1887

Abstract

Protein turnover plays an important role in cell metabolism by regulating metabolic fluxes. Furthermore, the energy costs for protein turnover have been estimated to account for up to a third of the total energy production during cell replication and hence may represent a major limiting factor in achieving either higher biomass or production yields. This work aimed to measure the specific growth rate (μ)-dependent abundance and turnover rate of individual proteins, estimate the ATP cost for protein production and turnover, and compare this with the total energy balance and other maintenance costs. The lactic acid bacteria model organism was used to measure protein turnover rates at μ = 0.1 and 0.5 h in chemostat experiments. Individual turnover rates were measured for ~75 % of the total proteome. On average, protein turnover increased by sevenfold with a fivefold increase in growth rate, whilst biomass yield increased by 35 %. The median turnover rates found were higher than the specific growth rate of the bacterium, which suggests relatively high energy consumption for protein turnover. We found that protein turnover costs alone account for 38 and 47 % of the total energy produced at μ = 0.1 and 0.5 h, respectively, and gene ontology groups Energy metabolism and Translation dominated synthesis costs at both growth rates studied. These results reflect the complexity of metabolic changes that occur in response to changes in environmental conditions, and signify the trade-off between biomass yield and the need to produce ATP for maintenance processes.

Funding
This study was supported by the:
  • European Regional Development Fund (Award EU29994)
  • SA Archimedes (Award 3.2.0701.11-0018)
  • Ministry of Education, Estonia (Award SF0140090s08)
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2014-07-01
2024-12-04
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References

  1. Adamberg K., Seiman A., Vilu R. ( 2012). Increased biomass yield of Lactococcus lactis by reduced overconsumption of amino acids and increased catalytic activities of enzymes. PLoS ONE 7:e48223 [View Article][PubMed]
    [Google Scholar]
  2. Arike L., Valgepea K., Peil L., Nahku R., Adamberg K., Vilu R. ( 2012). Comparison and applications of label-free absolute proteome quantification methods on Escherichia coli. J Proteomics 75:5437–5448 [View Article][PubMed]
    [Google Scholar]
  3. Bahey-El-Din M. ( 2012). Lactococcus lactis-based vaccines from laboratory bench to human use: an overview. Vaccine 30:685–690 [View Article][PubMed]
    [Google Scholar]
  4. Belle A., Tanay A., Bitincka L., Shamir R., O’Shea E. K. ( 2006). Quantification of protein half-lives in the budding yeast proteome. Proc Natl Acad Sci U S A 103:13004–13009 [View Article][PubMed]
    [Google Scholar]
  5. Bermúdez-Humarán L. G., Kharrat P., Chatel J.-M., Langella P. ( 2011). Lactococci and lactobacilli as mucosal delivery vectors for therapeutic proteins and DNA vaccines. Microb Cell Fact 10:Suppl 1S4 [View Article][PubMed]
    [Google Scholar]
  6. Bermúdez-Humarán L. G., Aubry C., Motta J.-P., Deraison C., Steidler L., Vergnolle N., Chatel J.-M., Langella P. ( 2013). Engineering lactococci and lactobacilli for human health. Curr Opin Microbiol 16:278–283 [View Article][PubMed]
    [Google Scholar]
  7. Bolotin A., Wincker P., Mauger S., Jaillon O., Malarme K., Weissenbach J., Ehrlich S. D., Sorokin A. ( 2001). The complete genome sequence of the lactic acid bacterium Lactococcus lactis ssp. lactis IL1403. Genome Res 11:731–753 [View Article][PubMed]
    [Google Scholar]
  8. Buonaguro L., Wang E., Tornesello M. L., Buonaguro F. M., Marincola F. M. ( 2011). Systems biology applied to vaccine and immunotherapy development. BMC Syst Biol 5:146 [View Article][PubMed]
    [Google Scholar]
  9. Burton R. E., Siddiqui S. M., Kim Y. I., Baker T. A., Sauer R. T. ( 2001). Effects of protein stability and structure on substrate processing by the ClpXP unfolding and degradation machine. EMBO J 20:3092–3100 [View Article][PubMed]
    [Google Scholar]
  10. Canelas A. B., Harrison N., Fazio A., Zhang J., Pitkänen J.-P., van den Brink J., Bakker B. M., Bogner L., Bouwman J. & other authors ( 2010). Integrated multilaboratory systems biology reveals differences in protein metabolism between two reference yeast strains. Nat Commun 1:145 [View Article][PubMed]
    [Google Scholar]
  11. Cargile B. J., Bundy J. L., Grunden A. M., Stephenson J. L. Jr ( 2004). Synthesis/degradation ratio mass spectrometry for measuring relative dynamic protein turnover. Anal Chem 76:86–97 [View Article][PubMed]
    [Google Scholar]
  12. Cox J., Mann M. ( 2008). MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26:1367–1372 [View Article][PubMed]
    [Google Scholar]
  13. Dressaire C., Redon E., Milhem H., Besse P., Loubière P., Cocaign-Bousquet M. ( 2008). Growth rate regulated genes and their wide involvement in the Lactococcus lactis stress responses. BMC Genomics 9:343 [View Article][PubMed]
    [Google Scholar]
  14. Dressaire C., Picard F., Redon E., Loubière P., Queinnec I., Girbal L., Cocaign-Bousquet M. ( 2013). Role of mRNA stability during bacterial adaptation. PLoS ONE 8:e59059 [View Article][PubMed]
    [Google Scholar]
  15. Gerth U., Kock H., Kusters I., Michalik S., Switzer R. L., Hecker M. ( 2008). Clp-dependent proteolysis down-regulates central metabolic pathways in glucose-starved Bacillus subtilis. J Bacteriol 190:321–331 [View Article][PubMed]
    [Google Scholar]
  16. González-Ramos D., van den Broek M., van Maris A. J., Pronk J. T., Daran J.-M. G. ( 2013). Genome-scale analyses of butanol tolerance in Saccharomyces cerevisiae reveal an essential role of protein degradation. Biotechnol Biofuels 6:48 [View Article][PubMed]
    [Google Scholar]
  17. Helbig A. O., Daran-Lapujade P., van Maris A. J., de Hulster E. A., de Ridder D., Pronk J. T., Heck A. J., Slijper M. ( 2011). The diversity of protein turnover and abundance under nitrogen-limited steady-state conditions in Saccharomyces cerevisiae. Mol Biosyst 7:3316–3326 [View Article][PubMed]
    [Google Scholar]
  18. Hong K.-K., Hou J., Shoaie S., Nielsen J., Bordel S. ( 2012). Dynamic 13C-labeling experiments prove important differences in protein turnover rate between two Saccharomyces cerevisiae strains. FEMS Yeast Res 12:741–747 [View Article][PubMed]
    [Google Scholar]
  19. Hughes C., Krijgsveld J. ( 2012). Developments in quantitative mass spectrometry for the analysis of proteome dynamics. Trends Biotechnol 30:668–676 [View Article][PubMed]
    [Google Scholar]
  20. Karr J. R., Sanghvi J. C., Macklin D. N., Gutschow M. V., Jacobs J. M., Bolival B. Jr, Assad-Garcia N., Glass J. I., Covert M. W. ( 2012). A whole-cell computational model predicts phenotype from genotype. Cell 150:389–401 [View Article][PubMed]
    [Google Scholar]
  21. Khmelinskii A., Keller P. J., Bartosik A., Meurer M., Barry J. D., Mardin B. R., Kaufmann A., Trautmann S., Wachsmuth M. & other authors ( 2012). Tandem fluorescent protein timers for in vivo analysis of protein dynamics. Nat Biotechnol 30:708–714 [View Article][PubMed]
    [Google Scholar]
  22. Lahtvee P.-J., Adamberg K., Arike L., Nahku R., Aller K., Vilu R. ( 2011). Multi-omics approach to study the growth efficiency and amino acid metabolism in Lactococcus lactis at various specific growth rates. Microb Cell Fact 10:12 [View Article][PubMed]
    [Google Scholar]
  23. Lowry O. H., Rosebrough N. J., Farr A. L., Randall R. J. ( 1951). Protein measurement with the Folin phenol reagent. J Biol Chem 193:265–275[PubMed]
    [Google Scholar]
  24. Maier T., Schmidt A., Güell M., Kühner S., Gavin A.-C., Aebersold R., Serrano L. ( 2011). Quantification of mRNA and protein and integration with protein turnover in a bacterium. Mol Syst Biol 7:511 [View Article][PubMed]
    [Google Scholar]
  25. Martin S. F., Munagapati V. S., Salvo-Chirnside E., Kerr L. E., Le Bihan T. ( 2012). Proteome turnover in the green alga Ostreococcus tauri by time course 15N metabolic labeling mass spectrometry. J Proteome Res 11:476–486 [View Article][PubMed]
    [Google Scholar]
  26. Mehmeti I., Faergestad E. M., Bekker M., Snipen L., Nes I. F., Holo H. ( 2012). Growth rate-dependent control in Enterococcus faecalis: effects on the transcriptome and proteome, and strong regulation of lactate dehydrogenase. Appl Environ Microbiol 78:170–176 [View Article][PubMed]
    [Google Scholar]
  27. Menon A. S., Goldberg A. L. ( 1987). Binding of nucleotides to the ATP-dependent protease La from Escherichia coli. J Biol Chem 262:14921–14928[PubMed]
    [Google Scholar]
  28. Neves A. R., Pool W. A., Kok J., Kuipers O. P., Santos H. ( 2005). Overview on sugar metabolism and its control in Lactococcus lactis – the input from in vivo NMR. FEMS Microbiol Rev 29:531–554[PubMed]
    [Google Scholar]
  29. O’Sullivan E., Condon S. ( 1999). Relationship between acid tolerance, cytoplasmic pH, and ATP and H+-ATPase levels in chemostat cultures of Lactococcus lactis. Appl Environ Microbiol 65:2287–2293[PubMed]
    [Google Scholar]
  30. Oh E., Lu M., Park C., Park C., Oh H. B., Lee S. Y., Lee J. ( 2011). Dynamic modeling of lactic acid fermentation metabolism with Lactococcus lactis. J Microbiol Biotechnol 21:162–169 [View Article][PubMed]
    [Google Scholar]
  31. Ong S.-E., Blagoev B., Kratchmarova I., Kristensen D. B., Steen H., Pandey A., Mann M. ( 2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386 [View Article][PubMed]
    [Google Scholar]
  32. Orij R., Urbanus M. L., Vizeacoumar F. J., Giaever G., Boone C., Nislow C., Brul S., Smits G. J. ( 2012). Genome-wide analysis of intracellular pH reveals quantitative control of cell division rate by pHc in Saccharomyces cerevisiae. Genome Biol 13:R80 [View Article][PubMed]
    [Google Scholar]
  33. Pirt S. J. ( 1965). The maintenance energy of bacteria in growing cultures. Proc R Soc Lond B Biol Sci 163:224–231 [View Article][PubMed]
    [Google Scholar]
  34. Pratt J. M., Petty J., Riba-Garcia I., Robertson D. H., Gaskell S. J., Oliver S. G., Beynon R. J. ( 2002). Dynamics of protein turnover, a missing dimension in proteomics. Mol Cell Proteomics 1:579–591 [View Article][PubMed]
    [Google Scholar]
  35. Rappsilber J., Mann M., Ishihama Y. ( 2007). Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc 2:1896–1906 [View Article][PubMed]
    [Google Scholar]
  36. Russell J. B., Cook G. M. ( 1995). Energetics of bacterial growth: balance of anabolic and catabolic reactions. Microbiol Rev 59:48–62[PubMed]
    [Google Scholar]
  37. Schwanhäusser B., Busse D., Li N., Dittmar G., Schuchhardt J., Wolf J., Chen W., Selbach M. ( 2011). Global quantification of mammalian gene expression control. Nature 473:337–342 [View Article][PubMed]
    [Google Scholar]
  38. Schwanhäusser B., Wolf J., Selbach M., Busse D. ( 2013). Synthesis and degradation jointly determine the responsiveness of the cellular proteome. BioEssays 35:597–601 [View Article][PubMed]
    [Google Scholar]
  39. Stephanopoulos G., Aristidou A. A., Nielsen J. H. ( 1998). Metabolic Engineering: Principles and Methodologies New York: Academic Press;
    [Google Scholar]
  40. Stouthamer A. H. ( 1973). A theoretical study on the amount of ATP required for synthesis of microbial cell material. Antonie van Leeuwenhoek 39:545–565 [View Article][PubMed]
    [Google Scholar]
  41. Taymaz-Nikerel H., Borujeni A. E., Verheijen P. J. T., Heijnen J. J., van Gulik W. M. ( 2010). Genome-derived minimal metabolic models for Escherichia coli MG1655 with estimated in vivo respiratory ATP stoichiometry. Biotechnol Bioeng 107:369–381 [View Article][PubMed]
    [Google Scholar]
  42. Thompson J. ( 1978). In vivo regulation of glycolysis and characterization of sugar: phosphotransferase systems in Streptococcus lactis . J Bacteriol 136:465–476[PubMed]
    [Google Scholar]
  43. Trötschel C., Albaum S. P., Wolff D., Schröder S., Goesmann A., Nattkemper T. W., Poetsch A. ( 2012). Protein turnover quantification in a multilabeling approach: from data calculation to evaluation. Mol Cell Proteomics 11:512–526 [View Article][PubMed]
    [Google Scholar]
  44. Trötschel C., Albaum S. P., Poetsch A. ( 2013). Proteome turnover in bacteria: current status for Corynebacterium glutamicum and related bacteria. Microb Biotechnol 6:708–719 [View Article][PubMed]
    [Google Scholar]
  45. Valgepea K., Adamberg K., Seiman A., Vilu R. ( 2013). Escherichia coli achieves faster growth by increasing catalytic and translation rates of proteins. Mol Biosyst 9:2344–2358 [View Article][PubMed]
    [Google Scholar]
  46. van Bodegom P. ( 2007). Microbial maintenance: a critical review on its quantification. Microb Ecol 53:513–523 [View Article][PubMed]
    [Google Scholar]
  47. Vizcaíno J. A., Côté R. G., Csordas A., Dianes J. A., Fabregat A., Foster J. M., Griss J., Alpi E., Birim M. & other authors ( 2013). The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res 41:D1D1063–D1069 [View Article][PubMed]
    [Google Scholar]
  48. Wiśniewski J. R., Zougman A., Nagaraj N., Mann M. ( 2009). Universal sample preparation method for proteome analysis. Nat Methods 6:359–362 [View Article][PubMed]
    [Google Scholar]
  49. Wiśniewski J. R., Ostasiewicz P., Duś K., Zielińska D. F., Gnad F., Mann M. ( 2012). Extensive quantitative remodeling of the proteome between normal colon tissue and adenocarcinoma. Mol Syst Biol 8:611 [View Article][PubMed]
    [Google Scholar]
  50. Wodke J. A., Puchałka J., Lluch-Senar M., Marcos J., Yus E., Godinho M., Gutiérrez-Gallego R., dos Santos V. A., Serrano L. & other authors ( 2013). Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling. Mol Syst Biol 9:653 [View Article][PubMed]
    [Google Scholar]
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