Identifying Stress Responsive Genes using Overlapping Communities in Co-expression Networks

TL;DR

This paper introduces a workflow that extends WGCNA to identify overlapping gene modules in co-expression networks, pinpointing treatment-responsive genes in plants, exemplified by salt stress response in rice.

Contribution

It presents a novel approach combining hierarchical link clustering and LASSO regression to detect overlapping gene modules linked to specific treatments, improving target gene identification.

Findings

Identified 19 salt stress-responsive rice genes.

Genes are grouped into modules associated with salt tolerance traits.

Workflow reduces candidate genes for experimental validation.

Abstract

This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed approach is both a generalization and an extension of WGCNA. It aims to identify specific modules of overlapping communities underlying the co-expression network of genes. Module detection is achieved by using Hierarchical Link Clustering. The overlapping nature of the systems' regulatory domains that generate co-expression can be identified by such modules. LASSO regression is employed to analyze phenotypic responses of modules to treatment. Results. The workflow is applied to rice (Oryza sativa), a major food source known to be highly sensitive to salt stress. The workflow identifies 19 rice genes that seem relevant in the response to salt stress. They are distributed across 6 modules: 3 modules, each grouping together 3 genes, are associated to shoot K content; 2 modules of 3 genes are associated to shoot biomass; and 1 module of 4 genes is associated to root biomass. These genes represent target genes for the improvement of salinity tolerance in rice. Conclusion. A more effective framework to reduce the search-space for target genes that respond to a specific treatment is introduced. It facilitates experimental validation by restraining efforts to a smaller subset of genes of high potential relevance.