Expression patterns and action analysis of genes associated with hepatitis virus infection during rat liver regeneration.

AIM: To study the action of hepatitis virus infection-associated genes at transcription level during liver regeneration (LR). METHODS: Hepatitis virus infection-associated genes were obtained by collecting the data from databases and retrieving the correlated articles, and their expression changes in the regenerating rat liver were detected with the rat genome 230 2.0 array. RESULTS: Eighty-eight genes were found to be associated with liver regeneration. The number of genes initially and totally expressed during initial LR [0.5-4 h after partial hepatectomy (PH)], transition from G0 to G1 (4-6 h after PH), cell proliferation (6-66 h after PH), cell differentiation and reorganization of structure-function (66-168 h after PH) was 37, 8, 48, 3 and 37, 26, 80, 57, respectively, indicating that the genes were mainly triggered at the early stage of LR (0.5-4 h after PH), and worked at different phases. These genes were classified into 5 types according to their expression similarity, namely 37 up-regulated, 9 predominantly up-regulated, 34 down-regulated, 6 predominantly down-regulated and 2 up/down-regulated genes. Their total up- and down-regulation frequencies were 359 and 149 during LR, indicating that the expression of most genes was enhanced, while the expression of a small number of genes was attenuated during LR. According to time relevance, they were classified into 12 groups (0.5 and 1 h, 2 and 4 h, 6 h, 8 and 12 h, 16 and 96 h, 18 and 24 h, 30 and 42 h, 36 and 48 h, 54 and 60 h, 66 and 72 h, 120 and 144 h, 168 h), demonstrating that the cellular physiological and biochemical activities during LR were fluctuated. According to expression changes of the genes, their expression patterns were classified into 23 types, suggesting that the cellular physiological and biochemical activities during LR were diverse and complicated. CONCLUSION: The anti-virus infection capacity of regenerating liver can be enhanced and 88 genes play an important role in LR.

Impact of water content and temperature on the degradation of Cry1Ac protein in leaves and buds of Bt cotton in the soil.

Determining the influence of soil environmental factors on degradation of Cry1Ac protein from Bt cotton residues is vital for assessing the ecological risks of this commercialized transgenic crop. In this study, the degradation of Cry1Ac protein in leaves and in buds of Bt cotton in soil was evaluated under different soil water content and temperature settings in the laboratory. An exponential model and a shift-log model were used to fit the degradation dynamics of Cry1Ac protein and estimate the DT50 and DT90 values. The results showed that Cry1Ac protein in the leaves and buds underwent rapid degradation in the early stage (before day 48), followed by a slow decline in the later stage under different soil water content and temperature. Cry1Ac protein degraded the most rapidly in the early stage at 35°C with 70% soil water holding capacity. The DT50 values were 12.29 d and 10.17 d and the DT90 values were 41.06 d and 33.96 d in the leaves and buds, respectively. Our findings indicated that the soil temperature was a major factor influencing the degradation of Cry1Ac protein from Bt cotton residues. Additionally, the relative higher temperature (25°C and 35°C) was found to be more conducive to degradation of Cry1Ac protein in the soil and the greater water content (100%WHC) retarded the process. These findings suggested that under appropriate soil temperature and water content, Cry1Ac protein from Bt cotton residues will not persist and accumulate in soil.

Integrating remote sensing and GIS for prediction of winter wheat (Triticum aestivum) protein contents in Linfen (Shanxi), China.

In this study, relationships between normalized difference vegetation index (NDVI) and plant (winter wheat) nitrogen content (PNC) and between PNC and grain protein content (GPC) were investigated using multi-temporal moderate-resolution imaging spectroradiometer (MODIS) data at the different stages of winter wheat in Linfen (Shanxi, P. R. China). The anticipating model for GPC of winter wheat was also established by the approach of NDVI at the different stages of winter wheat. The results showed that the spectrum models of PNC passed F test. The NDVI4.14 regression effect of PNC model of irrigated winter wheat was the best, and that in dry land was NDVI4.30. The PNC of irrigated and dry land winter wheat were significantly (P<0.01) and positively correlated to GPC. Both of protein spectral anticipating model of irrigated and dry land winter wheat passed a significance test (P<0.01). Multiple anticipating models (MAM) were established by NDVI from two periods of irrigated and dry land winter wheat and PNC to link GPC anticipating model. The coefficient of determination R(2) (R) of MAM was greater than that of the other two single-factor models. The relative root mean square error (RRMSE) and relative error (RE) of MAM were lower than those of the other two single-factor models. Therefore, test effects of multiple proteins anticipating model were better than those of single-factor models. The application of multiple anticipating models for predication of protein content (PC) of irrigated and dry land winter wheat was more accurate and reliable. The regionalization analysis of GPC was performed using inverse distance weighted function of GIS, which is likely to provide the scientific basis for the reasonable winter wheat planting in Linfen city, China.