Transcriptomics of developing wild sunflower seeds from the extreme ends of a latitudinal gradient differing in seed oil composition
Bibliographic record
Abstract
Abstract Seed oil composition, an important agronomic trait in cultivated sunflower, varies latitudinally across the native range of its wild progenitor. This pattern is thought to be driven by selection for a higher proportion of saturated fatty acids in southern populations compared to northern populations, likely due to the different temperatures experienced during seed germination. To investigate whether these differences in fatty acid composition between northern and southern populations correspond to transcriptional variation in the expression of genes involved in fatty acid metabolism, we sequenced RNA from developing seeds of sunflowers from Texas, USA and Saskatchewan, Canada (the extreme ends of sunflower’s latitudinal range) grown in a common garden. Over 4,000 genes were found to be differentially expressed between Texas and Canada, including several genes involved in lipid metabolism. Many differentially expressed oil metabolism genes colocalized with known oil QTL. The genes producing stearoyl-ACP-desaturases ( SAD ) were of particular interest because of their known role in the conversion of fully saturated into unsaturated fatty acids. Two SAD genes were more highly expressed in seeds from Canadian populations, consistent with the observation of increased levels of unsaturated fatty acids in seeds from that region. We also constructed a gene co-expression network to investigate regional variation in network modules. The results of this analysis revealed regional differentiation for eight of twelve modules, but no clear relationship with oil biosynthesis. Overall, the differential expression of SAD genes offers a partial explanation for the observed differences in seed oil composition between Texas and Canada, while the expression patterns of other metabolic genes suggest complex regulation of fatty acid production and usage across latitudes.
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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".