An RNA-Seq transcriptome analysis revealing novel insights into fluorine absorption and transportation in the tea plant
Bibliographic record
Abstract
The tea plant [Camellia sinensis (L.) O. Kuntze] is a species with a high concentration of fluorine in its leaves, especially in the mature leaves. The physiological mechanisms for fluorine absorption and accumulation have been well studied, but the related molecular mechanisms are poorly understood in the tea plant. In this study, transcriptome analysis by RNA-Seq following exposure to 16 mg/L of fluorine for 0, 3, 6, and 24 h was performed to identify the candidate genes involved in the transmembrane transportation of fluorine. More than 1.23 billion high-quality reads were generated, and 259.84 million unigenes were assembled de novo, with 518 216 of them being annotated in the seven databases used. Meanwhile, a large number of transporters, transcription factors, and heat-shock proteins with differential expression in response to high levels of fluorine (P ≤ 0.05) were identified. Comparative transcriptome analysis showed that the uptake of fluorine is related to photosynthesis, plant hormone signal transduction, and glutathione metabolism. Further systematic analysis of nitrate and potassium transporter genes revealed that many of these genes regulate fluorine transportation in roots and leaves. Gene expression and fluorine content analysis in different cultivars revealed CsNRT1/PTR 3.1 and CsPT 8 as the key genes regulating the transmembrane transportation of fluorine in 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".