Distinguishing between resistance, tolerance and persistence to antibiotic treatment

Journal name:
Nature Reviews Microbiology
Volume:
14,
Pages:
320–330
Year published:
DOI:
doi:10.1038/nrmicro.2016.34
Published online

Abstract

Antibiotic tolerance is associated with the failure of antibiotic treatment and the relapse of many bacterial infections. However, unlike resistance, which is commonly measured using the minimum inhibitory concentration (MIC) metric, tolerance is poorly characterized, owing to the lack of a similar quantitative indicator. This may lead to the misclassification of tolerant strains as resistant, or vice versa, and result in ineffective treatments. In this Opinion article, we describe recent studies of tolerance, resistance and persistence, outlining how a clear and distinct definition for each phenotype can be developed from these findings. We propose a framework for classifying the drug response of bacterial strains according to these definitions that is based on the measurement of the MIC together with a recently defined quantitative indicator of tolerance, the minimum duration for killing (MDK). Finally, we discuss genes that are associated with increased tolerance — the 'tolerome' — as targets for treating tolerant bacterial strains.

At a glance

Figures

  1. Characteristic drug responses of resistance, tolerance and persistence.
    Figure 1: Characteristic drug responses of resistance, tolerance and persistence.

    The survival strategies of resistance, tolerance and persistence to antibiotic treatment each manifest as a characteristic drug response. a| The minimum inhibitory concentration (MIC) for a strain of bacteria that is resistant to an antibiotic is substantially higher than the MIC for a susceptible strain. Coloured wells represent bacterial growth, whereas wells in which the antibiotic concentration is high enough to kill the bacteria are in light brown. b| The MIC for a tolerant strain of bacteria is similar to that of a susceptible strain; however, the minimum duration for killing (MDK; for example for 99% of bacterial cells in the population (MDK99)) for a tolerant strain is substantially higher than the MDK99 for a susceptible strain. c| A persistent strain of bacteria has a similar MIC and a similar MDK99 to a susceptible strain; however, the MDK for 99.99% of bacterial cells in the population (MDK99.99) is substantially higher for a persistent strain than the MDK99.99 for a susceptible strain. Concentrations and timescales are chosen for illustration purposes only.

  2. Tolerance arises from slow growth or lag phase.
    Figure 2: Tolerance arises from slow growth or lag phase.

    a| The minimum duration for killing (MDK) for 99% of bacterial cells in a population (MDK99) is plotted against doubling time for several combinations of bacterial strain or species and antibiotic, as extracted from time–kill curves in the literature10, 26, 28, 29, 37, 38, 40, 56. The dashed line shows the best fit for the relationship between the MDK99 and the doubling time for strains of bacteria that are tolerant by slow growth, which demonstrates the correlation between these two variables. The shaded area highlights the distribution of bacterial strains that are tolerant by lag; these strains were detected by exposure to the drug directly on dilution from the stationary phase. b| A schematic growth curve that shows the importance of subculturing to reach strictly exponential growth. An initial 1 in 100,000 dilution of a bacterial population from a culture in the stationary phase of the growth cycle is followed by serial 1 in100 dilutions; in each instance, the colony is grown until the population density reaches 107 colony forming units (CFU) ml−1 before dilution. Each dilution reduces the number of residual non-growing bacterial cells — that is, cells in the lag phase — in the population and several dilution steps may be required until the population is composed only of cells in the exponential growth phase, with no cells remaining in the lag phase.

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Affiliations

  1. Asher Brauner, Ofer Fridman, Orit Gefen and Nathalie Q. Balaban are at the Racah Institute of Physics and the Harvey M. Kruger Family Center for Nanoscience and Nanotechnology, Edmond J. Safra Campus, The Hebrew University of Jerusalem, Jerusalem 91904, Israel.

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The authors declare no competing interests.

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Author details

  • Asher Brauner

    Asher Brauner is a physics student at The Hebrew University of Jerusalem, Israel. His work involves the development of a method to quantify tolerance and persistence.

  • Ofer Fridman

    Ofer Fridman completed his Ph.D. in physics at the Hebrew University of Jerusalem, Israel. His work revealed how bacteria adapt to the duration of antibiotic treatment by evolving a dormancy lag that matches the duration of the drug exposure.

  • Orit Gefen

    Orit Gefen is an adjunct scientist in physics at The Hebrew University of Jerusalem, Israel. She has worked on persistence and dormancy at the single-cell level for more than a decade. Her work involves the development of microfluidic devices and the identification of a constant residual activity in non-growing bacteria.

  • Nathalie Q. Balaban

    Nathalie Q. Balaban is Professor of Physics at The Hebrew University of Jerusalem, Israel. Her research interests focus on the development of experimental and theoretical approaches to the study of stochasticity in single cells, and the determination of the role of this stochasticity in disease and evolution. She holds a consolidator grant from the European Research Council. Nathalie Q. Balaban's homepage.

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