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

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.

Whole blood transcriptomic profiling identifies molecular pathways related to cardiovascular mortality in heart failure

“…Finally, to explore the potential of our approach to identify new drug treatments, we utilized the CMap‐L1000v1 7 in vitro drug signature database (https://clue.io/) as previously described 14 . Briefly, to represent the prognosis‐related transcriptomics signature, we used the 60 most upregulated and 60 most down‐regulated genes that were also present in the CMap‐L1000v1 database.…”

“…Briefly, to represent the prognosis‐related transcriptomics signature, we used the 60 most upregulated and 60 most down‐regulated genes that were also present in the CMap‐L1000v1 database. Choice of 60/60 genes is empirical and informed by our recent work on insulin resistance drug repurposing 14 that yielded a drug list composed of ∼50% true‐positive drug classes and/or drug targets. The matching score was calculated by aggregating the transcriptional pattern across nine independent cell lines for >2500 compounds, many of which include Food and Drug Administration approved drugs 14 .…”

“…Finally, to explore the potential of our approach to identify new drug treatments, we utilized the CMap-L1000v1 7 in vitro drug signature database (https://clue.io/) as previously described. 14 Briefly, to represent the prognosis-related transcriptomics signature, we used the 60 most upregulated and 60 most down-regulated genes that were also present in the CMap-L1000v1 database. Choice of 60/60 genes is empirical and informed by our recent work on insulin resistance drug repurposing 14 that yielded a drug list composed of ∼50% true-positive drug classes and/or drug targets.…”

“…Beyond these individual proteins, use of transcriptome signatures represents a promising alternative strategy to discover therapeutics and pathways that can modulate diseases. 7,14,42 In fact, many of the protein targets of the 29 drugs that reversed the mortality-related transcriptome in vitro, are not regulated in patients, but rather represent members of the same pathways regulated in those that died (Figure 4). Notably, several positively acting drugs are known to target heart failure-related molecular processes (online supplementary Table S4), and several have in vivo support for their potential utility in CHF, including a monoamine oxidase inhibitor which can be cardioprotective, 43 forskolin which elevates cAMP and cardiac function, 44 and the experimental epigenetic drug, chaetocin (a histone lysine methyltransferase inhibitor), which appears to be cardioprotective.…”

“…The matching score was calculated by aggregating the transcriptional pattern across nine independent cell lines for >2500 compounds, many of which include Food and Drug Administration approved drugs. 14 For each matching drug, the known protein targets were identified using PubChem and the small molecule suite. 15 A network of significant pathway interactions was derived using metascape.org, using the CHF survival associated genes and the top ranked known protein target of each drug as input values, and the detectable transcriptome as background.…”