Marine sponges (phylum Porifera) are a diverse, phylogenetically deep-branching clade known for forming intimate partnerships with complex communities of microorganisms. To date, 16S rRNA gene sequencing studies have largely utilised different extraction and amplification methodologies to target the microbial communities of a limited number of sponge species, severely limiting comparative analyses of sponge microbial diversity and structure. Here, we provide an extensive and standardised dataset that will facilitate sponge microbiome comparisons across large spatial, temporal, and environmental scales. Samples from marine sponges (n = 3569 specimens), seawater (n = 370), marine sediments (n = 65) and other environments (n = 29) were collected from different locations across the globe. This dataset incorporates at least 268 different sponge species, including several yet unidentified taxa. The V4 region of the 16S rRNA gene was amplified and sequenced from extracted DNA using standardised procedures. Raw sequences (total of 1.1 billion sequences) were processed and clustered with (i) a standard protocol using QIIME closed-reference picking resulting in 39 543 operational taxonomic units (OTU) at 97% sequence identity, (ii) a de novo clustering using Mothur resulting in 518 246 OTUs, and (iii) a new high-resolution Deblur protocol resulting in 83 908 unique bacterial sequences. Abundance tables, representative sequences, taxonomic classifications, and metadata are provided. This dataset represents a comprehensive resource of sponge-associated microbial communities based on 16S rRNA gene sequences that can be used to address overarching hypotheses regarding host-associated prokaryotes, including host specificity, convergent evolution, environmental drivers of microbiome structure, and the sponge-associated rare biosphere.

The dichotomy between high microbial abundance (HMA) and low microbial abundance (LMA) sponges has been observed in sponge-microbe symbiosis, although the extent of this pattern remains poorly unknown. We characterized the differences between the microbiomes of HMA (n = 19) and LMA (n = 17) sponges (575 specimens) present in the Sponge Microbiome Project. HMA sponges were associated with richer and more diverse microbiomes than LMA sponges, as indicated by the comparison of alpha diversity metrics. Microbial community structures differed between HMA and LMA sponges considering Operational Taxonomic Units (OTU) abundances and across microbial taxonomic levels, from phylum to species. The largest proportion of microbiome variation was explained by the host identity. Several phyla, classes, and OTUs were found differentially abundant in either group, which were considered “HMA indicators” and “LMA indicators.” Machine learning algorithms (classifiers) were trained to predict the HMA-LMA status of sponges. Among nine different classifiers, higher performances were achieved by Random Forest trained with phylum and class abundances. Random Forest with optimized parameters predicted the HMA-LMA status of additional 135 sponge species (1,232 specimens) without a priori knowledge. These sponges were grouped in four clusters, from which the largest two were composed of species consistently predicted as HMA (n = 44) and LMA (n = 74). In summary, our analyses shown distinct features of the microbial communities associated with HMA and LMA sponges. The prediction of the HMA-LMA status based on the microbiome profiles of sponges demonstrates the application of machine learning to explore patterns of host-associated microbial communities.

The systematics of the poriferan Order Haplosclerida (Class Demospongiae) has been under scrutiny for a number of years without resolution. Molecular data suggests that the order needs revision at all taxonomic levels. Here, we provide a comprehensive view of the phylogenetic relationships of the marine Haplosclerida using many species from across the order, and three gene regions. Gene trees generated using 28S rRNA, nad1 and cox1 gene data, under maximum likelihood and Bayesian approaches, are highly congruent and suggest the presence of four clades. Clade A is comprised primarily of species of Haliclona and Callyspongia, and clade B is comprised of H. simulans and H. vansoesti (Family Chalinidae), Amphimedon queenslandica (Family Niphatidae) and Tabulocalyx (Family Phloeodictyidae), Clade C is comprised primarily of members of the Families Petrosiidae and Niphatidae, while Clade D is comprised of Aka species. The polyphletic nature of the suborders, families and genera described in other studies is also found here.

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The natural distribution of the honeybee (Apis mellifera L.) has been changed by humans in recent decades to such an extent that the formerly widest-spread European subspecies, Apis mellifera mellifera, is threatened by extinction through introgression from highly divergent commercial strains in large tracts of its range. Conservation efforts for A. m. mellifera are underway in multiple European countries requiring reliable and cost-efficient molecular tools to identify purebred colonies. Here, we developed four ancestry-informative SNP assays for high sample throughput genotyping using the iPLEX Mass Array system. Our customized assays were tested on DNA from individual and pooled, haploid and diploid honeybee samples extracted from different tissues using a diverse range of protocols. The assays had a high genotyping success rate and yielded accurate genotypes. Performance assessed against whole-genome data showed that individual assays behaved well, although the most accurate introgression estimates were obtained for the four assays combined (117 SNPs). The best compromise between accuracy and genotyping costs was achieved when combining two assays (62 SNPs). We provide a ready-to-use cost-effective tool for accurate molecular identification and estimation of introgression levels to more effectively monitor and manage A. m. mellifera conservatories.

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The emergence of antibiotic resistance and viruses with high epidemic potential made unexplored marine environments an appealing target source for new metabolites. Marine fungi represent one of the most suitable sources for the discovery of new compounds. Thus, the aim of this work was (i) to isolate and identify fungi associated with the Atlantic sponge Grantia compressa; (ii) to study the fungal metabolites by applying the OSMAC approach (one strain; many compounds); (iii) to test fungal compounds for their antimicrobial activities. Twenty-one fungal strains (17 taxa) were isolated from G. compressa. The OSMAC approach revealed an astonishing metabolic diversity in the marine fungus Eurotium chevalieri MUT 2316, from which 10 compounds were extracted, isolated, and characterized. All metabolites were tested against viruses and bacteria (reference and multidrug-resistant strains). Dihydroauroglaucin completely inhibited the replication of influenza A virus; as for herpes simplex virus 1, total inhibition of replication was observed for both physcion and neoechinulin D. Six out of 10 compounds were active against Gram-positive bacteria with isodihydroauroglaucin being the most promising compound (minimal inhibitory concentration (MIC) 4–64 µg/mL) with bactericidal activity. Overall, G. compressa proved to be an outstanding source of fungal diversity. Marine fungi were capable of producing different metabolites; in particular, the compounds isolated from E. chevalieri showed promising bioactivity against well-known and emerging pathogens.

Hematopoietic stem cell transplant recipients frequently develop BK virus (BKV)-associated hemorrhagic cystitis, which coincides with BK viruria. However, the precise role of BKV in the etiology of hemorrhagic cystitis in hematopoietic stem cell transplant recipients remains unclear, since approximately 50% of all such adult transplant recipients excrete BKV, yet do not develop this clinical condition. In the present study, BKV were analyzed to determine if mutations in the non-coding control region (NCCR), and specific BKV sub-types defined by sequence analysis of major capsid protein VP1, were associated with development of hemorrhagic cystitis in hematopoietic stem cell transplant recipients. The regions encoding VP1 and NCCRs of BKV in urine samples collected from 15 hematopoietic stem cell transplant recipients with hemorrhagic cystitis and 20 without this illness were amplified and sequenced. Sequence variations in the NCCRs of BKV were identified in urine samples from those with and without hemorrhagic cystitis. Furthermore, five unique sequence variations within transcription factor binding sites in the canonical NCCR, O-P-Q-R-S, were identified, representing new BKV variants from a population of cloned quasi-species obtained from patients with and without hemorrhagic cystitis. Thirty-five BKV VP1 sequences were analyzed by phylogenetic analysis but no specific BKV sub-type was associated with hemorrhagic cystitis. Five previously unrecognized naturally occurring variants of the BKV are described which involve amplifications, deletions, and rearrangements of the archetypal BKV NCCRs in individuals with and without hemorrhagic cystitis. Architectural rearrangements in the NCCRs of BKV did not appear to be a prerequisite for development of hemorrhagic cystitis in hematopoietic stem cell transplant recipients.