Transcriptionally active enhancers in human cancer cells

Lidschreiber, Katja; Jung, Lisa Anna; Emde, Henrik von der; Dave, Kashyap; Taipale, Jussi; Cramer, Patrick; Lidschreiber, Michael

doi:10.15252/msb.20209873

Cited by 33 publications

(41 citation statements)

References 159 publications

(281 reference statements)

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“…The ATI assay in GP5d cells and processing of the data were done as previously described 17 . Transcribed enhancer regions defined using TT-seq data are based on Lidschreiber et al 55 . The experiments were performed from GP5d cells in two biological replicates as previously described 56 .…”

Section: Methodsmentioning

confidence: 99%

Sequence determinants of human gene regulatory elements

et al. 2022

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DNA can determine where and when genes are expressed, but the full set of sequence determinants that control gene expression is unknown. Here, we measured the transcriptional activity of DNA sequences that represent an ~100 times larger sequence space than the human genome using massively parallel reporter assays (MPRAs). Machine learning models revealed that transcription factors (TFs) generally act in an additive manner with weak grammar and that most enhancers increase expression from a promoter by a mechanism that does not appear to involve specific TF–TF interactions. The enhancers themselves can be classified into three types: classical, closed chromatin and chromatin dependent. We also show that few TFs are strongly active in a cell, with most activities being similar between cell types. Individual TFs can have multiple gene regulatory activities, including chromatin opening and enhancing, promoting and determining transcription start site (TSS) activity, consistent with the view that the TF binding motif is the key atomic unit of gene expression.

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Section: Methodsmentioning

confidence: 99%

Sequence determinants of human gene regulatory elements

et al. 2022

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“…Most cancer enhancers show cell-and/or stage selectivity in their activation patterns [125][126][127], therefore their associated genetic variability is ideal for assessing personalized predisposition or therapy. Since enhancers (and super-enhancers) function through DNA binding motifs, their activity is vulnerable to variation that modulates the binding capacity of transcription factor proteins, thus altering transcription of the target gene [128].…”

Section: Genetic Variability In Enhancersmentioning

confidence: 99%

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

Lange

Begolli

Giakountis

2021

ncRNA

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The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

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“…Recent progress of quantitative RNA labeling techniques enable the absolute measurements of the rates of RNA synthesis and degradation (Rabani et al , 2011; Schwanhäusser et al , 2011; Schwalb et al , 2016; Herzog et al , 2017; Muhar et al , 2018; Schofield et al , 2018). TT‐seq combines a short 4‐thiouridine (4sU) RNA labeling pulse with an RNA fragmentation step to capture newly synthesized RNA, and thereby detects also short‐lived transcripts and allows estimation of the kinetics of RNA metabolism (Schwalb et al , 2016; Michel et al , 2017; Choi et al , 2021; Lidschreiber et al , 2021). To study transcriptional regulation in SL‐2i and SL–mTORi transitions, we measured total RNA and newly synthesized RNA with TT‐seq, annotated transcription units (TUs) de novo , and examined how transcription of different TU classes responds to state transitions.…”

Section: Introductionmentioning

confidence: 99%

Distinct transcription kinetics of pluripotent cell states

Shao

Kumar

Lidschreiber

et al. 2022

Molecular Systems Biology

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Mouse embryonic stem cells (mESCs) can adopt naïve, ground, and paused pluripotent states that give rise to unique transcriptomes. Here, we use transient transcriptome sequencing (TT‐seq) to define both coding and non‐coding transcription units (TUs) in these three pluripotent states and combine TT‐seq with RNA polymerase II occupancy profiling to unravel the kinetics of RNA metabolism genome‐wide. Compared to the naïve state (serum), RNA synthesis and turnover rates are globally reduced in the ground state (2i) and the paused state (mTORi). The global reduction in RNA synthesis goes along with a genome‐wide decrease of polymerase elongation velocity, which is related to epigenomic features and alterations in the Pol II termination window. Our data suggest that transcription activity is the main determinant of steady state mRNA levels in the naïve state and that genome‐wide changes in transcription kinetics invoke ground and paused pluripotent states.

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Transcriptionally active enhancers in human cancer cells

Cited by 33 publications

References 159 publications

Sequence determinants of human gene regulatory elements

Sequence determinants of human gene regulatory elements

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

Distinct transcription kinetics of pluripotent cell states

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