Buffering of transcription rate by mRNA half-life is a conserved feature of Rett syndrome models
Bibliographic record
Abstract
Abstract Models of MECP2 dysfunction in Rett syndrome (RTT) assume that transcription rate changes directly correlate with altered steady-state mRNA levels. However, limited evidence suggests that transcription rate changes are buffered by poorly understood compensatory post-transcriptional mechanisms. Here we measure transcription rate and mRNA half-life changes in RTT patient neurons using RATE-seq, and reinterpret nuclear and whole-cell RNAseq from Mecp2 mice. Genes are dysregulated by changing transcription rate only or half-life only and are buffered when both are changed. We utilized classifier models to understand the direction of transcription rate changes based on gene-body DNA sequence, and combined frequencies of three dinucleotides were better predictors than contributions by CA and CG. MicroRNA and RNA-Binding Protein (RBP) motifs were enriched in 3’UTRs of genes with half-life changes. Motifs for nuclear localized RBPs were enriched on buffered genes with increased transcription rate. Our findings identify post-transcriptional mechanisms in humans and mice that alter half-life only or buffer transcription rate changes when a transcriptional modulator gene is mutated in a neurodevelopmental disorder.
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 | 0.001 |
| 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 | 0.000 | 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".