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Record W4386887058 · doi:10.1128/aem.01047-23

Metagenome meta-analysis reveals an increase in the abundance of some multidrug efflux pumps and mobile genetic elements in chemically polluted environments

2023· review· en· W4386887058 on OpenAlexafffund
Jéssica Subirats, Hannah Sharpe, Vera Tai, Michael Fruci, Edward Topp

Bibliographic record

VenueApplied and Environmental Microbiology · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsAgriculture and Agri-Food CanadaWestern University
FundersAgriculture and Agri-Food CanadaGovernment of Canada
KeywordsEffluxMetagenomicsResistomePollutionBiologyRelative species abundancePollutantAbundance (ecology)Mobile genetic elementsAntibiotic resistanceEcologyAntibioticsEnvironmental chemistryGeneGeneticsChemistryGenome

Abstract

fetched live from OpenAlex

ABSTRACT Many human activities contaminate terrestrial and aquatic environments with numerous chemical pollutants that not only directly alter the environment but also affect microbial communities in ways that are potentially concerning to human health, such as selecting for the spread of antibiotic-resistance genes (ARGs) through horizontal gene transfer. In the present study, metagenomes available in the public domain from polluted (with antibiotics, with petroleum, with metal mining, or with coal-mining effluents) and unpolluted terrestrial and aquatic environments were compared to examine whether pollution has influenced the abundance and composition of ARGs and mobile elements, with specific focus on IS26 and class 1 integrons ( intI 1). When aggregated together, polluted environments had a greater relative abundance of ARGs than unpolluted environments and a greater relative abundance of IS26 and intI 1. In general, chemical pollution, notably with petroleum, was associated with an increase in the prevalence of ARGs linked to multidrug efflux pumps. Included in the suite of efflux pumps were mexK , mexB , mexF , and mexW that are polyspecific and whose substrate ranges include multiple classes of critically important antibiotics. Also, in some instances, β-lactam resistance (TEM181 and OXA-541) genes increased, and genes associated with rifampicin resistance (RNA polymerases subunits rpoB and rpoB2 ) decreased in relative abundance. This meta-analysis suggests that different types of chemical pollution can enrich populations that carry efflux pump systems associated with resistance to multiple classes of medically critical antibiotics. IMPORTANCE The United Nations has identified chemical pollution as being one of the three greatest threats to environmental health, through which the evolution of antimicrobial resistance, a seminally important public health challenge, may be favored. While this is a very plausible outcome of continued chemical pollution, there is little evidence or research evaluating this risk. The objective of the present study was to examine existing metagenomes from chemically polluted environments and evaluate whether there is evidence that pollution increases the relative abundance of genes and mobile genetic elements that are associated with antibiotic resistance. The key finding is that for some types of pollution, particularly in environments exposed to petroleum, efflux pumps are enriched, and these efflux pumps can confer resistance to multiple classes of medically important antibiotics that are typically associated with Pseudomonas spp. or other Gram-negative bacteria. This finding makes clear the need for more investigation on the impact of chemical pollution on the environmental reservoir of ARGs and their association with mobile genetic elements that can contribute to horizontal gene transfer events.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.271
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations20
Published2023
Admission routes2
Has abstractyes

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