Response of bacterial colonization in Nematostella vectensis to development, environment and biogeography.

Environmental Microbiology Microbiology (2016) (2015) 18(6), 1764–1781

doi:10.1111/1462-2920.12926 doi:10.1111/1462-2920.12926

Response of bacterial colonization in Nematostella vectensis to development, environment and biogeography

Benedikt M. Mortzfeld,1 Szymon Urbanski,1 Adam M. Reitzel,2 Sven Künzel,3 Ulrich Technau4 and Sebastian Fraune1* 1 Zoological Institute, Christian-Albrechts University Kiel, Olshausenstrasse 40, Kiel 24098, Germany. 2 Department of Biological Sciences, The University of North Carolina at Charlotte, Woodward Hall 245, Charlotte, NC 28223, USA. 3 Max-Planck Institute for Evolutionary Biology, Plön 24306, Germany. 4 Department of Molecular Evolution and Development, Centre for Organismal Systems Biology, Faculty of Life Sciences, University of Vienna, Althanstrasse 14, Wien 1090, Austria. Summary The establishment of host–bacterial colonization during development is a fundamental process influencing the fitness of many organisms, but the factors controlling community membership and influencing the establishment of the microbial ecosystem during development are poorly understood. The starlet sea anemone Nematostella vectensis serves as a cnidarian model organism due to the availability of laboratory cultures and its high tolerance for broad ranges of salinity and temperature. Here, we show that the anemone’s epithelia are colonized by diverse bacterial communities and that the composition of its microbiota is tightly coupled to host development. Environmental variations led to robust adjustments in the microbial composition while still maintaining the ontogenetic core signature. In addition, analysis of bacterial communities of Nematostella polyps from five different populations revealed a strong correlation between host biogeography and bacterial diversity despite years of laboratory culturing. These observed variations in fine-scale community composition following environmental change and for individuals from different geographic origins could Received 17 February, 2015; revised 4 May, 2015; accepted 4 May, 2015. *For correspondence. E-mail [email protected]; Tel. (+49) 431 880 4149; Fax (+49) 431 880 4747. C 2015 Society for © for Applied Applied Microbiology Microbiology and and John John Wiley Wiley && Sons Sons Ltd Ltd V

represent the microbiome’s contribution to host acclimation and potentially adaptation, respectively, and thereby contribute to the maintenance of homeostasis due to environmental changes.

Introduction The diversity of microbes colonizing a multicellular organism is proposed to be a result of coevolution between the eukaryotic species and the associated microbial community, influenced by both environment and host (Ley et al., 2006). Recently, animal–microbe interactions have gained widespread recognition as significant drivers of animal evolution and diversification (Brucker and Bordenstein, 2012; 2013; McFall-Ngai et al., 2013). Epithelia in metazoans select their microbiota, resulting in a coevolved consortium of microbes enabling both invertebrates and vertebrates to expand the range of diet supply, to shape the immune system and to control pathogenic bacteria (Fraune and Bosch, 2010; McFall-Ngai et al., 2013). These discoveries point to a key role for bacteria in the fitness of an organism and imply that beneficial host– bacteria interactions should be considered as an integral part of development and evolution of host organisms. Mutualistic associations between animals and microbes can evolve by distinct selective forces. Because association with a beneficial microbiota increases the host’s fitness, selective pressures should act on regulatory mechanisms in the host, e.g. immune system, ensuring suitable bacterial colonization. Additionally, vertically transmitted bacteria are often selected for being beneficial to the host, since the increase in the host’s fitness ensures the future availability of the host as habitat (Ley et al., 2006). These interlinked dependencies between the host and its associated microbes led to the ‘hologenome theory of evolution’, considering the holobiont as a unit undergoing natural selection (Rosenberg et al., 2007). The genome of the host organism, together with the genomes of its associated microbes, provides immense potential for adaptation to changing environmental conditions (Rosenberg et al., 2007) by changes in the abundance of associated bacteria or uptake of new bacterial symbionts (Reshef et al., 2006).

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Cnidaria belong to an early branching group of metazoans and have preserved much of the genetic complexity of the common metazoan ancestor (Technau et al., 2005). Thus, general principles of epithelial host–microbe interactions that are functional in all metazoan life – including humans – can be investigated in this group. In addition, cnidarians are predominately coastal species and thus experience both natural and anthropogenic environmental variation that may result in shifts in the microbial community during acclimation to new conditions. Colonizing bacteria are a vital component of cnidarian holobionts (Lesser et al., 2004; Fraune and Bosch, 2007; Fiore et al., 2010; Fraune et al., 2014), and detailed analysis of the bacterial communities associated with different Hydra species has revealed that these associations are highly species-specific and reflect the phylogenetic relationships of its hosts, supporting a hypothesis for coevolution (Franzenburg et al., 2013b). Here, we used Nematostella vectensis, a sea anemone belonging to class Anthozoa in the phylum Cnidaria, to uncover general factors determining bacterial colonization, including host development, environment and host biogeography. Nematostella polyps inhabit sediments of brackish water, salt marshes and lagoons, and are commonly found along the Pacific, Atlantic and Gulf of Mexico coasts of North America and the southeastern portion of England. Thus, Nematostella occupies a large range of environmental conditions concerning temperature and salinity (Sheader et al., 1997; Reitzel et al., 2013). Nematostella populations show significant genetic differentiation among locations (Pearson et al., 2002; Darling et al., 2004; 2009; Reitzel et al., 2008) and strong phylogeographic structure (Reitzel et al., 2013). They also possess genetic variation indicative of selection shown by single nucleotide polymorphisms (SNPs) from expressed sequence tags and genome sequencing (Sullivan et al., 2009; Reitzel et al., 2010; 2013b). This genetic diversity is mirrored by phenotypic variation along the Atlantic coast, including significant differences in thermal tolerance dependent on latitude and a tenfold difference in temperature-dependent growth rates (Reitzel et al., 2013a). Although there is significant genetic and phenotypic variation between populations of Nematostella, individuals from geographically distant populations are interfertile (Hand and Uhlinger, 1994; Reitzel et al., 2008), supporting them as all belonging to the same species. Population genetic analyses as well as laboratory culturing have also shown that this species routinely reproduces both sexually and asexually. For sexual reproduction, eggs embedded in a matrix of gelatinous material and sperm are produced by germ line cells in the mesenteries and released into the surrounding water (Hand and Uhlinger, 1992). Spawning is induced by a shift in temperature and exposure to light (Fritzenwanker and Technau, 2002). The ontogeny of Nematostella is

Bacterial colonization in Nematostella vectensis 1765 characterized by two major developmental transitions. The first transition occurs approximately 6–14 days postfertilization (dpf) when the planula larvae metamorphose first into a primary polyp with two mesenteries and four tentacles, and later into a juvenile polyp with eight mesenteries and several tentacles (Reitzel et al., 2007). These juvenile polyps need up to six more months to reach the second major event, the transition from a juvenile to a sexually mature polyp. These characteristics, together with the exceptionally high acclimation potential to varying abiotic factors, support Nematostella as a suitable model organism to understand how environmental factors affect the composition and function of microbiota and the consequences of host–microbe interactions during rapid acclimation of a holobiont to changing environmental conditions and adaptation in separate geographic regions. To elucidate which factors influence bacterial community assembly in Nematostella, we profiled the composition of the microbiota at various developmental stages over a period of more than one year under different environmental conditions. In addition, we analysed the bacterial communities of Nematostella polyps from five different populations, revealing a strong correlation between host biogeography and bacterial diversity. To this end, we identified the bacterial succession pattern during Nematostella development and influencing environmental factors. Results Impact of development and environment on bacterial colonization To quantify the establishment of the microbiota throughout the ontogenetic development of Nematostella under different environmental impacts, we created 15 independent laboratory cultures under different environmental conditions altering temperature and salinity. Samples were preserved at specific time points from first cleavage events in embryogenesis (0.5 dpf) up to 401 dpf (reproductive adult) (Fig. 1A). Following total DNA extraction from five pooled animals, the microbial composition was determined via 454-pyrosequencing of the variable regions 1 and 2 of the bacterial 16S rRNA genes. This approach resulted in 331 554 sequences distributed over 88 samples. For analysis, the sequences were grouped into operational taxonomic units (OTUs) based on 97% sequence similarity. This resulted in a total of 508 bacterial OTUs present with at least 10 sequence reads. The numbers of reads per sample were normalized to the lowest coverage of 1500 reads per sample (Table S1). Determining the impact of developmental time and environmental conditions on the assemblage of the bacterial community, we performed principal coordinates of analysis (PCoA) (Fig. 1). Assigning the samples by the

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1766 B. M. Mortzfeld et al.

Bacterial colonization in Nematostella vectensis

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Fig. 1. Analysis of bacterial communities associated with Nematostella vectensis in the lab using principal coordinates analysis (PCoA) of the binary Pearson distance matrix. All three PCoA plots are based on the same distance matrix containing 88 samples. The per cent variation explained by the PCoA is indicated on the axes. A. PCoA illustrating similarity of bacterial communities based on developmental time points. B. PCoA illustrating similarity of bacterial communities based on developmental phase. C. PCoA illustrating similarity of bacterial communities based on environmental conditions.

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Bacterial colonization in Nematostella vectensis 1767


Table 1. Statistical analysis determining influence of developmental time points, developmental phases and environmental conditions on the bacterial colonization. Adonis

Time points

Dev. phase

Env. condition

2

Anosim

Metric

R

P value

R

P value

Binary Pearson Binary Jaccard Binary Sörensen-Dice Unweighted Unifrac Pearson Abund. Jaccard Bray–Curtis Weighted Unifrac Binary Pearson Binary Jaccard Binary Sörensen-Dice Unweighted Unifrac Pearson Abund. Jaccard Bray–Curtis Weighted Unifrac Binary Pearson Binary Jaccard Binary Sörensen-Dice Unweighted Unifrac Pearson Abund. Jaccard Bray–Curtis Weighted Unifrac

0.534 0.422 0.533 0.466 0.633 0.831 0.530 0.482 0.292 0.201 0.285 0.223 0.386 0.567 0.286 0.227 0.089 0.074 0.087 0.069 0.065 0.088 0.070 0.083

0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.039* 0.011* 0.003** 0.006**

0.838 0.832 0.832 0.830 0.602 0.794 0.705 0.511 0.773 0.751 0.751 0.673 0.569 0.697 0.630 0.371 0.174 0.179 0.179 0.105 −0.035 0.079 −0.011 0.059

0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.011* 0.815 0.028* 0.622 0.065

Levels of siginificance (*P < 0.05, **P < 0.01, ***P > 0.001)

developmental stage revealed a strong temporal, but conserved progression of the bacterial community from the earliest developmental time points up to 401 dpf (Fig. 1A). Using eight different beta diversity metrics, we found the bacterial colonization to be highly significantly influenced by developmental time (Table 1). Thereby, the bacterial communities are clustered into three major groups reflecting the three major developmental phases of Nematostella (Fig. 1B). The first group spans the early developmental stages until the metamorphosis into a juvenile polyp, while the second group includes the juvenile polyps (22–144 dpf), and the third group the adult, sexually mature polyps (253–401 dpf) (Fig. 1B). In contrast, environmental conditions are a weak predictor for bacterial colonization within the total dataset (Fig. 1C). Only three unweighted metrics, binary Pearson, binary Jaccard and binary Sörensen-Dice, are significantly influenced by environmental conditions, but with weak effect sizes (Table 1). Therefore, the changes in the bacterial colonization caused by the ontogenetic progression exceed the impact of the analysed abiotic factors, temperature and salinity, in Nematostella. Bacterial colonization during development is established in three major steps To uncover the patterns of bacterial assembly during ontogeny in more detail, we analysed the samples

(n = 46) taken under standard conditions (18°C/16‰) separately. We determined the composition of the associated bacterial species within the three developmental phases by combining samples that correspond to the developmental time points (Fig. 2A). In total, 15 bacterial phyla were identified, with the three most abundant being Proteobacteria, Bacteroidetes and Spirochaetes (Fig. 2A, Fig. S1). The most abundant phylum, the Proteobacteria, accounts for more than 55% of the total bacterial diversity in all developmental phases. While during developmental time β-Proteobacteria, Actinobacteria and Bacteroidetes decrease, the Spirochaetes became one of the most abundant bacterial groups in sexually mature polyps (≥ 253 dpf) (Fig. 2A, Fig. S1). To detect differential abundance of bacterial groups between the developmental phases, we used the linear discriminant analysis effect size (LEfSe) (Segata et al., 2011), a tool for biomarker discovery. LEfSe determines features most likely to explain differences between samples by using statistical significance, biological consistency and effect relevance. LEfSe detected seven bacterial classes with statistically significant differences between the three developmental phases of Nematostella (Fig. 2B). The bacterial classes with differential abundance in early developmental stages when compared with the other two developmental phases belonged to the bacterial phyla Bacteroidetes (Flavobacteriia) and to the both Gram-positive bacterial groups Firmicutes (Bacilli) and

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Fig. 2. Analysis of bacterial communities associated with Nematostella vectensis at different developmental stages. A. Pie charts representing the mean relative abundance (early stages n = 15, juvenile polyps n = 15, adult polyps n = 16) of bacterial classes at the three developmental phases. B. LEfSe results of bacterial colonization during development. The cladogram reports the taxa (highlighted by small circles and by shading) showing significant different abundance values (according to LEfSe) in the three developmental phases; for each taxon, the colour denotes significant overrepresentation of bacterial groups for one of the three developmental phases for both the circles and the shading. C. Venn diagram showing the numbers of OTUs specific for each developmental phase. OTUs were filtered using a threshold of at least 50 reads in all samples under standard environmental conditions (18°C/16‰). D. Chao1 estimated alpha diversities at the different developmental time points. Values were estimated using 1500 reads per sample. Statistics were carried out using one-way ANOVA with Dunnett’s multiple comparison test (**P < 0.01).


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Actinobacteria (Actinobacteria) (Fig. 2B). In contrast, β-Proteobacteria were significantly overrepresented in juvenile polyps, while Gram-positive bacteria were nearly completely eliminated. In adult polyps, bacterial classes of the phyla Proteobacteria (γ-Proteobacteria), Spirochaetes (Spirochaetes) and Planctomycetes (Phycisphaerae) were significantly more abundant (Fig. 2B). The three-step assembly of the bacterial community during life cycle was also reflected at the OTU level (Fig. 2C, Table S2). Using analysis of variance, we identified for each developmental phase indicator OTUs colonizing specifically the host tissue at each stage (Table S2). For the early developmental phase, we identified 18 specific OTUs (Fig. 2C), with the most abundant OTUs being 2214 (α-Proteobacteria, Rhodobacterales, Rhodobacteraceae) and 2298 (Bacteroidetes, Flavobacteriia, Flavobacteriales, Flavobacteriaceae) (Table S2). In contrast, after the metamorphosis to juvenile polyps, 13 different OTUs were significantly more abundant, with the most abundant OTUs being 1209 (γ-Proteobacteria, Vibrionales), 219 (Bacteroidetes, Flavobacteriia, Flavobacteriales, Cryomorphaceae) and 194 (α-Proteobacteria, Rhodobacterales, Rhodobacteraceae). Following sexual maturation, 10 other OTUs colonized the polyps. The two most abundant OTUs in adult polyps were OTU 1473 (Spirochaetes, Spirochaetes) and OTU 1576 (γ-Proteobacteria, Legionellales, Francisellaceae) (Table S2). To verify that the stepwise assembly of the bacterial community during development was also reflected with changes in the total bacterial diversity, we estimated the number of OTUs over ontogeny using the Chao1 metric (Fig. 2D). The alpha diversity was highest directly after fertilization and decreased significantly in the following 3 weeks from ∼ 125 OTUs to ∼ 60 OTUs. After 40 dpf, the number of OTUs rose again to ∼ 90 OTUs: after 50 dpf, the alpha diversity stabilized at 80–100 OTUs, with one peak at 253 dpf accounting for ∼ 120 OTUs during the phase of sexual maturation. Interestingly, the principal bottleneck of bacterial diversity occurred during the metamorphosis of Nematostella. While analysing the bacterial colonization of male and female polyps, no differences in their bacterial colonization were detectable (Fig. S2). Environmental variations and robust changes in bacterial colonization To investigate the influence of the abiotic factors, temperature and salinity, on the bacterial composition, we analysed the beta diversity of bacterial communities between the different environmental conditions at four different developmental time points separately (Table S3). We chose one time point within the early developmental phase (planula), two time points within juvenile stage (40 dpf, 123 dpf) and one time point of adult polyps

Bacterial colonization in Nematostella vectensis 1769 (385 dpf) (Figure S3). While at planula stage (4 dpf) no significant clustering of the bacterial communities was observed, bacterial communities clustered significantly according to environmental conditions at 40, 123 and 385 dpf (Table S3). Using the binary Pearson distance matrix, we calculated the distances between samples within and between all treatments (Fig. 3A) at all four developmental time points. Continuous development under different environmental conditions led to a significant increase in distances between treatments, while the distances within a treatment did not change significantly over developmental time (Fig. 3A). Therefore, the composition of the microbiota of each environmental condition becomes more distinct with developmental age (Table S3; Fig. 3A). In contrast, the alpha diversities of bacterial communities, measured by the Chao1 metric, were not affected by the different environmental conditions (Fig. 3B). To identify bacterial groups responding to salinity or temperature shifts in adult polyps, we analysed the bacterial communities of 385 dpf in more detail (Fig. 4, Fig. S4). The PCoA using the binary Pearson distance matrix (Fig. 4A) revealed that samples clustered together based on environmental conditions. While principal component 1 (PC1) mostly separates samples cultivated at high temperature (25°C) from samples cultivated at low temperature (18°C), PC2 correlates with differences in salinity (Fig. 4A). We determined the composition of the associated bacterial species within the four environmental conditions by mean relative abundance of bacterial phyla (Fig. 4B, Fig. S4). Main differences in response to temperature shifts could be observed by the increase of Bacteroidetes at 25°C and the higher abundance of γ-Proteobacteria at 18°C. Using LEfSe, we could confirm the significant higher abundance of four classes of the Bacteroidetes (Flavobacteriia, Sphingobacteriia, Rhodothermi and Saprospirae) and the significant lower abundance of four classes of Proteobacteria (Legionellales, Alteromonadales, Bdellovibrionales and Rhodobacterales) as a response to higher temperature (Fig. 4C). In contrast, no obvious changes at the phylum level could be detected in response to salinity (Fig. 4B). Nevertheless, using LEfSe, we detected six bacterial orders belonging to Bacteroidetes (Flavobacteriales), γ-Proteobacteria (Pseudomonadales, Alteromonadales and Aeromonadales), β-Proteobacteria (Burkholderiales) and Planctomycetes (Phycisphaerales), which responded significantly to changes in salinity (Fig. 4D). Geographic origin is determining bacterial colonization To determine the relative contribution of geography to the overall pattern of diversity between individual Nematostella polyps, we sequenced 16 polyps originating




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Fig. 3. Analysis of bacterial diversity at different environmental conditions and different developmental time points. A. Distance comparison plot of binary Pearson distances within and between treatments at different developmental time points. Increase in distance over time was tested by one-way ANOVA with Dunnett’s multiple comparison test (***P < 0.001) using planula as control. B. Chao1 estimated alpha diversities at the different environmental conditions at certain developmental time points. Values were estimated using 1500 reads per sample. Statistics were carried out using one-way ANOVA with Dunnett’s multiple comparison test.

from five different locations from the North American Atlantic coast (Fig. 5A, Table S4). The animals were maintained for at least 10 years in the lab and reached sexual maturation prior to DNA extraction. Most individuals were also of known sex based on laboratory culturing or histological analysis. First, we determined the composition of the associated bacterial taxa within the different sampling locations (Fig. 5B, Fig. S5). Based on this analysis, it became apparent that γ-Proteobacteria increased, while bacteria summarized in the group ‘other bacteria’ decreased in abundance in animals originating from the south in comparison to animals originating from the north (Fig. 5B). As an assessment of the influence of geography, we analysed the degree to which bacterial communities separate according to geographic location using both taxon- (OTU) and phylogenetic-based measures of beta diversity (Fig. 5C, Table 2). The samples displayed significant separation according to all eight beta diversity measures. We next investigated the influence of geographic distance on a continuous scale by applying Mantel tests to each of the eight measures of beta diversity (Table 2). Mantel tests revealed a significant relationship between geographic distance and beta diversity measures, and explained approximately 50–68% of the variation in seven out of eight beta diversity measures respectively (Table 2). Only the weighted Unifrac metric revealed no significant correlation between geographic

distance and bacterial composition. To detect OTUs contributing to the observed biogeographic differences in the bacterial communities, we performed a similarity percentages (SIMPER) analysis based on the Bray–Curtis similarity. Based on this analysis, we identified five OTUs, each explaining between 2% and 5% dissimilarity between the different populations. These five OTUs contributed mainly to the observed correlation between geographic distance and bacterial beta diversity. While OTU1590 (unclassified bacteria), 2187 (unclassified bacteria) and 1384 (γ-Proteobacteria, Xanthomonadales) were mainly found in animals originating from the northern populations, OTU485 (γ-Proteobacteria, Oceanospirillales) and 1320 (γ-Proteobacteria, Alteromonadales) were more abundant in animals originating from the south (Fig. 5D). Identification of a stable-associated microbiota with Nematostella To characterize a potential core microbiota associated with Nematostella, we compared the 20 most abundant bacterial 16S rRNA sequences from our laboratory cultured Nematostella, with the biogeographic samples collected from North America and separately cultured in the USA, and with sequences publicly available in the GenBank database (HQ189547–HQ189745) (Table S5). This


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Fig. 4. Analysis of bacterial diversity at different environmental conditions at 385 dpf. A. PCoA of the bacterial communities at 385 dpf of different environmental conditions using the binary Pearson distance matrix. The per cent variation explained by the PCoA is indicated on the axes. B. Pie charts representing the mean relative abundances (n = 5) of bacterial classes at the four different environmental conditions. C, D. LEfSe results of bacterial colonization at different temperatures (C) and different salinities (D). The cladograms report the bacterial groups (highlighted by small circles and by shading) showing significant different abundance values (according to LEfSe) at the different environmental conditions. For each taxon, the colour denotes significant overrepresentation of bacterial groups at one environmental condition for both the circles and the shading.

publicly available 16S rRNA library was sequenced from Nematostella polyps isolated from the Great Sippewissett Marsh in Massachusetts, USA. Interestingly, we identified 85% and 40%, respectively, of our sequences independently present in these both datasets (Table S5). Within these sequences, the OTU2187 represents the bacteria most widely present in different developmental stages and in individuals collected from various geo-

graphic regions. This OTU represents a bacterium detectable during all developmental phases in relatively constant abundance of 6.7%, 5.7% and 6.5% in early developmental stages, juvenile polyps and adult polyps, respectively, and in all animals isolated in the USA. In addition, it is present in Nematostella polyps previously sampled in Massachusetts, USA (HQ189732 and HQ189731) (Fig. 6). Interestingly, this OTU could not be assigned to any of the




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Fig. 5. Contribution of host biogeography to the overall pattern of bacterial diversity between individual Nematostella polyps sampled from the US east coast. A. Map showing the location of the sampled populations: Nova Scotia (NS), Maine (ME), Massachusetts (MA), New Jersey (NJ) and Maryland (MD). B. Pie charts representing the mean relative abundance of bacterial classes at the five different populations. C. Jackknife environment cluster tree (Bray–Curtis metric, based on 3000 sequences per sample) of the analysed bacterial communities. There were 1000 jackknife replicates calculated, nodes are marked with jackknife fractions. The branch length indicator displays distance between samples in Bray–Curtis units. Colours indicate different sampling locations corresponding to colours in (A). D. Relative abundance of the five detected OTUs contributing most to the observed biogeographic differences in the bacterial communities (based on SIMPER analysis). Each OTU is explaining between 2% and 5% dissimilarity between different populations.

known bacterial phyla using the short 16S rRNA gene fragment of the V1V2 region. Using primers to amplify most of the 16S RNA gene, we sequenced a 1480 bp amplicon of the corresponding 16S rRNA gene of OTU2187 (GenBank Acc. No. KR973435). Searching public databases using this sequence as query, we identified several similar sequences (Fig. 6), but without any clear phylogenetic affiliations. In a phylogenetic analysis including eight other 16S rRNA gene sequences from two corals, one ctenophore, two molluscs and one urochordate, OTU2187 form a monophyletic clade within the Mollicutes, which is not within a known Mollicutes genus (Fig. 6,

Fig. S6). Interestingly, all available sequences were isolated from marine animal tissues (Daniels and Breitbart, 2012; Fernandez-Piquer et al., 2012; Duperron et al., 2013; Dishaw et al., 2014), suggesting that this bacterial group lives in close association with a variety of marine animals. Because members of this lineage showed < 85% 16S rRNA gene sequence identity to known bacterial genera, we suggest the status of a new candidate genus, named ‘Marinoplasma’, to acknowledge the so far exclusive affiliation of these bacteria with species in the marine environment. ‘Marinoplasma’ is in a sister group relation to the genus Mycoplasma.

Table 2. Summary of statistical analysis testing the influence of geographic origin on the bacterial colonization. Adonis 2

Anosim

Geographic distance

Beta diversity measure

R

P value

R

P value

Mantel r

Mantel P

Binary Pearson Binary Jaccard Binary Sörensen-Dice Unweighted Unifrac Pearson Abund Jaccard Bray–Curtis Weighted Unifrac

0.490 0.437 0.507 0.457 0.635 0.708 0.514 0.557

0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.006**

0.812 0.774 0.774 0.725 0.694 0.600 0.767 0.699

0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001***

0.526 0.576 0.583 0.525 0.664 0.406 0.686 0.402

0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.001*** 0.067

Levels of siginificance (*P < 0.05, **P < 0.01, ***P > 0.001)




Fig. 6. Phylogenetic analysis of OTU2187. Phylogenetic position of OTU2187 (GenBank Acc. No. KR973435) within the Mollicutes (based on 16S rRNA gene, maximum likelihood using the general time reversible (GTR) model with gamma distribution and invariant sites (G + I). Bootstrap values are shown at the corresponding nodes. The branch-length indicator displays 0.05 substitutions per site. Note: Together with eight other sequences, OTU2187 forms a monophyletic group within the Mollicutes.

In addition, two previously published studies, analysing the Nematostella genome sequences, identified sequences with high similarities to known bacterial sequences (Starcevic et al., 2008; Artamonova and Mushegian, 2013). While the first study tends to infer that these sequences are the results of horizontal gene transfer (HGT) (Starcevic et al., 2008), the second study (Artamonova and Mushegian, 2013) proposes that these sequences originate from bacteria closely associated with Nematostella, most likely from Flavobacteriales (Bacteroidetes) and Pseudomonas (γ-Proteobacteria), that were sequenced and assembled into the first version of the Nematostella genome. Considering the origin of genomic DNA from primary polyps, which were used for the Nematostella genome project (Putnam et al., 2007), we screened our OTU table for the presence of corresponding bacteria in early developmental stages (see Table S2). Interestingly, we identified six OTUs belonging to Bacteroidetes and five

OTUs belonging to γ-Proteobacteria significantly overrepresented in the early developmental phase (Table S2). In addition, we identified the 16S rRNA sequence of OTU2298 (Bacteroidetes, Flavobacteriia, Flavobacteriales, Flavobacteriaceae) (100% similarity) partially present in the genomic contig c441902931.Contig1 (StellaBase) of Nematostella. In addition to the partial 16S rRNA gene, two open reading frames without introns are present on this contig. Both genes map to genes present in known Flavobacteria. Due to the independent occurrence of the same bacterial sequences within 16S rRNA gene libraries from animals kept in two different labs (Germany and USA) and the presence of this bacterial sequence within the assembled genome of Nematostella, we conclude that this bacterium is indeed part of the core microbiota of Nematostella and lives in close association with the host tissue of early developmental stages, and does not represent an HGT event.


1774 B. M. Mortzfeld et al. Discussion Many animals are associated with complex communities of microbial colonizers that vary in different parts of the body (Human Microbiome Project Consortium, 2012). In addition, it has been shown that microbial communities in hosts are dynamic over time and follow complex rules of assembly during ontogeny (Koenig et al., 2011; Franzenburg et al., 2013a; Chaparro et al., 2014). The factors that control community membership during the assembly process and influence the colonization pattern during development remain poorly understood despite the immense impact microbes may play in the development and physiology of multicellular hosts.

Host development is a robust predictor for bacterial colonization in Nematostella The microbial data from the earliest developmental stages to 1-year-old adult polyps clearly demonstrate the robust colonization process in Nematostella. The clear separation of early developmental stages, juvenile and adult polyps based on PCoA (Fig. 1B) is correlated to major developmental transitions within the development of Nematostella, mostly independent of two abiotic factors: temperature and salinity. Based on these results, we conclude that ontogenetic factors are substantially stronger compared with the investigated environmental factors in shaping the bacterial colonization in Nematostella. Factors determining the changes in the bacterial composition during development might be linked to physiological and immunological remodelling as shown during metamorphosis in amphibians (Rollins-Smith, 1998; Faszewski et al., 2008) and insects (Vigneron et al., 2014). In Nematostella, metamorphosis is characterized by the transition of a free-swimming larva to a settled polyp, with mesenteries and several tentacles providing new potential niches for colonization. In addition, in this developmental phase, animals start to feed, which may lead to changes in the metabolisms and to a new input of potential bacterial colonizers. The second change in bacterial colonization is correlated with the transition of juvenile polyps to gametes producing adult polyps. This transition is not characterized by any obvious morphological change, but accompanied by high proliferation of germ cells within the mesenteries of the polyps. While the female polyps produce between 500 and 2000 eggs embedded in a gelatinous matrix, the male polyps release their sperms into the surrounding water (Hand and Uhlinger, 1992; Fritzenwanker and Technau, 2002). Both processes are most likely depending on major changes in the animal’s physiology, which might be reflected in the bacterial composition. Interestingly, we could not detect any differences in the bacterial colonization between male


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and female polyps (Fig. S2), concluding that male and female germ cell proliferation have similar effects on bacterial colonization. Analyses of the alpha diversity of bacterial communities revealed that the earliest developmental time points (first cleavages and gastrula) are colonized by the highest bacterial diversity. While during mid-blastula transition the embryo starts to utilize its own transcriptional machinery, the bacterial alpha diversity decreases significantly reaching its minimum after metamorphosis at 22 dpf. Interestingly, Gram-positive bacteria (Firmicutes and Actinobacteria) are nearly eliminated during that time (Fig. 2A), suggesting a selective mechanism against Gram-positive bacteria established during early development. These observations lead to the hypothesis that the whole bacterial community associated with the early developmental phase undergoes a winnowing process during metamorphosis. This process might be hostdriven, but potential factors controlling the bacterial community assembly during ontogeny are not known in Nematostella. The modulation of symbiont density during development has previously been reported in several insect species (Stoll et al., 2010; Kim et al., 2014; Laughton et al., 2014). In the bean bug Riptortus pedestris, the decrease of symbiont density during molting is correlated with an induced expression of antimicrobial peptides (AMPs) (Kim et al., 2014). In contrast, a study using the cnidarian Hydra showed that the expression of species-specific AMPs led to the establishment of species-specific bacterial signatures, reflecting host phylogeny (Franzenburg et al., 2013b). Thus, AMPs are capable of mediating host–microbe homeostasis (Fraune and Bosch, 2010; Fraune et al., 2010; Salzman et al., 2010; Login et al., 2011; Franzenburg et al., 2013b; Mukherjee et al., 2014), and therefore are good candidates for mediating the assembly of certain bacterial communities during different developmental phases, e.g. by the start of the expression of AMPs specific for the polyp stage. Identification and expression of AMPs in Nematostella will require future research. Within the group of bacteria, which were not responding to developmental changes, only OTU2187 is present in higher abundance (see Table S2). Our phylogenetic analyses revealed that this bacterium forms with other not yet characterized marine bacteria a new monophyletic clade within the Mollicutes. All identified bacteria belonging to this new clade were characterized in association with marine invertebrates and vertebrates. Although Mollicutes are widespread commensals or pathogens of vertebrates and invertebrates (Razin et al., 1998; Citti and Blanchard, 2013), only few studies describe them as marine colonizers (Zbinden and CambonBonavita, 2003). Therefore, it is difficult to speculate on the role of these bacterial species in marine organisms.


12 B. M. Mortzfeld et al. Nevertheless, as they were recovered all from apparently healthy animals, they are probably non-pathogenic for the host. In line with that, terrestrial isopods harbouring a Mycoplasma-like symbiont have a fitness advantage on low-quality food in comparison to individuals lacking this symbiont (Fraune and Zimmer, 2008). Further research on these bacterial colonizers in Nematostella might elucidate their potential function as marine symbionts. Nevertheless, the intimacy of the interaction between Nematostella and bacteria during development points to the significance of this inter-kingdom association and implies that hosts depleted of their bacteria should be at a disadvantage. Animal development has traditionally been viewed as an autonomous process directed by the genome, but in many animals bacteria provide signals for multiple developmental steps, e.g. for the induction of settlement and metamorphosis of many marine invertebrate larvae (Hadfield, 2011). Observations in a number of invertebrates and vertebrates strongly support the view that development, at least in part, is an orchestration of both animal-encoded ontogeny and inter-kingdom communication. Environmental impact on bacterial colonization Nematostella lives in highly variable habitats including daily and seasonal shifts in temperature and salinity. It has been reported that Nematostella inhabits shallow and ephemeral salt marsh pools at temperature ranges from below 5°C to 42°C and salinity ranges from 2 to 42‰ (Sheader et al., 1997; Reitzel et al., 2013a). In our experiments, we detected no differences in overall mortality due to the different environmental conditions applied, confirming the high tolerance of Nematostella to variable environmental conditions. This is consistent with results showing that Nematostella exhibits a broad thermal tolerance for early developmental stages as well as for adult polyps (Reitzel et al., 2013a). The effects of these tremendous ranges of environmental conditions on the bacterial colonization in Nematostella and potential fitness consequences are not known. Here, we show that long-term acclimatization to different environmental conditions leads to a robust tuning of bacterial colonization, e.g. by the increase of rare species. Interestingly, similar trends were observed in sponge microbiomes, which show high stability to seasonal environmental changes, while the variability was restricted to rare bacterial colonizers (Erwin et al., 2012). These results show that members of the rare biosphere might have important roles in maintaining ecosystem processes after changes in the environmental conditions. The changes in bacterial colonization during environmental acclimation can be explained by two possible mechanisms, which might act in combination. First, bacte-

Bacterial colonization in Nematostella vectensis 1775 ria react directly according to their different physiological properties. Due to different physiological capacities, the fitness of a bacterium may change, and as a consequence bacterial community composition may change. In addition, new bacteria may enter the colonizing community, as some bacteria go locally extinct and niches become available under new environmental conditions. Second, the bacterial community composition may change indirectly by altered biotic bacteria–bacteria (Little et al., 2008) and host– bacteria interactions (Ley et al., 2006). Bacteria–bacteria interactions within the bacterial community may alter by changes in bacterial communication via quorum sensing and quorum quenching (Waters and Bassler, 2005; Hughes and Sperandio, 2008), metabolic competitions (Rendueles and Ghigo, 2012), or cooperation (Elias and Banin, 2012). In the cnidarian Hydra, frequencydependent bacteria–bacteria interactions appear to be a key feature involved in assembling of the bacterial community (Franzenburg et al., 2013a). These results imply the importance of frequency-dependent bacteria–bacteria interactions for structuring bacterial communities. Therefore, we can assume that changes in the fitness of certain bacteria in response to environmental variation may influence the complex bacteria–bacteria interactions in the holobiont Nematostella. In addition, altered host–bacteria interactions may also lead to compositional shifts in the bacterial community. The regulation of the microbial community by the host is performed by effector molecules of the innate immune system like AMPs (Salzman et al., 2010; Login et al., 2011; Franzenburg et al., 2013b; Mukherjee et al., 2014) or by the provision of selective nutrients by the host (Ley et al., 2006). In addition, the specific composition of complex carbohydrates on the boundary between epithelium and environment may have a huge impact on the colonization with bacterial species (Kashyap et al., 2013; Pickard et al., 2014). Such molecules appear in the mucus layer of the human gut (Johansson et al., 2008; 2011) or in the glycocalyx of the cnidarians Hydra (Böttger et al., 2012; Fraune et al., 2014) and Nematostella (Lang et al., 2007; Tucker et al., 2013). Changes in the host expression of immune factors or glycoproteins due to environmental changes may have direct effects on bacterial community structure. Despite the influences of abiotic conditions, the community structure in Nematostella is relatively stable in regard to environmental variance as the holobiont maintains the bacterial ontogenetic core signature. A general assumption in host–microbe assembly is that community stability (resilience) is often positively correlated to community diversity and functional redundancy (Girvan et al., 2005; Robinson et al., 2010). The sea anemone Nematostella is living in a highly variable environment, facing daily perturbations in temperature and salinity


1776 B. M. Mortzfeld et al. (Darling et al., 2005). Therefore, the host–bacteria interdependence in Nematostella might be less strong as in holobionts evolving under more constant conditions. This may lead to a higher functional redundancy in the microbiome of Nematostella compared with communities with highly specialized members, as observed in insects (Douglas, 2014) or sponges (Fan et al., 2013). Consequentially, the holobiont Nematostella is most likely less sensitive to environmental shifts than highly interdependent systems, which are potentially more vulnerable to stress situations (Bourne et al., 2008; Fan et al., 2013). Responding to the environmental changes via adjustments in the microbial composition while still maintaining the ontogenetic core signature could represent the microbiome’s way to buffer the impact of the environment to the holobiont. Shifts in bacterial composition can thus prevent growth of external pathogens (Woodhams et al., 2007; Harris et al., 2009; Fraune et al., 2014) can contribute to the thermotolerance of the whole holobiont (Dunbar et al., 2007), and thereby contribute to the maintenance of homeostasis. Future efforts should address the question of how the regulation of the different bacterial communities is achieved and what are the leading factors for the establishment of bacterial communities under different environmental conditions. Host biogeography strongly influences bacterial diversity in Nematostella Our analysis of bacterial colonization of Nematostella polyps from different populations of the North American coast revealed a strong correlation between host biogeography, measured by the geographic distance between sampling locations, and bacterial diversity, measured by beta diversity matrices. While host biogeography can be seen as the sum of environmental conditions, we controlled for proximate environmental factors by culturing the animals for years under identical laboratory conditions. Therefore, we can also exclude that the differences we observed in bacterial colonization are influenced by different developmental age of the sampled polyps. Nevertheless, polyps under laboratory conditions may still maintain a bacterial colonization reflecting the environmental conditions they were confronted with in the wild. In addition, biogeography is also highly correlated with population genetic structure in Nematostella, indicated by limited genetic exchange between different populations (Reitzel et al., 2008; 2013b). The importance of host genetics in shaping bacterial colonization has been demonstrated in various vertebrates (Ley et al., 2008; Ochman et al., 2010; Campbell et al., 2012) and invertebrates (Brucker and Bordenstein, 2013; Franzenburg et al., 2013b). One bacterial OTU, which might response to host genetics, is OTU2187 (Fig. 5D). This bacterium


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neither changed in abundance in response to different developmental stages (Table S2) nor to environmental conditions under laboratory conditions (Fig. 4). In contrast, OTU2187 responded significantly to differences in biogeographic origin of Nematostella polyps (Fig. 5D), indicating a potential influence of host genetics on OTU2187 abundance. We conclude that host biogeography in Nematostella can be seen as an approximation for host genetics and the sum of environmental factors the animals were confronted with, such as temperature, salinity, food and substrate. Future work will elucidate the contribution of each factor, host genetic and environment to overall bacterial diversity and identify host factors influencing bacterial colonization. Conclusion Nematostella is characterized by a stable-associated bacterial community, which is dynamic but highly conserved in response to host development. The adjustments in fine-scale community composition following environmental change could represent the microbiome’s way to buffer the impact of environmental changes. With these insights we can now focus on identifying the underlying mechanistic processes and address questions such as the following: Does the change in bacterial colonization due to changes in environmental conditions lead to functional differences within the bacterial community? What is the fitness consequence of an acclimatized bacterial colonization for the holobiont? Which factors determine the changes in the bacterial composition during development? To answer these questions, we will establish a germfree Nematostella model and use it to test animal performance as a function of associated bacterial colonizers. In addition, we will perform RNAseq experiments at different developmental stages in response to bacterial colonization to identify host responses, which are active during whole development, as well as specific responses of the three developmental stages. Experimental procedures Animals All experiments were carried out with Nematostella vectensis. The adult animals of the laboratory culture were F1 offspring of CH2XCH6 individuals collected from the Rhode River in Maryland, USA (Hand and Uhlinger, 1992; Fritzenwanker and Technau, 2002). They were kept under constant, artificial conditions without substrate or light in the Nematostella medium (NM), which was adjusted to 18°C and 16‰ salinity with Red Sea Salt® and Millipore H2O. Sexual reproduction was induced via temperature shift to 24°C and light exposure for 10 h (Fritzenwanker and Technau, 2002). For the experiments, 15 independent laboratory cultures were created by


14 B. M. Mortzfeld et al. fertilizing egg patches from 15 different female polyps with a sperm mixture of 10 different male polyps. The fertilized eggs were then permanently cultured under different environmental conditions altering temperature and salinity: 18°C/16‰ (n = 6), 18°C/25‰ (n = 3), 25°C/16‰ (n = 3), 25°C/25‰ (n = 3). Adult polyps were fed two to three times a week with first instar nauplius larvae of Artemia salina as prey (Ocean Nutrition Micro Artemia Cysts 430–500 g, Coralsands, Wiesbaden, Germany). The polyps analysed from different populations of the US east coast were sampled from 2003 to 2004 and maintained in the lab for the subsequent 10 years as clonal lines propagated by transverse bisection. All analysed animals were mature adult polyps and were kept under identical conditions.

DNA extraction and 454-pyrosequencing Before extraction, the animals were washed three times with 500 μl sterile filtered NM and frozen without liquid for at least 2 days at −80°C. The gDNA was extracted from whole animals at least 3 days after last feeding with the DNeasy® Blood & Tissue Kit (Qiagen, Hilden, Germany), as described in the manufacturer’s protocol. Elution was done in 50 μl. The eluate was frozen at −20°C until sequencing. For 454-pyrosequencing, the variable regions 1 and 2 (V1V2) of the bacterial 16S rRNA genes were amplified in a 25 μl PCR reaction from the extracted DNA with the forward primer V2_A_Pyro_27F 5′-CGTATCGCCTCCCTCGCGCCA TCAGTCAGAGTTTGATCCTGGCTCAG-3′ and the barcoded (N’s) reverse primers V2_B_338R 5′-CTATGCGCCT TGCCAGCCCGCTCAGNNNNNNNNNNCATGCTGCCTCCC GTAGGAGT-3′ using the Phusion® Hot Start II DNA Polymerase (Thermo Fisher Scientific, Waltham, MA, USA). Each PCR reaction was performed twice following the manufacturer’s protocol starting with an initial denaturation step (98°C, 20 s) followed by 30 cycles of amplification (denaturation: 98°C, 9 s; annealing: 55°C, 20 s; elongation: 72°C, 20 s) and a final elongation (72°C, 10 min). The duplicates were combined and distinct bands of ∼ 350 bp were extracted after gel electrophoresis using the MinElute Gel Extraction Kit (Qiagen), as recommended by the manufacturer. Elution was performed in 10 μl. The concentration was measured with the aid of the Quant-iT™ dsDNA BR Assay Kit (Qiagen) Nanodrop 3300 (Thermo Fisher Scientific). Equal amounts of DNA from each sample were pooled in one reaction tube and purified using Agencourt Ampure Beads (Beckman Coulter, Krefeld, Germany). The solution was analysed on a Bioanalyzer (Agilent Technologies, Waldbronn, Germany) and forwarded to an emulsion PCR. Finally, the sample was sequenced using a 454-GS-FLX-System and the Titanium Sequencing Chemistry Kit (Roche, Basel, Switzerland) following the manufacturer’s protocol.

Bacterial colonization in Nematostella vectensis 1777 10-nt barcodes to the corresponding sample as input, the sequences were analysed using the following parameters: length between 300 and 400 bp, no ambiguous bases, and no mismatch to the primer sequence. Chimeric sequences were identified using Chimera Slayer (Haas et al., 2011). Identified chimeric sequences were verified manually. Putative chimeric sequences, which were present in at least two independent samples, were retained in the analysis. The 454 data are deposited at Metagenomics RAST (ID No. 12115 and 12116).

Statistical analyses of bacterial communities The OTUs with < 10 reads were removed from the dataset to avoid false positive OTUs, which rely on the overall error rate of the sequencing method (Faith et al., 2013). The number of reads was normalized to 1500 sequences per sample. Sequences with at least 97% identity were grouped into OTUs and classified by the ribosomal database project (RDP) classifier. Alpha diversity was calculated using the Chao1 metric implemented in QIIME. Beta diversity was depicted in a PCoA by 100 jackknifed replicates using eight different matrices (see Tables 1, 2, Table S3). Statistical analysis of clustering was done using the statistical methods Adonis and Anosim. To test the significance of host biogeography contributing to the divergence in bacterial communities, we performed Mantel tests. To create a distance matrix containing pairwise geographic distances, we used the Vincenty formula for calculating the distance between two latitude/longitude points. The OTU-based community matrix was transferred to the programme PRIMER v.6.1.9 (Primer-E). The SIMPER analysis tool of PRIMER was used to identify bacterial OTUs explaining the dissimilarity between animals originating from different populations based on Bray–Curtis similarity.

Microbial biomarker discovery and visualization Potential bacterial groups associated with specific conditions were identified by the LEfSe (https://bitbucket.org/biobakery/ biobakery/wiki/lefse) (Segata et al., 2011). LEfSe couples robust tests for measuring statistical significance (Kruskal– Wallis test) with quantitative tests for biological consistency (Wilcoxon-rank sum test). The differentially abundant and biologically relevant bacterial groups are ranked by effect size after undergoing linear discriminant analysis. All P values were corrected for multiple hypotheses testing using Benjamini and Hochberg’s false discovery rate correction (q-value). A q-value of 0.25 and an effect size threshold of 3.5 (on a log10 scale) were used for all bacterial groups discussed in this study. Bacterial biomarkers are graphically represented on hierarchical trees reflecting the RDP taxonomy for 16S rRNA gene data.

16S rRNA analysis The 16S rRNA gene amplicon sequence analysis was conducted using the QIIME 1.8.0 package (Caporaso et al., 2010). All sequencing reads were denoised using the ‘denoise_wrapper.py’ script in QIIME. Using the sequence fasta-file, a quality file and a mapping file that assigned the

Phylogenetic analysis of OTU2187 The OTU2187 is of special interest, as it occurs in all developmental stages as well as in the samples analysed form the USA. Using the short V1V2 region of this OTU, it was not possible to assign it to a known bacterial group. For


1778 B. M. Mortzfeld et al. full-length amplification of the corresponding 16S rRNA gene of OTU2187, we chose the sample with the highest abundance of OTU2187 and amplified the bacterial 16S rRNA genes by using the bacterial primer 27F and 1492R. Ten bacterial clones were sequenced by Sanger sequencing and compared with OTU2187. One sequence showed 100% similarity to OTU2187 and was used for further analysis (GenBank Acc. No. KR973435). For phylogenetic analysis, a sequence alignment was generated using MEGA 5 (Tamura et al., 2007). A model test was used to estimate the best-fit substitution models for phylogenetic analyses. For the maximum-likelihood analyses, genes were tested using the general time reversible model with gamma distribution and invariant sites (G + I). A bootstrap test with 100 replicates for maximum likelihood and random seed was conducted.

Acknowledgements We are grateful to Thomas Bosch, René Augustin and Tim Lachnit for their support and critical discussions. We gratefully thank the anonymous referees for their constructive comments and suggestions. The work was partially funded by incentive funding from the University of North Carolina at Charlotte and by EPA Grant F5E11155 for field collections (grants to AMR) and by a grant from the Austrian Research Fund FWF (P24858-B21) (grant to UT). The authors declare no conflict of interest.

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Supporting information Additional Supporting Information may be found in the online version of this article at the publisher’s web-site: Fig. S1. Abundance of the major bacterial phyla and classes in Nematostella vectensis of all sequenced samples under standard conditions (18°C/16‰). The fraction of ‘Other Bacteria’ includes all rare fractions with a relative abundance of < 1% and unclassified bacteria.


18 B. M. Mortzfeld et al. Fig. S2. Principal coordinates analysis (PCoA) of the binary Pearson distance matrix of male and female samples. The per cent variation explained by the PCoA is indicated on the axes. Fig. S3. Analysis of bacterial diversity at different environmental conditions at planula stage, 40 dpf and 123 pf. Pie charts representing the mean relative abundances (n = 3) of bacterial classes at the four different environmental conditions at each developmental time point. Principal coordinates analysis (PCoA) of the bacterial communities at different environmental conditions using the binary Pearson distance matrix. The per cent variation explained by the PCoA is indicated on the axes. Fig. S4. Abundance of the major bacterial phyla and classes in Nematostella vectensis of all sequenced samples at 385 dpf at four different environmental conditions. The fraction of ‘Other Bacteria’ includes all rare fractions with a relative abundance of < 1% and unclassified bacteria. Fig. S5. Abundance of the major bacterial phyla and classes in Nematostella vectensis originating from different populations. The fraction of ‘Other Bacteria’ includes all rare fractions with a relative abundance of < 1% and unclassified bacteria.

Bacterial colonization in Nematostella vectensis 1781 Fig. S6. Expanded phylogenetic tree of the phylogenetic analysis of OTU2187. Phylogenetic position of OTU2187 within the Mollicutes (based on 16S rRNA gene, maximum likelihood using the general time reversible (GTR) model with gamma distribution and invariant sites (G + I). Bootstrap values are shown at the corresponding nodes. The branchlength indicator displays 0.05 substitutions per site. Table S1. OTU table for developmental and environmental samples. Table S2. Statistical analysis (ANOVA) analysing the presence of main bacterial OTUs (> 50 normalized reads) at different developmental phases. Table S3. Statistical analysis comparing the influence of environmental factors on the bacterial colonization at different developmental stages. Table S4. OTU table for biogeographic samples. Table S5. Presence of bacteria in three different Nematostella datasets. The presence of the 20 most abundant bacterial OTUs in the laboratory dataset was checked in the biogeographic dataset and in the publicly available dataset from the Janelle Thompson lab.


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