Transcriptomic and Phenotypic Analyses Reveal the Molecular Mechanism of Dwarfing in Tetraploid Robinia pseudoacacia L.
Bibliographic record
Abstract
Polyploid breeding techniques aid in the cultivation of new forestry cultivars, thus expanding the suite of strategies for the improvement of arboreal traits and innovation within the field of forestry. Compared to diploid Robinia pseudoacacia L. (black locust) ‘D26-5①’ (2×), its dwarfed homologous tetraploid ‘D26-5②’ (4×) variety has better application prospects in garden vegetation guardrails and urban landscape. However, the molecular mechanism of the generation and growth of this dwarf variety is still unclear. Here, plant growth and development as well as histological differences between the diploid and its autotetraploid were investigated. Levels of endogenous hormones at three different developmental stages (20, 40, and 70 days) of 2× and homologous 4× tissue culture plantlets were assessed, and it was found that the brassinosteroid (BR) contents of the former were significantly higher than the latter. Transcriptome sequencing data analysis of 2× and homologous 4× showed that differentially expressed genes (DEGs) were significantly enriched in plant hormone synthesis and signal transduction, sugar and starch metabolism, and the plant circadian rhythm pathway, which are closely related to plant growth and development. Therefore, these biological pathways may be important regulatory pathways leading to dwarfism and slow growth in tetraploids. Additionally, utilizing weighted gene coexpression network analysis (WGCNA), we identified three crucial differentially expressed genes (DEGs)—PRR5, CYP450, and SPA1—that potentially underlie the observed ploidy variation. This study provides a new reference for the molecular mechanism of dwarfism in dwarfed autotetraploid black locusts. Collectively, our results of metabolite analysis and comparative transcriptomics confirm that plant hormone signaling and the circadian rhythm pathway result in dwarfism in black locusts.
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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) | 0.000 | 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".