Sex-dependent effects of Neuroligin-2 absence on wake/sleep architecture and electrocorticographic spectral and multifractal activities
Bibliographic record
Abstract
Abstract Neuroligin-2 (NLGN2) regulates GABAergic neurotransmission and is linked to neurodevelopmental disorders. The absence of NLGN2 in male mice decreases time spent in slow wave sleep and alters electrocorticographic (ECoG) activity. We tested whether NLGN2 absence also impacts wake/sleep states in females and whether sleep phenotypes associate with specific omic signatures of the cerebral cortex. Nlgn2 knockout (KO) mice and wild-type (WT) littermates were implanted with ECoG electrodes, and ECoG signals were recorded for 48 hours. Other cohorts were submitted to motor cortex sampling followed by quantifications of the transcriptome or proteome. KO mice of both sexes spent less time in slow wave and paradoxical sleep, and KO males (but not females) showed more wake and slow wave sleep episodes during the light period. Mutant animals of both sexes showed widespread differences in wake/sleep ECoG spectral activity when compared to WT mice, including a slower theta peak frequency during paradoxical sleep. The most prominent Hurst exponent was significantly increased in Nlgn2 KO animals during all states, and Hurst exponents were less dispersed during paradoxical sleep only in KO females. Less than 31% of genes with expression changed in KO versus WT mice were shared between females and males (e.g., linked to MAPK/ERK pathway, protein regulation and neurotransmission), and it was less than 15% in the case of proteins (e.g., related to calcium signaling and excitatory synapse function). Moreover, KO males showed a higher number (> 2.5 fold) of genes with expression significantly increased and decreased compared to control mice than KO females, but only KO females presented significantly lower levels of a subset of core proteins regulating synapses (e.g., SYNGAP1, HOMER1). The findings indicate that effects of NLGN2 absence on wake/sleep phenotypes and omic landscapes depend on biological sex, and could help understanding the origin of sleep disturbances in neurodevelopmental disorders.
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) | 0.000 | 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) | 0.003 | 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".