Genome-wide identification of the peach LOB/LBD genes and the positive role of the PpNAP4–PpLOB1 module in peach fruit softening
Bibliographic record
Abstract
PpLOB1 positively regulates peach softening. PpNAP4 activates PpLOB1 expression. Softening of fleshy fruits during ripening and post-harvest is a programmed event that greatly affects quality and storage span. However, the molecular mechanism underlying peach softening remain largely unknown. Lateral organ boundary (LOB) domain (LBD) proteins are pivotal regulators of plant growth and development. To date, certain LOB/LBD transcription factors are seemingly implicated in fruit softening. In this study, we identified 42 LOB/LBD genes in the peach genome. Expression analysis showed a significant upregulation of PpLOB1 transcripts toward peach fruit ripening. PpLOB1 was classified into Class II subgroup, and showed high sequence similarity to several softening-related LOB/LBD transcription factors. Transient transformation assays showed that PpLOB1 positively modulates peach softening. Further experiments demonstrated that PpLOB1 directly targeted and activated the promoters of pectate lyase 1 ( PpPL1 ) and PpPL15 , thereby contributing to the regulation of fruit softening. Additionally, PpNAP4 up-regulated PpLOB1 expression by binding to its promoter. Meanwhile, our findings revealed that PpNAP4 and PpNAP6 cooperatively modulate the expression of PpLOB1 . Altogether, all results revealed a new regulatory module that involves PpNAP4 and PpLOB1, and contributes to peach fruit softening.
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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 | 0.000 |
| 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) | 0.001 | 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".