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

Detalhes bibliográficos
Autor(a) principal: Medeiros Filho, Fernando
Data de Publicação: 2021
Outros Autores: Nascimento, Ana Paula Barbosa do, Costa, Maiana de Oliveira Cerqueira e, Merigueti, Thiago Castanheira, Menezes, Marcio Argollo de, Nicolás, Marisa Fabiana, Santos, Marcelo Trindade dos, Assef, Ana Paula D’Alincourt Carvalho, Silva, Fabrício Alves Barbosa da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da FIOCRUZ (ARCA)
Texto Completo: https://www.arca.fiocruz.br/handle/icict/50842
Resumo: Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
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spelling Medeiros Filho, FernandoNascimento, Ana Paula Barbosa doCosta, Maiana de Oliveira Cerqueira eMerigueti, Thiago CastanheiraMenezes, Marcio Argollo deNicolás, Marisa FabianaSantos, Marcelo Trindade dosAssef, Ana Paula D’Alincourt CarvalhoSilva, Fabrício Alves Barbosa da2022-01-21T13:45:46Z2022-01-21T13:45:46Z2021MEDEIROS, 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. 2021.2296-889Xhttps://www.arca.fiocruz.br/handle/icict/5084210.3389/fmolb.2021.728129engFrontiers MediaPseudomonas aeruginosaRede metabólicaDados de transcriçãoModelo integradoAlvo terapêuticoPseudomonas aeruginosaMetabolic networkTranscriptome dataIntegrated modelTherapeutic targetA Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Modelsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleFundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.Laboratório Nacional de Computação Científica. Petrópolis, RJ, Brasil.Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.Universidade Federal Fluminense. Instituto de Física. Niterói, RJ, Brasil.Laboratório Nacional de Computação Científica. Petrópolis, RJ, Brasil.Laboratório Nacional de Computação Científica. Petrópolis, RJ, Brasil.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.Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.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.info:eu-repo/semantics/openAccessreponame:Repositório Institucional da FIOCRUZ (ARCA)instname:Fundação Oswaldo Cruz (FIOCRUZ)instacron:FIOCRUZLICENSElicense.txtlicense.txttext/plain; charset=utf-82991https://www.arca.fiocruz.br/bitstream/icict/50842/1/license.txt5a560609d32a3863062d77ff32785d58MD51ORIGINALAnaPaulaAssef_MarisaNicolas_etal_IOC_2021.pdfAnaPaulaAssef_MarisaNicolas_etal_IOC_2021.pdfapplication/pdf3023769https://www.arca.fiocruz.br/bitstream/icict/50842/2/AnaPaulaAssef_MarisaNicolas_etal_IOC_2021.pdf2f212360d3fab6b898b59d8e7379dec8MD52icict/508422022-01-21 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dc.title.pt_BR.fl_str_mv A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
title A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
spellingShingle A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
Medeiros Filho, Fernando
Pseudomonas aeruginosa
Rede metabólica
Dados de transcrição
Modelo integrado
Alvo terapêutico
Pseudomonas aeruginosa
Metabolic network
Transcriptome data
Integrated model
Therapeutic target
title_short A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
title_full A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
title_fullStr A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
title_full_unstemmed A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
title_sort A Systematic Strategy to Find Potential Therapeutic Targets for Pseudomonas aeruginosa Using Integrated Computational Models
author Medeiros Filho, Fernando
author_facet Medeiros Filho, Fernando
Nascimento, Ana Paula Barbosa do
Costa, Maiana de Oliveira Cerqueira e
Merigueti, Thiago Castanheira
Menezes, Marcio Argollo de
Nicolás, Marisa Fabiana
Santos, Marcelo Trindade dos
Assef, Ana Paula D’Alincourt Carvalho
Silva, Fabrício Alves Barbosa da
author_role author
author2 Nascimento, Ana Paula Barbosa do
Costa, Maiana de Oliveira Cerqueira e
Merigueti, Thiago Castanheira
Menezes, Marcio Argollo de
Nicolás, Marisa Fabiana
Santos, Marcelo Trindade dos
Assef, Ana Paula D’Alincourt Carvalho
Silva, Fabrício Alves Barbosa da
author2_role author
author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Medeiros Filho, Fernando
Nascimento, Ana Paula Barbosa do
Costa, Maiana de Oliveira Cerqueira e
Merigueti, Thiago Castanheira
Menezes, Marcio Argollo de
Nicolás, Marisa Fabiana
Santos, Marcelo Trindade dos
Assef, Ana Paula D’Alincourt Carvalho
Silva, Fabrício Alves Barbosa da
dc.subject.other.pt_BR.fl_str_mv Pseudomonas aeruginosa
Rede metabólica
Dados de transcrição
Modelo integrado
Alvo terapêutico
topic Pseudomonas aeruginosa
Rede metabólica
Dados de transcrição
Modelo integrado
Alvo terapêutico
Pseudomonas aeruginosa
Metabolic network
Transcriptome data
Integrated model
Therapeutic target
dc.subject.en.pt_BR.fl_str_mv Pseudomonas aeruginosa
Metabolic network
Transcriptome data
Integrated model
Therapeutic target
description Fundação Oswaldo Cruz. Programa de Computação Científica. Rio de Janeiro, RJ, Brasil.
publishDate 2021
dc.date.issued.fl_str_mv 2021
dc.date.accessioned.fl_str_mv 2022-01-21T13:45:46Z
dc.date.available.fl_str_mv 2022-01-21T13:45:46Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.citation.fl_str_mv 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.
dc.identifier.uri.fl_str_mv https://www.arca.fiocruz.br/handle/icict/50842
dc.identifier.issn.pt_BR.fl_str_mv 2296-889X
dc.identifier.doi.none.fl_str_mv 10.3389/fmolb.2021.728129
identifier_str_mv 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.
2296-889X
10.3389/fmolb.2021.728129
url https://www.arca.fiocruz.br/handle/icict/50842
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Frontiers Media
publisher.none.fl_str_mv Frontiers Media
dc.source.none.fl_str_mv reponame:Repositório Institucional da FIOCRUZ (ARCA)
instname:Fundação Oswaldo Cruz (FIOCRUZ)
instacron:FIOCRUZ
instname_str Fundação Oswaldo Cruz (FIOCRUZ)
instacron_str FIOCRUZ
institution FIOCRUZ
reponame_str Repositório Institucional da FIOCRUZ (ARCA)
collection Repositório Institucional da FIOCRUZ (ARCA)
bitstream.url.fl_str_mv https://www.arca.fiocruz.br/bitstream/icict/50842/1/license.txt
https://www.arca.fiocruz.br/bitstream/icict/50842/2/AnaPaulaAssef_MarisaNicolas_etal_IOC_2021.pdf
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repository.name.fl_str_mv Repositório Institucional da FIOCRUZ (ARCA) - Fundação Oswaldo Cruz (FIOCRUZ)
repository.mail.fl_str_mv repositorio.arca@fiocruz.br
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