Free amino acid profiles suggest a possible role for asparagine in the control of storage-product accumulation in developing seeds of low- and high-protein soybean lines
Bibliographic record
Abstract
Several approaches were taken to examine the role of N-assimilate supply in the control of soybean (Glycine max) seed composition. In the first study, developing seeds were grown in vitro with D-[U-14C]sucrose (Suc) and different concentrations of Gln. Light stimulated carbon flux into oil and protein, and was required to sustain Suc uptake and anabolic processes under conditions of elevated nitrogen supply. High Gln supply resulted in higher transcript levels of beta-conglycinin and oleosin. In the second study, analyses of soluble amino acid pools in two genetically related lines, NC103 and NC106 (low- and high-seed protein, respectively) showed that, in the light, NC106 accumulated higher levels of Asn and several other amino acids in developing cotyledons compared with NC103, whereas at the seed coat and apoplast levels both lines were similar. In the dark, NC103 accumulated Gln, Arg, and its precursors, suggesting a reduced availability of organic acids required for amino acid interconversions, while NC106 maintained higher levels of the pyruvate-derived amino acids Val, Leu, and Ile. Comparing NC103 and NC106, differences in seed composition were reflected in steady-state transcript levels of storage proteins and the lipogenic enzyme multi-subunit acetyl CoA carboxylase. In the third study, a positive correlation (P < or = 0.05) between free Asn in developing cotyledons and seed protein content at maturity was confirmed in a comparison of five unrelated field-grown cultivars. The findings support the hypothesis that high seed-protein content in soybean is determined by the capacity of the embryo to take up nitrogen sources and to synthesize storage proteins. Asn levels are probably tightly regulated in the embryo of high-protein lines, and may act as a metabolic signal of seed nitrogen status.
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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".