Identifying the Anti-inflammatory Effects of Astragalus Polysaccharides in Anti-N-Methyl-D-Aspartate Receptor Encephalitis: Network Pharmacology and Experimental Validation.

BACKGROUND: Astragalus polysaccharides (APS), a group of bioactive compounds obtained from the natural source Astragalus membranaceus(AM), exhibits numerous pharmacological actions in the central nervous system, such as anti-inflammatory, antioxidant, and immunomodulatory properties. Despite the remarkable benefits, the effectiveness of APS in treating anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and the corresponding mechanism have yet to be fully understood. As such, this study aims to investigate the impact of APS on anti-NMDAR encephalitis and explore the potential molecular network mechanism. METHODS: The impact of APS intervention on mice with anti-NMDAR encephalitis was assessed, and the possible molecular network mechanism was investigated utilizing network pharmacology and bioinformatics techniques such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG),protein-protein interaction (PPI) network, and molecular docking. Enzyme-linked immunosorbent assay (ELISA) was applied to detect the expression of core target proteins. RESULTS: APS significantly ameliorated cognitive impairment and reduced susceptibility to PTZ-induced seizures in mice with anti-NMDAR encephalitis, confirming the beneficial effect of APS on anti-NMDAR encephalitis. Seventeen intersecting genes were identified between APS and anti-NMDAR encephalitis. GO and KEGG analyses revealed the characteristics of the intersecting gene networks. STRING interaction in the PPI network was applied to find crucial molecules. The results of molecular docking suggested that APS may regulate interleukin-1ß (IL-1ß), tumor necrosis factor-α (TNF-α), and interleukin-6 (IL-6) as potential targets in anti-NMDAR encephalitis. Furthermore, the levels of IL-1ß, IL-6, and TNF-α detected by ELISA in anti-NMDAR encephalitis mice were significantly downregulated in response to the administration of APS. CONCLUSION: The findings of this study demonstrate the significant role of APS in the treatment of anti-NMDAR encephalitis, as it effectively suppresses inflammatory cytokines. These results suggest that APS has the potential to be considered as a viable herbal medication for the treatment of anti-NMDAR encephalitis.

White matter abnormalities and multivariate pattern analysis in anti-NMDA receptor encephalitis.

Objective: This study aimed to investigate white matter (WM) microstructural alterations and their relationship correlation with disease severity in anti-NMDA receptor (NMDAR) encephalitis. Multivariate pattern analysis (MVPA) was applied to discriminate between patients and healthy controls and explore potential imaging biomarkers. Methods: Thirty-two patients with anti-NMDAR encephalitis and 26 matched healthy controls underwent diffusion tensor imaging. Tract-based spatial statistics and atlas-based analysis were used to determine WM microstructural alterations between the two groups. MVPA, based on a machine-learning algorithm, was applied to classify patients and healthy controls. Results: Patients exhibited significantly reduced fractional anisotropy in the corpus callosum, fornix, cingulum, anterior limb of the internal capsule, and corona radiata. Moreover, mean diffusivity was increased in the anterior corona radiata and body of the corpus callosum. On the other hand, radial diffusivity was increased in the anterior limb of the internal capsule, cingulum, corpus callosum, corona radiata, and fornix. WM changes in the cingulum, fornix, and retrolenticular part of the internal capsule were correlated with disease severity. The accuracy, sensitivity, and specificity of fractional anisotropy-based classification were each 78.33%, while they were 67.71, 65.83, and 70% for radial diffusivity. Conclusion: Widespread WM lesions were detected in anti-NMDAR encephalitis. The correlation between WM abnormalities and disease severity suggests that these alterations may serve a key role in the pathophysiological mechanisms of anti-NMDAR encephalitis. The combination of tract-based spatial statistics and MVPA may provide more specific and complementary information at the group and individual levels.

Potential biomarkers of Alzheimer's disease and cerebral small vessel disease.

Background: Cerebral small vessel disease (CSVD) is associated with the pathogenesis of Alzheimer's disease (AD). Effective treatments to alleviate AD are still not currently available. Hence, we explored markers and underlying molecular mechanisms associated with AD by utilizing gene expression profiles of AD and CSVD patients from public databases, providing more options for early diagnosis and its treatment. Methods: Gene expression profiles were collected from GSE63060 (for AD) and GSE162790 (for CSVD). Differential analysis was performed between AD and mild cognitive impairment (MCI) or CSVD progression and CSVD no-progression. In both datasets, differentially expressed genes (DEGs) with the same expression direction were identified as common DEGs. Then protein-protein interaction (PPI) network was constructed for common DEGs. Differential immune cells and checkpoints were calculated between AD and MCI. Results: A total of 146 common DEGs were identified. Common DEGs were mainly enriched in endocytosis and oxytocin signaling pathways. Interestingly, endocytosis and metabolic pathways were shown both from MCI to AD and from CSVD no-progression to CSVD progression. Moreover, SIRT1 was identified as a key gene by ranking degree of connectivity in the PPI network. SIRT1 was associated with obesity-related genes and metabolic disorders. Additionally, SIRT1 showed correlations with CD8 T cells, NK CD56 bright cells, and checkpoints in AD. Conclusion: The study revealed that the progression of AD is associated with abnormalities in gene expression and metabolism and that the SIRT1 gene may serve as a promising therapeutic target for the treatment of AD.

Identification of Alzheimer's Disease Molecular Subtypes Based on Parallel Large-Scale Sequencing.

The incidence of Alzheimer's disease (AD) is constantly increasing as the older population grows, and no effective treatment is currently available. In this study, we focused on the identification of AD molecular subtypes to facilitate the development of effective drugs. AD sequencing data collected from the Gene Expression Omnibus (GEO) database were subjected to cluster sample analysis. Each sample module was then identified as a specific AD molecular subtype, and the biological processes and pathways were verified. The main long non-coding RNAs and transcription factors regulating each "typing pathway" and their potential mechanisms were determined using the RNAInter and TRRUST databases. Based on the marker genes of each "typing module," a classifier was developed for molecular typing of AD. According to the pathways involved, five sample clustering modules were identified (mitogen-activated protein kinase, synaptic, autophagy, forkhead box class O, and cell senescence), which may be regulated through multiple pathways. The classifier showed good classification performance, which may be useful for developing novel AD drugs and predicting their indications.

Corrigendum: Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication.

[This corrects the article DOI: 10.3389/fnagi.2021.731180.].

Microglia Mediate the Occurrence and Development of Alzheimer's Disease Through Ligand-Receptor Axis Communication.

Alzheimer's disease (AD) is a common neurodegenerative disease. Its onset is insidious and its progression is slow, making diagnosis difficult. In addition, its underlying molecular and cellular mechanisms remain unclear. In this study, clustering analysis was performed on single-cell RNA sequencing (scRNA-seq) data from the prefrontal cortex of 48 AD patients. Each sample module was identified to be a specific AD cell type, eight main brain cell types were identified, and the dysfunctional evolution of each cell type was further explored by pseudo-time analysis. Correlation analysis was then used to explore the relationship between AD cell types and pathological characteristics. In particular, intercellular communication between neurons and glial cells in AD patients was investigated by cell communication analysis. In patients, neuronal cells and glial cells significantly correlated with pathological features, and glial cells appear to play a key role in the development of AD through ligand-receptor axis communication. Marker genes involved in communication between these two cell types were identified using five types of modeling: logistic regression, multivariate logistic regression, least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). LASSO modeling identified CXCR4, EGFR, MAP4K4, and IGF1R as key genes in this communication. Our results support the idea that microglia play a role in the occurrence and development of AD through ligand-receptor axis communication. In particular, our analyses identify CXCR4, EGFR, MAP4K4, and IGF1R as potential biomarkers and therapeutic targets in AD.

Identification of Dysregulated Mechanisms and Potential Biomarkers in Ischemic Stroke Onset.

OBJECTIVE: Ischemic stroke (IS) is a major cause of severe disability. This study aimed to identify potential biomarkers closely related to IS diagnosis and treatment. METHODS: Profiles of gene expression were obtained from datasets GSE16561, GSE22255, GSE112801 and GSE110993. Differentially expressed mRNAs between IS and controls were then subjected to weighted gene co-expression network analysis as well as multiscale embedded gene co-expression network analysis. The intersection of the two sets of module genes was subjected to analyses of functional enrichment and of microRNAs (miRNAs) regulation. Then, the area under receiver operating characteristic curves (AUC) was calculated to assess the ability of genes to discriminate IS patients from controls. IS diagnostic signatures were constructed using least absolute shrinkage and selection operator regression. RESULTS: A total of 234 common co-expression network genes were found to be potentially associated with IS. Enrichment analysis found that these genes were mainly associated with inflammation and immune response. The aberrantly expressed miRNAs (hsa-miR-651-5p, hsa-miR-138-5p, hsa-miR-9-3p and hsa-miR-374a-3p) in IS had regulatory effects on IS-related genes and were involved in brain-related diseases. We used the criterion AUC > 0.7 to screen out 23 hub genes from IS-related genes in the GSE16561 and GSE22255 datasets. We obtained an 8-gene signature (ADCY4, DUSP1, ATP5F1, DCTN5, EIF3G, ELAVL1, EXOSC7 and PPIE) from the training set of GSE16561 dataset, which we confirmed in the validation set of GSE16561 dataset and in the GSE22255 dataset. The genes in this signature were highly accurate for diagnosing IS. In addition, the 8-gene signature significantly correlated with infiltration by immune cells. CONCLUSION: These findings provide new clues to molecular mechanisms and treatment targets in IS. The genes in the signature may be candidate markers and potential gene targets for treatments.

Identification of Potential Key Genes Involved in the Carotid Atherosclerosis.

PURPOSE: Carotid atherosclerosis is a kind of systemic atherosclerosis in the carotid arteries. However, the efficiency of treatment is insufficient. Therefore, it is urgent to find therapeutic targets and deepen the understanding of carotid atherosclerosis. MATERIALS AND METHODS: In this study, we analyzed differentially expressed genes (DEGs) between atheroma plaque and macroscopically intact tissue (control samples). Furthermore, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analysis based on the DEGs. Four methods were used to identify the hub genes in the protein-protein interaction networks of the DEGs. Furthermore, we also performed network module analysis to reveal carotid atherosclerosis-related gene modules and biological functions. RESULTS: The enrichment results showed that the biological functions were related to inflammation, immunity, chemokine and cell adhesion molecule, such as PIK-Akt signaling pathway, Rap1 signaling pathway, MAPK signaling pathway, NOD-like receptor signaling pathway and B cell receptor signaling pathway. In addition, we screened the hub genes. A total of 16 up-regulated genes (C3AR1, CCR1, CCR2, CD33, CD53, CXCL10, CXCL8, CXCR4, CYBB, FCER1G, FPR2, ITGAL, ITGAM, ITGAX, ITGB2, and LILRB2) were identified as hub genes. A total of 5 gene modules were obtained. We found that biological functions obtained for each cluster were mostly related to immunity, chemokines and cell adhesion molecules. CONCLUSION: The present study identified key DEGs in atheroma plaque compared with control samples. The key genes involved in the development of carotid atherosclerosis may provide valuable therapeutic targets for carotid atherosclerosis.

A Potential Autophagy-Related Competing Endogenous RNA Network and Corresponding Diagnostic Efficacy in Schizophrenia.

Competing endogenous RNA (ceRNA) and autophagy were related to neurological diseases. But the relationship among ceRNA, autophagy and Schizophrenia (SZ) was not clear. In this study, we obtained gene expression profile of SZ patients (GSE38484, GSE54578, and GSE16930) from Gene Expression Omnibus (GEO) database. Then we screened the autophagy-related differentially expressed lncRNA, miRNA, and mRNA (DElncRNA, DEmiRNA, and DEmRNA) combined with Gene database from The National Center for Biotechnology Information (NCBI). In addition, we performed enrichment analysis. The result showed that biological processes (BPs) mainly were associated with cellular responses to oxygen concentration. The enriched pathways mainly included ErbB, AMPK, mTOR signaling pathway and cell cycle. Furthermore, we constructed autophagy-related ceRNA network based on the TargetScan database. Moreover, we explored the diagnostic efficiency of lncRNA, miRNA and mRNA in ceRNA, through gene set variation analysis (GSVA). The result showed that the diagnostic efficiency was robust, especially miRNA (AUC = 0.884). The miRNA included hsa-miR-423-5p, hsa-miR-4532, hsa-miR-593-3p, hsa-miR-618, hsa-miR-4723-3p, hsa-miR-4640-3p, hsa-miR-296-5p, and hsa-miR-3943. The result of this study may be helpful for deepening the pathophysiology of SZ. In addition, our finding may provide a guideline for the clinical diagnosis of SZ.

Gene Set Index Based on Different Modules May Help Differentiate the Mechanisms of Alzheimer's Disease and Vascular Dementia.

PURPOSE: Alzheimer's disease (AD) and vascular dementia shared similar symptoms, the aim of the present study was to identify potential differences in the mechanisms underlying the two diseases. MATERIALS AND METHODS: The data set including AD, vascular dementia, and control samples was carried out gene differential expression analysis, weighted gene co-expression network analysis, functional enrichment, protein-protein interaction network construction, and least absolute shrinkage and selection operator analysis to reveal the differences in the mechanisms underlying the two diseases and potential diagnostic gene signature. RESULTS: We identified the gene modules related to AD or vascular dementia. Enrichment analysis of module genes and construction of a protein-protein interaction network suggested that the "brown" module may be involved in a chemokine pathway, the "blue" module may be involved in cortisol synthesis and secretion, and the "turquoise" module may be involved in cholinergic synapse transmission. The hub gene-based signature index may be a biomarker of AD and vascular dementia and may even differentiate the two diseases from each other with high area under curve. CONCLUSION: Our results identified not only core pathways involved in both AD and vascular disease, but also their potentially specific pathways. We proposed the hub gene-based signature index may be useful for diagnosing AD and vascular dementia.

Identification of Key Differentially Expressed Transcription Factors in Glioblastoma.

Glioblastoma (GBM) is the most frequent malignant brain tumor in adults. Our study focused on uncovering differentially expressed genes (DEGs) and their methylation in order to identify novel diagnostic biomarkers and potential treatment targets. Using GBM RNA-sequencing data from The Cancer Genome Atlas (TCGA) database, DEGs between GBM samples and paracancer tissue samples were analyzed. Enrichment analysis for DEGs and transcription factors (TFs) was performed. A total of 1029 upregulated genes and 1542 downregulated genes were identified, which were associated mainly with multiple tumor-related and immune-related pathways such as cell cycle, mitogen-activated protein kinase signaling pathway, leukocyte transendothelial migration, and autoimmune thyroid disease. These DEGs were enriched for 174 TFs, and six TFs were differentially expressed and identified as key TFs in GBM: HOXA3, EN1, ZIC1, and FOXD3 were upregulated, while HLF and EGR3 were downregulated. A total of 1978 DEGs were involved in the regulatory networks of the six key differentially expressed TFs. High expression of EN1 was associated with shorter overall survival, while high expression of EGR3 was associated with shorter recurrence-free survival. The six TFs were differentially methylated in GBM samples compared with paracancer tissues. Our study identifies numerous DEGs and their associated pathways as potential contributors to GBM, particularly the TFs EN1, EGR3, HOXA3, ZIC1, FOXD3, and HLF. The differential expression of these TFs may be unlikely driven by aberrant methylation. These TFs may be useful as diagnostic markers and treatment targets in GBM, and EN1 and EGR3 may have predictive prognostic value.

A Novel Schizophrenia Diagnostic Model Based on Statistically Significant Changes in Gene Methylation in Specific Brain Regions.

OBJECTIVE: The present study identified methylation patterns of schizophrenia- (SCZ-) related genes in different brain regions and used them to construct a novel DNA methylation-based SCZ diagnostic model. METHODS: Four DNA methylation datasets representing different brain regions were downloaded from the Gene Expression Omnibus. The common differentially methylated genes (CDMGs) in all datasets were identified to perform functional enrichment analysis. The differential methylation sites of 10 CDMGs involved in the largest numbers of neurological or psychiatric-related biological processes were used to construct a DNA methylation-based diagnostic model for SCZ in the respective datasets. RESULTS: A total of 849 CDMGs were identified in the four datasets, but the methylation sites as well as degree of methylation differed across the brain regions. Functional enrichment analysis showed CDMGs were significantly involved in biological processes associated with neuronal axon development, intercellular adhesion, and cell morphology changes and, specifically, in PI3K-Akt, AMPK, and MAPK signaling pathways. Four DNA methylation-based classifiers for diagnosing SCZ were constructed in the four datasets, respectively. The sample recognition efficiency of the classifiers showed an area under the receiver operating characteristic curve of 1.00 in three datasets and >0.9 in one dataset. CONCLUSION: DNA methylation patterns in SCZ vary across different brain regions, which may be a useful epigenetic characteristic for diagnosing SCZ. Our novel model based on SCZ-gene methylation shows promising diagnostic power.

Identification of molecular correlations of RBM8A with autophagy in Alzheimer's disease.

Our previous studies revealed RBM8A may play a role in various progressive neurological diseases. The present study aimed to explore the role of RBM8A in Alzheimer's disease (AD). RBM8A is significantly down-regulated in AD. Interestingly, 9186 differentially expressed genes are overlapped from comparisons of AD versus control and RBM8A-low versus RBM8A-high. Weight gene correlation analysis was performed and 9 functional modules were identified. Modules positively correlated with AD and RBM8A-low are significantly involved in the RAP1 signaling pathway, PI3K-AKT signaling pathway, hematopoietic cell lineage, autophagy and APELIN signaling pathway. Fifteen genes (RBM8A, RHBDF2, TNFRSF10B, ACP1, ANKRD39, CA10, CAMK4, CBLN4, LOC284214, NOVA1, PAK1, PPEF1, RGS4, TCEB1 and TMEM118) are identified as hub genes, and the hub gene-based LASSO model can accurately predict the occurrence of AD (AUC = 0.948). Moreover, the RBM8A-module-pathway network was constructed, and low expression of RBM8A down-regulates multiple module genes, including FIP200, Beclin 1, NRBF2, VPS15 and ATG12, which composes key complexes of autophagy. Thus, our study supports that low expression of RBM8A correlates with the decrease of the components of key complexes in autophagy, which could potentially contribute to pathophysiological changes of AD.

Association between polymorphisms in microRNAs and ischemic stroke in an Asian population: evidence based on 6,083 cases and 7,248 controls.

BACKGROUND: Polymorphisms in miR-146a (rs2910164), miR-196a2 (rs11614913), miR-149 (rs2292832) and miR-499 (rs3746444) have been associated with ischemic stroke (IS), but studies have given inconsistent results. METHODS: This meta-analysis investigated the possible association between IS risk and the four polymorphisms. A total of 14 case-control studies from Asian populations involving 6,083 cases and 7,248 controls for the four polymorphisms were included. RESULTS: Results showed that the GG genotype of miR-146a (rs2910164) may be associated with increased IS risk according to the recessive model (OR=1.20, 95% CI=1.02-1.42, P=0.03). Similarly, the CC genotype of miR-149 (rs2292832) may be associated with increased IS risk according to the recessive model (OR=1.28, 95% CI=1.08-1.52, P=0.005) and the homozygous model (OR=1.31, 95% CI=1.09-1.58, P=0.004). In contrast, miR-196a2 (rs11614913) and miR-499 (rs3746444) polymorphisms did not show significant association with IS risk in any of the five genetic models. CONCLUSION: These results indicate that the GG genotype of miR-146a (rs2910164) and CC genotype of miR-149 (rs2292832) may confer increased susceptibility to IS, while miR-196a2 (rs11614913) and miR-499 (rs3746444) polymorphisms may not be associated with IS risk in Asian populations. These conclusions should be verified in large and well-designed studies.