Volatile fingerprinting of human respiratory viruses from cell culture.
Identifieur interne : 000946 ( Main/Exploration ); précédent : 000945; suivant : 000947Volatile fingerprinting of human respiratory viruses from cell culture.
Auteurs : Giorgia Purcaro [États-Unis] ; Christiaan A. Rees ; Wendy F. Wieland-Alter ; Mark J. Schneider ; Xi Wang ; Pierre-Hugues Stefanuto ; Peter F. Wright ; Richard I. Enelow ; Jane E. HillSource :
- Journal of breath research [ 1752-7163 ] ; 2018.
Descripteurs français
- KwdFr :
- Analyse discriminante, Animaux, Composés organiques volatils (analyse), Humains, Infections à virus respiratoire syncytial (métabolisme), Lignée cellulaire, Métabolome, Métabolomique (), Méthode des moindres carrés, Souris, Techniques de culture cellulaire, Virus de la grippe A (physiologie), Virus respiratoires syncytiaux (métabolisme).
- MESH :
- analyse : Composés organiques volatils.
- métabolisme : Infections à virus respiratoire syncytial, Virus respiratoires syncytiaux.
- physiologie : Virus de la grippe A.
- Analyse discriminante, Animaux, Humains, Lignée cellulaire, Métabolome, Métabolomique, Méthode des moindres carrés, Souris, Techniques de culture cellulaire.
English descriptors
- KwdEn :
- Animals, Cell Culture Techniques, Cell Line, Discriminant Analysis, Humans, Influenza A virus (physiology), Least-Squares Analysis, Metabolome, Metabolomics (methods), Mice, Respiratory Syncytial Virus Infections (metabolism), Respiratory Syncytial Viruses (metabolism), Volatile Organic Compounds (analysis).
- MESH :
- chemical , analysis : Volatile Organic Compounds.
- metabolism : Respiratory Syncytial Virus Infections, Respiratory Syncytial Viruses.
- methods : Metabolomics.
- physiology : Influenza A virus.
- Animals, Cell Culture Techniques, Cell Line, Discriminant Analysis, Humans, Least-Squares Analysis, Metabolome, Mice.
Abstract
Volatile metabolites are currently under investigation as potential biomarkers for the detection and identification of pathogenic microorganisms, including bacteria, fungi, and viruses. Unlike bacteria and fungi, which produce distinct volatile metabolic signatures associated with innate differences in both primary and secondary metabolic processes, viruses are wholly reliant on the metabolic machinery of infected cells for replication and propagation. In the present study, the ability of volatile metabolites to discriminate between respiratory cells infected and uninfected with virus, in vitro, was investigated. Two important respiratory viruses, namely respiratory syncytial virus (RSV) and influenza A virus (IAV), were evaluated. Data were analyzed using three different machine learning algorithms (random forest (RF), linear support vector machines (linear SVM), and partial least squares-discriminant analysis (PLS-DA)), with volatile metabolites identified from a training set used to predict sample classifications in a validation set. The discriminatory performances of RF, linear SVM, and PLS-DA were comparable for the comparison of IAV-infected versus uninfected cells, with area under the receiver operating characteristic curves (AUROCs) between 0.78 and 0.82, while RF and linear SVM demonstrated superior performance in the classification of RSV-infected versus uninfected cells (AUROCs between 0.80 and 0.84) relative to PLS-DA (0.61). A subset of discriminatory features were assigned putative compound identifications, with an overabundance of hydrocarbons observed in both RSV- and IAV-infected cell cultures relative to uninfected controls. This finding is consistent with increased oxidative stress, a process associated with viral infection of respiratory cells.
DOI: 10.1088/1752-7163/aa9eef
PubMed: 29199638
Affiliations:
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Le document en format XML
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<term>Metabolomics (methods)</term>
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<front><div type="abstract" xml:lang="en">Volatile metabolites are currently under investigation as potential biomarkers for the detection and identification of pathogenic microorganisms, including bacteria, fungi, and viruses. Unlike bacteria and fungi, which produce distinct volatile metabolic signatures associated with innate differences in both primary and secondary metabolic processes, viruses are wholly reliant on the metabolic machinery of infected cells for replication and propagation. In the present study, the ability of volatile metabolites to discriminate between respiratory cells infected and uninfected with virus, in vitro, was investigated. Two important respiratory viruses, namely respiratory syncytial virus (RSV) and influenza A virus (IAV), were evaluated. Data were analyzed using three different machine learning algorithms (random forest (RF), linear support vector machines (linear SVM), and partial least squares-discriminant analysis (PLS-DA)), with volatile metabolites identified from a training set used to predict sample classifications in a validation set. The discriminatory performances of RF, linear SVM, and PLS-DA were comparable for the comparison of IAV-infected versus uninfected cells, with area under the receiver operating characteristic curves (AUROCs) between 0.78 and 0.82, while RF and linear SVM demonstrated superior performance in the classification of RSV-infected versus uninfected cells (AUROCs between 0.80 and 0.84) relative to PLS-DA (0.61). A subset of discriminatory features were assigned putative compound identifications, with an overabundance of hydrocarbons observed in both RSV- and IAV-infected cell cultures relative to uninfected controls. This finding is consistent with increased oxidative stress, a process associated with viral infection of respiratory cells.</div>
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<name sortKey="Stefanuto, Pierre Hugues" sort="Stefanuto, Pierre Hugues" uniqKey="Stefanuto P" first="Pierre-Hugues" last="Stefanuto">Pierre-Hugues Stefanuto</name>
<name sortKey="Wang, Xi" sort="Wang, Xi" uniqKey="Wang X" first="Xi" last="Wang">Xi Wang</name>
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