mProX™ Human GPR31 Stable Cell Line

Rubino M, et al. "Inhibition of Eicosanoid Degradation Mitigates Fibrosis of the Heart.." Circulation research, 2023.
Organ fibrosis, caused by an increase in extracellular matrix production by resident fibroblasts, is a major contributor to over 45% of deaths in the Western world, particularly related to heart failure. Through high-content imaging, a screening method was used to identify compounds that could suppress fibroblast activation in multiple organ systems. One such compound, SW033291, was further studied and shown to inhibit expression of activation markers, reduce cell contractility, and reverse constitutive activation of fibroblasts from patients with heart failure. Additionally, SW033291 was found to block cardiac fibrosis and improve diastolic function in mouse models. These effects were linked to the compound's ability to stimulate extracellular signal-regulated kinase 1/2 mitogen-activated protein kinase signaling, triggered by inhibition of the eicosanoid-degrading enzyme, 15-hydroxyprostaglandin dehydrogenase. Furthermore, the compound's ability to mimic the effects of the G protein-coupled receptor, GPR31, suggests that targeting eicosanoid degradation may be a potential therapeutic approach for treating pathological organ fibrosis, specifically in the heart.
Pubmed: 36475698   DOI: 10.1161/CIRCRESAHA.122.321475

Guo J, et al. "Typing characteristics of metabolism-related genes in osteoporosis.." Frontiers in pharmacology, 2022.
Osteoporosis is a prevalent musculoskeletal disorder that poses a significant burden on global healthcare systems. However, the mechanisms underlying the diverse metabolic etiology of osteoporosis remain largely unexplored and there is a lack of research investigating metabolic phenotypes associated with osteoporosis. Therefore, the aim of this study was to identify and characterize different osteoporosis metabolic subtypes and associated genes by utilizing machine learning techniques. Gene expression profiles were obtained from the GEO database and unsupervised clustering analysis and multi-omics enrichment were used to identify distinct metabolic gene subtypes and corresponding characteristic genes. The results were validated with external datasets and the inferred immune and stromal cell types of the signature genes were determined. Three distinct metabolic subtypes, namely lipid and steroid metabolism, glycolysis, and polysaccharide metabolism, were identified in osteoporosis patients. Additionally, 10 potential metabolic genes (GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9) were highlighted by the machine learning SVM method. This study provides valuable insights into the metabolic-related pathogenesis of osteoporosis and can serve as a potential avenue for further research on the disorder.
Pubmed: 36188607   DOI: 10.3389/fphar.2022.999157