Identification of potential target genes for ankylosing spondylitis treatment

Polysaccharides to postbiotics: Nurturing bone health via modulating "gut-immune axis"

The increasing prevalence of individuals affected by bone pathologies globally has sparked catastrophic concerns. Ankylosing spondylitis, osteoporosis, rheumatoid arthritis, osteoarthritis, and fractures alone impact an estimated 1.71 billion people worldwide. The gut microbiota plays a crucial role in interacting with the host through the synthesis of a diverse range of metabolites called gut-associated metabolites (GAMs), which originate from external dietary substrates or endogenous host compounds. Many metabolic disorders have been linked to alterations in the gut microbiota's activity and composition. The development of metabolic illnesses has been linked to certain microbiota-derived metabolites, such as branched-chain amino acids, bile acids, short-chain fatty acids, tryptophan, trimethylamine N-oxide, and indole derivatives. Moreover, the modulation of gut microbiota through biotics (prebiotics, probiotics and postbiotics) presents a promising avenue for therapeutic intervention. Biotics selectively promote the growth of beneficial gut bacteria, thereby enhancing the production of GAMs with potential beneficial effects on bone metabolism. Understanding the intricate interplay between GAMs, and bone-associated genes through molecular informatics holds significant promise for early diagnosis, prognosis, and novel treatment strategies for various bone disorders.

Exploration of the shared pathways and common biomarker PAN3 in ankylosing spondylitis and ulcerative colitis using integrated bioinformatics analysis

Background

Ulcerative colitis (UC) is a chronic autoimmune-related disease that causes inflammation of the intestine. Ankylosing spondylitis (AS) is a common extraintestinal complication of UC involving the sacroiliac joint. However, the pathogenesis of AS secondary to UC has not been studied. This study aimed to investigate the shared pathways and potential common biomarkers of UC and AS.

Methods

Microarray data downloaded from the Gene Expression Omnibus (GEO) database were used to screen differentially expressed genes (DEGs) in the UC and AS datasets. Weighted gene co-expression network analysis (WGCNA) was performed to identify co-expression modules related to UC and AS. Shared genes were then further analyzed for functional pathway enrichment. Next, the optimal common biomarker was selected using SVM-RFF and further validated using two independent GEO datasets. Finally, immune infiltration analysis was used to investigate the correlation of immune cell infiltration with common biomarkers in UC and AS.

Results

A total of 4428 and 2438 DEGs in UC and AS, respectively, were screened. Four modules were identified as significant for UC and AS using WGCNA. A total of 25 genes overlapped with the strongest positive and negative modules of UC and AS. KEGG analysis showed these genes may be involved in the mitogen-activated protein kinase (MAPK) signaling pathway. GO analysis indicated that these genes were significantly enriched for RNA localization. PAN3 was selected as the optimal common biomarker for UC and AS. Immune infiltration analysis showed that the expression of PAN3 was correlated with changes in immune cells.

Conclusion

This study first explored the common pathways and genetic diagnostic markers involved in UC and AS using bioinformatic analysis. Results suggest that the MAPK signaling pathway may be associated with both pathogeneses and that PAN3 may be a potential diagnostic marker for patients with UC complicated by AS.

New Insights into the Regulatory Role of Ferroptosis in Ankylosing Spondylitis via Consensus Clustering of Ferroptosis-Related Genes and Weighted Gene Co-Expression Network Analysis

Background: The pathogenesis of ankylosing spondylitis (AS) remains undetermined. Ferroptosis is a newly discovered form of regulated cell death involved in multiple autoimmune diseases. Currently, there are no reports on the connection between ferroptosis and AS. Methods: AS samples from the Gene Expression Omnibus were divided into two subgroups using consensus clustering of ferroptosis-related genes (FRGs). Weighted gene co-expression network analysis (WGCNA) of the intergroup differentially expressed genes (DEGs) and protein–protein interaction (PPI) analysis of the key module were used to screen out hub genes. A multifactor regulatory network was then constructed based on hub genes. Results: The 52 AS patients in dataset GSE73754 were divided into cluster 1 (n = 24) and cluster 2 (n = 28). DEGs were mainly enriched in pathways related to mitochondria, ubiquitin, and neurodegeneration. Candidate hub genes, screened by PPI and WGCNA, were intersected. Subsequently, 12 overlapping genes were identified as definitive hub genes. A multifactor interaction network with 45 nodes and 150 edges was generated, comprising the 12 hub genes and 32 non-coding RNAs. Conclusions: AS can be divided into two subtypes according to FRG expression. Ferroptosis might play a regulatory role in AS. Tailoring treatment according to the ferroptosis status of AS patients can be a promising direction.

Potential Pathogenic Genes and Mechanism of Ankylosing Spondylitis: A Study Based on WGCNA and Bioinformatics Analysis

OBJECTIVE

The purpose of this study is to explore the high-risk pathogenic driver genes for the occurrence and development of ankylosing spondylitis (AS) based on the bioinformatics method at the molecular level, to further elaborate the molecular mechanism of the pathogenesis of AS, and to provide potential biological targets for the diagnosis and treatment of clinical AS.

METHODS

The gene expression profile data GSE16879 were downloaded from the GEO (Gene Expression Omnibus) database, and weighted gene coexpression network analysis was performed. Highly correlated genes were divided into 14 modules, and 582 genes contained in the yellow (classic module) and 59 genes contained in grey60 (hematologic module) modules had the strongest correlation with AS. After protein-protein interaction (PPI) analysis, the top 20 genes with the highest scores were obtained from classic module and hematologic module, respectively. The DAVID (Database for Annotation, Visualization, and Integrated Discovery) database was used for Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes analysis to analyze the biological functions of high-risk genes related to AS.

RESULTS

The results showed that the process of signal recognition particle-dependent cotranslational protein targeting to membrane, ribosome, nicotinamide adenine diphosphate hydride dehydrogenase (ubiquinone) activity, platelet activation, integrin complex, and extracellular matrix binding were enriched.

CONCLUSIONS

In this study, weighted gene coexpression network analysis, an efficient system biology algorithm, was used to analyze the high-risk pathogenic driver gene of AS. We provide new targets for the diagnosis and treatment of clinical AS and new ideas for further study.

The Expression Levels of MicroRNAs Associated with T and B Cell Differentiation/stimulation in Ankylosing Spondylitis

Spondyloarthropathies (SpAs), are a group of chronic inflammatory diseases with a number of genetic, physiopathological, clinical and radiological features. Ankylosing spondylitis (AS) is the most common type of spondylo-arthropathies, and >90.0% of patients with ankylosing spondylitis are human leukocyte antigen-B27 (HLA-B2 7)-positive. In recent years, non-HLA genetic factors have been reported to have an effect on ankylosing spondylitis. MicroRNAs (miRNAs), are endogenous non coding RNA molecules containing 18-23 nucleotides that play a role in the post-transcriptional regulation of gene expression. In this study, we aimed to determine the expression levels of miRNAs associated with T- and B-cell differentiation/stimulation in peripheral blood mononuclear cells and their relationship with the etiology of the AS in patients and healthy controls. In a molecular study, peripheral blood mononuclear cell isolation, and total RNA isolation were performed first. In the second step, cDNA synthesis and quantitative real-time PCR (qPCR) expression analysis were completed. Ultimately, in the patient and control group, the expression levels of miR-142-5p and miR-143 were found to be significantly different (p <0.05). According to current knowledge, miR-142-5p andmiR-143 expressions were found to be important for those diseases that share similar etiology with AS. We suggest that miR-142-5p and miR-143 may play a role in the pathogenesis, especially miR- 142-5p may be a potential biomarker and a target molecule for the treatment.

Biomarker development for axial spondyloarthritis

The term axial spondyloarthritis (axSpA) encompasses a heterogeneous group of diseases that have variable presentations, extra-articular manifestations and clinical outcomes, and that will respond differently to treatments. The prototypical type of axSpA, ankylosing spondylitis, is thought to be caused by interaction between the genetically primed host immune system and gut microbiota. Currently used biomarkers such as HLA-B27 status, C-reactive protein and erythrocyte sedimentation rate have, at best, moderate diagnostic and predictive value. Improved biomarkers are needed for axSpA to assist with early diagnosis and to better predict treatment responses and long-term outcomes. Advances in a range of 'omics' technologies and statistical approaches, including genomics approaches (such as polygenic risk scores), microbiome profiling and, potentially, transcriptomic, proteomic and metabolomic profiling, are making it possible for more informative biomarker sets to be developed for use in such clinical applications. Future developments in this field will probably involve combinations of biomarkers that require novel statistical approaches to analyse and to produce easy to interpret metrics for clinical application. Large publicly available datasets from well-characterized case-cohort studies that use extensive biological sampling, particularly focusing on early disease and responses to medications, are required to establish successful biomarker discovery and validation programmes.