Biology subjects

van Vliet, S.

Publications and source records attributed to van Vliet, S..

15 recordsLinked to original sources

bioRxiv2026

Opposing range-dependent interactions create complex spatial patterns of antibiotic tolerance in multispecies biofilms

Many microbial communities form multispecies biofilms where cells interact through diffusible molecules. In these biofilms, multiple interactions, often with opposing effects, occur simultaneously, yet we lack quantitative frameworks to predict how they combine to shape community functions. Here, we hypothesized that complex spatial patterns can emerge when opposing interactions have distinct spatial ranges. To test this, we studied how two Pseudomonas aeruginosa exoproducts, HQNO and rhamnolipids, jointly modulate Staphylococcus aureus antibiotic tolerance by respectively increasing and decreasing it. Using microfluidics-based imaging, we quantified spatial-tolerance patterns at single-cell resolution and found that tolerance indeed shows a complex spatial pattern: S. aureus cells survived treatment only at intermediate distances from P. aeruginosa, while cells closer or farther away did not. Combining experiments and modelling, we showed that this remarkable pattern emerges because rhamnolipids have a stronger but short-ranged effect, while HQNO has a weaker but longer-ranged effect. We found that spatial arrangement affects overall tolerance by shifting the balance between the two opposing interactions. Finally, using bioprinting, we confirmed that HQNO and rhamnolipids modulate tolerance in highly mixed biofilms. In more segregated biofilms, spatial arrangement still strongly modulated tolerance, but independently of these compounds, suggesting additional interactions. Together, our results show that spatial-tolerance patterns emerge from the combined effect of opposing range-dependent interactions and cannot be predicted from either alone. By predicting how opposing interactions jointly determine community properties, our framework provides a foundation for understanding and ultimately engineering microbiome functions.

systems biology↗

bioRxiv2025

Coenzyme A depletion causes antibiotic tolerance in Pseudomonas aeruginosa

The widespread use of antibiotics promotes both resistance and tolerance. While resistance enables bacterial growth in the presence of drugs, tolerance allows survival during treatment, generating persisters that seed relapse and promote resistance. Despite its clinical relevance, the molecular basis of tolerance remains poorly understood. Using proteomic and metabolomic profiling combined with machine learning, we identified thiol oxidation as a robust predictor of tolerance in the human pathogen Pseudomonas aeruginosa. Single-cell analyses established a direct link between thiol oxidation and drug survival, indicating that redox imbalance drives persistence. Whereas depletion of coenzyme A (CoA), a central thiol-containing metabolite, scaled with tolerance, restoring CoA using engineered catalysts from Staphylococcus aureus abolished tolerance, establishing a causal relation between CoA availability and drug susceptibility. Thiol-based predictors also accurately capture tolerance of clinical P. aeruginosa isolates. These findings establish CoA-centered redox control as a key determinant of tolerance, opening opportunities for diagnostics and therapeutic interventions to prevent infection relapses.

microbiology↗

bioRxiv2025

When less is not more: Limits to the evolution of metabolic dependence in spatially structured microbial communities

In microbial communities, evolutionary processes can lead to loss of biosynthetic pathways, creating metabolic dependencies. The Black Queen Hypothesis suggests that such gene loss can confer a fitness advantage by reducing metabolic burden. However, how these dependencies evolve at the level of individual cells in spatially structured communities remains poorly understood. We used a combination of microfluidic single-cell imaging and mathematical modeling to examine the early fate of auxotrophic mutants within E. coli populations. We found that without external amino acids, auxotroph growth is strongly constrained by low amino acid leakage from wildtype neighbors, and further reduced when they form local clusters that drain this limited amino acid pool. A growth advantage was only observed when amino acids were added or when leakage from wildtypes exceeded a threshold. Together, our results reveal insights into determinants of mutant invasion fitness and the trade-offs between reducing metabolic costs and maintaining metabolic autonomy. HighlightsO_LISingle-cell imaging reveals how auxotrophs fare in spatially structured populations C_LIO_LIAuxotrophs grow slowly due to low amino acid leakage from wildtype neighbors C_LIO_LIMutant clustering intensifies local amino acid depletion and reduces auxotroph growth C_LIO_LIModel identifies leakage threshold required for auxotroph invasion from rare C_LI

evolutionary biology↗

bioRxiv2024

Engineering microbial consortia: uptake and leakage ratedifferentially shape community arrangement and composition

Bacteria often grow as communities in intricate spatial arrangements on surfaces and interact with each other through the local exchange of diffusible molecules. Yet, our understanding of how these interactions shape the properties of the communities remains limited. Here, we study synthetic communities of Escherichia coli amino acid auxotrophs interacting through the obligate exchange of amino acids. We genetically engineer these strains to alter their amino acid leakage and uptake abilities. We then characterise the spatial arrangement and composition of the communities when grown on a surface. By integrating experimental data with mathematical modeling, we demonstrate that amino acid uptake and leakage rates are crucial determinants of community structure. Our results show that while the spatial arrangement of the community is primarily governed by uptake rates, the community composition is predominantly influenced by leakage rates. These findings enhance our understanding of microbial community dynamics and provide a framework for predicting and engineering microbial consortia.

microbiology↗

bioRxiv2024

Cooperative colonization of the host and pathogen dissemination involves stochastic and spatially structured expression of virulence traits

Bacteria respond to changing environments by altering gene expression. Some responses display probabilistic cell-to-cell variation within isogenic populations. A few paradigmatic examples in animal pathogens have demonstrated that this phenotypic heterogeneity has biological relevance for virulence. We investigate single-cell flagellar expression in relation to type III secretion expression in the plant pathogen Pseudomonas syringae and describe that both systems undergo phenotypic heterogeneity throughout plant colonization. We establish that high expression of these system carries growth penalties. Stochastic, spatial and time factors shape dynamics of a phenotypically diverse population which displays division of labor during colonization: T3SSON bacteria effectors act as common goods to suppress immunity, allowing the increase of motile bacteria that actively leave the infected tissue before necrosis. This study provides a comprehensive view of how processes underlying bacterial specialization play out in the context of complex and changing environments of biological and applied relevance such as host colonization.

microbiology↗

bioRxiv2023

Experimentally Evolved Staphylococcus aureus Survives in the Presence of Pseudomonas aeruginosa by Acquiring Mutations in the Amino Acid Transporter, GltT

Staphylococcus aureus and Pseudomonas aeruginosa are the most common bacterial pathogens isolated from cystic fibrosis (CF) related lung infections. When both of these opportunistic pathogens are found in a coinfection, CF patients tend to have higher rates of pulmonary exacerbations and experience a more rapid decrease in lung function. When cultured together under standard laboratory conditions, it is often observed that P. aeruginosa effectively inhibits S. aureus growth. Previous work from our group revealed that S. aureus from CF infections have isolate-specific survival capabilities when cocultured with P. aeruginosa. In this study, we designed a serial transfer evolution experiment to identify mutations that allow S. aureus to adapt to the presence of P. aeruginosa. Using S. aureus USA300 JE2 as our ancestral strain, populations of S. aureus were repeatedly cocultured with fresh P. aeruginosa strain, PAO1. After 8 coculture periods, S. aureus populations that survived better in the presence of PAO1 were observed. We found two independent mutations in the highly conserved S. aureus aspartate transporter, gltT, that were unique to evolved P. aeruginosa-tolerant isolates. Subsequent phenotypic testing demonstrated that gltT mutants have reduced uptake of glutamate and outcompete wild-type S. aureus when glutamate is absent from chemically-defined media. These findings together demonstrate that the presence of P. aeruginosa exerts selective pressure on S. aureus to alter its uptake and metabolism of key amino acids when the two bacteria are cultured together. ImportanceStaphylococcus aureus and Pseudomonas aeruginosa are the two most common bacterial pathogens that infect people with the genetic disease, cystic fibrosis (CF). They are often found together in CF-associated polymicrobial infections that are associated with worse patient prognosis. Understanding how these very different opportunistic pathogens influence each other in a shared environment is pertinent to improving the treatment of polymicrobial infections. While much attention has been brought to the interspecific interactions between S. aureus and P. aeruginosa, few studies have used experimental evolution methods to identify determinants of their competition and coexistence. Here, we use a serial transfer experimental evolution approach and identified a single genetic change associated with improved survival of S. aureus in the presence of P. aeruginosa. Our findings implicate metabolism of shared resources as an important factor in S. aureuss ability to survive in the presence of P. aeruginosa.

microbiology↗

bioRxiv2022

A Novel Biosensor Reveals Dynamic Changes of C-di-GMP in Differentiating Cells with Ultra-High Temporal Resolution

Cyclic diguanylate (c-di-GMP) is a ubiquitous second messenger that regulates a wide range of biological processes in bacteria, including motility, surface attachment, virulence and persistence. The regulatory networks controlling c-di-GMP are generally complex and understudied. This is largely due to a lack of appropriate tools to monitor dynamic changes of c-di-GMP concentrations in vivo in a non-invasive manner. Here, we develop a genetically-encoded ratiometric c-di-GMP biosensor, called cdGreen2, by applying a powerful directed evolution approach based on iterative fluorescence-activated cell sorting (FACS) under alternating c-di-GMP regimes. We demonstrate that cdGreen2 can robustly track c-di-GMP dynamics in live cells with ultra-high temporal resolution over multiple generations. To validate its exquisite diagnostic power, we utilize cdGreen2 to dissect the regulatory networks driving bimodal developmental programs in the environmental model organism Caulobacter crescentus and the human pathogen Pseudomonas aeruginosa. These studies disclose the molecular determinants governing cell cycle-dependent c-di-GMP oscillations in C. crescentus and surface-induced c-di-GMP asymmetry in P. aeruginosa. The sensitivity and versatility of cdGreen2 will help unveil c-di-GMP dynamics in a wide range of organisms with unprecedented temporal resolution. The simple, yet powerful design principles underlying cdGreen2 will serve as a blueprint for the development of similar, orthogonal biosensors for other signaling molecules, metabolites or antibiotics, paving the way to uncover the complex interplay of small molecule-based networks with unprecedented spatiotemporal resolution.

microbiology↗

bioRxiv2022

Changes in interactions over ecological time scales influence single cell growth dynamics in a metabolically coupled marine microbial community

Microbial communities thrive in almost all habitats on earth. Within these communities, cells interact through the release and uptake of metabolites. These interactions can have synergistic or antagonistic effects on individual community members. The collective metabolic activity of microbial communities leads to changes in their local environment. As the environment changes over time, the nature of the interactions between cells can change. We currently lack understanding of how such dynamic feedbacks affect the growth dynamics of individual microbes and of the community as a whole. Here we study how interactions mediated by the exchange of metabolites through the environment change over time within a simple marine microbial community. We used a microfluidic-based approach that allows us to disentangle the effect cells have on their environment from how they respond to their environment. We found that the interactions between two species - a degrader of chitin and a cross-feeder that consumes metabolic by-products - changes dynamically over time as cells modify their environment. Cells initially interact positively and then start to compete at later stages of growth. Our results demonstrate that interactions between microbes are not static and depend on the state of the environment, emphasizing the importance of disentangling how modifications of the environment affects species interactions. This experimental approach can shed new light on how interspecies interactions scale up to community level processes in natural environments.

microbiology↗

bioRxiv2021

Multilevel selection favors fragmentation modes that maintain cooperative interactions in multispecies communities

Reproduction is one of the requirements for evolution and a defining feature of life. Yet, across the tree of life, organisms reproduce in many different ways. Groups of cells (e.g., multicellular organisms, colonial microbes, or multispecies biofilms) divide by releasing propagules that can be single-celled or multicellular. What conditions determine the number and size of reproductive propagules? In multicellular organisms, existing theory suggests that single-cell propagules prevent the accumulation of deleterious mutations (e.g., cheaters). However, groups of cells, such as biofilms, sometimes contain multiple metabolically interdependent species. This creates a reproductive dilemma: small daughter groups, which prevent the accumulation of cheaters, are also unlikely to contain the species diversity that is required for ecological success. Here, we developed an individual-based, multilevel selection model to investigate how such multi-species groups can resolve this dilemma. By tracking the dynamics of groups of cells that reproduce by fragmenting into smaller groups, we identified fragmentation modes that can maintain cooperative interactions. We systematically varied the fragmentation mode and calculated the maximum mutation rate that communities can withstand before being driven to extinction by the accumulation of cheaters. We find that for groups consisting of a single species, the optimal fragmentation mode consists of releasing single-cell propagules. For multi-species groups we find various optimal strategies. With migration between groups, single-cell propagules are favored. Without migration, larger propagules sizes are optimal; in this case, group-size dependent fissioning rates can prevent the accumulation of cheaters. Our work shows that multi-species groups can evolve reproductive strategies that allow them to maintain cooperative interactions. Author summaryIn order to reproduce, multicellular organisms and colonial bacteria fragment into offspring groups. Fragmentation modes in nature are very diverse: e.g. some organisms split into two halves, while others release single-celled propagules. These fragmentation modes can have fitness consequences, e.g. small propagules reduce the spread of deleterious mutants. However, the consequences of different fragmentation modes are not yet well understood for groups of cells containing several metabolically interdependent species, such as complex biofilms. We developed a multilevel selection model to investigate the effect of fragmentation mode on the accumulation of deleterious mutants when groups contain multiple species. In such groups, small propagules are not always a viable strategy, because they may harbor low species diversity. We find that alternative mechanisms, such as migration of cells between groups and group-size dependent fissioning rates, can prevent the accumulation of mutants. We also find that multilevel selection can lead to the evolution of fragmentation strategies that allow multi-species groups to thrive in the face of deleterious mutations.

evolutionary biology↗

bioRxiv2020

Predicting global dynamics of spatial microbial communities from local interaction rules

Interactions between cells drive biological processes across all of life, from microbes in the environment to cells in multicellular organisms. Interactions often arise in spatially structured settings, where cells mostly interact with their neighbors. A central question is how local interactions shape the properties of biological systems. This question is very relevant in the context of microbial communities, such as biofilms, where cells live close by in space and are connected via a dense network of biochemical interactions. To understand and control the functioning of these communities, it is essential to uncover how community-level properties, such as the community composition, spatial arrangement, and growth rate, arise from these interactions. Here, we develop a mathematical framework that can predict community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our predictions can qualitatively reproduce measurements from an experimental cross-feeding community. For these cross-feeding communities, the community growth rate is reduced when cells interact only with few neighbors; as a result, some communities can co-exist in a well-mixed system, where cells can interact with all other cells, but not in systems where cells can interact only with close by neighbors. In general, our framework shows that key molecular parameters underlying the cell-cell interactions (e.g. the uptake and leakage rates of molecules) determine community-level properties. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how community-level dynamics and biological functions emerge from microscopic interactions between single cells.

systems biology↗

bioRxiv2019

The role of multilevel selection in host microbiome evolution

Animals are associated with a microbiome that can affect their reproductive success. It is therefore important to understand how a host and its microbiome coevolve. According to the hologenome concept, hosts and their microbiome form an integrated evolutionary entity, a holobiont, on which selection can potentially act directly. However, this view is controversial and there is an active debate on whether the association between hosts and their microbiomes is strong enough to allow for selection at the holobiont level. Much of this debate is based on verbal arguments, but a quantitative framework is needed to investigate the conditions under which selection can act at the holobiont level. Here we use multilevel selection theory to develop such a framework. We found that selection at the holobiont level can in principle favor a trait that is costly to the microbes but that provides a benefit to the host. However, such scenarios require rather stringent conditions. The degree to which microbiome composition is heritable decays with time, and selection can only act at the holobiont level when this decay is slow enough, which occurs when vertical transmission is stronger than horizontal transmission. Moreover, the host generation time has to be short enough compared to the timescale of the evolutionary dynamics at the microbe level. Our framework thus allows us to quantitatively predict for what kind of systems selection could act at the holobiont level.

evolutionary biology↗

bioRxiv2019

Metabolic activity affects response of single cells to a nutrient switch in structured populations

Microbes live in ever-changing environments where they need to adapt their metabolism to different nutrient conditions. Many studies have characterized the response of genetically identical cells to nutrient switches in homogenous cultures, however in nature microbes often live in spatially structured groups such as biofilms where cells can create metabolic gradients by consuming and releasing nutrients. Consequently, cells experience different local microenvironments and vary in their phenotype. How does this phenotypic variation affect the ability of cells to cope with nutrient switches? Here we address this question by growing dense populations of Escherichia coli in microfluidic chambers and studying a switch from glucose to acetate at the single cell level. Before the switch, cells vary in their metabolic activity: some grow on glucose while others cross-feed on acetate. After the switch, only few cells can resume growth after a period of lag. The probability to resume growth depends on a cells phenotype prior to the switch: it is highest for cells crossfeeding on acetate, while it depends in a non-monotonic way on growth rate for cells growing on glucose. Our results suggest that the strong phenotypic variation in spatially structured populations might enhance their ability to cope with fluctuating environments.

systems biology↗

bioRxiv2019

Emergent microscale gradients give rise to metabolic cross-feeding and antibiotic tolerance in clonal bacterial populations

Bacteria often live in spatially structured groups such as biofilms. In these groups, cells can collectively generate gradients through the uptake and release of compounds. In turn, individual cells adapt their activities to the environment shaped by the whole group. Here we studied how these processes can generate phenotypic variation in clonal populations and how this variation contributes to the resilience of the population to antibiotics. We grew two-dimensional populations of Escherichia coli in microfluidic chambers where limiting amounts of glucose were supplied from one side. We found that the collective metabolic activity of cells created microscale gradients where nutrient concentration varied over a few cell lengths. As a result, growth rates and gene expression levels varied strongly between cells close in space. Furthermore, we found evidence for a metabolic cross-feeding interaction between glucose fermenting and acetate respiring subpopulations. Finally, we found that the populations were able to survive an antibiotic pulse that was lethal in well-mixed conditions, likely due to the presence of a slow growing subpopulation. Our work shows that emergent metabolic gradients can have important consequences for the functionality of bacterial populations as they create opportunities for metabolic interactions and increase the populations tolerance to environmental stressors.

microbiology↗

bioRxiv2019

Short-range interactions govern cellular dynamics in microbial multi-genotype systems

Ecosystem processes result from interaction between organisms. When interactions are local, the spatial organization of organisms defines their network of interactions, and thus influences the systems functioning. This can be especially relevant for microbial systems, which often consist of spatially structured communities of cells connected by a dense interaction network. Here we measured the spatial interaction network between cells in microbial systems and identify the factors that determine it. Combining quantitative single-cell analysis of synthetic bacterial communities with mathematical modeling, we find that cells only interact with other cells in their immediate neighbourhood. This short interaction range impacts the functioning of the whole system by reducing its ability to perform metabolic processes collectively. Our experiments and models demonstrate that the spatial scale of cell-to-cell interaction plays a fundamental role in understanding and controlling natural communities, and in engineering microbial systems for specific purposes. Significance StatementCommunities of interacting microbes perform fundamental processes on earth. We do not understand well how these processes emerge from the interactions between individual microbial cells. Our work investigates how strongly individual cells interact and how the strength of the interaction depends on the distance between cells. The discovery that individual cells live in a small world, i.e. they only interact with a small number of cells around them, changes our understanding of how cells in natural microbial communities are metabolically coupled and how their spatial arrangement determines emergent properties at the community level. Our quantitative single-cell approach allows to address central questions on systems composed of interacting genotypes and to increase our understanding and ability to control microbial communities.

systems biology↗

bioRxiv2017

Local interactions lead to spatially correlated gene expression levels in bacterial groups.

Many bacteria live in spatially structured assemblies where the microenvironment of a cell is shaped by the activities of its neighbors. Bacteria regulate their gene expression based on the inferred state of the environment. This raises the question whether the phenotypes of neighboring cells can become correlated through interactions via the shared microenvironment. Here, we addressed this question by following gene expression dynamics in Escherichia coli microcolonies. We observed strong spatial correlations in the expression dynamics for pathways involved in toxin production, SOS-stress response, and metabolism. These correlations can partly be explained by a combination of shared lineage history and spatial gradients in the colony. Interestingly, we also found evidence for cell-cell interactions in SOS-stress response, methionine biosynthesis and overall metabolic activity. Together our data suggests that intercellular feedbacks can couple the phenotypes of neighboring cells, raising the question whether gene-regulatory networks have evolved to spatially organize biological functions.

microbiology↗