Biology subjects

Hubmann, G.

Publications and source records attributed to Hubmann, G..

3 recordsLinked to original sources

bioRxiv2020

Unbiased metabolic flux inference through combined thermodynamic and 13C flux analysis

ABSTRACTQuantification of cellular metabolic fluxes, for instance with 13C-metabolic flux analysis, is highly important for applied and fundamental metabolic research. A current challenge in 13C-flux analysis is that the available experimental data are usually insufficient to resolve metabolic fluxes in large metabolic networks without making assumptions on flux directions and reversibility. To infer metabolic fluxes in a more unbiased manner, we devised an approach that does not require such assumptions. The developed three-step approach integrates thermodynamics, metabolome, physiological data, and 13C labelling data, and involves a novel method to comprehensively sample the complex thermodynamically-constrained metabolic flux space. Applying our approach to budding yeast with its compartmentalised metabolism and parallel pathways, we could resolve metabolic fluxes in an unbiased manner, we obtained an uncertainty estimate for each flux, and we found novel flux patterns that until now had remained unknown, likely due to assumptions made in previous 13C flux analysis studies. We expect that our approach will be an important step forward to determine metabolic fluxes with improved accuracy in microorganisms and possibly also in more complex organisms.View Full Text

systems biology↗

bioRxiv2019

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

Metabolic heterogeneity between individual cells of a population harbors offers significant challenges for fundamental and applied research. Identifying metabolic heterogeneity and investigating its emergence requires 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, by drawing on the robust cell-intrinsic correlation between glycolytic flux and levels of fructose-1,6-bisphosphate (FBP), and by transplanting the B. subtilis FBP-binding transcription factor CggR into yeast. As proof of principle, using fluorescence microscopy, we applied the sensor to identify metabolic subpopulations in yeast cultures. We anticipate that our biosensor will become a valuable tool to identify and study metabolic heterogeneity in cell populations.

synthetic biology↗

bioRxiv2018

Saccharomyces cerevisiae goes through distinct metabolic phases during its replicative lifespan

A comprehensive description of the phenotypic changes during cellular aging is key towards unraveling its causal forces. Using recently developed experimental tools, which previously had enabled us to map age related changes in proteome and transcriptome (Janssens et al., 2015), and model-based inference methods, here, we generated a comprehensive account of the metabolic changes during the entire replicative life of Saecharomyces cerevisiae. With age, we found decreasing metabolite levels, decreasing growth and substrate uptake rates accompanied by a switch from aerobic fermentation to a respiratory metabolism, with increased glycerol and acetate production. The identification of intracellular metabolic fluxes revealed an increase in redox cofactor turnover, likely to combat the increased production of reactive oxygen species. The identified metabolic changes possibly reflect a dynamic adaptation to the age-associated, non- homeostatic increase in volume. With metabolism being an important factor of the cellular phenotype, this work complements our recent mapping of the transcriptomic and proteomic changes towards a holistic description of the cellular processes during aging.

cell biology↗