Abstract
BackgroundSystems 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.ResultsWe 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.ConclusionAnti-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.
Highlights
Systems biology aims to analyse regulation mechanisms into the cell
To identify the impact of integrating anti-sense transcription in gene network inference, we propose to achieve an original differential network analysis where we compare two context-specific gene networks inferred from two sets of actors: the first set is only composed by sense transcripts, it represents a usual transcriptomic study; the second set is composed by both sense and anti-sense transcripts
AS-impacted genes and change motifs We propose a differential network analysis where the extended core network inferred from the sense only data (S) is compared to the extended core network inferred from the sense and anti-sense data (SAS)
Summary
Systems biology aims to analyse regulation mechanisms into the cell. Molecular classification of diseases may be achieved by looking for Differential network biology [3, 4] refers to a set of works that rely on differential network mapping to analyse interactions between components of a biological system. These works study the changes that can be observed. One type of approach in network biology is to integrate static interaction knowledge with dynamic changes in gene expression or metabolic fluxes. In [6], the integration of a static protein-protein interaction network with ageing-related gene expression data is used to identify cellular changes related to age. While the global network topology does not exhibit significant changes with age, the measures of local topology (node degree, clustering coefficient, graphlet degree, . . . ) reveal a small set of proteins whose centrality values are correlated with age
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