The influence of intraspecific sequence variation during DNA metabarcoding: A case study of eleven fungal species

DNA metabarcoding has become a powerful approach for analysing complex communities from environmental samples, but there are still methodological challenges limiting its full potential. While conserved DNA markers, like 16S and 18S, often are not able to discriminate among closely related species, other more variable markers - like the fungal ITS region, may include considerable intraspecific variation, which can lead to oversplitting of species during DNA metabarcoding analyses. Here we assessed the effects of intraspecific sequence variation in DNA metabarcoding by analysing local populations of eleven fungal species. We investigated the allelic diversity of ITS2 haplotypes using both Sanger sequencing and high throughput sequencing (HTS) coupled with error correction with the software dada2. All the eleven species, except one, included some level of intraspecific variation in the ITS2 region. Overall, we observed a high correspondence between haplotypes generated by Sanger sequencing and HTS, with the exception of a few additional haplotypes detected using either approach. These extra haplotypes, typically occurring in low frequencies, were probably due to PCR and sequencing errors or intragenomic variation in the rDNA region. The presence of intraspecific (and possibly intragenomic) variation in ITS2 suggest that haplotypes (or ASVs) should not be used as basic units in ITS-based fungal community analyses, but an extra clustering step is needed to approach species-level resolution.

Automated summary

DNA metabarcoding is a powerful tool for analyzing complex ecological communities, but methodological challenges arise from intraspecific variation, which can lead to oversplitting of species. The study on local populations of eleven fungal species revealed allelic diversity in the ITS2 region, showing high correspondence between Sanger sequencing and high throughput sequencing, with the need for additional clustering steps for species-level resolution.