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Balaban NQ, Helaine S, Lewis K, Ackermann M, Aldridge B, Andersson DI, Brynildsen MP, Bumann D, Camilli A, Collins JJ, Dehio C, Fortune S, Ghigo JM, Hardt WD, Harms A, Heinemann M, Hung DT, Jenal U, Levin BR, Michiels J, Storz G, Tan MW, Tenson T, Van Melderen L, Zinkernagel A. Definitions and guidelines for research on antibiotic persistence. Nat Rev Microbiol 2020; 17:441-448. [PMID: 30980069 PMCID: PMC7136161 DOI: 10.1038/s41579-019-0196-3] [Citation(s) in RCA: 629] [Impact Index Per Article: 157.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Increasing concerns about the rising rates of antibiotic therapy failure and advances in single-cell analyses have inspired a surge of research into antibiotic persistence. Bacterial persister cells represent a subpopulation of cells that can survive intensive antibiotic treatment without being resistant. Several approaches have emerged to define and measure persistence, and it is now time to agree on the basic definition of persistence and its relation to the other mechanisms by which bacteria survive exposure to bactericidal antibiotic treatments, such as antibiotic resistance, heteroresistance or tolerance. In this Consensus Statement, we provide definitions of persistence phenomena, distinguish between triggered and spontaneous persistence and provide a guide to measuring persistence. Antibiotic persistence is not only an interesting example of non-genetic single-cell heterogeneity, it may also have a role in the failure of antibiotic treatments. Therefore, it is our hope that the guidelines outlined in this article will pave the way for better characterization of antibiotic persistence and for understanding its relevance to clinical outcomes. Antibiotic persistence contributes to the survival of bacteria during antibiotic treatment. In this Consensus Statement, scientists working on the response of bacteria to antibiotics define antibiotic persistence and provide practical guidance on how to study bacterial persisters.
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Affiliation(s)
| | - Sophie Helaine
- MRC Centre for Molecular Bacteriology and Infection, Imperial College London, London, UK
| | - Kim Lewis
- Department of Biology, Northeastern University, Boston, MA, USA
| | - Martin Ackermann
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Zurich, Switzerland.,Department of Environmental Microbiology, Eawag, Dubendorf, Switzerland
| | - Bree Aldridge
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA
| | - Dan I Andersson
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Mark P Brynildsen
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
| | - Dirk Bumann
- Focal Area Infection Biology, Biozentrum of the University of Basel, Basel, Switzerland
| | - Andrew Camilli
- Department of Molecular Biology and Microbiology, Tufts University School of Medicine, Boston, MA, USA
| | - James J Collins
- Institute for Medical Engineering & Science, Department of Biological Engineering, and Synthetic Biology Center, Massachusetts Institute of Technology, Cambridge, MA, USA.,Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christoph Dehio
- Focal Area Infection Biology, Biozentrum of the University of Basel, Basel, Switzerland
| | - Sarah Fortune
- Department of Immunology and Infectious Diseases, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Jean-Marc Ghigo
- Institut Pasteur, Genetics of Biofilms Laboratory, Paris, France
| | | | - Alexander Harms
- Focal Area Infection Biology, Biozentrum of the University of Basel, Basel, Switzerland
| | - Matthias Heinemann
- Molecular Systems Biology, Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Groningen, Netherlands
| | | | - Urs Jenal
- Focal Area Infection Biology, Biozentrum of the University of Basel, Basel, Switzerland
| | - Bruce R Levin
- Department of Biology, Emory University, Atlanta, GA, USA
| | - Jan Michiels
- Center for Microbiology, KU Leuven-University of Leuven, Leuven, Belgium
| | - Gisela Storz
- Division of Molecular and Cellular Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA
| | - Man-Wah Tan
- Infectious Diseases Department, Genentech, South San Francisco, CA, USA
| | - Tanel Tenson
- Institute of Technology, University of Tartu, Tartu, Estonia
| | | | - Annelies Zinkernagel
- Division of Infectious Diseases, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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Saakian DB, Yakushkina T, Koonin EV. Allele fixation probability in a Moran model with fluctuating fitness landscapes. Phys Rev E 2019; 99:022407. [PMID: 30934266 DOI: 10.1103/physreve.99.022407] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Indexed: 06/09/2023]
Abstract
Evolution on changing fitness landscapes (seascapes) is an important problem in evolutionary biology. We consider the Moran model of finite population evolution with selection in a randomly changing, dynamic environment. In the model, each individual has one of the two alleles, wild type or mutant. We calculate the fixation probability by making a proper ansatz for the logarithm of fixation probabilities. This method has been used previously to solve the analogous problem for the Wright-Fisher model. The fixation probability is related to the solution of a third-order algebraic equation (for the logarithm of fixation probability). We consider the strong interference of landscape fluctuations, sampling, and selection when the fixation process cannot be described by the mean fitness. Such an effect appears if the mutant allele has a higher fitness in one landscape and a lower fitness in another, compared with the wild type, and the product of effective population size and fitness is large. We provide a generalization of the Kimura formula for the fixation probability that applies to these cases. When the mutant allele has a fitness (dis-)advantage in both landscapes, the fixation probability is described by the mean fitness.
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Affiliation(s)
- David B Saakian
- Laboratory of Applied Physics, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam
| | - Tatiana Yakushkina
- National Research University Higher School of Economics, Moscow 101000, Russia
| | - Eugene V Koonin
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
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