Computational Analysis of -omics Data to Identify Transcription Factors Regulating Secondary Metabolism in Rauvolfia serpentina

Rauvolfia serpentina has been known to produce therapeutically important indole alkaloids used in treatment of various diseases. Despite its medicinal importance, complete understanding of its secondary metabolism is challenging due to complex interplay among various transcription factors (TFs) and genes. However, weighted co-expression analysis of transcriptome along with integration of metabolomics data has proficiency to elucidate topological properties of complex regulatory interactions in secondary metabolism. We aimed to implement an integrative strategy using -omics data to identify TFs of unknown function and exemplify their role in regulation of valuable metabolites as well as metabolic traits. A total of 69 TFs were identified through significant thresholds and removal of false positives based on cis-regulatory motif analysis. Network-biology inspired analysis of co-expression network lead to generation of four statistically significant and biologically robust modules. Similar to known regulatory roles of WRKY and AP2-EREBP TF families in Catharanthus roseus, this study presented them to regulate synthesis of alkaloids in R. serpentina as well. Moreover, TFs in module 4 were observed to be regulating connecting steps between primary and secondary metabolic pathways in the synthesis of terpenoid indole alkaloids. Integration of metabolomics data further highlight the significance of module 1 since it was statistically predicted to be involved in synthesis of specialized metabolites, and associated genes may physically clustered on genome. Importantly, putative TFs in module 1 may modulate the major indole alkaloids synthesis in response to various environmental stimuli. The methodology implemented herein may provide a better reference to identify and explore functions of transcriptional regulators.