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Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress

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  • Ming Ni
  • Antoine L Decrulle
  • Fanette Fontaine
  • Alice Demarez
  • Francois Taddei
  • Ariel B Lindner

Abstract

Bacteria suffer various stresses in their unpredictable environment. In response, clonal populations may exhibit cell-to-cell variation, hypothetically to maximize their survival. The origins, propagation, and consequences of this variability remain poorly understood. Variability persists through cell division events, yet detailed lineage information for individual stress-response phenotypes is scarce. This work combines time-lapse microscopy and microfluidics to uniformly manipulate the environmental changes experienced by clonal bacteria. We quantify the growth rates and RpoH-driven heat-shock responses of individual Escherichia coli within their lineage context, stressed by low streptomycin concentrations. We observe an increased variation in phenotypes, as different as survival from death, that can be traced to asymmetric division events occurring prior to stress induction. Epigenetic inheritance contributes to the propagation of the observed phenotypic variation, resulting in three-fold increase of the RpoH-driven expression autocorrelation time following stress induction. We propose that the increased permeability of streptomycin-stressed cells serves as a positive feedback loop underlying this epigenetic effect. Our results suggest that stochasticity, pre-disposition, and epigenetic effects are at the source of stress-induced variability. Unlike in a bet-hedging strategy, we observe that cells with a higher investment in maintenance, measured as the basal RpoH transcriptional activity prior to antibiotic treatment, are more likely to give rise to stressed, frail progeny. Author Summary: Individual organisms of identical genetic background, living in a homogeneous constant environment, may nonetheless exhibit observable differences dubbed phenotypic plasticity or variability. When such a population is challenged with an unforeseen stress, the disparity among individuals may increase, yielding different strategies in response. This work addresses the occurrence and propagation of phenotypic variation as it affects bacterial survival in response to mild antibiotic treatments. We recorded images of single bacterial cells as they divide prior to and during exposure to a sub-lethal level of streptomycin, a ribosome-targeted antibiotic. We found that individual differences increase upon stress to the extent that cells may either die or survive the treatment. Differentiation events were traced back prior to exposure. We suggest that a positive feedback loop, governed by increased membrane permeability, underlies the transient cell memory observed. Cells with relatively high basal stress-response levels prior to stress are not primed for better survival, but are rather more likely to succumb to antibiotic treatment. As pathogens commonly encounter sub-lethal doses of antibiotics, their survival may be better understood in light of this study.

Suggested Citation

  • Ming Ni & Antoine L Decrulle & Fanette Fontaine & Alice Demarez & Francois Taddei & Ariel B Lindner, 2012. "Pre-Disposition and Epigenetics Govern Variation in Bacterial Survival upon Stress," PLOS Genetics, Public Library of Science, vol. 8(12), pages 1-11, December.
  • Handle: RePEc:plo:pgen00:1003148
    DOI: 10.1371/journal.pgen.1003148
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    References listed on IDEAS

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    1. Avigdor Eldar & Michael B. Elowitz, 2010. "Functional roles for noise in genetic circuits," Nature, Nature, vol. 467(7312), pages 167-173, September.
    2. Alex Sigal & Ron Milo & Ariel Cohen & Naama Geva-Zatorsky & Yael Klein & Yuvalal Liron & Nitzan Rosenfeld & Tamar Danon & Natalie Perzov & Uri Alon, 2006. "Variability and memory of protein levels in human cells," Nature, Nature, vol. 444(7119), pages 643-646, November.
    3. John R. S. Newman & Sina Ghaemmaghami & Jan Ihmels & David K. Breslow & Matthew Noble & Joseph L. DeRisi & Jonathan S. Weissman, 2006. "Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise," Nature, Nature, vol. 441(7095), pages 840-846, June.
    4. Vanessa M. D’Costa & Christine E. King & Lindsay Kalan & Mariya Morar & Wilson W. L. Sung & Carsten Schwarz & Duane Froese & Grant Zazula & Fabrice Calmels & Regis Debruyne & G. Brian Golding & Hendri, 2011. "Antibiotic resistance is ancient," Nature, Nature, vol. 477(7365), pages 457-461, September.
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