Guanshi Zhang

Dr. Guanshi Zhang is an Assistant Professor at the University of Texas Health Science Center at San Antonio (UTHSCSA), Department of Medicine, Division of Nephrology. He serves as Co-Director of the San Antonio Program for Undergraduate Research in Renal Science (SPURRS) and is actively seeking M.S. and Ph.D. students for his research program. Dr. Zhang is affiliated with the Center for Renal Precision Medicine, where he conducts cutting-edge research at the intersection of metabolomics, mitochondrial biology, and kidney disease.

Dr. Zhang's research focuses on understanding how hyperglycemia and aging lead to cellular dysregulation in different organ systems, with particular emphasis on diabetic complications and aging-associated diseases. His laboratory employs advanced techniques including mass spectrometry imaging-based spatial metabolomics and lipidomics to investigate metabolic alterations in both human kidney disease and veterinary models. Early in his career, Dr. Zhang was trained in metabolomics, and during his postdoctoral training at the University of California San Diego, he expanded his expertise to mitochondrial biology of diabetic complications.

His publication record demonstrates a dual focus: one strand of research examines diabetic kidney disease and metabolic alterations in human renal pathology, while another investigates metabolic biomarkers for diseases in dairy cows such as ketosis, mastitis, and lameness. His recent work has identified novel biomarkers and metabolic pathways involved in kidney injury, with particular attention to adenine metabolism, mitochondrial dysfunction, and spatial metabolomics in renal tissue. Dr. Zhang has made significant contributions to major collaborative efforts including the Kidney Precision Medicine Project.

Dr. Zhang has contributed significantly to methodological advancements in mass spectrometry imaging, particularly regarding sample preparation and analysis reproducibility. His technical publications address critical issues that affect the reliability of spatial metabolomics data, providing essential protocols for the research community.