Aims. To investigate the correlations among cytokines and regulatory T cells (T-regs) in ankylosing spondylitis (AS) patients, and their changes after anti-tumour necrosis factor-α (TNF-α) treatment. Methods. We included 72 AS patients with detailed medical records, disease activity score (Bath Ankylosing Spondylitis Disease Activity Index), functional index (Bath Ankylosing Spondylitis Functional Index), and laboratory data (interleukin (IL)-2, IL-4, IL-10, TNF-α, interferon (IFN)-γ, transforming growth factor (TGF)-β, ESR, and CRP). Their peripheral blood mononuclear cells (PBMCs) were marked with anti-CD4, anti-CD25, and anti-FoxP3 antibodies, and triple positive T cells were gated by flow cytometry as T-regs. Their correlations were calculated and the changes after anti-TNF-α therapy were compared. Results. The frequency of T-regs in PBMCs was positively correlated to ESR and CRP in AS (r = 0.35 and 0.43; p = 0.032 and 0.027, respectively), and there was also a significant correlation between serum level of TNF-α and CRP (p = 0.041). The frequency of T-regs in PBMCs positively correlated to serum levels of TNF-α, IL-10, and TGF-β, while IL-2, IL-4, and IFN-γ showed opposite results. After anti-TNF-α treatment, there were significantly lower serum levels of TNF-α, IL-10, TGF-β, and frequency of T-regs in PBMCs among these AS patients (p = 0.026, 0.032, 0.029, and 0.037, respectively). Conclusion. In AS patients, proinflammatory cytokine may give positive feedback to induce more T-reg production and anti-inflammatory cytokine
Degenerative cervical spondylosis (DCS) is a common musculoskeletal disease that encompasses a wide range of progressive degenerative changes and affects all components of the cervical spine. DCS imposes very large social and economic burdens. However, its genetic basis remains elusive. Predicted whole-blood and skeletal muscle gene expression and genome-wide association study (GWAS) data from a DCS database were integrated, and functional summary-based imputation (FUSION) software was used on the integrated data. A transcriptome-wide association study (TWAS) was conducted using FUSION software to assess the association between predicted gene expression and DCS risk. The TWAS-identified genes were verified via comparison with differentially expressed genes (DEGs) in DCS RNA expression profiles in the Gene Expression Omnibus (GEO) (Accession Number: GSE153761). The Functional Mapping and Annotation (FUMA) tool for genome-wide association studies and Meta tools were used for gene functional enrichment and annotation analysis.Aims
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This study aimed, through bioinformatics analysis and in vitro experiment validation, to identify the key extracellular proteins of intervertebral disc degeneration (IDD). The gene expression profile of GSE23130 was downloaded from the Gene Expression Omnibus (GEO) database. Extracellular protein-differentially expressed genes (EP-DEGs) were screened by protein annotation databases, and we used Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) to analyze the functions and pathways of EP-DEGs. STRING and Cytoscape were used to construct protein-protein interaction (PPI) networks and identify hub EP-DEGs. NetworkAnalyst was used to analyze transcription factors (TFs) and microRNAs (miRNAs) that regulate hub EP-DEGs. A search of the Drug Signatures Database (DSigDB) for hub EP-DEGs revealed multiple drug molecules and drug-target interactions.Aims
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Lumbar spinal stenosis (LSS) is a common skeletal system disease that has been partly attributed to genetic variation. However, the correlation between genetic variation and pathological changes in LSS is insufficient, and it is difficult to provide a reference for the early diagnosis and treatment of the disease. We conducted a transcriptome-wide association study (TWAS) of spinal canal stenosis by integrating genome-wide association study summary statistics (including 661 cases and 178,065 controls) derived from Biobank Japan, and pre-computed gene expression weights of skeletal muscle and whole blood implemented in FUSION software. To verify the TWAS results, the candidate genes were furthered compared with messenger RNA (mRNA) expression profiles of LSS to screen for common genes. Finally, Metascape software was used to perform enrichment analysis of the candidate genes and common genes.Aims
Methods