Genetic Variants of Nogo-66 Receptor with Possible Association to Schizophrenia Block Myelin Inhibition of Axon Growth
Bibliographic record
Abstract
In schizophrenia, genetic predisposition has been linked to chromosome 22q11 and myelin-specific genes are misexpressed in schizophrenia. Nogo-66 receptor 1 (NGR or RTN4R) has been considered to be a 22q11 candidate gene for schizophrenia susceptibility because it encodes an axonal protein that mediates myelin inhibition of axonal sprouting. Confirming previous studies, we found that variation at the NGR locus is associated with schizophrenia in a Caucasian case-control analysis, and this association is not attributed to population stratification. Within a limited set of schizophrenia-derived DNA samples, we identified several rare NGR nonconservative coding sequence variants. Neuronal cultures demonstrate that four different schizophrenia-derived NgR1 variants fail to transduce myelin signals into axon inhibition, and function as dominant negatives to disrupt endogenous NgR1. This provides the first evidence that certain disease-derived human NgR1 variants are dysfunctional proteins in vitro. Mice lacking NgR1 protein exhibit reduced working memory function, consistent with a potential endophenotype of schizophrenia. For a restricted subset of individuals diagnosed with schizophrenia, the expression of dysfunctional NGR variants may contribute to increased disease risk.
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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 | 0.001 |
| 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 | 0.000 |
| 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".