ANALYSIS OF GENETIC MARKERS ASSOCIATED

WITH KIDNEY TRANSPLANTATION OUTCOME

This thesis is submitted to the Sindh Institute of Medical

Sciences for the degree of Doctor of Philosophy

By

Ali Raza

Centre of Human Genetics and Molecular Medicine,

Sindh Institute of Medical Sciences (SIMS), Sindh Institute of Urology and Transplantation (SIUT),

Karachi, Pakistan.

2015

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Summary:

Allograft rejection remains a major hurdle in successful transplantation

despite improved immunosuppressive drugs and clinical care. The molecular

changes in the renal allograft that lead to graft rejection need to be

investigated. In the present work, polymorphisms in chemokine receptors and

urinary chemokine levels were investigated for association with rejection. This

study includes; (a) gene polymorphisms of chemokine receptors of CCR2 and

CCR5 (CCR2V64I and CCR5-59029G>A and CCR5Δ32), (b) urinary levels

of interferon induced protein-10 (IP-10), (c) urinary levels of monokine

induced by interferon-gamma (MIG) and (d) urinary levels of monocyte

chemotactic protein-1 (MCP-1). This is the first study on chemokine receptor

polymorphisms and the urinary chemokine levels (IP-10, MIG and MCP-1) in

cohorts of Pakistani renal transplant patients. The project was approved by the

Institutional Ethical Review Committee and informed consent was taken from

all the participants.

Briefly, the gene polymorphisms CCR2V64I, CCR5-59029G>A and

CCR5Δ32 were investigated in 606 renal transplant patients and their donors

by amplified fragments length polymorphisms (RFLP). The results showed

that the G/G genotype of CCR2V64I was associated with a high frequency of

allograft rejection (P=0.009). The Kaplan-Meier curve also indicated a

significant reduction in the overall time to rejection-free allograft survival for

patients with the G/G genotype of CCR2V64I as compared to the A/A or G/A

genotype (59.2±1.4 vs. 68±2.6 weeks, P=0.008) showing that individuals with

the A allele, either in the homozygous or heterozygous state, have a greater

chance to accept the graft.

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Human IP-10 is classified as the CXC chemokine sub-family. A total

of 206 urine samples of (a) rejection (n=96), (b) non-rejection (n=22) and (c)

controls (n=88) were quantified for IP-10 by enzyme-linked immunosorbent

assay (ELISA) for association with rejection. The results showed statistically

significant differences in the urinary IP-10 levels between the rejection vs.

non-rejection groups (P=0.004). The Receiver operating characteristic curve

(ROC) of IP-10 showed area under the curve (AUC) of 0.70±0.06 with 72%

sensitivity and 64% specificity, at a cut-off value of 27pg/ml.

Human MIG also belongs to the CXC chemokine sub-family. A total

of 266 urine samples from (a) rejection (n=108), (b) non-rejection (n=70), (c)

stable grafts (n=42) and (d) control groups (n=46) were quantified for MIG

and analyzed for association with rejection. The results indicated that although

urinary MIG levels were higher in patients with rejection the association was

not statistically significant (P>0.05). The ROC curve also showed AUC of

0.54±0.04 with low sensitivity (46%) and specificity (55%) at cut-off value of

6pg/ml.

Human MCP-1 belongs to the CC chemokine sub-family. A total of

409 urine samples of (a) rejection (n=165), (b) non-rejection (n=93), (c) stable

grafts (n=42) and (d) controls (n=109) were quantified for urinary level of

MCP-1 by ELISA. The results showed that MCP-1 levels were different

between the rejection and other groups (P<0.05). The ROC curve illustrated

the area under curve of 0.83±0.04 with a sensitivity and specificity of 84% and

74% respectively, at a cut-off value of 214pg/ml.

In conclusion, this work shows the usefulness of chemokine receptor

CCR2V64I polymorphism as a marker for the increased possibility of an

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immune response against an allograft. Urinary levels of MCP-1, the ligand of

CCR2, and IP-10 were increased and show good correlation with rejection.

While urinary MIG did not show any association with rejection. These

findings may help in developing new therapeutic strategies in renal

transplantation based on patient genetic makeup. Additionally, non-invasive

screening tests based on urinary levels of IP-10 and MCP-1 would help in the

assessment of the immune status of the graft.

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1. Introduction

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1.1. Renal Diseases:

The global burden of renal diseases and urinary tract abnormalities is

approximately 8.3 x 105 and 1.8 x 107 per annum respectively (Dirks et al.,

2006). Renal diseases are ranked 12th among the causes of mortality in both

males and females (Dirks et al., 2006). They are characterized by reduced

glomerular filtration rate (GFR: less than 60 ml/min./1.73m2) with or without

proteinuria, abnormality in the urine sediments, abnormality in urine and

blood chemistry measurements (creatinine, urea, electrolytes, proteins etc.)

and abnormal findings in imaging and renal histology (Levey et al., 2003).

The kidneys are under constant threat by environmental factors (drugs,

toxins, microbes), genetic factors (polycystic kidneys, alports syndrome),

congenital factors (reduced number of nephrons), dietary habits (low water in-

take, high salt) and lifestyle (smoking, alcoholism) (Lei et al., 1998; Orth et

al., 1998; Iseki, 2005; Song et al., 2009). These factors may culminate in

damage to kidney structure and function.

Renal diseases are also characterized as either acute (sudden onset) or

chronic. Chronic kidney disease (CKD) is further classified into 5 stages

depending on GFR and other associated clinical features. Ultimately, these

conditions lead to a complete shutdown of renal function and are termed “end

stage renal disease” (ESRD).

1.2. Renal Transplantation:

Renal transplantation (RT) is a therapeutic option for ESRD patients,

envisioned nearly half a century ago. In its initial stages, it was considered a

risky therapy. However, the subsequent surgical and technological

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advancements, based on increased knowledge underlying graft biology, turned

RT into a viable option. The first successful renal transplantation was

performed in Boston, USA, in 1954, when a kidney was transplanted between

identical twins (Murray et al., 2011). This opened a new era for RT in the

treatment of ESRD patients.

The success of RT has improved significantly with the identification of

immunological factors, which are major contributors in graft tolerance and

rejection. These immunological factors include human leukocyte antigens

(HLAs), ABO blood compatibility, immune cells (antigen presenting cells, T

and B cells), immune regulatory molecules (cytokines, chemokines) etc.

Additionally, discoveries of therapeutic agents/drugs against identified

immunological factors such as immunosuppressive drugs (Cyclosporine-A,

Tacrolimus, Azathioprine, Prednisolone), anti-interleukin-2 (IL-2; responsible

for activation/proliferation of T-cells and B-cells) and antithymocyte globulin

(ATG) to prevent graft rejection have revolutionized transplantation (Chinen

and Buckley, 2010). As a result of these discoveries, the patient survival rate

has increased by 90% and graft-survival by 80% (Clayton et al., 2010).

Long-term renal allograft-survival with preserved renal function is the

ultimate goal of transplantation. The main hurdles that impede long-term

graft-survival include genetic differences between the donor and recipient and

factors other than donor-recipient mismatch (drug toxicity, malignancies,

infections etc).

Genetic mismatches between donors and recipients particularly at

immune regulatory genes such as human leukocyte antigens (HLA-I and –II

loci) are key immune factors in graft rejection. The genetic differences

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between donor and recipient may be the result of polymorphisms, mutations or

epigenetic variation that lead to activation of the immune system.

Furthermore, genetic variations in the immunosuppressive drug-metabolizing

genes of the recipient produce different pharmacological responses. Hence,

genetic variations at both immune regulatory genes and drug metabolizing

genes, significantly contribute in immune regulation.

Factors such as drug toxicity, infections (BKV, CMV), recurrence of

primary renal disease [(focal segmental glomerulosclerosis

(FSGS), immunoglobulin-A (IgA) nephropathy; membrano-proliferative

glomerulonephritis (MPGN) type-I and -II etc.)], and post-transplant

malignancies, all affect renal dysfunction and activate immune response. Thus,

these factors have a major role in transplant outcome (rejection/graft loss).

Renal transplant patients need continuous monitoring of their graft

function as it is at risk of numerous adverse conditions associated with

immunological and non-immunological factors. Life-long monitoring of graft

functioning is necessary for successful transplantation. However, the initial

post–transplant duration (3 months) is the most important phase in transplant

outcome (Magee and Pascual, 2004). During this phase, the risk of rejection,

drug toxicity, infections and surgical complications is quite high. Therefore,

optimum immunosuppressive drug levels are essential to suppress the immune

system against the allograft while preventing infections and drug toxicity.

Currently, graft function is monitored by biochemical tests (creatinine,

urea, protein), imaging techniques and histopathological features associated

with the clinical presentation. However, any deranged levels of creatinine,

urea and/or presence of urinary protein (albumin) are considered as indicators

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(markers) of renal allograft dysfunction, especially creatinine. Creatinine is a

good renal-function marker but it has the disadvantage of being a late indicator

of renal injury, as allograft injury/damage may already have been initiated.

Another method for assessment of graft dysfunction is graft biopsy, a gold

standard technique. It is performed to determine the causes of unexplained

renal allograft dysfunction. The current therapeutic strategies depend on the

results of renal graft pathology. However, graft biopsy is an invasive

technique that restricts its usage in graft assessment.

To monitor the renal allograft functioning and immune status, there is a

need of marker(s) for an early and accurate diagnosis of rejection using non-

invasive methods. Currently urine and blood samples are being investigated

for the identification of markers for rejection and tolerance (Kurian et al.,

2009; Alachkar, 2012).

Advanced molecular technology (microarray) has identified various

markers for different histopathological conditions such as acute cellular

rejection, interstitial fibrosis and tubular atrophy and tolerance. It has also

helped in the identification of genes involved in complement the system and

fibrosis; cell signaling, stress and up-regulation of the immune system

(Famulski et al., 2006; Scherer et al., 2009; Nakorchevsky et al., 2010). The

strategies of integrating histopathological findings with molecular data (omics;

genomic/epigenomics, transcriptomics, proteomics and metabolomics) have

improved prediction of rejection outcome (Reeve et al., 2009; Sis et al., 2010;

de Freitas et al., 2012). In addition, new quantitative methods based on

microarray for assessment of rejection in renal transplantation have also

showed significant correlation (Mueller et al., 2007; Halloran et al., 2010).

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Despite these advancements, findings are still inconclusive about the

immunological quiescence that may support transplantation tolerance or

rejection. The prediction of long-term outcomes in RT is hindered as no robust

marker(s) is/are available to diagnose or predict rejection and to monitor the

inflammatory status of recipients against transplanted organ (Naesens and

Sarwal, 2010). The search for immunological and non-immunological players,

their genes and functional polymorphisms that result in variations in protein

expressions and regulation of molecular pathways are currently being

investigated to be used as rejection markers as well as for therapeutic

purposes.

The future perspect for RT is to provide better health with optimum

graft survival. The goals of personalized medicine in RT include the longevity

of the graft with minimal to no requirement of immunosuppressive drugs and

the detection of early graft injury using noninvasive methods so as to render

the invasive techniques such as renal biopsies obsolete. Thus, the

identification of non-invasive biomarker(s), their role in prognosis of graft

functioning and graft outcome is a major research area in the molecular

biology of kidney transplantation.

1.3. Chemokines and Chemokines Receptors:

In RT, chemokines and chemokine receptors play a central role in host

immune regulation against the allograft. Chemokines activate pro-

inflammatory cells and facilitate cell migration as well as differentiation and

activation of cytotoxic cells particularly in renal injury and rejection

mechanism. An increased level of chemokines at an early phase of

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injury/rejection and their quantification by using non-invasive sampling (blood

and urine) makes them ideal candidates to be used as biomarker for graft

function as well as monitoring immune response. Currently, chemokines and

chemokine receptors are being investigated as potential diagnostic, prognostic

and therapeutic markers in renal disease and RT (Krensky and Clayberger,

2011; Sayyed et al., 2011; Urushihara et al., 2011).

The human chemokine family consists of 50 chemoattractant cytokines

and at least 18 chemokine receptors. Chemokines are small signaling protein

molecules (60-100 amino acids) having molecular weights of 7-12 kilodalton

(kDa). These chemotactic proteins are produced constitutively in the non-

inflammatory state or may be induced in response to inflammatory signals.

The main functions of chemokines are to maintain tissue homeostasis, cellular

circulation and regulate immune system. They exert their biological activity

by interacting with specific chemokine receptors; G-protein coupled receptors

(GPCRs) on immune cells (Vassilatis et al., 2003).

The human genome project has identified, two major chemokine gene

clusters on chromosomal regions 4q13 and 17q12. Similarly, chemokine

receptor genes have also been identified as clusters on chromosome 3.

1.3.1. Chemokines Structure:

All chemokines are structurally similar. This structural homology is

mainly due to similar amino acid sequence among the chemokine families.

The chemokine structure consists of an amino terminus, 3 beta-pleated sheets

(β1-3), mucin stalk and an α-helix carboxyl terminus (Figure 1.1).

Chemokines possess 2 main regions essential for proper structural folding,

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interaction and activation of receptors. These include (a) a loop with the

peptide back bone stretching between 2 and 3 cysteine residues (b) amino

terminus (Baggiolini, 1998).

The structural conformation of chemokines is dependent on disulfide

bonds connecting conserved cysteine amino acids. These disulfide bonds are

formed between the first and third and the second and fourth conserved

cysteine residues. Chemokines are classified into four sub-families based on

the presence of these conserved cysteine amino acids at the amino terminal of

the primary protein structure. These chemokine families are named as CC,

CXC, CX3C and XC (Figure 1.2).

The CC chemokine family contains two conserved cysteine amino

acids adjacent to each other. However, CXC and CX3C chemokines family

contains a single and three amino acids respectively between two cysteine

amino acids. The XC chemokine contains a single cysteine amino acid at the

amino terminal.

1.3.2. Chemokines Function:

Functionally, chemokines are classified as homeostatic and

inflammatory. Homeostatic chemokines are involved in tissue homeostasis

and cellular recirculation under non-inflammatory conditions. So far, they are

limited in number. Most of the chemokines identified are inflammatory and

participate in immune regulation. Interestingly, the biological activity of both

types of chemokines is regulated by specific GPCRs.

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Figure 1.1: The genomic and structural organization of chemokine sub-

families. (Adapted from: Rollins, 1997)

Figure 1.2: Structure of chemokines sub-families: CC, CXC, CX3C and XC.

(Adapted from: Townson and Liptak, 2003).

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The chemokine families are encoded by various chromosomal regions.

The majority of chemokine ligands (L), encoded by specific chromosomal

loci, are mentioned in Table 1.1. These chemokines ligands interact with

corresponding signaling chemokine receptors present on specific immune

cells. For example, the CCL chemokine ligands, encoded by 17q11.2 mainly

act on monocytes; CXCL chemokine ligands located on 4q12-q13 act on

neutrophils, CX3CL chemokine ligands coded by 16q13 act on monocytes

and T-cells and the CL chemokine with T-cells (Locati et al., 2005).

Table 1.1: Chemokine family, chromosomal region and the target

immune cells

Chemokine family

Chromosomal region

Targeted immune cells

CCL 17q11.2 Monocytes, Macrophages, T-cells, Basophiles

CXCL 4q12-q13 4q21.21

Neutrophils, B-cells, NK cells, T-cells

CL 1q23 T-cells, NK cells

CX3CL 16q13 Monocytes, T-cells, NK cells

1.3.3. Chemokine Receptors:

All chemokine receptors are seven transmembrane proteins, associated

with G-protein signaling molecules. They are found on many immune cells

such as leukocytes, monocytes/macrophages and T-cells. All chemokine

ligands bind specifically with GPCRs. So far, 18 chemokine receptors based

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on G-protein dependent chemotactic activity have been identified (Zlotnik and

Yoshie, 2012).

Based on chemokine ligands’ interaction with receptors, chemokine

receptors are named as CCR, CXCR, CX3CR and XCR. The CCR (CCR1 to

10) and CXCR (CXCR1 to 6) receptor families are more frequent for CC and

CXC ligands where as a single receptor has been identified for the XCL

(XCR1) and CX3C (CX3CR1) families (Figure 1.3).

The structural conformation of chemokine receptors is very important

for their biological activity. Chemokine receptors consist of an amino terminal

domain, conserved regions/domains (aspartic acid residues; Thr-X-Pro, TXP;

Asp-Arg-Tyr, DRY) and a carboxyl terminal. The amino terminal is further

categorized as Glutamic acid-Leucine-Arginine (ELR) positive domain. These

chemokine receptors’ domains are essential for specific binding with

chemokine ligands (Figure 1.4).

A single chemokine can bind with multiple chemokine receptors

(Nomiyama et al., 2011). Similarly, chemokine receptors also bind with

multiple chemokine ligands to regulate specific immune cells (Figure 1.3). For

example, CCL chemokine attracts monocytes via CCR1, CCR2 and CCR5;

CXCL chemokine attracts neutrophils via CXCR1 and CXCR2, and CXCL9

and CXCL10 attract T-cells and NK cells by CXCR3. This redundancy

provides the molecular basis for a robust system which achieves the desired

outcome with alternative pathways (Mantovani et al., 1999).This explains why

immune cells express specific receptors on their surface, for example

neutrophils possess CXCR receptors and T-cells express both CXCR and CCR

to regulate their biological activity.

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Figure 1.3: Chemokines family ligands and chemokine receptors (Rostène et

al., 2007).

Figure 1.4: Chemokine ligand and chemokine receptors interaction with

a) ELR positive and b) ELR negative domains (Mantovani et al., 2006).

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Chemokine expression is regulated at the transcriptional and

translational levels (Schlöndorff et al., 1997). Similarly, the expression of

chemokine receptors is also regulated at the transcriptional and translational

levels. The expression of chemokine receptors on immune cells correlates with

the response to chemokines. For example; the expression level of chemokine

receptors CCR1 and CCR2 in circulating T-cells is regulated by IL-2

cytokines. IL-2 strongly up-regulates the expression of CCR1 and CCR2 on T-

cells and subsequently triggers recruitment into sites of immune and

inflammation reactions (Loetscher et al., 1996). This expression regulation

provides a control mechanism for the migration of leukocytes and other

immune cells.

Chemokine ligand-receptor interaction triggers activation signals

which cause dissociation of G-protein from receptor and activates a cascade of

signaling events in the cytoplasm of the cells. These signals ultimately result

in physiological processes such as migration, trafficking, differentiation and

degranulation of the cells.

1.3.4. Chemokines and Chemokine Receptors in Immuno-inflammatory

Mechanism during Renal Injury:

Inflammatory cells play a crucial role in renal injury as well as

allograft rejection. Renal injury could be the result of ischemia, toxins,

immunological factors and surgical procedures. These renal injuries generate

pro-inflammatory and inflammatory signals through chemokines, cytokines,

growth factors etc. from renal cells (epithelial cells, endothelial cells,

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mesangial cells, interstitial cells) to attract neutrophils, monocytes, NK cells

and T-cells to the site of kidney injury.

Renal injury affects renal function significantly. The severity of renal

injury also correlates with renal functioning. During injury, the expression

levels of chemokines and corresponding receptors, and the number of

infiltrating immune cells are increased which also correlate with renal injury

(Girlanda et al., 2008).

Chemokines contribute in three major aspects of graft biology a)

leukocytes recruitment in ischemia – reperfusion injury (IRI), b) mediation of

host response to infections and c) control of inflammatory components of

acute or chronic rejection (Gerard and Rollins 2001; Hu and Knechtle, 2006).

Chemokines, with their receptors, play a major role in leukocyte migration

into the renal allograft; facilitate migration of dendritic cells and T-cells

between lymph nodes and transplanted organ.

The interaction of chemokines and chemokine receptors ultimately

result either in resolution or progression of renal injury (Anders et al., 2003).

Rapid down-modulation of chemokines signals supports resolution of acute

inflammation. However, repeated episodes of renal injury result in continuous

chemokine secretion and leukocyte influx into the glomeruli or interstitial

spaces. These infiltrating leukocytes further contribute to injury by releasing

inflammatory and profibrotic factors. These signals significantly contribute to

renal rejection and graft dysfunction.

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1.3.5. Chemokines and Chemokine Receptors in Immuno-inflammatory

Mechanism during Renal Allograft Rejection:

Renal graft rejection is a highly complex immunological process that

results from the interaction between the graft and the host immune response.

This immunological response is suppressed by immunosuppressive drugs.

However, drug over-dosing (drug toxicity) is one of the main causes of renal

injury. Additionally, drug over-dosing suppresses the immune system which

may also increase the chance of renal infection (pyelonephritis). This may

further exacerbate renal injury and could activate the immune system involve

in the rejection mechanism. Rejection is characterized as a major

immunological cause of renal injury which leads to fibrosis or graft rejection.

Initially, surgical procedures of graft implantation cause trauma and

ischemic-reperfusion related injuries result in the production of pro-

inflammatory cytokines and chemokines and activation of vascular

endothelium. The pro-inflammatory stimuli by cytokines (Interferon gamma,

INF-γ and Tumor necrosis factor, TNF) rapidly induce other cytokines and

chemokines (CCL2/MCP-1, CXCL8/IL-8, CXCL10/interferon gamma

inducible protein, IP-10) within a few hours of renal injury and

CCL5/RANTES after 12-48 hours (Segerer et al., 2000; Song et al., 2000;

Fang et al., 2012). Additionally, growth factors, immune complexes and

complement activation factors also induce chemokine production. The pro-

inflammatory signals also activate vascular endothelium and tubular

epithelium of the renal allograft (Thorburn et al., 2009). The activation

includes up-regulation of HLA, co-stimulating molecules and adhesion

molecules, and the release of inflammatory chemokines (CXC and CC

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chemokines). During inflammation, the expression of chemokines (CCL2,

CXCL1, CXCL2) on epithelium and corresponding chemokine receptors

(CXCR and CXCR2) on monocytes and leukocytes are up-regulated and direct

immune cells to the inflamed renal site (Nguan and Du, 2009). This initial

innate immune response to renal injury subsides with the healing mechanism

in HLA-matched graft. However, in the HLA-mismatched graft, early non-

specific ischemic-reperfusion injury is reported to be an immunological cause

of graft rejection (Pascual et al., 2002).

HLA-mismatch between donor and recipient activates the adaptive

immune response against allograft. The adaptive immunity involves

infiltration of specific alloreactive T-cells, dendritic cells, NK cells and

macrophages. These immune cells are regulated by chemokine receptors

(CCR5, CXCR3) and ligands (CXCL9, CXCL10) to the inflamed site of

allograft (Mantovani et al., 2004). The infiltration of activated lymphocytes

causes significant renal injury/damage and stimulates fibroblast proliferation

and matrix synthesis. Histopathological findings such as presence of

leukocytes in the intrestitium and tubulitis are the hallmark of rejection

(Racusen et al., 1999). The occurrence of repeated episodes of rejection is

associated with loss of renal function and scarring, which ultimately results in

graft-loss.

Chemokines and chemokine receptors are crucial in the immuno-

regulatory response against the allograft. The expression of number of

chemokines and chemokine receptors have showed increased expression in

rejection (Inston and Cockwell, 2002; Panzer et al., 2004; Segerer et al., 2005;

Hoffmann et al., 2010; Krensky and Clayberger, 2011; Saxena et al., 2012).

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Interestingly, the levels of chemokines in blood and urine correlate with

histopathological findings at times of rejection. This indicates the role of

chemokines in modulating the rejection mechanism particularly during early

phase of rejection against the graft. Therefore, these chemokine factors could

be good early indicators for graft rejection.

1.3.6. Urinary Chemokines and Transplant Outcome

Chemokine receptors and their ligands are important mediators of

allograft injury. As the expression of chemokine receptors is up-regulated

during allograft injury, the measurement of their corresponding ligands could

be a potential tool for the surveillance of the graft damage. Using a non-

invasive sample (urine) for the quantification of chemokines would be ideal

for surveillance of immune response against allograft (Roedder et al., 2011;

Pereira et al., 2012). Several studies have been carried out to measure human

chemokines in the urine of kidney transplant patients for various pathological

conditions. These studies have showed an increased level of certain

chemokines (CXCL9, CXCL10, CXCL11) in patients with acute and chronic

rejection and infection compared to stable grafts and healthy individuals (Hu

et al., 2004; Schaub et al., 2009; Jackson et al., 2011; Ho et al., 2013).

Identification of acute rejection, chronic rejection and stable graft

functioning are important in renal transplantation. Increased urinary

chemokine levels have shown association with rejection. They also have a

good prediction power for rejection episodes much earlier than serum

creatinine (Kanmaz et al., 2004; Hauser et al., 2005). Therefore the response

of patients to anti-rejection therapy could be monitored by down regulation of

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chemokine levels (Sun et al., 2003). As chemokines and chemokine receptors

participate in pathological changes at particular points of renal injury, the

quantification of urinary chemokines serve as biomarkers needed for early

non-invasive diagnosis of rejection. This could also detect significant

subclinical, acute to chronic rejection and graft loss.

1.3.7. The Functional Polymorphisms in Chemokines and Chemokine

Receptors:

Genetic factors are well-known to play a key role in transplantation

including the genetic makeup of the recipients as well as genetic differences

between donors and recipients in immunoregulatory genes. These genetic

factors regulate innate and adaptive immunity. Genetic variations such as

single nucleotide polymorphisms (SNPs) can increase or decrease the

transcriptional activity of a gene as well as functional changes in a protein and

contribute to an individual’s immune response against an allograft. Several

SNPs in the genes of chemokine ligands and chemokine receptors which have

shown association with renal diseases and transplant outcome have been

identified (Simeoni et al., 2005; Ma Angeles et al., 2012).

Studies based on renal allograft biopsies with various histopathological

conditions such as acute cellular and chronic rejection have shown high

expression levels of chemokine receptors (CXCR3 and CCR5) and

corresponding ligands (CXCL9, CXCL10, CXCL11, CCL5) (Segerer et al.,

2000; Nelson et al., 2001; Akalin et al., 2001; Sarwal et al., 2003; Flechner et

al., 2004). This intra-graft expression showed the role of

chemokine/chemokine ligand pathways in the recruitment of effector cells to

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allograft (Obara et al., 2005; Wang et al., 2008; Bedognetti, 2011). The

CXCR3/CXCR3 ligand and CCR5/CCR5 ligand pathways are main

components of immunological constant of rejection (ICR). The various

expression levels during rejection episode could be influenced by

polymorphisms in the receptors of CCR2 and CCR5.

Various chemokine polymorphisms in CCR and CXCR have been

reported (Geraghty et al., 2002). However, certain polymorphisms increase the

expression levels of chemokine receptors on immune cells, and subsequently

participate in renal allograft dysfunction, repeated rejection and graft loss

(Abdi et al., 2002; Hancock, 2002; Kruger et al., 2002; Yigit et al., 2007; Cha

et al., 2009; Gorgi et al., 2011). While the chemokine polymorphism,

CCR5Δ32 of CCR5, reduces the risk of rejection in kidney allograft

(Fischereder et al., 2001). Some studies have shown a lack of association of

these polymorphisms with graft outcome (Brabcova et al., 2007; Azmandian

et al., 2012).

Genetic difference between donors and recipients in chemokines and

their chemokine receptors is another important factor in mediating response

against renal allograft. Multiple studies have reported the role of the donor’s

genetic makeup, particularly chemokine polymorphisms in the allograft

rejection (Hoffmann et al., 2004; Cha et al., 2009; Kang et al., 2008).

The functional polymorphisms in chemokine receptors (CCR5 and

CCR2) may help in predicting the host immune response against renal

allograft. Additionally, therapeutic strategies could be designed by identifying

the high risk renal transplant patients to minimize the incidence of rejection

episodes and prolong graft survival.

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1.4. Current Study:

The current study investigates the role of genetic variations

(polymorphisms; SNPs) in chemokine receptors. Chemokine receptors 2 and

5 were genotyped for CCR2V64I, CCR59029G>A and CCR5Δ32 (chapter 3)

and determined their association with renal transplant outcome. Additionally,

urinary chemokine levels of human interferon gamma inducible protein-10

(IP-10/CXCL-10; chapter 4); human monokine induced by interferon-gamma

(MIG/CXCL9; chapter 5) and human monocyte chemotactic protein-1 (MCP-

1/CCL2; chapter 6) were also quantified at different histopathologically

confirmed events. The measured urinary chemokine levels were used for

association studies with rejection, non-rejection and stable graft.

1.4.1. Chemokine Receptor Gene Polymorphisms (CCR2V64I, CCR5-

59029G>A and CCR5Δ32):

Chemokine receptor-2 (CCR2) and -5 (CCR5) are GPCRs. They are

encoded by two genes located in a cluster of chemokine receptor genes on

p21.3-p24 region of chromosome 3 (Liu et al. 1996; Samson et al., 1996 a and

b). The CCR2 and CCR5 genes comprise of 3 and 4 exons respectively,

spanning approximately 6-7 kb of genomic sequence (Mummidi et al. 1997;

Wong et al., 1997). The CCR2 gene encodes a receptor for monocytes

chemoattractant protein-1(MCP-1) where as CCR5 gene encodes a receptor

for regulated on activation normal T-cell expressed and secreted protein

(RANTES) and macrophage inflammatory protein-1 (MIP-1α and -β) ( Charo

et al., 1994). Both CCR receptors are expressed on the surface of T-cells, B-

cells, monocytes/macrophages, basophils and NK cells.

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Genetic variations such as SNPs and mutations in the coding region of

CCR2 and CCR5 chemokine receptors influence their expression and function.

These genetic variations have been implicated in kidney transplantation

rejection. The V64I polymorphism of CCR2, CCR5-59029G>A

polymorphism and CCR5 32 bp deletion (CCR5Δ32) have been widely

studied due to their association with kidney allograft rejection.

Smith et al. (1997) identified the V64I polymorphism in the first

transmembrane region of CCR2. The V64I polymorphism of CCR2 is due to a

single nucleotide change (G to A) at codon position of 64 that results in amino

acid change from valine to isoleuicine. This polymorphism has been shown to

be correlated with the reduction of CCR2 function in the anti-allograft

immune response and to be involved in the down modulation of CCR5

expression which is important in the graft rejection (Segerer et al., 2001;

Nakayama et al., 2004). Various renal transplant studies have shown the

association of CCR2V64I polymorphism with either allograft rejection or

stable allograft function (Prasad et al., 2007; Omrani et al., 2008; Gorgi et al.,

2011).

The CCR5-59029G>A is a polymorphism of CCR5 in the promoter

region that increases its expression level and subsequently its activity by

attracting monocytes and T-cells. Another mutation in the CCR5 gene is a 32

bp deletion (CCR5Δ32) that results in a frame shift and premature termination

of translation of the transcript (Samson et al., 1996 c). These genetic

variations in CCR5 have been associated with repeated rejection episodes,

graft rejection as well as graft survival (Fischereder et al., 2001; Prasad et al.,

2007; Omrani et al., 2008; Gorgi et al., 2011).

22

This study evaluates the association of CCR2V64I, CCR5-59029G>A

and CCR5Δ32 genetic variations with renal allograft rejection and survival in

our study groups. The premise of the study is that these chemokine receptor

polymorphisms help in identifying transplant patients at high risk for allograft

rejection and minimize the rejection incidences. Therapeutic strategies based

on chemokine biology may result in long-term allograft survival.

1.4.2. Urinary Interferon-Gamma Induced Protein-10 and its Association

with Renal Allograft Rejection:

Interferon-gamma inducible protein-10 (IP-10 or CXCL10) belongs to

CXC chemokine sub-group of chemokines super family. It has chemotactic

properties for T helper cells (Th1), NK cells, DC cells and macrophages

(Loetscher et al., 1996; Bonecchi et al., 1998; Romagnani et al., 2001). IP-10

is secreted by several immune cells (leukocytes, neutrophils) and non-immune

cells (epithelial cells, endothelial cells) and its biological activity is manifested

by binding to CXCR3 receptors. The CXCR3 receptors and IP-10 have a

central role in the immune response against transplanted organs (Panzer et al.,

2004; Romagnani and Crescioli, 2012).

IP-10 is named as it was identified as an early response gene induced

on treatment with interferon in many cells. The IP-10 gene is located on

chromosome 4q21.1 and consists of four (4) exons. The molecular size of IP-

10 protein is 10 kDa and it is composed of 98 amino acids.

IP-10 and its corresponding chemokine receptor (CXCR3) have a

central role in immune response against various transplant organs (renal, heart,

lung). IP-10 derived either from donor or recipient, is also associated with

rejection. As IP-10 has shown potent chemoattractant property for T cells,

23

donor derived IP-10 chemokine participate in initiating early rejection process

(Hancock et al., 2001). An increased IP-10 level along with other co-

stimulating cytokines also promotes survival and expansion of certain T-cell

subtypes and triggers apoptosis (Sidahmed et al., 2012). Therefore, it

contributes to rejection and early graft loss.

Multiple studies have shown increased expression of IP-10 in graft,

blood (pre-transplant and post-transplant) and significant association with

acute rejection, chronic allograft nephropathy and graft loss (Rotondi et al.,

2004; Tatapudi et al., 2004; Lazzeri et al., 2005; Matl et al., 2010; Lo et al.,

2011; Mao et al., 2011). It has been shown that IP-10 chemokine in urine

identifies early renal allograft inflammation and renal injury with better

sensitivity and predictability than serum IP-10 (Jackson et al., 2011).

Interestingly, it also indicates the response of patients to anti-rejection therapy

by down regulating the IP-10 levels (Matz et al., 2006; Mao et al., 2011).

Stability and reproducibility of IP-10 levels in non-invasive samples (urine

and blood) make it an ideal marker for transplantation studies.

1.4.3. Urinary Monokine Induced by Interferon-Gamma and its

Association with Renal Allograft Rejection:

The monokine induced by interferon-gamma (MIG or CXCL9) is a

major chemoattractant for T-lymphocytes. It is induced in response to INF-γ

by various cells such as monocytes/macrophages, neutrophils, endothelial

cells, epithelial cells, and also by liver and kidney. It has a role in many

diseases such as autoimmune diseases, renal diseases and allograft rejection

(Koga et al., 1999; Romagnani et al., 1999; Auerbach et al., 2009; Asaoka et

al., 2012).

24

The MIG gene is located on chromosome 4 at q12-21 (Lee and Farber,

1996; O'Donovan et al., 1999). The functional protein is composed of 103

amino acids and belongs to CXC chemokine sub-family. Its activity is

regulated by CXCR3-GPCRs on Th1 cells. Thus, it is regulated predominantly

by Th1 immune response. Interaction of MIG with CXCR3 causes

inflammatory cells infiltration that leads to renal disease and acute renal

allograft rejection (Lazzeri et al., 2002).

Various studies have shown that MIG levels are associated with renal

rejection and also identify subjects with high risk of rejection (Koga et al.,

1999; Rotondi et al., 2010; Jackson et al., 2011; De Muro et al., 2013). High

urinary levels of MIG and its association with rejection make it a potential

non-invasive biomarker for graft monitoring (Schaub et al., 2009). Multiple

studies have showed the diagnostic power of urinary MIG for rejection is

better than serum creatinine. Additionally, it could also reflect the immune

response to anti-rejection therapy (Hu et al., 2004; Hauser et al., 2005). On the

other hand, therapeutic approaches that neutralize MIG during early post

transplant periods have showed significant results in preventing chronic graft

vasculopathy and graft fibrosis (Miura et al., 2003).

1.4.4. Urinary MCP-1 and its Association with Renal Allograft Rejection:

Human monocyte chemotactic protein-1 (MCP-1/CCL2) is a

chemotactic ligand, and belongs to the CCL chemokine family. It is produced

in response to inflammatory mediators such as TNF and INF-γ (Rollins,

1997). MCP-1 is a major chemoattractant and activator of monocytes and

other immune cells (NK cells and T-cells) and contributes to inflammation and

recruitment of immune cells to the inflamed or injured site (Ajuebor et al.,

25

1998; Robertson et al., 2000). Various studies have shown the involvement of

MCP-1 in renal injury, wound healing and renal transplantation (Rovin et al.,

1996; Low et al., 2001; Rice et al., 2002; Eardley et al., 2006; Dantas et al.,

2007; Wu et al., 2010).

The MCP-1 ligand is protein in nature and its gene is located at q11.2

on chromosome 17. The MCP-1 is composed of 76 amino acids having

molecular size of 13kDa (Van Coillie et al., 1999). The MCP-1 ligand is

specific for the GPCR, CCR2. The ligand mediates its activity by interacting

with CCR2 of monocytes, NK cells and T-cells. Additionally, it also regulates

the expression levels of CCR2 on immune cells (Cho et al., 2007).

As MCP-1 is an early chemoattractant released after ischemia

reperfusion renal injury and initial allograft rejection, it contributes

significantly to immune regulation against renal transplantation. Various renal

transplant studies have shown MCP-1 association with transplant outcome

such as up-regulation of MCP-1 ligands and corresponding receptors (CCR2),

gene polymorphisms in MCP-1 gene, and also urinary levels of MCP-1

(Grandaliano et al., 1997; Kruger et al., 2002; Rüster et al., 2004; Lacha et al.,

2005; Dubiński et al., 2008; Kang et al., 2008).

The quantification of urinary MCP-1 levels of renal transplant patients

during rejection is a non-invasive immune monitoring approach. Multiple

studies have shown that urinary MCP-1 levels are associated with graft

dysfunction (Prodjosudjadi et al., 1996; Grandaliano et al., 1997; Sun et al.,

2003; Ho et al., 2010; Ho et al., 2012). Detection of urinary MCP-1 may

provide early and non-invasive diagnostic and prognostic approach for graft

dysfunction.

26

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40

Material and Methods

41

2.1. Subjects:

The project was approved by the Ethical Review Committee (E.R.C),

SIUT. The project was in accordance of Helsinki declaration as well as

institutional guidelines. Renal patients transplanted during June 2009 to

January 2013 were included in this study. Informed consent was taken from all

the participating kidney donors and recipients.

2. 2. Methods:

2.2.1. Specimens: Blood and urine samples were collected at (a) operation

time (b) just before graft biopsy and (c) 1-10, 14, 20 and 60 day post-

transplant. Blood samples (4 ml) were collected in EDTA containing

vacutainers (Becton Dickinson®, Franklin Lakes, NJ, USA) and stored at 4°C

until DNA extraction. Urine samples (100 ml) were collected in sterile

containers and centrifuged in 50 ml falcon tubes at 3200 rpm for 20 minutes at

4°C. The urine supernatant was aliquoted in two separate (2 ml)

microcentrifuge tubes and a 50 ml sterile container. The samples were stored

at -80°C and used for quantification of chemokines by immunoassay.

The methods used in this thesis such as DNA isolation and

purification, polymerase chain reaction (PCR), restriction fragment length

polymorphism (RFLP) and enzyme-linked immunosorbent assay (ELISA) are

described in standard laboratory manuals (Sambrook and Russell, 2001).

2.2.2. DNA Isolation:

Genomic DNA was purified from whole blood by the standard phenol-

chloroform-isoamyl alcohol method. Whole blood (4 ml) was transferred to

42

falcon tube and 12 ml of red blood cell lysis buffer (RCLB; 0.15M ammonium

chloride, 10mM potassium bicarbonate, 0.5M EDTA, pH 7.4) was added for

red blood cell lysis. The samples were placed on ice for 30 minutes. The blood

samples were centrifuged at 1200 rpm for 10 minutes at 4°C. The supernatant

was discarded and pellet was resuspended in 10 ml RCLB. The centrifugation

step was repeated and supernatant was again discarded.

Lymphocytes obtained centrifugation, were resuspended in 4.5 ml of

Saline-Tris-EDTA buffer (STE buffer; 3M NaCl, 1M Tris pH 8.0 and 0.5M

EDTA pH 8.0). 250 ul of 10% sodium dodecyl sulphate (SDS) was added

drop-wise with gentle vortexing, followed by 5 µl of 20 mg/ml Proteinase-K.

The samples were incubated overnight for Proteinase-K digestion in a shaking

water bath at 55oC.

The following day, first extraction was performed with equal volumes

of Tris-equilibrated phenol (pH 8.0), mixed gently for 10 minutes and kept on

ice for 10 minutes. The samples were centrifuged at 3200 rpm for 30 minutes

at 4oC. The aqueous layer was carefully removed with the help of cut-off

micropipette tips (to avoid shearing of DNA) to a sterile 15 ml falcon tube.

The second extraction was performed with an equal volume of chloroform-

isoamyl alcohol (24:1 v/v). The sample was mixed gently for 10 minutes,

placed on ice for 10 minutes and then centrifuged at 3200 rpm for 30 minutes

at 4oC. The aqueous layer was collected in another 15 ml falcon tube.

The nucleic acid (DNA) was precipitated by adding 10M ammonium

acetate (one tenth the volume of aqueous layer) and an equal volume of

isopropanol. It was mixed gently to precipitate DNA and stored at -20oC

overnight.

43

The next day, DNA was pelleted by centrifugation at 3200 rpm for 60

minutes at 4oC. The DNA pellet was washed with 70% ethanol and

centrifuged at 3200 rpm for 40 minutes. The DNA pellet was air dried and

resuspended in 500 ul of Tris-EDTA buffer (TE: 10mM Tris, 1mM EDTA, pH

8.0).

2.2.3. Quantification of DNA Samples:

The optical density (O.D) of DNA samples was measured at 260nm

and 280nm using a spectrophotometer (Lambda EZ-201, Perkin Elmer®, USA)

and DNA concentration was calculated by the following formula:

Absorbance at 260nm x dilution factor x 50 = DNA concentration (µg/ml)

Where, 50 = Correction factor for double stranded DNA.

The DNA absorbance ratio (OD260/OD280) of 1.7-2.0 was used for

chemokine receptor gene polymorphisms (CCR2V64I, CCR5-59029G>A and

CCR5Δ32).

2.2.4. Polymerase Chain Reaction:

The polymerase chain reaction was performed with GoTaq®, flexi

DNA polymerase kit (Promega®, Medison, WI, USA). The reaction

(Master Mix) for PCR was prepared, containing 1X PCR buffer, 1.5mM

MgCl2, 0.1mM dNTPs, 0.9U Taq Polymerase, 0.3µM of each Primers

(forward and reverse) and 50-75ng of genomic DNA in a total volume of 15µl.

44

The polymerase chain reaction was used to amplify polymorphic

region of a) CCR2V64I b) CCR5-59029G>A and c) CCR5Δ32 under specific

PCR conditions. The detailed methodology for genotyping of each gene is

provided in the following section.

2.2.4.1. Genotyping of CCR2V64I:

The CCR2V64I polymorphic region was amplified under specific PCR

conditions by using sequence specific primers (SSPs) described by Abdi et al.,

2002. The preparation of PCR master reaction mix were used are described in

Table 2.1.

Table 2.1: Polymerase chain reaction condition for genotyping of

CCR2V64I

PCR Reaction Mix Working Concentration

Required vol. per reaction

PCR Buffer II (10X ) 1X 1.50ul

MgCl2 (25mM) 1.5mM 0.90ul

dNTPs (2.5mM) 100uM 0.75ul

Taq Polymerase (2U/ul) 0.9U 0.45ul

Primers (forward; 10uM) 0.3uM 0.45ul

Primers (reverse;10uM) 0.3uM 0.45ul

Deionized water 9.2ul

DNA sample (50ng/ul) 65ng 1.3ul

Total volume 15.00ul

45

The PCR was performed using a thermal cycler (Gene Amp® PCR

System 9700, Applied Biosystems, USA). The thermal cycling conditions for

CCR2V64I are given below

Thermal Cycler Conditions:

1 cycle of 95C for 4 minutes (DNA denaturation step)

35 cycles of 94C for 30 seconds (DNA denaturation step)

65C for 45 seconds (Primer annealing step)

72C for 1 minute (Extension step)

1 cycle of 72C for 7 minutes (Final extension)

Hold 4ºC

The amplified PCR product size of CCR2V64I was 173 bp; and correct

fragment size was ascertained on agarose gel electrophoresis using 100 bp

DNA ladder (Promega®, Medison, WI, USA).

2.2.4.2. Genotyping of CCR5-59029G>A:

The PCR was used to amplify CCR5-59029G>A promoter region

under specific PCR conditions by using SSPs described by Abdi et al., 2002.

The master reaction mix for CCR5-59029G>A is described in Table 2.2.

Thermal cycler conditions were same as given above for CCR2V64I.

The PCR amplified product size of CCR5-59029G>A was 268 bp. Agarose

gel electrophoresis with a 100 bp ladder was performed to visualize the PCR

product of the CCR5 gene.

46

Table 2.2: Polymerase chain reaction condition for the genotyping of

CCR5-59029G>A

PCR Master Reaction Mix

Working

Concentration

Required vol. per

reaction

PCR Buffer (II) 10X 1X 1.50ul

MgCl2 (25mM) 1.5mM 0.90ul

dNTPs (2.5mM) 100uM 0.75ul

Taq Polymerase (2U/ul) 0.9U 0.45ul

Primers (forward;10uM) 0.3uM 0.45ul

Primers (reverse;10uM) 0.3uM 0.45ul

Deionized water 9.0ul

DNA sample (50ng/ul) 75ng 1.5ul

Total volume 15.00ul

47

2.2.4.3. Genotyping of CCR5Δ32:

The PCR was used to amplify CCR5Δ32 under specific PCR

conditions by using SSPs described by Abdi et al., 2002. The PCR master

reaction mix for CCR5Δ32 is described in Table 2.3.

Table 2.3: Polymerase chain reaction condition for the genotyping of

CCR5Δ32:

Thermal cycling conditions used for amplification of CCR5Δ32 were

1 cycle of 95C for 4 minutes (DNA denaturation step)

35 cycles of 94C for 30 seconds (DNA denaturation step)

57C for 45 seconds (Primer annealing step)

72C for 1 minute (Extension step)

1 cycle of 72C for 7 minutes (Final extension)

Hold 4ºC

PCR Master Reaction Mix Working Concentration

Required vol. per reaction

PCR Buffer II (5X) 1X 3.0ul

MgCl2 (25mM) 1.5mM 0.9ul

dNTPs (2mM) 100uM 0.75ul

GoTaqTM Polymerase (5U/ul) 0.5U 0.1ul

Primers (forward; 20uM) 0.3uM 0.225ul

Primers (reverse; 20uM) 0.3uM 0.225ul

Deionized water 8.8ul

DNA sample (100ng/ul) 50ng 0.5ul

Total volume 15.00ul

48

The PCR amplified products were analyzed on a 3% agarose gel

electrophoresis to identify CCR5Δ32.

The CCR5Δ32 polymorphism was scored on the presence or absence

of the 32 base pair fragment. The amplicon size of a 233 bp showed wild type

while a 201 bp indicated a 32 bp deletion fragment of CCR5. (Fig 2.1)

2.3. Agarose Gel Electrophoresis:

Agarose gel electrophoresis was used to ascertain amplification of

CCR2V64I, CCR5-59029G>A and CCR5Δ32. 3% agarose gel (Promega®,

Madison, WI, USA) was prepared by adding 9g of agarose to 300 ml with 1X

TBE buffer [(10X TBE: Tris base (0.6M), Boric acid (0.9M), disodium EDTA

(25mM) pH 8.0)]. The agarose suspension was heated in a loosely capped

bottle till completely dissolved. 5µl of Ethidium bromide (10mg/ml) was

added, mixed and then placed in a water shaking bath at 55ºC for 10 min. The

gel was poured into the casting tray of a horizontal gel electrophoresis (Sigma

Chemical Co. ST. Louis, USA.) having 3mm comb. The comb was withdrawn

from solidified gel and the gel was placed in a buffer tank containing

sufficient 1X TBE buffer to cover it.

The PCR product (5ul) was mixed with 5ul of Orange G loading dye

[6X Orange G loading dye containing 0.125% Orange-G, 20% Ficoll and

0.5M EDTA, pH 8.0) and loaded into the wells with the help of micro-

pipettor. 5ul of DNA molecular weight marker (100bp DNA ladder;

Promega®, Madison, WI, USA) having band size ranging from 100-1500bp

was loaded in each gel.

49

Gel electrophoresis was carried out at a constant 200 volts for 30

minutes. The gel bands were examined under ultraviolet light (UV). The gels

were annotated and recorded using a gel documentation system (Gel Doc

System®, Bio- Rad, Milan, Italy).

After getting the specific PCR product of CCR2V64I and CCR5-

59029G>A on gel electrophoresis, the PCR product was used for restriction

fragment polymorphisms analysis.

2.4. Restriction Fragments Length Polymorphisms (RFLP):

Restriction Fragment length polymorphisms is a method to detect

difference in homologous DNA sequence. This technique is based on a

nucleotide base change that results in the creation or abolition of restriction

site. This region is amplified by designing primers for a flanking region of

restriction site. The amplified products are subsequently digested with

appropriate restriction enzyme under appropriate conditions. The banding

patterns are analyzed using 3% agarose gel.

The functional polymorphisms of CCR2V64I and CCR5-59029G>A

were investigated by RFLPs method, using restriction enzymes. The detailed

condition for RFLPs for CCR2 and CCR5 are given below.

2.4.1. Restriction Fragments Length Polymorphisms of CCR2V64I:

The reaction mix and condition for the RFLPs assay of CCR2V64I are

described in Table 2.4.

50

Table 2.4: Reaction mix for restriction fragment polymorphism

CCR2V64I:

Reagents Volume / Reaction

CCR2V64I amplified product (173 bp)

Water

10X buffer O

BsaBI enzyme (10u/µl)

10.0µl

9.4µl

1.0µl

0.2µl

10µl of the PCR products were digested with 2U BseJI (BsaBI:

Fermentas®, Life Sciences) for CCR2V64I with 10X buffer O at 65ºC

overnight in water bath. The DNA banding pattern of CCR2V64I was

recorded using gel documentation system and results were used for statistical

Figure 2.2 analysis.

2.4.2. Restriction fragment length polymorphism of CCR5-59029G>A:

The PCR product (268bp) of CCR5-59029G>A was digested with

Bsp12861 (Fermentas®, Life Sciences) for overnight at 37°C to find out the

presence of polymorphism (G or A) at 59029 (Table 2.5).

10µl of the PCR products were digested with 2U of Bsp12861 for

CCR5-59029G>A with Buffer 10X (Bsp12861 at 37ºC overnight in water

bath. The digested products were run on 3% agarose gel at a constant 200

volts for 30 minutes. DNA banding pattern was recorded using gel

documentation system and data were analyzed for statistical analysis

51

Table 2.5: Reaction mix for restriction fragment length polymorphism of

CCR5-59029G>A

Reagents Volume / Reaction

PCR Product (268bp)

Water

Buffer 10X (Bsp12861)

Bsp12861 Enzyme (10u/µl)

10.0µl

18.8µl

2.0µl

0.2µl

.

52

Figure 2.1: Genotyping of CCR5Δ32. It shows heterozygous at CCR5Δ32

(double bands) and wild type (no CCR5Δ32 region; a single band)

53

Figure 2.2: Genotyping of CCR2V64I. The BsaBI restriction enzyme

digested the PCR product (173 bp) of CCR2V64I yielded two fragments of

149 bp and 24 bp due to the presence of homozygous AA genotype; whereas

the 173 bp amplicon of CCR2 remained uncut, indicate the presence of

homozygous GG genotype. Further, two bands of 173 bp and 149 bp were

observed in heterozygous GA genotype on 3% agarose gel.

Heterozygous GA Homozygous GG Homozygous AA

54

Figure 2.3: Genotyping of CCR5-59029G>A. The Bsp12861 result a single

fragment of (130 bp) for homozygous genotype (GG), two fragments of 130

bp and 258 bp for the heterozygous (GA) genotype and un-cut fragments for

homozygous (AA).

55

2.5. Quantification of Urinary Chemokines using Enzyme-Linked

Immunosorbent Assay (ELISA):

An enzyme-linked immunosorbent assay (ELISA) was used to measure

the urinary levels of a) human interferon induced protein-10 (CXCL10/IP-10)

b) human monokine induced by interferon-gamma (CXCL9/MIG) and c)

human CCL2/MCP-1 using R&D Systems ELISA kits (R&D Systems;

Minneapolis, MN, USA). The detailed methodology for each chemokine

analysis is described in the section below.

2.5.1. Human Interferon-Gamma Inducible Protein-10:

Urinary Human IP-10 concentration was measured in urine samples of

transplant patients using the Human urinary IP-10/CXCL10 ELISA kit

(Quantikine®, R&D Systems; Minneapolis, MN, USA). The IP-10 stock

standard, working standard dilutions and samples were prepared and assayed

as described in the kit manual.

Initially, the IP-10 stock standard of 5000pg/ml was prepared by

reconstituting 5ng of standard with 1ml of deionized water. This IP-10 stock

standard was used for making serial dilution of standards (STD) of various

concentrations such as 500pg/ml (STD-1), 250pg/ml (STD-2), 125pg/ml

(STD-3), 62.5pg/ml (STD-4), 31.2pg/ml (STD-5), 15.6pg/ml (STD-6) and

7.8pg/ml (STD-7).

The STD-1 (500pg/ml) was prepared by pipetting 100ul of IP-10 stock

standard (5000pg/ml) and mixed with 900ul RD5K calibrator diluents in a

1.5ml microcentrifuge tube. For Serial dilution of standards (STD-2 – STD-7)

500ul of calibrator diluent was pipetted into 6 separate 1.5ml microcentrifuge

56

tubes. The STD-2 was prepared by pipetting 500ul of STD-1 and mixed with

500ul of calibrator diluent in 1.5ml tube. Similarly, other working standards

(STD-3 – STD-7) were prepared.

The plate lay-out was prepared in such a way that first standard (high

concentration to the lowest concentration standards), then blanks (H1and H2)

and at last, urine samples (A3–H12) were assigned wells. All working

standards, blanks and samples were assayed in duplicate. Initially, 150uL of

Assay diluent RD1-56 (RD1-56: buffered protein base with preservative) was

added to each plate well. As per plate lay-out, 100ul of each STD (500pg/ml,

250pg/ml, 125pg/ml, 62.5pg/ml, 31.2pg/ml, 15.6pg/ml and 7.8pg/ml), 100ul

of assay diluent as a blank and 100ul of urine sample was added to the

respective wells. The plate was covered by an adhesive strip and incubated for

2 hours at room temperature.

After incubation period, each well was aspirated and washed 4 times

with wash buffer (wash buffer was prepared by taking 20ml of wash buffer

and q.s. to 500ml with deionized water). Finally, the plate was inverted and

blotted on tissue paper to soak any remaining buffer.

Anti-human IP-10 conjugate (200ul) was added into each well. The

plate was covered by an adhesive strip and incubated for 2 hours at room

temperature. After incubation, each well was aspirated and washed 4 times

with wash buffer and blotted as before. Freshly prepared Substrate solution

(200ul) was added to each well and incubated for 30 minutes at room

temperature in darkness. 50ul of stop solution was added to each well which

resulted in a change of colour from blue to yellow.

57

The optical density of each sample was determined using a microplate

reader (Synergy 2, BioTek® Instrument, Inc., USA). The plate was read at

450nm with the wavelength correction was set at 540nm. A standard curve

was generated using standards (STD-1 to STD-7) and concentration in urine

samples was calculated using Gen5 (version 1.08) data collection and analysis

software.

The measured urinary level of IP-10 was used for association with

renal outcome using statistical software.

2.5.2. Human Monokine Induced by Interferon-Gamma:

Human MIG concentration in urine samples was another chemokine

used to investigate for rejection and non-rejection, stable graft and controls

using Human CXCL9/MIG ELISA kit (Quantikine®, R&D Systems;

Minneapolis, MN, USA).

First the MIG stock standard and working standards were prepared.

MIG stock of 20,000pg/ml was prepared by reconstituting 20ng of standard

with 1ml of deionized water. The MIG stock standard was used for making

serial dilution of standards (STD) such as 2000pg/ml, 1000pg/ml, 500pg/ml,

250pg/ml, 125pg/ml, 62.5pg/ml, 31.2pg/ml and 15.6pg/ml.

The STD-1 (2000pg/ml) was prepared by adding 100ul of MIG stock

standard (20,000pg/ml) and mixed with 900ul RD5P (2.5X) calibrator diluent

[RD5P (2.5X); 10ml of RD5P q.s. to 20ml with deionized water)] in a 1.5ml

microcentrifuge tube. Serial dilutions of other working standards (STD-2 to

STD-8) was prepared by pipetting 500ul of calibrator diluent into 7 separate

microcentrifuge tubes of 1.5ml for each standard. The STD-2 was prepared by

58

pipetting 500ul of STD-1 and mixed with 500ul of calibrator diluents of STD-

2 tube. Similarly, other working standards from STD-3 to STD-8 were

prepared.

Initially, 100uL of assay diluent RD1W (RD1W: buffered protein base

with preservatives) were added to each plate well. 100ul of each standard

(2000pg/ml, 1000pg/ml, 500pg/ml, 250pg/ml, 125pg/ml, 62.5pg/ml,

31.2pg/ml and 15.5pg/ml) were pipetted from high concentration standard to

lowest concentration standard. For a blank, 100ul of assay diluent RD1W was

added to wells (H1 and H2). 100ul of urine sample was added to respective

wells. The plate was covered by adhesive strip and incubated for 2 hours at

room temperature.

After incubation, each well was aspirated and washed four times with

wash buffer (wash buffer; 20ml of wash buffer q.s. to 500ml with deionized

water). Finally, the plate was inverted and blotted against tissue paper to soak

any remaining buffer.

Anti-human MIG conjugate (200ul) was added into each well. The

plate was covered by adhesive strip and incubated for further 2 hours at room

temperature. After incubation, each well was aspirated completely and washed

four times with wash buffer. Substrate solution (200ul) was added to each

well and incubated for 30 minutes at room temperature in darkness. 50ul of

Stop solution was added to each well which resulted in a change of color from

blue to yellow.

The optical density of each sample was determined using the

microplate reader (Synergy 2, BioTek® Instrument, Inc., USA). The plate was

read at 450nm with the wavelength correction was set at 540nm. The standard

59

curve was generated based on standards (STD-1 to STD-8) and concentration

of samples was calculated using microplate data collection and analysis

software, Gen5 version 1.08.

The measured concentration of urinary level of MIG was used for

association with renal outcome using statistical software.

2.5.3. Human Monocyte Chemotactic Protein-1:

The quantitative determination of Human MCP-1/CCL2 concentration

was investigated in urine samples of transplant patients (stable graft, rejection

and control). Human urinary MCP-1/CCL2 was quantified by ELISA

(Quantikine®, R&D Systems; Minneapolis, MN, USA).

The MCP-1 stock standard (2000pg/ml) was prepared by

reconstituting 10ng of standard with 5ml of RD5L (1X) calibrator diluent

[RD5L (1X); 10ml of RD5L and 40ml of deionized water)]. This MCP-1 stock

standard was used for making serial dilution of various working standards

(STD) such as 1000pg/ml (STD-1), 500pg/ml (STD-2), 250pg/ml (STD-3),

125pg/ml (STD-4), 62.5pg/ml (STD-5) and 31.2pg/ml (STD-6).

The plate lay-out was prepared by assigning wells for MCP-1

standards (A1-G2), blanks (H1 and H2) and urine samples (A3-H12) and all

samples (standards, blanks and urine samples) were assayed in duplicate. As

per lay-out, 200ul of each working standards were pipetted from high

concentration standard (STD-1; 1000pg/ml, in A1 and A2 wells) to lowest

concentration standard (STD-6; 31.2pg/ml, in G1 and G2 wells). The 200ul of

calibrator diluent was added to each well (H1 and H2), as a blank. As 2-fold

dilution of urine samples was recommended, 100ul of calibrator diluent was

60

added followed by adding 100ul of urine sample to each well. The plate was

covered by adhesive strip and incubated for 2 hours at room temperature.

After the incubation period, fluid from each well was aspirated

completely, followed by washing steps. In the washing procedure, each well

was filled with 400ul of wash buffer (wash buffer was prepared by taking;

20ml of wash buffer and q.s. to 500ml with deionized water). The wash buffer

was aspirated completely from each well. This washing procedure was

repeated 3 times and, at last, the plate was inverted and blotted against tissue

paper to soak any remaining buffer.

Anti-human MCP-1 conjugate (200ul) was added to each well and

the plate was covered with adhesive strip and incubated for 1 hour at room

temperature. After incubation, fluid from each well was aspirated and washed

3 times as performed previously.

Freshly prepared 200ul of Substrate solution [(substrate solution;

12.5ml of stabilized hydrogen peroxide (solution A) was mixed with 12.5 ml

of stabilized chromogen, (tetramethylbenzidine; solution B)] were added in

each well and incubated for 20 minutes at room temperature in darkness. The

blue colour of various intensities was observed in working standards and

samples. 50ul of Stop solution (2N sulphuric acid) was added to each well

which resulted in change of blue colour to yellow colour. Finally, the plate

was read for the quantification of urinary MCP-1.

The optical density of samples was read using the microplate reader

(Synergy 2, BioTek® Instrument, Inc., USA). The plate was read at 450nm

with the wavelength correction set at 540nm. The standard curve was

generated based on standards and concentration of samples was calculated by

61

the microplate data collection and analysis software, Gen5 version 1.08.

The measured concentration of MCP-1 was multiplied by 2 (dilution

factor), as samples were diluted. The concentration of MCP-1 was used for

association with renal outcome using statistical software.

2.6. Histopathological Classifications:

Renal allograft biopsy is a standard gold method to evaluate

undetermined cause of renal dysfunctional or recurrent glomerular disease. It

is an invasive procedure which is performed on the renal graft by indication of

delayed graft functioning, increase in serum creatinine levels (acute or

chronic) by at least 20% of the baseline levels, proteinuria >1g (Racusen et

al., 1999; Serón et al., 2008; Kazi and Mubarak, 2012). In this thesis, all the

renal allograft biopsies were indicated (i.e. unknown cause of increased serum

creatinine levels by 20%).

Renal graft tissues were microscopically examined for pathological

features by pathologists, at the Department of Pathology, SIUT. The

pathological conditions were categorized according to Banff 97 classification

and its updates (Racusen et al., 1999). The major histopathological findings

include rejection (cellular or vascular rejection), drug toxicity, pyelonephritis,

interstitial fibrosis and tubular atrophy etc. These histopathological events

occurred as a single event or a combination of events.

62

2.7. Statistical Analysis:

The data were analyzed using Statistical Package for Social Sciences

for Windows® (SPSS® version 17; SPSS Inc., Chicago, IL, USA) and

MedCalc® statistical software (version 12.7.5) for Windows®

(http://www.medcalc.org/). The specific tests were applied to analyze data and

explained in detail in their respective chapters.

63

2.8. References:

Abdi R, Tran TB, Sahagun-Ruiz A, Murphy PM, Brenner BM, Milford EL, McDermott DH. (2002). Chemokine receptor polymorphism and risk of acute rejection in human renal transplantation. J Am Soc Nephrol. 13(3):754-8.

Kazi JI and Mubarak M (2012). Chapter 10: Pathology of Renal Transplantation. In: Topics in Renal Biopsy and Pathology. Eds. Dr. Muhammed Mubarak; 159 – 180.

Racusen LC, Solez K, Colvin RB, Bonsib SM, Castro MC, Cavallo T, Croker BP, Demetris AJ, Drachenberg CB, Fogo AB, Furness P, Gaber LW, Gibson IW, Glotz D,Goldberg JC, Grande J, Halloran PF, Hansen HE, Hartley B, Hayry PJ, Hill CM,Hoffman EO, Hunsicker LG, Lindblad AS, Yamaguchi Y, et al.( 1999). The Banff 97 working classification of renal allograft pathology. Kidney Int. 55(2):713-23

Sambrook J and Russell DW. (2001). Preparation and analysis of eukaryotic genomic DNA. In: “Molecular Cloning – A laboratory Manual.” 3rd edition. Eds. Sambrook J., Russell DW. Cold Spring Harbor Press, New York pp 6.1-6.62. Serón D, Anaya F, Marcén R, del Moral RG, Martul EV, Alarcón A, Andrés A, Burgos D, Capdevila L, Molina MG, Jiménez C, Morales JM, Oppenheimer F, Pallardó L, Fructuoso AS. (2008). Guidelines for indicating, obtaining, processing and evaluating kidney biopsies. Nefrologia.28:385-96.

64

3. The effect of chemokine receptors genes polymorphisms

(CCR2V64I, CCR5-59029G>A and CCR5Δ32) on renal

allograft survival in Pakistani transplant patients.

65

3.1. Introduction:

Recent advances in understanding the role of the immune system in

kidney allograft tolerance has markedly improved the short-term graft survival

rate (<15% acute rejection/year; Nankivell and Alexander, 2010). However,

long-term graft survival is still a major problem. Events that may influence the

short and long-term graft survival include acute rejection, chronic rejection,

interstitial fibrosis and tubular atrophy (IFTA) and delayed graft function.

Several immune-regulatory molecules have been implicated in allograft

rejection. Variations in the genes that encode these molecules have been

shown to influence their expression and function. Chemokines and their

receptors are implicated in kidney transplant rejection (Fischereder and

Schroppel, 2009; Goldfarb-Rumyantzev et al., 2010; Bedognetti, 2011).

Chemokine receptor 2 (CCR2) is specific for the monocyte

chemotactic protein-1 (MCP-1). It is expressed on the surface of monocytes,

activated memory T cells, B cells and basophils. It plays an important role in

attracting the monocytes and T-cells to the site of injury. A point mutation (G

to A) in the CCR2 gene at codon position 64 leads to an amino acid change

from valine to isoleucine (V64I) in the trans-membrane region of the protein.

This polymorphism has been shown to be correlated with the reduction of

CCR2 function in the antiallograft immune response and to be involved in the

down modulation of CCR5 expression which plays an important role in graft

rejection (Segerer et al., 2001; Nakayama et al., 2004). CCR2+/64I

(heterozygous for minor allele) and CCR2-64I/64I (homozygous for minor

allele) have been found to be associated with the reduced incidence of

rejection episodes (Hancock, 2002; Yigit et al., 2007; Singh et al., 2008).

66

Contradictory results have been reported by Gorgi et al. (2011), who have

concluded that the CCR2-64I allele is involved in an increased risk of acute

rejection in Tunisian allograft recipients.

The pro-inflammatory chemokines, RANTES (regulated on activation

normal T-cell expressed and secreted protein) and MIP-1 (macrophage

inflammatory protein-1) interact specifically with the CCR5 receptor. This

receptor is expressed on activated memory T cells, natural killer cells,

immature dendritic cells and monocytes/macrophages. It is involved in the

inflammatory response, immune cell trafficking, angiogenesis and metastasis.

Polymorphisms of the CCR5 gene have been shown to be associated with

renal allograft rejection (Krensky and Clayberger, 2011). A promoter region

polymorphism (59029G>A) of CCR5 increases the expression level and

subsequently its activity by attracting monocytes or T-cells. Therefore, the

allograft recipients carrying the A/A genotype of CCR5-59029G>A

polymorphism have poor graft survival or repeated rejection episodes (Cha et

al., 2009). Conflicting results have also been reported in different populations

that show a reduction of the incidence of AR in the A/A and G/A genotypes

(Abdi et al., 2002; Yigit et al., 2007; Omrani et al., 2008). The other mutant

form of CCR5 gene is CCR5Δ32, in which the deletion of a 32 base-pair

sequence results in a nonfunctional receptor (Omrani et al., 2008).

Fischereder et al., (2001) have shown that the mutant receptor significantly

influences renal allograft survival. These observations suggest that the same

genetic variation may play different roles in different ethnic groups in the

context of allograft outcome.

67

This study was carried out to determine the role of polymorphisms of

the CCR2 and CCR5 genes in long term graft survival among kidney allograft

recipients in our population. The effects of the donor’s genotype on the

allograft outcome were also evaluated.

3.2. Material and Methods:

This study was conducted on consecutive patients that received kidney

transplants between June 2009 and February 2011 at the Sindh Institute of

Urology and Transplantation (SIUT). The research protocol was approved by

the Institutional Review Board and conformed to the Tenets of the Declaration

of Helsinki. Written informed consent was obtained from all the subjects.

Inclusion and Exclusion Criteria of Patients and Donors:

All patients undergone live-related renal transplant procedure for the

first time. Recipients received kidney from their blood/law related donors

(N=606). Donors were selected after comprehensive pre-transplant evaluation

i.e. examination for the presence of any disease, donor-recipient

immunological match, non-obese (Body Mass Index of 18-25) etc.

During post-transplantation, patients with unknown cause of renal

dysfunction underwent renal graft biopsy procedure. Based on Banff 97

classification and its updates, the cases were categorized as rejection (non-

stable group) and non-rejection (stable group) (Racusen et al., 1999).

A total of one thousand two hundred and twelve (1212) individuals

from different regions of Pakistan have been included in this study. The cohort

consists of six hundred and six (n=606) renal transplant recipients and an

equal number of relatives who were living donors.

68

Complete demographic data including age, gender and relationship of

donors with the recipients are summarized in Table 3.1. Pre-operative data and

post-operative data are provided in Table 3.2.

Table 3.1. Description of Recipients and Donors

* Consanguineous ♦ Cousins Table 3.2. Pre- and Post-operative parameters of patients.

Cold Ischemic Time (minutes) 122.11±26.18 (minutes) HLA Match(A/B/DR)

Identical match 109 (18.1%)

Non-identical match 493 (81.8%) Induction

No induction 521 (86%)

ATG 51 (8.4%)

Simulect 34 (5.6%)

Immunosuppressive drugs therapy

Cyclosporine 461(76.1%)

Tacrolimus 59 (9.7%)

Cyclosporine to tacrolimus 85 (14%)

Tacrolimus to cyclosporine 1 (0.2%)

Donors Recipients

Total Number (n) 606 606

Gender

Female 257 (42.4%) 125 (20.6%)

Male 349 (57.6%) 481 (79.4%)

Mean Age 34.89 ± 10.15 30.13 ±10.73

Relation of recipient with donor

Sibling 332 (54.8%)

Parents 156 (25.7%)

Offspring 42 (7%)

Wife/ Husband * 58 (9.6%) / 8 (1.3%)

Other blood relatives♦ 10 (1.6%)

69

3.2.1. Genotyping of CCR2V64I, CCR5-59029G>A and CCR5Δ32

Polymorphisms:

Blood samples (4 ml) of each recipient and donor were collected in

EDTA containing vacutainers before renal transplantation. DNA was purified

by the standard phenol-chloroform-isoamyl alcohol method as described

earlier.

The genotyping of CCR2V64I, CCR5-59029G>A and CCR5Δ32

polymorphisms was performed with some minor modifications using specific

primers as described by Abdi et al. (2002). The detailed methods are given

above in the materials and methods section. The primer sequences for

amplification of CCR2V64I, CCR5-59029G>A and CCR5Δ32

polymorphisms and their product sizes are listed in Table 3.3.

70

Table 3.3: Primers sequences and product sizes of CCR2V64I, CCR5- 59029G>A and CCR5Δ32.

*Abdi et al., 2002;

FP = Forward Primer; RP = Reverse Primer; bp = base-pair

Polymorphisms Primer Sequence Product Size

CCR5-59029G>A* FP: 5'-CCCGTGAGCCCATAGTTAAAACTC-3'

RP: 5'-TCACAGGGCTTTTCAACAG'TAAGG-3' 268 bp

CCR2V64I* FP: 5'-TTGGTTTTGTGGGCAACATGATGG-3'

RP: 5'-CATTGCATTCCCAAAGACCCACTC-3' 173 bp

CCR5Δ32* FP: 5′-TGTTTGCGTCTCTCCCAG-3′

RP: 5′-CACAGCCCTGTGCCTCTT-3′

233bp (wild type)

201bp (del. type)

71

3.2.2 Statistical Analysis:

The clinical and genotyping data for the patients and controls were

analyzed using the Statistical Package for Social Sciences software (SPSS

version 17; SPSS Inc., Chicago, IL, USA). The χ² goodness of fit test was used

to check Hardy-Weinberg Equilibrium (HWE). The association of CCR2V64I,

CCR5-59029G>A and CCR5Δ32 with clinical features was analyzed using the

chi-squared test of independence with the appropriate degree of freedom. The

strength of the association was measured by odds ratio (OR) with a 95%

confidence interval (CI). A p-value of less than 0.05 was considered to be

significant. Statistical significant factors were selected as co-variable in binary

logistic regression to model the event probability for a categorical response

variable i.e. rejection or non-rejection. The cumulative rejection-free survival

time of renal allograft was calculated by Kaplan-Meier (KM) curve and the

log-rank test was used to calculate the statistical significance for the mean

time of renal allograft rejection among the CCR2 and CCR5 polymorphism

genotypes.

3.3. Results:

A total of 1212 kidney transplant recipients and their donors were

analyzed in this study. The patients’ data are summarized in Table 3.1 and 3.2.

All the allografts were from living, genetically related donors. This study was

categorized into different parts in order to better understand the role of CCR2

and CCR5 gene polymorphisms in transplant rejection. At first, the overall

frequencies of CCR2V64I, CCR5-59029G>A and CCR5Δ32 genotypes were

analyzed between donors and recipients as shown in Table 3.4. In the second

72

step, the transplant recipients were classified into two groups on the basis of

biopsy proven graft status; stable graft function group (non-rejecters) and non-

stable graft function group (rejecters). The demographic characteristics of

patients with stable and non-stable graft are given in Table 3.5a. Both groups

were compared for the chemokine receptor gene polymorphisms. The

genotypic frequencies of both the groups are given in Table 3.5b. The effects

of donor genotypes on the outcome and survival of allograft were also

analyzed (Table 3.6). Lastly, the role of the donor’s gender on the outcome of

allograft related to chemokine receptor gene polymorphisms was evaluated

(Table 3.7).

The overall occurrence of rejection in our patient cohort was 26%

while 74% patients had stable graft function. Out of the 26% allograft

rejection patients, only 6% patients required post transplant dialysis. The

stable graft group included 353 (78.6%) males and 96 (21.4%) females while

the non-stable graft group comprised of 128 (81.5%) males and 29 (18.5%)

females. There was no difference in the mean age of the stable graft vs. the

non-stable graft groups (30±10 years). The mean time for cold ischemia

between the stable and non-stable graft groups was almost the same (122±25

and 121±28 minutes respectively).

3.3.1. Association of CCR2V64I genotypes and alleles with renal allograft

rejection:

For the analysis of CCR2V64I polymorphism, DNA from 606 renal

allograft recipients and 573 donors was screened. The overall frequency of

CCR2-64V (G) allele was 91% in the recipients and 90.9% in the donors. The

73

CCR2-64I (A) allele was present in 9% recipients and 9.1% donors. The

frequencies for the three possible genotypes i.e. GG, GA, AA were 82.2%,

16% and 1.2% in the recipients and 82.5%, 16.8% and 0.7% in the donors

respectively (Table 3.4). The donor and recipient populations were in Hardy-

Weinberg Equilibrium (HWE) with respect to the CCR2V64I genotypes

(χ²=0.88, 1 df; P>0.05 and χ²=0.13, 1 df; P>0.05 respectively).

In the non-stable graft group, the frequency of CCR2 GG genotype

was higher (90%) when compared with the stable graft group (80%). The GG

genotype is significantly associated with a high risk of graft rejection with a

P=0.009, OR=2.14 (95% CI=1.2-3.7). However, the CCR2 G/A genotype

(P=0.005, OR=0.4, 95% CI=0.2-0.7) alone and in combination with the A/A

(P=0.009, OR=0.4, 95% CI=0.2-0.8) was associated with a reduced risk of

graft rejection (Table 3.5b).

Further, the effect of the genotype of the donor on the graft outcome

was also investigated. It was observed that the frequency of CCR2 G/G

genotype was slightly higher in the donors for the non-stable graft group

(85.2%) as compared to the stable graft group (81.6%) while the frequency of

CCR2 A/A genotype was higher in the donors of the stable graft group

(17.4%) compared with the non-stable graft group (14.7%). However, the

difference was not statistically significant (Table 3.6). Table 3.7 shows the

correlation between the CCR2V64I genotypes, the sex of the donor and the

incidence of graft non-stability. A higher risk of acute rejection was evident

when the donors were females with the CCR2 G/G genotype (P=0.02, OR 2.6,

95% CI=1.1-6.3). No significant association was found with any CCR2

genotype in the case of male donors. Moreover, both the G and A alleles of

74

CCR2 did not show any significant association with other risk factors such as,

the recipient’s age, the donor’s age, ethnic background and cold ischemia time

etc. for allograft failure.

3.3.2. Association of CCR5-59029G>A genotypes and alleles with

rejection:

A total of 605 recipients and 599 donors were analyzed for the CCR5-

59029 G>A polymorphism. The overall frequency of CCR5-59029G>A (G)

allele was 54.5% in the recipients and 52% in the donors while the A allele

was present in 45.5% in the recipients and 48% in the donors (Table 3.4).

When genotypic frequencies were compared among donors and recipients, it

was observed that the frequency of the GG genotype was slightly higher in the

recipients than the donors (33.8% vs. 30.7% respectively). However, there

was no difference in the frequencies between donors and recipients with the

AA and GA genotypes (Table 3.4).

No significant difference was observed between the recipients of stable

and non-stable graft groups with all the three genotypes (Table 3.5b).

Similarly no statistically significant difference was observed when the effect

of donor genotypes was analyzed on allograft outcome (Table 3.6). When the

G>A polymorphism was investigated in relation to the donor’s sex, it was

observed that the kidney from male donors with homozygous G allele had a

greater chance of graft rejection (P=0.04, OR=1.7, 95% CI=1.03-3.01; Table

3.7). Additionally, other risk factors for allograft failure such as, recipient’s

age, donor’s age, recipient’s sex, ethnic background, cold ischemic time etc.

were also checked for association with the CCR5 polymorphism. Results

75

showed no significant association between the CCR5 polymorphism and the

risk factors for graft failure.

3.3.3. Association of CCR5Δ32 genotypes and alleles with renal rejection:

For the analysis of CCR5Δ32 polymorphism, 593 recipient and 601

donor DNA samples were analyzed. The overall frequencies of the CCR5Δ32

wild type (+/+) allele and Δ32/Δ32 allele were similar in the recipients and

donors (98% for the +/+ allele and 2% for the Δ32/Δ32 allele respectively).

The frequencies for all three possible genotypes i.e. +/+, +/Δ32 and Δ32/Δ32

were 96.1%, 3.7% and 0.2% in the recipients and 96%, 4% and 0% in the

donors respectively (Table 3.4). The CCR5Δ32 genotypes of donors and

recipients were in HWE (χ²=2.46, 1 df; P>0.05 and χ²=0.23, 1 df; P>0.05

respectively).

No statistically significant association was observed between the

CCR5Δ32 allele frequencies and allograft outcome (Tables 3.5b and 3.6).

Similarly, no significant effect of CCR5Δ32 genotypes was observed with

respect to the donor’s sex on allograft outcome (Table 3.7).

76

Table 3.4: Genotypic and allelic frequencies of CCR2V64I, CCR5-59029G>A and

CCR5Δ32 in the allograft recipients and their donors.

Chemokine Receptors Recipient frequency (%) Donor frequency (%)

CCR2 V64I (n=606) (n=573)

G/G 502(82.2%) 473(82.5%) G/A 97 (16%) 96(16.8%) A/A 7 (1.2%) 4(0.7%) G allele 91% 90.9% A allele 9% 9.1%

CCR5- 59029G>A Recipients (n=605) Donors (n=599)

G/G 206 (33.8%) 186 (30.7%) G/A 247 (40.8%) 257 (41.8%) A/A 152 (25.1) 163 (26.5%) G allele 54.5% 52% A allele 45.5% 48%

CCR5 Δ32 Recipients (n=593) Donors (n=601)

+/+ 570 (96.1%) 578 (96%) +/Δ32 22 (3.7%) 23 (4%) Δ32/Δ32 01(0.2) 0(0) + allele 98% 98% Δ32 allele 2% 2%

77

Table-3.5a: Clinical characteristics of renal transplant patients and rejection group Non-Rejection

(n=449) Rejection (n=157) P-value OR (95% Cl)

Recipient age in years (mean ±SD) 30.5±10.6 29.4±10.7 0.286 --

Age Range ( min-max) (8-58) (8-60)

Recipient gender (M/F) 348(78.2%)/97(21.8%) 126(81%)/30(19%) 0.569 1.17 (0.741-1.85)

BMI 20.4±3.3 20.8±3.5 0.317 --

Range ( min-max) (11-38) (13-31)

Donor age in year (mean ± SD) 34.1±10.2 36.4±9.6 0.016* --

Age range (18-65) (15-58)

Donor gender (M/F) 273(61.3%)/172(38.7%) 78(50%)/78(50%) 0.014* 0.630(0.436-0.910) ESRD Unknown disease Stone disease Glomerular diseases

274(72.3%) 56(15.5%) 32(12.2%)

102(77%) 16(12%) 15(11%)

0.394 --

HLA Match at A/B/DR 1 H – 3 Ags 1 H – 4 Ags 1 H – 5 Ags

43(44.8%) 29(30.2%) 10(10.4%)

6(27.3%) 8(36.4%) 4(18.2%)

0.000*

Immunosuppressive drugs CsA, Aza, Pred CsA, MMF, Pred Tac, MMF, Pred

378(85%) 23(5.2%) 43(9.6%)

134(85.4%) 6(3.82%) 17(10.8%)

0.728

SD= Standard deviation; H= Haplotype; Ags= Antigens; CsA= Cyclosporin A; MMF=Mycophenolate;Aza = Azathioprine, Pred= Prednisolone; ESRD = End-stage renal disease; BMI= Body Mass Index

* indicates significant value

78

Table 3.5b: Genotypic frequencies in the groups of patients with and without rejection history

Genotype Stable graft Non-Stable graft P-value Odds Ratio 95%CI

CCR2V64I

GG 361 (80%) 141 (90%) 0.009 2.14 1.2-3.7

A/A 5(1.11%) 2(1.2%) 1 1.1 0.2-5.9

G/A 83 (18.5%) 14 (8.9%) 0.005 0.4 0.2-0.7

G/A+A/A 88(15.3%) 16(2.8%) 0.009 0.4 0.2-0.8

CCR5-59029G>A

GG 153 (34.1%) 53 (33.7%) 1 0.9 0.6-1.4

A/A 177 (39.5%) 34 (21.6) 0.28 0.7 0.5-1.1

G/A 118 (26.3%) 70 (44.5%) 0.29 1.2 0.8-1.7

CCR5 Δ32

+/+ 419(96%) 151(96.8%) 0.6 1.2 0.4-3.5

+/Δ32 17(3.9%) 5 (3.2%) 0.8 0.8 0.2-2.2

Δ32/Δ32 1 (0.2%) 0 (0%) - - -

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Table 3.6: Genotypic frequencies in the groups of donors with and without rejection history

Genotype Stable graft Non-Stable graft P-value

CCR2V64I

Donors = 573

G/G 346 (81.6%) 127 (85.2%) 0.358

A/A 74(17.4%) 22(14.7%)

G/A 4(0.94%) 0

CCR5-59029G>A

Donors = 599

G/G 131 (29.5%) 55(35.5%)

0.381 A/A 193(43.5%) 61 (39.3%)

G/A 120(27%) 39 (25.2%)

CCR5Δ32

Donor = 601

+/+ 426 (96.2%) 152 (96.2%) 1

+/Δ32 17(3.8%) 6 (3.8%)

Δ32/Δ32 0 0

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Table 3.7: Association of donor’s sex with CCR2V64I, CCR5-59029G>A and

CCR5Δ32 polymorphism

Stable graft Non-stable graft P value OR 95% CI

CCR2V64I

Male G/G 217(82.8%) 54(78.2%) 0.4 0.7 0.3-1.4 G/A 43(16.4) 15(21.7%) 0.3 1.4 0.7-2.7 A/A 2(0.76%) 0 - - -

Female

G/G 129(79.6%) 73(91.5%) 0.02 2.6 1.1-6.3 G/A 31(19.13%) 7(8.75%) 0.03 0.4 0.17-0.9 A/A 2(1.2%) 0 - - - CCR5-59029G>A Male G/G 77(28.3%) 30(41%) 0.04 1.7 1.03-3.01 G/A 119(43.7%) 20(27.3%) 0.015 0.4 0.2-0.8 A/A 76(28%) 23(31.5%) 0.5 1.1 0.6-2

Female

G/G 54(31.3%) 25(30.4%) 1 0.9 0.5-1.6 G/A 74(43%) 41(50%) 0.3 0.7 0.3-1.34 A/A 44(25.5%) 16(19.5%) 0.3 1.3 0.7-2.2 CCR5Δ32 Male +/+ 260(95.6%) 75(96%) 1 1.1 0.3-4.1 +/Δ32 12(4.4%) 3(4%) 1 0.8 0.2-3 Δ32/Δ32 0(0%) 0(0%) - - -

Female

+/+ 166(97%) 77(96.2%) 0 0.7 0.1-3 +/Δ32 5(3%) 3(3.8%) 0.9 1.2 0.3-5 Δ32/Δ32 0(0%) 0(0%) - - -

O.R = Odd Ratio

81

3.3.4 Demographic characteristics of stable graft and Non-stable graft:

The demographic characteristics of patients with stable and non-stable

graft were compared by univariate analysis. Results showed (i) donor’s age (p =

0.016) (ii) donor’s gender (p = 0.014) and (iii) HLA match (p = 0.000) were

significant between the two groups (Table 3.5a).

Binary Logistic Regression:

Variables found to be statistical significant were selected as co-variable in

binary logistic regression (LR). LR was applied to model the event probability for a

categorical response variable with two outcomes (dependable variable; rejection or

non-rejection).

The logistic regression showed that donor’s CCR5-59029 AA genotype,

recipient’s CCR2 V64I AA, HLA match at 1 Haplotype 5 Antigens (1H-5 Ags) and

identical match effect the likelihood that recipients have graft dysfunction. The

model explained 29% (Nagelkerke R2 variation in the dependent variable) variance in

renal rejection and correctly classified 76.3% of cases. Donor’s CCR5-59029 AA

genotype was 2.53 times more likely to exhibit renal rejection compared to CCR5-

59029 GG genotype. However, recipient’s CCR2 V64I AA (0.385 times), and HLA

match (1H-5 Ags match, 0.277 times and identical match 0.012 times) were

associated with a reduction in the likelihood of exhibiting renal rejection (Table 3.8).

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Table-3.8: Binary Logistic Regression

B S.E. Wald df Sig. Exp(B) 95% C.I. for EXP(B) Lower Upper

Recipient age -.014 .012 1.368 1 .242 .986 .963 1.010 Donor age .011 .011 .923 1 .337 1.011 .989 1.034 Recipient gender .361 .279 1.674 1 .196 1.435 .830 2.479 Donor gender -.203 .237 .738 1 .390 .816 .513 1.298 Recipient CCR5-59029 1.696 2 .428

CCR5-59029(GG) .109 .322 .114 1 .736 1.115 .593 2.094 CCR5-59029 (AA) .337 .267 1.590 1 .207 1.401 .830 2.365 Donor CCR5-59029 11.157 2 .004

CCR5-59029 (GG) .399 .324 1.517 1 .218 1.490 .790 2.812 CCR5-59029 (AA) .930 .288 10.428 1 .001 2.534 1.441 4.455 Recipient CCR2 V64I 6.582 2 .037

CCR2V64I (GG) -.065 .963 .005 1 .946 .937 .142 6.185 CCR2V64I (AA) -.954 .372 6.570 1 .010 .385 .186 .799 Donor CCR2 V64I .363 2 .834

CCR2 V64I (GG) .510 1.522 .112 1 .738 1.665 .084 32.889 CCR2 V64I (AA) .157 .299 .277 1 .599 1.170 .652 2.102

83

B S.E. Wald df Sig. Exp(B)

95% C.I. for EXP(B)

Lower Upper

BMI 2.578 2 .276

BMI (26-50) -.606 .377 2.576 1 .109 .546 .261 1.143 BMI (18-25) -.574 .491 1.366 1 .242 .563 .215 1.475 HLA Match 30.547 8 .000

1H-4 Ags Match -.550 .283 3.767 1 .052 .577 .331 1.005 1H-5 Ags Match -1.283 .360 12.733 1 .000 .277 .137 .561 1H-6 Ags Match -1.602 .813 3.880 1 .049 .201 .041 .992 Zero Match -.873 .764 1.306 1 .253 .418 .094 1.866 BHD-1 Ag Match -.522 .463 1.270 1 .260 .594 .240 1.470 BHD-2 Ags Match -.834 .515 2.623 1 .105 .434 .158 1.191 BHD-3 Ags Match -21.313 19248.583 .000 1 .999 .000 .000 . Identical Match -4.423 1.032 18.379 1 .000 .012 .002 .091 Constant -.303 .802 .143 1 .705 .738

1H-4 Ags = 1 Haplotype 4 Antigens; 1H-5 Ags = 1 Haplotype 5 Antigens; 1H-6 Ags = 1 Haplotype 6 Antigens; BHD-1 Ag Match; both haplotypes are different 1antigen match; BHD-2 Ags Match; both haplotypes are different 2 antigens match; BHD-3 Ags Match; Both haplotypes are different 3antigens match; BMI= Body Mass Index

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3.3.5. Effect of chemokine receptor gene polymorphism on graft survival:

Figure 3.1 shows the overall rejection-free graft survival in the patients

with respect to CCR2V64I polymorphism. As described previously by Singh

et al., (2008), the CCR2 GG genotype was compared with the GA and AA

(GG vs. GA + AA) genotypes. A significant reduction in the mean time of

rejection-free graft survival was observed in the CCR2 GG genotype (59±1.4

vs. 68±2.6 weeks; log-rank P=0.008).

CCR5-59029G>A and CCR5Δ32 genotypes had no effect on the mean

time of kidney rejection-free graft survival. The overall mean times of

rejection-free graft survival were 60±1.3 weeks (log-rank P=0.46) and 60±1.3

weeks (log-rank P=0.55) respectively.

85

Figure 3.1: Kaplan-Meier survival curve shows the overall rejection-free graft

survival of the patients with CCR2V64I polymorphism (GG vs. AA+GA

genotypes).

86

3.4. Discussion:

End stage renal disease (ESRD) is a major health concern in the world

(Schena, 2000). The estimated prevalence of ESRD in Pakistan is about 100

per million (Rizvi et al., 2002). The major causes of ESRD in the adult

population are renal stone, diabetes mellitus, hypertension and genetic factors.

Congenital urological anomalies, glomerulopathies and genetic factors are

common in children (Rizvi et al., 2002; Delucchi et al., 2006; Chacko et al.,

2007; USRDS, 2008).

The ultimate choice for the treatment of ESRD is renal transplantation.

Despite the progress in understanding rejection mechanisms and designing

targeted drugs, the success rate of long-term graft (5 years) survival varies

from 44% to 84% and patient survival varies from 74% to 100% globally. In

Pakistan, the graft survival rate is 65% and patient survival rate in 5 years is

75% (Rizvi et al., 2009). Allograft rejection mediated by the immune cells (T-

cells, B-cells), repeated infection episodes, interstitial fibrosis and tubular

atrophy (IFTA) are major causes of early graft loss (Otukesh et al., 2006;

Rizvi et al., 2009; Süsal et al., 2009).

Chemokines that are released after renal injury are considered to be

involved in the initiation of the immune response. These chemokines direct,

recruit and regulate the immunological processes that lead to the perseverance

or progression of renal disease (Anders et al., 2003). Certain chemokines and

their receptors have been found to have high expression levels during the

course of rejection (Bedognetti, 2011). It has been reported that CCR2 and

CCR5 are among those chemokines whose ligand and receptor expression

87

increases during rejection (Morgun et al., 2006; Reeve et al., 2009; Saint-

Mezard et al., 2009; Krensky and Clayberger, 2011).

This study reveals the effect of CCR2V64I polymorphism on renal

graft outcome. Patients carrying the CCR2 GG genotype have statistically

lower graft rejection time as compared to those that have other genotypes (GA

and AA). The genotypic frequencies of CCR2 G/A (heterozygous) alone and

in combination with CCR2 AA (homozygous mutant) were relatively higher in

stable allografts. The KM curve also showed a significant reduction in the

mean time of rejection-free graft survival in the GG genotype of CCR2

compared to the AA+GA genotypes (59±1.4 vs. 68±2.6 weeks, respectively).

These findings are in accordance to previous studies where it was shown that

individuals with the A allele, either in the homozygous or heterozygous state,

have a greater chance to accept the graft (Abdi et al., 2002; Hancock, 2002;

Yigit et al., 2007; Singh et al., 2008). In contrast, Krüger et al., (2002) and

Brabcova et al., (2007) have demonstrated that the CCR2V64I genotype does

not affect either graft rejection or acceptance.

Due to the low frequency of CCR5Δ32 homozygotes in the recipients

and absence in the donors, it was not possible to assess any association with

the increased or reduced risk of renal graft loss. The findings of this work

support the results of Abdi et al., (2002) and Singh et al., (2009) that

CCR5Δ32 had no effect on acute rejection. In contrast, a positive role of

homozygous CCR5Δ32 allele and long-term allograft survival were reported

by Fischereder et al., (2001).

Single nucleotide polymorphism of CCR5 in the promoter region

-59029G>A is another variant of interest in renal rejection/tolerance studies.

88

In vitro studies have shown that the CCR5-59029 A mutant type expressess

high levels of CCR5 receptors on CD4+ T-cells (Mcdermott et al., 1998;

Shieh et al., 2000). Recently, Gorgi et al., (2011) have reported an increased

risk of rejection with the -59029 A allele in combination with the CCR2V64I

A allele in the HLA matched recipients. In contrast to the above reports, the

increased expression of CCR5 receptors have been suggested to be associated

with a reduced rejection risk (Abdi et al., 2002; Yigit et al., 2007; Omrani et

al., 2008). Hoffmann et al., (2004) reported that donors with the A allele of

CCR5 experienced more episodes of allograft rejection (P=0.029) and also

increased chances to develop IFTA (P=0.04). In the present study, the

frequency of G and A alleles of CCR5 polymorphism were 54.5% and 45.5%

respectively and neither of the alleles was significantly associated with

allograft rejection or tolerance.

The results of this study revealed that the presence of the GG genotype

of CCR2V64I polymorphism in allograft recipients is associated with a

significantly higher number of rejection episodes. Donor’s gender may also

affect the allograft outcome. In our study, the CCR2V64I GG genotype of

female donors and CCR5-59029G>A GG genotype of male donors are found

to be associated with the rejection episodes. The association of polymorphisms

within these genes with the rejection episodes could be used for the early

detection of possible graft rejection episodes.

Further studies of immune-related genes (chemokines CXCL10, CCL2

cytokines TNF, IL-10 etc), polymorphisms and their expression (at mRNA or

protein) levels in our renal cohort patients would expand our knowledge

regarding our population genetics related to transplantation.

89

3.5. References: Abdi R, Tran TB, Sahagun-Ruiz A, Murphy PM, Brenner BM, Milford EL, McDermott DH. (2002). Chemokine receptor polymorphism and risk of acute rejection in human renal transplantation. J Am Soc Nephrol. 13(3):754-8.

Anders HJ, Vielhauer V, Schlöndorff D. (2003). Chemokines and chemokine receptors are involved in the resolution or progression of renal disease. Kidney Int. 63(2): 401-415.

Bedognetti D. (2011). Signature Associated with Acute Rejection: Allograft Rejection, In: Marincola, FM. and Wang, E. (Eds.), Immunologic Signatures of Rejection. Springer, New York, USA. 305-345.

Brabcova I, Petrasek J, Hribova P, Hyklova K, Bartosova K, Lacha J, Viklický O. (2007). Genetic variability of major inflammatory mediators has no impact on the outcome of kidney transplantation. Transplantation. 84(8): 1037-1044.

Chacko B, Rajamanickam T, Neelakantan N, Tamilarasi V, John GT. (2007). Pediatric renal transplantation: a single center experience of 15 yr from India. Pediatr. Transplant. 11(8): 844-849.

Cha RH, Yang SH, Kim HS, Kim SM, Park MH, Ha J, Kim YS. (2009). Genetic interactions between the donor and the recipient for susceptibility to acute rejection in kidney transplantation: polymorphisms of CCR5. Nephrol. Dial. Transplant. 24(9): 2919-2925.

Delucchi A, Ferrario M, Varela M, Cano F, Rodriguez E, Guerrero JL, Lillo AM, Wolff E, Godoy J, Buckel E, Gonzalez G, Rodriguez J, Cavada G. (2006). Pediatric renal transplantation: a single center experience over 14 years. Pediatr. Transplant. 10(2): 193-197.

Fischereder M, Luckow B, Hocher B, Wüthrich RP, Rothenpieler U, Schneeberger H, Panzer U, Stahl RA, Hauser IA, Budde K, Neumayer H, Krämer BK, Land W, Schlöndorff D. (2001). CC chemokine receptor 5 and renal transplant survival. Lancet. 357(9270): 1758-1761.

Fischereder M, Schroppel B. (2009). The role of chemokines in acute renal allograft rejection and chronic allograft injury. Front. Biosci. 14: 1807-1814. Goldfarb-Rumyantzev AS, Naiman N. (2010). Genetic predictors of acute renal transplant rejection. Nephrol. Dial. Transplant. 25(4):1039-47.

Gorgi Y, Sfar I, Jendoubi-Ayed S, Makhlouf M, Rhomdhane TB, Bardi R, Aouadi H, Abdallah TB, Abderrahim E, Ayed K. (2011). Allograft renal rejection and chemokine polymorphism. Saudi J. Kidney Dis. Transpl. 22(1): 18-23.

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Hancock WW (2002). Chemokines and transplant immunology. J. Am. Soc. Nephrol. 13(3): 821-824.

Hoffmann S, Park J, Jacobson LM, Muehrer RJ, Lorentzen D, Kleiner D, Becker YT, Hullett DA, Mannon R, Kirk AD, Becker BN. (2004). Donor genomics influence graft events: the effect of donor polymorphisms on acute rejection and chronic allograft nephropathy. Kidney Int. 66(4): 1686-1693.

Krensky AM, Clayberger C. (2011). Chemokines and cytotoxic effector molecules in rejection, In: Marincola, F.M. and Wang, E. (Eds.), Immunologic Signatures of Rejection. Springer, New York, USA, 77-92.

Krüger B, Schröppel B, Ashkan R, Marder B, Zülke C, Murphy B, Krämer BK, Fischereder M. (2002). A monocyte chemoattractant protein-1 (MCP-1) polymorphism and outcome after renal transplantation. J. Am. Soc. Nephrol. 13(10): 2585-2589.

McDermott DH, Zimmerman PA, Guignard F, Kleeberger CA, Leitman SF, Murphy PM. (1998). CCR5 promoter polymorphism and HIV-1 disease progression. Multicenter AIDS cohort study (MACS). Lancet. 352(9131): 866-870.

Morgun A, Shulzhenko N, Perez-Diez A, Diniz RV, Sanson GF, Almeida DR, Matzinger P, Gerbase-DeLima M. (2006). Molecular profiling improves diagnoses of rejection and infection in transplanted organs. Circ. Res. 98(12): E74-83.

Nakayama EE, Tanaka Y, Nagai Y, Iwamoto A, Shioda T. (2004). A CCR2-V64I polymorphism affects stability of CCR2A isoform. AIDS. 18(5): 729-738.

Nankivell BJ, Alexander SI. (2010). Rejection of the kidney allograft. N. Engl. J. Med. 363(15): 1451-1462.

Omrani MD, Mokhtari MR, Tagizadae A, Bagheri M, Ahmad-Poor P. (2008). Association of CCR5-59029 A/G and CCR2-V64I variants with renal allograft survival. Iran J. Immunol. 5(4): 201-206.

Otukesh H, Basiri A, Simfrosh N, Hoseini R, Sharifian M, Sadigh N, Golnari P, Rezai M, Fereshtenejad M. (2006). Outcome of pediatric renal transplantation in Labfi Nejad Hospital, Tehran, Iran. Pediatr. Nephrol. 21(10): 1459-1463.

Reeve J, Einecke G, Mengel M, Sis B, Kayser N, Kaplan B, Halloran PF. (2009). Diagnosing rejection in renal transplants: a comparison of molecular- and histopathology-based approaches. Am. J. Transplant. 9(8): 1802-1810.

Rizvi SA, Naqvi SA, Hussain Z, Hashmi A, Akhtar F, Zafar MN, Hussain M, Ahmed E, Kazi JI, Hasan AS, Khalid R, Aziz S, Sultan S. (2002).

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Living-related pediatric renal transplants: a single-center experience from a developing country. Pediatr. Transplant. 6(2): 101-110.

Rizvi SA, Zafar MN, Lanewala AA, Naqvi SA. (2009). Challenges in pediatric renal transplantation in developing countries. Curr. Opin. Organ Transplant. 14(5): 533-539.

Saint-Mezard P, Berthier CC, Zhang H, Hertig A, Kaiser S, Schumacher M, Wieczorek G, Bigaud M, Kehren J, Rondeau E, Raulf F, Marti HP. (2009). Analysis of independent microarray datasets of renal biopsies identifies a robust transcript signature of acute allograft rejection. Transpl. Int. 22(3): 293-302.

Schena FP. (2000). Epidemiology of end-stage renal disease: International comparisons of renal replacement therapy. Kidney Int. 57[Suppl], S39-S45.

Segerer S, Cui Y, Eitner F, Goodpaster T, Hudkins KL, Mack M, Cartron JP, Colin Y, Schlondorff D, Alpers CE. (2001). Expression of chemokines and chemokine receptors during human renal transplant rejection. Am. J. Kid. Dis. 37(3): 18-31.

Shieh B, Liau YE, Hsieh PS, Yan YP, Wang ST, Li C. (2000). Influence of nucleotide polymorphisms in the CCR2 gene and the CCR5 promoter on the expression of cell surface CCR5 and CXCR4. Int. Immunol. 12(9): 1311-1318.

Singh R, Kapoor R, Srivastava A, Mittal RD. (2008). Impact of chemokine receptor CCR2 and CCR5 gene polymorphism on allograft outcome in North Indian renal transplant recipients. Scand. J. Immunol. 69(1): 51-56.

Solez K, Axelsen RA, Benediktsson H, Burdick JF, Cohen AH, Colvin RB, Croker BP, Droz D, Dunnill MS, Halloran PF, et al. (1993).International standardization of criteria for the histologic diagnosis of renal allograft rejection: the Banff working classification of kidney transplant pathology. Kidney Int. 44(2): 411-422.

Süsal C, Döhler B, Sadeghi M, Ovens J, Opelz G. (2009). HLA antibodies and the occurrence of early adverse events in the modern era of transplantation. Transplantation. 87(9): 1367-1371.

USRDS Annual Data Report (2008). Incidence of reported end stage renal disease. ttp://www.usrds.org/2008/ref/A_Incidence_08.pdf.

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4. Urinary Interferon-gamma inducible protein-10 and its

association with renal allograft rejection.

93

4.1. Introduction:

Chemokines are signaling molecules that are released at the site of

renal injury during the course of kidney allograft rejection. The evaluation of

chemokine levels at the time of rejection has been reported to be a useful

indicator of rejection events (Roedder et al., 2011; Pereira et al., 2012).

Interferon-gamma inducible protein-10 (IP-10 or CXCL10) belongs to

the CXC sub-group of the chemokine super family. It has chemotactic

properties for T helper cells (Th1), natural killer cells (NK cells), dendritic

cells (DCs), γδ T cells and macrophages (Bonecchi et al., 1998; Loetscher et

al., 1998; Romagnani et al., 2001). It is secreted by several immune

(leukocytes, neutrophils) and non-immune cells (epithelial cells, endothelial

cells). It exerts its biological activity by binding to the CXCR3 receptors that

are expressed on the immune cells, particularly T-cells. Both the IP-10 and

CXCR3 receptors have a central role in the immune response against

transplanted organs as they are potent chemoattractant for T-cells that lead to

acute rejection (Panzer et al., 2004; Romagnani and Crescioli, 2012).

IP-10 is induced by interferon-gamma which indicates its early

involvement in allograft inflammation and renal injury. The finding of

increased expression levels of IP-10 in blood (pre-transplant and post-

transplant) and urine of the allograft recipients have been shown to be

associated with rejection, chronic allograft nephropathy and early graft loss

(Rotondi et al., 2004; Lazzeri et al., 2005 ; Matl et al., 2010; Lo et al., 2011;

Mao et al., 2011). Elevated urinary IP-10 levels reduce the need for

surveillance and indication biopsies by 61% and 64%, respectively (Hirt-

Minkowski et al., 2012). These findings suggest that the urinary IP-10

94

chemokine levels could provide a non-invasive and early monitoring marker

for graft function.

In the present study, urinary levels of IP-10 protein were determined in

(a) biopsy-proven allograft rejection (b) biopsy-proven non-rejection cases and

(c) in healthy controls. Further, IP-10 protein levels were also investigated for

their association with rejection.

4.2. Material and Methods:

This study was approved by the Institutional Review Board (SIUT-

ERC/A03-2009) and conformed to the tenets of the declaration of Helsinki.

Written informed consent was taken from all the participants. Based on our

inclusion and exclusion criteria, a total of 206 urine samples were included

from 206 individuals (118 transplant patients and 88 healthy donors). The

urine samples (n=206) were comprised of rejection (n=96), non-rejection

(n=22) and controls (n=88) (Figure 4.1).

4.2.1. Inclusion and Exclusion Criteria:

This study includes only live, related renal transplant patients

with biopsy-proven rejection and non-rejection events during the first post-

transplantation year. All were first transplants. The rejection cases were

classified according to Banff 97 classification and its updates (Racusen et al.,

1999), as acute T-cell mediated rejection (ACR), borderline (BLR) and acute

antibody-mediated rejection (ABMR). Cases showing intimal or transmural

arteritis were classified as acute vascular rejection (AVR: IIA, IIB or III).

Other histopathological diagnoses such as interstitial fibrosis and tubular

95

atrophy (IFTA), drug toxicity and bacterial or viral infection was excluded

from the study, as were the biopsies showing more than one pathology in

association with rejection.

Healthy individuals were included in the control group. This included

individuals selected as kidney donors with no known diagnosis of renal or

other diseases.

4.2.2. Samples Processing and IP-10 Quantification:

Early morning urine samples were collected form healthy donors as

well as patients who had undergone biopsy. Urine samples were collected

prior to performance of the biopsy procedure(s). A total of 206 samples were

collected and grouped into rejection (n=96), non-rejection (n=22) and controls

(n=88). The samples were processed and quantified for IP-10 concentration by

the enzyme-linked immunosorbent assay using human CXCL10/IP-10

Quantikine ELISA kit (R&D Systems; Minneapolis, USA), as described

earlier in methodology section.

The optical density of samples was determined at 450nm/540nm using

the microplate reader (Synergy 2, BioTek® Instrument, Inc, USA). A standard

curve was generated using the IP-10 standards and the concentration of

samples was calculated by the microplate data collection and analysis

software, Gen5® version 1.08. Each sample was assayed in duplicate for IP-10

protein levels and the average value was used for statistical analysis.

96

Figure 4.1: Schematic representation of samples for urinary IP-10 study. A total of 206 urine samples were included in this

study based on our criteria. It consists of 118 urine samples collected prior of graft biopsy procedure from renal transplant patients,

diagnosed as Rejection (n=96) and Non-Rejection (n=22). Eighty-eight samples of healthy donors as controls.

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4.2.3. Statistical Analysis:

The statistical analyses were carried out with Statistical Package for

Social Sciences SPSS for Windows (version 17; SPSS Inc., Chicago, IL,

USA). The mean values of IP-10 concentration in the rejection, non-rejection

and control cases were determined. Additionally, the mean concentration of

IP-10 protein across different sub-classifications of rejection was also

investigated. Urinary IP-10 concentrations in rejection, non-rejection and

control cases were compared using non-parametric tests (Kruskal-Wallis,

Mann-Whitney) at significance level of p<0.05. The Receiver Operating

Characteristic (ROC) curve analysis was used to determine the concentration

of IP-10 protein for predicting rejection. The diagnostic power of IP-10 (area

under curve (AUC), sensitivity and specificity) was calculated using

MedCalc® statistical software (version 12.7.5: http://www.medcalc.org/).

Based on the urinary IP-10 protein concentrations, the renal transplant

patients with biopsies cases (n=118) were classified into 3 categories as

<100pg/ml, 100-200pg/ml and >200pg/ml. The Kaplan-Meier (KM) and log-

rank tests were applied to determine the significant differences in renal

allograft rejection-free survival time among these categories.

4.3. Results:

4.3.1. Clinical Characteristics of Study Groups:

The detailed characteristics of the renal transplant patients and controls

are provided in Table 4.1. Renal transplant patients with rejection included 78

(81%) males and 18 (19%) females, with a mean age of 27±1 years. The non-

rejection patients consisted of all males with mean age of 31±2.3 years. The

mean age of the control group mean age was 35±1 years with 42 (48%) males

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and 46 (52%) females. The mean age of the donors in the rejection and non-

rejection groups was 37±1.1 and 29±1.6 years respectively. The serum

creatinine level was 1.9±0.1 (mg/dl) in rejection and 1.6±0.1 (mg/dl) in non-

rejection group.

Renal transplant patients were on standard triple drug

immunosuppressive regimen, consisting of calcineurin inhibitors

[Cyclosporine (CsA) or Tacrolimus (TAC)] with either Mycophenolate

mofetil (MMF) or Azathioprine (Aza) and steroids (Prednisone). Patients with

poor HLA match were given IL-2 receptor blocker (Simulect) or ATG

(Antithymocyte globulin) as induction therapy.

A total of 118 urine samples from renal transplant patients included 96

samples from rejection and 22 from non-rejection cases. The histopathological

findings of rejection were further categorized as cases of ACR IA+IB (n=63),

AVR IIA+IIB (n=9) and BLR (n=24). The biopsy-proven cases of acute T-cell

mediated rejection were treated with steroids (Methylprednisolone) and either

cyclosporine or tacrolimus. Cases showing steroid resistance and intimal

arteritis (AVR IIA or IIB) were treated with the ATG.

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Table 4.1: Clinical characteristics of patients across study groups

Rejection (n=96)

Non-Rejection (n=22) Control (n=88)

Age in years (mean ± Std Error) 27±1.0 31±2.3 35±0.96 Age range (years) 10-55yrs 18-48yrs 19-56yrs Gender (M/F) 78/18 22/0 42/46 ESRD Unknown Stone disease Glomerulonephritis

62(64.6%) 12(12.5%) 12(12.5%)

10(45.5%) 8(36.4%) 2(9.1%)

-

Pre-transplant Dialysis duration ( months) 4.2±0.7 3.7±0.7 - Donor Gender (M/F) 40/56 9/13 - Donor Age in year (mean ± Std Error) 37±1.1 29±1.6 - Donor-Recipient HLA match at A, B and DR locus 1 H – 3 Ags 1 H – 4 Ags 1 H – 5 Ags

43(44.8%) 29(30.2%) 10(10.4%)

6(27.3%) 8(36.4%) 4(18.2%)

-

Ischaemia Time (minutes) 120±5 96±6.6 - Serum Creatinine levels at time of biopsy (mg/dl; mean ± Std Error) 1.9±0.08 1.6±0.10 - Best Serum Creatinine levels (mg/dl; mean ± Std Error) 1.1±0.03 1.1±0.53 - Primary Immunosuppressive drugs CsA, Aza, Pred CsA, MMF, Pred Tac, MMF, Pred Tac, Aza, Pred

84(87.5%) 2(2.1%) 9(9.4%) 1(1.0%)

17(77.3%)

-- 4(18.2%) 1(4.5%)

-

Induction therapy No Induction therapy ATG Simulect

80(83.3%) 7(7.3%) 9(9.4%)

17(77.3%) 4(18.2%) 1(4.5%)

-

Anti-rejection therapy ATG Solumedrol (Methylprednisolone)

37(38.5%) 59(61.5%)

-- -- -

ESRD= end-stage renal disease, H= Haplotype, Ags = Antigens, CsA= Cyclosporine, Aza = Azathioprine, Pred= Prednisone, MMF= Mycophenolate mofetil, Tac = Tacrolimus, ATG= Antithymocyte globulin, Std Error = Standard Error.

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4.3.2. Urinary levels of IP-10 and their association with renal allograft

rejection:

The mean urinary IP-10 protein levels among the controls, rejection and

non-rejection cases are summarized in Table 4.2. The lowest value of IP-10 in

the controls was within a range of 0-112pg/ml. However, rejection cases

showed increased IP-10 levels with a mean concentration of 228±44pg/ml.

Among the sub-classification of rejection cases, the highest mean value of IP-

10 was detected in the acute tubulointerstitial rejection (ACR IA and IB)

(257±52pg/ml) followed by BLR (196±110pg/ml) and intimal arteritis (AVR

IIA and IIB) (113±54pg/ml). Further, the mean value of IP-10 for non-

rejection (NAD) samples was 60±25pg/ml.

Table 4.2: Urinary levels of IP-10 among different histopathological

groups and controls

Groups Mean IP-10 (pg/ml)

NAD (n=22) 60 ± 25

BLR (n=24) 196 ± 110

ACR (IA+IB) (n=63) 257 ± 52

AVR (IIA+IIB) (n=9) 113 ± 54

Control (n=88) 10.5 ± 2.5

NAD=No abnormality detected; BLR=Borderline rejection; ACR=Acute

cellular rejection; AVR=Acute vascular rejection; Mean ± Standard Error.

Statistical analysis showed significant differences in urinary IP-10 levels

between the allograft rejection and control cases (P<0.001) as well as the

rejection and non-rejection cases (P=0.004; Figure 4.2).

101

Analysis of IP-10 among different categories of graft rejection cases (i.e.

ACR [IA, IB], AVR [IIA, IIB] and BLR) with non-rejection (NAD) cases

showed significant differences between ACR (IA, IB) and NAD (P<0.001).

However, no significant differences were found in BLR vs. NAD (P=0.453)

and AVR (IIA, IIB) vs. NAD (P=0.086; Figure 4.3). Additionally, when sub-

classified rejection groups were compared, significant difference was found in

ACR (IA, IB) vs. BLR (P=0.019)while no significant difference was found in

ACR (IA, IB) vs. AVR (IIA, IIB) and AVR (IIA, IIB) vs. BLR.

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Figure 4.2: Urinary levels of IP-10 in rejection, non-rejection and control

groups. IP-10 levels of rejection were significantly higher from non-rejection

(P=0.004) and control (P<0.001) groups.

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Figure 4.3: Urinary IP-10 levels among histopathological and control

group. Significant difference was observed between ACR (IA+IB) vs. NAD

(P<0.001) group. No significant difference was found between BLR vs. NAD

(P=0.453), BLR vs. AVR (IIA+IIB) (P=0.392) and AVR (IIA+IIB) vs. NAD

(P=0.086).

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4.3.3. ROC curve analysis for the different histopathological categories:

The optimum cut-off point was selected which is defined as the value at

which both the sensitivity and specificity was found to be maximum. The

Youden’s index was used for the selection of optimum cut-off point,

calculated by MedCalc software. Table 4.3 lists the area under the curve

(AUC), specificity and sensitivity among the rejection sub-types. The

optimum cut-off point of IP-10 was found at 27 pg/ml with Sensitivity (72%)

and specificity (64%) and the area under the curve was 0.70±0.06 (95% CI:

0.60-0.77) (Figure 4.4). Interestingly, at the same cut-off value of IP-10

(27pg/ml), ACR (IA, IB) showed higher sensitivity (79%) with similar

specificity (64%) and the area under the curve was 0.74.

Table 4.3: ROC curve analysis for different histopathological categories

Group AUC ± S.E Cut off (pg/ml)

Sensitivity and

Specificity P-value

Rej vs. Non-Rej 0.70 ± 0.06 27 72/64 <0.001

Rej vs. Control 0.87 ± 0.03 8.5 82/78 <0.001

ACR(IA+IB) vs. NAD 0.74 ± 0.06 27 79/64 <0.001

BLR vs. NAD 0.56 ± 0.09 21 54/59 0.450

AVR(IIA+IIB) vs. NAD 0.70 ± 0.10 27 64/78 0.050

ACR(IA+IB) vs. BLR 0.66 ± 0.07 23 81/50 <0.001

ACR(IA+IB) vs. AVR (IIA+IIB) 0.60 ± 0.10 47 62/56 0.468

BLR vs. AVR(IIA+IIB) 0.60 ± 0.11 11 46/89 0.343

Rej = Rejection; AUC = Area under curve; S.E = Standard Error

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Figure 4.4: Receiver operating characteristic (ROC) curve of urinary IP-

10. The true positive results (sensitivity) and false positivity (1-specificity) for

urinary IP-10 levels as a marker for rejection are shown. It showed an area of

0.70±0.06 (95% confidence interval: 0.60-0.77) under the curve with 72%

sensitivity and 64% specificity at a cut-off level of 27pg/ml.

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4.3.4. Effect of urinary IP-10 level on rejection-free graft survival:

Rejection-free renal allograft survival time among the different

urinary levels of IP-10 (<100pg/ml, 100-200pg/ml and >200pg/ml) showed

statistically significant differences with rejection episodes (P<0.001; Figure

4.5). For rejection, the highest mean time of rejection-free graft survival was

observed with <100pg/ml IP-10 levels (9.26±1.1 weeks), which is followed by

3.3±0.8 weeks and 3.7±1.0 weeks for 100-200pg/ml and >200pg/ml

respectively. A significant difference was found in the mean-time of rejection-

free graft survival between <100pg/ml and >200pg/ml IP-10 levels (9.26±1.1

vs. 3.7±1.1 weeks; log-rank P<0.001) and <100pg/ml and 100-200pg/ml

(9.26±1.1 vs. 3.3±0.7 weeks; log-rank P=0.017). However, no significant

difference was found in the mean-time of rejection-free graft survival between

100-200pg/ml and >200pg/ml IP-10 levels (3.3±0.7 vs. 3.7±1.1 weeks; log-

rank P=0.968).

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Figure 4.5: Kaplan-Meier survival was constructed using urinary IP-10

levels (<100pg/ml, 100-200pg/ml and >200pg/ml). Number of events

(rejection episodes) /total cases (96/118) was 58/77, 10/12 and 28/29 in

<100pg/ml, 100-200pg/ml and >200pg/ml respectively. Recipients with IP-10

levels less than 100pg/ml showed significantly higher rejection-free graft

survival compared with patients having >200pg/ml (9.26±1.1 vs. 3.7±1.1

weeks; log-rank P<0.001) and 100-200pg/ml (9.26±1.1 vs. 3.3±0.7 weeks;

log-rank P=0.017) in 1 year of post-transplant duration.

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4.4. Discussion:

Although improved immunosuppressive drugs and advanced clinical

care have helped to reduce the acute rejection rate, it remains a major

challenge in transplantation. It has been shown that the early post-

transplantation period is the most critical for rejection (Varma et al., 2007). In

our previous study, the incidence of renal allograft rejection was 26% during

the first year post-transplantation (Firasat et al. 2012). In the present study, it

was observed that 83% of the allograft rejection episodes occurred within 3

months post-transplantation. Interestingly, the majority (66.7%) of the

rejection cases occurred within the first month post-transplantation. Although

early rejection episodes, predicted by urinary markers, may be prevented with

therapeutic interventions, it remains a major risk factor for subsequent graft

dysfunction/loss.

In the present study, IP-10 concentration in urine of renal transplant

patients at times of allograft dysfunction and biopsy findings of rejection and

non-rejection was quantified during the first year post-transplantation. Urinary

IP-10 levels were also determined in a healthy control group at the same time.

The highest mean IP-10 concentration was found in rejection cases

(228±44pg/ml) followed by non-rejection (60±25pg/ml) and controls

(10.5±2.5pg/ml). A similar pattern of IP-10 levels has also been reported in

other studies for rejection and non-rejection cases. These studies showed high

IP-l0 levels (309±506pg/ml; >100pg/ml) in rejection episodes and low levels

in chronic, stable and control cases (Hu et al., 2004; Khan, 2012).

Previous studies have reported a correlation of increased urinary IP-10

concentration with histopathological findings of rejection (Schaub et al., 2009;

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Ho et al., 2011). In our study, increased level was also detected in the rejection

group (P=0.004). However, when sub-classes of rejection groups i.e. ACR

(IA, IB), AVR ( IIA, IIB) and BLR were compared with the non-rejection

(NAD) group, only ACR (IA, IB) vs. NAD showed statistically significant

differences (P<0.001). No significant differences were observed in the AVR

(IIA, IIB) vs. NAD and BLR vs. NAD groups. Our study is in agreement with

Hirt-Minkowski et al (2012), even though the number of AVR cases (IIA, IIB)

is low [N=9 (7.6%)], urinary IP-10 is not a good indicator of acute vascular

rejection. This indicates that urinary IP-10 concentration does not reflect

inflammation of the renal vascular compartment. This could be restricted

inflammation of the vascular region without a spill over into the urine. This

results in low levels of IP-10 in urine

Various studies have shown the association of urinary IP-10 levels with

graft rejection and suggested it to be a clinically useful marker (Matz et al.,

2006). To delineate the clinical relevance of the test, ROC analysis in our

study showed sensitivity and specificity of 72% and 64% respectively at

27pg/ml. Similarly, Jackson et al. (2011) showed 80% sensitivity and 76%

specificity at 28pg/ml. Hu et al. (2004) also reported greater sensitivity

(86.4%) and specificity (91.3%) with rejection at >100pg/ml. Further studies

to evaluate the threshold levels at times of rejection would be helpful for

determining the optimal cutoff value for the biomarker.

Clinical and sub-clinical rejection can be predicted by IP-10-guided

biopsies which would help in reducing the necessity of graft biopsies by 64%

(Hirt-Minkowski et al., 2012). Suthanthiran et al. (2013) used a three-gene

signature study that showed that elevated levels of IP-10 mRNA, 18S rRNA

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and CD3ε mRNA could predict rejection with a sensitivity of 79% and

specificity of 78%. They further showed that urinary IP-10 is associated with

rejection.

Rotondi et al. (2004) have shown that high IP-10 level in serum lowers

graft survival. The increased concentration of urinary IP-10 has also been

shown to decrease graft survival. Our results show that cases with high IP-10

levels (>100pg/ml) have significantly lower rejection-free graft survival rates

as compared to the cases with low levels (<100pg/ml).

There are some limitations in this study. It includes (1) only IP-10 in

urine was determined as it provides non-invasive and convenient sample

collecting approach and may better reflect any renal injury, as urine is in direct

contact with transplanted renal organ. Measuring IP-10 in blood is also a less

invasive method and has been reported to be associated with rejection.

However, there are some draw backs with blood compared to urine, as the

concentration of molecular marker are diluted in blood and are also mixed

with other metabolites, proteins and peptides, that make it complicated to

identify the location of injury (Christians et al., 2011). Determination of

chemokines in urine as well as in blood at biopsy-proven rejection time and

their association with renal allograft outcome may reflect the better choice of

sample source.

This was our initial study for the identification of urinary biomarkers.

Number of non-rejection (n = 22) and also sub-types of rejection, AVR (n = 9)

cases were also limited. Study other immune regulatory molecules such as

Interferon-gamma, with increased sample size, would help to better

111

understand interferon-gamma-induced chemokines and their involvement in

rejection.

The present study is the first from Pakistan that shows elevated

expression of IP-10 protein at the time of renal allograft rejection. In addition

to other indicators like serum creatinine levels, it will be helpful as a routine

non-invasive marker to predict the necessity of renal graft biopsy.

112

4.5. References:

Bonecchi R, Bianchi G, Bordignon PP, D'Ambrosio D, Lang R, Borsatti A, Sozzani S, Allavena P, Gray PA, Mantovani A, Sinigaglia F. (1998). Differential expression of chemokine receptors and chemotactic responsiveness of type 1 T helper cells (Th1s) and Th2s. J Exp Med. 187(1): 129-34. Christians U, Klawitter J, Klawitter J, Brunner N, Schmitz V. (2011). Biomarkers of immunosuppressant organ toxicity after transplantation: status, concepts and misconceptions. Expert Opin Drug Metab Toxicol. 7(2):175-200 Firasat S, Raza A, Abid A, Aziz T, Mubarak M, Naqvi SA, Rizvi SA, Mehdi SQ, Khaliq S. (2012). The effect of chemokine receptor gene polymorphisms (CCR2V64I, CCR5-59029G>A and CCR5Δ32 on renal allograft survival in Pakistani transplant patients. Gene. 511(2): 314-319. Hirt-Minkowski P, Amico P, Ho J, Gao A, Bestland J, Hopfer H, Steiger J, Dickenmann M, Burkhalter F, Rush D, Nickerson P, Schaub S. (2012). Detection of clinical and subclinical tubulo-interstitial inflammation by the urinary CXCL10 chemokine in a real-life setting. Am J Transplant. 12(7): 1811-23. Ho J, Rush DN, Karpinski M, Storsley L, Gibson IW, Bestland J, Gao A, Stefura W, HayGlass KT, Nickerson PW. (2011). Validation of urinary CXCL10 as a marker of borderline, subclinical, and clinical tubulitis. Transplantation. 92(8): 878-882. Hu H, Aizenstein BD, Puchalski A, Burmania JA, Hamawy MM, Knechtle SJ. (2004). Elevation of CXCR3-binding chemokines in urine indicates acute renal-allograft dysfunction. Am J Transplant. 4(3): 432-437. Jackson JA, Kim EJ, Begley B, Cheeseman J, Harden T, Perez SD, Thomas S, Warshaw B, Kirk AD. (2011). Urinary chemokines CXCL9 and CXCL10 are noninvasive markers of renal allograft rejection and BK viral infection. Am J Transplant. 11(10): 2228-2234.

Khan A. (2012). Detection and quantitation of forty eight cytokines, chemokines, growth factors and nine acute phase proteins in healthy human plasma, saliva and urine. Proteomics. 75(15): 4802-4819.

Lazzeri E, Rotondi M, Mazzinghi B, Lasagni L, Buonamano A, Rosati A, Pradella F, Fossombroni V, La Villa G, Gacci M, Bertoni E, Serio M, Salvadori M, Romagnani P. (2005). High CXCL10 expression in rejected kidneys and predictive role of pretransplant serum CXCL10 for acute rejection and chronic allograft nephropathy. Transplantation. 79(9): 1215-1220. Lo DJ, Weaver TA, Kleiner DE, Mannon RB, Jacobson LM, Becker BN, Swanson SJ, Hale DA, Kirk AD. (2011).Chemokines and their receptors in human renal allotransplantation. Transplantation. 91(1): 70-77.

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Loetscher M, Gerber B, Loetscher P, Jones SA, Piali L, Clark-Lewis I, Baggiolini M, Moser B. (1996). Chemokine receptor specific for IP10 and mig: structure, function, and expression in activated T-lymphocytes. J Exp Med. 184(3): 963-969. Mao Y, Wang M, Zhou Q, Jin J, Wang Y, Peng W, Wu J, Shou Z, Chen J. (2011). CXCL10 and CXCL13 Expression were highly up-regulated in peripheral blood mononuclear cells in acute rejection and poor response to anti-rejection therapy. J Clin Immunol. 31(3): 414-418. Matl I, Hribova P, Honsova E, Brabcova I, Viklicky O. (2010). Potential predictive markers in protocol biopsies for premature renal graft loss. Kidney Blood Press Res. 33(1): 7-14. Matz M, Beyer J, Wunsch D, Mashreghi MF, Seiler M, Pratschke J, Babel N, Volk HD, Reinke P, Kotsch K. (2006). Early post-transplant urinary IP-10 expression after kidney transplantation is predictive of short- and long-term graft function. Kidney Int. 69(9): 1683-1690. Panzer U, Reinking RR, Steinmetz OM, Zahner G, Sudbeck U, Fehr S, Pfalzer B, Schneider A, Thaiss F, Mack M, Conrad S, Huland H, Helmchen U, Stahl RA. (2004). CXCR3 and CCR5 positive T-cell recruitment in acute human renal allograft rejection. Transplantation. 78(9): 1341-1350. Pereira AB, Teixeira AL, Rezende NA, Pereira RM, Miranda DM, Oliveira EA, Teixeira MM, Simões E Silva AC. (2012). Urinary chemokines and anti-inflammatory molecules in renal transplanted patients as potential biomarkers of graft function: a prospective study. Int Urol Nephrol. 44(5): 1539-1548.

Pereira AB, Teixeira AL, Rezende NA, Pereira RM, Miranda DM, Oliveira EA, Teixeira MM, Simões E Silva AC. (1999). The Banff 97 working classification of renal allograft pathology. Kidney Int. 55(2): 713-723.

Roedder S, Vitalone M, Khatri P, Sarwal MM. (2011). Biomarkers in solid organ transplantation: establishing personalized transplantation medicine. Genome Med. 3(6): 37. Romagnani P, Annunziato F, Lazzeri E, Cosmi L, Beltrame C, Lasagni L, Galli G, Francalanci M, Manetti R, Marra F, Vanini V, Maggi E, Romagnani S. (2001). Interferon-inducible protein 10, monokine induced by interferon gamma, and interferon-inducible T-cell alpha chemoattractant are produced by thymic epithelial cells and attract T-cell receptor (TCR) alphabeta+ CD8+ single-positive T cells, TCRgammadelta+ T cells, and natural killer-type cells in human thymus. Blood. 97(3): 601-607. Romagnani P, Crescioli C. (2012). CXCL10: A candidate biomarker in transplantation. Clin Chim Acta. 413(17-18): 1364-1373.

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Rotondi M, Rosati A, Buonamano A, Lasagni L, Lazzeri E, Pradella F, Fossombroni V, Cirami C, Liotta F, La Villa G, Serio M, Bertoni E, Salvadori M, Romagnani P. (2004). High pretransplant serum levels of CXCL10/IP-10 are related to increased risk of renal allograft failure. Am J Transplant. 4(9): 1466-1474.

Schaub S, Nickerson P, Rush D, Mayr M, Hess C, Golian M, Stefura W, Hayglass K. (2009). Urinary CXCL9 and CXCL10 levels correlate with the extent of subclinical tubulitis. Am J Transplant; 9(6): 1347-1353.

Suthanthiran M, Schwartz JE, Ding R, Abecassis M, Dadhania D, Samstein B, Knechtle SJ, Friedewald J, Becker YT, Sharma VK, Williams NM, Chang CS, Hoang C, Muthukumar T, August P, Keslar KS, Fairchild RL, Hricik DE, Heeger PS, Han L, Liu J, Riggs M, Ikle DN, Bridges ND, Shaked A; Clinical Trials in Organ Transplantation 04 (CTOT-04) Study Investigators. (2013). Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N Engl J Med. 369(1): 20-31.

Varma PP, Hooda AK, T Sinha, GS Chopra, SC Karan, GS Sethi, Badwal S, Kotwal A. (2007). Renal Transplantation - An Experience of 500 Patients. MJAFI. 63: 107-111.

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5. Urinary Monokine Induced by Interferon-Gamma and its

Association with Renal Allograft Rejection

116

5.1. Introduction:

Chemokines are major immunological players in the regulation of

immune response. These signaling molecules are released at the site of injury

that subsequently trigger immune activation. In transplantation, the activation

of host immune system is a major threat to allograft rejection/graft

dysfunction. The quantification of chemokines may be a useful indicator of

host immune response against allograft. Recent investigations have showed

that chemokines are good marker for graft functioning (Roedder et al., 2011;

Pereira et al., 2012; Suthanthiran et al., 2013).

Human Monokine induced by Interferon-Gamma (MIG or CXCL9) is

a 103 amino acid protein that is classified as a CXC chemokine sub-family

member (Farber, 1993). It is a chemoattractant for immune cells particularly

for T-lymphocytes that are induced in response to interferon–gamma (INF-γ)

(Whiting et al., 2004). MIG exerts its biological activity on T helper cells

(Th1) via CXCR3 receptors (G-protein coupled receptors). Infiltration and

accumulation of T-cells in/to transplant organ is the major phenomenon that

leads to allograft rejection (Koga et al., 1999; Romagnani et al., 1999; Lazzeri

et al., 2002; Auerbach et al., 2009; Asaoka et al., 2012).

Human MIG chemokine could be an ideal biomarker for graft

monitoring using non-invasive approach as it is involved in the early response

of immune system (Hauser et al., 2005). The quantification of MIG in urine in

rejection, non-rejection, stable graft and control groups could provide a

correlation of this marker with the immune status of transplant patients.

Additionally, urinary MIG level may help in identifying subject(s) with high

risk for rejection and also guide the clinicians about the graft immune status.

117

This study was carried out to determine the urinary levels of MIG in

rejection, non-rejection, stable grafts and control groups in order to investigate

its utility as a marker for the identification of graft rejection.

5.2. Material and Methods:

This study was approved by Institutional Review Board and conformed

to the tenets of the declaration of Helsinki. Informed consent was taken from

renal transplant patients as well as healthy renal donors. All patients were

transplanted at Sindh Institute of Urology and Transplantation, (SIUT). A total

of 266 urine samples from 266 participants were included based on inclusion

and exclusion criteria.

5.2.1. Inclusion and exclusion criteria:

This study includes (a) male or female of any age (b) first time renal

transplant with no other organ(s) transplanted previously and (c) only live and

blood related renal donors. Subjects were classified as (i) Rejection group,

cases with biopsy-proven rejection and subsequently treated with anti-

rejection therapy (ii) Non-Rejection group, cases with biopsy proven non-

rejection (iii) Stable graft group, cases that have no history of biopsy and

serum creatinine levels were maintained 0.5-1.5mg/dl during the first two

months of post-transplantation and (iv) Control group, renal donors with no

known diagnosis of renal disease and other diseases.

Cases were classified as rejection and non-rejection group based on

Banff 97 classification of renal graft biopsies (Racusen et al., 1999). Rejection

group includes acute cellular rejection (ACR), acute vascular rejection (AVR)

and borderline rejection (BLR). On the other hand, non-rejection group

118

includes no abnormality detected (NAD) and interstitial fibrosis and tubular

atrophy (IFTA).

Other histopathological diagnoses such as interstitial fibrosis and

tubular atrophy (IFTA), drug toxicity and bacterial or viral infection were

excluded from the study, as were the biopsies showing more than one

pathology in association with rejection.

5.2.2. Samples processing and MIG quantification:

Early morning urine samples were collected from healthy donors,

stable grafts and patients undergoing graft biopsy procedure(s). The samples

were processed as described earlier and stored at -80°C quantification of MIG.

A total of 266 urine samples were included based on inclusion and exclusion

criteria. Of these samples, 178 samples were classified histopathologically as

rejection (n=108) and non-rejection (n=70). A total of forty-two (n=42)

samples of stable graft patients and forty-six (n=46) samples of healthy renal

donors were also included (Figure 5.1). Rejection group was comprised of

ACR (n=71), AVR (n=10) and BLR (n=27) and non-rejection group included

NAD (n=37) and IFTA (n=33).

These samples were quantified for MIG levels by enzyme-linked

immunoassay using human MIG Quantikine® ELISA kits (R&D Systems;

Minneapolis, USA). The detailed method is described above in the

methodology section. The optical density of samples was measured at

450nm/540nm using the microplate reader (Synergy 2, BioTek® Instrument,

Inc, USA). A standard curve was generated using MIG standards and

subsequently the concentration of samples was calculated by the microplate

data collection and analysis software Gen5® version 1.08. As each sample was

119

assayed in duplicate for MIG levels, therefore the mean concentration was

used for statistical analyses.

5.2.4. Statistical Analysis:

The statistical analyses were carried out with Statistical Package for

Social Sciences software (SPSS version 17; SPSS Inc., Chicago, IL, USA).

The mean concentration of MIG was calculated for rejection, non-rejection,

stable graft and control groups. Non-parametric tests (Kruskal-Wallis and

Mann-Whitney) were applied to identify significant difference of MIG levels

across the groups. A p-value of less than 0.05 was considered to be significant.

Additionally, difference of MIG concentration across sub-classified rejection

groups (ACR, AVR and BLR) was also investigated.

The receiver operating characteristic (ROC) curve was constructed to

determine the area under the curve (AUC), the highest sensitivity and

specificity of MIG at certain threshold level using MedCalc® statistical

software (version 12.7.5) (http://www.medcalc.org/).

5.3. Results:

The clinical characteristics of patients among various groups and

healthy donors are listed in Table 5.1. Renal transplant patients in rejection

group included 91 (84%) males and 17 (16%) females, with a mean age of

25±10 years. On the other hand, non-rejection group contained 59 (84%)

males and 11 (16%) females, with a mean age of 29±11 years. Stable graft

group contained 29 (69%) males and 13(31%) females, with 30±11 years of

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mean age. In control group, the mean age was 31±8 years with equal numbers

of both genders i.e. 23 (50%).

Renal transplant patients were on standard triple drug

immunosuppressive regimen, consisting of calcineurin inhibitors

[Cyclosporine (CsA) or Tacrolimus (TAC)] with either Mycophenolate

mofetil (MMF) or Azathioprine (Aza) and steroids (Prednisone). Patients with

poor HLA match were given IL-2 receptor blocker (Simulect) or ATG

(Antithymocyte globulin) as induction therapy.

The biopsy-proven cases of acute T-cell mediated rejection (108) were

treated with steroids (Methylprednisolone) and either cyclosporine or

tacrolimus. Cases showing steroid resistance and intimal arteritis (AVR) were

treated with the ATG.

121

Figure 5.1: Schematic representation of samples for urinary MIG study. A total of 266 urine samples were included based

on our study criteria. It consists of biopsy-proven 178 urine samples, collected from renal transplant patients prior of graft

biopsy procedure. One hundred and eight (108) samples were diagnosed as rejection and seventy samples (70) as non-rejection.

Forty-two (42) samples of stable grafts patients and 46 samples of controls (healthy renal donors) were also included.

122

5.3.1. Urinary MIG levels and association with graft rejection:

The mean concentration of MIG among groups of rejection, non-

rejection, stable grafts and healthy donors are summarized in Table 5.2. The

lowest mean value of MIG was detected in the control group (5.5±0.4pg/ml),

followed by stable grafts (8.5±3.5pg/ml) and non-rejection group

22.56±6.4pg/ml (IFTA + NAD group). However, rejection group showed

increased MIG levels with mean concentration of 73.6±25pg/ml as compared

to other study groups.

The MIG concentrations were further analyzed to investigate any

significant differences between groups (Figure 5.2; Table 5.3). Results showed

no statistically significant difference between the rejection and other groups

[Rej vs. NAD (P=0.487), Rej vs. IFTA (P=0.316), Rej vs. stable grafts

(P=0.505), Rej vs. control (P=0.542)]. However, significant difference was

found between stable grafts vs. NAD (P<0.001), stable grafts vs. IFTA

(P<0.001) and stable grafts vs. control group (P<0.001).

123

Table 5.1: Clinical characteristics of study population REJ

(n = 108) NON-REJ

(n = 70) STABLE GRAFT

(n = 42)

RENAL DONORS (n = 46)

Gender Females 17 (16%) 11 (16%) 13 (31%) 23 (50%) Males 91 (84%) 59 (84%) 29 (69%) 23 (50%) Mean age of Recipients (Range)

25.13±10 (8-55yrs)

29.35±10 (8-50yrs)

29.9±11 (7-58yrs)

-

Mean age of Donors (Range)

34.84 ± 9.8 (20-55yrs)

35.2±9.5 (18-54yrs)

31.5±8.5 (18-55yrs)

31.3±8 (18-55yrs)

Etiology of primary disease Unknown cause 71 (66.4%) 43(61%) 26 (62%) - Stone disease 12 (11.2%) 10 (14%) 5 (12%) - Glomerular disease 6 (5.6%) 3 (4%) 3 (7%) - Donor-Recipient HLA match at A, B and DR locus 1Hap 3 Ags match 36 (33%) 23 (33%) 9 (21%) - 1Hap 4 Ags match 26 (24%) 17 (24%) 9 (21%) - 1Hap 5 Ags match 12 (11%) 8 (11%) 5 (12%) - Identical match 8 (7.5%) 6 (9%) 13 (31%) - Induction No induction 76 (70%) 62 (89%) 36 (86%) - Simulect 19 (18%) 6 (9%) 5 (12%) - ATG 13 (12%) 2 (3%) 1 (2%) - Primary drugs CYA/AZA/PRED 88 (82%) 62 (89%) 39 (93%) - TAC/MMF/PRED 12 (11%) 6 (9%) 2 (5%) - Anti-rejection therapy ATG 38 (35%) - -

Solumedrol 70 (65%) - - -

REJ = Rejection, NON-REJ = Non-Rejection, Hap = Haplotypes, Ags = Antigens

124

Table 5.2: Urinary levels of MIG among different histopathological

groups and control

Groups Mean MIG ± S.E (pg/ml)

NAD (n = 37) 28.3 ± 11

IFTA (n = 33 ) 16 ± 5.4

REJ (n = 108) 73.6 ± 25

Stable graft (n = 42) 8.5 ± 3.5

Control (n = 46) 5.5 ± 0.4

NAD = No abnormality detected, IFTA = Interstitial fibrosis and tubular

atrophy, REJ = Rejection, S.E = Standard Error

125

Table 5.3: Statistical significance difference in urinary MIG levels among

study groups

Groups

P-value

REJ vs. IFTA 0.316

REJ vs. NAD 0.487

REJ vs. Control 0.542

REJ vs. Stable graft 0.505

IFTA vs. Control 0.620

IFTA vs. NAD 0.930

NAD vs. Control 0.060

IFTA vs. Stable graft <0.001

NAD vs. Stable graft <0.001

Control vs. Stable graft <0.001

P-value <0.05 is considered as significant.

Mann-Whitney U test (two independent sample tests) was applied.

126

Figure 5.2: Box-plot for urinary MIG levels among different groups.

Results showed no statistically significant difference between rejection and

other groups [Rej vs. NAD (P=0.487), Rej vs. IFTA (P=0.316), Rej vs. stable

grafts (P=0.505), Rej vs. control (P=0.542)]. However, significant difference

was found between stable grafts vs. NAD (P<0.001), stable grafts vs. IFTA

(P<0.001) and stable grafts vs. control (P<0.001).

127

Additionally, IFTA (n=33) and NAD (n=37) groups were pooled together

as non-rejection and compared with rejection group to find significant

difference in MIG levels. Results showed no statistically significant difference

in MIG levels between rejection and non-rejection group (P=0.408) (Figure

5.3; Table 5.4).

Table 5.4: Statistical significance of MIG with rejection outcome

Urinary Marker

P-value

Outcome

MIG

0.408

Non-Significant

Rejection group (n=108) = ACR, BLR and AVR

Non-rejection group (n=70) = NAD and IFTA

Mann-Whitney U test (two independent sample tests)

128

Figure 5.3: Box-plot of urinary MIG levels in rejection (n=108) and non-

rejection group (n=70). MIG levels were higher in rejection as compared to

non-rejection group. Results showed non-significant difference in MIG levels

between two groups (P=0.408).

P=0.408

129

The MIG concentration was also determined among sub-classes of

rejection (i.e. ACR, AVR and BLR). The highest mean concentration of MIG

was detected in the AVR (86.7±33pg/ml) followed by ACR (73.4±33pg/ml)

and BLR (69.4±52pg/ml). Statistical analyses showed significant difference in

MIG levels between ACR vs. AVR (P=0.018) and AVR vs. BLR (P=0.005).

However, no significance difference in MIG was observed between ACR vs.

BLR (P=0.103) (Table 5.5).

Table 5.5: Significance levels of MIG among rejection sub-classes

Sub-classes of Rejection P-value Significant level

ACR vs. AVR 0.018 Significant

ACR vs. BLR 0.103 Non-significant

AVR vs. BLR 0.005 Significant

Mann-Whitney U test (two independent sample tests) was applied

130

Figure 5.4: Box-plot of urinary MIG levels in the sub-classes of rejection.

Statistical analyses showed significant difference in MIG levels between ACR

vs. AVR (P=0.018) and AVR vs. BLR (P=0.005). However, no significance

difference in MIG was observed between ACR vs. BLR (P=0.103)

P=0.005

P=0.103

P=0.018

131

5.3.2. ROC curve analyses of MIG:

The area under the curve (AUC), sensitivity, specificity and significant

level with rejection are listed in Table 5.6.

The AUC for MIG was 0.54±0.04 (AUC±S.E; 95% CI: 0.46-0.61) and

the optimum sensitivity (46%) and specificity (55%) was observed for

rejection group at cut-off of 6pg/ml. This shows that MIG is a not a good

predictive marker for rejection (Figure 5.5).

Table 5.6: Receiver operating characteristic curve for urinary MIG levels

Marker AUC ± S.E (95% CI)

Cut off (pg/ml)

Sensitivity/ Specificity

(%) P-value

MIG

0.54±0.04 (0.46-0.61)

6

46/55 0.385

AUC = Area Under the Curve

CI = Confidence Interval

S.E = Standard Error

132

Figure 5.5: Receiver operating characteristic of urinary MIG levels. It

showed an AUC of 0.54±0.04 (95% CI: 0.46-0.61) with 46% sensitivity and

55% specificity at cut-off level of 6pg/ml.

133

5.4. Discussion:

Despite immunosuppressive drugs and advanced clinical care, graft

rejection remains the major hurdle in successful organ transplantation. The

gold standard method for confirming the diagnosis of rejection is a graft

biopsy. However, the invasive nature of this procedure is a major limitation

that restricts its usage for diagnosis and monitoring of graft dysfunction.

Therefore, markers are needed based on non-invasive methods which correlate

with graft rejection and guide clinicians about the graft status.

This study is part of our effort to investigate urinary markers

associated with renal graft monitoring particularly for the early diagnosis of

rejection. Previously, our centre has reported that half (54%) of the cases that

underwent biopsy procedure showed histopathological features of acute

rejection (24%) and IFTA (29.8%) (Kazi and Mubarak, 2012). Therefore, it

was anticipated that by identifying markers associated with these features

(rejection and IFTA) would help to identify high risk group for rejection. In

addition, it would also reduce the number of graft biopsies performed as

recently reported by Hirt-Minkowski (Hirt-Minkowski et al., 2012).

Investigating the urinary levels of MIG showed varied levels

across the groups (rejection, non-rejection, stable graft and healthy donors)

ranging from 0-2098pg/ml., Increased mean MIG level was found in the

rejection group as compared to non-rejection and other groups (stable graft

and healthy donors). However, the difference was not statistically significant

(P<0.05). Previous studies have shown significant increases in MIG levels in

rejection. These studies suggested that MIG is a good marker for rejection

with (79%-93%) sensitivity and specificity (Hu et al., 2004; Hauser et al.,

134

2005; Jackson et al., 2011; De Muro et al., 2013). However, we found low

sensitivity and specificity of this marker for rejection group.

These conflicting results could be due to sample size (low rejection

cases) and variation in the techniques as well as analysis methods. In our

study, large number of rejection cases (n=108) were included compared to Hu

et al., 2004 (n=28), Hauser et al., 2005 (n=15), Schaub et al., 2009 (n=57),

Jackson et al., 2011(n=25) and Pierina De Muro et al., 2013(n=19). MIG was

quantified by ELISA technique and analyzed without normalization with

urinary creatinine. Only two studies were found (Hauser et al., 2005 and

Pierina De Muro et al., 2013) that used the same methodology. Further studies

using more number of rejection cases and uniform methodology would better

delineate its association with graft outcome.

The therapeutic approach of neutralizing MIG during an early post

transplant period has also been suggested to prevent chronic graft

vasculopathy and graft fibrosis (Miura et al., 2003). However, Ho et al.,

(2010) reported no such association of MIG with histology (interstitial fibrosis

and tubular atrophy) and graft outcome. Similarly, we also did not find any

association of MIG with IFTA group.

This is the first study on the association of urinary MIG with renal

allograft rejection from Pakistan. This study demonstrates that monitoring of

urinary MIG concentration is not a useful indicator for graft rejection.

135

5.5. References: Asaoka T, Marubashi S, Kobayashi S, Hama N, Eguchi H, Takeda Y, Tanemura M,Wada H, Takemasa I, Takahashi H, Ruiz P, Doki Y, Mori M, Nagano H. (2012). Intragraft transcriptome level of CXCL9 as biomarker of acute cellular rejection after liver transplantation. J Surg Res. 178(2):1003-1014. Auerbach MB, Shimoda N, Amano H, Rosenblum JM, Kish DD, Farber JM, Fairchild RL. (2009). Monokine induced by interferon-gamma (MIG/CXCL9) is derived from both donor and recipient sources during rejection of class II major Histocompatibility complex disparate skin allografts. Am J Pathol. 174(6):2172-81.

De Muro P, Faedda R, Masala A, Lepedda AJ, Zinellu E, Ciccarese M, Cossu M, Pala PG, Satta RP, Formato M. (2013). Kidney post-transplant monitoring of urinary glycosaminoglycans/proteoglycans and monokine induced by IFN-γ (MIG). Clin Exp Med. 13(1):59-65.

Farber JM. (1993). HuMig: a new human member of the chemokine family of cytokines. Biochem Biophys Res Commun. 192(1):223-30.

Hauser IA, Spiegler S, Kiss E, Gauer S, Sichler O, Scheuermann EH, Ackermann H, Pfeilschifter JM, Geiger H, Gröne HJ, Radeke HH. (2005). Prediction of acute renal allograft rejection by urinary monokine induced by IFN-gamma (MIG). J Am Soc Nephrol. 16(6):1849-58.

Hirt-Minkowski P, Amico P, Ho J, Gao A, Bestland J, Hopfer H, Steiger J, Dickenmann M, Burkhalter F, Rush D, Nickerson P, Schaub S. (2012). Detection of clinical and subclinical tubulo-interstitial inflammation by the urinary CXCL10 chemokine in a real-life setting. Am J. Transplant. 12(7):1811-23.

Ho J, Rush DN, Gibson IW, Karpinski M, Storsley L, Bestland J, Stefura W, HayGlass KT, Nickerson PW. (2010). Early urinary CCL2 is associated with the laterdevelopment of interstitial fibrosis and tubular atrophy in renal allografts.Transplantation. 90(4):394-400.

Hu H, Aizenstein BD, Puchalski A, Burmania JA, Hamawy MM, Knechtle SJ. (2004). Elevation of CXCR3-binding chemokines in urine indicates acute renal-allograft dysfunction. Am J Transplant. 4(3):432-437. Jackson JA, Kim EJ, Begley B, Cheeseman J, Harden T, Perez SD, Thomas S, Warshaw B, Kirk AD. (2011). Urinary chemokines CXCL9 and CXCL10 are noninvasive markers of renal allograft rejection and BK viral infection. Am J Transplant. 11(10):2228-2234. Kazi JI, Mubarak M. (2012). Biopsy Findings in Renal Allograft Dysfunction in a Live Related Renal Transplant Program. J Transplant Technol Res 2:108.

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Koga S, Auerbach MB, Engeman TM, Novick AC, Toma H, Fairchild RL. (1999). T cell infiltration into class II MHC-disparate allografts and acute rejection is dependent on the IFN-gamma-induced chemokine Mig. J Immunol. 163(9):4878-85. Lazzeri E, Rotondi M, Mazzinghi B, Lasagni L, Buonamano A, Rosati A, Pradella F, Fossombroni V, La Villa G, Gacci M, Bertoni E, Serio M, Salvadori M, Romagnani P. (2005). High CXCL10 expression in rejected kidneys and predictive role of pretransplant serum CXCL10 for acute rejection and chronic allograft nephropathy. Transplantation. 79(9):1215-20. Miura M, Morita K, Koyanagi T, Fairchild RL. (2003). Neutralization of monokine induced by interferon-gamma during the early post transplantation period prevents development of chronic allograft vasculopathy and graft fibrosis. Transplant Proc. 35(2):875-7. Pereira AB, Teixeira AL, Rezende NA, Pereira RM, Miranda DM, Oliveira EA, Teixeira MM, Simões E Silva AC. (2012). Urinary chemokines and anti-inflammatory molecules in renal transplanted patients as potential biomarkers of graft function: a prospective study. Int Urol Nephrol. 44(5):1539-1548.

Racusen LC, Solez K, Colvin RB, Bonsib SM, Castro MC, Cavallo T, Croker BP,Demetris AJ, Drachenberg CB, Fogo AB, Furness P, Gaber LW, Gibson IW, Glotz D,Goldberg JC, Grande J, Halloran PF, Hansen HE, Hartley B, Hayry PJ, Hill CM,Hoffman EO, Hunsicker LG, Lindblad AS, Yamaguchi Y, et al.( 1999). The Banff 97 working classification of renal allograft pathology. Kidney Int. 55(2):713-23

Roedder S, Vitalone M, Khatri P, Sarwal MM. (2011). Biomarkers in solid organ transplantation: establishing personalized transplantation medicine. Genome Med. 3(6):37.

Suthanthiran M, Schwartz JE, Ding R, Abecassis M, Dadhania D, Samstein B, Knechtle SJ, Friedewald J, Becker YT, Sharma VK, Williams NM, Chang CS, Hoang C, Muthukumar T, August P, Keslar KS, Fairchild RL, Hricik DE, Heeger PS, Han L, Liu J, Riggs M, Ikle DN, Bridges ND, Shaked A. (2013). Clinical Trials in Organ Transplantation 04 (CTOT-04) Study Investigators. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N Engl J Med. 369(1):20-31. Whiting D, Hsieh G, Yun JJ, Banerji A, Yao W, Fishbein MC, Belperio J, Strieter RM, Bonavida B, Ardehali A. (2004). Chemokine monokine induced by IFN-gamma/CXC chemokine ligand 9 stimulates T lymphocyte proliferation and effector cytokine production. J Immunol. 172(12):7417-24.

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6. Urinary monocyte chemotactic protein-1 levels and its

association with renal allograft rejection.

138

6.1. Introduction:

Currently, diagnosis of graft rejection is based on gold standard

technique of graft biopsy. However, invasive procedure of graft biopsy limits

its usage in transplantation. Hence, identification of rejection by marker(s)

using non-invasive samples (urine/blood) would be ideal to

prognosis/diagnosis of rejection early as well as accurately.

Human monocyte chemotactic protein-1 (MCP-1/CCL2) belongs to

chemokine C-C family (Gu et al., 1999). The gene product of MCP-1 is

composed of 76 amino acids having molecular size of 13kDa (Van Coillie et

al., 1999). This early chemokine is released in response to inflammatory

mediators (TNF and INF-γ) after surgical procedures (ischemic reperfusion)

(Rice et al., 2002). Many cell types also produce MCP-1 such as endothelial

cells, epithelial cells, fibroblasts and smooth muscle cells. As MCP-1 has high

chemoattractant potency for immune cells, it attracts/directs immune cells

(monocytes/macrophage, NK cells and T-cells) to the inflamed/injured site

(Ajuebor et al., 1998; Robertson et al., 2000).

MCP-1 is an important chemokine involved in the regulation of

allograft rejection phenomena. The intra-renal expression analysis has showed

an increased level of MCP-1 at time of injury/rejection(s) which correlates

with monocyte infiltration and renal damage (Rovin et al., 1996;

Prodjosudjadi et al., 1996; Grandaliano et al., 1997; Robertson et al., 2000;

Rüster et al., 2004). This high expression of MCP-1 ligand and its

corresponding chemokine receptors (CCR2), contribute in the graft outcome

(Segerer et al., 2001). Positive correlations have also been reported between

urinary MCP-1 levels and degree of leukocytes infiltration in the kidney

139

(Rovin et al., 1996; Wada et al., 1999; Tam et al., 2004). Similarly, increased

excretory MCP-1 level has also been reported with renal graft

rejection/dysfunction (Prodjosudjadi et al., 1996; Grandaliano et al., 1997;

Boratyńska 1998; Sun et al., 2003; Dubiński et al., 2008). Thus, urinary levels

of MCP-1 may reflect the immune status and provide a non-invasive tool for

graft monitoring.

This study was carried out to determine the urinary level of MCP-1 in

stable graft, rejection and non-rejection renal transplant patients and its utility

as a marker for the identification of graft rejection.

6.2. Material and Methods:

This study was approved by the Institutional Review Board and

conformed to the tenets of the declaration of Helsinki. Inform consent was

taken from all the participants at the Sindh Institute of Urology and

Transplantation, (S.I.U.T).

A total of 409 urine samples from 300 renal transplant patients and 109

healthy renal donors were included in this study. Among transplant patients,

258 patients underwent biopsy procedure(s) and diagnosed as rejection

(n=165) and non-rejection (n=93) based on Banff classification (Racusen et

al., 1999). However, 42 patients, categorized as stable grafts, had no history of

biopsy and serum creatinine levels were maintained at 0.5-1.5mg/dl during the

first two months of post-transplantation.

The samples were grouped as rejection (n=165), non-rejection (n=93),

stable graft (n=42) and controls (healthy donors; n=109) Figure 6.1. The non-

rejection groups were further sub-classified into IFTA (n=47) and NAD

(n=46).

140

6.2.1. Urine samples and MCP-1 quantification:

Early morning urine samples were collected from healthy donors,

stable grafts and patients who had undergone graft biopsy procedure. Urine

samples were processed and quantified for MCP-1 concentration by enzyme-

linked immunoassay using human CCL2/MCP-1 Quantikine ELISA kit (R&D

system; Minneapolis, MN, USA), as described earlier in material and method

section.

The optical density of samples was determined at 450nm with wave

length correction of 540nm using the microplate reader (Synergy 2, BioTek®

Instrument, Inc, USA). A standard curve was generated using MCP-1

standards and subsequently the concentration of samples was determined by

the microplate data collection and analysis software, Gen5® version 1.08. As

each sample was diluted and assayed in duplicate for MCP-1 levels, the

measured concentration was multiplied by 2 (dilution factor) and the average

concentration of MCP-1was used for statistical analysis.

141

Figure 6.1: Schematic representation of samples for urinary MCP-1 study. A total of 409 urine samples were included based on our

criteria. Among renal transplant patients, 258 patients underwent biopsy procedure(s) and diagnosed as rejection (n=165) and non-

rejection (n=93) based on Banff classification. Forty-two (n=42) patients of stable grafts and 109 healthy donors were also included.

142

6.2.2. Statistical Analysis:

Statistical analyses were carried out with Statistical Package for

Social Sciences software (SPSS version 17; SPSS Inc., Chicago, IL, USA).

The average concentration of MCP-1 was calculated for rejection, non-

rejection, stable grafts and control groups. Non-parametric tests (Kruskal-

Wallis and Mann-Whitney) were applied to identify significant difference of

MCP-1 levels across the groups. A p-value of less than 0.05 was considered to

be significant (P<0.05).

The receiver operating characteristic (ROC) curve was constructed to

determine the area under the curve (AUC), the optimum sensitivity and

specificity for urinary MCP-1 levels as a marker for rejection, at certain

threshold level, using MedCalc® statistical software (version 12.7.5)

(http://www.medcalc.org).

MCP-1 levels were further analyzed for their impact on rejection-free

graft survival. The cumulative survival time of renal allograft was calculated

by Kaplan-Meier (KM) curve and the log-rank test was applied to calculate the

statistical significance among the transplant patients.

6.3. Results:

6.3.1. Clinical characteristics of patients:

The clinical characteristics data of all participants such as age,

gender, relation with donor, HLA match, drug regimen, induction therapy and

anti-rejection therapy are provided in Table 6.1. Across the groups, transplant

patients include 69-87% males and 12-31% females; with a mean age range of

26-31±years. On the other hand, control group consisted of 48% male and

52% female donors with a mean age of 35 years.

143

Table 6.1: Clinical characteristics of study population

REJ (n=165)

NON-REJ

(n=93)

STABLE GRAFT (n=42)

CONTROL (n=109)

Gender Females 25(15%) 12(13%) 13(31%) 57(52%) Males 140(85%) 81(87%) 29(69%) 52(48%) Age of Recipient (Mean ± S.E) (Range)

26±0.75 (8-55yrs)

30±1.5 (8-53yrs)

29±1.68 (7-58yrs)

-

Etiology of ESRD Unknown cause 111(67%) 67(72%) 26(62%) - Stone disease 18(11%) 15(16%) 5(12%) - Glomerular disease 11(6.7%) 5(5.4%) 3(7%) - Donor-Recipient HLA match at A, B and DR locus 1Hap - 3 Ags Match 61(37%) 34(36.5%) 9(21.4%) - 1Hap - 4 Ags Match 40(24%) 22(23.6%) 9(21.4%) - 1Hap - 5Ags Match 19(11.5%) 11(12%) 5(12%) - Identical Match 9(5.5%) 7(7.5%) 13(31%) - Induction therapy No induction therapy 122(74%) 81(87%) 36(86%) - Simulect 24(14.5%) 6(6.4%) 5(12%) - ATG 19(11.5%) 6(6.4%) 1(2.4%) - Primary immunosuppressant drugs CYA/AZA/PRED 136(82.4%) 84(90%) 39(93%) - TAC/MMF/PRED 21(12.7%) 7(7.5%) 2(5%) - Anti-rejection therapy ATG 58(35%) - - - Solumedrol 107(65%) - - -

Rej = Rejection, CYA = Cyclosporine, AZA= Azathioprine, PRED = Prednisolone, Ags = Antigens, ATG = Antithymocyte globulin, S.E = Standard Error, Hap = Haplotype

144

6.3.2. Urinary levels of MCP-1 and their association with rejection:

Urinary levels of MCP-1 were determined in 409 urine samples of 353

participants of various groups (Table 6.2). The graft rejection group showed

high levels of MCP-1 concentration (953±69pg/ml) as compared to stable

grafts (254±57pg/ml), no abnormality detected (296±40pg/ml), IFTA

(228±49pg/ml) and control groups (38±13pg/ml).

Table 6.2:Urinary levels of MCP-1 among different study groups

Groups

MCP-1 pg/ml (Mean ± S.E)

NAD (n = 47) 296 ± 40

IFTA (n = 46 ) 228 ± 49

REJ (n = 165) 953 ± 69

Stable grafts (n= 42) 254 ± 57

Control (n = 109) 38 ± 13

NAD = No abnormality detected, IFTA = Interstitial fibrosis and tubular

atrophy, REJ = Rejection, S.E = Standard Error

The significant association of urinary MCP-1 levels with rejection group

was determined by comparing with non-rejection, stable grafts and control

groups using non-parametric test (Kruskal-Wallis and Mann-Whitney).

Results showed significant difference in MCP-1 levels between rejection and

non-rejection (NAD and IFTA) and other groups (control and stable grafts) i.e.

P<0.05 (Figure 6.2; Table 6.3). Significant difference in MCP-1 levels was

also observed between IFTA vs. NAD (P=0.030), IFTA vs. control (P<0.001),

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NAD vs. control (P<0.001) and stable grafts vs. control group (P<0.001). This

indicates that MCP-1 levels varied significantly across the groups and could

differentiate between histopathological conditions such as rejection vs. non-

rejection. No significant difference was observed between NAD vs. stable

grafts (P=0.120) and IFTA vs. stable grafts (P=0.810) Figure 6.3.

Table 6.3: Association levels of urinary MCP-1 among groups.

Groups

P-value

Outcome

REJ vs. NAD <0.001 Significant

REJ vs. IFTA <0.001 Significant

REJ vs. Control <0.001 Significant

REJ vs. stable graft <0.001 Significant

IFTA vs. NAD 0.030 Significant

IFTA vs. Control <0.001 Significant

IFTA vs. stable graft 0.810 Non-Significant

NAD vs. Control <0.001 Significant

NAD vs. stable graft 0.120 Non-Significant

Control vs. stable graft <0.001 Significant

P value less than 0.05(P<0.05) = Significant

Mann-Whitney U test (two independent sample tests)

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Figure 6.2: MCP-1 levels in rejection and non-rejection groups. MCP-1

levels were significantly higher in rejection group as compared non-

rejection (P<0.001).

147

Figure 6.3: Urinary levels of MCP-1 among various study groups.

Significant difference was found between rejection, non-rejection (NAD and

IFTA), control and stable graft i.e. P<0.05. Significant difference was also

observed between IFTA vs. NAD (P=0.030), IFTA vs. control (P<0.001),

NAD vs. control (P<0.001) and stable graft vs. control group (P<0.001). No

significant difference in MCP-1 level was observed between stable graft vs.

NAD (P=0.120) and stable graft vs. IFTA (P=0.810).

148

6.3.3. ROC Curve Analyses for MCP-1:

An area under the curve (AUC), specificity, sensitivity and the

significant level of MCP-1 are listed in Table 6.4.

The optimum sensitivity (84%) and specificity (74%) was observed

for the patients diagnosed as rejection at a cut-off 214pg/ml of MCP-1 with an

area under the curve of 0.83±0.04 (AUC±S.E) (95% CI: 0.77-0.88) when

compared with stable grafts (Figure 6.4). Similar sensitivity (87%) but low

specificity (62%) was observed when the rejection group was compared with

non-rejection group at 198pg/ml of MCP-1, with area under the curve of

0.81±0.03 (95% CI: 0.76 – 0.86). This indicates that urinary MCP-1 is a good

predictive marker for rejection episodes.

Table 6.4: Receiver operating characteristic for MCP-1

Group AUC±S.E (95% C.I)

Cut-off point (pg/ml)

Sensitivity &

Specificity (%) P-value

MCP-1 (Rej vs. Stable grafts)

0.83 ± 0.04 (0.77– 0.88)

214 84/74 <0.001

MCP-1 (Rej vs. Non-Rej)

0.81 ± 0.03 (0.76 – 0.86)

198 87/62 <0.001

AUC = Area under the curve S.E = Standard Error

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Figure 6.4: Receiver operating characteristic curve of urinary MCP-1 levels

analyzed for patients diagnosed as rejection. It showed an area of 0.83±0.04

under the curve with sensitivity of 84% and specificity of 74% at cut-off

214pg/ml for MCP-1.

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6.3.4. Effect of Urinary MCP-1 level on Graft Survival:

MCP-1 levels were further investigated to find out the effect on cumulative

rejection-free graft survival. Transplant patients were categorized into two

groups according to the cut-off value of 214pg/ml of MCP-1 (i.e. >214pg/ml

and <214pg/ml of MCP-1). Kaplan-Meier curve was constructed to determine

cumulative rejection-free graft survival and significant levels by log-rank test

for categorized groups (Table 6.5). It was observed that urinary MCP-1 levels

>214pg/ml had significantly lower rejection-free graft survival as compared to

patients with <214pg/ml at certain time periods such as 3 months, 6 months,

12 months and 24 months. Results indicate significant differences between

two groups at all time periods i.e. 4 vs. 9 weeks, log-rank P<0.001; 8 vs. 18

weeks, log-rank P<0.001; 10 vs. 27 weeks, log-rank P<0.001 and 27 vs. 70

weeks; log-rank P<0.001 respectively (Figure 6.5).

Table 6.5: Kaplan-Meier survival curve for transplant patients categorized into two groups based on MCP-1 levels.

MCP-1 Total N No. of

Events Censored

N Percent <214pg/ml 92 20 72 78.3% >214pg/ml 141 104 37 26.2% Overall 233 124 109 46.8%

Overall Comparisons Chi-Square d.f. Sig.

Log-rank (Mantel-Cox)

40.367

1

0.000

d.f = degree of freedom

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Figure 6.5: Kaplan-Meier survival curve for transplant patients categorized

into <214pg/ml and >214pg/ml MCP-1 levels in 24 months (104 weeks) post-

transplantation.

152

6.4. Discussion:

Molecular changes in a graft precede any pathological changes and are

ultimately reflected by clinical symptoms. Detection of molecular

changes/patterns associated with rejection before these manifest at

histopathological or clinical levels by non-invasive marker(s), is the most

active area of current transplant research. Chemokine levels are suggested to

reflect these molecular changes as they mediate immune response against

allograft. Increased expression levels of multiple chemokines and their

cognate receptors play a significant role in allograft rejection process.

MCP-1 is one of the most powerful chemoattractant that is involved in

the pathogenesis of renal diseases/failure (Segerer et al., 2000; Viedt et al.,

2002). MCP-1 is produced by proximal tubular epithelial cells which triggers a

series of inflammatory events. It includes secretion of other chemokines and

activation of transcription factors which ultimately progress to

tubulointerstitial damage (Viedt et al., 2002).

The main finding of this study is the association of MCP-1 with the

rejection group. An increased MCP-1 level was found which differentiated

rejection group from other study groups i.e. non-rejection, stable graft and

control groups. The ROC curve analyses also illustrated that MCP-1 is a good

marker for rejection having AUC of 0.83±0.04 with sensitivity and specificity

of 84% and 74% respectively. Despite limited studies on MCP-1 in renal

transplantation, our study findings are in accordance to previous studies that

showed that MCP-1 is associated with rejection (Rüster et al., 2004; Dubinski

et al., 2008; De Serres et al., 2012).

153

Ho et al., (2010, 2012) have also showed that the MCP-1 is associated

with development of IFTA as well as graft loss. In this study, we also found an

association of MCP-1 levels with IFTA development. MCP-1 levels

significantly differentiated IFTA from other groups such as NAD, control and

rejection groups. This indicates that MCP-1 levels could reflect the changes

that lead to development of IFTA. The concentration of MCP-1 was lower in

the IFTA group as compared to the rejection group. This may be due to the

fact that its expression level is significantly lower in IFTA as compared to

rejection (Rüster et al., 2004).

There are some limitations in this study. MCP-1 was studied on

exclusive diagnosed cases of single event on graft biopsy such as rejection

(acute T-cell mediated rejection), normal histology and interstitial

fibrosis/tubular atrophy. Due to low number (1%) of antibody mediated

rejection (AMR) cases in our centre, these cases were excluded (Kazi and

Mubarak 2012). Study MCP-1 in other histopathological diagnosed cases of

drug toxicity, infections and in other rejection types (AMR and chronic

rejection would help in understanding its role in graft outcome.

It would be informative to determine MCP-1 levels

(increased/decreased) at the end of the anti-rejection therapy, with biopsy

proven rejection resolved, to reflect/monitor the patient response to therapy.

However, our institute does not have a policy to perform protocol biopsy.

Unfortunately, lack of data regarding urinary biomarkers for renal and other

transplant organs from other centres are also a major limiting factor for

validation of any biomarker for rejection.

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This study as well as various previous studies have shown association

of urinary MCP-1 levels with graft rejection and suggest it as a clinically

useful diagnostic/prognostic marker. However, it is important to set up an

optimal threshold (cut-off) level of MCP-1 to delineate the clinical relevance

of the test. Further studies to evaluate the threshold levels at times of rejection

would be helpful for correct and early diagnosis.

155

6.5. References:

Ajuebor MN, Flower RJ, Hannon R, Christie M, Bowers K, Verity A, Perretti M. (1998). Endogenous monocyte chemoattractant protein-1 recruits monocytes in the zymosan peritonitis model. J Leukoc Biol. 63(1):108-116.

Boratyńska M. (1998).The role of monocyte chemotactic peptide (MCP-1) in chronic renal allograft rejection. Pol Arch Med Wewn. 99(4):272-80.

De Serres SA, Mfarrej BG, Grafals M, Riella LV, Magee CN, Yeung MY, Dyer C, Ahmad U, Chandraker A, Najafian N. (2012). Derivation and validation of a cytokine-based assay to screen for acute rejection in renal transplant recipients. Clin J Am Soc Nephrol. 7(6):1018-25.

Dubiński B, Boratyńska M, Kopeć W, Szyber P, Patrzałek D, Klinger M. (2008). Activated cells in urine and monocyte chemotactic peptide-1 (MCP-1)--sensitive rejection markers in renal graft recipients. Transpl Immunol. 18(3):203-7.

Grandaliano G, Gesualdo L, Ranieri E, Monno R, Stallone G, Schena FP. (1997). Monocyte chemotactic peptide-1 expression and monocyte infiltration in acute renal transplant rejection. Transplantation. 6(3):414-20.

Gu L, Tseng SC, Rollins BJ. (1999). Monocyte chemoattractant protein-1. Chem Immunol. 72:7-29.

Prodjosudjadi W, Daha MR, Gerritsma JS, Florijn KW, Barendregt JN, Bruijn JA,van der Woude FJ, van Es LA. (1996). Increased urinary excretion of monocyte chemoattractant protein-1 during acute renal allograft rejection. Nephrol DialTransplant. 11(6):1096-103.

Racusen LC, Solez K, Colvin RB, Bonsib SM, Castro MC, Cavallo T, Croker BP, Demetris AJ, Drachenberg CB, Fogo AB, Furness P, Gaber LW, Gibson IW, Glotz D,Goldberg JC, Grande J, Halloran PF, Hansen HE, Hartley B, Hayry PJ, Hill CM,Hoffman EO, Hunsicker LG, Lindblad AS, Yamaguchi Y, et al.( 1999). The Banff 97 working classification of renal allograft pathology.Kidney Int. 55(2):713-23

Rice JC, Spence JS, Yetman DL, Safirstein RL. (2002). Monocyte chemoattractant protein-1 expression correlates with monocyte infiltration in the post-ischemic kidney. Ren Fail. 24(6):703-23 Robertson H, Morley AR, Talbot D, Callanan K, Kirby JA. (2000). Renal allograft rejection: beta-chemokine involvement in the development of tubulitis. Transplantation. 69(4):684-687. Rollins BJ. (1997). Chemokines. Blood 90(3): 909-928.

Rovin BH, Doe N, Tan LC. (1996). Monocyte chemoattractant protein-1 levels in patients with glomerular disease. Am. J Kidney Dis. 27(5):640-6.

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Rüster M, Sperschneider H, Fünfstück R, Stein G, Gröne HJ. (2004). Differential expression of beta-chemokines MCP-1 and RANTES and their receptors CCR1, CCR2, CCR5 in acute rejection and chronic allograft nephropathy of human renal allografts. Clin Nephrol. 61(1):30-9.

Segerer S, Cui Y, Hudkins KL, Goodpaster T, Eitner F, Mack M, Schlöndorff D, Alpers CE. (2000). Expression of the chemokine monocyte chemoattractant protein-1 and its receptor chemokine receptor 2 in human crescentic glomerulonephritis. J Am Soc Nephrol. 11(12):2231-42.

Segerer S, Cui Y, Eitner F, Goodpaster T, Hudkins KL, Mack M, Cartron JP, Colin Y, Schlondorff D, Alpers CE. (2001). Expression of chemokines and chemokine receptors during human renal transplant rejection. Am. J Kidney Dis. 37(3):518-31.

Sun XH, Tan JM, Wu WZ, Lin WH, Li QS, Fang YH. (2003). [Significance of detecting urinary monocyte chemotactic peptide-1 in renal transplant recipient]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 19(6):563-4.

Tam FW, Sanders JS, George A, Hammad T, Miller C, Dougan T, Cook HT, Kallenberg CG, Gaskin G, Levy JB, Pusey CD. (2004). Urinary monocyte chemoattractant protein-1 (MCP-1) is a marker of active renal vasculitis. Nephrol Dial Transplant. 19(11):2761-8.

Van Coillie E, Van Damme J, Opdenakker G. (1999). The MCP/eotaxin subfamily of CC chemokines. Cytokine Growth Factor Rev. 10(1):61-86.

Viedt C, Dechend R, Fei J, Hänsch GM, Kreuzer J, Orth SR. (2002). MCP-1 induces inflammatory activation of human tubular epithelial cells:involvement of the transcription factors, nuclear factor-kappaB and activating protein-1.J Am Soc Nephrol. 13(6):1534-47.

Wada T, Furuichi K, Segawa-Takaeda C, Shimizu M, Sakai N, Takeda SI, Takasawa K, Kida H, Kobayashi KI, Mukaida N, Ohmoto Y, Matsushima K, Yokoyama H. (1999). MIP-1alpha and MCP-1 contribute to crescents and interstitial lesions in human crescentic glomerulonephritis. Kidney Int. 56(3):995-1003.

Wu Y, Yang L, Su T, Wang C, Liu G, Li XM. (2010). Pathological significance of a panel of urinary biomarkers in patients with drug-induced tubulointerstitial nephritis. Clin J Am Soc Nephrol. 5(11):1954-9. Varma PP, Hooda AK, T Sinha, GS Chopra, SC Karan, GS Sethi, Badwal S, Kotwal A. (2007). Renal Transplantation - An Experience of 500 Patients. MJAFI. 63: 107-111.

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Chapter-7: Discussion and Future Perspective

Renal transplantation is the most effective available treatment for

ESRD world-wide. In Pakistan, only 2.3 % of ESRD patients have access to

this therapy. The graft survival and patient survival rate is 65% and 75% at 5-

years of transplantation (Rizvi et al., 2009). The biggest hurdle in successful

transplantation is allograft rejection that occurs in 26 percent of cases during

the first post-transplantation year (Firasat et al., 2012). Early diagnosis of

rejection is crucial for optimum management of graft functioning. Currently,

serum creatinine is used as a diagnostic and monitoring marker for graft

functioning and graft biopsy is the gold standard technique for confirming

diagnosis of graft dysfunction. However both these tests have limitations as

serum creatinine is a late indicator of graft dysfunction and graft biopsy is an

invasive procedure which limits its usage (Manno et al., 2004).

The main aim of the study was to identify gene polymorphisms of

immune regulatory molecules associated with graft rejection. We investigated

the association of CCR2V64I, CCR5-59029G>A and CCR5Δ32

polymorphisms in the immune regulation of renal allograft to identify patients

at high risk for rejection based on their genetic make-up. Therapeutic

strategies that target specific immunological components involved in the

facilitation/provocation of rejection mechanism may reduce the rejection risk.

Thus, identification of such prognostic marker(s) would help in proper and

effective management of transplantation.

Another aim of this study was to identify early and non-invasive

markers associated with renal allograft rejection. Chemokine are released at

early phase of graft injury and their quantification by non-invasive method in

urine samples may reflect the immunological status of the kidney graft

158

(Jackson et al., 2011). Therefore, urinary chemokine levels could be used to

predict the onset of rejection earlier than serum creatinine. Such early non-

invasive markers would assist clinicians in graft assessment and help in early

diagnosis of rejection.

This is the first study investigating the gene polymorphisms of CCR2

and CCR5 and their role in the renal transplant patients from Pakistan. The

significant finding was the association of AA genotype of CCR2 with the

rejection outcome, which follows the same trends as reported from

neighboring country (India: Singh et al., 2008). Another important finding was

the significantly elevated levels of urinary chemokines of IP-10 and MCP-1

among biopsy-proven rejection. This would help clinician(s), in association

with other existing markers, in the graft assessment and guide regarding the

need of graft biopsy.

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7.1. References:

Firasat S, Raza A, Abid A, Aziz T, Mubarak M, Naqvi SA, Rizvi SA, Mehdi SQ, Khaliq S. (2012). The effect of chemokine receptor gene polymorphisms (CCR2V64I, CCR5-59029G>A and CCR5Δ32) on renal allograft survival in Pakistani transplant patients. Gene. 511(2):314-9.

Jackson JA, Kim EJ, Begley B, Cheeseman J, Harden T, Perez SD, Thomas S, Warshaw B, Kirk AD. (2011). Urinary chemokines CXCL9 and CXCL10 are noninvasive markers of renal allograft rejection and BK viral infection. Am J Transplant. 11(10):2228-2234

Manno C, Strippoli GF, Arnesano L, Bonifati C, Campobasso N, Gesualdo L, Schena FP. (2004) Predictors of bleeding complications in percutaneous ultrasound-guided renal biopsy. Kidney Int. 66(4):1570-7.

Rizvi SA, Zafar MN, Lanewala AA, Naqvi SA. (2009). Challenges in pediatric renal transplantation in developing countries. Curr. Opin. Organ Transplant. 14(5): 533-539.

Singh R, Kapoor R, Srivastava A, Mittal RD. (2008). Impact of chemokine receptor CCR2 and CCR5 gene polymorphism on allograft outcome in North Indian renal transplant recipients. Scand. J. Immunol. 69(1): 51-56.