Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach

Abstract

Background Glucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset. Methods We used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters. Results The density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

Identification of Potential Genes Encoding Protein Transporters in Arabidopsis thaliana Glucosinolate (GSL) Metabolism

“…It also highlighted the in silico-based approaches that employed the ‘guilt-by-association’ (GBA) principle in identifying transporters in plant specialised metabolism. In relation to GSL biosynthesis, the GBA approach has been used to identify regulators [ 50 , 51 , 52 , 53 , 54 , 55 ] and enzymes [ 56 , 57 , 58 , 59 ]. Identifying TPs using co-expressed genes successfully defined a boron transporter candidate in A. thaliana [ 71 ].…”

“…Therefore, we propose a ‘guilt-by-association’ (GBA) approach to identify and characterise possible GSL TPs involved in GSL metabolism [ 32 ]. The GBA principle has been used to identify regulators [ 50 , 51 , 52 , 53 , 54 , 55 ] and enzymes [ 56 , 57 , 58 , 59 ] involved in GSL biosynthesis. Detailed information on most of the molecular components related to GSL biosynthesis and metabolism can be found in SuCComBase, which is accessible at (accessed on 16 December 2021) [ 60 ].…”

Gene Co-Expression Network Analysis Reveals Key Regulatory Genes in Metisa plana Hormone Pathways

“…The overlapping clusters are described with several biological processes related to the hormone biosynthesis pathway. DPClusO constructs an undirected graph consisting of a finite set of nodes, N and a finite set of edges, E. Two key parameters, namely the density, d k and cluster property, cp nk , are applied in the DPClusO algorithm of the cluster network analysis [40,41]. The default cp value, 0.5 was used with reference to the previous studies [41][42][43].…”

Gene Co-Expression Network Tools and Databases for Crop Improvement

“…Although the whole genome sequence in crops is widely developed, plant gene annotation is still scarce. Previous studies have used the GCN as a source to annotate unknown and uncharacterised genes involved in specific biological roles [15,16] and to identify new genes involved in biological mechanisms [17][18][19][20][21][22]. This is due to the hypothesis that groups of genes that are co-expressed in a module may share a similar biological function [13].…”