ABSTRACT
Chronic asthma is characterized by airway hyperresponsiveness, inflammation, and remodeling. Previous studies have shown that mesenchymal stromal/stem cells (MSCs) exert anti-inflammatory effects on asthma via regulation of the immune cells. However, the therapeutic mechanism of MSCs, especially the mechanism of airway remodeling in chronic asthma, remains to be elucidated. Here, we aimed to investigate the therapeutic effect of MSCs on airway remodeling in chronic asthma and explored the mechanisms by analyzing the polarization phenotype of macrophages in the lungs. We established a mouse model of chronic asthma induced by ovalbumin (OVA) and evaluated the effect of MSCs on airway remodeling. The data showed that MSCs treatment before the challenge exerted protective effects on OVA-induced chronic asthma, i.e., decreased the inflammatory cell infiltration, Th2 cytokine levels, subepithelial extracellular matrix deposition, and transforming growth factor ß (TGF-ß)/Smad signaling. Additionally, we found that MSCs treatment markedly suppressed macrophage M2 polarization in lung tissue. At the same time, MSCs treatment inhibited NF-κB p65 nuclear translocation, ER stress, and oxidative stress in the OVA-induced chronic allergic airway remodeling mice model. In conclusion, these results demonstrated that MSCs treatment prevents OVA-induced chronic airway remodeling by suppressing macrophage M2 polarization, which may be associated with the dual inhibition of ER stress and oxidative stress. This discovery may provide a new theoretical basis for the future clinical application of MSCs.
ABSTRACT
Alzheimer's disease (AD) is a common neurodegenerative disease. The major problems that exist in the diagnosis of AD include the costly examinations and the high-invasive sampling tissue. Therefore, it would be advantageous to develop blood biomarkers. Because AD's pathological process is considered tightly related to autophagy; thus, a diagnostic model for AD based on ATGs may have more predictive accuracy than other models. We obtained GSE63060 dataset from the GEO database, ATGs from the HADb and screened 64 differentially expressed autophagy-related genes (DE-ATGs). We then applied them to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as DisGeNET and PaGenBase enrichment analyses. By using the univariate analysis, least absolute shrinkage and selection operator (LASSO) regression method and the multivariable logistic regression, nine DE-ATGs were identified as biomarkers, which are ATG16L2, BAK1, CAPN10, CASP1, RAB24, RGS19, RPS6KB1, ULK2, and WDFY3. We combined them with sex and age to establish a nomogram model. To evaluate the model's distinguishability, consistency, and clinical applicability, we applied the receiver operating characteristic (ROC) curve, C-index, calibration curve, and on the validation datasets GSE63061, GSE54536, GSE22255, and GSE151371 from GEO database. The results show that our model demonstrates good prediction performance. This AD diagnosis model may benefit both clinical work and mechanistic research.
