Skip to main content

Novel trends of genome evolution in highly complex tropical sponge microbiomes

A Correction to this article was published on 24 October 2022

This article has been updated



Tropical members of the sponge genus Ircinia possess highly complex microbiomes that perform a broad spectrum of chemical processes that influence host fitness. Despite the pervasive role of microbiomes in Ircinia biology, it is still unknown how they remain in stable association across tropical species. To address this question, we performed a comparative analysis of the microbiomes of 11 Ircinia species using whole-metagenomic shotgun sequencing data to investigate three aspects of bacterial symbiont genomes—the redundancy in metabolic pathways across taxa, the evolution of genes involved in pathogenesis, and the nature of selection acting on genes relevant to secondary metabolism.


A total of 424 new, high-quality bacterial metagenome-assembled genomes (MAGs) were produced for 10 Caribbean Ircinia species, which were evaluated alongside 113 publicly available MAGs sourced from the Pacific species Ircinia ramosa. Evidence of redundancy was discovered in that the core genes of several primary metabolic pathways could be found in the genomes of multiple bacterial taxa. Across hosts, the metagenomes were depleted in genes relevant to pathogenicity and enriched in eukaryotic-like proteins (ELPs) that likely mimic the hosts’ molecular patterning. Finally, clusters of steroid biosynthesis genes (CSGs), which appear to be under purifying selection and undergo horizontal gene transfer, were found to be a defining feature of Ircinia metagenomes.


These results illustrate patterns of genome evolution within highly complex microbiomes that illuminate how associations with hosts are maintained. The metabolic redundancy within the microbiomes could help buffer the hosts from changes in the ambient chemical and physical regimes and from fluctuations in the population sizes of the individual microbial strains that make up the microbiome. Additionally, the enrichment of ELPs and depletion of LPS and cellular motility genes provide a model for how alternative strategies to virulence can evolve in microbiomes undergoing mixed-mode transmission that do not ultimately result in higher levels of damage (i.e., pathogenicity) to the host. Our last set of results provides evidence that sterol biosynthesis in Ircinia-associated bacteria is widespread and that these molecules are important for the survival of bacteria in highly complex Ircinia microbiomes.

Video Abstract


Sponges (phylum Porifera) are exceptional examples of host-microbial associations in the aquatic environment. In some species, microbes can comprise up to 40% of the sponge biomass [1, 2], and the richness of operational taxonomic units (OTUs) in the microbiome can number in the thousands [3]. One such genus that has a particularly noteworthy relationship with symbiotic microbes is Ircinia. Tropical species of Ircinia contain abundant populations of Cyanobacteria and hundreds to thousands of other microbial species belonging to dozens of phyla [3,4,5,6,7]. Experiments measuring host growth in light vs. dark conditions in other genera have demonstrated that “Candidatus Synechococcus spongiarum” (Cyanobacteria) engages in a nutritional symbiosis with sponges [8, 9], a phenomenon that is likely present in Ircinia given the abundance of chlorophyll a in many species and the presence of “Ca. S. spongiarum” as evidenced by 16S rRNA barcoding [10], although it should be noted that some cyanobacterial symbionts of sponges, such as “Ca. Synechococcus feldmannii,” do not contribute to the host’s carbon budget [11]. The microbial symbionts in Ircinia also influence the host’s biology by contributing to nitrogen, carbon, and phosphorous cycling [12, 13] and by underlying the production of biologically active secondary metabolites [14, 15 ].

Recent research on tropical Ircinia has focused on the evolutionary impacts of microbial symbioses by investigating the host transcriptomes [4], 16S rRNA metabarcoding of microbiomes [4 , 5], microbial metagenomes [16], and host genomes using microsatellites [5] and restriction site-associated DNA sequencing (RADseq) [4 ]. In Caribbean Ircinia, two studies discovered that microbiome dissimilarities scale positively with host genetic distances, suggesting a potential role of the microbiome in ecological divergence among the host species [4, 5]. Additionally, an analysis mapping RADseq loci that were FST outliers to an Ircinia transcriptome assembly inferred selection in host genes that are part of the lipopolysaccharide (LPS)-induced Ras-Raf-MEK-ERK (MAPK/ERK) pathway and selection in other genes potentially relevant to the regulation of host-microbiome interactions [4]. Given the presence of this pathway, one possibility is that the bacteria in Ircinia have lost LPS to avoid digestion, a hypothesis corroborated by the depletion of LPS-related genes in “Ca. S. spongiarum,” which is exposed to the archaeocytes of the sponge [17, 18 ].

A previous study of the Pacific species Ircinia ramosa used a metagenomic approach to examine how microbes evolve within this host [16]. This study found redundancy in several primary metabolic pathways including nitrogen, sulfur, and carbon metabolism, which could result in a portfolio type effect [19] that buffers the core metabolic functioning from taxonomic fluctuations in the microbiome. The I. ramosa metagenome also contained ankyrin (ANK) and WD40 repeat-bearing eukaryotic-like proteins (ELPs), which are generally accepted as aiding prokaryotes in evading the host’s immune apparatuses by mimicking the host sponge’s molecular patterning [20]. The present study sought to expand our understanding of genome evolution in the bacterial symbionts of tropical Ircinia by performing a whole-shotgun metagenomic survey of ten Caribbean species that were previously investigated for genome-wide patterns of evolution in the sponge hosts [4], six of which are newly described species [21]. Given the high level of complexity within the microbiomes of tropical Ircinia, the evidence of selection in host genes that are involved in immune responses to microbial pathogens, the abundance of bioactive compounds in Ircinia, and their potential to influence host-microbe interactions, we hypothesized that within the metagenomes of Ircinia we would find further evidence for metabolic redundancy among multiple bacterial taxa, depletion of LPS biosynthesis and other genes related to pathogenicity, and purifying selection in genes involved in the biosynthesis of secondary metabolites.


Sample collections, DNA extractions, and sequencing

New metagenome-assembled genomes (MAGs) were produced for this study from two specimens of each of ten Ircinia species, resulting in a sample size of 20. In Bocas del Toro, Panama, specimens of Ircinia radix were collected from prop roots of the mangrove hammock at Inner Solarte (latitude, longitude: 9.3058, − 82.1732), individuals of Ircinia laeviconulosa and I. bocatorensis from seagrass beds near the Smithsonian Tropical Research Institute’s Bocas del Toro Research Station (9.3517, − 82.2590), and individuals of Ircinia lowi from patch reefs at Punta Caracol (9.3771, − 82.3023). In Florida, specimens of Ircinia campana and Ircinia cf. reteplana Topsent, 1923, were collected from a seagrass bed near the MOTE Marine Laboratory and Aquarium’s Elizabeth Moore International Center for Coral Reef Research & Restoration (24.6609, − 81.4563). In Belize, specimens of Ircinia vansoesti were collected from the prop roots of mangrove hammocks near the Blue Ground site (16.8083, − 88.1496), specimens of Ircinia strobilina and Ircinia ruetzleri were collected from the coral patch reefs of Blue Ground (16.8010, − 88.1461), and specimens of Ircinia felix were collected from the forereef of Carrie Bow Cay (16.8042, − 88.0796). Thumb-size fragments were cut from the outer region of the sponges’ bodies and stored in 90% EtOH that was replaced at the 24- and 48-h time points to ensure thorough inundation and preservation of the microbial cells. The exterior surfaces of the sponges were free of overgrowth with the exception of I. vansoesti, from which the loosely attached overgrowth easily sloughed away upon sample collection. Photos and descriptions of the growth forms and maps of the sampling locations can be found in [4]. The specimens used in the current study are present in the set of specimens used in [4].

DNA was extracted from a 0.5 cm × 0.5 cm × 0.5 cm fragment cut from the outermost region of each specimen using the Molzyme Ultra-Deep Microbiome Prep DNA isolation kit according to the manufacturer’s instructions. This kit enriches prokaryotic DNA by first lysing eukaryotic cells and destroying the then free-floating eukaryotic DNA and subsequently lysing the prokaryotic cells and isolating the DNA therein [22, 23]. The absence of host DNA in our samples was evaluated by amplifying the Molzyme DNA isolations with Ircinia-specific cytochrome oxidase c subunit 1 (CO1) primers cox1.IrcF (GAT AAT GCG GYT CGA GTT GK) and cox1.IrcR (CTA CCG GAT CAA AGA AAG AAG TRT) in HotStarTaq Master Mix (QIAGEN). PCR amplifications were conducted using cycles of 95 °C for 15 min; 35 cycles at 94 °C for 1 min, 62 °C for 1 min, and 72 °C for 1.5 min; and a 10-min-long final elongation step. To provide control isolation with host DNA, PCR amplifications were repeated for the same samples using bulk-tissue DNA isolations produced using the Wizard Genomic DNA Purification kit (Promega). Previous 16S rRNA metabarcoding of these same samples demonstrated that they contain only trace relative abundances of environmental seawater bacteria [4]; thus, contaminating DNA from the seawater microbial community is expected to be negligible. Whole-metagenome shotgun sequencing was performed for the Molzyme DNA extractions after confirming the absence of a CO1 amplification band at the Yale Center for Genome Analysis on a NovaSeq6000 with a target of 30 million 2 × 150 reads per specimen.

Metagenomic assembly, binning, dereplication, and annotation

Reads were filtered and trimmed using FastP [24] with default parameters. [25] was used to remove PhiX control reads and reads mapping to the Mus musculus and Homo sapiens genomes, which was performed as a precautionary step since the DNA libraries were prepared in a facility that routinely handles vertebrate model organisms. Metagenomic contigs were assembled independently for each specimen using Megahit v1.2.9 [26 ] and subsequently binned using MetaBAT 2 v2:2.15 [27] with default parameters. Contaminating contigs were identified on the basis of being outliers with regard to genomic content (i.e., %GC and tetranucleotide content) and removed from the bins using RefineM v0.0.25 [28]. The qualities of the refined bins were assessed using CheckM v1.1.2 [29 ]; only bins with quality scores equal to or higher than a cutoff score of 40, calculated as genome completeness—5× contamination [28]—and which had at least 85% genome completeness were retained for downstream analysis. MAGs were then dereplicated using dRep v2.5.2 [30] within each host species using a cutoff of 96.5% average nucleotide identity, an empirically derived estimate of maximum prokaryotic intraspecific genomic divergence [31]. Protein predictions and KEGG Orthology (KO) annotations ( were performed on the MAGs using EnrichM v0.5.0 [32]. Pfam annotations were performed on the protein domains using Interproscan v5.39-77 [33]. For proteins that contained duplicated identical domain annotations, only one domain was retained to avoid double-counting proteins. The relative abundances of each MAG per host specimen were evaluated with CoverM v0.4.0 ( using the concatenated dereplicated Caribbean Ircinia MAG dataset. We chose this scheme as previous research using 16S rRNA metabarcoding demonstrated a high degree of symbiont sharing, although at varying relative abundances [4].

Processing of publicly available I. ramosa symbiont and Tara oceans MAGs

Publicly available I. ramosa symbiont MAGs (NCBI BioProject ID PRJNA555144) and MAGs produced from pelagic samples collected during the Tara Oceans Expedition that correspond to North Atlantic and South Pacific oceanic provinces (NCBI BioProject ID PRJNA391943) [34] were downloaded from NCBI. The quality filtering, dereplication, and annotation steps were performed as outlined above. Taxonomy was assigned to the MAGs, and a whole-genome phylogenetic tree was produced for the Ircinia MAGs using GTDB-Tk v1.0.2 [35].

Enrichment analysis

We compared MAGs sourced from each Ircinia host species to Tara Oceans MAGs from the oceanic province that encompassed the collection sites of the sponges (North Atlantic for the ten Caribbean Ircinia species and South Pacific for I. ramosa). Pairwise permutational t-tests were run for 10,000 iterations that compared the average abundance (e.g., copy number) of a feature in the MAGs belonging to a given Ircinia species against the Tara Oceans datasets. To factor in differences in genome size, we normalized feature counts by dividing them by the total number of predicted proteins prior to the t-tests. p-values were corrected using the false discovery rate adjustment of Benjamini-Hochberg [36] using the total number of features within an annotation category (either KO or Pfam). Given the high number of multiple comparisons, we further decided to assign a conservative significance cutoff of p < 0.01. Additionally, we omitted the features that were present in less than 10% of both the Ircinia and Tara Oceans MAGs. The enrichment analysis pipeline was also performed at the phylum level (or class level, in the case of Proteobacteria) comparing Tara Oceans MAGs against Ircinia MAGs. To ensure sufficient sample sizes for the analysis, this analysis grouped samples across sources and required a minimum of ten MAGs belonging to each life history category.

Inference of selection, homology search, and phylogenetic analysis in CSGs

We subjected the most common cluster of steroid genes (CSGs) to variant analysis. In the order that they appear in the CSGs, these genes are delta14-sterol reductase [EC:] (TM7SF2/ERG24), sterol 14alpha-demethylase [EC:] (CYP51), and lanosterol synthase [EC:] (LSS/ERG7). We restricted the analysis to the ten Caribbean Ircinia species as they all shared the same DNA extraction, library prep, sequencing, and MAG refinement pipeline. Preparation for this dataset differed from the enrichment analysis dataset in that the MAGs underwent dereplication across the ten host species instead of within each host species. This dereplication scheme produced fewer MAGs, which reduced computational burden, a necessary consideration to keep the wall time of the analysis and storage space of the intermediate files within feasibility.

Variants were called by mapping the cleaned and filtered reads for each metagenomic sample to each MAG using BWA MEM v0.7.17 [37]. Alignments were converted to binary format and sorted by position with Samtools v1.9 [38]. PCR and optical duplicate reads were removed from the alignments using the “MarkDuplicates” utility in Picard v2.22.2 ( Variants were called using the “mpileup” and “call” functions in BCFtools v1.9 [39]. The resultant VCF files were also filtered using BCFtools to remove variants that had QUAL scores less than 20 or if read depth was anomalously high [40]. This pipeline was run for each replicated MAG using the Snakemake workflow management system v.5.13.0 [41]. Nucleotide diversity was measured for the three core genes within the CSGs as a function of average variable sites per nucleotide and was compared against the genome-wide average of nucleotide diversity for all coding sequences, less the three CSG genes, using t-tests.

The selection was also inferred in the three core genes contained in the CSGs by comparing the ratio of nonsynonymous (dN) to synonymous (dS) substitutions. Codon-aware alignments were constructed of each of the three sets of genes using Mafft v7.149 [42], implemented in translatorx v1.1 [43]. Maximum likelihood gene trees were inferred for each alignment using IQ-Tree v2.1.4 [44], with best-fitting substitution models chosen by ModelFinder [45] and branch support estimated using 1000 ultrafast bootstrap replicates [46]. Next, we used the CODEML program in PAML v4.10.0 [47] to estimate omega values (dN/dS) as a proxy of selection acting on each gene. CODEML was run using each alignment and gene tree with model 0 to infer a single estimate of omega for each gene. This pipeline was repeated for 1000 randomly chosen genes that were present in at least 10% of the Ircinia-sourced MAGs to provide a distribution of omega values for comparison. A quality filter was applied that required genes to have a tree-wide average ultrafast-bootstrap value of at least 70% to be retained for the final distribution.

To investigate the potential patterns of horizontal gene transmission (HGT), phylogenetic trees were produced via Bayesian inference for each of the three genes in the CSGs using the codon-aware alignments constructed for the aforementioned CODEML analysis. Prior to tree inference, the models of nucleotide evolution best fitting each alignment were selected using JModelTest v2.1.10, implemented in the CIPRES Science Gateway ( [48]. Two parallel runs each with four chains were then run MrBayes v3.2.6 [49] until the average standard deviation of split frequencies fell below 0.01.

We performed a sequence homology search to investigate the presence of the CSGs in other bacterial genomes by querying sequences of the complete three-gene CSGs to NCBI’s nucleotide collection (nr) database (accessed 12 August 2021) using the online version of BLASTn [50] with default settings. The query sequences we chose for this analysis were selected to represent each host species and bacterial phylum of the MAGs in which the CSGs were present. Additionally, we searched for the CSGs in the publicly available MAGs of three non-Ircinia host species (Aplysina aerophoba, NCBI BioProject ID PRJNA326328 and PRJNA274222; Spongia spongia, PRJEB18465; and Mycale hentscheli, PRJNA603662) and the Mediterranean Tara Oceans MAGs (the oceanic province from which A. aerophoba samples were collected) by subjecting them to the same quality control (QC), dereplication (within species), and annotation pipeline as above.


Metagenomic sequencing of ten Caribbean Ircinia species’ microbiomes

Whole-metagenome shotgun sequencing was performed on the microbiomes of ten Caribbean Ircinia species. Contigs binned into 895 MAGs (mean = 89.5 ± 30.8 [1 s.d.] per species), which were reduced to 426 MAGs upon dereplication and the CheckM-based QC steps (mean = 42.6 ± 17.1 [1 s.d.] per species) (Additional file 6: Table S1). The species with the fewest MAGs was I. strobilina (n = 16), and the species with the most MAGs was I. ruetzleri (n = 62). Only two archaeal MAGs were recovered; thus, we decided to only retain the remaining 424 bacterial MAGs for downstream analysis. The MAGs encompassed 14 bacterial phyla (Additional file 6: Table S1, Fig. 1). The most numerous phyla in the Caribbean Ircinia metagenomes in terms of MAG richness were Proteobacteria (68 Alphaproteobacteria and 49 Gammaproteobacteria), Chloroflexota (82 MAGs), and Poribacteria (40 MAGs). Additionally, a Trichodesmium (Cyanobacteria) that was initially present in the pre-QC I. cf. reteplana dataset was removed as it did not meet the 85% completeness threshold (jk18x7bins.61, quality = 77.0, completeness = 80.8%, contamination = 0.8%). Although the Trichodesmium and archaeal MAGs were not used in downstream analyses, we have included them in the NCBI BioProject.

Fig. 1
figure 1

Whole-genome phylogenetic tree of MAGs sourced from bacterial symbionts of Ircinia spp. and North Atlantic and South Pacific Tara Oceans MAGs, produced using the MAG dataset that was dereplicated within each source using GTDB-Tk. The inner ring indicates the source of the MAGs, the middle ring denotes the presence or absence of CSGs, and the outer ring denotes the GTDB-Tk phylum-level taxonomic annotations. Tree plotting and annotation were performed with the R package ggtree v2.0.4 [51]

On average, 57.5% ± 17.2 [1 s.d.] raw reads mapped back to the bacterial MAGs per host specimen during the CoverM analysis (Additional file 1: Fig. S1). 3.3% of MAGs were unique to only one host species whereas 77.4% of MAGs were found in at least half of the species (Additional file 2: Fig. S2A). 7.1% of MAGs were unique to only one of the three Caribbean locales, and 69.6% of MAGs were found across all three sites (Additional file 2: Fig. S2B). A PCA of the relative abundances of MAGs showed specimens clustering together by host species (Additional file 2: Fig. S2C). Synechococcus was by far the most abundant microbial genus in our dataset; on average, it comprised 21.6% ± 11.6 [1 s.d.] of the total microbiome composition per host specimen and was present in every host taxon except for I. strobilina. Multiple Synechococcus MAGs were found within a given specimen, although only a fraction of the Synechococcus MAGs were dominant in each host taxon with the exception of I. strobilina, which contained no Synechococcus (Additional file 7: Table S2).

General comparison of I. ramosa to Caribbean Ircinia metagenomes

A total of 113 bacterial MAGs passed the quality filtering and dereplication pipeline for I. ramosa (Additional file 6: Table S1). Of the 14 phyla found in Caribbean Ircinia, two were absent in I. ramosa: Binatota and Verrucomicrobiota. Fourteen MAGs belonging to the class Binatia (phylum Binatota) were recovered in the Caribbean Ircinia dataset, with at least one MAG from this class found in each host species’ dataset except for I. campana, although the CoverM analysis revealed that members of this class were present in this host species at low abundances. A MAG (jk18x7bins.13) belonging to the order Opitutales (phylum Verrucomicrobiota) was found in the set of MAGs from I. cf. reteplana, and the CoverM analysis showed the additional presence of this MAG in I. campana, I. ruetzleri, and I. felix.

Similar to I. ramosa, a metabolic characterization of the Caribbean Ircinia MAGs showed redundancy across bacterial taxa in genes involved in nitrogen metabolism (PATH:ko00910), sulfur metabolism (PATH:ko00920), and the six autotrophic carbon cycling pathways of prokaryotes: the reductive citric acid cycle, the Wood-Ljungdahl pathway, the 3-hydroxypropionate bicycle, the 3-hydroxypropionate/4-hydroxybutyrate cycle, the Calvin-Benson-Bassham cycle, and the dicarboxylate/4-hydroxybutyrate cycle (Additional file 1: Fig. S1). However, nickel-CODH (cooS, acsA, K00198) was absent from all MAGs (Additional file 1: Fig. S1), concurrent with previous reports of this gene and, consequently, the Wood-Ljungdahl pathway being absent from sponge microbiomes [11]. Additionally, we discovered a pattern of redundancy within methane metabolism (PATH:ko00680).

Enrichment patterns of tropical Ircinia metagenomes

To investigate the patterns of genomic enrichment in the symbiotic bacteria associated with tropical Ircinia, we compared the dataset of MAGs from the ten Caribbean Ircinia species and publicly available MAGs sourced from the Pacific species I. ramosa to MAGs assembled from samples collected during the Tara Oceans Expedition [34]. Since the Tara Oceans dataset was produced from DNA isolated from filtered oceanic seawater, we assume that these microbes are pelagic [34]. A similar analytical scheme has been adopted before to study the genomic enrichment patterns in Porites lutea microbial metagenomes [52]. The present analysis compared MAGs found within each host species to Tara Oceans MAGs from the oceanic province that encompassed the collection site of the host species and was performed within each species and independently for bacterial phyla with at least ten MAGs in each life history category. A gene was considered enriched if its average copy number was significantly greater in the sponge MAG dataset relative to the corresponding Tara Oceans dataset. Conversely, a gene was considered depleted in the Ircinia metagenomes if the average copy number was significantly lower relative to its average copy number in the Tara Oceans dataset.

Following quality filtering and dereplication, 101 bacterial MAGs (of the original 286) representing 12 phyla were recovered for the North Atlantic and 158 MAGs (of the original 474) representing 15 phyla were recovered from the South Pacific Tara Oceans datasets (Additional file 6: Table S1). A high degree of taxonomic overlap between the Ircinia and Tara Oceans MAGs was observed. 75.9% of the South Pacific MAGs belonged to the phyla found also in I. ramosa, and 85.6% of I. ramosa MAGs belonged to the phyla found in the South Pacific dataset. Likewise, 83.2% of the North Atlantic MAGs belonged to the phyla also present in the Caribbean Ircinia dataset, and 72.8% of the Caribbean Ircinia MAGs belonged to the phyla also found in the North Atlantic dataset. Four phyla contained at least ten free-living and ten sponge-associated MAGs (Actinobacteriota, 14 Tara Oceans and 51 Ircinia MAGs; Bacteroidota, 47 Tara Oceans and 18 Ircinia MAGs; Chloroflexota, 23 Tara Oceans and 115 Ircinia MAGs; and Proteobacteria). Proteobacteria was further split at the class level into Alphaproteobacteria (68 Tara Oceans and 73 Ircinia MAGs) and Gammaproteobacteria (38 Tara Oceans and 58 Ircinia MAGs).

The three major categories of genes that have been found to be enriched in other sponge-symbiont metagenomes and were investigated here are ELPs, cellular defense, and molecular transporters [20, 53,54,55,56,57,58,59,60,61,62,63,64,65]. Ircinia MAGs were characterized by a prevalence and diversity of ELPs (Fig. 2, Additional file 3: Fig. S3, Additional file 8: Table S3). In addition to the ankyrin-rich repeats (ANK) and WD40 domains that were previously reported in I. ramosa [16], multiple additional classes of ELPs that are enriched in the microbiomes of other host sponge species [20, 53,54,55,56,57,58, 60, 61] were enriched in Ircinia: leucine-rice repeats (LRR), fibronectin type III domain (fn3), cadherin domains (CAD), tetratricopeptide repeats (TPR), pyrrolo-quinoline quinone domains (PQQ), Lin-41 sequence repeats (NHL), and PD40. Additionally, two domains that were predicted to be ELPs based on the EffectiveDB database of ELPs [66], Eukaryotic type carbonic anhydrase (PF00194) and Calx-beta motif (PF03160), were enriched across multiple host species and bacterial phyla. Each host species displayed unique signatures of ELP enrichment; for example, enrichment in TPR domains was detected only in I. radix, I. vansoesti, I. ruetzleri, I. felix, and I. ramosa. In some cases, domains belonging to ELP classes were depleted, as seen in two CADs, PF17803 Cadherin_4 and PF17892 Cadherin_5, which were depleted in multiple host species, and in TPRs, which were depleted as a class in Alphaproteobacteria and PF09976 TPR_21, which was depleted in I. ramosa. Actinobacteriota were not enriched for ELPs, consistent with previous analyses of tropical sponge metagenomes [67].

Fig. 2
figure 2

Plots depicting the genes and domains that are enriched or depleted in tropical Ircinia spp. Each circle corresponds to a gene that was determined as being differentially abundant in either MAGs sourced from the microbiomes of Ircinia or Tara Oceans pelagic seawater samples in terms of normalized average gene copy number. The size of each circle is scaled to the log10-transformed ratio of the gene calculated as (avg. copy number in Ircinia MAGs)/(avg. copy number in Tara Oceans MAGs) for genes under the y = x diagonal and (avg. copy number in Tara Oceans MAGs)/(avg. copy number in Ircinia MAGs) for genes above the y = x diagonal. The ELP graph was constructed from the Interproscan annotations; all others were constructed using the KO annotations produced using EnrichM

Genes related to cellular defense were also enriched across Ircinia metagenomes (Fig. 2, Additional file 3: Fig. S3, Additional file 8: Table S3) including type II T-A systems (hicA-hicB, vapC-vapB, mazF-mazE, relE-relB, hipA-hipB, parE-parD, chpB-chpS), components of type II and type III restriction-modification (R-M) systems, and CRISPR-CAS-associated genes (cas1-4, csb1-2). Notable differences across hosts included the enrichment of all species except I. bocatorensis and I. strobilina for the three subunits of the EcoR124I type I restriction-modification enzyme (hsdM, hsdR, hsdS). I. felix and I. ramosa were enriched for both components of the type IV R-M system mcrBC and three dnd sulfur modification genes (dndB-D). Only I. ruetzleri was enriched for the CRISPR system Cascade subunits casA/C/D/E. Genes related to defense were also enriched across the five bacterial taxa analyzed independently, although Gammaproteobacteria were not enriched for CRISPR-CAS genes.

A similar signature of enrichment was present among molecular transporters (e.g., ATP-binding cassette transporters) (Fig. 2, Additional file 3: Fig. S3, Additional file 8: Table S3). These included the peptide/nickel transport system comprising the ddpABCDF operon (ddpF/D and ABC.PE.P/S/P1), which were enriched in all hosts except I. strobilina and were also found to be depleted in Bacteroidota, and amino acid transporters including the branched-chain amino acid transport system genes livF/G/H/K/M, which were enriched in I. ramosa, I. cf. reteplana, I. vansoesti, I. rutzleri, and I. laeviconulosa. Three of these species (I. ramosa, I. cf. reteplana, and I. laeviconulosa) also had strong enrichment patterns for the general l-amino acid transport system genes aapJ/M/P/Q. Six species (I. ramosa, I. cf. reteplana, I. vansoesti, I. ruetzleri, I. bocatorensis, and I. laeviconulosa) were enriched for the glycine betaine/proline transport system genes proV/W/X. Amino acid transporters were enriched in all five bacterial taxa that were evaluated independently. The putative spermidine/putrescine transport system genes ABC.SP.A/P/P1/S were enriched in I. cf. reteplana and I. ruetzleri, with two of these genes enriched in I. ramosa (ABC.SP.A/S) and I. vansoesti (ABC.SP.P/S). I. ramosa, I. sp. reteplana, and I. ruetzleri were enriched for sugar/saccharide transport system genes (with the exception of LPS transporters) including the raffinose/stachyose/melibiose transport system (msmE/F/G) and the multiple sugar transport system genes ABC.MS.P/P1/S. I. ramosa was also enriched for the two ribose transport system genes rbsB/C and the simple sugar transport system genes ABC.SS.A/P. I. radix was unique in that only this host was enriched for the molybdate transport system genes modA/B.

We investigated enrichment patterns for genes whose products are either targeted by host immune systems or are implicated in pathogenicity. We detected depletion of motility genes across tropical Ircinia metagenomes (Fig. 2, Additional file 8: Table S3), including the gene that codes for flagellin (fliC/hag) and 31 other genes involved in flagellar assembly, and 11 chemotaxis-associated genes. Two other categories of genes relevant to microbial pathogenicity that have not yet been subject to scrutiny under a large-scale comparative metagenomic analysis in sponges were depleted in Ircinia metagenomes: LPS transport (8 genes) and LPS biosynthesis pathways (19 genes) (Fig. 2, Additional file 8: Table S3). The only exception was waaQ (heptosyltransferase III), which was enriched in six of the species (I. cf. reteplana, I. vansoesti, I. ruetzleri, I. felix, I. bocatorensis, and I. radix). On the level of individual bacterial phyla, motility genes were only recovered as being completely depleted in Proteobacteria. The bacterial phyla were also depleted for LPS genes, although in Actinobacteriota LPS genes were largely absent and not detected as being enriched nor depleted.

Enrichment and evolution of steroid biosynthesis genes

Five genes belonging to the eukaryotic steroid biosynthesis pathway (PATH:ko00100) were discovered to be enriched in tropical Ircinia (Fig. 2, Additional file 3: Fig. S3, Additional file 8: Table S3). They were delta 14-sterol reductase (TM7SF2/ERG24), sterol 24-C-methyltransferase (SMT1/ERG6), farnesyl-diphosphate farnesyltransferase (FDFT1), lanosterol synthase (LSS/ERG7), and sterol 14alpha-demethylase (CYP51) (Fig. 2). These genes often occurred scattered around the bacterial chromosomes or in doublets, as with TM7SF2/ERG24 and FDFT1, but three genes regularly clustered together to form an operon: TM7SF2/ERG24, CYP51, and LSS/ERG7 (Fig. 3, Additional file 4: Fig. S4). These clusters of steroid genes (hereafter CSGs) were present in 115 of the 537 MAGs dereplicated within species and were found in 11 classes spanning 10 phyla (Acidimicrobiia (Actinobacteriota), Acidobacteriae (Acidobacteriota), Alphaproteobacteria (Proteobacteria), Binatia (Binatota), Gammaproteobacteria (Proteobacteria), Dehalococcoidia (Chloroflexota), Gemmatimonadetes (Gemmatimonadota), Nitrospiria (Nitrospirota), Rhodothermia (Bacteroidota), UBA8248 (UBA8248), Verrucomicrobiae (Verrucomicrobiota)) and in all 11 Ircinia species (Fig. 1, Additional file 4: Fig. S4). Despite being taxonomically widespread, the CSGs were conserved in length (mean length 5910 bp ± 74 bp [1 s.d.], min = 5293 bp, max = 6059) (Additional file 10: Data File S1). The CSGs were usually flanked by dihydroflavonol-4-reductase (DFR) and a hypothetical protein (in that order) at the 5′ end oriented on the same strand as the three core genes and by either SMT1/ERG6 or FDFT1 at the 3′ end, which were coded on the opposite strand (Fig. 3, Additional file 4: Fig. S4).

Fig. 3
figure 3

Representative CSGs are depicted on the alphaproteobacterial clade of the GTDB-Tk-inferred phylogeny. CSGs are depicted with other genes found in the eukaryotic steroid biosynthesis pathway (PATH:ko00100) that were commonly found flanking the three core genes (TM7SF2/ERG24, CYP51, and LSS/ERG7) and DFR (dihydroflavonol-4-reductase). “Others” refers to the genes not found in pathway ko00100. If no operon is shown for a given MAG, then a CSG was not detected. Arrows denote the direction of the coding sequence. Genes that overlap with the “steroid biosynthesis” panels of Fig. 2 are colored to match. Tree plotting and annotation were performed with the R packages ggtree [51]

Given the novelty of the CSGs, we decided to check publicly available sequences for homologous features. MAGs of three other non-Ircinia species (Aplysina aerophoba, 28 of 41 initial MAGs passing QC and dereplication; Spongia spongia, 10 of 10 MAGs; and Mycale hentscheli, 13 of 35 MAGs) were checked for the presence of CSGs, which were found in only two MAGs of A. aerophoba, GCA_002239025.1 (Chloroflexota) and GCA_002238765.1 (Nitrospirota), and none in the other non-Ircinia hosts or Tara Oceans MAGs (Additional file 4: Fig. S4). A homology search using BLASTn that queried representative CSGs from each class and host species against the NCBI nr database yielded the same best hit across queries (mean query cover = 73% ± 32% [1 s.d.], mean E-value = 5E−144 ± 1.7E−143, mean percent identity = 74% ± 4%), a CSG from a gammaproteobacterial symbiont of another sponge genus, Melophlus, (NCBI accession MT026193.1) (Additional file 9: Table S4) that codes for a low-abundance sarasinoside [68]. In all searches, the next best significant hits had low query covers of (typically less than 5%) and hence were unlikely to constitute homologous CSGs.

The lengths of the CYP51, LSS/ERG7, and TM7SF2/ERG24 codon-aware multiple sequence nucleotide alignments were 1698, 3765, and 1167 positions, respectively, with 12.4%, 21.5%, and 24.3% conserved sites. GTR+I+G [69] was identified by AIC [70] as the model best-fitting nucleotide evolution for all three alignments, calculated in jModelTest2. Bayesian phylogenetic tree inference using the predicted coding sequences for each of the three core genes recovered trees that were not consistent with the genome-wide phylogeny inferred by GTDB-Tk and provided evidence for horizontal gene transfer (HGT) (Additional file 5: Fig. S5). For each gene, clades were typically represented by sequences from only one bacterial class. However, in many cases, the sequences from a given bacterial class were distributed over multiple clades that shared common ancestors with sequences from different bacterial classes before meeting the common ancestor from which all the sequences from the same class were descended. Additionally, the CYP51 tree was divided into two major clades, each of which contained sequences from Rhodothermia, Gammaproteobacteria, Alphaproteobacteria, and Dehalococcoidia (Additional file 5: Fig. S5A).

Variant analysis was performed using a dataset of MAGs dereplicated across species by comparing the omega value of each gene relative to the omega values of genes not contained in the CSGs and by analyzing the proportion of variable sites within each gene relative to the MAG-wide average for protein-coding regions. The dereplication scheme resulted in 284 Caribbean Ircinia MAGs (mean MAGs per host species = 28.4 ± 15.2 [1 s.d.]), 59 of which (20.8%) had CSGs (Additional file 6: Table S1). A total of 344 non-steroid genes were present in the omega dataset after quality filtering based on tree-wide average ultrafast bootstrap values. The omega values were substantially lower for the three genes contained in the CSGs (TM7SF2/ERG24 omega = 0.13, LSS/ERG7 omega = 0.17, CYP51 omega = 0.26) (Fig. 4). This trend held in the pre-ultrafast bootstrap filter dataset of 1000 non-steroid genes. The three core CSG genes also generally had significantly lower rates of variability relative to MAG-wide rates across classes (Fig. 5). However, the proportion of variable sites differed across the CSGs by class. For example, in Binatia and Nitrospiria CYP51 had the lowest rate of variability relative to TM7SF2/ERG24 and LSS/ERG7, whereas in Dehalococcoidia LSS/ERG7 had the lowest rate of variability relative to TM7SF2/ERG24 and CYP51.

Fig. 4
figure 4

Histogram of omega values for genes with tree-wide average bootstrap scores > 70%. Gray bars correspond to non-steroid biosynthesis genes. The dark gray line marks the density distribution of omega values calculated for non-steroid genes

Fig. 5
figure 5

Plots depicting the proportions of nucleotide variability in CSGs compared to MAG-wide values for bacterial classes that had at least three MAGs with CSGs. The lines link the values within each MAG. FDR-corrected p-values are reported (*p < 0.05, **p < 0.01, and ***p < 0.001) for pairwise permutational t-tests each run for 10,000 iterations comparing the proportion of variable sites within each gene to the genome-wide proportion of variable sites that fall within coding regions. N.s., not significant. The CSG genes are positioned in the order in which they appear in the CSGs


The density and sheer taxonomic richness of tropical Ircinia microbiomes, combined with the many ecologically important metabolic processes that they drive such as biogeochemical cycling [12, 13] and photosynthesis [10, 71], render this genus an important study system for our understanding of tropical benthic ecology and host-microbial coevolution. To this end, our results illuminate trends of genome evolution of bacterial symbionts of Ircinia and provide insights into how highly complex microbiomes exist within this genus. Combined with prior research investigating genome evolution in Ircinia hosts [4], the current study helps complete our view of host-microbial evolution in sponges.

A yet unanswered question in sponge biology is how hosts can tolerate dense and diverse microbiomes. Four categories directly relevant to pathogenicity that were investigated were LPS biosynthesis, LPS transport, motility (including the flagellin gene fliC/hag and other flagellar assembly proteins), and chemotaxis, which were depleted in Ircinia microbiomes. Motility and chemotaxis genes have been found to be depleted in the metagenomes of Lamellodysidea herbaceae (particularly flagellar biosynthesis genes) [64], Petrosia ficiformis, Sarcotragus foetidus, and Aplysina aerophoba [63], in a sulfur-oxidizing gammaproteobacterial symbiont of Suberites sp. [72] and in a Rhodospirillaceae symbiont of Spongia officinalis [57]. Prior research also found that genes responsible for the biosynthesis of l-rhamnose, a component of the O-antigen of LPS, were missing from the genomes of four “Ca. S. spongiarum” strains, each inhabiting a different host sponge species: Theonella swinhoei, Ircinia variabilis, A. aerophoba, and Carteriospongia foliascens [17]. The modification to the LPS of “Ca. S. spongiarum” is speculated to prevent recognition and removal by host archaeocytes, a hypothesis supported by the finding that another cyanobacterial symbiont, “Ca. S. feldmannii,” which is intracellular and thus not exposed to archaeocytes, does in fact have conventional O-antigen based on the presence l-rhamnose biosynthesis genes [18]. While it is not known whether the present MAGs represent intra or extracellular bacteria, given that LPS and flagellar components elicit immune responses that can be damaging for both the host and symbionts [73,74,75,76] and that LPS-induced pathways are present in sponges [77,78,79] and are under selection in Caribbean Ircinia [4], the hypothesis could be extended to state that loss of LPS and motility genes are common features among bacterial symbionts of Ircinia and non-Ircinia hosts and represent another common strategy to avoid inciting the host’s immune system in addition to ELP enrichment, another widespread feature of sponge symbionts [20, 53,54,55,56,57,58, 60, 61].

The enrichment patterns of three categories of transporters (amino acid, spermidine/putrescine, sugar/saccharide) provide further indications into how the symbiotic microbes have evolved nutritional strategies within Ircinia and suggest that transporters are an adaption to sponges in general [20, 53,54,55,56,57,58,59,60,61,62,63,64,65]. Like other members of class Demospongiae, Ircinia possess high concentrations of free amino acids within their tissues [80], which could not only provide abundant sources of amino acids for protein biosynthesis but also the source molecules for the breakdown products spermidine and putrescine, two polyamines that are essential for cell growth [81] and which perform protective functions for host-associated prokaryotes [82]. Additionally, the enrichment of sugar transporters suggests that, like the symbionts of other sponges [83, 84], the microbiomes of Ircinia are active contributors in microbial processes that drive dissolved organic matter (DOM) turnover in tropical benthic environments.

Ircinia are known for their production of antibiotics (reviewed in [85]). The enrichment of genes within the peptide/nickel transporter category comprising the ddpABCDF operon could be an evolutionary response to the presence of such chemicals, as it is involved in cell wall restructuring that confers vancomycin resistance [86]. Curiously, no genes belonging to the biosynthesis of vancomycin group antibiotics pathway (PATH:ko01055) were enriched in sponge metagenomes, although several of these genes were indeed present in the MAGs of Ircinia symbionts.

Sterols are rare occurrences in bacteria, although they have been found to be produced by a handful of free-living and host-associated bacteria [68, 87]. The biogenic source of these compounds in sponges is still debated, especially for sterols with 24-C side-chain modifications such as 24-isopropylcholesterol, which is used as a biomarker to date sponge fossils to the Neoproterozoic Era [88]. SMT1/ERG6, a gene that can perform this modification, was found to be enriched in Ircinia bacterial MAGs, and furthermore, SMT copies derived from bacterial symbionts of A. aerophoba were recently demonstrated in vitro to perform sequential methylations at the 24-C position that produce the 24-isopropyl side-chain [89]. However, assignment of 24-isopropylcholesterol synthesis to symbiotic bacteria will require a more in-depth pathway characterization of sterol biosynthesis, and comparisons of molecular clock dating estimates of host and symbiont-derived SMT1/ERG6 copies could help investigate whether the genesis of bacterial copies are old enough to have contributed to 24-isopropylcholesterol in Neoproterozoic sponge fossils, as has already been done to date the earliest algal SMT gene duplication to the later Phanerozoic Eon [90].

Sterol production has been either observed or predicted based on genetic data for eight of the ten bacterial phyla containing CSGs in Ircinia [87, 91,92,93,94,95,96] but, to our knowledge, has never been predicted in Binatota or UBA8248. The CSGs are similar in gene content and are conserved in synteny relative to described putative bacterial steroid formation and modification operons [91], with the C-24 modification gene (SMT1/ERG6) co-occurring with the O2-dependent steps of cyclization (LSS/ERG7), C-14 demethylation (CYP51 and TM7SF2/ERG24) and, in several of the CSGs, the modification of desaturation step (ERG2). Our pipeline did not annotate SdmA and Sdmb, the genes involved in the C-4 demethylation step, although these could be annotated as the hypothetical proteins within the CSGs.

In addition to the conservation of organization and gene content within the CSGs, other lines of evidence in our data support the hypothesis that they are functionally important to bacterial symbionts of Ircinia. One interpretation of the evidence for HGT and the widespread distribution of taxa possessing the CSGs is that they are a necessity in the environment of the sponge interior, as is the case for antibiotic resistance genes that undergo HGT in pathogens [97,98,99,100]. This hypothesis is corroborated by the fact that the CSGs are absent from the Tara Oceans dataset and are otherwise a scarcity in the bacterial domain [87]. Additionally, the low omega values and low levels of nucleotide variance within the CSGs compared to MAG-wide averages suggest that they are undergoing purifying selection and are thus constrained in evolution at the level of the molecular residue [101,102,103].

A potential caveat of our enrichment analysis should be noted in that some phyla are overrepresented in Ircinia microbiomes relative to the Tara Oceans dataset that could be contributing unique signatures of gene content. This was partially captured in our analysis of individual bacterial phyla (and classes, in the case of Proteobacteria), which demonstrated that enrichment trends are not uniform across taxa. Thus, the patterns of genomic enrichment are best interpreted as being produced by a combination of environmental filtering, including selection by the host, and different patterns of genome evolution that are the result of selective regimes inside the sponge.

It should be noted that the sponge MAG dataset has a higher level of geographic resolution than the Tara Oceans MAG dataset. This is due to the fact that the authors of the latter dataset produced MAGs specific to the level of oceanic province [34]. Based on the coordinates of the sampling stations, these MAGs could include genomic content found only in bacteria that occur at higher latitudes and more eastward longitudes in the North Atlantic and South Pacific relative to the sampling locations of our sponge dataset, potentially introducing geographical bias. However, the inclusion of pelagic MAGs derived from a broader geographic range would also be expected to increase the probability that CSGs would be detected, which was not the case. Furthermore, our results are concurrent in several regards with patterns of genome evolution that have been detected in other sponge symbionts such as enrichment of ELPs, genes involved in cellular defense, and molecular transporters [20, 53,54,55,56,57,58,59,60,61,62,63,64]. Nevertheless, the testing of hypotheses regarding the effects of sponge inhabitation on bacterial genome evolution could be enhanced by future work that uses a free-living bacterial MAG dataset that has a level of geographic resolution matching that of the sponge dataset.


Tropical Ircinia are recognized for their roles in driving ecologically important metabolic processes that impact the coral reef, seagrass, and mangrove environments in which they reside. The current study provides an investigation of microbial genome evolution of Ircinia symbionts across multiple host species and has identified aspects of primary and secondary metabolism of tropical Ircinia microbiomes, including the putative capacity to synthesize sterols, that support this genus as being central to microbial and chemical diversity in benthic habitats. Our results also highlight the potential for Ircinia to buffer their environments against chemical fluctuations in the seawater [13]. In the Caribbean, this buffering capacity could be further supported by the existence of a regional pool of microbes that are shared among the host species and the metabolic redundancy within each species’ microbiome. These ecological roles, combined with the likely presence of undiscovered bioactive secondary compounds in Ircinia, underline the importance of protecting this genus and its habitats.

Availability of data and materials

The raw sequencing read and assembled MAG datasets supporting the conclusions of this article are available in NCBI’s GenBank repository under BioProject ID PRJNA768976 []. Physical vouchers of the sponge specimens used in this study are deposited with the Smithsonian National Museum of Natural History under the following accession numbers: I. campana: 1641986 and 1641983; I. cf. reteplana: 1641982 and 1641984; I. felix: 1642022 and 1641991; I. vansoesti (I. sp. “BZ1”): 1642005 and 1642012; I. ruetzleri (I. sp. “BZ2”): 1642000 and 1642003; I. lowi (I. sp. “Encrusting”): 1582269 and 1582270; I. laeviconulosa (I. sp. “Massive A green”): 1582283 and 1582286; I. radix (I. sp. “Massive A pink”): 1582259 and 1582260; I. bocatorensis (I. sp. “Massive B”): 1582289 and 1582291; and I. strobilina: 1642018 and 1642021. The code required to reproduce the analyses can be found under and .

Change history


  1. Vacelet J, Donadey C, Vacelet J, Donadey C. Electron microscope study of the association between some sponges and bacteria. J Exp Mar Biol Ecol. 1977;30:301–14.

    Article  Google Scholar 

  2. Wilkinson CR. Microbial associations in sponges. II. Numerical analysis of sponge and water bacterial populations. Mar Biol. 1978;49:169–76.

    Article  Google Scholar 

  3. Thomas T, Moitinho-Silva L, Lurgi M, Björk JR, Easson C, Astudillo-García C, et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat Commun. 2016;7:11870.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Kelly JB, Carlson DE, Low JS, Rice T, Thacker RW. The relationship between microbiomes and selective regimes in the sponge genus Ircinia. Front Microbiol. 2021;12:489.

    Article  Google Scholar 

  5. Griffiths S, Antwis R, Lenzi L, Lucaci A, Behringer DC, Butler M, et al. Host genetics and geography influence microbiome composition in the sponge Ircinia campana. J Anim Ecol. 2019;88:1684–95.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Thacker R, Diaz M, Rützler K, Erwin P, Kimble S, Pierce M, et al. Phylogenetic relationships among the filamentous cyanobacterial symbionts of Caribbean sponges and a comparison of photosynthetic production between sponges hosting filamentous and unicellular cyanobacteria. In: Custódio MR, Lôbo-Hajdu G, Hajdu E, Muricy G, editors. Porifera research: biodiversity, innovation and sustainability; 2007. p. 621–6.

    Google Scholar 

  7. Glasl B, Smith CE, Bourne DG, Webster NS. Exploring the diversity-stability paradigm using sponge microbial communities. Sci Rep. 2018;8:8425.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Freeman CJ, Thacker RW. Complex interactions between marine sponges and their symbiotic microbial communities. Limnol Oceanogr. 2011;56:1577–86.

    Article  Google Scholar 

  9. Erwin PM, Thacker RW. Phototrophic nutrition and symbiont diversity of two Caribbean sponge – cyanobacteria symbioses. Mar Ecol Prog Ser. 2008;362:139–47.

    Article  CAS  Google Scholar 

  10. Erwin PM, Thacker RW. Incidence and identity of photosynthetic symbionts in Caribbean coral reef sponge assemblages. J Mar Biol Assoc U K. 2007;87:1683–92.

    Article  CAS  Google Scholar 

  11. 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.

    Article  CAS  PubMed  Google Scholar 

  12. Southwell M, Weisz J, Martens C, Lindquist N. In situ fluxes of dissolved inorganic nitrogen from the sponge community on conch reef, key Largo, Florida. Limnol Oceanogr. 2008;53:986–96.

    Article  CAS  Google Scholar 

  13. Archer SK, Stevens JL, Rossi RE, Matterson KO, Layman CA. Abiotic conditions drive significant variability in nutrient processing by a common Caribbean sponge, Ircinia felix. Limnol Oceanogr. 2017;62:1783–93.

    Article  CAS  Google Scholar 

  14. De Rosa S, Mitova M, Tommonaro G. Marine bacteria associated with sponge as source of cyclic peptides. Biomol Eng. 2003;20:311–6.

    Article  PubMed  Google Scholar 

  15. Prokof’eva NG, Kalinovskaya NI, Luk’yanov PA, Shentsova EB, Kuznetsova TA. The membranotropic activity of cyclic acyldepsipeptides from bacterium Bacillus pumilus, associated with the marine sponge Ircinia sp. Toxicon. 1999;37:801–13.

    Article  PubMed  Google Scholar 

  16. 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.

  17. Burgsdorf I, Slaby BM, Handley KM, Haber M, Blom J, Marshall CW, et al. Lifestyle evolution in cyanobacterial symbionts of sponges. mBio. 2015;6:e00391–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ilia B, Handley MK, Rinat B-S, Erwin PM, Laura S, Seth B. Life at home and on the roam: genomic adaptions reflect the dual lifestyle of an intracellular, facultative symbiont. mSystems. 2019;4:e00057–19.

    Google Scholar 

  19. Tilman D. The ecological consequences of changes in biodiversity: a search for general principles. Ecology. 1999;80:1455–74.

    Google Scholar 

  20. Reynolds D, Thomas T. Evolution and function of eukaryotic-like proteins from sponge symbionts. Mol Ecol. 2016;25:5242–53.

    Article  CAS  PubMed  Google Scholar 

  21. Kelly JB, Thacker RW. New shallow water species of Caribbean Ircinia Nardo, 1833 (Porifera: Irciniidae). Zootaxa. 2021;5072:301–23.

    Article  PubMed  Google Scholar 

  22. Thoendel M, Jeraldo PR, Greenwood-Quaintance KE, Yao JZ, Chia N, Hanssen AD, et al. Comparison of microbial DNA enrichment tools for metagenomic whole genome sequencing. J Microbiol Methods. 2016;127:141–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Leo S, Gaïa N, Ruppé E, Emonet S, Girard M, Lazarevic V, et al. Detection of bacterial pathogens from broncho-alveolar lavage by next-generation sequencing. Int J Mol Sci. 2017;18.

  24. Chen S, Zhou Y, Chen Y, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34:i884–90.

    PubMed  PubMed Central  Google Scholar 

  25. Bushnell B. BBMap: a fast, accurate, splice-aware alignerUnited States; 2014.

    Google Scholar 

  26. Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.

    CAS  PubMed  Google Scholar 

  27. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.

    Article  CAS  PubMed  Google Scholar 

  29. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Varghese NJ, Mukherjee S, Ivanova N, Konstantinidis KT, Mavrommatis K, Kyrpides NC, et al. Microbial species delineation using whole genome sequences. Nucleic Acids Res. 2015;43:6761–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Boyd JA, Woodcroft BJ, Tyson GW. Comparative genomics using EnrichM. In prep; 2019.

    Google Scholar 

  33. Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Tully BJ, Graham ED, Heidelberg JF. The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. Scientific Data. 2018;5:170203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the genome taxonomy database. Bioinformatics. 2019;36:1925–7.

    PubMed Central  Google Scholar 

  36. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;57:289–300.

    Google Scholar 

  37. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM; 2013.

    Google Scholar 

  38. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10:giab008.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Li H. Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics. 2014;30:2843–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Köster J, Rahmann S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics. 2012;28:2520–2.

    Article  PubMed  Google Scholar 

  42. Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Abascal F, Zardoya R, Telford MJ. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 2010;38(Web Server issue):W7–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37:1530–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Minh BQ, Nguyen MAT, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30:1188–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–91.

    Article  CAS  PubMed  Google Scholar 

  48. Darriba D, Taboada GL, Doallo R, Posada D. jModelTest 2: more models, new heuristics and parallel computing. Nat Methods. 2012;9:772.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61:539–42.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008;36(Web Server issue):W5–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Yu G, Smith DK, Zhu H, Guan Y, Lam TT-Y. Ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2017;8:28–36.

    Article  Google Scholar 

  52. Robbins SJ, Singleton CM, Chan CX, Messer LF, Geers AU, Ying H, et al. A genomic view of the reef-building coral Porites lutea and its microbial symbionts. Nat Microbiol. 2019;4:2090–100.

    Article  PubMed  Google Scholar 

  53. Fan L, Reynolds D, Liu M, Stark M, Kjelleberg S, Webster NS, et al. Functional equivalence and evolutionary convergence in complex communities of microbial sponge symbionts. Proc Natl Acad Sci. 2012;109:E1878 LP–E1887.

    Article  Google Scholar 

  54. Karimi E, Keller-Costa T, Slaby BM, Cox CJ, da Rocha UN, Hentschel U, et al. Genomic blueprints of sponge-prokaryote symbiosis are shared by low abundant and cultivatable Alphaproteobacteria. Sci Rep. 2019;9:1999.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Karimi E, Ramos M, Gonçalves JMS, Xavier JR, Reis MP, Costa R. Comparative metagenomics reveals the distinctive adaptive features of the Spongia officinalis endosymbiotic consortium. Front Microbiol. 2017;8:2499.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Thomas T, Rusch D, DeMaere MZ, Yung PY, Lewis M, Halpern A, et al. Functional genomic signatures of sponge bacteria reveal unique and shared features of symbiosis. ISME J. 2010;4:1557–67.

    Article  CAS  PubMed  Google Scholar 

  57. Karimi E, Slaby BM, Soares AR, Blom J, Hentschel U, Costa R. Metagenomic binning reveals versatile nutrient cycling and distinct adaptive features in alphaproteobacterial symbionts of marine sponges. FEMS Microbiol Ecol. 2018;94.

  58. Gauthier M-EA, 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.

    Article  Google Scholar 

  59. Moitinho-Silva L, Díez-Vives C, Batani G, Esteves AIS, Jahn MT, Thomas T. Integrated metabolism in sponge–microbe symbiosis revealed by genome-centered metatranscriptomics. Isme J. 2017;11:1651.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Hentschel U, Piel J, Degnan SM, Taylor MW. Genomic insights into the marine sponge microbiome. Nat Rev Microbiol. 2012;10:641–54.

    Article  CAS  PubMed  Google Scholar 

  61. Díez-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.

    Article  PubMed  Google Scholar 

  62. Slaby BM, Hackl T, Horn H, Bayer K, Hentschel U. Metagenomic binning of a marine sponge microbiome reveals unity in defense but metabolic specialization. Nat Publ Group. 2017;11:2465–78.

    Google Scholar 

  63. Horn H, Slaby BM, Jahn MT, Bayer K, Moitinho-Silva L, Forster F, et al. An enrichment of CRISPR and other defense-related features in marine sponge-associated microbial metagenomes. Front Microbiol. 2016;7:1751.

    Article  PubMed  PubMed Central  Google Scholar 

  64. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Chaib De Mares M, Jiménez DJ, Palladino G, Gutleben J, Lebrun LA, Muller EEL, et al. Expressed protein profile of a Tectomicrobium and other microbial symbionts in the marine sponge Aplysina aerophoba as evidenced by metaproteomics. Sci Rep. 2018;8:11795.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Eichinger V, Nussbaumer T, Platzer A, Jehl M-A, Arnold R, Rattei T. Effective DB—updates and novel features for a better annotation of bacterial secreted proteins and type III, IV, VI secretion systems. Nucleic Acids Res. 2016;44:D669–74.

    Article  CAS  PubMed  Google Scholar 

  67. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Mohanty I, Podell S, Biggs JS, Garg N, Allen EE, Agarwal V. Multi-omic profiling of Melophlus sponges reveals diverse metabolomic and microbiome architectures that are non-overlapping with ecological neighbors. Mar Drugs. 2020;18:124.

    Article  CAS  PubMed Central  Google Scholar 

  69. Tavare S. Some probabilistic and statistical problems in the analysis of DNA sequences. Lectures Mathematics Life Sci. 1986;17:57–86.

    Google Scholar 

  70. Akaike H. In: Parzen E, Tanabe K, Kitagawa G, editors. Information theory and an extension of the maximum likelihood principle BT - selected papers of Hirotugu Akaike. New York: Springer New York; 1998. p. 199–213.

    Chapter  Google Scholar 

  71. Wilkinson C, Cheshire A. Comparisons of sponge populations across the barrier reefs of Australia and Belize: evidence for higher productivity in the Caribbean. Mar Ecol Prog Ser. 1990;67:285–94.

    Article  Google Scholar 

  72. Tian R-M, Zhang W, Cai L, Wong Y-H, Ding W, Qian P-Y. Genome reduction and microbe-host interactions drive adaptation of a sulfur-oxidizing bacterium associated with a cold seep sponge. mSystems. 2017;2:e00184–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Chaban B, Hughes HV, Beeby M. The flagellum in bacterial pathogens: for motility and a whole lot more. Semin Cell Dev Biol. 2015;46:91–103.

    Article  CAS  PubMed  Google Scholar 

  74. Ramos HC, Rumbo M, Sirard J-C. Bacterial flagellins: mediators of pathogenicity and host immune responses in mucosa. Trends Microbiol. 2004;12:509–17.

    Article  CAS  PubMed  Google Scholar 

  75. Miyake K. Innate recognition of lipopolysaccharide by toll-like receptor 4-MD-2. Trends Microbiol. 2004;12:186–92.

    Article  CAS  PubMed  Google Scholar 

  76. Medzhitov R, Preston-Hurlburt P, Janeway CA. A human homologue of the drosophila toll protein signals activation of adaptive immunity. Nature. 1997;388:394–7.

    Article  CAS  PubMed  Google Scholar 

  77. Böhm M, Hentschel U, Friedrich A, Fieseler L, Steffen R, Gamulin V, et al. Molecular response of the sponge Suberites domuncula to bacterial infection. Mar Biol. 2001;139:1037–45.

    Article  Google Scholar 

  78. Wiens M, Korzhev M, Perovic-Ottstadt S, Luthringer B, Brandt D, Klein S, et al. Toll-like receptors are part of the innate immune defense system of sponges (demospongiae: Porifera). Mol Biol Evol. 2007;24:792–804.

    Article  CAS  PubMed  Google Scholar 

  79. Schmittmann L, Franzenburg S, Pita L. Individuality in the immune repertoire and induced response of the sponge Halichondria panicea. Front Immunol. 2021;12:2300.

    Article  Google Scholar 

  80. Sica D, Zollo F. Free amino acids in some sponges. Biochem Syst Ecol. 1977;5:129–31.

    Article  CAS  Google Scholar 

  81. Cohen SS. Guide to the polyamines: Oxford University Press; 1998.

    Google Scholar 

  82. Shah P, Swiatlo E. A multifaceted role for polyamines in bacterial pathogens. Mol Microbiol. 2008;68:4–16.

    Article  CAS  PubMed  Google Scholar 

  83. Hentschel U, Usher KM, Taylor MW. Marine sponges as microbial fermenters. FEMS Microbiol Ecol. 2006;55:167–77.

    Article  CAS  PubMed  Google Scholar 

  84. de Goeij JM, van Oevelen D, Vermeij MJA, Osinga R, Middelburg JJ, de Goeij AFPM, et al. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science. 1979;2013(342):108–10.

    Google Scholar 

  85. Hardoim CCP, Costa R. Microbial communities and bioactive compounds in marine sponges of the family Irciniidae-a review. Marine Drugs. 2014;12:5089–122.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Lessard IA, Walsh CT. VanX, a bacterial D-alanyl-D-alanine dipeptidase: resistance, immunity, or survival function? Proc Natl Acad Sci U S A. 1999;96:11028–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Wei JH, Yin X, Welander PV. Sterol synthesis in diverse bacteria. Front Microbiol. 2016;7:990.

    Article  PubMed  PubMed Central  Google Scholar 

  88. Love GD, Grosjean E, Stalvies C, Fike DA, Grotzinger JP, Bradley AS, et al. Fossil steroids record the appearance of Demospongiae during the Cryogenian period. Nature. 2009;457:718–21.

    Article  CAS  PubMed  Google Scholar 

  89. Brown MO, Olagunju BO, Giner J-L, Welander PV. Sterol side chain alkylation by sponge and bacterial methyltransferases. bioRxiv. 2022:2022.05.16.491679.

  90. Gold DA, Grabenstatter J, de Mendoza A, Riesgo A, Ruiz-Trillo I, Summons RE. Sterol and genomic analyses validate the sponge biomarker hypothesis. Proc Natl Acad Sci U S A. 2016;113:2684–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Hoshino Y, Gaucher EA. Evolution of bacterial steroid biosynthesis and its impact on eukaryogenesis. Proc Natl Acad Sci. 2021;118:e2101276118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Lamb DC, Hargrove TY, Zhao B, Wawrzak Z, Goldstone JV, Nes WD, et al. Concerning P450 evolution: structural analyses support bacterial origin of sterol 14α-demethylases. Mol Biol Evol. 2021;38:952–67.

    Article  CAS  PubMed  Google Scholar 

  93. Patt TE, Hanson RS. Intracytoplasmic membrane, phospholipid, and sterol content of Methylobacterium organophilum cells grown under different conditions. J Bacteriol. 1978;134:636–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Bird CW, Lynch JM, Pirt FJ, Reid WW, Brooks CJW, Middleditch BS. Steroids and squalene in Methylococcus capsulatus grown on methane. Nature. 1971;230:473–4.

    Article  CAS  PubMed  Google Scholar 

  95. Banta AB, Wei JH, Gill CCC, Giner J-L, Welander PV. Synthesis of arborane triterpenols by a bacterial oxidosqualene cyclase. Proc Natl Acad Sci. 2017;114:245–50.

    Article  CAS  PubMed  Google Scholar 

  96. Schouten S, Bowman JP, Rijpstra WIC, Sinninghe Damsté JS. Sterols in a psychrophilic methanotroph, Methylosphaera hansonii. FEMS Microbiol Lett. 2000;186:193–5.

    Article  CAS  PubMed  Google Scholar 

  97. Aminov RI, Mackie RI. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol Lett. 2007;271:147–61.

    Article  CAS  PubMed  Google Scholar 

  98. Gyles C, Boerlin P. Horizontally transferred genetic elements and their role in pathogenesis of bacterial disease. Vet Pathol. 2013;51:328–40.

    Article  PubMed  Google Scholar 

  99. Ochman H, Lawrence JG, Groisman EA. Lateral gene transfer and the nature of bacterial innovation. Nature. 2000;405:299–304.

    Article  CAS  PubMed  Google Scholar 

  100. Davies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev. 2010;74:417–33.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Kimura M. Genetic variability maintained in a finite population due to mutational production of neutral and nearly neutral isoalleles. Genet Res. 1968;11:247–70.

    Article  CAS  PubMed  Google Scholar 

  102. Kimura M. Preponderance of synonymous changes as evidence for the neutral theory of molecular evolution. Nature. 1977;267:275–6.

    Article  CAS  PubMed  Google Scholar 

  103. Yang Z, Bielawski JP. Statistical methods for detecting molecular adaptation. Trends Ecol Evol. 2000;15:496–503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank Dr. Jackie L. Collier and Dr. Liliana Davalos-Alvarez for their input and comments on the manuscript; the staff of the Smithsonian Tropical Research Institute’s Bocas Research Station, the Smithsonian’s Carrie Bow Cay Field Station, and the MOTE Marine Laboratory and Aquarium’s Elizabeth Moore International Center for Coral Reef Research & Restoration for their support on the logistical aspects of the fieldwork; and Barrett Brooks (Smithsonian Museum of Natural History) and Karen Koltes (form. Office of the Secretary of the Interior) for their help with the specimen collections in Carrie Bow Cay. We thank the Yale Center for Genome Analysis for their attentiveness in the metagenomic DNA library preparation and sequencing; the staff of the SeaWulf HPC at SBU, which we used to perform the majority of our analyses; and our funders at


Open Access funding enabled and organized by Projekt DEAL. Fieldwork and DNA sequencing for this study were supported by the following grants awarded to JBK: Fellowship of Graduate Student Travel (Society for Integrative and Comparative Biology), Dr. David F. Ludwig Memorial Student Travel Scholarship (Association for Environmental Health and Sciences Foundation), and crowdfunding via Additional funding was provided by Stony Brook University. This work was also supported by grants to RWT from the US National Science Foundation (DEB-1622398, DEB-1623837, OCE-1756249).

Author information

Authors and Affiliations



JBK designed the study and generated the data. JBK and DEC performed the formal analysis. All authors contributed to the data conceptualization. JBK wrote the original manuscript. All authors contributed to the editing of the manuscript. Resources and funding were obtained by JBK and RWT. RWT supervised the study. The authors read and approved the final manuscript.

Authors’ information

Opted out.

Corresponding author

Correspondence to Joseph B. Kelly.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors have approved the manuscript and agree to its submission to Microbiome.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: "The author "David E. Carlson" should be affiliated to affiliation 2.

Supplementary Information

Additional file 1: Fig. S1.

Phylogenetic depiction of MAG distributions across Caribbean Ircinia species and redundancy in primary metabolic modes. The heatmap immediately to the right of the tree indicates the ln-transformed relative abundances of each MAG by host specimen. Species abbreviations accompanying specimen labels at the bottom of the heatmap are as follows: ICam (I. campana), IcfR (I. cf. reteplana), IRad (I. radix), ILae (I. laeviconulosa), IBoc (I. bocatorensis B), ILow (I. lowi), IVan (I. vansoesti), IRue (I. ruetzleri), IFel (I. felix), IStr (I. strobilina). To the right, the presence and absence of genes are annotated that belong to the six autotrophic carbon cycling pathways of prokaryotes (the Wood-Ljungdahl pathway, the dicarboxylate/4-hydroxybutyrate cycle, the 3-hydroxypropionate bicycle, the 3-hydroxypropionate/4-hydroxybutyrate cycle, the reductive citric acid cycle, and the Calvin–Benson–Bassham cycle), nitrogen metabolism (PATH:ko00910), sulfur metabolism (PATH:ko00920), and methane metabolism (PATH:ko00680). Plotting was performed using the GTDB-Tk bacterial phylogeny and ggtree [51].

Additional file 2: Fig. S2.

Plots depicting prevalence of MAGs associated with Caribbean Ircinia by source and region, inferred using the relative abundance matrix produced by CoverM. A. Bar chart depicting the percent of MAGs that are found across multiple host species. B. Bar chart depicting the regional specificity of MAGs. C. PCA of taxonomic community compositions of each host species’ microbiome.

Additional file 3: Fig. S3.

Plots depicting genes and domains that that are enriched or depleted in tropical Ircinia spp. at the level of bacterial phylum. Plotting scheme follows that of Fig. 2.

Additional file 4: Fig. S4.

CSGs plotted annotated on bacterial phylogeny comprising sponge-derived (Ircinia and non-Ircinia) and Tara Oceans MAGs, following tree construction and plotting scheme as outlined for Fig. 3.

Additional file 5: Fig. S5.

Midpoint-rooted phylogenetic trees for three genes found in CSGs, inferred in MrBayes. Nodes with less than 50% posterior probabilities are collapsed.

Additional file 6: Table S1

. Taxonomy, statistics, source metadata, and CSG occurrence in MAGs that passed QC and dereplication steps and were used in the final analyses. The archaeal MAGs and the Trichodesmium MAG that did not meet the completeness threshold are included.

Additional file 7: Table S2.

Table of relative abundances of Synechococcus MAGs in Ircinia, inferred via CoverM.

Additional file 8: Table S3.

Table of genes and domains that are significantly enriched or depleted in Ircinia, and which are plotted in Fig. 2.

Additional file 9: Table S4.

The top BLASTn hits for representative CSGs.

Additional file 10: Data File S1.

Fasta-formatted CSGs found in bacterial symbionts of Ircinia.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kelly, J.B., Carlson, D.E., Low, J.S. et al. Novel trends of genome evolution in highly complex tropical sponge microbiomes. Microbiome 10, 164 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: