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

James A Timmons,Claude-Henry Volmar,Robert J Brogan,Andrew Anighoro,Stuart M Phillips,David Gordon Farquhar,Jake Taylor-King,William E Kraus,Jack Stahl,Claes Wahlestedt

doi:10.7554/elife.68832

Abstract

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.

Highlights

Systemic insulin resistance (IR) is a multi-organ pathophysiological state and an early characteristic of type 2 diabetes mellitus (T2DM)
Biomarkers were ranked based on consistent direction and strength of association across two major human organs targeted by insulin because most orally dosed drugs will act systemically
We illustrate that a cell-line transcriptome-b ased high-throughput Drug repurposing (DR) assay yields interpretable and quantitative pharmacological data when designed around robust clinical RNA signatures, and that deep learning (DL)-b ased drug target predictions can be used to interpret assay scores

Summary

Introduction

Systemic insulin resistance (IR) is a multi-organ pathophysiological state and an early characteristic of type 2 diabetes mellitus (T2DM). IR contributes to the pathobiology of neurodegeneration (Norambuena et al, 2017), heart failure (Wamil et al, 2021) and viral infections, such as COVID-19 (Ceriello et al, 2020; Donath, 2021). Several T2DM drug treatments indirectly reduce IR following improved metabolic homeostasis, making them candidate treatments for various diseases (Donath, 2021; Everett et al, 2018; Norambuena et al, 2017).

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