Back to Search Start Over

Mapping single‐cell responses to population‐level dynamics during antibiotic treatment.

Authors :
Kim, Kyeri
Wang, Teng
Ma, Helena R
Şimşek, Emrah
Li, Boyan
Andreani, Virgile
You, Lingchong
Source :
Molecular Systems Biology. 7/11/2023, Vol. 19 Issue 7, p1-13. 13p.
Publication Year :
2023

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 cumulative probability of cell lysis increases sigmoidally 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. Synopsis: How do population‐level responses emerge from collective single‐cell responses upon antibiotic treatment? Measuring the kinetics of bacterial elongation and lysis in single cells treated with beta‐lactams allows quantitative mapping to temporal population biomass dynamics. When beta‐lactam antibiotics trigger bacterial filamentation and lysis, the cumulative lysis probability is sigmoidal over cell length.Parameters that describe such probability are unique to antibiotic conditions.Damage accumulation can describe how the probability arises from single‐cell responses.Mathematical mapping provides a quantitative understanding of population time‐kill curves from the single‐cell response parameters. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17444292
Volume :
19
Issue :
7
Database :
Academic Search Index
Journal :
Molecular Systems Biology
Publication Type :
Academic Journal
Accession number :
164876701
Full Text :
https://doi.org/10.15252/msb.202211475