Regulatory Enhancer–Core-Promoter Communication via Transcription Factors and Cofactors

Zabidi, Muhammad A.; Stark, Alexander

doi:10.1016/j.tig.2016.10.003

Cited by 184 publications

(149 citation statements)

References 163 publications

(164 reference statements)

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“…Many studies suggest that there are at least two broad classes of genes: housekeeping and highly-regulated genes (de Jonge et al, 2016; Eisenberg and Levanon, 2013; Huisinga and Pugh, 2004; Mencía et al, 2002; Ohtsuki et al, 1998; Zabidi and Stark, 2016). These two gene classes differ in their response to transcription activators, chromatin organization and modifications, and in promoter sequence elements.…”

Section: Introductionmentioning

confidence: 99%

Transcription of Nearly All Yeast RNA Polymerase II-Transcribed Genes Is Dependent on Transcription Factor TFIID

et al. 2017

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SUMMARY Previous studies suggested that expression of most yeast mRNAs is dominated by either transcription factor TFIID or SAGA. We reexamined the role of TFIID by rapid depletion of S. cerevisiae TFIID subunits and measurement of changes in nascent transcription. We find that transcription of nearly all mRNAs is strongly dependent on TFIID function. Degron-dependent depletion of Tafs 1,2,7,11, and 13 showed similar transcription decreases for genes in the Taf1-depleted, Taf1-enriched, TATA-containing and TATA-less gene classes. The magnitude of TFIID-dependence varies with growth conditions, although this variation is similar genome-wide. Many studies have suggested differences in gene regulatory mechanisms between TATA and TATA-less genes and these differences have been attributed in part to differential dependence on SAGA or TFIID. Our work indicates that TFIID participates in expression of nearly all yeast mRNAs and that differences in regulation between these two gene categories is due to other properties.

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Section: Introductionmentioning

confidence: 99%

Transcription of Nearly All Yeast RNA Polymerase II-Transcribed Genes Is Dependent on Transcription Factor TFIID

et al. 2017

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“…A prototypical TF possesses two different functionalities: (i) the recognition and sequence‐specific binding to short DNA sequence motifs; and (ii) the trans‐activation of transcription, typically via the recruitment of COFs (Chrivia et al , ; Conaway & Conaway, ; Zabidi & Stark, ) or the direct interaction with the PIC (Choy & Green, ). These two functions are distinct and typically mediated by two different protein domains (Fig A): a DNA‐binding domain (DBD) and a trans‐activation domain [tAD, often also TAD , which we avoid given the frequent use of TAD for topologically associating domains , meaning self‐associating chromosomal neighborhoods (Dixon et al , ; Nora et al , )].…”

Section: Introductionmentioning

confidence: 99%

A high‐throughput method to identify trans‐activation domains within transcription factor sequences

et al. 2018

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Even though transcription factors (TFs) are central players of gene regulation and have been extensively studied, their regulatory trans‐activation domains (tADs) often remain unknown and a systematic functional characterization of tADs is lacking. Here, we present a novel high‐throughput approach tAD‐seq to functionally test thousands of candidate tADs from different TFs in parallel. The tADs we identify by pooled screening validate in individual luciferase assays, whereas neutral regions do not. Interestingly, the tADs are found at arbitrary positions within the TF sequences and can contain amino acid (e.g., glutamine) repeat regions or overlap structured domains, including helix–loop–helix domains that are typically annotated as DNA‐binding. We also identified tADs in the non‐native reading frames, confirming that random sequences can function as tADs, albeit weakly. The identification of tADs as short protein sequences sufficient for transcription activation will enable the systematic study of TF function, which—particularly for TFs of different transcription activating functionalities—is still poorly understood.

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“…Due to the binding of regulatory proteins, enhancers exhibit DNase I hypersensitivity (DNase I HS) signal quantified by DNase I HS sequencing (DNase-seq) or Formaldehyde-Assisted Isolation of Regulatory Elements and sequencing (FAIRE-seq) [17,18]. These data sets have been adopted in several studies to locate enhancers [19,20,21,22,23,24,5,25,26]. The features measured by different techniques generate a set of signals along the genome.…”

Section: Introductionmentioning

confidence: 99%

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

Osmala

Lähdesmäki

2019

Preprint

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Background: The human genome is far from completely annotated. Specifically, the locations ofgenedistal regulatory enhancers are difficult to locate. Enhancers are binding sites of transcription factors and occupied by nucleosomes with modified histones. The binding sites of transcription factors (TFs) and the localization of histone modifications can be quantified by the chromatin immunoprecipitation assay coupled with next generation sequencing (ChIP-seq). The resulting data has been successfully adopted for genomewide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type, and they do not utilize the pattern of the ChIP-seq coverage profiles efficiently. It is also difficult to estimate the accuracy and specificity of the genome-wide enhancer predictions. Results:We developed PREPRINT, a PRobabilistic Enhancer PRedictIoN Tool. We considered the pattern of, for example, the ChIP-seq coverage profile around the enhancers. The data at the positive and negative examples of enhancers was utilized to probabilistically model the enhancer coverage pattern and to train a kernel-based classifier. We demonstrated the performance of the method using ENCODE data from two cell lines. The predicted enhancers were computationally validated based on the TFs and co-regulatory factor binding sites. We compared our enhancer predictions to the ones obtained by other methods. The effects of different parameter choices during training, testing and validation were studied, and finally, the approach to validate the genome-wide predictions was investigated. Conclusion: PREPRINT performed comparably to the state-of-the-art methods and provided probabilistic interpretation (i.e. uncertainty) for the predictions. PREPRINT generalized to data from cell type not utilized for training, and often the performance of PREPRINT was superior to RFECS. We observed that the choice of training data and the choice of parameter values at different steps of the enhancer prediction and validation influenced on the final set of predictions. PREPRINT identifed biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.

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Regulatory Enhancer–Core-Promoter Communication via Transcription Factors and Cofactors

Cited by 184 publications

References 163 publications

Transcription of Nearly All Yeast RNA Polymerase II-Transcribed Genes Is Dependent on Transcription Factor TFIID

Transcription of Nearly All Yeast RNA Polymerase II-Transcribed Genes Is Dependent on Transcription Factor TFIID

A high‐throughput method to identify trans‐activation domains within transcription factor sequences

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

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