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Ntini, E.* ; Budach, S.* ; Vang Ørom, U.A.* ; Marsico, A.

Genome-wide measurement of RNA dissociation from chromatin classifies transcripts by their dynamics and reveals rapid dissociation of enhancer lncRNAs.

Cell Syst. 14, 906-922.e6 (2023)
Postprint DOI PMC
Open Access Green
Long non-coding RNAs (lncRNAs) are involved in gene expression regulation in cis. Although enriched in the cell chromatin fraction, to what degree this defines their regulatory potential remains unclear. Furthermore, the factors underlying lncRNA chromatin tethering, as well as the molecular basis of efficient lncRNA chromatin dissociation and its impact on enhancer activity and target gene expression, remain to be resolved. Here, we developed chrTT-seq, which combines the pulse-chase metabolic labeling of nascent RNA with chromatin fractionation and transient transcriptome sequencing to follow nascent RNA transcripts from their transcription on chromatin to release and allows the quantification of dissociation dynamics. By incorporating genomic, transcriptomic, and epigenetic metrics, as well as RNA-binding protein propensities, in machine learning models, we identify features that define transcript groups of different chromatin dissociation dynamics. Notably, lncRNAs transcribed from enhancers display reduced chromatin retention, suggesting that, in addition to splicing, their chromatin dissociation may shape enhancer activity.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Rna Processing ; Rna-binding Protein Interactions ; Chromatin Dissociation Dynamics ; Co-transcriptional Splicing ; Enhancer ; Enhancer-associated Lncrnas ; Lncrnas ; Machine Learning ; Nascent Rna Transcription ; Predictive Models; Long Noncoding Rnas; Messenger-rna; Polymerase-ii; Splicing Kinetics; Gene-expression; Termination; Polyadenylation; Localization; Binding; Principles
Sprache englisch
Veröffentlichungsjahr 2023
HGF-Berichtsjahr 2023
ISSN (print) / ISBN 2405-4712
e-ISSN 2405-4720
Zeitschrift Cell Systems
Quellenangaben Band: 14, Heft: 10, Seiten: 906-922.e6 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Maryland Heights, MO
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Fondation Sante Research Grant
DFG
DOI 10.1016/j.cels.2023.09.005
WOS ID 001096473000001
Scopus ID 85173908042
PubMed ID 37857083
Erfassungsdatum 2023-11-28
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