viruses, [26, 27, 54, 7 Endosome 59, 64] Lysosome/ ssRNA RNA viruses, [26, 27, 54, 8 Endosome 59, 64] Lysosome/ CpG DNA Gram [26, 27, 54, Endosome (unmethylated) negative/positive 59, 65] bacterial and viral DNA, mycobacteria, 9 Viruses, parasite (protozoans),

Hemazoin, Plasmodium sp., 10 Surface Unknown Unknown [26, 27, 54]

Heterodimer/Combination Surface Triacyl LPP, Bacteria and [27, 51-54] 1/2 mycobacteria Surface Zymosan, Mycoplasma sp., [27, 51-54] LTA, Saccromyces 2/6 Diacyl LPP, LTA, cerevisiae, Group B streptococcus, Surface Glucoronoxylomannan, Trypanosome sp., [26, 27, 54] 2/4 GPI Cryptococcus neoformans, 7-8 Endosome ssRNA RNA viruses [26, 27, 54]

1.9. TLR signaling pathways

LRRs are the key motifs responsible for PAMPs recognition[15]. Upon binding to associated ligands, LRR recruit associated adaptor molecules (Table 4) [66-68] to their intracellular signaling domain leading to the activation of several kinases[15] that finally result in the activation of Necrosis Factor-kappa B (NF-κB), Cytokinin

Specific Binding Protein-Mitogen Associated Protein Kinase (p38-MAPK), Jun N-

Terminus Kinase (JnK) and interferon reactivating factor (IRF)[69] which are transferred into the nucleus, priming immunological activities such as inflammation, proliferation, regulation and survival (Figure 4)[15]. These adaptor proteins cause the phosphorylation of NF-κβ inhibitor, which frees the transcription factors NF-κβ, c-Jun

N-terminal kinase (JNK) and Mitogen-activated protein kinases (p38-MAPK) to enter the nucleus. NF-κB, JnK and p35-MAPK promotes the transcription of many inflammatory response genes including interleukins, tumor necrosis factor (TNF) and

INF[66-68]. Cytokines like interleukins and antimicrobial peptides (AMP) - beta defensins and cathelictins are released resulting in the opsonosis or direct killing of the pathogens. Opsonosis of the pathogens tags the pathogens and or harmful substances to be engulfed and destroyed by phagocytes or reactive intermediates released by leucocytes. The phagocytes then present the digested microbial fragments on their surfaces which are detected by the B and T cells. This leads to further immune activation by forming a bridge to the adaptive immune system. This activates the adaptive immune system for the production of antigens that are specific to the invading pathogenic microbe or harmful substance[15].

Table 4: TLRs and associated adaptor recruitment

PRR PAMP Adaptor TLR1 Bacterial triacyl LPP MyD88/TIRAP TLR2 Bacterial glycolipids MyD88/TIRAP Bacterial LPP Bacterial LP Bacterial LTA Fungal zymosan TLR3 Viral dsRNA TRIF/MyD88 Viral polyinosinic-polycytidylic acid ⱡ TLR4 Bacterial LPS MyD88/TIRAP/ Heat Shock Protein 70 and 90 &TRIF/TRAM Fibrinogen Heparan sulfate ⱡ Hyaluronic acid ⱡ TLR5 Bacterial flagellin MyD88 TLR6 Mycoplasma diacyl LPP MyD88/TIRAP TLR7 Viral ssRNA MyD88 Imidazoquinoline ⱡ Loxoribine ⱡ Bropirimine ⱡ TLR8 Viral ssRNA MyD88 TLR9 Bacterial CpG DNA MyD88 TLR10 Unknown Unknown ⱡ Synthetic homolog of the naturally occurring proteins/compounds-TLR ligands

Figure 4: TLR pathway

1.10. SNPs, gene expression and functionality

SNP is a position in the genome where certain individuals/populations in an area or country etc have one nucleotide whilst others have a different nucleotide. For example, at a certain locus in the gene DNA sequence, some individuals/populations in an area or a country etc will have Cysteine (C) nucleotide whilst others would have

Thymine (T). SNPs are variations that occur more than 1% in a population.

A SNP is defined as a major allele frequency (MAF) when the allele variation in certain population occurs at a frequency of greater than 1.0-5.0%[70, 71]. It is estimated that approximately 30 million SNPs exist in the human genome[72].

In the last decade, human genome sequence has shifted genomic research efforts towards understanding how sequence variation contributes to the expression, functionality and regulation of proteins and more so to understand sequence variation and its association to diseases. Efforts are ongoing to understand SNP genetic diversity in different human populations in the hope to identify genomic regions that predispose or prevent human beings to common disorders such as obesity, diabetes, inflammatory bowel disease (IBD) syndrome and infectious diseases.

Genome Wide Association Study (GWAS) is usually performed to determine whether and how SNPs correlate to specific clinical conditions. It is important to note that, it may not be just a single SNP that has the overall phenotypic expressivity but many hundreds of thousands of SNPs acting together to give a phenotypic trait. GWAS is a complex approach and remains a costly undertaking.

SNPs within the coding region (exons) are believed to potentially alter the coding sequence of the gene, protein expression and ultimately protein function. However, studies have shown that variants outside of exons can also affect gene function by promoting or suppressing protein expression and function. The completion of human genome sequence allows a more detailed look at the variation between individuals and their effects.

Genomic analysis studies have shown that SNPs can have the following effects[73-

75];

(1): up regulation of gene expression, high transcription and expression resulting in high responses,

(2): down regulation of gene expression, lower cytokines and chemokine production,

(3): structural instability,

(4): altered structural orientation,

(5): altered structural conformation,

(6): early termination of protein synthesis,

(7): late termination of protein synthesis, and

(8): affinity.

These modifications in genes resulted in abnormal responsiveness to ligands, which the downstream effect leads to protection, susceptibility, severity and progression of diseases [76].

Having mentioned all these, it is suggestive that high-throughput the GWAS approach may not allow us to understand the mechanism by which all SNPs cause their effect, but its application will allow to some degree in confirming the association results with a measurable effect on gene function. This can be employed as biomarkers in clinical diagnosis. By studying SNP profiles (eg: haplotypes) associated with a disease phenotype relevant gene pattern associated with a disease may be revealed.

1.11. SNP analysis-Genome Wide Association Studies

In 2001, over 1.5 million SNPs had been identified in the human genome and were made available in many public databases[77] http://www.ncbi.nlm.nih.gov/SNP. This has opened the possibility of doing association genetic studies. GWAS are normally

done with the aim to infer that having a particular SNP in the candidate gene leads to certain outcome of interest[77-79].

GWAS can be done in two ways;

1) assumption which is more focused on the world population rather than on a certain region, country or population in having a specific predisposition genetic factor to the outcome of interest, and

2) assumption which is more region, country and population specific. It is usually developed from a prior knowledge about the predisposing genetic factor. This approach is more target specific than the first approach.

However, GWAS interpreting association studies should not conclude causality

(cause and effect) but just an association. For causality, interpretation needs further and advanced studies to support and prove it. This can be done by observing those who had the genetic factor of interest and how likely the outcome can be reached over time. Also, in vivo assays can be done through gene silencing and knockout techniques in animal models.

1.12. TLR genes SNPs and disease association

There has been increased interest in identifying TLR SNPs and making association with diseases[80-82]. Of the 10 TLR identified in human, TLR4 SNPs has been the most studied, particularly SNPs Argenine299Guanine (Arg299Gua) and

Thymine399Isoleucine (Thr399Iso)[82]. Arg299Gua and Thr399Iso SNPs are missense mutations at nucleotides +896 and +1196 respectively that impact the extracellular domain and show phenotypic differences in LPS responsiveness.

It is very well established that multiple SNPs along the gene coding region or across the whole gene can be involved to a particular phenotype. So the design that is often followed in GWAS is to test and analyze multiple locus SNPs on a gene by doing haplotype analysis. Haplotype analysis gives extra strength to analysis of individual

SNP with the following advantages[77, 78, 83], common genetic variation can be structured into haplotypes within blocks of strong linkage disequilibrium (LD) and the functional properties of a protein can be determined by the linear sequence of amino acids corresponding to DNA variation on a haplotype.

Many candidate TLR genes SNPs have been investigated for a possible association with infectious diseases where several are tabulated in Table 5.

Table 5: Summary of studies that have been done and what was found with infection/disease

(Susceptibility) TLR Infections/Diseases Association Organ dysfunction, death, and gram-positive Yes[84] infection in sepsis. Prostate cancer risk (Very large sample sizes are Yes (There is needed for casual relationship). trend)[85] Decreased incidence of leprosy. Yes (Protective)[86] 1 Susceptibility to recurrent urinary tract infection Yes [87-89] (UTI/s) and endomeritis. Protection from pyelonephritis. (Protective) Malaria susceptibility. Yes[90] Increased risk of developing tuberculosis (TB). Yes[91]

Increased asymptomatic bacterial infection (ASB) Yes[92] risk. Decreased lipopeptide-induced signaling. (Protective) Susceptibility to systolic blood pressure (SBP) in Yes[93] cirrhotic patients with ascites, microsatellite in TLR2. Extra-pulmonary TB. Yes[94-96] Affect hepatitis C virus (HCV) viral loads and Yes[93, 97] increase the risk for Hepatocellular Carcinoma (HCC) in HCV genotype-1 infected patients. 2 Bacterial (Gram-positive) infection in newborn Yes[98] infants. Acute/self-remitting sarcoidosis. Yes[99] Susceptibility to SBP in cirrhotic patients with Yes[93] ascites. Clinical relevance in patients with major trauma Yes[92, 94, 100] with sepsis. Endometritis and UGTI. Significant in African Yes[89] American race. Vaccine-induced immune response. Yes[101]

Measles-specific humoral and cellular immunity and Yes[102] 3 production of cytokines.

Confers immunologically mediated protection from Yes (Protection) HIV-1. [103] Immunology responses. Yes[104] Haplotype was associated with hepatitis B virus Yes, when (HBV). analyzed as haplotype[105] Susceptibility to HCV infection. Yes[106]

Increased risk of Chlamydia trachomatis (C. Yes[88] trachomatis) infection and pelvic inflammatory disease (PID) in African American. Genetic variations in TLR4 genes in relation to Yes[107] atopic manifestations. Modulate risk of liver fibrosis Yes[108] Trichomas vaginalis serostatus and risk of prostate Yes (Trend cancer (large study required). observed) [85, 109] Severity of HBV chronic infection. Yes[110, 111] Surgical patients and association with risk of Yes[60, 112] infection, clinical course and outcome and severity of severe infections and sepsis. Increased ASB risk, associated with decreased Yes[92] lipopeptide-induced signaling. 4 Progression to advanced fibrosis in patients with Yes[108] chronic HCV (Caucasian and African Americans) Susceptibility to UTIs, altered risks of acquiring rUTI Yes and pyelonephritis. (Susceptibility/ Associated with protection from rUTI, but not Protective) [87] pyelonephritis. Risk of tubal pathology following C. trachomatis Yes (Needs large infection. study) [113] Active TB. Yes[114] HIV-1 peak viral load and HIV disease progression. Yes[115] Occurrence of serious infections. Yes[116] Mother-to-child transmission (MTCT) of HIV-1. Yes[117] Variability in airway responsiveness to inhaled LPS Yes[118] in humans. Poor outcome in an intensive care unit (ICU). Yes[60]

Vaginal concentration of anaerobic Gram-negative Yes[119] rods, Prevotella, Bacteroides and Porphyromonas. T.vaginalis and prostate cancer risk. Yes (Trend of association) [109] Contributes to translational regulation of TLR4. Yes[120] Periodontitis. Yes[120] Pertussis toxin-specific immunoglobulin G response Yes[121] after whole-cell pertussis vaccination. Different types of cancer. Yes[122]

Measles-specific humoral and cellular immunity and Yes[102] 6 production of cytokines. Infection and the natural course of disease. Yes[123] Aetiology of asthma and related disorders. Yes[124] 7 Systemic lupus erythromytes (SLE) susceptibility in Yes[125] women of European descent.

Activated bacterial phagocytosis and its role in the Yes[126] modulation of TLR8-dependent microbicide response of infected macrophages. TB susceptibility in males. Yes[127] Systemic lupus erythromytes (SLE) susceptibility in Yes[125] 8 women of European descent. Susceptibility to asthma and related atopic Yes[124] disorders. Susceptibility to pulmonary TB. Yes[127] Inflammatory Bowel Disease (IBD) Yes[128] MTCT of HIV-1. Yes[117, 129]

Asthma susceptibility. Yes[130, 131] SLE in Taiwanese, Chinese patients. Yes[125, 132, 133] Risk of incidence and mortality in prostate cancer in Yes[134] Sweden cohort. 9 Earlier spontaneous HbeAg seroconversion. Yes[110]

Direct interaction between T. gondii and pro- Yes[135] inflammatory responses to severe pathologies in ocular disease.

Renal and immunologic disorders. Yes[136] Risk of tubal pathology following C. trachomatis Yes (There is a infection. trend) [113] MTCT of HIV-1. Yes[117, 129] Susceptibility to infections. Yes[137] HIV-1 disease progression. Yes[115, 138, 139]

Presented in Table 5 showed the importance of knowing the genetic make-up of an individual, population or race which can provide some knowledge into better understanding the predisposing factors that an individual have towards certain diseases and disorders. Knowing the genetic make-up can help physicians predict the disease that a person is predisposed and thus can help in directing medication.

1.13. Thesis Objectives

The project was aimed at answering the question of whether having certain TLR

SNPs have any associations with HIV infection status.

2.0 Methods

2.1. Selection of TLR gene SNPs

A panel of TLR genes were selected based on the association of the TLR gene SNPs with the outcome that we are interested in, susceptibility to or protection from infections/diseases and HIV. Thereby, our literature search was focused on the effect of the selected SNPs with disease outcomes in HIV patients and other infectious and to some extent, non-infectious diseases. Literature searches were done in Medical

Literature Analysis and Retrieval System Online for Public (MEDLINE /PubMed) and other related websites http://www.ncbi.nlm.nih.gov/pubmed, http://scholar.google.com. The search was done with these following inputs; [hiv

AND tlr snps], [opportunistic infections AND hiv AND tlr snps], [tlr snps AND infectious diseases]. Articles showing association with TLR gene SNPs and diseases were referenced.

Reference sequence number (rs#), assigned to the SNPs in the data base for single nucleotide polymorphism (dbSNP) and HapMap database used in those articles were noted and a further search was done to confirm the SNPs, and additionally to obtain other biological and clinical information. The additional information gathered was the position of the gene on the chromosome, position of the SNP within the gene, the resulting amino acid change and the functionality of the SNPs. Within the gene, the region to which the SNP lays whether in the untranslated, promoter, enhancer, intron or the exon region was noted.

We then looked up the frequency of occurrence of these SNPs in the two population of interest, the Caucasian Americans (CA) and the African Americans (AfA). For CA

population, Caucasian European descendant was used. For AfA population, AfA in

South West America descendants was used. In collating the SNPs frequencies, we made sure that both populations were run in the same project for SNP frequency determination. A SNP was accepted if the number of individuals used in the initial sequencing panel was greater than 20 individuals and the frequency was greater than 0.1 (10%). For example, in a population of 100 individuals, 10 of them should be positive for that particular SNP. This criterion was set to ensure sufficient power to observe the minor allele frequency in our genotyping assay. The reason for having multiple SNP in a gene for our analysis was to enable us to perform haplotype analysis to see if several/sets of SNPs on a particular gene were associated with a particular phenotype. Additional, LD analysis was performed to infer better interpretation of haplotypes.

2.2. Sample preparation for genotyping

Genomic deoxyribonucleic acid (gDNA) samples were prepared according to the manufacturers’ protocol (Illumina,USA). The requirements for Illmina Golden Gate

Bead Express Assays are as follows; 15µL of 50-100ng/µL gDNA freshly isolated from either newly or archived biological samples. If the extracted gDNA does not have the required concentration, then the sample was concentrated from a larger volume to the final volume of 15 µL with the expected concentration of 50-100ng/µL gDNA.

Concentration measurements were done using nanodrop and Qubit. The quality of the DNA was determined using the 260/280 ratio. A ratio of 1.6-2.0 infers that the sample quality is good http://www.illumina.com.

Quality and accurate concentration is vital in this particular assay because some of the queried SNPs can occur in multiple copies and thus can have adverse effects on the performance of the assay. Studies have shown that polyploid genomes can generate cluster patterns whereby the software as well as the researcher may find it difficult to interpret http://www.illumina.com.

2.3. Genotyping assay

The Golden Gate Vera Code Bead Express Platform Genotyping Assay[140] http://www.illumina.com was selected for this project based on a number of factors, the compatibility of the assay to the number of SNPs planned to be genotyped, the cost of running the samples and the user friendly approach of the assay. Also,

Golden Gate assay is robust and reagents are generic and can be applied to a wide variety of organisms http://www.illumina.com.

2.3.1. Golden Gate Vera Code Bead Express Platform

The assay is based on bead fluorophore/fluorescence reading. Bead Express output is normally a reading obtained where the PCR products are labeled with illuminating dye at the extension end of the amplicon. Each of the illuminating dyes is tagged with different Illuminocode address sequence. The products are hybridized to small glass rods that are coupled to the oligonucleotides complementary to the

Illuminocodes. The beads are then washed, re-suspended and loaded onto the reader for the final read out which is presented to the monitor for interpretation.

Detection of the samples is through the capture of the fluorophores emitted by the

illuminating dye by laser scanner. The relative signals from the illuminating dye determine the sample genotype. The system compiles a virtual representation of the plate, acquires fluorescence data, records the information and allows for exporting data for downstream analysis[140], http://www.illumina.com.

2.3.2. Establishing the Bead Express Genotyping Assay

Firstly, a file of selected SNPs was created and provided to Illumina. Using the list of genes and SNPs we provided, Illumina generated SNP specific PCR products, Figure

5[140] http://www.illumina.com. The products generated were then hybridized in solution form (BeadExpress, Illumina, USA). Three oligonucleotides are designed for each SNP. Two allele specific oligonucleotides (ASOs) that define each SNP and a locus specific sequence (LSO) which is found downstream of the SNP[140] http://www.illumina.com. The ASO and LSO were primed with the target sequence called universal primers. The LSO oligonucleotides created by Illumina are usually coded, containing complementary sequences and attached to beads[140] http://www.illumina.com. After creating the oligonucleotides, the oligonucleotides were pooled into a single reaction. Thereby, when a sample is put into the cocktail of these oligonucleotides, the complementary oligos containing the beads hybridize to the gDNA in a single sample reaction well. Following allele specific primer extension and ligation reactions, florescent labeled primers added to the reaction and the PCR was carried out generating multiple labeled amplicons representing hundreds of different SNPs. The fluorescently labelled primers were then combined with Illumina beads which address the sequence within the PCR amplicons to their similar

sequence on the bead and the fluorescence on each bead was quantified resulting in a signal associated with a particular address sequence. Each address was translated to a particular locus and the presence of one signal or both signals on a given bead indicates the genotype which is presented as AA, Aa or aa (Figure 5).

Figure 5: Principle chemistry in genotyping assay (adopted from http://www.illumina.com)

2.4. Sample

The samples were collected in the USA. The healthy controls samples were obtained from random blood donors in North America through the Red Cross, Maryland area.

The cases were collected from a cohort of HIV positive patients at the Case Western

Reserve University/University Hospitals of Cleveland, Cleveland, Ohio.

Table 6: Individual demography in the study

Characteristics HIV+ HIV- Race Caucasian - American 54 48 African - American 102 48 Others 24 0 Sex Male 135 47 Female 41 47 Unknown 4 2 180 96

Cases are defined as those individuals, who were clinically positive for HIV/AIDS.

They may or may not have other diseases, particularly oral manifestations. Controls were healthy individuals, defined as those without HIV/AIDS who may or may not have other diseases.

2.5. Genotype data processing

Genotyping was performed on a 96-well plate format. Some samples were run in triplicates purposely to assess the reproducibility of the assay. After genotyping, the outputs were saved and imported into Genome Studio (genomic analysis software,

Illumina, USA) for analysis.

In Genome Studio, data cleaning was first performed. Poorly performing biological samples and SNPs were removed or revisited for further downstream analysis. This was done by sorting the samples according to call rates, and those samples and SNPs that had a call rate of less than 85% were manually removed. Those that had a call rate above 90% were imported for further analysis. Samples with call rates between

85-89% were revisited and compared against the internal controls. This comparison

was made to ensure that the low call rates are not due to processing or sample inequality error. The poorly performing samples were then compared side-by-side with the chip that the call was associated with and the positions of the chip. The later was done to ensure that the samples that were performing badly were not due to the chip itself.

A technical problem would occur if there were; inhibitors in the reaction, the quality of the DNA was poor or the sample concentration was too low thus giving lower readings/call rates.

Once sample by sample analysis and cleaning was done, samples were analyzed SNP- by-SNP through clustering analysis. SNPs that repeatedly fell outside the cluster were excluded from the analysis. SNP clustering was done to determine the frequency for all alleles per sample. Clustering gates were adjusted to cater for the majority of the samples so that there would be enough samples for downstream analysis. Also the genotyping calls and the distribution of the alleles AA, Aa and aa clusters, representing homozygous (AA, aa) and heterozygous (Aa) was performed. This was done to make sure that the genotypes are tightly clustered into the different clustering groups, AA, Aa or aa. When all the adjustments had been performed and data was of sufficient quality (genotype calls being above 85%), the data was exported and converted in a file document that is compatible and readable with

PLINK (population linkage analysis) software http://pngu.mgh.harvard.edu.

2.6. Statistical association analysis

Since we had two plates being analyzed, a Fishers Exact test statistical analysis was performed to check for batch-to-batch effect. The significance p-value was set at p <

0.05 and we expected to see no difference between plates. The differences between plates may be attributed by the volume of sample added and the concentration which may vary from one plate to another. Additionally, the reagents used in one reaction could be different from the other in pH, reagent concentrations, and the reaction temperature. If we observed no significant difference, then this implied that all conditions mentioned afore were consistent between the two plates, and could be analyzed together.

Using population linkage analysis software (PLINK), Hardy Weinberg Equilibrium

(HWE) and frequency analysis was performed. The HWE and frequency obtained was compared to HWE and frequency analysis data reported in HapMap and dbSNP database.

Logistic regression statistical analysis was done through PLINK to find associations of significance in those SNPs. In logistic regression analysis, the analysis was first performed without adjusting for any factors. Secondly, the analysis was performed by adjusting for race and finally the analysis was stratified for race to compare SNP frequency between HIV+ and HIV- status within the CA and AfA racial population.

Two genetic model test was performed, the addictive genetic model test (ADD) and dominant genetic model test (DOM) to determine the association of the allele to HIV status. Under the ADD test, analysis was performed taking the three genotypes, AA

(homozygous dominant), Aa (heterozygous dominant), aa (homozygous recessive)

independently and making comparison between them. Under the DOM test, the AA and Aa genotypes were combined and compared to aa genotype.

Additionally, the “haplo.stat” package in R statistical software was employed to test for the different haplotype combination and their association to HIV status. During haplotype analysis, tests were performed for each TLR gene SNPs and in the heterodimers TLR1-TLR2 and TLR2-TLR6. Haplotype was performed in heterodimers because TLR have been observed to combine their proteins to produce a single functional biological unit. For all these analyses, a Bonferroni correction was used to adjust for multiple comparisons setting the significant p < 0.001 and the default significant p < 0.05.

Linkage disequilibrium (LD) analysis was also performed using SHEsis

(http://analysis.bio-x.cn/SHEsisMain.htm) to determine the occurrence frequencies for all allele combinations.

3.0 Results

3.1. Batch Effect (Plate - Plate)

Batch-to-batch test was performed to check for variability between plates showed that there was no significant difference between plates (p > 0.05) of p = 0.81 (AA), p

= 0.83 (Aa) and p = 0.87 (aa) (Table 7).

Table 7: Fishers Exact t-test plate to plate variation results

Genotype Plate 1 Plate 2 p-value AA 0.35 0.36 0.81 Aa 0.26 0.25 0.83 aa 0.32 0.33 0.87 AA = homozygous dominant Aa = heterozygous dominant aa = homozygous recessive

3.2. Hardy Weinberg Equilibrium Test (HWE)

HWE test showed no deviation when compared to allele frequencies reported in dbSNP, GVS and HapMap databases.

3.3. Allele frequencies

The frequencies for each of the genotyped alleles differed between HIV status category, HIV+ and HIV- in general irrespective of race, CA or AfA (Table 8).

Furthermore, the allele frequency results showed to be different between HIV statuses within the two racial populations (Table 8).

Table 8: Observed allele frequency in HIV status and within each population

Gene SNP rs number MAF (all) MAF (all) MAF (CA) MAF (AfA) (CHR #, ID) (alternative name) HIV+ HIV- HIV+ CA HIV- CA HIV+ AfA HIV- AfA TLR1 -7202G>A rs5743551 0.38 (A) 0.44 (G) 0.37 (G) 0.16 (G) 0.24 (A) 0.27 (A) (#4, 7096) -2192T>C rs5743595 0.11 (C) 0.09 (C) 0.20 (C) 0.14 (C) 0.05 (C) 0.05 (C) 239G>C rs5743611 0.05 (C) 0.05 (C) 0.09 (C) 0.09 (C) 0.03 (C) 0.01 (C) 1805G>T rs5743618 0.31 (G) 0.49 (T) 0.44 (G) 0.21 (T) 0.16 (G) 0.23 (G)

TLR2 -16934T>A rs4696480 0.46 (A) 0.39 (A) 0.49 (A) 0.40 (A) 0.44 (A) 0.39 (A) (#4, 7097) -15607A>G rs1898830 0.22 (G) 0.26 (G) 0.27 (G) 0.36 (G) 0.18 (G) 0.14 (G) 597T>C rs3804099 0.44 (T) 0.48 (T) 0.45 (T) 0.46 (C) 0.42 (T) 0.42 (T) 1350T>C rs3804100 0.05 (C) 0.05 (C) 0.04 (C) 0.03 (C) 0.04 (C) 0.06 (C) 2258G>A rs5743708 0.01 (A) 0.02 (A) 0.01 (A) 0.03 (A) 0.01 (A) 0.01 (A)

TLR3 -8921A>T rs5743303 0.16 (T) 0.18 (T) 0.20 (T) 0.22 (T) 0.15 (T) 0.15 (T) (#4, 7098) -8441T>A (-926T>A) rs5743305 0.38 (A) 0.37 (A) 0.43 (A) 0.45 (A) 0.37 (A) 0.29 (A) -299698G>T (-7C>A) rs3775296 0.14 (T) 0.19 (T) 0.18 (T) 0.22 (T) 0.13 (T) 0.16 (T) 1234C>T rs3775291 0.16 (T) 0.19 (A) 0.29 (T) 0.27 (A) 0.07 (A) 0.11 (A)

TLR4 -3612A>G rs2770150 0.23 (G) 0.22 (G) 0.32 (G) 0.31 (G) 0.18 (G) 0.14 (G) (#9, 7099) -2604G>A rs2737190 0.40 (A) 0.50 (A) 0.43 (G) 0.26 (G) 0.28 (A) 0.26 (A) -1607T>C rs10759932 0.23 (C) 0.14 (C) 0.21 (C) 0.05 (C) 0.24 (C) 0.23 (C) 896A>G (1063A>G) rs4986790 0.06 (G) 0.08 (G) 0.06 (G) 0.08 (G) 0.06 (G) 0.08 (G) 1196C>T (1363C>T) rs4986791 0.02 (T) 0.05 (T) 0.03 (T) 0.08 (T) 0.01 (T) 0.01 (T) +11381G>C (3725G>C) rs11536889 0.06 (C) 0.11 (C) 0.08 (C) 0.19 (C) 0.03 (C) 0.04 (C) +12186C>G (4530C>G) rs7873784 0.22 (C) 0.14 (C) 0.18 (C) 0.15 (C) 0.25 (C) 0.13 (C)

TLR6 -2079T>A rs5743789 0.21 (A) 0.18 (A) 0.13 (A) 0.22 (A) 0.25 (A) 0.12 (A) (#4, 10333) -1401G>A rs5743795 0.11 (A) 0.10 (A) 0.19 (A) 0.16 (A) 0.05 (A) 0.04 (A) -673C>T rs5743806 0.50 (C) 0.42 (C) 0.38 (C) 0.24 (C) 0.43 (T) 0.40 (T) -502T>C rs1039559 0.30 (C) 0.41 (C) 0.40 (C) 0.41 (T) 0.23 (C) 0.23 (C) 745T>C rs5743810 0.17 (T) 0.33 (T) 0.32 (T) 0.49 (C) 0.09 (T) 0.15 (T) 1083C>G rs3821985 0.48 (C) 0.44 (G) 0.39 (G) 0.25 (G) 0.39 (C) 0.38 (C) 1263A>G rs3775073 0.47 (A) 0.45 (G) 0.40 (G) 0.25 (G) 0.38 (A) 0.35 (A) 1932T>G rs5743818 0.17 (G) 0.17 (G) 0.28 (G) 0.22 (G) 0.10 (G) 0.11 (G) 4224C>T rs2381289 0.34 (T) 0.33 (T) 0.50 (T) 0.40 (T) 0.24 (T) 0.26 (T)

TLR7 1-120T>G rs2302267 0.03 (G) 0.07 (G) 0.05 (G) 0.10 (G) 0.02 (G) 0.04 (G) (X, 51284) 2403G>A rs864058 0.12 (A) 0.17 (A) 0.09 (A) 0.14 (A) 0.16 (A) 0.19 (A)

TLR8 1A>G rs3764880 0.28 (G) 0.26 (G) 0.39 (G) 0.18 (G) 0.24 (G) 0.34 (G) (X, 51311) +3121T>C rs1548731 0.50 (T) 0.41 (T) 0.30 (T) 0.30 (T) 0.39 (C) 0.48 (C) 28A>G rs5744077 0.06 (G) 0.06 (G) 0.00 (G) 0.00 (G) 0.10 (G) 0.12 (G) 354C>T rs2159377 0.11 (T) 0.18 (T) 0.16 (T) 0.16 (T) 0.09 (T) 0.21 (T) 645C>T rs5744080 0.38 (C) 0.46 (C) 0.45 (C) 0.35 (C) 0.30 (C) 0.27 (C) 1953G>C rs2407992 0.28 (G) 0.41 (G) 0.39 (G) 0.35 (C) 0.17 (G) 0.16 (G) 2253C>A rs3747414 0.33 (A) 0.30 (A) 0.44 (A) 0.25 (A) 0.30 (A) 0.36 (A)

TLR9 -1486C>T rs187084 0.38 (C) 0.36 (C) 0.37 (C) 0.35 (C) 0.39 (C) 0.38 (C) (#3, 54106) -1237C>T rs5743836 0.26 (C) 0.23 (C) 0.17 (C) 0.11 (C) 0.33 (C) 0.34 (C) +1174G>A rs352139 0.44 (A) 0.44 (A) 0.49 (A) 0.47 (G) 0.39 (A) 0.35 (A) 1635G>A (2848G>A) rs352140 0.43 (T) 0.43 (A) 0.47 (A) 0.47 (A) 0.43 (A) 0.40 (A)

IL6 -174G>C rs1800795 0.24 (C) 0.24 (C) 0.30 (C) 0.38 (C) 0.21 (C) 0.10 (C) (#7,3569)

IL10 -1082A>G (-1117A>G) rs1800896 0.40 (G) 0.41 (G) 0.38 (G) 0.47 (G) 0.41 (G) 0.35 (G) (#1,3586) -592C>A (-627C>A) rs1800872 0.35 (A) 0.31 (A) 0.37 (A) 0.24 (A) 0.34 (A) 0.39 (A) CHR# = Chromosome number (eg: Chromosome 4 = #4), ID = Gene idenification number (eg: 7096 = TLR1), rs number = dbSNP reference number, MAF = Minor allele frequency, SNP = Single nucleotide polymorphism, HIV+ = HIV positive, HIV- = HIV negative, CA = Caucasian American, AfA = African Americans

3.4. Statistical analysis - Logistic regression

3.4.1. Unadjusted

Using logistic regression statistical analysis on all 276 samples without adjusting for any factors, the analysis revealed 3 SNPs to be significantly associated with HIV status at p < 0.001: TLR1 rs5743618 (1805G>T, OR = 1.71, p = 0.00013), TLR1 rs5743551 (-7202G>A, OR = 1.76, p = 0.00047), and TLR6 rs5743810 (745T>C, OR =

0.47, p = 0.00015) (Table 9). But, after adjusting for race as a covariate, no SNP was found to be significant at p < 0.001 level, bonferroni correction. However at p < 0.05, some SNPs were found to be significant (Table 9). Interestingly, most of these significantly (p < 0.05) associating SNPs are found in TLR1, TLR4, TLR6 and TLR8 gene and nothing in the other TLRs analyzed.

Table 9: Logistic regression statistical analysis results showing only significant alleles at p < 0.05

TLR (CHR) SNP rs# Position Allele OR L95 U95 P 1 (4) rs5743618 38798648 G 1.71 0.39 0.74 0.00013 1 (4) rs5743551 38807654 A 1.76 0.41 0.78 0.00047 4 (9) rs11536889 120478131 C 0.44 0.23 0.84 0.01340 4 (9) rs7873784 120478936 C 1.75 1.08 2.84 0.02350 4 (9) rs10759932 120465144 C 1.70 1.07 2.69 0.02517 4 (9) rs2737190 120464181 A 0.71 0.51 0.99 0.04213 6 (4) rs5743810 38830350 T 0.47 0.32 0.70 0.00015 6 (4) rs1039559 38831596 C 0.64 0.45 0.91 0.01222 8 (23) rs2407992 12939112 G 0.64 0.42 0.97 0.03612 8 (23) rs2302267 12885578 G 0.33 0.11 0.98 0.04645 CHR = Chromosome, SNP rs# = SNP reference sequence number, Position = SNP position on the gene, ADD/DOM = Additive/Dominant genetic model test, OR = Odds Ratio, L95/U95 = Lower/Upper Limit Confidency interval, P = p-value < 0.05

3.4.2. Adjusted: Stratified by race with HIV status as covariant

3.4.2.1. African American

After stratifying the data by race and adjusting for HIV status as the covariant, the analysis revealed TLR4 SNP rs7873784 (+2186C>G) to be associated with HIV+ status under both ADD and DOM test, at p < 0.05 (Table 10). The OR showed that individuals with TLR4 rs7873784 allele C has 2.37 and 2.31 (ADD and DOM) increased odds of being HIV positive compared to an individual without C allele. TLR8 SNP rs2159377 (354C>T, Asp118Asp) was found to be significant under the ADD test but lost significance under DOM test (Table 10).

Table 10: Additive and Dominant genetic model test statistical analysis results showing significant alleles at p < 0.05 in AfA population

TLR (CHR) SNP rs# Position Allele TEST OR L95 U95 P 4 (9) rs7873784 120478936 C ADD 2.37 1.16 4.84 0.0184 8 (23) rs2159377 12937513 T ADD 0.39 0.16 0.92 0.0312

4 (9) rs7873784 120478936 C DOM 2.31 1.06 5.01 0.0346 CHR = Chromosome, SNP rs# = SNP reference sequence number, Position = SNP position on the gene ADD/DOM = Additive/Dominant genetic model test, OR = Odds Ratio L95/U95 = Lower/Upper Limit Confidency interval, P = p-value < 0.05

3.4.2.2. Caucasian American

In contrast to AfA group, several SNPs were found to be significant under both the

ADD and DOM test in the CA group. Five SNPs, TLR1 rs5743551, rs5743618, TLR4 rs10759932, and TLR6 rs5743810, rs1039559 were significant under both genetic analysis model tests (ADD and DOM).

Under DOM test (Table 11), majority of the SNPs had ORs in the range of 2-4. This implied that an individual with that allele has 2-4 times increased odds of being HIV

positive compared to an individual without that allele. For example, an individual with TLR1 rs5743618 allele T has 2.51 and 2.52 times increased odds (ADD and DOM) of being HIV positive compared to an individual without the T allele. On the other hand, there are SNPs showing protective effect. The OR for the protective allele ranges from 0.28-0.31 (69-72%) under DOM test implying that an individual with that minor allele has 69-72% decreased odds of being HIV positive compared to an individual without that allele. For example, an individual with TLR6 rs5743810 allele

T has 72% decreased odds (DOM) of being HIV positive compared to an individual without the T allele.

Table 11: Additive and Dominant genetic model test statistical analysis results showing significant alleles at p < 0.05 in CA population

TLR (CHR) SNP rs# Position Allele TEST OR L95 U95 P 1 (4) rs5743618 38798648 T ADD 2.51 1.29 4.88 0.0066 1 (4) rs5743551 38807654 G ADD 2.69 1.31 5.53 0.0069 4 (9) rs10759932 120465144 C ADD 4.03 1.40 11.59 0.0098 6 (4) rs1039559 38831596 C ADD 0.41 0.22 0.79 0.0073 6 (4) rs5743810 38830350 T ADD 0.45 0.24 0.83 0.0104 6 (4) rs5743806 38831767 C ADD 2.09 1.03 4.23 0.0404 6 (4) rs3775073 38829832 G ADD 2.05 1.02 4.09 0.0427 8 (23) rs3764880 12924826 G ADD 3.01 1.16 7.83 0.0237 8 (23) rs2407992 12939112 C ADD 2.43 1.10 5.36 0.0284

1 (4) rs5743618 38798648 T DOM 2.52 1.05 6.10 0.0395 1 (4) rs5743551 38807654 G DOM 2.75 1.11 6.82 0.0284 4 (9) rs10759932 120465144 C DOM 4.23 1.31 13.68 0.0159 6 (4) rs1039559 38831596 C DOM 0.31 0.11 0.88 0.0280 6 (4) rs5743810 38830350 T DOM 0.28 0.11 0.73 0.0096 CHR = Chromosome, SNP rs# = SNP reference sequence number, Position = SNP position on the gene ADD/DOM = Additive/Dominant genetic model test, OR = Odds Ratio L95/U95 = Lower/Upper Limit Confidency interval, P = p-value < 0.05

3.4.3. Haplotype analysis

In the haplotype analysis, the gene-by-gene analysis showed two haplotypes in TLR1 and one haplotype in TLR4 to be associated with HIV status in CA. Additionally,

heterodimer haplotype analysis showed one haplotype in TLR2-TLR6 heterodimer to be significantly associated with HIV status in CA. However, nothing was observed in

AfA race group (Table 12).

Table 12: Haplotype analysis results showing only significant alleles at p < 0.05 in CA population in CA group

Hap-Freq Hap-Freq Hap-Freq Hap-Score Haplotype All HIV+ HIV- Hap-Score p-value Inference TLR1 GGTA 0.575 0.467 0.698 -3.313 0.00092 Protective TGTG 0.093 0.157 0.021 2.198 0.02794 Susceptibility TLR4 TGCACGG 0.098 0.083 0.028 2.529 0.01143 Susceptibility TLR2-6 TGTTG-CCACGTTT 0.139 0.012 0.028 -2.839 0.00452 Protective Hap-Freq = Haplotype frequency Hap-Score = Haplotype score p-vlaue = p-value < 0.05

Based on the Hap-score (Table 12), it may be inferred that some haplotypes had protective effects while others had susceptibility effect. According to the definition of Hap-score, a negative hap-score implies protection[141]. This is proven by the frequencies of this haplotype being more in the HIV- than the HIV+ individuals. On the other hand, a positive hap-score implies susceptibility[141]. This is proven by the high frequency of this haplotype in the HIV+ than the HIV- individuals.

3.5. Linkage Disequilibrium analysis

The LD analysis revealed several SNPs to be in LD using the Lewontin’s D’ and correlation r2 test[142]. High LD indicated that the allele combination occurs more

frequently together. According to our definition, those LD that had a D’ > 0.8 and r2 >

0.5 where accepted as significant (Table 13).

Table 13: LD results showing only significant alleles at D’ > 0.8 and r2 > 0.5 in CA and AfA population

Gene SNP pair D' (r 2) HIV+ CA HIV− CA HIV+ AfA HIV− AfA TLR1 rs5743551-rs5743618 0.90 (0.59) 0.99 (0.70) - 0.99 (0.80) rs5743551-rs5743595 - 1 (0.84) - rs5743595-rs5743618 - 0.99 (0.59) -

TLR3 rs5743303-rs3775296 0.99 (0.83) 1 (1) 0.73 (0.50) -

TLR4 rs4986790-rs4986791 - 1 (1) - -

TLR6 rs5743806-rs3821985 0.95 (0.88) 0.99 (0.94) 0.99 (0.84) 1 (0.91) rs5743806-rs3775073 1 (0.92) 0.99 (0.94) 0.97 (0.77) 1 (0.83) rs3821985-rs3775073 1 (0.96) 1 (1) 0.97 (0.89) 1 (0.91) rs5743806-rs5743818 0.93 (0.55) 0.99 (0.88) - - rs1039559-rs5743810 0.94 (0.65) 0.99 (0.71) - 1 (0.57) rs1039559-rs2381289 0.99 (0.66) 0.99 (0.95) - - rs3775073-rs5743818 0.93 (0.50) 1 (0.84) - - rs5743810-rs2381289 - 0.99 (0.68) - - rs3821985-rs5743818 - 1 (0.84) - - rs1039559-rs3775073 - - - 0.99 (0.54)

TLR8 rs5744080-rs2407992 1 (0.82) 1 (0.99) - 0.99 (0.51) rs5743080-rs3747414 - 0.99 (0.64) - - rs2407992-rs3747414 - 0.99 (0.64) - -

TLR9 rs187084-rs352139 0.99 (0.56) 0.99 (0.62) - - rs352139-rs352140 0.95 (0.75) 1 (1) - - rs187084-rs352140 - 0.99 (0.62) - -

TLR1-6 rs5743595-rs5743795 93 (77) 83 (70) - 99 (79) rs5743595-rs5743795 - 100 (84) - - rs5743618-rs5743795 - 91 (58) - - rs5743618-rs5743810 - - - 99 (57) 2 - = D' and r <0.8 and/or <0.5, respectively.

A D’ > 0.8 indicates that the haplotype are dependent and thus are more likely to occur together. r2 < 0.5 indicates that the haplotype are in correlation and supports the D’ result. The haplotypes for the combination of alleles occurring together is more pronounced in the CA and few in the AfA group (Table 13). Furthermore, there is more LD occurring in the HIV- than the HIV+ group within each racial group.

4.0 Discussion

TLR have gained more interest in clinical research studies than other receptors because of its clinical importance of recognizing and eliminating a wide range of invading pathogenic microbes[143]. There is an increasing amount of data showing associations in TLR gene SNPs and increased risk or protection from bacterial, viral and fungal infections and inflammatory/autoimmune diseases (Table 5). Of the 10

TLR identified in humans, there are few SNPs in certain TLR genes that have been the focus of many association studies. Of those TLR gene SNPs commonly studied, TLR4

SNPs Asp299Gly (896A/G) and Thr399Ile (1196C/T) have been the focus of many studies[57, 60, 144-148]. These two TLR4 SNPs have been studied down to the molecular level where the effects of the SNP to molecular structure and orientation have been identified. These two TLR4 SNPs alter the folding of the horse-shoe extracellular domain LRR structure. Structural analysis of this TLR SNP showed that the horse-shoe structure is tighter because of 8-hydrogen bond formation in the mutant structure (containing the two TLR4 SNPs) compared to 3-hydrogen bonds in wild type[149, 150], leading to the receptor not binding to its ligand, in this case LPS.

It is very well known that all protein molecules interact with each other in a lock and key fashion. Thereby, if the protein structure and conformation are affected, then the protein molecule would not be able to recognize and bind to its protein ligands thus resulting in non-responsiveness to associated PAMPs. Study by Yamakawa N et al, 2012 implied that the same might be occurring to other TLR gene SNPs[149].

Table 5 showed some of the TLR genes SNPs that have been studied but it also revealed that there are not many studies being done in relation to HIV infection.

Furthermore, many of these studies have been narrowly focused. That is, many studies are focused on TLR gene SNPs and their association with bacterial or viral or fungal infections/diseases only. Also, the numbers of SNPs analyzed per study are usually less than 5 per TLR gene.

Most of TLR SNPs and HIV association studies performed on in European population cohorts[116, 117, 139, 151, 152] and several in African population cohorts[153-155].

To our knowledge, there’s only one study that was done in North American population cohort[115]. This raises the questions of population diversity and how reproducible the results going be, taking into consideration the geographical separation of the same race, AfA versus Africans in Africa and CA versus European

Caucasians. Interestingly, the study done in North American population was looking at the association of TLR gene SNPs to the clinical outcome of peak viral load and disease progression. The limitations of that study are, only four TLR genes and few numbers of SNPs in these genes were analyzed.

As mentioned above, majority of the studies use few number of TLR gene SNPs or are rather more focused on few certain TLR gene SNPs. In this study, we tried to take into consideration several candidate SNPs that have been reported to be associated with HIV infection or HIV associated infections/diseases and other infectious diseases and genetic disorders. Comparing Pine SO et al, 2009[115] done in North

American cohort study to ours, we established the foundation in identifying the TLR gene SNPs and haplotypes that are associated with HIV status in CA and AfA racial group in North America population. Additionally, this study population had SNPs in almost all TLR gene (TLR1, 2, 3, 4, 6, 7, 8, and 9) and 4-9 SNPs per TLR gene. Lastly,

we were able to perform haplotype analysis within individual gene and between heterodimer forming TLRs. This is important because, it is known that several SNPs act together to give a phenotype. So a haplotype analysis provides a better view of the impact of the SNPs on a particular gene[147, 156, 157]

From studies done on individual SNP elsewhere in different racial populations, they were able to show that TLR4 SNPs rs4986790 (896A>G , Asp299Gly), rs4986791

(1196C>T,Thr399Ile)[115, 116], TLR7 SNPs rs179008 (130A>T, Gln11Leu), rs1634319

(4-596T>C)[151, 153], TLR8 SNPs rs3764880 (1A>G,Met1Val)[115, 153] and TLR9 rs352139 4 (-44G>A) and rs352140 (1635G>A ,Pro545Pro) were being associated with HIV-1 peak viral load, mother to child transmission (MTCT) and disease progression in HIV infected individuals[117, 137, 139, 153]. On the other hand, there are studies that showed a protective effect of TLR gene SNPs to HIV-1 peak viral load,

MTCT and disease progression[152, 153].

In this study, our analyses showed that certain TLR gene SNPs may be associated with protection or susceptibility to HIV infection, based on OR and hap-score (Table

9). Of the TLR gene SNPs listed above from other studies, only TLR8 rs3764880

(1A>G, Met1Val) was found to be significantly associated with HIV+ status under the

ADD test but lost significant under DOM test (Table 11) in our study. This association was observed only in the CA group.

4.1. Logistic regression, Linkage Disequilibrium and Haplotype analysis

Of the 41 TLR SNPs analyzed by logistic regression without adjustments, only 3 SNP showed to be significantly associated with HIV at p < 0.001 after bonferroni

correction; TLR1 rs5743618, rs5743551 and TLR6 rs5743810 (Table 9). Something worth mentioning is, several SNPs were found to be significantly associating with either HIV+ or HIV- status at p < 0.05 level (Table 9 - DOM) in TLR1, TLR4, and TLR6.

When stratified for ethnicity, these SNPs were found to be significant only in the CA group and not the AfA group at p < 0.05. However, when the analysis was adjusted for race and having HIV status as the covariant, several SNPs showed to be significant under the ADD test and furthermore under DOM test.

In the AfA population, only TLR4 rs7873784 (+12186C) was observed to be significant under both genetic models respectively (Table 10) p < 0.05. The biological relevance of this SNP was studied in cancer but no study has analyzed this SNP in HIV or HIV related infections[122, 158, 159]. Study by Tsilidis KK et al, 2009 showed that, TLR4

SNP rs7873784 was associated with colorectal cancer by decreased activation of IL-

10, an anti-inflammatory cytokines[159]. The latter associated with an increase in inflammatory cytokines action which was thought be responsible for colorectal cancer.

In CA group, 9 SNPs were found to be significant under ADD test, (Table 11) p < 0.05.

Of those, 5 of them were found to be significant under the DOM test and they are,

TLR1 rs5743551 (-7202G, OR = 2.75, p = 0.0284), TLR1 rs5743618 (1805T, OR = 2.52, p = 0.0395), TLR4 rs10759932 (-1607C, OR = 4.23, p = 0.0159), TLR6 rs5743810 (745T,

OR = 0.28, p = 0.0096) and TLR6 rs1039559 (-502C, OR = 0.31, p = 0.028). Under the

DOM test, it showed that individuals having TLR1 rs5743551 (-7202G), TLR1 rs5743618 (1805T, Ser602Ile), and TLR4 rs10759932 (-1607C) alleles had 2.75, 2.52

and 4.32 increased odds of being HIV+ respectively compared to individuals without these alleles (Table 11). On the other hand, TLR6 rs1039559 (-502C) and TLR6 rs5743810 (745T) under the DOM test (Table 11) showed that individuals with these alleles have 69% and 72% decreased odds of being HIV+ respectively. Being statistically significant under both ADD and DOM test for these 5 SNPs, TLR1 rs5743618 (1805T), TLR4 rs10759932 (-1607C), TLR1 rs5743551 (-7202G), TLR6 rs1039559 (-502C) and TLR1 rs5743810 (745T) (Table 11) confirmed what we saw is a true significantly associating allele to the respective HIV status.

Based on haplotype frequencies, (Table 12 - positive and negative hap-score), the frequencies of these haplotypes differ between the HIV+ and HIV- group within each racial group. Two haplotypes, TLR1 GGTA and TLR2-6 TGTTGCCACGTTT had negative hap-scores of -3.313 and -2.839 respectively. These two haplotype may be inferred to as protective haplotypes. On the other hand, two haplotypes, TLR1 TGTG and

TLR4 TCGACGG had positive hap-scores of 2.198 and 2.529 respectively. These two haplotype may be inferred to as susceptibility haplotypes. The inference of protection against or susceptibility to HIV can be confirmed by the frequency of occurrence of these haplotypes in HIV+ and or HIV- group. For instance, haplotype

TLR1 GGTA has a hap-score of -3.313 which may infer a protective haplotype where the frequency of occurrence this haplotype is greater in HIV- (69.8%) than HIV+

(46.6%) group (Table 12). On the other hand for example, TLR1 TGTG haplotype has a positive hap-score of 2.198 which may infer a susceptibility haplotype where the occurrence of this haplotype is greater in HIV+ (15.7%) than HIV- (2%) group (Table

12).

In support of the above mentioned analysis, the LD analysis showed certain SNPs were in LD with each other. Interestingly, those alleles that are in strong LD (Table

13) have been found to be significantly associated with HIV status (Table 9). Very good examples are SNPs TLR1 rs5743551 and TLR1 rs5743618 where in the logistic regression analysis (Table 9), both were found to be significantly associated with

HIV+ status. This imply that, the association of TLR SNPs and being HIV positive can be the outcome of the presence of these two SNPs acting together.

An interesting thing revealed in the LD analysis was, few SNPs are in LD in the AfA group compared to CA which supports the theory of admixture in the AfA group[160]. Furthermore, the LD analysis to some extends supports the findings that are presented in the association analysis (OR) and haplotype analysis (hap.score) where the analysis revealed significant association in having those SNPs and HIV status in the CA group and almost nothing in the AfA group.

SNPs found significant in this study were not reported before in association with HIV infection. Also, this study did not find SNPs that were associated with HIV and HIV related/associated infections and clinical outcome that was observed in other studies. The reason for the difference and making this study unique might be the population, North American compared to European and African population.

Additionally, the uniqueness of this study was the fact that the association was made to HIV status, either HIV+ or HIV- , compared to other studies where clinical outcomes such as MTCT, high peak viral load and disease progression was measured within the HIV+ cohorts.

Therefore, the biological significance of this study to HIV disease and associated diseases and what can be interpreted about the biological outcome of genotype data with HIV status remained crucial to be determined.

4.2. Biological importance of statistical significant SNPs in this study

4.2.1. TLR1 rs5743618

TLR1 rs5743618 is located at aa position 602 in the gene length of 786 aa where this sequence region of the gene is required for the synthesis of cytoplasmic

(intracellular) topological domain www.uniprot.org www.genecards.org. TLR1 rs5743618 1805G/T (Ser602Ile) is located within the transmembrane domain of TLR1 gene and it was predicted to alter protein function by the PolyPhen program www.uniprot.org www.genecards.org. TLR1 recognizes LPP (Table 3), so it is possible that mutation in this region of the gene can result in the TLR signal not recruiting the associated adaptor and thus halting downstream signaling or it may continuously produce signals resulting in increased cytokine production. Study by Taylor BD et al,

2013, reported that AfA women were more likely to carry TLR1 rs5743618 1805TT genotype to TLR1 rs5743618 (Ser602Ile)[89]. The TLR1 rs5743618 (Ser602Ile) was commonly observed in European population[86]. From previous study, his team had found that functional TLR1 SNP rs5743618 (1805TT) genotype was associated with

UGTI, chlamydial infection and reduced pregnancy in AfA women[88, 89]. In another independent study, Hawn TR et al, 2009 reported that the TLR1 rs5743618 T allele was associated with significantly greater NF-κB signaling in transfected HEK293 cells

compared to the G allele[87, 92]. The adverse effect of increased expression of NK-

ĸB can lead to septic shock and inflammatory diseases.

A protective effect was observed in European and Indian population[161]. Study by

John CM et al, 2007[86] showed that TLR1 gene SNP rs5743618 (Ser602Ile) affect receptor translocation to the cell surface and was associated with protection against leprosy.

4.2.2. TLR1 rs5743551

TLR1 rs5743551 (-27202A/G) SNP is located in the 5’ near gene in the promoter region www.dbsnp.org, www.uniprot.org, www.genecards.org.

Wurfel MM et al, 2008 study showed that TLR1 rs5743551 (-27202G) is strongly associated with increased mortality and organ dysfunction in patients with sepsis and septic shock[84]. Supporting this finding, an independent study showed the SNP to be in strong association with sepsis induced acute lung[162]. These studies together showed the up regulation effect of TLR1 rs5743551 leading to hyper inflammatory, predisposing patient with sepsis to other complicated clinical outcomes. Specifically, TLR1 rs5743551 (-27202G) G allele showed a higher hypermorphic effect with increased IL-6 production in the homozygotes individuals,

TLR1 rs5743551 (-27202GG) [84, 162].

These TLR1 rs5743618 and rs5743551 have been reported to be in strong LD and this was observed in this study too. The latter implies that, variations in TLR1 gene leads to increased cytokine production and expression and is thought to have mediate the

association between AfA race and upper genital tract infection and inflammation[88,

89].

4.2.3. TLR4 rs10759932

TLR4 rs10759932 SNP is located in the 5’ near gene of the promoter region. Study by

Penders J et al, 2007, found a statistically significant multiplicative interaction for

TLR4 rs10759932 and Escherichia coli (E. coli) exposure with allergic sensitization[107]. E. coli colonization was associated with decreased risk of sensitization in children homozygous for the TLR4 rs10759932 T allele[107]. By contrast, heterozygotes children were at increased risk of sensitization, although not statistically significant, a trend was observed[107, 121].

4.2.4. TLR6 rs5743810

TLR6 rs5743810 occurred at amino acid position 249 in the gene length of 796 amino acids. The region of the gene is required for the synthesis of LRR5 www.uniprot.org, www.genecards.org.

Studies have found that AfA are more likely to carry alleles that up regulate proinflammatory cytokines IL1, IL6 and TNF. This may result in an imbalanced immune response, leading to severe inflammation that can be harmful. As AfA are at increased risk for inflammatory diseases, genetic variations that lead to increased inflammatory responses in the presence of infectious agents may also be more frequent in this group[89].

In contrast, AfA women were significantly less likely to be homozygous or heterozygous for TLR6 rs5743810 T alleles. Those women who are homozygous or heterozygous for TLR6 rs5743810 T allele had decreased odds of endometritis and/or

UGTI[89].

In another study, they showed an association between TLR6 rs5743810 (Ser249Pro) and development of neutralizing antibodies after 24 months of therapy in males but not in females[158].

4.2.5. TLR6 rs1039559

TLR6 rs1039559 SNP is located in the promoter region. It was speculated to affect disease characteristics of ischemic stroke[163]. In another study in a Dutch population, TLR6 rs1039559 CC allele, was found to be associated with increased risk of developing high total immunoglobulin (Ig)E level[164].These two SNPs are in high

LD in our analysis and so might be relevant in HIV infection.

4.2.6 Biological summary

From discussing the TLR gene SNP functionality, it raises more questions than answers to translate the functional effects of these SNPs to HIV infection and disease progression. For instance, this study revealed several SNPs that have not been reported or studied in TLR1 and TLR6. Mentioning the biological importance of these

SNPs in other diseases, it can be speculated that this SNPs can have positive or negative impact on HIV disease susceptibility and progression which this study

showed susceptibility and protective effect. More important would be the SNPs association with HIV opportunistic infections like oral candidiasis, TB and others.

5.0 Conclusion

This study revealed that some TLR SNPs may be associated with protection and or susceptibility to HIV-1 infection. Majority of the HIV-1 associating SNPs reported in this study have not been studied in other HIV related studies. Additionally, this study did not find significance in TLR SNPs that had been reported to be associated with

HIV-1 infection and disease outcomes in other studies. Interestingly, this study had furthermore revealed that TLR1 and TLR6 SNP were associated with HIV infection.

Despite the fact that, AfA are proportionately infected with HIV-1[165, 166] www.cdc.gov, this study found only TLR4 rs7873784 (+12186C) to be associated with

HIV-1 infection. This could be attributed by sample size where the sample size may not be large enough to capture any associating SNP in the AfA population. The other factor can be the heterogeneity of the AfA group[160], shown by our LD analysis

(Table 13) where fewer SNPs are in LD. Additionally, it would be because of unequal distribution of the HIV status individuals (Table 6). On the other hand, 5 SNPs were found to be associated with HIV in CA population.

Even though this study has limitations in sample size and unequal distribution of sex in each HIV status group within each racial group, the importance of the association of the SNPs cannot be overlooked.

This study lays the foundation where further studies needs to be done in terms of the biological importance of these significant SNPs by doing independent association studies and correlating these genotype data to a phenotype and/or clinical outcomes. Given the central role of innate receptors, it seemed possible that the functional abnormalities of these cells could trigger altered responses to

opportunistic pathogens in HIV-1-coinfected subjects[167-169]. Moreover, HIV-1 infection is known to be associated with reduced innate and adaptive immune responses to pathogens, which can promote complicated and severe manifestations of opportunistic infection and HIV disease progression - time to AIDS. In addition, HIV infection can lead to markedly reduced numbers and altered functions of innate immune cells like CD4+ cells[170-172].This is very important and it remained to be determined.

The future direction of this study is to analyze TLR SNPs in individuals to whom their clinical measures are available, such as HIV infection, periodontal diseases and other clinical measures such as CD4+ count, viral load and microbiological reports to piece the genotype and phenotype data together. This will help in making better association and understanding the effects of these SNPs.

In summary, this study showed that several TLR SNPs are associated with HIV status at p < 0.05 in the CA racial population and only one in AfA population. Furthermore, this study provides comprehensive insights into the role of TLR SNPs and HIV disease association. Thus, further studies are needed to be done to measure the association effect, genotype to phenotypic/clinical outcomes.

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