Sufficiency analysis of estrogen responsive enhancers using synthetic activators
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA551016 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR9601240.fastq.gz | Fastqsanger.gz | |||
| SRR9601250.fastq.gz | Fastqsanger.gz | |||
| SRR9601260.fastq.gz | Fastqsanger.gz | |||
| SRR9601270.fastq.gz | Fastqsanger.gz | |||
| SRR9601231.fastq.gz | Fastqsanger.gz |
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Project description:Multiple regulatory regions bound by the same transcription factor have been shown to simultaneously control a single gene’s expression. However, it remains unclear how these regulatory regions combine to regulate transcription. Here we test the sufficiency of promoter-distal estrogen receptor α (ER)-binding sites (ERBS) for activating gene expression by recruiting synthetic activators in the absence of estrogens. Targeting either dCas9-VP16(10x) or dCas9-p300(core) to ERBS induces H3K27ac and activates nearby expression in a manner similar to an estrogen induction, with dCas9-VP16(10x) acting as a stronger activator. The sufficiency of individual ERBS is highly correlated with their necessity, indicating an inherent activation potential. By targeting ERBS combinations, we found that ERBS work independently to control gene expression. The sufficiency results contrast necessity assays that show synergy between these ERBS, suggesting that synergy occurs between ERBS in terms of activator recruitment, whereas directly recruiting activators leads to independent effects on gene expression.
2019-06-26 | GSE133300 | GEO
Project description:Multiple regulatory regions bound by the same transcription factor have been shown to simultaneously control a single gene's expression. However, it remains unclear how these regulatory regions combine to regulate transcription. Here, we test the sufficiency of promoter-distal estrogen receptor ?-binding sites (ERBSs) for activating gene expression by recruiting synthetic activators in the absence of estrogens. Targeting either dCas9-VP16(10x) or dCas9-p300(core) to ERBS induces H3K27ac and activates nearby expression in a manner similar to an estrogen induction, with dCas9-VP16(10x) acting as a stronger activator. The sufficiency of individual ERBSs is highly correlated with their necessity, indicating an inherent activation potential that is associated with the binding of RNA polymerase II and several transcription factors. By targeting ERBS combinations, we found that ERBSs work independently to control gene expression when bound by synthetic activators. The sufficiency results contrast necessity assays that show synergy between these ERBSs, suggesting that synergy occurs between ERBSs in terms of activator recruitment, whereas directly recruiting activators leads to independent effects on gene expression.
| S-EPMC6769255 | biostudies-literature
Project description:We used gene expression profiling to investigate whether the molecular effects induced by estrogens of different provenance are intrinsically similar. In this article we show that the physiologic estrogen 17-beta-estradiol, the phytoestrogen genistein, and the synthetic estrogen diethylstilbestrol alter the expression of the same 179 genes in the intact immature mouse uterus under conditions where each chemical has produced an equivalent gravimetric and histologic uterotrophic effect, using the standard 3-day assay protocol. Data are also presented indicating the limitations associated with comparison of gene expression profiles for different chemicals at times before the uterotrophic effects are fully realized. We conclude that the case has yet to be made for regarding synthetic estrogens as presenting a unique human hazard compared with phytoestrogens and physiologic estrogens. Key words: diethylstilbestrol, estrogen, gene expression, genistein, microarray, phytoestrogen, toxicogenomics, uterus.
2022-11-20 | E-AFMX-13 | biostudies-arrayexpress
Project description:The human genome encodes an order of magnitude more gene expression enhancers than promoters, suggesting that most genes are regulated by the combined action of multiple enhancers. We have previously shown that neighboring estrogen-responsive enhancers, which are approximately 5,000 basepairs apart, exhibit complex synergistic contributions to the production of an estrogenic transcriptional response. Here we sought to determine the molecular underpinnings of the observed enhancer cooperativity. We generated genetic deletions of individual estrogen receptor (ER) bound enhancers and found that enhancers containing full estrogen response element (ERE) motifs control ER binding at neighboring sites, while enhancers with pre-existing histone acetylation/accessibility confer a permissible chromatin environment to the neighboring enhancers. Genome engineering revealed that a cluster of two enhancers with half EREs could not compensate for the lack of a full ERE site within the cluster. In contrast, two enhancers with full EREs produced a transcriptional response greater than the wild-type locus. By swapping genomic sequences between enhancers, we found that the genomic location in which a full ERE resides strongly influences enhancer activity. Our results lead to a model in which a full ERE is required for ER recruitment, but the presence of pre-existing histone acetylation within an enhancer cluster is also needed in order for estrogen-driven gene regulation to occur.
2020-03-18 | GSE147141 | GEO
Project description:The human genome encodes an order of magnitude more gene expression enhancers than promoters, suggesting that most genes are regulated by the combined action of multiple enhancers. We have previously shown that neighboring estrogen-responsive enhancers, which are approximately 5,000 basepairs apart, exhibit complex synergistic contributions to the production of an estrogenic transcriptional response. Here we sought to determine the molecular underpinnings of the observed enhancer cooperativity. We generated genetic deletions of individual estrogen receptor (ER) bound enhancers and found that enhancers containing full estrogen response element (ERE) motifs control ER binding at neighboring sites, while enhancers with pre-existing histone acetylation/accessibility confer a permissible chromatin environment to the neighboring enhancers. Genome engineering revealed that a cluster of two enhancers with half EREs could not compensate for the lack of a full ERE site within the cluster. In contrast, two enhancers with full EREs produced a transcriptional response greater than the wild-type locus. By swapping genomic sequences between enhancers, we found that the genomic location in which a full ERE resides strongly influences enhancer activity. Our results lead to a model in which a full ERE is required for ER recruitment, but the presence of pre-existing histone acetylation within an enhancer cluster is also needed in order for estrogen-driven gene regulation to occur.
2020-03-18 | GSE147140 | GEO
Project description:Regulatory Sharing Between Estrogen Receptor α Bound Enhancers
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Project description:Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. A single enhancer, of a few hundred base pairs in length, can autonomously and independently of its location and orientation drive cell-type specific expression of a gene or transgene. It has been a long-standing goal in the field to decode the regulatory logic of an enhancer and to understand the details of how spatiotemporal gene expression is encoded in an enhancer sequence. Recently, deep learning models have yielded unprecedented insight into the enhancer code, and well-trained models are reaching a level of understanding that may be close to complete. As a consequence, we hypothesized that deep learning models can be used to guide the directed design of synthetic, cell type specific enhancers, and that this process would allow for a detailed tracing of all enhancer features at nucleotide-level resolution. Here we implemented and compared three different design strategies, each built on a deep learning model: (1) directed sequence evolution; (2) directed iterative motif implanting; and (3) generative design. We evaluated the function of fully synthetic enhancers to specifically target Kenyon cells or glial cells in the fruit fly brain using transgenic animals. We then exploited this concept further by creating “dual-code” enhancers that target two cell types, and minimal enhancers smaller than 50 base pairs that are fully functional. By examining the trajectories followed during state space searches towards functional enhancers, we could accurately define the enhancer code as the optimal strength, combination, and relative distance of TF activator motifs, and the absence of TF repressor motifs. Finally, we applied the same three strategies to successfully design human enhancers, finding highly similar design principles as in Drosophila. In conclusion, enhancer design guided by deep learning leads to better understanding of how enhancers work and shows that their code can be exploited to manipulate cell states.
2023-11-19 | GSE240003 | GEO
Project description:Functional testing of synthetic liver enhancers
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Project description:Regulatory Sharing Between Estrogen Receptor α Bound Enhancers [ChIP-seq]
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Project description:Regulatory Sharing Between Estrogen Receptor α Bound Enhancers [ATAC-seq]
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