Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells

Nature Biotechnology volume 29, pages 436–442 (2011)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Changes in transcription rates during the response of dendritic cells to LPS.
Figure 2: Changes in transcription rate account for most expression changes; changes in degradation rate contribute to 'peaked' responses.
Figure 3: Genome-wide analysis of RNA transcription and degradation rates using RNA- and 4sU-Seq.
Figure 4: Genome-wide analysis of RNA processing rates.

Similar content being viewed by others

Accession codes

Accessions

Gene Expression Omnibus

References

  1. Kim, H.D., Shay, T., O'Shea, E.K. & Regev, A. Transcriptional regulatory circuits: predicting numbers from alphabets. Science 325, 429–432 (2009).

    Article  CAS  Google Scholar 

  2. Wang, R.S., Zhang, X.S. & Chen, L. Inferring transcriptional interactions and regulator activities from experimental data. Mol. Cells 24, 307–315 (2007).

    CAS  PubMed  Google Scholar 

  3. Raghavan, A. et al. Genome-wide analysis of mRNA decay in resting and activated primary human T lymphocytes. Nucleic Acids Res. 30, 5529–5538 (2002).

    Article  CAS  Google Scholar 

  4. Shalem, O. et al. Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation. Mol. Syst. Biol. 4, 223 (2008).

    Article  Google Scholar 

  5. Amit, I. et al. A module of negative feedback regulators defines growth factor signaling. Nat. Genet. 39, 503–512 (2007).

    Article  CAS  Google Scholar 

  6. Mellman, I. & Steinman, R.M. Dendritic cells: specialized and regulated antigen processing machines. Cell 106, 255–258 (2001).

    Article  CAS  Google Scholar 

  7. Amit, I. et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science 326, 257–263 (2009).

    Article  CAS  Google Scholar 

  8. Ernst, J., Vainas, O., Harbison, C.T., Simon, I. & Bar-Joseph, Z. Reconstructing dynamic regulatory maps. Mol. Syst. Biol. 3, 74 (2007).

    Article  Google Scholar 

  9. Nachman, I., Regev, A. & Friedman, N. Inferring quantitative models of regulatory networks from expression data. Bioinformatics 20 Suppl 1, i248–i256 (2004).

    Article  CAS  Google Scholar 

  10. Nilsson, R. et al. Transcriptional network dynamics in macrophage activation. Genomics 88, 133–142 (2006).

    Article  CAS  Google Scholar 

  11. Ramsey, S.A. et al. Uncovering a macrophage transcriptional program by integrating evidence from motif scanning and expression dynamics. PLoS Comput. Biol. 4, e1000021 (2008).

    Article  Google Scholar 

  12. Segal, E., Raveh-Sadka, T., Schroeder, M., Unnerstall, U. & Gaul, U. Predicting expression patterns from regulatory sequence in Drosophila segmentation. Nature 451, 535–540 (2008).

    Article  CAS  Google Scholar 

  13. Barenco, M. et al. Dissection of a complex transcriptional response using genome-wide transcriptional modelling. Mol. Syst. Biol. 5, 327 (2009).

    Article  Google Scholar 

  14. Elkon, R., Zlotorynski, E., Zeller, K.I. & Agami, R. Major role for mRNA stability in shaping the kinetics of gene induction. BMC Genomics 11, 259 (2010).

    Article  Google Scholar 

  15. Garcia-Martinez, J., Aranda, A. & Perez-Ortin, J.E. Genomic run-on evaluates transcription rates for all yeast genes and identifies gene regulatory mechanisms. Mol. Cell 15, 303–313 (2004).

    Article  CAS  Google Scholar 

  16. Cheadle, C. et al. Control of gene expression during T cell activation: alternate regulation of mRNA transcription and mRNA stability. BMC Genomics 6, 75 (2005).

    Article  Google Scholar 

  17. Hao, S. & Baltimore, D. The stability of mRNA influences the temporal order of the induction of genes encoding inflammatory molecules. Nat. Immunol. 10, 281–288 (2009).

    Article  CAS  Google Scholar 

  18. Core, L.J., Waterfall, J.J. & Lis, J.T. Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322, 1845–1848 (2008).

    Article  CAS  Google Scholar 

  19. Wang, Y. et al. Precision and functional specificity in mRNA decay. Proc. Natl. Acad. Sci. USA 99, 5860–5865 (2002).

    Article  CAS  Google Scholar 

  20. Pelechano, V. & Perez-Ortin, J.E. The transcriptional inhibitor thiolutin blocks mRNA degradation in yeast. Yeast 25, 85–92 (2008).

    Article  CAS  Google Scholar 

  21. Amorim, M.J., Cotobal, C., Duncan, C. & Mata, J. Global coordination of transcriptional control and mRNA decay during cellular differentiation. Mol. Syst. Biol. 6, 380 (2010).

    Article  Google Scholar 

  22. Cleary, M.D., Meiering, C.D., Jan, E., Guymon, R. & Boothroyd, J.C. Biosynthetic labeling of RNA with uracil phosphoribosyltransferase allows cell-specific microarray analysis of mRNA synthesis and decay. Nat. Biotechnol. 23, 232–237 (2005).

    Article  CAS  Google Scholar 

  23. Dolken, L. et al. High-resolution gene expression profiling for simultaneous kinetic parameter analysis of RNA synthesis and decay. RNA 14, 1959–1972 (2008).

    Article  Google Scholar 

  24. Friedel, C.C. & Dolken, L. Metabolic tagging and purification of nascent RNA: implications for transcriptomics. Mol. Biosyst. 5, 1271–1278 (2009).

    Article  CAS  Google Scholar 

  25. Friedel, C.C., Dolken, L., Ruzsics, Z., Koszinowski, U.H. & Zimmer, R. Conserved principles of mammalian transcriptional regulation revealed by RNA half-life. Nucleic Acids Res. 37, e115 (2009).

    Article  Google Scholar 

  26. Kenzelmann, M. et al. Microarray analysis of newly synthesized RNA in cells and animals. Proc. Natl. Acad. Sci. USA 104, 6164–6169 (2007).

    Article  CAS  Google Scholar 

  27. Geiss, G.K. et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat. Biotechnol. 26, 317–325 (2008).

    Article  CAS  Google Scholar 

  28. Levin, J.Z. et al. Comprehensive comparative analysis of strand-specific RNA sequencing methods. Nat. Methods 7, 709–715 (2010).

    Article  CAS  Google Scholar 

  29. Chechik, G. & Koller, D. Timing of gene expression responses to environmental changes. J. Comput. Biol. 16, 279–290 (2009).

    Article  CAS  Google Scholar 

  30. Chechik, G. et al. Activity motifs reveal principles of timing in transcriptional control of the yeast metabolic network. Nat. Biotechnol. 26, 1251–1259 (2008).

    Article  CAS  Google Scholar 

  31. Pruitt, K.D., Tatusova, T. & Maglott, D.R. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2007).

    Article  CAS  Google Scholar 

  32. Nam, K., Lee, G., Trambley, J., Devine, S.E. & Boeke, J.D. Severe growth defect in a Schizosaccharomyces pombe mutant defective in intron lariat degradation. Mol. Cell. Biol. 17, 809–818 (1997).

    Article  CAS  Google Scholar 

  33. Kim, Y.J., Bjorklund, S., Li, Y., Sayre, M.H. & Kornberg, R.D. A multiprotein mediator of transcriptional activation and its interaction with the C-terminal repeat domain of RNA polymerase II. Cell 77, 599–608 (1994).

    Article  CAS  Google Scholar 

  34. Kim, H.C., Kim, G.M., Yang, J.M. & Ki, J.W. Cloning, expression, and complementation test of the RNA lariat debranching enzyme cDNA from mouse. Mol. Cells 11, 198–203 (2001).

    CAS  PubMed  Google Scholar 

  35. Clement, J.Q., Qian, L., Kaplinsky, N. & Wilkinson, M.F. The stability and fate of a spliced intron from vertebrate cells. RNA 5, 206–220 (1999).

    Article  CAS  Google Scholar 

  36. Guttman, M. et al. Ab initio reconstruction of cell type-specific transcriptomes in mouse reveals the conserved multi-exonic structure of lincRNAs. Nat. Biotechnol. 28, 503–510 (2010).

    Article  CAS  Google Scholar 

  37. Lai, W.S., Parker, J.S., Grissom, S.F., Stumpo, D.J. & Blackshear, P.J. Novel mRNA targets for tristetraprolin (TTP) identified by global analysis of stabilized transcripts in TTP-deficient fibroblasts. Mol. Cell. Biol. 26, 9196–9208 (2006).

    Article  CAS  Google Scholar 

  38. Carballo, E., Lai, W.S. & Blackshear, P.J. Feedback inhibition of macrophage tumor necrosis factor-alpha production by tristetraprolin. Science 281, 1001–1005 (1998).

    Article  CAS  Google Scholar 

  39. Lagarias, J.C., Reeds, J.A., Wright, M.H. & Wright, P.E. Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J. Optimiz. 9, 112–147 (1998).

    Article  Google Scholar 

  40. Hodges, J.L. & Lehmann, E.L. Basic Concepts of Probability and Statistics. 2nd edn. (Holden-Day, 1970).

    Google Scholar 

  41. Wilks, S.S. The large sample distribution of the likelihood ratio for testing composite hypotheses. Ann. Math. Stat. 9, 60–62 (1938).

    Article  Google Scholar 

  42. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    Article  CAS  Google Scholar 

  43. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S.L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  44. Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J. & Wheeler, D.L. GenBank. Nucleic Acids Res. 36, D25–D30 (2008).

    Article  CAS  Google Scholar 

  45. Rhead, B. et al. The UCSC Genome Browser database: update 2010. Nucleic Acids Res. 38, D613–D619 (2010).

    Article  CAS  Google Scholar 

  46. Gardner, P.P. et al. Rfam: updates to the RNA families database. Nucleic Acids Res. 37, D136–D140 (2009).

    Article  CAS  Google Scholar 

  47. Ye, J., McGinnis, S. & Madden, T.L. BLAST: improvements for better sequence analysis. Nucleic Acids Res. 34, W6 (2006).

    Article  CAS  Google Scholar 

  48. Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

    Article  CAS  Google Scholar 

  49. Vandesompele, J. et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol 3, RESEARCH0034 (2002).

    Article  Google Scholar 

  50. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank S. Schwartz for assistance in analyzing splicing signals, J. Bochicchio for project management and the Broad Sequencing Platform for all sequencing work. I.A. was supported by the Human Frontiers Science Program. Work was supported by the Howard Hughes Medical Institute, a National Institutes of Health PIONEER DP1-00003958-01 award, a Burroughs Wellcome Fund Career Award at the Scientific Interface and the Merkin Foundation for Stem Cell Research at the Broad Institute (A.R.) by a US-Israel Bi-national Science Foundation award (N.F. and A.R.) and the EU FP7 “MODEL-IN” consortium grant (N.F.).

Author information

Author notes

  1. Nir Friedman, Ido Amit and Aviv Regev: These authors contributed equally to this work.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Michal Rabani, Joshua Z Levin, Lin Fan, Xian Adiconis, Raktima Raychowdhury, Manuel Garber, Andreas Gnirke, Chad Nusbaum, Nir Hacohen, Ido Amit & Aviv Regev

  2. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Michal Rabani

  3. School of Computer Science, Hebrew University, Jerusalem, Israel

    Nir Friedman

  4. Institute of Life Sciences, Hebrew University, Jerusalem, Israel

    Nir Friedman

  5. Department of Biology, Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Aviv Regev

Authors
  1. Michal Rabani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joshua Z Levin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xian Adiconis
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Raktima Raychowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Manuel Garber
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Andreas Gnirke
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chad Nusbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Nir Hacohen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nir Friedman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ido Amit
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Aviv Regev
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.R., I.A. and A.R. conceived and designed the study. M.R. and I.A. conducted the experiments. M.R., N.F. and A.R. designed the computational methods. M.R. developed and implemented the computational methods. R.R. made the cell cultures. J.Z.L., X.A., L.F., A.G. and C.N. constructed and sequenced the cDNA libraries. N.H. contributed experimental methods and reagents. M.G. contributed computational methods for RNA-Seq analysis.

Corresponding authors

Correspondence to Nir Friedman, Ido Amit or Aviv Regev.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Methods, Supplementary Notes and Supplementary Figs. 1–24 (PDF 6164 kb)

Supplementary Table 1

The 254 genes in the 'signature' set. (XLS 28 kb)

Supplementary Table 2

nCounter measurements data. (XLS 375 kb)

Supplementary Table 3

Standard RNA-Seq sequencing libraries statistics. (XLS 14 kb)

Supplementary Table 4

4sU-Seq sequencing libraries statistics. (XLS 15 kb)

Supplementary Table 5

Functional enrichments in the 8 expression clusters. (XLS 51 kb)

Supplementary Table 6

Functional enrichments in 10 deciles with distinct (fixed) degradation rates and in the group of genes that reject the 'constant degradation' model. (XLS 173 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rabani, M., Levin, J., Fan, L. et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nat Biotechnol 29, 436–442 (2011). https://doi.org/10.1038/nbt.1861

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt.1861

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research
pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy