Flavonoid Metabolism in Tetrastigma hemsleyanum Diels et Gilg Based on Metabolome Analysis and Transcriptome Sequencing
Bibliographic record
Abstract
Tetrastigma hemsleyanum Diels et Gilg, known as a “plant antibiotic”, possesses several attractive properties including anti-inflammatory, anti-tumor, and antioxidant effects, with its efficacy being attributed to flavonoids. However, the flavonoid biosynthesis of T. hemsleyanum has rarely been studied. In this study, we investigated the flavonoid metabolism of T. hemsleyanum through metabolome analysis and transcriptome sequencing. The metabolomic results showed differences in the flavonoids of the leaves and root tubers of T. hemsleyanum. A total of 22 flavonoids was detected, and the concentrations of most flavonoids in the leaves were higher than those in the root tubers. Transcriptome analysis revealed that differentially expressed genes (DEGs) in the leaves and root tubers were enriched in photosynthesis-antenna proteins. Pearson correlation analysis indicated that the expression levels of chalcone isomerase (CHI) and UDP-glycose flavonoid glycosyltransferase (UFGT) were highly correlated with the concentrations of most flavonoids. Further, this study found that the photosynthesis-antenna proteins essentially contributed to the difference in the flavonoids in T. hemsleyanum. The gene expressions and concentrations of the total flavonoids of leaves and root tubers in Hangzhou, Jinhua, Lishui, and Taizhou in Zhejiang Province, China, showed that CHI (CL6715.Contig1_All, Unigene19431_All, CL921.Contig4_All) and UFGT (CL11556.Contig3_All, CL11775.Contig1_All) were the potential key genes of accumulation of most flavonoids in T. hemsleyanum.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".