Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle

• Published in: Molecular cell Vol. 84; no. 14; p. 2765
• Main Authors: Ietswaart, Robert, Smalec, Brendan M, Xu, Albert, Choquet, Karine, McShane, Erik, Jowhar, Ziad Mohamoud, Guegler, Chantal K, Baxter-Koenigs, Autum R, West, Emma R, Fu, Becky Xu Hua, Gilbert, Luke, Floor, Stephen N, Churchman, L Stirling
• Format: Journal Article
• Language: English
• Published: United States 25-07-2024
• Subjects: Active Transport, Cell Nucleus; Animals; Cell Nucleus - genetics; Cell Nucleus - metabolism; Chromatin - genetics; Chromatin - metabolism; Cytoplasm - genetics; Cytoplasm - metabolism; DEAD-box RNA Helicases - genetics; DEAD-box RNA Helicases - metabolism; Exosomes - genetics; Exosomes - metabolism; Humans; Machine Learning; Mice; Polyribosomes - genetics; Polyribosomes - metabolism; RNA Stability; RNA, Messenger - genetics; RNA, Messenger - metabolism; RNA-Binding Proteins - genetics; RNA-Binding Proteins - metabolism; RNA dynamics; DDX3X; poly(A) tails; nuclear export; subcellular TimeLapse-seq; translation; RNA-binding proteins; RNA flow; nuclear RNA degradation; kinetic modeling
• ISSN: 1097-4164
• DOI: 10.1016/j.molcel.2024.06.008

Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.