Microfluidic-based transcriptomics reveal force-independent bacterial rheosensing

Abstract

Multiple cell types sense fluid flow as an environmental cue. Flow can exert shear force (or stress) on cells, and the prevailing model is that biological flow sensing involves the measurement of shear force1,2. Here, we provide evidence for force-independent flow sensing in the bacterium Pseudomonas aeruginosa. A microfluidic-based transcriptomic approach enabled us to discover an operon of P. aeruginosa that is rapidly and robustly upregulated in response to flow. Using a single-cell reporter of this operon, which we name the flow-regulated operon (fro), we establish that P. aeruginosa dynamically tunes gene expression to flow intensity through a process we call rheosensing (as rheo- is Greek for flow). We further show that rheosensing occurs in multicellular biofilms, involves signalling through the alternative sigma factor FroR, and does not require known surface sensors. To directly test whether rheosensing measures force, we independently altered the two parameters that contribute to shear stress: shear rate and solution viscosity. Surprisingly, we discovered that rheosensing is sensitive to shear rate but not viscosity, indicating that rheosensing is a kinematic (force-independent) form of mechanosensing. Thus, our findings challenge the dominant belief that biological mechanosensing requires the measurement of forces.

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Fig. 1: Flow triggers the induction of gene expression in P. aeruginosa.
Fig. 2: The shear rate rapidly and dynamically tunes rheosensing.
Fig. 3: fro induction requires the sigma factor FroR and anti-sigma factor FroI, but not known surface sensors.
Fig. 4: Rheosensing is a force-independent sensory modality.

Data availability

The data supporting the findings of the study are available in this article and its Supplementary Information files. All of the RNA-Seq data used to reach the conclusions of this paper are freely available under the National Center for Biotechnology Information Sequence Read Archive accession number PRJNA530209. Additionally, the raw data that support the findings of this study are available from the corresponding author upon request.

Code availability

The custom MATLAB routines used for processing and analysing the fluorescence microscopy data are freely available from the corresponding author upon request. The custom Python and Perl scripts used for processing and analysing the RNA-Seq data are freely available from the corresponding author upon request.

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Acknowledgements

We thank K. Kim for assistance with generating the flow-shielded and biofilm streamer microfluidic channels. We also thank members of the Gitai laboratory, J. Shaevitz, N. Wingreen, D. Kearns and L. Wiltbank for helpful discussions and comments on the manuscript. This work was supported by a grant (DP1AI124669) from the National Institutes of Health (to Z.G.). Additional funding came from the National Science Foundation (PHY-1734030 to B.P.B. and M.D.K.), Glenn for Aging Research (B.P.B.), DFG award KO5239/1-1 from the German Research Foundation (to M.D.K.), and National Institutes of Health grants K22AI112816 (to A.S.) and R21AI121828 (to B.P.B. and M.D.K.).

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J.E.S., A.L., M.D.K., A.S., H.A.S. and Z.G. designed the experiments. J.E.S., A.L., M.D.K. and A.S. performed the experiments. B.P.B. and A.S. conducted the computational analyses. J.E.S. and Z.G. wrote the paper.

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Correspondence to Zemer Gitai.

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Supplementary information

Supplementary Information

Supplementary Figures 1–11, Supplementary Tables 3–5 and Supplementary References.

Reporting Summary

Supplementary Dataset 1

Genes induced greater than threefold after four hours of flow.

Supplementary Dataset 2

Genes induced greater than threefold after 20 min of flow.

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Sanfilippo, J.E., Lorestani, A., Koch, M.D. et al. Microfluidic-based transcriptomics reveal force-independent bacterial rheosensing. Nat Microbiol 4, 1274–1281 (2019). https://doi.org/10.1038/s41564-019-0455-0

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