1887

Abstract

sv. Typhimurium is an established model organism for Gram-negative, intracellular pathogens. Owing to the rapid spread of resistance to antibiotics among this group of pathogens, new approaches to identify suitable target proteins are required. Based on the genome sequence of Typhimurium and associated databases, a genome-scale metabolic model was constructed. Output was based on an experimental determination of the biomass of when growing in glucose minimal medium. Linear programming was used to simulate variations in the energy demand while growing in glucose minimal medium. By grouping reactions with similar flux responses, a subnetwork of 34 reactions responding to this variation was identified (the ). This network was used to identify sets of one and two reactions that when removed from the genome-scale model interfered with energy and biomass generation. Eleven such sets were found to be essential for the production of biomass precursors. Experimental investigation of seven of these showed that knockouts of the associated genes resulted in attenuated growth for four pairs of reactions, whilst three single reactions were shown to be essential for growth.

Funding
This study was supported by the:
  • , Danish Research Council for Technology and Production , (Award 274-07-0328)
  • , Animal Health Veterinary Laboratory Agency
  • , Oxford Brookes University
Loading

Article metrics loading...

/content/journal/micro/10.1099/mic.0.076091-0
2014-06-01
2021-01-24
Loading full text...

Full text loading...

/deliver/fulltext/micro/160/6/1252.html?itemId=/content/journal/micro/10.1099/mic.0.076091-0&mimeType=html&fmt=ahah

References

  1. AbuOun M., Suthers P. F., Jones G. I., Carter B. R., Saunders M. P., Maranas C. D., Woodward M. J., Anjum M. F. ( 2009). Genome scale reconstruction of a Salmonella metabolic model: comparison of similarity and differences with a commensal Escherichia coli strain. J Biol Chem 284:29480–29488 [CrossRef][PubMed]
    [Google Scholar]
  2. Andries K., Verhasselt P., Guillemont J., Göhlmann H. W. H., Neefs J.-M., Winkler H., Van Gestel J., Timmerman P., Zhu M. & other authors ( 2005). A diarylquinoline drug active on the ATP synthase of Mycobacterium tuberculosis. Science 307:223–227 [CrossRef][PubMed]
    [Google Scholar]
  3. Baothman O. A. S., Rolfe M. D., Green J. ( 2013). Characterization of Salmonella enterica serovar Typhimurium aconitase A. Microbiology 159:1209–1216 [CrossRef][PubMed]
    [Google Scholar]
  4. Bauchop T., Elsden S. R. ( 1960). The growth of micro-organisms in relation to their energy supply. J Gen Microbiol 23:457–469 [CrossRef][PubMed]
    [Google Scholar]
  5. Becker D., Selbach M., Rollenhagen C., Ballmaier M., Meyer T. F., Mann M., Bumann D. ( 2006). Robust Salmonella metabolism limits possibilities for new antimicrobials. Nature 440:303–307 [CrossRef][PubMed]
    [Google Scholar]
  6. Bowden S. D., Rowley G., Hinton J. C. D., Thompson A. ( 2009). Glucose and glycolysis are required for the successful infection of macrophages and mice by Salmonella enterica serovar Typhimurium. Infect Immun 77:3117–3126 [CrossRef][PubMed]
    [Google Scholar]
  7. Bowden S. D., Ramachandran V. K., Knudsen G. M., Hinton J. C., Thompson A. ( 2010). An incomplete TCA cycle increases survival of Salmonella Typhimurium during infection of resting and activated murine macrophages. PLoS ONE 5:e13871 [CrossRef][PubMed]
    [Google Scholar]
  8. Cano D. A., Pucciarelli M. G., Martínez-Moya M., Casadesús J., García-del Portillo F. ( 2003). Selection of small-colony variants of Salmonella enterica serovar Typhimurium in nonphagocytic eucaryotic cells. Infect Immun 71:3690–3698 [CrossRef][PubMed]
    [Google Scholar]
  9. Chaudhuri R. R., Peters S. E., Pleasance S. J., Northen H., Willers C., Paterson G. K., Cone D. B., Allen A. G., Owen P. J. & other authors ( 2009). Comprehensive identification of Salmonella enterica serovar Typhimurium genes required for infection of BALB/c mice. PLoS Pathog 5:e1000529 [CrossRef][PubMed]
    [Google Scholar]
  10. Cohen, P. S. (2010).
  11. Datsenko K. A., Wanner B. L. ( 2000). One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc Natl Acad Sci U S A 97:6640–6645 [CrossRef][PubMed]
    [Google Scholar]
  12. Edwards J. S., Palsson B. O. ( 2000). The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Natl Acad Sci U S A 97:5528–5533 [CrossRef][PubMed]
    [Google Scholar]
  13. Feist A. M., Henry C. S., Reed J. L., Krummenacker M., Joyce A. R., Karp P. D., Broadbelt L. J., Hatzimanikatis V., Palsson B. O. ( 2007). A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol 3:121 [CrossRef][PubMed]
    [Google Scholar]
  14. Fell D. A., Small J. R. ( 1986). Fat synthesis in adipose tissue. An examination of stoichiometric constraints. Biochem J 238:781–786[PubMed]
    [Google Scholar]
  15. Fuhrer T., Sauer U. ( 2009). Different biochemical mechanisms ensure network-wide balancing of reducing equivalents in microbial metabolism. J Bacteriol 191:2112–2121 [CrossRef][PubMed]
    [Google Scholar]
  16. García-del Portillo F., Núñez-Hernández C., Eisman B., Ramos-Vivas J. ( 2008). Growth control in the Salmonella-containing vacuole. Curr Opin Microbiol 11:46–52 [CrossRef][PubMed]
    [Google Scholar]
  17. Gevorgyan A., Poolman M. G., Fell D. A. ( 2008). Detection of stoichiometric inconsistencies in biomolecular models. Bioinformatics 24:2245–2251 [CrossRef][PubMed]
    [Google Scholar]
  18. Görke B., Stülke J. ( 2008). Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat Rev Microbiol 6:613–624 [CrossRef][PubMed]
    [Google Scholar]
  19. Gruer M. J., Bradbury A. J., Guest J. R. ( 1997). Construction and properties of aconitase mutants of Escherichia coli.. Microbiology 143:1837–1846 [CrossRef][PubMed]
    [Google Scholar]
  20. Holzhütter H.-G. ( 2004). The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks. Eur J Biochem 271:2905–2922 [CrossRef][PubMed]
    [Google Scholar]
  21. Holzhütter H.-G. ( 2006). The generalized flux-minimization method and its application to metabolic networks affected by enzyme deficiencies. Biosystems 83:98–107 [CrossRef][PubMed]
    [Google Scholar]
  22. Huerta-Cepas J., Dopazo J., Gabaldón T. ( 2010). ete: a Python environment for tree exploration. BMC Bioinformatics 11:24 [CrossRef][PubMed]
    [Google Scholar]
  23. Hughes J. A. ( 2006). In vivo hydrolysis of S-adenosyl-l-methionine in Escherichia coli increases export of 5-methylthioribose. Can J Microbiol 52:599–602 [CrossRef][PubMed]
    [Google Scholar]
  24. Jelsbak L., Thomsen L. E., Wallrodt I., Jensen P. R., Olsen J. E. ( 2012). Polyamines are required for virulence in Salmonella enterica serovar Typhimurium. PLoS ONE 7:e36149 [CrossRef][PubMed]
    [Google Scholar]
  25. Karp P. D., Paley S., Romero P. ( 2002). The Pathway Tools software. Bioinformatics 18:Suppl. 1S225–S232 [CrossRef][PubMed]
    [Google Scholar]
  26. Klamt S., Gilles E. D. ( 2004). Minimal cut sets in biochemical reaction networks. Bioinformatics 20:226–234 [CrossRef][PubMed]
    [Google Scholar]
  27. Maloney P. C. ( 1987). Coupling to an energized membrane: role of ion-motive gradients in the tranduction of metabolic energy. Escherichia Coli and Salmonella Typhimurium. Cellular and Molecular Biology vol. 1222–243 Neidhardt F. C. Washington, DC: American Society for Microbiology;
    [Google Scholar]
  28. McClelland M., Sanderson K. E., Spieth J., Clifton S. W., Latreille P., Courtney L., Porwollik S., Ali J., Dante M. & other authors ( 2001). Complete genome sequence of Salmonella enterica serovar Typhimurium LT2. Nature 413:852–856 [CrossRef][PubMed]
    [Google Scholar]
  29. Mercado-Lubo R., Gauger E. J., Leatham M. P., Conway T., Cohen P. S. ( 2008). A Salmonella enterica serovar Typhimurium succinate dehydrogenase/fumarate reductase double mutant is avirulent and immunogenic in BALB/c mice. Infect Immun 76:1128–1134 [CrossRef][PubMed]
    [Google Scholar]
  30. Miller K. A., Phillips R. S., Mrázek J., Hoover T. R. ( 2013). Salmonella utilizes d-glucosaminate via a mannose family phosphotransferase system permease and associated enzymes. J Bacteriol 195:4057–4066 [CrossRef][PubMed]
    [Google Scholar]
  31. Murphy D. J., Brown J. R. ( 2007). Identification of gene targets against dormant phase Mycobacterium tuberculosis infections. BMC Infect Dis 7:84 [CrossRef][PubMed]
    [Google Scholar]
  32. Norrby S. R., Nord C. E., Finch R. European Society of Clinical Microbiology and Infectious Diseases ( 2005). Lack of development of new antimicrobial drugs: a potential serious threat to public health. Lancet Infect Dis 5:115–119 [CrossRef][PubMed]
    [Google Scholar]
  33. Paterson D. L. ( 2006). Resistance in gram-negative bacteria: Enterobacteriaceae. Am J Med 119:Suppl. 1S20–S28 [CrossRef][PubMed]
    [Google Scholar]
  34. Pfeiffer T., Sánchez-Valdenebro I., Nuño J. C., Montero F., Schuster S. ( 1999). metatool: for studying metabolic networks. Bioinformatics 15:251–257 [CrossRef][PubMed]
    [Google Scholar]
  35. Poolman M. G. ( 2006). ScrumPy: metabolic modelling with Python. Syst Biol (Stevenage) 153:5375–378 [CrossRef][PubMed]
    [Google Scholar]
  36. Poolman M. G., Sebu C., Pidcock M. K., Fell D. A. ( 2007). Modular decomposition of metabolic systems via null-space analysis. J Theor Biol 249:691–705 [CrossRef][PubMed]
    [Google Scholar]
  37. Poolman M. G., Miguet L., Sweetlove L. J., Fell D. A. ( 2009). A genome-scale metabolic model of Arabidopsis and some of its properties. Plant Physiol 151:1570–1581 [CrossRef][PubMed]
    [Google Scholar]
  38. Poolman M. G., Kundu S., Shaw R., Fell D. A. ( 2013). Responses to light intensity in a genome-scale model of rice metabolism. Plant Physiol 162:1060–1072 [CrossRef][PubMed]
    [Google Scholar]
  39. Prost L. R., Sanowar S., Miller S. I. ( 2007). Salmonella sensing of anti-microbial mechanisms to promote survival within macrophages. Immunol Rev 219:55–65 [CrossRef][PubMed]
    [Google Scholar]
  40. Raghunathan A., Reed J., Shin S., Palsson B., Daefler S. ( 2009). Constraint-based analysis of metabolic capacity of Salmonella typhimurium during host–pathogen interaction. BMC Syst Biol 3:38 [CrossRef][PubMed]
    [Google Scholar]
  41. Rao S. P. S., Alonso S., Rand L., Dick T., Pethe K. ( 2008). The protonmotive force is required for maintaining ATP homeostasis and viability of hypoxic, nonreplicating Mycobacterium tuberculosis. Proc Natl Acad Sci U S A 105:11945–11950 [CrossRef][PubMed]
    [Google Scholar]
  42. Richardson E. J., Limaye B., Inamdar H., Datta A., Manjari K. S., Pullinger G. D., Thomson N. R., Joshi R. R., Watson M., Stevens M. P. ( 2011). Genome sequences of Salmonella enterica serovar Typhimurium, Choleraesuis, Dublin, and Gallinarum strains of well-defined virulence in food-producing animals. J Bacteriol 193:3162–3163 [CrossRef][PubMed]
    [Google Scholar]
  43. Steeb B., Claudi B., Burton N. A., Tienz P., Schmidt A., Farhan H., Mazé A., Bumann D. ( 2013). Parallel exploitation of diverse host nutrients enhances Salmonella virulence. PLoS Pathog 9:e1003301 [CrossRef][PubMed]
    [Google Scholar]
  44. Tchawa Yimga M., Leatham M. P., Allen J. H., Laux D. C., Conway T., Cohen P. S. ( 2006). Role of gluconeogenesis and the tricarboxylic acid cycle in the virulence of Salmonella enterica serovar Typhimurium in BALB/c mice. Infect Immun 74:1130–1140 [CrossRef][PubMed]
    [Google Scholar]
  45. Thiele I., Hyduke D. R., Steeb B., Fankam G., Allen D. K., Bazzani S., Charusanti P., Chen F. C., Fleming R. M. & other authors ( 2011). A community effort towards a knowledge-base and mathematical model of the human pathogen Salmonella Typhimurium LT2. BMC Syst Biol 5:8 [CrossRef][PubMed]
    [Google Scholar]
  46. Tierrez A., García-del Portillo F. ( 2005). New concepts in Salmonella virulence: the importance of reducing the intracellular growth rate in the host. Cell Microbiol 7:901–909 [CrossRef][PubMed]
    [Google Scholar]
  47. Varma A., Palsson B. O. ( 1993a). Metabolic capabilities of Escherichia coli: I. Synthesis of biosynthetic precursors and cofactors. J Theor Biol 165:477–502 [CrossRef][PubMed]
    [Google Scholar]
  48. Varma A., Palsson B. O. ( 1993b). Metabolic capabilities of Escherichia coli: II. Optimal growth patterns. J Theor Biol 165:503–522 [CrossRef]
    [Google Scholar]
  49. Wallis T. S., Paulin S. M., Plested J. S., Watson P. R., Jones P. W. ( 1995). The Salmonella dublin virulence plasmid mediates systemic but not enteric phases of salmonellosis in cattle. Infect Immun 63:2755–2761[PubMed]
    [Google Scholar]
  50. Watson M. R. ( 1986). A discrete model of bacterial metabolism. Comput Appl Biosci 2:23–27[PubMed]
    [Google Scholar]
  51. Wayne L. G., Sohaskey C. D. ( 2001). Nonreplicating persistence of Mycobacterium tuberculosis. . Annu Rev Microbiol 55:139–163 [CrossRef][PubMed]
    [Google Scholar]
  52. Weinstein E. A., Yano T., Li L.-S., Avarbock D., Avarbock A., Helm D., McColm A. A., Duncan K., Lonsdale J. T., Rubin H. ( 2005). Inhibitors of type II NADH : menaquinone oxidoreductase represent a class of antitubercular drugs. Proc Natl Acad Sci U S A 102:4548–4553 [CrossRef][PubMed]
    [Google Scholar]
  53. Yu B. J., Sung B. H., Lee J. Y., Son S. H., Kim M. S., Kim S. C. ( 2006). sucAB and sucCD are mutually essential genes in Escherichia coli . FEMS Microbiol Lett 254:245–250 [CrossRef][PubMed]
    [Google Scholar]
  54. Zomorrodi A. R., Suthers P. F., Ranganathan S., Maranas C. D. ( 2012). Mathematical optimization applications in metabolic networks. Metab Eng 14:672–686 [CrossRef][PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/micro/10.1099/mic.0.076091-0
Loading
/content/journal/micro/10.1099/mic.0.076091-0
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error