Souvik Bhattacharyya

Our multidisciplinary research combines genetics, molecular biology, evolution, systems biology, big data, and mathematical modeling to study how bacterial behaviors impact antimicrobial resistance (AMR). Bacteria are rapidly developing resistance to existing antimicrobials, depleting our arsenal of effective drugs. Therefore, it is critical to develop new antibiotics and slow down the evolution of AMR. Bacteria can become resistant genetically by acquiring new mutations or resistant genes. Additionally, there are underexplored transient bacterial behaviors that allow them to survive under very high concentrations of antibiotics. Working on these transient behaviors, we have made some fascinating discoveries. We uncovered the phenomenon of ‘necrosignaling,’ where bacterial cells killed by antibiotics release a danger signal that warns live cells. We also found that this transient behavior activates a genetic program to increase the mutation rate, thus accelerating AMR evolution. These findings have significant implications for understanding and combating AMR. Currently, we are working on several projects within this broad field: 1. Phenotypic Vulnerabilities: Since antibiotics target essential genes, the resistance mutations come at a fitness cost to bacteria, making them vulnerable to defects in other key functions. We use next-gen sequencing to identify these vulnerabilities in antibiotic-resistant strains. 2. Necrosignaling: We aim to identify necrosignals in other bacteria and its clinical significance. 3. Bacterial Memory: We recently discovered that bacteria can have a deterministic memory that increases their antibiotic survival. We aim to find its molecular mechanism and prevalence across different species. 4. Global Warming & AMR: We have found that surface moisture can impact mutation rates in surface-dwelling bacteria. The recent rise in global temperatures has indicated an increase in humidity and surface moisture. We aim to test if global warming-like fluctuations would increase mutation rates in bacteria.