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Local and global consequences of flow on bacterial quorum sensing

Nature Microbiology volume 1, Article number: 15005 (2016) Cite this article

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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 studies5–7 have shown that sufficiently strong flow represses QS. 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.

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Figure 1: Fluid flows locally repress QS by advecting signalling molecules.
Figure 2: Forming a thick biofilm promotes QS under flow.
Figure 3: Flows assist long-distance QS by enhancing autoinducer accumulation.
Figure 4: QS is activated inside crevices.

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Acknowledgements

The authors acknowledge the Novick Laboratory for providing S. aureus strains and the pJL-agr-GFP plasmid. The authors thank B. Wang, J. Chen, K. Papenfort and J. Yan for technical advice and discussions, and K. Drescher for discussions and for providing strain KDV028. The authors thank A. Ismail for providing the image that appears in Fig. 4a,i and W. Lee for the image in Fig. 4a,ii. The authors thank members of the B.L.B. and H.A.S. laboratories for suggestions. This work was supported by National Science Foundation grants 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.), National Institutes of Health grant R01GM065859 (B.L.B.) and an STX fellowship (M.K.K.).

Author information

Authors and Affiliations

  1. Department of Chemistry, Princeton University, Princeton, 08544, New Jersey, USA

    Minyoung Kevin Kim & Aishan Zhao

  2. Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, 08544, New Jersey, USA

    François Ingremeau & Howard A. Stone

  3. Department of Molecular Biology, Princeton University, Princeton, 08544, New Jersey, USA

    Bonnie L. Bassler

  4. Howard Hughes Medical Institute, Chevy Chase, 20815, Maryland, USA

    Bonnie L. Bassler

Authors
  1. Minyoung Kevin Kim
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  4. Bonnie L. Bassler
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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. analysed the data and wrote the manuscript. All authors discussed and approved the results.

Corresponding authors

Correspondence to Bonnie L. Bassler or Howard A. Stone.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Figures 1–8, Tables 1–3, Equations, Methods and References. (PDF 2444 kb)

Supplementary Video 1

Fluid flows locally repress QS by advecting signalling molecules. Time series of merged fluorescence images of S. aureus under no flow and flow conditions for 12 h after inoculation (see Fig. 1b for details). The time interval between each image is 10 min. (AVI 5172 kb)

Supplementary Video 2

Forming a thick biofilm promotes QS under flow. Series of merged fluorescence images of S. aureus biofilms for the xy planes as a function of z-axis. The same biofilm region is taken under flow for 13 h after inoculation (left) and following 13 h of flow and 3 h of no flow (right). The interval between each frame is 3 µm in z-axis. (AVI 1639 kb)

Supplementary Video 3

Flows assist long-distance QS by enhancing autoinducer accumulation. Time series of merged images of S. aureus under a constant flow at different downstream locations along a microfluidic channel (Q[fluid] = 0.5 µl per min): from left to right in the video, indicating 50 mm, 240 mm, 280 mm and 300 mm from the inlet, respectively. The time interval between each image is 20 min. (AVI 3022 kb)

Supplementary Video 4

QS is activated inside crevices. Time series of merged fluorescence images of S. aureus in a complex topography. The time interval between each image is 10 min (AVI 5327 kb)

Supplementary Video 5

QS dynamics of S. aureus MRSA in a complex topography with continuous injection of 1 µM antagonist (AIP-II). Time series of merged fluorescence images of S. aureus MRSA inside of crevices in the absence of antagonist (right) and when 1 µM antagonist (AIP-II; left) was continuously flowed into the channel beginning 16 h after inoculation. The time interval between each image is 20 min. (AVI 4509 kb)

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Kim, M., Ingremeau, F., Zhao, A. et al. Local and global consequences of flow on bacterial quorum sensing. Nat Microbiol 1, 15005 (2016). https://doi.org/10.1038/nmicrobiol.2015.5

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