Comparison of Two 16S rRNA Primers (V3–V4 and V4–V5) for Studies of Arctic Microbial Communities

Abstract

Microbial communities of the Arctic Ocean are poorly characterized in comparison to other aquatic environments as to their horizontal, vertical, and temporal turnover. Yet, recent studies showed that the Arctic marine ecosystem harbors unique microbial community members that are adapted to harsh environmental conditions, such as near-freezing temperatures and extreme seasonality. The gene for the small ribosomal subunit (16S rRNA) is commonly used to study the taxonomic composition of microbial communities in their natural environment. Several primer sets for this marker gene have been extensively tested across various sample sets, but these typically originated from low-latitude environments. An explicit evaluation of primer-set performances in representing the microbial communities of the Arctic Ocean is currently lacking. To select a suitable primer set for studying microbiomes of various Arctic marine habitats (sea ice, surface water, marine snow, deep ocean basin, and deep-sea sediment), we have conducted a performance comparison between two widely used primer sets, targeting different hypervariable regions of the 16S rRNA gene (V3–V4 and V4–V5). We observed that both primer sets were highly similar in representing the total microbial community composition down to genus rank, which was also confirmed independently by subgroup-specific catalyzed reporter deposition-fluorescence in situ hybridization (CARD-FISH) counts. Each primer set revealed higher internal diversity within certain bacterial taxonomic groups (e.g., the class Bacteroidia by V3–V4, and the phylum Planctomycetes by V4–V5). However, the V4–V5 primer set provides concurrent coverage of the archaeal domain, a relevant component comprising 10–20% of the community in Arctic deep waters and the sediment. Although both primer sets perform similarly, we suggest the use of the V4–V5 primer set for the integration of both bacterial and archaeal community dynamics in the Arctic marine environment.

Spatial Distribution of Arctic Bacterioplankton Abundance Is Linked to Distinct Water Masses and Summertime Phytoplankton Bloom Dynamics (Fram Strait, 79°N)

“…However, other taxonomic groups (e.g., Alteromonadaceae ) showed two- to threefold lower relative abundance in the molecular study ( Supplementary Figure 1 ). These discrepancies can be explained by previously conducted direct methodological comparison between 16S rRNA gene observations and CARD-FISH counts ( Fadeev et al, 2021 ), which suggested potential over-representation of the SAR11 clade in the microscopy counts that could affect the proportional representation of other taxonomic groups in the dataset. Alternatively, the potentially higher cellular activity (and thus higher ribosomal content) of phytoplankton bloom-associated taxonomic groups (e.g., Bacteroidetes ) may have altered their representation in the PCR-based 16S rRNA gene dataset ( Rosselli et al, 2016 ), and thus potentially lower sequence proportion of other taxonomic groups.…”

“…To date, the majority of Arctic bacterioplankton studies are performed using high-throughput sequencing of the 16S rRNA gene, which cannot be directly converted to absolute standing stock abundances of specific taxonomic groups due to polymerase chain reaction (PCR) primers selection ( Fadeev et al, 2021 ), as well as other quantitative biases ( Gloor et al, 2017 ; Kumar et al, 2017 ; Piwosz et al, 2020 ). Here we used semi-automatic CAtalyzed Reporter Deposition-Fluorescence In Situ Hybridization (CARD-FISH; Pernthaler et al, 2002 ).…”

Microbial Community Dynamics during Biodegradation of Crude Oil and Its Response to Biostimulation in Svalbard Seawater at Low Temperature

“…A growing body of Arctic marine microbiology research is characterizing microbial communities using data from either amplicon-based or shotgun metagenomic sequencing [11,[13][14][15]42], and the latter is also used for functional profiling. For the taxonomic assignment of shotgun metagenomic data, numerous classifiers and reference databases are available that fall into several categories: (i) DNA-to-DNA methods, where perfect matches between sequence stretches and reference sequences (k-mers) are sought (e.g., Kraken2, Bracken, and PathSeq); (ii) DNA-to-protein methods, where sequence reads are compared with protein-coding sequences (e.g., Kaiju and DIAMOND); and (iii) DNA-tomarker methods, including only specific marker gene families in reference databases (e.g., MetaPhlAn2) [43,44].…”

Exposure to environmentally relevant oxytetracycline induced gut histopathological damages and microbiota alterations of Pelophylax nigromaculatus larvae

“…16S rRNA gene target region has a major impact on the gut microbiota NGS results and V4-V5 region has better overall performance in species identification (Rintala et al, 2017). Meanwhile, the main advantage of the V4-V5 primer set is its comprehensive coverage of the bacteria domain without compromising the detection of other taxonomic groups (Fadeev et al, 2021). On the basis of NGS results, common features of post-antibiotic gut microbiota dysbiosis almost include the changes of species richness and diversity, disorders of community structures and loss of taxa (Becattini et al, 2016).…”