TP53 and p21 (CDKN1A) polymorphisms and the risk of systemic lupus erythematosus

Mesenchymal stem cells in autoimmune disease: A systematic review and meta-analysis of pre-clinical studies

Mesenchymal Stem Cells (MSCs) are of interest in the clinic because of their immunomodulation capabilities, capacity to act upstream of inflammation, and ability to sense metabolic environments. In standard physiologic conditions, they play a role in maintaining the homeostasis of tissues and organs; however, there is evidence that they can contribute to some autoimmune diseases. Gaining a deeper understanding of the factors that transition MSCs from their physiological function to a pathological role in their native environment, and elucidating mechanisms that reduce their therapeutic relevance in regenerative medicine, is essential. We conducted a Systematic Review and Meta-Analysis of human MSCs in preclinical studies of autoimmune disease, evaluating 60 studies that included 845 patient samples and 571 control samples. MSCs from any tissue source were included, and the study was limited to four autoimmune diseases: multiple sclerosis, rheumatoid arthritis, systemic sclerosis, and lupus. We developed a novel Risk of Bias tool to determine study quality for in vitro studies. Using the International Society for Cell & Gene Therapy's criteria to define an MSC, most studies reported no difference in morphology, adhesion, cell surface markers, or differentiation into bone, fat, or cartilage when comparing control and autoimmune MSCs. However, there were reported differences in proliferation. Additionally, 308 biomolecules were differentially expressed, and the abilities to migrate, invade, and form capillaries were decreased. The findings from this study could help to explain the pathogenic mechanisms of autoimmune disease and potentially lead to improved MSC-based therapeutic applications.

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.