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Microbiomes of microscopic marine invertebrates do not reveal signatures of phylosymbiosis

Abstract

Animals and microorganisms often establish close ecological relationships. However, much of our knowledge about animal microbiomes comes from two deeply studied groups: vertebrates and arthropods. To understand interactions on a broader scale of diversity, we characterized the bacterial microbiomes of close to 1,000 microscopic marine invertebrates from 21 phyla, spanning most of the remaining tree of metazoans. Samples were collected from five temperate and tropical locations covering three marine habitats (sediment, water column and intertidal macroalgae) and bacterial microbiomes were characterized using 16S ribosomal RNA gene sequencing. Our data show that, despite their size, these animals harbour bacterial communities that differ from those in the surrounding environment. Distantly related but coexisting invertebrates tend to share many of the same bacteria, suggesting that guilds of microorganisms preferentially associated with animals, but not tied to any specific host lineage, are the main drivers of the ecological relationship. Host identity is a minor factor shaping these microbiomes, which do not show the same correlation with host phylogeny, or ‘phylosymbiosis’, observed in many large animals. Hence, the current debate on the varying strength of phylosymbiosis within selected lineages should be reframed to account for the possibility that such a pattern might be the exception rather than the rule.

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Fig. 1: Collection of over 1,000 specimens representing most animal phyla.
Fig. 2: Impact of host taxonomy and environmental variables on microbiome composition.
Fig. 3: Microbiome overlap between animals and their environment.
Fig. 4: Microbiome composition differences do not reflect host family identity.
Fig. 5: Weak signals of host genus- and species-specific bacteria.

Data availability

All sequence data are deposited in the NCBI Short Read Archive under the BioProject accession number PRJNA746569. Specimen photographs are deposited at Dryad (https://doi.org/10.5061/dryad.ngf1vhhv6).

Code availability

No custom code has been used during this work. All analyses were conducted with publicly accessible packages in R and have been cited in the Methods.

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Acknowledgements

We wish to thank F. Bergmeier, B. Brenzinger, S. Fujimoto, U. Jondelius, M. Kolicka, C. Nielsen, A. Schmidt-Rhaesa, M. Sørensen and W. Sterrer for additional taxonomic expertise, the Hakai Institute and CARMABI and their helpful staff (in particular, N. Acharya-Patel, C. Janusson and C. Prentice from Hakai for assistance with DNA extractions) and C. Wall and G. Buckholtz for laboratory procedures at the University of British Columbia. This project was funded by the Tula Foundation’s Hakai Institute (P.J.K. and B.S.L.), the Natural Sciences and Engineering Research Council (NSERC 2019-03896 to B.S.L. and NSERC 2019-03994 to P.J.K.), the Gordon and Betty Moore Foundation (https://doi.org/10.37807/GBMF9201, P.J.K.) and the Canadian Graduate Scholarship programme (N.A.T.I.).

Author information

Authors and Affiliations

Authors

Contributions

The project was conceived and planned by V.B., N.W.L.V.S., M.H., B.S.L. and P.J.K. B.S.L. and P.J.K. are the senior authors and provided supervision and funding. V.B., N.W.L.V.S., M.H. and N.A.T.I. performed most of the field and laboratory work. C.C.H. conducted the analyses. V.M., N.O. and R.S.P. contributed to field and laboratory work. N.W.L.V.S., M.H., P.A.C., K.G., O.H., A.K. and K.W. identified the animals. V.B., C.C.H. and P.J.K. wrote the original draft. All authors edited iterative versions of the final manuscript.

Corresponding authors

Correspondence to V. Boscaro or P. J. Keeling.

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The authors declare no competing interests.

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Nature Microbiology thanks Diego Fontaneto, Holly Lutz, Owen Wangensteen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Examples of animals collected during the survey.

a, Examples of collected animals from non-arthropod phyla. b, Examples of collected animals from arthropod lineages. Bars stand for 250 μm, except for a7, a9, a11, a12, a13, a15, and a18, where they stand for 50 μm (hatched bars), and a20 and b1, where they stand for 1,000 μm (red bars).

Extended Data Fig. 2 Observed number of ASVs in host-associated and environmental microbial communities.

Bacterial richness, expressed as ASV counts, of the microbial communities, grouped according to: a, host phylum; b, host order within the phylum Annelida and Nematoda; c, location (for both animal-associated and environmental data); and d, habitat (for both animal-associated and environmental data). The dashed box in a separates phyla with less than five sampled specimens each. Microbial community Shannon-diversity (Fig. 2) and ASV numbers are considerably higher in environmental communities than in invertebrate hosts.

Extended Data Fig. 3 Diversity and richness of host-associated and environmental microbial communities do not correlate.

a, Individual animal microbiome Shannon-diversity index and b, ASV counts plotted against the averaged values from corresponding environmental communities. c,d, same plots separated according to host phylum. Differences in Shannon-diversity and richness of animal-associated microbiomes are not tied to Shannon-diversity and richness of background environmental communities. The grey area shows the 95% confidence interval (default geom_smooth se parameter). n = 877 specimens. n of specimens per phylum as in Fig. 2d.

Extended Data Fig. 4 Microbiome overlap between animals and their environment in locations other than Quadra Island.

a-e, Proportion of bacterial Amplicon Sequence Variants shared between individual invertebrates and their environment, separated by habitat for locations with multiple sampled habitats. f-j, Proportion of bacterial ASVs shared between individual specimens and all other animals in the same sample, separated by habitat for locations with multiple sampled habitats. Solid, black lines in circular plots indicate overall average. Dashed lines indicate 25%, 50%, and 75% thresholds, for scale. Black circles plot phylum average.

Extended Data Fig. 5 Influence of keystone environmental bacteria in animal-associated microbiomes isolated from macroalgae and water.

SPIEC-EASI co-occurrence network of key environmental ASVs found in Quadra Island a, macroalgae (n = 253 ASVs) and b, water column (n = 228 ASVs) samples. Each node represents a single ASV. Lines connecting two nodes (edges) indicate an association between the two ASVs. Node size is scaled to eigen-centrality, which considers the number of connecting nodes as well as their subsequent connections. c,d, prevalence and abundance (both as %) of the same environmental ASVs (respective of each habitat) in animals from the same habitat and location. Individual ASVs (on the x axis) are ordered according to their eigen-centrality in the environmental network, and may be represented by multiple datapoints in the abundance plot (on the right) to reflect their varying abundance in multiple host phyla. Grey arrowheads in prevalence plots indicate environmental ASVs that are absent in host-associated microbiomes. Point colour indicates host phyla. As is the case in sediments from the same location (see text), keystone environmental bacteria are not particularly abundant nor prevalent in animal-associated microbiomes.

Extended Data Fig. 6 Correlation of ASVs shared between animals and those shared between animals and the environment.

The proportion of bacterial ASVs shared between individual invertebrates collected in Quadra Island and all other co-occurring animals in the same sample plotted against: a, the proportion of bacterial ASVs shared between animals and their environment; b, the proportion of shared ASVs between animals that are also shared with the environment. Both coloured and separated according to host phylum. While there is a tendency for co-occurring animals to share more ASVs in samples where more ASVs are also shared with the environment, the ASVs responsible for both overlaps do not increase in number accordingly, and hence are not necessarily the same. n of specimens per phylum as in Fig. 2d.

Extended Data Fig. 7 Predicting host taxonomy and environmental factors with random forest models.

Out-of-bag error rates of random forest models using microbial community ASVs to predict potential groupings. From left to right: all data, predicting community type (host-associated vs. environmental); animal-associated data, predicting host phylum, host phylum restricted to phyla that only include more than 20 specimens, host class, and host order; both animal-associated and environmental data, predicting location and habitat. The models can confidently discriminate microbial community type as well as environmental parameters from environmental communities. They fare poorly when discriminating any parameter from host-associated microbiomes, especially those related to host taxonomy.

Extended Data Fig. 8 Phylogenetic relationships used in phylogeny-based phylosymbiosis tests.

Phylogenetic trees (topology only) among a, phyla investigated in this work32. b, families in Annelida;67 c, families in Nematoda;68 d, species in Astrotorhynchus69. Mantel tests, results of which are shown under each panel, compare the phylogenetic topologies shown with similarity dendrograms of microbiomes from corresponding specimens, as shown in d. Microbiome data from specimens belonging to the same taxon (phylum, family, or species) were mapped in 0-branch lengths polytomies in each tree, as shown for Astrotorhynchus (numbers of specimens per polytomy are reported within dark triangles in a–c). All the performed analyses showed a very low degree of covariation (R value) between host phylogeny and microbiome similarity.

Extended Data Fig. 9 Potential phylosymbiosis signal in Echinoderes (Kinorhyncha).

Principal Coordinates Analysis using Bray–Curtis dissimilarity of microbiomes from Echinoderes specimens largely clustered according to host species. Ellipses group specimens of the same species.

Extended Data Fig. 10 Core bacterial families found in invertebrate phyla.

Prevalence of bacterial families at increasing relative abundance thresholds within each invertebrate phylum. Only families present in the majority of specimens (>50%) at or above 0.005% relative abundance are included. Actual prevalence values are included at each threshold with colour denoting degree of prevalence. Families occurring in all specimens at a given abundance threshold (prevalence value = 1) are indicated by a dark grey outline.

Supplementary information

Supplementary Information

Supplementary Notes 1–3 and Figs. 1–3.

Reporting Summary

Supplementary Tables

Supplementary Table 1: Metadata on the 46 samples processed during the survey. Each sample corresponds to 15–29 individual animal specimens and 5–9 environmental aliquots. Supplementary Table 2: Metadata on the 1,000+ invertebrates and 250+ environmental aliquots investigated. Morphology-based taxonomy is only reported for annelids, platyhelminths, gnathostomulids, rotifers, gastrotrichs, bryozoans, kinorhynchs, priapulids, tardigrades, nematodes and hemichordates. Partial 18S ribosomal gene sequences for 232 specimens are also reported. Supplementary Table 3: Analysis of compositions of microbiomes with bias correction (ANCOMBC) estimated separately for each habitat. Log-fold changes with respect to specimens are reported. Standard error, test statistic (standardized effect size), P value (two-way Z-test) and P value adjusted for multiple tests (Holm–Bonferroni method) are also shown for each comparison. Supplementary Table 4: Dataframe listing the prevalence and identity of potential symbionts described in the manuscript. Taxonomic identification indicated by best BLAST hit and DADA2 assignment using the RDP classifier and the SILVA database (v.138). Bootstrap support for taxonomic assignment is also included. Supplementary Table 5: Dataframe listing ASVs assigned to Rickettsiales, Holosporales, Chlamydiae and Endozoicomonas by DADA2 and the RDP classifier using the SILVA database (v.138). Bootstrap support for taxonomic assignment is also included. Best BLAST hit against the full nt database is also reported. Host phylum, location and habitat columns include all occurrences of each ASV.

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Boscaro, V., Holt, C.C., Van Steenkiste, N.W.L. et al. Microbiomes of microscopic marine invertebrates do not reveal signatures of phylosymbiosis. Nat Microbiol 7, 810–819 (2022). https://doi.org/10.1038/s41564-022-01125-9

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