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RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection

Author

Listed:
  • Kemal Avican

    (Umeå University)

  • Jehad Aldahdooh

    (University of Helsinki
    University of Helsinki)

  • Matteo Togninalli

    (ETH Zürich
    Swiss Institute for Bioinformatics)

  • A. K. M. Firoj Mahmud

    (Umeå University)

  • Jing Tang

    (University of Helsinki
    University of Helsinki)

  • Karsten M. Borgwardt

    (ETH Zürich
    Swiss Institute for Bioinformatics)

  • Mikael Rhen

    (Karolinska Institute)

  • Maria Fällman

    (Umeå University)

Abstract

Bacterial processes necessary for adaption to stressful host environments are potential targets for new antimicrobials. Here, we report large-scale transcriptomic analyses of 32 human bacterial pathogens grown under 11 stress conditions mimicking human host environments. The potential relevance of the in vitro stress conditions and responses is supported by comparisons with available in vivo transcriptomes of clinically important pathogens. Calculation of a probability score enables comparative cross-microbial analyses of the stress responses, revealing common and unique regulatory responses to different stresses, as well as overlapping processes participating in different stress responses. We identify conserved and species-specific ‘universal stress responders’, that is, genes showing altered expression in multiple stress conditions. Non-coding RNAs are involved in a substantial proportion of the responses. The data are collected in a freely available, interactive online resource (PATHOgenex).

Suggested Citation

  • Kemal Avican & Jehad Aldahdooh & Matteo Togninalli & A. K. M. Firoj Mahmud & Jing Tang & Karsten M. Borgwardt & Mikael Rhen & Maria Fällman, 2021. "RNA atlas of human bacterial pathogens uncovers stress dynamics linked to infection," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23588-w
    DOI: 10.1038/s41467-021-23588-w
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    Cited by:

    1. Amir Bar & Liron Argaman & Michal Eldar & Hanah Margalit, 2023. "TRS: a method for determining transcript termini from RNAtag-seq sequencing data," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

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