Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review

The Journal of Antibiotics volume 74, pages 95–104 (2021)Cite this article

Subjects

Abstract

Antimicrobial resistance (AMR) threatens the effective prevention and treatment of a wide range of infections. Governments around the world are beginning to devote effort for innovative treatment development to treat these resistant bacteria. Systems biology methods have been applied extensively to provide valuable insights into metabolic processes at system level. Genome-scale metabolic models serve as platforms for constraint-based computational techniques which aid in novel drug discovery. Tools for automated reconstruction of metabolic models have been developed to support system level metabolic analysis. We discuss features of such software platforms for potential users to best fit their purpose of research. In this work, we focus to review the development of genome-scale metabolic models of Gram-negative pathogens and also metabolic network approach for identification of antimicrobial drugs targets.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. World Health Organization. Antimicrobial resistance: global report on surveillance 2014. 2016:257. https://apps.who.int/iris/bitstream/handle/10665/112642/9789241564748_eng.pdf?sequence=1.

  2. World Health Organization. Global strategy for containment of antimicrobial resistance. 2001. https://apps.who.int/iris/bitstream/handle/10665/66860/WHO_CDS_CSR_DRS_2001.2.pdf?sequence=1.

  3. Jubeh B, Breijyeh Z, Karaman R. Antibacterial prodrugs to overcome bacterial resistance. Molecules. 2020;25. https://doi.org/10.3390/molecules25071543.

  4. Giraldi G, Montesano M, Napoli C, Frati P, La Russa R, Santurro A, et al. Healthcare-associated infections due to multidrug-resistant organisms: a surveillance study on extra hospital stay and direct costs. Curr Pharm Biotechnol. 2019;20:643–52. https://doi.org/10.2174/1389201020666190408095811.

    Article  CAS  PubMed  Google Scholar 

  5. Khan HA, Baig FK, Mehboob R. Nosocomial infections: epidemiology, prevention, control and surveillance. Asian Pac J Trop Biomed. 2017;7:478–82. https://doi.org/10.1016/j.apjtb.2017.01.019.

    Article  Google Scholar 

  6. Bassetti M, Peghin M, Vena A, Giacobbe DR. Treatment of infections due to MDR Gram-negative bacteria. Front Med. 2019;6. https://doi.org/10.3389/fmed.2019.00074.

  7. World Health Organization. The evolving threat of antimicrobial resistance: options for action. 2012. https://apps.who.int/iris/handle/10665/44812.

  8. World Health Organization. Adopt AwaRe: handle antibiotics with care. 2019. https://adoptaware.org/.

  9. World Health Organization. Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. 2017. https://www.who.int/medicines/publications/global-priority-list-antibiotic-resistant-bacteria/en/.

  10. Papp B, Szappanos B, Notebaart RA. Use of genome-scale metabolic models in evolutionary systems biology. Methods Mol Biol. New York: Humana Press; 2011. pp. 483–97. https://doi.org/10.1007/978-1-61779-173-4_27.

  11. Gu C, Kim GB, Kim WJ, Kim HU, Lee SY. Current status and applications of genome-scale metabolic models. Genome Biol. 2019;20. https://doi.org/10.1186/s13059-019-1730-3.

  12. O’Brien EJ, Monk JM, Palsson BO. Using genome-scale models to predict biological capabilities. Genome Biol. 2015;161:971–87. https://doi.org/10.1016/j.cell.2015.05.019.

    Article  CAS  Google Scholar 

  13. Mobegi FM, van Hijum SAFT, Burghout P, Bootsma HJ, de Vries SPW, van der Gaast-de Jongh CE, et al. From microbial gene essentiality to novel antimicrobial drug targets. BMC Genom. 2014;15:958. https://doi.org/10.1186/1471-2164-15-958.

    Article  CAS  Google Scholar 

  14. Fields FR, Lee SW, McConnell MJ. Using bacterial genomes and essential genes for the development of new antibiotics. Biochem Pharmacol. 2017;134:74–86. https://doi.org/10.1016/j.bcp.2016.12.002.

    Article  CAS  PubMed  Google Scholar 

  15. Moir DT, Shaw KJ, Hare RS, Vovis GF. Genomics and antimicrobial drug discovery. Antimicrob Agents Chemother. 1999;43:439–46. https://doi.org/10.1128/aac.43.3.439.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Oberhardt MA, Puchałka J, Fryer KE, Martins Dos Santos VAP, Papin JA. Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J Bacteriol. 2008;190:2790–803. https://doi.org/10.1128/JB.01583-07.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Oberhardt MA, Puchałka J, dos Santos VAPM, Papin JA. Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis. PLoS Comput Biol. 2011;7:e1001116. https://doi.org/10.1371/journal.pcbi.1001116.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bartell JA, Blazier AS, Yen P, Thøgersen JC, Jelsbak L, Goldberg JB, et al. Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat Commun. 2017;8. https://doi.org/10.1038/ncomms14631.

  19. Zhu Y, Czauderna T, Zhao J, Klapperstueck M, Maifiah MHM, Han ML, et al. Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa. Gigascience. 2018;7:1–18. https://doi.org/10.1093/gigascience/giy021.

    Article  CAS  PubMed  Google Scholar 

  20. Presta L, Bosi E, Mansouri L, Dijkshoorn L, Fani R, Fondi M. Constraint-based modeling identifies new putative targets to fight colistin-resistant A. baumannii infections. Sci Rep. 2017;7. https://doi.org/10.1038/s41598-017-03416-2.

  21. Norsigian CJ, Kavvas E, Seif Y, Palsson BO, Monk JM. iCN718, an updated and improved genome-scale metabolic network reconstruction of Acinetobacter baumannii AYE. Front Genet. 2018;9. https://doi.org/10.3389/fgene.2018.00121.

  22. Zhu Y, Zhao J, Maifiah MHM, Velkov T, Schreiber F, Li J. Metabolic responses to polymyxin treatment in Acinetobacter baumannii ATCC 19606: integrating transcriptomics and metabolomics with genome-scale metabolic modeling. mSystems. 2019;4:e00157–18. https://doi.org/10.1128/msystems.00157-18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Liao YC, Huang TW, Chen FC, Charusanti P, Hong JSJ, Chang HY, et al. An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228. J Bacteriol. 2011;193:1710–7. https://doi.org/10.1128/JB.01218-10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Henry CS, Rotman E, Lathem WW, Tyo KEJ, Hauser AR, Mandel MJ. Generation and validation of the iKp1289 Metabolic model for Klebsiella pneumoniae KPPR1. J Infect Dis. 2017;215:S37–43. https://doi.org/10.1093/infdis/jiw465.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ramos PIP, Fernández Do Porto D, Lanzarotti E, Sosa EJ, Burguener G, Pardo AM, et al. An integrative, multi-omics approach towards the prioritization of Klebsiella pneumoniae drug targets. Sci Rep. 2018;8. https://doi.org/10.1038/s41598-018-28916-7.

  26. Norsigian CJ, Attia H, Szubin R, Yassin AS, Palsson B, Aziz RK, et al. Comparative genome-scale metabolic modeling of metallo-beta-lactamase-producing multidrug-resistant Klebsiella pneumoniae clinical isolates. Front Cell Infect Microbiol. 2019;9. https://doi.org/10.3389/fcimb.2019.00161.

  27. Dunphy LJ, Papin JA. Biomedical applications of genome-scale metabolic network reconstructions of human pathogens. Curr Opin Biotechnol. 2018;51:70–9. https://doi.org/10.1016/j.copbio.2017.11.014.

    Article  CAS  PubMed  Google Scholar 

  28. Feist AM, Herrgård MJ, Thiele I, Reed JL, Palsson B. Reconstruction of biochemical networks in microorganisms. Nat Rev Microbiol. 2009;7:129–43. https://doi.org/10.1038/nrmicro1949.

    Article  CAS  PubMed  Google Scholar 

  29. Chavali AK, D’Auria KM, Hewlett EL, Pearson RD, Papin JA. A metabolic network approach for the identification and prioritization of antimicrobial drug targets. Trends Microbiol. 2012;20:113–23. https://doi.org/10.1016/j.tim.2011.12.004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Karp PD, Paley S, Romero P. The pathway tools software. Bioinformatics. 2002;18:S225–32. https://doi.org/10.1093/bioinformatics/18.suppl_1.S225.

    Article  PubMed  Google Scholar 

  31. Pinney JW, Shirley MW, McConkey GA, Westhead DR. metaSHARK: software for automated metabolic network prediction from DNA sequence and its application to the genomes of Plasmodium falciparum and Eimeria tenella. Nucleic Acids Res. 2005;33:1399–409. https://doi.org/10.1093/nar/gki285.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Kanehisa M. From genomics to chemical genomics: new developments in KEGG. Nucleic Acids Res. 2006;34:D354–7. https://doi.org/10.1093/nar/gkj102.

    Article  CAS  Google Scholar 

  33. Karp PD, Billington R, Caspi R, Fulcher CA, Latendresse M, Kothari A, et al. The BioCyc collection of microbial genomes and metabolic pathways. Brief Bioinform. 2019;20:1085–93. https://doi.org/10.1093/bib/bbx085.

    Article  CAS  PubMed  Google Scholar 

  34. Henry CS, Dejongh M, Best AA, Frybarger PM, Linsay B, Stevens RL. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat Biotechnol. 2010;28:977–82. https://doi.org/10.1038/nbt.1672.

    Article  CAS  PubMed  Google Scholar 

  35. Machado D, Andrejev S, Tramontano M, Patil KR. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 2018;46:7542–53. https://doi.org/10.1093/nar/gky537.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Thiele I, Palsson B. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat Protoc. 2010;5:93–121. https://doi.org/10.1038/nprot.2009.203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Faria JP, Rocha M, Rocha I, Henry CS. Methods for automated genome-scale metabolic model reconstruction. Biochem Soc Trans. 2018;46. https://doi.org/10.1042/BST20170246.

  38. Wang H, Marcišauskas S, Sánchez BJ, Domenzain I, Hermansson D, Agren R, et al. RAVEN 2.0: a versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor. PLoS Comput Biol. 2018;14:e1006541 https://doi.org/10.1371/journal.pcbi.1006541.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dias O, Rocha M, Ferreira EC, Rocha I. Reconstructing genome-scale metabolic models with merlin. Nucleic Acids Res. 2015;43:3899–910.

    Article  CAS  Google Scholar 

  40. Aite M, Chevallier M, Frioux C, Trottier C, Got J, Cortés MP, et al. Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models. PLoS Comput Biol. 2018;14:e1006146. https://doi.org/10.1371/journal.pcbi.1006146.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Hanemaaijer M, Olivier BG, Röling WFM, Bruggeman FJ, Teusink B. Model-based quantification of metabolic interactions from dynamic microbial-community data. PLoS ONE. 2017;12:e0173183. https://doi.org/10.1371/journal.pone.0173183.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Mendoza SN, Olivier BG, Molenaar D, Teusink B. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol. 2019;20. https://doi.org/10.1186/s13059-019-1769-1.

  43. Hucka M, Bergmann FT, Chaouiya C, Dräger A, Hoops S, Keating SM, et al. The Systems Biology Markup Language (SBML): language specification for level 3 version 2 core release 2. J Integr Bioinform. 2019;16. https://doi.org/10.1515/jib-2019-0021.

  44. Wilkinson MD, Dumontier M, Aalbersberg IjJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3. https://doi.org/10.1038/sdata.2016.18.

  45. Hamilton JJ, Reed JL. Software platforms to facilitate reconstructing genome-scale metabolic networks. Environ Microbiol. 2014;16:49–59. https://doi.org/10.1111/1462-2920.12312.

    Article  PubMed  Google Scholar 

  46. Ling ML, Apisarnthanarak A, Madriaga G. The burden of healthcare-associated infections in southeast Asia: a systematic literature review and meta-analysis. Clin Infect Dis. 2015;60:1690–9. https://doi.org/10.1093/cid/civ095.

    Article  PubMed  Google Scholar 

  47. Exner M, Bhattacharya S, Christiansen B, Gebel J, Goroncy-Bermes P, Hartemann P, et al. Antibiotic resistance: what is so special about multidrug-resistant Gram-negative bacteria? GMS Hyg Infect Control. 2017;12. https://doi.org/10.3205/dgkh000290.

  48. Seif Y, Monk JM, Machado H, Kavvas E, Palsson BO. Systems biology and pangenome of Salmonella O-antigens. MBio. 2019;10:e01247–19. https://doi.org/10.1128/mBio.01247-19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Cesur MF, Siraj B, Uddin R, Durmuş S, Çakır T. Network-based metabolism-centered screening of potential drug targets in Klebsiella pneumoniae at genome scale. Front Cell Infect Microbiol. 2020;9. https://doi.org/10.3389/fcimb.2019.00447.

  50. Xavier JC, Patil KR, Rocha I. Metabolic models and gene essentiality data reveal essential and conserved metabolism in prokaryotes. PLoS Comput Biol. 2018;14:e1006556 https://doi.org/10.1371/journal.pcbi.1006556.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Willsey GG, Ventrone S, Schutz KC, Wallace AM, Ribis JW, Suratt BT, et al. Pulmonary surfactant promotes virulence gene expression and biofilm formation in Klebsiella pneumoniae. Infect Immun. 2018;86:e00135–18. https://doi.org/10.1128/IAI.00135-18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Vornhagen J, Sun Y, Breen P, Forsyth V, Zhao L, Mobley HLT, et al. The Klebsiella pneumoniae citrate synthase gene, gltA, influences site specific fitness during infection. PLoS Pathog. 2019;15:e1008010.

    Article  CAS  Google Scholar 

  53. Nordmann P, Poirel L. Emerging carbapenemases in Gram-negative aerobes. Clin Microbiol Infect. 2002;8:321–31. https://doi.org/10.1046/j.1469-0691.2002.00401.x.

    Article  CAS  PubMed  Google Scholar 

  54. Ahmed-Bentley J, Chandran AU, Joffe AM, French D, Peirano G, Pitout JDD. Gram-negative bacteria that produce carbapenemases causing death attributed to recent foreign hospitalization. Antimicrob Agents Chemother. 2013;57:3085–91. https://doi.org/10.1128/AAC.00297-13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Khan AU, Maryam L, Zarrilli R. Structure, genetics and worldwide spread of New Delhi Metallo-β-lactamase (NDM): a threat to public health. BMC Microbiol. 2017;17. https://doi.org/10.1186/s12866-017-1012-8.

  56. Boucher HW, Talbot GH, Bradley JS, Edwards JE, Gilbert D, Rice LB, et al. Bad Bugs, no drugs: no ESKAPE! An update from the infectious Diseases Society of America. Clin Infect Dis. 2009;48:1–12. https://doi.org/10.1086/595011.

    Article  PubMed  Google Scholar 

  57. Kim HU, Kim TY, Lee SY. Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Mol Biosyst. 2010;6:339–48. https://doi.org/10.1039/b916446d.

    Article  CAS  PubMed  Google Scholar 

  58. Farrugia DN, Elbourne LDH, Hassan KA, Eijkelkamp BA, Tetu SG, Brown MH, et al. The complete genome and phenome of a community-acquired Acinetobacter baumannii. PLoS ONE. 2013;8:e58628. https://doi.org/10.1371/journal.pone.0058628.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Gallagher LA, Ramage E, Weiss EJ, Radey M, Hayden HS, Held KG, et al. Resources for genetic and genomic analysis of emerging pathogen Acinetobacter baumannii. J Bacteriol. 2015;197:2027–35. https://doi.org/10.1128/JB.00131-15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Uwingabiye J, Frikh M, Lemnouer A, Bssaibis F, Belefquih B, Maleb A, et al. Acinetobacter infections prevalence and frequency of the antibiotics resistance: comparative study of intensive care units versus other hospital units. Pan Afr Med J. 2016;23. https://doi.org/10.11604/pamj.2016.23.191.7915.

  61. Dortet L, Potron A, Bonnin RA, Plesiat P, Naas T, Filloux A, et al. Rapid detection of colistin resistance in Acinetobacter baumannii using MALDI-TOF-based lipidomics on intact bacteria. Sci Rep. 2018;8. https://doi.org/10.1038/s41598-018-35041-y.

  62. Hancock REW. Peptide antibiotics. Lancet. 1997;349:418–22. https://doi.org/10.1016/S0140-6736(97)80051-7.

    Article  CAS  PubMed  Google Scholar 

  63. Jensen PA, Papin JA. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics. 2011;27:541–7. https://doi.org/10.1093/bioinformatics/btq702.

    Article  CAS  PubMed  Google Scholar 

  64. Moffatt JH, Harper M, Harrison P, Hale JDF, Vinogradov E, Seemann T, et al. Colistin resistance in Acinetobacter baumannii is mediated by complete loss of lipopolysaccharide production. Antimicrob Agents Chemother. 2010;54:4971–7. https://doi.org/10.1128/AAC.00834-10.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Cheah SE, Johnson MD, Zhu Y, Tsuji BT, Forrest A, Bulitta JB, et al. Polymyxin resistance in Acinetobacter baumannii: genetic mutations and transcriptomic changes in response to clinically relevant dosage regimens. Sci Rep. 2016;6. https://doi.org/10.1038/srep26233.

  66. Weinstein RA, Gaynes R, Edwards JR. Overview of nosocomial infections caused by Gram-negative bacilli. Clin Infect Dis. 2005;41:848–54. https://doi.org/10.1086/432803.

    Article  Google Scholar 

  67. Klockgether J, Cramer N, Wiehlmann L, Davenport CF, Tümmler B. Pseudomonas aeruginosa genomic structure and diversity. Front Microbiol. 2011;2. https://doi.org/10.3389/fmicb.2011.00150.

  68. Moradali MF, Ghods S, Rehm BHA. Pseudomonas aeruginosa lifestyle: a paradigm for adaptation, survival, and persistence. Front Cell Infect Microbiol. 2017;7. https://doi.org/10.3389/fcimb.2017.00039.

  69. The Center for Disease, Dynamics Economics & Policy. ResistanceMap: antibiotic resistance of Pseudomonas aeruginosa. 2020. https://resistancemap.cddep.org/AntibioticResistance.php.

  70. Mesaros N, Van Bambeke F, Avrain L, Glupczynski G, Vanhoof R, Plésiat P, et al. L’effl ux actif des antibiotiques et la résistance bactérienne: état de la question et implications. La Lett l’infectiologue. 2005;4:117–26.

  71. Vital-Lopez FG, Reifman J, Wallqvist A. Biofilm formation mechanisms of Pseudomonas aeruginosa predicted via genome-scale kinetic models of bacterial metabolism. PLoS Comput Biol. 2015;11:e1004452. https://doi.org/10.1371/journal.pcbi.1004452.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Biggs MB, Papin JA. Novel multiscale modeling tool applied to Pseudomonas aeruginosa biofilm formation. PLoS ONE. 2013;8:e78011. https://doi.org/10.1371/journal.pone.0078011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Robinson CV, Elkins MR, Bialkowski KM, Thornton DJ, Kertesz MA. Desulfurization of mucin by Pseudomonas aeruginosa: influence of sulfate in the lungs of cystic fibrosis patients. J Med Microbiol. 2012;61:1644–53. https://doi.org/10.1099/jmm.0.047167-0.

    Article  CAS  PubMed  Google Scholar 

  74. Hussein M, Han ML, Zhu Y, Zhou Q, Lin YW, Hancock REW, et al. Metabolomics Study of the Synergistic Killing of Polymyxin B in Combination with Amikacin against Polymyxin-Susceptible and -Resistant Pseudomonas aeruginosa. Antimicrob Agents Chemother. 2019;64:e01587–19. https://doi.org/10.1128/AAC.01587-19.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Maifiah MHM, Creek DJ, Nation RL, Forrest A, Tsuji BT, Velkov T, et al. Untargeted metabolomics analysis reveals key pathways responsible for the synergistic killing of colistin and doripenem combination against Acinetobacter baumannii. Sci Rep. 2017;7. https://doi.org/10.1038/srep45527.

  76. Raetz CRH, Reynolds CM, Trent MS, Bishop RE, Lipid A. Modification systems in Gram-negative bacteria. Annu Rev Biochem. 2007;76:295–329. https://doi.org/10.1146/annurev.biochem.76.010307.145803.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Mack SG, Turner RL, Dwyer DJ. Achieving a predictive understanding of antimicrobial stress physiology through systems biology. Trends Microbiol. 2018;26:296–312. https://doi.org/10.1016/j.tim.2018.02.004.

    Article  CAS  PubMed  Google Scholar 

  78. Stokes JM, Lopatkin AJ, Lobritz MA, Collins JJ. Bacterial metabolism and antibiotic efficacy. Cell Metab. 2019;30:251–9. https://doi.org/10.1016/j.cmet.2019.06.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Peng B, Li H, Peng XX. Functional metabolomics: from biomarker discovery to metabolome reprogramming. Protein Cell. 2015;6:628–37. https://doi.org/10.1007/s13238-015-0185-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the financial support by the Fundamental Research Grant Scheme, Ministry of Higher Education, Malaysia (FRGS/1/2019/SKK11/TAYLOR/03/1).

Author information

Authors and Affiliations

  1. School of Pharmacy, Taylor’s University, 47500, Subang Jaya, Selangor, Malaysia

    Wan Yean Chung

  2. Biomedicine Discovery Institute, Infection and Immunity Program and Department of Microbiology, Monash University, Melbourne, 3800, VIC, Australia

    Yan Zhu

  3. International Institute for Halal Research and Training (INHART), International Islamic University Malaysia (IIUM), 53100, Jalan Gombak, Selangor, Malaysia

    Mohd Hafidz Mahamad Maifiah

  4. Center for Drug Discovery and Molecular Pharmacology (CDDMP), Faculty of Health and Medical Sciences, Taylor’s University, 47500, Subang Jaya, Selangor, Malaysia

    Naveen Kumar Hawala Shivashekaregowda

  5. School of Medicine, Taylor’s University, 47500, Subang Jaya, Selangor, Malaysia

    Eng Hwa Wong

  6. Faculty of Pharmacy, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Nusaibah Abdul Rahim

Authors
  1. Wan Yean Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mohd Hafidz Mahamad Maifiah
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Naveen Kumar Hawala Shivashekaregowda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Eng Hwa Wong
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nusaibah Abdul Rahim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Eng Hwa Wong or Nusaibah Abdul Rahim.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chung, W.Y., Zhu, Y., Mahamad Maifiah, M.H. et al. Novel antimicrobial development using genome-scale metabolic model of Gram-negative pathogens: a review. J Antibiot 74, 95–104 (2021). https://doi.org/10.1038/s41429-020-00366-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41429-020-00366-2

This article is cited by

Search

Advanced search

Quick links