Spatial Organization Plasticity as an Adaptive Driver of Surface Microbial Communities

Abstract

Biofilms are dynamic habitats which constantly evolve in response to environmental fluctuations and thereby constitute remarkable survival strategies for microorganisms. The modulation of biofilm functional properties is largely governed by the active remodeling of their three-dimensional structure and involves an arsenal of microbial self-produced components and interconnected mechanisms. The production of matrix components, the spatial reorganization of ecological interactions, the generation of physiological heterogeneity, the regulation of motility, the production of actives enzymes are for instance some of the processes enabling such spatial organization plasticity. In this contribution, we discussed the foundations of architectural plasticity as an adaptive driver of biofilms through the review of the different microbial strategies involved. Moreover, the possibility to harness such characteristics to sculpt biofilm structure as an attractive approach to control their functional properties, whether beneficial or deleterious, is also discussed.

Keywords: adaptative response; microbial biofilm; spatial dynamic; structure/function.

FIGURE 1

Modeling of diffusion in biofilm of various architecture. CLSM sections of three characteristic biofilm structures were displayed in the first column: (A) a flat (Escherichia coli), (B) a mushroom-like (Pseudomonas putida and Pseudomonas aeruginosa) and (C) a egg-like structure (Salmonella enterica). Those images are used as an input of a modeling pipeline which simulates the diffusion of a chemical component through the biofilm, from a bulk source located in the upper boundary of the image. Based on the biofilm images, we construct for each structure a heterogeneous diffusion coefficient map that reflects the diffusive capabilities of the biofilm: the higher the local bacterial density, the lower the local diffusion coefficient. Next, this tensor is inserted in a reaction-diffusion equation together with a reaction function that mimics the consumption of the component by the bacteria. The consumption rate also varies with the local bacterial density. We display three snapshots of the simulated component distribution, at time t = 10, 50, and 150 s when the steady-state is reached. Isolines are displayed every 0.1 to better represent the distribution gradients. We finally display a cut in z of the component distribution in each biofilm at steady-state (D). The cut plane of a given biofilm crosses its point of minimal component concentration at steady-state. To facilitate the comparison, we normalized the z-coordinates of the different graphs. We can see that the biofilm structure is a determinant driver of the component density map at steady-state, presenting a diversity of situation, from quasi-uniform distributions (structure C) to strong gradients (structures A and B).

FIGURE 2

Biofilm architectural modulations in response to environmental stimuli or depending on bacterial composition. (A) Impact of cell morphology on biofilm spatial organization. Images displayed 2D sections from simulations where biofilms exponentially grown from 1:1 mixtures of red- and blue-labeled strains form distinct 3D patterns depending on the coccal (S) or rod-like (L) morphology of the strain (Adapted from Smith et al., 2017). (B) Impact of substrate availability (High, moderate and low availability) on biofilm architecture and lineage segregation. Simulations were started with a 1:1 mixture of red and blue cells, where cell color served a neutral marker for lineage segregation. Substrate concentration decrease was associated to a higher spatial segregation of cell lineages (Adapted from Nadell et al., 2010). (C) Impact of disturbance frequency on Vibrio cholerae biofilm spatial organization and strain competition. Images are optical sections taken from the bottom cell layer of biofilms initiated with a 1:1:1 mixture of wild-type strain (teal): a mutant strain hyper-secreting biofilm matrix (red): a mutant strain that is unable to produce extracellular matrix (yellow) cells in microfluidic devices (scale bar: 20 μm). Biofilms grew under continuous nutrient provision (left), or underwent disturbance events every 12 h (middle) or every 6 h (right). Each disturbance event consist in stopping the flow during 2 h to lead to nutrient limitation. Cells were then allowed to disperse to a new microfluidic chamber by pumping the dispersed cells from the initial chamber to the new chamber. After a 2 h-incubation, flow was resumed to pump fresh medium in the newly colonized chamber and enable biofilm growth (adapted from Yan et al., 2017). (D) Impact of toxic on EPS production and biofilm structure in Thiomonas sp. CB2. Images show three dimensional confocal reconstruction of 7 day-old biofilms cultivated in the absence, or in the presence of 1.33 and 2.67 mM Arsenic [As(III)]. Biofilms were stained using SYTO9 (cells, green) and ConA (exopolysaccharides, red) (Adapted from Marchal et al., 2011).

FIGURE 3

Sculpting biofilm architecture to control their function. (A) Role of biosurfactants in Pseudomonas putida biofilm architecture. Images displayed vertical sections of biofilms grew in flow chamber during 3 days in presence or absence of rhamnoses. IsoF correspond to the wild-type strain and PL2 strain to a conditional mutant in which the native promoter region of psoA (a gene coding a large non-ribosomal peptide synthethase which directs the biosynthesis of the two cyclic lipopeptide biosurfactants putisolvin I and II) has been replaced with the rhamnose-inducible PrhaB promoter (adapted from Carcamo-Oyarce et al., 2015). Addition of 0.2% rhamnose in growth medium of PL2 lead to the recovery of the flat wild-type biofilm structure suggesting that putisolvins promote the colonization of the substratum. (B) Predation by protozoa affects biofilms spatial organization during gravity-driven dead-end ultrafiltration and induces higher permeate fluxes (adapted from Derlon et al., 2012). (C) Green bacilli creates transient pores in the biofilm matrix of Staphylococcus aureus, leading to an increased sensitivity to biocide action as described in Houry et al. (2012) (courtesy of Julien Deschamps, INRA).