Transcriptomic response of Pseudomonas nicosulfuronedens LAM1902 to the sulfonylurea herbicide nicosulfuron
Bibliographic record
Abstract
The overuse of the herbicide nicosulfuron has become a global environmental concern. As a potential bioremediation technology, the microbial degradation of nicosulfuron shows much promise; however, the mechanism by which microorganisms respond to nicosulfuron exposure requires further study. An isolated soil-borne bacteria Pseudomonas nicosulfuronedens LAM1902 displaying nicosulfuron, chlorimuron-ethyl, and cinosulfuron degradabilities in the presence of glucose, was used to determine the transcriptional responses to nicosulfuron exposure. RNA-Seq results indicated that 1102 differentially expressed genes (DEGs) were up-regulated and 702 down-regulated under nicosulfuron stress. DEGs were significantly enriched in "ABC transporters", "sulfur metabolism", and "ribosome" pathways (p ≤ 0.05). Several pathways (glycolysis and pentose phosphate pathways, a two-component regulation system, as well as in bacterial chemotaxis metabolisms) were affected by nicosulfuron exposure. Surprisingly, nicosulfuron exposure showed positive effects on the production of oxalic acid that is synthesized by genes encoding glycolate oxidase through the glyoxylate cycle pathway. The results suggest that P. nicosulfuronedens LAM1902 adopt acid metabolites production strategies in response to nicosulfuron, with concomitant nicosulfuron degradation. Data indicates that glucose metabolism is required during the degradation and adaptation of strain LAM1902 to nicosulfuron stress. The present studies provide a glimpse at the molecular response of microorganisms to sulfonylurea pesticide toxicity and a potential framework for future mechanistic studies.
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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 | 0.001 |
| 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".