Evidence map›Paper›PMID 35194207›Full record

ArticleNature chemical biology2022

Combining CRISPRi and metabolomics for functional annotation of compound libraries.

Miquel Anglada-Girotto, Gabriel Handschin, Karin Ortmayr, Adrian I Campos, Ludovic Gillet, Pablo Manfredi, Claire V Mulholland, Michael Berney, Urs Jenal, Paola Picotti and 1 more

Erratum issuedAbstract read
In one paragraph

Article in Nature chemical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

29 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  18. Chemical Composition of Commercial Cannabis.Journal of agricultural and food chemistry · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Miquel Anglada-Girotto *Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-1885-8649
Gabriel Handschin *Institute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
Karin OrtmayrInstitute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0002-0603-1073
Adrian I CamposInstitute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.ORCID http://orcid.org/0000-0003-3468-8619
Ludovic GilletInstitute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
Pablo ManfrediBiozentrum, University of Basel, Basel, Switzerland.
Claire V MulhollandDepartment of Microbiology and Immunology, Albert Einstein College of Medicine, New York, NY, USA.
Michael BerneyDepartment of Microbiology and Immunology, Albert Einstein College of Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0002-4833-0573
Urs JenalBiozentrum, University of Basel, Basel, Switzerland.ORCID http://orcid.org/0000-0002-1637-3376
Paola PicottiInstitute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland.
Mattia ZampieriInstitute of Molecular Systems Biology, Department of Biology, ETH Zurich, Zurich, Switzerland. zampieri@imsb.biol.ethz.ch.ORCID http://orcid.org/0000-0002-9339-068X

Funding

Eradicating persistent M. tuberculosis by synthetic lethality of terminal respiratory oxidasesR01AI139465 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI BERNEY, MICHAEL · 2019 to 2023
$3.2M
Identification of new inhibitors of essential functions in M. tuberculosis by high-throughput metabolic profilingR01AI173328 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE · PI Michael Berney · 2023 to 2026
$2.6M
Rapid screening for modes-of-action of M. tuberculosis inhibitors by high-throughput metabolic fingerprintingR21AI133191 · NIAID · ALBERT EINSTEIN COLLEGE OF MEDICINE, INC · PI BERNEY, MICHAEL · 2018 to 2019
$462k
European Research Council 866004NIAID NIH HHS R01 AI139465NIAID NIH HHS R01 AI173328NIAID NIH HHS R21 AI133191
6 · The paper itself

Abstract

Molecular profiling of small molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small-molecule direct targets and secondary effects. However, current profiling methods are limited in either the number of measurable parameters or throughput. Here we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from CRISPR interference (CRISPRi) with 352 genes in all major essential biological processes. Next, on the basis of the comparison of genetic changes with 1,342 drug-induced metabolic changes, we made de novo predictions of compound functionality and revealed antibacterials with unconventional modes of action (MoAs). We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality from bacteria to human cell lines.

Indexed as

Clustered Regularly Interspaced Short Palindromic RepeatsEscherichia coliCRISPR-Cas SystemsHumansMetabolomics

Identifiers

PMID35194207
PMCPMC7612681

What Socratic holds

Textmetadata
LicenceTDM
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 Socratic graph.