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

Abstract

Deep-sequencing technologies are becoming nearly routine to describe microbial community composition in environmental samples. The 18S ribosomal DNA (rDNA) pyrosequencing has revealed a vast diversity of infrequent sequences, leading to the proposition of the existence of an extremely diverse microbial 'rare biosphere'. Although 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 nonexistent. 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 and both classical cloning and sequencing and pyrosequencing of two different markers, the 18S rDNA and the internal transcribed spacer (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 artifacts. Our results suggest that a careful and rigorous analysis of pyrosequencing data sets, including data denoising and sequence clustering with well-adjusted parameters, is necessary to accurately describe microbial biodiversity using this molecular approach.

Figure 1

Frequencies of the different tintinnid families found in the VilleFr-43 and Ioni-7 samples by morphology (cells), 18S rDNA library sequencing (clones) and pyrosequencing of 18S rDNA and ITS regions. Sequences were assigned to families by BLAST searches against a curated tintinnid sequence database. Micrographs on the right show representative morphologies encountered in each family. Complete absence of a family in a sample is indicated by 0.

Figure 2

Maximum likelihood phylogenetic tree of 18S rDNA sequences of detected OTUs and their closest tintinnid relatives, based on 1200 aligned positions. Sequences from this study are in bold. Relative proportions of the different OTUs in each sample estimated from clone libraries and 18S rDNA pyroreads are indicated by circles of proportional size on the right. Relative proportions of the different tintinnid species estimated from cell counts under the microscope are indicated by hexagons of proportional size on the right. The numbers at nodes are bootstrap values (values <50% are omitted). Accession numbers are provided in brackets. The scale bar represents the number of substitutions for a unit branch length.