VeA is involved in anti-tumor activity by regulating adenylosuccinate lyase to mediate the synthesis of Acadesine in endophytic <i>Fusarium solani</i>
Bibliographic record
Abstract
Acadesine (AICAR) is a promising candidate for new drugs in Phase III clinical trials. The purpose of this study is to analyse the steps in the biosynthesis pathway of AICAR. Our previous study found that overexpression of veA, a gene encoding a global regulator, significantly increased AICAR production of endophytic Fusarium solani HB1-J1 and the anti-tumor activity of its extracts. Transcriptome and metabolome analysis of FsveAOE14, a veA overexpressing F. solani strain, revealed a 10-step AICAR synthesis pathway, with adenylosuccinate lyase PurB as a key enzyme. Generally, overexpressing purB (the gene encoding adenylosuccinate lyase) enhances AICAR synthesis. However, in FsveAOE14, despite down-regulation of purB, AICAR content increased, which is contradictory. Further studies revealed that expression levels of purB homologs gene, pro06469 and pro10879, were upregulated in FsveAOE14. This suggests that although veA overexpression leads to purB down-regulation, their up-regulation may compensate for the reduction of purB, thus affecting AICAR synthesis. Additionally, compared to the wild type, overexpressing purB significantly enhances the inhibitory activity of the strain’s extracts against the nonsmall-cell lung cancer cell line A549. Furthermore, it also increases the metabolic levels of other anti-tumor compounds, including 3-methyladenine, taurine, and others. These results indicate that VeA regulates AICAR biosynthesis via key enzymes like PurB, enhancing AICAR and other anti-tumor compound production, thus increasing the anti-tumor activity of F. solani extracts .
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".