Sponge microbiomes contribute to host health, nutrition, and defense through the production of secondary metabolites. Chlamydiae, a phylum of obligate intracellular bacteria ranging from animal pathogens to endosymbionts of microbial eukaryotes, are frequently found associated with sponges. However, sponge-associated chlamydial diversity has not yet been investigated at the genomic level and host interactions thus far remain unexplored. Here, we sequenced the microbiomes of three sponge species and found high, though variable, Chlamydiae relative abundances of up to 18.7% of bacteria. Using genome-resolved metagenomics 18 high-quality sponge-associated chlamydial genomes were reconstructed, covering four chlamydial families. Among these, Candidatus Sororchlamydiaceae shares a common ancestor with Chlamydiaceae animal pathogens, suggesting long-term co-evolution with animals. Based on gene content, sponge-associated chlamydiae resemble members from the same family more than sponge-associated chlamydiae of other families, and have greater metabolic versatility than known chlamydial animal pathogens. Sponge-associated chlamydiae are also enriched in genes for degrading diverse compounds found in sponges. Unexpectedly, we identified widespread genetic potential for secondary metabolite biosynthesis across Chlamydiae, which may represent an unexplored source of novel natural products. This finding suggests that Chlamydiae members may partake in defensive symbioses and that secondary metabolites play a wider role in mediating intracellular interactions. Furthermore, sponge-associated chlamydiae relatives were found in other marine invertebrates, pointing towards wider impacts of the Chlamydiae phylum on marine ecosystems.
Porifera (i.e., sponges) are ubiquitous filter-feeding metazoans that provide essential ecosystem services. These animals have complex, deeply integrated, and essential microbiomes that play important roles in processes such as nutrient cycling [1,2,3,4]. The sponge microbiome also produces secondary (or specialized) metabolites that may contribute to host chemical defence . Generally, sponges are known as a source of novel secondary metabolites with medical and industrial relevance [6,7,8,9]. With increasing exposure to anthropogenic threats, further investigation of the sponge microbiome is essential for understanding host impacts, from acting as detrimental opportunists to providing resilience against dysbiosis and disease [4, 10]. Despite cultivation efforts, many sponge-associated microbial groups remain uncultured to date [11, 12], and have only recently been explored through cultivation-independent sequencing approaches [13,14,15,16,17]. In a recent large-scale survey, sponge-associated microbial communities were shown to be diverse, yet structured, and composed of taxonomic groups with both generalist and specialist host ranges . One of these generalist phyla is Chlamydiae, which is found at high relative abundance in some sponge species [19, 20]. Yet, the genomic diversity of sponge-associated chlamydiae has not been previously investigated.
Chlamydiae is a bacterial phylum of obligate eukaryotic endosymbionts well-known for animal pathogens, such as Chlamydia trachomatis and other Chlamydiaceae [21,22,23,24]. Many Chlamydiae members instead infect microbial eukaryotes and have more extensive metabolic repertoires [21,22,23]. Chlamydial environmental distribution and abundance has been underestimated, as their small-subunit (SSU) rRNA genes are often missed by primers used to survey microbial diversity [25,26,27]. However, with the recent use of metagenomic and single-cell approaches sequenced chlamydial genomic diversity is quickly expanding, resulting in a widening view of the potential lifestyles of uncultivated chlamydial groups [27,28,29,30,31,32]. Retrieving additional genomes affiliated with the Chlamydiae is needed to further our understanding of chlamydial ecological impacts, range of host interactions along the parasite-mutualist continuum, and the evolution of endosymbiosis and pathogenicity in this major bacterial group within the Planctomycetes, Verrucomicrobia, Chlamydiae (PVC) superphylum [33, 34].
The sponge species Halichondria panicea, Haliclona oculata, and Haliclona xena, sampled from an estuary in the Netherlands, were previously found to have high relative abundances of Chlamydiae . In the present study, we performed genome-centered analyses of the microbial communities from these sponge species in order to gain insight into chlamydial sponge-associated lifestyles. Comparative analyses of 18 resulting high-quality sponge-associated chlamydiae draft genomes revealed degradative genes found in other sponge symbionts. In addition, these Chlamydiae genomes share metabolic features with other members from their respective chlamydial families, and encode genes absent in known chlamydial families composed strictly of animal endosymbionts (i.e., Chlamydiaceae and Parilichlamydiaceae). Unexpectedly, we also identified extensive genetic potential for secondary metabolite biosynthesis across the Chlamydiae phylum. Finally, we found that relatives of these sponge-associated chlamydiae are also associated with additional sponge species and other marine invertebrates. Together, our results suggest that Chlamydiae members play ecological roles that impact animals found in marine ecosystems and represent an untapped source for secondary metabolite discovery.
Results and discussion
Specific Chlamydiae lineages vary in relative abundance across three sponge species
Using bacteria-specific SSU rRNA gene amplicon sequencing, high relative abundances of Chlamydiae were found in the sponges H. panicea P_S1 (5.3%), H. panicea P_S2 (3.3%), and H. oculata O_S4 (18.7%) (Fig. 1a), which had been collected during the same sampling event as a prior study  (Fig. S1 and Data S1). However, Chlamydiae relative abundance was substantially lower in three additional sponge specimens, H. panicea P_S3 (0.80%), H. oculata O_S5 (0.31%), and H. xena X_S6 (0.34%) (Fig. 1a), that were collected from a similar location, but at different dates (Fig. S1 and Data S1). This variation was unexpected, as consistently high relative abundances of Chlamydiae were previously found across all three sponge species . Nevertheless, Chlamydiae were not detected in other studies of the H. panicea microbiome . Beyond Chlamydiae, bacterial compositions across the sponge species were consistent with a prior investigation, with the same phyla represented and Proteobacteria as the dominant phylum (Fig. 1a) . SSU rRNA gene amplicon sequences were subsequently clustered into operational taxonomic units (OTUs) at species-level (97% identity) (Data S2). This revealed variability in the specific bacterial OTUs present in different sponge specimens (Fig. 1b). However, three specific Chlamydiae-affiliated OTUs with high relative abundances (up to 8%; Data S3) were found in all samples with higher overall abundances of Chlamydiae (P_S1, P_S2, and O_S4) (Fig. 1a, b). Yet, these OTUs were largely undetected in amplicon data from the other sponge specimens (P_S3, O_S5, and X_S6) (Data S3). In a prior study, these same chlamydial lineages were found at high relative abundance across all specimens of the three sponge species, but were largely undetected in surrounding seawater . Together, these results support a close association of these specific chlamydial lineages with two of the investigated sponge species (H. oculata and H. panicea), while prior work suggests associations with all three sponge species .
Chlamydiae have also been detected with varying presence and abundance across a diverse range of other sponge species . Despite this, sponge-associated chlamydiae have not been previously investigated at the genome level. With the aim of exploring sponge-associated chlamydial genomic diversity, we sequenced and assembled seven high-quality metagenomes from the six sampled sponge specimens, with two generated for O_S4 (O_S4.1 and O_S4.2) (Fig. S1 and Data S1). We assessed microbial diversity in the resulting metagenomes by identifying contigs encoding ribosomal protein gene clusters, and thus representing distinct microbial lineages. Ribosomal protein sequences from each contig were concatenated and a maximum-likelihood (ML) phylogenetic tree reconstructed (Fig. 1c and Data S4–S5). The relative abundance of each microbial lineage was inferred by comparing coverage of these contigs from each sponge metagenome (Fig. 1c and Data S4). These analyses confirmed the larger patterns in sponge microbial community composition seen in the SSU rRNA gene amplicon results. However, several phyla had lower (i.e., Bacteroides, Cyanobacteria, and Proteobacteria) and higher (i.e., Actinobacteria and Chlamydiae) relative abundances in metagenomes than amplicons, perhaps due to differences in SSU rRNA gene copy number . Overall, the metagenomes confirmed the chlamydial patterns seen in amplicon data, with three distinct Chlamydiae-affiliated lineages found with high relative abundance when Chlamydiae were present (P_S1, O_S4.1, and O_S4.2) (Fig. 1c and Data S4–S5).
Together, investigations of sponge-associated microbial communities from amplicon and metagenome data support variable associations of diverse chlamydial lineages with several sponge species. These observed differences in Chlamydiae relative abundance indicate that chlamydial presence is non-essential across these sponge species and were unexpected, given that healthy sponges typically have stable microbiomes . Several different factors could be affecting Chlamydiae abundance patterns and we are unable to resolve between them here. Variation between samples from the same species could stem from persistent, and thereby still stable, associations of these chlamydial lineages with specific sponge individuals or populations. Conversely, it could suggest temporal changes in the presence of these chlamydial lineages, as a result of seasonality or specific environmental conditions. It is also possible that sponge-associated chlamydiae remain present, but only increase in abundance under specific environmental conditions. As part of their conserved lifestyles, Chlamydiae members have both an intracellular dividing phase and an extracellular non-dividing phase where they remain as elementary bodies [21, 24]. Chlamydiae members can also enter states of persistence inside host cells when under environmental stress and refrain from dividing . Thus, sponge-associated chlamydiae may still be present in all sponge individuals, but remain at low abundances as elementary bodies or in persistence states. In addition, variation in presence and abundance could also be the result of these chlamydiae infecting a different eukaryotic host that is itself associated with the sponges.
Genome-resolved metagenomics of sponges expands sequenced chlamydial diversity
To gain further insight into the Chlamydiae-affiliated diversity associated with these sponge species, we then used genome-resolved metagenomics to retrieve microbial genomes. Metagenome-assembled genomes (MAGs) were obtained from each of the seven sponge metagenome assemblies using differential coverage profiles and consensus results from several binning tools (Fig. S1). This resulted in 106 medium to high-quality MAGs (median 89% completeness and 1.4% redundancy) (Data S4). MAGs affiliated with Chlamydiae were further collected, manually curated, and reassembled (from P_S1, O_S4.1, and O_S4.2; Fig. S1). This resulted in 18 high-quality draft Chlamydiae genomes with high contiguity (median 35 contigs), high completeness (median 98.7%), and low redundancy (median 1.01%) (Data S6). Exceptionally, Chlamydiae bacterium O_S4.1_1 and O_S1_54 were 99% complete and retrieved on only three contigs each. Phylogenomic trees were then inferred to determine the placement of sponge-associated chlamydiae MAGs within the Chlamydiae phylum, using four subsets of concatenated marker proteins with chlamydial and outgroup species representatives (Figs. S2–S3, and Data S6–S7). The obtained species tree topology was consistent across ML reconstructions and the placement of sponge-associated chlamydiae MAGs was highly supported (Figs. 2a and S3, and Data S5). A Bayesian phylogenetic tree was also inferred (Fig. 2a) using the smallest subset of marker proteins (n = 15), which included those that best resolved the phyla present in the dataset (Fig. S2 and Data S7). Here, species topology was overall consistent with the ML trees, with a few exceptions for long-branching taxa.
Sponge-associated chlamydiae MAGs were placed in four distinct chlamydial families (Fig. 2a and Fig. S3). Two MAGs were affiliated with the recently described Metagenomic Chlamydial Family D (MCF-D) , which includes a MAG from the glass sponge Vazella pourtalesii , indicating a more widespread association of this family with sponges (Fig. 2a). Three additional MAGs and Chlamydiae bacterium 1063924-70  formed a well-supported clade with Simkaniaceae from marine, coastal, and host-associated environments, including invertebrates (Fig. 2a and Fig. S3, and Data S6) [31, 38, 39]. However, five other MAGs consistently form a group sister to Simkaniaceae with several uncharacterized freshwater chlamydiae (Fig. 2a and Fig. S3, and Data S6) [31, 40]. Based on its well-supported phylogenetic position and conserved gene content, we propose this Simkaniaceae-like sister clade as a new chlamydial family Candidatus Parasimkaniaceae (Figs. 2, 3 and Figs. S4–S5). A few long-branching taxa, including one sponge-associated MAG (Chlamydiae bacterium O_S4.2_37) were part of the Ca. Parasimkaniaceae, with high support in the Bayesian tree, yet clustered together with Simkaniaceae in ML phylogenies (Fig. 2a and Fig. S3). Additional genomes are needed to confidently resolve whether these putative Ca. Parasimkaniaceae members are indeed affiliated with this family or instead with Simkaniaceae.
The remaining seven sponge-associated chlamydiae MAGs formed a clade with other chlamydial MAGs with unknown hosts that were obtained from marine sediment  and fungal mycelium  metagenomes. This group shares a common ancestor with Chlamydiaceae and was previously referred to as Chlamydiae Clade IV (Fig. 2a and Fig. S3) . We propose to name this family Candidatus Sororchlamydiaceae on the basis of its consistent phylogenetic position, improved taxon sampling in our study, distinct genomic characteristics such as genome size, and conserved gene content (Figs. 2b, 3 and Figs. S4–S5). Identifying sponge-associated Ca. Sororchlamydiaceae also has potential evolutionary implications. Ca. Sororchlamydiaceae share a common ancestor with exclusively animal-associated chlamydiae including Clavichlamydia salmonicola, a fish pathogen , and the Chlamydiaceae family, that have thus far only been obtained from tetrapods (e.g., mammals, birds, and reptiles) . If many Ca. Sororchlamydiaceae are indeed sponge symbionts, this could indicate an ancestral association of these chlamydiae from the Chlamydiales order with Metazoa, and subsequent long-term evolution with animal hosts.
Overall, the sponge-associated chlamydiae MAGs affiliated with four different chlamydial families (Fig. 2a). Among these, were four phylogenetically distinct chlamydial species groups with a high level of relatedness in species phylogenies and with at least 95% average nucleotide identity (ANI) (Fig. 2a and Data S6). Two of these species’ groups affiliated with Ca. Sororchlamydiaceae, and one each with the Simkaniaceae and Ca. Parasimkaniaceae (Fig. 2a). A representative from each group was obtained from both the H. panicea (P_S1) and H. oculata (O_S4.1 and O_S4.2) metagenomes, which had high overall Chlamydiae relative abundances (Fig. 1a). In addition, SSU rRNA genes from two of these species’ groups corresponded to abundant OTUs identified in the amplicon data (Ca. Sororchlamydiaceae species group 2 to OTU_6_5108 and Simkaniaceae species group 4 to OTU_42_730; Fig. 2a and Data S4). No SSU rRNA genes were obtained in Ca. Parasimkaniaceae species group 3 MAGs. However, we suspect that they correspond to OTU_2241 based on the affiliation of this amplicon sequence with other Ca. Parasimkaniaceae members in an SSU rRNA gene tree (Data S5). Together, our results present a widened view on sequenced chlamydial genomic diversity with the addition of 18 high-quality genomes affiliated with the Chlamydiae phylum. Furthermore, this expanded diversity has allowed us to resolve and describe two additional chlamydial families, the Ca. Sororchlamydiaceae and Ca. Parasimkaniaceae.
Sponge-associated chlamydiae gene content corresponds to family affiliation
To gain information about the putative lifestyles of these sponge-associated chlamydiae and potential for sponge interactions, we then performed comparative genomics analyses to investigate their gene content. We found that within chlamydial families a large proportion of gene content is shared and that few genes are unique to sponge-associated lineages, with more genes shared between closely related species (Fig. S4). Thus, the sponge-associated chlamydiae accessory genome corresponds more to phylogenetic affiliation than to an ecological association with sponges. This also indicates that sponge-associated chlamydiae have similar lifestyles to other chlamydiae. Supporting this, sponge-associated chlamydiae genomes encode hallmark genes associated with endosymbiosis and the typical chlamydial lifecycle that are found conserved across Chlamydiae [27, 31]. These include nucleotide transporters (NTTs) that can be used for energy parasitism of ATP, the UhpC transporter that can be used to uptake host-derived glucose-6-phosphate, the transcription factor EUO that acts as the master regulator of the chlamydial biphasic lifecycle, and a type III secretion system that can be used to mediate host interactions through the secretion of effectors [21, 23] (Data S8). The presence of these genes strongly suggests that sponge-associated chlamydiae are likewise endosymbionts with the potential for an intracellular lifestyle in sponge hosts. Chlamydiae could be actively acquired from the surrounding water by the filtering action of the sponge. Ca. Sororchlamydiaceae genomes also encode flagellar genes (Data S8), in contrast to most other chlamydiae [27, 32], which they could use to encounter new sponge hosts.
We then compared the presence of central metabolic pathways across Chlamydiae-affiliated genomes to see if sponge-associated chlamydiae MAGs resembled previously sequenced groups in terms of core metabolism. Indeed, metabolic profiles of sponge-associated chlamydiae were comparable to other members from the same chlamydial families (Fig. 3 and Fig. S4–S5, and Data S9). More generally, central metabolic pathway presence and completeness was similar across chlamydial families, with some exceptions. Our results mirrored prior findings [21, 29], with protist-infecting chlamydiae (i.e., Ca. Amoebachlamydiales) genomes encoding more complete metabolic pathways, and Chlamydiaceae and Parilichlamydiaceae genomes encoding fewer, consistent with their specialized lifestyles as pathogens with specific animal hosts (Fig. 3 and Fig. S5, and Data S9). Relative to other chlamydial families, Ca. Sororchlamydiaceae and MCF-D members encode a larger diversity of genes for the de novo biosynthesis of coenzymes and unlike most chlamydiae they encode asparagine biosynthesis genes. Several Ca. Sororchlamydiaceae genomes also encode more genes for pyrimidine biosynthesis than most other chlamydiae (Fig. 3 and Fig. S5, and Data S9).
Despite generally conserved core metabolism, genes from certain metabolic pathways are more common in sponge-associated chlamydiae genomes, such as genes involved in de novo biosynthesis of aromatic amino acids (e.g., tryptophan) in sponge-associated Simkaniaceae (Fig. 3 and Fig. S5, and Data S9). It has been suggested that members of the sponge microbiome exchange aromatic amino acids  and sponge-associated Simkaniaceae may likewise do so or provide them to the sponge host. Similarly, most sponge-associated Ca. Parasimkaniaceae encode a sodium-transporting NADH dehydrogenase, which is absent in all other members of the family (Fig. 3 and Fig. S5, and Data S9). Conversely, the first three genes of the TCA cycle (i.e., citrate synthase, aconitase, and isocitrate dehydrogenase) are absent in most sponge-associated Ca. Parasimkaniaceae genomes and present in other family members (Data S9). These genes are also absent in Chlamydiaceae, which depend on the uptake of host-derived TCA cycle intermediates  and this could likewise be the case for sponge-associated Ca. Parasimkaniaceae. Overall, sponge-associated chlamydiae metabolic profiles were not convergent, and instead, these chlamydiae largely resembled relatives from affiliated families. Similarly, sponge-associated chlamydiae genomes did not resemble known chlamydial groups thus far composed exclusively of animal pathogens (i.e., Chlamydiaceae and Parilichlamydiaceae).
Degradative genes indicate potential for interactions between Chlamydiae members and sponges
Genes more common among sponge-associated chlamydiae, relative to other Chlamydiae members, were further investigated to provide additional information about their potential lifestyles. This revealed that sponge-associated chlamydiae encode genes related to taurine, inositol, and xylose degradation (Fig. 4 and Data S8), which have previously been found in the genomes of sponge symbionts. Taurine dioxygenase (TauD), an enzyme that degrades the sulfonate taurine to sulfite, is widespread among members of the sponge microbiome who can use it to degrade host-derived taurine , which is found in nearly all marine metazoans . Ca. Sororchlamydiaceae genomes encode tauD (Fig. 4 and Data S8), suggesting they can use host-derived taurine to acquire sulfite. However, tauACB ABC transporter genes (K15551, K15552, and K10831) were not identified (Data S8). This mirrors recent findings of more widespread identification of tauD among sponge-associated MAGs than tauACB . Nevertheless, in Ca. Sororchlamydiaceae members we did identify components of the larger gene families that include sulfonate ABC transporters (COG1116 and COG0600), alongside inconsistent identification of functional orthologs (K02051 and K02049) (Data S8). Further investigation is needed to determine whether these putative sulfonate ABC transporters can transport taurine or if chlamydial TauD is instead used to degrade an alternative sulfonate . Inositols are common in eukaryotes and rare in bacteria, and the sponge host has been suggested as the source of inositols for inositol-degrading bacterial symbionts [14, 46, 47]. Ca. Sororchlamydiaceae species group 2 MAGs (Fig. 2a) have the genetic potential to degrade scyllo-inositols using a putative scyllo-inositol 2-dehydrogenase (IolW) (Fig. 4 and Data S8). The gene iolW has previously been found in sponge symbionts such as Ca. Poribacteria members, which reside in the sponge extracellular matrix [14, 47, 48]. Ca. Poribacteria members can also degrade the sugar xylose, using the concerted action of xylose isomerase (XylA) and xylulokinase (XylB) . Simkaniaceae species group 4 MAGs (Fig. 2a) encode both xylA and xylB and thus have the genetic potential to degrade xylose (Fig. 4 and Data S8). Genes for degrading taurine, scyllo-inositol, and xylose are rare or completely absent outside of sponge-associated chlamydiae genomes (Fig. 4 and Data S8). Finding these genes in sponge-associated chlamydiae is thus consistent with a sponge-symbiont lifestyle and the use of host-derived compounds.
Eukaryotic-like proteins and domains are thought to mediate host-microbe interactions and corresponding genes are abundant in the genomes of sponge symbionts . Similarly, such genes are abundant across Chlamydiae-affiliated genomes . Here, we identified several additional typically eukaryotic genes as enriched in sponge-associated chlamydiae genomes, including sterol reductases and carnitine O-acetyltransferase (Fig. 4 and Data S8). Sterol reductase genes were identified across Ca. Sororchlamydiaceae genomes and in Simkaniaceae species group 4 MAGs (Fig. 2a). Sterol reductases perform the final step in ergosterol or cholesterol biosynthesis and have previously been found in intracellular bacteria, including several Chlamydiae members . In phylogenetic trees of these sterol reductases (K00223/K00213 and K09828), chlamydial sequences branch together with both bacterial and eukaryotic homologs, and could have been acquired by horizontal gene transfer (HGT) from either (Data S5). Carnitine is abundant in animal tissues and in bacteria it is used as an osmoprotectant or metabolized, although bacteria cannot synthesize carnitine de novo . Genes related to carnitine degradation have been found in members of the sponge microbiome and carnitine is also abundant in the sponge extracellular matrix . In animals, carnitine is used in the carnitine shuttle where it is reversibly acylated by carnitine O-acetyltransferase to allow fatty acid transport across the mitochondrial matrix for oxidation . Unexpectedly, we also identified carnitine O-acetyltransferase genes (K00624) in Simkaniaceae species group 4 MAGs and Ca. Parasimkaniaceae member Chlamydiae bacterium S4.2_102 (Figs. 2a and 4, and Data S8). In a phylogenetic tree, these chlamydial sequences branch within eukaryotic homologs suggesting that the genes were obtained by HGT from a eukaryotic host (Data S5). We likewise identified the ABC transporter OpuC (K05845 and PF04069) in these same chlamydial lineages, which can uptake osmoprotectants including carnitine  (Data S8). However, we did not detect genes for carnitine degradation (Data S8). Together, this suggests that some sponge-associated chlamydiae may obtain host carnitine to use as an osmoprotectant, or potentially, use carnitine O-acetyltransferase to mediate host metabolism or to acquire host fatty acids. Overall, the presence of these eukaryotic-like genes in sponge-associated chlamydiae genomes indicate host interactions and represent potential targets for future studies to elucidate chlamydiae-sponge interactions.
Genes for degrading acetoin were also identified as enriched in sponge-associated chlamydiae genomes and are rare in other Chlamydiae members (Fig. 4 and Data S8). Acetoin is a volatile organic compound that some bacteria can use as an energy and carbon storage compound, and which can act as a sole carbon source under glucose-limitation [54, 55]. Chlamydiae affiliated with the Ca. Sororchlamydiaceae, MCF-D, and Simkaniaceae species group 4 MAGs (Fig. 2a) may be acetoin-degrading bacteria as most encode an acetoin dehydrogenase complex (AcoA-B) (Fig. 4 and Data S8). AcoA-B is used to degrade acetoin to acetaldehyde and acetyl-CoA, with the concerted reduction of NAD+ to NADH [54,55,56]. The resulting acetaldehyde can undergo further fermentation to ethanol by alcohol dehydrogenases, such as those encoded in Ca. Sororchlamydiaceae and MCF-D genomes (Fig. 4 and Data S8), acetyl-CoA can enter the TCA cycle, and NADH can be used as a reducing agent in other reactions or enter the electron transport chain (Fig. 4). Some sponge-associated Ca. Sororchlamydiaceae and Simkaniaceae may also produce acetoin through the fermentation of pyruvate to acetolactate using acetolactate synthase (Fig. 4). Acetolactate is then converted to acetoin spontaneously under aerobic conditions or through the action of acetolactate decarboxylase , which is encoded in some Simkaniaceae genomes, including sponge-associated members (Fig. 4). Together, these genes suggest that sponge-associated chlamydiae can degrade acetoin as a carbon and energy source, which could be obtained from members of the sponge microbiome.
In addition, several genes involved in degrading organic pollutants and toxic compounds were also enriched in sponge-associated chlamydiae genomes and rare in other Chlamydiae members. A putative S-(hydroxymethyl)glutathione dehydrogenase gene was found in several chlamydiae affiliated with Ca. Sororchlamydiaceae, MCF-D, and a few other chlamydial families (Fig. 4 and Data S8). This enzyme is involved in oxidizing formaldehyde, a toxic compound that can be produced as an intermediate during methylotrophy , genes for which have been found expressed by sponge symbionts . We also identified genes involved in chlorocatechol and catechol degradation (i.e., dienelactone hydrolase, 3-oxoadipate enol-lactonase, and catechol 2,3-dioxygenase) in a few sponge-associated chlamydiae genomes (Fig. 4 and Data S8). Anthropogenic contamination is a growing concern in marine ecosystems, and microbially mediated degradation of such organic pollutants and other aromatics have a branching point at chlorocatechol and catechol . Genes involved in these degradative pathways have previously been found in members of the sponge microbiome, and may also be used to detoxify compounds produced by other microbiome members . Dienelactone hydrolases perform a key step by degrading dienelactones to maleylacetate, which can then be further catabolised before entering the TCA cycle [61, 62]. Simkaniaceae species group 4 MAGs (Fig. 2a) and Chlamydiae bacterium 1063924-70, which was obtained from a marine environment (Data S6), encode dienelactone hydrolase (Fig. 4 and Data S8). In addition, genes with the dienelactone hydrolase family protein domain (PF01738) are found in most sponge-associated Ca. Sororchlamydiaceae genomes (Data S8). Sponge-associated chlamydiae may use these genes to protect themselves, or the sponge host, from toxic compounds, whether they originate from external sources or other microbial community members.
Widespread potential for secondary metabolite biosynthesis across the phylum Chlamydiae
Sponges are well-known as reservoirs for the discovery of natural products with medical and industrial importance, many of which are secondary metabolites produced by microbiome members [6,7,8]. Typically, genes for producing secondary metabolites are organized in biosynthetic gene clusters (BGCs) . Recently, metagenomic analyses have revealed that BGCs are found across a broad phylogenetic range of bacteria in sponge microbiomes . To gain further perspective on the genetic capacity for secondary metabolite biosynthesis across Chlamydiae, we used antiSMASH  to investigate BGCs in representative genomes and our sponge-associated chlamydiae MAGs (Fig. 5 and Data S10). Unexpectedly, our analysis revealed that BGCs are widespread across the phylum Chlamydiae, with many chlamydial genomes encoding multiple and different types of BGCs, most with unknown functions (Fig. 5a and Data S10).
Genomes of Ca. Sororchlamydiaceae and Ca. Amoebachlamydiales encode the most conserved set of BGCs, with many encoding NRPS-like (non-ribosomal peptide synthetase), RiPP-like (ribosomally synthesized and post-translationally modified peptide), and T3PKS (type III polyketide synthase) BGCs (Fig. 5a). However, BGCs were few in Ca. Anoxychlamydiaceae, largely absent in Ca. Parasimkaniaceae and sponge-associated Simkaniaceae, and completely absent in all Chlamydiaceae and Parilichlamydiaceae genomes (Fig. 5a). We found additional PKS and NRPS genes, which are central in the biosynthesis of various secondary metabolites, in some sponge-associated chlamydiae MAGs. For example, MCF-D member Chlamydiae bacterium S4.2_7 encodes NRPS gene homologs related to those found in Simkania negevensis (Data S8). One group of PKS genes, SnoaL-like polyketide cyclases (PF07366), were found encoded in many sponge-associated chlamydiae MAGs (Data S8). Phylogenetic analysis of this protein family showed that it has been gained multiple times by different chlamydial groups and from diverse potential HGT partners (Fig. 5b). Despite BGCs not being widely identified in Ca. Parasimkaniaceae, some genomes do encode this PKS gene (Data S8 and Fig. 5b).
Phosphoenolpyruvate mutase (PepM) performs the first committed step for synthesizing fosfomycin and other phosphonates . Fosfomycin inhibits bacterial cell wall biosynthesis by binding to the active site of MurA, which performs the initial step in peptidoglycan biosynthesis . Two Ca. Sororchlamydiaceae sponge MAGs (Chlamydiae bacterium S1_42 and S4.2_81) encode a phosphonate BGC (Fig. 5a), with closest homology to BGCs used to produce the antibiotic fosfomycin (0.61 similarity score and 50% PepM sequence identity; Data S10). In a phylogenetic tree, chlamydial PepM homologs formed a well-supported clade together with known PepM sequences, indicating that they likely have the same function (Fig. 5c). However, we were unable to determine the HGT donor, since chlamydial homologs formed a clade with diverse bacteria primarily represented by MAGs (Fig. 5c). Chlamydia spp. are resistant to very large quantities of fosfomycin due to conserved changes in the MurA active site . Further investigation into MurA sequence evolution and the genetic potential for fosfomycin production across Chlamydiae could elucidate whether Chlamydiaceae MurA resistance may be connected to an ancestral capacity for fosfomycin production. However, it remains to be confirmed if this BGC is indeed used to produce fosfomycin or instead a different phosphonate antimicrobial.
Overall, our results show that many Chlamydiae members have the potential to produce secondary metabolites, which may play a previously unrecognized role in their endosymbiotic lifestyles. As far as we are aware, secondary metabolite biosynthesis had not been previously noted or investigated in the Chlamydiae phylum. This could be explained by the absence of these gene clusters in the most-studied family, the Chlamydiaceae. The Chlamydiae phylum is part of the PVC superphylum and other PVC members are also associated with sponges [18, 34]. In particular, Planctomycetes phylum members have been identified as a potential reservoir of novel secondary metabolites [68, 69]. Despite Planctomycetes-affiliated genomes being substantially larger , a comparable number of BGCs were identified in Chlamydiae-affiliated genomes (Fig. 5a). The Chlamydiae phylum may thus likewise represent a reservoir for the discovery of secondary metabolites.
Based on their endosymbiotic lifestyles and smaller genome sizes, it was unexpected to identify BGCs as common in many Chlamydiae genomes. These chlamydial BGCs could function in inter-microbial warfare, in communication, or in mediating host interactions . In addition, chlamydial BGCs could function in providing chemical defence to the host. Members of the sponge microbiome have been suggested to provide chemical defence to the sponge host . Recently, an endosymbiosis was identified between a Haliclona sponge species and a gammaproteobacterium mediated by chemical defence through antibiotic production . Sponge-associated chlamydiae genomes that encode BGCs (i.e., Ca. Sororchlamydiaceae and MCF-D family members; Fig. 5a) may likewise participate in host-beneficial defensive endosymbioses. MAGs from other putatively endosymbiotic bacteria (e.g., affiliated with Legionellales and Rickettsiales) were also obtained from the metagenomes (Data S4), and specifically from samples that had high Chlamydiae relative abundances (Fig. 1a). This suggests the potential for intracellular interactions between co-infecting endosymbionts, perhaps mediated by secondary metabolites. Alternatively, the co-occurrence of putatively endosymbiotic bacteria in some sponge metagenomes could simply indicate increased susceptibility to infection under certain environmental conditions or in some sponge individuals. Furthermore, it could also suggest co-infection of a different eukaryotic host, whose association with the sponges is in turn variable. Novel antimicrobial compounds have also been isolated from sponges that are active against chlamydial species , and could instead indicate antagonistic interactions with the sponge host or members of the sponge microbiome. Some protist-infecting Parachlamydiaceae members have previously been shown to be mutualists that protect their host amoeba against Legionella infection through an unknown mechanism [73, 74]. This mechanism may have a basis in the production of secondary metabolites as many BGCs were identified in Parachlamydiaceae-affiliated genomes (Fig. 5a). Some sponge-associated chlamydiae could offer similar defensive benefits against co-infection to their host.
Chlamydiae is associated with other sponge species and marine invertebrates
To determine whether sponge-associated chlamydiae are associated with other hosts or environments we screened publicly available SSU rRNA gene amplicon datasets for close relatives (≥95% identity) (Fig. 6a and Data S11). Relatives of sponge-associated chlamydiae were found almost exclusively in marine environments, with higher prevalence in marine invertebrates such as sponges, corals, sea squirts, and molluscs (Fig. 6a). Sponge-associated chlamydiae affiliated with MCF-D were present in a higher proportion and wider range of environments, yet still primarily of marine origin. Unfortunately, no SSU rRNA gene was obtained for any of the sponge-associated Ca. Parasimkaniaceae MAGs, and their environmental distribution could not be assessed. Despite the clear association with marine environments here, Chlamydiae have also been found associated with sponges from freshwater lakes . Relatively few environments were identified with higher abundances of sponge-associated chlamydiae relatives (Data S11), possibly due to Chlamydiae being undetected and underestimated by many common primers for surveying microbial diversity [25,26,27]. Those identified included incubations of Great Barrier Reef lagoon water  and humic acid amended aquaculture systems (Data S11). In addition to our study, and the previous study of these sponge species , Chlamydiae have also previously been found in high relative abundance in association with sponges. This includes Suberites zeteki, a marine sponge invasive in Hawaii , and H. panicea after ex situ cultivation . In addition, several chlamydial MAGs have previously been obtained from sponge metagenomes. This includes Simkaniaceae bacterium CLI4_bin_1 and Chlamydiia bacterium bin105s from the sponges Cliona orientalis and Vazella pourtalesii, and affiliated with Simkaniaceae and MCF-D families (Fig. 2a), respectively [16, 37]. These studies and ours suggest that more chlamydial genomes will likely be uncovered from further metagenomic investigation of sponge-associated microbial communities.
Although Chlamydiae has been found across different sponge species and relatives of sponge-associated chlamydiae were detected in various marine invertebrates, it is possible that they instead have another sponge-associated eukaryotic host. To help elucidate this, eukaryotic SSU rRNA genes across the metagenomes were collected and classified (Fig. 6b and Data S4). Some of the identified eukaryotes are present in multiple samples. However, importantly, no other eukaryotes apart from the sponge were found across the samples with high Chlamydiae relative abundances (Figs. 1a and 6b). Still, additional eukaryotes may have been missed in the metagenomes, for example due to DNA extraction biases. We did identify a mitochondrial cytochrome c oxidase subunit 1 (CO1) gene most closely related to the green algae Picochlorum in samples with high Chlamydiae relative abundances, and this could represent an alternative host (Data S4). Chlamydiae members have not previously been identified in green algae, which thus far have relatively few known endosymbionts . However, this could be the result of sampling bias. Recently, chlamydiae affiliated with Simkaniaceae were detected in some cultures of Symbiodiniaceae, a group of dinoflagellate microalgae that are endosymbionts of cnidarians, such as corals . There is thus some precedent for an association between Chlamydiae members and algae. In addition, diverse eukaryotes have been found associated with sponges that could act as alternative hosts. For example, eukaryotes found associated with H. panicea include diatoms, algae, ciliates, nematodes, turbellaria, amphipods, and copepod and polychaete larvae [80, 81]. Chlamydiae has also previously been detected in a sponge-associated polychaete microbiome . Indeed, several Simkaniaceae-affiliated MAGs have been retrieved from marine worm metagenomes (Data S6). Fungi were also found in other H. panicea and H. xena samples from the Oosterschelde estuary, although at low relative abundance . While we found a clear association of some Chlamydiae lineages and sponge species, further work is needed to determine whether this is through an alternative eukaryotic host.
Chlamydiae was found across a wide range of sponge microbiomes in a recent amplicon survey . This prompted us to examine whether specific chlamydial groups are associated with sponges. To answer this question we inferred a SSU rRNA gene phylogeny using chlamydial sequences obtained from this wide survey , from our study, and from the prior study of these sponge species . We then placed them in the context of a representative dataset of bacterial and chlamydial SSU rRNA gene sequences [25, 26], and sequenced Chlamydiae species representatives (Data S6). In the resulting phylogenetic tree a separation is apparent, with the vast majority of sponge-associated sequences affiliating with less studied chlamydial families (Fig. 6c). No sponge sequences grouped together with the well-studied Chlamydiaceae family, and few sequences affiliated with the protist-infecting Ca. Amoebachlamydiales families (i.e., Criblamydiaceae, Waddliaceae, and Parachlamydiaceae) (Fig. 6c). As expected, sequences from the present study and previous study of these sponge species  clustered together in the Ca. Sororchlamydiaceae, MCF-D, Simkaniaceae, and Ca. Parasimkaniaceae clades (Fig. 6c). Chlamydial sequences from the wide survey of sponge microbiomes  primarily grouped together with the Ca. Parasimkaniaceae, Simkaniaceae, Rhabdochlamydiaceae, and in unclassified clades (Fig. 6c). Altogether, these observations provide evidence for widespread associations between less-studied chlamydial groups and sponges.
Conclusions and future perspectives
Using genome-resolved metagenomics we have expanded sequenced Chlamydiae diversity with 18 high-quality genomes that provide insight into chlamydial associations with animal hosts. This allowed us to uncover genomic diversity from four distinct chlamydial families and to describe two that previously had poor representation, Ca. Sororchlamydiaceae and Ca. Parasimkaniaceae. All cultivated Chlamydiae members are obligate endosymbionts  and based on their gene content sponge-associated chlamydiae likely are as well. Sponge-associated chlamydiae are capable of acquiring carbon and energy directly from eukaryotic hosts (e.g., through the action of NTTs and UhpC etc.). Thus, their capacity for degrading a wide range of sponge-derived compounds and compounds present in the larger microbial community was unexpected. The presence of genes for degrading toxins and pollutants, and BGCs in some sponge-associated chlamydiae could suggest host-beneficial effects. More generally, our findings open the door for further exploration of BGCs across Chlamydiae, and suggest larger roles for secondary metabolites in endosymbiotic interactions. Relatives of sponge-associated chlamydiae are prevalent in other sponges and marine invertebrates, where they have unknown effects.
We found that sponge-associated chlamydiae vary in their relative abundance across specimens of the same sponge species. This points to important unanswered questions about the nature of sponge-chlamydiae associations. Foremost, it is currently unclear whether the presence of sponge-associated chlamydiae is beneficial or detrimental to the sponge, and if their presence is an indicator of environmental conditions and host health? Future studies are also needed to confirm whether these chlamydiae are directly associated with the sponge host and are endosymbionts, or if their interaction is secondary as symbionts of a different eukaryotic host living in the sponges. This important distinction can be elucidated, for example, through direct experimental evidence from fluorescence in situ hybridization, transmission electron microscopy, or single-cell genomics of sponge cells. Our genomic investigation has brought new insights to sponge-associated chlamydiae and suggests chlamydial effects on sponges, whether direct or indirect, that warrant further investigation. Altogether, our work represents a first step in untangling the potentially wide impacts of Chlamydiae on marine ecosystems.
Description of Candidatus Sororchlamydiaceae fam. nov
(So.ror.chla.my.di.a.ce’ae. L. fem. n. soror sister; Chlamydiaceae taxonomic name of a bacterial family; L. suff. -aceae ending to denote a family; Ca. Sororchlamydiaceae referring to the close relationship to the bacterial family Chlamydiaceae)
The family Candidatus Sororchlamydiaceae (formerly Chlamydiae Clade IV) represents a distinct monophyletic lineage, sister to Chlamydiaceae, supported by concatenated marker gene phylogenies in the present study (Fig. 2a and Fig. S3) and in prior work [27, 31]. Ca. Sororchlamydiaceae members share conserved gene content and metabolism, and have larger genome sizes and greater metabolic potential than Chlamydiaceae members (Fig. 2b, 3 and Fig. S4–S5) .
Description of Candidatus Parasimkaniaceae fam. nov
(Par.a.sim.ka.ni. a.ce’ae. Gr. prep. para alike, alongside of; N.L. fem. n. Simkania taxonomic name of a bacterial genus; L. suff. -aceae ending to denote a family; Ca. Parasimkaniaceae referring to the close relationship to the bacterial family Simkaniaceae).
The family Candidatus Parasimkaniaceae represents a distinct monophyletic lineage supported by concatenated marker gene phylogenies and is closely related to the Simkaniaceae family (Fig. 2a and Fig. S3). Ca. Parasimkaniaceae members share conserved gene content, and have on average smaller genome sizes and reduced metabolic potential relative to Simkaniaceae members (Figs. 2b, 3 and Figs. S4–S5).
Materials and methods
Sponge collection and DNA extraction
Six sponge specimens from three sponge species (Halichondria panicea – P_S1, P_S2, and P_S3; Haliclona oculata – O_S4 and O_S5; Haliclona xena – X_S6) were collected from the Oosterschelde estuary in the Netherlands, washed in autoclaved seawater, and stored at −80 °C (Fig. S1 and Data S1). P_S1, P_S2, and O_S4 were sampled at the same timepoint and location as those investigated in a previous study (Fig. S1) . The sponge SSU rRNA gene from each metagenome was used to confirm sponge species identification (Data S1).
For generating SSU rRNA gene amplicons, DNA was extracted from 0.2 g of each specimen using the DNAeasy PowerLyzer Powersoil Kit (QIAGEN) according to manufacturer’s protocols, with DNA elution in water and bead beating with the PowerLyzer 24 homogenizer (MO BIO) at 4000 rpm for 45 s. For O_S4 (O_S4.2), O_S5, and X_S5 this DNA was also used for metagenomic sequencing (Fig. S1). Due to high levels of fragmentation an additional extraction optimized for high-molecular-weight DNA was performed for metagenomic sequencing of P_S1, P_S2, P_S3, and a second O_S4 extraction (O_S4.1) (Fig. S1). Thus, two different DNA extraction methods were performed on sponge specimen O_S4 and both were later sequenced (O_S4.1 and O_S4.2). In the high-molecular-weight DNA extraction, bead beating with the DNAeasy PowerLyser Powersoil Kit (QIAGEN) was used as described above, but with the addition of 0.2 M ethylenediamine tetraacetic acid (EDTA) (1:1 ratio) prior to the lysis step to inhibit DNases. After bead beating, 10% cetyltrimethylammonium bromide buffer (CTAB) (1:4 ratio), 0.5 M NaCl (1:2 ratio), 0.1 M EDTA (1:4 ratio), 10 μL β-mercaptoethanol (100%), and 5 μL proteinase K (600 mAU/mL) were added and the samples were incubated overnight at 56 °C. RNase A was then added (final concentration of 0.3 ng/μL) and the sample incubated for 30 min at 37 °C. Two rounds of chloroform/isoamyl alcohol 24:1 (1:1 ratio) addition, incubation for 2 min at room temperature, centrifugation (10000 × g for 10 min), and transfer of the aqueous phase were then performed. DNA was precipitated using isopropanol (6 h) and pelleted with centrifugation for 15 min at 10000 × g at 4 °C, before being washed twice with 80% ethanol and eluted in water.
Generation of bacterial SSU rRNA gene amplicons
A two-step PCR approach was used to obtain SSU rRNA gene fragments for amplicon sequencing (Fig. S1), using the bacterial-specific primers S-D-0564-a-S-15 (AYTGGGYDTAAAGNG) and S-D-Bact-1061-a-A-17 (CRRCACGAGCTGACGAC)  that capture most chlamydial lineages . HotStarTaq DNA Polymerase (QIAGEN) was used with the following reaction conditions: initial heat activation at 95 °C (15 min), followed by 28 cycles of denaturation at 94 °C (60 s), a step-down to 70 °C (1 s), a ramping rate of 0.4 °C/s to 50 °C for annealing (60 s), and a ramping rate of 0.8 °C/s to 72 °C for extension (60 s), with a final extension at 72 °C (10 min). A second PCR reaction was performed according to the manufacturers’ protocol to obtain sequence libraries with adaptor sequences from the TruSeq DNA LT Sample Prep Kit (Illumina). PCR products were purified using magnetic Agencourt AMPure XP beads (Beckman Coulter) and sequencing performed on the MiSeq System (Illumina) using the v3 chemistry (2 × 300 bp).
Sequence reads were demultiplexed and quality control performed using Cutadapt v. 1.10  to remove remaining adaptor and primer sequences, trim 3’ read ends to a minimum Phred quality score of 10, and remove reads shorter than 100 bp in length. VSEARCH v. 1.11.1  was then used to merge forward and reverse reads (–fastq-minovlen 16), to de-replicate reads (–derep_fulllength), and to obtain centroid OTU clusters at 97% identity. Chimeric sequences were detected and removed using UCHIME  with the SILVA123.1_SSUref_tax:99 database . OTUs were taxonomically classified using CREST v. 4.2.1  with the default ‘silvamod138’ database, which is a modified version of the SILVA nr SSU Ref v138 database  (Data S2). OTU read counts and relative abundances across samples are available in Data S3.
Metagenome sequencing and assembly
The Nextera DNA Library Prep Kit (Illumina) was used to prepare sequence libraries with 25 ng of input DNA, followed by sequencing with the NovaSeq6000 System (Illumina) for P_S1, P_S2, P_S3, and O_S4 (O_S4.1), and with the HiSeq2500 System (Illumina) for O_S4 (O_S4.2), O_S5, and X_S6. Quality control of resulting sequence reads was performed to remove adaptors and low-quality sequences using Trimmomatic v. 0.35  with the options: ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGDOWN:4:15 MINLEN:50. Read quality was assessed using FastQC v0.11.4 . Resulting paired sequence reads were then assembled using MEGAHIT v3.13  (--meta --only-assembler), and assembly statistics obtained with QUAST v5.0.2  (Data S1). Protein sequences were predicted using Prodigal v2.6.3 . Barrnap v. 0.9  was used to identify metagenomic SSU rRNA genes, which were classified using the LCAClassifier from CREST v. 3.1.0  using the default modified 'silvamod138' version of the SILVA nr SSU Ref v138 database  (Data S4).
Binning of metagenome-assembled genomes
Differential read coverage was obtained by mapping each set of sequence reads against each assembled metagenome using Bowtie2 v2.2.6 . MAGs from each metagenome were obtained using differential coverage binning with metaBAT 2.12.1 , CONCOCT v. 1.1.0 , and MaxBin v. 2.2.7  (Fig. S1). The metaWRAP  v. 1.2.4 “bin_refinement” module was then used to consolidate resulting bins into hybridized bin sets. The highest quality hybridized or original bin was selected with a cut-off of 70% completeness and 10% redundancy (Data S4). Several assemblies had smaller sizes (P1_S2 and P1_S3) and few MAGs above quality thresholds were obtained for these (Data S1 and S4). Further manual refinement of chlamydial MAGs was performed using anvi’o [101, 102] v.6.2, followed by reassembly with the metaWRAP  v. 1.2.4 “reassemble_bins” module, and additional manual curation (Fig. S1).
Genome characteristics and annotation
Chlamydiae MAGs were annotated using Prokka  v1.14.6. In addition, protein-coding genes were annotated with the NCBI NR protein database , top Blastp hits , and Pfam  and TIGRFAM  database domains identified by InterProScan  5.47-82.0. Comparative genomic analyses were performed between sponge-associated chlamydiae MAGs and a set of Chlamydiae species representatives with high-quality genomes. Genome characteristics and genome quality were determined with miComplete  v. 1.1.1 using a marker gene-set conserved in complete chlamydial genomes  (Data S6). Genes were also assigned to eggNOG  4.5 NOGs at the universal-level using eggNOG-mapper 1.0.3 (“-d NOG”) , and KEGG KOs  identified using GhostKOALA  (Chlamydiae representatives) and BlastKOALA (chlamydial genomes from the present study). Genes of interest were identified (Data S8) and COGs were also assigned to COG pathways (Data S9) . AntiSMASH 6 beta  was used to identify BGCs, and top hits to MIBiG clusters  where found (Data S10). Chlamydial species groups with ≥95% ANI were identified using dRep  v3.2.2 “compare” and the options “--SkipMash” and “-sa 0.95”, and only ANI comparisons with 50% query and reference coverage further considered (Data S6).
Ribosomal protein phylogeny of metagenomic contigs
Microbial community composition was assessed by identifying metagenomic contigs encoding ribosomal proteins (at least 5 of 15), typically found together in a conserved gene cluster , using a previously described pipeline  (Data S4). Ribosomal proteins from each contig were concatenated and a ML phylogeny inferred using RAxML  8.2.4 with the PROTCATLG model of evolution, and 100 rapid bootstrap replicates (Data S5). Read coverages of these metagenomic contigs were compared to measure relative abundances (Data S4).
Chlamydiae species phylogeny
Protein sequences from 74 single-copy marker genes (Data S7), found conserved across Chlamydiae MAGs and PVC species representatives (Data S6), were each aligned using MAFFT-L-INS-i  v7.471, and trimmed with BMGE  (entropy of 0.6). IQ-TREE  v. 1.6.12 was used to infer single-gene trees with 1000 ultrafast bootstraps inferred , and using ModelFinder  for model selection from LG  and LG profile mixture models (C10 to C60) , with gamma or free-distributed rates (+G or +R) , and with or without empirically determined amino acid frequencies (+F). Trees were manually inspected, divergent sequences removed, and the process repeated where necessary (Data S7). To determine which marker genes had the strongest phylogenetic signal, the monophyly of PVC phyla in the single-gene trees was assessed (Data S7). Four marker gene datasets were chosen to include all genes (74 NOGs), genes where Chlamydiae was monophyletic (60 NOGs), genes where Chlamydiae and most other PVC phyla were monophyletic (40 NOGs), and genes where all PVC phyla were monophyletic (15 NOGs) (Data S7). ML species phylogenies were then inferred as described above for concatenated alignments of each dataset.
The topology across all species trees was consistent and the placement of sponge-associated chlamydiae MAGs supported (Fig. S3 and Data S5). Further analyses were thus performed with only the smallest concatenated dataset (15 NOGs). A ML tree was inferred using IQ-TREE  v. 1.6.12 with 100 non-parametric bootstraps under the PMSF approximation  of the LG + C60 + F + R4 model of evolution (Fig. 2a and Data S5). A Bayesian phylogeny was also inferred using PhyloBayes-MPI  1.7b and the CAT + GTR + Γ4 model of evolution , with four independent MCMC chains. After approximately 100,000 generations the four chains had not converged (with a burn-in of 5000, and sampling every 10 generations) (Data S5). However, two chains had converged (maxdiff of 0.16) (Fig. 2a), and the topology of both deeper nodes and the placement of sponge-associated chlamydiae was consistent across all chains (Data S5).
Single-protein phylogenies of specific genes
The eukaryotic affiliation of typically eukaryotic genes was confirmed using a phylogenetic workflow (https://github.com/jennahd/HGT_trees) (Data S5). Additionally, phylogenetic trees were inferred for protein sequences with SnoaL-like PKS (PF07366) and PepM (PF13714) protein domains (Data S5). Here, DIAMOND Blastp  v0.9.36 searches (with “max-target-seqs 2000” and “more-sensitive”) were performed against NCBI’s NR database , and sequence redundancy removed using CD-HIT  v. 4.8.1 at 80% identity. For PepM, the top 100 hits to the Swiss-Prot database, of proteins with curated annotations, were additionally retrieved . Sequences were aligned and trimmed as above. For SnoaL-like PKS an initial tree was inferred using FastTree2  v2.1.11 and a subset of more distantly related sequences removed. ML phylogenies were then inferred using IQ-TREE  v.1.6.12, with model selection by ModelFinder  as described above. The LG + C60 + F + R4 model was selected in both cases, and 1000 ultrafast bootstraps inferred  (Data S5).
Small subunit rRNA gene phylogeny of sponge-associated Chlamydiae diversity
Chlamydial SSU rRNA gene amplicon OTUs were collected from the present study, the prior study of these sponge species , and from a survey of sponge microbial diversity . These were combined with full-length and near full-length SSU rRNA genes from Chlamydiae MAGs and reference genomes (Data S6), and prior surveys of chlamydial diversity . Sequences were added to a previously published bacterial SSU rRNA gene alignment of 85% sequence identity representatives  using MAFFT-L-INS-i  v7.471 (“--add” for near full-length sequences, and “--addfragments” for amplicon OTUs). The alignment was trimmed using trimAl  v1.4.rev15 with a gap threshold of 0.1. A ML phylogeny was then inferred using IQ-TREE  v. 1.6.12, with the GTR + F + R10 model selected from GTR models  by ModelFinder  and 1000 ultrafast bootstraps .
The environmental distribution of specific chlamydial lineages was assessed using IMNGS . Here, environmental samples were screened for sequences with at least 95% identity to SSU rRNA genes from several sponge-associated chlamydiae MAGs (Data S11). Samples with at least 0.1% relative abundance of these chlamydial lineages were also identified (Data S11).
Data visualization and availability
Phylogenetic trees were visualized using the ETE3 Toolkit , iTOL , and Figtree v1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/). Plots were generated using R version 4.0.3 (R Core Team, 2020), with ggplot2  v. 3.3.2, and with UpSetR  v. 1.4.0 for intersection plots. Assembled metagenomes, metagenome sequence reads, amplicon sequence reads, and MAGs generated from each sponge sample can be found deposited under BioProject PRJNA742377. Accessions for data analyzed or obtained in this study can be found in Data S1, S4, and S6. Whole Genome Shotgun projects for sponge metagenome assemblies P_S1, P_S2, P_S3, O_S4.1, O_S4.2, O_S5, and X_S6 have been deposited at DDBJ/ENA/GenBank under the accessions JAHZIM000000000, JAHZIN000000000, JAHZIO000000000, JAHZIP000000000, JAHZIS000000000, JAHZIQ000000000, and JAHZIR000000000, respectively. The versions described in this paper are versions JAHZIM010000000, JAHZIN010000000, JAHZIO010000000, JAHZIP010000000, JAHZIS010000000, JAHZIQ010000000, and JAHZIR010000000. All sequence alignments and phylogenetic trees in Newick format, sponge-associated chlamydiae genome annotations, antiSMASH and eggNOG-mapper output files, and KEGG KOs across Chlamydiae species representatives are provided at the figshare data repository: https://doi.org/10.6084/m9.figshare.14939475.v1.
Hentschel U, Piel J, Degnan SM, Taylor MW. Genomic insights into the marine sponge microbiome. Nat Rev Microbiol. 2012;10:641–54.
Reveillaud J, Maignien L, Eren AM, Huber JA, Apprill A, Sogin ML, et al. Host-specificity among abundant and rare taxa in the sponge microbiome. ISME J. 2014;8:1198–209.
Moitinho-Silva L, Nielsen S, Amir A, Gonzalez A, Ackermann GL, Cerrano C, et al. The sponge microbiome project. Gigascience 2017;6:1–7.
Pita L, Rix L, Slaby BM, Franke A, Hentschel U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome 2018;6:46.
Paul VJ, Freeman CJ, Agarwal V. Chemical ecology of marine sponges: new opportunities through “-omics”. Integr Comp Biol. 2019;59:765–76.
Taylor MW, Radax R, Steger D, Wagner M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol Mol Biol Rev. 2007;71:295–347.
Palazzotto E, Weber T. Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms. Curr Opin Microbiol. 2018;45:109–16.
Kenshole E, Herisse M, Michael M, Pidot SJ. Natural product discovery through microbial genome mining. Curr Opin Chem Biol. 2021;60:47–54.
Leal MC, Puga J, Serodio J, Gomes NCM, Calado R. Trends in the discovery of new marine natural products from invertebrates over the last two decades - where and what are we bioprospecting? PLoS One. 2012;7:e30580.
Egan S, Gardiner M. Microbial dysbiosis: rethinking disease in marine ecosystems. Front Microbiol. 2016;7:991.
Schippers KJ, Sipkema D, Osinga R, Smidt H, Pomponi SA, Martens DE, et al. Cultivation of sponges, sponge cells and symbionts: achievements and future prospects. Adv Mar Biol. 2012;62:273–337.
Gutleben J, Loureiro C, Ramirez Romero LA, Shetty S, Wijffels RH, Smidt H, et al. Cultivation of bacteria from Aplysina aerophoba: effects of oxygen and nutrient gradients. Front Microbiol. 2020;11:175.
Slaby BM, Hackl T, Horn H, Bayer K, Hentschel U. Metagenomic binning of a marine sponge microbiome reveals unity in defense but metabolic specialization. ISME J. 2017;11:2465–78.
Podell S, Blanton JM, Neu A, Agarwal V, Biggs JS, Moore BS, et al. Pangenomic comparison of globally distributed Poribacteria associated with sponge hosts and marine particles. ISME J 2019;13:468–81.
Engelberts JP, Robbins SJ, de Goeij JM, Aranda M, Bell SC, Webster NS. Characterization of a sponge microbiome using an integrative genome-centric approach. ISME J. 2020;14:1100–10.
Robbins SJ, Song W, Engelberts JP, Glasl B, Slaby BM, Boyd J, et al. A genomic view of the microbiome of coral reef demosponges. ISME J. 2021;15:1641–54.
Burgsdorf I, Sizikov S, Squatrito V, Britstein M, Slaby BM, Cerrano C, et al. Lineage-specific energy and carbon metabolism of sponge symbionts and contributions to the host carbon pool. ISME J. 2022;16:1163–75.
Thomas T, Moitinho-Silva L, Lurgi M, Bjork JR, Easson C, Astudillo-Garcia C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun. 2016;7:11870.
Zhu P, Li Q, Wang G. Unique microbial signatures of the alien Hawaiian marine sponge Suberites zeteki. Micro Ecol. 2008;55:406–14.
Naim MA, Morillo JA, Sorensen SJ, Waleed AA, Smidt H, Sipkema D. Host-specific microbial communities in three sympatric North Sea sponges. FEMS Microbiol Ecol. 2014;90:390–403.
Omsland A, Sixt BS, Horn M, Hackstadt T. Chlamydial metabolism revisited: interspecies metabolic variability and developmental stage-specific physiologic activities. FEMS Microbiol Rev. 2014;38:779–801.
Taylor-Brown A, Vaughan L, Greub G, Timms P, Polkinghorne A. Twenty years of research into Chlamydia-like organisms: a revolution in our understanding of the biology and pathogenicity of members of the phylum Chlamydiae. Pathog Dis. 2015;73:1–15.
Collingro A, Kostlbacher S, Horn M. Chlamydiae in the environment. Trends Microbiol. 2020;28:877–88.
Elwell C, Mirrashidi K, Engel J. Chlamydia cell biology and pathogenesis. Nat Rev Microbiol. 2016;14:385–400.
Lagkouvardos I, Weinmaier T, Lauro FM, Cavicchioli R, Rattei T, Horn M. Integrating metagenomic and amplicon databases to resolve the phylogenetic and ecological diversity of the Chlamydiae. ISME J. 2014;8:115–25.
Schulz F, Eloe-Fadrosh EA, Bowers RM, Jarett J, Nielsen T, Ivanova NN, et al. Towards a balanced view of the bacterial tree of life. Microbiome 2017;5:140.
Dharamshi JE, Tamarit D, Eme L, Stairs CW, Martijn J, Homa F, et al. Marine sediments illuminate chlamydiae diversity and evolution. Curr Biol. 2020;30:1032–48. e7
Taylor-Brown A, Madden D, Polkinghorne A. Culture-independent approaches to chlamydial genomics. Micro Genom. 2018;4:e000145.
Taylor-Brown A, Pillonel T, Greub G, Vaughan L, Nowak B, Polkinghorne A. Metagenomic analysis of fish-associated Ca. Parilichlamydiaceae reveals striking metabolic similarities to the terrestrial Chlamydiaceae. Genome Biol Evol. 2018;10:2587–95.
Taylor-Brown A, Spang L, Borel N, Polkinghorne A. Culture-independent metagenomics supports discovery of uncultivable bacteria within the genus Chlamydia. Sci Rep. 2017;7:10661.
Köstlbacher S, Collingro A, Halter T, Schulz F, Jungbluth SP, Horn M. Pangenomics reveals alternative environmental lifestyles among chlamydiae. Nat Commun. 2021;12:4021.
Collingro A, Kostlbacher S, Mussmann M, Stepanauskas R, Hallam SJ, Horn M. Unexpected genomic features in widespread intracellular bacteria: evidence for motility of marine chlamydiae. ISME J. 2017;11:2334–44.
Wagner M, Horn M. The Planctomycetes, Verrucomicrobia, Chlamydiae and sister phyla comprise a superphylum with biotechnological and medical relevance. Curr Opin Biotechnol. 2006;17:241–9.
Devos DP, Ward NL. Mind the PVCs. Environ Microbiol. 2014;16:1217–21.
Wichels A, Wurtz S, Dopke H, Schutt C, Gerdts G. Bacterial diversity in the breadcrumb sponge Halichondria panicea (Pallas). FEMS Microbiol Ecol. 2006;56:102–18.
Louca S, Doebeli M, Parfrey LW. Correcting for 16S rRNA gene copy numbers in microbiome surveys remains an unsolved problem. Microbiome 2018;6:41.
Bayer K, Busch K, Kenchington E, Beazley L, Franzenburg S, Michels J, et al. Microbial strategies for survival in the glass sponge Vazella pourtalesii. mSystems 2020;5:e00473–20.
Israelsson O. Chlamydial symbionts in the enigmatic Xenoturbella (Deuterostomia). J Invertebr Pathol. 2007;96:213–20.
Pizzetti I, Schulz F, Tyml T, Fuchs BM, Amann R, Horn M, et al. Chlamydial seasonal dynamics and isolation of ‘Candidatus Neptunochlamydia vexilliferae’ from a Tyrrhenian coastal lake. Environ Microbiol. 2016;18:2405–17.
Anantharaman K, Brown CT, Hug LA, Sharon I, Castelle CJ, Probst AJ, et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat Commun. 2016;7:13219.
Karlsen M, Nylund A, Watanabe K, Helvik JV, Nylund S, Plarre H. Characterization of ‘Candidatus Clavochlamydia salmonicola’: an intracellular bacterium infecting salmonid fish. Environ Microbiol. 2008;10:208–18.
Nunes A, Gomes JP. Evolution, phylogeny, and molecular epidemiology of Chlamydia. Infect Genet Evol. 2014;23:49–64.
Gauthier MEA, Watson JR, Degnan SM. Draft genomes shed light on the dual bacterial symbiosis that dominates the microbiome of the coral reef sponge Amphimedon queenslandica. Front Mar Sci. 2016;3:196.
Clifford EL, Hansell DA, Varela MM, Nieto-Cid M, Herndl GJ, Sintes E. Crustacean zooplankton release copious amounts of dissolved organic matter as taurine in the ocean. Limnol Oceanogr. 2017;62:2745–58.
Eichhorn E, van der Ploeg JR, Kertesz MA, Leisinger T. Characterization of alpha-ketoglutarate-dependent taurine dioxygenase from Escherichia coli. J Biol Chem. 1997;272:23031–6.
Michell RH. Inositol derivatives: evolution and functions. Nat Rev Mol Cell Biol. 2008;9:151–61.
Kamke J, Sczyrba A, Ivanova N, Schwientek P, Rinke C, Mavromatis K, et al. Single-cell genomics reveals complex carbohydrate degradation patterns in poribacterial symbionts of marine sponges. ISME J. 2013;7:2287–300.
Fieseler L, Horn M, Wagner M, Hentschel U. Discovery of the novel candidate phylum “Poribacteria” in marine sponges. Appl Environ Microbiol. 2004;70:3724–32.
Diez-Vives C, Moitinho-Silva L, Nielsen S, Reynolds D, Thomas T. Expression of eukaryotic-like protein in the microbiome of sponges. Mol Ecol. 2017;26:1432–51.
Martyn JE, Gomez-Valero L, Buchrieser C. The evolution and role of eukaryotic-like domains in environmental intracellular bacteria: the battle with a eukaryotic cell. FEMS Microbiol Rev. 2022;46:fuac012.
Gilk SD, Beare PA, Heinzen RA. Coxiella burnetii expresses a functional Delta24 sterol reductase. J Bacteriol. 2010;192:6154–9.
Meadows JA, Wargo MJ. Carnitine in bacterial physiology and metabolism. Microbiology 2015;161:1161–74.
Chen CL, Beattie GA. Characterization of the osmoprotectant transporter OpuC from Pseudomonas syringae and demonstration that cystathionine-beta-synthase domains are required for its osmoregulatory function. J Bacteriol. 2007;189:6901–12.
Xiao ZJ, Xu P. Acetoin metabolism in bacteria. Crit Rev Microbiol. 2007;33:127–40.
Yang T, Rao Z, Zhang X, Xu M, Xu Z, Yang ST. Metabolic engineering strategies for acetoin and 2,3-butanediol production: advances and prospects. Crit Rev Biotechnol. 2017;37:990–1005.
Xiao Z, Lu JR. Strategies for enhancing fermentative production of acetoin: a review. Biotechnol Adv. 2014;32:492–503.
Chistoserdova L. Modularity of methylotrophy, revisited. Environ Microbiol. 2011;13:2603–22.
Moitinho-Silva L, Seridi L, Ryu T, Voolstra CR, Ravasi T, Hentschel U. Revealing microbial functional activities in the Red Sea sponge Stylissa carteri by metatranscriptomics. Environ Microbiol. 2014;16:3683–98.
Ufarte L, Laville E, Duquesne S, Potocki-Veronese G. Metagenomics for the discovery of pollutant degrading enzymes. Biotechnol Adv. 2015;33:1845–54.
Podell S, Blanton JM, Oliver A, Schorn MA, Agarwal V, Biggs JS, et al. A genomic view of trophic and metabolic diversity in clade-specific Lamellodysidea sponge microbiomes. Microbiome 2020;8:97.
Moiseeva OV, Solyanikova IP, Kaschabek SR, Groning J, Thiel M, Golovleva LA, et al. A new modified ortho cleavage pathway of 3-chlorocatechol degradation by Rhodococcus opacus 1CP: genetic and biochemical evidence. J Bacteriol. 2002;184:5282–92.
Arora PK, Bae H. Bacterial degradation of chlorophenols and their derivatives. Micro Cell Fact. 2014;13:31.
Rust M, Helfrich EJN, Freeman MF, Nanudorn P, Field CM, Ruckert C, et al. A multiproducer microbiome generates chemical diversity in the marine sponge Mycale hentscheli. Proc Natl Acad Sci USA. 2020;117:9508–18.
Medema MH, Blin K, Cimermancic P, de Jager V, Zakrzewski P, Fischbach MA, et al. antiSMASH: rapid identification, annotation and analysis of secondary metabolite biosynthesis gene clusters in bacterial and fungal genome sequences. Nucleic Acids Res. 2011;39:W339–46.
Metcalf WW, van der Donk WA. Biosynthesis of phosphonic and phosphinic acid natural products. Annu Rev Biochem. 2009;78:65–94.
Falagas ME, Athanasaki F, Voulgaris GL, Triarides NA, Vardakas KZ. Resistance to fosfomycin: mechanisms, frequency and clinical consequences. Int J Antimicrob Agents. 2019;53:22–8.
McCoy AJ, Sandlin RC, Maurelli AT. In vitro and in vivo functional activity of Chlamydia MurA, a UDP-N-acetylglucosamine enolpyruvyl transferase involved in peptidoglycan synthesis and fosfomycin resistance. J Bacteriol. 2003;185:1218–28.
Graca AP, Calisto R, Lage OM. Planctomycetes as novel source of bioactive molecules. Front Microbiol. 2016;7:1241.
Wiegand S, Jogler M, Jogler C. On the maverick Planctomycetes. FEMS Microbiol Rev. 2018;42:739–60.
O’Brien J, Wright GD. An ecological perspective of microbial secondary metabolism. Curr Opin Biotechnol. 2011;22:552–8.
Tianero MD, Balaich JN, Donia MS. Localized production of defence chemicals by intracellular symbionts of Haliclona sponges. Nat Microbiol. 2019;4:1149–59.
Abdelmohsen UR, Cheng C, Reimer A, Kozjak-Pavlovic V, Ibrahim AK, Rudel T, et al. Antichlamydial sterol from the Red Sea sponge Callyspongia aff. implexa. Planta Med. 2015;81:382–7.
Maita C, Matsushita M, Miyoshi M, Okubo T, Nakamura S, Matsuo J, et al. Amoebal endosymbiont Neochlamydia protects host amoebae against Legionella pneumophila infection by preventing Legionella entry. Microbes Infect. 2018;20:236–44.
Konig L, Wentrup C, Schulz F, Wascher F, Escola S, Swanson MS, et al. Symbiont-mediated defense against Legionella pneumophila in amoebae. mBio 2019;10:e00333–19.
Gaikwad S, Shouche YS, Gade WN. Microbial community structure of two freshwater sponges using Illumina MiSeq sequencing revealed high microbial diversity. AMB Express. 2016;6:40.
Angly FE, Pantos O, Morgan TC, Rich V, Tonin H, Bourne DG, et al. Diuron tolerance and potential degradation by pelagic microbiomes in the Great Barrier Reef lagoon. PeerJ 2016;4:e1758.
Knobloch S, Johannsson R, Marteinsson V Bacterial diversity in the marine sponge Halichondria panicea from Icelandic waters and host-specificity of its dominant symbiont “Candidatus Halichondribacter symbioticus”. FEMS Microbiol Ecol. 2019;95:fiy220.
Husnik F, Tashyreva D, Boscaro V, George EE, Lukes J, Keeling PJ. Bacterial and archaeal symbioses with protists. Curr Biol. 2021;31:R862–R77.
Maire J, Girvan SK, Barkla SE, Perez-Gonzalez A, Suggett DJ, Blackall LL, et al. Intracellular bacteria are common and taxonomically diverse in cultured and in hospite algal endosymbionts of coral reefs. ISME J. 2021;15:2028–42.
Peattie ME, Hoare R. The sublittoral ecology of the Menai Strait: II. The sponge Halichondria panicea (Pallas) and its associated fauna. Estuar Coast Shelf Sci. 1981;13:621–35.
Barthel D, Wolfrath B. Tissue sloughing in the sponge Halichondria panicea - a fouling organism prevents being fouled. Oecologia 1989;78:357–60.
Turon M, Uriz MJ, Martin D. Multipartner symbiosis across biological domains: looking a the eukaryotic associations from a microbial perspective. mSystems 2019;4:e00148–19.
Naim MA, Smidt H, Sipkema D. Fungi found in Mediterranean and North Sea sponges: how specific are they? Peerj 2017;5:e3722.
Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 2011;17:10–2.
Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 2016;4:e2584.
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011;27:2194–200.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
Lanzen A, Jorgensen SL, Huson DH, Gorfer M, Grindhaug SH, Jonassen I, et al. CREST-classification resources for environmental sequence tags. PLoS One. 2012;7:e49334.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30:2114–20.
Andrews S FastQC: a quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc.
Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015;31:1674–6.
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 2013;29:1072–5.
Hyatt D, Chen GL, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinform. 2010;11:119.
Seemann T barrnap 0.8: rapid ribosomal RNA prediction. https://github.com/tseemann/barrnap.
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.
Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 2019;7:e7359.
Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.
Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 2016;32:605–7.
Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 2018;6:158.
Eren AM, Kiefl E, Shaiber A, Veseli I, Miller SE, Schechter MS, et al. Community-led, integrated, reproducible multi-omics with anvi’o. Nat Microbiol. 2021;6:3–6.
Eren AM, Esen OC, Quince C, Vineis JH, Morrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 2015;3:e1319.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.
Sayers EW, Beck J, Bolton EE, Bourexis D, Brister JR, Canese K, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2021;49:D10–D7.
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinforma. 2009;10:421.
El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–D32.
Haft DH, Selengut JD, White O. The TIGRFAMs database of protein families. Nucleic Acids Res. 2003;31:371–3.
Jones P, Binns D, Chang HY, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 2014;30:1236–40.
Hugoson E, Lam WT, Guy L. miComplete: weighted quality evaluation of assembled microbial genomes. Bioinformatics 2020;36:936–7.
Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.
Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol Biol Evol. 2017;34:2115–22.
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–62.
Kanehisa M, Sato Y, Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequence. J Mol Biol. 2016;428:726–31.
Galperin MY, Wolf YI, Makarova KS, Vera Alvarez R, Landsman D, Koonin EV. COG database update: focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Res. 2021;49:D274–D81.
Kautsar SA, Blin K, Shaw S, Navarro-Munoz JC, Terlouw BR, van der Hooft JJJ, et al. MIBiG 2.0: a repository for biosynthetic gene clusters of known function. Nucleic Acids Res. 2020;48:D454–8.
Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 2017;11:2864–8.
Nomura M, Morgan EA. Genetics of bacterial ribosomes. Annu Rev Genet. 1977;11:297–347.
Martijn J, Vosseberg J, Guy L, Offre P, Ettema TJG. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 2018;557:101–5.
Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014;30:1312–3.
Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.
Criscuolo A, Gribaldo S. BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments. BMC Evol Biol. 2010;10:210.
Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.
Minh BQ, Nguyen MA, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30:1188–95.
Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.
Le SQ, Gascuel O. An improved general amino acid replacement matrix. Mol Biol Evol. 2008;25:1307–20.
Quang le S, Gascuel O, Lartillot N. Empirical profile mixture models for phylogenetic reconstruction. Bioinformatics 2008;24:2317–23.
Le SQ, Dang CC, Gascuel O. Modeling protein evolution with several amino acid replacement matrices depending on site rates. Mol Biol Evol. 2012;29:2921–36.
Wang HC, Minh BQ, Susko E, Roger AJ. Modeling site heterogeneity with posterior mean site frequency profiles accelerates accurate phylogenomic estimation. Syst Biol. 2018;67:216–35.
Lartillot N, Rodrigue N, Stubbs D, Richer J. PhyloBayes MPI: phylogenetic reconstruction with infinite mixtures of profiles in a parallel environment. Syst Biol. 2013;62:611–5.
Lartillot N, Philippe H. A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol Biol Evol. 2004;21:1095–109.
Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.
Li W, Godzik A. CD-HIT: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006;22:1658–9.
UniProt C. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49:D480–9.
Price MN, Dehal PS, Arkin AP. FastTree 2-approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490.
Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 2009;25:1972–3.
Tavaré S. Some probabilistic and statistical problems in the analysis of DNA sequences. Lect Math Life Sci. 1986;17:57–86.
Lagkouvardos I, Joseph D, Kapfhammer M, Giritli S, Horn M, Haller D, et al. IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci Rep. 2016;6:33721.
Huerta-Cepas J, Serra F, Bork P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol Biol Evol. 2016;33:1635–8.
Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–5.
Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. 2016.
Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 2017;33:2938–40.
We thank Klaske Schippers and Rene Wijffels for obtaining the sponge samples examined in this study. We thank Daniel Tamarit for useful advice and discussions. Sequencing was performed at the Uppsala Genome Centre, a sequencing platform that is hosted at Uppsala University as part of the National Genomics Infrastructure at the Science for Life Laboratory.
The national infrastructure used is supported by the Swedish Research Council (VR-RFI) and the Knut and Alice Wallenberg Foundation. Computational resources were provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX with the project number SNIC 2020/15-158, PDC with the project numbers SNIC 2019/3-474 and SNIC 2020/5-473, and HPC2N with the project number SNIC 2019/5-114. This work was supported by grants from the European Research Council (ERC Starting and Consolidator grants 310039 and 817834, respectively), and the Swedish Research Council (VR grant 2015‐04959). Open access funding provided by Uppsala University.
The authors declare no competing interests.
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Dharamshi, J.E., Gaarslev, N., Steffen, K. et al. Genomic diversity and biosynthetic capabilities of sponge-associated chlamydiae. ISME J 16, 2725–2740 (2022). https://doi.org/10.1038/s41396-022-01305-9