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

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• dc.contributor.author De Toma, Ilario
• dc.contributor.author Sierra, Cesar
• dc.contributor.author Dierssen, Mara
• dc.date.accessioned 2022-06-10T10:11:43Z
• dc.date.available 2022-06-10T10:11:43Z
• dc.date.issued 2021
• dc.description.abstract 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.
• dc.description.sponsorship Funding: The lab of MD is supported by the Departament d'Universitats, Recerca i Societat de la Informació (DURSI) de la Generalitat de Catalunya (Grups consolidats 2017 SGR 926). We acknowledge the support of the Agencia Estatal de Investigación (PID2019-110755RB-I00/AEI/ 10.13039/501100011033), H2020 SC1 (GO-DS21- 848077), Jerôme Lejeune Foundation (#2002), NIH (#1R01EB 028159-01), Fundació La Marató-TV3 (#2016/20-30), JPND Heroes AC170006 Ministerio de Ciencia Innovación y Universidades (#RTC2019-007230-1 and #RTC2019-007329-1). We acknowledge support of the Spanish Ministry of Science and Innovation to the EMBL partnership, the Centro de Excelencia Severo Ochoa and the CERCA Programme / Generalitat de Catalunya
• dc.format.mimetype application/pdf
• dc.identifier.citation De Toma I, Sierra C, Dierssen M. Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome. PLoS Comput Biol. 2021 Sep 27;17(9):e1009317. DOI:10.1371/journal.pcbi.1009317
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pcbi.1009317
• dc.identifier.issn 1553-734X
• dc.identifier.uri http://hdl.handle.net/10230/53450
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/848077
• dc.rights © 2021 Ilario De Toma, Cesar Sierra, Mara Dierssen. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.other Down, Síndrome de
• dc.subject.other Genètica
• dc.subject.other Proteïnes
• dc.title Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion