Widespread reorganisation of pluripotent factor binding and gene regulatory interactions between human pluripotent states.
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ABSTRACT:
SUBMITTER: Chovanec P
PROVIDER: S-EPMC8026613 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The transition from naive to primed pluripotency is accompanied by an extensive reorganisation of transcriptional and epigenetic programmes. However, the role of transcriptional enhancers and three-dimensional chromatin organisation in coordinating these developmental programmes remains incompletely understood. Here, we generated a high-resolution atlas of gene regulatory interactions, chromatin profiles and transcription factor occupancy in naive and primed human pluripotent stem cells, and developed a network-graph approach to examine the atlas at multiple spatial scales. We uncovered highly connected promoter hubs that changed substantially in interaction frequency and in transcriptional co-regulation between pluripotent states. Small hubs frequently merged to form larger networks in primed cells, often linked by newly-formed Polycomb-associated interactions. We identified widespread state-specific differences in enhancer activity and interactivity that corresponded with an extensive reconfiguration of OCT4, SOX2 and NANOG binding and target gene expression. These findings provide new, multilayered insights into the chromatin-based gene regulatory control of human pluripotent states.
2021-01-21 | GSE133126 | GEO
Project description:Widespread reorganisation of pluripotent factor binding and gene regulatory interactions between human pluripotent states
| PRJNA550039 | ENA
Project description:Recent experimental and theoretical efforts have highlighted the fact that binding of transcription factors to DNA can be more accurately described by continuous measures of their binding affinities, rather than a discrete description in terms of binding sites. While the binding affinities can be predicted from a physical model, it is often desirable to know the distribution of binding affinities for specific sequence backgrounds. In this paper, we present a statistical approach to derive the exact distribution for sequence models with fixed GC content. We demonstrate that the affinity distribution of almost all known transcription factors can be effectively parametrized by a class of generalized extreme value distributions. Moreover, this parameterization also describes the affinity distribution for sequence backgrounds with variable GC content, such as human promoter sequences. Our approach is applicable to arbitrary sequences and all transcription factors with known binding preferences that can be described in terms of a motif matrix. The statistical treatment also provides a proper framework to directly compare transcription factors with very different affinity distributions. This is illustrated by our analysis of human promoters with known binding sites, for many of which we could identify the known regulators as those with the highest affinity. The combination of physical model and statistical normalization provides a quantitative measure which ranks transcription factors for a given sequence, and which can be compared directly with large-scale binding data. Its successful application to human promoter sequences serves as an encouraging example of how the method can be applied to other sequences.
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Project description:In developing bilaterans, the Hox transcription factor family regulates batteries of downstream genes to diversify serially repeated units. Given Hox homeodomains bind a wider array of DNA binding sites in vitro than are regulated by the full-length protein in vivo, regions outside the homeodomain must aid DNA site selection. Indeed, we find affinity for disparate DNA sequences varies less than 3-fold for the homeodomain isolated from the Drosophila Hox protein Ultrabithorax Ia (UbxHD), whereas for the full-length protein (UbxIa) affinity differs by more than 10-fold. The rank order of preferred DNA sequences also differs, further demonstrating distinct DNA binding preferences. The increased specificity of UbxIa can be partially attributed to the I1 region, which lies adjacent to the homeodomain and directly impacts binding energetics. Each of three segments within I1-the Extradenticle-binding YPWM motif, the six amino acids immediately N-terminal to this motif, and the eight amino acids abutting the YPWM C-terminus-uniquely contribute to DNA specificity. Combination of these regions synergistically modifies DNA binding to further enhance specificity. Intriguingly, the presence of the YPWM motif in UbxIa inhibits DNA binding only to Ubx-Extradenticle heterodimer binding sites, potentially functioning in vivo to prevent Ubx monomers from binding and misregulating heterodimer target genes. However, removal of the surrounding region allows the YPWM motif to also inhibit binding to Hox-only recognition sequences. Despite a modular domain design for Hox proteins, these results suggest that multiple Hox protein regions form a network of regulatory interactions that coordinate context- and gene-specific responses. Since most nonhomeodomain regions are not conserved between Hox family members, these regulatory interactions have the potential to diversify binding by the highly homologous Hox homeodomains.
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Project description:MotivationIdentifying the target genes regulated by transcription factors (TFs) is the most basic step in understanding gene regulation. Recent advances in high-throughput sequencing technology, together with chromatin immunoprecipitation (ChIP), enable mapping TF binding sites genome wide, but it is not possible to infer function from binding alone. This is especially true in mammalian systems, where regulation often occurs through long-range enhancers in gene-rich neighborhoods, rather than proximal promoters, preventing straightforward assignment of a binding site to a target gene.ResultsWe present EMBER (Expectation Maximization of Binding and Expression pRofiles), a method that integrates high-throughput binding data (e.g. ChIP-chip or ChIP-seq) with gene expression data (e.g. DNA microarray) via an unsupervised machine learning algorithm for inferring the gene targets of sets of TF binding sites. Genes selected are those that match overrepresented expression patterns, which can be used to provide information about multiple TF regulatory modes. We apply the method to genome-wide human breast cancer data and demonstrate that EMBER confirms a role for the TFs estrogen receptor alpha, retinoic acid receptors alpha and gamma in breast cancer development, whereas the conventional approach of assigning regulatory targets based on proximity does not. Additionally, we compare several predicted target genes from EMBER to interactions inferred previously, examine combinatorial effects of TFs on gene regulation and illustrate the ability of EMBER to discover multiple modes of regulation.AvailabilityAll code used for this work is available at http://dinner-group.uchicago.edu/downloads.html.
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Project description:Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted. Few cancer drivers in non-coding regions have been identified so far. Here, the authors develop a transcription factor-aware burden test to predict non-coding variants and analyze the impact on transcription factor binding - especially ETS factors - as well as their impact on transcriptional activity.
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Project description:Deciphering gene regulatory networks (GRNs) is both a promise and challenge of systems biology. The promise lies in identifying key transcription factors (TFs) that enable an organism to react to changes in its environment. The challenge lies in validating GRNs that involve hundreds of TFs with hundreds of thousands of interactions with their genome-wide targets experimentally determined by high-throughput sequencing. To address this challenge, we developed ConnecTF, a species-independent, web-based platform that integrates genome-wide studies of TF-target binding, TF-target regulation, and other TF-centric omic datasets and uses these to build and refine validated or inferred GRNs. We demonstrate the functionality of ConnecTF by showing how integration within and across TF-target datasets uncovers biological insights. Case study 1 uses integration of TF-target gene regulation and binding datasets to uncover TF mode-of-action and identify potential TF partners for 14 TFs in abscisic acid signaling. Case study 2 demonstrates how genome-wide TF-target data and automated functions in ConnecTF are used in precision/recall analysis and pruning of an inferred GRN for nitrogen signaling. Case study 3 uses ConnecTF to chart a network path from NLP7, a master TF in nitrogen signaling, to direct secondary TF2s and to its indirect targets in a Network Walking approach. The public version of ConnecTF (https://ConnecTF.org) contains 3,738,278 TF-target interactions for 423 TFs in Arabidopsis, 839,210 TF-target interactions for 139 TFs in maize (Zea mays), and 293,094 TF-target interactions for 26 TFs in rice (Oryza sativa). The database and tools in ConnecTF will advance the exploration of GRNs in plant systems biology applications for model and crop species.
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Project description:Myelin-gene Regulatory Factor (MyRF) is one of the master transcription factors controlling myelin formation and development in oligodendrocytes which is crucial for the powerful brain functions. The N-terminal of MyRF, which contains a proline-rich region and a DNA binding domain (DBD), is auto-cleaved from the ER membrane, and then enters the nucleus to participate in transcription regulation of the myelin genes. Here we report the crystal structure of MyRF DBD. It shows an Ig-fold like architecture which consists of two antiparallel ?-sheets with 7 main strands, packing against each other, forming a ?-sandwich. Compared to its homolog, Ndt80, MyRF has a smaller and less complex DBD lacking the helices and the big loops outside the core. Structural alignment reveals that MyRF DBD possess less interaction sites with DNA than Ndt80 and may bind only at the major groove of DNA. Moreover, the structure reveals a trimeric assembly, agreeing with the previous report that MyRF DBD functions as a trimer. The mutant that we designed based on the structure disturbed trimer formation, but didn't affect the auto-cleavage reaction. It demonstrates that the activation of self-cleavage reaction of MyRF is independent of the presence of its N-terminal DBD homotrimer. The structure reported here will help to understand the molecular mechanism underlying the important roles of MyRF in myelin formation and development.
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