Large scale study of anti-sense regulation by differential network analysis

TitreLarge scale study of anti-sense regulation by differential network analysis
Type de publicationArticle de revue
AuteurLegeay, Marc
1, 2
, Aubourg, Sébastien , Renou, Jean-Pierre , Duval, Béatrice
EditeurBMC
TypeArticle scientifique dans une revue à comité de lecture
Année2018
LangueAnglais
Date20 Nov. 2018
NuméroS5
Pagination95
Volume12
Titre de la revueBMC Systems Biology
ISSN1752-0509
Mots-clésAnti-sense regulation, Differential network analysis, Functional Analysis
Résumé en anglais

BACKGROUND: Systems biology aims to analyse regulation mechanisms into the cell. By mapping interactions observed in different situations, differential network analysis has shown its power to reveal specific cellular responses or specific dysfunctional regulations. In this work, we propose to explore on a large scale the role of natural anti-sense transcription on gene regulation mechanisms, and we focus our study on apple (Malus domestica) in the context of fruit ripening in cold storage.

RESULTS: We present a differential functional analysis of the sense and anti-sense transcriptomic data that reveals functional terms linked to the ripening process. To develop our differential network analysis, we introduce our inference method of an Extended Core Network; this method is inspired by C3NET, but extends the notion of significant interactions. By comparing two extended core networks, one inferred with sense data and the other one inferred with sense and anti-sense data, our differential analysis is first performed on a local view and reveals AS-impacted genes, genes that have important interactions impacted by anti-sense transcription. The motifs surrounding AS-impacted genes gather transcripts with functions mostly consistent with the biological context of the data used and the method allows us to identify new actors involved in ripening and cold acclimation pathways and to decipher their interactions. Then from a more global view, we compute minimal sub-networks that connect the AS-impacted genes using Steiner trees. Those Steiner trees allow us to study the rewiring of the AS-impacted genes in the network with anti-sense actors.

CONCLUSION: Anti-sense transcription is usually ignored in transcriptomic studies. The large-scale differential analysis of apple data that we propose reveals that anti-sense regulation may have an important impact in several cellular stress response mechanisms. Our data mining process enables to highlight specific interactions that deserve further experimental investigations.

URL de la noticehttp://okina.univ-angers.fr/publications/ua18219
DOI10.1186/s12918-018-0613-7
Lien vers le document

https://bmcsystbiol.biomedcentral.com/articles/10.1186/s12918-018-0613-7

Autre titreBMC Syst Biol
Identifiant (ID) PubMed30458828