Microbial inefficient substrate use through the perspective of resource allocation models

Microorganisms extract energy from substrates following strategies that may seem suboptimal at first glance. Beyond the so-called yield-rate trade-off, resource allocation models, which focus on assigning different functional roles to the limited number of enzymes that a cell can support, offer a framework to interpret the inefficient substrate use by microorganisms. We review here relevant examples of substrate conversions where a significant part of the available energy is not utilised and how resource allocation models offer a mechanistic interpretation thereof, notably for open mixed cultures. Future developments are identified, in particular, the challenge of considering metabolic flexibility towards uncertain environmental changes instead of strict fixed optimality objectives, with the final goal of increasing the prediction capabilities of resource allocation models. Finally, we highlight the relevance of resource allocation to understand and enable a promising biorefinery platform revolving around lactate, which would increase the flexibility of waste-to-chemical biorefinery schemese authors would like to acknowledge the support of the Spanish Ministry of Education (FPU14/05457) and project CONSERVAL (INTERREG V-A Spain-Portugal, POCTEP), co-financed by the ERDF (Ref: 2352). The authors belong to the Galician Competitive Research Group (ED431C2017/029) and to the CRETUS Strategic Partnership (ED431E 2018/01), both programmes are co-funded by Xunta de Galicia and ERDF (EU)S

Reconstruction and modeling protein translocation and compartmentalization in Escherichia coli at the genome-scale

BackgroundMembranes play a crucial role in cellular functions. Membranes provide a physical barrier, control the trafficking of substances entering and leaving the cell, and are a major determinant of cellular ultra-structure. In addition, components embedded within the membrane participate in cell signaling, energy transduction, and other critical cellular functions. All these processes must share the limited space in the membrane; thus it represents a notable constraint on cellular functions. Membrane- and location-based processes have not yet been reconstructed and explicitly integrated into genome-scale models.ResultsThe recent genome-scale model of metabolism and protein expression in Escherichia coli (called a ME-model) computes the complete composition of the proteome required to perform whole cell functions. Here we expand the ME-model to include (1) a reconstruction of protein translocation pathways, (2) assignment of all cellular proteins to one of four compartments (cytoplasm, inner membrane, periplasm, and outer membrane) and a translocation pathway, (3) experimentally determined translocase catalytic and porin diffusion rates, and (4) a novel membrane constraint that reflects cell morphology. Comparison of computations performed with this expanded ME-model, named iJL1678-ME, against available experimental data reveals that the model accurately describes translocation pathway expression and the functional proteome by compartmentalized mass.ConclusioniJL1678-ME enables the computation of cellular phenotypes through an integrated computation of proteome composition, abundance, and activity in four cellular compartments (cytoplasm, periplasm, inner and outer membrane). Reconstruction and validation of the model has demonstrated that the iJL1678-ME is capable of capturing the functional content of membranes, cellular compartment-specific composition, and that it can be utilized to examine the effect of perturbing an expanded set of network components. iJL1678-ME takes a notable step towards the inclusion of cellular ultra-structure in genome-scale models

Metabolic perturbations in mutants of glucose transporters and their applications in metabolite production in Escherichia coli

Background: Most microorganisms have evolved to maximize growth rate, with rapid consumption of carbon sources from the surroundings. However, fast growing phenotypes usually feature secretion of organic compounds. For example, E. coli mainly produced acetate in fast growing condition such as glucose rich and aerobic condition, which is troublesome for metabolic engineering because acetate causes acidification of surroundings, growth inhibition and decline of production yield. The overflow metabolism can be alleviated by reducing glucose uptake rate. Results: As glucose transporters or their subunits were knocked out in E. coli, the growth and glucose uptake rates decreased and biomass yield was improved. Alteration of intracellular metabolism caused by the mutations was investigated with transcriptome analysis and C-13 metabolic flux analysis (C-13 MFA). Various transcriptional and metabolic perturbations were identified in the sugar transporter mutants. Transcription of genes related to glycolysis, chemotaxis, and flagella synthesis was downregulated, and that of gluconeogenesis, Krebs cycle, alternative transporters, quorum sensing, and stress induced proteins was upregulated in the sugar transporter mutants. The specific production yields of value-added compounds (enhanced green fluorescent protein, gamma-aminobutyrate, lycopene) were improved significantly in the sugar transporter mutants. Conclusions: The elimination of sugar transporter resulted in alteration of global gene expression and redirection of carbon flux distribution, which was purposed to increase energy yield and recycle carbon sources. When the pathways for several valuable compounds were introduced to mutant strains, specific yield of them were highly improved. These results showed that controlling the sugar uptake rate is a good strategy for ameliorating metabolite production.11Ysciescopu

Systems biology of yeast metabolism - Understanding metabolism through proteomics and constraint-based modeling

Metabolism is the set of all chemical reactions that occur inside of cells. By providing all the building blocks that are required for sustaining a cellular state and cell proliferation, metabolism is at the core of cellular function. Therefore, in order to understand cellular function it is important to understand cellular metabolism. The cellular metabolic network comprises thousands of reactions even in the simplest of organisms. Due to the high complexity, a holistic approach is required to study and understand the interactions between different parts of metabolism giving rise to cellular phenotypes.In this thesis, a systems biology approach to study metabolism in yeast, mainly with a focus on Saccharomyces cerevisiae (baker’s yeast), was used. This approach consisted of combining proteomic analysis with constraint-based modeling to gain insights into different aspects of metabolism. First, the role of mitochondria in cellular metabolism throughout diauxic growth was evaluated, showing that mitochondria balance their role as a biosynthetic hub and center for energy generation depending on the mode of cellular metabolism. Next, the construction of a model of mitochondrial metabolism describing the essential mitochondrial processes of protein import and cofactor metabolism as well as proton motive force driving the generation of free energy (in the form of ATP) is described and evaluated. The model was used to investigate the dynamics in mitochondrial metabolism and the requirement of these processes.Second, the constraints placed on cellular metabolism arising from finite protein resources is investigated in two studies. The first study evaluates the effect of amino acid supplementation of the physiology and allocation of protein resources. This study showed that as the burden of producing amino acids is relieved, the cells can allocate more protein to the translation, which allows the cells to grow faster. In the second study, a quantitative comparison of four yeast species was performed to evaluate the underlying causes of overflow metabolism, which is the seemingly wasteful strategy of aerobic fermentation instead of using the more efficient respiratory pathway for glucose utilization. We showed that overflow metabolism in yeast is linked to adaptations in metabolism and protein translation This phenomenon is seen in cells ranging from bacteria to yeast and cancer cells, and the insights provided in our study could therefore be valuable in understanding the metabolism not only in yeast but in more complex systems

Understanding global resource allocation in fission yeast through data analysis and coarse-grained mathematical modelling

Unicellular organisms can grow in a large variety of environments. Even in those supporting robust growth, cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate and the cell size. I have approached this topic from two angles, namely a comprehensive analysis of a gene expression data set and the construction of coarse-grained resource allocation models (C-GRAMs). First, I studied a combined data set of protein and transcript abundances during growth of the fission yeast Schizosaccharomyces pombe on various abundant nitrogen sources. Approximately half of gene expression was significantly correlated with the growth rate, and this came alongside wide-spread nutrient-specific expression. Genes positively correlated with the growth rate participated in protein production, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, the expression of metabolic enzymes was mainly condition specific. Second, C-GRAMs are simple models of single cells, where large components of the macromolecular composition are abstracted into single entities. The dynamics and steady-state behaviour of such models can then be easily explored. A minimal C-GRAM with nitrogen and carbon pathways converging on biomass production described the effects of the uptake of sugars, ammonium, and/or compound nutrients such as amino acids on the translational resource allocation towards proteome sectors that maximised the growth rate. Prompted by new observations that the relation between cell volume and the growth rate was identical for both carbon and nitrogen perturbations, but that the surface-to-volume ratio was elevated in low-nitrogen conditions, I extended this to a C-GRAM that additionally accounted for the cell cycle, cell division, cell wall biosynthesis, and the effect of molecular crowding on the ribosomal efficiency.Open Acces

Is energy excess the initial trigger of carbon overflow metabolism? Transcriptional network response of carbon-limited Escherichia coli to transient carbon excess

Background: Escherichia coli adapted to carbon-limiting conditions is generally geared for energy-efficient carbon utilization. This includes also the efficient utilization of glucose, which serves as a source for cellular building blocks as well as energy. Thus, catabolic and anabolic functions are balanced under these conditions to minimize wasteful carbon utilization. Exposure to glucose excess interferes with the fine-tuned coupling of anabolism and catabolism leading to the so-called carbon overflow metabolism noticeable through acetate formation and eventually growth inhibition. Results: Cellular adaptations towards sudden but timely limited carbon excess conditions were analyzed by exposing slow-growing cells in steady state glucose-limited continuous culture to a single glucose pulse. Concentrations of metabolites as well as time-dependent transcriptome alterations were analyzed and a transcriptional network analysis performed to determine the most relevant transcription and sigma factor combinations which govern these adaptations. Down-regulation of genes related to carbon catabolism is observed mainly at the level of substrate uptake and downstream of pyruvate and not in between in the glycolytic pathway. It is mainly accomplished through the reduced activity of CRP-cAMP and through an increased influence of phosphorylated ArcA. The initiated transcriptomic change is directed towards down-regulation of genes, which contribute to active movement, carbon uptake and catabolic carbon processing, in particular to down-regulation of genes which contribute to efficient energy generation. Long-term changes persisting after glucose depletion and consumption of acetete encompassed reduced expression of genes related to active cell movement and enhanced expression of genes related to acid resistance, in particular acid resistance system 2 (GABA shunt) which can be also considered as an inefficient bypass of the TCA cycle. Conclusions: Our analysis revealed that the major part of the trancriptomic response towards the glucose pulse is not directed towards enhanced cell proliferation but towards protection against excessive intracellular accumulation of potentially harmful concentration of metabolites including among others energy rich compounds such as ATP. Thus, resources are mainly utilized to cope with “overfeeding” and not for growth including long-lasting changes which may compromise the cells future ability to perform optimally under carbon-limiting conditions (reduced motility and ineffective substrate utilization)

Quantitative modelling of bacterial growth physiology, cell size and shape control

Bacteria are highly adaptive microorganisms that proliferate in a wide range of environmental conditions via changes in cell size, shape and molecular composition. How bacterial cell size, shapes and physiological properties are regulated in diverse environmental conditions are questions of longstanding interest. Regulation of cell size and shape imply cellular control mechanisms that couple bacterial growth and division processes to their cellular environment and molecular composition. Studies in the past decades have revealed many fundamental principles of bacterial growth physiology, in particular the relationship between cellular growth rate, proteome composition and the nutrient environment. However, the quantitative relations defining the interdependence of cell growth and morphology, together with the molecular mechanisms underlying the control of bacterial cell morphology remain poorly understood. In this thesis I develop quantitative theory and models for bacterial growth dynamics that link cellular proteome with cell size and division control (Chapter 2), cell shape control (Chapter 3), regulation of bacterial growth and morphology in the presence of antibiotic stress (Chapter 4), and energy allocation strategies for cellular growth and shape control (Chapter 5). My work reveals that cell size maintenance under nutrient perturbations requires a balanced trade-off between ribosomes and division protein synthesis. Deviations from this tradeoff relationship are predicted under translation inhibition, leading to distinct modes of cell morphological changes, in agreement with single-cell data on Escherichia coli growth and cell morphology. Using the particular example of ribosome-targeting antibiotics, I present a systems-level model for the regulation of cell shape and growth physiology under antibiotic stress, and uncover various feedback mechanisms that bacteria can harness to increase their fitness in the presence of antibiotics

A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions

Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the ‘post antibiotic era’. Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in in vivo versus in vitro environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of in vitro studies and to streamline lab work. Here, we review the current status of mathematical modelling and highlight that data on mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modellers, smoothing the road to regulatory approval and widespread use of phage therapy

Mechanistic understanding of mixed-culture fermentations by metabolic modelling

Biorefineries are set to become an important agent in the shift towards a circular economy due to their potential to valorise organic wastes into marketable products. Anaerobic fermentations yielding volatile fatty acids are a key process in this production scheme as their products act as intermediates between the organic wastes and the final biorefinery products. However, their product selectivity is highly influenced by the environmental conditions and the mechanisms governing the process remain unknown. In this thesis, predictive tools were developed with the objective of understanding the mechanisms governing anaerobic fermentations and of designing processes targeting specific volatile fatty acids with high productivity