Dataset Information

Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence.

ABSTRACT: In the face of antibiotics, bacterial populations avoid extinction by harboring a subpopulation of dormant cells that are largely drug insensitive. This phenomenon, termed "persistence," is a major obstacle for the treatment of a number of infectious diseases. The mechanism that generates both actively growing as well as dormant cells within a genetically identical population is unknown. We present a detailed study of the toxin-antitoxin module implicated in antibiotic persistence of Escherichia coli. We find that bacterial cells become dormant if the toxin level is higher than a threshold, and that the amount by which the threshold is exceeded determines the duration of dormancy. Fluctuations in toxin levels above and below the threshold result in coexistence of dormant and growing cells. We conclude that toxin-antitoxin modules in general represent a mixed network motif that can serve to produce a subpopulation of dormant cells and to supply a mechanism for regulating the frequency and duration of growth arrest. Toxin-antitoxin modules thus provide a natural molecular design for implementing a bet-hedging strategy.

SUBMITTER: Rotem E

PROVIDER: S-EPMC2906590 | biostudies-literature | 2010 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence.

Rotem Eitan E Loinger Adiel A Ronin Irine I Levin-Reisman Irit I Gabay Chana C Shoresh Noam N Biham Ofer O Balaban Nathalie Q NQ

Proceedings of the National Academy of Sciences of the United States of America 20100628 28

In the face of antibiotics, bacterial populations avoid extinction by harboring a subpopulation of dormant cells that are largely drug insensitive. This phenomenon, termed "persistence," is a major obstacle for the treatment of a number of infectious diseases. The mechanism that generates both actively growing as well as dormant cells within a genetically identical population is unknown. We present a detailed study of the toxin-antitoxin module implicated in antibiotic persistence of Escherichia ...[more]

PMID: 20616060

Similar Datasets

Project description:Ecological and evolutionary dynamics have been historically regarded as unfolding at broadly separated timescales. However, these two types of processes are nowadays well-documented to intersperse much more tightly than traditionally assumed, especially in communities of microorganisms. Advancing the development of mathematical and computational approaches to shed novel light onto eco-evolutionary problems is a challenge of utmost relevance. With this motivation in mind, here we scrutinize recent experimental results showing evidence of rapid evolution of tolerance by lag in bacterial populations that are periodically exposed to antibiotic stress in laboratory conditions. In particular, the distribution of single-cell lag times-i.e., the times that individual bacteria from the community remain in a dormant state to cope with stress-evolves its average value to approximately fit the antibiotic-exposure time. Moreover, the distribution develops right-skewed heavy tails, revealing the presence of individuals with anomalously large lag times. Here, we develop a parsimonious individual-based model mimicking the actual demographic processes of the experimental setup. Individuals are characterized by a single phenotypic trait: their intrinsic lag time, which is transmitted with variation to the progeny. The model-in a version in which the amplitude of phenotypic variations grows with the parent's lag time-is able to reproduce quite well the key empirical observations. Furthermore, we develop a general mathematical framework allowing us to describe with good accuracy the properties of the stochastic model by means of a macroscopic equation, which generalizes the Crow-Kimura equation in population genetics. Even if the model does not account for all the biological mechanisms (e.g., genetic changes) in a detailed way-i.e., it is a phenomenological one-it sheds light onto the eco-evolutionary dynamics of the problem and can be helpful to design strategies to hinder the emergence of tolerance in bacterial communities. From a broader perspective, this work represents a benchmark for the mathematical framework designed to tackle much more general eco-evolutionary problems, thus paving the road to further research avenues.

Project description:Somatic missense mutations in PIK3CA, which encodes the p110α catalytic subunit of phosphoinositide 3-kinases, occur frequently in human cancers. Activating mutations spread across multiple domains, some of which are located at inhibitory contact sites formed with the regulatory subunit p85α. PIK3R1, which encodes p85α, also has activating somatic mutations. We find a strong correlation between lipid kinase and lipid-binding activities for both wild-type (WT) and a representative set of oncogenic mutant complexes of p110α/p85α. Lipid binding involves both electrostatic and hydrophobic interactions. Activation caused by a phosphorylated receptor tyrosine kinase (RTK) peptide binding to the p85α N-terminal SH2 domain (nSH2) induces lipid binding. This depends on the polybasic activation loop as well as a conserved hydrophobic motif in the C-terminal region of the kinase domain. The hotspot E545K mutant largely mimics the activated WT p110α. It shows the highest basal activity and lipid binding, and is not significantly activated by an RTK phosphopeptide. Both the hotspot H1047R mutant and rare mutations (C420R, M1043I, H1047L, G1049R and p85α-N564D) also show increased basal kinase activities and lipid binding. However, their activities are further enhanced by an RTK phosphopeptide to levels markedly exceeding that of activated WT p110α. Phosphopeptide binding to p110β/p85α and p110δ/p85α complexes also induces their lipid binding. We present a crystal structure of WT p110α complexed with the p85α inter-SH2 domain and the inhibitor PIK-108. Additional to the ATP-binding pocket, an unexpected, second PIK-108 binding site is observed in the kinase C-lobe. We show a global conformational change in p110α consistent with allosteric regulation of the kinase domain by nSH2. These findings broaden our understanding of the differential biological outputs exhibited by distinct types of mutations regarding growth factor dependence, and suggest a two-tier classification scheme relating p110α and p85α mutations with signalling potential.

Dataset Information

Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence.

Publications

Regulation of phenotypic variability by a threshold-based mechanism underlies bacterial persistence.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets