Dr. Sonika Bhatnagar

Dr. Sonika Bhatnagar |Clyto Access

NSIT, India


Expertise: computational and structural aspects of protein-protein interactions as drug targets in cardiovascular disease and bacterial stress response.


Dr. Sonika Bhatnagar completed Ph.D. (Biophysics) from A.I.I.M.S., New Delhi. She specialized in molecular modeling and drug design during her post doctoral research. She joined as faculty at NSIT and was recipient of Innocentive (Eli Lilly) award for prioritization of drug targets against obesity. She currently works as Associate Professor in Division of Biological Sciences & Engineering.



Title: Integrative Omics for Identification of Therapeutic Targets and Prognostic Markers in Hyperlipidemia.


Lipids have an indispensable physiological role in metabolism and signaling. Hyperlipidemia (HL) is a condition featuring excessive lipids in the blood stream. Its is a rapidly growing problem worldwide and in India due to increase in sedentary life style, and poor nutrition. HL is the most fundamental lifestyle problem leading to the development of atherosclerosis, which later develops into cardiovascular disease. This requires mandatory lipid profile assessment for diagnosis of many CVDs. While separate lipid-protein and protein-protein interactions have been determined in HL, a system-wide view has been overlooked. In order to obtain an informatics driven view if all possible interactions in HL, a lipid- protein-protein interaction network (LPPIN) consisting of 4088 proteins, 67 lipids and 20,259 edges was constructed. The protein-protein interaction network was developed from pathway linker using transcriptomics data from HL patients while the lipid-protein interactions were mined using STITCH 4.0. The LPPIN provides a clear look at the positions of the lipids and proteins essential for pathogenesis of HL. It also localizes the current generation drug targets for HL. It also shows a path for the development of Alzheimer’s disease, cancer, diabetes and CVD from HL. Analysis of the integrated LPPIN revealed that cholesterol, diacylglycerol, phosphatidylinositol-bis- phosphate and inositol-triphosphate had the largest impact on central signaling nodes in the LPPIN. The Gastrin-CREB signaling pathway emerged as a novel pathway in the pathogenesis of HL. A rationalized approach was used for repurposing of approved HL drug targets and novel drug targets were proposed using a machine learning approach. Next, a domain crucial for lipid binding in HL was selected from the network. A three dimensional atomic model of this domain (SR1) was developed using standard homology modeling approach. Its stable native and lipid bound substrate has been modeled using molecular dynamics simulations in order to gain understanding of its structure and function. Protein clusters associated with CVD, cancer, Alzheimer& disease and type-2- diabetes were observed to be clustered in the network. The lipids associated with these clusters consisted of triacylglycerol, cholesterol, oleic acid, linoleic acid, arachidonic acid, palmitate, inositol triphosphate, inositol-1,4- bisphosphate and phosphatidinositol-4- phosphate. Separately, a comparison of lipid profiles in normolipidemia, hyperlipidemia and CVD from lipidomics studies was carried out to identify novel lipid biomarkers. Our analysis revealed that palmitoyl- lysophosphatidylcholine was decreased while free fatty acids and ceramides are maintained at are elevated in HL and CVD in comparison with normolipidemic profiles. Changes in lipid composition increased level of saturated diacylglycerol, triacylglycerol and phospholipids in HL and CVD. Also, CVD was associated with increased level of small chain fatty acids with low double bond content in triacylglycerol, cholesterylester and sphingomyelin. Our integrative omics approach lends novel insights and potential therapeutic benefit from genomic studies, protein interactions, lipidomic abundance and chemical interactions.


Related Conferences :

International Biotechnology and Pharmaceutical Industry Forum