Date/Time
Date(s) - 12/02/2024
3:00 pm - 4:00 pm
Location
Communicore, C1-15
Abstract: The COVID-19 pandemic and other recent outbreaks such as Mpox and H1N1 have underscored the need for globally scalable early detection and continued surveillance of endemic and emerging infectious diseases and rapidly evaluating efficacy of novel vaccines and therapeutics. Current pathogen detection-based diagnostic and monitoring methods are complex, expensive and, critically, are limited by potential sampling biases that may occur over time relative to course of disease and can miss pathogens of low abundance. Monitoring host immune responses provides a simpler, amplified and longer-lasting readout of infection. Current serological assays, however, measure only the presence or titer of antibodies, are unable to distinguish current infection from pre-existing immunity, which is a critical concern, especially, in endemic regions.
In our recent work, we have discovered the remarkable ability of pathogen-specific antibody glycosylation to specifically mark active infections. We have shown that integrating this with biophysical and functional properties of antibodies (both Fab & Fc region – including antigen-specificity, isotype/subtype, Fc receptor and complement binding), using AI/machine-learning based methods, defines a multivariate, disease-state specific ‘antibody-omic’ signature. Measuring this signature for large sets of pathogen-specific antibodies, however, currently requires intractably large sample volumes, laborious sample preparation and expensive and complex analytical methods. To resolve this bottleneck, we have recently developed a sample-sparing antibody-omic assay as well as inexpensive multiplexed electronic and optical detection microchip formats for implementing this at the point-of-care (POC) for use in resource-poor settings.
Here, I will present our recent findings demonstrating the broad applicability of the above technologies to diagnosis, prognostic monitoring and defining underlying disease state in several infectious diseases including viral (COVID-19), mycobacterial (Tuberculosis) and helminth-mediated (Schistosomiasis) infections. Additionally, time permitting, I will also outline recent work in applying these techniques to monitoring of vaccine efficacy and longevity.
Bio:
Dr. Aniruddh Sarkar is an Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University. He received his B.Tech. and M.Tech. in Electrical Engineering from IIT Bombay and his Ph.D. in Electrical Engineering and Computer Science from MIT. Dr. Sarkar joined the faculty at Georgia Tech in 2019 after completing postdoctoral training at Harvard Medical School. His research program exploits microscale and nanoscale physical phenomena to develop technology for precision biology and medicine, with a specific focus on addressing healthcare disparities. Most recently, this has resulted in the discovery of a novel class of diagnostic and prognostic biomarkers for infectious diseases (e.g. Tuberculosis, COVID-19, NTDs) and methods for their inexpensive point-of-care detection. His work has been published in leading journals such as Cell, Science Translational Medicine and Nature Communications. Dr. Sarkar is recipient of the Bernie Marcus Early Career Professorship in Therapeutic Cell Characterization and Manufacturing.