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Mapping single-cell responses to population-level dynamics during antibiotic treatment

View ORCID ProfileKyeri Kim, View ORCID ProfileTeng Wang, View ORCID ProfileHelena R. Ma, View ORCID ProfileEmrah Şimşek, View ORCID ProfileBoyan Li, View ORCID ProfileVirgile Andreani, View ORCID ProfileLingchong You
doi: https://doi.org/10.1101/2022.11.18.517151
Kyeri Kim
1Department of Biomedical Engineering, Duke University, USA
2Center for Quantitative Biodesign, Duke University, USA
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Teng Wang
1Department of Biomedical Engineering, Duke University, USA
2Center for Quantitative Biodesign, Duke University, USA
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  • ORCID record for Teng Wang
Helena R. Ma
1Department of Biomedical Engineering, Duke University, USA
2Center for Quantitative Biodesign, Duke University, USA
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Emrah Şimşek
1Department of Biomedical Engineering, Duke University, USA
2Center for Quantitative Biodesign, Duke University, USA
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Boyan Li
3Integrated Science Program, Yuanpei College, Peking University, China
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Virgile Andreani
4Biomedical Engineering Department, Boston University, USA
5Biological Design Center, Boston University, USA
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Lingchong You
1Department of Biomedical Engineering, Duke University, USA
2Center for Quantitative Biodesign, Duke University, USA
6Center for Genomic and Computational Biology, Duke University, USA
7Department of Molecular Genetics and Microbiology, Duke University School of Medicine, USA
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  • For correspondence: you@duke.edu
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Abstract

Treatment of sensitive bacteria with beta-lactam antibiotics often leads to two salient population-level features: a transient increase in total population biomass before a subsequent decline, and a linear correlation between growth and killing rates. However, it remains unclear how these population-level responses emerge from collective single-cell responses. During beta-lactam treatment, it is well recognized that individual cells often exhibit varying degrees of filamentation before lysis. We show that the probability of cell lysis increases with the extent of filamentation and that this dependence is characterized by unique parameters that are specific to bacterial strain, antibiotic dose, and growth condition. Modeling demonstrates how the single-cell lysis probabilities can give rise to population-level biomass dynamics, which were experimentally validated. This mapping provides insights into how the population biomass time-kill curve emerges from single cells and allows the representation of both single-and population-level responses with universal parameters.

Competing Interest Statement

The authors have declared no competing interest.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 19, 2022.
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Mapping single-cell responses to population-level dynamics during antibiotic treatment
Kyeri Kim, Teng Wang, Helena R. Ma, Emrah Şimşek, Boyan Li, Virgile Andreani, Lingchong You
bioRxiv 2022.11.18.517151; doi: https://doi.org/10.1101/2022.11.18.517151
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Mapping single-cell responses to population-level dynamics during antibiotic treatment
Kyeri Kim, Teng Wang, Helena R. Ma, Emrah Şimşek, Boyan Li, Virgile Andreani, Lingchong You
bioRxiv 2022.11.18.517151; doi: https://doi.org/10.1101/2022.11.18.517151

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