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Peer-reviewedCC BYNature communications

Deep-learning single-cell profiling uncovers interbacterial and drug-driven reshaping of pathogen virulence.

  • Bence Bognár1,2,3
  • Terézia Kovács1,2
  • Réka Muresan4
  • Ede Migh1
  • Tímea Balogh1
  • Nándor Milán Kovács1,2
  • Áron Somogyi4
  • Árpád Molnár1,2
  • Bálint Csörgő1
  • Csaba Pál1
  • Péter Horváth1,5,6,7
  • Szilvia Juhász8,9
1 more authors
  • Viktória Lázár10,11
11 affiliations
  1. 1Synthetic and Systems Biology Unit, Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary.
  2. 2HCEMM-BRC Pharmacodynamic Drug Interaction Research Group, Szeged, Hungary.
  3. 3Doctoral School of Biology, University of Szeged, Szeged, Hungary.
  4. 4HCEMM Cancer Microbiome Core Group, Szeged, Hungary.
  5. 5Single-Cell Technologies Ltd., Szeged, Hungary.
  6. 6Institute of AI for Health, Helmholtz Zentrum München, Neuherberg, Germany.
  7. 7Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.
  8. 8Synthetic and Systems Biology Unit, Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary. [email protected].
  9. 9HCEMM Cancer Microbiome Core Group, Szeged, Hungary. [email protected].
  10. 10Synthetic and Systems Biology Unit, Institute of Biochemistry, HUN-REN Biological Research Centre, Szeged, Hungary. [email protected].
  11. 11HCEMM-BRC Pharmacodynamic Drug Interaction Research Group, Szeged, Hungary. [email protected].
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Abstract

Bacterial invasion and genotoxicity are key contributors to infectious disease progression and carcinogenesis. Yet current methods do not quantify these processes systematically and at single-cell resolution, limiting our ability to explore how interplay of virulence factors and drug effects shape pathogenicity. Here, we present MALVINA, a high-throughput deep-learning workflow integrating host-pathogen co-culture and high-content imaging to jointly measure bacterial invasion and host DNA damage. Applied to Escherichia coli isolates and human colorectal epithelial cells, we demonstrate MALVINA's versatility by deciphering (1) strains with elevated invasive or genotoxic potential, (2) interbacterial modulation of virulence, (3) pro-virulent drug effects, (4) virulence-targeting drugs, and (5) bacterial potentiation of drug toxicity. MALVINA revealed strain-specific virulence profiles and colibactin-dependent suppression of competitors' invasion coupled with enhanced genotoxicity. It also enabled identification of drug-induced modulation of bacterial invasion and genotoxicity, offering a new perspective for virulence-aware drug profiling. Overall, MALVINA offers an adaptable framework for dissecting pathogen-host-drug interactions.

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Cite

BibTeXRIS
BibTeX
@article{bognar2026deeplearning,
  title = {{Deep-learning single-cell profiling uncovers interbacterial and drug-driven reshaping of pathogen virulence.}},
  author = {Bence Bognár and Terézia Kovács and Réka Muresan and Ede Migh and Tímea Balogh and Nándor Milán Kovács and Áron Somogyi and Árpád Molnár and Bálint Csörgő and Csaba Pál and Péter Horváth and Szilvia Juhász and Viktória Lázár},
  journal = {Nature communications},
  year = {2026},
  month = sep,
  doi = {10.1038/s41467-026-77989-w},
  url = {https://europepmc.org/article/MED/42957675},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Deep-learning single-cell profiling uncovers interbacterial and drug-driven reshaping of pathogen virulence.
AU  - Bence Bognár
AU  - Terézia Kovács
AU  - Réka Muresan
AU  - Ede Migh
AU  - Tímea Balogh
AU  - Nándor Milán Kovács
AU  - Áron Somogyi
AU  - Árpád Molnár
AU  - Bálint Csörgő
AU  - Csaba Pál
AU  - Péter Horváth
AU  - Szilvia Juhász
AU  - Viktória Lázár
PY  - 2026
DA  - 2026/09/29
JO  - Nature communications
DO  - 10.1038/s41467-026-77989-w
UR  - https://europepmc.org/article/MED/42957675
AB  - Bacterial invasion and genotoxicity are key contributors to infectious disease progression and carcinogenesis. Yet current methods do not quantify these processes systematically and at single-cell resolution, limiting our ability to explore how interplay of virulence factors and drug effects shape pathogenicity. Here, we present MALVINA, a high-throughput deep-learning workflow integrating host-pathogen co-culture and high-content imaging to jointly measure bacterial invasion and host DNA damage. Applied to Escherichia coli isolates and human colorectal epithelial cells, we demonstrate MALVINA's versatility by deciphering (1) strains with elevated invasive or genotoxic potential, (2) interbacterial modulation of virulence, (3) pro-virulent drug effects, (4) virulence-targeting drugs, and (5) bacterial potentiation of drug toxicity. MALVINA revealed strain-specific virulence profiles and colibactin-dependent suppression of competitors' invasion coupled with enhanced genotoxicity. It also enabled identification of drug-induced modulation of bacterial invasion and genotoxicity, offering a new perspective for virulence-aware drug profiling. Overall, MALVINA offers an adaptable framework for dissecting pathogen-host-drug interactions.
SN  - 2041-1723
ER  -