Goldmine integrates information placing genomic ranges into meaningful biological contexts

Epigenetic regulation of asymmetric cell division by the LIBR-BRD4 axis

Asymmetric cell division (ACD) is a mechanism used by stem cells to maintain the number of progeny. However, the epigenetic mechanisms regulating ACD remain elusive. Here we show that BRD4, a BET domain protein that binds to acetylated histone, is segregated in daughter cells together with H3K56Ac and regulates ACD. ITGB1 is regulated by BRD4 to regulate ACD. A long noncoding RNA (lncRNA), LIBR (LncRNA Inhibiting BRD4), decreases the percentage of stem cells going through ACD through interacting with the BRD4 mRNAs. LIBR inhibits the translation of BRD4 through recruiting a translation repressor, RCK, and inhibiting the binding of BRD4 mRNAs to polysomes. These results identify the epigenetic regulatory modules (BRD4, lncRNA LIBR) that regulate ACD. The regulation of ACD by BRD4 suggests the therapeutic limitation of using BRD4 inhibitors to treat cancer due to the ability of these inhibitors to promote symmetric cell division that may lead to tumor progression and treatment resistance.

OGRE: calculate, visualize, and analyze overlap between genomic input regions and public annotations

BACKGROUND

Modern genome sequencing leads to an ever-growing collection of genomic annotations. Combining these elements with a set of input regions (e.g. genes) would yield new insights in genomic associations, such as those involved in gene regulation. The required data are scattered across different databases making a manual approach tiresome, unpractical, and prone to error. Semi-automatic approaches require programming skills in data parsing, processing, overlap calculation, and visualization, which most biomedical researchers lack. Our aim was to develop an automated tool providing all necessary algorithms, benefiting both bioinformaticians and researchers without bioinformatic training.

RESULTS

We developed overlapping annotated genomic regions (OGRE) as a comprehensive tool to associate and visualize input regions with genomic annotations. It does so by parsing regions of interest, mining publicly available annotations, and calculating possible overlaps between them. The user can thus identify location, type, and number of associated regulatory elements. Results are presented as easy to understand visualizations and result tables. We applied OGRE to recent studies and could show high reproducibility and potential new insights. To demonstrate OGRE's performance in terms of running time and output, we have conducted a benchmark and compared its features with similar tools.

CONCLUSIONS

OGRE's functions and built-in annotations can be applied as a downstream overlap association step, which is compatible with most genomic sequencing outputs, and can thus enrich pre-existing analyses pipelines. Compared to similar tools, OGRE shows competitive performance, offers additional features, and has been successfully applied to two recent studies. Overall, OGRE addresses the lack of tools for automatic analysis, local genomic overlap calculation, and visualization by providing an easy to use, end-to-end solution for both biologists and computational scientists.

Over-expression of ADAR1 in mice does not initiate or accelerate cancer formation in vivo

Adenosine to inosine editing (A-to-I) in regions of double stranded RNA (dsRNA) is mediated by adenosine deaminase acting on RNA 1 (ADAR1) or ADAR2. ADAR1 and A-to-I editing levels are increased in many human cancers. Inhibition of ADAR1 has emerged as a high priority oncology target, however, whether ADAR1 overexpression enables cancer initiation or progression has not been directly tested. We established a series of in vivo models to allow overexpression of full-length ADAR1, or its individual isoforms, to test if increased ADAR1 expression was oncogenic. Widespread over-expression of ADAR1 or the p110 or p150 isoforms individually as sole lesions was well tolerated and did not result in cancer initiation. Therefore, ADAR1 overexpression alone is not sufficient to initiate cancer. We demonstrate that endogenous ADAR1 and A-to-I editing increased upon immortalization in murine cells, consistent with the observations from human cancers. We tested if ADAR1 over-expression could co-operate with cancer initiated by loss of tumour suppressors using a model of osteosarcoma. We did not see a disease potentiating or modifying effect of overexpressing ADAR1 or its isoforms in the models assessed. We conclude that increased ADAR1 expression and A-to-I editing in cancers is most likely a consequence of tumor formation.

The phenotype of the most common human ADAR1p150 Zα mutation P193A in mice is partially penetrant

ADAR1 -mediated A-to-I RNA editing is a self-/non-self-discrimination mechanism for cellular double-stranded RNAs. ADAR mutations are one cause of Aicardi-Goutières Syndrome, an inherited paediatric encephalopathy, classed as a "Type I interferonopathy." The most common ADAR1 mutation is a proline 193 alanine (p.P193A) mutation, mapping to the ADAR1p150 isoform-specific Zα domain. Here, we report the development of an independent murine P195A knock-in mouse, homologous to human P193A. The Adar1P195A/P195A mice are largely normal and the mutation is well tolerated. When the P195A mutation is compounded with an Adar1 null allele (Adar1P195A/- ), approximately half the animals are runted with a shortened lifespan while the remaining Adar1P195A/- animals are normal, contrasting with previous reports. The phenotype of the Adar1P195A/- animals is both associated with the parental genotype and partly non-genetic/environmental. Complementation with an editing-deficient ADAR1 (Adar1P195A/E861A ), or the loss of MDA5, rescues phenotypes in the Adar1P195A/- mice.

Multimodal CRISPR perturbations of GWAS loci associated with coronary artery disease in vascular endothelial cells

Genome-wide association studies have identified >250 genetic variants associated with coronary artery disease (CAD), but the causal variants, genes and molecular mechanisms remain unknown at most loci. We performed pooled CRISPR screens to test the impact of sequences at or near CAD-associated genetic variants on vascular endothelial cell functions. Using CRISPR knockout, inhibition and activation, we targeted 1998 variants at 83 CAD loci to assess their effect on three adhesion proteins (E-selectin, ICAM1, VCAM1) and three key endothelial functions (nitric oxide and reactive oxygen species production, calcium signalling). At a false discovery rate ≤10%, we identified significant CRISPR perturbations near 42 variants located within 26 CAD loci. We used base editing to validate a putative causal variant in the promoter of the FES gene. Although a few of the loci include genes previously characterized in endothelial cells (e.g. AIDA, ARHGEF26, ADAMTS7), most are implicated in endothelial dysfunction for the first time. Detailed characterization of one of these new loci implicated the RNA helicase DHX38 in vascular endothelial cell senescence. While promising, our results also highlighted several limitations in using CRISPR perturbations to functionally dissect GWAS loci, including an unknown false negative rate and potential off-target effects.

Intestinal stem cell aging signature reveals a reprogramming strategy to enhance regenerative potential

The impact of aging on intestinal stem cells (ISCs) has not been fully elucidated. In this study, we identified widespread epigenetic and transcriptional alterations in old ISCs. Using a reprogramming algorithm, we identified a set of key transcription factors (Egr1, Irf1, FosB) that drives molecular and functional differences between old and young states. Overall, by dissecting the molecular signature of aged ISCs, our study identified transcription factors that enhance the regenerative capacity of ISCs.

Altered hydroxymethylome in the substantia nigra of Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disorder, and aging and genetic and environmental exposure can contribute to its pathogenesis. DNA methylation has been suggested to play a pivotal role in neurodevelopment and neurodegenerative diseases. 5-hydroxymethylcytosine (5hmC) is generated through 5-methylcytosine (5mC) oxidization by ten-eleven translocation proteins and is particularly enriched in the brain. Although 5hmC has been linked to multiple neurological disorders, little is known about 5hmC alterations in the substantia nigra of patients with PD. To determine the specific alterations in DNA methylation and hydroxymethylation in PD brain samples, we examined the genome-wide profiles of 5mC and 5hmC in the substantia nigra of patients with PD and Alzheimer's disease (ad). We identified 4119 differentially hydroxymethylated regions (DhMRs) and no differentially methylated regions (DMRs) in the postmortem brains of patients with PD compared with those of controls. These DhMRs were PD-specific when compared with the results of AD. Gene ontology analysis revealed that several signaling pathways, such as neurogenesis and neuronal differentiation, were significantly enriched in PD DhMRs. KEGG enrichment analysis revealed substantial alterations in multiple signaling pathways, including phospholipase D (PLD), cAMP and Rap1. In addition, using a PD Drosophila model, we found that one of the 5hmC-modulated genes, PLD1, modulated α-synuclein toxicity. Our analysis suggested that 5hmC may act as an independent epigenetic marker and contribute to the pathogenesis of PD.

MethReg: estimating the regulatory potential of DNA methylation in gene transcription

Epigenome-wide association studies often detect many differentially methylated sites, and many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated that CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here, we present MethReg, an R/Bioconductor package that analyzes matched DNA methylation and gene expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize and annotate CpG sites with high regulatory potential. At these CpG sites, TF-target gene associations are often only present in a subset of samples with high (or low) methylation levels, so they can be missed by analyses that use all samples. Using colorectal cancer and Alzheimer's disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

Dynamic transcriptional activity and chromatin remodeling of regulatory T cells after varied duration of interleukin-2 receptor signaling

Regulatory T (Treg) cells require (interleukin-2) IL-2 for their homeostasis by affecting their proliferation, survival and activation. Here we investigated transcriptional and epigenetic changes after acute, periodic and persistent IL-2 receptor (IL-2R) signaling in mouse peripheral Treg cells in vivo using IL-2 or the long-acting IL-2-based biologic mouse IL-2-CD25. We show that initially IL-2R-dependent STAT5 transcription factor-dependent pathways enhanced gene activation, chromatin accessibility and metabolic reprogramming to support Treg cell proliferation. Unexpectedly, at peak proliferation, less accessible chromatin prevailed and was associated with Treg cell contraction. Restimulation of IL-2R signaling after contraction activated signature IL-2-dependent genes and others associated with effector Treg cells, whereas genes associated with signal transduction were downregulated to somewhat temper expansion. Thus, IL-2R-dependent Treg cell homeostasis depends in part on a shift from more accessible chromatin and expansion to less accessible chromatin and contraction. Mouse IL-2-CD25 supported greater expansion and a more extensive transcriptional state than IL-2 in Treg cells, consistent with greater efficacy to control autoimmunity.

GenomicDistributions: fast analysis of genomic intervals with Bioconductor

Background

Epigenome analysis relies on defined sets of genomic regions output by widely used assays such as ChIP-seq and ATAC-seq. Statistical analysis and visualization of genomic region sets is essential to answer biological questions in gene regulation. As the epigenomics community continues generating data, there will be an increasing need for software tools that can efficiently deal with more abundant and larger genomic region sets. Here, we introduce GenomicDistributions, an R package for fast and easy summarization and visualization of genomic region data.

Results

GenomicDistributions offers a broad selection of functions to calculate properties of genomic region sets, such as feature distances, genomic partition overlaps, and more. GenomicDistributions functions are meticulously optimized for best-in-class speed and generally outperform comparable functions in existing R packages. GenomicDistributions also offers plotting functions that produce editable ggplot objects. All GenomicDistributions functions follow a uniform naming scheme and can handle either single or multiple region set inputs.

Conclusions

GenomicDistributions offers a fast and scalable tool for exploratory genomic region set analysis and visualization. GenomicDistributions excels in user-friendliness, flexibility of outputs, breadth of functions, and computational performance. GenomicDistributions is available from Bioconductor (https://bioconductor.org/packages/release/bioc/html/GenomicDistributions.html).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08467-y.

AnnoMiner is a new web-tool to integrate epigenetics, transcription factor occupancy and transcriptomics data to predict transcriptional regulators

Gene expression regulation requires precise transcriptional programs, led by transcription factors in combination with epigenetic events. Recent advances in epigenomic and transcriptomic techniques provided insight into different gene regulation mechanisms. However, to date it remains challenging to understand how combinations of transcription factors together with epigenetic events control cell-type specific gene expression. We have developed the AnnoMiner web-server, an innovative and flexible tool to annotate and integrate epigenetic, and transcription factor occupancy data. First, AnnoMiner annotates user-provided peaks with gene features. Second, AnnoMiner can integrate genome binding data from two different transcriptional regulators together with gene features. Third, AnnoMiner offers to explore the transcriptional deregulation of genes nearby, or within a specified genomic region surrounding a user-provided peak. AnnoMiner’s fourth function performs transcription factor or histone modification enrichment analysis for user-provided gene lists by utilizing hundreds of public, high-quality datasets from ENCODE for the model organisms human, mouse, Drosophila and C. elegans. Thus, AnnoMiner can predict transcriptional regulators for a studied process without the strict need for chromatin data from the same process. We compared AnnoMiner to existing tools and experimentally validated several transcriptional regulators predicted by AnnoMiner to indeed contribute to muscle morphogenesis in Drosophila. AnnoMiner is freely available at http://chimborazo.ibdm.univ-mrs.fr/AnnoMiner/.

The majority of A-to-I RNA editing is not required for mammalian homeostasis

BACKGROUND

Adenosine-to-inosine (A-to-I) RNA editing, mediated by ADAR1 and ADAR2, occurs at tens of thousands to millions of sites across mammalian transcriptomes. A-to-I editing can change the protein coding potential of a transcript and alter RNA splicing, miRNA biology, RNA secondary structure and formation of other RNA species. In vivo, the editing-dependent protein recoding of GRIA2 is the essential function of ADAR2, while ADAR1 editing prevents innate immune sensing of endogenous RNAs by MDA5 in both human and mouse. However, a significant proportion of A-to-I editing sites can be edited by both ADAR1 and ADAR2, particularly within the brain where both are highly expressed. The physiological function(s) of these shared sites, including those evolutionarily conserved, is largely unknown.

RESULTS

To generate completely A-to-I editing-deficient mammals, we crossed the viable rescued ADAR1-editing-deficient animals (Adar1E861A/E861AIfih1-/-) with rescued ADAR2-deficient (Adarb1-/-Gria2R/R) animals. Unexpectedly, the global absence of editing was well tolerated. Adar1E861A/E861AIfih1-/-Adarb1-/-Gria2R/R were recovered at Mendelian ratios and age normally. Detailed transcriptome analysis demonstrated that editing was absent in the brains of the compound mutants and that ADAR1 and ADAR2 have similar editing site preferences and patterns.

CONCLUSIONS

We conclude that ADAR1 and ADAR2 are non-redundant and do not compensate for each other's essential functions in vivo. Physiologically essential A-to-I editing comprises a small subset of the editome, and the majority of editing is dispensable for mammalian homeostasis. Moreover, in vivo biologically essential protein recoding mediated by A-to-I editing is an exception in mammals.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

Identification of Human Lineage-Specific Transcriptional Coregulators Enabled by a Glossary of Binding Modules and Tunable Genomic Backgrounds

Transcription factors (TFs) control cellular processes by binding specific DNA motifs to modulate gene expression. Motif enrichment analysis of regulatory regions can identify direct and indirect TF binding sites. Here, we created a glossary of 108 non-redundant TF-8mer "modules" of shared specificity for 671 metazoan TFs from publicly available and new universal protein binding microarray data. Analysis of 239 ENCODE TF chromatin immunoprecipitation sequencing datasets and associated RNA sequencing profiles suggest the 8mer modules are more precise than position weight matrices in identifying indirect binding motifs and their associated tethering TFs. We also developed GENRE (genomically equivalent negative regions), a tunable tool for construction of matched genomic background sequences for analysis of regulatory regions. GENRE outperformed four state-of-the-art approaches to background sequence construction. We used our TF-8mer glossary and GENRE in the analysis of the indirect binding motifs for the co-occurrence of tethering factors, suggesting novel TF-TF interactions. We anticipate that these tools will aid in elucidating tissue-specific gene-regulatory programs.

Genome-wide brain DNA methylation analysis suggests epigenetic reprogramming in Parkinson disease

Objective

Given the known strong relationship of DNA methylation with environmental exposure, we investigated whether brain regions affected in Parkinson disease (PD) were differentially methylated between PD cases and controls.

Methods

DNA chip arrays were used to perform a genome-wide screen of DNA methylation on the dorsal motor nucleus of the vagus (DMV), substantia nigra (SN), and cingulate gyrus (CG) of pathologically confirmed PD cases and controls selected using the criteria of Beecham et al. Analysis examined differentially methylated regions (DMRs) between cases and controls for each brain area. RNA sequencing and pathway analysis were also performed for each brain area.

Results

Thirty-eight PD cases and 41 controls were included in the analysis. Methylation studies revealed 234 significant DMR in the DMV, 44 in the SN, and 141 in the CG between cases and controls (Sidak p < 0.05). Pathway analysis of these genes showed significant enrichment for the Wnt signaling pathway (FDR < 0.01).

Conclusions

Our data suggest that significant DNA methylation changes exist between cases and controls in PD, especially in the DMV, one of the areas affected earliest in PD. The etiology of these methylation changes is not yet known, but the predominance of methylation changes occurring in the DMV supports the hypothesis that vagus nerve function, perhaps involving the gastrointestinal system, is important in PD pathogenesis. These data also give independent support that genes involved in Wnt signaling are a likely factor in the neurodegenerative processes of PD.

Associations between Maternal Tobacco Smoke Exposure and the Cord Blood [Formula: see text] DNA Methylome

BACKGROUND

Maternal tobacco smoke exposure has been associated with altered DNA methylation. However, previous studies largely used methylation arrays, which cover a small fraction of CpGs, and focused on whole cord blood.

OBJECTIVES

The current study examined the impact of in utero exposure to maternal tobacco smoke on the cord blood [Formula: see text] DNA methylome.

METHODS

The methylomes of 20 Hispanic white newborns ([Formula: see text] exposed to any maternal tobacco smoke in pregnancy; [Formula: see text] unexposed) from the Maternal and Child Health Study (MACHS) were profiled by whole-genome bisulfite sequencing (median coverage: [Formula: see text]). Statistical analyses were conducted using the Regression Analysis of Differential Methylation (RADMeth) program because it performs well on low-coverage data (minimizes false positives and negatives).

RESULTS

We found that 10,381 CpGs were differentially methylated by tobacco smoke exposure [neighbor-adjusted p-values that are additionally corrected for multiple testing based on the Benjamini-Hochberg method for controlling the false discovery rate (FDR) [Formula: see text]]. From these CpGs, RADMeth identified 557 differentially methylated regions (DMRs) that were overrepresented ([Formula: see text]) in important regulatory regions, including enhancers. Of nine DMRs that could be queried in a reduced representation bisulfite sequencing (RRBS) study of adult [Formula: see text] cells ([Formula: see text] smokers; [Formula: see text] nonsmokers), four replicated ([Formula: see text]). Additionally, a CpG in the promoter of SLC7A8 (percent methylation difference: [Formula: see text] comparing exposed to unexposed) replicated ([Formula: see text]) in an EPIC (Illumina) array study of cord blood [Formula: see text] cells ([Formula: see text] exposed to sustained maternal tobacco smoke; [Formula: see text] unexposed) and in a study of adult [Formula: see text] cells across two platforms (EPIC: [Formula: see text] smokers; [Formula: see text] nonsmokers; 450K: [Formula: see text] smokers; [Formula: see text] nonsmokers).

CONCLUSIONS

Maternal tobacco smoke exposure in pregnancy is associated with cord blood [Formula: see text] DNA methylation in key regulatory regions, including enhancers. While we used a method that performs well on low-coverage data, we cannot exclude the possibility that some results may be false positives. However, we identified a differentially methylated CpG in amino acid transporter SLC7A8 that is highly reproducible, which may be sensitive to cigarette smoke in both cord blood and adult [Formula: see text] cells. https://doi.org/10.1289/EHP3398.

5-Hydroxymethylcytosine preferentially targets genes upregulated in isocitrate dehydrogenase 1 mutant high-grade glioma

Gliomas demonstrate epigenetic dysregulation exemplified by the Glioma CpG Island Methylator Phenotype (G-CIMP) seen in IDH1 mutant tumors. 5-Hydroxymethylcytosine (5hmC) is implicated in glioma pathogenesis; however, its role in IDH1 mutant gliomas is incompletely understood. To characterize 5hmC in IDH1 mutant gliomas further, we examine 5hmC in a cohort of IDH1 mutant and wild-type high-grade gliomas (HGG) using a quantitative locus-specific approach. Regions demonstrating high 5hmC abundance and differentially hydroxymethylated regions (DHMR) enrich for enhancers implicated in glioma pathogenesis. Among these regions, IDH1 mutant tumors possess greater 5hmC compared to wild type. 5hmC contributes to overall methylation status of G-CIMP genes. 5hmC targeting gene body regions correlates significantly with increased gene expression. In particular, a strong correlation between increased 5hmC and increased gene expression is identified for genes highly expressed in the IDH1 mutant cohort. Overall, locus-specific gain of 5hmC targeting regulatory regions and associated with overexpressed genes suggests a significant role for 5hmC in IDH1 mutant HGG.

annotatr: genomic regions in context

Motivation

Analysis of next-generation sequencing data often results in a list of genomic regions. These may include differentially methylated CpGs/regions, transcription factor binding sites, interacting chromatin regions, or GWAS-associated SNPs, among others. A common analysis step is to annotate such genomic regions to genomic annotations (promoters, exons, enhancers, etc.). Existing tools are limited by a lack of annotation sources and flexible options, the time it takes to annotate regions, an artificial one-to-one region-to-annotation mapping, a lack of visualization options to easily summarize data, or some combination thereof.

Results

We developed the annotatr Bioconductor package to flexibly and quickly summarize and plot annotations of genomic regions. The annotatr package reports all intersections of regions and annotations, giving a better understanding of the genomic context of the regions. A variety of graphics functions are implemented to easily plot numerical or categorical data associated with the regions across the annotations, and across annotation intersections, providing insight into how characteristics of the regions differ across the annotations. We demonstrate that annotatr is up to 27× faster than comparable R packages. Overall, annotatr enables a richer biological interpretation of experiments.

Availability and Implementation

http://bioconductor.org/packages/annotatr/ and https://github.com/rcavalcante/annotatr.

Contact

rcavalca@umich.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Base-Resolution Analysis of DNA Methylation Patterns Downstream of Dnmt3a in Mouse Naïve B Cells

The DNA methyltransferase, Dnmt3a, is dynamically regulated throughout mammalian B cell development and upon activation by antigenic stimulation. Dnmt3a inactivation in hematopoietic stem cells has been shown to drive B cell-related malignancies, including chronic lymphocytic leukemia, and associates with specific DNA methylation patterns in transformed cells. However, while it is clear that inactivation of Dnmt3a in hematopoietic stem cells has profound functional effects, the consequences of Dnmt3a inactivation in cells of the B lineage are unclear. To assess whether loss of Dnmt3a at the earliest stages of B cell development lead to DNA methylation defects that might impair function, we selectively inactivated Dnmt3a early in mouse B cell development and then utilized whole genome bisulfite sequencing to generate base-resolution profiles of Dnmt3a^+/+ and Dnmt3a^−/− naïve splenic B cells. Overall, we find that global methylation patterns are largely consistent between Dnmt3a^+/+ and Dnmt3a^−/− naïve B cells, indicating a minimal functional effect of DNMT3A in mature B cells. However, loss of Dnmt3a induced 449 focal DNA methylation changes, dominated by loss-of-methylation events. Regions found to be hypomethylated in Dnmt3a^−/− naïve splenic B cells were enriched in gene bodies of transcripts expressed in B cells, a fraction of which are implicated in B cell-related disease. Overall, the results from this study suggest that factors other than Dnmt3a are the major drivers for methylome maintenance in B cell development.

Interplay between maternal Slc6a4 mutation and prenatal stress: a possible mechanism for autistic behavior development

The low activity allele of the maternal polymorphism, 5HTTLPR, in the serotonin transporter, SLC6A4, coupled with prenatal stress is reported to increase the risk for children to develop autism spectrum disorder (ASD). Similarly, maternal Slc6a4 knock-out and prenatal stress in rodents results in offspring demonstrating ASD-like characteristics. The present study uses an integrative genomics approach to explore mechanistic changes in early brain development in mouse embryos exposed to this maternal gene-environment phenomenon. Restraint stress was applied to pregnant Slc6a4 ^+/+ and Slc6a4 ^+/- mice and post-stress embryonic brains were assessed for whole genome level profiling of methylome, transcriptome and miRNA using Next Generation Sequencing. Embryos of stressed Slc6a4 ^+/+ dams exhibited significantly altered methylation profiles and differential expression of 157 miRNAs and 1009 genes affecting neuron development and cellular adhesion pathways, which may function as a coping mechanism to prenatal stress. In striking contrast, the response of embryos of stressed Slc6a4 ^+/- dams was found to be attenuated, shown by significantly reduced numbers of differentially expressed genes (458) and miRNA (0) and genome hypermethylation. This attenuated response may pose increased risks on typical brain development resulting in development of ASD-like characteristics in offspring of mothers with deficits in serotonin related pathways during stressful pregnancies.

UROPA: a tool for Universal RObust Peak Annotation

The annotation of genomic ranges of interest represents a recurring task for bioinformatics analyses. These ranges can originate from various sources, including peaks called for transcription factor binding sites (TFBS) or histone modification ChIP-seq experiments, chromatin structure and accessibility experiments (such as ATAC-seq), but also from other types of predictions that result in genomic ranges. While peak annotation primarily driven by ChiP-seq was extensively explored, many approaches remain simplistic ("most closely located TSS"), rely on fixed pre-built references, or require complex scripting tasks on behalf of the user. An adaptable, fast, and universal tool, capable to annotate genomic ranges in the respective biological context is critically missing. UROPA (Universal RObust Peak Annotator) is a command line based tool, intended for universal genomic range annotation. Based on a configuration file, different target features can be prioritized with multiple integrated queries. These can be sensitive for feature type, distance, strand specificity, feature attributes (e.g. protein_coding) or anchor position relative to the feature. UROPA can incorporate reference annotation files (GTF) from different sources (Gencode, Ensembl, RefSeq), as well as custom reference annotation files. Statistics and plots transparently summarize the annotation process. UROPA is implemented in Python and R.