Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

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

doi:10.1128/msphere.00443-21

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

Journal Article · Wed Aug 25 00:00:00 EDT 2021 · mSphere

DOI:https://doi.org/10.1128/msphere.00443-21· OSTI ID:2469714

Sastry, Anand V. ^[1]; ^[2]; Anand, Amitesh ^[1]; Poudel, Saugat ^[1]; Hefner, Ying ^[1]; Xu, Sibei ^[1]; Szubin, Richard ^[1]; Feist, Adam M. ^[3]; Nizet, Victor ^[1]; ^[3]

University of California, San Diego, La Jolla, CA (United States)
University of California, San Diego, La Jolla, CA (United States); University of Texas, Richardson, Dallas, TX (United States)
University of California, San Diego, La Jolla, CA (United States); Technical University of Denmark, Lyngby (Denmark)

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.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities (SUF); Novo Nordisk Foundation; National Institutes of Health (NIH)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 2469714

Journal Information:: mSphere, Journal Name: mSphere Journal Issue: 4 Vol. 6; ISSN 2379-5042

Publisher:: American Society for MicrobiologyCopyright Statement

Country of Publication:: United States

Language:: English

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59 BASIC BIOLOGICAL SCIENCES
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Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

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