Evolving generalists in switching rugged landscapes

Area of Science:

• Evolutionary biology
• Computational biology
• Population genetics

Background:

• Evolving systems constantly adapt to changing environments, seeking solutions in dynamic fitness landscapes.
• Generalists maintain fitness across varied selective pressures, analogous to broadly neutralizing antibodies or multi-drug resistant microbes.
• Understanding conditions favoring the evolution of generalists in complex, time-varying landscapes remains a challenge.

Purpose of the Study:

• To investigate how environmental cycling influences the evolution of generalists in rugged fitness landscapes.
• To determine the role of epistasis in non-randomly varying environments.
• To develop a model for predicting the emergence of adaptable genotypes.

Main Methods:

• Development of a generative model to assess the propensity of evolving generalists.
• Analysis of rugged fitness landscapes with tunable relatedness and slow environmental alternation.
• Estimation of environmental correlations from experimental fitness landscape topography.

Main Results:

• Environmental cycling significantly enhances the search for fit generalists by expanding their effective basins of attraction.
• Optimal emergence of high-performing generalists occurs at intermediate levels of landscape ruggedness and environmental relatedness.
• The study provides a method to estimate environmental correlations from fitness landscape data.

Conclusions:

• Environmental cycling is a key factor facilitating the evolution of adaptable generalists.
• Intermediate ruggedness and environmental relatedness are optimal conditions for generalist emergence.
• This framework offers strategies for developing broadly neutralizing antibodies and combating antibiotic resistance.