Comprehensive transcriptomics and proteomics analyses of pollinated and parthenocarpic litchi (Litchi chinensis Sonn.) fruits during early development
Bibliographic record
Abstract
Litchi (Litchi chinensis Sonn.) is an important fruit that is widely cultivated in tropical and subtropical areas. In this study, we used RNA-Seq and iTRAQ technologies to compare the transcriptomes and proteomes of pollinated (polLFs) and parthenocarpic (parLFs) litchi fruits during early development (1 day, 2 days, 4 days and 6 days). We identified 4,864 DEGs in polLFs and 3,672 in parLFs, of which 2,835 were shared and 1,051 were specifically identified in parLFs. Compared to po1LFs, 768 DEGs were identified in parLFs. iTRAQ analysis identified 551 DEPs in polLFs and 1,021 in parLFs, of which 305 were shared and 526 were exclusively identified in parLFs. We found 1,127 DEPs in parLFs compared to polLFs at different stages. Further analysis revealed some DEGs/DEPs associated with abscisic acid, auxin, ethylene, gibberellin, heat shock protein (HSP), histone, ribosomal protein, transcription factor and zinc finger protein (ZFP). WGCNA identified a large set of co-expressed genes/proteins in polLFs and parLFs. In addition, a cross-comparison of transcriptomic and proteomic data identified 357 consistent DEGs/DEPs in polLFs and parLFs. This is the first time that protein/gene changes have been studied in polLFs and parLFs, and the findings improve our understanding of litchi parthenocarpy.
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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".