Insights into the transcriptional responses of a microbial community to silver nanoparticles in a freshwater microcosm
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) are widely used because of their excellent antibacterial properties . They are, however, easily discharged into the water environment, causing potential adverse environmental effects. Meta-transcriptomic analyses are helpful to study the transcriptional response of prokaryotic and eukaryotic aquatic microorganisms to AgNPs . In the present study, microcosms were used to investigate the toxicity of AgNPs to a natural aquatic microbial community . It was found that a 7-day exposure to 10 μg L −1 silver nanoparticles (AgNPs) dramatically affected the structure of the microbial community. Aquatic micro eukaryota (including eukaryotic algae, fungi, and zooplankton) and bacteria (i.e., heterotrophic bacteria and cyanobacteria) responded differently to the AgNPs stress. Meta-transcriptomic analyses demonstrated that eukaryota could use multiple cellular strategies to cope with AgNPs stress, such as enhancing nitrogen and sulfur metabolism , over-expressing genes related to translation, amino acids biosynthesis , and promoting bacterial-eukaryotic algae interactions. By contrast, bacteria were negatively affected by AgNPs with less signs of detoxification than in case of eukaryota; various pathways related to energy metabolism, DNA replication and genetic repair were seriously inhibited by AgNPs. As a result, eukaryotic algae (mainly Chlorophyta) dominated over cyanobacteria in the AgNPs treated microcosms over the 7-d exposure. The present study helps to understand the effects of AgNPs on aquatic microorganisms and provides insights into the contrasting AgNPs toxicity in eukaryota and bacteria.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".