Alpha-neurexins in health and disease
Bibliographic record
Abstract
Alpha-neurexins (α-Nrxns) are synaptic adhesion molecules that play crucial roles in synapse organization, specificity, and function. This review provides a comprehensive overview of α-Nrxns, covering their gene organization, molecular architecture, and roles in both physiological and pathological contexts. We begin by detailing the unique structural properties of α-Nrxns, particularly their large extracellular regions and complex alternative splicing, which facilitate diverse trans-synaptic interactions. We then examine their critical roles in regulating presynaptic neurotransmitter release, postsynaptic receptor function, and overall synaptic organization. While deletion of α-Nrxns in mice results in only modest morphological brain abnormalities, it causes profound deficits in synaptic function, underscoring their role in fine-tuning neural circuit activity in a context-dependent manner. We also explore how specific α-Nrxn ligands such as neurexophilins or IgSF21 contribute to synaptic diversity. Furthermore, we discuss emerging evidence linking α-NRXNs to various neurodevelopmental and psychiatric disorders, including autism spectrum disorder, schizophrenia, and intellectual disability. These links are supported by both genetic association studies and behavioral analyses in α-Nrxn mutant mice, which exhibit phenotypes that partially mirror symptoms observed in human disorders. Finally, we highlight recent advances in human induced pluripotent stem cell (hiPSC)-derived neuronal models, which offer powerful platforms to investigate α-NRXN-associated disease mechanisms at the cellular level. These models enable the study of patient-specific neurobiological alterations and support the development of targeted therapeutic strategies. Collectively, this review emphasizes the pivotal role of α-Nrxns in maintaining synaptic integrity and demonstrates how their dysfunction contributes to a broad spectrum of brain disorders, providing valuable insights for future translational research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".