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. 2017 Jan 13;6:e21855. doi: 10.7554/eLife.21855

Flow environment and matrix structure interact to determine spatial competition in Pseudomonas aeruginosa biofilms

Carey D Nadell 1,†, Deirdre Ricaurte 2,†, Jing Yan 2, Knut Drescher 1, Bonnie L Bassler 2,3,*
Editor: Sara Mitri4
PMCID: PMC5283829  PMID: 28084994

Abstract

Bacteria often live in biofilms, which are microbial communities surrounded by a secreted extracellular matrix. Here, we demonstrate that hydrodynamic flow and matrix organization interact to shape competitive dynamics in Pseudomonas aeruginosa biofilms. Irrespective of initial frequency, in competition with matrix mutants, wild-type cells always increase in relative abundance in planar microfluidic devices under simple flow regimes. By contrast, in microenvironments with complex, irregular flow profiles – which are common in natural environments – wild-type matrix-producing and isogenic non-producing strains can coexist. This result stems from local obstruction of flow by wild-type matrix producers, which generates regions of near-zero shear that allow matrix mutants to locally accumulate. Our findings connect the evolutionary stability of matrix production with the hydrodynamics and spatial structure of the surrounding environment, providing a potential explanation for the variation in biofilm matrix secretion observed among bacteria in natural environments.

DOI: http://dx.doi.org/10.7554/eLife.21855.001

Research Organism: Other

eLife digest

Bacteria often live together – attached to surfaces like river rocks, water pipes, the lining of the gut and catheters – in communities called biofilms. These groups of bacteria are small-scale ecosystems in which cells cooperate and compete with one another to obtain resources, such as food and space to grow. Within a biofilm, a sticky glue-like substance called the matrix binds the cells to each other and to the surface. Cells that make the matrix typically have an advantage over those that do not because they can better resist the shearing forces experienced when liquid flows over the surface. The matrix also helps cells to capture nutrients from the passing liquid. Nevertheless, not all strains of bacteria make matrix, despite its advantages.

Because of where they can grow, biofilms are fundamentally important in the environment, in industry and in infections. Resolving why some bacteria make matrix while others do not could therefore allow scientists and engineers to re-design the surfaces involved in these settings to discourage harmful biofilms or to encourage beneficial ones.

Nadell, Ricaurte et al. have now used a bacterium called Pseudomonas aeruginosa to explore how the properties of the surface and the flowing liquid affect matrix production among cells in biofilms. P. aeruginosa typically lives in soil and can cause infections in people, especially in hospital patients and people who have weakened immune systems. Nadell, Ricaurte et al. studied normal P. aeruginosa bacteria and a mutant strain that is unable to make matrix. The strains were labeled with fluorescent markers and put into special chambers that simulated different environments. The proportion of each strain was measured after three days of biofilm growth. When biofilms were grown under flowing liquid in simple environments with flat surfaces, matrix producers always outcompeted non-producers. However, the two strains coexisted in more complex and porous environments, like those found in soil.

Nadell, Ricaurte et al. went on to show that the strains could co-exist because the matrix producers made biofilms that created areas within the environment where the liquid flows very slowly or not at all. In these regions, non-producing cells could compete successfully because resistance to shearing forces is less important when flow is weak or absent, and so the non-producing cells were not washed away. The results begin to explain why matrix production among cells in environmental settings is diverse and highlight that the environment is important in the evolution of bacterial biofilms.

DOI: http://dx.doi.org/10.7554/eLife.21855.002

Introduction

In nature, bacteria predominantly exist in biofilms, which are surface-attached or free-floating communities of cells held together by a secreted matrix (Hall-Stoodley et al., 2004; Nadell et al., 2009; Flemming et al., 2016). The extracellular matrix defines biofilm structure, promotes cell-cell and cell-surface adhesion, and confers resistance to chemical and physical insults (Flemming and Wingender, 2010; Hobley et al., 2015; Teschler et al., 2015; Tseng et al., 2013; Doroshenko et al., 2014; Landry et al., 2006). The matrix also plays a role in the social evolution and population dynamics of biofilm-dwelling bacteria (Nadell et al., 2009; Steenackers et al., 2016; Nadell et al., 2016; Ghoul and Mitri, 2016; Mitri et al., 2016, 2013, 2011). In some species, such as the soil bacterium Bacillus subtilis, matrix materials are readily shared among cells, leading to public goods dilemmas in which non-producing strains can outcompete producing strains (van Gestel et al., 2015, 2014; Kovács, 2014). In other species, including the pathogens Vibrio cholerae, Pseudomonas fluorescens, and P. aeruginosa, matrix-secreting cell lineages privatize most matrix components, allowing them to smother or laterally displace other cell lineages and, in so doing, outcompete non-producing cells (Xavier and Foster, 2007; Nadell and Bassler, 2011; Nadell et al., 2015; Kim et al., 2014c; Schluter et al., 2015; Irie et al., 2016; Drescher et al., 2016; Yan et al., 2016; Madsen et al., 2015; Oliveira et al., 2015). Interestingly, not all wild and clinical isolates of these species produce a biofilm matrix, despite the clear ecological and competitive benefits of possessing a matrix (Mann and Wozniak, 2012; Yawata et al., 2014; Chowdhury et al., 2016). Theory and experiments investigating bacterial colonies on agar show that constrained movement can promote coexistence of different strains and species (Levin, 1974; Levin and Paine, 1974; Durrett and Levin, 1994, 1998; Kerr et al., 2002; Kim et al., 2008; Poltak and Cooper, 2011). Fitness trade-offs between the benefits of being adhered to surfaces and the ability to disperse to new locations can cause variability in matrix production (Nadell and Bassler, 2011; Yawata et al., 2014; Levin, 1974; Cohen and Levin, 1991), but it is not well understood how selective forces within the biofilm environment itself might drive the coexistence of strains that make matrix with strains that do not. Here, we explore how selection for matrix production occurs within biofilms on different surface geometries and under different flow regimes, including those that are relevant inside host organisms and in abiotic environments such as soil.

The local hydrodynamics associated with natural environments can have dramatic effects on biofilm matrix organization. This phenomenon has been particularly well established for P. aeruginosa, a common soil bacterium (Green et al., 1974) and opportunistic pathogen that thrives in open wounds (Fazli et al., 2009; Burmølle et al., 2010), on sub-epithelial medical devices (Guaglianone et al., 2010), and in the lungs of cystic fibrosis patients (Harmsen et al., 2010; Ciofu et al., 2013; Folkesson et al., 2012; Stacy et al., 2015; McNally et al., 2014). Under steady laminar flow in simple microfluidic channels, P. aeruginosa forms biofilms with intermittent mushroom-shaped tower structures (Harmsen et al., 2010; Friedman and Kolter, 2004; Miller et al., 2012; Parsek and Tolker-Nielsen, 2008). Under irregular flow regimes in more complex environments, however, P. aeruginosa also produces sieve-like biofilm streamers that protrude into the liquid phase above the substratum (Persat et al., 2015; Kim et al., 2014a, 2014b; Rusconi et al., 2010). These streamers – whose structure depends on the secreted matrix – are proficient at catching cells, nutrients, and debris that pass by, leading to clogging and termination of local flow (Drescher et al., 2013).

The spatial and temporal characteristics of flow thus combine to alter matrix morphology, which, in turn, feeds back to alter local hydrodynamics and nutrient advection and diffusion (Nadell et al., 2016; Hellweger et al., 2016; Stewart and Franklin, 2008). By modifying community structure and solute transport in and around biofilms (Stewart, 2012), this feedback could have a significant influence on the evolutionary dynamics of matrix secretion in natural environments (Coyte et al., 2016). Here, we study within-biofilm competition as a function of flow regime using strains of P. aeruginosa PA14 that differ only in their production of Pel, a viscoelastic matrix polysaccharide that serves as the primary structural element for biofilm and streamer formation (Friedman and Kolter, 2004; Drescher et al., 2013; Chew et al., 2014; Jennings et al., 2015). Using a combination of fluid flow visualization and population dynamics analyses, we reveal a novel interaction between hydrodynamic conditions, biofilm architecture, and competition within bacterial communities.

Results/discussion

We performed competition experiments with wild type PA14 and an otherwise isogenic strain deleted for pelA, which is required for synthesis of Pel (Franklin et al., 2011). We focused on Pel because it is the key structural polysaccharide in PA14 biofilms, and it is necessary for streamer formation under complex flow regimes. Deletion of pelA significantly impairs biofilm formation in PA14, which does not naturally produce Psl, an additional matrix polysaccharide secreted by other P. aeruginosa isolates (Colvin et al., 2012, 2011). Wild-type cells produced GFP, and ∆pelA mutants produced mCherry. Experiments in shaken liquid culture using genetically identical wild-type cells producing GFP or mCherry confirmed that fluorescent protein expression constructs had no measurable effect on growth rate (Figure 1—figure supplement 1). Our first goal was to compare the population dynamics of the wild-type and ∆pelA strains in typical planar microfluidic devices, which have simple parabolic flow regimes, and in porous environments containing turns and corners, which have irregular flow profiles and better reflect the packed soil environments that P. aeruginosa often occupies (Green et al., 1974; Das and Mukherjee, 2007; Stover et al., 2000). To approximate the latter environment, we used microfluidic chambers containing column obstacles. The size and spacing distributions of the column obstacles were specifically designed to simulate soil or sand (see Materials and methods). Analogous methods have been used previously to study bacterial growth (Vos et al., 2013) and the behavior of Caenorhabditis elegans (Lockery et al., 2008) in realistic environments while maintaining accessibility to microscopy. In our setup, flow was maintained through these chambers at rates comparable to those experienced by P. aeruginosa in a soil environment (Heath, 1983).

The flow regime alters selection for matrix production in biofilms

Several approaches are available to study how competitive dynamics differ in particular flow environments. Most commonly, one would monitor biofilm co-cultures of wild-type and ∆pelA PA14 cells over time until their strain compositions reached steady state. Performing such time-series experiments was not possible here due to a combination of low-fluorescence output in the early phases of biofilm growth coupled with phototoxicity incurred by cells during epifluorescence imaging. To circumvent this issue, we measured the change in frequency of wild-type and ∆pelA cells over a fixed 72 hr time period as a function of their initial ratio in both planar chambers and soil-mimicking chambers containing column obstacles. Population composition was quantified in all cases using microscopy, as described in the Materials and methods section. From these measurements, we could infer the final stable states of Pel-producing and non-producing cells as a function of surface topography and flow conditions. This method is commonly used to evaluate the behavior of dynamical systems, and it has been employed in a variety of related experimental applications (Nadell and Bassler, 2011; Nadell et al., 2015; Madsen et al., 2015; Chuang et al., 2009; Sanchez and Gore, 2013; Drescher et al., 2014).

In planar chambers with simple parabolic flows, wild-type PA14 increased in relative abundance regardless of initial population composition, indicating uniform positive selection for Pel secretion (Figure 1A,B). This result is consistent with recent studies of V. cholerae and Pseudomonas spp. demonstrating that – in these species – core structural polysaccharides of the secreted matrix cannot be readily exploited by non-producing mutants (Nadell and Bassler, 2011; Nadell et al., 2015; Kim et al., 2014c; Schluter et al., 2015; Irie et al., 2016; Yan et al., 2016; Madsen et al., 2015). Confocal microscopy revealed that Pel-producers mostly excluded non-Pel-producing cells from biofilm clusters in planar chambers (Figure 2A,B), although some ∆pelA mutants resided on the periphery of wild-type biofilms. The liquid effluent from these chambers contained an over-representation of the ∆pelA mutant relative to wild type, consistent with the interpretation that ∆pelA strain was displaced from the substratum over time (Figure 2C). When wild-type and ∆pelA cells competed in microfluidic devices simulating porous microenvironments, by contrast, there was a pronounced shift to negative frequency-dependent selection for Pel production (Figure 1A,C). Wild-type PA14 was selectively favored at initial frequencies below ~0.6. Above this critical frequency, the ∆pelA mutant was favored. From this result, we can infer that in this porous environment, ∆pelA null mutants can grow and stably coexist with wild type Pel-producers.

Figure 1. Wild-type P. aeruginosa PA14 outcompetes the ΔpelA mutant under simple flow conditions, but the two strains coexist under complex flow conditions.

(A) Wild-type and ∆pelA strains were co-cultured at a range of initial frequencies in the simple planar (black data) or column-containing (blue data) microfluidic chambers. In both cases, fresh minimal M9 medium with 0.5% glucose was introduced at flow rates adjusted to equalize the volume of medium flowing through each chamber per unit time across all experiments. The diagonal gray line denotes the maximum possible increase in wild-type frequency for a given initial condition. Each data point is an independent biological replicate. (B) A maximum intensity projection (top-down view) of a confocal z-stack of wild type (green) and ∆pelA (red) biofilms in simple flow chambers. (C) An epifluorescence micrograph (top-down view) of wild type (green) and ∆pelA (red) biofilms after 72 hr growth in a flow chamber containing column obstacles to simulate a porous environment with irregular flows. Images in (B) and (C) were taken from chambers in which the wild type was inoculated at a frequency of 0.7.

DOI: http://dx.doi.org/10.7554/eLife.21855.003

Figure 1—source data 1. Change in WT frequency as a function of initial frequency in two flow conditions.
DOI: http://dx.doi.org/10.7554/eLife.21855.004
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DOI: 10.7554/eLife.21855.004

Figure 1.

Figure 1—figure supplement 1. The maximum growth rates of P. aeruginosa PA14 wild type and ∆pelA cells in mixed liquid culture.

Figure 1—figure supplement 1.

n = 4 biological x 6 technical replicates (different overnight cultures and microtiter plate wells, respectively (bars denote means ± S.E.). Growth rates were measured as the maximum slope of growth curves of cultures whose optical density at 600 nm was taken every 30 min until stationary phase. Cultures were grown in minimal M9 medium with 0.5% glucose at ambient room temperature (24°C). The maximum growth rates of wild-type cells expressing GFP and wild-type cells expressing mCherry were not different, indicating that the fluorescent protein constructs did not introduce growth rate bias (two-sample t = 0.235, df = 6, p=0.822). A two-sample t-test comparing the maximum growth rate of the ∆pelA strain against the pooled data for wild type (t = 2.31, df = 10, p=0.0432) suggests a growth rate decrement on the part of wild type relative to ∆pelA, but was not statistically significant at a critical p<0.05 threshold with Bonferroni correction for two pairwise comparisons.
DOI: http://dx.doi.org/10.7554/eLife.21855.005
Figure 1—figure supplement 1—source data 1. Maximum liquid culture growth rates of study strains.
DOI: http://dx.doi.org/10.7554/eLife.21855.006
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DOI: 10.7554/eLife.21855.006
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Figure 2. Matrix production confers a competitive advantage to wild-type P. aeruginosa PA14 in biofilms under simple flow conditions.

Figure 2.

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(A) Absolute abundances of wild-type and ΔpelA strains in monoculture and co-culture in planar microfluidic flow-chambers (bars denote means ± S.D. for n = 3–6). The two strains were inoculated alone (left two bars) or together at a 1:1 ratio (right two bars). (B) Single optical plane 3 μm from the surface, and z-projections at right and bottom respectively, of the wild-type (green) and ΔpelA (red) strains grown in co-culture for 48 hr after inoculation at a 1:1 ratio. (C) Relative abundance of wild-type and ∆pelA strains in the liquid effluent of planar microfluidic devices (points denote means ± S.D. for n = 3). Wild-type and ∆pelA cells were combined at a 1:1 initial ratio and co-inoculated on the glass substratum of simple flow chambers. At 0, 24, and 48 hr, 5 µL of effluent was collected from chamber outlets. Wild-type frequency was calculated within biofilms and within the liquid effluent for each time point.

DOI: http://dx.doi.org/10.7554/eLife.21855.007

Figure 2—source data 1. Biofilm production of WT and Pel-deficient P. aeruginosa in mono-culture and co-culture; cell counts in chamber effluents.
(A) WT and ΔpelA biomass accumulation. (C) WT and ΔpelA cell counts in effluent.
DOI: http://dx.doi.org/10.7554/eLife.21855.008
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DOI: 10.7554/eLife.21855.008

Wild-type P. aeruginosa obstructs porous environments, generating low-shear regions that Pel-deficient mutants can occupy

Previous work has shown that in environments with flow and with corners, wild-type P. aeruginosa produces Pel-dependent biofilm streamers that extrude from the surface into the passing liquid (Kim et al., 2014a; Rusconi et al., 2010; Drescher et al., 2013). In our experiments, streamers were produced by wild-type cells and could be readily detected via microscopy throughout column-containing chambers, but not planar chambers. Streamers are known to catch cells and debris that pass by (Drescher et al., 2013), and although ∆pelA cells could be found in the streamers in our experiments, they were not abundant. Cell capture by streamers therefore cannot account for the observed coexistence of the two strains (Figure 3—figure supplement 1). This result suggests that streamers do indeed catch debris, consistent with prior studies (Kim et al., 2014b; Drescher et al., 2013), but that in our system, ∆pelA cells are not present at high enough density in the passing liquid phase to accumulate substantial population sizes by this mechanism.

Our microscopy-based observation of chambers containing column obstacles suggested that wild-type biofilms gradually obstructed some of the regions located between columns over time. We hypothesized that partial clogging could render those portions of the chambers more suitable for growth of the ∆pelA strain, which was previously shown to be sensitive to removal by shear (Colvin et al., 2012, 2011). This hypothesis predicts that ∆pelA cells should be found predominantly in regions of the chamber that have been clogged by wild type biofilms. To test this prediction, we repeated our co-culture competition experiment with wild-type and ∆pelA cells in chambers containing columns, and we measured the distribution of each strain as above. We next introduced fluorescent beads into the chambers by connecting new influent syringes to the inflow tubing. By tracking the beads with high frame-rate microscopy, we could distinguish areas in which flow was present from areas in which flow was absent or very low, and then we could superimpose this information onto the spatial distributions of wild-type and ∆pelA mutant cells (Figure 3—figure supplement 2).

Wild-type biofilms accumulated intermittently, often with clusters of ∆pelA cells in close proximity. Importantly, and in support of our prediction, ∆pelA biofilm clusters occurred significantly more often in regions in which flow was blocked by wild-type biofilms than in regions in which flow was not interrupted (Figure 3A,B). As shown previously (Drescher et al., 2013), ∆pelA cells did not clog chambers when grown in isolation, supporting the interpretation that ∆pelA accumulation relies on and only occurs after clogging by the wild type. This result is consistent with prior indications that Pel-producers have higher shear tolerance than Pel-deficient cells (Colvin et al., 2012, 2011), which we confirmed in our system by growing each strain in isolation under varying shear stress in planar chambers (Figure 3C). Further supporting our interpretation, the ∆pelA strain can outcompete the wild type when the two are grown together in planar microfluidic chambers in the absence of flow (Figure 3—figure supplement 3). This experiment approximates the clogged areas of complex flow environments, and the results explain how – on spatial scales encompassing areas of high flow and low or no flow – the wild-type and the ∆pelA strains can coexist.

Figure 3. Pel-deficient mutants occupy locations protected from flow due to local clogging by wild-type P. aeruginosa biofilms.

(A) Wild-type (green) and ∆pelA (red) P. aeruginosa strain mixtures were inoculated into complex flow chambers with irregularly-spaced column obstacles. Biofilms were imaged using confocal microscopy, after which fluorescent beads were flowed through the chamber. The presence or absence of flow was monitored through averaging successive exposures of bead tracks (white lines are bead tracks; blue arrows highlight flow trajectories). (B) Analysis of co-occurrence of flow and wild-type or ∆pelA cell growth at the end of 1:1 competition experiments in complex flow chambers with column obstacles, as illustrated by the micrograph in (A). The occurrence of wild-type (gray) and ∆pelA (white) cell clusters are shown as a function of whether local flow has been blocked or remained open after 72 hr of competition (bars denote means ± S.E. for n = 3). These occurrence frequency data are normalized to the total area of blocked versus open flow in the microfluidic devices, as determined by the presence or absence of fluorescent bead tracks. There is no significant difference in wild type occurrence in regions in which flow is unobstructed and in regions in which flow is blocked (two-sample t = 0.995, df = 4, p=0.376), but the ∆pelA strain is significantly more likely to occur in regions in which flow is blocked at a p<0.05 threshold with Bonferroni correction for two pairwise comparisons (two-sample t = 3.60, df = 4, p=0.0227). (C) Biofilm growth of wild-type P. aeruginosa PA14 (gray) and the ΔpelA mutant (white) in monoculture in planar flow chambers under different shear stress exposure treatments (bars denote means ± S.D. for n = 5–10).

DOI: http://dx.doi.org/10.7554/eLife.21855.009

Figure 3—source data 1. Occurrence of WT and Pel-deficient P. aeruginosa in areas with flow blocked versus areas with flow open.
Biomass accumulation of WT and Pel-deficient P. aeruginosa under different degrees of shear stress.
(B) WT and ΔpelA biomass accumulation. (C) WT and ΔpelA growth under variable shear
DOI: http://dx.doi.org/10.7554/eLife.21855.010
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DOI: 10.7554/eLife.21855.010

Figure 3.

Figure 3—figure supplement 1. Streamer structures produced by wild-type P. aeruginosa PA14 (green) in microfluidic chambers with complex flow profiles do not capture large numbers of co-cultured ∆pelA mutants (red) over 72 hr of biofilm growth (black circles are column obstacles).

Figure 3—figure supplement 1.

DOI: http://dx.doi.org/10.7554/eLife.21855.011
Figure 3—figure supplement 2. Analysis procedure for correlating local flow and accumulation of ∆pelA and wild-type cells.

Figure 3—figure supplement 2.

Liquid enters the chamber on the left and exits on the right. (A) First, the positions of the columns were identified from the fluorescence image. The centers of the columns served as nodes to divide the spaces into triangular regions. Within each region, the area covered by the column was removed. (B) The corresponding image with fluorescent beads (white) to track local flows. Typically, 10 to 15 images were taken and integrated to cover the flow regions sampled by the beads. Overlaid are triangular regions showing ∆pelA cell accumulation (from (C)). (C) Averaged intensity within each sampling region was calculated, and a threshold was set to determine if biofilm accumulation occurred in each region. White triangles correspond to regions identified as containing biofilms using this method. ∆pelA cells accumulated primarily in regions lacking flow due to upstream clogging or in regions immediately downstream of obstructed areas. (D) Wild-type cells accumulated both in areas of high flow and areas of low flow.
DOI: http://dx.doi.org/10.7554/eLife.21855.012
Figure 3—figure supplement 3. Change in frequency of WT cells from a 1:1 starting population with ∆pelA with and without flow.

Figure 3—figure supplement 3.

P. aeruginosa PA14 and the ∆pelA strain were inoculated at a 1:1 ratio and allowed to form biofilms in planar microfluidic devices with or without flow for 72 hr. At that time, the biofilms were imaged, and the change in relative frequency of the wild-type strain was calculated (as in Figure 1 in the main text). When flow was present, the WT outcompeted the ∆pelA strain, and thus, the WT strain tended to show a positive change in frequency within biofilms (gray). When flow was absent, the WT strain tended to show a negative change in frequency (white). Bars denote means ± S.D. for n = 3–4.
DOI: http://dx.doi.org/10.7554/eLife.21855.013
Figure 3—figure supplement 3—source data 1. Comparison of competition in simple chambers with or without flow.
DOI: http://dx.doi.org/10.7554/eLife.21855.014
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DOI: 10.7554/eLife.21855.014
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Conclusions

Biofilm growth is ubiquitous in porous microenvironments and often causes clogging in natural and industrial contexts, including soil beds and water filtration systems (Knowles et al., 2011). Here, we have shown that the clogging process can dramatically influence population dynamics within growing biofilms by generating a feedback between hydrodynamic flow, biofilm spatial architecture, and competition (Coyte et al., 2016). Our findings suggest that when P. aeruginosa wild type and ΔpelA mutants experience irregular flow in heterogeneous environments, wild-type biofilm formation causes partial clogging, regionally reducing local flow speed. The lack of flow generates favorable conditions for the ∆pelA strain, whose biofilms would otherwise be removed by shear forces, presumably enabling it to proliferate locally if sufficient nutrients for growth diffuse from other areas of the chamber in which medium continues to flow (Bottero et al., 2013).

P. aeruginosa is notorious as an opportunistic pathogen of plants and animals, including humans (Xavier, 2016). It also thrives outside of hosts, for example, in porous niches such as soil (Fierer et al., 2007). Despite the well-documented ecological benefits of matrix secretion during biofilm formation, environmental and clinical isolates of P. aeruginosa exhibit considerable variation in their production of matrix components, including loss or overexpression of Pel (Mann and Wozniak, 2012; Chew et al., 2014). Our results offer an explanation for natural variation in the ability of P. aeruginosa to produce extracellular matrix, particularly among bacteria in porous microhabitats: the evolutionary stable states of extracellular matrix secretion vary with the topographical complexity of the flow environment in which the bacteria reside.

Materials and methods

Strains

All strains are derivatives of Pseudomonas aeruginosa PA14 (RRID:WB_PA14). Wild-type PA14 strains constitutively producing fluorescent proteins (Drescher et al., 2013) were provided by Albert Siryaporn (UC Irvine), and they harbor genes encoding either EGFP or mCherry under the control of the PA1/04/03 promoter in single copy on the chromosome (Choi and Schweizer, 2006). The ∆pelA strain was constructed using the lambda red system modified for P. aeruginosa (Lesic and Rahme, 2008).

Liquid growth rate experiments

To determine maximum growth rates and the potential for fluorescent protein production to cause fitness differences, bacterial strains were grown overnight in M9 minimal medium with 0.5% glucose at 37°C. Overnight cultures were back-diluted into minimal M9 medium with 0.5% glucose at room temperature and monitored until their optical densities at 600 nm were ~0.2, corresponding to logarithmic phase. Cultures were back diluted again into minimal M9 medium with 0.5% glucose and transferred to 96-well plates at room temperature. This experiment was repeated for four biological replicates (different overnight inoculation cultures), each repeated for six technical replicates (different wells within a 96-well plate). Measurements of culture optical density at 600 nm were taken once per 10 min until saturation, corresponding to stationary phase. Matlab (Natick, MA) curve fitting software was used to calculate the maximum growth rate of each strain (wild type [GFP]: 0.00716 h−1, wild type [mCherry]: 0.00733 h−1, ∆pelA [mCherry]: 0.00842 h−1). These experiments confirmed that the fluorescent protein markers had no measurable effect on growth rates and thus did not contribute to competitive outcomes in our experiments.

Microfluidics and competition experiments

Microfluidic devices consisting of poly(dimethylsiloxane) (PDMS) bonded to 36 mm x 60 mm glass slides were constructed using standard soft photolithography techniques (Sia and Whitesides, 2003). We used planar microfluidic devices with no obstacles to simulate environments with simple parabolic flow profiles, and we used devices with PDMS pillars interspersed throughout the chamber volume to simulate environments with complex (i.e. irregular, non-parabolic) flow profiles. The size and spatial distributions of these column obstacles were determined by taking a cross-section through a simulated volume of packed beads mimicking a simple soil environment. Flow rates through these two chamber types were adjusted to equalize the average initial flow velocities, although the local flow velocity within each chamber varied as biofilms grew during experiments (see main text).

For all competition experiments, bacterial strains were grown overnight. The following morning, aliquots of the overnight cultures were added to Eppendorf (Hamburg, Germany) tubes, and their optical densities were equalized prior to preparation of defined mixtures of wild-type and ∆pelA cells. 100 µL volumes of the wild-type strain alone, the ∆pelA strain alone, or mixtures of the two strains (for competition experiments), were introduced into microfluidic chambers using 1 mL syringes and Cole-Parmer (Vernon Hills, IL) polytetrafluoroethylene tubing (inner diameter = 0.30 mm; outer diameter = 0.76 mm). After 3 hr, fresh tubing connected to syringes containing fresh minimal M9 medium with 0.5% glucose were inserted into the inlet channels. The syringes (3 mL BD Syringe, 27G; Becton, Dickinson and Co.; Franklin Lakes, NJ) were mounted onto high-precision syringe pumps (Harvard Apparatus; Holliston, MA), which were used to tune flow speeds according to empirical measurements of flow speeds in soil (Heath, 1983). In our experiments, the average flow speed was 150–200 µm/s, unless noted otherwise. In Figure 3C, to alter shear, we varied the average flow speed; shear was estimated using standard calculations for surface shear stress under fluid flow: τ(y)=α∂u∂y= ΔpL H2 where τ is the shear stress, y is the height above the surface (evaluated in this case for y=0), α is the dynamic viscosity of the fluid, and u is the fluid flow velocity field, calculated for a rectangular channel in terms of the pressure decrease Δp/L across the length L and height H of the channel. The pressure decrease was calculated for our channel dimensions and flow rates using previously published results (Fuerstman et al., 2007). Biofilms were grown at room temperature. It should be noted that microfluidic experiments in the obstacle-containing chambers experience a high failure rate, in which no biofilms appear to grow after the 72 hr period of the experiment. No data could be extracted from such chambers, which were omitted from analysis. This problem was overcome by performing the experiment at high replication. Sufficient data were thus collected to populate the relevant panels in Figures 1 and 3 of the main text. In the case of competition experiments, one replicate was defined as the output from one independently inoculated microfluidic chamber (e.g. Figure 1A). For experiments in which biofilm growth was measured as a function of flow-mediated shear stress (Figure 3C), one replicate was defined as the output from one imaging location within a microfluidic chamber, with two to three locations per chamber being sampled.

Design of soil-mimicking microfluidic chambers

To obtain spatial patterns of column obstacles that mimic soil or sand, we first generated a 3D model of packed spheres. The centers of the spheres were positioned such that they had equal radii of 1 (arbitrary units), in a close-packed arrangement. Soil grains, however, are not all the same size. To include heterogeneity in sphere size in our model, we adjusted each sphere’s radius using uniformly distributed random numbers to generate a range of sphere radii varying from 0.4 to 1.0. For a plane that is oblique to any of the symmetry planes defined by the centers of the spheres, we generated a cross-section through the 3D packed-sphere model. This cross-section of the spheres was used to define the borders of the columns in our soil-mimicking microfluidic devices. To convert the arbitrarily sized spheres from the 3D model to the actual sizes of physical columns in our microfluidic chambers, we chose column radii that varied from 80 to 200 μm, corresponding to particle sizes of fine- and medium-grain sand.

Microscopy and image analysis

Mature biofilms were imaged using a Nikon (Tokyo, Japan) Ti-E inverted microscope via a widefield epifluorescence light path (using a 10x objective) or a Borealis-modified Yokogawa CSU-X1 (Tokyo, Japan) spinning disk confocal scanner (using a 60x TIRF objective). A 488-nm laser line was used to excite EGFP, and a 594-nm laser line was used to excite mCherry. Quantification of biofilm composition was performed using Matlab and Nikon NIS Elements analysis software (Drescher et al., 2014). Imaging of biofilms could only be performed once for each experiment, precluding time-series analyses, due to phototoxicity effects after multiple rounds of imaging. Phototoxicity was a particularly notable issue here due to dimness of the fluorescent proteins in P. aeruginosa, which made long exposures necessary to capture images of sufficient quality for later analysis. For this reason, we opted for inferential population dynamics analysis as described in the main text.

Effluent measurements

To measure strain frequencies in the biofilm effluent of planar chambers (Figure 2C), 1:1 strain mixtures of wild-type and ∆pelA cells were prepared and inoculated into simple flow chambers according to the procedure outlined above for competition experiments. At 0, 24, and 48 hr, 5 µL samples were collected from the microfluidic chamber outlet tubing, mixed vigorously by vortex, and plated onto agar in serial dilution. After overnight growth at 37°C, plates were imaged with an Image Quant LAS 4000 (GE Healthcare Bio-Sciences; Pittsburgh, PA). Cy3 and Cy5 fluorescence settings were used for EGFP and mCherry excitation, respectively. Image Quant TL Colony Counting software was used to measure the relative abundance of each strain.

Flow tracking experiments

1:1 mixtures of the wild-type and the ∆pelA mutant were prepared and introduced into obstacle-containing flow chambers according to the procedure described above. Minimal M9 medium with 0.5% glucose was introduced into the chambers for 72 hr as described above. The entire chamber was then imaged using widefield epifluorescence microscopy to document the locations of wild-type and ∆pelA cell clusters. Subsequently, the influent syringes were replaced with syringes containing yellow-green fluorescent beads (sulfate-modified, diameter = 2 µm; Invitrogen; Carlsbad, CA) at a concentration of 0.3%, and bead suspensions were flowed into the microfluidic chambers. To determine the presence or absence of flow with respect to the spatial distributions of wild-type and ∆pelA cells, and to obtain large images for statistics, the entire chamber was imaged with a 1 s exposure time, over which traveling beads were captured as streaks. It should be noted that this experiment also has a high failure rate due to the sensitivity of the microfluidic chambers to removal and re-insertion of syringes, and required optimization to execute successfully. Custom Matlab code was written to correlate the presence or absence of fluid flow with the accumulation of wild-type and ∆pelA cells. In brief, the positions of the columns were first identified and used to divide the chamber into triangular sampling areas using a network structure in which columns served as nodes and straight lines between column centers served as edges. Within each sampling triangle, the area covered by columns was first removed, and subsequently, the averaged ∆pelA and wild-type fluorescence intensities in the remaining area were used to determine if a region had wild type and/or ∆pelA accumulation. In parallel, each sampling area was scored for the presence of flow in the corresponding bead tracking images (Figure 3—figure supplement 2).

Data display and statistical tests

In all cases where displayed, bars denote the mean values of the measurements taken, and with the exception of Figure 3B and Figure 1—figure supplement 1, the error bars denote standard deviations. In Figure 3B and Figure 1—figure supplement 1, error bars denote standard errors. In Figure 3B, we report the results of a two-tailed t-test comparing the wild type occurrence frequency in regions of soil-mimicking chambers where flow was obstructed, versus the wild type occurrence frequency in regions where flow was unobstructed. A second t-test was performed to make the same comparison for the ∆pelA cells. The p-values from these tests were evaluated against a critical threshold of p<0.05 adjusted by Bonferroni correction for two pairwise comparisons. Two t-tests were also performed on the data in Figure 1—figure supplement 1 measuring the maximum growth rates of our strains in liquid culture.

Acknowledgements

We thank members of the BLB laboratory, as well as Thomas Bartlett and Alvaro Banderas for helpful discussions. This work was supported by the Alexander von Humboldt Foundation (CDN), the Human Frontier Science Program Grant CDA00084/2015-C (KD), the Max Planck Society (KD), the Deutsche Forschungsgemeinschaft Grant SFB987 (KD), the Howard Hughes Medical Institute (BLB), NIH Grant 2 R37GM065859 (BLB), National Science Foundation Grant MCB-0948112 (BLB), National Science Foundation Grant MCB-1344191 (BLB), and the Alexander von Humboldt Foundation, the Max Planck Society, and the Federal Ministry of Education and Research (BLB). JY holds a Career Award at the Scientific Interface from the Burroughs Wellcome Fund.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Funding Information

This paper was supported by the following grants:

  • Alexander von Humboldt-Stiftung to Carey D Nadell, Bonnie L Bassler.

  • Human Frontier Science Program CDA00084/2015-C to Knut Drescher.

  • Max-Planck-Gesellschaft to Knut Drescher, Bonnie L Bassler.

  • Deutsche Forschungsgemeinschaft SFB987 to Knut Drescher.

  • Howard Hughes Medical Institute to Bonnie L Bassler.

  • National Institutes of Health 2R37GM065859 to Bonnie L Bassler.

  • National Science Foundation MCB-0948112 to Bonnie L Bassler.

  • National Science Foundation MCB-1344191 to Bonnie L Bassler.

Additional information

Competing interests

The authors declare that no competing interests exist.

Author contributions

CDN, Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing.

DR, Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing.

JY, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing.

KD, Data curation, Software, Formal analysis, Funding acquisition, Validation, Investigation, Methodology, Writing—original draft, Writing—review and editing.

BLB, Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Writing—original draft, Project administration, Writing—review and editing.

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eLife. 2017 Jan 13;6:e21855. doi: 10.7554/eLife.21855.015

Decision letter

Editor: Sara Mitri1

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Flow environment and matrix structure interact to determine spatial competition in Pseudomonas aeruginosa biofilms" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom served as Guest Editor, and the evaluation has been overseen by Richard Losick as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This is a very well written manuscript on biofilm formation and strain co-existence in the bacterium Pseudomonas aeruginosa. The article studies the advantage of P. aeruginosa producing extracellular polymeric substances in different flow environments. By competing a wild-type producer strain with a non-producing mutant, the authors show that while the wild-type out-competes the mutant in an environment where flow is unobstructed, introducing barriers and creating an irregular flow environment can change these predictions to the advantage of the mutant, leading to co-existence. They argue that this is because shear is reduced in the complex environment, creating small pockets where the mutant can grow without being washed away.

The three reviewers all agree that this work is novel and intriguing. While the dynamics of bacterial biofilms within porous environments has long been studied, how genotypes compete at the pore scale has largely been ignored. Previous work has largely focused on Darcy scale phenomena and lack clear mechanistic insights into the processes that shape community structure. The message of the paper is also important, since it shows that selection pressures in simplified environmental conditions in the lab may change in more complex environments. The data also nicely explain natural diversity in biofilm production phenotypes. Finally, the article is clear and well written. However, the reviewers have made a few recommendations that would greatly improve the manuscript.

Essential revisions:

1) Matrix secretion is interesting from a social evolution perspective because matrix secretion is costly, but can potentially benefit other cells in the vicinity including non-matrix producers. Spatial structure has therefore previously been shown to foster strain co-existence in bacterial communities (e.g. Kerr et al. 2002 Nature, Kim et al. 2008 PNAS, Poltak & Cooper 2011 ISME Journal). The dynamics reported here are somewhat different, since previous studies were conducted in the absence of flow. Liquid flow changes the story substantially because fitness now depends on a strain's ability to stay put, and other secretions – which may be important public goods – are washed away. This social component and a clear comparison to previous literature is not well covered in the paper. The authors mention this aspect here and there, but do not clearly introduce the social principles. This should be changed.

The authors should also be careful in stating that secreted matrix cannot be exploited. First, there is evidence that matrix can efficiently be exploited (Van Gestel et al. 2014 ISME Journal). Second, the authors' results do not disprove exploitation, but simply show that it would not occur in this setup.

2) The fitness advantages of the pelA mutant versus the WT in the different scenarios are unclear. Why does the fraction of WT cells decrease when they are inoculated at frequencies larger than 0.6 (even if the effect is not huge)? It seems that this could have many causes: measurements in liquid culture show that pelA cells do have a slightly larger growth rate than WT cells, but this difference was not statistically significant. Could the difference between the two genotypes be larger in a biofilm that is not exposed to flow (note the huge error bar in Figure 3C)? Or perhaps the architecture of the WT biofilm in porous environments is somehow playing a role? It seems that the basic mechanism behind this observation are not discussed in the manuscript.

There is one experiment that may make the story complete: competing the two strains in a regular microfluidic channel with no flow. In this scenario, biofilm can form, but cells will not be washed away. Will the mutant grow as in liquid (Figure 1—figure supplement 1) or are there other advantages to biofilm growth? Will they grow even more relative to the wild-type than in liquid? The authors already show mono-culture data under this condition (Figure 3C). This would substantiate their claim in the conclusion that "Pel non-producers do not appear to directly exploit the matrix material produced by wild type cells, but they can take advantage of the low-shear conditions that wild type matrix producers generate in complex flow environments".

We realize how onerous suggesting additional experiments can be and the lead author is now working in a different lab, but it does seem that having some insight on dynamics of competition on flat surfaces and the mechanisms at play in the porous media would make this a much better paper.

3) We are still left with questions about the fundamental mechanisms behind the empirical observations. Though the tradeoffs between surface colonization and dispersal are mentioned in the introduction, it is not clear whether the pel mutants, which are trapped in low flow regions, have relegated themselves to a low nutrient/low dispersal environment. Do the authors have an idea as to whether the cells trapped within these low velocity regions suffer from decreased reproductive fitness? The authors measured cells from the effluent in the flat microfluidic devices, is there a particular reason why similar observations could not be made in the porous channels?

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Flow environment and matrix structure interact to determine spatial competition in Pseudomonas aeruginosa biofilms" for further consideration at eLife. Your revised article has been favorably evaluated by Richard Losick (Senior editor) and a Reviewing editor.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

How do the data in the new Figure (Figure 3—figure supplement 3) compare to Figure 3C and to growth in liquid? Is there a bigger advantage? The best would also be to use similar plots for all growth comparisons (Figure 1—figure supplement 1, Figure 3B and the new figure).

I apologize for not noticing this earlier, but the details of flow rates appear to be missing. What flow rate are you using by default? And how does this change to you achieve the differences in shear in Figure 3C? How do you measure and verify differences in shear?

In your response, you state that "Our experiments directly demonstrate that cells in the flow-blocked regions continue to grow". I do not see these data. You have shown – and this is well-presented in the Results section – that there is a higher concentration of pelA mutants in areas with reduced flow. You also show in Figure 1 that the frequency of the wildtype is lower in the complex compared to the planar environment. But you have not provided data to show that the population size of pelA mutants is increasing over time in the complex environment. If you have these data, please add them as a supplementary figure. Otherwise, unless I have misunderstood something, the conclusions section needs to be modified: what you state "[flow] enabl[es] [DpelA] to proliferate locally using nutrients diffusing from the bulk liquid phase that are replenished by flow in other regions of the environment" has not been shown here. I think changing the sentence to something like "potentially enabling it to proliferate locally assuming that sufficient nutrients are diffusing from the bulk liquid phase…" would be more fitting and sufficient given your current results.

eLife. 2017 Jan 13;6:e21855. doi: 10.7554/eLife.21855.016

Author response

Essential revisions:

1) Matrix secretion is interesting from a social evolution perspective because matrix secretion is costly, but can potentially benefit other cells in the vicinity including non-matrix producers. Spatial structure has therefore previously been shown to foster strain co-existence in bacterial communities (e.g. Kerr et al. 2002 Nature, Kim et al. 2008 PNAS, Poltak & Cooper 2011 ISME Journal). The dynamics reported here are somewhat different, since previous studies were conducted in the absence of flow. Liquid flow changes the story substantially because fitness now depends on a strain's ability to stay put, and other secretions – which may be important public goods – are washed away. This social component and a clear comparison to previous literature is not well covered in the paper. The authors mention this aspect here and there, but do not clearly introduce the social principles. This should be changed.

The authors should also be careful in stating that secreted matrix cannot be exploited. First, there is evidence that matrix can efficiently be exploited (Van Gestel et al. 2014 ISME Journal). Second, the authors' results do not disprove exploitation, but simply show that it would not occur in this setup.

We thank the referees for pointing out this idea for addition to our manuscript. We agree in full that the social evolution perspective is important to understanding biofilm matrix production. New sentences have been added in the introduction to address the reviewers' concerns. Our goal was to include the social evolution perspective while not making social evolution the central theme of the manuscript, which aims more specifically to show how flow conditions alter spatial competition in biofilms. We are also more careful now to emphasize that biofilm matrix material is not always privatized, which can lead to public goods dilemmas with cheating cells outgrowing cooperative cells, as was observed in the important van Gestel et al. 2014 ISME J paper from the Kovacs group which is now cited.

New text can be found in the Introduction section.

2) The fitness advantages of the pelA mutant versus the WT in the different scenarios are unclear. Why does the fraction of WT cells decrease when they are inoculated at frequencies larger than 0.6 (even if the effect is not huge)? It seems that this could have many causes: measurements in liquid culture show that pelA cells do have a slightly larger growth rate than WT cells, but this difference was not statistically significant. Could the difference between the two genotypes be larger in a biofilm that is not exposed to flow (note the huge error bar in Figure 3C)? Or perhaps the architecture of the WT biofilm in porous environments is somehow playing a role? It seems that the basic mechanism behind this observation are not discussed in the manuscript.

There is one experiment that may make the story complete: competing the two strains in a regular microfluidic channel with no flow. In this scenario, biofilm can form, but cells will not be washed away. Will the mutant grow as in liquid (Figure 1—figure supplement 1) or are there other advantages to biofilm growth? Will they grow even more relative to the wild-type than in liquid? The authors already show mono-culture data under this condition (Figure 3C). This would substantiate their claim in the conclusion that "Pel non-producers do not appear to directly exploit the matrix material produced by wild type cells, but they can take advantage of the low-shear conditions that wild type matrix producers generate in complex flow environments".

We realize how onerous suggesting additional experiments can be and the lead author is now working in a different lab, but it does seem that having some insight on dynamics of competition on flat surfaces and the mechanisms at play in the porous media would make this a much better paper.

We thank the referees for this suggestion. We have performed the requested experiment, which shows clearly that Pel non-producing cells outcompete wild type cells when they are grown together in planar microfluidic chambers with no flow. This result is consistent with the existing logic of our conclusions and provides the additional needed support for our interpretation that the referees were seeking in their request. New text has also been added to describe these results (subsection “Wild type P. aeruginosa obstructs porous environments, generating low-shear regions that Pel deficient mutants can occupy”). We also added a new supplemental figure (Figure 3—figure supplement 3) with the results of the new experiment.

3) We are still left with questions about the fundamental mechanisms behind the empirical observations. Though the tradeoffs between surface colonization and dispersal are mentioned in the introduction, it is not clear whether the pel mutants, which are trapped in low flow regions, have relegated themselves to a low nutrient/low dispersal environment. Do the authors have an idea as to whether the cells trapped within these low velocity regions suffer from decreased reproductive fitness? The authors measured cells from the effluent in the flat microfluidic devices, is there a particular reason why similar observations could not be made in the porous channels?

An important point is that our manuscript is concerned with local competition for space and resources in the biofilm environment: we are exploring how the stable relative abundances of wild type and matrix mutants change as a function of environmental condition. The fact that Pel mutants can locally coexist with wild type cells in the complex flow environment, due to the clogging which reduces flow, whereas they cannot coexist with wild type in the simple flow environment, means by definition that Pel mutants have a higher reproductive fitness under complex flow conditions than they do under simple flow conditions. This result and its underlying causes are the core conclusions of our paper. Regarding nutrient status within flow-blocked areas, we note also that even though bulk transport of nutrients has been reduced or terminated, diffusion of nutrients from neighboring regions continues. Our experiments directly demonstrate that cells in the flow-blocked regions continue to grow.

We have modified existing text and added new sentences in the Introduction section and Conclusion section to emphasize that we are studying within-biofilm competition effects.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below:

How do the data in the new Figure (Figure 3—figure supplement 3) compare to Figure 3C and to growth in liquid? Is there a bigger advantage? The best would also be to use similar plots for all growth comparisons (Figure 1—figure supplement 1, Figure 3B and the new figure).

We appreciate the request for clarification here. The data shown in Figure 3—figure supplement 3 are changes in wild type population frequency from an initially 1:1 mixed population, i.e., with wild type at an initial frequency of 0.5. These data are most directly comparable to Figure 1, except only one initial population composition is shown in Figure 3—figure supplement 3, rather than nine as in Figure 1. We believe it is important to retain the different plot formats for the three figures mentioned because each plot conveys a different type of information. Figure 1—figure supplement 1 shows growth rate as extracted from time series data of growth in liquid culture. Figure 3C shows the absolute biomass accumulation after a fixed period of biofilm growth under different shear conditions, and lastly, the new Figure 3—figure supplement 3 shows changes in the relative abundance of wild type versus the pelA mutant in biofilms with no flow. To make it clear why the graphs are different, we have now provided additional detail in the figure legend of Figure 3—figure supplement 3 to explain exactly what that graph depicts.

I apologize for not noticing this earlier, but the details of flow rates appear to be missing. What flow rate are you using by default? And how does this change to you achieve the differences in shear in Figure 3C? How do you measure and verify differences in shear?

In response to the Editor’s question, we have included exact information regarding flow (subsection “Microfluidics and competition experiments”). Shear rates in the planar flow chambers are calculated on the basis of flow rate, rather than being measured directly (which cannot be done in our apparatus). As such they are estimates, but importantly, the estimate of shear under each condition is identical irrespective of which strain is being tested. Thus, the inter-strain comparison (our point of interest) remains correct.

In the revised text, we wrote: “In our experiments, the average flow speed was 150-200 µm/sec, unless noted otherwise. In Figure 3C, to alter shear, we varied the average flow speed; shear was estimated using standard calculations for surface shear stress under fluid flow: τ(y)=α∂u∂y= �pL H2 where τ is the shear stress, y is the height above the surface (evaluated in this case for y=0), α is the dynamic viscosity of the fluid, and u is the fluid flow velocity field, calculated for a rectangular channel in terms of the pressure decrease �p/L across the length L and height H of the channel. The pressure decrease was calculated for our channel dimensions and flow rates using previously published results. Lab Chip 7, 1479-1489.)”

In your response, you state that "Our experiments directly demonstrate that cells in the flow-blocked regions continue to grow". I do not see these data. You have shown – and this is well-presented in the Results section – that there is a higher concentration of pelA mutants in areas with reduced flow. You also show in Figure 1 that the frequency of the wildtype is lower in the complex compared to the planar environment. But you have not provided data to show that the population size of pelA mutants is increasing over time in the complex environment. If you have these data, please add them as a supplementary figure. Otherwise, unless I have misunderstood something, the conclusions section needs to be modified: what you state "[flow] enabl[es] [DpelA] to proliferate locally using nutrients diffusing from the bulk liquid phase that are replenished by flow in other regions of the environment" has not been shown here. I think changing the sentence to something like "potentially enabling it to proliferate locally assuming that sufficient nutrients are diffusing from the bulk liquid phase…" would be more fitting and sufficient given your current results.

In all of our frequency experiments, the total is 100%, thus if one strain decreases in frequency, the other must have increased in frequency. In the case of complex flow chambers, when the wild type strain decreased in frequency relative to the initial population composition, the pelA mutant had therefore increased in population size disproportionately relative to the wild type. This interpretation was the basis for our text statement. However, in light of the confusion this experiment/text has caused, we agree with the editor, that altering our concluding sentence to shift the tone toward interpretation, rather than demonstration, is more appropriate. We have made the suggested change in the main text (Conclusions section). We used the word presumably rather than the word potentially (presumably enabling it to proliferate).

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Figure 1—source data 1. Change in WT frequency as a function of initial frequency in two flow conditions.

    DOI: http://dx.doi.org/10.7554/eLife.21855.004

    elife-21855-fig1-data1.xlsx (43.2KB, xlsx)
    DOI: 10.7554/eLife.21855.004
    Figure 1—figure supplement 1—source data 1. Maximum liquid culture growth rates of study strains.

    DOI: http://dx.doi.org/10.7554/eLife.21855.006

    elife-21855-fig1-figsupp1-data1.xlsx (40.3KB, xlsx)
    DOI: 10.7554/eLife.21855.006
    Figure 2—source data 1. Biofilm production of WT and Pel-deficient P. aeruginosa in mono-culture and co-culture; cell counts in chamber effluents.

    (A) WT and ΔpelA biomass accumulation. (C) WT and ΔpelA cell counts in effluent.

    DOI: http://dx.doi.org/10.7554/eLife.21855.008

    elife-21855-fig2-data1.xlsx (44.4KB, xlsx)
    DOI: 10.7554/eLife.21855.008
    Figure 3—source data 1. Occurrence of WT and Pel-deficient P. aeruginosa in areas with flow blocked versus areas with flow open.

    Biomass accumulation of WT and Pel-deficient P. aeruginosa under different degrees of shear stress.

    (B) WT and ΔpelA biomass accumulation. (C) WT and ΔpelA growth under variable shear

    DOI: http://dx.doi.org/10.7554/eLife.21855.010

    elife-21855-fig3-data1.xlsx (48.1KB, xlsx)
    DOI: 10.7554/eLife.21855.010
    Figure 3—figure supplement 3—source data 1. Comparison of competition in simple chambers with or without flow.

    DOI: http://dx.doi.org/10.7554/eLife.21855.014

    elife-21855-fig3-figsupp3-data1.xlsx (39.4KB, xlsx)
    DOI: 10.7554/eLife.21855.014

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