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在线非靶向代谢组学监测大肠杆菌琥珀酸发酵过程。

On-line untargeted metabolomics monitoring of an Escherichia coli succinate fermentation process.

机构信息

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

Institute of Infection, Immunity and Inflammation, Glasgow Polyomics, University of Glasgow, Glasgow, UK.

出版信息

Biotechnol Bioeng. 2022 Oct;119(10):2757-2769. doi: 10.1002/bit.28173. Epub 2022 Jul 15.

Abstract

The real-time monitoring of metabolites (RTMet) is instrumental for the industrial production of biobased fermentation products. This study shows the first application of untargeted on-line metabolomics for the monitoring of undiluted fermentation broth samples taken automatically from a 5 L bioreactor every 5 min via flow injection mass spectrometry. The travel time from the bioreactor to the mass spectrometer was 30 s. Using mass spectrometry allows, on the one hand, the direct monitoring of targeted key process compounds of interest and, on the other hand, provides information on hundreds of additional untargeted compounds without requiring previous calibration data. In this study, this technology was applied in an Escherichia coli succinate fermentation process and 886 different m/z signals were monitored, including key process compounds (glucose, succinate, and pyruvate), potential biomarkers of biomass formation such as (R)-2,3-dihydroxy-isovalerate and (R)-2,3-dihydroxy-3-methylpentanoate and compounds from the pentose phosphate pathway and nucleotide metabolism, among others. The main advantage of the RTMet technology is that it allows the monitoring of hundreds of signals without the requirement of developing partial least squares regression models, making it a perfect tool for bioprocess monitoring and for testing many different strains and process conditions for bioprocess development.

摘要

实时监测代谢物(RTMet)对于生物基发酵产品的工业生产至关重要。本研究首次应用无靶向在线代谢组学对 5L 生物反应器中未经稀释的发酵液样品进行自动在线监测,每 5 分钟采集一次,采用流动注射质谱法。从生物反应器到质谱仪的传输时间为 30 秒。质谱法一方面可以直接监测目标关键过程化合物,另一方面可以提供数百种无需预先校准数据的额外无靶向化合物的信息。在本研究中,该技术应用于大肠杆菌琥珀酸发酵过程,监测了 886 种不同的 m/z 信号,包括关键过程化合物(葡萄糖、琥珀酸和丙酮酸)、生物量形成的潜在生物标志物(R)-2,3-二羟基异戊酸和(R)-2,3-二羟基-3-甲基戊酸以及戊糖磷酸途径和核苷酸代谢等化合物。RTMet 技术的主要优势在于它可以在不开发偏最小二乘回归模型的情况下监测数百种信号,使其成为生物过程监测的理想工具,并可用于测试许多不同的菌株和生物过程开发的过程条件。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d6d/9541951/ad325f7d9c1b/BIT-119-2757-g009.jpg

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