Constraint-based modeling identifies new putative targets to fight colistin-resistant A. baumannii infections

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

  Presta, Luana ^a   Bosi, Emanuele ^a   Mansouri, Leila ^a   Dijkshoorn, Lenie ^b   Fani, Renato ^a   Fondi, Marco ^a  

^a UNIVERSITY OF FLORENCE   (Italy)

^b LEIDEN UNIVERSITY MEDICAL CENTER   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ANTIINFECTIVE AGENT; COLISTIN;

ACINETOBACTER BAUMANNII; ACINETOBACTER INFECTION; ANTIBIOTIC RESISTANCE; BACTERIAL GENOME; DRUG EFFECT; GENETICS; GENOMICS; HUMAN; MICROBIAL SENSITIVITY TEST; MICROBIOLOGY; PHENOTYPE; PROCEDURES;

ACINETOBACTER BAUMANNII; ACINETOBACTER INFECTIONS; ANTI-BACTERIAL AGENTS; COLISTIN; DRUG RESISTANCE, BACTERIAL; GENOME, BACTERIAL; GENOMICS; HUMANS; MICROBIAL SENSITIVITY TESTS; PHENOTYPE;

EID: 85020854104     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-03416-2     Document Type: Article

References (42)

1. Dijkshoorn, L., Nemec, A. & Seifert, H. An increasing threat in hospitals: multidrug-resistant Acinetobacter baumannii. Nat. Rev. Microbiol. 5, 939-951, doi:10.1038/nrmicro1789 (2007).
2. Gonzalez-Villoria, A. M. & Valverde-Garduno, V. Antibiotic-Resistant Acinetobacter baumannii Increasing Success Remains a Challenge as a Nosocomial Pathogen. J. Pathog 2016, 1-10, doi:10.1155/2016/7318075 (2016).
3. Rice, L. B. Federal funding for the study of antimicrobial resistance in nosocomial pathogens: no ESKAPE. J. Infect. Dis. 197, 1079-81 (2008).
4. Cai, Y., Chai, D., Wang, R., Liang, B. & Bai, N. Colistin resistance of Acinetobacter baumannii: Clinical reports, mechanisms and antimicrobial strategies. J. Antimicrob. Chemother. 67, 1607-1615, doi:10.1093/jac/dks084 (2012).
5. Thiele, I. & Palsson, B. O. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5, 93-121, doi:10.1038/nprot.2009.203 (2010).
6. Orth, J. D., Thiele, I. & Palsson, B. O. What is flux balance analysis? Nat. Biotechnol. 28, 245-8, doi:10.1038/nbt.1614 (2010).
7. Thiele, I., Vo, T. D., Price, N. D. & Palsson, B. O. Expanded metabolic reconstruction of Helicobacter pylori (iIT341 GSM/GPR): An in silico genome-scale characterization of single- and double-deletion mutants. J. Bacteriol. 187, 5818-5830, doi:10.1128/ JB.187.16.5818-5830.2005 (2005).
8. Oberhardt, M. A. et al. Applications of genome-scale metabolic reconstructions. Mol. Syst. Biol. 5, 320, doi:10.1038/msb.2009.77 (2009).
9. Raman, K., Rajagopalan, P. & Chandra, N. Flux Balance Analysis of Mycolic Acid Pathway: Targets for Anti-Tubercular Drugs. PLoS Comput. Biol. 1, 0349-0358, doi:10.1371/journal.pcbi.0010046 (2005).
10. Lee, D.-S. et al. Comparative genome-scale metabolic reconstruction and flux balance analysis of multiple Staphylococcus aureus genomes identify novel antimicrobial drug targets. J. Bacteriol. 191, 4015-24, doi:10.1128/JB.01743-08 (2009).
11. Machado, D. & Herrgard, M. Systematic Evaluation of Methods for Integration of Transcriptomic Data into Constraint-Based Models of Metabolism. PLoS Comput. Biol. 10, e1003580, doi:10.1371/journal.pcbi.1003580 (2014).
12. Tängdén, T. Combination antibiotic therapy for multidrug-resistant Gram-negative bacteria. Ups. J. Med. Sci. 9734, 149-153, doi: 10.3109/03009734.2014.899279 (2014).
13. Motaouakkil, S. et al. Colistin and rifampicin in the treatment of nosocomial infections from multiresistant Acinetobacter baumannii. J. Infect. 53, 274-278, doi:10.1016/j.jinf.2005.11.019 (2006).
14. Henry, R. et al. The transcriptomic response of Acinetobacter baumannii to colistin and doripenem alone and in combination in an in vitro pharmacokinetics/pharmacodynamics model. J. Antimicrob. Chemother. 70, 1303-1313, doi:10.1093/jac/dku536 (2014).
15. Peleg, A. Y. et al. The success of acinetobacter species; genetic, metabolic and virulence attributes. PLoS One 7, e46984, doi:10.1371/ journal.pone.0046984 (2012).
16. Reed, J. L., Vo, T. D., Schilling, C. H. & Palsson, B. O. An expanded genome-scale model of Escherichia coli K-12 (i JR904 GSM/ GPR). Genome Biol. 4, R54, doi:10.1186/gb-2003-4-9-r54 (2003).
17. Feist, A. M. et al. A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol. Syst. Biol. 3, 121, doi:10.1038/msb4100155 (2007).
18. Durot, M. et al. Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data. BMC Syst. Biol. 2, 85, doi:10.1186/1752-0509-2-85 (2008).
19. Kim, H. U., Kim, T. Y. & Lee, S. Y. Genome-scale metabolic network analysis and drug targeting of multi-drug resistant pathogen Acinetobacter baumannii AYE. Mol. Biosyst. 6, 339-348, doi:10.1039/b916446d (2010).
20. Barve, A., Rodrigues, J. F. M. & Wagner, A. Superessential reactions in metabolic networks. Proc. Natl. Acad. Sci. USA 109, E1121-30, doi:10.1073/pnas.1113065109 (2012).
21. Wang, N., Ozer, E. A., Mandel, M. J. & Hauser, A. R. Genome-wide identification of Acinetobacter baumannii genes necessary for persistence in the lung. MBio 5, e01163-14, doi:10.1128/mBio.01163-14 (2014).
22. Monk, J. M. et al. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. Genome-scale metabolic reconstructions of multiple Escherichia coli strains highlight strain-specific adaptations to nutritional environments. 110, 20338-43, doi:10.1073/pnas.1307797110/-/DCSupplemental.www.pnas.org/cgi/ doi/10.1073/pnas.1307797110 (2013).
23. Dorsey, C. W., Tomaras, A. P. & Actis, L. a. Genetic and Phenotypic Analysis of Acinetobacter baumannii Insertion Derivatives Generated with a Transposome System Genetic and Phenotypic Analysis of Acinetobacter baumannii Insertion Derivatives Generated with a Transposome System. Appl. Environ. Microbiol. 68, 6353-6360, doi:10.1128/AEM.68.12.6353-6360.2002 (2002).
24. Jensen, P. A. & Papin, J. A. Functional integration of a metabolic network model and expression data without arbitrary thresholding. Bioinformatics 27, 541-7, doi:10.1093/bioinformatics/btq702 (2011).
25. Mahadevan, R. & Schilling, C. H. The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab. Eng. 5, 264-276, doi:10.1016/j.ymben.2003.09.002 (2003).
26. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403-10, doi:10.1016/S0022-2836(05)80360-2 (1990).
27. Chohnan, S., Kurusu, Y., Nishihara, H. & Takamura, Y. Cloning and characterization of mdc genes encoding malonate decarboxylase from Pseudomonas putida. FEMS Microbiol. Lett 174, 311-9, doi:10.1111/fml.1999.174.issue-2 (1999).
28. Moffatt, J. H. et al. Colistin resistance in Acinetobacter baumannii is mediated by complete loss of lipopolysaccharide production. Antimicrob. Agents Chemother. 54, 4971-4977, doi:10.1128/AAC.00834-10 (2010).
29. Davenport, K. W. et al. Draft Genome Assembly of Acinetobacter baumannii ATCC 19606. Genome Announc 2, e00832-14, doi:10.1128/genomeA.00832-14 (2014).
30. Moretti, S. et al. MetaNetX/MNXref - reconciliation of metabolites and biochemical reactions to bring together genome-scale metabolic networks. Nucleic Acids Res 44, D523-D526, doi:10.1093/nar/gkv1117 (2016).
31. King, Z. A. et al. BiGG Models: A platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res 44, D515-D522, doi:10.1093/nar/gkv1049 (2016).
32. Henry, C. S. et al. High-throughput generation, optimization and analysis of genome-scale metabolic models. Nat. Biotechnol. 28, 977-982, doi:10.1038/nbt.1672 (2010).
33. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45, D353-D361, doi:10.1093/nar/gkw1092 (2017).
34. Remm, M., Storm, C. E. V. & Sonnhammer, E. L. L. Automatic clustering of orthologs and in-paralogs from pairwise species comparisons. J. Mol. Biol. 314, 1041-52, doi:10.1006/jmbi.2000.5197 (2001).
35. Saier, M. H., Tran, C. V. & Barabote, R. D. TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Res 34, D181-D186, doi:10.1093/nar/gkj001 (2006).
36. Ren, Q., Chen, K. & Paulsen, I. T. TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res 35, D274-D279, doi:10.1093/nar/gkl925 (2007).
37. Ebrahim, A., Lerman, J. A., Palsson, B. O. & Hyduke, D. R. COBRApy: COnstraints-Based Reconstruction and Analysis for Python. (2013).
38. Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6, 1290-1307, doi:10.1038/nprot.2011.308 (2011).
39. James, S. & Simmons., A. Culture Medium for Differentiating Organisms of Typhoid-Colon Aerogenes Groups and for Isolation of Certain Fungi. J. Infect. Dis 39, 209-214, doi:10.1093/infdis/39.3.209 (1926).
40. Segrè, D., Vitkup, D. & Church, G. M. Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. USA 99, 15112-7, doi:10.1073/pnas.232349399 (2002).
41. Snitkin, E. S. et al. Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions. Genome Biol. 9, R140, doi:10.1186/gb-2008-9-9-r140 (2008).
42. Wang, Z. et al. Why Is the Correlation between Gene Importance and Gene Evolutionary Rate So Weak? PLoS Genet. 5, e1000329, doi:10.1371/journal.pgen.1000329 (2009).