Identification of PTGS2 as a genetically supported protective biomarker and potential therapeutic target in myasthenia gravis

BACKGROUND: Myasthenia Gravis (MG) is an autoimmune disease that damages the neuromuscular junction (NMJ), reduces the transmission of nerve impulses to muscles, and thus causes fluctuating muscle weakness and fatigue. The main types of MG are autoantibodies that target necessary components of the postsynaptic membrane, such as acetylcholine receptors (AChRs) and muscle-specific kinase (MuSK). The above immune-mediated alterations disrupt synaptic transmission and reduce muscle contraction. Study the molecular and cellular mechanisms of MG to find genes that regulate the immune system, cause inflammation, or affect NMJ homeostasis and may serve as biomarkers. These biomarkers can provide more information on the course of a disease and help to customise diagnosis and treatment according to this information. METHODS: The transcriptomic data in this study were obtained from the Gene Expression Omnibus (GEO) database under accession number GSE85452 (GPL10558), which contains peripheral blood gene expression profiles of MG patients and healthy controls. Differential Expression Analysis was conducted to find genes in *M. fitumendi* related to MG. Preprocess and normalise the raw data before the following comparisons. Mendelian Randomisation (MR) was employed to investigate whether the candidate genes causally affected MG risk. PTGS2 was found to be a protective factor (OR < 1) and selected for further study. Gene set enrichment analysis (GSEA) was then carried out to identify related pathways, and single-sample gene set enrichment analysis (ssGSEA) was used to explore associations with the immune system. Single-cell RNA sequencing (scRNA-seq) was performed to find out which cells expressed PTGS2, how the proportions of different cell types in the MG microenvironment were changed, and what inter-cellular communication occurred. A network-based virtual PTGS2 overexpression analysis was also carried out in MG cells with scTenifoldNet. Single-cell gene regulatory networks were built from the raw count data and denoised by tensor decomposition. PTGS2 regulatory activity increased due to a doubling of the weight of the positive regulatory edge. Genes with an adjusted P-value <0.05 were regarded as significantly altered and subjected to KEGG and Gene Ontology enrichment analysis. RESULTS: Using a sensitive threshold of |log₂FC| > 0.38 for the initial screening of the MG transcriptome, a particular set of differentially expressed genes in patients was identified compared with healthy individuals. Mendelian Randomisation analysis also showed that PTGS2 is associated with a reduced risk of MG and has a negative causal association with the disease. Functional enrichment analysis linked PTGS2-associated molecular signatures to immune and inflammatory pathways. Immune infiltration analysis showed variations in the proportion of immune cells in MG, and PTGS2 expression was significantly correlated with several subsets of immune cells. Gene-disease association mapping links PTGS2 to multiple immune-related disorders. Single-cell RNA sequencing also showed cell-type-specific expression and different distributions of PTGS2 in MG patients and healthy controls. Overexpression of virtual PTGS2 mainly modified genes and pathways in the myeloid cell, such as IL-17 signalling, chemotaxis and neutrophil migration. Therefore, PTGS2 may be involved in the regulation of inflammatory myeloid responses in MG. SUMMARY: Transcriptome and gene analysis identified PTGS2 as a protective gene for myasthenia gravis. PTGS2 is linked to immune-inflammatory signals, and single-cell data have identified specific cell types in the MG immune microenvironment that express it. Network-based virtual PTGS2 overexpression mainly altered myeloid-associated genes and pathways of IL-17 signalling, chemotaxis and neutrophil migration. PTGS2 may be a biomarker and a possible therapeutic target for MG.