Suppressor mutations in <i>Mecp2</i> -null mice reveal that the DNA damage response is key to Rett syndrome pathology
Bibliographic record
Abstract
Abstract Mutations in X-linked methyl-CpG-binding protein 2 ( MECP2) cause Rett syndrome (RTT). We carried out a genetic screen for secondary mutations that improved phenotypes in Mecp2 /Y mice after mutagenesis with N -ethyl- N -nitrosourea (ENU), aiming to identify potential therapeutic entry points. Here we report the isolation of 106 founder animals that show suppression of Mecp2 -null traits from screening 3,177 Mecp2 /Y genomes. Using exome sequencing, genetic crosses and association analysis, we identify 33 candidate genes in 30 of the suppressor lines. A network analysis shows that 61% of the candidate genes cluster into the functional categories of transcriptional repression, chromatin modification or DNA repair, delineating a pathway relationship with MECP2. Many mutations lie in genes that are predicted to modulate synaptic signaling or lipid homeostasis. Surprisingly, mutations in genes that function in the DNA damage response (DDR) also improve symptoms in Mecp2/Y mice. The combinatorial effects of multiple loci can be resolved by employing association analysis. One line, which was previously reported to carry a suppressor mutation in a gene required for cholesterol synthesis, Sqle , carries a second mutation in retinoblastoma binding protein 8 ( Rbbp8 or CtIP ), which regulates a DDR choice in double stranded break (DSB) repair. Cells from Mecp2 /Y mice have increased DSBs, so this finding suggests that the balance between homology directed repair and non-homologous end joining is important for neuronal cells. In this and other lines, the presence of two suppressor mutations confers better symptom improvement than one locus alone, suggesting that combination therapies could be effective in RTT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".