Article (Scientific journals)
Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning.
Beccaria, Marco; Franchina, Flavio; Nasir, Mavra et al.
2021In Molecules, 26 (15), p. 4600
Peer reviewed
 

Files


Full Text
molecules-26-04600-v2.pdf
Author postprint (2.19 MB)
Download

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
GC-MS; SPME; VOCs; features reduction; machine learning; mycobacteria species; random forest; Biomarkers; Volatile Organic Compounds; Biomarkers/analysis; Gas Chromatography-Mass Spectrometry/methods; Machine Learning/statistics & numerical data; Mycobacterium/chemistry; Mycobacterium/metabolism; Mycobacterium abscessus/chemistry; Mycobacterium abscessus/metabolism; Mycobacterium avium/chemistry; Mycobacterium avium/metabolism; Mycobacterium avium Complex/chemistry; Mycobacterium avium Complex/metabolism; Mycobacterium bovis/chemistry; Mycobacterium bovis/metabolism; Principal Component Analysis; Solid Phase Microextraction; Volatile Organic Compounds/classification; Volatile Organic Compounds/isolation & purification; Volatile Organic Compounds/metabolism; Metabolome; Gas Chromatography-Mass Spectrometry; Mycobacterium; Mycobacterium abscessus; Mycobacterium avium; Mycobacterium avium Complex; Mycobacterium bovis; Analytical Chemistry; Chemistry (miscellaneous); Molecular Medicine; Pharmaceutical Science; Drug Discovery; Physical and Theoretical Chemistry; Organic Chemistry
Abstract :
[en] 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.
Disciplines :
Chemistry
Microbiology
Author, co-author :
Beccaria, Marco  ;  Université de Liège - ULiège > Molecular Systems (MolSys) ; Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
Franchina, Flavio  ;  Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique, organique et biologique ; Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
Nasir, Mavra;  Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
Mellors, Theodore;  Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
Hill, Jane E;  Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA ; Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
Purcaro, Giorgia  ;  Université de Liège - ULiège > TERRA Research Centre > Chimie des agro-biosystèmes ; Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
Language :
English
Title :
Investigating Bacterial Volatilome for the Classification and Identification of Mycobacterial Species by HS-SPME-GC-MS and Machine Learning.
Publication date :
29 July 2021
Journal title :
Molecules
eISSN :
1420-3049
Publisher :
MDPI AG, Switzerland
Volume :
26
Issue :
15
Pages :
4600
Peer reviewed :
Peer reviewed
Available on ORBi :
since 06 July 2022

Statistics


Number of views
29 (1 by ULiège)
Number of downloads
24 (0 by ULiège)

Scopus citations®
 
5
Scopus citations®
without self-citations
5
OpenCitations
 
2

Bibliography


Similar publications



Contact ORBi