Glucose is an important substrate for organisms to acquire energy needed for cellular growth. Despite the importance of this metabolite, single-cell information at a fast time-scale about the dynamics of intracellular glucose levels is difficult to obtain as the current available sensors have drawbacks in terms of pH sensitivity or unmatched glucose affinity. To address this, we developed a convenient method to create and screen biosensor libraries using yeast as a workhorse. This resulted in TINGL (Turquoise INdicator for GLucose), a robust and specific biosensor with an affinity that is compatible with intracellular glucose detection. We show that the sensor can be calibrated in vivo (i.e., intracellular) through equilibration of internal and external glucose in a yeast mutant unable to phosphorylate glucose. Using this method, we estimated dynamic glucose levels in budding yeast during transitions to glucose. We found that glucose concentrations reached levels up to approximately 1 mM as previously determined biochemically. Furthermore, the sensor showed that intracellular glucose dynamics differ based on whether cells are glucose-repressed or not. Finally, the human codon-optimized version (THINGL, Turquoise Human INdicator for GLucose) also showed a robust response after glucose addition to starved human cells, showing the versatility of the sensors. We believe that this sensor can aid researchers interested in cellular carbohydrate metabolism.

Fluorescent proteins (FPs) have become indispensable to biological research. Since the discovery of the first FP, Aequorea victoria green fluorescent protein (avGFP), new fluorescent proteins have been continuously being developed. To achieve optimal experimental results, selecting an FP based on specific characteristics─such as brightness, photostability, photochromicity, monomericity, pH robustness, and fluorescence lifetime─is essential. However, a thorough side-by-side comparison of these characteristics is missing for the latest generation of FPs. Here, we provide a comprehensive characterization in yeast of the most recently developed FPs, including FPs that were codon-optimized for yeast. We believe that this provides an excellent compendium for choosing the most suitable FP for research purposes.

Microbial communities carry out important ecological functions. Their activities emerge from interactions between species, often potentiated by metabolic traits. We lack a quantitative understanding of how these traits shape community properties. Here, we present theory for microbial communities, leveraging concepts from quantitative microbial physiology. We focus on how steady-state metabolic exchanges between species determine their fractional abundances, given their biomass and byproduct yields on nutrients. We start by deriving formal conditions for the steady states of communities of microbes that grow, die, and cross-feed metabolites. We describe the metabolic stoichiometry of nutrient uptake and the formation of biomass and byproducts for each species in terms of charge- and chemical-element balanced reactions (macrochemical reactions). Byproducts function as nutrients for other species. Next, we express the relative abundances of species (living and dead), the net metabolic conversion of a community, and the biomass carrying capacity in terms of the metabolic stoichiometry, growth rates and death rates of the species. We show how niche creation can emerge from stoichiometric imbalances in cross-feeding communities. Finally, we discuss how relative species abundances depend on the Adenosine Triphosphate (ATP) stoichiometries of intracellular metabolism.

To drastically reduce the carbon footprint of the food production chain, a major shift towards alternatives to conventional meat and dairy products is required. The use of plant-based proteins is a promising route, but it also comes with challenges: Plant-based proteins often contain antinutritional factors and off-flavors, which can negatively impact consumer acceptance. Fermentation is broadly used to improve the quality of these products. However, how these unwanted molecules are synthesized and degraded is poorly understood, but this knowledge is essential for fermentation-based strategies to improve the sensory and nutritional value of plant-based products. This review provides a comprehensive overview of synthesis and degradation pathways of key antinutritional factors and off-flavor compounds in plant-based substrates, including aldehydes, furans, sulfur compounds, pyrazines, glycoalkaloids (GAs), pyrimidine glycosides, polyphenols, saponins, glucosinolates (GSLs), phytic acid (PA), oxalates, lectins, and protease and amylase inhibitors. With this we identified the research gaps in the field, which can be divided into three types: (i) degradation pathways that are unknown (furans, alkyl-methoxypyrazines, and dimethyl trisulfide), (ii) well-characterized pathways but typically not found in food-grade organisms (dimethyl sulfide, dimethyl disulfide, and isothiocyanates derived from GSLs), and (iii) pathways that are only described partially (GAs, saponins, polyphenols, PA, and pyrimidine glycosides). Other molecule classes, like aldehydes, alcohols, and oxalate, have well-characterized degradation pathways in food-grade organisms. Focusing future research on compounds with poorly understood degradation pathways will help to accelerate the development of more rationally designed cultures for producing healthy and sustainable plant-based foods.

Glycolysis is a fundamental metabolic pathway central to the bioenergetics and physiology of virtually all living organisms. In this comprehensive review, we explore the intricate biochemical principles and evolutionary origins of glycolytic pathways, from the classical Embden-Meyerhof-Parnas (EMP) pathway in humans to various prokaryotic and alternative glycolytic routes. By examining glycolysis across the tree of life, we explore its presence and adaptation in prokaryotes, archaea, bacteria, animals and plants, and the extension of glycolysis into sulfosugar metabolism. Further, we discuss the role of unwanted side reactions, thermodynamic principles, and metabolic control principles that underpin glycolysis and the broader metabolic network, and summarise advanced methods for quantifying glycolytic activity, including new analytical methods, alongside kinetic, constraint-based, and machine-learning based modelling. With a focus on the Pasteur, Crabtree, and Warburg effects, this review further discusses the roles of glycolysis in health and disease, highlighting its impact on global metabolic operations, inborn errors, and various pathologies as well as its role in biotechnology and metabolic engineering.

Cells regulate metabolic fluxes by changing enzyme levels according to their needs, but how do they regulate something that cannot be measured directly? In this issue of Nature Metabolism, Palme et al. address the fundamental question of how metabolic flux sensing is coupled to gene expression.

Genome-scale metabolic models (GSMMs) can mechanistically explain phenotypic differences among closely related bacterial strains. However, high-throughput multi-strain reconstructions of GSMMs are still challenging: reference-based methods inherit curated information while missing new contents; alternatively (universe-based), reference-free methods could cover strain-specific reactions, but they disregard curated information. Ideally, references should be curated pan-GSMMs for species (or genus), but their reconstruction is extremely demanding, making them still rare in the literature. Here, Gempipe is presented, a computational tool streamlining the multi-strain reconstruction and analysis of GSMMs, going through the production of a pan-GSMM. Its reconstruction method is hybrid; as an optional reference, GSMM is automatically expanded with extra reactions taken from a reference-free reconstruction. Gempipe also downloads, filters, and annotates genomes; performs in-depth gene recovery; annotates models' contents; and predicts strain-specific capabilities. The companion programming interface includes functions ranging from the (pan-)GSMMs' curation to the multi-strain analysis. Gempipe was validated using multi-strain data sets, showing improved accuracy when compared with state-of-the-art tools. Moreover, metabolic diversities within Limosilactobacillus reuteri were explored, grouping strains into metabolically coherent clusters and systematically predicting health-related metabolites' biosynthesis.IMPORTANCEAvailable genome-scale metabolic model (GSMM) reconstruction tools present major limitations in the context of multi-strain modeling. Gempipe surpasses these limitations by implementing a novel, hybrid reconstruction strategy. Not only does it produce more accurate strain-specific GSMMs, but it also produces pan-GSMMs when the only available reference is a manually curated model for a single strain, which is currently the most common case. With the vast availability of genome sequences, the high-throughput, multi-strain GSMM reconstruction and analysis approach provided by Gempipe will facilitate large-scale studies of exploration and bioprospecting of strain-level bacterial metabolic diversity, moving a step forward in strains' screening and rational selection.

The understanding of cellular behavior relies on the integration of metabolism and its regulation. Multi-omics data provide a detailed snapshot of the molecular processes underpinning cellular functions and their regulation, describing the current state of the cell. While Machine Learning (ML) models can uncover complex patterns and relationships within these data, they require large datasets for training and often lack interpretability. On the other hand, mathematical models, such as Genome-Scale Metabolic Models (GEMs), offer a structured framework for analyzing the organization and dynamics of specific cellular mechanisms. At the same time, they don't allow for seamless integration of omics information. Recently, a new framework to embed GEMs in a neural network has been introduced: these hybrid models combine the strengths of mechanistic and data-driven approaches, offering a promising platform for integrating different data sources with mechanistic knowledge. In this study, we present a Metabolic-Informed Neural Network (MINN) that utilizes multi-omics data to predict metabolic fluxes in Escherichia coli, under different growth rates and gene knockouts. We test its performances against pure ML and parsimonious Flux Balance Analysis (pFBA), demonstrating its efficacy in improving prediction performances. We also highlight how conflicts can emerge between the data-driven and the mechanistic objectives, and we propose different solutions to mitigate them. Finally, we illustrate a strategy to couple the MINN with pFBA, enhancing the interpretability of the solution.

Protein allostery, present in all three domains of life, is key to the regulation of metabolism by allowing fast and precise control of catalysis in response to cellular demands. While metabolic pathways are frequently equipped with multiple allosterically regulated catalytic steps, experimental studies often focus on a single step, failing to capture how regulations exerted at multiple steps interact with each other for tuning pathways. Using the nearly ubiquitous Embden-Meyerhof-Parnas pathway of glycolysis as a paradigm, the present study unveils a remarkable regulatory synergy between multiple allosteric proteins of a metabolic pathway and demonstrates its impact on cell survival in dynamic environments. By using complete pathway complementation, as well as single-gene complementation, the essential regulatory steps were identified to be glucokinase, phosphofructokinase, and pyruvate kinase. Expression of these enzymes together, even in the context of the Saccharomyces cerevisiae pathway, led to imbalances in glycolysis that could only be overcome by lowering the glucokinase activity. Integrating these results with kinetic modeling and microfluidics experiments, the present work reveals the key synergistic role played by allosteric regulations in preventing glycolytic imbalance in the model eukaryote Saccharomyces cerevisiae and highlights the power of synthetic biology in addressing long-standing questions in systems biology.IMPORTANCEAll forms of life are equipped with intricate molecular mechanisms that tune their cellular responses to external and internal cues. These mechanisms are key to cells' survival in natural environments and important for the performance of bioprocesses, which are characterized by variable environments (e.g., nutrient availability). One of these molecular mechanisms, protein allostery, enables rapid fine-tuning of the rate of cellular processes by modulating protein activity in response to metabolites in vivo. Using the industrial yeast and model eukaryote Saccharomyces cerevisiae as a paradigm, the present work reveals that, in the major route for sugar utilization known as glycolysis, three distinct allosteric regulations are critical to yeast cell survival when transitioning between carbon sources. These three regulations, while not required for pathway operation per se, allow efficient and balanced pathway operation under dynamic conditions. These findings, therefore, reveal a new aspect of yeast glycolysis, one of the best-studied metabolic pathways.

Microbial communities carry out important ecological functions. Their activities emerge from complex interactions between species, often potentiated by metabolic traits. We lack a quantitative understanding of how these traits shape community properties. Here, we present the theory for microbial communities, leveraging concepts from quantitative microbial physiology. We derive formal conditions for the steady states of microbial communities. We express the relative abundances of species (living and dead), the net metabolic conversion of a community, and the biomass carrying capacity in terms of the metabolic stoichiometry of the species and their growth and death rates. We show how niche creation can emerge from stoichiometric imbalances in cross-feeding communities. Finally, we discuss how relative species abundances depend on the ATP stoichiometries of intracellular metabolism. ### Competing Interest Statement The authors have declared no competing interest.

Wild-type cells allocate their limited resources to express proteins that support growth and survival in their natural environments. In contrast, biotechnology aims to maximize key performance indicators such as yield, productivity, or titer. Maximizing performance indicators, however, inevitably encounters physical, biochemical, genetic, and evolutionary constraints that create trade-offs between competing objectives. A central challenge in microbial biotechnology is therefore to align cellular behavior with production goals, which can be achieved by manipulating cultivation conditions and intracellular resource allocation strategies through targeted metabolic engineering or adaptive laboratory evolution. Resource allocation models provide a theoretical framework to understand and guide such optimization efforts. Here, we review the current state of resource allocation modeling, including tools, methods, and theoretical foundations, and discuss their current applications in microbial biotechnology.

Using genome-wide metabolic models and Flux Balance Analysis, we are devising an Escherichia coli strain to produce L-threonine through previously undescribed routes. In silico simulations and preliminary in vivo experiments show that diverting carbon flux away from serine metabolism may be beneficial to enhance threonine production. This system may be further explored not only to produce threonine itself, but also to develop an Escherichia coli platform capable of producing high value-added products which require threonine as a starting substrate. In order to do this we took advantage of the increased threonine production to sustain the production of the high value-added chemical 2,5-dimethylpyrazine (DMP) from glucose. ### Competing Interest Statement The authors have declared no competing interest.

For unicellular organisms, the reproduction rate and growth are crucial fitness determinants and functional manifestations of the organism genotype. Using the budding yeast Saccharomyces cerevisiae as a model organism, we integrated metabolism, which provides energy and building blocks for growth, with cell mass growth and cell cycle progression into a low-granularity, multiscale (from cell to population) computational model. This model predicted that cells with constitutive respiration do not modulate cell size according to the growth conditions. We experimentally validated the model predictions using mutants with defects in the upper part of glycolysis or glucose transport. Plugging in molecular details of cellular subsystems allowed us to refine predictions from the cellular to the molecular level. Our hybrid multiscale modeling approach provides a framework for structuring molecular knowledge and predicting cell phenotypes under various genetic and environmental conditions.

Many arthropods, including insects, harbor endosymbiotic bacteria. As bacteria transitioned to live inside host cells, free-living bacterial ancestors of endosymbionts reduced their genomes in a process known as streamlining. Contemporary endosymbioses represent at least two distinct life strategies: endosymbionts either become unilaterally dependent on host-derived resources (uni-obligate) or enter bidirectionally obligate mutualistic relationships (bi-obligate endosymbiosis). It is an open question whether these paths taken by free-living bacteria can be sequential, or alternatively, if commitment takes place early, making these strategies mutually exclusive. In this study, we quantified metabolic capacity of different endosymbionts to contextualize genome streamlining in terms of organismal function. We show that uni-obligate endosymbionts of insects have lost substantial parts of their metabolic capacity that are present both in free-living bacteria and bi-obligate endosymbionts. On the contrary, genomes of uni-obligate endosymbionts contain more genes which can be attributed to scavenging host resources. In sum, we challenge the existing notion that bi-obligate endosymbionts have emerged from uni-obligate ones, and rather, propose a stochastic ratchet model: in early stages of endosymbiosis, bacteria get locked into either a more mutualistic or more parasitic relationship with the host depending on the first losses during streamlining. ### Competing Interest Statement The authors have declared no competing interest. * GEM : genome-scale metabolic model ORF : open reading frame str : strain

Lactate dehydrogenase (LDH) from Schistosoma mansoni has peculiar properties for a eukaryotic LDH. Schistosomal LDH (SmLDH) isolated from schistosomes, and the recombinantly expressed protein, are strongly inhibited by ATP, which is neutralized by fructose-1,6-bisphosphate (FBP). In the conserved FBP/anion binding site we identified two residues in SmLDH (Val187 and Tyr190) that differ from the conserved residues in LDHs of other eukaryotes, but are identical to conserved residues in FBP-sensitive prokaryotic LDHs. Three-dimensional (3D) models were generated to compare the structure of SmLDH with other LDHs. These models indicated that residues Val187, and especially Tyr190, play a crucial role in the interaction of FBP with the anion pocket of SmLDH. These 3D models of SmLDH are also consistent with a competitive model of SmLDH inhibition in which ATP (inhibitor) and FBP (activator) compete for binding in a well-defined anion pocket. The model of bound ATP predicts a distortion of the nearby key catalytic residue His195, resulting in enzyme inhibition. To investigate a possible physiological role of this allosteric regulation of LDH in schistosomes we made a kinetic model in which the allosteric regulation of the glycolytic enzymes can be varied. The model showed that inhibition of LDH by ATP prevents fermentation to lactate in the free-living stages in water and ensures complete oxidation via the Krebs cycle of the endogenous glycogen reserves. This mechanism of allosteric inhibition by ATP prevents the untimely depletion of these glycogen reserves, the only fuel of the free-living cercariae. Neutralization by FBP of this ATP inhibition of LDH prevents accumulation of glycolytic intermediates when S. mansoni schistosomula are confronted with the sudden large increase in glucose availability upon penetration of the final host. It appears that the LDH of S. mansoni is special and well suited to deal with the variations in glucose availability the parasite encounters during its life cycle.

S. cerevisiae (or budding yeast) is an important micro-organism for sucrose-based fermentation in biotechnology. Yet, it is largely unknown how budding yeast adapts to sucrose transitions. Sucrose can only be metabolized when the invertase or the maltose machinery are expressed and we propose that the Gpr1p receptor signals extracellular sucrose availability via the cAMP peak to adapt cells accordingly. A transition to sucrose or glucose gave a transient cAMP peak which was maximally induced for sucrose. When transitioned to sucrose, cAMP signalling mutants showed an impaired cAMP peak together with a lower growth rate, a longer lag phase and a higher final OD600 compared to a glucose transition. These effects were not caused by altered activity or expression of enzymes involved in sucrose metabolism and imply a more general metabolic adaptation defect. Basal cAMP levels were comparable among the mutant strains, suggesting that the transient cAMP peak is required to adapt cells correctly to sucrose. We propose that the short-term dynamics of the cAMP signalling cascade detects long-term extracellular sucrose availability and speculate that its function is to maintain a fermentative phenotype at continuously low glucose and fructose concentrations.

The expression of metabolic proteins is controlled by genetic circuits, matching metabolic demands and changing environmental conditions. Ideally, this regulation brings about a competitive level of metabolic fitness. Understanding how cells can achieve a robust (close-to-optimal) functioning of metabolism by appropriate control of gene expression aids synthetic biology by providing design criteria of synthetic circuits for biotechnological purposes. It also extends our understanding of the designs of genetic circuitry found in nature such as metabolite control of transcription factor activity, promoter architectures and transcription factor dependencies, and operon composition (in bacteria). Here, we review, explain and illustrate an approach that allows for the inference and design of genetic circuitry that steers metabolic networks to achieve a maximal flux per unit invested protein across dynamic conditions. We discuss how this approach and its understanding can be used to rationalize Escherichia coli’s strategy to regulate the expression of its ribosomes and infer the design of circuitry controlling gene expression of amino-acid biosynthesis enzymes. The inferred regulation indeed resembles E. coli’s circuits, suggesting that these have evolved to maximize amino-acid production fluxes per unit invested protein. We end by an outlook of the use of this approach in metabolic engineering applications.

Under excess glucose conditions, many yeasts switch from high-yield respiratory metabolism to low-yield fermentation, a phenomenon called the Crabtree effect in yeast, or the Warburg effect in mammalian cells. Cellular constraints and limited resources are generally believed to govern the metabolic strategies of cells to adapt to environmental conditions, but which constraints drive this switch is still under debate. Here we study the Crabtree-negative, fully respiratory yeast Pichia kluyveri and compare it to the Crabtree-positive yeast Saccharomyces cerevisiae from a resource allocation perspective. By integrating quantitative physiology and proteomics into whole-cell proteome-constrained models, we find that the Crabtree effect is determined by the composition and catalytic efficiency of the electron transport chain. We find that the subsequent proteome efficiency of respiration versus fermentation varies between these species. The variation in parameters and composition of the respiratory machinery likely reflects the evolutionary and ecological history of these yeast species. This study advances our understanding of the role of proteome constraints and proteome efficiency in governing cellular metabolism of yeasts, and that of eukaryotic cells at large. ### Competing Interest Statement The authors have declared no competing interest.

Since its conception some fifty years ago, metabolic control analysis (MCA) aims to understand how cells control their metabolism by adjusting the activity of their enzymes. Here we extend its scope to a whole-cell context. We consider metabolism in the evolutionary context of growth-rate maximisation by optimisation of protein concentrations. This framework allows for the prediction of flux control coefficients from proteomics data or stoichiometric modelling. Since genes compete for finite biosynthetic resources, we treat all protein concentrations as interdependent. We show that elementary flux modes (EFMs) emerge naturally as the optimal metabolic networks in the whole-cell context and we derive their control properties. In the evolutionary optimum, the number of expressed EFMs is determined by the number of protein-concentration constraints that limit growth rate. We use published glucose-limited chemostat data of S. cerevisiae to illustrate that it uses only two EFMs prior to the onset of fermentation and that it uses four EFMs during fermentation. We discuss published enzyme-titration data to show that S. cerevisiae and E. coli indeed can express proteins at growth-rate maximising concentrations. Accordingly, we extend MCA to elementary flux modes operating at an optimal state. We find that the expression of growth-unassociated proteins changes results from classical metabolic control analysis. Finally, we show how flux control coefficients can be estimated from proteomics and ribosome-profiling data. We analyse published proteomics data of E. coli to provide a whole-cell perspective of the control of metabolic enzymes on growth rate. We hope that this paper stimulates a renewed interest in metabolic control analysis, so that it can serve again the purpose it once had: to identify general principles that emerge from the biochemistry of the cell and are conserved across biological species.

Microbial growth requires energy for maintaining the existing cells and producing components for the new ones. Microbes therefore invest a considerable amount of their resources into proteins needed for energy harvesting. Growth in different environments is associated with different energy demands for growth of yeast Saccharomyces cerevisiae , although the cross-condition differences remain poorly characterized. Furthermore, a direct comparison of the energy costs for the biosynthesis of the new biomass across conditions is not feasible experimentally; computational models, on the contrary, allow comparing the optimal metabolic strategies and quantify the respective costs of energy and nutrients. Thus in this study, we used a resource allocation model of S. cerevisiae to compare the optimal metabolic strategies between different conditions. We found that S. cerevisiae with respiratory-impaired mitochondria required additional energetic investments for growth, while growth on amino acid-rich media was not affected. Amino acid supplementation in anaerobic conditions also was predicted to rescue the growth reduction in mitochondrial respiratory shuttle-deficient mutants of S. cerevisiae . Collectively, these results point to elevated costs of resolving the redox imbalance caused by de novo biosynthesis of amino acids in mitochondria. To sum up, our study provides an example of how resource allocation modeling can be used to address and suggest explanations to open questions in microbial physiology.