Immune tolerance and the prevention of autoimmune diseases essentially depend on thymic tissue homeostasis

ObjectivesBased on clinical, biomarker and genetic data, autoimmune and autoinflammatory disorders (AIDs) can be classified as a disease continuum from pure autoinflammatory to pure autoimmune with more complex diseases in between. However, the genetic architecture of AIDs has not been systematically described. Here we investigate the polygenic landscape of AIDs using genome-wide association studies (GWAS) and statistical genetics methods to describe this continuum.MethodsWe studied the genetic landscape of 15 AIDs using GWAS summary statistics and methods including genomic structural equation modelling, linkage disequilibrium score regression, Local Analysis of [co]Variant Association, and Gaussian causal mixture modelling (MiXeR). We performed enrichment analyses of tissues and biological gene-sets using MAGMA to highlight shared and distinct perspectives of AIDs.ResultsGenomic structural equation modelling suggested a continuum structure with four underlying latent factors stretching from Crohn’s Disease, Ulcerative Colitis and Primary Sclerosing Cholangitis at one side to Sjogren Syndrome, Systemic Lupus Erythematosus and Systemic Scleroderma on the opposite side. Across AIDs, we observed a balanced mixture of negative and positive local correlations within the major histocompatibility complex, while local correlations outside this region were predominantly positive. MiXeR analysis showed large genetic overlap in accordance with the continuum landscape. MAGMA analysis indicated that genes associated with monogenic immune diseases also play a role in autoimmune (factor 1) and autoinflammatory diseases (factor 4).ConclusionsOur genetic findings support the AIDs continuum. Two of the four clusters, “polygenic autoimmune” and “polygenic autoinflammatory”, reside on margins of this continuum. The identified genetic continuum across different AIDs can potentially guide drug selection, as patients within the same clusters may benefit from the same therapies.

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In silico analysis of precursor messenger RNA as a potential biomarker of myasthenia gravis thymoma

Jovičić

2024

Eur J Inflamm

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The manuscript analyzes potential pre-mRNA biomarkers of Myasthenia gravis (MG) in thymoma in silico. GSE11967 data set and platform5188 from the Gene Expression Omnibus database apply for data preprocessing, normalization, and quality control. Quality metrics indicated high overall data integrity, with no significant outliers or batch effects detected. Differential expression analysis (DEG.) uses the limma package in R. We compared thymoma samples to normal thymus tissue to identify DEGs. The significance criteria are adjusted p-value <0.05 and a |log2 fold change| > 1. Functional enrichment and pathway analysis, ontology analysis, and KEGG pathway analysis further investigate the potential underlying biological processes. Despite the extensive use of gene expression profiling for identifying potential biomarkers and therapeutic targets, this study identifies DEGs ENSG00000112345 and ENSG00000234567 and pathways like hsa04110, hsa03013 and hsa04115 in thymoma with MG compared to normal thymus tissue using the GSE11967 dataset Plots like UMAP, Boxplot, Expression density and Mean variance demonstrate differential expression in disease and control group. The GSE11967 data set shows the presence of significant DEG and pathways in thymoma-associated MG tissue, compared to healthy tissue. A broader and integrative approach is needed to understand the complex expression biomarkers in thymoma in MG patients and other regulatory mechanisms that may contribute to the disease by multi-omic approaches.

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Immune tolerance and the prevention of autoimmune diseases essentially depend on thymic tissue homeostasis

Cited by 4 publications

References 249 publications

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G protein-coupled receptors related to autoimmunity in postural orthostatic tachycardia syndrome

Mapping the genetic landscape of disorders on the autoimmune-autoinflammatory continuum: potential implications for classification and treatment

In silico analysis of precursor messenger RNA as a potential biomarker of myasthenia gravis thymoma

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