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Enhancing the tropism of bacteria via genetically programmed biosensors

Nature Biomedical Engineering volume 6, pages 94–104 (2022)Cite this article

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Abstract

Engineered bacteria for therapeutic applications would benefit from control mechanisms that confine the growth of the bacteria within specific tissues or regions in the body. Here we show that the tropism of engineered bacteria can be enhanced by coupling bacterial growth with genetic circuits that sense oxygen, pH or lactate through the control of the expression of essential genes. Bacteria that were engineered with pH or oxygen sensors showed preferential growth in physiologically relevant acidic or oxygen conditions, and reduced growth outside the permissive environments when orally delivered to mice. In syngeneic mice bearing subcutaneous tumours, bacteria engineered with both hypoxia and lactate biosensors coupled through an AND gate showed increased tumour specificity. The multiplexing of genetic circuits may be more broadly applicable for enhancing the localization of bacteria to specified niches.

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Fig. 1: Schematic of biosensors for engineered bacteria tropism.
Fig. 2: Design and characterization of hypoxia, lactate and pH biosensors.
Fig. 3: Engineering biosensor-dependent containment circuits and multiplexing for AND logic gate growth in vitro.
Fig. 4: Engineered biosensors respond to physiological cues.
Fig. 5: The multiplexed biosensor achieves enhanced specificity of bacteria tumour colonization.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw fluorescence and OD600 data in Fig. 2b, and the raw c.f.u. counts and measured weights in Supplementary Fig. 12 are provided as Supplementary Information. Additional data are available from the corresponding author on request.

Code availability

The MATLAB code used in this study is available from the corresponding author on request.

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Acknowledgements

We thank J. Zhang and W. Mather for technical assistance and the members of the Danino laboratory for reading the manuscript. This work was supported by the DoD Idea Development Award (LC160314), DoD Era of Hope Scholar Award (BC160541), Honjo International Foundation Scholarship (to T.H.) and NIH F99CA253756 (to T.H.).

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  1. These authors contributed equally: Tiffany Chien, Tetsuhiro Harimoto.

Authors and Affiliations

  1. Department of Biomedical Engineering, Columbia University, New York, NY, USA

    Tiffany Chien, Tetsuhiro Harimoto, Benjamin Kepecs, Kelsey Gray, Courtney Coker, Nicholas Hou, Kelly Pu, Tamjeed Azad, Andoni Nolasco & Tal Danino

  2. Biostatistics Department, Columbia University, New York, NY, USA

    Martina Pavlicova

  3. Data Science Institute, Columbia University, New York, NY, USA

    Tal Danino

  4. Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA

    Tal Danino

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Contributions

T.C., T.H. and T.D. conceived and designed the study. T.C., T.H., B.K. and T.A. performed in vitro characterization of biosensor. T.C., T.H., K.G., C.C., K.P. and A.N. performed in vivo experiments for this study. T.C., T.H., N.H. and T.D. developed the computational model for this study. T.C., T.H., M.P. and T.D. analysed experimental data. T.C., T.H. and T.D. wrote the manuscript.

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Correspondence to Tal Danino.

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Competing interests

T.C., T.H. and T.D. have filed a provisional patent application (number 62/930,665) with the US Patent and Trademark Office related to this work.

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Peer review information Nature Biomedical Engineering thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Computational modelling of biosensors.

Biosensors are modelled under regulation of transcription activators (FNR, CadC) or repressors (LldR) with varying environmental conditions. (Left) biosensor circuit schematics as shown in Fig. 1 with (middle) modeling in silico predictions compared to (right) in vitro experimental results (n = 3, ±S.E.M). See supplementary materials for detailed equations and parameters used in this study. Lactate and pH biosensor in vitro GFP fluorescent result is normalized by OD. Hypoxia biosensor in vitro GFP fluorescence was measured in an anaerobic chamber and are normalized by data from constitutive promoter pTac.

Extended Data Fig. 2 Computational simulation of three 2-input AND-gate containment circuits.

a, Computational modelling of lactate hypoxia AND gate containment strain growth over time in permissive (high lactate and hypoxia) and non-permissive (none, high lactate or hypoxia only) conditions. b, Growth simulation of pH hypoxia AND gate containment strain in permissive (low pH and hypoxia) and non-permissive (none, low pH or hypoxia only) conditions. c, Growth simulation of lactate pH AND gate containment strain in permissive (high lactate and low pH) and non-permissive (none, high lactate or low pH only) conditions. Lines not shown are covered by overlapping lines.

Supplementary information

Supplementary Information

Supplementary figures, tables and references.

Reporting Summary

Supplementary Data 1

Relevant gene sequences used in this study.

Supplementary Data 2

Raw fluorescence and OD600 data for Fig. 2b.

Supplementary Data 3

Raw c.f.u. counts and measured weight (in g) for Supplementary Fig. 12.

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Chien, T., Harimoto, T., Kepecs, B. et al. Enhancing the tropism of bacteria via genetically programmed biosensors. Nat Biomed Eng 6, 94–104 (2022). https://doi.org/10.1038/s41551-021-00772-3

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