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The spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to causedeaths each year globally. Its surrogates, antibiotic resistance genes ARGsare 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 Rdsistance 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.

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. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved Antibiotic Resistance And How We Can Develop, 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. We 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.

Introduction

We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. The spread of antibiotic resistance has become one of the most pressing threats to global health, estimated to causedeaths Antibioitc year globally, with this number projected to increase to 10 million by if no action is taken [ 12 ]. Antibiotic resistance genes ARGs are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics [ 34567 ]. Accurately identifying ARGs is thus an indispensable step to understanding the ecology and transmission of ARGs between environmental and human-associated reservoirs [ 8 ].

Background

Metagenomic high-throughput sequencing technologies have provided a quick and sensitive way to explore the ARGs in a single genome or metagenomic samples, and many bioinformatic tools were proposed to annotate genes from the metagenomic datasets. Most methods fall into two categories: assembly-based methods, those that assemble sequencing reads into contiguous fragments and then search against reference databases, and the read-based methods, those that align raw reads Devekop reference alleles directly [ 9 ].

Such methods scale well for complex metagenomic samples, but they may cause a large number of false positives due to local sequence similarity.]