Co-occurrence of antibiotic and metal resistance genes revealed in complete genome collection

Li, Li-Guan; Xia, Yu; Zhang, Tong

doi:10.1038/ismej.2016.155

Cited by 390 publications

(205 citation statements)

References 72 publications

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“…High quality ARGs (High): A UNI-gene is tagged with a "High" annotation factor if it has ≥ 90% identity to a CARD/ARDB-ARG over its entire length. This similarity cutoff has been used in other studies to identify relevant ARGs (Pal et al, 2015;Li et al, 2017) and is stricter than that used in the construction of the ARDB database (Liu and Pop, 2009).…”

Section: Uniprot Gene Annotationmentioning

confidence: 99%

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

Arango-Argoty

Garner

Pruden

et al. 2017

Preprint

126

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Growing 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 sequencebased 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 . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/149328 doi: bioRxiv preprint first posted online Jun. 12, 2017; 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.

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Section: Uniprot Gene Annotationmentioning

confidence: 99%

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

Arango-Argoty

Garner

Pruden

et al. 2017

Preprint

126

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“…We further searched ARGs in all available NCBI 15,738 plasmids and other mobile genetic element (MGE) databases 35,36 by usearch v11.0, diamond 0.9.24 and blast 2.5.0+ [62][63][64] . The search cutoff was set for genomes and MGEs as e-value 1e-5, 90% aa similarity over 80% aa hit length; and for metagenomes as e-value 1e-7, 80% aa similarity over 75% aa hit length 7,8,37,65 . The abundance of ARGs was normalized into copy of genes per bacterial cell (ARGs-OAP 7,8 ) using Equation 1.…”

Section: Methodsmentioning

confidence: 99%

Choosing Your Battles: Which Resistance Genes Warrant Global Action?

Zhang

Yin

et al. 2019

Preprint

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The increasing accumulation of antibiotic resistance genes (ARGs) in pathogens poses a severe threat to the treatment of bacterial infections. However, not all ARGs do not pose the same threats to human health. Here, we present a framework to rank the risk of ARGs based on three factors: "anthropogenic enrichment", "mobility", and "host pathogenicity". The framework is informed by all available bacterial genomes (55,000), plasmids (16,000), integrons (3,000), and 850 metagenomes covering diverse global eco-habitats. The framework prioritizes 3% of all known ARGs in Rank I (the most at risk of dissemination amongst pathogens) and 0.3% of ARGs in Rank II (high potential emergence of new resistance in pathogens). We further validated the framework using a list of 38 ARG families previously identified as high risk by the World Health Organization and published literature, and found that 36 of them were properly identified as top risk (Rank I) in our approach. Furthermore, we identified 43 unreported Rank I ARG families that should be prioritized for public health interventions. Within the same gene family, homologous genes pose different risks, host range, and ecological distributions, indicating the need for high resolution surveillance into their sequence variants. Finally, to help strategize the policy interventions, we studied the impact of industrialization on high risk ARGs in 1,120 human gut microbiome metagenomes of 36 diverse global populations. Our findings suggest that current policies on controlling the clinical antimicrobial consumptions could effectively control Rank I, while greater antibiotic stewardship in veterinary settings could help control Rank II. Overall, our framework offered a straightforward evaluation of the risk posed by ARGs, and prioritized a shortlist of current and emerging threats for global action to fight ARGs.

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“…Metals such as lead, copper, zinc, cadmium, and metalloid arsenic are often used in agriculture and aquaculture (Singer, Shaw, Rhodes, & Hart, 2016). Since resistance to antibiotics and metals can share certain mechanisms (Singer et al, 2016), the phenomenon called coselection between them has been reported, and the stress of widespread metals in the environment may promote this phenomenon (Li, Xia, & Zhang, 2017). Especially, Cu and Zn that were reported to have an extensive positive correlation with antibiotic resistance (Ji et al, 2012;Wang et al, 2016;Zhu et al, 2013) in the samples from livestock farms.…”

Section: Correlations Between Antibiotic Resistance and Abiotic Facmentioning

confidence: 99%

Prevalence and proliferation of antibiotic resistance genes in the subtropical mangrove wetland ecosystem of South China Sea

Zhao

Yan

et al. 2019

MicrobiologyOpen

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The emerging pollutants antibiotic resistance genes (ARGs) are prevalent in aquatic environments such as estuary. Coastal mangrove ecosystems always serve as natural wetlands for receiving sewage which always carry ARGs. Currently, the research considering ARG distribution in mangrove ecosystems gains more interest. In this work, we investigated the diversity of ARGs in an urban estuary containing mangrove and nonmangrove areas of the South China Sea. A total of 163 ARGs that classified into 22 resistance types and six resistance mechanisms were found. ARG abundance of the samples in the estuary is between 0.144 and 0.203. This is within the general range of Chinese estuaries. The difference analysis showed that abundances of total ARGs, six most abundant ARGs (mtrA, rpoB, rpoC, rpsL, ef‐Tu, and parY), the most abundant resistance types (elfamycin, multidrug, and peptide), and the most abundant resistance mechanism (target alteration) were significantly lower in mangrove sediment than that in nonmangrove sediment (p < 0.05). Network and partial redundancy analysis showed that sediment properties and mobile genetic elements were the most influential factors impacting ARG distribution rather than microbial community. The two factors collectively explain 51.22% of the differences of ARG distribution. Our study indicated that mangrove sediments have the capacity to remove ARGs. This work provides a research paradigm for analysis of ARG prevalence and proliferation in the subtropical marine coastal mangrove ecosystem.

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Co-occurrence of antibiotic and metal resistance genes revealed in complete genome collection

Cited by 390 publications

References 72 publications

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

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

Choosing Your Battles: Which Resistance Genes Warrant Global Action?

Prevalence and proliferation of antibiotic resistance genes in the subtropical mangrove wetland ecosystem of South China Sea

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