Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat and F1 hybrids at different growth stages
Bibliographic record
Abstract
Abstract Being a major source of protein and calories in the human diet, bread wheat is cultivated worldwide, and its yield is showing dwindling notion due to various environmental challenges. Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. Interestingly the genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored. In this study, we assessed the differential expression profiles of yield-related negative regulators in elite cultivars of wheat and their F1 hybrids and identified various cis-regulatory signatures that might influence these expression patterns. Furthermore, the differential and preferential expression patterns of the homeologs showed dynamic expression profiles of A, B, and D subgenomes at six different growth stages, portraying the different homeolog-dominance at the same stage in the different cultivars and their F1 hybrids. Based on the expression profiling, hybrid progenies of elite cultivars were developed that showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents and the F2 segregating populations demonstrated varying yields from high-yielding parents. These results suggest the significance of dynamic transcriptional expression patterns and the regulatory signatures of major genes that can substantially contribute and provide insights for the selective introduction of better cultivars into hybrid breeding programs which can be harnessed for the improvement of future wheat.
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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".