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The Sources and Potential Hosts Identification of Antibiotic Resistance Genes in the Yellow River, Revealed by Metagenomic Analysis.

ABSTRACT: The fate of antibiotic resistance genes (ARGs) has been revealed in various environmental media in recent years. Namely, the emergence of genes that resist colistin and carbapenems has attracted wide attention. However, the pollution condition of ARGs and sources in the Yellow River is still little understood, despite the river being the second longest in China. The present study determined the levels of ARG pollution in the Henan section of the Yellow River and evaluated the role of the aquaculture industry in the spread of ARGs. As revealed by the results, a total of 9 types of ARGs were detected in the sediments of the Yellow River, and the total ARG content in the Yellow River ranges from 7.27 to 245.45 RPKM. Sul1 and sul2 are the dominant ARGs, and the huge usage of sulfonamides, horizontal gene transfer, and wide bacteria host contribute to the prevalence of these two genes. The results of Spearman correlation analysis indicate that the breeding industry has little influence on ARGs in the Yellow River. Network analysis reveals that the opportunistic pathogen Pseudomonas is the potential host of sul1, tetG, and ANT(3'')-IIa, which can pose a risk to human health.

SUBMITTER: Zhang K

PROVIDER: S-EPMC9408424 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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The Sources and Potential Hosts Identification of Antibiotic Resistance Genes in the Yellow River, Revealed by Metagenomic Analysis.

Zhang Kai K Li Kuangjia K Liu Ziyi Z Li Qidi Q Li Wenpeng W Chen Qi Q Xia Yangchun Y Hu Feiyue F Yang Fengxia F

International journal of environmental research and public health 20220821 16

The fate of antibiotic resistance genes (ARGs) has been revealed in various environmental media in recent years. Namely, the emergence of genes that resist colistin and carbapenems has attracted wide attention. However, the pollution condition of ARGs and sources in the Yellow River is still little understood, despite the river being the second longest in China. The present study determined the levels of ARG pollution in the Henan section of the Yellow River and evaluated the role of the aquacul ...[more]

PMID: 36012061

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Project description:Yellow-feathered chickens have great nutritional value and are widely and traditionally used in China, on an industrial scale as broilers. The presence of intestinal microbes has been shown to correlate with poultry performance and serves as an essential reservoir of antibiotic resistance genes (ARGs). Antibiotic resistance is a major public health concern. Here, we investigated functional characteristics of the gut microbiome of indigenous Chinese yellow-feathered chickens (the Huiyang Bearded, Xinghua, Huaixiang, Zhongshan Shanlan, Qingyuan Partridge, and Yangshan chickens) through metagenomic sequencing and reconstructed 409 draft genomes, including 60 novel species and 6 novel genera. Furthermore, we assessed the functions of the intestinal microbial communities and examined the ARGs within them. The results showed that the microbial populations of yellow-feathered broilers were primarily dominated by Bacteroidetes and Firmicutes at the phylum level and Bacteroides at the genus level. Furthermore, the Qingyuan Partridge chicken showed a significantly higher abundance of Prevotella than the other five breeds of chicken. Principal coordinates analysis indicated significant differences in the structures of microbial communities and ARGs, based on the binary Jaccard distance, among the six chicken breeds. Moreover, 989 ARGs conferring tetracycline, multidrug, and aminoglycoside resistance were identified, which represented more than 80% of the faecal resistomes; the most abundant gene in the yellow-feathered chickens was tet(Q). In addition, we found the greatest abundance of resistance genes in Xinghua chickens, indicating that Xinghua chickens are highly resistant to antibiotics. Overall, our findings revealed differences in the gut microbial community structure of indigenous Chinese yellow-feathered broiler breeds and the composition and characteristics of ARGs and antibiotic resistance that enabled us to reconstruct the yellow-feathered chicken gut microbial community genomes. The current data significantly improves our knowledge of the gut microbiome and antibiotic resistance of popular broiler breeds in China.

Project description:IntroductionThe Brahmaputra, a major transboundary river of the Himalayas flowing predominantly through Northeast India, particularly Assam, is increasingly endangered by contamination due to rapid urbanization and anthropogenic pressures. These environmental changes pose significant risks at the microbial level, affecting nutrient cycling and productivity, and thereby impacting river ecosystem health. The next-generation sequencing technology using a metagenomics approach has revolutionized our understanding of the microbiome and its critical role in various aquatic environments.MethodsThe present study aimed to investigate the structure of the bacterial community and its functional potentials within the sediments of the Brahmaputra River, India, using high-throughput shotgun metagenomics. Additionally, this study sought to explore the presence of antimicrobial resistance genes in the river's sediment.Results and discussionShotgun metagenomics revealed a diverse bacterial community comprising 31 phyla, 52 classes, 291 families, 1,016 genera, and 3,630 species. Dominant phyla included Pseudomonadota (62.47-83.48%), Actinobacteria (11.10-24.89%), Bacteroidetes (0.97-3.82%), Firmicutes (0.54-3.94%), Cyanobacteria (0.14-1.70%), and Planctomycetes (0.30-0.78%). Functional profiling highlighted significant involvement in energy metabolism, amino acid and central carbon metabolism, stress response, and degradation pathways, emphasizing the microbial community's role in ecosystem functioning and resilience. Notably, 50 types of antibiotic resistance genes (ARGs) were detected, with resistance profiles spanning multidrug, aminoglycoside, β-lactam, fluoroquinolone, rifampicin, sulfonamide, and tetracycline classes. Network analysis underscored the intricate relationships among ARG subtypes, suggesting potential mechanisms of resistance propagation. Furthermore, plasmid-related genes and 185 virulence factor genes (VFGs) were identified, indicating additional layers of microbial adaptation and potential pathogenicity within the river sediments. This comprehensive microbial and functional profiling of the Brahmaputra's sediment metagenome provides crucial insights into microbial diversity, resistance potential, and ecological functions, offering a foundation for informed management and mitigation strategies to preserve river health and mitigate pollution impacts.

Project description:BackgroundAssembly of metagenomic samples can provide essential information about the mobility potential and taxonomic origin of antibiotic resistance genes (ARGs) and inform interventions to prevent further spread of resistant bacteria. However, similar to other conserved regions, such as ribosomal RNA genes and mobile genetic elements, almost identical ARGs typically occur in multiple genomic contexts across different species, representing a considerable challenge for the assembly process. Usually, this results in many fragmented contigs of unclear origin, complicating the risk assessment of ARG detections. To systematically investigate the impact of this issue on detection, quantification and contextualization of ARGs, we evaluated the performance of different assembly approaches, including genomic-, metagenomic- and transcriptomic-specialized assemblers. We quantified recovery and accuracy rates of each tool for ARGs both from in silico spiked metagenomic samples as well as real samples sequenced using both long- and short-read sequencing technologies.ResultsThe results revealed that none of the investigated tools can accurately capture genomic contexts present in samples of high complexity. The transcriptomic assembler Trinity showed a better performance in terms of reconstructing longer and fewer contigs matching unique genomic contexts, which can be beneficial for deciphering the taxonomic origin of ARGs. The currently commonly used metagenomic assembly tools metaSPAdes and MEGAHIT were able to identify the ARG repertoire but failed to fully recover the diversity of genomic contexts present in a sample. On top of that, in a complex scenario MEGAHIT produced very short contigs, which can lead to considerable underestimation of the resistome in a given sample.ConclusionsOur study shows that metaSPAdes and Trinity would be the preferable tools in terms of accuracy to recover correct genomic contexts around ARGs in metagenomic samples characterized by uneven coverages. Overall, the inability of assemblers to reconstruct long ARG-containing contigs has impacts on ARG quantification, suggesting that directly mapping reads to an ARG database should be performed as a complementary strategy to get accurate ARG abundance and diversity measures.

Project description:BackgroundGrowing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively.ResultsEvaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories.ConclusionsThe deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg .

Dataset Information

The Sources and Potential Hosts Identification of Antibiotic Resistance Genes in the Yellow River, Revealed by Metagenomic Analysis.

Publications

The Sources and Potential Hosts Identification of Antibiotic Resistance Genes in the Yellow River, Revealed by Metagenomic Analysis.

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