Channeling in native microbial pathways: Implications and challenges for metabolic engineering

Precise metabolic modeling in post-omics era: accomplishments and perspectives

Microbes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules.

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.

Metabolic heterogeneity in tumor microenvironment - A novel landmark for immunotherapy

The surrounding non-cancer cells and tumor cells that make up the tumor microenvironment (TME) have various metabolic rhythms. TME metabolic heterogeneity is influenced by the intricate network of metabolic control within and between cells. DNA, protein, transport, and microbial levels are important regulators of TME metabolic homeostasis. The effectiveness of immunotherapy is also closely correlated with alterations in TME metabolism. The response of a tumor patient to immunotherapy is influenced by a variety of variables, including intracellular metabolic reprogramming, metabolic interaction between cells, ecological changes within and between tumors, and general dietary preferences. Although immunotherapy and targeted therapy have made great strides, their use in the accurate identification and treatment of tumors still has several limitations. The function of TME metabolic heterogeneity in tumor immunotherapy is summarized in this article. It focuses on how metabolic heterogeneity develops and is regulated as a tumor progresses, the precise molecular mechanisms and potential clinical significance of imbalances in intracellular metabolic homeostasis and intercellular metabolic coupling and interaction, as well as the benefits and drawbacks of targeted metabolism used in conjunction with immunotherapy. This offers insightful knowledge and important implications for individualized tumor patient diagnosis and treatment plans in the future.

Optimizing the conversion of phosphoenolpyruvate to lactate by enzymatic channeling with mixed nanoparticle display

Co-assembling enzymes with nanoparticles (NPs) into nanoclusters allows them to access channeling, a highly efficient form of multienzyme catalysis. Using pyruvate kinase (PykA) and lactate dehydrogenase (LDH) to convert phosphoenolpyruvic acid to lactic acid with semiconductor quantum dots (QDs) confirms how enzyme cluster formation dictates the rate of coupled catalytic flux (kflux) across a series of differentially sized/shaped QDs and 2D nanoplatelets (NPLs). Enzyme kinetics and coupled flux were used to demonstrate that by mixing different NP systems into clusters, a >10× improvement in kflux is observed relative to free enzymes, which is also ≥2× greater than enhancement on individual NPs. Cluster formation was characterized with gel electrophoresis and transmission electron microscopy (TEM) imaging. The generalizability of this mixed-NP approach to improving flux is confirmed by application to a seven-enzyme system. This represents a powerful approach for accessing channeling with almost any choice of enzymes constituting a multienzyme cascade.

Regulation of T16H subcellular localization for promoting its catalytic efficiency in yeast cells

To investigate the effect of subcellular localization on the transformation efficiency of heterologous expressed functional P450s in yeast. Microbial biotransformation offers a promising substitute for the direct extraction of natural products, but its viability in industrial applications depends on achieving high transformation efficiencies. To investigate the influence of subcellular microenvironments on the activity of heterologously expressed P450s, Catharanthus roseus tabersonine 16-hydroxylase (T16H) was chosen, and its subcellular localization was regulated by fusing organelle-localization signals. Interestingly, this manipulation had no effect on the gene expression levels of T16H, but resulted in varying conversion rates from tabersonine to 16-hydroxy tabersonine. Notably, the highest transformation efficiency was observed in yeast cells expressing peroxisome-localized T16H. Given the alkaline pH optimum for P450s, the alkaline peroxisomal lumen could be a suitable compartment for P450s reactions to achieve high transformation efficiency using yeast cells. Different organelle-localization of T16H in yeast cells resulted in varying conversion rates, suggesting that compartmentalizing the expression of target enzymes could be a viable approach to increase transformation efficiency in yeast.

Deuterated water as a substrate-agnostic isotope tracer for investigating reversibility and thermodynamics of reactions in central carbon metabolism

Stable isotope tracers are a powerful tool for the quantitative analysis of microbial metabolism, enabling pathway elucidation, metabolic flux quantification, and assessment of reaction and pathway thermodynamics. 13C and 2H metabolic flux analysis commonly relies on isotopically labeled carbon substrates, such as glucose. However, the use of 2H-labeled nutrient substrates faces limitations due to their high cost and limited availability in comparison to 13C-tracers. Furthermore, isotope tracer studies in industrially relevant bacteria that metabolize complex substrates such as cellulose, hemicellulose, or lignocellulosic biomass, are challenging given the difficulty in obtaining these as isotopically labeled substrates. In this study, we examine the potential of deuterated water (2H2O) as an affordable, substrate-neutral isotope tracer for studying central carbon metabolism. We apply 2H2O labeling to investigate the reversibility of glycolytic reactions across three industrially relevant bacterial species -C. thermocellum, Z. mobilis, and E. coli-harboring distinct glycolytic pathways with unique thermodynamics. We demonstrate that 2H2O labeling recapitulates previous reversibility and thermodynamic findings obtained with established 13C and 2H labeled nutrient substrates. Furthermore, we exemplify the utility of this 2H2O labeling approach by applying it to high-substrate C. thermocellum fermentations -a setting in which the use of conventional tracers is impractical-thereby identifying the glycolytic enzyme phosphofructokinase as a major bottleneck during high-substrate fermentations and unveiling critical insights that will steer future engineering efforts to enhance ethanol production in this cellulolytic organism. This study demonstrates the utility of deuterated water as a substrate-agnostic isotope tracer for examining flux and reversibility of central carbon metabolic reactions, which yields biological insights comparable to those obtained using costly 2H-labeled nutrient substrates.

Isotopically non-stationary 13 C metabolic flux analysis of two closely related fast-growing cyanobacteria, Synechococcus elongatus PCC 11801 and 11802

Synechococcus elongatus PCC 11801 and 11802 are closely related cyanobacterial strains that are fast-growing and tolerant to high light and temperature. These strains hold significant promise as chassis for photosynthetic production of chemicals from carbon dioxide. A detailed quantitative understanding of the central carbon pathways would be a reference for future metabolic engineering studies with these strains. We conducted isotopic non-stationary 13 C metabolic flux analysis to quantitively assess the metabolic potential of these two strains. This study highlights key similarities and differences in the central carbon flux distribution between these and other model/non-model strains. The two strains demonstrated a higher Calvin-Benson-Bassham (CBB) cycle flux coupled with negligible flux through the oxidative pentose phosphate pathway and the photorespiratory pathway and lower anaplerosis fluxes under photoautotrophic conditions. Interestingly, PCC 11802 shows the highest CBB cycle and pyruvate kinase flux values among those reported in cyanobacteria. The unique tricarboxylic acid (TCA) cycle diversion in PCC 11801 makes it ideal for the large-scale production of TCA cycle-derived chemicals. Additionally, dynamic labeling transients were measured for intermediates of amino acid, nucleotide, and nucleotide sugar metabolism. Overall, this study provides the first detailed metabolic flux maps of S. elongatus PCC 11801 and 11802, which may aid metabolic engineering efforts in these strains.

From Protein Film Electrochemistry to Nanoconfined Enzyme Cascades and the Electrochemical Leaf

Protein film electrochemistry (PFE) has given unrivalled insight into the properties of redox proteins and many electron-transferring enzymes, allowing investigations of otherwise ill-defined or intractable topics such as unstable Fe-S centers and the catalytic bias of enzymes. Many enzymes have been established to be reversible electrocatalysts when attached to an electrode, and further investigations have revealed how unusual dependences of catalytic rates on electrode potential have stark similarities with electronics. A special case, the reversible electrochemistry of a photosynthetic enzyme, ferredoxin-NADP⁺ reductase (FNR), loaded at very high concentrations in the 3D nanopores of a conducting metal oxide layer, is leading to a new technology that brings PFE to myriad enzymes of other classes, the activities of which become controlled by the primary electron exchange. This extension is possible because FNR-based recycling of NADP(H) can be coupled to a dehydrogenase, and thence to other enzymes linked in tandem by the tight channelling of cofactors and intermediates within the nanopores of the material. The earlier interpretations of catalytic wave-shapes and various analogies with electronics are thus extended to initiate a field perhaps aptly named "cascade-tronics", in which the flow of reactions along an enzyme cascade is monitored and controlled through an electrochemical analyzer. Unlike in photosynthesis where FNR transduces electron transfer and hydride transfer through the unidirectional recycling of NADPH, the "electrochemical leaf" (e-Leaf) can be used to drive reactions in both oxidizing and reducing directions. The e-Leaf offers a natural way to study how enzymes are affected by nanoconfinement and crowding, mimicking the physical conditions under which enzyme cascades operate in living cells. The reactions of the trapped enzymes, often at very high local concentration, are thus studied electrochemically, exploiting the potential domain to control rates and direction and the current-rate analogy to derive kinetic data. Localized NADP(H) recycling is very efficient, resulting in very high cofactor turnover numbers and new opportunities for controlling and exploiting biocatalysis.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

Machine learning alternative to systems biology should not solely depend on data

In recent years, artificial intelligence (AI)/machine learning has emerged as a plausible alternative to systems biology for the elucidation of biological phenomena and in attaining specified design objective in synthetic biology. Although considered highly disruptive with numerous notable successes so far, we seek to bring attention to both the fundamental and practical pitfalls of their usage, especially in illuminating emergent behaviors from chaotic or stochastic systems in biology. Without deliberating on their suitability and the required data qualities and pre-processing approaches beforehand, the research and development community could experience similar 'AI winters' that had plagued other fields. Instead, we anticipate the integration or combination of the two approaches, where appropriate, moving forward.

Dolit S, K. Khamis A, Abd-Aziz N, R. Azman N, A. Asli U. Regenerating Soil Microbiome: Balancing Microbial CO2 Sequestration and Emission

Soil microbiome plays a significant role in soil’s ecosystem for soils to be physically and biologically healthy. Soil health is fundamental for plant growth and crops productivity. In the introduction part, the roles and dynamics of the microbial community in soils, primarily in the cycle of soil organic carbon and CO2 release and absorption, are deliberated. Next, the impact of crop management practices and climate change on the soil carbon balance are described, as well as other issues related to soil degradation, such as unbalanced nutrient recycling and mineral weathering. In response to these issues, various approaches to soil regeneration have been developed in order to foster an efficient and active soil microbiome, thereby balancing the CO2 cycle and carbon sequestration in the soil ecosystem.

A Universal Method for Developing Autoinduction Expression Systems Using AHL-Mediated Quorum-Sensing Circuits

A major challenge in engineering microorganisms for the desirable product is maintaining the rational balance between cell growth and production. Quorum sensing (QS)-based dynamic regulations provide a pathway-independent genetic control technology to rebalance metabolic flux for biomass and product synthesis. However, the lack of a universal method for screening QS elements and the complex design of autoinduction circuits limit their applications in metabolic engineering. Here, we developed a universal method for simple and rapid screening and evaluating various QS systems from Gram-negative bacteria, and the largest library containing 195 combinations of receiving device/signal molecules was constructed and evaluated in Escherichia coli. A simple logical circuit with different inducer synthesis rates was established to dynamically regulate gene expression levels, leading to efficient protein expression and product synthesis. The system was further applied in Pseudomonas putida, which indicated it could be widely accommodated in other microorganisms. Therefore, the method could be used in diverse Gram-negative strains for the desired biosynthesis.

Fine-tuning gene expression for improved biosynthesis of natural products: From transcriptional to post-translational regulation

Microbial production of natural compounds has attracted extensive attention due to their high value in pharmaceutical, cosmetic, and food industries. Constructing efficient microbial cell factories for biosynthesis of natural products requires the fine-tuning of gene expressions to minimize the accumulation of toxic metabolites, reduce the competition between cell growth and product generation, as well as achieve the balance of redox or co-factors. In this review, we focus on recent advances in fine-tuning gene expression at the DNA, RNA, and protein levels to improve the microbial biosynthesis of natural products. Commonly used regulatory toolsets in each level are discussed, and perspectives for future direction in this area are provided.

Cyanobacteria as cell factories: the roles of host and pathway engineering and translational research

Cyanobacteria, a group of photoautotrophic prokaryotes, are attractive hosts for the sustainable production of chemicals from carbon dioxide and sunlight. However, the rates, yields, and titers have remained well below those needed for commercial deployment. We argue that the following areas will be central to the development of cyanobacterial cell factories: engineered and well-characterized host strains, model-guided pathway design, and advanced synthetic biology tools. Although several foundational studies report improved strain properties, translational research will be needed to develop engineered hosts and deploy them for metabolic engineering. Further, the recent developments in metabolic modeling and synthetic biology of cyanobacteria will enable nimble strategies for strain improvement with the complete cycle of design, build, test, and learn.

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach

Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO₂ fixation within oxygenic photosynthetic organisms. The cyanobacteria, Synechocystis sp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of ¹³C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.

Stable and Temporary Enzyme Complexes and Metabolons Involved in Energy and Redox Metabolism

Significance: Alongside well-characterized permanent multimeric enzymes and multienzyme complexes, relatively unstable transient enzyme-enzyme assemblies, including metabolons, provide an important mechanism for the regulation of energy and redox metabolism. Critical Issues: Despite the fact that enzyme-enzyme assemblies have been proposed for many decades and experimentally analyzed for at least 40 years, there are very few pathways for which unequivocal evidence for the presence of metabolite channeling, the most frequently evoked reason for their formation, has been provided. Further, in contrast to the stronger, permanent interactions for which a deep understanding of the subunit interface exists, the mechanism(s) underlying transient enzyme-enzyme interactions remain poorly studied. Recent Advances: The widespread adoption of proteomic and cell biological approaches to characterize protein-protein interaction is defining an ever-increasing number of enzyme-enzyme assemblies as well as enzyme-protein interactions that likely identify factors which stabilize such complexes. Moreover, the use of microfluidic technologies provided compelling support of a role for substrate-specific chemotaxis in complex assemblies. Future Directions: Embracing current and developing technologies should render the delineation of metabolons from other enzyme-enzyme complexes more facile. In parallel, attempts to confirm that the findings reported in microfluidic systems are, indeed, representative of the cellular situation will be critical to understanding the physiological circumstances requiring and evoking dynamic changes in the levels of the various transient enzyme-enzyme assemblies of the cell. Antioxid. Redox Signal. 35, 788-807.

Metabolic engineering of Escherichia coli for quinolinic acid production by assembling L-aspartate oxidase and quinolinate synthase as an enzyme complex

Quinolinic acid (QA) is a key intermediate of nicotinic acid (Niacin) which is an essential human nutrient and widely used in food and pharmaceutical industries. In this study, a quinolinic acid producer was constructed by employing comprehensive engineering strategies. Firstly, the quinolinic acid production was improved by deactivation of NadC (to block the consumption pathway), NadR (to eliminate the repression of L-aspartate oxidase and quinolinate synthase), and PtsG (to slow the glucose utilization rate and achieve a more balanced metabolism, and also to increase the availability of the precursor phosphoenolpyruvate). Further modifications to enhance quinolinic acid production were investigated by increasing the oxaloacetate pool through overproduction of phosphoenolpyruvate carboxylase and deactivation of acetate-producing pathway enzymes. Moreover, quinolinic acid production was accelerated by assembling NadB and NadA as an enzyme complex with the help of peptide-peptide interaction peptides RIAD and RIDD, which resulted in up to 3.7 g/L quinolinic acid being produced from 40 g/L glucose in shake-flask cultures. A quinolinic acid producer was constructed in this study, and these results lay a foundation for further engineering of microbial cell factories to efficiently produce quinolinic acid and subsequently convert this product to nicotinic acid for industrial applications.

Mimicking natural strategies to create multi-environment enzymatic reactors: From natural cell compartments to artificial polyelectrolyte reactors

Engineering microenvironments for sequential enzymatic reactions has attracted specific interest within different fields of research as an effective strategy to improve the catalytic performance of enzymes. While in industry most enzymatic reactions occur in a single compartment carrier, living cells are however able to conduct multiple reactions simultaneously within confined sub-compartments, or organelles. Engineering multi-compartments with regulated environments and transformation properties enhances enzyme activity and stability and thus increases the overall yield of final products. In this review, we discuss current and potential methods to fabricate artificial cells for sequential enzymatic reactions, which are inspired by mechanisms and metabolic pathways developed by living cells. We aim to advance the understanding of living cell complexity and its compartmentalization and present solutions to mimic these processes in vitro. Particular attention has been given to layer-by-layer assembly of polyelectrolytes for developing multi-compartments. We hope this review paves the way for the next steps toward engineering of smart artificial multi-compartments with adoptive stimuli-responsive properties, mimicking living cells to improve catalytic properties and efficiency of the enzymes and enhance their stability.

Integrated knowledge mining, genome-scale modeling, and machine learning for predicting Yarrowia lipolytica bioproduction

Predicting bioproduction titers from microbial hosts has been challenging due to complex interactions between microbial regulatory networks, stress responses, and suboptimal cultivation conditions. This study integrated knowledge mining, feature extraction, genome-scale modeling (GSM), and machine learning (ML) to develop a model for predicting Yarrowia lipolytica chemical titers (i.e., organic acids, terpenoids, etc.). First, Y. lipolytica production data, including cultivation conditions, genetic engineering strategies, and product information, was manually collected from literature (~100 papers) and stored as either numerical (e.g., substrate concentrations) or categorical (e.g., bioreactor modes) variables. For each case recorded, central pathway fluxes were estimated using GSMs and flux balance analysis (FBA) to provide metabolic features. Second, a ML ensemble learner was trained to predict strain production titers. Accurate predictions on the test data were obtained for instances with production titers >1 g/L (R² = 0.87). However, the model had reduced predictability for low performance strains (0.01-1 g/L, R² = 0.29) potentially due to biosynthesis bottlenecks not captured in the features. Feature ranking indicated that the FBA fluxes, the number of enzyme steps, the substrate inputs, and thermodynamic barriers (i.e., Gibbs free energy of reaction) were the most influential factors. Third, the model was evaluated on other oleaginous yeasts and indicated there were conserved features for some hosts that can be potentially exploited by transfer learning. The platform was also designed to assist computational strain design tools (such as OptKnock) to screen genetic targets for improved microbial production in light of experimental conditions.

Animal Fatty Acid Synthase: A Chemical Nanofactory

Fatty acids are crucial molecules for most living beings, very well spread and conserved across species. These molecules play a role in energy storage, cell membrane architecture, and cell signaling, the latter through their derivative metabolites. De novo synthesis of fatty acids is a complex chemical process that can be achieved either by a metabolic pathway built by a sequence of individual enzymes, such as in most bacteria, or by a single, large multi-enzyme, which incorporates all the chemical capabilities of the metabolic pathway, such as in animals and fungi, and in some bacteria. Here we focus on the multi-enzymes, specifically in the animal fatty acid synthase (FAS). We start by providing a historical overview of this vast field of research. We follow by describing the extraordinary architecture of animal FAS, a homodimeric multi-enzyme with seven different active sites per dimer, including a carrier protein that carries the intermediates from one active site to the next. We then delve into this multi-enzyme's detailed chemistry and critically discuss the current knowledge on the chemical mechanism of each of the steps necessary to synthesize a single fatty acid molecule with atomic detail. In line with this, we discuss the potential and achieved FAS applications in biotechnology, as biosynthetic machines, and compare them with their homologous polyketide synthases, which are also finding wide applications in the same field. Finally, we discuss some open questions on the architecture of FAS, such as their peculiar substrate-shuttling arm, and describe possible reasons for the emergence of large megasynthases during evolution, questions that have fascinated biochemists from long ago but are still far from answered and understood.

Kinetic modeling of anaerobic degradation of plant-derived aromatic mixtures by Rhodopseudomonas palustris

Rhodopseudomonas palustris is a model microorganism for studying the anaerobic metabolism of aromatic compounds. While it is well documented which aromatics can serve as sole organic carbon sources, co-metabolism of other aromatics is poorly understood. This study used kinetic modeling to analyze the simultaneous degradation of aromatic compounds present in corn stover hydrolysates and model the co-metabolism of aromatics not known to support growth of R. palustris as sole organic substrates. The simulation predicted that p-coumaroyl amide and feruloyl amide were hydrolyzed to p-coumaric acid and ferulic acid, respectively, and further transformed via p-coumaroyl-CoA and feruloyl-CoA. The modeling also suggested that metabolism of p-hydroxyphenyl aromatics was slowed by substrate inhibition, whereas the transformation of guaiacyl aromatics was inhibited by their p-hydroxyphenyl counterparts. It also predicted that substrate channeling may occur during degradation of p-coumaroyl-CoA and feruloyl-CoA, resulting in no detectable accumulation of p-hydroxybenzaldehyde and vanillin, during the transformation of these CoA ligated compounds to p-hydroxybenzoic acid and vanillic acid, respectively. While the simulation correctly represented the known transformation of p-hydroxybenzoic acid via the benzoyl-CoA pathway, it also suggested co-metabolism of vanillic acid and syringic acid, which are known not to serve as photoheterotrophic growth substrate for R. palustris.

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

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

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Biosynthesis of terpene compounds using the non-model yeast Yarrowia lipolytica: grand challenges and a few perspectives

Yarrowia lipolytica has emerged as an important non-model host for terpene production. However, three main challenges remain in industrial production using this yeast. First, considerable knowledge gaps exist in metabolic flux across multiple compartments, cofactor generation, and catabolism of non-sugar carbon sources. Second, many enzymatic steps in the complex-terpene synthesis pathway can pose rate-limitations, causing accumulation of toxic intermediates and increased metabolic burdens. Third, metabolic shifts, morphological changes, and genetic mutations are poorly characterized under industrial fermentation conditions. To overcome these challenges, systems metabolic analysis, protein engineering, novel pathway engineering, model-guided strain design, and fermentation optimization have been attempted with some successes. Further developments that address these challenges are needed to advance the Yarrowia lipolytica platform for industrial-scale production of high-value terpenes, including those with highly complex structures such as anticancer molecules withanolides and insecticidal limonoids.

Enzyme Assembly for Compartmentalized Metabolic Flux Control

Enzyme assembly by ligand binding or physically sequestrating enzymes, substrates, or metabolites into isolated compartments can bring key molecules closer to enhance the flux of a metabolic pathway. The emergence of enzyme assembly has provided both opportunities and challenges for metabolic engineering. At present, with the development of synthetic biology and systems biology, a variety of enzyme assembly strategies have been proposed, from the initial direct enzyme fusion to scaffold-free assembly, as well as artificial scaffolds, such as nucleic acid/protein scaffolds, and even some more complex physical compartments. These assembly strategies have been explored and applied to the synthesis of various important bio-based products, and have achieved different degrees of success. Despite some achievements, enzyme assembly, especially in vivo, still has many problems that have attracted significant attention from researchers. Here, we focus on some selected examples to review recent research on scaffold-free strategies, synthetic artificial scaffolds, and physical compartments for enzyme assembly or pathway sequestration, and we discuss their notable advances. In addition, the potential applications and challenges in the applications are highlighted.

Redox rebalance against genetic perturbations and modulation of central carbon metabolism by the oxidative stress regulation

The micro-aerophilic organisms and aerobes as well as yeast and higher organisms have evolved to gain energy through respiration (via oxidative phosphorylation), thereby enabling them to grow much faster than anaerobes. However, during respiration, reactive oxygen species (ROSs) are inherently (inevitably) generated, and threaten the cell's survival. Therefore, living organisms (or cells) must furnish the potent defense systems to keep such ROSs at harmless level, where the cofactor balance plays crucial roles. Namely, NADH is the source of energy generation (catabolism) in the respiratory chain reactions, through which ROSs are generated, while NADPH plays important roles not only for the cell synthesis (anabolism) but also for detoxifying ROSs. Therefore, the cell must rebalance the redox ratio by modulating the fluxes of the central carbon metabolism (CCM) by regulating the multi-level regulation machinery upon genetic perturbations and the change in the growth conditions. Here, we discuss about how aerobes accomplish such cofactor homeostasis against redox perturbations. In particular, we consider how single-gene mutants (including pgi, pfk, zwf, gnd and pyk mutants) modulate their metabolisms in relation to cofactor rebalance (and also by adaptive laboratory evolution). We also discuss about how the overproduction of NADPH (by the pathway gene mutation) can be utilized for the efficient production of useful value-added chemicals such as medicinal compounds, polyhydroxyalkanoates, and amino acids, all of which require NADPH in their synthetic pathways. We then discuss about the metabolic responses against oxidative stress, where αketoacids play important roles not only for the coordination between catabolism and anabolism, but also for detoxifying ROSs by non-enzymatic reactions, as well as for reducing the production of ROSs by repressing the activities of the TCA cycle and respiration (via carbon catabolite repression). Thus, we discuss about the mechanisms (basic strategies) that modulate the metabolism from respiration to respiro-fermentative metabolism causing overflow, based on the role of Pyk activity, affecting the NADPH production at the oxidative pentose phosphate (PP) pathway, and the roles of αketoacids for the change in the source of energy generation from the oxidative phosphorylation to the substrate level phosphorylation.

Cyanobacterial carboxysome mutant analysis reveals the influence of enzyme compartmentalization on cellular metabolism and metabolic network rigidity

Cyanobacterial carboxysomes encapsulate carbonic anhydrase and ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO). Genetic deletion of the major structural proteins encoded within the ccm operon in Synechococcus sp. PCC 7002 (ΔccmKLMN) disrupts carboxysome formation and significantly affects cellular physiology. Here we employed both metabolite pool size analysis and isotopically nonstationary metabolic flux analysis (INST-MFA) to examine metabolic regulation in cells lacking carboxysomes. Under high CO₂ environments (1%), the ΔccmKLMN mutant could recover growth and had a similar central flux distribution as the control strain, with the exceptions of moderately decreased photosynthesis and elevated biomass protein content and photorespiration activity. Metabolite analyses indicated that the ΔccmKLMN strain had significantly larger pool sizes of pyruvate (>18 folds), UDPG (uridine diphosphate glucose), and aspartate as well as higher levels of secreted organic acids (e.g., malate and succinate). These results suggest that the ΔccmKLMN mutant is able to largely maintain a fluxome similar to the control strain by changing in intracellular metabolite concentrations and metabolite overflows under optimal growth conditions. When CO₂ was insufficient (0.2%), provision of acetate moderately promoted mutant growth. Interestingly, the removal of microcompartments may loosen the flux network and promote RuBisCO side-reactions, facilitating redirection of central metabolites to competing pathways (i.e., pyruvate to heterologous lactate production). This study provides important insights into metabolic regulation via enzyme compartmentation and cyanobacterial compensatory responses.

Engineering microbial consortia by division of labor

During microbial applications, metabolic burdens can lead to a significant drop in cell performance. Novel synthetic biology tools or multi-step bioprocessing (e.g., fermentation followed by chemical conversions) are therefore needed to avoid compromised biochemical productivity from over-burdened cells. A possible solution to address metabolic burden is Division of Labor (DoL) via natural and synthetic microbial consortia. In particular, consolidated bioprocesses and metabolic cooperation for detoxification or cross feeding (e.g., vitamin C fermentation) have shown numerous successes in industrial level applications. However, distributing a metabolic pathway among proper hosts remains an engineering conundrum due to several challenges: complex subpopulation dynamics/interactions with a short time-window for stable production, suboptimal cultivation of microbial communities, proliferation of cheaters or low-producers, intermediate metabolite dilution, transport barriers between species, and breaks in metabolite channeling through biosynthesis pathways. To develop stable consortia, optimization of strain inoculations, nutritional divergence and crossing feeding, evolution of mutualistic growth, cell immobilization, and biosensors may potentially be used to control cell populations. Another opportunity is direct integration of non-bioprocesses (e.g., microbial electrosynthesis) to power cell metabolism and improve carbon efficiency. Additionally, metabolic modeling and ¹³C-metabolic flux analysis of mixed culture metabolism and cross-feeding offers a computational approach to complement experimental research for improved consortia performance.

Can enzyme proximity accelerate cascade reactions?

The last decade has seen an exponential expansion of interest in conjugating multiple enzymes of cascades in close proximity to each other, with the overarching goal being to accelerate the overall reaction rate. However, some evidence has emerged that there is no effect of proximity channeling on the reaction velocity of the popular GOx-HRP cascade, particularly in the presence of a competing enzyme (catalase). Herein, we rationalize these experimental results quantitatively. We show that, in general, proximity channeling can enhance reaction velocity in the presence of competing enzymes, but in steady state a significant enhancement can only be achieved for diffusion-limited reactions or at high concentrations of competing enzymes. We provide simple equations to estimate the effect of channeling quantitatively and demonstrate that proximity can have a more pronounced effect under crowding conditions in vivo, particularly that crowding can enhance the overall rates of channeled cascade reactions.

Post-PKS enzyme complexes

This review highlights the protein–protein interactions between type II post-PKS tailoring enzymes with an emphasis on gilvocarcin and mithramycin.

Regulation of glycolytic flux and overflow metabolism depending on the source of energy generation for energy demand

Overflow metabolism is a common phenomenon observed at higher glycolytic flux in many bacteria, yeast (known as Crabtree effect), and mammalian cells including cancer cells (known as Warburg effect). This phenomenon has recently been characterized as the trade-offs between protein costs and enzyme efficiencies based on coarse-graining approaches. Moreover, it has been recognized that the glycolytic flux increases as the source of energy generation changes from energetically efficient respiration to inefficient respiro-fermentative or fermentative metabolism causing overflow metabolism. It is highly desired to clarify the metabolic regulation mechanisms behind such phenomena. Metabolic fluxes are located on top of the hierarchical regulation systems, and represent the outcome of the integrated response of all levels of cellular regulation systems. In the present article, we discuss about the different levels of regulation systems for the modulation of fluxes depending on the growth rate, growth condition such as oxygen limitation that alters the metabolism towards fermentation, and genetic perturbation affecting the source of energy generation from respiration to respiro-fermentative metabolism in relation to overflow metabolism. The intracellular metabolite of the upper glycolysis such as fructose 1,6-bisphosphate (FBP) plays an important role not only for flux sensing, but also for the regulation of the respiratory activity either directly or indirectly (via transcription factors) at higher growth rate. The glycolytic flux regulation is backed up (enhanced) by unphosphorylated EIIA and HPr of the phosphotransferase system (PTS) components, together with the sugar-phosphate stress regulation, where the transcriptional regulation is further modulated by post-transcriptional regulation via the degradation of mRNA (stability of mRNA) in Escherichia coli. Moreover, the channeling may also play some role in modulating the glycolytic cascade reactions.

Engineering Microorganisms for Enhanced CO2 Sequestration

Microbial CO₂ sequestration not only provides a green and sustainable approach for ameliorating global warming but also simultaneously produces biofuels and chemicals. However, the efficiency of microbial CO₂ fixation is still very low. In addition, concomitant microbial CO₂ emission decreases the carbon yield of desired chemicals. To address these issues, strategies including engineering CO₂-fixing pathways and energy-harvesting systems have been developed to improve the efficiency of CO₂ fixation in autotrophic and heterotrophic microorganisms. Furthermore, metabolic pathways and energy metabolism can be rewired to reduce microbial CO₂ emissions and increase the carbon yield of value-added products. This review highlights the potential of biotechnology to promote microbial CO₂ sequestration and provides guidance for the broader use of microorganisms as attractive carbon sinks.

The role of dynamic enzyme assemblies and substrate channelling in metabolic regulation

Transient physical association between enzymes appears to be a cardinal feature of metabolic systems, yet the purpose of this metabolic organisation remains enigmatic. It is generally assumed that substrate channelling occurs in these complexes. However, there is a lack of information concerning the mechanisms and extent of substrate channelling and confusion regarding the consequences of substrate channelling. In this review, we outline recent advances in the structural characterisation of enzyme assemblies and integrate this with new insights from reaction-diffusion modelling and synthetic biology to clarify the mechanistic and functional significance of the phenomenon.

A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data

New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise. Here, we show that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.

Advances and prospects in metabolic engineering of Zymomonas mobilis

Biorefinery of biomass-based biofuels and biochemicals by microorganisms is a competitive alternative of traditional petroleum refineries. Zymomonas mobilis is a natural ethanologen with many desirable characteristics, which makes it an ideal industrial microbial biocatalyst for commercial production of desirable bioproducts through metabolic engineering. In this review, we summarize the metabolic engineering progress achieved in Z. mobilis to expand its substrate and product ranges as well as to enhance its robustness against stressful conditions such as inhibitory compounds within the lignocellulosic hydrolysates and slurries. We also discuss a few metabolic engineering strategies that can be applied in Z. mobilis to further develop it as a robust workhorse for economic lignocellulosic bioproducts. In addition, we briefly review the progress of metabolic engineering in Z. mobilis related to the classical synthetic biology cycle of "Design-Build-Test-Learn", as well as the progress and potential to develop Z. mobilis as a model chassis for biorefinery practices in the synthetic biology era.

Glucose repression can be alleviated by reducing glucose phosphorylation rate in Saccharomyces cerevisiae

Microorganisms commonly exhibit preferential glucose consumption and diauxic growth when cultured in mixtures of glucose and other sugars. Although various genetic perturbations have alleviated the effects of glucose repression on consumption of specific sugars, a broadly applicable mechanism remains unknown. Here, we report that a reduction in the rate of glucose phosphorylation alleviates the effects of glucose repression in Saccharomyces cerevisiae. Through adaptive evolution under a mixture of xylose and the glucose analog 2-deoxyglucose, we isolated a mutant strain capable of simultaneously consuming glucose and xylose. Genome sequencing of the evolved mutant followed by CRISPR/Cas9-based reverse engineering revealed that mutations in the glucose phosphorylating enzymes (Hxk1, Hxk2, Glk1) were sufficient to confer simultaneous glucose and xylose utilization. We then found that varying hexokinase expression with an inducible promoter led to the simultaneous utilization of glucose and xylose. Interestingly, no mutations in sugar transporters occurred during the evolution, and no specific transporter played an indispensable role in simultaneous sugar utilization. Additionally, we demonstrated that slowing glucose consumption also enabled simultaneous utilization of glucose and galactose. These results suggest that the rate of intracellular glucose phosphorylation is a decisive factor for metabolic regulations of mixed sugars.

Engineering and systems-level analysis of Pseudomonas chlororaphis for production of phenazine-1-carboxamide using glycerol as the cost-effective carbon source

BACKGROUND

Glycerol, an inevitable byproduct of biodiesel, has become an attractive feedstock for the production of value-added chemicals due to its availability and low price. Pseudomonas chlororaphis HT66 can use glycerol to synthesize phenazine-1-carboxamide (PCN), a phenazine derivative, which is strongly antagonistic to fungal phytopathogens. A systematic understanding of underlying mechanisms for the PCN overproduction will be important for the further improvement and industrialization.

RESULTS

We constructed a PCN-overproducing strain (HT66LSP) through knocking out three negative regulatory genes, lon, parS, and prsA in HT66. The strain HT66LSP produced 4.10 g/L of PCN with a yield of 0.23 (g/g) from glycerol, which was of the highest titer and the yield obtained among PCN-producing strains. We studied gene expression, metabolomics, and dynamic ¹³C tracer in HT66 and HT66LSP. In response to the phenotype changes, the transcript levels of phz biosynthetic genes, which are responsible for PCN biosynthesis, were all upregulated in HT66LSP. Central carbon was rerouted to the shikimate pathway, which was shown by the modulation of specific genes involved in the lower glycolysis, the TCA cycle, and the shikimate pathway, as well as changes in abundances of intracellular metabolites and flux distribution to increase the precursor availability for PCN biosynthesis. Moreover, dynamic ¹³C-labeling experiments revealed that the presence of metabolite channeling of 3-phosphoglyceric acid to phosphoenolpyruvate and shikimate to trans-2,3-dihydro-3-hydroxyanthranilic acid in HT66LSP could enable high-yielding synthesis of PCN.

CONCLUSIONS

The integrated analysis of gene expression, metabolomics, and dynamic ¹³C tracer enabled us to gain a more in-depth insight into complex mechanisms for the PCN overproduction. This study provides important basis for further engineering P. chlororaphis for high PCN production and efficient glycerol conversion.

Genome-scale modeling and transcriptome analysis of Leuconostoc mesenteroides unravel the redox governed metabolic states in obligate heterofermentative lactic acid bacteria

Obligate heterofermentative lactic acid bacteria (LAB) are well-known for their beneficial health effects in humans. To delineate the incompletely characterized metabolism that currently limits their exploitation, at systems-level, we developed a genome-scale metabolic model of the representative obligate heterofermenting LAB, Leuconostoc mesenteroides (iLME620). Constraint-based flux analysis was then used to simulate several qualitative and quantitative phenotypes of L. mesenteroides, thereby evaluating the model validity. With established predictive capabilities, we subsequently employed iLME620 to elucidate unique metabolic characteristics of L. mesenteroides, such as the limited ability to utilize amino acids as energy source, and to substantiate the role of malolactic fermentation (MLF) in the reduction of pH-homeostatic burden on F0F1-ATPase. We also reported new hypothesis on the MLF mechanism that could be explained via a substrate channelling-like phenomenon mainly influenced by intracellular redox state rather than the intermediary reactions. Model simulations further revealed possible proton-symporter dependent activity of the energy efficient glucose-phosphotransferase system in obligate heterofermentative LAB. Moreover, integrated transcriptomic analysis allowed us to hypothesize transcriptional regulatory bias affecting the intracellular redox state. The insights gained here about the low ATP-yielding metabolism of L. mesenteroides, dominantly controlled by the cellular redox state, could potentially aid strain design for probiotic and cell factory applications.

Synthetic biology for manufacturing chemicals: constraints drive the use of non-conventional microbial platforms

Genetically modified microbes have had much industrial success producing protein-based products (such as antibodies and enzymes). However, engineering microbial workhorses for biomanufacturing of commodity compounds remains challenging. First, microbes cannot afford burdens with both overexpression of multiple enzymes and metabolite drainage for product synthesis. Second, synthetic circuits and introduced heterologous pathways are not yet as "robust and reliable" as native pathways due to hosts' innate regulations, especially under suboptimal fermentation conditions. Third, engineered enzymes may lack channeling capabilities for cascade-like transport of metabolites to overcome diffusion barriers or to avoid intermediate toxicity in the cytoplasmic environment. Fourth, moving engineered hosts from laboratory to industry is unreliable because genetic mutations and non-genetic cell-to-cell variations impair the large-scale fermentation outcomes. Therefore, synthetic biology strains often have unsatisfactory industrial performance (titer/yield/productivity). To overcome these problems, many different species are being explored for their metabolic strengths that can be leveraged to synthesize specific compounds. Here, we provide examples of non-conventional and genetically amenable species for industrial manufacturing, including the following: Corynebacterium glutamicum for its TCA cycle-derived biosynthesis, Yarrowia lipolytica for its biosynthesis of fatty acids and carotenoids, cyanobacteria for photosynthetic production from its sugar phosphate pathways, and Rhodococcus for its ability to biotransform recalcitrant feedstock. Finally, we discuss emerging technologies (e.g., genome-to-phenome mapping, single cell methods, and knowledge engineering) that may facilitate the development of novel cell factories.