Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.

The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incom...

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Autores principales:	Dafne Pérez-Montarelo, Nicholas J Hudson, Ana I Fernández, Yuliaxis Ramayo-Caldas, Brian P Dalrymple, Antonio Reverter
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/927873ca1d6f41038800ff58b8318d27
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spelling	oai:doaj.org-article:927873ca1d6f41038800ff58b8318d272021-11-18T08:13:50ZPorcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.1932-620310.1371/journal.pone.0046159https://doaj.org/article/927873ca1d6f41038800ff58b8318d272012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23049964/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species.Dafne Pérez-MontareloNicholas J HudsonAna I FernándezYuliaxis Ramayo-CaldasBrian P DalrympleAntonio ReverterPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 9, p e46159 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dafne Pérez-Montarelo Nicholas J Hudson Ana I Fernández Yuliaxis Ramayo-Caldas Brian P Dalrymple Antonio Reverter Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.
description	The processes that drive tissue identity and differentiation remain unclear for most tissue types. So are the gene networks and transcription factors (TF) responsible for the differential structure and function of each particular tissue, and this is particularly true for non model species with incomplete genomic resources. To better understand the regulation of genes responsible for tissue identity in pigs, we have inferred regulatory networks from a meta-analysis of 20 gene expression studies spanning 480 Porcine Affymetrix chips for 134 experimental conditions on 27 distinct tissues. We developed a mixed-model normalization approach with a covariance structure that accommodated the disparity in the origin of the individual studies, and obtained the normalized expression of 12,320 genes across the 27 tissues. Using this resource, we constructed a network, based on the co-expression patterns of 1,072 TF and 1,232 tissue specific genes. The resulting network is consistent with the known biology of tissue development. Within the network, genes clustered by tissue and tissues clustered by site of embryonic origin. These clusters were significantly enriched for genes annotated in key relevant biological processes and confirm gene functions and interactions from the literature. We implemented a Regulatory Impact Factor (RIF) metric to identify the key regulators in skeletal muscle and tissues from the central nervous systems. The normalization of the meta-analysis, the inference of the gene co-expression network and the RIF metric, operated synergistically towards a successful search for tissue-specific regulators. Novel among these findings are evidence suggesting a novel key role of ERCC3 as a muscle regulator. Together, our results recapitulate the known biology behind tissue specificity and provide new valuable insights in a less studied but valuable model species.
format	article
author	Dafne Pérez-Montarelo Nicholas J Hudson Ana I Fernández Yuliaxis Ramayo-Caldas Brian P Dalrymple Antonio Reverter
author_facet	Dafne Pérez-Montarelo Nicholas J Hudson Ana I Fernández Yuliaxis Ramayo-Caldas Brian P Dalrymple Antonio Reverter
author_sort	Dafne Pérez-Montarelo
title	Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.
title_short	Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.
title_full	Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.
title_fullStr	Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.
title_full_unstemmed	Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.
title_sort	porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/927873ca1d6f41038800ff58b8318d27
work_keys_str_mv	AT dafneperezmontarelo porcinetissuespecificregulatorynetworksderivedfrommetaanalysisofthetranscriptome AT nicholasjhudson porcinetissuespecificregulatorynetworksderivedfrommetaanalysisofthetranscriptome AT anaifernandez porcinetissuespecificregulatorynetworksderivedfrommetaanalysisofthetranscriptome AT yuliaxisramayocaldas porcinetissuespecificregulatorynetworksderivedfrommetaanalysisofthetranscriptome AT brianpdalrymple porcinetissuespecificregulatorynetworksderivedfrommetaanalysisofthetranscriptome AT antonioreverter porcinetissuespecificregulatorynetworksderivedfrommetaanalysisofthetranscriptome
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Porcine tissue-specific regulatory networks derived from meta-analysis of the transcriptome.

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