De Novo Analysis Reveals Transcriptomic Responses in Eriobotrya japonica Fruits during Postharvest Cold Storage
Bibliographic record
Abstract
Cold storage is the primary preservation method of postharvest loquat fruits. However, cold storage also results in many chilling injury physiological disorders called lignification, which decreases the quality and economic value of the fruits. Few studies to date have focused on the transcriptomic responses associated with lignification except lignin synthesis pathways. This study aimed to explore the changes of loquat transcriptome during long-term cold storage. Our results showed that the gene expression patterns were differed among the five stages. The differentially expressed genes (DEGs) in response to cold storage were more intense and complex in earlier stage. The membrane-related genes preferentially responded to low temperature and were followed by intracellular-located genes. The cold-induced pathways were mainly concerned with signal transduction and secondary metabolism (i.e., lignin, pectin, cellulose, terpenoid, carotenoid, steroid) in the first three stages and were chiefly related to primary metabolism in the later two stages, especially energy metabolism. Further investigation suggested that 503 protein kinases, 106 protein phosphatases, and 40 Ca2+ signal components were involved in the cold signal transduction of postharvest loquat fruits. We predicted a pathway including 649 encoding genes of 49 enzymes, which displayed the metabolisms of major sugars and polysaccharides in cold-stored loquat fruits. The coordinated expression patterns of these genes might contribute to the changes of saccharides in the pathway. These results provide new insight into the transcriptomic changes of postharvest loquat fruits in response to cold storage environment, which may be helpful for improving the postharvest life of loquat in the future.
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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 | 0.001 |
| 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".