| Are Oligotypes Meaningful Ecological and Phylogenetic Units? A Case Study of Microcystis in Freshwater Lakes - :16141 | Office of Oceanic and Atmospheric Research (OAR)
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Are Oligotypes Meaningful Ecological and Phylogenetic Units? A Case Study of Microcystis in Freshwater Lakes
  • Published Date:
  • Source:
    Frontiers in Microbiology, 8, 7.
Filetype[PDF-1.16 MB]

  • Description:
    Oligotyping is a computational method used to increase the resolution of marker gene microbiome studies. Although oligotyping can distinguish highly similar sequence variants, the resulting units are not necessarily phylogenetically and ecologically informative due to limitations of the selected marker gene. In this perspective, we examine how oligotyping data is interpreted in recent literature, and we illustrate some of the method's constraints with a case study of the harmful bloom-forming cyanobacterium Microcystis. We identified three Microcystis oligotypes from a western Lake Erie bacterial community 16S rRNA gene (V4 region) survey that had previously clustered into one OTU. We found the same three oligotypes and two additional sequence variants in 46 Microcystis cultures isolated from Michigan inland lakes spanning a trophic gradient. In Lake Erie, shifts in Microcystis oligotypes corresponded to spatial nutrient gradients and temporal transitions in bloom toxicity. In the cultures, Microcystis oligotypes showed preferential distributions for different trophic states, but genomic data revealed that the oligotypes identified in Lake Erie did not correspond to toxin gene presence. Thus, oligotypes could not be used for inferring toxic ecotypes. Most strikingly, Microcystis oligotypes were not monophyletic. Our study supports the utility of oligotyping for distinguishing sequence types along certain ecological features, while it stresses that 16S rRNA gene sequence types may not reflect ecologically or phylogenetically cohesive populations. Therefore, we recommend that studies employing oligotyping or related tools consider these caveats during data interpretation.

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