Reconstruction of the Transcriptional Regulatory Network in Arabidopsis thaliana Aliphatic Glucosinolate Biosynthetic Pathway

Aliphatic glucosinolate is an important secondary metabolite responsible in plant defense mechanism and carcinogenic activity. It plays a crucial role in plant adaptation towards changes in the environment such as salinity and drought. However, in many plant genomes, there are thousands of genes encoding proteins still with putative functions and incomplete annotations. Therefore, the genome of Arabidopsis thaliana was selected to be investigated further to identify any putative genes that are potentially involved in the aliphatic glucosinolate biosynthesis pathway, most of its gene are with incomplete annotation. Known genes for aliphatic glucosinolates were retrieved from KEGG and AraCyc databases. Three co-expression databases i.e., ATTED-II, GeneMANIA and STRING were used to perform the co-expression network analysis. The integrated co-expression network was then being clustered, annotated and visualized using Cytoscape plugin, MCODE and ClueGO. Then, the regulatory network of A. thaliana from AtRegNet was mapped onto the co-expression network to build the transcriptional regulatory network. This study showed that a total of 506 genes were co-expressed with the 61 aliphatic glucosinolate biosynthesis genes. Five transcription factors have been predicted to be involved in the biosynthetic pathway of aliphatic glucosinolate, namely SEPALLATA 3 (SEP3), PHYTOCHROME INTERACTING FACTOR 3-like 5 (AtbHLH15/PIL5), ELONGATED HYPOCOTYL 5 (HY5), AGAMOUS-like 15 (AGL15) and GLABRA 3 (GL3). Meanwhile, three other genes with high potential to be involved in the aliphatic glucosinolates biosynthetic pathway were identified, i.e., methylthioalkylmalate-like synthase 4 (MAML-4) and aspartate aminotransferase (ASP1 and ASP4). These findings can be used to complete the aliphatic glucosinolate biosynthetic pathway in A. thaliana and to update the information on the glucosinolate-related pathways in public metabolic databases.