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Network models of TEM ?-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.

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 ?-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, ?-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.

SUBMITTER: Guthrie VB

PROVIDER: S-EPMC3178621 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.

Guthrie Violeta Beleva VB Allen Jennifer J Camps Manel M Karchin Rachel R

PLoS computational biology 20110922 9

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 (p ...[more]

PMID: 21966264

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Project description:Extended-spectrum beta-lactamases (ESBL) constitute a key antibiotic-resistance mechanism affecting Gram-negative bacteria, and also an excellent model for studying evolution in real time. A shift in the epidemiology of ESBLs is being observed, which is characterized by the explosive diversification and increase in frequency of the CTX-M-type beta-lactamases in different settings. This provides a unique opportunity for studying a protein evolutionary radiation by the sequential acquisition of specific mutations enhancing protein efficiency and fitness concomitantly. The existence of driver antibiotic molecules favoring protein divergence has been investigated by combining evolutionary analyses and experimental site-specific mutagenesis. Phylogenetic reconstruction with all the CTX-M variants described so far provided a hypothetical evolutionary scenario showing at least three diversification events. CTX-M-3 was likely the enzyme at the origin of the diversification in the CTX-M-1 cluster, which was coincident with positive selection acting on several amino acid positions. Sixty-three CTX-M-3 derivatives containing all combinations of mutations under positively selected positions were constructed, and their phenotypic efficiency was evaluated. The CTX-M-3 diversification process can only be explained in a complex selective landscape with at least two antibiotics (cefotaxime and ceftazidime), indicating the need to invoke mixtures of selective drivers in order to understand the final evolutionary outcome. Under this hypothesis, we found congruent results between the in silico and in vitro analyses of evolutionary trajectories. Three pathways driving the diversification of CTX-M-3 towards the most complex and efficient variants were identified. Whereas the P167S pathway has limited possibilities of further diversification, the D240G route shows a robust diversification network. In the third route, drift may have played a role in the early stages of CTX-M-3 evolution. Antimicrobial agents should not be considered only as selectors for efficient mechanisms of resistance but also as diversifying agents of the evolutionary trajectories. Different trajectories were identified using a combination of phylogenetic reconstructions and directed mutagenesis analyses, indicating that such an approach might be useful to fulfill the desirable goal of predicting evolutionary trajectories in antimicrobial resistance.

Dataset Information

Network models of TEM ?-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.

Publications

Network models of TEM β-lactamase mutations coevolving under antibiotic selection show modular structure and anticipate evolutionary trajectories.

Similar Datasets

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