A novel insight into nitrogen and auxin signaling in lateral root formation in tea plant [Camellia sinensis (L.) O. Kuntze]
Bibliographic record
Abstract
BACKGROUND: Tea plant (Camellia sinensis) is one of the most popular non-alcoholic beverages worldwide. In tea, lateral roots (LRs) are the main organ responsible for the absorption of moisture and mineral nutrients from the soil. Lateral roots formation and development are regulated by the nitrogen and auxin signaling pathways. In order to understand the role of auxin and nitrogen signaling in LRs formation and development, transcriptome analysis was employed to investigate the differentially expressed genes involved in lateral roots of tea plants treated with indole-3-butyric acid (IBA), N-1-naphthylphthalamic acid (NPA), low and high concentrations of nitrogen. RESULTS: A total of 296 common differentially expressed genes were identified and annotated to four signaling pathways, including nitrogen metabolism, plant hormone signal transduction, glutathione metabolism and transcription factors. RNA-sequencing results revealed that majority of differentially expressed genes play important roles in nitrogen metabolism and hormonal signal transduction. Low nitrogen condition induced the biosynthesis of auxin and accumulation of transcripts, thereby, regulating lateral roots formation. Furthermore, metabolism of cytokinin and ethylene biosynthesis were also involved in lateral roots development. Transcription factors like MYB genes also contributed to lateral roots formation of tea plants through secondary cell wall biosynthesis. Reversed phase ultra performance liquid chromatography (RP-UPLC) results showed that the auxin concentration increased with the decreased nitrogen level in lateral roots. Thus, tea plant lateral roots formation could be induced by low nitrogen concentration via auxin biosynthesis and accumulation. CONCLUSION: This study provided insights into the mechanisms associated with nitrogen and auxin signaling pathways in LRs formation and provides information on the efficient utilization of nitrogen in tea plant at the genetic level.
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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 | 0.000 | 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) | 0.000 | 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".