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Predictable properties of fitness landscapes induced by adaptational tradeoffs.

ABSTRACT: Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of Escherichia coli strains, and previous observations on antibiotic resistance mutations. Our model generates a succession of landscapes with predictable properties as antibiotic concentration is varied. The landscape is nearly smooth at low and high concentrations, but the tradeoff induces a high ruggedness at intermediate antibiotic concentrations. Despite this high ruggedness, however, all the fitness maxima in the landscapes are evolutionarily accessible from the wild type. This implies that selection for antibiotic resistance in multiple mutational steps is relatively facile despite the complexity of the underlying landscape.

SUBMITTER: Das SG

PROVIDER: S-EPMC7297540 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Predictable properties of fitness landscapes induced by adaptational tradeoffs.

Das Suman G SG Direito Susana Ol SO Waclaw Bartlomiej B Allen Rosalind J RJ Krug Joachim J

eLife 20200519

Fitness effects of mutations depend on environmental parameters. For example, mutations that increase fitness of bacteria at high antibiotic concentration often decrease fitness in the absence of antibiotic, exemplifying a tradeoff between adaptation to environmental extremes. We develop a mathematical model for fitness landscapes generated by such tradeoffs, based on experiments that determine the antibiotic dose-response curves of <i>Escherichia coli</i> strains, and previous observations on a ...[more]

PMID: 32423531

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Project description:BACKGROUND: Tumorigenesis requires multiple genetic changes. Mutator mutations are mutations that increase genomic instability, and according to the mutator hypothesis, accelerate tumorigenesis by facilitating oncogenic mutations. Alternatively, repeated lineage selection and expansion without increased mutation frequency may explain observed cancer incidence. Mutator lineages also risk increased deleterious mutations, leading to extinction, thus providing another counterargument to the mutator hypothesis. Both selection and extinction involve changes in lineage fitness, which may be represented as "trajectories" through a "fitness landscape" defined by genetics and environment. METHODOLOGY/PRINCIPAL FINDINGS: Here I systematically analyze the relative efficiency of tumorigenesis with and without mutator mutations by evaluating archetypal fitness trajectories using deterministic and stochastic mathematical models. I hypothesize that tumorigenic mechanisms occur clinically in proportion to their relative efficiency. This work quantifies the relative importance of mutator pathways as a function of experimentally measurable parameters, demonstrating that mutator pathways generally enhance efficiency of tumorigenesis. An optimal mutation rate for tumor evolution is derived, and shown to differ from that for species evolution. CONCLUSIONS/SIGNIFICANCE: The models address the major counterarguments to the mutator hypothesis, confirming that mutator mechanisms are generally more efficient routes to tumorigenesis than non-mutator mechanisms. Mutator mutations are more likely to occur early, and to occur when more oncogenic mutations are required to create a tumor. Mutator mutations likely occur in a minority of premalignant lesions, but these mutator premalignant lesions are disproportionately likely to develop into malignant tumors. Tumor heterogeneity due to mutator mutations may contribute to therapeutic resistance, and the degree of heterogeneity of tumors may need to be considered when therapeutic strategies are devised. The model explains and predicts important biological observations in bacterial and mouse systems, as well as clinical observations.

Dataset Information

Predictable properties of fitness landscapes induced by adaptational tradeoffs.

Publications

Predictable properties of fitness landscapes induced by adaptational tradeoffs.

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