Protein and gene expression patterns of endo-β-mannanase following germination of rice
Bibliographic record
Abstract
Abstract The rice (Oryza sativa L.) cv. Taichung 65, a japonica subspecies, was used to characterize the isoform, protein and gene expression patterns of endo-β-mannanase during and after seed germination. Activity assays and isoform analyses of whole grains or seed parts (scutellum, aleurone layer and starchy endosperm) revealed that seeds began to express endo-β-mannanase activity at 48 h from the start of imbibition at 25°C, after the completion of germination of most seeds. Three isoforms of endo-β-mannanase (pI 8.86, pI 8.92 and pI 8.98) were detected in the aleurone layer and starchy endosperm, but only two (pI 8.86 and pI 8.92) were present in the scutellum. The endo-β-mannanase in the starchy endosperm was mainly from the aleurone layer. Western blot analysis, using a tomato anti-endo-β-mannanase antibody, indicated that an endo-β-mannanase protein was present in an inactive form in dry grains. The amount of this protein decreased in the scutellum, but increased in the aleurone layer during and after germination. Thus, the increase in endo-β-mannanase activity in rice grains may be due to the activation of extant proteins and/or the de novo synthesis of the enzyme. Northern blot analysis showed that four putative rice endo-β-mannanase genes (OsMAN1, OsMAN2, OsMAN6 and OsMANP) were expressed in germinating and germinated rice grains. However, OsMANP was not expressed in the scutellum. The amount of OsMAN6 mRNA decreased after the completion of germination and paralleled the decline in endo-β-mannanase protein. In the aleurone layer, the increase of OsMAN2, OsMAN6 and OsMANP mRNA was prior to the increase of endo-β-mannanase protein.
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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".