tetano
Editor, Senior Moderator
Sci Rep
. 2024 Jul 24;14(1):17051.
doi: 10.1038/s41598-024-67219-y. High and low pathogenicity avian influenza virus discrimination and prediction based on volatile organic compounds signature by SIFT-MS: a proof-of-concept study
Fabien Filaire[SUP] 1 2 3 [/SUP], Aurélie Sécula[SUP] 4 [/SUP], Pierre Bessière[SUP] 4 [/SUP], Marielle Pagès-Homs[SUP] 5 [/SUP], Jean-Luc Guérin[SUP] 4 [/SUP], Frederic Violleau[SUP] 6 [/SUP], Ugo Till[SUP] 7 [/SUP]
Affiliations
High and low pathogenicity avian influenza viruses (HPAIV, LPAIV) are the primary causes of poultry diseases worldwide. HPAIV and LPAIV constitute a major threat to the global poultry industry. Therefore, early detection and well-adapted surveillance strategies are of the utmost importance to control the spread of these viruses. Volatile Organic Compounds (VOCs) released from living organisms have been investigated over the last decades as a diagnostic strategy. Mass spectrometry instruments can analyze VOCs emitted upon viral infection. Selected ion flow tube mass spectrometry (SIFT-MS) enables direct analysis of cell headspace in less than 20 min. As a proof-of-concept study, we investigated the ability of a SIFT-MS coupled sparse Partial Least Square-Discriminant Analysis analytical workflow to discriminate IAV-infected cells. Supernatants of HPAIV, LPAIV, and control cells were collected from 1 to 72 h post-infection and analyzed using our analytical workflow. At each collection point, VOCs' signatures were first identified based on four independent experiments and then used to discriminate the infectious status of external samples. Our results indicate that the identified VOCs signatures successfully discriminate, as early as 1-h post-infection, infected cells from the control cells and differentiated the HPAIV from the LPAIV infection. These results suggest a virus-dependent VOCs signature. Overall, the external samples' status was identified with 96.67% sensitivity, 100% specificity, and 97.78% general accuracy.
Keywords: Avian influenza; Infection; Multivariate analysis; SIFT-MS; Virus; Volatile organic
. 2024 Jul 24;14(1):17051.
doi: 10.1038/s41598-024-67219-y. High and low pathogenicity avian influenza virus discrimination and prediction based on volatile organic compounds signature by SIFT-MS: a proof-of-concept study
Fabien Filaire[SUP] 1 2 3 [/SUP], Aurélie Sécula[SUP] 4 [/SUP], Pierre Bessière[SUP] 4 [/SUP], Marielle Pagès-Homs[SUP] 5 [/SUP], Jean-Luc Guérin[SUP] 4 [/SUP], Frederic Violleau[SUP] 6 [/SUP], Ugo Till[SUP] 7 [/SUP]
Affiliations
- PMID: 39048690
- DOI: 10.1038/s41598-024-67219-y
High and low pathogenicity avian influenza viruses (HPAIV, LPAIV) are the primary causes of poultry diseases worldwide. HPAIV and LPAIV constitute a major threat to the global poultry industry. Therefore, early detection and well-adapted surveillance strategies are of the utmost importance to control the spread of these viruses. Volatile Organic Compounds (VOCs) released from living organisms have been investigated over the last decades as a diagnostic strategy. Mass spectrometry instruments can analyze VOCs emitted upon viral infection. Selected ion flow tube mass spectrometry (SIFT-MS) enables direct analysis of cell headspace in less than 20 min. As a proof-of-concept study, we investigated the ability of a SIFT-MS coupled sparse Partial Least Square-Discriminant Analysis analytical workflow to discriminate IAV-infected cells. Supernatants of HPAIV, LPAIV, and control cells were collected from 1 to 72 h post-infection and analyzed using our analytical workflow. At each collection point, VOCs' signatures were first identified based on four independent experiments and then used to discriminate the infectious status of external samples. Our results indicate that the identified VOCs signatures successfully discriminate, as early as 1-h post-infection, infected cells from the control cells and differentiated the HPAIV from the LPAIV infection. These results suggest a virus-dependent VOCs signature. Overall, the external samples' status was identified with 96.67% sensitivity, 100% specificity, and 97.78% general accuracy.
Keywords: Avian influenza; Infection; Multivariate analysis; SIFT-MS; Virus; Volatile organic