RNA-Seq Reveals Transcription Factors Involved in Temperature-mediated Anthocyanin Accumulation and Biosynthesis in Purple Pakchoi (<i>Brassica campestris</i> ssp. <ichinensis</i> Makino.)
Bibliographic record
Abstract
Temperature is a main environmental factor that affects anthocyanin biosynthesis and accumulation in purple pakchoi (Brassica campestris ssp. Chinensis Makino.). Purple pakchoi is one of the most popular vegetables with high content of anthocyanin in China. Recently, we found that the purple color of purple pakchoi cultivar, "ziyi", deepened after 10-day low temperature(5°C, LT) treatment with increased anthocyanin content compared to plants after 20°C (normal temperature, NT, control) treatment. Contractly, the color of pakchoi treated with 10-day heat temperature (35°C, HT) became lighter with decreased anthocyanin content than that of control. The transcriptiom analysis revealed a total of 51008 unigenes from plants treated with NT，LT, and HT by RNA-seq. A total of 4321 and 8455 differentially expressed genes (DEGs) were identified from HT and LT compared to NT, respectively. Among these DEGs, 173 unigenes were downregulated in LT and upregulated in HT compared to NT. 218 unigenes were upregulated in LT and downregulated in HT. Further Gene Ontology enrichment analysis revealed a series of candidate genes that may be involve in temperature-mediated anthocyanin accumulation, including structural genes and 20 transcription factors. Collectively, our study provide a global view of transcriptiomic resources in response to temperature-induced anthocyanin accumulation in purple pakchoi.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".