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在线靶向代谢组学实时监测发酵过程中相关化合物。

On-line targeted metabolomics for real-time monitoring of relevant compounds in fermentation processes.

机构信息

School of Biological Sciences, Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Edinburgh, UK.

Glasgow Polyomics, University of Glasgow, Glasgow, UK.

出版信息

Biotechnol Bioeng. 2024 Feb;121(2):683-695. doi: 10.1002/bit.28599. Epub 2023 Nov 22.

Abstract

Fermentation monitoring is a powerful tool for bioprocess development and optimization. On-line metabolomics is a technology that is starting to gain attention as a bioprocess monitoring tool, allowing the direct measurement of many compounds in the fermentation broth at a very high time resolution. In this work, targeted on-line metabolomics was used to monitor 40 metabolites of interest during three Escherichia coli succinate production fermentation experiments every 5 min with a triple quadrupole mass spectrometer. This allowed capturing high-time resolution biological data that can provide critical information for process optimization. For nine of these metabolites, simple univariate regression models were used to model compound concentration from their on-line mass spectrometry peak area. These on-line metabolomics univariate models performed comparably to vibrational spectroscopy multivariate partial least squares regressions models reported in the literature, which typically are much more complex and time consuming to build. In conclusion, this work shows how on-line metabolomics can be used to directly monitor many bioprocess compounds of interest and obtain rich biological and bioprocess data.

摘要

发酵监测是生物过程开发和优化的有力工具。在线代谢组学作为一种生物过程监测工具,开始受到关注,它可以非常高的时间分辨率直接测量发酵液中的许多化合物。在这项工作中,使用靶向在线代谢组学每隔 5 分钟用三重四极杆质谱仪监测 3 次大肠杆菌琥珀酸生产发酵实验中的 40 种感兴趣的代谢物。这允许捕获高时间分辨率的生物数据,这些数据可为过程优化提供关键信息。对于其中的 9 种代谢物,使用简单的单变量回归模型从它们的在线质谱峰面积来模拟化合物浓度。这些在线代谢组学单变量模型的性能与文献中报道的振动光谱多元偏最小二乘回归模型相当,而这些模型通常构建起来要复杂得多,也耗时得多。总之,这项工作表明了如何使用在线代谢组学来直接监测许多生物过程中的感兴趣的化合物,并获得丰富的生物和生物过程数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd5c/10953439/7f4afa41450f/BIT-121-683-g002.jpg

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