S100A8 and S100A12 Proteins as Biomarkers of High Disease Activity in Patients with Rheumatoid Arthritis That Can Be Regulated by Epigenetic Drugs

The Role of Alarmins in the Pathogenesis of Rheumatoid Arthritis, Osteoarthritis, and Psoriasis

Alarmins are immune-activating factors released after cellular injury or death. By secreting alarmins, cells can interact with immune cells and induce a variety of inflammatory responses. The broad family of alarmins involves several members, such as high-mobility group box 1, S100 proteins, interleukin-33, and heat shock proteins, among others. Studies have found that the concentrations and expression profiles of alarmins are altered in immune-mediated diseases. Furthermore, they are involved in the pathogenesis of inflammatory conditions. The aim of this narrative review is to present the current evidence on the role of alarmins in rheumatoid arthritis, osteoarthritis, and psoriasis. We discuss their potential involvement in mechanisms underlying the progression of these diseases and whether they could become therapeutic targets. Moreover, we summarize the impact of pharmacological agents used in the treatment of these diseases on the expression of alarmins.

Computational Deciphering of the Role of S100A8 and S100A9 Proteins and Their Changes in the Structure Assembly Influences Their Interaction with TLR4, RAGE, and CD36

S100A8 and S100A9 belong to the calcium-binding, damage associated molecular pattern (DAMP) proteins shown to aggravate the pathogenesis of rheumatoid arthritis (RA) through their interaction with the TLR4, RAGE and CD36 receptors. S100A8 and S100A9 proteins tend to exist in monomeric, homo and heterodimeric forms, which have been implicated in the pathogenesis of RA, via interacting with Pattern Recognition receptors (PRRs). The study aims to assess the influence of changes in the structure and biological assembly of S100A8 and S100A9 proteins as well as their interaction with significant receptors in RA through computational methods and surface plasmon resonance (SPR) analysis. Molecular docking analysis revealed that the S100A9 homodimer and S100A8/A9 heterodimer showed higher binding affinity towards the target receptors. Most S100 proteins showed good binding affinity towards TLR4 compared to other receptors. Based on the 50 ns MD simulations, TLR4, RAGE, and CD36 formed stable complexes with the monomeric and dimeric forms of S100A8 and S100A9 proteins. However, SPR analysis showed that the S100A8/A9 heterodimers formed stable complexes and exhibited high binding affinity towards the receptors. SPR data also indicated that TLR4 and its interactions with S100A8/A9 proteins may play a primary role in the pathogenesis of RA, with additional contributions from CD36 and RAGE interactions. Subsequent in vitro and in vivo investigations are warranted to corroborate the involvement of S100A8/A9 and the expression of TLR4, RAGE, and CD36 in the pathophysiology of RA.

Generation and Application of Monoclonal Antibodies against Porcine S100A8, S100A9, and S100A12 Proteins Using Hybridoma Technology

S100A8, S100A9, and S100A12 proteins are important members of the S100 protein family, act primarily as congenital immunomodulators, and are closely related to the occurrence of infectious diseases. There have been few reports on the functional properties of S100A8, S100A9, and S100A12 proteins in swine, but it is certain that porcine S100A8, S100A9, and S100A12 proteins are highly expressed in diseased swine. To address the current lack of reliable and timely detection tools for these three proteins, we generated monoclonal antibodies specific to the porcine S100A8, S100A9, and S100A12 proteins using hybridoma technology. The results of serum sample testing showed that the above monoclonal antibodies specifically recognize the proteins S100A8, S100A9, and S100A12 in the serum and were able to evaluate the content change of these proteins during the infection process. This provides the basis for the use of porcine S100A8, S100A9, and S100A12 in the surveillance and diagnosis of swine diseases and laid a foundation for further understanding their roles in infection, immunity, and inflammation, as well as their potential applications in preventing or treating gastrointestinal tract or inflammatory diseases in swine.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.