Publicação
Profiling airway microbiome composition through volatilomics
| dc.contributor.author | Neto, Filipa | |
| dc.contributor.author | Ferraz, Ricardo | |
| dc.contributor.author | Vieira, Mónica | |
| dc.contributor.author | Prudêncio, Cristina | |
| dc.contributor.author | Rufo, João | |
| dc.contributor.author | Almeida Vieira, Mónica Andreia | |
| dc.contributor.author | Cavaleiro Rufo, João | |
| dc.date.accessioned | 2026-07-28T09:15:39Z | |
| dc.date.available | 2026-07-28T09:15:39Z | |
| dc.date.issued | 2026-05-29 | |
| dc.description.abstract | The airway microbiome is known to mediate respi-ratory health. However, current available methods for microbiome analysis are time-consuming and/or rep-resent significant costs for a generalized application in clinical practice. Volatilomics has been suggested as a rapid and low-cost approach to screen microbial pro-files in human samples. Therefore, we aimed to study the efficacy of volatilomics in discriminating microbi-al isolates collected from human breath condensate samples. Bacterial strains showing significant growth under conditions simulating the respiratory environ-ment were isolated. Each strain was standardised to an inoculum of 10⁸ CFU/mL and analysed using an elec-tronic nose equipped with a six-sensor matrix. Data were explored through principal component analysis, cluster analysis, pattern analysis, random forests and recursive partitioning regression. One sensor was re-moved from the analysis due to high collinearity. Prin-cipal component analysis was able to separate strains and the control mainly through the second principal component (p = 0.024), characterized by high MQ3 and MQ8 sensor responses. Sensor profile maps showed distinct volatile patterns across strains (Fig-ure 1), suggesting the presence of distinct microbial signatures. However, reproducibility was low between replicas and time since culture. Recursive partitioning for separating sterile controls from inoculated sam-ples showed the highest accuracy (AUC = 0.73). These results show the potential of separation of microbial strains based on volatilomics. Nonetheless, relative robustness was only achieved for the discrimination of sterile vs inoculated samples. | eng |
| dc.identifier.citation | Neto, F., Ferraz, R., Vieira, M., Prudêncio, C., & Rufo, J. (2026). Profiling airway microbiome composition through volatilomics. Book of Abstracts of the 8th Meeting on Medicinal Biotechnology, 24. https://edicoes.ipp.pt/index.php/books/catalog/book/251 | |
| dc.identifier.doi | 10.26537/ed.p.porto.251 | |
| dc.identifier.isbn | 978-989-9226-20-3 | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/32637 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Politema | |
| dc.relation | 2024.15890.PEX | |
| dc.relation.hasversion | https://edicoes.ipp.pt/index.php/books/catalog/book/251 | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Electronic nose | |
| dc.subject | Metabolomics | |
| dc.subject | Microbiome | |
| dc.subject | Volatile organic compounds | |
| dc.title | Profiling airway microbiome composition through volatilomics | eng |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2026-05-29 | |
| oaire.citation.conferencePlace | Porto | |
| oaire.citation.endPage | 24 | |
| oaire.citation.startPage | 24 | |
| oaire.citation.title | Book of Abstracts of the 8th Meeting on Medicinal Biotechnology | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| person.familyName | Almeida Vieira | |
| person.familyName | Cavaleiro Rufo | |
| person.givenName | Mónica Andreia | |
| person.givenName | João | |
| person.identifier | R-00F-QED | |
| person.identifier.ciencia-id | A01E-9178-9B48 | |
| person.identifier.ciencia-id | 0F14-AD98-862F | |
| person.identifier.orcid | 0000-0002-8687-4811 | |
| person.identifier.orcid | 0000-0003-1175-242X | |
| person.identifier.rid | K-7994-2013 | |
| person.identifier.scopus-author-id | 56549851500 | |
| relation.isAuthorOfPublication | 861e9c68-4ecc-4be1-a794-852343368e9a | |
| relation.isAuthorOfPublication | 762b2c6e-6710-4061-bc68-74084b460ccf | |
| relation.isAuthorOfPublication.latestForDiscovery | 861e9c68-4ecc-4be1-a794-852343368e9a |