Unraveling the regulatory network of barley grain metabolism through the integrative analysis of multiomics and mQTL
Bibliographic record
Abstract
Although metabolites of barley grains have been analyzed, the changes in metabolite abundance and gene expression regulation mechanisms during barley grain development have not been elucidated. Here, we explore the dynamic accumulation patterns of metabolites in barley grains at six different developmental stages by analyzing high-resolution metabolomic and transcriptomic data from Huadamai6 and Huaai11. We detect 986 metabolites and 18,868 co-expressed genes. Meanwhile, we identify 1057 mQTLs associated with 553 metabolites in a double haploid (DH) population derived from these two cultivars, and integrate metabolome, transcriptome, and mQTL information to construct a global co-expression regulatory network of barley grain metabolism. Using this dataset, we excavate transcription factors and structural genes that regulate flavonoid metabolism pathway, and also reveal the complex mechanism of HvC1-1 and HvMYC-1 regulating grain color differentiation in the DH population. Our findings reveal the metabolic regulatory network of barley grains development, and provide valuable resources for future nutritional quality improvement and molecular design breeding of barley. This study establishes a dynamic regulatory network for barley grain development integrating genetic, metabolic, and gene expression information, revealing the regulatory role of key transcription factors in barley grain flavonoid metabolism and grain color formation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".