Genome-scale transcriptional analyses of first-generation interspecific sunflower hybrids reveals broad regulatory compatibility
Bibliographic record
Abstract
BACKGROUND: Interspecific hybridization creates individuals harboring diverged genomes. The interaction of these genomes can generate successful evolutionary novelty or disadvantageous genomic conflict. Annual sunflowers Helianthus annuus and H. petiolaris have a rich history of hybridization in natural populations. Although first-generation hybrids generally have low fertility, hybrid swarms that include later generation and fully fertile backcross plants have been identified, as well as at least three independently-originated stable hybrid taxa. We examine patterns of transcript accumulation in the earliest stages of hybridization of these species via analyses of transcriptome sequences from laboratory-derived F1 offspring of an inbred H. annuus cultivar and a wild H. petiolaris accession. RESULTS: While nearly 14% of the reference transcriptome showed significant accumulation differences between parental accessions, total F1 transcript levels showed little evidence of dominance, as midparent transcript levels were highly predictive of transcript accumulation in F1 plants. Allelic bias in F1 transcript accumulation was detected in 20% of transcripts containing sufficient polymorphism to distinguish parental alleles; however the magnitude of these biases were generally smaller than differences among parental accessions. CONCLUSIONS: While analyses of allelic bias suggest that cis regulatory differences between H. annuus and H. petiolaris are common, their effect on transcript levels may be more subtle than trans-acting regulatory differences. Overall, these analyses found little evidence of regulatory incompatibility or dominance interactions between parental genomes within F1 hybrid individuals, although it is unclear whether this is a legacy or an enabler of introgression between species.
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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".