Suppressor mutations in <i>Mecp2</i> -null mice implicate the DNA damage response in Rett syndrome pathology
Bibliographic record
Abstract
Mutations in X-linked methyl-CpG-binding protein 2 ( MECP2) cause Rett syndrome (RTT). To identify functional pathways that could inform therapeutic entry points, we carried out a genetic screen for secondary mutations that improved phenotypes in Mecp2 /Y mice after mutagenesis with N -ethyl- N -nitrosourea (ENU). Here, we report the isolation of 106 founder animals that show suppression of Mecp2 -null traits from screening 3177 Mecp2 /Y genomes. Whole-exome sequencing, genetic crosses, and association analysis identified 22 candidate genes. Additional lesions in these candidate genes or pathway components associate variant alleles with phenotypic improvement in 30 lines. A network analysis shows that 63% of the 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 modulate synaptic signaling or lipid homeostasis. Mutations in genes that function in the DNA damage response (DDR) also improve phenotypes in Mecp2/Y mice. Association analysis was successful in resolving combinatorial effects of multiple loci. One line, which carries a suppressor mutation in a gene required for cholesterol synthesis, Sqle , carries a second mutation in retinoblastoma binding protein 8, endonuclease ( Rbbp8 , also known as 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 nonhomologous end joining is important for neuronal cells. In this and other lines, two suppressor mutations confer greater improvement than one 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 | 0.000 |
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".