Literature DB >> 19396373

Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001.

Ali Navid1, Eivind Almaas.   

Abstract

The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one of the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from the literature. Our model demonstrates excellent agreement with Y. pestis' known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cell's phenotypic behavior under different circumstances and identify metabolic weaknesses that may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.

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Year:  2009        PMID: 19396373     DOI: 10.1039/b818710j

Source DB:  PubMed          Journal:  Mol Biosyst        ISSN: 1742-2051


  17 in total

Review 1.  A metabolic network approach for the identification and prioritization of antimicrobial drug targets.

Authors:  Arvind K Chavali; Kevin M D'Auria; Erik L Hewlett; Richard D Pearson; Jason A Papin
Journal:  Trends Microbiol       Date:  2012-01-31       Impact factor: 17.079

2.  An experimentally validated genome-scale metabolic reconstruction of Klebsiella pneumoniae MGH 78578, iYL1228.

Authors:  Yu-Chieh Liao; Tzu-Wen Huang; Feng-Chi Chen; Pep Charusanti; Jay S J Hong; Hwan-You Chang; Shih-Feng Tsai; Bernhard O Palsson; Chao A Hsiung
Journal:  J Bacteriol       Date:  2011-02-04       Impact factor: 3.490

3.  Yeast dynamic metabolic flux measurement in nutrient-rich media by HPLC and accelerator mass spectrometry.

Authors:  Benjamin J Stewart; Ali Navid; Kenneth W Turteltaub; Graham Bench
Journal:  Anal Chem       Date:  2010-11-09       Impact factor: 6.986

4.  Yersinia pestis Resists Predation by Acanthamoeba castellanii and Exhibits Prolonged Intracellular Survival.

Authors:  Javier A Benavides-Montaño; Viveka Vadyvaloo
Journal:  Appl Environ Microbiol       Date:  2017-06-16       Impact factor: 4.792

5.  A Beginner's Guide to the COBRA Toolbox.

Authors:  Ali Navid
Journal:  Methods Mol Biol       Date:  2022

6.  Curating COBRA Models of Microbial Metabolism.

Authors:  Ali Navid
Journal:  Methods Mol Biol       Date:  2022

7.  Reconstruction and flux-balance analysis of the Plasmodium falciparum metabolic network.

Authors:  Germán Plata; Tzu-Lin Hsiao; Kellen L Olszewski; Manuel Llinás; Dennis Vitkup
Journal:  Mol Syst Biol       Date:  2010-09-07       Impact factor: 11.429

Review 8.  Accomplishments in genome-scale in silico modeling for industrial and medical biotechnology.

Authors:  Caroline B Milne; Pan-Jun Kim; James A Eddy; Nathan D Price
Journal:  Biotechnol J       Date:  2009-12       Impact factor: 4.677

Review 9.  Applications of genome-scale metabolic reconstructions.

Authors:  Matthew A Oberhardt; Bernhard Ø Palsson; Jason A Papin
Journal:  Mol Syst Biol       Date:  2009-11-03       Impact factor: 11.429

10.  Integrative genomic analysis identifies isoleucine and CodY as regulators of Listeria monocytogenes virulence.

Authors:  Lior Lobel; Nadejda Sigal; Ilya Borovok; Eytan Ruppin; Anat A Herskovits
Journal:  PLoS Genet       Date:  2012-09-06       Impact factor: 5.917

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