Recruitment of chromatin remodelers by XIST B-repeat region is variably dependent on HNRNPK
Bibliographic record
Abstract
X-chromosome inactivation is triggered by the long non-coding RNA XIST, whose structure is characterized by tandem repeats that modularly recruit different proteins and chromatin remodelers. Previously, we reported that the addition of the mouse PID region to a transgene with human repeat regions A, F and E (miniXIST; 5.1 kb) enabled binding of HNRNPK and also enabled the induction of silencing and recruitment of H3K27me3, UbH2A and H4K20me1, but only partially. As the 680 bp PID region enabled so many features of inactivation, we hypothesized that augmenting the PID with more mouse or human sequences rich in CCC motifs would allow us to design a short transgene which was as effective as Full XIST. Three new transgenes using the A, F and E human domains as a backbone were tested for ability to induce silencing and heterochromatic mark recruitment. The all human-derived BhB-BhB transgene (4.9 kb) was as good as our previous miniXIST, suggesting that these domains are the human equivalent of the mouse PID region. A PID-PID transgene (5.8 kb) was not statistically different from Full XIST and could be potentially used for chromosome therapy. Adding BhB to PID (BhB-PID, 5.4 kb) had an intermediate efficacy compared to the other two transgenes, suggesting that the most important component for silencing and heterochromatic mark recruitment is the number of CCC motifs, not the species of origin. Finally, we created a heterozygous HNRNPK deletion and observed a disproportionate impact on HNRNPK and UbH2A recruitment to XIST, reflecting complex roles for the PID and HNRNPK in X-chromosome inactivation.
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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".