Autism spectrum disorders (ASDs) are four times more common in males than in females, but the underlying mechanisms are poorly understood. We characterized sexually dimorphic changes in mice carrying a heterozygous mutation in Chd8 (Chd8) that was first identified in human CHD8 (Asn2373LysfsX2), a strong ASD-risk gene that encodes a chromatin remodeler. Notably, although male mutant mice displayed a range of abnormal behaviors during pup, juvenile, and adult stages, including enhanced mother-seeking ultrasonic vocalization, enhanced attachment to reunited mothers, and isolation-induced self-grooming, their female counterparts do not. This behavioral divergence was associated with sexually dimorphic changes in neuronal activity, synaptic transmission, and transcriptomic profiles. Specifically, female mice displayed suppressed baseline neuronal excitation, enhanced inhibitory synaptic transmission and neuronal firing, and increased expression of genes associated with extracellular vesicles and the extracellular matrix. Our results suggest that a human CHD8 mutation leads to sexually dimorphic changes ranging from transcription to behavior in mice.

SUMMARY Synaptic adhesion molecules regulate diverse aspects of synapse development and plasticity. SALM3 is a PSD-95-interacting synaptic adhesion molecule known to induce presynaptic differentiation in contacting axons, but little is known about its presynaptic receptors and in vivo functions. Here, we identify an interaction between SALM3 and LAR family receptor protein tyrosine phosphatases (LAR-RPTPs) that requires the mini-exon B splice insert in LAR-RPTPs. In addition, SALM3-dependent presynaptic differentiation requires all three types of LAR-RPTPs. SALM3 mutant (Salm3−/−) mice display markedly reduced excitatory synapse number but normal synaptic plasticity in the hippocampal CA1 region. Salm3−/− mice exhibit hypoactivity in both novel and familiar environments but perform normally in learning and memory tests administered. These results suggest that SALM3 regulates excitatory synapse development and locomotion behavior.

Background Anesthesia during the synaptogenic period induces dendritic spine formation, which may affect neurodevelopment. The authors, therefore, evaluated whether changes in synaptic transmission after dendritic spine formation induced by sevoflurane were associated with long-term behavioral changes. The effects of sevoflurane on mitochondrial function were also assessed to further understand the mechanism behind spinogenesis. Methods Postnatal day 16 to 17 mice were exposed to sevoflurane (2.5% for 2 h), and synaptic transmission was measured in the medial prefrontal cortex 6 h or 5 days later. The expression of postsynaptic proteins and mitochondrial function were measured after anesthesia. Long-term behavioral changes were assessed in adult mice. Results Sevoflurane increased the expression of excitatory postsynaptic proteins in male and female mice (n = 3 to 5 per group). Sevoflurane exposure in male mice transiently increased miniature excitatory postsynaptic current frequency (control: 8.53 ± 2.87; sevoflurane: 11.09 ± 2.58) but decreased miniature inhibitory postsynaptic current frequency (control: 10.18 ± 4.66; sevoflurane: 6.88 ± 2.15). Unexpectedly, sevoflurane increased miniature inhibitory postsynaptic current frequency (control: 1.81 ± 1.11; sevoflurane: 3.56 ± 1.74) in female mice (neurons, n = 10 to 21 per group). Sevoflurane also increased mitochondrial respiration in male mice (n = 5 to 8 per group). However, such changes from anesthesia during the critical period did not induce long-term behavioral consequences. Values are presented as mean ± SD. Conclusions Sevoflurane exposure during the critical period induces mitochondrial hyperactivity and transient imbalance of excitatory/inhibitory synaptic transmission, without long-lasting behavioral consequences. Further studies are needed to confirm sexual differences and to define the role of mitochondrial activity during anesthesia-induced spine formation.

Objective Schizophrenia is a chronic and debilitating neuropsychiatric disorder. It has been suggested that impaired brain connectivity underlies the pathophysiology of schizophrenia. Network analysis has thus recently emerged in the field of schizophrenia research. Methods We investigated 48 schizophrenia patients and 24 healthy controls using network analysis and a machine learning method. A number of global and nodal network properties were estimated from graphs that were reconstructed using probabilistic brain tractography. These network properties were then compared between groups and used for machine learning to classify schizophrenia patients and healthy controls. Results In classifying schizophrenia patients and healthy controls via network properties, the support vector machine, random forest, naïve Bayes, and gradient boosting machine learning models showed an encouraging level of performance. The overall connectivity was revealed as the most significant contributing feature to this classification among the global network properties. Among the nodal network properties, although the relative importance of each region of interest was not identical, there were still some patterns. Conclusion In conclusion, the possibility exists to classify schizophrenia patients and healthy controls using network properties, and we have found that there is a provisional pattern of involved brain regions among patients with schizophrenia.