Skip to main content
PMC home page
Oncotarget logoLink to Oncotarget
. 2017 Oct 19;8(59):100079–100094. doi: 10.18632/oncotarget.21941

Associations between HVEM/LIGHT/BTLA/CD160 polymorphisms and the occurrence of antibody-mediate rejection in renal transplant recipients

Zijie Wang 1,*, Ke Wang 1,*, Haiwei Yang 1,*, Zhijian Han 1, Jun Tao 1, Hao Chen 1, Yuqiu Ge 2, Miao Guo 3, Chuanjian Suo 1, Ji-Fu Wei 3, Ruoyun Tan 1, Min Gu 1
PMCID: PMC5725004  PMID: 29245962

Abstract

Antibody-mediated rejection (ABMR) is a serious complications that can occur following renal transplantation. The production of donor-specific antibodies by the humoral immune response can trigger costimulatory signals, which are crucial in activating immune cells, and therefore, playing a potential role in ABMR. To investigate the role of HVEM/LIGHT/BTLA/CD160 polymorphisms in ABMR, we retrospectively analyzed 200 renal transplant recipients. We adopted next-generation sequencing (NGS) to identify HVEM/LIGHT/BTLA/CD160 single-nucleotide polymorphisms (SNPs) in the genotypes of these patients. We divided the patients into two groups: those with ABMR and those who were stable. We adopted multiple models and performed regression analysis after adjusting for multiple confounding variables, to determine the correlation between the SNPs and ABMR. We obtained 41 high-quality SNPs readouts. However, we did not observe any significant association between these polymorphisms and the pathogenesis of ABMR in any of the models.Nevertheless, since there is evidence suggesting the involvement of costimulatory signals in graft rejection, further research should be conducted to better understand how genetic polymorphisms may be involved in ABMR.

Keywords: kidney transplantation, antibody-mediated rejection, costimulatory signals, single-nucleotide polymorphisms next generation sequencing

INTRODUCTION

Kidney transplantation is an optimal choice for patients with end-stage renal disease. It is considered superior to dialysis, due to the reduced complications, lower mortality rates and improvement to patient quality of life [1]. However, antibody-mediated rejection (ABMR), also termed as humoral rejection, poses a substantial threat to post-transplant patients and inevitably leads to allograft loss [2]. The precise pathogenesis of ABMR remains unclear. Generally, ABMR is closely associated with antibodies ligating to donor antigens, which mediate allograft damage via activation of the complement system or cytotoxic cells [3, 4]. These antibodies are directed against human leukocyte antigens (HLAs) and major histocompatibility complex (MHC) class I and II antigens, termed as donor-specific antibodies (DSAs) [5]. Meanwhile, they can also be directed against other stimulators, such as minor histocompatibility antigens, ABO group antigens and endothelia cell antigens [6, 7]. Although ABMR occurs in less than 10% of renal transplant recipients, 30% of them ultimately suffer from graft loss [3]. As a result, ABMR impacts the long-term graft survival in kidney transplantation and is one of the most challenging clinical events following renal transplant [8].

Activation of both T and B cells after transplantation is a tightly regulated process consisting of multiple distinct but interrelated signals [9]. Secondary signals, also named costimulatory signals, play an important role in activation and inhibition of immune cells. Recently, much attention has been placed on HVEM (herpes virus entry mediator) and LIGHT (homologous to lymphotoxin, which exhibit inducible expression and compete with HSV glycoprotein D for binding to HVEM, a receptor expressed on T lymphocytes), BTLA (B and T lymphocyte attenuator) and CD160 costimulatory pathways. HVEM and LIGHT belong to the TNFR superfamily, while BTLA and CD160 are members of the Ig superfamily. The functions and structures of these costimulatory molecules can be divided into positive and negative costimulatory pathways [10]. The binding of HVEM on T cells to membrane-bound LIGHT delivers positive signals through HVEM that promotes T-cell survival, while the conjugation of HVEM to CD160/BTLA on T cells delivers a coinhibitory signal that deactivates T-cells [11–14]. There is substantial evidence that suggests disorder of the HVEM/LIGHT/BTLA/CD160 signaling system is essential in the development of autoimmune diseases and allograft rejection [15, 16]. Costimulatory signals are widely investigated in T cell mediated immunity. However, regarding humoral immunity, the role of the HVEM/LIGHT/BTLA/CD160 costimulatory system in B cell activation and allograft transplantation remains unclear. Studies suggest that HVEM is expressed at high levels in all peripheral blood B cells, while at low levels in germinal center (GC) B lymphocytes, which may be activated since GC is where dendritic cells (DC), T cells and B cells interact [17]. It is postulated that LIGHT expression on DC and T cells causes HVEM engagement on naïve B cells, which costimulates B cell proliferation and Ig secretion, as a result, enhancing humoral immune responses [13]. It has also been suggested that de novo DSAs is needed in the cognate interaction between CD4+ T follicular helper cells (Tfh), which are primed by donor alloantigens and presented as host antigen presenting cells and B lymphocytes that recognize soluble and membrane-bound alloantigens. This suggests the possibility that HVEM/LIGHT/BTLA/CD160 participates in the modulation of DSAs and humoral immune response [18–20].

Until now, the association between HVEM/LIGHT/BTLA/CD160 gene polymorphisms and ABMR in renal transplant recipients has remained unexplored. Here, we evaluated the association between a total 41 single nucleotide polymorphisms (SNPs) of HVEM/LIGHT/BTLA/CD160 genes and occurrence of ABMR and investigated its role in the formation of DSAs and pathogenesis of ABMR in renal transplantation recipients.

RESULTS

Demographic and clinical characteristics

The demographic characteristics of the renal transplant recipients are shown in Table 1. This study included 200 patients from the Chinese Han population: 69 renal transplant recipients had ABMR (40 men and 29 women), while 131 were considered stable (82 men and 49 women). The immunosuppressive protocols administered in stable and ABMR groups are also presented. Among patients in ABMR groups, we further collected ABMR-related clinical information, such as C4d scoring, histological classifications and the level of serum DSAs, and reported them in Table 1. We did not observe any significant differences (P>0.05) in age, sex, donor type and immunosuppressive protocol between the stable and ABMR group.

Table 1. Basic characteristics of patients included in our study.

Characteristics Stable group ABMR group P value
Case number 131 69 NS
Age (years; mean ± SD) 38.56 ± 1.40 38.92 ± 1.02 NS
Male (%) 62.60 57.97 NS
PRA (%) 0 0 NS
Donor type NS
 Living-related 16 7
 DCD 115 62
Immunosuppressive protocol NS
Pred + MMF + CsA 62 26
Pred + MMF + TAC 60 35
Pred + MMF + CsA + SIR 5 6
Pred + MMF + TAC + SIR 4 2
Type of ABMR*
 Acute ABMR - 23
 Chronic active ABMR - 46
Grade of morphologic tissue injury*
 Grade I - 25
 Grade II - 34
 Grade III - 10
C4d Scroing by IF*
 C4d1 - 5
 C4d2 - 17
 C4d3 - 47
Criculating DSAs (MFI, mean ± SD)
 Class I - 1368.12 ± 550.96
 Class II - 1191.23 ± 655.88
Open in a new tab

Abbreviations: ABMR, antibody-mediated rejection; NS, not significant; SD, standard deviation; PRA, panel reactive antibody; Pred, prednisone; MMF, Mycophenolate Mofetil; CsA, Cyclosporin A; TAC, tacrolimus; SIR, sirolimus; IF, immunofluorescence; DSA, donor-specific antibody.

*The classification of ABMR are in accordance with Banff 2007 criteria.

Association of HVEM/ LIGHT/ BTLA/ CD160 SNPs with ABMR

Previous investigations into HVEM/LIGHT/BTLA SNPs have been limited to rs2234163, rs2234165 and rs2234167 for HVEM SNPs, rs344560 and rs2291667 for LIGHT SNPs, and rs9288952, rs2171513 and rs76844316 for BTLA SNPs. However, in our study, we screened the genetic distribution of 41 HVEM/LIGHT/BTLA/CD160 SNPs, which we show in Table 2. All genotype frequencies in the control group conformed to the Hardy-Weinberg equilibrium (HWE) (P>0.05; Table 2). In logistic regression analysis and corrected for age, sex, and immunosuppressive protocols (Table 3), we did not find any significant associations (P<0.05) between the occurrence of ABMR and polymorphisms in any of the 41 HVEM/LIGHT/BTLA/CD160 SNPs among the different models.

Table 2. Genetic distributions of HVEM/LIGHT/BTLA/CD160 polymorphisms between the ABMR and stable group.

Genotype Chromosome Position Stable group (n=131) ABMR group (n=69) HWE for the stable group
Χ2 P value
HVEM
rs4870 Chr1 2488153 0.74 0.69
AA 38 24
AG 70 34
GG 23 11
rs2234158 Chr1 2489200 <0.01 0.99
CC 103 69
CT 1 0
rs376994775 Chr1 2489746 <0.01 0.99
CC 130 69
CT 1 0
rs754021885 Chr1 2489961 <0.01 0.99
CC 130 68
CT 1 1
rs572222644 Chr1 2491163 <0.01 0.99
CC 131 68
CT 0 1
rs2234161 Chr1 2491205 <0.01 0.99
CC 36 20
CT 65 35
TT 30 14
rs2234162 Chr1 2491305 <0.01 0.99
CC 130 69
CT 1 0
rs2234163 Chr1 2491306 0.23 0.89
GG 123 64
GA 8 5
rs2234165 Chr1 2492276 0.39 0.82
GG 120 63
GA 11 6
rs575127151 Chr1 2492935 <0.01 0.99
GG 131 68
GA 0 1
rs375010878 Chr1 2493087 <0.01 0.99
CC 130 68
CT 1 1
rs2234167 Chr1 2494330 0.16 0.92
GG 123 66
GA 8 3
rs8725 Chr1 2494785 0.01 0.99
GG 37 19
GA 64 35
AA 30 15
rs376495994 Chr1 2496492 <0.01 0.99
GG 131 68
GA 0 1
rs186536172 Chr1 2496521 <0.01 0.99
CC 130 68
CT 1 1
rs7544646 Chr1 2496649 0.38 0.83
CC 47 19
CG 59 35
GG 25 15
rs7515633 Chr1 2496653 0.18 0.91
AA 43 19
AG 61 35
GG 27 15
LIGHT
rs344560 Chr19 6665020 0.55 0.76
TC 13 7
CC 118 62
rs772372888 Chr19 6665098 <0.01 0.99
CC 130 69
CT 1 0
rs61761328 Chr19 6665099 0.02 0.99
GG 127 69
GA 4 0
rs183886666 Chr19 6665336 <0.01 0.99
GG 131 68
GA 0 1
rs8101047 Chr19 6665481 1.24 0.54
AA 4 0
AG 38 25
GG 89 44
rs542346038 Chr19 6667076 <0.01 0.99
GG 130 69
GA 1 0
rs2291668 Chr19 6669934 4.37 0.11
GG 63 26
GA 57 38
AA 11 5
rs2291667 Chr19 6669986 0.05 0.98
GG 128 66
GA 3 3
rs748673655 Chr19 6669992 <0.01 0.99
CC 130 69
CT 1 0
rs344558 Chr19 6670253 0.04 0.98
AA 114 56
AC 17 12
CC 0 1
rs563748272 Chr19 6677752 <0.01 0.99
GG 130 69
GT 1 0
BTLA
rs2971205 Chr3 112184772 <0.01 0.99
AA 130 69
AG 1 0
rs2171513 Chr3 112184927 3.07 0.22
AA 15 3
AG 42 25
GG 74 41
rs770019001 Chr3 112184932 <0.01 0.99
CC 130 69
CG 1 0
rs9288952 Chr3 112185025 3.87 0.14
GG 24 5
GA 48 29
AA 59 35
rs76844316 Chr3 112188609 0.02 0.99
TT 110 61
TG 20 8
GG 1 0
rs16859629 Chr3 112190380 <0.01 0.99
TT 130 69
TC 1 0
rs9851198 Chr3 117448419 <0.01 0.99
GG 131 68
AA 0 1
CD160
rs2231375 Chr1 145696694 0.67 0.71
GG 96 55
GA 31 13
AA 4 1
rs3766526 Chr1 145698637 <0.01 0.99
GG 131 68
GA 0 1
rs368476773 Chr1 145698914 <0.01 0.99
CC 131 68
CT 0 1
rs193141418 Chr1 145698935 0.06 0.97
CC 125 68
CT 6 1
rs587741068 Chr1 145703913 <0.01 0.99
AA 130 69
AG 1 0
rs587727931 Chr1 145704474 <0.01 0.99
GG 130 69
GA 1 0
Open in a new tab

Abbreviations: ABMR, antibody-mediated rejection; HVEM, herpes virus entry mediator; LIGHT, homologous to lymphotoxin (lymphotoxin-like), exhibits inducible expression and competes with HSV glycoprotein D for binding to herpesvirus entry mediator, a receptor expressed on T lymphocytes; BTLA, B and T lymphocyte attenuator; CD, cluster of differentiation; NA, not available; HWE, hardy-weinberg equilibrium.

Table 3. Regression analysis for age-, sex- and immunosuppressive protocol-adjusted BTLA/HVEM/CD160/LIGHT genetic polymorphisms among recipients with ABMR.

SNPs model OR 95%CIs P value
rs2971205
Additive NA NA 1.00
Dominant NA NA 1.00
rs2171513
Additive 0.81 0.51, 1.29 0.38
Dominant 0.92 0.50, 1.69 0.79
Recessive 0.36 0.10, 1.33 0.13
HET 1.11 0.59, 2.11 0.74
HOM 0.38 0.10, 1.41 0.15
rs770019001
Additive NA NA 1.00
Dominant NA NA 1.00
rs9288952
Additive 0.74 0.48, 1.15 0.18
Dominant 0.85 0.47, 1.55 0.60
Recessive 0.37 0.13, 1.03 0.06
HET 1.08 0.57, 2.04 0.82
HOM 0.38 0.13, 1.11 0.08
rs76844316
Additive 0.72 0.30, 1.70 0.45
Dominant 0.74 0.30, 1.81 0.51
Recessive NA NA 1.00
HET 0.77 0.31, 1.89 0.57
HOM NA NA 1.00
rs16859629
Additive NA NA 1.00
Dominant NA NA 1.00
rs9851198
Additive NA NA 1.00
Dominant NA NA 1.00
rs4870
Additive 0.83 0.53, 1.28 0.39
Dominant 0.77 0.41, 1.45 0.42
Recessive 0.80 0.36, 1.78 0.58
HET 0.80 0.41, 1.56 0.50
HOM 0.69 0.28, 1.70 0.42
rs2234158
Additive NA NA 1.00
Dominant NA NA 1.00
rs376994775
Additive NA NA 1.00
Dominant NA NA 1.00
rs754021885
Additive 3.06 0.18, 52.98 0.44
Dominant 3.06 0.18, 52.98 0.44
rs572222644
Additive NA NA 1.00
Dominant NA NA 1.00
rs2234161
Additive 0.92 0.60, 1.40 0.68
Dominant 0.96 0.49, 1.86 0.90
Recessive 0.81 0.39, 1.68 0.58
HET 1.02 0.51, 2.06 0.95
HOM 0.82 0.35, 1.94 0.66
rs2234162
Additive NA NA 1.00
Dominant NA NA 1.00
rs2234163
Additive 1.83 0.66, 5.04 0.24
Dominant 1.66 0.54, 5.11 0.38
rs2234165
Additive 1.01 0.35, 2.91 0.99
Dominant 1.01 0.35, 2.91 0.99
rs575127151
Additive NA NA 1.00
Dominant NA NA 1.00
rs375010878
Additive 2.39 0.14, 40.69 0.55
Dominant 2.39 0.14, 40.69 0.55
rs2234167
Additive 0.80 0.20, 3.17 0.75
Dominant 0.80 0.20, 3.17 0.75
rs8725
Additive 0.98 0.65, 1.50 0.94
Dominant 1.05 0.54, 2.05 0.88
Recessive 0.90 0.44, 1.83 0.76
HET 1.10 0.54, 2.23 0.79
HOM 0.95 0.41, 2.22 0.91
rs376495994
Additive NA NA 1.00
Dominant NA NA 1.00
rs186536172
Additive 2.28 0.13, 39.34 0.57
Dominant 2.28 0.13, 39.34 0.57
rs7544646
Additive 1.22 0.81, 1.85 0.34
Dominant 1.48 0.77, 2.84 0.24
Recessive 1.12 0.54, 2.34 0.76
HET 1.50 0.75, 3.13 0.23
HOM 1.43 0.61, 3.35 0.41
rs7515633
Additive 1.12 0.74, 1.69 0.60
Dominant 1.29 0.67, 2.49 0.44
Recessive 1.02 0.49, 2.11 0.96
HET 1.33 0.66, 2.68 0.42
HOM 1.21 0.52, 2.83 0.66
rs2231375
Additive 0.71 0.37, 1.34 0.29
Dominant 0.68 0.33, 1.40 0.29
Recessive 0.59 0.06, 5.47 0.64
HET 0.70 0.33, 1.46 0.34
HOM 0.54 0.06, 5.09 0.59
rs3766526
Additive NA NA 1.00
Dominant NA NA 1.00
rs368476773
Additive NA NA 1.00
Dominant NA NA 1.00
rs193141418
Additive 0.27 0.03, 2.35 0.24
Dominant 0.27 0.03, 2.35 0.24
rs587741068
Additive NA NA 1.00
Dominant NA NA 1.00
rs587727931
Additive NA NA 1.00
Dominant NA NA 1.00
rs344560
Additive 1.01 0.38, 2.69 0.99
Dominant 1.01 0.38, 2.69 0.99
rs772372888
Additive NA NA 1.00
Dominant NA NA 1.00
rs61761328
Additive NA NA 1.00
Dominant NA NA 1.00
rs183886666
Additive NA NA 1.00
Dominant NA NA 1.00
rs8101047
Additive 1.02 0.58, 1.81 0.94
Dominant 1.18 0.63, 2.21 0.60
Recessive NA NA 1.00
HET 1.31 0.70, 2.47 0.40
HOM NA NA 1.00
rs542346038
Additive NA NA 1.00
Dominant NA NA 1.00
rs2291668
Additive 1.20 0.74, 1.94 0.46
Dominant 1.51 0.82, 2.79 0.18
Recessive 0.66 0.21, 2.07 0.48
HET 1.65 0.88, 3.08 0.12
HOM 0.87 0.26, 2.88 0.82
rs2291667
Additive 2.32 0.43, 12.64 0.33
Dominant 2.32 0.43, 12.64 0.33
rs748673655
Additive NA NA 1.00
Dominant NA NA 1.00
rs344558
Additive 1.77 0.80, 3.95 0.16
Dominant 1.67 0.72, 3.89 0.23
Recessive NA NA 1.00
HET 1.53 0.65, 3.62 0.33
HOM NA NA 1.00
rs563748272
Additive NA NA 1.00
Dominant NA NA 1.00
Open in a new tab

Abbreviations: SNPs, single nuclear polymorphisms; OR, odds ratio; CIs: confidential intervals.

DISCUSSION

To the best of our knowledge, this is the first study that deploys next-generation sequencing (NGS) technology to investigate the association between HVEM/LIGHT/BLTA/CD160 SNPs and ABMR in renal transplant recipients. We screened a total 41 SNPs, previously unexplored in the context of ABMR, and show that none of the polymorphisms were significantly associated with the onset of ABMR in renal transplant recipients.

HVEM belongs to the TNF receptor superfamily and acts as a shared ligand for the costimulatory and coinhibitory receptor [13]. Human HVEM is a type 1 transmembrane glycoprotein with four pseudo repeats of the cysteine-rich domain (CRD) in its extracellular domain. It is expressed widely on T cells, B cells and other hematopoietic (DC, Tregs, monocytes, neutrophils, and NK cells) and nonhematopoietic cells (parenchymal cells) [13]. HVEM serves a central role in the HVEM/LIGHT/BTLA/CD160 costimulatory pathway, directing both positive (LIGHT) and negative (BTLA/CD160) costimulatory signals depending its receptor [21]. Rs2234163, rs2234165 and rs2234167, which are included in our study, have been researched in association with HVEM polymorphism and sporadic breast cancer [22]. In this instance, Dalin Li et al. reported that rs2234167, which is in the exon of the HVEM gene, is significantly associated with increased breast cancer risk, and presumed to influence the binding affinity between HVEM and BTLA/LIGHT/CD160 [22]. In our study, however, we did not find any significant association between 17 SNPs of HVEM and the onset of ABMR in renal transplant recipients.

LIGHT, a member of the TNF cytokine superfamily, is a type II transmembrane glycoprotein that is widely expressed on hematopoietic cells at certain periods of cell differentiation, including T cells, B cells, DC, NK cells and platelets, acting as a key cytokine with multiple functions [13, 23]. LIGHT-deficient mice survived slightly longer than control mice (10 days versus 7 days) in fully MHC-mismatched cardiac transplantation, implying that the HVEM/LIGHT pathway has potential functions in transplantation [24]. Meanwhile, in the humoral immune response, recent work suggests that LIGHT participates in B cell expansion and promotes Ig production [17]. LIGHT binds to three receptors: HVEM, LTβR and DcR3. The human LIGHT gene is situated on a segment of chromosome 19p13.3, which is paralogous to the MHC immune response loci [23]. Previous investigations have demonstrated that rs344560, located near the receptor-binding region of LIGHT, directly influences the binding avidity to LTβR, whereas rs2291667, positioned in the cytosolic domain, which could decrease the binding avidity to DcR3 and lowers the expression of LIGHT on the cell membrane [25]. Heterotrimers of SNPs are associated with lower DcR3 avidity and the increased LIGHT bioavailability, contributing to the pathogenesis of inflammatory diseases, such as rheumatoid arthritis. However, in our study, we did not find any significant differences in SNP distributions on LIGHT genes between the ABMR and control group of renal transplant recipients, calling for a deeper investigation into the functions of LIGHT in ABMR.

The BTLA gene is located on chromosome 3 in q13.2 with five exons [26]. BTLA is a member of the immunoglobulin superfamily and is constitutively expressed on naïve T and B cells, NK cells, macrophages and dendritic cells at low levels [10]. BTLA is up-regulated on activated T cells, but when conjugated with HVEM, a co-inhibitory signal suppresses T cell activation and differentiation in vitro [12]. Studies regarding the genetic variations of BTLA have mainly focused on its role in cancer (for example, lymphocytic leukemia [27] and breast cancer [28]) and susceptibility to autoimmune diseases (for example, rheumatoid arthritis [29, 30], systemic lupus erythematosus and type 1 diabetes mellitus [31]). In particular, the majority of investigation have focused on rs9288952 and its role in increasing breast cancer risk in Chinese populations [28] and rheumatoid arthritis in Japanese and Taiwanese populations [30, 31]. Inuo et al. revealed no relationship between rs2171513 and susceptibility to lupus erythematosus and type 1 diabetes mellitus in Japanese populations [31]. While, Oki et al. showed rs76844316 is significantly related to rheumatoid arthritis in Japanese populations [29]. Our study is the first to investigate the association between BTLA SNPs with the development of ABMR in renal transplant recipients. None of the seven BTLA SNPs we screened, including the three SNPs mentioned above, showed any association with ABMR.

CD160 is another member of the Ig superfamily and is glycosylphosphatidylinositol anchored on the cell membrane [32]. It is also the second co-inhibitory ligand of HVEM, commonly associated with cytolytic activity in NK, NKT, and CD8+ T cells [33]. A recent study suggests that CD160 signaling is vital in activating CD28-independent effector/memory CD8+ alloreactive T cells. This is because CD160Ig inhibits alloreactive CD8+ T cell proliferation and IFN-γ production in vitro, particularly in the absence of CD28 costimulation, resulting in the prolonged survival of fully mismatched cardiac allograft in CD4-/-, CD28-/- knockout and CTLA4Ig treated wild type recipients [34]. However, there are no studies available that address the association between CD160 and humoral immunity, including polymorphism of CD160 and the onset of allograft rejection. In our study, none of the six CD160 SNPs showed any significant association with the occurrence of ABMR in renal transplant recipients.

This study is a first attempt in addressing the functions of HVEM/LIGHT/BTLA/CD160 cosignaling pathway in the pathogenesis of ABMR in renal transplant patients. While our results suggest that the 41 HVEM/LIGHT/BTLA/CD160 SNPs that we screened are not associated with the onset of ABMR, there are several advantages in our approach. First, we collected sufficient baseline information about the patients and included an adequate number of control patients. Second, we adopted NGS technology, which allows high-throughput and large-scale analysis of the genotypes, increasing the reliability of our findings. Third, we used regression analysis after adjusting the data for multiple confounding factors to obtain more detailed clinical information. Moreover, considering various causing contribute to the pathogenesis of post-transplant ABMR, we failed to collect more ABMR-related information to analysis the distributions of these causing and its relationship with SNPs in our study. Therefore, further studies are required with larger sample sizes from different populations to fully understand the role of these genes in ABMR onset.

In summary, through a case-control study on 69 renal transplant recipients with ABMR and 131 control recipients, we provide the first study to explore the association between HVEM/LIGHT/BTLA/CD160 gene polymorphisms and ABMR in renal transplant recipients. We showed that none of the 41 HVEM/LIGHT/BTLA/CD160 gene polymorphisms were associated with ABMR. Since there are limited studies investigating the role of the costimulatory signaling pathways in graft rejection, we recommend further research is required to gain a deeper understanding of the role of these genes and its variants in ABMR after kidney transplantation.

MATERIALS AND METHODS

Ethics statement

The procedures followed in our study were in accordance with the ethical standards of the Declarations of Helsinki and Istanbul. The study was limited to the living-related transplantation of kidney tissues to lineal or collateral relatives not beyond the third degree of kinship, or transplantation of kidney tissues from cadaveric allograft donors after cardiac death. The protocols followed were approved by the local ethics committee of The First Affiliated Hospital with Nanjing Medical University. We obtained written informed consent from all transplant recipients. None of the transplant donors were considered vulnerable.

Collection of patient data

The study included 200 renal transplant recipients who underwent kidney transplantation between February 2008 and December 2015 at the Kidney Transplant Center of The First Affiliated Hospital of Nanjing Medical University. At least two clinicians critically reviewed the transplant recipients’ medical records, and extracted relevant data, including age, gender, transplant date, duration of transplantation, number of transplants, and immunosuppressive protocol, for patient selection. They also extracted data on panel reactive antibodies and HLA mismatch during the pre-transplant period.

Methylprednisolone was intravenously administered at a dosage of 500 mg/day during surgery and for two days following the procedure. Following this, the dosage was reduced to 400 mg, 300 mg, 200 mg, and then 80 mg on each subsequent day. This was followed by administration of prednisone at a dosage of 30 mg/day as maintenance therapy. In addition, basiliximab (20 mg) was intravenously administered 30 min before the procedure and on the fourth day after the procedure. All recipients received a three-drug or four-drug immunosuppressive regimen: cyclosporin A (n = 101) or tacrolimus (n = 99) combined with mycophenolate mofetil and prednisone, with or without sirolimus (n = 17). The starting dose of cyclosporine A and tacrolimus was 8 mg·kg-1·day-1 and 0.2 mg·kg-1·day-1, respectively; these doses were later adjusted according to serum creatinine levels. In patients where ABMR episodes occurred, methylprednisolone was intravenously administered at a dosage of 200 mg/day for three to five days.

Diagnosis of antibody-mediated rejection

We considered an increase in serum creatinine by 20% from the baseline (not attributable to other causes), fever, proteinuria and pain in the region of the transplanted kidney to be indicative of ABMR. To confirm the diagnosis, we analyzed allograft biopsies according to the Banff 07 classification criteria, which included positive C4d staining, presence of circulating DSAs and morphological evidence of acute tissue injury [35]. Moreover, patients diagnosed with either acute ABMR or chronic active ABMR were all included in our study.

Sample collection, preparation and NGS

We collected peripheral blood samples (2 mL) from each recipient and extracted DNA using the QIAmp DNA mini kit (Qiagen, Hilden, Germany). We quantitatively analyzed the concentration and purity of genomic DNA (gDNA) using NanoDrop ND2000 (Thermo, MA, USA), and assessed gene integrity using agarose gel electrophoresis. We considered gDNA samples with a total mass of ≥1 μg and A260/A280 absorbance ratio of ≥1.80 and ≤2.0 as acceptable. Then, we selected a pool containing upstream and downstream oligonucleotides specific to the target regions of interest as gDNA hybrids. We next fragmented gDNA using a Bioruptor Interrupt instrument (Diagenode, Belgium), and performed quantitative detection to ensure that the average fragment size was 150–250 bp. We then performed end repair, dA-tailing and sequencing adaptor ligation using the ABI 9700 PCR instrument (ABI, USA). We amplified the adapter-ligated DNA by selective, limited-cycle PCR for five cycles, before quantitatively analyzing using the Qubit dsDNA HS Assay Kit (Invitrogen, USA). We hybridized the prepared library (750 ng) with 11 μL of hybridization blocking buffer (Allwegene, China), 20 μL of hybridization buffer (Allwegene, China) and a mixture of 5 μL RNase block (Invitrogen, USA) and 2 μL probes (Allwegene, China) overnight (at least 8–16 h) at 65°C. We mixed the hybridized products with 200 μL Dynabeads MyOne Streptavidin T1 magnetic beads (Invitrogen, USA) for 30 min at room temperature. The products were then washed twice with a wash buffer (Allwegene, China), before the mixture was amplified for 16 PCR cycles and quantitatively assessed using the Qubit dsDNA HS Assay Kit (Invitrogen, USA). We denatured the captured libraries and loaded them onto an Illumina cBot instrument at a concentration of 12 to 16 pmol/L for cluster generation, according to the manufacturer’s instructions. We sequenced up to 20 WUCaMP libraries per HiSeq lane. A PhiX control (Illumina) was added to lane 8 of each flow cell.

Analysis of NGS data

We analyzed sequencing data, including the number of altered chromosomes, genomic alterations and the depth of the sequencing coverage. We based all analyses on the human reference sequence UCSC hg19 assembly (NCBI build 37.2) using the Burrows-Wheeler Aligner. We performed local alignment and duplication removal using the Genome Analysis Tool Kit and Picard software. We detected SNPs using dbSNP 132. We used Gemini software to detect damaging or deleterious SNPs and prediction tools such as Sorting Intolerant from Tolerant and Polymorphism Phenotyping to analyze all human non-synonymous SNPs. In addition, we detected putative somatic variant calls with two separate programs: MuTect 1.1.5 and VarScan 2.3.6, by pairing each sample with its matched blood sample.

Statistical analysis

We determined conformance to the HWE using genotype frequencies obtained from a single gene. We used the chi-square test to compare the observed and expected values. We performed genotype association analysis using a dominant model (minor allele homozygotes plus heterozygotes vs. major allele homozygotes), recessive model (minor allele homozygotes vs. heterozygotes plus major homozygotes), additive model (major homozygotes vs. heterozygotes vs. minor homozygotes), HET model (major homozygotes vs. heterozygotes) and HOM model (major homozygotes vs. minor homozygotes). We compared genotypic frequencies between the control and ABMR group using the chi-square test. In addition, we explored linkage disequilibrium blocks using Haploview version 4.2. We calculated odds ratios (ORs) and 95% confidence intervals (95% CIs) using the SPSS 13.0 software (SPSS Inc., Chicago, IL, USA). We considered P<0.05 to indicate statistical significance. The OR provides an effect estimate: a value of less than one assumes a protective effect, while a value of more than one assumes an increased disease risk. In addition, we analyzed the genotypic distributions of C4 SNPs in recipients with ABMR and stable recipients using logistic regression models adjusted for age, sex and immunosuppressive protocol.

Abbreviations

ABMR

antibody-mediated rejection

HLA

human leukocyte antigens

MHC

major histocompatibility complex

DSA

donor-specific antibodies

HVEM

herpes virus entry mediator

BTLA

B and T lymphocyte attenuator

LIGHT

homologous to lymphotoxin, which exhibit inducible expression and compete with HSV glycoprotein D for binding to HVEM, a receptor expressed on T lymphocytes)

GC

germinal center

DC

dendritic cells

Tfh

helper cells

SNP

single nucleotide polymorphisms

HWE

Hardy-Weinberg equilibrium

NGS

next-generation sequencing

CRD

cysteine-rich domain

Footnotes

Author contributions

Zijie Wang: sample collection, statistical analysis and manuscript preparation;

Ke Wang: sample collection and manuscript preparation;

Haiwei Yang: gene testing and study design;

Zhijian Han: statistical analysis;

Jun Tao: statistical analysis;

Hao Chen: sample collection;

Yuqiu Ge: statistical analysis;

Miao Guo: statistical analysis and gene testing;

Chuanjian Suo: sample collection;

Ruoyun Tan: funding and study design;

Ji-Fu Wei: study design, gene testing and manuscript preparation;

Min Gu: study design, funding and manuscript preparation.

CONFLICTS OF INTERESTS

The authors have declared that no competing interests exist.

FUNDING

This work was supported by the National Natural Science Foundation of China [grant numbers 81570676, 81100532, 81470981], the Science and Education Health Project of Jiangsu Province for Important Talent [grant number RC2011055], the “333 High Level Talents Project” in Jiangsu Province, China [grant numbers BRA2015469, BRA2016514 (2011 and 2013)], the Standardized Diagnosis and Treatment Research Program of Key Diseases in Jiangsu Province, China [grant number BE2016791], the Open Project Program of Health Department of Jiangsu Province, China [grant number JSY-2-2016-099], the Jiangsu Province Six Talents Peak from Department of Human Resources, Social Security Office of Jiangsu Province, China [grant numbers 2010WSN-56, 2011-WS-033], the General Program of Health Department of Jiangsu Province, China [grant number H2009907], and the Priority Academic Program Development of Jiangsu Higher Education Institutions [grant number JX10231801]. National Key R&D Plan for Precision Medicine [grant number 2017YFC0910001].

REFERENCES

  • 1.Tonelli M, Wiebe N, Knoll G, Bello A, Browne S, Jadhav D, Klarenbach S, Gill J. Systematic review: kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transplant. 2011;11:2093–2109. doi: 10.1111/j.1600-6143.2011.03686.x. https://doi.org/10.1111/j.1600-6143.2011.03686.x. [DOI] [PubMed] [Google Scholar]
  • 2.Garces JC, Giusti S, Staffeld-Coit C, Bohorquez H, Cohen AJ, Loss GE. Antibody-Mediated Rejection: A Review. Ochsner. J. 2017;17:46–55. [PMC free article] [PubMed] [Google Scholar]
  • 3.Kim M, Martin ST, Townsend KR, Gabardi S. Antibody-mediated rejection in kidney transplantation: a review of pathophysiology, diagnosis, and treatment options. Pharmacotherapy. 2014;34:733–744. doi: 10.1002/phar.1426. https://doi.org/10.1002/phar.1426. [DOI] [PubMed] [Google Scholar]
  • 4.Halloran PF. The clinical importance of alloantibody-mediated rejection. Am J Transplant. 2003;3:639–640. doi: 10.1034/j.1600-6143.2003.00172.x. [DOI] [PubMed] [Google Scholar]
  • 5.Davis S, Cooper JE. Acute antibody-mediated rejection in kidney transplant recipients. Transplantation reviews. 2017;31:47–54. doi: 10.1016/j.trre.2016.10.004. https://doi.org/10.1016/j.trre.2016.10.004. [DOI] [PubMed] [Google Scholar]
  • 6.Sigdel TK, Sarwal MM. Moving beyond HLA: a review of nHLA antibodies in organ transplantation. Human immunology. 2013;74:1486–1490. doi: 10.1016/j.humimm.2013.07.001. https://doi.org/10.1016/j.humimm.2013.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Colvin RB, Smith RN. Antibody-mediated organ-allograft rejection. Nature reviews. Immunology. 2005;5:807–817. doi: 10.1038/nri1702. https://doi.org/10.1038/nri1702. [DOI] [PubMed] [Google Scholar]
  • 8.Chehade H, Pascual M. The Challenge of Acute Antibody-Mediated Rejection in Kidney Transplantation. Transplantation. 2016;100:264–265. doi: 10.1097/TP.0000000000000959. https://doi.org/10.1097/tp.0000000000000959. [DOI] [PubMed] [Google Scholar]
  • 9.Li XC, Rothstein DM, Sayegh MH. Costimulatory pathways in transplantation: challenges and new developments. Immunol Rev. 2009;229:271–293. doi: 10.1111/j.1600-065X.2009.00781.x. https://doi.org/10.1111/j.1600-065X.2009.00781.x. [DOI] [PubMed] [Google Scholar]
  • 10.Pilat N, Sayegh MH, Wekerle T. Costimulatory pathways in transplantation. Semin Immunol. 2011;23:293–303. doi: 10.1016/j.smim.2011.04.002. https://doi.org/10.1016/j.smim.2011.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Adams AB, Larsen CP, Pearson TC, Newell KA. The role of TNF receptor and TNF superfamily molecules in organ transplantation. American journal of transplantation. 2002;2:12–18. doi: 10.1034/j.1600-6143.2002.020104.x. [DOI] [PubMed] [Google Scholar]
  • 12.Watanabe N, Gavrieli M, Sedy JR, Yang J, Fallarino F, Loftin SK, Hurchla MA, Zimmerman N, Sim J, Zang X, Murphy TL, Russell JH, Allison JP. BTLA is a lymphocyte inhibitory receptor with similarities to CTLA-4 and PD-1. Nature immunology. 2003;4:670–679. doi: 10.1038/ni944. https://doi.org/10.1038/ni944. [DOI] [PubMed] [Google Scholar]
  • 13.del Rio ML, Lucas CL, Buhler L, Rayat G, Rodriguez-Barbosa JI. HVEM/LIGHT/BTLA/CD160 cosignaling pathways as targets for immune regulation. Journal of leukocyte biology. 2010;87:223–235. doi: 10.1189/jlb.0809590. https://doi.org/10.1189/jlb.0809590. [DOI] [PubMed] [Google Scholar]
  • 14.Tamada K, Shimozaki K, Chapoval AI, Zhai Y, Su J, Chen SF, Hsieh SL, Nagata S, Ni J, Chen L. LIGHT, a TNF-like molecule, costimulates T cell proliferation and is required for dendritic cell-mediated allogeneic T cell response. J Immunol. 2000;164:4105–4110. doi: 10.4049/jimmunol.164.8.4105. [DOI] [PubMed] [Google Scholar]
  • 15.Steinberg MW, Cheung TC, Ware CF. The signaling networks of the herpesvirus entry mediator (TNFRSF14) in immune regulation. Immunological reviews. 2011;244:169–187. doi: 10.1111/j.1600-065X.2011.01064.x. https://doi.org/10.1111/j.1600-065X.2011.01064.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.del Rio ML, Buhler L, Gibbons C, Tian J, Rodriguez-Barbosa JI. PD-1/PD-L1, PD-1/PD-L2, and other co-inhibitory signaling pathways in transplantation. Transpl Int. 2008;21:1015–1028. doi: 10.1111/j.1432-2277.2008.00726.x. https://doi.org/10.1111/j.1432-2277.2008.00726.x. [DOI] [PubMed] [Google Scholar]
  • 17.Duhen T, Pasero C, Mallet F, Barbarat B, Olive D, Costello RT. LIGHT costimulates CD40 triggering and induces immunoglobulin secretion; a novel key partner in T cell-dependent B cell terminal differentiation. European journal of immunology. 2004;34:3534–3541. doi: 10.1002/eji.200425598. https://doi.org/10.1002/eji.200425598. [DOI] [PubMed] [Google Scholar]
  • 18.Conlon TM, Saeb-Parsy K, Cole JL, Motallebzadeh R, Qureshi MS, Rehakova S, Negus MC, Callaghan CJ, Bolton EM, Bradley JA, Pettigrew GJ. Germinal center alloantibody responses are mediated exclusively by indirect-pathway CD4 T follicular helper cells. J Immunol. 2012;188:2643–2652. doi: 10.4049/jimmunol.1102830. https://doi.org/10.4049/jimmunol.1102830. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Steele DJ, Laufer TM, Smiley ST, Ando Y, Grusby MJ, Glimcher LH, Auchincloss H., Jr Two levels of help for B cell alloantibody production. The Journal of experimental medicine. 1996;183:699–703. doi: 10.1084/jem.183.2.699. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.de Graav GN, Dieterich M, Hesselink DA, Boer K, Clahsen-van Groningen MC, Kraaijeveld R, Litjens NH, Bouamar R, Vanderlocht J, Tilanus M, Houba I, Boonstra A, Roelen DL, et al. Follicular T helper cells and humoral reactivity in kidney transplant patients. Clinical and experimental immunology. 2015;180:329–340. doi: 10.1111/cei.12576. https://doi.org/10.1111/cei.12576. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.McGrath MM, Najafian N. The role of coinhibitory signaling pathways in transplantation and tolerance. Frontiers in immunology. 2012;3:47. doi: 10.3389/fimmu.2012.00047. https://doi.org/10.3389/fimmu.2012.00047. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Li D, Fu Z, Chen S, Yuan W, Liu Y, Li L, Pang D, Li D. HVEM gene polymorphisms are associated with sporadic breast cancer in Chinese women. PloS one. 2013;8:e71040. doi: 10.1371/journal.pone.0071040. https://doi.org/10.1371/journal.pone.0071040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Granger SW, Butrovich KD, Houshmand P, Edwards WR, Ware CF. Genomic characterization of LIGHT reveals linkage to an immune response locus on chromosome 19p13.3 and distinct isoforms generated by alternate splicing or proteolysis. J Immunol. 2001;167:5122–5128. doi: 10.4049/jimmunol.167.9.5122. [DOI] [PubMed] [Google Scholar]
  • 24.Ye Q, Fraser CC, Gao W, Wang L, Busfield SJ, Wang C, Qiu Y, Coyle AJ, Gutierrez-Ramos JC, Hancock WW. Modulation of LIGHT-HVEM costimulation prolongs cardiac allograft survival. The Journal of experimental medicine. 2002;195:795–800. doi: 10.1084/jem.20012088. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Cheung TC, Coppieters K, Sanjo H, Oborne LM, Norris PS, Coddington A, Granger SW, Elewaut D, Ware CF. Polymorphic variants of LIGHT (TNF superfamily-14) alter receptor avidity and bioavailability. J Immunol. 2010;185:1949–1958. doi: 10.4049/jimmunol.1001159. https://doi.org/10.4049/jimmunol.1001159. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Partyka A, Woszczyk D, Strzała T, Szczepańska A, Tomkiewicz A, Frydecka I, Karabon L. Gene polymorphisms of novel immunotolerant molecule BTLA: distribution of alleles, genotypes and haplotypes in Polish Caucasian population. Arch Immunol Ther Exp (Warsz) 2015;63:73–78. doi: 10.1007/s00005-014-0300-3. https://doi.org/10.1007/s00005-014-0300-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Karabon L, Partyka A, Jasek M, Lech-Maranda E, Grzybowska-Izydorczyk O, Bojarska-Junak A, Pawlak-Adamska E, Tomkiewicz A, Robak T, Rolinski J, Frydecka I. Intragenic Variations in BTLA Gene Influence mRNA Expression of BTLA Gene in Chronic Lymphocytic Leukemia Patients and Confer Susceptibility to Chronic Lymphocytic Leukemia. Arch Immunol Ther Exp (Warsz) 2016;64:137–145. doi: 10.1007/s00005-016-0430-x. https://doi.org/10.1007/s00005-016-0430-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Fu Z, Li D, Jiang W, Wang L, Zhang J, Xu F, Pang D, Li D. Association of BTLA gene polymorphisms with the risk of malignant breast cancer in Chinese women of Heilongjiang Province. Breast Cancer Res Treat. 2010;120:195–202. doi: 10.1007/s10549-009-0462-6. https://doi.org/10.1007/s10549-009-0462-6. [DOI] [PubMed] [Google Scholar]
  • 29.Oki M, Watanabe N, Owada T, Oya Y, Ikeda K, Saito Y, Matsumura R, Seto Y, Iwamoto I, Nakajima H. A functional polymorphism in B and T lymphocyte attenuator is associated with susceptibility to rheumatoid arthritis. Clinical & developmental immunology. 2011;2011:305656. doi: 10.1155/2011/305656. https://doi.org/10.1155/2011/305656. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Lin SC, Kuo CC, Chan CH. Association of a BTLA gene polymorphism with the risk of rheumatoid arthritis. Journal of biomedical science. 2006;13:853–860. doi: 10.1007/s11373-006-9113-7. https://doi.org/10.1007/s11373-006-9113-7. [DOI] [PubMed] [Google Scholar]
  • 31.Inuo M, Ihara K, Matsuo T, Kohno H, Hara T. Association study between B- and T-lymphocyte attenuator gene and type 1 diabetes mellitus or systemic lupus erythematosus in the Japanese population. International journal of immunogenetics. 2009;36:65–68. doi: 10.1111/j.1744-313X.2008.00809.x. https://doi.org/10.1111/j.1744-313X.2008.00809.x. [DOI] [PubMed] [Google Scholar]
  • 32.Maïza H, Leca G, Mansur IG, Schiavon V, Boumsell L, Bensussan A. A novel 80-kD cell surface structure identifies human circulating lymphocytes with natural killer activity. The Journal of experimental medicine. 1993;178:1121–1126. doi: 10.1084/jem.178.3.1121. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Krummey SM, Ford ML. New insights into T-cell cosignaling in allograft rejection and survival. Current opinion in organ transplantation. 2015;20:43–48. doi: 10.1097/MOT.0000000000000151. https://doi.org/10.1097/mot.0000000000000151. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.D'Addio F, Ueno T, Clarkson M, Zhu B, Vergani A, Freeman GJ, Sayegh MH, Ansari MJ, Fiorina P, Habicht A. CD160Ig fusion protein targets a novel costimulatory pathway and prolongs allograft survival. PloS one. 2013;8:e60391. doi: 10.1371/journal.pone.0060391. https://doi.org/10.1371/journal.pone.0060391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Solez K, Colvin RB, Racusen LC, Haas M, Sis B, Mengel M, Halloran PF, Baldwin W, Banfi G, Collins AB, Cosio F, David DS, Drachenberg C, et al. Banff 07 classification of renal allograft pathology: updates and future directions. American journal of transplantation. 2008;8:753–760. doi: 10.1111/j.1600-6143.2008.02159.x. https://doi.org/10.1111/j.1600-6143.2008.02159.x. [DOI] [PubMed] [Google Scholar]

Articles from Oncotarget are provided here courtesy of Impact Journals, LLC

RESOURCES