Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted. Few cancer drivers in non-coding regions have been identified so far. Here, the authors develop a transcription factor-aware burden test to predict non-coding variants and analyze the impact on transcription factor binding - especially ETS factors - as well as their impact on transcriptional activity. Large scale projects such as the Cancer Genome Atlas (TCGA) and the International Genome Consortium (ICGC) have identified millions of somatic variants in tumors6. However, in most cases, it is not known whether these mutations affect any cellular function, confer growth advantage, or are causally implicated in cancer development7. The difficulty in annotating variants is that only a few cancer driver mutations are needed to initiate tumor growth, development, and metastasis and these mutations must be distinguished from thousands of passenger mutations that do not alter fitness7. Even though more than 90% of somatic variants are in non-coding regions, few non-coding cancer drivers have been identified9, highlighting the need for approaches to identify and validate non-coding variants (NCVs) in cancer.Cancer initiation and progression are often associated with environmentally induced or spontaneous mutations, and inherited genomic variants that increase cancer risk18. Methods have employed a range of different parameters to estimate the background mutational frequency in CREs, including cancer-specific mutational signatures, sequence conservation, functional annotations, mutational frequencies in neighboring regions or other “similar” genomic regions, replication timing, and expression levels19. Despite these varied approaches to estimate mutational burden and the increasing number of sequenced tumor samples, studies have only identified ~100 driver NCVs. For example, burden tests within specific cancer types have identified NCVs in the promoters of TERT, FOXA1, HES1, SDHD, and PLEKHS122. Further, a global analysis of 2568 cancer whole genome samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) identified driver NCVs in the promoters of TERT, HES1 and seven additional genes9. A more recent analysis of 3949 tumors from PCAWG and the Hartwig Medical Foundation identified driver NCVs in the promoters and enhancers of 52 genes19. Additionally, driver NCVs have been identified in the super-enhancers of BCL6, BCL2, CXCR4 in diffuse large B-cell lymphomas23. Whether this somewhat limited number of driver NCVs is due to a modest contribution of NCVs to cancer or to limitations of current approaches to identify and validate NCV drivers remains to be determined.Mutational burden tests have been used to predict driver NCVs. These tests are based on determining an increased mutational frequency in DNA regions of interest ) compared to a background mutational frequency8. For example, TERT overexpression, a major contributor to cancer, is caused by multiple NCVs in its promoter that create ETS factor binding sites27. We hypothesize that an approach to assess NCV burden that accounts for changes in TF binding may improve the sensitivity to detect mutational burden.NCVs in CREs likely affect the binding of transcription factors (TFs) and the recruitment of regulatory cofactors (COFs) leading to changes in gene expression29. We applied our TFA-BT to promoter NCVs from the PCAWG datasets and predicted 2555 cancer driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related and essential genes, and their expression levels are associated with cancer prognosis. To evaluate our TFA-BT NCVs, we used an integrative approach that combines two high-throughput experimental approaches to assay the impact of NCVs on gene expression and the disruption of TF-COF regulatory complexes. Using MPRAs  we found that 765 TFA-BT NCVs altered transcriptional activity, which is a similar validation rate to known driver NCVs. Further, using the microarray-based CASCADE (comprehensive assessment of complex assembly at DNA elements) assay, we found that 510 TFA-BT NCVs lead to differential binding of TF-COF regulatory complexes, and impact primarily the binding of ETS factors. Together, our integrated computational and experimental approach shows that cancer NCVs are a more widespread driver mechanism than previously recognized.Here, we present a TF-aware burden test (TFA-BT) based on the assumption that creating (or disrupting) binding sites for a particular TF at different positions within a CRE will have similar transcriptional effects and should therefore be grouped together in the burden analysis. Indeed, it has been reported that TF binding sites (TFBSs) in CREs frequently occur in homotypic clusters and regulate gene expression through cooperative and non-cooperative mechanismsWe developed a TFA-BT that identifies CREs containing a higher-than-expected number of NCVs across patients that alter  TFBSs for a particular TF. We applied our TFA-BT to somatic NCVs in the promoters of protein-coding genes (from −2000 to +250 bp of the transcription start site). Briefly, for each TF-promoter pair  our method counts the number of NCVs predicted to alter the binding of a specific TF (A) within a promoter (B). We then determine the probability of this observation given (1) the total number of observed NCVs in promoter B across a set of patient samples, and (2) the probability that a random NCV in B  alters a binding site for TF A Fig. . These T6. Predictions were performed per cancer type and in a pan-cancer analysis. In total, we predicted 2555 TFA-BT NCVs in the promoters of 813 genes, which altered the binding sites of 404 TFs (Supplementary Data 30.We applied the TFA-BT to predict cancer driver NCVs (hereafter referred to as TFA-BT NCVs) in the promoters of protein-coding genes using 2654 tumor samples from the PCAWG cohort corresponding to 20 cancer typesBCL2 and BCL6, individual TFA-BT NCVs are generally not recurrent but affect the binding of the same TFs at different positions in the promoter across tumor samples. In other cases, such as TERT, a few TFA-BT NCVs are highly recurrent including the widely reported chr5:1295228 C > T and chr5:1295250 C > T mutations , TEDC2 (skin cancer), and PES1 (skin cancer) 38. Finally, we found a significant enrichment in gene ontologies associated with general and cancer-related cellular processes , SK-MEL-28 (melanoma), and HT-29  cell lines, which match the cancer types with the most TFA-BT NCVs CVs Fig. . NCVs thMost burden tests can identify genomic regions enriched in cancer mutations but cannot determine which of the many mutations in a particular region are actually functional. Interestingly, TFA-BT NCVs validated at a higher rate than random patient-derived NCVs in the promoters of genes reported to have high mutational burden  than for NCVs that gain TFBSs  or NCVs that lead to gain and loss of TFBSs  BCL2, DERL1, and IFI44L, in which the presence of TFA-BT NCVs was associated with differences in gene expression . To determine whether changes in gene expression caused by TFA-BT NCVs were allele-specific, we measured expression allelic imbalance. We focused on BCL2 as this was the gene with the highest number of patients with TFA-BT NCVs in PCAWG and found that lymphoma patients with TFA-BT NCVs in the BCL2 promoter showed a marked allelic expression imbalance of BCL2 not observed in patients with other or no NCV in the BCL2 promoter  incubated with cell nuclear extracts are used to profile the differential recruitment of regulatory COFs e.g., BRD4) to Ref/Alt DNA probe sets to Ref/A).To identify differentially bound NCVs, we profiled the recruitment of six COFs spanning a range of functional categories: SRC1 (NCOA1) is a transcriptional coactivator with acetyl-transferase activity; BRD4 is a chromatin reader and regulatory scaffold; MOF (KAT8) is a histone acetyltransferase; NCOR1 is a transcriptional corepressor; RBBP5 is a core member of the MLL/SET histone methyltransferase complexes; TBL1XR1 is a member of the NCoR corepressor complex. COF recruitment was profiled using nuclear extracts from Jurkat and SK-MEL-28 cells to 2956 paired Ref/Alt probe sets that included: 2555 TFA-BT NCVs, 17 literature-reported driver NCVs, and 384 background NCVs predicted to not impact TF binding. NCVs that lead to significant differential recruitment (either gain or loss) of any single COF were classified as a bmVar (binding-modulating variant) , with highest bmVar enrichment in our predicted TFA-BT group which was enriched well beyond the background NCVs. Our CASCADE approach is cell-type dependent, and results will vary based on the expression levels and interaction strengths of the TFs and COFs assayed. We identified more bmVars using Jurkat cell extracts but the general trends across probe groups were consistent for both cell types. Of the 510 TFA-BT bmVars we identified, the majority were disruptions in which the NCV led to loss of binding  demonstrates that alteration of regulatory complex binding is strongly predictive of a change in gene expression  and suggests possible mechanisms for the observed gene expression effects. Importantly, TFA-BT genes with NCVs classified as emVars or bmVars displayed a higher enrichment in essential, fitness, and prognostic genes than all TFA-BT genes . 98% of the COF motifs matched ETS-family motifs, while the remaining ones resembled ETS motifs but matched similar looking motifs .Our TFA-BT approach is based on identifying NCVs that alter TF binding motifs. In our original analysis, we predicted TFBS alterations for 404 TFs from multiple TF families. For 48.7% of the NCVs we predicted binding changes in two or more TFs, and for some NCVs up to 62 TFs. Therefore, prediction alone is not sufficient to determine the TF whose binding is altered by an NCV. To address the identity of the TF affected by each NCVs, we used CASCADE to determine binding motifs impacted by the 359 NCVs identified as significant by both CASCADE and MPRAs  validate by MPRAs at similar or higher rates than other TBA-BT NCVs . For example, all validated NCVs in the ity Fig. . This isEGR1 promoter led to reduced recruitment of BRD4, MOF, NCOR, and SCR1, showing mechanistic convergence between different mutations within the same promoter  only evaluate a small 200 bp sequence fragment and are missing neighboring chromatin context41, (ii) many (40%) NCVs reside in elements that do not exhibit activity by MPRA and are thus unable to be evaluated, and (iii) we evaluated only three cell lines in this study. We also found that one fifth of the TFA-BT NCVs lead to altered DNA binding of TF-COF complexes assayed by CASCADE. This is also likely a conservative estimate as only six COFs were profiled and NCVs show COF specificity. Altogether, these results show that the TFA-BT can prioritize NCVs that lead to altered gene expression and binding of regulatory complexes. The success of the TFA-BT approach highlights the importance of using regulatory models in NCV burden tests.In this study, we developed a TFA-BT which we applied to 2654 tumor samples from the PCAWG cohort57. Our results suggest that mutations in the promoters of translation genes constitute a potential cancer mechanism.Genes containing TFA-BT NCVs are enriched in translation and rRNA processing genes. Mutations in the promoters of these genes may alter their expression leading not only to changes in protein synthesis which can affect cell proliferation, but also to an imbalance in ribosome components and free ribosomal proteins. Free ribosomal proteins caused by altered gene expression or copy number variation have been shown to affect cell cycle, apoptosis, and DNA repair leading to cancer59. Interestingly, we found that 52 of the TFA-BT NCVs are biallelic 60 and 290 pairs of TFA-BT NCVs are within 10 nt and affect the same TFBS in at least one donor. This suggests that in many cases, TFA-BT NCVs affect both alleles either at the same nucleotide position or at different positions within a TFBS, likely leading to biallelic disruption of gene expression.Most of the TFA-BT NCVs for which we detected altered transcriptional activity reduced gene expression in MPRAs. Given that the vast majority of cancer mutations are heterozygous, this suggests that a partial reduction in the expression of most TFA-BT genes may be sufficient to have a functional role in cancer. Indeed, the haploinsufficiency of multiple genes caused by copy number variation or promoter methylation has been widely associated with cancer63. Increased and decreased activity of different ETS factors has been implicated in all stages of tumorigenesis via diverse mechanisms, including gene rearrangement and amplification, feed-forward signaling loops, gain-of-function co-regulatory complexes, and cis-acting NCVs in ETS target gene promoters64. Our studies further identified the disruption of ETS binding sites in gene promoters as a widespread cancer mechanism. Whether disruption of ETS factor binding sites is also frequent in enhancers, which often bind a different TF repertoire than promoters, remains to be determined. A large fraction of ETS binding disruption is associated with UV-light mutational signatures and are concentrated primarily in the GG doublet of the canonical 5’-GGAA-3’ ETS box and downstream bases, as has been reported49. Mutations at these positions have been associated with increased mutational rates at sites of ETS factor binding and potentially reduced DNA repair66, but are mostly considered non-functional and are, therefore, excluded from most burden tests. Here, we show that these frequent ETS-disrupting mutations have the largest transcriptional effects and disruption of COF binding. This suggests that excluding these mutations, as well as those associated with other mutational signatures such as APOBEC and POLE, may not be warranted.We found that NCVs impacting gene expression and regulatory complex binding primarily disrupted ETS-factor binding sites. This is consistent with the known role of ETS factors in cancer initiation and progressionTFA-BT is based on the hypothesis that creating (or disrupting) a TFBS at different positions within a gene promoter is likely to lead to similar effects on target gene expression. However, some of these NCVs may reside in TFBSs that are not bound or functional in vivo. We consider this not to be the major driver of our findings as non-functional NCVs would, in general, not be enriched across patients given that TFA-BT considers the overall promoter mutational burden as background. Another possibility is that binding sites predicted to affect the same TF in a promoter may actually bind TF paralogs with different effector functions. However, this does not seem to occur frequently, as most TFA-BT NCVs in a promoter tend to perturb transcriptional activity in the same direction (activation or repression).Although TFA-BT is focused on individual TFs, NCVs that affect the binding of different TFs within a promoter can also have a similar effect on gene expression. This may be the case for NCVs within a promoter that alter the recruitment of similar COFs. Indeed, we found that different TFA-BT NCVs within a promoter often share similar changes in COF recruitment, suggesting shared mechanisms. This supports a potential extension of our approach to develop a COF-aware burden test. This type of test would require knowledge of the COFs that are highly active in a tumor sample as well as the TFs involved in the recruitment of such COFs. Future studies incorporating information on TF-COF complexes will allow us to extend our predictions to other CREs and TFs that may not necessarily function through homotypic clusters.67 and their corresponding TF family. Given a PWM of length n and a genomic position (hs37d5 from the 1000 Genome Project), for each of the 2n-1 DNA sequences on each strand of length n that overlap with the genomic position, we determined a TF binding score using the function:s is a genomic sequence of length n, M is the PWM with n columns and each column in M contains the frequency of each nucleotide in each position i = 1,…,n, and bsi is the background frequency of nucleotide si assuming a uniform distribution. The highest score obtained for the 4n-2 sequences (2n-1 sequences in forward and reverse strands) was assigned as the binding score corresponding to the PWM for the reference or alternate NCV alleles. Significant scores were selected and reported based on TFM-pvalue68 score thresholds determined using a significance level α = 10-4. This method was applied for each reference position and the three possible alternative alleles for the entire human genome (hs37d5) to create an altered TFBS database, a genome-wide catalog of NCV-TF effects. Custom C scripts were developed to generate this dataset using GPUs and the data was stored in the Hadoop servers at Boston University .To predict the effect of all possible NCVs in the human genome on TF binding, for each possible NCV and each TF with available position weight matrices (PWMs), we determined the binding score corresponding to the reference and alternative sequences. We downloaded 1898 PWMs corresponding to human TFs from CIS-BP on April 3, 201869 for 14 datasets (55 experiments) performed in cell lines with normal karyotype 71 and BCF Tools (v.1.9)12. The intersection of variants from both tools was used to extract the allele read counts for each variant. Allelic imbalance analysis was performed for heterozygous positions in promoters for each experiment. A binomial test was used to identify NCVs located in positions where reads were not evenly distributed .To estimate optimal threshold(s) of motif scores differences for a given PWM between a reference allele and alternative allele to predict allelic imbalance in TF binding, we used available ChIP-seq experimental data. ChIP-seq experiment FASTQ files were downloaded from the ENCODE Project68, or 2) when at least one allele passed the motif binding threshold and the difference in score between alleles  is above a certain value ranging from 0 to 7. To benchmark our predictions, for each TF, we used NCVs in allelic imbalance in ChIP-seq as true positives and those not in allelic imbalance as true negatives, and compared to predicted gain/loss of TFBSs in the same direction as the allelic imbalance. F-values and relative accuracies were calculated for all thresholds. Based on the F-values, we selected three parameter settings: 1) either the reference or alternate allele pass the binding threshold for the motif determined by TFM-pvalue, 2) at least one allele passed the motif binding threshold and the difference in score between alleles was greater than two, and 3) at least one allele passed the motif binding threshold and the difference in score between alleles was greater than three. These three parameter settings were independently used for the TF-aware burden test (TFA-BT) as described below in section titled “Development of the TF-aware burden test.”Differential predicted binding events were calculated by comparing the motif score of each alternative to its reference allele. Thresholds of two types were generated for gain/disruption of TFBSs to determine their ability to predict ChIP-seq allelic imbalance: 1) when only the reference or alternate allele pass the binding threshold for the motif determined by TFM-pvalue6 using the ICGC portal5 . We considered only single-nucleotide variants (SNVs) and excluded multi-nucleotide variants from the analyzes. To identify NCVs in promoter regions, we used BEDTools (v.2.27.0) intersection command72. Promoters from protein-coding genes were defined as regions between –2 kb to +250 bp from the transcription start sites (TSSs) annotated in GENCODE v1973. In the case of overlapping alternative promoters, promoter regions were merged to prevent over-counting. To avoid considering protein-coding regions, in the case of alternative promoters, we filtered “coding_regions” using the GENCODE v1973  annotation. We used the R package IRanges (v.2.12)74 to determine the promoter coordinates, and BEDTools (v.2.27.0)72 was used to remove promoter coordinates overlapping with coding regions , where n is number of NCVs in promoter B across patients, and p is the probability that an NCV in B creates (or disrupts) a binding site for PWM A.We designed the TFA-BT to determine whether the number of NCVs observed in promoter B that led to creation (or disruption) of a binding site for PWM A is more than expected by chance, given the total number of mutations observed in promoter B across samples within a certain cancer type. The number of promoter NCVs that create (or disrupt) a binding site for PWM A in promoter B follows a binomial distribution p) was estimated as:F is the probability of changing the reference base at position i in promoter B to the mutated base Mj, C is 1 if mutating Bi to Mj leads the creation (or disruption) of a binding site for PWM A and 0 otherwise, and L is the nucleotide length of promoter B. F was calculated based on the genome-wide mutational frequencies in a cancer type, whereas C was determined by calculating the motif score difference between the sequence surrounding position i for the reference and alternate alleles. These motif scores were obtained by querying the altered TFBS database. We used thresholds obtained from the TFMp-value algorithm68 to determine whether a motif score is significant, and the three different thresholds selected from the ChIP-seq allelic imbalance analysis. For a given set of tumor samples, we calculated P for each PWM-promoter pair using each of three thresholds selected independently, followed by multiple hypothesis testing correction using FDR. Of note, given that for a particular threshold NCVs are deemed significant only after comparing to the expected background using the same threshold, NCVs identified as significant using the three thresholds are overlapping but not subsets of each other. For robustness and to increase confidence in our predictions, only PWM-promoter associations that were significant with an FDR < 0.01 using all three score thresholds independently were considered in subsequent analyzes. Then, we selected the NCVs from the PCAWG samples6 located in the promoters with significant promoter-PWM associations that were associated with differential scores of the corresponding PWM. Finally, we applied the TFA-BT to tumor samples from each of the 20 cancer types, as well as to all PCAWG samples in a pan-cancer analysis to identify predicted driver NCVs (TFA-BT NCVs). Supplementary Data The probability . The list of genes identified as essential in all cell lines in the DepMap Achilles project was downloaded from the DepMap 21Q4 release 76. The list of fitness genes was derived from the Fitness/Non-Fitness Binary Matrix (filename binaryDepScores.tsv) downloaded from the DepMap ProjectScore website77. Only genes designated as “fitness” genes in at least 10 cell lines were considered “fitness” genes for the enrichment analyzes. The list of prognostic genes was derived from the pathology data from the Human Protein Atlas version 21.035 (filename pathology.tsv). Genes with reported p-values  for one or no cancer types were discarded. For the remaining gene-cancer pairs, p-values associated with favorable or unfavorable prognosis were adjusted using an FDR correction and further filtered for q-values of less than 0.01. Genes passing this threshold in at least one cancer type were considered prognostic.We also compared enrichments of essential, fitness, and prognosis genes between TFA-BT, Cancer Gene CensusOdds ratios and p-values for enrichments of essential, fitness, and prognostic genes among the TFA-BT, Cancer Gene Census, and IntOGen genes were computed using Fisher’s exact tests. Enrichments of essential genes used the list of all protein-coding genes as the background, enrichments of fitness genes used the list of all genes in the unfiltered file downloaded from the ProjectScore website, and enrichments of prognostic genes used the list of all genes in the unfiltered file downloaded from the Human Protein Atlas website. Confidence intervals for the proportions of enriched genes were computed using Wald intervals.38 , and considered genes with altered gene expression in more than five cancer types. We then calculated the enrichment of these genes in the 813 TFA-BT gene set using a proportional comparison test.Structural variation has been associated with changes in gene expression. We obtained genes associated with changes in gene expression caused by structural variation across 21 TCGA cohorts70. Then, Salmon78 was used to quantify the expression of the human transcriptome  in transcripts per million (TPM). The expression of each gene transcript was added to obtain the gene TPM expression. For each gene, differential expression was determined between patients with and without TFA-BT NCVs in the gene promoter. Only genes with at least five ICGC donors within the same type of cancer and at least one TFA-BT NCV in the promoter were considered. A student’s t-test was performed to determine differential expression and P-values were adjusted with FDR. Expression allelic imbalance for BCL2 in lymphoid cancer was calculated as the ratio between reads corresponding to alleles of common SNVs within transcribed regions reported by PCAWG.Aligned BAM files corresponding to 1366 samples were downloaded from ICGC. BAM files were converted to FASTQ files using the SAMtools fastq function39. Briefly, oligos were synthesized (Agilent Technologies) as 230 bp sequences containing 200 bp of genomic sequences and 15 bp of adaptor sequence on either end. Unique 20 bp barcodes were added by PCR along with additional constant sequences for subsequent incorporation into a backbone vector (addgene #109035) by Gibson assembly.The MPRA library was constructed as previously describedThe oligo library was expanded by electroporation into NEB 10-beta E. coli, and the resulting plasmid library was sequenced by Illumina 2 × 150 bp chemistry to acquire oligo-barcode pairings. The library underwent restriction digestion using AsiSI, and GFP with a minimal TATA promoter was inserted by Gibson assembly resulting in the 200 bp oligo sequence positioned directly upstream of the promoter and the 20 bp barcode residing in the 3’ UTR of GFP. After library expansion in E. coli, the final MPRA plasmid library was sequenced by Illumina 1 × 26 bp chemistry to acquire a baseline representation of each oligo-barcode pair within the library.The library is comprised of nine sets of test sequences: 1) TFA-BT NCVs (2555 NCVs); 2) TFA-BT – unobserved (534 NCVs), corresponding to random NCVs that correspond to significant TF-promoter pairs by TFA-BT but that were not observed in the PCAWG cohort; 3) ChIP-seq allelic imbalance (281 NCVs), corresponding to a subset of NCVs that showed allelic imbalance in TF binding in ENCODE; 4) Reported driver NCVs 17 NCVs), corresponding to well characterized cancer driver NCVs; 5) Reported germline NCVs (97 NCVs), corresponding to disease-associated NCVs that show differential transcriptional activity or TF binding by enhanced yeast one-hybrid assays; 6) Random NCVs in reported genes (1298 NCVs), corresponding to random NCVs observed in PCAWG within the promoters of genes reported to have high mutational burden; 7) No predicted TF binding (500 NCVs), corresponding to NCVs that do not reside within TF binding sites; 8) No differential binding (500 NCVs), corresponding to NCVs with no predicted differential TF binding; and 9) Non-driver differential TF binding (500 NCVs), corresponding to random NCVs in non-driver gene promoters with predicted differential TF binding  Negative controls (506 sequences) were selected from previous experiments for not having activity across multiple cell types. 2) Positive activity controls (119 sequences) were selected for activity in multiple cell types from previous experiments. Elements were selected across the activity spectrum . 3) MPRA emVar controls (64 NCVs) measuring allelic effect variants selected for showing allelic skew in multiple cell types from previous experiments (Supplementary Data Jurkat cells (ATCC - TIB-152) were grown in RPMI with 10% FBS to a density of 1 million cells per mL prior to transfection. HT-29 cells (ATCC - HTB-38) were cultured in Mocoy’s 5a media with 10% FBS, and SK-MEL-28 cells (ATCC - HTB-72) in EMEM supplemented with 10% FBS. Six electroporation replicates were performed on separate days by collecting 90 million cells and splitting across nine 100 uL transfections each containing 10 ug of MPRA plasmid. Cells were electroporated with the Neon Transfection System (100 μl kit) using three pulses at 1350 V for 10 ms for Jurkat cells, two pulses at 1300 V for 20 ms for HT-29 cells, and one pulse at 1200 V for 40 ms for SK-MEL-28 cells. After transfection each replicate was split between two T-175 flasks with 150 mL of culture media for recovery. After 48 h, the cells were pelleted, washed three times with PBS, and stored at –80 °C for later RNA extraction.RNA for all cell lines was extracted from frozen cell pellets using the Qiagen RNeasy Maxi kit. Half of the isolated total RNA underwent DNase treatment and a mixture of three GFP-specific biotinylated primers 79 and replicates were median normalized followed by an additional normalization of the RNA samples to center the average RNA/DNA activity distribution of the 506 negative control sequences over a log2 fold change of zero. This normalization was performed independently for each cell type. Dispersion-mean relationships were modeled for each cell type independently and used by DESeq2 in a negative binomial distribution to identify oligos showing differential expression relative to the plasmid input. Oligos passing a false discovery rate (FDR) threshold of 1% were considered to be active. For sequences that displayed significant MPRA activity, a paired t-test was applied on the log-transformed RNA/plasmid ratios for each experimental replicate to test whether the reference and alternate allele had similar activity . The cells were grown in suspension in RPMI 1640 Glutamax media  with 10% heat-inactivated fetal bovine serum . T175  non-treated flasks were used when culturing Jurkat cells for experiments. Cells were grown in 50 mL of media when being cultured in T175 flasks.SK-MEL-28 cells were obtained from the Tewhey lab to ensure the same cells used for the MPRA experiments were used for the CASCADE experiments. The cells were cultured using EMEM media  with 10% heat-inactivated fetal bovine serum . Cells were grown in 30 mL of media when being cultured in T225 flasks for adherent cells .80, with modifications detailed below. To harvest nuclear extracts from Jurkat cells, the cells were collected in a falcon tube and placed on ice. To harvest nuclear extracts from SK-MEL-28 cells, the media was aspirated off and the cells were washed once with 1X PBS . Once the 1X PBS used to wash the cells was aspirated off, enough 1X PBS was mixed with 0.1 mM Protease Inhibitor  to cover the cells was added to each flask. A cell scraper was used to dislodge the cells from the flask, and cells were collected in a falcon tube and placed on ice. Jurkat and SK-MEL28 cells were pelleted by centrifugation at 500 × g for 5 min at 4 °C. Both pellets were washed with 2 mL of 1X PBS with Protease Inhibitor and pelleted again at 500 × g for 2 min at 4 °C. To lyse the plasma membrane, the cells were resuspended in Buffer A  , 0.5 mM DTT  and incubated for 10 min on ice. After the 10 min incubation, Igepal detergent  was added to the cell and Buffer A mixture and vortexed for 10 s. To separate the cytosolic fraction from the nuclei, the sample was centrifuged at 500 × g for 5 min at 4 °C to pellet the nuclei. The cytosolic fraction was collected into a separate microcentrifuge tube. The pelleted nuclei were then resuspended in Buffer C (100 µL for Jurkat nuclei and 150 µL for SK-MEL-28 nuclei)  and then vortexed for 30 s. To extract the nuclear proteins , the nuclei were incubated in Buffer C for 1 h while mixing at 4 °C. To separate the nuclear extract from the nuclear debris, the mixture was centrifuged at 21,000 × g for 20 min at 4 °C. The nuclear extract was collected in a separate microcentrifuge tube and flash frozen using liquid nitrogen. Nuclear extracts were stored at ‒80 °C.Nuclear extracts were obtained as previously described81. PBM experiments using cell extracts were performed following the protocols previously described82 and outlined below. The double-stranded microarray was pre-wetted in HBS + TX-100  for 5 min and then de-wetted in an HBS bath. Each of the microarray chambers were then incubated with 180 μL of nuclear extract binding mixture for 1 h in the dark. Nuclear extract binding mixture (per chamber): 400–600 μg of nuclear extract; 20 mM HEPES (pH 7.9); 100 mM NaCl; 1 mM DTT; 0.2 mg/mL BSA; 0.02% Triton X-100; 0.4 mg/mL salmon testes DNA . The microarray was then rinsed in an HBS bath containing 0.1% Tween-20 and subsequently de-wetted in an HBS bath. After the nuclear extract incubation, the microarray was incubated for 20 min in the dark with 20μg/mL primary antibody for the TF or COF of interest diluted in 180 μL of 2% milk in HBS . DNA microarrays were double stranded as described in Berger et al.CASCADE experiments were performed using custom-designed microarrays . Microarray probes are all 60 nucleotides (nt) long and of the format: “GCCTAG” 5-prime flank sequence–- 26-nt variable sequence–- “CTAG” 3-prime flank sequence–- “GTCTTGATTCGCTTGACGCTGCTG” 24-nt common primer (Supplementary Data This microarray was designed to profile the impact of NCVs on COF binding by comparing the binding to reference (Ref) and alternate (Alt) probes. The design included 2956 Ref/Alt paired probe sets that include: 2555 TFA-BT NCVs, 17 literature-reported driver NCVs, and 384 background NCVs  probes of a defined genomic locus are included as probes in the microarray. By profiling the differential binding of a COF to all SV probes we can directly determine a motif/logo for that COF and genomic loci as described in Bray & Hook et al. (details below). The design included probes to evaluate motifs at 359 NCVs identified as significant by both CASCADE  and MPRAs   using the fluorescence distribution of a set of background probes included on each microarray.Image analysis and spatial detrending of the microarray fluorescence intensities were performed as previously describedt-test between the 5 replicate probes for the Ref and Alt alleles. Finally, an aggregate, multiple hypothesis-corrected p-value for differential binding was determined using Fisher’s method  and the six independent p-values. The magnitude of the differential binding was quantified using a “ Δz-score” computed as the difference in the mean z-score for the Ref probes  and the Alt probes. Therefore, for each NCV we assessed the magnitude (Δz-score) and significance  of the differential COF binding. We annotated NCVs as differentially bound in each experiment if they met the following criteria: (1) the z-score of Ref or Alt allele > 2.0; (2) delta z-score > 2.0; (3) aggregate p-value <10-3. NCVs were called differentially bound if they met the above criteria in both replicate CASCADE experiments.To determine differential COF binding due to each NCV, probe intensities were compared between the Ref and Alt probes. For each NCV, differential binding was assessed independently to all six sequences representing that NCV . For each of the six sequences, the significance of the differential binding was assessed using a two-sided Student’s 80. COF motifs can either be represented as a Δz-score matrix, which is akin to an energy matrix that evaluates the change in binding magnitude for each nucleotide variant, or as a position probability matrix (PPM) that is based on a probabilistic model relating base frequencies and binding energies84. We use Δz-score matrices to directly show of the impact of base identify on binding and use PPMs to compare against motifs in public databases which almost exclusively represent motifs as PPMs. Δz-score matrices for a locus are determined using z-scores from the seed probe (zseed) and three SV probes at each of the 26 base positions across the locus. The Δz-score matrix values are based on the z-score differences from the median, calculated independently for each position (i) along the probe:i indicates the nucleotide position (1 to 26) and j indicates the nucleotide . The median at position i is determined over the seed sequence and three probes with variant nucleotide at position i. PPMs are determined by transforming the same z-scores in a different manner:i indicates the nucleotide position (1 to 26), j indicates the nucleotide , and β is an empirically determined scaling parameter:COF motifs were determined by evaluating the z-scores for the seed and SV probes representing each NCV as previously described85 using the TomTom86 algorithm  using the “meme” package87 implemented in R.PPMs for each locus were compared against PPMs from JASPARStatistical analyzes and figures were generated using GraphPad Prism (v.9.2) and R stats (v.4.2).Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.Supplementary informationPeer review fileDescription to Additional Supplementary InformationDataset 1Dataset 2Dataset 3Dataset 4Dataset 5Dataset 6Dataset 7Dataset 8Dataset 9Reporting summary


Epigenetic regulation plays an essential role in driving precise transcriptional programs during development and homeostasis. Among epigenetic mechanisms, histone mono-ubiquitination has emerged as an important post-transcriptional modification. Two major histone mono-ubiquitination events are the mono-ubiquitination of histone H2A at lysine 119 (H2AK119ub), placed by Polycomb repressive complex 1 (PRC1), and histone H2B lysine 120 mono-ubiquitination (H2BK120ub), placed by the heteromeric RNF20/RNF40 complex. Both of these events play fundamental roles in shaping the chromatin epigenetic landscape and cellular identity. In this review we summarize the current understandings of molecular concepts behind histone mono-ubiquitination, focusing on their recently identified roles in tissue development and pathologies. The mammalian genome is organized and packed as a highly dynamic nucleoprotein complex–chromatin, whose architecture is constantly altered by epigenetic regulators in response to various cues ,2,3,4. THistone mono-ubiquitination involves the covalent attachment of ubiquitin, a highly conserved 76 amino acid polypeptide, to a lysine residue in the modified histone ,11. ThisSimilar to other histone PTMs, histone ubiquitination is a dynamic and reversible process. The ubiquitin modification can be removed by a group of specialized deubiquitinating enzymes (DUBs), that hydrolyze the peptide bond between the ubiquitin and its histone substrate ,49. InteAmong the various histone mono-ubiquitination modifications, two of the most well-studied events that regulate transcription are the mono-ubiquitination of histone H2A at lysine 119 (H2AK119ub) by the PRC1 complex and the mono-ubiquitination of histone H2B at lysine 120 (H2BK120ub) by the RNF20/RNF40 complex ,61,62,63The deposition of H2AK119 mono-ubiquitination is catalyzed by PRC1—a multimeric complex that functions in concert with the PRC2 complex and together represent one of the most studied evolutionarily conserved epigenetic mechanisms for gene silencing ,65,66. PThe deposition of H2AK119ub on histone H2A tails is a reversible process. H2AK119ub-specific DUBs, ubiquitin specific peptidase 16 (USP16) and 2A-DUB , were shown to be associated with promoters of active genes and to counteract the deposition of H2AK119ub ,93. H2AKAlthough it is well-known that the H2AK119ub histone modification regulates transcriptional repression, the mechanisms of this regulation are not fully understood. In part, H2AK119ub regulates transcription by promoting the recruitment of PRC2 to chromatin through interactions with the PRC2 subunit JARID2. JARID2 can recognize H2AK119ub-containing nucleosomes via its UBD motif, leading to the deposition of PRC2-mediated H3K27me3 on H2AK119ub-marked genomic loci A 54,55,,100. In RNF20 and RNF40 are two RING finger E3 ubiquitin ligases that cooperate to form a heteromeric complex that catalyzes H2BK120 mono-ubiquitination. While RNF20 and RNF40 are paralogues that share amino acid identity of 72% and a similarity of 86% , both seRNF20 and RNF40 are tightly linked to the transcriptional machinery , and theWW domain-containing adaptor with coiled-coil (WAC) is another functional partner of RNF20/40 that is required for its E3 ligase activity. WAC directly interacts with RNF20/40 and provides a molecular link between the RNF20/40 complex and RNA polymerase II, through the binding of WAC to the phosphorylated serine 2 residue of RNA polymerase II CTD domain . SimilarThe deposition of H2BK120ub is also involved in epigenetic crosstalk with histone methyltransferase complexes that regulate gene expression. Specifically, H2BK120ub stimulates the catalytic activity of disruptor of telomere silencing 1 (DOTL1) towards the methylation of histone H3 at lysine 79 (H3K79me), which is associated with the transcriptional machinery and transcriptional elongation ,132,133.Interestingly, the DUB module of the SAGA complex regulates H2BK120ub both in the context of nucleosomes and in disassembled H2A-H2B dimers that are associated with the facilitates chromatin transcription (FACT) complex , indicatThe PRC1 complex have been shown to play critical roles in stem cells and tissue development, starting from its requirement for the progression past the gastrulation stage to the control of proper spatiotemporal tissue patterning during development ,155,156.The deposition of H2AK119ub can be detected as early as the process of oogenesis as shown in growing oocytes of 7-days old mouse females, with increased levels in fully grown oocytes . The depRing1b mutants, Illingworth and colleagues demonstrated that the deposition of H2AK119ub by the RING1B subunit of PRC1 is essential for late but not early mouse development [Ring1b results in early embryonic lethality before gastrulation [Ring1b mutants progress past gastrulation. In line, transcriptional analysis in catalytic-inactive Ring1b mutant ESCs demonstrated that the majority of genes upregulated in Ring1b-null ESCs remained silent when only RING1B catalysis was impaired [Ring1b mutant mice progressed past gastrulation, later during development mutant mice showed developmental defects and prenatal lethality [drosophila, where the repression of homeotic genes normally upregulated upon the loss of PRC1 global activity was maintained in PRC1 catalytic inactive mutant flies during early development, but resulted in late developmental defects and lethality [Ring1b catalytic activity in a Ring1a-null ESCs background, was shown to result in a massive upregulation of PRC1 target genes, similar to the upregulation of PRC1 target genes observed in Ring1a/b-null ECSs [PRC1-mediated H2AK119ub also plays context-dependent roles in development. Using catalytic-inactive elopment . While crulation , catalytimpaired . Howeverethality . Similarethality . It is wull ECSs . Thus, dA tissue-specific requirement for PRC1 catalysis and H2AK119ub in somatic stem cell differentiation was recently reported in the developing skin epithelium. On the molecular level, the deletion of PRC1 catalysis in embryonic epidermal progenitors resulted in a rapid loss of H2AK119ub and a broad upregulation of canonical Polycomb target genes , indicatIn adult stem cells, epigenetic regulators are thought to play fundamental roles in facilitating changes in gene expression to maintain tissue homeostasis in response to internal and external cues ,184,185.Increased epidermal pigmentation is a process that occurs upon exposure to UV, where epidermal cells become pigmented by melanocytes to prevent DNA damage that can lead to skin cancer ,188. IntRnf20 was embryonic lethal [The activity of RNF20/40 and H2BK120ub have been reported to control various cellular processes, including differentiation, proliferation, survival, DNA repair, and inflammatory responses ,192,193.Rnf20 in mouse germ cells resulted in reduced testis size, impaired meiotic recombination, and infertility. Interestingly, chromatin relaxation using chloroquine partially rescued the impaired spermatogenesis process in Rnf20-deficient spermatocytes, resulting in meiotic recombination and increased numbers of mature sperm cells [Rnf20, H2BK120-deubiquitinase Usp44, or the ectopic expression of mutant H2BK120R inhibits ESC differentiation, emphasizing the importance of H2BK120ub in this process [Rnf40 impaired early gene activation and iPSC generation [The activity of RNF20/40 and H2BK120ub plays a role in various stages of embryonic development, from spermatogenesis to lineage-specific differentiation processes. RNF20 and H2BK120ub levels are high in precursor spermatocytes, but low in round spermatids and mature sperms , suggestrm cells . Likewisrm cells ,202,203.neration . Similarneration . Taken tRnf20 in the mouse brain impaired astrocyte differentiation, while the overexpression of Rnf20 in primary neural precursor cells promoted their differentiation into astrocytes [Rnf40 in Schwann cells resulted in impaired postnatal induction of Schwan cells terminal differentiation, reduced myelin production, and progressive axonal degradation. Mice lacking Rnf40 in Schwann cells exhibited progressive gait abnormalities, ataxia, tremor, and hindlimb clasping during early postnatal stages, and by postnatal day 56 they showed severe movement problems and did not reach normal age [One example of the involvement of H2B120ub in tissue-specific stem cell differentiation was shown in the production of astrocytes, a sub-type of glia cells differentiated from neural precursor cells . Specifitrocytes . The lintrocytes . Molecultrocytes ,209, andtrocytes . The conrmal age .Rnf40 in mice during early osteoblast differentiation stages resulted in defects in differentiation and decreased osteocytes number and function, leading to impaired bone formation [Rnf40 during later stages of osteoblast differentiation using Bglap-driven expression of Cre recombinase did not affect osteocytes number and no bone formation defects were observed, although some increase in cortical and trabecular bone was detected. The deletion of Rnf40 in late-stage osteoblast and osteocytes, however, did not result in gross changes or bone mass [Another example is the case of osteoblast differentiation, where RNF40 was required for the induction of specific differentiation markers and transcriptional reprogramming during early stages of human mesenchymal stem cells differentiation in vitro  and mousormation . Interesone mass . Taken tRnf20 and Rnf40 genes were identified as regulators of cardiomyocytes maturation. Disruption of RNF20 or RNF40 expression impaired cardiomyocyte maturation in young mouse neonates, causing cardiac dysfunction. RNF20 was also required for maintenance of adult cardiomyocytes maturity features [Xenopus and mice [Finally, RNF20 and RNF40 are also involved in heart development. Using an in vivo forward genetics screen, both features . Furtherand mice . Human gand mice ,211. On and mice  althoughAs reviewed extensively in the literature, there is a tight connection between the activity of RNF20/40 and H2BK120ub and cancer development ,149,213.The mechanism of tumor suppression by RNF20/40 may be through its association with tumor suppressor genes, such as the p53 tumor suppressor gene, involved in DDR and cell cycle arrest. Specifically, the RNF20/40 complex was shown to interact with p53 and regulate p53 target genes, while depletion of RNF20 decrease the expression of canonical p53 target genes ,191,217.In addition to the extensive cancer development research using mainly in vitro methods, there are several important studies that used mouse models to determine the roles of RNF20/40 in this process. We hereby focus on published work including heterozygous mice or conditional deletion in mice.Rnf20+/− mice showed increased proinflammatory transcriptional response, and were shown to be more prone to severe colonic inflammation compared to wild-type mice following dextran sodium sulfate (DSS) treatment [Rnf20+/− under these conditions, and their T cell functionality was reduced. Colons of Rnf20+/− mice showed compromised barrier function and were shorter. Treatment with a chemical carcinogen and DSS resulted in more colorectal polyps in +/−Rnf20 mice, which often progressed to inflammation-associated colorectal cancer [Rnf20 or Rnf40 were conditionally deleted in the mouse intestine, spontaneous colorectal inflammation occurred, and these knockout mice displayed significantly decreased survival [Rnf20 or Rnf40 mice did not show similar phenotypes. Furthermore, RNA-seq results showed that the deletion of Rnf20 or Rnf40 promoted inflammatory bowel disease-related gene expression programs [Rnf40, resulted in lower weight loss, milder epithelial damage, and reduced colon inflammation when compared to control mice, indicating that RNF40 activity promotes inflammatory pathways in the mouse colon [Tumor initiation and progression may result in some cases from chronic inflammation. RNF20 or RNF40 knockdown in human mammary MCF10A epithelial cells was shown to downregulate non-inflammatory genes and upregulated mature cytokine mRNA transcripts. Moreover, heterozygous reatment . More iml cancer . In corrl cancer ,200. Simse colon .Rnf40, either heterozygous or homozygous, in a mammary carcinoma mouse model increased their tumor-free survival and decreased the number of tumors they developed along with the kinetics of tumor growth. Complete deletion of Rnf40 showed better results than partial deletion in this case, and no marked morphological changes were observed [A specific connection between RNF20/40 and cancer was also shown from studies of human breast cancer patient’s samples, as well as from in vivo experiments ,216,220.observed . Taken tTissue development and homeostasis in multicellular organisms relies on the ability to activate distinct sets of transcriptional programs ,221,222.While H2A119ub is generally associated with gene repression, H2BK120ub is mostly associated with transcriptional activation and elongation. Their opposing roles may be linked to the recognition by different readers, as well as their localization on opposing edges of the nucleosome ,223,224.The use of genetic mouse models has also proven to be instrumental in elucidating the roles of PRC1-mediated H2AK119ub, as well as RNF20/40-mediated H2BK120ub, in somatic stem cells and their differentiated progenies . Further


A recent International Diabetes Federation report suggests that more than 463 million people between 20 and 79 years have diabetes. Of the 20 million women affected by hyperglycemia during pregnancy, 84% have gestational diabetes. In addition, more than 1.1 million children or adolescents are affected by type 1 diabetes. Factors contributing to the increase in diabetes prevalence are complex and include contributions from genetic, environmental, and epigenetic factors. However, molecular regulatory mechanisms influencing the progression of an individual towards increased susceptibility to metabolic diseases such as diabetes are not fully understood. Recent studies suggest that the pathogenesis of diabetes involves epigenetic changes, resulting in a persistently dysregulated metabolic phenotype. This review summarizes the role of epigenetic mechanisms, mainly DNA methylation and histone modifications, in the development of the pancreas, their contribution to the development of diabetes, and the potential employment of epigenetic modulators in diabetes treatment. ASHabsent small and homeotic disks protein 1 homologBETbromodomain extra‐terminal proteinCARM1coactivator‐associated arginine methyltransferase 1CMLchronic myeloid leukaemiaCpa1carboxypeptidase A1CREBcamp response element‐bindingCTLA4cytotoxic T‐lymphocyte‐associated protein 4DMRsdifferentially methylated regionsDNMTDNA methyltransferaseDOT1LDOT1 like histone lysine methyltransferaseEHMT2euchromatic histone lysine methyltransferase 2EHMT2euchromatic histone lysine methyltransferase 2Enpp1ectonucleotide pyrophosphatase/phosphodiesterase 1ERendoplasmic reticulumEZH2enhancer of Zeste 2 Polycomb Repressive Complex 2 SubunitFOXA2forkhead box protein A2GABPAcalmodulin kinase II GA binding protein transcription factor subunit alphaGATA4GATA binding protein 4GCGglucagonGDMgestational diabetes mellitusGLP1glucagon‐like peptideGOgene ontologyGWASgenome‐wide association studiesG‐CSFgranulocyte colony‐stimulating factorHdacshistone deacetylasesHDMShistone demethylasesHFDhigh‐fat dietHGPhepatic glucose productionHLAhuman leukocyte antigenHLAhuman leukocyte antigenHMTshistone methyltransferasesIDDMinsulin‐dependent diabetes mellitusIFN‐gammainterferon gammaIL2RBinterleukin‐2 receptor βIL6interleukin‐6InsinsulinIUGRintrauterine growth retardation modelJmjd3jumonji domain‐containing protein 3Kdacihistone lysine deacetylase inhibitorsKH2hexokinase 2 HK2LBWlow birth weightLDLclow‐density lipoprotein cLINElong interspersed nuclear elementsLPLlipoprotein lipaseMAFBMAF bzip transcription factor BMeDIPmethylated DNA immunoprecipitationMetSmetabolic syndromeMLL1‐5mixed‐lineage leukaemia 1‐5MSK‐1/2mitogen and stress‐activated kinase 1/2 Embryonic day 12.5 E12.5MVPmultivariate positionsNabsodium butyrateNabsodium butyrateNAFLDnonalcoholic fatty liver diseaseNeurog3neurogenin 3NNATneuronatinNODnonobese diabeticNSD1nuclear receptor binding SET domain protein 1Pcldx1phosphatidylinositol specific phospholipase c x domain containing 1PCNAproliferating cell nuclear antigenPDX1duodenal homeobox 1Prmtsprotein arginine methyltransferasesRIZ1retinoblastoma protein‐binding zinc finger protein 1SAHAsuberoylanilide hydroxamic acidSET1ASET domain containing 1ASET1BSET domain containing 1BSET2SET domain containing 2SET7enhancer‐of‐zeste, trithorax domain‐containing lysine methyltransferase 7SHHsonic hedgehogSIRT1sirtuin 1Slc27a2solute carrier family 27 member 2SNPsingle‐nucleotide polymorphismSOX9SRY‐box transcription factor 9SREBF2sterol regulatory element‐binding transcription factor 2STZstreptozotocinSTZstreptozotocinSUV39H1suppressor of variegation 3‐9 homolog 1SUV39H2suppressor of variegation 3‐9 homolog 2T1Dtype 1 diabetesT2Dtype 2 diabetesTLRtoll‐like receptorTNFALPHAtumor necrosis factorTSAtrichostatin ATxnipthioredoxin interacting proteinUHRF1ubiquitin‐like with PHD and ring finger domains 1VPAvalproic acidWpcweeks postconception1Metabolism is a broad term used to describe a set of life‐sustaining chemical reactions occurring in the body. Metabolism is a tightly controlled process involving different systems such as the nervous, digestive, and endocrine systems. Any errors in metabolism would cause an imbalance in the functional homeostasis of the organism and result in diseases collectively called metabolic syndrome (MetS) that include insulin resistance, hyperglycemia, cardiovascular diseases, hyperinsulinemia, obesity, hyperlipidemia, and hypertension.Diabetes mellitus is a disease caused by abnormal glucose metabolism, resulting in consistently high blood glucose levels. Insufficient insulin levels caused by reduced insulin secretion from the pancreas lead to Type 1 diabetes (T1D), whereas defective insulin signalling causing inefficient utilization of glucose by muscles leads to Type 2 diabetes (T2D). Gestational diabetes mellitus (GDM) is a kind of diabetes that develops during pregnancy. Diabetes affects the nervous system, cardiovascular system, kidneys, eyes, bones, and immune systems and is caused by the combination of genetic, environmental, and epigenetic factors.2Histones are basic globular proteins that aid in packaging long DNA in an orderly manner within the nucleus. This, in turn, is responsible for the regulation of gene expression within the cell. The nucleosome structural unit consists of approximately 146 base pairs of DNA wrapped around an octamer that is made up of two copies of each of the four core histone proteins  in a left‐handed super‐helical turn.Histone methyltransferases (HMTs) are enzymes that catalyze histone methylation by transferring one to three methyl groups from S‐adenosyl methionine to the lysine or arginine residues of the histone tails. Histone lysine methyltransferases include histone H3K4 methyltransferases such as mixed‐lineage leukemia 1‐5 (MLL1‐5), SET domain‐containing 1A, 1B  and Absent small and homeotic disks protein 1 homolog (ASH), histone H3K9 methyltransferases such as euchromatic histone lysine methyltransferase 2 (EHMT2), Suppressor of variegation 3‐9 homolog 1, 2 , and retinoblastoma protein‐binding zinc finger protein 1 (RIZ1), histone H3K36 methyltransferases SET domain containing 2 (SET2), and nuclear receptor binding set domain protein 1 (NSD1), H3K79 methyltransferase, DOT1 like histone lysine methyltransferase (DOT1L), enhancer of zeste 2 polycomb repressive complex 2 subunit (EZH2) which methylates H3K27. Histone methylation is generally associated with transcriptional repression , although methylation of some lysine's  and arginine residues on H3 and H4 are shown to be associated with transcriptional activation.The acetylation of histones generally influences gene expression through the activation of transcription. The acetylation of lysine residues catalyzed by histone acetyltransferases neutralizes the positive charge on histones. This, in turn, decreases the interaction of the N‐termini of histones with negatively charged DNA phosphate groups. This changed tail transforms compact chromatin into an open structure and gives access to transcriptional machinery, which results in gene transcription.The phosphorylation of histones by ATP‐dependent protein kinases also plays a role in regulating histone biological activity, which mainly occurs during mitosis and meiosis.The monoubiquitination of histones H2A and H2B had been shown to play critical roles in DNA repair, gene transcription, and messenger RNA (mRNA) translation.2.1PDX1 expression to restrain the induction of the ventral pancreas and allow liver specification.Jmjd3), an H3K27me3 demethylase, results in impaired differentiation of progenitor cells to endocrine cell transition and islet formation.Ins1) expression in mouse embryonic pancreas explants. BETs, especially BRD4, is known to modulate RNA polymerase II (RNA pol II) activity by its interaction with the transcription initiation complex or by releasing a paused RNA pol II. The reduced expression of Ins1 in inhibitor‐treated cells could be because of the diminished RNA pol II modulatory effects of BETs. It is also possible that increases in Ngn3 in the pancreatic primordium cause premature differentiation into endocrine cells at the expense of progenitors, reducing the pool of cells available to become endocrine and exocrine cells. Thus, pancreata from inhibitor‐treated explants may harbour higher percentages of endocrine cells but lower numbers, accounting for reduced expression of insulin. Acinar cells were also increased by the BET inhibition, as indicated by the increase in the cells with the expression of carboxypeptidase A1 (Cpa1) and CelA markers.Pancreas development begins at Carnegie stage 10 (CS10) in humans, with the pancreatic bud developing from the foregut region of the endoderm, which is marked by diminishing levels of sonic hedgehog and increasing levels of pancreatic and duodenal homeobox 1 (PDX1).Hdacs have shown that the loss of Hdac5 and Hdac9 had an increase in β‐cell mass, and the mice lacking Hdac4 and Hdac9 had more δ‐cells.Setd7 is observed in endodermal cells before Pdx1 transcription and leads to changes in specific epigenetic marks in chromatin, leading to pancreatic cell‐specific differentiation.Msk1 led to an increase in pancreatic α‐cell mass, and monoallelic loss of it increased β‐cell mass.In humans, NEUROG3 increases rapidly in the trunk cells during β‐cell mass formation. This is followed by the vascularization of β‐cell mass by 10‐week postconception (wpc) and the disappearance of SRY‐box transcription factor 9 (SOX9) in the trunk cells. By 12–13 wpc, islets become fully developed with α‐, β‐, γ‐, δ‐ and ε‐ cells.Sirt1 impairs pancreatic β‐cell function, and its ectopic expression increases glucose tolerance. Moreover, it induces insulin secretion in response to glucose stimulation by posttranslational modification (deacetylation)of forkhead box protein A2 (FoxA2), a transcription factor regulating Pdx1, eventually leading to regulating Pdx1.Sirt6‐deficient mice showed decreased levels of glucose‐induced insulin secretion without any marked reduction in pancreatic β‐cell mass, probably suggesting a role for Sirt6 in the regulation of glucose sensitivity of pancreatic β cells.Sirtuins are NAD(+)‐dependent deacetylases known to function in regulating cellular metabolism.INS and glucagon (GCG) in human islets. They found that in human pancreatic α‐cells, the promoter of MAF bZIP transcription factor  is occupied with H3K4me3 modification and leads to the activation of MAFB and eventually the glucagon gene, whereas in the β‐cells, the PDX1 enhancer displayed enrichment of H3K4me1.NEUROG3, showed enrichment of H3K27me3 concordant with its downregulation in adult pancreatic islets.The pancreas is a highly heterogeneous organ composed of different types of cells performing different functions. It is the center for the hormonal regulation of glucose homeostasis. Thus, a tight regulation of gene expression is required for the precise hormonal synthesis and secretion from the pancreas. Contrasting views are available regarding the epigenetic modifications of the genes encoding pancreatic hormones. An absence or low levels of active promoter modifications of the genes encoding islet‐specific hormones were reported, suggesting a role for distinct regulatory mechanisms.INS gene compared to HeLa cells. Although H3K4me3 was also high in this region, it was more strongly associated with the INS promoter region. The distribution of H4 tetra‐acetylation  and H3 di‐acetylation at this locus was similar to the H3K4me3 distribution. These open chromatin signatures extended from nearly 55 kb upstream to nearly 25 kb downstream of INS, suggesting that this entire region is in an open chromatin conformation in pancreatic β‐cells. Moreover, this region displayed low levels of H3K27me3, a signature of heterochromatin. The regulatory regions in the INS gene locus were associated with high levels of H3K4me1, a signature chromatin modification for enhancer regions.Prmt1 in mice resulted in the loss of H4R3me2 and impairment of β‐cell function because of the restricted accessibility of key β‐cell transcription factors such as Nkx6.1, Mafa, Pdx1, and Neurod1.Prmt5 in mice leads to reduced insulin expression, impaired glucose tolerance, and glucose‐stimulated insulin secretion (GSIS) through histone methylation‐related chromatin remodelling.Ins and Irs2 in β‐cells and stimulates β‐cell proliferation as well as insulin signalling.However, several others have reported extensive histone modifications around pancreatic hormone loci and attributed these modifications to their precise regulation.2.2+ T cells from peripheral blood of normal and T1D human samples suggested decreased HDAC1 expression in T1D samples and an impaired T cell function.HLA‐DRB1 and HLA‐DQB1 belonging to the IDDM1 group showed changes in H3K9ac in monocytes of T1D patients, resulting in altered expression of these proteins in them and thus altering their ability for antigen presentation.CTLA4) expression is upregulated in T1D because of changes in H3K9me2 at its T promoter region and dysregulation of CD8 T cell‐mediated immune reactions, leading to enhanced autoimmune reactions.Genetic and epigenetic factors are involved in the pathogenesis of T1D. Histone‐modifying enzymes play a critical role in regulating immune cell reactions, which contribute to the development of T1D. Comparative analysis of CD42.3T2D is characterized by a decrease in β‐cell mass, impaired insulin secretion, and insulin resistance resulting from impaired β‐cell function. Although more than 70 loci have been shown to be associated with increased T2D risk by genome‐wide association studies, this explains only ~11% of all T2D cases.HDAC7 was observed in pancreatic β cells from T2D patients. Insulin signalling controls pancreatic β cell mass and function by increasing β cell division and insulin secretion.IRS2 promoter resulting in repression of IRS2 expression. Treatment with HDAC inhibitors increased lysine acetylation of IRS2 with a concomitant decrease in phosphorylation of tyrosines of IRS2 protein.Pdx1 gene expression was downregulated in growth‐retarded rats because of the loss of Usf1 (transcription factor regulating Pdx1) binding at the Pdx1 proximal promoter region, Hdac1 recruitment to the promoter, and deacetylation of H3 and H4 at the Pdx1 promoter in the pancreatic β‐cells. Postnatally, the Pdx1 promoter region undergoes H3K4 demethylation and H3K9 methylation, and in adulthood, CpGs at the Pdx1 proximal promoter region is methylated, leading to the permanent silencing of Pdx1.Pdx1 and Glut2.HK2) increased its expression by twofolds in response to insulin. Similarly, treating rat L6 myotubes with insulin repressed IRS2 and PIK3R2 expression and treatment with trichostatin, a potent and reversible inhibitor of histone deacetylase, resulted in increased histone acetylation at the promoters of Irs2 and Pik3R2 and increased their expression.Pepck, G6P and pyruvate carboxylase by changing H4K8 acetylation at the PEPCK gene promoter. HDAC6 regulates glucocorticoid‐induced hepatic gluconeogenesis by controlling the acetylation of Hsp90 and the subsequent nuclear translocation of the glucocorticoid receptor (GR). Deacetylated Hsp90 binds to GR and for a nuclear translocating complex. Deletion of HDAC6 or inhibition by tubacin, a hydroxamic acid HDAC inhibitor that inhibits only HDAC6 activity, ameliorates glucose homeostasis.PDX1 promoter. Inhibiting the deacetylation in MIN6 and INS‐1 cell lines by valproic acid (VPA), an HDAC inhibitor, increased PDX1 expression and insulin secretion.HDACs regulate pancreatic endocrine function, and glucose homeostasis and their dysregulations have been shown to be associated with T2D. For example, increased expression of Neurod1, neuronatin (Nnat), ectonucleotide pyrophosphatase/phosphodiesterase 1 (Enpp1), Enpp2, phosphatidylinositol specific phospholipase c x domain containing 1 (Pcldx1), and solute carrier family 27 member 2 (Slc27a2) were downregulated in these mice.HMT also play a role in the pathogenesis of T2D. Mll2 is an H3K4 methyltransferase that participates in the mono‐, di‐, and trimethylation of H3K4. Haploinsufficiency of Mll2 led to hyperglycemia and hyperinsulinemia at fasting and impaired glucose tolerance, and blunted insulin secretion in response to glucose loading. Haploinsufficient mice for Mll2 showed peripheral insulin resistance. Genes involved in β‐cell regulation, glucose metabolism, and fatty acid transport, such as Pdx1, is regulated by the histone methyltransferase, Nuclear receptor‐binding set domain protein 2 (NSD2), and the downregulation of NSD2 expression can lead to T2D.Glut4, the cellular glucose transporter, is regulated by trithorax domain‐containing lysine methyltransferase 7 (SET7).H3K9 methylations mark heterochromatin formation and transcriptional gene silencing and thus play a major role in controlling gene expression. Histone methyltransferase and demethylase enzymes regulate the dynamics of histone methylation. JmjCdomain‐containing histone demethylase 2A (Jhdm2a) catalyzes the removal of H3K9 methylation marks and regulates peroxisome proliferator‐activated receptor α (Pparα) and Apolipoprotein C1 (ApoC1), which regulate energy homeostasis, including adipogenesis, regulation of fat storage and glucose transport. The absence of Jhdm2a leads to obesity, hypertriglyceridemia, hypercholesterolemia, hyperinsulinemia, and hyperleptinemia in mice.Glut4. Glut4 activates calmodulin kinase II (CaMKII), leading to histone acetylation at the NRF2‐binding regions of the promoter of Mef2, the major transcription regulator of Glut4, leading to the upregulation of Mef2 and eventually Glut4.Nrf1, GA binding protein transcription factor subunit alpha (Gabpα), and Mef2A‐binding regions of DNA, and decreased acetylation was found at the MAFK binding region, a known negative regulator of β‐cell function.Exercise is known to protect against T2D by inducing epigenetic mechanisms to upregulate the expression of the glucose transporter Although only a few studies have been conducted regarding histone modifications associated with GDM, it is known that epigenetic changes induced during pregnancy can induce GDM and lead to congenital diseases in offspring. A comparison of placentas from GDM and control mothers showed that GDM babies had reduced H3K9ac expression, although H3K4me3 expression was not significantly different.3The failure to maintain true epigenetic marks leads to changes in the genetic information of the cell and inappropriate gene expression, resulting in different diseases.There are several ways by which DNA methylation regulates gene expression. Interaction between Dnmts and histone‐modifying enzymes such as HMTs and HDACs could suppress gene repression. Dnmt1 and Dnmt3a bind to SUV39H1, a HMT that methylates H3K9 and consequently restricts gene expression.3.1PFKFB3), involved in insulin secretion and at the proximal promoters of aminoacylase 3 (ACY3), hepatocyte nuclear factor 1‐alpha (HNF1A), and hepatocyte nuclear factor 4‐alpha (HNF4A), genes with key roles in pancreas development, were among the hypomethylated group, whereas the promoter region of NK6 homeobox 1 (NKX6.1), an important gene in β‐cell development, was among the hypermethylated dDMR group.Dnmt1 in the pancreatic progenitors during the early stages of development greatly inhibited the development of the pancreas. In Dnmt1 knockout mice, pancreatic progenitor cells were arrested at the G2/M stage of the cell cycle and were removed by apoptosis,Arx promoter region favoured β‐cell differentiation, whereas hypomethylation favoured α‐cell differentiation.Development‐related differentially methylated regions (dDMRs) undergo dynamic changes during the development of the pancreas. In humans, at 22 weeks of development  that are free of methylation and are enriched with active histone modifications, suggesting that these could be regulatory elements for the gene cluster at the INS locus or a boundary element for open chromatin at the INS locus.Conditional deletion of 3.2INS, and of the seven CpG sites close to its transcription start sites verified, it was found that in the pancreatic islets of T1D donors, hypomethylation occurred at CpGs at −19, −135, and −234, whereas hypermethylation occurred at −180. No changes in the methylation pattern were observed at −69, −180, and −206 compared to the controls. In these studies, the extent of CpG methylations was not correlated to the levels of glycated haemoglobin or the duration of T1D.HLA, INS, IL2RB, and CD226, most of which are critical for initiating immune reactions against autoantigens.INS‐IGF2, SH2B3, MEG3, and ORMDL3) in the affected twins.Ins1 and Ins2 genes changed over time during the development of T1D.ITGB3BP), AF4/FMR2 family member 3 (AFF3), protein tyrosine phosphatase non‐receptor type 2 (PTPN2), cathepsin H (CTSH), and cytotoxic T‐lymphocyte associated protein 4 (CTLA4) genes in human peripheral blood lymphocytes also increased the risk of T1D.Over the last decade, there was a drastic increase in the incidence of T1D, and even monozygotic twins only show 40% concordance for T1D occurrence, suggesting a role for environmental factors and epigenetic mechanisms in the aetiology of the disease.3.3(PPARGC1A) caused by DNMT3B. Non‐CpG methylations are a significant but smaller fraction of nucleotide methylations seen in the CpT, CpA, and CpC asymmetric (non‐CpG) dinucleotides. It has been shown that these non‐CpG methylations caused a downregulation in the expression of PGC‐1α in skeletal muscle cells and myotubes upon exposure to TNFα or free fatty acids but not insulin or glucose.PPARGC1A.INS expression. The reduced insulin expression in pancreatic islet cells of T2D donors was shown to correlate with the methylation at four CpG sites located 234, 180, and 102 bp upstream and 63 bp downstream of the transcription start site  of the insulin gene.GLRA1), dexamethasone‐induced ras‐related protein 1 (RASD1), VAC14, solute carrier organic anion transporter family member 5A1 (SLCO5A1), and cholinergic receptor nicotinic alpha 5 subunits (CHRNA5). An increase in methylation at the CpG sites of the PDX1 gene was also observed when these islets were exposed to high amounts of glucose.CDKN1A, PDE7B, and SEPT9, displayed upregulation of their expression in islets from T2D patients concordant with the hypomethylation of CpGs at their promoters. Overexpression of some of these genes led to impaired glucose‐stimulated insulin secretion or β‐cell proliferation, confirming the role of their dysregulated expression in the pathogenesis of T2D.TXNIP),SLC30A8),GLP1R),PPARG), potassium voltage‐gated channel subfamily q member 1 (KCNQ1), transcription factor 7‐like 2 (TCF7L2), and insulin receptor 1 (IRS1) have been observed in T2D patients.T2D is a complex metabolic disease involving dysregulated expression of multiple genes involved in oxidative phosphorylation; carbohydrate, amino acid, lipid metabolism; inflammation; and glycan degradation.NR4A3), Parkinson disease‐2 (PARK2), phosphotyrosine interaction domain containing 1 (PID1), solute carrier family 2 member 2 (SLC2A2), and suppressor of cytokine signalling 2, that had both DMRs and associated changes in gene expression in T2D.KCNQ1 locus, a region in the human genome considered to be highly associated with the risk of T2D development, was inversely associated with insulin sensitivity and serum adiponectin. A SNP at rs231840 in this region ablated this association, suggesting that this site regulates insulin sensitivity and adiponectin levels through CpG methylation.LETM1), RNA‐binding motif protein 20 (RBM20), IRS2, mannosidase alpha Class 2a member 2 (MAN2A2), and the 1q25.3 regions of the genome. It also identified that methylations at CpGs in Fc receptor‐like 6 , signalling lymphocytic activation molecule family member 1 (SLAMF1), apolipoprotein B mRNA‐editing enzyme catalytic polypeptide 3 (APOBEC3H), and the 15q26.1 region are associated with blood glucose levels. Further, in silico analysis predicted a connection between the expression of these genes and the adaptive immune system.Ankrd26) gene, a gene associated with the development of T2D, leading to its downregulation.Pparγ, and age‐dependent fat accumulation in mice.Whole‐genome bisulfite sequencing of human pancreatic islet cells identified 457 genes, including nuclear receptor subfamily 4 group A member 3 (PDGFA) and the consequent enhancement in its expression observed in T2D patients have been associated with hyperinsulinemia, increased insulin resistance, and increased steatohepatitis risk.HNF4A gene and the consequent upregulation of its fetal version, the P2 isoform, have been attributed to the dysregulation of HGP in T2D patients.T2D, a complex metabolic disease, is known to affect metabolic tissues other than the pancreas, such as the liver, muscles, and adipose tissues.MAPK1, MYO18B, HOXC6, and the AMP‐activated protein kinase subunit PRKAB1 with known function in muscles, such as the formation of sarcomeres, displayed differential methylation in men with the family history of T2D compared to men without any family history of T2D.PPARGC1A in muscles and HNF4A in adipose tissue.HNF4A has been shown as a regulator of fatty acid metabolism in hepatocytes through the regulation of PPARγ and PPARα expression.PPARGC1A was also reported in the subcutaneous adipose tissue of low birth weight (LBW) men who tended to develop T2D.SIRT1, N‐acetyltransferase 6 [NAT6], phospholipase A2 group XIIB [PLA2G12B], and nuclear factor of activated T cells calcineurin‐dependent 1 [NFATC1]) were identified to be differentially methylated between the muscles of T2D and control samples. Analysis of transcription factor binding sites for the methylation changes revealed that the binding sites of zinc finger E‐box binding homeobox 1 (ZEB1) were hypermethylated, whereas the binding sites of three TFs  were significantly hypomethylated.A study conducted among individuals with a family history of T2D revealed altered DNA methylation of genes in biological pathways, including mitogen‐activated protein kinase (MAPK), insulin, and calcium signalling in their muscle tissues. Moreover, individual genes such as 3.4RBP4), GLUT3, Resistin, PPARα,P2RX5), CCDC15, and ADAM12.PIWIL3), cytochrome b‐245 alpha chain (CYBα), glutathione s‐transferase mu 1 (GSTM1), GSTM6, potassium voltage‐gated channel subfamily E regulatory subunit 1 (KCNE1), and nucleoredoxin (NXN) expression. A study to underpin the effects of GDM on birth weight of offspring, and maternal GDM on the development of T2D, identified 75 differentially methylated CpG loci compared to normal offspring, representing 72 genes with relevance to altered growth and metabolism.PPARGC1A and the corresponding reduction in its expression was linked to the abnormal glucose metabolism in the offspring of GDM mothers.Agap2), 1‐phosphatidylinositol‐4,5‐bisphosphate phosphodiesterase beta‐1 (Plcbr1), Hnf1b, guanine nucleotide‐binding protein, alpha stimulating (Gnas) fructose‐bisphosphatase 2 (Fbp2), cadherin‐13 (Cdh13), wingless‐type 2 (Wnt2), Kcnq1, luteinizing hormone/choriogonadotropin receptor (Lhcgr), and iroquois‐class homeodomain protein (Irx3) genes, which eventually led to increased susceptibility to T2D in adulthood.PLIN1) was negatively correlated with maternal weight and an increase in insulin levels during gestation. The methylation levels of cg17586860 and cg18197392 in the 5′‐UTR region of somatostatin receptor 4 (SSTR4), an insulin resistance‐related gene, were negatively correlated with changes in dietary carbohydrate intake and glycemic load across gestation, whereas cg14631053 methylation positively correlated with mRNA expression of SSTR4 in the placenta.GDM, which has a high incidence and adversely affects the quality of life of pregnant women during pregnancy, is also known to affect fetal health. Studies show that epigenetic changes play a major role in the development of GDM. The methylation status of the placenta plays a key role in maintaining glucose homeostasis phenotype among mothers. It regulates the “maternal‐fetal conflict” for nutrient shunting by choosing between the methylation of maternal and paternal alleles. Maternal and paternal alleles in the placenta are imprinted for resolving this conflict in an amicable way for both the mother and the offspring. In the placenta, the genes that are expressed from paternal alleles act to allocate more nutrients to the maintenance of the fetus, while the gene expression coordinated by maternal alleles helps the mother to conserve her resources.We have utilized all the genes which have been shown to have changes in expression and DNA methylation levels and were also linked to T1D, T2D and GDM and generated the gene network using STRING v114The most prescribed drug for T2D is metformin. It reduces T2D by reducing intestinal glucose absorption, enhancing glucose uptake, decreasing plasma insulin levels at fasting, and increasing insulin sensitivity. It also inhibits gluconeogenesis. However, owing to side effects such as lactic acidosis, the search for better treatment options is still ongoing. Although epigenetic causes have been significantly attributed to the development and progression of diabetes, the field of epigenetic therapy for diabetes has not evolved much. This is probably because of the multifactorial cause of diabetes and the lack of personalized epigenetic information about patients. However, epigenetic therapy in combination with immunotherapy or other conventional therapeutic methods could be a good option for treating diabetes.ClinicalTrials.gov Identifier: NCT04105023). However, more studies are required to reveal its potential as an epigenetic modulator in diabetic patients.Genistein, an isoflavone of plant origin, has been found to be effective in treating obesity and insulin resistance by regulating the activities of enzymes for HGP and lipogenesis.Decitabine  is an analogue of deoxycytidine, and when incorporated into the DNA, it inhibits DNA methylation. It has been approved by the Food and Drug Administration (FDA), USA, for the treatment of Chronic Myeloid Leukaemia (CML) and tested for its treatment potential against other types of cancers.ClinicalTrials.gov Identifier: NCT04073927), and the results are awaited.NaB is a four‐carbon fatty acid that possesses general HDAC inhibitor activity.SIRT1 and downregulating the expression of the proinflammatory NF‐κB pathway.Pdx‐1 resulting from the increased recruitment of the repressive chromatin modification H3K9me2 and reduced recruitment of activating chromatin modifications such as H3K4me3 and H3 acetylation at its promoter region. Treatment with liraglutide could reverse these epigenetic modifications and activate Pdx‐1 expression enhancing β cell function.ClinicalTrials.gov Identifier: NCT01299012), where the drug was subcutaneously administered daily at a rate of 0.6 mg, showed improved blood glucose control in T1D patients. Another study  was conducted to understand the effect of liraglutide on insulin treatment‐associated weight gain in patients with T2D, and it showed that treatment with 1.8 mg of liraglutide improved glycemic control and reduced weight gain. A Phase 4 study  focused on the use of liraglutide as an additional treatment to insulin in patients with autoimmune diabetes mellitus is still ongoing. A phase 2 study to understand the impact of liraglutide on glucose tolerance and the risk of T2D in women with previous pregnancy‐induced diabetes is ongoing .Subcutaneous injection of liraglutide, an analogue of glucagon‐like peptide (GLP‐1), is being used for treating T2D. Administration of it once a day helps to maintain both fasting and postprandial blood glucose at normal levels for 24 h, owing to its slow release and reduced renal filtration.The use of HDAC inhibitors also has the potential as therapeutic agents for diabetes as they promote β‐cell differentiation, proliferation, regeneration, and function and improve insulin resistance. The immunomodulatory action HDAC inhibitors such as suberoylanilide hydroxamic acid (SAHA) and Givinostat offers protection from developing T1D by preventing β‐cell destruction.5Together, the studies presented substantiate the role of epigenetic regulatory mechanisms in normal development, differentiation, and the function of the pancreas and changes in them leading to the metabolic disease diabetes. DNA methyltransferases and histone‐modifying enzymes mediate gene expression, and through the regulatory interactions of transcription factors, they coordinate cell fate decisions and the differentiation of the pancreas. Genome‐wide studies have generated whole‐genome epigenomic maps that are good resources for understanding pancreatic development and differentiation and the contribution of epigenetics in the process. It also suggests how changes in their regulation can result in different diseases, including diabetes. Over the years, attempts have been made to establish methods by which damaged or destroyed β‐cells could be functionally replaced or compensated through the reprogramming of other types of cells to β‐cells, directed differentiation of stem cells to functional β‐cells, or stimulation of β‐cell proliferation to compensate for the loss.Although previous studies have led to a better understanding of the development and differentiation of the pancreas and the transcription factors that are critical for each stage of pancreas specification as well as the chromatin states of the different cell populations during development and differentiation, which include stem cells, pancreatic progenitors, and different cell types of the islets, recent studies have only begun to assess whether chromatin regulators may be used as therapeutic targets for diabetes. Studies have shown that epigenetic reversion has the potential to improve cancer treatment, and thus, discovering epigenetic agents with the potential to change the epigenetic effect could be applied to treat diabetes and related pathologies. It is also becoming clearer it is the combination of DNA methylation and histone modifications together drive changes in regulatory mechanisms resulting in metabolic diseases such as diabetes. However, such studies are only becoming to be addressed.Together, the fundamental studies and clinical observations of epigenetic regulatory mechanisms have provided new insights into the development and differentiation of the pancreas and how changes in them can contribute to diseases. These studies have also opened up new disease biomarkers and therapeutic targets that could promote the design of novel therapeutic strategies.The authors declare that there are no conflict of interests.Suneesh Kaimala and Challagandla Anil Kumar: wrote the initial version of the paper with contributions from Bright Starling Emerald, Mohammed Z. Allouh, and Suraiya Anjum Ansari. Challagandla Anil Kumar: generated the figures with help from Bright Starling Emerald. All authors reviewed, edited the manuscript and approved the final manuscript.All authors contributed to the study design and data collection.