Biological control of rice bacterial blight by plant-associated bacteria producing 2,4-diacetylphloroglucinol
Bibliographic record
Abstract
Certain plant-associated strains of fluorescent Pseudomonas spp. are known to produce the antimicrobial antibiotic 2,4-diacetylphloroglucinol (DAPG). It has antibacterial, antifungal, antiviral, and antihelminthic properties and has played a significant role in the biological control of tobacco, wheat, and sugar beet diseases. It has never been reported from India and has not been implicated in the biological suppression of a major disease of the rice crop. Here, we report that a subpopulation of 27 strains of plant-associated Pseudomonas fluorescens screened in a batch of 278 strains of fluorescent pseudomonads produced DAPG. The DAPG production was detected by a PCR-based screening method that used primers Phl2a and Phl2b and amplified a 745-bp fragment characteristic of DAPG. HPLC, 1H NMR, and IR analyses provided further evidence for its production. We report also that this compound inhibited the growth of the devastating rice bacterial blight pathogen Xanthomonas oryzae pv. oryzae in laboratory assays and suppressed rice bacterial blight up to 59%-64% in net-house and field experiments. Tn5 mutants defective in DAPG production (Phl-) of P. fluorescens PTB 9 were much less effective in their suppression of rice bacterial blight.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.000 | 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".