Temporal-spatial transcriptomics reveals key gene regulation for grain yield and quality in wheat
Bibliographic record
Abstract
Abstract Cereal grain size and quality are important agronomic traits in crop production. The development of wheat grains is underpinned by complex regulatory networks. The precise spatial and temporal coordination of diverse cell types is essential for the formation of functional compartments. To provide comprehensive spatiotemporal information about biological processes in developing wheat grain, we performed a spatial transcriptomics study during the early grain development stage from 4 to 12 days after pollination. We defined a set of tissue-specific marker genes and discovered that certain genes or gene families exhibit specific spatial expression patterns over time. Weighted gene co-expression network and motif enrichment analyses identified specific groups of genes potentially regulating wheat grain development. The embryo and surrounding endosperm specifically expressed transcription factor TaABI3-3B negatively regulates embryo and grain size. In Chinese breeding programs, a haplotype associated with higher grain weight was identified, linked to altered expression levels of TaABI3-3B . Data and knowledge obtained from the proposed study will provide pivotal insights into yield improvement and serve as important genetic information for future wheat breeding.
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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".