High‐throughput metabolomics predicts drug–target relationships for eukaryotic proteins. Issue 2 (23rd February 2022)

• Record Type: Journal Article
• Authors: Holbrook‐Smith, Duncan; Durot, Stephan; Sauer, Uwe
• Is Part Of: Molecular systems biology. Volume 18:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-02-23
• Subjects: chemical screening -- drug–targets -- metabolomics -- overexpression -- signaling; Molecular biology -- Periodicals; Systems biology -- Periodicals; 572.8
• DOI: 10.15252/msb.202110767
• Languages: English
• ISSNs: 1744-4292

Abstract

Abstract: Chemical probes are important tools for understanding biological systems. However, because of the huge combinatorial space of targets and potential compounds, traditional chemical screens cannot be applied systematically to find probes for all possible druggable targets. Here, we demonstrate a novel concept for overcoming this challenge by leveraging high‐throughput metabolomics and overexpression to predict drug–target interactions. The metabolome profiles of yeast treated with 1, 280 compounds from a chemical library were collected and compared with those of inducible yeast membrane protein overexpression strains. By matching metabolome profiles, we predicted which small molecules targeted which signaling systems and recovered known interactions. Drug–target predictions were generated across the 86 genes studied, including for difficult to study membrane proteins. A subset of those predictions were tested and validated, including the novel targeting of GPR1 signaling by ibuprofen. These results demonstrate the feasibility of predicting drug–target relationships for eukaryotic proteins using high‐throughput metabolomics. Synopsis: High‐throughput metabolome profiling identifies potential drug‐target relationships by comparing drug‐treated and induced overexpression S. cerevisiae strains. The approach retrieves known as well as new potential drug‐gene relationships. High‐throughput metabolomics is used to profile the metabolomes of S. cerevisiae perturbed by induced overexpression of 80 genes, or treatment with 1, 280 drugs. Through the comparison of the metabolome profiles, known as well as new drug‐target relationships are detected. A new predicted relationship is that between the sugar sensor GPR1 and the drug ibuprofen, providing insight into the effect of the drug on filamentous growth. The presented approach is suitable for analyzing hard‐to‐study membrane proteins, and for performing genome‐scale studies. Abstract : High‐throughput metabolome profiling identifies potential drug‐target relationships by comparing drug‐treated and induced overexpression S. cerevisiae strains. The approach retrieves known as well as new potential drug‐gene relationships.