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Engineering synthetic breath biomarkers for respiratory disease

Nature Nanotechnology volume 15, pages 792–800 (2020)Cite this article

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Abstract

Human breath contains many volatile metabolites. However, few breath tests are currently used in the clinic to monitor disease due to bottlenecks in biomarker identification. Here we engineered breath biomarkers for respiratory disease by local delivery of protease-sensing nanoparticles to the lungs. The nanosensors shed volatile reporters upon cleavage by neutrophil elastase, an inflammation-associated protease with elevated activity in lung diseases such as bacterial infection and alpha-1 antitrypsin deficiency. After intrapulmonary delivery into mouse models with acute lung inflammation, the volatile reporters are released and expelled in breath at levels detectable by mass spectrometry. These breath signals can identify diseased mice with high sensitivity as early as 10 min after nanosensor administration. Using these nanosensors, we performed serial breath tests to monitor dynamic changes in neutrophil elastase activity during lung infection and to assess the efficacy of a protease inhibitor therapy targeting neutrophil elastase for the treatment of alpha-1 antitrypsin deficiency.

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Fig. 1: Schematic of the approach.
Fig. 2: vABNs are activated by human NE and release volatile reporters detectable by mass spectrometry.
Fig. 3: In silico and in vivo investigation of parameters contributing to the breath signal facilitate vABN optimization.
Fig. 4: Serial vABN breath tests enable monitoring of pulmonary NE activity during lung infection.
Fig. 5: vABN-derived breath signal can be used to assess duration of NE inhibition following A1AT treatment.
Fig. 6: vABN breath tests can resolve normal and pathologic NE activity for assessment of A1AT augmentation therapies.

Data availability

Research data is available online at https://doi.org/10.5281/zenodo.3840591. Source data are provided with this paper.

Code availability

Code for the PBPK model is available online at https://github.com/NN19092108A/PBPKmodel. Source data are provided with this paper.

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Acknowledgements

We thank H. Fleming (MIT) for critical editing of the manuscript; H. Ko (MIT) for assistance with experiments; C. Buss (MIT), M. Zieger (U Mass), D. Kotton (BU) and A. Wilson (BU) for helpful discussion; Q. Smith (MIT) for assistance with confocal imaging; and E. Roche (MIT), J. Dudani and A. Bekdemir (MIT) for feedback on the PBPK model. We thank C. Mueller (U Mass) for providing us with AATD mouse models. We thank the Koch Institute’s Robert A. Swanson (1969) Biotechnology Center for technical support, specifically the Hope Babette Tang (1983) Histology Facility, the Biopolymers & Proteomics Core Facility, and the Microscopy Core Facility. We also thank N. Watson at the Whitehead Institute W.M. Keck Microscopy Facility for her TEM imaging services. This study was supported in part by a Global Health Innovation Partnership (GHIP) grant from the Bill and Melinda Gates Foundation, Massachusetts General Hospital and the Ragon Institute; funding from Janssen Research and Development; and funding from the Kathy and Curt Marble Cancer Research Fund to S.N.B. L.W.C. acknowledges support from the National Institute of Health Pathway to Independence Award (K99 EB28311). M.N.A. thanks the National Science Foundation Graduate Research Fellowship Program for support. S.N.B. is a Howard Hughes Institute Investigator.

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Authors and Affiliations

  1. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Leslie W. Chan, Melodi N. Anahtar & Sangeeta N. Bhatia

  2. Harvard–MIT Division of Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Melodi N. Anahtar, Kelsey E. Hern & Sangeeta N. Bhatia

  3. Biological and Chemical Technologies Group, Massachusetts Institute of Technology Lincoln Laboratory, Lexington, MA, USA

    Ta-Hsuan Ong & Roderick R. Kunz

  4. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sangeeta N. Bhatia

  5. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Sangeeta N. Bhatia

  6. Broad Institute, Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    Sangeeta N. Bhatia

  7. Howard Hughes Medical Institute, Cambridge, MA, USA

    Sangeeta N. Bhatia

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Contributions

L.W.C. and S.N.B. conceived the study with suggestions from R.R.K. L.W.C. synthesized and characterized the nanoparticle sensors. L.W.C. and T.-H.O. carried out in vitro experiments. M.N.A. built the multicompartment model for in silico predictions of breath signal output, extracted in vivo parameters for reporter partitioning and completed in silico experiments. L.W.C. and K.E.H. carried out in vivo experiments. L.W.C., M.N.A. and T.-H.O. analysed the data. L.W.C. wrote the paper with contributions from S.N.B. and M.N.A. and feedback from all authors.

Corresponding author

Correspondence to Sangeeta N. Bhatia.

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Competing interests

S.N.B., L.W.C., M.N.A. and R.R.K. are listed as inventors on patent applications related to the content of this work. S.N.B. holds equity in Glympse Bio and Impilo Therapeutics; is a director at Vertex; consults for Cristal, Maverick and Moderna; and receives sponsored research funding from Johnson and Johnson.

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Peer review information Nature Nanotechnology thanks Marcin Drag, Hossam Haick and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Figs. 1–13, Tables 1–2, text (model description), materials, methods and refs. 1–8.

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Chan, L.W., Anahtar, M.N., Ong, TH. et al. Engineering synthetic breath biomarkers for respiratory disease. Nat. Nanotechnol. 15, 792–800 (2020). https://doi.org/10.1038/s41565-020-0723-4

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