A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

• Published in: eLife Vol. 11
• Main Authors: Timmons, James A, Anighoro, Andrew, Brogan, Robert J, Stahl, Jack, Wahlestedt, Claes, Farquhar, David Gordon, Taylor-King, Jake, Volmar, Claude-Henry, Kraus, William E, Phillips, Stuart M
• Format: Journal Article
• Language: English
• Published: England eLife Science Publications, Ltd 17.01.2022; eLife Sciences Publications Ltd; eLife Sciences Publications, Ltd
• Subjects: Adipose Tissue - metabolism; Analysis; Biomarkers; Biomarkers - metabolism; Biopsy; Cardiovascular diseases; Cell cycle; Computational and Systems Biology; Deep learning; Dementia; Dementia disorders; Diabetes; Diabetes mellitus; Diabetes therapy; Disease; Disease resistance; Drug Repositioning; drug repurposing; Drug therapy; Drugs; Epidermal growth factor; Exercise; Gene expression; Genes; Genetic research; Genomes; Genomics; Health aspects; Heart failure; Homeostasis; Humans; Infection; Insulin; insulin biology; Insulin Resistance; Kinases; Medical research; Medicine; Medicine, Experimental; Metabolic Diseases - drug therapy; Metabolic Diseases - genetics; Metabolic disorders; Metabolism; Muscles - metabolism; Protein binding; Protein-tyrosine kinase; Proteins; Rankings; Short Report; Therapeutic targets; Transcriptome; Transcriptomics; Type 2 diabetes; Tyrosine; Virus diseases; Canada; COVID-19; computational biology; systems biology; deep learning; Exercise; drug repurposing; Transcriptomics; insulin biology; medicine; Diabetes; human
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.68832

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.