Background: Paternal aging has been consistently linked to an increased risk of neurodevelopmental disorders, including autism spectrum disorder (ASD), in offspring. While de novo mutations arising from paternal aging have been extensively studied, recent evidence has highlighted the role of epigenetic factors. We have previously revealed age-related DNA hypomethylation in mouse sperm and alterations in histone modifications during spermatogenesis. Objectives: This study aimed to investigate age-related alterations in the microRNA (miRNA) profile of mouse sperm and analyze the target genes regulated by differentially expressed miRNAs. Materials and Methods: Microarray analyses were conducted to identify differentially expressed miRNAs in sperm samples from mice at different ages: 3 months (3M), over 12M, and beyond 20M. The expression levels of genes potentially regulated by these miRNAs were also accessed. Comprehensive bioinformatic analyses were performed to unravel the intricate relationship between differentially expressed miRNAs and their target genes. Results: We identified 26 miRNAs exhibiting differential expression between the 3M and 20M, 34 miRNAs between the 12M and 20M, and 2 miRNAs between the 3M and 12M mice. Notably, the target genes regulated by these differentially expressed miRNAs were significantly associated with apoptosis and ferroptosis pathways. Some of these target genes were relevant to the nervous system and potentially involvement in ASD. Discussion: The findings suggest that aging induces alterations in the sperm miRNA profiles . Importantly, the target genes regulated by these differentially expressed miRNAs are associated with cell death and ASD, implying a potential link between paternal aging and an increased risk of neurodevelopmental disorders. Conclusion: The observed age-related changes in sperm miRNA profiles have the potential to impact sperm quality and subsequently affect offspring development. Further validation and through investigations are necessary to gain a comprehensive understanding of the precise effects of paternal aging on subsequent generations.

BackgroundEvaluating and controlling confounders are necessary when investigating molecular pathogenesis using human postmortem brain tissue. Particularly, tissue pH and RNA integrity number (RIN) are valuable indicators for controlling confounders. However, the influences of these indicators on the expression of each gene in postmortem brain have not been fully investigated. Therefore, we aimed to assess these effects on gene expressions of human brain samples.MethodsWe isolated total RNA from occipital lobes of 13 patients with schizophrenia and measured the RIN and tissue pH. Gene expression was analyzed and gene sets affected by tissue pH and RIN were identified. Moreover, we examined the functions of these genes by enrichment analysis and upstream regulator analysis.ResultsWe identified 2,043 genes (24.7%) whose expressions were highly correlated with pH; 3,004 genes (36.3%) whose expressions were highly correlated with RIN; and 1,293 genes (15.6%) whose expressions were highly correlated with both pH and RIN. Genes commonly affected by tissue pH and RIN were highly associated with energy production and the immune system. In addition, genes uniquely affected by tissue pH were highly associated with the cell cycle, whereas those uniquely affected by RIN were highly associated with RNA processing.ConclusionThe current study elucidated the influence of pH and RIN on gene expression profiling and identified gene sets whose expressions were affected by tissue pH or RIN. These findings would be helpful in the control of confounders for future postmortem brain studies.

Schizophrenia is a multifactorial disorder, the genetic architecture of which remains unclear. Although many studies have examined the etiology of schizophrenia, the gene sets that contribute to its symptoms have not been fully investigated. In this study, we aimed to identify each gene set associated with corresponding symptoms of schizophrenia using the postmortem brains of 26 patients with schizophrenia and 51 controls. We classified genes expressed in the prefrontal cortex (analyzed by RNA-seq) into several modules by weighted gene co-expression network analysis (WGCNA) and examined the correlation between module expression and clinical characteristics. In addition, we calculated the polygenic risk score (PRS) for schizophrenia from Japanese genome-wide association studies, and investigated the association between the identified gene modules and PRS to evaluate whether genetic background affected gene expression. Finally, we conducted pathway analysis and upstream analysis using Ingenuity Pathway Analysis to clarify the functions and upstream regulators of symptom-related gene modules. As a result, three gene modules generated by WGCNA were significantly correlated with clinical characteristics, and one of these showed a significant association with PRS. Genes belonging to the transcriptional module associated with PRS significantly overlapped with signaling pathways of multiple sclerosis, neuroinflammation, and opioid use, suggesting that these pathways may also be profoundly implicated in schizophrenia. Upstream analysis indicated that genes in the detected module were profoundly regulated by lipopolysaccharides and CREB. This study identified schizophrenia symptom-related gene sets and their upstream regulators, revealing aspects of the pathophysiology of schizophrenia and identifying potential therapeutic targets.