David Buchner earned his Ph.D. in Human Genetics at the University of Michigan in Miriam Meisler's lab, where he identified and characterized a modifier gene for neurological disease. He completed postdoctoral training with David Ginsburg (HHMI, University of Michigan) and Joe Nadeau (Case Western Reserve University), using animal models to discover novel genes underlying bleeding disorders and metabolic disease. As an Assistant Research Scientist in Alan Saltiel's lab at the University of Michigan’s Life Sciences Institute, he studied the pathophysiology of obesity and type 2 diabetes. David joined the faculty of Case Western Reserve University’s School of Medicine in 2013 and is currently as Associate Professor in the Department of Genetics and Genome Sciences, with secondary appointments in Biochemistry and the Research Institute for Children's Health.
Our lab is interested in identifying genes that underlie endocrine disorders ranging from rare monogenic diseases to complex polygenic traits. We also seek to understand the cellular mechanisms underlying these disorders, with a particular focus on insulin signaling and glucose regulation in adipocytes.
Research Information
Research Interests
Our lab investigates the mechanisms that drive metabolic disease, integrating molecular studies of adipocyte biology and nuclear receptor signaling with systems genetics of complex traits. We focus on how gene regulation, sex differences, and gene-gene interactions shape obesity, diabetes, and related cardiometabolic phenotypes, using multi-omics, computational methods, and complementary animal models. Ultimately, we aim to connect variants to pathways to improve risk prediction and identify targets for precision prevention and therapy.
Research Projects
1) Adipocyte Biology: Pathways to Obesity and Type 2 Diabetes
We study how gene regulation and nuclear receptor signaling govern adipocyte development and function, and how these processes shape metabolic health in obesity and type 2 diabetes. Adipocytes normally protect against metabolic disease by safely storing lipids and secreting adipokines; when adipose tissue fails to expand and remodel appropriately, ectopic fat, inflammation, and insulin resistance emerge. Using integrated genomics, molecular biology, and mouse models, we map pathways that control adipose tissue plasticity and systemic glucose homeostasis. Our goal is to translate these mechanisms into strategies that restore adipocyte health and prevent metabolic disease.
Representative publications:
Xu X, Charrier A, Congrove S, Ockunzzi J, Buchner DA. (2024)
Cell-state-dependent regulation of PPARγ signaling by the transcription factor ZBTB9 in adipocytes. Journal of Biological Chemistry. 300(12): 107985 PMCID: PMC11681874
Charrier A, Xu X, Guan BJ, Ngo J, Wynshaw-Boris A, Hatzoglou M, Buchner DA. (2021)
Adipocyte-specific deletion of zinc finger protein 407 results in lipodystrophy and insulin resistance in mice. Molecular and Cellular Endocrinology; 521:111109. PMCID: PMC7813145
2) System Genetics of Metabolic Health
We study the genetic architecture of complex traits with an emphasis on metabolic disease, examining how sex differences and gene-gene interactions shape obesity, diabetes, and related cardiometabolic phenotypes. Using large-scale human datasets and complementary mouse models, we map additive and epistatic effects and develop sex-stratified, interaction-aware methods to detect polygenic signals beyond traditional thresholds. By integrating statistical genetics with functional genomics, we link variants to mechanisms in metabolic tissues to improve risk prediction and guide precision medicine.
Representative publications:
Miller AK, Bartlett J, Pan C, Lusis AJ, Crawford DC, Williams SM, Buchner DA. (2025)
Concordance between male- and female-specific GWAS results helps define underlying genetic architecture of complex traits. Nature Communications; 16(1): 8696. PMCID: PMC12485208
Chen A, Liu Y, Williams SM, Morris N, Buchner DA. (2017)
Widespread epistasis regulates glucose homeostasis and gene expression. PLOS Genetics. 13:e1007025. PMCID: PMC5636166