Secret arsenal of a cereal killer- cryptic activation of secondary metabolism biosynthesis in Fusarium graminearum
Bibliographic record
Abstract
Fusarium graminearum is a fungal pathogen and is a major causal agent of diseases in several agriculturally important crop species.In addition to the disease that diminishes grain yield, this pathogen produces secondary metabolites that are harmful to both plants and animals.Secondary metabolites are not essential for survival, instead, they enable the pathogen to successfully infect its host.In fungi, genes necessary to produce secondary metabolites are often arranged together in the genome, forming secondary metabolic clusters (SMCs).The F. graminearum genome contains 76 such clusters with a potential to produce a diverse array of secondary metabolites (SMs).However, given high functional specificity and energetic cost, most of these clusters remain silent, or "cryptic," unless the organism is subjected to an environment conductive to SM production.Alternatively, SMCs can be activated by genetically manipulating their activators or repressors.The goal of this dissertation is to establish the transcriptional factor TRI6 and the MAP kinase MGV1 as regulators of secondary metabolism by genetically altering their expression and thus activating cryptic SMCs in F. graminearum.TRI6 is a transcriptional factor that regulates the trichothecene group of mycotoxins and other non-trichothecene genes.MGV1 is a MAP kinase, implicated in regulation of diverse cellular responses, including secondary metabolite biosynthesis.We used transcriptomic and metabolomic analyses to identify SMCs regulated by TRI6 and MGV1.We discovered that at the transcriptional level, MGV1 and TRI6 co-regulate biosynthesis of four SMs; however, MGV1 also exerts its control of three SMCs at the post-transcriptional level.Finally, at the mechanistic level, we demonstrate that TRI6 regulates the trichothecene genes by directly binding to the promoters of the genes of the cluster.However, the regulation of other SMCs such as gramillin is achieved indirectly, through physical binding of TRI6 to the cluster-specific protein GRA2.
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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) | 0.002 | 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".