Efficient induction and rapid identification of haploid grains in tetraploid wheat by editing genes TtMTL and pyramiding anthocyanin markers
Bibliographic record
Abstract
Doubled haploid (DH) technology provides an effective way to generate homozygous genetic and breeding materials over a short period of time. We produced three types of homozygous TtMTL gene-edited mutants (mtl-a, mtl-b, and mtl-ab) by CRISPR/Cas9 in durum wheat. PCR restriction enzymes and sequencing confirmed that the editing efficiency was up to 53.5%. The seed-setting rates of the three types of mutants ranged from 20% to 60%. Abnormal grain phenotypes of kernel, embryo, and both embryo and endosperm abortions were observed in the progenies of the mutants. The average frequency of embryo-less grains was 25.3%. Chromosome counting, guard cell length, and flow cytometry confirmed that the haploid induction rate was in the range of 3%–21% in the cross- and self-pollinated progenies of the mtl mutants (mtl-a and mtl-ab). Furthermore, we co-transformed two vectors, pCRISPR/Cas9-MTL and pBD68-(ZmR + ZmC1), into durum wheat, to pyramide Ttmtl-edited mutations and embryo-specifically expressed anthocyanin markers, and developed a homozygous durum haploid inducer with purple embryo (DHIPE). Using DHIPE as the male parent to be crossed with the wild-type Kronos, the grains with white embryos were identified as haploid, while the grains with purple embryos were diploid. These findings will promote the breeding of new tetraploid wheat varieties.
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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".