Sub-MIC antibiotics influence the microbiome, resistome and structure of riverine biofilm communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".