Estimating effects of yeast extract compositions on Escherichia coli growth by a metabolomics approach

Tachibana, Seiga; Watanabe, Katsuhiro; Konishi, Masaaki

doi:10.1016/j.jbiosc.2019.03.012

Cited by 22 publications

(22 citation statements)

References 25 publications

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“…To confirm the concept, Tachibana et al (2019) demonstrated that the component of different brands and lots of yeast extract (YE-a, YE-b, YE-c, YE-d, YE-e, and YE-f) estimated E. coli growth in batch culture in flasks by using gas chromatograph-mass spectrometry (GC-MS) and partial least square regression. E. coli NBRC 3301, synonym K12, was used for cultivation.…”

Section: Influences Of Component Variation In Yeast Extract On Eschermentioning

confidence: 99%

“…The numerous data in omics analysed using statistical analysis between food compositions and evaluation functions. In the issues for the difference of raw material compositions, the composition data of raw materials, named "substratome", can estimate the enhancing and inhibiting the cultivations, by metabolomics like approaches (Tachibana et al, 2019). We defined the approach as "substratemics", which can estimate the cultivating results from "substratome" of initial media components ( Figure 1).…”

Section: "Substratome" Concept For Bioprocess Engineeringmentioning

confidence: 99%

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Modelling Bioprocesses Using Component Profiles in Natural Media, Named “Substratome”, Obtained by Performing Comprehensive Non-Targeted Analysis

2021

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Natural media are often used for various commercial bioprocesses by manufacturers to cut raw material cost. However, the components of the raw materials varies between lot-to-lots and brand-to-brands. The varieties of raw materials influence to the cell growths and materials productivities, and results in unstable production across batches in manufacturing processes. To ensure the quality of raw materials among batches, it is necessary to perform a laboratory screening to purchasing the optimal one, and ensure a desirable performance in industrial process. To solve the serious problems in bioprocesses, it is developing that a modelling methodology using composition of raw materials, named us “substratome”, obtained by non-targeted metabolomicslike methods can estimate the cell growth and bio-productions. Here, we will present that two model studies: [1] Escherichia coli growths have been estimated from hydrophilic components in yeast extract obtained by gas chromatography-mass spectrometry (GC-MS), and [2] bioethanol production have been estimated by the volatile components in corncob and corn stover hydrolysates obtained by GC-MS; by partial least square regression (PLS-R). Additionally, we will present preliminary results to solve the same issues by using artificial intelligence.

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Section: Influences Of Component Variation In Yeast Extract On Eschermentioning

confidence: 99%

Section: "Substratome" Concept For Bioprocess Engineeringmentioning

confidence: 99%

Modelling Bioprocesses Using Component Profiles in Natural Media, Named “Substratome”, Obtained by Performing Comprehensive Non-Targeted Analysis

2021

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“…To identify the hydrophilic components of yeast extract, non-targeted GC-MS analyses were performed after trimethylsilylation according to a previous report. [15] Each 5.0 g/L yeast extract sample (E1, E2, E3, E4, M1, M2, M3, and M4) and mixed samples (E1-E4, E2-E4, E3-E4, E4-M1, E4-M2, E4-M3, E1-M3, E2-M3, E3-M3, M3-M1, M3-M2, and M3-M4) were prepared and autoclaved at 121degC for 20 min. The sample (100 μL) was combined with 20 mg/mL ribitol (60 μL).…”

Section: Gc-msmentioning

confidence: 99%

“…In previous studies, we successfully used metabolomics-based approaches with non-targeted analyses via gas chromatography-mass spectrometry (GC-MS) and machine learning to estimate the effect of yeast extract on microbial growth. [15,16,17] We demonstrated that 165 peaks were observed using GC-MS when E. coli was cultivated in 24 different medium compositions with 6 different yeast extracts. The data fit well to the partial least squares regression (PLS) model with reasonable accuracies.…”

Section: Introductionmentioning

confidence: 99%

“…Because they are important medium components, the PLS model estimated several amino acids, and some of these amino acids were found to influence E. coli growth in validation experiments. [15] This approach was also applied to bioethanol production. In the model fitting of PLS and DNN, [16,17] the volatile components of hydrolysates derived from lignocellulosic biomass served as independent variables and ethanol and cell yields served as dependent variables.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the effects of media formulated with various yeast extracts on heterologous protein production by Escherichia coli using machine learning

Tachibana¹,

Chiou²,

Konishi³

2020

Preprint

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Precision fermentation with mass spectrometry‐based spent media analysis

Dodia

Sunder²,

Borkar³

et al. 2023

Biotech & Bioengineering

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Optimization and monitoring of bioprocesses requires the measurement of several process parameters and quality attributes. Mass spectrometry (MS)‐based techniques such as those coupled to gas chromatography (GCMS) and liquid Chromatography (LCMS) enable the simultaneous measurement of hundreds of metabolites with high sensitivity. When applied to spent media, such metabolome analysis can help determine the sequence of substrate uptake and metabolite secretion, consequently facilitating better design of initial media and feeding strategy. Furthermore, the analysis of metabolite diversity and abundance from spent media will aid the determination of metabolic phases of the culture and the identification of metabolites as surrogate markers for product titer and quality. This review covers the recent advances in metabolomics analysis applied to the development and monitoring of bioprocesses. In this regard, we recommend a stepwise workflow and guidelines that a bioprocesses engineer can adopt to develop and optimize a fermentation process using spent media analysis. Finally, we show examples of how the use of MS can revolutionize the design and monitoring of bioprocesses.

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Estimating effects of yeast extract compositions on Escherichia coli growth by a metabolomics approach

Cited by 22 publications

References 25 publications

Modelling Bioprocesses Using Component Profiles in Natural Media, Named “Substratome”, Obtained by Performing Comprehensive Non-Targeted Analysis

Modelling Bioprocesses Using Component Profiles in Natural Media, Named “Substratome”, Obtained by Performing Comprehensive Non-Targeted Analysis

Modeling the effects of media formulated with various yeast extracts on heterologous protein production by Escherichia coli using machine learning

Precision fermentation with mass spectrometry‐based spent media analysis

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