Environment determines evolutionary trajectory in a constrained phenotypic space

Fraebel, David T.; Mickalide, Harry; Schnitkey, Diane; Merritt, Jason; Kuhlman, Thomas E.; Kuehn, Seppe

doi:10.7554/elife.24669

Cited by 87 publications

(124 citation statements)

References 73 publications

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“…12 of those targets were affected in multiple strains, and of those, 8 were affected across strains with different selection conditions. Unlike previous studies [22], we did not observe mutations with an obvious interpretation in terms of their impact on evolved phenotypes. Therefore, we took a statistical approach to interpreting our sequencing data.…”

Section: Mutations Present In Evolved Strains Cannot Predict Phenotypcontrasting

confidence: 99%

“…Population-level migration through soft agar depends on both growth and motility of individual cells. Therefore, selection for faster migration can be driven by enhancements to growth rate, chemotactic response and undirected motility [15,22]. For example, increases in running speed or tumble frequency are known to drive faster migration through soft agar [23].…”

Section: What Makes a Generalist?mentioning

confidence: 99%

“…Reads were demultiplexed and adapter trimmed with the onboard Illumina software. Sequence data for the ancestral strain was used from our previous work, where we sequenced the founder with an average depth of 553x by aggregating reads from four separate sequencing reactions [22].…”

Section: Whole Genome Sequencing and Analysismentioning

confidence: 99%

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Evolution of generalists by phenotypic plasticity

Fraebel

Gowda

Mani³

et al. 2019

Preprint

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Adapting organisms face a tension between specializing their phenotypes for certain ecological tasks or developing generalist strategies which permit persistence in multiple environmental conditions. Understanding when and how generalists or specialists evolve is therefore an important question in evolutionary dynamics. Here we study the evolution of bacterial range expansions by selecting Escherichia coli for faster migration through porous media containing one of four different sugars supporting growth and chemotaxis. We find that selection in any one sugar drives the evolution of faster migration in all sugars relative to the ancestral strain. Measurements of growth and motility of all evolved lineages in all nutrient conditions reveal that the ubiquitous evolution of fast migration arises via phenotypic plasticity. Phenotypic plasticity permits evolved strains to exploit distinct phenotypic strategies to achieve fast migration in each environment irrespective of the environment in which they were evolved. Our work suggests that plasticity plays an important role in the evolution of generalist phenotypes.

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Section: Mutations Present In Evolved Strains Cannot Predict Phenotypcontrasting

confidence: 99%

Section: What Makes a Generalist?mentioning

confidence: 99%

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Evolution of generalists by phenotypic plasticity

Fraebel

Gowda

Mani³

et al. 2019

Preprint

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“…These laws constrain evolutionary trajectories in trait space of species [48,49], i.e. a species cannot adopt any combination of strategies at the same time.…”

Section: Introductionmentioning

confidence: 99%

Trade-off shapes diversity in eco-evolutionary dynamics

Farahpour

Saeedghalati

Brauer

et al. 2017

Preprint

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We introduce an Interaction and Trade-off based Eco-Evolutionary Model (ITEEM), in which species are competing for resources in a well-mixed system, and their evolution in interaction trait space is subject to a life-history trade-off between replication rate and competitive ability.We demonstrate that the strength of the trade-off has a fundamental impact on eco-evolutionary dynamics, as it imposes four phases of diversity, including a sharp phase transition. Despite its minimalism, ITEEM produces without further ad hoc features a remarkable range of observed patterns of eco-evolutionary dynamics. Most notably we find self-organization towards structured communities with high and sustainable diversity, in which competing species form interaction cycles similar to rock-paper-scissors games.

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“…A quantitative analysis of particulate trajectory data is increasingly common in biological studies. This type of data spans length and time scales from bacterial chemotaxis (1)(2)(3)(4)(5)(6) to the motion of ms2 spots in Drosophila embryos (7). When analyzing particulate trajectories, on a basic level, one would like to know how the particles are moving (e.g.…”

Section: Introductionmentioning

confidence: 99%

A jump distance based parameter inference scheme for particulate trajectories in biological settings

Menssen

Mani

2017

Preprint

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Modern biology is a treasure trove of data. With all this data, it is helpful to have analytical tools that are applicable regardless of context. One type of data that needs more quantitative analytical tools is particulate trajectories. This type of data appears in many different contexts and across scales in biology: from inferring statistics of a bacteria performing chemotaxis to the mobility of ms2 spots within nuclei. Presently, most analyses performed on data of this nature has been limited to mean square displacement (MSD) analyses. While simple, MSD analysis has several pitfalls, including difficulty in selecting between competing models, how to handle systems with multiple distinct sub-populations, and parameter extraction from limited time-series data sets. Here, we provide an alternative to MSD analysis using the jump distance distribution (JDD) [1,2]. The JDD resolves several issues: one can select between competing models of motion, have composite models that allow for multiple populations, and have improved error bounds on parameter estimates when data is limited. A major consequence is that you can perform analyses using a fraction of the data required to get similar results using MSD analyses, thereby giving access to a larger range of temporal dynamics when the underlying stochastic process is not stationary. In this paper, we construct and validate a derivation of the JDD for different transport models, explore the dependence on dimensionality of the process (1-3 dimensions), and implement a parameter estimation and model selection scheme. Finally, we discuss extensions of our scheme and its applications to biological data. Author summaryMean square displacement (MSD) analyses have been the standard for analyzing particulate trajectories, where its shortcomings have been overlooked in light of its simplicity. The Jump Distance Distribution (JDD) has been proposed by others in the past as a new way to analyze particulate trajectories, but has not been sufficiently analyzed in varying numbers of dimensions or given a robust analysis on performance and how it compares to MSD analysis. We present the forms of the JDD in 1, 2, and 3 dimensions for three different models for transport: pure diffusion, directed diffusion, and anomalous diffusion. We also discuss how to select between competing models, and verify our method with a rigorous analysis. Through this, we have a method that is superior to a MSD analysis. This method works across a wide range of parameters, which should make it broadly applicable to any system where the underlying motion is stochastic.

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Environment determines evolutionary trajectory in a constrained phenotypic space

Cited by 87 publications

References 73 publications

Evolution of generalists by phenotypic plasticity

Evolution of generalists by phenotypic plasticity

Trade-off shapes diversity in eco-evolutionary dynamics

A jump distance based parameter inference scheme for particulate trajectories in biological settings

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