Advertisement

Computational Systems Biology of Metabolism in Infection

  • Müberra Fatma Cesur
  • Ecehan Abdik
  • Ünzile Güven-Gülhan
  • Saliha Durmuş
  • Tunahan Çakır
Chapter
Part of the Experientia Supplementum book series (EXS, volume 109)

Abstract

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

Keywords

Genome-scale metabolic network Flux balance analysis Drug target Metabolomics Fluxomics Pathogen-host interaction 

Notes

Acknowledgments

This work was financially supported by the Turkish Academy of Sciences—Outstanding Young Scientists Award Program (TUBA-GEBIP), and by TUBITAK, The Scientific and Technological Research Council of Turkey (Project Code: 316S005).

References

  1. Abdelrazig S, Ortori CA, Davey G, Deressa W, Mulleta D, Barrett DA, Amberbir A, Fogarty AW (2017) A metabolomic analytical approach permits identification of urinary biomarkers for Plasmodium falciparum infection: a case-control study. Malar J 16(1):229.  https://doi.org/10.1186/s12936-017-1875-zPubMedPubMedCentralCrossRefGoogle Scholar
  2. Ahn S, Jung J, Jang IA, Madsen EL, Park W (2016) Role of glyoxylate shunt in oxidative stress response. J Biol Chem 291(22):11928–11938.  https://doi.org/10.1074/jbc.M115.708149PubMedPubMedCentralCrossRefGoogle Scholar
  3. Ahn YY, Lee DS, Burd H, Blank W, Kapatral V (2014) Metabolic network analysis-based ıdentification of antimicrobial drug targets in category a bioterrorism agents. PLoS One 9(1):e85195.  https://doi.org/10.1371/journal.pone.0085195PubMedPubMedCentralCrossRefGoogle Scholar
  4. Atkinson TP, Balish MF, Waites KB (2008) Epidemiology, clinical manifestations, pathogenesis and laboratory detection of Mycoplasma pneumoniae infections. FEMS Microbiol Rev 32(6):956–973.  https://doi.org/10.1111/j.1574-6976.2008.00129.xPubMedCrossRefGoogle Scholar
  5. Aurrecoechea C, Brestelli J, Brunk BP, Dommer J, Fischer S, Gajria B, Gao X, Gingle A, Grant G, Harb OS, Heiges M, Innamorato F, Iodice J, Kissinger JC, Kraemer E, Li W, Miller JA, Nayak V, Pennington C, Pinney DF, Roos DS, Ross C, Stoeckert CJ Jr, Treatman C, Wang H (2009) PlasmoDB: a functional genomic database for malaria parasites. Nucleic Acids Res 37:539–543.  https://doi.org/10.1093/nar/gkn814CrossRefGoogle Scholar
  6. Banerjee D, Parmar D, Bhattacharya N, Ghanate AD, Panchagnula V, Raghunathan A (2017) A scalable metabolite supplementation strategy against antibiotic resistant pathogen Chromobacterium violaceum induced by NAD+/NADH+ imbalance. BMC Syst Biol 11(1):51.  https://doi.org/10.1186/s12918-017-0427-zPubMedPubMedCentralCrossRefGoogle Scholar
  7. Banoei MM, Vogel HJ, Weljie AM, Kumar A, Yende S, Angus DC, Winston BW, Canadian Critical Care Translational Biology Group (CCCTBG) (2017) Plasma metabolomics for the diagnosis and prognosis of H1N1 influenza pneumonia. Crit Care 21(1):97.  https://doi.org/10.1186/s13054-017-1672-7PubMedPubMedCentralCrossRefGoogle Scholar
  8. Bartell JA, Blazier AS, Yen P, Thøgersen JC, Jelsbak L, Goldberg JB, Papin JA (2017) Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat Commun 8:14631.  https://doi.org/10.1038/ncomms14631PubMedPubMedCentralCrossRefGoogle Scholar
  9. Bäumler A, Fang FC (2013) Host specificity of bacterial pathogens. Cold Spring Harb Perspect Med 3(12):a010041PubMedPubMedCentralCrossRefGoogle Scholar
  10. Bäumler A, Sperandio V (2016) Interactions between the microbiota and pathogenic bacteria in the gut. Nature 535(7610):85–93.  https://doi.org/10.1038/nature18849PubMedPubMedCentralCrossRefGoogle Scholar
  11. Bazzani S, Hoppe A, Holzhütter H (2012) Network-based assessment of the selectivity of metabolic drug targets in Plasmodium falciparum with respect to human liver metabolism. BMC Syst Biol 6:118PubMedPubMedCentralCrossRefGoogle Scholar
  12. Becker SA, Palsson BO (2008) Context-specific metabolic networks are consistent with experiments. PLoS Comput Biol 4(5):e1000082.  https://doi.org/10.1371/journal.pcbi.1000082PubMedPubMedCentralCrossRefGoogle Scholar
  13. Becker SA, Palsson BØ (2005) Genome-scale reconstruction of the metabolic network in Staphylococcus aureus N315: an initial draft to the two-dimensional annotation. BMC Microbiol 5:8.  https://doi.org/10.1186/1471-2180-5-8PubMedPubMedCentralCrossRefGoogle Scholar
  14. Bendouah Z, Barbeau J, Hamad WA, Desrosiers M (2006) Biofilm formation by Staphylococcus aureus and Pseudomonas aeruginosa is associated with an unfavorable evolution after surgery for chronic sinusitis and nasal polyposis. Otolaryngol Head Neck Surg 134(6):991–996.  https://doi.org/10.1016/j.otohns.2006.03.001PubMedCrossRefGoogle Scholar
  15. Berger A, Dohnt K, Tielen P, Jahn D, Becker J, Wittmann C (2014) Robustness and plasticity of metabolic pathway flux among uropathogenic isolates of Pseudomonas aeruginosa. PLoS One 9(4):e88368.  https://doi.org/10.1371/journal.pone.0088368PubMedPubMedCentralCrossRefGoogle Scholar
  16. Bergkessel M, Basta DW, Newman DK (2016) The physiology of growth arrest: uniting molecular and environmental microbiology. Nat Rev Microbiol 14(9):549–562.  https://doi.org/10.1038/nrmicro.2016.107PubMedCrossRefGoogle Scholar
  17. Beste D, Nöh K, Niedenführ S, Mendum T, Hawkins N, Ward J, Beale M, Wiechert W, McFadden J (2013) 13C-flux spectral analysis of host-pathogen metabolism reveals a mixed diet for ıntracellular Mycobacterium tuberculosis. Chem Biol 20(8):1012–1021PubMedPubMedCentralCrossRefGoogle Scholar
  18. Bhagirath AY, Li Y, Somayajula D, Dadashi M, Badr S, Duan K (2016) Cystic fibrosis lung environment and Pseudomonas aeruginosa infection. BMC Pulm Med 16(1):174.  https://doi.org/10.1186/s12890-016-0339-5PubMedPubMedCentralCrossRefGoogle Scholar
  19. Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N (2010) Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions. Mol Syst Biol 6:422.  https://doi.org/10.1038/msb.2010.68PubMedPubMedCentralCrossRefGoogle Scholar
  20. Bosi E, Monk JM, Aziz RK, Fondi M, Nizet V, Palsson BØ (2016) Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specificmetabolic capabilities linked to pathogenicity. Proc Natl Acad Sci USA 113(26):3801–3809.  https://doi.org/10.1073/pnas.1523199113CrossRefGoogle Scholar
  21. Bouatra S, Aziat F, Mandal R, Guo AC, Wilson MR, Knox C, Bjorndahl TC, Krishnamurthy R, Saleem F, Liu P, Dame ZT, Poelzer J, Huynh J, Yallou FS, Psychogios N, Dong E, Bogumil R, Roehring C, Wishart DS (2013) The human urine metabolome. PLoS One 8(9).  https://doi.org/10.1371/journal.pone.0073076
  22. Bozzetto S, Pirillo P, Carraro S, Berardi M, Cesca L, Stocchero M, Giordano G, Zanconato S, Baraldi E (2017) Metabolomic profile of children with recurrent respiratory infections. Pharmacol Res 115:162–167PubMedCrossRefGoogle Scholar
  23. Butt AM, Nasrullah I, Tahir S, Tong Y (2012) Comparative genomics analysis of Mycobacterium ulcerans for the ıdentification of putative essential genes and therapeutic candidates. PLoS One 7(8).  https://doi.org/10.1371/journal.pone.0043080
  24. Cañigral I, Moreno Y, Alonso J, González A, Ferrús M (2010) Detection of Vibrio vulnificus in seafood, seawater and wastewater samples from a Mediterranean coastal area. Microbiol Res 165(8):657–664.  https://doi.org/10.1016/j.micres.2009.11.012PubMedCrossRefGoogle Scholar
  25. Caspi R, Billington R, Ferrer L, Foerster H, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Mueller A, Ong Q, Paley S, Subhraveti P, Weaver DS, Karp D (2016) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 44(1):471–480.  https://doi.org/10.1093/nar/gkv1164CrossRefGoogle Scholar
  26. Cassman M, Arkin A, Doyle F, Katagiri F, Lauffenburger D, Stokes C (2007) Systems biology: international research and development. Springer Science & Business Media, DordrechtCrossRefGoogle Scholar
  27. Chandrasekaran S, Price ND (2010) Probabilistic integrative modeling of genome-scale metabolic and regulatory networks in Escherichia coli and Mycobacterium tuberculosis. Proc Natl Acad Sci USA 107(41):17845–17850.  https://doi.org/10.1073/pnas.1005139107PubMedPubMedCentralCrossRefGoogle Scholar
  28. Chang A, Scheer M, Grote A, Schomburg I (2009) BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009. Nucleic Acids Res 37:588–592.  https://doi.org/10.1093/nar/gkn820CrossRefGoogle Scholar
  29. Chaves-Moreno D, Wos-oxley ML, Jáuregui R, Medina E, Oxley AP (2015) Application of a novel “pan-genome”-based strategy for assigning rnaseq transcript reads to Staphylococcus aureus strains. PLoS One 10(12):e0145861.  https://doi.org/10.1371/journal.pone.0145861PubMedPubMedCentralCrossRefGoogle Scholar
  30. Cheung YW, Tanner JA (2011) Targeting glutamate synthase for tuberculosis drug development. Hong Kong Med J 17(Suppl 2):32–34PubMedGoogle Scholar
  31. Choi SY, Yoon K, Lee JI, Mitchell RJ (2015) Violacein: properties and production of a versatile bacterial pigment. Biomed Res Int 2015:465056.  https://doi.org/10.1155/2015/465056PubMedPubMedCentralGoogle Scholar
  32. Cloete R, Oppon E, Murungi E, Schubert W, Christoffels A (2016) Resistance related metabolic pathways for drug target identification in Mycobacterium tuberculosis. BMC Bioinformatics 17(75).  https://doi.org/10.1186/s12859-016-0898-8
  33. Conlan S, Mijares LA, Comparative N, Program S, Becker J, Blakesley RW, Bouffard GG, Brooks S, Coleman H, Gupta J, Gurson N, Park M, Schmidt B, Thomas PJ, Otto M, Kong HH, Murray PR, Segre JA (2012) Staphylococcus epidermidis pan-genome sequence analysis reveals diversity of skin commensaland hospital infection-associated isolates. Genome Biol 13(7):R64.  https://doi.org/10.1186/gb-2012-13-7-r64PubMedPubMedCentralCrossRefGoogle Scholar
  34. Creczynski-pasa TB, Antônio RV (2004) Energetic metabolism of Chromobacterium violaceum. Genet Mol Res 3(1):162–166PubMedGoogle Scholar
  35. Cribbs SK, Uppal K, Li S, Jones DP, Huang L, Tipton L, Fitch A, Greenblatt RM, Kingsley L, Guidot DM, Ghedin E, Morris A (2016) Correlation of the lung microbiota with metabolic profiles in bronchoalveolar lavage fluid in HIV infection. Microbiome 4:3.  https://doi.org/10.1186/s40168-016-0147-4PubMedPubMedCentralCrossRefGoogle Scholar
  36. Cui L, Hou J, Fang J, Lee Y, Costa V, Won L, Chen Q, Ooi E, Tannenbaum S, Chen J, Ong C (2017) Serum metabolomics ınvestigation of humanized mouse model of dengue virus infection. J Virol 91(14):e00386-1.  https://doi.org/10.1128/JVI.00386-17CrossRefGoogle Scholar
  37. Dostálová A, Volf P (2012) Leishmania development in sand flies: parasite-vector interactions overview. Parasit Vectors 5:276PubMedPubMedCentralCrossRefGoogle Scholar
  38. Doyle MA, Macrae JI, De Souza DP, Saunders EC, Mcconville MJ, Likić VA (2009) LeishCyc: a biochemical pathways database for Leishmania major. BMC Syst Biol 3:57.  https://doi.org/10.1186/1752-0509-3-57PubMedPubMedCentralCrossRefGoogle Scholar
  39. Durmus S, Çakir T, Özgür A, Guthke R (2015) A review on computational systems biology of pathogen-host interactions. Front Microbiol 6:235.  https://doi.org/10.3389/fmicb.2015.00235PubMedPubMedCentralGoogle Scholar
  40. Dutow P, Schmidl SR, Ridderbusch M, Stülke J (2010) Interactions between glycolytic enzymes of Mycoplasma pneumoniae. J Mol Microbiol Biotechnol 19(3):134–139.  https://doi.org/10.1159/000321499PubMedCrossRefGoogle Scholar
  41. Ehlers S, Schaible UE (2013) The granuloma in tuberculosis: dynamics of a host-pathogen collusion. Front Immunol 3:411.  https://doi.org/10.3389/fimmu.2012.00411PubMedPubMedCentralCrossRefGoogle Scholar
  42. Fadiel A, Eichenbaum KD, El Semary N, Epperson B (2007) Mycoplasma genomics: tailoring the genome for minimal life requirements through reductive evolution. Front Biosci 12:2020–2028.  https://doi.org/10.2741/2207PubMedCrossRefGoogle Scholar
  43. Fattuoni C, Palmas F, Noto A, Barberini L, Mussap M, Grapov D, Dessì A, Casu M, Casanova A, Furione M, Arossa A, Spinillo A, Baldanti F, Fanos V, Zavattoni M (2016) Primary HCMV infection in pregnancy from classic data towards metabolomics: an exploratory analysis. Clin Chim Acta 460:23–32PubMedCrossRefGoogle Scholar
  44. Ferrarini MG, Siqueira FM, Mucha SG, Palama TL, Jobard É, Elena-herrmann B, Vasconcelos ATR, Tardy F, Schrank IS, Zaha A, Sagot MF (2016) Insights on the virulence of swine respiratory tract mycoplasmas through genome-scale metabolic modeling. BMC Genomics 17:353.  https://doi.org/10.1186/s12864-016-2644-zPubMedPubMedCentralCrossRefGoogle Scholar
  45. Francke C, Siezen RJ, Teusink B (2005) Reconstructing the metabolic network of a bacterium from its genome. Trends Microbiol 13(11):550–558.  https://doi.org/10.1016/j.tim.2005.09.001PubMedCrossRefGoogle Scholar
  46. Frank KL, Colomer-winter C, Grindle SM, Lemos JA, Schlievert PM, Dunny GM (2014) Transcriptome analysis of Enterococcus faecalis during mammalian ınfection shows cells undergo adaptation and exist in a stringent response state. PLoS One 9(12):e115839.  https://doi.org/10.1371/journal.pone.0115839PubMedPubMedCentralCrossRefGoogle Scholar
  47. Gal-mor O, Boyle EC, Grassl GA (2014) Same species, different diseases: how and why typhoidal and non-typhoidal Salmonella enterica serovars differ. Front Microbiol 5:391.  https://doi.org/10.3389/fmicb.2014.00391PubMedPubMedCentralCrossRefGoogle Scholar
  48. Gaurav K, Hasija Y (2017) Comparative analysis of metabolic network of pathogens. Front Biol 12(2):139–150.  https://doi.org/10.1007/s11515-017-1440-8CrossRefGoogle Scholar
  49. Gengenbacher M, Kaufmann S (2012) Mycobacterium tuberculosis: success through dormancy. FEMS Microbiol Rev 36(3):514–532.  https://doi.org/10.1111/j.1574-6976.2012.00331.x.MycobacteriumPubMedPubMedCentralCrossRefGoogle Scholar
  50. Gerdes SY, Scholle MD, Souza MD, Bernal A, Baev MV, Farrell M, Kurnasov OV, Daugherty MD, Mseeh F, Polanuyer BM, Campbell JW, Anantha S, Shatalin KY, Chowdhury SAK, Fonstein MY, Osterman AL (2002) From genetic footprinting to antimicrobial drug targets: examples in cofactor biosynthetic pathways. J Bacteriol 184(16):4555–4572.  https://doi.org/10.1128/JB.184.16.4555PubMedPubMedCentralCrossRefGoogle Scholar
  51. Ginsburg H (2006) Progress in in silico functional genomics: the malaria metabolic pathways database. Trends Parasitol 22(6):238–240PubMedCrossRefGoogle Scholar
  52. Goldman E, Green L (2015) Practical handbook of microbiology. CRC Press, Boca Raton, FLGoogle Scholar
  53. Goto S, Okuno Y, Hattori M, Nishioka T, Kanehisa M (2002) LIGAND: database of chemical compounds and reactions in biological pathways. Nucleic Acids Res 30(1):402–404.  https://doi.org/10.1093/nar/30.1.402PubMedPubMedCentralCrossRefGoogle Scholar
  54. Götz A, Eylert E, Eisenreich W, Goebel W (2010) Carbon metabolism of Enterobacterial human pathogens growing in epithelial colorectal adenocarcinoma (Caco-2) cells. PLoS One 5(5):e10586.  https://doi.org/10.1371/journal.pone.0010586PubMedPubMedCentralCrossRefGoogle Scholar
  55. Großeholz R, Koh C, Veith N, Fiedler T, Strauss M, Olivier B, Collins BC, Schubert OT, Bergmann F, Kreikemeyer B, Aebersold R, Kummer U (2016) Integrating highly quantitative proteomics and genome-scale metabolic modeling to study pH adaptation in the human pathogen Enterococcus faecalis. NPJ Syst Biol Appl 2:16017.  https://doi.org/10.1038/npjsba.2016.17PubMedPubMedCentralCrossRefGoogle Scholar
  56. Großhennig S, Schmidl SR, Schmeisky G, Busse J, Stülke J (2013) Implication of glycerol and phospholipid transporters in mycoplasma pneumoniae growth and virulence. Infect Immun 81(3):896–904.  https://doi.org/10.1128/IAI.01212-12PubMedPubMedCentralCrossRefGoogle Scholar
  57. Guimarães LC, Florczak-Wyspianska J, de Jesus LB, Viana MV, Silva A, Ramos RT, de Carvalho Azevedode Castro Soares S, de Carvalho Azevedo VA (2015) Inside the pan-genome - methods and software overview. Curr Genomics 16(4):245–252PubMedPubMedCentralCrossRefGoogle Scholar
  58. Gupta M, Prasad Y, Sharma SK, Kumar C (2017) Identification of phosphoribosyl-amp cyclohydrolase, as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis. J Biomol Struct Dyn 35(2):287–299.  https://doi.org/10.1080/07391102.2015.1137229PubMedCrossRefGoogle Scholar
  59. Hadizadeh M, Tabatabaiepour SN, Tabatabaiepour SZ, Hosseini Nave H, Mohammadi M, Sohrabi S (2017) Genome-wide Identification of potential drug target in Enterobacteriaceae family: a homology-based method. Microb Drug Resist.  https://doi.org/10.1089/mdr.2016.0259
  60. Hartman HB, Fell DA, Rossell S, Jensen PR, Woodward MJ, Thorndahl L, Jelsbak L, Olsen JE, Raghunathan A, Daefler S, Poolman MG (2014) Identification of potential drug targets in Salmonella enterica sv. Typhimurium using metabolic modelling and experimental validation. Microbiology 160(Pt 6):1252–1266.  https://doi.org/10.1099/mic.0.076091-0PubMedCrossRefGoogle Scholar
  61. Harvey R, Champe P, Fisher B (2007) Lippincotts illustrated reviews: microbiology. Lippincott Williams & Wilkins, Philadelphia, PAGoogle Scholar
  62. Henriksen S, Liu J, Estiu G, Oltvai Z, Wiest O (2010) Identification of novel bacterial histidine biosynthesis inhibitors using docking, ensemble rescoring, and whole-cell assays. Bioorg Med Chem 18(14):5148–5156.  https://doi.org/10.1016/j.bmc.2010.05.060.IdentificationPubMedPubMedCentralCrossRefGoogle Scholar
  63. Henry CS, Dejongh M, Best AA, Frybarger PM, Linsay B, Stevens RL (2010) High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol 28(9):977–982.  https://doi.org/10.1038/nbt.1672PubMedCrossRefGoogle Scholar
  64. Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, Ojima Y, Tanaka K, Tanaka S, Aoshima K, Oda Y, Kakazu Y, Kusano M, Tohge T, Matsuda F, Sawada Y, Hirai M, Nakanishi H, Ikeda K, Akimoto N, Maoka T, Takahash H, Ara T, Sakurai N, Suzuki H, Shibata D, Neumann S, Iida T, Tanaka K, Funatsu K, Matsuura F, Soga T, Taguchi R, Saito K, Nishioka T (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45(7):703–714.  https://doi.org/10.1002/jms.1777PubMedCrossRefGoogle Scholar
  65. Huthmacher C, Hoppe A, Bulik S, Holzhütter H (2010) Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis. BMC Syst Biol 4:120PubMedPubMedCentralCrossRefGoogle Scholar
  66. Hwang M, Damte D, Cho M, Kim Y, Park S (2010) Optimization of culture media of pathogenic Mycoplasma hyopneumoniae by a response surface methodology. J Vet Sci 11(4):327–332.  https://doi.org/10.4142/jvs.2010.11.4.327PubMedPubMedCentralCrossRefGoogle Scholar
  67. Irwin JJ, Shoichet BK, Mysinger MM, Huang N, Colizzi F, Wassam P, Cao Y (2009) Automated docking screens: a feasibility study. J Med Chem 52(18):5712–5720.  https://doi.org/10.1021/jm9006966PubMedPubMedCentralCrossRefGoogle Scholar
  68. Jamshidi N, Palsson BØ (2007) Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets. BMC Syst Biol 1(26).  https://doi.org/10.1186/1752-0509-1-26
  69. Jensen PA, Papin JA (2011) Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27(4):541–547.  https://doi.org/10.1093/bioinformatics/btq702PubMedCrossRefGoogle Scholar
  70. Jerby L, Ruppin E (2012) Predicting drug targets and biomarkers of cancer via genome-scale metabolic modeling. Clin Cancer Res 18(20):5572–5584.  https://doi.org/10.1158/1078-0432.CCR-12-1856PubMedCrossRefGoogle Scholar
  71. Jones MK, Oliver JD (2009) Vibrio vulnificus: disease and pathogenesis. Infect Immun 77(5):1723–1733.  https://doi.org/10.1128/IAI.01046-08PubMedPubMedCentralCrossRefGoogle Scholar
  72. Josling GA, Llinás M (2015) Sexual development in Plasmodium parasites: knowing when its time to commit. Nat Rev Microbiol 13(9):573–587.  https://doi.org/10.1038/nrmicro3519PubMedCrossRefGoogle Scholar
  73. Kahlon R (2016) Pseudomonas: molecular and applied biology. Springer, Switzerland, pp 82–83CrossRefGoogle Scholar
  74. Kajihara T, Nakamura S, Iwanaga N, Oshima K, Takazono T, Miyazaki T, Izumikawa K, Yanagihara K, Kohno N, Kohno S (2015) Clinical characteristics and risk factors of enterococcal infections in Nagasaki, Japan: a retrospective study. BMC Infect Dis 15:426.  https://doi.org/10.1186/s12879-015-1175-6PubMedPubMedCentralCrossRefGoogle Scholar
  75. Kaltdorf M, Srivastava M, Gupta SK, Liang C, Krappmann S, Dandekar T (2016) Systematic identification of anti-fungal drug targets by a metabolic network approach. Front Mol Biosci 3:22.  https://doi.org/10.3389/fmolb.2016.00022PubMedPubMedCentralCrossRefGoogle Scholar
  76. Kamminga T, Slagman S, Bijlsma J, Martins Dos Santos V, Suarez-Diez M, Schaap P (2017) Metabolic modeling of energy balances in Mycoplasma hyopneumoniae shows that pyruvate addition increases growth rate. Biotechnol Bioeng 114(10):2339–2347.  https://doi.org/10.1002/bit.26347PubMedCrossRefGoogle Scholar
  77. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y (2008) KEGG for linking genomes to life and the environment. Nucleic Acids Res 36:480–484.  https://doi.org/10.1093/nar/gkm882CrossRefGoogle Scholar
  78. Kao D, Ismond KP, Tso V, Millan B, Hotte N, Fedorak RN (2016) Urine-based metabolomic analysis of patients with Clostridium difficile infection: a pilot study. Metabolomics 12:135.  https://doi.org/10.1007/s11306-016-1080-zCrossRefGoogle Scholar
  79. Karp PD, Ouzounis CA, Moore-Kochlacs C, Goldovsky L, Kaipa P, Ahrén D, Tsoka S, Darzentas N, Kunin V, López-Bigas N (2005) Expansion of the BioCyc collection of pathway/genome databases to 160 genomes. Nucleic Acids Res 33(19):6083–6089.  https://doi.org/10.1093/nar/gki892PubMedPubMedCentralCrossRefGoogle Scholar
  80. Kauffman KJ, Prakash P, Edwards JS (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14(5):491–496.  https://doi.org/10.1016/j.copbio.2003.08.001PubMedCrossRefGoogle Scholar
  81. Kim HU, Kim T, Lee YS (2010) Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Mol Biosyst 6(2):339–348.  https://doi.org/10.1039/b916446dPubMedCrossRefGoogle Scholar
  82. Kim HU, Kim SY, Jeong H, Kim TY, Kim JJ, Choy HE, Yi KY, Rhee JH, Lee SY (2011) Integrative genome-scale metabolic analysis of Vibrio vulnificus for drug targeting and discovery. Mol Syst Biol 7:460.  https://doi.org/10.1038/msb.2010.115PubMedPubMedCentralCrossRefGoogle Scholar
  83. Kim M, Lun D (2014) Methods for integration of transcriptomic data in genome-scale metabolic models. Comput Struct Biotechnol J 11(18):59–65.  https://doi.org/10.1016/j.csbj.2014.08.009PubMedPubMedCentralCrossRefGoogle Scholar
  84. Kim Y, Schmidt B, Kidwai A, Jones M, Deatherage Kaise B, Brewer H, Mitchell H, Palsson B, McDermott J, Heffron F, Smith R, Peterson S, Ansong C, Hyduk D, Metz T, Adkins J (2013) Salmonella modulates metabolism during growth under conditions that induce expression of virulence genes. Mol Biosyst 9(6):1522–1534.  https://doi.org/10.1039/c3mb25598kPubMedPubMedCentralCrossRefGoogle Scholar
  85. King ZA, Lu J, Miller P, Federowicz S, Lerman JA, Ebrahim A, Palsson BO, Lewis NE (2016) BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44:515–522.  https://doi.org/10.1093/nar/gkv1049CrossRefGoogle Scholar
  86. Kitano H (2002) Computational systems biology. Nature 420(6912):206–210PubMedCrossRefGoogle Scholar
  87. Klamt S, Gilles ED (2004) Minimal cut sets in biochemical reaction networks. Bioinformatics 20(2):226–234.  https://doi.org/10.1093/bioinformatics/btg395PubMedCrossRefGoogle Scholar
  88. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, Djoumbou Y, Eisner R, Guo AC, Wishart DS (2011) DrugBank 3.0: a comprehensive resource for ‘omics’ research on drugs. Nucleic Acids Res 39:1035–1041.  https://doi.org/10.1093/nar/gkq1126CrossRefGoogle Scholar
  89. Kumar A, Suthers PF, Maranas CD (2012) MetRxn: a knowledgebase of metabolites and reactions spanning metabolic models and databases. BMC Bioinformatics 13:6.  https://doi.org/10.1186/1471-2105-13-6PubMedPubMedCentralCrossRefGoogle Scholar
  90. Landeck L, Kneip C, Reischl J, Asadullah K (2016) Biomarkers and personalized medicine: current status and further perspectives with special focus on dermatology. Exp Dermatol 25(5):333–339.  https://doi.org/10.1111/exd.12948PubMedCrossRefGoogle Scholar
  91. Larocque M, Chénard T, Najmanovich R (2014) A curated C. difficile strain 630 metabolic network: prediction of essential targets and inhibitors. BMC Syst Biol 8:117PubMedPubMedCentralCrossRefGoogle Scholar
  92. Le Carrou J, Laurentie M, Kobisch M, Gautier-Bouchardon AV (2006) Persistence of Mycoplasma hyopneumoniae in experimentally ınfected pigs after marbofloxacin treatment and detection of mutations in the parC gene. Antimicrob Agents Chemother 50(6):1959–1966.  https://doi.org/10.1128/AAC.01527-05PubMedPubMedCentralCrossRefGoogle Scholar
  93. Le Novère N, Bornstein B, Broicher A, Courtot M, Donizelli M, Dharuri H, Li L, Sauro H, Schilstra M, Shapiro B, Snoep J, Hucka M (2006) BioModels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Res 34(Database issue):D689–D691.  https://doi.org/10.1093/nar/gkj092PubMedCrossRefGoogle Scholar
  94. Lee DS, Burd H, Liu J, Almaas E, Wiest O, Barabási AL, Oltvai ZN, Kapatral V (2009) Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J Bacteriol 191(12):4015–4024.  https://doi.org/10.1128/JB.01743-08PubMedPubMedCentralCrossRefGoogle Scholar
  95. Lewis NE, Nagarajan H, Palsson BO (2012) Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods. Nat Rev Microbiol 10(4):291–305.  https://doi.org/10.1038/nrmicro2737PubMedPubMedCentralCrossRefGoogle Scholar
  96. Li X, Mishra SK, Wu M, Zhang F, Zheng J (2014) Syn-lethality: an integrative knowledge base of synthetic lethality towards discovery of selective anticancer therapies. Biomed Res Int 2014:19603.  https://doi.org/10.1155/2014/196034Google Scholar
  97. Liao Y, Huang T, Chen F, Charusanti P, Hong JSJ, Chang H, Tsai S, Palsson BO, Hsiung CA (2011) An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228. J Bacteriol 193(7):1710–1717.  https://doi.org/10.1128/JB.01218-10PubMedPubMedCentralCrossRefGoogle Scholar
  98. Lin H, Ding H, Guo FB, Huang J (2010) Prediction of subcellular location of mycobacterial protein using feature selection techniques. Mol Divers 14(4):667–671.  https://doi.org/10.1007/s11030-009-9205-1PubMedCrossRefGoogle Scholar
  99. Liu A, Archer AM, Biggs MB, Papin JA (2017) Growth-altering microbial interactions are responsive to chemical context. PLoS One 12(3):e0164919PubMedPubMedCentralCrossRefGoogle Scholar
  100. Lunardi J, Nunes J, Bizarro C, Basso L, Santos D, Machado P (2013) Targeting the histidine pathway in Mycobacterium tuberculosis. Curr Top Med Chem 13(22):2866–2884.  https://doi.org/10.2174/15680266113136660203PubMedCrossRefGoogle Scholar
  101. Ma S, Minch KJ, Rustad TR, Hobbs S, Zhou SL, Sherman DR, Price ND (2015) Integrated modeling of gene regulatory and metabolic networks in Mycobacterium tuberculosis. PLoS Comput Biol 11(11):e1004543.  https://doi.org/10.1371/journal.pcbi.1004543PubMedPubMedCentralCrossRefGoogle Scholar
  102. Matthews L, Gopinath G, Gillespie M, Caudy M, Croft D, De Bono B, Garapati P, Hemish J, Hermjakob H, Jassal B, Kanapin A, Lewis S, Mahajan S, May B, Schmidt E, Vastrik I, Wu G, Birney E, Stein L, D’Eustachio P (2009) Reactome knowledgebase of human biological pathways and processes. Nucleic Acids Res 37:619–622.  https://doi.org/10.1093/nar/gkn863CrossRefGoogle Scholar
  103. Mehla K, Ramana J (2015) Novel drug targets for food-borne pathogen Campylobacter jejuni: an Integrated subtractive genomics and comparative metabolic pathway study. OMICS 19(7):393–406PubMedPubMedCentralCrossRefGoogle Scholar
  104. Mehla K, Ramana J (2017) Tapping into Salmonella typhimurium LT2 genome in a quest to explore its therapeutic arsenal: a metabolic network modeling approach. Biomed Pharmacother 86:57–66.  https://doi.org/10.1016/j.biopha.2016.11.129PubMedCrossRefGoogle Scholar
  105. Mortensen N, Mercier K, Mcritchie S, Cavallo T, Pathmasiri W, Stewart D, Sumner SJ (2016) Microfluidics meets metabolomics to reveal the impact of Campylobacter jejuni infection on biochemical pathways. Biomed Microdevices 18(3):51.  https://doi.org/10.1007/s10544-016-0076-9PubMedPubMedCentralCrossRefGoogle Scholar
  106. Murphy DJ, Brown JR (2007) Identification of gene targets against dormant phase Mycobacterium tuberculosis infections. BMC Infect Dis 7:84.  https://doi.org/10.1186/1471-2334-7-84PubMedPubMedCentralCrossRefGoogle Scholar
  107. Ng M, Saunders E, Olshansky M, McConville M, Likic V (2016) 13C metabolic flux ratio analysis by direct measurement of free metabolic intermediates in L. mexicana using gas chromatography-mass spectrometry. Bio Rxiv:73676.  https://doi.org/10.1101/073676
  108. Nikel PI, Chavarría M, Fuhrer T, Sauer U, De Lorenzo V (2015) Pseudomonas putida KT2440 strain metabolizes glucose through a cycle formed by enzymes of the entner- doudoroff, embden-meyerhof-parnas, and pentose phosphate pathways. J Biol Chem 290(43):25920–25932.  https://doi.org/10.1074/jbc.M115.687749PubMedPubMedCentralCrossRefGoogle Scholar
  109. Nishiumi S, Yoshida M, Azuma T (2017) Alterations in metabolic pathways in stomach of mice infected with Helicobacter pylori. Microb Pathog 109:78–85PubMedCrossRefGoogle Scholar
  110. Noto A, Dessi A, Puddu M, Mussap M, Fanos V (2014) Metabolomics technology and their application to the study of the viral infection. J Matern Fetal Neonatal Med 27(Suppl 2):53–57.  https://doi.org/10.3109/14767058.2014.955963PubMedCrossRefGoogle Scholar
  111. Oberhardt M, Puchałka J, Fryer K, Martins dos Santos V, Papin J (2008) Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol 190(8):2790–2803.  https://doi.org/10.1128/JB.01583-07PubMedPubMedCentralCrossRefGoogle Scholar
  112. Oberhardt M, Goldberg J, Hogardt M, Papin J (2010) Metabolic network analysis of Pseudomonas aeruginosa during chronic cystic fibrosis lung ınfection. J Bacterio 192(20):5534–5548.  https://doi.org/10.1128/JB.00900-10CrossRefGoogle Scholar
  113. Opperman M, Shachar-Hill Y (2016) Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates. Metab Eng 38:251–263.  https://doi.org/10.1016/j.ymben.2016.09.002PubMedCrossRefGoogle Scholar
  114. Otto M (2010) Staphylococcus colonization of the skin and antimicrobial peptides. Expert Rev Dermatol 5(2):183–195.  https://doi.org/10.1586/edm.10.6.StaphylococcusPubMedPubMedCentralCrossRefGoogle Scholar
  115. Overbeek R, Larsen N, Walunas T, Souza M, Pusch G, Selkov E Jr, Liolios K, Joukov V, Kaznadzey D, Anderson I, Bhattacharyya A, Burd H, Gardner W, Hanke P, Kapatral V, Mikhailova N, Vasieva O, Osterman A, Vonstein V, Fonstein M, Ivanova N, Kyrpides N (2003) The ERGOTM genome analysis and discovery system. Nucleic Acids Res 31(1):164–171.  https://doi.org/10.1093/nar/gkg148PubMedPubMedCentralCrossRefGoogle Scholar
  116. Phalak P, Chen J, Carlson R, Henson M (2016) Metabolic modeling of a chronic wound biofilm consortium predicts spatial partitioning of bacterial species. BMC Syst Biol 10(1):90.  https://doi.org/10.1186/s12918-016-0334-8PubMedPubMedCentralCrossRefGoogle Scholar
  117. Pienaar E, Matern WM, Linderman JJ, Bader JS, Kirschner DE (2016) Multiscale model of Mycobacterium tuberculosis ınfection maps metabolite and gene perturbations to granuloma sterilization. Infect Immun 84(5):1650–1669.  https://doi.org/10.1128/IAI.01438-15.EditorPubMedPubMedCentralCrossRefGoogle Scholar
  118. Pieper U, Webb BM, Barkan DT, Schneidman-duhovny D, Schlessinger A, Braberg H, Yang Z, Meng EC, Pettersen EF, Huang CC, Datta RS, Sampathkumar P, Ferrin TE, Burley SK, Madhusudhan MS, Sjölander K, Ferrin TE, Burley SK, Sali A (2011) ModBase, a database of annotated comparative protein structure models, and associated resources. Nucleic Acids Res 39(Database issue):D465–D474.  https://doi.org/10.1093/nar/gkq1091PubMedCrossRefGoogle Scholar
  119. Plata G, Hsiao T-L, Olszewski KL, Llinás M, Vitkup D (2010) Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network. Mol Syst Biol 6:408.  https://doi.org/10.1038/msb.2010.60PubMedPubMedCentralCrossRefGoogle Scholar
  120. Quek L-E, Wittmann C, Nielsen LK, Krömer JO (2009) OpenFLUX: efficient modelling software for 13C-based metabolic flux analysis. Microb Cell Fact 8:25.  https://doi.org/10.1186/1475-2859-8-25PubMedPubMedCentralCrossRefGoogle Scholar
  121. 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.  https://doi.org/10.1186/1752-0509-3-38PubMedPubMedCentralCrossRefGoogle Scholar
  122. Rajendran R, May A, Sherry L, Kean R, Williams C, Jones BL, Burgess KV, Heringa J, Abeln S, Brandt BW, Munro CA, Ramage G (2016) Integrating Candida albicans metabolism with biofilm heterogeneity by transcriptome mapping. Sci Rep 6:35436.  https://doi.org/10.1038/srep35436PubMedPubMedCentralCrossRefGoogle Scholar
  123. Reed JL, Vo TD, Schilling CH, Palsson BO (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4(9):R54PubMedPubMedCentralCrossRefGoogle Scholar
  124. Reid AJ, Vermont SJ, Cotton JA, Harris D, Hill-Cawthorne GA, Latham SM, Mourier T, Norton R, Quail MA, Sanders M, Shanmugam D, Sohal A, Wasmuth JD, Brunk B, Grigg ME, Howard JC, Parkinson J, Roos DS, Trees AJ, Berriman M, Pain A, Wastling JM (2012) Comparative genomics of the apicomplexan parasites Toxoplasma gondii and Neospora caninum: Coccidia differing in host range and transmission strategy. PLoS Pathog 8(3):e1002567.  https://doi.org/10.1371/journal.ppat.1002567PubMedPubMedCentralCrossRefGoogle Scholar
  125. Ren Q, Chen K, Paulsen IT (2007) TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res 35(Database issue):D274–D279.  https://doi.org/10.1093/nar/gkl925PubMedCrossRefGoogle Scholar
  126. Reynolds L, Redpath S, Yurist-Doutsch S, Gill N, Brown E, Van Der Heijden J, Brosschot TP, Han J, Marshall N, Woodward S, Valdez Y, Borchers C, Perona-Wright G, Finlay B (2017) Enteric helminths promote Salmonella coinfection by altering the intestinal metabolome. J Infect Dis 215(8):1245–1254PubMedCrossRefGoogle Scholar
  127. Richardson A, Somerville G, Sonenshein A (2015) Regulating the ıntersection of metabolism and pathogenesis in Gram-positive bacteria. Microbiol Spectr 3(3).  https://doi.org/10.1128/microbiolspec.MBP-0004-2014.Regulating
  128. Riley CP, Gough ES, He J, Jandhyala SS, Kennedy B, Orcun S, Ouzzani M, Buck C, Roumani AM, Zhang X (2010) The proteome discovery pipeline – a data analysis pipeline for mass spectrometry-based differential proteomics discovery. Open Proteomics J 3:8–19Google Scholar
  129. Sachs G, Keeling D (2001) Ion motive ATPases: V- and P-type ATPases. eLS, Wiley.Google Scholar
  130. Saier MH, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-hagelsieb G (2016) The Transporter Classification Database (TCDB): recent advances. Nucleic Acids Res 44(Database issue):D372–D379.  https://doi.org/10.1093/nar/gkv1103PubMedCrossRefGoogle Scholar
  131. Samant S, Lee H, Ghassemi M, Chen J, Cook JL, Mankin AS, Neyfakh AA (2008) Nucleotide biosynthesis is critical for growth of bacteria in human blood. PLoS Pathog 4(2):e37.  https://doi.org/10.1371/journal.ppat.0040037PubMedPubMedCentralCrossRefGoogle Scholar
  132. Sasse A, Hamer S, Amich J, Binder J, Krappmann S (2016) Mutant characterization and in vivo conditional repression identify aromatic amino acid biosynthesis to be essential for Aspergillus fumigatus virulence. Virulence 7(1):56–62.  https://doi.org/10.1080/21505594.2015.1109766PubMedCrossRefGoogle Scholar
  133. Sassetti CM, Boyd DH, Rubin EJ (2003) Genes required for mycobacterial growth defined by high density mutagenesis. Mol Microbiol 48(1):77–84PubMedCrossRefGoogle Scholar
  134. Scarborough JA, Paul J, Spencer J (2017) Evolution of the ability to modulate host chemokine networks via gene duplication in human cytomegalovirus (HCMV). Infect Genet Evol 51:46–53.  https://doi.org/10.1016/j.meegid.2017.03.013PubMedCrossRefGoogle Scholar
  135. Seekatz A, Casey M, Krishna R, Yu-Ming C, Alison E, John Y, Vincent B, Freeman E, Kao JY, Young VB (2017) Inter-individual recovery of the microbiota and metabolome over time following fecal microbiota transplantation ın patients with recurrent Clostridium difficile infection. bioRxiv.  https://doi.org/10.1101/141846
  136. Segrè D, Vitkup D, Church GM (2002) Analysis of optimality in natural and perturbed. Proc Natl Acad Sci USA 99(23):15112–15117PubMedPubMedCentralCrossRefGoogle Scholar
  137. Sharma M, Shaikh N, Yadav S, Singh S, Garg P (2017) A systematic reconstruction and constraint-based analysis of Leishmania donovani metabolic network: identification of potential antileishmanial drug targets. Mol Biosyst 13(5):955–969.  https://doi.org/10.1039/C6MB00823BPubMedCrossRefGoogle Scholar
  138. Shen Y, Liu J, Estiu G, Isin B, Ahn Y, Lee D, Barabási A, Kapatral V, Wiest O, Oltvai Z (2010) Blueprint for antimicrobial hit discovery targeting metabolic networks. Proc Natl Acad Sci USA 107(3):1082–1087.  https://doi.org/10.1073/pnas.0909181107PubMedPubMedCentralCrossRefGoogle Scholar
  139. Shreiner A, Kao J, Young V (2015) The gut microbiome in health and in disease. Curr Opin Gastroenterol 31(1):69–75.  https://doi.org/10.1097/MOG.0000000000000139.ThePubMedPubMedCentralCrossRefGoogle Scholar
  140. Shrinet J, Shastri J, Gaind R, Bhavesh N, Sunil S (2016) Serum metabolomics analysis of patients with chikungunya and dengue mono/co-infections reveals distinct metabolite signatures in the three disease conditions. Sci Rep 6:36833.  https://doi.org/10.1038/srep36833PubMedPubMedCentralCrossRefGoogle Scholar
  141. Silva Miranda M, Breiman A, Allain S, Deknuydt F, Altare F (2012) The tuberculous granuloma: an unsuccessful host defence mechanism providing a safety shelter for the bacteria? Clin Dev Immunol 2012:13912.  https://doi.org/10.1155/2012/139127CrossRefGoogle Scholar
  142. Simeonidis E, Chandrasekaran S, Price N (2013) A guide to integrating transcriptional regulatory and metabolic networks using PROM (probabilistic regulation of metabolism). Methods Mol Biol:103–112.  https://doi.org/10.1007/978-1-62703-299-5
  143. Singh S, Singh D, Singh A, Gautam B, Ram G, Dwivedi S, Ramteke P (2016) An approach for identification of novel drug targets in Streptococcus pyogenes SF370 through pathway analysis. Interdiscip Sci 8(4):388–394PubMedCrossRefGoogle Scholar
  144. Siqueira F, Thompson C, Virginio V, Gonchoroski T, Reolo L, Almeida L, da Fonsêca M, de Souza R, Prosdocimi F, Schrank I, Ferreira H, de Vasconcelos A, Zaha A (2013) New insights on the biology of swine respiratory tract mycoplasmas from a comparative genome analysis. BMC Genomics 14:175PubMedPubMedCentralCrossRefGoogle Scholar
  145. Siqueira F, Gerber A, Guedes R, Almeida L, Schrank I, Vasconcelos A, Zaha A (2014) Unravelling the transcriptome profile of the Swine respiratory tract mycoplasmas. PLoS One 9(10):e110327.  https://doi.org/10.1371/journal.pone.0110327PubMedPubMedCentralCrossRefGoogle Scholar
  146. Smith C, OMaille G, Want E, Qin C, Trauger S, Brandon T, Custodio D, Abagyan R, Siuzdak G (2005) METLIN: a metabolite mass spectral database. Ther Drug Monit 27(6):747–751PubMedCrossRefGoogle Scholar
  147. Sousa A, Pereira M (2014) Pseudomonas aeruginosa diversification during ınfection development in cystic fibrosis lungs – a review. Pathogens 3(3):680–703.  https://doi.org/10.3390/pathogens3030680PubMedPubMedCentralCrossRefGoogle Scholar
  148. Steinway S, Biggs M, Loughran TP Jr, Papin JA, Albert R (2015) Inference of network dynamics and metabolic interactions in the gut microbiome. PLoS Comput Bio 11(5):e1004338.  https://doi.org/10.1371/journal.pcbi.1004338CrossRefGoogle Scholar
  149. Sud M, Fahy E, Cotter D, Dennis E, Subramaniam S (2012) LIPID MAPS-Nature lipidomics gateway: an online resource for students and educators interested in lipids. J Chem Educ 89(2):291–292.  https://doi.org/10.1021/ed200088u.LIPIDPubMedCrossRefGoogle Scholar
  150. Sussman JL, Lin D, Jiang J, Manning NO, Prilusky J (1998) Protein Data Bank (PDB): database of three-dimensional structural ınformation of biological macromolecules. Acta Crystallogr D Biol Crystallogr 54(Pt 6 Pt 1):1078–1084.  https://doi.org/10.1107/S0907444998009378PubMedCrossRefGoogle Scholar
  151. Szklarczyk D, Morris J, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva N, Roth A, Bork P, Jensen L, Mering C (2017) The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res 45(D1):D362–D368.  https://doi.org/10.1093/nar/gkw937PubMedCrossRefGoogle Scholar
  152. The Uniprot Consortium (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45(D1):D158–D169.  https://doi.org/10.1093/nar/gkw1099CrossRefGoogle Scholar
  153. Thiele I, Vo TD, Price ND, Palsson BØ (2005) Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): an in silico genome-scale characterization of single- and double-deletion mutants. J Bacteriol 187(16):5818–5830.  https://doi.org/10.1128/JB.187.16.5818PubMedPubMedCentralCrossRefGoogle Scholar
  154. Tilley L, Dixon MW, Kirk K (2011) The Plasmodium falciparum-infected red blood cell. Int J Biochem Cell Biol 43(6):839–842.  https://doi.org/10.1016/j.biocel.2011.03.012PubMedCrossRefGoogle Scholar
  155. Tymoshenko S, Oppenheim R, Agren R, Nielsen J, Soldati-Favre D, Hatzimanikatis V (2015) Metabolic needs and capabilities of Toxoplasma gondii through combined computational and experimental analysis. PLoS Comput Biol 11(5):e1004261.  https://doi.org/10.1371/journal.pcbi.1004261PubMedPubMedCentralCrossRefGoogle Scholar
  156. Uddin R, Saeed K, Khan W, Azam S, Wadood A (2015) Metabolic pathway analysis approach: identification of novel therapeutic target against methicillin resistant Staphylococcus aureus. Gene 556(2):213–226PubMedCrossRefGoogle Scholar
  157. Varnum S, Streblow D, Monroe M, Smith P, Auberry K, Pasa-Tolic L, Wang D, Camp D 2nd, Rodland K, Wiley S, Britt W, Shenk T, Smith R, Nelson JA (2004) Identification of proteins in human cytomegalovirus (HCMV) particles: the HCMV proteome. J Virol 78(20):10960–10966.  https://doi.org/10.1128/JVI.78.20.10960PubMedPubMedCentralCrossRefGoogle Scholar
  158. Veith N, Solheim M, Van Grinsven KWA, Olivier BG, Levering J, Grosseholz R, Hugenholtz J, Holo H, Nes I, Teusink B (2015) 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(5):1622–1633.  https://doi.org/10.1128/AEM.03279-14PubMedPubMedCentralCrossRefGoogle Scholar
  159. Voge N, Perera R, Mahapatra S, Gresh L, Balmaseda A, Loro MA, Hopf-jannasch A, Belisle J, Harris E, Blair C, Beaty B (2016) Metabolomics-based discovery of small molecule biomarkers in serum associated with dengue virus infections and disease outcomes. PLoS Negl Trop Dis 10(2):e0004449.  https://doi.org/10.1371/journal.pntd.0004449PubMedPubMedCentralCrossRefGoogle Scholar
  160. Webb B, Sali A, Francisco S (2017) Comparative protein structure modeling using MODELLER. Curr Protoc Bioinformatics 54.  https://doi.org/10.1002/cpbi.3.Comparative
  161. Wilking H, Thamm M, Stark K, Aebischer T, Seeber F (2016) Prevalence, incidence estimations, and risk factors of Toxoplasma gondii infection in Germany: a serological study. Sci Rep 6(22551).  https://doi.org/10.1038/srep22551
  162. Winsor G, Lam D, Fleming L, Lo R, Whiteside M, Yu N, Hancock R, Brinkman F (2011) Pseudomonas genome database: improved comparative analysis and population genomics capability for Pseudomonas genomes. Nucleic Acids Res 39(Database issue):D596–D600.  https://doi.org/10.1093/nar/gkq869PubMedCrossRefGoogle Scholar
  163. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M, Tg C (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36(Database issue):D901–D906.  https://doi.org/10.1093/nar/gkm958PubMedCrossRefGoogle Scholar
  164. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A (2013) HMDB 3.0 — the human metabolome database in 2013. Nucleic Acids Res 41(Database issue):D801–D807.  https://doi.org/10.1093/nar/gks1065PubMedGoogle Scholar
  165. Wodke J, Puchałka J, Lluch-Senar M, Marcos J, Yus E, Godinho M, Gutiérrez-Gallego R, dos Santos V, Serrano L, Klipp E, Maier T (2013) Dissecting the energy metabolism in Mycoplasma pneumoniae through genome-scale metabolic modeling. Mol Syst Biol 9:653.  https://doi.org/10.1038/msb.2013.6PubMedPubMedCentralCrossRefGoogle Scholar
  166. Wunderlich Z, Mirny L (2006) Using the topology of metabolic networks to predict viability of mutant strains. Biophys J 91(6):2304–2311.  https://doi.org/10.1529/biophysj.105.080572PubMedPubMedCentralCrossRefGoogle Scholar
  167. Xavier J (2016) Sociomicrobiology and pathogenic bacteria. Microbiol Spectr 4(3).  https://doi.org/10.1128/microbiolspec.VMBF-0019-2015.Sociomicrobiology
  168. Xia J, Sinelnikov IV, Han B, Wishart DS (2015) MetaboAnalyst 3.0 –– making metabolomics more meaningful. Nucleic Acids Res 43(W1):W251–W257.  https://doi.org/10.1093/nar/gkv380PubMedPubMedCentralCrossRefGoogle Scholar
  169. Yang C, Li Y (2011) Chromobacterium violaceum infection: a clinical review of an important but neglected infection. J Chin Med Assoc 74(10):435–441.  https://doi.org/10.1016/j.jcma.2011.08.013PubMedCrossRefGoogle Scholar
  170. Yeh I, Hanekamp T, Tsoka S, Karp P, Altman R (2004) Computational analysis of Plasmodium falciparum metabolism: organizing genomic information to facilitate drug discovery. Genome Res 14(5):917–924.  https://doi.org/10.1101/gr.2050304PubMedPubMedCentralCrossRefGoogle Scholar
  171. Yu C, Chen Y, Lu C, Hwang J (2006) Prediction of protein subcellular localization. Proteins 64(3):643–651.  https://doi.org/10.1002/protPubMedCrossRefGoogle Scholar
  172. Zampieri M, Enke T, Chubukov V, Ricci V, Piddock L, Sauer U (2017) Metabolic constraints on the evolution of antibiotic resistance. Mol Syst Biol 13(3):917.  https://doi.org/10.15252/msb.20167028PubMedPubMedCentralCrossRefGoogle Scholar
  173. Zhang A, Sun H, Han Y, Yan G, Wang X (2013a) Urinary metabolic biomarker and pathway study of hepatitis B virus infected patients based on UPLC-MS system. PLoS One 8(5):e64381.  https://doi.org/10.1371/journal.pone.0064381PubMedPubMedCentralCrossRefGoogle Scholar
  174. Zhang A, Sun H, Han Y, Yan G, Yuan Y, Song G, Yuan X, Xie N, Wang X (2013b) Ultraperformance liquid chromatography–mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets. Anal Chem 85(15):7606–7612.  https://doi.org/10.1021/ac401793dPubMedCrossRefGoogle Scholar
  175. Zhang R, Ou H, Zhang C (2004) DEG: a database of essential genes. Nucleic Acids Res 32(Database issue):D271–D272.  https://doi.org/10.1093/nar/gkh024PubMedPubMedCentralCrossRefGoogle Scholar
  176. Zhang S, Wang D, Wang Y, Hasman H, Aarestrup F, Alwathnani H, Zhu Y, Rensing C (2015) Genome sequences of copper resistant and sensitive Enterococcus faecalis strains isolated from copper-fed pigs in Denmark. Stand Genomic Sci 10:35.  https://doi.org/10.1186/s40793-015-0021-1PubMedPubMedCentralCrossRefGoogle Scholar
  177. Zhao G, Usui ML, Lippman SI, James GA, Stewart PS, Fleckman P, Olerud JE (2013) Biofilms and inflammation in chronic wounds. Adv Wound Care 2(7):389–399.  https://doi.org/10.1089/wound.2012.0381CrossRefGoogle Scholar
  178. Zhou C, Zhou D, Elsheikha H, Liu G, Suo X (2015) Global metabolomic profiling of mice brains following experimental infection with the cyst-forming Toxoplasma gondii. PLoS One 10(10):e0139635.  https://doi.org/10.1371/journal.pone.0139635PubMedPubMedCentralCrossRefGoogle Scholar
  179. Zhou C, Zhou D, Elsheikha H, Zhao Y, Suo X (2016) Metabolomic profiling of mice serum during toxoplasmosis progression using liquid spectrometry. Sci Rep 6:19557.  https://doi.org/10.1038/srep19557PubMedPubMedCentralCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Müberra Fatma Cesur
    • 1
  • Ecehan Abdik
    • 1
  • Ünzile Güven-Gülhan
    • 1
  • Saliha Durmuş
    • 1
  • Tunahan Çakır
    • 1
  1. 1.Computational Systems Biology Group, Department of BioengineeringGebze Technical UniversityGebzeTurkey

Personalised recommendations