bioRxivGeneticsPreprint
A dish-to-biobank framework links β-cell nutrient-stress programs to genetic and dietary risk for Type 2 Diabetes
Xianming Wang, Hanbyul Lee, Ann Le, Berk TurhaniD, Nan Hu, Pretty S. Garcia, Xuewei CaoiD, D K Liu, Thahmina A. AliiD, Nan ZhangiD, Breanna Williams, Caleb A. LareauiD, G M WangiD, Danwei HuangfuiD, Kushal K. DeyiD
Abstract
Type 2 diabetes (T2D) arises from genetic susceptibility and chronic metabolic stress, but whether these converge on shared molecular programs in human populations remains unclear. Here, we develop a dish-to-biobank framework linking controlled {beta}-cell perturbation to population-scale disease genetics through the circulating plasma proteome, and apply it to T2D. scRNA-seq of human stem cell-derived islets under factorial glucose and palmitate exposure identifies their combination (glucolipotoxicity) as the condition eliciting the strongest SC-{beta} cell transcriptional response, with glucolipotoxicity-upregulated genes uniquely enriched for T2D heritability, monogenic diabetes genes, and rare-variant burden signals. CRISPR knockout of {beta}-cell identity regulators PAX6 and PDX1 aligns with this program, establishing convergence of environmental and genetic perturbations on a shared disease-relevant state. We then used the plasma proteome as an accessible population-scale readout of these experimentally defined {beta}-cell stress programs, scoring 45,956 UK Biobank White British participants. We define heritable stress signatures that associate with refined carbohydrate and saturated fat intake, and undergo trans-tissue genetic regulation, with a subset of variants showing diet-dependent effects. Together, these findings establish glucolipotoxicity as a genetically anchored model of {beta}-cell dysfunction and provide a generalizable framework for linking controlled cellular perturbations to human disease genetics at population scale.
- organism: human
- assay: scRNA-seq
- assay: proteomics
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: database / resource
- approach: statistical model
- approach: experimental assay
- Citations
- 0
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