Enhancer RNA profiling predicts transcription factor activity

Azofeifa, Joseph; Allen, Mary A.; Hendrix, Josephina R.; Read, Timothy D.; Rubin, Jonathan; Dowell, Robin

doi:10.1101/gr.225755.117

Cited by 105 publications

(163 citation statements)

References 52 publications

Supporting

Mentioning

154

Contrasting

Unclassified

Order By: Relevance

“…Further, p53 depletion does not alter basal CRE-associated chromatin modifications or chromatin structure at the large majority of CRE (48,54). Enhancer-derived RNA (eRNA) have been identified as a strong predictor of transcription factor binding and CRE activity (67). In further support of a multi-factor model, eRNA are transcribed from p53-regulated CREs in the absence of p53 (2,48).…”

Section: The Requirement For Other Transcription Factors Co-regulatinmentioning

confidence: 94%

Locally acting transcription factors are required for p53-dependent cis-regulatory element activity

Catizone

Uzunbas

Celadova

et al. 2019

Preprint

View full text Add to dashboard Cite

The master tumor suppressor p53 controls transcription of a wide-ranging gene network involved in apoptosis, cell cycle arrest, DNA damage repair, and senescence. Recent studies revealed pervasive binding of p53 to cis-regulatory elements (CRE), which are non-coding segments of DNA that spatially and temporally control transcription through the combinatorial binding of local transcription factors (TFs). Although the role of p53 as a strong trans-activator of gene expression is well known, the coregulatory factors and local sequences acting at p53-bound CREs are comparatively understudied. We designed and executed a massively parallel reporter assay (MPRA) to investigate the effect of transcription factor binding motifs and local sequence context on p53-bound CRE activity. Our data indicate that p53-bound CREs are both positively and negatively affected by alterations in local sequence context and changes to co-regulatory TF motifs. We identified a SP1/KLF family motif located in an intronic p53 CRE that is required for the endogenous expression of the p53-dependent gene CCNG1. We also identified ATF3 as a factor that co-regulates the expression of the p53-dependent gene GDF15 through binding with p53 in an upstream CRE. Loss of either p53 or ATF3 severely reduces CRE activity and alters endogenous GDF15 mRNA levels in the cell. Our data suggests that p53 has the flexibility to cooperate with a variety of transcription factors in order to regulate CRE activity. By utilizing different sets of co-factors across CREs, we hypothesize that p53 activity is guarded against loss of any one regulatory partner allowing for dynamic and redundant control of p53mediated transcription.Recent evidence suggests that sequence variation within cis-regulatory elements (CREs) can influence p53 binding, transcriptional activity, and tumor suppressor function (5-7) . The critical nature of the core p53 response element (p53RE) on p53 binding and CRE activity is well understood (8-10), but the influence of local sequence variation and the role of transcription factor motifs outside of the p53RE on p53 activity remains an open and vital question.Cis-regulatory elements, such as promoters and enhancers, govern gene expression through temporal, spatial, and quantitative control of transcription (11,12). While multiple models for CRE function have been proposed, the majority involve cooperative binding and activity of multiple transcription factors and cofactors acting locally to fine-tune gene expression (11)(12)(13). The presence and availability of transcription factors, repressors, and other cofactors vary across cell states such as development, stress, disease, and cell type (14-17). This variability provides a mechanism for differential CRE activity and downstream gene expression. Loss of transcription factor binding within a CRE, through variation in DNA sequence or through changes in trans-factor availability, can strongly influence CRE activity and gene expression (11,12,18), with direct implications in numerous developmental and d...

show abstract

Section: The Requirement For Other Transcription Factors Co-regulatinmentioning

confidence: 94%

Locally acting transcription factors are required for p53-dependent cis-regulatory element activity

Catizone

Uzunbas

Celadova

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Importantly, this process can both give rise to new transcripts and alter the levels of existing transcripts. Nascent transcription assays show that when a TF is activated, transcripts arise immediately proximal to the corresponding TF motif [1,6]. In this work we introduce TFEA, which quantifies positional enrichment of TF motifs across an ordered list of regions ( Figure 1).…”

Section: Overviewmentioning

confidence: 99%

“…Likewise, some histone marks have been associated with actively transcribed regions, such as H3K27ac and H3K4me1/3 [11]. In principle, differential signals from these assays inform on the underlying mechanistic activity of TFs [6].…”

Section: Introductionmentioning

confidence: 99%

“…Historically, detecting motif enrichment in this way relied on sequences being classified into either signal or background and then calculating motif enrichment in signal sequences relative to background [11,14]. More sophisticated approaches can take advantage of two additional factors: 1) positional information -where the motif is located relative to a region of interest [8,6] and 2) differential information -the amount of change occurring within that region of interest [45,12]. Relatively few techniques encode both types of information [39,52,24] and these currently provide no easily accessible software package or web-based application.…”

Section: Introductionmentioning

confidence: 99%

“…Our method, which we refer to as transcription factor enrichment analysis (TFEA) draws inspiration from the popular gene set enrichment analysis (GSEA) algorithm [56]. TFEA improves on our previous position based approach [6] and shows performance comparable to the state of the art motif enrichment approaches. Additionally, TFEA can be applied to a number of regulatory data types including PRO-seq, CAGE, DNAse-seq and ChIP-seq.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment

Rubin

Stanley

Sigauke

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Detecting differential activation of transcription factors (TFs) in response to perturbation provides insight into cellular processes. Transcription Factor Enrichment Analysis (TFEA) is a robust and reliable computational method that detects differential activity of hundreds of TFs given any set of perturbation data. TFEA draws inspiration from GSEA and detects positional motif enrichment within a list of ranked regions of interest (ROIs). As ROIs are typically inferred from the data, we also introduce muMerge, a statistically principled method of generating a consensus list of ROIs from multiple replicates and conditions. TFEA is broadly applicable to data that informs on transcriptional regulation including nascent (eg. PRO-Seq), CAGE, ChIP-Seq, and accessibility (e.g. ATAC-Seq). TFEA not only identifies the key regulators responding to a perturbation, but also temporally unravels regulatory networks with time series data. Consequently, TFEA serves as a hypothesis-generating tool that provides an easy, rigorous, and cost-effective means to broadly assess TF activity yielding new biological insights.

show abstract

Multi‐omics analysis reveals the functional transcription and potential translation of enhancers

Yang

et al. 2020

Intl Journal of Cancer

View full text Add to dashboard Cite

Enhancer can transcribe RNAs, however, most of them were neglected in traditional RNA‐seq analysis workflow. Here, we developed a Pipeline for Enhancer Transcription (PET, http://fun-science.club/PET) for quantifying enhancer RNAs (eRNAs) from RNA‐seq. By applying this pipeline on lung cancer samples and cell lines, we showed that the transcribed enhancers are enriched with histone marks and transcription factor motifs (JUNB, Hand1‐Tcf3 and GATA4). By training a machine learning model, we demonstrate that enhancers can predict prognosis better than their nearby genes. Integrating the Hi‐C, ChIP‐seq and RNA‐seq data, we observe that transcribed enhancers associate with cancer hallmarks or oncogenes, among which LcsMYC‐1 (Lung cancer‐specific MYC eRNA‐1) potentially supports MYC expression. Surprisingly, a significant proportion of transcribed enhancers contain small protein‐coding open reading frames (sORFs) and can be translated into microproteins. Our study provides a computational method for eRNA quantification and deepens our understandings of the DNA, RNA and protein nature of enhancers.

show abstract

Enhancer RNA profiling predicts transcription factor activity

Cited by 105 publications

References 52 publications

Locally acting transcription factors are required for p53-dependent cis-regulatory element activity

Locally acting transcription factors are required for p53-dependent cis-regulatory element activity

Transcription factor enrichment analysis (TFEA): Quantifying the activity of hundreds of transcription factors from a single experiment

Multi‐omics analysis reveals the functional transcription and potential translation of enhancers

Contact Info

Product

Resources

About