Sinha Rupika, Singh Shalini, Srivastava Pradeep
School of Biochemical Engineering, Indian Institute of Technology (Banaras Hindu University), Varanasi, 221005, India.
Bioprocess Biosyst Eng. 2014 May;37(5):829-40. doi: 10.1007/s00449-013-1051-y. Epub 2013 Sep 19.
Rapamycin is a high-value product finding immense use as a drug, in organ transplantation, and as a potential immunosuppressant. Optimization of fermentation parameters of rapamycin production by Streptomyces hygroscopicus NRRL 5491 has been carried out. The low titer value of rapamycin in the original producer strain limits its applicability at industrial level. This study aims at improving the production of rapamycin by optimizing the nutrient requirements. Addition of L-lysine increased the production of rapamycin up to a significant level which supports the fact that it acts as precursor for rapamycin production, as found in previous studies. Effect of optimized medium on the Streptomyces growth rate as well as rapamycin production has been studied. The optimization study incorporates one at a time parameter optimization studies followed by tool-based hybrid methodology. This methodology includes the Plackett-Burman design (PBD) method, artificial neural networks (ANN), and genetic algorithms (GA). PBD screened mannose, soyabean meal, and L-lysine concentrations as significant factors for rapamycin production. ANN was used to construct rapamycin production model. This strategy has led to a significant increase of rapamycin production up to 320.89 mg/L at GA optimized concentrations of 25.47, 15.39, and 17.48 g/L for mannose, soyabean meal, and L-lysine, respectively. The present study must find its application in scale-up study for industrial level production of rapamycin.
雷帕霉素是一种高价值产品，在药物、器官移植以及作为潜在免疫抑制剂方面有广泛应用。已对吸水链霉菌NRRL 5491生产雷帕霉素的发酵参数进行了优化。原始生产菌株中雷帕霉素的低滴度值限制了其在工业层面的应用。本研究旨在通过优化营养需求来提高雷帕霉素的产量。添加L - 赖氨酸可将雷帕霉素产量提高到显著水平，这支持了它作为雷帕霉素生产前体的事实，正如先前研究所发现的。研究了优化培养基对链霉菌生长速率以及雷帕霉素产量的影响。优化研究包括一次一个参数的优化研究，随后采用基于工具的混合方法。该方法包括Plackett - Burman设计（PBD）法、人工神经网络（ANN）和遗传算法（GA）。PBD筛选出甘露糖、豆粕和L - 赖氨酸浓度是雷帕霉素生产的显著因素。ANN用于构建雷帕霉素生产模型。在GA优化浓度下，甘露糖、豆粕和L - 赖氨酸分别为25.47、15.39和17.48 g/L时，该策略使雷帕霉素产量显著提高至320.89 mg/L。本研究必定会在雷帕霉素工业规模生产的放大研究中找到其应用。
