Gene-environment interactions within a precision environmental health framework.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study.

The correlations among individual exposures in the exposome, which refers to all exposures an individual encounters throughout life, are important for understanding the landscape of how exposures co-occur, and how this impacts health and disease. Exposome-wide association studies (ExWAS), which are analogous to genome-wide association studies (GWAS), are increasingly being used to elucidate links between the exposome and disease. Despite increased interest in the exposome, tools and publications that characterize exposure correlations and their relationships with human disease are limited, and there is a lack of data and results sharing in resources like the GWAS catalog. To address these gaps, we developed the PEGS Explorer web application to explore exposure correlations in data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS) that were rigorously calculated to account for differing data types and previously published results from ExWAS. Through globe visualizations, PEGS Explorer allows users to explore correlations between exposures found to be associated with complex diseases. The exposome data used for analysis includes not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The web application addresses the lack of accessible data and results sharing, a major challenge in the field, and enables users to put results in context, generate hypotheses, and, importantly, replicate findings in other cohorts. PEGS Explorer will be updated with additional results as they become available, ensuring it is an up-to-date resource in exposome science.

Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study.

The exposome collectively refers to all exposures, beginning in utero and continuing throughout life, and comprises not only standard environmental exposures such as point source pollution and ozone levels but also exposures from diet, medication, lifestyle factors, stress, and occupation. The exposome interacts with individual genetic and epigenetic characteristics to affect human health and disease, but large-scale studies that characterize the exposome and its relationships with human disease are limited. To address this gap, we used extensive questionnaire data from the diverse North Carolina-based Personalized Environment and Genes Study (PEGS, n = 9, 429) to evaluate exposure associations in relation to common diseases. We performed an exposome-wide association study (ExWAS) to examine single exposure models and their associations with 11 common complex diseases, namely allergic rhinitis, asthma, bone loss, fibroids, high cholesterol, hypertension, iron-deficient anemia, ovarian cysts, lower GI polyps, migraines, and type 2 diabetes. Across diseases, we found associations with lifestyle factors and socioeconomic status as well as asbestos, various dust types, biohazardous material, and textile-related exposures. We also found disease-specific associations such as fishing with lead weights and migraines. To differentiate between a replicated result and a novel finding, we used an AI-based literature search and database tool that allowed us to examine the current literature. We found both replicated findings, especially for lifestyle factors such as sleep and smoking across diseases, and novel findings, especially for occupational exposures and multiple diseases.

Race/ethnicity-stratified fine-mapping of the MHC locus reveals genetic variants associated with late-onset asthma.

Introduction: Asthma is a chronic disease of the airways that impairs normal breathing. The etiology of asthma is complex and involves multiple factors, including the environment and genetics, especially the distinct genetic architecture associated with ancestry. Compared to early-onset asthma, little is known about genetic predisposition to late-onset asthma. We investigated the race/ethnicity-specific relationship among genetic variants within the major histocompatibility complex (MHC) region and late-onset asthma in a North Carolina-based multiracial cohort of adults. Methods: We stratified all analyses by self-reported race (i.e., White and Black) and adjusted all regression models for age, sex, and ancestry. We conducted association tests within the MHC region and performed fine-mapping analyses conditioned on the race/ethnicity-specific lead variant using whole-genome sequencing (WGS) data. We applied computational methods to infer human leukocyte antigen (HLA) alleles and residues at amino acid positions. We replicated findings in the UK Biobank. Results: The lead signals, rs9265901 on the 5' end of HLA-B, rs55888430 on HLA-DOB, and rs117953947 on HCG17, were significantly associated with late-onset asthma in all, White, and Black participants, respectively (OR = 1.73, 95%CI: 1.31 to 2.14, p = 3.62 × 10-5; OR = 3.05, 95%CI: 1.86 to 4.98, p = 8.85 × 10-6; OR = 19.5, 95%CI: 4.37 to 87.2, p = 9.97 × 10-5, respectively). For the HLA analysis, HLA-B*40:02 and HLA-DRB1*04:05, HLA-B*40:02, HLA-C*04:01, and HLA-DRB1*04:05, and HLA-DRB1*03:01 and HLA-DQB1 were significantly associated with late-onset asthma in all, White, and Black participants. Conclusion: Multiple genetic variants within the MHC region were significantly associated with late-onset asthma, and the associations were significantly different by race/ethnicity group.

Questionnaire-Based Polyexposure Assessment Outperforms Polygenic Scores for Classification of Type 2 Diabetes in a Multiancestry Cohort.

OBJECTIVE: Environmental exposures may have greater predictive power for type 2 diabetes than polygenic scores (PGS). Studies examining environmental risk factors, however, have included only individuals with European ancestry, limiting the applicability of results. We conducted an exposome-wide association study in the multiancestry Personalized Environment and Genes Study to assess the effects of environmental factors on type 2 diabetes. RESEARCH DESIGN AND METHODS: Using logistic regression for single-exposure analysis, we identified exposures associated with type 2 diabetes, adjusting for age, BMI, household income, and self-reported sex and race. To compare cumulative genetic and environmental effects, we computed an overall clinical score (OCS) as a weighted sum of BMI and prediabetes, hypertension, and high cholesterol status and a polyexposure score (PXS) as a weighted sum of 13 environmental variables. Using UK Biobank data, we developed a multiancestry PGS and calculated it for participants. RESULTS: We found 76 significant associations with type 2 diabetes, including novel associations of asbestos and coal dust exposure. OCS, PXS, and PGS were significantly associated with type 2 diabetes. PXS had moderate power to determine associations, with larger effect size and greater power and reclassification improvement than PGS. For all scores, the results differed by race. CONCLUSIONS: Our findings in a multiancestry cohort elucidate how type 2 diabetes odds can be attributed to clinical, genetic, and environmental factors and emphasize the need for exposome data in disease-risk association studies. Race-based differences in predictive scores highlight the need for genetic and exposome-wide studies in diverse populations.

Association of distance to swine concentrated animal feeding operations with immune-mediated diseases: An exploratory gene-environment study.

BACKGROUND: Concentrated animal feeding operations (CAFOs) are a source of environmental pollution and have been associated with a variety of health outcomes. Immune-mediated diseases (IMD) are characterized by dysregulation of the normal immune response and, while they may be affected by gene and environmental factors, their association with living in proximity to a CAFO is unknown. OBJECTIVES: We explored gene, environment, and gene-environment (GxE) relationships between IMD, CAFOs, and single nucleotide polymorphisms (SNPs) of prototypical xenobiotic response genes AHR, ARNT, and AHRR and prototypical immune response gene PTPN22. METHODS: The exposure analysis cohort consisted of 6,464 participants who completed the Personalized Environment and Genes Study Health and Exposure Survey and a subset of 1,541 participants who were genotyped. We assessed the association between participants' residential proximity to a CAFO in gene, environment, and GxE models. We recombined individual associations in a transethnic model using METAL meta-analysis. RESULTS: In White participants, ARNT SNP rs11204735 was associated with autoimmune diseases and rheumatoid arthritis (RA), and ARNT SNP rs1889740 was associated with RA. In a transethnic genetic analysis, ARNT SNPs rs11204735 and rs1889740 and PTPN22 SNP rs2476601 were associated with autoimmune diseases and RA. In participants living closer than one mile to a CAFO, the log-distance to a CAFO was associated with autoimmune diseases and RA. In a GxE interaction model, White participants with ARNT SNPs rs11204735 and rs1889740 living closer than eight miles to a CAFO had increased odds of RA and autoimmune diseases, respectively. The transethnic model revealed similar GxE interactions. CONCLUSIONS: Our results suggest increased risk of autoimmune diseases and RA in those living in proximity to a CAFO and a potential role of the AHR-ARNT pathway in conferring risk. We also report the first association of ARNT SNPs rs11204735 and rs1889740 with RA. Our findings, if confirmed, could allow for novel genetically-targeted or other preventive approaches for certain IMD.

The skin is no barrier to mixtures: Air pollutant mixtures and reported psoriasis or eczema in the Personalized Environment and Genes Study (PEGS).

BACKGROUND: Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution. METHODS: We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant's address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures. RESULTS: Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E-2); however, the conditional odds ratio for the combined mixture components of PM2.5 (black carbon, sulfate, sea salt, and soil), CO, SO2, benzene, toluene, and ethylbenzene is 1.10 (p-value = 5.4E-3). SIGNIFICANCE: While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases. SIGNIFICANCE AND IMPACT STATEMENT: The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.

Questionnaire-based exposome-wide association studies (ExWAS) reveal expected and novel risk factors associated with cardiovascular outcomes in the Personalized Environment and Genes Study.

While multiple factors are associated with cardiovascular disease (CVD), many environmental exposures that may contribute to CVD have not been examined. To understand environmental effects on cardiovascular health, we performed an exposome-wide association study (ExWAS), a hypothesis-free approach, using survey data on endogenous and exogenous exposures at home and work and data from health and medical histories from the North Carolina-based Personalized Environment and Genes Study (PEGS) (n = 5015). We performed ExWAS analyses separately on six cardiovascular outcomes (cardiac arrhythmia, congestive heart failure, coronary artery disease, heart attack, stroke, and a combined atherogenic-related outcome comprising angina, angioplasty, atherosclerosis, coronary artery disease, heart attack, and stroke) using logistic regression and a false discovery rate of 5%. For each CVD outcome, we tested 502 single exposures and built multi-exposure models using the deletion-substitution-addition (DSA) algorithm. To evaluate complex nonlinear relationships, we employed the knockoff boosted tree (KOBT) algorithm. We adjusted all analyses for age, sex, race, BMI, and annual household income. ExWAS analyses revealed novel associations that include blood type A (Rh-) with heart attack (OR[95%CI] = 8.2[2.2:29.7]); paint exposures with stroke (paint related chemicals: 6.1[2.2:16.0], acrylic paint: 8.1[2.6:22.9], primer: 6.7[2.2:18.6]); biohazardous materials exposure with arrhythmia (1.8[1.5:2.3]); and higher paternal education level with reduced risk of multiple CVD outcomes (stroke, heart attack, coronary artery disease, and combined atherogenic outcome). In multi-exposure models, trouble sleeping and smoking remained important risk factors. KOBT identified significant nonlinear effects of sleep disorder, regular intake of grapefruit, and a family history of blood clotting problems for multiple CVD outcomes (combined atherogenic outcome, congestive heart failure, and coronary artery disease). In conclusion, using statistics and machine learning, these findings identify novel potential risk factors for CVD, enable hypothesis generation, provide insights into the complex relationships between risk factors and CVD, and highlight the importance of considering multiple exposures when examining CVD outcomes.

SIRT1 regulates cardiomyocyte alignment during maturation.

Cardiomyocyte elongation and alignment, a critical step in cardiomyocyte maturation starting from the perinatal stage, is crucial for formation of the highly organized intra- and inter-cellular structures for spatially and temporally ordered contraction in adult cardiomyocytes. However, the mechanism(s) underlying the control of cardiomyocyte alignment remains elusive. Here, we report that SIRT1, the most conserved NAD+-dependent protein deacetylase highly expressed in perinatal heart, plays an important role in regulating cardiomyocyte remodeling during development. We observed that SIRT1 deficiency impairs the alignment of cardiomyocytes/myofibrils and disrupts normal beating patterns at late developmental stages in an in vitro differentiation system from human embryonic stem cells. Consistently, deletion of SIRT1 at a late developmental stage in mouse embryos induced the irregular distribution of cardiomyocytes and misalignment of myofibrils, and reduced the heart size. Mechanistically, the expression of several genes involved in chemotaxis, including those in the CXCL12/CXCR4 and CCL2/CCR2/CCR4 pathways, was dramatically blunted during maturation of SIRT1-deficient cardiomyocytes. Pharmacological inhibition of CCL2 signaling suppressed cardiomyocyte alignment. Our study identifies a regulatory factor that modulates cardiomyocyte alignment at the inter-cellular level during maturation.

UV-exposure, endogenous DNA damage, and DNA replication errors shape the spectra of genome changes in human skin.

Human skin is continuously exposed to environmental DNA damage leading to the accumulation of somatic mutations over the lifetime of an individual. Mutagenesis in human skin cells can be also caused by endogenous DNA damage and by DNA replication errors. The contributions of these processes to the somatic mutation load in the skin of healthy humans has so far not been accurately assessed because the low numbers of mutations from current sequencing methodologies preclude the distinction between sequencing errors and true somatic genome changes. In this work, we sequenced genomes of single cell-derived clonal lineages obtained from primary skin cells of a large cohort of healthy individuals across a wide range of ages. We report here the range of mutation load and a comprehensive view of the various somatic genome changes that accumulate in skin cells. We demonstrate that UV-induced base substitutions, insertions and deletions are prominent even in sun-shielded skin. In addition, we detect accumulation of mutations due to spontaneous deamination of methylated cytosines as well as insertions and deletions characteristic of DNA replication errors in these cells. The endogenously induced somatic mutations and indels also demonstrate a linear increase with age, while UV-induced mutation load is age-independent. Finally, we show that DNA replication stalling at common fragile sites are potent sources of gross chromosomal rearrangements in human cells. Thus, somatic mutations in skin of healthy individuals reflect the interplay of environmental and endogenous factors in facilitating genome instability and carcinogenesis.

Ancestry-dependent gene expression correlates with reprogramming to pluripotency and multiple dynamic biological processes.

Induced pluripotent stem cells (iPSCs) can be derived from differentiated cells, enabling the generation of personalized disease models by differentiating patient-derived iPSCs into disease-relevant cell lines. While genetic variability between different iPSC lines affects differentiation potential, how this variability in somatic cells affects pluripotent potential is less understood. We generated and compared transcriptomic data from 72 dermal fibroblast-iPSC pairs with consistent variation in reprogramming efficiency. By considering equal numbers of samples from self-reported African Americans and White Americans, we identified both ancestry-dependent and ancestry-independent transcripts associated with reprogramming efficiency, suggesting that transcriptomic heterogeneity can substantially affect reprogramming. Moreover, reprogramming efficiency-associated genes are involved in diverse dynamic biological processes, including cancer and wound healing, and are predictive of 5-year breast cancer survival in an independent cohort. Candidate genes may provide insight into mechanisms of ancestry-dependent regulation of cell fate transitions and motivate additional studies for improvement of reprogramming.

Correction: Repair of multiple simultaneous double-strand breaks causes bursts of genome-wide clustered hypermutation.

[This corrects the article DOI: 10.1371/journal.pbio.3000464.].

ORSO (Online Resource for Social Omics): A data-driven social network connecting scientists to genomics datasets.

High-throughput sequencing has become ubiquitous in biomedical sciences. As new technologies emerge and sequencing costs decline, the diversity and volume of available data increases exponentially, and successfully navigating the data becomes more challenging. Though datasets are often hosted by public repositories, scientists must rely on inconsistent annotation to identify and interpret meaningful data. Moreover, the experimental heterogeneity and wide-ranging quality of high-throughput biological data means that even data with desired cell lines, tissue types, or molecular targets may not be readily interpretable or integrated. We have developed ORSO (Online Resource for Social Omics) as an easy-to-use web application to connect life scientists with genomics data. In ORSO, users interact within a data-driven social network, where they can favorite datasets and follow other users. In addition to more than 30,000 datasets hosted from major biomedical consortia, users may contribute their own data to ORSO, facilitating its discovery by other users. Leveraging user interactions, ORSO provides a novel recommendation system to automatically connect users with hosted data. In addition to social interactions, the recommendation system considers primary read coverage information and annotated metadata. Similarities used by the recommendation system are presented by ORSO in a graph display, allowing exploration of dataset associations. The topology of the network graph reflects established biology, with samples from related systems grouped together. We tested the recommendation system using an RNA-seq time course dataset from differentiation of embryonic stem cells to cardiomyocytes. The ORSO recommendation system correctly predicted early data point sources as embryonic stem cells and late data point sources as heart and muscle samples, resulting in recommendation of related datasets. By connecting scientists with relevant data, ORSO provides a critical new service that facilitates wide-ranging research interests.

Repair of multiple simultaneous double-strand breaks causes bursts of genome-wide clustered hypermutation.

A single cancer genome can harbor thousands of clustered mutations. Mutation signature analyses have revealed that the origin of clusters are lesions in long tracts of single-stranded (ss) DNA damaged by apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC) cytidine deaminases, raising questions about molecular mechanisms that generate long ssDNA vulnerable to hypermutation. Here, we show that ssDNA intermediates formed during the repair of gamma-induced bursts of double-strand breaks (DSBs) in the presence of APOBEC3A in yeast lead to multiple APOBEC-induced clusters similar to cancer. We identified three independent pathways enabling cluster formation associated with repairing bursts of DSBs: 5' to 3' bidirectional resection, unidirectional resection, and break-induced replication (BIR). Analysis of millions of mutations in APOBEC-hypermutated cancer genomes revealed that cancer tolerance to formation of hypermutable ssDNA is similar to yeast and that the predominant pattern of clustered mutagenesis is the same as in resection-defective yeast, suggesting that cluster formation in cancers is driven by a BIR-like mechanism. The phenomenon of genome-wide burst of clustered mutagenesis revealed by our study can play an important role in generating somatic hypermutation in cancers as well as in noncancerous cells.

Genome-wide mutagenesis resulting from topoisomerase 1-processing of unrepaired ribonucleotides in DNA.

Ribonucleotides are the most common non-canonical nucleotides incorporated into DNA during replication, and their processing leads to mutations and genome instability. Yeast mutation reporter systems demonstrate that 2-5 base pair deletions (Δ2-5bp) in repetitive DNA are a signature of unrepaired ribonucleotides, and that these events are initiated by topoisomerase 1 (Top1) cleavage. However, a detailed understanding of the frequency and locations of ribonucleotide-dependent mutational events across the genome has been lacking. Here we present the results of genome-wide mutational analysis of yeast strains deficient in Ribonucleotide Excision Repair (RER). We identified mutations that accumulated over thousands of generations in strains expressing either wild-type or variant replicase alleles (M644G Pol ε, L612M Pol δ, L868M Pol α) that confer increased ribonucleotide incorporation into DNA. Using a custom-designed mutation-calling pipeline called muver (for mutationes verificatae), we observe a number of surprising mutagenic features. This includes a 24-fold preferential elevation of AG and AC relative to AT dinucleotide deletions in the absence of RER, suggesting specificity for Top1-initiated deletion mutagenesis. Moreover, deletion rates in di- and trinucleotide repeat tracts increase exponentially with tract length. Consistent with biochemical and reporter gene mutational analysis, these deletions are no longer observed upon deletion of TOP1. Taken together, results from these analyses demonstrate the global impact of genomic ribonucleotide processing by Top1 on genome integrity.

DNA methylation in mice is influenced by genetics as well as sex and life experience.

DNA methylation is an essential epigenetic process in mammals, intimately involved in gene regulation. Here we address the extent to which genetics, sex, and pregnancy influence genomic DNA methylation by intercrossing 2 inbred mouse strains, C57BL/6N and C3H/HeN, and analyzing DNA methylation in parents and offspring using whole-genome bisulfite sequencing. Differential methylation across genotype is detected at thousands of loci and is preserved on parental alleles in offspring. In comparison of autosomal DNA methylation patterns across sex, hundreds of differentially methylated regions are detected. Comparison of animals with different histories of pregnancy within our study reveals a CpG methylation pattern that is restricted to female animals that had borne offspring. Collectively, our results demonstrate the stability of CpG methylation across generations, clarify the interplay of epigenetics with genetics and sex, and suggest that CpG methylation may serve as an epigenetic record of life events in somatic tissues at loci whose expression is linked to the relevant biology.

Revealing a human p53 universe.

p53 transcriptional networks are well-characterized in many organisms. However, a global understanding of requirements for in vivo p53 interactions with DNA and relationships with transcription across human biological systems in response to various p53 activating situations remains limited. Using a common analysis pipeline, we analyzed 41 data sets from genome-wide ChIP-seq studies of which 16 have associated gene expression data, including our recent primary data with normal human lymphocytes. The resulting extensive analysis, accessible at p53 BAER hub via the UCSC browser, provides a robust platform to characterize p53 binding throughout the human genome including direct influence on gene expression and underlying mechanisms. We establish the impact of spacers and mismatches from consensus on p53 binding in vivo and propose that once bound, neither significantly influences the likelihood of expression. Our rigorous approach revealed a large p53 genome-wide cistrome composed of >900 genes directly targeted by p53. Importantly, we identify a core cistrome signature composed of genes appearing in over half the data sets, and we identify signatures that are treatment- or cell-specific, demonstrating new functions for p53 in cell biology. Our analysis reveals a broad homeostatic role for human p53 that is relevant to both basic and translational studies.

Muver, a computational framework for accurately calling accumulated mutations.

BACKGROUND: Identification of mutations from next-generation sequencing data typically requires a balance between sensitivity and accuracy. This is particularly true of DNA insertions and deletions (indels), that can impart significant phenotypic consequences on cells but are harder to call than substitution mutations from whole genome mutation accumulation experiments. To overcome these difficulties, we present muver, a computational framework that integrates established bioinformatics tools with novel analytical methods to generate mutation calls with the extremely low false positive rates and high sensitivity required for accurate mutation rate determination and comparison. RESULTS: Muver uses statistical comparison of ancestral and descendant allelic frequencies to identify variant loci and assigns genotypes with models that include per-sample assessments of sequencing errors by mutation type and repeat context. Muver identifies maximally parsimonious mutation pathways that connect these genotypes, differentiating potential allelic conversion events and delineating ambiguities in mutation location, type, and size. Benchmarking with a human gold standard father-son pair demonstrates muver's sensitivity and low false positive rates. In DNA mismatch repair (MMR) deficient Saccharomyces cerevisiae, muver detects multi-base deletions in homopolymers longer than the replicative polymerase footprint at rates greater than predicted for sequential single-base deletions, implying a novel multi-repeat-unit slippage mechanism. CONCLUSIONS: Benchmarking results demonstrate the high accuracy and sensitivity achieved with muver, particularly for indels, relative to available tools. Applied to an MMR-deficient Saccharomyces cerevisiae system, muver mutation calls facilitate mechanistic insights into DNA replication fidelity.

Widespread transcriptional pausing and elongation control at enhancers.

Regulation by gene-distal enhancers is critical for cell type-specific and condition-specific patterns of gene expression. Thus, to understand the basis of gene activity in a given cell type or tissue, we must identify the precise locations of enhancers and functionally characterize their behaviors. Here, we demonstrate that transcription is a nearly universal feature of enhancers in Drosophila and mammalian cells and that nascent RNA sequencing strategies are optimal for identification of both enhancers and superenhancers. We dissect the mechanisms governing enhancer transcription and discover remarkable similarities to transcription at protein-coding genes. We show that RNA polymerase II (RNAPII) undergoes regulated pausing and release at enhancers. However, as compared with mRNA genes, RNAPII at enhancers is less stable and more prone to early termination. Furthermore, we found that the level of histone H3 Lys4 (H3K4) methylation at enhancers corresponds to transcriptional activity such that highly active enhancers display H3K4 trimethylation rather than the H3K4 monomethylation considered a hallmark of enhancers. Finally, our work provides insights into the unique characteristics of superenhancers, which stimulate high-level gene expression through rapid pause release; interestingly, this property renders associated genes resistant to the loss of factors that stabilize paused RNAPII.

Methionine metabolism is essential for SIRT1-regulated mouse embryonic stem cell maintenance and embryonic development.

Methionine metabolism is critical for epigenetic maintenance, redox homeostasis, and animal development. However, the regulation of methionine metabolism remains unclear. Here, we provide evidence that SIRT1, the most conserved mammalian NAD+-dependent protein deacetylase, is critically involved in modulating methionine metabolism, thereby impacting maintenance of mouse embryonic stem cells (mESCs) and subsequent embryogenesis. We demonstrate that SIRT1-deficient mESCs are hypersensitive to methionine restriction/depletion-induced differentiation and apoptosis, primarily due to a reduced conversion of methionine to S-adenosylmethionine. This reduction markedly decreases methylation levels of histones, resulting in dramatic alterations in gene expression profiles. Mechanistically, we discover that the enzyme converting methionine to S-adenosylmethionine in mESCs, methionine adenosyltransferase 2a (MAT2a), is under control of Myc and SIRT1. Consistently, SIRT1 KO embryos display reduced Mat2a expression and histone methylation and are sensitive to maternal methionine restriction-induced lethality, whereas maternal methionine supplementation increases the survival of SIRT1 KO newborn mice. Our findings uncover a novel regulatory mechanism for methionine metabolism and highlight the importance of methionine metabolism in SIRT1-mediated mESC maintenance and embryonic development.