MSGD: a manually curated database of genomic, transcriptomic, proteomic and drug information for multiple sclerosis.

Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the central nervous system. 'Omics' technologies (genomics, transcriptomics, proteomics) and associated drug information have begun reshaping our understanding of multiple sclerosis. However, these data are scattered across numerous references, making them challenging to fully utilize. We manually mined and compiled these data within the Multiple Sclerosis Gene Database (MSGD) database, intending to continue updating it in the future. We screened 5485 publications and constructed the current version of MSGD. MSGD comprises 6255 entries, including 3274 variant entries, 1175 RNA entries, 418 protein entries, 313 knockout entries, 612 drug entries and 463 high-throughput entries. Each entry contains detailed information, such as species, disease type, detailed gene descriptions (such as official gene symbols), and original references. MSGD is freely accessible and provides a user-friendly web interface. Users can easily search for genes of interest, view their expression patterns and detailed information, manage gene sets and submit new MS-gene associations through the platform. The primary principle behind MSGD's design is to provide an exploratory platform, aiming to minimize filtration and interpretation barriers while ensuring highly accessible presentation of data. This initiative is expected to significantly assist researchers in deciphering gene mechanisms and improving the prevention, diagnosis and treatment of MS. Database URL: http://bio-bigdata.hrbmu.edu.cn/MSGD.

Neutrophil Membrane-Camouflaged Polyprodrug Nanomedicine for Inflammation Suppression in Ischemic Stroke Therapy.

Neuroinflammation has emerged as a major concern in ischemic stroke therapy because it exacebates neurological dysfunction and suppresses neurological recovery after ischemia/reperfusion. Fingolimod hydrochloride (FTY720) is an FDA-approved anti-inflammatory drug which exhibits potential neuroprotective effects in ischemic brain parenchyma. However, delivering a sufficient amount of FTY720 through the blood-brain barrier into brain lesions without inducing severe cardiovascular side effects remains challenging. Here, a neutrophil membrane-camouflaged polyprodrug nanomedicine that can migrate into ischemic brain tissues and in situ release FTY720 in response to elevated levels of reactive oxygen species. This nanomedicine delivers 15.2-fold more FTY720 into the ischemic brain and significantly reduces the risk of cardiotoxicity and infection compared with intravenously administered free drug. In addition, single-cell RNA-sequencing analysis identifies that the nanomedicine attenuates poststroke inflammation by reprogramming microglia toward anti-inflammatory phenotypes, which is realized via modulating Cebpb-regulated activation of NLRP3 inflammasomes and secretion of CXCL2 chemokine. This study offers new insights into the design and fabrication of polyprodrug nanomedicines for effective suppression of inflammation in ischemic stroke therapy.

Identification of diagnostic signatures for ischemic stroke by machine learning algorithm.

OBJECTIVE: Ischemic stroke (IS) is one of the major diseases threatening human health and survival and a leading cause of acquired mortality and disability in adults. The aim of this study was to screen diagnostic features of IS and to explore the characteristics of immune cell infiltration in IS pathogenesis. METHODS: The microarray data of IS (GSE16561, GSE58294, GSE37587, and GSE124026) in the GEO database were merged after removing the batch effect. Then integrated bioinformatic analysis and machine-learning strategies were adopted to analyze the functional correlation and select diagnostic signatures. The WGCNA was used to identify the co-expression modules related to IS. The CIBERSORT algorithm was performed to assess the inflammatory state of IS and to investigate the correlation between diagnostic signatures and infiltrating immune cells. RESULTS: Functional analysis of dysregulated genes showed that immune response-regulating signaling pathway and pattern recognition receptor activity were enriched in the pathophysiology of IS. The turquoise module was identified as the significant module with IS. By using Lasso and SVM-RFE learning methods, we finally obtained four diagnostic genes, including LAMP2, CR1, CLEC4E, and F5. The corresponding results of AUC of ROC prediction model in training and validation cohort were 0.954 and 0.862, respectively. The immune cell infiltration analysis suggested that plasma cells, resting and activated NK cells, activated dendritic cells, memory B cells, CD8+ T cells, naïve CD4+ T cells, and resting mast cells may be involved in the development of IS. Additionally, these diagnostic signatures might be correlated with multiple immune cells in varying degrees. CONCLUSION: We identified four biologically relevant genes (LAMP2, CR1, CLEC4E, and F5) with diagnostic effects for IS, our results further provide novel insights regarding molecular mechanisms associated with various immune cells that related to IS for future investigations.

Identification of potential immune-related hub genes in Parkinson's disease based on machine learning and development and validation of a diagnostic classification model.

BACKGROUND: Parkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model. METHODS: We downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples). RESULTS: Ultimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes. CONCLUSION: In this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.