Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells
Open accessAbstract
Cellular RNA levels are determined by the interplay of RNA production, processing and degradation. However, because most studies of RNA regulation do not distinguish the separate contributions of these processes, little is known about how they are temporally integrated. Here we combine metabolic labeling of RNA at high temporal resolution with advanced RNA quantification and computational modeling to estimate RNA transcription and degradation rates during the response of mouse dendritic cells to lipopolysaccharide. We find that changes in transcription rates determine the majority of temporal changes in RNA levels, but that changes in degradation rates are important for shaping sharp 'peaked' responses. We used sequencing of the newly transcribed RNA population to estimate temporally constant RNA processing and degradation rates genome wide. Degradation rates vary significantly between genes and contribute to the observed differences in the dynamic response. Certain transcripts, including those encoding cytokines and transcription factors, mature faster. Our study provides a quantitative approach to study the integrative process of RNA regulation.
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Mouse bone marrow-derived dendritic cells are mostly post-mitotic, and LPS synchronizes them and triggers broad regulatory changes.
“DCs are mostly post-mitotic, and LPS synchronizes them (27) and causes dramatic regulatory changes from the expression of thousands of transcripts (22, 24, 25) to protein phosphorylation (26).”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [22]
The higher intronic read fraction in nuclei reflects enrichment of nascent transcripts in the nucleus.
“reflecting the enrichment of nascent transcripts in the nucleus3,13–16”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [13]
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- Authors
- Rabani, M., Levin, J. Z., Fan, L., Adiconis, X., Raychowdhury, R., Garber, M., Gnirke, A., Nusbaum, C., Hacohen, N., Friedman, N., Amit, I., & Regev, A.
- Affiliations
- Broad Institute (US)
- Hebrew University of Jerusalem (IL)
- Corresponding author
- Friedman, Nir
- Amit, Ido
- Regev, Aviv
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2011-04-24
- Volume
- 29
- Issue
- 5
- Pages
- 436-442
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC3114636
- OpenAlex
- W1561227903
- Cited by
- 583
- References
- 46
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 29 · 2023: 28 · 2022: 44 · 2021: 33 · 2020: 57 · 2019: 32 · 2018: 41 · 2017: 48 · 2016: 34 · 2015: 40 · 2014: 46 · 2013: 45 · 2012: 40
- FWCI
- 18.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (1dp2od002230-01, DP1OD003958)
- National Institute of Allergy and Infectious Diseases (U54AI057159)
- National Center for Research Resources (S10RR026688)
- National Center for Chronic Disease Prevention and Health Promotion (DP1-00003958-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- dendritic cells, lipopolysaccharide response, transcription rate, gene expression regulation
- MeSH terms
- Animals, Cells, Cultured, Dendritic Cells, Female, Lipopolysaccharides, Mice, Inbred C57BL, Models, Molecular, RNA, RNA Polymerase II, RNA, Messenger, Transcription Factors, Transcription, Genetic, Down-Regulation, Up-Regulation, Sequence Analysis, RNA, Computational Biology, Biotinylation, Mice, Genetic Association Studies
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