Expression and Enzyme Activity of Catalase in Chilo suppressalis (Lepidoptera: Crambidae) Is Responsive to Environmental Stresses.

Catalase (CAT) is an important antioxidant enzyme that protects organisms against oxidative stresses by eliminating hydrogen peroxide. In this study, we cloned and characterized a full-length cDNA of CAT from Chilo suppressalis (CsCAT) and examined the influence of environmental stresses on CsCAT expression and enzyme activity. The cDNA contains a 1659-bp open reading frame encoding a polypeptide of 553 amino acids most closely related (90.14%) to Papilio polytes catalases. The CsCAT was expressed in all developmental stages with the highest expression in the fat body, and the CsCAT enzyme activity closely mirrored its observed mRNA expression patterns. The CsCAT mRNA was up-regulated when the larvae were exposed to high temperature (≥30 °C), insecticides (abamectin and chlorantraniliprole), chemicals (H2O2, CHP, CdCl2, and CuSO4), and a dead-end trap plant (vetiver grass), and the CsCAT enzyme activity again mirrored the observed CsCAT expression patterns. These results suggest that up-regulation of CsCAT may enhance the defense response of C. suppressalis by weakening the effects of environmental stresses, and provide insight into the role of CsCAT during development of C. suppressalis.

Prognostic Value of SETD2 Expression in Patients with Metastatic Renal Cell Carcinoma Treated with Tyrosine Kinase Inhibitors.

PURPOSE: Mutations of SETD2 occur in 3% to 16% of clear cell renal cell carcinoma cases. Previous studies identified an association between SETD2 mutation and prognosis of patients with nonmetastatic clear cell renal cell carcinoma. In this study we explored the prognostic and predictive value of SETD2 expression in patients with metastatic renal cell carcinoma treated with targeted therapy. MATERIALS AND METHODS: We retrospectively enrolled 138 patients with metastatic renal cell carcinoma treated with sunitinib or sorafenib at a single institution from 2007 to 2014. SETD2 expression was assessed by immunohistochemistry on tissue microarrays. RESULTS: After excluding those patients with loss of followup or unavailable tissue samples, 111 were included in the study. Low SETD2 expression was associated with reduced overall survival (p <0.001) and progression-free survival (p=0.001). After adjustment for histological type, Heng risk group and drugs used for targeted therapy, SETD2 was defined as an independent prognostic marker for overall survival (HR 2.535 [95% CI 1.429-4.497], p=0.001) and progression-free survival (HR 1.755 [95% CI 1.031-2.988], p=0.038). Its prognostic value for overall survival was more predominant in patients with clear cell renal cell carcinoma (p <0.001) or patients in the intermediate risk group of Heng risk criteria (p <0.001), while its predictive value for progression-free survival was more predominant in patients treated with sorafenib (p <0.001). SETD2 could also be combined with the Heng risk model for better overall survival prediction. CONCLUSIONS: SETD2 is a potential prognostic biomarker for overall survival and progression-free survival prediction in patients with metastatic renal cell carcinoma receiving targeted therapy. However, it remains to be seen whether this is generalizable to other ethnicities and prospective external validation is required.

[Nondestructive identification of different oil content maize kernels by near-infrared spectra].

Using 220 maize single kernels, containing 75 common maize single kernels, 72 high-oil maize single kernels and 73 super high-oil maize single kernels as study materials, BPANN identification model was set up for maize single kernel with different oil content based on principal components of near infrared (NIR) spectra. Four fifths of the samples were randomly selected as training set and the other samples as prediction set. Fourteen principal components from the second to the fifteenth were selected as nets input and -1, 0, 1 as nets output. Ten models were set up like this and the accurate identification rate of all the training sets can reach 100%. For prediction sets, fifteen common corn grain samples had an average accurate identification rate of 99.33%, fourteen high-oil corn grain samples had an average accurate identification rate of 97.88%, fourteen super high-oil corn grain samples had an average accurate identification rate of 91.43%, and total maize grains in prediction set had an average accurate identification rate of over 95%. Results showed that NIR spectroscopy combined with BP-ANN technology could identify maize kernels fast and nondestructively according to oil content, which offered a very useful classification method for maize seed breeding. The effect of different principal component on BPANN models was also studied. Results told us that the first principal component with over 99% of variance contribution had negative effect on the identification model. The predictive ability of identification models set up by different principal component was discriminatory, although the learning accurate identification rates were all 100%. So it is necessary to choose correlative principal component to set up identification model.