Identification of basic helix-loop-helix transcription factors that activate betulinic acid biosynthesis by RNA-sequencing of hydroponically cultured <i>Lotus japonicus</i>
Bibliographic record
Abstract
Abstract Although triterpenes are ubiquitous in plant kingdom, their biosynthetic regulatory mechanisms are limitedly understood. Here, we found that hydroponic culture dramatically activated betulinic acid (BA) biosynthesis in the model Fabaceae Lotus japonicus , and investigated its transcriptional regulation. Fabaceae plants develop secondary aerenchyma (SA) on the surface of hypocotyls and roots during flooding for root air diffusion. Hydroponic culture induced SA in L. japonicus and simultaneously increased the accumulation of BA and the transcript levels of its biosynthetic genes. RNA-sequencing of soil-grown and hydroponically cultured plant tissues, including SA collected by laser microdissection, revealed that several transcription factor genes were co-upregulated with BA biosynthetic genes. Overexpression of LjbHLH32 and LjbHLH50 in L. japonicus transgenic hairy roots upregulated the expression of BA biosynthesis genes, resulting in enhanced BA accumulation. However, transient luciferase reporter assays in Arabidopsis mesophyll cell protoplasts showed that LjbHLH32 transactivated promoters of biosynthetic genes in the soyasaponin pathway but not the BA pathway, like its homolog GubHLH3, a soyasaponin biosynthesis regulator in Glycyrrhiza uralensis . This suggested the evolutionary origin and complex regulatory mechanisms of BA biosynthesis in Fabaceae. This study sheds light on the unrevealed biosynthetic regulatory mechanisms of triterpenes in Fabaceae plants. Highlight Hydroponic culture enhanced betulinic acid synthesis in Lotus japonicus . RNA-sequencing and functional characterization experiments suggest that LjbHLH32 and LjbHLH50 are the transcription factors activating betulinic acid biosynthesis.
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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 | 0.000 |
| 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".