Bacterial Adaptation through Distributed Sensing of Metabolic Fluxes

Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Molecular Systems Biology (Impact Factor: 10.87). 09/2010; 6(9):355. DOI: 10.1038/msb.2010.10
Source: PubMed


The recognition of carbon sources and the regulatory adjustments to recognized changes are of particular importance for bacterial survival in fluctuating environments. Despite a thorough knowledge base of Escherichia coli's central metabolism and its regulation, fundamental aspects of the employed sensing and regulatory adjustment mechanisms remain unclear. In this paper, using a differential equation model that couples enzymatic and transcriptional regulation of E. coli's central metabolism, we show that the interplay of known interactions explains in molecular-level detail the system-wide adjustments of metabolic operation between glycolytic and gluconeogenic carbon sources. We show that these adaptations are enabled by an indirect recognition of carbon sources through a mechanism we termed distributed sensing of intracellular metabolic fluxes. This mechanism uses two general motifs to establish flux-signaling metabolites, whose bindings to transcription factors form flux sensors. As these sensors are embedded in global feedback loop architectures, closed-loop self-regulation can emerge within metabolism itself and therefore, metabolic operation may adapt itself autonomously (not requiring upstream sensing and signaling) to fluctuating carbon sources.

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    • "resent in a pathway , then its flux change should be positively ( respectively , negatively ) correlated with the flux change in that pathway ( see Supplementary Material for details ) . It has been proposed that allosteric intermediates function as flux - signaling metabolites that directly translate flux information to metabolite concentration ( Kotte et al . , 2010 ; Matsuoka and Shimizu , 2015 ) . The method , named allosteric regulation FBA ( arFBA ) , is a vari - ation of parsimonious FBA ( pFBA ) ( Lewis et al . , 2010 ) where the objective function is extended as follows :"
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    ABSTRACT: Modeling cellular metabolism is fundamental for many biotechnological applications, including drug discovery and rational cell factory design. Central carbon metabolism (CCM) is particularly important as it provides the energy and precursors for other biological processes. However, the complex regulation of CCM pathways has still not been fully unraveled and recent studies have shown that CCM is mostly regulated at post-transcriptional levels. In order to better understand the role of allosteric regulation in controlling the metabolic phenotype, we expand the reconstruction of CCM in Escherichia coli with allosteric interactions obtained from relevant databases. This model is used to integrate multi-omics datasets and analyze the coordinated changes in enzyme, metabolite, and flux levels between multiple experimental conditions. We observe cases where allosteric interactions have a major contribution to the metabolic flux changes. Inspired by these results, we develop a constraint-based method (arFBA) for simulation of metabolic flux distributions that accounts for allosteric interactions. This method can be used for systematic prediction of potential allosteric regulation under the given experimental conditions based on experimental data. We show that arFBA allows predicting coordinated flux changes that would not be predicted without considering allosteric regulation. The results reveal the importance of key regulatory metabolites, such as fructose-1,6-bisphosphate, in controlling the metabolic flux. Accounting for allosteric interactions in metabolic reconstructions reveals a hidden topology in metabolic networks, improving our understanding of cellular metabolism and fostering the development of novel simulation methods that account for this type of regulation.
    Frontiers in Bioengineering and Biotechnology 10/2015; 3. DOI:10.3389/fbioe.2015.00154
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    • "Additionally, enzyme kinetics and performance of nonnative genes/proteins are currently not covered in GEMs. Although the first genome-scale and reduced constraint-based models that take regulation into account have emerged for E. coli (Kotte et al. 2010; Covert et al. 2001) they have so far only been created for certain regulatory systems present in yeast (Christensen et al. 2009). Although the lack of regulatory predicting power in GEMs hinders the quality of predictions for metabolic engineering purposes, the combination of modeling data with real experiments can precisely identify the regions which are highly regulated at post-transcriptional level and allows one to investigate those regions more thoroughly using bottom-up approaches (described below). "
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    ABSTRACT: Generally, a microorganism's phenotype can be described by its pattern of metabolic fluxes. Although fluxes cannot be measured directly, inference of fluxes is well established. In biotechnology, often, the aim is to increase the capacity of specific fluxes. For this, metabolic engineering methods have been developed and applied extensively. Many of these rely on balancing of intracellular metabolites, redox and energy fluxes, using genome-scale models (GEMs) that in combination with appropriate objective functions and constraints can be used to predict potential gene targets for obtaining a preferred flux distribution. These methods point to strategies for altering gene expression, however, fluxes are often controlled by post-transcriptional events. Moreover, GEMs are usually not taking into account metabolic regulation, thermodynamics and enzyme kinetics. To facilitate metabolic engineering, tools from synthetic biology have emerged, enabling integration and assembly of naturally non-existent, but well characterized components into a living organism. To describe these systems kinetic models are often used, and to integrate these systems with the standard metabolic engineering approach it is necessary to expand the modeling of metabolism to consider kinetics of individual processes. This review will give an overview about models available for metabolic engineering of yeast and discusses their applications.This article is protected by copyright. All rights reserved.
    FEMS Yeast Research 08/2014; 15(1). DOI:10.1111/1567-1364.12199 · 2.82 Impact Factor
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    • "With the concept of ‘sytems biology’ coming to the stage of biological research, construction of kinetic models has been the primary focus in a substantial number of studies [1-4]. Kinetic models are mechanistic representations of biological systems. "
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