This study was aimed to identify pET21b-HPV16E2/BL21(DE3) strain and to optimize the expression of human papillomavirus type 16 (HPV16) E2 protein by orthogonal analysis. Four influence factors on two levels were selected to increase the target protein quantity. The four factors were induction time, induction temperature, inductor concentration and cell density. The quantity of HPV16 E2 protein was used as the evaluation parameter. Induced by IPTG, HPV16 E2 protein was analyzed by SDS-PAGE and Western Blot. Target protein was analyzed by GIS imaging system to quantify the protein level. SPSS13. 0 software was applied to analyze the result. Data showed that the expression strain pET211rHPV16 E2/BL21(DE3) was identified correctly. HPV16 E2 protein expressed mainly at insoluble form. The 42KD protein band was identified by SDS-PAGE and Western blot. Orthogonal test was applied on influence factor analysis and expression optimization successfully. Main influence factors were inductor concentration and induction temperature. The optimimum condition of maximum expression quantity was 37 degrees C, 7h, 1.0 mmol/L IPTG and OD600 1.0. In this experiment, orthogonal test could not only be used to analyze the influential factors and promote the target protein expression, but also be used to provide a better experiment method for molecular biological study.
DNA-Binding Proteins ; biosynthesis ; genetics ; Genetic Vectors ; genetics ; Human papillomavirus 16 ; metabolism ; Humans ; Oncogene Proteins, Viral ; biosynthesis ; genetics ; Papillomavirus Infections ; virology ; Recombinant Proteins ; biosynthesis ; genetics

ObjectiveTo clarify the spatial distribution characteristics of medicinal plant resources in Gansu province， analyze the causes， changing trends， and driving factors of the spatial differentiation， and thus lay a scientific basis for the rational development and sustainable development of medicinal plant resources in this province. MethodBased on the data of The Fourth National Survey of Chinese Medicine Resources， the richness and spatial distribution difference of medicinal plant resources in 87 counties （districts） of Gansu province were analyzed via the global spatial autocorrelation analysis， trend surface analysis， local spatial autocorrelation analysis， and hotspot analysis. Moreover， the correlation of vegetation type， soil texture， annual average temperature， annual average precipitation， and altitude with the spatial distribution pattern of the medicinal plant resources was discussed. ResultCounties （districts） with high or low richness of medicinal plant resources in Gansu province were respectively clustered together. To be specific， counties （districts） with high richness of the medicinal resources were mainly in southeastern Gansu， while those with low richness in northwestern Gansu. The leading driving factors affecting the cold and hot spots included vegetation type， soil texture， and average annual rainfall. ConclusionThe species richness of medicinal plant resources in Gansu province rises from west to east and from north to south. The natural driving factors are the key to the diversity and spatial distribution pattern of medicinal plant resources， which show significant influence on them.