Unveiling the mechanism of melatonin impacts on maize seedling growth: sugar metabolism as a case
Bibliographic record
Abstract
Melatonin regulates growth in many plants; however, the mechanism remains unclear. In this study, exogenous melatonin feeding resulted in both promotional (≤10 μm) and inhibitory (≥100 μm) effects on maize seedling growth. Initial analyses suggested positive correlations between the amount of melatonin and sucrose synthesis and hydrolysis-related gene expression, enzyme activities, and sucrose metabolites. However, assays of photosynthetic rate, hexokinase (HxK) activity, expression of photosynthetic marker genes, and HxK-related genes showed opposite effects under 10 μm (positive) and 100 μm (negative) melatonin treatments. Similarly, 10 μm melatonin accelerated starch catabolism at night, whereas 100 μm melatonin significantly decreased this process and led to starch accumulation in photosynthetic tissues. Furthermore, expression analysis of genes related to sucrose phloem loading resulted in a slight upregulation of sucrose transporters (SUT1 and SUT2) when seedlings were induced with 10 μm melatonin, while treatment with 100 μm melatonin resulted in significant downregulation of these sucrose transporter genes (SUT1 and SUT2), as well as tie-dyed2 (Tdy2) and sucrose export defective 1. Taken together, these results suggest that low doses of melatonin benefit maize seedling growth by promoting sugar metabolism, photosynthesis, and sucrose phloem loading. Conversely, high doses of melatonin inhibit seedling growth by inducing the excessive accumulation of sucrose, hexose and starch, suppressing photosynthesis and sucrose phloem loading.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".