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...
Autores principales: | , , , , , , , , |
---|---|
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 |