Molecular characterization of an anthocyanin-related glutathione S-transferase gene in Japanese gentian with the CRISPR/Cas9 system
Bibliographic record
Abstract
BACKGROUND: The blue pigmentation of Japanese gentian flowers is due to a polyacylated anthocyanin, gentiodelphin, and all associated biosynthesis genes and several regulatory genes have been cloned and characterized. However, the final step involving the accumulation of anthocyanins in petal vacuoles remains unclear. We cloned and analyzed the glutathione S-transferases (GSTs) in Japanese gentian that are known to be involved in anthocyanin transport in other plant species. RESULTS: We cloned GST1, which is expressed in gentian flower petals. Additionally, this gene belongs to the Phi-type GST clade related to anthocyanin biosynthesis. We used the CRISPR/Cas9-mediated genome editing system to generate loss-of-function GST1 alleles. The edited alleles were confirmed by Sanger and next-generation sequencing analyses. The GST1 genome-edited lines exhibited two types of mutant flower phenotypes, severe (almost white) and mild (pale blue). The phenotypes were associated with decreased anthocyanin accumulation in flower petals. In the GST1 genome-edited lines, sugar-induced stress conditions inhibited the accumulation of anthocyanins in stems and leaves, suggestvhing that GST1 is necessary for stress-related anthocyanin accumulation in organs other than flowers. These observations clearly demonstrate that GST1 is the gene responsible for anthocyanin transport in Japanese gentian, and is necessary for the accumulation of gentiodelphin in flowers. CONCLUSIONS: In this study, an anthocyanin-related GST gene in Japanese gentian was functionally characterized. Unlike other biosynthesis genes, the functions of GST genes are difficult to examine in in vitro studies. Thus, the genome-editing strategy described herein may be useful for in vivo investigations of the roles of transport-related genes in gentian plants.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".