Identification and expression analysis of the <i>LRR-RLK</i> gene family in tomato (<i>Solanum lycopersicum</i>) Heinz 1706
Bibliographic record
Abstract
As the largest subfamily of receptor-like kinases (RLKs), leucine-rich repeat receptor-like kinases (LRR-RLKs) regulate the growth, development, and stress responses of plants. Through a reiterative process of sequence analysis and re-annotation, 234 LRR-RLK genes were identified in the genome of tomato (Solanum lycopersicum) 'Heinz 1706', which were further grouped into 10 major groups based on their sequence similarity. In comparison to the significant role of tandem duplication in the expansion process of this gene family in other species, only approximately 12% (29 out of 234) of SlLRR-RLK genes arose from tandem duplication. Using the multiple expectation maximization for motif elicitation (MEME) method, the motif composition and arrangement were found to be variably conserved within each SlLRR-RLK group, indicating their different extent of functional divergence. Expression profiling analyses by qRT-PCR data revealed that SlLRR-RLK genes were differentially expressed in various tomato organs and tissues, and some SlLRR-RLK genes exhibited preferential expression in fruits at distinct developmental stages, suggesting that SlLRR-RLK may take important roles in fruit development and ripening process. The results of this study provide an overview of the LRR-RLK gene family in tomato Heinz 1706, one important species of Solanaceae, and will be helpful for future functional analysis of this important protein family in fleshy fruit-bearing species.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".