Cryptogamie, Algologie, 2017, 38 (1): 1-18 © 2017 Adac. Tous droits réservés
Diatom resting stages in surface sediments: a pilot study comparing Next Generation Sequencing and Serial Dilution Cultures
Roberta PIREDDA a*, Diana SARNO a, Carina B. LANGE b,a, Maria Paola TOMASINO c,a,d, Adriana ZINGONE a & Marina MONTRESOR a
aStazione Zoologica Anton Dohrn, Department of Integrative Marine Ecology, Villa Comunale, 80121 Naples, Italy
bDepartment of Oceanography, COPAS Sur-Austral & Centro FONDAP-IDEAL, University of Concepción, Barrio Universitario, Concepcion, Chile
cInstitute of Biomembranes and Bioenergetics, National Research Council, Bari, Italy
dCurrent address: CIIMAR – Interdisciplinary Center of Marine and Environmental Research, University of Porto, Portugal
Abstract – Several diatom species produce resting stages as part of their life cycle. These resting stages accumulate in the sediments where they can remain for a long time before eventually being re-suspended in the water column and switching to active growth. Until now, the abundance and diversity of viable diatom resting stages have been assessed using the Serial Dilution Culture (SDC) method. In the present study, surface sediment samples from the Gulf of Naples were used to compare results obtained with the SDC method with those provided by HTS metabarcoding based on DNA extracted from the same sediment sample; the marker used was the V4 region of 18S rDNA. HTS metabarcoding showed a marked dominance of polar centric diatoms, among which Chaetoceros species were the most represented, in terms of both sequence and ribotype number. Almost all the most abundant ribotypes identified with metabarcoding matched records of species observed in SDCs. In some cases, however, this marker region could not distinguish between morphologically and phylogenetically distinct species, e.g., Skeletonema pseudocostatum and S. tropicum. As expected, molecular analysis provided a higher number of ribotypes as compared to the number of taxa recorded by SDC. Despite the well-known biases of both methodologies for quantitative assessments, our results show that DNA metabarcoding via HTS sequencing is a promising approach to explore the diversity of diatom resting stages. This study also confirms the importance of curated reference sequences to fully interpret the diversity stored in environmental sediment samples.
Diatoms / HTS / metabarcoding / V4 / 18S rDNA resting stages / SDC / sediments
*Corresponding author: [email protected]
doi/10.7872/crya/v38.iss1.2017.1 2 R. Piredda et al.
INTRODUCTION
Many unicellular eukaryotes produce dormant stages within their life cycle (Lennon & Jones 2011) in the form of spores, resting cells, cysts, akinetes, which can survive in bottom sediments for up to 100 years (Härnström et al., 2011; Ribeiro et al., 2011; Miyazono et al., 2012). The formation of benthic dormant stages is prompted by the onset of adverse environmental conditions for growth (Kuwata et al., 1993; Kremp et al., 2009) or by resource limitation (Gutiérrez et al., 1990). Germination is regulated, in turn, by the perception of environmental signals, such as light, temperature or oxygen availability (Shikata et al., 2011; Moore et al., 2015). Surface sediments host reservoirs of species and populations produced in different seasons and years, which eventually switch to active growth thus contributing in shaping local plankton biodiversity and successional patterns (e.g., Jones & Lennon, 2011). Diatoms produce two different kinds of dormant stages: spores and resting cells. Spores are morphologically different from vegetative cells, while resting cells are virtually identical to vegetative cells. Both stages, however, share physiological features such as reduced respiration and photosynthesis, high content of storage materials that allow them to survive for long time in the sediments (McQuoid & Hobson, 1996). Due to the small size of diatom dormant stages (usually ≤ 20 µm), their identification and enumeration is extremely difficult through direct observation of natural sediment samples. The abundance of viable resting stages can only be estimated applying an approach based on the Serial Dilution Culture (SDC) method, coupled with assessment of the Most Probable Number (MPN) (McQuoid, 2002; Andersen & Throndsen, 2003). This approach has been applied in different geographic areas, where it has allowed estimation of the composition of dormant diatom populations at different spatial and temporal scales (e.g., Itakura et al., 1997; McQuoid et al., 2002; Ishikawa & Furuya, 2004; Chen et al., 2009). Montresor et al. (2013) recorded a considerable fraction of the planktonic diatom species as resting stages in surface sediments at the Long Term Ecological Research station MareChiara (LTER-MC) in the Gulf of Naples (Tyrrhenian Sea, Mediterranean Sea), where Skeletonema pseudocostatum, various Chaetoceros species and small-celled Thalassiosira were the dominant taxa. When applying the SDC-MPN method, one has to consider a number of possible limitations. Surface sediments not only host resting stages, but also contain recently settled vegetative cells and, in relatively shallow areas where light reaches the bottom, an actively growing benthic diatom community. Moreover, in order to be detected, resting stages must be able to germinate under the experimental conditions applied in SDC. Upon germination, competition among different taxa may also occur in the mixed cultures, thus representing another potential bias in the assessment of the MPN of viable resting stages. As an alternative to SDC, meta- DNA barcoding is potentially a promising and powerful approach to estimate protist diversity, including diatom dormant stages, in natural sediment samples (Forster et al., 2016). This method, made possible by the recent development of High Throughput Sequencing (HTS), is based on deep sequencing of diagnostic DNA or cDNA fragments, generally the V4 and/or V9 regions of the 18S rDNA. For these two fragments, a large number of protist sequences is available (Guillou et al., 2013) and they can thus provide a good taxonomic coverage. The V4 region has been proposed as a pre-barcoding marker for protists (Pawlowski et al., 2012) and has been used in several studies focusing on marine protists (Bittner et al., 2013; Logares et al., 2014; Kopf et al., 2015; Massana et al., 2015). Diatom resting stages in surface sediments 3
With the aim of testing the performance of the HTS-metabarcoding approach for the identification of diatom resting stages, we carried out a pilot study at the LTER-MC station in the Gulf of Naples, comparing the results obtained from the SDC-MPN method with those provided by HTS-metabarcoding of the V4 region of the 18S rDNA extracted from the same surface sediment sample.
MATERIALS AND METHODS
Sediments were collected with a gravity corer equipped with three collectors, each 4 cm in diameter, at LTER station MareChiara in the Gulf of Naples (depth 70 m; 40°48.5’ N, 14° 15’ E; Tyrrhenian Sea, Mediterranean Sea) on February 7th 2014 (Fig. 1). The three collectors containing the sediment cores were kept in the dark and brought to the laboratory. The surface sediment (0-1 cm) of each of the three cores were pooled together, gently mixed and placed in a Falcon tube (Corning Life Sciences, Corning, NY, USA). Before carrying out DNA extraction and the SDC-MPN test, samples were stored in the dark at 10°C for 7 weeks in order to ensure that only resting stages survived in the sediments.
Fig. 1 Map of the Gulf of Naples (Tyrrhenian Sea, Mediterranean Sea) showing location of the LTER sampling site MareChiara (MC). Lines represent bathymetry in meters. 4 R. Piredda et al.
Serial dilution cultures (SDC) The abundance of viable diatom resting stages was estimated using a modified version of the SDC method (Montresor et al., 2013). The germination experiments were performed using triplicate 24-well tissue culture plates (Corning Life Sciences, Corning, NY, USA) filled with 1800 μl of f/4 medium (50:50 v:v dilution of f/2 medium, Guillard & Ryther, 1962). One gram (wet weight) of the pooled surface sediment was transferred to a Falcon tube and diluted with f/4 medium to a final volume of 10 ml (first dilution step 1:10). This sample was used to inoculate the four rows of each culture plate with progressive dilutions of the sample. The three culture plates were sealed with Parafilm M® and incubated at 18-20°C at an irradiance of about 5 μmol photons·m−2·s−1 for one week. The plates were subsequently transferred to higher irradiance (50 μmol photons·m−2·s−1). The culture plates were first inspected after 3 days of exposure at higher irradiance, and subsequently after 7, 14 and 21 days. Diatom cells were identified at the species level whenever possible. The abundance of the individual species was assessed from their presence in the different dilution wells using the MPN method (Throndsen, 1978).
DNA extraction and sequencing One subsample of 5 g of sediment sample was used to extract DNA. The sample was pre-treated according to Fortin et al. (2004) to remove most of the PCR- inhibiting compounds and the extracellular DNA from the sediment. DNA extraction was performed with the PowerMax™ soil DNA isolation kits (MoBio, Laboratories, Carlsbad, CA, USA). DNA yield and purity were evaluated by NanoDrop ND-1000 (Thermo Scientific, DE, USA). The V4 region of eukaryote SSU rDNA gene was amplified using primers listed in Stoeck et al. (2010) with slight modifications aimed at maximizing specificity and avoiding different annealing temperature in the V4 pair (Piredda et al., 2017). Illumina sequencing and pre-processing data analysis were performed with the protocol described in Piredda et al. (2017). Briefly, the Illumina Nextera’s protocol (Illumina, San Diego, CA, USA) was modified to obtain the V4 amplicon library for sequencing on the Illumina MiSeq platform as indicated in Kozich et al. (2013) and Manzari et al. (2015). Illumina paired-end reads (2x250 bp) were processed using mothur v.1.33.0 software (Schloss et al., 2009). Primer sequences were removed, no ambiguous bases were allowed, the maximum homopolymer size was 8 bp, and the remaining sequences were dereplicated. The pre-clustering algorithm (Huse et al., 2010) was used to further denoise sequences, allowing 1 nucleotide difference for every 100 bp of sequence, and the resulting sequences were screened for chimeras using UCHIME in de novo mode (Edgar et al., 2011). Ribotypes with sequence abundance lower than 10 were excluded from downstream analyses. Taxonomic assignments were determined BLASTing ribotypes (blastn) against the PR2 database (Guillou et al., 2013) integrated with 80 Bacillariophyta private sequences. Sequences were considered as successfully assigned if the similarity with the reference was ≥ 90% and the query coverage was ≥ 70%. Sequences that did not match these criteria were assigned to the phylum rank (minimum similarity 80%). Ribotypes assigned to the same reference were subsequently clustered at 98% of similarity in order to reduce variability due to intraspecific polymorphism and/or sequencing errors. Nomenclature and terms of ranks of the PR2 database mainly follow the classification of eukaryotes proposed by Adl et al. (2012). Diatom resting stages in surface sediments 5
RESULTS
Protists in the sediments: NGS data Filtering procedure generated a final curated dataset including 228,420 sequences. After the removal of ribotypes with total abundance lower than 10, 212,402 sequences were obtained (corresponding to 1,290 unique ribotypes), which represented 93% of the dataset. After removal of metazoa, which accounted for 44,316 sequences, the number of protist sequences was 168,086 (1,166 unique ribotypes) (Table 1). The sequence composition of the protist groups recorded in the sediment sample at the highest taxonomic level is illustrated in Figure 2a. Each group included sequences that satisfied the taxonomic assignment criteria and some that did not, which were assigned to phylum rank (Fig. 2a). Stramenopiles, Alveolata and Rhizaria constituted the largest fraction of the recovered sequences, accounting for 50, 27, and 15%, respectively, of the total. Few sequences of Archaeplastida were recorded
Table 1. General description of the V4 dataset
Total Total cleaned Ribotypes with Protists Diatoms raw data data > 10 sequences (no metazoa) Sequences 414,015 228,420 212,402 168,086 31,185 Unique ribotypes 14,173 1,290 1,166 82
Fig. 2. a) The V4 sequence composition of the protist groups at the highest taxonomic level. Each group includes sequences that satisfied the taxonomic assignment criteria (filled colour) and sequences that did not, which were assigned to the phylum rank (same colour, hatched). b) The V4 sequence composition of the Stramenopiles. 6 R. Piredda et al. and Haptophyta sequences were not present. Alveolata were dominated by Dinophyta (68.5%) and Ciliophora (23%). Rhizaria were largely dominated by Cercozoa (97.5%), while Radiolaria represented only a minor fraction of the sequences (2.5%). Foraminifera were not recorded, due to the known failure of the V4 primers with this group (Pawlowski et al., 2011). Within Stramenopiles, Labyrinthulea and Bacillariophyta (diatoms) comprised the largest fraction of sequences, while MAST (MArine STramenopiles), Chrysophyceae, Dictyochophyceae, and Raphidophyceae represented only a very small fraction accounting, in total, for 2.3% of the sequences (Fig. 2b).
Diatoms in the sediments: NGS data The vast majority of V4 diatom sequences could be assigned to benchmark references with high value of similarity ranging from 100 to 97% (data not shown). Polar centrics were by far the most abundant group, in terms of both sequence and ribotype numbers (Fig. 3a). Within this group, Chaetoceros, subgenus Hyalochaete, was the most abundant genus (67% of diatom sequences), followed by Thalassiosira, Skeletonema and Biddulphia (Fig. 3b). Radial centric diatoms only included three ribotypes, of which Leptocylindus danicus was the dominant one (0.7% of diatom sequences). Also araphid pennates were poorly represented with only six ribotypes, each including relatively few sequences. Twenty ribotypes were assigned to raphid pennates (Fig. 3a); these ribotypes, containing few sequences, were assigned to benthic diatoms identified to the genus level only or to environmental sequences of raphid diatoms. When clustering ribotypes assigned to the same reference at 98% of similarity, their total number decreased from 53 to 39 for polar and from 20 to 18 for raphid diatoms; those of radial and araphids did not change (Table 2). For only one species, C. curvisetus, HTS sequences were assigned to two different reference sequences available for this species (C. curvisetus clade 1 and clade 2). Chaetoceros
Fig. 3. a) Diatom V4 sequence composition; in parenthesis the number of ribotypes before clustering at 98% of similarity. b) Main genera represented in polar centric diatoms after clustering at 98% of similarity. Diatom resting stages in surface sediments 7 socialis, C. diadema, C. curvisetus clade 2 were the dominant diatom species in terms of V4 sequences recorded in the sediments, followed by an unidentified Thalassiosira sp. 1, Skeletonema pseudocostatum and/or S. tropicum (not distinct in V4), Biddulphia alternans and C. costatus. Few sequences of freshwater diatoms (Cymbella, Prestauroneis integra, Diademis gallica, Sellaphora pupula, Amphora pediculus, Entomoneis ornata and Staurosira elliptica) were recorded in the dataset, but the attribution to reference sequences was generally based on similarity values < 98%.
Diatoms in the sediments: SDC-MPN data The results of the SDC incubations carried out on three replicate subsamples showed considerable diversity between replicas. However, Skeletonema pseudocostatum, Arcocellulus mammifer and small-sized (≤ 10 µm) Thalassiosira species were the most abundant taxa in all replicates with viable cell abundances > 105 cells·g-1 wet sediment (Table 2). Chaetoceros socialis and Papiliocellulus sp. reached comparably high abundances only in one of the three replicas. Two other diatom taxa, Chaetoceros curvisetus and Skeletonema menzelii, showed average abundances > 103 cells·g-1 wet sediment. Fourteen additional diatoms recorded in the samples at lower abundance could be identified at the species level: four species of Chaetoceros, three Thalassiosira, Asterionellopsis glacialis, Odontella aurita, Lithodesmium variabile, Biddulphia alternans, Trieres mobiliensis, Leptocylindrus danicus/hargarvesii, and three taxa could be identified at the genus level only (Chaetoceros sp., Papiliocellulus sp., Psammodictyon cf. panduriforme) and various pennate diatoms were grouped based on their cell size (Table 2).
DISCUSSION
The analysis of the V4 region of the 18S rDNA of diatoms from surface sediments at the LTER-MC station showed a marked dominance of sequences of polar centrics, among which Chaetoceros species were the most represented. The vast majority of diatom sequences could be taxonomically assigned to species or genus level. All the most abundant ribotypes identified in metabarcoding data matched records obtained with the SDC-MPN method, with some minor exceptions discussed below. Storage in the dark and at relatively low temperature was effective in selecting resting stages while eliminating recently settled vegetative cells, as indicated by i) the marked dominance of sequences belonging to species for which the formation of resting stages is known, and ii) the absence of sequences of key planktonic genera for which the formation of resting stages has never been reported, e.g. Pseudo-nitzschia.
Taxonomic coverage and resolution of the metabarcode dataset The V4 sequences from the examined sediment sample were identified based on reference sequences obtained from the PR2 database (Guillou et al., 2013) with the addition of a number of sequences obtained in recent years for various planktonic diatom taxa from the Gulf of Naples. The high value of similarity with 8 R. Piredda et al.
Table 2. Number of V4 sequences attributed to reference sequences after clustering at 98% (sequences), their percentage over the total diatom sequences (% diatoms), the average concentration of viable diatom resting stages·g-1 wet sediment (SDC-MPN) and the standard deviation of three incubations (STD)
Polar centric diatoms Sequences % Diatoms SDC-MPN STD Chaetoceros socialis 7570 24.27 17100 25062 Chaetoceros diadema 5283 16.94 290 59 Chaetoceros curvisetus clade 1 (a) 4514 14.47 1217 404 Thalassiosira sp. 1 (b) 1961 6.29 54833 56636 Biddulphia alternans 1895 6.08 67 58 Skeletonema pseudocostatum/tropicum (c) 1766 5.66 64333 49136 Chaetoceros costatus 1543 4.95 282 73 Arcocellulus mammifer 563 1.81 23833 5485 Polar centric-Mediophyceae X sp. 1496 1.59 –– Polar centric-Mediophyceae X sp. 2488 1.56 –– Chaetoceros sp. 1 441 1.41 –– Thalassiosira pseudonana (b) 431 1.38 –– Thalassiosira allenii (b) 366 1.17 –– Odontella aurita 268 0.86 183 72 Chaetoceros vixvisibilis 260 0.83 –– Lithodesmium variabile 223 0.72 67 58 Chaetoceros curvisetus clade 2 (a) 217 0.70 –– Chaetoceros protuberans 190 0.61 33 58 Chaetoceros sp. 2 169 0.54 –– Skeletonema menzelii 162 0.52 1133 480 Thalassiosira sp. 2 strain 14 162 0.52 –– Chaetoceros sp. 3 159 0.51 –– Chaetoceros lauderi 1100.35 –– Cerataulus smithii 101 0.32 –– Trieres mobiliensis 101 0.32 33 58 Chaetoceros diversus 94 0.30 –– Chaetoceros seiracanthus 82 0.26 –– Papiliocellulus simplex (d) 67 0.21 9333 1443 Chaetoceros cf. constrictus 66 0.21 –– Chaetoceros pseudocurvisetus 61 0.20 –– Polar centric-Mediophyceae X sp. 3550.18 –– Skeletonema dohrnii 50 0.16 –– Thalassiosira rotula 42 0.13 67 115 Chaetoceros sp. 4 35 0.11 –– Thalassiosira mediterranea 27 0.09 357 29 Chaetoceros muellerii strain 2 21 0.07 –– Chaetoceros sp. strain 13 21 0.07 –– Biddulphia tridens 21 0.07 –– Chaetoceros muellerii strain1 17 0.05 –– Chaetoceros affinis ––283 491 Chaetoceros sp. ––63 55 Thalassiosira cf. eccentrica ––33 58 Diatom resting stages in surface sediments 9
Polar centric diatoms Sequences % Diatoms SDC-MPN STD
RADIAL CENTRIC DIATOMS Leptocylindrus danicus (e) 216 0.69 127 64 Actinocyclus curvatulus 29 0.09 –– Paralia sulcata 24 0.08 ––
RAPHID PENNATE DIATOMS Raphid pennate X sp. 1 103 0.33 –– Prestauroneis integra 64 0.21 –– Raphid pennate X sp. 2530.17 –– Nitzschia sp. 1 53 0.17 –– Cymbella sp. 1 (f) 42 0.13 33 58 Psammodictyon sp. 1 38 0.12 –– Bacillariophyta sp. 38 0.12 –– Raphid pennate X sp. 3290.09 –– Amphora pediculus 24 0.08 –– Navicula gregaria 24 0.08 –– Entomoneis ornata 23 0.07 –– Sellaphora pupula 23 0.07 –– Raphid pennate X sp. 1190.06 –– Raphid pennate X sp. 2170.05 –– Psammodictyon sp. (g) 15 0.05 75 130 Fallacia monoculata 14 0.04 –– Diadesmis gallica 12 0.04 –– Entomoneis ornata 10 0.03 ––
ARAPHID PENNATE DIATOMS Plagiogramma sp. 86 0.28 –– Rhaphoneis sp. 50 0.16 –– Dimeregramma sp. 27 0.09 –– Staurosira elliptica 26 0.08 –– Dimeregramma sp. 15 0.05 –– Asterionellopsis glacialis 13 0.04 243 212
UNIDENTIFIED PENNATE DIATOMS Pennate diatom (10 µm) ––163 203 cf. Grammatophora (15 µm) ––160 204 Pennate diatom (30 µm) ––130 225 Pennate diatom in bundles ––75 130 Pennate diatom (25 µm) ––33 58
(a) The morphotype identified as Chaetoceros curvisetus corresponds to two cryptic genetic clades, i.e. Chaetoceros curvisetus clade 1 and Chaetoceros curvisetus clade 2. (b) The morphotype identified as Thalassiosira sp. 1 can include different Thalassiosira species ≤ 10 µm (i.e., T. allenii, T. pseudonana and Thalassiosira sp. 1). (c) As Skeletonema pseudocostatum in SDC identification. (d) As Papiliocellulus sp. in SDC identification. (e) As Leptocylindrus cf. danicus in SDC identification, since L. danicus cannot be distinguished from L. hargravesii in light microscopy (Nanjappa et al. 2013). (f) As Cf. Cymbella in SDC identification. (g) As Psammodictyon cf. panduriforme in SDC identification. 10 R. Piredda et al. specific benchmark references proves that the reference database for V4 barcode region has a good taxonomic coverage for planktonic diatoms from the study area. However, several sequences were assigned to benchmark sequences identified only at the genus level, and a number of sequences within both polar centric and raphid pennates were attributed to sequences so-far only found in environmental DNA samples (environmental sequences) included in the PR2 database. Moreover, a number of sequences were assigned to freshwater taxa, but with low similarity values, thus suggesting that those sequences may belong to phylogenetically closely related marine species. This pilot study provides also examples for the different taxonomic resolution capability of the V4 barcode region in different genera. In some cases the V4 region allows the discrimination between species that are indistinguishable in light microscopy, i.e. Leptocylindrus danicus and L. hargravesii (Nanjappa et al., 2013), and also between cryptic clades of Chaetoceros curvisetus previously described in the study area (Kooistra et al., 2010). In other cases, this marker region cannot distinguish between morphologically and phylogenetically distinct species, e.g., Skeletonema pseudocostatum and S. tropicum. In particular, these two species do differ in the 18S rDNA region, which is used in phylogenetic analyses (Sarno et al., 2005), but share the same V4 barcode region. We have tentatively assigned these sequences to S. pseudocostatum considering the high abundance of this species in the SDC cultures. However, the presence of S. tropicum in the sample cannot be excluded, since both vegetative cells and resting stages of this species have been recorded in our study area (Montresor et al., 2013). The application of the 98% similarity threshold to cluster ribotypes assigned to the same reference sequence provided a good match with diatom morpho-species detected in the SDC. However, there is not a fixed similarity value that would allow discriminating intraspecific variability and/or sequencing errors from cryptic species; therefore, different clustering criteria have been applied in different studies and lineages (Caron et al., 2009, Kemarrek et al., 2013, Massana et al., 2015). As already pointed out by various authors (e.g., Cowart et al., 2015, Leray & Knowlton, 2016), the lack of coverage at the species level for many taxa may hinder a correct interpretation of metabarcoding data. Although the reference database used in this study has a good coverage for most planktonic diatoms of the study area, there was still a considerable number of sequences that failed to be assigned even at the genus level, particularly within the polar centric and raphid diatoms. Especially for benthic taxa, reference sequences from strains well characterized morphologically are still missing in many cases.
SDC and metabarcode
The SDC-MPN method, implemented for bacteriological research, has been applied to estimate abundance and diversity of protists especially in cases of problematic light microscopy identification and counting. Examples are the quantification of small sized flagellates and coccoid cells that cannot be identified in light microscopy (e.g., Cerino & Zingone, 2006), the evaluation of diatom viable resting stages in sediments (e.g., Montresor et al., 2013), and the assessment of the effectiveness of ballast water treatments (Cullen & MacIntyre, 2016). Cullen and Macintyre (2016) broadly discussed the principles, assumptions and applications of SDC-MPN method, outlining its advantages and limitations. Indeed, results from SDC can be biased due to the incapability of the target species to grow in the Diatom resting stages in surface sediments 11 selected growth medium and at the selected experimental (e.g., temperature, irradiance) conditions, as well as by interspecific competition that may take place in the culture wells. Nonetheless, in the case of diatom spores, their small size and difficulty of separating them from the sediments has made SDC the only possible approach to assess their diversity and abundance. Environmental DNA metabarcoding could be a useful approach to improve both the identification and quantitative assessment of diatom resting stages. This method also has some drawbacks, since it only provides data in terms of relative abundance of the different ribotypes in one sample. In addition, the proportional abundance can be biased by the copy number of the marker tags in different organisms, which is known to be variable but the extent of this variability has not been assessed in most cases (Zhu et al., 2005, Medinger et al., 2010). Finally, in the case of resting stages a possible bias could also derive from a less effective DNA extraction than for vegetative cells, due to the presence of the heavily silicified silica shells. Although many methods have been published and tested, the clean extraction of nucleic acids from environmental samples, particularly from soils, is still a challenge (Forster et al., 2016). Extraction and purification of DNA are crucial steps in the analysis of marine sediments, where also the presence of high organic matter loads, organic pollutants and heavy metals can interfere with the recovery of DNA and reduce the effectiveness of PCR (Luna et al., 2012). To avoid these problems, we first treated the sample with the washing protocol proposed by Fortin et al. (2004), which efficiently removes most of the PCR-inhibiting compounds and the extracellular DNA (Luna et al., 2012). We then applied the standardized MoBio PowerMax™ soil DNA isolation protocol that provides a good combination of DNA yield, purity and diversity coverage of eukaryotes and is therefore well adapted for diversity surveys in sediments (Lekang et al., 2015). The applied method should have eliminated a large fraction of dissolved DNA as shown by the composition of metazoan sequences. Out of the about 40,000 metazoan sequences, about 20,000 were assigned to aquatic crustaceans, with ca. 16,000 sequences of the planktonic copepod Acartia clausii, which could have been present in the sample as eggs, dead organisms, or body fragments. Another fraction of the metazoan sequences was attributed to nematodes, which could also have been in the sediment sample. Finally, about 3500 sequences were assigned to a single platyhelminth species. Had large amounts of extracellular DNA been present in the sediment sample, a much larger diversity of metazoan taxa would have been recorded. An even clearer indication of the limited presence of free DNA in the sample was the absence of sequences of planktonic taxa that do not form resting stages but are abundant in the study area (e.g., Pseudo-nitzschia spp., Thalassionema spp., or Leptocylindrus aporus; Ribera et al., 2004, Nanjappa et al., 2014). The results of our pilot comparative study showing the spore-forming Chaetoceros as the dominant ribotypes in the sediments suggest that the extraction protocol applied was effective. In addition, a good agreement was found between the list of taxa obtained from the SDC-MPN and the metabarcoding approach. All morphotypes identified at a species level in the SDC experiment were in fact also detected with the metabarcoding approach. The only exception was C. affinis for which 18S reference sequences are not available. Nevertheless, discrepancies were also recorded when comparing the concentrations of some species in the SDC with the relative abundance of the respective sequences in the metabarcoding dataset. This was the case of Cymatosiraceae, i.e. Arcocellulus mammifer and Papiliocellulus sp., which were abundant in the SDC but accounted for only 2% of the sequences, or Chaetoceros 12 R. Piredda et al. diadema and Biddulphia alternans, very abundant in the sequence dataset but scarcely represented in SDC. However, neither SDC-MPN nor sequencing are strictly quantitative methods, and in addition they are prone to different kinds of bias, which may lead to divergent results in some cases. In some cases, differences can be explained by the different resolution capabilities of the two methods. As an example, the morphotype identified as Thalassiosira sp. 1 in SDC possibly included different small Thalassiosira species, i.e., T. allenii, T. pseudonana and Thalassiosira sp. 1, which were well discriminated by metabarcoding. Interestingly, both the SDC study and the metabarcoding approach provided evidence for the presence of pennate diatoms in a relatively deep (70 m) sediment sample, as already reported in other studies (Sánchez et al., 2009, Ishikawa et al., 2011). This is not surprising, since benthic diatoms can live at rather low irradiance levels; in the Gulf of Marseille (Mediterranean Sea), viable pennate diatoms were recorded between 75 and 100 m depth (Plante-Cuny, 1969). What is more surprising is that they survived seven weeks of storage in dark and cool conditions. This suggests that pennate diatoms either have resting stages, although reports in the literature are scanty (McQuoid & Hobson, 1996), or may survive in complete darkness, as reported for other diatoms (Peters 1996; Kamp et al., 2011). As expected, the number of ribotypes – 82 based on the taxonomic assignment criteria and 66 after clustering at 98% similarity – was higher than the number of taxa recorded in the SDC (30 taxa). Besides the above-mentioned problems related to cultivation, this difference can be explained by the lower identification power allowed by optical methods, which is also evident in the presence of several unidentified supra-generic taxa in the SDC list, especially for pennate diatoms, which most likely encompassed more than one species (Table 2). In addition, HTS is capable to address also cryptic and pseudo-cryptic diversity, leading to a more complete list of species than allowed by light microscopy. Most of the diversity was detected in the genus Chaetoceros, for which nineteen V4 ribotypes were identified in the sediment sample vs. only seven morphospecies in the SDC experiment. This finding reflects the high diversity of this planktonic spore- forming diatom group, which is currently largely underestimated. In spite of the absence of many species that do not form resting stages in the HTS data, the number of diatom ribotypes in sediments was higher than that detected by HTS in individual plankton samples from the same area (Piredda et al., 2017). This point at the role of sediments as seed banks, which integrate resting stages produced over time.
The resting community in the sediments
In the present study, the sediment sample was stored in the dark, at a temperature close to that recorded at the bottom of LTER-MC station previous to DNA extraction and SDC inoculation. This procedure selected the autotrophic species that can form resting stages. However, the community in the sediments also includes heterotrophic protists with a benthic habit that can survive and even increase their number during the storage period. One example are Labyrinthulea, a group of heterotrophic Stramenopiles that showed very high sequence numbers in our sample. However, the vast majority of these sequences could be assigned to only four very abundant ribotypes, suggesting that these Labyrinthulea might have grown in the sediment sample during the dark storage period prior to DNA extraction. Cercozoa is another group of heterotrophic protists that includes amoeboids and flagellate taxa belonging to Granofilosea, Imbricatea and Endomyxa; as expected, they were very Diatom resting stages in surface sediments 13 abundant in the sediments (Forster et al., 2016). In contrast to Labyrinthulea, Cercozoa were instead represented by a considerable number of ribotypes that presumably reflect their diversity in the natural sediment sample. Acknowledgements. The authors thank W.H.C.F. Kooistra, C. Gaonkar, I. Percopo and M.V. Ruggiero (Stazione Zoologica Anton Dohrn) for providing unpublished diatom sequences; the Monitoring and Environmental Data Unit (MEDA) for collecting the sediment samples. The sequencing activity was carried out in the Molecular Biodiversity Lab (MoBiLab) of the ESFRI LifeWatch-Italy (Bari, Italy); we thank Giuseppe Sgaramella and Teresa De Filippis for technical assistance. MPT was supported by LifeWatch-Italy. This study was funded by the Italian project MIUR-FIRB Biodiversitalia (RBAP10A2T4). CBL acknowledges support by ASSEMBLE-Marine Grant Agreement 1555 and CNRS/LIA- MORFUN Mission Nr. 7569.
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