Combining CRISPRi and metabolomics for functional annotation of compound libraries
Open accessAbstract
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.
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Most functional metabolomics studies rely on indirect measurements and correlation analyses, leading to ambiguity in the precise definition of functional metabolomics.
“However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics.”
From Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products (2024) · cites this paper as [15]
The Extended Data section discusses correlation-based similarity metrics and warns that indirect effects can bias them. But it does not say that most functional metabolomics studies rely on indirect measurements or that this creates ambiguity in the definition of functional metabolomics. (LLM, conf=0.75)
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- Authors
- Anglada-Girotto, M., Handschin, G., Ortmayr, K., Campos, A. I., Gillet, L., Manfredi, P., Mulholland, C. V., Berney, M., Jenal, U., Picotti, P., & Zampieri, M.
- Affiliations
- ETH Zurich (CH)
- University of Basel (CH)
- Albert Einstein College of Medicine (US)
- Corresponding author
- Zampieri, Mattia
- ORCID
- Venue
- Nature Chemical Biology
- Published
- 2022-02-22
- Volume
- 18
- Issue
- 5
- Pages
- 482-491
- ISSN
- 1552-4450 (ISSN-L)
- 1552-4469
- PMC
- PMC7612681
- OpenAlex
- W4213458101
- Cited by
- 60
- References
- 60
- Citations by year
- 2026: 13 · 2025: 7 · 2024: 18 · 2023: 15 · 2022: 7
- FWCI
- 3.84
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-04-08)
- Open access
- green
- edoc.unibas.ch
- Licence
- Locations
- Nature Chemical Biology (publishedVersion)
- PubMed (publishedVersion)
- edoc (University of Basel) (acceptedVersion, other-oa)
- PubMed Central (acceptedVersion)
- edoc (University of Basel)
- Funders
- National Institute of Allergy and Infectious Diseases (R01AI139465, R21AI133191, R01AI173328)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Bacterial Genetics and Biotechnology
- Keywords
- CRISPR interference, metabolomics, Escherichia coli, antibacterial mechanism, drug-induced metabolic changes, gene silencing, high-throughput screening
- MeSH terms
- Escherichia coli, Humans, Metabolomics, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems
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