Literature DB >> 26213639

Quantifying selective pressures driving bacterial evolution using lineage analysis.

Guillaume Lambert1, Edo Kussell2.   

Abstract

Organisms use a variety of strategies to adapt to their environments and maximize long-term growth potential, but quantitative characterization of the benefits conferred by the use of such strategies, as well as their impact on the whole population's rate of growth, remains challenging. Here, we use a path-integral framework that describes how selection acts on lineages -i.e. the life-histories of individuals and their ancestors- to demonstrate that lineage-based measurements can be used to quantify the selective pressures acting on a population. We apply this analysis to E. coli bacteria exposed to cyclical treatments of carbenicillin, an antibiotic that interferes with cell-wall synthesis and affects cells in an age-dependent manner. While the extensive characterization of the life-history of thousands of cells is necessary to accurately extract the age-dependent selective pressures caused by carbenicillin, the same measurement can be recapitulated using lineage-based statistics of a single surviving cell. Population-wide evolutionary pressures can be extracted from the properties of the surviving lineages within a population, providing an alternative and efficient procedure to quantify the evolutionary forces acting on a population. Importantly, this approach is not limited to age-dependent selection, and the framework can be generalized to detect signatures of other trait-specific selection using lineage-based measurements. Our results establish a powerful way to study the evolutionary dynamics of life under selection, and may be broadly useful in elucidating selective pressures driving the emergence of antibiotic resistance and the evolution of survival strategies in biological systems.

Entities:  

Year:  2015        PMID: 26213639      PMCID: PMC4511495          DOI: 10.1103/PhysRevX.5.011016

Source DB:  PubMed          Journal:  Phys Rev X        ISSN: 2160-3308            Impact factor:   15.762


  31 in total

1.  Bacterial persistence as a phenotypic switch.

Authors:  Nathalie Q Balaban; Jack Merrin; Remy Chait; Lukasz Kowalik; Stanislas Leibler
Journal:  Science       Date:  2004-08-12       Impact factor: 47.728

2.  Phenotypic diversity, population growth, and information in fluctuating environments.

Authors:  Edo Kussell; Stanislas Leibler
Journal:  Science       Date:  2005-08-25       Impact factor: 47.728

Review 3.  Morphological plasticity as a bacterial survival strategy.

Authors:  Sheryl S Justice; David A Hunstad; Lynette Cegelski; Scott J Hultgren
Journal:  Nat Rev Microbiol       Date:  2008-02       Impact factor: 60.633

Review 4.  The fixation probability of beneficial mutations.

Authors:  Z Patwa; L M Wahl
Journal:  J R Soc Interface       Date:  2008-11-06       Impact factor: 4.118

5.  Unifying life-history analyses for inference of fitness and population growth.

Authors:  Ruth G Shaw; Charles J Geyer; Stuart Wagenius; Helen H Hangelbroek; Julie R Etterson
Journal:  Am Nat       Date:  2008-07       Impact factor: 3.926

6.  Selection and covariance.

Authors:  G R Price
Journal:  Nature       Date:  1970-08-01       Impact factor: 49.962

Review 7.  Measuring selection in contemporary human populations.

Authors:  Stephen C Stearns; Sean G Byars; Diddahally R Govindaraju; Douglas Ewbank
Journal:  Nat Rev Genet       Date:  2010-08-03       Impact factor: 53.242

Review 8.  The mechanism of the irreversible antimicrobial effects of penicillins: how the beta-lactam antibiotics kill and lyse bacteria.

Authors:  A Tomasz
Journal:  Annu Rev Microbiol       Date:  1979       Impact factor: 15.500

9.  Individual histories and selection in heterogeneous populations.

Authors:  Stanislas Leibler; Edo Kussell
Journal:  Proc Natl Acad Sci U S A       Date:  2010-07-02       Impact factor: 11.205

10.  Bacterial persistence: a model of survival in changing environments.

Authors:  Edo Kussell; Roy Kishony; Nathalie Q Balaban; Stanislas Leibler
Journal:  Genetics       Date:  2005-01-31       Impact factor: 4.562

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  19 in total

1.  Populations adapt to fluctuating selection using derived and ancestral allelic diversity.

Authors:  Wei-Hsiang Lin; Mark J Rocco; Amelia Bertozzi-Villa; Edo Kussell
Journal:  Evolution       Date:  2015-05-27       Impact factor: 3.694

Review 2.  Experimental Design, Population Dynamics, and Diversity in Microbial Experimental Evolution.

Authors:  Bram Van den Bergh; Toon Swings; Maarten Fauvart; Jan Michiels
Journal:  Microbiol Mol Biol Rev       Date:  2018-07-25       Impact factor: 11.056

3.  Eco-evolutionary dynamics of a population with randomly switching carrying capacity.

Authors:  Karl Wienand; Erwin Frey; Mauro Mobilia
Journal:  J R Soc Interface       Date:  2018-08       Impact factor: 4.118

4.  Costs of Clock-Environment Misalignment in Individual Cyanobacterial Cells.

Authors:  Guillaume Lambert; Justin Chew; Michael J Rust
Journal:  Biophys J       Date:  2016-08-23       Impact factor: 4.033

5.  Noise-driven growth rate gain in clonal cellular populations.

Authors:  Mikihiro Hashimoto; Takashi Nozoe; Hidenori Nakaoka; Reiko Okura; Sayo Akiyoshi; Kunihiko Kaneko; Edo Kussell; Yuichi Wakamoto
Journal:  Proc Natl Acad Sci U S A       Date:  2016-03-07       Impact factor: 11.205

6.  Robust, linear correlations between growth rates and β-lactam-mediated lysis rates.

Authors:  Anna J Lee; Shangying Wang; Hannah R Meredith; Bihan Zhuang; Zhuojun Dai; Lingchong You
Journal:  Proc Natl Acad Sci U S A       Date:  2018-04-02       Impact factor: 11.205

7.  The interplay of phenotypic variability and fitness in finite microbial populations.

Authors:  Ethan Levien; Jane Kondev; Ariel Amir
Journal:  J R Soc Interface       Date:  2020-05-13       Impact factor: 4.118

8.  To grow is not enough: impact of noise on cell environmental response and fitness.

Authors:  Nash Rochman; Fangwei Si; Sean X Sun
Journal:  Integr Biol (Camb)       Date:  2016-10-10       Impact factor: 2.192

9.  Stochastic expression of a multiple antibiotic resistance activator confers transient resistance in single cells.

Authors:  Imane El Meouche; Yik Siu; Mary J Dunlop
Journal:  Sci Rep       Date:  2016-01-13       Impact factor: 4.379

10.  Antibiotic resistance: a physicist's view.

Authors:  Rosalind Allen; Bartłomiej Waclaw
Journal:  Phys Biol       Date:  2016-08-11       Impact factor: 2.583

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