A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models.

Autor:	Medeiros Filho F; Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil., do Nascimento APB; Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil., Costa MOCE; Laboratório Nacional de Computação Científica, Petrópolis, Brazil., Merigueti TC; Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil., de Menezes MA; Instituto de Física, Universidade Federal Fluminense, Niterói, Brazil., Nicolás MF; Laboratório Nacional de Computação Científica, Petrópolis, Brazil., Dos Santos MT; Laboratório Nacional de Computação Científica, Petrópolis, Brazil., Carvalho-Assef APD; Laboratório de Pesquisa Em Infecção Hospitalar, Instituto Oswaldo Cruz, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil., da Silva FAB; Programa de Computação Científica, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil.
Jazyk:	angličtina
Zdroj:	Frontiers in molecular biosciences [Front Mol Biosci] 2021 Sep 20; Vol. 8, pp. 728129. Date of Electronic Publication: 2021 Sep 20 (Print Publication: 2021).
DOI:	10.3389/fmolb.2021.728129
Abstrakt:	Pseudomonas aeruginosa is an opportunistic human pathogen that has been a constant global health problem due to its ability to cause infection at different body sites and its resistance to a broad spectrum of clinically available antibiotics. The World Health Organization classified multidrug-resistant Pseudomonas aeruginosa among the top-ranked organisms that require urgent research and development of effective therapeutic options. Several approaches have been taken to achieve these goals, but they all depend on discovering potential drug targets. The large amount of data obtained from sequencing technologies has been used to create computational models of organisms, which provide a powerful tool for better understanding their biological behavior. In the present work, we applied a method to integrate transcriptome data with genome-scale metabolic networks of Pseudomonas aeruginosa . We submitted both metabolic and integrated models to dynamic simulations and compared their performance with published in vitro growth curves. In addition, we used these models to identify potential therapeutic targets and compared the results to analyze the assumption that computational models enriched with biological measurements can provide more selective and (or) specific predictions. Our results demonstrate that dynamic simulations from integrated models result in more accurate growth curves and flux distribution more coherent with biological observations. Moreover, identifying drug targets from integrated models is more selective as the predicted genes were a subset of those found in the metabolic models. Our analysis resulted in the identification of 26 non-host homologous targets. Among them, we highlighted five top-ranked genes based on lesser conservation with the human microbiome. Overall, some of the genes identified in this work have already been proposed by different approaches and (or) are already investigated as targets to antimicrobial compounds, reinforcing the benefit of using integrated models as a starting point to selecting biologically relevant therapeutic targets. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2021 Medeiros Filho, Nascimento, Costa, Merigueti, Menezes, Nicolás, Santos, Carvalho-Assef and Silva.)
Databáze:	MEDLINE
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