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CPR and DPANN Have an Overlooked Role in Corals’ Microbial Community Structure

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Abstract

Understanding how microbial communities are structured in coral holobionts is important to estimate local and global impacts and provide efficient environment management strategies. Several studies investigated the relationship between corals and their microbial communities, including the environmental drivers of shifts in this relationship, associated with diseases and coral cover loss. However, these studies are often geographically or taxonomically restricted and usually focused on the most abundant microbial groups, neglecting the rare biosphere, including archaea in the group DPANN and the recently discovered bacterial members of the candidate phyla radiation (CPR). Although it is known that rare microbes can play essential roles in several environments, we still lack understanding about which taxa comprise the rare biosphere of corals’ microbiome. Here, we investigated the host-related and technical factors influencing coral microbial community structure and the importance of CPR and DPANN in this context by analyzing more than a hundred coral metagenomes from independent studies worldwide. We show that coral genera are the main biotic factor shaping coral microbial communities. We also detected several CPR and DPANN phyla comprising corals’ rare biosphere for the first time and showed that they significantly contribute to shaping coral microbial communities.

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Fig. 1
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Data availability

We used metagenomic and genomic data that were publicly available in online repositories to perform our analyses. All the IDs are available in Online Resource 2 and 3.

Code Availability

The bioinformatic pipeline and statistical analysis are available at http://www.github.com/meirelleslab/CPR_DPANN_coral_holobiont.

References

  1. Sato Y, Ling EYS, Turaev D, Laffy P, Weynberg KD, Rattei T, Willis BL, Bourne DG (2017) Unraveling the microbial processes of black band disease in corals through integrated genomics. Sci Rep 7:1–14. https://doi.org/10.1038/srep40455

    Article  CAS  Google Scholar 

  2. Carlos C, Torres TT, Ottoboni LMM (2013) Bacterial communities and species-specific associations with the mucus of Brazilian coral species. Sci Rep 3. https://doi.org/10.1038/srep01624

  3. Zaneveld JR, Burkepile DE, Shantz AA, Pritchard CE, McMinds R, Payet JP, Welsh R, Correa AMS, Lemoine NP, Rosales S, Fuchs C, Maynard JA, Thurber RV (2016) Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat Commun 7:11833. https://doi.org/10.1038/ncomms11833

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Littman R, Willis BL, Bourne DG (2011) Metagenomic analysis of the coral holobiont during a natural bleaching event on the Great Barrier Reef. Environ Microbiol Rep 3:651–660. https://doi.org/10.1111/j.1758-2229.2010.00234.x

    Article  CAS  PubMed  Google Scholar 

  5. Mascarenhas R, Ruziska FM, Moreira EF, Campos AB, Loiola M, Reis K, Trindade-Silva AE, Barbosa FAS, Salles L, Menezes R, Veiga R, Coutinho FH, Dutilh BE, Guimarães Jr PR, Assis APA, Ara A, Miranda JGV, Andrade RFS, Vilela B, Meirelles PM (2020) Integrating computational methods to investigate the macroecology of microbiomes. Front Genet 10:1–24. https://doi.org/10.3389/fgene.2019.01344

    Article  Google Scholar 

  6. Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, Küsel K, Rillig MC, Rivett DW, Salles JF, van der Heijden MGA, Youssef NH, Zhang X, Wei Z, Hol WHG (2017) Where less may be more: how the rare biosphere pulls ecosystems strings. ISME J 11:853–862. https://doi.org/10.1038/ismej.2016.174

    Article  PubMed  PubMed Central  Google Scholar 

  7. Hug LA, Baker BJ, Anantharaman K, Brown CT, Probst AJ, Castelle CJ, Butterfield CN, Hernsdorf AW, Amano Y, Ise K, Suzuki Y, Dudek N, Relman DA, Finstad KM, Amundson R, Thomas BC, Banfield JF (2016) A new view of the tree of life. Nat Microbiol 1:16048. https://doi.org/10.1038/nmicrobiol.2016.48

    Article  CAS  PubMed  Google Scholar 

  8. Castelle CJ, Brown CT, Anantharaman K, Probst AJ, Huang RH, Banfield JF (2018) Biosynthetic capacity, metabolic variety and unusual biology in the CPR and DPANN radiations. Nat Rev Microbiol 16:629–645. https://doi.org/10.1038/s41579-018-0076-2

    Article  CAS  PubMed  Google Scholar 

  9. Schmieder R, Edwards R (2011) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27:863–864. https://doi.org/10.1093/bioinformatics/btr026

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Wood DE, Salzberg SL (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15:R46. https://doi.org/10.1186/GB-2014-15-3-R46

    Article  PubMed  PubMed Central  Google Scholar 

  11. Ramirez KS, Knight CG, De Hollander M et al (2018) Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat Microbiol 3:189–196. https://doi.org/10.1038/s41564-017-0062-x

    Article  CAS  PubMed  Google Scholar 

  12. Katz BM, McSweeney M (1980) A multivariate Kruskal-Wallis test with post hoc procedures. Multivariate Behav Res 15:281–297. https://doi.org/10.1207/s15327906mbr1503_4

    Article  CAS  PubMed  Google Scholar 

  13. Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2:37–52. https://doi.org/10.1016/0169-7439(87)80084-9

    Article  CAS  Google Scholar 

  14. Richard A, Wichern D (2007) Applied multivariate statistical analysis6th edn. Pearson Educational Inc, New Jersey

    Google Scholar 

  15. Breiman L (2001) Random Forests. Mach Learn 45:5–32

    Article  Google Scholar 

  16. Li J, Chen Q, Zhang S et al (2013) Highly heterogeneous bacterial communities associated with the South China Sea reef corals Porites lutea, Galaxea fascicularis and Acropora millepora. PLoS One 8:e71301. https://doi.org/10.1371/journal.pone.0071301

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lynch MDJ, Neufeld JD (2015) Ecology and exploration of the rare biosphere. Nat Rev Microbiol 13:217–229. https://doi.org/10.1038/nrmicro3400

    Article  CAS  PubMed  Google Scholar 

  18. Hillebrand H (2004) On the generality of the latitudinal diversity gradient. Am Nat 163:192–211. https://doi.org/10.1007/978-3-642-61320-3_6

    Article  PubMed  Google Scholar 

  19. Hernandez-Agreda A, Gates RD, Ainsworth TD (2017) Defining the core microbiome in corals’ microbial soup. Trends Microbiol 25:125–140. https://doi.org/10.1016/j.tim.2016.11.003

    Article  CAS  PubMed  Google Scholar 

  20. Mahmoud HM, Kalendar AA (2016)Coral-associated actinobacteria: diversity, abundance, and biotechnological potentials. Frontiers in Microbiology 7:204. https://doi.org/10.3389/fmicb.2016.00204

    Article  PubMed  PubMed Central  Google Scholar 

  21. Thurber RV, Willner-Hall D, Rodriguez-Mueller B, Desnues C, Edwards RA, Angly F, Dinsdale E, Kelly L, Rohwer F (2009) Metagenomic analysis of stressed coral holobionts. Environmental Microbiology 11(8):2148–2163. https://doi.org/10.1111/j.1462-2920.2009.01935.x

    Article  CAS  Google Scholar 

  22. Gignoux-Wolfsohn SA, Aronson FM, Vollmer SV (2017) Complex interactions between potentially pathogenic, opportunistic, and resident bacteria emerge during infection on a reef-building coral. FEMS microbiology ecology 93(7). https://doi.org/10.1093/femsec/fix080

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Acknowledgements

This work was primarily supported by the PROPESQ-UFBA (11268). ABC was supported by the National Council for Scientific and Technological Development (CNPq-132261/2018-9). ATS thanks Coordination of Superior Level Staff Improvement (CAPES-88887.301758/2018-00). PMM thanks Serrapilheira Institute (grant number Serra-1709-17818). Authors thank MCTIC/FINEP/CT-Infra 01/2013 project 0761/13 for the computational infrastructure. We acknowledge the National Laboratory for Scientific Computing (LNCC/MCTI, Brazil) for providing HPC resources of the SDumont supercomputer, which have contributed to the research results reported within this article (URL: http://sdumont.lncc.br). Authors thank anonymous reviewers for comments on the manuscript.

Funding

This work was primarily supported by the PROPESQ-UFBA (11268). ABC was supported by CNPq (132261/2018-9). ATS thanks CAPES (88887.301758/2018-00). PMM thanks Serrapilheira Institute (grant number Serra-1709-17818).

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Author notes

  1. Amanda Barreto Campos and Letícia Costa Cavalcante contributed equally to this work.

Authors and Affiliations

  1. Institute of Biology, Federal University of Bahia, Salvador, Brazil

    Amanda Barreto Campos, Letícia Costa Cavalcante, Miguel Loiola & Pedro Milet Meirelles

  2. National Institute for Interdisciplinary and Transdisciplinary Studies in Ecology and Evolution (IN-TREE), Salvador, Brazil

    Amanda Barreto Campos, Amaro Emiliano Trindade Silva & Pedro Milet Meirelles

  3. Institute of Mathematics and Statistics, Federal University of Bahia, Salvador, Brazil

    Arthur R. de Azevedo & Anderson Ara

Authors
  1. Amanda Barreto Campos
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  2. Letícia Costa Cavalcante
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  3. Arthur R. de Azevedo
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  4. Miguel Loiola
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  5. Amaro Emiliano Trindade Silva
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  6. Anderson Ara
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  7. Pedro Milet Meirelles
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Contributions

ABC wrote the manuscript. LCC performed the data collection and the bioinformatic analysis. AAR and AA performed the statistical analysis. ABC, LCC, ATS, ML, and PMM interpreted the results. PMM conceived the idea and supervised the work. All authors revised the paper and agreed with its content.

Corresponding author

Correspondence to Pedro Milet Meirelles.

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Campos, A.B., Cavalcante, L.C., de Azevedo, A.R. et al. CPR and DPANN Have an Overlooked Role in Corals’ Microbial Community Structure. Microb Ecol 83, 252–255 (2022). https://doi.org/10.1007/s00248-021-01737-4

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  • DOI: https://doi.org/10.1007/s00248-021-01737-4

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