| Author | Medeiros Filho, Fernando | |
| Author | Nascimento, Ana Paula Barbosa do | |
| Author | Costa, Maiana de Oliveira Cerqueira e | |
| Author | Merigueti, Thiago Castanheira | |
| Author | Menezes, Marcio Argollo de | |
| Author | Nicolás, Marisa Fabiana | |
| Author | Santos, Marcelo Trindade dos | |
| Author | Assef, Ana Paula D’Alincourt Carvalho | |
| Author | Silva, Fabrício Alves Barbosa da | |
| Access date | 2022-01-21T13:45:46Z | |
| Available date | 2022-01-21T13:45:46Z | |
| Document date | 2021 | |
| Citation | MEDEIROS, FILHO, Fernando et al. A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models. Frontiers in Molecular Biosciences, v. 8, Article 728129, p. 1 - 14, Sept. 2021. | pt_BR |
| ISSN | 2296-889X | pt_BR |
| URI | https://www.arca.fiocruz.br/handle/icict/50842 | |
| Language | eng | pt_BR |
| Publisher | Frontiers Media | pt_BR |
| Rights | open access | |
| Subject in Portuguese | Pseudomonas aeruginosa | pt_BR |
| Subject in Portuguese | Rede metabólica | pt_BR |
| Subject in Portuguese | Dados de transcrição | pt_BR |
| Subject in Portuguese | Modelo integrado | pt_BR |
| Subject in Portuguese | Alvo terapêutico | pt_BR |
| Title | A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models | pt_BR |
| Type | Article | |
| DOI | 10.3389/fmolb.2021.728129 | |
| Abstract | 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 topranked
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. | pt_BR |
| Affilliation | Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil. | pt_BR |
| Affilliation | Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil. | pt_BR |
| Affilliation | Laboratório Nacional de Computação Científica. Petrópolis, RJ, Brasil. | pt_BR |
| Affilliation | Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil. | pt_BR |
| Affilliation | Universidade Federal Fluminense. Instituto de Física. Niterói, RJ, Brasil. | pt_BR |
| Affilliation | Laboratório Nacional de Computação Científica. Petrópolis, RJ, Brasil. | pt_BR |
| Affilliation | Laboratório Nacional de Computação Científica. Petrópolis, RJ, Brasil. | pt_BR |
| Affilliation | Fundação Oswaldo Cruz. Insituto Oswaldo Cruz. Laboratório de Pesquisa em Infecção Hospitalar. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Programa de Pós-Graduação em Biologia Parasitária. Rio de Janeiro, RJ, Brasil. | pt_BR |
| Affilliation | Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil. | pt_BR |
| Subject | Pseudomonas aeruginosa | pt_BR |
| Subject | Metabolic network | pt_BR |
| Subject | Transcriptome data | pt_BR |
| Subject | Integrated model | pt_BR |
| Subject | Therapeutic target | pt_BR |
