Combining metabolomics and transcriptomics to study selenium response mechanisms in germinating watermelon seeds (Citrullus mucospermus)
Bibliographic record
Abstract
Watermelon seeds are readily accepted even after selenium enrichment, thereby potentially helping meet daily selenium requirements of adults. However, no existing studies have elucidated the molecular-level metabolic changes in watermelon seeds induced by selenium enrichment. In this study, UPLC-MS/MS was employed to identify and quantify metabolite alterations in watermelon seeds. A total of 1036 metabolites were identified, and 81 differentially expressed metabolites (DEMs) being screened out. Gene identification and expression analysis were conducted using the Illumina NovaSeq 6000 platform. A total of 18,686 genes were identified, and 999 differentially expressed genes (DEGs) were screened out. Joint omics analysis revealed that DEMs and DEGs were enriched in 25 pathways, primarily involving flavonoid biosynthesis and amino acid metabolism. Genes like PAL, 4CL, CYP73A, and CHS were the key genes for the biosynthesis of flavonoids under selenium treatment, resulting in a significant decrease in the expression level of chlorogenic acid in the pathway. In the amino acid metabolism pathway, genes such as AGXT2, glnA, speE, and thrC played crucial roles, causing significant alterations in the expression levels of metabolites such as L-Alanine, Threonine, and Tryptophan in the pathway. This study provides a theoretical basis for developing watermelon seeds with both selenium-enrichment function and balanced nutrition. The selenium enrichment process can be improved by regulating the expression levels of key genes such as PAL and AGXT2, and the amino acid composition can also be regulated.
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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 | 0.001 | 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) | 0.000 | 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 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".