The transcriptomic signature of developing soybean seeds reveals the genetic basis of seed trait adaptation during domestication
Bibliographic record
Abstract
Cultivated soybean has undergone many transformations during domestication. In this paper we report a comprehensive assessment of the evolution of gene co-expression networks based on the analysis of 40 transcriptomes from developing soybean seeds in cultivated and wild soybean accessions. We identified 2680 genes that are differentially expressed during seed maturation and established two cultivar-specific gene co-expression networks. Through analysis of the two networks and integration with quantitative trait locus data we identified two potential key drivers for seed trait formation, GA20OX and NFYA. GA20OX encodes an enzyme in a rate-limiting step of gibberellin biosynthesis, and NFYA encodes a transcription factor. Overexpression of GA20OX and NFYA enhanced seed size/weight and oil content, respectively, in seeds of transgenic plants. The two genes showed significantly higher expression in cultivated than in wild soybean, and the increases in expression were associated with genetic variations in the promoter region of each gene. Moreover, the expression of GA20OX and NFYA in seeds of soybean accessions correlated with seed weight and oil content, respectively. Our study reveals transcriptional adaptation during soybean domestication and may identify a mechanism of selection by expression for seed trait formation, providing strategies for future breeding practice.
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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) | 0.000 | 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".