Escape from X-chromosome inactivation at <i>KDM5C</i> is driven by promoter-proximal DNA elements and enhanced by domain context
Bibliographic record
Abstract
Over 20% of human X-linked genes escape from X-chromosome inactivation (XCI), and are important contributors to sex differences in gene expression. Candidate factors involved in escape have been identified through enrichment analyses and include both regional as well as promoter-proximal elements; however, functional testing is limited. Using both in vivo and in vitro mouse models, we refine a region of just 2.6 kb of the human escape gene KDM5C as able to drive escape from XCI. Transgenes of mouse Kdm5c escape XCI; however, human KDM5C is one of three escape genes in a more than 200 kb region, so we initially tested a BAC transgene containing a full-length version of the gene with a reporter insertion. Contrary to our expectation, this transgene failed to escape from XCI. To understand why, we moved to a mouse embryonic stem cell system and tested the BAC transgene without the reporter cassette. Despite being separated from other human escape genes, and also being tested in a different species, human KDM5C was able to escape from XCI, suggesting that the reporter integration disrupted or separated critical escape elements. We refined escape-essential sequences to only 2.6 kb including the promoter, exon 1 and contiguous 1.6 kb of the first intron, consistent with previous studies demonstrating local elements are sufficient for escape. Interestingly, dual copy insertions showed higher escape, suggesting that while local elements are important drivers for escape, the size or number of escape genes in a region can boost inactive X expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".