Epistatic Relationship between MGV1 and TRI6 in the Regulation of Biosynthetic Gene Clusters in Fusarium graminearum
Bibliographic record
Abstract
Genetic studies have shown that the MAP kinase MGV1 and the transcriptional regulator TRI6 regulate many of the same biosynthetic gene clusters (BGCs) in Fusarium graminearum. This study sought to investigate the relationship between MGV1 and TRI6 in the regulatory hierarchy. Transgenic F. graminearum strains constitutively expressing MGV1 and TRI6 were generated to address both independent and epistatic regulation of BGCs by MGV1 and TRI6. We performed a comparative transcriptome analysis between axenic cultures grown in nutrient-rich and secondary metabolite-inducing conditions. The results indicated that BGCs regulated independently by Mgv1 included genes of BGC52, whereas genes uniquely regulated by TRI6 included the gene cluster (BGC49) that produces gramillin. To understand the epistatic relationship between MGV1 and TRI6, CRISPR/Cas9 was used to insert a constitutive promoter to drive TRI6 expression in the Δmgv1 strain. The results indicate that BGCs that produce deoxynivalenol and fusaoctaxin are co-regulated, with TRI6 being partially regulated by MGV1. Overall, the findings from this study indicate that MGV1 provides an articulation point to differentially regulate various BGCs. Moreover, TRI6, embedded in one of the BGCs provides specificity to regulate the expression of the genes in the BGC.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".