Schizophrenia is a neurodevelopmental disorder that NMDA receptor (NMDAR) hypofunction appears centrally involved. Schizophrenia typically emerges in adolescence or early adulthood. Electrophysiological and several neurochemical changes have linked the GABA deficits to abnormal behaviors induced by NMDAR hypofunction. However, few studies have systematically investigated the molecular basis of GABA deficits, especially during adolescence. To address this issue, we transiently administrated MK-801 to mice on PND 10, which exhibited schizophrenia-relevant deficits in adolescence. Slice recording showed reduced GABA transmission and PVI+ hypofunction, indicating GABAergic hypofunction. Cortical proteomic evaluation combined with analysis of single cell data from the Allen Brain showed that various metabolic processes were enriched in top ranks and differentially altered in excitatory neurons, GABAergic interneurons, and glial cells. Notably, the GABA-related amino acid metabolic process was disturbed in both astrocytes and interneurons, in which we found a downregulated set of GABA-related proteins (GAD65, SYNPR, DBI, GAT3, SN1, and CPT1A). They synergistically regulate GABA synthesis, release, reuptake, and replenishment. Their downregulation indicates impaired GABA cycle and homeostasis regulated by interneuron-astrocyte communication in adolescence. Our findings on molecular basis of GABA deficits could provide potential drug targets of GABAergic rescue for early prevention and intervention.
Background Single-cell RNA sequencing (scRNA-Seq) provides new perspectives and ideas to investigate the interactions between different cell types and organisms. By integrating scRNA-seq with new computational frameworks or specific technologies, better Alzheimer’s disease (AD) treatments may be developed. Methods The single-cell sequencing dataset GSE158234 was obtained from the GEO database. Preprocessing, quality control, dimensionality-reducing clustering, and annotation to identify cell types were performed on it. RNA-seq profiling dataset GSE238013 was used to determine the components of specific cell subpopulations in diverse samples. A set of genes included in the OMIM, Genecards, CTD, and DisGeNET databases were selected as highly plausible AD-related genes. Then, ROC curves were created to predict the diagnostic value using the significantly expressed genes in the KO group as hub genes. The genes mentioned above were mapped to the Coremine Medical database to forecast prospective therapeutic Chinese medicines, and a “Chinese medicine-ingredient-target” network was constructed to screen for potential therapeutic targets. The last step was to undertake Mendelian randomization research to determine the causal link between the critical gene IL1B and AD in the genome-wide association study. Results Using the scRNA-seq dataset, five unique cell clusters were discovered. These clusters were further subdivided into four distinct cell types using marker genes. The KO group showed a more substantial differential subgroup of macrophages than the WT group. By using the available datasets and PPI network analysis, 54 common genes were discovered. Four clusters were identified using the MCODE approach, and correlation analysis showed that seven genes in those four clusters had a significantly negative correlation with macrophages. Six genes in four sets had a significantly positive correlation. Five genes had different levels of expression in the WT and KO groups. The String database was used to identify the regulatory relationships between the four genes (IL10, CX3CR1, IL1B, and IL6) that were finally selected as AD hub genes. Screening identified potential traditional Chinese medicine to intervene in the transformation process of AD, including Radix Salviae, ginseng, Ganoderma, licorice, Coptidis Rhizoma, and Scutellariae Radix, in addition to promising therapeutic targets, such as PTGS1, PTGS2, and RXRA. Finally, it was shown that IL1B directly correlated with immune cell infiltration in AD. In inverse variance weighting, we found that IL1B was associated with a higher risk of AD, with an OR of 1.003 (95% CI = 1.001–1.006, p = 0.038). Conclusion Our research combined network pharmacology and the scRNA-seq computational framework to uncover pertinent hub genes and prospective traditional Chinese medicine potential therapeutic targets for AD. These discoveries may aid in understanding the molecular processes behind AD genes and the development of novel medications to treat the condition.
Anti-angiogenesis therapy that targets VEGF is one of the important treatment strategies in advanced ovarian cancer. However, depending on the pharmaceutical agent, treatment can have undesirable side effects. SEMA4D has recently gained interest for its role in promoting angiogenesis. Here, we try to further understand the mechanism by which SEMA4D promotes angiogenesis in ovarian cancer. Correlation and western blot assaya were used to detect the relationship between VEGF and SEMA4D in clinical tissues and cells. Vasculogenic mimicry and transwell migration analyses were used to detect the roles of VEGF, SEMA4D and plexin-B1 on vasculogenic mimicry and migration. Vascular density and SEMA4D expression was determined using immunofluorescence staining in clinical tissues of EOC. Western blot was used to detect the expressions of CD31, MMP2 and VE-cadherin. We also analyzed the relationship between VEGF-SEMA4D and malignant tumor prognosis. We found that knockdown of VEGF could suppress SEMA4D expression and that the expressions of VEGF and SEMA4D have a positive correlation in EOC cancer tissues. Vasculogenic mimicry and transwell migration analyses showed that SEMA4D and VEGF have a synergistic effect on the promotion of angiogenesis in A2780 and HUVEC cells. Soluble SEMA4D (sSEMA4D) could promote VM and migration in A2780 and HUVEC cells via the SEMA4D/plexin-B1 pathway, but the effect was not noted in stably transfected shR-plexin-B1 cells. In clinical tissues of EOC, the vascular density and SEMA4D/plexin-B1 expression were higher. When VEGF, SEMA4D and plexin-B1 was knocked down, the expression of CD31, MMP2 and VE-cadherin, which are the markers and initiators of angiogenesis and the epithelial–mesenchymal transition (EMT) process were reduced. VEGF and SEMA4D had a positive correlation with the malignant degree of ovarian cancer, and SEMA4D can serve as an independent prognostic factor. VEGF and SEMA4D have synergistic effects on the promotion of angiogenesis in epithelial ovarian cancer. Targeting VEGF and the SEMA4D signaling pathway could be important for the therapy for EOC.
Abstract Schizophrenia, a complex neuropsychiatric disorder, frequently experiences a high rate of misdiagnosis due to subjective symptom assessment. Consequently, there is an urgent need for innovative and objective diagnostic tools. In this study, we used cutting-edge extracellular vesicles’ (EVs) proteome profiling and XGBoost-based machine learning to develop new markers and personalized discrimination scores for schizophrenia diagnosis and prediction of treatment response. We analysed plasma and plasma-derived EVs from 343 participants, including 100 individuals with chronic schizophrenia, 34 first-episode and drug-naïve patients, 35 individuals with bipolar disorder, 25 individuals with major depressive disorder and 149 age- and sex-matched healthy controls. Our innovative approach uncovered EVs-based complement changes in patients, specific to their disease-type and status. The EV-based biomarkers outperformed their plasma counterparts, accurately distinguishing schizophrenia individuals from healthy controls with an area under curve (AUC) of 0.895, 83.5% accuracy, 85.3% sensitivity and 82.0% specificity. Moreover, they effectively differentiated schizophrenia from bipolar disorder and major depressive disorder, with AUCs of 0.966 and 0.893, respectively. The personalized discrimination scores provided a personalized diagnostic index for schizophrenia and exhibited a significant association with patients’ antipsychotic treatment response in the follow-up cohort. Overall, our study represents a significant advancement in the field of neuropsychiatric disorders, demonstrating the potential of EV-based biomarkers in guiding personalized diagnosis and treatment of schizophrenia.
Objective: Epilepsy prediction algorithms offer patients with drug-resistant epilepsy a way to reduce unintended harm from sudden seizures. The purpose of this study is to investigate the applicability of transfer learning (TL) technique and model inputs for different deep learning (DL) model structures, which may provide a reference for researchers to design algorithms. Moreover, we also attempt to provide a novel and precise Transformer-based algorithm. Methods: Two classical feature engineering methods and the proposed method which consists of various EEG rhythms are explored, then a hybrid Transformer model is designed to evaluate the advantages over pure convolutional neural networks (CNN)-based models. Finally, the performances of two model structures are analyzed utilizing patient-independent approach and two TL strategies. Results: We tested our method on the CHB-MIT scalp EEG database, the results showed that our feature engineering method gains a significant improvement in model performance and is more suitable for Transformer-based model. In addition, the performance improvement of Transformer-based model utilizing fine-tuning strategies is more robust than that of pure CNN-based model, and our model achieved an optimal sensitivity of 91.7% with false positive rate (FPR) of 0.00/h. Conclusion: Our epilepsy prediction method achieves excellent performance and demonstrates its advantage over pure CNN-based structure in TL. Moreover, we find that the information contained in the gamma (γ) rhythm is helpful for epilepsy prediction. Significance: We propose a precise hybrid Transformer model for epilepsy prediction. The applicability of TL and model inputs is also explored for customizing personalized models in clinical application scenarios.
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