eQuilibrator--the biochemical thermodynamics calculator

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Design of a synthetic enzyme cascade for the in vitro fixation of formaldehyde to acetoin

Formaldehyde (FALD) has gained prominence as an essential C1 building block in the synthesis of valuable chemicals. However, there are still challenges in converting FALD into commodities. Recently, cell-free biocatalysis has emerged as a popular approach for producing such commodities. Acetoin, also known as 3-hydroxy-2-butanone, has been widely used in food, cosmetic, agricultural and the chemical industry. It is valuable to develop a process to produce acetoin from FALD. In this study, a cell-free multi-enzyme catalytic system for the production of acetoin using FALD as the substrate was designed and constructed. It included three scales: FALD utilization pathway, glycolysis pathway and acetoin synthesis pathway. After the optimization of the reaction system, 20.17 mM acetoin was produced from 122 mM FALD, with a yield of 0.165 mol/mol, reaching 99.0% of the theoretical yield. The pathway provides a new approach for high-yield acetoin production from FALD, which consolidates the foundation for the production of high value-added chemicals using cheap one-carbon compounds.

Hydrogen production pathways in Clostridia and their improvement by metabolic engineering

Biological production of hydrogen has a tremendous potential as an environmentally sustainable technology to generate a clean fuel. Among the different available methods to produce biohydrogen, dark fermentation features the highest productivity and can be used as a means to dispose of organic waste biomass. Within this approach, Clostridia have the highest theoretical H2 production yield. Nonetheless, most strains show actual yields far lower than the theoretical maximum: improving their efficiency becomes necessary for achieving cost-effective fermentation processes. This review aims at providing a survey of the metabolic network involved in H2 generation in Clostridia and strategies used to improve it through metabolic engineering. Together with current achievements, a number of future perspectives to implement these results will be illustrated.

The radical impact of oxygen on prokaryotic evolution-enzyme inhibition first, uninhibited essential biosyntheses second, aerobic respiration third

Molecular oxygen is a stable diradical. All O2-dependent enzymes employ a radical mechanism. Generated by cyanobacteria, O2 started accumulating on Earth 2.4 billion years ago. Its evolutionary impact is traditionally sought in respiration and energy yield. We mapped 365 O2-dependent enzymatic reactions of prokaryotes to phylogenies for the corresponding 792 protein families. The main physiological adaptations imparted by O2-dependent enzymes were not energy conservation, but novel organic substrate oxidations and O2-dependent, hence O2-tolerant, alternative pathways for O2-inhibited reactions. Oxygen-dependent enzymes evolved in ancestrally anaerobic pathways for essential cofactor biosynthesis including NAD+, pyridoxal, thiamine, ubiquinone, cobalamin, heme, and chlorophyll. These innovations allowed prokaryotes to synthesize essential cofactors in O2-containing environments, a prerequisite for the later emergence of aerobic respiratory chains.

Integrating Experimental and Computational Analyses of Yeast Protein Profiles for Optimizing the Production of High-Quality Microbial Proteins

Microbial proteins represent a promising solution to address the escalating global demand for protein, particularly in regions with limited arable land. Yeasts, such as Saccharomyces cerevisiae, are robust and safe protein-producing strains. However, the utilization of non-conventional yeast strains for microbial protein production has been hindered, partly due to a lack of comprehensive understanding of protein production traits. In this study, we conducted experimental analyses focusing on the growth, protein content, and amino acid composition of nine yeast strains, including one S. cerevisiae strain, three Yarrowia lipolytica strains, and five Pichia spp. strains. We identified that, though Y. lipolytica and Pichia spp. strains consumed glucose at a slower rate compared to S. cerevisiae, Pichia spp. strains showed a higher cellular protein content, and Y. lipolytica strains showed a higher glucose-to-biomass/protein yield and methionine content. We further applied computational approaches to explain that metabolism economy was the main underlying factor for the limited amount of scarce/carbon-inefficient amino acids (such as methionine) within yeast cell proteins. We additionally verified that the specialized metabolism was a key reason for the high methionine content in Y. lipolytica strains, and proposed Y. lipolytica strain as a potential producer of high-quality single-cell protein rich in scarce amino acids. Through experimental evaluation, we identified Pichia jadinii CICC 1258 as a potential strain for high-quality protein production under unfavorable pH/temperature conditions. Our work suggests a promising avenue for optimizing microbial protein production, identifying the factors influencing amino acid composition, and paving the way for the use of unconventional yeast strains to meet the growing protein demands.

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.

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of 'bottom up' parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard 'macroscopic' kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and 'microscopic' rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.

Chlamydomonas reinhardtii chloroplast factory construction for formate bioconversion

The photosynthetic autotrophic production of microalgae is limited by the effective supply of carbon and light energy, and the production efficiency is lower than the theoretical value. Represented by methanol, C1 compounds have been industrially produced by artificial photosynthesis with a solar energy efficiency over 10%, but the complexity of artificial products is weak. Here, based on a construction of chloroplast factory, green microalgae Chlamydomonas reinhardtii CC137c was modified for the bioconversion of formate for biomass production. By screening the optimal combination of chloroplast transport peptides, the cabII-1 cTP1 fusion formate dehydrogenase showed significant enhancement on the conversion of formate with a better performance in the maintenance of light reaction activity. This work provided a new way to obtain bioproducts from solar energy and CO2 with potentially higher-than-nature efficiency by the artificial-natural hybrid photosynthesis.

A synthetic methylotrophic Escherichia coli as a chassis for bioproduction from methanol

Methanol synthesized from captured greenhouse gases is an emerging renewable feedstock with great potential for bioproduction. Recent research has raised the prospect of methanol bioconversion to value-added products using synthetic methylotrophic Escherichia coli, as its metabolism can be rewired to enable growth solely on the reduced one-carbon compound. Here we describe the generation of an E. coli strain that grows on methanol at a doubling time of 4.3 h-comparable to many natural methylotrophs. To establish bioproduction from methanol using this synthetic chassis, we demonstrate biosynthesis from four metabolic nodes from which numerous bioproducts can be derived: lactic acid from pyruvate, polyhydroxybutyrate from acetyl coenzyme A, itaconic acid from the tricarboxylic acid cycle and p-aminobenzoic acid from the chorismate pathway. In a step towards carbon-negative chemicals and valorizing greenhouse gases, our work brings synthetic methylotrophy in E. coli within reach of industrial applications.

Dynamic lid domain of Chloroflexus aurantiacus Malonyl-CoA reductase controls the reaction

Malonyl-Coenzyme A Reductase (MCR) in Chloroflexus aurantiacus, a characteristic enzyme of the 3-hydroxypropionate (3-HP) cycle, catalyses the reduction of malonyl-CoA to 3-HP. MCR is a bi-functional enzyme; in the first step, malonyl-CoA is reduced to the free intermediate malonate semialdehyde by the C-terminal region of MCR, and this is further reduced to 3-HP by the N-terminal region of MCR. Here we present the crystal structures of both N-terminal and C-terminal regions of the MCR from C. aurantiacus. A catalytic mechanism is suggested by ligand and substrate bound structures, and structural and kinetic studies of MCR variants. Both MCR structures reveal one catalytic, and one non-catalytic SDR (short chain dehydrogenase/reductase) domain. C-terminal MCR has a lid domain which undergoes a conformational change and controls the reaction. In the proposed mechanism of the C-terminal MCR, the conversion of malonyl-CoA to malonate semialdehyde is based on the reduction of malonyl-CoA by NADPH, followed by the decomposition of the hemithioacetal to produce malonate semialdehyde and coenzyme A. Conserved arginines, Arg734 and Arg773 are proposed to play key roles in the mechanism and conserved Ser719, and Tyr737 are other essential residues forming an oxyanion hole for the substrate intermediates.

Calcium oscillations optimize the energetic efficiency of mitochondrial metabolism

Energy transduction is central to living organisms, but the impact of enzyme regulation and signaling on its thermodynamic efficiency is generally overlooked. Here, we analyze the efficiency of ATP production by the tricarboxylic acid cycle and oxidative phosphorylation, which generate most of the chemical energy in eukaryotes. Calcium signaling regulates this pathway and can affect its energetic output, but the concrete energetic impact of this cross-talk remains elusive. Calcium enhances ATP production by activating key enzymes of the tricarboxylic acid cycle while calcium homeostasis is ATP-dependent. We propose a detailed kinetic model describing the calcium-mitochondria cross-talk and analyze it using nonequilibrium thermodynamics: after identifying the effective reactions driving mitochondrial metabolism out of equilibrium, we quantify the mitochondrial thermodynamic efficiency for different conditions. Calcium oscillations, triggered by extracellular stimulation or energy deficiency, boost the thermodynamic efficiency of mitochondrial metabolism, suggesting a compensatory role of calcium signaling in mitochondrial bioenergetics.

Metabolic flux optimization of iterative pathways through orthogonal gene expression control: Application to the β-oxidation reversal

Balancing relative expression of pathway genes to minimize flux bottlenecks and metabolic burden is one of the key challenges in metabolic engineering. This is especially relevant for iterative pathways, such as reverse β-oxidation (rBOX) pathway, which require control of flux partition at multiple nodes to achieve efficient synthesis of target products. Here, we develop a plasmid-based inducible system for orthogonal control of gene expression (referred to as the TriO system) and demonstrate its utility in the rBOX pathway. Leveraging effortless construction of TriO vectors in a plug-and-play manner, we simultaneously explored the solution space for enzyme choice and relative expression levels. Remarkably, varying individual expression levels led to substantial change in product specificity ranging from no production to optimal performance of about 90% of the theoretical yield of the desired products. We obtained titers of 6.3 g/L butyrate, 2.2 g/L butanol and 4.0 g/L hexanoate from glycerol in E. coli, which exceed the best titers previously reported using equivalent enzyme combinations. Since a similar system behavior was observed with alternative termination routes and higher-order iterations, we envision our approach to be broadly applicable to other iterative pathways besides the rBOX. Considering that high throughput, automated strain construction using combinatorial promoter and RBS libraries remain out of reach for many researchers, especially in academia, tools like the TriO system could democratize the testing and evaluation of pathway designs by reducing cost, time and infrastructure requirements.

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.

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.

Artificial carbon assimilation: From unnatural reactions and pathways to synthetic autotrophic systems

Synthetic biology is being increasingly used to establish novel carbon assimilation pathways and artificial autotrophic strains that can be used in low-carbon biomanufacturing. Currently, artificial pathway design has made significant progress from advocacy to practice within a relatively short span of just over ten years. However, there is still huge scope for exploration of pathway diversity, operational efficiency, and host suitability. The accelerated research process will bring greater opportunities and challenges. In this paper, we provide a comprehensive summary and interpretation of representative one-carbon assimilation pathway designs and artificial autotrophic strain construction work. In addition, we propose some feasible design solutions based on existing research results and patterns to promote the development and application of artificial autotrophy.

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.

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.

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.

Design and Construction of Artificial Biological Systems for One-Carbon Utilization

The third-generation (3G) biorefinery aims to use microbial cell factories or enzymatic systems to synthesize value-added chemicals from one-carbon (C1) sources, such as CO2, formate, and methanol, fueled by renewable energies like light and electricity. This promising technology represents an important step toward sustainable development, which can help address some of the most pressing environmental challenges faced by modern society. However, to establish processes competitive with the petroleum industry, it is crucial to determine the most viable pathways for C1 utilization and productivity and yield of the target products. In this review, we discuss the progresses that have been made in constructing artificial biological systems for 3G biorefineries in the last 10 years. Specifically, we highlight the representative works on the engineering of artificial autotrophic microorganisms, tandem enzymatic systems, and chemo-bio hybrid systems for C1 utilization. We also prospect the revolutionary impact of these developments on biotechnology. By harnessing the power of 3G biorefinery, scientists are establishing a new frontier that could potentially revolutionize our approach to industrial production and pave the way for a more sustainable future.

Personalised modelling of clinical heterogeneity between medium-chain acyl-CoA dehydrogenase patients

BACKGROUND

Monogenetic inborn errors of metabolism cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we mainly focus on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of the mitochondrial fatty acid oxidation (mFAO). It is an enigma why some MCADD patients-if untreated-are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients.

RESULTS

We built and experimentally validated, for the first time, a kinetic model of the human liver mFAO. Metabolites were partitioned according to their water solubility between the bulk aqueous matrix and the inner membrane. Enzymes are also either membrane-bound or in the matrix. This metabolite partitioning is a novel model attribute and improved predictions. MCADD substantially reduced pathway flux and CoASH, the latter due to the sequestration of CoA as medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed an accumulation of medium- and short-chain acylcarnitines, just like the acyl-CoA pool in the MCADD model. The model suggested some rescues that increased flux and CoASH, notably increasing short-chain acyl-CoA dehydrogenase (SCAD) levels. Proteome analysis of MCADD patient-derived fibroblasts indeed revealed elevated levels of SCAD in a patient with a clinically asymptomatic state. This is a rescue for MCADD that has not been explored before. Personalised models based on these proteomics data confirmed an increased pathway flux and CoASH in the model of an asymptomatic patient compared to those of symptomatic MCADD patients.

CONCLUSIONS

We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides a novel explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management and monitoring for inborn errors of metabolism.

Engineering the biological conversion of formate into crotonate in Cupriavidus necator

To advance the sustainability of the biobased economy, our society needs to develop novel bioprocesses based on truly renewable resources. The C1-molecule formate is increasingly proposed as carbon and energy source for microbial fermentations, as it can be efficiently generated electrochemically from CO2 and renewable energy. Yet, its biotechnological conversion into value-added compounds has been limited to a handful of examples. In this work, we engineered the natural formatotrophic bacterium C. necator as cell factory to enable biological conversion of formate into crotonate, a platform short-chain unsaturated carboxylic acid of biotechnological relevance. First, we developed a small-scale (150-mL working volume) cultivation setup for growing C. necator in minimal medium using formate as only carbon and energy source. By using a fed-batch strategy with automatic feeding of formic acid, we could increase final biomass concentrations 15-fold compared to batch cultivations in flasks. Then, we engineered a heterologous crotonate pathway in the bacterium via a modular approach, where each pathway section was assessed using multiple candidates. The best performing modules included a malonyl-CoA bypass for increasing the thermodynamic drive towards the intermediate acetoacetyl-CoA and subsequent conversion to crotonyl-CoA through partial reverse β-oxidation. This pathway architecture was then tested for formate-based biosynthesis in our fed-batch setup, resulting in a two-fold higher titer, three-fold higher productivity, and five-fold higher yield compared to the strain not harboring the bypass. Eventually, we reached a maximum product titer of 148.0 ± 6.8 mg/L. Altogether, this work consists in a proof-of-principle integrating bioprocess and metabolic engineering approaches for the biological upgrading of formate into a value-added platform chemical.

The Architecture of Metabolic Networks Constrains the Evolution of Microbial Resource Hierarchies

Microbial strategies for resource use are an essential determinant of their fitness in complex habitats. When facing environments with multiple nutrients, microbes often use them sequentially according to a preference hierarchy, resulting in well-known patterns of diauxic growth. In theory, the evolutionary diversification of metabolic hierarchies could represent a mechanism supporting coexistence and biodiversity by enabling temporal segregation of niches. Despite this ecologically critical role, the extent to which substrate preference hierarchies can evolve and diversify remains largely unexplored. Here, we used genome-scale metabolic modeling to systematically explore the evolution of metabolic hierarchies across a vast space of metabolic network genotypes. We find that only a limited number of metabolic hierarchies can readily evolve, corresponding to the most commonly observed hierarchies in genome-derived models. We further show how the evolution of novel hierarchies is constrained by the architecture of central metabolism, which determines both the propensity to change ranks between pairs of substrates and the effect of specific reactions on hierarchy evolution. Our analysis sheds light on the genetic and mechanistic determinants of microbial metabolic hierarchies, opening new research avenues to understand their evolution, evolvability, and ecology.

Unravelling the hidden power of esterases for biomanufacturing of short-chain esters

Microbial production of esters has recently garnered wide attention, but the current production metrics are low. Evidently, the ester precursors (organic acids and alcohols) can be accumulated at higher titers by microbes like Escherichia coli. Hence, we hypothesized that their 'direct esterification' using esterases will be efficient. We engineered esterases from various microorganisms into E. coli, along with overexpression of ethanol and lactate pathway genes. High cell density fermentation exhibited the strains possessing esterase-A (SSL76) and carbohydrate esterase (SSL74) as the potent candidates. Fed-batch fermentation at pH 7 resulted in 80 mg/L of ethyl acetate and 10 mg/L of ethyl lactate accumulation by SSL76. At pH 6, the total ester titer improved by 2.5-fold, with SSL76 producing 225 mg/L of ethyl acetate, and 18.2 mg/L of ethyl lactate, the highest reported titer in E. coli. To our knowledge, this is the first successful demonstration of short-chain ester production by engineering 'esterases' in E. coli.

Synthetic NAD(P)(H) Cycle for ATP Regeneration

ATP is the energy currency of the cell and new methods for ATP regeneration will benefit a range of emerging biotechnology applications including synthetic cells. We designed and assembled a membraneless ATP-regenerating enzymatic cascade by exploiting the substrate specificities of selected NAD(P)(H)-dependent oxidoreductases combined with substrate-specific kinases. The enzymes in the NAD(P)(H) cycle were selected to avoid cross-reactions, and the cascade was driven by irreversible fuel oxidation. As a proof-of-concept, formate oxidation was chosen as the fueling reaction. ATP regeneration was accomplished via the phosphorylation of NADH to NADPH and the subsequent transfer of the phosphate to ADP by a reversible NAD⁺ kinase. The cascade was able to regenerate ATP at a high rate (up to 0.74 mmol/L/h) for hours, and >90% conversion of ADP to ATP using monophosphate was also demonstrated. The cascade was used to regenerate ATP for use in cell free protein synthesis reactions, and the ATP production rate was further enhanced when powered by the multistep oxidation of methanol. The NAD(P)(H) cycle provides a simple cascade for the in vitro regeneration of ATP without the need for a pH-gradient or costly phosphate donors.

Lactate oxidation is linked to energy conservation and to oxygen detoxification via a putative terminal cytochrome oxidase in Methanosarcina acetivorans

The marine archaeon Methanosarcina acetivorans contains a putative NAD ⁺ -independent d-lactate dehydrogenase (D-iLDH/glycolate oxidase) encoded by the MA4631 gene, belonging to the FAD-oxidase C superfamily. Nucleotide sequences similar to MA4631 gene, were identified in other methanogens and Firmicutes with >90 and 35-40% identity, respectively. Therefore, the lactate metabolism in M. acetivorans is reported here. Cells subjected to intermittent pulses of oxygen (air-adapted; AA-Ma cells) consumed lactate only in combination with acetate, increasing methane production and biomass yield. In AA-Ma cells incubated with d-lactate plus [¹⁴C]-l-lactate, the radioactive label was found in methane, CO₂ and glycogen, indicating that lactate metabolism fed both methanogenesis and gluconeogenesis. Moreover, d-lactate oxidation was coupled to O₂-consumption which was sensitive to HQNO; also, AA-Ma cells showed high transcript levels of gene dld and those encoding subunits A (MA1006) and B (MA1007) of a putative cytochrome bd quinol oxidase, compared to anaerobic control cells. An E. coli mutant deficient in dld complemented with the MA4631 gene, grew with d-lactate as carbon source and showed membrane-bound d-lactate:quinone oxidoreductase activity. The product of the MA4631 gene is a FAD-containing monomer showing activity of iLDH with preference to d-lactate. The results suggested that air adapted M. acetivorans is able to co-metabolize lactate and acetate with associated oxygen consumption by triggering the transcription and synthesis of the D-iLDH and a putative cytochrome bd: methanophenazine (quinol) oxidoreductase. Biomass generation and O₂ consumption, suggest a potentially new oxygen detoxification mechanism coupled to energy conservation in this methanogen.

Characterisation of acetogen formatotrophic potential using Eubacterium limosum

Formate is a promising energy carrier that could be used to transport renewable electricity. Some acetogenic bacteria, such as Eubacterium limosum, have the native ability to utilise formate as a sole substrate for growth, which has sparked interest in the biotechnology industry. However, formatotrophic metabolism in E. limosum is poorly understood, and a system-level characterisation in continuous cultures is yet to be reported. Here, we present the first steady-state dataset for E. limosum formatotrophic growth. At a defined dilution rate of 0.4 d^-1, there was a high specific uptake rate of formate (280 ± 56 mmol/gDCW/d; gDCW = gramme dry cell weight); however, most carbon went to CO₂ (150 ± 11 mmol/gDCW/d). Compared to methylotrophic growth, protein differential expression data and intracellular metabolomics revealed several key features of formate metabolism. Upregulation of phosphotransacetylase (Pta) appears to be a futile attempt of cells to produce acetate as the major product. Instead, a cellular energy limitation resulted in the accumulation of intracellular pyruvate and upregulation of pyruvate formate ligase (Pfl) to convert formate to pyruvate. Therefore, metabolism is controlled, at least partially, at the protein expression level, an unusual feature for an acetogen. We anticipate that formate could be an important one-carbon substrate for acetogens to produce chemicals rich in pyruvate, a metabolite generally in low abundance during syngas growth. KEY POINTS: First Eubacterium limosum steady-state formatotrophic growth omics dataset High formate specific uptake rate, however carbon dioxide was the major product Formate may be the cause of intracellular stress and biofilm formation.

Hydrogenase-based oxidative biocatalysis without oxygen

Biocatalysis-based synthesis can provide a sustainable and clean platform for producing chemicals. Many oxidative biocatalytic routes require the cofactor NAD⁺ as an electron acceptor. To date, NADH oxidase (NOX) remains the most widely applied system for NAD⁺ regeneration. However, its dependence on O₂ implies various technical challenges in terms of O₂ supply, solubility, and mass transfer. Here, we present the suitability of a NAD⁺ regeneration system in vitro based on H₂ evolution. The efficiency of the hydrogenase-based system is demonstrated by integrating it into a multi-enzymatic cascade to produce ketoacids from sugars. The total NAD⁺ recycled using the hydrogenase system outperforms NOX in all different setups reaching up to 44,000 mol per mol enzyme. This system proves to be scalable and superior to NOX in terms of technical simplicity, flexibility, and total output. Furthermore, the system produces only green H₂ as a by-product even in the presence of O₂.

Rapid prototyping enzyme homologs to improve titer of nicotinamide mononucleotide using a strategy combining cell-free protein synthesis with split GFP

Engineering biological systems to test new pathway variants containing different enzyme homologs is laborious and time-consuming. To tackle this challenge, a strategy was developed for rapidly prototyping enzyme homologs by combining cell-free protein synthesis (CFPS) with split green fluorescent protein (GFP). This strategy featured two main advantages: (1) dozens of enzyme homologs were parallelly produced by CFPS within hours, and (2) the expression level and activity of each homolog was determined simultaneously by using the split GFP assay. As a model, this strategy was applied to optimize a 3-step pathway for nicotinamide mononucleotide (NMN) synthesis. Ten enzyme homologs from different organisms were selected for each step. Here, the most productive homolog of each step was identified within 24 h rather than weeks or months. Finally, the titer of NMN was increased to 1213 mg/L by improving physiochemical conditions, tuning enzyme ratios and cofactor concentrations, and decreasing the feedback inhibition, which was a more than 12-fold improvement over the initial setup. This strategy would provide a promising way to accelerate design-build-test cycles for metabolic engineering to improve the production of desired products.

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.

Machine learning for metabolic pathway optimization: A review

Optimizing the metabolic pathways of microbial cell factories is essential for establishing viable biotechnological production processes. However, due to the limited understanding of the complex setup of cellular machinery, building efficient microbial cell factories remains tedious and time-consuming. Machine learning (ML), a powerful tool capable of identifying patterns within large datasets, has been used to analyze biological datasets generated using various high-throughput technologies to build data-driven models for complex bioprocesses. In addition, ML can also be integrated with Design-Build-Test-Learn to accelerate development. This review focuses on recent ML applications in genome-scale metabolic model construction, multistep pathway optimization, rate-limiting enzyme engineering, and gene regulatory element designing. In addition, we have discussed some limitations of these methods as well as potential solutions.

ATP-Independent and Cell-Free Biosynthesis of β-Hydroxy Acids Using Vinyl Esters as Smart Substrates

In vitro biosynthetic pathways that condense and reduce molecules through coenzyme A (CoASH) activation demand energy and redox power in the form of ATP and NAD(P)H, respectively. These coenzymes must be orthogonally recycled by ancillary reactions that consume chemicals, electricity, or light, impacting the atom economy and/or the energy consumption of the biosystem. In this work, we have exploited vinyl esters as dual acyl and electron donor substrates to synthesize β-hydroxy acids through a non-decarboxylating Claisen condensation, reduction and hydrolysis stepwise cascade, including a NADH recycling step, catalyzed by a total of 4 enzymes. Herein, the chemical energy to activate the acyl group with CoASH and the redox power for the reduction are embedded into the vinyl esters. Upon optimization, this self-sustaining cascade reached a titer of (S)-3-hydroxy butyrate of 24 mM without requiring ATP and simultaneously recycling CoASH and NADH. This work illustrates the potential of in vitro biocatalysis to transform simple molecules into multi-functional ones.

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.

NADH dehydrogenases drive inward electron transfer in Shewanella oneidensis MR-1

Shewanella oneidensis MR-1 is a promising chassis organism for microbial electrosynthesis because it has a well-defined biochemical pathway (the Mtr pathway) that can connect extracellular electrodes to respiratory electron carriers inside the cell. We previously found that the Mtr pathway can be used to transfer electrons from a cathode to intracellular electron carriers and drive reduction reactions. In this work, we hypothesized that native NADH dehydrogenases form an essential link between the Mtr pathway and NADH in the cytoplasm. To test this hypothesis, we compared the ability of various mutant strains to accept electrons from a cathode and transfer them to an NADH-dependent reaction in the cytoplasm, reduction of acetoin to 2,3-butanediol. We found that deletion of genes encoding NADH dehydrogenases from the genome blocked electron transfer from a cathode to NADH in the cytoplasm, preventing the conversion of acetoin to 2,3-butanediol. However, electron transfer to fumarate was not blocked by the gene deletions, indicating that NADH dehydrogenase deletion specifically impacted NADH generation and did not cause a general defect in extracellular electron transfer. Proton motive force (PMF) is linked to the function of the NADH dehydrogenases. We added a protonophore to collapse PMF and observed that it blocked inward electron transfer to acetoin but not fumarate. Together these results indicate a link between the Mtr pathway and intracellular NADH. Future work to optimize microbial electrosynthesis in S. oneidensis MR-1 should focus on optimizing flux through NADH dehydrogenases.

Paving the way for synthetic C1 - Metabolism in Pseudomonas putida through the reductive glycine pathway

One-carbon (C1) compounds such as methanol, formate, and CO₂ are alternative, sustainable microbial feedstocks for the biobased production of chemicals and fuels. In this study, we engineered the carbon metabolism of the industrially important bacterium Pseudomonas putida to modularly assimilate these three substrates through the reductive glycine pathway. First, we demonstrated the functionality of the C1-assimilation module by coupling the growth of auxotrophic strains to formate assimilation. Next, we extended the module in the auxotrophic strains from formate to methanol-dependent growth using both NAD and PQQ-dependent methanol dehydrogenases. Finally, we demonstrated, for the first time, engineered CO₂-dependent formation of part of the biomass through CO₂ reduction to formate by the native formate dehydrogenase, which required short-term evolution to rebalance the cellular NADH/NAD ⁺ ratio. This research paves the way to further engineer P. putida towards full growth on formate, methanol, and CO₂ as sole feedstocks, thereby substantially expanding its potential as a sustainable and versatile cell factory.

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.

Conversion of Escherichia coli into Mixotrophic CO2 Assimilation with Malate and Hydrogen Based on Recombinant Expression of 2-Oxoglutarate:Ferredoxin Oxidoreductase Using Adaptive Laboratory Evolution

We report the mixotrophic growth of Escherichia coli based on recombinant 2-oxoglutarate:ferredoxin oxidoreductase (OGOR) to assimilate CO₂ using malate as an auxiliary carbon source and hydrogen as an energy source. We employ a long-term (~184 days) two-stage adaptive evolution to convert heterotrophic E. coli into mixotrophic E. coli. In the first stage of evolution with serine, diauxic growth emerges as a prominent feature. At the end of the second stage of evolution with malate, the strain exhibits mixotrophy with CO₂ as an essential substrate for growth. We expect this work will open new possibilities in the utilization of OGOR for microbial CO₂ assimilation and future hydrogen-based electro-microbial conversion.

Metabolic reconstruction of the human pathogen Candida auris: using a cross-species approach for drug target prediction

Candida auris is an emerging human pathogen, associated with antifungal drug resistance and hospital candidiasis outbreaks. In this work, we present iRV973, the first reconstructed Genome-scale metabolic model (GSMM) for C. auris. The model was manually curated and experimentally validated, being able to accurately predict the specific growth rate of C. auris and the utilization of several sole carbon and nitrogen sources. The model was compared to GSMMs available for other pathogenic Candida species and exploited as a platform for cross-species comparison, aiming the analysis of their metabolic features and the identification of potential new antifungal targets common to the most prevalent pathogenic Candida species. From a metabolic point of view, we were able to identify unique enzymes in C. auris in comparison with other Candida species, which may represent unique metabolic features. Additionally, 50 enzymes were identified as potential drug targets, given their essentiality in conditions mimicking human serum, common to all four different Candida models analysed. These enzymes represent interesting drug targets for antifungal therapy, including some known targets of antifungal agents used in clinical practice, but also new potential drug targets without any human homolog or drug association in Candida species.

Biomanufacturing by In Vitro Biotransformation (ivBT) Using Purified Cascade Multi-enzymes

In vitro biotransformation (ivBT) refers to the use of an artificial biological reaction system that employs purified enzymes for the one-pot conversion of low-cost materials into biocommodities such as ethanol, organic acids, and amino acids. Unshackled from cell growth and metabolism, ivBT exhibits distinct advantages compared with metabolic engineering, including but not limited to high engineering flexibility, ease of operation, fast reaction rate, high product yields, and good scalability. These characteristics position ivBT as a promising next-generation biomanufacturing platform. Nevertheless, challenges persist in the enhancement of bulk enzyme preparation methods, the acquisition of enzymes with superior catalytic properties, and the development of sophisticated approaches for pathway design and system optimization. In alignment with the workflow of ivBT development, this chapter presents a systematic introduction to pathway design, enzyme mining and engineering, system construction, and system optimization. The chapter also proffers perspectives on ivBT development.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Recommendations for performing measurements of apparent equilibrium constants of enzyme-catalyzed reactions and for reporting the results of these measurements

The measurement of values of apparent equilibrium constants K' for enzyme-catalyzed reactions involve a substantial number of critical details, neglect of which could lead to systematic errors. Here, interferences, impurities in the substances used, and failure to achieve equilibrium are matters of substantial consequence. Careful reporting of results is of great importance if the results are to have archival value. Thus, attention must be paid to the identification of the substances, specification of the reaction(s), the conditions of reaction, the definition of the equilibrium constant(s) and standard states, the use of standard nomenclature, symbols, and units, and uncertainties. This document contains a general discussion of various aspects of these equilibrium measurements as well as STRENDA (Standards for Reporting Enzymology Data) recommendations regarding the measurements and the reporting of results.

BioBulkFoundary: a customized webserver for exploring biosynthetic potentials of bulk chemicals

SUMMARY

Advances in metabolic engineering have boosted the production of bulk chemicals, resulting in tons of production volumes of some bulk chemicals with very low prices. A decrease in the production cost and overproduction of bulk chemicals makes it necessary and desirable to explore the potential to synthesize higher-value products from them. It is also useful and important for society to explore the use of design methods involving synthetic biology to increase the economic value of these bulk chemicals. Therefore, we developed 'BioBulkFoundary', which provides an elaborate analysis of the biosynthetic potential of bulk chemicals based on the state-of-art exploration of pathways to synthesize value-added chemicals, along with associated comprehensive technology and economic database into a user-friendly framework.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://design.rxnfinder.org/biobulkfoundary/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Building the uracil skeleton in primitive ponds at the origins of life: carbamoylation of aspartic acid

A large set of nucleobases and amino acids is found in meteorites, implying that several chemical reservoirs are present in the solar system. The "geochemical continuity" hypothesis explores how protometabolic paths developed from so-called "bricks" in an enzyme-free prebiotic world and how they affected the origins of life. In the living cell, the second step of synthesizing uridine and cytidine RNA monomers is a carbamoyl transfer from a carbamoyl donor to aspartic acid. Here we compare two enzyme-free scenarios: aqueous and mineral surface scenarios in a thermal range up to 250 °C. Both processes could have happened in ponds under open atmosphere on the primeval Earth. Carbamoylation of aspartic acid with cyanate in aqueous solutions at 25 °C gives high N-carbamoyl aspartic acid yields within 16 h. It is important to stress that, while various molecules could be efficient carbamoylating agents according to thermodynamics, kinetics plays a determining role in selecting prebiotically possible pathways.

Optimizing microbial networks through metabolic bypasses

Metabolism has long been considered as a relatively stiff set of biochemical reactions. This somewhat outdated and dogmatic view has been challenged over the last years, as multiple studies exposed unprecedented plasticity of metabolism by exploring rational and evolutionary modifications within the metabolic network of cell factories. Of particular importance is the emergence of metabolic bypasses, which consist of enzymatic reaction(s) that support unnatural connections between metabolic nodes. Such novel topologies can be generated through the introduction of heterologous enzymes or by upregulating native enzymes (sometimes relying on promiscuous activities thereof). Altogether, the adoption of bypasses resulted in an expansion in the capacity of the host's metabolic network, which can be harnessed for bioproduction. In this review, we discuss modifications to the canonical architecture of central carbon metabolism derived from such bypasses towards six optimization purposes: stoichiometric gain, overcoming kinetic limitations, solving thermodynamic barriers, circumventing toxic intermediates, uncoupling product synthesis from biomass formation, and altering redox cofactor specificity. The metabolic costs associated with bypass-implementation are likewise discussed, including tailoring their design towards improving bioproduction.

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.

Revealing the Characteristics of Glucose- and Lactate-Based Chain Elongation for Caproate Production by Caproicibacterium lactatifermentans through Transcriptomic, Bioenergetic, and Regulatory Analyses

Caproate, an important medium-chain fatty acid, can only be synthesized by limited bacterial species by using ethanol, lactate, or certain saccharides. Caproicibacterium lactatifermentans is a promising caproate producer due to its glucose and lactate utilization capabilities. However, the global cellular responses of this bacterium to different carbon sources were not well understood. Here, C. lactatifermentans showed robust growth on glucose but more active caproate synthesis on lactate. Comparative transcriptome revealed that the genes involved in reverse β-oxidation for caproate synthesis and V-type ATPase-dependent ATP generation were upregulated under lactate condition, while several genes responsible for biomass synthesis were upregulated under glucose condition. Based on metabolic pathway reconstructions and bioenergetics analysis, the biomass accumulation on glucose condition may be supported by sufficient supplies of ATP and metabolite intermediates via glycolysis. In contrast, the ATP yield per glucose equivalent from lactate conversion into caproate was only 20% of that from glucose. Thus, the upregulation of the reverse β-oxidation genes may be essential for cell survival under lactate conditions. Furthermore, the remarkably decreased lactate utilization was observed after glucose acclimatization, indicating the negative modulation of lactate utilization by glucose metabolism. Based on the cotranscription of the lactate utilization repressor gene lldR with sugar-specific PTS genes and the opposite expression patterns of lldR and lactate utilization genes, a novel regulatory mechanism of glucose-repressed lactate utilization mediated via lldR was proposed. The results of this study suggested the molecular mechanism underlying differential physiologic and metabolic characteristics of C. lactatifermentans grown on glucose and lactate. IMPORTANCE Caproicibacterium lactatifermentans is a unique and robust caproate-producing bacterium in the family Oscillospiraceae due to its lactate utilization capability, whereas its close relatives such as Caproicibacterium amylolyticum, Caproiciproducens galactitolivorans, and Caproicibacter fermentans cannot utilize lactate but produce lactate as the main fermentation end product. Moreover, C. lactatifermentans can also utilize several saccharides such as glucose and maltose. Although the metabolic versatility of the bacterium makes it to be a promising industrial caproate producer, the cellular responses of C. lactatifermentans to different carbon sources were unknown. Here, the molecular mechanisms of biomass synthesis supported by glucose utilization and the cell survival supported by lactate utilization were revealed. A novel insight into the regulatory machinery in which glucose negatively regulates lactate utilization was proposed. This study provides a valuable basis to control and optimize caproate production, which will contribute to achieving a circular economy and environmental sustainability.

A Multiscale Spatiotemporal Model Including a Switch from Aerobic to Anaerobic Metabolism Reproduces Succession in the Early Infant Gut Microbiota

The human intestinal microbiota starts to form immediately after birth and is important for the health of the host. During the first days, facultatively anaerobic bacterial species generally dominate, such as Enterobacteriaceae. These are succeeded by strictly anaerobic species, particularly Bifidobacterium species. An early transition to Bifidobacterium species is associated with health benefits; for example, Bifidobacterium species repress growth of pathogenic competitors and modulate the immune response. Succession to Bifidobacterium is thought to be due to consumption of intracolonic oxygen present in newborns by facultative anaerobes, including Enterobacteriaceae. To study if oxygen depletion suffices for the transition to Bifidobacterium species, here we introduced a multiscale mathematical model that considers metabolism, spatial bacterial population dynamics, and cross-feeding. Using publicly available metabolic network data from the AGORA collection, the model simulates ab initio the competition of strictly and facultatively anaerobic species in a gut-like environment under the influence of lactose and oxygen. The model predicts that individual differences in intracolonic oxygen in newborn infants can explain the observed individual variation in succession to anaerobic species, in particular Bifidobacterium species. Bifidobacterium species became dominant in the model by their use of the bifid shunt, which allows Bifidobacterium to switch to suboptimal yield metabolism with fast growth at high lactose concentrations, as predicted here using flux balance analysis. The computational model thus allows us to test the internal plausibility of hypotheses for bacterial colonization and succession in the infant colon. IMPORTANCE The composition of the infant microbiota has a great impact on infant health, but its controlling factors are still incompletely understood. The frequently dominant anaerobic Bifidobacterium species benefit health, e.g., they can keep harmful competitors under control and modulate the intestinal immune response. Controlling factors could include nutritional composition and intestinal mucus composition, as well as environmental factors, such as antibiotics. We introduce a modeling framework of a metabolically realistic intestinal microbial ecology in which hypothetical scenarios can be tested and compared. We present simulations that suggest that greater levels of intraintestinal oxygenation more strongly delay the dominance of Bifidobacterium species, explaining the observed variety of microbial composition and demonstrating the use of the model for hypothesis generation. The framework allowed us to test a variety of controlling factors, including intestinal mixing and transit time. Future versions will also include detailed modeling of oligosaccharide and mucin metabolism.