High-resolution 13C metabolic flux analysis

Increased Ammonium Toxicity in Response to Exogenous Glutamine in Metastatic Breast Cancer Cells

Several cancers, including breast cancers, show dependence on glutamine metabolism. The purpose of the present study was to determine the mechanistic basis and impact of differential glutamine metabolism in nonmetastatic and metastatic murine mammary cancer cells. Universally labeled 13C5-glutamine metabolic tracing, qRT-PCR, measures of reductive-oxidative balance, and exogenous ammonium chloride treatment were used to assess glutamine reprogramming. Results show that 4 mM media concentration of glutamine, compared with 2 mM, reduced viability only in metastatic cells, and that this decrease in viability was accompanied by increased incorporation of glutamine-derived carbon into the tricarboxylic acid (TCA) cycle. While increased glutamine metabolism in metastatic cells occurred in tandem with a decrease in the reduced/oxidized glutathione ratio, treatment with the antioxidant molecule N-acetylcysteine did not rescue cell viability. However, the viability of metastatic cells was more sensitive to ammonium chloride treatment compared with nonmetastatic cells, suggesting a role of metabolic reprogramming in averting nitrogen cytotoxicity in nonmetastatic cells. Overall, these results demonstrate the ability of nonmetastatic cancer cells to reprogram glutamine metabolism and that this ability may be lost in metastatic cells.

Multi-Strain and -Species Investigation of Volatile Metabolites Emitted from Planktonic and Biofilm Candida Cultures

Candida parapsiliosis is a prevalent neonatal pathogen that attains its virulence through its strain-specific ability to form biofilms. The use of volatilomics, the profiling of volatile metabolites from microbes is a non-invasive, simple way to identify and classify microbes; it has shown great potential for pathogen identification. Although C. parapsiliosis is one of the most common clinical fungal pathogens, its volatilome has never been characterised. In this study, planktonic volatilomes of ten clinical strains of C. parapsilosis were analysed, along with a single strain of Candida albicans. Headspace-solid-phase microextraction coupled with gas chromatography-mass spectrometry were employed to analyse the samples. Species-, strain-, and media- influences on the fungal volatilomes were investigated. Twenty-four unique metabolites from the examined Candida spp. (22 from C. albicans; 18 from C. parapsilosis) were included in this study. Chemical classes detected across the samples included alcohols, fatty acid esters, acetates, thiols, sesquiterpenes, and nitrogen-containing compounds. C. albicans volatilomes were most clearly discriminated from C. parapsilosis based on the detection of unique sesquiterpene compounds. The effect of biofilm formation on the C. parapsilosis volatilomes was investigated for the first time by comparing volatilomes of a biofilm-positive strain and a biofilm-negative strain over time (0–48 h) using a novel sampling approach. Volatilomic shifts in the profiles of alcohols, ketones, acids, and acetates were observed specifically in the biofilm-forming samples and attributed to biofilm maturation. This study highlights species-specificity of Candida volatilomes, and also marks the clinical potential for volatilomics for non-invasively detecting fungal pathogens. Additionally, the range of biofilm-specificity across microbial volatilomes is potentially far-reaching, and therefore characterising these volatilomic changes in pathogenic fungal and bacterial biofilms could lead to novel opportunities for detecting severe infections early.

In Silico Design Strategies for the Production of Target Chemical Compounds Using Iterative Single-Level Linear Programming Problems

The optimization of metabolic reaction modifications for the production of target compounds is a complex computational problem whose execution time increases exponentially with the number of metabolic reactions. Therefore, practical technologies are needed to identify reaction deletion combinations to minimize computing times and promote the production of target compounds by modifying intracellular metabolism. In this paper, a practical metabolic design technology named AERITH is proposed for high-throughput target compound production. This method can optimize the production of compounds of interest while maximizing cell growth. With this approach, an appropriate combination of metabolic reaction deletions can be identified by solving a simple linear programming problem. Using a standard CPU, the computation time could be as low as 1 min per compound, and the system can even handle large metabolic models. AERITH was implemented in MATLAB and is freely available for non-profit use.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ²-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Measuring metabolic reaction fluxes in living cells is difficult, yet important. The gold standard is to label extracellular metabolites with ¹³C, to use mass spectrometry to find out where the ¹³C-atoms ends up, and finally use mathematical modelling to calculate how quickly each reaction must have flowed, for the ¹³C-atoms to end up like that. This measurement thus relies on usage of the right mathematical model, which must be selected among various candidate models. In this manuscript, we present a new way to do this model selection step, utilizing validation data. Using an adopted approach to calculate the uncertainty of model predictions, we identify new validation experiments, which are neither too similar, nor too dissimilar, compared to the previous training data. The model candidate that is best at predicting this new validation data is the one chosen. Tests on simulated data where the true model is known, shows that the validation-based method is robust when the magnitude of the error in the measurement uncertainty is unknown, something that conventional methods are not. This improvement is important since true uncertainties can be difficult to estimate for these data. Finally, we demonstrate how the new method can be used on real data, to identify fluxes and important reactions.

Attenuation of glutamine synthetase selection marker improves product titer and reduces glutamine overflow in Chinese hamster ovary cells

The glutamine synthetase (GS) expression system is commonly used to ensure stable transgene integration and amplification in CHO host lines. Transfected cell populations are typically grown in the presence of the GS inhibitor, methionine sulfoximine (MSX), to further select for increased transgene copy number. However, high levels of GS activity produce excess glutamine. We hypothesized that attenuating the GS promoter while keeping the strong IgG promoter on the GS-IgG expression vector would result in a more efficient cellular metabolic phenotype. Herein, we characterized CHO cell lines expressing GS from either an attenuated promoter or an SV40 promoter and selected with/without MSX. CHO cells with the attenuated GS promoter had higher IgG specific productivity and lower glutamine production compared to cells with SV40-driven GS expression. Selection with MSX increased both specific productivity and glutamine production, regardless of GS promoter strength. ¹³ C metabolic flux analysis (MFA) was performed to further assess metabolic differences between these cell lines. Interestingly, central carbon metabolism was unaltered by the attenuated GS promoter while the fate of glutamate and glutamine varied depending on promoter strength and selection conditions. This study highlights the ability to optimize the GS expression system to improve IgG production and reduce wasteful glutamine overflow, without significantly altering central metabolism. Additionally, a detailed supplementary analysis of two "lactate runaway" reactors provides insight into the poorly understood phenomenon of excess lactate production by some CHO cell cultures. This article is protected by copyright. All rights reserved.

Trypanosoma brucei: Metabolomics for analysis of cellular metabolism and drug discovery

BACKGROUND

Trypanosoma brucei is the causative agent of Human African Trypanosomiasis (also known as sleeping sickness), a disease causing serious neurological disorders and fatal if left untreated. Due to its lethal pathogenicity, a variety of treatments have been developed over the years, but which have some important limitations such as acute toxicity and parasite resistance. Metabolomics is an innovative tool used to better understand the parasite's cellular metabolism, and identify new potential targets, modes of action and resistance mechanisms. The metabolomic approach is mainly associated with robust analytical techniques, such as NMR and Mass Spectrometry. Applying these tools to the trypanosome parasite is, thus, useful for providing new insights into the sleeping sickness pathology and guidance towards innovative treatments.

AIM OF REVIEW

The present review aims to comprehensively describe the T. brucei biology and identify targets for new or commercialized antitrypanosomal drugs. Recent metabolomic applications to provide a deeper knowledge about the mechanisms of action of drugs or potential drugs against T. brucei are highlighted. Additionally, the advantages of metabolomics, alone or combined with other methods, are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Compared to other parasites, only few studies employing metabolomics have to date been reported on Trypanosoma brucei. Published metabolic studies, treatments and modes of action are discussed. The main interest is to evaluate the metabolomics contribution to the understanding of T. brucei's metabolism.

Glucocorticoids coordinate macrophage metabolism through the regulation of the tricarboxylic acid cycle

OBJECTIVES

Glucocorticoids (GCs) are one of the most widely prescribed anti-inflammatory drugs. By acting through their cognate receptor, the glucocorticoid receptor (GR), GCs downregulate the expression of pro-inflammatory genes and upregulate the expression of anti-inflammatory genes. Metabolic pathways have recently been identified as key parts of both the inflammatory activation and anti-inflammatory polarization of macrophages, immune cells responsible for acute inflammation and tissue repair. It is currently unknown whether GCs control macrophage metabolism, and if so, to what extent metabolic regulation by GCs confers anti-inflammatory activity.

METHODS

Using transcriptomic and metabolomic profiling of macrophages, we identified GC-controlled pathways involved in metabolism, especially in mitochondrial function.

RESULTS

Metabolic analyses revealed that GCs repress glycolysis in inflammatory myeloid cells and promote tricarboxylic acid (TCA) cycle flux, promoting succinate metabolism and preventing intracellular accumulation of succinate. Inhibition of ATP synthase attenuated GC-induced transcriptional changes, likely through stalling of TCA cycle anaplerosis. We further identified a glycolytic regulatory transcription factor, HIF1α, as regulated by GCs, and as a key regulator of GC responsiveness during inflammatory challenge.

CONCLUSIONS

Our findings link metabolism to gene regulation by GCs in macrophages.

Adapting isotopic tracer and metabolic flux analysis approaches to study C1 metabolism

Single-carbon (C1, or one-carbon) substrates are promising feedstocks for sustainable biofuel and biochemical production. Crucial to the goal of engineering C1-utilizing strains for improved production is a quantitative understanding of the organization, regulation and rates of the reactions that underpin C1 metabolism. ¹³C Metabolic flux analysis (MFA) is a well-established platform for interrogating these questions with multi-carbon substrates, and uses the differential labeling of metabolites that results from feeding a substrate with position-specific incorporation of ¹³C in order to infer quantitative fluxes and pathway topology. Adapting isotopic tracer approaches to C1 metabolism, where position-specific substrate labeling is impossible, requires additional experimental considerations. Here we review recent studies that have developed isotopic tracer approaches to overcome the challenge of uniform metabolite labeling and provide quantitative insight into C1 metabolism.

Engineering Yeast Yarrowia lipolytica for Methanol Assimilation

Conferring methylotrophy on industrial microorganisms would enable the production of diverse products from one-carbon feedstocks and contribute to establishing a low-carbon society. Rebuilding methylotrophs, however, requires a thorough metabolic refactoring and is highly challenging. Only recently was synthetic methylotrophy achieved in model microorganisms─Escherichia coli and baker's yeast Saccharomyces cerevisiae. Here, we have engineered industrially important yeast Yarrowia lipolytica to assimilate methanol. Through rationally constructing a chimeric assimilation pathway, rewiring the native metabolism for improved precursor supply, and laboratory evolution, we improved the methanol assimilation from undetectable to a level of 1.1 g/L per 72 h and enabled methanol-supported cellular maintenance. By transcriptomic analysis, we further found that fine-tuning of methanol assimilation and ribulose monophosphate/xylulose monophosphate (RuMP/XuMP) regeneration and strengthening formate dehydrogenation and the serine pathway were beneficial for methanol assimilation. This work paves the way for creating synthetic methylotrophic yeast cell factories for low-carbon economy.

Coordinated reprogramming of metabolism and cell function in adipocytes from proliferation to differentiation

Adipose tissue plays a major role in regulating lipid and energy homeostasis by storing excess nutrients, releasing energetic substrates through lipolysis, and regulating metabolism of other tissues and organs through endocrine and paracrine signaling. Adipocytes within fat tissues store excess nutrients through increased cell number (hyperplasia), increased cell size (hypertrophy), or both. The differentiation of pre-adipocytes into mature lipid-accumulating adipocytes requires a complex interaction of metabolic pathways that is still incompletely understood. Here, we applied parallel labeling experiments and ¹³C-metabolic flux analysis to quantify precise metabolic fluxes in proliferating and differentiated 3T3-L1 cells, a widely used model to study adipogenesis. We found that morphological and biomass composition changes in adipocytes were accompanied by significant shifts in metabolic fluxes, encompassing all major metabolic pathways. In contrast to proliferating cells, differentiated adipocytes 1) increased glucose uptake and redirected glucose utilization from lactate production to lipogenesis and energy generation; 2) increased pathway fluxes through glycolysis, oxidative pentose phosphate pathway and citric acid cycle; 3) reduced lactate secretion, resulting in increased ATP generation via oxidative phosphorylation; 4) rewired glutamine metabolism, from glutaminolysis to de novo glutamine synthesis; 5) increased cytosolic NADPH production, driven mostly by increased cytosolic malic enzyme flux; 6) increased production of monounsaturated C16:1; and 7) activated a mitochondrial pyruvate cycle through simultaneous activity of pyruvate carboxylase, malate dehydrogenase and malic enzyme. Taken together, these results quantitatively highlight the complex interplay between pathway fluxes and cell function in adipocytes, and suggest a functional role for metabolic reprogramming in adipose differentiation and lipogenesis.

Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics

Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.

A genome-scale nutrient minimization forecast algorithm for controlling essential amino acid levels in CHO cell cultures

Mammalian cell culture processes rely heavily on empirical knowledge in which process control remains a challenge due to the limited characterization/understanding of cell metabolism and inability to predict the cell behaviors. This study facilitates control of Chinese hamster ovary (CHO) processes through a forecast-based feeding approach that predicts multiple essential amino acids levels in the culture from easily acquired viable cell density data. Multiple cell growth behavior forecast extrapolation approaches are considered with logistic curve fitting found to be the most effective. Next, the nutrient-minimized CHO genome-scale model is combined with the growth forecast model to generate essential amino acid forecast profiles of multiple CHO batch cultures. Comparison of the forecast with the measurements suggests that this algorithm can accurately predict the concentration of most essential amino acids from cell density measurement with error mitigated by incorporating off-line amino acids concentration measurements. Finally, the forecast algorithm is applied to CHO fed-batch cultures to support amino acid feeding control to control the concentration of essential amino acids below 1-2 mM for lysine, leucine, and valine as a model over a 9-day fed batch culture while maintaining comparable growth behavior to an empirical-based culture. In turn, glycine production was elevated, alanine reduced and lactate production slightly lower in control cultures due to metabolic shifts in branched-chain amino acid degradation. With the advantage of requiring minimal measurement inputs while providing valuable and in-advance information of the system based on growth measurements, this genome model-based amino acid forecast algorithm represent a powerful and cost-effective tool to facilitate enhanced control over CHO and other mammalian cell-based bioprocesses.

Stable isotope tracing to assess tumor metabolism in vivo

Cancer cells undergo diverse metabolic adaptations to meet the energetic demands imposed by dysregulated growth and proliferation. Assessing metabolism in intact tumors allows the investigator to observe the combined metabolic effects of numerous cancer cell-intrinsic and -extrinsic factors that cannot be fully captured in culture models. We have developed methods to use stable isotope-labeled nutrients (e.g., [¹³C]glucose) to probe metabolic activity within intact tumors in vivo, in mice and humans. In these methods, the labeled nutrient is introduced to the circulation through an intravenous catheter prior to surgical resection of the tumor and adjacent nonmalignant tissue. Metabolism within these tissues during the infusion transfers the isotope label into metabolic intermediates from pathways supplied by the infused nutrient. Extracting metabolites from surgical specimens and analyzing their isotope labeling patterns provides information about metabolism in the tissue. We provide detailed information about this technique, from introduction of the labeled tracer through data analysis and interpretation, including streamlined approaches to quantify isotope labeling in informative metabolites extracted from tissue samples. We focus on infusions with [¹³C]glucose and the application of mass spectrometry to assess isotope labeling in intermediates from central metabolic pathways, including glycolysis, the tricarboxylic acid cycle and nonessential amino acid synthesis. We outline practical considerations to apply these methods to human subjects undergoing surgical resections of solid tumors. We also discuss the method's versatility and consider the relative advantages and limitations of alternative approaches to introduce the tracer, harvest the tissue and analyze the data.

Non-destructive quantification of anaerobic gut fungi and methanogens in co-culture reveals increased fungal growth rate and changes in metabolic flux relative to mono-culture

BACKGROUND

Quantification of individual species in microbial co-cultures and consortia is critical to understanding and designing communities with prescribed functions. However, it is difficult to physically separate species or measure species-specific attributes in most multi-species systems. Anaerobic gut fungi (AGF) (Neocallimastigomycetes) are native to the rumen of large herbivores, where they exist as minority members among a wealth of prokaryotes. AGF have significant biotechnological potential owing to their diverse repertoire of potent lignocellulose-degrading carbohydrate-active enzymes (CAZymes), which indirectly bolsters activity of other rumen microbes through metabolic exchange. While decades of literature suggest that polysaccharide degradation and AGF growth are accelerated in co-culture with prokaryotes, particularly methanogens, methods have not been available to measure concentrations of individual species in co-culture. New methods to disentangle the contributions of AGF and rumen prokaryotes are sorely needed to calculate AGF growth rates and metabolic fluxes to prove this hypothesis and understand its causality for predictable co-culture design.

RESULTS

We present a simple, microplate-based method to measure AGF and methanogen concentrations in co-culture based on fluorescence and absorbance spectroscopies. Using samples of < 2% of the co-culture volume, we demonstrate significant increases in AGF growth rate and xylan and glucose degradation rates in co-culture with methanogens relative to mono-culture. Further, we calculate significant differences in AGF metabolic fluxes in co-culture relative to mono-culture, namely increased flux through the energy-generating hydrogenosome organelle. While calculated fluxes highlight uncertainties in AGF primary metabolism that preclude definitive explanations for this shift, our method will enable steady-state fluxomic experiments to probe AGF metabolism in greater detail.

CONCLUSIONS

The method we present to measure AGF and methanogen concentrations enables direct growth measurements and calculation of metabolic fluxes in co-culture. These metrics are critical to develop a quantitative understanding of interwoven rumen metabolism, as well as the impact of co-culture on polysaccharide degradation and metabolite production. The framework presented here can inspire new methods to probe systems beyond AGF and methanogens. Simple modifications to the method will likely extend its utility to co-cultures with more than two organisms or those grown on solid substrates to facilitate the design and deployment of microbial communities for bioproduction and beyond.

Engineering Bacteroides thetaiotaomicron to produce non-native butyrate based on a genome-scale metabolic model-guided design

Bacteroides thetaiotaomicron represents a major symbiont of the human gut microbiome that is increasingly viewed as a promising candidate strain for microbial therapeutics. Here, we engineer B. thetaiotaomicron for heterologous production of non-native butyrate as a proof-of-concept biochemical at therapeutically relevant concentrations. Since B. thetaiotaomicron is not a natural producer of butyrate, we heterologously expressed a butyrate biosynthetic pathway in the strain, which led to the production of butyrate at the final concentration of 12 mg/L in a rich medium. Further optimization of butyrate production was achieved by a round of metabolic engineering guided by an expanded genome-scale metabolic model (GEM) of B. thetaiotaomicron. The in silico knock-out simulation of the expanded model showed that pta and ldhD were the potent knock-out targets to enhance butyrate production. The maximum titer and specific productivity of butyrate in the pta-ldhD double knockout mutant increased by nearly 3.4 and 4.8 folds, respectively. To our knowledge, this is the first engineering attempt that enabled butyrate production from a non-butyrate producing commensal B. thetaiotaomicron. The study also highlights that B. thetaiotaomicron can serve as an effective strain for live microbial therapeutics in human.

Prospects for multi-omics in the microbial ecology of water engineering

Advances in high-throughput sequencing technologies and bioinformatics approaches over almost the last three decades have substantially increased our ability to explore microorganisms and their functions - including those that have yet to be cultivated in pure isolation. Genome-resolved metagenomic approaches have enabled linking powerful functional predictions to specific taxonomical groups with increasing fidelity. Additionally, related developments in both whole community gene expression surveys and metabolite profiling have permitted for direct surveys of community-scale functions in specific environmental settings. These advances have allowed for a shift in microbiome science away from descriptive studies and towards mechanistic and predictive frameworks for designing and harnessing microbial communities for desired beneficial outcomes. Water engineers, microbiologists, and microbial ecologists studying activated sludge, anaerobic digestion, and drinking water distribution systems have applied various (meta)omics techniques for connecting microbial community dynamics and physiologies to overall process parameters and system performance. However, the rapid pace at which new omics-based approaches are developed can appear daunting to those looking to apply these state-of-the-art practices for the first time. Here, we review how modern genome-resolved metagenomic approaches have been applied to a variety of water engineering applications from lab-scale bioreactors to full-scale systems. We describe integrated omics analysis across engineered water systems and the foundations for pairing these insights with modeling approaches. Lastly, we summarize emerging omics-based technologies that we believe will be powerful tools for water engineering applications. Overall, we provide a framework for microbial ecologists specializing in water engineering to apply cutting-edge omics approaches to their research questions to achieve novel functional insights. Successful adoption of predictive frameworks in engineered water systems could enable more economically and environmentally sustainable bioprocesses as demand for water and energy resources increases.

Microbial production of riboflavin: Biotechnological advances and perspectives

Riboflavin is an essential nutrient for humans and animals, and its derivatives flavin mononucleotide (FMN) and flavin adenine dinucleotide (FAD) are cofactors in the cells. Therefore, riboflavin and its derivatives are widely used in the food, pharmaceutical, nutraceutical and cosmetic industries. Advances in biotechnology have led to a complete shift in the commercial production of riboflavin from chemical synthesis to microbial fermentation. In this review, we provide a comprehensive review of biotechnologies that enhance riboflavin production in microorganisms, as well as representative examples. Firstly, the synthesis pathways and metabolic regulatory processes of riboflavin in microorganisms; and the current strategies and methods of metabolic engineering for riboflavin production are systematically summarized and compared. Secondly, the using of systematic metabolic engineering strategies to enhance riboflavin production is discussed, including laboratory evolution, histological analysis and high-throughput screening. Finally, the challenges for efficient microbial production of riboflavin and the strategies to overcome these challenges are prospected.

How to Tackle Underdeterminacy in Metabolic Flux Analysis? A Tutorial and Critical Review

Metabolic flux analysis is often (not to say almost always) faced with system underdeterminacy. Indeed, the linear algebraic system formed by the steady-state mass balance equations around the intracellular metabolites and the equality constraints related to the measurements of extracellular fluxes do not define a unique solution for the distribution of intracellular fluxes, but instead a set of solutions belonging to a convex polytope. Various methods have been proposed to tackle this underdeterminacy, including flux pathway analysis, flux balance analysis, flux variability analysis and sampling. These approaches are reviewed in this article and a toy example supports the discussion with illustrative numerical results.

Hypoxia promotes breast cancer cell growth by activating a glycogen metabolic program

Hypoxia is known to be commonly present in breast tumor microenvironments. Stem-like cells that repopulate breast tumors, termed tumor-repopulating cells (TRC), thrive under hypoxic conditions, but the underlying mechanism remains unclear. Here we show that hypoxia promotes the growth of breast TRCs through metabolic reprogramming. Hypoxia mobilized transcription factors HIF-1α and FoxO1 and induced epigenetic reprogramming to upregulate cytosolic phosphoenolpyruvate carboxykinase (PCK1), a key enzyme that initiates gluconeogenesis. PCK1 subsequently triggered retrograde carbon flow from gluconeogenesis to glycogenesis, glycogenolysis, and the pentose phosphate pathway. The resultant NADPH facilitated reduced glutathione production, leading to a moderate increase of reactive oxygen species that stimulated hypoxic breast TRC growth. Notably, this metabolic mechanism was absent in differentiated breast tumor cells. Targeting PCK1 synergized with paclitaxel to reduce the growth of triple-negative breast cancer (TNBC). These findings uncover an altered glycogen metabolic program in breast cancer, providing potential metabolic strategies to target hypoxic breast TRCs and TNBC.

Enhancing in silico strain design predictions through next generation metabolic modeling approaches

The reconstruction and analysis of metabolic models has garnered increasing attention due to the multitude of applications in which these have proven to be practical. The growing number of generated metabolic models has been accompanied by an exponentially expanding arsenal of tools used to analyze them. In this work, we discussed the biological relevance of a number of promising modeling frameworks, focusing on the questions and hypotheses each method is equipped to address. To this end, we critically analyzed the steady-state modeling approaches focusing on resource allocation and incorporation of thermodynamic considerations which produce promising results and aid in the generation and experimental validation of numerous predictions. For smaller networks involving more complex regulation, we addressed kinetic modeling techniques which show encouraging results in addressing questions outside the scope of steady-state modeling. Finally, we discussed the potential application of the discussed frameworks within the field of strain design. Adoption of such methodologies is believed to significantly enhance the accuracy of in silico predictions and hence decrease the number of design-build-test cycles required.

Simultaneous 3-Nitrophenylhydrazine Derivatization Strategy of Carbonyl, Carboxyl and Phosphoryl Submetabolome for LC-MS/MS-Based Targeted Metabolomics with Improved Sensitivity and Coverage

Metabolomics is a powerful and essential technology for profiling metabolic phenotypes and exploring metabolic reprogramming, which enables the identification of biomarkers and provides mechanistic insights into physiology and disease. However, its applications are still limited by the technical challenges particularly in its detection sensitivity for the analysis of biological samples with limited amount, necessitating the development of highly sensitive approaches. Here, we developed a highly sensitive liquid chromatography tandem mass spectrometry method based on a 3-nitrophenylhydrazine (3-NPH) derivatization strategy that simultaneously targets carbonyl, carboxyl, and phosphoryl groups for targeted metabolomic analysis (HSD^ccp-TM) in biological samples. By testing 130 endogenous metabolites including organic acids, amino acids, carbohydrates, nucleotides, carnitines, and vitamins, we showed that the derivatization strategy resulted in significantly improved detection sensitivity and chromatographic separation capability. Metabolic profiling of merely 60 oocytes and 5000 hematopoietic stem cells primarily isolated from mice demonstrated that this method enabled routine metabolomic analysis in trace amounts of biospecimens. Moreover, the derivatization strategy bypassed the tediousness of inferring the MS fragmentation patterns and simplified the complexity of monitoring ion pairs of metabolites, which greatly facilitated the metabolic flux analysis (MFA) for glycolysis, the tricarboxylic acid (TCA) cycle, and pentose phosphate pathway (PPP) in cell cultures. In summary, the novel 3-NPH derivatization-based method with high sensitivity, good chromatographic separation, and broad coverage showed great potential in promoting metabolomics and MFA, especially in trace amounts of biospecimens.

Integrating Systems and Synthetic Biology to Understand and Engineer Microbiomes

Microbiomes are complex and ubiquitous networks of microorganisms whose seemingly limitless chemical transformations could be harnessed to benefit agriculture, medicine, and biotechnology. The spatial and temporal changes in microbiome composition and function are influenced by a multitude of molecular and ecological factors. This complexity yields both versatility and challenges in designing synthetic microbiomes and perturbing natural microbiomes in controlled, predictable ways. In this review, we describe factors that give rise to emergent spatial and temporal microbiome properties and the meta-omics and computational modeling tools that can be used to understand microbiomes at the cellular and system levels. We also describe strategies for designing and engineering microbiomes to enhance or build novel functions. Throughout the review, we discuss key knowledge and technology gaps for elucidating the networks and deciphering key control points for microbiome engineering, and highlight examples where multiple omics and modeling approaches can be integrated to address these gaps.

Substrate usage determines carbon flux via the citrate cycle in Helicobacter pylori

Helicobacter pylori displays a worldwide infection rate of about 50%. The Gram-negative bacterium is the main reason for gastric cancer and other severe diseases. Despite considerable knowledge about the metabolic inventory of H. pylori, carbon fluxes through the citrate cycle (TCA cycle) remained enigmatic. In this study, different ¹³ C-labeled substrates were supplied as carbon sources to H. pylori during microaerophilic growth in a complex medium. After growth, ¹³ C-excess and ¹³ C-distribution were determined in multiple metabolites using GC-MS analysis. [U-¹³ C₆ ]Glucose was efficiently converted into glyceraldehyde but only less into TCA cycle-related metabolites. In contrast, [U-¹³ C₅ ]glutamate, [U-¹³ C₄ ]succinate, and [U-¹³ C₄ ]aspartate were incorporated at high levels into intermediates of the TCA cycle. The comparative analysis of the ¹³ C-distributions indicated an adaptive TCA cycle fully operating in the closed oxidative direction with rapid equilibrium fluxes between oxaloacetate-succinate and α-ketoglutarate-citrate. ¹³ C-Profiles of the four-carbon intermediates in the TCA cycle, especially of malate, together with the observation of an isocitrate lyase activity by in vitro assays, suggested carbon fluxes via a glyoxylate bypass. In conjunction with the lack of enzymes for anaplerotic CO₂ fixation, the glyoxylate bypass could be relevant to fill up the TCA cycle with carbon atoms derived from acetyl-CoA.

Measurement of lipogenic flux by deuterium resolved mass spectrometry

De novo lipogenesis (DNL) is disrupted in a wide range of human disease. Thus, quantification of DNL may provide insight into mechanisms and guide interventions if it can be performed rapidly and noninvasively. DNL flux is commonly measured by ²H incorporation into fatty acids following deuterated water (²H₂O) administration. However, the sensitivity of this approach is limited by the natural abundance of ¹³C, which masks detection of ²H by mass spectrometry. Here we report that high-resolution Orbitrap gas-chromatography mass-spectrometry resolves ²H and ¹³C fatty acid mass isotopomers, allowing DNL to be quantified using lower ²H₂O doses and shorter experimental periods than previously possible. Serial measurements over 24-hrs in mice detects the nocturnal activation of DNL and matches a ³H-water method in mice with genetic activation of DNL. Most importantly, DNL is detected in overnight-fasted humans in less than an hour and is responsive to feeding during a 4-h study. Thus, ²H specific MS provides the ability to study DNL in settings that are currently impractical.

13C isotope-based metabolic flux analysis revealing cellular landscape of glucose metabolism in human liver cells exposed to perfluorooctanoic acid

Perfluorooctanoic acid (PFOA) is well known to break glucose homeostasis. However, the effects of PFOA on glucose metabolism are difficult to be evaluated because related metabolites may be synthesized from other nutritional substrates. Here, the relative contribution of glucose to metabolites (e.g., pyruvate and citrate) in the PFOA-treated human liver cells (HepG2) was determined using the ¹³C isotope-based metabolic flux analysis (MFA), i.e., pathway activities. The relative percentage of [U-¹³C₆] glucose-derived pyruvate in cells exposed to PFOA was not significantly different from that in the controls, indicating that the metabolic pattern of glycolysis was not substantially changed by PFOA. The pathway activity of [U-¹³C₆] glucose-driven tricarboxylic acid (TCA) cycle was dramatically inhibited by PFOA. Consequently, mitochondrial respiratory function was phenotypically impaired by PFOA, as observed from the decreasing basal oxygen consumption rate (OCR), ATP-linked OCR and spare respiratory capacity. This study suggests that PFOA may cause the abnormal glucose metabolism via altering the metabolic pattern of TCA cycle instead of glycolysis. The MFA is strongly recommended as a promising and robust tool to address the toxicity mechanisms of contaminants associated with glucose metabolism.

Exploring the Glucose Fluxotype of the E. coli y-ome Using High-Resolution Fluxomics

We have developed a robust workflow to measure high-resolution fluxotypes (metabolic flux phenotypes) for large strain libraries under fully controlled growth conditions. This was achieved by optimizing and automating the whole high-throughput fluxomics process and integrating all relevant software tools. This workflow allowed us to obtain highly detailed maps of carbon fluxes in the central carbon metabolism in a fully automated manner. It was applied to investigate the glucose fluxotypes of 180 Escherichia coli strains deleted for y-genes. Since the products of these y-genes potentially play a role in a variety of metabolic processes, the experiments were designed to be agnostic as to their potential metabolic impact. The obtained data highlight the robustness of E. coli’s central metabolism to y-gene deletion. For two y-genes, deletion resulted in significant changes in carbon and energy fluxes, demonstrating the involvement of the corresponding y-gene products in metabolic function or regulation. This work also introduces novel metrics to measure the actual scope and quality of high-throughput fluxomics investigations.

Utilizing tandem mass spectrometry for metabolic flux analysis

Metabolic flux analysis (MFA) aims at revealing the metabolic reaction rates in a complex biochemical network. To do so, MFA uses the input of stable isotope labeling patterns of the intracellular metabolites. Elementary metabolic unit (EMU) is the computational framework to simulate the metabolite labeling patterns in a network, which was originally designed for simulating mass isotopomer distributions (MIDs) at the MS1 level. Recently, the EMU framework is expanded to simulate tandem mass spectrometry data. Tandem mass spectrometry has emerged as a new experimental approach to provide information on the positional isotope labeling of metabolites and therefore greatly improves the precision of MFA. In this review, we will discuss the new EMU framework that can accommodate the tandem mass isotopomer distributions (TMIDs) data. We will also analyze the improvement on the MFA precision by using TMID. Our analysis shows that combining the MIDs of the parent and daughter ions and the TMID for the MFA is more powerful than using TMID alone.

Analysis of metabolic network disruption in engineered microbial hosts due to enzyme promiscuity

Increasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Transforming organisms such as Escherichia coli into microbial factories is achieved via several engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed Metabolic Disruption Workflow (MDFlow), for discovering interactions and network disruptions arising from enzyme promiscuity – the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We demonstrate how enzyme promiscuity may aid cells in adapting to disruptions of essential metabolic functions. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropionic acid (3-HP) production. Using MDFlow, we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. As we demonstrate, MDFlow provides an innovative workflow to systematically identify incompatibilities between the native metabolism of the host and its engineered modifications due to enzyme promiscuity.

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Engineering modifications to cellular hosts result in undesirable byproducts.

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Metabolic Disruption: changes in engineered host due to enzyme promiscuity.

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Metabolic Disruption Workflow (MDFlow) uncovers metabolic disruption.

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MDFlow corroborates previously experimentally verified promiscuous interactions.

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MDFlow compares disruption due to heterologous pathways targeting 3-HP production.

Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by ¹³C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi.

IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati. Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

Improving the Methanol Tolerance of an Escherichia coli Methylotroph via Adaptive Laboratory Evolution Enhances Synthetic Methanol Utilization

There is great interest in developing synthetic methylotrophs that harbor methane and methanol utilization pathways in heterologous hosts such as Escherichia coli for industrial bioconversion of one-carbon compounds. While there are recent reports that describe the successful engineering of synthetic methylotrophs, additional efforts are required to achieve the robust methylotrophic phenotypes required for industrial realization. Here, we address an important issue of synthetic methylotrophy in E. coli: methanol toxicity. Both methanol, and its oxidation product, formaldehyde, are cytotoxic to cells. Methanol alters the fluidity and biological properties of cellular membranes while formaldehyde reacts readily with proteins and nucleic acids. Thus, efforts to enhance the methanol tolerance of synthetic methylotrophs are important. Here, adaptive laboratory evolution was performed to improve the methanol tolerance of several E. coli strains, both methylotrophic and non-methylotrophic. Serial batch passaging in rich medium containing toxic methanol concentrations yielded clones exhibiting improved methanol tolerance. In several cases, these evolved clones exhibited a > 50% improvement in growth rate and biomass yield in the presence of high methanol concentrations compared to the respective parental strains. Importantly, one evolved clone exhibited a two to threefold improvement in the methanol utilization phenotype, as determined via ¹³C-labeling, at non-toxic, industrially relevant methanol concentrations compared to the respective parental strain. Whole genome sequencing was performed to identify causative mutations contributing to methanol tolerance. Common mutations were identified in 30S ribosomal subunit proteins, which increased translational accuracy and provided insight into a novel methanol tolerance mechanism. This study addresses an important issue of synthetic methylotrophy in E. coli and provides insight as to how methanol toxicity can be alleviated via enhancing methanol tolerance. Coupled improvement of methanol tolerance and synthetic methanol utilization is an important advancement for the field of synthetic methylotrophy.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions

Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and ¹³C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between ¹³C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.

Biotechnology has benefitted from the development of high throughput methods characterising living systems at different levels (e.g. concerning genes or proteins), allowing the industrial production of chemical commodities. Recently, focus has been placed on determining reaction rates (or metabolic fluxes) in the metabolic network of certain microorganisms, in order to identify bottlenecks hindering their exploitation. Two main approaches are commonly used, termed metabolic flux analysis (MFA) and flux balance analysis (FBA), based on measuring and estimating fluxes, respectively. While the influence of thermodynamics in living systems was accepted several decades ago, its application to study biochemical networks has only recently been enabled. In this sense, a multitude of different approaches constraining well-established modelling methods with thermodynamics has been suggested. However, physicochemical parameters are generally not properly adjusted to the experimental conditions, which might affect their predictive capabilities. In this study, we have explored the reliability of currently available tools by investigating the impact of varying said parameters in the simulation of metabolic fluxes and metabolite concentration values. Additionally, our in-depth analysis allowed us to highlight limitations and potential solutions that should be considered in future studies.

Recent advances in systems and synthetic biology approaches for developing novel cell-factories in non-conventional yeasts

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae, is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus, K. lactis, Yarrowia lipolytica, Pichia pastoris, Scheffersomyces stipitis, Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using ¹³C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology.

Adaptive laboratory evolution of methylotrophic Escherichia coli enables synthesis of all amino acids from methanol-derived carbon

Recent attempts to create synthetic Escherichia coli methylotrophs identified that de novo biosynthesis of amino acids, in the presence of methanol, presents significant challenges in achieving autonomous methylotrophic growth. Previously engineered methanol-dependent strains required co-utilization of stoichiometric amounts of co-substrates and methanol. As such, these strains could not be evolved to grow on methanol alone. In this work, we have explored an alternative approach to enable biosynthesis of all amino acids from methanol-derived carbon in minimal media without stoichiometric coupling. First, we identified that biosynthesis of threonine was limiting the growth of our methylotrophic E. coli. To address this, we performed adaptive laboratory evolution to generate a strain that grew efficiently in minimal medium with methanol and threonine. Methanol assimilation and growth of the evolved strain were analyzed, and, interestingly, we found that the evolved strain synthesized all amino acids, including threonine, from methanol-derived carbon. The evolved strain was then further engineered through overexpression of an optimized threonine biosynthetic pathway. We show that the resulting methylotrophic E. coli strain has a methanol-dependent growth phenotype with homoserine as co-substrate. In contrast to previous methanol-dependent strains, co-utilization of homoserine is not stoichiometrically linked to methanol assimilation. As such, future engineering of this strain and successive adaptive evolution could enable autonomous growth on methanol as the sole carbon source. KEY POINTS: • Adaptive evolution of E. coli enables biosynthesis of all amino acids from methanol. • Overexpression of threonine biosynthesis pathway improves methanol assimilation. • Methanol-dependent growth is seen in minimal media with homoserine as co-substrate.

Analysis of plant secondary metabolism using stable isotope-labelled precursors

INTRODUCTION

Analysis of biochemical pathways typically involves feeding a labelled precursor to an organism, and then monitoring the metabolic fate of the label. Initial studies used radioisotopes as a label and then monitored radioactivity in the metabolic products. As analytical equipment improved and became more widely available, preference shifted the use stable 'heavy' isotopes like deuterium (² H)-, carbon-13 (¹³ C)- and nitrogen-15 (¹⁵ N)-atoms as labels. Incorporation of the labels could be monitored by mass spectrometry (MS), as part of a hyphenated tool kits, e.g. Liquid chromatography (LC)-MS, gas chromatography (GC)-MS, LC-MS/MS. MS offers great sensitivity but the exact location of an isotope label in a given metabolite cannot always be unambiguously established. Nuclear magnetic resonance (NMR) can also be used to pick up signals of stable isotopes, and can give information on the precise location of incorporated label in the metabolites. However, the detection limit for NMR is quite a bit higher than that for MS.

OBJECTIVES

A number of experiments involving feeding stable isotope-labelled precursors followed by NMR analysis of the metabolites is presented. The aim is to highlight the use of NMR analysis in identifying the precise fate of isotope labels after precursor feeding experiments. As more powerful NMR equipment becomes available, applications as described in this review may become more commonplace in pathway analysis.

CONCLUSION AND PROSPECTS

NMR is a widely accepted tool for chemical structure elucidation and is now increasingly used in metabolomic studies. In addition, NMR, combined with stable isotope feeding, should be considered as a tool for metabolic flux analyses.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Improved production of 2,3-butanediol and isobutanol by engineering electron transport chain in Escherichia coli

The electron transport chain (ETC) is one of the major energy generation pathways in microorganisms under aerobic condition. Higher yield of ATP can be achieved through oxidative phosphorylation with consumption of NADH than with substrate level phosphorylation. However, most value-added metabolites are in an electrochemically reduced state, which requires reducing equivalent NADH as a cofactor. Therefore, optimal production of value-added metabolites should be balanced with ETC in terms of energy production. In this study, we attempted to reduce the activity of ETC to secure availability of NADH. The ETC mutants exhibited poor growth rate and production of fermentative metabolites compared to parental strain. Introduction of heterologous pathways for synthesis of 2,3-butanediol and isobutanol to ETC mutants resulted in increased titres and yields of the metabolites. ETC mutants yielded higher NADH/NAD+ ratio but similar ATP content than that by the parental strain. Furthermore, ETC mutants operated fermentative metabolism pathways independent of oxygen supply in large-scale fermenter, resulting in increased yield and titre of 2,3-butanediol. Thus, engineering of ETC is a useful metabolic engineering approach for production of reduced metabolites.

Automated engineering of synthetic metabolic pathways for efficient biomanufacturing

Metabolic engineering involves the engineering and optimization of processes from single-cell to fermentation in order to increase production of valuable chemicals for health, food, energy, materials and others. A systems approach to metabolic engineering has gained traction in recent years thanks to advances in strain engineering, leading to an accelerated scaling from rapid prototyping to industrial production. Metabolic engineering is nowadays on track towards a truly manufacturing technology, with reduced times from conception to production enabled by automated protocols for DNA assembly of metabolic pathways in engineered producer strains. In this review, we discuss how the success of the metabolic engineering pipeline often relies on retrobiosynthetic protocols able to identify promising production routes and dynamic regulation strategies through automated biodesign algorithms, which are subsequently assembled as embedded integrated genetic circuits in the host strain. Those approaches are orchestrated by an experimental design strategy that provides optimal scheduling planning of the DNA assembly, rapid prototyping and, ultimately, brings forward an accelerated Design-Build-Test-Learn cycle and the overall optimization of the biomanufacturing process. Achieving such a vision will address the increasingly compelling demand in our society for delivering valuable biomolecules in an affordable, inclusive and sustainable bioeconomy.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. ¹³C-metabolic flux analysis (¹³C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice.

Low Entropy Sub-Networks Prevent the Integration of Metabolomic and Transcriptomic Data

The constantly and rapidly increasing amount of the biological data gained from many different high-throughput experiments opens up new possibilities for data- and model-driven inference. Yet, alongside, emerges a problem of risks related to data integration techniques. The latter are not so widely taken account of. Especially, the approaches based on the flux balance analysis (FBA) are sensitive to the structure of a metabolic network for which the low-entropy clusters can prevent the inference from the activity of the metabolic reactions. In the following article, we set forth problems that may arise during the integration of metabolomic data with gene expression datasets. We analyze common pitfalls, provide their possible solutions, and exemplify them by a case study of the renal cell carcinoma (RCC). Using the proposed approach we provide a metabolic description of the known morphological RCC subtypes and suggest a possible existence of the poor-prognosis cluster of patients, which are commonly characterized by the low activity of the drug transporting enzymes crucial in the chemotherapy. This discovery suits and extends the already known poor-prognosis characteristics of RCC. Finally, the goal of this work is also to point out the problem that arises from the integration of high-throughput data with the inherently nonuniform, manually curated low-throughput data. In such cases, the over-represented information may potentially overshadow the non-trivial discoveries.

Regulatory interventions improve the biosynthesis of limiting amino acids from methanol carbon to improve synthetic methylotrophy in Escherichia coli

Synthetic methylotrophy aims to engineer methane and methanol utilization pathways in platform hosts like Escherichia coli for industrial bioprocessing of natural gas and biogas. While recent attempts to engineer synthetic methylotrophs have proved successful, autonomous methylotrophy, that is, the ability to utilize methane or methanol as sole carbon and energy substrates, has not yet been realized. Here, we address an important limitation of autonomous methylotrophy in E. coli: the inability of the organism to synthesize several amino acids when grown on methanol. We targeted global and local amino acid regulatory networks. Those include removal of amino acid allosteric feedback inhibition (argA^H15Y , ilvA^L447F , hisG^E271K , leuA^G462D , proB^D107N , thrA^S345F , trpE^S40F ), knockouts of transcriptional repressors (ihfA, metJ); and overexpression of amino acid biosynthetic operons (hisGDCBHAFI, leuABCD, thrABC, trpEDCBA) and transcriptional regulators (crp, purR). Compared to the parent methylotrophic E. coli strain that was unable to synthesize these amino acids from methanol carbon, these strategies resulted in improved biosynthesis of limiting proteinogenic amino acids (histidine, leucine, lysine, methionine, phenylalanine, threonine, tyrosine) from methanol carbon. In several cases, improved amino acid biosynthesis from methanol carbon led to improvements in methylotrophic growth in methanol minimal medium supplemented with a small amount of yeast extract. This study addresses a key limitation currently preventing autonomous methylotrophy in E. coli and possibly other synthetic methylotrophs and provides insight as to how this limitation can be alleviated via global and local regulatory modifications.

Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage

Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.