Characterising the Canine Oral Microbiome by Direct Sequencing of Reverse-Transcribed rRNA Molecules

Abstract

PCR amplification and sequencing of phylogenetic markers, primarily Small Sub-Unit ribosomal RNA (SSU rRNA) genes, has been the paradigm for defining the taxonomic composition of microbiomes. However, ‘universal’ SSU rRNA gene PCR primer sets are likely to miss much of the diversity therein. We sequenced a library comprising purified and reverse-transcribed SSU rRNA (RT-SSU rRNA) molecules from the canine oral microbiome and compared it to a general bacterial 16S rRNA gene PCR amplicon library generated from the same biological sample. In addition, we have developed BIONmeta, a novel, open-source, computer package for the processing and taxonomic classification of the randomly fragmented RT-SSU rRNA reads produced. Direct RT-SSU rRNA sequencing revealed that 16S rRNA molecules belonging to the bacterial phyla Actinobacteria, Bacteroidetes, Firmicutes, Proteobacteria and Spirochaetes, were most abundant in the canine oral microbiome (92.5% of total bacterial SSU rRNA). The direct rRNA sequencing approach detected greater taxonomic diversity (1 additional phylum, 2 classes, 1 order, 10 families and 61 genera) when compared with general bacterial 16S rRNA amplicons from the same sample, simultaneously provided SSU rRNA gene inventories of Bacteria, Archaea and Eukarya, and detected significant numbers of sequences not recognised by ‘universal’ primer sets. Proteobacteria and Spirochaetes were found to be under-represented by PCR-based analysis of the microbiome, and this was due to primer mismatches and taxon-specific variations in amplification efficiency, validated by qPCR analysis of 16S rRNA amplicons from a mock community. This demonstrated the veracity of direct RT-SSU rRNA sequencing for molecular microbial ecology.

Modified RNA-seq method for microbial community and diversity analysis using rRNA in different types of environmental samples

“…Since the first use of next-generation sequencing in metatranscriptomics, the use of RNA-seq approaches has provided insights into the function of microorganisms in diverse environmental samples. However, for RNA-seq analyses, a high input quantity (>100 ng) of purified RNA is usually required [ 15 , 16 , 33 , 34 ], while SSU rRNA enriched method requires even higher amounts [ 11 , 20 ]. Because adaptor ligation generally occurs after random-primed double-stranded cDNA synthesis in standard protocols [ 35 ], contaminated DNA should be removed before library preparation.…”

“…The limitation for microbial population analysis that rely on the SSU rRNA-based sequence method is the difficulty in obtaining high quantities of enriched SSU rRNA from various environmental samples [ 11 ]. The total NA method was applied to different environmental samples with relatively low contents of total RNA ( S2 Table ) without DNase I treatment.…”

“…To enrich SSU rRNA reads in the metatranscriptome, our group developed a gel-extracted SSU rRNA reverse-transcription method for microbial population analysis of active sludge and anaerobic sludge samples [ 20 ]. The enriched SSU rRNA-based RNA-seq method has also been applied to the canine mouth [ 11 ] and an anaerobic bioreactor [ 12 ].…”

“…However, microbial population analysis utilizing SSU rRNA within RNA-seq data has not been broadly applied in the field of microbial ecology because its accuracy has not been confirmed using a mock community. Additional reasons for the limited application of this method include the requirement for high qualities of RNA [ 11 ] and the difficulty of performing alpha- and beta-diversity analyses due to differences in the regions covered by the sequences, although we already used V3 region covered sequences (32–36% of the reverse-transcribed SSU rRNA sequences) in a previous study [ 20 ].…”

Dietary Inulin and Trichuris suis Infection Promote Beneficial Bacteria Throughout the Porcine Gut

“…In order to determine the bacterial composition only highquality reads were mapped using the BION v. 16.03 package (Danish Genome Institute, Denmark) (McDonald et al, 2016). The workflow consisted of several steps.…”