Identifying stress responsive genes using overlapping communities in co-expression networks

Abstract

Background 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. Conclusions 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.

The Developmental Delay of Seedlings With Cotyledons Only Confers Stress Tolerance to Suaeda aralocaspica (Chenopodiaceae) by Unique Performance on Morphology, Physiology, and Gene Expression

“…Three sets of genes involved in development, photosynthesis, and salt stress response were analyzed, and the results revealed that most of the development-related (DR) genes were upregulated in seedling growth compared with those related to response to stress, especially the SYNC1 (asparaginyl-tRNA synthetase) and LST8 (lethal with SEC13 protein 8) genes, which play important roles in regulating amino acid accumulation and nutrition synthesis (Moreau et al, 2012 ; Arifin et al, 2019 ), whereas another DR gene, XDH1 (xanthine dehydrogenase) was downregulated in cotyledon development, which is generally upregulated in leaf senescence (Nakagawa et al, 2007 ), suggesting that cotyledons of S. aralocaspica seedlings are not aging. Genes associated with salt tolerance may limit the uptake and transport of ions and adjust osmotic and ionic balance (Qiu et al, 2017 ; Riccio-Rengifo et al, 2021 ). In this study, most of the salt response genes, e.g., BADH4 (betaine aldehyde dehydrogenase 4) and SKD1 (suppressor of K + transport growth defect 1, encoding for salt-induced AAA-Type ATPase), were upregulated with increasing NaCl concentration.…”

Gene communities in co-expression networks across different tissues

“…For example, enforcing pillar or semi-pillar communities such that all the genes belonging to the same multilayer community are present in the same set of layers [44,91] may facilitate biological interpretation of obtained results. Allowing overlapping of communities [131,132] and genes not belonging to any community may be another choice. For example, overlapping community detection in single-layer networks has been shown to be better at identifying biologically relevant disease modules than non-overlapping community detection [131].…”

A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology

“…On the other hand, since a gene (or protein) is involved in multiple functions, overlapping gene modules (i.e. a gene can belong to one or more modules) are more reflective of the real biological system [14,15]. Based on independent component analysis (ICA), principal component analysis (PCA), and independent principal component analysis (IPCA), Nguyen et al proposed a method for the identification of overlapping gene co-expression modules: oCEM, which had good performance in detecting clinically relevant co-expression modules [3].…”

“…Based on independent component analysis (ICA), principal component analysis (PCA), and independent principal component analysis (IPCA), Nguyen et al proposed a method for the identification of overlapping gene co-expression modules: oCEM, which had good performance in detecting clinically relevant co-expression modules [3]. Camila et al further extended WGCNA, using hierarchical link clustering to identify communities (modules) in co-expression networks, and identified 19 rice genes associated with salt stress [15]. The above methods had achieved good results in the identification of gene co-expression modules, but they only considered the similarity between gene expressions in the clustering process, while ignoring the functional similarity within the modules.…”