Cargando…

Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples

16S rRNA gene profiling, which contains nine hypervariable regions (V1–V9), is the gold standard for identifying taxonomic units by high-throughput sequencing. Microbiome studies combine two or more region sequences (usually V3–V4) to increase the resolving power for identifying bacterial taxa. We c...

Descripción completa

Detalles Bibliográficos
Autores principales: López-Aladid, Ruben, Fernández-Barat, Laia, Alcaraz-Serrano, Victoria, Bueno-Freire, Leticia, Vázquez, Nil, Pastor-Ibáñez, Roque, Palomeque, Andrea, Oscanoa, Patricia, Torres, Antoni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998635/
https://www.ncbi.nlm.nih.gov/pubmed/36894603
http://dx.doi.org/10.1038/s41598-023-30764-z
_version_ 1784903508338671616
author López-Aladid, Ruben
Fernández-Barat, Laia
Alcaraz-Serrano, Victoria
Bueno-Freire, Leticia
Vázquez, Nil
Pastor-Ibáñez, Roque
Palomeque, Andrea
Oscanoa, Patricia
Torres, Antoni
author_facet López-Aladid, Ruben
Fernández-Barat, Laia
Alcaraz-Serrano, Victoria
Bueno-Freire, Leticia
Vázquez, Nil
Pastor-Ibáñez, Roque
Palomeque, Andrea
Oscanoa, Patricia
Torres, Antoni
author_sort López-Aladid, Ruben
collection PubMed
description 16S rRNA gene profiling, which contains nine hypervariable regions (V1–V9), is the gold standard for identifying taxonomic units by high-throughput sequencing. Microbiome studies combine two or more region sequences (usually V3–V4) to increase the resolving power for identifying bacterial taxa. We compare the resolving powers of V1–V2, V3–V4, V5–V7, and V7–V9 to improve microbiome analyses in sputum samples from patients with chronic respiratory diseases. DNA were isolated from 33 human sputum samples, and libraries were created using a QIASeq screening panel intended for Illumina platforms (16S/ITS; Qiagen Hilden, Germany). The analysis included a mock community as a microbial standard control (ZymoBIOMICS). We used the Deblur algorithm to identify bacterial amplicon sequence variants (ASVs) at the genus level. Alpha diversity was significantly higher for V1–V2, V3–V4, and V5–V7 compared with V7–V9, and significant compositional dissimilarities in the V1–V2 and V7–V9 analyses versus the V3–V4 and V5–V7 analyses. A cladogram confirmed these compositional differences, with the latter two being very similar in composition. The combined hypervariable regions showed significant differences when discriminating between the relative abundances of bacterial genera. The area under the curve revealed that V1–V2 had the highest resolving power for accurately identifying respiratory bacterial taxa from sputum samples. Our study confirms that 16S rRNA hypervariable regions provide significant differences for taxonomic identification in sputum. Comparing the taxa of microbial community standard control with the taxa samples, V1–V2 combination exhibits the most sensitivity and specificity. Thus, while third generation full-length 16S rRNA sequencing platforms become more available, the V1–V2 hypervariable regions can be used for taxonomic identification in sputum.
format Online
Article
Text
id pubmed-9998635
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-99986352023-03-11 Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples López-Aladid, Ruben Fernández-Barat, Laia Alcaraz-Serrano, Victoria Bueno-Freire, Leticia Vázquez, Nil Pastor-Ibáñez, Roque Palomeque, Andrea Oscanoa, Patricia Torres, Antoni Sci Rep Article 16S rRNA gene profiling, which contains nine hypervariable regions (V1–V9), is the gold standard for identifying taxonomic units by high-throughput sequencing. Microbiome studies combine two or more region sequences (usually V3–V4) to increase the resolving power for identifying bacterial taxa. We compare the resolving powers of V1–V2, V3–V4, V5–V7, and V7–V9 to improve microbiome analyses in sputum samples from patients with chronic respiratory diseases. DNA were isolated from 33 human sputum samples, and libraries were created using a QIASeq screening panel intended for Illumina platforms (16S/ITS; Qiagen Hilden, Germany). The analysis included a mock community as a microbial standard control (ZymoBIOMICS). We used the Deblur algorithm to identify bacterial amplicon sequence variants (ASVs) at the genus level. Alpha diversity was significantly higher for V1–V2, V3–V4, and V5–V7 compared with V7–V9, and significant compositional dissimilarities in the V1–V2 and V7–V9 analyses versus the V3–V4 and V5–V7 analyses. A cladogram confirmed these compositional differences, with the latter two being very similar in composition. The combined hypervariable regions showed significant differences when discriminating between the relative abundances of bacterial genera. The area under the curve revealed that V1–V2 had the highest resolving power for accurately identifying respiratory bacterial taxa from sputum samples. Our study confirms that 16S rRNA hypervariable regions provide significant differences for taxonomic identification in sputum. Comparing the taxa of microbial community standard control with the taxa samples, V1–V2 combination exhibits the most sensitivity and specificity. Thus, while third generation full-length 16S rRNA sequencing platforms become more available, the V1–V2 hypervariable regions can be used for taxonomic identification in sputum. Nature Publishing Group UK 2023-03-09 /pmc/articles/PMC9998635/ /pubmed/36894603 http://dx.doi.org/10.1038/s41598-023-30764-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
López-Aladid, Ruben
Fernández-Barat, Laia
Alcaraz-Serrano, Victoria
Bueno-Freire, Leticia
Vázquez, Nil
Pastor-Ibáñez, Roque
Palomeque, Andrea
Oscanoa, Patricia
Torres, Antoni
Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples
title Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples
title_full Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples
title_fullStr Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples
title_full_unstemmed Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples
title_short Determining the most accurate 16S rRNA hypervariable region for taxonomic identification from respiratory samples
title_sort determining the most accurate 16s rrna hypervariable region for taxonomic identification from respiratory samples
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9998635/
https://www.ncbi.nlm.nih.gov/pubmed/36894603
http://dx.doi.org/10.1038/s41598-023-30764-z
work_keys_str_mv AT lopezaladidruben determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT fernandezbaratlaia determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT alcarazserranovictoria determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT buenofreireleticia determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT vazqueznil determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT pastoribanezroque determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT palomequeandrea determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT oscanoapatricia determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples
AT torresantoni determiningthemostaccurate16srrnahypervariableregionfortaxonomicidentificationfromrespiratorysamples