A biophysical limit for quorum sensing in biofilms

Narla, Avaneesh V.; Borenstein, David Bruce; Wingreen, Ned S.

doi:10.1101/2020.11.01.364125

Cited by 4 publications

(5 citation statements)

References 40 publications

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“…Such simulations have been used to show how mechanical interactions lead to cellular re-ordering in dense colonies [19], how cell shape affects the spatial partitioning of different species within microbial communities [20], and how mechanical interactions and cell shape together lead to fractal patterning in bacterial layers [21]. Furthermore, agent-based simulations have been used to account for feedback of mechanical interactions on cell growth [22] and interplay of mechanics with with other forms of interaction like quorum sensing [23]. Nevertheless, the combined effects of mechanical interactions and heritable phenotypic differences, including different degrees of motility, remain poorly understood.…”

Section: Introductionmentioning

confidence: 99%

Computational model of fractal interface formation in bacterial biofilms

Brooks

McCool

Gillman

et al. 2022

Preprint

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Bacterial colonies benefit from cellular heterogeneity, with cells differentiating into diverse states of physiology and gene expression. As colonies grow, such cells in distinct states arrange into spatial patterns. To uncover the functional role of these emergent patterns, we must understand how they arise from cellular growth, phenotypic inheritance, and mechanical interactions among cells. Here we present a simple, agent-based model to predict patterns formed by motile and extracellular matrix-producing cells in developing populations of Bacillus subtilis bacteria. By incorporating phenotypic inheritance, differential mechanical interactions of the two cell types, and the escape of peripheral motile cells, our model predicts the emergence of a pattern: matrix cells surround a fractal-like population of interior motile cells. We find that, while some properties of the emergent motile-matrix interface depend on the initial spatial arrangement of cells, the distribution of motile cells at large radii are a product solely of the model’s growth mechanism. Using a box-counting analysis, we find that the emergent motile-matrix interface exhibits a fractal dimension that increases as biofilms grow but eventually reaches a maximum as the thickness of the peripheral layer of matrix exceeds the capacity of the inner cells to push matrix cells out of the way. We find that the presence of the fractal interface correlates with a larger colony growth rate and increases the local proximity of motile and matrix cells, which could promote resource sharing. Our results show that simple computational models can account for morphological features of active systems like bacterial colonies, where colony-level phenotypes emerge from single cell-level properties and cells modifying their own environment.Author summaryLike cells in our bodies, bacterial cells can differentiate into different cell types, which perform different roles in colonies. During the growth of Bacillus subtilis colonies, motile cells, which can swim, and matrix cells, which produce sticky polymers to adhere cells together, form reproducible spatial patterns. Multiple factors could drive the formation of these patterns, including inheritance of the motility and matrix states as cells divide, and different mechanical interactions between different cell types as they push each other around during growth. We created an agent-based computational model, in which we represent bacterial cells as occupying squares within a grid. We find that through inheritance of motile and matrix state and greater resistance to physical pushing by matrix cells, our model produces patterns similar to those observed in experiments—an exterior population of matrix cells surrounding an interior group of motile cells with fractal arms that branch into the outer matrix layer. Our results show that simple models can account for complex phenomena like the growth of heterogeneous bacterial colonies.

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Section: Introductionmentioning

confidence: 99%

Computational model of fractal interface formation in bacterial biofilms

Brooks

McCool

Gillman

et al. 2022

Preprint

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“…4A-B and D-E). Moreover, we note that our analysis thus far has focused on the case in which autoinducer production is nutrient-dependent; however, this process may sometimes be nutrient-independent [87]. In this case, we expect that our overall analysis still applies, but with the onset of biofilm formation specified by only the dispersal parameter J -as confirmed in Fig.…”

Section: Discussionmentioning

confidence: 74%

A biophysical threshold for biofilm formation

Ott¹,

Chiu²,

Amchin³

et al. 2021

Preprint

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Bacteria are ubiquitous in our daily lives, either as motile planktonic cells or as immobilized surface-attached biofilms. These different phenotypic states play key roles in agriculture, environment, industry, and medicine; hence, it is critically important to be able to predict the conditions under which bacteria transition from one state to the other. Unfortunately, these transitions depend on a dizzyingly complex array of factors that are determined by the intrinsic properties of the individual cells as well as those of their surrounding environments, and are thus challenging to describe. To address this issue, here, we develop a generally-applicable biophysical model of the interplay between motility-mediated dispersal and biofilm formation under positive quorum sensing control. Using this model, we establish a universal rule predicting how the onset and extent of biofilm formation depend collectively on cell concentration and motility, nutrient diffusion and consumption, chemotactic sensing, and autoinducer production. Our work thus provides a key step toward quantitatively predicting and controlling biofilm formation in diverse and complex settings.

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“…Moreover, microbial cells can communicate through small diffusible chemical signal molecules known as the autoinducers that regulate the quorum sensing (QS) mechanism involved in the coordination of the community activities and in monitoring their population density ( Afonso et al., 2020 ; Palla et al., 2018 ). QS was shown to regulate spore germination, the metabolite productions as well as the development of an antimicrobial resistance ( Narla et al., 2021 ; Preda and Sãndulescu, 2019 ). Studies have demonstrated that the inhibition of QS signaling molecules suppressed the initiation of sporulation as well as spores' germination ( Vesty et al., 2020 ; Wang et al., 2018 ; Xiong et al., 2021 ).…”

Section: Search Strategiesmentioning

confidence: 99%

“…The matrix acts a protective barrier against different harmful and/or stressful conditions, provides nutrients, favors horizontal gene transfer and metabolic cooperation between bacterial species ( Khelissa et al., 2017 ; Sandasi et al., 2011 ). Moreover, increased cell densities in biofilm structure, favors the QS signaling that could control tightly the expression of many genes promoting biofilm formation, as well as the transfer of genetic material between cells ( Afonso et al., 2020 ; Li and Tian, 2012 ; Narla et al., 2021 ).…”

Section: Search Strategiesmentioning

confidence: 99%