Dataset Information

Kotte2010_Ecoli_Metabolic_Adaption

ABSTRACT: This is the model described in: Bacterial adaptation through distributed sensing of metabolic fluxes Oliver Kotte, Judith B Zaugg and Matthias Heinemann;Mol Sys Biol2010;6:355. doi:10.1038/msb.2010.10; Abstract: 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. In its current form this SBML model is parametrized for the glucose to acetate transition and to simulate the extended diauxic shift as shown in figure 3 and scenario 6 of the attached matlab file. In this scenario the cells first are grown from an OD600 (BM) of 0.03 with a starting glucose concentration of 0.5 g/l for 8.15 h (29340 sec). Then a medium containing 5 g/l acetate is inoculated with these cells to an OD600 of 0.03 and grown for another 19.7 hours (70920 sec). Finally the cells are shifted to a medium containing both glucose and acetate at a concentration of 3 g/l with a starting OD600 of 0.0005. The shifts where implemented using events triggering at the times determined by the parameters shift1 and shift2 (in hours). To simulate other scenarios the initial conditions need to be changed as described in the supplemental materials (supplement 1) The original SBML model and the MATLAB file used for the calculations can be down loaded as supplementary materials of the publication from the MSB website. (supplement 2). The units of the external metabolites are in [g/l], those of the biomass in optical density,OD600, taken as dimensionless, and [micromole/(gramm dry weight)] for all intracellular metabolites. As the latter cannot be implemented in SBML, it was chosen to be micromole only and the units of the parameters are left mostly undefined. This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

SUBMITTER: Lukas Endler

PROVIDER: BIOMD0000000244 | BioModels | 2010-03-14

REPOSITORIES: BioModels

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Bacterial adaptation through distributed sensing of metabolic fluxes.

Kotte Oliver O Zaugg Judith B JB Heinemann Matthias M

Molecular systems biology 20100309

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 ...[more]

PMID: 20212527

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Project description:Background Lignocellulosic biomass is a promising renewable feedstock for biofuel production. Acetate is one of the major inhibitors liberated from hemicelluloses during hydrolysis. An understanding of the toxic effects of acetate on the fermentation microorganism and the efficient utilization of mixed sugars of glucose and xylose in the presence of hydrolysate inhibitors is crucial for economic biofuel production. Results A new microarray was designed including both coding sequences and intergenic regions to investigate the acetate stress responses of Zymomonas mobilis 8b when using single carbon sources of glucose or xylose, or mixed sugars of both glucose and xylose. With the supplementation of exogenous acetate, 8b can utilize all the glucose with a similar ethanol yield, although the growth, final biomass, and ethanol production rate were reduced. However, xylose utilization was inhibited in both media containing xylose or a mixed sugar of glucose and xylose, although the performance of 8b was better in mixed sugar than xylose-only media. The presence of acetate caused genes related to biosynthesis, the flagellar system, and glycolysis to be downregulated, and genes related to stress responses and energy metabolism to be upregulated. Unexpectedly, xylose seems to pose more stress on 8b, recruiting more genes for xylose utilization, than does acetate. Several gene candidates based on transcriptome results were selected for genetic manipulation, and a TonB-dependent receptor knockout mutant was confirmed to have a slight advantage regarding acetate tolerance. Conclusions Our results indicate Z. mobilis utilized a different mechanism for xylose utilization, with an even more severe impact on Z. mobilis than that caused by acetate treatment. Our study also suggests redox imbalance caused by stressful conditions may trigger a metabolic reaction leading to the accumulation of toxic intermediates such as xylitol, but Z. mobilis manages its carbon and energy metabolism through the control of individual reactions to mitigate the stressful conditions. We have thus provided extensive transcriptomic datasets and gained insights into the molecular responses of Z. mobilis to the inhibitor acetate when grown in different sugar sources, which will facilitate future metabolic modeling studies and strain improvement efforts for better xylose utilization and acetate tolerance.

Project description:In contrast to batch cultivation, chemostat cultivation allows the identification of carbon source responses without interference by carbon-catabolite repression, accumulation of toxic products, and differences in specific growth rate. This study focuses on the yeast Saccharomyces cerevisiae, grown in aerobic, carbon-limited chemostat cultures. Genome-wide transcript levels and in vivo fluxes were compared for growth on two sugars, glucose and maltose, and for two C2-compounds, ethanol and acetate. In contrast to previous reports on batch cultures, few genes (180 genes) responded to changes of the carbon source by a changed transcript level. Very few transcript levels were changed when glucose as the growth-limiting nutrient was compared with maltose (33 transcripts), or when acetate was compared with ethanol (16 transcripts). Although metabolic flux analysis using a stoichiometric model revealed major changes in the central carbon metabolism, only 117 genes exhibited a significantly different transcript level when sugars and C2-compounds were provided as the growthlimiting nutrient. Despite the extensive knowledge on carbon source regulation in yeast, many of the carbon source-responsive genes encoded proteins with unknown or incompletely characterized biological functions. In silico promoter analysis of carbon source-responsive genes confirmed the involvement of several known transcriptional regulators and suggested the involvement of additional regulators. Transcripts involved in the glyoxylate cycle and gluconeogenesis showed a good correlation with in vivo fluxes. This correlation was, however, not observed for other important pathways, including the pentose-phosphate pathway, tricarboxylic acid cycle, and, in particular, glycolysis. These results indicate that in vivo fluxes in the central carbon metabolism of S. cerevisiae grown in steadystate, carbon-limited chemostat cultures are controlled to a large extent via post-transcriptional mechanisms. Experiment Overall Design: Cultivation of microorganisms in chemostats offers numerous advantages for studying the structure and regulation of metabolic networks (11). In chemostat cultures, individual culture parameters can be changed, while keeping other relevant physical and chemical culture parameters (composition of synthetic medium, pH, temperature, aeration, etc.) constant. An especially important parameter in this respect is the specific growth rate, which, in a chemostat, is equal to the dilution rate, which can be accurately controlled. This allows the experimenter to investigate the effects of environmental changes or genetic interventions at a fixed specific growth rate, even if these changes result in different specific growth rates in batch cultures. In a chemostat, growth can be limited by a single, selected nutrient. The very low residual concentrations of this growth-limiting nutrient in chemostat cultures alleviate effects of catabolite repression and inactivation. Furthermore, these low residual substrate concentrations prevent substrate toxicity, which, for example, occurs when S. cerevisiae is grown on ethanol or acetate as the carbon source in batch cultures . Experiment Overall Design: The central goal of the present study is to assess to what extent carbon source-dependent regulation of fluxes through central carbon metabolism in S. cerevisiae is regulated at the level of transcription. To this end, we compare the transcriptome of carbon-limited, aerobic chemostat cultures grown on four different carbon sources: glucose, maltose, ethanol, and acetate. Data from the transcriptome analysis are compared with flux distribution profiles calculated with a stoichiometric metabolic network model. Questions that will be addressed are as follows: (i) does glucose-limited aerobic cultivation lead to a complete alleviation of glucose-catabolite repression; (ii) how (in)complete is our understanding of the genes involved in the transcriptional response of S. cerevisiae to four of the most common carbon sources for this yeast; and (iii) to what extent do transcriptome analyses with microarrays provide a reliable indication of flux distribution in metabolic networks?

Project description:Background: Lignocellulosic biomass is a promising renewable feedstock for the microbial production of fuels. To release the major fermentable sugars such as glucose and xylose, pretreatment and enzymatic hydrolysis of biomass feedstock are needed. During this process, many toxic compounds are produced or introduced which subsequently inhibit microbial growth and eventually the production rate and yield. Acetate is one of the major inhibitors liberated from hemicelluloses during dilute acid pretreatment. An understanding of the toxic effects of acetate on the fermentation microorganism is critical to improving biofuel yields in the process. In addition, the efficient utilization of mixed sugars of glucose and xylose in the presence of hydrolysate inhibitors is crucial for economic biofuel production. Results: We have observed previously that some pretreatment inhibitors affect growth and performance in Zymomonas mobilis 8b differently when different sugars (e.g. glucose or xylose) are used as substrate. To investigate this phenomenon at the cellular level, microarray technology was used to investigate the acetate stress responses of Z. mobilis 8b when using single carbon sources of glucose or xylose, and mixed sugars of glucose and xylose. We designed a microarray based on the most up-to-date genome annotation for both coding sequences and intergenic regions. In the presence of acetate, 8b still can utilize all the glucose (though xylose utilization was inhibited) with similar ethanol yield although the growth, final biomass, and ethanol production rate were reduced. The presence of acetate caused genes related to biosynthesis, flagellar system, and glycolysis to be downregulated, and genes related to stress responses and energy metabolism to be upregulated. Our result indicates that Z. mobilis utilized different mechanism for xylose utilization compared to that of glucose, with even more dramatic results than those caused by treatment of the culture with the inhibitor acetate. Our study also suggests that redox imbalance caused by stressful conditions may trigger a metabolic reaction that leads to the accumulation of toxic intermediates such as xylitol, but Z. mobilis appears to be capable of managing its carbon and energy metabolism through the control of individual reactions to overcome the inhibition caused by stressful conditions. Several target gene candidates based on transcriptomic result have been selected for genetic manipulation and a TonB-dependent receptor knockout mutant was confirmed to have advantage on acetate tolerance. Conclusions: We have gained insights into the molecular responses of the model ethanologenic bacterium Z. mobilis to the inhibitor acetate when grown in different sugar sources. These insights will facilitate future metabolic modeling studies and help further strain metabolic engineering efforts for better xylose utilization and acetate tolerance. Two series of microarray studies using total RNA extracted from Zymomonas mobilis subsp mobilis 8b (an xylose-utilizing recombinant) were carried out to investigate the effect of carbon source and acetate on Z. mobilis. One study compared the acetate effect in either glucose or xylose at exponential phase and another study investigated the acetate effect in mixed sugar of glucose and xylose at three growth phases of exponential, transition, and stationary. Tthree biological replicates were used for each condition.

Dataset Information

Kotte2010_Ecoli_Metabolic_Adaption

Publications

Bacterial adaptation through distributed sensing of metabolic fluxes.

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