Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Pyrosequencing Characterization of the Microbiota from Atlantic Intertidal Marine Sponges Reveals High Microbial Diversity and the Lack of Co-Occurrence Patterns

  • Anoop Alex,

    Affiliations CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Rua dos Bragas 177, 4050–123, Porto, Portugal, Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo, Alegre, 4169–007, Porto, Portugal

  • Agostinho Antunes

    Affiliations CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Rua dos Bragas 177, 4050–123, Porto, Portugal, Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo, Alegre, 4169–007, Porto, Portugal


Sponges are ancient metazoans that host diverse and complex microbial communities. Sponge-associated microbial diversity has been studied from wide oceans across the globe, particularly in subtidal regions, but the microbial communities from intertidal sponges have remained mostly unexplored. Here we used pyrosequencing to characterize the microbial communities in 12 different co-occurring intertidal marine sponge species sampled from the Atlantic coast, revealing a total of 686 operational taxonomic units (OTUs) at 97% sequence similarity. Taxonomic assignment of 16S ribosomal RNA tag sequences estimated altogether 26 microbial groups, represented by bacterial (75.5%) and archaeal (22%) domains. Proteobacteria (43.4%) and Crenarchaeota (20.6%) were the most dominant microbial groups detected in all the 12 marine sponge species and ambient seawater. The Crenarchaeota microbes detected in three Atlantic Ocean sponges had a close similarity with Crenarchaeota from geographically separated subtidal Red Sea sponges. Our study showed that most of the microbial communities observed in sponges (73%) were also found in the surrounding ambient seawater suggesting possible environmental acquisition and/or horizontal transfer of microbes. Beyond the microbial diversity and community structure assessments (NMDS, ADONIS, ANOSIM), we explored the interactions between the microbial communities coexisting in sponges using the checkerboard score (C-score). Analyses of the microbial association pattern (co-occurrence) among intertidal sympatric sponges revealed the random association of microbes, favoring the hypothesis that the sponge-inhabiting microbes are recruited from the habitat mostly by chance or influenced by environmental factors to benefit the hosts.


Sponge microbial ecology is attaining momentum with the advancement of sequencing technology and the keen interest in unlocking the global sponge-associated microbial diversity. The sponge-associated microorganisms can contribute to nearly 40% of the total sponge biomass [1] and may benefit the hosts with various functional roles. Culture-independent 16S rRNA (ribosomal RNA) gene based molecular techniques, such as Sanger sequence analyses derived from clone library [24], denaturing gradient gel electrophoresis [5], terminal restriction fragment length polymorphism [3,6] and culture-dependent isolation procedures [2,4] provided insight into the complex sponge microbial consortium. In recent years, 454 tag sequencing of the 16S rRNA revealed an unexplored exceptional diversity of microbial assemblages residing within the sponge body [79]. Apart from cataloging the microbial diversity, the potential pharmacological application of bioactive compounds isolated from the sponges and associated microbes further intensified the effort to understand the sponge-associated microbial world [10].

Irrespective of the enormous amount of microbial community data from widely distributed sponges, the mechanism of symbiotic establishment and its exact role within the sponge host remains mostly unclear [11,12]. It has been suggested that microbial interaction may provide the sponges with benefits that include the incorporation of dissolved organic matter [13,14], production of photosynthates [15,16], antifeedants [1720] and removal of waste products [2123]. Sponge-associated microbes has been classified as photosynthetic bacteria [24], heterotrophic bacteria, archaea [25] and non-representative candidate phyla [26,27].

Sponge-microbe association, its variability and similarity have been assessed by the sampling of (i) distinct sponge species from different oceans [9], (ii) congeneric sponges from different oceans [28] (iii) congeneric sponges from the same ocean but sampled from neighboring sites [29] and (iv) sympatric distantly related sponges from the same habitat [30]. Early studies hypothesized that sponges host a “uniform bacterial community” [31,32], however subsequent studies questioned the true nature of these microbes due to the presence of sponge-specific microbes in seawater samples [7]. Further investigation of sponge-microbe association detected the existence of common symbiotic microbial taxa across various sponge lineages, suggesting the lack of host specificity and described a ‘mix of specialist and generalist’ microbiota among sponge hosts [3,4,29]. However, beyond alpha and beta diversity analyses, to our knowledge no studies have been performed to examine the interactions between microbial taxa (co-occurrence pattern) coexisting in sponge hosts. Determining the bacterial community structure and structure-function relationship is crucial to infer the ecological niche of bacterial taxa and their interactions [3335].

Here, we have used 454 pyrosequencing (i) to characterize the bacterial community from 12 different intertidal marine sponge species (class Demospongiae) inhabiting the same habitat (single sampling site collection) in the Atlantic coast of Portugal and (ii) to determine co-occurrence patterns among the microbial communities in sponges. In depth sequencing from unexplored Atlantic intertidal marine sponges revealed the presence of diverse microbial consortium including several groups of bacteria, candidate phyla and Crenarchaeota group. Crenarchaeotal symbionts associated with sponge species in our current study were compared with to those associated with sponges in the Red Sea to test for symbiont similarity between geographically and taxonomically distinct sponges. Furthermore, we tested for the nature of the association (random or non-random) prevailing among the microbial communities in sponge species.

Materials and Methods

Ethics statement

The study did not involve any kind of endangered or protected species. No specific scientific research permits were required to conduct the field study and sampling of the sponges from the rocky beaches.

Sample collection and 454 pyrosequencing

Sampling of different sponge species (while sponges were exposed during low tide, n = 12, Table 1) and surrounding ambient seawater (one or two specimens each) has been performed on 14th January 2013 at Praia da Memória (41.2308206N 8.7216926W), an Atlantic Ocean rocky beach from Portugal. Within one hour of sampling and transportation to the lab in an insulated container, sponge tissues of 1cm3 were washed thoroughly with sterile seawater prior to DNA extraction. Approximately 2.5 liters of seawater from the sample location was filtered through 0.45μm sterile filter followed by DNA extraction with PureLinkTM Genomic DNA kit (Invitrogen). Amplicon libraries for 454 pyrosequencing were constructed with unique barcoded (S1 Table) universal primers U789F (5'-TAGATACCCSSGTAGTCC-3') and reverse primer U1068R (5'-CTGACGR CRGCCATGC-3') 16S rRNA gene (Baker et al. 2003) targeting at hypervariable region V6 of bacteria and archaea. Triplicate PCR reactions were performed in a total volume of 100 μl constituting 5 U of Pfx50 DNA polymerase (Invitrogen), 1X Pfx50 PCR mix, 0.3 mM of dNTPs (NZYTech), 0.5 μM of each barcoded primers and 30 ng of metagenomic DNA. Thermocycler conditions, initial denaturation at 95°C for 5 minutes and 26 cycles of 94°C for 15 seconds, 63°C for 30 seconds, 68°C for 45 seconds and a final extension at 68°C for 5 minutes were used. Gel purified PCR (Macherey-Nagel) products were pooled and performed pyrosequencing on ROCHE 454 GS-FLX Titanium platform. Raw pyrosequencing reads were submitted to the NCBI Short Reads Archive database (SRR949132).

454 tag sequence processing and OTU picking

Pyrosequencing data analyses were performed with QIIME v.1.6.0 [36]. Briefly, raw multiplexed sequences (138,615 reads) were pre-processed by trimming with an average quality threshold score of 25, removing reads containing ambiguous bases, sequences shorter than 100 bp and unassigned reads. Final sequences of an average read length of 285.5 bp were assigned to samples based on barcodes for downstream analysis. Before proceeding with diversity analysis, pre-processed dataset was screened by denoising [37] to avoid over representation of species diversity. De-multiplexed reads were checked for chimeras using UCHIME [38] against 16S “Gold” database (reference database in the Broad Microbiome Utilities, version microbiomeutil-r20110519; and clustered into operational taxonomic units (OTUs) using a 97% similarity threshold with USEARCH algorithm [39]. Taxonomic assignment to phylum, class, order, family and genus level was implemented with naïve Bayesian classifier [40] on a set of trained Greengenes reference sequences and taxonomy [41] by mothur method at 80% similarity confidence [42].

Microbial diversity and co-occurrence analysis

The BIOM (Biological Observation Matrix) file obtained after clustering the reads at 97% similarity was further used for downstream analysis. Briefly, microbial richness indices namely observed species richness (S.obs), expected richness with Chao1 estimator (S.Chao1) [43], abundance-based coverage estimator (S.ACE) [44] and diversity measures (shannon and simpson indices) [45,46] were executed by plot_richness function in phyloseq package v 1.5.15 [47]. The non-metric multidimensional scaling (NMDS) was performed to visually compare the microbial community dissimilarity among different sponge species and the ambient seawater using Bray-Curtis distance.

Non-random association pattern of microbes among sponges was tested by calculating the checkerboard score (C-score) [48] using the co-occurrence [49] module in EcoSim v 7.71 [50]. We evaluated this relationship between microbes that were identified as core i.e., OTUs present in at least 50% of the sponge samples. Data was transformed into a presence-absence matrix (PAM) and was used to generate the C-score [48] under a null model. Representation using PAM could be insightful and useful, particularly when the association patterns shaping the species distribution are influenced mostly by both environmental and ecological factors [51]. Observed C-scores were compared with expected C-scores by fixed rows-equiprobable columns simulation algorithm. We used the C-score to investigate whether the microbes are randomly distributed among the sponge samples. Significantly larger observed C-score than the expected C-score indicates the possible segregation (non-random pattern) of microbial taxa and smaller observed C-score than the expected C-score implies the possible aggregation (random pattern) of microbes.

Crenarchaeota microbial community from the Atlantic Ocean and the Red Sea sponges

Due to the high abundance of Crenarchaeota in our study, we performed a comparative case study to evaluate the similarity of Crenarchaeota from our study and that previously detected in the Red Sea sponges [8]. Same read length size of 16S rRNA amplicons (~ 300 bp encompassing the V6 hypervariable region) from the Atlantic Ocean (current samples) and the Red Sea samples, improved the taxonomic resolution and diversity analyses. We retrieved the raw pyrosequencing data of the Red Sea sponges (SRA012874.2) from the NCBI Short Reads Archive database. The sequencing reads were pre-processed with NCBI SRA toolkit ( and analyzed as mentioned above using QIIME v.1.6.0 [36].

Reads assigned to Crenarchaeota were retrieved from both data sets and merged to generate a final OTU table for further analyses. We employed UPGMA (Unweighted Pair Group Method using arithmetic Averages) cluster analysis [52] with the Crenarchaeotal dataset using Bray-Curtis distance matrix. UPGMA performed the clustering of the microbes (Crenarchaeota) represented in different samples based on the overall similarities among the microbial taxa. The closely related sponge species are grouped together and illustrated as a dendrogram. Statistical significance of the sample groupings (Atlantic Ocean and Red Sea samples) was tested with nonparametric ADONIS and ANOSIM functions using the vegan package v 2.0.7 [53] in QIIME v.1.6.0 [36]. We used ADONIS, a robust permutation analyses to test the differentiation between the means of two or more groups of data, to explain the percentage of variation by computing the effective size (R2) and a p-value. Whereas, the ANOSIM tests whether two groups (Atlantic Ocean and Red Sea samples) are significantly different by comparing the ranks of distances between the groups and within the groups. Analyses were conducted with 1000 permutations.


OTUs and bacterial richness

454 sequencing and quality sorting of the microbial communities associated with the sponges and seawater derived 84,199 sequence reads clustered at 97% identity into 686 unique OTUs. Clustering of the reads at lower sequence similarity thresholds resulted in lower number of OTUs (S1 Fig). The total number of reads retrieved and OTUs (at 3% sequence divergence) from each sample are provided in S2 Fig. The highest number of OTUs was observed in the sponge Amphilectus fucorum (AMF), representing up to 370 OTUs and minimum of 29 OTUs from the sponge Cliona celata (CCL) (Fig 1). OTU based alpha diversity measures, abundance-based coverage estimator (ACE) and Chao1 presented the higher richness of microbial species in the sponge AMF and lowest in the sponge CCL. Most of the sponge species and seawater (SW) showed richness ranging from 100–400. Microbial community diversity, Shannon and Simpson indices indicated higher dominance in seawater (SW) compared to the sponge species sampled from the same location (Fig 1). Rank-abundance curve suggested the presence of a few abundant bacterial species in the sponge samples and in the surrounding seawater (S3 Fig).

Fig 1. Alpha diversity indices of microbial communities from the 12 sponge species and seawater (SW).

Community richness was estimated using observed species (S.obs), chao 1 estimator (S.chao1) and abundance-based coverage estimator (S.ACE). Community diversity was calculated using Shannon and Simpson indices. Analyses were executed with phyloseq package v 1.5.15 [47] in the R environment ( and plotted using ggplot2 [85]. Sample description is provided in Table 1.

Taxonomic composition of pyrosequencing reads

Taxonomic assignment of qualified reads (84,199) with Greengenes reference sequences at a confidence threshold of 97% similarity classified the reads into two major domains- bacteria (75.5%) and archaea (22.0%), with 22 bacterial, 3 archaeal and 1 unknown group. The known bacterial phyla- Actinobacteria, Acidobacteria, Bacteroidetes, Chlamydiae, Cyanobacteria, Chloroflexi, Firmicutes, Fusobacteria, Nitrospirae, Planctomycetes, Proteobacteria, Spirochaetes, Synergistetes, Verrucomicrobia and several candidate bacterial phyla- NKB19, OP3, SBR1093, TM7, TM6, ZB2 and WS3 were detected in the sponge samples and ambient seawater (Fig 2). The bacterial group Proteobacteria dominated in all samples comprising the major class- Alphaproteobacteria (12.1%), Gammaproteobacteria (10.6%), Betaproteobacteria (2.7%), Deltaproteobacteria (1.3%), other Proteobacteria (16.7%) and least represented by Epsilonproteobacteria (0.2%), but only in the sponge AMF (S4 Fig). Planctomycetes (15.8%) and Cyanobacteria (1.9%) were also retrieved from the sponges studied. The photosynthetic bacterial reads affiliated to Prochlorococcus were present in all samples except in the sponges Aaptos papillata (AAP) and C. celata.

Fig 2. Taxonomic compositions and relative abundance of the OTUs from the sponge species and surrounding seawater.

Different color pellets are assigned to represent bacterial groups. Detailed information about sample codes is provided in Table 1.

In this study both seawater and sponges harbored the archaeal group of the phylum Crenarchaeota (20.6%) and Euryarchaeota (1.1%) (Fig 2, S5A Fig). Crenarchaeota were observed with high frequency in all samples, whereas, Euryarchaeota reads were found in lower abundance and were notably absent in a few sponge species including CCL, AAP, POLY and TED (S5A Fig). Nitrosopumilus sp. was the most abundant archaeon found among the samples analyzed in this study (S5B Fig).

Bacterial community similarity and random distribution

The non-metric multidimensional scaling (NMDS) plot clearly discriminated the bacterial communities among sample sources (represented by the lower value of stress 0.05891) (Fig 3). Microbial community differentiation using phylogenetic information (p-test) showed significant distinction (p < 0.01) of the microbes associated with the sponges and seawater (S6 Fig). In addition, we detected a bacterial community difference among three intrageneric sponge hosts (Polymastia sp.) studied (S7 Fig). The UPGMA cluster analysis of Crenarchaeota obtained here (Atlantic Ocean) and Crenarchaeota from the Red Sea sponges suggested the similarity of archaeal communities, but only between two Red Sea sponge species- Hyrtios erectus (HE-2) and Xestospongia testudinaria (XT-1), and three Atlantic Ocean sponge species- Ophlitaspongia papilla (OPT), Tedania pillarriosae (TED) and C. celata (CCL). Most of the sponges from both oceans were grouped separately indicating distinct Crenarchaeota communities (Fig 4). This pattern is further confirmed by nonparametric ADONIS (p = 0.0009, R2 = 0.2) and ANOSIM (p = 0.001, R = 0.4206) analyses, revealing significant difference in the archaea communities from different oceans (S2 Table). We analyzed the distribution of core microbes among the sponge hosts, which are present in at least 50–100% of the samples. At the phylum level, both Crenarchaeota and Planctomycetes were found in all the sponge samples irrespective of the different core bacterial ranges defined (Fig 5).

Fig 3. Microbial community differentiations among the intertidal Atlantic Ocean samples.

Non-metric multidimensional scaling plots showing the pattern of microbial communities recovered from the sponge samples and seawater. Each sample code and its designated colored dots were delimited by an ellipse for visualization purpose. Stress value < 0.05 shows an excellent representation in NMDS analysis, while stress value < 0.01 gives a good representation. Analyses were executed with phyloseq package v 1.5.15 [47] in the R environment ( and plotted using ggplot2 [85].

Fig 4. UPGMA cluster analysis of Crenarchaeotal communities associated with the Atlantic Ocean and the Red Sea samples.

The bold letters indicate the samples from the current study. Detailed information about sample codes is provided in Table 1 (this study). Sequence data from the Red Sea samples were obtained from previous study [8].

Fig 5. Core bacterial communities assigned to the sponge samples.

The x-axis represents the core microbiata assignment defined as the distributions of the OTUs that are present in 50–100% of the sponge samples studied.

The co-occurrence analysis did not support the non-random microbial distribution hypothesis among the sponge samples analyzed. At the phylum level, the observed C-score (0.74242) was significantly lower than the simulated C-score (1.78472; p < 0.001), suggesting a random microbial distribution pattern (Fig 6). Further analysis at the species level also supported the random distribution (null hypothesis) of sponge-associated bacteria (observed C-score = 2.885 and simulated C-score = 5.172; p < 0.001).

Fig 6. Histogram representation of the co-occurrence analysis.

The observed C-score from the real data set (sponge associated microbes) is represented by an arrow on to the simulated C-scores.


Microbial consortium from sponges

Microbial assemblages in wide oceans are being documented both from open water (free-living bacteria) and in association with marine animals. Recently, sponges have been a major focus of study due to their microbial abundance, ecological role and biotechnological significance [32]. Here, we investigated the sponge-associated microbial assemblage from 12 different co-occurring intertidal marine sponges sampled from the Atlantic coast of Portugal and compared it with the surrounding ambient seawater from the sampling location. Tag pyrosequencing revealed altogether 26 different bacterial and archaeal phyla on the sponge communities (Fig 2) suggesting a complex microbial consortium. Proteobacteria formed the most abundant group of bacteria, distributed among all five major classes of Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Deltaproteobacteria and Epsilonproteobacteria. This bacterial group was very prominent in almost every sponges studied previously, irrespective of the habitat [32], and predominated during isolation techniques [54]. At the class level classification, the Alphaproteobacteria prevailed among all sponge samples followed by Gammaproteobacteria (S4 Fig). The second most diverse microbial group, Crenarchaeota, predominated both in seawater and sponge species. Other studies showed increasing evidence for the ubiquitous and abundant nature of archaeal communities [55,56]; which were previously thought to be present only in extreme conditions [57,58]. For instance, Crenarchaeotal microbes have been recovered from the open surface waters of temperate and polar seas [59,60], and other invertebrates beside sponges [61]. Interestingly, archaea are being found in association with marine sponges from diverse locations, such as Korea [62], Brazil [63] Antarctica [64] and the Red Sea [8]. We reported here for the first time the association of archaea, particularly the diverse Crenarchaeota, among 12 different intertidal marine sponge species from the European Atlantic coast (S5A Fig). High abundance of Crenarchaeota has previously been reported in the North Atlantic Ocean, playing an important role in the oxidation of ammonia to nitrite, indicated by the dominance of archaeal ammonia monooxygenase (amoA) genes [65]. The majority of archaeal reads (20.2%; S5B Fig) grouped in the genus Nitrosopumilus and were mostly found in the sponge A. papillata and less represented in C. celata. Overall, our study showed the presence of archaea among the intertidal sponges sampled from the Atlantic coast.

The high abundance of Crenarchaeota found in the Atlantic Ocean (this study) and the Red Sea sponges [8] prompted us to further investigate a possible similarity between archaeal communities from geographically separated sponges. A few samples from both oceans grouped differently indicating a clear distinction of multiple Crenarchaeota (Fig 4) microbial communities. The clustering of sponge samples from both the Red Sea (HE-1 and XT-1) and the Atlantic Ocean (OPT, CCL and TED) indicates the presence of similar microbial members of the archaeal lineage. However, the detection of Crenarchaeota may also suggest the generalist nature of these microbes. It is noteworthy that the retrieval of Crenarchaeotal group in this study could be due to their abundance and cosmopolitan distribution in the world’s oceans [55,59].

Planctomycetes and Cyanobacteria were also detected frequently in the sponge samples recovered from the intertidal region. Various studies have shown the association of photosynthetic bacteria and their benefits to sponges [15] inhabiting both in tropical [24] and temperate conditions [4,66,67]. It is evident that the majority of the sponges studied here (10 out of 12) harbored cyanobacteria (1.9%, including chloroplast; Fig 2). Lower level taxonomic classification revealed the presence of Prochlorococcus, a representative symbiotic cyanobacterial genera reported to colonize the widely studied sponges [68]. It is also possible that the detected cyanobacteria in the sponges sampled could be simply filtered food-particles. Since the intertidal sponges we studied are recurrently exposed to light during low tide, the prominence of photosymbionts might support the previous hypothesis of the cyanobacterial role in supplementing the sponges with required energy and protecting them from UV radiation during environmental exposure [24]. Moreover, we detected Candidatus Entotheonella from the sponges Aplysilla rosea, Polymastia sp. and Aaptos papillata in very low abundance (< 0.1%). Entotheonella sp. was previously identified in the marine sponge Theonella swinhoei [69] and Discodermia sp. [70,71].

Our data also revealed the ‘rare’ phyla Chlamydiae and WS3 at very low abundance (< 0.1%). Representatives of uncultivated bacterial members formed the ‘candidate divisions’ [72] that have been frequently reported from sponges [79] and other environments. Candidate bacteria of division TM7 previously reported to transmit vertically in the sponge Xestospongia muta [73] was also recovered from 10 sponge species and seawater sample in our study. However, retrieval of TM7 bacterial group might indicate the possible horizontal transmission of these microbes. Electron microscopic examination [7476] and fluorescence in situ hybridization (FISH) [73,77] of the adult as well as the sponge larvae could explain the mode of microbial transmission in the current sponge samples. Moreover, we detected the presence of two new sponge-associated uncultivated bacterial groups: the candidate phyla NKB19 and ZB2, which highlights the usefulness of deep sequencing in our study.

Lack of non-random association among sponge microbiota

The majority of microbes co-exist in different mode of association within the ecosystem and non-random patterns of interaction are known to exist across all domains of life [78]. These associations play an important role in the structuring of the microbial communities [79] through microbe-microbe and microbe-metazoan interactions. The cataloging of such ecological patterns is important for understanding the ecosystem dynamics and the evolutionary ecology of individual organisms [80]. Considering the exceptionally high microbial diversity in sponges, it is always tempting to conclude a non-random pattern of microbial association, where microbial taxa are segregated and exhibit close relationships. However, our results do not support a non-random microbial association in the sponges studied (i.e., significantly less observed C-score; Fig 6), which suggests that other factors may influence the structuring of the microbial assemblage in sponges. Co-occurrence analysis considered the bacterial communities among varying gradients like different human genotypes [81] and different soil types [35], validating the non-random association. Even though we tested the co-occurrence hypothesis among the sponges sampled from similar external gradients (temperature, salinity and pH), there might be oscillating internal biotic factors among the sponge species influencing the microbial community structuring. Abiotic factors, mainly temperature, can substantially influence the sponge holobionts due to the removal of symbionts and the immediate introduction of opportunistic bacteria [82]. Although there are widespread competition between microbes for resources, its detection in natural environment is not trivial [80]. Our current dataset is not exhaustive to explain the co-occurrence pattern in sponge microbes, and further studies should follow with wide sampling from different ecosystems.

Sponge-associated microbes are not restricted to its host

It is noteworthy that nearly 73% of the microbial groups retrieved in this study were represented both in sponges and seawater samples. A decade ago sponge microbiology proposed two definitions—‘sponge-specific’ and ‘sponge-species-specific’ [31] to describe the particular nature of the microbial association with sponges which was further validated later [83]. In our study we have not defined the “sponge-specific” or “sponge-species-specific” microbial communities due to the recurrent presence of similar microbes among the sponge species and ambient seawater. It is clear from our dataset that Chlamydiae, Nitrospirae, and the two candidate phyla SBR1093 and TM6 were the only microbial groups found exclusively in association with the sponges. A recent comprehensive study [84] substantiated the widespread (but rare) existence of microbes in diverse marine environments previously thought to be present only in sponges. The presence of such microbes suggests its ability to survive outside the sponge host, which may serve as a ‘seed bank’ for the colonization of sponges [7]. Further rigorous sampling and deep-sequencing could provide valuable knowledge to understand the nature of the microbial specificity among these sponges.


Here, we provide the first report on the sponge-associated microbial communities in intertidal Atlantic sponges. 16S rRNA tag pyrosequencing exposed a diverse and complex nature of sponge-associated microbes among 12 different co-occurring intertidal sponges (class Demospongiae) from the Atlantic coast of Portugal. OTU definition of microbial communities at 97% similarity threshold revealed altogether 26 different microbial groups, including new bacterial groups (candidate phyla NKB19 and ZB2) not detected previously in sponges. Comparison of the sponge-associated archaeal communities suggests similarity of Crenarchaeota between geographically isolated Atlantic Ocean and distant Red Sea sponges. However, the observation of sponge-associated microbial communities in ambient seawater suggests that they can be widespread in marine environments, existing outside of the sponge body either in an active or inactive state. The flexibility of sponge-associated microbes to either flourish in seawater or in association with sponge host in later stages of life may contribute to the lack of co-occurrence patterns. Further detailed evaluation using different gradients at spatial and temporal scale would be insightful to clarify the co-occurrence pattern among sponges.

Supporting Information

S1 Fig. Total number of OTUs represented at different similarity thresholds.

Pyrosequencing reads are grouped at various sequence similarity revealing lesser number of OTUs with less sequence similarities.


S2 Fig. Graph representing total number of sequencing reads and OTUs recovered from the samples.

A. Number of reads retrieved from each sample after quality filtering. B. Number of OTUs represented in each sample. Detailed information about sample codes is provided in Table 1.


S3 Fig. Rank abundance curve showing diversity of bacterial communities associated with sponge species and seawater (SW).

Detailed information about sample codes is provided in Table 1.


S4 Fig. Relative abundance of Proteobacteria among the sample sources.

Detailed information about sample codes is provided in Table 1.


S5 Fig. Relative abundance of archaea bacteria from the samples.

(A) Relative abundance of the phyla Crenarchaeota and Euryarchaeota. (B) Abundance of archaea bacteria at lower taxonomic level. Sample code details are given in Table 1.


S6 Fig. Pairwise comparison of the microbial communities amongst the different sponge species and seawater (SW).

Heat map representing the p-values obtained from the p-test [86] performed for each pair of samples. Comparison of differentiation among microbes in all possible pair of samples was calculated using phylogenetic information. Similarities between microbial communities are estimated as the number of parsimony changes and the p-values (< 0.01) explains the probability that the assigned sample pairs are dissimilar. Colors are coded in the cell according to significance values. Sample code details are given in Table 1.


S7 Fig. Heat map showing pairwise dissimilarity between intergeneric sponge-associated bacterial communities.

Beta-diversity metrics, pearson distance was used to compute the differences among microbes in three different sponge species, Polymastia agglutinans (PAG), Polymastia penicillus (POLY) and Polymastia sp. (POL). The resulting distance matrices were plotted as a heat map, with increasing grades of blue color representing greater dissimilarity.


S1 Table. List of samples and respective barcodes.

Multiplex Identifiers (MID) were attached with 16S rRNA primer for amplifying each sample and used like a barcode to identify amplicons or samples during pyrosequencing.


S2 Table. Statistical test of sample groupings.

Statistical significance of grouping of the Atlantic Ocean (this study) and the Red Sea samples analyzed with function in QIIME using Bray-Curtis distance matrix derived from the Crenarchaeota communities. The symbol ‘*’ represents significant p-values obtained from the test.



We thank Ana Regueiras and Sofia Costa for help with the sponge sampling and the anonymous reviewers for helpful comments on an earlier version of the manuscript.

Author Contributions

Conceived and designed the experiments: A. Alex A. Antunes. Performed the experiments: A. Alex. Analyzed the data: A. Alex A. Antunes. Contributed reagents/materials/analysis tools: A. Antunes. Wrote the paper: A. Alex A. Antunes.


  1. 1. Vacelet J, Donadey C. Electron microscope study of the association between some sponges and bacteria. J Exp Mar Biol Ecol. 1977;30: 301–314.
  2. 2. Zhu P, Li Q, Wang G. Unique microbial signatures of the alien Hawaiian marine sponge Suberites zeteki. Microb Ecol. 2008;55: 406–414. pmid:17676375
  3. 3. Erwin PM, Olson JB, Thacker RW. Phylogenetic diversity, host-specificity and community profiling of sponge-associated bacteria in the northern Gulf of Mexico. PloS One. 2011;6: e26806. pmid:22073197
  4. 4. Alex A, Silva V, Vasconcelos V, Antunes A. Evidence of unique and generalist microbes in distantly related sympatric intertidal marine sponges (porifera: demospongiae). PloS One. 2013;8: e80653. pmid:24265835
  5. 5. Li Z-Y, He L-M, Wu J, Jiang Q. Bacterial community diversity associated with four marine sponges from the South China Sea based on 16S rDNA-DGGE fingerprinting. J Exp Mar Biol Ecol. 2006;329: 75–85.
  6. 6. Anderson SA, Northcote PT, Page MJ. Spatial and temporal variability of the bacterial community in different chemotypes of the New Zealand marine sponge Mycale hentscheli. FEMS Microbiol Ecol. 2010;72: 328–342. pmid:20412301
  7. 7. Webster NS, Taylor MW, Behnam F, Lücker S, Rattei T, Whalan S, et al. Deep sequencing reveals exceptional diversity and modes of transmission for bacterial sponge symbionts. Environ Microbiol. 2010;12: 2070–2082. pmid:21966903
  8. 8. Lee OO, Wang Y, Yang J, Lafi FF, Al-Suwailem A, Qian P-Y. Pyrosequencing reveals highly diverse and species-specific microbial communities in sponges from the Red Sea. ISME J. 2011;5: 650–664. pmid:21085196
  9. 9. Schmitt S, Tsai P, Bell J, Fromont J, Ilan M, Lindquist N, et al. Assessing the complex sponge microbiota: core, variable and species-specific bacterial communities in marine sponges. ISME J. 2012;6: 564–576. pmid:21993395
  10. 10. Hentschel U, Usher KM, Taylor MW. Marine sponges as microbial fermenters. FEMS Microbiol Ecol. 2006;55: 167–177. pmid:16420625
  11. 11. Taylor MW, Hill RT, Piel J, Thacker RW, Hentschel U. Soaking it up: the complex lives of marine sponges and their microbial associates. ISME J. 2007;1: 187–190. pmid:18043629
  12. 12. Webster NS, Taylor MW. Marine sponges and their microbial symbionts: love and other relationships. Environ Microbiol. 2012;14: 335–346. pmid:21443739
  13. 13. Wilkinson CC, Garrone RR. Nutrition of marine sponges. Involvement of symbiotic bacteria in the uptake of dissolved carbon. Nutr Low Metazoa-Pages 157–161. 1980;
  14. 14. Yahel G, Sharp JH, Marie D, Häse C, Genin A. In situ feeding and element removal in the symbiont-bearing sponge Theonella swinhoei: Bulk DOC is the major source for carbon. Limnol Oceanogr. 2003;48: 141–149.
  15. 15. Wilkinson CR. Net primary productivity in coral reef sponges. Science. 1983;219: 410–412. pmid:17815320
  16. 16. Venn AA, Loram JE, Douglas AE. Photosynthetic Symbioses in Animals. J Exp Bot. 2008;59: 1069–1080. pmid:18267943
  17. 17. Unson MD, Holland ND, Faulkner DJ. A brominated secondary metabolite synthesized by the cyanobacterial symbiont of a marine sponge and accumulation of the crystalline metabolite in the sponge tissue. Mar Biol. 1994;119: 1–11.
  18. 18. Chanas B, Pawlik JR, Lindel T, Fenical W. Chemical defense of the Caribbean sponge Agelas clathrodes (Schmidt). J Exp Mar Biol Ecol. 1997;208: 185–196.
  19. 19. Becerro MA, Thacker RW, Turon X, Uriz MJ, Paul VJ. Biogeography of sponge chemical ecology: comparisons of tropical and temperate defenses. Oecologia. 2003;135: 91–101. pmid:12647108
  20. 20. Pawlik JR. The Chemical Ecology of Sponges on Caribbean Reefs: Natural Products Shape Natural Systems. BioScience. 2011;61: 888–898.
  21. 21. Wilkinson CR. Microbial associations in sponges. I. Ecology, physiology and microbial populations of coral reef sponges. Mar Biol. 1978;49: 161–167.
  22. 22. Hoffmann F, Radax R, Woebken D, Holtappels M, Lavik G, Rapp HT, et al. Complex nitrogen cycling in the sponge Geodia barretti. Environ Microbiol. 2009;11: 2228–2243. pmid:19453700
  23. 23. Schläppy M-L, Schöttner SI, Lavik G, Kuypers MMM, de Beer D, Hoffmann F. Evidence of nitrification and denitrification in high and low microbial abundance sponges. Mar Biol. 2010;157: 593–602. pmid:24391241
  24. 24. Steindler L, Beer S, Ilan M. Photosymbiosis in intertidal and subtidal tropical sponges. Symbiosis. 2002;33: 263–273.
  25. 25. Preston CM, Wu KY, Molinski TF, DeLong EF. A psychrophilic crenarchaeon inhabits a marine sponge: Cenarchaeum symbiosum gen. nov., sp. nov. Proc Natl Acad Sci. 1996;93: 6241–6246. pmid:8692799
  26. 26. Fieseler L, Horn M, Wagner M, Hentschel U. Discovery of the Novel Candidate Phylum “Poribacteria” in Marine Sponges. Appl Env Microbiol. 2004;70: 3724–3732. pmid:15184179
  27. 27. Enticknap JJ, Kelly M, Peraud O, Hill RT. Characterization of a Culturable Alphaproteobacterial Symbiont Common to Many Marine Sponges and Evidence for Vertical Transmission via Sponge Larvae. Appl Environ Microbiol. 2006;72: 3724–3732. pmid:16672523
  28. 28. Montalvo NF, Hill RT. Sponge-Associated Bacteria Are Strictly Maintained in Two Closely Related but Geographically Distant Sponge Hosts. Appl Environ Microbiol. 2011;77: 7207–7216. pmid:21856832
  29. 29. Erwin PM, López-Legentil S, González-Pech R, Turon X. A specific mix of generalists: bacterial symbionts in Mediterranean Ircinia spp. FEMS Microbiol Ecol. 2012;79: 619–637. pmid:22092516
  30. 30. Lee OO, Wong YH, Qian P-Y. Inter- and Intraspecific Variations of Bacterial Communities Associated with Marine Sponges from San Juan Island, Washington. Appl Environ Microbiol. 2009;75: 3513–3521. pmid:19363076
  31. 31. Hentschel U, Hopke J, Horn M, Friedrich AB, Wagner M, Hacker J, et al. Molecular Evidence for a Uniform Microbial Community in Sponges from Different Oceans. Appl Env Microbiol. 2002;68: 4431–4440. pmid:12200297
  32. 32. Taylor MW, Radax R, Steger D, Wagner M. Sponge-Associated Microorganisms: Evolution, Ecology, and Biotechnological Potential. Microbiol Mol Biol Rev. 2007;71: 295–347. pmid:17554047
  33. 33. Fuhrman JA, Steele JA. Community structure of marine bacterioplankton: patterns, networks, and relationships to function. Aquat Microb Ecol. 2008;53: 69–81.
  34. 34. Chaffron S, Rehrauer H, Pernthaler J, von Mering C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 2010;20: 947–959. pmid:20458099
  35. 35. Barberán A, Bates ST, Casamayor EO, Fierer N. Using network analysis to explore co-occurrence patterns in soil microbial communities. ISME J. 2012;6: 343–351. pmid:21900968
  36. 36. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7: 335–336. pmid:20383131
  37. 37. Reeder J, Knight R. Rapidly denoising pyrosequencing amplicon reads by exploiting rank-abundance distributions. Nat Methods. 2010;7: 668–669. pmid:20805793
  38. 38. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27: 2194–2200. pmid:21700674
  39. 39. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinforma Oxf Engl. 2010;26: 2460–2461.
  40. 40. Wang Q, Garrity GM, Tiedje JM, Cole JR. Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Appl Environ Microbiol. 2007;73: 5261–5267. pmid:17586664
  41. 41. McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6: 610–618. pmid:22134646
  42. 42. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75: 7537–7541. pmid:19801464
  43. 43. Chao A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics. 1987;43: 783–791. pmid:3427163
  44. 44. Chao A, Lee S-M. Estimating the Number of Classes via Sample Coverage. J Am Stat Assoc. 1992;87: 210–217.
  45. 45. Shannon CE. A Mathematical Theory of Communication. Bell Syst Tech J. 1948;27: 379–423.
  46. 46. Simpson EH. Measurement of diversity. Nature. 1949;163: 688.
  47. 47. McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE. 2013;8: e61217. pmid:23630581
  48. 48. Stone L, Roberts A. The checkerboard score and species distributions. Oecologia. 1990;85: 74–79.
  49. 49. Gotelli NJ. Null model analysis of species co-occurence patterns. Ecology. 2000;81: 2606–2621.
  50. 50. Gotelli NJ, Entsminger GL. EcoSim: Null models software for ecology. [Internet]. Acquired Intelligence Inc. & Kesey-Bear. Jericho, VT 05465.; Available:
  51. 51. Soberón J. Pairwise versus presence–absence approaches for analysing biodiversity patterns. J Biogeogr. 2015; n/a–n/a.
  52. 52. Sokal R, Michener C. A statistical method for evaluating systematic relationships. Univ Kans Sci Bull. 1958;28: 1409–1438.
  53. 53. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, et al. vegan: Community Ecology Package [Internet]. 2013. Available:
  54. 54. Webster NS, Hill RT. The culturable microbial community of the Great Barrier Reef sponge Rhopaloeides odorabile is dominated by an α-Proteobacterium. Mar Biol. 2001;138: 843–851.
  55. 55. Karner MB, DeLong EF, Karl DM. Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature. 2001;409: 507–510. pmid:11206545
  56. 56. Galand PE, Casamayor EO, Kirchman DL, Potvin M, Lovejoy C. Unique archaeal assemblages in the Arctic Ocean unveiled by massively parallel tag sequencing. ISME J. 2009;3: 860–869. pmid:19322244
  57. 57. Blöchl E, Burggraf S, Fiala G, Lauerer G, Huber G, Huber R, et al. Isolation, taxonomy and phylogeny of hyperthermophilic microorganisms. World J Microbiol Biotechnol. 1995;11: 9–16. pmid:24414408
  58. 58. Fuchs T, Huber H, Teiner K, Burggraf S, Stetter KO. Metallosphaera prunae, sp. nov., a Novel Metal-mobilizing, Thermoacidophilic Archaeum, Isolated from a Uranium Mine in Germany. Syst Appl Microbiol. 1995;18: 560–566.
  59. 59. DeLong EF. Archaea in coastal marine environments. Proc Natl Acad Sci U S A. 1992;89: 5685–5689. pmid:1608980
  60. 60. DeLong EF, Wu KY, Prézelin BB, Jovine RV. High abundance of Archaea in Antarctic marine picoplankton. Nature. 1994;371: 695–697. pmid:7935813
  61. 61. McInerney JO, Wilkinson M, Patching JW, Embley TM, Powell R. Recovery and phylogenetic analysis of novel archaeal rRNA sequences from a deep-sea deposit feeder. Appl Environ Microbiol. 1995;61: 1646–1648. pmid:7538283
  62. 62. Lee E-Y, Lee HK, Lee YK, Sim CJ, Lee J-H. Diversity of symbiotic archaeal communities in marine sponges from Korea. Biomol Eng. 2003;20: 299–304. pmid:12919812
  63. 63. Turque AS, Batista D, Silveira CB, Cardoso AM, Vieira RP, Moraes FC, et al. Environmental Shaping of Sponge Associated Archaeal Communities. PLoS ONE. 2010;5: e15774. pmid:21209889
  64. 64. Webster NS, Negri AP, Munro MMHG, Battershill CN. Diverse microbial communities inhabit Antarctic sponges. Environ Microbiol. 2004;6: 288–300. pmid:14871212
  65. 65. Agogué H, Brink M, Dinasquet J, Herndl GJ. Major gradients in putatively nitrifying and non-nitrifying Archaea in the deep North Atlantic. Nature. 2008;456: 788–791. pmid:19037244
  66. 66. Lemloh M-L, Fromont J, Brümmer F, Usher K. Diversity and abundance of photosynthetic sponges in temperate Western Australia. BMC Ecol. 2009;9: 4. pmid:19196460
  67. 67. Alex A, Vasconcelos V, Tamagnini P, Santos A, Antunes A. Unusual Symbiotic Cyanobacteria Association in the Genetically Diverse Intertidal Marine Sponge Hymeniacidon perlevis (Demospongiae, Halichondrida). PloS One. 2012;7: e51834. pmid:23251637
  68. 68. Steindler L, Huchon D, Avni A, Ilan M. 16S rRNA phylogeny of sponge-associated cyanobacteria. Appl Environ Microbiol. 2005;71: 4127–4131. pmid:16000832
  69. 69. Schmidt EW, Obraztsova AY, Davidson SK, Faulkner DJ, Haygood MG. Identification of the antifungal peptide-containing symbiont of the marine sponge Theonella swinhoei as a novel δ-proteobacterium, “Candidatus Entotheonella palauensis.” Mar Biol. 2000;136: 969–977.
  70. 70. Schirmer A, Gadkari R, Reeves CD, Ibrahim F, DeLong EF, Hutchinson CR. Metagenomic analysis reveals diverse polyketide synthase gene clusters in microorganisms associated with the marine sponge Discodermia dissoluta. Appl Environ Microbiol. 2005;71: 4840–4849. pmid:16085882
  71. 71. Bruck WM, Sennett SH, Pomponi SA, Willenz P, McCarthy PJ. Identification of the bacterial symbiont Entotheonella sp. in the mesohyl of the marine sponge Discodermia sp. ISME J. 2008;2: 335–339. pmid:18256706
  72. 72. Hugenholtz P, Pitulle C, Hershberger KL, Pace NR. Novel division level bacterial diversity in a Yellowstone hot spring. J Bacteriol. 1998;180: 366–376. pmid:9440526
  73. 73. Schmitt S, Angermeier H, Schiller R, Lindquist N, Hentschel U. Molecular microbial diversity survey of sponge reproductive stages and mechanistic insights into vertical transmission of microbial symbionts. Appl Environ Microbiol. 2008;74: 7694–7708. pmid:18820053
  74. 74. Ereskovsky AV, Gonobobleva E, Vishnyakov A. Morphological evidence for vertical transmission of symbiotic bacteria in the viviparous sponge Halisarca dujardini Johnston (Porifera, Demospongiae, Halisarcida). Mar Biol. 2005;146: 869–875.
  75. 75. De Caralt S, Uriz MJ, Wijffels RH. Vertical transmission and successive location of symbiotic bacteria during embryo development and larva formation in Corticium candelabrum (Porifera: Demospongiae). J Mar Biol Assoc UK. 2007;87.
  76. 76. Croué J, West NJ, Escande M-L, Intertaglia L, Lebaron P, Suzuki MT. A single betaproteobacterium dominates the microbial community of the crambescidine-containing sponge Crambe crambe. Sci Rep. 2013;3.
  77. 77. Sharp KH, Eam B, Faulkner DJ, Haygood MG. Vertical transmission of diverse microbes in the tropical sponge Corticium sp. Appl Environ Microbiol. 2007;73: 622–629. pmid:17122394
  78. 78. Horner-Devine MC, Silver JM, Leibold MA, Bohannan BJM, Colwell RK, Fuhrman JA, et al. A comparison of taxon co-occurrence patterns for macro- and microorganisms. Ecology. 2007;88: 1345–1353. pmid:17601127
  79. 79. Prosser JI, Bohannan BJM, Curtis TP, Ellis RJ, Firestone MK, Freckleton RP, et al. The role of ecological theory in microbial ecology. Nat Rev Microbiol. 2007;5: 384–392. pmid:17435792
  80. 80. Konopka A. What is microbial community ecology? ISME J. 2009;3: 1223–1230. pmid:19657372
  81. 81. Ren T, Glatt DU, Nguyen TN, Allen EK, Early SV, Sale M, et al. 16S rRNA survey revealed complex bacterial communities and evidence of bacterial interference on human adenoids. Environ Microbiol. 2013;15: 535–547. pmid:23113966
  82. 82. Fan L, Liu M, Simister R, Webster NS, Thomas T. Marine microbial symbiosis heats up: the phylogenetic and functional response of a sponge holobiont to thermal stress. ISME J. 2013;7: 991–1002. pmid:23283017
  83. 83. Simister RL, Deines P, Botté ES, Webster NS, Taylor MW. Sponge-specific clusters revisited: a comprehensive phylogeny of sponge-associated microorganisms. Environ Microbiol. 2012;14: 517–524. pmid:22151434
  84. 84. Taylor MW, Tsai P, Simister RL, Deines P, Botte E, Ericson G, et al. “Sponge-specific” bacteria are widespread (but rare) in diverse marine environments. ISME J. 2013;7: 438–443. pmid:23038173
  85. 85. Wickham H. ggplot2: elegant graphics for data analysis [Internet]. Springer New York; 2009. Available:
  86. 86. Martin AP. Phylogenetic Approaches for Describing and Comparing the Diversity of Microbial Communities. Appl Environ Microbiol. 2002;68: 3673–3682. pmid:12147459