scholarly journals DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

Microbiome ◽  
2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Gustavo Arango-Argoty ◽  
Emily Garner ◽  
Amy Pruden ◽  
Lenwood S. Heath ◽  
Peter Vikesland ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Metagenomic Data ◽  
Learning Approach

scholarly journals DeepARG: A deep learning approach for predicting antibiotic resistance genes from metagenomic data

2017 ◽  
Author(s):  
G. A. Arango-Argoty ◽  
E. Garner ◽  
A. Pruden ◽  
L. S. Heath ◽  
P. Vikesland ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Genes ◽  
Agricultural Waste ◽  
Antibiotic Resistance Gene ◽  
False Negative ◽  
Antibiotic Resistance Genes ◽  
Direct Access ◽  
Metagenomic Data ◽  
Community Research

ABSTRACTGrowing concerns regarding increasing rates of antibiotic resistance call for global monitoring efforts. Monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is of particular interest as these media can serve as sources of potential novel antibiotic resistance genes (ARGs), as hot spots for ARG exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequence-based monitoring has recently enabled direct access and profiling of the total metagenomic DNA pool, where ARGs are identified or predicted based on the “best hits” of homology searches against existing databases. Unfortunately, this approach tends to produce high rates of false negatives. To address such limitations, we propose here a deep leaning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two models, deepARG-SS and deepARG-LS, were constructed for short read sequences and full gene length sequences, respectively. Performance evaluation of the deep learning models over 30 classes of antibiotics demonstrates that the deepARG models can predict ARGs with both high precision (>0.97) and recall (>0.90) for most of the antibiotic resistance categories. The models show advantage over the traditional best hit approach by having consistently much lower false negative rates and thus higher overall recall (>0.9). As more data become available for under-represented antibiotic resistance categories, the deepARG models’ performance can be expected to be further enhanced due to the nature of the underlying neural networks. The deepARG models are available both in command line version and via a Web server at http://bench.cs.vt.edu/deeparg. Our newly developed ARG database, deepARG-DB, containing predicted ARGs with high confidence and high degree of manual curation, greatly expands the current ARG repository. DeepARG-DB can be downloaded freely to benefit community research and future development of antibiotic resistance-related resources.AbbreviationsARGantibiotic resistance gene

scholarly journals HMD-ARG: hierarchical multi-task deep learning for annotating antibiotic resistance genes

Microbiome ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Yu Li ◽  
Zeling Xu ◽  
Wenkai Han ◽  
Huiluo Cao ◽  
Ramzan Umarov ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Third Party ◽  
Superior Performance ◽  
Beta Lactamase ◽  
Learning Framework ◽  
Sequence Encoding ◽  
Global Threat

Abstract Background The 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. Results Here, 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. Conclusions 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. 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/.

scholarly journals Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models

2021 ◽  
pp. 117001
Author(s):  
Jiyi Jang ◽  
Ather Abbas ◽  
Minjeong Kim ◽  
Jingyeong Shin ◽  
Young Mo Kim ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Genes ◽  
Antibiotic Resistance Genes ◽  
Learning Models ◽  
Recreational Beach

scholarly journals Genome-Resolved Metagenomics and Antibiotic Resistance Genes Analysis in Reclaimed Water Distribution Systems

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3477
Author(s):  
Changzhi Wang ◽  
Pei-Ying Hong
Keyword(s):  
Antibiotic Resistance ◽  
Relative Abundance ◽  
Resistance Genes ◽  
Distribution Systems ◽  
Water Distribution ◽  
Reclaimed Water ◽  
Water Distribution Systems ◽  
Antibiotic Resistance Genes ◽  
Quality Data ◽  
Metagenomic Data

Water reuse is increasingly pursued to alleviate global water scarcity. However, the wastewater treatment process does not achieve full removal of biological contaminants from wastewater, hence microorganisms and their genetic elements can be disseminated into the reclaimed water distribution systems (RWDS). In this study, reclaimed water samples are investigated via metagenomics to assess their bacterial diversity, metagenome-assembled genomes (MAGs) and antibiotic resistance genes (ARGs) at both point of entry (POE) and point of use (POU) in 3 RWDS. The number of shared bacterial orders identified by metagenome was higher at the POE than POU among the three sites, indicating that specific conditions in RWDS can cause further differentiation in the microbial communities at the end of the distribution system. Two bacterial orders, namely Rhizobiales and Sphingomonadales, had high replication rates in two of the examined RWDS (i.e., site A and B), and were present in higher relative abundance in POU than at POE. In addition, MAG and ARG relative abundance exhibited a strong correlation (R2 = 0.58) in POU, indicating that bacteria present in POU may have a high incidence of ARG. Specifically, resistance genes associated with efflux pump mechanisms (e.g., adeF and qacH) increased in its relative abundance from POU to POE at two of the RWDS (i.e., site A and B). When correlated with the water quality data that suggests a significantly lower dissolved organic carbon (DOC) concentration at site D than the other two RWDS, the metagenomic data suggest that low DOC is needed to maintain the biological stability of reclaimed water along the distribution network.

scholarly journals Ontology-Aware Deep Learning Enables Novel Antibiotic Resistance Gene Discovery Towards Comprehensive Profiling of ARGs

2021 ◽  
Author(s):  
Yuguo Zha ◽  
Cheng Chen ◽  
Qihong Jiao ◽  
Xiaomei Zeng ◽  
Xuefeng Cui ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Deep Learning ◽  
Resistance Gene ◽  
Resistance Genes ◽  
Antibiotic Resistance Gene ◽  
Antibiotic Resistance Genes ◽  
Oral Microbiome ◽  
Gram Positive Bacteria ◽  
Wet Lab ◽  
Deep Learning Model

Antibiotic resistance genes (ARGs) have emerged in pathogens and spread faster than expected, arousing a worldwide concern. Current methods are suitable mainly for the discovery of close homologous ARGs and have limited utility for discovery of novel ARGs, thus rendering the profiling of ARGs incomprehensive. Here, an ontology-aware deep learning model, ONN4ARG (http://onn4arg.xfcui.com/), is proposed for the discovery of novel ARGs based on multi-level annotations. Experiments based on billions of candidate microbial genes collected from various environments show the superiority of ONN4ARG in comprehensive ARG profiling. Enrichment analyses show that ARGs are both environment-specific and host-specific. For example, resistance genes for rifamycin, which is an important antibacterial agent active against gram-positive bacteria, are enriched in Actinobacteria and in soil environment. Case studies verified ONN4ARG's ability for novel ARG discovery. For example, a novel streptomycin resistance gene was discovered from oral microbiome samples and validated through wet-lab experiments. ONN4ARG provides a complete picture of the prevalence of ARGs in microbial communities as well as guidance for detection and reduction of the spread of resistance genes.

Quintuplex PCR to Detect Antibiotic Resistance Genes in Streptococcus pneumoniae

2016 ◽  
Vol 1 (2) ◽  
pp. 22 ◽  
Author(s):  
Navindra Kumari Palanisamy ◽  
Parasakthi Navaratnam ◽  
Shamala Devi Sekaran
Keyword(s):  
Antibiotic Resistance ◽  
Streptococcus Pneumoniae ◽  
Resistance Genes ◽  
Bacterial Pathogen ◽  
Antibiotic Resistance Genes ◽  
Pcr Amplification ◽  
Penicillin Resistance ◽  
Penicillin Binding Proteins ◽  
Efflux Mechanism ◽  
Mic Value

Introduction: Streptococcus pneumoniae is an important bacterial pathogen, causing respiratory infection. Penicillin resistance in S. pneumoniae is associated with alterations in the penicillin binding proteins, while resistance to macrolides is conferred either by the modification of the ribosomal target site or efflux mechanism. This study aimed to characterize S. pneumoniae and its antibiotic resistance genes using 2 sets of multiplex PCRs. Methods: A quintuplex and triplex PCR was used to characterize the pbp1A, ermB, gyrA, ply, and the mefE genes. Fifty-eight penicillin sensitive strains (PSSP), 36 penicillin intermediate strains (PISP) and 26 penicillin resistance strains (PRSP) were used. Results: Alteration in pbp1A was only observed in PISP and PRSP strains, while PCR amplification of the ermB or mefE was observed only in strains with reduced susceptibility to erythromycin. The assay was found to be sensitive as simulated blood cultures showed the lowest level of detection to be 10cfu. Conclusions: As predicted, the assay was able to differentiate penicillin susceptible from the non-susceptible strains based on the detection of the pbp1A gene, which correlated with the MIC value of the strains.

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