Evolutionary constraints in variable environments, from proteins to networks

Taute, Katja M.; Gude, Sebastian; Nghe, Philippe; Tans, Sander J.

doi:10.1016/j.tig.2014.04.003

Cited by 31 publications

(30 citation statements)

References 60 publications

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“…The fitness of an organism, however, is notably difficult to measure directly [2–4], especially when the selective pressure is weak or depends indirectly on an organism’s phenotype and external environment. In microorganisms, conventional measures of fitness typically rely on the instantaneous growth rate in a constant environment, but this approach fails to provide a complete measure of fitness that accounts for natural environmental fluctuations [5–7]. In order to optimally respond to unstable environments, cells have to optimize the fitness tradeoffs involved in the use of strategies –such as phase variation [8], phenotypic switching [9], or epigenetic inheritability [10]– which stochastically generate heterogeneity within the population at typically low rate to adapt to changing environments [11–13].…”

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confidence: 99%

Quantifying Selective Pressures Driving Bacterial Evolution Using Lineage Analysis

Lambert

Kussell

2015

Phys. Rev. X

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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.

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confidence: 99%

Quantifying Selective Pressures Driving Bacterial Evolution Using Lineage Analysis

Lambert

Kussell

2015

Phys. Rev. X

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“…For instance, bacteria can tolerate highly diverse conditions by recognizing specific combinations of stressors such as pH and osmotic pressure 3 , and plants can elongate above dense canopies by responding to particular combinations of light intensity and wavelength 4 . However, it is not straightforward to establish whether a particular regulatory network is able to optimally respond to the multiplicity of signals presented by a complex environment [5][6][7][8][9] , and consequently how evolutionary constraints limit the range of tolerated environments. Constraints on the adaptation abilities of regulatory networks have been studied both experimentally, by targeted mutagenesis or knock-out of its constituent components 7,10,11 , and computationally, by varying parameters in kinetic models [12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Evolutionary constraints in regulatory networks defined by partial order between phenotypes

Kogenaru

Nghe

et al. 2019

Preprint

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Gene regulation networks allow organisms to adapt to diverse environmental niches. However, the constraints underlying the evolution of regulatory phenotypes remain ill-defined both theoretically and experimentally. Here, we show that the concept of partial order identifies such constraints, and test the predictions by experimentally evolving an engineered signal-integrating network in multiple environments. We find that populations: 1) expand in fitness space along the Pareto-optimal front predicted by conflicts in regulatory demands, by fine-tuning binding affinities within the network, 2) expand beyond this constraint by changes in the network structure, thus allowing access to new fitness domains. Strikingly, the constraint predictions are based on whether the network output increases or decreases in response to the different signals, and do not require information on the network architecture or underlying genetics. Overall, our findings show that limited knowledge on current regulatory phenotypes can provide predictions on future evolutionary constraints.

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“…The fitness of an organism, however, is notably difficult to measure directly [2][3][4], especially when the selective pressure is weak or depends indirectly on an organism's phenotype and external environment. In microorganisms, conventional measures of fitness typically rely on the instantaneous growth rate in a constant environment, but this approach fails to provide a complete measure of fitness that accounts for natural environmental fluctuations [5][6][7]. In order to optimally adapt to changing environments, cells have to optimize the fitness trade-offs involved in the use of strategies such as phase variation [8], phenotypic switching [9], or epigenetic inheritability [10], which stochastically generate heterogeneity within the population at typically low rate in response to unstable environments [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

History is Written by the Survivors

Salman

2015

Physics

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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 Escherichia 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.

show abstract

Evolutionary constraints in variable environments, from proteins to networks

Cited by 31 publications

References 60 publications

Quantifying Selective Pressures Driving Bacterial Evolution Using Lineage Analysis

Quantifying Selective Pressures Driving Bacterial Evolution Using Lineage Analysis

Evolutionary constraints in regulatory networks defined by partial order between phenotypes

History is Written by the Survivors

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