Pathway thermodynamics highlights kinetic obstacles in central metabolism

Expression and characterization of monofunctional alcohol dehydrogenase enzymes in Clostridium thermocellum

Clostridium thermocellum is a thermophilic anaerobic bacterium that could be used for cellulosic biofuel production due to its strong native ability to consume cellulose, however its ethanol production ability needs to be improved to enable commercial application. In our previous strain engineering work, we observed a spontaneous mutation in the native adhE gene that reduced ethanol production. Here we attempted to complement this mutation by heterologous expression of 18 different alcohol dehydrogenase (adh) genes. We were able to express all of them successfully in C. thermocellum. Surprisingly, however, none of them increased ethanol production, and several actually decreased it. Our findings contribute to understanding the correlation between C. thermocellum ethanol production and Adh enzyme cofactor preferences. The identification of a set of adh genes that can be successfully expressed in this organism provides a foundation for future investigations into how the properties of Adh enzymes affect ethanol production.

Mitochondrial ATP generation is more proteome efficient than glycolysis

Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.

Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase in Chlamydia trachomatis persistence

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence reflects an adaptive response or a lack thereof. To understand this, transcriptomics data were collected for CTL grown under nutrient-replete and nutrient-starved conditions. Applying K-means clustering on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions in the absence of any canonical global stress regulator. This is consistent with previous data that suggested that CTL's stress response is due to a lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed that phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence state. Our data indicate that pgm has the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm in the presence or absence of tryptophan revealed the importance of this gene in modulating persistence. Hence, this work, for the first time, introduces thermodynamics and enzyme cost as tools to gain a deeper understanding on CTL persistence.

IMPORTANCE

This study uses a metabolic model to investigate factors that contribute to the persistence of Chlamydia trachomatis serovar L2 (CTL) under tryptophan and iron starvation conditions. As CTL lacks many canonical transcriptional regulators, the model was used to assess two prevailing hypotheses on persistence-that the chlamydial response to nutrient starvation represents a passive response due to the lack of regulators or that it is an active response by the bacterium. K-means clustering of stress-induced transcriptomics data revealed striking evidence in favor of the lack of adaptive (i.e., a passive) response. To find the metabolic signature of this, metabolic modeling pin-pointed pgm as a potential regulator of persistence. Thermodynamic driving force, enzyme cost, and CRISPRi knockdown of pgm supported this finding. Overall, this work introduces thermodynamic driving force and enzyme cost as a tool to understand chlamydial persistence, demonstrating how systems biology-guided CRISPRi can unravel complex bacterial phenomena.

Is the TCA cycle malate dehydrogenase-citrate synthase metabolon an illusion?

This review discusses the intriguing yet controversial concept of metabolons, focusing on the malate dehydrogenase-citrate synthase (MDH-CISY) metabolon as a model. Metabolons are multienzyme complexes composed of enzymes that catalyze sequential reactions in metabolic pathways. Metabolons have been proposed to enhance metabolic pathway efficiency by facilitating substrate channeling. However, there is skepticism about the presence of metabolons and their functionality in physiological conditions in vivo. We address the skepticism by reviewing compelling evidence supporting the existence of the MDH-CISY metabolon and highlighting its potential functions in cellular metabolism. The electrostatic interaction between MDH and CISY and the intermediate oxaloacetate, channeled within the metabolon, has been demonstrated using various experimental techniques, including protein-protein interaction assays, isotope dilution studies, and enzyme coupling assays. Regardless of the wealth of in vitro evidence, further validation is required to elucidate the functionality of MDH-CISY metabolons in living systems using advanced structural and spatial analysis techniques.

Probabilistic and maximum entropy modeling of chemical reaction systems: Characteristics and comparisons to mass action kinetic models

We demonstrate and characterize a first-principles approach to modeling the mass action dynamics of metabolism. Starting from a basic definition of entropy expressed as a multinomial probability density using Boltzmann probabilities with standard chemical potentials, we derive and compare the free energy dissipation and the entropy production rates. We express the relation between entropy production and the chemical master equation for modeling metabolism, which unifies chemical kinetics and chemical thermodynamics. Because prediction uncertainty with respect to parameter variability is frequently a concern with mass action models utilizing rate constants, we compare and contrast the maximum entropy model, which has its own set of rate parameters, to a population of standard mass action models in which the rate constants are randomly chosen. We show that a maximum entropy model is characterized by a high probability of free energy dissipation rate and likewise entropy production rate, relative to other models. We then characterize the variability of the maximum entropy model predictions with respect to uncertainties in parameters (standard free energies of formation) and with respect to ionic strengths typically found in a cell.

Should the standard model of cellular energy metabolism be reconsidered? Possible coupling between the pentose phosphate pathway, glycolysis and extra-mitochondrial oxidative phosphorylation

The process of cellular respiration occurs for energy production through catabolic reactions, generally with glucose as the first process step. In the present work, we introduce a novel concept for understanding this process, based on our conclusion that glucose metabolism is coupled to the pentose phosphate pathway (PPP) and extra-mitochondrial oxidative phosphorylation in a closed-loop process. According to the current standard model of glycolysis, glucose is first converted to glucose 6-phosphate (glucose 6-P) and then to fructose 6-phosphate, glyceraldehyde 3-phosphate and pyruvate, which then enters the Krebs cycle in the mitochondria. However, it is more likely that the pyruvate will be converted to lactate. In the PPP, glucose 6-P is branched off from glycolysis and used to produce NADPH and ribulose 5-phosphate (ribulose 5-P). Ribulose 5-P can be converted to fructose 6-P and glyceraldehyde 3-P. In our view, a circular process can take place in which the ribulose 5-P produced by the PPP enters the glycolysis pathway and is then retrogradely converted to glucose 6-P. This process is repeated several times until the complete degradation of glucose 6-P. The role of mitochondria in this process is to degrade lipids by beta-oxidation and produce acetyl-CoA; the function of producing ATP appears to be only secondary. This proposed new concept of cellular bioenergetics allows the resolution of some previously unresolved controversies related to cellular respiration and provides a deeper understanding of metabolic processes in the cell, including new insights into the Warburg effect.

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects

The construction of high-performance microbial cell factories (MCFs) is the centerpiece of biomanufacturing. However, the complex metabolic regulatory network of microorganisms poses great challenges for the efficient design and construction of MCFs. The genome-scale metabolic network models (GSMs) can systematically simulate the metabolic regulation process of microorganisms in silico, providing effective guidance for the rapid design and construction of MCFs. In this review, we summarized the development status of 16 important industrial microbial GSMs, and further outline the technologies or methods that continuously promote high-quality GSMs construction from five aspects: I) Databases and modeling tools facilitate GSMs reconstruction; II) evolving gap-filling technologies; III) constraint-based model reconstruction; IV) advances in algorithms; and V) developed visualization tools. In addition, we also summarized the applications of GSMs in guiding metabolic engineering from four aspects: I) exploring and explaining metabolic features; II) predicting the effects of genetic perturbations on metabolism; III) predicting the optimal phenotype; IV) guiding cell factories construction in practical experiment. Finally, we discussed the development of GSMs, aiming to provide a reference for efficiently reconstructing GSMs and guiding metabolic engineering.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

From metagenomes to metabolism: Systematically assessing the metabolic flux feasibilities for "Candidatus Accumulibacter" species during anaerobic substrate uptake

With the rapid growing availability of metagenome assembled genomes (MAGs) and associated metabolic models, the identification of metabolic potential in individual community members has become possible. However, the field still lacks an unbiassed systematic evaluation of the generated metagenomic information to uncover not only metabolic potential, but also feasibilities of these models under specific environmental conditions. In this study, we present a systematic analysis of the metabolic potential in species of "Candidatus Accumulibacter", a group of polyphosphate-accumulating organisms (PAOs). We constructed a metabolic model of the central carbon metabolism and compared the metabolic potential among available MAGs for "Ca. Accumulibacter" species. By combining Elementary Flux Modes Analysis (EFMA) with max-min driving force (MDF) optimization, we obtained all possible flux distributions of the metabolic network and calculated their individual thermodynamic feasibility. Our findings reveal significant variations in the metabolic potential among "Ca. Accumulibacter" MAGs, particularly in the presence of anaplerotic reactions. EFMA revealed 700 unique flux distributions in the complete metabolic model that enable the anaerobic uptake of acetate and its conversion into polyhydroxyalkanoates (PHAs), a well-known phenotype of "Ca. Accumulibacter". However, thermodynamic constraints narrowed down this solution space to 146 models that were stoichiometrically and thermodynamically feasible (MDF > 0 kJ/mol), of which only 8 were strongly feasible (MDF > 7 kJ/mol). Notably, several novel flux distributions for the metabolic model were identified, suggesting putative, yet unreported, functions within the PAO communities. Overall, this work provides valuable insights into the metabolic variability among "Ca. Accumulibacter" species and redefines the anaerobic metabolic potential in the context of phosphate removal. More generally, the integrated workflow presented in this paper can be applied to any metabolic model obtained from a MAG generated from microbial communities to objectively narrow the expected phenotypes from community members.

Optimal enzyme profiles in unbranched metabolic pathways

How to optimize the allocation of enzymes in metabolic pathways has been a topic of study for many decades. Although the general problem is complex and nonlinear, we have previously shown that it can be solved by convex optimization. In this paper, we focus on unbranched metabolic pathways with simplified enzymatic rate laws and derive analytic solutions to the optimization problem. We revisit existing solutions based on the limit of mass-action rate laws and present new solutions for other rate laws. Furthermore, we revisit a known relationship between flux control coefficients and enzyme abundances in optimal metabolic states. We generalize this relationship to models with density constraints on enzymes and metabolites, and present a new local relationship between optimal reaction elasticities and enzyme amounts. Finally, we apply our theory to derive simple kinetics-based formulae for protein allocation during bacterial growth.

Coenzyme A Thioester Intermediates as Platform Molecules in Cell-Free Chemical Biomanufacturing

The in vitro synthesis of Coenzyme A (CoA)-thioester intermediates opens new avenues to transform simple molecules into more complex and multifunctional ones by assembling cell-free biosynthetic cascades. In this review, we have systematically cataloged known CoA-dependent enzyme reactions that have been successfully implemented in vitro. To faciliate their identification, we provide their UniProt ID when available. Based on this catalog, we have organized enzymes into three modules: activation, modification, and removal. i) The activation module includes enzymes capable of fusing CoA with organic molecules. ii) The modification module includes enzymes capable of catalyzing chemical modifications in the structure of acyl-CoA intermediates. And iii) the removal module includes enzymes able to remove the CoA and release an organic molecule different from the one activated in the upstream. Based on these reactions, we constructed a reaction network that summarizes the most relevant CoA-dependent biosynthetic pathways reported until today. From the information available in the articles, we have plotted the total turnover number of CoA as a function of the product titer, observing a positive correlation between both parameters. Therefore, the success of a CoA-dependent in vitro pathway depends on its ability to regenerate CoA, but also to regenerate other cofactors such as NAD(P)H and ATP.

Glycine homeostasis requires reverse SHMT flux

The folate-dependent enzyme serine hydroxymethyltransferase (SHMT) reversibly converts serine into glycine and a tetrahydrofolate-bound one-carbon unit. Such one-carbon unit production plays a critical role in development, the immune system, and cancer. Using rodent models, here we show that the whole-body SHMT flux acts to net consume rather than produce glycine. Pharmacological inhibition of whole-body SHMT1/2 and genetic knockout of liver SHMT2 elevated circulating glycine levels up to eight-fold. Stable-isotope tracing revealed that the liver converts glycine to serine, which is then converted by serine dehydratase into pyruvate and burned in the tricarboxylic acid cycle. In response to diets deficient in serine and glycine, de novo biosynthetic flux was unaltered, but SHMT2- and serine-dehydratase-mediated catabolic flux was lower. Thus, glucose-derived serine synthesis is largely insensitive to systemic demand. Instead, circulating serine and glycine homeostasis is maintained through variable consumption, with liver SHMT2 a major glycine-consuming enzyme.

Reaction thermodynamics as a constraint on piscine steroidogenesis flux distributions

While a considerable amount is known of the dynamics of piscine steroidogenesis during reproduction, the influence of thermodynamics constraints on its control has not been studied. In this manuscript, Gibbs free energy change of reactions was calculated for piscine steroidogenesis using the in silico eQuilibrator thermodynamics calculator. The analysis identified cytochrome P450 (cyp450) oxidoreductase reactions to have more negative Gibbs free energy changes relative to hydroxysteroid (HSD) and transferase reactions. In addition, a more favorable Gibbs free energy change was predicted for the Δ5 (cyp450 catalyzed) vs. Δ4 (HSD catalyzed) steroidogenesis branch-point, which converts pregnenolone to 17α-hydroxypregnenolone or progesterone respectively. Comparison of in silico predictions with in vivo experimentally measured flux across the Δ5 vs. Δ4 branch-point showed higher flux through the thermodynamically more favorable Δ5 pathway in reproducing or spawning vs. non-spawning fathead minnows (Pimephales promelas). However, the exposure of fish to endocrine stressors such as hypoxia or the synthetic estrogen 17α-ethinylestradiol (EE2), resulted in increased flux through both Δ5 and Δ4 pathways, indicating an adaptive response to increase steroidogenic redundancy. The correspondence of elevated flux through the Δ5 branch-point in spawning fish indicated the use of a thermodynamically favorable pathway to optimize steroid hormone productions during reproduction. We hypothesize that such selective use of a thermodynamically favorable steroidogenesis pathway may conserve reduced equivalents or transcriptional costs for investment to other biosynthetic or catabolic reactions to support reproduction. If generalizable, such an approach can provide novel insights into the structural principles and regulation of steroidogenesis or other metabolic pathways.

Engineering a synthetic energy-efficient formaldehyde assimilation cycle in Escherichia coli

One-carbon (C1) substrates, such as methanol or formate, are attractive feedstocks for circular bioeconomy. These substrates are typically converted into formaldehyde, serving as the entry point into metabolism. Here, we design an erythrulose monophosphate (EuMP) cycle for formaldehyde assimilation, leveraging a promiscuous dihydroxyacetone phosphate dependent aldolase as key enzyme. In silico modeling reveals that the cycle is highly energy-efficient, holding the potential for high bioproduct yields. Dissecting the EuMP into four modules, we use a stepwise strategy to demonstrate in vivo feasibility of the modules in E. coli sensor strains with sarcosine as formaldehyde source. From adaptive laboratory evolution for module integration, we identify key mutations enabling the accommodation of the EuMP reactions with endogenous metabolism. Overall, our study demonstrates the proof-of-concept for a highly efficient, new-to-nature formaldehyde assimilation pathway, opening a way for the development of a methylotrophic platform for a C1-fueled bioeconomy in the future.

Machine Learning and Metabolic Model Guided CRISPRi Reveals a Central Role for Phosphoglycerate Mutase in Chlamydia trachomatis Persistence

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence is an adaptive response or lack of it. To understand that transcriptomics data were collected for nutrient-sufficient and nutrient-starved CTL. Applying machine learning approaches on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions without having any global stress regulator. This indicated that CTL's stress response is due to lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence. Later, pgm was found to have the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm and tryptophan starvation experiments revealed the importance of this gene in inducing persistence. Hence, this work, for the first time, introduced thermodynamics and enzyme-cost as tools to gain deeper understanding on CTL persistence.

A multi-organ maize metabolic model connects temperature stress with energy production and reducing power generation

Climate change has adversely affected maize productivity. Thereby, a holistic understanding of metabolic crosstalk among its organs is important to address this issue. Thus, we reconstructed the first multi-organ maize metabolic model, iZMA6517, and contextualized it with heat and cold stress transcriptomics data using expression distributed reaction flux measurement (EXTREAM) algorithm. Furthermore, implementing metabolic bottleneck analysis on contextualized models revealed differences between these stresses. While both stresses had reducing power bottlenecks, heat stress had additional energy generation bottlenecks. We also performed thermodynamic driving force analysis, revealing thermodynamics-reducing power-energy generation axis dictating the nature of temperature stress responses. Thus, a temperature-tolerant maize ideotype can be engineered by leveraging the proposed thermodynamics-reducing power-energy generation axis. We experimentally inoculated maize root with a beneficial mycorrhizal fungus, Rhizophagus irregularis, and as a proof-of-concept demonstrated its efficacy in alleviating temperature stress. Overall, this study will guide the engineering effort of temperature stress-tolerant maize ideotypes.

Design and thermodynamic analysis of a pathway enabling anaerobic production of poly-3-hydroxybutyrate in Escherichia coli

Utilizing anaerobic metabolisms for the production of biotechnologically relevant products presents potential advantages, such as increased yields and reduced energy dissipation. However, lower energy dissipation may indicate that certain reactions are operating closer to their thermodynamic equilibrium. While stoichiometric analyses and genetic modifications are frequently employed in metabolic engineering, the use of thermodynamic tools to evaluate the feasibility of planned interventions is less documented. In this study, we propose a novel metabolic engineering strategy to achieve an efficient anaerobic production of poly-(R)-3-hydroxybutyrate (PHB) in the model organism Escherichia coli. Our approach involves re-routing of two-thirds of the glycolytic flux through non-oxidative glycolysis and coupling PHB synthesis with NADH re-oxidation. We complemented our stoichiometric analysis with various thermodynamic approaches to assess the feasibility and the bottlenecks in the proposed engineered pathway. According to our calculations, the main thermodynamic bottleneck are the reactions catalyzed by the acetoacetyl-CoA β-ketothiolase (EC 2.3.1.9) and the acetoacetyl-CoA reductase (EC 1.1.1.36). Furthermore, we calculated thermodynamically consistent sets of kinetic parameters to determine the enzyme amounts required for sustaining the conversion fluxes. In the case of the engineered conversion route, the protein pool necessary to sustain the desired fluxes could account for 20% of the whole cell dry weight.

Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments

Metabolic network models have become increasingly precise and accurate as the most widespread and practical digital representations of living cells. The prediction functions were significantly expanded by integrating cellular resources and abiotic constraints in recent years. However, if unreasonable modeling methods were adopted due to a lack of consideration of biological knowledge, the conflicts between stoichiometric and other constraints, such as thermodynamic feasibility and enzyme resource availability, would lead to distorted predictions. In this work, we investigated a prediction anomaly of EcoETM, a constraints-based metabolic network model, and introduced the idea of enzyme compartmentalization into the analysis process. Through rational combination of reactions, we avoid the false prediction of pathway feasibility caused by the unrealistic assumption of free intermediate metabolites. This allowed us to correct the pathway structures of l-serine and l-tryptophan. A specific analysis explains the application method of the EcoETM-like model and demonstrates its potential and value in correcting the prediction results in pathway structure by resolving the conflict between different constraints and incorporating the evolved roles of enzymes as reaction compartments. Notably, this work also reveals the trade-off between product yield and thermodynamic feasibility. Our work is of great value for the structural improvement of constraints-based models.

Next-generation feedstocks methanol and ethylene glycol and their potential in industrial biotechnology

Microbial fermentation processes are expected to play an important role in reducing dependence on fossil-based raw materials for the production of everyday chemicals. In order to meet the growing demand for biotechnological products in the future, alternative carbon sources that do not compete with human nutrition must be exploited. The chemical conversion of the industrially emitted greenhouse gas CO2 into microbially utilizable platform chemicals such as methanol represents a sustainable strategy for the utilization of an abundant carbon source and has attracted enormous scientific interest in recent years. A relatively new approach is the microbial synthesis of products from the C2-compound ethylene glycol, which can also be synthesized from CO2 and non-edible biomass and, in addition, can be recovered from plastic waste. Here we summarize the main chemical routes for the synthesis of methanol and ethylene glycol from sustainable resources and give an overview of recent metabolic engineering work for establishing natural and synthetic microbial assimilation pathways. The different metabolic routes for C1 and C2 alcohol-dependent bioconversions were compared in terms of their theoretical maximum yields and their oxygen requirements for a wide range of value-added products. Assessment of the process engineering challenges for methanol and ethylene glycol-based fermentations underscores the theoretical advantages of new synthetic metabolic routes and advocates greater consideration of ethylene glycol, a C2 substrate that has received comparatively little attention to date.

Design, Evaluation, and Implementation of Synthetic Isopentyldiol Pathways in Escherichia coli

Isopentyldiol (IPDO) is an important raw material in the cosmetic industry. So far, IPDO is exclusively produced through chemical synthesis. Growing interest in natural personal care products has inspired the quest to develop a biobased process. We previously reported a biosynthetic route that produces IPDO via extending the leucine catabolism (route A), the efficiency of which, however, is not satisfactory. To address this issue, we computationally designed a novel non-natural IPDO synthesis pathway (route B) using RetroPath RL, the state-of-the-art tool for bioretrosynthesis based on artificial intelligence methods. We compared this new pathway with route A and two other intuitively designed routes for IPDO biosynthesis from various perspectives. Route B, which exhibits the highest thermodynamic driving force, least non-native reaction steps, and lowest energy requirements, appeared to hold the greatest potential for IPDO production. All three newly designed routes were then implemented in the Escherichia coli BL21(DE3) strain. Results show that the computationally designed route B can produce 2.2 mg/L IPDO from glucose but no IPDO production from routes C and D. These results highlight the importance and usefulness of in silico design and comprehensive evaluation of the potential efficiencies of candidate pathways in constructing novel non-natural pathways for the production of biochemicals.

Improved 2,3-Butanediol Production Rate of Metabolically Engineered Saccharomyces cerevisiae by Deletion of RIM15 and Activation of Pyruvate Consumption Pathway

Saccharomyces cerevisiae is a promising host for the bioproduction of higher alcohols, such as 2,3-butanediol (2,3-BDO). Metabolically engineered S. cerevisiae strains that produce 2,3-BDO via glycolysis have been constructed. However, the specific 2,3-BDO production rates of engineered strains must be improved. To identify approaches to improving the 2,3-BDO production rate, we investigated the factors contributing to higher ethanol production rates in certain industrial strains of S. cerevisiae compared to laboratory strains. Sequence analysis of 11 industrial strains revealed the accumulation of many nonsynonymous substitutions in RIM15, a negative regulator of high fermentation capability. Comparative metabolome analysis suggested a positive correlation between the rate of ethanol production and the activity of the pyruvate-consuming pathway. Based on these findings, RIM15 was deleted, and the pyruvate-consuming pathway was activated in YHI030, a metabolically engineered S. cerevisiae strain that produces 2,3-BDO. The titer, specific production rate, and yield of 2,3-BDO in the test tube-scale culture using the YMS106 strain reached 66.4 ± 4.4 mM, 1.17 ± 0.017 mmol (g dry cell weight h)-1, and 0.70 ± 0.03 mol (mol glucose consumed)-1. These values were 2.14-, 2.92-, and 1.81-fold higher than those of the vector control, respectively. These results suggest that bioalcohol production via glycolysis can be enhanced in a metabolically engineered S. cerevisiae strain by deleting RIM15 and activating the pyruvate-consuming pathway.

The potential of CO2-based production cycles in biotechnology to fight the climate crisis

Rising CO2 emissions have pushed scientists to develop new technologies for a more sustainable bio-based economy. Microbial conversion of CO2 and CO2-derived carbon substrates into valuable compounds can contribute to carbon neutrality and sustainability. Here, we discuss the potential of C1 carbon sources as raw materials to produce energy, materials, and food and feed using microbial cell factories. We provide an overview of potential microbes, natural and synthetic C1 utilization pathways, and compare their metabolic driving forces. Finally, we sketch a future in which C1 substrates replace traditional feedstocks and we evaluate the costs associated with such an endeavor.

Is distance from equilibrium a good indicator for a reaction's flux control?

By analysing a large set of models obtained from the JWS Online and Biomodels databases, we tested to what extent the disequilibrium ratio can be used as an estimator for the flux control of a reaction, a discussion point that was already raised by Kacser and Burns, and Heinrich and Rapoport in their seminal MCA manuscripts. Whereas no functional relation was observed, the disequilibrium ratio can be used as an estimator for the maximal flux control of a reaction step. We extended the original analysis of the relationship by incorporating the overall pathway disequilibrium ratio in the expression, which made it possible to make explicit expressions for flux control coefficients.

Minimizing enzyme mass to decompose flux distribution for identifying biologically relevant elementary flux modes

The flux distribution in metabolic network can be decomposed as non-negative linear combinations of elementary flux modes (EFMs). Identifying biologically relevant EFM combination by decomposing flux distribution in metabolic network is a useful method to study metabolisms in systems biology. However, the occurrence of biologically irrelevant EFMs hinders the application of such methods. In this paper, we introduce a novel method for identifying EFM combination by minimizing enzyme mass. Our proposed method, called EMMD (Enzyme Mass Minimization Decomposition), takes into consideration both thermodynamic and enzymatic constraints in stoichiometry metabolic models. By implementing EMMD, we can decompose the flux distributions in metabolic network to detect biologically relevant EFM combinations. We demonstrate the effectiveness of our method by applying it to the core Escherichia coli metabolic network and show that the optimal EFM combinations identified by EMMD are unique. Moreover, the optimal EFM combination identified by EMMD not only aligns more closely with experimental values in terms of estimated growth rate, but it also demonstrates more favorable thermodynamics. Finally, we investigated the growth of the core Escherichia coli metabolic network in Luria-Bertani medium containing different carbon sources, revealing the impact of various carbon sources on the growth rate of flux distribution. EMMD thus could be a promising complement to the existing flux decomposition tools.

Network-wide thermodynamic constraints shape NAD(P)H cofactor specificity of biochemical reactions

The ubiquitous coexistence of the redox cofactors NADH and NADPH is widely considered to facilitate an efficient operation of cellular redox metabolism. However, it remains unclear what shapes the NAD(P)H specificity of specific redox reactions. Here, we present a computational framework to analyze the effect of redox cofactor swaps on the maximal thermodynamic potential of a metabolic network and use it to investigate key aspects of redox cofactor redundancy in Escherichia coli. As one major result, our analysis suggests that evolved NAD(P)H specificities are largely shaped by metabolic network structure and associated thermodynamic constraints enabling thermodynamic driving forces that are close or even identical to the theoretical optimum and significantly higher compared to random specificities. Furthermore, while redundancy of NAD(P)H is clearly beneficial for thermodynamic driving forces, a third redox cofactor would require a low standard redox potential to be advantageous. Our approach also predicts trends of redox-cofactor concentration ratios and could facilitate the design of optimal redox cofactor specificities.

Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions

Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the synthesis of biomass building blocks. FDM allowed for a detailed quantification of the energy and biosynthesis budget for growing Escherichia coli cells. Surprisingly, the ATP generated during the biosynthesis of building blocks from glucose almost balances the demand from protein synthesis, the largest energy expenditure known for growing cells. This leaves the bulk of the energy generated by fermentation and respiration unaccounted for, thus challenging the common notion that energy is a key growth-limiting resource. Moreover, FDM together with proteomics enables the quantification of enzymes contributing towards each metabolic function, allowing for a first-principle formulation of a coarse-grained model of global protein allocation based on the structure of the metabolic network.

Achieving net zero CO2 emission in the biobased production of reduced platform chemicals using defined co-feeding of methanol

Next-generation bioprocesses of a future bio-based economy will rely on a flexible mix of readily available feedstocks. Renewable energy can be used to generate sustainable CO₂-derived substrates. Metabolic engineering already enables the functional implementation of different pathways for the assimilation of C1 substrates in various microorganisms. In addition to feedstocks, the benchmark for all future bioprocesses will be sustainability, including the avoidance of CO₂ emissions. Here we review recent advances in the utilization of C1-compounds from different perspectives, considering both strain and bioprocess engineering technologies. In particular, we evaluate methanol as a co-feed for enabling the CO₂ emission-free production of acetyl-CoA-derived compounds. The possible metabolic strategies are analyzed using stoichiometric modeling combined with thermodynamic analysis and prospects for industrial-scale implementation are discussed.

Optimal evaluation of energy yield and driving force in microbial metabolic pathway variants

This work presents a methodology to evaluate the bioenergetic feasibility of alternative metabolic pathways for a given microbial conversion, optimising their energy yield and driving forces as a function of the concentration of metabolic intermediates. The tool, based on thermodynamic principles and multi-objective optimisation, accounts for pathway variants in terms of different electron carriers, as well as energy conservation (proton translocating) reactions within the pathway. The method also accommodates other constraints, some of them non-linear, such as the balance of conserved moieties. The approach involves the transformation of the maximum energy yield problem into a multi-objective mixed-integer linear optimisation problem which is then subsequently solved using the epsilon-constraint method, highlighting the trade-off between yield and rate in metabolic reactions. The methodology is applied to analyse several pathway alternatives occurring during propionate oxidation in anaerobic fermentation processes, as well as to the reverse TCA cycle pathway occurring during autotrophic microbial CO2 fixation. The results obtained using the developed methodology match previously reported literature and bring about insights into the studied pathways.

Turn air-captured CO2 with methanol into amino acid and pyruvate in an ATP/NAD(P)H-free chemoenzymatic system

The use of gaseous and air-captured CO₂ for technical biosynthesis is highly desired, but elusive so far due to several obstacles including high energy (ATP, NADPH) demand, low thermodynamic driving force and limited biosynthesis rate. Here, we present an ATP and NAD(P)H-free chemoenzymatic system for amino acid and pyruvate biosynthesis by coupling methanol with CO₂. It relies on a re-engineered glycine cleavage system with the NAD(P)H-dependent L protein replaced by biocompatible chemical reduction of protein H with dithiothreitol. The latter provides a higher thermodynamic driving force, determines the reaction direction, and avoids protein polymerization of the rate-limiting enzyme carboxylase. Engineering of H protein to effectively release the lipoamide arm from a protected state further enhanced the system performance, achieving the synthesis of glycine, serine and pyruvate at g/L level from methanol and air-captured CO₂. This work opens up the door for biosynthesis of amino acids and derived products from air.

Engineering a new-to-nature cascade for phosphate-dependent formate to formaldehyde conversion in vitro and in vivo

Formate can be envisioned at the core of a carbon-neutral bioeconomy, where it is produced from CO₂ by (electro-)chemical means and converted into value-added products by enzymatic cascades or engineered microbes. A key step in expanding synthetic formate assimilation is its thermodynamically challenging reduction to formaldehyde. Here, we develop a two-enzyme route in which formate is activated to formyl phosphate and subsequently reduced to formaldehyde. Exploiting the promiscuity of acetate kinase and N-acetyl-γ-glutamyl phosphate reductase, we demonstrate this phosphate (P_i)-based route in vitro and in vivo. We further engineer a formyl phosphate reductase variant with improved formyl phosphate conversion in vivo by suppressing cross-talk with native metabolism and interface the P_i route with a recently developed formaldehyde assimilation pathway to enable C2 compound formation from formate as the sole carbon source in Escherichia coli. The P_i route therefore offers a potent tool in expanding the landscape of synthetic formate assimilation.

A Hitchhiker's Guide to Supplying Enzymatic Reducing Power into Synthetic Cells

The construction from scratch of synthetic cells by assembling molecular building blocks is unquestionably an ambitious goal from a scientific and technological point of view. To realize functional life-like systems, minimal enzymatic modules are required to sustain the processes underlying the out-of-equilibrium thermodynamic status hallmarking life, including the essential supply of energy in the form of electrons. The nicotinamide cofactors NAD(H) and NADP(H) are the main electron carriers fueling reductive redox reactions of the metabolic network of living cells. One way to ensure the continuous availability of reduced nicotinamide cofactors in a synthetic cell is to build a minimal enzymatic module that can oxidize an external electron donor and reduce NAD(P)+. In the diverse world of metabolism there is a plethora of potential electron donors and enzymes known from living organisms to provide reducing power to NAD(P)+ coenzymes. This perspective proposes guidelines to enable the reduction of nicotinamide cofactors enclosed in phospholipid vesicles, while avoiding high burdens of or cross-talk with other encapsulated metabolic modules. By determining key requirements, such as the feasibility of the reaction and transport of the electron donor into the cell-like compartment, we select a shortlist of potentially suitable electron donors. We review the most convenient proteins for the use of these reducing agents, highlighting their main biochemical and structural features. Noting that specificity toward either NAD(H) or NADP(H) imposes a limitation common to most of the analyzed enzymes, we discuss the need for specific enzymes─transhydrogenases─to overcome this potential bottleneck.

Self assembling nanoparticle enzyme clusters provide access to substrate channeling in multienzymatic cascades

Access to efficient enzymatic channeling is desired for improving all manner of designer biocatalysis. We demonstrate that enzymes constituting a multistep cascade can self-assemble with nanoparticle scaffolds into nanoclusters that access substrate channeling and improve catalytic flux by orders of magnitude. Utilizing saccharification and glycolytic enzymes with quantum dots (QDs) as a model system, nanoclustered-cascades incorporating from 4 to 10 enzymatic steps are prototyped. Along with confirming channeling using classical experiments, its efficiency is enhanced several fold more by optimizing enzymatic stoichiometry with numerical simulations, switching from spherical QDs to 2-D planar nanoplatelets, and by ordering the enzyme assembly. Detailed analyses characterize assembly formation and clarify structure-function properties. For extended cascades with unfavorable kinetics, channeled activity is maintained by splitting at a critical step, purifying end-product from the upstream sub-cascade, and feeding it as a concentrated substrate to the downstream sub-cascade. Generalized applicability is verified by extending to assemblies incorporating other hard and soft nanoparticles. Such self-assembled biocatalytic nanoclusters offer many benefits towards enabling minimalist cell-free synthetic biology.

Thermodynamic and Kinetic Modeling Directs Pathway Optimization for Isopropanol Production in a Gas-Fermenting Bacterium

Highly efficient bioproduction from gaseous substrates (e.g., hydrogen and carbon oxides) will require systematic optimization of the host microbes. To date, the rational redesign of gas-fermenting bacteria is still in its infancy, due in part to the lack of quantitative and precise metabolic knowledge that can direct strain engineering.

Pickaxe: a Python library for the prediction of novel metabolic reactions

BACKGROUND

Biochemical reaction prediction tools leverage enzymatic promiscuity rules to generate reaction networks containing novel compounds and reactions. The resulting reaction networks can be used for multiple applications such as designing novel biosynthetic pathways and annotating untargeted metabolomics data. It is vital for these tools to provide a robust, user-friendly method to generate networks for a given application. However, existing tools lack the flexibility to easily generate networks that are tailor-fit for a user's application due to lack of exhaustive reaction rules, restriction to pre-computed networks, and difficulty in using the software due to lack of documentation.

RESULTS

Here we present Pickaxe, an open-source, flexible software that provides a user-friendly method to generate novel reaction networks. This software iteratively applies reaction rules to a set of metabolites to generate novel reactions. Users can select rules from the prepackaged JN1224min ruleset, derived from MetaCyc, or define their own custom rules. Additionally, filters are provided which allow for the pruning of a network on-the-fly based on compound and reaction properties. The filters include chemical similarity to target molecules, metabolomics, thermodynamics, and reaction feasibility filters. Example applications are given to highlight the capabilities of Pickaxe: the expansion of common biological databases with novel reactions, the generation of industrially useful chemicals from a yeast metabolome database, and the annotation of untargeted metabolomics peaks from an E. coli dataset.

CONCLUSION

Pickaxe predicts novel metabolic reactions and compounds, which can be used for a variety of applications. This software is open-source and available as part of the MINE Database python package ( https://pypi.org/project/minedatabase/ ) or on GitHub ( https://github.com/tyo-nu/MINE-Database ). Documentation and examples can be found on Read the Docs ( https://mine-database.readthedocs.io/en/latest/ ). Through its documentation, pre-packaged features, and customizable nature, Pickaxe allows users to generate novel reaction networks tailored to their application.

ImmCellFie: A user-friendly web-based platform to infer metabolic function from omics data

Understanding cellular metabolism is important across biotechnology and biomedical research and has critical implications in a broad range of normal and pathological conditions. Here, we introduce the user-friendly web-based platform ImmCellFie, which allows the comprehensive analysis of metabolic functions inferred from transcriptomic or proteomic data. We explain how to set up a run using publicly available omics data and how to visualize the results. The ImmCellFie algorithm pushes beyond conventional statistical enrichment and incorporates complex biological mechanisms to quantify cell activity. For complete details on the use and execution of this protocol, please refer to Richelle et al. (2021).1.

In vivo implementation of a synthetic metabolic pathway for the carbon-conserving conversion of glycolaldehyde to acetyl-CoA

Ethylene glycol (EG) derived from plastic waste or CO₂ can serve as a substrate for microbial production of value-added chemicals. Assimilation of EG proceeds though the characteristic intermediate glycolaldehyde (GA). However, natural metabolic pathways for GA assimilation have low carbon efficiency when producing the metabolic precursor acetyl-CoA. In alternative, the reaction sequence catalyzed by EG dehydrogenase, d-arabinose 5-phosphate aldolase, d-arabinose 5-phosphate isomerase, d-ribulose 5-phosphate 3-epimerase (Rpe), d-xylulose 5-phosphate phosphoketolase, and phosphate acetyltransferase may enable the conversion of EG into acetyl-CoA without carbon loss. We investigated the metabolic requirements for in vivo function of this pathway in Escherichia coli by (over)expressing constituting enzymes in different combinations. Using ¹³C-tracer experiments, we first examined the conversion of EG to acetate via the synthetic reaction sequence and showed that, in addition to heterologous phosphoketolase, overexpression of all native enzymes except Rpe was required for the pathway to function. Since acetyl-CoA could not be reliably quantified by our LC/MS-method, the distribution of isotopologues in mevalonate, a stable metabolite that is exclusively derived from this intermediate, was used to probe the contribution of the synthetic pathway to biosynthesis of acetyl-CoA. We detected strong incorporation of ¹³C carbon derived from labeled GA in all intermediates of the synthetic pathway. In presence of unlabeled co-substrate glycerol, 12.4% of the mevalonate (and therefore acetyl-CoA) was derived from GA. The contribution of the synthetic pathway to acetyl-CoA production was further increased to 16.1% by the additional expression of the native phosphate acyltransferase enzyme. Finally, we demonstrated that conversion of EG to mevalonate was feasible albeit at currently extremely small yields.

Enzymatic Conversion of CO2: From Natural to Artificial Utilization

Enzymatic carbon dioxide fixation is one of the most important metabolic reactions as it allows the capture of inorganic carbon from the atmosphere and its conversion into organic biomass. However, due to the often unfavorable thermodynamics and the difficulties associated with the utilization of CO₂, a gaseous substrate that is found in comparatively low concentrations in the atmosphere, such reactions remain challenging for biotechnological applications. Nature has tackled these problems by evolution of dedicated CO₂-fixing enzymes, i.e., carboxylases, and embedding them in complex metabolic pathways. Biotechnology employs such carboxylating and decarboxylating enzymes for the carboxylation of aromatic and aliphatic substrates either by embedding them into more complex reaction cascades or by shifting the reaction equilibrium via reaction engineering. This review aims to provide an overview of natural CO₂-fixing enzymes and their mechanistic similarities. We also discuss biocatalytic applications of carboxylases and decarboxylases for the synthesis of valuable products and provide a separate summary of strategies to improve the efficiency of such processes. We briefly summarize natural CO₂ fixation pathways, provide a roadmap for the design and implementation of artificial carbon fixation pathways, and highlight examples of biocatalytic cascades involving carboxylases. Additionally, we suggest that biochemical utilization of reduced CO₂ derivates, such as formate or methanol, represents a suitable alternative to direct use of CO₂ and provide several examples. Our discussion closes with a techno-economic perspective on enzymatic CO₂ fixation and its potential to reduce CO₂ emissions.

Glycine homeostasis requires reverse SHMT flux

The folate-dependent enzyme serine hydroxymethyltransferase (SHMT) reversibly converts serine into glycine and a tetrahydrofolate-bound one-carbon unit. Such one-carbon unit production plays a critical role in development, the immune system, and cancer. Here we show that the whole-body SHMT flux acts to net consume rather than produce glycine. Pharmacological inhibition of whole-body SHMT1/2 and genetic knockout of liver SHMT2 elevated circulating glycine levels up to eight-fold. Stable isotope tracing revealed that the liver converts glycine to serine, which is then converted by serine dehydratase into pyruvate and burned in the tricarboxylic acid cycle. In response to diets deficient in serine and glycine, de novo biosynthetic flux was unaltered but SHMT2- and serine dehydratase-mediated catabolic flux was lower. Thus, glucose-derived serine synthesis does not respond to systemic demand. Instead, circulating serine and glycine homeostasis is maintained through variable consumption, with liver SHMT2 as a major glycine-consuming enzyme.

Increasing the Thermodynamic Driving Force of the Phosphofructokinase Reaction in Clostridium thermocellum

The ability to control the distribution of thermodynamic driving force throughout a metabolic pathway is likely to be an important tool for metabolic engineering. The phosphofructokinase reaction is a key enzyme in Embden-Mayerhof-Parnas glycolysis and therefore improving the thermodynamic driving force of this reaction in C. thermocellum is believed to enable higher product titers.

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

Reconstruction and Analysis of Thermodynamically Constrained Models Reveal Metabolic Responses of a Deep-Sea Bacterium to Temperature Perturbations

Microbial acclimation to different temperature conditions can involve broad changes in cell composition and metabolic efficiency. A systems-level view of these metabolic responses in nonmesophilic organisms, however, is currently missing. In this study, thermodynamically constrained genome-scale models were applied to simulate the metabolic responses of a deep-sea psychrophilic bacterium, Shewanella psychrophila WP2, under suboptimal (4°C), optimal (15°C), and supraoptimal (20°C) growth temperatures. The models were calibrated with experimentally determined growth rates of WP2. Gibbs free energy change of reactions (Δ_rG'), metabolic fluxes, and metabolite concentrations were predicted using random simulations to characterize temperature-dependent changes in the metabolism. The modeling revealed the highest metabolic efficiency at the optimal temperature, and it suggested distinct patterns of ATP production and consumption that could lead to lower metabolic efficiency under suboptimal or supraoptimal temperatures. The modeling also predicted rearrangement of fluxes through multiple metabolic pathways, including the glycolysis pathway, Entner-Doudoroff pathway, tricarboxylic acid (TCA) cycle, and electron transport system, and these predictions were corroborated through comparisons to WP2 transcriptomes. Furthermore, predictions of metabolite concentrations revealed the potential conservation of reducing equivalents and ATP in the suboptimal temperature, consistent with experimental observations from other psychrophiles. Taken together, the WP2 models provided mechanistic insights into the metabolism of a psychrophile in response to different temperatures. IMPORTANCE Metabolic flexibility is a central component of any organism's ability to survive and adapt to changes in environmental conditions. This study represents the first application of thermodynamically constrained genome-scale models in simulating the metabolic responses of a deep-sea psychrophilic bacterium to various temperatures. The models predicted differences in metabolic efficiency that were attributed to changes in metabolic pathway utilization and metabolite concentration during growth under optimal and nonoptimal temperatures. Experimental growth measurements were used for model calibration, and temperature-dependent transcriptomic changes corroborated the model-predicted rearrangement of metabolic fluxes. Overall, this study highlights the utility of modeling approaches in studying the temperature-driven metabolic responses of an extremophilic organism.

Contribution of the genomic and nutritional differentiation to the spatial distribution of bacterial colonies

Colony growth is a common phenomenon of structured populations dispersed in nature; nevertheless, studies on the spatial distribution of colonies are largely insufficient. Here, we performed a systematic survey to address the questions of whether and how the spatial distribution of colonies was influenced by the genome and environment. Six Escherichia coli strains carrying either the wild-type or reduced genomes and eight media of varied nutritional richness were used to evaluate the genomic and environmental impacts, respectively. The genome size and nutritional variation contributed to the mean size and total area but not the variation and shape of size distribution of the colonies formed within the identical space and of equivalent spatial density. The spatial analysis by means of the Voronoi diagram found that the Voronoi correlation remained nearly constant in common, in comparison to the Voronoi response decreasing in correlation to genome reduction and nutritional enrichment. Growth analysis at the single colony level revealed positive correlations of the relative growth rate to both the maximal colony size and the Voronoi area, regardless of the genomic and nutritional variety. This result indicated fast growth for the large space assigned and supported homeostasis in the Voronoi correlation. Taken together, the spatial distribution of colonies might benefit efficient clonal growth. Although the mechanisms remain unclear, the findings provide quantitative insights into the genomic and environmental contributions to the growth and distribution of spatially or geographically isolated populations.

Thermodynamic limitations of PHB production from formate and fructose in Cupriavidus necator

The chemolithotroph Cupriavidus necator H16 is known as a natural producer of the bioplastic-polymer PHB, as well as for its metabolic versatility to utilize different substrates, including formate as the sole carbon and energy source. Depending on the entry point of the substrate, this versatility requires adjustment of the thermodynamic landscape to maintain sufficiently high driving forces for biological processes. Here we employed a model of the core metabolism of C. necator H16 to analyze the thermodynamic driving forces and PHB yields from formate for different metabolic engineering strategies. For this, we enumerated elementary flux modes (EFMs) of the network and evaluated their PHB yields as well as thermodynamics via Max-min driving force (MDF) analysis and random sampling of driving forces. A heterologous ATP:citrate lyase reaction was predicted to increase driving force for producing acetyl-CoA. A heterologous phosphoketolase reaction was predicted to increase maximal PHB yields as well as driving forces. These enzymes were then verified experimentally to enhance PHB titers between 60 and 300% in select conditions. The EFM analysis also revealed that PHB production from formate may be limited by low driving forces through citrate lyase and aconitase, as well as cofactor balancing, and identified additional reactions associated with low and high PHB yield. Proteomics analysis of the engineered strains confirmed an increased abundance of aconitase and cofactor balancing. The findings of this study aid in understanding metabolic adaptation. Furthermore, the outlined approach will be useful in designing metabolic engineering strategies in other non-model bacteria.

Engineering Embden-Meyerhof-Parnas Glycolysis to Generate Noncanonical Reducing Power

Noncanonical cofactors such as nicotinamide mononucleotide (NMN+) supplant the electron-transfer functionality of the natural cofactors, NAD(P)+, at a lower cost in cell-free biomanufacturing and enable orthogonal electron delivery in whole-cell metabolic engineering. Here, we redesign the high-flux Embden-Meyerhof-Parnas (EMP) glycolytic pathway to generate NMN+-based reducing power, by engineering Streptococcus mutans glyceraldehyde-3-phosphate dehydrogenase (Sm GapN) to utilize NMN+. Through iterative rounds of rational design, we discover the variant GapN Penta (P179K-F153S-S330R-I234E-G210Q) with high NMN+-dependent activity and GapN Ortho (P179K-F153S-S330R-I234E-G214E) with ~3.4 × 106-fold switch in cofactor specificity from its native cofactor NADP+ to NMN+. GapN Ortho is further demonstrated to function in Escherichia coli only in the presence of NMN+, enabling orthogonal control of glucose utilization. Molecular dynamics simulation and residue network connectivity analysis indicate that mutations altering cofactor specificity must be coordinated to maintain the appropriate degree of backbone flexibility to position the catalytic cysteine. These results provide a strategy to guide future designs of NMN+-dependent enzymes and establish the initial steps toward an orthogonal EMP pathway with biomanufacturing potential.

Free-Energy Transduction in Chemical Reaction Networks: from Enzymes to Metabolism

We provide a rigorous definition of free-energy transduction and its efficiency in arbitrary---linear or nonlinear---open chemical reaction networks (CRNs) operating at steady state. Our method is based on the knowledge of the stoichiometric matrix and of the chemostatted species (i.e. the species maintained at constant concentration by the environment) to identify the fundamental currents and forces contributing to the entropy production. Transduction occurs when the current of a stoichiometrically balanced process is driven against its spontaneous direction (set by its force) thanks to other processes flowing along their spontaneous direction. In these regimes, open CRNs operate as thermodynamic machines. After exemplifying these general ideas using toy models, we analyze central energy metabolism. We relate the fundamental currents to metabolic pathways and discuss the efficiency with which they are able to transduce free energy.

MINE 2.0: Enhanced biochemical coverage for peak identification in untargeted metabolomics

SUMMARY

Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource.

AVAILABILITY AND IMPLEMENTATION

The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API), and https://github.com/tyo-nu/MINE-app (web app).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Activating Silent Glycolysis Bypasses in Escherichia coli

All living organisms share similar reactions within their central metabolism to provide precursors for all essential building blocks and reducing power. To identify whether alternative metabolic routes of glycolysis can operate in E. coli, we complementarily employed in silico design, rational engineering, and adaptive laboratory evolution. First, we used a genome-scale model and identified two potential pathways within the metabolic network of this organism replacing canonical Embden-Meyerhof-Parnas (EMP) glycolysis to convert phosphosugars into organic acids. One of these glycolytic routes proceeds via methylglyoxal and the other via serine biosynthesis and degradation. Then, we implemented both pathways in E. coli strains harboring defective EMP glycolysis. Surprisingly, the pathway via methylglyoxal seemed to immediately operate in a triosephosphate isomerase deletion strain cultivated on glycerol. By contrast, in a phosphoglycerate kinase deletion strain, the overexpression of methylglyoxal synthase was necessary to restore growth of the strain. Furthermore, we engineered the "serine shunt" which converts 3-phosphoglycerate via serine biosynthesis and degradation to pyruvate, bypassing an enolase deletion. Finally, to explore which of these alternatives would emerge by natural selection, we performed an adaptive laboratory evolution study using an enolase deletion strain. Our experiments suggest that the evolved mutants use the serine shunt. Our study reveals the flexible repurposing of metabolic pathways to create new metabolite links and rewire central metabolism.

Structural Thermokinetic Modelling

To translate metabolic networks into dynamic models, the Structural Kinetic Modelling framework (SKM) assumes a given reference state and replaces the reaction elasticities in this state by random numbers. A new variant, called Structural Thermokinetic Modelling (STM), accounts for reversible reactions and thermodynamics. STM relies on a dependence schema in which some basic variables are sampled, fitted to data, or optimised, while all other variables can be easily computed. Correlated elasticities follow from enzyme saturation values and thermodynamic forces, which are physically independent. Probability distributions in the dependence schema define a model ensemble, which allows for probabilistic predictions even if data are scarce. STM highlights the importance of variabilities, dependencies, and covariances of biological variables. By varying network structure, fluxes, thermodynamic forces, regulation, or types of rate laws, the effects of these model features can be assessed. By choosing the basic variables, metabolic networks can be converted into kinetic models with consistent reversible rate laws. Metabolic control coefficients obtained from these models can tell us about metabolic dynamics, including responses and optimal adaptations to perturbations, enzyme synergies and metabolite correlations, as well as metabolic fluctuations arising from chemical noise. To showcase STM, I study metabolic control, metabolic fluctuations, and enzyme synergies, and how they are shaped by thermodynamic forces. Considering thermodynamics can improve predictions of flux control, enzyme synergies, correlated flux and metabolite variations, and the emergence and propagation of metabolic noise.

Innovation and tinkering in the evolution of oxidases

Although molecular oxygen is a relative newcomer to the biosphere, it has had a profound impact on metabolism. About 700 oxygen‐dependent enzymatic reactions are known, the vast majority of which emerged only after the appearance of oxygen in the biosphere, circa 3 billion years ago. Oxygen was a major driving force for evolutionary innovation—~60% of all known oxygen‐dependent enzyme families emerged as such; that is, the founding ancestor was an O₂‐dependent enzyme. The other 40% seem to have diverged by tinkering from pre‐existing proteins whose function was not related to oxygen. Here, we focus on the latter. We describe transitions from various enzyme classes, as well as from non‐enzymatic proteins, and we explore these transitions in terms of catalytic chemistry, metabolism, and protein structure. These transitions vary from subtle ones, such as simply repurposing oxidoreductases by replacing an electron acceptor such as NAD by O₂, to drastic changes in reaction mechanism, such as turning carboxylases and hydrolases into oxidases. The latter is more common and can occur with strikingly minor changes, for example, only one mutation in the active site. We further suggest that engineering enzymes to harness the extraordinary reactivity of oxygen may yield higher catabolic power and versatility.