Multi-omics analysis reveals a link between Brassica -specific miR1885 and rapeseed tolerance to low temperature
Bibliographic record
Abstract
Brassica crops include various edible vegetable and plant oil crops, and their production is limited by low temperature. The key regulators of low-temperature resistance in Brassica remain largely unexplored. To identify post-transcriptional regulators of plant response to low temperature, we performed small RNA profiling, and found that 16 known miRNAs responded to cold treatment in Brassica rapa . The cold response of seven of those miRNAs were further confirmed by qRT-PCR and/or northern blotting analyses. In parallel, a genome-wide association study of 220 accessions of Brassica napus identified four candidate MIRNA genes, all of which were cold-responsive, at the loci associated with low temperature resistance. Specifically, these large-scale data analyses revealed a link between miR1885 and the plant response to low temperature in both B. rapa and B. napus . Using 5′ rapid amplification of cDNA ends approach, we validated that miR1885 can cleave its putative target gene transcripts, Bn.TIR.A09 and Bn.TNL.A03, in B. napus . Furthermore, overexpression of miR1885 in B. napus decreased the mRNA abundance of Bn.TIR.A09 and Bn.TNL.A03 and resulted in increased sensitivity to low temperature. Knocking down of miR1885 in B. napus led to increased mRNA abundance of its targets and improved rapeseed tolerance to low temperature. Together, our results suggested that the loci of miR1885 and its targets could be potential candidates for the molecular breeding of low temperature-tolerant 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".