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.

  • Review Article
  • Published:

mRNAs, proteins and the emerging principles of gene expression control

Nature Reviews Genetics volume 21, pages 630–644 (2020)Cite this article

Subjects

Abstract

Gene expression involves transcription, translation and the turnover of mRNAs and proteins. The degree to which protein abundances scale with mRNA levels and the implications in cases where this dependency breaks down remain an intensely debated topic. Here we review recent mRNA–protein correlation studies in the light of the quantitative parameters of the gene expression pathway, contextual confounders and buffering mechanisms. Although protein and mRNA levels typically show reasonable correlation, we describe how transcriptomics and proteomics provide useful non-redundant readouts. Integrating both types of data can reveal exciting biology and is an essential step in refining our understanding of the principles of gene expression control.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Overview of the gene expression pathway.
Fig. 2: Across-gene and within-gene correlations between mRNA and protein levels.
Fig. 3: Quantitative parameters of the gene expression pathway.
Fig. 4: Predicting protein levels from mRNA levels.
Fig. 5: Contextual confounders of mRNA–protein correlations.
Fig. 6: Mechanisms of buffering between mRNA and protein levels.

Similar content being viewed by others

References

  1. Abbott, S. & Fairbanks, D. J. Experiments on plant hybrids by Gregor Mendel. Genetics 204, 407–422 (2016).

    PubMed  PubMed Central  Google Scholar 

  2. Lester, G. & Bonner, D. M. The occurrence of beta-galactosidase in Escherichia coli. J. Bacteriol. 63, 759–769 (1952).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Jacob, F. & Monod, J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 3, 318–356 (1961).

    CAS  PubMed  Google Scholar 

  4. Gann, A. Jacob and Monod: from operons to EvoDevo. Curr. Biol. 20, R718–R723 (2010).

    CAS  PubMed  Google Scholar 

  5. Montgomery, S. B. & Dermitzakis, E. T. From expression QTLs to personalized transcriptomics. Nat. Rev. Genet. 12, 277–282 (2011).

    CAS  PubMed  Google Scholar 

  6. Koch, L. Genomics: adding another dimension to gene regulation. Nat. Rev. Genet. 16, 563 (2015).

    CAS  PubMed  Google Scholar 

  7. Bentley, D. L. Coupling mRNA processing with transcription in time and space. Nat. Rev. Genet. 15, 163–175 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hentze, M. W., Castello, A., Schwarzl, T. & Preiss, T. A brave new world of RNA-binding proteins. Nat. Rev. Mol. Cell Biol. 19, 327–341 (2018).

    CAS  PubMed  Google Scholar 

  9. Tahmasebi, S., Khoutorsky, A., Mathews, M. B. & Sonenberg, N. Translation deregulation in human disease. Nat. Rev. Mol. Cell Biol. 19, 791–807 (2018).

    CAS  PubMed  Google Scholar 

  10. Teixeira, F. K. & Lehmann, R. Translational control during developmental transitions. Cold Spring Harb. Perspect. Biol. 11, a032987 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Emmott, E., Jovanovic, M. & Slavov, N. Ribosome stoichiometry: from form to function. Trends Biochem. Sci. 44, 95–109 (2019).

    CAS  PubMed  Google Scholar 

  12. Schwartz, A. L. & Ciechanover, A. Targeting proteins for destruction by the ubiquitin system: implications for human pathobiology. Annu. Rev. Pharmacol. Toxicol. 49, 73–96 (2009).

    CAS  PubMed  Google Scholar 

  13. Pohl, C. & Dikic, I. Cellular quality control by the ubiquitin-proteasome system and autophagy. Science 366, 818–822 (2019).

    CAS  PubMed  Google Scholar 

  14. Ryan, C. J. et al. High-resolution network biology: connecting sequence with function. Nat. Rev. Genet. 14, 865–879 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Mann, M. & Jensen, O. N. Proteomic analysis of post-translational modifications. Nat. Biotechnol. 21, 255–261 (2003).

    CAS  PubMed  Google Scholar 

  16. Alberts, B. et al. Molecular Biology of the Cell (Garland Press, 2002).

  17. Aebersold, R. et al. How many human proteoforms are there? Nat. Chem. Biol. 14, 206–214 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Salovska, B. et al. Isoform-resolved correlation analysis between mRNA abundance regulation and protein level degradation. Mol. Syst. Biol. 16, e9170 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Kempfer, R. & Pombo, A. Methods for mapping 3D chromosome architecture. Nat. Rev. Genet. 21, 207–226 (2019).

    PubMed  Google Scholar 

  20. Goodwin, S., McPherson, J. D. & McCombie, W. R. Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17, 333–351 (2016).

    CAS  PubMed  Google Scholar 

  21. Stark, R., Grzelak, M. & Hadfield, J. RNA sequencing: the teenage years. Nat. Rev. Genet. 20, 631–656 (2019).

    CAS  PubMed  Google Scholar 

  22. Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016). This review provides an overview of mass spectrometry-based proteomic technologies and their biomedical applications.

    CAS  PubMed  Google Scholar 

  23. Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Liu, Y., Beyer, A. & Aebersold, R. On the dependency of cellular protein levels on mRNA abundance. Cell 165, 535–550 (2016).

    CAS  PubMed  Google Scholar 

  25. Fortelny, N., Overall, C. M., Pavlidis, P. & Freue, G. V. C. Can we predict protein from mRNA levels? Nature 547, E19–E20 (2017). This study questions the utility of protein-to-mRNA ratios and argues that these are likely to be of little use when one is attempting to make within-gene estimates of protein levels from mRNA.

    CAS  PubMed  Google Scholar 

  26. Franks, A., Airoldi, E. & Slavov, N. Post-transcriptional regulation across human tissues. PLoS Comput. Biol. 13, e1005535 (2017).

    PubMed  PubMed Central  Google Scholar 

  27. Gygi, S. P., Rochon, Y., Franza, B. R. & Aebersold, R. Correlation between protein and mRNA abundance in yeast. Mol. Cell. Biol. 19, 1720–1730 (1999). One of the original studies attempting to correlate proteins with mRNA abundance. Gygi and colleagues note that coverage bias may greatly affect across-gene correlations.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Edfors, F. et al. Gene-specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol. 12, 883 (2016).

    PubMed  PubMed Central  Google Scholar 

  29. Zhang, B. et al. Proteogenomic characterization of human colon and rectal cancer. Nature 513, 382–387 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Mertins, P. et al. Proteogenomics connects somatic mutations to signalling in breast cancer. Nature 534, 55–62 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Zhang, H. et al. Integrated proteogenomic characterization of human high-grade serous ovarian cancer. Cell 166, 755–765 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Huang, K.-L. et al. Proteogenomic integration reveals therapeutic targets in breast cancer xenografts. Nat. Commun. 8, 14864 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. Archer, T. C. et al. Proteomics, post-translational modifications, and integrative analyses reveal molecular heterogeneity within medulloblastoma subgroups. Cancer Cell 34, 396–410.e8 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Mun, D.-G. et al. Proteogenomic characterization of human early-onset gastric cancer. Cancer Cell 35, 111–124.e10 (2019).

    CAS  PubMed  Google Scholar 

  35. Vasaikar, S. et al. Proteogenomic analysis of human colon cancer reveals new therapeutic opportunities. Cell 177, 1035–1049.e19 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Khan, Z. et al. Primate transcript and protein expression levels evolve under compensatory selection pressures. Science 342, 1100–1104 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Battle, A. et al. Genomic variation. Impact of regulatory variation from RNA to protein. Science 347, 664–667 (2015). This study looks at how mRNA variation, arising from germline DNA variation in a population of humans, is buffered at the translational and protein levels.

    CAS  PubMed  Google Scholar 

  38. Chick, J. M. et al. Defining the consequences of genetic variation on a proteome-wide scale. Nature 534, 500–505 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Wang, D. et al. A deep proteome and transcriptome abundance atlas of 29 healthy human tissues. Mol. Syst. Biol. 15, e8503 (2019).

    PubMed  PubMed Central  Google Scholar 

  40. Ankney, J. A., Astor Ankney, J., Muneer, A. & Chen, X. Relative and absolute quantitation in mass spectrometry–based proteomics. Annu. Rev. Anal. Chem. 11, 49–77 (2018). This review addresses the pros and cons of different absolute and relative proteomic quantification methods.

    CAS  Google Scholar 

  41. 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).

    CAS  PubMed  Google Scholar 

  42. Fu, Y., Wu, P.-H., Beane, T., Zamore, P. D. & Weng, Z. Elimination of PCR duplicates in RNA-seq and small RNA-seq using unique molecular identifiers. BMC Genomics 19, 531 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. Benjamini, Y. & Speed, T. P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Tang, H. et al. A computational approach toward label-free protein quantification using predicted peptide detectability. Bioinformatics 22, e481–e488 (2006).

    CAS  PubMed  Google Scholar 

  45. Zimmer, D., Schneider, K., Sommer, F., Schroda, M. & Mühlhaus, T. Artificial intelligence understands peptide observability and assists with absolute protein quantification. Front. Plant. Sci. 9, 1559 (2018).

    PubMed  PubMed Central  Google Scholar 

  46. Peng, M. et al. Protease bias in absolute protein quantitation. Nat. Methods 9, 524–525 (2012). This study specifically interrogates intensity-based quantification methods in mass-spectrometry-based proteomics and how these may differ widely depending on the mode of enzyme digestion.

    CAS  PubMed  Google Scholar 

  47. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011). This study combines metabolic pulse labelling and absolute quantification of both mRNAs and proteins with mathematical modelling to quantify the major stages of mammalian gene expression control.

    PubMed  Google Scholar 

  48. Li, J. J., Bickel, P. J. & Biggin, M. D. System wide analyses have underestimated protein abundances and the importance of transcription in mammals. PeerJ 2, e270 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. Jovanovic, M. et al. Dynamic profiling of the protein life cycle in response to pathogens. Science 347, 1259038 (2015). This study integrates absolute quantification of mRNAs and proteins along with protein turnover information in the context of LPS stimulation using ordinary differential equations to comprehensively assess gene expression regulation.

    PubMed  PubMed Central  Google Scholar 

  50. Ahrné, E., Molzahn, L., Glatter, T. & Schmidt, A. Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics 13, 2567–2578 (2013).

    PubMed  Google Scholar 

  51. Zeiler, M., Straube, W. L., Lundberg, E., Uhlen, M. & Mann, M. A protein epitope signature tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines. Mol. Cell. Proteom. 11, O111.009613 (2012).

    Google Scholar 

  52. Lin, C. Y. et al. Transcriptional amplification in tumor cells with elevated c-Myc. Cell 151, 56–67 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Crick, F. Central dogma of molecular biology. Nature 227, 561–563 (1970). This study provides the original postulate of the central dogma of molecular biology, not to be confused with gene expression in general.

    CAS  PubMed  Google Scholar 

  54. Cobb, M. 60 years ago, Francis Crick changed the logic of biology. PLoS Biol. 15, e2003243 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. Davidson, E. H. Emerging properties of animal gene regulatory networks. Nature 468, 911–920 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Lindeboom, R. G. H. et al. Integrative multi-omics analysis of intestinal organoid differentiation. Mol. Syst. Biol. 14, e8227 (2018).

    PubMed  PubMed Central  Google Scholar 

  57. Becker, K. et al. Quantifying post-transcriptional regulation in the development of Drosophila melanogaster. Nat. Commun. 9, 4970 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. Ingolia, N. T., Lareau, L. F. & Weissman, J. S. Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147, 789–802 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Cheng, Z. et al. Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress. Mol. Syst. Biol. 12, 855 (2016).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Hausser, J., Mayo, A., Keren, L. & Alon, U. Central dogma rates and the trade-off between precision and economy in gene expression. Nat. Commun. 10, 68 (2019). This study analyses the parametric landscape of gene expression across genes, investigating the overall strategy evolution has selected, for example, to regulate highly expressed genes.

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Schwanhäusser, B., Wolf, J., Selbach, M. & Busse, D. Synthesis and degradation jointly determine the responsiveness of the cellular proteome. Bioessays 35, 597–601 (2013).

    PubMed  Google Scholar 

  63. Beyer, A., Hollunder, J., Nasheuer, H.-P. & Wilhelm, T. Post-transcriptional expression regulation in the yeast Saccharomyces cerevisiae on a genomic scale. Mol. Cell. Proteomics 3, 1083–1092 (2004).

    CAS  PubMed  Google Scholar 

  64. Zecha, J. et al. Peptide level turnover measurements enable the study of proteoform dynamics. Mol. Cell. Proteomics 17, 974–992 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Kristensen, A. R., Gsponer, J. & Foster, L. J. Protein synthesis rate is the predominant regulator of protein expression during differentiation. Mol. Syst. Biol. 9, 689 (2013).

    PubMed  PubMed Central  Google Scholar 

  66. Baum, K., Schuchhardt, J., Wolf, J. & Busse, D. Of gene expression and cell division time: a mathematical framework for advanced differential gene expression and data analysis. Cell Syst. 9, 569–579 (2019). This study formalizes the role of cell cycle time in the context of gene expression.

    CAS  PubMed  Google Scholar 

  67. Wilhelm, M. et al. Mass-spectrometry-based draft of the human proteome. Nature 509, 582–587 (2014). This study presents an in-depth proteomics and transcriptomics dataset of 12 tissues and is one of the first to posit that protein-to-mRNA ratios can be used to estimate absolute protein abundances from mRNA.

    CAS  PubMed  Google Scholar 

  68. Eraslan, B. et al. Quantification and discovery of sequence determinants of protein-per-mRNA amount in 29 human tissues. Mol. Syst. Biol. 15, e8513 (2019).

    PubMed  PubMed Central  Google Scholar 

  69. Buszczak, M., Signer, R. A. J. & Morrison, S. J. Cellular differences in protein synthesis regulate tissue homeostasis. Cell 159, 242–251 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Peshkin, L. et al. On the relationship of protein and mRNA dynamics in vertebrate embryonic development. Dev. Cell 35, 383–394 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Xiao, H. et al. Differential proteomic analysis of human saliva using tandem mass tags quantification for gastric cancer detection. Sci. Rep. 6, 22165 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Zhao, M. et al. A comprehensive analysis and annotation of human normal urinary proteome. Sci. Rep. 7, 3024 (2017).

    PubMed  PubMed Central  Google Scholar 

  73. Cso˝sz, É. et al. Quantitative body fluid proteomics in medicine - a focus on minimal invasiveness. J. Proteomics 153, 30–43 (2017).

    PubMed  Google Scholar 

  74. Geyer, P. E., Holdt, L. M., Teupser, D. & Mann, M. Revisiting biomarker discovery by plasma proteomics. Mol. Syst. Biol. 13, 942 (2017).

    PubMed  PubMed Central  Google Scholar 

  75. Yao, C. et al. Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat. Commun. 9, 3268 (2018).

    PubMed  PubMed Central  Google Scholar 

  76. Meissner, F., Scheltema, R. A., Mollenkopf, H.-J. & Mann, M. Direct proteomic quantification of the secretome of activated immune cells. Science 340, 475–478 (2013).

    CAS  PubMed  Google Scholar 

  77. Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Moritz, C. P., Mühlhaus, T., Tenzer, S., Schulenborg, T. & Friauf, E. Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause. J. Neurochem. 149, 582–604 (2019).

    CAS  PubMed  Google Scholar 

  79. Zappulo, A. et al. RNA localization is a key determinant of neurite-enriched proteome. Nat. Commun. 8, 583 (2017).

    PubMed  PubMed Central  Google Scholar 

  80. Holt, C. E., Martin, K. C. & Schuman, E. M. Local translation in neurons: visualization and function. Nat. Struct. Mol. Biol. 26, 557–566 (2019).

    CAS  PubMed  Google Scholar 

  81. Chekulaeva, M. & Landthaler, M. Eyes on translation. Mol. Cell 63, 918–925 (2016).

    CAS  PubMed  Google Scholar 

  82. Sysoev, V. O. et al. Global changes of the RNA-bound proteome during the maternal-to-zygotic transition in Drosophila. Nat. Commun. 7, 12128 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Stoeckius, M. et al. Global characterization of the oocyte-to-embryo transition in Caenorhabditis elegans uncovers a novel mRNA clearance mechanism. EMBO J. 33, 1751–1766 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Koch, S., Acebron, S. P., Herbst, J., Hatiboglu, G. & Niehrs, C. Post-transcriptional Wnt signaling governs epididymal sperm maturation. Cell 163, 1225–1236 (2015).

    CAS  PubMed  Google Scholar 

  85. Liu, X. et al. Regulation of mitochondrial biogenesis in erythropoiesis by mTORC1-mediated protein translation. Nat. Cell Biol. 19, 626–638 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Gautier, E.-F. et al. Comprehensive proteomic analysis of human erythropoiesis. Cell Rep. 16, 1470–1484 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Nguyen, A. T. et al. UBE2O remodels the proteome during terminal erythroid differentiation. Science 357, eaan0218 (2017).

    PubMed  PubMed Central  Google Scholar 

  88. Yanagitani, K., Juszkiewicz, S. & Hegde, R. S. UBE2O is a quality control factor for orphans of multiprotein complexes. Science 357, 472–475 (2017). Nguyen et al. (2017) and Yanagitani et al. (2017) characterize the mechanism of action and role of UBE2O as a ubiquitin ligase responsible for clearing surplus protein complex subunits.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Mathieson, T. et al. Systematic analysis of protein turnover in primary cells. Nat. Commun. 9, 689 (2018).

    PubMed  PubMed Central  Google Scholar 

  90. Dörrbaum, A. R., Kochen, L., Langer, J. D. & Schuman, E. M. Local and global influences on protein turnover in neurons and glia. eLife 7, e34202 (2018).

    PubMed  PubMed Central  Google Scholar 

  91. Marguerat, S. et al. Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. Cell 151, 671–683 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Félix, M.-A. & Barkoulas, M. Pervasive robustness in biological systems. Nat. Rev. Genet. 16, 483–496 (2015).

    PubMed  Google Scholar 

  93. Rogers, J. & Gibbs, R. A. Comparative primate genomics: emerging patterns of genome content and dynamics. Nat. Rev. Genet. 15, 347–359 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Waszak, S. M. et al. Population variation and genetic control of modular chromatin architecture in humans. Cell 162, 1039–1050 (2015).

    CAS  PubMed  Google Scholar 

  95. Raj, A., Peskin, C. S., Tranchina, D., Vargas, D. Y. & Tyagi, S. Stochastic mRNA synthesis in mammalian cells. PLoS Biol. 4, e309 (2006).

    PubMed  PubMed Central  Google Scholar 

  96. Raser, J. M. & O’Shea, E. K. Control of stochasticity in eukaryotic gene expression. Science 304, 1811–1814 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Kustatscher, G., Grabowski, P. & Rappsilber, J. Pervasive coexpression of spatially proximal genes is buffered at the protein level. Mol. Syst. Biol. 13, 937 (2017). This study outlines how covariation in mRNA owing to chromosomal location of source genes is lost at the protein level.

    PubMed  PubMed Central  Google Scholar 

  98. Geiger, T., Cox, J. & Mann, M. Proteomic changes resulting from gene copy number variations in cancer cells. PLoS Genet. 6, e1001090 (2010). This is the first systematic analysis showing that the levels of some proteins are resistant to DNA copy number changes in mammalian cell lines.

    PubMed  PubMed Central  Google Scholar 

  99. Stingele, S. et al. Global analysis of genome, transcriptome and proteome reveals the response to aneuploidy in human cells. Mol. Syst. Biol. 8, 608 (2012).

    PubMed  PubMed Central  Google Scholar 

  100. Dephoure, N. et al. Quantitative proteomic analysis reveals posttranslational responses to aneuploidy in yeast. eLife 3, e03023 (2014).

    PubMed  PubMed Central  Google Scholar 

  101. Gonçalves, E. et al. Widespread post-transcriptional attenuation of genomic copy-number variation in cancer. Cell Syst. 5, 386–398.e4 (2017). This study presents a reanalysis of Clinical Proteomic Tumor Analysis Consortium (CPTAC) breast, ovarian and colorectal cancer studies and finds that ~20–30% of mRNA changes caused by aneuploidy are buffered at the protein level and further leverage this information to predict protein–protein interactions.

    PubMed  PubMed Central  Google Scholar 

  102. Liu, Y. et al. Systematic proteome and proteostasis profiling in human Trisomy 21 fibroblast cells. Nat. Commun. 8, 1212 (2017).

    PubMed  PubMed Central  Google Scholar 

  103. Schlattl, A., Anders, S., Waszak, S. M., Huber, W. & Korbel, J. O. Relating CNVs to transcriptome data at fine resolution: assessment of the effect of variant size, type, and overlap with functional regions. Genome Res. 21, 2004–2013 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Fehrmann, R. S. N. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015). This study demonstrates, across tens of thousands of microarray and RNA-seq samples, the direct and dosage-sensitive effects of somatic copy number alterations (aneuploidy) on mRNA and underlines almost no buffering between the DNA and mRNA levels.

    CAS  PubMed  Google Scholar 

  105. Grönlund, A., Lötstedt, P. & Elf, J. Transcription factor binding kinetics constrain noise suppression via negative feedback. Nat. Commun. 4, 1864 (2013).

    PubMed  Google Scholar 

  106. Müller-McNicoll, M., Rossbach, O., Hui, J. & Medenbach, J. Auto-regulatory feedback by RNA-binding proteins. J. Mol. Cell Biol. 11, 930–939 (2019).

    PubMed  PubMed Central  Google Scholar 

  107. Jumaa, H. & Nielsen, P. J. The splicing factor SRp20 modifies splicing of its own mRNA and ASF/SF2 antagonizes this regulation. EMBO J. 16, 5077–5085 (1997). This is one of the early studies analysing the autoregulatory capability of many splicing factors.

    CAS  PubMed  PubMed Central  Google Scholar 

  108. Lareau, L. F., Inada, M., Green, R. E., Wengrod, J. C. & Brenner, S. E. Unproductive splicing of SR genes associated with highly conserved and ultraconserved DNA elements. Nature 446, 926–929 (2007).

    CAS  PubMed  Google Scholar 

  109. de Bie, P. & Ciechanover, A. Ubiquitination of E3 ligases: self-regulation of the ubiquitin system via proteolytic and non-proteolytic mechanisms. Cell Death Differ. 18, 1393–1402 (2011).

    PubMed  PubMed Central  Google Scholar 

  110. Signor, S. A. & Nuzhdin, S. V. The evolution of gene expression in cis and trans. Trends Genet. 34, 532–544 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Bader, D. M. et al. Negative feedback buffers effects of regulatory variants. Mol. Syst. Biol. 11, 785 (2015).

    PubMed  PubMed Central  Google Scholar 

  112. Battich, N., Stoeger, T. & Pelkmans, L. Control of transcript variability in single mammalian cells. Cell 163, 1596–1610 (2015).

    CAS  PubMed  Google Scholar 

  113. Artieri, C. G. & Fraser, H. B. Evolution at two levels of gene expression in yeast. Genome Res. 24, 411–421 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. McManus, C. J., May, G. E., Spealman, P. & Shteyman, A. Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome Res. 24, 422–430 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Taggart, J. C. & Li, G.-W. Production of protein-complex components is stoichiometric and lacks general feedback regulation in eukaryotes. Cell Syst. 7, 580–589.e4 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Juszkiewicz, S. & Hegde, R. S. Quality control of orphaned proteins. Mol. Cell 71, 443–457 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. McShane, E. et al. Kinetic analysis of protein stability reveals age-dependent degradation. Cell 167, 803–815.e21 (2016).

    CAS  PubMed  Google Scholar 

  118. Taggart, J. C., Zauber, H., Selbach, M., Li, G.-W. & McShane, E. Keeping the proportions of protein complex components in check. Cell Syst. 10, 125–132 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Santaguida, S., Vasile, E., White, E. & Amon, A. Aneuploidy-induced cellular stresses limit autophagic degradation. Genes Dev. 29, 2010–2021 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Santaguida, S. et al. Chromosome mis-segregation generates cell-cycle-arrested cells with complex karyotypes that are eliminated by the immune system. Dev. Cell 41, 638–651.e5 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    CAS  PubMed  Google Scholar 

  122. Marx, V. A dream of single-cell proteomics. Nat. Methods 16, 809–812 (2019).

    CAS  PubMed  Google Scholar 

  123. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).

    PubMed  PubMed Central  Google Scholar 

  126. Popovic, D., Koch, B., Kueblbeck, M., Ellenberg, J. & Pelkmans, L. Multivariate control of transcript to protein variability in single mammalian cells. Cell Syst. 7, 398–411.e6 (2018).

    CAS  PubMed  Google Scholar 

  127. Albayrak, C. et al. Digital quantification of proteins and mRNA in single mammalian cells. Mol. Cell 61, 914–924 (2016).

    CAS  PubMed  Google Scholar 

  128. Genshaf, A. S. et al. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol. 17, 188 (2016).

    Google Scholar 

  129. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    CAS  PubMed  Google Scholar 

  130. Sopko, R. et al. Mapping pathways and phenotypes by systematic gene overexpression. Mol. Cell 21, 319–330 (2006).

    CAS  PubMed  Google Scholar 

  131. Brennan, C. M. et al. Protein aggregation mediates stoichiometry of protein complexes in aneuploid cells. Genes Dev. 33, 1031–1047 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Klosin, A. et al. Phase separation provides a mechanism to reduce noise in cells. Science 367, 464–468 (2020).

    CAS  PubMed  Google Scholar 

  133. Nusinow, D. P. et al. Quantitative proteomics of the cancer cell line encyclopedia. Cell 180, 387–402.e16 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Carter, S. L., Eklund, A. C., Kohane, I. S., Harris, L. N. & Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat. Genet. 38, 1043–1048 (2006).

    CAS  PubMed  Google Scholar 

  135. Ben-David, U. et al. The landscape of chromosomal aberrations in breast cancer mouse models reveals driver-specific routes to tumorigenesis. Nat. Commun. 7, 12160 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  136. Gehre, M., Buccitelli, C., Diaz, N., Korbel, J. & Noh, K.-M. Efficient strategies to detect genome editing and integrity in CRISPR-Cas9 engineered ESCs. Preprint at bioRxiv https://doi.org/10.1101/635151 (2019).

  137. Wang, J. et al. Proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Mol. Cell. Proteom. 16, 121–134 (2017).

    CAS  Google Scholar 

  138. Lapek, J. D. Jr et al. Detection of dysregulated protein-association networks by high-throughput proteomics predicts cancer vulnerabilities. Nat. Biotechnol. 35, 983–989 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. Kustatscher, G. et al. Co-regulation map of the human proteome enables identification of protein functions. Nat. Biotechnol. 37, 1361–1371 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  140. Roumeliotis, T. I. et al. Genomic determinants of protein abundance variation in colorectal cancer cells. Cell Rep. 20, 2201–2214 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Romanov, N. et al. Disentangling genetic and environmental effects on the proteotypes of individuals. Cell 177, 1308–1318.e10 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Ori, A. et al. Spatiotemporal variation of mammalian protein complex stoichiometries. Genome Biol. 17, 47 (2016).

    PubMed  PubMed Central  Google Scholar 

  143. Doll, S., Gnad, F. & Mann, M. The case for proteomics and phospho-proteomics in personalized cancer medicine. Proteomics Clin. Appl. 13, e1800113 (2019).

    PubMed  Google Scholar 

  144. Welker, F. et al. Enamel proteome shows that Gigantopithecus was an early diverging pongine. Nature 576, 262–265 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Olsen, J. V. et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127, 635–648 (2006).

    CAS  PubMed  Google Scholar 

  146. Meyer, K. & Selbach, M. Quantitative affinity purification mass spectrometry: a versatile technology to study protein-protein interactions. Front. Genet. 6, 237 (2015).

    PubMed  PubMed Central  Google Scholar 

  147. Yang, J., Wagner, S. A. & Beli, P. Illuminating spatial and temporal organization of protein interaction networks by mass spectrometry-based proteomics. Front. Genet. 6, 344 (2015).

    PubMed  PubMed Central  Google Scholar 

  148. Dettmer, K., Aronov, P. A. & Hammock, B. D. Mass spectrometry-based metabolomics. Mass. Spectrom. Rev. 26, 51–78 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Zelezniak, A. et al. Machine learning predicts the yeast metabolome from the quantitative proteome of kinase knockouts. Cell Syst. 7, 269–283.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. Senft, D., Qi, J. & Ronai, Z. A. Ubiquitin ligases in oncogenic transformation and cancer therapy. Nat. Rev. Cancer 18, 69–88 (2017).

    PubMed  PubMed Central  Google Scholar 

  151. Krönke, J. et al. Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells. Science 343, 301–305 (2014).

    PubMed  Google Scholar 

  152. Lu, G. et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 343, 305–309 (2014).

    CAS  PubMed  Google Scholar 

  153. Chamberlain, P. P. & Hamann, L. G. Development of targeted protein degradation therapeutics. Nat. Chem. Biol. 15, 937–944 (2019).

    CAS  PubMed  Google Scholar 

  154. Tang, Y.-C., Williams, B. R., Siegel, J. J. & Amon, A. Identification of aneuploidy-selective antiproliferation compounds. Cell 144, 499–512 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Ben-David, U. & Amon, A. Context is everything: aneuploidy in cancer. Nat. Rev. Genet. 21, 44–62 (2020).

    CAS  PubMed  Google Scholar 

  156. Hasin, Y., Seldin, M. & Lusis, A. Multi-omics approaches to disease. Genome Biol. 18, 83 (2017).

    PubMed  PubMed Central  Google Scholar 

  157. Vitrinel, B. et al. Exploiting interdata relationships in next-generation proteomics analysis. Mol. Cell. Proteomics 18, S5–S14 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  159. Herzog, V. A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Mann, M. Functional and quantitative proteomics using SILAC. Nat. Rev. Mol. Cell Biol. 7, 952–958 (2006).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  162. Schwanhäusser, B., Gossen, M., Dittmar, G. & Selbach, M. Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9, 205–209 (2009).

    PubMed  Google Scholar 

  163. Doherty, M. K., Hammond, D. E., Clague, M. J., Gaskell, S. J. & Beynon, R. J. Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC. J. Proteome Res. 8, 104–112 (2009).

    CAS  PubMed  Google Scholar 

  164. Boisvert, F.-M. et al. A quantitative spatial proteomics analysis of proteome turnover in human cells. Mol. Cell. Proteomics 11, M111.011429 (2012).

    PubMed  Google Scholar 

  165. Savitski, M. M. et al. Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 173, 260–274.e25 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. Dieterich, D. C., Link, A. J., Graumann, J., Tirrell, D. A. & Schuman, E. M. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl Acad. Sci. USA 103, 9482–9487 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  167. Eichelbaum, K., Winter, M., Berriel Diaz, M., Herzig, S. & Krijgsveld, J. Selective enrichment of newly synthesized proteins for quantitative secretome analysis. Nat. Biotechnol. 30, 984–990 (2012).

    CAS  PubMed  Google Scholar 

  168. Brar, G. A. & Weissman, J. S. Ribosome profiling reveals the what, when, where and how of protein synthesis. Nat. Rev. Mol. Cell Biol. 16, 651–664 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  169. van Heesch, S. et al. The translational landscape of the human heart. Cell 178, 242–260.e29 (2019).

    PubMed  Google Scholar 

  170. Calviello, L. et al. Detecting actively translated open reading frames in ribosome profiling data. Nat. Methods 13, 165–170 (2016).

    CAS  PubMed  Google Scholar 

  171. Diament, A. et al. The extent of ribosome queuing in budding yeast. PLoS Comput. Biol. 14, e1005951 (2018).

    PubMed  PubMed Central  Google Scholar 

  172. Riba, A. et al. Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates. Proc. Natl Acad. Sci. USA 116, 15023–15032 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  173. Duncan, C. D. S. & Mata, J. Effects of cycloheximide on the interpretation of ribosome profiling experiments in Schizosaccharomyces pombe. Sci. Rep. 7, 10331 (2017).

    PubMed  PubMed Central  Google Scholar 

  174. Ingolia, N. T. Tracking the missing footprints of idle ribosomes. Cell Syst. 4, 583–584 (2017).

    CAS  PubMed  Google Scholar 

  175. Liu, T.-Y. et al. Time-resolved proteomics extends ribosome profiling-based measurements of protein synthesis dynamics. Cell Syst. 4, 636–644.e9 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  176. Anscombe, F. J. Graphs in statistical analysis. Am. Stat. 27, 17–21 (1973).

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the extensive contributions of the scientific community to the topic of this Review and apologize for being unable to reference all pertinent articles. The authors thank P. Mertins, J. Wolf, D. Harnett (all from the Max Delbrück Center for Molecular Medicine) and E. McShane (Harvard Medical School) for feedback. They also thank T. Melder (Max Delbrück Center for Molecular Medicine) for help with the chemical structures and all other members of the Selbach laboratory for helpful discussions.

Author information

Authors and Affiliations

  1. Proteome Dynamics, Max Delbrück Center for Molecular Medicine, Berlin, Germany

    Christopher Buccitelli & Matthias Selbach

  2. Charité — Universitätsmedizin Berlin, Berlin, Germany

    Matthias Selbach

Authors
  1. Christopher Buccitelli
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthias Selbach
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Matthias Selbach.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information

Nature Reviews Genetics thanks Gary Churchill, Yansheng Liu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Glossary

Genotype

The complement of DNA possessed by an organism.

Phenotype

The observable characteristics of an organism, which results from the genotype and its interaction with the environment.

Non-coding RNAs

RNAs transcribed from the genome that do not serve as templates for proteins (for example, microRNAs).

Ubiquitin–proteasome system

An ancient system conserved across species that is responsible for the regulated catabolism of individual proteins. Classes of enzymes (E1, E2 and E3) function in specifically ubiquitylating proteins within the cell, thus targeting these clients for destruction by the proteasome.

Autophagy

A form of cellular catabolism that is responsible for the removal of large cellular components (for instance, protein aggregates or damaged organelles). Autophagy involves the inclusion of these components within double-membraned vesicles (termed ‘autophagosomes’), which then undergo fusion with lysosomes.

Omics technologies

A generic term referring to the multitude of technologies to systematically measure multiple biological molecules simultaneously.

Precision

The closeness of repeated measurements to each other. High precision means that measurements are highly reproducible.

Accuracy

The closeness of the average of repeated measurements to the true value. High accuracy means that the measurements are on average in good agreement with the true quantity.

GC correction

A computational approach used to account for sequencing depth biases due to the guanine/cytosine composition of a particular region of the genome.

Shotgun proteomics

In ‘shotgun’ or ‘bottom-up’ proteomics, the proteins in a sample are cleaved into peptides before being analysed by mass spectrometry. Peptides are simpler than proteins, which facilitates their analysis by mass spectrometry and makes the shotgun approach particularly popular.

Proteases

Enzymes that digest proteins into smaller fragments. In shotgun proteomics, proteins are digested into peptides by sequence-specific proteases (such as trypsin, which cleaves proteins at the carboxy-terminal side of lysine and arginine residues).

Lipopolysaccharide

(LPS). A structural component of the outer membrane of Gram-negative bacteria. LPS may be sensed by specialized cells of the mammalian immune system (for example, dendritic cells), triggering both transcriptional and post-transcriptional responses so as to combat an imminent infection.

Transcription rates

The rates at which mRNA transcripts are generated for given genes.

Half-lives

The times it takes for a set of molecules (mRNAs and proteins in the context of this Review) to reduce in number to half of their original quantity via degradation. The term implies that degradation follows first-order kinetics, which is not always true.

Translation rate constants

The rates at which proteins are synthesized, as a function of transcript number (expressed as protein copies per mRNA per hour).

Secretomes

The complement of the proteome produced and secreted by cells.

Polarized cells

A difference in the distribution of cellular materials across a cell (for example, of organelles or proteins).

Maternal to zygotic transition

The point at which a developing zygote transitions from relying on maternally imparted proteins and mRNAs to gene products encoded by and transcribed from its own genome.

Erythropoiesis

The process involving the development and differentiation of red blood cells (erythrocytes).

Quiescent

Referring to quiescence, which is a cellular state occurring in most organisms that is characterized by a temporary exit from the cell cycle.

Quantitative trait loci

(QTLs). Loci in the genome for which the genotype correlates with variation in a quantifiable trait of an organism. Expression QTLs (eQTLs) and protein QTLs (pQTLs) are loci that correlate with variation in mRNA and protein levels, respectively.

Aneuploidy

The abnormal copy number of either a segment of or the entire chromosome in the genome.

Stoichiometry

With reference to protein complexes, the proportion of the individual subunits that make up a protein complex.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buccitelli, C., Selbach, M. mRNAs, proteins and the emerging principles of gene expression control. Nat Rev Genet 21, 630–644 (2020). https://doi.org/10.1038/s41576-020-0258-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41576-020-0258-4

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