EpiFactors : a comprehensive database of human epigenetic factors and complexes

Altres ajuts: Russian Fund For Basic Research(RFFI)grant 14-04-0018 i grant 15-34-20423, Ake Olsson's foundation, Swedish Cancer foundation, Swedish Childhood cancer foundation, Dynasty Foundation Fellowship, RIKEN Omics Science Center, RIKEN Preventive Medicine and Diagnosis Innovation Program i RIKEN Center for Life Science Technologies.Abstract: Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians

Functional annotation of human long noncoding RNAs via molecular phenotyping

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-todate lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.Peer reviewe

The Constrained Maximal Expression Level Owing to Haploidy Shapes Gene Content on the Mammalian X Chromosome.

X chromosomes are unusual in many regards, not least of which is their nonrandom gene content. The causes of this bias are commonly discussed in the context of sexual antagonism and the avoidance of activity in the male germline. Here, we examine the notion that, at least in some taxa, functionally biased gene content may more profoundly be shaped by limits imposed on gene expression owing to haploid expression of the X chromosome. Notably, if the X, as in primates, is transcribed at rates comparable to the ancestral rate (per promoter) prior to the X chromosome formation, then the X is not a tolerable environment for genes with very high maximal net levels of expression, owing to transcriptional traffic jams. We test this hypothesis using The Encyclopedia of DNA Elements (ENCODE) and data from the Functional Annotation of the Mammalian Genome (FANTOM5) project. As predicted, the maximal expression of human X-linked genes is much lower than that of genes on autosomes: on average, maximal expression is three times lower on the X chromosome than on autosomes. Similarly, autosome-to-X retroposition events are associated with lower maximal expression of retrogenes on the X than seen for X-to-autosome retrogenes on autosomes. Also as expected, X-linked genes have a lesser degree of increase in gene expression than autosomal ones (compared to the human/Chimpanzee common ancestor) if highly expressed, but not if lowly expressed. The traffic jam model also explains the known lower breadth of expression for genes on the X (and the Z of birds), as genes with broad expression are, on average, those with high maximal expression. As then further predicted, highly expressed tissue-specific genes are also rare on the X and broadly expressed genes on the X tend to be lowly expressed, both indicating that the trend is shaped by the maximal expression level not the breadth of expression per se. Importantly, a limit to the maximal expression level explains biased tissue of expression profiles of X-linked genes. Tissues whose tissue-specific genes are very highly expressed (e.g., secretory tissues, tissues abundant in structural proteins) are also tissues in which gene expression is relatively rare on the X chromosome. These trends cannot be fully accounted for in terms of alternative models of biased expression. In conclusion, the notion that it is hard for genes on the Therian X to be highly expressed, owing to transcriptional traffic jams, provides a simple yet robustly supported rationale of many peculiar features of X's gene content, gene expression, and evolution

Jaccard index based similarity measure to compare transcription factor binding site models

BACKGROUND: Positional weight matrix (PWM) remains the most popular for quantification of transcription factor (TF) binding. PWM supplied with a score threshold defines a set of putative transcription factor binding sites (TFBS), thus providing a TFBS model. TF binding DNA fragments obtained by different experimental methods usually give similar but not identical PWMs. This is also common for different TFs from the same structural family. Thus it is often necessary to measure the similarity between PWMs. The popular tools compare PWMs directly using matrix elements. Yet, for log-odds PWMs, negative elements do not contribute to the scores of highly scoring TFBS and thus may be different without affecting the sets of the best recognized binding sites. Moreover, the two TFBS sets recognized by a given pair of PWMs can be more or less different depending on the score thresholds. RESULTS: We propose a practical approach for comparing two TFBS models, each consisting of a PWM and the respective scoring threshold. The proposed measure is a variant of the Jaccard index between two TFBS sets. The measure defines a metric space for TFBS models of all finite lengths. The algorithm can compare TFBS models constructed using substantially different approaches, like PWMs with raw positional counts and log-odds. We present the efficient software implementation: MACRO-APE (MAtrix CompaRisOn by Approximate P-value Estimation). CONCLUSIONS: MACRO-APE can be effectively used to compute the Jaccard index based similarity for two TFBS models. A two-pass scanning algorithm is presented to scan a given collection of PWMs for PWMs similar to a given query. AVAILABILITY AND IMPLEMENTATION: MACRO-APE is implemented in ruby 1.9; software including source code and a manual is freely available at http://autosome.ru/macroape/ and in supplementary materials

Genome-wide map of human and mouse transcription factor binding sites aggregated from ChIP-Seq data

Abstract Objectives Mammalian genomics studies, especially those focusing on transcriptional regulation, require information on genomic locations of regulatory regions, particularly, transcription factor (TF) binding sites. There are plenty of published ChIP-Seq data on in vivo binding of transcription factors in different cell types and conditions. However, handling of thousands of separate data sets is often impractical and it is desirable to have a single global map of genomic regions potentially bound by a particular TF in any of studied cell types and conditions. Data description Here we report human and mouse cistromes, the maps of genomic regions that are routinely identified as TF binding sites, organized by TF. We provide cistromes for 349 mouse and 599 human TFs. Given a TF, its cistrome regions are supported by evidence from several ChIP-Seq experiments or several computational tools, and, as an optional filter, contain occurrences of sequence motifs recognized by the TF. Using the cistrome, we provide an annotation of TF binding sites in the vicinity of human and mouse transcription start sites. This information is useful for selecting potential gene targets of transcription factors and detecting co-regulated genes in differential gene expression data

EpiFactors : a comprehensive database of human epigenetic factors and complexes

Altres ajuts: Russian Fund For Basic Research(RFFI)grant 14-04-0018 i grant 15-34-20423, Ake Olsson's foundation, Swedish Cancer foundation, Swedish Childhood cancer foundation, Dynasty Foundation Fellowship, RIKEN Omics Science Center, RIKEN Preventive Medicine and Diagnosis Innovation Program i RIKEN Center for Life Science Technologies.Abstract: Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians

The single nucleotide variant rs12722489 determines differential estrogen receptor binding and enhancer properties of an IL2RA intronic region.

We studied functional effect of rs12722489 single nucleotide polymorphism located in the first intron of human IL2RA gene on transcriptional regulation. This polymorphism is associated with multiple autoimmune conditions (rheumatoid arthritis, multiple sclerosis, Crohn's disease, and ulcerative colitis). Analysis in silico suggested significant difference in the affinity of estrogen receptor (ER) binding site between alternative allelic variants, with stronger predicted affinity for the risk (G) allele. Electrophoretic mobility shift assay showed that purified human ERα bound only G variant of a 32-bp genomic sequence containing rs12722489. Chromatin immunoprecipitation demonstrated that endogenous human ERα interacted with rs12722489 genomic region in vivo and DNA pull-down assay confirmed differential allelic binding of amplified 189-bp genomic fragments containing rs12722489 with endogenous human ERα. In a luciferase reporter assay, a kilobase-long genomic segment containing G but not A allele of rs12722489 demonstrated enhancer properties in MT-2 cell line, an HTLV-1 transformed human cell line with a regulatory T cell phenotype

A holistic view of mouse enhancer architectures reveals analogous pleiotropic effects and correlation with human disease.

BACKGROUND Efforts to elucidate the function of enhancers in vivo are underway but their vast numbers alongside differing enhancer architectures make it difficult to determine their impact on gene activity. By systematically annotating multiple mouse tissues with super- and typical-enhancers, we have explored their relationship with gene function and phenotype. RESULTS Though super-enhancers drive high total- and tissue-specific expression of their associated genes, we find that typical-enhancers also contribute heavily to the tissue-specific expression landscape on account of their large numbers in the genome. Unexpectedly, we demonstrate that both enhancer types are preferentially associated with relevant 'tissue-type' phenotypes and exhibit no difference in phenotype effect size or pleiotropy. Modelling regulatory data alongside molecular data, we built a predictive model to infer gene-phenotype associations and use this model to predict potentially novel disease-associated genes. CONCLUSION Overall our findings reveal that differing enhancer architectures have a similar impact on mammalian phenotypes whilst harbouring differing cellular and expression effects. Together, our results systematically characterise enhancers with predicted phenotypic traits endorsing the role for both types of enhancers in human disease and disorders