Accuracy of protist diversity assessments: morphology compared to cloning and direct pyrosequencing of 18S rRNA genes and ITS regions using the conspicuous tintinnid ciliates as a case study

Sampling

Deep-sequencing technologies are becoming nearly routine to describe microbial community composition in environmental samples. 18S rDNA pyrosequencing has revealed a vast diversity of infrequent sequences, leading to the proposition of the existence of an extremely diverse microbial "rare biosphere". While rare microbes no doubt exist, critical views suggest that many rare sequences may actually be artifacts. However, information about how diversity revealed by molecular methods relates to that revealed by classical morphology approaches is practically non-existent. To address this issue, we used different approaches to assess the diversity of tintinnid ciliates, a species-rich group in which species can be easily distinguished morphologically. We studied two Mediterranean marine samples with different patterns of tintinnid diversity. We estimated tintinnid diversity in these samples employing morphological observations as well as both classical cloning and sequencing and pyrosequencing of two different markers, the 18S rDNA and the ITS regions, applying a variety of computational approaches currently used to analyze pyrosequence reads. We found that both molecular approaches were efficient in detecting the tintinnid species observed by microscopy and revealed similar phylogenetic structures of the tintinnid community at the species level. However, depending on the method used to analyze the pyrosequencing results, we observed discrepancies with the morphology-based assessments up to several orders of magnitude. In several cases, the inferred number of operational taxonomic units (OTUs) largely exceeded the total number of tintinnid cells in the samples. Such inflation of the OTU numbers corresponded to "rare biosphere" taxa, composed largely of artefacts. Our results suggest that a careful and rigorous analysis of pyrosequencing datasets, including data denoising and sequence clustering with well-adjusted parameters, is necessary to accurately describe microbial biodiversity using this molecular approach.

Method steps

1. Pipeline used: https://www.ebi.ac.uk/metagenomics/pipelines/5.0