Analyses génomiques intégratives pour l'identification et la hiérarchisation des ARN non codants longs associés à l'autisme

Aperçu: G.M.

Les études génétiques ont identifié de nombreux loci de risque pour le "trouble du spectre de l'autisme" (TSA), bien que les facteurs causaux dans la plupart des cas soient encore inconnus. Actuellement, les gènes de risque TSA connus sont tous des gènes codant pour la protéine; Cependant, la grande majorité des transcriptions chez l'homme sont des ARN non codants (ncRNA) qui ne codent pas les protéines. Récemment, de longs ARN non codants (lncRNAs) se sont révélés très exprimés dans le cerveau humain et essentiels au développement normal du cerveau.  

En utilisant une approche intégrative composée d'une analyse d'expression différentielle dans les tissus affectés et des métriques de connectivité à partir d'un réseau de co-expression du développement, l'équipe a classé par ordre de priorité un ensemble d'ARNnc associés aux TSA . L'identification des lncRNAs en tant que nouveaux gènes de sensibilité aux TSA pourrait aider à expliquer la pathogenèse génétique des TSA.

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PLoS One. 2017 May 31;12(5):e0178532. doi: 10.1371/journal.pone.0178532. eCollection 2017.

Integrative genomic analyses for identification and prioritization of long non-coding RNAs associated with autism

Gudenas BL¹, Srivastava AK², Wang L¹.

Author information

1

Department of Genetics and Biochemistry, Clemson University, Clemson, South Carolina, United States of America.

2

J.C. Self Research Institute of Human Genetics, Greenwood Genetic Center, Greenwood, South Carolina, United States of America.

Abstract

Genetic studies have identified many risk loci for autism spectrum disorder (ASD) although causal factors in the majority of cases are still unknown. Currently, known ASD risk genes are all protein-coding genes; however, the vast majority of transcripts in humans are non-coding RNAs (ncRNAs) which do not encode proteins. Recently, long non-coding RNAs (lncRNAs) were shown to be highly expressed in the human brain and crucial for normal brain development. We have constructed a computational pipeline for the integration of various genomic datasets to identify lncRNAs associated with ASD. This pipeline utilizes differential gene expression patterns in affected tissues in conjunction with gene co-expression networks in tissue-matched non-affected samples. We analyzed RNA-seq data from the cortical brain tissues from ASD cases and controls to identify lncRNAs differentially expressed in ASD. We derived a gene co-expression network from an independent human brain developmental transcriptome and detected a convergence of the differentially expressed lncRNAs and known ASD risk genes into specific co-expression modules. Co-expression network analysis facilitates the discovery of associations between previously uncharacterized lncRNAs with known ASD risk genes, affected molecular pathways and at-risk developmental time points. In addition, we show that some of these lncRNAs have a high degree of overlap with major CNVs detected in ASD genetic studies. By utilizing this integrative approach comprised of differential expression analysis in affected tissues and connectivity metrics from a developmental co-expression network, we have prioritized a set of candidate ASD-associated lncRNAs. The identification of lncRNAs as novel ASD susceptibility genes could help explain the genetic pathogenesis of ASD.

PMID: 28562671

DOI: 10.1371/journal.pone.0178532