Nathan Basisty Ph.D., is currently a tenure track Investigator at the NIA (NIH) and head of the Translational Geroproteomics Unit (TGU). He received his Ph.D., in Pathology and B.S. in Biochemistry from the University of Washington, where he investigated the role of protein turnover in mammalian aging and longevity using novel combinations of metabolic labeling, LC-MS/MS, and software tools. In 2015, he joined The Buck Institute for Research on Aging, where he did his postdoctoral fellowship in the labs of Dr. Birgit Schilling and Dr. Judith Campisi. There he developed novel and specialized proteomic approaches to understand aging processes and age-related diseases, including the application of data-independent acquisition (DIA) or SWATH workflows to identify and quantify PTMs and secretomes. Dr. Basisty has been recognized for his work investigating the role of protein turnover in mammalian aging and longevity using a combination of metabolic labeling, LC-MS/MS, and software tools.
Loss of protein homeostasis is a hallmark of aging and age-related conditions, including neurodegeneration, sarcopenia, and type 2 diabetes. However, alterations in markers of proteostasis machinery do not necessarily reflect rates of protein turnover. Therefore, methods to measure the turnover rates of proteins directly, rather than surrogate measurements of translation and degradation machinery, are critically needed to accurately examine the stability of the proteome during aging and disease processes. Conducting a protein turnover study in multicellular organisms in vivo remains very computationally complex and difficult for most scientists. The development of versatile computational tools on widely accessible, open-source platforms is needed to make this approach more user-friendly.
Read More In this presentation Dr Nathan Basisty introduces a new computational tool – TurnoveR – for the accurate calculation of protein turnover rates from mass spectrometry analysis of metabolic labeling experiments in the Skyline software platform. Using data generated from metabolic labeling of mice with heavy leucine in independent experiments, we demonstrate how this tool enables the calculation of protein turnover rates seamlessly within a Skyline workspace using raw data acquired on multiple mass spectrometric platforms and derive new biological insights into age-related diseases.
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