Overexpression of the global regulator FnVeA upregulates antitumor substances in endophytic <i>Fusarium nematophilum</i>
Bibliographic record
Abstract
The special niche of endophytic fungi promotes their potential to produce antitumor compounds with novel structure and significant bioactivity for screening of new antitumor drugs. In our previous studies, we isolated a Fusarium strain from the roots of the medicinal plant Nothapodytes pittosporoides and identified it as Fusarium nematophilum. We found that the crude extract of F. nematophilum had significant antitumor activity on A549 cancer cells, and overexpressing the global regulatory factor FnVeA (the VeA gene of the fungus F. nematophilum) resulted in a significant increase in the antitumor activity, which was approximately fivefold higher than wild strain for relative inhibition rate. In FnVeAOE, the accumulation of indole, alkene, alkaloid, steroid, and flavonoid metabolites with potential antitumor activity was significantly upregulated compared with wild type via metabolomic analysis. Moreover, the transcriptome analysis showed that 134 differential genes were considered to be closely related to the biosynthesis of antitumor substances, of which 59 differential genes were considered as candidate key genes, and related to tryptophan dimethylallyltransferase, cytochrome P450 monooxygenase, polyketide synthases, and transcription factors. Taken together, we suggest that FnVeA may regulate the biosynthesis of antitumor substances by mediating the expression of genes related to secondary metabolic pathways in F. nematophilum.
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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".