Characterization of protein-protein interactions between the nucleocapsid protein and membrane protein of the avian infectious bronchitis virus

Avian infectious bronchitis virus (IBV) is one of the major viral respiratory diseases of chickens. Better understanding of the molecular mechanism of viral pathogenesis may contribute significantly to the development of prophylactic, therapeutic and diagnostic reagents as well as help in infection control. Avian IBV belongs to the Coronaviridaes and is similar to the other known coronaviruses. Previous studies have indicated that protein–protein interactions between nucleocapsid (N) and the membrane (M) proteins in coronavirus are related to coronavirus viral assembly. However, cases of IBV are seldom reported. In this study, yeast two-hybrid and  co-immunoprecipitation techniques were applied to investigate possible interactions between IBV N and M proteins. We found that interaction of the N and M proteins took place in vivo and the residues 168 – 225 of the M protein and the residues 150 - 210 of the N protein were determined to be involved in their interaction. These results may provide some useful information on the molecular mechanism of IBV’s N and M proteins, which will facilitate therapeutic strategies aiming at the disruption of the association between membrane and nucleocapsid proteins and indicate a new drug target for IBV.Key words: Co-immunoprecipitation, membrane protein, nucleocapsid protein, protein-protein interaction, yeast two-hybrid

Effect of Aspect Ratio on Field Emission Properties of ZnO Nanorod Arrays

ZnO nanorod arrays are prepared on a silicon wafer through a multi-step hydrothermal process. The aspect ratios and densities of the ZnO nanorod arrays are controlled by adjusting the reaction times and concentrations of solution. The investigation of field emission properties of ZnO nanorod arrays revealed a strong dependency on the aspect ratio and their density. The aspect ratio and spacing of ZnO nanorod arrays are 39 and 167 nm (sample C), respectively, to exhibit the best field emission properties. The turn-on field and threshold field of the nanorod arrays are 3.83 V/μm and 5.65 V/μm, respectively. Importantly, the sample C shows a highest enhancement of factorβ, which is 2612. The result shows that an optimum density and aspect ratio of ZnO nanorod arrays have high efficiency of field emission

Salmonella Type III Effector AvrA Stabilizes Cell Tight Junctions to Inhibit Inflammation in Intestinal Epithelial Cells

Salmonella Typhimurium is a major cause of human gastroenteritis. The Salmonella type III secretory system secretes virulence proteins, called effectors. Effectors are responsible for the alteration of tight junction (TJ) structure and function in intestinal epithelial cells. AvrA is a newly described bacterial effector found in Salmonella. We report here that AvrA expression stabilizes cell permeability and tight junctions in intestinal epithelial cells. Cells colonized with an AvrA-deficient bacterial strain (AvrA−) displayed decreased cell permeability, disruption of TJs, and an increased inflammatory response. Western blot data showed that TJ proteins, such as ZO-1, claudin-1, decreased after AvrA- colonization for only 1 hour. In contrast, cells colonized with AvrA-sufficient bacteria maintained cell permeability with stabilized TJ structure. This difference was confirmed in vivo. Fluorescent tracer studies showed increased fluorescence in the blood of mice infected with AvrA- compared to those infected with the AvrA-sufficient strains. AvrA- disrupted TJ structure and function and increased inflammation in vivo, compared to the AvrA- sufficient strain. Additionally, AvrA overexpression increased TJ protein expression when transfected into colonic epithelial cells. An intriguing aspect of this study is that AvrA stabilized TJs, even though the other TTSS proteins, SopB, SopE, and SopE2, are known to disrupt TJs. AvrA may play a role in stabilizing TJs and balancing the opposing action of other bacterial effectors. Our findings indicate an important role for the bacterial effector AvrA in regulation of intestinal epithelial cell TJs during inflammation. The role of AvrA represents a highly refined bacterial strategy that helps the bacteria survive in the host and dampen the inflammatory response

Soil Moisture Inversion Based on Data Augmentation Method Using Multi-Source Remote Sensing Data

Soil moisture is an important land environment characteristic that connects agriculture, ecology, and hydrology. Surface soil moisture (SSM) prediction can be used to plan irrigation, monitor water quality, manage water resources, and estimate agricultural production. Multi-source remote sensing is a crucial tool for assessing SSM in agricultural areas. The field-measured SSM sample data are required in model building and accuracy assessment of SSM inversion using remote sensing data. When the SSM samples are insufficient, the SSM inversion accuracy is severely affected. An SSM inversion method suitable for a small sample size was proposed. The alpha approximation method was employed to expand the measured SSM samples to offer more training data for SSM inversion models. Then, feature parameters were extracted from Sentinel-1 microwave and Sentinel-2 optical remote sensing data, and optimized using three methods, which were Pearson correlation analysis, random forest (RF), and principal component analysis. Then, three common machine learning models suitable for small sample training, which were RF, support vector regression, and genetic algorithm-back propagation neural network, were built to retrieve SSM. Comparison experiments were carried out between various feature optimization methods and machine learning models. The experimental results showed that after sample augmentation, SSM inversion accuracy was enhanced, and the combination of utilizing RF for feature screening and RF for SSM inversion had a higher accuracy, with a coefficient of determination of 0.7256, a root mean square error of 0.0539 cm3/cm3, and a mean absolute error of 0.0422 cm3/cm3, respectively. The proposed method was finally used to invert the regional SSM of the study area. The inversion results indicated that the proposed method had good performance in regional applications with a small sample size