Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.

Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcript...

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Autores principales:	Naresh Doni Jayavelu, Nadav Bar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2da9ecc6d6c44ccda5b5568609c6afa4
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spelling	oai:doaj.org-article:2da9ecc6d6c44ccda5b5568609c6afa42021-11-18T08:38:42ZDynamics of regulatory networks in gastrin-treated adenocarcinoma cells.1932-620310.1371/journal.pone.0078349https://doaj.org/article/2da9ecc6d6c44ccda5b5568609c6afa42014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24416123/?tool=EBIhttps://doaj.org/toc/1932-6203Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.Naresh Doni JayaveluNadav BarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 1, p e78349 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naresh Doni Jayavelu Nadav Bar Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
description	Understanding gene transcription regulatory networks is critical to deciphering the molecular mechanisms of different cellular states. Most studies focus on static transcriptional networks. In the current study, we used the gastrin-regulated system as a model to understand the dynamics of transcriptional networks composed of transcription factors (TFs) and target genes (TGs). The hormone gastrin activates and stimulates signaling pathways leading to various cellular states through transcriptional programs. Dysregulation of gastrin can result in cancerous tumors, for example. However, the regulatory networks involving gastrin are highly complex, and the roles of most of the components of these networks are unknown. We used time series microarray data of AR42J adenocarcinoma cells treated with gastrin combined with static TF-TG relationships integrated from different sources, and we reconstructed the dynamic activities of TFs using network component analysis (NCA). Based on the peak expression of TGs and activity of TFs, we created active sub-networks at four time ranges after gastrin treatment, namely immediate-early (IE), mid-early (ME), mid-late (ML) and very late (VL). Network analysis revealed that the active sub-networks were topologically different at the early and late time ranges. Gene ontology analysis unveiled that each active sub-network was highly enriched in a particular biological process. Interestingly, network motif patterns were also distinct between the sub-networks. This analysis can be applied to other time series microarray datasets, focusing on smaller sub-networks that are activated in a cascade, allowing better overview of the mechanisms involved at each time range.
format	article
author	Naresh Doni Jayavelu Nadav Bar
author_facet	Naresh Doni Jayavelu Nadav Bar
author_sort	Naresh Doni Jayavelu
title	Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
title_short	Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
title_full	Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
title_fullStr	Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
title_full_unstemmed	Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
title_sort	dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/2da9ecc6d6c44ccda5b5568609c6afa4
work_keys_str_mv	AT nareshdonijayavelu dynamicsofregulatorynetworksingastrintreatedadenocarcinomacells AT nadavbar dynamicsofregulatorynetworksingastrintreatedadenocarcinomacells
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Dynamics of regulatory networks in gastrin-treated adenocarcinoma cells.

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