A locus on mouse Ch10 influences susceptibility to limbic seizure severity: fine mapping and<i>in silico</i>candidate gene analysis
Bibliographic record
Abstract
Identification of genes contributing to mouse seizure susceptibility can reveal novel genes or pathways that provide insight into human epilepsy. Using mouse chromosome substitution strains and interval-specific congenic strains (ISCS), we previously identified an interval conferring pilocarpine-induced limbic seizure susceptibility on distal mouse chromosome 10 (Ch10). We narrowed the region by generating subcongenics with smaller A/J Ch10 segments on a C57BL/6J (B6) background and tested them with pilocarpine. We also tested pilocarpine-susceptible congenics for 6-Hz ECT (electroconvulsive threshold), another model of limbic seizure susceptibility, to determine whether the susceptibility locus might have a broad effect on neuronal hyperexcitability across more than one mode of limbic seizure induction. The ISCS Line 1, which contained the distal 2.7 Mb segment from A/J (starting at rs29382217), was more susceptible to both pilocarpine and ECT. Line 2, which was a subcongenic of Line 1 (starting at rs13480828), was not susceptible, thus defining a 1.0 Mb critical region that was unique to Line 1. Bioinformatic approaches identified 45 human orthologs within the unique Line 1 susceptibility region, the majority syntenic to human Ch12. Applying an epilepsy network analysis of known and suspected excitability genes and examination of interstrain genomic and brain expression differences revealed novel candidates within the region. These include Stat2, which plays a role in hippocampal GABA receptor expression after status epilepticus, and novel candidates Pan2, Cdk2, Gls2 and Cs, which are involved in neural cell differentiation, cellular remodeling and embryonic development. Our strategy may facilitate discovery of novel human epilepsy genes.
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 | 0.001 |
| 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 | 0.000 |
| 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".