High-resolution 13C metabolic flux analysis

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.

Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis

Metabolic networks are regulated to ensure the dynamic adaptation of biochemical reaction fluxes to maintain cell homeostasis and optimal metabolic fitness in response to endogenous and exogenous perturbations. To this end, metabolism is tightly controlled by dynamic and intricate regulatory mechanisms involving allostery, enzyme abundance and post-translational modifications. The study of the molecular entities involved in these complex mechanisms has been boosted by the advent of high-throughput technologies. The so-called omics enable the quantification of the different molecular entities at different system layers, connecting the genotype with the phenotype. Therefore, the study of the overall behavior of a metabolic network and the omics data integration and analysis must be approached from a holistic perspective. Due to the close relationship between metabolism and cellular phenotype, metabolic modelling has emerged as a valuable tool to decipher the underlying mechanisms governing cell phenotype. Constraint-based modelling and kinetic modelling are among the most widely used methods to study cell metabolism at different scales, ranging from cells to tissues and organisms. These approaches enable integrating metabolomic data, among others, to enhance model predictive capabilities. In this review, we describe the current state of the art in metabolic modelling and discuss future perspectives and current challenges in the field.

A guide to deciphering microbial interactions and metabolic fluxes in microbiome communities

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.

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.

Nitrate reduction by Paracoccus thiophilus strain LSL 251 under aerobic condition: Performance and intracellular central carbon flux pathways

The intracellular carbon metabolic flux pathways of denitrifying bacteria under aerobic conditions remain unclear. Here, a newly strain LSL251 was identified as Paracoccus thiophilus. Strain LSL251 removed 94.79% and 98.78% of total organic carbon and nitrate. 74.66% of nitrogen in culture system was lost as gaseous nitrogen. Moreover, ¹³C stable isotopic labeling and metabolic flux analyses revealed that the primary intracellular carbon metabolic pathways were the Entner-Doudoroff pathway and the tricarboxylic acid (TCA) cycle. Electrons are primarily donated as direct electron donor-NADH through the TCA cycle. Furthermore, response surface methodology modeled that the highest total nitrogen removal efficiency was 98.43%, where the optimal parameters were C/N ratio of 8.00, 32.98 °C, 50.18 rpm, and initial pH of 7.73. All together, these results have shed new lights on intracellular central carbon metabolic distribution and flux pathways of aerobic denitrifying bacteria.

Nitrate removal characteristics and 13C metabolic pathways of aerobic denitrifying bacterium Paracoccus denitrificans Z195

Strain Z195 was isolated and identified as Paracoccus denitrificans. Z195 exhibited efficient aerobic denitrification and carbon removal abilities, and removed 93.74% of total nitrogen (TN) and 97.81% of total organic carbon.71.88% of nitrogen was lost as gaseous products.¹³C-metabolic flux analysis revealed that 95% and 132% of the carbon fluxes entered the Entner-Doudoroff (ED) pathway and tricarboxylic acid (TCA) cycle, respectively. Electrons produced by carbon metabolism markedly promoted the processes of nitrogen metabolism process and aerobic respiration. A response surface methodology model demonstrated that the optimal conditions for the maximum TN removal were a C/N ratio of 7.47, shaking speed of 108 rpm, temperature of 31 °C and initial pH of 8.02. Additionally, the average TN and chemical oxygen demand removal efficiencies of raw wastewater were 89% and 91%, respectively. The results give new insight for understanding metabolic flux analysis of aerobic denitrifying bacteria.

Triggering the stringent response enhances synthetic methanol utilization 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, 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.

Glycogen metabolism regulates macrophage-mediated acute inflammatory responses

Our current understanding of how sugar metabolism affects inflammatory pathways in macrophages is incomplete. Here, we show that glycogen metabolism is an important event that controls macrophage-mediated inflammatory responses. IFN-γ/LPS treatment stimulates macrophages to synthesize glycogen, which is then channeled through glycogenolysis to generate G6P and further through the pentose phosphate pathway to yield abundant NADPH, ensuring high levels of reduced glutathione for inflammatory macrophage survival. Meanwhile, glycogen metabolism also increases UDPG levels and the receptor P2Y₁₄ in macrophages. The UDPG/P2Y₁₄ signaling pathway not only upregulates the expression of STAT1 via activating RARβ but also promotes STAT1 phosphorylation by downregulating phosphatase TC45. Blockade of this glycogen metabolic pathway disrupts acute inflammatory responses in multiple mouse models. Glycogen metabolism also regulates inflammatory responses in patients with sepsis. These findings show that glycogen metabolism in macrophages is an important regulator and indicate strategies that might be used to treat acute inflammatory diseases.

Investigating E. coli Coculture for Resveratrol Production with 13C Metabolic Flux Analysis

Resveratrol, a phytoalexin produced by plants, has several beneficial effects in humans. It can be produced using Escherichia coli by introducing only three heterologous genes: TAL, 4CL, and STS. However, the resveratrol synthesis pathway requires two precursors, tyrosine and acetyl-CoA, which are produced by two branched central metabolic pathways. Therefore, overexpression of these genes in E. coli results in the production of only trace amounts of resveratrol. In this study, we attempted to produce resveratrol via coculture of two engineered strains in which the two metabolic pathways are activated. The first strain was engineered to produce p-coumaric acid using tyrosine as a precursor, which can be synthesized by the pentose phosphate pathway. The second strain produced resveratrol by combining p-coumaric acid from the first strain and malonyl-CoA synthesized from acetyl-CoA, which is produced by the glycolytic pathway. In total, 55.7 mg/L of resveratrol was produced from 20 g/L of glucose via coculture of these two strains in glucose minimal medium without any supplements. The metabolic fluxes in each of the strains producing resveratrol were successfully investigated by ¹³C metabolic flux analysis. The results showed that the balance between the citric acid cycle and the malonyl-CoA supply node was important for resveratrol production.

Engineering Escherichia coli for methanol-dependent growth on glucose for metabolite production

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 ¹³C-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 ¹³C-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.

The Entner-Doudoroff and Nonoxidative Pentose Phosphate Pathways Bypass Glycolysis and the Oxidative Pentose Phosphate Pathway in Ralstonia solanacearum

Understanding the metabolic versatility of Ralstonia solanacearum is important, as it regulates the trade-off between virulence and metabolism (1, 2) in a wide range of plant hosts. Due to a lack of clear evidence until this work, several published research papers reported on the potential roles of glycolysis and the oxidative pentose phosphate pathway (OxPPP) in R. solanacearum (3, 4). This work provided evidence from ¹³C stable isotope feeding and genome annotation-based comparative metabolic network analysis that the Entner-Doudoroff pathway and non-OxPPP bypass glycolysis and OxPPP during the oxidation of glucose, a component of the host xylem pool that serves as a potential carbon source (5). The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with ¹³C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes. The study highlights the need to critically examine phytopathogens whose metabolism is poorly understood.

In Ralstonia solanacearum, a devastating phytopathogen whose metabolism is poorly understood, we observed that the Entner-Doudoroff (ED) pathway and nonoxidative pentose phosphate pathway (non-OxPPP) bypass glycolysis and OxPPP under glucose oxidation. Evidence derived from ¹³C stable isotope feeding and genome annotation-based comparative metabolic network analysis supported the observations. Comparative metabolic network analysis derived from the currently available 53 annotated R. solanacearum strains, including a recently reported strain (F1C1), representing the four phylotypes, confirmed the lack of key genes coding for phosphofructokinase (pfk-1) and phosphogluconate dehydrogenase (gnd) enzymes that are relevant for glycolysis and OxPPP, respectively. R. solanacearum F1C1 cells fed with [¹³C]glucose (99% [1-¹³C]glucose or 99% [1,2-¹³C]glucose or 40% [¹³C₆]glucose) followed by gas chromatography-mass spectrometry (GC-MS)-based labeling analysis of fragments from amino acids, glycerol, and ribose provided clear evidence that rather than glycolysis and the OxPPP, the ED pathway and non-OxPPP are the main routes sustaining metabolism in R. solanacearum. The ¹³C incorporation in the mass ions of alanine (m/z 260 and m/z 232), valine (m/z 288 and m/z 260), glycine (m/z 218), serine (m/z 390 and m/z 362), histidine (m/z 440 and m/z 412), tyrosine (m/z 466 and m/z 438), phenylalanine (m/z 336 and m/z 308), glycerol (m/z 377), and ribose (m/z 160) mapped the pathways supporting the observations. The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with ¹³C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes.

IMPORTANCE Understanding the metabolic versatility of Ralstonia solanacearum is important, as it regulates the trade-off between virulence and metabolism (1, 2) in a wide range of plant hosts. Due to a lack of clear evidence until this work, several published research papers reported on the potential roles of glycolysis and the oxidative pentose phosphate pathway (OxPPP) in R. solanacearum (3, 4). This work provided evidence from ¹³C stable isotope feeding and genome annotation-based comparative metabolic network analysis that the Entner-Doudoroff pathway and non-OxPPP bypass glycolysis and OxPPP during the oxidation of glucose, a component of the host xylem pool that serves as a potential carbon source (5). The outcomes help better define the central carbon metabolic network of R. solanacearum that can be integrated with ¹³C metabolic flux analysis as well as flux balance analysis studies for defining the metabolic phenotypes. The study highlights the need to critically examine phytopathogens whose metabolism is poorly understood.

Improving synthetic methylotrophy via dynamic formaldehyde regulation of pentose phosphate pathway genes and redox perturbation

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 ¹³C-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 vivo¹³C-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.

Quantifying Methane and Methanol Metabolism of "Methylotuvimicrobium buryatense" 5GB1C under Substrate Limitation

Methanotrophic metabolism has been under investigation for decades using biochemical and genetic approaches. Recently, a further step has been taken toward understanding methanotrophic metabolism in a quantitative manner by means of flux balance analysis (FBA), a mathematical approach that predicts fluxes constrained by mass balance and a few experimental measurements. However, no study has previously been undertaken to experimentally quantitate the complete methanotrophic central metabolism. The significance of this study is to fill such a gap by performing ¹³C INST-MFA on a fast-growing methanotroph. Our quantitative insights into the methanotrophic carbon and energy metabolism will pave the way for future FBA studies and set the stage for rational design of methanotrophic strains for industrial applications. Further, the experimental strategies can be applied to other methane or methanol utilizers, and the results will offer a unique and quantitative perspective of diverse methylotrophic metabolism.

Methanotrophic bacteria are a group of prokaryotes capable of using methane as their sole carbon and energy source. Although efforts have been made to simulate and elucidate their metabolism via computational approaches or ¹³C tracer analysis, major gaps still exist in our understanding of methanotrophic metabolism at the systems level. Particularly, direct measurements of system-wide fluxes are required to understand metabolic network function. Here, we quantified the central metabolic fluxes of a type I methanotroph, “Methylotuvimicrobium buryatense” 5GB1C, formerly Methylomicrobium buryatense 5GB1C, via ¹³C isotopically nonstationary metabolic flux analysis (INST-MFA). We performed labeling experiments on chemostat cultures by switching substrates from ¹²C to ¹³C input. Following the switch, we measured dynamic changes of labeling patterns and intracellular pool sizes of several intermediates, which were later used for data fitting and flux calculations. Through computational optimizations, we quantified methane and methanol metabolism at two growth rates (0.1 h⁻¹ and 0.05 h⁻¹). The resulting flux maps reveal a core consensus central metabolic flux phenotype across different growth conditions: a strong ribulose monophosphate cycle, a preference for the Embden-Meyerhof-Parnas pathway as the primary glycolytic pathway, and a tricarboxylic acid cycle showing small yet significant fluxes. This central metabolic consistency is further supported by a good linear correlation between fluxes at the two growth rates. Specific differences between methane and methanol growth observed previously are maintained under substrate limitation, albeit with smaller changes. The substrate oxidation and glycolysis pathways together contribute over 80% of total energy production, while other pathways play less important roles.

IMPORTANCE Methanotrophic metabolism has been under investigation for decades using biochemical and genetic approaches. Recently, a further step has been taken toward understanding methanotrophic metabolism in a quantitative manner by means of flux balance analysis (FBA), a mathematical approach that predicts fluxes constrained by mass balance and a few experimental measurements. However, no study has previously been undertaken to experimentally quantitate the complete methanotrophic central metabolism. The significance of this study is to fill such a gap by performing ¹³C INST-MFA on a fast-growing methanotroph. Our quantitative insights into the methanotrophic carbon and energy metabolism will pave the way for future FBA studies and set the stage for rational design of methanotrophic strains for industrial applications. Further, the experimental strategies can be applied to other methane or methanol utilizers, and the results will offer a unique and quantitative perspective of diverse methylotrophic metabolism.