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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/107924
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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 |
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article |
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eng |
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eng |
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openAccess |
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http://creativecommons.org/licenses/by/4.0/ Creative Commons Attribution 4.0 International (CC BY 4.0) |
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