Abstract
Understanding how novel functions evolve (genetic adaptation) is a critical goal of evolutionary biology. Among asexual organisms, genetic adaptation involves multiple mutations that frequently interact in a non-linear fashion (epistasis). Non-linear interactions pose a formidable challenge for the computational prediction of mutation effects. Here we use the recent evolution of β-lactamase under antibiotic selection as a model for genetic adaptation. We build a network of coevolving residues (possible functional interactions), in which nodes are mutant residue positions and links represent two positions found mutated together in the same sequence. Most often these pairs occur in the setting of more complex mutants. Focusing on extended-spectrum resistant sequences, we use network-theoretical tools to identify triple mutant trajectories of likely special significance for adaptation. We extrapolate evolutionary paths (n = 3) that increase resistance and that are longer than the units used to build the network (n = 2). These paths consist of a limited number of residue positions and are enriched for known triple mutant combinations that increase cefotaxime resistance. We find that the pairs of residues used to build the network frequently decrease resistance compared to their corresponding singlets. This is a surprising result, given that their coevolution suggests a selective advantage. Thus, β-lactamase adaptation is highly epistatic. Our method can identify triplets that increase resistance despite the underlying rugged fitness landscape and has the unique ability to make predictions by placing each mutant residue position in its functional context. Our approach requires only sequence information, sufficient genetic diversity, and discrete selective pressures. Thus, it can be used to analyze recent evolutionary events, where coevolution analysis methods that use phylogeny or statistical coupling are not possible. Improving our ability to assess evolutionary trajectories will help predict the evolution of clinically relevant genes and aid in protein design.
Highlights
Evolutionary biology seeks to understand how proteins rapidly evolve novel functions and adapt to new environments, while retaining their functional specificity [1,2,3,4]
We collect a database of mutations that contribute to the evolution of b-lactamase resistance to inhibitors and to new b-lactam antibiotics in bacterial pathogens, such as Escherichia coli
We compiled a database of TEM b-lactamase sequences evolved under antibiotic pressure and identified functional interactions between individual residue positions
Summary
Evolutionary biology seeks to understand how proteins rapidly evolve novel functions and adapt to new environments, while retaining their functional specificity [1,2,3,4]. A few residues have a large impact on increasing fitness under selective conditions, whereas the contribution of most residues is more modest [6] The difference between these two classes of mutations cannot always be explained only by properties of the specific sites: The impact of mutations is context-dependent and reflects a complex network of interactions between multiple residues within a protein [1,4,7]. The reason is that acquisition of resistance to inhibitors and newer b-lactam antibiotics [7,8] requires only a small number of mutations. This is a system where the impacts of individual mutations on adaptive fitness can be readily assessed
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