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近红外光谱学作为高通量技术用于筛选木糖发酵重组酿酒酵母菌株。

Near infrared spectroscopy as high-throughput technology for screening of xylose-fermenting recombinant Saccharomyces cerevisiae strains.

机构信息

Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, 1-1 Rokkodai, Nada, Kobe 657-8501, Japan.

出版信息

Anal Chem. 2011 Jun 1;83(11):4023-9. doi: 10.1021/ac103128p. Epub 2011 May 11.

Abstract

Recently, genetic engineering efforts have been made to develop recombinant Saccharomyces cerevisiae strains able to utilize xylose, an inexpensive and abundant carbon source. However, their construction and selection processes are limited by the speed and expenses of the existing testing methods, thus a rapid and equally precise method will significantly increase the number of tested strains. Here, near infrared (NIR) spectroscopy is proposed as a successful alternative method for screening recombinant xylose-fermenting S. cerevisiae. Supernatant samples of fermentation solutions from one diploid and three haploid recombinant strains were collected along the fermentation process. NIR spectra of the diluted supernatant provided effective differentiation of strains consistent with their phenotypic and genotypic features. This result could be used as a feedback for multicomponent analysis, in order to develop regression model for quantification of consumed glucose and xylose, produced ethanol, glycerol, and xylitol. Robust partial least-squares regression models with high prediction accuracy that are effective with any strain were achieved for all components when the modeling was performed with combined data of all strains, achieving 0.21-1.49 g/L of standard error of prediction with calibration, prediction, limit of detection and limit of quantification in the range of 1.0-4.5 and 3.0-13.4 g/L, respectively.

摘要

最近,人们通过基因工程手段,致力于开发能够利用木糖的重组酿酒酵母菌株,木糖是一种廉价且丰富的碳源。然而,其构建和选择过程受到现有测试方法的速度和费用的限制,因此一种快速且同样精确的方法将显著增加测试菌株的数量。在这里,近红外(NIR)光谱被提议作为筛选重组木糖发酵酿酒酵母的一种成功替代方法。在发酵过程中,收集了来自一个二倍体和三个单倍体重组菌株的发酵液上清液的样本。稀释上清液的 NIR 光谱能够有效地对菌株进行区分,这与它们的表型和基因型特征一致。这一结果可作为多元分析的反馈,以开发用于定量消耗的葡萄糖和木糖、产生的乙醇、甘油和木糖醇的回归模型。当使用所有菌株的组合数据进行建模时,对于所有成分都实现了具有高预测准确性的稳健偏最小二乘回归模型,在 1.0-4.5 和 3.0-13.4 g/L 的范围内,校准、预测、检测限和定量限的预测标准误差分别为 0.21-1.49 g/L。

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