Combining CRISPRi and metabolomics for functional annotation of compound libraries

Area of Science:

• Microbiology
• Metabolomics
• Systems Biology

Background:

• Molecular profiling of small molecules is crucial for understanding compound function and targets.
• Current profiling methods face limitations in measurable parameters and throughput.
• A need exists for high-throughput, unbiased frameworks for compound functional annotation.

Purpose of the Study:

• To develop a multiplexed, unbiased framework for high-throughput functional annotation of compound libraries.
• To link genetic perturbations to metabolic changes for predicting compound functionality.
• To identify antibacterials with unconventional modes of action.

Main Methods:

• Generated a reference map of metabolic changes using CRISPR interference (CRISPRi) targeting 352 genes in Escherichia coli.
• Linked genetic-induced metabolic changes with 1,342 drug-induced metabolic changes.
• Employed a computational framework for de novo prediction of compound functionality.

Main Results:

• Successfully created a high-throughput framework for annotating compound libraries.
• Identified antibacterials with novel and unconventional modes of action.
• Demonstrated the framework's adaptability for diverse biological systems.

Conclusions:

• The developed framework enables high-throughput functional annotation of small molecules by linking genetic and metabolic data.
• This approach facilitates the discovery of compounds with new mechanisms of action.
• The strategy is adaptable for analyzing compound functionality across different organisms, including human cell lines.