Effect of the defoliant tribufos on the reproductive ability of Japanese quail (Coturnix japonica).

As a cotton defoliator, tribufos (S,S,S-tributyl phosphorotrithioate) is widespread in the environment. It can cause neurotoxicity in chickens, reproductive toxicity in rats, and can also cause headaches and nausea in humans. However, little is known about its impact on the reproduction of birds. Here, by analyzing the differences in reproductive indexs and histopathological characteristics, we investigated the chronic effects of 32 mg a.i./kg, 160 mg a.i./kg and 800 mg a.i./kg tribufos treatment on the reproductive ability of Japanese quail (Coturnix japonica). The results indicated that 32 mg a.i./kg and 160 mg a.i./kg tribufos treatment significantly reduced the food intake of quails, significantly increased the broken egg rate, and had adverse effects on gonads and liver tissue. The 160 mg a.i./kg tribufos treatment also significantly reduced the average egg production. Moreover, 800 mg a.i./kg treatment had significant negative effects on feed intake (FI), body weight (BW), eggshell thickness, egg production (EP), fertilization rate, hatchability and progeny 14-d survival rate, and it also significantly increased the broken egg rate. In addition, tribufos exposure caused lesions in quail gonads and liver tissue. Overall, our results revealed that tribufos had adverse effects on the reproductive ability of Japanese quail, especially at high concentrations.

Co-Culture of White Rot Fungi Pleurotus ostreatus P5 and Bacillus amyloliquefaciens B2: A Strategy to Enhance Lipopeptide Production and Suppress of Fusarium Wilt of Cucumber.

Fusarium wilt, caused by Fusarium oxysporum f. sp. cucumerinum (FOC), poses a serious threat to cucumber productivity. Compared to traditional chemical pesticides, biological control strategies have attracted more attention recently owing to their effectiveness against pathogens and their environmental safety. This study investigated the effect of white rot fungi Pleurotus ostreatus P5 on the production of cyclic lipopeptides (CLPs) of Bacillus amyloliquefaciens B2 and the potential co-culture filtrate of strains B2 and P5 to control cucumber Fusarium wilt. A PCR amplification of CLP genes revealed that B. amyloliquefaciens B2 had two antibiotic biosynthesis genes, namely, ituA and srf, which are involved in iturin A and surfactin synthesis. Liquid chromatography with tandem mass spectrometry (LC-MS/MS) revealed that CLPs derived from strain B2 contained two families, iturin A (C14, C15) and surfactin (C12-C17). The co-culture exhibited an enhanced accumulation of iturin A and surfactin compared to the monoculture of strain B2. Furthermore, the gene expressions of ituA and srf were both significantly upregulated when co-cultured with the fungus compared to monocultures. In an in vitro experiment, the co-culture filtrate and monoculture filtrate of B. amyloliquefaciens B2 inhibited mycelial growth by 48.2% and 33.2%, respectively. In a greenhouse experiment, the co-culture filtrate was superior to the monoculture filtrate in controlling cucumber Fusarium wilt disease and in the promotion of plant growth. Co-culture filtrate treatment significantly enhanced the microbial metabolic activity and decreased the abundance of FOC in the rhizosphere soil. These results show that the co-culture of P. ostreatus P5 and B. amyloliquefaciens B2 has great potential in cucumber Fusarium wilt disease prevention by enhancing the production of bacterial CLPs.

Study on QSTR of benzoic acid compounds with MCI.

Quantitative structure-toxicity relationship (QSTR) plays an important role in toxicity prediction. With the modified method, the quantum chemistry parameters of 57 benzoic acid compounds were calculated with modified molecular connectivity index (MCI) using Visual Basic Program Software, and the QSTR of benzoic acid compounds in mice via oral LD(50) (acute toxicity) was studied. A model was built to more accurately predict the toxicity of benzoic acid compounds in mice via oral LD(50): 39 benzoic acid compounds were used as a training dataset for building the regression model and 18 others as a forecasting dataset to test the prediction ability of the model using SAS 9.0 Program Software. The model is LogLD(50) = 1.2399 x (0)J(A) +2.6911 x (1)J(A) - 0.4445 x J(B) (R(2) = 0.9860), where (0)J(A) is zero order connectivity index, (1)J(A) is the first order connectivity index and J(B) = (0)J(A) x (1)J(A) is the cross factor. The model was shown to have a good forecasting ability.