Tracking the Sources of Antibiotic Resistance Genes in an Urban Stream during Wet Weather using Shotgun Metagenomic Analyses

Baral, Darshan; Dvorak, Bruce I.; Admiraal, David M.; Jia, Shangang; Zhang, Chi; Li, Xu

doi:10.1021/acs.est.8b01219

Cited by 74 publications

(36 citation statements)

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“…In addition to domains, one could also easily imagine splitting datasets for mSourceTracker analysis by phylogenetic groups at a lower taxonomic level (e.g., methanogens or the proteobacteria) or even using non-organismal datasets (e.g., untargeted chemical or metabolic datasets). This is similar in principle to the approach taken by previous research studying the origins of antibiotic resistance markers (Gou et al, 2018;Baral et al, 2018;Li, Yin & Zhang, 2018). The identification of distinct source origins for different taxonomic groups in the same ''sink'' samples is not without precedent in the literature.…”

Section: Discussionmentioning

confidence: 65%

“…SourceTracker was designed for use with bacterial 16S rRNA marker genes and has primarily been used with these data. However, it has been applied to a few shotgun metagenomic studies, including one that tracked the source origins of antibiotic resistance gene markers (Baral et al, 2018). While more expensive to generate and more computationally intensive to analyze, shotgun metagenomic data provide a much broader potential array of microbial diversity (Bacteria, Archaea, Eukaryota, and viruses) for use in microbial source tracking.…”

Section: Introductionmentioning

confidence: 99%

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Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics

McGhee

Rawson

Bailey

et al. 2020

PeerJ

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Background Microbial source tracking methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach uses deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an extensive diversity of studies, from beach contamination to human behavior. Methods Here, we demonstrate a novel application of SourceTracker to work with metagenomic datasets and tested this approach using sink samples from a study of coastal marine environments. Source environment metagenomes were obtained from metagenomics studies of gut, freshwater, marine, sand and soil environments. As part of this effort, we implemented features for determining the stability of source proportion estimates, including precision visualizations for performance optimization, and performed domain-specific source-tracking analyses (i.e., Bacteria, Archaea, Eukaryota and viruses). We also applied SourceTracker to metagenomic libraries generated from samples collected from the International Space Station (ISS). Results SourceTracker proved highly effective at predicting the composition of known sources using shotgun metagenomic libraries. In addition, we showed that different taxonomic domains sometimes presented highly divergent pictures of environmental source origins for both the coastal marine and ISS samples. These findings indicated that applying SourceTracker to separate domains may provide a deeper understanding of the microbial origins of complex, mixed-source environments, and further suggested that certain domains may be preferable for tracking specific sources of contamination.

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Section: Discussionmentioning

confidence: 65%

Section: Introductionmentioning

confidence: 99%

Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics

McGhee

Rawson

Bailey

et al. 2020

PeerJ

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“…S7). SourceTracker analysis has been performed to identify the extent of contribution of each source to the sink, as previously reported [36][37][38]. It can be used for estimating the relative contributions of taxa and ARGs from exposed environments (sources) in the human airway sputum (sinks).…”

Section: Sourcetracker Analysis Of Bacteria and Antibiotic Resistancementioning

confidence: 99%

Antibiotic resistance associated with air quality and transferred by airborne particulate matter

Zhou

Liu

Lin

et al. 2020

Preprint

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Background: Environmental spread of antibiotic resistance has become a public health problem, but the relationship between air quality and antibiotic resistance has not been fully investigated, the effect and mechanism of air pollutants on horizontal genes transfer of antibiotic resistance are rarely mentioned. Results: In the present study, significant correlation ( P < 0.05) between resistance rates of 10 clinical antibiotic-resistant pathogens and number of days when air quality index was below 100 were observed in an extensive survey from 2014 to 2017 in China. In addition, characterization of antibiotic resistance genes (ARGs), mobile genetic elements (MGEs), and bacterial communities in airborne particulate matter (PM), dust, and human airway sputum samples were profiled in a hospital. PM 2.5 and PM 10 were shown to contain ARGs and MGEs high relative abundance and diversity index. Importantly, transferable multi-resistant plasmids were identified from air, e.g., pTAir-3 with 26 MGEs and 10 ARGs, using conjugative mating assays and nanopore sequencing . Furthermore, PM 2.5 and PM 10 significantly enhanced the conjugative transfer frequencies between bacteria, via inducing increased levels of reactive oxygen species and cell membrane permeability; upregulated gene expression levels were confirmed by genome-wide RNA sequencing. Conclusions: These findings provide a new perspective on antibiotic resistance research and have profound implications for antibiotic resistance control in clinics and environments.

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“…One could also easily imagine splitting not only by domain, but also by specific phylogenetic groups (e.g., methanogens or the proteobacteria) or even multiple independent datasets (e.g., untargeted chemical or metabolic datasets). This is similar in principle to the approach taken by previous research to study the origins of antibiotic resistance markers (Gou et al, 2018;Baral et al, 2018;Li, Yin & Zhang, 2018). The identification of distinct origin sources for different taxonomic groups in the same "sink" samples is not without precedent in the literature.…”

Section: Discussionmentioning

confidence: 70%

“…SourceTracker was Abstract    designed for use with bacterial 16S rRNA marker genes and has primarily been used with these data. However, it has been applied to a few shotgun metagenomic studies, including one that tracked the source origins of antibiotic resistance gene markers (Baral et al, 2018). While more expensive and computationally intensive, shotgun metagenomic data allows for a much broader potential array of microbial diversity (bacteria, archaea, eukaryotes and viruses) to be used in microbial source tracking.…”

Section: Introductionmentioning

confidence: 99%

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Background. Microbial source tracking methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach uses deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an extensive diversity of studies, from beach contamination to studying human behavior. Methods. Here, we developed metagenomic-SourceTracker (mSourceTracker), an expanded SourceTracker approach for shotgun metagenomic datasets. We tested mSourceTracker using sink samples from coastal marine environment metagenomes and source environment metagenomes collected from freshwater, marine, soil, sand and gut environments. We also implemented features for determining the stability of source proportion estimates using new techniques that split metagenomic data for domain-specific analyses (i.e., Bacteria, Archaea, Eukarya and viruses). The added features allow users to visualize the precision of mSourceTracker and assess ways to optimize performance. Results.Our results found mSourceTracker to be highly effective at predicting the composition of known sources using shotgun metagenomic libraries. In addition, we showed that different taxonomic domains sometimes presented highly divergent pictures of source origins. These findings indicated that applying mSourceTracker to separate domains may provide a deeper understanding of the microbial origins of complex, mixed-source environments, and further suggested that certain domains may be preferable for tracking specific sources of contamination.

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Tracking the Sources of Antibiotic Resistance Genes in an Urban Stream during Wet Weather using Shotgun Metagenomic Analyses

Cited by 74 publications

References 74 publications

Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics

Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics

Antibiotic resistance associated with air quality and transferred by airborne particulate matter

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