Temporal dynamics of genetic architecture governing leaf development in <i>Populus</i>
Bibliographic record
Abstract
Leaf development is a multifaceted and dynamic process orchestrated by a myriad of genes to shape the proper size and morphology. The dynamic genetic network underlying leaf development remains largely unknown. Utilizing a synergistic genetic approach encompassing dynamic genome-wide association study (GWAS), time-ordered gene co-expression network (TO-GCN) analyses and gene manipulation, we explored the temporal genetic architecture and regulatory network governing leaf development in Populus. We identified 42 time-specific and 18 consecutive genes that displayed different patterns of expression at various time points. We then constructed eight TO-GCNs that covered the cell proliferation, transition, and cell expansion stages of leaf development. Integrating GWAS and TO-GCN, we postulated the functions of 27 causative genes for GWAS and identified PtoGRF9 as a key player in leaf development. Genetic manipulation via overexpression and suppression of PtoGRF9 revealed its primary influence on leaf development by modulating cell proliferation. Furthermore, we elucidated that PtoGRF9 governs leaf development by activating PtoHB21 during the cell proliferation stage and attenuating PtoLD during the transition stage. Our study provides insights into the dynamic genetic underpinnings of leaf development and understanding the regulatory mechanism of PtoGRF9 in this dynamic process.
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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".