Investigating xylose metabolism in recombinant Saccharomyces cerevisiae via 13C metabolic flux analysis

Ferulic acid triggering a co-production of 4-vinyl guaiacol and fumaric acid from lignocellulose-based carbon source by Rhizopus oryzae

Plant secondary metabolites have attracted considerable attention due to the increasing demand for finite fossil resources and environmental concerns. However, the biosynthesis of aromatic aldehydes or alcohols from renewable resources remains challenging and costly. This study explores a novel approach performed by the aromatic catabolizing organism Rhizopus oryzae, which enables a ferulic acid-activated co-production of 4-vinyl guaiacol (4-VG) and fumaric acid. The strain produced 4.60 g/L 4-VG and 11.25 g/L fumaric acid from a mixed carbon source of glucose and xylose, suggesting that this new pathway allows the potential production of natural 4-VG from low-cost substrates. This green route, which utilizes Rhizopus oryzae's ability to efficiently convert various renewable resources into valuable chemicals, paves the way for improved catalytic efficiency in 4-VG production.

Segmentation and evaluation of pathway module efficiency: Quantitative approach to monitor and overcome evolving bottlenecks in xylose to ethanol pathway

Engineering microbes that can efficiently ferment xylose to ethanol is critical to the development of renewable fuels from lignocellulosic biomass. To accelerate the strain optimization process, a method termed Segmentation and Evaluation of Pathway Module Efficiency (SEPME) was developed to enable rapid and iterative identification and removal of metabolic bottlenecks. Using SEPME, the overall pathway was segmented into two modules: the upstream xylose assimilation pathway and the downstream pentose phosphate pathway, glycolysis, and fermentation. The efficiencies of both modules were then quantified to identify the rate controlling module, followed by analyses of control coefficients, reaction rates, and byproduct concentrations to narrow down targets within the module. SEPME analysis revealed that as the strain was engineered with increasing xylose-to-ethanol yields, the bottlenecks shifted within a module and across the two modules. Guided by SEPME, these bottlenecks were removed one by one, and a strain approaching the theoretical ethanol yield was obtained.

A versatile in situ cofactor enhancing system for meeting cellular demands for engineered metabolic pathways

Cofactor imbalance obstructs the productivities of metabolically engineered cells. Herein, we employed a minimally perturbing system, xylose reductase and lactose (XR/lactose), to increase the levels of a pool of sugar phosphates which are connected to the biosynthesis of NAD(P)H, FAD, FMN, and ATP in Escherichia coli. The XR/lactose system could increase the amounts of the precursors of these cofactors and was tested with three different metabolically engineered cell systems (fatty alcohol biosynthesis, bioluminescence light generation, and alkane biosynthesis) with different cofactor demands. Productivities of these cells were increased 2-4-fold by the XR/lactose system. Untargeted metabolomic analysis revealed different metabolite patterns among these cells, demonstrating that only metabolites involved in relevant cofactor biosynthesis were altered. The results were also confirmed by transcriptomic analysis. Another sugar reducing system (glucose dehydrogenase) could also be used to increase fatty alcohol production but resulted in less yield enhancement than XR. This work demonstrates that the approach of increasing cellular sugar phosphates can be a generic tool to increase in vivo cofactor generation upon cellular demand for synthetic biology.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Deletion of NGG1 in a recombinant Saccharomyces cerevisiae improved xylose utilization and affected transcription of genes related to amino acid metabolism

Production of biofuels and biochemicals from xylose using yeast cell factory is of great interest for lignocellulosic biorefinery. Our previous studies revealed that a natural yeast isolate Saccharomyces cerevisiae YB-2625 has superior xylose-fermenting ability. Through integrative omics analysis, NGG1, which encodes a transcription regulator as well as a subunit of chromatin modifying histone acetyltransferase complexes was revealed to regulate xylose metabolism. Deletion of NGG1 in S. cerevisiae YRH396h, which is the haploid version of the recombinant yeast using S. cerevisiae YB-2625 as the host strain, improved xylose consumption by 28.6%. Comparative transcriptome analysis revealed that NGG1 deletion down-regulated genes related to mitochondrial function, TCA cycle, ATP biosynthesis, respiration, as well as NADH generation. In addition, the NGG1 deletion mutant also showed transcriptional changes in amino acid biosynthesis genes. Further analysis of intracellular amino acid content confirmed the effect of NGG1 on amino acid accumulation during xylose utilization. Our results indicated that NGG1 is one of the core nodes for coordinated regulation of carbon and nitrogen metabolism in the recombinant S. cerevisiae. This work reveals novel function of Ngg1p in yeast metabolism and provides basis for developing robust yeast strains to produce ethanol and biochemicals using lignocellulosic biomass.

Genome-scale metabolic network models: from first-generation to next-generation

Over the last two decades, thousands of genome-scale metabolic network models (GSMMs) have been constructed. These GSMMs have been widely applied in various fields, ranging from network interaction analysis, to cell phenotype prediction. However, due to the lack of constraints, the prediction accuracy of first-generation GSMMs was limited. To overcome these limitations, the next-generation GSMMs were developed by integrating omics data, adding constrain condition, integrating different biological models, and constructing whole-cell models. Here, we review recent advances of GSMMs from the first generation to the next generation. Then, we discuss the major application of GSMMs in industrial biotechnology, such as predicting phenotypes and guiding metabolic engineering. In addition, human health applications, including understanding biological mechanisms, discovering biomarkers and drug targets, are also summarized. Finally, we address the challenges and propose new trend of GSMMs. KEY POINTS: •This mini-review updates the literature on almost all published GSMMs since 1999. •Detailed insights into the development of the first- and next-generation GSMMs. •The application of GSMMs is summarized, and the prospects of integrating machine learning are emphasized.

Engineering Yarrowia lipolytica to produce fuels and chemicals from xylose: A review

The production of chemicals and fuels from lignocellulosic biomass has great potential industrial applications due to its economic feasibility and environmental attractiveness. However, the utilized microorganisms must be able to use all the sugars present in lignocellulosic hydrolysates, especially xylose, the second most plentiful monosaccharide on earth. Yarrowia lipolytica is a good candidate for producing various valuable products from biomass, but this yeast is unable to catabolize xylose efficiently. The development of metabolic engineering facilitated the application of Y. lipolytica as a platform for the bioconversion of xylose into various value-added products. Here, we reviewed the research progress on natural xylose-utilization pathways and their reconstruction in Y. lipolytica. The progress and emerging trends in metabolic engineering of Y. lipolytica for producing chemicals and fuels are further introduced. Finally, challenges and future perspectives of using lignocellulosic hydrolysate as substrate for Y. lipolytica are discussed.

Different transcriptional responses of haploid and diploid S. cerevisiae strains to changes in cofactor preference of XR

Background

Xylitol accumulation is a major barrier for efficient ethanol production through heterologous xylose reductase-xylitol dehydrogenase (XR-XDH) pathway in recombinant Saccharomyces cerevisiae. Mutated NADH-preferring XR is usually employed to alleviate xylitol accumulation. However, it remains unclear how mutated XR affects the metabolic network for xylose metabolism. In this study, haploid and diploid strains were employed to investigate the transcriptional responses to changes in cofactor preference of XR through RNA-seq analysis during xylose fermentation.

Results

For the haploid strains, genes involved in xylose-assimilation (XYL1, XYL2, XKS1), glycolysis, and alcohol fermentation had higher transcript levels in response to mutated XR, which was consistent with the improved xylose consumption rate and ethanol yield. For the diploid strains, genes related to protein biosynthesis were upregulated while genes involved in glyoxylate shunt were downregulated in response to mutated XR, which might contribute to the improved yields of biomass and ethanol. When comparing the diploids with the haploids, genes involved in glycolysis and MAPK signaling pathway were significantly downregulated, while oxidative stress related transcription factors (TFs) were significantly upregulated, irrespective of the cofactor preference of XR.

Conclusions

Our results not only revealed the differences in transcriptional responses of the diploid and haploid strains to mutated XR, but also provided underlying basis for better understanding the differences in xylose metabolism between the diploid and haploid strains.

Improving lysine production through construction of an Escherichia coli enzyme-constrained model

Microbial cell factories are widely used for the production of high-value chemicals. However, maximizing production titers is made difficult by the complicated regulatory mechanisms of these cell platforms. Here, k_cat values were incorporated to construct an Escherichia coli enzyme-constrained model. The resulting ec_iML1515 model showed that the protein demand and protein synthesis rate were the key factors affecting lysine production. By optimizing the expression of the 20 top-demanded proteins, lysine titers reached 95.7 ± 0.7 g/L, with a 0.45 g/g glucose yield. Moreover, adjusting NH₄ ⁺ and dissolved oxygen levels to regulate the synthesis rate of energy metabolism-related proteins caused lysine titers and glucose yields to increase to 193.6 ± 1.8 g/L and 0.74 g/g, respectively. The ec_iML1515 model provides insight into how enzymes required for the biosynthesis of certain products are distributed between and within metabolic pathways. This information can be used to accurately predict and rationally design lysine production.

Comprehensive understanding of Saccharomyces cerevisiae phenotypes with whole-cell model WM_S288C

Biological network construction for Saccharomyces cerevisiae is a widely used approach for simulating phenotypes and designing cell factories. However, due to a complicated regulatory mechanism governing the translation of genotype to phenotype, precise prediction of phenotypes remains challenging. Here, we present WM_S288C, a computational whole-cell model that includes 15 cellular states and 26 cellular processes and which enables integrated analyses of physiological functions of Saccharomyces cerevisiae. Using WM_S288C to predict phenotypes of S. cerevisiae, the functions of 1140 essential genes were characterized and linked to phenotypes at five levels. During the cell cycle, the dynamic allocation of intracellular molecules could be tracked in real-time to simulate cell activities. Additionally, one-third of non-essential genes were identified to affect cell growth via regulating nucleotide concentrations. These results demonstrated the value of WM_S288C as a tool for understanding and investigating the phenotypes of S. cerevisiae.

Tracing metabolic flux through time and space with isotope labeling experiments

Metabolism is dynamic and must function in context-specific ways to adjust to changes in the surrounding cellular and ecological environment. When isotopic tracers are used, metabolite flow (i.e. metabolic flux) can be quantified through biochemical networks to assess metabolic pathway operation. The cellular activities considered across multiple tissues and organs result in the observed phenotype and can be analyzed to discover emergent, whole-system properties of biology and elucidate misconceptions about network operation. However, temporal and spatial challenges remain significant hurdles and require novel approaches and creative solutions. We survey current investigations in higher plant and animal systems focused on dynamic isotope labeling experiments, spatially resolved measurement strategies, and observations from re-analysis of our own studies that suggest prospects for future work. Related discoveries will be necessary to push the frontier of our understanding of metabolism to suggest novel solutions to cure disease and feed a growing future world population.

Metabolic flux analysis for metabolome data validation of naturally xylose-fermenting yeasts

BACKGROUND

Efficient xylose fermentation still demands knowledge regarding xylose catabolism. In this study, metabolic flux analysis (MFA) and metabolomics were used to improve our understanding of xylose metabolism. Thus, a stoichiometric model was constructed to simulate the intracellular carbon flux and used to validate the metabolome data collected within xylose catabolic pathways of non-Saccharomyces xylose utilizing yeasts.

RESULTS

A metabolic flux model was constructed using xylose fermentation data from yeasts Scheffersomyces stipitis, Spathaspora arborariae, and Spathaspora passalidarum. In total, 39 intracellular metabolic reactions rates were utilized validating the measurements of 11 intracellular metabolites, acquired by mass spectrometry. Among them, 80% of total metabolites were confirmed with a correlation above 90% when compared to the stoichiometric model. Among the intracellular metabolites, fructose-6-phosphate, glucose-6-phosphate, ribulose-5-phosphate, and malate are validated in the three studied yeasts. However, the metabolites phosphoenolpyruvate and pyruvate could not be confirmed in any yeast. Finally, the three yeasts had the metabolic fluxes from xylose to ethanol compared. Xylose catabolism occurs at twice-higher flux rates in S. stipitis than S. passalidarum and S. arborariae. Besides, S. passalidarum present 1.5 times high flux rate in the xylose reductase reaction NADH-dependent than other two yeasts.

CONCLUSIONS

This study demonstrated a novel strategy for metabolome data validation and brought insights about naturally xylose-fermenting yeasts. S. stipitis and S. passalidarum showed respectively three and twice higher flux rates of XR with NADH cofactor, reducing the xylitol production when compared to S. arborariae. Besides then, the higher flux rates directed to pentose phosphate pathway (PPP) and glycolysis pathways resulted in better ethanol production in S. stipitis and S. passalidarum when compared to S. arborariae.

Production of biofuels and chemicals from xylose using native and engineered yeast strains

Numerous metabolic engineering strategies have allowed yeasts to efficiently assimilate xylose, the second most abundant sugar component of lignocellulosic biomass. During the investigation of xylose utilization by yeasts, a global rewiring of metabolic networks upon xylose cultivation has been captured, as opposed to a pattern of glucose repression. A clear understanding of the xylose-induced metabolic reprogramming in yeast would shed light on the optimization of yeast-based bioprocesses to produce biofuels and chemicals using xylose. In this review, we delved into the characteristics of yeast xylose metabolism, and potential benefits of using xylose as a carbon source to produce various biochemicals with examples. Transcriptomic and metabolomic patterns of xylose-grown yeast cells were distinct from those on glucose-a conventional sugar of industrial biotechnology-and the gap might lead to opportunities to produce biochemicals efficiently. Indeed, limited glycolytic metabolic fluxes during xylose utilization could result in enhanced production of metabolites whose biosynthetic pathways compete for precursors with ethanol fermentation. Also, alleviation of glucose repression on cytosolic acetyl coenzyme A (acetyl-CoA) synthesis, and respiratory energy metabolism during xylose utilization enhanced production of acetyl-CoA derivatives. Consideration of singular properties of xylose metabolism, such as redox cofactor imbalance between xylose reductase and xylitol dehydrogenase, is necessary to maximize these positive xylose effects. This review argues the importance and benefits of xylose utilization as not only a way of expanding a substrate range, but also an effective environmental perturbation for the efficient production of advanced biofuels and chemicals in yeasts.

Promiscuous activities of heterologous enzymes lead to unintended metabolic rerouting in Saccharomyces cerevisiae engineered to assimilate various sugars from renewable biomass

BACKGROUND

Understanding the global metabolic network, significantly perturbed upon promiscuous activities of foreign enzymes and different carbon sources, is crucial for systematic optimization of metabolic engineering of yeast Saccharomyces cerevisiae. Here, we studied the effects of promiscuous activities of overexpressed enzymes encoded by foreign genes on rerouting of metabolic fluxes of an engineered yeast capable of assimilating sugars from renewable biomass by profiling intracellular and extracellular metabolites.

RESULTS

Unbiased metabolite profiling of the engineered S. cerevisiae strain EJ4 revealed promiscuous enzymatic activities of xylose reductase and xylitol dehydrogenase on galactose and galactitol, respectively, resulting in accumulation of galactitol and tagatose during galactose fermentation. Moreover, during glucose fermentation, a trisaccharide consisting of glucose accumulated outside of the cells probably owing to the promiscuous and transglycosylation activity of β-glucosidase expressed for hydrolyzing cellobiose. Meanwhile, higher accumulation of fatty acids and secondary metabolites was observed during xylose and cellobiose fermentations, respectively.

CONCLUSIONS

The heterologous enzymes functionally expressed in S. cerevisiae showed promiscuous activities that led to unintended metabolic rerouting in strain EJ4. Such metabolic rerouting could result in a low yield and productivity of a final product due to the formation of unexpected metabolites. Furthermore, the global metabolic network can be significantly regulated by carbon sources, thus yielding different patterns of metabolite production. This metabolomic study can provide useful information for yeast strain improvement and systematic optimization of yeast metabolism to manufacture bio-based products.

Genome-scale metabolic modelling common cofactors metabolism in microorganisms

The common cofactors ATP/ADP, NAD(P)(H), and acetyl-CoA/CoA are indispensable participants in biochemical reactions in industrial microbes. To systematically explore the effects of these cofactors on cell growth and metabolic phenotypes, the first genome-scale cofactor metabolic model, icmNX6434, including 6434 genes, 1782 metabolites, and 6877 reactions, was constructed from 14 genome-scale metabolic models of 14 industrial strains. The origin, consumption, and interactions of these common cofactors in microbial cells were elucidated by the icmNX6434 model, and they played important roles in cell growth. The essential cofactor modules contained 2480 genes and 2948 reactions; therefore, improving cofactor biosynthesis, directing these cofactors into essential metabolic pathways, as well as avoiding cofactor utilization during byproduct biosynthesis and futile cycles, are three ways to increase cell growth. The effects of these common cofactors on the distribution and rate of the carbon flux in four universal modes, as well as an optimized metabolic flux, could be obtained by manipulating cofactor availability and balance. Significant changes in the ATP, NAD(H), NADP(H), or acetyl-CoA concentrations triggered relevant metabolic responses to acidic, oxidative, heat, and osmotic stress. Globally, the model icmNX6434 provides a comprehensive platform to elucidate the physiological effects of these cofactors on cell growth, metabolic flux, and industrial robustness. Moreover, the results of this study are a further example of using a consensus genome-scale metabolic model to increase our understanding of key biological processes.

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.

Transcriptomes of a xylose-utilizing industrial flocculating Saccharomyces cerevisiae strain cultured in media containing different sugar sources

Lignocellulosic hydrolysates used for bioethanol production contain a mixture of sugars, with xylose being the second most abundant after glucose. Since xylose is not a natural substrate for Saccharomyces cerevisiae, recombinant S. cerevisiae strongly prefers glucose over xylose, and the fermentation rate and ethanol yield with xylose are both lower than those with glucose. To determine the molecular basis for glucose and xylose fermentation, we used microarrays to investigate the transcriptional difference of a xylose-utilizing industrial strain cultured in both single sugar media and a mixed sugar medium of glucose and xylose. The transcriptomes were nearly identical between glucose metabolizing cells in the glucose alone medium and those in the glucose fermentation phase in the mixed-sugar medium. Whereas the transcriptomes highly differed between the xylose metabolizing cells in the xylose alone medium and those in the xylose fermentation phase in the mixed sugar medium, and the differences mainly involved sulfur metabolism. When the transcriptional profiles were compared between glucose fermentation state and xylose fermentation state, we found the expression patterns of hexose transporters and glucose signaling pathway differed in response to different sugar sources, and the expression levels of the genes involved in gluconeogenesis, the glyoxylate and tricarboxylic acid cycles and respiration increased with xylose, indicating that the xylose-metabolizing cells had high requirements for maintenance energy and lacked the carbon catabolite repression capability. The effect of carbon catabolite repression by glucose lasted after glucose depletion for specific genes to different extents.

Recent applications of metabolomics to advance microbial biofuel production

Biofuel production from plant biomass is a promising source of renewable energy [1]. However, efficient biofuel production involves the complex task of engineering high-performance microorganisms, which requires detailed knowledge of metabolic function and regulation. This review highlights the potential of mass-spectrometry-based metabolomic analysis to guide rational engineering of biofuel-producing microbes. We discuss recent studies that apply knowledge gained from metabolomic analyses to increase the productivity of engineered pathways, characterize the metabolism of emerging biofuel producers, generate novel bioproducts, enable utilization of lignocellulosic feedstock, and improve the stress tolerance of biofuel producers.

Comprehensive analysis of glucose and xylose metabolism in Escherichia coli under aerobic and anaerobic conditions by 13C metabolic flux analysis

Glucose and xylose are the two most abundant sugars derived from the breakdown of lignocellulosic biomass. While aerobic glucose metabolism is relatively well understood in E. coli, until now there have been only a handful of studies focused on anaerobic glucose metabolism and no ¹³C-flux studies on xylose metabolism. In the absence of experimentally validated flux maps, constraint-based approaches such as MOMA and RELATCH cannot be used to guide new metabolic engineering designs. In this work, we have addressed this critical gap in current understanding by performing comprehensive characterizations of glucose and xylose metabolism under aerobic and anaerobic conditions, using recent state-of-the-art techniques in ¹³C metabolic flux analysis (¹³C-MFA). Specifically, we quantified precise metabolic fluxes for each condition by performing parallel labeling experiments and analyzing the data through integrated ¹³C-MFA using the optimal tracers [1,2-¹³C]glucose, [1,6-¹³C]glucose, [1,2-¹³C]xylose and [5-¹³C]xylose. We also quantified changes in biomass composition and confirmed turnover of macromolecules by applying [U-¹³C]glucose and [U-¹³C]xylose tracers. We demonstrated that under anaerobic growth conditions there is significant turnover of lipids and that a significant portion of CO₂ originates from biomass turnover. Using knockout strains, we also demonstrated that β-oxidation is critical for anaerobic growth on xylose. Quantitative analysis of co-factor balances (NADH/FADH₂, NADPH, and ATP) for different growth conditions provided new insights regarding the interplay of energy and redox metabolism and the impact on E. coli cell physiology.

Investigate the Metabolic Reprogramming of Saccharomyces cerevisiae for Enhanced Resistance to Mixed Fermentation Inhibitors via 13C Metabolic Flux Analysis

The fermentation inhibitors from the pretreatment of lignocellulosic materials, e.g., acetic acid and furfural, are notorious due to their negative effects on the cell growth and chemical production. However, the metabolic reprogramming of the cells under these stress conditions, especially metabolic response for resistance to mixed inhibitors, has not been systematically investigated and remains mysterious. Therefore, in this study, 13C metabolic flux analysis (13C-MFA), a powerful tool to elucidate the intracellular carbon flux distributions, has been applied to two Saccharomyces cerevisiae strains with different tolerances to the inhibitors under acetic acid, furfural, and mixed (i.e., acetic acid and furfural) stress conditions to unravel the key metabolic responses. By analyzing the intracellular carbon fluxes as well as the energy and cofactor utilization under different conditions, we uncovered varied metabolic responses to different inhibitors. Under acetate stress, ATP and NADH production was slightly impaired, while NADPH tended towards overproduction. Under furfural stress, ATP and cofactors (including both NADH and NADPH) tended to be overproduced. However, under dual-stress condition, production of ATP and cofactors was severely impaired due to synergistic stress caused by the simultaneous addition of two fermentation inhibitors. Such phenomenon indicated the pivotal role of the energy and cofactor utilization in resisting the mixed inhibitors of acetic acid and furfural. Based on the discoveries, valuable insights are provided to improve the tolerance of S. cerevisiae strain and further enhance lignocellulosic fermentation.

Rapid and efficient galactose fermentation by engineered Saccharomyces cerevisiae

In the important industrial yeast Saccharomyces cerevisiae, galactose metabolism requires energy production by respiration; therefore, this yeast cannot metabolize galactose under strict anaerobic conditions. While the respiratory dependence of galactose metabolism provides benefits in terms of cell growth and population stability, it is not advantageous for producing fuels and chemicals since a substantial fraction of consumed galactose is converted to carbon dioxide. In order to force S. cerevisiae to use galactose without respiration, a subunit (COX9) of a respiratory enzyme was deleted, but the resulting deletion mutant (Δcox9) was impaired in terms of galactose assimilation. Interestingly, after serial sub-cultures on galactose, the mutant evolved rapidly and was able to use galactose via fermentation only. The evolved strain (JQ-G1) produced ethanol from galactose with a 94% increase in yield and 6.9-fold improvement in specific productivity as compared to the wild-type strain. (13)C-metabolic flux analysis demonstrated a three-fold reduction in carbon flux through the TCA cycle of the evolved mutant with redirection of flux toward the fermentation pathway. Genome sequencing of the JQ-G1 strain revealed a loss of function mutation in a master negative regulator of the Leloir pathway (Gal80p). The mutation (Glu348*) in Gal80p was found to act synergistically with deletion of COX9 for efficient galactose fermentation, and thus the double deletion mutant Δcox9Δgal80 produced ethanol 2.4 times faster and with 35% higher yield than a single knockout mutant with deletion of GAL80 alone. When we introduced a functional COX9 cassette back into the JQ-G1 strain, the JQ-G1-COX9 strain showed a 33% reduction in specific galactose uptake rate and a 49% reduction in specific ethanol production rate as compared to JQ-G1. The wild-type strain was also subjected to serial sub-cultures on galactose but we failed to isolate a mutant capable of utilizing galactose without respiration. We concluded that the metabolic "death valley" (i.e. no galactose utilization by the Δcox9 mutant) is a necessary intermediate phenotype to facilitate galactose utilization without respiration in yeast. The results in this study demonstrate a promising approach for directing adaptive evolution toward fermentative metabolism and for generating evolved yeast strains with improved phenotypes under anaerobic conditions.

High-throughput evaluation of synthetic metabolic pathways

A central challenge in the field of metabolic engineering is the efficient identification of a metabolic pathway genotype that maximizes specific productivity over a robust range of process conditions. Here we review current methods for optimizing specific productivity of metabolic pathways in living cells. New tools for library generation, computational analysis of pathway sequence-flux space, and high-throughput screening and selection techniques are discussed.

¹³C Pathway Analysis for the Role of Formate in Electricity Generation by Shewanella Oneidensis MR-1 Using Lactate in Microbial Fuel Cells

Microbial fuel cell (MFC) is a promising technology for direct electricity generation from organics by microorganisms. The type of electron donors fed into MFCs affects the electrical performance, and mechanistic understanding of such effects is important to optimize the MFC performance. In this study, we used a model organism in MFCs, Shewanella oneidensis MR-1, and (13)C pathway analysis to investigate the role of formate in electricity generation and the related microbial metabolism. Our results indicated a synergistic effect of formate and lactate on electricity generation, and extra formate addition on the original lactate resulted in more electrical output than using formate or lactate as a sole electron donor. Based on the (13)C tracer analysis, we discovered decoupled cell growth and electricity generation in S. oneidensis MR-1 during co-utilization of lactate and formate (i.e., while the lactate was mainly metabolized to support the cell growth, the formate was oxidized to release electrons for higher electricity generation). To our best knowledge, this is the first time that (13)C tracer analysis was applied to study microbial metabolism in MFCs and it was demonstrated to be a valuable tool to understand the metabolic pathways affected by electron donors in the selected electrochemically-active microorganisms.

Metabolic engineering of Saccharomyces cerevisiae to produce 1-hexadecanol from xylose

Background

An advantageous but challenging approach to overcome the limited supply of petroleum and relieve the greenhouse effect is to produce bulk chemicals from renewable materials. Fatty alcohols, with a billion-dollar global market, are important raw chemicals for detergents, emulsifiers, lubricants, and cosmetics production. Microbial production of fatty alcohols has been successfully achieved in several industrial microorganisms. However, most of the achievements were using glucose, an edible sugar, as the carbon source. To produce fatty alcohols in a renewable manner, non-edible sugars such as xylose will be a more appropriate feedstock.

Results

In this study, we aim to engineer a Saccharomyces cerevisiae strain that can efficiently convert xylose to fatty alcohols. To this end, we first introduced the fungal xylose utilization pathway consisting of xylose reductase (XR), xylitol dehydrogenase (XDH), and xylulose kinase (XKS) into a fatty alcohol-producing S. cerevisiae strain (XF3) that was developed in our previous studies to achieve 1-hexadecanol production from xylose at 0.4 g/L. We next applied promoter engineering on the xylose utilization pathway to optimize the expression levels of XR, XDH, and XKS, and increased the 1-hexadecanol titer by 171 %. To further improve the xylose-based fatty alcohol production, two optimized S. cerevisiae strains from promoter engineering were evolved with the xylose as the sole carbon source. We found that the cell growth rate was improved at the expense of decreased fatty alcohol production, which indicated 1-hexadecanol was mainly produced as a non-growth associated product. Finally, through fed-batch fermentation, we successfully achieved 1-hexadecanol production at over 1.2 g/L using xylose as the sole carbon source, which represents the highest titer of xylose-based 1-hexadecanol reported in microbes to date.

Conclusions

A fatty alcohol-producing S. cerevisiae strain was engineered in this study to produce 1-hexadecanol from xylose. Although the xylose pathway we developed in this study could be further improved, this proof-of-concept study, for the first time to our best knowledge, demonstrated that the xylose-based fatty alcohol could be produced in S. cerevisiae with potential applications in developing consolidated bioprocessing for producing other fatty acid-derived chemicals.

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-016-0423-9) 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

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

(13)C metabolic flux analysis of the extremely thermophilic, fast growing, xylose-utilizing Geobacillus strain LC300

Thermophiles are increasingly used as versatile hosts in the biotechnology industry. One of the key advantages of thermophiles is the potential to achieve high rates of feedstock conversion at elevated temperatures. The recently isolated Geobacillus strain LC300 grows extremely fast on xylose, with a doubling time of less than 30 min. In the accompanying paper, the genome of Geobacillus LC300 was sequenced and annotated. In this work, we have experimentally validated the metabolic network model using parallel (13)C-labeling experiments and applied (13)C-metabolic flux analysis to quantify precise metabolic fluxes. Specifically, the complete set of singly labeled xylose tracers, [1-(13)C], [2-(13)C], [3-(13)C], [4-(13)C], and [5-(13)C]xylose, was used for the first time. Isotopic labeling of biomass amino acids was measured by gas chromatography mass spectrometry (GC-MS). Isotopic labeling of carbon dioxide in the off-gas was also measured by an on-line mass spectrometer. The (13)C-labeling data was then rigorously integrated for flux elucidation using the COMPLETE-MFA approach. The results provided important new insights into the metabolism of Geobacillus LC300, its efficient xylose utilization pathways, and the balance between carbon, redox and energy fluxes. The pentose phosphate pathway, glycolysis and TCA cycle were found to be highly active in Geobacillus LC300. The oxidative pentose phosphate pathway was also active and contributed significantly to NADPH production. No transhydrogenase activity was detected. Results from this work provide a solid foundation for future studies of this strain and its metabolic engineering and biotechnological applications.

Metabolic engineering of yeast to produce fatty acid-derived biofuels: bottlenecks and solutions

Fatty acid-derived biofuels can be a better solution than bioethanol to replace petroleum fuel, since they have similar energy content and combustion properties as current transportation fuels. The environmentally friendly microbial fermentation process has been used to synthesize advanced biofuels from renewable feedstock. Due to their robustness as well as the high tolerance to fermentation inhibitors and phage contamination, yeast strains such as Saccharomyces cerevisiae and Yarrowia lipolytica have attracted tremendous attention in recent studies regarding the production of fatty acid-derived biofuels, including fatty acids, fatty acid ethyl esters, fatty alcohols, and fatty alkanes. However, the native yeast strains cannot produce fatty acids and fatty acid-derived biofuels in large quantities. To this end, we have summarized recent publications in this review on metabolic engineering of yeast strains to improve the production of fatty acid-derived biofuels, identified the bottlenecks that limit the productivity of biofuels, and categorized the appropriate approaches to overcome these obstacles.

Multi -omics and metabolic modelling pipelines: challenges and tools for systems microbiology

Integrated -omics approaches are quickly spreading across microbiology research labs, leading to (i) the possibility of detecting previously hidden features of microbial cells like multi-scale spatial organization and (ii) tracing molecular components across multiple cellular functional states. This promises to reduce the knowledge gap between genotype and phenotype and poses new challenges for computational microbiologists. We underline how the capability to unravel the complexity of microbial life will strongly depend on the integration of the huge and diverse amount of information that can be derived today from -omics experiments. In this work, we present opportunities and challenges of multi -omics data integration in current systems biology pipelines. We here discuss which layers of biological information are important for biotechnological and clinical purposes, with a special focus on bacterial metabolism and modelling procedures. A general review of the most recent computational tools for performing large-scale datasets integration is also presented, together with a possible framework to guide the design of systems biology experiments by microbiologists.

13C pathway analysis of biofilm metabolism of Shewanella oneidensis MR-1

Biofilm metabolism ofShewanellawas analyzedvia¹³C tracing experiments for the first time.

Investigating oxalate biosynthesis in the wood-decaying fungus Gloeophyllum trabeum using 13C metabolic flux analysis

Oxalate synthesis was rigorously investigated in a wood-decaying fungus, Gloeophyllum trabeum, using ¹³C metabolic flux analysis, a method not previously explored in this type of system.

Metabolomic and (13)C-metabolic flux analysis of a xylose-consuming Saccharomyces cerevisiae strain expressing xylose isomerase

Over the past two decades, significant progress has been made in the engineering of xylose-consuming Saccharomyces cerevisiae strains for production of lignocellulosic biofuels. However, the ethanol productivities achieved on xylose are still significantly lower than those observed on glucose for reasons that are not well understood. We have undertaken an analysis of central carbon metabolite pool sizes and metabolic fluxes on glucose and on xylose under aerobic and anaerobic conditions in a strain capable of rapid xylose assimilation via xylose isomerase in order to investigate factors that may limit the rate of xylose fermentation. We find that during xylose utilization the flux through the non-oxidative Pentose Phosphate Pathway (PPP) is high but the flux through the oxidative PPP is low, highlighting an advantage of the strain employed in this study. Furthermore, xylose fails to elicit the full carbon catabolite repression response that is characteristic of glucose fermentation in S. cerevisiae. We present indirect evidence that the incomplete activation of the fermentation program on xylose results in a bottleneck in lower glycolysis, leading to inefficient re-oxidation of NADH produced in glycolysis.

Re-assessment of YAP1 and MCR1 contributions to inhibitor tolerance in robust engineered Saccharomyces cerevisiae fermenting undetoxified lignocellulosic hydrolysate

Development of robust yeast strains that can efficiently ferment lignocellulose-based feedstocks is one of the requirements for achieving economically feasible bioethanol production processes. With this goal, several genes have been identified as promising candidates to confer improved tolerance to S. cerevisiae. In most of the cases, however, the evaluation of the genetic modification was performed only in laboratory strains, that is, in strains that are known to be quite sensitive to various types of stresses. In the present study, we evaluated the effects of overexpressing genes encoding the transcription factor (YAP1) and the mitochondrial NADH-cytochrome b5 reductase (MCR1), either alone or in combination, in an already robust and xylose-consuming industrial strain of S. cerevisiae and evaluated the effect during the fermentation of undiluted and undetoxified spruce hydrolysate. Overexpression of either gene resulted in faster hexose catabolism, but no cumulative effect was observed with the simultaneous overexpression. The improved phenotype of MCR1 overexpression appeared to be related, at least in part, to a faster furaldehyde reduction capacity, indicating that this reductase may have a wider substrate range than previously reported. Unexpectedly a decreased xylose fermentation rate was also observed in YAP1 overexpressing strains and possible reasons behind this phenotype are discussed.

Elucidation of intrinsic biosynthesis yields using 13C-based metabolism analysis

This paper discusses the use of 13C-based metabolism analysis for the assessment of intrinsic product yields - the actual carbon contribution from a single carbon substrate to the final product via a specific biosynthesis route - in the following four cases. First, undefined nutrients (such as yeast extract) in fermentation may contribute significantly to product synthesis, which can be quantified through an isotopic dilution method. Second, product and biomass synthesis may be dependent on the co-metabolism of multiple-carbon sources. 13C labeling experiments can track the fate of each carbon substrate in the cell metabolism and identify which substrate plays a main role in product synthesis. Third, 13C labeling can validate and quantify the contribution of the engineered pathway (versus the native pathway) to the product synthesis. Fourth, the loss of catabolic energy due to cell maintenance (energy used for functions other than production of new cell components) and low P/O ratio (Phosphate/Oxygen Ratio) significantly reduces product yields. Therefore, 13C-metabolic flux analysis is needed to assess the influence of suboptimal energy metabolism on microbial productivity, and determine how ATP/NAD(P)H are partitioned among various cellular functions. Since product yield is a major determining factor in the commercialization of a microbial cell factory, we foresee that 13C-isotopic labeling experiments, even without performing extensive flux calculations, can play a valuable role in the development and verification of microbial cell factories.