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Characterizing algal microbiomes using long-read nanopore sequencing
van der Loos, L.M.; D'hondt, S.; Willems, A.; De Clerck, O. (2021). Characterizing algal microbiomes using long-read nanopore sequencing. Algal Research 59: 102456. https://dx.doi.org/10.1016/j.algal.2021.102456
In: Algal Research. Elsevier: Amsterdam. ISSN 2211-9264, more
Peer reviewed article  
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Keywords
    Aquaculture
    Seaweed
    Ulva Linnaeus, 1753 [WoRMS]
Author keywords
    Marine seaweed; Microbiome; Oxford Nanopore Technologies
Authors  Top 
Abstract
    Microbes are vitally important for seaweed growth, functioning and reproduction, and are likely to have a big impact on aquaculture. Algae-associated bacteria, however, remain mostly unmonitored in aquaculture. Here, we studied the microbiomes of Ulva australis and Ulva lacinulata, three natural populations and an aquaculture set-up, based on full-length 16S rRNA gene sequences. The microbiome of cultivated Ulva was pronouncedly different from natural populations, and was specifically associated with higher relative abundances of known growth-promoting bacteria Sulfitobacter and Roseobacter. On a smaller scale, there were species-specific differences as well. In general, Ulva-associated communities were highly distinct from environmental seawater and sediment reference samples. We demonstrated a workflow generating full-length 16S rRNA sequences in real-time using Oxford Nanopore sequencing. We compared 3 different reference databases to assign taxonomy with Kraken2 (SILVA, Greengenes and NCBI). In addition, we used Nanopore's cloud-based EPI2ME workflow for comparison. All four methods yielded comparable results in terms of relative abundances on phylum and order level, but differed widely in alpha diversity indices at genus level. Using the NCBI 16S database, especially in combination with the EPI2ME workflow, resulted in a high proportion of false identifications of cyanobacteria due to chloroplast contamination. Based on our results, we recommend assigning taxonomy of Nanopore-derived long-reads with Kraken2 and the SILVA database in seaweed-microbiome studies. The protocols used in this study provide results within 24 h and may be applicable for rapid microbial surveys in aquaculture.
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