RACK1 promotes porcine reproductive and respiratory syndrome virus infection in Marc-145 cells through ERK1/2 activation.

Porcine reproductive and respiratory syndrome (PRRS) is an acute infectious disease that spreads rapidly among pigs and seriously threatens the pig industry. Activation of ERK1/2 is a hallmark of most viral infections. RACK1 interacts with a variety of kinases and membrane receptors that closely associated with viral infections and the development and progression of cancer. However, no studies have clearly defined whether RACK1 can regulate PRRSV infection through ERK1/2 activation. In our study, using RT-qPCR, immunoblotting, indirect fluorescent staining, siRNA knockdown and protein overexpression techniques, we found that downregulation of cellular RACK1 inhibited ERK1/2 activation and subsequently suppressed PRRSV infection, while overexpression of RACK1 enhanced ERK1/2 activation and PRRSV infection. Bioinformatic and Co-immunoprecipitation experimental analysis revealed that cellular RACK1 could interact with viral N protein to exert its function. We elaborated that RACK1 promoted PRRSV replication in Marc-145 cells through ERK1/2 activation. Our study provides new insights into regulating the innate antiviral immune responses during PRRSV infection and contributes to further understanding of the molecular mechanisms underlying PRRSV replication.

Genetic Algorithm in Multimedia Dynamic Prediction of Groundwater in Open-Pit Mine.

This study is aiming at the nonlinear mapping relationship between the groundwater level and its influencing factors. Through the design and calculation process of matlab7 platform, taking the monitoring wells distributed in an open-pit mining area as an example, the short-term prediction of groundwater dynamics in the study area is carried out by using BP neural network model and BP neural network model based on genetic algorithm. Root mean squared error (RMSE), Mean absolute percent-age error (MAPE) and Nash-Sutcliffe efficiency (NSE) are used coefficients,, and the results were compared with BP neural network and stepwise regression model. From the results of the comparative analysis, the genetic algorithm optimized the BP neural network model in the training phase and the test phase, the RMSE was 0.25 and 0.36, the MAPE was 6.7 and 8.13%, and the NSE was 0.87 and 0.72, respectively. The BP neural network model optimized by genetic algorithm is obviously superior to the BP neural network model, which is an ideal prediction model for short-term groundwater level. This model can provide a prediction method for groundwater dynamic prediction and has a good application prospect.