The Role of Dynamic DNA Methylation in Liver Transplant Rejection in Children : Transplantation Direct

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Liver Transplantation

The Role of Dynamic DNA Methylation in Liver Transplant Rejection in Children

Ningappa, Mylarappa PhD1; Shao, Xiaojian PhD2; Ashokkumar, Chethan PhD1; Xu, Qingyong PhD3; Zeevi, Adriana PhD4; Grundberg, Elin PhD5; Pastinen, Tomi MD, PhD5; Sindhi, Rakesh MD1

Author Information
doi: 10.1097/TXD.0000000000001394

Abstract

INTRODUCTION

Epigenetic changes may be better suited to aid management of liver transplant (LT) rejection in children, who are at risk for cumulative toxicity of lifelong immunosuppression.1 These changes regulate gene transcription by affecting the binding of transcription factors (TFs) and should therefore precede transcription and its clinical consequences. In turn, patterns of TF binding or the loci‚ which bind to these factors can reveal the particular mechanism of rejection in a given recipient, and the immunosuppressant best able to target that mechanism. This task is not fulfilled by currently available diagnostic tests. Cell function assays predict rejection-risk and not the appropriate immunosuppressant.2 Molecular diagnostics detect rejection and its progression using predominantly upregulated genes, which may not be mechanistic.3,4

As a clinically usable epigenetic mechanism, DNA methylation is attractive for several reasons.

Epigenetic regulation turns genes on to allow transcription or turns off genes to silence them. DNA methylation is a common epigenetic change that causes genes to compact around histones, thereby silencing gene expression. Hypomethylation unwinds gene that promote transcription. Epigenetic regulation can also be inferred from chromatin states but requires complex sample preparation procedures.5

As a test substrate, DNA can be easily acquired from several sources: blood, biopsy or body fluids. Commercially available arrays use small quantities of DNA to screen methylation status of nearly a million methylation (CpG) sites genome-wide.6 Early DNA methylation studies have identified differentially methylated regions (DMRs) near genes encoding cytokines as well as other genes within interferon and mammalian target of rapamycin signaling pathways, as being relevant in renal transplant rejection.7,8 No such work has been performed in pediatric LT recipients.

We and others have shown that trait-associated and dynamic epigenetic variants are enriched in distal regulatory enhancer regions and we further showed that these features are also seen downstream of the transcription start sites—both genomic regions being underrepresented on commercially available arrays.5-11 To overcome this limitation, we implemented the methylC-capture sequencing (MCC-Seq) approach for customized DNA methylation profiling of millions of CpGs located in regulatory elements known to be active specifically in immune cells.11,12 Specifically, this immune cell MCC-Seq panel covers (1) the majority of human gene promoters, blood-cell-lineage–specific enhancer regions and methylation footprint regions observed in peripheral blood,12 (2) CpGs from Illumina Human Methylation 450 Bead Chips, and (3) published autoimmune-related single nucleotide polymorphisms (SNPs) and SNPs in their linkage disequilibrium regions with r2 > 0.8. Here, we test whether MCC-Seq applied to pre- and post-LT blood samples obtained in the first 90 d after LT from 17 children with LT can reveal potential mechanisms of LT rejection for further investigation. Four children experienced early biopsy-proven acute cellular rejection (rejectors [Rs]) and 13 did not (nonrejector [NRs]).

MATERIALS AND METHODS

Human Subjects

Archived DNA extracted from blood samples from 17 children with LT was tested under University of Pittsburgh Institutional Review Board approved study #19030279. Mean ± SD age of subjects was 6.3 ± 8.2 y and male:female gender distribution was 6:11. Indications for LT included primary diagnoses such as biliary atresia in 6, maple syrup urine disease in 4, urea cycle in 5, Propionic acedimia in 1, and cystic fibrosis in 1. Four of the 17 children experienced acute cellular rejection within 90 d of transplant and were termed rejectors (Rs). Thirteen of the 17 children had no rejection. DNA samples were collected before and within the first 90 d after LT. For the 4 rejectors, post-LT samples were collected at 9.75 ± 10.8 d before the rejection event. All 4 patients experienced steroid-responsive rejection. None needed antilymphocyte treatment for steroid-resistant rejection. The number of HLA mismatches in Rs and NRs was not significant (Table S1, SDC,https://links.lww.com/TXD/A466).

MCC-Seq

One µg of DNA was used for whole genome bisulfite sequencing library preparation (KAPA Biosystems, Wilmington, MA), bisulphite conversion (Epitect Fast DNA Bisulfite Kit [Qiagen]), and enrichment (12-plex) using the custom probes10,13 according to the Roche NimbleGen SeqCapEpi Enrichment System protocol (Wilmington, MA). We compared DNA methylation differences between Rs and NRs using a general linear regression model adjusted for age and sex. Correction of immune cell proportion was performed with constrained linear projection via the projectMix function of the RefFreeEWAS package (version 2.2), using a custom panel of 30 455 cell-type–specific hypomethylated and hypermethylated CpGs.14 The blood reference epigenome profiles include megakaryocyte, neutrophil, monocyte, B cell, and T cell. Differentially methylated CpGs (DMCs) were further filtered if the DNA methylation profile is correlated with any of the estimated blood proportion at nominal P <0.05. To assess potential regional clustering of significant DMCs, candidate regions surrounding the blood corrected DMCs were expanded for up to 200-bp distance both upstream and downstream. Within these candidate regions, all consecutive CpGs with methylation changes in the same direction and with nominal P <0.01 were merged. Regions with at least 3 CpGs fulfilling these criteria were considered DMRs. Gene ontology function enrichment analysis of rejection-associated DMCs was performed using Genomic Regions Enrichment of Annotations Tool.15

RESULTS

CpGs that Characterize Rejectors are Enriched in Distal Regions and Hypomethylated After LT

Of the ~2.5 million autosomal CpGs tested in the association analysis, we identified 3357 DMCs including 1894 hypomethylated and 1463 hypermethylated CpGs in post-LT samples from R compared with NR. The genome-wide distribution of significant CpGs is shown in Figure 1A. Among the 3357 DMCs, 1117 were significantly correlated with blood cell proportions and were excluded. Among the remaining 2238 DMCs, 1108 were hypermethylated and 1130 were hypomethylated (Tables 1 and Table S2, SDC,https://links.lww.com/TXD/A466). To further explore how the DMCs mapped to regions, we identified DMRs containing clusters of 3 or more DMCs with the same directional change in methylation. Of 216 DMRs that clearly distinguished R and NR groups, 113 (52%) were hypermethylated and 103 (48%) were hypomethylated (Figure 1B, Table S3, SDC,https://links.lww.com/TXD/A466). Examples include intronic regions in ADAP1 (50 DMCs, chr7:948678-949115, Figure 1C) and LHX6 (34 DMCs) and the intergenic region near ARRB2 (24 DMCs) (Table S3, SDC, https://links.lww.com/TXD/A466).16-18 Enrichment analysis of genomic regions revealed that rejection-associated DMCs and DMRs were enriched in the distal regulatory regions and depleted in the promoter regions (Table S3, SDC,https://links.lww.com/TXD/A466). Next, we identified significant genome-wide DMCs in pre-LT samples from Rs compared with NRs. After filtering DMCs (n = 665)‚ which correlated significantly with blood proportions, 2620 DMCs remained‚ of which 1413 were hypermethylated and 1207 were hypomethylated (Table S4, SDC,https://links.lww.com/TXD/A466). Among these DMCs, we identified 282 DMRs consisting of 221 (78%) hypermethylated and 61 (22%) hypomethylated DMRs (Table S5, SDC, https://links.lww.com/TXD/A466). Thus, as proportion of all DMRs, hypomethylated DMRs increased from 61/282 (22%) in pre-LT to 103/216 (48%, P < 0.0001) in post-LT samples.

TABLE 1. - Summary of differentially methylated CpGs and regions
Post-LT Pre-LT
No. CpGs 2 544 280 2 545 085
No. DMCs 2238 2620
No. Hypermethylated DMCs 1108 1413
No. Hypomethylated DMCs 1130 1207
No. DMRs 216 282
No. Hypermethylated DMRs 113 221
No. Hypomethylated DMRs 103 61
DMC, differentially methylated CpG; DMR, differentially methylated region; LT, liver transplant.Counts of DMCs (Tables S2 and S4, SDC, https://links.lww.com/TXD/A466) and DMRs (Tables S3 and S5, SDC, https://links.lww.com/TXD/A466) in samples obtained from 4 rejectors compared with those from 13 nonrejectors within 90 d post LT) and pre LT.

F1
FIGURE 1.:
Identification of differentially methylated CpGs (DMCs) for LT rejection. (A) Manhattan plot of P values from association analysis to identify differentially methylated CpGs. (B) Heatmap of DMCs in methylation profiles for all individuals. Phenotype features including sex, age onset, and LT rejection status are illustrated in the bars on top of the heatmap. (C). DNA methylation pattern at the differentially methylated region (DMR) for ADAP1 (chr7:948678-949115). This region shows consistent hypermethylation among R (pink) compared with NR (blue) over 50 DMCs. CpGs, methylations; LT, liver transplant; LTNR, liver transplant nonrejector; LTR, liver transplant rejection; NR, nonrejector.

Functional Enrichment Analysis Reveals Mechanisms for Additional Investigation

The genes associated with DMCs located within the DMRs regions in posttransplant samples were enriched for the biological processes of antigen processing and presentation of exogenous peptide antigen via major histocompatibility complex (MHC) class I, (P = 4.4 × 10−21) and cellular component of “MHC protein complex” (P = 1.7 × 10−23) and molecular function of peptide antigen binding (P = 7.9 × 10−17) (Figure 2, Table S6, SDC, https://links.lww.com/TXD/A466). DMCs in pretransplant samples were enriched for the top-ranked biological processes of “regulation of thyroid-stimulating hormone secretion” (P < 3.1 × 10−58) and several developmental and morphogenesis processes. “Antigen processing and presentation of exogenous peptide antigen via MHC class” ranked 17th among these processes (P < 9.5 × 10−34) (Figure 3, Table S7, SDC, https://links.lww.com/TXD/A466). Enriched top-ranked cellular components included “MHC class I protein complex” (P < 9.1 × 10−36), and top-ranked molecular functions included “thyroid-stimulating hormone receptor activity” (P < 2.6 × 10−55)” and “peptide antigen binding” (P < 1.8 × 10−30) (Figure 3, Table S7, SDC, https://links.lww.com/TXD/A466). The top-ranked enriched pathways among pretransplant DMC were consistent with the developmental nature of liver disease affecting children who present for transplantation. Liver development is regulated by thyroid hormones, manifested in the top-ranked pathways of regulation of thyroid-stimulating hormone secretion P = 4.06E-54 (Table S7, SDC, https://links.lww.com/TXD/A466).19,20 Liver components like hepatic stellate cells and sinusoidal epithelium originate from the mesoderm, as does the metanephric collecting duct system, represented by the pathway “regulation of metanephric nephron tubule epithelial cell differentiation, P = 3.7 × 10−36” (Table S7, SDC, https://links.lww.com/TXD/A466).21 Enrichment of DMRs in MHC class I molecules among rejectors was demonstrable in coding sequences of HLA-C and intronic reqions of HLA-F in pretransplant samples and the HLA-B promoter sequence in posttransplant samples.

F2
FIGURE 2.:
Enrichment in posttransplant samples. Top-ranked 20 gene ontology (GO) bological processes (BP), cellular component (CC)‚ and molecular function (MF) enriched in R compared with NR in DMCs located within regions of DMRs in posttransplant samples. DMC, differentially methylated CpG; DMR, differentially methylated region; NR, nonrejector; R, rejector.
F3
FIGURE 3.:
Enrichment in pretransplant samples. Top-ranked 20 gene ontology (GO) bological processes (BP), cellular component (CC), and molecular function (MF) enriched in R compared with NR in DMCs located within regions of DMRs in pretransplant samples. DMC, differentially methylated CpG; DMR, differentially methylated region; NR, nonrejector; R, rejector.

Public databases reveal that all 3 HLA DMRs (HLA-C, HLA-F, and HLA-B) contain known single nucleotide polymorphisms, which are known to bind to TFs, alter expression of the corresponding gene, or affect peptide antigen binding (SNPs) (Table S8A–C, SDC,https://links.lww.com/TXD/A466, Figure S1, SDC,https://links.lww.com/TXD/A465). SNPs associated with altered gene expression or expressed quantitative trait loci are shown in Figure S1, SDC, https://links.lww.com/TXD/A465. However, the differential methylation between R versus NR was not associated with the degree of mismatches between the donor and recipient in traditional pretransplant HLA typing of HLA-B (mean ± SD 1.5 ± 0.58 versus 1.69 ± 0.48, P = 0.577, (not significant) and HLA-C alleles (mean ± SD 1 ± 0.82 versus 1.38 ± 0.51, P = 0.42, not significant).

DISCUSSION

Our study shows that rejection after LT in children is associated with differential DNA methylation affecting distal regions of genes, and with 3-fold increase in hypomethylated: hypermethylated regions (103:113) compared with pretransplant status (61:221; P = 0.0001) (Figure 1B and Table 1). This is to be expected because dynamic changes in methylation that are induced by disease predominantly affect distal regions such as enhancers, for which the MCC-seq platform is especially suited. Differentially methylated loci also reveal several new directions for future study. First, DMRs containing the highest number of DMCs are present in several loci that could regulate the rejection alloresponse (Table S3, SDC, https://links.lww.com/TXD/A466). These loci include ADAP1 (Figure 1C)‚ which participates in B cell receptor signaling17; ARRB2, which negatively regulates inflammatory responses of many immune cells including T cell and B cell18; and LHX6 which regulates the development of many cells including lymphoid cells.22 A larger association study and functional experiments are needed to confirm this possibility.

Second, enrichment analyses suggest that posttransplant rejection may recruit MHC I molecules, which classically present endogenous antigen, to present exogenous (transplant) antigens.23 Exogenous antigens are usually presented by MHC class II molecules. This possibility is based on the emergence of transporter associated with antigen processing (TAP)–independent antigen processing and presentation of peptide antigen via MHC class I, as the top-ranked biological process, and MHC class I protein complex and peptide antigen binding as the top-ranked cellular component and molecular function, respectively, in posttransplant samples from rejectors compared with NRs (Figure 2, Table S6, SDC, https://links.lww.com/TXD/A466). The TAP proteins bind to the complex between MHC class I molecules and endogenous peptide antigens, aiding transport of this complex to the cell surface.16 Presentation of exogenous antigen via MHC class I does not require TAP. Consistent with a shift to TAP-independent antigen presentation after transplantation, pretransplant DMCs are enriched for molecular functions of TAP1- and TAP2-binding and peptide antigen binding, and the cellular component, MHC class I protein complex. Pre-LT samples were also enriched for several biological processes related to morphogenesis, for example, pronephric field specification and nephron morphogenesis, reflecting the contribution of underlying developmental diseases that require liver transplantation, to the pretransplant blood methylome (Figure 3, Table S7, SDC, https://links.lww.com/TXD/A466).

Third, these enrichment results suggests that dynamic methylation could add to the clinical utility of the MHC locus by predicting transplant rejection. Currently, donor–recipient mismatches at polymorphic HLA alleles are used to assess histocomatibility. These mismatches lead to donor-specific anti-HLA antibodies (DSA), which are increasingly implicated in late graft loss, and DSA specificity.23-28 Highly polymorphic HLA genes, for which corresponding proteins show high expression on the cell surface induce DSA more frequently (HLA-DR, HLA-DQ, and HLA-B).29-31 Polymorphisms in HLA-B, which is enriched for DMRs in posttransplant samples from rejectors are also associated with TAP-independent MHC class I antigen processing and presentation of exogenous antigens and have been used to explain CD8-mediated cytotoxicity in a variety of autoimmune diseases.32-34 TAP-independent class I antigen presentation after LT may also explain why alloresponsive CD8-memory cells are also being used clinically to predict acute LT rejection in children.2 Others HLA genes are less polymorphic and have low surface expression because of predominantly intracellular location, which is influenced in part by binding to TAP. These genes are either associated with a low incidence of DSA (HLA-C), or have no known function (HLA-F). HLA-C is largely intracellular where it remains bound to TAP.29HLA-F expression is restricted to the B cell lineage in the resting state, also partly influenced by binding to TAP.30 DMRs in HLA-C and HLA-F are enriched in pretransplant samples from rejectors in our study.

Finally, public databases reveal how sequence variants in DMR of the HLA genes may affect the rejection alloresponse via dynamic methylation, highlighting a complexity that has yet to be understood fully. The expression of MHC molecules is regulated by binding of TFs to regulatory elements.35-37 This binding is influenced by polymorphisms at TF binding sites. An example is the CCCTC binding TF, CTCF, which regulates the expression of several MHC genes.38-39 We have previously shown the effect of altered methylation on CTCF-mediated expression of HLA-DOA, a B cell–specific MHC gene, and the relationship of SNPs in these regulatory loci to B cell presentation of donor antigen during LT rejection in children.40,41 Consistent with these reports, each DMR in the HLA-B, -C, and -F genes contains known SNPs, which either bind TFs, bring about amino acid changes in the peptide-biding regions of corresponding MHC molecules, or may explain cell–cell interactions such as those between HLA-F and natural killer cells, which underlie immunity and tolerance (Table S8, SDC, https://links.lww.com/TXD/A466). For example, the SNPs rs2076177 and rs1736924 in the DMR of HLA-F in rejectors are, respectively, associated with enhanced and decreased expression and splicing in public databases (Figure S1, SDC, https://links.lww.com/TXD/A465). Thus, the study of dynamic DNA methylation can reveal potential causal links between genetic variation and transcriptional regulation, and facilitate preventive intervention by predicting rejection.

In conclusion, and consistent with several previous studies in nontransplant settings, our study localizes dynamic DNA methylation associated with LT rejection in children, to mostly nonpromoter distal regulatory regions. The emergence of the well-known transplant-relevant MHC-dependent antigen presentation as an enriched pathway, and of associated MHC class I-dependent mechanisms among rejectors in this exploratory cohort adds to the validity of DNA methylation as an investigative tool. Given that this enrichment is associated with novel but plausible differentially methylated regulatory loci, DNA methylation analysis can identify novel mechanisms and hypotheses for further investigation in the rare pediatric LT subcohort of transplant recipients. Limitations to be addressed in follow-up studies include a larger sample size, and evaluation of methylation status of DNA derived from intragraft immune cells in addition to DNA derived from unfractionated peripheral blood leukocytes.

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