Metabolic-flux dependent regulation of microbial physiology

Litsios, Athanasios; Ortega, Álvaro Darío; Wit, Ernst; Heinemann, Matthias

doi:10.1016/j.mib.2017.10.029

Cited by 59 publications

(61 citation statements)

References 57 publications

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“…Microorganisms display mechanisms that sense metabolic fluxes and translate this flux information into a cellular response (Fung et al, 2005;Kotte et al, 2010). Specifically, the concentrations of certain so-called flux-signaling metabolites correlate with the flux through the respective metabolic pathway (Litsios et al, 2017). A recognized flux-signaling metabolite is the glycolytic intermediate fructose-1,6-bisphosphate (FBP), whose concentration linearly correlates with the flux through glycolysis in Bacillus subtilis (Chubukov et al, 2013) and Escherichia coli (Kotte et al, 2010;Kochanowski et al, 2013), dynamically varying in a broad concentration range: from 0.01 mM to around 15 mM (Bennett et al, 2009;Kleijn et al, 2009;Meyer et al, 2014;Link et al, 2015;Kochanowski et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Assessment of the interaction between the flux‐signaling metabolite fructose‐1,6‐bisphosphate and the bacterial transcription factors CggR and Cra

Folly

Ortega

Hubmann

et al. 2018

Molecular Microbiology

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Bacteria regulate cell physiology in response to extra- and intracellular cues. Recent work showed that metabolic fluxes are reported by specific metabolites, whose concentrations correlate with flux through the respective metabolic pathway. An example of a flux-signaling metabolite is fructose-1,6-bisphosphate (FBP). In turn, FBP was proposed to allosterically regulate master regulators of carbon metabolism, Cra in Escherichia coli and CggR in Bacillus subtilis. However, a number of questions on the FBP-mediated regulation of these transcription factors is still open. Here, using thermal shift assays and microscale thermophoresis we demonstrate that FBP does not bind Cra, even at millimolar physiological concentration, and with electrophoretic mobility shift assays we also did not find FBP-mediated impairment of Cra's affinity for its operator site, while fructose-1-phosphate does. Furthermore, we show for the first time that FBP binds CggR within the millimolar physiological concentration range of the metabolite, and decreases DNA-binding activity of this transcription factor. Molecular docking experiments only identified a single FBP binding site CggR. Our results provide the long thought after clarity with regards to regulation of Cra activity in E. coli and reveals that E. coli and B. subtilis use distinct cellular mechanism to transduce glycolytic flux signals into transcriptional regulation.

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Section: Introductionmentioning

confidence: 99%

Assessment of the interaction between the flux‐signaling metabolite fructose‐1,6‐bisphosphate and the bacterial transcription factors CggR and Cra

Folly

Ortega

Hubmann

et al. 2018

Molecular Microbiology

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“…The signals that drive the regulation of the metabolic fluxes in P. putida are unclear. It has been proposed that the concentration and/or the flow of certain 'flux-signalling' metabolites through particular enzymes may serve as signals to control metabolic fluxes in microbes (for a review, see Litsios et al, 2018). The activity of the Crc/Hfq system can change not only according to nutrient availability and internal signals (Monteagudo-Cascales et al, 2019), but to temperature as well, which has important consequences for cell physiology (Fonseca et al, 2011(Fonseca et al, , 2013.…”

Section: Discussionmentioning

confidence: 99%

Influence of the Crc global regulator on substrate uptake rates and the distribution of metabolic fluxes in Pseudomonas putida KT2440 growing in a complete medium

Molina

Rosa

Nogales

et al. 2019

Environmental Microbiology

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When the soil bacterium Pseudomonas putida grows in a complete medium, it prioritizes the assimilation of preferred carbon sources, optimizing its metabolism and growth. This regulatory process is orchestrated by the Crc and Hfq proteins. The present work examines the changes that occur in metabolic fluxes when the crc gene is inactivated and cells grow exponentially in LB complete medium. Analyses were performed at three different moments during exponential growth, examining the assimilation rates for the compounds present in LB, changes in the proteome, and the changes in metabolic fluxes predicted by the iJN1411 metabolic model for P. putida KT2440. During the early exponential phase, consumption rates for sugars, many organic acids and most amino acids were higher in a Crc‐null strain than in the wild type, leading to an overflow of the metabolic pathways and the leakage of pyruvate and acetate. These accelerated consumption rates decreased during the mid‐exponential phase, when cells mostly used sugars and alanine. At later times, pyruvate was recovered from the medium and utilized. The higher consumption rates of the Crc‐null strain reduced the growth rate. The lack of the Crc/Hfq regulatory system thus led to unbalanced metabolism with poorly optimized metabolic fluxes.

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“…Meanwhile, these metabolite‐protein interactions can be exploited to design biosensors for metabolic flux‐sensing and flux‐dependent regulation. For instance, the levels of the above‐mentioned flux‐sensing metabolite, FBP, correlate linearly with glycolytic flux across a broad range of microbial species, fluxes, conditions and even in short‐term glycolytic perturbations (Litsios, Ortega, Wit, & Heinemann, ). Recently, D. Yang et al () developed a malonyl‐CoA biosensor to dynamically rerouting metabolic flux in Escherichia coli, Pseudomonas putida , and Corynebacterium glutamicum and aimed at achieving enhanced production of malonyl‐CoA‐derived chemicals including fatty acids, flavonoids, nonribosomal polyketides and phenylpropanoids.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, these metaboliteprotein interactions can be exploited to design biosensors for metabolic flux-sensing and flux-dependent regulation. For instance, the levels of the above-mentioned flux-sensing metabolite, FBP, correlate linearly with glycolytic flux across a broad range of microbial species, fluxes, conditions and even in short-term glycolytic perturbations (Litsios, Ortega, Wit, & Heinemann, 2018 (Kummel, Panke, & Heinemann, 2006). Also, a metabolic burden usually places hidden constraints on host productivity, which in turn can be used to engineer cell metabolism for bioproduction with less consumption of ATP and biomass precursors and more gain of energetic efficiency (Wu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

Wang

Haringa

Tang

et al. 2019

Biotech & Bioengineering

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Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind. K E Y W O R D SCFD, Euler-Langrange, metabolic model, metabolomics, population heterogeneity, scale down

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Metabolic-flux dependent regulation of microbial physiology

Cited by 59 publications

References 57 publications

Assessment of the interaction between the flux‐signaling metabolite fructose‐1,6‐bisphosphate and the bacterial transcription factors CggR and Cra

Assessment of the interaction between the flux‐signaling metabolite fructose‐1,6‐bisphosphate and the bacterial transcription factors CggR and Cra

Influence of the Crc global regulator on substrate uptake rates and the distribution of metabolic fluxes in Pseudomonas putida KT2440 growing in a complete medium

Coupled metabolic‐hydrodynamic modeling enabling rational scale‐up of industrial bioprocesses

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