Fantastic Venoms... And Where to Find Them - Mass Spectrometry Imaging of Venom Systems to Decipher Evolutionary Biology of Toxins for Spatial Differentiation

Venoms, honed through millions of years, offer potent adaptations for predation and defense secreted by highly specialized glandular systems. Despite extensive research, there is still limited knowledge of the functional biology of animal toxins, including their venom production and storage. Major aspect is convergent evolution, where disparate species develop analogous venom components, providing insights into selective pressures shaping these biological arsenals. Venom proteomics, at the nexus of molecular biology and evolution, investigates the intricate compositions of venoms across species and underscores the evolutionary importance of venomous adaptations and highlights mass spectrometry (MS) as a key innovation for unraveling venom complexities. MS imaging (MSI), a cutting edge technology, stands out for its ability to provide spatially resolved information to map toxin distribution within organisms and reveal nuances in venom production and secretion dynamics. Comparative MSI venom analyses can elucidate evolutionary relationships between species, providing a molecular perspective on phylogenetic connections. In conclusion, venomics in combination with MSI, transforms our understanding of evolutionary venom significance. This research not only enriches our insights into species evolution but also holds promise for drug development, showcasing the broader implications of this interdisciplinary approach in biology and medicine.

Our research is focused on the development of new mass spectrometry (MS) tools to discover biodiverse natural toxins and systematically determine the role of specific biomolecules in different types of pathogenesis. I have a broad background in biological chemistry, with specific training and expertise in untargeted proteomics and metabolomics analyses to gain a profound understanding of disease mechanisms on the molecular level. As postdoctoral fellow, I laid the groundwork for the proposed research by developing a robust workflow to spatially localize molecular alterations and identify proteomic-derived predictive classifier. The current application builds logically on my prior work and expands our research work to implement novel MS-based techniques towards individual precision medicine. I directly applied state-of-the-art MS imaging methods in the context of natural toxins, but also computational methods for effective downstream analysis for treatment classification and resistance prediction.