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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.

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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.

References

  1. Pereira, J. et al. Breath analysis as a potential and non-invasive frontier in disease diagnosis: an overview. Metabolites 5, 3–55 (2014).

    Google Scholar 

  2. Amann, A. et al. Analysis of exhaled breath for disease detection. Annu. Rev. Anal. Chem. 7, 455–482 (2014).

    CAS  Google Scholar 

  3. van Oort, P. M. et al. The potential role of exhaled breath analysis in the diagnostic process of pneumonia—a systematic review. J. Breath Res. 12, 024001 (2018).

  4. Hanna, G. B., Boshier, P. R., Markar, S. R. & Romano, A. Accuracy and methodologic challenges of volatile organic compound-based exhaled breath tests for cancer diagnosis: a systematic review and meta-analysis. JAMA Oncol. 5, e182815 (2019).

    Google Scholar 

  5. Gaude, E. et al. Targeted breath analysis: exogenous volatile organic compounds (EVOC) as metabolic pathway-specific probes. J. Breath Res. 13, 032001 (2019).

    CAS  Google Scholar 

  6. Savarino, V., Vigneri, S. & Celle, G. The 13C urea breath test in the diagnosis of Helicobacter pylori infection. Gut 45, 18–23 (1999).

    Google Scholar 

  7. Lock, J. et al. Interpretation of non-invasive breath tests using 13C-labeled substrates—a preliminary report with 13C-methacetin. Eur. J. Med. Res. 14, 547–550 (2009).

    CAS  Google Scholar 

  8. Ghoshal, U. C. How to interpret hydrogen breath tests. J. Neurogastroenterol. Motil. 17, 312–317 (2011).

    Google Scholar 

  9. Timmins, G. S. Stable isotope biomarker breath tests for human metabolic and infectious diseases: a review of recent patent literature. Expert. Opin. Ther. Pat. 26, 1393–1398 (2016).

    CAS  Google Scholar 

  10. Lange, J. et al. Volatile organic compound based probe for induced volatolomics of cancers. Angew. Chem. Int. Ed. 58, 17563–17566 (2019).

    CAS  Google Scholar 

  11. Raissy, H. H. et al. A proof of concept study to detect urease producing bacteria in lungs using aerosolized 13C-urea. Pediatr. Allergy Immunol. Pulmonol. 29, 68–73 (2016).

    Google Scholar 

  12. Bishai, W. R. & Timmins, G. S. Potential for breath test diagnosis of urease positive pathogens in lung infections. J. Breath Res. 13, 032002 (2019).

    Google Scholar 

  13. Aberle, D. R. et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N. Engl. J. Med. 365, 395–409 (2011).

    Google Scholar 

  14. Fuchs, S. I. et al. Lung clearance index for monitoring early lung disease in alpha-1-antitrypsin deficiency. Respir. Med. 116, 93–99 (2016).

  15. Lagier, J. et al. Current and past strategies for bacterial culture in clinical microbiology. Clin. Microbiol. Rev. 28, 208–236 (2015).

    CAS  Google Scholar 

  16. Taggart, C. C., Greene, C. M., Carroll, T. P., O’Neill, S. J. & McElvaney, N. G. Elastolytic proteases: inflammation resolution and dysregulation in chronic infective lung disease. Am. J. Respir. Crit. Care Med. 171, 1070–1076 (2005).

    Google Scholar 

  17. Houghton, A. M. et al. Neutrophil elastase–mediated degradation of IRS-1 accelerates lung tumor growth. Nat. Med. 16, 219–223 (2010).

    CAS  Google Scholar 

  18. Kwong, G. A. et al. Mass-encoded synthetic biomarkers for multiplexed urinary monitoring of disease. Nat. Biotechnol. 31, 63–70 (2013).

    CAS  Google Scholar 

  19. Kwon, E. J. & Dudani, J. S. Ultrasensitive tumour-penetrating nanosensors of protease activity. Nat. Biomed. Eng. 1, 0054 (2017).

    CAS  Google Scholar 

  20. Buss, C. G., Dudani, J. S., Akana, R. T. K., Fleming, H. E. & Bhatia, S. N. Protease activity sensors noninvasively classify bacterial infections and antibiotic responses. EBioMedicine 38, 248–256 (2018).

    Google Scholar 

  21. Loynachan, C. N. et al. Renal clearable catalytic gold nanoclusters for in vivo disease monitoring. Nat. Nanotechnol. 14, 883–890 (2019).

    CAS  Google Scholar 

  22. Kirkpatrick, J. D. et al. Urinary detection of lung cancer in mice via noninvasive pulmonary protease profiling. Sci. Transl. Med. 12, eaaw0262 (2020).

    CAS  Google Scholar 

  23. Pham, C. T. N. Neutrophil serine proteases: specific regulators of inflammation. Nat. Rev. Immunol. 6, 541–550 (2006).

    CAS  Google Scholar 

  24. Rubenfeld, G. D. et al. Incidence and outcomes of acute lung injury. N. Engl. J. Med. 353, 1685–1693 (2005).

    CAS  Google Scholar 

  25. Laurell, C.-B. & Eriksson, S. The electrophoretic α1-globulin pattern of serum in α1-antitrypsin deficiency. Scandinav. J. Clin. Lab. Invest. 15, 132–140 (1963).

    CAS  Google Scholar 

  26. Kasperkiewicz, P., Poreba, M., Snipas, S. J., Parker, H. & Winterbourn, C. C. Design of ultrasensitive probes for human neutrophil elastase through hybrid combinatorial substrate library profiling. Proc. Natl Acad. Sci. USA 111, 2518–2523 (2014).

    CAS  Google Scholar 

  27. Anderson, B. M. et al. Application of a chemical probe to detect neutrophil elastase activation during inflammatory bowel disease. Sci. Rep. 9, 13295 (2019).

    Google Scholar 

  28. Patton, J. S. The lungs as a portal of entry for systemic drug delivery. Proc. Am. Thorac. Soc. 1, 338–344 (2004).

    CAS  Google Scholar 

  29. Gursahani, H., Riggs-Sauthier, J., Pfeiffer, J. & Lechuga-Ballesteros, D. Absorption of polyethylene glycol (PEG) polymers: the effect of PEG size on permeability. J. Pharm. Sci. 98, 2847–2856 (2009).

    CAS  Google Scholar 

  30. Pelfrêne, A., Cave, M. R., Wragg, J. & Douay, F. In vitro investigations of human bioaccessibility from reference materials using simulated lung fluids. Int. J. Environ. Res. Public Health 14, 112 (2017).

    Google Scholar 

  31. Ng, A. W., Bidani, A. & Heming, T. A. Innate host defense of the lung: effects of lung-lining fluid pH. Lung 182, 297–317 (2004).

    Google Scholar 

  32. Teeguarden, J. G., Bogdanffy, M. S., Covington, T. R., Tan, C. & Jarabek, A. M. A PBPK model for evaluating the impact of aldehyde dehydrogenase polymorphisms on comparative rat and human nasal tissue acetaldehyde dosimetry. Inhal. Toxicol. 20, 375–390 (2008).

    CAS  Google Scholar 

  33. Chiu, W. A. et al. Physiologically based pharmacokinetic (PBPK) modeling of interstrain variability in trichloroethylene metabolism in the mouse. Environ. Health Perspect. 122, 456–463 (2014).

    CAS  Google Scholar 

  34. Nong, A. et al. Physiologically based modeling of the inhalation pharmacokinetics of ethylbenzene in B6C3F1 mice. J. Toxicol. Environ. Heal. Part A 70, 1838–1848 (2007).

    CAS  Google Scholar 

  35. Krishnan, K., Gargas, M. L., Fennell, T. R. & Andersen, M. E. A physiologically based description of ethylene oxide dosimetry in the rat. Toxicol. Ind. Health 8, 121–140 (1992).

    CAS  Google Scholar 

  36. Reddy, M. B. et al. Inhalation dosimetry modeling with decamethylcyclopentasiloxane in rats and humans. Toxicol. Sci. 105, 275–285 (2018).

    Google Scholar 

  37. Beauchamp, J. Real-time breath gas analysis for pharmacokinetics: monitoring exhaled breath by on-line proton-transfer-reaction mass spectrometry after ingestion of eucalyptol-containing capsules. J. Breath Res. 4, 026006 (2010).

    CAS  Google Scholar 

  38. Stein, R. L. & Strimpler, A. M. Catalysis by human leukocyte elastase. Aminolysis of acyl-enzymes by amino acid amides and peptides. Biochemistry 26, 2238–2242 (1987).

    CAS  Google Scholar 

  39. Hammond, P. D., Stutzenberger, F. & Butler, R. N. Use of a disposable syringe as a novel economic breath-collection chamber for mice. Contemp. Top. Lab. Anim. Sci. 37, 70–71 (1998).

    Google Scholar 

  40. Santos, A. M. et al. Role of 13C-urea breath test in experimental model of Helicobacter pylori infection in mice. Helicobacter 16, 320–326 (2011).

    Google Scholar 

  41. Polverino, E., Rosales-mayor, E., Dale, G. E. & Dembowsky, K. The role of neutrophil elastase inhibitors in lung diseases. Chest 152, 249–262 (2017).

    Google Scholar 

  42. Dudani, J. S., Warren, A. D. & Bhatia, S. N. Harnessing protease activity to improve cancer care. Annu. Rev. Cancer Biol. 2, 353–376 (2017).

    Google Scholar 

  43. Sandhaus, R. A. et al. Clinical practice guidelines: the diagnosis and management of alpha-1 antitrypsin deficiency in the adult. J. COPD Found. 3, 668–682 (2016).

    Google Scholar 

  44. Campos, M. A. et al. The biological effects of double-dose alpha-1 antitrypsin augmentation therapy: a pilot study. Am. J. Respir. Crit. Care Med. 200, 318–326 (2019).

    CAS  Google Scholar 

  45. Jonigk, D. et al. Anti-inflammatory and immunomodulatory properties of α1-antitrypsin without inhibition of elastase. Proc. Natl Acad. Sci. USA 110, 15007–15012 (2013).

    CAS  Google Scholar 

  46. Borel, F. et al. Editing out five Serpina1 paralogs to create a mouse model of genetic emphysema. Proc. Natl Acad. Sci. USA 115, 2788–2793 (2018).

    CAS  Google Scholar 

  47. Shvartsburg, A. A. et al. Ultrafast differential ion mobility spectrometry at extreme electric fields in multichannel microchips. Anal. Chem. 81, 6489–6495 (2009).

    CAS  Google Scholar 

  48. Dudani, J. S., Ibrahim, M., Kirkpatrick, J., Warren, A. D. & Bhatia, S. N. Classification of prostate cancer using a protease activity nanosensor library. Proc. Natl Acad. Sci. USA 115, 8954–8959 (2018).

  49. Ong, T. et al. Use of mass spectrometric vapor analysis to improve canine explosive detection efficiency. Anal. Chem. 89, 6482–6490 (2017).

    CAS  Google Scholar 

  50. Gargas, M. L., Burgess, R. J., Voisard, D. E., Cason, G. H. & Andersen, M. E. Partition coefficients of low-molecular-weight volatile chemicals in various liquids and tissues. Toxicol. Appl. Pharmacol. 98, 87–99 (1989).

    CAS  Google Scholar 

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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 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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