Gene target discovery with network analysis in Toxoplasma gondii

Autores
Alonso, Andrés Mariano; Corvi, Maria M.; Diambra, Luis Aníbal
Año de publicación
2019
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis.
Centro Regional de Estudios Genómicos
Materia
Ciencias Exactas
Biología
Toxoplasma gondii
pathogen biology
network analysis
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/138645

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network_name_str SEDICI (UNLP)
spelling Gene target discovery with network analysis in Toxoplasma gondiiAlonso, Andrés MarianoCorvi, Maria M.Diambra, Luis AníbalCiencias ExactasBiologíaToxoplasma gondiipathogen biologynetwork analysisInfectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis.Centro Regional de Estudios Genómicos2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/138645enginfo:eu-repo/semantics/altIdentifier/issn/2045-2322info:eu-repo/semantics/altIdentifier/doi/10.1101/397398info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/4.0/Creative Commons Attribution 4.0 International (CC BY 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:31:52Zoai:sedici.unlp.edu.ar:10915/138645Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:31:52.259SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Gene target discovery with network analysis in Toxoplasma gondii
title Gene target discovery with network analysis in Toxoplasma gondii
spellingShingle Gene target discovery with network analysis in Toxoplasma gondii
Alonso, Andrés Mariano
Ciencias Exactas
Biología
Toxoplasma gondii
pathogen biology
network analysis
title_short Gene target discovery with network analysis in Toxoplasma gondii
title_full Gene target discovery with network analysis in Toxoplasma gondii
title_fullStr Gene target discovery with network analysis in Toxoplasma gondii
title_full_unstemmed Gene target discovery with network analysis in Toxoplasma gondii
title_sort Gene target discovery with network analysis in Toxoplasma gondii
dc.creator.none.fl_str_mv Alonso, Andrés Mariano
Corvi, Maria M.
Diambra, Luis Aníbal
author Alonso, Andrés Mariano
author_facet Alonso, Andrés Mariano
Corvi, Maria M.
Diambra, Luis Aníbal
author_role author
author2 Corvi, Maria M.
Diambra, Luis Aníbal
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Exactas
Biología
Toxoplasma gondii
pathogen biology
network analysis
topic Ciencias Exactas
Biología
Toxoplasma gondii
pathogen biology
network analysis
dc.description.none.fl_txt_mv Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis.
Centro Regional de Estudios Genómicos
description Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogens biology. In this work, we propose a system biology approach to analyze transcriptomic data of the parasite Toxoplasma gondii . By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of the parasite were embedded into the dynamics of a gene network model. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 339 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small subnetwork module that controls the parasite9s life cycle. These new findings can contribute to understand of parasite pathogenesis.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/138645
url http://sedici.unlp.edu.ar/handle/10915/138645
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2045-2322
info:eu-repo/semantics/altIdentifier/doi/10.1101/397398
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
Creative Commons Attribution 4.0 International (CC BY 4.0)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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