Sub‐inhibitory concentrations of different pharmaceutical products affect the meta‐transcriptome of river biofilm communities cultivated in rotating annular reactors
Bibliographic record
Abstract
Surface waters worldwide are contaminated by pharmaceutical products that are released into the environment from wastewater treatment plants. Here, we hypothesize that pharmaceutical products have effects on organisms as well as genes related to nutrient cycling in complex microbial communities. To test this hypothesis, biofilms were grown in reactors and subjected low concentrations of three antibiotics [erythromycin, ER, sulfamethoxazole, SL and sulfamethazine, SN) and a lipid regulator (gemfibrozil, GM). Total community RNA was extracted and sequenced together with PCR amplicons of the 16S rRNA gene using 454 pyrosequencing. Exposure to pharmaceutical products resulted in very little change in bacterial community composition at the phylum level based on 16S rRNA gene amplicons, even though some genera were significantly affected. In contrast, large shifts were observed in the active community composition based on taxonomic affiliations of mRNA sequences. Consequently, expression of gene categories related to N, P and C cycling were strongly affected by the presence of pharmaceutical products, with each treatment having specific effects. These results indicate that low pharmaceutical product concentrations rapidly provoke a variety of functional shifts in river bacterial communities. In the longer term these shifts in gene expression and microbial activity could lead to a disruption of important ecosystem processes like nutrient cycling.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".