Biology subjects

de Groot, D. H.

Publications and source records attributed to de Groot, D. H..

11 recordsLinked to original sources

bioRxiv2025

Side-by-side systematic characterization of novel FPs in budding yeast

Fluorescent proteins (FPs) have become indispensable for biological research. Since the discovery of the first FP, Aequorea victoria green fluorescent protein (avGFP), new fluorescent proteins are continuously being developed. To achieve optimal experimental results, selecting an FP based on specific characteristics--such as brightness, photostability, photochromicity, monomericity, pH robustness, and fluorescence lifetime--is essential. However, a thorough side-by-side comparison of these characteristics is missing for the latest generation of FPs. Here, we provide a comprehensive characterization in yeast of the most recently developed FPs, including FPs that were codon-optimized for yeast. We believe this provides an excellent compendium for choosing the most suitable FP for research purposes.

molecular biology↗

bioRxiv2025

Bonsai: Tree representations for distortion-free visualization and exploratory analysis of single-cell omics data

Single-cell omics methods promise to revolutionize our understanding of gene regulatory processes during cell differentiation, but analysis of such data continues to pose a major challenge. Apart from technical challenges such as the sparsity and heterogeneous noise properties of these data, the crucial problem is that we know little about the potentially very complex high-dimensional structures that the data represent. Consequently, there is an urgent need for exploratory analysis methods that allow rigorous representation and visualization of the structure in the data. However, currently popular methods such as UMAP and t-SNE are unsatisfactory because they are ad hoc, stochastic, uninterpretable, and known to severely distort the structure in the data. Here we show that these challenges can be overcome by representing the data on tree structures and present Bonsai : a novel method that reconstructs the most likely tree relating any set of high-dimensional objects while rigorously accounting for heterogeneous measurement noise. We show that, in contrast to other visualization methods, distances along the Bonsai trees accurately represent true distances between the objects in high-dimensional space across many types of datasets. Moreover, Bonsai automatically regularizes measurement noise, outperforming even methods specifically designed for that purpose on tasks such as nearest-neighbor identification. By analyzing a blood cell dataset, we show that Bonsai trees not only capture known lineage relationships but also provide novel biological insights. For example, Bonsai uncovers that different subsets of NK cells derive from the myeloid and lymphoid lineage, and pinpoints genes that distinguish myeloid-NK from lymphoid-NK cells. Bonsai is free from tunable parameters and scales to datasets of hundreds of thousands of cells. The accompanying tool, Bonsai-scout, provides visualizations of the Bonsai trees and allows for interactive data exploration such as identifying subclades and their markers, visualizing features along the tree, changing the tree layout, and zooming in on substructures. Finally, application to a dataset of football statistics shows the generality of Bonsai in successfully capturing complex structures in high-dimensional data.

bioinformatics↗

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

Coupling phenotype stability to growth rate overcomes limitations of bet-hedging strategies

Microbes in the wild face highly variable and unpredictable environments, and are naturally selected for their average growth rate across environments. Apart from using sensory-regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: increasing the phenotype switching rate increases the rate at which maladapted cells explore alternative phenotypes, but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are only effective when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype switching rates may systematically decrease with growth rate. We here show that such growth rate dependent stability (GRDS) can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. GRDS allows cells to be more explorative when maladapted, and more phenotypically stable when well-adapted. We further show that even a small decrease in switching rates of faster growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

evolutionary biology↗

bioRxiv2021

Metabolic constraints on nitrogen fixation by rhizobia in legume nodules

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which use plant-derived dicarboxylates as energy and electron sources for reduction of atmospheric N2 into ammonia for secretion to plants. Using heterogeneous genome-scale datasets, we reconstructed a model of bacteroid metabolism to investigate the effects of varying dicarboxylate and oxygen supply on carbon and nitrogen allocation. Modelling and 13C metabolic flux analysis in bacteroids indicate that microaerobiosis restricts the decarboxylating arm of the TCA cycle and limits ammonia assimilation into glutamate. Catabolism of dicarboxylates induces a higher oxygen demand but also a higher NADH/NAD+ ratio compared to sugars. Carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules with alanine secretion increasing as oxygen tension decreases. Our results provide a framework for understanding fundamental constraints on rhizobial metabolism during symbiotic nitrogen fixation.

microbiology↗

bioRxiv2020

Elementary Conversion Modes enable exploration of all metabolic capabilities of any organism

The metabolic capabilities of cells determine their biotechnological potential, fitness in ecosystems, pathogenic threat levels, and function in multicellular organisms. Their comprehensive experimental characterisation is generally not feasible, particularly for unculturable organisms. In principle, the full range of metabolic capabilities can be computed from an organisms annotated genome using metabolic network reconstruction. However, current computational methods cannot deal with genome-scale metabolic networks. Part of the problem is that these methods aim to enumerate all metabolic pathways, while computation of all (elementally balanced) conversions between nutrients and products would suffice. Indeed, the elementary conversion modes (ECMs, defined by Urbanczik and Wagner) capture the full metabolic capabilities of a network, but the use of ECMs has not been accessible, until now. We extend and explain the theory of ECMs, implement their enumeration in ecmtool, and illustrate their applicability. This work contributes to the elucidation of the full metabolic footprint of any cell.

systems biology↗

bioRxiv2019

The common message of constraint-based optimization approaches: overflow metabolism is caused by two growth-limiting constraints

Living cells can express different metabolic pathways that support growth. The criteria that determine which pathways are selected in which environment remain unclear. One recurrent selection is overflow metabolism: the simultaneous usage of an ATP-efficient and -inefficient pathway, shown for example in Escherichia coli, Saccharomyces cerevisiae and cancer cells. Many models, based on different assumptions, can reproduce this observation. Therefore, they provide no conclusive evidence which mechanism is causing overflow metabolism. We compare the mathematical structure of these models. Although ranging from Flux Balance Analyses to self-fabricating Metabolism and Expression models, we can rewrite all models into one standard form. We conclude that all models predict overflow metabolism when two, model-specific, growth-limiting constraints are hit. This is consistent with recent theory. Thus, identifying these two constraints is essential for understanding overflow metabolism. We list all imposed constraints by these models, so that they can hopefully be tested in future experiments.

systems biology↗

bioRxiv2019

Cell fate determination by Lamarckian molecule-inheritance and chance

Single, isogenic cells can differ in their survival and adaptation capacity. This phenotypic diversity is generally due to stochastic molecular events. Since mother cells on average pass half of their molecular content on to their daughters, the states of progeny cells strongly correlate with that of mother cells (Lamarckian inheritance). Why a particular cell deviates qualitatively from others therefore requires consideration of chance events along its ancestral lineage. Here we develop theory to understand cellular heterogeneity in terms of stochastic ancestral events of molecule synthesis, molecule degradation and cell divisions. We find that cell growth stochasticity has profound consequences for molecular heterogeneity in isogenic populations of cells, especially for long-lived molecules such as proteins. For instance, the lower bound on noise in molecule copy numbers that has often been observed experimentally is shown to be solely determined by the probability distribution for the generation times of cells. Thus, copy-number noise is unavoidable, even in high-copy number circuits. Stochastic cell-fate and cell-differentiation decisions are therefore not necessarily due to noise in genetic circuits. We conclude that consideration of past chance events along cell lineages improves our understanding of how adaptive and mal-adaptive phenotypic heterogeneity arises in populations of isogenic cells.

biophysics↗

bioRxiv2019

Elementary Growth Modes provide a molecular description of cellular self-fabrication

A major aim of biology is to predict phenotype from genotype. Here we ask if we can describe all possible molecular states (phenotypes) for a cell that fabricates itself at a constant rate, given its enzyme kinetics and the stoichiometry of all reactions (the genotype). For this, we must understand the autocatalytic process of cellular growth which is inherently nonlinear: steady-state self-fabrication requires a cell to synthesize all of its components, including metabolites, enzymes and ribosomes, in the proportions that exactly match its own composition - the growth demand thus depends on the cellular composition. Simultaneously, the concentrations of these components should be tuned to accomplish this synthesis task - the cellular composition thus depends on the growth demand. We here derive a theory that describes all phenotypes that solve this circular problem; the basic equations show how the concentrations of all cellular components and reaction rates must be balanced to get a constant self-fabrication rate. All phenotypes can be described as a combination of one or more minimal building blocks, which we call Elementary Growth Modes (EGMs). EGMs can be used as the theoretical basis for all models that explicitly model self-fabrication, such as the currently popular Metabolism and Expression models. We then used our theory to make concrete biological predictions: we find that natural selection for maximal growth rate drives microorganisms to states of minimal phenotypic complexity: only one EGM will be active when cellular growth rate is maximised. The phenotype of a cell is only extended with one more EGM whenever growth becomes limited by an additional biophysical constraint, such as a limited solvent capacity of a cellular compartment. Our theory starts from basic biochemical and evolutionary considerations, and describes unicellular life, both in growth-promoting and in stress-inducing environments, in terms of EGMs, the universal building blocks of self-fabrication and a cells phenotype.

systems biology↗

bioRxiv2018

In vivo characterisation of fluorescent proteins in budding yeast.

Fluorescent proteins (FPs) are widely used in many organisms, but are commonly characterised in vitro. However, the in vitro properties may poorly reflect in vivo performance. Therefore, we characterised 27 FPs in vivo using Saccharomyces cerevisiae as model organism. We linked the FPs via a T2A peptide to a control FP, producing equimolar expression of the 2 FPs from 1 plasmid. Using this strategy, we characterised the FPs for brightness, photostability, photochromicity and pH-sensitivity, achieving a comprehensive in vivo characterisation. Many FPs showed different in vivo properties compared to existing in vitro data. Additionally, various FPs were photochromic, which affects readouts due to complex bleaching kinetics. Finally, we codon optimized the best performing FPs for optimal expression in yeast, and found that codon-optimization alters FP characteristics. These FPs improve experimental signal readout, opening new experimental possibilities. Our results may guide future studies in yeast that employ fluorescent proteins.

molecular biology↗

bioRxiv2017

Maximal growth rate requires minimal metabolic complexity

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized -in a static environment under mass-conservation and enzyme expression constraints- we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.\n\nAuthor summaryThe microbial genome encodes for a large network of enzyme-catalyzed reactions. The reaction rates depend on concentrations of enzymes and metabolites, which in turn depend on those rates. Cells face a number of biophysical constraints on enzyme expression, for example due to a limited membrane area or cytosolic volume. Considering this complexity and nonlinearity of metabolism, how is it possible, that experimental data can often be described by simple linear models? We show that it is evolution itself that selects for simplicity. When reproductive rate is maximised, the number of active independent metabolic pathways is bounded by the number of growth-limiting enzyme constraints, which is typically small. A small number of pathways automatically generates the measured simple relations. We identify the importance of growth-limiting constraints in shaping microbial behaviour, by focussing on their mechanistic nature. We demonstrate that overflow metabolism - an important phenomenon in bacteria, yeasts, and cancer cells - is caused by two constraints on enzyme expression. We derive experimental guidelines for constraint identification in microorganisms. Knowing these constraints leads to increased understanding of metabolism, and thereby to better predictions and more effective manipulations.

systems biology↗