Gene target discovery with network analysis in <i>Toxoplasma gondii</i>

Autores
Alonso, Andrés Mariano; Corvi, María 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 efective in many cases. In fact, traditional scientifc 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 pathogen biology. In this paper, we apply a gene regulatory network 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 T. gondii were embedded into the dynamics of a gene regulatory network. 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 338 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identifed a small gene circuit that drives a series of phenotypic transitions that characterize the life cycle of this pathogen. These new fndings can contribute to the understanding of parasite pathogenesis.
Facultad de Ciencias Exactas
Centro Regional de Estudios Genómicos
Materia
Ciencias Exactas
Biología
Toxoplasma gondii
transcriptomic data
pathogens
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:10915/107924

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network_name_str SEDICI (UNLP)
spelling Gene target discovery with network analysis in <i>Toxoplasma gondii</i>Alonso, Andrés MarianoCorvi, María M.Diambra, Luis AníbalCiencias ExactasBiologíaToxoplasma gondiitranscriptomic datapathogensInfectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not efective in many cases. In fact, traditional scientifc 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 pathogen biology. In this paper, we apply a gene regulatory network approach to analyze transcriptomic data of the parasite <i>Toxoplasma gondii</i>. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of <i>T. gondii</i> were embedded into the dynamics of a gene regulatory network. 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 338 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identifed a small gene circuit that drives a series of phenotypic transitions that characterize the life cycle of this pathogen. These new fndings can contribute to the understanding of parasite pathogenesis.Facultad de Ciencias ExactasCentro 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/10915/107924enginfo:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC6345969&blobtype=pdfinfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-018-36671-yinfo:eu-repo/semantics/altIdentifier/issn/2045-2322info:eu-repo/semantics/altIdentifier/pmid/30679502info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-018-36671-yinfo: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:UNLP2025-10-15T11:15:46Zoai:sedici.unlp.edu.ar:10915/107924Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:15:46.687SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
title Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
spellingShingle Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
Alonso, Andrés Mariano
Ciencias Exactas
Biología
Toxoplasma gondii
transcriptomic data
pathogens
title_short Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
title_full Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
title_fullStr Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
title_full_unstemmed Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
title_sort Gene target discovery with network analysis in <i>Toxoplasma gondii</i>
dc.creator.none.fl_str_mv Alonso, Andrés Mariano
Corvi, María M.
Diambra, Luis Aníbal
author Alonso, Andrés Mariano
author_facet Alonso, Andrés Mariano
Corvi, María M.
Diambra, Luis Aníbal
author_role author
author2 Corvi, María M.
Diambra, Luis Aníbal
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Exactas
Biología
Toxoplasma gondii
transcriptomic data
pathogens
topic Ciencias Exactas
Biología
Toxoplasma gondii
transcriptomic data
pathogens
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 efective in many cases. In fact, traditional scientifc 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 pathogen biology. In this paper, we apply a gene regulatory network approach to analyze transcriptomic data of the parasite <i>Toxoplasma gondii</i>. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of <i>T. gondii</i> were embedded into the dynamics of a gene regulatory network. 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 338 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identifed a small gene circuit that drives a series of phenotypic transitions that characterize the life cycle of this pathogen. These new fndings can contribute to the understanding of parasite pathogenesis.
Facultad de Ciencias Exactas
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 efective in many cases. In fact, traditional scientifc 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 pathogen biology. In this paper, we apply a gene regulatory network approach to analyze transcriptomic data of the parasite <i>Toxoplasma gondii</i>. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of <i>T. gondii</i> were embedded into the dynamics of a gene regulatory network. 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 338 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identifed a small gene circuit that drives a series of phenotypic transitions that characterize the life cycle of this pathogen. These new fndings can contribute to the understanding 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
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format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/107924
url http://sedici.unlp.edu.ar/handle/10915/107924
dc.language.none.fl_str_mv eng
language eng
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info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41598-018-36671-y
info:eu-repo/semantics/altIdentifier/issn/2045-2322
info:eu-repo/semantics/altIdentifier/pmid/30679502
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-018-36671-y
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)
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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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