Comprehensive silk gland multi-omics comparison illuminates two alternative mechanisms in silkworm heterosis
Bibliographic record
Abstract
Heterosis is a common phenomenon in plants and animals with diverse underlying mechanisms. Here, we applied two widely used silkworm hybrid systems and performed multi-omics analysis to identify possible intrinsic associations between different hybrid strategies and epigenetic mechanisms with silkworm heterosis. We found significant differences in the silk gland transcriptomic landscape between the two systems, including differentially expressed genes and expression patterns in the hybrid offspring compared to their parents. In the quaternary hybrid system, hybrid vigor was primarily due to up-regulated genes and the parent-dominant up-regulated expression pattern, involving multiple transport processes, cellular nitrogen compound catabolism, glucose metabolism, and tricarboxylic acid cycle. In the binary system, hybrid vigor was mainly due to the down-regulated genes and transgressively down-regulated expression pattern, mainly involving basic nitrogen synthesis metabolism and body function. We also demonstrated that DNA methylation may affect hybrid vigor by regulating the expression of several heterosis-related genes. Thus, this study revealed two alternative mechanisms that may contribute to silkworm heterosis, both of which facilitate the efficient utilization of energy and nitrogen for silk production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".