TFregulomeR reveals transcription factors' context-specific features and functions.
TL;DR: It is demonstrated that TFs’ target gene ontologies can differ notably depending on their partners and, by re-analyzing well characterized TFs, it is brought to light that numerous leucine zipper TFBSs derived from ChIP-seq experiments documented in current databases were inadequately characterized.
Abstract: Transcription factors (TFs) are sequence-specific DNA binding proteins, fine-tuning spatiotemporal gene expression. Since genomic occupancy of a TF is highly dynamic, it is crucial to study TF binding sites (TFBSs) in a cell-specific context. To date, thousands of ChIP-seq datasets have portrayed the genomic binding landscapes of numerous TFs in different cell types. Although these datasets can be browsed via several platforms, tools that can operate on that data flow are still lacking. Here, we introduce TFregulomeR (https://github.com/benoukraflab/TFregulomeR), an R-library linked to an up-to-date compendium of cistrome and methylome datasets, implemented with functionalities that facilitate integrative analyses. In particular, TFregulomeR enables the characterization of TF binding partners and cell-specific TFBSs, along with the study of TF's functions in the context of different partnerships and DNA methylation levels. We demonstrated that TFs' target gene ontologies can differ notably depending on their partners and, by re-analyzing well characterized TFs, we brought to light that numerous leucine zipper TFBSs derived from ChIP-seq experiments documented in current databases were inadequately characterized, due to the fact that their position weight matrices were assembled using a mixture of homodimer and heterodimer binding sites. Altogether, analyses of context-specific transcription regulation with TFregulomeR foster our understanding of regulatory network-dependent TF functions.
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TL;DR: This work presents the approach that earned a shared first rank in the “ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge” in 2017 and benchmark the influence of different feature sets and finds that chromatin accessibility and binding motifs are sufficient to yield state-of-the-art performance.
Abstract: Prediction of cell type-specific, in vivo transcription factor binding sites is one of the central challenges in regulatory genomics. Here, we present our approach that earned a shared first rank in the “ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge” in 2017. In post-challenge analyses, we benchmark the influence of different feature sets and find that chromatin accessibility and binding motifs are sufficient to yield state-of-the-art performance. Finally, we provide 682 lists of predicted peaks for a total of 31 transcription factors in 22 primary cell types and tissues and a user-friendly version of our approach, Catchitt, for download.
85 citations
TL;DR: It is shown that targeted demethylation of the promoter-exon 1-intron 1 (PrExI) region initiates an epigenetic wave of local chromatin remodeling and distal long-range interactions, culminating in gene-locus specific activation in the p16 gene.
Abstract: Aberrant DNA methylation in the region surrounding the transcription start site is a hallmark of gene silencing in cancer. Currently approved demethylating agents lack specificity and exhibit high toxicity. Herein we show, using the p16 gene as an example, that targeted demethylation of the promoter-exon 1-intron 1 (PrExI) region initiates an epigenetic wave of local chromatin remodeling and distal long-range interactions, culminating in gene-locus specific activation. Through development of CRISPR-DiR (DNMT1-interacting RNA), in which ad hoc edited guides block methyltransferase activity in a locus-specific fashion, we demonstrate that demethylation is coupled to epigenetic and topological changes. These results suggest the existence of a specialized “demethylation firing center (DFC)” which can be switched on by an adaptable and selective RNA-mediated approach for locus-specific transcriptional activation. One Sentence Summary Locus demethylation via CRISPR-DiR reshapes chromatin structure and specifically reactivates its cognate gene.
6 citations
Cites methods from "TFregulomeR reveals transcription f..."
...TF direct binding motifs surrounding p16 transcription start site were searched out using the TFregulomeR package, which is a TF motif analysis tool linking to 1,468 public TF ChIP-seq datasets in human (24)....
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...CTCF binding was analyzed in our study using ChIP-Seq data from cell lines analyzed by TFregulomeR (FB8470, GM12891, GM19240, prostate epithelial cells, and H1-derived mesenchymal stem cells)....
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...Bioinformatic analysis TF bindings and motif Analysis TF direct binding motifs surrounding p16 transcription start site were searched out using the TFregulomeR package, which is a TF motif analysis tool linking to 1,468 public TF ChIP-seq datasets in human (24)....
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...WGBS methylation data were collected from SNU-398 cells (both wild type and Decitabine treated) performed in our study; histone mark enrichments determined by ChIP-seq cross 7 cell lines (GM12878, H1-hESC, HSMM, HUVEC, K562, NHEK, NHLF) obtained from ENCODE; CTCF binding was analyzed in our study using ChIP-Seq data from cell lines analyzed by TFregulomeR (FB8470, GM12891, GM19240, prostate epithelial cells, and H1-derived mesenchymal stem cells)....
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...Using the motif analysis tool TFregulomeR (24), a TF binding site analysis tool linking to a large compendium of ChIP-seq data, we found CTCF (CCCTC-binding factor) binding peaks in exon 1 across five different cell lines (Fig....
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TL;DR: JUNB and ATF4::CEBPβ are proposed as candidate transcription factors that modulate the BMSC-induced transformation of the regulome linked to the transcriptional response that may lead to promising novel therapeutic targets for the treatment of MM.
Abstract: Simple Summary The bone marrow (BM) microenvironment provides a protective sanctuary for multiple myeloma (MM) against therapeutic agents. MM cells interact with BM stromal cells (BMSCs) and the interaction is sufficient to confer de novo multi-drug resistance with epigenetic mechanisms as one of the contributors yet to be elucidated. We profiled genome-wide landscapes of gene expression (transcriptome) and chromatin accessibility (regulome) for MM cells interacting with BMSCs and characterized the induced signatures. We evaluated the contributions from soluble factors derived from BMSCs and compared these results to physical adhesion to the BMSC-induced changes in the transcriptome and regulome. The multi-omics approach further identified candidate transcription factors that regulate the BMSC-induced transcriptome through modulating the regulome, which may lead to promising novel therapeutic targets for the treatment of MM. Abstract Multiple myeloma (MM) is a hematological cancer with inevitable drug resistance. MM cells interacting with bone marrow stromal cells (BMSCs) undergo substantial changes in the transcriptome and develop de novo multi-drug resistance. As a critical component in transcriptional regulation, how the chromatin landscape is transformed in MM cells exposed to BMSCs and contributes to the transcriptional response to BMSCs remains elusive. We profiled the transcriptome and regulome for MM cells using a transwell coculture system with BMSCs. The transcriptome and regulome of MM cells from the upper transwell resembled MM cells that coexisted with BMSCs from the lower chamber but were distinctive to monoculture. BMSC-induced genes were enriched in the JAK2/STAT3 signaling pathway, unfolded protein stress, signatures of early plasma cells, and response to proteasome inhibitors. Genes with increasing accessibility at multiple regulatory sites were preferentially induced by BMSCs; these genes were enriched in functions linked to responses to drugs and unfavorable clinic outcomes. We proposed JUNB and ATF4::CEBPβ as candidate transcription factors (TFs) that modulate the BMSC-induced transformation of the regulome linked to the transcriptional response. Together, we characterized the BMSC-induced transcriptome and regulome signatures of MM cells to facilitate research on epigenetic mechanisms of BMSC-induced multi-drug resistance in MM.
6 citations
16 Dec 2020
TL;DR: Chen et al. as mentioned in this paper used the notion of gene presence and geneabsence in addition to log2 ratios of gene expression values for the prediction of the Up/Down targets of a transcription factor in a given biological context.
Abstract: Identifying target genes of a transcription factor is crucial in biomedical research. Thanks to ChIP-seq technology, scientists can estimate potential genome-wide target genes of a transcription factor. However, finding the consistently behaving Up/Down targets of a transcription factor in a given biological context is difficult because it requires analysis of a large number of studies under the same or comparable context. We present a transcription target prediction method, called Cohort-based TF target prediction system (cTAP). This method assumes that the pathway involving the transcription factor of interest is featured with multiple functional groups of marker genes pertaining to the concerned biological process. It uses the notion of gene-presence and gene-absence in addition to log2 ratios of gene expression values for the prediction. Target prediction is made by applying multiple machine-learning models that learn the patterns of genepresence and gene-absence from log2 ratio and four types of Z scores from the normalized cohort’s gene expression data. The learned patterns are then associated with the putative targets of the concerned transcription factor to elicit genes exhibiting Up/Down gene regulation patterns “consistently” within the cohort. Totally 11 publicly available GEO data sets related to osteoclastogenesis are used in our experiment. The learned models using gene-presence and gene-absence produce target genes different from using only log2 ratios such as CASP1, BID, and IRF5. Our literature survey reveals that all these predicted targets have known roles in bone remodeling, specifically related to immune and osteoclasts, suggesting confidence in our method and potential merit for a wet-lab experiment for validation.
2 citations
TL;DR: In this paper , the authors investigated a molecular mechanism driving the downregulation of the NDUFA13 gene, due to hypermethylation, which is associated with multiple cancers, and identified a hypermethylated region containing the binding sites of two transcription factors, CEBPB and E2F1-DP1, located 130 b.p from the gene transcription start site.
Abstract: Abstract Abstract Selective DNA binding by transcription factors (TFs) is crucial for the correct regulation of DNA transcription. In healthy cells, promoters of active genes are hypomethylated. A single CpG methylation within a TF response element (RE) may change the binding preferences of the protein, thus causing the dysregulation of transcription programs. Here, we investigate a molecular mechanism driving the downregulation of the NDUFA13 gene, due to hypermethylation, which is associated with multiple cancers. Using bioinformatic analyses of breast cancer cell line MCF7, we identify a hypermethylated region containing the binding sites of two TFs dimers, CEBPB and E2F1-DP1, located 130 b.p. from the gene transcription start site. All-atom extended MD simulations of wild type and methylated DNA alone and in complex with either one or both TFs dimers provide mechanistic insights into the cooperative asymmetric binding order of the two dimers; the CEBPB binding should occur first to facilitate the E2F1-DP1–DNA association. The CpG methylation within the E2F1-DP1 RE and the linker decrease the cooperativity effects and renders the E2F1-DP1 binding site less recognizable by the TF dimer. Taken together, the identified CpG methylation site may contribute to the downregulation of the NDUFA13 gene.
1 citations
References
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TL;DR: This work presents Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer, and uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions.
Abstract: We present Model-based Analysis of ChIP-Seq data, MACS, which analyzes data generated by short read sequencers such as Solexa's Genome Analyzer. MACS empirically models the shift size of ChIP-Seq tags, and uses it to improve the spatial resolution of predicted binding sites. MACS also uses a dynamic Poisson distribution to effectively capture local biases in the genome, allowing for more robust predictions. MACS compares favorably to existing ChIP-Seq peak-finding algorithms, and is freely available.
13,008 citations
"TFregulomeR reveals transcription f..." refers methods in this paper
...Briefly, relying on the Irreproducibility Discovery Rate (IDR) (24), ChIP-seq peaks were standardly called using MACS2 (25), followed by a motif de novo enrichment analysis with MEME-ChIP (11), focusing on regions defined by a ±100 bp window around peak summits....
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TL;DR: It is demonstrated in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions.
9,620 citations
TL;DR: The Genomic Regions Enrichment of Annotations Tool (GREAT) is developed to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome.
Abstract: We developed the Genomic Regions Enrichment of Annotations Tool (GREAT) to analyze the functional significance of cis-regulatory regions identified by localized measurements of DNA binding events across an entire genome. Whereas previous methods took into account only binding proximal to genes, GREAT is able to properly incorporate distal binding sites and control for false positives using a binomial test over the input genomic regions. GREAT incorporates annotations from 20 ontologies and is available as a web application. Applying GREAT to data sets from chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) of multiple transcription-associated factors, including SRF, NRSF, GABP, Stat3 and p300 in different developmental contexts, we recover many functions of these factors that are missed by existing gene-based tools, and we generate testable hypotheses. The utility of GREAT is not limited to ChIP-seq, as it could also be applied to open chromatin, localized epigenomic markers and similar functional data sets, as well as comparative genomics sets.
3,730 citations
"TFregulomeR reveals transcription f..." refers methods in this paper
...At last, objects generated by the TFregulomeR package are compatible with thirdparty packages such as TFBSTools, which provides means for TFBS matrix handling and motif scanning (30), and rGREAT, for ontology analysis (32)....
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...Functional annotation was achieved using rGREAT, a GREAT analysis API (32)....
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...TFregulomeR compendium is accessible via an effective R-package application program interface (API), called TFregulomeR....
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...This API offers the required functions for easy data access, analysis and integration in pipelines....
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TL;DR: A large-scale chromatin immunoprecipitation assay based on direct ultrahigh-throughput DNA sequencing was developed, which was then used to map in vivo binding of the neuron-restrictive silencer factor (NRSF; also known as REST) to 1946 locations in the human genome.
Abstract: In vivo protein-DNA interactions connect each transcription factor with its direct targets to form a gene network scaffold. To map these protein-DNA interactions comprehensively across entire mammalian genomes, we developed a large-scale chromatin immunoprecipitation assay (ChIPSeq) based on direct ultrahigh-throughput DNA sequencing. This sequence census method was then used to map in vivo binding of the neuron-restrictive silencer factor (NRSF; also known as REST, for repressor element–1 silencing transcription factor) to 1946 locations in the human genome. The data display sharp resolution of binding position [±50 base pairs (bp)], which facilitated our finding motifs and allowed us to identify noncanonical NRSF-binding motifs. These ChIPSeq data also have high sensitivity and specificity [ROC (receiver operator characteristic) area ≥ 0.96] and statistical confidence (P <10^(–4)), properties that were important for inferring new candidate interactions. These include key transcription factors in the gene network that regulates pancreatic islet cell development.
2,789 citations
TL;DR: This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them, highlighting the importance of continued effort to understand TF-mediated gene regulation.
1,833 citations