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

• Published in: Scientific reports Vol. 13; no. 1; pp. 3974 - 10
• Main Authors: López-Aladid, Ruben, Fernández-Barat, Laia, Alcaraz-Serrano, Victoria, Bueno-Freire, Leticia, Vázquez, Nil, Pastor-Ibáñez, Roque, Palomeque, Andrea, Oscanoa, Patricia, Torres, Antoni
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.03.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 631/326; 631/326/2565; 631/326/2565/2134; 692/699; Bacteria; Genera; High-Throughput Nucleotide Sequencing; Humanities and Social Sciences; Humans; Microbiomes; Microbiota - genetics; multidisciplinary; Next-generation sequencing; Respiratory diseases; Respiratory System; RNA, Ribosomal, 16S - genetics; rRNA 16S; Science; Science (multidisciplinary); Sequence Analysis, DNA; Sputum; Taxa; Taxonomy
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-30764-z

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.