Ravi Pathak
Cancer Biologist/Scientific Director
FEATURED PROJECTS
I have generated a triple transgenic conditional knock out mouse model harboring a conditional Pten allele. The Pten gene is conditionally deleted in vivo from the lung epithelial cells using the doxycycline dependent Cre/Loxp approach. Using this model, I identified a leptin driven feed-forward signaling loop in the lung epithelial cells. Leptin mediated leptin/lepr gene expression, likely amplifying leptin signaling that may contribute to the pathogenesis and severity of lung diseases, resulting in poor clinical outcomes. The findings were published in the Journal of Biochemistry and recommended to F1000 prime for its impact and significance.
Chicken Egg Model - Hepatocellular Carcinoma
In this project, I developed an in ovo xenograft model of HCC that reliably produce growth of three-dimensional, vascularized tumors that histologically resemble undifferentiated Hepatocellular Carcinoma. The findings were published in the Journal of Visualized Experiment (JoVE). Based on this work, I further developed the technology to generate patient derived xenografts.
CANCER MODELS
One of the key focus areas of my research over the last five years has been the development of in vivo cancer models.
SYSTEMS BIOLOGY AND NETWORK ANALYSIS
One of my significant contributions as a researcher is the integration of omics, network, systems and computational biology data to differentially diagnose, interpret, and prognosticate diseases. Working closely with interdisciplinary teams, I have developed novel methods of pattern recognition, methods to functionally cluster and analyze high throughput proteomic/genomic data, and pipelines to correlate protein structure with function using novel network-based pipelines.
PATTERN RECOGNITION
Nitrate response at the plant level is mediated by the transcriptional regulation of several hundreds of genes, but the mechanism(s) of nitrate signaling is not known and no known response elements have been identified till date. Nitrate response element (NRE) was originally reported to be comprised of an Ag/cTCA core sequence motif preceded by a 7-bp AT rich region, based on promoter deletion analyses in nitrate and nitrite reductases from Arabidopsis thaliana and birch. Using a novel pattern recognition pipeline that I co-developed with my collaborators this work conclusively proved the randomness of previously proposed motifs. The findings were published in Molecular Genetics and Genomics, and Physiology and Molecular Biology of Plants.
NETWORK ANALYSIS AND FUNCTIONAL INTERACTOMES
Human diseases are often the result of ‘misfolded’ proteins, which often become toxic. Therefore the question of treating the misfolded proteins and studying disorderliness in protein structure is vital for drug designing. I have developed a computational pipeline using theories similar to social networking phenomena to study the impact of disorder on neighboring partners, and the biological network it features in. Using a novel approach to drug discovery that exploits systems and network biology at the structural, topological and functional level, I identified intrinsic disorder in the C-tail region of PTEN (a tumor suppressor genes) and several hub proteins in PTEN-driven molecular network implicated in human diseases as therapeutic targets. This finding has greatly enhanced the repertoire of clinically relevant biological targets for pharmacotherapy. More recently, I used this approach define intrinsically disordered regions (IDRs) in structurally hybrid kinases, which also revealed that 65% of kinases have an IDR adjacent to their kinase domain (KD). The findings reveal that spatially and evolutionarily conserved IDRs in kinases may influence their functions, which can be exploited for targeted therapies in diseases including those that involve aberrant cytoskeletal remodeling. The research based on this approach has been published in Nature Scientific Reports and Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics.
FUNCTIONAL GENOMICS
As a graduate student in India, my research focused on studying plant signal transduction and elucidating the molecular mechanisms of nitrate response in rice. Plant signaling is a less understood area and has major implications for fertilizer use and productivity. I set-up a plant molecular biology lab, and established high throughput microarray technology in my laboratory. Generating one of the earliest whole genome transcriptomic profiles of rice using microarrays, I identified the first genome-wide nitrate responsive gene expression pattern in a major crop plant under different nutrient and environmental stimulus. In addition to optimizing the experimental workflow, I also built the bioinformatics pipeline used to analyze the data. This was particularly challenging given that rice genome sequencing projects were not completed, and annotations were largely unavailable. My work laid the foundation for future characterization efforts, and the findings have been recently published in PLOS One, Plant Molecular Biology and Frontiers in Plant Science.