Identification of Candidate Genes Responsible for Flower Colour Intensity in Gentiana triflora
Bibliographic record
Abstract
Gentians cultivated in Japan (Gentiana trifloraandGentiana scabraand hybrids) have blue flowers, but flower colour intensity differs among cultivars. The molecular mechanism underlying the variation in flower colour intensity is unclear. Here, we produced F2progeny derived from an F1cross of intense- and faint-blue lines and attempted to identify the genes responsible for flower colour intensity using RNA-sequencing analyses. Comparative analysis of flower colour intensity and transcriptome data revealed differentially expressed genes (DEGs), although known flavonoid biosynthesis-related genes showed similar expression patterns. From quantitative RT-PCR (qRT-PCR) analysis, we identified two and four genes with significantly different expression levels in the intense- and faint-blue flower lines, respectively. We conducted further analyses on one of the DEGs, termedGtMIF1, which encodes a putative mini zinc-finger protein homolog, which was most differently expressed in faint-blue individuals. Functional analysis ofGtMIF1was performed by producing stable tobacco transformants.GtMIF1-overexpressing tobacco plants showed reduced flower colour intensity compared with untransformed control plants. DNA-marker analysis also confirmed that theGtMIF1allele of the faint-blue flower line correlated well with faint flower colour in F2progeny. These results suggest thatGtMIF1is one of the key genes involved in determining the flower colour intensity of gentian.
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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".