Evidence map›Paper›PMID 35194925›Full record

ArticleMolecular systems biology2022

High-throughput metabolomics predicts drug-target relationships for eukaryotic proteins.

Duncan Holbrook-Smith, Stephan Durot, Uwe Sauer

Open access · goldAbstract read
In one paragraph

Article in Molecular systems biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
4.1field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
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  7. Plant Metabolomics: The Future of Anticancer Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2024
    Review
  8. Review
  9. The metabolic domestication syndrome of budding yeast.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Duncan Holbrook-SmithInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.ORCID 0000-0003-0787-3165
Stephan DurotInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.ORCID 0000-0003-2307-7070
Uwe SauerInstitute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.ORCID 0000-0002-5923-0770
ETH Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

MetabolomicsSaccharomyces cerevisiaeMetabolomeProteinsSmall Molecule LibrariesProteinsSmall Molecule Librarieschemical screeningdrug-targetsmetabolomicsoverexpressionsignaling

Identifiers

PMID35194925
PMCPMC8864444
OpenAlexW4213458745

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.