Biology subjects

Dal Co, A.

Publications and source records attributed to Dal Co, A..

6 recordsLinked to original sources

bioRxiv2024

Local DNA compaction creates TF-DNA clusters that enable transcription

Transcription factor (TF) clusters have been suggested to facilitate transcription. The mechanisms driving the formation of TF clusters and their impact on transcription, however, remain largely unclear. This is mostly due to the lack of a tractable system. Here, we exploit the transcriptional activation of mir430 in zebrafish embryos to simultaneously follow the dynamic formation of a large Nanog cluster, the underlying DNA, and transcription output by live imaging at high temporal and spatial resolution. We find that the formation of a Nanog cluster that can support transcription requires local DNA compaction. This brings more Nanog-binding sites into the cluster, and therefore more Nanog. Importantly, we find that Nanog stabilizes this TF-DNA cluster, which emphasizes the interdependent relationship between TFs and DNA dynamics in cluster formation. Once the Nanog-DNA cluster at the mir430 locus reaches a maximum amount of Nanog, transcription begins. This maximum is a locus-intrinsic feature, which shows that the locus self-regulates the recruitment of an optimal amount of Nanog. Our study supports a model in which endogenous TF clusters positively impact transcription and form through a combination of DNA binding and local DNA compaction.

cell 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

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↗