TPS5 and TOR signaling components are determinants of Populus balsamifera leaf morphology
Bibliographic record
Abstract
Variation in leaf morphology in plant species predicts long-term growth and yield. Consequently, we aim to understand the genetic basis of natural variation in poplar leaf morphology as an avenue to maximize biomass accrual. Multilocus GWAS and deep learning genomic prediction were used to investigate the genetic architecture of twelve correlated traits representing leaf size and shape in a diverse population of 313 Populus balsamifera L. genotypes. 94 significant associations were detected, with 70 associations unique to a single trait, and 24 were detected in association with more than one trait. We developed genomic selection models to predict leaf morphology in novel genotypes using a strategy called GWADL (Genome-Wide Association enriched Deep Learning). We detected significant SNP-trait associations in the poplar TOR orthologue and likely upstream activating kinases SnRK3 and SnAK1 . The most significant polymorphism, explaining variance in tip angle, leaf mass-per-area, and serration density, was detected in association with TERPENE SYNTHASE5, PbTPS5 . Exogenous application of sesquiterpenes β-eudesmol and 1αH,5αH-Guaia-6-ene-4β,10β-diol in developing young poplar leaves resulted in significantly smaller mature leaves. This study provides a genetic and mathematical foundation for improving poplar performance by optimizing leaf morphology, and importantly identified a novel role for the sesquiterpene synthase PbTPS5 in normal plant growth and development.
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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".