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Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples.
Perrone, Maria Rita; Romano, Salvatore; De Maria, Giuseppe; Tundo, Paolo; Bruno, Anna Rita; Tagliaferro, Luigi; Maffia, Michele; Fragola, Mattia.
Afiliación
  • Perrone MR; Department of Mathematics and Physics, University of Salento, 73100 Lecce, Italy.
  • Romano S; Department of Mathematics and Physics, University of Salento, 73100 Lecce, Italy.
  • De Maria G; Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy.
  • Tundo P; Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy.
  • Bruno AR; Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy.
  • Tagliaferro L; Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy.
  • Maffia M; Department of Biological and Environmental Sciences and Technologies, University of Salento, 73100 Lecce, Italy.
  • Fragola M; Department of Mathematics and Physics, University of Salento, 73100 Lecce, Italy.
Article en En | MEDLINE | ID: mdl-36011742
The compositional analysis of 16S rRNA gene sequencing datasets is applied to characterize the bacterial structure of airborne samples collected in different locations of a hospital infection disease department hosting COVID-19 patients, as well as to investigate the relationships among bacterial taxa at the genus and species level. The exploration of the centered log-ratio transformed data by the principal component analysis via the singular value decomposition has shown that the collected samples segregated with an observable separation depending on the monitoring location. More specifically, two main sample clusters were identified with regards to bacterial genera (species), consisting of samples mostly collected in rooms with and without COVID-19 patients, respectively. Human pathogenic genera (species) associated with nosocomial infections were mostly found in samples from areas hosting patients, while non-pathogenic genera (species) mainly isolated from soil were detected in the other samples. Propionibacterium acnes, Staphylococcus pettenkoferi, Corynebacterium tuberculostearicum, and jeikeium were the main pathogenic species detected in COVID-19 patients' rooms. Samples from these locations were on average characterized by smaller richness/evenness and diversity than the other ones, both at the genus and species level. Finally, the ρ metrics revealed that pairwise positive associations occurred either between pathogenic or non-pathogenic taxa.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Microbiota / COVID-19 Límite: Humans Idioma: En Revista: Int J Environ Res Public Health Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Microbiota / COVID-19 Límite: Humans Idioma: En Revista: Int J Environ Res Public Health Año: 2022 Tipo del documento: Article País de afiliación: Italia Pais de publicación: Suiza