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Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations

Nature volume 513, pages 418–421 (2014)Cite this article

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Abstract

The great therapeutic achievements of antibiotics have been dramatically undercut by the evolution of bacterial strategies that overcome antibiotic stress1,2. These strategies fall into two classes. ‘Resistance’ makes it possible for a microorganism to grow in the constant presence of the antibiotic, provided that the concentration of the antibiotic is not too high. ‘Tolerance’ allows a microorganism to survive antibiotic treatment, even at high antibiotic concentrations, as long as the duration of the treatment is limited. Although both resistance and tolerance are important reasons for the failure of antibiotic treatments3,4,5,6, the evolution of resistance7,8,9 is much better understood than that of tolerance. Here we followed the evolution of bacterial populations under intermittent exposure to the high concentrations of antibiotics used in the clinic and characterized the evolved strains in terms of both resistance and tolerance. We found that all strains adapted by specific genetic mutations, which became fixed in the evolved populations. By monitoring the phenotypic changes at the population and single-cell levels, we found that the first adaptive change to antibiotic stress was the development of tolerance through a major adjustment in the single-cell lag-time distribution, without a change in resistance. Strikingly, we found that the lag time of bacteria before regrowth was optimized to match the duration of the antibiotic-exposure interval. Whole genome sequencing of the evolved strains and restoration of the wild-type alleles allowed us to identify target genes involved in this antibiotic-driven phenotype: ‘tolerance by lag’ (tbl). Better understanding of lag-time evolution as a key determinant of the survival of bacterial populations under high antibiotic concentrations could lead to new approaches to impeding the evolution of antibiotic resistance.

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Figure 1: Resistance and tolerance of the evolved strains.
Figure 2: Changes in the single-cell lag-time distributions underlie antibiotic tolerance.
Figure 3: Optimization of lag time.

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BioProject

Data deposits

Whole genome sequence data of KLY (ancestral), tbl3a tbl5a and tbl8a, as well as reconstructed ancestral genome sequence data, have been deposited in the BioProject database under the accession number PRJNA229104.

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Acknowledgements

We thank I. Levin-Riesman for discussions and technical help with the ScanLag system, S. Belkin for strains, and the National BioResource Project and National Institute of Genetics, Japan, for the Keio collection deletion mutants. The work was supported by the European Research Council (Starting Grant no. 260871) and the Israel Science Foundation (no. 592/10). O.F. acknowledges support from the Levtzion Fellowship.

Author information

Authors and Affiliations

  1. Racah Institute of Physics, The Sudarsky Center for Computational Biology and the Center for NanoScience, Edmond J. Safra Campus, The Hebrew University, Jerusalem 91904, Israel,

    Ofer Fridman, Amir Goldberg, Irine Ronin & Nathalie Q. Balaban

  2. Broad Institute of Harvard and MIT, Cambridge, 02142, Massachusetts, USA

    Noam Shoresh

Authors
  1. Ofer Fridman
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  2. Amir Goldberg
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  5. Nathalie Q. Balaban
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Contributions

N.Q.B. and O.F. designed the experiments. O.F. performed the experiments. O.F. and N.Q.B. analysed the data. O.F. and N.S. performed the theoretical analysis. O.F. and A.G. analysed the whole genome sequencing data. I.R. made the genetic reconstructions. N.Q.B. and N.S. wrote the manuscript.

Corresponding author

Correspondence to Nathalie Q. Balaban.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1 Killing curves of the evolved clones indicate tolerance to ampicillin treatment.

a, Cultures were grown overnight from a single colony for each evolved tbl clone. The survival of the cultures was determined as previously described2. Data are presented as the mean ± s.d. of two independent experiments. b, Determining whether the evolved strains show a persistent phenotype: a sub-population of persisters is characterized by a significant slowing of the killing rate compared with the rate at which the bulk of the bacterial population (99%) is dying from the treatment, as indicated by the straight line on each graph. The point (on the y axis) at which an upward departure from this line occurs indicates the size of that subpopulation. The hipA7 strain (orange) shows such a slowing corresponding to 10−2–10−1 of the cells being persistent. The wild-type ancestral strain (grey) also exhibits this behaviour but at a persister population that is at least two orders of magnitude smaller. None of the evolved strains shows a marked decrease in the killing rate at frequencies comparable to or larger than the ancestral persistence level. Therefore, the increased survival of the evolved strains is characterized as tolerance.

Extended Data Figure 2 Extended phenotypic analysis of the ancestral strain and the clones evolved under cyclic antibiotic exposure.

a, The growth rates of evolved clones show no significant difference from the ancestral strain. The cultures of each clone were grown in a 24-well plate and the optical density at 630 nm (OD630) was measured over time. The doubling times were extracted from the fit to the exponential growth phase. Data are presented as the mean ± s.d. of four replicates. b, Survival rates after 10 h under 8 µg ml−1 norfloxacin treatment of the ancestral strain and the evolved clones. Data are presented as the mean ± s.d. of two independent experiments.

Extended Data Figure 3 Evolution under the same protocol without antibiotic exposure shows no lag extension.

Two parallel bacterial lines (‘No antibiotic’, orange shades) were subjected to the same protocol as the cyclic antibiotic exposure but without antibiotics. Instead, the cultures were diluted 1:100 in fresh medium without ampicillin. The colony appearance time was continuously monitored by ScanLag, an automated scanner system16. The histograms show the fraction of CFUs detected at each time point for the ancestral strain (grey), line one (dark orange) and line two (light orange) (n = 1,080, 1,320 and 1,833, respectively).

Extended Data Figure 4 Restoration of tbl gene wild-type alleles restores the ancestral single-cell lag-time distribution.

Colony appearance time was continuously monitored by ScanLag, an automated scanner system16. The histograms show the fraction of CFUs detected at each time point: ancestral strain (grey), tbl5a (red), tbl5a with restored metG (orange) (n = 1,409, 1,099 and 1,029, respectively; temperature (T) = 32 °C) (a); ancestral strain (grey), tbl8a (blue), tbl8a with restored prsA (orange) (n = 2,245, 1,288 and 2,285, respectively) (b); ancestral strain (grey), tbl3b (green), tbl3b with restored prsA (orange) (n = 2,245, 441 and 1,936, respectively) (c); and ancestral strain (grey), tbl3a (green), tbl3a with deletion of toxin–antitoxin vapBC (n = 1,140, 1,571 and 902, respectively; T = 32 °C) (d). All evolved bacterial clones form colonies at later times, whereas restoration of the wild-type alleles, or deletion of the mutated module, abolishes the long lag.

Extended Data Table 1 tbl genes identified by whole genome sequencing of evolved strains
Full size table
Extended Data Table 2 Additional mutations identified by whole genome sequencing of evolved strains
Full size table
Extended Data Table 3 Evaluation of fixation frequencies of the tbl genes
Full size table

Supplementary information

Supplementary Information

This file contains Supplementary Methods, Text and Data, a list of the Strains and Primers used and additional references. (PDF 295 kb)

ScanLag imaging of colony appearance for ancestral and evolved strain tbl5a

This video shows typical appearance of colonies on an LBL agar plate for the ancestral strain (left) and evolved strain (right). Appearance of evolved bacterial colonies is stretched over many hours. (AVI 2517 kb)

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Fridman, O., Goldberg, A., Ronin, I. et al. Optimization of lag time underlies antibiotic tolerance in evolved bacterial populations. Nature 513, 418–421 (2014). https://doi.org/10.1038/nature13469

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