Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

Anand V. Sastry, Nicholas Dillon, Amitesh Anand, Saugat Poudel, Ying Hefner, Sibei Xu, Richard Szubin, Adam M. Feist, Victor Nizet, Bernhard Palssona*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available. IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
Issue number4
Publication statusPublished - 25 Aug 2021

Bibliographical note

Funding Information:
This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-05CH11231. This study was funded by the Novo Nordisk Foundation Center for Biosustainability and the Technical University of Denmark (NNF10CC1016517) and by the NIH NIAID (1-U01-AI124316). Nick Dillon was additionally supported by grant T32-NIH-5T32HD087978-05. We declare there are no competing interests.

Publisher Copyright:
© 2021. Sastry et al. This is an openaccess article distributed under the terms of the Creative Commons Attribution 4.0 International license.

Other keywords

  • antibiotics
  • independent component analysis
  • iron regulation
  • machine learning
  • RNA-seq
  • transcriptional regulation


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