Dataset Information

Evolutionary trajectories of beta-lactamase CTX-M-1 cluster enzymes: predicting antibiotic resistance.

ABSTRACT: 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.

SUBMITTER: Novais A

PROVIDER: S-EPMC2809773 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Evolutionary trajectories of beta-lactamase CTX-M-1 cluster enzymes: predicting antibiotic resistance.

Novais Angela A Comas Iñaki I Baquero Fernando F Cantón Rafael R Coque Teresa M TM Moya Andrés A González-Candelas Fernando F Galán Juan-Carlos JC

PLoS pathogens 20100122 1

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 sp ...[more]

PMID: 20107608

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Project description: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.

Project description:β-lactam resistance is a significant global public health issue. Outbreaks of bacteria resistant to extended-spectrum β-lactams and carbapenems are serious health concerns that not only complicate medical care but also impact patient outcomes. The primary objective of this work was to express and purify two soluble recombinant representative serine β‑lactamases using Escherichia coli strain as an expression host and pET101/D as a cloning vector. Furthermore, a second objective was to evaluate the potential, innovative, and safe use of galloylquinic acid (GQA) from Copaifera lucens as a potential β-lactamase inhibitor.In the present study, blaCTX-M-15 and blaKPC-2 represented genes encoding for serine β-lactamases that were cloned from parent isolates of E. coli and K. pneumoniae, respectively, and expression as well as purification were performed. Moreover, susceptibility results demonstrated that recombinant cells became resistant to all test carbapenems (MICs; 64-128 µg/mL) and cephalosporins (MICs; 128-512 µg/mL). The MICs of the tested β-lactam antibiotics were determined in combination with 4 µg/mL of GQA, clavulanic acid, or tazobactam against E. coli strains expressing CTX-M-15 or KPC-2-β-lactamases. Interestingly, the combination with GQA resulted in an important reduction in the MIC values by 64-512-fold to the susceptible range with comparable results for other reference inhibitors. Additionally, the half-maximal inhibitory concentration of GQA was determined using nitrocefin as a β-lactamase substrate. Data showed that the test agent was similar to tazobactam as an efficient inhibitors of the test enzymes, recording smaller IC50 values (CTX-M-15; 17.51 for tazobactam, 28.16 µg/mL for GQA however, KPC-2; 20.91 for tazobactam, 24.76 µg/mL for GQA) compared to clavulanic acid. Our work introduces GQA as a novel non-β-lactam inhibitor, which interacts with the crucial residues involved in β-lactam recognition and hydrolysis by non-covalent interactions, complementing the enzyme's active site. GQA markedly enhanced the potency of β-lactams against carbapenemase and extended-spectrum β-lactamase-producing strains, reducing the MICs of β-lactams to the susceptible range. The β-lactamase inhibitory activity of GQA makes it a promising lead molecule for the development of more potent β-lactamase inhibitors.

Dataset Information

Evolutionary trajectories of beta-lactamase CTX-M-1 cluster enzymes: predicting antibiotic resistance.

Publications

Evolutionary trajectories of beta-lactamase CTX-M-1 cluster enzymes: predicting antibiotic resistance.

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