Robust, linear correlations between growth rates and β-lactam-mediated lysis rates.

Academic Article


  • It is widely acknowledged that faster-growing bacteria are killed faster by β-lactam antibiotics. This notion serves as the foundation for the concept of bacterial persistence: dormant bacterial cells that do not grow are phenotypically tolerant against β-lactam treatment. Such correlation has often been invoked in the mathematical modeling of bacterial responses to antibiotics. Due to the lack of thorough quantification, however, it is unclear whether and to what extent the bacterial growth rate can predict the lysis rate upon β-lactam treatment under diverse conditions. Enabled by experimental automation, here we measured >1,000 growth/killing curves for eight combinations of antibiotics and bacterial species and strains, including clinical isolates of bacterial pathogens. We found that the lysis rate of a bacterial population linearly depends on the instantaneous growth rate of the population, regardless of how the latter is modulated. We further demonstrate that this predictive power at the population level can be explained by accounting for bacterial responses to the antibiotic treatment by single cells. This linear dependence of the lysis rate on the growth rate represents a dynamic signature associated with each bacterium-antibiotic pair and serves as the quantitative foundation for designing combination antibiotic therapy and predicting the population-structure change in a population with mixed phenotypes.
  • Authors

  • Lee, Anna J
  • Wang, Shangying
  • Meredith, Hannah R
  • Zhuang, Bin
  • Dai, Zhuojun
  • You, Lingchong
  • Status

    Publication Date

  • April 17, 2018
  • Keywords

  • Anti-Bacterial Agents
  • Bacterial Load
  • Bacteriolysis
  • Biomass
  • Carbenicillin
  • Culture Media
  • Escherichia coli
  • High-Throughput Screening Assays
  • Kinetics
  • Nephelometry and Turbidimetry
  • Robotics
  • Temperature
  • antibiotic resistance
  • beta-lactams
  • quantitative biology
  • systems biology
  • Digital Object Identifier (doi)

    Start Page

  • 4069
  • End Page

  • 4074
  • Volume

  • 115
  • Issue

  • 16