Transcriptome sequencing revealed the regulation of stem internode length associated with mechanical harvesting in three tea varieties
Bibliographic record
Abstract
Introduction Internode length of tea plant is one of the key traits affecting the mechanical harvesting effect, but there are relatively few reports on their molecular regulatory. Methods Transcriptome sequencing (RNA-seq) was performed on it in order to explain its molecular mechanism. GO, KEGG enrichment and WGCNA were used to identify key genes, and their expression levels in three tea tree varieties were validated by qRT-PCR. Result In this study, we measured the internode lengths of the three varieties and found that their internode lengths were ‘ Feiyun ’ > ‘ Qiancha 1 ’ > ‘ Longjing 43 ’. 10,518 differentially expressed genes were identified through transcriptome sequencing and analysis. GO and KEGG enrichment showed that these differentially expressed genes were mainly enriched in plant hormone signal transduction and DNA-binding transcription factor activity pathways. WGCNA analysis identified two modules significantly correlated with internode length. Combining enrichment analysis with WGCNA results, 28 candidate genes associated with internode length were identified. In addition, it was found that the expression levels of DELLA and GA3ox were highly expressed in ‘ Longjing 43 ’, while GA2ox , WRKY , and ERF were highly expressed in ‘ Feiyun ’, showing significant positive and negative correlations with internode length, respectively. Discussion Our results provide candidate genes for studying the molecular mechanism of stem elongation, and provide a theoretical basis for selection machine harvested tea varieties and improvement of mechanical harvesting efficiency.
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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 | 0.001 | 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".