Survey of gene, lncRNA and transposon transcription patterns in four mouse organs highlights shared and organ-specific sex-biased regulation
Bibliographic record
Abstract
ABSTRACT Background Sex-biased gene regulation is the basis of sexual dimorphism in phenotypes and has been studied across different cell types and different developmental stages. However, sex-biased expression of transposable elements (TEs) that represent nearly half of the mammalian genome and have the potential of influencing genome integrity and regulation, remains underexplored. Results Here, we report a survey of gene, lncRNA and TE expression in four organs from mice with different combinations of gonadal and genetic sex. Data show remarkable variability among organs with respect to the impact of gonadal sex on transcription with the strongest effects observed in liver. In contrast, the X-chromosome dosage alone had modest influence on sex-biased transcription across different organs, albeit interaction between X-dosage and gonadal sex cannot be ruled out. The presence of the Y chromosome influenced TE, but not gene or lncRNA expression in liver. Notably, 90% of sex-biased TEs (sDETEs) reside in clusters. Moreover, 54% of these clusters overlap or reside close (<100 kb) to sex-biased genes or lncRNAs, share the same sex bias, and also have higher expression levels than sDETE clusters that do not co-localize with other types of sex-biased transcripts. We also tested the heterochromatic sink hypothesis that predicts higher expression of TEs in XX individuals and found no evidence to support it. Conclusions Our data show that sex-biased expression of TEs varies among organs with highest numbers of sDETEs found in liver following the trends observed for genes and lncRNAs. It is enhanced by proximity to other types of sex-biased transcripts.
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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 | 0.001 | 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) | 0.002 | 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".