Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells.

Characterizing the multifaceted contribution of genetic and epigenetic factors to disease phenotypes is a major challenge in human genetics and medicine. We carried out high-resolution genetic, epigenetic, and transcriptomic profiling in three major human immune cell types (CD14+ monocytes, CD16+ neutrophils, and naive CD4+ T cells) from up to 197 individuals. We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. Finally, we demonstrate colocalization of molecular trait QTLs at 345 unique immune disease loci. This expansive, high-resolution atlas of multi-omics changes yields insights into cell-type-specific correlation between diverse genomic inputs, more generalizable correlations between these inputs, and defines molecular events that may underpin complex disease risk.

Integrative COVID-19 biological network inference with probabilistic core decomposition.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein-protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.

Genomic variants associated with age at diagnosis of childhood-onset type 1 diabetes.

Age at diagnosis (AAD) of Type 1 diabetes (T1D) is determined by the age at onset of the autoimmune attack and by the rate of beta cell destruction that follows. Twin studies found that T1D AAD is strongly influenced by genetics, notably in young children. In young UK, Finnish, Sardinian patients AAD-associated genomic variants were previously identified, which may vary across populations and with time. In 1956 children of European ancestry born in mainland France in 1980-2008 who declared T1D before 15 years, we tested 94 T1D-associated SNPs for their association with AAD using nonparametric Kruskal-Wallis test. While high-risk HLA genotypes were not found to be associated with AAD, fourteen SNPs located in 12 non-HLA loci showed a strong association (2.9 × 10-12 < P < 1.4 × 10-3 after FDR correction). Four of these loci have been associated with AAD in previous cohorts (GSDMB, IL2, TNFAIP3, IL1), supporting a partially shared genetic influence on AAD of T1D in the studied European populations. In contrast, the association of 8 new loci CLEC16A, TYK2, ERBB3, CCR7, FCRL3, DNAH2, FGF3/4, and HPSE2 with AAD is novel. The 12 protein-coding genes located within these loci are involved in major immune pathways or in predisposition to other autoimmune diseases, which suggests a prominent role for these genes in the early immune mechanisms of beta cell destruction.

Use of NAD tagSeq II to identify growth phase-dependent alterations in E. coli RNA NAD+ capping.

Recent findings regarding nicotinamide adenine dinucleotide (NAD+)-capped RNAs (NAD-RNAs) indicate that prokaryotes and eukaryotes employ noncanonical RNA capping to regulate gene expression. Two methods for transcriptome-wide analysis of NAD-RNAs, NAD captureSeq and NAD tagSeq, are based on copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry to label NAD-RNAs. However, copper ions can fragment/degrade RNA, interfering with the analyses. Here we report development of NAD tagSeq II, which uses copper-free, strain-promoted azide-alkyne cycloaddition (SPAAC) for labeling NAD-RNAs, followed by identification of tagged RNA by single-molecule direct RNA sequencing. We used this method to compare NAD-RNA and total transcript profiles of Escherichia coli cells in the exponential and stationary phases. We identified hundreds of NAD-RNA species in E. coli and revealed genome-wide alterations of NAD-RNA profiles in the different growth phases. Although no or few NAD-RNAs were detected from some of the most highly expressed genes, the transcripts of some genes were found to be primarily NAD-RNAs. Our study suggests that NAD-RNAs play roles in linking nutrient cues with gene regulation in E. coli.

DpCoA tagSeq: Barcoding dpCoA-Capped RNA for Direct Nanopore Sequencing via Maleimide-Thiol Reaction.

Recent discoveries of noncanonical RNA caps, such as nicotinamide adenine dinucleotide (NAD+) and 3'-dephospho-coenzyme A (dpCoA), have expanded our knowledge of RNA caps. Although dpCoA has been known to cap RNAs in various species, the identities of its capped RNAs (dpCoA-RNAs) remained unknown. To fill this gap, we developed a method called dpCoA tagSeq, which utilized a thiol-reactive maleimide group to label dpCoA cap with a tag RNA serving as the 5' barcode. The barcoded RNAs were isolated using a complementary DNA strand of the tag RNA prior to direct sequencing by nanopore technology. Our validation experiments with model RNAs showed that dpCoA-RNA was efficiently tagged and captured using this protocol. To confirm that the tagged RNAs are capped by dpCoA and no other thiol-containing molecules, we used a pyrophosphatase NudC to degrade the dpCoA cap to adenosine monophosphate (AMP) moiety before performing the tagSeq protocol. We identified 44 genes that transcribe dpCoA-RNAs in mouse liver, demonstrating the method's effectiveness in identifying and characterizing the capped RNAs. This strategy provides a viable approach to identifying dpCoA-RNAs that allows for further functional investigations of the cap.

Blood DNA methylation at TXNIP and glycemic changes in response to weight-loss diet interventions: the POUNDS lost trial.

BACKGROUND: Thioredoxin Interacting Protein (TXNIP) functions as a master regulator for glucose homeostasis. Hypomethylation at the 5'-cytosine-phosphate-guanine-3' (CpG) site cg19693031 of TXNIP has been consistently related to islet dysfunction, hyperglycemia, and type 2 diabetes. DNA methylation (DNAm) may reveal the missing mechanistic link between obesity and type 2 diabetes. We hypothesize that baseline DNAm level at TXNIP in blood may be associated with glycemic traits and their changes in response to weight-loss diet interventions. METHODS: We included 639 adult participants with overweight or obesity, who participated in a 2-year randomized weight-loss diet intervention. Baseline blood DNAm levels were profiled by high-resolution methylC-capture sequencing. We defined the regional DNAm level of TXNIP as the average methylation level over CpGs within 500 bp of cg19693031. Generalized linear regression models were used for main analyses. RESULTS: We found that higher regional DNAm at TXNIP was significantly correlated with lower fasting glucose, HbA1c, and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) at baseline (P < 0.05 for all). Significant interactions were observed between dietary protein intake and DNAm on changes in insulin (P-interaction = 0.007) and HOMA-IR (P-interaction = 0.009) at 6 months. In participants with the highest tertile of regional DNAm at TXNIP, average protein (15%) intake was associated with a greater reduction in insulin (ß: -0.14; 95% CI: -0.24, -0.03; P = 0.011) and HOMA-IR (ß: -0.15; 95% CI: -0.26, -0.03; P = 0.014) than high protein (25%) intake, whereas no significant associations were found in those with the lower tertiles (P > 0.05). The interaction was attenuated to be non-significant at 2 years, presumably related to decreasing adherence to the diet intervention. CONCLUSIONS: Our data indicate that higher regional DNAm level at TXNIP was significantly associated with better fasting glucose, HbA1c, and HOMA-IR; and people with higher regional DNAm levels benefited more in insulin and HOMA-IR improvement by taking the average-protein weight-loss diet.

NAD tagSeq reveals that NAD+-capped RNAs are mostly produced from a large number of protein-coding genes in Arabidopsis.

The 5' end of a eukaryotic mRNA transcript generally has a 7-methylguanosine (m7G) cap that protects mRNA from degradation and mediates almost all other aspects of gene expression. Some RNAs in Escherichia coli, yeast, and mammals were recently found to contain an NAD+ cap. Here, we report the development of the method NAD tagSeq for transcriptome-wide identification and quantification of NAD+-capped RNAs (NAD-RNAs). The method uses an enzymatic reaction and then a click chemistry reaction to label NAD-RNAs with a synthetic RNA tag. The tagged RNA molecules can be enriched and directly sequenced using the Oxford Nanopore sequencing technology. NAD tagSeq can allow more accurate identification and quantification of NAD-RNAs, as well as reveal the sequences of whole NAD-RNA transcripts using single-molecule RNA sequencing. Using NAD tagSeq, we found that NAD-RNAs in Arabidopsis were produced by at least several thousand genes, most of which are protein-coding genes, with the majority of these transcripts coming from <200 genes. For some Arabidopsis genes, over 5% of their transcripts were NAD capped. Gene ontology terms overrepresented in the 2,000 genes that produced the highest numbers of NAD-RNAs are related to photosynthesis, protein synthesis, and responses to cytokinin and stresses. The NAD-RNAs in Arabidopsis generally have the same overall sequence structures as the canonical m7G-capped mRNAs, although most of them appear to have a shorter 5' untranslated region (5' UTR). The identification and quantification of NAD-RNAs and revelation of their sequence features can provide essential steps toward understanding the functions of NAD-RNAs.

Quantitative Profiling of Protein-Derived Electrophilic Cofactors in Bacterial Cells with a Hydrazine-Derived Probe.

Post-translational modification of proteins can form electrophilic cofactors that serve as a catalytic center. The derived electrophilic cofactors greatly expand protein activities and functions. However, there are few studies concerning how to profile the electrophiles in bacteria. Herein, we utilized a clickable probe called propargyl hydrazine to profile the protein-derived electrophilic cofactors in Escherichia coli (E. coli) cells. Since the cofactors are mostly carbonyl groups, the hydrazine-based probe can specifically react with the cofactors to form a Schiff base. The labeled proteins were then pulled down for mass spectrometry (MS) analysis. Fourteen proteins were shown to undergo enrichment by the probe and competitive binding by its analogue, propyl hydrazine. The identified proteins were further analyzed with targeted proteomics based on parallel reaction monitoring (PRM). Using this strategy, we obtained a global portrait of protein electrophiles in bacterial cells, among which the proteins of speD and panD were previously reported to derive pyruvoyl group as an electrophilic center while lpp can retain N-terminal formyl methionine. This quantitative chemical proteomics strategy can be used to find out protein electrophiles in bacteria and holds great potential to further characterize the protein functions.

Integration of Metabolomics and Lipidomics Reveals Metabolic Mechanisms of Triclosan-Induced Toxicity in Human Hepatocytes.

Triclosan (TCS), an extensively used antimicrobial agent, has raised considerable concern due to its hepatocarcinogenic potential. However, previous hepatotoxicity studies primarily focused on the activation of specific intracellular receptors, the underlying mechanisms still warrant further investigation at the metabolic level. Herein, we applied metabolomics in combination with lipidomics to unveil TCS-related metabolic responses in human normal and cancerous hepatocytes. Endogenous and exogenous metabolites were analyzed for the identification of metabolic biomarkers and biotransformation products. In L02 normal cells, TCS exposure induced the up-regulation of purine metabolism and amino acid metabolism, caused lipid accumulation, and disturbed energy metabolism. These metabolic disorders in turn enhanced the overproduction of reactive oxygen species (ROS), leading to the alteration of antioxidant enzyme activities, down-regulation of endogenous antioxidants, and peroxidation of lipids. TCS-induced oxidative stress is thus considered to be one crucial factor for hepatotoxicity. However, in HepG2 cancer cells, TCS underwent fast detoxification through phase II metabolism, accompanied by the enhancement of energy metabolism and elevation of antioxidant defense system, which contributed to the potential effects of TCS on human hepatocellular carcinoma development. These different responses of metabolism between normal and cancerous hepatocytes provide novel and robust perspectives for revealing the mechanisms of TCS-triggered hepatotoxicity.

Integrative Chemical Proteomics-Metabolomics Approach Reveals Acaca/Acacb as Direct Molecular Targets of PFOA.

Identification of the direct molecular targets of environmental pollutants is of great importance for toxicity mechanism studies. Despite numerous studies have been conducted to investigate the toxicity mechanism of perfluorinated compounds (PFCs), their direct-binding protein targets which trigger downstream toxicity effects remain largely unknown. Herein, we present a systematic chemical proteomic study to profile the target proteins of PFCs by taking PFOA as a representative. Considering its electrophilicity, PFOA could preferentially bind to reactive cysteine-containing proteins. Therefore, two complementary cysteine-targeting probes, iodoacetamide alkyne (IAA) and ethynyl benziodoxolone azide (EBX), were selected to enrich the putative target proteins in the absence or presence of PFOA. Quantitative proteomic analysis of the enriched proteins identified Acaca and Acacb as novel target proteins of PFOA. We then applied parallel reaction monitoring (PRM)-based targeted proteomics study combined with thermal shift assay-based chemical proteomics to verify Acaca and Acacb as bona fide binding targets. These findings afford a plausible explanation for the PFOA-induced liver toxicity, especially regarding abnormal fatty acid metabolism that was validated by targeted metabolomics analysis. The present study documents an integrative chemical proteomics-metabolomics platform that facilitates the authentic identification of proteins that are targeted by small molecules and its potential to be applied for toxicity mechanism studies of environmental pollutants.

Optimizing ChIP-seq peak detectors using visual labels and supervised machine learning.

Motivation: Many peak detection algorithms have been proposed for ChIP-seq data analysis, but it is not obvious which algorithm and what parameters are optimal for any given dataset. In contrast, regions with and without obvious peaks can be easily labeled by visual inspection of aligned read counts in a genome browser. We propose a supervised machine learning approach for ChIP-seq data analysis, using labels that encode qualitative judgments about which genomic regions contain or do not contain peaks. The main idea is to manually label a small subset of the genome, and then learn a model that makes consistent peak predictions on the rest of the genome. Results: We created 7 new histone mark datasets with 12 826 visually determined labels, and analyzed 3 existing transcription factor datasets. We observed that default peak detection parameters yield high false positive rates, which can be reduced by learning parameters using a relatively small training set of labeled data from the same experiment type. We also observed that labels from different people are highly consistent. Overall, these data indicate that our supervised labeling method is useful for quantitatively training and testing peak detection algorithms. Availability and Implementation: Labeled histone mark data http://cbio.ensmp.fr/~thocking/chip-seq-chunk-db/ , R package to compute the label error of predicted peaks https://github.com/tdhock/PeakError. Contacts: toby.hocking@mail.mcgill.ca or guil.bourque@mcgill.ca. Supplementary information: Supplementary data are available at Bioinformatics online.

Systematic DNA methylation analysis of multiple cell lines reveals common and specific patterns within and across tissues of origin.

DNA methylation is a key functional regulatory mechanism in human genome, which plays critical roles in development, differentiation and many diseases. With rapid progress of large-scale projects (e.g. ENCODE), many DNA methylation data across diverse cell lines have been produced. However, common methylation patterns, cell lineage- and cell line-specific DNA methylation patterns across multiple cell lines have not yet been explored completely. Using the DNA methylation data across 54 human cell lines, we identified 35 276 local DNA methylation regions called local clusters of CpG sites (LCCSs). We constructed an LCCS co-methylation network and investigated the common DNA methylation patterns across all cell lines, which reveal two distinct groups in terms of their methylation level and genomic characteristics. We further detected diverse sets of cell lineage-specific high- and low-methylation patterns, which were depleted in promoter, CpG island (CGI) and repeat regions but enriched in gene body and non-CGI regions, especially the CGI shore regions. We discovered that the cell lineage-specific low-methylated LCCSs were enriched with functional transcriptional factor binding motif regions. Moreover, the detected cell line-specific high- and low-methylated patterns show distinct enrichments in cell line-specific chromatin states and functional relevance with the corresponding cell lines.

The dynamics of DNA methylation fidelity during mouse embryonic stem cell self-renewal and differentiation.

The faithful transmission of DNA methylation patterns through cell divisions is essential for the daughter cells to retain a proper cell identity. To achieve a comprehensive assessment of methylation fidelity, we implemented a genome-scale hairpin bisulfite sequencing approach to generate methylation data for DNA double strands simultaneously. We show here that methylation fidelity increases globally during differentiation of mouse embryonic stem cells (mESCs), and is particularly high in the promoter regions of actively expressed genes and positively correlated with active histone modification marks and binding of transcription factors. The majority of intermediately (40%-60%) methylated CpG dinucleotides are hemi-methylated and have low methylation fidelity, particularly in the differentiating mESCs. While 5-hmC and 5-mC tend to coexist, there is no significant correlation between 5-hmC levels and methylation fidelity. Our findings may shed new light on our understanding of the origins of methylation variations and the mechanisms underlying DNA methylation transmission.

Deciphering the heterogeneity in DNA methylation patterns during stem cell differentiation and reprogramming.

BACKGROUND: Human induced pluripotent stem cells (iPSCs) have a wide range of applications throughout the fields of basic research, disease modeling and drug screening. Epigenetic instable iPSCs with aberrant DNA methylation may divide and differentiate into cancer cells. Unfortunately, little effort has been taken to compare the epigenetic variation in iPSCs with that in differentiated cells. Here, we developed an analytical procedure to decipher the DNA methylation heterogeneity of mixed cells and further exploited it to quantitatively assess the DNA methylation variation in the methylomes of adipose-derived stem cells (ADS), mature adipocytes differentiated from ADS cells (ADS-adipose) and iPSCs reprogrammed from ADS cells (ADS-iPSCs). RESULTS: We observed that the degree of DNA methylation variation varies across distinct genomic regions with promoter and 5'UTR regions exhibiting low methylation variation and Satellite showing high methylation variation. Compared with differentiated cells, ADS-iPSCs possess globally decreased methylation variation, in particular in repetitive elements. Interestingly, DNA methylation variation decreases in promoter regions during differentiation but increases during reprogramming. Methylation variation in promoter regions is negatively correlated with gene expression. In addition, genes showing a bipolar methylation pattern, with both completely methylated and completely unmethylated reads, are related to the carbohydrate metabolic process, cellular development, cellular growth, proliferation, etc. CONCLUSIONS: This study delivers a way to detect cell-subset specific methylation genes in a mixed cell population and provides a better understanding of methylation dynamics during stem cell differentiation and reprogramming.

DMEAS: DNA methylation entropy analysis software.

SUMMARY: DMEAS is the first user-friendly tool dedicated to analyze the distribution of DNA methylation patterns for the quantification of epigenetic heterogeneity. It supports the analysis of both locus-specific and genome-wide bisulfite sequencing data. DMEAS progressively scans the mapping results of bisulfite sequencing reads to extract DNA methylation patterns for contiguous CpG dinucleotides. It determines the DNA methylation level and calculates methylation entropy for genomic segments to enable the quantitative assessment of DNA methylation variations observed in cell populations. AVAILABILITY AND IMPLEMENTATION: DMEAS program, user guide and all the testing data are freely available from http://sourceforge.net/projects/dmeas/files/

Prediction of posttranslational modification sites from amino acid sequences with kernel methods.

Post-translational modification (PTM) is the chemical modification of a protein after its translation and one of the later steps in protein biosynthesis for many proteins. It plays an important role which modifies the end product of gene expression and contributes to biological processes and diseased conditions. However, the experimental methods for identifying PTM sites are both costly and time-consuming. Hence computational methods are highly desired. In this work, a novel encoding method PSPM (position-specific propensity matrices) is developed. Then a support vector machine (SVM) with the kernel matrix computed by PSPM is applied to predict the PTM sites. The experimental results indicate that the performance of new method is better or comparable with the existing methods. Therefore, the new method is a useful computational resource for the identification of PTM sites. A unified standalone software PTMPred is developed. It can be used to predict all types of PTM sites if the user provides the training datasets. The software can be freely downloaded from http://www.aporc.org/doc/wiki/PTMPred.

iHyd-PseAAC: predicting hydroxyproline and hydroxylysine in proteins by incorporating dipeptide position-specific propensity into pseudo amino acid composition.

Post-translational modifications (PTMs) play crucial roles in various cell functions and biological processes. Protein hydroxylation is one type of PTM that usually occurs at the sites of proline and lysine. Given an uncharacterized protein sequence, which site of its Pro (or Lys) can be hydroxylated and which site cannot? This is a challenging problem, not only for in-depth understanding of the hydroxylation mechanism, but also for drug development, because protein hydroxylation is closely relevant to major diseases, such as stomach and lung cancers. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods to address this problem. In view of this, a new predictor called "iHyd-PseAAC" (identify hydroxylation by pseudo amino acid composition) was proposed by incorporating the dipeptide position-specific propensity into the general form of pseudo amino acid composition. It was demonstrated by rigorous cross-validation tests on stringent benchmark datasets that the new predictor is quite promising and may become a useful high throughput tool in this area. A user-friendly web-server for iHyd-PseAAC is accessible at http://app.aporc.org/iHyd-PseAAC/. Furthermore, for the convenience of the majority of experimental scientists, a step-by-step guide on how to use the web-server is given. Users can easily obtain their desired results by following these steps without the need of understanding the complicated mathematical equations presented in this paper just for its integrity.

Integrative chemoproteomics reveals anticancer mechanisms of silver(i) targeting the proteasome regulatory complex.

Silver compounds have favorable properties as promising anticancer drug candidates, such as low side effects, anti-inflammatory properties, and high potential to overcome drug resistance. However, the exact mechanism by which Ag(i) confers anticancer activity remains unclear, which hinders further development of anticancer applications of silver compounds. Here, we combine thermal proteome profiling, cysteine profiling, and ubiquitome profiling to study the molecular mechanisms of silver(i) complexes supported by non-toxic thiourea (TU) ligands. Through the formation of AgTU complexes, TU ligands deliver Ag+ ions to cancer cells and tumour xenografts to elicit inhibitory potency. Our chemical proteomics studies show that AgTU acts on the ubiquitin-proteasome system (UPS) and disrupts protein homeostasis, which has been identified as a main anticancer mechanism. Specifically, Ag+ ions are released from AgTU in the cellular environment, directly target the 19S proteasome regulatory complex, and may oxidize its cysteine residues, thereby inhibiting proteasomal activity and accumulating ubiquitinated proteins. After AgTU treatment, proteasome subunits are massively ubiquitinated and aberrantly aggregated, leading to impaired protein homeostasis and paraptotic death of cancer cells. This work reveals the unique anticancer mechanism of Ag(i) targeting the 19S proteasome regulatory complex and opens up new avenues for optimizing silver-based anticancer efficacy.

Increased CpG methylation at the CDH1 locus in inflamed ileal mucosa of patients with Crohn disease.

BACKGROUND: E-cadherin, a major actor of cell adhesion in the intestinal barrier, is encoded by the CDH1 gene associated with susceptibility to Crohn Disease (CD) and colorectal cancer. Since epigenetic mechanisms are suspected to contribute to the multifactorial pathogenesis of CD, we studied CpG methylation at the CDH1 locus. The methylation of the CpG island (CGI) and of the 1st enhancer, two critical regulatory positions, was quantified in surgical specimens of inflamed ileal mucosa and in peripheral blood mononuclear cells (PBMC) of 21 CD patients. Sixteen patients operated on for a non-inflammatory bowel disease, although not normal controls, provided a macroscopically normal ileal mucosa and PBMC for comparison. RESULTS: In ileal mucosa, 19/21 (90%) CD patients vs 8/16 control patients (50%) (p < 0.01) had a methylated CDH1 promoter CGI. In PBMC, CD patients with methylated CGI were 11/21 (52%) vs 7/16 controls (44%), respectively. Methylation in the 1st enhancer of CDH1 was also higher in the CD group for each of the studied CpGs and for their average value (45 ± 17% in CD patients vs 36 ± 17% in controls; p < 0.001). Again, methylation was comparable in PBMC. Methylation of CGI and 1st enhancer were not correlated in mucosa or PBMC. CONCLUSIONS: Methylation of several CpGs at the CDH1 locus was increased in the inflamed ileal mucosa, not in the PBMC, of CD patients, suggesting the association of CDH1 methylation with ileal inflammation. Longitudinal studies will explore if this increased methylation is a risk marker for colorectal cancer.