Ciprofloxacin-driven purifying selection on viral genomes accelerates soil N <sub>2</sub> O production
Bibliographic record
Abstract
Viruses are ubiquitous regulators of microbial dynamics and may thus greatly influence global microbial-driven greenhouse gas emissions. Anthropogenic stressors, such as chemical contamination, are likely to amplify these viral contributions; however, their global significance and underlying mechanisms remain elusive. Utilizing 15 N tracing, metagenomics, and laboratory assays, we explore soil viral communities and their evolutionary potential under the stress from antibiotic ciprofloxacin (CIP), focusing on their roles in regulating nitrogen cycling and N 2 O production. Through isolation and reinoculation of soil viruses, we demonstrate that CIP stimulates soil denitrification-derived N 2 O production, with 18 to 29% of the increase attributed to viral activity. Under CIP stress, soil viruses shift toward a lysogenic lifestyle, promoting mutualism with denitrifiers by horizontally transferring viral denitrification-related auxiliary metabolic genes (AMGs). The observed synonymous mutations in these AMGs, driven by CIP, suggest enhanced purifying selection, likely optimizing codon usage to align with host preferences. This optimization likely enhances the expression of denitrifying AMGs and increases N 2 O production. This study provides insights into the overlooked role of viral dynamics and genomic mutations in modulating N 2 O production under stressful environments, highlighting their evolutionary significance and impact on biogeochemical cycles in the Anthropocene.
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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".