Relationship between the cholesterol ester transfer protein TaqIB polymorphism and the lipid-lowering effect of atorvastatin in patients with coronary atherosclerotic heart disease.

This study aimed to investigate the relationship between the cholesterol ester transfer protein (CETP) gene TaqIB polymorphism and the lipid-lowering effect of atorvastatin in patients with coronary atherosclerotic heart disease. Two hundred eighty-eight patients were divided into a control group, an acute coronary syndrome (ACS) group, and a stable coronary heart disease (CHD) group. Blood biochemical indices were determined using the enzyme method, and polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) analysis was performed to study the TaqIB polymorphism of the CETP gene. The ACS and stable CHD groups were treated with atorvastatin, and blood lipid levels were reexamined after three months. Plasma levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and lipoprotein(a) were all significantly higher in the ACS and stable CHD groups compared to the control group (P < 0.05 or P < 0.01). After three months of treatment with atorvastatin, plasma levels of TC, LDL-C, triglycerides (TG) (only in patients with genotype B2B2), and lipoprotein(a) (only in patients with genotype B1B2) were all significantly decreased (P < 0.05 or P < 0.01). After treatment, the plasma level of TG was lower in patients with genotype B2B2 compared to patients with genotypes B1B1 or B1B2 (B1 carriers) (P < 0.01). Therefore, the CETP TaqIB polymorphism is associated with the lipid-lowering effect of atorvastatin in patients with CHD.

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions.

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

Tetramethylpyrazine production from glucose by a newly isolated Bacillus mutant.

2,3,5,6-Tetramethylpyrazine (TTMP) was produced using a newly isolated Bacillus mutant. Culture medium optimization studies showed that soytone, an enzyme-hydrolysate of soybean meal, with the supplementation of vitamins, can fully replace yeast extract plus peptone in supporting TTMP production from glucose. In a 5-l fermenter, using the optimized medium which contained 20% glucose, 5% soytone, 3% (NH(4))(2)HPO(4), and vitamin supplements, fermentations were carried out with stirring at 700 rpm, air flow at 1.0 vvm, controlled pH at 7.0, and temperature at 37 degrees C. TTMP reached 4.33 g l(-1) after 64.6 h cultivation. A product recovery method was described, which involved evaporation, crystallization, and lyophilization. The product purity was 99.88%, determined by GC with the normalization method. The main impurities were 2,3,5-trimethylpyrazine (0.09%) and 2-ethyl-3,5,6-trimethylpyrazine (0.02%), which were identified by GC/MS. (13)C NMR determination also gave a consistent result. Natural and high purity of the product and the utilization of cheap green renewable materials make this process promising to compete with TTMP chemical synthetic methods.