Integrative Transcriptomic and Metabolomic Analysis Reveals Regulatory Networks and Metabolite Dynamics in Gastrodia elata Flower Development
Bibliographic record
Abstract
Flower development, a vital phase in the plant life cycle, involves intricate physiological and morphogenetic processes driven by dynamic molecular and metabolic processes. However, the specific molecular mechanisms and metabolite accumulation patterns during Gastrodia elata flower development remain largely unknown. This study utilized Illumina’s next-generation sequencing to analyze the G. elata flower transcriptome across three critical developmental stages, capturing gene expression changes, particularly those related to transcription factors that regulate flower formation and metabolite accumulation. FPKM analysis showed significant transcriptomic changes during G. elata flower development, while targeted metabolomics identified key metabolites with stage-specific variations via widely targeted metabolic profiling. Here, integrative transcriptome and metabolome analyses were performed to investigate floral genes and compounds in G. elata flowers at three different developmental stages. The differentially expressed genes (DEGs) and significant changes in metabolites (SCMs) involved in key biological pathways were identified. This approach aimed to identify functional genes or pathways jointly enriched in metabolites, thereby defining pathways linked to crucial biological phenotypes. By mapping DEGs and SCMs to KEGG pathways, the comprehensive network was constructed, uncovering functional relationships between gene expression and metabolite accumulation. This study proposes dynamic models of transcriptomic and metabolite changes, revealing key regulatory networks that govern G. elata flower development and potential applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".