Dataset Information

Rhizosphere suppression hinders antibiotic resistance gene (ARG) spread under bacterial invasion.

ABSTRACT: The rhizosphere is an extremely important component of the "one health" scenario by linking the soil microbiome and plants, in which the potential enrichment of antibiotic resistance genes (ARGs) might ultimately flow into the human food chain. Despite the increased occurrence of soil-borne diseases, which can lead to increased use of pesticides and antibiotic-producing biocontrol agents, the understanding of the dynamics of ARG spread in the rhizosphere is largely overlooked. Here, tomato seedlings grown in soils conducive and suppressive to the pathogen Ralstonia solanacearum were selected as a model to investigate ARG spread in the rhizosphere with and without pathogen invasion. Metagenomics data revealed that R. solanacearum invasion increased the density of ARGs and mobile genetic elements (MGEs). Although we found ARGs originating from human pathogenic bacteria in both soils, the enrichment was alleviated in the suppressive soil. In summary, the suppressive soil hindered ARG spread through pathogen suppression and had a lower number of taxa carrying antibiotic resistance.

SUBMITTER: Li Y

PROVIDER: S-EPMC9845992 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Rhizosphere suppression hinders antibiotic resistance gene (ARG) spread under bacterial invasion.

Li Yuchan Y Deng Xuhui X Zhang Na N Shen Zongzhuan Z Li Rong R Shen Qirong Q Salles Joana Falcao JF

One health (Amsterdam, Netherlands) 20230102

The rhizosphere is an extremely important component of the "one health" scenario by linking the soil microbiome and plants, in which the potential enrichment of antibiotic resistance genes (ARGs) might ultimately flow into the human food chain. Despite the increased occurrence of soil-borne diseases, which can lead to increased use of pesticides and antibiotic-producing biocontrol agents, the understanding of the dynamics of ARG spread in the rhizosphere is largely overlooked. Here, tomato seedl ...[more]

PMID: 36683960

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Project description:BackgroundThe spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs.ResultsHere, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method.ConclusionsWe propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/ . Video abstract (MP4 50984 kb).

Dataset Information

Rhizosphere suppression hinders antibiotic resistance gene (ARG) spread under bacterial invasion.

Publications

Rhizosphere suppression hinders antibiotic resistance gene (ARG) spread under bacterial invasion.

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