A combined sequence-based and fragment-based characterization of microbial eukaryote assemblages provides taxonomic context for the Terminal Restriction Fragment Length Polymorphism (T-RFLP) method

Abstract

Microbial eukaryotes in seawater samples collected from two depths (5m and 500m) at the USC Microbial Observatory off the coast of Southern California, USA, were characterized by cloning and sequencing of 18S rRNA genes, as well as DNA fragment analysis of these genes. The sequenced genes were assigned to operational taxonomic units (OTUs), and taxonomic information for the sequence-based OTUs was obtained by comparison to public sequence databases. The sequences were then subjected to in silico digestion to predict fragment sizes, and that information was compared to the results of the T-RFLP method applied to the same samples in order to provide taxonomic context for the environmental T-RFLP fragments. A total of 663 and 678 sequences were analyzed for the 5m and 500m samples, respectively, which clustered into 157 OTUs and 183 OTUs. The sequences yielded substantially fewer taxonomic units as in silico fragment lengths (i.e., following in silico digestion), and the environmental T-RFLP resulted in the fewest unique OTUs (unique fragments). Bray–Curtis similarity analysis of protistan assemblages was greater using the T-RFLP dataset compared to the sequence-based OTU dataset, presumably due to the inability of the fragment method to differentiate some taxa and an inability to detect many rare taxa relative to the sequence-based approach. Nonetheless, fragments in our analysis generally represented the dominant sequence-based OTUs and putative identifications could be assigned to a majority of the fragments in the environmental T-RFLP results. Our empirical examination of the T-RFLP method identified limitations relative to sequence-based community analysis, but the relative ease and low cost of fragment analysis make this method a useful approach for characterizing the dominant taxa within complex assemblages of microbial eukaryotes in large datasets.