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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Available from: Judith B Zaugg
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    • "However, these data are typically not available in most applications and the analysis of transient behavior in this approach still remains target of future research. In the last years, models based on less detailed descriptions integrating all kinds of biological networks (metabolic, regulatory and signaling) started also to appear (Covert et al., 2009;Kotte et al., 2010;Lee et al., 2008). Although there are initiatives for models that integrate different kinds of biological networks, this has a limited scope to particular biological processes. "
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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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    • "The TF which positively and/or negatively influences more than 260 operons (for example, see database Ecocyc ) and is the most important regulator for carbohydrate metabolism. Mathematical models for carbohydrate uptake and control are already available[Baldazzi et al., 2010;Bettenbrock et al., 2006;Kotte et al., 2010]and provide a quantitative approach for the understanding of the complex interaction scheme between metabolism, signaling and gene expression. In contrast, the role of the PTS Ntr is less well understood and mathematical models are scarce[Kremling et al., 2012]. "
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