<i>Ne2</i>, a typical CC–NBS–LRR‐type gene, is responsible for hybrid necrosis in wheat
Bibliographic record
Abstract
Summary Hybrid necrosis, caused by complementary genes Ne1 and Ne2, is a serious barrier for combining desirable traits from different genotypes of wheat, affecting the full utilisation of heterosis. To date, both Ne1 and Ne2 are still not isolated although they were documented decades ago. We report here the map‐based cloning and functional characterisation of Ne2, encoding a coiled coil–nucleotide‐binding site–leucine‐rich repeat (CC–NBS–LRR) protein. Homozygous frameshift mutations generated using the CRISPR/Cas9 approach confirmed the Ne2‐inducing hybrid necrosis in wheat. Upregulated expression of Ne2 induced by Ne1 and excess hydrogen peroxide accumulation are associated with the necrosis formation. Genetic analyses of a Ne2 allele (Ne2m) and leaf rust resistance gene LrLC10/Lr13 revealed that they might be the same gene. Furthermore, we demonstrated that the frequency of the Ne2 allele was much lower in landraces (2.00%) compared with that in modern cultivars (13.62%), suggesting that Ne2 allele has been partially applied in wheat genetic improvement. Our findings open opportunities of thoroughly investigating the molecular mechanism of hybrid necrosis, selecting Lr13 and simultaneously avoiding hybrid necrosis in wheat breeding through marker‐assisted selection.
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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 | 0.001 |
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".