产γ-氨基丁酸的乳酸菌菌株选育及其发酵条件优化
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摘要
γ-氨基丁酸(γ-amino butyric acid, 简称GABA, 也称氨酪酸)是中枢神经系统一种重要的抑制性神经递质,本文对产GABA的菌种选育和发酵过程进行了初步研究。
从鲜奶中分离得到一株产GABA的乳酸菌株hjxi-01,经初步鉴定为短乳杆菌。实验结果表明,此乳酸菌细胞具有合成GABA的能力。在此基础上,先后使用了紫外线和γ射线对出发菌株进行了诱变处理。最终得到一株高产突变株hixi-08119,经连续传代12次,遗传性状稳定,GABA平均产量达到17g/L,较出发菌株hjxj-01提高142.9%。
运用神经网络结合粒子群算法,对乳酸菌产GABA的摇瓶发酵培养条件进行了优化。以Plackett-Burman设计对15个相关影响因素进行评价,并从中筛选出具有显著正效应的葡萄糖、L-谷氨酸钠(L-glutamic acid sodium,简称为L-MSG)和四水硫酸锰等三个因素,并通过逐一单因素实验确定这三个因素的合适取值区域。按照Hybrid设计,选取11组训练样本来建立人工神经网络(Artificial neural
network,简称ANN)模型,结果表明,Hybrid设计选取样本比随意取样更合理,所建立的模型准确可靠,预测的平均误差为2.37%。最后依赖于模型由粒子群算法(Particle swarm optimization,简称PSO)进行全局搜索寻优得到最优培养条件。在优化后的条件下,GABA积累浓度达到33.4g/L,较优化前提高96.5%,同网络模型预测相比,误差仅为1.49%。
优化了乳酸菌hixi-08119产GABA的间歇发酵培养条件。首先对谷氨酸代谢流量分布进行估算,用来决定理论上控制哪几步局部代谢反应步骤对优化是有利的。研究发现,从谷氨酸转化为GABA和GABA转化琥珀酸这两步局部途径对GABA的摩尔得率和积累速率有重要影响。接着对可能影响途径相关酶(谷氨酸脱羧酶、GABA转氨酶和琥珀酸半醛脱氢酶)活性的23个因素做了进一步优化研究。通过Packett-Burman设计、Hybrid designs、人工神经网络和PSO等优化方法分析得知,FeCl_3和磷酸吡哆醛(pyridoxal phosphate,简称PLP)浓度,以及pH值对GABA的摩尔得率有重大影响,当间歇发酵20h时控制这三个变量水平为:
γ-amino butyric acid(GABA) is a major inhibitory neurotransmitter in the central nervous system. In this paper, GABA-producing strain breeding and fermentationg process were studied.A GABA-yielding strain hjxj-01 was isolated from the milk samples and identified as Lactobacillus brevis. It was shown that Lactobacillus brevis hjxj-Ol had the ablity of producing GABA. Then, UV and ~(60)Co y-rays were used to treat the original strain. After several times of mutagenesis, a mutant strain hjxj-08119 was bred by GABA resistance selection. Moreover, the GABA-producing capacity could be maintained stably after 12 generations. The fermentation results indicated that the average yield of GABA was 17 g/L, which was increased 142.9% compared with that of the origin strain hjxj-Ol.Artificial neural network (ANN) and particle swarm optimization (PSO) were applied to optimize GABA production by Lactobacillus brevis in shake flask cultures. Firstly, glucose, sodium glutamate and MnSO_4H_2O, which influenced GABA production positively were screened from 15 related factors by using Plackett-Burman design. The reasonable ranges of these three factors were determined by single factors experiment. Then according to hybrid design, 11 samples were selected for training network, and ANN was modeled. The results indicated that hybrid design was more rational than random design, and the model built by hybrid design shown good performance. The average error between the experimental value and predictive values was 2.37%. Finally, based on the model, the optimized condition was predicted by particle swarm optimization. Verification experiment showed that the optimized condition leaded the average yield of GABA to be increased from 17.06g/L to 33.4g/L, and in the optimized condition, the relative discrepancy was only 1.49% between the experimental value and the predictive value of GABA yield.The effects of culture condition on GABA yield of Lactobacillus hjxj-08119 in
batch fermentation was investigated. Evaluation of flux distribution for glutamate metabolism was used first to determine which metabolic pathways, significant for optimization in theory, should be controlled. More specifically, control for the local pathways from glutamate to GABA and from GABA to succinate were very important with regard to the GABA molar yield and GABA production rate. Subsequently, twenty three factors, including pH, temperature, pyridoxal phosphate(PLP), 2-ketoglutarate, etc, which might influence the activity of glutamate decarboxylase, 4-aminobutyrate aminotransferase and succinate semialdehyde dehydrogenase were investigated employing Plackett-Burman designs (PBD), hybrid designs (HD), artificial neural networks (ANN) and particle swarm optimization (PSO). According to the PBD analysis, FeCl_3, PLP and pH were proved to be significant parameters for the GABA molar yield. When the condition was controlled as pH=5.08, c(FeCl3)=5.1 mmol/L and c(PLP)=0.71 mmol/L after 20h cluture in batch fermentation, the GABA molar yield and GABA production rate between 20h and 40h were to be increased by 13.1% and 45.9% (from 71% to 80.27% and from 8.25 mmol/(L·h) to 12.04 mmol/(L-h) respectively compared with those obtained in the original condition. Based on the optimal condition in batch fermentation, the fed- batch fermentation was investigated too. The addition of glucose and L-MSG could result in accumulation of GABA to reach as high as 107.5g/L after fermentation culture of 91.5 h.A non-structured model was proposed to simulate the growth of Lactobacillus hjxj-08119, the consumption of glucose, the consumption of L-MSG and the accumulation of GABA in batch fermentation. The parameters of fermentation kinetics (μ_(max)、α 、 β 、 m_1 、 m_2、 Y_(x/s_1) 、 Y_(p/s_2) and Y_(s_1/s_2)) was estimated usingPSO.
从鲜奶中分离得到一株产GABA的乳酸菌株hjxi-01,经初步鉴定为短乳杆菌。实验结果表明,此乳酸菌细胞具有合成GABA的能力。在此基础上,先后使用了紫外线和γ射线对出发菌株进行了诱变处理。最终得到一株高产突变株hixi-08119,经连续传代12次,遗传性状稳定,GABA平均产量达到17g/L,较出发菌株hjxj-01提高142.9%。
运用神经网络结合粒子群算法,对乳酸菌产GABA的摇瓶发酵培养条件进行了优化。以Plackett-Burman设计对15个相关影响因素进行评价,并从中筛选出具有显著正效应的葡萄糖、L-谷氨酸钠(L-glutamic acid sodium,简称为L-MSG)和四水硫酸锰等三个因素,并通过逐一单因素实验确定这三个因素的合适取值区域。按照Hybrid设计,选取11组训练样本来建立人工神经网络(Artificial neural
network,简称ANN)模型,结果表明,Hybrid设计选取样本比随意取样更合理,所建立的模型准确可靠,预测的平均误差为2.37%。最后依赖于模型由粒子群算法(Particle swarm optimization,简称PSO)进行全局搜索寻优得到最优培养条件。在优化后的条件下,GABA积累浓度达到33.4g/L,较优化前提高96.5%,同网络模型预测相比,误差仅为1.49%。
优化了乳酸菌hixi-08119产GABA的间歇发酵培养条件。首先对谷氨酸代谢流量分布进行估算,用来决定理论上控制哪几步局部代谢反应步骤对优化是有利的。研究发现,从谷氨酸转化为GABA和GABA转化琥珀酸这两步局部途径对GABA的摩尔得率和积累速率有重要影响。接着对可能影响途径相关酶(谷氨酸脱羧酶、GABA转氨酶和琥珀酸半醛脱氢酶)活性的23个因素做了进一步优化研究。通过Packett-Burman设计、Hybrid designs、人工神经网络和PSO等优化方法分析得知,FeCl_3和磷酸吡哆醛(pyridoxal phosphate,简称PLP)浓度,以及pH值对GABA的摩尔得率有重大影响,当间歇发酵20h时控制这三个变量水平为:
γ-amino butyric acid(GABA) is a major inhibitory neurotransmitter in the central nervous system. In this paper, GABA-producing strain breeding and fermentationg process were studied.A GABA-yielding strain hjxj-01 was isolated from the milk samples and identified as Lactobacillus brevis. It was shown that Lactobacillus brevis hjxj-Ol had the ablity of producing GABA. Then, UV and ~(60)Co y-rays were used to treat the original strain. After several times of mutagenesis, a mutant strain hjxj-08119 was bred by GABA resistance selection. Moreover, the GABA-producing capacity could be maintained stably after 12 generations. The fermentation results indicated that the average yield of GABA was 17 g/L, which was increased 142.9% compared with that of the origin strain hjxj-Ol.Artificial neural network (ANN) and particle swarm optimization (PSO) were applied to optimize GABA production by Lactobacillus brevis in shake flask cultures. Firstly, glucose, sodium glutamate and MnSO_4H_2O, which influenced GABA production positively were screened from 15 related factors by using Plackett-Burman design. The reasonable ranges of these three factors were determined by single factors experiment. Then according to hybrid design, 11 samples were selected for training network, and ANN was modeled. The results indicated that hybrid design was more rational than random design, and the model built by hybrid design shown good performance. The average error between the experimental value and predictive values was 2.37%. Finally, based on the model, the optimized condition was predicted by particle swarm optimization. Verification experiment showed that the optimized condition leaded the average yield of GABA to be increased from 17.06g/L to 33.4g/L, and in the optimized condition, the relative discrepancy was only 1.49% between the experimental value and the predictive value of GABA yield.The effects of culture condition on GABA yield of Lactobacillus hjxj-08119 in
batch fermentation was investigated. Evaluation of flux distribution for glutamate metabolism was used first to determine which metabolic pathways, significant for optimization in theory, should be controlled. More specifically, control for the local pathways from glutamate to GABA and from GABA to succinate were very important with regard to the GABA molar yield and GABA production rate. Subsequently, twenty three factors, including pH, temperature, pyridoxal phosphate(PLP), 2-ketoglutarate, etc, which might influence the activity of glutamate decarboxylase, 4-aminobutyrate aminotransferase and succinate semialdehyde dehydrogenase were investigated employing Plackett-Burman designs (PBD), hybrid designs (HD), artificial neural networks (ANN) and particle swarm optimization (PSO). According to the PBD analysis, FeCl_3, PLP and pH were proved to be significant parameters for the GABA molar yield. When the condition was controlled as pH=5.08, c(FeCl3)=5.1 mmol/L and c(PLP)=0.71 mmol/L after 20h cluture in batch fermentation, the GABA molar yield and GABA production rate between 20h and 40h were to be increased by 13.1% and 45.9% (from 71% to 80.27% and from 8.25 mmol/(L·h) to 12.04 mmol/(L-h) respectively compared with those obtained in the original condition. Based on the optimal condition in batch fermentation, the fed- batch fermentation was investigated too. The addition of glucose and L-MSG could result in accumulation of GABA to reach as high as 107.5g/L after fermentation culture of 91.5 h.A non-structured model was proposed to simulate the growth of Lactobacillus hjxj-08119, the consumption of glucose, the consumption of L-MSG and the accumulation of GABA in batch fermentation. The parameters of fermentation kinetics (μ_(max)、α 、 β 、 m_1 、 m_2、 Y_(x/s_1) 、 Y_(p/s_2) and Y_(s_1/s_2)) was estimated usingPSO.
引文
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