Discovering metabolic disease gene interactions by correlated effects on cellular morphology

Yang Jiao, Umer Ahmed, M.F. Michelle Sim, Andrea Bejar, Xiaolan Zhang, M. Mesbah Uddin Talukder, Robert Rice, Jason Flannick, Anna I. Podgornaia, Dermot F. Reilly, Jesse M. Engreitz, Maria Kost-Alimova, Kate Hartland, Josep-Maria Mercader, Sara Georges, Vilas Wagh, Marija Tadin-Strapps, John G. Doench, J. Michael Edwardson, Justin J. Rochford, Evan D. Rosen, Amit R. Majithia

A plethora of disease relevant genetic loci have been identified by genetic association studies. Jiao and colleagues hypothesized that perturbing these loci/genes in adipocytes in vitro and assessing the effect on morphologic features would enable disease relevant functional annotation. Here, they demonstrate the utility of this approach in metabolic disease. They ablated 125 genes in human pre-adipocytes using CRISPR/CAS9 and profiled the effect on cellular morphology using morphologic similarity to identify mechanistic interactions between genes. They demonstrate that this morphometric approach is capable of surveying diverse cellular mechanisms by validating both a protein-protein interaction on the lipid droplet surface and a transcriptional regulatory interaction in the DNA.

Objective: Impaired expansion of peripheral fat contributes to the pathogenesis of insulin resistance and Type 2 Diabetes (T2D). We aimed to identify novel disease–gene interactions during adipocyte differentiation.

Methods: Genes in disease-associated loci for T2D, adiposity and insulin resistance were ranked according to expression in human adipocytes. The top 125 genes were ablated in human pre-adipocytes via CRISPR/CAS9 and the resulting cellular phenotypes quantified during adipocyte differentiation with high-content microscopy and automated image analysis. Morphometric measurements were extracted from all images and used to construct morphologic profiles for each gene.

Results: Over 107 morphometric measurements were obtained. Clustering of the morphologic profiles accross all genes revealed a group of 14 genes characterized by decreased lipid accumulation, and enriched for known lipodystrophy genes. For two lipodystrophy genes, BSCL2 and AGPAT2, sub-clusters with PLIN1 and CEBPA identifed by morphological similarity were validated by independent experiments as novel protein–protein and gene regulatory interactions.

Conclusions: A morphometric approach in adipocytes can resolve multiple cellular mechanisms for metabolic disease loci; this approach enables mechanistic interrogation of the hundreds of metabolic disease loci whose function still remains unknown.