Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013 Feb 4:13:25.
doi: 10.1186/1471-2180-13-25.

Quantitative analysis of persister fractions suggests different mechanisms of formation among environmental isolates of E. coli

Niels Hofsteenge  1 , Erik van Nimwegen, Olin K Silander
Affiliations

Quantitative analysis of persister fractions suggests different mechanisms of formation among environmental isolates of E. coli

Niels Hofsteenge et al. BMC Microbiol. .

Abstract

Background: Bacterial persistence describes a phenomenon wherein a small subpopulation of cells is able to survive a challenge with high doses of an antibiotic (or other stressor) better than the majority of the population. Previous work has shown that cells that are in a dormant or slow-growing state are persistent to antibiotic treatment and that populations with higher fractions of dormant cells exhibit higher levels of persistence. These data suggest that a major determinant of the fraction of persisters within a population is the rate at which cells enter and exit from dormancy. However, it is not known whether there are physiological changes in addition to dormancy that influence persistence. Here, we use quantitative measurements of persister fractions in a set of environmental isolates of E. coli together with a mathematical model of persister formation to test whether a single general physiological change, such as cell dormancy, can explain the differences in persister phenotypes observed in different strains.

Results: If a single physiological change (e.g. cell dormancy) underlies most persister phenotypes, then strains should exhibit characteristic fractions of persister cells: some strains will consistently have high fractions of persisters (dormant cells), whereas others will have low fractions. Although we found substantial variation in the fraction of persisters between different environmental isolates of E. coli, these fractions were not correlated across antibiotics. Some strains exhibited high persister fractions in one antibiotic, but low persister fractions in a second antibiotic. Surprisingly, no correlation in persister fractions was observed between any two drugs, even for antibiotics with nearly identical modes of action (ciprofloxacin and nalidixic acid).

Conclusions: These data support the hypothesis that there is no single physiological change that determines the persistence level in a population of cells. Instead, the fraction of cells that survive antibiotic treatment (persist) depends critically on the specific antibiotic that is used, suggesting that physiological changes in addition to dormancy can underlie persister phenotypes.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Environmental isolates exhibit substantial variation in persister fractions after treatment with 100 ug ampicillin. The kill curves are characterized by biphasic behavior, implying that there are at least two distinct populations of cells with differing death rates. The plot shows the killing data of six replicate cultures for three strains (SC552, SC649 and MG1655); the lines indicate the best-fit models for each replicate.
Figure 2
Figure 2
Environmental isolates exhibit different fractions of persisters after treatment with ciprofloxacin or nalidixic acid. The plots show six replicates for each of the three strains shown in Figure 1. A: Killing dynamics during 48 hours of treatment with ciprofloxacin. Biphasic dynamics, similar to those observed in Figure 1, are observed. B: Killing dynamics during 48 hours of treatment with nalidixic acid. There are large differences in persister fractions between the two antibiotics, with strain SC649 exhibiting a low fraction of persisters in ciprofloxacin, but a high fraction in nalidixic acid.
Figure 3
Figure 3
No correlation is observed between persister fractions in different antibiotics. We found that although the calculated persister fractions are repeatable, there is no consistent correlation between the fractions of persisters in any two antibiotics. The plots show the correlations in persister fractions. A: ampicillin and ciprofloxacin; B: ampicillin and nalidixic acid; and C: ciprofloxacin and nalidixic acid. Only one strain exhibits a very high fraction of persisters in two antibiotics; however, these antibiotics are ciprofloxacin and ampicillin, members of two different classes. The error bars indicate standard errors for the biological replicates. The values of Spearman’s rho and the corresponding p-value are shown in each plot.
Figure 4
Figure 4
Kill curves in combinations of antibiotics are biphasic and vary between treatments. We used combinations of antibiotics to examine the dynamics of cell killing. These dynamics are similar to those observed in single antibiotics. A–C: Killing dynamics of all replicate cultures upon treatment of strains SC552 with all pairwise combinations of the three antibiotics. D-F: Killing dynamics of strain SC649.
Figure 5
Figure 5
A subset of persister cells is multidrug tolerant. The persister fractions estimated from the killing dynamics are shown for single or combinations of antibiotics. A: strain SC552; B: SC649. For both strains, there is a subset of persisters that appear to be resistant to both antibiotics.
Figure 6
Figure 6
Known persister loci are rapidly gained and/or lost within the E. coli clade. Grey boxes indicate the presence of the orthologue in the indicated genome; white indicates absence. The data suggests that toxin – antitoxin loci undergo rapid loss and/or gain within the E. coli clade. Orthologue presence – absence of toxin-antitoxin loci is based on a bidirectional best-hit analyses [33] for 14 E. coli and Shigella taxa and E. fergusonii.
Figure 7
Figure 7
The primary determinant of the persister fraction is the rate of switching to the persister state. A: The rate of switching from the normal cellular state to the persister state is strongly correlated with the fraction of persisters in the population. B: There is little to no correlation between the rate of switching from the persister state to the normal state and the fraction of persisters. C: No correlation exists between the rate of death of normal cells and the persister fraction.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Bigger JW. Treatment of staphylococcal infections with penicillin - by intermittent sterilisation. Lancet. 1944;2:497–500.
    1. del Pozo JL, Patel R. The challenge of treating biofilm-associated bacterial infection. Clin Pharmacol Ther. 2007;82(2):204–209. doi: 10.1038/sj.clpt.6100247. - DOI - PubMed
    1. Lewis K. Persister cells. Annu Rev Microbiol. 2010;64:357–372. doi: 10.1146/annurev.micro.112408.134306. - DOI - PubMed
    1. Mulcahy LR, Burns JL, Lory S, Lewis K. Emergence of pseudomonas aeruginosa strains producing high levels of persister cells in patients with cystic fibrosis. J Bacteriol. 2010;192(23):6191–6199. doi: 10.1128/JB.01651-09. - DOI - PMC - PubMed
    1. Tuomanen E, Cozens R, Tosch W, Zak O, Tomasz A. The rate of killing of escherichia-coli by beta-lactam antibiotics is strictly proportional to the rate of bacterial-growth. J Gen Microbiol. 1986;132:1297–1304. - PubMed

MeSH terms

Substances