Alternative polyadenylation of MeCP2: influence of cis-acting elements and trans-acting factors
Bibliographic record
Abstract
The human MeCP2 gene encodes a ubiquitously expressed methyl CpG binding protein. Mutations in this gene cause a neurodevelopmental disorder called Rett Syndrome (RS). Mutations identified in the coding region of MeCP2 account for approximately 65% of all RS cases. However, 35% of all patients do not show mutations in the coding region of MeCP2, suggesting that mutations in non-coding regions likely exist that affect MeCP2 expression rather than protein function. The gene is unusual in that is has a >8.5 kb 3' untranslated region (3' UTR), and the size of the 3'UTR is differentially regulated in various tissues because of distinct polyadenylation signals. We have identified putative cis-acting auxiliary regulatory elements that play a role in alternative polyadenylation of MeCP2 using an in vivo polyadenylation reporter assay and in a luciferase assay. These cis-acting auxiliary elements are found both upstream and downstream of the core CPSF binding sites. Mutation of one of these cis-acting auxiliary elements, a G-rich element (GRS) significantly reduced MeCP2 polyadenylation efficiency in vivo. We further investigated what trans-acting factor(s) might be binding to this cis-acting element and found that hnRNP F protein binds specifically to the element. We next investigated the MeCP2 3' UTRs by performing quantitative real-time PCR; the data suggest that altered RNA stability is not a major factor in differential MeCP2 3' UTR usage. In sum, the mechanism(s) of regulated alternative 3'UTR usage of MeCP2 are complex, and insight into these mechanisms will aid our understanding of the factors that influence MeCP2 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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".