Transcriptomic evidence for the control of soybean root isoflavonoid content by regulation of overlapping phenylpropanoid pathways
Bibliographic record
Abstract
BACKGROUND: Isoflavonoids are a class of specialized metabolites found predominantly in legumes. They play a role in signaling for symbiosis with nitrogen-fixing bacteria and inhibiting pathogen infection. RESULTS: A transcriptomic approach using soybean cultivars with high (Conrad and AC Colombe) and low (AC Glengarry and Pagoda) root isoflavonoid content was used to find elements that underlie this variation. Two genes, encoding the flavonoid-metabolizing enzymes, flavonoid 3'-hydroxylase (GmF3'H) and dihydroflavonol 4-reductase (GmDFR), had lower expression levels in high isoflavonoid cultivars. These enzymes compete with isoflavonoid biosynthetic enzymes for the important branch-point substrate naringenin and its derivatives. Differentially expressed genes, between the two sets of cultivars, encode transcription factors, transporters and enzymatic families of interest, such as oxidoreductases, hydrolases and transferases. In addition, genes annotated with stress and disease response were upregulated in high isoflavonoid cultivars. CONCLUSIONS: Coordinated regulation of genes involved in flavonoid metabolism could redirect flux into the isoflavonoid branch of the phenylpropanoid pathway, by reducing competition for the flavanone substrate. These candidate genes could help identify mechanisms to overcome the endogenous bottleneck to isoflavonoid production, facilitate biosynthesis in heterologous systems, and enhance crop resistance against pathogenic infections.
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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".