Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis

npj Systems Biology and Applications

Volumn 2, Issue , 2016, Pages

  Großeholz, Ruth ^a   Koh, Ching Chiek ^b   Veith, Nadine ^a   Fiedler, Tomas ^c   Strauss, Madlen ^c   Olivier, Brett ^d   Collins, Ben C ^b   Schubert, Olga T ^b   Bergmann, Frank ^a   Kreikemeyer, Bernd ^c   Aebersold, Ruedi ^b,e   Kummer, Ursula ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

^b ETH ZURICH   (Switzerland)

^c ROSTOCK UNIVERSITY MEDICAL CENTER   (Germany)

^d AMSTERDAM INSTITUTE FOR MOLECULES   (Netherlands)

^e UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85014570449     PISSN: None     EISSN: 20567189     Source Type: Journal    
DOI: 10.1038/npjsba.2016.17     Document Type: Article

References (48)

1. Durot, M., Bourguignon, P.-Y. & Schachter, V. Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiol. Rev. 33, 164–190 (2009).
2. Flahaut, N. a. L. et al. Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation. Appl. Microbiol. Biotechnol. 97, 8729–8739 (2013).
3. Lobel, L., Sigal, N., Borovok, I., Ruppin, E. & Herskovits, A. A. Integrative genomic analysis identifies isoleucine and CodY as regulators of Listeria monocytogenes virulence. PLoS Genet. 8, e1002887 (2012).
4. Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B. Ø. & Ruppin, E. Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol. 26, 1003–1010 (2008).
5. Zur, H., Ruppin, E. & Shlomi, T. iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140–3142 (2010).
6. Chandrasekaran, S. & Price, N. D. Probabilistic integrative modeling of genomescale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc. Natl Acad. Sci. USA 107, 17845–17850 (2010).
7. Chandrasekaran, S. & Price, N. D. Metabolic constraint-based refinement of transcriptional regulatory networks. PLoS Comput. Biol. 9, e1003370 (2013).
8. Colijn, C. et al. Interpreting expression data with metabolic flux models: predicting mycobacterium tuberculosis mycolic acid production. PLoS Comput. Biol. 5, e1000489 (2009).
9. Jensen, P. A. & Papin, J. A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27, 541–547 (2011).
10. Becker, S. A. & Palsson, B. O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 4, e1000082 (2008).
11. Taniguchi, Y. et al. Quantifiying E. coli proteome and transcriptome with single-molecule sensitivity in single cells. Science (80-.) 329, 533–539 (2011).
12. Vogel, C. & Marcotte, E. M. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).
13. Akbani, R. et al. A pan-cancer proteomic perspective on The Cancer Genome Atlas. Nat. Commun. 5, 3887 (2014).
14. Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell Proteomics 11, O111.016717 (2012).
15. Rosenberger, G. et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data 1, 140031 (2014).
16. Schubert, O. T. et al. Absolute proteome composition and dynamics during dormancy and resuscitation of Mycobacterium tuberculosis. Cell Host Microbe 18, 96–108 (2015).
17. Mardinoglu, A. et al. Integration of clinical data with a genome-scale metabolic model of the human adipocyte. Mol. Syst. Biol. 9, 649 (2013).
18. Agren, R. et al. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol. Syst. Biol. 10, 1–13 (2014).
19. Simons, M. et al. Assessing the metabolic impact of nitrogen availability using a compartmentalized maize leaf genome-scale model. Plant Physiol. 166, 1659–1674 (2014).
20. Fondi, M. et al. Genome scale metabolic reconstruction and constraints-based modelling of the Antarctic bacterium Pseudoalteromonas haloplanktis TAC125. Environ. Microbiol. 17, n/a–n/a (2014).
21. Jamet, E. et al. Prevalence and characterization of antibiotic resistant Enterococcus faecalis in French cheeses. Food Microbiol. 31, 191–198 (2012).
22. Giraffa, G. Enterococci from foods. FEMS Microbiol. Rev. 26, 163–171 (2002).
23. McHugh, C. P., Zhang, P., Michalek, S. & Eleazer, P. D. pH required to kill Enterococcus faecalis in vitro. J. Endod. 30, 218–219 (2004).
24. Fiedler, T. et al. Characterization of three lactic acid bacteria and their i sogenic ldh deletion mutants shows optimization for YATP (cell mass produced per mole of ATP) at their physiological pHs. Appl. Environ. Microbiol. 77, 612–617 (2011).
25. Kobayashi, H., Suzuki, T., Kinoshita, N. & Unemoto, T. Amplification of the Streptococcus faecalis proton-translocating ATPase by a decrease in cytoplasmic pH. J. Bacteriol. 158, 1157–1160 (1984).
26. Kobayashi, H. A proton-translocating ATPase regulates pH of the bacterial cytoplasm. J. Biol. Chem. 260, 72–76 (1985).
27. Veith, N. et al. Using a genome-scale metabolic model of enterococcus faecalis V583 to assess amino acid uptake and its impact on central metabolism. Appl. Environ. Microbiol. 81, 1622–1633 (2015).
28. Marquis, R. E., Bender, G. R., Murray, D. R. & Wong, A. Arginine deiminase system and bacterial adptation to acid environments. Appl. Environ. Microbiol. 53 198-200 (1987).
29. Marquis, R. E., Bender, G. R., Murray, D. R. & Wong, A. Arginine deiminase system and bacterial adptation to acid environments. Appl. Environ. Microbiol 53, 53–56 (1987)
30. Simpson, S. & Vink, R. Lactate efflux stimulates ATP exchange in Streptococcus faecalis membrane vesicles. FEMS Microbiol. 19, 111–114 (1983).
31. Levering, J. et al. Role of phosphate in the central metabolism of two lactic acid bacteria—A comparative systems biology approach. FEBS J. 279, 1274–1290 (2012).
32. Even, S., Lindley, N. D. & Cocaign-bousquet, M. Molecular physiology of sugar catabolism in Lactococcus lactis IL1403. J. Bacteriol. 183, 3817–3824 (2001).
33. Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219–223 (2014).
34. Teusink, B. et al. Analysis of growth of Lactobacillus plantarum WCFS1 on a complex medium using a genome-scale metabolic model. J. Biol. Chem. 281, 40041–40048 (2006).
35. Vesić, D. & Kristich, C. J. A Rex family transcriptional repressor influences H2O2 accumulation by Enterococcus faecalis. J. Bacteriol. 195, 1815–1824 (2013).
36. Von Heijne, G. The amino acid composition of membrane-penetrating segments. Eur. J. Biochem. 278, 275–278 (1981).
37. Yizhak, K., Benyamini, T., Liebermeister, W., Ruppin, E. & Shlomi, T. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 26, 255–260 (2010).
38. Akesson, M., Förster, J. & Nielsen, J. Integration of gene expression data into genome-scale metabolic models. Metab. Eng. 6, 285–293 (2004).
39. Ogier, J.-C. & Serror, P. Safety assessment of dairy microorganisms: the Enterococcus genus. Int. J. Food Microbiol. 126, 291–301 (2008).
40. Teusink, B., Wiersma, A., Jacobs, L., Notebaart, R. A. & Smid, E. J. Understanding the Adaptive growth strategy of lactobacillus plantarum by in silico optimisation. PLoS Comput. Biol. 5, 1–8 (2009).
41. Jendresen, C. B., Martinussen, J. & Kilstrup, M. The PurR regulon in Lactococcus lactis—transcriptional regulation of the purine nucleotide metabolism and translational machinery. Microbiology 158, 2026–2038 (2012).
42. Martinussen, J. A. N. A transcriptional activator, homologous to the Bacillus subtilis PurR repressor, is required for expression of purine biosynthetic genes in Lactococcus lactis. J. Bacteriol. 180, 3907–3916 (1998).
43. Gitton, C. et al. Proteomic signature of Lactococcus lactis NCDO763 cultivated in milk proteomic signature of lactococcus lactis ncdo763 cultivated in milk. Appl Environ Microbiol 71, 7152–7163 (2005).
44. Suárez, C. a., Blancato, V. S., Poncet, S., Deutscher, J. & Magni, C. CcpA represses the expression of the divergent cit operons of Enterococcus faecalis through multiple cre sites. BMC Microbiol. 11, 227 (2011).
45. Kaper, J. B. & Sperandio, V. Bacterial cell-to-cell signaling in the gastrointestinal tract. Infect. Immun. 73, 3197–3209 (2005).
46. Feldman-Salit, A. et al. Regulation of the activity of lactate dehydrogenases from four lactic acid bacteria. J. Biol. Chem. 288, 21295–21306 (2013).
47. Garrigues, C., Loubiere, P., Lindley, N. D. & Cocaign-Bousquet, M. Control of the shift from homolactic acid to mixed-acid fermentation in Lactococcus lactis: Predominant role of the NADH/NAD+ ratio. J. Bacteriol. 179, 5282–5287 (1997).
48. Fozo, E. M., Kajfasz, J. K. & Quivey, R. G. Low pH-induced membrane fatty acid alterations in oral bacteria. FEMS Microbiol. Lett. 238, 291–295 (2004).