The use of different 16S rRNA gene variable regions in biogeographical studies

• Published in: Environmental microbiology reports Vol. 15; no. 3; pp. 216 - 228
• Main Authors: Varliero, Gilda, Lebre, Pedro H., Stevens, Mark I., Czechowski, Paul, Makhalanyane, Thulani, Cowan, Don A.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2023
• Subjects: Bacteria; Bacteria - genetics; Datasets; Environmental surveys; Gene sequencing; Genes, rRNA; Microorganisms; Phylogenetics; Phylogeny; RNA, Ribosomal, 16S - genetics; rRNA 16S; Sequence Analysis, DNA; Spatial data; Taxonomy; Variable region; Veganism; Prince Charles Mountains
• ISSN: 1758-2229; 1758-2229
• DOI: 10.1111/1758-2229.13145

16S rRNA gene amplicon sequencing is routinely used in environmental surveys to identify microbial diversity and composition of the samples of interest. The dominant sequencing technology of the past decade (Illumina) is based on the sequencing of 16S rRNA hypervariable regions. Online sequence data repositories, which represent an invaluable resource for investigating microbial distributional patterns across spatial, environmental or temporal scales, contain amplicon datasets from diverse 16S rRNA gene variable regions. However, the utility of these sequence datasets is potentially reduced by the use of different 16S rRNA gene amplified regions. By comparing 10 Antarctic soil samples sequenced for five different 16S rRNA amplicons, we explore whether sequence data derived from diverse 16S rRNA variable regions can be validly used as a resource for biogeographical studies. Patterns of shared and unique taxa differed among samples as a result of variable taxonomic resolutions of the assessed 16S rRNA variable regions. However, our analyses also suggest that the use of multi‐primer datasets for biogeographical studies of the domain Bacteria is a valid approach to explore bacterial biogeographical patterns due to the preservation of bacterial taxonomic and diversity patterns across different variable region datasets. We deem composite datasets useful for biogeographical studies.