Anti-MAPK1 (C-terminal) polyclonal antibody

Cells experience mechanical stresses in different physiological and pathological settings. Clathrin-coated structures (CCSs) are sensitive to such perturbations in a way that often results in a mechanical impairment of endocytic budding. Compressive stress is a mechanical perturbation that leads to increased membrane tension and promotes proliferative signals. Here, we report that compression leads to frustration of CCSs and that CCSs are required to potentiate receptor-mediated signaling in these conditions. We show that cell compression stalled CCS dynamics and slowed down the dynamic exchange of CCS components. As previously reported, compression-induced paracrine activation of the epidermal growth factor receptor (EGFR) was the primary cause of ERK (ERK1 and ERK2, also known as MAPK3 and MAPK1, respectively) activation in these conditions. We observed that EGFR was efficiently recruited at CCSs upon compression and that CCSs were required for full ERK activation. In addition, we demonstrated that compression-induced frustrated CCSs could also increase ligand-dependent signaling of other receptors. We thus propose that CCS frustration resulting from mechanical perturbations can potentiate signaling through different receptors, with potential important consequences for the adaptation of the cell to its environment. This article has an associated First Person interview with the first author of the paper.

Background Gastric cancer (GC) is the fifth most common cancer type worldwide. The aim of this study was to identify gastric-related therapeutic indicators on the basis of the ego network analysis. Material and methods The microarray data related to GC was downloaded from ArrayExpress database. All human protein-protein interaction (PPI) networks were downloaded from the STRING database. Ego genes were identified on the basis of PPI networks and the gene expression in GC, and then co-expression networks (ego networks) were constructed using these ego genes. On the basis of ego networks, the optimal GO terms and genes were predicted by affinity predictions and cold read predictions. Finally, the predicted genes as effective biomarkers for GC were verified by the bioinformatics analysis. Results The differential expression networks were conducted and comprised of 365 edges and 232 nodes, which resulted in 218 ego genes. Although there was no significant difference in the expression of top ten ego genes among different groups of GC samples, it was eventually confirmed that top three optimal GO terms with highest cool read values were translational termination (cool read value = 0.987), translational elongation (cool read value = 0.986), and macromolecular complex disassembly (cool read value = 0.985) and top five optimal genes were UBA52, RPS27A, MAPK1, UBC, and UBB. UBA52, RPS27A, and MAPK1 were verified by the bioinformatics analysis to be related to the progression and metastasis of GC. Conclusions An ego network analysis approach is a very effective method for screening GC and the screened genes might be biomarkers for GC diagnosis and treatment.