Natural Variation in Gene Expression Between Wild and Weedy Populations of <i>Helianthus annuus</i>
Bibliographic record
Abstract
The molecular genetic changes underlying the transformation of wild plants into agricultural weeds are poorly understood. Here we use a sunflower cDNA microarray to detect variation in gene expression between two wild (non-weedy) Helianthus annuus populations from Utah and Kansas and four weedy H. annuus populations collected from agricultural fields in Utah, Kansas, Indiana, and California. When grown in a common growth chamber environment, populations differed substantially in their gene expression patterns, indicating extensive genetic differentiation. Overall, 165 uni-genes, representing approximately 5% of total genes on the array, showed significant differential expression in one or more weedy populations when compared to both wild populations. This subset of genes is enriched for abiotic/biotic stimulus and stress response proteins, which may underlie niche transitions from the natural sites to agricultural fields for H. annuus. However, only a small proportion of the differentially expressed genes overlapped in multiple wild vs. weedy comparisons, indicating that most of the observed expression changes are due to local adaptation or neutral processes, as opposed to parallel genotypic adaptation to agricultural fields. These results are consistent with an earlier phylogeographic study suggesting that weedy sunflowers have evolved multiple times in different regions of the United States and further indicate that the evolution of weedy sunflowers has been accompanied by substantial gene expression divergence in different weedy populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".