BACKGROUND:Elucidating the dynamics of gene expression across developmental stages, including the genomic characteristics of brain expression during infancy, is pivotal in deciphering human psychiatric and neurological disorders and providing insights into developmental disorders. RESULTS:Leveraging comprehensive human GWAS associations with temporal and spatial brain expression data, we discovered a distinctive co-expression cluster comprising 897 genes highly expressed specifically during infancy, enriched in functions related to the neuronal system. This gene cluster notably harbors the highest ratio of genes linked to psychiatric and neurological disorders. Through computational analysis, MYT1L emerged as a potential central transcription factor governing these genes. Remarkably, the infancy-specific expressed genes, including SYT1, exhibit prominent colocalization within human accelerated regions. Additionally, chromatin state analysis unveiled prevalent epigenetic markers associated with enhancer-specific modifications. In addition, this cluster of genes has demonstrated to be specifically highly expressed in cell-types including excitatory neurons, medial ganglionic eminence and caudal ganglionic eminence. CONCLUSIONS:This study comprehensively characterizes the genomics and epigenomics of genes specifically expressed during infancy, identifying crucial hub genes and transcription factors. These findings offer valuable insights into early detection strategies and interventions for psychiatric and neurological disorders.

BackgroundNeuropathic pain (NP) due to nerve injury, disrupts neural plasticity by triggering the release of inflammatory mediators. Alongside the hypothesis that neuro-inflammation contributes to this disruption, Andrographolide (Andro), a traditional bioactive compound derived from Andrographis paniculata, has garnered attention for its potent anti-inflammatory properties. However, whether Andro could ameliorate NP by regulating neuroinflammation remains unknown.PurposeThis study aimed to investigate whether and how Andro regulates neuroinflammation and alleviates NP.MethodsThe analgesic effects of Andro on NP were evaluated using both the spinal nerve ligation (SNL) and formalin rat models. A combination of network pharmacology, RNA sequencing, and experimental validation was employed to elucidate the underlying mechanism behind Andro's analgesic effects. Additionally, various techniques such as functional ultrasound, immunohistochemistry, quantitative real-time polymerase chain reaction (qPCR), patch clamp, and electron microscopy were employed to investigate the specific neural cell types, neural functions, and changes in neural plasticity influenced by Andro.ResultsNetwork pharmacology analysis unveiled the crucial roles played by shared targets of Andro and pain in regulating pain-related inflammation, including microglia activation, neuroinflammation, immune modulation, and synaptic transmission. Furthermore, we confirmed Andro's superior efficacy in pain relief compared to the traditional analgesic drug, Gabapentin. In these models, Andro was observed to modulate the haemodynamic response triggered by SNL. Transcriptome analysis and molecular docking studies indicated the involvement of major histocompatibility complex class II (MHCII) genes (Db1, Da, and Bb). Electron microscopy revealed improvements in synaptic ultrastructure, and electrophysiological investigations showed a selective reduction in glutamatergic transmission in neuropathic rats after following Andro treatment. The integration of systems pharmacology analysis and biological validation collectively demonstrated that the mechanism of pain relief involves immune modulation, enhancement of synaptic plasticity, and precise regulation of excitatory neurotransmission.ConclusionIn conclusion, this study has demonstrated that Andro, by targeting MHCII genes, may serve as a promising therapeutic candidate for neuropathic pain.

With the advancements in Artificial Intelligence (AI) technology, Large Language Models (LLMs) provide outstanding capabilities for natural language understanding and generation, enhancing various domains. In psychiatry, LLMs can empower healthcare by analyzing vast amounts of medical data to improve diagnostic accuracy, enhance therapeutic communication, and personalize patient care with their strength in understanding and generating human-like text. In clinical AI, developing and utilizing robust and interpretable models has been a longstanding challenge. This survey investigates the current psychiatric practice of LLMs, along with a series of corpus resources that could be used for training psychiatric LLMs. We discuss the limitations concerning LLM reproducibility, capabilities, usability, interpretability in clinical settings, and ethical considerations. Additionally, we propose potential future directions for research, clinical application, and education in psychiatric LLMs. Finally, we discuss the challenge of integrating LLMs into the evolving landscape of healthcare in real-world scenarios.

The pathogenesis of schizophrenia (SCZ) is heavily influenced by genetic factors. Ring finger protein 4 (RNF4) and squamous cell carcinoma antigen recognized by T cells 3 (SART3) are thought to be involved in nervous system growth and development via oxidative stress pathways. Moreover, they have previously been linked to SCZ. Yet the role of RNF4 and SART3 in SCZ remains unclear. Here, we investigated how these two genes are involved in SCZ by studying their variants observed in patients. We first observed significantly elevated mRNA levels of RNF4 and SART3 in the peripheral blood in both first-episode (n=30) and chronic (n=30) SCZ patients compared to controls (n=60). Next, we targeted-sequenced three single nucleotide polymorphisms (SNPs) in SART3 and six SNPs in RNF4 for association with SCZ using the genomic DNA extracted from peripheral blood leukocytes from SCZ participants (n=392) and controls (n=572). We observed a combination of SNPs that included rs1203860, rs2282765 (both in RNF4), and rs2287550 (in SART3) was associated with increased risk of SCZ, suggesting common pathogenic mechanisms between these two genes. We then conducted experiments in HEK293T cells to better understand the interaction between RNF4 and SART3. We observed that SART3 lowered the expression of RNF4 through ubiquitination and downregulated the expression of nuclear factor E2-related factor 2 (NRF2), a downstream factor of RNF4, implicating the existence of a possible shared regulatory mechanism for RNF4 and SART3. In conclusion, our study provides evidence that the interaction between RNF4 and SART3 contributes to the risk of SCZ. The findings shed light on the underlying molecular mechanisms of SCZ and may lead to the development of new therapies and interventions for this disorder.

BACKGROUND:Neuroimmune plays an important role in major depressive disorders (MDD). N-linked protein glycosylation (NLG) might contribute to depression by regulating the neuroinflammatory response. As microglia is the main executor of neuroimmune function in the central neural system (CNS), targeting the process of N-linked protein glycosylation of microglia in the mice used for studying depression might potentially offer new avenues for the strategy for MDD. METHODS:The chronic unpredictable mild stress (CUMS) mouse model was established for the whole brain microglia isolating. Then, RNA samples of microglia were extracted for transcriptome sequencing and mRNA analysis. Immunofluorescence (IF) was used to identify the expression level of NLG-related enzyme, B4galt1, in microglia. RESULTS:The data showed that NLG was positively related to depression. Moreover, the NLG-related gene, B4galt1 increased expression in the microglia of CUMS mice. Then, the inhibition of NLG reversed the depressive behavior in CUMS mice. The expression level of B4galt1 in CUMS mice was upregulating following the NLG-inhibitor treatment. Similar results haven't been observed in neurons. Information obtained from these experiments showed increasing expression of B4galt1 in microglia following depressive-like behaviors. CONCLUSIONS:These findings indicate that NLG in microglia is associated with MDD, and suggest that therapeutically targeting NLG might be an effective strategy for depression. LIMITATIONS:How to modulate the B4galt1 or NLG pathways in microglia efficiently and economically request new technologies.

IntroductionObsessive–compulsive disorder (OCD), characterized by the presence of obsessions and/or compulsions, is often difficult to diagnose and treat in routine clinical practice. The candidate circulating biomarkers and primary metabolic pathway alteration of plasma in OCD remain poorly understood.MethodsWe recruited 32 drug-naïve patients with severe OCD and 32 compared healthy controls and applied the untargeted metabolomics approach by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) to assess their circulating metabolic profiles. Both univariate and multivariate analyses were then utilized to filtrate differential metabolites between patients and healthy controls, and weighted Correlation Network Analysis (WGCNA) was utilized to screen out hub metabolites.ResultsA total of 929 metabolites were identified, including 34 differential metabolites and 51 hub metabolites, with an overlap of 13 metabolites. Notably, the following enrichment analyses underlined the importance of unsaturated fatty acids and tryptophan metabolism alterations in OCD. Metabolites of these pathways in plasma appeared to be promising biomarkers, such as Docosapentaenoic acid and 5-Hydroxytryptophan, which may be biomarkers for OCD identification and prediction of sertraline treatment outcome, respectively.ConclusionOur findings revealed alterations in the circulating metabolome and the potential utility of plasma metabolites as promising biomarkers in OCD.

Precision medicine based on personalized genomics provides promising strategies to enhance the efficacy of molecular-targeted therapies. However, the clinical effectiveness of drugs has been severely limited due to genetic variations that lead to drug resistance. Predicting the impact of missense mutations on clinical drug response is an essential way to reduce the cost of clinical trials and understand genetic diseases. Here, we present Emden, a novel method integrating graph and transformer representations that predicts the effect of missense mutations on drug response through binary classification with interpretability. Emden utilized protein sequences-based features and drug structures as inputs for rapid prediction, employing competitive representation learning and demonstrating strong generalization capabilities and robustness. Our study showed promising potential for clinical drug guidance and deep insight into computer-assisted precision medicine. Emden is freely available as a web server at https://www.psymukb.net/Emden.

Identifying pathogenetic variants and inferring their impact on protein–protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences of protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. MIPPI was specifically trained to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein–protein interactions. We demonstrate the accuracy of MIPPI and provide interpretation through the analysis of learned attention weights, which exhibit correlations with the amino acids interacting with the variant. Moreover, we showed the practicality of MIPPI in prioritizing de novo mutations associated with complex neurodevelopmental disorders and the potential to determine the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI emerges as a versatile, robust, and interpretable model, capable of effectively predicting mutation impacts on protein–protein interactions and facilitating the discovery of clinically actionable variants.

Clinical high-risk psychosis (CHR-P)1 refers to a heterogeneous state where patients exhibit psychotic syndromes such as hallucination and delusion not reaching the diagnostic criteria, with the ability of self-reasoning and help-seeking partially maintained. These characteristics make CHR-P an ideal phase for the early treatment of schizophrenia. Researchers have made great efforts to study peripheral molecular markers with the advantages of feasibility and objectivity2 that could predict CHR-P outcomes, such as using proteome3 and metabolome4 with success. For RNA, studies such as Influence du Cannabis sur l'émergence de symptômes psychopathologiques des Adolescents et jeunes Adultes présentant un état mental à Risque (ICAAR)5 and the NAPLS (North American Prodrome Longitudinal Study) cohort6 have already shown that RNA and microRNA had significant alterations during CHR-P conversion to psychosis, suggesting that blood transcriptome had the potentiality of being used as a biomarker. To evaluate whether blood RNA could serve as CHR-P biomarkers, we collected blood samples of patients with CHR-P from the Shanghai at-risk psychosis (SHARP) longitudinal cohort7 and applied RNA sequencing. Syndrome severity was evaluated by Structured Interview for Prodromal Syndromes (SIPS). We removed 10 samples with RNA integrity number (RIN) <6. With peripheral expression data and clinical assessment at both baseline and at 1- and 2-year follow-up, we built a model to evaluate the psychotic symptom alteration by differential expression analysis and step regression, then explored the biological functions associated with this model by gene set enrichment analysis (GSEA). We then built a model to predict the 2-year prognosis by baseline gene expression, which is achieved by limma-voom8 differential expression and Lasso regression. The model robustness was evaluated by cross-validation and test set validation (for the first model) or premutation (for the second model) (Fig. S1). We obtained RNA expression data of the peripheral blood from 56 patients collected at baseline and at 1-year follow-up. These patients had a mean SIPS-psychotic symptom score of 9.8 (SE of 3.9) at baseline and 4.2 (SE of 4.0) at 1-year follow-up (Table S1). After adjusting for known and surrogate confounders (Figs S2 and S3, Tables S2 and S3), we applied stepwise regression and built a 12-gene model (Fig. 1a and Table S4), including genes such as SDK1 (sidekick cell adhesion molecule 1; standardized coefficient τ = 2.22) and NOTCH4 (Notch receptor 4; τ = 0.86), to reflect symptom alteration (R2 = 0.76, F = 7.84, P = 3.06 × 10−6). This model was robust against subsampling (Fig. 1a) and achieved R2 = 0.56 to 0.70 (P < 10−4) in leave-one-out and cross-validation tests and R2 = 0.40 (P = 0.012) in the test set (Fig. S4). GSEA found that genes showing a negative correlation with model-predicted symptom scores were enriched in synaptic vesicle recycling (Table S5–6, adjusted P = 0.04, Fig. 1b), suggesting that synapse functions were suppressed during CHR-P symptom deterioration. When predicting for the CHR-P conversion, we applied Lasso regression on 10 differential expression genes (Fig. S3) and obtained a five-gene model that included SYP (gene encoding Synaptophysin; P = 6.47 × 10−4) and CNTNAP3 (contactin-associated protein family member 3; P = 1.15 × 10−3) (Fig. 1c, S5, and S6, and Table S7), and achieved area under the curve (AUC) = 0.98. For models containing top 4, 3, 2, and 1 gene, we observed an AUC of 0.96, 0.95, 0.91, and 0.90, respectively. In the leave-one-out and cross-validation tests, the model with top two (SYP and IFNW1 [interferon omega 1]) or top three (by including gene CNTNAP3) genes achieved AUC scores between 0.80 and 0.92 (Fig. 1d). In contrast, the AUC scores of models with more genes dropped to 0.69 from 0.85. Accordingly, the permutation P-value reached the lowest (P = 0.03) at the three-gene model. We concluded that the three-gene model was the optimal predictive model. Additionally, no predictive models using noncoding RNA yielded less accurate results (Figs S7–S10). In this study, we confirmed that blood RNA has the potentiality to serve as a biomarker for CHR-P conversion risk. Thus, blood transcriptome is valuable to CHR-P's clinical intervention, where treatment choices are dependent on the objective evaluation of symptom severity and conversion risk.9 In addition, our symptom association model also reflected the underlying biological mechanism, such as downregulation of synaptic vesicle recycling during CHR-P progression. This result indicated that synaptic functions were significantly suppressed during schizophrenia pathology. Furthermore, the low expression level of a synaptic gene SYN is significantly associated with the future conversion of CHR-P, in line with the previous meta-analysis, which revealed low expression of Synaptophysin in the hippocampus of patients with schizophrenia.10 It should be noted that because of the relatively small sample size and the lack of a validation cohort, we only obtained a suggestive model that was valuable for further validation and could not be considered as immediately applicable. The follow-up period should also be extended to achieve a more precise end point. In sum, our results indicate that blood transcriptome is valuable for the prognosis prediction of CHR-P, and suggests that inhibition of synaptic functions plays an important role in the CHR-P pathology. Highlighted biomarkers and models are highly valuable for future studies. This work is supported by grants from the National Key R&D Program of China (No. 2016YFC1307005 and 2016YFC1306803), the National Natural Science Foundation of China (No. 81971257, 81971292 and 82150610506), the Natural Science Foundation of Shanghai (No. 21ZR1428600), Shanghai Jiao Tong University Medical Engineering (Science) Cross Research Fund (YG2019ZDA30), the Shanghai Municipal Science and Technology Major Project (grant No. 2018SHZDZX01), and the Shanghai Clinical Research Center for Mental Health (No. 19MC1911100). All the authors declared no conflicts of interest. This study was approved by the research ethics committee at the Shanghai Mental Health Centre. Appendix S1. Supplementary Material: Contains supplementary methods, supplementary results. Fig. S1. Flowchart of the study. Fig. S2. Symptom evaluation model of protein-coding genes. Fig. S4. WGCNA result for confounder identification. Fig. S5. Correlation matrix of potential confounders. Fig. S6. Distribution of τ of prediction model in subsampling tests. Fig. S7. Stepwise F test for lincRNA symptom evaluation model. Fig. S8. subsampling test for lincRNA symptom evaluation model. Fig. S9. Prediction model by lincRNA. Fig. S10. Comparison of prediction model of protein-coding genes and lincRNA. Table S2. Result of symptom evaluation model. Table S4. Correlation between confounders and outcome. Table S7. Full result of GSEA. Fig. S3. Prognosis model and its biological interpretations. Table S1. Clinical assessment, demographic characteristics, and confounders of all participants. Table S3. Result of prognosis prediction model. Table S5. Result of confounder selection by MARS. Table S6. Correlation between gene expression, SScore and RScore. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DNMs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPIs harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We observed that PsychiPPIs preferentially affected the human protein interactome network hub proteins. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.

AbstractIdentifying pathogenetic variants and inferring their impact on protein–protein interactions sheds light on their functional consequences on diseases. Limited by the availability of experimental data on the consequences on protein interaction, most existing methods focus on building models to predict changes in protein binding affinity. Here, we introduced MIPPI, an end-to-end, interpretable transformer-based deep learning model that learns features directly from sequences by leveraging the interaction data from IMEx. We trained MIPPI to determine the types of variant impact (increasing, decreasing, disrupting, and no effect) on protein-protein interactions, and demonstrated its accuracy and interpreted the learned attention weights, which correlate with the amino acids interacting with the variant. We further showed the utility of MIPPI in prioritizingde novomutations associated with complex neurodevelopmental disorders and the potential in determining the pathogenic and driving mutations. Finally, we experimentally validated the functional impact of several variants identified in patients with such disorders. Overall, MIPPI is a versatile, robust, and interpretable model suitable for prediction tasks for mutation impact on protein–protein interaction and the discovery of clinically actionable variants. A webserver for MIPPI is available from https://www.psymukb.net/MIPPI.

大多数神经精神疾病的病因复杂,病理机制研究难度大,相应药物或疗法的研发进展十分有限,因此在诊断及治疗方面都面临着巨大的未满足的临床需求.这一困境部分可归结于大脑各区域中细胞类型的多样性和高度异质性.以往对于神经精神疾病的研究主要基于解剖学、组织学、神经电生理或在细胞群体水平进行的传统测序等技术,无法在细胞亚型或单细胞水平开展转录组分析,从而鉴定神经细胞在各种神经精神疾病中的病理性改变.近年来,单细胞测序技术的快速发展为该领域相关研究提供了新的思路和手段.单细胞转录组测序可以测定单个细胞的基因表达图谱,从而可能鉴定出新的疾病关联细胞亚型,同时可以在同一疾病条件下比较各个细胞亚群的数目变化及各亚群内转录组水平的改变,从而有力推动了神经精神疾病发病机制的探索、疾病分型的重新定义、治疗新靶点的发现.本研究从阿尔兹海默症等几种重要神经精神疾病的发病机制研究角度,对单细胞转录组测序技术在神经精神疾病中的应用进展进行综述,并展望该技术体系在未来精准诊断和治疗中的应用前景.

Anxiety disorders are the most common psychiatric disorders, and the change in the activity of the prefrontal cortex (PFC) is considered as the underlying pathological mechanism. Parvalbumin-expressing (PV+) inhibition contributes to the overall activity of the PFC. However, the molecular mechanism underlying the excitation-inhibition imbalance of PV+ neurons in the PFC is unknown. Efnb2 is a membrane-bound molecule that plays an important role in the nervous system through binding the Eph receptor. To investigate whether the loss of Efnb2 in PV+ affects anxiety, we examined the behavior of wild type and Efnb2 in PV+ neurons knockout (KO) mice. We monitored the defensive responses to aversive stimuli of elevated plus maze (EPM) and found that KO mice exhibited obvious fearless and anxiolytic behaviors. To further investigate the underlying regulatory mechanism, we performed RNA sequencing, analyzed the differentially expressed genes (DEGs), and constructed the weighted gene co-expression network analysis (WGCNA). The WGCNA identified 12 characteristic modules. Among them, the MEgreen module showed the most significant correlation with KO mice of EPM stimuli. The Gene Ontology enrichment and the Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that this was related to the distal axon, Ras signaling pathway and insulin signaling pathway. Furthermore, the whole-cell voltage clamp recordings also proved that Efnb2 gene knock-out could affect synaptic function. Together with the transcriptomic analysis of mice with Efnb2 knockout on PV+ neurons, our findings suggest that Efnb2 gene in the PV+ neuron of PFC may be a crucial factor for fear and anxiety, which provide an insight into anxiety pathophysiology.

Nuclear receptor SET domain protein (NSD2) plays a fundamental role in the pathogenesis of Wolf–Hirschhorn Syndrome (WHS) and is overexpressed in multiple human myelomas, but its protein–protein interaction (PPI) patterns, particularly at the isoform/exon levels, are poorly understood. We explored the subcellular localizations of four representative NSD2 transcripts with immunofluorescence microscopy. Next, we used label-free quantification to perform immunoprecipitation mass spectrometry (IP-MS) analyses of the transcripts. Using the interaction partners for each transcript detected in the IP-MS results, we identified 890 isoform-specific PPI partners (83% are novel). These PPI networks were further divided into four categories of the exon-specific interactome. In these exon-specific PPI partners, two genes, RPL10 and HSPA8, were successfully confirmed by co-immunoprecipitation and Western blotting. RPL10 primarily interacted with Isoforms 1, 3, and 5, and HSPA8 interacted with all four isoforms, respectively. Using our extended NSD2 protein interactions, we constructed an isoform-level PPI landscape for NSD2 to serve as reference interactome data for NSD2 spliceosome-level studies. Furthermore, the RNA splicing processes supported by these isoform partners shed light on the diverse roles NSD2 plays in WHS and myeloma development. We also validated the interactions using Western blotting, RPL10, and the three NSD2 (Isoform 1, 3, and 5). Our results expand gene-level NSD2 PPI networks and provide a basis for the treatment of NSD2-related developmental diseases.

Characterizing the natural selection of complex traits is important for understanding human evolution and both biological and pathological mechanisms. We leveraged genome-wide summary statistics for 870 polygenic traits and attempted to quantify signals of selection on traits of different forms in European ancestry across four periods in human history and evolution. We found that 88% of these traits underwent polygenic change in the past 2,000-3,000 years. Recent selection was associated with ancient selection signals in the same trait. Traits related to pigmentation, body measurement and nutritional intake exhibited strong selection signals across different time scales. Our findings are limited by our use of exclusively European data and the use of genome-wide association study data, which identify associations between genetic variants and phenotypes that may not be causal. In sum, we provide an overview of signals of selection on human polygenic traits and their characteristics across human evolution, based on a European subset of human genetic diversity. These findings could serve as a foundation for further populational and medical genetic studies.

Schizophrenia (SCZ) is a severe chronic psychiatric illness with heterogeneous symptoms. However, the pathogenesis of SCZ is unclear, and the number of well-defined SCZ risk factors is limited. We hypothesized that an abnormal behavior (AB) gene set verified by mouse model experiments can be used to better understand SCZ risks. In this work, we carried out an integrative bioinformatics analysis to study two types of risk genes that are either differentially expressed (DEGs) in the case-control study data or carry reported SCZ genetic variants (MUTs). Next, we used RNA-Seq expression data from the hippocampus (HIPPO) and dorsolateral prefrontal cortex (DLPFC) to define the key genes affected by different types (DEGs and MUTs) in different brain regions (DLPFC and HIPPO): DLPFC-kDEG, DLPFC-kMUT, HIPPO-kDEG, and HIPPO-kMUT. The four hub genes (SHANK1, SHANK2, DLG4, and NLGN3) of the biological functionally enriched terms were strongly linked to SCZ via gene co-expression network analysis. Then, we observed that specific spatial expressions of DLPFC-kMUT and HIPPO-kMUT were convergent in the early stages and divergent in the later stages of development. In addition, all four types of key genes showed significantly larger average protein–protein interaction degrees than the background. Comparing the different cell types, the expression of four types of key genes showed specificity in different dimensions. Together, our results offer new insights into potential risk factors and help us understand the complexity and regional heterogeneity of SCZ.

Recurrent deletions and duplications of chromosome 7q11.23 copy number variants (CNVs) are associated with several psychiatric disorders. Although phenotypic abnormalities have been observed in patients, causal genes responsible for CNV-associated diagnoses and traits are still poorly understood. Furthermore, the targeted human brain regions, developmental stages, protein networks, and signaling pathways, influenced by this CNV remain unclear. Previous works showed GTF2I involved in Williams-Beuren syndrome, but pathways affected by GTF2I are indistinct. We first constructed dynamic spatiotemporal networks of 7q11.23 genes by combining data from the brain developmental transcriptome with physical interactions of 7q11.23 proteins. Topological changes were observed in protein-protein interaction (PPI) networks throughout different stages of brain development. Early and late fetal periods of development in the cortex, striatum, hippocampus, and amygdale were observed as the vital periods and regions for 7q11.23 CNV proteins. CNV proteins and their partners are significantly enriched in DNA repair pathway. As a driver gene, GTF2I interacted with PRKDC and BRCA1 to involve in DNA repair pathway. The physical interaction between GTF2I with PRKDC was confirmed experimentally by the liquid chromatography-tandem mass spectrometry (LC-MS/MS). We identified that early and late fetal periods are crucial for 7q11.23 genes to affect brain development. Our results implicate that 7q11.23 CNV genes converge on the DNA repair pathway to contribute to the pathogenesis of psychiatric diseases.

Background:Neuropathic pain is a common chronic pain, which is related to hypersensitivity to stimulus and greatly affects the quality of life of patients. Maladaptive gene changes and molecular signaling underlie the sensitization of nociceptive pathways. We previously found that the activation of microglial glucagon-like peptide 1 receptor (GLP-1R) could potently relieve formalin-, bone cancer-, peripheral nerve injury-, and diabetes-induced pain hypersensitivity. So far, little is known about how the gene profile changes upon the activation of GLP-1R signaling in the pathophysiology of neuropathic pain.Methods:Spinal nerve ligation (SNL) was performed to induce neuropathic pain in rats. Mechanical allodynia was assessed using von Frey filaments. The expression of IL-10, β-endorphin, and μ-opioid receptor (MOR) was examined by real-time quantitative polymerase chain reaction (qPCR) and whole-cell recording. Measurements of cellular excitability of the substantia gelatinosa (SG) neurons by whole-cell recording were carried out. R packages of differential gene expression analysis based on the negative binomial distribution (DESeq2) and weighted correlation network analysis (WGCNA) were used to analyze differential gene expression and the correlated modules among GLP-1R clusters in neuropathic pain.Results:The GLP-1R agonist, exenatide, has an antiallodynic effect on neuropathic pain, which could be reversed by intrathecal injections of the microglial inhibitor minocycline. Furthermore, differential gene expression analysis (WGCNA) indicated that intrathecal injections of exenatide could reverse the abnormal expression of 591 genes in the spinal dorsal horn induced by nerve injury. WGCNA revealed 58 modules with a close relationship between the microglial GLP-1R pathway and features of nerve injuries, including pain, ligation, paw withdrawal latency (PWL), and anxiety. The brown module was identified as the highest correlated module, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that inflammatory responses were most correlated with PWL. To further unravel the changes of hyperalgesia-related neuronal electrophysiological activity mediated by microglia GLP-1 receptors, whole-cell recording identified that MOR agonism stimulated a robust outward current in the sham groups compared with the spinal nerve ligation (SNL) groups. This inhibitory effect on the SNL group was more sensitive than that of the sham group after bath application of β-endorphin.Conclusions:Our results further confirmed that the GLP-1R pathway is involved in alleviating pain hypersensitivity mediated by spinal microglia activation, and inflammatory responses were the most correlated pathway associated with PWL changes in response to exenatide treatment. We found that the identification of gene regulation in response to GLP-1R activation is an effective strategy for identifying new therapeutic targets for neuropathic pain. Investigation for the activation of spinal microglial GLP-1R which might ameliorate inflammatory responses through gene expression and structural changes is providing a potential biomarker in pain management.

The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer's Disease (AD). We applied summary-based and two-sample Mendelian Randomization to estimate the associations between blood molecules and brain disorders. We identified 524 RNA, 807 methylation sites, 29 proteins, seven cytokines, and 22 metabolites having a significant association with at least one of 14 brain disorders. Simulation analyses indicated that a cross-omics combination of biomarkers had better performance for most disorders, and different disorders could associate with different omics. We identified an 11-methylation-site model for SCZ diagnosis (Area Under Curve, AUC = 0.74) by analyzing selected candidate markers in published datasets (total N = 6098). Moreover, we constructed an 18-methylation-sites model that could predict the prognosis of elders with mild cognitive impairment (hazard ratio = 2.32). We provided an association landscape between blood cross-omic biomarkers and 14 brain disorders as well as a suggestion guide for future clinical discovery and application.

The chromosome 22q11.21 copy number variant (CNV) is a vital risk factor that can be a genetic predisposition to neurodevelopmental disorders (NDD). As the 22q11.21 CNV affects multiple genes, causal disease genes and mechanisms affected are still poorly understood. Thus, we aimed to identify the most impactful 22q11.21 CNV genes and the potential impacted human brain regions, developmental stages and signaling pathways. We constructed the spatiotemporal dynamic networks of 22q11.21 CNV genes using the brain developmental transcriptome and physical protein–protein interactions. The affected brain regions, developmental stages, driver genes and pathways were subsequently investigated via integrated bioinformatics analysis. As a result, we first identified that 22q11.21 CNV genes affect the cortical area mainly during late fetal periods. Interestingly, we observed that connections between a driver gene, DGCR8, and its interacting partners, MECP2 and CUL3, also network hubs, only existed in the network of the late fetal period within the cortical region, suggesting their functional specificity during brain development. We also confirmed the physical interaction result between DGCR8 and CUL3 by liquid chromatography-tandem mass spectrometry. In conclusion, our results could suggest that the disruption of DGCR8-dependent microRNA biogenesis plays a vital role in NDD for late fetal cortical development.