Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction
Abstract
In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimummore »
- Authors:
-
- US Army Medical Research and Material Command, Fort Detrick, MD (united States). Dept. of Defense Biotechnology High Performance Computing Software Applications Inst., Telemedicine and Advanced Technology Research Center
- U.S. Army Medical Research and Materiel Command, Fort Detrick, MD (United States). Telemedicine and Advanced Technology Research Center. Dept. of Defense Biotechnology High Performance Computing Software Applications Inst.
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Pathogen Bioinformatics
- New York Structural Biology Center, New York, NY (United States)
- General Electric Company, Niskayuna, NY (United States). GE Global Research. Diagnostics and Biomedical Technologies. Computational Biology and Biostatistics Lab.
- U.S. Army Medical Research Inst. for Infectious Diseases, Fort Detrick, MD (United States). Bacteriology Division
- Mount Sinai School of Medicine, New York, NY (United States)
- Publication Date:
- Research Org.:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1627608
- Grant/Contract Number:
- AC52-07NA27344
- Resource Type:
- Accepted Manuscript
- Journal Name:
- PLoS ONE
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 5; Journal ID: ISSN 1932-6203
- Publisher:
- Public Library of Science
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 59 BASIC BIOLOGICAL SCIENCES; Enzyme metabolism; Francisella tularensis; drug metabolism; crystal structure; drug discovery; enzymes; protein structure; metabolic networks
Citation Formats
Chaudhury, Sidhartha, Abdulhameed, Mohamed Diwan M., Singh, Narender, Tawa, Gregory J., D’haeseleer, Patrik M., Zemla, Adam T., Navid, Ali, Zhou, Carol E., Franklin, Matthew C., Cheung, Jonah, Rudolph, Michael J., Love, James, Graf, John F., Rozak, David A., Dankmeyer, Jennifer L., Amemiya, Kei, Daefler, Simon, and Wallqvist, Anders. Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction. United States: N. p., 2013.
Web. doi:10.1371/journal.pone.0063369.
Chaudhury, Sidhartha, Abdulhameed, Mohamed Diwan M., Singh, Narender, Tawa, Gregory J., D’haeseleer, Patrik M., Zemla, Adam T., Navid, Ali, Zhou, Carol E., Franklin, Matthew C., Cheung, Jonah, Rudolph, Michael J., Love, James, Graf, John F., Rozak, David A., Dankmeyer, Jennifer L., Amemiya, Kei, Daefler, Simon, & Wallqvist, Anders. Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction. United States. https://doi.org/10.1371/journal.pone.0063369
Chaudhury, Sidhartha, Abdulhameed, Mohamed Diwan M., Singh, Narender, Tawa, Gregory J., D’haeseleer, Patrik M., Zemla, Adam T., Navid, Ali, Zhou, Carol E., Franklin, Matthew C., Cheung, Jonah, Rudolph, Michael J., Love, James, Graf, John F., Rozak, David A., Dankmeyer, Jennifer L., Amemiya, Kei, Daefler, Simon, and Wallqvist, Anders. Tue .
"Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction". United States. https://doi.org/10.1371/journal.pone.0063369. https://www.osti.gov/servlets/purl/1627608.
@article{osti_1627608,
title = {Rapid Countermeasure Discovery against Francisella tularensis Based on a Metabolic Network Reconstruction},
author = {Chaudhury, Sidhartha and Abdulhameed, Mohamed Diwan M. and Singh, Narender and Tawa, Gregory J. and D’haeseleer, Patrik M. and Zemla, Adam T. and Navid, Ali and Zhou, Carol E. and Franklin, Matthew C. and Cheung, Jonah and Rudolph, Michael J. and Love, James and Graf, John F. and Rozak, David A. and Dankmeyer, Jennifer L. and Amemiya, Kei and Daefler, Simon and Wallqvist, Anders},
abstractNote = {In the future, we may be faced with the need to provide treatment for an emergent biological threat against which existing vaccines and drugs have limited efficacy or availability. To prepare for this eventuality, our objective was to use a metabolic network-based approach to rapidly identify potential drug targets and prospectively screen and validate novel small molecule antimicrobials. Our target organism was the fully virulent Francisella tularensis subspecies tularensis Schu S4 strain, a highly infectious intracellular pathogen that is the causative agent of tularemia and is classified as a category A biological agent by the Centers for Disease Control and Prevention. We proceeded with a staggered computational and experimental workflow that used a strain-specific metabolic network model, homology modeling and X-ray crystallography of protein targets, and ligand- and structure-based drug design. Selected compounds were subsequently filtered based on physiological-based pharmacokinetic modeling, and we selected a final set of 40 compounds for experimental validation of antimicrobial activity. We began screening these compounds in whole bacterial cell-based assays in biosafety level 3 facilities in the 20th week of the study and completed the screens within 12 weeks. Six compounds showed significant growth inhibition of F. tularensis, and we determined their respective minimum inhibitory concentrations and mammalian cell cytotoxicities. The most promising compound had a low molecular weight, was non-toxic, and abolished bacterial growth at 13 mM, with putative activity against pantetheine-phosphate adenylyltransferase, an enzyme involved in the biosynthesis of coenzyme A, encoded by gene coaD. The novel antimicrobial compounds identified in this study serve as starting points for lead optimization, animal testing, and drug development against tularemia. Our integrated in silico/in vitro approach had an overall 15% success rate in terms of active versus tested compounds over an elapsed time period of 32 weeks, from pathogen strain identification to selection and validation of novel antimicrobial compounds.},
doi = {10.1371/journal.pone.0063369},
journal = {PLoS ONE},
number = 5,
volume = 8,
place = {United States},
year = {Tue May 21 00:00:00 EDT 2013},
month = {Tue May 21 00:00:00 EDT 2013}
}
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Works referencing / citing this record:
Accelerating the Drug Development Pipeline with Genome-Scale Metabolic Network Reconstructions
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- Systems Biology