Mycobacterium bovis BCG is the only vaccine against tuberculosis. The variable forms of cultivation throughout the years, before seed-lots were developed, allowed in vitro evolution of the original strain, generating a family of vaccines with different phenotypic and genotypic characteristics. Molecular studies revealed regions of difference (RDs) in the genomes of the various BCG strains. This work aims to characterize the gene pair rv3407-rv3408 (vapB47-vapC47), coding for a toxin–antitoxin system of the VapBC family, and to evaluate possible transcriptional effects due to the adjacent BCG Moreau-specific genomic deletion RD16. We show that these genes are co-transcribed in BCG strains Moreau and Pasteur, and that the inactivation of an upstream transcriptional repressor (Rv3405c) due to RD16 has a polar effect, leading to increased vapBC47 expression. Furthermore, we detect VapB47 DNA binding in vitro, dependent on a 5′ vapB47 sequence that contributes to a palindrome, spanning the promoter and coding region. Our data shed light on the regulation of VapBC systems and on the impact of the BCG Moreau RD16 deletion in the expression of adjacent genes, contributing to a better understanding of BCG Moreau physiology. Tuberculosis (TB) is a widespread infectious disease estimated to affect one third of the global population. It ranked first among infectious diseases causing deaths worldwide, only now surpassed by COVID-19. Despite great efforts, the TB death toll is still exceedingly high, with an estimated 1.3 million deaths among HIV-negative people and an additional 214,000 among HIV-positive people in 2020. The decline in TB incidence achieved in previous years has almost stopped  glucose and 50 µg/mL kanamycin at 37 °C until OD600nm = 0.7. Cells were recovered by centrifugation and transferred to 500 mL Overnight Express™ Instant TB Medium  containing 50 µg/mL kanamycin. The culture was incubated at 37 °C for 16 h with agitation. Bacterial cells were harvested, resuspended in 30 mL of lysis buffer , and cells were disrupted by sonication. The lysate was clarified at 12,000× g for 20 min at 4 °C and the soluble fraction was loaded into a Ni2+ immobilized-metal affinity chromatography (IMAC) column, which was washed in buffer A containing 20 mM Tris-CL pH 7.5 and 100 mM KCl. The elution of rRv3407 was achieved by applying a linear imidazole gradient (20 mM–500 mM) in buffer A. After evaluation by electrophoresis in reductive/SDS-denaturant polyacrylamige gel (SDS-PAGE), fractions containing purified rRv3407 were pooled and the protein concentrated using an Amicon Ultra–4 ultracel 30 kDa . Buffer was exchanged four times using an Amicon Ultra–4 ultracel 5 kDa  to 10 mM Tris-Cl, pH 7.5, 100 mM NaCl, 0.05% (v/v) Tween 20.For cloning, expression and purification of recombinant Rv3407 (rRv3407) containing a C-terminal His-tag, the rv3406 gene (62 bp) and the intergenic region between rv3406 and rv3407 . The second, using primers 07promFOR biotinylated and 07promREV2 (TCCACAAGCCCAACGGTAGC) yielded a fragment of 122 bp, containing the same region described above plus the first 26 bp of rv3407. Cycling conditions for PCR were: 95 °C/4 min (1 cycle); 95 °C/30 s, 53 °C/30 s, 72 °C/30 s (35 cycles); 72 °C/4 min (1 cycle). The PCR products were used as probes in EMSA reactions. Briefly, the reactions were set up at 4 °C for 1 h in 20 mM Tris-Cl pH 7.5, 100 mM KCl, 2.5% (v/v) glycerol, 0.05 µg/µL poly dI-dC, 0.5 µM of the shorter or longer biotin-labelled DNA fragments, and purified rRv3407, in different concentrations . Pre-electrophoresis of 5% polyacrylamide native gel was performed in 0.5 X TBE buffer at 4 °C for 30 min at constant 100 V. Reactions were resolved by native 5% PAGE, run at 85 V at 4 °C in TBE 0.5 X, and the results were visualized with the LightShift® Chemiluminescent EMSA kit  [For EMSA assays, two biotinylated DNA fragments were obtained by amplification using PCR Master Mix 2 X . The first, using primers 07promFOR biotinylated (TCGACGTGTACGGGCAGGCT) and 07promREV1 (GGTGCTACATAATTGCGTTG) yielded a fragment of 96 bp comprising the end of the MA, USA) .rv3405c, encoding a TetR family transcriptional repressor. Immediately downstream is rv3406, a gene known to be repressed by Rv3405c, followed by rv3407 and rv3408, coding for the VapBC47 TA pair  TA-pair, we performed quantitative real-time PCR (RT-qPCR) (rv3407 and rv3408 transcripts were detected in BCG Moreau compared to BCG Pasteur.To assess whether the loss in regulation due to RD16 could have a polar effect on the transcription levels of the adjacent RT-qPCR)  on RNA eMtb [vapB47 upstream region with the MFold program [rv3406 and rv3407 (IR06-07) and the 5´ end of the rv3407 coding region, similar to what is described for FitAB from Neisseria gonorrhoeae (PDB 2H1O) [Inverted repeats are often present in the regulatory regions of TA genes, such as described for Mtb . Analysi program  identifiDB 2H1O) .rv3406 and the IR06-07 region, but 07promF-R2 also contains the initial 26 bp of the rv3407 coding region  were performed with purified recombinant VapB47 and biotin-labeled DNA fragments 07promF-R1 (96 bp) and 07promF-R2 (122 bp). Both fragments include the last 62 bp of g region A. rVapB4g region B but is  , adjacent to the BCG Moreau-specific RD16 genomic deletion, is noteworthy. We previously described the impact of RD16 on rv3406 expression due to inactivation of its repressor protein, Rv3405c, in BCG Moreau. Here we investigated a possible polar effect of this loss of regulation on the downstream orthologues, rv3407-rv3408, encoding this TA pair.Due to their potential role as targets for antibacterial therapy, toxin–antitoxin systems are being extensively studied in Mtb ,37,38,39Mtb  allowed rv3406 and rv3407 in BCG Moreau, due to the loss of repression by Rv3405c and consequent constitutive rv3406 expression. This directly affects the transcription of vapB47 (rv3407) and vapC47 (rv3408), increasing their transcription levels in comparison to the reference strain, BCG Pasteur , including the first 26 nucleotides of the rv3407 coding region, shows the presence of palindromic sequences and motifs characteristic of regulator sequences within promoter regions  and antitoxin (VapB22) results in reduced expression of various proteins, such as Esx-I, Esx-V, KasA, KasB and AcpM, as the antitoxin can regulate expression of these genes [Mtb replication. Repression of these genes is therefore related to a faster bacterial clearance by the innate immune response, resulting in reduced immunopathology [rv3406 constitutive expression alters rv3407-rv3408 levels, and an imbalance in these proteins’ ratio could impact the production of other virulence-associated genes that remain in the BCG genome. However, this hypothesis remains to be verified.It was proposed for VapBC22 in se genes . These vathology . If a siMtb TA system, VapBC46, shows that besides producing the toxin, distinct VapC46 expression levels can lead to different pathways within the bacteria life cycle. Under a toxin threshold, VapC46 modulates the expression of several genes, while maintaining metabolic active cells; above this level, cells are directed to a dormancy state [Cys, reducing its level in the cytosol, leading to an environment that mimics Cys starvation, directing cells to an overall mechanism similar to oxidative- and copper stress responses [A study on another cy state . If simiesponses . TherefoMtb VapBC21. In this case, VapC21 can interact with its cognate VapB21, but also with the non-cognate VapB32 [Another interesting VapBC feature was discovered for e VapB32 . This shrv3407-rv3408 make up an operon and that its expression is increased in BCG Moreau because of the RD16 deletion. As previously shown, this deletion leads to rv3406 constitutive expression in Moreau; here we corroborate that this mutation has a wider polar effect than previously detected, also interfering in the transcription of rv3407 (vapB47) and rv3408 (vapC47). Thus, in this strain the fine-tuned auto-regulation of the toxin–antitoxin expression by the antitoxin is likely bypassed, with unknown consequences to the physiology and immunomodulatory properties of this strain. We also show that rVapB47 binds DNA as predicted for this type of protein. Additionally, we identify the requirement of a palindrome in the rv3407 5´ region for rVapB47 DNA binding. These findings expand the current knowledge on the functional impact of the genomic differences between these important BCG vaccine strains and contribute to a better understanding of TA pairs functions.Most studies about the VapBC family are focused on the structure and function of VapC toxins and their interaction with cognate VapB antitoxins ,46,47,48


SETD2, H3K36me3, DNMT3B, and TET1 binding. This mechanism is validated in SETD2 knockdown cells and SETD2-mutated patients. Selective DNA methylation loss in CG-poor regions makes the chromatin accessible for alternative transcription. We show alternative promoters can control tumor transcriptomes and their regulatory architecture.Transcriptional regulation is commonly governed by alternative promoters. However, the regulatory architecture in alternative and reference promoters, and how they differ, remains elusive. In 100 CAGE-seq libraries from hepatocellular carcinoma patients, here we annotate 4083 alternative promoters in 2926 multi-promoter genes, which are largely undetected in normal livers. These genes are enriched in oncogenic processes and predominantly show association with overall survival. Alternative promoters are narrow nucleosome depleted regions, CpG island depleted, and enriched for tissue-specific transcription factors. Globally tumors lose DNA methylation. We show hierarchical retention of intragenic DNA methylation with CG-poor regions rapidly losing methylation, while CG-rich regions retain it, a process mediated by differential  The regulatory mechanisms of alternative promoters remain to be investigated. Here, the authors explore the sequence and epigenetics landscape of alternative promoters and how they regulate gene expression in hepatocellular carcinoma. Genome-wide profiling of HCC patients has helped to build a molecular map of mutations, dysregulated genes, and DNA methylomes5. Additional efforts to map histone modifications6, chromatin accessibility7, transposon activation8, and RNA N6-methyladenosine (m6A) methylation9 are ongoing and important steps to understanding mechanisms of gene regulation in cancer. Promoters are gateways to start transcription and regulate gene expression in a temporal and spatial manner, but little is known about promoter regulation in cancer. Therefore, we aimed to understand how the regulatory architecture of promoter usage impacts gene regulation using HCC as a model.Hepatocellular carcinoma (HCC) accounts for about 90% of primary liver cancers and is the third leading cause of cancer-related deathcis-regulatory elements to ensure precise gene regulation10. Cap Analysis of Gene Expression Sequencing (CAGE-seq) determines the 5’-ends of TSSs at single nucleotide resolution12 and quantifies gene expression similar to RNA-seq13. CAGE-seq also detects differently regulated transcription initiation events within the same core promoter15, and thus, it can accurately detect alternative promoters. In addition to reference promoters, many genes utilize alternative promoters for specific processes, such as cell fate transitions in yeast16 and mammalian cells17, in vertebrate embryogenesis18, and may have important roles in cancer19. Widespread activation of alternative promoters is known in different contexts; however, it is unclear whether their (epi-)genetic states are different compared to the reference promoter and if alternative promoters are under a different regulatory architecture. Alternative promoters have dynamic intragenic DNA methylation across human tissues20, and loss of DNA methyltransferase 3B (DNMT3B) in mouse embryonic stem (ES) cells was shown to result in spurious initiation of alternative promoters21. Therefore, precise regulation of alternative promoters is important to ensure correct gene expression. Transcription of alternative promoters is widespread in cancer19. Our understanding of the mechanism(s) of activation and alternative promoters’ impact on gene expression is unknown.Transcription is facilitated by a reference promoter, which is the region proximal to the transcription start site (TSS), integrating In this study, we analyze 100 CAGE-seq libraries from HCC patients and annotate 4083 (including 1031 novel) alternative promoters, representing 2926 multi-promoter (MP) genes, which are supported by histone modifications, Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq), RNA Pol2 ChiP, and DNA methylation across patient cohorts and HepG2 cells. Transcription of alternative promoters is dominant outside CpG islands (CGIs), enriched for genes important in hepatocarcinogenesis, and their activation frequently results in the downregulation of the reference promoter. We show that CG-poor regions preferentially lose DNA methylation in tumor tissues, followed by the accessibility of the chromatin and Pol2 binding, resulting in alternative transcription from CG-poor regions. Collectively, our study elucidates the mechanism of activation and preferential underlying DNA sequence for alternative promoter activation in cancer.8  from GSEA30 were overrepresented in MP genes  by MP genes  with the expression of alternative promoters   12,493; 80.3%) or MP  genes   Fig. . At the ma) Fig. . We lowema) Fig. .Fig. 2ThCTNNBL1 ) between tumor and tumor-adjacent tissues  upregulated  act as tumor-specific alternative promoter regions in HCC. Compared to 80% of reference promoters, only 48.6% (1987 out of 4083) of alternative promoters overlapped with CGIs  was also shared with reference promoters  among novel alternative promoters  lower in novel alternative promoters  in reference promoters compared to its alternative promoters  was enriched at similar levels in both SP and MP genes while initiating Pol2 modified by phosphorylation of serine 5 (Pol2-Ser5) and elongating Pol2 modified by phosphorylation of serine 2 (Pol2-Ser2) on its carboxy-terminal domain (CTD) were higher among MP genes  at TSSs both in HCC patients6  across gene bodies. To this end, we compared the DNA methylation landscapes of SETD2 mutated HCC patients (n = 15) with SETD2 wild-type HCC patients (n = 362). We observed that SETD2-mutant tumors were significantly hypomethylated and that demethylated CpGs were enriched in gene bodies overlapping CGIs  alternative promoters. Since there is no consensus on which promoter should be assigned as a reference or alternative, a highly expressed promoter is often annotated as the reference promoter36 or gene upstream CGIs61 in adult tissues. Human promoters are mostly CG-rich62 and have distinct promoter architecture and regulation11 compared to CG-poor promoters which are mostly tissue-specific17. Accordingly, a majority of HCC nonCGI alternative promoters are detected in lower proportion across other tumor types  and alternative (low CG density) promoters. Depletion of HCC alternative promoters from intragenic CGIs is notably different than alternative promoters usage from intragenic CGIs during developmental stagespes Fig. , indicat47, while we showed that intragenic CGIs lack H3K4me3 marks due to the retention of high DNA methylation in the tumor. It is known that aged cells (and cancer cells) generally lose DNA methylation from gene bodies59. We described a hierarchy that explained preferential loss or retention of intragenic DNA methylation dependent upon the flanking CG density where CG-rich regions retain high tumor DNA methylation. This preferential retention of intragenic DNA methylation is regulated by SETD2, H3K36me3, and DNMT3B as they preferentially bind to CG-rich regions. This leads to intragenic CG-rich regions with more repressive chromatin marks, and that would require higher levels of DNA demethylating enzyme to make their chromatin accessible. Instead, these intragenic CG-rich regions have low levels of TET1 binding and, thus, have reduced active demethylation. Thus, intragenic CGIs are less favorable to act as alternative promoters in cancer, though they can act as alternative promoters during embryonic development and cell differentiation36. On the contrary, intragenic CG-poor regions have lower levels of repressive chromatin state and higher levels of TET1 binding, which collectively makes their DNA sensitive to active demethylation and accessible chromatin to experience pervasive Pol2 binding64 that, upon the availability of a tissue-specific transcription factor will efficiently initiate and elongate these Pol2. We propose that other tumors have similar mechanisms in activating alternative promoters, while their preferred location will be dependent upon DNA demethylated sites that are enriched for tissue-specific transcription factors of the specific tumor tissue.We also found that epigenetic (histone modifications and DNA methylation) landscapes of reference and alternative promoters are dramatically different, which is due to their differences in CG density. The H3K4me3 marks are enriched on CGIs65. However, alternative promoters are widespread in cancer, raising an interesting question as to whether it is advantageous for tumor cells. We discussed different scenarios where alternative promoters offer functional advantages for tumors. Firstly, alternative promoters can downregulate reference promoters through transcriptional interference, which often coincides with important genes, including hepatocyte-specific markers  with bowtie267 by allowing up to two mismatches. On average, around 80-90% of sequenced reads were mapped, resulting in an average of 15-16 million mapped reads  and had a minimum of 3 TPM at least in 1 sample.To define TCs for each sample, we clustered CTSSs in the same strand that overlapped within 20 nucleotidesA gene with a single promoter was classified as a single promoter gene, and its promoter was assigned the reference  promoter. Genes with two or more promoters were classified as MP genes. The promoter with the highest mean expression level at the population level, which is also expressed in normal liver tissue, was assigned as the reference promoter. The remaining promoters of that gene were assigned as alternative  promoters of that gene. The terms reference TSS and alternative TSS have been used ambiguously.23, and 5′ ends capping of snoRNAs17. Thousands of intragenic CAGE TCs are detected within exons that represent post-transcriptional processing and are characterized by GG-initiation12. We filtered intragenic CAGE TCs with GG-initiation and those overlapping with coding exons and annotated enhancers70. We also annotated novel enhancers from CAGE TCs by computing directionality24. Briefly, the directionality score (DS) for intragenic TCs was calculated by measuring the expression level of TCs in forward and reverse strands, where DS = (Forward − Reverse)/(Forward + Reverse). CAGE TCs with directionality scores between 0.5 and −0.5 were classified as enhancer RNAs. On the remaining TCs, we only retained those that have YR-initiation or YC-initiation initiation motif15. The remaining 1077 TCs represent high-confidence true promoter tags that act as alternative TSSs to annotated genes. We clustered proximal TCs with 300 bases, thus resulting in 1031 novel alternative TSSs. To provide evidence of RNA transcripts for these 1031 alternative TSSs, we analyzed transcript models from RNA-seq26 and expressed sequence tags27. We excluded transcripts that are proximal (within 300 bases apart) to 5′ ends of annotated Ensembl transcripts. The remaining transcripts with distinct 5′ ends overlapped within 300 bases of novel alternative TSSs.To annotate novel alternative promoters, we implemented a pipeline Fig.  that sys70 across different tissues and cell types, and ChromHMM71 annotated enhancers on HepG2 cells. We intersected ChromHMM HepG2 enhancers with other enhancers and ensured that enhancers were annotated across multiple datasets. We excluded enhancers that overlapped with gene promoters. The remaining 21682 enhancers were divided into two groups based on overlap with CGIs. Enhancers that overlapped (a minimum of 10% of enhancer length) with CGIs were annotated as CGI enhancers (n = 791). The remaining non-overlapping (n = 20891) enhancers were annotated as nonCGI enhancers.We downloaded previously annotated enhancers6. We downloaded raw sequence reads of H3K36me3 marks on SETD2 wild type and SETD2 knockdown conditions on HepG2 cells72. The raw sequence reads were mapped using bowtie267 and excluded multi-mapping reads.We downloaded the raw sequence reads of H3K4me1, H3K4me3, H3K27ac, and H3K27me3 ChIP-seq data for four HCC patients73. The methylation levels were computed as beta values in the range of 0–1. The reduced RRBS of HepG2 cells was downloaded from ENCODE. The TET-assisted reduced RRBS (TAB-RRBS) of HCC patients was downloaded as a beta value6. For each CpG, we computed the average methylation levels across the cohort, separately for tumor and tumor-adjacent tissues. Differentially methylated CpGs around promoter regions were identified using a t-test on individual methylation levels between tumor and tumor-adjacent tissues. The P-value was adjusted for multiple corrections and P-value less than 0.05 was defined as significantly methylated CpGs. For visualization of average methylation levels around TSSs and CGIs, the mean beta value was converted into bigwig tracks and plotted average beta value using deepTools74.The Illumina 450 K DNA methylation data from the TCGA HCC cohort was downloaded from UCSC Xena hubSETD2 mutations, we separated TCGA HCC patients into two groups, namely, SETD2 mutants (n = 16) and SETD2 wild type (n = 352). Differentially methylated CpGs were identified using a t-test on individual methylation levels between SETD2 mutants and SETD2 wild-type tumors. The P-value was adjusted for multiple corrections and a P-value less than 0.05 was defined as significantly methylated CpGs.For the analyses of DNA methylation landscapes induced by 74. From mapped BAM files, we first generated coverage as wig tracks which are normalized as RPKM using default parameters from deepTools. For the genes and CGIs metaplot, variable lengths of genes and CGIs were scaled between start and end.All metaplots across genes and CGIs were plotted using deepTools75. To calculate the density of TFs, we intersected TFs peaks with promoter regions by using bedtools76. Different TFs overlapping promoters were summed to determine the total number of TFs per promoter. To identify overrepresented transcription factors, we performed motif analyses using HOMER77. We used 500 bases around TSSs as the search region to detect motifs. The background regions were controlled for nucleotide composition and selected by default by HOMER. We first identified motifs using the following parameter “findMotifsGenome.pl -chopify -len 8,10,12 -S 25 -size −500,500”. To compare these identified motifs with known motifs, we reran prediction using the following command “findMotifsGenome.pl -chopify -len 8,10,12 -S 25 -mcheck -mknown -size −500,500”. For visualization of motifs, we computed the matrix of p-value across promoter types and plotted them as heatmaps.We downloaded transcription factors (TFs) ChIP-seq peaks for HepG2 from ENCODE28, Hoshida signature genes29, sorafenib resistance signature31, and GSEA cancer hallmark genes30 were downloaded from the literature. We are interested in these gene signatures with annotated SP and MP genes. We computed Fisher’s exact test to determine the statistical significance of the overlap between signature genes with single and MP genes.Curated signature genes for hepatocytes78 and transcript expression levels from UCSC Xena hub73. Each transcript was sorted based on its expression levels and classified into high-expression and low-expression groups. We associated the expression levels of these transcripts from two (high-expression and low-expression) groups with the survival status of patients and performed Kaplan–Meier analyses. For MP genes, we performed Kaplan–Meier analysis on the reference transcript and on the alternative transcript.To compute the OS of patients associated with expression level for each transcript, we downloaded TCGA LIHC clinical data32. The significance cut-off was defined at an adjusted P-value of 0.05 and log2 fold-change of absolute (0.5). For gene level analysis, we summed the expression levels of reference and alternatives promoter for each MP gene.Differentially expressed genes and promoters were identified using DE-seq2To compare whether alternative promoters altered the N-terminus of the protein, we analyzed only those alternative transcripts that have assigned UniProt protein domains and hence excluded noncoding transcripts and novel alternative promoters. We compared the N-terminus of the reference and alternative promoters, and if they had different start codons, they were assigned as different N-terminus proteins.27. We intersected CGIs with assigned promoter regions of annotated transcripts, and those overlapping promoters were assigned as promoter CGIs. The remaining CGIs that overlapped gene bodies were classified as intragenic CGIs. The remaining CGIs were classified as intergenic CGIs. The observed/expected (O/E) CG dinucleotides for a given genomic window were calculated using the following formula O/E CG = (CG count) (genomic window)/(C count * G count). We used “bedtools nuc” function from Bedtools76 to measure these values and computed the O/E CG ratio for each genomic window.Annotated CGIs were downloaded from the UCSC databaseFurther information on research design is available in the Supplementary InformationReporting SummarySupplementary TablesSupplementary Table 2


Helicobacter pylori (H. pylori) there is no information regarding what transcription factors induce/upregulate the expression of CLDN6. We aimed to identify whether CREB and Yin Yang1 (YY1) regulate the expression of CLDN6 and the site where they bind to the promoter sequence. Bioinformatics analysis, H. pylori lipopolysaccharide (LPS), YY1 and CREB silencing, Western blot, luciferase assays, and chromatin immunoprecipitation experiments were performed using the stomach gastric adenocarcinoma cell line AGS. A gen reporter assay suggested that the initial 2000 bp contains the regulatory sequence associated with CLDN6 transcription; the luciferase assay demonstrated three different regions with transcriptional activity, but the −901 to −1421 bp region displayed the maximal transcriptional activity in response to LPS. Fragment 1279–1421 showed CREB and, surprisingly, YY1 occupancy. Sequential Chromatin Immunoprecipitation (ChIP) experiments confirmed that YY1 and CREB interact in the 1279–1421 region. Our results suggest that CLDN6 expression is regulated by the binding of YY1 and CREB in the 901–1421 enhancer, in which a non-described interaction of YY1 with CREB was established in the 1279–1421 region.Aberrant expression of the tight junction protein claudin 6 (CLDN6) is a hallmark of gastric cancer progression. Its expression is regulated by the cAMP response element-binding protein (CREB). In gastric cancer induced by  H. pylori LPS increases the expression of CLDN6 in AGS cells [Epithelial tight junctions are dynamic structures formed via the aggregation of several different transmembrane proteins  . ClaudinGS cells .H. pylori show up-regulation and activation of CREB that correlate with early cellular inflammation and ulceration [claudin 6 expression is regulated by CREB, amongst other transcription factors [Mongolian gerbils infected with ceration . In siliceration , and tha factors . Interes factors , a pathw factors .Diverse nuclear processes, including enhancer–promoter interactions, are regulated, among others, by CCCTC-binding factor (CTCF) . YY1 conH. pylori-induced gastric cancer, there is no information regarding what transcription factors induce/upregulate the expression of CLDN6. The aim of this paper is to identify whether CREB and YY1 transcription factors regulate the expression of CLDN6 and to identify the site where they bind to the promoter sequence.The interaction between YY1 and CREB has been established in viral infections ,35 and gCLDN6 transcription is initiated; because the number of possible binding sites was extensive, we selected sites with a minimal similitude value of 0.8 for YY1 and CREB. Nine YY1 binding sites were found, but one with a similitude value of 0.9 was in a site very close to where transcription is initiated. Concerning CREB, 17 binding sites with a similitude value between 0.80 and 0.89, and three with a similitude value greater than 0.9, were selected. Interestingly, a luciferase assay determined that none of the 0.9-value YY1 or CREB participated in the transcriptional activity after LPS stimulation (CLDN6 promoter).As YY1 interacts with CREB, a Transcription Factor Binding (TFBind) analysis was performed to determine all the possible YY1 and CREB binding sites in a promoter region close to the location where H. pylori LPS [p < 0.05). Interestingly, this increase was apparently subordinated to a significant up-regulation (p < 0.01) of YY1 and CREB transcription factors that initiated 4 h after LPS exposure and reached its maximum value after 12 and 24 h   A. Transf−901 bp) B. The ap−901 bp) C. The diCLDN6 transcription regulation, silencing experiments using specific siRNAs were done. p < 0.0001).To determine the role of YY1 and CREB in As the luciferase analysis had already shown, the 901–1421 region possessed the elements that control CLDN6 expression in LPS-treated cells. The chromatin immunoprecipitation assays showed YY1 and CREB occupancy sites in the 901–1018 bp fragment; the 1018–1149 fragment only had YY1 occupancy, and the 1149–1279 fragment did not show occupancy signals for either transcription factor; these sites corresponded to those predicted via the bioinformatic analysis. The 1279–1421 fragment showed CREB occupancy as predicted, but surprisingly, YY1 occupancy was also demonstrated despite not being predicted via the bioinformatic analysis .Because of the surprising presence of a YY1 occupancy site in 1279–1421, we looked for possible YY1 binding sites with a minimal similitude value of 0.5 onwards in this region; the analysis did not show a putative binding site. Because ample bibliographic information validates the spatial interaction of both proteins, we performed sequential ChIP experiments in all three regions to determine whether the presence of YY1 in the 1279–1421 region is secondary to its interaction with CREB. The results confirmed, as expected, that YY1 and CREB interact in the 1279–1421 region, thus confirming our hypothesis .claudin 6 promoter [Claudins, a large family of transmembrane structural proteins, are fundamental to tight junction function and regulation linked to associated regulatory and scaffolding proteins . They expromoter . CLDN6 epromoter ,43,44. Cpromoter ,19, but CLDN6. The bioinformatics analysis results showed that CREB and YY1 have 17 and 9, respectively, probable binding sites 2000 bp upstream of the site where transcription of CLDN6 is initiated. Our results confirmed that H. pylori LPS induced, in the context of a chronic inflammatory process, the expression of CLDN6 in the AGS cell line [A previous analysis of claudin expression in the AGS cell line suggested that the transcription factors CREB and YY1 could be regulators of ell line . Still, YY1 is a zinc finger protein that can activate or repress transcription, depending on its interactions with other transcription factors ,46,47, sH. pylori LPS. lL-1b and TNFα regulate gene transcription via the C/EBP1, C/EBP2, and YY1 elements in epithelial cells [H. pylori capable of inducing gastric carcinogenesis; cytotoxin-associated gene A (CagA) protein, another major and substantial component of H. pylori, is known to provoke genomic hypermutations, DNA double strand breaks, subverted DNA damage responses that include the down-regulation of DNA repair genes, and transcriptomic and proteomic alterations that increase the chance of generating a “hit-and-run mechanism” for gastric carcinogenesis [Bacterial LPS regulates gene transcription by binding YY1 complexes to the CCAAT enhancer binding protein 1 (C/EBP1) or long non-coding RNA (lncRNA) via inflammatory cytokines ,52. Our al cells . YY1 funal cells . LPS is ogenesis ,56,57.CLDN6. The ChIP assays confirmed the results and revealed a YY1 binding site in the 1279–1421 bp region not predicted via the bioinformatics analysis. The distance between these CREB and YY1 sites was identified, suggesting that the 1279–1421 region is an enhancer [CLDN6 transcription, but the precise mechanism remains to be solved. YY1 has been recognized as a structural regulator of enhancer–promoter loops [To identify the specific regulatory regions of the 2000 bp promoter regions where CREB and YY1 binding sites were found, the complete promoter region and two fragments were cloned in the pMetLuc2 vector and evaluated via a luciferase activity assay. The results suggested that two CREB sites and two YY1 sites in the DNA fragment corresponding to the 901 bp–1421 bp region are significantly important for regulating the enhancer . Gene exenhancer . Enhanceenhancer . YY1 is enhancer . Our reser loops . YY1 ander loops .H. pylori LPS is involved in the transcriptional initiation and activation of CLDN6, a perfectly defined tight junction protein associated with enhanced gastric cancer progression and invasiveness [The mechanism through which YY1 functions both as a transcriptional activator and repressor depends in its acetylation/deacetylation . Still, siveness .Our results suggest that CLDN6 expression, a protein associated with gastric epithelial cancer progression, is regulated by the binding of YY1 and CREB in the 901–1421 enhancer in which a non-described interaction of YY1 with CREB was established in the 1279–1421 region.Dulbecco’s modified Eagle’s medium (DMEM), fetal bovine serum (FBS), L-glutamine, sodium pyruvate, insulin, Dulbecco’s phosphate-buffered saline (PBS), streptomycin–penicillin, bovine albumin, and total antibody compensation beads were from Invitrogen . Monoclonal anti-claudin-6 (sc-393671), anti-YY1 (sc-7341), anti-GAPDH (sc-47724), anti-actin (sc-32251), and goat anti-mouse IgG were from Santa Cruz Biotechnology . Monoclonal anti-CREB (MA1-083) and Lipofectamine 2000 (11668027) were from ThermoFisher Scientific . The Molecular Biology Kit, EZ-10 Spin Column Plasmid DNA Miniprep Kit was from Southern Labware . The ready-to-glow secreted luciferase reporter assay Cat # 631727) and pMetLuc2 reporter vector (Cat # 631729) were from Takara Bio USA . Primers were designed using TFBIND [1727 and 6) were cultured in sterile P-100 Petri dishes with DMEM supplemented with 5% FBS, 0.1 U/mL of insulin, a 1% streptomycin–penicillin solution, 2 mmol/L of L-glutamine, and a 2 mmol sodium pyruvate solution at 37 °C in a humid environment containing 5% CO2, until reaching >90% confluence. Luciferase experiments were performed with 8 × 105 cells/well seeded in 6 well Tissue Culture Plates  for 48 h until they reached 90% confluence, which represents 1 × 106 cells/well. The LPS treatment of AGS cells was performed in p-100 culture dishes once they reached 80–90% confluence. All the experiments were immediately performed with these confluent AGS cell cultures in their third passage.Human gastric adenocarcinoma (AGS)  cells . We used a 0.8 cut-off instead of the 0.5 value for both transcription factors.The search for sequences with potential transcriptional relevance in the present study was performed using TFBIND  were used to transfect AGS cells. 3 × 10Helicobacter pylori (strain J99) LPS were kindly donated by Dr. Victor R. Coria, Instituto Nacional de Pediatría, México. AGS cells were treated with 10 ng/mL of LPS H. pylori for 4 h, 8 h, 12 h, 24 h, and 48 h.g (4 °C). The supernatants were recovered, and a Bio-Rad Protein Assay determined the total protein concentration .Control, LPS-treated, and transcription factors silenced AGS cells were washed twice with PBS and scraped with 1 mL of an ice-cold lysis buffer (150 mM NaCl/50 mM Tris/1 mM EGTA/1 mM EDTA/1% IGEPAL/0.1% Sodium deoxycholate/0.1% SDS + protease and phosphatases inhibitors). Cell suspensions were sonicated for 1 min at 25% amplitude, followed by centrifugation for 30 min at 17,000× For Western blot analysis, 20 µg of protein was resolved on 13% SDS/PAGE and transferred to nitrocellulose membranes in a Bio-Rad semi-dry blotting system for 1 h at 120 mA. Membranes were blocked with 5% non-fat dry milk in Tris-buffered saline (TBS) for 1 h, washed twice with TBS, and incubated with the relevant primary antibody diluted in Tween 20/TBS (TTBS) overnight at 4 °C (anti claudin-6 (1:200), anti-YY1 (1:400), anti-CREB (1:500), anti-GAPDH (1:5000), and anti α-actin (1:500)). Membranes were washed with TTBS and incubated with horseradish peroxidase-labeled secondary goat anti-mouse IgG (1:4000) diluted in TTBS for 1 h at room temperature. Afterward, membranes were washed thrice with TTBS and once with TBS before antibody binding was detected via chemiluminescence using the Supersignal West Dura Extended Duration Substrate  as a substrate. Equal protein loading was confirmed in all the experiments by determining β-actin as a loading control. All experiments were done in triplicate. The quantitative analysis of the Western blot bands was performed using ImageJ v 1.53 software, which calculates the intensity of the gel band, measuring the amount of pixels/sq.in. The values were determined for each band. The results are expressed as dots per point (DPP) and intensity.claudin 6 promoter were amplified from AGS genomic DNA using the following primers: −2000 forward: 5′CCAGCCGGTGATCTAGTCC3′; −1421 forward: 5′GCCACTACAGCTTTGTTAAGGG3′; and 901 forward: 5′CGGGCACCTGTAGTAGTCC3′. We used the same reverse primer: 5′AATTCCTAGGCCGAGTGTCG3′. The amplified DNA was cloned in pMetLuc2 plasmid using DNA ligase T4. The result of this ligation was used to transform competent E. coli DH5α to get transformed colonies. Plasmid DNA was extracted using the Southern Labware EZ-10 Molecular Biology Kit, according to the manufacturer´s instructions. The purified DNA was used to transfect AGS cells using lipofectamine 2000; these transfected AGS cells were treated with H. pylori LPS 10 ng/mL for 4, 8, 12, 24, and 48 h. Luciferase activity was measured using the Ready-to-Glow secreted luciferase reporter assay  in a Perkin Elmer Wallac 1420 Victor Spectrophotometer. The values obtained from these experiments were normalized according to Schagat T. of Promega Corporation .The −2000, −1421, and −901 regions of the H. pylori LPS 10 ng/mL for 24 h before being fixed with 3.7% formaldehyde, lysed, and sonicated, as previously mentioned. Immunoprecipitation was then performed with CREB and YY1 antibodies, and purified DNA fragments were analyzed via qPCR, using Ruby Hot Start Master (2×) according to the manufacturer’s instructions. The following primers were used: 901–1018 forward: 5′GCACTACAGCTTTGTTAAGGG3′; reverse: 5′CACTACCACGCCCGGCTAAC3′; 1018–1149 forward: GTTAGCCGGGCGTGGTAGTGGGC; reverse: CGCCCAGTCTGGAGTGCAATGG; 1149–1279 forward: CCATTGCACTCCAGACTGGGCG; reverse: TTTCCTGACCTCGTGATCTGCCC; and 1279–1421 forward: GGGCAGATCACGAGGTCAGGAAA; reverse: GGGACTACTACAGGTGCCCG. The results of these assays were analyzed in a 3.5% agarose gel, and the quantitative analysis of the bands’ images was performed using ImageJ v 1.53 software, which calculates the intensity of the gel band, measuring the amount of pixels/sq.in. The values were determined for each band. The results are expressed as dots per point (DPP) and intensity.A ChIP assay was performed using Millipore EZ-ChIP kit  according to the manufacturer’s instructions. In brief, AGS cells were treated with p < 0.05 (*), p < 0.01 (**), p < 0.001 (***) or p < 0.0001 (****) versus the control condition, and “n” represents the number of independent experiments.Statistical analyses were performed using the GraphPad Prism software, version 4 . All values are expressed as means ± standard deviations (SDs). Statistical significance in a one-way analysis of variance (ANOVA), followed by a post hoc Dunett’s test and selected pairs comparison test, was set to


In recent years, increasingly more non-coding RNAs have been detected with the development of high-throughput sequencing technology, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), small nucleolar RNAs (snoRNAs), and piwi-interacting RNA (piRNAs). The discovery of enhancer RNAs (eRNAs) in 2010 has further broadened the range of non-coding RNAs revealed. eRNAs are non-coding RNA molecules produced by the transcription of DNA cis-acting elements, enhancer fragments. Recent studies revealed that the transcription of eRNAs may be a biological marker responding to enhancer activity that can participate in the regulation of coding gene transcription. In this review, we discussed the biological characteristics of eRNAs, their functions in transcriptional regulation, the regulation factors of eRNAs production, and the research progress of eRNAs in different diseases.Video AbstractThe online version contains supplementary material available at 10.1186/s12964-023-01206-0. Cell ability to respond to specific developmental or environmental stimuli requires strict regulation of gene expression, which is controlled by a variety of genomic regulatory elements, including enhancers, promoters, and silencers. Enhancer is a type of non-coding DNA cis-acting element. The 72-bp sequence, isolated from Simian Virus 40 (SV40) in 1981, is the first enhancer to be cloned, with its function tested . Later sGenomic analysis revealed that many enhancers can be combined by RNA polymerase II (RNA Pol II) and actively transcribed to produce non-coding RNAs, namely eRNAs , 14. A nDuring the individual development of eukaryotes, enhancers can activate or enhance gene transcription by binding to transcription factors, cofactors, and chromatin complexes to act on promoters. The dynamic interaction between an enhancer and the corresponding promoter determines its tissue- or cell-specific action mode –28. HoweSuper-enhancers are a class of cis-regulatory elements with strong transcriptional activation properties, which were first proposed by scholar Richard A. Young in 2013 . They arUsing ChIP-seq, two studies in 2010 found that RNA polymerase II could bind to enhancer regions, and the new non-coding transcripts, eRNAs, were detected through RNA-seq technology , 14. SinAccording to cap-analysis gene expression (CAGE) technology, the number of eRNAs in humans is approximately 40,000–65,000, which is extremely large , 51. MosCompared with messenger RNA(mRNAs) and lncRNAs, eRNAs mainly present following characteristics: (a) eRNAs are mainly produced by genomic regions marked by high H3K4me1 and low H3K4me3 histone modifications, whereas the promoter regions of lncRNAs genes usually have high levels of H3K4me3 histone modifications ; (b) eRNAlthough RNA-seq technology has a wide range of applications, it has not been employed on a large scale in the detection of eRNAs mainly due to their poor stability. Methods such as global run-on sequencing (GRO-seq) and its derivative, the precision nuclear run-on sequencing (PRO-seq) have been used to detect the presence of eRNAs, that is, to determine the transcription at a certain stage of cell development by detecting the sequence of new transcripts in the cell , 61–65. Distal-acting enhancers are key elements in the regulation of time- and cell type-specific gene expression patterns. The genome-wide identification of active enhancers is necessary to understand gene expression and developmental and disease-related processes. Initially, researchers assessed the enhancer activity mainly by analyzing the recruitment of stimuli-induced transcriptional activators, epigenetic modifications of histones, and the transcription levels of nearby genes. Later studies found that enhancer transcription and the production of bidirectional eRNAs were also closely related to the enhancer activity , 73. In Traditionally, enhancers can be combined with transcription factors, which in turn interact with mediator complexes or other transcription co-activators to promote the recruitment of RNA Pol II and chromatin-modifying enzymes to the promoter region and form chromatin loop (enhancer-promoter loop) to foster the transcription of enhancer target genes. The discovery of eRNAs reveals the complex connection between enhancers that produce eRNAs and target genes, as well as the importance of eRNAs in promoting the transcription of adjacent target genes . In thisStudies have found that eRNAs accelerated the transcription process by fostering enhancer-promoter interaction , 67. ForCERNA) and that by distal enhancers (DRR) (DRRRNA). By curbing the expression of eRNAs, the authors found that the reduction in CERNA levels significantly decreased the occupancy rate of RNA Pol II in the proximal region of the MyoD1 gene. On the other hand, the knockdown of DRRRNA diminished the occupancy rate of the proximal RNA Pol II of the MyoG gene, which is the downstream gene of MyoD1. The authors also assessed the influence of eRNAs on molecular events before RNA Pol II assembly, namely chromatin accessibility. They found that the knockdown of DRRRNA significantly reduced the chromatin accessibility of MyoG. After the decrease in CERNA expression, the cells were less susceptible to Deoxyribonuclease I (DNase I) treatment at MyoD1 and MyoG. The results of this study shows that eRNAs from the MyoD1 regulatory region promote chromatin recombination/depolymerization and RNA Pol II assembly at specific sites in the muscle-derived gene regulatory network, thereby affecting gene transcription. These findings also suggest that eRNAs can form an open chromatin environment by recruiting key transcription factors and/or chromatin remodeling complexes  and N6-methyladenosine (m6A). Aguilo et al. [5C modification. NSUN7 depletion caused an abrupt decrease of eRNAs expression levels and a loss of m5C modification on eRNAs. Lee et al. [6A signals on eRNAs by using methylation inscribed nascent transcripts sequencing (MINT-Seq). They identified the m6A motif of eRNAs as "GGACT". MINT-Seq results revealed the presence of m6A peaks was on the entire eRNA transcript, but with the highest abundance in the middle of the transcript. In addition, m6A-marked eRNAs (m6A-eRNAs) were longer and possessed higher transcript abundance than non-m6A-marked eRNAs. RNA half-life experiments further confirmed that m6A-eRNAs were more stable. Mechanistically, m6A-eRNAs could recruit the m6A reader protein YTHDC1 to active enhancer regions to promote enhancer transcription and facilitate the formation of transcriptional activator condensates, which in turn promote target genes activation. Interestingly, Xu et al. [6A methyltransferase complex (MTC) and m6A demethylase ALKBH5 located in the enhancer region could regulate eRNAs m6A methylation and demethylation, respectively.Chemical modifications occur on eRNAs can stabilize eRNAs Fig. C, which o et al.  demonstre et al.  have detu et al.  showed tStudies have gradually revealed the regulatory role of eRNAs in a variety of diseases, such as tumors, inflammation, neurological diseases and muscular diseases. We have summarized the eRNAs with confirmed regulatory role in Table Enhancers play specific roles in human cells, and their mutations or misregulation can lead to the occurrence of malignant tumors. Therefore, it would be crucially important to understand whether the eRNAs produced by them affect the tumorigenesis process. In a previous study on this topic, Zhang et al.  pooled dMeanwhile, a number of studies have confirmed the regulatory roles of eRNAs in tumor progression at the molecular biological level. For example, the HPSE eRNA produced by heparanase can promote cancer progression by driving the chromatin loop and regulating the hnRNPU/p300/EGR1/HPSE axis ; KallikreRNAs also play a role in the progression of inflammation. For instance, Nicholas et al.  found thYao et al.  discoverCERNA could active MyoD expression in cis by promoting chromatin remodeling and increasing RNA polymerase II occupancy and DNA accessibility, which was a crucial transcription factor in myogenesis. Tsai et al. [DRReRNA which was transcribed from the distal enhancer of MyoD (located on mouse chromosome 7) did not regulate the expression of MyoD, but localized to Myogenin locus (located on mouse chromosome 1) and regulated the expression of Myogenin in trans. Furthermore, they found that DRReRNA could bind to SMC3, which was one member of Cohesin complex, and recruit Cohesin at Myogenin in differentiated C2C12 cells, thus promoting myotube differentiation.As we mentioned above, Mousavi et al. . revealei et al.  demonstrIt is only more than a decade since the discovery of eRNAs. Although increasing research on eRNAs has been performed in recent years and many of their biological functions have been revealed and confirmed, there are still issues that need to be addressed. For example, most of the functions of eRNAs reported in those literature cannot exist independently of enhancers. Thus, it is critical to elucidate whether they can serve as functional genes similarly to other non-coding RNAs, because most eRNAs have poor stability. In addition, it is worthwhile to establish and develop better tools for the manipulation of eRNAs expression, which would be crucial to the success of functional eRNAs research. In addition, although Zhang et al. has suggested the widespread presence of eRNAs in tumor cells through database integration, the specific mechanisms by which eRNAs function in malignant tumors remain to be explored. Furthermore, the lineage-specific expression signatures of eRNAs may guide the molecular typing and diagnosis of tumors. Their transcriptional regulatory role in tumor genes also suggests the potential to be employed as a target for tumor diagnosis or therapy. In conclusion, many unknowns remain regarding the biological functions and specific mechanisms of eRNAs activities, which require further continuous exploration.


RELA impacts the beta cell transcriptional landscape and provides network control over glucoregulation.NF-κB activation unites metabolic and inflammatory responses in many diseases yet less is known about the role that NF-κB plays in normal metabolism. In this study we investigated how Rela gene, encoding the canonical NF-κB transcription factor p65 (βp65KO mice), or the Ikbkg gene, encoding the NF-κB essential modulator NEMO (βNEMOKO mice), as well as βA20Tg mice that carry beta cell-specific and forced transgenic expression of the NF-κB-negative regulator gene Tnfaip3, which encodes the A20 protein. Mouse studies were complemented by bioinformatics analysis of human islet chromatin accessibility (assay for transposase-accessible chromatin with sequencing [ATAC-seq]), promoter capture Hi-C (pcHi-C) and p65 binding (chromatin immunoprecipitation–sequencing [ChIP-seq]) data to investigate genome-wide control of the human beta cell metabolic programme.We generated novel mouse lines harbouring beta cell-specific deletion of either the Rela deficiency resulted in complete loss of stimulus-dependent inflammatory gene upregulation, consistent with its known role in governing inflammation. However, Rela deletion also rendered mice glucose intolerant because of functional loss of insulin secretion. Glucose intolerance was intrinsic to beta cells as βp65KO islets failed to secrete insulin ex vivo in response to a glucose challenge and were unable to restore metabolic control when transplanted into secondary chemical-induced hyperglycaemic recipients. Maintenance of glucose tolerance required Rela but was independent of classical NF-κB inflammatory cascades, as blocking NF-κB signalling in vivo by beta cell knockout of Ikbkg (NEMO), or beta cell overexpression of Tnfaip3 (A20), did not cause severe glucose intolerance. Thus, basal p65 activity has an essential and islet-intrinsic role in maintaining normal glucose homeostasis. Genome-wide bioinformatic mapping revealed the presence of p65 binding sites in the promoter regions of specific metabolic genes and in the majority of islet enhancer hubs (~70% of ~1300 hubs), which are responsible for shaping beta cell type-specific gene expression programmes. Indeed, the islet-specific metabolic genes Slc2a2, Capn9 and Pfkm identified within the large network of islet enhancer hub genes showed dysregulated expression in βp65KO islets.RELA as a regulator of islet-specific transcriptional programmes necessary for the maintenance of healthy glucose metabolism. These findings have clinical implications for the use of anti-inflammatories, which influence NF-κB activation and are associated with diabetes.These data demonstrate an unappreciated role for The online version of this article (10.1007/s00125-023-05931-6) contains peer-reviewed but unedited supplementary material. Regulation of correct blood glucose levels is essential to fuel the metabolic demands of the body and is therefore essential for life. A normal physiological glucose range is maintained through the coordinated efforts of glucose-sensing and glucose-responding tissues, including the brain, the liver, muscle and the pancreatic islets of Langerhans . DisruptRELA (p65), REL (c-Rel) and NFKB1 (p105), as well as transcription factors activated by non-canonical signalling, such as those encoded by RELB (RelB) and NFKB2 (p100) [Rela, in hepatocytes [Map3k14, which encodes NIK, renders mice hypoglycaemic and improves glucose tolerance [Rela impacts the beta cell transcriptional landscape and provides network control over glucoregulation.The NF-κB transcription factor family comprises DNA-binding components activated by canonical signalling, such as those encoded by 2 p100) . Classic2 (p100) . DecreasRELA p6, REL (c-2 (p100) . Furtheratocytes  but not atocytes  improves0 . Classatocytes  and impaatocytes , whereasolerance , 14, hypolerance , amyloidolerance  and tranolerance  can actiolerance . These dTnfaip3  [Animal studies using 6- to 12-week-old male and female mice housed under specific pathogen-free (SPF) conditions were approved by the Garvan Institute Animal Ethics Committee (no. 17_24). C57BL/6 mice were purchased from the Animal BioResource Centre  and βA20Tg knock-in mice were generated at Ozgene  using murine oxP/loxP  and IkbkIn brief, and as described previously , 80 handIn brief, and as described previously , i.p. GThttps://imagej.nih.gov) on consecutive pancreatic serial sections cut at 200 µm intervals. Beta cell mass (per mg) was calculated by multiplying the relative insulin-positive area by the mass of the isolated pancreas before fixation. Images were captured using a Leica DM 4000 or Leica DM 6000 Power Mosaic microscope (Leica Microsystems).In brief, and as described previously , paralleMembranes were incubated using standard techniques, diluents and buffers with anti-A20 (56305/D13H3), anti-IκBα (9242), anti-phospho-IκBα (2859/I4D4), anti-IKKγ (2585), anti-JNK (9252), anti-phospho-JNK (9255), anti-p65 (6956/L8F6) all sourced from Cell Signaling Technology, anti-mCherry (ab183628) (Abcam) or anti-β-actin (AC-15) (Sigma-Aldrich) antibodies, followed by labelling with the HRP-conjugated secondary antibody goat-anti-mouse IgG Fc (Pierce Antibodies) or donkey-anti-rabbit IgG (GE Life Sciences). HRP conjugates were visualised using an ECL detection kit (GE Life Sciences).PPIA  and ACTB were used as housekeeping genes and data were analysed using the 2–ΔΔCt method. Initial denaturation was performed at 95°C for 10 s; this was followed by a three-step cycle consisting of 95°C for 15 s , 63°C for 30 s  and 72°C for 30 s . A melting curve analysis was performed after completion of 45 cycles using the following conditions: 95°C for 2 min, 40°C for 3 min and a gradual increase to 95°C with 25 acquisitions/°C.Total RNA was extracted using the RNeasy Plus Mini Kit (Qiagen) and reverse transcribed using the Quantitect Reverse Transcription Kit (Qiagen). Primers were designed using sequences from GenBank and synthesised by Sigma-Aldrich (ESM Table Human islet gene networks were investigated using the assay for transposase-accessible chromatin with sequencing (ATAC-seq) based on genomic datasets including eight donors without diabetes generated by Bysani et al  downloadhttps://github.com/marcelm/cutadapt; TU Dortmund University). Trimmed reads were aligned to the GRCh37 genome using Bowtie2  with an ‘-X 2000’ setting. Quality trimming was performed with option ‘-q 10’ and ‘-F 2828’ using Samtools (https://github.com/samtools/samtools). Duplicate reads were removed using Picard . Mitochondrial reads, reads mapping to regions with an anomalous and unstructured signal (ENCODE hg19) and high signal regions on the nuclear genome that show sequence homology with the mitochondrial genome were excluded using BEDTools . For peak calling the read start sites were adjusted to represent the centre of the Tn5 transposase binding event. Peaks were called from ATAC-seq data using MACS2 , and HINT-ATAC  was used to identify footprints within the ATAC-seq peaks called from merged reads extracted from nucleosomal-free regions (Nfr) and regions bound by one nucleosome (1N) with the parameters ‘--atac-seq --paired-end --organism=hg19’. Intersection of genomic regions was performed using BEDTools. Enrichment of Gene Ontology (GO) terms and gene sets was performed using the ChIP-Enrich tool .The libraries were trimmed to remove Nextera adapters using cutadapt  software .All data are presented as means ± SEM or ± SD. Two-way Student’s Rela gene, which encodes the p65 protein. Islets isolated from βp65KO mice showed reduced levels of Rela mRNA and a >90% reduction in p65 protein  subunit of the activating IκB kinase (IKK) protein complex [Ikbkg mRNA and NEMO protein levels  and human p65 binding (ChIP-seq) datasets [CXCL10, ICAM1 and TNFAIP3; Fig. CXCL1 or TNF loci  mediates the uptake of glucose into beta cells, is essential for GSIS and is associated with an increased risk for type 2 diabetes [SLC2A2 interacts strongly with the gene promoter of TNIK (which encodes TRAF2- and NCK-interacting kinase) in 3D space  that harbours chromatin interactions stemming from the MIR569, TNIK, RNU1-70P and PLD1 promoters  in 3D space   can influence metabolic control , which r TNFAIP3  contribu TNFAIP3 , 25, whiications . Furthern islets , but is n islets , suggestn islets  and, of n islets , and betn islets . These dRELA among a suite of factors that influence healthy metabolism, including external factors such as exercise [+ channels [We identified GO terms for unexpected gene sets within the islet enhancer hubs, including ‘hallmark_estrogen_response_early’. Enrichment for oestrogen response genes matched with the more severe glucose intolerance observed in βp65KO female mice and, of interest, oestrogen has been reported to play a glucose-lowering role in humans and mice  by enhanexercise , antibioexercise , as wellchannels .Nnt mutation can display defects in glucose tolerance and insulin secretion [Tnfaip3/A20 transgenic lines, backcrossed onto C57BL/6N mice, and by using alternative Cre lines (e.g. Ins1Cre line).This study has some limitations with regard to the use of animal strains and lines in the generation of beta cell-specific targeted gene deletions. Some Cre lines have been reported to exhibit glucose tolerance defects , and it ecretion . To ruleecretion . The preThe new concept that p65 plays a role in healthy metabolic homeostasis is intriguing in light of the central role that p65 specifically, and the NF-κB pathway in general, plays in regulating islet inflammatory homeostasis. Deletion or blocking of NF-κB/p65 activation in beta cells and islets prevents activation of islet-intrinsic inflammation , 47 and Supplementary file1 (PDF 1568 KB)Below is the link to the electronic supplementary material.


Mtrrgt allele is known to disrupt one-carbon metabolism, and thus methyl group availability, leading to several developmental phenotypes . Remarkably, previous studies showed that some of the phenotypes were transgenerationally inherited. Here, we explored the genome-wide epigenetic impact of one-carbon metabolism in placentas associated with fetal growth phenotypes and determined whether specific DNA methylation changes were inherited. Firstly, methylome analysis of Mtrrgt/gt homozygous placentas revealed genome-wide epigenetic instability. Several differentially methylated regions (DMRs) were identified including at the Cxcl1 gene promoter and at the En2 gene locus, which may have phenotypic implications. Importantly, we discovered hypomethylation and ectopic expression of a subset of ERV elements throughout the genome of Mtrrgt/gt placentas with broad implications for genomic stability. Next, we determined that known spermatozoan DMRs in Mtrrgt/gt males were reprogrammed in the placenta with little evidence of direct or transgenerational germline DMR inheritance. However, some spermatozoan DMRs were associated with placental gene misexpression despite normalisation of DNA methylation, suggesting the inheritance of an alternative epigenetic mechanism. Integration of published wildtype histone ChIP-seq datasets with Mtrrgt/gt spermatozoan methylome and placental transcriptome datasets point towards H3K4me3 deposition at key loci. These data suggest that histone modifications might play a role in epigenetic inheritance in this context. Overall, this study sheds light on the mechanistic complexities of one-carbon metabolism in development and epigenetic inheritance.One-carbon metabolism, including the folate cycle, has a crucial role in fetal development though its molecular function is complex and unclear. The hypomorphic  It is well established that the vitamin folate  is important for fetal development. A highly recognisable example is increased risk of neural tube closure defects  in babies that result from maternal dietary folate deficiency . In factMtrrgt) , fetal growth enhancement (FGE), or developmental delay)  . During rrgt) . Methionrrgt) . Importao-factor . The hypcontrols . Consequteinemia  and wideteinemia . Addition humans  or humanutations  includinutations  and neurutations . Beyond l delay) , complicl delay) , haemorrl delay) . TherefoMtrrgt mouse line is also a unique mammalian model of transgenerational epigenetic inheritance that occurs via the maternal grandparental lineage  initiates multigenerational inheritance of developmental phenotypes in their wildtype (Mtrr+/+) grandprogeny   to influence gene expression during development  . This efrations) . In geneelopment . Given tpression . The effpression . It was t males , which ot males . HoweverMtrrgt/gt homozygous mice and in F2 Mtrr+/+ mice derived from F0 Mtrr+/gt maternal grandfathers. In doing so, we probe the underlying impact on fetal growth and whether the differentially methylated regions (DMRs) are functionally important and/or inherited. We reveal that the Mtrrgt/gt placental methylome is unstable with implications for phenotype establishment, transposable element regulation, and genetic stability. We also determine that specific DMRs observed in spermatozoa of Mtrrgt/gt males are reprogrammed in Mtrrgt/gt placentas and as a result, we explore other epigenetic mechanisms for inheritance including histone methylation (H3K4me3). We integrate published ChIP-seq datasets from wildtype embryonic and trophoblast lineages with our Mtrrgt/gt spermatozoan methylome and placental transcriptome datasets. Overall, these analyses delve into the mechanistic complexities of one-carbon metabolism during development and epigenetic inheritance of phenotype.In this study, we use genome-wide approaches to investigate the global impact of one-carbon metabolism on the placental methylome in This research was regulated under the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body.MtrrGt(XG334)Byg (MGI:3526159) mouse line, referred to as the Mtrrgt mouse line, was generated when a β-geo gene-trap (gt) vector was inserted into intron 9 of the Mtrr gene in 129P2Ola/Hsd embryonic stem cells (ESCs)  were used as controls and were bred in-house and maintained separately from the Mtrrgt mouse line. The effects of the maternal grandpaternal Mtrrgt allele were determined by the following pedigree: F0 Mtrr+/gt males were mated to C57Bl/6J females. The resulting F1 Mtrr+/+ females were mated to C57Bl/6J males to generate F2 Mtrr+/+ conceptuses. Genotyping for Mtrr+ and Mtrrgt alleles was performed using PCR on DNA extracted from ear tissue or yolk sac using a three-primer reaction resulting in a wildtype band at 252 bp and a mutant band at 383 bp (a (5′-GAGATTGGGTCCCTCTTCCAC), primer b (5′-GCTGCGCTTCTGAATCCACAG), and primer c (5′-CG ACTTCCGGAGCGGATCTC) : 1.6 g choline, 2.73 mg folic acid, 26.8 μg vitamin B12, 3.4 g methionine, 51.3 mg zinc.s (ESCs) . Mtrrgerations . Mtrr+l effect , C57Bl/6t 383 bp . Primer GATCTC) . All micNoon of the day that the vaginal plug was detected was defined as embryonic (E) day 0.5. Mice were euthanized by cervical dislocation. Fetuses and placentas were dissected in cold 1x phosphate buffered saline (PBS) at E10.5 using a Zeiss SteReo Discovery V8 microscope, scored for phenotypes, and photographed. Fetuses and placentas were weighed and measured separately and snap frozen in liquid nitrogen (stored at −80°C). Both male and female placentas were assessed since no phenotypic sexual dimorphism was identified at E10.5 .Conceptuses were rigorously scored for gross phenotypes during dissection and allocated to the phenotypic categories that were previously defined, including phenotypically normal (PN), fetal growth enhancement (FGE), fetal growth restriction (FGR), developmental delay, severe abnormalities , and resorption . Notablyhttps://www.emouseatlas.org/emap/home.html). PN fetuses at E10.5 contained 30–39 somite pairs and had crown-rump lengths that were within two standard deviations (sd) from the mean of C57Bl/6J fetuses at E10.5, putting them within the normal range for growth. All PN conceptuses lacked abnormalities identified via gross assessment.Fetuses and placentas met all developmental milestones appropriate for the developmental stage according to e-Mouse Atlas Project . Yet, the fetuses displayed crown-rump lengths that were ≥2 sd below (for FGR) or above (for FGE) the mean crown-rump length for C57Bl/6J fetuses . ConceptAB_2616058) or mouse immunoglobulin G (IgG) control and 10 μL Dynabeads coupled with M-280 sheep anti-mouse antibody (Invitrogen). Pulled down DNA was amplified for 12 cycles (meDIP) or 15 cycles (IgG control) with adapter-specific indexed primers. Final clean-up and size selection was carried out with AMPure-XP SPRI beads (Beckman Coulter). Libraries were quantified and assessed using the Kapa Library Quantification Kit (Kapa Biosystems) and Bioanalyzer 2100 System (Agilent). Indexed libraries were sequenced (50-bp paired-end) on an Illumina HiSeq 2500 sequencer. Raw fastq data were trimmed with TrimGalore (v0.6.6), using default parameters, and unique reads mapped to the Mus musculus GRCm38 genome assembly using Bowtie2 (v2.4.1). Data analysis was carried out using SeqMonk software (www.bioinformatics.babraham.ac.uk).Whole placentas at E10.5 were homogenized using a MagNA Lyser Instrument (Roche) and incubated on an Eppendorf ThermoMixer at 1,000 rpm at 56°C for 10 min. Genomic DNA was extracted using a QIAamp Fast DNA Tissue kit (Qiagen) following the manufacturer’s instructions. MeDIP-Seq was carried out as described previously . BrieflyWhole male placentas at E10.5 were homogenized using lysing matrix D beads. RNA library preparation and sequencing was performed by Cambridge Genomic Services, Department of Pathology, University of Cambridge. The concentration and purity of RNA was determined by a SpectroStar spectrophotometer (BMG LABTECH) and the RNA integrity was determined by an Agilent Tapestation Bioanalyzer (Aligent Technologies LDA United Kingdom Ltd.). Libraries were prepared using 200 ng of total RNA and TruSeq stranded mRNA Library Preparation kit (Illumina). A unique index sequence was added to each RNA library to allow for multiplex sequencing. Libraries were pooled and sequenced on the Illumina NextSeq500 platform with 75 bp single-end reads. Sequencing was performed in duplicate to provide >18 million reads per sample. To monitor sequencing quality control, 1% PhiX Control (Illumina) spike-in was used. Quality control of Fastq files was performed using FastQC and fastq_screen. Sequences were trimmed with Trim Galore! and aligned to GRCm38 mouse genome using STAR aligner. Alignments were processed using custom ClusterFlow (v0.5dev) pipelines and assessed using MultiQC (0.9. dev0). Gene quantification was determined with HTSeq- Counts (v0.6.1p1). Additional quality control was performed with rRNA and mtRNA counts script, feature counts (v 1.5.0- p2) and qualimap (v2.2). Differential gene expression was performed with DESeq2 package . Read counts were normalised on the estimated size factors.RLTR4_Mm and RLTR4_MM-int elements. RNA-seq data was analysed using SQuIRE  or were FGR based on crown-rump length  with default settings  assessing 500 bp contiguous regions. The resulting DMRs were further filtered for regions that displayed a log2 fold change (FC) > 1 in DNA methylation compared to controls. Only a few DMRs were present in Mtrrgt/gt placentas , though both hyper- and hypomethylated regions were observed . Firstly, only one placental DMR associated with the misexpression of a protein-coding gene Cxcl1; . Secondlceptuses . Since tat E10.5 , they weat E10.5 . Strikinat E10.5  with impMtrrgt/gt genotype had a gene regulatory effect, we carried out RNA-seq on Mtrrgt/gt placentas at E10.5 associated with PN and FGR fetuses. Using DESeq, the RNA-seq data was assessed for differentially expressed genes that were within 2 kb of a DMR (identified in Mtrrgt/gt placentas) and had transcript levels with a log2FC > 0.6 compared to control placentas. The Cxcl1 gene [chemokine (C-X-C motif) ligand 1] was the only dysregulated gene identified in this context. We observed that hypomethylation at the DMR located in the promoter of Cxcl1 was associated with a modest upregulation of Cxcl1 transcripts  enrichment and potential interactions of the DMR with neighbouring genes. To do this, we analysed published H3K4me3 and H3K27me3 ChIP-seq datasets and promoter capture Hi-C datasets from wildtype mouse trophoblast stem cells (TSCs)  . TSCs arplacenta , and theassessed . Accordilacentas  indicatiEn2 DMR is prevalent in different tissue types including mature spermatozoa of Mtrrgt/gt males and Mtrrgt/gt embryos and placentas at E10.5 , Rbm33 (RNA binding motif 33), and the developmental regulator Shh (sonic hedgehog)  . These dedgehog) . TherefoMtrrgt/gt placentas at E10.5 that were associated with ERV elements  for meDIP-seq data and an expectation-maximisation algorithm [SQuIRE  suggesting that germline transposon silencing is maintained and unlikely to play a key role in epigenetic inheritance mechanisms in the Mtrrgt mouse line. Overall, these results indicated that placenta and spermatozoa DMRs in Mtrrgt/gt mice were tissue-specific and that most of the spermatozoa DMRs were effectively reprogrammed in the pre-implantation embryo or during placental development, notwithstanding the shared Mtrrgt/gt genotype of the parental and offspring generations. These data might negate DNA methylation as a mechanistic factor in epigenetic inheritance within the Mtrrgt mouse line. Instead, altered heritability of other epigenetic factors, such as histone modifications or small non-coding RNA content in germ cells, might be an alternative or additional mechanism.In our previous study of epigenetic inheritance in the use line , we founuse line  and placositives , were alositives . Similarositives , DNA metlacentas . This filacentas . ConversMtrrgt/gt placental RNA-seq dataset was assessed for differentially expressed genes that were within 2 kb of a sperm DMR from Mtrrgt/gt males and had transcript levels with a log2FC > 0.6 compared to control placentas. Five misexpressed genes , Tshz3 , Ovol2 , Osm , and Ugt1a7c  met these criteria but only in Mtrrgt/gt placentas with FGR  overlapped with an H3K4me3 peak in wildtype prospermatogonia, the majority which were located within gene bodies  were among those enriched for H3K4me3 in both prospermatogonia and extraembryonic ectoderm . This finding more broadly supports a role for H3K4me3 in inheritance of epimutations from germ cells to the placenta. Future mechanistic experiments should focus on multigenerational patterns of H3K4me3 in the Mtrrgt mouse line.Next, we explored whether histone modifications were present in the developing germline at regions demarcated by spermatozoa DMRs to better understand the broader epigenetic context of these regions. To do this, ChIP-seq datasets were analysed for histone mark enrichment in developing wildtype male germ cells   at specie bodies . This vae bodies . Since He bodies , it was e bodies . Indeed,e bodies . Remarkaectoderm . We infeMtrr+/+ placentas indicated significant alteration of DNA methylation patterns caused by either a maternal grandfather or maternal grandmother Mtrrgt allele  at E10.5, there was no DMR overlap . It is possible that placental DNA methylation is less sensitive than male germ cells to impaired one-carbon metabolism. DNA in trophoblast cells is globally hypomethylated compared to other cell types  retrotransposons are also considerably shifted in Mtrrgt/gt mice as determined by bisulfite pyrosequencing (Mtrrgt locus contains a cluster of 129P2Ola/Hsd-derived KRAB zinc finger proteins (ZFPs) in an otherwise C57Bl/6J background because of the mutagenesis process  and in F2 Mtrr+/+ embryos and adult livers  . Our datrm cells . The lact livers  despite ficiency .de novo DNA methylation patterns or in gene expression in early cell lineages of the placenta and/or embryo. The most drastic epigenetic changes likely lead to altered lineage decisions and developmental phenotypes. Since a wide spectrum of phenotypes are observed in Mtrrgt mouse line, disrupting one-carbon metabolism might cause stochastic epigenetic changes across the genome, affecting different cell types in different individuals. The most striking data that reinforces a potential role for histone H3K4me in epigenetic inheritance comes from a study whereby wildtype mice were fed a folate-deficient diet. Mature spermatozoa from folate-deficient males displayed alterations in histone H3K4me3 patterns specifically at developmental genes and putative enhancers, a subset of which were retained in the F1 8-cell embryos and were associated with gene misexpression (Mtrrgt/gt males. This may be due to the differences in the mouse models employed, with our genetic approach causing a more severe metabolic effect than dietary deficiency. Regardless, this finding suggests that genomic hotspots regulated by one-carbon metabolism are unlikely and that the epigenome is differently or stochastically affected in these models. To fully understand the mechanisms involved in epigenetic inheritance, histone methylation should be explored as an inherited epigenetic mechanism in the Mtrrgt mouse model.We propose a model whereby impaired one-carbon metabolism alters H3K4me3 deposition in developing male germ cells, which then drives the changes in DNA methylation through modified access of DNA methyltransferases. In the pre-implantation embryo when DNA methylation is reprogrammed, a subset of abnormal H3K4me3 marks may persist, driving further changes in establishing aberrant pression . We did Mtrrgt mouse line.Overall, this study together with our previously published work  indicate


H19 and Igf2 imprinted domain in the soma, where insulation of the Igf2 promoter from its enhancers occurs by CTCF in the maternally inherited unmethylated chromosome but not the paternally inherited methylated allele. The molecular mechanism that targets paternal methylation imprint establishment to the imprinting control region (ICR) in the male germline is unknown. We tested the function of prospermatogonia-specific broad low-level transcription in this process using mouse genetics. Paternal imprint establishment was abnormal when transcription was stopped at the entry point to the ICR. The germline epimutation persisted into the paternal allele of the soma, resulting in reduced Igf2 in fetal organs and reduced fetal growth, consistent with the insulator model and insulin-like growth factor 2 (IGF2)’s role as fetal growth factor. These results collectively support the role of broad low-level transcription through the H19/Igf2 ICR in the establishment of its paternal methylation imprint in the male germ line, with implications for Silver-Russell syndrome.The insulator model explains the workings of the  Genetic experiments find that paternal imprint establishment requires broad low-level transcription in prospermatogonia. Imprinted genes often cluster in domains and have a special expression pattern of being transcribed from one parental chromosome only, the one inherited from the father, or the one inherited from the mother. The two chromosomes carry different epigenetic markings in the form of DNA methylation at the imprinting control region (ICR) that regulates parental allele–specific expression of genes along the domain. Those imprinted germline differentially methylated regions (gDMRs) originate in the male or female germ lines and are carried into the zygote from the sperm or the oocyte. DMRs are later maintained in the paternally (PAT) or maternally (MAT) inherited chromosome during development of the soma. One outstanding question is why PAT and MAT gDMR establishment differ in the male and female germ lines.H19 and Igf2 genes depends on an ICR, which behaves as a maternal allele–specific CTCF-mediated insulator in somatic cells  mouse prospermatogonia (fig. S1), at the time when the establishment of PAT gDMRs, such as the H19/Igf2 ICR, is initiated  experiments (nitiated  and testH19/Igf2 ICR sequences (fig. S2) (We carried out a gene targeting experiment in mouse embryonic stem (ES) cells. We inserted a transcription terminator cassette (Ter)  transgene, crossed in from the TgOG2 transgenic mouse . We also noted that the H19Ter+neo/H19Ter+neo prospermatogonia exhibited no transcription across the Pgkneo cassette in the forward direction (fig. S3). No transcript exited into neighboring sequences out of the cassettes in either forward or reverse direction (fig. S3). In response to the terminator insertion, transcription was diminished across the ICR in the prospermatogonia of H19Ter+neo/H19Ter+neo and H19Ter/H19Ter fetuses . The weight of the mutant H19+/H19Ter+neo pups, males and females, was significantly lower compared with WT siblings. The number of H19+/H19Ter+neo mutant pups was much less than expected at weaning. This bias likely resulted from early postnatal lethality, because of the 17 dead pups found during the first 3 days of birth, 12 were H19+/H19Ter+neo and 5 were WT. The H19+/H19Ter+neo but not H19+/H19Ter weanling pups and adults showed a broader distribution of weights than their WT siblings at 3 and 8 weeks. The weight of H19+/H19Ter+neo adults was also reduced compared to WT littermates, and, in males but not females, the difference was significant. We noticed that the WT siblings of H19+/H19Ter mice, both females (P = 8.2959 × 10−7) and males (P = 2.1692 × 10−5), were significantly smaller at weaning and at 8 weeks of age  than the WT siblings of H19+/H19Ter+neo mice. The reason for this difference is not known. It did not initiate at fetal stage, as those WT fetuses and their placentas were not significantly different between crosses (P = 0.8040 and P = 0.2896). The H19+/H19Ter mice and H19+/H19Ter+neo mutant mice were not different from each other. These results collectively suggest that defective methylation establishment in prospermatogonia has a lasting phenotypic effect in the next generation, as paternal inheritance of the Ter insertion results in reduced fetal and placental growth in both mutant lines and reduced postnatal weight in the H19+/H19Ter+neo and perhaps also the H19+/H19Ter mutant mice.On the basis of the insulator model  (12–15),18.5 dpc . The weiH19+/H19Ter+neo and H19+/H19Ter fetuses at 18.5 dpc. Upon paternal transmission, both the H19Ter+neo and H19Ter alleles were variably hypomethylated compared to the WT allele in control fetuses . These results demonstrate that compromised methylation imprint establishment in prospermatogonia has severe consequences to the expression of both H19 and Igf2 imprinted genes upon paternal inheritance.The insulator model  predicts fetuses . Becauser father . H19 lev fetuses . Derepre fetuses . Relaxat fetuses . ParentaH19-Igf2 gDMR in prospermatogonia: (i) The RNA-terminator cassette prevents the broad low-level transcript from entering the ICR; (ii) DNA methylation establishment is reduced at the ICR in fetal male germ cells; (iii) DNA hypomethylation is inherited to the offspring in the paternal allele; (iv) ICR hypomethylation in the soma results in biallelic insulation and causes reduced Igf2 and biallelic H19 transcription; and (v) reduced Igf2 levels result in fetal growth retardation.This work supports a role of transcription through the ICR in the establishment of de novo DNA methylation at the Igf2 transcription with partially reduced fetus and placenta weight. DNA methylation was established at a variable degree in the prospermatogonia of different mutant fetuses and resulted in variable size offspring out of mutant germ cells. Smaller fetuses had more profound hypomethylation at the ICR in their perinatal prospermatogonia. This model provides a useful experimental system for studying the heritability of epigenetic states between generations.The stochastic epigenetic effects in this mouse model resemble SRS cases with epigenetic molecular features. We found that DNA methylation of the ICR was partially reduced in both mutant lines. Although the terminator cassette stopped the broad low-level transcript before entering the ICR or the Pgkneo cassette (fig. S3), it did not prevent the restarting of the transcript in the ICR , which, if it occurs in one germ cell but not in another, can underlie the stochastic nature of the effect on DNA methylation establishment. A partial ICR hypomethylation in the soma allows partial gain of insulation and partial loss of H19-Igf2, Dlk1-Gtl2, and part of the Rasgrf1 DMR (TerH19/H19Ter mutant prospermatogonia (fig. S7), we detected no difference in H3K36me2 (or H3K36me3 and H3K27me3) levels along the H19/Igf2 gDMR or surrounding sequences. This finding suggests that H3K36me2 affects de novo DNA methylation at those sequences independent of broad low-level transcription. Future experiments will test whether the prospermatogonia-specific transcripts recruit DNMT3A and perhaps NSD1 to other paternal gDMRs. Our RNA-seq experiment detected specific transcription through each PAT methylated imprinted gDMR  methylation in the sperm and a further reduced DNA methylation by the two-cell stage and became unmethylated in the paternal allele of the offspring  protein levels in SRS fetuses, as well as the fetal growth retardation and microsomia symptoms. This SRS model will allow epigenetic, genetic, or pharmacological corrections of growth retardation in SRS of epigenetic origin. In these cases, therapeutically correcting IGF2 signaling at the fetal stage could be beneficial . Female and male uterus-mate fetuses from multiple litters were used as biological replicates in the experiments that compare RNA levels, allelic expression, DNA methylation, and weight between the genotypes. Fetus, newborn, and weanling numbers are provided in the figures.H19Ter+neo and H19Ter mouse models were generated using gene targeting in 129SI ES cells as we did earlier Mann/J transgenic mouse line  was used for distinguishing parental alleles due to the presence of single-nucleotide polymorphisms (SNPs) in its genome compared with our mouse model. For germ cell collection, the H19Ter+neo and H19Ter lines were crossed with the TgOG2 transgenic mouse B6;CBA-Tg(Pou5f1-EGFP)2Mnn that allows purification of prospermatogonia by FACS (The Ten micrograms of genomic DNA from the polymerase chain reaction (PCR)–positive ES clones were digested with restriction enzyme, then separated by 0.8% agarose gel electrophoresis, and transferred to Amersham Hybond nylon membrane , and then Southern blot hybridization experiments were performed using dioxigenin-labeled probes. Visualization was done by exposing to x-ray film for 30 min. PCR primers for generating these probes were as follows: probe 1 (outside short arm): 5′ GCA ATC CGT TTT AGG ACT GCG ATG TAC GAG AC 3′ and 5′ GCT ACA TTC ACA CGA GCA TCC AGG AGG C 3′ to detect an 8.7-kb WT and 11.5-kb mutant Bam HI band; probe 2 (neo cassette): 5′ CGG CAG GAG CAA GGT GAG ATG AC 3′ and 5′ CGC TTG GGT GGA GAG GCT ATT CG 3′; probe 3 (inside long arm): 5′ CAG AGA GCA GCA GAG AAG TGT TAG CTC TTT GGG 3′ and 5′ GTA AGT GTC TGT CCC GCT CGT GGT CA 3′ to detect a 4-kb WT and 7-kb mutant Hind III band.2 and using PCR cycles at 94°C for 1 min,  for 35 cycles and 72°C for 10 min. Mutant bands were resolved in a 0.8% agarose gel. Mouse tail tip samples were collected at weaning time, boiled in 100 μl of tail digestion buffer ]; Neo47 with Neo48 also detected the H19Ter+neo allele (293 bp); and ON36 with ON37 detected the H19Ter allele (371 bp). The primer sequences were the following: ON34, 5′ GGG GCT GCT AAA GCG CAT GC 3′; ON36, 5′ AAC AGC CCC CTG CTG TCC A 3′; and ON37, 5′ AAG CTT TGA GTA CCC CAG GTT CAA CAA AGG 3′.Oligonucleotide primers ON39 (5′ TCA GTG AGA TGA GTT GGG AGC ACT ACC A 3′), ON40 (5′ AGA ACA ATC AAG GGT CCC CAA ACT CAC C 3′), and ON41 (5′ GAG CGT GCA GGG CAC TTA CAC C 3′) confirmed the correct recombination along the long arm by amplifying a 14-kb fragment in the WT versus a 12-kb fragment in the mutant in 500 ng of ES cell DNA using the LA Taq DNA Polymerase  in 1× LA PCR buffer with MgClH19Ter+neo/H19Ter+neo or H19Ter/H19Ter females that carry the Pou5f1 promoter-driven EGFP transgene from the TgOG2  from FACS sorted 15.5 dpc male germ cells, followed by deoxyribonuclease I treatment. Libraries were prepared by the VAI Genomics Core from 500 ng of total RNA using the KAPA RNA HyperPrep Kit . Ribosomal RNA material was reduced using the QIAseq FastSelect –rRNA HMR Kit . RNA was sheared to 300 to 400 bp. Before PCR amplification, cDNA fragments were ligated to IDT for Illumina TruSeq UD indexed adapters  and amplified with 8 cycles of PCR. Quality and quantity of the finished libraries were assessed using a combination of Agilent DNA High Sensitivity chip (Agilent Technologies Inc.) and QuantiFluor dsDNA System . Individually indexed libraries were pooled, and 50-bp, paired-end sequencing was performed on an Illumina NovaSeq 6000 sequencer to an average depth of 100 million raw paired reads per transcriptome. Base calling was done by Illumina RTA3, and output of noncrystallographic symmetry was demultiplexed and converted to FastQ format with Illumina Bcl2fastq v1.9.0.https://github.com/FelixKrueger/TrimGalore). Trimmed reads were aligned to the mm10 reference genome using STAR  and used for paired-end library construction (NEBNext). Libraries were sequenced (150-bp paired-end) on the NovaSeq 6000 platform. Reads were clipped to 100 bp using Fastp (https://doi.org/10.1002/imt2.107) and then aligned using GenPipes’ chip.py pipeline , flash-frozen in liquid nitrogen and stored at −80°C until use. Chromatin was prepared from 100,000 cells and divided into four fractions for H3K36me2, H3K36me3, and H3K27me3 for ultralow-input native ChIP-seq . Contaminating DNA was removed with the DNA-free DNA Removal Kit (Ambion). cDNA was prepared from 400 ng of total RNA using the SuperScript III Random Primer Synthesis kit (Invitrogen) for the quantitative PCR assays as we did earlier  allelotyping assays  according to the manufacturer’s instructions. PCR was performed using the genotype-specific forward primers and the shared reverse primer (table S3) and Zymo Taq hot start polymerase  to amplify the converted DNA. The purified PCR fragment was ligated into pGEM-T vector . Sanger sequencing of 15 independent colonies per region was performed by GENEWIZ. The QUMA online software was used for methylation analysis. Colonies that had identical pattern of the occasional incomplete conversion were excluded from the displays.Fetal kidney DNA was purified by Proteinase K digestion, phenol-chloroform extraction, and ethanol precipitation. The EZ DNA methylation kit  was used for the bisulfite conversion according to the instructions of the kit. Multiplex PCR was performed with indexed primers (table S4) and Zymo Taq hot start polymerase  to amplify the converted DNA. The amplified DNA fragments were resolved using a 1% agarose gel, and the DNA was isolated from the gel slices using Monarch DNA Gel Extraction Kit . Fifty nanograms of each fragment was amplified with different index primers as listed below for Amplicon-EZ sequencing by GENEWIZ.https://github.com/FelixKrueger/TrimGalore) (Reads were trimmed using TrimGalore v0.6.0 (