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Post-transcriptional regulation across human tissues

Alexander Franks, Edoardo Airoldi, Nikolai Slavov

PLOS Computational Biology · 2017 · CC BY

doi:10.1101/020206

Abstract

Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across-tissues variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissues variability. The reliable quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets, suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.

References 50

  1. 1.

    Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proceedings of the National Academy of Sciences. 2001;98(19):10869–10874.

    Not in our library10.1073/pnas.191367098

  2. 2.

    Slavov N, Dawson KA. Correlation signature of the macroscopic states of the gene regulatory network in cancer. Proceedings of the National Academy of Sciences. 2009;106(11):4079–4084.

    Not in our library10.1073/pnas.0810803106

  3. 3.

    Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, et al Comprehensive identification of cell cycle–regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular biology of the cell. 1998;9(12):3273–3297.

    Not in our library10.1091/mbc.9.12.3273

  4. 4.

    Slavov N, Macinskas J, Caudy A, Botstein D. Metabolic cycling without cell division cycling in respiring yeast. Proceedings of the National Academy of Sciences of the United Statesof America. 2011;108(47):19090–19095.

    Not in our library10.1073/pnas.1116998108

  5. 5.

    Slavov N, Airoldi EM, van Oudenaarden A, Botstein D. A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes. Molecular Biology of the Cell. 2012;23(10):1986–1997.

    Not in our library10.1091/mbc.e11-11-0961

  6. 6.

    Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, et al Landscape of transcription in human cells. Nature. 2012;489(7414):101–108.

    Not in our library10.1038/nature11233

  7. 7.

    Alberts B, Johnson A, Morgan JLD, Raff M, Roberts K, Walter P.Molecular Biology of the Cell.6th ed Garland; 2014.

    Not in our library

  8. 8.

    Kuersten S, Goodwin EB. The power of the 3′ UTR: translational control and development. Nature Reviews Genetics. 2003;4(8):626–637.

    Not in our library10.1038/nrg1125

  9. 9.

    Hengst L, Reed SI. Translational control of p27Kip1 accumulation during the cell cycle. Science. 1996;271(5257):1861–1864.

    Not in our library10.1126/science.271.5257.1861

  10. 10.

    Polymenis M, Schmidt EV. Coupling of cell division to cell growth by translational control of the G1 cyclin CLN3 in yeast. Genes & development. 1997;11(19):2522

    Not in our library10.1101/gad.11.19.2522

  11. 11.

    Daran-Lapujade P, Rossell S, van Gulik WM, Luttik MA, de Groot MJ, Slijper M,et al The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proceedings of the National Academy of Sciences. 2007;104(40):15753–15758.

    Not in our library10.1073/pnas.0707476104

  12. 12.
  13. 13.

    Gygi SP, Rochon Y, Franza BR, Aebersold R. Correlation between protein and mRNA abundance in yeast. Molecular and cellular biology. 1999;19(3):1720–1730.

    Not in our library10.1128/mcb.19.3.1720

  14. 14.

    Smits AH, Lindeboom RG, Perino M, van Heeringen SJ, Veenstra GJC, Vermeulen M. Global absolute quantification reveals tight regulation of protein expression in single Xenopus eggs. Nucleic acids research. 2014;42(15):9880–9891.

    Not in our library10.1093/nar/gku661

  15. 15.

    Schwanhäusser B, Busse D, Li N, Dittmar G, Schuchhardt J, Wolf J, et al Global quantification of mammalian gene expression control. Nature. 2011;473(7347):337–342.

    Not in our library10.1038/nature10098

  16. 16.
  17. 17.
  18. 18.

    Jovanovic M, Rooney MS, Mertins P, Przybylski D, Chevrier N, Satija R, et al Dynamic profiling of the protein life cycle in response to pathogens. Science. 2015;347(6226):1259038

    Not in our library10.1126/science.1259038

  19. 19.
  20. 20.

    Wilhelm M, Schlegl J, Hahne H, Gholami A, Lieberenz M, et al Mass-spectrometry-based draft of the human proteome. Nature. 2014;509:582–587.

    Not in our library10.1038/nature13319

  21. 21.

    Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, et al A draft map of the human proteome. Nature. 2014;509(7502):575–581.

    Not in our library10.1038/nature13302

  22. 22.
  23. 23.

    Blyth CR. On Simpson’s paradox and the sure-thing principle. Journal of the American Statistical Association. 1972;67(338):364–366.

    Not in our library10.1080/01621459.1972.10482387

  24. 24.

    Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome research. 2008;18(9):1509–1517.

    Not in our library10.1101/gr.079558.108

  25. 25.

    Consortium SI, et al A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium. Nature Biotechnology. 2014;32(9):903–914.

    Not in our library10.1038/nbt.2957

  26. 26.

    Peng M, Taouatas N, Cappadona S, van Breukelen B, Mohammed S, Scholten A,et al Protease bias in absolute protein quantitation. Nature methods. 2012;9(6):524–525.

    Not in our library10.1038/nmeth.2031

  27. 27.

    Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H, Pandey A, et al Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & cellular proteomics. 2002;1(5):376–386.

    Not in our library10.1074/mcp.m200025-mcp200

  28. 28.

    Blagoev B, Ong SE, Kratchmarova I, Mann M. Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nature biotechnology. 2004;22(9):1139–1145.

    Not in our library10.1038/nbt1005

  29. 29.

    Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, et al Landscape of transcription in human cells. Nature. 2012;489(7414):101–108.

    Not in our library10.1038/nature11233

  30. 30.

    Fagerberg L, Hallström BM, Oksvold P, Kampf C, Djureinovic D, Odeberg J,et al Analysis of the human tissue-specific expression by genome-wide integration of transcriptomics and antibody-based proteomics. Molecular & Cellular Proteomics. 2014;13(2):397–406.

    Not in our library10.1074/mcp.m113.035600

  31. 31.

    Consortium GO, et al The Gene Ontology (GO) database and informatics resource. Nucleic acids research. 2004;32(suppl 1):D258–D261.

    Not in our library10.1093/nar/gkh036

  32. 32.

    Hall JE. Guyton and Hall Textbook of Medical Physiology: Enhanced E-book.Elsevier Health Sciences; 2010.

    Not in our library

  33. 33.
  34. 34.

    McIsaac RS, Silverman SJ, McClean MN, Gibney PA, Macinskas J, Hickman MJ,et al Fast-acting and nearly gratuitous induction of gene expression and protein depletion in Saccharomyces cerevisiae. Molecular biology of the cell. 2011;22(22):4447–4459.

    Not in our library10.1091/mbc.e11-05-0466

  35. 35.

    Mauro VP, Edelman GM. The ribosome filter hypothesis. Proceedings of the National Academy of Sciences. 2002;99(19):12031–12036.

    Not in our library10.1073/pnas.192442499

  36. 36.

    Mauro VP, Matsuda D. Translation regulation by ribosomes: Increased complexity and expanded scope. RNA biology. 2016;13(9):748–755.

    Not in our library10.1080/15476286.2015.1107701

  37. 37.

    Slavov N, Semrau S, Airoldi E, Budnik B, van Oudenaarden A. Differential stoichiometry among core ribosomal proteins. Cell Reports. 2015;13:865–873.

    Not in our library10.1016/j.celrep.2015.09.056

  38. 38.

    Preiss T. All Ribosomes Are Created Equal. Really? Trends in biochemical sciences. 2016;41(2):121–123.

    Not in our library10.1016/j.tibs.2015.11.009

  39. 39.

    Gebauer F, Hentze MW. Molecular mechanisms of translational control. Nature reviews Molecular cell biology. 2004;5(10):827–835.

    Not in our library10.1038/nrm1488

  40. 40.

    Rojas-Duran MF, Gilbert WV. Alternative transcription start site selection leads to large differences in translation activity in yeast. RNA. 2012;18(12):2299–2305.

    Not in our library10.1261/rna.035865.112

  41. 41.

    Castello A, Fischer B, Eichelbaum K, Horos R, Beckmann BM, Strein C, et al Insights into RNA biology from an atlas of mammalian mRNA–binding proteins. Cell. 2012;149(6):1393–1406.

    Not in our library10.1016/j.cell.2012.04.031

  42. 42.

    Arribere JA, Gilbert WV. Roles for transcript leaders in translation and mRNA decay revealed by transcript leader sequencing. Genome research. 2013;23(6):977–987.

    Not in our library10.1101/gr.150342.112

  43. 43.

    Katz Y, Li F, Lambert NJ, Sokol ES, Tam WL, Cheng AW, et al Musashi proteins are post-transcriptional regulators of the epithelial-luminal cell state. eLife. 2014;3:e03915

    Not in our library10.7554/elife.03915

  44. 44.

    Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nature biotechnology. 2008;26(12):1367–1372.

    Not in our library10.1038/nbt.1511

  45. 45.

    Franks AM, Csárdi G, Drummond DA, Airoldi EM. Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology. Journal of the American Statistical Association. 2015;110(509):27–44.

    Not in our library10.1080/01621459.2014.964404

  46. 46.

    Spearman C. The proof and measurement of association between two things. Am J Psychol. 1904;15:72–101.

    Not in our library10.2307/1412159

  47. 47.

    Zimmerman D, Williams R. Properties of the spearman correction for attenuation for normal and realistic non-normal distributions. Applied Psychological Measurement. 1997;21(3):253270

    Not in our library10.1177/01466216970213005

  48. 48.

    Slavov N, Botstein D. Coupling among growth rate response, metabolic cycle, and cell division cycle in yeast. Molecular Biology of the Cell. 2011;22(12):1997–2009.

    Not in our library10.1091/mbc.e11-02-0132

  49. 49.

    Storey JD. The positive false discovery rate: A Bayesian interpretation and the q-value. Annals of statistics. 2003; p. 2013–2035.

    Not in our library10.1214/aos/1074290335

  50. 50.

    Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Annals of statistics. 2001; p. 1165–1188.

    Not in our library

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