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

Sarahani Harun; Nor Afiqah-Aleng; Mohammad Bozlul Karim; Md Altaf Ul Amin; Shigehiko Kanaya; Zeti-Azura Mohamed-Hussein

doi:10.7717/peerj.11876

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

PeerJ. 2021 Aug 4:9:e11876. doi: 10.7717/peerj.11876. eCollection 2021.

Authors

Sarahani Harun¹, Nor Afiqah-Aleng², Mohammad Bozlul Karim³, Md Altaf Ul Amin³, Shigehiko Kanaya³, Zeti-Azura Mohamed-Hussein^{1

4}

Affiliations

¹ Centre for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia.
² Institute of Marine Biotechnology, Universiti Malaysia Terengganu, Kuala Nerus, Terengganu, Malaysia.
³ Graduate School of Science and Technology & NAIST Data Science Center, Nara Institute of Science and Technology, Nara, Japan.
⁴ Department of Applied Physics, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM Bangi, Selangor, Malaysia.

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.

Keywords: Aliphatic glucosinolates; Gene network analysis; Graph clustering; Indolic glucosinolates; Nitrogen-containing compounds; Secondary metabolites.

Grants and funding

This work was supported by the Malaysian Ministry of Higher Education (ERGS/1/2013/STG07/UKM/02/3) awarded to Zeti-Azura Mohamed-Hussein. Sarahani Harun is funded by JASSO for her attachment at NAIST to perform this experiment. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.