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Article

Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning

by
Marco Beccaria
1,*,†,
Flavio A. Franchina
1,*,†,
Mavra Nasir
2,
Theodore Mellors
1,
Jane E. Hill
1,2,‡,§ and
Giorgia Purcaro
1,‡,‖
1
Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
2
Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
*
Authors to whom correspondence should be addressed.
†
Current address: Department of Chemical, Pharmaceutical, and Agricultural Sciences, University of Ferrara, Via L. Borsari 46, 44121 Ferrara, Italy.
‡
These authors contributed equally to this work.
§
Current address: Department of Chemical and Biological Engineering, University of British Columbia, 2360 E Mall, Vancouver, BC V6T 1Z3, Canada.
‖
Current address: Laboratory of Analytical Chemistry, AgroBioChem Department, Gembloux Agro-Bio Tech University of Liège, Passage des Deportes 2, 5030 Gembloux, Belgium.
Molecules 2021, 26(15), 4600; https://doi.org/10.3390/molecules26154600
Submission received: 15 April 2021 / Revised: 19 July 2021 / Accepted: 23 July 2021 / Published: 29 July 2021
(This article belongs to the Special Issue Cutting-Edge Chromatographic Techniques for Untargeted Analysis)
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Abstract

Species of Mycobacteriaceae cause disease in animals and humans, including tuberculosis and leprosy. Individuals infected with organisms in the Mycobacterium tuberculosis complex (MTBC) or non-tuberculous mycobacteria (NTM) may present identical symptoms, however the treatment for each can be different. Although the NTM infection is considered less vital due to the chronicity of the disease and the infrequency of occurrence in healthy populations, diagnosis and differentiation among Mycobacterium species currently require culture isolation, which can take several weeks. The use of volatile organic compounds (VOCs) is a promising approach for species identification and in recent years has shown promise for use in the rapid analysis of both in vitro cultures as well as ex vivo diagnosis using breath or sputum. The aim of this contribution is to analyze VOCs in the culture headspace of seven different species of mycobacteria and to define the volatilome profiles that are discriminant for each species. For the pre-concentration of VOCs, solid-phase micro-extraction (SPME) was employed and samples were subsequently analyzed using gas chromatography–quadrupole mass spectrometry (GC-qMS). A machine learning approach was applied for the selection of the 13 discriminatory features, which might represent clinically translatable bacterial biomarkers.
Keywords: GC-MS; mycobacteria species; machine learning; random forest; SPME; VOCs; features reduction GC-MS; mycobacteria species; machine learning; random forest; SPME; VOCs; features reduction

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MDPI and ACS Style

Beccaria, M.; Franchina, F.A.; Nasir, M.; Mellors, T.; Hill, J.E.; Purcaro, G. Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning. Molecules 2021, 26, 4600. https://doi.org/10.3390/molecules26154600

AMA Style

Beccaria M, Franchina FA, Nasir M, Mellors T, Hill JE, Purcaro G. Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning. Molecules. 2021; 26(15):4600. https://doi.org/10.3390/molecules26154600

Chicago/Turabian Style

Beccaria, Marco, Flavio A. Franchina, Mavra Nasir, Theodore Mellors, Jane E. Hill, and Giorgia Purcaro. 2021. "Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning" Molecules 26, no. 15: 4600. https://doi.org/10.3390/molecules26154600

APA Style

Beccaria, M., Franchina, F. A., Nasir, M., Mellors, T., Hill, J. E., & Purcaro, G. (2021). Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning. Molecules, 26(15), 4600. https://doi.org/10.3390/molecules26154600

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