Gene expression profiling reveals subgenome dominance during <i>Brassica napus</i> seed development
Bibliographic record
Abstract
Abstract We profiled the gene regulatory landscape of Brassica napus reproductive development using RNA sequencing. Comparative analysis of this nascent amphidiploid across the plant lifecycle revealed the contribution of each subgenome to plant reproduction. Global mRNA profiling revealed lower accumulation of C n subgenome transcripts relative to the A n subgenome. Subgenome-specific transcriptional networks identified distinct transcription factor families enriched in each of the A n and C n subgenome early in seed development. Global gene expression profiling of laser-microdissected seed subregions further reveal subgenome expression dynamics in the embryo, endosperm, and seed coat of early stage seeds. Transcription factors predicted to be regulators encoded by the A n subgenome are expressed primarily in the seed coat whereas regulators encoded by the C n subgenome were expressed primarily in the embryo. Data suggest subgenome bias are characteristic features of the B. napus seed throughout development, and that such bias might not be universal across the embryo, endosperm, and seed coat of the developing seed. Whole genome transcription factor networks identified BZIP11 as a transcriptional regulator of early B. napus seed development. Knockdown of BZIP11 using RNA interference resulted in a similar reduction in gene activity of predicted gene targets, and a reproductive-lethal phenotype. Taken together, transcriptional networks spanning both the A n and C n genomes of the B. napus seed can identify valuable targets for seed development research and that-omics level approaches to studying gene regulation in B. napus can benefit from both broad and high-resolution analyses. One Sentence Summary Global RNA sequencing coupled with laser microdissection provides a critical resource to study subgenome bias in whole seeds and specific tissues of polyploid plants.
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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".