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

Area of Science:

• Systems biology
• Genomics
• Plant science

Background:

• Systems biology analyzes cellular regulation through interaction mapping.
• Differential network analysis identifies specific cellular responses and dysfunctions.
• Natural anti-sense transcription's role in gene regulation is often overlooked.

Purpose of the Study:

• To explore the large-scale role of natural anti-sense transcription in gene regulation.
• To focus on apple (Malus domestica) fruit ripening under cold storage conditions.
• To identify new regulatory mechanisms and actors involved in ripening and cold acclimation.

Main Methods:

• Differential functional analysis of sense and anti-sense transcriptomic data.
• Inference of an Extended Core Network, inspired by C3NET, to analyze significant interactions.
• Comparison of networks inferred with sense data versus sense and anti-sense data.
• Identification of anti-sense transcription (AS)-impacted genes and surrounding functional motifs.
• Computation of Steiner trees to analyze the rewiring of AS-impacted genes.

Main Results:

• Differential analysis revealed functional terms linked to the apple fruit ripening process.
• The method identified AS-impacted genes, highlighting genes with interactions affected by anti-sense transcription.
• New actors and interactions involved in ripening and cold acclimation pathways were discovered.
• Analysis of Steiner trees elucidated the rewiring of AS-impacted genes within the network, including anti-sense actors.

Conclusions:

• Anti-sense transcription, often ignored, significantly impacts cellular stress response mechanisms.
• Large-scale differential analysis of apple data demonstrates the importance of anti-sense regulation.
• The data mining process highlights specific interactions warranting further experimental investigation.