Metabolic heterogeneity in clonal microbial populations

Takhaveev, Vakil; Heinemann, Matthias

doi:10.1016/j.mib.2018.02.004

Cited by 89 publications

(95 citation statements)

References 81 publications

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“…Increasing evidence suggests that individual cells in a population can be metabolically very different (Nikolic et al, 2013;van Heerden et al, 2014;Solopova et al, 2014;Kotte et al, 2015;Takhaveev & Heinemann, 2018). Metabolic heterogeneity has been found, for instance, not only in microbial cultures used for biotechnological processes (Xiao et al, 2016), but also in cells of human tumors (Strickaert et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Here, particularly knowing the flux through glycolysis would be valuable, as this flux has been shown to correlate with highly productive phenotypes (Gupta et al, 2017) and cancer (Pavlova & Thompson, 2016). While nowadays metabolic fluxes can be resolved in ensembles of cells, for instance, by means of 13 C flux analysis (Antoniewicz, 2015), inference of fluxes in individual cells, however, is not possible until today (Takhaveev & Heinemann, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

Monteiro

Hubmann

Takhaveev

et al. 2019

Molecular Systems Biology

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Metabolic heterogeneity between individual cells of a population harbors significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence require tools to zoom into metabolism of individual cells. While methods exist to measure metabolite levels in single cells, we lack capability to measure metabolic flux, i.e., the ultimate functional output of metabolic activity, on the single‐cell level. Here, combining promoter engineering, computational protein design, biochemical methods, proteomics, and metabolomics, we developed a biosensor to measure glycolytic flux in single yeast cells. Therefore, drawing on the robust cell‐intrinsic correlation between glycolytic flux and levels of fructose‐1,6‐bisphosphate (FBP), we transplanted the B. subtilis FBP‐binding transcription factor CggR into yeast. With the developed biosensor, we robustly identified cell subpopulations with different FBP levels in mixed cultures, when subjected to flow cytometry and microscopy. Employing microfluidics, we were also able to assess the temporal FBP/glycolytic flux dynamics during the cell cycle. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

Monteiro

Hubmann

Takhaveev

et al. 2019

Molecular Systems Biology

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“…A recent review article by Heins and Weuster‐Botz () has summarized several mechanisms, such as (a) bet‐hedging; (b) noise in gene expression; (c) persistence; (d) bistability, behind population heterogeneity in microbial bioprocesses. Also, recent findings have concluded that stochastic fluctuations in the molecular level can propagate by both regulatory proteins and metabolic reactions and the inherently stochastic cellular metabolism could be a generic source of phenotypic heterogeneity (Kiviet et al, ; Takhaveev & Heinemann, ). For example, Nikolic et al () showed that in clonal bacterial populations such metabolic specialization can be driven by stochastic gene expression, and consequently, large variation in metabolic activities between single cells was found in terms of the assimilation of different sugars (glucose and arabinose).…”

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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“…In particular the determination of steady-state cAMP levels remains difficult using currently available methods, and for this reason many of the functional implications of basal cAMP levels are still under debate 4,[46][47][48] . Finally, it is still unknown whether heterogeneity occurs in single-cell cAMP responses; Cell-to-cell heterogeneity is especially relevant for industrial bioprocessing where glucose-signalling heterogeneity can affect industrial efficiency [49][50][51][52] .…”

Section: Introductionmentioning

confidence: 99%

A yeast FRET biosensor enlightens cAMP signalling

Botman

O’Toole²,

Goedhart

et al. 2019

Preprint

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The cAMP-PKA signalling cascade in budding yeast regulates adaptation to changing environments. Many questions remain about the function of cAMP dynamics, largely because no robust method for in vivo cAMP measurements exists for yeast. Here we developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We show that this biosensor can be used in flow cytometry, allowing for highthroughput single cell-level quantification that complements microscopy measurements. We quantified both steady-state cAMP levels and dynamic changes in response to sudden nutrient transitions. Steadystate cAMP levels were found to correlate with growth rate (rather than glycolytic flux) and were independent of extracellular sensing. During transitions, a cAMP-peak differentiates between different carbon source transitions, and is generated only when switching from non-or slowly-fermentable sugars to rapidly fermentable ones. Furthermore, generation of the cAMP peak is mediated by a combination of extracellular sensing and metabolism; deficits in either results in a largely diminished response. Moreover, the cAMP peak follows Weber's law; its height scales with the relative, and not the absolute, change in glucose. This means that cells respond to small changes in extracellular glucose when the initial level is low, but ignore these changes when this level is high. Lastly, we speculate that the cAMP peak height conveys information about prospective growth rates, especially for fermentable sugars. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signalling and metabolic adaptation.

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Metabolic heterogeneity in clonal microbial populations

Cited by 89 publications

References 81 publications

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

Measuring glycolytic flux in single yeast cells with an orthogonal synthetic biosensor

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

A yeast FRET biosensor enlightens cAMP signalling

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