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Mol Metab. 2019 Jun;24:108-119. doi: 10.1016/j.molmet.2019.03.001. Epub 2019 Mar 13.

Discovering metabolic disease gene interactions by correlated effects on cellular morphology.

Author information

1
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
2
University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK.
3
Department of Pharmacology, University of Cambridge, Cambridge CB2 1PD, UK.
4
Genetics and Pharmacogenomics, Merck & Co., Inc., Boston, MA 02115, USA.
5
University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, CB2 0QQ, UK; Rowett Institute and the Aberdeen Cardiovascular and Diabetes Centre, University of Aberdeen, Foresterhill, Aberdeen AB25 2ZD, UK.
6
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Endocrinology, Diabetes and Obesity, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA; Harvard Medical School, Department of Genetics, Boston, MA 02215, USA.
7
Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Endocrinology, Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA. Electronic address: amajithia@ucsd.edu.

Abstract

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.

KEYWORDS:

Functional genomics; Gene discovery; Genetic screen; High content imaging; Insulin resistance; Lipodystrophy; Metabolic syndrome; Type 2 diabetes

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