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

Abstract

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.

Keywords: Anti-sense regulation; Differential network analysis; Functional analysis.

Fig. 1

Differential network analysis. In order to identify change motifs, we realise a differential network analysis from gene networks inferred by Extended Core Network on Sense data in one hand, and Sense and Anti-Sense data in an other hand

Fig. 2

Box plots of F₁ scores for C3NET and ECN with different accepting rates. The number following ECN indicates the accepting rates. The C3NET method is the first on the left, then the ECN methods are sorted beginning with ECN_0. Box plots are obtained from 500 simulations on two datasets : E. coli (a and b) and S. cerevisiae (c and d). Accepting rates from 0 to 100% with a 10% step are tested (a and c), and from 0 to 20% with a 1% step (b and d)

Fig. 3

Illustration of a change motif in the Extended Core Network. A sense node is represented in blue, an anti-sense node is represented in purple. The orange triangle-shaped node is an AS-impacted gene. A red link is only present in the Sense network. A green link is present in the Sense and Anti-Sense network. The link $S1⊸S2$ means that the mutual information between S1 and S2 is maximal for S1

Fig. 4

Extended Core Network with a 5% accepting rate for sense-only 60DAH experiment. Orange triangle-shaped nodes represent AS-impacted genes: they are connected to one or several sense nodes in this graph, but in the SAS network, they only have anti-sense neighbors

Fig. 5

Change motifs from the 60DAH experiment. Orange and blue nodes represent sense nodes, an orange node being an AS-impacted gene. Purple nodes represent anti-sense nodes. A red link is a link only from the sense network. A green link is a link only from the sense and anti-sense network. A gray link is a link from both networks. Each apple gene (MDP) is associated with its best homolog in Arabidopsis thaliana. a Change motif #1. The AS-impacted gene is MDP 0000251669_r. b Change motif #2. The AS-impacted gene is MDP 0000205588_r. c Change motif #3. The AS-impacted gene is MDP 0000917574_r

Fig. 6

Steiner tree of an AS-impacted sub-graph from the 60DAH experiment. Orange nodes are AS-impacted genes, blue nodes are Steiner sense nodes, purple nodes are Steiner anti-sense nodes. Gray links are connections from the SAS network, red links are connections from the S network. Bigger nodes corresponds to the nodes from the change motif #3