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Teusink, B.

Publications and source records attributed to Teusink, B..

At least 19 recordsLinked to original sources

Glutamine-derived NADH fuels lactate production to maximize ATP yield under oxygen consumption constraints in head and neck cancer

Metabolic reprogramming is one of the hallmarks of cancer. A well-known change is the Warburg effect: glucose is consumed at increased rates and fermented to lactate instead of being respired to carbon dioxide, despite the presence of sufficient oxygen (O2). We show that head and neck squamous cell carcinoma (HNSCC) cells excrete more carbon in the form of lactate than is consumed as glucose, indicating the presence of additional carbon sources for lactate. Amino acid uptake and [U-13C]-glutamine labelling data show that glutamine is highly consumed and oxidized in the tricarboxylic acid (TCA) cycle, but remarkably does not contribute to lactate carbon. To explain these observations, we generated a cell-line specific metabolic model to understand the link between lactate production and glutamine metabolism. In the model, we show how low cellular O2 consumption capacity can cause increased lactate production with glutamine as NADH source. The model suggests that ATP production under limited O2 consumption is augmented by increased glutamine oxidation which yields ATP, FADH2 and NADH. FADH2 can only be respired, despite yielding less ATP than NADH respiration. NADH can be exported from the mitochondrion to be reoxidized by lactate dehydrogenase, permitting more cycles of glutamine oxidation, thereby increasing the total ATP yield. By experimentally inhibiting mitochondrial respiration, we confirmed that non-glucose carbon sources are used for lactate production under decreased O2 consumption capacity. This unexpected feature of O2-constrained ATP production provides a credible mechanism for the increased lactate production via a novel redox cycle in HNSCC cells.

systems biology↗

Stoichiometric analysis of microbial communities: interrelating community function, structure and biomass carrying capacity

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.

ecology↗

Gempipe: a tool for drafting, curating and analyzing pan and multi-strain genome-scale metabolic models.

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 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; 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 datasets, 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 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 it produces more accurate strain-specific GSMMs, but also 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 the exploration and bioprospecting of strain-level bacterial metabolic diversity, moving a step forward in strains screening and rational selection.

microbiology↗

Side-by-side systematic characterization of novel FPs in budding yeast

Fluorescent proteins (FPs) have become indispensable for biological research. Since the discovery of the first FP, Aequorea victoria green fluorescent protein (avGFP), new fluorescent proteins are 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 this provides an excellent compendium for choosing the most suitable FP for research purposes.

molecular biology↗

MINN: A METABOLIC-INFORMED NEURAL NETWORK FOR INTEGRATING OMICS DATA INTO GENOME-SCALE METABOLIC MODELING

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 dont 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.

systems biology↗

A stochastic ratchet during genome streamlining can commit insect endosymbionts to a parasitic or mutualistic fate

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.

systems biology↗

An mTurquoise2-based glucose biosensor

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 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 calibrated the sensor in vivo through equilibration of internal and external glucose in a yeast mutant unable to phosphorylate glucose. Using this method, we measured dynamic glucose levels in budding yeast during transitions to glucose. We found that glucose concentrations reached levels up to 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.

biochemistry↗

Mitochondrial efficiency determines Crabtree effect across yeasts

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.

systems biology↗

cAMP in budding yeast: a messenger for sucrose metabolism?

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 yield 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.

microbiology↗

A modular model integrating metabolism, growth, and cell cycle predicts that fermentation is required to modulate cell size in yeast populations

For unicellular organisms, the reproduction rate and growth are crucial determinants of fitness and, therefore, essential 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.

systems biology↗

A fast method to distinguish between fermentative and respiratory metabolisms in single yeast cells

Saccharomyces cerevisiae adapts its metabolism according to nutrient availability. Typically, it rapidly ferments glucose to ethanol, and then shifts to respiration when glucose becomes limited. However, our understanding of the regulation of metabolism is largely based on population averages, whereas nutrient transitions may cause heterogeneous responses at the individual cell level. Although protein expression can be followed at the single-cell level as a proxy for metabolic modes, direct assessment of the contribution of respiration or (respiro)fermentation to energy metabolism is lacking. Here we describe a method to quickly differentiate between fermentative and respiratory metabolisms in individual cells of budding yeast. The method explores the use of the fluorescent FRET-based biosensor yAT1.03 to measure cytosolic ATP, coupled with the respiratory inhibitor Antimycin A. For the method validation, we used cells under fermentative and respiratory regimes from batch and chemostat cultures. Upon Antimycin A addition, we observed a sharp decrease of the normalized FRET ratio for respiratory cells; respirofermentative cells showed no response. Next, we tracked the changes in metabolism during the diauxic shift of a glucose pre-grown batch culture. Following glucose exhaustion, the entire cell population experienced a progressive rise in intracellular ATP produced via respiration, suggesting a uniform and gradual increase in respiratory capacity as cells pick up growth in a medium with ethanol as the sole carbon source. Overall, the combination of yAT1.03 with Antimycin A is a robust tool to distinguish fermentative from respiratory yeast cells, offering a new single-cell opportunity to study yeast metabolism. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/546324v1_figs1.gif" ALT="Figure 1"> View larger version (37K): org.highwire.dtl.DTLVardef@1db7087org.highwire.dtl.DTLVardef@1b8b839org.highwire.dtl.DTLVardef@46d641org.highwire.dtl.DTLVardef@9c4aac_HPS_FORMAT_FIGEXP M_FIG Identification of fermentative and respiratory metabolisms in yeast cells using an ATP sensor coupled with a respiration inhibitor. (a) yAT1.03 consists of a donor (tdTomato) and an acceptor (ymTq2{Delta}11) domain linked by a binding domain with affinity to ATP. When ATP binds to the binding domain, donor and acceptor come together and the Forster energy is transferred from the first to the second domain. When expressed in in vivo cells the sensor allows real time measurements of ATP changes. (b) Depending on the growth conditions, yeast cells expressing yAT1.03 show a distinct response after being pulsed with the respiratory inhibitor Antimycin A (AA). The drop in ATP levels in respiratory cells caused by AA results from the inhibition of the mitochondrial electron transport chain. (c) Distinct metabolic responses to an AA pulse pre-, during and post-diauxic shift reveal distinct metabolic phenotypes. C_FIG

systems biology↗

standard-GEM: standardization of open-source genome-scale metabolic models

The field of metabolic modelling at the genomescale continues to grow with more models being created and curated. This comes with an increasing demand for adopting common principles regarding transparency and versioning, in addition to standardisation efforts regarding file formats, annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR principles (findability, accessibility, interoperability, and reusability), and incorporating bestpractices. standard-GEM facilitates the reuse of GEMs across web services and platforms in the metabolic modelling field and enables automatic validation of GEMs. The use of this template for new models, and its adoption for existing ones, paves the way for increasing model quality, openness, and accessibility with minimal effort. Availabilitystandard-GEM is available from github.com/MetabolicAtlas/standard-GEM under the conditions of the CC BY 4.0 licence along with additional supporting material.

systems biology↗

Elevated energy costs of biomass production in mitochondrial-respiration deficient Saccharomyces cerevisiae

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.

systems biology↗

Using the AKAR3-EV biosensor to assess Sch9- & PKA-signalling in budding yeast

Budding yeast uses the well-conserved TORC1-Sch9 and cAMP-PKA signalling pathways to regulate adaptations to changing nutrient environments. Dynamic and single-cell measurements of the activity of these two cascades will improve our understanding of cellular adaptation of yeast. Here, we employed the AKAR3-EV biosensor developed for mammalian cells to measure the cellular phosphorylation status determined by Sch9 and PKA activity in budding yeast. Using various mutant strains and inhibitors, we show that AKAR3-EV robustly measures the Sch9- and PKA-dependent phosphorylation status in intact yeast cells. At the single-cell level, we found that the phosphorylation responses are homogenous for glucose, sucrose and fructose, but highly heterogeneous for mannose. The Sch9 and PKA pathways have a relatively high affinity for glucose (K0.5 of 0.24 mM) under glucose derepressed conditions. Lastly, steady-state FRET levels of AKAR3-EV seem to be independent of growth rates, suggesting that the Sch9- and PKA-dependent phosphorylation activity are transient responses to nutrient transitions. We believe that the AKAR3-EV sensor is an excellent addition to the biosensor arsenal for illuminating cellular adaptation in single yeast cells.

cell biology↗

Large excess capacity of glycolytic enzymes in Saccharomyces cerevisiae under glucose-limited conditions

In Nature, microbes live in very nutrient-dynamic environments. Rapid scavenging and consumption of newly introduced nutrients therefore offer a way to outcompete competitors. This may explain the observation that many microorganisms, including the budding yeast Saccharomyces cerevisiae, appear to keep "excess" glycolytic proteins at low growth rates, i.e. the maximal capacity of glycolytic enzymes (largely) exceeds the actual flux through the enzymes. However, such a strategy requires investment into preparatory protein expression that may come at the cost of current fitness. Moreover, at low nutrient levels, enzymes cannot operate at high saturation, and overcapacity is poorly defined without taking enzyme kinetics into account. Here we use computational modeling to suggest that in yeast the overcapacity of the glycolytic enzymes at low specific growth rates is a genuine excess, rather than the optimal enzyme demand dictated by enzyme kinetics. We found that the observed expression of the glycolytic enzymes did match the predicted optimal expression when S. cerevisiae exhibits mixed respiro-fermentative growth, while the expression of tricarboxylic acid cycle enzymes always follows the demand. Moreover, we compared the predicted metabolite concentrations with the experimental measurements and found the best agreement in glucose-excess conditions. We argue that the excess capacity of glycolytic proteins in glucose-scarce conditions is an adaptation of S. cerevisiae to fluctuations of nutrient availability in the environment.

systems biology↗

Glycolysis revisited: from steady state growth to glucose pulses

Kinetic metabolic models of central metabolism have been proposed to understand how Saccharomyces cerevisiae navigates through nutrient perturbations. Yet, these models lacked important variables that constrain metabolism under relevant physiological conditions and thus have limited operational use such as in optimization of industrial fermentations. In this work, we developed a physiologically informed kinetic model of yeast glycolysis connected to central carbon metabolism by including the effect of anabolic reactions precursors, mitochondria and the trehalose cycle. A parameter estimation pipeline was developed, consisting of a divide and conquer approach, supplemented with regularization and global optimization. We show how this first mechanistic description of a growing yeast cell captures experimental dynamics at different growth rates and under a strong glucose perturbation, is robust to parametric uncertainty and explains the contribution of the different pathways in the network. Our work suggests that by combining multiple types of data and computational methods, complex but physiologically representative and robust models can be achieved.

systems biology↗

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

The fission yeast Schizosaccharomyces pombe is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the bakers yeast Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually-curated high-quality reconstruction of S. pombes metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes, and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these use cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behaviour and the role of resource allocation in driving different strategies in fission yeast.

systems biology↗

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here we developed a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions (16304 reactions in total). The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data revealed that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation - known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providing a framework to understand metabolic adaptation in eukaryal cells.

systems biology↗