Dynamics of gene silencing during X inactivation using allele-specific RNA-seq
Bibliographic record
Abstract
BACKGROUND: During early embryonic development, one of the two X chromosomes in mammalian female cells is inactivated to compensate for a potential imbalance in transcript levels with male cells, which contain a single X chromosome. Here, we use mouse female embryonic stem cells (ESCs) with non-random X chromosome inactivation (XCI) and polymorphic X chromosomes to study the dynamics of gene silencing over the inactive X chromosome by high-resolution allele-specific RNA-seq. RESULTS: Induction of XCI by differentiation of female ESCs shows that genes proximal to the X-inactivation center are silenced earlier than distal genes, while lowly expressed genes show faster XCI dynamics than highly expressed genes. The active X chromosome shows a minor but significant increase in gene activity during differentiation, resulting in complete dosage compensation in differentiated cell types. Genes escaping XCI show little or no silencing during early propagation of XCI. Allele-specific RNA-seq of neural progenitor cells generated from the female ESCs identifies three regions distal to the X-inactivation center that escape XCI. These regions, which stably escape during propagation and maintenance of XCI, coincide with topologically associating domains (TADs) as present in the female ESCs. Also, the previously characterized gene clusters escaping XCI in human fibroblasts correlate with TADs. CONCLUSIONS: The gene silencing observed during XCI provides further insight in the establishment of the repressive complex formed by the inactive X chromosome. The association of escape regions with TADs, in mouse and human, suggests that TADs are the primary targets during propagation of XCI over the X chromosome.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".