Biology subjects

Heinemann, M.

Publications and source records attributed to Heinemann, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Accessible Gibbs energy at metabolic activation limits long-term cell growth

When exposed to a nutrient, cells activate metabolism by reorganizing metabolite pools and enzyme expression to approach the maximal growth rate permitted by physicochemical constraints. While these constraints define reachable steady states, here we propose that the Gibbs energy accessible at activation further limits which states are reached. Using minimal metabolic models, we find that limited accessible Gibbs energy can trap cells in low-growth states by constraining metabolic reorganization and imposing a proteomic burden on transport and phosphorylation reactions. To investigate this experimentally, we reconstituted the arginine deiminase pathway in vesicles, revealing that the size of a conserved pool of interconverting metabolites (arginine, citrulline, and ornithine) determines accessible Gibbs energy and constrains steady-state ATP production rate, a proxy for growth. Together, these results indicate that cellular metabolism retains memory of its initial energetic state, with accessible Gibbs energy at activation acting as a thermodynamic constraint on long-term growth.

systems biology↗

bioRxiv2026

Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data is essential. Unfortunately, accurate resolution of cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires inherently imperfect cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3373 proteins, using cross-validation to determine regularisation parameters, and identified 563 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

systems biology↗

bioRxiv2026

Cell cycle dynamics of redox state and lipid metabolism in S. cerevisiae, S. pombe and murine leukaemia cells

Coordination of metabolism, cell growth and cell division is essential to life. Recent single-cell measurements in S. cerevisiae have shown that metabolic processes and the cellular redox state are dynamic along the cell cycle. However, it is unknown whether similar metabolic oscillations also occur in other organisms. Until now, the dynamics of metabolism in other eukaryotes have predominantly been studied in cell cycle synchronised populations. Since cell cycle synchronisation methods can perturb metabolism, they may also introduce artefacts in the recorded dynamics. Here, we performed time-lapse microscopy analyses of exponentially growing single cells of the budding yeast S. cerevisiae, the fission yeast S. pombe and murine leukaemia L1210 cells. Measuring the NAD(P)H autofluorescence and the cell surface area growth rate in unsynchronised cells, we discovered oscillations along the cell cycle of the cellular redox state and lipid metabolism, respectively. Thus, our work shows that metabolism is dynamic along the cell cycle of these three evolutionarily distant eukaryotic organisms. This finding suggests that such metabolic oscillations could be a conserved characteristic among eukaryotes.

cell biology↗

bioRxiv2025

Thermo-Flux: generation and analysis of comprehensive thermodynamic-stoichiometric metabolic network models

Metabolic modelling, particularly through genome-scale stoichiometric models and flux balance analysis (FBA), has greatly advanced our understanding of metabolism. Yet, there is a continued quest to further improve the predictive capabilities of FBA. While thermodynamic constraints can allow for improved predictions, their addition to metabolic models has so far required cumbersome manual curation. To circumvent manual curation, we introduce Thermo-Flux, a semi-automated Python package, which converts stoichiometric metabolic network models into comprehensive thermodynamic-stoichiometric models for improved predictions. Thermo-Flux enables (i) automated mass and charge balancing while considering physical and biochemical parameters, (ii) definition of transporter variants and Gibbs energies for membrane transport, (iii) robust handling of metabolites with unknown structures or Gibbs energies, and integrates (iv) recent advances in determining Gibbs energies and their respective uncertainties. To guide users in the conversion of stoichiometric models into comprehensive thermodynamic-stoichiometric models, we provide detailed instructions on how to use the Thermo-Flux pipeline and include background information to enable appropriate modeling assumptions. By converting 87 stoichiometric models from the BiGG database and demonstrating improved flux predictions for a genome-scale yeast model (iMM904) converted into a thermodynamic-stoichiometric model, we showcase the applicability of Thermo-Flux. We expect Thermo-Flux to support fundamental metabolic research and biotechnological applications. The package is available at https://github.com/molecular-systems-biology/thermo-flux along with tutorials.

systems biology↗

bioRxiv2025

Lipid droplet dynamics during the budding yeast cell cycle influence the timing of cell cycle START

Recent work has revealed that metabolism is dynamic over the budding yeast cell cycle, showing that the NAD(P)H autofluorescence oscillates, and protein and lipid biosynthesis are dynamic. Dynamic storage and liquidation of neutral lipids over the cell cycle could contribute to these metabolic dynamics. However, the dynamics of neutral lipids over the cell cycle are as yet unknown. To elucidate them, we established mNeonGreen-Tgl3 and Pln1-mNeonGreen as protein markers for lipid droplets (LDs) and determined LD dynamics during the cell cycle with single-cell time-lapse microscopy. We found oscillations in the LD number over the cell cycle, with a notable trough around START. Deletion of the genes responsible for either the synthesis of triacylglycerol (TAG) or its mobilisation from LDs lowered LD numbers and abolished the oscillation in LD number. Moreover, in these deletion mutants, we found START to be delayed, suggesting that the mobilisation of TAG from LDs is required for its timely occurrence. The influence of LD dynamics on the timing of START emphasises that research studying cell cycle commitment should consider storage lipid metabolism as a potential contributor to cell cycle START.

cell biology↗

bioRxiv2025

Detecting Nuclear Pore Complex assembly in living cells

The formation of nuclear pore complexes (NPCs) -- vital gateways regulating nuclear-cytoplasmic transport -- is a highly orchestrated process requiring the integration of hundreds of nucleoporins into the nuclear envelope. A major challenge in studying this assembly process in living cells has been the difficulty to distinguish newly forming NPCs from their mature counterparts. Here, we present a powerful nanobody-based approach that overcomes this limitation. We demonstrate that a nanobody targeting the nucleoporin Nic96 from Saccharomyces cerevisiae selectively binds newly synthesized Nic96 subcomplexes prior to its incorporation into NPCs in vivo. Importantly, nanobody-bound Nic96 is incorporated in NPCs, and expression of the nanobody does not disrupt nuclear transport, cell growth, or lifespan, nor does it show genetic interactions with known NPC assembly surveillance pathways -- making it an ideal, non-perturbing tool to study NPC biogenesis in yeast. Illustrating the use of the Nic96 nanobody we report novel aspects of the early stages of assembly, including co-recruitment of Kap121 and VHH[Nic96] to putative assembly sites. In addition, we show local enrichment of newly synthesized nucleoporins on nuclear envelope proximal lipid droplets, including a specific subset of Pdr16- and Ldo16-positive lipid droplets near the nucleus-vacuole junction. When NPC assembly is delayed, association of Nic96 subcomplexes with lipid droplets increases. These findings illustrate how the strategy to pulse-label newly forming NPCs opens new avenues for dissecting the spatiotemporal regulation of NPC assembly and misassembly.

cell biology↗

bioRxiv2025

OsTIR1F74G Expression Controls Basal and Auxin-Induced Degradation Rates of the AID System in Yeast

The auxin-inducible degron (AID) system has been widely used to conditionally and dynamically deplete proteins in yeast. In this system, the plant hormone auxin promotes OsTIR1-mediated degradation of proteins carrying an auxin-inducible degron tag. However, "basal" degradation of AID-tagged proteins in the absence of auxin has hampered work with essential proteins due to defective or non-viable strains. A second-generation AID system based on the OsTIRF47G mutant was recently introduced to overcome the limitations of basal degradation in budding yeast. However, it remained unclear to what extent the use of OsTIRF47G eliminates basal degradation and how it impacts auxin-induced degradation. Here, by performing a quantitative characterization of the basal and auxin-induced degradation dynamics using OsTIR1F74G in budding yeast, we find that basal degradation is still detectable and that it depends on OsTIR1F74G expression levels. We show that also the auxin-induced degradation rates and auxin-induced steady-state concentrations of AID-tagged proteins depend on OsTIR1F74G expression levels, in addition to the type of auxin used. Lastly, we showcase how increased basal degradation of AID-tagged functional proteins can impair cell growth and lead to unwanted phenotypes. We anticipate that our findings will guide future applications of the AID system in budding yeast by helping researchers select appropriate OsTIR1F74G expression levels, particularly in studies focused on precise control of degradation dynamics.

molecular biology↗

bioRxiv2025

Condition-dependent, amorphous protein agglomerates control cytoplasmic rheology

Molecular crowding in the bacterial cytoplasm restricts the diffusion of large molecules, impacting cellular processes. However, how nutrient availability influences cytoplasmic rheology is not well understood. With single-particle tracking in Escherichia coli, we observed a threefold variation in the diffusion of a 40-nm particle across exponential growth conditions. Previously suggested determinants of rheology did not account for this variation; instead, we found a strong anticorrelation between the diffusion coefficient and the abundance of amino acid metabolism proteins, persisting upon genetic perturbations and showing that lower diffusion is associated with increased viscoelasticity. Photoactivated light microscopy revealed that some amino acid metabolism proteins form clusters. Electron microscopy showed that these proteins could form amorphous agglomerates at physiological concentrations in vitro, likely driven by their low intrinsic disorder, high compactness and hydropathy score. These findings show that protein agglomerates regulate cytoplasmic rheology in a condition-dependent manner, suggesting an underappreciated level of cytoplasmic organization. HighlightsO_LIDiffusion of 40-nm particles varies threefold across growth conditions in E. coli C_LIO_LICytoplasmic diffusion inversely correlates with COG-E protein abundance COG-E proteins form agglomerates that increase cytoplasmic viscoelasticity C_LIO_LIProtein compactness and hydrophobicity predict condition-dependent crowding effects C_LI

biophysics↗

bioRxiv2024

Alternatives to photorespiration: A systems-level analysis reveals mechanisms of enhanced plant productivity

Photorespiration causes a significant decrease in crop yield due to mitochondrial decarboxylation. Alternative pathways (APs) have been designed to relocate the decarboxylating step or even fix additional carbon. To improve the success of transferring those engineered APs from model species to crops we must understand how they will interact with metabolism and how the plant physiology affects their performance. Here we used multiple mathematical modelling techniques to analyse and compare existing AP designs. We show that carbon-fixing APs are the most promising candidates to replace native photorespiration in major crop species. Our results demonstrate the different metabolic routes that APs employ to increase yield and which plant physiology can profit most from them. We anticipate our results to guide the design of new APs and to help improve existing ones. Codehttps://gitlab.com/gain4crops/2024-paper

plant biology↗

bioRxiv2024

Contribution of cellular macromolecules to the diffusion of a 40 nm particle in Escherichia coli

Due to the high concentration of proteins, nucleic acids and other macromolecules, the bacterial cytoplasm is typically described as a crowded environment. However, the extent to which each of these macromolecules individually affects the mobility of macromolecular complexes, and how this depends on growth conditions, is presently unclear. In this study, we sought to quantify the crowding experienced by an exogenous 40 nm fluorescent particle in the cytoplasm of E. coli under different growth conditions. By performing single particle tracking measurements in cells selectively depleted of DNA and/or mRNA, we determined the contribution to crowding of mRNA, DNA and remaining cellular components, i.e., mostly proteins and ribosomes. To estimate this contribution to crowding, we quantified the difference of the particles diffusion coefficient in conditions with and without those macromolecules. We found that the contributions of the three classes of components were of comparable magnitude, being largest in the case of proteins and ribosomes. We further found that the contributions of mRNA and DNA to crowding were significantly larger than expected based on their volumetric fractions alone. Finally, we found that the crowding contributions change only slightly with the growth conditions. These results reveal how various cellular components partake in crowding of the cytoplasm and the consequences this has for the mobility of large macromolecular complexes. Statement of SignificanceThe mobility of a particle of interest in the cytoplasm depends on a variety of factors that include the concentration, shape and physicochemical properties of crowding obstacles. Different macromolecules in the cell are therefore expected to hinder the mobility of a given particle to different extents. However, an accurate and systematic investigation of these hindrances to mobility in vivo has not been yet carried out. In this work, through a novel combination of experimental and computational approaches, we determine the diffusion coefficient of a 40 nm particle in the cytoplasm of E. coli under conditions of selective removal of some macromolecules. This allows us to quantify the hindering effect of each of the depleted macromolecules on the mobility of the said particle. For DNA, mRNA, and remaining macromolecules, we observe that this effect is of comparable magnitude, being largest in the latter case. This work sheds light on the interplay between intracellular composition and the physical properties of the cytoplasm at the 40 nm scale.

biophysics↗

bioRxiv2022

Comparison of two sampling, quenching and extraction methods for quantitative yeasts metabolomics

Knowing intracellular metabolite concentrations is important for fundamental metabolism research as well as for biotechnology. The first steps in a quantitative metabolomics workflow, i.e., sampling, quenching, and extraction, are key to obtaining unbiased quantitative data. A qualified quenching and extraction method not only needs to rapidly terminate the in vivo biochemical reaction activities to preserve the endogenous metabolite levels but also has to fully extract all metabolites from cells. Recently, two different filtration-based sampling, quenching, and extraction methods have been proposed and used for quantitative yeast metabolomics. One method integrates the quenching and extraction into one step using a methanol-acetonitrile-water solution after a filtration step, while the other --more conventional-- method quenches with cold methanol and extracts with boiling ethanol. In this study, we tested whether these two methods are equally well suited for quantitative metabolome analyses with yeast. Using isotope dilution mass spectrometry (IDMS) with GC-MS and LC-MS as analytical methods, in combination with precise quantification of cell volumes, we determined absolute concentrations of 63 intracellular metabolites covering amino acids, organic acids, phosphorylated sugars, and nucleotides in two S. cerevisiae strains with different physiology. By analyzing the data from samples generated with the two methods, we found that while both methods yielded essentially identical concentrations for amino and organic acids, the cold-solvent extraction yielded significantly lower concentrations for particularly phosphorylated sugars and nucleotides, presumably because of lower quenching or extraction efficiency of this method. Our results show that methanol-quenching combined with boiling-ethanol-extraction is the more accurate approach when aiming to quantify a broad range of different metabolites from yeast cells. The significant discrepancy observed between both common metabolite extraction methods demonstrates the importance of method optimization for quantitative studies in particular when working with microbes with rigid cell walls such as those found in yeast.

systems biology↗

bioRxiv2021

A new sugar for an old phage: A c-di-GMP dependent polysaccharide pathway sensitizes E. coli for bacteriophage infection

Bacteriophages are ubiquitous parasites of bacteria and major drivers of bacterial ecology and evolution. Despite an ever-growing interest in their biotechnological and therapeutic applications, detailed knowledge of the molecular mechanisms underlying phage-host interactions remains scarce. Here, we show that bacteriophage N4 exploits a novel surface glycan, NGR, as a receptor to infect its host Escherichia coli. We demonstrate that this process is regulated by the second messenger c-di-GMP and that N4 infection is specifically stimulated by the diguanylate cyclase DgcJ while the phosphodiesterase PdeL effectively protects E. coli from N4-mediated killing. PdeL-mediated protection requires its catalytic activity to reduce c-di-GMP and includes a secondary role as a transcriptional repressor. We demonstrate that PdeL binds to and represses the promoter of the wec operon, which encodes components of the ECA exopolysaccharide pathway. However, only the acetylglucosamine epimerase WecB but none of the other ECA components is required for N4 infection. Based on this, we postulate that NGR is an N-acetylmannosamine-based carbohydrate polymer that is produced and exported to the cell surface of E. coli in a c-di-GMP dependent manner where it serves as a receptor for N4. This novel carbohydrate pathway is conserved in E. coli and other bacterial pathogens, serves as the primary receptor for a range of N4-like bacteriophages, and is induced at elevated temperature and by specific amino acid-based nutrients. These studies provide an entry point into understanding how bacteria use specific regulatory mechanisms to balance costs and benefits of highly conserved surface structures. ImportanceBecause bacterial surface glycans are in direct contact with the environment they can provide essential protective functions during infections or against competing bacteria. But such structures are also Achilles heels as they can serve as primary receptors for bacteriophages. Bacteria thus need to carefully control the exposure of conserved surface glycans to balance costs and benefits. Here, we identify a novel exopolysaccharide that is widely conserved in E. coli and is used by N4 and related bacteriophages as primary receptor. We demonstrate that the synthesis of NGR (N4 glycan receptor) is tightly controlled by the second messenger c-di-GMP in a highly specific manner and by a single diguanylate cyclase. These studies provide an example of how bacteria can alleviate the strong selective pressure imposed on them by bacteriophages entering through conserved surface structures by carefully regulating their synthesis and secretion.

microbiology↗

bioRxiv2021

A photo-switchable yeast isocitrate dehydrogenase to control metabolic flux through the citric acid cycle

For various research questions in metabolism, it is highly desirable to have means available, with which the flux through specific pathways can be perturbed dynamically, in a reversible manner, and at a timescale that is consistent with the fast turnover rates of metabolism. Optogenetics, in principle, offers such possibility. Here, we developed an initial version of a photo-switchable isocitrate dehydrogenase (IDH) aimed at controlling the metabolic flux through the citric acid cycle in budding yeast. By inserting a protein-based light switch (LOV2) into computationally identified active/regulatory-coupled sites of IDH and by using in vivo screening in Saccharomyces cerevisiae, we obtained a number of IDH enzymes whose activity can be switched by light. Subsequent in-vivo characterization and optimization resulted in an initial version of photo-switchable (PS) IDH. While further improvements of the enzyme are necessary, our study demonstrates the efficacy of the overall approach from computational design, via in vivo screening and characterization. It also represents one of the first few examples, where optogenetics were used to control the activity of a metabolic enzyme.

synthetic biology↗

bioRxiv2020

A synthetic RNA-based biosensor for fructose-1,6-bisphosphate that reports glycolytic flux

Metabolic heterogeneity, the occurrence of different metabolic phenotypes among cells, represents a key challenge in health and biotechnology. To unravel its molecular basis, tools probing metabolism of single cells are needed. While RNA devices harbor huge potential for the development of such tools, until today, it is challenging to create in vivo-functional sensors for any given metabolite. Here, we developed from scratch an RNA-based sensor for fructose-1,6-bisphosphate (FBP), a doubly phosphorylated intermediate of glycolysis. Starting from in vitro selection of an RNA aptamer and its structural analyses, we developed libraries of RNA-based regulatory devices with this aptamer and the hammerhead ribozyme as an actuator. Through FACS-seq-based high-throughput screening in yeast, we identified in vivo-functional FBP-sensing devices that generate fluorescent readout dependent on intracellular FBP concentration. As FBP reports the flux through glycolysis, the developed RNA device can be used to sense the glycolytic rate in single cells, offering unprecedented possibilities to investigate the causes of metabolic heterogeneity.

synthetic biology↗

bioRxiv2020

A robust method for generating, quantifying and testing large amounts of Escherichia coli persisters

Bacteria can exhibit phenotypes, which makes them tolerant against antibiotics. However, often only a few cells of a bacterial population show such so-called persister phenotype, which makes it difficult to study this health-threatening phenotype. We recently found that certain abrupt nutrient-shifts generate E. coli populations that consist of almost only antibiotic tolerant persister cells. Such nearly homogeneous persister cell populations enable assessment with population-averaging experimental methods, such as high-throughput methods. In this paper, we provide a detailed protocol of how to generate such large fraction of tolerant cells using the nutrient-switch approach. Furthermore, we describe how to determine the fraction of cells that enter the tolerant state upon a sudden nutrient shift and describe a new way to assess antibiotic tolerance with flow cytometry. We envision that these methods facilitate research into the important and exciting phenotype of bacterial cells.Competing Interest StatementThe authors have declared no competing interest.View Full Text

molecular biology↗

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↗