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Beyss, M.

Publications and source records attributed to Beyss, M..

5 recordsLinked to original sources

Does specific membrane surface area constrain Escherichia coli energy spillage and therefore aerobic and anaerobic growth rates?

The physical dimensions of bacterial cells are finite, tightly regulated, and constrain phenotype based on cell surface area to volume ratios. Additionally, bacterial membranes have limited capacity to host proteins, constraining enzymatic potential per area. We combine these biophysical properties into an aggregate parameter termed specific membrane surface area (sMSA, nm2 gcdw-1) which is readily integrated into systems biology analyses including constraint-based stoichiometric models. sMSA-based analyses have remarkable agency to predict and interpret bacterial phenotypes including maximum growth rate, respiration efficiency, and potential for maintenance energy generation. The presented Escherichia coli research 1) quantitatively recreates the membrane proteome from central metabolism as a function of growth rate, 2) demonstrates that the maximum growth rate of both aerobic and anaerobic E. coli cultures abides by similar cell biology constraints including a Pareto tradeoff between simultaneous synthesis of biomass macromolecules and generation of maintenance energy, 3) quantifies the areal capacity of E. coli membranes to dissipate Gibbs energy during enthalpic and entropic dominated metabolisms, and 4) presents evidence that sMSA theory could permit isogenic populations to host multiple concurrent phenotypes with equivalent fitness metrics.The results support our sMSA theory, based on cell geometry and membrane enzyme capacity, as a substantial driver of metabolism in both oxic and anoxic environments.

systems biology↗

Multi-omic data fusion reveals the in vivo enzyme kinetics of Vibrio natriegens at the genome-scale

Vibrio natriegens is a halophilic, Gram-negative marine bacterium that is increasingly used in metabolic engineering applications due to its fast growth rate. In sparse minimal medium the organism has a doubling time of 25 minutes, which is about twice as fast as Escherichia coli under similar conditions. Given that its protein density is similarly constrained to that of E. coli, this necessitates that its metabolic enzymes are able to catalyze flux at a higher rate to sustain its metabolism. In this work, we measure the apparent turnover numbers of metabolically active enzymes in V. natriegens under a variety of growth conditions. The apparent turnover numbers of V. natriegens enzymes were measured in vivo by conducting coupled quantitative proteomics and 13C metabolic flux analysis experiments under seven different carbon source conditions in sparse minimal medium. A high quality genome-scale metabolic model was constructed and curated using additional experimental data. This model was extended with enzyme constraints, and subsequently used to find kinetic parameters that minimize the difference between model predictions and experimental observations. This model guided data fusion approach enabled the estimation of 357 apparent turnover numbers for metabolically active enzymes in V. natriegens. Our results reveal that the metabolic enzymes of V. natriegens are in median 14-fold faster than those of E. coli under similar conditions. Moreover, we show that machine learning generated turnover number estimates substantially underestimate the kinetics of V. natriegens. Our turnover number estimates were used to parameterize multiple condition dependent enzyme constrained flux balance analysis models of V. natriegens, which improved their predictive accuracy compared to the machine learning parameterisation. The combined experimental-computational approach employed here sheds light on the mechanism V. natriegens uses to accelerate its growth. This approach can also be extended to other bacteria, increasing the availability of in vivo measured enzyme turnover numbers, and improving the predictive accuracy of enzyme constrained metabolic models of other microbes.

systems biology↗

MooViE - Engine for single-view visual analysis of multivariate data

SummaryUnderstanding input-output relationships within multivariate datasets is an ubiquitous task in the life and data sciences. For this, visual analysis is indispensable for providing expressive summaries and preparing decision-making. We present the visual analysis approach and software MooViE, which is designed to strike the balance between being tailored to the specific data semantic and while being broadly applicable. MooViE supports the data exploration process for extracting important information from the data and captures the result in a fresh single-view visualization. MooViE is implemented in C++ to facilitate fast access and effective interaction with comprehensive multivariate datasets. We showcase the engine for various application fields, relevant to the life sciences. Availability and ImplementationThe source code is available under MIT license at https://jugit.fz-juelich.de/IBG-1/ModSim/MooViE and https://github.com/JuBiotechMooViE, with detailed documentation and usage instructions (https://moovie.readthedocs.io), as well as zenodo-archived releases (https://doi.org/10.5281/zenodo.10997388). Platform independent Docker images are also available (jugit-registry.fz-juelich.de/ibg-1/modsim/moovie/moovie). ContactKatharina Noh k.noeh@fz-juelich.de

bioinformatics↗

Bayesian multi-model-based 13C15N-metabolic flux analysis quantifies carbon-nitrogen metabolism in mycobacteria

Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the pathogen Mycobacterium tuberculosis (Mtb), this is particularly important in informing the development of effective drugs targeting Mtbs metabolism. Here we performed 13C15N dual isotopic labelling of mycobacterial steady state cultures and quantified intracellular carbon-nitrogen (CN) and nitrogen (N) fluxes in addition to carbon (C) fluxes and inferred their reaction bidirectionalities. The combination of 13C15N-MFA with a Bayesian multi-model approach allowed us to resolve C and N fluxes simultaneously which was not possible with classical 13C-MFA. We quantified CN fluxes for amino acid and, for the first time, nucleotide biosynthesis. Our analysis identified glutamate as the central CN and N node in mycobacteria, and improved resolution of the anaplerotic node. Our study describes a powerful platform to measure carbon and nitrogen metabolism in any biological system with statistical rigor.

systems biology↗

A mixed nitrogen diet and compartmentalized utilization for Mycobacterium tuberculosis replicating in host cells: results of a systems-based analysis

Nitrogen metabolism of Mycobacterium tuberculosis (Mtb) is crucial for the survival and virulence of this pathogen inside host macrophages but little is known about the nitrogen sources acquired from the host or their route of assimilation. Here we performed a systems-based analysis of nitrogen metabolism in intracellullar Mtb and developed 15N-Flux Spectral Ratio Analysis (FSRA) to probe the metabolic cross-talk between the host cell and Mtb. We demonstrate that intracellular Mtb acquires nitrogen from multiple amino acids in the macrophage including glutamate, glutamine, aspartate, alanine, glycine and valine, with glutamine being the predominant nitrogen donor. Each nitrogen source is uniquely assimilated into specific intracellular pools indicating compartmentalised metabolism. This was not observed for in vitro-grown Mtb indicating that there is a switch in nitrogen metabolism when the pathogen enters the intracellular environment. These results provide clues about the potential metabolic targets for development of innovative anti-tuberculosis therapies.

microbiology↗