A transcriptomic analysis reveals soybean seed pre-harvest deterioration resistance pathways under high temperature and humidity stress
Bibliographic record
Abstract
Pre-harvest soybean seeds in the field are susceptible to high temperature and humidity (HTH) stress, leading to pre-harvest seed deterioration, which will result in a reduction in grain quality, yield, and seed vigor. To understand the gene expression involved in seed deterioration response under HTH stress, in this study, we conducted an RNA-Seq analysis using two previously screened soybean cultivars with contrasting seed deterioration resistance. HTH stress induced 1081 and 357 differentially expressed genes (DEGs) in the sensitive cultivar Ningzhen No. 1 and resistant cultivar Xiangdou No. 3, respectively. The majority of DEGs in the resistant cultivar were up-regulated, while down-regulated DEGs were predominant in the sensitive cultivar. KEGG pathway analysis revealed that metabolic pathways, biosynthesis of secondary metabolites, and protein processing in endoplasmic reticulum were the predominant pathways in both cultivars during seed deterioration under HTH stress. The genes involved in photosynthesis, carbohydrate metabolism, lipid metabolism, and heat shock proteins pathways might contribute to the different response to seed deterioration under HTH treatment in the two soybean cultivars. Our study extends the knowledge of gene expression in soybean seed under HTH stress and further provides insight into the molecular mechanism of seed deterioration as well as new strategies for breeding soybean with improved seed deterioration resistance.
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".