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Amino acid auxotrophy refers to an organism's inability to synthesize one or more amino acids that are required for cell growth. In microbiome research, co-cultures of amino acid auxotrophs are often used to investigate metabolite cross-feeding interactions and model community dynamics. Thus far, it has been implicitly assumed that amino acids are mainly cross-fed between these auxotrophs. However, this assumption has not been fully verified. For example, it could be that intermediates of amino acid biosynthesis pathways are exchanged instead, or in addition to amino acids. If true, this would significantly increase the complexity of metabolic interactions that needs to be considered. Here, we show that metabolic pathway intermediates are indeed exchanged in many co-cultures of amino acid auxotrophs. To demonstrate this, we selected 25 E. coli single gene knockouts that are auxotrophic for five different amino acids: arginine, histidine, isoleucine, proline, and tryptophan. In co-culture experiments, we paired strains that shared the same amino acid auxotrophy and monitored cell growth. We observed growth in 23 out of 55 strain pairings, indicating that pathway intermediates were exchanged between the strains. To provide further support for cross-feeding of pathway intermediates, auxotrophic E. coli strains were cultured in media supplemented with commercially available metabolic pathway intermediates at different concentrations. Supplementing media with these metabolites recovered cell growth as was predicted from the co-culture experiments. Most of these metabolites supported high growth rates, even when present at low concentrations (10 μM), suggesting the presence of high affinity transporters for these metabolites. In total, we identified eight metabolic pathway intermediates that were likely exchanged between the auxotrophic E. coli strains and verified six of these, including histidinol, N-acetyl-L-ornithine, L-ornithine, L-citrulline, keto-isoleucine and anthranilate. Taken together, this work demonstrates that exchange of metabolic pathway intermediates is more common than has been assumed so far. In future, these exchanges must be explicitly considered when constructing models of metabolite cross-feeding interactions in microbial communities and when interpreting results from microbiome studies involving auxotrophic strains., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2025 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Hypoxia is a common feature in various pathophysiological contexts, including tumor microenvironment, and IFN-γ is instrumental for anti-tumor immunity. HIF1α has long been known as a primary regulator of cellular adaptive responses to hypoxia, but its role in IFN-γ induction in hypoxic T cells is unknown. Here, we show that the HIF1α-glycolysis axis controls IFN-γ induction in both human and mouse T cells, activated under hypoxia. Specific deletion of HIF1α in T cells (Hif1α -/- ) and glycolytic inhibition suppresses IFN-γ induction. Conversely, HIF1α stabilization by hypoxia and VHL deletion in T cells (Vhl -/- ) increases IFN-γ production. Hypoxic Hif1α -/- T cells are less able to kill tumor cells in vitro, and tumor-bearing Hif1α -/- mice are not responsive to immune checkpoint blockade (ICB) therapy in vivo. Mechanistically, loss of HIF1α greatly diminishes glycolytic activity in hypoxic T cells, resulting in depleted intracellular acetyl-CoA and attenuated activation-induced cell death (AICD). Restoration of intracellular acetyl-CoA by acetate supplementation re-engages AICD, rescuing IFN-γ production in hypoxic Hif1α -/- T cells and re-sensitizing Hif1α -/- tumor-bearing mice to ICB. In summary, we identify HIF1α-regulated glycolysis as a key metabolic control of IFN-γ production in hypoxic T cells and ICB response., (© 2024. The Author(s).)

Mammalian cell culture processes are widely utilized for biotherapeutics production, disease diagnostics, and biosensors, and hence, should be optimized to support robust cell growth and viability. However, toxic by-products accumulate in cultures due to inefficiencies in metabolic activities and nutrient utilization. In this study, we applied comprehensive 13 C stable-isotope tracing of amino acids and glucose to two Immunoglobulin G (IgG) producing Chinese Hamster Ovary (CHO) cell lines to identify secreted by-products and trace their origins. CHO cells were cultured in media formulations missing a single amino acid or glucose supplemented with a 13 C-tracer of the missing substrate, followed by gas chromatography-mass spectrometry (GC-MS) analysis to track labeled carbon flows and identify by-products. We tracked the sources of all secreted by-products and verified the identity of 45 by-products, majority of which were derived from glucose, leucine, isoleucine, valine, tyrosine, tryptophan, methionine, and phenylalanine. In addition to by-products identified previously, we identified several metabolites including 2-hydroxyisovaleric acid, 2-aminobutyric acid, L-alloisoleucine, ketoisoleucine, 2-hydroxy-3-methylvaleric acid, desmeninol, and 2-aminobutyric acid. When added to CHO cell cultures at different concentrations, certain metabolites inhibited cell growth while others including 2-hydroxy acids, surprisingly, reduced lactate accumulation. In vitro enzymatic analysis indicated that 2-hydroxy acids were metabolized by lactate dehydrogenase suggesting a possible mechanism for lowered lactate accumulation, e.g., competitive substrate inhibition. The 13 C-labeling assisted metabolomics pipeline developed and the metabolites identified will serve as a springboard to reduce undesirable by-products accumulation and alleviate inefficient substrate utilization in mammalian cultures used for biomanufacturing and other applications through altered media formulations and pathway engineering strategies., Competing Interests: Competing interests statement:Current employment of authors: J.E.G. is currently affiliated with Takeda Pharmaceutical Company; H.M.N. is currently affiliated with IQVIA; E.H.O. is currently affiliated with Genentech; V.G.D. is currently affiliated with Pfizer; B.O.M. is currently affiliated with Amgen; S.K. is currently affiliated with Sanofi.

Cysteine and cystine are essential amino acids present in mammalian cell cultures. While contributing to biomass synthesis, recombinant protein production, and antioxidant defense mechanisms, cysteine poses a major challenge in media formulations owing to its poor stability and oxidation to cystine, a cysteine dimer. Due to its poor solubility, cystine can cause precipitation of feed media, formation of undesired products, and consequently, reduce cysteine bioavailability. In this study, a highly soluble cysteine containing dipeptide dimer, Ala-Cys-Cys-Ala (ACCA), was evaluated as a suitable alternative to cysteine and cystine in CHO cell cultures. Replacing cysteine and cystine in basal medium with ACCA did not sustain cell growth. However, addition of ACCA at 4 mM and 8 mM to basal medium containing cysteine and cystine boosted cell growth up to 15% and 27% in CHO-GS and CHO-K1 batch cell cultures respectively and led to a proportionate increase in IgG titer. 13 C-Metabolic flux analysis revealed that supplementation of ACCA reduced glycolytic fluxes by 20% leading to more efficient glucose metabolism in CHO-K1 cells. In fed-batch cultures, ACCA was able to replace cysteine and cystine in feed medium. Furthermore, supplementation of ACCA at high concentrations in basal medium eliminated the need for any cysteine equivalents in feed medium and increased cell densities and viabilities in fed-batch cultures without any significant impact on IgG charge variants. Taken together, this study demonstrates the potential of ACCA to improve CHO cell growth, productivity, and metabolism while also facilitating the formulation of cysteine- and cystine-free feed media. Such alternatives to cysteine and cystine will pave the way for enhanced biomanufacturing by increasing cell densities in culture and extending the storage of highly concentrated feed media as part of achieving intensified bioproduction processes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Metabolic rewiring during the proliferation-to-quiescence transition is poorly understood. Here, using a model of contact inhibition-induced quiescence, we conducted 13 C-metabolic flux analysis in proliferating (P) and quiescent (Q) mouse embryonic fibroblasts (MEFs) to investigate this process. Q cells exhibit reduced glycolysis but increased TCA cycle flux and mitochondrial respiration. Reduced glycolytic flux in Q cells correlates with reduced glycolytic enzyme expression mediated by yes-associated protein (YAP) inhibition. The increased TCA cycle activity and respiration in Q cells is mediated by induced mitochondrial pyruvate carrier (MPC) expression, rendering them vulnerable to MPC inhibition. The malate-to-pyruvate flux, which generates NADPH, is markedly reduced by modulating malic enzyme 1 (ME1) dimerization in Q cells. Conversely, the malate dehydrogenase 1 (MDH1)-mediated oxaloacetate-to-malate flux is reversed and elevated in Q cells, driven by high mitochondrial-derived malate levels, reduced cytosolic oxaloacetate, elevated MDH1 levels, and a high cytoplasmic NAD + /NADH ratio. Transcriptomic analysis revealed large number of genes are induced in Q cells, many of which are associated with the extracellular matrix (ECM), while YAP-dependent and cell cycle-related genes are repressed. The results suggest that high TCA cycle flux and respiration in Q cells are required to generate ATP and amino acids to maintain de-novo ECM protein synthesis and secretion., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The rapidly growing market of biologics including monoclonal antibodies has stimulated the need to improve biomanufacturing processes including mammalian host systems such as Chinese Hamster Ovary (CHO) cells. Cell culture media formulations continue to be enhanced to enable intensified cell culture processes and optimize cell culture performance. Amino acids, major components of cell culture media, are consumed in large amounts by CHO cells. Due to their low solubility and poor stability, certain amino acids including tyrosine, leucine, and phenylalanine can pose major challenges leading to suboptimal bioprocess performance. Dipeptides have the potential to replace amino acids in culture media. However, very little is known about the cleavage, uptake, and utilization kinetics of dipeptides in CHO cell cultures. In this study, replacing amino acids, including leucine and tyrosine by their respective dipeptides including but not limited to Ala-Leu and Gly-Tyr, supported similar cell growth, antibody production, and lactate profiles. Using 13 C labeling techniques and spent media studies, dipeptides were shown to undergo both intracellular and extracellular cleavage in cultures. Extracellular cleavage increased with the culture duration, indicating cleavage by host cell proteins that are likely secreted and accumulate in cell culture over time. A kinetic model was built and for the first time, integrated with 13 C labeling experiments to estimate dipeptide utilization rates, in CHO cell cultures. Dipeptides with alanine at the N-terminus had a higher utilization rate than dipeptides with alanine at the C-terminus and dipeptides with glycine instead of alanine at N-terminus. Simultaneous supplementation of more than one dipeptide in culture led to reduction in individual dipeptide utilization rates indicating that dipeptides compete for the same cleavage enzymes, transporters, or both. Dipeptide utilization rates in culture and cleavage rates in cell-free experiments appeared to follow Michaelis-Menten kinetics, reaching a maximum at higher dipeptide concentrations. Dipeptide utilization behavior was found to be similar in cell-free and cell culture environments, paving the way for future testing approaches for dipeptides in cell-free environments prior to use in large-scale bioreactors. Thus, this study provides a deeper understanding of the fate of dipeptides in CHO cell cultures through an integration of cell culture, 13 C labeling, and kinetic modeling approaches providing insights in how to best use dipeptides in media formulations for robust and optimal mammalian cell culture performance., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Evonik Industries Ag. Published by Elsevier Inc. All rights reserved.)

Tricarboxylic acid cycle (TCA cycle) plays an important role for aerobic growth of heterotrophic bacteria. Theoretically, eliminating TCA cycle would decrease carbon dissipation and facilitate chemicals biosynthesis. Here, we construct an E. coli strain without a functional TCA cycle that can serve as a versatile chassis for chemicals biosynthesis. We first use adaptive laboratory evolution to recover aerobic growth in minimal medium of TCA cycle-deficient E. coli. Inactivation of succinate dehydrogenase is a key event in the evolutionary trajectory. Supply of succinyl-CoA is identified as the growth limiting factor. By replacing endogenous succinyl-CoA dependent enzymes, we obtain an optimized TCA cycle-deficient E. coli strain. As a proof of concept, the strain is engineered for high-yield production of four separate products. This work enhances our understanding of the role of the TCA cycle in E. coli metabolism and demonstrates the advantages of using TCA cycle-deficient E. coli strain for biotechnological applications., (© 2024. The Author(s).)

Summary: The analysis of stable isotope labeling experiments requires accurate, efficient, and reproducible quantification of mass isotopomer distributions (MIDs), which is not a core feature of general-purpose metabolomics software tools that are optimized to quantify metabolite abundance. Here, we present PIRAMID (Program for Integration and Rapid Analysis of Mass Isotopomer Distributions), a MATLAB-based tool that addresses this need by offering a user-friendly, graphical user interface-driven program to automate the extraction of isotopic information from mass spectrometry (MS) datasets. This tool can simultaneously extract ion chromatograms for various metabolites from multiple data files in common vendor-agnostic file formats, locate chromatographic peaks based on a targeted list of characteristic ions and retention times, and integrate MIDs for each target ion. These MIDs can be corrected for natural isotopic background based on the user-defined molecular formula of each ion. PIRAMID offers support for datasets acquired from low- or high-resolution MS, and single (MS) or tandem (MS/MS) instruments. It also enables the analysis of single or dual labeling experiments using a variety of isotopes (i.e. 2H, 13C, 15N, 18O, 34S)., Data Availability and Implementation: MATLAB p-code files are freely available for non-commercial use and can be downloaded from https://mfa.vueinnovations.com/. Commercial licenses are also available. All the data presented in this publication are available under the "Help_menu" folder of the PIRAMID software., (© The Author(s) 2023. Published by Oxford University Press.)

Chinese hamster ovary (CHO) cells, predominant hosts for recombinant biotherapeutics production, generate lactate as a major glycolysis by-product. High lactate levels adversely impact cell growth and productivity. The goal of this study was to reduce lactate in CHO cell cultures by adding chemical inhibitors to hexokinase-2 (HK2), the enzyme catalyzing the conversion of glucose to glucose 6-phosphate, and examine their impact on lactate accumulation, cell growth, protein titers, and N-glycosylation. Five inhibitors of HK2 enzyme at different concentrations were evaluated, of which 2-deoxy- d-glucose (2DG) and 5-thio- d-glucose (5TG) successfully reduced lactate accumulation with only limited impacts on CHO cell growth. Individual 2DG and 5TG supplementation led to a 35%-45% decrease in peak lactate, while their combined supplementation resulted in a 60% decrease in peak lactate. Inhibitor supplementation led to at least 50% decrease in moles of lactate produced per mol of glucose consumed. Recombinant EPO-Fc titers peaked earlier relative to the end of culture duration in supplemented cultures leading to at least 11% and as high as 32% increase in final EPO-Fc titers. Asparagine, pyruvate, and serine consumption rates also increased in the exponential growth phase in 2DG and 5TG treated cultures, thus, rewiring central carbon metabolism due to low glycolytic fluxes. N-glycan analysis of EPO-Fc revealed an increase in high mannose glycans from 5% in control cultures to 25% and 37% in 2DG and 5TG-supplemented cultures, respectively. Inhibitor supplementation also led to a decrease in bi-, tri-, and tetra-antennary structures and up to 50% lower EPO-Fc sialylation. Interestingly, addition of 2DG led to the incorporation of 2-deoxy-hexose (2DH) on EPO-Fc N-glycans and addition of 5TG resulted in the first-ever observed N-glycan incorporation of 5-thio-hexose (5TH). Six percent to 23% of N-glycans included 5TH moieties, most likely 5-thio-mannose and/or 5-thio-galactose and/or possibly 5-thio-N-acetylglucosamine, and 14%-33% of N-glycans included 2DH moieties, most likely 2-deoxy-mannose and/or 2-deoxy-galactose, for cultures treated with different concentrations of 5TG and 2DG, respectively. Our study is the first to evaluate the impact of these glucose analogs on CHO cell growth, protein production, cell metabolism, N-glycosylation processing, and formation of alternative glycoforms., (© 2023 The Authors. Biotechnology and Bioengineering published by Wiley Periodicals LLC.)

Hypoxia has been identified as a major factor in the pathogenesis of adipose tissue inflammation, which is a hallmark of obesity and obesity-linked type 2 diabetes mellitus. In this study, we have investigated the impact of hypoxia (1% oxygen) on the physiology and metabolism of 3T3-L1 adipocytes, a widely used cell culture model of adipose. Specifically, we applied parallel labeling experiments, isotopomer spectral analysis, and 13 C-metabolic flux analysis to quantify the impact of hypoxia on adipogenesis, de novo lipogenesis and metabolic flux reprogramming in adipocytes. We found that 3T3-L1 cells can successfully differentiate into lipid-accumulating adipocytes under hypoxia, although the production of lipids was reduced by about 40%. Quantitative flux analysis demonstrated that short-term (1 day) and long-term (7 days) exposure to hypoxia resulted in similar reprogramming of cellular metabolism. Overall, we found that hypoxia: 1) reduced redox and energy generation by more than 2-fold and altered the patterns of metabolic pathway contributions to production and consumption of energy and redox cofactors; 2) redirected glucose metabolism from pentose phosphate pathway and citric acid cycle to lactate production; 3) rewired glutamine metabolism, from net glutamine production to net glutamine catabolism; 4) suppressed branched chain amino acid consumption; and 5) reduced biosynthesis of odd-chain fatty acids and mono-unsaturated fatty acids, while synthesis of saturated even-chain fatty acids was not affected. Together, these results highlight the profound impact of extracellular microenvironment on adipocyte metabolic activity and function., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2023 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.

Carbon dioxide-fixing acetogenic bacteria (acetogens) utilizing the Wood-Ljungdahl Pathway (WLP) play an important role in CO 2 fixation in the biosphere and in the development of biological processes - alone or in cocultures, under both autotrophic and mixotrophic conditions - for production of chemicals and fuels. To date, limited work has been reported in experimentally validating and quantifying reaction fluxes of their core metabolic pathways. Here, the core metabolic model of the acetogen Clostridium ljungdahlii was interrogated using 13 C-metabolic flux analysis ( 13 C-MFA), which required the development of a new defined culture medium. Autotrophic, heterotrophic, and mixotrophic growth in defined medium was possible by adding 1 mM methionine to replace yeast extract. Our 13 C-MFA found an incomplete TCA cycle and inactive core pathways/reactions, notably those of the oxidative pentose phosphate pathway, Entner-Doudoroff pathway, and malate dehydrogenase. 13 C-MFA during mixotrophic growth using the parallel tracers [1- 13 C]fructose, [1,2- 13 C]fructose, [1,2,3- 13 C]fructose, and [U- 13 C]asparagine found that externally supplied CO 2 contributed the majority of carbon consumed. All internally-produced CO 2 from the catabolism of asparagine and fructose was consumed by the WLP. While glycolysis of fructose was active, it was not a major contributor to overall production of ATP, NADH, and acetyl-CoA. Gluconeogenic reactions were active despite the availability of organic carbon. Asparagine was catabolized equally via conversion to threonine and subsequent cleavage to produce acetaldehyde and glycine, and via deamination to fumarate and then the anaplerotic conversion of malate to pyruvate. Both pathways for asparagine catabolism produced acetyl-CoA, either directly via pyruvate or indirectly via the WLP. Cofactor stoichiometry based on our data predicted an essentially zero flux through the ferredoxin-dependent transhydrogenase (Nfn) reaction. Instead, nearly all of NADPH generated from the hydrogenase reaction was consumed by the WLP. Reduced ferredoxin produced by the hydrogenase reaction and glycolysis was mostly used for ATP generation via the RNF/ATPase system, with the remainder consumed by the WLP. NADH produced by RNF/ATPase was entirely consumed via the WLP., (Copyright © 2022 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

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 13 C-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., (Copyright © 2021 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

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 13 C-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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Bennett, Gregory, Gonzalez, Har, Antoniewicz and Papoutsakis.)

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.

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. 13 C-metabolic flux analysis ( 13 C-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., (Copyright © 2020 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

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., (© 2020 Wiley Periodicals LLC.)

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, i.e. 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. By activating the stringent/stress response via ppGpp overproduction, or DksA and RpoS overexpression, we demonstrate improved biosynthesis of proteinogenic amino acids via endogenous upregulation of amino acid synthesis pathway genes. Thus, we were able to achieve biosynthesis of several limiting amino acids from methanol-derived carbon, in contrast to the control methylotrophic E. coli strain. 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 stringent/stress response activation., (Copyright © 2020 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Microbiomes occupy nearly all environments on Earth. These communities of interacting microorganisms are highly complex, dynamic biological systems that impact and reshape the molecular composition of their habitats by performing complex biochemical transformations. The structure and function of microbiomes are influenced by local environmental stimuli and spatiotemporal changes. In order to control the dynamics and ultimately the function of microbiomes, we need to develop a mechanistic and quantitative understanding of the ecological, molecular, and evolutionary driving forces that govern these systems. Here, we describe recent advances in developing computational and experimental approaches that can promote a more fundamental understanding of microbial communities through comprehensive model-based analysis of heterogeneous data types across multiple scales, from intracellular metabolism, to metabolite cross-feeding interactions, to the emergent macroscopic behaviors. Ultimately, harnessing the full potential of microbiomes for practical applications will require developing new predictive modeling approaches and better tools to manipulate microbiome interactions., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

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 methanol auxotrophs have proved successful, these studies focused on scarce and expensive co-substrates. Here, we engineered E. coli for methanol-dependent growth on glucose, an abundant and inexpensive co-substrate, via deletion of glucose 6-phosphate isomerase (pgi), phosphogluconate dehydratase (edd), and ribose 5-phosphate isomerases (rpiAB). Since the parental strain did not exhibit methanol-dependent growth on glucose in minimal medium, we first achieved methanol-dependent growth via amino acid supplementation and used this medium to evolve the strain for methanol-dependent growth in glucose minimal medium. The evolved strain exhibited a maximum growth rate of 0.15 h -1 in glucose minimal medium with methanol, which is comparable to that of other synthetic methanol auxotrophs. Whole genome sequencing and 13 C-metabolic flux analysis revealed the causative mutations in the evolved strain. A mutation in the phosphotransferase system enzyme I gene (ptsI) resulted in a reduced glucose uptake rate to maintain a one-to-one molar ratio of substrate utilization. Deletion of the e14 prophage DNA region resulted in two non-synonymous mutations in the isocitrate dehydrogenase (icd) gene, which reduced TCA cycle carbon flux to maintain the internal redox state. In high cell density glucose fed-batch fermentation, methanol-dependent acetone production resulted in 22% average carbon labeling of acetone from 13 C-methanol, which far surpasses that of the previous best (2.4%) found with methylotrophic E. coli Δpgi. This study addresses the need to identify appropriate co-substrates for engineering synthetic methanol auxotrophs and provides a basis for the next steps toward industrial one-carbon bioprocessing., (Copyright © 2020 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Escherichia coli is an ideal choice for constructing synthetic methylotrophs capable of utilizing the non-native substrate methanol as a carbon and energy source. All current E. coli-based synthetic methylotrophs require co-substrates. They display variable levels of methanol-carbon incorporation due to a lack of native regulatory control of biosynthetic pathways, as E. coli does not recognize methanol as a proper substrate despite its ability to catabolize it. Here, using the E. coli formaldehyde-inducible promoter P frm , we implement dynamic expression control of select pentose-phosphate genes in response to the formaldehyde produced upon methanol oxidation. Genes under P frm control exhibited 8- to 30-fold transcriptional upregulation during growth on methanol. Formaldehyde-induced episomal expression of the B. methanolicus rpe and tkt genes involved in the regeneration of ribulose 5-phosphate required for formaldehyde fixation led to significantly improved methanol assimilation into intracellular metabolites, including a 2-fold increase of 13 C-methanol into glutamate. Using a simple strategy for redox perturbation by deleting the E. coli NAD-dependent malate dehydrogenase gene maldh, we demonstrate 5-fold improved biomass formation of cells growing on methanol in the presence of a small concentration of yeast extract. Further improvements in methanol utilization are achieved via adaptive laboratory evolution and heterologous rpe and tkt expression. A short-term in vivo 13 C-methanol labeling assay was used to determine methanol assimilation activity for Δmaldh strains, and demonstrated dramatically higher labeling in intracellular metabolites, including a 6-fold and 1.8-fold increase in glycine labeling for the rpe/tkt and evolved strains, respectively. The combination of formaldehyde-controlled pentose phosphate pathway expression and redox perturbation with the maldh knock-out greatly improved both growth benefit with methanol and methanol carbon incorporation into intracellular metabolites., (Copyright © 2019 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Precise quantification of metabolic pathway fluxes in biological systems is of major importance in guiding efforts in metabolic engineering, biotechnology, microbiology, human health, and cell culture. 13 C metabolic flux analysis ( 13 C-MFA) is the predominant technique used for determining intracellular fluxes. Here, we present a protocol for 13 C-MFA that incorporates recent advances in parallel labeling experiments, isotopic labeling measurements, and statistical analysis, as well as best practices developed through decades of experience. Experimental design to ensure that fluxes are estimated with the highest precision is an integral part of the protocol. The protocol is based on growing microbes in two (or more) parallel cultures with 13 C-labeled glucose tracers, followed by gas chromatography-mass spectrometry (GC-MS) measurements of isotopic labeling of protein-bound amino acids, glycogen-bound glucose, and RNA-bound ribose. Fluxes are then estimated using software for 13 C-MFA, such as Metran, followed by comprehensive statistical analysis to determine the goodness of fit and calculate confidence intervals of fluxes. The presented protocol can be completed in 4 d and quantifies metabolic fluxes with a standard deviation of ≤2%, a substantial improvement over previous implementations. The presented protocol is exemplified using an Escherichia coli ΔtpiA case study with full supporting data, providing a hands-on opportunity to step through a complex troubleshooting scenario. Although applications to prokaryotic microbial systems are emphasized, this protocol can be easily adjusted for application to eukaryotic organisms.

Methanol is an attractive and broadly available substrate for large-scale bioproduction of fuels and chemicals. It contains more energy and electrons per carbon than carbohydrates and can be cheaply produced from natural gas. Synthetic methylotrophy refers to the development of non-native methylotrophs such as Escherichia coli and Corynebacterium glutamicum to utilize methanol as a carbon source. Here, we discuss recent advances in engineering these industrial hosts to assimilate methanol for growth and chemicals production through the introduction of the ribulose monophosphate (RuMP) cycle. In addition, we present novel strategies based on flux coupling and adaptive laboratory evolution to engineer new strains that can grow exclusively on methanol., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Kinetic models of metabolic networks offer the promise of quantitative phenotype prediction. The mechanistic characterization of enzyme catalyzed reactions allows for tracing the effect of perturbations in metabolite concentrations and reaction fluxes in response to genetic and environmental perturbation that are beyond the scope of stoichiometric models. In this study, we develop a two-step computational pipeline for the rapid parameterization of kinetic models of metabolic networks using a curated metabolic model and available 13C-labeling distributions under multiple genetic and environmental perturbations. The first step involves the elucidation of all intracellular fluxes in a core model of E. coli containing 74 reactions and 61 metabolites using 13C-Metabolic Flux Analysis (13C-MFA). Here, fluxes corresponding to the mid-exponential growth phase are elucidated for seven single gene deletion mutants from upper glycolysis, pentose phosphate pathway and the Entner-Doudoroff pathway. The computed flux ranges are then used to parameterize the same (i.e., k-ecoli74) core kinetic model for E. coli with 55 substrate-level regulations using the newly developed K-FIT parameterization algorithm. The K-FIT algorithm employs a combination of equation decomposition and iterative solution techniques to evaluate steady-state fluxes in response to genetic perturbations. k-ecoli74 predicted 86% of flux values for strains used during fitting within a single standard deviation of 13C-MFA estimated values. By performing both tasks using the same network, errors associated with lack of congruity between the two networks are avoided, allowing for seamless integration of data with model building. Product yield predictions and comparison with previously developed kinetic models indicate shifts in flux ranges and the presence or absence of mutant strains delivering flux towards pathways of interest from training data significantly impact predictive capabilities. Using this workflow, the impact of completeness of fluxomic datasets and the importance of specific genetic perturbations on uncertainties in kinetic parameter estimation are evaluated., Competing Interests: The authors have declared that no competing interests exist.

Despite remarkable progress in mapping biochemistry and gene-protein-reaction relationships, quantitative systems-level understanding of microbial metabolism remains a persistent challenge. Here, 13 C-metabolic flux analysis was applied to interrogate metabolic responses to 20 genetic perturbations in all viable Escherichia coli single gene knockouts in upper central metabolic pathways. Strains with severe growth defects displayed highly altered intracellular flux patterns and were the most difficult to predict using current constraint-based modeling approaches. In the ΔpfkA strain, an unexpected glucose-secretion phenotype was identified. The broad range of flux rewiring responses that were quantified suggest that some compensating pathways are more flexible than others, resulting in a more robust physiology. The fact that only 2 out of 20 strains displayed an increased net pathway-flux capacity points to a fundamental rate limitation of E. coli core metabolism. In cataloguing the various cellular responses, our results provide a critical resource for kinetic model development and efforts focused on genotype-to-phenotype predictions., (Copyright © 2019 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Constraint-based modeling has been applied to analyze metabolism of numerous organisms via flux balance analysis and genome-scale metabolic models, including mammalian cells such as the Chinese hamster ovary (CHO) cells-the principal cell factory platform for therapeutic protein production. Unfortunately, the application of genome-scale model methodologies using the conventional biomass objective function is challenged by the presence of overly-restrictive constraints, including essential amino acid exchange fluxes that can lead to improper predictions of growth rates and intracellular flux distributions. In this study, these constraints are found to be reliably predicted by an "essential nutrient minimization" approach. After modifying these constraints with the predicted minimal uptake values, a series of unconventional objective functions are applied to minimize each individual non-essential nutrient uptake rate, revealing useful insights about metabolic exchange rates and flows across different cell lines and culture conditions. This unconventional uptake-rate objective functions (UOFs) approach is able to distinguish metabolic differences between three distinct CHO cell lines (CHO-K1, -DG44, and -S) not directly observed using the conventional biomass growth maximization solutions. Further, a comparison of model predictions with experimental data from literature correctly correlates with the specific CHO-DG44-derived cell line used experimentally, and the corresponding dual prices provide fruitful information concerning coupling relationships between nutrients. The UOFs approach is likely to be particularly suited for mammalian cells and other complex organisms which contain multiple distinct essential nutrient inputs, and may offer enhanced applicability for characterizing cell metabolism and physiology as well as media optimization and biomanufacturing control., Competing Interests: Competing interestsThe authors declare no competing interests.

Overcoming carbon catabolite repression presents a significant challenge, largely due to the complex regulatory networks governing substrate catabolism, even in microbial cells. In this work, we have engineered an E. coli strain, which we have named X2G, that not only exhibits a reversed substrate preference for xylose over glucose, but also demonstrates an unusual ability to produce significant amounts of glucose. We obtained this non-intuitive phenotype by deleting four genes in upper central metabolism: ptsI, glk, pfkA, and zwf, which respectively encode Enzyme I of the phosphotransferase system, glucokinase, the dominant isozyme of phosphofructokinase, and glucose-6-phosphate dehydrogenase. The deletion of ptsI and glk blocks glucose uptake in E. coli, while the deletion of pfkA and zwf prevents the reassimilation of carbons through glycolysis and the oxidative pentose phosphate pathway, respectively. Our strain X2G is capable of converting 34% of the carbon it takes up as xylose into exported glucose. This corresponds to a glucose production rate of 1.4 ± 0.3 mmol/g DW /h at a specific growth rate of 0.25 ± 0.03 h -1 , or about 1.8 ± 0.1 mM of glucose accumulated for every unit increase in OD 600 . Despite a 22% decrease in xylose uptake rate, a 33% decrease in biomass yield, and a 52% decrease in acetate production rate relative to the wild-type, the intracellular flux profile and cofactor allocation of X2G remain largely unperturbed, as elucidated through 13 C-metabolic flux analysis. Further quantification of the pool sizes of key intracellular metabolites revealed that glucose secretion by X2G is likely driven by the substantial accumulation of intracellular glucose 6-phosphate, fructose 6-phosphate, glucose and fructose at levels greater than 20x of that in wild-type E. coli. Combined, our results shed light on the flexibility of central metabolism, and the opportunities this affords for producing value-added pentose- and hexose-derived products from lignocellulosic feedstocks., (Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

In the past two decades, 13 C metabolic flux analysis ( 13 C-MFA) has matured into a powerful and widely used scientific tool in metabolic engineering and systems biology. Traditionally, metabolic fluxes have been determined from measurements of isotopic labeling by means of mass spectrometry (MS) or nuclear magnetic resonance (NMR). In recent years, tandem MS has emerged as a new analytical technique that can provide additional information for high-resolution quantification of metabolic fluxes in complex biological systems. In this paper, we present recent advances in methods and algorithms for incorporating tandem MS measurements into existing 13 C-MFA approaches that are based on the elementary metabolite units (EMU) framework. Specifically, efficient EMU-based algorithms are presented for simulating tandem MS data, tracing isotopic labeling in biochemical network models and for correcting tandem MS data for natural isotope abundances.

Adaptive laboratory evolution (ALE) has emerged as a powerful tool in basic microbial research and strain development. In the context of metabolic science and engineering, it has been applied to study gene knockout responses, expand substrate ranges, improve tolerance to process conditions, and to improve productivity via designed growth coupling. In recent years, advancements in ALE methods and systems biology measurement technologies, particularly genome sequencing and 13 C metabolic flux analysis ( 13 C-MFA), have enabled detailed study of the mechanisms and dynamics of evolving metabolism. In this review, we discuss a range of studies that have applied flux analysis to adaptively evolved strains, as well as modeling frameworks developed to predict and interpret evolved fluxes. These efforts link mutations to fitness-enhanced phenotypes, identify bottlenecks and approaches to resolve them, and address systems concepts such as optimality., Competing Interests: CONFLICT OF INTEREST The authors declare no conflict of interest.

In this study, we have investigated for the first time the metabolism of E. coli grown on agar using 13 C metabolic flux analysis ( 13 C-MFA). To date, all 13 C-MFA studies on microbes have been performed with cells grown in liquid culture. Here, we extend the scope of 13 C-MFA to biological systems where cells are grown in dense microbial colonies. First, we identified new optimal 13 C tracers to quantify fluxes in systems where the acetate yield cannot be easily measured. We determined that three parallel labeling experiments with the tracers [1,2- 13 C]glucose, [1,6- 13 C]glucose, and [4,5,6- 13 C]glucose permit precise estimation of not only intracellular fluxes, but also of the amount of acetate produced from glucose. Parallel labeling experiments were then performed with wild-type E. coli and E. coli ΔackA grown in liquid culture and on agar plates. Initial attempts to fit the labeling data from wild-type E. coli grown on agar did not produce a statistically acceptable fit. To resolve this issue, we employed the recently developed co-culture 13 C-MFA approach, where two E. coli subpopulations were defined in the model that engaged in metabolite cross-feeding. The flux results identified two distinct E. coli cell populations, a dominant cell population (92% of cells) that metabolized glucose via conventional metabolic pathways and secreted a large amount of acetate (~40% of maximum theoretical yield), and a second smaller cell population (8% of cells) that consumed the secreted acetate without any glucose influx. These experimental results are in good agreement with recent theoretical simulations. Importantly, this study provides a solid foundation for future investigations of a wide range of problems involving microbial biofilms that are of great interest in biotechnology, ecology and medicine, where metabolite cross-feeding between cell populations is a core feature of the communities., (Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

In the originally published version of this Article, the colours of the bars in Fig. 4b were inadvertently switched during the production process, such that 'HK2-Dox' and 'HK2+Dox' were depicted in red and 'Nt-Dox' and 'Nt+Dox' were depicted in blue. These errors have now been corrected in both the PDF and HTML versions of the Article.

Cancer metabolism is significantly altered from normal cellular metabolism allowing cancer cells to adapt to changing microenvironments and maintain high rates of proliferation. In the past decade, stable-isotope tracing and network analysis have become powerful tools for uncovering metabolic pathways that are differentially activated in cancer cells. In particular, 13 C metabolic flux analysis ( 13 C-MFA) has emerged as the primary technique for quantifying intracellular fluxes in cancer cells. In this review, we provide a practical guide for investigators interested in getting started with 13 C-MFA. We describe best practices in 13 C-MFA, highlight potential pitfalls and alternative approaches, and conclude with new developments that can further enhance our understanding of cancer metabolism.

Hepatocellular carcinoma (HCC) cells are metabolically distinct from normal hepatocytes by expressing the high-affinity hexokinase (HK2) and suppressing glucokinase (GCK). This is exploited to selectively target HCC. Hepatic HK2 deletion inhibits tumor incidence in a mouse model of hepatocarcinogenesis. Silencing HK2 in human HCC cells inhibits tumorigenesis and increases cell death, which cannot be restored by GCK or mitochondrial binding deficient HK2. Upon HK2 silencing, glucose flux to pyruvate and lactate is inhibited, but TCA fluxes are maintained. Serine uptake and glycine secretion are elevated suggesting increased requirement for one-carbon contribution. Consistently, vulnerability to serine depletion increases. The decrease in glycolysis is coupled to elevated oxidative phosphorylation, which is diminished by metformin, further increasing cell death and inhibiting tumor growth. Neither HK2 silencing nor metformin alone inhibits mTORC1, but their combination inhibits mTORC1 in an AMPK-independent and REDD1-dependent mechanism. Finally, HK2 silencing synergizes with sorafenib to inhibit tumor growth.

Unraveling the mechanisms of microbial adaptive evolution following genetic or environmental challenges is of fundamental interest in biological science and engineering. When the challenge is the loss of a metabolic enzyme, adaptive responses can also shed significant insight into metabolic robustness, regulation, and areas of kinetic limitation. In this study, whole-genome sequencing and high-resolution 13 C-metabolic flux analysis were performed on 10 adaptively evolved pgi knockouts of Escherichia coli Pgi catalyzes the first reaction in glycolysis, and its loss results in major physiological and carbon catabolism pathway changes, including an 80% reduction in growth rate. Following adaptive laboratory evolution (ALE), the knockouts increase their growth rate by up to 3.6-fold. Through combined genomic-fluxomic analysis, we characterized the mutations and resulting metabolic fluxes that enabled this fitness recovery. Large increases in pyridine cofactor transhydrogenase flux, correcting imbalanced production of NADPH and NADH, were enabled by direct mutations to the transhydrogenase genes sthA and pntAB The phosphotransferase system component crr was also found to be frequently mutated, which corresponded to elevated flux from pyruvate to phosphoenolpyruvate. The overall energy metabolism was found to be strikingly robust, and what have been previously described as latently activated Entner-Doudoroff and glyoxylate shunt pathways are shown here to represent no real increases in absolute flux relative to the wild type. These results indicate that the dominant mechanism of adaptation was to relieve the rate-limiting steps in cofactor metabolism and substrate uptake and to modulate global transcriptional regulation from stress response to catabolism., Competing Interests: The authors declare no conflict of interest.

Methane, the main component of natural gas, can be used to produce methanol which can be further converted to other valuable products. There is increasing interest in using biological systems for the production of fuels and chemicals from methanol, termed methylotrophy. In this work, we have examined methanol assimilation metabolism in a synthetic methylotrophic E. coli strain. Specifically, we applied 13 C-tracers and evaluated 25 different co-substrates for methanol assimilation, including amino acids, sugars and organic acids. In particular, co-utilization of threonine significantly enhanced methylotrophy. Through our investigations, we proposed specific metabolic pathways that, when activated, correlated with increased methanol assimilation. These pathways are normally repressed by the leucine-responsive regulatory protein (lrp), a global regulator of metabolism associated with the feast-or-famine response in E. coli. By deleting lrp, we were able to further enhance the methylotrophic ability of our synthetic strain, as demonstrated through increased incorporation of 13 C carbon from 13 C-methanol into biomass., (Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Synthetic methylotrophy aims to develop non-native methylotrophic microorganisms to utilize methane or methanol to produce chemicals and biofuels. We report two complimentary strategies to further engineer a previously engineered methylotrophic E. coli strain for improved methanol utilization. First, we demonstrate improved methanol assimilation in the presence of small amounts of yeast extract by expressing the non-oxidative pentose phosphate pathway (PPP) from Bacillus methanolicus. Second, we demonstrate improved co-utilization of methanol and glucose by deleting the phosphoglucose isomerase gene (pgi), which rerouted glucose carbon flux through the oxidative PPP. Both strategies led to significant improvements in methanol assimilation as determined by 13 C-labeling in intracellular metabolites. Introduction of an acetone-formation pathway in the pgi-deficient methylotrophic E. coli strain led to improved methanol utilization and acetone titers during glucose fed-batch fermentation., (Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Phototrophic organisms exhibit a highly dynamic proteome, adapting their biomass composition in response to diurnal light/dark cycles and nutrient availability. Here, we used experimentally determined biomass compositions over the course of growth to determine and constrain the biomass objective function (BOF) in a genome-scale metabolic model of Chlorella vulgaris UTEX 395 over time. Changes in the BOF, which encompasses all metabolites necessary to produce biomass, influence the state of the metabolic network thus directly affecting predictions. Simulations using dynamic BOFs predicted distinct proteome demands during heterotrophic or photoautotrophic growth. Model-driven analysis of extracellular nitrogen concentrations and predicted nitrogen uptake rates revealed an intracellular nitrogen pool, which contains 38% of the total nitrogen provided in the medium for photoautotrophic and 13% for heterotrophic growth. Agreement between flux and gene expression trends was determined by statistical comparison. Accordance between predicted flux trends and gene expression trends was found for 65% of multisubunit enzymes and 75% of allosteric reactions. Reactions with the highest agreement between simulations and experimental data were associated with energy metabolism, terpenoid biosynthesis, fatty acids, nucleotides, and amino acid metabolism. Furthermore, predicted flux distributions at each time point were compared with gene expression data to gain new insights into intracellular compartmentalization, specifically for transporters. A total of 103 genes related to internal transport reactions were identified and added to the updated model of C. vulgaris , i CZ946, thus increasing our knowledgebase by 10% for this model green alga., (© 2018 American Society of Plant Biologists. All Rights Reserved.)

Thermophilic organisms are being increasingly investigated and applied in metabolic engineering and biotechnology. The distinct metabolic and physiological characteristics of thermophiles, including broad substrate range and high uptake rates, coupled with recent advances in genetic tool development, present unique opportunities for strain engineering. However, poor understanding of the cellular physiology and metabolism of thermophiles has limited the application of systems biology and metabolic engineering tools to these organisms. To address this concern, we applied high resolution 13 C metabolic flux analysis to quantify fluxes for three divergent extremely thermophilic bacteria from separate phyla: Geobacillus sp. LC300, Thermus thermophilus HB8, and Rhodothermus marinus DSM 4252. We performed 18 parallel labeling experiments, using all singly labeled glucose tracers for each strain, reconstructed and validated metabolic network models, measured biomass composition, and quantified precise metabolic fluxes for each organism. In the process, we resolved many uncertainties regarding gaps in pathway reconstructions and elucidated how these organisms maintain redox balance and generate energy. Overall, we found that the metabolisms of the three thermophiles were highly distinct, suggesting that adaptation to growth at high temperatures did not favor any particular set of metabolic pathways. All three strains relied heavily on glycolysis and TCA cycle to generate key cellular precursors and cofactors. None of the investigated organisms utilized the Entner-Doudoroff pathway and only one strain had an active oxidative pentose phosphate pathway. Taken together, the results from this study provide a solid foundation for future model building and engineering efforts with these and related thermophiles., (Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Vibrio natriegens is a fast-growing, non-pathogenic bacterium that is being considered as the next-generation workhorse for the biotechnology industry. However, little is known about the metabolism of this organism which is limiting our ability to apply rational metabolic engineering strategies. To address this critical gap in current knowledge, here we have performed a comprehensive analysis of V. natriegens metabolism. We constructed a detailed model of V. natriegens core metabolism, measured the biomass composition, and performed high-resolution 13 C metabolic flux analysis ( 13 C-MFA) to estimate intracellular fluxes using parallel labeling experiments with the optimal tracers [1,2- 13 C]glucose and [1,6- 13 C]glucose. During exponential growth in glucose minimal medium, V. natriegens had a growth rate of 1.70 1/h (doubling time of 24min) and a glucose uptake rate of 3.90g/g/h, which is more than two 2-fold faster than E. coli, although slower than the fast-growing thermophile Geobacillus LC300. 13 C-MFA revealed that the core metabolism of V. natriegens is similar to that of E. coli, with the main difference being a 33% lower normalized flux through the oxidative pentose phosphate pathway. Quantitative analysis of co-factor balances provided additional insights into the energy and redox metabolism of V. natriegens. Taken together, the results presented in this study provide valuable new information about the physiology of V. natriegens and establish a solid foundation for future metabolic engineering efforts with this promising microorganism., (Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Adaptive laboratory evolution (ALE) is a widely-used method for improving the fitness of microorganisms in selected environmental conditions. It has been applied previously to Escherichia coli K-12 MG1655 during aerobic exponential growth on glucose minimal media, a frequently used model organism and growth condition, to probe the limits of E. coli growth rate and gain insights into fast growth phenotypes. Previous studies have described up to 1.6-fold increases in growth rate following ALE, and have identified key causal genetic mutations and changes in transcriptional patterns. Here, we report for the first time intracellular metabolic fluxes for six such adaptively evolved strains, as determined by high-resolution 13 C-metabolic flux analysis. Interestingly, we found that intracellular metabolic pathway usage changed very little following adaptive evolution. Instead, at the level of central carbon metabolism the faster growth was facilitated by proportional increases in glucose uptake and all intracellular rates. Of the six evolved strains studied here, only one strain showed a small degree of flux rewiring, and this was also the strain with unique genetic mutations. A comparison of fluxes with two other wild-type (unevolved) E. coli strains, BW25113 and BL21, showed that inter-strain differences are greater than differences between the parental and evolved strains. Principal component analysis highlighted that nearly all flux differences (95%) between the nine strains were captured by only two principal components. The distance between measured and flux balance analysis predicted fluxes was also investigated. It suggested a relatively wide range of similar stoichiometric optima, which opens new questions about the path-dependency of adaptive evolution., (Copyright © 2017 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Engineered microbes offer a practical and sustainable alternative to traditional industrial approaches. To increase the economic feasibility of biological processes, microbial isolates are engineered to take up inexpensive feedstocks (including lignocellulosic biomass, syngas, methane, and carbon dioxide), and convert them into substrates of central metabolism and further into value-added products. To trace the metabolism of these feedstocks into products, isotopic tracers are applied together with isotopomer analysis techniques such as 13 C-metabolic flux analysis to provide a detailed picture of pathway utilization. Flux data is then integrated with kinetic models and constraint-based approaches to identify metabolic bottlenecks, propose novel metabolic engineering strategies, and improve process performance., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The bacterial phosphoenolpyruvate-carbohydrate phosphotransferase system (PTS) consists of cascading phosphotransferases that couple the simultaneous import and phosphorylation of a variety of sugars to the glycolytic conversion of phosphoenolpyruvate (PEP) to pyruvate. As the primary route of glucose uptake in E. coli, the PTS plays a key role in regulating central carbon metabolism and carbon catabolite repression, and is a frequent target of metabolic engineering interventions. Here we show that Enzyme I, the terminal phosphotransferase responsible for the conversion of PEP to pyruvate, is responsible for a significant in vivo flux in the reverse direction (pyruvate to PEP) during both gluconeogenic and glycolytic growth. We use 13 C alanine tracers to quantify this back-flux in single and double knockouts of genes relating to PEP synthetase and PTS components. Our findings are relevant to metabolic engineering design and add to our understanding of gene-reaction connectivity in E. coli., Competing Interests: The authors declare no competing financial interests.

Methanol is an attractive substrate for biological production of chemicals and fuels. Engineering methylotrophic Escherichia coli as a platform organism for converting methanol to metabolites is desirable. Prior efforts to engineer methylotrophic E. coli were limited by methanol dehydrogenases (Mdhs) with unfavorable enzyme kinetics. We engineered E. coli to utilize methanol using a superior NAD-dependent Mdh from Bacillus stearothermophilus and ribulose monophosphate (RuMP) pathway enzymes from B. methanolicus. Using 13 C-labeling, we demonstrate this E. coli strain converts methanol into biomass components. For example, the key TCA cycle intermediates, succinate and malate, exhibit labeling up to 39%, while the lower glycolytic intermediate, 3-phosphoglycerate, up to 53%. Multiple carbons are labeled for each compound, demonstrating a cycling RuMP pathway for methanol assimilation to support growth. By incorporating the pathway to synthesize the flavanone naringenin, we demonstrate the first example of in vivo conversion of methanol into a specialty chemical in E. coli., (Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

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 13 C-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 13 C metabolic flux analysis ( 13 C-MFA). Specifically, we quantified precise metabolic fluxes for each condition by performing parallel labeling experiments and analyzing the data through integrated 13 C-MFA using the optimal tracers [1,2- 13 C]glucose, [1,6- 13 C]glucose, [1,2- 13 C]xylose and [5- 13 C]xylose. We also quantified changes in biomass composition and confirmed turnover of macromolecules by applying [U- 13 C]glucose and [U- 13 C]xylose tracers. We demonstrated that under anaerobic growth conditions there is significant turnover of lipids and that a significant portion of CO 2 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/FADH 2 , 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., (Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

13 C metabolic flux analysis ( 13 C-MFA) is a widely used tool for quantitative analysis of microbial and mammalian metabolism. Until now, 13 C-MFA was based mainly on measurements of isotopic labeling of amino acids derived from hydrolyzed biomass proteins and isotopic labeling of extracted intracellular metabolites. Here, we demonstrate that isotopic labeling of glycogen and RNA, measured with gas chromatography-mass spectrometry (GC-MS), provides valuable additional information for 13 C-MFA. Specifically, we demonstrate that isotopic labeling of glucose moiety of glycogen and ribose moiety of RNA greatly enhances resolution of metabolic fluxes in the upper part of metabolism; importantly, these measurements allow precise quantification of net and exchange fluxes in the pentose phosphate pathway. To demonstrate the practical importance of these measurements for 13 C-MFA, we have used Escherichia coli as a model microbial system and CHO cells as a model mammalian system. Additionally, we have applied this approach to determine metabolic fluxes of glucose and xylose co-utilization in the E. coli ΔptsG mutant. The convenience of measuring glycogen and RNA, which are stable and abundant in microbial and mammalian cells, offers the following key advantages: reduced sample size, no quenching required, no extractions required, and GC-MS can be used instead of more costly LC-MS/MS techniques. Overall, the presented approach for 13 C-MFA will have widespread applicability in metabolic engineering and biomedical research., (Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

13 C-Metabolic flux analysis ( 13 C-MFA) is a widely used approach in metabolic engineering for quantifying intracellular metabolic fluxes. The precision of fluxes determined by 13 C-MFA depends largely on the choice of isotopic tracers and the specific set of labeling measurements. A recent advance in the field is the use of parallel labeling experiments for improved flux precision and accuracy. However, as of today, no systemic methods exist for identifying optimal tracers for parallel labeling experiments. In this contribution, we have addressed this problem by introducing a new scoring system and evaluating thousands of different isotopic tracer schemes. Based on this extensive analysis we have identified optimal tracers for 13 C-MFA. The best single tracers were doubly 13 C-labeled glucose tracers, including [1,6- 13 C]glucose, [5,6- 13 C]glucose and [1,2- 13 C]glucose, which consistently produced the highest flux precision independent of the metabolic flux map (here, 100 random flux maps were evaluated). Moreover, we demonstrate that pure glucose tracers perform better overall than mixtures of glucose tracers. For parallel labeling experiments the optimal isotopic tracers were [1,6- 13 C]glucose and [1,2- 13 C]glucose. Combined analysis of [1,6- 13 C]glucose and [1,2- 13 C]glucose labeling data improved the flux precision score by nearly 20-fold compared to widely use tracer mixture 80% [1- 13 C]glucose +20% [U- 13 C]glucose., (Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using 13 C-metabolic flux analysis ( 13 C-MFA) aided by multiple tracer experiments and integrated data analysis. Using 13 C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different 13 C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of 13 C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems., (Copyright © 2016 the American Physiological Society.)

Maximizing the conversion of biogenic carbon feedstocks into chemicals and fuels is essential for fermentation processes as feedstock costs and processing is commonly the greatest operating expense. Unfortunately, for most fermentations, over one-third of sugar carbon is lost to CO 2 due to the decarboxylation of pyruvate to acetyl-CoA and limitations in the reducing power of the bio-feedstock. Here we show that anaerobic, non-photosynthetic mixotrophy, defined as the concurrent utilization of organic (for example, sugars) and inorganic (for example, CO 2 ) substrates in a single organism, can overcome these constraints to increase product yields and reduce overall CO 2 emissions. As a proof-of-concept, Clostridium ljungdahlii was engineered to produce acetone and achieved a mass yield 138% of the previous theoretical maximum using a high cell density continuous fermentation process. In addition, when enough reductant (that is, H 2 ) is provided, the fermentation emits no CO 2 . Finally, we show that mixotrophy is a general trait among acetogens., Competing Interests: White Dog Labs is commercializing a mixotrophy-based fermentation process and has filed a patent application PCT:US2016:019760.