Prokaryote composition and predicted metagenomic content of two Cinachyrella Morphospecies and water from West Papuan Marine Lakes

Prokaryote composition and predicted metagenomic content of two Cinachyrella Morphospecies and... Abstract Certain sponge species are difficult to identify using classical taxonomic characters, and other techniques are often necessary. Here we used 454-pyrosequencing of the 16S rRNA gene to investigate bacterial and archaeal communities of two distinct Cinachyrella morphospecies collected from two Indonesian marine lakes with differing degrees of connection to the surrounding sea. Our main goal was to assess whether these morphospecies hosted distinct bacterial and archaeal communities and to what extent these differed from those found in lake water. A recently developed bioinformatic tool (PICRUSt) was used to predict metagenomic gene content of the studied communities. Compositionally, sponge samples clustered according to morphospecies as opposed to marine lake indicating that each morphospecies hosted distinct bacterial and archaeal communities. There were, however, also differences in higher taxon abundance among lakes. In the less connected lake, for example, both Cinachyrella morphospecies had higher levels of the order Synechococcales. With respect to metabolic gene content, although there were pronounced differences in predicted enrichment between both morphospecies, both were putatively enriched for KOs involved in pathways related to stress response, energy metabolism, environmental information processing and the production of secondary metabolites compared to prokaryote communities in water. These morphospecies may thus prove to be interesting sources of novel compounds of potential pharmaceutical and/or biotechnological importance. Archaea, Bacteria, Porifera, Indonesia, Misool, PICRUSt INTRODUCTION Marine lakes are small bodies of landlocked seawater, often located within islands, that are isolated to varying degrees from the surrounding marine environment. At present, there are an estimated 200 marine lakes known to exist in the world, most of which are found in tropical and subtropical regions including parts of Indonesia, Vietnam and Palau (Becking et al.2011). Much of these lakes are believed to have formed following the last glacial maximum, which was ∼19 000 years before present (Smith et al.2011). In the 5000 years following this maximum, glacial melting led to a global rise in sea level, which flooded natural depressions leading to the formation of marine lakes, particularly in areas of karstic limestone (van Hengstum, Scott and Gröcke 2011). Marine lakes can be distinguished based on their connection to the open sea. Connections can vary from lakes with pronounced water exchange through large open tunnels, caves or channels to lakes with highly limited exchange through small cracks and fissures in the island bedrock. The degree of connection has been shown to have a profound effect on the lake environment and fauna present (Becking et al.2011). Lakes with large, open connections to the open sea tend to have environmental conditions in terms of temperature, salinity, pH and tidal amplitude that are similar to the open sea. These lakes also house organisms including hard corals that are similar to those found in the open sea. Marine lakes with limited connections to the open sea, in contrast, have been shown to have lower pH, lower salinities (through the influx of freshwater) and higher temperatures than the surrounding open sea (Becking et al.2011). Lakes with limited connectivity to the open sea are of particular interest because they represent island systems of marine water and represent natural laboratories to test theories of biodiversity, biogeography and evolution (Dawson et al.2009). The physical isolation of marine lakes has set the stage for rapidly evolving populations of numerous taxa including shrimps, sponges, mussels and jellyfish (Holthuis 1973; Dawson and Hamner 2005; Becking et al.2013, 2016). The current study focuses on sponges and seawater collected in two marine lakes in the Raja Ampat region of West Papua, Indonesia. The Raja Ampat region harbours some of the most diverse marine environments in the world (Mangubhai et al.2012). Located in the ‘coral triangle’ (Bellwood, Renema and Rosen 2012), it encompasses more than 15 000 islands over an area of ca. 40 000 km2. Both lakes sampled in this study are located in the Misool area of southern Raja Ampat. Much work has focused on the distinct flora and fauna of marine lakes, which includes numerous endemic species. In addition to the flora and fauna, prokaryotes form an important component of the lake ecosystem and may be present in the sediment, water and as symbionts of the flora and fauna. Cleary et al. (2013, 2015a, 2016) previously described the bacterioplankton and host-associated bacterial communities of sponges, mussels and jellyfish found in the marine lakes of Berau, Indonesia. prokaryote symbionts are a topic of particular interest due to their ecological importance and biotechnological potential, particularly in association with sponges (Taylor et al.2007). prokaryotes play crucial roles in key biogeochemical cycles and are a key link between dissolved organic carbon and the autotrophic-grazer food chain (Currie and Kalff 1984; Danger et al.2007). Within their host organisms, prokaryotes play important roles in nutrition, immunity, defence and reproduction (Reiswig 1975; Hurst and Werren 2001; Baumann 2005; Macdonald and Monteleone 2005; Scarborough, Ferrari and Godfray 2005). Sponges are one of the most studied hosts of prokaryote symbionts and are an excellent model to study host–prokaryote associations (Taylor et al.2007). Sponges are sedentary, filter-feeding organisms that use a single layer of flagellated cells to pump water through their bodies. They are a conspicuous component of rocky coasts, coral reefs and marine lakes (Bergquist 1978; Becking, Cleary and de Voogd 2013). Sponges can host very dense populations of prokaryotes, in certain instances comprising up to 40% of total sponge biomass (Vacelet and Donadey 1977) and have been a focus of interest due to the biotechnological and pharmaceutical potential of their (prokaryote derived) secondary metabolites (Blunt and Munro 1998; van Soest and Braekman 1999; Faulkner et al.2000; Hentschel et al.2002; Sipkema et al.2005; Taylor et al.2007). In this study, we assessed bacterial and archaeal communities of bacterial two morphospecies belonging to the genus Cinachyrella. Cinachyrella, together with the genus Paratetilla, are also known as moon sponges because of their globular morphology with crater-like depressions. These sponges are common tropical reef denizens, but it is difficult to visually identify species in the field, and generally requires microscopical analysis of the spicules. Recently, two distinct prokaryote communities were revealed in the Caribbean sponge Cinachyrella kuekenthali, which on closer examination turned out to be two different sponge species (Cuvelier et al.2014). A similar pattern of distinct bacterial communities distinguishing different Paratetilla species was observed in northern Australia (Chambers et al.2013). Cinachyrella australiensis, in particular, is common in coastal waters of the Indo-Pacific region, although several morphospecies are known to occur (de Voogd and Cleary 2008; Becking, Cleary and de Voogd 2013). Although it was concluded that most tropical representatives were actually part of a single intertropical species, namely C. australiensis, it is highly likely that many Cinachyrella species, hiding under this species umbrella, are actually valid species (e.g. C. lindgreni Lendenfeld 1903; C. porosa Lendenfeld 1888; C. providentiae Dendy 1922). Moon sponges are common in marine lakes in Indonesia, and in this study we compared the bacterial and archaeal communities of different specimens of the genus Cinachyrella collected from two different marine lakes from a remote region in eastern Indonesia to investigate whether we could reveal distinct bacterial and archaeal communities and link these to clear species. In addition to the above, we compared the bacterial and archaeal communities found in Cinachyrella sponges with those found in lake water and used a bioinformatic tool, PICRUSt, to predict metagenomic gene content (Langille et al.2013). MATERIAL AND METHODS Study site The equatorial location of Misool means that the main seasonal influence is driven by monsoons (Prentice and Hope 2007). Misool is most influenced by the southeast monsoon from May to October which is characterised by cooler sea surface temperatures (SSTs), persistent winds and strong ocean swell. The annual rainfall in Papua averages 2500–4500 mm with interannual variability in rainfall due to the El Niño Southern Oscillation (Prentice and Hope 2007). There are seasonal differences in SSTs with an average SST of 29.0°C (Mangubhai et al.2012). Data collection Samples of Cinachyrella spp. were collected by snorkeling from 13 to 18 September 2013 in Southeast Misool, Raja Ampat region, West Papua province in Indonesia. SE Misool is part of a marine protected area of 343 200 ha established in 2009 (KKPD Misool Timur-Selatan). Samples of Cinachyrella (Demospongiae: Tetractinellida: Tetillidae) were collected at a depth of 1–3 m from two marine lakes located in two different islands in the Southeast Misool region (Becking, de Leeuw and Vogler 2015). The salinity in the waters surrounding the islands in which both marine lakes are found varies from 33 to 35 ppt. The lakes, here called lake K and lake M due to the lack of formal names, are separated by 33 km they are referred to as lake 1 and 2 respectively by Becking, de Leeuw and Vogler (2015). Marine lake K (02° 13΄ 15″S 130° 27΄ 31″E) is located in the island Karawop; another lake, located 100 m from this lake, has been entirely converted into a fish aquaculture facility. The lake is 72 by 64 m at its widest point with a maximum depth of 4.5 m. There is a moderate connection to the sea with a tidal amplitude of 45 cm in lake K compared to 100 cm in the open sea and a temporal delay of ∼3 h. Temperature and salinity (measured in May 2011) were 30°C and 31.5 ppt respectively (Klei 2016). Marine lake M (01° 56΄ 16″S 130° 22΄ 28″) is located on the eastern tip of the island of Misool and is ∼200 by 80 m at its widest point with a maximum depth of 7.5 m. The tidal amplitude is only 35 cm compared to 100 cm in the surrounding sea with a temporal delay in tide of at least 3 h. Temperature and salinity were 31°C to 33°C and 29 ppt, respectively. The dissolved oxygen in lake M was 80% at the surface to 2 m depth but declined to 0% at 3 to 6 m depth (Klei 2016). The environmental parameters indicate that lake M is less connected to the sea than lake K. Although pH was not measured during the initial survey, less connected lakes tend to have lower pH levels, higher temperatures and lower salinities than better connected lakes (Becking et al.2011). Voucher specimens of all Cinachyrella specimens sampled have been deposited in the sponge collection of the Naturalis Biodiversity Center (RMNH POR.10495–10500). Sponge samples were separated into two distinct morphospecies referred to as Cinachyrella sp. A and Cinachyrella sp. B (Fig. 1) using classical taxonomic characters. The dimension and morphology of the silicious spicules were measured and compared with one another and with type material of various Cinachyrella species including C. australiensis Carter, 1886. Cinachyrella sp. B is characterised by the presence of acanthose microxeas (150–200 μm) and relatively large sigmaspires (18–25 μm) in addition to megascleres. Cinachyrella sp. B is similar to C. australiensis; however, this species has smaller sigmaspires and was described from southern Australia. Cinachyrella anatriaenilla Fernandez et al. 2017 was recently described from American Samoa; although this species has overlapping dimensions with Cinachyrella sp. B, the megascleres are much smaller in size and thus represents a different species. Morphotype Cinachyrella sp. A does not have acanthose microxeas and has very small sigmaspires (7–10 μm) and is probably closely related to C. albatridens Lendenfeld 1907 described from the deep sea near Chagos in the Indian Ocean and C. arabica from Oman. Three specimens each of Cinachyrella sp. A and Cinachyrella sp. B were collected from lakes K and M. Two specimens of Cinachyrella sp. A were collected from lake K and one from lake M. One specimen of Cinachyrella sp. B was collected from lake K and two were collected from lake M. Figure 1. View largeDownload slide (a). Cinachyrella sp. B sigmaspire (RMNH POR.10500); (b). Cinachyrella sp. A sigmaspire (RMNH POR.10498); (c) detail of acanthose microxea. Figure 1. View largeDownload slide (a). Cinachyrella sp. B sigmaspire (RMNH POR.10500); (b). Cinachyrella sp. A sigmaspire (RMNH POR.10498); (c) detail of acanthose microxea. Lake water samples were collected by filtering 1 l of seawater through a Millipore® White Isopore Membrane Filter (GTTP04700, 47 mm diameter, 0.22 μm pore size). Samples were stored in 96% EtOH. After sampling, tubes containing the samples were frozen or carried in ice during travel between fieldwork lodging and the Netherlands and Portugal, where the samples were stored at –80°C until processing. Three water samples were collected in total, namely, one from lake K and two from lake M. A summary of the sponge and water samples collected is presented in Table S1 (Supporting Information). DNA extraction and pyrosequencing Total community-DNA extraction and 16S rRNA gene barcoded pyrosequencing We isolated PCR-ready total community DNA (TC-DNA) from three water and six sponge (three Cinachyrella sp. A and three Cinachyrella sp. B) samples using the FastDNA® SPIN Kit (MP Biomedicals) following the manufacturer's instructions. Briefly, the membrane filter (water sample) and sponge samples were each cut into small pieces. The whole membrane filter and 500 mg of sponge were transferred to Lysing Matrix E tubes containing a mixture of ceramic and silica particles. The microbial cell lysis was performed in the FastPrep® Instrument (Q Biogene) for 80 s at the speed of 6.0. Extracted DNA was eluted into DNase/Pyrogen-Free Water to a final volume of 50 μl and stored at –20°C until use. For the archaeal 16S rRNA gene amplification, the first PCR amplification was performed using DNA with archaea-specific forward (ARC344f-mod) and reverse (Arch958R-mod) primers (Pires et al.2012). After a denaturation step at 94°C during 5 min, 30 thermal cycles of 1 min at 94°C, 1 min at 56°C and 1 min at 72°C were carried out followed by an extension step at 72°C for 7 min (Pires et al.2012). Using the amplicons of the archaeal 16S rRNA gene as template, the V3-V4 regions were amplified using the barcoded fusion forward (524F-10-ext: 5΄- TGYCAGCCGCCGCGGTAA -3΄; Pires et al.2012) and reverse (Arch958R-mod: 5΄-CCGGCGTTGAVTCCAATT -3΄; Pires et al.2012) primers. After 4-min denaturation at 94°C, 30 cycles of 94°C for 30 s, 50°C for 45 s and 68°C for 60s and a final extension at 68°C for 10 min were carried with GS 454 FLX Titanium chemistry, according to manufacturer's instructions (Roche, 454 Life Sciences, Brandford, CT, USA). For the bacterial 16S rRNA gene amplification, the first PCR amplification was performed from DNA using the F-27 and R-1494 primers (Gomes et al.2001). After a denaturation step at 94°C for 5 min, 25 thermal cycles of 45 s at 94°C, 45 s at 56°C and 1:30 min at 72°C were carried out followed by an extension step at 72°C for 10 min. Using the amplicons of the bacterial 16S rRNA gene as template, the V3-V4 region was amplified, with the barcoded fusion forward (V3: 5΄ -ACTCCTACGGGAGGCAG-3΄; Yu et al.2005) and reverse (V4: 5΄ -TACNVRRGTHTCTAATYC-3΄; Vaz_Moreira et al.2011) primers. After a denaturation step at 94°C during 4 min, 25 thermal cycles of 30 s at 94°C, 45 s at 44°C and 1 min at 68°C and a final extension at 68°C for 10 min (Cleary et al.2013) were carried out with GS 454 FLX Titanium chemistry, according to manufacturer's instructions (Roche, 454 Life Sciences, Brandford, CT, USA). A summary of the primers used is presented in Table S2 (Supporting Information). Barcoded pyrosequencing libraries were analysed using the QIIME (Quantitative Insights Into Microbial Ecology; Caporaso et al.2010) software package (http://www.qiime.org/; last visited 20 January 2014). In QIIME, separate fasta and qual files for Bacteria and Archaea were used as input for the split_libraries.py script. Default arguments were used (e.g. for the number of ambiguous bases and maximum length of homopolymer run; n = 6) except for the minimum sequence length, which was set at 218 bp for both the bacterial and archaeal data after removal of forward primers and barcodes; backward primers were removed using the ‘truncate only’ argument and a sliding window test of quality scores was enabled with a value of 50 as suggested in the QIIME description for the script. In addition to user-defined cut-offs, the split_libraries.py script performs several quality filtering steps (http://qiime.org/scripts/split_libraries.html). OTUs were selected using UPARSE with usearch7 (Edgar 2013). The UPARSE sequence analysis tool (Edgar 2013) provides clustering, chimera checking and quality filtering on de-multiplexed sequences. Chimera checking was performed using the UCHIME algorithm (Edgar et al.2011). The quality filtering, as implemented in usearch7, filters noisy reads and preliminary results suggest that it gives results comparable to other denoisers such as AmpliconNoise, but is much less computationally expensive (http://drive5.com/usearch/features.html; last visited 20 January 2014). First, reads were filtered with the -fastq_filter command and the following arguments -fastq_trunclen 250 -fastq_maxee 0.5 -fastq_truncqual 15. Sequences were then dereplicated and sorted using the -derep_fulllength and -sortbysize commands. OTU clustering was performed using the -cluster_otus command. An additional chimera check was subsequently applied using the -uchime_ref command with the gold.fa database (http://drive5.com/uchime/gold.fa). AWK scripts were then used to convert the OTU files to the QIIME format. In QIIME, representative sequences were selected using the pick_rep_set.py script in QIIME using the ‘most_abundant’ method. Taxonomy was assigned to reference sequences of OTUs using default arguments in the assign_taxonomy.py script in QIIME with the rdp method (Wang et al.2007). In the assign_taxonomy.py function, we used a fasta file containing reference sequences from the Greengenes 13_8 release and the rdp classifier method. We used a modified version of the taxonomy file supplied with the Greengenes 13_8 release to map sequences to the assigned taxonomy. Finally, we used the make_otu_table.py script in QIIME to generate a square matrix of OTUs × samples. This was subsequently used as input for further analyses using the R package (R Core Team 2013). Sequence identifiers of closely related taxa of numerically dominant OTUs for Archaea (≥10 sequences) and Bacteria (≥200 sequences) were downloaded using the NCBI Basic Local Alignment Search Tool (BLAST) command line ‘blastn’ tool with the -db argument set to nt (Zhang et al.2000). The DNA sequences generated in this study can be downloaded from the NCBI SRA: SRP069346. We used PICRUSt (Langille et al.2013), a bioinformatics tool that uses marker genes, in this case 16S rRNA, to predict metagenome gene functional content. A detailed description of these methods has been published previously (Langille et al.2013; Polónia et al.2015; Cleary et al.2015b). In PICRUSt, we used the pick_closed_reference_otus.py script with reverse strand matching enabled and a 97% cut-off threshold (genus level). As optional argument in the pick_closed_reference_otus.py, we included the Greengenes 13_8 representative set of reference sequences against which to create an OTU table. We used the normalize_by_copy_number.py script to normalise the OTU table by marker gene copy number. The normalised data was used as input for the predict_metagenomes.py script, which produces a table of metagenome functional predictions for a given OTU table. Output of the predict_metagenomes.py script consists of a table of gene (or functional) counts assigned to KEGG orthologues (KOs). KOs are sets of orthologous (high-sequence similarity and consistent phylogenetic position; Smit and Mushegian 2000) biosynthesis genes that have been shown to catalyse the same reaction within the same pathway and are thus functionally correlated (Aoki-Kinoshita and Kanehisa 2009). These orthologue groups are graphically represented as nodes in KEGG individual pathways (Kanehisa and Goto 2000). Note that because of functional overlap, some KOs can be represented in more than one pathway. Note also that the PICRUSt results, as presented here, are predictive and thus provide information on potential enrichment and putative function as opposed to measuring actual gene presence/expression and function. In this study, we selected 24 KOs that had a high relative abundance and were involved in the nitrogen metabolism, environmental information processing (transporters), stress response and the biosynthesis of secondary metabolites. A number of these KOs were predicted to be enriched in other sponge species (de Voogd et al.2015; Cleary et al.2015b). Previous studies have shown that sponges and their microbial symbionts play important roles in the nitrogen metabolism and the biosynthesis of compounds of biotechnological and pharmaceutical interest (Wilkinson and Fay 1979; Sipkema et al.2005; Taylor et al.2007). Adaptation to potentially stressful environments, such as low pH marine lakes, furthermore requires mechanisms to cope with and adapt to stress. Statistical analyses Tables containing the presence and abundance per sample of all bacterial and archaeal OTUs were imported into R using the read.csv() function. Plant organelles, mitochondria, sequences not classified as Bacteria (or Archaea) and sequences not assigned to a domain or phylum were removed prior to statistical analysis. We used a self-written function in R (Gomes et al.2010) to estimate rarefied OTU richness for each sample. Care, however, should be taken in the interpretation of richness estimates based on sequence data given the prevalence of sequencing errors (Edgar 2013). OTU matrices of Bacteria and Archaea were loge (x + 1) transformed (in order to normalise the distribution of the data), and distance matrices were constructed using the Bray-Curtis index with the vegdist() function in the VEGAN package (Oksanen et al.2009) in R. The Bray-Curtis index is one of the most frequently applied (dis)similarity indices used in ecology (Legendre and Gallagher 2001; Cleary 2003; Polónia et al.2015). Variation in OTU composition among biotopes (Cinachyrella sp. A and B and water samples) was assessed with principal coordinates analysis using the cmdscale() function in R with the Bray-Curtis distance matrix as input. Variation in bacterial and archaeal composition among biotopes (Cinachyrella sp. A, Cinachyrella sp. B and water) was tested for significance using the adonis() function in vegan. In the adonis analysis, the Bray-Curtis distance matrix of species composition was the response variable with biotope as independent variable; the strata (block) argument was set to site (marine lake) so that randomisations were constrained to occur within each site and not across both sites. Detailed descriptions of the functions used here can be found in R (e.g. ?cmdscale) and online in reference manuals (http://cran.r-project.org/web/packages/vegan/index.html; 29 May 2015). RESULTS The sequencing effort yielded 58 636 bacterial and 38 796 archaeal sequences, which were assigned to 1134 bacterial and 87 archaeal OTUs after quality control. For Bacteria, we recorded 13 phyla, 22 classes and 35 orders in Cinachyrella sp. A compared to 26 phyla, 53 classes and 72 orders in Cinachyrella sp. B. In water, we recorded 23 phyla, 42 classes and 63 orders. In line with this, OTU richness was substantially higher in Cinachyrella sp. B and water samples than Cinachyrella sp. A samples (Fig. S1, Supporting Information). The most abundant phylum overall was Proteobacteria where mean relative abundance ranged from 53.8 ± 0.2% in Cinachyrella sp. B to 72.6 ± 5.0% in water and 91.4 ± 9.2% in Cinachyrella sp. A (Fig. 2). The percentage of Cyanobacteria ranged from 4.4 ± 0.8% in water to 6.1 ± 8.0% in Cinachyrella sp. A and 35.7 ± 5.4% in Cinachyrella sp. B. Actinobacterial abundance was also higher in Cinachyrella sp. B (6.8 ± 1.9%) than Cinachyrella sp. A (1.3 ± 0.9%) or water (1.5 ± 0.8%). There were also pronounced differences at the class and order level between Cinachyrella sp. A and Cinachyrella sp. B. Cinachyrella sp. A had a greater abundance of Alphaproteobacteria (34.9 ± 7.2% versus 4.4 ± 2.8%) (Fig. S2, Supporting Information), the gammproteobacterial order HTCC2188 (4.3 ± 2.5% versus 0.3 ± 0.3%) and the betaproteobacterial order EC94 (5.1 ± 2.2% versus 0.0 ± 0.0%) than Cinachyrella sp. B. Cinachyrella sp. B, in turn, had a much greater abundance of the cyanobacterial order Synechococcales than Cinachyrella sp. A (35.3 ± 0.9% versus 6.0 ± 8.0%) (Fig. S3, Supporting Information). There were also differences among lakes with Cinachyrella specimens of both morphospecies having a much higher abundance of Synechococcophycideae in lake M than lake K (Fig. S2, Supporting Information). Figure 2. View largeDownload slide Relative abundance of the most abundant bacterial phyla in Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. Figure 2. View largeDownload slide Relative abundance of the most abundant bacterial phyla in Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. For Archaea, we recorded two phyla, six classes and seven orders in Cinachyrella sp. A compared to two phyla, seven classes and eight orders in Cinachyrella sp. B. In water, we recorded two phyla, five classes and seven orders. Archaeal OTU richness was somewhat higher in water than both Cinachyrella morphospecies but there was considerable overlap among samples from different biotopes (Fig. S4, Supporting Information). The two most abundant phyla were the Euryarchaeota and Crenarchaeota. The Euryarchaeota was far more abundant in water samples (99.8 ± 0.1%) than in Cinachyrella sp. A (4.2 ± 3.9%) or Cinachyrella sp. B (5.8 ± 2.7%). In contrast, Crenarchaeota dominated both Cinachyrella sp. A (95.8 ± 3.9%) and Cinachyrella sp. B (94.2 ± 2.7%) samples, but only made up 0.2 ± 0.1% of the archaeal community in water samples. Importance of biotopes in structuring composition There was a significant difference in bacterial composition among biotopes (Adonis: F2,6 = 14.44, P = 0.002, R2 = 0.828). Variation among biotopes thus explained almost 83% of the variation in bacterial composition. The main axis (axis 1) separated samples of water from samples of both Cinachyrella morphospecies (Fig. 3a). The second axis (axis 2) separated samples of both Cinachyrella morphospecies. There were a number of OTUs that were abundant in both Cinachyrella morphospecies that were absent or rare in water including OTUs 2, 3, 16, 20, 68, 87 and 3145 (Table 1). OTU 3 was the most abundant OTU overall with 12 219 sequences and was related (sequence similarity = 96.22%) to an organism obtained from the sponge Plakortis halichondrioides in the Bahamas. All of the OTUs restricted to both Cinachyrella morphospecies were, in fact, closely related to organisms obtained from other sponges with sequence similarities ranging from 91.92% to 98.58% (Table 1). In addition to the above, both morphospecies housed OTUs restricted to those morphospecies. These included OTUs 5, 18, 23, 59 and 229 in Cinachyrella sp. A and OTUs 6, 7, 15, 42, 53, 92 and 2324 in Cinachyrella sp. B. These OTUs were related to organisms obtained from sponges, an octocoral and sediment with sequence similarities ranging from 90.33% to 98.82%. In contrast to the above, OTUs found mainly in water or in both water and sponge samples had high sequence similarities (>99%) with organisms found mainly in seawater. Figure 3. View largeDownload slide Ordination showing the first two axes of the principal coordinates analysis of (a) bacterial OTU composition and (b) archaeal OTU composition. Light grey symbols represent operational taxonomic unit (OTU) scores with the symbol size representing their abundance (number of sequence reads). Figure 3. View largeDownload slide Ordination showing the first two axes of the principal coordinates analysis of (a) bacterial OTU composition and (b) archaeal OTU composition. Light grey symbols represent operational taxonomic unit (OTU) scores with the symbol size representing their abundance (number of sequence reads). Table 1. List of abundant (≥200 sequence reads) bacterial OTUs and closely related organisms identified using BLAST search. OTU  Sum  Group  Green  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  87  6172  Cin  404788  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  806637511  99.76  Lake sediment, Japan:Shizuoka, Hamamatsu, Lake Sanaru  2  6167  Cin  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  28557443  99.76  Seawater  3  12 219  Cina  4483818  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  16  1862  Cina  4360584  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  400269037  94.41  Sponge: Cymbastella coralliophila, Australia: Orpheus Island, Great Barrier Reef  20  1820  Cina  4376233  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  168997213  91.92  Sponge: Tethya californiana, USA: California, Monterey  3145  317  Cina  4483818  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  68  216  Cina  545247  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269153  98.58  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  5  3416  CinAa  1106953  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269119  95.25  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  23  830  CinAa  744558  Proteobacteria  Gammaproteobacteria  HTCC2188  HTCC2089  Unclassified  350542298  93.51  Marine surface sediment, South Korea: Ulleung Basin  18  826  CinAa  1148138  Proteobacteria  Betaproteobacteria  EC94  Unclassified  Unclassified  290575709  93.05  Sponge, the Red sea  229  518  CinAa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  91.65  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  59  226  CinAa  1111200  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  134290589  91.33  Sponge: Xestospongia muta, USA: Key Largo, FL  7  2930  CinBa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  90.33  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  6  2685  CinBa  4447845  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  148732264  98.82  Site S25 near Coco's Island, Costa Rica  15  1260  CinBa  4303805  Actinobacteria  Acidimicrobiia  Acidimicrobiales  Unclassified  Unclassified  402169730  96.68  Intertidal surface sediment, China  42  306  CinBa  2369769  Proteobacteria  Deltaproteobacteria  Unclassified  Unclassified  Unclassified  400269180  97.54  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  53  284  CinBa  499915  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  164653427  91.19  Sponge: Haliclona simulans, Ireland  92  241  CinBa  1686480  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269113  94.7  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  2324  209  CinBa  4425801  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  441084656  90.91  Sponge: Dysidea avara, Mediterranean Sea: Medas Islands  13  859  Water  1109476  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  592749174  100  Shrimp pond, China  48  438  Water  316815  Proteobacteria  Alphaproteobacteria  Rickettsiales  Pelagibacteraceae  Unclassified  909637544  100  Marine sediment, shallow hydrothermal vent, Hot Lake, high temperature site, Italy:Panarea, Tyrrhenian Sea  28  224  Water  621743  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  530540087  100  Sponge: Haliclona sp., India: Gulf of Mannar  67  217  Water  4306324  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  451775277  100  Coastal tropical surface seawater, Pacific Ocean  12  2212  Wide  4441050  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  429144095  100  Estuary in middle of river, China: Jiulong River  24  864  Wide  346431  Proteobacteria  Alphaproteobacteria  Rhodobacterales  Rhodobacteraceae  Unclassified  831181535  100  Seawater, coral reef, Japan: Wakayama, Kushimoto  38  435  Wide  263082  Actinobacteria  Acidimicrobiia  Acidimicrobiales  C111  Unclassified  592748882  100  Shrimp pond, China  36  410  Wide  123788  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  703891050  100  Seawater east of Marquesas Island in the South Pacific Ocean  799  371  Wide  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  891166367  100  Seawater, Japan: East China Sea  25  349  Wide  216695  Proteobacteria  Gammaproteobacteria  HTCC2188  Unclassified  Unclassified  388955260  100  Seawater; next to dolphin Y, USA: San Diego, CA  60  322  Wide  4481997  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  429143959  99.55  Estuary in middle of river, China: Jiulong River  171  277  Wide  118817  Bacteroidetes  Flavobacteriia  Flavobacteriales  Flavobacteriaceae  Unclassified  817254067  99.76  Adriatic bottom seawater, Italy  121  215  Wide  267884  Proteobacteria  Gammaproteobacteria  Alteromonadales  OM60  Unclassified  592748867  100  Shrimp pond, China  561  206  Wide  538178  Proteobacteria  Alphaproteobacteria  Rhodospirillales  Rhodospirillaceae  Unclassified  440547316  100  Seawater, China: Zhanjiang Bay  OTU  Sum  Group  Green  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  87  6172  Cin  404788  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  806637511  99.76  Lake sediment, Japan:Shizuoka, Hamamatsu, Lake Sanaru  2  6167  Cin  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  28557443  99.76  Seawater  3  12 219  Cina  4483818  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  16  1862  Cina  4360584  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  400269037  94.41  Sponge: Cymbastella coralliophila, Australia: Orpheus Island, Great Barrier Reef  20  1820  Cina  4376233  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  168997213  91.92  Sponge: Tethya californiana, USA: California, Monterey  3145  317  Cina  4483818  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  68  216  Cina  545247  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269153  98.58  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  5  3416  CinAa  1106953  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269119  95.25  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  23  830  CinAa  744558  Proteobacteria  Gammaproteobacteria  HTCC2188  HTCC2089  Unclassified  350542298  93.51  Marine surface sediment, South Korea: Ulleung Basin  18  826  CinAa  1148138  Proteobacteria  Betaproteobacteria  EC94  Unclassified  Unclassified  290575709  93.05  Sponge, the Red sea  229  518  CinAa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  91.65  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  59  226  CinAa  1111200  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  134290589  91.33  Sponge: Xestospongia muta, USA: Key Largo, FL  7  2930  CinBa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  90.33  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  6  2685  CinBa  4447845  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  148732264  98.82  Site S25 near Coco's Island, Costa Rica  15  1260  CinBa  4303805  Actinobacteria  Acidimicrobiia  Acidimicrobiales  Unclassified  Unclassified  402169730  96.68  Intertidal surface sediment, China  42  306  CinBa  2369769  Proteobacteria  Deltaproteobacteria  Unclassified  Unclassified  Unclassified  400269180  97.54  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  53  284  CinBa  499915  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  164653427  91.19  Sponge: Haliclona simulans, Ireland  92  241  CinBa  1686480  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269113  94.7  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  2324  209  CinBa  4425801  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  441084656  90.91  Sponge: Dysidea avara, Mediterranean Sea: Medas Islands  13  859  Water  1109476  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  592749174  100  Shrimp pond, China  48  438  Water  316815  Proteobacteria  Alphaproteobacteria  Rickettsiales  Pelagibacteraceae  Unclassified  909637544  100  Marine sediment, shallow hydrothermal vent, Hot Lake, high temperature site, Italy:Panarea, Tyrrhenian Sea  28  224  Water  621743  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  530540087  100  Sponge: Haliclona sp., India: Gulf of Mannar  67  217  Water  4306324  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  451775277  100  Coastal tropical surface seawater, Pacific Ocean  12  2212  Wide  4441050  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  429144095  100  Estuary in middle of river, China: Jiulong River  24  864  Wide  346431  Proteobacteria  Alphaproteobacteria  Rhodobacterales  Rhodobacteraceae  Unclassified  831181535  100  Seawater, coral reef, Japan: Wakayama, Kushimoto  38  435  Wide  263082  Actinobacteria  Acidimicrobiia  Acidimicrobiales  C111  Unclassified  592748882  100  Shrimp pond, China  36  410  Wide  123788  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  703891050  100  Seawater east of Marquesas Island in the South Pacific Ocean  799  371  Wide  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  891166367  100  Seawater, Japan: East China Sea  25  349  Wide  216695  Proteobacteria  Gammaproteobacteria  HTCC2188  Unclassified  Unclassified  388955260  100  Seawater; next to dolphin Y, USA: San Diego, CA  60  322  Wide  4481997  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  429143959  99.55  Estuary in middle of river, China: Jiulong River  171  277  Wide  118817  Bacteroidetes  Flavobacteriia  Flavobacteriales  Flavobacteriaceae  Unclassified  817254067  99.76  Adriatic bottom seawater, Italy  121  215  Wide  267884  Proteobacteria  Gammaproteobacteria  Alteromonadales  OM60  Unclassified  592748867  100  Shrimp pond, China  561  206  Wide  538178  Proteobacteria  Alphaproteobacteria  Rhodospirillales  Rhodospirillaceae  Unclassified  440547316  100  Seawater, China: Zhanjiang Bay  OTU: OTU number; sum: number of sequence reads; group: biotope or biotopes where the OTUs were mainly found (Cin: both Cinachyrella morphospecies; CnA: Cinachyrella sp. A; CnB: Cinachyrella sp. B; wide: found in all biotopes; superscript a: an OTU only present in a particular biotope); green: Greengenes reference number; GI: Genbank identification numbers of closely related organisms identified using BLAST; Seq: sequence similarity of these organisms with our representative OTU sequences; source: isolation source of organisms identified using BLAST. View Large As with Bacteria, there was a significant difference in archaeal composition among biotopes (Adonis: F2,6 = 9.91, P = 0.004, R2 = 0.768). The ordination of Archaea also closely resembled that of Bacteria with the main axis separating samples of water from samples of both Cinachyrella morphospecies and the second axis separating samples of both Cinachyrella morphospecies. Both Cinachyrella morphospecies were distinguished by the presence of two highly abundant crenarchaeotal OTUs, one restricted to each morphospecies (Fig. 3b and Table 2). One of these OTUs, found mainly in Cinachyrella sp. A, was related to an organism obtained from the sponge Geodia barretti in Norway with a sequence similarity of 96.74% while the other OTU, found mainly in Cinachyrella sp. B, had 96.04% sequence similarity with an organism obtained from marine sediment. Table 2. List of abundant ( ≥ 10 sequence reads) archaeal OTUs and closely related organisms identified using BLAST search. OTU  Green  Sum  Group  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  1  553746  13 287  CnA  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.74  Sponge: Geodia barretti, Norway  586  324034  11  CnAa  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.51  Sponge: Geodia barretti, Norway  439  553746  11 854  CnB  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  525332479  96.04  Marine sediment  8  4462888  563  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546629  100  Coral: Montastraea annularis complex, Virgin Islands  10  4369009  32  Wide  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Nitrosopumilus  757399660  100  Transition zone between Pearl River estuary and coastal South China Sea  14  104677  76  Wide  Crenarchaeota  Thaumarchaeota  Nitrososphaerales  Nitrososphaeraceae  Candidatus Nitrososphaera  298105138  100  Sponge: Halichondria oshoro  56  4462887  30  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  83416103  99.3  Sponge: Axechina raspailoides NTM Z4460  2  104364  4683  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  100  Bacterioplankton 5m, North Atlantic Ocean  5  4428688  3976  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  100  Surface seawater  6  112246  1106  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933392  100  Surface sea water, South Pacific Ocean  7  147038  204  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  757399666  100  Transition zone between Pearl River estuary and coastal South China Sea  11  147038  534  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  597709959  99.53  Carbonate chimney in Prony Hydrothermal field, New Caledonia  17  2380278  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159153  100  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  24  4462887  178  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394999593  97.2  Seawater, Pacific Ocean: near central Chile  27  110525  18  Water  Euryarchaeota  Thermoplasmata  E2  Marine group III  Unassigned  394999409  100  Seawater, Pacific Ocean: near central Chile  38  2781721  63  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546727  100  Coral Diploria strigosa, Virgin Islands  301  4462887  471  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159150  99.77  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  344  4462887  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  759  4462887  1377  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  100  Surface sea water, South Pacific Ocean  872  4462887  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  993  4428688  28  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  99.77  Surface seawater  1050  4428688  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394998924  99.77  Seawater, Pacific Ocean: near Peru  853  104364  15  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  990  104364  14  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  OTU  Green  Sum  Group  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  1  553746  13 287  CnA  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.74  Sponge: Geodia barretti, Norway  586  324034  11  CnAa  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.51  Sponge: Geodia barretti, Norway  439  553746  11 854  CnB  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  525332479  96.04  Marine sediment  8  4462888  563  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546629  100  Coral: Montastraea annularis complex, Virgin Islands  10  4369009  32  Wide  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Nitrosopumilus  757399660  100  Transition zone between Pearl River estuary and coastal South China Sea  14  104677  76  Wide  Crenarchaeota  Thaumarchaeota  Nitrososphaerales  Nitrososphaeraceae  Candidatus Nitrososphaera  298105138  100  Sponge: Halichondria oshoro  56  4462887  30  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  83416103  99.3  Sponge: Axechina raspailoides NTM Z4460  2  104364  4683  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  100  Bacterioplankton 5m, North Atlantic Ocean  5  4428688  3976  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  100  Surface seawater  6  112246  1106  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933392  100  Surface sea water, South Pacific Ocean  7  147038  204  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  757399666  100  Transition zone between Pearl River estuary and coastal South China Sea  11  147038  534  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  597709959  99.53  Carbonate chimney in Prony Hydrothermal field, New Caledonia  17  2380278  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159153  100  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  24  4462887  178  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394999593  97.2  Seawater, Pacific Ocean: near central Chile  27  110525  18  Water  Euryarchaeota  Thermoplasmata  E2  Marine group III  Unassigned  394999409  100  Seawater, Pacific Ocean: near central Chile  38  2781721  63  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546727  100  Coral Diploria strigosa, Virgin Islands  301  4462887  471  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159150  99.77  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  344  4462887  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  759  4462887  1377  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  100  Surface sea water, South Pacific Ocean  872  4462887  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  993  4428688  28  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  99.77  Surface seawater  1050  4428688  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394998924  99.77  Seawater, Pacific Ocean: near Peru  853  104364  15  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  990  104364  14  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  OTU: OTU number; Green: Greengenes reference number; sum: number of sequence reads; group: biotope or biotopes where the OTUs were mainly found (CnA: Cinachyrella sp. A; CnB: Cinachyrella sp. B; wide: found in all biotopes; superscript a: an OTU only present in a particular biotope); GI: Genbank identification numbers of closely related organisms identified using BLAST; Seq: sequence similarity of these organisms with our representative OTU sequences; source: isolation source of organisms identified using BLAST. View Large Predictive metagenome analysis For Bacteria, mean (and standard deviation) NSTI values for the sampled biotopes were 0.120 (0.008) for Cinachyrella sp. A, 0.075 (0.017) for Cinachyrella sp. B and 0.122 (0.016) for water. KOs that were putatively enriched for both Cinachyrella morphospecies compared to water included K04517, K04748, K02044 (phosphonate transport), K03799 (heat shock), K03893 (arsenical pump) and K07334 (proteic killer suppression protein). KOs that were predicted to be enriched for Cinachyrella sp. A compared to Cinachyrella sp. B and water were K14266 (FADH2 O2-dependent halogenase), K02014 (iron complex recepter), K03406 (methyl-accepting chemotaxis), K00799 (glutathione) and K07233 (copper resistance) (Fig. 4 and Table 3). KOs that were predicted to be enriched for Cinachyrella sp. B compared to Cinachyrella sp. A and water included K01582 (lysine decarboxylase), K05356 (trans-octaprenyltranstransferase), K00366 (ferredoxin-nitrite reductase), K00367 (ferredoxin-nitrate reductase), K02006 (cobalt/nickel transport), K02074 (zinc/manganese transport), K02075 (zinc/manganese transport), K03321 (sulfate permease), K08226 (MFS transporter), K03711 (Fur family transcriptional regulator) and K03969 (phage shock protein A). Results for KO enrichment based on archaeal data are shown in Fig. S5 (Supporting Information). For the selected KOs based on archaeal data, there were pronounced differences between the Cinachyrella morphospecies and water, but relatively little difference between both Cinachyrella morphospecies. Figure 4. View largeDownload slide Mean predicted relative gene count abundance for selected KEGG orthologs based on bacterial data for samples from Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. (a) K01582: lysine decarboxylase [EC:4.1.1.18], (b) K04517: prephenate dehydrogenase [EC:1.3.1.12], (c) K05356: all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85], (d) K14266: tryptophan halogenase [EC:1.14.19.9], (e) K00366: ferredoxin-nitrite reductase [EC:1.7.7.1], (f) K00367: ferredoxin-nitrate reductase [EC:1.7.7.2], (g) K01915: glutamine synthetase [EC:6.3.1.2], (h) K04748: nitric oxide reductase NorQ protein [EC:1.14.19.9], (i) K08738: cytochrome c, (j) K02006: cobalt/nickel transport system ATP-binding protein, (k) K02014: iron complex outermembrane recepter protein, (l) K02044: phosphonate transport system substrate-binding protein, (m) K02074: zinc/manganese transport system ATP-binding protein, (n) K02075: zinc/manganese transport system permease protein, (o) K03321: sulfate permease, (p) K03406: methyl-accepting chemotaxis protein, (q) K08226: MFS transporter, (r) K00799: glutathione S-transferase [EC:2.5.1.18], (s) K03711: Fur family transcriptional regulator, (t) K03799: heat shock protein HtpX [EC:3.4.24.-], (u) K03893: arsenical pump membrane protein, (v) K03969: phage shock protein A, (w) K07233: copper resistance protein B, and (x) K07334: proteic killer suppression protein. Figure 4. View largeDownload slide Mean predicted relative gene count abundance for selected KEGG orthologs based on bacterial data for samples from Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. (a) K01582: lysine decarboxylase [EC:4.1.1.18], (b) K04517: prephenate dehydrogenase [EC:1.3.1.12], (c) K05356: all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85], (d) K14266: tryptophan halogenase [EC:1.14.19.9], (e) K00366: ferredoxin-nitrite reductase [EC:1.7.7.1], (f) K00367: ferredoxin-nitrate reductase [EC:1.7.7.2], (g) K01915: glutamine synthetase [EC:6.3.1.2], (h) K04748: nitric oxide reductase NorQ protein [EC:1.14.19.9], (i) K08738: cytochrome c, (j) K02006: cobalt/nickel transport system ATP-binding protein, (k) K02014: iron complex outermembrane recepter protein, (l) K02044: phosphonate transport system substrate-binding protein, (m) K02074: zinc/manganese transport system ATP-binding protein, (n) K02075: zinc/manganese transport system permease protein, (o) K03321: sulfate permease, (p) K03406: methyl-accepting chemotaxis protein, (q) K08226: MFS transporter, (r) K00799: glutathione S-transferase [EC:2.5.1.18], (s) K03711: Fur family transcriptional regulator, (t) K03799: heat shock protein HtpX [EC:3.4.24.-], (u) K03893: arsenical pump membrane protein, (v) K03969: phage shock protein A, (w) K07233: copper resistance protein B, and (x) K07334: proteic killer suppression protein. Table 3. KEGG orthologues (KO) shown in Fig. 4. KO  Categ  Group  Definition  K02044  Env  CN  Phosphonate transport system substrate-binding protein  K03799  Stress  CN  Heat shock protein HtpX [EC:3.4.24.-]  K03893  Stress  CN  Arsenical pump membrane protein  K04517  Biosyn  CN  Prephenate dehydrogenase [EC:1.3.1.12]  K04748  Energy  CN  Nitric oxide reductase NorQ protein; nitric-oxide reductase NorQ protein [EC:1.7.99.7]  K07334  Stress  CN  Proteic killer suppression protein  K00799  Stress  CnA  Glutathione S-transferase [EC:2.5.1.18]  K02014  Env  CnA  Iron complex outermembrane recepter protein  K03406  Env  CnA  Methyl-accepting chemotaxis protein  K07233  Stress  CnA  Copper resistance protein B  K14266  Biosyn  CnA  FADH2 O2-dependent halogenase I [EC:1.14.14.7]  K08738  Energy  CnA, Wat  Cytochrome c  K00366  Energy  CnB  Ferredoxin-nitrite reductase [EC:1.7.7.1]  K00367  Energy  CnB  Ferredoxin-nitrate reductase [EC:1.7.7.2]  K01582  Biosyn  CnB  Lysine decarboxylase [EC:4.1.1.18]  K02006  Env  CnB  Cobalt/nickel transport system ATP-binding protein  K02074  Env  CnB  Zinc/manganese transport system ATP-binding protein  K02075  Env  CnB  Zinc/manganese transport system permease protein  K03321  Env  CnB  Sulfate permease  K03711  Stress  CnB  Fur family transcriptional regulator  K03969  Stress  CnB  Phage shock protein A  K05356  Biosyn  CnB  Trans-octaprenyltranstransferase [EC:2.5.1.11]; all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85]  K08226  Env  CnB  MFS transporter  K01915  Energy  Wat  Glutamine synthetase [EC:6.3.1.2]  KO  Categ  Group  Definition  K02044  Env  CN  Phosphonate transport system substrate-binding protein  K03799  Stress  CN  Heat shock protein HtpX [EC:3.4.24.-]  K03893  Stress  CN  Arsenical pump membrane protein  K04517  Biosyn  CN  Prephenate dehydrogenase [EC:1.3.1.12]  K04748  Energy  CN  Nitric oxide reductase NorQ protein; nitric-oxide reductase NorQ protein [EC:1.7.99.7]  K07334  Stress  CN  Proteic killer suppression protein  K00799  Stress  CnA  Glutathione S-transferase [EC:2.5.1.18]  K02014  Env  CnA  Iron complex outermembrane recepter protein  K03406  Env  CnA  Methyl-accepting chemotaxis protein  K07233  Stress  CnA  Copper resistance protein B  K14266  Biosyn  CnA  FADH2 O2-dependent halogenase I [EC:1.14.14.7]  K08738  Energy  CnA, Wat  Cytochrome c  K00366  Energy  CnB  Ferredoxin-nitrite reductase [EC:1.7.7.1]  K00367  Energy  CnB  Ferredoxin-nitrate reductase [EC:1.7.7.2]  K01582  Biosyn  CnB  Lysine decarboxylase [EC:4.1.1.18]  K02006  Env  CnB  Cobalt/nickel transport system ATP-binding protein  K02074  Env  CnB  Zinc/manganese transport system ATP-binding protein  K02075  Env  CnB  Zinc/manganese transport system permease protein  K03321  Env  CnB  Sulfate permease  K03711  Stress  CnB  Fur family transcriptional regulator  K03969  Stress  CnB  Phage shock protein A  K05356  Biosyn  CnB  Trans-octaprenyltranstransferase [EC:2.5.1.11]; all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85]  K08226  Env  CnB  MFS transporter  K01915  Energy  Wat  Glutamine synthetase [EC:6.3.1.2]  Categ: category of KO: env—environmental information processing; stress—stress; energy—energy metabolism; biosyn—biosynthesis of secondary metabolites. Group: biotope where the KO was most abundant: CN—both Cinachyrella morphospecies; CnA—Cinachyrella sp. A; CnB—Cinachyrella sp. B; Wat—water. Definition: definition of KO. View Large DISCUSSION A large number of Cinachyrella species have been recorded from the Indo-Pacific region under a large variety of names, with little distinction between them. Burton (1934) concluded that most tropical representatives should all be synomised with Cinachyrella australiensis, a species originally described from southern Australia. However, Burton only made the distinction based on the possession/absence of roughened or smooth micro-oxea of which he dismissed their importance in distinguishing different species. Other highly variable characters such as overall size, colour and dimension of the crater like pits (porocalises) failed to delineate species. Consequently, C. australiensis has been the most common name used for moon sponges present in the Indo-Pacific region bearing acanthose microxeas. In the present paper, we were able to examine type material of valid and non-valid Cinachyrella species including C. australiensis and compare it to our freshly collected Cinachyrella specimens from the anchialine systems in Raja Ampat, Indonesia. Moon sponges can be abundant in anchialine and coastal waters because they can survive in highly sediment impacted areas. We were able to distinguish different morphospecies based on size differences of the microscleres (sigmaspires and presence/absence of the acanthose microxeas). We could, however, not identify our samples to any of the examined type specimens and also not to the supposed intertropical C. australiensis. Cleary et al. (2013) showed that C. australiensis housed different bacterial communities inside two marine lakes and the surrounding sea. However, we re-examined the specimens and found that the specimens from marine lakes were actually a different species. Moreover, these specimens were identical to morphospecies Cinachyrella sp. A in the present paper. It is likely that isolation and limited gene flow between the lakes and the sea of this particular species could have led to a divergence into different lineages or species. Microbial communities appear to be highly conserved in different moon sponges (Chambers et al.2013; Cuvelier et al.2014) and could possibly be used to separate different species in addition to classical taxonomic characters. This was also the case in this study where bacterial and archaeal communities clustered according to morphospecies as opposed to lake. The archaeal communities of both Cinachyrella species were dominated by a single highly abundant and different OTU. Both of these OTUs were assigned to the family Cenarchaeaceae. Previous studies of other low microbial abundance (LMA) sponges have also shown them to be dominated by a single highly dominant cenarchaeaceal OTU (Polónia et al. 2014, 2015; Moitinho-Silva et al.2017). The bacterial communities of both sponge species were dominated by OTUs assigned to the phylum Proteobacteria, particularly the classes Gamma- and Alphaproteobacteria. Cinachyrella sp. B also had consistently high abundance of OTUs assigned to the class Synechococcophycideae. This is common for LMA sponges such as Cinachyrella spp., which have been shown to host less diverse microbial communities, mainly consisting of species belonging to the Proteobacteria and Cyanobacteria (Cleary et al.2013). LMA sponges, in general, have higher pumping rates, wider aquiferous canals and greater choanocyte chamber density than high microbial abundance (HMA) sponges indicative of a more heterotrophic feeding mode (Vacelet and Donadey 1977; Ribes et al.2012; Poppell et al.2014). LMA sponges also, in general, consume less oxygen and are smaller, more fragile, softer and more brittle than HMA sponges, which are often massive, firm and fleshy (Gloeckner et al.2014). HMA sponges are also believed to be more dependent on their microbial symbionts for energy acquisition than LMA sponges, which are believed to be more dependent on their high pumping rates and thus heterotrophy (Weisz et al.2007, Poppell et al.2014, Ribes et al.2015). In the bacterial community, a large number of OTUs were absent from seawater but present in both sponge species. All of these were mainly related to organisms found in other LMA sponge species belonging to the genera Tethya and Cinachyra. In addition to this, a number of OTUs were restricted to either sponge host. This included OTUs related to organisms found in sponges, but also a number found in other biotopes including octocorals and sediment. A large number of abundant OTUs had also relatively low sequence similarity to organisms in GenBank. The taxonomic threshold for species is 98.7%, genera 94.5% and families 86.5% (Yarza et al.2014). In this study, nine abundant OTUs had sequence similarities lower than 94.5%. This suggests that both Cinachyrella morphospecies house a number of abundant, novel bacterial genera that have not been found in other environmental samples present in the GenBank database. The proportion of potentially novel taxa is much higher than that found for other LMA and HMA sponge species sampled outside the marine lakes of Misool, where the sequence similarity for organisms in GenBank was >98% for all abundant OTUs (Cleary et al.2017). Although the results for both Bacteria and Archaea were clear and point to both morphospecies housing distinct microbial communities, it is important to remember that sample size was limited to only three specimens per morphospecies. Future research should attempt to increase sample size and compare samples of Cinachyrella from a greater number of habitats. The main betaproteobacterial OTU in Cinachyrella sp. A was assigned to the order EC94. Relatively little is known about this order, but previous studies have shown it to be abundant in various sponge species including Callyspongia sp. from Guam and several sponges from Korea (Jeong, Kim and Park 2013, 2015; Steinert et al.2016). In addition to EC94, Chromatiales and the gammaproteobacterial order HTCC2188 were also more abundant in Cinachyrella sp. A. The HTCC2188 order, which has been characterised as oligotrophic (Cho and Giovannoni 2004), has been recorded from a number of sponge species including the Cinachyrella species inhabiting marine lakes in Berau (Cleary et al.2013) and Cinachyra sp. and Coelocarteria singaporensis from Papua New Guinea (Morrow et al.2015). The Chromatiales, in turn, are important sulphur-oxidising organisms (Thomas et al.2014) that have also been found as symbionts in a number of sponge species including the LMA species Stylissa carteri and S. massa and the HMA species Xestospongia testudinaria and Hyrtios erectus (de Voogd et al.2015; Cleary et al.2015b). Specimens of both Cinachyrella morphospecies in the less connected lake M were characterised by a greater abundance of Synechococcales. Importantly, previous studies have shown that the relative abundance of Synechococcales in various sponge species is much higher under low pH conditions (Morrow et al.2015; Ribes et al.2016). For example, in an aquarium study with normal and low pH treatments, the relative abundance of Synechococcales in the sponge Dysidea avara increased in response to low pH conditions and proteobacterial abundance decreased. In the same experiment, another species, Agelas oroides, revealed a reduction in Chloroflexi abundance and an increase in alphaproteobacterial (mainly Rhodobacterales) abundance. A third species, Chondrosia reniformis, subjected to the same treatments only had a limited ability to acquire novel microbes and was severely affected by the low pH treatment (Ribes et al.2016). Ribes et al. (2016) suggested that the ability of Dysidea avara and Agelas oroides to horizontally acquire novel microbes left them relatively unaffected by experimental low pH manipulation, whereas C. reniformis that lacked this ability was severely affected. In Papua New Guinea, both the sponge species Cinachyra sp. and C. singaporensis increased in abundance at a low pH hydrothermal CO2 seep compared to a control site. Both species also showed a marked increase in the relative abundance of Cyanobacteria (mainly Synechococcales) (Morrow et al.2015). Morrow et al. (2015) suggested that the increase in photosynthetic Synechococcales provided the sponges with nutrition and enhanced their ability to grow under low pH conditions. The variation in composition exhibited by both Cinachyrella morphospecies in this study suggests that they possess a flexible, but still distinct, bacterial community that enables them to adapt to low pH conditions as found in the marine lakes of Misool (Becking et al.2011) by recruiting low pH-tolerant symbionts such as members of the Synechococcophycideae. In line with the differences in composition between both Cinachyrella morphospecies, there were also pronounced differences in predicted metagenomic gene content using PICRUSt. In addition to predicting metagenomic gene content, PICRUSt also provides a quality control using weighted Nearest Sequenced Taxon Index (NSTI) scores. NSTI was developed to evaluate the predictive accuracy of PICRUSt and calculates dissimilarity between reference genomes and the metagenome under study. Langille et al. (2013) showed that PICRUSt accuracy decreases as NSTI scores increase, but in their study still produced reliable results for a dataset of soil samples with a mean NSTI score of 0.17. All of our scores for sponge and water samples were well below this value thus indicating that our results can be considered reasonably reliable. The estimated accuracy provided by Langille et al. (2013) was, however, based on different environments and no sponge studies were included in their paper. It is possible that the relationship between genome representatives and OTUs could be different for sponge microbiomes. Although the sequence similarities of certain sponge-specific OTUs were relatively low to organisms in GenBank, these taxa largely belonged to groups such as Alpha- and Gammaproteobacteria that are very well represented in the greengenes database. Both Cinachyrella morphospecies were predicted to be enriched for a number of KOs involved in various pathways including pathways involved in stress response, environmental information processing and the energy metabolism. Stress response KOs included the heat shock protein (K03799), proteic killer suppression protein (K07334), nitric oxide reductase (K04748) and arsenical pump membrane protein (K03893). Arsenic is an ubiquitous environmental toxin. It is a substrate analogue of phosphate and as such is taken up via phosphate transporters (which were also predicted to be enriched in both Cinachyrella morphospecies) into the bacterial cell (Rosenberg, Gerdes and Chegwidden 1977). Arsenic detoxification occurs via the arsenical pump membrane protein, which catalyses the extrusion of arsenic from the bacterial cell (Meng, Liu and Rosen 2004). All living organisms have systems of arsenic detoxification. Enrichment of gene copies encoding for the arsenical pump membrane protein in both sponge species reflects the environmental amplitude of Cinachyrella species, which can be found in pristine and perturbed environments where they are often embedded in sediment (McDonald, Hooper and McGuinness 2002; Bell and Smith 2004; Fromont, Vanderklift and Kendrick 2006; de Voogd and Cleary 2008, 2009). In addition to both morphospecies being putatively enriched for selected KOs compared to water, there were also marked differences between the morphospecies. Cinachyrella sp. A was predicted to be enriched for KOs involved in stress response, namely K00799 (glutathione S-transferase) and K07233 (copper resistance), and environmental information processing, namely K02014 (iron complex recepter) and K03406 (methyl-accepting chemotaxis). Glutathione is a powerful antioxidant that can prevent damage to cells by stressors including free radicals, peroxides, lipid peroxides and heavy metals (Pompella et al.2003; Yadav 2010). K03406 is part of the two-component regulatory system, which is a stimulus-response coupling mechanism that enables organisms to respond to shifting environmental conditions. Cinachyrella sp. B, in turn, was predicted to be enriched for a number of KOs involved in stress response, environmental information processing, the energy metabolism and biosynthesis of other secondary metabolites. KOs involved in stress response that were predicted to be enriched in Cinachyrella sp. B included K03969 (phage shock protein) and K03711 (Fur family transcriptional regulator). The FUR (ferric uptake regulator) family of transcriptional regulators is involved in the regulation of iron and zinc metabolism through control by Fur and Zur proteins. Control of metal homeostasis is crucial to all living organisms. In addition to the above, FUR homologues control defence against peroxide stress and play a key role in microbial survival under adverse environmental conditions (Fillat 2014). Both sponge morphospecies were predicted to be selectively enriched for KOs involved in the biosynthesis of various secondary metabolites. Cinachyrella sp. A was putatively enriched for K14266, which is involved in the biosynthesis of Staurosporine. Staurosporine is an indolocarbazole, which inhibits protein kinases by preventing ATP binding to the kinase. It has a wide range of biological activities including antifungal and antibacterial activity (Rüegg and Burgess 1989). Cinachyrella sp. B, in turn, was predicted to be enriched for K05356, which is involved in Terpenoid backbone biosynthesis. Terpenoids are a highly diverse class of organic chemicals. They represent the largest group of natural products, 60% of which are terpenoids (Firn 2010). Terpene synthases have also been shown to be widely distributed in bacteria and represent a potentially important source for the discovery of novel natural products (Yamada et al.2015). Natural products previously found in Cinachyrella species include two fatty acids that had been unknown prior to their discovery in C. alloclada (Barnathan et al.1992), novel prototype galactins (Ueda et al.2013) and an alkaloid (Cinachyramine; Shimogawa et al.2006) isolated from Cinachyrella spp. in Japan. A novel phosphate-containing macrolide was also isolated from the sponge C. enigmatica, in Papua New Guinea. The compound called Enigmazole A represents a novel structural family of marine phosphomacrolides with significant antitumor activity (Oku et al.2010). The predicted enrichment for KOs involved in the biosynthesis of various secondary metabolites in this study suggests that both sponge species studied here may prove to be interesting sources for novel compounds with potentially important pharmaceutical and/or biotechnological properties. In summary, this study shows that two distinct Cinachyrella morphospecies inhabiting Papuan marine lakes housed distinct communities of Bacteria and Archaea. In addition to this, the predicted metagenomic gene content of both morphospecies differed. Specimens thus of the same morphospecies from different lakes were more similar to one another than different morphospecies within the same lake indicating that the specific traits of each morphospecies were more important in structuring bacterial and archaeal composition than environmental differences between lakes. Presumed environmental differences between lakes, however, did appear to have some effect on composition with specimens in the less connected and presumably more acidic lake, for example, having a greater percentage of sequences assigned to the cyanobacterial genus Synechococcus. These results highlight the potential of the sponge microbiome to be used as a means of separating species of the genus Cinachyrella and suggest that Cinachyrella and the closely related Paratetilla may be interesting subjects to study sponge–microbe evolution, in particular with respect to what traits are important in structuring microbial composition. The limited sample size, however, of this study makes it difficult to draw hard conclusions. Future studies would benefit from sampling more morphospecies across a greater range of habitats, inside and outside of marine lakes. SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. Acknowledgements We would like to thank Lisa Becking for collecting the specimens in the field and Ristek and LIPI, Indonesia for supporting the fieldwork. FUNDING This work was supported by European Funds through COMPETE [FCOMP-01-0124-FEDER-008657] and by National Funds through the Portuguese Foundation for Science and Technology (FCT) within the LESS CORAL project [PTDC/AAC-AMB/115304/2009]. ARMP was supported by a postdoctoral scholarship [SFRH/BPD/117563/2016] funded by FCT, Portugal (QREN-POPH—Type 4.1 – Advanced Training, subsidized by the European Social Fund and national funds MCTES). 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Prokaryote composition and predicted metagenomic content of two Cinachyrella Morphospecies and water from West Papuan Marine Lakes

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Abstract

Abstract Certain sponge species are difficult to identify using classical taxonomic characters, and other techniques are often necessary. Here we used 454-pyrosequencing of the 16S rRNA gene to investigate bacterial and archaeal communities of two distinct Cinachyrella morphospecies collected from two Indonesian marine lakes with differing degrees of connection to the surrounding sea. Our main goal was to assess whether these morphospecies hosted distinct bacterial and archaeal communities and to what extent these differed from those found in lake water. A recently developed bioinformatic tool (PICRUSt) was used to predict metagenomic gene content of the studied communities. Compositionally, sponge samples clustered according to morphospecies as opposed to marine lake indicating that each morphospecies hosted distinct bacterial and archaeal communities. There were, however, also differences in higher taxon abundance among lakes. In the less connected lake, for example, both Cinachyrella morphospecies had higher levels of the order Synechococcales. With respect to metabolic gene content, although there were pronounced differences in predicted enrichment between both morphospecies, both were putatively enriched for KOs involved in pathways related to stress response, energy metabolism, environmental information processing and the production of secondary metabolites compared to prokaryote communities in water. These morphospecies may thus prove to be interesting sources of novel compounds of potential pharmaceutical and/or biotechnological importance. Archaea, Bacteria, Porifera, Indonesia, Misool, PICRUSt INTRODUCTION Marine lakes are small bodies of landlocked seawater, often located within islands, that are isolated to varying degrees from the surrounding marine environment. At present, there are an estimated 200 marine lakes known to exist in the world, most of which are found in tropical and subtropical regions including parts of Indonesia, Vietnam and Palau (Becking et al.2011). Much of these lakes are believed to have formed following the last glacial maximum, which was ∼19 000 years before present (Smith et al.2011). In the 5000 years following this maximum, glacial melting led to a global rise in sea level, which flooded natural depressions leading to the formation of marine lakes, particularly in areas of karstic limestone (van Hengstum, Scott and Gröcke 2011). Marine lakes can be distinguished based on their connection to the open sea. Connections can vary from lakes with pronounced water exchange through large open tunnels, caves or channels to lakes with highly limited exchange through small cracks and fissures in the island bedrock. The degree of connection has been shown to have a profound effect on the lake environment and fauna present (Becking et al.2011). Lakes with large, open connections to the open sea tend to have environmental conditions in terms of temperature, salinity, pH and tidal amplitude that are similar to the open sea. These lakes also house organisms including hard corals that are similar to those found in the open sea. Marine lakes with limited connections to the open sea, in contrast, have been shown to have lower pH, lower salinities (through the influx of freshwater) and higher temperatures than the surrounding open sea (Becking et al.2011). Lakes with limited connectivity to the open sea are of particular interest because they represent island systems of marine water and represent natural laboratories to test theories of biodiversity, biogeography and evolution (Dawson et al.2009). The physical isolation of marine lakes has set the stage for rapidly evolving populations of numerous taxa including shrimps, sponges, mussels and jellyfish (Holthuis 1973; Dawson and Hamner 2005; Becking et al.2013, 2016). The current study focuses on sponges and seawater collected in two marine lakes in the Raja Ampat region of West Papua, Indonesia. The Raja Ampat region harbours some of the most diverse marine environments in the world (Mangubhai et al.2012). Located in the ‘coral triangle’ (Bellwood, Renema and Rosen 2012), it encompasses more than 15 000 islands over an area of ca. 40 000 km2. Both lakes sampled in this study are located in the Misool area of southern Raja Ampat. Much work has focused on the distinct flora and fauna of marine lakes, which includes numerous endemic species. In addition to the flora and fauna, prokaryotes form an important component of the lake ecosystem and may be present in the sediment, water and as symbionts of the flora and fauna. Cleary et al. (2013, 2015a, 2016) previously described the bacterioplankton and host-associated bacterial communities of sponges, mussels and jellyfish found in the marine lakes of Berau, Indonesia. prokaryote symbionts are a topic of particular interest due to their ecological importance and biotechnological potential, particularly in association with sponges (Taylor et al.2007). prokaryotes play crucial roles in key biogeochemical cycles and are a key link between dissolved organic carbon and the autotrophic-grazer food chain (Currie and Kalff 1984; Danger et al.2007). Within their host organisms, prokaryotes play important roles in nutrition, immunity, defence and reproduction (Reiswig 1975; Hurst and Werren 2001; Baumann 2005; Macdonald and Monteleone 2005; Scarborough, Ferrari and Godfray 2005). Sponges are one of the most studied hosts of prokaryote symbionts and are an excellent model to study host–prokaryote associations (Taylor et al.2007). Sponges are sedentary, filter-feeding organisms that use a single layer of flagellated cells to pump water through their bodies. They are a conspicuous component of rocky coasts, coral reefs and marine lakes (Bergquist 1978; Becking, Cleary and de Voogd 2013). Sponges can host very dense populations of prokaryotes, in certain instances comprising up to 40% of total sponge biomass (Vacelet and Donadey 1977) and have been a focus of interest due to the biotechnological and pharmaceutical potential of their (prokaryote derived) secondary metabolites (Blunt and Munro 1998; van Soest and Braekman 1999; Faulkner et al.2000; Hentschel et al.2002; Sipkema et al.2005; Taylor et al.2007). In this study, we assessed bacterial and archaeal communities of bacterial two morphospecies belonging to the genus Cinachyrella. Cinachyrella, together with the genus Paratetilla, are also known as moon sponges because of their globular morphology with crater-like depressions. These sponges are common tropical reef denizens, but it is difficult to visually identify species in the field, and generally requires microscopical analysis of the spicules. Recently, two distinct prokaryote communities were revealed in the Caribbean sponge Cinachyrella kuekenthali, which on closer examination turned out to be two different sponge species (Cuvelier et al.2014). A similar pattern of distinct bacterial communities distinguishing different Paratetilla species was observed in northern Australia (Chambers et al.2013). Cinachyrella australiensis, in particular, is common in coastal waters of the Indo-Pacific region, although several morphospecies are known to occur (de Voogd and Cleary 2008; Becking, Cleary and de Voogd 2013). Although it was concluded that most tropical representatives were actually part of a single intertropical species, namely C. australiensis, it is highly likely that many Cinachyrella species, hiding under this species umbrella, are actually valid species (e.g. C. lindgreni Lendenfeld 1903; C. porosa Lendenfeld 1888; C. providentiae Dendy 1922). Moon sponges are common in marine lakes in Indonesia, and in this study we compared the bacterial and archaeal communities of different specimens of the genus Cinachyrella collected from two different marine lakes from a remote region in eastern Indonesia to investigate whether we could reveal distinct bacterial and archaeal communities and link these to clear species. In addition to the above, we compared the bacterial and archaeal communities found in Cinachyrella sponges with those found in lake water and used a bioinformatic tool, PICRUSt, to predict metagenomic gene content (Langille et al.2013). MATERIAL AND METHODS Study site The equatorial location of Misool means that the main seasonal influence is driven by monsoons (Prentice and Hope 2007). Misool is most influenced by the southeast monsoon from May to October which is characterised by cooler sea surface temperatures (SSTs), persistent winds and strong ocean swell. The annual rainfall in Papua averages 2500–4500 mm with interannual variability in rainfall due to the El Niño Southern Oscillation (Prentice and Hope 2007). There are seasonal differences in SSTs with an average SST of 29.0°C (Mangubhai et al.2012). Data collection Samples of Cinachyrella spp. were collected by snorkeling from 13 to 18 September 2013 in Southeast Misool, Raja Ampat region, West Papua province in Indonesia. SE Misool is part of a marine protected area of 343 200 ha established in 2009 (KKPD Misool Timur-Selatan). Samples of Cinachyrella (Demospongiae: Tetractinellida: Tetillidae) were collected at a depth of 1–3 m from two marine lakes located in two different islands in the Southeast Misool region (Becking, de Leeuw and Vogler 2015). The salinity in the waters surrounding the islands in which both marine lakes are found varies from 33 to 35 ppt. The lakes, here called lake K and lake M due to the lack of formal names, are separated by 33 km they are referred to as lake 1 and 2 respectively by Becking, de Leeuw and Vogler (2015). Marine lake K (02° 13΄ 15″S 130° 27΄ 31″E) is located in the island Karawop; another lake, located 100 m from this lake, has been entirely converted into a fish aquaculture facility. The lake is 72 by 64 m at its widest point with a maximum depth of 4.5 m. There is a moderate connection to the sea with a tidal amplitude of 45 cm in lake K compared to 100 cm in the open sea and a temporal delay of ∼3 h. Temperature and salinity (measured in May 2011) were 30°C and 31.5 ppt respectively (Klei 2016). Marine lake M (01° 56΄ 16″S 130° 22΄ 28″) is located on the eastern tip of the island of Misool and is ∼200 by 80 m at its widest point with a maximum depth of 7.5 m. The tidal amplitude is only 35 cm compared to 100 cm in the surrounding sea with a temporal delay in tide of at least 3 h. Temperature and salinity were 31°C to 33°C and 29 ppt, respectively. The dissolved oxygen in lake M was 80% at the surface to 2 m depth but declined to 0% at 3 to 6 m depth (Klei 2016). The environmental parameters indicate that lake M is less connected to the sea than lake K. Although pH was not measured during the initial survey, less connected lakes tend to have lower pH levels, higher temperatures and lower salinities than better connected lakes (Becking et al.2011). Voucher specimens of all Cinachyrella specimens sampled have been deposited in the sponge collection of the Naturalis Biodiversity Center (RMNH POR.10495–10500). Sponge samples were separated into two distinct morphospecies referred to as Cinachyrella sp. A and Cinachyrella sp. B (Fig. 1) using classical taxonomic characters. The dimension and morphology of the silicious spicules were measured and compared with one another and with type material of various Cinachyrella species including C. australiensis Carter, 1886. Cinachyrella sp. B is characterised by the presence of acanthose microxeas (150–200 μm) and relatively large sigmaspires (18–25 μm) in addition to megascleres. Cinachyrella sp. B is similar to C. australiensis; however, this species has smaller sigmaspires and was described from southern Australia. Cinachyrella anatriaenilla Fernandez et al. 2017 was recently described from American Samoa; although this species has overlapping dimensions with Cinachyrella sp. B, the megascleres are much smaller in size and thus represents a different species. Morphotype Cinachyrella sp. A does not have acanthose microxeas and has very small sigmaspires (7–10 μm) and is probably closely related to C. albatridens Lendenfeld 1907 described from the deep sea near Chagos in the Indian Ocean and C. arabica from Oman. Three specimens each of Cinachyrella sp. A and Cinachyrella sp. B were collected from lakes K and M. Two specimens of Cinachyrella sp. A were collected from lake K and one from lake M. One specimen of Cinachyrella sp. B was collected from lake K and two were collected from lake M. Figure 1. View largeDownload slide (a). Cinachyrella sp. B sigmaspire (RMNH POR.10500); (b). Cinachyrella sp. A sigmaspire (RMNH POR.10498); (c) detail of acanthose microxea. Figure 1. View largeDownload slide (a). Cinachyrella sp. B sigmaspire (RMNH POR.10500); (b). Cinachyrella sp. A sigmaspire (RMNH POR.10498); (c) detail of acanthose microxea. Lake water samples were collected by filtering 1 l of seawater through a Millipore® White Isopore Membrane Filter (GTTP04700, 47 mm diameter, 0.22 μm pore size). Samples were stored in 96% EtOH. After sampling, tubes containing the samples were frozen or carried in ice during travel between fieldwork lodging and the Netherlands and Portugal, where the samples were stored at –80°C until processing. Three water samples were collected in total, namely, one from lake K and two from lake M. A summary of the sponge and water samples collected is presented in Table S1 (Supporting Information). DNA extraction and pyrosequencing Total community-DNA extraction and 16S rRNA gene barcoded pyrosequencing We isolated PCR-ready total community DNA (TC-DNA) from three water and six sponge (three Cinachyrella sp. A and three Cinachyrella sp. B) samples using the FastDNA® SPIN Kit (MP Biomedicals) following the manufacturer's instructions. Briefly, the membrane filter (water sample) and sponge samples were each cut into small pieces. The whole membrane filter and 500 mg of sponge were transferred to Lysing Matrix E tubes containing a mixture of ceramic and silica particles. The microbial cell lysis was performed in the FastPrep® Instrument (Q Biogene) for 80 s at the speed of 6.0. Extracted DNA was eluted into DNase/Pyrogen-Free Water to a final volume of 50 μl and stored at –20°C until use. For the archaeal 16S rRNA gene amplification, the first PCR amplification was performed using DNA with archaea-specific forward (ARC344f-mod) and reverse (Arch958R-mod) primers (Pires et al.2012). After a denaturation step at 94°C during 5 min, 30 thermal cycles of 1 min at 94°C, 1 min at 56°C and 1 min at 72°C were carried out followed by an extension step at 72°C for 7 min (Pires et al.2012). Using the amplicons of the archaeal 16S rRNA gene as template, the V3-V4 regions were amplified using the barcoded fusion forward (524F-10-ext: 5΄- TGYCAGCCGCCGCGGTAA -3΄; Pires et al.2012) and reverse (Arch958R-mod: 5΄-CCGGCGTTGAVTCCAATT -3΄; Pires et al.2012) primers. After 4-min denaturation at 94°C, 30 cycles of 94°C for 30 s, 50°C for 45 s and 68°C for 60s and a final extension at 68°C for 10 min were carried with GS 454 FLX Titanium chemistry, according to manufacturer's instructions (Roche, 454 Life Sciences, Brandford, CT, USA). For the bacterial 16S rRNA gene amplification, the first PCR amplification was performed from DNA using the F-27 and R-1494 primers (Gomes et al.2001). After a denaturation step at 94°C for 5 min, 25 thermal cycles of 45 s at 94°C, 45 s at 56°C and 1:30 min at 72°C were carried out followed by an extension step at 72°C for 10 min. Using the amplicons of the bacterial 16S rRNA gene as template, the V3-V4 region was amplified, with the barcoded fusion forward (V3: 5΄ -ACTCCTACGGGAGGCAG-3΄; Yu et al.2005) and reverse (V4: 5΄ -TACNVRRGTHTCTAATYC-3΄; Vaz_Moreira et al.2011) primers. After a denaturation step at 94°C during 4 min, 25 thermal cycles of 30 s at 94°C, 45 s at 44°C and 1 min at 68°C and a final extension at 68°C for 10 min (Cleary et al.2013) were carried out with GS 454 FLX Titanium chemistry, according to manufacturer's instructions (Roche, 454 Life Sciences, Brandford, CT, USA). A summary of the primers used is presented in Table S2 (Supporting Information). Barcoded pyrosequencing libraries were analysed using the QIIME (Quantitative Insights Into Microbial Ecology; Caporaso et al.2010) software package (http://www.qiime.org/; last visited 20 January 2014). In QIIME, separate fasta and qual files for Bacteria and Archaea were used as input for the split_libraries.py script. Default arguments were used (e.g. for the number of ambiguous bases and maximum length of homopolymer run; n = 6) except for the minimum sequence length, which was set at 218 bp for both the bacterial and archaeal data after removal of forward primers and barcodes; backward primers were removed using the ‘truncate only’ argument and a sliding window test of quality scores was enabled with a value of 50 as suggested in the QIIME description for the script. In addition to user-defined cut-offs, the split_libraries.py script performs several quality filtering steps (http://qiime.org/scripts/split_libraries.html). OTUs were selected using UPARSE with usearch7 (Edgar 2013). The UPARSE sequence analysis tool (Edgar 2013) provides clustering, chimera checking and quality filtering on de-multiplexed sequences. Chimera checking was performed using the UCHIME algorithm (Edgar et al.2011). The quality filtering, as implemented in usearch7, filters noisy reads and preliminary results suggest that it gives results comparable to other denoisers such as AmpliconNoise, but is much less computationally expensive (http://drive5.com/usearch/features.html; last visited 20 January 2014). First, reads were filtered with the -fastq_filter command and the following arguments -fastq_trunclen 250 -fastq_maxee 0.5 -fastq_truncqual 15. Sequences were then dereplicated and sorted using the -derep_fulllength and -sortbysize commands. OTU clustering was performed using the -cluster_otus command. An additional chimera check was subsequently applied using the -uchime_ref command with the gold.fa database (http://drive5.com/uchime/gold.fa). AWK scripts were then used to convert the OTU files to the QIIME format. In QIIME, representative sequences were selected using the pick_rep_set.py script in QIIME using the ‘most_abundant’ method. Taxonomy was assigned to reference sequences of OTUs using default arguments in the assign_taxonomy.py script in QIIME with the rdp method (Wang et al.2007). In the assign_taxonomy.py function, we used a fasta file containing reference sequences from the Greengenes 13_8 release and the rdp classifier method. We used a modified version of the taxonomy file supplied with the Greengenes 13_8 release to map sequences to the assigned taxonomy. Finally, we used the make_otu_table.py script in QIIME to generate a square matrix of OTUs × samples. This was subsequently used as input for further analyses using the R package (R Core Team 2013). Sequence identifiers of closely related taxa of numerically dominant OTUs for Archaea (≥10 sequences) and Bacteria (≥200 sequences) were downloaded using the NCBI Basic Local Alignment Search Tool (BLAST) command line ‘blastn’ tool with the -db argument set to nt (Zhang et al.2000). The DNA sequences generated in this study can be downloaded from the NCBI SRA: SRP069346. We used PICRUSt (Langille et al.2013), a bioinformatics tool that uses marker genes, in this case 16S rRNA, to predict metagenome gene functional content. A detailed description of these methods has been published previously (Langille et al.2013; Polónia et al.2015; Cleary et al.2015b). In PICRUSt, we used the pick_closed_reference_otus.py script with reverse strand matching enabled and a 97% cut-off threshold (genus level). As optional argument in the pick_closed_reference_otus.py, we included the Greengenes 13_8 representative set of reference sequences against which to create an OTU table. We used the normalize_by_copy_number.py script to normalise the OTU table by marker gene copy number. The normalised data was used as input for the predict_metagenomes.py script, which produces a table of metagenome functional predictions for a given OTU table. Output of the predict_metagenomes.py script consists of a table of gene (or functional) counts assigned to KEGG orthologues (KOs). KOs are sets of orthologous (high-sequence similarity and consistent phylogenetic position; Smit and Mushegian 2000) biosynthesis genes that have been shown to catalyse the same reaction within the same pathway and are thus functionally correlated (Aoki-Kinoshita and Kanehisa 2009). These orthologue groups are graphically represented as nodes in KEGG individual pathways (Kanehisa and Goto 2000). Note that because of functional overlap, some KOs can be represented in more than one pathway. Note also that the PICRUSt results, as presented here, are predictive and thus provide information on potential enrichment and putative function as opposed to measuring actual gene presence/expression and function. In this study, we selected 24 KOs that had a high relative abundance and were involved in the nitrogen metabolism, environmental information processing (transporters), stress response and the biosynthesis of secondary metabolites. A number of these KOs were predicted to be enriched in other sponge species (de Voogd et al.2015; Cleary et al.2015b). Previous studies have shown that sponges and their microbial symbionts play important roles in the nitrogen metabolism and the biosynthesis of compounds of biotechnological and pharmaceutical interest (Wilkinson and Fay 1979; Sipkema et al.2005; Taylor et al.2007). Adaptation to potentially stressful environments, such as low pH marine lakes, furthermore requires mechanisms to cope with and adapt to stress. Statistical analyses Tables containing the presence and abundance per sample of all bacterial and archaeal OTUs were imported into R using the read.csv() function. Plant organelles, mitochondria, sequences not classified as Bacteria (or Archaea) and sequences not assigned to a domain or phylum were removed prior to statistical analysis. We used a self-written function in R (Gomes et al.2010) to estimate rarefied OTU richness for each sample. Care, however, should be taken in the interpretation of richness estimates based on sequence data given the prevalence of sequencing errors (Edgar 2013). OTU matrices of Bacteria and Archaea were loge (x + 1) transformed (in order to normalise the distribution of the data), and distance matrices were constructed using the Bray-Curtis index with the vegdist() function in the VEGAN package (Oksanen et al.2009) in R. The Bray-Curtis index is one of the most frequently applied (dis)similarity indices used in ecology (Legendre and Gallagher 2001; Cleary 2003; Polónia et al.2015). Variation in OTU composition among biotopes (Cinachyrella sp. A and B and water samples) was assessed with principal coordinates analysis using the cmdscale() function in R with the Bray-Curtis distance matrix as input. Variation in bacterial and archaeal composition among biotopes (Cinachyrella sp. A, Cinachyrella sp. B and water) was tested for significance using the adonis() function in vegan. In the adonis analysis, the Bray-Curtis distance matrix of species composition was the response variable with biotope as independent variable; the strata (block) argument was set to site (marine lake) so that randomisations were constrained to occur within each site and not across both sites. Detailed descriptions of the functions used here can be found in R (e.g. ?cmdscale) and online in reference manuals (http://cran.r-project.org/web/packages/vegan/index.html; 29 May 2015). RESULTS The sequencing effort yielded 58 636 bacterial and 38 796 archaeal sequences, which were assigned to 1134 bacterial and 87 archaeal OTUs after quality control. For Bacteria, we recorded 13 phyla, 22 classes and 35 orders in Cinachyrella sp. A compared to 26 phyla, 53 classes and 72 orders in Cinachyrella sp. B. In water, we recorded 23 phyla, 42 classes and 63 orders. In line with this, OTU richness was substantially higher in Cinachyrella sp. B and water samples than Cinachyrella sp. A samples (Fig. S1, Supporting Information). The most abundant phylum overall was Proteobacteria where mean relative abundance ranged from 53.8 ± 0.2% in Cinachyrella sp. B to 72.6 ± 5.0% in water and 91.4 ± 9.2% in Cinachyrella sp. A (Fig. 2). The percentage of Cyanobacteria ranged from 4.4 ± 0.8% in water to 6.1 ± 8.0% in Cinachyrella sp. A and 35.7 ± 5.4% in Cinachyrella sp. B. Actinobacterial abundance was also higher in Cinachyrella sp. B (6.8 ± 1.9%) than Cinachyrella sp. A (1.3 ± 0.9%) or water (1.5 ± 0.8%). There were also pronounced differences at the class and order level between Cinachyrella sp. A and Cinachyrella sp. B. Cinachyrella sp. A had a greater abundance of Alphaproteobacteria (34.9 ± 7.2% versus 4.4 ± 2.8%) (Fig. S2, Supporting Information), the gammproteobacterial order HTCC2188 (4.3 ± 2.5% versus 0.3 ± 0.3%) and the betaproteobacterial order EC94 (5.1 ± 2.2% versus 0.0 ± 0.0%) than Cinachyrella sp. B. Cinachyrella sp. B, in turn, had a much greater abundance of the cyanobacterial order Synechococcales than Cinachyrella sp. A (35.3 ± 0.9% versus 6.0 ± 8.0%) (Fig. S3, Supporting Information). There were also differences among lakes with Cinachyrella specimens of both morphospecies having a much higher abundance of Synechococcophycideae in lake M than lake K (Fig. S2, Supporting Information). Figure 2. View largeDownload slide Relative abundance of the most abundant bacterial phyla in Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. Figure 2. View largeDownload slide Relative abundance of the most abundant bacterial phyla in Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. For Archaea, we recorded two phyla, six classes and seven orders in Cinachyrella sp. A compared to two phyla, seven classes and eight orders in Cinachyrella sp. B. In water, we recorded two phyla, five classes and seven orders. Archaeal OTU richness was somewhat higher in water than both Cinachyrella morphospecies but there was considerable overlap among samples from different biotopes (Fig. S4, Supporting Information). The two most abundant phyla were the Euryarchaeota and Crenarchaeota. The Euryarchaeota was far more abundant in water samples (99.8 ± 0.1%) than in Cinachyrella sp. A (4.2 ± 3.9%) or Cinachyrella sp. B (5.8 ± 2.7%). In contrast, Crenarchaeota dominated both Cinachyrella sp. A (95.8 ± 3.9%) and Cinachyrella sp. B (94.2 ± 2.7%) samples, but only made up 0.2 ± 0.1% of the archaeal community in water samples. Importance of biotopes in structuring composition There was a significant difference in bacterial composition among biotopes (Adonis: F2,6 = 14.44, P = 0.002, R2 = 0.828). Variation among biotopes thus explained almost 83% of the variation in bacterial composition. The main axis (axis 1) separated samples of water from samples of both Cinachyrella morphospecies (Fig. 3a). The second axis (axis 2) separated samples of both Cinachyrella morphospecies. There were a number of OTUs that were abundant in both Cinachyrella morphospecies that were absent or rare in water including OTUs 2, 3, 16, 20, 68, 87 and 3145 (Table 1). OTU 3 was the most abundant OTU overall with 12 219 sequences and was related (sequence similarity = 96.22%) to an organism obtained from the sponge Plakortis halichondrioides in the Bahamas. All of the OTUs restricted to both Cinachyrella morphospecies were, in fact, closely related to organisms obtained from other sponges with sequence similarities ranging from 91.92% to 98.58% (Table 1). In addition to the above, both morphospecies housed OTUs restricted to those morphospecies. These included OTUs 5, 18, 23, 59 and 229 in Cinachyrella sp. A and OTUs 6, 7, 15, 42, 53, 92 and 2324 in Cinachyrella sp. B. These OTUs were related to organisms obtained from sponges, an octocoral and sediment with sequence similarities ranging from 90.33% to 98.82%. In contrast to the above, OTUs found mainly in water or in both water and sponge samples had high sequence similarities (>99%) with organisms found mainly in seawater. Figure 3. View largeDownload slide Ordination showing the first two axes of the principal coordinates analysis of (a) bacterial OTU composition and (b) archaeal OTU composition. Light grey symbols represent operational taxonomic unit (OTU) scores with the symbol size representing their abundance (number of sequence reads). Figure 3. View largeDownload slide Ordination showing the first two axes of the principal coordinates analysis of (a) bacterial OTU composition and (b) archaeal OTU composition. Light grey symbols represent operational taxonomic unit (OTU) scores with the symbol size representing their abundance (number of sequence reads). Table 1. List of abundant (≥200 sequence reads) bacterial OTUs and closely related organisms identified using BLAST search. OTU  Sum  Group  Green  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  87  6172  Cin  404788  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  806637511  99.76  Lake sediment, Japan:Shizuoka, Hamamatsu, Lake Sanaru  2  6167  Cin  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  28557443  99.76  Seawater  3  12 219  Cina  4483818  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  16  1862  Cina  4360584  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  400269037  94.41  Sponge: Cymbastella coralliophila, Australia: Orpheus Island, Great Barrier Reef  20  1820  Cina  4376233  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  168997213  91.92  Sponge: Tethya californiana, USA: California, Monterey  3145  317  Cina  4483818  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  68  216  Cina  545247  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269153  98.58  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  5  3416  CinAa  1106953  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269119  95.25  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  23  830  CinAa  744558  Proteobacteria  Gammaproteobacteria  HTCC2188  HTCC2089  Unclassified  350542298  93.51  Marine surface sediment, South Korea: Ulleung Basin  18  826  CinAa  1148138  Proteobacteria  Betaproteobacteria  EC94  Unclassified  Unclassified  290575709  93.05  Sponge, the Red sea  229  518  CinAa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  91.65  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  59  226  CinAa  1111200  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  134290589  91.33  Sponge: Xestospongia muta, USA: Key Largo, FL  7  2930  CinBa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  90.33  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  6  2685  CinBa  4447845  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  148732264  98.82  Site S25 near Coco's Island, Costa Rica  15  1260  CinBa  4303805  Actinobacteria  Acidimicrobiia  Acidimicrobiales  Unclassified  Unclassified  402169730  96.68  Intertidal surface sediment, China  42  306  CinBa  2369769  Proteobacteria  Deltaproteobacteria  Unclassified  Unclassified  Unclassified  400269180  97.54  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  53  284  CinBa  499915  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  164653427  91.19  Sponge: Haliclona simulans, Ireland  92  241  CinBa  1686480  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269113  94.7  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  2324  209  CinBa  4425801  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  441084656  90.91  Sponge: Dysidea avara, Mediterranean Sea: Medas Islands  13  859  Water  1109476  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  592749174  100  Shrimp pond, China  48  438  Water  316815  Proteobacteria  Alphaproteobacteria  Rickettsiales  Pelagibacteraceae  Unclassified  909637544  100  Marine sediment, shallow hydrothermal vent, Hot Lake, high temperature site, Italy:Panarea, Tyrrhenian Sea  28  224  Water  621743  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  530540087  100  Sponge: Haliclona sp., India: Gulf of Mannar  67  217  Water  4306324  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  451775277  100  Coastal tropical surface seawater, Pacific Ocean  12  2212  Wide  4441050  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  429144095  100  Estuary in middle of river, China: Jiulong River  24  864  Wide  346431  Proteobacteria  Alphaproteobacteria  Rhodobacterales  Rhodobacteraceae  Unclassified  831181535  100  Seawater, coral reef, Japan: Wakayama, Kushimoto  38  435  Wide  263082  Actinobacteria  Acidimicrobiia  Acidimicrobiales  C111  Unclassified  592748882  100  Shrimp pond, China  36  410  Wide  123788  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  703891050  100  Seawater east of Marquesas Island in the South Pacific Ocean  799  371  Wide  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  891166367  100  Seawater, Japan: East China Sea  25  349  Wide  216695  Proteobacteria  Gammaproteobacteria  HTCC2188  Unclassified  Unclassified  388955260  100  Seawater; next to dolphin Y, USA: San Diego, CA  60  322  Wide  4481997  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  429143959  99.55  Estuary in middle of river, China: Jiulong River  171  277  Wide  118817  Bacteroidetes  Flavobacteriia  Flavobacteriales  Flavobacteriaceae  Unclassified  817254067  99.76  Adriatic bottom seawater, Italy  121  215  Wide  267884  Proteobacteria  Gammaproteobacteria  Alteromonadales  OM60  Unclassified  592748867  100  Shrimp pond, China  561  206  Wide  538178  Proteobacteria  Alphaproteobacteria  Rhodospirillales  Rhodospirillaceae  Unclassified  440547316  100  Seawater, China: Zhanjiang Bay  OTU  Sum  Group  Green  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  87  6172  Cin  404788  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  806637511  99.76  Lake sediment, Japan:Shizuoka, Hamamatsu, Lake Sanaru  2  6167  Cin  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  28557443  99.76  Seawater  3  12 219  Cina  4483818  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  16  1862  Cina  4360584  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  400269037  94.41  Sponge: Cymbastella coralliophila, Australia: Orpheus Island, Great Barrier Reef  20  1820  Cina  4376233  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  168997213  91.92  Sponge: Tethya californiana, USA: California, Monterey  3145  317  Cina  4483818  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  745791420  96.22  Sponge: Plakortis halichondrioides, Bahamas  68  216  Cina  545247  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269153  98.58  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  5  3416  CinAa  1106953  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269119  95.25  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  23  830  CinAa  744558  Proteobacteria  Gammaproteobacteria  HTCC2188  HTCC2089  Unclassified  350542298  93.51  Marine surface sediment, South Korea: Ulleung Basin  18  826  CinAa  1148138  Proteobacteria  Betaproteobacteria  EC94  Unclassified  Unclassified  290575709  93.05  Sponge, the Red sea  229  518  CinAa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  91.65  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  59  226  CinAa  1111200  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  134290589  91.33  Sponge: Xestospongia muta, USA: Key Largo, FL  7  2930  CinBa  991785  Proteobacteria  Gammaproteobacteria  Unclassified  Unclassified  Unclassified  152963946  90.33  Octocoral: Eunicea fusca, USA: Florida, Hillsboro Ledge  6  2685  CinBa  4447845  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  148732264  98.82  Site S25 near Coco's Island, Costa Rica  15  1260  CinBa  4303805  Actinobacteria  Acidimicrobiia  Acidimicrobiales  Unclassified  Unclassified  402169730  96.68  Intertidal surface sediment, China  42  306  CinBa  2369769  Proteobacteria  Deltaproteobacteria  Unclassified  Unclassified  Unclassified  400269180  97.54  Sponge: Cinachyra sp., Australia: Orpheus Island, Great Barrier Reef  53  284  CinBa  499915  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  164653427  91.19  Sponge: Haliclona simulans, Ireland  92  241  CinBa  1686480  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  400269113  94.7  Sponge: Coelocarteria singaporensis, Australia: Orpheus Island, Great Barrier Reef  2324  209  CinBa  4425801  Proteobacteria  Unclassified  Unclassified  Unclassified  Unclassified  441084656  90.91  Sponge: Dysidea avara, Mediterranean Sea: Medas Islands  13  859  Water  1109476  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  592749174  100  Shrimp pond, China  48  438  Water  316815  Proteobacteria  Alphaproteobacteria  Rickettsiales  Pelagibacteraceae  Unclassified  909637544  100  Marine sediment, shallow hydrothermal vent, Hot Lake, high temperature site, Italy:Panarea, Tyrrhenian Sea  28  224  Water  621743  Bacteroidetes  Flavobacteriia  Flavobacteriales  Cryomorphaceae  Unclassified  530540087  100  Sponge: Haliclona sp., India: Gulf of Mannar  67  217  Water  4306324  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  451775277  100  Coastal tropical surface seawater, Pacific Ocean  12  2212  Wide  4441050  Proteobacteria  Gammaproteobacteria  Chromatiales  Unclassified  Unclassified  429144095  100  Estuary in middle of river, China: Jiulong River  24  864  Wide  346431  Proteobacteria  Alphaproteobacteria  Rhodobacterales  Rhodobacteraceae  Unclassified  831181535  100  Seawater, coral reef, Japan: Wakayama, Kushimoto  38  435  Wide  263082  Actinobacteria  Acidimicrobiia  Acidimicrobiales  C111  Unclassified  592748882  100  Shrimp pond, China  36  410  Wide  123788  Proteobacteria  Alphaproteobacteria  Unclassified  Unclassified  Unclassified  703891050  100  Seawater east of Marquesas Island in the South Pacific Ocean  799  371  Wide  557211  Cyanobacteria  Synechococcophycideae  Synechococcales  Synechococcaceae  Synechococcus  891166367  100  Seawater, Japan: East China Sea  25  349  Wide  216695  Proteobacteria  Gammaproteobacteria  HTCC2188  Unclassified  Unclassified  388955260  100  Seawater; next to dolphin Y, USA: San Diego, CA  60  322  Wide  4481997  Proteobacteria  Gammaproteobacteria  Oceanospirillales  Unclassified  Unclassified  429143959  99.55  Estuary in middle of river, China: Jiulong River  171  277  Wide  118817  Bacteroidetes  Flavobacteriia  Flavobacteriales  Flavobacteriaceae  Unclassified  817254067  99.76  Adriatic bottom seawater, Italy  121  215  Wide  267884  Proteobacteria  Gammaproteobacteria  Alteromonadales  OM60  Unclassified  592748867  100  Shrimp pond, China  561  206  Wide  538178  Proteobacteria  Alphaproteobacteria  Rhodospirillales  Rhodospirillaceae  Unclassified  440547316  100  Seawater, China: Zhanjiang Bay  OTU: OTU number; sum: number of sequence reads; group: biotope or biotopes where the OTUs were mainly found (Cin: both Cinachyrella morphospecies; CnA: Cinachyrella sp. A; CnB: Cinachyrella sp. B; wide: found in all biotopes; superscript a: an OTU only present in a particular biotope); green: Greengenes reference number; GI: Genbank identification numbers of closely related organisms identified using BLAST; Seq: sequence similarity of these organisms with our representative OTU sequences; source: isolation source of organisms identified using BLAST. View Large As with Bacteria, there was a significant difference in archaeal composition among biotopes (Adonis: F2,6 = 9.91, P = 0.004, R2 = 0.768). The ordination of Archaea also closely resembled that of Bacteria with the main axis separating samples of water from samples of both Cinachyrella morphospecies and the second axis separating samples of both Cinachyrella morphospecies. Both Cinachyrella morphospecies were distinguished by the presence of two highly abundant crenarchaeotal OTUs, one restricted to each morphospecies (Fig. 3b and Table 2). One of these OTUs, found mainly in Cinachyrella sp. A, was related to an organism obtained from the sponge Geodia barretti in Norway with a sequence similarity of 96.74% while the other OTU, found mainly in Cinachyrella sp. B, had 96.04% sequence similarity with an organism obtained from marine sediment. Table 2. List of abundant ( ≥ 10 sequence reads) archaeal OTUs and closely related organisms identified using BLAST search. OTU  Green  Sum  Group  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  1  553746  13 287  CnA  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.74  Sponge: Geodia barretti, Norway  586  324034  11  CnAa  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.51  Sponge: Geodia barretti, Norway  439  553746  11 854  CnB  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  525332479  96.04  Marine sediment  8  4462888  563  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546629  100  Coral: Montastraea annularis complex, Virgin Islands  10  4369009  32  Wide  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Nitrosopumilus  757399660  100  Transition zone between Pearl River estuary and coastal South China Sea  14  104677  76  Wide  Crenarchaeota  Thaumarchaeota  Nitrososphaerales  Nitrososphaeraceae  Candidatus Nitrososphaera  298105138  100  Sponge: Halichondria oshoro  56  4462887  30  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  83416103  99.3  Sponge: Axechina raspailoides NTM Z4460  2  104364  4683  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  100  Bacterioplankton 5m, North Atlantic Ocean  5  4428688  3976  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  100  Surface seawater  6  112246  1106  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933392  100  Surface sea water, South Pacific Ocean  7  147038  204  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  757399666  100  Transition zone between Pearl River estuary and coastal South China Sea  11  147038  534  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  597709959  99.53  Carbonate chimney in Prony Hydrothermal field, New Caledonia  17  2380278  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159153  100  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  24  4462887  178  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394999593  97.2  Seawater, Pacific Ocean: near central Chile  27  110525  18  Water  Euryarchaeota  Thermoplasmata  E2  Marine group III  Unassigned  394999409  100  Seawater, Pacific Ocean: near central Chile  38  2781721  63  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546727  100  Coral Diploria strigosa, Virgin Islands  301  4462887  471  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159150  99.77  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  344  4462887  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  759  4462887  1377  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  100  Surface sea water, South Pacific Ocean  872  4462887  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  993  4428688  28  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  99.77  Surface seawater  1050  4428688  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394998924  99.77  Seawater, Pacific Ocean: near Peru  853  104364  15  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  990  104364  14  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  OTU  Green  Sum  Group  Phylum  Class  Order  Family  Genus  GI  Seq (%)  Source  1  553746  13 287  CnA  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.74  Sponge: Geodia barretti, Norway  586  324034  11  CnAa  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  379771585  96.51  Sponge: Geodia barretti, Norway  439  553746  11 854  CnB  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Unassigned  525332479  96.04  Marine sediment  8  4462888  563  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546629  100  Coral: Montastraea annularis complex, Virgin Islands  10  4369009  32  Wide  Crenarchaeota  Thaumarchaeota  Cenarchaeales  Cenarchaeaceae  Nitrosopumilus  757399660  100  Transition zone between Pearl River estuary and coastal South China Sea  14  104677  76  Wide  Crenarchaeota  Thaumarchaeota  Nitrososphaerales  Nitrososphaeraceae  Candidatus Nitrososphaera  298105138  100  Sponge: Halichondria oshoro  56  4462887  30  Wide  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  83416103  99.3  Sponge: Axechina raspailoides NTM Z4460  2  104364  4683  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  100  Bacterioplankton 5m, North Atlantic Ocean  5  4428688  3976  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  100  Surface seawater  6  112246  1106  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933392  100  Surface sea water, South Pacific Ocean  7  147038  204  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  757399666  100  Transition zone between Pearl River estuary and coastal South China Sea  11  147038  534  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  597709959  99.53  Carbonate chimney in Prony Hydrothermal field, New Caledonia  17  2380278  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159153  100  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  24  4462887  178  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394999593  97.2  Seawater, Pacific Ocean: near central Chile  27  110525  18  Water  Euryarchaeota  Thermoplasmata  E2  Marine group III  Unassigned  394999409  100  Seawater, Pacific Ocean: near central Chile  38  2781721  63  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  39546727  100  Coral Diploria strigosa, Virgin Islands  301  4462887  471  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  321159150  99.77  Hydrothermal vent plume, Japan:Okinawa, off Taketomi Island  344  4462887  73  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  759  4462887  1377  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  100  Surface sea water, South Pacific Ocean  872  4462887  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  383933309  99.77  Surface sea water, South Pacific Ocean  993  4428688  28  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  662721932  99.77  Surface seawater  1050  4428688  13  Water  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  394998924  99.77  Seawater, Pacific Ocean: near Peru  853  104364  15  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  990  104364  14  Watera  Euryarchaeota  Thermoplasmata  E2  Marine group II  Unassigned  160213227  99.77  Bacterioplankton 5m, North Atlantic Ocean  OTU: OTU number; Green: Greengenes reference number; sum: number of sequence reads; group: biotope or biotopes where the OTUs were mainly found (CnA: Cinachyrella sp. A; CnB: Cinachyrella sp. B; wide: found in all biotopes; superscript a: an OTU only present in a particular biotope); GI: Genbank identification numbers of closely related organisms identified using BLAST; Seq: sequence similarity of these organisms with our representative OTU sequences; source: isolation source of organisms identified using BLAST. View Large Predictive metagenome analysis For Bacteria, mean (and standard deviation) NSTI values for the sampled biotopes were 0.120 (0.008) for Cinachyrella sp. A, 0.075 (0.017) for Cinachyrella sp. B and 0.122 (0.016) for water. KOs that were putatively enriched for both Cinachyrella morphospecies compared to water included K04517, K04748, K02044 (phosphonate transport), K03799 (heat shock), K03893 (arsenical pump) and K07334 (proteic killer suppression protein). KOs that were predicted to be enriched for Cinachyrella sp. A compared to Cinachyrella sp. B and water were K14266 (FADH2 O2-dependent halogenase), K02014 (iron complex recepter), K03406 (methyl-accepting chemotaxis), K00799 (glutathione) and K07233 (copper resistance) (Fig. 4 and Table 3). KOs that were predicted to be enriched for Cinachyrella sp. B compared to Cinachyrella sp. A and water included K01582 (lysine decarboxylase), K05356 (trans-octaprenyltranstransferase), K00366 (ferredoxin-nitrite reductase), K00367 (ferredoxin-nitrate reductase), K02006 (cobalt/nickel transport), K02074 (zinc/manganese transport), K02075 (zinc/manganese transport), K03321 (sulfate permease), K08226 (MFS transporter), K03711 (Fur family transcriptional regulator) and K03969 (phage shock protein A). Results for KO enrichment based on archaeal data are shown in Fig. S5 (Supporting Information). For the selected KOs based on archaeal data, there were pronounced differences between the Cinachyrella morphospecies and water, but relatively little difference between both Cinachyrella morphospecies. Figure 4. View largeDownload slide Mean predicted relative gene count abundance for selected KEGG orthologs based on bacterial data for samples from Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. (a) K01582: lysine decarboxylase [EC:4.1.1.18], (b) K04517: prephenate dehydrogenase [EC:1.3.1.12], (c) K05356: all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85], (d) K14266: tryptophan halogenase [EC:1.14.19.9], (e) K00366: ferredoxin-nitrite reductase [EC:1.7.7.1], (f) K00367: ferredoxin-nitrate reductase [EC:1.7.7.2], (g) K01915: glutamine synthetase [EC:6.3.1.2], (h) K04748: nitric oxide reductase NorQ protein [EC:1.14.19.9], (i) K08738: cytochrome c, (j) K02006: cobalt/nickel transport system ATP-binding protein, (k) K02014: iron complex outermembrane recepter protein, (l) K02044: phosphonate transport system substrate-binding protein, (m) K02074: zinc/manganese transport system ATP-binding protein, (n) K02075: zinc/manganese transport system permease protein, (o) K03321: sulfate permease, (p) K03406: methyl-accepting chemotaxis protein, (q) K08226: MFS transporter, (r) K00799: glutathione S-transferase [EC:2.5.1.18], (s) K03711: Fur family transcriptional regulator, (t) K03799: heat shock protein HtpX [EC:3.4.24.-], (u) K03893: arsenical pump membrane protein, (v) K03969: phage shock protein A, (w) K07233: copper resistance protein B, and (x) K07334: proteic killer suppression protein. Figure 4. View largeDownload slide Mean predicted relative gene count abundance for selected KEGG orthologs based on bacterial data for samples from Cinachyrella sp. A (CnA), Cinachyrella sp. B (CnB) and water (Wat) in lakes K and M. (a) K01582: lysine decarboxylase [EC:4.1.1.18], (b) K04517: prephenate dehydrogenase [EC:1.3.1.12], (c) K05356: all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85], (d) K14266: tryptophan halogenase [EC:1.14.19.9], (e) K00366: ferredoxin-nitrite reductase [EC:1.7.7.1], (f) K00367: ferredoxin-nitrate reductase [EC:1.7.7.2], (g) K01915: glutamine synthetase [EC:6.3.1.2], (h) K04748: nitric oxide reductase NorQ protein [EC:1.14.19.9], (i) K08738: cytochrome c, (j) K02006: cobalt/nickel transport system ATP-binding protein, (k) K02014: iron complex outermembrane recepter protein, (l) K02044: phosphonate transport system substrate-binding protein, (m) K02074: zinc/manganese transport system ATP-binding protein, (n) K02075: zinc/manganese transport system permease protein, (o) K03321: sulfate permease, (p) K03406: methyl-accepting chemotaxis protein, (q) K08226: MFS transporter, (r) K00799: glutathione S-transferase [EC:2.5.1.18], (s) K03711: Fur family transcriptional regulator, (t) K03799: heat shock protein HtpX [EC:3.4.24.-], (u) K03893: arsenical pump membrane protein, (v) K03969: phage shock protein A, (w) K07233: copper resistance protein B, and (x) K07334: proteic killer suppression protein. Table 3. KEGG orthologues (KO) shown in Fig. 4. KO  Categ  Group  Definition  K02044  Env  CN  Phosphonate transport system substrate-binding protein  K03799  Stress  CN  Heat shock protein HtpX [EC:3.4.24.-]  K03893  Stress  CN  Arsenical pump membrane protein  K04517  Biosyn  CN  Prephenate dehydrogenase [EC:1.3.1.12]  K04748  Energy  CN  Nitric oxide reductase NorQ protein; nitric-oxide reductase NorQ protein [EC:1.7.99.7]  K07334  Stress  CN  Proteic killer suppression protein  K00799  Stress  CnA  Glutathione S-transferase [EC:2.5.1.18]  K02014  Env  CnA  Iron complex outermembrane recepter protein  K03406  Env  CnA  Methyl-accepting chemotaxis protein  K07233  Stress  CnA  Copper resistance protein B  K14266  Biosyn  CnA  FADH2 O2-dependent halogenase I [EC:1.14.14.7]  K08738  Energy  CnA, Wat  Cytochrome c  K00366  Energy  CnB  Ferredoxin-nitrite reductase [EC:1.7.7.1]  K00367  Energy  CnB  Ferredoxin-nitrate reductase [EC:1.7.7.2]  K01582  Biosyn  CnB  Lysine decarboxylase [EC:4.1.1.18]  K02006  Env  CnB  Cobalt/nickel transport system ATP-binding protein  K02074  Env  CnB  Zinc/manganese transport system ATP-binding protein  K02075  Env  CnB  Zinc/manganese transport system permease protein  K03321  Env  CnB  Sulfate permease  K03711  Stress  CnB  Fur family transcriptional regulator  K03969  Stress  CnB  Phage shock protein A  K05356  Biosyn  CnB  Trans-octaprenyltranstransferase [EC:2.5.1.11]; all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85]  K08226  Env  CnB  MFS transporter  K01915  Energy  Wat  Glutamine synthetase [EC:6.3.1.2]  KO  Categ  Group  Definition  K02044  Env  CN  Phosphonate transport system substrate-binding protein  K03799  Stress  CN  Heat shock protein HtpX [EC:3.4.24.-]  K03893  Stress  CN  Arsenical pump membrane protein  K04517  Biosyn  CN  Prephenate dehydrogenase [EC:1.3.1.12]  K04748  Energy  CN  Nitric oxide reductase NorQ protein; nitric-oxide reductase NorQ protein [EC:1.7.99.7]  K07334  Stress  CN  Proteic killer suppression protein  K00799  Stress  CnA  Glutathione S-transferase [EC:2.5.1.18]  K02014  Env  CnA  Iron complex outermembrane recepter protein  K03406  Env  CnA  Methyl-accepting chemotaxis protein  K07233  Stress  CnA  Copper resistance protein B  K14266  Biosyn  CnA  FADH2 O2-dependent halogenase I [EC:1.14.14.7]  K08738  Energy  CnA, Wat  Cytochrome c  K00366  Energy  CnB  Ferredoxin-nitrite reductase [EC:1.7.7.1]  K00367  Energy  CnB  Ferredoxin-nitrate reductase [EC:1.7.7.2]  K01582  Biosyn  CnB  Lysine decarboxylase [EC:4.1.1.18]  K02006  Env  CnB  Cobalt/nickel transport system ATP-binding protein  K02074  Env  CnB  Zinc/manganese transport system ATP-binding protein  K02075  Env  CnB  Zinc/manganese transport system permease protein  K03321  Env  CnB  Sulfate permease  K03711  Stress  CnB  Fur family transcriptional regulator  K03969  Stress  CnB  Phage shock protein A  K05356  Biosyn  CnB  Trans-octaprenyltranstransferase [EC:2.5.1.11]; all-trans-nonaprenyl-diphosphate synthase [EC:2.5.1.84 2.5.1.85]  K08226  Env  CnB  MFS transporter  K01915  Energy  Wat  Glutamine synthetase [EC:6.3.1.2]  Categ: category of KO: env—environmental information processing; stress—stress; energy—energy metabolism; biosyn—biosynthesis of secondary metabolites. Group: biotope where the KO was most abundant: CN—both Cinachyrella morphospecies; CnA—Cinachyrella sp. A; CnB—Cinachyrella sp. B; Wat—water. Definition: definition of KO. View Large DISCUSSION A large number of Cinachyrella species have been recorded from the Indo-Pacific region under a large variety of names, with little distinction between them. Burton (1934) concluded that most tropical representatives should all be synomised with Cinachyrella australiensis, a species originally described from southern Australia. However, Burton only made the distinction based on the possession/absence of roughened or smooth micro-oxea of which he dismissed their importance in distinguishing different species. Other highly variable characters such as overall size, colour and dimension of the crater like pits (porocalises) failed to delineate species. Consequently, C. australiensis has been the most common name used for moon sponges present in the Indo-Pacific region bearing acanthose microxeas. In the present paper, we were able to examine type material of valid and non-valid Cinachyrella species including C. australiensis and compare it to our freshly collected Cinachyrella specimens from the anchialine systems in Raja Ampat, Indonesia. Moon sponges can be abundant in anchialine and coastal waters because they can survive in highly sediment impacted areas. We were able to distinguish different morphospecies based on size differences of the microscleres (sigmaspires and presence/absence of the acanthose microxeas). We could, however, not identify our samples to any of the examined type specimens and also not to the supposed intertropical C. australiensis. Cleary et al. (2013) showed that C. australiensis housed different bacterial communities inside two marine lakes and the surrounding sea. However, we re-examined the specimens and found that the specimens from marine lakes were actually a different species. Moreover, these specimens were identical to morphospecies Cinachyrella sp. A in the present paper. It is likely that isolation and limited gene flow between the lakes and the sea of this particular species could have led to a divergence into different lineages or species. Microbial communities appear to be highly conserved in different moon sponges (Chambers et al.2013; Cuvelier et al.2014) and could possibly be used to separate different species in addition to classical taxonomic characters. This was also the case in this study where bacterial and archaeal communities clustered according to morphospecies as opposed to lake. The archaeal communities of both Cinachyrella species were dominated by a single highly abundant and different OTU. Both of these OTUs were assigned to the family Cenarchaeaceae. Previous studies of other low microbial abundance (LMA) sponges have also shown them to be dominated by a single highly dominant cenarchaeaceal OTU (Polónia et al. 2014, 2015; Moitinho-Silva et al.2017). The bacterial communities of both sponge species were dominated by OTUs assigned to the phylum Proteobacteria, particularly the classes Gamma- and Alphaproteobacteria. Cinachyrella sp. B also had consistently high abundance of OTUs assigned to the class Synechococcophycideae. This is common for LMA sponges such as Cinachyrella spp., which have been shown to host less diverse microbial communities, mainly consisting of species belonging to the Proteobacteria and Cyanobacteria (Cleary et al.2013). LMA sponges, in general, have higher pumping rates, wider aquiferous canals and greater choanocyte chamber density than high microbial abundance (HMA) sponges indicative of a more heterotrophic feeding mode (Vacelet and Donadey 1977; Ribes et al.2012; Poppell et al.2014). LMA sponges also, in general, consume less oxygen and are smaller, more fragile, softer and more brittle than HMA sponges, which are often massive, firm and fleshy (Gloeckner et al.2014). HMA sponges are also believed to be more dependent on their microbial symbionts for energy acquisition than LMA sponges, which are believed to be more dependent on their high pumping rates and thus heterotrophy (Weisz et al.2007, Poppell et al.2014, Ribes et al.2015). In the bacterial community, a large number of OTUs were absent from seawater but present in both sponge species. All of these were mainly related to organisms found in other LMA sponge species belonging to the genera Tethya and Cinachyra. In addition to this, a number of OTUs were restricted to either sponge host. This included OTUs related to organisms found in sponges, but also a number found in other biotopes including octocorals and sediment. A large number of abundant OTUs had also relatively low sequence similarity to organisms in GenBank. The taxonomic threshold for species is 98.7%, genera 94.5% and families 86.5% (Yarza et al.2014). In this study, nine abundant OTUs had sequence similarities lower than 94.5%. This suggests that both Cinachyrella morphospecies house a number of abundant, novel bacterial genera that have not been found in other environmental samples present in the GenBank database. The proportion of potentially novel taxa is much higher than that found for other LMA and HMA sponge species sampled outside the marine lakes of Misool, where the sequence similarity for organisms in GenBank was >98% for all abundant OTUs (Cleary et al.2017). Although the results for both Bacteria and Archaea were clear and point to both morphospecies housing distinct microbial communities, it is important to remember that sample size was limited to only three specimens per morphospecies. Future research should attempt to increase sample size and compare samples of Cinachyrella from a greater number of habitats. The main betaproteobacterial OTU in Cinachyrella sp. A was assigned to the order EC94. Relatively little is known about this order, but previous studies have shown it to be abundant in various sponge species including Callyspongia sp. from Guam and several sponges from Korea (Jeong, Kim and Park 2013, 2015; Steinert et al.2016). In addition to EC94, Chromatiales and the gammaproteobacterial order HTCC2188 were also more abundant in Cinachyrella sp. A. The HTCC2188 order, which has been characterised as oligotrophic (Cho and Giovannoni 2004), has been recorded from a number of sponge species including the Cinachyrella species inhabiting marine lakes in Berau (Cleary et al.2013) and Cinachyra sp. and Coelocarteria singaporensis from Papua New Guinea (Morrow et al.2015). The Chromatiales, in turn, are important sulphur-oxidising organisms (Thomas et al.2014) that have also been found as symbionts in a number of sponge species including the LMA species Stylissa carteri and S. massa and the HMA species Xestospongia testudinaria and Hyrtios erectus (de Voogd et al.2015; Cleary et al.2015b). Specimens of both Cinachyrella morphospecies in the less connected lake M were characterised by a greater abundance of Synechococcales. Importantly, previous studies have shown that the relative abundance of Synechococcales in various sponge species is much higher under low pH conditions (Morrow et al.2015; Ribes et al.2016). For example, in an aquarium study with normal and low pH treatments, the relative abundance of Synechococcales in the sponge Dysidea avara increased in response to low pH conditions and proteobacterial abundance decreased. In the same experiment, another species, Agelas oroides, revealed a reduction in Chloroflexi abundance and an increase in alphaproteobacterial (mainly Rhodobacterales) abundance. A third species, Chondrosia reniformis, subjected to the same treatments only had a limited ability to acquire novel microbes and was severely affected by the low pH treatment (Ribes et al.2016). Ribes et al. (2016) suggested that the ability of Dysidea avara and Agelas oroides to horizontally acquire novel microbes left them relatively unaffected by experimental low pH manipulation, whereas C. reniformis that lacked this ability was severely affected. In Papua New Guinea, both the sponge species Cinachyra sp. and C. singaporensis increased in abundance at a low pH hydrothermal CO2 seep compared to a control site. Both species also showed a marked increase in the relative abundance of Cyanobacteria (mainly Synechococcales) (Morrow et al.2015). Morrow et al. (2015) suggested that the increase in photosynthetic Synechococcales provided the sponges with nutrition and enhanced their ability to grow under low pH conditions. The variation in composition exhibited by both Cinachyrella morphospecies in this study suggests that they possess a flexible, but still distinct, bacterial community that enables them to adapt to low pH conditions as found in the marine lakes of Misool (Becking et al.2011) by recruiting low pH-tolerant symbionts such as members of the Synechococcophycideae. In line with the differences in composition between both Cinachyrella morphospecies, there were also pronounced differences in predicted metagenomic gene content using PICRUSt. In addition to predicting metagenomic gene content, PICRUSt also provides a quality control using weighted Nearest Sequenced Taxon Index (NSTI) scores. NSTI was developed to evaluate the predictive accuracy of PICRUSt and calculates dissimilarity between reference genomes and the metagenome under study. Langille et al. (2013) showed that PICRUSt accuracy decreases as NSTI scores increase, but in their study still produced reliable results for a dataset of soil samples with a mean NSTI score of 0.17. All of our scores for sponge and water samples were well below this value thus indicating that our results can be considered reasonably reliable. The estimated accuracy provided by Langille et al. (2013) was, however, based on different environments and no sponge studies were included in their paper. It is possible that the relationship between genome representatives and OTUs could be different for sponge microbiomes. Although the sequence similarities of certain sponge-specific OTUs were relatively low to organisms in GenBank, these taxa largely belonged to groups such as Alpha- and Gammaproteobacteria that are very well represented in the greengenes database. Both Cinachyrella morphospecies were predicted to be enriched for a number of KOs involved in various pathways including pathways involved in stress response, environmental information processing and the energy metabolism. Stress response KOs included the heat shock protein (K03799), proteic killer suppression protein (K07334), nitric oxide reductase (K04748) and arsenical pump membrane protein (K03893). Arsenic is an ubiquitous environmental toxin. It is a substrate analogue of phosphate and as such is taken up via phosphate transporters (which were also predicted to be enriched in both Cinachyrella morphospecies) into the bacterial cell (Rosenberg, Gerdes and Chegwidden 1977). Arsenic detoxification occurs via the arsenical pump membrane protein, which catalyses the extrusion of arsenic from the bacterial cell (Meng, Liu and Rosen 2004). All living organisms have systems of arsenic detoxification. Enrichment of gene copies encoding for the arsenical pump membrane protein in both sponge species reflects the environmental amplitude of Cinachyrella species, which can be found in pristine and perturbed environments where they are often embedded in sediment (McDonald, Hooper and McGuinness 2002; Bell and Smith 2004; Fromont, Vanderklift and Kendrick 2006; de Voogd and Cleary 2008, 2009). In addition to both morphospecies being putatively enriched for selected KOs compared to water, there were also marked differences between the morphospecies. Cinachyrella sp. A was predicted to be enriched for KOs involved in stress response, namely K00799 (glutathione S-transferase) and K07233 (copper resistance), and environmental information processing, namely K02014 (iron complex recepter) and K03406 (methyl-accepting chemotaxis). Glutathione is a powerful antioxidant that can prevent damage to cells by stressors including free radicals, peroxides, lipid peroxides and heavy metals (Pompella et al.2003; Yadav 2010). K03406 is part of the two-component regulatory system, which is a stimulus-response coupling mechanism that enables organisms to respond to shifting environmental conditions. Cinachyrella sp. B, in turn, was predicted to be enriched for a number of KOs involved in stress response, environmental information processing, the energy metabolism and biosynthesis of other secondary metabolites. KOs involved in stress response that were predicted to be enriched in Cinachyrella sp. B included K03969 (phage shock protein) and K03711 (Fur family transcriptional regulator). The FUR (ferric uptake regulator) family of transcriptional regulators is involved in the regulation of iron and zinc metabolism through control by Fur and Zur proteins. Control of metal homeostasis is crucial to all living organisms. In addition to the above, FUR homologues control defence against peroxide stress and play a key role in microbial survival under adverse environmental conditions (Fillat 2014). Both sponge morphospecies were predicted to be selectively enriched for KOs involved in the biosynthesis of various secondary metabolites. Cinachyrella sp. A was putatively enriched for K14266, which is involved in the biosynthesis of Staurosporine. Staurosporine is an indolocarbazole, which inhibits protein kinases by preventing ATP binding to the kinase. It has a wide range of biological activities including antifungal and antibacterial activity (Rüegg and Burgess 1989). Cinachyrella sp. B, in turn, was predicted to be enriched for K05356, which is involved in Terpenoid backbone biosynthesis. Terpenoids are a highly diverse class of organic chemicals. They represent the largest group of natural products, 60% of which are terpenoids (Firn 2010). Terpene synthases have also been shown to be widely distributed in bacteria and represent a potentially important source for the discovery of novel natural products (Yamada et al.2015). Natural products previously found in Cinachyrella species include two fatty acids that had been unknown prior to their discovery in C. alloclada (Barnathan et al.1992), novel prototype galactins (Ueda et al.2013) and an alkaloid (Cinachyramine; Shimogawa et al.2006) isolated from Cinachyrella spp. in Japan. A novel phosphate-containing macrolide was also isolated from the sponge C. enigmatica, in Papua New Guinea. The compound called Enigmazole A represents a novel structural family of marine phosphomacrolides with significant antitumor activity (Oku et al.2010). The predicted enrichment for KOs involved in the biosynthesis of various secondary metabolites in this study suggests that both sponge species studied here may prove to be interesting sources for novel compounds with potentially important pharmaceutical and/or biotechnological properties. In summary, this study shows that two distinct Cinachyrella morphospecies inhabiting Papuan marine lakes housed distinct communities of Bacteria and Archaea. In addition to this, the predicted metagenomic gene content of both morphospecies differed. Specimens thus of the same morphospecies from different lakes were more similar to one another than different morphospecies within the same lake indicating that the specific traits of each morphospecies were more important in structuring bacterial and archaeal composition than environmental differences between lakes. Presumed environmental differences between lakes, however, did appear to have some effect on composition with specimens in the less connected and presumably more acidic lake, for example, having a greater percentage of sequences assigned to the cyanobacterial genus Synechococcus. These results highlight the potential of the sponge microbiome to be used as a means of separating species of the genus Cinachyrella and suggest that Cinachyrella and the closely related Paratetilla may be interesting subjects to study sponge–microbe evolution, in particular with respect to what traits are important in structuring microbial composition. The limited sample size, however, of this study makes it difficult to draw hard conclusions. Future studies would benefit from sampling more morphospecies across a greater range of habitats, inside and outside of marine lakes. SUPPLEMENTARY DATA Supplementary data are available at FEMSEC online. Acknowledgements We would like to thank Lisa Becking for collecting the specimens in the field and Ristek and LIPI, Indonesia for supporting the fieldwork. FUNDING This work was supported by European Funds through COMPETE [FCOMP-01-0124-FEDER-008657] and by National Funds through the Portuguese Foundation for Science and Technology (FCT) within the LESS CORAL project [PTDC/AAC-AMB/115304/2009]. ARMP was supported by a postdoctoral scholarship [SFRH/BPD/117563/2016] funded by FCT, Portugal (QREN-POPH—Type 4.1 – Advanced Training, subsidized by the European Social Fund and national funds MCTES). 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