(13)C-metabolic flux analysis in S-adenosyl-L-methionine production by Saccharomyces cerevisiae

Effect of Methionine on Gene Expression in Komagataella phaffii Cells

Komagataella phaffii yeast plays a prominent role in modern biotechnology as a recombinant protein producer. For efficient use of this yeast, it is essential to study the effects of different media components on its growth and gene expression. We investigated the effect of methionine on gene expression in K. phaffii cells using RNA-seq analysis. Several gene groups exhibited altered expression when K. phaffii cells were cultured in a medium with methanol and methionine, compared to a medium without this amino acid. Methionine primarily affects the expression of genes involved in its biosynthesis, fatty acid metabolism, and methanol utilization. The AOX1 gene promoter, which is widely used for heterologous expression in K. phaffii, is downregulated in methionine-containing media. Despite great progress in the development of K. phaffii strain engineering techniques, a sensitive adjustment of cultivation conditions is required to achieve a high yield of the target product. The revealed effect of methionine on K. phaffii gene expression is important for optimizing media recipes and cultivation strategies aimed at maximizing the efficiency of recombinant product synthesis.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by ¹³C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Analogs of S-Adenosyl-L-Methionine in Studies of Methyltransferases

Methyltransferases (MTases) play an important role in the functioning of living systems, catalyzing the methylation reactions of DNA, RNA, proteins, and small molecules, including endogenous compounds and drugs. Many human diseases are associated with disturbances in the functioning of these enzymes; therefore, the study of MTases is an urgent and important task. Most MTases use the cofactor S‑adenosyl‑L‑methionine (SAM) as a methyl group donor. SAM analogs are widely applicable in the study of MTases: they are used in studies of the catalytic activity of these enzymes, in identification of substrates of new MTases, and for modification of the substrates or substrate linking to MTases. In this review, new synthetic analogs of SAM and the problems that can be solved with their usage are discussed.

mfapy: An open-source Python package for 13C-based metabolic flux analysis

¹³C-based metabolic flux analysis (¹³C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In ¹³C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for ¹³C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of ¹³C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy).

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An open-source Python package, mfapy, is developed for ¹³C-MFA.

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mfapy enables users to write Python codes for data analysis procedures of ¹³C-MFA.

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mfapy has a flexibility and extensibility to support various data analysis procedures.

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Computer simulations of ¹³C-MFA experiments is supported for experimental design.

Comparative 13 C-metabolic flux analysis indicates elevation of ATP regeneration, carbon dioxide, and heat production in industrial Saccharomyces cerevisiae strains

BACKGROUND

Various industrial Saccharomyces cerevisiae strains are used for specific processes, such as sake, wine brewing and bread making. Understanding mechanisms underlying the fermentation performance of these strains would be useful for further engineering of the S. cerevisiae metabolism. However, the relationship between the fermentation performance, intra-cellular metabolic states, and other phenotypic characteristics of industrial yeasts is still unclear. In this study, ¹³ C-metabolic flux analysis of four diploid yeast strains-laboratory, sake, bread, and wine yeasts-was conducted.

RESULTS

While the Crabtree effect was observed for all strains, the metabolic flux level of glycolysis was elevated in bread and sake yeast. Furthermore, increased flux levels of the TCA cycle were commonly observed in the three industrial strains. The specific rates of CO₂ production, net ATP regeneration, and metabolic heat generation estimated from the metabolic flux distribution were two to three times greater than those of the laboratory strain. The elevation in metabolic heat generation was correlated with the tolerance to low-temperature stress.

CONCLUSION

These results indicate that the metabolic flux distribution of sake and bread yeast strains contributes to faster production of ethanol and CO₂ . It is also suggested that the generation of metabolic heat is preferable under the actual industrial fermentation conditions.

Regulation of intracellular ATP supply and its application in industrial biotechnology

Efficient cell factories are the core of industrial biotechnology. In recent years, synthetic biology develops rapidly, and more and more modified microbial cell factories are employed in industrial biotechnology. ATP plays vital roles in biosynthesis, metabolism regulation, and cellular maintenance. Regulating cellular ATP supply can effectively modify cellular metabolism. This paper presents a review of recent studies on the regulation of the intracellular ATP supply and its application in industrial biotechnology. Detailed strategies for regulating the ATP supply and the resulting impact on bioproduction are introduced. It is observed that regulating the cellular ATP supply can provide great possibilities for making microbial cells into efficient factories. Future perspectives for further understanding the function of ATP are also discussed.

Principal metabolic flux mode analysis

Motivation

In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples.

Results

We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches.

Availability and implementation

Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Metabolic regulatory mechanisms and physiological roles of functional amino acids and their applications in yeast

In yeast, amino acid metabolism and its regulatory mechanisms vary under different growth environments by regulating anabolic and catabolic processes, including uptake and export, and the metabolic styles form a complicated but robust network. There is also crosstalk with various metabolic pathways, products and signal molecules. The elucidation of metabolic regulatory mechanisms and physiological roles is important fundamental research for understanding life phenomenon. In terms of industrial application, the control of amino acid composition and content is expected to contribute to an improvement in productivity, and to add to the value of fermented foods, alcoholic beverages, bioethanol, and other valuable compounds (proteins and amino acids, etc.). This review article mainly describes our research in constructing yeast strains with high functionality, focused on the metabolic regulatory mechanisms and physiological roles of "functional amino acids", such as l-proline, l-arginine, l-leucine, l-valine, l-cysteine, and l-methionine, found in yeast.

Methionine supplementation stimulates mitochondrial respiration

Mitochondria play essential metabolic functions in eukaryotes. Although their major role is the generation of energy in the form of ATP, they are also involved in maintenance of cellular redox state, conversion and biosynthesis of metabolites and signal transduction. Most mitochondrial functions are conserved in eukaryotic systems and mitochondrial dysfunctions trigger several human diseases. By using multi-omics approach, we investigate the effect of methionine supplementation on yeast cellular metabolism, considering its role in the regulation of key cellular processes. Methionine supplementation induces an up-regulation of proteins related to mitochondrial functions such as TCA cycle, electron transport chain and respiration, combined with an enhancement of mitochondrial pyruvate uptake and TCA cycle activity. This metabolic signature is more noticeable in cells lacking Snf1/AMPK, the conserved signalling regulator of energy homeostasis. Remarkably, snf1Δ cells strongly depend on mitochondrial respiration and suppression of pyruvate transport is detrimental for this mutant in methionine condition, indicating that respiration mostly relies on pyruvate flux into mitochondrial pathways. These data provide new insights into the regulation of mitochondrial metabolism and extends our understanding on the role of methionine in regulating energy signalling pathways.

13C-metabolic flux analysis of ethanol-assimilating Saccharomyces cerevisiae for S-adenosyl-L-methionine production

BACKGROUND

Saccharomyces cerevisiae is a host for the industrial production of S-adenosyl-L-methionine (SAM), which has been widely used in pharmaceutical and nutritional supplement industries. It has been reported that the intracellular SAM content in S. cerevisiae can be improved by the addition of ethanol during cultivation. However, the metabolic state in ethanol-assimilating S. cerevisiae remains unclear. In this study, ¹³C-metabolic flux analysis (¹³C-MFA) was conducted to investigate the metabolic regulation responsible for the high SAM production from ethanol.

RESULTS

The comparison between the metabolic flux distributions of central carbon metabolism showed that the metabolic flux levels of the tricarboxylic acid cycle and glyoxylate shunt in the ethanol culture were significantly higher than that of glucose. Estimates of the ATP balance from the ¹³C-MFA data suggested that larger amounts of excess ATP was produced from ethanol via increased oxidative phosphorylation. The finding was confirmed by the intracellular ATP level under ethanol-assimilating condition being similarly higher than glucose.

CONCLUSIONS

These results suggest that the enhanced ATP regeneration due to ethanol assimilation was critical for the high SAM accumulation.

Analysis of Fluxomic Experiments with Principal Metabolic Flux Mode Analysis

In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and assume the stoichiometric steady state of the metabolic network.We will discuss a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also discuss a sparse variant of PMFA that favors flux modes that contain a small number of reactions. PMFA has several benefits: (1) it can be applied to large metabolic network in efficient way as PMFA does not enumerate elementary modes, (2) the method is more robust to the steady-state violations than competing approaches, and (3) can compactly capture the variation in the data by a few factors. This chapter will describe the detailed steps how to do the above task on experimental data from fluxomic and gene expression measurements.

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, ¹³C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

Metabolome analysis of Saccharomyces cerevisiae and optimization of culture medium for S-adenosyl-L-methionine production

S-Adenosyl-l-methionine (SAM) is a fine chemical used as a nutritional supplement and a prescription drug. It is industrially produced using Saccharomyces cerevisiae owing to its high SAM content. To investigate the optimization of culture medium components for higher SAM production, metabolome analysis was conducted to compare the intracellular metabolite concentrations between Kyokai no. 6 (high SAM-producing) and laboratory yeast S288C (control) under different SAM production conditions. Metabolome analysis and the result of principal component analysis showed that the rate-limiting step for SAM production was ATP supply and the levels of degradation products of adenosine nucleotides were higher in Kyokai 6 strain than in the S288C strain under the l-methionine supplemented condition. Analysis of ATP accumulation showed that the levels of intracellular ATP in the Kyokai 6 strain were also higher compared to those in the S288C strain. Furthermore, as expected from metabolome analysis, the SAM content of Kyokai 6 strain cultivated in the medium without yeast extract increased by 2.5-fold compared to that in the additional condition, by increasing intracellular ATP level with inhibited cell growth. These results suggest that high SAM production is attributed to the enhanced ATP supply with l-methionine condition and high efficiency of intracellular ATP consumption.

Rapid and Quantitative Determination of S-Adenosyl-L-Methionine in the Fermentation Process by Surface-Enhanced Raman Scattering

Concentrations of S-Adenosyl-L-Methionine (SAM) in aqueous solution and fermentation liquids were quantitatively determined by surface-enhanced Raman scattering (SERS) and verified by high-pressure liquid chromatography (HPLC). The Ag nanoparticle/silicon nanowire array substrate was fabricated and employed as an active SERS substrate to indirectly measure the SAM concentration. The linear relationship between the integrated intensity of peak centered at ~2920 cm-1 in SERS spectra and the SAM concentration was established, and the limit of detections of SAM concentrations was analyzed to be ~0.1 g/L. The concentration of SAM in real solution could be predicted by the linear relationship and verified by the HPLC detection method. The relative deviations (δ) of the predicted SAM concentration are less than 13% and the correlation coefficient is 0.9998. Rolling-Circle Filter was utilized to subtract fluorescence background and the optimal results were obtained when the radius of the analyzing circle is 650 cm-1.

A principal components method constrained by elementary flux modes: analysis of flux data sets

Background

Non-negative linear combinations of elementary flux modes (EMs) describe all feasible reaction flux distributions for a given metabolic network under the quasi steady state assumption. However, only a small subset of EMs contribute to the physiological state of a given cell.

Results

In this paper, a method is proposed that identifies the subset of EMs that best explain the physiological state captured in reaction flux data, referred to as principal EMs (PEMs), given a pre-specified universe of EM candidates. The method avoids the evaluation of all possible combinations of EMs by using a branch and bound approach which is computationally very efficient. The performance of the method is assessed using simulated and experimental data of Pichia pastoris and experimental fluxome data of Saccharomyces cerevisiae. The proposed method is benchmarked against principal component analysis (PCA), commonly used to study the structure of metabolic flux data sets.

Conclusions

The overall results show that the proposed method is computationally very effective in identifying the subset of PEMs within a large set of EM candidates (cases with ~100 and ~1000 EMs were studied). In contrast to the principal components in PCA, the identified PEMs have a biological meaning enabling identification of the key active pathways in a cell as well as the conditions under which the pathways are activated. This method clearly outperforms PCA in the interpretability of flux data providing additional insights into the underlying regulatory mechanisms.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1063-0) contains supplementary material, which is available to authorized users.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, ¹³C Metabolic Flux Analysis (¹³C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many ¹³C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of ¹³C-MFA and illustrate how ¹³C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms

ATP regulation in bioproduction

Adenosine-5'-triphosphate (ATP) is consumed as a biological energy source by many intracellular reactions. Thus, the intracellular ATP supply is required to maintain cellular homeostasis. The dependence on the intracellular ATP supply is a critical factor in bioproduction by cell factories. Recent studies have shown that changing the ATP supply is critical for improving product yields. In this review, we summarize the recent challenges faced by researchers engaged in the development of engineered cell factories, including the maintenance of a large ATP supply and the production of cell factories. The strategies used to enhance ATP supply are categorized as follows: addition of energy substrates, controlling pH, metabolic engineering of ATP-generating or ATP-consuming pathways, and controlling reactions of the respiratory chain. An enhanced ATP supply generated using these strategies improves target production through increases in resource uptake, cell growth, biosynthesis, export of products, and tolerance to toxic compounds.