Speakers

Biography

Mamta Masand is a passionate Ph.D. research scholar working under the supervision of Dr. Ram Kumar Sharma at the CSIR Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh. She completed her M.Sc. in Bioinformatics from the Central University of Himachal Pradesh. She was awarded the prestigious ICMR-SRF direct fellowship in 2022 to support her Ph.D. studies. Her research is focused on the genetic improvement of Stevia rebaudiana, a plant known for its natural sweeteners. She employs transcriptomics, genome studies, and molecular docking studies to uncover and characterize the key regulators involved in the biosynthesis of desirable Steviol glycosides. Her work aims to improve the quality and sweetness of Stevia, addressing the challenge of its bitter aftertaste. She has published nine research articles and has presented her findings at an international conference.

Abstract

Sugar-rich diets and modern lifestyles contribute to various metabolic disorders. A widely adopted global strategy to reduce sugar intake is using low/no-calorie sweeteners (LNCSs). Among the various plant-derived LNCSs, Stevia rebaudiana is commercially popular due to its ability to accumulate more than 60 Steviol glycosides (SGs). However, the acceptability of these SGs is limited by their bitter aftertaste. Therefore, current breeding efforts focus on creating superior cultivars that increase the accumulation of desirable SGs while eliminating the bitter aftertaste. Gene co-expression network studies are valuable for uncovering gene correlations, identifying plausible candidate genes, and understanding the molecular mechanisms of complex traits. In this study, we first constructed the phased genome of a superior Stevia cultivar. We then predicted a genome-wide interlog protein-protein interaction (PPI) network (~12000 proteins with 1.2 lakh interactions). A high-confidence network, hc-StPIN, consisting of (~5,000 nodes with ~80,000 interactions), was predicted by combining interlog and domain-based approaches.

Furthermore, RNA sequencing data were used to construct a gene co-expression network (GCN) regulating SG biosynthesis. Overall, 30 co-expression modules were identified, with five modules pivotal in the targeted accumulation of desired SGs. The network's reliability was assessed, and significant hub proteins associated with SG biosynthesis were identified. This study enhances our understanding of the molecular networks regulating SGs and identifies key regulators promoting higher accumulation of desired SGs.