Flavonoid Biosynthesis Pathway Participating in Salt Resistance in a Landrace Sweet Sorghum Revealed by RNA-Sequencing Comparison With Grain Sorghum
Bibliographic record
Abstract
Abiotic stresses affect crop productivity worldwide. Plants have developed defense mechanisms against environmental stresses by altering the gene expression pattern which leads to regulation of certain metabolic and defensive pathways. Sorghum [Sorghum bicolor (L.) Moench] is an important crop in those regions irrigated by salty water. Sweet sorghum is a variant of common grain sorghum and is relatively more adapted to marginal growing conditions. Here, we compared the different response to salt stress of sweet and grain sorghum. We investigated six traits related with seed germination under salt-stress and normal conditions, conducted a genome-wide research on the salt effect on the gene expression of a landrace sweet and two grain sorghum by RNA-sequencing at seedling stage. The results showed that salt stress had significant inhibition to sorghum seed germination capability, and the inhibition to grain sorghum was greater. By comparing sweet and grain sorghum and the KEGG pathway analysis based on the DEGs, six genes involved in flavonoid biosynthesis pathway to tannins and anthocyanins from phenylalanine were identified in the landrace sweet sorghum, which expression was significant different with that in grain sorghum. Quantitative real-time PCR (qRT-PCR) data were closely in accordance with the transcript patterns estimated from the RNA-seq data. Tannins accumulation changes were associated with the genes expression under salt stress and control. These suggested that flavonoid biosynthesis pathway was involved in the sorghum resistance to salt stress. The present results suggested that flavonoid biosynthesis plays an important role in the sweet sorghum capacity for salt tolerance.
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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".