Article | Published:

Global quantification of mammalian gene expression control

Nature volume 473, pages 337342 (19 May 2011) | Download Citation

  • A Corrigendum to this article was published on 13 February 2013

Abstract

Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Sequence Read Archive

Data deposits

Sequences have been deposited in the Sequence Read Archive under accession code SRA030871.

References

  1. 1.

    & Modeling the dynamics of transcriptional gene regulatory networks for animal development. Dev. Biol. 325, 317–328 (2009)

  2. 2.

    & Coupling and coordination in gene expression processes: a systems biology view. Nature Rev. Genet. 9, 38–48 (2008)

  3. 3.

    , , & Global signatures of protein and mRNA expression levels. Mol. Biosyst. 5, 1512–1526 (2009)

  4. 4.

    , & Correlation of mRNA and protein in complex biological samples. FEBS Lett. 583, 3966–3973 (2009)

  5. 5.

    , , , & Quantification of protein half-lives in the budding yeast proteome. Proc. Natl Acad. Sci. USA 103, 13004–13009 (2006)

  6. 6.

    et al. Decay rates of human mRNAs: correlation with functional characteristics and sequence attributes. Genome Res. 13, 1863–1872 (2003)

  7. 7.

    , , , & Global protein stability profiling in mammalian cells. Science 322, 918–923 (2008)

  8. 8.

    , & Quantitative proteomics by metabolic labeling of model organisms. Mol. Cell. Proteomics 9, 11–24 (2010)

  9. 9.

    & Metabolic labeling of proteins for proteomics. Mol. Cell. Proteomics 4, 857–872 (2005)

  10. 10.

    , , , & Conserved principles of mammalian transcriptional regulation revealed by RNA half-life. Nucleic Acids Res. 37, e115 (2009)

  11. 11.

    Functional and quantitative proteomics using SILAC. Nature Rev. Mol. Cell Biol. 7, 952–958 (2006)

  12. 12.

    , , , & Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC. J. Proteome Res. 8, 104–112 (2009)

  13. 13.

    , , & The turnover kinetics of major histocompatibility complex peptides of human cancer cells. Mol. Cell. Proteomics 5, 357–365 (2006)

  14. 14.

    , , & Analysis of nucleolar protein dynamics reveals the nuclear degradation of ribosomal proteins. Curr. Biol. 17, 749–760 (2007)

  15. 15.

    , , & Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9, 205–209 (2009)

  16. 16.

    et al. Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58–63 (2008)

  17. 17.

    & MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nature Biotechnol. 26, 1367–1372 (2008)

  18. 18.

    , , , & Analysis of proteome dynamics in the mouse brain. Proc. Natl Acad. Sci. USA 107, 14508–14513 (2010)

  19. 19.

    , , , & Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis. Anal. Chem. 76, 4951–4959 (2004)

  20. 20.

    , , , & Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods 5, 621–628 (2008)

  21. 21.

    , , , & Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nature Biotechnol. 25, 117–124 (2007)

  22. 22.

    et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762–765 (2009)

  23. 23.

    et al. Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol. Syst. Biol. 6, 400 (2010)

  24. 24.

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

  25. 25.

    et al. In vivo dynamics of RNA polymerase II transcription. Nature Struct. Mol. Biol. 14, 796–806 (2007)

  26. 26.

    , , & Dissecting eukaryotic translation and its control by ribosome density mapping. Nucleic Acids Res. 33, 2421–2432 (2005)

  27. 27.

    , & Integrative analyses of posttranscriptional regulation in the yeast Saccharomyces cerevisiae using transcriptomic and proteomic data. Curr. Microbiol. 57, 18–22 (2008)

  28. 28.

    , & Weighing in on ubiquitin: the expanding role of mass-spectrometry-based proteomics. Nature Cell Biol. 7, 750–757 (2005)

  29. 29.

    & The ubiquitin system. Annu. Rev. Biochem. 67, 425–479 (1998)

  30. 30.

    , , & How proteolysis drives the cell cycle. Science 274, 1652–1659 (1996)

  31. 31.

    & The stability of mRNA influences the temporal order of the induction of genes encoding inflammatory molecules. Nature Immunol. 10, 281–288 (2009)

  32. 32.

    , , & Recurrent design patterns in the feedback regulation of the mammalian signalling network. Mol. Syst. Biol. 4, 190 (2008)

  33. 33.

    Energy constraints on the evolution of gene expression. Mol. Biol. Evol. 22, 1365–1374 (2005)

  34. 34.

    & Effects of molecular memory and bursting on fluctuations in gene expression. Science 319, 339–343 (2008)

  35. 35.

    , , & Dissecting the expression dynamics of RNA-binding proteins in posttranscriptional regulatory networks. Proc. Natl Acad. Sci. USA 106, 20300–20305 (2009)

  36. 36.

    , , , & Diverse RNA-binding proteins interact with functionally related sets of RNAs, suggesting an extensive regulatory system. PLoS Biol. 6, e255 (2008)

  37. 37.

    , & Balancing acts: molecular control of mammalian iron metabolism. Cell 117, 285–297 (2004)

  38. 38.

    , , & Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009)

  39. 39.

    & Nascent transcript sequencing visualizes transcription at nucleotide resolution. Nature 469, 368–373 (2011)

  40. 40.

    & Molecular mechanisms of translational control. Nature Rev. Mol. Cell Biol. 5, 827–835 (2004)

  41. 41.

    & Regulation of translation initiation in eukaryotes: mechanisms and biological targets. Cell 136, 731–745 (2009)

  42. 42.

    et al. Systems-level dynamic analyses of fate change in murine embryonic stem cells. Nature 462, 358–362 (2009)

  43. 43.

    , & Negative autoregulation speeds the response times of transcription networks. J. Mol. Biol. 323, 785–793 (2002)

  44. 44.

    et al. Metabolic labeling of RNA uncovers principles of RNA production and degradation dynamics in mammalian cells. Nature Biotechnol. 10.1038/nbt.1861 (24 April 2011).

Download references

Acknowledgements

We thank N. Rajewsky and L. Dölken for fruitful discussions and C. Sommer for technical assistance. M.S. and W.C. are supported by the Helmholtz Association, the German Ministry of Education and Research (BMBF) and the Senate of Berlin by funds aimed at establishing the Berlin Institute of Medical Systems Biology (BIMSB) (grant number 315362A). J.W. is supported by the ForSys-programme of the German Ministry of Education and Research (grant number 315289); D.B. by the Helmholtz Alliance on Systems Biology/MSBN; and N.L. by the China Scholarship Council CSC.

Author information

Affiliations

  1. Max Delbrück Center for Molecular Medicine, Robert-Rössle-Str. 10, D-13092 Berlin, Germany

    • Björn Schwanhäusser
    • , Dorothea Busse
    • , Na Li
    • , Gunnar Dittmar
    • , Jana Wolf
    • , Wei Chen
    •  & Matthias Selbach
  2. MicroDiscovery GmbH, Marienburger Str. 1, D-10405 Berlin, Germany

    • Johannes Schuchhardt

Authors

  1. Search for Björn Schwanhäusser in:

  2. Search for Dorothea Busse in:

  3. Search for Na Li in:

  4. Search for Gunnar Dittmar in:

  5. Search for Johannes Schuchhardt in:

  6. Search for Jana Wolf in:

  7. Search for Wei Chen in:

  8. Search for Matthias Selbach in:

Contributions

M.S. conceived, designed and supervised the experiments. B.S. performed wet-lab experiments, mass spectrometry and proteomic data analysis. D.B. and J.W. developed and employed the mathematical model. N.L. performed RNA-seq experiments. W.C. designed and supervised RNA-seq experiments. B.S., D.B., J.S., W.C. and M.S. analysed genome-wide data. G.D. helped in cycloheximide chase experiments and data analysis. B.S., D.B., J.S., J.W., W.C. and M.S. interpreted the data. M.S. wrote the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Jana Wolf or Wei Chen or Matthias Selbach.

Supplementary information

PDF files

  1. 1.

    Supplementary Figures

    This file contains Supplementary Figures 1-12 with legends. This file was replaced on 13 February 2013 - see Selbach 11848 corrigendum for details.

  2. 2.

    Supplementary Methods

    This file contains Supplementary Methods and Data, Supplementary Figures 1-4 with legends and additional references.

Excel files

  1. 1.

    Supplementary Table 1

    This table displays an overview of data reproducibility. This file was replaced on 13 February 2013 - see Selbach 11848 corrigendum for details.

  2. 2.

    Supplementary Table 2

    This table displays categories enriched in bins of genes with the specified combinations of protein and mRNA half-lives.

  3. 3.

    Supplementary Table 3

    This table displays protein and mRNA copy numbers, half-lives, transcription rates and translation rate constants in mouse fibroblasts (NIH 3T3). This file was replaced on 13 February 2013 - see Selbach 11848 corrigendum for details.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature10098

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.