Critical Issues in Mycobiota Analysis

Abstract

Fungi constitute an important part of the human microbiota and they play a significant role for health and disease development. Advancements made in the culture-independent analysis of microbial communities have broadened our understanding of the mycobiota, however, microbiota analysis tools have been mainly developed for bacteria (e.g., targeting the 16S rRNA gene) and they often fall short if applied to fungal marker-gene based investigations (i.e., internal transcribed spacers, ITS). In the current paper we discuss all major steps of a fungal amplicon analysis starting with DNA extraction from specimens up to bioinformatics analyses of next-generation sequencing data. Specific points are discussed at each step and special emphasis is placed on the bioinformatics challenges emerging during operational taxonomic unit (OTU) picking, a critical step in mycobiota analysis. By using an in silico ITS1 mock community we demonstrate that standard analysis pipelines fall short if used with default settings showing erroneous fungal community representations. We highlight that switching OTU picking to a closed reference approach greatly enhances performance. Finally, recommendations are given on how to perform ITS based mycobiota analysis with the currently available measures.

Keywords: 16S rRNA gene; DNA isolation; OTU picking; formalin-fixed paraffin-embedded tissue (FFPE); internal transcribed spacer (ITS); microbiota; multiple sequence alignment (MSA); mycobiota.

Figure 1

Schematic representations of rRNA operons and their variability assessed by multiple sequence alignments (MSA). (A) Prokaryotic and (B) eukaryotic rRNA operons. Position and orientation of oligonucleotide primers used for ITS amplification are schematically indicated (for sequence information see Table 1). SSU, small subunit; LSU, large subunit; tRNA, transfer RNA; V1-V9, variable regions; ITS, internal transcribed spacer; bps, base-pairs. (C) Multiple sequence alignment (MSA) of the entire 16S rRNA operon of five different bacterial species (encompassing five different phyla). Variable regions (V1–V9) are highlighted in blue, conserved regions in yellow, positions according to the E. coli 16S rRNA (GenBank acc. no.: J01695.2). (D) MSA of the complete internal transcribed spacer region of five different fungal species of the same genus (Hydnum sp.). (E) MSA of the complete ITS region of seven fungal taxa representing different phyla. Information about sequences used for MSA generation (C,D) is given as Supplementary Tables S3–S5.

Figure 2

DNA isolation from human FFPE skin samples and ITS PCR amplification influenced by beat beating. (A) Significant difference in overall DNA yield from FFPE skin samples (n = 10) with and without bead beating (^**p < 0.005 by Mann Whitney test; data are mean + SEM). (B) Significantly increased detection of fungal DNA isolated without bead beating by ITS2 qPCR (n = 10; ^*p < 0.05, ^***p < 0.005, Kruskal-Wallis test; data are mean + SEM). NTC, negative control.

Figure 3

The four main steps of a typical amplicon analysis workflow. Individual steps and features of (1) pre-processing, (2) OTU picking, (3) taxonomic annotation, as well as, (4) visualization and statistics are indicated and discussed in the manuscript.

Figure 4

Phylogenetic resolution of five different fungal species is impaired when clustering ITS sequences. (A) Tree based on the corresponding NCBI taxonomy information using NCBI's Common Tree. Treeing is congruent with the phylogenetic study performed by Diezmann et al. (2004). (B) LSU based treeing recapitulates largely the NCBI taxonomy. (C) ITS based treeing impairs phylogeny. Trees of subfigures (B,C) are based on MSA of LSU and ITS2 fragments, respectively (taxon IDs and accession numbers are given as Data Sheets S3–S5).

Figure 5

Schematic overview of the experimental set-up testing the performance of mothur, QIIME, and MICCA to resolve the ITS1 mock community. ITS1 fragments were extracted from the UNITE ITS reference collection (v.7) and analyzed with mothur (default workflow), QIIME, and MICCA (default and closed reference based workflow).

Figure 6

Recommended workflow to analyze ITS amplicons. (i) Pre-processing of fungal ITS amplicons can be performed using standard tools. (ii) For OTU picking a closed reference strategy is needed. (iii) Classification can either be done using the clustering information from the used reference database or by re-classification of representative reads using the ITS RDP classifier. (iv) Obtained OTU profiles (OTU tables) can be further analyzed by common visualization and statistical analysis techniques, except phylogenetic treeing methods based on distance matrices.