The Zn(II)2Cys6 putative transcription factor is involved in the regulation of leucinostatin production and pathogenicity of the nematophagous fungus <i>Paecilomyces lilacinus</i>
Bibliographic record
Abstract
Zn(II)2Cys6 transcription factor genes encode transcription regulators that manage the infection potential and production of secondary metabolites such as toxins in fungi. In this study, a gene named rolP that encodes a putative Zn(II)2Cys6 transcription factor regulating leucinostatin production in the filamentous fungus Paecilomyces lilacinus was characterized by a gene knockout approach. The deduced proprotein consists of 705 amino acids and is highly homologous to the Zn(II)2Cys6 transcription factor of the entomopathogenic fungus Metarhizium brunneum. Predictive analysis of the secondary structure of the proportion showed that it has two domains. Paecilomyces lilacinus can produce nematotoxins leucinostatins A and B. Deletion of rolP from the P. lilacinus wild-type strain Pl36-1 leads to the absence of leucinostatin A, while a large increase in the leucinostatin A level was achieved in the rolP overexpression strain Ov-Pl36-1. During the process of nematode infection, rolP showed high expression levels at 48–72 h and peaked at 48 h. Bioassay tests confirmed the requirement of rolP for leucinostatin production. However, the root-knot second-stage juvenile-toxicity was minimized to 35.3% in ΔrolP; toxicity was 97.2% with Ov-Pl36-1 compared with 68.4% with the wild type strain Pl36-1. Interestingly, neither knockout nor overexpression of the rolP gene affected the growth or sporulation of P. lilacinus. Additionally, fungal nutrition and acidic media might stimulate the rolP activity of the wild type strain against nematodes. These findings suggested that the rolP gene is required for the induction and production of leucinostatins and is considered a leucinostatin regulatory gene in P. lilacinus.
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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".