Metabolomic Analyses Provide New Insights into Signaling Mechanisms for Nutrient Uptake by Lateral Roots of Pruned Tea Plant (<i>Camellia sinensis</i>)
Bibliographic record
Abstract
Pruning is an important plant management practice in tea cultivation. However, the mechanism underlying the dynamics of nutrient uptake by roots of pruned tea is unknown. This study investigated the metabolic alterations in lateral roots of pruned tea to unveil the mechanism of nutrient uptake. Elemental analysis revealed that pruning significantly increases the uptake of nutrients by lateral roots. Metabolic profiling showed significant metabolic variations in lateral roots of pruned tea. Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analysis revealed that flavonoid biosynthesis, phenylpropanoid biosynthesis, and amino acid metabolism were differentially regulated in lateral roots. Caffeine metabolism was significantly hindered, while ethylene signaling was significantly induced in lateral roots of pruned plants. In addition, intermediates in the tricarboxylic acid (TCA) cycle were upregulated, indicating high rates of the TCA cycle. Therefore, pathways related to phenylpropanoid biosynthesis, TCA cycle, ethylene biosynthesis, and metabolism of amino acids contribute to higher nutrient uptake by lateral roots of the tea plant.
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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 | 0.001 |
| 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".