Transcriptomics-based Analysis of the Response of Sugar Content in Litchi Pulp to Foliar Calcium Fertilizer Treatment
Bibliographic record
Abstract
Ten 16-year-old trees were used as test materials to investigate the effect of foliar calcium fertilizer on the sugar content of ‘Feizixiao’ litchi ( Litchi chinensis Sonn.) pulp. The experiment began 35 days after anthesis (DAA) in 2020 and 2021, and the treatment was a foliar spray application of 0.3% CaCl 2 aqueous solution, whereas the control was a foliar spray application of water. The sugar content, sucrose-metabolizing enzymes, and ATP-dependent phosphofructokinase (PFK) activities in pulp were measured in 2020 and 2021. Transcriptome sequencing (RNA-seq) was performed on RNA samples from treatment and control fruit pulps at 35, 63, and 69 DAA (full mature stage) in 2020, and 10 genes were chosen for confirmation by real-time polymerase chain reaction (PCR) in 2020 and 2021. At full maturity, the soluble sugar content in the calcium-treated group was extremely significantly or significantly higher than that in the control group. After 63 DAA, the net sucrose-metabolizing enzyme activity in the calcium-treated group was significantly higher than that in the control group. Furthermore, at full maturity, the calcium-treated group had significantly higher sucrose synthase cleavage activity and significantly lower PFK activity than the control group. Fifty-four highly expressed genes in the glycolytic pathway (EMP) were screened from transcriptome data, including hexokinase, PFK, and pyruvate kinase genes; 87% of these genes were downregulated in the treatment group compared with the control group at 69 DAA in 2020. The linear regression between RNA-seq and real-time PCR results was significant in 2020 ( r = 0.9292) and 2021 ( r = 0.8889). When the fruit is fully ripe, calcium treatment increases net sucrose-metabolizing enzyme activity by increasing sucrose synthase cleavage activity, promoting the accumulation of reducing sugars, and it downregulates phosphofructokinase gene expression in EMP, promoting sugar accumulation.
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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 | 0.001 |
| 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) | 0.001 | 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".