Absolute Quantification of Protein and mRNA Abundances Demonstrate Variability in Gene-Specific Translation Efficiency in Yeast

Cell Systems

Volumn 4, Issue 5, 2017, Pages 495-504.e5

  Lahtvee, Petri Jaan ^a   Sánchez, Benjamín J ^a   Smialowska, Agata ^a,b   Kasvandik, Sergo ^c   Elsemman, Ibrahim E ^d   Gatto, Francesco ^a,e   Nielsen, Jens ^a,d  

^a CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

^b SCIENCE FOR LIFE LABORATORY   (Sweden)

^c UNIVERSITY OF TARTU   (Estonia)

^d Technical University of Denmark   (Denmark)

^e UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

absolute proteome; absolute transcriptome; genome scale metabolic modeling; integrative data analysis; protein degradation rates; protein turnover; translation efficiency; translational control

Indexed keywords

MESSENGER RNA; SACCHAROMYCES CEREVISIAE PROTEIN; PROTEOME; TRANSCRIPTOME;

ARTICLE; CONTROLLED STUDY; NONHUMAN; PRIORITY JOURNAL; PROTEIN METABOLISM; PROTEIN SYNTHESIS REGULATION; QUANTITATIVE ANALYSIS; RNA TRANSLATION; SACCHAROMYCES CEREVISIAE; TRANSLATION ELONGATION; GENE EXPRESSION REGULATION; GENETICS; METABOLISM; PHYSIOLOGY; PROTEIN DEGRADATION; PROTEIN PROCESSING; PROTEIN SYNTHESIS; PROTEOMICS;

GENE EXPRESSION REGULATION, FUNGAL; PROTEIN BIOSYNTHESIS; PROTEIN PROCESSING, POST-TRANSLATIONAL; PROTEOLYSIS; PROTEOME; PROTEOMICS; RNA, MESSENGER; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; TRANSCRIPTOME;

EID: 85016638812     PISSN: 24054712     EISSN: 24054720     Source Type: Journal    
DOI: 10.1016/j.cels.2017.03.003     Document Type: Article

References (71)

1. Anders, S., Huber, W., Differential expression analysis for sequence count data. Genome Biol., 11, 2010, R106.
2. Aung, H.W., Henry, S.A., Walker, L.P., Revising the representation of fatty acid, glycerolipid, and glycerophospholipid metabolism in the consensus model of yeast metabolism. Ind. Biotechnol. (New Rochelle N. Y.) 9 (2013), 215–228.
3. Barabasi, A.-L., Oltvai, Z.N.Z.N., Barabási, A.-L., Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5 (2004), 101–113.
4. Belle, A., Tanay, A., Bitincka, L., Shamir, R., O'Shea, E.K., Quantification of protein half-lives in the budding yeast proteome. Proc. Natl. Acad. Sci. USA 103 (2006), 13004–13009.
5. Benaroudj, N., Zwickl, P., Seemüller, E., Baumeister, W., Goldberg, A.L., ATP hydrolysis by the proteasome regulatory complex PAN serves multiple functions in protein degradation. Mol. Cell 11 (2003), 69–78.
6. Benjamini, Y., Hochberg, Y., Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 57 (1995), 289–300.
7. Bordel, S., Agren, R., Nielsen, J., Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes. PLoS Comput. Biol., 6, 2010, e1000859.
8. Brar, G.A., Yassour, M., Friedman, N., Regev, A., Ingolia, N.T., Weissman, J.S., High-resolution view of the yeast meiotic program revealed by ribosome profiling. Science 335 (2012), 552–557.
9. Bryan, A.K., Goranov, A., Amon, A., Manalis, S.R., Measurement of mass, density, and volume during the cell cycle of yeast. Proc. Natl. Acad. Sci. USA 107 (2010), 999–1004.
10. Charif, D., Lobry, J.R., SeqinR 1.0-2: a contributed package to the R project for statistical computing devoted to biological sequences retrieval and analysis. Bastolla, U., Porto, M., Roman, E., Vendruscolo, M., (eds.) Structural Approaches to Sequence Evolution: Molecules, Networks, Populations, 2007, Springer, 207–232.
11. Cherry, J.M., Hong, E.L., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E.T., Christie, K.R., Costanzo, M.C., Dwight, S.S., Engel, S.R., et al. Saccharomyces genome database: the genomics resource of budding yeast. Nucleic Acids Res. 40 (2012), 1–6.
12. Chong, Y.T., Koh, J.L.Y., Friesen, H., Duffy, K., Cox, M.J., Moses, A., Moffat, J., Boone, C., Andrews, B.J., Yeast proteome dynamics from single cell imaging and automated analysis. Cell 161 (2015), 1413–1424.
13. Chubukov, V., Uhr, M., Le Chat, L., Kleijn, R.J., Jules, M., Link, H., Aymerich, S., Stelling, J., Sauer, U., Transcriptional regulation is insufficient to explain substrate-induced flux changes in Bacillus subtilis. Mol. Syst. Biol., 9, 2013, 709.
14. Cox, J., Mann, M., MaxQuant enables high peptide identification rates, individualized ppb-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26 (2008), 1367–1372.
15. Crooks, G., Hon, G., Chandonia, J., Brenner, S., NCBI GenBank FTP Site\nWebLogo: a sequence logo generator. Genome Res. 14 (2004), 1188–1190.
16. Csárdi, G., Franks, A., Choi, D.S., Airoldi, E.M., Drummond, D.A., Accounting for experimental noise reveals that mRNA levels, amplified by post-transcriptional processes, largely determine steady-state protein levels in yeast. PLoS Genet., 11, 2015, e1005206.
17. Edfors, F., Danielsson, F., Hallström, B.M., Käll, L., Lundberg, E., Pontén, F., Uhlén, M., Gene-specific correlation of RNA and protein levels in human cells and tissues. Mol. Syst. Biol., 12, 2016, 883.
18. Forster, J., Famili, I., Palsson, B.O., Nielsen, J., Genome-scale reconstruction of the Saccharomyces cerevisiae Metabolic Network. Genome Res. 13 (2003), 244–253.
19. Ghaemmaghami, S., Huh, W., Bower, K., Howson, R.W., Belle, A., Dephoure, N., O'Shea, E.K., Weissman, J.S., Global analysis of protein expression in yeast. Nature 108 (2003), 737–741.
20. Gingold, H., Pilpel, Y., Determinants of translation efficiency and accuracy. Mol. Syst. Biol., 7, 2011, 481.
21. Goodwin, S., McPherson, J.D., McCombie, W.R., Coming of age: ten years of next-generation sequencing technologies. Nat. Rev. Genet. 17 (2016), 333–351.
22. Helbig, A.O., Daran-Lapujade, P., van Maris, A.J., de Hulster, E.A., de Ridder, D., Pronk, J.T., Heck, A.J.R., Slijper, M., The diversity of protein turnover and abundance under nitrogen-limited steady-state conditions in Saccharomyces cerevisiae. Mol. Biosyst. 7 (2011), 3316–3326.
23. Hock, M.B., Kralli, A., Transcriptional control of mitochondrial biogenesis and function. Annu. Rev. Genet. 71 (2009), 177–203.
24. Hong, K.-K., Hou, J., Shoaie, S., Nielsen, J., Bordel, S., Dynamic (13) C-labeling experiments prove important differences in protein turnover rate between two Saccharomyces cerevisiae strains. FEMS Yeast Res. 12 (2012), 741–747.
25. Hui, S., Silverman, J.M., Chen, S.S., Erickson, D.W., Basan, M., Wang, J., Hwa, T., Williamson, J.R., Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol. Syst. Biol., 11, 2015, 784.
26. Ingolia, N.T., Ghaemmaghami, S., Newman, J.R.S., Weissman, J.S., Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324 (2009), 218–223.
27. Kerkhoven, E.J., Lahtvee, P.-J., Nielsen, J., Applications of computational modeling in metabolic engineering of yeast. FEMS Yeast Res. 15 (2015), 1–13.
28. Khmelinskii, A., Keller, P.J., Bartosik, A., Meurer, M., Barry, J.D., Mardin, B.R., Kaufmann, A., Trautmann, S., Wachsmuth, M., Pereira, G., et al. Tandem fluorescent protein timers for in vivo analysis of protein dynamics. Nat. Biotechnol. 30 (2012), 708–714.
29. Khoomrung, S., Chumnanpuen, P., Jansa-Ard, S., Ståhlman, M., Nookaew, I., Borén, J., Nielsen, J., Rapid quantification of yeast lipid using microwave-assisted total lipid extraction and HPLC-CAD. Anal. Chem. 85 (2013), 4912–4919.
30. Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., Salzberg, S.L., TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol., 14, 2013, R36.
31. Klis, F.M., de Koster, C.G., Brul, S., Cell wall-related bionumbers and bioestimates of Saccharomyces cerevisiae and Candida albicans. Eukaryot. Cell 13 (2014), 2–9.
32. Lahtvee, P.-J., Adamberg, K., Arike, L., Nahku, R., Aller, K., Vilu, R., Multi-omics approach to study the growth efficiency and amino acid metabolism in Lactococcus lactis at various specific growth rates. Microb. Cell Fact, 10, 2011, 12.
33. Lahtvee, P.-J., Seiman, A., Arike, L., Adamberg, K., Vilu, R., Protein turnover forms one of the highest maintenance costs in Lactococcus lactis. Microbiology 160 (2014), 1501–1512.
34. Lahtvee, P.J., Kumar, R., Hallström, B.M., Nielsen, J., Adaptation to different types of stress converge on mitochondrial metabolism. Mol. Biol. Cell 27 (2016), 2505–2514.
35. Liao, Y., Smyth, G.K., Shi, W., featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30 (2014), 923–930.
36. Liu, Y.-Y., Slotine, J.-J., Barabási, A.-L., Controllability of complex networks. Nature 473 (2011), 167–173.
37. Liu, Y.-Y., Slotine, J.-J., Barabási, A.-L., Observability of complex systems. Proc. Natl. Acad. Sci. USA 110 (2013), 2460–2465.
38. Maier, T., Güell, M., Serrano, L., Correlation of mRNA and protein in complex biological samples. FEBS Lett. 583 (2009), 3966–3973.
39. Marguerat, S., Schmidt, A., Codlin, S., Chen, W., Aebersold, R., Bähler, J., Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells. Cell 151 (2012), 671–683.
40. 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 (2014), 422–430.
41. McMurrough, I., Rose, A.H., Effect of growth rate and substrate limitation on the composition and structure of the cell wall of Saccharomyces cerevisiae. Biochem. J. 105 (1967), 189–203.
42. Milborrow, S. (2011). Package ‘earth’3.2–1. Multivariate adaptive regression spline models.
43. Milo, R., What is the total number of protein molecules per cell volume? A call to rethink some published values. BioEssays 35 (2013), 1050–1055.
44. Nielsen, J., It is all about metabolic fluxes. J. Bacteriol. 185 (2003), 7031–7035.
45. Nielsen, J., Transcriptional control of metabolic fluxes. Mol. Syst. Biol., 7, 2011, 478.
46. Nilsson, A., Nielsen, J., Metabolic trade-offs in yeast are caused by F1F0-ATP synthase. Sci. Rep., 6, 2016, 22264.
47. Orth, J.D., Thiele, I., Palsson, B.Ø., What is flux balance analysis?. Nat. Biotechnol. 28 (2010), 245–248.
48. Peebo, K., Valgepea, K., Maser, A., Nahku, R., Adamberg, K., Vilu, R., Proteome reallocation in Escherichia coli with increasing specific growth rate. Mol. Biosyst. 11 (2015), 1184–1193.
49. Pelechano, V., Wei, W., Steinmetz, L.M., Widespread co-translational RNA decay reveals ribosome dynamics. Cell 161 (2015), 1400–1412.
50. Picotti, P., Bodenmiller, B., Mueller, L.N., Domon, B., Aebersold, R., Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138 (2009), 795–806.
51. Pratt, J.M., Petty, J., Riba-Garcia, I., Robertson, D.H.L., Gaskell, S.J., Oliver, S.G., Beynon, R.J., Dynamics of protein turnover, a missing dimension in proteomics. Mol. Cell. Proteomics 1 (2002), 579–591.
52. Reimand, J., Arak, T., Vilo, J., G: Profiler—a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res. 39 (2011), 307–315.
53. Reuter, J.A., Spacek, D.V., Snyder, M.P., High-throughput sequencing technologies. Mol. Cell 58 (2015), 586–597.
54. Robbins-Pianka, A., Rice, M.D., Weir, M.P., The mRNA landscape at yeast translation initiation sites. Bioinformatics 26 (2010), 2651–2655.
55. Sánchez, B.J., Nielsen, J., Genome scale models of yeast: towards standardized evaluation and consistent omic integration. Integr. Biol. 7 (2015), 846–858.
56. Schellenberger, J., Que, R., Fleming, R.M.T., Thiele, I., Orth, J.D., Feist, A.M., Zielinski, D.C., Bordbar, A., Lewis, N.E., Rahmanian, S., et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6 (2011), 1290–1307.
57. Schmidt, A., Kochanowski, K., Vedelaar, S., Ahrne, E., Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold, R., Heinemann, M., The quantitative and condition-dependent Escherichia coli proteome. Nat. Biotechnol. 34 (2016), 104–110.
58. Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., Selbach, M., Global quantification of mammalian gene expression control. Nature 473 (2011), 337–342.
59. Sharp, P.M., Cowe, E., Synonymous codon usage in Saccharomyces cerevisiae. Yeast 7 (1991), 657–678.
60. Simeonidis, E., Price, N.D., Genome-scale modeling for metabolic engineering. J. Ind. Microbiol. Biotechnol. 42 (2015), 327–338.
61. Stephanopoulos, G., Aristidou, A.A., Nielsen, J.H., Metabolic Engineering: Principles and Methodologies. 1998, Academic Press.
62. Stouthamer, A.H., A theoretical study on the amount of ATP required for synthesis of microbial cell material. Antonie Van Leeuwenhoek 39 (1973), 545–565.
63. Trapnell, C., Williams, B.A., Pertea, G., Mortazavi, A., Kwan, G., Van Baren, M.J., Salzberg, S.L., Wold, B.J., Pachter, L., Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28 (2010), 511–515.
64. Tuller, T., Waldman, Y.Y., Kupiec, M., Ruppin, E., Translation efficiency is determined by both codon bias and folding energy. TL-107. Proc. Natl. Acad. Sci. USA 107 (2010), 3645–3650.
65. Valgepea, K., Adamberg, K., Seiman, A., Vilu, R., Escherichia coli achieves faster growth by increasing catalytic and translation rates of proteins. Mol. Biosyst. 9 (2013), 2344–2358.
66. Vizcaíno, J.A., Côté, R.G., Csordas, A., Dianes, J.A., Fabregat, A., Foster, J.M., Griss, J., Alpi, E., Birim, M., Contell, J., et al. The Proteomics Identifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res. 41 (2013), D1063–D1069.
67. Vogel, C., de Sousa Abreu, R., Ko, D., Le, S.-Y., Shapiro, B.A., Burns, S.C., Sandhu, D., Boutz, D.R., Marcotte, E.M., Penalva, L.O., Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line. Mol. Syst. Biol., 6, 2010, 400.
68. Weinberg, D.E., Shah, P., Eichhorn, S.W., Hussmann, J.A., Plotkin, J.B., Bartel, D.P., Improved ribosome-footprint and mRNA measurements provide insights into dynamics and regulation of yeast translation. Cell Rep. 14 (2016), 1787–1799.
69. Wilhelm, M., Schlegl, J., Hahne, H., Gholami, A.M., Lieberenz, M., Savitski, M.M., Mathieson, T., Mass-spectrometry-based draft of the human proteome. Nature 509 (2014), 582–587.
70. Yassour, M., Kaplan, T., Fraser, H.B., Levin, J.Z., Pfiffner, J., Adiconis, X., Schroth, G., Luo, S., Khrebtukova, I., Gnirke, A., et al. Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing. Proc. Natl. Acad. Sci. USA 106 (2009), 3264–3269.
71. Zur, H., Tuller, T., New universal rules of eukaryotic translation initiation fidelity. PLoS Comput. Biol., 9, 2013, e1003136.