Evolving generalists in switching rugged landscapes

Evolving systems, be it an antibody repertoire in the face of mutating pathogens or a microbial population exposed to varied antibiotics, constantly search for adaptive solutions in time-varying fitness landscapes. Generalists refer to genotypes that remain fit across diverse selective pressures; while multi-drug resistant microbes are undesired yet prevalent, broadly-neutralizing antibodies are much wanted but rare. However, little is known about under what conditions such generalists with a high capacity to adapt can be efficiently discovered by evolution. In addition, can epistasis-the source of landscape ruggedness and path constraints-play a different role, if the environment varies in a non-random way? We present a generative model to estimate the propensity of evolving generalists in rugged landscapes that are tunably related and alternating relatively slowly. We find that environmental cycling can substantially facilitate the search for fit generalists by dynamically enlarging their effective basins of attraction. Importantly, these high performers are most likely to emerge at intermediate levels of ruggedness and environmental relatedness. Our approach allows one to estimate correlations across environments from the topography of experimental fitness landscapes. Our work provides a conceptual framework to study evolution in time-correlated complex environments, and offers statistical understanding that suggests general strategies for eliciting broadly neutralizing antibodies or preventing microbes from evolving multi-drug resistance. Author summary Generalists are robust performers under varied environmental conditions, even though they are less fit than specialists in any particular environment. For better (e.g. induction of broadly neutralizing antibody response) or worse (e.g. emergence of multi-drug resistance in microbes), it is important to be able to evolve generalists efficiently. Yet, whether and when environmental changes select generalists over specialists remain largely unknown. Here we develop a dynamic landscape model to study the evolutionary discovery of generalists in time-varying correlated environments. We demonstrate that cycling rugged fitness landscapes can enhance the propensity of evolving fit generalists, via dynamic augmentation of their attractors. We find that high performers are most reliably evolved under intermediate environmental correlations, reflecting a tension between diversity and accessibility. Our approach offers design principles for choosing correlated environments in dynamic protocols to speed or slow generalist evolution in diverse contexts.