Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

Volkmer, Benjamin; Heinemann, Matthias

doi:10.1371/journal.pone.0023126

Cited by 299 publications

(309 citation statements)

References 32 publications

Supporting

Mentioning

294

Contrasting

Order By: Relevance

“…8a). Second, the size distribution of double-labelled cells determined as the forward scatter (FSC) in the flow cytometer 25 was congruent with the size distribution of EGFP-labelled WT cells of E. coli. Third, analysing cocultures of pairwise cross-feeding consortia within E. coli or between A. baylyi and E. coli under the fluorescence microscope revealed individual cells that fluoresced in both red and green colour (Fig.…”

Section: Construction Of Synthetic Cross-feeding Interactionsmentioning

confidence: 69%

Metabolic cross-feeding via intercellular nanotubes among bacteria

et al. 2015

View full text Add to dashboard Cite

Bacteria frequently exchange metabolites by diffusion through the extracellular environment, yet it remains generally unclear whether bacteria can also use cell-cell connections to directly exchange nutrients. Here we address this question by engineering cross-feeding interactions within and between Acinetobacter baylyi and Escherichia coli, in which two distant bacterial species reciprocally exchange essential amino acids. We establish that in a well-mixed environment E. coli, but likely not A. baylyi, can connect to other bacterial cells via membranederived nanotubes and use these to exchange cytoplasmic constituents. Intercellular connections are induced by auxotrophy-causing mutations and cease to establish when amino acids are externally supplied. Electron and fluorescence microscopy reveal a network of nanotubular structures that connects bacterial cells and enables an intercellular transfer of cytoplasmic materials. Together, our results demonstrate that bacteria can use nanotubes to exchange nutrients among connected cells and thus help to distribute metabolic functions within microbial communities.

show abstract

Section: Construction Of Synthetic Cross-feeding Interactionsmentioning

confidence: 69%

Metabolic cross-feeding via intercellular nanotubes among bacteria

et al. 2015

View full text Add to dashboard Cite

show abstract

“…The GC-MS-derived mass isotope distributions of proteinogenic amino acids were then corrected for naturally occurring isotopes (46), and the flux ratio of glycolysis to pentose phosphate pathway (as serine derived through glycolysis) was calculated with FiatFlux software (15). To calculate the absolute glycolytic flux, glucose uptake rates were calculated converting OD 600 into cellular volume according to a recent publication (47) and then multiplied with the flux ratio obtained above, assuming no residual glucose as was found by others (13).…”

mentioning

confidence: 99%

“…In the scoring phase (quantification), proteins of interest were quantified from digested cell lysates (1 μg per analysis) across all samples using selected reaction monitoring and spiked in heavy labeled reference peptides on a Vantage LC-MS platform (Thermo Fisher Scientific) as previously described (51,52). Condition-dependent intracellular volumes were taken from a recent publication (47).…”

mentioning

confidence: 99%

Functioning of a metabolic flux sensor in Escherichia coli

Kochanowski

Volkmer

Gerosa

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

191

183

View full text Add to dashboard Cite

Regulation of metabolic operation in response to extracellular cues is crucial for cells' survival. Next to the canonical nutrient sensors, which measure the concentration of nutrients, recently intracellular "metabolic flux" was proposed as a novel impetus for metabolic regulation. According to this concept, cells would have molecular systems ("flux sensors") in place that regulate metabolism as a function of the actually occurring metabolic fluxes. Although this resembles an appealing concept, we have not had any experimental evidence for the existence of flux sensors and also we have not known how these flux sensors would work in detail. Here, we show experimental evidence that supports the hypothesis that Escherichia coli is indeed able to measure its glycolytic flux and uses this signal for metabolic regulation. Combining experiment and theory, we show how this flux-sensing function could emerge from an aggregate of several molecular mechanisms: First, the system of reactions of lower glycolysis and the feedforward activation of fructose-1,6-bisphosphate on pyruvate kinase translate flux information into the concentration of the metabolite fructose-1,6-bisphosphate. The interaction of this "flux-signaling metabolite" with the transcription factor Cra then leads to flux-dependent regulation. By responding to glycolytic flux, rather than to the concentration of individual carbon sources, the cell may minimize sensing and regulatory expenses. R egulation of metabolic operation is crucial for cells' survival. The canonical view is that this regulation occurs in response to extracellular cues, where, for instance, nutrient-specific transmembrane or intracellular receptors sense the presence of a nutrient and transfer respective commands to the regulatory machinery (1-5).Recently, however, a novel impetus for metabolic regulation was proposed: cells could regulate their metabolism as a function of the actually occurring intracellular metabolic fluxes (6, 7). According to this concept, changes in extracellular nutrient abundances would first-in a rather passive manner-result in changes in intracellular metabolic fluxes. In a second instance, the metabolic fluxes would be sensed by molecular systems ("flux sensors"), which in turn would transmit the sensed "flux signal" to the regulatory machinery that consequently would adjust metabolic operation (6). On the basis of a detailed mathematical model of Escherichia coli's central metabolism and its regulation, Kotte et al. suggested that this organism would have such flux sensors in place that establish a correlation between a metabolic flux and the concentration of certain so-called flux-signaling metabolites, which in turn affect the activity of transcription factors and thus would allow for transcriptional regulation in a flux-dependent manner.Using intracellular flux to regulate metabolism is an appealing concept as it would omit the need for nutrient-specific sensors for many different nutrients, and would allow the integration of multiple nutrient inputs directl...

show abstract

“…This distribution may change over time and with the experimental conditions. For instance, in conditions supporting a higher growth rate, the average size of the cell in the population is larger [6,43]. Several models of the cell size distribution and its dependence on the experimental conditions have been proposed, based on different hypotheses about the criterion determining when a cell divides (reviewed in [42,44]).…”

Section: Growth Of Microbial Populationsmentioning

confidence: 99%

Mathematical modelling of microbes: metabolism, gene expression and growth

Jong

Casagranda

Giordano

et al. 2017

J. R. Soc. Interface.

View full text Add to dashboard Cite

The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research.

show abstract

Condition-Dependent Cell Volume and Concentration of Escherichia coli to Facilitate Data Conversion for Systems Biology Modeling

Cited by 299 publications

References 32 publications

Metabolic cross-feeding via intercellular nanotubes among bacteria

Metabolic cross-feeding via intercellular nanotubes among bacteria

Functioning of a metabolic flux sensor in Escherichia coli

Mathematical modelling of microbes: metabolism, gene expression and growth

Contact Info

Product

Resources

About