Quantification and Classification of E. coli Proteome Utilization and Unused Protein Costs across Environments

PLoS Computational Biology

Volumn 12, Issue 6, 2016, Pages

  O’Brien, Edward J ^a,b   Utrilla, Jose ^a,c   Palsson, Bernhard O ^a,d,e  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c INSTITUTO DE CIENCIAS FÍSICAS   (Mexico)

^d Chalmers University of Technology   (Denmark)

^e UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COSTS; ESCHERICHIA COLI; GROWTH RATE; HEALTH; MOLECULAR BIOLOGY;

% REDUCTIONS; 'CURRENT; CELLULAR GROWTH RATE; COST AND BENEFITS; E. COLI; FITNESS COSTS; GENOME-SCALE MODEL; PROTEIN EXPRESSIONS; PROTEOMICS; SPECIFIC COST;

PROTEINS;

PROTEOME; ESCHERICHIA COLI PROTEIN;

ARTICLE; BACTERIAL GROWTH; CONTROLLED STUDY; COST BENEFIT ANALYSIS; ENVIRONMENTAL CHANGE; ESCHERICHIA COLI; LIMIT OF QUANTITATION; NONHUMAN; PROTEIN EXPRESSION; PROTEIN FUNCTION; PROTEOMICS; BIOLOGICAL MODEL; BIOLOGY; CLASSIFICATION; GENETICS; METABOLISM; PHYSIOLOGY; PROCEDURES; PROTEIN DATABASE;

COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; ESCHERICHIA COLI; ESCHERICHIA COLI PROTEINS; MODELS, BIOLOGICAL; PROTEOME;

EID: 84978861815     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004998     Document Type: Article

References (48)

1. Dekel E, Alon U, Optimality and evolutionary tuning of the expression level of a protein. Nature. 2005;436: 588–592. 16049495
2. Neidhardt FC, Bacterial growth: constant obsession with dN/dt. J Bacteriol. 1999;181: 7405–7408. 10601194
3. Li G-W, Burkhardt D, Gross C, Weissman JS, Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell. 2014;157: 624–635. doi: 10.1016/j.cell.2014.02.03324766808
4. Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T, Interdependence of cell growth and gene expression: origins and consequences. Science. 2010;330: 1099–1102. doi: 10.1126/science.119258821097934
5. Bienick MS, Young KW, Klesmith JR, Detwiler EE, Tomek KJ, Whitehead TA, The interrelationship between promoter strength, gene expression, and growth rate. PLoS One. 2014;9: e109105. doi: 10.1371/journal.pone.010910525286161
6. Valgepea K, Peebo K, Adamberg K, Vilu R, Lean-proteome strains—next step in metabolic engineering. Front Bioeng Biotechnol. 2015;3: 11. doi: 10.3389/fbioe.2015.0001125705616
7. O’Brien EJ, Lerman JA, Chang RL, Hyduke DR, Palsson BØ, Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction. Mol Syst Biol. Wiley Online Library; 2013;9. Available: http://onlinelibrary.wiley.com/doi/10.1038/msb.2013.52/full
8. Schmidt A, Kochanowski K, Vedelaar S, Ahrné E, Volkmer B, Callipo L, et al. The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol. 2015; doi: 10.1038/nbt.3418
9. O’Brien EJ, Palsson BO, Computing the functional proteome: recent progress and future prospects for genome-scale models. Curr Opin Biotechnol. 2015;34C: 125–134.
10. Mahadevan R, Schilling CH, The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng. 2003;5: 264–276. 14642354
11. Reed JL, Palsson BØ, Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res. 2004;14: 1797–1805. 15342562
12. O’Brien EJ, Monk JM, Palsson BO, Using Genome-scale Models to Predict Biological Capabilities. Cell. 2015;161: 971–987. doi: 10.1016/j.cell.2015.05.01926000478
13. Valgepea K, Adamberg K, Seiman A, Vilu R, Escherichia coli achieves faster growth by increasing catalytic and translation rates of proteins. Mol Biosyst. 2013;9: 2344–2358. doi: 10.1039/c3mb70119k23824091
14. Klumpp S, Scott M, Pedersen S, Hwa T, Molecular crowding limits translation and cell growth. Proc Natl Acad Sci U S A. 2013;110: 16754–16759. doi: 10.1073/pnas.131037711024082144
15. LaCroix RA, Sandberg TE, O’Brien EJ, Utrilla J, Ebrahim A, Guzman GI, et al. Discovery of key mutations enabling rapid growth of Escherichia coli K-12 MG1655 on glucose minimal media using adaptive laboratory evolution. Appl Environ Microbiol. 2014;
16. Lewis NE, Hixson KK, Conrad TM, Lerman JA, Charusanti P, Polpitiya AD, et al. Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Mol Syst Biol. EMBO Press; 2010;6: 390.
17. Cheng K-K, Lee B-S, Masuda T, Ito T, Ikeda K, Hirayama A, et al. Global metabolic network reorganization by adaptive mutations allows fast growth of Escherichia coli on glycerol. Nat Commun. 2014;5: 3233. doi: 10.1038/ncomms423324481126
18. Utrilla J, Edward J. O’Brien KC, McCloskey D, Cheung J, Wang H, Armenta-Medina D, et al. Proteome and Energy Re-allocation by Adaptive Regulatory Mutations Reveals a Fitness Trade-off.
19. Yang L, Tan J, O’Brien EJ, Monk JM, Kim D, Li HJ, et al. Systems biology definition of the core proteome of metabolism and expression is consistent with high-throughput data. Proc Natl Acad Sci U S A. 2015; doi: 10.1073/pnas.1501384112
20. Shimada T, Fujita N, Yamamoto K, Ishihama A, Novel roles of cAMP receptor protein (CRP) in regulation of transport and metabolism of carbon sources. PLoS One. 2011;6: e20081. doi: 10.1371/journal.pone.002008121673794
21. Chubukov V, Gerosa L, Kochanowski K, Sauer U, Coordination of microbial metabolism. Nat Rev Microbiol. 2014;12: 327–340. doi: 10.1038/nrmicro323824658329
22. Reitzer L, Nitrogen assimilation and global regulation in Escherichia coli. Annu Rev Microbiol. 2003;57: 155–176. 12730324
23. You C, Okano H, Hui S, Zhang Z, Kim M, Gunderson CW, et al. Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature. 2013;500: 301–306. doi: 10.1038/nature1244623925119
24. Keseler IM, Mackie A, Peralta-Gil M, Santos-Zavaleta A, Gama-Castro S, Bonavides-Martínez C, et al. EcoCyc: fusing model organism databases with systems biology. Nucleic Acids Res. 2013;41: D605–12. doi: 10.1093/nar/gks102723143106
25. Monod J, The Growth of Bacterial Cultures. Annu Rev Microbiol. 1949;3: 371–394.
26. Alper H, Stephanopoulos G, Global transcription machinery engineering: a new approach for improving cellular phenotype. Metab Eng. 2007;9: 258–267. 17292651
27. Edwards JS, Ibarra RU, Palsson BO, In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol. 2001;19: 125–130. 11175725
28. Ibarra RU, Edwards JS, Palsson BO, Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth. Nature. 2002;420: 186–189. 12432395
29. Adadi R, Volkmer B, Milo R, Heinemann M, Shlomi T, Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters. PLoS Comput Biol. 2012;8: e1002575. doi: 10.1371/journal.pcbi.100257522792053
30. Zarecki R, Oberhardt MA, Yizhak K, Wagner A, Shtifman Segal E, Freilich S, et al. Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms. PLoS One. 2014;9: e98372. doi: 10.1371/journal.pone.009837224866123
31. Aidelberg G, Towbin BD, Rothschild D, Dekel E, Bren A, Alon U, Hierarchy of non-glucose sugars in Escherichia coli. BMC Syst Biol. 2014;8: 133. doi: 10.1186/s12918-014-0133-z25539838
32. Applebee MK, Joyce AR, Conrad TM, Pettigrew DW, Palsson BØ, Functional and metabolic effects of adaptive glycerol kinase (GLPK) mutants in Escherichia coli. J Biol Chem. 2011;286: 23150–23159. doi: 10.1074/jbc.M110.19530521550976
33. Conrad TM, Lewis NE, Palsson BØ, Microbial laboratory evolution in the era of genome-scale science. Mol Syst Biol. 2011;7: 509. doi: 10.1038/msb.2011.4221734648
34. Gresham D, Hong J, The functional basis of adaptive evolution in chemostats. FEMS Microbiol Rev. 2015;39: 2–16. doi: 10.1111/1574-6976.1208225098268
35. Shachrai I, Zaslaver A, Alon U, Dekel E, Cost of unneeded proteins in E. coli is reduced after several generations in exponential growth. Mol Cell. 2010;38: 758–767. doi: 10.1016/j.molcel.2010.04.01520434381
36. New AM, Cerulus B, Govers SK, Perez-Samper G, Zhu B, Boogmans S, et al. Different levels of catabolite repression optimize growth in stable and variable environments. PLoS Biol. 2014;12: e1001764. doi: 10.1371/journal.pbio.100176424453942
37. Berney M, Weilenmann H-U, Ihssen J, Bassin C, Egli T, Specific growth rate determines the sensitivity of Escherichia coli to thermal, UVA, and solar disinfection. Appl Environ Microbiol. 2006;72: 2586–2593. 16597961
38. Lindqvist R, Barmark G, Specific growth rate determines the sensitivity of Escherichia coli to lactic acid stress: implications for predictive microbiology. Biomed Res Int. 2014;2014: 471317. doi: 10.1155/2014/47131725110680
39. Poole K, Stress responses as determinants of antimicrobial resistance in Gram-negative bacteria. Trends Microbiol. 2012;20: 227–234. doi: 10.1016/j.tim.2012.02.00422424589
40. Tagkopoulos I, Liu Y-C, Tavazoie S, Predictive behavior within microbial genetic networks. Science. 2008;320: 1313–1317. doi: 10.1126/science.115445618467556
41. Mitchell A, Romano GH, Groisman B, Yona A, Dekel E, Kupiec M, et al. Adaptive prediction of environmental changes by microorganisms. Nature. 2009;460: 220–224. doi: 10.1038/nature0811219536156
42. Perkins TJ, Swain PS, Strategies for cellular decision-making. Mol Syst Biol. 2009;5: 326. doi: 10.1038/msb.2009.8319920811
43. Goel A, Eckhardt TH, Puri P, de Jong A, Branco Dos Santos F, Giera M, et al. Protein costs do not explain evolution of metabolic strategies and regulation of ribosomal content: does protein investment explain an anaerobic bacterial Crabtree effect?Mol Microbiol. 2015;97: 77–92. doi: 10.1111/mmi.1301225828364
44. Schubert OT, Ludwig C, Kogadeeva M, Zimmermann M, Rosenberger G, Gengenbacher M, et al. Absolute Proteome Composition and Dynamics during Dormancy and Resuscitation of Mycobacterium tuberculosis. Cell Host Microbe. 2015; doi: 10.1016/j.chom.2015.06.001
45. King T, Ishihama A, Kori A, Ferenci T, A regulatory trade-off as a source of strain variation in the species Escherichia coli. J Bacteriol. 2004;186: 5614–5620. 15317765
46. Kassen R, The experimental evolution of specialists, generalists, and the maintenance of diversity. J Evol Biol. Blackwell Science Ltd.; 2002;15: 173–190.
47. Volkmer B, Heinemann M, Condition-dependent cell volume and concentration of Escherichia coli to facilitate data conversion for systems biology modeling. PLoS One. 2011;6: e23126. doi: 10.1371/journal.pone.002312621829590
48. Bar-Even A, Noor E, Savir Y, Liebermeister W, Davidi D, Tawfik DS, et al. The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry. 2011;50: 4402–4410. doi: 10.1021/bi200228921506553