#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast


Cells respond to their environment by making proteins using transcription and translation of mRNA. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy and contain missing values. Here we show that when methods that account for noise are used to analyze much of the same data, mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels as commonly assumed, but rise much more rapidly. Regulation of translation achieves amplification of, rather than competition with, transcriptional signals. Our results suggest that for this set of conditions, mRNA sets protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.


Vyšlo v časopise: Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast. PLoS Genet 11(5): e32767. doi:10.1371/journal.pgen.1005206
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1005206

Souhrn

Cells respond to their environment by making proteins using transcription and translation of mRNA. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy and contain missing values. Here we show that when methods that account for noise are used to analyze much of the same data, mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels as commonly assumed, but rise much more rapidly. Regulation of translation achieves amplification of, rather than competition with, transcriptional signals. Our results suggest that for this set of conditions, mRNA sets protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.


Zdroje

1. de Sousa Abreu R, Penalva L, Marcotte E, Vogel C (2009) Global signatures of protein and mRNA expression levels. Mol Biosyst 5: 1512–1526. doi: 10.1039/b908315d 20023718

2. Belle A, Tanay A, Bitincka L, Shamir R, O’Shea EK (2006) Quantification of protein half-lives in the budding yeast proteome. Proc Natl Acad Sci U S A 103: 13004–13009. doi: 10.1073/pnas.0605420103 16916930

3. Schwanhausser B, Busse D, Li N, Dittmar G, Schuchhardt J, et al. (2011) Global quantification of mammalian gene expression control. Nature 473: 337–342. doi: 10.1038/nature10098 21593866

4. Beyer A, Hollunder J, Nasheuer HP, Wilhelm T (2004) Post-transcriptional expression regulation in the yeast Saccharomyces cerevisiae on a genomic scale. Mol Cell Proteomics 3: 1083–1092. doi: 10.1074/mcp.M400099-MCP200 15326222

5. Yu EZ, Burba AEC, Gerstein M (2007) PARE: a tool for comparing protein abundance and mRNA expression data. BMC bioinformatics 8: 309. doi: 10.1186/1471-2105-8-309 17718915

6. Gygi S, Rochon Y, Franza B, Aebersold R (1999) Correlation between protein and mRNA abundance in yeast. Mol Cell Biol 19: 1720–1730. 10022859

7. Maier T, Guell M, Serrano L (2009) Correlation of mRNA and protein in complex biological samples. FEBS Lett 583: 3966–3973. doi: 10.1016/j.febslet.2009.10.036 19850042

8. Siwiak M, Zielenkiewicz P (2010) A comprehensive, quantitative, and genome-wide model of translation. PLoS Comput Biol 6: e1000865. doi: 10.1371/journal.pcbi.1000865 20686685

9. Vogel C, Marcotte E (2012) Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet 13: 227–232. doi: 10.1038/nrg3185 22411467

10. Nie L, Wu G, Zhang W (2006) Correlation between mRNA and protein abundance in Desulfovib-rio vulgaris: a multiple regression to identify sources of variations. Biochemical and biophysical research communications 339: 603–10. doi: 10.1016/j.bbrc.2005.11.055 16310166

11. Brockmann R, Beyer A, Heinisch J, Wilhelm T (2007) Posttranscriptional expression regulation: what determines translation rates? PLoS Comput Biol 3: e57. doi: 10.1371/journal.pcbi.0030057 17381238

12. Schmidt MW, Houseman A, Ivanov AR, Wolf DA (2007) Comparative proteomic and transcriptomic profiling of the fission yeast Schizosaccharomyces pombe. Molecular systems biology 3: 79. doi: 10.1038/msb4100117 17299416

13. Castrillo J, Zeef L, Hoyle D, Zhang N, Hayes A, et al. (2007) Growth control of the eukaryote cell: a systems biology study in yeast. J Biol 6: 4. doi: 10.1186/jbiol54 17439666

14. Wu G, Nie L, Zhang W (2008) Integrative analyses of posttranscriptional regulation in the yeast Saccharomyces cerevisiae using transcriptomic and proteomic data. Current microbiology 57: 18–22. doi: 10.1007/s00284-008-9145-5 18363056

15. Vogel C, Abreu Rde S, Ko D, Le SY, Shapiro BA, et al. (2010) Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol Syst Biol 6: 400. doi: 10.1038/msb.2010.59 20739923

16. Wang M, Weiss M, Simonovic M, Haertinger G, Schrimpf S, et al. (2012) Paxdb, a database of protein abundance averages across all three domains of life. Molecular & Cellular Proteomics 11: 492–500. doi: 10.1074/mcp.O111.014704

17. Lu P, Vogel C, Wang R, Yao X, Marcotte E (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol 25: 117–124. doi: 10.1038/nbt1270 17187058

18. Ingolia N, Ghaemmaghami S, Newman J, Weissman J (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324: 218–223. doi: 10.1126/science.1168978 19213877

19. Greenbaum D, Colangelo C, Williams K, Gerstein M (2003) Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol 4: 117. doi: 10.1186/gb-2003-4-9-117 12952525

20. Futcher B, Latter G, Monardo P, McLaughlin C, Garrels J (1999) A sampling of the yeast proteome. Mol Cell Biol 19: 7357–7368. 10523624

21. Li J, Bickel P, Biggin M (2014) System wide analyses have underestimated protein abundances and the importance of transcription in mammals. PeerJ 2: e270. doi: 10.7717/peerj.270 24688849

22. Leek J, Scharpf R, Bravo H, Simcha D, Langmead B, et al. (2010) Tackling the widespread and critical impact of batch effects in highthroughput data. Nat Rev Genet 11: 733–739. doi: 10.1038/nrg2825 20838408

23. Li JJ, Biggin MD (2015) Statistics requantitates the central dogma. Science 347: 1066–1067. doi: 10.1126/science.aaa8332 25745146

24. Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15: 72–101. doi: 10.2307/1412159

25. Pearson K (1903) I. Mathematical Contributions to the Theory of Evolution.XI On the Influence of Natural Selection on the Variability and Correlation of Organs. Philosophical Transactions of the Royal Society of London Series A 200: 1–66. doi: 10.1098/rsta.1903.0001

26. Alexander RA, Hanges PJ, Alliger GM (1985) Correcting for Restriction of Range in Both X and Y When the Unrestricted Variances are Unknown. Applied Psychological Measurement 9: 317–323. doi: 10.1177/014662168500900310

27. Franks AM, Csárdi G, Drummond DA, Airoldi EM (2014) Estimating a structured covariance matrix from multi-lab measurements in high-throughput biology. Journal of the American Statistical Association:00–00.

28. Plotkin J (2010) Transcriptional regulation is only half the story. Mol Syst Biol 6: 406. doi: 10.1038/msb.2010.63 20739928

29. Jovanovic M, Rooney M, Mertins P, Przybylski D, Chevrier N, et al. (2015) Dynamic profiling of the protein life cycle in response to pathogens. Science 347: 1259038-. doi: 10.1126/science.1259038 25745177

30. Ghaemmaghami S, Huh W, Bower K, Howson R, Belle A, et al. (2003) Global analysis of protein expression in yeast. Nature 425: 737–741. doi: 10.1038/nature02046 14562106

31. Hutcheon J, Chiolero A, Hanley J (2010) Random measurement error and regression dilution bias. BMJ 340: c2289. doi: 10.1136/bmj.c2289 20573762

32. Weisberg S (2005) Applied Linear Regression. Hoboken, NJ: John Wiley & Sons, Inc., third edit edition. URL http://onlinelibrary.wiley.com/book/10.1002/0471704091.

33. Legendre P, Legendre L, Legendre L, Legendre L (1998) Numerical ecology. Amsterdam, New York: Elsevier, 2nd English edition.

34. Sokal R, Rohlf F (1995) Biometry. New York: W. H. Freeman and Co., 3rd edition.

35. Velculescu V, Zhang L, Zhou W, Vogelstein J, Basrai M, et al. (1997) Characterization of the yeast transcriptome. Cell 88: 243–251. doi: 10.1016/S0092-8674(00)81845-0 9008165

36. Holstege F, Jennings E, Wyrick J, Lee T, Hengartner C, et al. (1998) Dissecting the regulatory circuitry of a eukaryotic genome. Cell 95: 717–728. doi: 10.1016/S0092-8674(00)81641-4 9845373

37. Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, et al. (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320: 1344–1349. doi: 10.1126/science.1158441 18451266

38. Lipson D, Raz T, Kieu A, Jones D, Giladi E, et al. (2009) Quantification of the yeast transcriptome by single-molecule sequencing. Nat Biotechnol 27: 652–658. doi: 10.1038/nbt.1551 19581875

39. Newman J, Ghaemmaghami S, Ihmels J, Breslow D, Noble M, et al. (2006) Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441: 840–846. doi: 10.1038/nature04785 16699522

40. de Godoy L, Olsen J, Cox J, Nielsen M, Hubner N, et al. (2008) Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455: 1251–1254. doi: 10.1038/nature07341 18820680

41. Lee M, Topper S, Hubler S, Hose J, Wenger C, et al. (2011) A dynamic model of proteome changes reveals new roles for transcript alteration in yeast. Mol Syst Biol 7: 514. doi: 10.1038/msb.2011.48 21772262

42. Cherry J, Hong E, Amundsen C, Balakrishnan R, Binkley G, et al. (2012) Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Res 40: D700–705. doi: 10.1093/nar/gkr1029 22110037

43. Yassour M, Kaplan T, Fraser H, Levin J, Pfiffner J, et al. (2009) Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing. Proc Natl Acad Sci USA 106: 3264–3269. doi: 10.1073/pnas.0812841106 19208812

44. Marioni J, Mason C, Mane S, Stephens M, Gilad Y (2008) Rna-seq: An assessment of technical reproducibility and comparison with gene expression arrays. Genome Research 18: 1509–1517. doi: 10.1101/gr.079558.108 18550803

45. Archer K, Dumur C, Taylor G, Chaplin M, Guiseppi-Elie, et al. (2008) A disattenuated correlation estimate when variables are measured with error: illustration estimating cross-platform correlations. Stat med 27: 1026–1039. doi: 10.1002/sim.2984 17600855

46. Schmidt F, Hunter J (1999) Theory testing and measurement error. Intelligence 27: 183198. doi: 10.1016/S0160-2896(99)00024-0

47. Muchinsky P (1996) The correction for attenuation. Educational and psychological measurement 56: 63–75. doi: 10.1177/0013164496056001004

48. Zimmerman D, Williams R (1997) Properties of the spearman correction for attenuation for normal and realistic non-normal distributions. Applied Psychological Measurement 21: 253270. doi: 10.1177/01466216970213005

49. Drummond DA, Bloom JD, Adami C, Wilke CO, Arnold FH (2005) Why highly expressed proteins evolve slowly. Proc Natl Acad Sci USA 102: 14338–14343. doi: 10.1073/pnas.0504070102 16176987

50. Adolph S, Hardin J (2007) Estimating phenotypic correlations: correcting for bias due to intrain-dividual variability. Functional Ecology 21: 178–184. doi: 10.1111/j.1365-2435.2006.01209.x

51. Archer K, Dumur C, Taylor G, Chaplin M, Guiseppi-Elie A, et al. (2007) Application of a correlation correction factor in a microarray crossplatform reproducibility study. BMC Bioinformatics 8: 447. doi: 10.1186/1471-2105-8-447 18005444

52. Behseta S, Berdyyeva T, Olson C, Kass R (2009) Bayesian correction for attenuation of correlation in multi-trial spike count data. J neurophysiol 101: 2186–2193. doi: 10.1152/jn.90727.2008 19129297

53. Zenklusen D, Larson D, Singer R (2008) Single-RNA counting reveals alternative modes of gene expression in yeast. Nat Struct Mol Biol 15: 1263–1271. doi: 10.1038/nsmb.1514 19011635

54. Miura F, Kawaguchi N, Yoshida M, Uematsu C, Kito K, et al. (2008) Absolute quantification of the budding yeast transcriptome by means of competitive PCR between genomic and complementary DNAs. BMC Genomics 9: 574. doi: 10.1186/1471-2164-9-574 19040753

55. Johnston GC, Pringle JR, Hartwell LH (1977) Coordination of growth with cell division in the yeast Saccharomyces cerevisiae. Experimental cell research 105: 79–98. doi: 10.1016/0014-4827(77)90154-9 320023

56. von der Haar T, McCarthy J (2002) Intracellular translation initiation factor levels in Saccharomyces cerevisiae and their role in cap-complex function. Mol Microbiol 46: 531–544. doi: 10.1046/j.1365-2958.2002.03172.x 12406227

57. Picotti P, Bodenmiller B, Mueller L, Domon B, Aebersold R (2009) Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138: 795–806. doi: 10.1016/j.cell.2009.05.051 19664813

58. Gerashchenko M, Lobanov A, Gladyshev V (2012) Genome-wide ribosome profiling reveals complex translational regulation in response to oxidative stress. Proc Natl Acad Sci USA 109: 17394–17399. doi: 10.1073/pnas.1120799109 23045643

59. Gerashchenko MV, Gladyshev VN (2014) Translation inhibitors cause abnormalities in ribosome profiling experiments. Nucleic acids research 42: e134. doi: 10.1093/nar/gku671 25056308

60. McManus CJ, May GE, Spealman P, Shteyman A (2014) Ribosome profiling reveals post-transcriptional buffering of divergent gene expression in yeast. Genome research 24: 422–30. doi: 10.1101/gr.164996.113 24318730

61. Subtelny AO, Eichhorn SW, Chen GR, Sive H, Bartel DP (2014) Poly(A)-tail profiling reveals an embryonic switch in translational control. Nature 508: 66–71. doi: 10.1038/nature13007 24476825

62. Ahrné E, Molzahn L, Glatter T, Schmidt A (2013) Critical assessment of proteome-wide label-free absolute abundance estimation strategies. Proteomics 13: 2567–78. doi: 10.1002/pmic.201300135 23794183

63. Warner JR (1999) The economics of ribosome biosynthesis in yeast. Trends Biochem Sci 24: 437–440. doi: 10.1016/S0968-0004(99)01460-7 10542411

64. Marguerat S, Schmidt A, Codlin S, Chen W, Aebersold R, et al. (2012) Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. CELL 151: 671–683. doi: 10.1016/j.cell.2012.09.019 23101633

65. Akashi H (2003) Translational selection and yeast proteome evolution. Genetics 164: 1291–1303. 12930740

66. Wallace EW, Airoldi EM, Drummond DA (2013) Estimating selection on synonymous codon usage from noisy experimental data. Mol Biol Evol 30: 1438–1453. doi: 10.1093/molbev/mst051 23493257

67. Gu W, Zhou T, Wilke C (2010) A universal trend of reduced mrna stability near the translation-initiation site in prokaryotes and eukaryotes. PLoS Computational Biology 6: e1000664. doi: 10.1371/journal.pcbi.1000664 20140241

68. Goodman DB, Church GM, Kosuri S (2013) Causes and effects of N-terminal codon bias in bacterial genes. Science 342: 475–479. doi: 10.1126/science.1241934 24072823

69. Kudla G, Murray AW, Tollervey D, Plotkin JB (2009) Coding-sequence determinants of gene expression in Escherichia coli. Science 324: 255–258. doi: 10.1126/science.1170160 19359587

70. Frank KR, Mills D (1978) Ribosome activity and degradation in meiotic cells of Saccharomyces cerevisiae. Molecular & General Genetics 160: 59–65.

71. R Core Team (2014) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

72. Gelman A, Jakulin A, Pittau M, Su Y (2008) A weakly informative default prior distribution for logistic and other regression models. Annals of Applied Statistics 2: 1360–1383. doi: 10.1214/08-AOAS191

73. Barnard J, McCulloch R, Meng X (2000) Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Statistica Sinica 10: 1281–1311.

74. Gelman A, Carlin J, Stern H, Rubin D (2003) Bayesian data analysis. Chapman and Hall/CRC, 2 edition.

75. Peng J, Elias J, Thoreen C, Licklider L, Gygi S (2003) Evaluation of multidimensional chromatog-raphy coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome. J Proteome Res 2: 43–50. doi: 10.1021/pr025556v 12643542

76. Causton H, Ren B, Koh S, Harbison C, Kanin E, et al. (2001) Remodeling of yeast genome expression in response to environmental changes. Mol Biol Cell 12: 323–337. doi: 10.1091/mbc.12.2.323 11179418

77. Dudley A, Aach J, Steffen M, Church G (2002) Measuring absolute expression with microarrays with a calibrated reference sample and an extended signal intensity range. Proc Natl Acad Sci USA 99: 7554–7559. doi: 10.1073/pnas.112683499 12032321

78. Garcia-Martinez J, Aranda A, Perez-Ortin J (2004) Genomic run-on evaluates transcription rates for all yeast genes and identifies gene regulatory mechanisms. Mol Cell 15: 303–313. doi: 10.1016/j.molcel.2004.06.004 15260981

79. Ingolia NT (2010) Genome-wide translational profiling by ribosome footprinting. Methods Enzymol 470: 119–142. doi: 10.1016/S0076-6879(10)70006-9 20946809

80. MacKay V, Li X, Flory M, Turcott E, Law G, et al. (2004) Gene expression analyzed by high-resolution state array analysis and quantitative proteomics: response of yeast to mating pheromone. Mol Cell Proteomics 3: 478–489. doi: 10.1074/mcp.M300129-MCP200 14766929

81. Pelechano V, Pérez-Ortín J (2010) There is a steady-state transcriptome in exponentially growing yeast cells. Yeast 27: 413–422. doi: 10.1002/yea.1768 20301094

82. Roth F, Hughes J, Estep P, Church G (1998) Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat Biotechnol 16: 939–945. doi: 10.1038/nbt1098-939 9788350

83. Nagaraj N, Kulak N, Cox J, Neuhauser N, Mayr K, et al. (2012) System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Mol Cell Proteomics 11: M111.013722 doi: 10.1074/mcp.M111.013722 22021278

84. Thakur S, Geiger T, Chatterjee B, Bandilla P, Frohlich F, et al. (2011) Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol Cell Proteomics 10: M110.003699 doi: 10.1074/mcp.M110.003699 21586754

85. Washburn M, Wolters D, Yates J 3rd (2001) Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat Biotechnol 19: 242–247. doi: 10.1038/85686 11231557

Štítky
Genetika Reprodukčná medicína

Článok vyšiel v časopise

PLOS Genetics


2015 Číslo 5
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#