Competing Roles of Ca2+ and Nonmuscle Myosin IIA on the Dynamics of the Metastasis-Associated Protein S100A4

Secreted S100A4 causes asthmatic airway epithelial barrier dysfunction induced by house dust mite extracts via activating VEGFA/VEGFR2 pathway

The airway epithelial barrier dysfunction plays a crucial role in pathogenesis of asthma and causes the amplification of downstream inflammatory signal pathway. S100 calcium binding protein A4 (S100A4), which promotes metastasis, have recently been discovered as an effective inflammatory factor and elevated in bronchoalveolar lavage fluid in asthmatic mice. Vascular endothelial growth factor-A (VEGFA), is considered as vital regulator in vascular physiological activities. Here, we explored the probably function of S100A4 and VEGFA in asthma model dealt with house dust mite (HDM) extracts. Our results showed that secreted S100A4 caused epithelial barrier dysfunction, airway inflammation and the release of T-helper 2 cytokines through the activation of VEGFA/VEGFR2 signaling pathway, which could be partial reversed by S100A4 polyclonal antibody, niclosamide and S100A4 knockdown, representing a potential therapeutic target for airway epithelial barrier dysfunction in asthma.

Extracting Dynamical Correlations and Identifying Key Residues for Allosteric Communication in Proteins by correlationplus

Extracting dynamical pairwise correlations and identifying key residues from large molecular dynamics trajectories or normal-mode analysis of coarse-grained models are important for explaining various processes like ligand binding, mutational effects, and long-distance interactions. Efficient and flexible tools to perform this task can provide new insights about residues involved in allosteric regulation and protein function. In addition, combining and comparing dynamical coupling information with sequence coevolution data can help to understand better protein function. To this aim, we developed a Python package called correlationplus to calculate, visualize, and analyze pairwise correlations. In this way, the package aids to identify key residues and interactions in proteins. The source code of correlationplus is available under LGPL version 3 at https://github.com/tekpinar/correlationplus. The current version of the package (0.2.0) can be installed with common installation methods like conda or pip in addition to source code installation. Moreover, docker images are also available for usage of the code without installation.