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A kinase bioscavenger provides antibiotic resistance by extremely tight substrate binding.

ABSTRACT: Microbial communities are self-controlled by repertoires of lethal agents, the antibiotics. In their turn, these antibiotics are regulated by bioscavengers that are selected in the course of evolution. Kinase-mediated phosphorylation represents one of the general strategies for the emergence of antibiotic resistance. A new subfamily of AmiN-like kinases, isolated from the Siberian bear microbiome, inactivates antibiotic amicoumacin by phosphorylation. The nanomolar substrate affinity defines AmiN as a phosphotransferase with a unique catalytic efficiency proximal to the diffusion limit. Crystallographic analysis and multiscale simulations revealed a catalytically perfect mechanism providing phosphorylation exclusively in the case of a closed active site that counteracts substrate promiscuity. AmiN kinase is a member of the previously unknown subfamily representing the first evidence of a specialized phosphotransferase bioscavenger.

SUBMITTER: Terekhov SS

PROVIDER: S-EPMC7314540 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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A kinase bioscavenger provides antibiotic resistance by extremely tight substrate binding.

Terekhov Stanislav S SS Mokrushina Yuliana A YA Nazarov Anton S AS Zlobin Alexander A Zalevsky Arthur A Bourenkov Gleb G Golovin Andrey A Golovin Andrey A Belogurov Alexey A Osterman Ilya A IA Kulikova Alexandra A AA Mitkevich Vladimir A VA Lou Hua Jane HJ Turk Benjamin E BE Wilmanns Matthias M Smirnov Ivan V IV Altman Sidney S Gabibov Alexander G AG

Science advances 20200624 26

Microbial communities are self-controlled by repertoires of lethal agents, the antibiotics. In their turn, these antibiotics are regulated by bioscavengers that are selected in the course of evolution. Kinase-mediated phosphorylation represents one of the general strategies for the emergence of antibiotic resistance. A new subfamily of AmiN-like kinases, isolated from the Siberian bear microbiome, inactivates antibiotic amicoumacin by phosphorylation. The nanomolar substrate affinity defines Ami ...[more]

PMID: 32637600

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Project description:BackgroundThe interconnectivities of built and natural environments can serve as conduits for the proliferation and dissemination of antibiotic resistance genes (ARGs). Several studies have compared the broad spectrum of ARGs (i.e., "resistomes") in various environmental compartments, but there is a need to identify unique ARG occurrence patterns (i.e., "discriminatory ARGs"), characteristic of each environment. Such an approach will help to identify factors influencing ARG proliferation, facilitate development of relative comparisons of the ARGs distinguishing various environments, and help pave the way towards ranking environments based on their likelihood of contributing to the spread of clinically relevant antibiotic resistance. Here we formulate and demonstrate an approach using an extremely randomized tree (ERT) algorithm combined with a Bayesian optimization technique to capture ARG variability in environmental samples and identify the discriminatory ARGs. The potential of ERT for identifying discriminatory ARGs was first evaluated using in silico metagenomic datasets (simulated metagenomic Illumina sequencing data) with known variability. The application of ERT was then demonstrated through analyses using publicly available and in-house metagenomic datasets associated with (1) different aquatic habitats (e.g., river, wastewater influent, hospital effluent, and dairy farm effluent) to compare resistomes between distinct environments and (2) different river samples (i.e., Amazon, Kalamas, and Cam Rivers) to compare resistome characteristics of similar environments.ResultsThe approach was found to readily identify discriminatory ARGs in the in silico datasets. Also, it was not found to be biased towards ARGs with high relative abundance, which is a common limitation of feature projection methods, and instead only captured those ARGs that elicited significant profiles. Analyses of publicly available metagenomic datasets further demonstrated that the ERT approach can effectively differentiate real-world environmental samples and identify discriminatory ARGs based on pre-defined categorizing schemes.ConclusionsHere a new methodology was formulated to characterize and compare variances in ARG profiles between metagenomic data sets derived from similar/dissimilar environments. Specifically, identification of discriminatory ARGs among samples representing various environments can be identified based on factors of interest. The methodology could prove to be a particularly useful tool for ARG surveillance and the assessment of the effectiveness of strategies for mitigating the spread of antibiotic resistance. The python package is hosted in the Git repository: https://github.com/gaarangoa/ExtrARG.

Dataset Information

A kinase bioscavenger provides antibiotic resistance by extremely tight substrate binding.

Publications

A kinase bioscavenger provides antibiotic resistance by extremely tight substrate binding.

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