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Record W4385457599 · doi:10.3389/fmicb.2023.1194952

Sub-MIC antibiotics influence the microbiome, resistome and structure of riverine biofilm communities

2023· article· en· W4385457599 on OpenAlexaff
Gabriela Flores-Vargas, Darren R. Korber, Jordyn Bergsveinson

Bibliographic record

VenueFrontiers in Microbiology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsResistomeBiologyMetagenomicsAntibioticsMicrobiologyMicrobiomeAntibiotic resistanceProteobacteriaBiofilmMicrobial ecologyBacteriaBioinformaticsGenetics16S ribosomal RNA

Abstract

fetched live from OpenAlex

The effects of sub-minimum inhibitory concentrations (sub-MICs) of antibiotics on aquatic environments is not yet fully understood. Here, we explore these effects by employing a replicated microcosm system fed with river water where biofilm communities were continuously exposed over an eight-week period to sub-MIC exposure (1/10, 1/50, and 1/100 MIC) to a mix of common antibiotics (ciprofloxacin, streptomycin, and oxytetracycline). Biofilms were examined using a structure-function approach entailing microscopy and metagenomic techniques, revealing details on the microbiome, resistome, virulome, and functional prediction. A comparison of three commonly used microbiome and resistome databases was also performed. Differences in biofilm architecture were observed between sub-MIC antibiotic treatments, with an overall reduction of extracellular polymeric substances and autotroph (algal and cyanobacteria) and protozoan biomass, particularly at the 1/10 sub-MIC condition. While metagenomic analyses demonstrated that microbial diversity was lowest at the sub-MIC 1/10 antibiotic treatment, resistome diversity was highest at sub-MIC 1/50. This study also notes the importance of benchmarking analysis tools and careful selection of reference databases, given the disparity in detected antimicrobial resistance genes (ARGs) identity and abundance across methods. Ultimately, the most detected ARGs in sub-MICs exposed biofilms were those that conferred resistance to aminoglycosides, tetracyclines, β-lactams, sulfonamides, and trimethoprim. Co-occurrence of microbiome and resistome features consistently showed a relationship between Proteobacteria genera and aminoglycoside ARGs. Our results support the hypothesis that constant exposure to sub-MICs antibiotics facilitate the transmission and promote prevalence of antibiotic resistance in riverine biofilms communities, and additionally shift overall microbial community metabolic function.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.009
GPT teacher head0.235
Teacher spread0.225 · 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 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

Citations24
Published2023
Admission routes1
Has abstractyes

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