Multiomics plasticity in seed traits of pan-genome wheat cultivars
Bibliographic record
Abstract
Abstract The molecular basis of cultivar-level variations in polyploid wheat that enables environmental adaptation while maintaining yield and quality in polyploid wheat remains poorly understood. We conducted a detailed phenotypic assessment and multiomics analysis of nine pan-genome polyploid wheat cultivars grown under control and drought conditions. We aimed to investigate the subgenome-level variations, cultivar differences and biochemical mechanisms affecting plant fitness under moderate drought stress. Intrinsic water use efficiency, grain yield, and grain protein content and quality differed among cultivars, supporting the plasticity of drought stress responses. Biased proteome and metabolome abundance changes in response to moderate drought stress during the vegetative stage indicate different strategies for the utilization of homeologous protein isoforms assigned to the A, B, and D subgenomes. Drought effects were detected at the protein level, but significant changes were observed in central carbon pathway metabolites and micronutrient profiles. The subgenomic localization of seed storage proteins highlight differences in nutrient reservoir accumulation and emphasizes the enhanced role of S-rich prolamins in the stress response. Subgenomic variations define cultivar phenotypes by producing molecules that accumulate and enable the underlying trade-offs between environmental adaptation and yield- or quality-related traits. These variations can be used to select crops with increased stress resistance without compromising yield.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".