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. Author manuscript; available in PMC: 2016 Sep 2.
Published in final edited form as: Nat Microbiol. 2016 Jan 11;1:15005. doi: 10.1038/nmicrobiol.2015.5

Local and Global Consequences of Flow on Bacterial Quorum Sensing

Minyoung Kevin Kim 1, Francois Ingremeau 2, Aishan Zhao 1, Bonnie L Bassler 3,4,*, Howard A Stone 2,*
PMCID: PMC5010089  NIHMSID: NIHMS807831  PMID: 27571752

Abstract

Bacteria use a chemical communication process called quorum sensing (QS) to control collective behaviours, such as pathogenesis and biofilm formation1,2. QS relies on the production, release, and group-wide detection of signal molecules called autoinducers. To date, studies of bacterial pathogenesis in well-mixed cultures have revealed virulence factors and the regulatory circuits controlling them, including the overarching role of QS3. Although flow is ubiquitous to nearly all living systems4, much less explored is how QS influences pathogenic traits in scenarios that mimic host environments, for example, under fluid flow and in complex geometries. Previous studies have showed that sufficiently strong flow represses QS5–7. Nonetheless, it is not known how QS functions under constant or intermittent flow, how it varies within biofilms or as a function of position along a confined flow, or how surface topography (grooves, crevices, pores) influence QS-mediated communication. We explore these questions using two common pathogens Staphylococcus aureus and Vibrio cholerae. We identify conditions where flow represses QS and other conditions where QS is activated despite flow, including characterizing geometric and topographic features that influence the QS response. Our studies highlight that, under flow, genetically identical cells do not exhibit phenotypic uniformity with respect to QS in space and time, leading to complex patterns of pathogenesis and colonization. Understanding the ramifications of spatially and temporally non-uniform QS responses in realistic environments will be crucial for successful deployment of synthetic pro- and anti-QS strategies.

Flow locally washes away autoinducers and represses QS

To study QS dynamics under flow, we introduced Staphylococcus aureus into microfluidic devices8 and continuously flowed sterile medium through the chambers with flow speeds comparable to those found in hosts9,10. The S. aureus strain harbored an agr P3-gfpmut2 transcriptional fusion11, which is activated in response to the endogenously produced QS autoinducer called AIP-I2 (Supplementary Fig. 1a), and a constitutively expressed sarA P1-mKate transcriptional fusion12, which enabled us to normalize measurements of QS responses by correcting for the effects of growth (Supplementary Fig. 1c and 1d).

S. aureus had a doubling time of 2.36 h in flow compared to 3.39 h in the absence of flow presumably because flow provided oxygen and nutrients (Fig. 1a). After 6 h, near the inlet, there were about three times more cells under flow than in the no-flow case. Nonetheless, high-cell-density dependent QS was only activated under the no-flow condition (Fig. 1b,c and Supplementary Video 1). We obtained identical result – no QS – under flow rates even as low as 0.5 µL/min, suggesting that flow effectively removes endogenously-produced autoinducers. Results similar to this have been reported previously6,7,13. To confirm this idea, QS autoinducers or autoinducer antagonists were exogenously introduced into the flow (Supplementary Fig. 2a) and, irrespective of the flow rate we used, QS was activated and repressed, respectively. Analogous results were obtained using the Gram-negative pathogen Vibrio cholerae (Supplementary Fig. 2b), suggesting, as has previous work6,7, that this is a general phenomenon. QS represses biofilm formation in both S. aureus and V. cholerae, underscoring why robust biofilms form under flow14–16 (Fig. 1c and Supplementary Fig. 2b).

Figure 1. Fluid flows locally repress QS by advecting signaling molecules.

Figure 1

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a, (i) Growth curves obtained by measuring mKate fluorescence intensity from the S. aureus sarA P1-mKate reporter under different flow conditions; flow rates of 0–10 µL/min. Relative cell number denotes the number of cells divided by the initial cell number. Cell counts were obtained as dividing the summed mKate fluorescence intensity by the experimentally determined average fluorescence signal of individual cells. (ii) Normalized QS output is the GFP fluorescence intensity from the QS-controlled agr P3-gfpmut2 reporter divided by the cell counts as a function of time for different flow conditions. We imaged the surface area of 400 µm × 500 µm, which is one quarter of the area of the channel. Data points indicate means and error bars denote standard deviations with n = 4 independent replicates. b, Fluorescence images of S. aureus under no flow and flow conditions 6 h after inoculation. Left panels (i) and (iv) show the constitutive sarA P1-mKate reporter. Middle panels (ii) and (v) show the QS-controlled agr P3-gfpmut2 reporter. Right two panels (iii) and (vi) show the merged images for the left and middle panels. Top panels (i), (ii), and (iii); no flow, bottom panels (iv), (v), and (vi); flow rate of 1 µL/min. The images are based on n = 4 independent replicates.

QS can be expected to be repressed under sufficiently strong flow due to the advection of autoinducers. Considering the ubiquity of QS mechanisms in bacteria, we wondered if there exist realistic scenarios in which QS is activated even under flowing systems. The remainder of this work explores several examples of such responses and makes the case that more sophisticated assessment of the influence of flow, in conjunction with a consideration of geometry and surface topography, is required to capture and understand how such features drive non-uniform spatial and temporal dynamics of bacterial QS.

Forming a thick biofilm promotes QS under flow

We investigated whether S. aureus biofilm formation affects QS dynamics under constant flow. We found that QS was locally repressed for cells that were close to the flowing fluid, but QS was activated at the base of thick biofilms (Fig. 2a and 2b (i), and Supplementary Video 2). We interpret this result, highlighting the spatial distribution of QS, to mean that advection of autoinducers by flow is efficient at the interface between the biofilms and the flowing fluid13,17,18, but is not efficient at the base13,19, where autoinducers inside biofilms can accumulate, increasing in concentration over time.

Figure 2. Forming a thick biofilm promotes QS under flow.

Figure 2

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a, A three-dimensional rendering of biofilms (127 µm × 127 µm × 50 µm) of S. aureus grown in a microfluidic channel (Qfluid = 0.1 µl/min). The figure is the merged image (see Fig. 1 for details); red indicates QS-off cells, and yellow indicates QS-on cells. The images are based on n = 4 independent replicates. b, Central merged images show single optical sections of the xy plane, 10 µm above the surface-biofilm interface, with z-projections shown at right (xz plane) and below (yz plane). The same biofilm region is shown (i) under flow for 13 h after inoculation and (ii) following 13 h of flow and 3 h of no flow. The images are based on n = 4 independent replicates. c, Merged images for the base of biofilms grown under different flow rates, (i) 0.1 µL/min and (ii) 10 µL/min, were taken 13 h after inoculation. The images are based on n = 4 independent replicates. d, Normalized QS output was measured as a function of cell number in a volume of 5000 µm3 (100 µm2 area × 50 µm height).

To explore QS induction inside biofilms, we terminated the flow at 13 h, waited for an additional 3 h, during which diffusion was the only transport mechanism, and then examined the QS reporter output. Diffusion was less efficient than advection at removing autoinducers, and QS activation indeed extended further into the inner region of the biofilm (Fig. 2b (ii)). If, by contrast, we maintained the flow for the entire 16 h, QS activation was restricted to the cells at the bottom of the biofilm (Supplementary Fig. 3). Under 100-fold higher flow speed, QS activation within the biofilm did not occur (Fig. 2c). The heterogeneous QS activation that occurs within biofilms under flow also depends on biofilm thickness. Under constant flow, increasing the biofilm thickness over a fixed area increases QS activation (Fig. 2d). Thus, the interplay between flow strength and biofilms leads to a spatially structured QS activation under flow.

Our findings could underscore a curious biofilm phenotype known for S. aureus. Activation of QS in S. aureus leads to upregulation of components that degrade the biofilm extracellular matrix20,21 (Supplementary Fig. 1a), which is presumed to enable cells to escape, spread, and colonize new regions22,23. Our results show that cells that are in contact with flow are in QS-off mode, so they produce robust biofilms, which shield cells inside the biofilm from autoinducer advection. Presumably, this feature enables internal cells to be in the QS-on mode, to launch the gene expression program required for dispersal, facilitating flow-driven dissemination during infection.

In a separate set of experiments we also found that, regardless of biofilm thickness, intermittent flow promotes temporal fluctuations in and out of the QS-mode, ultimately leading to QS activation, albeit following an initial delay and only sub-maximal activation occurs (Supplementary Fig. 4). Thus, our studies suggest that time-varying flows that mimic rain, digestion, and urination can lead to non-uniform temporal QS activation.

QS responses vary as a function of distance to the flow inlet

Bacterial niches with connectivity over long distances are common in confined geometries, for example, bacterial contamination in piping, microbial distribution in soil, porous media, phloem in plants, or the intestinal tracts of animals. We wondered whether constant flow represses QS uniformly over long distances. To test this idea, we designed extended chambers with physiologically relevant length scales (~ 0.3 m). We found that QS is locally repressed by flow near the inlet, but in these confined environments, QS is activated downstream (Fig. 3a,b and Supplementary Video 3). Relatively more biofilm formation was observed in the upstream region than downstream at later time points presumably due to the consequences of QS-driven differences in biofilm regulation. As fluid travels over a surface colonized by bacteria that produce autoinducers, the average autoinducer concentration in the flowing fluid increases downstream over time. We changed the flow speeds to examine if QS activation was affected. The autoinducer concentration at distance x is approximately proportional to the travel time t of flowing fluid (t ∝ x/Qfluid). As we increase the flow rate Qfluid, the travel time to distance x decreases so the autoinducer concentration at distance x decreases. Thus, as flow rate increases, the critical distance Dc to initiate QS activation increases (Supplementary Fig. 5a).

Figure 3. Flows assist long-distance QS by enhancing autoinducer accumulation.

Figure 3

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a, Merged images (see Fig. 1 for details) at different downstream locations along a microfluidic channel (Qfluid = 0.5 µl/min): (i) 50 mm, (ii) 240 mm, (iii) 260 mm, (iv) 280 mm and (v) 300 mm 14 h after the start of the experiment. The images are based on n = 3 independent replicates. b, Spatio-temporal evolution of the normalized QS output (Qfluid = 0.5 µl/min). Data indicate means of normalized QS output for triplicate data. c, Model prediction for the autoinducer concentration profile as a function of space and time (see Supplementary Equations).

To understand how autoinducer concentration increases as fluid travels downstream, we developed a mathematical model of the kinetics and convection (see Supplementary equations). The model predicts the concentration profiles as a function of distance and time, showing accumulation downstream, which promotes specific regions in which QS is induced (Fig. 3c and Supplementary Fig. 5b). Our findings suggest that, compared with the no-flow scenario in which QS-mediated communication is driven exclusively by diffusion, the transport of autoinducers by flow in confined configurations increases the length scale of bacterial communication so that QS-directed processes are non-uniform along natural flow pathways. This finding might explain why Pseudomonas aeruginosa is motile upstream (a QS-off phenotype), but forms biofilms downstream (a QS-on phenotype) in flowing networks24.

QS is activated in crevices despite flow

Next we considered whether complex topographies (grooves, crevices, pores) might also influence QS-mediated communication. Crevices such as cracks in rocks, tooth cavities, intestinal crypts, and corrugated pipes are a few examples where bacterial colonization can occur (Fig. 4a). In the present context, S. aureus colonizes human intestinal crypts and this behavior is central to its pathogenic lifestyle25. We designed chambers with crevices, and initially examined flow patterns by imaging particles in the fluid (Fig. 4b). Outside the crevices, particles moved with the flow with convection being the dominant feature (flow-dominant region), while inside the crevices, the tracer particles remained at rest and diffusion dominated (flow-limited region)26. When we seeded bacteria into the chamber, QS was repressed outside the crevices, similar to what we found under constant flow (Fig. 1). However, QS was activated inside the crevices (Fig. 4c and Supplementary Video 4). The results show that autoinducer accumulates inside crevices as a result of the reduction of flow, which depends on the aspect ratio of the cavities, Lc/Wc. In crevices with low Lc/Wc, in which flow can penetrate and advection is dominant, autoinducers were washed away, and QS was repressed (Supplementary Fig. 6a).

Figure 4. QS is activated inside crevices.

Figure 4

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a, Flow networks with crevices or pores in (i) the small intestine of mice (image courtesy of Dr. Anisa Ismail), (ii) tooth cavities (image courtesy of Dr. Wonhee Lee), (iii) corrugated industrial pipes, and (iv) cracks in rocks. Scale bars: 120 µm, 10 mm, 2 cm, and 5 cm, respectively. b, Image of fluorescent 1 µm diameter beads flowing into a corrugated microfluidic channel under a flow rate of 1 µL/min. The images based on n = 4 independent replicates. c, Merged images of S. aureus in a complex topography. As in Fig. 1, red shows QS-off cells, and yellow shows QS-on cells. The images are based on n = 6 independent replicates. d, Normalized QS output inside of crevices in the absence of antagonist (black) and when 1 µM antagonist (AIP-II) was continuously flowed in beginning 8 h after inoculation (red). Data points indicate means and error bars denote standard deviations with n = 4 independent experiments.

Our findings could underscore a pathogenic behavior of S. aureus. Inside crypts, S. aureus engages in QS and activates the production of enterotoxin B, which functions to increase the depth of the crypt3,27. According to our findings, this activity further insulates S. aureus from flow, which is a mechanism that presumably enables QS-control of pathogenicity specifically inside crypts (Fig. 4c). The consequences of topography are apparently generalizable: the Gram-negative enteric pathogen V. cholerae, which also colonizes human intestines and uses QS to control virulence28,29, also activates QS inside, but not outside, crevices (Supplementary Fig. 6b).

To explore the health consequences of QS in complex topographies, we introduced into the flow a molecule that acts as an antagonist (AIP-II) of the S. aureus autoinducer30. In response to antagonist diffusion into crevices, QS was repressed (Fig. 4d). Analogous results were obtained using the clinical pathogen S. aureus MRSA (Supplementary Fig. 6c,d and Supplementary Video 5), which highlights the generality of our results and approach.

We have discovered unexpected QS dynamics under different flow regimes, which arise from the interactions between bacterial signaling pathways, small molecule diffusion, convection, the geometry and thickness of the biofilms, the length of the diffusive path within cell clusters, and the distances and topographies characteristic of flow networks. These findings enable us to predict how QS will play out in different spatio-temporal contexts which will have significant implications for the development of strategies for controlling beneficial and pathogenic bacterial behaviors in realistic environments in nature, in medicine and in industry. Our results specifically suggest that bacterial colonization and biofilm development under flow can lead to heterogeneous QS activation (Supplementary Fig. 7), which promotes diversity in the genetic programs that bacteria enact. As a consequence, genetically identical bacteria exhibit remarkably different behaviors at particular regions and at particular times under flow.

Methods

Bacterial strains, plasmids, and autoinducer peptides

The strains and plasmids used are listed in Supplementary Table 1. Staphylococcus aureus strains RN4220 and RN6390b, and the plasmid pJL-agr-GFP were gifts from Dr. Richard Novick's group (New York University)10. DNA polymerase, dNTPs, and restriction enzymes were purchased from New England Biolabs (NEB, Ipswich, MA). DNA extraction and purification kits were acquired from Qiagen (Valencia, CA). DNA oligonucleotides were purchased from Integrated DNA Technologies (Coralville, Iowa). Sequences of plasmids were verified by Genewiz (South Plainfield, NJ). The autoinducer AIP-I and the autoinducer antagonist AIP-II were synthesized as previously described30, using standard solid-phase approaches.

A plasmid carrying a transcriptional fusion to monitor S. aureus QS activity was constructed by replacing the gfpmut2 gene from vector pJL-agr-GFP with a fragment containing agr P3-mKate. This plasmid is called pMK004. To make this plasmid, the mKate gene was amplified by PCR from pCN00431 using primers MKF012/MKR012 followed by overlap extension PCR cloning32. A plasmid constitutively expressing mKate under the sarA P1 promotor (pMK014), was constructed by replacing the agr P3 promoter in pMK004 with the constitutive promoter sarA P1. The sarA P1 sequence corresponded to −306 to −62 upstream of sarA, and was amplified from S. aureus RN6390b using primers MKF001/MKR00110,11,33, followed by overlap extension PCR cloning. The final plasmid harboring both the agr P3-gfpmut2 and the sarA P1-mKate fusions (pMK021) was constructed by introducing the sarA P1-mKate fragment onto pJL-agr-GFP using Gibson cloning (New England Biolabs, Ipswich, MA)34. All plasmids were introduced into Escherichia coli DH5α using chemical transformation (New England Biolabs, Ipswich, MA) followed by selection with ampicillin. Vectors were purified from E. coli, introduced into S. aureus strain RN4220 by electroporation, and selected with erythromycin35. Subsequently, vectors were transduced into S. aureus strain RN6390b or the MRSA strain using standard phage transduction techniques with phage 80α35.

Growth conditions

S. aureus strains were grown overnight at 37 °C with shaking in Tryptic Soy Broth (TSB; Difco, Franklin Lakes, NJ) with 10 µg/ml erythromycin to maintain plasmids, back-diluted 1:2000, and re-grown for 3 h (to OD600 ~ 0.05–0.1). V. cholerae strains were grown overnight at 37 °C with shaking in Tryptone Broth (TB; Difco, Franklin Lakes, NJ), back-diluted 1:1000, and re-grown for 3 h to early exponential phase.

Fluorescence reporter assay

Transcription from fluorescence reporter genes was measured in S. aureus strain RN6390b carrying pMK021 (MK121). Overnight S. aureus cultures were diluted 1:1000 into fresh TSB with 10 µg/ml erythromycin, and 100 µL of diluted cultures were distributed into wells of 96 well plates (MatTek, Ashland, MA), followed by addition of 50 µL of mineral oil (Sigma, St. Louis, MO) to prevent evaporation. Using a Synergy 2 plate reader (Biotek, Winooski, VT), OD600 was monitored, and GFP and mKate levels were measured at 484 nm/528 nm and 588 nm/633 nm, respectively. Measurements were conducted with 15 min intervals at 25 °C with shaking.

Microscopy

Imaging was performed using a Nikon Eclipse Ti microscope (Melville, NY) fitted with a Yokogawa CSU X-1 spinning disk confocal scanning unit (Biovision Technologies, Exton, PA), CFI Apo TIRF 60X Oil objective (Nikon, Melville, NY), and DU-897 X-9351 camera (Andor, Concord, MA). Laser lines at 488 and 593 nm were used to excite the GFP and mKate fluorescent proteins, respectively. We imaged the surface area of 400 µm × 500 µm, 250 µm × 250 µm, 125 µm × 250 µm, and 750 µm × 625 µm for Fig. 1, 2, 3 and 4, respectively. For Fig. 1, 2, and 4, images were taken at the bottom of the channels. Cell counts and quantitation of QS output were processed using custom software written in Matlab (Mathworks, Natick, MA). Fluorescence protein signals were distinguished from background noise by subtraction of background signals. To calculate cell counts, the constitutive mKate fluorescence from the total imaged bacterial biomass area was summed and divided by the experimentally determined average fluorescence signal of single cells. Normalized QS output is defined as the normalized GFP intensity, which was obtained by summing the QS-controlled GFP fluorescence intensity from the total imaged bacterial biomass area followed by subtraction of background noise and division by the cell counts. Each replicate was performed using independent bacterial cultures in individual chambers. For experiments exploring crevice geometries, we measured fluorescence intensities from three crevices and combined the data to become a single replicate. All experiments were conducted at room temperature.

Microfluidic experiments

Details of fabrication and experiments are provided in supplementary information. Channels were loaded with cultures for 10 min, after which sterile TSB containing 10 µg/ml erythromycin was flowed steadily into the devices to remove unattached planktonic cells. The flow into the main channel was constant or periodic with a range of Qfluid = 0.1–100 µL/min. The flow was driven by syringe pumps. For V. cholerae experiments, sterile TB was used as the medium.

Supplementary Material

Supplemental Information

Acknowledgments

We thank the Novick laboratory for generously providing S. aureus strains and the pJL-agr-GFP plasmid. We thank B. Wang, J. Chen, K. Papenfort and J. Yan for technical advice and discussions, and we thank K. Drescher for discussions and for generously providing strain KDV028. We thank A. Ismail for providing the image that appears in Fig. 4a (i) and W. Lee for the image in Fig. 4a (ii). We are grateful to members of the B.L.B. and H.A.S. laboratories for suggestions. This work was supported by NSF grant MCB-1119232 (B.L.B. and H.A.S.) and MCB-1344191 (B.L.B. and H.A.S), the Howard Hughes Medical Institute (B.L.B.), NIH grant R01GM065859 (B.L.B.), and STX fellowship (M.K.K.).

Footnotes

The authors declare no competing financial interests.

Author Contributions M.K.K., F.I., B.L.B., and H.A.S. designed the experiments. M.K.K. and F.I. performed the experiments. A.Z. synthesized the AIP. F.I. constructed the quantitative model, and H.A.S. revised it. M.K.K., F.I., B.L.B., and H.A.S. analyzed the data, and wrote the manuscript. All authors discussed and approved the results.

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