Publications by authors named "Alma Dal Co"

SUMMARYCommunities of microorganisms (microbiota) are present in all habitats on Earth and are relevant for agriculture, health, and climate. Deciphering the mechanisms that determine microbiota dynamics and functioning within the context of their respective environments or hosts (the microbiomes) is crucially important. However, the sheer taxonomic, metabolic, functional, and spatial complexity of most microbiomes poses substantial challenges to advancing our knowledge of these mechanisms.

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Microbial communities are fundamental to life on Earth. Different strains within these communities are often connected by a highly connected metabolic network, where the growth of one strain depends on the metabolic activities of other community members. While distributed metabolic functions allow microbes to reduce costs and optimize metabolic pathways, they make them metabolically dependent.

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Most bacteria live in dense, spatially structured communities such as biofilms. The high density allows cells to alter the local microenvironment, whereas the limited mobility can cause species to become spatially organized. Together, these factors can spatially organize metabolic processes within microbial communities so that cells in different locations perform different metabolic reactions.

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Since acidic environments often serve as an important line of defense against bacterial pathogens, it is important to fully understand how the latter manage to mount and evolve acid resistance mechanisms. Escherichia coli, a species harboring many pathovars, is typically equipped with the acid fitness island (AFI), a genomic region encoding the GadE master regulator together with several GadE-controlled functions to counter acid stress. This study reveals that and consequently AFI functions are heterogeneously expressed even in the absence of any prior acid stress, thereby preemptively creating acid-resistant subpopulations within a clonal E.

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Most microbes live in spatially structured communities (e.g., biofilms) in which they interact with their neighbors through the local exchange of diffusible molecules.

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Nutrient limitation is one of the most common triggers of antibiotic tolerance and persistence. Here, we present two microfluidic setups to study how spatial and temporal variation in nutrient availability lead to increased survival of bacteria to antibiotics. The first setup is designed to mimic the growth dynamics of bacteria in spatially structured populations (e.

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In bacterial communities, cells often communicate by the release and detection of small diffusible molecules, a process termed quorum-sensing. Signal molecules are thought to broadly diffuse in space; however, they often regulate traits such as conjugative transfer that strictly depend on the local community composition. This raises the question how nearby cells within the community can be detected.

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Communities of interacting microorganisms play important roles across all habitats on Earth. These communities typically consist of a large number of species that perform different metabolic processes. The functions of microbial communities ultimately emerge from interactions between these different microorganisms.

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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.

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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 homogeneous 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.

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The emergence of subpopulations that perform distinct metabolic roles has been observed in populations of genetically identical bacteria.

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Gene expression levels in clonal bacterial groups have been found to be spatially correlated. These correlations can partly be explained by the shared lineage history of nearby cells, although they could also arise from local cell-cell interactions. Here, we present a quantitative framework that allows us to disentangle the contributions of lineage history, long-range spatial gradients, and local cell-cell interactions to spatial correlations in gene expression.

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While we have good understanding of bacterial metabolism at the population level, we know little about the metabolic behavior of individual cells: do single cells in clonal populations sometimes specialize on different metabolic pathways? Such metabolic specialization could be driven by stochastic gene expression and could provide individual cells with growth benefits of specialization. We measured the degree of phenotypic specialization in two parallel metabolic pathways, the assimilation of glucose and arabinose. We grew Escherichia coli in chemostats, and used isotope-labeled sugars in combination with nanometer-scale secondary ion mass spectrometry and mathematical modeling to quantify sugar assimilation at the single-cell level.

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Article Synopsis
  • Vaccine-induced high-avidity IgA protects against bacterial infections by neutralizing toxins or blocking bacterial interactions with gut tissues through a process called 'immune exclusion.'
  • IgA facilitates the formation of bacterial clumps by connecting daughter cells after division, which helps in clearing pathogens from the gut, even at low bacterial densities typically found during infections.
  • This mechanism not only accelerates pathogen clearance but also hinders the spread of antibiotic resistance by preventing the transfer of plasmids between bacteria, highlighting the potential of oral vaccines in this fight.
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Timing is essential for many cellular processes, from cellular responses to external stimuli to the cell cycle and circadian clocks. Many of these processes are based on gene expression. For example, an activated gene may be required to reach in a precise time a threshold level of expression that triggers a specific downstream process.

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