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

doi:10.1016/j.molmet.2019.03.001

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. RochfordEvan D. Rosen, Amit R. Majithia (Corresponding Author)

The Rowett Institute of Nutrition and Health

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Pages (from-to)	108-119
Number of pages	12
Journal	Molecular Metabolism
Volume	24
Early online date	13 Mar 2019
DOIs	https://doi.org/10.1016/j.molmet.2019.03.001
Publication status	Published - Jun 2019

Bibliographical note

Supported by grants from the National Institute of Diabetes, Digestive, and Kidney Diseases (1K08DK102877-01 to Dr. Majithia), the Merck-Broad adipocyte collaboration (to Dr. Majithia and Dr. Rosen), the Medical Research Council (MR/L002620/1 to Dr. Rochford), the Biotechnology and Biological Sciences Research Council (BB/K017772/1 to Dr. Rochford), a Merit Scholarship from the Islamic Development Bank (to Dr. Talukder) and by the Agency for Science, Technology and Research, Singapore (to Dr. Sim).

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Discovering metabolic disease gene interactions by correlated effects on cellular morphology
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
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Cite this

Jiao, Y., Ahmed, U., Sim, M. F. M., Bejar, A., Zhang, X., Talukder, M. M. U., Rice, R., Flannick, J., Podgornaia, A. I., Reilly, D. F., Engreitz, J. M., Kost-Alimova, M., Hartland, K., Mercader, J.-M., Georges, S., Wagh, V., Tadin-Strapps, M., Doench, J. G., Edwardson, J. M., ... Majithia, A. R. (2019). Discovering metabolic disease gene interactions by correlated effects on cellular morphology. Molecular Metabolism, 24, 108-119. https://doi.org/10.1016/j.molmet.2019.03.001

Jiao, Y, Ahmed, U, Sim, MFM, Bejar, A, Zhang, X, Talukder, MMU, Rice, R, Flannick, J, Podgornaia, AI, Reilly, DF, Engreitz, JM, Kost-Alimova, M, Hartland, K, Mercader, J-M, Georges, S, Wagh, V, Tadin-Strapps, M, Doench, JG, Edwardson, JM, Rochford, JJ, Rosen, ED & Majithia, AR 2019, 'Discovering metabolic disease gene interactions by correlated effects on cellular morphology', Molecular Metabolism, vol. 24, pp. 108-119. https://doi.org/10.1016/j.molmet.2019.03.001

@article{d4612d35593744099ec9dc649ae9c449,

title = "Discovering metabolic disease gene interactions by correlated effects on cellular morphology",

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

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

note = "Supported by grants from the National Institute of Diabetes, Digestive, and Kidney Diseases (1K08DK102877-01 to Dr. Majithia), the Merck-Broad adipocyte collaboration (to Dr. Majithia and Dr. Rosen), the Medical Research Council (MR/L002620/1 to Dr. Rochford), the Biotechnology and Biological Sciences Research Council (BB/K017772/1 to Dr. Rochford), a Merit Scholarship from the Islamic Development Bank (to Dr. Talukder) and by the Agency for Science, Technology and Research, Singapore (to Dr. Sim). ",

year = "2019",

month = jun,

doi = "10.1016/j.molmet.2019.03.001",

language = "English",

volume = "24",

pages = "108--119",

journal = "Molecular Metabolism",

issn = "2212-8778",

publisher = "Elsevier GmbH",

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T1 - Discovering metabolic disease gene interactions by correlated effects on cellular morphology

AU - Jiao, Yang

AU - Ahmed, Umer

AU - Sim, M.F. Michelle

AU - Bejar, Andrea

AU - Zhang, Xiaolan

AU - Talukder, M. Mesbah Uddin

AU - Rice, Robert

AU - Flannick, Jason

AU - Podgornaia, Anna I.

AU - Reilly, Dermot F.

AU - Engreitz, Jesse M.

AU - Kost-Alimova, Maria

AU - Hartland, Kate

AU - Mercader, Josep-Maria

AU - Georges, Sara

AU - Wagh, Vilas

AU - Tadin-Strapps, Marija

AU - Doench, John G.

AU - Edwardson, J. Michael

AU - Rochford, Justin J.

AU - Rosen, Evan D.

AU - Majithia, Amit R.

N1 - Supported by grants from the National Institute of Diabetes, Digestive, and Kidney Diseases (1K08DK102877-01 to Dr. Majithia), the Merck-Broad adipocyte collaboration (to Dr. Majithia and Dr. Rosen), the Medical Research Council (MR/L002620/1 to Dr. Rochford), the Biotechnology and Biological Sciences Research Council (BB/K017772/1 to Dr. Rochford), a Merit Scholarship from the Islamic Development Bank (to Dr. Talukder) and by the Agency for Science, Technology and Research, Singapore (to Dr. Sim).

PY - 2019/6

Y1 - 2019/6

N2 - 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.

AB - 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.

KW - Gene discovery

KW - Functional genomics

KW - Metabolic syndrome

KW - Insulin resistance

KW - Type 2 diabetes

KW - Genetic screen

KW - High content imaging

KW - Lipodystrophy

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Discovering metabolic disease gene interactions by correlated effects on cellular morphology

Abstract

Bibliographical note

Keywords

UN SDGs

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Justin Rochford

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Discovering metabolic disease gene interactions by correlated effects on cellular morphology

Abstract

Bibliographical note

Keywords

UN SDGs

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Fingerprint

Profiles

Justin Rochford

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