An IgD-Fc-Ig fusion protein restrains the activation of T and B cells by inhibiting IgD-IgDR-Lck signaling in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by synovitis and the destruction of small joints. Emerging evidence shows that immunoglobulin D (IgD) stimulation induces T-cell activation, which may contribute to diseases pathogenesis in RA. In this study, we investigated the downstream signaling pathways by which IgD activated T cells as well as the possible role of IgD in the T-B interaction. Peripheral blood mononuclear cells were isolated from peripheral blood of healthy controls and RA patients. We demonstrated that IgD activated T cells through IgD receptor (IgDR)-lymphocyte-specific protein tyrosine kinase (Lck)-zeta-associated protein 70 (ZAP70)/phosphatidylinositol 3-kinase (PI3K)/nuclear factor kappa-B (NF-κB) signaling pathways; IgD-induced CD4+ T cells promoted the proliferation of CD19+ B cells in RA patients. A novel fusion protein IgD-Fc-Ig (composed of human IgD-Fc domain and IgG1 Fc domain, which specifically blocked the IgD-IgDR binding) inhibited the coexpression of IgDR and phosphorylated Lck (p-Lck) and the expression levels of p-Lck, p-ZAP70, p-PI3K on CD4+ T cells, and decreased NF-κB nuclear translocation in Jurkat cells. Meanwhile, IgD-Fc-Ig downregulated the expression levels of CD40L on CD4+ T cells as well as CD40, CD86 on CD19+ B cells in RA patients and healthy controls. It also decreased the expression levels of CD40L on CD4+ T cells and CD40 on CD19+ B cells from spleens of collagen-induced arthritis (CIA) mice and reduced IL-17A level in mouse serum. Moreover, administration of IgD-Fc-Ig (1.625-13 mg/kg, iv, twice a week for 4 weeks) in CIA mice dose-dependently decreased the protein expression levels of CD40, CD40L, and IgD in spleens. IgD-Fc-Ig restrains T-cell activation through inhibiting IgD-IgDR-Lck-ZAP70-PI3K-NF-κB signaling, thus inhibiting B-cell activation. Our data provide experimental evidences for application of IgD-Fc-Ig as a highly selective T cell-targeting treatment for RA.

Prediction of RNA-protein interactions by combining deep convolutional neural network with feature selection ensemble method.

RNA-protein interaction (RPI) plays an important role in the basic cellular processes of organisms. Unfortunately, due to time and cost constraints, it is difficult for biological experiments to determine the relationship between RNA and protein to a large extent. So there is an urgent need for reliable computational methods to quickly and accurately predict RNA-protein interaction. In this study, we propose a novel computational method RPIFSE (predicting RPI with Feature Selection Ensemble method) based on RNA and protein sequence information to predict RPI. Firstly, RPIFSE disturbs the features extracted by the convolution neural network (CNN) and generates multiple data sets according to the weight of the feature, and then use extreme learning machine (ELM) classifier to classify these data sets. Finally, the results of each classifier are combined, and the highest score is chosen as the final prediction result by weighting voting method. In 5-fold cross-validation experiments, RPIFSE achieved 91.87%, 89.74%, 97.76% and 98.98% accuracy on RPI369, RPI2241, RPI488 and RPI1807 data sets, respectively. To further evaluate the performance of RPIFSE, we compare it with the state-of-the-art support vector machine (SVM) classifier and other exiting methods on those data sets. Furthermore, we also predicted the RPI on the independent data set NPInter2.0 and drew the network graph based on the prediction results. These promising comparison results demonstrated the effectiveness of RPIFSE and indicated that RPIFSE could be a useful tool for predicting RPI.