UNIVERSITY OF CALGARY. Characteristics of Donor-Specific anti-hla Antibodies (DSA) Impacting different Renal. Allograft Outcomes. Salim S.

Transcription

1 UNIVERSITY OF CALGARY Characteristics of Donor-Specific anti-hla Antibodies (DSA) Impacting different Renal Allograft Outcomes by Salim S. Ghandorah A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE GRADUATE PROGRAM IN MEDICAL SCIENCE CALGARY, ALBERTA April, 2015 Salim Ghandorah 2015

2 Abstract AMR is the major complication of renal transplantation that significantly affects the allograft survival. Complement fixation of Donor specific anti-hla antibodies is the hallmark of AMR. Different methods have been developed to detect harmful DSAs. However, not all DSAs have the same detrimental effect, as different antibody characteristics (timing, specificity, strength, and complement-fixing ability) might trigger distinct immune responses. Here, we assessed DSA characteristics in renal transplant patients. Thirty-nine patients with de novo DSA were analyzed for the DSA MFI values, C1q positivity and IgG subclasses. IgG1 was the predominant IgG subclass (49.4%), followed by IgG3 (24.7%), IgG2 (16%), and IgG4 (9.9%). Among DSA characteristics, DSA MFI and IgG1 subclass were strongly correlated with C1q positivity (p= 0.01 and p= 0.009, respectively). Further studies are needed to investigate the clinical relevance of high MFI DSA and IgG1 subclass in improving the utility of the C1q assay in predicting graft outcomes. ii

3 Acknowledgements I would like to thank my supervisor, Dr. Noureddine Berka, for allowing me the time to pursue the MSc degree and supporting me throughout the process, I am very grateful. The experience I have gained through working under his supervision has inspired me to strive for excellence and taught me to think critically. I am also immensely grateful to my co-supervisor, Dr. Faisal Khan, whose guidance, support and advice from beginning to end was invaluable in maintaining my improvement and motivation. I will be always thankful for all their support. I would like to extend my appreciation to Dr. Serdar Yilmaz for being an extremely valuable member of my MSc committee and for providing important clinical data and detailed graft outcomes data that were crucial for this thesis. I am also grateful to Dr. Lee Anne Tibbles for her support and for helpful feedback during committee meetings. I would like to thank Dr. Abdulnaser Al-Abadi for providing the clinical data and his closer suggestions and advices. This project would not have been possible without the co-operation of Tissue Typing Lab fellows and staff from the Calgary Lab Services. I would like to thank Dr. Hamid Liacini Dr. Jinguo Wang and Taleb Rahmanian for diligent, close support and training with my experiments. I would like to thank Dr. Poonam Dharmani, Dr. Rehan Faridi and all lab members of Drs. Storek and Khan Lab for their inspiration, support and friendship throughout the years. I particularly thank Taylor Kemp for her close help, advice and support throughout my study. Finally, I thank my parents, who have tolerated my long absences during this very demanding time in my life. In particular, I thank my father, Sami, for his unwavering faith in my ability to succeed and for his valuable advice and overall unlimited help and support. I am also thankful for having a caring and wonderful Mom, Abdyiah, in my life. I am sorry to keep your kind heart waiting for me all this long time. I will come back.. I promise! I would like to thank my brothers Rami, Marwan, Raed, Rabiea, Rayan and my one and only sister Bashaer for being in my life, I am grateful to have such a caring and close family. Lastly, I thank my supportive, understanding, and caring wife, Afnan. Being in my life you have inspired me and brought joys and happiness. I will make you proud! iii

4 Dedication I would like to dedicate this work to my beloved wife Afnan.. to my supportive and dedicated father and mother.. to all my family and friends.. Thank you all!! iv

5 Table of Contents Abstract... ii Acknowledgements... iii Dedication... iv Table of Contents...v List of Figures and Illustrations... vii List of Abbreviations... viii CHAPTER ONE: INTRODUCTION The immune system Innate immunity Adaptive Immunity Major histocompatibility complex (MHC) HLA polymorphism The role of HLA in solid organ transplantation Current State of knowledge Rationale DSA strength (MFI values) DSA Complement-fixing ability C1q binding ability IgG Subclasses Hypothesis of the Thesis Aim of the study:...16 CHAPTER TWO: MATERIALS AND METHODS Subjects and specimens DSA analysis and clinical endpoints IgG Donor Specific anti-hla Abs monitoring C1q-SAB analysis IgG subclasses-sab assay analysis Clinical and pathological monitoring Statistical analysis...23 CHAPTER THREE: RESULTS Demographical and Clinical characteristics of kidney allograft recipients Characteristics of anti-hla Abs Frequency of IgG Subclasses, their patterns and biological groups Determinants for C1q Positivity...28 CHAPTER FOUR: DISCUSSION...31 CHAPTER FIVE: CONCLUSION...38 Tables and Figures. 39 References.. 59 v

6 List of Tables Table 1: Demographical and Clinical characteristics of kidney allograft recipients Table 2: Characteristics of patients with anti-hla Abs (Distribution and range) Table 3: Characteristics of anti-hla Abs (among the 3 biopsies) Table 4: Correlation of DSA characteristics with C1q positivity Table 5: Correlation of class I IgG subclasses with C1q positivity Table 6: Correlation of class II IgG subclasses with C1q positivity vi

7 List of Figures and Illustrations Figure 1: Schematic representations of patient cohorts in our study Figure 2: Illustration of the Luminex assay Figure 3: Illustration of the C1q assay Figure 4: Illustration of the modified SAB assay to analyze IgG subclasses Figure 5: Correlation of DSA MFI with overall Class I and Class II anti-hla Abs Figure 6: Correlation of DSA MFI with HLA-A, B, C, and HLA-DR, DQ, DP Figure 7: IgG subclass distribution among class I and class II anti-hla antibodies Figure 8: IgG subclass combination and pattern Figure 9: Correlation of DSA MFI with C1q positivity Figure 10: Correlation of DSA MFI of class I and class II with C1q positivity Figure 11: Correlation of DSA MFI with C1q positivity in each biopsy Figure 12: Correlation of IgG subclasses with C1q positivity Figure 13: Correlation of C1q MFI with IgG1 subclass (the predominant IgG subclass that strongly correlates with C1q positivity) Figure 14: Histograms of IgG SAB and C1q assays vii

8 List of Abbreviations Symbol Definition AA Amino Acids Abs Antibodies Ag Antigen AMR Antibody-mediated rejection APC Antigen Presenting Cells AR Acute Rejection BCR B-cell receptor CD4 Cluster of Differentiation 4 CDC Complement-Dependant Cytotoxicity CR Chronic rejection CRP C-reactive proteins DSA Donor specific antibodies ELISA Enzyme-linked immune-sorbent assay FC Flow Cytometry FXM Flow crossmatch HAR Hyperacute Rejection HLA Human Leukocyte Antigens IFN-γ Interferon gamma Ig Immunoglobulin MAC Membrane attack complex MBL Mannose-binding lectin MFI Mean Fluorescent Intensity MHC Major histocompatibility complex MMF Mycophenolate Mofetil NC Negative Control PBS Phosphate Buffered Saline PRA Panel reactive antibody PTC Peritubular capillaritis SABs Single Antigen Beads SPAs Solid Phase Assays SCr Serum Creatinine TCR T-cell receptors TNF Tumor necrosis factors TSBs Transplant Surveillance Biopsies viii

9 Chapter One: Introduction 1.1 The immune system The human immune system is a dynamic network of cells, tissues, and organs that employs a variety of biological mechanisms to defend its host against invading pathogens and diseases. The immune system normally has a remarkable ability to distinguish between the self and non-self. Malfunction of the immune system may lead to cancer, autoimmune, and inflammatory diseases. The immune system has two arms of defense: innate immune response (immediate) and adaptive immune response (long-term). Both innate and adaptive immune systems are very critical to refine the immune response and improve its recognition of the encountered pathogen by providing a quick and efficient response(1) Innate immunity The innate immune response is an immediate and a non-specific representing the first line of defence of the immune system. It is uncommon for innate immunity to recognize self antigens (Ags) as foreign antigens(2). The innate immune system includes cells and the physical barriers, which prevent organisms from entering into the body. If a pathogen penetrates the physical barrier, the innate immune system provides an immediate and quick, but non-specific response to eradicate the encountered pathogens(3). If the innate system fails to defend against the evading pathogen, the next level of defence is provided, known as the adaptive immune system. Complement system is another major component of the innate immune response that can assist in defending the body against encountered pathogens. Complements are plasma proteins that are involved in an enzymatic cascade leading to cell lysis. These soluble proteins also help in 1

10 recruiting additional phagocytic cells to the site of inflammation(4). Complement cascade can mediate an innate immune response if the first complement component in the complement pathway C1q binds to C-reactive proteins (CRP) or mannose-binding lectin (MBL) on the surface of the microbe(5). Complement activation is a general effector mechanism of the immune system, which can be associated with adaptive immune response in addition to the innate immunity(4) Adaptive Immunity The adaptive immune response involves complex and specialised mechanisms to selectively recognize a specific antigen. The cellular components of the adaptive immunity are characterized by the increase selectivity for a particular antigen, diversity, and their capacity to form of an immunological memory enabling the immune system to provide faster and stronger responses each time the same pathogen is encountered(1). The specificity and diversity of adaptive immune responses depend on the presence of antigen-specific receptors. Adaptive immunity are subdivided into two categories; antibody-mediated immunity (also referred as humoral immunity), which is the production of antibodies against extracellular microorganisms, and cell-mediated immunity, which uses specialized lymphocytes against intracellular microorganisms(6). Antibody-mediated immunity is mediated by microbial antigens that are bound to B cell immunoglobulin receptors (BCR). B cells can act as an APC and uptake these antigens, process them and present them to T cells through the Major Histocompatibility Complex (MHC) class I or class II molecules (7-9). Antigen-bound BCR interacts with T cells and via CD40 is required 2

11 to activate the B cell. Upon activation, a B cell can differentiate into antibody-producing plasma cells and memory B cells. Plasma cells have the ability to produce a single and specific antibody with unique specificity to the stimulating epitope, which subsequently neutralize, or opsonise, or activate the complement cascade that enhances the process of phagocytosis and lysis(10). Secreted antibodies are considered the main effector molecules of the humoral branch of the immune. Antibodies are glycoproteins consisting of a pair of heavy chains and a pair of light chains. There are disulfide bonds are present to hold the heavy chain and the light chain together. Antigens bind to the highly variable ends of the variable region on the heavy chains and the light chains. During B cells maturation, a series of random gene rearrangements occur to determine the specificity of each B cell(11). As a result, each individual B cell can express an antibody molecule with unique specificity on its surface. The specificity of an antibody can be enhanced through single nucleotide changes within the variable region and this process is called somatic hypermutation(12-14). 1.2 Major histocompatibility complex (MHC) Major Histocompatibility Complex (MHC), known as Human Leukocyte Antigens (HLA) in humans, is a complex of genes that plays a central role in the human immune system. The HLA molecules function as antigen presenters and are encoded in the short arm of human chromosome 6(15). There are two major MHC classes, MHC class I and MHC class II, each with a specific structure and function. Structurally, MHC class I molecule composed of a transmembrane heavy α chain constructing three extracellular domains, α1, α2, and α3. The two domains α1 and α2 3

12 form the peptide-binding region. The third domain, a-3, is non-covalently bound to β2 microglobulin molecule and provides the binding site for CD8+ T cells co-receptors(16, 17). MHC class II, on the other hand, is composed of two identical transmembrane α and β heavy chains; each has two extracellular domains, α1, α2, β1 and β2. The folding of the distal domains, α1 and β1, forms the peptide-binding site. The β2 domain functions as a binding site for CD4+ T cells co-receptors(18). MHC class I molecules, which are expressed by most nucleated cells, present intracellular peptides to CD8+ T cells. However, MHC class II molecules, which are expressed only by antigen-presenting cells, present extracellular peptides to CD4+ T cells. Proteins derived from different pathogens must be processed into small peptides and presented through MHC class I and MHC class II to be recognized by a T cell receptor (TCR). Initiation of immune response against a particular antigen can be enhanced by the conformational changes of peptides bound to MHC molecules, acquired through HLA polymorphisms to ensure a of wide range of pathogens are covered(19-21) HLA polymorphism There are three major classical genes, HLA A, B and C, which encode for HLA class I molecules. Further, HLA-DR, DQ and DP genes on chromosome 6 encode for HLA class II molecules. Two main mechanisms can lead to the generation of thousands of HLA allelic variants causing the HLA system the most polymorphic system(22). The first mechanism occurs when two genes are expressed from each loci, one is inherited from the father and one from the mother leading to the expression of a total of twelve different HLA molecules from the six 4

13 classical HLA gene loci(23). The second mechanism is the genetic polymorphisms that are generated due to the changes in the nucleotide components of each HLA gene loci leading to alteration of the amino acid structure. This HLA polymorphism leads to the extensive variability of HLA molecules among different individuals(22) The role of HLA in solid organ transplantation Transplantation refers to the process of transferring tissues or organs from one site to another. Transplantation is the most effective and life-enhancing treatment that facilitates emergency lifesaving interventions for patient (recipient) through the implementation of healthy tissue or organ (graft) from a healthy individual (donor)(24). Among many allografts, kidney is the most commonly transplanted allograft(25). Kidney transplantation is considered the remarkable treatment for several diseases, such as diabetes that would otherwise lead to the end-stage kidney failure(26). Kidneys can be obtained from living donors or cadaver, whether they are related or unrelated. However, because the kidney is heavily vascularised, there is a huge risk in triggering the immune response after transplantation. In general, there are two primary stages involved in the process of initiating an immune response to an allograft. The first stage is the sensitization stage, where an alloantigen is presented directly or indirectly to the host naïve T cells. The second stage occurs after the recognition of an alloantigen, the effector stage, which leads to the proliferation and activation of host T cells and consequently, activation of B cells. The activated B cells mature into plasma cells and produce antibodies. The subsequent binding of antibodies to HLA antigens on allograft lead to the activation of the complement cascade(27). 5

14 These antibodies have direct and indirect cytotoxic effects mediated by the membrane attack complex (MAC) of complement. This complex attracts inflammatory cells and activates phagocytosis. The damaged endothelial cells secrete Von Willebrand factor and the exposed basal membrane induces aggregation and adhesion of platelets, leading to thrombosis and vascular occlusion(28). The presence of Abs specific to the donor s HLA has been strongly associated with allograft rejection and loss in renal transplant recipients(29-32). Since the diversity in HLA antigens are the major stimulus of graft rejection, it is important to determine these differences to avoid unfavourable consequences(33). The expressed class I and class II allotypes by an individual can be referred to as HLA type. Expression of HLA molecules on the cell membrane plays an important role in the immune response to foreign tissue. Patient exposure to HLA molecules from a genetically unrelated individual can lead to the development of anti-hla Abs (34, 35). Several mechanisms can account for the exposure to HLA antigens that lead to the production of anti-hla antibodies. A renal transplant recipient with repeated pregnancies may be at higher risk to develop anti-hla antibodies due to prior sensitization to paternal HLA antigens of the fetus. Repeated transfusion can sometimes cause the development of anti-hla antibodies against the HLA antigens on transfused blood. Finally, a recipient with previous transplant may have significant levels of anti-hla antibodies from the previous grafts. These sensitizing events can lead to the activation of B cells and the secretion of anti-hla antibodies causing the recipient to become highly sensitized. Sensitization may negatively impact the success of kidney transplantation(36). The degree of sensitization proportionally increases with waiting time for patients on a waitlist, significantly reducing graft survival(37). 6

15 A patient can be sensitized prior and after transplant. Anti-HLA Abs present before transplantation are called preexisting Abs and the anti-hla Abs that are developed after transplantation are called de novo anti-hla Abs. Donor specific antibodies (DSA) are referred to the anti-hla antibodies developed against donor s HLA antigens. An increasing number of clinical studies have demonstrated inferior allograft survival in patients with circulating anti- HLA Abs compared to those without anti-hla Abs whether formed pre-transplant or developed de novo post-transplant(38-41). 1.3 Current State of knowledge Reported allograft rejections, due to donor specific HLA Abs, have resulted in Ab screening being required as part of the pre-transplant test regimen(42). Once preexisting HLA Abs are detected, it is crucial to determine their specificity using different assays. The information obtained from these assays can be used to identify patients who are at risk for allograft rejection. Of these assays, complement-dependent cytotoxicity (CDC) is a cellular assay that detects HLA Abs present in the patient s serum by reactivity with a panel of cells collected from cell donors with common or rare HLA antigens defined as Panel Reactive Antibody (PRA). However, although the CDC assay is considered the gold standard for identifying clinically relevant antibodies that dramatically decrease the incidence of graft rejection, it still has major limitations(43). The major limitations of the CDC assay, rendering it insensitive and nonspecific, are the inability to discriminate between HLA Abs and other cytotoxic Abs, interference from therapeutic Abs, and failure to detect HLA Abs that do not activate complement(44). Further, Flow Cytometry (FC) is a more sensitive assay, which detects the presence of recipient Abs on the surface of donor lymphocytes independent of complement fixation(45). 7

16 Currently, new technologies, known as solid-phase assays (SPAs), have routinely been used in transplant laboratories to clearly define the presence of pre-transplant anti-hla antibodies and monitor the development of anti-hla antibodies in patients sera against purified or recombinant HLA antigens fixed to a solid surface. The Enzyme-linked immune-sorbent assay (ELISA) was the first solid-phase assay to detect HLA Abs with greater sensitivity than CDC. Later sensitivity was considerably increased with the introduction of Luminex assay, which detects anti-hla Abs using specific-colored polystyrene beads coated with HLA Ags. Luminex beads may be coated with many different HLA Ags, to screen for anti-hla Abs, or an individual Ag, to identify the exact specificities of anti-hla Abs in a patient s serum. Briefly, in Luminex, there are three different panels that vary in the composition of target HLA antigens. The first panel is the screening panel that uses pooled antigens of two or more different bead populations. Each bead is coated with either class I (HLA-A, HLA-B, and HLA-C) or HLA class II (HLA-DR, HLA-DQ and HLA-DP). The affinity purified HLA antigens are obtained from multiple individual cell lines. They are relatively inexpensive and provide a tool to analyze the presence and the absence of the anti-hla Abs. The second panel is the phenotype panel in which each bead contains either class I or class II antigens. Unlike the screening panel, the HLA antigens in the phenotype panel are obtained from a cell line derived from a single individual. The last panel is the single antigen panel. Each bead is coated by a single cloned allelic HLA class I or class II antigens. The single antigen panel provide a precise analysis of the antibody specificities present(46). The Luminex assay, to a certain degree, is considered the most sensitive and specific technique. The introduction of the current sensitive assays shows a strong promise for improving the ability 8

17 to identify patients at risk of poor graft outcome, allowing finer definition of Ab specificity and monitoring patient s response to therapy. Sensitive assays have the advantage of detecting specificities, undetected by CDC, which may have implications for graft rejection in the early transplant period. Additionally, these techniques are also useful to detect low levels of Abs, which may lead to a positive Flow cross-match (FXM), even with a negative CDC(47). It has become clear that the presence of anti-hla Abs were considered a risk factor rather than a contraindication for transplantation(48). However, despite the great sensitivity, not all HLA DSA have the same detrimental effect and, therefore, not all Abs detected by Luminex are clinically relevant(49-53). With the introduction of sensitive techniques, the number of patients with detectable HLA antibodies has increased over time(54, 55). The correlation between anti-hla antibodies and graft rejection is evident but does not identify which antibodies are clinically relevant in causing rejection. Several studies have investigated the correlation between anti-hla antibodies and different graft outcomes. However, the findings of these studies show a remarkable variability(38). 1.4 Rationale The introduction of new sensitive assays for the detection of HLA Abs on the basis of their binding to purified HLA molecules bound to beads have had a remarkable impact on the decision-making process when selecting donors for sensitized patients. Several studies have reported patients with DSA detected by SPA, but not by CDC, developed to graft loss(29, 56-58). So far, many concerns have been expressed because no consensus exists on the clinical 9

18 relevance of DSA detectable using SPA, particularly Luminex assay. It is widely considered to be too sensitive, as it detects a wide range of specificities. However, not all Abs have the same detrimental clinical impact. In fact, many patients with DSA maintained a very stable graft function with no rejection(59-64). This raises the question of how to define clinically relevant DSA. A better interpretation of the results obtained through Luminex would increase the significance of this technique and provide a better assessment of patients. As different Ab characteristics might trigger distinct immune responses, it is crucial to analyze the detailed profile of the biological and functional properties of the Abs. Therefore, the detailed characterization of an Ab might provide a better tool for predicting different graft outcomes. To improve the utilization of Luminex assay, different approaches have been used to define the threshold for Ab positivity. Furthermore, there are several characteristics that can play a significant role in affecting the functionality of an Ab(65). However, the Ab strength and its complement-fixing ability might play a critical role in defining different graft outcomes DSA strength (MFI values) In an attempt to determine relevant DSAs detected by Luminex, researchers have relied on the Ab strength, as a determinant factor to make this distinction(66). Defining a value that describes the Ab strength, referred to as Mean Fluorescent Intensity (MFI) has been suggested as a determinative factor in defining acceptable and unacceptable Abs, and therefore, predicting the development of different graft outcomes(67). However, the ability to analyze the strength of Abs has added a dimension of complexity to the equation. There is a debate over the assignment of positive and negative reactions that are clinically relevant(60, 61, 63, 68-70). 10

19 Numerous studies have defined MFI cutoff values to discriminate between relevant and irrelevant DSA in correlation to graft outcomes. However, the reported MFI values have broad ranges and they usually overlap, which may affect patients on the extremities. Recent studies have suggested that DSAs detected by Luminex alone are not sufficient to predict the risk of clinical outcomes. Interestingly, neither the number, duration of DSA, nor MFI value proved predictive for graft rejection(71). These results suggest that almost half of DSA as defined solely by Luminex are clinically irrelevant and do not lead to graft rejection(50). There are several technical limitations that may count for the difficulties of the standardization of the results obtained by Luminex(69, 70, 72-75). The quantity of HLA molecules on beads may not represent actual distribution on cells. Soluble HLA antigens often lose their native structure when they are bound to the bead, causing conformational variability(76, 77). Additionally, few HLA Ags including HLA-C and HLA-non-DRB1 (DRB3,4,5) have low cell membrane expression(78-80). In order for Luminex to be more informative, many approaches have been developed in order to analyze the functional properties of the Abs. These approaches mainly focus on the assessment of complement fixation and IgG subclasses distribution(59, 75, 81-86) DSA Complement-fixing ability C1q binding ability The complement system plays a crucial role in host immune defense. The ability of an Ab to initiate complement activation is an important effector mechanism. Briefly, Abs initiate complement through the classical pathway by the binding of C1q to the Fc subunit of the 11

20 preexisting Abs, which leads to the formation of the membrane attack complex (MAC) complex and, consequently, cell lysis and death(87, 88). Tissue injury promotes an inflammatory response and enhances the adaptive immune system. Assessing the ability of a given Ab to bind and activate can determine clinically relevant DSA. Indeed, some studies showed that complementfixing DSA are more relevant than non-complement-fixing DSA(89, 90). In an effort to have a complement-dependent HLA Ab screening assay, with sensitivity and specificity equivalent to that seen with the conventional IgG binding assay, a C1q assay has been developed to assess the clinical relevance of complement-fixing ability of a given DSA(91-94). C1q assay detects only IgG Abs capable of binding C1q, the first step in the classical complement cascade, without any further functional step (C4d). C1q binding ability of an Ab has been strongly correlated with the strength of DSA as measured according to the MFI(29, 85). Supporting the general principle of complement activation, several studies have reported a significant association of C1q binding with the increased risk of AMR and graft loss(75, 82, 95, 96). However, recent studies have reported a poor correlation between C1q binding with MFI and different graft outcomes (74, 75, 85, 95, 97). An important reason could be that the C4d deposition lacks the sensitivity for humoral rejection(98, 99). Other determinative factors concern the complement-fixing ability, which is often not reported, and the immunoglobulin (Ig) subclasses may contribute in causing the positive reactions(100, 101). Especially the fact that some IgG subclasses are able to fix complement and others are not may be a determinative factor. 12

21 IgG Subclasses IgG is the predominant HLA specific Ab. Complement activation is possibly the most important biological function of IgG. There are 4 IgG subclasses (IgG1, IgG2, IgG3 and IgG4), which differ substantially in their functional and biological properties, half-lives and serum concentrations, as a result of different heavy chain gene usage(102). The germ line of IgG heavy chain genes are in the order Cγ3, Cγ1, Cγ2, and Cγ4 with subclass switching being sequential, in this order, and antigen driven, under T cell control. Variations among the characteristics of IgG subclasses could have a significant impact on their individual strength, which in turn, will influence its binding ability. IgG subclasses differ in their complement-fixing ability; IgG3 is the strongest complementfixing subclass followed by IgG1, then, IgG2 and IgG4 with the weakest complement-fixing ability(103). The complement-fixing IgGs (IgG1 and IgG3) are correlated with higher incidence of graft rejection and poor graft survival compared to non-complement-fixing Abs (IgG2 and IgG4)( ). However, the role of weak/non-complement fixing IgG2 and IgG4 must not be completely ignored, as it has been correlated with the development of early graft failure (107) ( ). Altogether, this emphasizes that distinct characteristics of DSA might have different impacts on allograft outcomes. Relying solely on the sheer presence of DSA obtained from the highly sensitive SPAs to define relevant Abs is not sufficient and needs reassessment. Our existing knowledge about the different characteristics that define relevant DSAs in correlation to allograft outcomes is still limited. To date, no conclusive study has been conducted to assess the 13

22 detailed analysis of DSA characteristics including functional assessment of DSA in renal transplant recipients. Such a study is warranted because - The complement-fixing ability of a given Ab is influenced by many factors, including: IgG subclass, C1q binding ability and the Ab strength. Although some reports did not find a strong correlation between the degree of initial DSA and C4d or peritubular capillaritis (PTC), most studies relied only on one parameter to correlate with different graft outcomes(107). - Previous studies did not analyze the association between DSA specificity, Class I or class II, with C1q binding in correlation with graft outcomes(75). DSA class I are more likely to cause an early AMR, whereas class II are more associated with microvascular injury and slower progression to graft loss(114). - No significant correlation between C4d deposition in biopsy with different methods that determine the complement fixing ability of an individual s DSA (CDC, C1q and C4d assays) (101, 107). - Some studies have assessed C4d positivity using protocol biopsies only (i.e. no biopsies were performed during an episode of active rejection)(115). - The clinical relevance of DSA to HLA Ags that have low cell surface expression (HLA-C, DP and HLA-DRB3,4,5) is unknown. - A detailed explanation of the IgG subclasses profile in correlation with different graft outcomes might provide a clear explanation. No previous study has looked at different IgG subclasses to assess its effect on the strength of DSAs and different graft outcomes. 14

23 Additionally, previous studies analyzing IgG subclasses have ignored the concept of IgG class switching and the role of the weak complement-fixing IgG subclasses(81, 116, 117). - The use of less sensitive techniques and incomplete HLA typing to detect DSA positivity. - The analysis performed relied on a relatively small sample size, a short follow-up period, and an incomplete picture of graft outcomes to draw conclusions(75, 115, 118). 1.5 Hypothesis of the Thesis Based on these facts, in the present study, we propose to evaluate the following hypothesis in the setting of renal transplant patients who have DSA below 7500 MFI and are qualified as a virtual CDC-AHG negative: Hypothesis: Complement binding ability of HLA-DSA instead of sheer presence of DSA will be a better biomarker of renal allograft outcomes. This hypothesis is based on the concept that not all DSA have the same detrimental effect on renal allograft. Therefore, parameters that distinguish pathogenic from presumably irrelevant DSA are required for accurate pre-transplant and post-transplant risk assessment. To date, readily available parameters such as DSA specificity, number, and strength are not informative. Including other functional characteristics like DSA complement-fixing ability through analyzing C1q and IgG subclasses could accurately define more clinically relevant Abs, thereby providing better predictive values for different adverse graft outcomes. 15

24 1.5.1 Aim of the study: The main aim of the study is to generate a detailed profile of DSA characteristics of renal allograft recipients. 16

25 2.1 Subjects and specimens Chapter Two: Materials and Methods The ALTRA program in Calgary Alberta performs over 50 kidney transplantations each year for patients with end-stage renal diseases or failure. A total of 315 consecutive renal transplant patients who received kidney transplant between January 2007 and December 2012 were accrued for the study. Sera of patients with DSA, defined as presence of antibody against donor specific antigen with an MFI value equal or above 1000 were included and retrospectively analyzed in the study. The inclusion criteria includes that each subject should have a follow-up of 24 months post-transplant, have at least two serum samples drawn (one pre-transplant and one posttransplant), and histopathological analysis of at least one kidney transplant biopsy (Surveillance biopsy or for-cause biopsy) should be available. Of the 315, 110 patients had anti-hla Abs; either preexisting or de novo Abs. 21 patients were excluded for either not meeting our study inclusion criteria or for incomplete information. A total of 89 patients met the study inclusion criteria (as described in Figure 1). The sera obtained from 47 patients who developed DSAs (MFI 1000) either pre-transplant, post-transplant, or both (3, 39, 5; respectively) were analyzed for different antibody characteristics. The source for the diagnosis of all subjects was patients records and the renal transplant database - ALTRAbase. Data including all demographical information such as gender, age, ethnicity, date of transplant and clinical information (history/type of disease, transplant-specific data, HLA typing for both donors and patients, crossmatch results and method used for 17

26 crossmatch, anti-hla Abs including DSAs with their MFIs, histopathological scores for biopsies and diagnosis, graft type, and post-transplant events such as infection) was obtained from ALTRAbase. An ethics approval from the Conjoint Health Research Ethics Board (CHREB) at University of Calgary was obtained. 2.2 DSA analysis and clinical endpoints Sera of patients with DSA were retrospectively assessed for various characteristics including time of appearance (preexisting vs de novo DSA, early vs late post-transplant de novo DSA), specificity (anti-hla class I; A, B, C and class II; DR, DQ, DP), and strength (MFI). These analyses were carried out using anti-hla DSA data generated for the renal transplant recipients as part of routine clinical assessment. Complement-fixing ability was assessed by analyzing recipient s sera for C1q-fixing ability (C1q positive or negative) and IgG subclasses (complement-fixing IgG1/IgG3 and weak/non-complement-fixing IgG2/IgG4). Both C1q and IgG subclass assessment assays were performed using Luminex platform based commercially available assays. 2.3 IgG Donor Specific anti-hla Abs monitoring The Tissue Typing Laboratory (TTL) routinely performs Ab screening for renal transplant recipients in the center. The Ab screening protocol includes determining the anti-hla Ab specificity using Luminex screening for class I and class II kits (Luminex LABScreen mixed class I and class II, One-Lambda Inc., CA, USA). DSA was determined using Luminex LABScreen Single Antigen (One-Lambda Inc., CA, USA). 18

27 Serum samples were collected and analyzed at different time points. First, pre-transplant sample, which is typically, collected one-several times for clinical investigations. For the present study we used the pre-transplant sample that was analyzed for the final crossmatch. Second, posttransplant sample, which includes protocol sample, the serum sample that was collected and analyzed whenever the surveillance protocol biopsy was performed (typically 6 months and 18 months post-transplantation) and for-cause sample, the serum sample that was collected and analyzed whenever for-cause biopsy is performed. Sera of patients with anti-hla Abs were retrospectively assessed for various characteristics including time of appearance (preexisting vs de novo anti-hla Abs; early vs late post-transplant de novo anti-hla Abs), specificity (anti-hla Abs class I; A, B, C and anti-hla Abs class II; DRB1, DRB3, DRB4, DRB5, DQA1, DQB1, DPA1, DPB1), and anti-hla Ab strength (MFI). The analysis was carried out using HLA DSA data (ALTRAbase) generated for the renal transplant recipients as part of routine clinical assessment. The Luminex LABScreen Single Antigen assay contains a population of beads coated with a purified HLA molecule representing a single cloned allelic HLA class I or class II antigens that enable precise Ab specificity analysis. Briefly, patients sera were incubated with the LABScreen Single Antigen beads at room temperature for 30 minutes with gently shaking. PE-conjugated anti-human IgG was added and incubated for another 30 minutes at room temperature. Samples were washed and analyzed using Luminex platform (figure 2). In our lab, cut off value of 1000 MFI is used to detect anti-hla Abs. the IgG assay enables the detection of all anti-hla IgG Abs irrespectively to their functional (complement-fixing) ability. 19

28 2.4 C1q-SAB analysis C1q, bound by Ag-Ab complex in a primary incubation, is bound to PE-labelled anti-c1q Ab in a secondary incubation. Its florescence intensity, measured on a LABScan analyzer or FC, indicates the relative amount of Ab bound to the test sample (figure 3). C1q Screen kit (One Lambda Inc., CA, USA) for Luminex-based analysis was used. The kit contains purified human C1q (PEPC1Q, lot 010), PE-conjugated anti-c1q (PEPAC1Q, lot 011), positive control beads (ref PEPC1QPCB, lot 006), negative control (Ref LS-NC, lot 013), HEPES buffer (ref HEPBUF, lot 013), LABScreen Single Antigen Class I-combi (ref LS1A04), and LABScreen Single Antigen Class II-Group I (ref LS2A01). Sera were heat inactivated at 56 o C for 30 minutes and centrifuged at 13,000g for 8 minutes. The C1q assay was performed according to the instructions of the manufacturer except that 4µL of diluted human C1q (1:10 dilution) was used to maintain consistent and adequate C1q concentrations among all samples. In a 96-well plate, mix diluted C1q with 5µL heat-inactivated sera. After 20 minutes incubation, 5µL of PE-conjugated purified anti-c1q was added to each test well and incubated again for 20 minutes at room temperature with gentle shaking. Finally, 70 µl BPS was used for washing and resuspension before analysis. Data were analyzed as for the IgG SAB assay using Luminex 200 platform based commercially available assay. No standard cutoff value was used for C1q positivity, but rather, values were compared to the background MFI value. The C1q assay detects only anti-hla Abs that has the ability to bind C1q. In our analysis, we used MFI of 1500 as a "cut-off" value for samples with high background (NC above 1000 MFI). For samples with low background, we used a cut-off value of 1000 (NC less 20

29 than 1000)(119). IgM was never removed and it could contribute the high negative control (NC) as well as MFI values on SAB beads. None of the samples were absorbed or treated prior analysis. 2.5 IgG subclasses-sab assay analysis Sera positive for DSA were further evaluated for IgG subclasses. To perform this analysis, we used a modified Luminex-SAB technology in which the conventional PE-conjugated antihuman IgG was replaced with PE-conjugated mouse monoclonal IgG (IgG1 clone HP6001, IgG2 clone HP6002, IgG3 clone HP6050, and IgG4 clone HP6025; Southern Biotech, Birmingham, AL, USA) (figure 4). All secondary IgG subclasses used have been previously shown to produce minimal crossreactivity(81). A single test for each IgG subclass was performed separately with LABScreen SABs following the same manufacture s protocol used for the detection of anti-hla IgG antibodies. The assay was performed in a 96-well microtiter plate. Briefly, 5µL of well-mixed SABs (LabScreen SAB LSP1AB04 Lot008 and LSP2AB01 Lot010) was added to 20µL of patient serum and gently agitated in the dark for 30 min at room temperature. Working wash buffer was prepared during incubation based on number of samples in each run by mixing stock wash buffer (provided by OneLambda) with distilled water. 150µL working wash buffer was added, and the plate was centrifuged for 5 minutes at 13000g. After discarding the supernatant, two more washing steps were performed using 200µL working wash buffer. 100µL of appropriately diluted PE-conjugated mouse monoclonal IgG1, IgG2, IgG3 and IgG4 subclass (concentration: anti-igg1, IgG3, and IgG4 0.5µg/mL; anti-igg µg/ml) was added and incubated in the 21

30 dark at room temperature for 30 minutes. After two washing steps, SABs were dissolved in 80 µl phosphate-buffered saline (PBS) and mixed very gently. Finally, data were acquired on the Luminex100 analyzer. The cut-off value was generated for each IgG subclass and for every individual bead by using the negative control serum in 4 separate runs (NC 1-4). The cut-off value was calculated using this formula: mean (NC 1-4) multiplied by 3 standard deviations (NC 1-4) as described earlier(120). A positive result was defined by a MFI value above the cut-off value. To determine the amount of IgG subclasses, we used the ratio above the corresponding cut-off (Ratio= MFI of IgG subclass divided by corresponding MFI cut-off). The positive control was the same as the one routinely used for Single Antigen antibody analysis and prepared from pooled sera from previously known highly sensitized renal transplant recipients. As reported before, in the majority of IgG subclass testing, the values of positive controls and negative controls were significantly lower than the values obtained when the generic PE- IgG was used(120). 2.6 Clinical and pathological monitoring We perform two types of biopsies in Calgary Transplant surveillance biopsies (TSBs) and for cause biopsies. The kidney biopsies are performed after renal transplants in consenting patients to allow early diagnosis of subclinical acute and chronic histologic changes and subsequent intervention. Since 1998, renal transplant recipients have been routinely monitored using two types of prospective biopsies at our center. A TSB biopsy is defined as a biopsy performed in patients with a steady state, without any context of acute graft failure or recent immunological 22

31 events (at 6 months and 18 months post-transplantation). A for-cause biopsy is performed when clinically indicated by a rise in serum Creatinine (SCr) and/or proteinuria. TSBs consist of two core samples obtained with 18-gauge needles using ultrasound guidance in the Radiology Department in our institution. Paraffin and plastic sections of TSBs are prepared and stained with heamatoxylin-eosin, Masson s trichrome, periodic acid-schiff, and periodic acid-schiffmethanamine-silver stains. A transplant pathologist examines each biopsy under light microscopy(121). 2.7 Statistical analysis Continuous variables were described as mean and median values for data. All DSA characteristics including DSA and C1q positivity, specificity, presence or absence of IgG subclasses, and timing of appearance were described as categorical variables. Categorical variables were described as frequency and percentage. Chi-squared or 2-test Fisher's exact test was used to test correlation among categorical variables. DSA and C1q MFIs were described as ordinal values. Student t test (or Mann-Whitney U test) was used to compare the difference between groups for continuous data. All univariate correlations with a p-value <0.05 are regarded as statistically significant. Statistical analyses were performed using SPSS software, version 22 (SPSS Inc., Chicago, IL). 23

32 Chapter Three: Results 3.1 Demographical and Clinical characteristics of kidney allograft recipients A total of 315 renal transplant recipients were included in this retrospective study. All patients were transplanted between January 2007 and December 2012 at the Southern Alberta Transplant Program. The demographical and pertinent clinical characteristics of the patients are summarized in Table 1. All patients were followed up for a period of two years to include as many kidney transplant biopsies as possible (Surveillance biopsy and/or for-cause biopsy). Of the 315 patients, 205 (65.1%) patients did not have circulating anti-hla Abs (negative) and 110 (34.9%) were found with anti-hla Abs (positive). There were no significant differences between the two groups for the majority of the demographical variables and pre- or posttransplantation characteristics including recipient s age (p= 0.24) and gender (p=0.91), donor age (p= 0.88), donor type (0.91), and graft type (p=0.28). Of the 315 transplant recipients, 194 (62%) were male, 121 (38%) female and 30 (10%) of them had previous transplant. The median recipient age was 47 years (range, 9-75 years). The median donor age was 43 years (range, years). Nearly 46% of transplant recipients received kidney from deceased donor and 54% received kidney for living donors. The majority (69%) of transplants were performed between donor-recipient pairs with at least 2 class I and class II HLA mismatches (Table 3.1). Nearly 15% of the transplant recipients received ATG and IVIG, 35% received steroid, 10% received Basiliximab, and 20% received Daclizumab as induction regimen. Standard 24

33 maintenance therapy was comprised of a triple therapy regime of calcineurin inhibitor (cyclosporine or tacrolimus), an anti-proliferative agent (primarily mycophenolate mofetil, MMF) and prednisone. 3.2 Characteristics of anti-hla Abs Sixty-three patients with anti-hla Abs did not have DSAs or did not meet our study criteria and were excluded from the study, whereas only 47 patients had DSAs and were included. A summary of the distributions and characteristics of anti-hla Abs is shown in Tables 2 and 3. Among the 47 patients with DSA, 39 (83%) patients had de novo DSAs, 3 (6%) patients with preexisting DSAs, and 5 (11%) patients had some preexisting and some de novo DSAs (p= ). When we analyzed the time of the DSA appearance posttransplant, 30 patients (77%) developed DSAs after one-year posttransplant and 9 patients (23%) developed DSAs within oneyear posttransplant (p=0.0001)(table 2). Based on the specificity of anti-hla Abs, the majority of patients had DSAs against class II HLA. Thirty-eight of 47 patients (81%) had DSA against class II HLA and 21/47 patients (45%) had DSAs against class I HLA (p= ). Fourteen patients (29%) had DSAs against both class I and class II. There was no significant difference between the maximum MFI of class I and class II anti-hla Abs. The median MFI of class I anti-hla Abs was 2285 ( MFI) compared to class II anti-hla Abs 4397 ( MFI) (P= 0.6) (Figure 5). Table 3.3 shows the distribution of class I and class II anti-hla Abs among the three biopsies. A total of 55 biopsies were performed among 39 patients with DSA. These biopsies include 25

34 protocol (either 6 or 18 months) and diagnostic (when clinically indicated by a raise of SCr and proteinuria) biopsies. At the time of biopsy, 20 sera were analyzed in patients who had their first biopsy, 22 sera with the second biopsy and 13 sera were analyzed with the third biopsy. All de novo DSAs against class I (A, B and C) were observed in 8 out of 20 (40%) sera at the first biopsy, 7 out of 22 (32%) sera at the second biopsy and 4 out of 13 (31%) sera at the third biopsy with a median MFI of 3015 ( ), 2285 ( ), and 2348 ( ), respectively. HLA-A and HLA-B were the predominant HLA class I specificities in all three biopsies. HLA-A was present in 4 (20%) at the first biopsy, 4 (18%) at the second biopsy and 2 (15%) at the third biopsy. HLA-B was present in 5 (25%) at the first biopsy, 4 (18%) at the second biopsy and 3 (23%) at the third biopsy. The lower expressed HLA-C has lower median MFI and was observed in only 1 (5%), 2 (9%) and 1 (8%) at the first, second and third biopsy, respectively. There was no significant difference in median MFI among HLA-A, HLA-B and HLA-C (figure 6). DSAs against class II (DR, DQ, and DP) were observed in 16 (80%) sera at the first biopsy, 20 (91%) at the second biopsy and 13 (100%) at the third biopsy with a median MFI of 5290 ( ), 4038 ( ), and 3364 ( ), respectively. HLA-DQ was observed in the majority of sera, followed by HLA-DR. HLA-DQ was present in 14 (70%) at the first biopsy, 16 (73%) at the second biopsy and 12 (92%) at the third biopsy. HLA-DR was present in 5 (25%) at the first biopsy, 11 (50%) at the second biopsy and 5 (38%) at the third biopsy. HLA-DP was found only in 2 (9%) of the second biopsy with the lowest median MFI (1672). There was a 26

35 significant difference between the median MFI of HLA-DR vs. HLA-DQ, HLA-DQ vs. HLA- DP, and HLA-DR vs. HLA-DP (p=0.01, p=0.006 and p=0.4, respectively) (figure 6). 3.3 Frequency of IgG Subclasses, their patterns and biological groups All 4 IgG subclasses were detected in most of the analyzed sera. IgG1, IgG2, IgG3 and IgG4 were all observed in class I and class II. IgG1 was the predominant IgG subclass, followed by IgG3, IgG2 and then, IgG4 (figure 7). Among all 4 IgG subclasses, IgG1 anti-hla Abs was the predominant IgG subclass (49.4%), followed by IgG3 (24.7%), IgG2 (16%), and finally, IgG4 (9.9%). IgG1 subclass was the most common IgG subclass among HLA-A, B and C, and HLA- DR and DQ antibodies while, HLA-DP included both complement-fixing IgG subclasses IgG1 and IgG3 subclasses. The investigated 39 patients had in total 117 DSAs. There were 10 different combinations of IgG subclasses observed (figure 8A). According to their complement-fixing ability, IgG subclasses were further divided into three major groups: (1) strong complement-fixing group (IgG1 and IgG3); (2) weak or non-complement-fixing group (IgG2 and IgG4); and (3) multiple IgG subclasses (mixture of strong, weak and noncomplement-fixing IgG subclasses). The majority of DSAs appeared in the strong complementfixing group (58%), followed by the mixture group (23%) and the weak/non-complement-fixing group (6%) (figure 8B). Thirteen percent (13%) of DSAs, which were detected by the standard SABs assay using the generic IgG Ab, were negative in all IgG subclasses. These DSAs revealed MFI by the standard SABs assay (median, 1596; range, ). 27

36 3.4 Determinants for C1q Positivity We correlated different DSA characteristics (HLA class, timing of appearance, DSA MFI and IgG subclasses) with C1q positivity (Table 4). The investigated 39 patients with de novo DSAs had a total of 55 sera obtained at the time of the biopsy, reaching an average of 1.4 sera per patient. Among these sera, 49/55 sera found with DSAs against class II HLA and 19 DSAs against class I HLA. Of those, 28/49 (57%) class II DSAs and 9/19 (47%) class I DSAs were positive for C1q (p= 0.058). We analyzed the time of appearance of anti-hla Abs in correlation with C1q positivity: early, within one-year posttransplant (n=13), or late, after one-year posttransplant (n=42). Only 8/13 early DSAs and 22/42 late DSAs were C1q positive. Whether the anti-hla Abs were developed early or late posttransplant failed to show any association with C1q positivity (p= 0.75) (Table 4). Beside specificity of anti-hla Abs and time of anti-hla Abs appearance, the MFI of a given DSA is another characteristic that could impact C1q positivity. The correlation of the overall anti-hla Abs MFI was strongly correlated with C1q positivity as shown in figure 9 (p= 0.01). The same pattern was found when we analyzed the MFI in each biopsy and among class I and class II anti-hla Abs (p= and 0.001, respectively) (figure 10). Additionally, analyzing the DSA MFI and C1q positivity among the first, second and third biopsies showed a significant correlation (p= 0.006, p= 0.002, and p= 0.001, respectively) (figure 11). In summary, out of the different DSA characteristics, only DSA MFI demonstrated a strong correlation with C1q positivity. 28

37 When we correlated IgG subclasses with C1q positivity, the complement-fixing IgG1 was the predominant IgG subclass. Table 3.5 shows the correlation of IgG subclasses of class I anti-hla Abs with C1q positivity. Among all four IgG subclasses, only IgG1 subclass showed a significant correlation with C1q positivity in all three biopsies (p= 0.009, p= 0.01, p=0.01, respectively). None of the other IgG subclasses demonstrated any correlation with C1q positivity (Table 5). We aimed to analyze the presence of both complement-fixing (IgG1 and IgG3) and both noncomplement-fixing (IgG2 and IgG4) IgG subclasses together at the same time. Neither the complement-fixing IgG subclasses nor the non-complement-fixing IgG subclasses showed a correlation with C1q positivity in all three biopsies (Table 5). Collectively, when we correlated IgG subclasses of class II anti-hla Abs with C1q positivity, the results were similar to the results obtained when we correlated the IgG subclasses of class I anti-hla Abs (Table 6). IgG1 subclass showed a significant correlation with C1q positivity in the first biopsy only. In the second and third biopsy, although the majority of IgG1 positive DSAs were able to produce C1q positive, it failed to reach a significant correlation (p= 0.07) (Table 6). In general, IgG1 subclass is the predominant IgG subclass and is the strongest complement-fixing Ab that increases C1q positivity. Similar to class I, when we combined IgG subclasses according to their complement-fixing ability, class II anti-hla Abs failed to show any correlation with C1q positivity (Table 6). However, analyzing class II sera with multiple IgG subclasses showed a correlation only in the second biopsy (p= 0.03) (Table 6). 29

38 Next, we analyzed sera that have only one IgG subclass with C1q positivity to eliminate the possibility of synergetic affect. We correlated the presence of the complement-fixing subclasses IgG1 or IgG3 or both at the same time with C1q positivity. The presence of IgG1 alone and the presence of both IgG1 and IgG3 together showed an increase in class I and class II C1q positivity. However, it failed to reach a significant correlation (p= 0.6 and 1; p= 0.1 and 0.6, respectively). Interestingly, the strong complement-fixing IgG3 did not show any correlation with C1q positivity and this could be due the limited number of detected IgG subclasses (p= 1 and 0.1) (figure 12). Additionally, we correlated the presence of the weak/non-complement-fixing IgG2 or IgG4 or both in the absence of IgG1 and IgG3. Unfortunately, no sera with C1q positive class I and class II DSAs were found with IgG2, IgG4 or both in the absence of IgG1 and IgG3 (figure 12). The C1q positivity in class II was primarily due to isolated IgG1, both IgG1 and IgG3, and minimally isolated IgG3. However, in class I, C1q positivity was primarily due to the presence of IgG1 and the presence of both IgG1 and IgG3 together (figure 12B). Among other IgG subclasses, IgG1 subclass was the predominant IgG subclass and showed significant correlation with C1q positivity. When we correlated presence of IgG1 with C1q MFI, it showed a strong correlation (p= 0.009) (figure 13). Median MFI for IgG1 positive was (range ). However, the median MFI for IgG1 negative was 0. There was only one DSA with IgG1 negative showed a C1q MFI of Interestingly, this DSA was negative in all IgG subclasses. 30

39 Chapter Four: Discussion The introduction of new sensitive assays for the detection of anti-hla Abs has had a remarkable impact on the decision-making process. So far, many concerns have been expressed because no consensus exists on the clinical relevance of DSA detectable using Luminex assay. This raises the question of how to define clinically relevant DSA. Relying solely on the sheer presence of DSA obtained from Luminex assay is not sufficient and needs reassessment. Our existing knowledge about the different characteristics that define relevant DSAs is still limited. The main aim of our study is to generate a detailed profile of DSA characteristics of renal allograft recipients in a well-characterized single center cohort. Complement-fixing ability of a given DSA is determined by the C1q-binding ability and the IgG subclass composition. In this study, we evaluated the correlation of different anti-hla Abs characteristics (HLA class, timing of DSA, DSA strength and IgG subclasses) with C1q positivity. The major finding was that among all other DSA characteristics, the strength of DSA represented by MFI and the presence of IgG1 subclass strongly correlated with C1q positivity. To the best of our knowledge, this is the first study generating detailed profile of de novo posttransplant DSA characteristics in a reasonably large, clinically and pathologically welldefined cohort of renal transplant patients. In our study, we examined the timing of appearance of de novo DSA whether it was developed within one-year or after one-year posttransplantation. No previous studies have examined the association between the timing of DSA appearance and the C1q positivity. Patients who developed DSAs within a year after transplantation were associated with poor graft 31

40 outcomes(122). Significantly, late biopsies were associated with microcirculation damage and graft loss(123, 124). Based on our observation, the timing of DSA appearance did not correlate with C1q positivity. The class I anti-hla Abs are more associated with early graft rejection, whereas class II anti- HLA Abs are more associated with slow progression to graft loss(114). Additionally, C1qbinding ability was shown to be correlated with early AMR(75). However, the study by Loupe et al. did not provide any correlation of HLA class with C1q positivity and their follow up time was only up to one-year after transplantation(75). It was suggested that correlating C1q positivity in longer follow-up time could capture the role of both anti-hla class I and class II Abs(125, 126). However, when we correlated HLA class and the presence of C1q positive de novo DSA, we did not find any association. Therefore, analyzing the C1q positivity even in a longer follow-up time will not add any value to the clinical relevance of DSA in correlation with poor graft outcomes. Even though, we did not find a correlation between C1q positivity and overall HLA specificity, HLA-DQ increased the C1q positivity. In accordance with previous studies, among other HLA class II specificities, DSAs against HLA-DQ were observed in the majority of sera (76%) with a higher median MFI (5622)( ). The presence of DSA against HLA-DQ was reported to be associated with an increased risk of allograft loss( ). In our study, the majority of anti- HLA-DQ DSAs was IgG1 subclass (57%) and IgG3 subclass (22%). The findings from our study were in accordance to a previous study was published reporting that the majority of patients with IgG1 and IgG3 anti-hla-dq DSAs developed graft rejection and loss(130). Due to the predominance of anti-hla-dq, the HLA specificity could be an important DSA characteristic that should be considered. 32

41 Our observation was consistent with previous studies that reported a strong association of the DSA MFI with C1q positivity(29, 85). Clearly, this means that the high MFI of DSA will increase the likelihood of detecting Abs with C1q-binding ability. This could be explained by the physic-chemical properties of C1q, which requires a minimal density of IgG to bind(133). The MFI represents the binding of anti-hla Abs to the bead, which is related to the amount and avidity of the Ab. However, relying only on binding cannot reflect the actual density of the anti-hla Ab bound to the bead(101). The ability to activate complement depends largely on several additional parameters (e.g., synergistic effect of several antibodies targeting different epitopes on the same HLA molecule, the conformational changes of certain HLA epitopes and the strength of binding to the target HLA molecule) (101, 120, 134). This might explain why some DSAs with lower MFI did not correlate with C1q positivity. Additionally, it can explain why some studies didn t find a correlation between C1q assay and CDC, while others did (135, 136). An alternative explanation could be the limited sensitivity of C1q assay, which detects the C1q binding capacity only when Abs are present in high titre. C1q negativity, however, does not indicate the detected DSA is not-clinically relevant. Indeed, 95% of C1q negative DSAs were complement-fixing IgG1 and IgG3. This could mean that these DSAs were found in low titers and have not reached the capacity to bind C1q. Therefore, considering these DSAs as not clinically relevant could be misleading as the posttransplant memory response and the antigenicity of the target HLA alloepitope could significantly increase the amount of DSA reaching the capacity to induce C1q positivity(137, 138). In line with previous studies, the complement-fixing IgG1was the predominant IgG subclass(81). The IgG1 subclass is a strong complement-fixing Ab and this was best demonstrated by the 33

42 strong correlation with C1q positivity and its MFI. This predominance of the IgG1 subclass could be another factor that contributed to the strong correlation with C1q positivity. Therefore, including the IgG subclass information could marginally improve the ability to predict C1qbindiong ability of a given DSA. As expected, weak and non-complement-fixing subclasses, isolated IgG2, IgG4, or both, did not show correlation with C1q positivity. None of the weak/non-complement-fixing group was able to produce C1q positivity. This could be due to the low prevalence of weak/non-complementfixing IgG2 and IgG4 (i.e. 6%). Complement-fixing IgG1 is the first IgG subclass secreted in response to Ag stimulation followed by IgG3 and later, by the weak/non-complement-fixing IgG2 and IgG4. The change in IgG profile is dynamic and might occur during the transplantation period. The secretion of IgG2 and IgG4 could be a result of an immunoglobulin class switching process from previously induced IgG1and IgG3 class at the time of transplantation. This can explain the low prevalence of weak/non-complement-fixing IgG subclasses. Although there was no correlation of weak/non-complement-fixing IgG subclasses with C1q positivity, the role of weak/non-complement fixing IgG2 and IgG4 must not be completely ignored. The weak/non-complement-fixing IgG subclasses have been correlated with the development of early graft failure( ). Furthermore, the presence or absence of each subclass indicates the degree of heavy chain class switching. As class switching is T-cell dependent, progression from IgG3 to IgG4 suggests enhanced T cell activation while a subclass limited response implies limited T cell activation. The tendency for multiple subclasses to be stimulated by a graft may be due to a great degree of specific T cell activation. Therefore, the incidence of clinical CMR, together with AMR, should be expected(101, 139). 34

43 We observed that thirteen percent (13%) of DSAs, which were detected by the standard SABs assay using the generic IgG Ab, were negative in all IgG subclasses. Generally, these DSAs revealed low MFI values by the standard SABs assay (median, 1596; range, ). This suggests a lower sensitivity of the IgG subclass assay. Additionally, this could be explained by the different binding properties of the reporter antibody, which target different epitopes on the IgG molecule. It is important to mention that IgG2, IgG3 and IgG4 revealed very low MFIs compared to those obtained by the conventional SAB assay. Therefore, we determined the positivity of each IgG subclass and every individual bead by using four negative control sera. Another observation in our study was that 23% of our patients composed of multiple IgG subclasses (figure 8). However, the correlation of the presence of multiple IgG subclasses with C1q positivity was significant only in one biopsy with class II DSA (Table 3.6). The significance is impeded by the small number of sera containing multiple IgG subclasses. It was suggested that complement-fixing and weak/non-complement-fixing IgG subclasses might work synergistically to induce C1q positivity(140). The diversity of the IgG subclass patterns provides evidence that the humoral immune response to foreign HLA antigen often involves the production of more than one IgG subclass (figure 8A). Due to this heterogeneity among different patients, identifying a significant IgG subclass pattern in correlation to C1q positivity might be difficult. Our study has a few limitations. The first limitation was that the study was not designed to provide kinetics of the capacity of anti-hla Abs to bind complement or the effect of treatment on these Abs. This would require further investigations. Therefore, it is difficult to assess Abs that have the ability to bind and activate complement in comparison to Abs that can only bind without actually activating complement cascade(95). The second limitation is the sample size, 35

44 which is not large enough to explore the clinical impact of posttransplant isolated weak/noncomplement-fixing DSAs. Finally, due to the lower prevalence, we couldn t analyze the correlation of HLA-C and HLA-non-DRB1 (DRB3,4,5), which have relatively lower expression on cells, with C1q positivity. The findings of our study may have implications on identifying clinically relevant DSAs through demining the functional relevance of DSAs rather relying on their biological properties. Analyzing the IgG subclass distribution and the DSA MFI could provide a better predictive for clinically relevant DSAs. Further studies are needed to investigate whether a specific change of IgG subclasses from pre- to posttransplant correlates with C1q positivity, and consequently, predictive of different graft outcomes. The dynamic of pre- and posttransplant IgG subclasses can explain the discrepancies in the correlation between C1q and CDC assays and C4d (75, 95, 101). Additionally, the correlation of the predominant IgG1 subclass and C1q positivity with different graft outcomes should be analyzed. The findings from this study should be further verified in a larger cohort. Our observations can be best illustrated by two cases of renal transplant patients with de novo DSAs. The first case was a female patient with de novo DSA and had HLA mismatched DR10 and DQ9. Persistent DSA was positive using the conventional SAB assay (MFI, 7025) and C1q assay (MFI, 25973). Analyzing sera with the IgG subclass assay detected the presence of an isolated IgG1 subclass. The patient had difficulties maintaining the graft and features were suggestive of AMR and C4d positivity during the second biopsy. The second case was a female patient with de novo DSA and had HLA mismatched at A3 and B62. Although, DSA was positive using conventional SAB assay, C1q was negative. The IgG subclass assay was negative 36

45 with all IgG subclasses. The biopsies showed no signs of rejection with C4d negative (Figure 14). 37

46 Chapter Five: Conclusion In conclusion, we evaluated different DSA characteristics after transplantation in kidney transplant recipients. Based on our observation, among different DSA characteristics, DSA MFI and the presence of the complement-fixing IgG1 subclass after transplantation were strongly correlated with C1q positivity. None of the other DSA characteristics were correlated with C1q positivity. The complement-fixing IgG1 subclass was associated with the majority of C1q positive cases. It could be due its complement-fixing ability and its predominance among other IgG subclasses. Therefore, the presence of IgG1 subclass and /or high titre of IgG are potential DSA characteristics that could provide substantial value beyond the standard IgG SAB assay for determining clinically relevant DSAs. Further studies are needed to investigate the clinical relevance of high MFI and the presence of IgG1 subclass in improving the utility of the C1q assay in predicting different graft outcomes in renal transplant recipients. 38

47 Tables and Figures Figure 1: Schematic representations of patient cohorts in our study 39

48 Figure 2: Illustration of the Luminex assay. The figure shows the HLA-bound bead attached to anti-hla Ab from patient serum. The bead will be analyzed using 2 lasers. The first laser to detect the bead is being analyzed. The second laser will measure the amount of anti-hla Abs attached to the analyzed bead. 40

49 Patient s Serum PE-conjugated anti-c1q Ab Phycoerythrin (PE) Figure 3: Illustration of the C1q assay This figure explains the C1q assay that detects anti-hla Abs with C1q-binding ability. Briefly, patient serum is mixed with HLA-coated beads and spiked with purified human C1q. then, add the PE-conjugated anti-c1q Ab, wash and analyze using the Luminex assay. 41

50 Figure 4: Illustration of the modified SAB assay to analyze IgG subclasses Briefly, it uses the same principle as the conventional method to analyze anti-hla Abs. the only modification is the substitution of the secondary PE-human IgG with diluted PE-conjugated mouse monoclonal IgG1, IgG2, IgG3 and IgG4 subclass (concentration: anti-igg1, IgG3, and IgG4 0.5µg/mL; anti-igg µg/ml). Finally, data were acquired on the Luminex100 analyzer. 42

51 Table 1: Demographical and Clinical characteristics of kidney allograft recipients Distribution and Range Complete HLA Antibody negative, HLA antibody p value cohort, n n (%) positive, n (%) Total number of subjects (65%) 110 (35%) -- Recipient Recipient age, median (range) 47 (9-75) 50 (16-66) 47 (9-75) 0.24 Recipient gender Male 194 (62%) 127 (62%) 67 (61%) Female 121 (38%) 78 (38%) 43 (39%) 0.91 Previous Transplants Yes 30 (10%) 1 (0.5%) 30 (27%) No 285 (90%) 204 (99.5%) 80 (73%) 0.001* Donor Donor age, median (range) 43 (12-76) 43 (19-57) 45 (12-76) 0.88 Donor Gender Male 152 (48%) 86 (42%) 66 (60%) Female 163 (52%) 119 (58%) 44 (40%) 0.003* Pre-transplant variables Graft type Kidney 314 (99%) 191 (93%) 98 (89%) Kidney and Pancreas/heart/Liver 26 (8%) 14 (7%) 12 (11%) 0.28 Donor Type CAD 145 (46%) 97 (47%) 51 (46%) Living 170 (54%) 108 (53%) 59 (54%) 0.91 HLA Mismatch 0 antigen mismatched 37 (12%) 30 (15%) 7 (6%) 0.04* 1-2 antigen mismatched 61 (19%) 45 (22%) 16 (15%) 0.14 >2 antigen mismatched 217 (69%) 130 (63%) 87 (79%) 0.005* Induction Therapy ATG/IVIG 47 (15%) 37 (18%) 10 (9%) 0.045* Steroid 110 (35%) 49 (24%) 61 (55%) 0.001* Basiliximab 33 (10%) 27 (13%) 6 (5%) 0.035* Daclizumab 63 (20%) 43 (21%) 20 (18%) 0.65 None 137 (43%) 116 (57%) 21 (19%) 0.001* *Significant p-value (p<0.05) 43

52 Table 2: Characteristics of patients with anti-hla Abs (Distribution and range) HLA characteristics Distribution n** (%) P value Total of patients with HLA Antibodies 89 Donor Specific Antibodies (DSA) with DSA 47 (53%) without DSA 42 (47%) DSA Class Class I 21 (45%) Class II 38 (81%) Both 14 (29%) Appearance of DSA Preexisting 3 (6%) de novo 39 (83%) Both 5 (11%) Timing of de novo DSA Early (<1year) 9 (23%) Late ( 1year) 30 (77%) Mean Fluorescence Intensity (MFI) class I median MFI (range) 2285 ( ) class II median MFI (range) 4397 ( ) *Significant p-value (p<0.05) ** based on number of patients * * * 44

53 Table 3: Characteristics of anti-hla Abs (among the 3 biopsies) Biopsy 1 (n=20) Biopsy 2 (n=22) Biopsy 3 (n=13) HLA antibody characteristics N (%) Median (Range) MFI N (%) Median (Range) MFI N (%) Median (Range) MFI Class I DSA 8 (40%) 3015 ( ) 7 (32%) 2285 ( ) 4 (31%) 2348 ( ) A 4 (20%) 4351 ( ) 4 (18%) 1693 ( ) 2 (15%) 6406 ( ) B 5 (25%) 4579 ( ) 4 (18%) 3144 ( ) 3 (23%) 2156 ( ) Cw 1 (5%) (9%) 2072 ( ) 1 (8%) 2653 Class II DSA 16 (80%) 5290 ( ) 20 (91%) 4038 ( ) 13 (100%) 3364 ( ) DR 5 (25%) 5061 ( ) 11 (50%) 1984 ( ) 5 (38%) 3756 ( ) DQ 14 (70%) 5622 ( ) 16 (73%) 5438 ( ) 12 (92%) 4225 ( ) DP (9%) 1672 ( ) 0 (0%) 0 45

54 Table 4: Correlation of DSA characteristics with C1q positivity Total Number of sera (n=55) Number of C1q+ (%) p-value DSA specificity Class I (47%) Class II (57%) Timing Late (52%) Early (62%)

55 Table 5: Correlation of class I IgG subclasses with C1q positivity Biopsy 1 Biopsy 2 Biopsy 3 Total Number Number of C1q+ p value Total Numbe r Number of C1q+ p value Total Number Number of C1q+ p value IgG1 positivity IgG (60%) 0.009* 6 3 (50%) 0.01* 4 3 (30%) 0.01* IgG (0%) 16 0 (0%) 9 0 (0%) IgG2 positivity IgG (50%) (100%) (10%) 0.2 IgG (11%) 21 2 (10%) 12 2 (20%) IgG3 positivity IgG (50%) (100%) (10%) 0.2 IgG (11%) 21 2 (10%) 12 2 (20%) IgG4 positivity IgG (0%) ns 0 0 (0%) ns 0 0 (0%) ns IgG (15%) 22 3 (14%) 13 3 (30%) IgG1 and 3 IgG1/ (12%) (50%) (0%) 1 IgG1/ (18%) 18 1 (6%) 10 2 (17%) IgG 2 and 4 IgG 2/ (0%) ns 0 0 (0%) ns 0 0 (0%) ns IgG 2/ (16%) 21 3 (14%) 12 2 (17%) multiple IgG subclasses (22%) (22%) (0%) (9%) 13 1 (8%) 9 3 (33%) *Significant p-value (p<0.05) 47

56 Table 6: Correlation of class II IgG subclasses with C1q positivity Biopsy 1 Biopsy 2 Biopsy 3 Total Number Number C1q+ of p value Total Number Number of C1q+ p value Total Number Number of C1q+ p value IgG1 positivity IgG (100%) 0.026* (73%) (67%) 0.07 IgG1-9 2 (22%) 7 2 (29%) 4 0 (0%) IgG2 positivity IgG (50%) (80%) (67%) 0.5 IgG (44%) 17 9 (53%) 10 4 (40%) IgG3 positivity IgG (64%) (67%) (75%) 0.2 IgG3-9 2 (22%) 16 9 (56%) 9 3 (33%) IgG4 positivity IgG (100%) (100%) (100%) 0.1 IgG (42%) (53%) 11 4 (36%) IgG1 and 3 IgG1/ (63%) (75%) (100%) 0.1 IgG1/ (36%) (56%) 10 3 (30%) IgG 2 and 4 IgG 2/ (0%) ns 0 0 (0%) ns 0 0 (0%) ns IgG 2/ (47%) (62%) 12 5 (42%) Multiple IgG subclasses (56%) (89%) 0.031* 3 2 (67%) (36%) 13 5 (38%) 9 3 (33%) *Significant p-value (p<0.05) 48

57 Figure 5: Correlation of DSA MFI with overall Class I and Class II anti-hla Abs 49

58 Figure 6: Correlation of DSA MFI with HLA-A, B, C, and HLA-DR, DQ, DP 50

59 Figure 7: IgG subclass distribution among class I and class II anti-hla antibodies 51

60 Figure 8: IgG subclass combination and pattern. (A) The different combinations of DSA IgG subclass. Ten different combinations were observed in the 39 patients with DSA. (B) The different patterns and distributions of IgG subclasses based on their biological features: (i) Complement-fixing IgG subclasses (IgG1 and/or IgG3); (ii) weak/non-complement-fixing IgG subclasses (IgG2 and/or/igg4); and (iii) mixture group (IgG1 and IgG3; and IgG2 and IgG4). 52

61 Figure 9: Correlation of DSA MFI with C1q positivity 53

62 Figure 10: Correlation of DSA MFI of class I and class II with C1q positivity The correlation of DSA MFI with C1q positivity in [A] class I (p= 0.007); and [B] class II (p= 0.001). 54

63 Figure 11: Correlation of DSA MFI with C1q positivity in each biopsy. The figure demonstrates the correlation of DSA MFI values with C1q positivity in [A] first biopsy (p= 0.006); [B] second biopsy (p= 0.002); and [C] third biopsy (p= 0.001). 55

64 Figure 12: Correlation of IgG subclasses with C1q positivity. This figure demonstrates the presence of the complement-fixing subclasses isolated IgG1 or IgG3 or both at the same time with C1q positivity in [A] class I; and [B] class II. The bi-chart on the right shows the IgG subclasses that correlate with C1q positivity in both class I and class II. 56

65 Figure 13: Correlation of C1q MFI with IgG1 subclass (the predominant IgG subclass that strongly correlates with C1q positivity) 57

66 Patient One: Patient Two: Figure 14: Histograms of IgG SAB and C1q assays. These histograms represent the results obtained from the IgG SAB and C1q assays for the 2 patients in [A] first biopsy, left panels; and [B] second biopsy, right panels. 58