高产吩嗪-1-羧酸（PCA）假单胞菌株M18G发酵优化模型的构建

吩嗪-1-羧酸（phenazine-1-carboxylic acid, PCA）是促进根际生长假单胞菌分泌的重要抗菌物质。采用Plackett-Burman（PB）设计和响应面法（response surface method, RSM）对假单胞菌株M18（Pseudomonas sp. M18）的gacA基因突变株M18G的次级代谢产物PCA发酵的营养条件进行建模。运用Plackett-Burman（PB）设计试验，从12个营养成分中筛选出4个关键的组分，进而采用RSM 法对这4个因素进行中心组合设计试验，建立回归方程并进行统计学分析，绘制各营养因子之间的关系图。实验结果表明：建立的模型能合理地模拟并优化发酵中各参数及其浓度，确定发酵培养基的成分和浓度为：黄豆粉33.4g/L，葡萄糖12.7g/L，大豆蛋白胨10.9g/L和乙醇13.8 g/L, M18G菌株经60 h发酵培养，最高PCA产率能达到1.89 g/L，比优化前提高了6倍左右。各营养因子的等值线图表明黄豆粉和乙醇在PCA高产发酵中起到更为关键的作用，因此提供了提高PCA发酵产量的有效方法，并为其未来商业化应用奠定了基础。

Optimal Model Design for High Production of Phenazine-1-Caboxylic Acid (PCA) by Pseudomas.spp M18G

Model on secondary metabolite phenazine-1-carboxylic acid (PCA) fermentation nutrition conditions of gacA inactivate mutant Pseudomonas sp. M18G was constructed by Plackett-Burman design (PB) and Response Surface Method (RSM). In PB design four key components selected from 12 different factors have shown to play an important role for promoting PCA production. Center Composite Design (CCD) was adopted to establish a fermentation model for the four nutrition components using RSM. The optimal concentration of the four components based on analysis of regression equation were determined that soybean meal 33.4g/L , glucose 12.7g/L, soy peptone10.9g/L, ethanol13.8g/L, and the highest PCA production could reached 1.89g/L after 60h fermentation and the yield increased to 6 fold over that before optimization. The contour graphs depicted interactions of the two nutrition components showed that soybean meal and ethanol played an even more crucial role for the highest production of PCA in fermentation.