Are long‐lived trees poised for evolutionary change? Single locus effects in the evolution of gene expression networks in spruce
Bibliographic record
Abstract
Genetic variation in gene expression traits contributes to phenotypic diversity and may facilitate adaptation following environmental change. This is especially important in long-lived organisms where adaptation to rapid changes in the environment must rely on standing variation within populations. However, the extent of expression variation in most wild species remains to be investigated. We address this question by measuring the segregation of expression levels in white spruce [Picea glauca (Moench), Voss] in a transcriptome-wide manner and examining the underlying evolutionary and biological processes. We applied a novel approach for the genetic analysis of expression variation by measuring its segregation in haploid meiotic seed tissue. We identified over 800 transcripts whose abundances are most likely controlled by variants in single loci. Cosegregation analysis of allelic expression levels was used to construct regulatory associations between genes and define regulatory networks. The majority (67%) of segregating transcripts were under linkage. Regulatory associations were typically among small groups of genes (2-3 transcripts), indicating that most segregating expression levels can evolve independently from one another. One notable exception was a large putative trans effect that altered the expression of 180 genes that includes key regulators of protein metabolism, highlighting a regulatory cascade affected by variation in a single locus in this conserved metabolic pathway. Overall, segregating expression variation was associated with stress response- and duplicated genes, whose evolution may be linked to functional innovations. These observations indicate that expression variation might be important in facilitating diversity of molecular responses to environmental stresses in wild trees.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".