Identification of lncRNAs in response to infection by <i>Plasmodiophora brassicae</i> in <i>Brassica napus</i> and development of lncRNA-based SSR markers
Bibliographic record
Abstract
Clubroot resistance in spring canola has been introgressed from different Brassica sources; however, molecular mechanism underlying this resistance, especially the involvement of long non-coding RNAs (lncRNAs), is yet to be understood. We identified 464 differentially expressed (DE) lncRNAs from the roots of clubroot-resistant canola, carrying resistance on chromosome BnaA03, and susceptible canola lines challenged with Plasmodiophora brassicae pathotype 3. Pathway enrichment analysis showed that most of the target genes regulated by these DE lncRNAs belonged to plant–pathogen interaction and hormone signaling, as well as primary and secondary metabolic pathways. Comparative analysis of these lncRNAs with 530 previously reported DE lncRNAs, identified using resistance located on BnaA08, detected 12 lncRNAs that showed a similar trend of upregulation in both types of resistant lines; these lncRNAs probably play a fundamental role in clubroot resistance. We identified SSR markers within 196 DE lncRNAs. Genotyping of two DH populations carrying resistance on BnaA03 identified a marker capable of detecting the resistance in 98% of the DH lines. To our knowledge, this is the first report of the identification of SSRs within lncRNAs responsive to P. brassicae infection, demonstrating the potential use of lncRNAs in the breeding of Brassica crops.
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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) | 0.001 | 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".