A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

Smallbone, Kieran; Messiha, Hanan L.; Carroll, Kathleen M.; Winder, Catherine; Malys, Naglis; Dunn, Warwick B.; Murabito, Ettore; Swainston, Neil; Dada, Joseph O.; Khan, F. Nawaz; Pir, Pınar; Simeonidis, Evangelos; Spasić, Irena; Wishart, Jill A.; Weichart, Dieter; Hayes, Neil W.; Jameson, Daniel; Broomhead, D. S.; Oliver, Stephen G.; Gaskell, Simon J.; McCarthy, John E.G.; Paton, Norman W.; Westerhoff, Hans V.; Kell, Douglas B.; Mendes, Pedro

doi:10.1016/j.febslet.2013.06.043

Cited by 120 publications

(193 citation statements)

References 86 publications

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“…A recent example of the latter strategy is the study by Smallbone et al (53), who combined proteomics and metabolomics data on yeast glycolysis with enzyme kinetic assays to obtain in vivo values for k cat and K m which, when integrated into a dynamic kinetic model, allowed to refine the picture of the control of yeast glycolysis, distributed more widely over the whole system than originally thought. Another example is the data set of Ishii et al, who measured in parallel enzyme and metabolite concentrations as well as fluxes and mRNA levels in CCM of E. coli (54).…”

Section: Discussionmentioning

confidence: 99%

Mass Spectrometry-based Workflow for Accurate Quantification of Escherichia coli Enzymes: How Proteomics Can Play a Key Role in Metabolic Engineering

Trauchessec

Jaquinod

Bonvalot

et al. 2014

Molecular & Cellular Proteomics

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Metabolic engineering aims to design high performance microbial strains producing compounds of interest. This requires systems-level understanding; genome-scale models have therefore been developed to predict metabolic fluxes. However, multi-omics data including genomics, transcriptomics, fluxomics, and proteomics may be required to model the metabolism of potential cell factories. Recent technological advances to quantitative proteomics have made mass spectrometry-based quantitative assays an interesting alternative to more traditional immuno-affinity based approaches. This has improved specificity and multiplexing capabilities. In this study, we developed a quantification workflow to analyze enzymes involved in central metabolism in Escherichia coli (E. coli). This workflow combined full-length isotopically labeled standards with selected reaction monitoring analysis. First, full-length 15 N labeled standards were produced and calibrated to ensure accurate measurements. Liquid chromatography conditions were then optimized for reproducibility and multiplexing capabilities over a single 30-min liquid chromatography-MS analysis. This workflow was used to accurately quantify 22 enzymes involved in E. coli central metabolism in a wild-type reference strain and two derived strains, optimized for higher NADPH production. In combination with measurements of metabolic fluxes, proteomics data can be used to assess different levels of regulation, in particular enzyme abundance and catalytic rate. This provides information that can be used to design specific strains used in biotechnology. In addition, accurate measurement of absolute enzyme concentrations is key to the development of predictive kinetic models in the context of metabolic engineering. Molecular & Cellular Proteomics

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Section: Discussionmentioning

confidence: 99%

Mass Spectrometry-based Workflow for Accurate Quantification of Escherichia coli Enzymes: How Proteomics Can Play a Key Role in Metabolic Engineering

Trauchessec

Jaquinod

Bonvalot

et al. 2014

Molecular & Cellular Proteomics

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“…In very few cases are all the kinetic rate equations known with any precision (see e.g. [53] for yeast glycolysis), but when they are one may resort to ODE-type modelling, using tools such as Copasi [54,55]. Another strategy is to use surrogate kinetic rate equations for each step, lin-log being both common and effective [56].…”

Section: Systems Biology Of Metabolic Networkmentioning

confidence: 99%

Control of metabolite efflux in microbial cell factories: current advances and future prospects

Kell¹

2018

Preprint

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The yield and ease of purification of biotechnological products are typically greatly enhanced if they can be secreted into the external medium. Although not universally appreciated, however, small molecule biotechnological products do not ‘float across’ the phospholipid bilayer portion of biological membranes, and thus they need transporters to assist their passage into the extramembrane and extracellular spaces. Some of these transporters may be reversible, equilibrative transporters that might more normally be used for uptake, while others may have an efflux directionality imposed on them via suitable energy coupling mechanisms. Despite the energetic costs of small molecule synthesis, natural evolution does in fact provide a number of mechanisms by which the secretion of such products can actually enhance fitness. Where available these provide useful starting points, and assaying for such activities is crucial. In particular, in a systems biology approach, we first need to identify such/suitable activities before we can seek to increase them. They may then be improved through promoter engineering, via manipulation of control elements, or by directed evolution of the transporter proteins themselves. Modern methods of synthetic biology provide enormous opportunities for all kinds of efflux transporter engineering; they are just beginning to be realised.

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“…These approximations systematically ignore any errors resulting from the differences between in vitro and in vivo environments. Approximations of this sort may also introduce significant errors, as k cat values can deviate by orders of magnitude between isozymes in the same organism as well as across organisms (17)(18)(19).…”

mentioning

confidence: 99%

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k _cat measurements

Davidi

Noor

Liebermeister

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

235

382

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Turnover numbers, also known as k cat values, are fundamental properties of enzymes. However, k cat data are scarce and measured in vitro, thus may not faithfully represent the in vivo situation. A basic question that awaits elucidation is: how representative are k cat values for the maximal catalytic rates of enzymes in vivo? Here, we harness omics data to calculate k vivo max , the observed maximal catalytic rate of an enzyme inside cells. Comparison with k cat values from Escherichia coli, yields a correlation of r 2 = 0.62 in log scale (p < 10 −10 ), with a root mean square difference of 0.54 (3.5-fold in linear scale), indicating that in vivo and in vitro maximal rates generally concur. By accounting for the degree of saturation of enzymes and the backward flux dictated by thermodynamics, we further refine the correspondence between k vivo max and k cat values. The approach we present here characterizes the quantitative relationship between enzymatic catalysis in vitro and in vivo and offers a highthroughput method for extracting enzyme kinetic constants from omics data. (1-6). Many models of cellular metabolism include k cat values, the maximal turnover rates of enzymes, as key inputs to predict the behavior of metabolic pathways and networks (7-9). However, most values have never been measured experimentally. Escherichia coli is the most intensely biochemically characterized organism, but k cat values are available for only about 10% of its ≈ 2, 000 enzyme-reaction pairs (Dataset S1). Indeed, k cat values are missing for several central metabolic enzymes. The scarcity of kinetic data limits the scope of models and necessitates generic parameter assignments that significantly reduce the predictive power of cellular models.Even if a larger collection of k cat values was made available, their current use poses a major difficulty: k cat values are measured through in vitro enzyme assays, representing the initial rate of the reaction, i.e., full substrates saturation and negligible levels of products. Such assays may underrepresent factors like cellular metabolite concentrations, thermodynamic constraints, posttranslational modifications, chaperones, cellular crowding, and activating and inhibiting molecules, which can substantially alter enzyme kinetics in vivo. These omissions call into question the relevance of k cat measurements in vivo (10-12). Furthermore, an effort to measure a large number of k cat values under in vivo-like conditions presents a daunting challenge, given how many unknown biochemical factors might be involved.Several studies grapple with missing k cat values by sampling from the distribution of k cat values measured in vitro or by using measurements of the same enzyme from related species (13-16). These approximations systematically ignore any errors resulting from the differences between in vitro and in vivo environments. Approximations of this sort may also introduce significant errors, as k cat values can deviate by orders of magnitude between isozymes in the same organism as well a...

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A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

Cited by 120 publications

References 86 publications

Mass Spectrometry-based Workflow for Accurate Quantification of Escherichia coli Enzymes: How Proteomics Can Play a Key Role in Metabolic Engineering

Mass Spectrometry-based Workflow for Accurate Quantification of Escherichia coli Enzymes: How Proteomics Can Play a Key Role in Metabolic Engineering

Control of metabolite efflux in microbial cell factories: current advances and future prospects

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k _cat measurements

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A model of yeast glycolysis based on a consistent kinetic characterisation of all its enzymes

Cited by 120 publications

References 86 publications

Mass Spectrometry-based Workflow for Accurate Quantification of Escherichia coli Enzymes: How Proteomics Can Play a Key Role in Metabolic Engineering

Mass Spectrometry-based Workflow for Accurate Quantification of Escherichia coli Enzymes: How Proteomics Can Play a Key Role in Metabolic Engineering

Control of metabolite efflux in microbial cell factories: current advances and future prospects

Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k cat measurements

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Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro k _cat measurements