Welcome to the Molecular Biotechnology Lab led by Dr. Paul. Our research mission is to unravel the complex biological mechanisms underlying breast cancer initiation, progression, and treatment response. We focus particularly on the interplay between host genetics, the microbiome, and molecular pathways regulating cancer development. Our approach is inherently interdisciplinary, integrating cutting-edge techniques in molecular biology, microbiology, genomics, and bioinformatics with advanced data science methodologies to generate novel insights and explore potential therapeutic strategies.
Our work currently focuses on several key areas:
Microbiome Interactions in Breast Cancer: We investigate how specific bacterial pathogens, particularly those originating from the oral cavity like Fusobacterium nucleatum, may influence breast cancer progression, metastasis, and patient outcomes. This involves studying bacterial virulence factors, their impact on the tumor microenvironment and host immune system using a combination of microbiology, molecular assays, genomic analysis, and bioinformatic/data science approaches to interpret complex datasets.
SNP Genotyping and Cancer Susceptibility: We perform Single Nucleotide Polymorphism (SNP) genotyping to identify genetic variants, such as those in the CYP19A1 (aromatase) gene, that may be linked to breast cancer susceptibility or predict response to specific therapies. This research aims to contribute to personalized medicine approaches.
Computer-Aided Drug Design (CADD) for Novel Inhibitors: Leveraging computational tools, we focus on designing and evaluating potential inhibitors for critical enzymes involved in estrogen synthesis, specifically Aromatase and 17-beta-Hydroxysteroid Dehydrogenase (17β-HSD). We primarily explore phytocompounds (plant-derived compounds) sourced from medicinal plants like Swietenia macrophylla and Centella asiatica, aiming to identify safer, potentially plant-based therapeutic options for hormone-sensitive breast cancer. This involves in silico screening, molecular docking, dynamics simulations, and potential subsequent in vitro validation.
We actively apply data science, machine learning, and bioinformatics techniques to address specific biomedical questions, often in collaboration with clinical experts.
Predicting Diabetic Retinopathy Biomarkers: Developing an ML model using blood parameters. (Collaboration: Dr. Kaviarasan, SRIHER)
Hepatitis B Clinical Classification (ML): Classifying acute vs. chronic cases using clinical and laboratory data. (Collaboration: Dr. John Fletcher, CMC Vellore)
Hepatitis B Mutation Analysis (ML): Identifying mutations associated with acute/chronic Hepatitis B status. (Collaboration: Dr. John Fletcher, CMC Vellore)
Hepatitis B Core Antigen Antibody Profiling: Analyzed antibody responses in acute and chronic Hepatitis B cases/flares. (Collaboration: Dr. John Fletcher, CMC Vellore) View Analysis on GitHub
Breast Cancer Microbiome Analysis: Investigated the association of Fusobacterium nucleatum with breast cancer progression using patient data. (Collaboration: Dr. Manickavasagam M, SRIHER) View Microbiome Analysis on GitHub | Kaggle | (Note: Presented by PhD Scholar Kiruthika Vijayakumar, awarded 2nd prize at Rencontres de Quy Nhon VII, 2024 - [Presentation], [Abstract])
Breast Cancer Data Analysis: Analyzed public Kaggle dataset exploring age, hormone status, and methylation patterns. (Google Data Analytics Professional Certificate Capstone) View Analysis on GitHub | Kaggle
Enterococcus faecalis Virulence Gene Analysis: Assessed the incidence of virulence genes in dental isolates. (Collaboration: Dr. Anbarasi K, SRIHER) View Analysis on GitHub
Mallur, Dhrithi Jayasimha, B. Lavanya, Sheshadri S. Temkar, V. Arun, and Benedict C. Paul. "Exploring okra-derived compounds as prospective aromatase inhibitors: a computational study for enhanced breast cancer therapy." Journal of Biomolecular Structure and Dynamics (2024): 1-9.
Temkar, Sheshadri S., Amruta Sridhara, Dhrithi Jayasimha Mallur, Deepak Ishwara Shivaprakash, Divya Iyengar, Nritam Das, and Benedict Paul C. 2024. “Potential Aromatase Inhibitors from Centella Asiatica with Non-Synonymous SNPS - A Computational Approach.” Current Computer-Aided Drug Design 20 (4): 341–58. doi: 10.2174/1573409919666230525151933. PMID: 37231718.
For a full list of publications, click here.