Transcriptional activity of antifungal metabolite-encoding genes phlD and hcnBC in Pseudomonas spp. using qRT-PCR
Bibliographic record
Abstract
Production of 2,4-diacetylphloroglucinol (2,4-DAPG) and hydrogen cyanide (HCN) by Pseudomonas spp. shows great potential for controlling soilborne plant pathogens. However, little is known about the transcriptional activity of phl and hcn genes encoding 2,4-DAPG and HCN, respectively. To progress toward a better understanding of what triggers phl and hcn expression under rhizosphere conditions, novel PCR primers and TaqMan probes were designed to monitor relative phlD and hcnBC expression in quantitative real time-PCR assays. Transcriptional activity of phlD and hcnBC was studied in time-course confrontational assays using combinations of Pseudomonas spp. isolated in this study: LBUM300 (producing 2,4-DAPG and HCN) and LBUM647 (producing HCN only); pathogens Phytophthora cactorum and Verticillium dahliae; and solid growth media King's B medium and potato dextrose agar. In correlation with the antagonistic activity observed, expression of phlD and hcnBC and production of 2,4-DAPG was detected throughout the 14-day course of the experiment in LBUM300 on both media, while hcnBC expression diminished to undetectable levels in LBUM647. In LBUM300 expression of phlD and hcnBC significantly changed over time and was also influenced by the presence of pathogen and growth media following time-dependent responses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".