Biology subjects

van Heerden, J. H.

Publications and source records attributed to van Heerden, J. H..

8 recordsLinked to original sources

bioRxiv2026

A rapid HPLC-based method to determine NAD(P)(H) and NMN redox cofactor concentrations and ratios in microbes

Redox cofactors are a key part of cellular physiology as they are involved in most metabolic pathways, and their ratios are linked to cellular robustness. However, measuring their levels in cells remains a challenge. Here, we describe a novel method to rapidly measure NAD(H), NADP(H) and nicotinamide mononucleotide (NMN) levels and their oxidized/reduced ratios using an HPLC connected to a fluorescence detector. By extensively characterizing this method and benchmarking it against the classical iodonitrotetrazolium (INT) assay, we show that this method results in accurate and reproducible measurements of NAD+, NADP+ and NMN levels in bacteria. We further demonstrate that this method can be used to determine intracellular NADH and NADPH concentrations and ratios of nicotinamide nucleotide cofactors in engineered Escherichia coli strains, as well as other bacterial species such as Pseudomonas putida.

biochemistry↗

bioRxiv2024

Mitochondrial efficiency determines Crabtree effect across yeasts

Under excess glucose conditions, many yeasts switch from high-yield respiratory metabolism to low-yield fermentation, a phenomenon called the Crabtree effect in yeast, or the Warburg effect in mammalian cells. Cellular constraints and limited resources are generally believed to govern the metabolic strategies of cells to adapt to environmental conditions, but which constraints drive this switch is still under debate. Here we study the Crabtree-negative, fully respiratory yeast Pichia kluyveri and compare it to the Crabtree-positive yeast Saccharomyces cerevisiae from a resource allocation perspective. By integrating quantitative physiology and proteomics into whole-cell proteome-constrained models, we find that the Crabtree effect is determined by the composition and catalytic efficiency of the electron transport chain. We find that the subsequent proteome efficiency of respiration versus fermentation varies between these species. The variation in parameters and composition of the respiratory machinery likely reflects the evolutionary and ecological history of these yeast species. This study advances our understanding of the role of proteome constraints and proteome efficiency in governing cellular metabolism of yeasts, and that of eukaryotic cells at large.

systems biology↗

bioRxiv2023

Growth consequences of the inhomogeneous organization of the bacterial cytoplasm

In many bacteria, translating ribosomes are excluded from the nucleoid, while amino-acid and energy-supplying metabolic enzymes spread evenly throughout the cytoplasm. Here we show with time-lapse fluorescence microscopy that this inhomogeneous organisation of the cytoplasm can cause single Escherichia coli cells to experience an imbalance between biosynthesis and metabolism when they divide, resulting in cell size-dependent growth rate perturbations. After division, specific growth rate and ribosome concentration correlates negatively with birthsize, and positively with each other. These deviations are compensated during the cell-cycle, but smaller-than-average cells do so with qualitatively different dynamics than larger-thanaverage cells. A mathematical model of cell growth, division and regulation of biosynthetic and metabolic resource allocation reproduces our experimental findings, suggesting a simple mechanism through which long-term growth rate homeostasis is maintained while heterogeneity is continuously generated. This work shows that the life of single bacterial cells is intrinsically out-of-steady-state, dynamic and reliant on cytoplasmic organization. Popular summaryClassical, population-level studies of the metabolism and growth of bacteria indicate that the average cell in a growing population operates at steady state and can be viewed as an homogeneous bag of enzymes. Here we show that this view does not capture the lives of single cells. At birth, they are perturbed from the steady state of their mother cell after which they need their entire cell cycle to return to this state by active regulation. Then they divide and their daughters are perturbed again; a never ending cycle that is inescapable and akin to a Sisyphean task. This behaviouremerges from the delicate interplay of the intrinsic randomness of (uneven) cell division, the inhomogeneous localisation of metabolic and ribosomal proteins in the cell, unbalanced metabolism, and compensatory steering of gene expression.

microbiology↗

bioRxiv2022

A computational toolbox to investigate the metabolic potential and resource allocation in fission yeast

The fission yeast Schizosaccharomyces pombe is a popular eukaryal model organism for cell division and cell cycle studies. With this extensive knowledge of its cell and molecular biology, S. pombe also holds promise for use in metabolism research and industrial applications. However, unlike the bakers yeast Saccharomyces cerevisiae, a major workhorse in these areas, cell physiology and metabolism of S. pombe remain less explored. One way to advance understanding of organism-specific metabolism is construction of computational models and their use for hypothesis testing. To this end, we leverage existing knowledge of S. cerevisiae to generate a manually-curated high-quality reconstruction of S. pombes metabolic network, including a proteome-constrained version of the model. Using these models, we gain insights into the energy demands for growth, as well as ribosome kinetics in S. pombe. Furthermore, we predict proteome composition and identify growth-limiting constraints that determine optimal metabolic strategies under different glucose availability regimes, and reproduce experimentally determined metabolic profiles. Notably, we find similarities in metabolic and proteome predictions of S. pombe with S. cerevisiae, which indicate that similar cellular resource constraints operate to dictate metabolic organization. With these use cases, we show, on the one hand, how these models provide an efficient means to transfer metabolic knowledge from a well-studied to a lesser-studied organism, and on the other, how they can successfully be used to explore the metabolic behaviour and the role of resource allocation in driving different strategies in fission yeast.

systems biology↗

bioRxiv2019

An improved ATP FRET sensor for yeast shows heterogeneity during nutrient transitions

Adenosine 5-triphosphate (ATP) is the main free energy carrier in metabolism. In budding yeast, shifts to glucose-rich conditions cause dynamic changes in ATP levels, but it is unclear how heterogeneous these dynamics are at the single-cell level. Furthermore, pH also changes and affects readout of fluorescence-based biosensors for single-cell measurements. To measure ATP changes reliably in single yeast cells, we developed yAT1.03, an adapted version of the AT1.03 ATP biosensor, that is pH-insensitive. We show that pregrowth conditions largely affect ATP dynamics during transitions. Moreover, single-cell analyses showed a large variety in ATP responses, which implies large differences of glycolytic startup between individual cells. We found three clusters of dynamic responses, and show that a small subpopulation of wild type cells reached an imbalanced state during glycolytic startup, characterised by low ATP levels. These results confirm the need for new tools to study dynamic responses of individual cells in dynamic environments.

molecular biology↗

bioRxiv2019

A new FRET biosensor enlightens cAMP signalling in budding yeast

The cAMP-PKA signalling cascade in budding yeast regulates adaptation to changing environments. We developed yEPAC, a FRET-based biosensor for cAMP measurements in yeast. We used this sensor with flow cytometry for high-throughput single cell-level quantification during dynamic changes in response to sudden nutrient transitions. We found that the characteristic cAMP peak differentiates between different carbon source transitions, and is rather homogenous among single-cells, especially for transitions to glucose. The peaks are mediated by a combination of extracellular sensing and intracellular metabolism. Moreover, the cAMP peak follows Webers law; its height scales with the relative, and not the absolute, change in glucose. Lastly, our results suggest that the cAMP peak height conveys information about prospective growth rates. In conclusion, our yEPAC-sensor makes possible new avenues for understanding yeast physiology, signalling and metabolic adaptation.

microbiology↗

bioRxiv2019

Growth-rate and protein-synthesis dynamics of single Bacillus subtilis cells along their cell-cycle

The growth rate of single bacterial cells is continuously disturbed by random fluctuations in biosynthesis rates and by deterministic cell-cycle events, such as division, genome duplication, and septum formation. It is not understood whether, and how, bacteria reject these disturbances. Here we quantified growth and constitutive protein expression dynamics of single Bacillus subtilis cells, as a function of cell-cycle-progression. Variation in birth size and growth rate, resulting from unequal cell division, is largely compensated for when cells divide again. We analysed the cell-cycle-dynamics of these compensations and found that both growth and protein expression exhibited biphasic behaviour. During a first phase of variable duration, the absolute rates were approximately constant and cells behaved as sizers. In the second phase, rates increased and growth behaviour exhibited characteristics of a timer-strategy. This work shows how cell-cycle-dependent rate adjustments of biosynthesis and growth are integrated to compensate for physio-logical disturbances caused by cell division.\n\nIMPORTANCEUnder constant conditions, bacterial populations can maintain a fixed average cell size and constant exponential growth rate. At the single cell-level, however, cell-division can cause significant physiological perturbations, requiring compensatory mechanisms to restore the growth-related characteristics of individual cells toward that of the average cell. Currently, there is still a major gap in our understanding of the dynamics of these mechanisms, i.e. how adjustments in growth, metabolism and biosynthesis are integrated during the bacterial cell-cycle to compensate the disturbances caused by cell division. Here we quantify growth and constitutive protein expression in individual bacterial cells at sub-cell-cycle resolution. Significantly, both growth and protein production rates display structured and coordinated cell-cycle-dependent dynamics. These patterns reveal the dynamics of growth rate and size compensations during cell-cycle progression. Our findings provide a dynamic cell-cycle perspective that offers novel avenues for the interpretation of physiological processes that underlie cellular homeostasis in bacteria.

microbiology↗

bioRxiv2017

Statistics and simulation of growth of single bacterial cells: illustrations with B. subtilis and E. coli

The inherent stochasticity of molecular reactions prevents us from predicting the exact state of single-cells in a population. However, when a population grows at steady-state, the probability to observe a cell with particular combinations of properties is fixed. Here we validate and exploit existing theory on the statistics of single-cell growth in order to predict the probability of phenotypic characteristics such as cell-cycle times, volumes, accuracy of division and cell-age distributions, using real-time imaging data for Escherichia coli and Bacillus subtilis. Our results show that single-cell growth-statistics can accurately be predicted from a few basic measurements. These equations relate different phenotypic characteristics, and can therefore be used in consistency tests of experimental single-cell growth data and prediction of single-cell statistics. We also exploit these statistical relations in the development of a fast stochastic-simulation algorithm of single-cell growth and protein expression. This algorithm greatly reduces computational burden, by recovering the statistics of growing cell-populations from the simulation of only one of its lineages. Our approach is validated by comparison of simulations and experimental data. This work illustrates a methodology for the prediction, analysis and tests of consistency of single-cell growth and protein expression data from a few basic statistical principles.

microbiology↗