Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.

Trisomy of human chromosome 21 (HSA21) causes Down syndrome (DS). The trisomy does not simply result in the upregulation of HSA21--encoded genes but also leads to a genome-wide transcriptomic deregulation, which affect differently each tissue and cell type as a result of epigenetic mechanisms and pr...

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Autores principales:	Ilario De Toma, Cesar Sierra, Mara Dierssen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/adf6eac23e61475ba75237b4c74df385
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spelling	oai:doaj.org-article:adf6eac23e61475ba75237b4c74df3852021-12-02T19:58:14ZMeta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.1553-734X1553-735810.1371/journal.pcbi.1009317https://doaj.org/article/adf6eac23e61475ba75237b4c74df3852021-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009317https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Trisomy of human chromosome 21 (HSA21) causes Down syndrome (DS). The trisomy does not simply result in the upregulation of HSA21--encoded genes but also leads to a genome-wide transcriptomic deregulation, which affect differently each tissue and cell type as a result of epigenetic mechanisms and protein-protein interactions. We performed a meta-analysis integrating the differential expression (DE) analyses of all publicly available transcriptomic datasets, both in human and mouse, comparing trisomic and euploid transcriptomes from different sources. We integrated all these data in a "DS network". We found that genome wide deregulation as a consequence of trisomy 21 is not arbitrary, but involves deregulation of specific molecular cascades in which both HSA21 genes and HSA21 interactors are more consistently deregulated compared to other genes. In fact, gene deregulation happens in "clusters", so that groups from 2 to 13 genes are found consistently deregulated. Most of these events of "co-deregulation" involve genes belonging to the same GO category, and genes associated with the same disease class. The most consistent changes are enriched in interferon related categories and neutrophil activation, reinforcing the concept that DS is an inflammatory disease. Our results also suggest that the impact of the trisomy might diverge in each tissue due to the different gene set deregulation, even though the triplicated genes are the same. Our original method to integrate transcriptomic data confirmed not only the importance of known genes, such as SOD1, but also detected new ones that could be extremely useful for generating or confirming hypotheses and supporting new putative therapeutic candidates. We created "metaDEA" an R package that uses our method to integrate every kind of transcriptomic data and therefore could be used with other complex disorders, such as cancer. We also created a user-friendly web application to query Ensembl gene IDs and retrieve all the information of their differential expression across the datasets.Ilario De TomaCesar SierraMara DierssenPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 9, p e1009317 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ilario De Toma Cesar Sierra Mara Dierssen Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.
description	Trisomy of human chromosome 21 (HSA21) causes Down syndrome (DS). The trisomy does not simply result in the upregulation of HSA21--encoded genes but also leads to a genome-wide transcriptomic deregulation, which affect differently each tissue and cell type as a result of epigenetic mechanisms and protein-protein interactions. We performed a meta-analysis integrating the differential expression (DE) analyses of all publicly available transcriptomic datasets, both in human and mouse, comparing trisomic and euploid transcriptomes from different sources. We integrated all these data in a "DS network". We found that genome wide deregulation as a consequence of trisomy 21 is not arbitrary, but involves deregulation of specific molecular cascades in which both HSA21 genes and HSA21 interactors are more consistently deregulated compared to other genes. In fact, gene deregulation happens in "clusters", so that groups from 2 to 13 genes are found consistently deregulated. Most of these events of "co-deregulation" involve genes belonging to the same GO category, and genes associated with the same disease class. The most consistent changes are enriched in interferon related categories and neutrophil activation, reinforcing the concept that DS is an inflammatory disease. Our results also suggest that the impact of the trisomy might diverge in each tissue due to the different gene set deregulation, even though the triplicated genes are the same. Our original method to integrate transcriptomic data confirmed not only the importance of known genes, such as SOD1, but also detected new ones that could be extremely useful for generating or confirming hypotheses and supporting new putative therapeutic candidates. We created "metaDEA" an R package that uses our method to integrate every kind of transcriptomic data and therefore could be used with other complex disorders, such as cancer. We also created a user-friendly web application to query Ensembl gene IDs and retrieve all the information of their differential expression across the datasets.
format	article
author	Ilario De Toma Cesar Sierra Mara Dierssen
author_facet	Ilario De Toma Cesar Sierra Mara Dierssen
author_sort	Ilario De Toma
title	Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.
title_short	Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.
title_full	Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.
title_fullStr	Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.
title_full_unstemmed	Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.
title_sort	meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in down syndrome.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/adf6eac23e61475ba75237b4c74df385
work_keys_str_mv	AT ilariodetoma metaanalysisoftranscriptomicdatarevealsclustersofconsistentlyderegulatedgeneanddiseaseontologiesindownsyndrome AT cesarsierra metaanalysisoftranscriptomicdatarevealsclustersofconsistentlyderegulatedgeneanddiseaseontologiesindownsyndrome AT maradierssen metaanalysisoftranscriptomicdatarevealsclustersofconsistentlyderegulatedgeneanddiseaseontologiesindownsyndrome
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Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome.

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