Psl trails guide exploration and microcolony formation in Pseudomonas aeruginosa biofilms

Abstract

Bacterial biofilms are surface-associated, multicellular, morphologically complex microbial communities. Biofilm-forming bacteria such as the opportunistic pathogen Pseudomonas aeruginosa are phenotypically distinct from their free-swimming, planktonic counterparts. Much work has focused on factors affecting surface adhesion, and it is known that P. aeruginosa secretes the Psl exopolysaccharide, which promotes surface attachment by acting as 'molecular glue'. However, how individual surface-attached bacteria self-organize into microcolonies, the first step in communal biofilm organization, is not well understood. Here we identify a new role for Psl in early biofilm development using a massively parallel cell-tracking algorithm to extract the motility history of every cell on a newly colonized surface. By combining this technique with fluorescent Psl staining and computer simulations, we show that P. aeruginosa deposits a trail of Psl as it moves on a surface, which influences the surface motility of subsequent cells that encounter these trails and thus generates positive feedback. Both experiments and simulations indicate that the web of secreted Psl controls the distribution of surface visit frequencies, which can be approximated by a power law. This Pareto-type behaviour indicates that the bacterial community self-organizes in a manner analogous to a capitalist economic system, a 'rich-get-richer' mechanism of Psl accumulation that results in a small number of 'elite' cells becoming extremely enriched in communally produced Psl. Using engineered strains with inducible Psl production, we show that local Psl concentrations determine post-division cell fates and that high local Psl concentrations ultimately allow elite cells to serve as the founding population for initial microcolony development.

Figure 1. Efficiency of surface coverage by bacterial trajectories and correlation with Psl trails

a–d. Cumulative surface coverage at 0.5 (a, b) and 5 hours (c, d). Top row is for WT and bottom row for ΔpslD. Red and black colors indicate used (i.e. covered by bacterial trajectories) and fresh surface, respectively. Bacteria in the current frame are shown in green. e. Reconstructed bacterial trajectories of WT generated between 16.3 and 18.7 hours after inoculation (color bar indicates the time a given cell spent at each point). f. Psl trail left behind by bacteria in the same period, stained by fluorescently conjugated HHA lectin. Scale bars are 10 μm.

Figure 2. Visit frequency distribution and its effect on bacterial movement

a. Visit frequency map of WT for the first 15.7 hours post inoculation, when microcolonies were just starting to form (example outlined by black square). b. Bright-field image for WT at t ~ 15.7 hours. c. Visit frequency distribution from (a). Solid line shows a power-law decay with exponent –2.9. Green arrow indicates where the curve begins to deviate from this power law. d. Visit frequency distributions for ΔP_psl/P_BAD-psl at arabinose concentrations 0% (Δ), 0.1% (□), 1% (○). e. Simulation results of visit frequency distributions at Psl deposition rates (arbitrary units, see Supplementary Methods) 0 (*), 10^–5 (+), 5 × 10^–5 (×). In (d) and (e), each data set is normalized by the total number of visits (roughly the same as for (a)) and solid lines show power-law decay. f. Schematic graph showing that distributions with steep slopes are more egalitarian, while those with shallow slopes are more hierarchical. g./h. Fitted power-law exponents of visit frequency distributions from experiments at different arabinose concentrations (g) and simulations at different Psl deposition rates (h). i. Fluorescent lectin-stained image showing hierarchical distribution of Psl (ΔP_psl/P_BAD-psl at 1% arabinose). j. Psl map from simulations (Psl deposition rate 5 × 10^–5). Scale bars are 10 μm.

Figure 3. Local Psl levels determine post-division cell fates

a. Visit frequency map of Psl++ for the first 14 hours post inoculation. Microcolonies have already started to form. b. Bright-field image for Psl++ at t ~ 14 hours. c. Visit frequency distributions of Psl++ from (a) for experiments (left) and simulations (right) agree. Solid line is an exponential fit to the second part of the data (green). Inset shows a power-law fit to the first part of the data (red). d. Probability of post-division cells’ fates: “stay” (solid rod) or “leave” (dashed rod envelope) for WT (red), Psl++ (blue) and ΔpslD (green). Error bars are estimated from 1/√N_div, with N_div the total number of division events during the time period of interest (N_div ≥ 90). e. WT and Psl++ microcolonies have drastically different compositions, as depicted by color-coded cell division lineages at early stages of microcolony formation (top row). For Psl++ the microcolony is dominated by a single lineage, whereas the WT microcolony has 20 different lineages. Bottom row depicts more developed microcolonies at the same location 3.3 hours later. Scale bar is 10 μm.