Mouse Survival Motor Neuron Alleles That Mimic SMN2 Splicing and Are Inducible Rescue Embryonic Lethality Early in Development but Not Late
Bibliographic record
Abstract
Spinal muscular atrophy (SMA) is caused by low survival motor neuron (SMN) levels and patients represent a clinical spectrum due primarily to varying copies of the survival motor neuron-2 (SMN2) gene. Patient and animals studies show that disease severity is abrogated as SMN levels increase. Since therapies currently being pursued target the induction of SMN, it will be important to understand the dosage, timing and cellular requirements of SMN for disease etiology and potential therapeutic intervention. This requires new mouse models that can induce SMN temporally and/or spatially. Here we describe the generation of two hypomorphic Smn alleles, Smn(C-T-Neo) and Smn(2B-Neo). These alleles mimic SMN2 exon 7 splicing, titre Smn levels and are inducible. They were specifically designed so that up to three independent lines of mice could be generated, herein we describe two. In a homozygous state each allele results in embryonic lethality. Analysis of these mutants indicates that greater than 5% of Smn protein is required for normal development. The severe hypomorphic nature of these alleles is caused by inclusion of a loxP-flanked neomycin gene selection cassette in Smn intron 7, which can be removed with Cre recombinase. In vitro and in vivo experiments demonstrate these as inducible Smn alleles. When combined with an inducible Cre mouse, embryonic lethality caused by low Smn levels can be rescued early in gestation but not late. This provides direct genetic evidence that a therapeutic window for SMN inductive therapies may exist. Importantly, these lines fill a void for inducible Smn alleles. They also provide a base from which to generate a large repertoire of SMA models of varying disease severities when combined with other Smn alleles or SMN2-containing mice.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".