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NGS-Based Biodiversity and Community Structure Analysis of Meiofaunal Eukaryotes in Shell Sand from Hållö Island, Smögen, and Soft Mud from Gullmarn Fjord, Sweden

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NGS-Based Biodiversity and Community Structure Analysis of Meiofaunal Eukaryotes in Shell Sand from Hållö Island, Smögen, and Soft Mud from Gullmarn Fjord, Sweden Biodiversity Data Journal 5: e12731 doi: 10.3897/BDJ.5.e12731 Research Article NGS-based biodiversity and community structure analysis of meiofaunal eukaryotes in shell sand from Hållö island, Smögen, and soft mud from Gullmarn Fjord, Sweden Quiterie Haenel‡, Oleksandr Holovachov§§, Ulf Jondelius , Per Sundberg|,¶, Sarah J. Bourlat|,¶ ‡ Zoological Institute, University of Basel, Basel, Switzerland § Swedish Museum of Natural History, Stockholm, Sweden | Department of Marine Sciences, University of Gothenburg, Gothenburg, Sweden ¶ SeAnalytics AB, Bohus-Björkö, Sweden Corresponding author: Sarah J. Bourlat ([email protected]) Academic editor: Urmas Kõljalg Received: 15 Mar 2017 | Accepted: 06 Jun 2017 | Published: 08 Jun 2017 Citation: Haenel Q, Holovachov O, Jondelius U, Sundberg P, Bourlat S (2017) NGS-based biodiversity and community structure analysis of meiofaunal eukaryotes in shell sand from Hållö island, Smögen, and soft mud from Gullmarn Fjord, Sweden. Biodiversity Data Journal 5: e12731. https://doi.org/10.3897/BDJ.5.e12731 Abstract Aim: The aim of this study was to assess the biodiversity and community structure of Swedish meiofaunal eukaryotes using metabarcoding. To validate the reliability of the metabarcoding approach, we compare the taxonomic resolution obtained using the mitochondrial cytochrome oxidase 1 (COI) ‘mini-barcode’ and nuclear 18S small ribosomal subunit (18S) V1-V2 region, with traditional morphology-based identification of Xenacoelomorpha and Nematoda. Location: 30 samples were analysed from two ecologically distinct locations along the west coast of Sweden. 18 replicate samples of coarse shell sand were collected along the north- eastern side of Hållö island near Smögen, while 12 replicate samples of soft mud were collected in the Gullmarn Fjord near Lysekil. © Haenel Q et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 2 Haenel Q et al Methods: Meiofauna was extracted using flotation and siphoning methods. Both COI and 18S regions were amplified from total DNA samples using Metazoan specific primers and subsequently sequenced using Illumina MiSeq, producing in total 24 132 875 paired-end reads of 300 bp in length, of which 15 883 274 COI reads and 8 249 601 18S reads. These were quality filtered resulting in 7 954 017 COI sequences and 890 370 18S sequences, clustered into 2805 and 1472 representative OTUs respectively, yielding 190 metazoan OTUs for COI and 121 metazoan OTUs for 18S using a 97% sequence similarity threshold. Results: The Metazoan fraction represents 7% of the total dataset for COI (190 OTUs) and 8% of sequences for 18S (121 OTUs). Annelida (30% of COI metazoan OTUs and 23.97% of 18S metazoan OTUs) and Arthropoda (27.37% of COI metazoan OTUs and 11.57% of 18S metazoan OTUs), were the most OTU rich phyla identified in all samples combined. As well as Annelida and Arthropoda, other OTU rich phyla represented in our samples include Mollusca, Platyhelminthes and Nematoda. In total, 213 COI OTUs and 243 18S OTUs were identified to species using a 97% sequence similarity threshold, revealing some non-native species and highlighting the potential of metabarcoding for biological recording. Taxonomic community composition shows as expected clear differentiation between the two habitat types (soft mud versus coarse shell sand), and diversity observed varies according to choice of meiofaunal sampling method and primer pair used. Keywords Meiofaunal biodiversity, community structure, Illumina Mi-Seq, Metabarcoding, COI, 18S Introduction Microscopic interstitial marine organisms, also termed ‘meiofauna’, are often defined as animals that pass a 1mm mesh but are retained on a 45 µm sieve (Higgins 1988). Meiofauna are an important component of sedimentary and benthic habitats due to their small size, abundance and rapid turnover rates. Moreover, meiofaunal surveys represent a useful tool for environmental impact assessments, underlying the urgent need for reliable, reproducible and rapid analytical methods. The breadth of taxonomic groups present in marine sediments makes meiofauna an ideal tool for detecting the effects of ecological impacts on marine biodiversity (Moreno et al. 2008). However, traditional morphology based taxonomy assignment methods are labour intensive and time consuming, leading us to explore recently developed metabarcoding methods for whole community analysis. Metabarcoding has previously been used to characterize plankton assemblages (Lindeque et al. 2013, de Vargas et al. 2015), marine benthic meiofaunal assemblages (Creer et al. 2010, Fonseca et al. 2014, Fonseca et al. 2010, Brannock and Halanych 2015, Cowart et al. 2015), meiofaunal communities colonizing autonomous reef monitoring structures (Leray and Knowlton 2015) or fish gut contents (Leray et al. 2013). The vast majority of studies have employed Roche 454 due to its long read lengths compared to other technologies (Table 1; Shokralla et al. 2012), but Illumina MiSeq is now able to provide NGS-based biodiversity and community structure analysis of meiofaunal eukaryotes ... 3 similarly long reads using paired-end sequencing (2x300 base pairs). As summarized in Table 1, there is no standardized method for metabarcoding of marine fauna, and a variety of sample extraction methods, sequencing platforms, molecular markers, bioinformatics pipelines and OTU clustering thresholds have been used to date, making these studies difficult to compare (Table 1). Table 1. Methodological comparison of benthic and pelagic metabarcoding studies of marine fauna published to date Authors Sample Sample Sequencing Marker Marker Chimera OTU Database type extraction platform size (bp) screening clustering method method and threshold Leray et Coral reef Dissection of Roche 454 COI 313 UCHIME CROP Moorea al. 2013 fish gut fish gut GS FLX 92-94% Biocode contents Database, GenBank Leray Autonomous 4 fractions Ion Torrent COI 313 BOLD, and reef (Sessile, GenBank Knowlton monitoring 2mm, 2015 structures 500μm, 106μm) Lindeque Zooplankton 200μm mesh Roche 454 18S 450 ChimeraSlayer UCLUST Silva 108, et al. from 50m to WP2 GS FLX (V1-V2 (QIIME 1.3.0) 97% GenBank 2013 the surface plankton net regions) (QIIME 1.3.0) de Plankton 3 fractions Paired-end 18S USEARCH V9_PR2, V9 Vargas et (5-20μm, Illumina (V9 rDNA, al. 2015 20-180μm, Genome region) Protistan 180-2000μm) Analyser IIx Ribosomal system Reference Database Fonseca Marine Decanting Roche 454 18S 364 OCTOPUS OCTOPUS GenBank et al. benthic 45μm sieve GS FLX (V1-V2 (250-500) 96% 2010 meiofauna Ludox regions) Fonseca Marine Decanting Roche 454 18S 450 Amplicon- Amplicon- GenBank et al. benthic 45μm sieve GS FLX (V1-V2 Noise Noise 2014 meiofauna Ludox regions) 99% and 96% Brannock Marine Directly from Paired-end 18S 87-187 [1 USEARCH UPARSE Silva 111 and benthic sediment, 100 bp (V9 3] 6.1. (QIIME 97% Halanych meiofauna elutriated on reads region) 1.8) UCLUST 2015 45μm sieve Illumina and HiSeq USEARCH (QIIME 1.8) 4 Haenel Q et al Cowart Benthic 2mm sieve, Roche 454 COI 450 USEARCH 6.1 UCLUST GenBank et al. meiofauna 1mm sieve, GS FLX 18S 710 (QIIIME 1.7) de novo Silva 115 2015 from 0.5mm sieve (QIIME seagrass 1.7) meadows This Meiofauna Siphoning Paired-end COI 313 UCHIME CROP BOLD, study from coarse 125μm, Illumina Mi- 18S 364 (part of COI: SweBol and shell sand flotation Seq (V1-V2 USEARCH 92-94% own and muddy (MgCl2) regions) 6.1.) 18S: databases benthic 125μm, (QIIME 1.9.1) 95-97% for sediment flotation Nemertea, (H2O) Acoela, 45μm/70μm Oligochaeta), Genbank Silva 111 In this study we used samples from muddy and sandy marine sediments to examine how results of metabarcoding based surveys of meiofaunal communities are impacted by three different meiofaunal extraction methods and three different primer pairs for COI and 18S. In order to validate the reliability of the metabarcoding approach, we compare the results obtained with traditional morphology-based taxonomic assignment for two test groups, Xenacoelomorpha and Nematoda, the latter previously shown to be the dominant taxon in meiofaunal communities in terms of number of OTUs (Fonseca et al. 2010). Materials and Methods Sampling Samples were collected in two ecologically distinct locations along the west coast of Sweden in August 2014. Hållö island samples: Coarse shell sand was sampled by dredging at 7-8m depth along the north-eastern side of Hållö island near Smögen, Sotenäs municipality, Västra Götalands county (N 58° 20.32-20.38', E 11° 12.73-12.68'). Gullmarn Fjord samples: Soft mud was collected using a Waren dredge at 53 m depth in the Gullmarn Fjord near Lysekil, Lysekil municipality, Västra Götalands county (N 58° 15.73', E 11°26.10'). Meiofaunal extraction Hållö island. Hållö island samples were extracted in the lab using two different variations of the flotation (decanting and sieving) technique. Flotation (freshwater): Freshwater was used to induce an osmotic shock in meiofaunal organisms and force them to detach from heavy sediment particles. 200 mL of sediment were placed in a large volume of fresh water and thoroughly mixed to suspend meiofauna NGS-based biodiversity and community structure analysis of meiofaunal eukaryotes ... 5 and lighter sediment particles. The supernatant was sieved through a 1000 µm sieve to separate the macrofaunal fraction, which was then discarded. The filtered sample was sieved again through a 45 µm sieve
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