USING COMPLEX SELECTION DYNAMICS TO REVEAL THE FITNESS LANDSCAPE AND CROSS EVOLUTIONARY VALLEYS

Nature repurposes proteins via evolutionary processes. During evolution, the fitness landscapes of proteins change dynamically. Selection for new functionality leaves the protein susceptible to genetic drift in the absence of selective pressure for the former function. Drift is considered to be a driver of evolution, and functional tradeoffs are common during selection. We measured the effect on ampicillin resistance of ~12,500 unique mutants of alleles of TEM-1 β-lactamase along an adaptive path in the evolution of cefotaxime resistance. This series of shifting protein fitness landscapes provides a systematic, quantitative description of genetic drift and pairwise/tertiary intragenic epistasis involving adaptive mutations. Our study provides insight into the relationships between mutation, protein structure, protein stability, epistasis, and drift and reveals the tradeoffs inherent in the evolution of new functions. We further use principles of ruggedness and dynamic change in fitness landscapes to develop and evaluate novel directed evolution strategies using complex selection dynamics. Interestingly, the strategy that included negative selection relative to the original landscape yielded more highly active variants of β-lactamase than the other four selection strategies. We reconstructed evolutionary pathways leading to this highly active allele, confirmed the presence of a fitness valley, and found an initially deleterious mutation that serves as an epistatic bridge to cross this fitness valley. The ability of negative selection and changing environments to provide access to novel fitness peaks has important implications for applied directed evolution as well as the natural evolutionary mechanisms, particularly of antibiotic resistance. We finally applied principles of the influence of selection environment to a clinically relevant system by comparing the evolutionary pathways of cells evolving competitively and continuously to single antibiotics, a cocktail of two antibiotics, or alternating cycles of the two antibiotics. We find evidence for distinct evolutionary pathways between antibiotic strategies. Specifically, we suggest that cocktail strategies appear to select for “specialists” of varying activity while cycling more stringently selects for “generalists.” We hypothesize that this result is due to separately emerging populations to evolve in distinct niches during cocktail therapies. Our results have direct relevance to informing clinical antibiotic regimens