Silicon ameliorates clubroot responses in canola (<i>Brassica napus</i>): A “multi‐omics”‐based investigation into possible mechanisms
Bibliographic record
Abstract
Clubroot disease, caused by Plasmodiophora brassicae Woronin, results in severe yield losses in Brassica crops, including canola. Silicon (Si) mitigates several stresses and enhances plant resistance to phytopathogens. We investigated the effects of Si on clubroot disease symptoms in canola at two concentrations of Si, Si: soil in 1: 100 w/w (Si1.0) and Si: soil in 1:200 w/w (Si0.5) under greenhouse conditions. In addition, the effects of Si on P. brassicae-induced gene expression, endogenous levels of phytohormones and metabolites were studied using "omics" approaches. Si application reduced clubroot symptoms and improved plant growth parameters. Gene expression analysis revealed increased transcript-level responses in Si1.0 compared to Si0.5 plants at 7-, 14-, and 21-days post-inoculation (dpi). Pathogen-induced transcript-level changes were affected by Si treatment, with genes related to antioxidant activity (e.g., POD, CAT), phytohormone biosynthesis and signalling (e.g., PDF1.2, NPR1, JAZ, IPT, TAA), nitrogen metabolism (e.g., NRT, AAT), and secondary metabolism (e.g., PAL, BCAT4) exhibiting differential expression. Endogenous levels of phytohormones (e.g., auxin, cytokinin), a majority of the amino acids and secondary metabolites (e.g., glucosinolates) were increased at 7 dpi, followed by a decrease at 14- and 21-dpi due to Si-treatment. Stress hormones such as abscisic acid (ABA), salicylic acid (SA), and jasmonic acid (JA) also decreased at the later time points in Si0.5, and Si1.0 treated plants. Si appears to improve clubroot symptoms while enhancing plant growth and associated metabolic processes, including nitrogen metabolism and secondary metabolite biosynthesis.
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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 | 0.002 |
| 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".