Metagenome-validated Parallel Amplicon Sequencing and Text Mining-based Annotations for Simultaneous Profiling of Bacteria and Fungi: Vaginal Microbiome and Mycobiota in Healthy Women

Abstract

Background: Amplicon sequencing of kingdom-specific tags such as 16S rRNA gene for bacteria and internal transcribed spacer (ITS) region for fungi are widely used for investigating microbial populations. So far most human studies have focused on bacteria while studies on host-associated fungi in health and disease have only recently started to accumulate. To enable cost-effective parallel analysis of bacterial and fungal communities in human and environmental samples, we developed a method where 16S rRNA gene and ITS-1 amplicons were pooled together for a single Illumina MiSeq or HiSeq run and analysed after primer-based segregation. Taxonomic assignments were performed with Blast in combination with an iterative text-extraction based filtration approach, which uses extensive literature records from public databases to select the most probable hits that were further validated by shotgun metagenomic sequencing. Results: Using 50 vaginal samples, we show that the combined run provides comparable results on bacterial composition and diversity to conventional 16S rRNA gene amplicon sequencing. The text-extraction-based taxonomic assignment guided tool provided ecosystem specific annotations that were confirmed by Metagenomic Phylogenetic Analysis (MetaPhlAn). The metagenome analysis revealed distinct functional differences between the bacterial community types while fungi were undetected, despite being identified in all samples based on ITS amplicons. Co-abundance analysis of bacteria and fungi did not show strong between-kingdom correlations within the vaginal ecosystem of healthy women. Conclusion: Combined amplicon sequencing for bacteria and fungi provides a simple and cost-effective method for simultaneous analysis of microbiota and mycobiota within the same samples. Text extraction-based annotation tool facilitates the characterization and interpretation of defined microbial communities from rapidly accumulating sequencing and metadata readily available through public databases.