Association of Human Leukocyte Antigen-G Polymorphisms and Clinical Outcomes Post-

Transplantation

by

Julieta Lazarte

A thesis submitted in conformity with the requirements for the degree of Master of Science

Institute of Medical Science University of Toronto

© Copyright by Julieta Lazarte, 2016

ii

Association of Human Leukocyte Antigen-G Polymorphisms and Clinical Outcomes Post-Transplantation

Julieta Lazarte

Master of Medical Science

Institute of Medical Science University of Toronto

2016

Abstract

Human leukocyte antigen (HLA)-G has been shown to inhibit cardiac cells injury in

vitro, suggestive of protection against cardiac allograft vasculopathy (CAV). The

expression of HLA-G is regulated by single nucleotide polymorphisms (SNPs), and

their association with CAV remains unknown. The objective was to determine the

association between donor and recipient genotypes and diagnosis of CAV. We

retrospectively analyzed 251 heart recipients of whom 196 had their corresponding

donors and the association was evaluated with parametric hazard regression

models. At 10 years after transplantation, freedom from severe CAV,

retransplantation or death was 64% over a mean follow-up of 5.2 ± 3.6 years. In

multivariable analysis, the presence of donor-recipient SNP -201 (CC-CC) matching

was associated with an increased risk of severe CAV. This is the first investigation

to identify an association and it may reveal a pathway to be explored for potential

diagnostic and therapeutic strategies for CAV.

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Acknowledgments The present thesis was made possible with the help and support of many individuals. To start, I would like to thank my family for providing me with unconditional support and encouragement; I would not have made it this far along my journey without them.

To the Rao lab, Laura Tumiati, Hiroyuki Kawajiri, Arash Ghashghaia and Liza Grosman-Rimon, thank you all for welcoming me, always helping me and for all the support along the way. Special thanks to collaborators, Swan Cot from Clinical Genomics Centre for genotyping the gene and HLA laboratory staff Dr. Kathryn Tinckam and Ms. Alice Van Oosterwijk for your tremendous support to get approval and retrieve the samples.

To the staff at the Division of Cardiovascular Surgery and Division of Cardiology at the Peter Munk Cardiac Centre, specially to Dr. Livia Goldraich, Dr. Cedric Manlhiot and Dr. Heather Ross, thank you all very much for all your support along the way, for critically evaluating my work, for your continuous guidance and mentorship.

To my committee members, Dr. Fillio Billia, Dr. Candice Silversides and Dr. Seema Mital, a heartfelt thank you for critically evaluating my work, helping me learn along the way, providing a great deal of additional support and for helping me towards my career goals. I appreciate the welcoming environment you provided while challenging my knowledge and ability.

To my supervisor Dr. Vivek Rao and co-supervisor Dr. Delgado, I owe both of you a sincere amount of gratitude for taking me on as a Master’s student. I am grateful of the amazing mentors I had, I learned a great deal from both of you reading the landscape of research and the persistence and patience it takes to sustain being a clinical scientist. I am very appreciative of the opportunities you have encouraged

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me to be a part of and all your support and guidance towards my future aspirations. I am truly grateful that I had the opportunity to work under their mentorship.

To my parents, Monica and Alberto, and brother, Franscisco, with love and gratitude.

v

Contributions HLA laboratory staff including Dr. Kathryn Tinckam and Ms. Alice Van Oosterwijk: for helping me retrieve DNA samples from donors and heart transplant patients from the laboratory.

Swan Cot from Clinical Genomics Centre: for your enormous assistance and insight in identifying the SNPs and for genotyping all the samples.

Dr. Manlhiot from CV Data Management Centre for assisting in all of the statistical analysis, drafting and revisions. The project could not have been completed without all your insight.

Dr. Delgado for overseeing all the angiography reports and helping me to categorize all of them to the current international standards.

Dr. Billia, Dr. Ross and Dr. Goldraich provided tremendous support in scientifically evaluating my thesis project, in the drafting and revisions.

Lastly Dr. Rao and Dr. Delgado for their continual guidance on the overall thesis project, drafting and revisions of all the documents I had to submit.

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Table of Contents PAGE

ABSTRACT ii

ACKNOWLEDGMENTS iii

CONTRIBUTIONS v

TABLE OF CONTENTS vi

LIST OF TABLES x

LIST OF FIGURES xii

LIST OF APPENDICES xiii

LIST OF ABBREVIATIONS xiv

CHAPTER 1: LITERATURE REVIEW 1

1. Introduction 1

1.1 Overview 1

1.2 Background 2

1.2.1 Immunology Basics 2

1.2.2 Human Leukocyte Antigen-G 3

1.2.3 HLA-G Structure 5

1.3 HLA-G Immune Inhibition 10

1.3.1 HLA-G Receptors 10

vii

1.3.2 Immune Modulation 13

1.4 HLA-G Polymorphisms 16

1.4.1 5’-Upstream Promoter Region 16

1.4.2 3’-Untranslated Region 19

1.4.3 Coding Region 21

1.4.4 Haplotypes 23

1.5 The Role of HLA-G in Pregnancy 25

1.6 The Role of HLA-G in Cancer 27

1.7 The Role of HLA-G in Heart Transplantation 28

1.8 The Role of HLA-G in Lung, Kidney and Liver Transplantation 32

1.9 The Role of HLA-G Polymorphisms in Heart Transplantation 34

1.10 The Role of HLA-G Polymorphisms in Lung, Kidney and Liver 35

Transplantation

1.11 The Role of Donor HLA-G Expression 36

1.12 The Role of Environmental Factors on Expression 38

1.13 The Role of Immunosuppressive Therapy 39

1.14 Heart Transplantation 40

1.14.1 Risk Factors for Survival Post-Transplantation 41

1.15 Rejection Outcomes Post-Transplantation 43

1.15.1 Cellular Mediated Rejection 43

1.15.2 Donor Specific Antibody 44

1.15.3 Cardiac Allograft Vasculopathy 45

CHAPTER 2: RATIONALE AND HYPOTHESIS 47

viii

2. The Association of Donor and Recipient HLA-G Polymorphisms 47

and Cardiac Allograft Vasculopathy

2.1 Summary and Rationale 47

2.2 Hypothesis 49

CHAPTER 3: METHODS 50

3. Study Design 50

3.1 Population of Interest 50

3.2 DNA Collection 51

3.3 HLA-G Polymorphisms 51

3.3.1 SNP Selection 51

3.3.2 RS1233333 52

3.3.3 RS1233334 53

3.3.4 RS41551813 53

3.3.5 RS12722477 54

3.3.6 RS41557518 54

3.3.7 RS12722482 55

3.3.8 RS371194629 55

3.3.9 RS1063320 56

3.3.10 RS9380142 56

3.3.11 SNP Genotyping 56

3.4 Study Outcome 58

3.5 Statistical Analysis 59

ix

CHAPTER 4: RESULTS 62

4. 62

4.1 General Patient, Donor and Pre-transplant Characteristics 62

4.2 Transplant Characteristics, Outcomes and Medical Therapy 66

4.3 Freedom from Mild CAV 69

4.4 Freedom From Severe CAV 71

4.5 HLA-G Polymorphisms 73

4.6 Predictors of Mild Cardiac Allograft Vasculopathy 77

4.7 Predictors of Severe Cardiac Allograft Vasculopathy 85

CHAPTER 5: DISCUSSION AND CONCLUSION 94

5. General Discussion 94

5.1 Research Aims 94

5.2 Proposed Mechanism 98

5.3 Clinical Significance 104

5.4 Limitation 105

5.5 Future Directions 108

5.6 Conclusion 112

REFERENCES 113

COPYRIGHT ACKNOWLEDGMENTS 137

x

List of Tables

TABLE PAGE

1 Recipient, donor and pre-transplant characteristics of the study 62

cohort

2 Transplant characteristics, outcomes and medical therapy of the 66

recipient cohort

3 Frequency of outcomes (Mild CAV, death/re-transplantation and 71

alive no CAV) with 95% confidence interval from competing risk model.

4 Frequency of outcomes (Severe CAV, death/re-transplantation and 73

alive no CAV) with 95% confidence interval from competing risk model.

5 HLA-G polymorphisms genotype, frequency, MAF and HWE for 74

recipients and donors.

6 Distribution (frequency and percentage) of haplotypes in the recipient 76

and donor cohorts.

7 Frequency of match genotypes between donor and recipient 77

8 Univariate analysis risk factors for mild CAV. 77

9 Multivariate model for the diagnosis of mild cardiac allograft 84

vasculopathy.

xi

10 Univariate analysis risk factors for severe CAV. 85

11 Multivariate model for the diagnosis of severe cardiac allograft 91

vasculopathy.

xii

List of Figures FIGURE PAGE

1 HLA-G gene locus and the HLA-G isoforms. 7

2 HLA-G isoforms. 9

3 HLA-G interactions with target receptors and the modulation of 12

immune activity.

4 HLA-G 5’- Upstream promoter region. 15

5 HLA-G 3’-Untranslated region sequence. 19

6 HLA-G extended haplotypes. 23

7 Pathway for the development of CAV. 30

8 Adult heart-transplant relative incidence of leading causes of death. 42

9 Competing outcomes after heart transplantation. 70

10 Competing outcomes after heart transplantation. 72

11 Predicted freedom from CAV from donor-recipient SNP -201 (CC-CC) 93

matching.

12 Proposed mechanism of the role of HLA-G SNPs. 103

xiii

List of Appendices ITEM

1 Permission to Use Material From Copyright Owner

xiv

List of Abbreviations

14bp 14 base pair

AMR Antibody mediated rejection

ART Assisted reproductive techniques

B-cells B-lymphocytes

BMI Body mass index

CAV Cardiac allograft vasculopathy

CHD Congenital heart disease

CMR Cellular mediated rejection

CMV Cytomegavirus

CNI Calcineurin inhibitor

CNS Central nervous system

CRE/TRE cAMP Respond Element/TPA Response element

CsA Cyclosporine

CVA Cerebrovascular accident

DC Dendritic cells

DEL Deletion

DNA Deoxyribonucleic acid

DSA Donor specific antibody

EnhA Enhancer A

HLA Human leukocyte antigen

HLA-G Human Leukocyte Antigen-G

HR Hazard ratio

xv

HWE Hardy-Weinberg equilibrium

IFN Interferon

IL Interleukin

ILT2/ILT4 Immunoglobulin-like transcript-2 or -4

INS Insertion

INDEL Insertion-deletion

ISHLT International Society of Heart and Lung Transplantation

ISRE Interferon-stimulated response element

KIR2DL4 Killer cell immunoglobulin-like receptor 2DL4

kDa kiloDalton

LCL Lower confidence limit

MAF Minor allele frequency

MHC Major Histocompatibility complex

mRNA Messenger ribonucleic acid

MMF Mycophenolate mofetil

MPA Mycophenolic acid analog

NK Natural killer

PRA Panel reactive antibody

PCR Polymerase chain reaction

PSI Proliferation signal inhibitor

RREB1 Ras Responsive Element Binding 1

SAP Shrimp alkaline phosphatase

sHLA-G Soluble Human Leukocyte Antigen-G

SNP Single nucleotide polymorphism

sNTPs Deoxynucleotide triphosphates

xvi

T-cells T-Lymphocytes

T-reg Regulatory T

TGF Transforming growth factor

TNF Tumor necrosis factor

UTR Untranslated region

UCL Upper confidence limit

VAD Ventricular assist device

Excerpts from this chapter are taken from Lazarte, J., Tumiati, L.C., Rao, V. & Delgado, D. (2015) New Developments in HLA-G in Cardiac Transplantation. Human Immunology: HLA-G Special Issue. In Press

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Chapter 1

Literature Review

1 Introduction

1.1 Overview

Human Leukocyte Antigen-G (HLA-G) is a non-classical (class Ib) protein found

within the major histocompatibility complex (MHC). It is located on chromosome 6

(6p21.31) (Roitt, 2001). HLA-G interacts with specific receptors on immune cells to

exert its immunosuppressive effects (Rebmann, da Silva Nardi, Wagner, & Horn,

2014). It is upregulated in various pathological situations such as transplantation,

infection, pregnancy and malignancy where it is thought to modulate the immune

response (Donadi et al., 2011). In addition, a number of polymorphisms have been

described that function to regulate HLA-G expression (Carosella, Rouas-Freiss,

Roux, Moreau, & LeMaoult, 2015; Castelli, Veiga-Castelli, Yaghi, Moreau, &

Donadi, 2014).

The ability of HLA-G to inhibit the immune response holds great potential for its

utilization against rejection in the setting of transplantation and could enhance

current diagnostic, preventive and therapeutic strategies.

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1.2 Background

1.2.1 Immunology Basics

The human immunologic system encompasses two defense pathways, the innate

and the adaptive pathways (Roitt, 2001). The innate pathway is primitive and non-

specific, and interacts with many different pathogenic cells as it is the first line of

defense the body has (Roitt, 2001). Leukocytes that participate in the innate

response are the phagocytic cells such as the monocytes, macrophages and others.

The adaptive pathway is highly specific to particular pathogens, unlike the innate

response, it can modulate its activity with successive interactions with the same

pathogen (Roitt, 2001). The main cells involved in the adaptive response are

lymphocytes such as T-lymphocytes and B-lymphocytes. There are many activities

that T-lymphocytes undertake such as the control of B-lymphocyte development,

antibody production, interaction with phagocytes and recognition and destruction of

infected cells (Afzali, Lechler, & Hernandez-Fuentes, 2007). T-lymphocytes

recognize antigens only when presented by proteins from the major

histocompatibility complex (MHC) mainly through their T-cell antigen receptor

(Grey, Buus, Colon, Miles, & Sette, 1989; Roitt, 2001; Townsend & McMichael,

1985).

The major histocompatibility complex (MHC) is a gene complex with more than 100

gene loci located in chromosome 6. The main function of the genes in the MHC

(class I and II) is to present antigens that are recognized by T-lymphocytes and

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initiate the activation of the immune system (Roitt, 2001). MHC class I genes are

further subdivided into (a) classical and (b) non-classical (Lefranc et al., 2005).

HLA-E, -F, -G and –H genes are part of the non-classical and less polymorphic, in

addition, HLA-E and –G are also uniquely recognized by NK cells (Roitt, 2001). All

the MHC molecules are co-dominantly expressed (Hviid, Moller, Sorensen, &

Morling, 1998).

In transplantation, the immune system recognizes foreign antigens expressed on

the graft resulting in an immune response, either cell-mediated or antibody-

mediated. This immunologic response was first identified by Medawar (1944). The

graft cells and the host cells present antigens and subsequently stimulate T-

lymphocytes (Roitt, 2001). Rejection is a time-dependent event occurring with

decreasing frequency with increasing time from transplant. Hyper acute rejection

occurs immediately after cross clamp removal, acute rejection can occur at any time

post-transplant but is more common in the early weeks. Although, donor and

recipient HLA matching, especially in renal transplantation, has led to a dramatic

decrease in rejection this is not feasible in heart transplantation (Roitt, 2001).

Hence, allograft rejection remains a major survival-limiting factor.

1.2.2 Human Leukocyte Antigen-G

Unlike the other HLA proteins, the major function of HLA-G is to inhibit the

immune cells activity rather than to present peptides (Ishitani et al., 2003). Indeed

HLA-G is limited to the type of peptides it presents, in particular, presented

peptides originate from single cytokine related proteins, histones H2A, nuclear and

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ribosomal proteins and cytokine receptors (Diehl et al., 1996; Ishitani et al., 2003;

Lee et al., 1995). Furthermore, HLA-G differs from the rest of the HLA molecules,

with a conserved coding region, limited expression, alternative splicing produces

seven different protein isoforms and, most importantly to transplantation, it

promotes tolerance (Donadi et al., 2011; Kovats et al., 1990b; Paul et al., 2000).

Transcription of HLA-G occurs in all cells, however, translation is restricted to a

few cell types (Carosella et al., 2003). HLA-G was initially identified in the cells

that line the maternal and fetal boundary (Geraghty, Koller, & Orr, 1987) and later

in amnion epithelial cells (Houlihan, Biro, Harper, Jenkinson, & Holmes, 1995),

erythroid progenitor (Menier et al., 2004), endothelial precursors cells (Blaschitz et

al., 1997; Menier et al., 2004), cornea cells (Le Discorde, Moreau, Sabatier, Legeais,

& Carosella, 2003), nail matrix cells (Ito et al., 2005), pancreas cells (Cirulli et al.,

2006) and thymus cells (Crisa, McMaster, Ishii, Fisher, & Salomon, 1997).

Interestingly, HLA-G expression has been shown to be induced post-transplantation

(Lila et al., 2002), in pregnancy complications (Abbas, Tripathi, Naik, & Agrawal,

2004; Goldman-Wohl, Ariel, Greenfield, Hanoch, & Yagel, 2000), viral infections

(LeBouder et al., 2009; Yan, Lin, Chen, & Chen, 2009), inflammatory diseases

(Rizzo et al., 2008) and malignancies (Dias, Castelli, Collares, Moreau, & Donadi,

2015).

Expression of HLA-G is modified by various genetic and environmental factors.

Genetic factors include nucleotide variations that affect the binding of regulatory

factors, microRNAs and DNA methylation and histone modification (Carosella et

al., 2015). On the other hand, environmental inducers of HLA-G expression include

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growth factors, anti-inflammatory, pro-inflammatory and hormones such as

progesterone (Sheshgiri, Rao, et al., 2008), interleukin 10 (IL-10) (Moreau et al.,

1999; Rizzo et al., 2005), interferons (IFNs) (Lefebvre et al., 2001), hypoxia

(Nagamatsu et al., 2004) and others (Moreau, Flajollet, & Carosella, 2009). Lastly,

in the settings of transplantation, immunosuppressive therapy has also been

recognized to induce expression (Sheshgiri et al., 2009).

1.2.3 HLA-G Structure

The 39 kilo Dalton (kDa) HLA-G protein is composed of a heavy chain linked to the

β2-microglobulin light chain (Geraghty et al., 1987). The HLA-G gene locus

resembles high degree of similarity to the classical HLA class I genes however

discrepancies remain regarding the location of the initiation of the HLA-G mRNA

transcript, the initiation of the 3’-untranslated region and the nomenclature for the

exons and introns (Carosella et al., 2015). These discrepancies and lack of consensus

are clearly noted in the Immunogenetic Database (IMGT/HLA) and the National

Center for Biotechnology Information (NCBI). According to the IMGT/HLA

database, exon 1 codes the leader peptide, exons 2-4 code the three α domains (α1,

α2, α3) and exon 5 codes the transmembrane domain (Figure 1)(Geraghty et al.,

1987). Exons 6 encodes for the cytoplasmic tail and contains a stop codon that

shortens the final protein product (Carosella et al., 2015).

Alternative splicing of the mRNA creates 7 protein isoforms (Figure 1) (Paul et al.,

2000). These constitute four membrane-bound (HLA-G1 to G4) and three soluble

(HLA-G5 to G7) isoforms (Paul et al., 2000). HLA-G1 and HLA-G5 contain the full-

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length protein structure and are composed of a heavy chain of three α globular

domains non-covalently linked to β2 microglobulin and a peptide (Paul et al., 2000).

The HLA-G2 and HLA-G6 isoforms lack the α2 globular domain, HLA-G3 and HLA-

G7 lack the α2 and α3 globular domains and HLA-G4 lacks the α3 globular domain

with no soluble counterpart unlike the other isoforms (Ishitani & Geraghty, 1992;

Kirszenbaum, Moreau, Gluckman, Dausset, & Carosella, 1994; Paul et al., 2000).

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Figure 1: HLA-G gene locus and the HLA-G isoforms. The HLA-G gene locus contains 7 introns and 8 exons, typical of HLA class I gene locus. Exon 1 codes the signal peptide, exons 2-4 code three α domains (α1, α2, α3) and exons 5-6 code the transmembrane and cytoplasmic region. Exons 7 and 8 are not coded due to the presence of a stop codon in exon 6. Alternative splicing of the mRNA creates 7 protein isoforms. These constitute four membrane-bound (HLA-G1 to G4) and three soluble (HLA-G5 to G7) isoforms. Adapted from Copyright © The Author(s) 2010 - Donadi et al. (2011).

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All the soluble counterparts (HLA-G5, HLA-G6 and HLA-G7) further lose the

transmembrane and cytoplasmic domains due to a stop codon in intron 4 (Fujii,

Ishitani, & Geraghty, 1994; Paul et al., 2000). Lastly, metalloproteases can shed-off

membrane-bound HLA-G1 resulting in a soluble protein (sHLA-G1) (Dong et al.,

2003). Therefore, the HLA-G molecule can be found as 7 different isoforms;

membrane-bound, soluble or shed. In addition, the HLA-G molecule is capable of

dimerization due to the presence of an unpaired cysteine at position 42 in the α1

domain (Figure 2) (Shiroishi, Kuroki, Ose, et al., 2006). A second cysteine in

position 147 in the α3 domain may also be involved in the dimerization although to

a lesser capacity (Boyson et al., 2002). These two cysteine (position 42 and 147) are

in addition to the characteristic ones in α2 and α3 domains of the HLA class I

molecules (Geraghty et al., 1987). Dimerization shifts the binding site to an oblique

orientation, which increases its binding specificity to receptors (Shiroishi, Kuroki,

Ose, et al., 2006). Indeed HLA-G receptors have a higher affinity for dimers as

opposed to the monomer configuration (Shiroishi, Kuroki, Ose, et al., 2006).

Furthermore, the association rate is prolonged with dimerization (Shiroishi, Kuroki,

Ose, et al., 2006). The importance of dimer formation is supported by two

observations. First no allele contains a polymorphism at codon 42 (TGT), therefore,

all isomers can create dimers (Howangyin et al., 2012). Secondly, once a variation is

introduced at codon 42 the activity of the molecule is significantly reduced (Gonen-

Gross et al., 2003). Therefore it was proposed that dimerization is required for

interaction with its receptors (Gonen-Gross et al., 2003). In summary, the HLA-G

molecule can be present in various forms. This feature will become important at a

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later time when defining the expression of HLA-G with its associated

polymorphisms and clinical outcomes.

Figure 2: HLA-G isoforms and dimers produced through the unpaired cysteine at position 42 and 147. Adapted from Copyright © 2012 Informa Healthcare USA, Inc. -Gonzalez et al. (2012).

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1.3 HLA-G Immune Inhibition

1.3.1 HLA-G Receptors

Several different pathways have been described demonstrating how HLA-G exerts

its protective effect. HLA-G is the main ligand for immunoglobulin-like transcript-2

(ILT2 or LILRB1) and -4 (ILT4 or LILRB2), the killer immunoglobulin-like receptor

(KIR2DL4/CD158d) and CD8 co-receptor (Figure 2)(Colonna et al., 1997; Colonna et

al., 1998; Rajagopalan & Long, 1999; Sanders, Giblin, & Kavathas, 1991). ILT2 and

ILT4 receptors interact with the α3 domain only found in the full-length protein,

HLA-G1, and its soluble counterpart, HLA-G5 (Shiroishi et al., 2003). In addition,

ILT2 recognizes the β2m structure while ILT4 recognizes the free heavy chains

(Gonen-Gross et al., 2005; Shiroishi, Kuroki, Rasubala, et al., 2006). ILT2 receptors

are present in B-lymphocytes (B-cells), T-lymphocytes (T-cells), natural killer (NK)

cells, monocytes, and dendritic cells (DC) (Colonna et al., 1997). ILT4 receptors are

expressed on monocytes, DC and macrophages (Colonna et al., 1998). Indeed, ILT2

and ILT4 receptors have the highest affinity for HLA-G over any other HLA class I

molecules (Shiroishi et al., 2003).

Due to the HLA-G interaction with the KIR2DL4 receptor (found primarily in NK

cells), HLA-G was recognized for protecting the fetus from the mother’s immune

system (Rajagopalan & Long, 1999). The KIR2DL4 receptor only recognized the α1

domain (which all protein isoforms have). However, the function of KIR2DL4, bound

to HLA-G, remains controversial with stimulatory and inhibitory effects described

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rather than just inhibitory ones given HLA-G’s known protective effect (Le Page,

Goodridge, John, Christiansen, & Witt, 2014; Rajagopalan & Long, 1999). The CD8

co-receptor is found in activated T-cells and subpopulations of NK cells and

recognizes the α3 domain (Sanders et al., 1991). HLA-G also binds to CD160 found

in CD8+T-cells and NK cells (Contini et al., 2003; Fons et al., 2006). By interacting

with these receptors, HLA-G can exert an immune inhibitory effect. This is

summarized in Figure 3.

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Figure 3: Soluble HLA-G interactions with target receptors on various immune cells and the modulation of their immune activity. Adapted from Copyright © 2007 Elsevier Ltd. - Pistoia, Morandi, Wang, and Ferrone (2007).

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1.3.2 Immune Modulation

HLA-G creates a time-dependent tolerant environment. This is achieved once HLA-

G binds to the ILT2, ILT4, KIR2DL4, CD8 and CD160 receptors in T-cells, NK cells,

B-cells, monocytes/macrophages and DC cells (Figure 3) (Gonzalez et al., 2012).

Indeed the tolerant effect lasts as long as HLA-G is bound to the aforementioned

receptors. Various investigations describe the response of the immune cells once

HLA-G interacts with their receptors. CD8+ T-cell and CD8+ NK cells undergo

apoptosis (Contini et al., 2003). CD4+ T-cell activity is repressed, regardless of the

presence of CD8+ T-cells (Bainbridge, Ellis, & Sargent, 2000). NK cell-mediated

lysis is suppressed (Rouas-Freiss, Goncalves, Menier, Dausset, & Carosella, 1997).

Dendritic cell maturation is inhibited (Liang et al., 2008). T-cells’ cytotoxic response

of allo-proliferation is inhibited (Riteau et al., 1999). Furthermore, proliferation,

differentiation and antibody secretion in B-cells is suppressed (Naji et al., 2014).

Overall, the binding of HLA-G to its receptors changes the cells’ activity to promote

tolerance.

Long-term modulation by HLA-G involves the formation of regulatory/suppressor

cells that continue inhibiting the immune system, even in the absence of HLA-G

(Gonzalez et al., 2012; Rebmann et al., 2014). Often times, the switch to regulatory

cells is triggered by trogocytosis. Trogocytosis occurs when a portion of the

membrane (with bound HLA-G) is transferred from cell to cell (Caumartin et al.,

2007; LeMaoult et al., 2007). In this process, the recipient cell acquires membrane-

embedded HLA-G molecules from a donor cell and for a brief period can utilize the

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HLA-G (Caumartin et al., 2007). Again various investigations highlight HLA-G’s

effect once in contact with the immune cells. T-cells allo-proliferation is inhibited

and the cells switch to a regulatory behavior promoting a tolerogenic behavior onto

other cells (Le Rond et al., 2006). Activation through trogocytosis leads CD4+ and

CD8+ T-cells to become unresponsive over the long-term and switch their phenotype

to regulatory T-cells (LeMaoult et al., 2007; LeMaoult, Krawice-Radanne, Dausset,

& Carosella, 2004). NK cells’ cytotoxic activity is terminated as they change their

behavior to regulatory activity and stimulate other NK cells (Caumartin et al.,

2007). DC cell maturation is terminated. The resulting DC cells then stimulate the

differentiation of T-cells into regulatory T-cells (Liang, Baibakov, & Horuzsko, 2002;

Liang et al., 2008). Altogether, the association of HLA-G with these cells initiates a

cascade effect that promotes long-term tolerance in the environment.

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Figure 4: HLA-G 5’- upstream promoter region with the known regulatory elements that bind to the gene and the current polymorphisms identified accordingly to the 1000 Genome data. Adapted from Copyright © 2014 Erick C. Castelli et al. (2014).

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1.4 HLA-G Polymorphisms

HLA-G gene expression is co-dominant. In this case, both paternal and maternal

alleles are expressed together and the phenotype is a combination of the two (Hviid

et al., 1998). Unlike HLA class I genes, the HLA-G gene has limited polymorphisms.

The coding region is the most conserved region while the 5’-upstream promoter

region and 3’-untranslated region demonstrate some variability (Castelli et al.,

2014). Due to this particular characteristic, variations in the gene are considered to

be instrumental in generating variable expression among individuals (Castelli et

al., 2014).

1.4.1 5’-Upstream Promoter Region

The 5’-upstream promoter region determines the rate of synthesis of the mRNA by

interacting with transcription factors (Castelli et al., 2014). The region contains two

main regulatory modules, the cis regulatory element, which includes the Enhancer

A with the interferon-stimulated response element (ISRE), and the second

regulatory element is the SXY module (Figure 4) (Castelli et al., 2014). These

regulatory elements are modified in contrast to typical HLA class I promoter

regions (S. J. Gobin & van den Elsen, 2000; Solier et al., 2001). The modifications

render them unresponsive to common HLA modulators such as nuclear factor (NF) -

κB and IFN-y (Castelli et al., 2014; S. J. Gobin, Keijsers, Cheong, van Zutphen, &

Van den Elsen, 1999; S. J. P. Gobin, van Zutphen, Woltman, & van den Elsen, 1999;

van den Elsen, Gobin, van Eggermond, & Peijnenburg, 1998). For instance, the

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Enhancer A (EnhA) element only interacts with p50/p50 homodimers in the HLA-G

gene in comparison with the vast array of homo and heterodimer factors that the

HLA class I genes interact with such as p65/p65 homodimers and p65/p50

heterodimers. Furthermore, the p50/p50 homodimers are recognized to be

ineffective promoters of transcription compared to p65/p65 and p65/p50 (Castelli et

al., 2014). Other elements in the promoter region constitute the heat shock element,

progesterone receptor element, hypoxia response element, locus control region

candidate, the CRE/TRE candidate sites (cAMP Respond Element/TPA Response

element), the RREB1 (Ras Responsive Element Binding 1) and GLI-3 repressor (a

signal transducer of the Hedgehog pathway) all which presume to regulate the

expression of HLA-G (Castelli et al., 2014). The heat shock element and hypoxia

response element are unique to the HLA-G gene (Ibrahim, Morange, Dausset,

Carosella, & Paul, 2000; Moreau et al., 2009). Although the heat shock factor

increases the expression of the HLA-G molecule, the functionality of the element is

unknown (Moreau et al., 2009). Indeed, the effect on HLA-G expression varies for

each element. For instance, the Ras Responsive element once activated decreases

HLA-G transcription and subsequent expression (Flajollet, Poras, Carosella, &

Moreau, 2009). While, the presence of progesterone upregulates the expression of

the protein (Yie, Xiao, & Librach, 2006).

Various polymorphisms are present in close proximity to regulatory elements,

modifying their affinity to the gene and subsequently affecting the overall

expression of HLA-G. A number of investigations have identified the role of various

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single nucleotide polymorphisms (SNPs) in modulating expression levels of HLA-G

and correlate their role in disease states (Donadi et al., 2011; Hviid, Hylenius,

Rorbye, & Nielsen, 2003; Z. Tan, Shon, & Ober, 2005). The G allele for the SNP-725

has been associated with an increased risk of miscarriages and risk for multiple

sclerosis (Ober, Billstrand, Kuldanek, & Tan, 2006; Wisniewski et al., 2010). Ober

et al. (2006) indicated that the switch of C for G creates a greater chance of

methylation at this SNP that could be inhibiting the transcription and subsequently

lead to lower HLA-G expression (Moreau et al., 2003). SNP-201 is located close to

regulatory elements P50 (a nuclear factor NF- Κ-B p105 subunit), SP1

(Transcription factor specificity protein 1) and hypoxia response element and has

been identified to potentially affect their association to the gene (Moreau et al.,

2009). Lastly, a variation at position -964 has been associated with asthma in

children and their respective mothers potentially because the presence of the A

allele decreases the methylation compared to the G allele (Nicolae et al., 2005; Ober

et al., 2003; Z. Tan et al., 2005).

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Figure 5: HLA-G 3’-untranslated region sequence with the location of the polymorphisms bolded. Adapted from Copyright © 2010 Macmillan Publishers Limited - Castelli et al. (2010).

1.4.2 3’-Untranslated Region

The 3’-untranslated region (UTR) establishes the degradation rate of the mRNA

product (Castelli et al., 2014). The 3’-UTR region begins past the stop codon in exon

6, since exon 7 and 8 are not present in the mature mRNA and it contains

regulatory elements such as AU-rich motifs and a poly-A signal (Alvarez, Piedade,

Balseiro, Ribas, & Regateiro, 2009; Geraghty et al., 1987; Kuersten & Goodwin,

2003). In addition, there are binding sites for micro-RNA (Castelli, Moreau,

Chiromatzo, et al., 2009) that down regulate mRNA expression (Gonzalez et al.,

2012; Veit & Chies, 2009). An investigation by Porto et al. (2015) analyzed the

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relation between the various microRNAs and the HLA-G polymorphisms. It was

proposed that a panel of microRNAs bind to some of the polymorphisms and may be

therefore be affected by them while others microRNAs bind to the gene

irrespectively of the nucleotide variations (Porto et al., 2015). Therefore in order to

fully predict the expression of HLA-G, besides sequencing the HLA-G gene for

polymorphisms, it was suggested that microRNAs should be profiled separately

since expression of HLA-G is a result of a combination of all these factors

influencing expression (Porto et al., 2015).

Several polymorphisms have been identified to modulate the expression of HLA-G

and are scattered in this region, such as the SNP 3142, SNP 3187, SNP 3196 and

the 14bp insertion-deletion (INDEL) sequence (Figure 5). However, other SNPs

have also been studied and associated to a lesser degree. For instance, the presence

of allele CC at position SNP 3027 was associated with higher soluble HLA-G levels

in a healthy donor population compared to the other alleles in that same position

(Martelli-Palomino et al., 2013). SNP 3142 has been associated with regulating

HLA-G expression. The presence of a GG at this SNP results in lower protein

production in a multiple sclerosis cohort (Rizzo et al., 2012) and may be related to

the fact that this SNP is targeted by micro-RNAs when guanine is present (Z. Tan

et al., 2007; Veit & Chies, 2009). The A allele for SNP 3187 is associated with pre-

eclampsia and AA genotype is associated with increased severity of the disease (Yie,

Li, Xiao, & Librach, 2008). Indeed, it was also observed that the A allele for this

SNP is associated with lower protein expression in vitro through alterations in

mRNA stability (Yie et al., 2008). SNP 3196 is located closed by and to the right of

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an AU-rich segment in the HLA-G gene that is affected by microRNAs. Indeed, the

presence of guanine at SNP 3196 position is associated with severe pre-eclampsia in

primiparas (Larsen, Hylenius, Andersen, & Hviid, 2010).

The 14bp INDEL (5’-ATTTGTTCATGCCT-3’) polymorphism has been extensively

studied. Found in the 3’ end of the gene, it spans from position +2961 to +2974

(Harrison, Humphrey, Jakobsen, & Cooper, 1993). Martelli-Palomino et al. (2013)

found that individuals homozygous for the deletion (DEL) allele have significantly

higher HLA-G expression than individuals homozygous for the insertion (INS)

allele. The INS allele is associated with a stable mRNA with lower mRNA

production and subsequently lower expression overall, as compared to the DEL

allele (Hviid et al., 2003).

1.4.3 Coding Region

The HLA-G coding region is known for its low degree of polymorphism, this

characteristic requires a strong selective pressure for invariance. Worldwide, 50

coding region alleles have been identified that have either non-synonymous or

synonymous polymorphisms combining to a total of 16 distinct proteins (G*01:01,

G*01:02, G*01:03, G*01:04, G*01:05N, G*01:06, G*01:07, G*01:08, G*01:09,

G*01:10, G*01:11, G*01:12, G*01:13, G*01:14, G*01:15, G*01:16, G*01:17, G*01:18)

(IMGT/HLA Database version 3.21.0). The most prominent alleles in the worldwide

population are G*01:01, G*01:03, G*01:04 and G*01:06 (Donadi et al., 2011). Two

alleles are associated with partial or no molecule expression. Allele G*01:05N has a

single nucleotide deletion in codon 130 which causes a frame shift and the

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formation of a stop codon (Suarez et al., 1997). Partial production of the protein

isoforms is observed with this allele (Suarez et al., 1997). Null allele G*01:13 has a

single nucleotide exchange, which leads to the formation of a stop codon in position

54 (Lajoie, Jeanneau, Faucher, Moreau, & Roger, 2008). No molecule is produced

with this allele (Lajoie et al., 2008).

In general, polymorphisms in the coding region have been found close to the three α-

globular domains regions, where the variations can cause modifications to the

interactions of the protein with the receptors (Donadi et al., 2011). There are 4

mutations in codons 13, 27, 31 and 54 that can affect α1 domain. The α2 domain has

six mutations in codons 100, 104, 105, 110, 130, 159 and 169. And lastly, mutations

on codons 185,189, 219 and 258 can affect the α3 domain (Donadi et al., 2011).

Interestingly, the signal peptide and transmembrane portion remain highly

conserved (Donadi et al., 2011). A few alleles from the coding region have been

associated with modulating protein expression (Rebmann et al., 2001). Rebmann et

al. (2001) was the first investigation to associate alleles in the coding region to

high/low HLA-G expression (Rebmann et al., 2001). The presence of allele G*01:04

was related to higher HLA-G expression, while alleles G*01:05N and G*01:01:03,

with low HLA-G in a healthy cohort (Rebmann et al., 2001). Furthermore, alleles

from the coding region have been associated with detrimental or beneficial

outcomes in various diseases. For instance, G*01:05N, G*01:01:08 and G*01:01:03

were found in higher frequency in a population of women with recurrent

spontaneous abortion compared to a healthy cohort indicating that the alleles may

be detrimental (Abbas et al., 2004). Another investigation identified an increase risk

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for miscarriages with alleles G*01:04 or G*01:05N (Aldrich et al., 2001). Lastly

Castelli, Mendes-Junior, Viana de Camargo, and Donadi (2008) recognized allele

G*01:04 to be associated with patients whose tumor had progressed to a higher

grade while allele G*01:03 was associated with high-grade tumors in patients that

reported smoking. Indeed, though limited, the coding region polymorphisms do

appear to have an effect in the HLA-G molecule expression and subsequent disease

outcome.

Figure 6: HLA-G extended haplotypes created with polymorphisms from the 5’-upstream promoter, 3’-untranslated and coding region with their frequency in a healthy volunteer population. Adapted from Copyright © 2012 American Society for Histocompatibility and Immunogenetics - Di Cristofaro et al. (2013).

1.4.4 Haplotypes

Several investigations have identified variations in polymorphisms in the 5’-

upstream promoter region, coding region and 3’-untranslated region, that always

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occur together as formed haplotypes, a phenomenon called linkage disequilibrium

(LD) (Castelli et al., 2011). Indeed, it is reasonable to consider that the effect of a

single nucleotide variation is not independent of the rest and that the overall

expression of the HLA-G gene may be better understood once all regions are

considered together. Several SNP variations (from all regions of the gene) were

grouped into 8 different haplotypes and related to levels of HLA-G expression

(Figure 6)(Di Cristofaro et al., 2013). The presence of a single haplotype 5 and

homozygous haplotype 1/haplotype 1 were associated with significantly higher

HLA-G expression compared to other haplotypes (Di Cristofaro et al., 2013). A

single haplotype 5 allele indicates its strong dominant effect (Di Cristofaro et al.,

2013). The haplotype 5 has SNP -725T, -716T, -201G, -56T, INS, 3142G, 3187A,

3196C and G*01:03 (Di Cristofaro et al., 2013). The effect of haplotype 5 may be, in

part, due to SNP -56 and -725, since the T allele for SNP -56 and the T allele for

SNP -725 are only present in haplotype 5 (Di Cristofaro et al., 2013). For haplotype

1, the allele includes SNP -725C, -716T, -201G, -56C, DEL, 3142C, 3187G, 3196C

and G*01:01 (Di Cristofaro et al., 2013). Interestingly, the presence of the INS allele

in haplotype 5 and 1 do not corroborate with low HLA-G levels as seen in previous

investigations (Di Cristofaro et al., 2013). Lastly, patients with haplotype

2/haplotype 2 had significantly lower HLA-G (Di Cristofaro et al., 2013). Haplotype

2 consisted of SNP -725C, -716G, -201A, -56C, INS, 3142G, 3187A, 3196G and

G*01:01/G*01:05N/ G*01:06 (Di Cristofaro et al., 2013). This may indicate that the

presence of GG at SNP 3196 is associated with significantly lower than normal

HLA-G levels, since it only occurs in haplotype 2 (Di Cristofaro et al., 2013). The

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remaining haplotypes, which consist of a combination of variations in SNP -725G/C,

-716T/G, -201C/T, DEL/INS, 3142C/G, 3187G/A and G*01:01/G*01:04, had HLA-G

levels in between the high and low category and significantly different from either

(Di Cristofaro et al., 2013). Indeed analyzing haplotypes or SNPs from all the

regions of the gene is currently the most comprehensive way of understanding the

effect of HLA-G polymorphisms compared to investigating a single polymorphisms

because it provides the full picture of the effect of polymorphisms throughout the

gene.

1.5 The Role of HLA-G in Pregnancy

Initially the expression of the HLA-G molecule was found in the trophoblast cells of

the placenta where it was recognized to be a key player in maternal tolerance

(Moreau et al., 1995). Trophoblast cells lack the expression of HLA class I peptides

such as HLA-A and -B on their cell surface but they do have HLA-G, -E –F, and –C

(King et al., 1996; Kovats et al., 1990a). As such, maternal T-cells are unable to

initiate an immune response against the fetus which is in essence a semi allograft

(Ljunggren & Karre, 1990). However, NK cells can stimulate an immune response

by recognizing cells that don’t present antigens (Ljunggren & Karre, 1990). This is

where HLA-G plays a key role in immune tolerance. HLA-G binds specifically to NK

cell inhibitory receptor (KIRKDL4), which in turn terminates the NK cell immune

activation (RouasFreiss, Goncalves, Menier, Dausset, & Carosella, 1997). Indeed

halting HLA-G interaction with the NK cell receptor in vitro, in turn led to an

increase in NK cytotoxic activity demonstrating its inhibitory effect (Sun, Han,

- 26 -

Chen, & Yao, 2008). The interaction of HLA-G with KIRKDL4 receptor protects the

trophoblast cells in the maternal-fetal interface and thus allows for a successful

pregnancy (Roussev & Coulam, 2007).

HLA-G levels increase throughout development starting at the pre-implantation

stage (Shaikly et al., 2008; Yao, Barlow, & Sargent, 2005). Interestingly varying

levels of HLA-G have been associated with different pregnancy outcomes. High

levels of HLA-G pre-implantation are associated with successful pregnancy rates

(Jurisicova, Casper, MacLusky, & Librach, 1996; Yao et al., 2005). Recurrent

spontaneous abortions and pre-eclampsia have been associated with lower levels of

HLA-G, specifically at first week of gestation (Goldman-Wohl, Ariel, Greenfield,

Hanoch, et al., 2000; Goldman-Wohl, Ariel, Greenfield, Hochner-Celnikier, et al.,

2000; Yie, Li, Li, & Librach, 2004). This association indicates that pregnancies with

low HLA-G are at a higher risk of such problems than pregnancies with normal

HLA-G levels (Gonzalez et al., 2012). Women with recurrent spontaneous abortion

had significantly higher frequency of the INS allele for the 14bp INDEL

polymorphisms than healthy control women (Hviid, Hylenius, Lindhard, &

Christiansen, 2004). In fact, low HLA-G expression is linked with the INS allele and

has been associated with pregnancy complications in various investigations (Fan,

Li, Huang, & Chen, 2014; Hviid, Hylenius, et al., 2004; Hviid et al., 2003; Wang,

Jiang, & Zhang, 2013).

Research in the area of Assisted Reproductive Techniques (ART) also corroborates

the importance of HLA-G in pregnancy outcomes. The presence of soluble HLA-G in

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the embryo has been found to be crucial for a successful pregnancy. HLA-G presence

increases the chances of a successful pregnancy from 45 to 72% (Dahl & Hviid,

2012; Fuzzi et al., 2002; Rizzo et al., 2007).

1.6 The Role of HLA-G in Cancer

In particularly the field of cancer, HLA-G expression was initially identified in

melanoma cells (Paul et al., 1998) but now it is known that it is expressed on

various tumor cell types (Gonzalez et al., 2012). It is considered a cancer biomarker,

as the expression of HLA-G is significantly higher in cancer patients compared to

healthy individuals (Ibrahim et al., 2004; Rebmann, Wagner, & Grosse-Wilde,

2007). More specifically, it is upregulated in choriocarcinona (Mao, Kurman, Huang,

Lin, & Shih Ie, 2007), breast cancer (He et al., 2010), renal cell cancer (Hanak et al.,

2009), ovarian cancer (Menier, Prevot, Carosella, & Rouas-Freiss, 2009), lung

cancer (Cao et al., 2011), liver cancer (Lin et al., 2010), multiple myeloma (Leleu et

al., 2005) and various other tumors (Dias et al., 2015).

HLA-G is a strong immune inhibitor that terminates T-cells and NK cell cytotoxic

activity allowing the tumour an opportunity to ‘hide’ from the immune system (Dias

et al., 2015). Indeed, across various tumour cell types, there is an association

between high levels of HLA-G and poor prognosis (Dias et al., 2015; He et al., 2010).

Poor prognosis is an umbrella term for detrimental outcomes such as shorter

survival time, higher chances of tumor reoccurrence, metastasis, invasion and

others (Dias et al., 2015).

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With the exception of the 14bp INDEL polymorphism, the role of various

polymorphisms has been studied to a lesser extent in relation to cancer outcomes.

To that point a recent breast cancer meta-analysis identified the 14bp INDEL

polymorphism to be an important factor in assessing risk of developing cancer in

the Asian population (Ge et al., 2014). In another investigation, patients with

hepatocellular carcinoma screened for the 14bp INDEL polymorphism were found to

have increased tissue expression of HLA-G when they had the DEL genotype (Jiang

et al., 2011). This suggests a potential detrimental role of the 14bp INDEL

polymorphism in cancer, however further work in this area of study is needed to

clarify inconsistences. In the context of transplantation, immunosuppressive

treatment has increased the likelihood of patients developing cancer post-

transplant (Stehlik et al., 2012). It is unknown however, how HLA-G expression

post-transplant affects the development and diagnosis of cancer.

1.7 The Role of HLA-G in Heart Transplantation

The immune inhibitory activity of HLA-G was first studied in the context of cardiac

transplantation in 2000 (Lila et al., 2000). Sheshgiri, Rouas-Freiss, et al. (2008) was

the first to identify in vitro that myocardial smooth muscle cells and cardiac

endothelial cells can be stimulated to express HLA-G in the presence of increasing

progesterone. These findings prompted the idea of progesterone as a novel

therapeutic treatment to stimulate graft tolerance (Sheshgiri, Rao, et al., 2008).

Indeed, as mention before there is a progesterone receptor element in the 5’-

upstream promoter region and it maybe through it that progesterone affects HLA-G

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expression (Castelli et al., 2014). Interestingly, these cells did not respond to other

environmental factors known to modulate HLA-G expression, such as interferon γ

and interleukin 10 (Sheshgiri, Rao, et al., 2008). Continuing with this line of work,

our team has explored the relation and response of human coronary smooth muscle

cells to HLA-G. When these cells were treated with everolimus, a potent

immunosuppressant, they expressed HLA-G (Mociornita et al., 2011). In addition,

expression of HLA-G in these cells led to the inhibition of their proliferative activity,

which was dependent on the concentration of HLA-G (Mociornita, Tumiati,

Papageorgiou, Grosman, et al., 2013). Lastly, HLA-G was shown to inhibit

neutrophil adhesion to injured human coronary artery endothelial cells (as a TNF-α-

induced inflammatory response) (Mociornita, Tumiati, Papageorgiou, Grosman-

Rimon, et al., 2013). In summary, HLA-G inhibitory activity in these observations,

suggests its potential role against crucial pathways in the development of CAV

(Figure 7).

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Figure 7: Pathway for the development of CAV starting from various stressors that lead to vascular inflammation followed by smooth muscle cell phenotypic change and their migration and proliferation into the vascular intima. Permission granted from Copyright © 2013 Mociornita, Tumiati, Papageorgiou, Grosman, et al. (2013).

Ischemia-reperfusion

EC injury

Acute rejection

Donor disease

Infection

Metabolic disorders

Hypertension

Donor age

Preservation damage

Immunosuppressants

Vascular inflammation

Proinflammatory cytokines

Adhesion molecules Chemokines

Growth factors

TcellsLeukocytes Macrophages

Phenotypic change

SMC migration into vascular intima and proliferation

CAV

- 31 -

In transplantation, survival is limited by rejection of the allograft, which occurs via

cellular-mediated or antibody-mediated processes, additionally, late term survival is

significantly limited by the development of cardiac allograft vasculopathy; which

involves both immune and non-immune factors (Chih et al., 2012; Lund et al., 2014;

Ramzy et al., 2005). In the clinical setting, 18% of heart transplant patients

expressed HLA-G in endomyocardial biopsy specimens (Lila et al., 2002). When

analyzing the association of HLA-G and outcomes post-transplant, patients with no

soluble HLA-G levels had significantly more episodes of acute cellular rejection than

those with positive levels (Lila et al., 2002). Our team demonstrated a strong benefit

to HLA-G expression, since 86% of those patients had no acute cellular rejection

episodes of Grade ≥ 2R (Sheshgiri, Rouas-Freiss, et al., 2008). This association of

positive expression of HLA-G and lower episodes of rejection was corroborated by

Luque et al. (2006) who concluded that high levels of HLA-G, assessed both pre- and

post-transplant, could be a potential clinical marker indicating favorable transplant

prognosis.

Our team explored the association between HLA-G expression and C4d staining, a

marker of antibody-mediated rejection (Sheshgiri, Rao, Mociornita, Ross, &

Delgado, 2010). The results revealed a negative correlation, suggesting that

increased expression of HLA-G blocked antibody processes as inferred from the

absence of C4d staining on endomyocardial biopsy (Sheshgiri et al., 2010). A recent

investigation has validated this observation by demonstrating that HLA-G has a

powerful role in inhibiting proliferation and antibody production by B-cells (Naji et

- 32 -

al., 2014). However, there is a need for further exploration of the role, if any, of

HLA-G and antibody-mediated rejection events post-transplantation.

In regards to the association of HLA-G and cardiac allograft vasculopathy, patients

with high levels of HLA-G had lower incidence of allograft vasculopathy at 1 year

post-transplant (Blanco-Garcia et al., 2013). This suggests that CAV development

is inhibited by the presence of HLA-G, which was also indirectly suggested by our

team’s in vitro analysis (Mociornita, Tumiati, Papageorgiou, Grosman, et al., 2013).

These results have been observed in another investigation where patients with

positive HLA-G expression had no evidence of cardiac vasculopathy (Lila et al.,

2002). Thus, we conclude that expression of HLA-G is associated with beneficial

outcomes in the heart transplant population by inhibiting the immune system and

promoting allograft tolerance.

1.8 The Role of HLA-G in Lung, Kidney and Liver

Transplantation

In the world of transplant, the relevance of HLA-G expression in allograft protection

has been corroborated. In liver-kidney transplantation, HLA-G expression was

noted in 14 out of 40 liver and 5 out of 9 kidney biopsy specimens (Creput,

Durrbach, et al., 2003). The presence of HLA-G was significantly correlated with

the absence of acute cellular or chronic rejection (Creput, Durrbach, et al., 2003).

HLA-G expression in biopsy specimens of lung, liver and kidney transplant

recipients was associated with a significant decrease in cellular mediated rejection

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episodes, suggesting HLA-G’s potential role in the protection of the allograft

(Brugiere et al., 2009; Hu, Wu, Su, Pang, & Zhang, 2014; Xiao et al., 2013). As

previously mentioned, the HLA-G molecule has various isoforms that exist and that

interact with immune cells, however most investigations examine monomers and

only two isoforms (HLA-G1 and HLA-G5) (Carosella et al., 2015). Importantly, there

are differences in the potential effect HLA-G isoforms have; for instance, immune

receptors have higher affinity for dimers over monomers (Shiroishi, Kuroki,

Rasubala, et al., 2006). The first investigation to analyze the association between

HLA-G dimers and outcomes was in a kidney transplant cohort. Dimers in the

plasma were associated with prolonged survival of the allograft (Ezeakile et al.,

2014). These studies demonstrate that there is still much to learn about HLA-G

isoforms and their variable roles with regulating immune cells and receptors.

The effect of HLA-G in antibody-mediated rejection and chronic allograft

dysfunction was also investigated in kidney and lung transplantation. The presence

of HLA-G was related to lower levels of anti-HLA antibodies, indicating an

inhibition of antibody-mediated rejection in a kidney transplant cohort (Qiu et al.,

2006). Furthermore in renal transplants, an inverse relationship between HLA-G

and chronic allograft nephropathy was observed (Crispim, Duarte, et al., 2008). In

lung transplantation, bronchial epithelial cells with presence of HLA-G were

associated with fewer episodes of cellular rejection and bronchiolitis obliterans

syndrome (Brugiere et al., 2009). The same group concluded that early presence of

HLA-G in the lung graft correlated with a better long-term prognosis and less

rejection episodes (Brugiere et al., 2015). Taken all together, these studies indicate

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that HLA-G plays a key role in regulation of the immune response and that higher

HLA-G expression is associated with fewer cases of cellular- and/or antibody-

mediated rejection and chronic allograft dysfunction.

1.9 The Role of HLA-G Polymorphisms in Heart

Transplantation

In order to understand what regulates the expression of HLA-G and identify

markers for disease outcomes or stages, nucleotide variations in the gene have been

investigated. Our team analyzed the 14bp INDEL polymorphism and its association

with cellular mediated rejection in a large population of heart transplants recipients

(Twito et al., 2011). A significant correlation between the prevalence of the DEL

allele in the recipients and decreased acute cellular rejection was identified. This is

in line with the findings that the DEL allele is associated with increased levels of

HLA-G and subsequently these high levels of HLA-G inhibit cellular mediated

rejection (Twito et al., 2011). The 14bp INDEL polymorphism could be a useful

clinical marker to identify patients at risk for higher rejection episodes (Twito et al.,

2011). In terms of cardiac allograft vasculopathy, no association was found with the

14bp INDEL polymorphism (Mociornita, Lim-Shon, et al., 2013). However,

analyzing multiple SNPs or haplotypes instead of a single polymorphism may

provide a better understanding of the effect of polymorphisms on HLA-G expression

and their association with outcomes.

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1.10 The Role of HLA-G Polymorphisms in Lung, Kidney and

Liver Transplantation

HLA-G polymorphisms were also investigated in lung and kidney transplant

cohorts. Again, the most studied polymorphism has been the 14bp INDEL

polymorphism. The presence of INS allele for the 14bp INDEL polymorphisms was

linked with increased risk for acute cellular rejection in kidney transplants patients

(Crispim, Mendes-Junior, et al., 2008; Misra et al., 2013). However, the findings

with respect to the 14bp INDEL polymorphism have not been consistent across all

organs. Bone marrow transplant patients with the DEL allele had an increased risk

of developing severe graft versus host disease (GvHD), while the INS allele and the

heterozygous allele had low risk of GvHD (La Nasa et al., 2007). Azarpira, Aghdaie,

Kazemi, Geramizadeh, and Darai (2014) in a cohort of liver transplant patients

found no significant association between the 14bp INDEL polymorphism and

rejection. Finally, a kidney transplant meta-analysis suggested no direct association

between the risk of rejection and the 14bp INDEL polymorphism (Hou, Huang, Ji,

& Zhou, 2014).

From the 3’-untranslated region, the GA genotype at SNP 3187 was associated with

increased episodes of acute cellular and chronic rejection and the CC genotype at

SNP 3035 was associated with chronic rejection only in kidney transplant patients

(Ciliao Alves et al., 2012). From the coding region, alleles G*01:01:01:03,

G*01:01:02, G*01:06 and G*01:05N were significantly associated with increased

risk of cellular rejection in kidney transplant patients (Misra et al., 2014). Another

- 36 -

investigation, focusing on coding region alleles, concluded that patients

heterozygous for a synonymous allele (G*01:01) and a non-synonymous allele (for

example G*01:04 or G*01:05N) were at higher risk for episodes of cellular rejection

(Pirri, Contieri, Benvenutti, & Bicalho Mda, 2009). Lastly in the 5’- upstream

regulatory region, increased rejection episodes were linked with allele C for SNP -

725 and allele G for SNP -964 (Misra et al., 2013).

A recent report investigated HLA-G haplotypes and their clinical significance in

transplant outcomes. The investigation concluded that haplotype HLA-

G*01:04~UTR3 was an independent risk factor for a chronic rejection in lung

transplant patients (Di Cristofaro et al., 2015). This haplotype consists of SNP -

725C, -716G, -201A, -56C, DEL, 3142G, 3187A, 3196C and G*01:04 alleles and

patients with the haplotype were found to have significantly lower HLA-G

expression than patients with the other haplotypes (Di Cristofaro et al., 2015).

Interestingly, allele G*01:04 and DEL in this haplotype did not corroborate previous

findings of high HLA-G expression indicating that potentially investigations of

individual polymorphisms may not be comparable with investigations looking at

haplotypes since haplotypes consider the overall effect of various polymorphisms (Di

Cristofaro et al., 2015; Martelli-Palomino et al., 2013; Rebmann et al., 2001).

1.11 The Role of Donor HLA-G Expression

The role that the donor HLA-G genotype plays in regulating the expression of HLA-

G and mediating tolerance has not been investigated thoroughly in the context of

transplantation and rejection outcomes. Sheshgiri, Rouas-Freiss, et al. (2008) and

- 37 -

Mociornita et al. (2011) identified that myocardial cells, cardiac endothelial cells

and human coronary smooth muscle cells are capable of expressing HLA-G. In liver

and kidney allografts, epithelial cells from liver biliary and renal tubular cells

expressed HLA-G (Creput, Durrbach, et al., 2003; Creput, Le Friec, et al., 2003).

Lastly, bronchial epithelial cells from transplanted lungs were also found to express

HLA-G (Brugiere et al., 2009). Undeniably, the expression of HLA-G by the

allograft must be regulated by the genotype of the cells, which are donor-derived

and therefore will express the donor genotype. Therefore, the donor HLA-G

genotype is likely of paramount importance when studying the impact of HLA-G

and outcomes post-transplant. What determines the outcome may be the overall

tolerance produced from the combined expression of the different players (the donor

and the recipient).

To date, the interaction between donor and recipient genotype has not been

extensively investigated. Potentially, a specific combination of genotype from donor

and recipient could result in benefit, detriment or no effect, on the patient’s

rejection outcome. Pirri et al. (2009) explored if matching HLA-G genotypes, from

the coding region only, between the donor and the recipient would increase or

decrease the chances of recurrent rejection in a kidney transplant group. When the

patient and donor alleles both matched there was a lower risk of rejection than

those with one match or zero matches (Pirri et al., 2009). This was the first

investigation to elucidate the role of the donor HLA-G genotype in rejection,

however the findings were limited by the methodology.

- 38 -

1.12 The Role of Environmental Factors on Expression

The expression of HLA-G is modified by various environmental factors. HLA-G

expression is induced and inhibited by growth factors, anti-inflammatory, pro-

inflammatory factors and hormones such as progesterone (Sheshgiri, Rao, et al.,

2008), interleukin 10 (IL-10) (Moreau et al., 1999; Rizzo et al., 2005), interferons

(IFNs) (Lefebvre et al., 2001), hypoxia (Nagamatsu et al., 2004) and others (Moreau

et al., 2009). Progesterone was recognized in vitro to induce the expression of HLA-

G in myocardial smooth muscle cells and cardiac endothelial cells (Sheshgiri, Rao,

et al., 2008). IL-10 is anti-inflammatory and immune inhibitory agent like HLA-G

(Hviid, Rizzo, et al., 2004). IL-10 is known to stimulate expression of HLA-G in

monocytes (Moreau et al., 1999), mesenchymal stem cells (Selmani et al., 2008),

renal carcinoma progenitor cells (Dunker et al., 2008), mononuclear cells (Sebti et

al., 2003), decidua stroma cells (Blanco et al., 2008) and finally in acute

myeloblastic and lymphoblastic leukemia (Blanco et al., 2008). HLA-G was also

observed to induce the expression of IL-10 when HLA-G is expressed in CD4+ T-reg

cells (Pankratz et al., 2014). Interestingly, coronary artery endothelial and smooth

muscle cells did not respond to IL-10 and interferon γ in vitro (Sheshgiri, Rao, et al.,

2008). Although various factors have been recognized to modify the expression of

HLA-G, they appear to be cell type dependent with variable results in different

investigations.

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1.13 The Role of Immunosuppressive Therapy

Immunosuppressive therapy is a fundamental part of the patient’s post-transplant

regimen. Few investigations have indicated potential immunosuppressive therapies

that induce HLA-G expression. An investigation by our group on patients treated

with either everolimus or mycophenolate mofetil therapy demonstrated a marked

difference in HLA-G expression among the groups (Sheshgiri et al., 2009). Sheshgiri

et al. (2009) identified high levels of soluble HLA-G in those patients on everolimus

versus mycophenolate mofetil. Furthermore, Mociornita et al. (2011) identified that

everolimus stimulated HLA-G expression in human coronary smooth muscle cells.

Indeed, treatment with rapamycin (a sister drug of everolimus) induced expression

of ILT4 receptors on DC cells and increased the release of soluble HLA-G (Fedoric &

Krishnan, 2008; Stallone et al., 2014). Another investigation found treatment with

belatacept to be associated with stimulating HLA-G expression in kidney transplant

patients (Bahri et al., 2009). Interestingly, therapeutic treatment, such as

cyclosporine (CsA), did not have an effect on HLA-G levels in heart transplant

patients, unlike that seen in a liver transplant cohort (Basturk et al., 2006;

Sheshgiri et al., 2009). Indeed, the possible induction of HLA-G, when patients are

treated with certain immunosuppressive therapies, has to be considered when

analyzing HLA-G expression and genotype in transplant patients to clearly

distinguish the role of HLA-G polymorphisms independently of the

immunosuppressive treatment. Immunosuppressive therapy induction of HLA-G is

a novel area of research and could provide a new direction in treatment that

specifically stimulates the production of this natural immune inhibitor.

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1.14 Heart Transplantation

Transplantation is currently the treatment of choice for patients with end-stage

heart failure who are failing optimal medical therapy (Arnold et al., 2007). The

commonest heart diseases requiring transplant are cardiomyopathy and coronary

artery disease (Lund et al., 2014). The 2014 International Society for Heart and

Lung Transplantation (ISHLT) Registry reported the median survival for adult

heart transplant recipients to be 11 years and 14 for those that survived the first

year (Lund et al., 2014). The increase in first year survival seen over the past

decade is likely due to improvements in the selection and matching of recipient and

donors (Lund et al., 2014). The leading causes of death are graft failure, cardiac

allograft vasculopathy, infection, renal failure, multiple organ failure and

malignancy (Figure 8) (Lund et al., 2014). Early morbidity and mortality are due to

graft failure, infection and multi-organ failure (Stehlik et al., 2012). Acute rejection

is directly responsible for 11% of deaths early post-transplant and may also be

linked to deaths categorized as graft failure (Lund et al., 2014). At the 5-year mark,

predictors of mortality include acute rejection, need for dialysis, infection and

cardiac allograft vasculopathy (Lund et al., 2014). Long-term survival is limited by

cardiac allograft vasculopathy, renal failure and malignancy (Lund et al., 2014). By

10 years post transplant, 28% of patients are diagnosed with malignancy, which is

associated with the chronic use of immunosuppressive therapies (Lund et al., 2014;

Stehlik et al., 2012). In conclusion, over the last four decades more than 100,000

heart transplants were done worldwide (Lund et al., 2014). Scarcity of donors and

challenges with long-term survival continue to limit outcomes (Lund et al., 2014).

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1.14.1 Risk Factors for Survival Post-Transplantation

Several factors affect survival post-transplantation, including both recipient and

donor factors. Pre-transplant recipient disease etiology and recipient age remain

significant factors for transplant survival (Lund et al., 2014). As recipient age

increases, survival decreases, not in a linear fashion but in a U-shaped relation

(Lund et al., 2014). Younger and older patients have a higher risk of mortality at 1-

year compared to patients in their 40s and 50s (Lund et al., 2014). Recipient gender

also plays a key role, male recipients have lower survival (Lund et al., 2014).

Additionally renal function, measured by recipient serum creatinine, bilirubin pre-

transplantation, and the requirement for mechanical assist device as bridge to

transplantation are factors associated with a greater risk of mortality (Lund et al.,

2014). In 2012, a total of 41% of patients required mechanical assist device (Lund et

al., 2014). Factors that increase risk linearly from the donor and transplantation,

include increasing donor age and ischemic time (past the 200min) (Lund et al.,

2014). In summation, several factors limit long-term survival in the transplant

population.

- 42 -

Figure 8: Adult heart-transplant relative incidence of leading causes of death (censored deaths from January 1994 to June 2013). Adapted from Copyright © 2003 International Society of Heart & Lung Transplantation - Lund et al. (2014).

0

10

20

30

40

50

0-30Days(N=5,609)

31Days– 1Year

(N=4,800)

>1Year– 3Years

(N=3,511)

>3Years– 5Years

(N=3,085)

>5Years–10Years(N=7,117)

>10– 15Years

(N=5,186)

>15Years(N=2,959)

%ofdeaths

CAVAcuteRejectionMalignancy(non-Lymph/PTLD)Infection(non-CMV)GraftFailureMultipleOrganFailure

- 43 -

1.15 Rejection Outcomes Post-Transplantation

1.15.1 Cellular Mediated Rejection

Acute cellular mediated rejection (CMR) is a mononuclear inflammatory response in

which predominantly T-lymphocytes infiltrate the myocardium (Schuurman et al.,

1989; Stewart et al., 2005). The T-lymphocytes are CD4 and CD8 positive and have

a high affinity for interleukin-2 receptors (Schuurman et al., 1989). Acute cellular

mediated rejection is quite common early post-transplant (Kubo et al., 1995).

According to the ISHLT report, 25% of patients had rejection of any type and 13%

had a 2R or 3R rejection in the first year (Lund et al., 2014). Cellular rejection is

associated with increased morbidity and mortality (DePasquale, Schweiger, & Ross,

2014). Fortunately, with the advances in immunosuppressive treatment, episodes of

acute cellular mediated rejection have declined (Lund et al., 2014). Risk factors for

acute rejection are recipient gender (Jarcho et al., 1994; Kubo et al., 1995), donor

gender (Jarcho et al., 1994; Kobashigawa et al., 1993; Kubo et al., 1995), recipient

ethnicity (Jarcho et al., 1994), recipient age (Jarcho et al., 1994; Kobashigawa et al.,

1993; Kubo et al., 1995) and number of HLA mismatches (Jarcho et al., 1994;

Sheldon et al., 1999). The profile of a patient at higher risk for CMR was

determined to be younger, race (non-white, non-Asian), female and HLA

mismatches (Kilic et al., 2012).

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1.15.2 Donor Specific Antibody

Donor specific antibodies (DSA) are widely known to be associated with poor

prognosis, antibody mediated rejection and cardiac allograft vasculopathy (Tambur

et al., 2005; Terasaki, Ozawa, & Castro, 2007). They may exist pre- and/or develop

post-transplant (Chih et al., 2012). Their interaction with allograft cells causes

injury and dysfunction by activating the complement cascade (Soleimani, Lechler,

Hornick, & George, 2006; Taylor, Yowell, Kfoury, Hammond, & Renlund, 2000;

Wehner, Morrell, Reynolds, Rodriguez, & Baldwin, 2007). The complement pathway

involves the activation of proteases that once they are cleaved produce C4d and C3d

molecules that bind to target proteins (Wasowska, 2010). C4d staining is associated

with the diagnosis of antibody-mediated rejection (AMR) and the presence of DSA

(Gupta et al., 2009). An even stronger signal is given when C4d coupled with C3d

staining is investigated (Rodriguez et al., 2005; C. D. Tan et al., 2009).

Patients diagnosed with AMR are more likely to have DSA (Nath et al., 2010). This

situation is due to the phenomenon of accommodation (Koch, Khalpey, & Platt,

2004). Interestingly, the presence of DSA does not always infer antibody mediated

injury and the absence of DSA does not mean an absence of AMR, as the antibody

may be bound on the graft (Bocrie et al., 2007; Koch et al., 2004).

Patients with class I DSA have a significantly greater chance of graft failure, and

those with only class II DSA have a higher risk of CAV (Frank et al., 2013; Zhang et

al., 2011). DSA frequency in transplant patients ranges from 4 to more than 50%

(Cardarelli et al., 2005). It is estimated that post-transplant, 31% of patients

developed DSA (Everly et al., 2009). Therapies targeting a reduction in DSA such as

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desensitization were associated with a significant improvement in long term

survival, supporting concerns about the detrimental effect of DSA on long term

outcomes (Kimball, Baker, Wagner, & King, 2011).

1.15.3 Cardiac Allograft Vasculopathy

Cardiac allograft vasculopathy (CAV) is a disease of the donor coronary arteries

characterized by intimal hyperplasia, vascular remodeling and stenosis (Ramzy et

al., 2005). Immune and non-immune pathogenic factors are involved in the

development of cardiac allograft vasculopathy (Ramzy et al., 2005). It is one of the

main reasons long-term survival is limited and it is one of the leading causes of

death (Lund et al., 2014). CAV diagnosis at the 1st, 5th and 10th year mark are 8%,

30%, and 50%, respectively (Lund et al., 2014). There has been a statistical

decrease in the incidence of CAV and patient survival (post a diagnosis of CAV) has

improved likely due to new available treatment options (Stehlik et al., 2012).

However, there are significant limitations to the treatment options and thus in

highly selected individuals the optimal treatment when all medication fails remains

retransplantation (Lund et al., 2014).

There are various factors that increase the risk for the development of CAV. Of

those increasing donor age, donor hypertension and certain immunosuppressive

treatment have been identified to predispose the patient to a greater risk of CAV

development (Stehlik et al., 2012). Indeed, hearts from older donors are associated

with higher chances of development of intimal thickness in the first year and the

subsequent progression of CAV (Gao et al., 1997; Kobashigawa et al., 2005).

- 46 -

Additionally, patients with CMV mismatch are at higher risk as well (Stehlik et al.,

2012). However there are also factors associated with lower risk such as the

diagnosis of some etiologies like congenital heart diseases, a female donor and a

female recipient (Stehlik et al., 2012). Certain events post transplantation have also

been associated with increase chances of CAV. Within the first year, recurrent

cellular rejection, donor specific antibodies and cytomegalovirus (CMV) infections

are associated with an increased risk of developing CAV (Kobashigawa et al., 1995;

Michaels et al., 2003; Toyoda et al., 1997). Indeed the presence of class II donor

specific antibodies has been identified as potential risk factor (Frank et al., 2013).

Interestingly a recent meta-analysis of CAV risk factors found them to be

inconsistently associated with CAV development (Braga, Santos, McDonald, Shah,

& Ross, 2012). This supports the need for further research to better understand

cardiac allograft vasculopathy.

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Chapter 2

Rationale and Hypothesis

2 The Association of Donor and Recipient HLA-G Polymorphisms and Cardiac Allograft Vasculopathy

2.1 Summary and Rationale

Human Leukocyte Antigen-G (HLA-G) is a naturally occurring immune modulator

(Carosella et al., 2015). Genetic polymorphisms in the HLA-G gene control its

expression and may be associated with different post-transplant outcomes (Castelli

et al., 2014; Donadi et al., 2011). HLA-G has been proven to terminate the cytotoxic

activity of NK cells, cytotoxic T-cells, macrophages and mononuclear cells through

cell-to-cell contact (Rebmann et al., 2014).

In vitro studies by our team demonstrated HLA-G’s cardiac vasculature protection

effect that potentially indicates its role in CAV development. When human coronary

smooth muscle cells were treated with everolimus, a potent immunosuppressant,

they were induced to express HLA-G (Mociornita et al., 2011). The expression of

HLA-G in these cells led to decreased proliferation that was dependent on the

concentration of HLA-G (Mociornita, Tumiati, Papageorgiou, Grosman, et al., 2013).

In addition, HLA-G was shown to inhibit neutrophil adhesion to injured human

coronary artery endothelial cells (as a tumour necrosis factor (TNF)-α-induced

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inflammatory response) (Mociornita, Tumiati, Papageorgiou, Grosman-Rimon, et

al., 2013). Clinically, patients with high levels of HLA-G have a lower incidence of

allograft vasculopathy at 1 year post-transplant (Blanco-Garcia et al., 2013).

However, the expression of HLA-G varies among patients indicating that there are

genetic factors that are influencing the expression of this protein. When exploring

the association between the 14bp INDEL polymorphism and cardiac allograft

vasculopathy, we found no correlation with the 14bp INDEL polymorphism

(Mociornita, Lim-Shon, et al., 2013). However, there are several other SNPs in the

gene that could be potential markers for CAV.

The role of the donor HLA-G genotype in regulating the expression of HLA-G and

mediating tolerance has never been investigated. Indeed allograft cells have been

found to express HLA-G (Brugiere et al., 2009; Creput, Durrbach, et al., 2003;

Creput, Le Friec, et al., 2003). Clearly the expression of HLA-G in the donor organ

is regulated by the donor genotype. To that point, the donor HLA-G genotype is of

importance since the overall outcome is determined by the degree of tolerance

produced from the combination the donor and the recipient genotypes. This

relationship has not been extensively investigated.

In one report, Pirri et al. (2009) explored the interaction of donor and recipient

HLA-G genotypes, from the coding region only, in kidney transplant patients. When

the patient and donor alleles both matched there was a lower risk of rejection than

those with one match or zero matches (Pirri et al., 2009). Despite limitations to this

study, the results allude to the potential significant role of the interaction between

the donor-recipient genotype in transplant outcomes. Understanding the association

- 49 -

between polymorphisms and cardiac allograft vasculopathy as evaluated by

coronary angiography may provide valuable knowledge for potential prognosis of

CAV in patients and in tailoring treatment and resources. Therefore, the rationale

for this study was to identify polymorphisms as predictors of CAV outcomes post-

transplantation in heart transplant recipients.

2.2 Hypothesis

We hypothesized that donor and recipient HLA-G polymorphisms are associated

with CAV outcomes in heart transplant recipients.

- 50 -

Chapter 3

Methods

3 Study Design

3.1 Population of Interest

This single center retrospective cohort included patients who underwent

transplantation at Toronto General Hospital and the patient’s corresponding heart

donors between January 2001 and December 2013. The institutional ethics research

board approved the study protocol. DNA was obtained from the institutional

biobank where patients consented pre transplant for their DNA to be stored for use

in future research.

During the study period, there were 287 adults (≥18 years) that received a heart

transplant at Toronto General Hospital from 287 donors and after excluding those

with intraoperative deaths (n=6), those with inadequate DNA to enable SNP

genotyping (n=28) and recipients of multiorgan transplants (n=2), there were 251

heart recipient samples for analysis. Of the 287 heart donors, the exclusion criteria

encompassed inadequate DNA to enable SNP genotyping (n= 55) and donor’s

without their corresponding recipient in the analysis (n=36). In total the cohort

included 251 patients and of those 196 had their corresponding heart donors.

Recipients demographics, clinical data and immunosuppressive treatment were

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captured until the end of 2014, unless the patient died or discontinued care at our

institution prior to that date.

Our standard immunosuppressive protocol consists of low dose induction with

thymoglobulin (1 mg/kg for 3 days), prednisone, mycophenolic acid analog and a

calcineurin inhibitor (cyclosporine or tacrolimus). Sirolimus or everolimus are

routinely introduced in cases of CAV, renal failure, malignancy history or recurrent

rejection. Statin is universally prescribed to all recipients, unless otherwise

contraindicated.

3.2 DNA Collection

DNA was stored in the biobank laboratory at -80°C. DNA concentration and purity

(A260/A280 ratio) were determined by the NanoDrop™ Spectrophotometer

(ThermoFisher Scientific, Waltham, MA). Only samples with a DNA concentration

of 15 ng/ul or higher were used for SNP genotyping. For each sample, 30 uL was

aliquoted in 96-well plates.

3.3 HLA-G Polymorphisms

3.3.1 SNP Selection

A literature review of all published research (transplant related or not) with HLA-G

SNPs was performed. SNPs were selected from the 5’-upstream promoter region,

coding region and 3’-untranslated region. We excluded SNPs with minor allele

frequency (MAF) less than 10% except for SNPs from the coding region. Our final

list included 2 SNPs from the 5’-upstream regulatory region (rs1233333 &

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rs1233334) and 4 SNPs from the 3’-untranslated region (rs1610696, rs9380142,

rs1063320 & rs66554220). From the coding region 4 SNPs were selected

(rs41551813, rs12722477, rs144244894 & rs12722482). Each individual with a

variation in a SNPs from the coding region were categorized into 6 haplotypes,

individuals with no mutant in any of the SNPs were haplotype 1 (G*01:01/G*01:01).

Those with a mutation in SNP 31 were haplotype 2 (G*01:01/G*01:03), with a

mutation in SNP 110 were haplotype 3 (G*01:01/G*01:04), with a mutation in SNP

130 were haplotypes 4 (G*01:01/G*01:05N), with a mutation in SNP 258 were

haplotype 5 (G*01:01/G*01:06) and lastly individuals with more than one mutation

were categorized to haplotype 6 (G*01:03/ G*0106 or G*01:04/ G*0106 or G*01:04/

G*01:03 or G*01:04/ G*01:04). SNP 3142 was sequenced in only about half of the

recipient (125) and donor (114) cohorts. Lastly, genotype matching between

recipient and donor was investigated. A full match of recipient-donor genotype was

defined as matching of both alleles for a particular SNP (i.e. CC recipient- CC

donor) and mismatch was any other possible combination.

The location of the HLA-G gene is in chromosome 6: 29,794,744-29,798,902 forward

strand. The following polymorphism information was obtained from NCBI website

available from: http://www.ncbi.nlm.nih.gov.

3.3.2 RS1233333

This SNP is located in 5’-upstream promoter region at position -201 of the HLA-G

gene. The ancestral allele is cytosine and the mutation is thymine. The reference

SNP allele is the reverse therefore the MAF is T=0.4972/1082.

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Sequence:

AATCCCCAACGCGGGGCCTCCCCAA[C/T]CCATACACCGCCTGTGGGGCCTGAG

This particular SNP is close to regulatory elements such as P50, SP1 and the

hypoxia response (HRE) element therefore variations in this SNP can potentially be

affecting the association of the regulatory elements to the gene (Castelli et al., 2014;

Moreau et al., 2009).

3.3.3 RS1233334

This SNP is located in 5’-upstream promoter region at position -725 of the HLA-G

gene. The ancestral allele is guanine and the mutation is cytosine or adenine since

it is a triallilec. The reference SNP allele is the reverse therefore the MAF is

C=0.0727/364.

Sequence:

AAATGCATCTAAAAGCATTACAACA[A/C/G]GACTCACAAAGCTCTTAAGTTTCA

C

The presence of the guanine allele creates a greater chance of methylation at this

SNP that could be inhibiting the transcription and subsequently lead to lower

expression (Ober et al., 2006).

3.3.4 RS41551813

This SNP is located at codon 31 (position 292) in exon 2 of the coding region of the

HLA-G gene. It defines allele HLA-G*01:03. The ancestral allele is adenine and the

mutation is thymine. It constitutes a synonymous substitution. The MAF is

T:0.0539/270.

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Sequence:

CATCGCCATGGGCTACGTGGACGAC[A/T]CGCAGTTCGTGCGGTTCGACAGCGA

Allele HLA-G*01:03 is one of the most common alleles in the worldwide population

(Donadi et al., 2011).

3.3.5 RS12722477

This SNP is located at codon 110 (position 755) in exon 3 of the coding region of the

HLA-G gene. It defines allele HLA-G*01:04. The ancestral allele is cytosine and the

mutation is adenine. The MAF is A=0.2027/1015.

Sequence:

CGACCTGGGGTCCGACGGACGCCTC[A/C]TCCGCGGGTATGAACAGTATGCCTA

This particular SNP was originally identified with higher soluble HLA-G levels by

(Rebmann et al., 2001) in a healthy population. Allele HLA-G*01:04 is one of the

most common alleles in the worldwide population (Donadi et al., 2011).

3.3.6 RS41557518

This SNP is located at codon 130 (position 814) in exon 3 of the coding region of the

HLA-G gene. It defines allele HLA-G*01:05N. The ancestral allele is cytosine and

the mutation is a deletion. The MAF is (-)=0.0284/141. It is a frame shift mutation.

Sequence: AGGATTACCTCGCCCTGAACGAGGA[-

/C]CTGCGCTCCTGGACCGCAGCGGACA

Allele G*01:05N has a single nucleotide deletion in codon 130 which causes a frame

shift and the formation of a stop codon which subsequently produces partial

expression of the protein isoforms (Suarez et al., 1997).

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3.3.7 RS12722482

This SNP is located at codon 258 (position 1799) in exon 4 of the coding region of the

HLA-G gene. It defines allele HLA-G*01:06. The ancestral allele was cytosine and

mutation is thymine. The MAF is T=0.0507/254.

Sequence:

CCTTCTGGAGAGGAGCAGAGATACA[C/T]GTGCCATGTGCAGCATGAGGGGCT

G

Allele HLA-G*01:06 is one of the most common alleles in the worldwide population

(Donadi et al., 2011).

3.3.8 RS371194629

This SNP is located in 3’-untranslated region of the HLA-G gene. The ancestral

allele is unknown. The MAF is insertion= 0.3942/1974. It is a deletion/insertion

variation.

Sequence: GCCCTGTGTGGGACTGAGTGGCAAG[ATTTGTTCATGCC[T/-

]TCCCTTTGTGACTTCAAGAACCCTGA

The polymorphism has been associated with variable mRNA production, stability

and subsequently variable protein expression (Hviid et al., 2003). It is also the

target site for various microRNAs (Castelli, Moreau, Oya e Chiromatzo, et al.,

2009).

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3.3.9 RS1063320

This SNP is located in 3’-untranslated region at position 3142 of the HLA-G gene.

The ancestral allele is cytosine and the mutation is guanine. The reference SNP

allele is the reverse therefore the MAF is G=0.4020/2013.

Sequence:

TACAGAAGTAAGTTATAGCTCAGTG[C/G]ACCACAAATTTGAGACAGAGACGGA

The presence of a guanine-guanine at this SNP is related with a lower protein

production potentially caused by the increased target of microRNAs when guanine

is present (Rizzo et al., 2012; Z. Tan et al., 2007; Veit & Chies, 2009).

3.3.10 RS9380142

This SNP is located in 3’-untranslated region at position 3187 of the HLA-G gene.

The ancestral allele is adenine and the mutation is guanine. The MAF is

G=0.2582/1293.

Sequence:

TCTGTATTAAAATTAGAATCTGAGT[A/G]TAAATTTACTTTTTCAAATTATTTC

It was also observed that the adenine allele for this SNP is associated with lower

protein expression in vitro because it affects the mRNA stability (Yie et al., 2008).

3.3.11 SNP Genotyping

SNP sequences were retrieved using the Sequenom Online Assay Design Suite

(Agena Bioscience, Inc., San Diego, CA) to generate PCR and extension primers for

the multiplex reaction. The iPLEX assay reaction relies on a single termination mix

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and universal reaction conditions for the SNPs selected. This allows for allele

specific differences in mass between extension products.

PCR amplification was carried out using 5-10 ng of template DNA, in 5 ul reactions

containing 1.25X PCR Buffer (Qiagen), 1.625 mM MgCl2 (Qiagen), 500 uM dNTP

mix (Fermentas), 100 nM primer mix (IDT) and 0.5 U Hotstar Taq polymerase

(Qiagen). The reactions were incubated in a standard thermocycler using the

following cycling conditions: initial denaturation at 94°C for 15 minutes, followed

by 45 cycles of 94°C for 20 seconds, 56°C for 30 seconds, 72°C for 1 minute, followed

by a final extension at 72°C for 3 minutes.

Unincorporated deoxynucleotide triphosphates (dNTPs) were dephosphorylated by

treatment with shrimp alkaline phosphatase following PCR. The 5 ul PCR reaction

was incubated with 2 ul of SAP mix (Agena Bioscience, Inc., San Diego, CA),

containing 0.85X SAP buffer and 0.3 U SAP enzyme (Sequenom), in a standard

thermocycler at 37°C for 20 minutes, followed by a 5 minute heat inactivation at

85°C.

Following SAP treatment, 2 ul of iPLEX extension cocktail was added to the PCR

reaction to a final concentration of 0.222X iPLEX buffer, 1X iPLEX termination

mix, 0.625 uM, 0.833 uM, 1.04 uM or 1.25 uM of each primer, and 1X iPLEX

enzyme (Sequenom). The primer concentrations in the multiplex reactions were

adjusted based on the primer mass. A higher concentration (1.04 uM or 1.25 uM)

was used for high mass primers. The reaction conditions for primer extension were

as follows: initial denaturation at 94°C for 30 seconds, followed by a 40 cycle

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program consisting of a single denaturation at 94°C for 5 seconds and 5 cycles of

52°C for 5 seconds and 80°C for 5 seconds. A final extension is performed at 72°C

for 3 minutes.

To remove salts from the iPLEX reaction products, the samples were diluted with

16 ul of water and 6 mg of Clean Resin (Sequenom) was added to each reaction. The

reactions were rotated for at least 10 minutes, followed by centrifugation at 5000

rpm for 5 minutes. The reaction products were dispensed onto a 384-element

SpectroCHIP bioarray (Sequenom) using the Sequenom RS-1000 MassARRAY

Nanodispenser and analyzed using the Sequenom MassARRAY Analyzer Compact

(Agena Bioscience, Inc., San Diego, CA). The data was then analyzed using the

Typer 4.0 Software (Agena Bioscience, Inc., San Diego, CA), which identifies SNP

alleles at the expected mass signal peaks according to the molecular weight of the

extension products.

3.4 Study Outcome

As per our Institutional protocol, screening for CAV included coronary angiography

performed at 1, 5 and 10-years post-transplant and on a per cause basis (abnormal

yearly stress test, new onset symptoms or unexplained drop in graft function).

Cardiac allograft vasculopathy (CAV) was classified according to the ISHLT grading

system as CAV 0, 1, 2 or 3 by a physician blinded to the SNP results (Mehra et al.,

2010). For the first model the outcome was the diagnosis of mild CAV, defined as

category CAV 1. For the second model the outcome was the diagnosis of severe CAV,

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defined as category CAV 2 or CAV 3. Additionally, for both models a combined

clinical outcome of CAV diagnosis, retransplantation or death was also examined.

3.5 Statistical Analysis

The data reported is described as follows: continuous data are described as mean ±

standard-deviation, medians with interquartile ranges and categorical data are

reported as frequencies throughout the report unless otherwise stated. The first

analysis was to identify the proper amount of patients at-risk of mild CAV and

severe CAV. Thus, a competing risk model was used to account for the concurrent

risk of death, re-transplantation or CAV (mild CAV and severe CAV); this strategy

allowed for proper estimation of patients remaining at-risk of outcomes over time

(McGiffin et al., 1997). Death and re-transplantation were censored since they are

expected outcomes post transplantation. Given the low number of patients

undergoing re-transplantation, re-transplantation and death were modeled

together. Rate of time-related events (mortality, retransplantation, CAV) were

modeled in multiphase parametric hazard models that decompose risk over time in

up to 3 additive, overlapping phases of risk (descriptively labeled as early, constant

and late but actually representing different mathematical functions to model

specific patterns of event distribution over time). This analysis was performed using

the HAZARD procedure for SAS available from:

https://www.lerner.ccf.org/qhs/software/hazard/

Complete mathematical description and validation of the procedure have previously

been published (Blackstone, Naftel, & Turner, 1986).

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In regards to the association between HLA-G genotype and mild and severe CAV

outcomes, 3 models were created for each polymorphism: 1) recessive effect

(homozygosity of the minor allele); 2) dominant effect (homozygosity or

heterozygosity of the minor allele), and; 3) allelic effect (number of minor alleles).

With the 3 different models for each polymorphism, a data-driven analysis was

created. For some of the rare genotypes, models with less than 10 events were not

included in the analysis, this applies primarily to SNPs that had a minor allelic

frequency of less than 10%. The model building strategy started with the evaluation

of the association between hazard of mild or severe CAV and clinical and genetic

risk factors in univariable parametric hazard regression models. From there factors

with p-values <0.10 and that were clinically relevant to the investigation were then

included in a multivariable parametric hazard regression model with backward

selection of variables to obtain a multivariable model. The backward selection

strategy allowed the variables in the model to be adjusted for cofounding factors

(which is important for clinical analysis) and subsequently increased the type-2

error chances. Internal validity of multivariable regression models was assessed

using bootstrap resampling (1000 samples) with a minimum threshold of 50%

reliability to remain in the final model. The bootstrap resampling of 1000 samples

repeats the analysis with a subsample of the cohort. By repeating the measurement

multiple times and getting the same result, this decreases the chances type-1 error

and strengthens the validity of each variable in the final model. Bootstrap is not a

direct correction of type-1 error, but decreases the chances of making type-1 error

significantly. For instance, the chances of making a type-1 error are 1/20, if you

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repeat the analysis a second time the chances in total are 1/20 x 1/20 so 1/400 and

in this way the chances of getting type-1 error decrease over time. Mean imputation

of variable was used to account for missing values in multivariable regression

models. In conclusion, all statistical analyses were performed using SAS v.9.4 (SAS

Statistical Software, Cary, NC).

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Chapter 4

Results

4

4.1 General Patient, Donor and Pre-Transplant

Characteristics

General patient and donor characteristics are shown in Table 1. Mean recipient age

at transplant was 48 ± 12 years, while mean donor age was 35 ± 14 years. Prior to

transplant, 21% of recipients were bridged with ventricular assist device, and 4%

and 2% were highly sensitized (>80%) for PRA class I and II, respectively.

Table 1: Recipient, donor and pre-transplant characteristics of the study cohort

General recipient characteristics N All patients

Sex (male) 251 174 (69%)

Age at transplant (years) 251 48.2 ± 12.1

Height (cm) 246 171.4 ± 10.1

Weight (kg) 247 72.7 ± 16.8

Body mass index (BMI) (kg/m2) 246 24.6 ± 4.7

Primary Diagnosis 251

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Congenital Heart Disease 17 (7%)

Idiopathic 114 (45%)

Ischemic 61 (24%)

Other 59 (24%)

Blood group 251

A 112 (45%)

AB 11 (4%)

B 43 (17%)

O 85 (34%)

Race 251

Black 7 (3%)

Caucasian 98 (39%)

Other 14 (6%)

Undisclosed 132 (53%)

General donor characteristics

Donor sex (male) 186 115 (62%)

Age of donor (years) 251 35.5 ± 14.3

Donor height (cm) 249 173 ± 11.5

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Donor weight (kg) 250 77.4 ± 18.1

Donor Body Mass Index (kg/m2) 249 25.8 ± 5.8

Blood group of donor 251

A 99 (39%)

AB 3 (1%)

B 38 (15%)

O 111 (44%)

Donor cause of death 249

Anoxia 33 (13%)

CNS tumor 2 (1%)

CVA/stroke 95 (38%)

Head trauma 102 (41%)

Other 17 (7%)

Pre-transplant characteristics

Cancer diagnosis 251 16 (6%)

Induction therapy (basiliximab vs.

thymoglobulin) 251 29 (12%)

Ventricular assist device 251 52 (21%)

CMV recipient positive status 249 151 (61%)

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CMV donor positive status 249 111 (45%)

CMV negative status vs. any positive 251 202 (80%)

Panel reactive antibodies (PRA) class I

values

157 0 (0-66)

Not sensitized (0%) 89 (57%)

Mildly sensitized (1-10%) 22 (14%)

Sensitized (11-79%) 40 (25%)

Highly sensitized (80-100%) 6 (4%)

Panel reactive antibodies (PRA) class II

values

157 0 (0-52)

Not sensitized (0%) 124 (79%)

Mildly sensitized (1-10%) 10 (6%)

Sensitized (11-79%) 20 (13%)

Highly sensitized (80-100%) 3 (2%)

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate. Legend: CNS: central nervous system, CVA: cerebrovascular accident, CMV: cytomegalovirus infection.

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4.2 Transplant Characteristics, Outcome and Medical

Therapy

Transplant characteristics, outcomes and immunosuppressive therapy are described

in Table 2. Mean ischemic time was 221 ± 66 min. Patients on any proliferation

signal inhibitor for more than 6 months amounted to 51% and of those 82% were on

sirolimus.

Table 2: Transplant characteristics, outcomes and medical therapy of the recipient

cohort

Transplant characteristics and outcomes N All patients

Ischemic time (minutes) 117 221 ± 66

First CAV diagnosis: CAV 1 251 102 (41%)

Remained at CAV1 102 90 (88%)

Progressed to CAV2/3 102 12 (12%)

First CAV diagnosis: CAV 2/3 251 8 (3%)

CAV 2 or 3 end of follow-up 251 20 (8%)

Percutaneous coronary intervention treatment 20 9 (45%)

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Re-transplantation 251 1 (0%)

Mortality 251 57 (33%)

Cause of death 57

Cancer 7 (12%)

Graft failure 15 (26%)

Multiple systemic organ failure 13 (23%)

Other 8 (14%)

Rejection/CAV 6 (11%)

Sepsis 8 (14%)

Medical therapy 251

Cyclosporine only 92 (37%)

Tacrolimus only 139 (55%)

None 20 (7%)

Any MPA 251 220 (88%)

MPA within 1 week of transplant 251 158 (63%)

MPA within 1 month of transplant 251 202 (80%)

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Any PSI for >6 months 251 127 (51%)

Sirolimus 127 104 (82%)

Everolimus 127 23 (18%)

PSI within 6 months of transplant 251 70 (28%)

PSI within 2 years of transplant 251 96 (38%)

PSI within 6 months of transplant (>6 months

treatment)

251 69 (27%)

PSI within 2 years of transplant (>6 months

treatment)

251 93 (37%)

Any statin 251 224 (89%)

Statin within 1 week of transplant 251 112 (45%)

Statin within 1 month of transplant 251 203 (81%)

Steroids stopped within 2 years of transplant 251 31 (12%)

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate.

Legend: MPA: mycophenolic acid analog, PSI: proliferation signal inhibitor.

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4.3 Freedom from Mild CAV

Duration of follow-up was 5.2 ± 3.6 years, with a median of 5.0 years (range 1 days

to 13.2 years). Freedom from mild CAV and death/retransplantation was 26.8% at

10 years after transplantation (Figure 9) (Table 3).

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Figure 9. Competing outcomes after heart transplantation. The hazard function for

mortality was characterized by a high early hazard of death or re-transplantation

immediately after transplantation followed by an ongoing constant hazard over

time. The hazard function for mild CAV was characterized by a strong early hazard

with a lower but accelerating increasing hazard over time.

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11 12

Prop

ortio

nofpatients

Timesincetransplantation(years)

Freefromretransplantation,deathorCAV

CAV

DeathwithoutCAV

- 71 -

Table 3: Frequency of outcomes (Mild CAV, death/re-transplantation and alive no

CAV) with 95% confidence interval from competing risk model.

Time since transplant Mild CAV Death/Re-

transplantation Alive without CAV

1 year 8.5 (5.9-12.2) 12.5 (9.0-17.0) 79.1 (72.0-84.7)

2 years 25.3 (20.2-31.0) 14.0 (10.4-18.7) 60.9 (53.2-68.1)

5 years 38.7 (32.9-44.7) 16.6 (12.5-21.8) 45.0 (37.8-52.4)

8 years 49.4 (42.0-56.7) 18.1 (13.3-24.1) 33.0 (26.1-40.7)

10 years 55.0 (46.3-63.3) 18.7 (13.5-25.4) 26.8 (20.2-34.7)

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate. Legend: CAV: cardiac allograft vasculopathy.

4.4 Freedom from Severe CAV

Duration of follow-up was 5.2 ± 3.6 years, with a median of 5.0 years (range 1 days

to 13.2 years). Freedom from severe CAV and death/retransplantation was 64% at

10 years after transplantation (Figure 10) (Table 4).

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Figure 10. Competing outcomes after heart transplantation. The hazard function for

mortality was characterized by a high early hazard of death or re-transplantation

immediately after transplantation followed by an ongoing constant hazard over

time. The hazard function for severe CAV was characterized by a single

progressively increasing hazard over time (late hazard function).

- 73 -

Table 4: Frequency of outcomes (Severe CAV, death/re-transplantation and alive no

CAV) with 95% confidence interval from competing risk model.

Time since transplant Severe CAV Death/Re-

transplantation Alive without CAV

1 year 0.4 (0.1-1.5) 12.5 (9.1-17.1) 87.1 (72.6-94.5)

2 years 1.2 (0.5-3.0) 14.5 (10.8-19.1) 84.3 (73.0-91.5)

5 years 4.9 (2.9-8.0) 18.4 (14.2-23.6) 76.7 (68.1-83.6)

8 years 9.6 (6.2-14.4) 21.2 (16.3-27.0) 69.3 (60.2-77.1)

10 years 13.3 (8.5-20.1) 22.8 (17.4-29.2) 64.1 (53.8-73.2)

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate. Legend: CAV: cardiac allograft vasculopathy.

4.5 HLA-G Polymorphisms

Frequencies of SNPs for recipients and donors are shown in Table 5. The

frequencies and percentages of the haplotypes for recipient and donor are shown in

Table 6. All were in Hardy-Weinberg equilibrium (HWE). Genotype frequencies for

SNP -201 CC, CT and TT were 25%, 51% and 24% for recipients and, 27%, 52% and

22% for donors, respectively. For SNP -201, 68 (35%) of recipients had genotype

matching with their donors. Matching frequencies for CC-CC, CT-CT and TT-TT

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were 7%, 28% and 51%, respectively. There were 106 (54%) matches for SNP -725,

84 (43%) for SNP 3196, 94 (48%) for SNP 3187, 38 (33%) for SNP 3142, 80 (41%) for

SNP 14bp and, lastly, 68 (35%) matched haplotype categories.

Table 5: HLA-G polymorphisms genotype, frequency, MAF and HWE for recipients and donors.

SNP Gene Location Major

Genotype

Minor

Genotype

Major

Genotype

Freq.

Heterozygous

Genotype

Freq.

Minor

Genotype

Freq.

MAF HWE

Recipient

201 RS1233333 CC TT 64 (25%) 128 (51%) 59 (24%) 49.0% 0.75

725 RS1233334 GG CC/AA 177 (71%) 46 (18%) 28 (11%) 20.3% -

3196 RS1610696 GG CC 117 (47%) 112 (45%) 22 (9%) 31.1% 0.51

3187 RS9380142 AA GG 131 (52%) 93 (37%) 27 (11%) 29.3% 0.10

3142 RS1063320 CC GG 43 (34%) 61 (49%) 21 (17%) 41.2% 0.94

14bp RS66554220 DEL INS 79 (31%) 126 (50%) 46 (18%) 43.4% 0.73

31 RS41551813 AA TT 234 (93%) 16 (6%) 1 (0%) 3.6% 0.22

110 RS12722477 CC AA 206 (82%) 40 (16%) 5 (2%) 10.0% 0.08

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130 RS144244894 CC DEL 243 (97%) 8 (3%) 0 (0%) 1.6% 0.80

258 RS12722482 CC TT 207 (82%) 44 (18%) 0 (0%) 8.8% 0.13

Donor

201 RS1233333 CC TT 52 (26.5%) 101 (51.5%) 43 (22%) 47.7% 0.65

725 RS1233334 GG CC/AA 146 (75%) 36 (18%) 14 (7%) 16.3% -

3196 RS1610696 GG CC 99 (50%) 78 (40%) 19 (10%) 29.6% 0.53

3187 RS9380142 AA GG 90 (46%) 84 (43%) 22 (11%) 32.7% 0.72

3142 RS1063320 CC GG 27 (24%) 62 (54%) 25 (22%) 49.1% 0.35

14bp RS66554220 DEL INS 65 (33%) 97 (50%) 34 (17%) 42.1% 0.83

31 RS41551813 AA TT 181 (92%) 15 (8%) 0 (0%) 3.8% 0.58

110 RS12722477 CC AA 157 (80%) 38 (20%) 1 (0%) 10.2% 0.42

130 RS144244894 CC DEL 190 (97%) 6 (3%) 0 (0%) 1.5% 0.83

258 RS12722482 CC TT 161 (82%) 35 (18%) 0 (0%) 8.9% 0.17

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate.

Legend: SNP: single nucleotide polymorphisms, MAF: minor allele frequency, HWE: Hardy-Weinberg equilibrium.

- 76 -

Table 6: Distribution (frequency and percentage) of haplotypes in the recipient and

donor cohorts.

Recipient Donor

Haplotype

Genotype

Frequency

Genotype

Percent

Genotype

Frequency

Genotype

Percent

G*01:01/G*01:01 147 59 109 56

G*01:01/G*01:03 14 6 11 6

G*01:01/G*01:04 35 14 35 18

G*01:01/G*01:05N 8 3 4 2

G*01:01/G*01:06 37 15 31 16

Other 10 4 6 3

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate.

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Table 7: Frequency of match genotypes between donor and recipient for each SNP

and for the haplotypes.

Name SNP N Frequency of Match

201 RS1233333 196 68 (35%)

725 RS1233334 196 106 (54%)

3196 RS1610696 196 84 (43%)

3187 RS9380142 196 94 (48%)

3142 RS1063320 114 38 (33%)

14bp RS66554220 196 80 (41%)

Haplotypes - 196 68 (35%)

4.6 Predictors of Mild Cardiac Allograft Vasculopathy

All variables (SNPs and relevant clinical data) tested in univariable parametric

hazard regression models for the outcome are listed in Table 8. Variables obtained

from the multivariable regression model are listed in Table 9.

Table 8: Univariate analysis risk factors for mild CAV.

General recipient characteristics HR LCL UCL p-value

Sex 0.82 0.55 1.25 0.36

Age at transplant (years) 1.00 0.99 1.02 0.67

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Weight (kg) 1.01 1.00 1.02 0.06

BMI (kg/m2) 1.03 0.99 1.07 0.12

Year of transplantation 1.09 1.02 1.16 0.01

Blood group: A 1.21 0.83 1.76 0.32

Blood group: B 0.97 0.59 1.57 0.89

Blood group: O 0.81 0.53 1.23 0.31

Primary diagnosis: CHD 1.19 0.78 1.81 0.42

Primary diagnosis: Idiopathic 1.00 0.69 1.46 0.99

Primary diagnosis: Ischemic 0.54 0.20 1.46 0.22

General donor characteristics

Donor sex 1.06 0.68 1.64 0.80

Age of donor (years) 1.03 1.02 1.04 <0.001

Donor weight (kg) 1.01 1.00 1.02 0.01

Donor BMI (kg/m2) 1.03 1.01 1.06 0.02

Blood group of donor: A 0.99 0.68 1.46 0.97

Blood group of donor: B 0.98 0.60 1.62 0.95

Blood group of donor: O 1.10 0.75 1.61 0.63

Donor cause of death: CVA/stroke 1.44 0.98 2.11 0.06

Donor cause of death: Trauma 1.51 0.21 10.88 0.68

Donor cause of death: Anoxia 0.68 0.37 1.28 0.23

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Pre-transplant characteristics

Pre-creatine level (umol/L) 1.00 1.00 1.01 0.67

Pre-bilirubin level (umol/L) 1.00 0.99 1.02 0.80

Pre-transplant diagnosis of cancer 0.24 0.06 1.00 0.05

Pre-transplant use of VAD 0.99 0.60 1.62 0.96

PRA class I 0.99 0.98 1.01 0.27

PRA class II 0.99 0.98 1.01 0.35

Recipient CMV status 0.85 0.58 1.25 0.41

Donor CMV status 0.90 0.62 1.32 0.59

CMV negative status vs. any positive 0.97 0.60 1.57 0.90

Ratio of recipient height over donor height 1.59 0.11 23.62 0.74

Ratio of recipient height over donor height >,< 1 0.98 0.67 1.45 0.94

Transplant characteristics

Induction therapy (basiliximab vs.

thymoglobulin) 0.55 0.29 1.01 0.05

Ischemic time (minutes) 1.00 0.99 1.00 0.55

Post-transplant characteristics

Non-compliance of patient- recorded from chart 0.83 0.49 1.41 0.49

Post-transplant diagnosis of diabetes 0.82 0.55 1.20 0.30

Post-transplant diagnosis of hypertension 1.05 0.68 1.61 0.82

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Post-transplant diagnosis of cancer 1.01 0.62 1.64 0.97

Medical therapy

Cyclosporine only 0.85 0.58 1.25 0.40

Tacrolimus only 1.06 0.71 1.58 0.77

Switched between tacrolimus and cyclosporine 1.23 0.74 2.02 0.42

Any MPA 0.63 0.33 1.22 0.17

MPA within 1 week of transplant 0.83 0.56 1.22 0.34

MPA within 1 month of transplant 0.80 0.48 1.33 0.39

Any PSI for >6 months 1.93 1.28 2.89 0.00

Sirolimus 1.59 1.09 2.32 0.02

PSI within 6 months of transplant 1.36 0.92 2.03 0.13

PSI within 6 months of transplant (>6 months

treatment) 1.37 0.92 2.03 0.12

PSI within 2 years of transplant (>6 months

treatment) 1.58 1.08 2.30 0.02

PSI within 2 years of transplant 1.54 1.06 2.24 0.02

Any statin 0.35 0.05 2.67 0.31

Statin within 1 week of transplant 0.68 0.46 1.00 0.05

Statin within 1 month of transplant 1.41 0.80 2.48 0.23

Steroids stopped within 2 years of transplant 0.71 0.40 1.24 0.23

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Recipient HLA-G SNPs

SNP -201 allelic effect 1.19 0.90 1.57 0.21

SNP -201 recessive effect 0.91 0.57 1.44 0.68

SNP -201 dominant effect 0.72 0.48 1.08 0.11

SNP -725 allelic effect 0.87 0.68 1.11 0.25

SNP -725 recessive effect 1.13 0.67 1.91 0.64

SNP -725 dominant effect 1.36 0.91 2.04 0.13

SNP 3196 allelic effect 1.23 0.90 1.67 0.20

SNP 3196 recessive effect 0.78 0.36 1.68 0.52

SNP 3196 dominant effect 0.78 0.54 1.14 0.20

SNP 3187 allelic effect 1.05 0.79 1.41 0.71

SNP 3187 recessive effect 1.10 0.60 2.01 0.76

SNP 3187 dominant effect 0.88 0.60 1.28 0.50

SNP 3142 allelic effect 0.81 0.50 1.30 0.38

SNP 3142 recessive effect 1.21 0.50 2.92 0.68

SNP 3142 dominant effect 1.43 0.68 3.00 0.34

14bp allelic effect 1.01 0.76 1.34 0.96

14bp recessive effect 1.25 0.77 2.04 0.37

14bp dominant effect 0.85 0.57 1.27 0.44

SNP 31 dominant effect 1.41 0.68 2.90 0.35

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SNP 110 dominant effect 0.72 0.42 1.23 0.23

SNP 130 allelic effect 1.38 0.44 4.35 0.59

SNP 258 allelic effect 0.79 0.50 1.25 0.31

Recipient Haplotypes

G*01:01/G*01:01 0.99 0.67 1.45 0.95

G*01:01/G*01:03 1.54 0.71 3.32 0.27

G*01:01/G*01:04 0.76 0.41 1.38 0.36

G*01:01/G*01:05N 0.73 0.23 2.30 0.59

G*01:01/G*01:06 1.17 0.70 1.93 0.55

Other 1.05 0.43 2.58 0.92

Donor HLA-G SNPs

SNP -201 allelic effect 0.98 0.73 1.33 0.91

SNP -201 recessive effect 0.93 0.56 1.55 0.78

SNP -201 dominant effect 1.12 0.68 1.84 0.65

SNP -725 allelic effect 1.44 0.98 2.13 0.06

SNP -725 recessive effect 0.63 0.27 1.44 0.27

SNP -725 dominant effect 0.56 0.32 0.98 0.04

SNP 3196 allelic effect 1.04 0.75 1.42 0.83

SNP 3196 recessive effect 0.87 0.44 1.75 0.70

SNP 3196 dominant effect 0.99 0.64 1.52 0.96

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SNP 3187 allelic effect 0.80 0.59 1.09 0.16

SNP 3187 recessive effect 1.35 0.71 2.55 0.36

SNP 3187 dominant effect 1.33 0.87 2.05 0.19

SNP 3142 allelic effect 0.77 0.46 1.28 0.31

SNP 3142 recessive effect 1.40 0.64 3.02 0.40

SNP 3142 dominant effect 1.44 0.59 3.50 0.42

14bp allelic effect 1.08 0.79 1.46 0.63

14bp recessive effect 0.94 0.54 1.62 0.82

14bp dominant effect 0.88 0.56 1.39 0.59

SNP 31 allelic effect 1.57 0.57 4.32 0.38

SNP 110 recessive effect 1.26 0.74 2.15 0.40

SNP 130 allelic effect 1.08 0.34 3.45 0.89

Donor Haplotypes

G*01:01/G*01:01 1.17 0.81 1.71 0.40

G*01:01/G*01:03 0.22 0.03 1.61 0.14

G*01:01/G*01:04 1.32 0.76 2.29 0.32

G*01:01/G*01:05N 0.97 0.24 3.96 0.97

G*01:01/G*01:06 0.98 0.53 1.84 0.96

Other 1.78 0.65 4.89 0.26

Matched SNPs

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SNP -201 1.47 0.95 2.27 0.08

SNP -725 1.09 0.71 1.68 0.68

SNP 3196 0.81 0.52 1.25 0.34

SNP 3187 1.00 0.65 1.53 1.00

SNP 3142 1.00 0.47 2.11 0.99

14bp 1.06 0.68 1.63 0.81

Legend: HR: Hazard ratio, LCL: lower confidence limit; UCL: upper confidence limit, CHD: congenital heart disease, BMI: body mass index, CVA: cerebrovascular accident VAD: ventricular assist device, PRA: panel reactive antibody, CMV: cytomegalovirus infection, MPA: mycophenolic acid analog, PSI: proliferation signal inhibitor, SNP: single nucleotide polymorphism.

Table 9: Multivariate model for the diagnosis of mild cardiac allograft vasculopathy.

Factors Bootstrap

reliability HR LCL UCL p-value

Donor age (per 10-years increase) 98% 1.31 1.14 1.51 <0.001

Any PSI exposure >6 months 93% 1.95 1.27 3.01 0.002

Donor weight (per 5-kg increase) 75% 1.13 1.02 1.26 0.02

Statin treatment started within 1 54% 0.66 0.44 0.99 0.04

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week of transplant

Pre-transplant cancer history 93% 0.17 0.04 0.77 0.01

Year of transplantation 100% 1.16 1.09 1.24 <0.001

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate. Legend: SNP: single nucleotide polymorphism, PRA: panel reactive antibody, HR: hazard ratio, LCL: lower confidence limit; UCL: upper confidence limit.

4.7 Predictors of Severe Cardiac Allograft Vasculopathy

All variables (SNPs and relevant clinical data) tested in univariable parametric

hazard regression model for the outcome are listed in Table 10. Variables obtained

from the multivariable regression model are listed in Table 11. Donor-recipient SNP

-201 (CC-CC) genotype matching was identified as an independent predictor of

severe cardiac allograft vasculopathy in the survival analysis (Figure 11).

Table 10: Univariate analysis risk factors for severe CAV.

General recipient characteristics HR LCL UCL p-value

Sex 0.97 0.37 2.54 0.95

Age at transplant (years) 1.02 0.98 1.06 0.39

Weight (kg) 1.03 1.00 1.06 0.02

Year of transplantation 1.11 0.92 1.33 0.27

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Blood group: B 1.88 0.72 4.90 0.20

Blood group: O 0.97 0.37 2.54 0.95

Blood group: A 0.86 0.35 2.11 0.74

Primary diagnosis: CHD 0.67 0.22 2.02 0.48

Primary diagnosis: Idiopathic 1.81 0.75 4.39 0.19

General donor characteristics

Donor sex 0.74 0.26 2.14 0.58

Age of donor (years) 1.04 1.01 1.08 0.01

Donor weight (kg) 1.02 1.00 1.05 0.07

Donor BMI (kg/m2) 1.06 1.00 1.12 0.03

Blood group of donor: B 1.60 0.58 4.44 0.36

Blood group of donor: A 0.65 0.25 1.69 0.37

Blood group of donor: O 1.23 0.51 3.02 0.64

Donor cause of death: CVA/stroke 0.96 0.38 2.42 0.93

Donor cause of death: Trauma 12.60 1.62 97.84 0.02

Donor cause of death: Anoxia 0.36 0.05 2.72 0.32

Pre-transplant characteristics

Pre-creatine level (umol/L) 1.01 1.00 1.02 0.08

Pre-bilirubin level (umol/L) 0.99 0.95 1.03 0.58

Pre-transplant use of VAD 1.23 0.41 3.70 0.71

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PRA class I 1.02 0.99 1.04 0.14

PRA class II 1.02 1.00 1.04 0.01

Recipient CMV status 1.90 0.68 5.30 0.22

Donor CMV status 0.98 0.40 2.36 0.96

CMV negative status vs. any positive 1.39 0.41 4.78 0.60

Ratio of recipient height over donor height 13.56 0.02 8073.74 0.42

Ratio of recipient height over donor height >,< 1 0.43 0.16 1.17 0.10

Transplant characteristics

Induction therapy (basiliximab vs. thymoglobulin)

1.40 0.48 4.05 0.53

Ischemic time (minutes) 1.00 1.00 1.01 0.35

Post-transplant characteristics

Non-compliance of patient- recorded from chart 0.52 0.12 2.26 0.38

Post-transplant diagnosis of diabetes 0.65 0.25 1.71 0.39

Post-transplant diagnosis of hypertension 0.95 0.34 2.63 0.92

Post-transplant diagnosis of cancer 0.99 0.33 2.97 0.98

Medical therapy

Cyclosporine only 0.49 0.20 1.24 0.13

Tacrolimus only 1.62 0.60 4.36 0.34

Switched between tacrolimus and cyclosporine 1.62 0.58 4.54 0.36

- 88 -

Any MPA 1.54 0.20 11.78 0.68

MPA within 1 week of transplant 0.80 0.33 1.94 0.62

MPA within 1 month of transplant 3.50 0.46 26.42 0.22

Any PSI for >6 months 1.73 0.62 4.78 0.29

Sirolimus 2.27 0.87 5.93 0.09

PSI within 6 months of transplant 1.56 0.63 3.87 0.34

PSI within 2 years of transplant (>6 months treatment)

1.52 0.62 3.70 0.36

PSI within 6 months of transplant (>6 months treatment)

1.56 0.63 3.87 0.34

PSI within 2 years of transplant 1.41 0.58 3.43 0.45

Statin within 1 week of transplant 0.57 0.21 1.50 0.25

Recipient HLA-G SNPs

SNP -201 allelic effect 1.06 0.56 2.00 0.87

SNP -201 recessive effect 1.30 0.47 3.60 0.61

SNP -201 dominant effect 0.72 0.28 1.80 0.47

SNP -725 allelic effect 1.50 0.72 3.14 0.28

SNP -725 recessive effect 0.27 0.04 2.03 0.20

SNP -725 dominant effect 0.67 0.22 2.00 0.47

SNP 3196 allelic effect 1.69 0.76 3.74 0.20

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SNP 3196 recessive effect 0.67 0.09 5.04 0.70

SNP 3196 dominant effect 0.52 0.21 1.30 0.16

SNP 3187 allelic effect 0.74 0.40 1.36 0.33

SNP 3187 recessive effect 1.26 0.37 4.31 0.71

SNP 3187 dominant effect 1.67 0.66 4.20 0.28

SNP 3142 allelic effect 0.90 0.29 2.83 0.86

SNP 3142 recessive effect 1.09 0.13 9.46 0.93

SNP 3142 dominant effect 1.19 0.22 6.53 0.84

14bp allelic effect 0.97 0.51 1.85 0.92

14bp recessive effect 1.47 0.49 4.43 0.49

14bp dominant effect 0.84 0.34 2.08 0.71

SNP 31 dominant effect 2.42 0.56 10.54 0.24

SNP 110 dominant effect 1.52 0.55 4.18 0.42

SNP 258 allelic effect 4.71 0.62 35.48 0.13

Recipient Haplotypes

G*01:01/G*01:01 1.23 0.49 3.09 0.66

G*01:01/G*01:03 3.03 0.70 13.17 0.14

G*01:01/G*01:04 1.76 0.58 5.29 0.32

Other 1.03 0.14 7.74 0.98

Donor HLA-G SNPs

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SNP -201 allelic effect 4.40 1.78 10.86 0.00

SNP -201 dominant effect 0.20 0.07 0.55 0.00

SNP -725 allelic effect 0.96 0.47 1.96 0.91

SNP -725 recessive effect 0.52 0.07 3.98 0.53

SNP -725 dominant effect 1.42 0.49 4.10 0.52

SNP 3196 allelic effect 2.85 1.04 7.84 0.04

SNP 3196 dominant effect 0.33 0.11 1.04 0.06

SNP 3187 allelic effect 0.27 0.13 0.55 0.00

SNP 3187 recessive effect 5.62 2.02 15.67 0.00

SNP 3187 dominant effect 6.74 1.53 29.82 0.01

SNP 3142 allelic effect 0.10 0.01 0.80 0.03

SNP 3142 recessive effect 13.14 1.46 118.28 0.02

14bp allelic effect 3.48 1.43 8.47 0.01

14bp dominant effect 0.27 0.10 0.74 0.01

SNP 110 dominant effect 0.35 0.05 2.65 0.31

Donor Haplotypes

G*01:01/G*01:01 2.40 0.95 6.06 0.06

G*01:01/G*01:04 0.49 0.06 3.65 0.48

G*01:01/G*01:06 1.03 0.24 4.44 0.97

Matched SNPs

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SNP -201 4.46 1.55 12.90 0.01

SNP -725 1.08 0.40 2.91 0.88

SNP 3196 2.87 0.99 8.30 0.05

SNP 3187 2.03 0.70 5.88 0.19

SNP 3142 0.56 0.06 5.02 0.60

14bp 1.53 0.57 4.08 0.40

Legend: HR: Hazard ratio, LCL: lower confidence limit; UCL: upper confidence limit, CHD: congenital heart disease, BMI: body mass index, CVA: cerebrovascular accident VAD: ventricular assist device, PRA: panel reactive antibody, CMV: cytomegalovirus infection, MPA: mycophenolic acid analog, PSI: proliferation signal inhibitor, SNP: single nucleotide polymorphism.

Table 11: Multivariate model for the diagnosis of severe cardiac allograft

vasculopathy.

Factors Bootstrap

reliability HR LCL UCL p-value

Donor-recipient SNP -201 (CC-CC) matching 100% 11.85 4.27 32.94 <.001

Level of pre-transplant PRA II (per 10%-

increase) 69% 1.40 1.09 1.81 0.009

Donor age (per 10-year increase) 77% 1.38 1.01 1.88 0.04

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Recipient weight (per 5-kg increase) 74% 1.15 1.00 1.34 0.05

Data is described as mean ± standard deviation, median (25th-75th percentiles) and frequencies, as appropriate. Legend: SNP: single nucleotide polymorphism, PRA: panel reactive antibody, HR: hazard ratio, LCL: lower confidence limit; UCL: upper confidence limit.

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Figure 11. Predicted freedom from CAV from donor-recipient SNP -201 (CC-CC) matching. In this analysis, the hazard function obtained from the parametric hazard regression model for severe CAV over time was resolved according to SNP-201 donor-recipient matching status.

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Chapter 5

Discussion and Conclusion

5 General Discussion

5.1 Research Aims

The relevance of HLA-G in transplantation is attributed to its immune inhibitory

activity. Differences in expression of HLA-G are associated with variable protection

against allograft rejection (Lila et al., 2002). The varying expression levels seen in

patients are potentially prompted by the few nucleotide variations in the conserved

gene sequence (Castelli et al., 2014). With this in mind, we investigated the role of

HLA-G SNPs (from recipient and donor) on CAV diagnosis.

We identified donor-recipient SNP -201 (CC-CC) genotype matching to be an

independent risk factor for severe CAV diagnosis. The other polymorphisms

investigated were not associated with severe CAV even though previous

investigations suggested their role in determining transplant outcome or in

modifying expression such as the 14bp INDEL polymorphisms (Misra et al., 2014;

Twito et al., 2011). Though immunosuppressive therapy has been recognized to

induce expression of HLA-G, in this analysis, variations in the immunosuppressive

regimen did not emerge to be a significant factor for severe CAV (Basturk et al.,

2006; Sheshgiri et al., 2009). We reasoned that probably the therapy levels that

induced higher expression levels are significantly higher than the therapy doses

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given in practice as it was observed in the in vitro analysis of Mociornita et al.

(2011).

No HLA-G polymorphism was identified as a predictor for diagnosis of mild CAV.

Patients with mild CAV were diagnosed within the first year with the majority

remaining CAV 1 over the follow-up period except for 8 patients who progressed to

severe CAV. Factors identified in the model to be predictors for mild CAV were

immunosuppression therapy and donor factors. We hypothesize that the CAV 1

diagnosis in this cohort is attributed to donor transmitted coronary disease rather

than CAV, though we are limited by the lack of baseline angiography. Medical

therapy, specifically PSI prescribed for > 6 months may be in part responsible for

the stability of mild CAV seen during follow-up given its anti-proliferative effects.

The matching of SNP -201 was identified as a risk factor in the second model. SNP -

201 is located in 5’- upstream promoter region at position -201. The 5’-regulatory

region contains two main regulatory modules, the cis regulatory element, which

includes the Enhancer A with the interferon-stimulated response element (ISRE),

and the second regulatory element is the SXY module (Castelli et al., 2014). These

regulatory elements are modified in contrast to typical HLA class I promoter

regions and the modifications render them unresponsive to common HLA

modulators such as nuclear factor (NF) - κB and interferon-γ (Castelli et al., 2014).

Other elements in the promoter region constitute the heat shock element, hypoxia

response element, and others, all which are presumed to regulate the expression of

HLA-G (Castelli et al., 2014). The heat shock element and hypoxia response

element are unique to the HLA-G gene (Ibrahim et al., 2000; Moreau et al., 2009).

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SNP -201 is particularly adjacent to Enhancer A element where p50 and the

hypoxia response (HRE) element bind therefore variations in this SNP can

potentially be affecting the association of the regulatory elements to the gene

(Castelli et al., 2014; Moreau et al., 2009). Interestingly, Enhancer A (EnhA)

element, only interacts with p50/p50 homodimers in the HLA-G gene in comparison

with the vast array of homo and heterodimer factors that the HLA class I genes

interact with (p65-p50 or p65-p65). In addition, the p50/p50 homodimers do not

have a transactivator therefore cannot activate transcription (Castelli et al., 2014).

The heat shock element appears to be unique to the HLA-G gene and binds between

-242 and -238. Although the heat shock element increases the expression of the

HLA-G molecule, the functionality of the element is unknown (Moreau et al., 2009).

Besides the location and potential effect SNP -201 could have in expression, it has,

thus far, never been found individually associated with expression levels or

transplant outcomes or any disease outcome. However the SNP has been

investigated as part of a group in formed haplotypes. A recent investigation by Di

Cristofaro et al. (2015) found the CC genotype was protective and instead genotype

TT was a risk factor for chronic lung rejection. Indeed, our analysis is significantly

different from this haplotype investigation in that we analyzed the effect of the

donor and considered the SNP individually, all which affected our final model.

Certainly, the one drawback of analyzing haplotypes is that the effect of individual

SNPs can be masked by the effect of other SNPs. Thus, certainly more needs to be

understood of HLA-G genetics and polymorphisms, in particular, SNP -201

biological mechanism.

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The findings of the donor-recipient SNP -201 (CC-CC) genotype matching as a risk

factor for severe CAV corroborate the potential role for HLA-G in the development

of CAV, as suggested in previous investigations. In vitro studies by our group

identified that expression of HLA-G in human coronary smooth muscle cells led to

the inhibition of proliferative activity and the inhibition was dependent on the

concentration of HLA-G (Mociornita, Tumiati, Papageorgiou, Grosman, et al., 2013).

Lastly, HLA-G was shown to inhibit neutrophil adhesion to injured human coronary

artery endothelial cells (as a TNF-α-induced inflammatory response) (Mociornita,

Tumiati, Papageorgiou, Grosman-Rimon, et al., 2013). The inhibitory effect of HLA-

G demonstrated in these in vitro analyses highlights its postulated role in the

development of CAV. Additionally Mociornita, Tumiati, Papageorgiou, Grosman, et

al. (2013) demonstrated the crucial importance of HLA-G’s concentration in

determining the final outcome. Smooth muscle cell proliferation was significantly

inhibited once HLA-G levels reached a specific concentration, anything below that

level did not modify their behaviour (Mociornita, Tumiati, Papageorgiou, Grosman,

et al., 2013). This observation demonstrates the intricacy of HLA-G’s effect, which is

suggestive of HLA-G possible role in CAV process. Lastly, two clinical investigations

in the heart transplant population demonstrated how higher HLA-G levels were

always associated with no or low incidence of CAV diagnosis, once again linking the

potential protective effect of HLA-G to CAV (Blanco-Garcia et al., 2013; Lila et al.,

2002).

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5.2 Proposed Mechanism

This investigation identified a novel association of the combination of donor-

recipient HLA-G genotypes in the development of CAV. Intuitively we propose a

mechanism for the association of donor-recipient SNP -201 (CC-CC) genotype

matching. Polymorphisms throughout the gene influence the expression of HLA-G

along with other factors, leading to high, medium or low expression levels (Di

Cristofaro et al., 2013; Rebmann et al., 2001). The donor genotype regulates

expression of HLA-G in allograft cells, while the recipient genotype controls the host

immune cell’s HLA-G expression (Carosella et al., 2015). Combined expression of

HLA-G in their respective cells, creates a “protective shield” at the primary target

site (Carosella et al., 2015). If the combined donor and recipient genotypes do not

produce enough HLA-G for the allograft to be protected, this might hinder HLA-G’s

inhibitory role because the less HLA-G molecules are available to interact with the

immune receptors, the immune inhibitory effect is lessened (Carosella et al., 2015;

Mociornita, Tumiati, Papageorgiou, Grosman, et al., 2013). Hindering HLA-G’s

protective role might leave the allograft susceptible to rejection and long-term

development of CAV (Blanco-Garcia et al., 2013). Thus, both recipient and donor

genotypes are equally important to understand the overall environmental tolerance

produced by HLA-G.

The rationale for the proposed mechanism is based on three important results

identified in the literature. Specifically heart and allograft cells express HLA-G,

variations in soluble HLA-G levels determine patient’s outcomes and variations are

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caused in part by SNPs in the HLA-G gene. Numerous investigations highlighted

that heart and graft cells express HLA-G. Sheshgiri, Rouas-Freiss, et al. (2008) and

Mociornita et al. (2011) identified myocardial cells, cardiac endothelial cells and

human coronary smooth muscle cells to be induced by environmental factors to

express HLA-G. These findings demonstrated how medical therapy and

progesterone can induce HLA-G expression in cells not normally known for

expressing it. In liver and kidney allografts, epithelial cells from liver, biliary and

renal tubular cells were identified to express HLA-G (Creput, Durrbach, et al.,

2003; Creput, Le Friec, et al., 2003). Lastly, bronchial epithelial cells from allograft

lungs were also found to express HLA-G (Brugiere et al., 2009). Again,

demonstrating the significant expression of HLA-G in cells not known for its

expression. If the allograft organ expresses HLA-G, it is reasonable to suspect that

the donor genotype may be dictating the expression levels (in addition to

environmental factors) and thus ultimately participating in the graft tolerance.

Thus far, only one other investigation has explored the effect of donor and recipient

matching of HLA-G in transplant outcomes. The analysis explored whether

compatibility of HLA-G loci alleles was important, specifically HLA-A, –B and –DR

loci compatibility (Pirri et al., 2009). Patient and donor matching coding region

alleles were at a lower risk of rejection than those patients that did not match HLA-

G loci in the kidney cohort (Pirri et al., 2009). This was the first investigation to

ever consider genotyping the donor for HLA-G however not with the same

reasoning. Indeed, it was the first indication that considering combined genotype

may play a crucial role in cellular rejection incidence. However, there were several

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limitations with the analysis that limit the findings. First, the sample size of the

cohort was quite small thus limiting the strength of the results. In addition, the

study was restricted by only analyzing coding region alleles and not looking at

polymorphisms in the 5’-upstream promoter region and 3’-untranslated regions of

the gene, which are also known for influencing expression levels and associated

with outcomes (Castelli et al., 2014). Although, our analysis identify that matching

of SNP -201 was detrimental (the opposite to the investigation), our model did

consider the effect of coding region alleles and coding region matching and not

matching and still did not find either as a factor for severe CAV. Possible reasoning

for the different findings is that, Pirri et al. (2009) analyzed different outcomes from

our analysis, cellular rejection versus CAV, and that in itself may influence the

findings. In summary, donor genotype not previously considered of importance in

transplantation, appears to be a key player in HLA-G expression and should be

considered in transplant investigations.

Secondly, varying levels of HLA-G are associated with variable outcomes. When

analyzing the association of HLA-G and outcomes post-transplant, patients with no

HLA-G expression had significantly more episodes of acute cellular rejection than

those with positive levels (Lila et al., 2002). We have shown a strong benefit in

expressing HLA-G, since 86% of those patients had no acute cellular rejection

episodes Grade≥2R (Sheshgiri, Rouas-Freiss, et al., 2008). This association of HLA-

G and lower episodes of rejection was further corroborated in liver, and kidney

transplant recipients, were the presence of HLA-G was significantly correlated with

absence of acute cellular or chronic rejection (Creput, Durrbach, et al., 2003).

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Indeed, HLA-G expression in biopsy specimens of lung, liver and kidney allografts

were associated with a significant decrease in cellular-mediated rejection episodes,

indicating HLA-G protection of the allograft (Brugiere et al., 2009; Hu et al., 2014;

Xiao et al., 2013). Particular to our outcome, heart and lung transplant patients

with high expression of HLA-G had significantly lower incidence of allograft

vasculopathy (Blanco-Garcia et al., 2013; Brugiere et al., 2015). Again, suggesting

that HLA-G’s levels determine rejection incidence. Low expression of HLA-G

diminishes graft tolerance given that HLA-G inhibitory activity is surpassed by the

number of immune cells generating an immune response.

The final piece of evidence for the proposed mechanism is that production and

degradation of the HLA-G protein is modified by polymorphisms that are close to or

where regulatory elements bind to the gene (Donadi et al., 2011). It is important to

clarify that expression is not only influenced by nucleotide variations but also by

microRNAs and epigenetics (DNA methylation and histone modification) (Carosella

et al., 2015). Indeed the effect is reciprocal since microRNAs and DNA methylation

is also influenced by SNPs variations (Carosella et al., 2015). For instance, an

investigation by Ober et al. (2006) indicated that the switch of C for G for SNP -725

increases the chances of the SNP to be methylated which inhibits transcription and

subsequently leads to lower expression of the molecule (Moreau et al., 2003). In the

3’-untranslated region of the gene, Martelli-Palomino et al. (2013) found that

individuals homozygous for the DEL allele, in the 14bp INDEL polymorphism, had

significantly higher HLA-G expression than individuals homozygous for the INS

allele. The INS allele was associated with decreased mRNA production compared to

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the DEL allele possibly due to the effect of various microRNAs that bind around

that region (Hviid et al., 2003). Lastly, in a healthy population, the presence of

allele G*01:04 from the coding region was associated with higher HLA-G levels

while allele G*01:05N and G*01:01:03 with low (Rebmann et al., 2001). These

observations demonstrate how variations in the gene can ultimately affect the

expression levels of the molecule.

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Figure 12: Proposed mechanism of the role of HLA-G SNPs from the recipient and donor in the expression of HLA-G and subsequently how lower expression diminishes the potential inhibitory role of HLA-G in process such as smooth muscle

Ischemia-reperfusion

EC injury

Acute rejection

Donor disease

Infection

Metabolic disorders

Hypertension

Donor age

Preservation damage

Immunosuppressants

Vascular inflammation

Proinflammatory cytokines

Adhesion molecules Chemokines

Growth factors

TcellsLeukocytes Macrophages

Phenotypic change

SMC migration into vascular intima and proliferation

CAV

ê HLA-G

DONOR SNPs

RECIPIENT SNPs

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cell proliferation that suggest HLA-G’s potential role in CAV development. Adapted with permission granted from Copyright © 2013 Mociornita, Tumiati, Papageorgiou, Grosman, et al. (2013).

5.3 Clinical Significance

The work described herein identified two clinically relevant findings. Firstly, there

is a novel role for the donor HLA-G genotype in CAV outcomes. This is the first

investigation to analyze the HLA-G donor genotype with the understanding that the

donor genotype controls the expression of HLA-G in the allograft cells.

Furthermore, HLA-G SNP matching between the recipient and donor is an

independent risk factor, which proves our hypothesis that both recipient and donor

are involved in HLA-G’s tolerance role. This underscores previous studies that may

have missed tolerance effectors by not adjusting for donor HLA-G genotype. We

anticipate that our analysis will prompt other investigators to consider donor

genotype when investigating the effect of HLA-G polymorphisms and transplant

outcomes. There is a definite need for more comprehensive investigations in this

field in order to draw meaningful conclusions.

Secondly, the finding of the crucial role of donor-recipient SNP -201 (CC-CC)

genotype matching proves the important role of polymorphisms on disease outcome

and expression. This SNP is located close to regulatory factors and potential

variation in this SNP could influence the binding of regulatory elements to the

gene. The CC genotype is the ancestral allele. We hypothesize that the ancestral

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allele role is not to stimulate the binding of regulatory factors such as p50-p50 to

the gene for the purpose to maintain low transcription levels. As previously

mentioned, expressing low HLA-G renders the molecule’s immune inhibiting

activity ineffective against the vast amount of activated immune cells. It is

reasonable to believe that the ancestral genotype would not stimulate for mRNA

production since naturally this molecule is not expressed in the vast majority of

cells (Carosella et al., 2003). However, this is speculation and further

investigations are needed to draw meaningful conclusions.

5.4 Limitation

There were several limitations in this investigation. For the first model of mild

CAV, the main limitation to the findings was the lack of baseline angiography done

4-6 weeks post transplant that would determine if the mild disease observed a year

later is truly donor disease. Of course it would have strengthened the analysis if

HLA-G expression had been measured to corroborate if the levels vary between the

mild CAV cohort and the no CAV cohort.

For the severe CAV model, inability to measure HLA-G levels, due to the

retrospective nature of the study, limits the ability to corroborate our proposed

mechanism. It would have strengthened the final outcome, if patients with donor-

recipient SNP -201 (CC-CC) genotype matching had significantly lower levels of

HLA-G than the rest of the cohort. However, current laboratory techniques and

commercially available tests to measure expression of HLA-G are neither optimized

nor standardized providing narrow results. As previously mentioned, HLA-G

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molecule has 7 different isoforms, which can be found shed, soluble and membrane-

bound, and in addition as a homo and/or hetero-dimers (Carosella et al., 2015). To

that point, there is no current standard for HLA-G measuring techniques and some

techniques available only measure a few of the HLA-G variants (Carosella et al.,

2015). For instance, majority of investigations use enzyme-linked immunosorbent

assay that measures only HLA-G1 and HLA-G5 variants and only monomers,

however, a current investigation by Ezeakile et al. (2014) identified that dimers

have a crucial role in transplant tolerance in a kidney cohort over monomers.

Therefore it appears that the role of the HLA-G isoforms vary in the context of the

disease investigated. We could infer that potentially the levels of HLA-G are

underreported but there is a lot that needs to be further investigated about HLA-G

expression levels to potentially state that. For instance, a recent report by Di

Cristofaro et al. (2015) concluded that haplotype 3 was a significant risk factor for

the diagnosis of CLAD post lung transplantation. In addition, this haplotype was

associated with lower HLA-G levels (Di Cristofaro et al., 2015). However, two alleles

that compose haplotype 3 are DEL allele for 14bp INDEL polymorphism and

G*01:04 from the coding region and they had been previously associated with

significantly high HLA-G expression in numerous investigations (Di Cristofaro et

al., 2015; Rebmann et al., 2001; Twito et al., 2011). Indeed, a lot more needs to be

investigated about the expression of the HLA-G.

As for the genetic section, thus far there is a bulk of investigations that either focus

on a single polymorphisms like the 14bp INDEL polymorphism or on a specific

region of the gene. The current line of thinking identifies such analysis as missing

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the full picture and rightly so, because the expression of HLA-G is influenced by the

effect of all the SNPs in its gene combined given that the polymorphisms are not

independent of each other (Castelli et al., 2014). Currently several important SNPs

(the majority investigated in this analysis) were grouped together to form 8

different haplotypes. Thus far, one study has categorized the haplotypes into high,

low and normal expression levels (Di Cristofaro et al., 2013). Investigating the role

of polymorphisms combined into haplotypes has advantages and disadvantages. The

advantage is that the analysis is closer to understanding the overall effect of the

genetics on the expression of HLA-G. The disadvantage surfaces from the

limitations in the statistics since sometimes, depending the outcome investigated,

haplotypes do not give enough power to draw meaningful conclusions. For this

analysis, we did not group the SNPs into defined haplotypes by Di Cristofaro et al.

(2013) for that same reason since it was better to investigate each SNP individually.

Indeed, a recent investigation identified haplotype HLA-G*01:04~UTR3 as

independent risk factor (HR 3.39) for chronic lung rejection (CLAD) (Di Cristofaro et

al., 2015). However, the investigation did not analyze the genotype of the donor,

which we identified to be an important factor in the determination of rejection

outcomes. Therefore the findings are unadjusted for the effect of the donor genotype

and could be missing an important signal. In particular given that HLA-G

expression was identified in bronchial epithelial cells on transplanted lungs, clearly

indicating that allograft tissue expresses HLA-G (Brugiere et al., 2009).

Two other important factors of a genetic investigation are adjusting for ethnicity of

the cohort and replicating the investigation in another cohort. Indeed, ethnicity of

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the studied cohort is primordial since it is known that there are variations in the

frequency of the SNPs depending the ethnicity of the group studied (Donadi et al.,

2011). However, we were not able to obtain the ethnicity of the donors since it is not

disclosed by the hospital. As for the patients we had 50% unknowns since it is a

choice for the patient to disclose their ethnicity. This is a clear limitation to the

analysis since we won’t know if the frequencies observed in our cohort were linked

to a specific ethnicity. The second factor is the lack of a replication analysis. A

replication cohort gives validity to the investigation by demonstrating that the

identified signal is reproducible in cohorts in other centers. We were not able to

complete a replication cohort analysis for this Master’s thesis but we are

considering it as a future analysis.

The final limitation to this investigation was that the mechanistic pathway of SNP -

201 has not been extensively investigated. SNP-201 is located beside regulatory

elements such as P50, SP1 and the hypoxia response element (HRE), which are

known to regulate HLA-G expression (Castelli et al., 2014; Moreau et al., 2009).

However, there is currently no investigation that suggests the potential effects of a

nucleotide variation in this SNP in terms of the binding of the regulatory elements

to the gene and the effects to the expression levels.

5.5 Future Directions

Future directions for research in HLA-G are required to translate this molecule to

clinical significance and utilization. A lot still needs to be investigated about HLA-

G, which will be delineated in the next few paragraphs. Indeed, investigations are

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warranted to identify the biological mechanism by which SNP -201 might influence

the severity of CAV. Furthermore, expression levels of SNP -201 variants need to be

investigated to corroborate the proposed mechanism. In summation, SNP -201

must be further investigated to understand the missing piece in this analysis.

Indeed in vitro and in vivo analysis would most certainly provide the answer to our

questions. An investigation on various heart cell lines such as human coronary

smooth muscle cells and endothelial cells may help us finally understand the effect

of the nucleotide variation on expression of the molecule. Initially, the exploration of

the phenotype of each genotype in the various heart cell lines is significant to

corroborate our analysis. If our hypothesis is correct, then cell lines with the CC

genotype should produce significantly lower HLA-G than those with CT/TT. Since

potentially, the heart cells would have to be stimulated to express HLA-G

(potentially with immunosuppressive therapy or hormones such as progesterone),

various control groups would be needed. Once it is established in the in vitro

analysis, the next step would be to do in vivo analysis in a mouse or rat line

(modified to express HLA-G). The in vivo analysis would consist of transplanting

the mouse/rat with a known genotype and creating the CC-CC donor-recipient

group, and the various other groups. Measuring of HLA-G would be primordial were

the donor heart and recipient tissue are in contact and it should be done at various

time points post-transplant. Experiments such as the discussed above would clarify

the role of HLA-G and its significance in allograft tolerance.

Given HLA-G’s diverse forms and a recent finding indicating that dimers are key

players in the transplant tolerance process, previous investigations (limited by

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techniques and tests) may have under reported HLA-G expression (Carosella et al.,

2015; Ezeakile et al., 2014). Therefore, the techniques to measure expression need

to be improved and standardized to subsequently enhance our understanding of the

role of HLA-G expression in influencing transplant outcomes (Carosella et al.,

2015). This will be an ongoing limitation in most investigations of HLA-G until it

gets resolved. Indeed, I would suggest to rely less on soluble and membrane

measurements in the next few years and concentrate on the genetics of the HLA-G

molecule.

Lastly, for HLA-G immune inhibitory activity to be translated to clinical practice,

future investigations must be more comprehensive and include in the analysis the

donor genotype and adjust for the immunosuppressive therapy of the patients.

Clearly our analysis demonstrated the crucial role the donor genotype has in

outcome determination and it is known from previous investigations that HLA-G

expression can be influenced by immunosuppressive therapy. Furthermore, if

looking at the genetics of the molecule, future investigation would be more

comprehensive if either analyzed previously identified SNPs from all regions of the

gene or considered the formed haplotypes. It no longer makes sense to investigate a

single SNP since it is known that many of the HLA-G SNPs are in linkage

equilibrium. Indeed, considering more than one SNP provides a more

comprehensive picture of the real effect (Castelli et al., 2014). In summation, HLA-

G and transplant outcomes investigations have advanced in the last few years

however, for this molecule to progress to clinical practice, more investigations are

warranted. Therefore I would propose to repeat the same study in a lung transplant

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cohort to validate our findings. The investigation would be clinical, retrospective

and single-centered to replicate what was undertook in the cardiac study. The

purpose would be to validate our findings in another population and highlight HLA-

G relevance in transplantation. Chronic rejection (chronic allograft lung

dysfunction) would be the study outcome since it is the equivalent of cardiac

allograft vasculopathy for the lung transplant patients. Given the incidence of

chronic rejection in lung transplantation, the study does not need to go further than

5 years back to actually get an incidence of 40% in the cohort. In addition with the

number of patients transplanted and the relatively high incidence of the disease, in

this project the effect of haplotypes could also be investigated. Haplotypes could not

be investigated in the cardiac project however it is very important to understand

what is the effect of all the polymorphisms combined. In summation, repeating the

project in the lung population, would potentially validate our findings and allow for

the investigation of haplotypes and donor genotype not previously investigated.

As for me, my careers goals are to work as a physician-scientist in the future, thus I

plan to do medical school and complete a PhD degree. Indeed for this year, I plan to

undergo the same investigation I did but in a lung transplant cohort for the purpose

to answer some of the questions raised in my heart project and validate our

findings. Validating the findings is a very important step to potentially move HLA-

G to a clinical significance especially in genetics studies since it is important to

make sure the factor identified is important in different cohorts/samples. Within the

next years, I plan to further my education by completing a PhD degree, hopefully

continuing in the field of HLA-G since there is so much potential with this molecule.

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5.6 Conclusion

In this investigation, the diagnosis of mild CAV was not associated with HLA-G

polymorphisms. The major factor that may explain the lack of association between

HLA-G SNPs and mild CAV in our model was the inability to distinguish mild CAV

from donor transmitted disease. Donor-recipient SNP -201 (CC-CC) genotype

matching is an independent risk factor, which proves our hypothesis that both

recipient and donor are involved in HLA-G’s tolerance role and appears to have a

significant association with the development of severe CAV. Our investigation

identifies the potential novel role of donor HLA-G genotype in influencing

transplant outcomes and the interaction of donor-recipient SNP -201 (CC-CC)

genotype matching has not yet been described. Indeed, these findings may help to

tailor care for this subset of patients potentially mitigating the high risk of

developing severe CAV.

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Appendices Permission to Use Material From Copyright Owner: Figure 6, 5, 1 and 3:

Figure 2:

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Figure 4:

Figure 8:

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Figure 7 and 12: Dear Ms. Lazarte, You have my permission to use the material described below in your Master's thesis. Sincerely, Amelia Mociornita Sent from Yahoo Mail on Android

From: “Julieta Lazarte" <julieta.lazarte@mail.utoronto.ca> Date:Fri, Sep 25, 2015 at 1:26 PM Subject:Permision to Use Material From Copyright Owner Date: Friday September 25th, 2015 Re: Permission to Use Copyrighted Material in a Master’s Thesis Dear Ms. Mociornita, I am a University of Toronto graduate student completing my Master’s thesis entitled " Association of Human Leukocyte Antigen-G Polymorphisms and Clinical Outcomes Post-Transplantation”. My thesis will be available in full text on the internet for reference, study and / or copy. Except in situations where a thesis is under embargo or restriction, the electronic version will be accessible

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through the U of T Libraries we pages, the Library’s web catalogue, and also through web search engines. I will also be granting Library and Archives Canada and Proquest/UMI a non-exclusive license to reproduce, loan, distribute, or sell single copies of my thesis by any means or any form or format. These rights will in no way restrict re-publication of the material in any other form by you or by others authorized by you. I would like permission to allow inclusion of the following material in my thesis: Figure 2 Mechanism of CAV. in Mociornita, A.G. (2013) thesis entitled: The Role of Human Leukocyte Antigen-G in Cardiac Allograft Vasculopathy. This material will be attributed through a citation. Please confirm in by email that these arrangements with your approval. Sincerely, Julieta Lazarte