Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Microbiome diversity and host immune functions influence survivorship of sponge holobionts under future ocean conditions

Abstract

The sponge-associated microbial community contributes to the overall health and adaptive capacity of the sponge holobiont. This community is regulated by the environment and the immune system of the host. However, little is known about the effect of environmental stress on the regulation of host immune functions and how this may, in turn, affect sponge–microbe interactions. In this study, we compared the bacterial diversity and immune repertoire of the demosponge, Neopetrosia compacta, and the calcareous sponge, Leucetta chagosensis, under varying levels of acidification and warming stress based on climate scenarios predicted for 2100. Neopetrosia compacta harbors a diverse microbial community and possesses a rich repertoire of scavenger receptors while L. chagosensis has a less diverse microbiome and an expanded range of pattern recognition receptors and immune response-related genes. Upon exposure to RCP 8.5 conditions, the microbiome composition and host transcriptome of N. compacta remained stable, which correlated with high survival (75%). In contrast, tissue necrosis and low survival (25%) of L. chagosensis was accompanied by microbial community shifts and downregulation of host immune-related pathways. Meta-analysis of microbiome diversity and immunological repertoire across poriferan classes further highlights the importance of host–microbe interactions in predicting the fate of sponges under future ocean conditions.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Sponge holobiont features and survivorship under future ocean conditions.
Fig. 2: Microbial community structure across stress treatments.
Fig. 3: Host immune responses under different treatment conditions.
Fig. 4: Microbiome diversity and immunological repertoire of diverse sponge species.

Data availability

Raw sequence data from this study are accessible through the NCBI Sequence Read Archive under BioProject PRJNA689294. The datasets we generated and analyzed are available in Figshare (https://figshare.com/projects/Sponge_holobionts_under_future_ocean_conditions/95796).

References

  1. 1.

    Le Quéré C, Moriarty R, Andrew RM, Canadell JG, Sitch S, Korsbakken JI, et al. Global carbon budget 2015. Earth Syst Sci Data. 2015;7:349–96.

    Article  Google Scholar 

  2. 2.

    Hoegh-Guldberg O, Mumby PJ, Hooten AJ, Steneck RS, Greenfield P, Gomez E, et al. Coral reefs under rapid climate change and ocean acidification. Science. 2007;318:1737–42.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  3. 3.

    Bell JJ, Bennett HM, Rovellini A, Webster NS. Sponges to be winners under near-future climate scenarios. Bioscience. 2018;68:955–68.

    Article  Google Scholar 

  4. 4.

    Bell JJ. The functional roles of marine sponges. Estuar Coast Shelf Sci. 2008;79:341–53.

    Article  Google Scholar 

  5. 5.

    Pita L, Rix L, Slaby BM, Franke A, Hentschel U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome. 2018;6:46.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Smith AM, Berman J, Key MM Jr, Winter DJ. Not all sponges will thrive in a high-CO2 ocean: Review of the mineralogy of calcifying sponges. Palaeogeogr Palaeoclimatol Palaeoecol. 2013;392:463–72.

    Article  Google Scholar 

  7. 7.

    Webster NS, Thomas T. The sponge hologenome. MBio. 2016;7:e00135–16.

    PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

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

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  9. 9.

    Thompson JR, Rivera HE, Closek CJ, Medina M. Microbes in the coral holobiont: partners through evolution, development, and ecological interactions. Front Cell Infect Microbiol. 2014;4:176.

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Fan L, Liu M, Simister R, Webster NS, Thomas T. Marine microbial symbiosis heats up: the phylogenetic and functional response of a sponge holobiont to thermal stress. ISME J. 2013;7:991–1002.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Egan S, Gardiner M. Microbial dysbiosis: rethinking disease in marine ecosystems. Front Microbiol. 2016;7:991.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Voolstra CR, Ziegler M. Adapting with microbial help: microbiome flexibility facilitates rapid responses to environmental change. Bioessays. 2020;42:e2000004.

    PubMed  Article  Google Scholar 

  13. 13.

    Botte ES, Nielsen S, Abdul Wahab MA, Webster J, Robbins S, Thomas T, et al. Changes in the metabolic potential of the sponge microbiome under ocean acidification. Nat Commun. 2019;10:4134.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  14. 14.

    Morrow KM, Bourne DG, Humphrey C, Botté ES, Laffy P, Zaneveld J, et al. Natural volcanic CO2 seeps reveal future trajectories for host–microbial associations in corals and sponges. ISME J. 2015;9:894–908.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  15. 15.

    Pollock FJ, Lamb JB, van de Water J, Smith HA, Schaffelke B, Willis BL, et al. Reduced diversity and stability of coral-associated bacterial communities and suppressed immune function precedes disease onset in corals. R Soc Open Sci. 2019;6:190355.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Pinzon JH, Kamel B, Burge CA, Harvell CD, Medina M, Weil E, et al. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. R Soc Open Sci. 2015;2:140214.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17.

    Pita L, Hoeppner MP, Ribes M, Hentschel U. Differential expression of immune receptors in two marine sponges upon exposure to microbial-associated molecular patterns. Sci Rep. 2018;8:16081.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  18. 18.

    Guzman C, Conaco C. Gene expression dynamics accompanying the sponge thermal stress response. PLoS ONE. 2016;11:e0165368.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  19. 19.

    Riesgo A, Farrar N, Windsor PJ, Giribet G, Leys SP. The analysis of eight transcriptomes from all poriferan classes reveals surprising genetic complexity in sponges. Mol Biol Evol. 2014;31:1102–20.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  20. 20.

    Germer J, Cerveau N, Jackson DJ. The holo-transcriptome of a calcified early branching metazoan. Front Mar Sci. 2017;4:81.

  21. 21.

    Ryu T, Seridi L, Moitinho-Silva L, Oates M, Liew YJ, Mavromatis C, et al. Hologenome analysis of two marine sponges with different microbiomes. BMC Genomics. 2016;17:158.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  22. 22.

    Hooper JNA, Van Soest RWM. Systema Porifera. A guide to the classification of sponges. In: Hooper JNA, Van Soest RWM, editors. Systema Porifera. New York, NY: Springer; 2002. p. 1–7.

  23. 23.

    Pachauri RK, Allen MR, Barros VR, Broome J, Cramer W, Christ R, et al. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Geneva: IPCC; 2014.

  24. 24.

    Pierrot DE, Lewis E, Wallace DWR. MS Excel program developed for CO2 system calculations. Oak Ridge, TN: Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, ORNL/CDIAC-IOS; 2006.

  25. 25.

    Herlemann DP, Labrenz M, Jurgens K, Bertilsson S, Waniek JJ, Andersson AF. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 2011;5:1571–9.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.

    CAS  PubMed  Article  Google Scholar 

  29. 29.

    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.

    Article  Google Scholar 

  31. 31.

    McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS Comput Biol. 2014;10:e1003531.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  32. 32.

    Douglas GM, Maffei VJ, Zaneveld JR, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2 for prediction of metagenome functions. Nat Biotechnol. 2020;38:685–8.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Asshauer KP, Wemheuer B, Daniel R, Meinicke P. Tax4Fun: predicting functional profiles from metagenomic 16S rRNA data. Bioinformatics. 2015;31:2882–4.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  35. 35.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, et al. De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. 2013;8:1494.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  37. 37.

    Conesa A, Gotz S. Blast2GO: A comprehensive suite for functional analysis in plant genomics. Int J Plant Genomics. 2008;2008:619832.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  38. 38.

    Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, et al. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–D30.

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Eddy SR. Profile hidden Markov models. Bioinformatics. 1998;14:755–63.

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. 42.

    Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Alexa A, Rahnenführer J. Gene set enrichment analysis with topGO. Bioconductor Improv. 2009;27:1–26.

  44. 44.

    Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47:D607–D13.

    CAS  Article  Google Scholar 

  45. 45.

    Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. 46.

    Moitinho-Silva L, Nielsen S, Amir A, Gonzalez A, Ackermann GL, Cerrano C, et al. The sponge microbiome project. Gigascience. 2017;6:1–7.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  47. 47.

    Lurgi M, Thomas T, Wemheuer B, Webster NS, Montoya JM. Modularity and predicted functions of the global sponge-microbiome network. Nat Commun. 2019;10:992.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  48. 48.

    Srivastava M, Simakov O, Chapman J, Fahey B, Gauthier ME, Mitros T, et al. The Amphimedon queenslandica genome and the evolution of animal complexity. Nature. 2010;466:720–6.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Guzman C, Conaco C. Comparative transcriptome analysis reveals insights into the streamlined genomes of haplosclerid demosponges. Sci Rep. 2016;6:18774.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Fortunato SA, Adamski M, Ramos OM, Leininger S, Liu J, Ferrier DE, et al. Calcisponges have a ParaHox gene and dynamic expression of dispersed NK homeobox genes. Nature. 2014;514:620–3.

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Voigt O, Fradusco B, Gut C, Kevrekidis C, Vargas S, Wörheide G. Carbonic anhydrases: an ancient tool in calcareous sponge biomineralization. Front Genet. 2021;12:624533.

  52. 52.

    Yuen B, Bayes JM, Degnan SM. The characterization of sponge NLRs provides insight into the origin and evolution of this innate immune gene family in animals. Mol Biol Evol. 2014;31:106–20.

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Madeira F, Park YM, Lee J, Buso N, Gur T, Madhusoodanan N, et al. The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 2019;47:W636–41.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics. 2011;27:1164–5.

    CAS  PubMed  Article  Google Scholar 

  55. 55.

    Ronquist F, Huelsenbeck JP. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003;19:1572–4.

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Wickham H. ggplot2: elegant graphics for data analysis. New York: Springer; 2016.

  57. 57.

    McDevitt-Irwin JM, Baum JK, Garren M, Vega Thurber RL. Responses of coral-associated bacterial communities to local and global stressors. Front Mar Sci. 2017;4:262.

  58. 58.

    Hori K, Matsumoto S. Bacterial adhesion: from mechanism to control. Biochem Eng J. 2010;48:424–34.

    CAS  Article  Google Scholar 

  59. 59.

    Yao J, Allen C. Chemotaxis is required for virulence and competitive fitness of the bacterial wilt pathogen Ralstonia solanacearum. J Bacteriol. 2006;188:3697–708.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  60. 60.

    Chu H, Mazmanian SK. Innate immune recognition of the microbiota promotes host-microbial symbiosis. Nat Immunol. 2013;14:668–75.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61.

    Bazzoni F, Beutler B. The tumor necrosis factor ligand and receptor families. N Engl J Med. 1996;334:1717–25.

    CAS  PubMed  Article  Google Scholar 

  62. 62.

    Hayden MS, Ghosh S. Regulation of NF-kappaB by TNF family cytokines. Semin Immunol. 2014;26:253–66.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  63. 63.

    Parrish AB, Freel CD, Kornbluth S. Cellular mechanisms controlling caspase activation and function. Cold Spring Harb Perspect Biol. 2013;5:a008672.

  64. 64.

    Wiens M, Korzhev M, Krasko A, Thakur NL, Perovic-Ottstadt S, Breter HJ, et al. Innate immune defense of the sponge Suberites domuncula against bacteria involves a MyD88-dependent signaling pathway. Induction of a perforin-like molecule. J Biol Chem. 2005;280:27949–59.

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Muller WE, Muller IM. Origin of the metazoan immune system: identification of the molecules and their functions in sponges. Integr Comp Biol. 2003;43:281–92.

    PubMed  Article  Google Scholar 

  66. 66.

    Yuen B Deciphering the genomic toolkit underlying animal-bacteria interactions – insights through the demosponge Amphimedon queenslandica. Saint Lucia, QLD: School of Biological Sciences, The University of Queensland; 2016.

  67. 67.

    Gauthier ME, Du Pasquier L, Degnan BM. The genome of the sponge Amphimedon queenslandica provides new perspectives into the origin of Toll-like and interleukin 1 receptor pathways. Evol Dev. 2010;12:519–33.

    CAS  PubMed  Article  Google Scholar 

  68. 68.

    Roue M, Quevrain E, Domart-Coulon I, Bourguet-Kondracki ML. Assessing calcareous sponges and their associated bacteria for the discovery of new bioactive natural products. Nat Prod Rep. 2012;29:739–51.

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Steinert G, Busch K, Bayer K, Kodami S, Arbizu PM, Kelly M, et al. Compositional and quantitative insights into bacterial and archaeal communities of South Pacific deep-sea sponges (Demospongiae and Hexactinellida). Front Microbiol. 2020;11:716.

    PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

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

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71.

    Yap NV, Whelan FJ, Bowdish DM, Golding GB. The evolution of the scavenger receptor cysteine-rich domain of the class a scavenger receptors. Front Immunol. 2015;6:342.

    PubMed  PubMed Central  Google Scholar 

  72. 72.

    Brown GD, Willment JA, Whitehead L. C-type lectins in immunity and homeostasis. Nat Rev Immunol. 2018;18:374–89.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  73. 73.

    von Moltke J, Ayres JS, Kofoed EM, Chavarria-Smith J, Vance RE. Recognition of bacteria by inflammasomes. Annu Rev Immunol. 2013;31:73–106.

    Article  CAS  Google Scholar 

  74. 74.

    Robertson SJ, Rubino SJ, Geddes K, Philpott DJ. Examining host-microbial interactions through the lens of NOD: from plants to mammals. Semin Immunol. 2012;24:9–16.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  75. 75.

    Ting JP, Lovering RC, Alnemri ES, Bertin J, Boss JM, Davis BK, et al. The NLR gene family: a standard nomenclature. Immunity. 2008;28:285–7.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Messier-Solek C, Buckley KM, Rast JP. Highly diversified innate receptor systems and new forms of animal immunity. Semin Immunol. 2010;22:39–47.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  77. 77.

    Bennett HM, Altenrath C, Woods L, Davy SK, Webster NS, Bell JJ. Interactive effects of temperature and pCO2 on sponges: from the cradle to the grave. Glob Chang Biol. 2017;23:2031–46.

    PubMed  Article  PubMed Central  Google Scholar 

  78. 78.

    Luter HM, Andersen M, Versteegen E, Laffy P, Uthicke S, Bell JJ, et al. Cross-generational effects of climate change on the microbiome of a photosynthetic sponge. Environ Microbiol. 2020;22:4732–44.

  79. 79.

    Girvan MS, Campbell CD, Killham K, Prosser JI, Glover LA. Bacterial diversity promotes community stability and functional resilience after perturbation. Environ Microbiol. 2005;7:301–13.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  80. 80.

    Ziegler M, Grupstra CGB, Barreto MM, Eaton M, BaOmar J, Zubier K, et al. Coral bacterial community structure responds to environmental change in a host-specific manner. Nat Commun. 2019;10:3092.

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  81. 81.

    Ribes M, Calvo E, Movilla J, Logares R, Coma R, Pelejero C. Restructuring of the sponge microbiome favors tolerance to ocean acidification. Environ Microbiol Rep. 2016;8:536–44.

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  82. 82.

    Vega Thurber R, Willner-Hall D, Rodriguez-Mueller B, Desnues C, Edwards RA, Angly F, et al. Metagenomic analysis of stressed coral holobionts. Environ Microbiol. 2009;11:2148–63.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  83. 83.

    van de Water J, Chaib De Mares M, Dixon GB, Raina JB, Willis BL, Bourne DG, et al. Antimicrobial and stress responses to increased temperature and bacterial pathogen challenge in the holobiont of a reef-building coral. Mol Ecol. 2018;27:1065–80.

    PubMed  Article  CAS  PubMed Central  Google Scholar 

  84. 84.

    Weisz JB, Lindquist N, Martens CS. Do associated microbial abundances impact marine demosponge pumping rates and tissue densities? Oecologia. 2008;155:367–76.

    PubMed  Article  PubMed Central  Google Scholar 

  85. 85.

    Ludeman DA, Reidenbach MA, Leys SP. The energetic cost of filtration by demosponges and their behavioural response to ambient currents. J Exp Biol. 2017;220:995–1007.

    PubMed  Article  Google Scholar 

  86. 86.

    Perea-Blazquez A, Davy SK, Bell JJ. Estimates of particulate organic carbon flowing from the pelagic environment to the benthos through sponge assemblages. PLoS ONE. 2012;7:e29569.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87.

    Morganti TM, Ribes M, Yahel G, Coma R. Size is the major determinant of pumping rates in marine sponges. Front Physiol. 2019;10:1474.

    PubMed  PubMed Central  Article  Google Scholar 

  88. 88.

    Peck LS, Clark MS, Power D, Reis J, Batista FM, Harper EM. Acidification effects on biofouling communities: winners and losers. Glob Chang Biol. 2015;21:1907–13.

    PubMed  PubMed Central  Article  Google Scholar 

  89. 89.

    Ribeiro B, Padua A, Barno A, Villela H, Duarte G, Rossi A, et al. Assessing skeleton and microbiome responses of a calcareous sponge under thermal and pH stresses. ICES J Mar Sci. 2020:fsaa231.

  90. 90.

    Lanna E, Klautau M. Life history and reproductive dynamics of the cryptogenic calcareous sponge Sycettusa hastifera (Porifera, Calcarea) living in tropical rocky shores. J Mar Biol Assoc UK. 2018;98:505–14.

    Article  Google Scholar 

  91. 91.

    Pörtner HO, Langenbuch M, Michaelidis B. Synergistic effects of temperature extremes, hypoxia, and increases in CO2 on marine animals: from Earth history to global change. J Geophys Res. 2005;110:C09S10.

  92. 92.

    Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.

    PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank Francis Kenith Adolfo, Robert Valenzuela, and Ronald De Guzman for field and hatchery assistance and staff of the Bolinao Marine Laboratory for logistical support. This study was funded by the Department of Science and Technology Philippine Council for Agriculture, Aquatic and Natural Resources Research and Development (QMSR-MRRD-MEC-295-1449) to CC.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Cecilia Conaco.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Posadas, N., Baquiran, J.I.P., Nada, M.A.L. et al. Microbiome diversity and host immune functions influence survivorship of sponge holobionts under future ocean conditions. ISME J (2021). https://doi.org/10.1038/s41396-021-01050-5

Download citation

Search

Quick links