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Record W2799614685 · doi:10.3389/fmicb.2018.00852

Human Activity Determines the Presence of Integron-Associated and Antibiotic Resistance Genes in Southwestern British Columbia

2018· article· en· W2799614685 on OpenAlexafffundabout
Miguel Uyaguari, Matthew A. Croxen, Zhiyao Luo, Kirby Cronin, Michael W.Y. Chan, Waren N. Baticados, Matthew J. Nesbitt, Shaorong Li, Kristina M. Miller, Damion Dooley, William Hsiao, Judith L. Isaac‐Renton, Patrick Tang, Natalie Prystajecky

Bibliographic record

VenueFrontiers in Microbiology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsPublic Health Agency of CanadaProvincial Laboratory of Public HealthVancouver Island UniversityUniversity of AlbertaPublic Health OntarioBC Centre for Disease ControlUniversity of British Columbia
FundersMitacsBritish Columbia Centre for Disease ControlGenome British ColumbiaGenome Canada
KeywordsResistomeMetagenomicsIntegronBiologyAntibiotic resistanceMicrobiomeMicrobial geneticsBacteriaMicrobiologyEcologyGeneGenetics

Abstract

fetched live from OpenAlex

The dissemination of antibiotic resistance bacteria from anthropogenic sources into the environment poses an emerging public health threat. Antibiotic resistance genes (ARGs) and gene-capturing systems such as integron-associated integrase genes (intI) play a key role in alterations of microbial communities and spread of antibiotic resistant bacteria into the environment. In order to assess the effect of anthropogenic activities on watersheds in southwestern British Columbia, the presence of putative antibiotic resistance and integrase genes was analyzed in the microbiome of agricultural, urban influenced and protected watersheds. A metagenomics approach and high-throughput quantitative PCR (HT qPCR) were used to screen for elements of resistance including ARGs and intI. Metagenomic sequencing of bacterial genomic DNA was used to characterize the resistome of microbial communities present in watersheds over a one-year period. There was a low prevalence of ARGs relative to the microbial population (<1%). Analysis of the metagenomic sequences detected a total of 60 elements of resistance including 46 ARGs, intI1 and groEL/intI1 genes and 12 quaternary ammonium compounds (qac) resistance genes across all watershed locations. The relative abundance and richness of ARGs was found to be highest in agriculture impacted watersheds compared to urban and protected watersheds. A downstream transport pattern was observed in the impacted watersheds (urban and agricultural) during dry months. Similar to other reports, this study found a strong association between intI1 and ARGs (e.g., sul1), an association which may be used as a proxy for anthropogenic activities. Chemical analysis of water samples for three major groups of antibiotics was below the detection limit. However, the high richness and gene copy numbers of ARGs in impacted sites suggest effects of effluents on microbial communities are occurring even at low concentrations of antimicrobials in the water column. Antibiotic resistance and integrase genes in a year-long metagenomic study showed that ARGs were driven mainly by environmental factors from anthropogenized sites in agriculture and urban watersheds. Environmental factors such as land-use and water quality parameters accounted for 45% of the variability observed in watershed locations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.317
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.243
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2018
Admission routes3
Has abstractyes

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