Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

To address drug-resistant tuberculosis, the leading single-pathogen infectious killer, drugs with novel modes of action (MoAs) are urgently needed. Phenotypic screening of chemical libraries can identify antimicrobial compounds, but standard screens cannot reveal the MoA of hits, limiting targeted selection of compounds with novel MoAs. Here, we develop a deep learning (DL) model to screen drug-treated Corynebacterium glutamicum ( Cglu ), a surrogate for Mycobacterium tuberculosis . We train our DL model to distinguish between MoAs directly from high-throughput images. Our model robustly classifies MoAs of established antibiotics and recognizes the MoA of previously unseen antibiotics. Inhibitors with a previously unseen MoA cluster together and apart from reference drugs, enabling the detection of novel MoAs. Moreover, our model links images of chemical (drugs) and genetic (mutants) perturbations targeting similar pathways, supporting mutant-based target prediction of compounds with novel MoAs directly from images. Last, our DL model recovers known biological relationships from images alone using the Cglu cell cycle as a case study.

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Publication Details

Journal
Science Advances
Published
2026-09-25
DOI
https://doi.org/10.1126/sciadv.aeg6806
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
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article

Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

Pedro M. Alzari, Julienne Petit, Nathalie Aulner, Anne Marie Wehenkel et al.
Science Advances
Cell Image Analysis Techniques
article

Deep learning extracts MoA-specific signatures from high-throughput images of chemically and genetically perturbed Corynebacteria

Pedro M. Alzari, Julienne Petit, Nathalie Aulner, Anne Marie Wehenkel, Elodie Sadowski, Agnès Zettor, S. Petrella, Yves‐Marie Boudehen, Daniel Krentzel, Jeanne Chiaravalli, Alexandra Aubry, Christophe Zimmer, Nassim Mahtal
article en

Abstract

To address drug-resistant tuberculosis, the leading single-pathogen infectious killer, drugs with novel modes of action (MoAs) are urgently needed. Phenotypic screening of chemical libraries can identify antimicrobial compounds, but standard screens cannot reveal the MoA of hits, limiting targeted selection of compounds with novel MoAs. Here, we develop a deep learning (DL) model to screen drug-treated Corynebacterium glutamicum ( Cglu ), a surrogate for Mycobacterium tuberculosis . We train our DL model to distinguish between MoAs directly from high-throughput images. Our model robustly classifies MoAs of established antibiotics and recognizes the MoA of previously unseen antibiotics. Inhibitors with a previously unseen MoA cluster together and apart from reference drugs, enabling the detection of novel MoAs. Moreover, our model links images of chemical (drugs) and genetic (mutants) perturbations targeting similar pathways, supporting mutant-based target prediction of compounds with novel MoAs directly from images. Last, our DL model recovers known biological relationships from images alone using the Cglu cell cycle as a case study.

Science AdvancesVol. 12(39)
Centre National de la Recherche Scientifique (FR), Inserm (FR), Institut Pasteur (FR), Université Paris Cité (FR), University of Würzburg (DE), Sorbonne Université (FR), Assistance Publique – Hôpitaux de Paris (FR), Laboratoire National de Référence (MA), Centre d'Immunologie et des Maladies Infectieuses (FR), CNR de la Résistance aux Antibiotiques (FR), Chimie Biologique pour le Vivant (FR), Biologie Moléculaire Structurale et Processus Infectieux (FR)
Good health and well-being
Openalex Percentile: Top 13%
Cell Image Analysis Techniques
DOIOpen Access PDFOpenAlex
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