Genomic insights into the evolution of Chinese sweetgum and its autumn leaf coloration
Bibliographic record
Abstract
Chinese sweetgum (Liquidambar formosana) is valued as a source of resin and timber and is an important ornamental tree due to its showy fall foliage. Here, we report the chromosome-level assembly of the Chinese sweetgum genome. Phylogenomic analyses showed the basal phylogenetic position of Chinese sweetgum in core eudicots. Comparative genomic analyses revealed that the well-known gamma event in the common ancestors of core eudicots is evident in the Chinese sweetgum genome, and ancestral triplicated blocks resulting from that event are more intact in Chinese sweetgum than in grapevine (Vitis vinifera). Because of its conserved genome structure, very slow rate of evolution, and basal phylogenetic position, the Chinese sweetgum genome is a good reference for comparative genome studies. Further, we reconstructed the entire metabolic pathway for anthocyanins and potential regulatory networks of autumn leaf coloration of this species via metabolomics and transcriptomics. The transcription factors LfMYB69, basic helix-loop-helix (LfbHLH4), and WD40-repeat (LfWDR1) may collectively regulate the transcription of anthocyanin biosynthetic genes. The regulation of chalcone synthase genes (LfCHS1-3) and dihydroflavonol 4-reductase genes (LfDFR1-2) by the LfMYB69-LfbHLH4-LfWDR1 complex was confirmed by luciferase assays. Epigenomic analyses revealed that 5 structural genes, including LfCHS1, and 2 regulatory LfMYBs are epigenetically regulated. This study expands our understanding of autumn leaf coloration and provides valuable genomic resources for comparative biology, breeding, and biotechnology.
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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 | 0.001 |
| 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".