Analysis of systemic epigenetic alterations in inflammatory bowel disease: defining geographical, genetic, and immune-inflammatory influences on the circulating methylome

doi:10.21203/rs.3.rs-537439/v1

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Analysis of systemic epigenetic alterations in inflammatory bowel disease: defining geographical, genetic, and immune-inflammatory influences on the circulating methylome

https://doi.org/10.21203/rs.3.rs-537439/v1

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published 23 Aug, 2022

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Gene-environment interactions play a pivotal role in the pathogenesis of Inflammatory Bowel Disease (IBD). Using the IBD Character inception cohort (295 controls, 154 CD, 161 UC, 28 IBD-U) we confirm the presence of a highly characteristic DNA methylation profile in peripheral blood implicating 137 differentially methylated positions (DMP) in IBD, including VMP1/MIR21 (p = 9.11×10^− 15) and RPS6KA2 (6.43×10^− 13); with consistency seen across Scandinavia and UK. Cell of origin analysis preferentially implicated the monocyte lineage. Dysregulated loci demonstrate strong genetic influence, notably VMP1 (p = 1.53×10^− 15). Age acceleration is seen in IBD (coefficient 0.94, p < 2.2x10^− 16). Several immuno-active genes demonstrated highly significant correlations between methylation and gene expression in IBD, in particular OSM: IBD r -0.32, p 3.64×10^− 7 vs. non-IBD r -0.14, p = 0.77). Multi-omic integration of methylome, genome and transcriptome also implicate specific pathways that associate with immune activation, response and regulation at disease inception. At follow up, a signature of 3 DMPs (TAP1, TESPA1, RPTOR) associated with treatment escalation to biological agents or surgery (hazard ratio of 5.19 (CI:2.14–12.56, logrank p = 9.70×10^− 4).

Gastroenterology & Hepatology

DNA methylation

genetics

inflammatory bowel diseases (IBD)

prognosis

methylation quantitative trait loci

gene expression

epigenetic clock

mendelian randomisation

Inflammatory Bowel Diseases (IBD) phenotypically classified in two main entities, Crohn’s disease (CD) and Ulcerative Colitis (UC) represent an important public health concern, with an projected prevalence of 1% in Western populations by 2030. There are significant implications for healthcare planning as costs, particularly of new treatments, increase¹. These considerations have accelerated global efforts in better understanding the aetiology of IBD. Unequivocal data now implicate the interaction of host susceptibility with the exposome in the development of IBD². These data have inevitably stimulated studies to explore the potential importance of epigenetic mechanisms, including DNA methylation, in pathogenesis. DNA methylation may regulate gene expression through its effect on the chromatin state as well as the accessibility of the transcription binding sites^3–5 and can be influenced by many pertinent environmental factors including smoking and age^6,7.

We have previously identified several alterations in the IBD-associated circulating epigenome. Our initial study in the high-prevalence Scottish population characterised a replicable pattern of DNA alterations in children with CD, with highly significant enrichment of methylation changes around GWAS single nucleotide polymorphisms, in particular the HLA region and VMP1/MIR21⁸. More recently, our adult epigenome-wide study within the Scottish population identified distinct differential methylation across key IBD genes⁹. Certain signals were highly cell-specific; RPS6KA2 was shown to be a CD14⁺ monocyte specific signal in IBD⁹. These top differentially expressed methylation signals were independently confirmed and replicated in a treatment-naïve paediatric CD cohort in North America (RISK consortium)¹⁰, providing strong stimulus for further research in this field. Importantly, the extent to which the findings can be generalised to other populations is largely unknown. Furthermore, the timing, stability, and functional importance of epigenetic alterations in affecting gene transcription have not been fully investigated.

With the advancing therapeutic repertoire in IBD, there is keen interest in risk stratifying patients at diagnosis in order to allow personalised medicine, with recent optimism that genetic, transcriptional, glycomic or serological markers may predict disease course, and help position new therapies^11–18. Promising data have emerged that epigenetic alterations are helpful in other diseases, notably colorectal cancer. The potential clinical utility of these epigenetic marks as biomarkers in immune- mediated diseases is yet to be determined.

In our multi-centre study, we aim to extend current understanding of the pathogenetic and translational importance of the circulating epigenome in IBD. We firstly aim to assess the consistency of DNA methylation alterations in IBD across geographically distinct populations in UK, Scandinavia and Spain. We investigate environmental modifiers of the methylome including inflammation, smoking and aging. Furthermore, we define the genetic contribution to these alterations, their association with gene expression and define the methylome of disease progression in IBD. Whilst focussed on IBD, the findings have potential implications beyond this disease field, to other complex and immune-mediated diseases.

Study demographics

The IBD Character cohort represents a multi-centre inception cohort in which 247 (72%) of the 343 IBD patients were treatment naïve at recruitment. Genome-wide methylation was measured in 638 DNA samples extracted from peripheral blood (295 controls, 154 CD, 161 UC and 28 IBD-unclassified (IBD-U) patients). Table 1 summarises study recruitment and patient demographics. The mean age in patients with IBD (n=343) was 34 years (range 7–79), and 33 years (range 3-79) in controls (n= 295). A total of 27% of CD patients had a colonic disease phenotype at recruitment while 42% of patients with UC exhibited extensive colitis. Two hundred individuals were recruited in the UK, 367 in Scandinavia and 66 in Spain at presentation for investigation of suspected IBD. A total of 33% of those recruited in the UK had confirmed IBD after investigation , while 58% and 39% had IBD in the Scandinavian and Spanish cohort respectively (Table 1).

Differentially methylated probes in Inflammatory Bowel Disease

Across the entire cohort, 137 probes exhibited Holm significant IBD-associated methylation differences in comparing IBD with controls (Supplementary Table 1). These include probes mapping at the loci VMP1/MIR21 (p=9.11×10^-15), SBNO2 (2.70×10^-14), RPS6KA2 (6.43×10^-13), and TNFSF10 (7.72×10^-8), thereby replicating and validating our previous findings⁹. Novel findings include differential methylation of PHOSPHO1 (3.43×10^-9) and SELPLG (2.54×10^-7). Table 2 summarises the top differentially methylated positions (DMPs).

Similar analyses were performed to identify UC and CD specific DMPs. There were 72 DMPs that differentiated CD from controls (Supplementary Table 2) and 67 DMPs that differentiated UC from controls (Supplementary Table 3).There were 24 DMPs that demonstrated overlap across UC and CD analyses (Supplementary Figure 1). There were no probes that differentiated UC from CD. Analysis was performed of Differentially Methylated Regions (DMRs), defined as regions with ≥ 3 contiguous probes with FDR corrected p<0.05 within a distance threshold based on expected probe density. The VMP1 locus on chromosome 17 was the only DMR identified within our dataset that remained significant using these criteria.

Next, we compared our dataset with the previous findings from our group on DMPs in adult and paediatric IBD by correlation analyses of the top 1000 methylation probe beta values in the 3 cohorts (Supplementary Figure 2)^8,9. Strong correlation was seen across populations (adult BIOM cohort⁹ r 0.96, p< 2.20×10^-308; paediatric cohort r 0.81, p<2.20×10^-308).

Consistency of Differentially methylated positions (DMPs) across Northern Europe

We then analysed the consistency of methylation across Europe (Figure 1) by splitting our cohort based on geographic area (Scandinavia vs UK vs Spain). Independent DMP analysis using all probes was performed in Scandinavia, identifying 34 probes that differentiated IBD from controls with a Holm p<0.05 (Table 3). These included SBNO2 (p= 2.88×10^-9), VMP1 (p=6.89×10^-8) and RPS6KA2 (p=1.57×10^-3). A total of 26 of these probes were significant in the UK cohort (n=200). In the Spanish cohort (n=66) only one (RPS6KA2) remained significant (Holm p=0.03). Power calculations were performed in view of these findings, taking into consideration the effect sizes noted in the Scandinavian cohort. This post-hoc analysis confirmed that the Spanish cohort was adequately powered to detect significant differences in 11 of the 34 DMPs identified in the Scandinavian dataset (power=0.8, alpha=0.05, effect size cut-off=0.8; Supplementary Table 4). DMPs consistently replicated across samples from UK and Scandinavia notably ZEB2, SBNO2 and ZPLD1 were not detected in samples from Spain despite being adequately powered to detect these DMPs.

Deciphering tissue and immune cell specificity of differentially methylated probes in IBD

Blood cell-type-specific DNase hypersensitive sites (DHS) enrichment testing using the eFORGE v2.0 online tool¹⁹ was performed for our top 137 differentially expressed methylation probes. This demonstrated that 62 of the 137 IBD associated CpGs are enriched in DHSs within monocytes (binomial p=2.04×10^-8). These encompassed the top-most differentially expressed DMPs in disease such as OSM, VMP1, SELPLG (PSGL-1) and AIM2. No enrichments were seen in other immune cells such as T-cells, B-cells, NK cells or haematopoietic stem cells (Supplementary Figure 3).

We then examined for overlap between the circulating blood DMPs in CD and UC in this study and those differentially methylated in human colonic intraepithelial cells (IECs) in CD and UC cohorts²⁰. A total of 38 and 5 probes overlapped in UC and CD respectively (Supplementary table 5). Top IEC signals with overlap in circulating methylome across both UC and colonic CD included SBNO2, RPS6KA2 and SELPLG. Overlapping probes unique to UC included AIM2, MAD1L1, MIR4470 and MIR3679.

The association of DNA methylation with inflammation

In order to better understand the influence of inflammation on the top differentially methylated probes, 3 distinct analyses were performed. These included correlations with inflammatory markers, DMP analysis using inflammatory markers such as hsCRP as covariates and DMP overlap with published inflammation associated methylation probes.

We investigated the correlation of the 137 differentially expressed DMPs in IBD with inflammatory markers, i.e. high sensitivity c-reactive protein (hsCRP) and albumin, in individuals with complete data (n=591; Supplementary Figure 4). A total of 98 probes correlated with concentration of hsCRP and 118 with albumin levels. Most significant correlations with hsCRP included LIPC (cg27307975, r -0.56, p= <2.22 x10^-16) and ZEB2 (cg20995564, r -0.48, p= <2.22 x10^-16). Top DMPs such as VMP1 (cg12054453, r 0.25, p= 3.78x10^-8) and RPS6KA2 (cg17501210, r -0.30, p= 7.81x10^-12) showed moderate correlation with hsCRP. There were however 39 probes that did not correlate with hsCRP and included another VMP1 probe (cg02782634, r -0.13, p=0.08) (Supplementary Table 6). In order to adjust for inflammatory activity, we also performed DMP analysis with previously described covariates and with the addition of hsCRP. A total of 59 probes remained significant and included SBNO2(p=5.26×10^-14), RPS6KA2 (p=4.51×10^-11) and VMP1 (p=5.36×10^-10) (Supplementary Table 7).

We then compared our top DMPs to those that have been reported to be associated with CRP levels²¹. A total of 43 of the 218 DMPs identified in the Lighthart et al were differentially methylated in our study. There were however 94 probes that still associated with IBD and were independent of inflammation associated probes (Supplementary Table 8). These included CLU (cg16292768), SBNO2 (cg12170787) and VMP1(cg02782634).

Finally, in order to identify inflammation independent DMPs, we excluded probes that correlated with hsCRP or reported inflammation-associated DMPs and identified 30 DMPs that showed no overlap with published inflammation associated DMPs or any correlation with inflammation (Supplementary Table 9).

The association of DNA methylation with smoking

Smoking can affect DNA methylation at certain CpG sites in blood and a recent systematic review summarised a total of 1460 smoking-associated CpG sites²². None of the 137 probes identified in this study overlap with the reported smoking-associated CpGs. Given the divergent effects of smoking on IBD subtypes, we performed analyses for probes that associate with CD or UC by including smoking, age, sex and cell proportions as covariates. In CD, 107 DMPs remained significant; SBNO2, RPS6KA2 and VMP1 being the top probes (Supplementary Table 10). In UC, 59 probes remained significant: SBNO2, VMP1 and ZEB2 being the most significant probes (Supplementary Table 11).

‘Epigenetic age’ acceleration and its association with Inflammatory Bowel Disease

The ‘epigenetic age’ of the patients was calculated using the methodology described by Horvath et al⁷. There was a strong correlation between actual age and ‘epigenetic age’ in this cohort (r=0.94, CI: 0.93-0.95, p<2.20x10^-16; Supplementary Figure 5). Age acceleration (AgeAccel) was seen in IBD (coefficient 0.94, p<2.2x10^-16). DiffAge, defined as the difference between predicted biological age and chronological age was determined for IBD and controls (including subtypes)^7,23. There were significant DiffAge seen between IBD and controls (non-IBD: median 4.34 years (IQR:3.83-4.70) vs. IBD: 5.28 years(IQR: 4.72-5.64); p<2.20x10^-16). Differences were also seen between IBD subtypes compared to non-IBD (vs. UC: 5.08 years (IQR: 4.51-5.49); p<2.20x10^-16and vs. CD: 5.53 years (IQR: 4.99-5.79); p<2.20x10^-16; Supplementary Figure 5). There was poor correlation seen between DiffAge and inflammatory markers in the entire cohort (hsCRP: r 0.09, p=0.03; Alb: r -0.13, p=2.88 x10^-3). No correlations were seen between DiffAge and treatment exposure in UC or CD (UC p=0.89; CD p=0.21).

Germline variations show a strong correlation with DNA methylation (meQTLs)

Using paired genetic and methylation data for the entire cohort (n=638) and age, sex as covariates, meQTLs were generated using the top DMPs using the entire cohort (DMPs n=137). A total of 2991 cis-meQTLs were identified. After applying a MAF >0.05 and holm adjustment, 341 cis-meQTLs remained significant across 21 unique genes, indicating a strong genetic influence on methylation. Several key loci that were significantly differentially methylated in IBD had a strong genetic influence including ITGB2 (7 cis-meQTLs; top p=2.83×10^-16), and 143 VMP1/MIR21 cis-meQTLs across 6 probes (Supplementary Table 12). This includes meQTLs with a known GWAS single nucleotide polymorphism (snp) rs1292053 and also with its previously reported LD snp ( rs8078424, r²=0.43, top p=1.48×10^-20)⁹. Other novel IBD relevant associations include AIM2(16 cis-meQTLs; top p=2.83×10^-16).

In order to determine the causal role of DNA methylation in IBD, Mendelian randomisation was applied to our dataset using TwoSampleMR²⁴. The most significant meQTLs for each CpG (sentinel meQTL) were generated using all snps and methylation probes independent of a diagnosis of IBD (Supplementary Table 13). Using sentinel meQTLs as the instrument variable, methylation as exposure variable and IBD as outcome variable, no causal associations were identified in our dataset.

IBD associated genes are differentially methylated and correlate with gene expression

In this study, paired whole blood gene expression data was available in 590 patients. Of the 137 DMPs identified, 24 probes were located either 200 or 1500 nucleotides upstream of the transcriptional start sites (TSS200 and TSS1500), within regulatory regions of a gene (5’untranslated region (5’UTR), 3’ untranslated region (3’UTR)) or body of the gene. In this study, we discovered 81 highly significant correlations (Supplementary Table 14) with a series of novel genes demonstrating high correlation between methylation alterations and expression including CD247 (Body, r= -0.69, p=4.70×10^-80), SELPLG ( 5’UTR, r= -0.56, p=3.10×10^-46), OSM (TSS1500, r= -0.33, p=2.48×10^-14) and AIM2 (TSS1500, r= -0.51, p=6.32×10^-37). Further sub-analysis was performed investigating the correlation differences in IBD and non-IBD cohorts (Supplementary Table 15). SELPLG expression and methylation demonstrated a relative higher negative correlation within IBD than non-IBD (IBD r -0.53, p=2.24×10^-22 vs -0.37, p=6.34×10^-8; 5’UTR). A total of 14 probes demonstrated significant correlation in IBD patients but no correlation in non-IBD (Figure 2). Examples include ZEB2 demonstrated negative correlation in IBD (r -0.45, p=6.43×10^-15; gene body) and no significant correlation in controls (r -0.16, p=0.29). Similarly, OSM expression negative correlated with methylation within IBD cases but no correlation seen within controls (IBD r -0.32, p 3.64×10^-7 vs. non-IBD r -0.14, p=0.77; TSS1500). There were 6 genes with methylation probes within TSS200 region, all demonstrating negative correlations with DNA methylation apart from SYNE2 (IBD r 0.31, p=2.89×10^-6 vs. non-IBD r 0.22, p=0.02).

Integrative analysis identify immune cell related activation in IBD

Multi-omics analysis was performed using Multi-omics Factor Analysis (MOFA)²⁵. Integration of IBD-related SNPs and mRNAs around the 137 DMPs produced ten factors, of which the first four explained most of the variability in the dataset (Fig. 3A). Factor1 and Factor 4 strongly correlated with IBD and hsCRP (Fig. 3B&E). These 2 factors were mostly influenced by DMPs and mRNAs (Fig. 3C) and remained independent of each other (r=0.052, Fig. 3D). Factor1 was higher in IBD compared with controls regardless of inflammation (Fig. 3F; p<2.20×10^-16) and appeared specific for IBD. On the contrary, at null hsCRP, Factor 4 was similar in IBD patients and controls (p=0.374 when < 0.5 mg/L) but was found to be elevated in IBD overall (Fig. 3D; p=4.21×10^-13) due to higher values relative to control starting at even marginally increased hsCRP. In UC, Factor1 reflected the extent of colitis (Fig. 3G). Thus, multi-omics integration revealed DMP-driven, IBD- and hsCRP-associated factors 1 and 4.

Factor 1 was defined by greater methylation of genes such as CXCR6 and CD247, and reduced methylation of ZEB2 (Fig. 3H-I). The transcriptomic repertoire of Factor 1 prominently featured S100 proteins and matrix metalloproteinases. Only one IBD GWAS SNP contributed to Factor 1: rs7495132 (CRTC3). In gene ontology, Factor1 was related to inflammation with activation of undifferentiated leukocytes, and lipid metabolism (Fig. 3J).

Reduced methylation at VMP1 (TMEM49) and the interferon-inducible inflammasome trigger AIM2 were driving Factor 4, which also shared lowered ZEB2 methylation with Factor1 (Fig. 3H-I). Gene expression behind Factor 4 included the immunosuppressive CD274 (PD-L1), along with Fc gamma receptor 1 (FCGR1A, FCGR1B) and AIM2 (overexpressed, consistent with lower methylation; Fig. 3J). The GWAS polymorphism rs1801274 (FCGR2A) was negatively correlated with Factor 4. Pathway analysis uncovered potential roles for Factor 4 in immune regulation, lipid metabolism, and cell death (Fig. 3K). The composition of Factor 4 shows that IBD-related differential methylation at VMP1 may relate to opsonization and phagocytosis and testifies of close coupling of pro- and anti-inflammatory responses.

DNA methylation associates with disease course in Inflammatory Bowel Disease

Follow up data were available for 291 patients with IBD in order to identify methylation markers that predicted treatment escalation in IBD over a median follow-up period of 526 days (IQR:223-775)(Table 4). Thirty nine patients with a diagnosis of CD, 26 patients with UC and 2 with IBD-U required escalation after a median follow-up time of 98 days (IQR:40-229). The median age in this group was 28 years (range: 18-67) and 58% were male (n=39). For downstream analysis, data were split into training (2/3^rd) and testing set (1/3^rd) for signal validation. In order to investigate DMPs that associate with treatment escalation, principal component (PC) analysis using all methylation probes was performed to identify PCs that associate with treatment escalation. In the training set (n=194; 40 escalations), 11 PCs significantly associated with treatment escalation (Supplementary Figure 6). The first 2 PCs correlate with cell proportions, however certain PCs such as PC17 only associate with treatment escalation. Probes that represent top/bottom 5% of variance within this principal component are shown in Supplementary Figure 7.

Probes that represented the top 5% of variance across all 11PCs that associate with treatment escalation in the training set were selected (n=55) for further LASSO penalised Cox regression. A total of 3 methylation probes remained significant predictors (Table 5) in IBD. These included Transporter associated with Antigen Processing 1 (TAP1; HR: 12.79), Regulatory protein of MTOR complex 1(RPTOR; HR:1.47) and Thymocyte expressed, positive selection associated 1 (TESPA1; HR: 1.29). A model incorporating these 3 probes, was then tested in an independent testing set (n=97; 27 escalations). The 3-probe combined model remained a significant predictor of treatment escalation in the testing set with a HR of 5.19 (CI:2.14-12.56, logrank p=9.70×10^-4; Figure 4). Adjusting for treatment naivety did not influence the top differentially methylated probes among patients with IBD. Similar analyses were performed to generate a model that included known clinical predictors: age, sex, hsCRP and albumin in the training set (n=174) and validated in the testing set (n=88) where data for covariates were complete. This clinical model had an HR of 4.55 (CI:2.00-10.35; p=1.03×10^-4) and performed at par with the methylation model. Supplementary Table 16 shows correlations of the top 3 prognostic DMPs with conventional inflammatory markers.

Are geographical replicability and variability preferentially driven by germline variation or alterations in the exposome?

In complex diseases such as IBD, DNA methylation potentially represents a mechanism at the interface between genetics and the environment. The strengths of this prospective case-control study include recruitment of individuals with a new diagnosis of IBD, mostly naïve to medical therapy, across multiple clinical centres in Europe. Importantly our data strongly replicate, validate and extend previous key DMP findings from children with CD and adults in the index Scottish population where our initial data were generated^8,9 to Scandinavia. We demonstrate significant evidence for dysregulation of several previously implicated loci, notably VMP1, RPS6KA2 and SBNO2 across Scandinavia and UK, populations with a shared genetic ancestry. Replication was less evident in the smaller Southern European cohort recruited in Spain. Despite being adequately powered to detect significant differences in 11 of the 34 probes tested, significant differential methylation for top probes such as ZEB2 and SBNO2 were not seen in the Spanish cohort. RPS6KA2 remains the only consistent signal across UK, Scandinavia and Spain. These differences may be in line with the North-South gradient in IBD that have been reported across USA and Europe^26–28. Whilst more detailed studies in Southern Europe are needed, these data may suggest population specificity of several methylation changes in IBD and heighten the focus on exploring genetic influences and factors within the exposome in these populations.

DMPs implicated in our study such as VMP1, RPS6KA2 and SBNO2 were also highly significant in the paediatric studies at diagnosis (RISK consortium; USA and Canada)¹⁰. It remains to be seen whether the methylation differences seen within our predominantly Caucasian non-Jewish cohort are replicated in other non-Caucasian populations. Population-specific methylation differences at 439 CpG sites were reported in a study that included Caucasian-American, African-American and Han Chinese-American healthy individuals and associated these changes with distinct phenotypic characteristics such as drug metabolism, disease susceptibility and appearance²⁹. Although, the majority of these differences were due to underlying genetic variations, up to 1/3^rd of the DMPs were independent of germline variations, bringing into focus the role of non-genetic influences such as the exposome on the epivariance across populations. Similar findings were also reported in a Hispanic origin cohort where genetic ancestry explained up to 3/4^th of the variance in methylation³⁰ and an Indonesian cohort where up to 10% of genes showed differential expression and methylation patterns between islands, likely attributed to small-scale environmental differences³¹. It remains to be determined whether the differences which are apparent between Northern Europe and Southern Europe within our study are genetically determined or whether these are related to the exposome. Further multiregional epigenome studies are now needed to explore this further in IBD.

The influence of genetics on DNA methylation in IBD

The differences between populations focus interest on the relative importance of germline variation on methylation; and in turn on gene expression. Our data also shed new light on the relationship between germline variation at specific loci and epigenetic alterations in complex disease. A key locus, VMP1 showed 143 cis-meQTLs across 6 probes and includes meQTLs with a known GWAS snp rs1292053 and its LD snp( rs8078424, top p=1.48×10^-20)⁹. However, causal inference using mendelian randomisation could not be determined. A recent study in CD demonstrated that DNA methylation at 3 CpG sites causally associated with a GWAS snp rs1819333 using mendelian randomisation analysis, likely through transcriptional regulation of RPS6KA2 expression^10,32. This snp was not on our genotyping platform and further validation could not be performed in this study. RPS6KA2 specific hypomethylation has consistently been demonstrated in IBD across several independent cohorts and across all ages^8–10. This gene encodes for a ribosomal kinase, a member of the serine/threonine kinase family and regulates the autophagy associated mTOR pathway and particularly relevant in CD³³. These data provide insight into the complex interaction of genetics and epigenetics in the pathophysiology of IBD.

Pro-inflammatory pathways

Of particular interest within the field of DNA methylation has been to address whether the changes seen are causal or a consequence of disease initiation. Somineni et al explored this in the paediatric cohort through several methods including longitudinal dynamics of methylation, correlation with known inflammatory markers and mendelian randomisation¹⁰. Although the vast majority of methylation signals in the Somineni et al study represented a consequence of disease initiation through inflammation, there were 10 CpGs that were unchanged at follow up³⁴. In our studies, some of the methylation alterations have been shown to associate with CRP levels²¹. Even though these changes may be a consequence of inflammation, the genes implicated are of great interest as potential targets for future drug discovery and the mechanisms of how these genes associate with inflammation, disease onset and disease course need further exploration. An example includes OSM, a gene that has been recently shown to be upregulated in inflamed intestinal tissue in IBD and is pro-inflammatory. This gene is able to predict anti-TNF response in IBD, making it a useful clinical biomarker and a potential drug target³⁵. In our study, a 450K probe close to the TSS (TSS1500, p=8.6´10^-11) of OSM is differentially methylated in IBD compared to controls and demonstrates correlation with OSM gene expression in IBD compared to controls (IBD r -0.32, p 3.64×10^-7 vs. non-IBD r -0.14, p=0.77; TSS1500). Other signals linked with CRP-associated DMPs have relevance in IBD pathogenesis. One of the top signals is P-selectin glycoprotein ligand-1(PSGL-1 or SELPLG) and plays a critical role in immune cell recruitment to sites of tissue inflammation³⁶. Anti-PSGL-1 antibody is currently in phase 2 drug trial for the treatment of CD (NIH project no. 1R44DK085845-01A1). Furthermore, differential methylation within this gene correlated with PSGL-1 expression in our study(IBD r -0.53, p=2.24×10^-22 vs -0.37, p=6.34×10^-8; 5’UTR). It is known that a normal level of DNA methylation is required to control differential expression of maternal and paternal alleles of imprinted genes³⁷ and plays an important role in cell differentiation and embryonic development. Studies have shown that methylation at gene promoter regions can vary depending on cell type with hypermethylation corresponding with low or no gene transcription^38,39.

Multi-omic data integration using MOFA revealed 2 factors (Factor 1 and 4) strongly associated with IBD and pro-inflammatory pathways. While Factor 1 was defined by greater methylation within CXCR6 and CD247 genes, reduced methylation of ZEB2 (Fig. 3H-I) and mRNA expression predominantly featuring S100 proteins and matrix metalloproteinases, Factor 4 however was defined by reduced methylation at VMP1 (TMEM49) and the interferon-inducible inflammasome trigger AIM2. The CpGs and mRNA repertoire that define these 2 factors highlight the role immune cell activation, cellular response and regulation play in shaping the circulatory methylome and transcriptome in patients with IBD at disease inception. These data provide a repertoire of novel targets to develop future drug therapies that target these pro-inflammatory pathways at disease onset.

Age acceleration in Inflammatory Bowel Disease

There is emerging interest in studying disease-associated age acceleration, defined as the difference between predicted age (determined by DNA methylation patterns) and chronological age. Recent studies have linked epigenetic age acceleration to all cause mortality^40,41 and cardiovascular disease outcomes⁴². Epigenetic age acceleration is yet to be explored in the context of immune-mediated diseases such as IBD. In our cohort, significant DiffAge was seen between IBD and controls (non-IBD: median 4.34 years (IQR:3.83-4.70) vs. IBD: 5.28 years(IQR: 4.72-5.64); p<2.20x10^-16); this was consistently seen within CD and UC when compared to non-IBD (vs. UC: 5.08 years(IQR: 4.51-5.49); p<2.20x10^-16and vs. CD: 5.53 years(IQR: 4.99-5.79); p<2.20x10^-16). Of particular interest was the absence of its association with drug exposure both in CD and UC (UC p=0.89, CD p=0.24) and poor correlation with inflammatory indices at disease inception. Further studies measuring serial methylation profiles may help determine whether these differences persist after treatment, over time.

The epigenome of treatment escalation

Of clinical interest, DNA methylation alterations were investigated for their performance as prognostic markers. A unique strength of our study lies in the evaluation of methylation in predicting an aggressive disease course in a treatment-naïve unselected inception cohort. We identify a simple molecular signature worthy of further attention as a potential predictive biomarker. The reported prognostic signals have relevance in the pathogenesis of IBD. TAP1 is essential in HLA class I proteins for generation of cytotoxic T-cell mediated immune response. Differential expression of this gene has been shown to associate with immune escape and poor prognosis in colorectal cancer and an indicator of an aggressive breast tumour^43,44 (Table 5). In IBD, TAP1 differential methylation has been previously reported as a CD specific signal in IECs from small intestine and colon and reported to be a regulatory DMR (rDMR: differentially methylated region (DMRs) that is located within 10kb of the transcription start site of a differentially expressed gene)²⁰. RPTOR gene forms part of the mTORC1 complex which has been shown to promote UC through COX-2 mediated Th17 responses⁴⁵. Depletion of RPTOR inactivates mTORC1 and ameliorates UC⁴⁵. These differentially methylated genes also differ from those implicated in disease susceptibility. Similar trends have been seen in GWAS in CD where unique prognostic genes have been identified that do not overlap with disease susceptibility¹⁴.

Several studies including GWAS have utilised the CD8⁺ T-cell derived transcriptome criteria for escalation, defined as the need for 2 or more immunosuppressants’ after initial disease remission^{13,14,46–48}. These studies collectively have provided the first lines of evidence for a circulating biomarker profile to define a subset of patients that require treatment escalation who perhaps may benefit from a ‘top-down’ approach to management at an early stage. Our data complement these findings and provide further molecular depth by defining their methylome signature.

Within the paediatric CD group, DNA methylation in separated circulating CD8⁺ T cells has been shown not to predict outcomes in CD⁴⁹. Given that CD8⁺ cells only represent a small proportion of the cellular compartment in whole blood, our signals warrant further exploration of the prognostic methylome in other immune cells. It is noteworthy in this context, the e-FORGE analysis in our dataset preferentially implicates monocytes. Furthermore, our prognostic analysis includes UC; the molecular prognostic profile of this subtype has not been examined previously. Further large multicentre studies are now needed to explore these findings further both in adults and children.

Strengths and limitations

Our study has recruited over four years one of the largest multi-centre European inception cohorts reported to date, and generated IBD specific methylation signals, associated with disease onset and progression. We have explored geographical, genetic and non-genetic correlates as mechanistic as well as translational implications. We have previously demonstrated that the most dysregulated areas implicated are cell-specific⁹. There are certain methodological considerations to take into account when interpreting our data. Firstly, our dataset is adequately powered in Scandinavia and UK, but we acknowledge that the samples size will detect some, but not all DMPs in Southern Europe (Spain). We have demonstrated, in the Spanish cohort, replication for RPS6KA2, one of the most robust findings in Northern Europe and North America, whilst excluding a significant effect for ten other markers all highly significant in Scandinavia.

One of the challenges of methylation studies in inflammatory diseases is to disentangle the effect of inflammation on the top differentially expressed DMPs. We have approached this in appropriate detail by several methods including correlation analyses with inflammatory markers such as hsCRP and matching our top probes with publicly available inflammation-associated DMPs.

There are also potential challenges in the prognostic analysis of data from multi-centre studies associated with the concern that decision-making can vary across centres. However, in our study all European centres used similar criteria and guidelines for decisions on escalation in IBD (also known as ‘step-up’ approach) where increments are made in therapy based on response to the initial treatment. Although we were able to identify and internally validate IBD-specific prognostic signals in this analysis , IBD subtype analysis and validation was not feasible due to the small number of patients who escalated therapy within each subtype. It is worth noting, however, that certain prognostic signals such as TAP1 have been shown to be CD specific in the published literature. Future studies are needed to further validate our findings.

This study highlights the stability of the IBD-specific circulating methylome across regions with shared ancestry. We demonstrate a close association of the methylome with inflammation and through integrative multi-omic analyses we identify key pro-inflammatory genes that are upregulated in IBD at inception. Furthermore, differential methylation within certain genes such as TAP1 associate with disease course over time. These data provide a rich resource for future translational studies investigating the epigenome in IBD; and potentially represent a paradigm for analysis in other complex diseases.

Study design

Patients were recruited prospectively as part of the IBD-CHARACTER inception cohort (reference 305676) from gastroenterology appointments across 7 centres in Europe (Table 1). All IBD cases met the standard diagnostic criteria for either UC or CD following thorough clinical, microbiological, endoscopic, histological, and radiological evaluation. The Lennard-Jones, Montreal and Paris criteria were used for diagnosis and classification of clinical phenotypes. The control group consisted of symptomatic controls attending gastroenterology clinic during the same period with no evidence of IBD after further investigations and at follow up. Healthy volunteers were also recruited into the study.

We collected patient demographics including sex, age at diagnosis, and date of diagnosis. Details of drug therapy and concomitant medications were recorded. Treatment naïvety within the IBD cohort was defined as no exposure to any IBD-related medical therapies such as oral and topical steroids, oral and topical 5-ASA therapies, biological therapies and immunomodulators. High-sensitivity CRP (hsCRP), albumin were re-assayed in a single batch at the end of recruitment. Other routine markers including haemoglobin, white cell count were also recorded.

Patients with IBD were followed prospectively and information on clinical outcomes were collected during follow-up. Treatment escalation was defined as the need for a biologic, ciclosporin or surgery, instituted for disease flare after initial induction therapy and aiming to induce disease remission. In UC, the definition of treatment escalation also included colectomy during index admission.

All patients and controls provided written, informed consent with local ethical approval at each centre.

Genome-wide methylation profiling

Peripheral blood leukocyte DNA was bisulphite converted and analysed using the Illumina HumanMethylation450 platform (Illumina, San Diego, CA, USA).⁵⁰ Cases and controls were randomly distributed across chips. Data were processed using the meffil package⁵¹ in R (R Foundation for Statistical Computing, Vienna). Samples containing >1% probes with detection p values >0.01 were removed. Probes with bead counts <3 in 10% samples, or detection p values>0.01 in 10% samples were also removed. Sex mismatches were identified by analysing the median intensities of the sex chromosome probes and removed from further analyses. Genotypes were compared with genotyping probes on the methylation array. Probes containing snps with a minor allele frequency of ≥0.01 in the European population in the 1000 Genomes Project were also removed.

Three samples with low signal intensities were removed (>1% probes with detection p values >0.01) and 684 probes were filtered out due to low signal. There were n=15 failed quality control that were removed from further analyses.

Differentially methylated positions and regions

Cell proportions were estimated from methylation data using the Houseman algorithm⁵² using the meffil package in R. Differentially methylated position analysis (DMP, single CpG probe) was performed using age, sex and cell proportions as covariates⁵³. Statistical significance was set at p<0.05 following adjustment for multiple testing using Holm correction⁵⁴ for whole blood data.

Differentially methylated regions (DMRs) were identified using the Lasso function from the ChaMP pipeline^55,56 (distinct from the lasso function described below in the biomarker discovery section) and defined as three or more contiguous probes within a distance threshold based on expected probe density, each achieving a FDR corrected p<0.05 in DMP analysis.

Epigenetic clock and Age acceleration

Age acceleration (AgeAccel) is defined as the residual resulting from a linear regression model, regressing the Horvath estimate of epigenetic age (biological age) on chronological age⁷. A positive value for AgeAccel indicates that the observed epigenetic age is higher than that predicted, based on chronological age. DiffAge according to Horvath was defined as the differences between predicted biological age and chronological age⁷. A positive value for DiffAge is seen for individuals with an increased biological age compared to their chronological age²³.

Methylation Quantitative Trait Loci Analyses (MeQTL) and Mendelian Randomisation

Whole blood leukocyte DNA were extracted using the Nucleon BACC 3 DNA extraction kit (GE Healthcare, Buckinghamshire, UK). Patients were genotyped using the Illumina HumanOmniExpressExome-8 Bead Chips (Illumina, San Diego, CA, USA). A sex-check was performed using PLINK to identify and remove sex-mismatches. MeQTLs and eQTLs were estimated using the Matrix-eQTL package ⁵⁷, with a distance threshold of 1Mb. snps with a minor allele frequency of <5% were filtered from downstream analyses. Age and sex were included as covariates in order to identify specific disease-associated meQTLs. A Holm corrected p <0.05 was used as the threshold for statistical significance for disease associated meQTLs.

Mendelian randomisation was performed using TwoSampleMR to determine causal inference²⁴ for the top meQTLs. For this analysis meQTLs, independent of IBD were obtained, using the same filtering thresholds as described above. MeQTL snps were used as the instrumental variable, CpG as the exposure, and IBD as the outcome variable.

Gene expression profiling

Whole blood RNA underwent targeted RNA sequencing was performed using Ion AmpliSeq Human Gene Expression Core Panel, containing 20,802 genes. Quality control was performed using the Ion Library Taqman™ Quantitation kit. Sequence reads were aligned using the Torrent Suite Software (TSS) and the number of matches per amplicon were quantified. After filtering, 14,182 transcripts were available for further analysis.

Cell-of-origin analysis

We used eFORGE v2.0 (http://eforge.cs.ucl.ac.uk/) to identify if the replicated CpGs were enriched in DNase I hypersensitive sites (DHSs) (markers of active regulatory regions) and loci with overlapping histone modifications (H3Kme1, H3Kme4, H3K9me3, H3K27me3, and H3K36me3) across available cell lines and tissues from Roadmap Epigenomics Project, BLUEPRINT Epigenome, and ENCODE (Encyclopedia of DNA Elements) consortia data.

Data integrative and pathway analysis

Integration of genomic, methylomic, and transcriptomic layers was done with Multi-Omics Factor Analysis v2 (MOFA). The following data were selected for cross-omics integration: all the 137 probes differentially methylated between IBD and controls, all the GWAS-related single nucleotide polymorphisms with minor allele frequency > 5% (SNPs, n=99), and all the available IBD-related transcripts (based on DisGeNET, n=1021). The integration was conducted using Multi-Omics Factor Analysis v2 (MOFA+)²⁵. Prioritized features were subject to gene ontology analysis using gene set enrichment analysis from the Broad Institute (Cambridge, USA). Further details are available in supplementary methods.

Prognostication analysis

For IBD prognostic marker analysis, data were randomly split to training (2/3^rd) and testing set (1/3^rd). Unsupervised principal component analyses were performed to examine correlates of treatment escalation and clinical predictors of disease severity using the PCAtools package(https://github.com/kevinblighe/PCAtools). Principal components (PC) that associate with treatment escalation were selected to identify methylation probes that explain the top 5% variance within each PC. These methylation probes were then selected to fit a model using least absolute shrinkage and selection operator (LASSO) penalised Cox regression with age and sex as covariates and 1000 cross validated iterations. Results of the Cox regression were then validated in the testing cohort by generating a combined model using all significant variables. Hazard ratios (HRs) were calculated from Cox regression coefficients. We then performed survival analyses based on the need for treatment escalation as defined above. Comparison for treatment escalation was performed using a log-rank test (1df) and graphically represented with a Kaplan-Meier plot. The performance of the methylation model was also assessed in the IBD subtypes. Conventional predictors of treatment escalation including age, sex, hsCRP and albumin in CD and age, sex, hsCRP, albumin and extent of colitis in UC were also examined.

Author Contributions: Study design RK, JH, MDA, MV, JS. Patient recruitment and sample processing NTV, RK, NAK, DB, SV, ATA CO, FG, JSO, MP, PR. Experimental work OA, FH, CP, RK, NTV, ATA, NAK, JKN. Data Analysis RK, ATA, JKN. RK, ATA and JKN wrote the manuscript. All authors were involved in critical review, editing, revision and approval of the final manuscript.

Conflict of interest: R. Kalla Financial support for research: EC IBD-Character, Lecture fee(s): Ferring, N. Kennedy Financial support for research: Wellcome Trust, Conflict with: Abbvie, MSD, Warner Chilcott, Ferring speaker fees. Shire Travel bursary, A. Adams: None Declared, J. Satsangi Financial support for research: EC grant IBD-BIOM,IBD-Character, Wellcome, CSO, MRC, Conflict with: Consultant for: Takeda, Conflict with: MSD speaker fees. Shire travelling expenses

Funding: The study has been funded by the following EU FP7 grant: IBD-CHARACTER (contract # 2858546). NAK was funded by the Wellcome Trust (grant number WT097943MA).

Corresponding authors: Professor J Satsangi, Dr Rahul Kalla, Dr Alex Adams.

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Table 1: Study demographics and study recruitment according to clinical centre

Study demographics
Variables	Inflammatory Bowel Diseases (n=343)	Controls (n=295)
Males (%)	185(54)	130(44)
Subtype IBD (CD:UC:IBD-U)	154:161:28
Subtype Controls (HC: Non-IBD)		54:241
Smoking status (current: never: ex: missing)	54:147:103:39	53:147:58:37
Mean age (range)	34(7-79)	33(3-79)
Montreal classification for CD
L1	48, 1 (31%, 1%)
L2, +L4	41, 5 (27%, 3%)
L3, +L4	46, 10 (30%, 6%)
Isolated L4	3 (2%)
Montreal Behaviour for CD
B1, B1p (non-stricturing & non-penetrating, +perianal)	120,6 (78%,4%)
B2, B2p (stricturing, +perianal)	12, 0 (8%, 0%)
B3, B3p (penetrating, +perianal)	6, 6( 4%, 4%)
Not available	4 (2%)
Paris Extent for UC
E1	41 (25%)
E2	52 (32%)
E3	67 (42%)
Not available	1 (1%)

Centre	CD	UC	IBD-U	Controls	Total
Edinburgh	47	57	10	86	200
Linkoping	11	4	1	41	57
Maastricht	4	1	0	0	5
Orebro	24	21	9	65	119
Oslo	54	68	6	63	191
IACS	14	10	2	40	66
Total	154	161	28	295	638

Footnote: Numbers denote the recruitment figures per centre within the study

Table 2: Top 10 differentially methylation positions (DMPs) in patients with Inflammatory Bowel Diseases versus controls.

Probe ID	Chr	Gene ID	Log FC	P value	Holm P	Region	Relation to CpG island
cg07573872	19	SBNO2	-0.05	3.07E-23	1.05E-17	Body	S_Shelf
cg09349128	22	CRELD2	-0.03	3.87E-21	1.32E-15		N_Shore
cg17501210	6	RPS6KA2	-0.04	1.25E-19	4.26E-14	Body	OpenSea
cg12054453	17	VMP1	-0.07	4.89E-19	1.67E-13	Body	OpenSea
cg16292768	8	CLU	-0.03	4.85E-18	1.66E-12	Body	OpenSea
cg18608055	19	SBNO2	-0.04	8.67E-17	2.96E-11	Body	OpenSea
cg16936953	17	VMP1	-0.07	2.32E-16	7.91E-11	Body	OpenSea
cg19821297	19	HOOK2	-0.04	5.32E-16	1.81E-10		S_Shore
cg27469606	19	SBNO2	-0.03	1.09E-15	3.73E-10	5'UTR	Island
cg12170787	19	SBNO2	-0.02	4.29E-15	1.47E-09	Body	OpenSea

Footnote: Positive value indicated increased methylation in cases compared to controls, negative values indicated hypomethylation in IBD cases versus controls. NA denotes methylation probes with no annotated gene symbol. Body: within body of the gene. 5’UTR: 5’untranslated region

Table 3: Top 10 differentially methylation positions (DMPs) in patients with Inflammatory Bowel Diseases versus controls that were tested in the Scandinavian cohort (NE Holm represents holm corrected p value) and replicated across UK (UK Holm represents p value) and Sain (SE Holm). The highlighted probe remains significant across all cohorts.

Probe ID	Chr	Gene	Region	Relation to CpG Island	Log FC	NE P value	NE Holm	UK Holm	SE Holm
cg17501210	6	RPS6KA2	Body	OpenSea	-0.04	4.61E-09	1.57E-03	5.26E-08	2.51E-02
cg12992827	3	ZLPD1		OpenSea	-0.05	1.81E-09	6.18E-04	9.89E-04	2.13E-01
cg02464912	14	SYNE2	TSS200	N_Shore	-0.01	7.46E-08	2.55E-02	1.00E-01	3.28E-01
cg12054453	17	VMP1	Body	OpenSea	-0.07	2.02E-13	6.89E-08	2.37E-04	5.08E-01
cg01101459	1	LINC01132		OpenSea	0.03	3.78E-08	1.29E-02	5.72E-02	5.08E-01
cg02782634	17	VMP1	Body	OpenSea	-0.02	1.04E-07	3.56E-02	1.00E-01	5.55E-01
cg18942579	17	VMP1	Body	OpenSea	-0.05	1.77E-08	6.03E-03	8.90E-05	5.55E-01
cg09349128	22	CRELD2		N_Shore	-0.04	3.80E-17	1.30E-11	3.07E-03	6.88E-01
cg18608055	19	SBNO2	Body	OpenSea	-0.03	7.08E-09	2.42E-03	5.21E-06	6.88E-01
cg16936953	17	VMP1	Body	OpenSea	-0.07	3.43E-11	1.17E-05	2.23E-04	8.08E-01

Footnote: Positive value indicated increased methylation in cases compared to controls, negative values indicated hypomethylation in IBD cases versus controls. NA denotes methylation probes with no annotated gene symbol. TSS200: 200bp from transcription start site. Body: within body of the gene

Table 4: Patient demographics for predicting disease course in Inflammatory Bowel Disease

Predicting Disease Course in Inflammatory Bowel Disease
Variables	IBD escalation group (n=67)	Non-escalation group (n=224)
Subtype IBD(CD:UC:IBD-U)	39:26:2	77:127:20
Edin:Oslo:Orebro:Spain	28:21:14:4	85:77:41:21
Males (%)	39(58)	118(53)
Smoking status (current: never: ex: missing)	16:35:15:1	37:104:82:1
Mean age (range)	32(18-67)	36(18-79)
Montreal classification for CD
L1	13	27
L2	9	23
L3	17	26
L4	0	1
Not available	0	0
Montreal Behaviour for CD
B1+B1p	29	66
B2	6	4
B3+B3p	4	5
Not available	0	2
Paris Extent for UC
E1	0	41
E2	8	41
E3	18	45
Not available	0	0

Table 5: Top 3 CpGs associated with escalation in treatment (surgery or anti-TNF therapies) in Inflammatory Bowel Disease (IBD). HR: hazard ratio; Chr: chromosome

CpG probe	Gene	Chr	IBD HR Training Set	Function/Relevance in Inflammatory Bowel Disease
cg26033526	TAP1	6	12.79	MHC-II encoded gene are essential in HLA class I proteins for generation of cytotoxic T-cell mediated immune reponse.
cg20502501	RPTOR	17	1.47	Encodes an mTOR binding protein and is part of the mammalian target of rapamycin complex 1 (mTORC1); mTORC1 plays a role in the pathogenesis of UC⁴⁵.
cg06352538	TESPA1	12	1.29	Plays a role in development and maturation of T-cells and T-cell antigen receptor mediated activation of ERK and NFAT signalling⁵⁸.

There is NO Competing Interest.

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Analysis of systemic epigenetic alterations in inflammatory bowel disease: defining geographical, genetic, and immune-inflammatory influences on the circulating methylome

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