Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast

PLoS Computational Biology

Volumn 13, Issue 1, 2017, Pages

  Gonçalves, Emanuel ^a   Raguz Nakic, Zrinka ^b   Zampieri, Mattia ^b   Wagih, Omar ^a   Ochoa, David ^a   Sauer, Uwe ^b   Beltrao, Pedro ^a   Saez Rodriguez, Julio ^a,c  

^a EUROPEAN BIOINFORMATICS INSTITUTE   (United Kingdom)

^b ETH ZURICH   (Switzerland)

^c RWTH AACHEN UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ASSOCIATION REACTIONS; ENZYME ACTIVITY; LEARNING SYSTEMS; PHYSIOLOGY; SIGNAL TRANSDUCTION; YEAST;

CONDITION; EXTRACELLULAR; IN-SILICO; METABOLIC ADAPTATION; METABOLOMICS; PHOSPHOPROTEOMICS; POST-TRANSCRIPTIONAL REGULATIONS; REGULATORY MECHANISM; SYSTEMATIC ANALYSIS; TIME-RESOLVED;

METABOLISM;

PHEROMONE; PHOSPHATASE; PHOSPHOTRANSFERASE; PROTEIN; SODIUM CHLORIDE; TRANSCRIPTION FACTOR;

ARTICLE; BINDING SITE; COMPUTER MODEL; CONTROLLED STUDY; ENZYME ACTIVITY; FUNGUS GROWTH; GROWTH RATE; INTRACELLULAR SIGNALING; METABOLIC REGULATION; METABOLOMICS; NITROGEN METABOLISM; NONHUMAN; PREDICTIVE VALUE; PROTEIN PROCESSING; PROTEIN PROTEIN INTERACTION; PROTEOMICS; TRANSCRIPTION REGULATION; TRANSCRIPTOMICS; YEAST; DRUG EFFECTS; GENE EXPRESSION REGULATION; GENETIC DATABASE; GENETICS; METABOLISM; PROCEDURES; SACCHAROMYCES CEREVISIAE; SYSTEMS BIOLOGY;

DATABASES, GENETIC; GENE EXPRESSION REGULATION, FUNGAL; METABOLOMICS; PHEROMONES; PROTEINS; SACCHAROMYCES CEREVISIAE; SODIUM CHLORIDE; SYSTEMS BIOLOGY; TRANSCRIPTION FACTORS;

EID: 85011355677     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005297     Document Type: Article

References (69)

1. Chubukov V, Gerosa L, Kochanowski K, Sauer U, Coordination of microbial metabolism. Nat Rev Microbiol. 2014;12: 327–340. doi: 10.1038/nrmicro323824658329
2. Herrgård MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol. 2008;26: 1155–1160. doi: 10.1038/nbt149218846089
3. Daran-Lapujade P, Rossell S, van Gulik WM, Luttik MAH, de Groot MJL, Slijper M, et al. The fluxes through glycolytic enzymes in Saccharomyces cerevisiae are predominantly regulated at posttranscriptional levels. Proc Natl Acad Sci U S A. 2007;104: 15753–15758. doi: 10.1073/pnas.070747610417898166
4. Bodenmiller B, Wanka S, Kraft C, Urban J, Campbell D, Pedrioli PG, et al. Phosphoproteomic Analysis Reveals Interconnected System-Wide Responses to Perturbations of Kinases and Phosphatases in Yeast. Sci Signal. AAAS; 2010;3: rs4–rs4.
5. Kemmeren P, Sameith K, van de Pasch LAL, Benschop JJ, Lenstra TL, Margaritis T, et al. Large-scale genetic perturbations reveal regulatory networks and an abundance of gene-specific repressors. Cell. Elsevier Inc.; 2014;157: 740–752.
6. Schulz JC, Zampieri M, Wanka S, von Mering C, Sauer U, Large-scale functional analysis of the roles of phosphorylation in yeast metabolic pathways. Sci Signal. AAAS; 2014;7: rs6.
7. Oliveira AP, Ludwig C, Picotti P, Kogadeeva M, Aebersold R, Sauer U, Regulation of yeast central metabolism by enzyme phosphorylation. Mol Syst Biol. Nature Publishing Group; 2012;8.
8. Zelezniak A, Sheridan S, Patil KR, Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes. PLoS Comput Biol. 2014;10: e1003572. doi: 10.1371/journal.pcbi.100357224762675
9. Patil KR, Nielsen J, Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc Natl Acad Sci U S A. 2005;102: 2685–2689. doi: 10.1073/pnas.040681110215710883
10. Gerosa L, Haverkorn van Rijsewijk BRB, Christodoulou D, Kochanowski K, Schmidt TSB, Noor E, et al. Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data. Cell Systems. Elsevier; 2015;1: 270–282.
11. Oliveira AP, Dimopoulos S, Busetto AG, Christen S, Dechant R, Falter L, et al. Inferring causal metabolic signals that regulate the dynamic TORC1-dependent transcriptome. Mol Syst Biol. EMBO Press; 2015;11: 802.
12. Oliveira AP, Ludwig C, Zampieri M, Weisser H, Aebersold R, Sauer U, Dynamic phosphoproteomics reveals TORC1-dependent regulation of yeast nucleotide and amino acid biosynthesis. Sci Signal. AAAS; 2015;8: rs4.
13. Daran-Lapujade P, Jansen MLA, Daran J-M, van Gulik W, de Winde JH, Pronk JT, Role of transcriptional regulation in controlling fluxes in central carbon metabolism of Saccharomyces cerevisiae. A chemostat culture study. J Biol Chem. 2004;279: 9125–9138. doi: 10.1074/jbc.M30957820014630934
14. Machado D, Herrgård M, Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism. PLoS Comput Biol. Public Library of Science; 2014;10: e1003580.
15. Yugi K, Kubota H, Toyoshima Y, Noguchi R, Kawata K, Komori Y, et al. Reconstruction of insulin signal flow from phosphoproteome and metabolome data. Cell Rep. 2014;8: 1171–1183. doi: 10.1016/j.celrep.2014.07.02125131207
16. Cheng C, Alexander R, Min R, Leng J, Yip KY, Rozowsky J, et al. Understanding transcriptional regulation by integrative analysis of transcription factor binding data. Genome Res. 2012;22: 1658–1667. doi: 10.1101/gr.136838.11122955978
17. Schacht T, Oswald M, Eils R, Eichmüller SB, König R, Estimating the activity of transcription factors by the effect on their target genes. Bioinformatics. 2014;30: i401–7. doi: 10.1093/bioinformatics/btu44625161226
18. Casado P, Rodriguez-Prados J-C, Cosulich SC, Guichard S, Vanhaesebroeck B, Joel S, et al. Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells. Sci Signal. 2013;6: rs6. doi: 10.1126/scisignal.200357323532336
19. Mischnik M, Sacco F, Cox J, Schneider H-C, Schäfer M, Hendlich M, et al. IKAP: A heuristic framework for inference of kinase activities from Phosphoproteomics data. Bioinformatics. 2016;32: 424–431. doi: 10.1093/bioinformatics/btv69926628587
20. Brückner S, Kern S, Birke R, Saugar I, Ulrich HD, Mösch H-U, The TEA transcription factor Tec1 links TOR and MAPK pathways to coordinate yeast development. Genetics. 2011;189: 479–494. doi: 10.1534/genetics.111.13362921840851
21. Saito H, Posas F, Response to hyperosmotic stress. Genetics. 2012;192: 289–318. doi: 10.1534/genetics.112.14086323028184
22. Vaga S, Bernardo-Faura M, Cokelaer T, Maiolica A, Barnes CA, Gillet LC, et al. Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast. Mol Syst Biol. 2014;10: 767. doi: 10.15252/msb.2014511225492886
23. D’Aquino KE, Monje-Casas F, Paulson J, Reiser V, Charles GM, Lai L, et al. The Protein Kinase Kin4 Inhibits Exit from Mitosis in Response to Spindle Position Defects. Mol Cell. 2005;19: 223–234. doi: 10.1016/j.molcel.2005.06.00516039591
24. Buescher JM, Moco S, Sauer U, Zamboni N, Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites. Anal Chem. American Chemical Society; 2010;82: 4403–4412.
25. Fuhrer T, Heer D, Begemann B, Zamboni N, High-Throughput, Accurate Mass Metabolome Profiling of Cellular Extracts by Flow Injection–Time-of-Flight Mass Spectrometry. Anal Chem. 2011;83: 7074–7080. doi: 10.1021/ac201267k21830798
26. Mo ML, Palsson BO, Herrgård MJ, Connecting extracellular metabolomic measurements to intracellular flux states in yeast. BMC Syst Biol. BioMed Central Ltd; 2009;3: 37.
27. Park JO, Rubin SA, Xu Y-F, Amador-Noguez D, Fan J, Shlomi T, et al. Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nat Chem Biol. 2016;
28. Kanshin E, Bergeron-Sandoval L-P, Isik SS, Thibault P, Michnick SW, A cell-signaling network temporally resolves specific versus promiscuous phosphorylation. Cell Rep. 2015;10: 1202–1214. doi: 10.1016/j.celrep.2015.01.05225704821
29. Mitchell A, Wei P, Lim WA, Oscillatory stress stimulation uncovers an Achilles’ heel of the yeast MAPK signaling network. Science. 2015;
30. Hohmann S, Osmotic stress signaling and osmoadaptation in yeasts. Microbiol Mol Biol Rev. Am Soc Microbiol; 2002;66: 300–372.
31. Merlini L, Dudin O, Martin SG, Mate and fuse: how yeast cells do it. Open Biol. 2013;3: 130008. doi: 10.1098/rsob.13000823466674
32. Bardwell L, A walk-through of the yeast mating pheromone response pathway. Peptides. 2005;26: 339–350. 15690603
33. Beltrao P, Albanèse V, Kenner LR, Swaney DL, Burlingame A, Villén J, et al. Systematic functional prioritization of protein posttranslational modifications. Cell. 2012;150: 413–425. doi: 10.1016/j.cell.2012.05.03622817900
34. Sadowski I, Breitkreutz B-J, Stark C, Su T-C, Dahabieh M, Raithatha S, et al. The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update. Database. 2013;2013: bat026. doi: 10.1093/database/bat02623674503
35. Ochoa D, Jonikas M, Lawrence RT, El Debs B, Selkrig J, Typas A, et al. An atlas of human kinase regulation. Mol Syst Biol. 2016;12: 888. doi: 10.15252/msb.2016729527909043
36. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102: 15545–15550. doi: 10.1073/pnas.050658010216199517
37. Beck T, Hall MN, The TOR signalling pathway controls nuclear localization of nutrient-regulated transcription factors. Nature. 1999;402: 689–692. doi: 10.1038/4528710604478
38. Schmidt A, Beck T, Koller A, Kunz J, Hall MN, The TOR nutrient signalling pathway phosphorylates NPR1 and inhibits turnover of the tryptophan permease. EMBO J. 1998;17: 6924–6931. doi: 10.1093/emboj/17.23.69249843498
39. Pedruzzi I, Dubouloz F, Cameroni E, Wanke V, Roosen J, Winderickx J, et al. TOR and PKA signaling pathways converge on the protein kinase Rim15 to control entry into G0. Mol Cell. 2003;12: 1607–1613. 14690612
40. Martin DE, Soulard A, Hall MN, TOR regulates ribosomal protein gene expression via PKA and the Forkhead transcription factor FHL1. Cell. 2004;119: 969–979. doi: 10.1016/j.cell.2004.11.04715620355
41. O’Duibhir E, Lijnzaad P, Benschop JJ, Lenstra TL, van Leenen D, Groot Koerkamp MJA, et al. Cell cycle population effects in perturbation studies. Mol Syst Biol. 2014;10: 732. doi: 10.15252/msb.2014517224952590
42. Brauer MJ, Huttenhower C, Airoldi EM, Rosenstein R, Matese JC, Gresham D, et al. Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol Biol Cell. 2008;19: 352–367. doi: 10.1091/mbc.E07-08-077917959824
43. Friden P, Schimmel P, LEU3 of Saccharomyces cerevisiae activates multiple genes for branched-chain amino acid biosynthesis by binding to a common decanucleotide core sequence. Mol Cell Biol. 1988;8: 2690–2697. 3043190
44. Nielsen PS, van den Hazel B, Didion T, de Boer M, Jørgensen M, Planta RJ, et al. Transcriptional regulation of the Saccharomyces cerevisiae amino acid permease gene BAP2. Mol Gen Genet. 2001;264: 613–622. 11212916
45. Mülleder M, Calvani E, Alam MT, Wang RK, Eckerstorfer F, Zelezniak A, et al. Functional Metabolomics Describes the Yeast Biosynthetic Regulome. Cell. 2016;167: 553–565.e12. doi: 10.1016/j.cell.2016.09.00727693354
46. Huang HL, Brandriss MC, The regulator of the yeast proline utilization pathway is differentially phosphorylated in response to the quality of the nitrogen source. Mol Cell Biol. 2000;20: 892–899. 10629046
47. Axelrod JD, Majors J, Brandriss MC, Proline-independent binding of PUT3 transcriptional activator protein detected by footprinting in vivo. Mol Cell Biol. 1991;11: 564–567. 1986247
48. Siddiqui AH, Brandriss MC, The Saccharomyces cerevisiae PUT3 activator protein associates with proline-specific upstream activation sequences. Mol Cell Biol. 1989;9: 4706–4712. 2689862
49. Chatr-Aryamontri A, Breitkreutz B-J, Oughtred R, Boucher L, Heinicke S, Chen D, et al. The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2015;43: D470–8. doi: 10.1093/nar/gku120425428363
50. Oughtred R, Chatr-aryamontri A, Breitkreutz B-J, Chang CS, Rust JM, Theesfeld CL, et al. Use of the BioGRID Database for Analysis of Yeast Protein and Genetic Interactions. Cold Spring Harb Protoc. 2016;2016: db.prot088880.
51. Oughtred R, Chatr-Aryamontri A, Breitkreutz B-J, Chang CS, Rust JM, Theesfeld CL, et al. BioGRID: A Resource for Studying Biological Interactions in Yeast. Cold Spring Harb Protoc. 2016;2016: db.top080754.
52. Jensen LJ, Kuhn M, Stark M, Chaffron S, Creevey C, Muller J, et al. STRING 8—a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009;37: D412–6. doi: 10.1093/nar/gkn76018940858
53. Chavel CA, Caccamise LM, Li B, Cullen PJ, Global regulation of a differentiation MAPK pathway in yeast. Genetics. 2014;198: 1309–1328. doi: 10.1534/genetics.114.16825225189875
54. Conrad M, Schothorst J, Kankipati HN, Van Zeebroeck G, Rubio-Texeira M, Thevelein JM, Nutrient sensing and signaling in the yeast Saccharomyces cerevisiae. FEMS Microbiol Rev. 2014;38: 254–299. doi: 10.1111/1574-6976.1206524483210
55. Broach JR, Nutritional control of growth and development in yeast. Genetics. 2012;192: 73–105. doi: 10.1534/genetics.111.13573122964838
56. Swinnen E, Wanke V, Roosen J, Smets B, Dubouloz F, Pedruzzi I, et al. Rim15 and the crossroads of nutrient signalling pathways in Saccharomyces cerevisiae. Cell Div. 2006;1: 3. doi: 10.1186/1747-1028-1-316759348
57. Byrne KP, Wolfe KH, The Yeast Gene Order Browser: combining curated homology and syntenic context reveals gene fate in polyploid species. Genome Res. 2005;15: 1456–1461. doi: 10.1101/gr.367230516169922
58. Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, et al. JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res. 2014;42: D142–7. doi: 10.1093/nar/gkt99724194598
59. Wasserman WW, Sandelin A, Applied bioinformatics for the identification of regulatory elements. Nat Rev Genet. 2004;5: 276–287. doi: 10.1038/nrg131515131651
60. Hu Z, Killion PJ, Iyer VR, Genetic reconstruction of a functional transcriptional regulatory network. Nat Genet. 2007;39: 683–687. doi: 10.1038/ng201217417638
61. Chua G, Morris QD, Sopko R, Robinson MD, Ryan O, Chan ET, et al. Identifying transcription factor functions and targets by phenotypic activation. Proc Natl Acad Sci U S A. 2006;103: 12045–12050. doi: 10.1073/pnas.060514010316880382
62. Harbison CT, Gordon DB, Lee TI, Rinaldi NJ, Macisaac KD, Danford TW, et al. Transcriptional regulatory code of a eukaryotic genome. Nature. 2004;431: 99–104. doi: 10.1038/nature0280015343339
63. Rhee HS, Pugh BF, Comprehensive genome-wide protein-DNA interactions detected at single-nucleotide resolution. Cell. 2011;147: 1408–1419. doi: 10.1016/j.cell.2011.11.01322153082
64. Tachibana C, Yoo JY, Tagne J-B, Kacherovsky N, Lee TI, Young ET, Combined global localization analysis and transcriptome data identify genes that are directly coregulated by Adr1 and Cat8. Mol Cell Biol. 2005;25: 2138–2146. doi: 10.1128/MCB.25.6.2138-2146.200515743812
65. Venters BJ, Wachi S, Mavrich TN, Andersen BE, Jena P, Sinnamon AJ, et al. A comprehensive genomic binding map of gene and chromatin regulatory proteins in Saccharomyces. Mol Cell. 2011;41: 480–492. doi: 10.1016/j.molcel.2011.01.01521329885
66. Christie KR, Weng S, Balakrishnan R, Costanzo MC, Dolinski K, Dwight SS, et al. Saccharomyces Genome Database (SGD) provides tools to identify and analyze sequences from Saccharomyces cerevisiae and related sequences from other organisms. Nucleic Acids Res. 2004;32: D311–4. doi: 10.1093/nar/gkh03314681421
67. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. JMLR.org; 2011;12: 2825–2830.
68. Hunter JD, Matplotlib: A 2D Graphics Environment. Computing in Science Engineering. AIP Publishing; 2007;9: 90–95.
69. Waskom M, Botvinnik O, Hobson P, Cole JB, Halchenko Y, Hoyer S, et al. seaborn: v0.5.0 (November 2014) [Internet]. ZENODO; 2014.