摘要
为实现乳铁蛋白N叶的大规模制备,本研究对表达乳铁蛋白N叶的工程菌枯草芽孢杆菌pMA0911-D60Y/Y92D进行了发酵工艺的优化。确定了最佳的培养条件:以葡萄糖为最佳碳源,以胰蛋白胨为最佳氮源,在pH 7.0、温度28℃、发酵25.5 h条件下诱导表达目的蛋白,目的蛋白的IOD值高达68.03%。在10L发酵罐上对重组菌株的发酵条件进行优化,获得如下的最佳发酵工艺,即采用300 r/min转速,0-7 h时,在pH 7.5、30℃条件下培养菌体;7-25 h时,在pH 7.0、28℃条件下诱导表达目的蛋白。发酵结束后,收集细胞并破碎后取上清液用HisTrapHP亲和层析及SuperdexTM200(10/300GL)亲和层析法对细胞上清液进行纯化至均一条带,获得了纯度>94%的重组乳铁蛋白N叶,1 L菌体能制备23.5 mg纯蛋白。本研究为重组牛乳铁蛋白N-叶的高效制备奠定了基础。
To achieve an efficient preparation of lactoferrin N-lobe,we optimized the fermentation process for a recombinant Bacillus subtilis pMA0911-D60Y/Y92D producing lactoferrin N-lobe.The IOD of the lactoferrin N-lobe reached 68.03%under the optimized cultural conditions,that is using glucose and tryptone as the best carbon and nitrogen source,respectively,and conduct the fermentation under pH 7.0,28°C,for 25.5 h.An optimized fermentation process was obtained through fermentation optimization on a 10 L fermenter.That is,culturing the recombinant strain at 30°C,pH 7.5 within 0-7 h,and switching to induction at 28°C,pH 7.5 within 7-25 h for production of lactoferrin N-lobe,using an agitation speed of 300 r/min throughout the fermentation.After the fermentation,the cells were collected and disrupted,followed by purification of the lactoferrin N-lobe to homogeneity by using HisTrap HP-affinity and a SuperdexTM 200(10/300 GL)-affinity chromatography.The purified lactoferrin N-lobe proteins with over 94%purity were obtained.One liter culture of recombinant B.subtilis pMA0911-D60Y/Y92D produced 23.5 mg of pure protein.This study may facilitate the fermentative production of the recombinant lactoferrin N-lobe.
作者
金亮
李利宏
张荣珍
徐岩
JIN Liang;LI Lihong;ZHANG Rongzhen;XU Yan(Laboratory of Brewing Microbiology and Applied Enzymology,School of Biotechnology,Jiangnan University,Wuxi 214122,Jiangsu,China)
出处
《生物工程学报》
CAS
CSCD
北大核心
2022年第7期2628-2638,共11页
Chinese Journal of Biotechnology
基金
国家自然科学基金(31970045)。
关键词
牛乳铁蛋白N叶
枯草芽孢杆菌
蛋白表达与纯化
发酵工艺优化
响应面分析
bovine lactoferrin N-lobe
Bacillus subtilis
protein expression and purification
fermentation process optimization
response surface analysis