Molecular Mechanisms Controlling the Disease Cycle in the Vascular Pathogen <i>Verticillium dahliae</i> Characterized Through Forward Genetics and Transcriptomics
Bibliographic record
Abstract
The soil-borne pathogen Verticillium dahliae has a worldwide distribution and a plethora of hosts of agronomic value. Molecular analysis of virulence processes can identify targets for disease control. In this work, we compared the global gene transcription profile of random T-DNA insertion mutant strain D-10-8F, which exhibits reduced virulence and alterations in microsclerotium formation and polar growth, with that of the wild-type strain. Three genes identified as differentially expressed were selected for functional characterization. To produce deletion mutants, we developed an updated version of one-step construction of Agrobacterium-recombination-ready plasmids (OSCAR) that included the negative selection marker HSVtk (herpes simplex virus thymidine kinase gene) to prevent ectopic integration of the deletion constructs. Deletion of VdRGS1 (VDAG_00683), encoding a regulator of G protein signaling (RGS) protein and highly upregulated in the wild type versus D-10-8F, resulted in phenotypic alterations in development and virulence that were indistinguishable from those of the random T-DNA insertion mutant. In contrast, deletion of the other two genes selected, vrg1 (VDAG_07039) and vvs1 (VDAG_01858), showed that they do not play major roles in morphogenesis or virulence in V. dahliae. Taken together the results presented here on the transcriptomic analysis and phenotypic characterization of D-10-8F and ∆VdRGS1 strains provide evidence that variations in G protein signaling control the progression of the disease cycle in V. dahliae. We propose that G protein–mediated signals induce the expression of multiple virulence factors during biotrophic growth, whereas massive production of microsclerotia at late stages of infection requires repression of G protein signaling via upregulation of VdRGS1 activity.
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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 | 0.001 | 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".