Profiling of the Transcriptomic Responses of Clonostachys rosea Upon Treatment With Fusarium graminearum Secretome
Bibliographic record
Abstract
Clonostachys rosea strain ACM941 is a fungal bio-control agent patented against the causative agent of Fusarium Head Blight, Fusarium graminearum. Although the molecular details remain enigmatic, previous studies have suggested that C. rosea may secrete F. graminearum growth inhibitors. Further toward this, experiments described herein show that induction of C. rosea cultures by the addition of an aliquot of F. graminearum(Fg)-spent media (including macroconidia), yield C. rosea (Cr)-spent media that elicited higher anti-F. graminearum activity than either control or deoxynivalenol (DON)-induced Cr-spent media. To gain additional insight into the genetic and metabolic factors modulating this interaction, transcriptomic (RNAseq) profiles of C. rosea in response to DON and Fg-spent media treatment, were developed. This analysis revealed 24,112 C. rosea unigenes, of which 5,605 and 6,285 were differentially regulated by DON and F-spent media, respectively. More than half of these unigenes were up-regulated, with annotations, most notably in the Fg-spent media treatment data, suggesting enhancement of polyketide (PK) and non-ribosomal peptide (NRP) secondary metabolite precursor synthesis, and PK/NRP-like synthases. Four ABC transporters were also up-regulated in response to Fg-spent media. Further analysis showed that the PK and NRP-like synthases belong to three gene clusters that also include ABC transporters, and other genes known to tailor secondary metabolite biosynthesis. The RNAseq data was further validated using quantitative RT-qPCR. Taken together, these results show that C. rosea responds to the presence of Fg-spent media (and to a lesser extent, DON-alone) by up-regulating unique aspects of its secondary metabolism-related genetic repertoire. The identities and roles of C. rosea secondary metabolites produced by the targeted gene clusters are now under investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".