Biology subjects

Teusink, B.

Publications and source records attributed to Teusink, B..

36 records · Page 2Linked to original sources

bioRxiv2021

Selection for cell yield does not reduce overflow metabolism in E. coli

Overflow metabolism is ubiquitous in nature, and it is often considered inefficient because it leads to a relatively low biomass yield per consumed carbon. This metabolic strategy has been described as advantageous because it supports high growth rates during nutrient competition. Here we experimentally evolved bacteria without nutrient competition by repeatedly growing and mixing millions of parallel batch cultures of E. coli. Each culture originated from a water-in-oil emulsion droplet seeded with a single cell. Unexpectedly we found that overflow metabolism (acetate production) did not change. Instead the numerical cell yield during the consumption of the accumulated acetate increased as a consequence of a reduction in cell size. Our experiments and a mathematical model show that fast growth and overflow metabolism followed by the consumption of the overflow metabolite, leads to a higher numerical cell yield and therefore a higher fitness compared to full respiration of the substrate. This provides an evolutionary scenario where overflow metabolism can be favourable even in the absence of nutrient competition.

microbiology↗

bioRxiv2021

A multi-phase multi-objective dynamic genome-scale model shows different redox balancing among yeast species in fermentation

Yeasts constitute over 1500 species with great potential for biotechnology. Still, the yeast Saccharomyces cerevisiae dominates industrial applications and many alternative physiological capabilities of lesser-known yeasts are not being fully exploited. While comparative genomics receives substantial attention, little is known about yeasts metabolic specificity in batch cultures. Here we propose a multi-phase multi-objective dynamic genome-scale model of yeast batch cultures that describes the uptake of carbon and nitrogen sources and the production of primary and secondary metabolites. The model integrates a specific metabolic reconstruction, based on the consensus Yeast8, and a kinetic model describing the time-varying culture environment. Besides, we proposed a multi-phase multi-objective flux balance analysis to compute the dynamics of intracellular fluxes. We then compared the metabolism of S. cerevisiae and S. uvarum strains in a rich medium fermentation. The model successfully explained the experimental data and brought novel insights into how cryotolerant strains achieve redox balance. The proposed model (along with the corresponding code) provides a comprehensive picture of the main steps occurring inside the cell during batch cultures and offers a systematic approach to prospect or metabolically engineering novel yeast cell factories. IMPORTANCENon-conventional yeast species hold the promise to provide novel metabolic routes to produce industrially relevant compounds and tolerate specific stressors, such as cold temperatures. This work presented and validated the first multi-phase multi-objective genome-scale dynamic model to describe carbon and nitrogen metabolism throughout batch fermentation. To test and illustrate its performance, we considered the comparative metabolism of three yeast strains of the Saccharomyces genus in rich medium fermentation. The study revealed that cryotolerant Saccharomyces species might use the GABA shunt and the production of reducing equivalents as alternative routes to achieve redox balance, a novel biological insight worth being explored further. The proposed model (along with the provided code) can be applied to a wide range of batch processes started with different yeast species and media, offering a systematic and rational approach to prospect non-conventional yeast species metabolism and engineering novel cell factories.

systems biology↗

bioRxiv2021

A multi-phase multi-objective genome-scale model shows diverse redox balance strategies in yeasts

systems biology↗

bioRxiv2020

Proteome constraints reveal targets for improving microbial fitness in nutrient-rich environments

Cells adapt to different conditions via gene expression that tunes metabolism and stress resistance for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade-offs1; Resource allocation under proteome constraints has emerged as a powerful paradigm to explain regulatory strategies in bacteria2. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient-rich conditions, i.e., multiple available nitrogen sources, such as the lactic acid bacterium Lactococcus lactis. Here we present an approach to identify preferred nutrients from integration of experimental data with a proteome-constrained genome-scale metabolic model of L. lactis (pcLactis), which explicitly accounts for gene expression processes and associated constraints. Using glucose-limited chemostat data3, we identified the uptake of glucose and arginine as dominant constraints, whose pathway proteins were indeed upregulated in evolved mutants. However, above a growth rate of 0.5 h-1, pcLactis suggests that available enzymes function at their maximum capacity, which allows an increase in growth rate only by altering gene expression to change metabolic fluxes, as was mainly observed for arginine metabolism. Thus, our integrative analysis of flux and proteomics data with a proteome-constrained model is able to identify and explain the constraints that form targets of regulation and fitness improvement in nutrient-rich growth environments.

systems biology↗

bioRxiv2020

Elementary Conversion Modes enable exploration of all metabolic capabilities of any organism

The metabolic capabilities of cells determine their biotechnological potential, fitness in ecosystems, pathogenic threat levels, and function in multicellular organisms. Their comprehensive experimental characterisation is generally not feasible, particularly for unculturable organisms. In principle, the full range of metabolic capabilities can be computed from an organisms annotated genome using metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods aim to enumerate all metabolic pathways, while computation of all (elementally balanced) conversions between nutrients and products would suffice. Indeed, the elementary conversion modes (ECMs, defined by Urbanczik and Wagner) capture the full metabolic capabilities of a network, but the use of ECMs has not been accessible, until now. We extend and explain the theory of ECMs, implement their enumeration in ecmtool, and illustrate their applicability. This work contributes to the elucidation of the full metabolic footprint of any cell.

systems biology↗

bioRxiv2020

Microbial competition reduces interaction distances to the low {micro}m-range

Metabolic interactions between cells affect microbial community compositions and hence their function in ecosystems. It is well-known that under competition for the exchanged metabolite, concentration gradients constrain the distances over which interactions can occur. However, interaction distances are typically quantified in two-dimensional systems or without accounting for competition or other metabolite-removal, conditions which may not very often match natural ecosystems. We here analyze the impact of cell-to-cell distance on unidirectional cross-feeding in a three-dimensional system with competition for the exchanged metabolite. Effective interaction distances were computed with a reaction-diffusion model and experimentally verified by growing a synthetic consortium of 1 {micro}m-sized metabolite producer, receiver and competitor cells in different spatial structures. We show that receivers cannot interact with producers [~]15 {micro}m away from them, as product concentration gradients flatten close to producer cells. We developed an aggregation protocol and created variants of the receiver cells import system, to show that within producer-receiver aggregates even low affinity receiver cells could interact with producers. These results show that competition or other metabolite-removal of a public good in a three-dimensional system reduces the interaction distance to the low micrometer-range, highlighting the importance of concentration gradients as physical constraint for cellular interactions.

microbiology↗

bioRxiv2019

An improved ATP FRET sensor for yeast shows heterogeneity during nutrient transitions

Adenosine 5-triphosphate (ATP) is the main free energy carrier in metabolism. In budding yeast, shifts to glucose-rich conditions cause dynamic changes in ATP levels, but it is unclear how heterogeneous these dynamics are at the single-cell level. Furthermore, pH also changes and affects readout of fluorescence-based biosensors for single-cell measurements. To measure ATP changes reliably in single yeast cells, we developed yAT1.03, an adapted version of the AT1.03 ATP biosensor, that is pH-insensitive. We show that pregrowth conditions largely affect ATP dynamics during transitions. Moreover, single-cell analyses showed a large variety in ATP responses, which implies large differences of glycolytic startup between individual cells. We found three clusters of dynamic responses, and show that a small subpopulation of wild type cells reached an imbalanced state during glycolytic startup, characterised by low ATP levels. These results confirm the need for new tools to study dynamic responses of individual cells in dynamic environments.

molecular biology↗

bioRxiv2019

A new FRET biosensor enlightens cAMP signalling in budding yeast

The cAMP-PKA signalling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions. We found that the characteristic cAMP peak differentiates between different carbon source transitions, and is rather homogenous among single-cells, especially for transitions to glucose. The peaks are mediated by a combination of extracellular sensing and intracellular metabolism. Moreover, the cAMP peak follows Webers law; its height scales with the relative, and not the absolute, change in glucose. Lastly, our results suggest that the cAMP peak height conveys information about prospective growth rates. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signalling and metabolic adaptation.

microbiology↗

bioRxiv2019

The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected in which environment remain unclear. One recurrent selection is overflow metabolism: the simultaneous usage of an ATP-efficient and -inefficient pathway, shown for example in Escherichia coli, Saccharomyces cerevisiae and cancer cells. Many models, based on different assumptions, can reproduce this observation. Therefore, they provide no conclusive evidence which mechanism is causing overflow metabolism. We compare the mathematical structure of these models. Although ranging from Flux Balance Analyses to self-fabricating Metabolism and Expression models, we can rewrite all models into one standard form. We conclude that all models predict overflow metabolism when two, model-specific, growth-limiting constraints are hit. This is consistent with recent theory. Thus, identifying these two constraints is essential for understanding overflow metabolism. We list all imposed constraints by these models, so that they can hopefully be tested in future experiments.

systems biology↗

bioRxiv2019

Elementary Growth Modes provide a molecular description of cellular self-fabrication

A major aim of biology is to predict phenotype from genotype. Here we ask if we can describe all possible molecular states (phenotypes) for a cell that fabricates itself at a constant rate, given its enzyme kinetics and the stoichiometry of all reactions (the genotype). For this, we must understand the autocatalytic process of cellular growth which is inherently nonlinear: steady-state self-fabrication requires a cell to synthesize all of its components, including metabolites, enzymes and ribosomes, in the proportions that exactly match its own composition - the growth demand thus depends on the cellular composition. Simultaneously, the concentrations of these components should be tuned to accomplish this synthesis task - the cellular composition thus depends on the growth demand. We here derive a theory that describes all phenotypes that solve this circular problem; the basic equations show how the concentrations of all cellular components and reaction rates must be balanced to get a constant self-fabrication rate. All phenotypes can be described as a combination of one or more minimal building blocks, which we call Elementary Growth Modes (EGMs). EGMs can be used as the theoretical basis for all models that explicitly model self-fabrication, such as the currently popular Metabolism and Expression models. We then used our theory to make concrete biological predictions: we find that natural selection for maximal growth rate drives microorganisms to states of minimal phenotypic complexity: only one EGM will be active when cellular growth rate is maximised. The phenotype of a cell is only extended with one more EGM whenever growth becomes limited by an additional biophysical constraint, such as a limited solvent capacity of a cellular compartment. Our theory starts from basic biochemical and evolutionary considerations, and describes unicellular life, both in growth-promoting and in stress-inducing environments, in terms of EGMs, the universal building blocks of self-fabrication and a cells phenotype.

systems biology↗

bioRxiv2019

A Systematic Assessment Of Current Genome-Scale Metabolic Reconstruction Tools

Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research. In this work, we performed a systematic assessment of the current genome-scale reconstruction software platforms. To meet our goal, we first defined a list of features for assessing software quality related to genome-scale reconstruction, which we expect to be useful for the potential users of these tools. Subsequently, we used the feature list to evaluate the performance of each tool. In order to assess the similarity of the draft reconstructions to high-quality models, we compared each tools output networks with that of the high-quality, manually curated, models of Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We showed that none of the tools outperforms the others in all the defined features and that model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. Author SummaryMetabolic networks that comprise biochemical reactions at genome-scale have become very useful to study and predict the phenotype of important microorganisms. Several software platforms exist to build these metabolic networks. Based on different approaches and utilizing a variety of databases it is, unfortunately, unclear what are the best scenarios to use each of these tools. Hence, to understand the potential uses of these tools, we created a list of relevant features for metabolic reconstruction and we evaluated the tools in all these categories. Here, we show that none of the tools is better than the other in all the evaluated categories; instead, each tool is more suitable for particular purposes. Therefore, users should carefully select the tool(s) that best fit the purpose of their research. This is the first time these tools are systematically evaluated and this overview can be used as a guide for selecting the correct tool(s) for each case.

systems biology↗

bioRxiv2019

Emergence of stable coexistence in a complex microbial community through metabolic cooperation and spatio-temporal niche partitioning

Microbial communities in nature often feature complex compositional dynamics yet also stable coexistence of diverse species. The mechanistic underpinnings of such dynamic stability remain unclear as system-wide studies have been limited to small engineered communities or synthetic assemblies. Here we show how kefir, a natural milk-fermenting community, realizes stable coexistence through spatio-temporal orchestration of species and metabolite dynamics. During milk fermentation, kefir grains (a polysaccharide matrix synthesized by kefir microbes) grow in mass but remain unchanged in composition. In contrast, the milk is colonized in a dynamic fashion with early members opening metabolic niches for the followers. Through large-scale mapping of metabolic preferences and inter-species interactions, we show how microbes poorly suited for milk survive in, and even dominate, the community through metabolic cooperation and uneven partitioning between the grain and the liquid phase. Overall, our findings reveal how spatio-temporal dynamics promote stable coexistence and have implications for deciphering and modulating complex microbial ecosystems.

microbiology↗

bioRxiv2018

In vivo characterisation of fluorescent proteins in budding yeast.

Fluorescent proteins (FPs) are widely used in many organisms, but are commonly characterised in vitro. However, the in vitro properties may poorly reflect in vivo performance. Therefore, we characterised 27 FPs in vivo using Saccharomyces cerevisiae as model organism. We linked the FPs via a T2A peptide to a control FP, producing equimolar expression of the 2 FPs from 1 plasmid. Using this strategy, we characterised the FPs for brightness, photostability, photochromicity and pH-sensitivity, achieving a comprehensive in vivo characterisation. Many FPs showed different in vivo properties compared to existing in vitro data. Additionally, various FPs were photochromic, which affects readouts due to complex bleaching kinetics. Finally, we codon optimized the best performing FPs for optimal expression in yeast, and found that codon-optimization alters FP characteristics. These FPs improve experimental signal readout, opening new experimental possibilities. Our results may guide future studies in yeast that employ fluorescent proteins.

molecular biology↗

bioRxiv2017

Low affinity membrane transporters can increase net substrate uptake rate by reducing efflux

Cells require membrane-located transporter proteins to import nutrients from the environment. Many organisms have several similar transporters for the same nutrient, which differ in their affinity. Typically, high affinity transporters are expressed when substrate is scarce and low affinity ones when substrate is more abundant. The benefit of using low affinity transporters when high affinity ones are available has so far remained unclear. Here, we investigate two hypotheses. First, it was previously hypothesized that a trade-off between the affinity and the maximal catalytic rate explains this phenomenon. We find some theoretical and experimental support for this hypothesis, but no conclusive evidence. Secondly, we propose a new hypothesis: for uptake by facilitated diffusion, at saturating extracellular substrate concentrations, lowering the affinity enhances the net uptake rate by reducing the substrate efflux rate. As a consequence, there exists an optimal, external substrate concentration dependent transporter affinity. An in silico analysis of glycolysis in Saccharomyces cerevisiae shows that using the low affinity HXT3 transporter instead of the high affinity HXT6 enhances the steady-state flux by 36%. We tried to test this hypothesis using yeast strains expressing a single glucose transporter that was modified to have either a high or a low affinity. Due to the intimate and reciprocal link between glucose perception and metabolism, direct experimental proof for this hypothesis remained inconclusive in our hands. Still, our theoretical results provide a novel reason for the presence of low affinity transport systems which might have more general implications for enzyme catalyzed conversions.

microbiology↗

bioRxiv2017

Maximal growth rate requires minimal metabolic complexity

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized -in a static environment under mass-conservation and enzyme expression constraints- we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.\n\nAuthor summaryThe microbial genome encodes for a large network of enzyme-catalyzed reactions. The reaction rates depend on concentrations of enzymes and metabolites, which in turn depend on those rates. Cells face a number of biophysical constraints on enzyme expression, for example due to a limited membrane area or cytosolic volume. Considering this complexity and nonlinearity of metabolism, how is it possible, that experimental data can often be described by simple linear models? We show that it is evolution itself that selects for simplicity. When reproductive rate is maximised, the number of active independent metabolic pathways is bounded by the number of growth-limiting enzyme constraints, which is typically small. A small number of pathways automatically generates the measured simple relations. We identify the importance of growth-limiting constraints in shaping microbial behaviour, by focussing on their mechanistic nature. We demonstrate that overflow metabolism - an important phenomenon in bacteria, yeasts, and cancer cells - is caused by two constraints on enzyme expression. We derive experimental guidelines for constraint identification in microorganisms. Knowing these constraints leads to increased understanding of metabolism, and thereby to better predictions and more effective manipulations.

systems biology↗

bioRxiv2017

Bifurcation analysis of metabolic pathways: an illustration from yeast glycolysis

In microorganisms such as bacteria or yeasts, metabolic rates are tightly coupled to growth rate, and therefore to fitness. Although the topology of central pathways are largely conserved across organisms, the enzyme kinetics and their parameters generally vary. This prevents us to understand and predict (changes in) metabolic dynamics. The analytical treatment of metabolic pathways is generally restricted to small models, containing maybe two to four equations. Since such small core models involve much coarse graining, their biological interpretation is often hampered. In this paper we aim to bridge the gap between analytical, more in-depth treatment of small core models and biologically more realistic and detailed models by developing new methods. We illustrate these methods for a model of glycolysis in Saccharo-myces cerevisiae yeast, arguably the best characterised metabolic pathway in the literature. The model is more involved than in previous studies, and involves both ATP/ADP and NADH/NAD householding.\n\nA detailed analysis of the steady state equations sheds new light on two recently studied biological phenomena in yeast glycolysis: whether it is to be expected that fructose-1,6-biphosphate (FBP) parameterises all steady states, and the occurrence of bistability between a regular steady state and imbalanced steady state in which glycolytic intermediates keep accumulating.\n\nThis work shows that the special structure of metabolic pathways does allow for more in-depth bifurcation analyses than is currently the norm. We especially emphasise which of the techniques developed here scale to larger pathways, and which do not.

systems biology↗

bioRxiv2017

Glucose limitation in Lactococcus shapes a single-peaked fitness landscape exposing membrane occupancy as a constraint

A central theme in biology is to understand the molecular basis of fitness: which strategies succeed under which conditions; how are they mechanistically implemented; and which constraints shape trade-offs between alternative strategies. We approached these questions with parallel bacterial evolution experiments in chemostats. Chemostats provide a constant environment with a defined resource limitation (glucose), in which the growth rate can be controlled. Using Lactococcus lactis, we found a single mutation in a global regulator of carbon metabolism, CcpA, to confer predictable fitness improvements across multiple growth rates. In silico protein structural analysis complemented with biochemical and phenotypic assays, show that the mutation reprograms the CcpA regulon, specifically targeting transporters. This supports that membrane occupancy, rather than biosynthetic capacity, is the dominant constraint for the observed fitness enhancement. It also demonstrates that cells can modulate a pleiotropic regulator to work around limiting constraints.

genomics↗

bioRxiv2017

Maintaining maximal metabolic flux by gene expression control

Many evolutionarily successful bacteria attain high growth rates across growth-permissive conditions. They express metabolic networks that synthesise all cellular components at a high rate. Metabolic reaction rates are bounded by the concentration of the catalysing enzymes and cells have finite resources available for enzyme synthesis. Therefore, bacteria that grow fast should express needed metabolic enzymes at precisely tuned concentrations. To maintain fast growth in a dynamic environment, cells should adjust gene expression of metabolic enzymes. The activity of many of the associated transcription factors is regulated by their binding to intracellular metabolites. We study optimal metabolite-mediated regulation of metabolic-gene expression that preserves maximisation of metabolic fluxes across varying conditions. We logically derive the underlying control logic of this type of optimal regulation, which we term Specific Flux (q) Optimization by Robust Adaptive Control (qORAC), and illustrate it with several examples. We show that optimal metabolic flux can be maintained in the face of K changing parameters only if the number of transcription-factor-binding metabolites is at least equal to K. qORAC-regulation of metabolism can generally be achieved with basic biochemical interactions, indicating that metabolism can operate close to optimality. The theory that we present is directly applicable to synthetic biology, biotechnology and fundamental studies of the regulation of metabolism.

systems biology↗