Compositional Data Analysis of 16S rRNA Gene Sequencing Results from Hospital Airborne Microbiome Samples

Maria Rita Perrone, Salvatore Romano, Giuseppe De Maria, Paolo Tundo, Anna Rita Bruno, Luigi Tagliaferro, Michele Maffia and Mattia Fragola ()
Additional contact information
Maria Rita Perrone: Department of Mathematics and Physics, University of Salento, 73100 Lecce, Italy
Salvatore Romano: Department of Mathematics and Physics, University of Salento, 73100 Lecce, Italy
Giuseppe De Maria: Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy
Paolo Tundo: Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy
Anna Rita Bruno: Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy
Luigi Tagliaferro: Presidio Ospedaliero Santa Caterina Novella, Azienda Sanitaria Locale Lecce, 73013 Galatina, Italy
Michele Maffia: Department of Biological and Environmental Sciences and Technologies, University of Salento, 73100 Lecce, Italy
Mattia Fragola: Department of Mathematics and Physics, University of Salento, 73100 Lecce, Italy

IJERPH, 2022, vol. 19, issue 16, 1-21

Abstract: 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.

Keywords: 16S rRNA gene sequencing; Aitchison distance; CLR transformation; singular value decomposition; alpha-diversity; airborne microbiome; compositional data; ? metrics (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/16/10107/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/16/10107/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:16:p:10107-:d:889164

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().