Marine protist diversity in European coastal waters and sediments as revealed by high‐throughput sequencing

Abstract

Summary Although protists are critical components of marine ecosystems, they are still poorly characterized. Here we analysed the taxonomic diversity of planktonic and benthic protist communities collected in six distant European coastal sites. Environmental deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from three size fractions (pico‐, nano‐ and micro/mesoplankton), as well as from dissolved DNA and surface sediments were used as templates for tag pyrosequencing of the V4 region of the 18S ribosomal DNA. Beta‐diversity analyses split the protist community structure into three main clusters: picoplankton‐nanoplankton‐dissolved DNA, micro/mesoplankton and sediments. Within each cluster, protist communities from the same site and time clustered together, while communities from the same site but different seasons were unrelated. Both DNA and RNA‐based surveys provided similar relative abundances for most class‐level taxonomic groups. Yet, particular groups were overrepresented in one of the two templates, such as marine alveolates (MALV)‐I and MALV‐II that were much more abundant in DNA surveys. Overall, the groups displaying the highest relative contribution were Dinophyceae, Diatomea, Ciliophora and Acantharia. Also, well represented were Mamiellophyceae, Cryptomonadales, marine alveolates and marine stramenopiles in the picoplankton, and Monadofilosa and basal Fungi in sediments. Our extensive and systematic sequencing of geographically separated sites provides the most comprehensive molecular description of coastal marine protist diversity to date.

Parasites dominate hyperdiverse soil protist communities in Neotropical rainforests

“…Samples from European near-shore marine sites (see Supplementary Code 1 for coordinates of each sample) came from the BioMarKs consortium 28,56 . We re-sequenced the samples here to obtain deeper sequencing.…”

Implementing and Innovating Marine Monitoring Approaches for Assessing Marine Environmental Status

“…Originally, most of the studies based on metabarcoding focused on prokaryotes (e.g., Sogin et al, 2006;Gilbert et al, 2009;Brazelton et al, 2010;Salazar et al, 2016), but, more recently, eukaryotes have also been investigated, including marine protists (e.g., Amaral-Zettler et al, 2009;Stoeck et al, 2010;Logares et al, 2014a;de Vargas et al, 2015;Massana et al, 2015) and metazoans (Thomsen et al, 2012;Lindeque et al, 2013;Hirai et al, 2015;Pearman and Irigoien, 2015). The development of highthroughput sequencing (HTS) technologies and of standardized procedures could allow metabarcoding analyses to be included in routine monitoring programmes (Visco et al, 2015;Zaiko et al, 2015a,b).…”

Novel Diversity of Deeply Branching Holomycota and Unicellular Holozoans Revealed by Metabarcoding in Middle Paraná River, Argentina

“…Both V4 and V9 are considered suitable regions for biodiversity studies given their great variability and the combination of high and low entropy in these sections, which allows forgood primer binding (Behnke et al, 2011;Hadziavdic et al, 2014;Tanabe et al, 2016). They also have been widely used in molecular diversity studies, so the research community knows the advantages and limitations of both regions (AmaralZettler et al, 2009;Stoeck et al, 2010;Massana et al, 2015). In fact, we overcame one of the main limitations of the V9 region, which is its short length (Amaral-Zettler et al, 2009;Pawlowski et al, 2011), by sequencing the V8 region as well.…”

“…PCR amplification of either the V4 or V8-9 regions of 18S rDNA were performed as previously described (Kim et al, 2016). Both V4 and V8-9 regions of the 18S rRNA gene were sequenced because of their extensive use for eukaryotic diversity (Nickrent and Sargent, 1991;Amaral-Zettler et al, 2009;Massana et al, 2015) and to compensate the existing primer bias of each region. Amplicon libraries were prepared as in Kim et al (2016) (further details about library preparation can be found in Supplementary Table).…”

“…These clean reads were then clustered into Operational Taxonomic Units (OTUs) using UPARSE with a 97% similarity threshold, resulting in a total of 4,598 OTUs for V4 and 4,176 OTUs for V8-9 region. Even though there is no clear consensus on the optimal threshold to use in clustering algorithms Brown et al, 2015), we decided to use 97% because it has been widely used (Stoeck et al, 2010;Kammerlander et al, 2015;Massana et al, 2015;Giner et al, 2016;Kim et al, 2016). We are aware that this threshold might group species or even genus into the same OTU.…”