Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data

Cinquemani, Eugenio; Laroute, Valérie; Cocaign‐Bousquet, Muriel; Jong, Hidde de; Ropers, Delphine

doi:10.1093/bioinformatics/btx250

Cited by 7 publications

(6 citation statements)

References 36 publications

(59 reference statements)

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“…In principle other approaches can be used to derive uptake/secretion rates of metabolites in the supernatant, like those described in ref. 65 , which can explicitly take into account a priori knowledge on the time at which rapid metabolic changes are expected.…”

Section: Methodsmentioning

confidence: 99%

Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli

et al. 2019

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How microbes dynamically coordinate uptake and simultaneous utilization of nutrients in complex nutritional ecosystems is still an open question. Here, we develop a constraint-based modeling approach that exploits non-targeted exo-metabolomics data to unravel adaptive decision-making processes in dynamic nutritional environments. We thereby investigate metabolic adaptation of Escherichia coli to continuously changing conditions during batch growth in complex medium. Unexpectedly, model-based analysis of time resolved exo-metabolome data revealed that fastest growth coincides with preferred catabolism of amino acids, which, in turn, reduces glucose uptake and increases acetate overflow. We show that high intracellular levels of the amino acid degradation metabolites pyruvate and oxaloacetate can directly inhibit the phosphotransferase system (PTS), and reveal their functional role in mediating regulatory decisions for uptake and catabolism of alternative carbon sources. Overall, the proposed methodology expands the spectrum of possible applications of flux balance analysis to decipher metabolic adaptation mechanisms in naturally occurring habitats and diverse organisms.

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Section: Methodsmentioning

confidence: 99%

Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli

et al. 2019

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“…The AEC was quantified as previously described ( 35 ). Specific growth rates and consumption and production rates were determined as previously described ( 46 ).…”

Section: Methodsmentioning

confidence: 99%

The Csr System Regulates Escherichia coli Fitness by Controlling Glycogen Accumulation and Energy Levels

Morin

Ropers

Cinquemani

et al. 2017

mBio

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In the bacterium Escherichia coli, the posttranscriptional regulatory system Csr was postulated to influence the transition from glycolysis to gluconeogenesis. Here, we explored the role of the Csr system in the glucose-acetate transition as a model of the glycolysis-to-gluconeogenesis switch. Mutations in the Csr system influence the reorganization of gene expression after glucose exhaustion and disturb the timing of acetate reconsumption after glucose exhaustion. Analysis of metabolite concentrations during the transition revealed that the Csr system has a major effect on the energy levels of the cells after glucose exhaustion. This influence was demonstrated to result directly from the effect of the Csr system on glycogen accumulation. Mutation in glycogen metabolism was also demonstrated to hinder metabolic adaptation after glucose exhaustion because of insufficient energy. This work explains how the Csr system influences E. coli fitness during the glycolysis-gluconeogenesis switch and demonstrates the role of glycogen in maintenance of the energy charge during metabolic adaptation.

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“…Finally, cell-specific external rates and their errors are calculated from cultivation data (concentration time courses of extracellular metabolites, off-gas analysis, biomass composition etc.) by means of simple regression (Murphy and Young, 2013), differentiation after smoothing (Llaneras and Picó, 2007), stochastic filtering (Cinquemani et al, 2017), or tailored bioprocess models (Noack et al, 2011).…”

Section: Decisions On the Design Of Fluxmlmentioning

confidence: 99%

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

et al. 2019

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13 C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. 13 C MFA, however, stands out from other “-omics sciences,” in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying 13 C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of 13 C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an “all-around carefree package” for 13 C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of 13 C MFA.

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Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data

Cited by 7 publications

References 36 publications

Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli

Regulatory mechanisms underlying coordination of amino acid and glucose catabolism in Escherichia coli

The Csr System Regulates Escherichia coli Fitness by Controlling Glycogen Accumulation and Energy Levels

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

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