in

An ancient anthozoan protein reveals an alternative evolutionary path of antiviral signalling


Abstract

How antiviral immunity first arose in animals is a central question in evolutionary biology. Here, using the sea anemone Nematostella vectensis, we identify CARDIB, a previously uncharacterized gene located next to RLRb—a cnidarian homologue of the vertebrate RIG-I-like receptor family. This conserved genomic linkage across Anthozoa reveals an ancient coupling between immune sensing and regulation. Despite sequence similarity to vertebrate MAVS, CARDIB performs an opposing function: it represses immune genes under basal conditions yet is essential for activation upon viral challenge. CARDIB binds RLRb through a single CARD domain, forming a repressive complex. Loss of either gene abolishes antiviral transcription, disrupts apoptosis and elevates viral load under laboratory conditions. Both genes, as well as the RLRb paralogue RLRa, are essential for antiviral defence under native conditions. Phylogeny places the cnidarian CARDs distinctly from the vertebrate RLR–MAVS families, revealing an ancient mechanism that regulates the antiviral response through CARD-based signalling.

Access through your institution

Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

USD 39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Conserved RLRb-CARDIB genomic organization in anthozoans, the generation of CARDIB mutant lines and role in the immune response.
Fig. 2: Interactions between CARDIB and RLRb, and the role of CARDIB in repressing the immune response.
Fig. 3: Phylogeny and clustering of CARDs from Anthozoa and other metazoa.
Fig. 4: Knockout of CARDIB and RLRb suppresses the immune system of N.vectensis.
Fig. 5: Viral load sequencing-based measurements in wild-type and mutant N.vectensis lines.

Similar content being viewed by others

Functional characterization of specialized immune cells in a cnidarian reveals an ancestral antiviral program

Regulation of antiviral innate immune signaling and viral evasion following viral genome sensing

A new-disease-causing dominant-negative variant in CARD11 gene in a Chinese case with recurrent fever

Data availability

All sequencing data generated in this work are publicly available under accessions PRJNA1250240 and PRJNA1262874 at the SRA database of the National Center for Biotechnology Information (NCBI) under BioProject: an ancient anthozoan protein reveals an alternative evolutionary path of antiviral signalling and BioProject: N.vectensis Total RNA-seq from a mesocosm study conducted in a South Carolina estuary. The scRNA-seq data analysed in this study were generated previously and are available in the corresponding publication31 and associated repositories. Processed data used for correlation analyses are provided in Supplementary Table 6. Source data are provided with this paper.

Code availability

Bioinformatic processing scripts and figure generation are available via GitHub at https://github.com/sydneybirch/Nematostella_virome_Mesocosm_2023, https://github.com/adrianjaimes/Cnidarian-immune-system.

References

  1. Koonin, E. V. & Dolja, V. V. A virocentric perspective on the evolution of life. Curr. Opin. Virol. 3, 546–557 (2013).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  2. Tenthorey, J. L., Emerman, M. & Malik, H. S. Evolutionary landscapes of host-virus arms races. Annu. Rev. Immunol. 40, 271–294 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  3. Broecker, F. & Moelling, K. What viruses tell us about evolution and immunity: beyond Darwin?. Ann. N. Y. Acad. Sci. 1447, 53–68 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  4. Yoneyama, M. et al. Shared and unique functions of the DExD/H-Box helicases RIG-I, MDA5, and LGP2 in antiviral innate immunity. J. Immunol. 175, 2851–2858 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  5. Kang, D. et al. mda-5: an interferon-inducible putative RNA helicase with double-stranded RNA-dependent ATPase activity and melanoma growth-suppressive properties. Proc. Natl Acad. Sci. USA 99, 637–642 (2002).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  6. Luo, D. et al. Structural insights into RNA recognition by RIG-I. Cell 147, 409–422 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  7. Wu, B. & Hur, S. How RIG-I like receptors activate MAVS. Curr. Opin. Virol. 12, 91–98 (2015).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  8. Hou, F. et al. MAVS forms functional prion-like aggregates to activate and propagate antiviral innate immune response. Cell 146, 448–461 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  9. Zeng, W. et al. Reconstitution of the RIG-I pathway reveals a signaling role of unanchored polyubiquitin chains in innate immunity. Cell 141, 315–330 (2010).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  10. Mukherjee, K., Korithoski, B. & Kolaczkowski, B. Ancient origins of vertebrate-specific innate antiviral immunity. Mol. Biol. Evol. 31, 140–153 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  11. Seth, R. B., Sun, L., Ea, C.-K. & Chen, Z. J. Identification and characterization of MAVS, a mitochondrial antiviral signaling protein that activates NF-κB and IRF3. Cell 122, 669–682 (2005).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  12. Korithoski, B. et al. Evolution of a novel antiviral immune-signaling interaction by partial-gene duplication. PLoS ONE 10, e0137276 (2015).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  13. Li, X.-D., Sun, L., Seth, R. B., Pineda, G. & Chen, Z. J. Hepatitis C virus protease NS3/4A cleaves mitochondrial antiviral signaling protein off the mitochondria to evade innate immunity. Proc. Natl Acad. Sci. USA 102, 17717–17722 (2005).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  14. Wang, B. et al. Enterovirus 71 protease 2Apro targets MAVS to inhibit anti-viral type I interferon responses. PLoS Pathog. 9, e1003231 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  15. Ding, S. et al. Rotavirus VP3 targets MAVS for degradation to inhibit type III interferon expression in intestinal epithelial cells. eLife 7, e39494 (2018).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  16. Sharma, A., Kontodimas, K. & Bosmann, M. The MAVS immune recognition pathway in viral infection and sepsis. Antioxid. Redox Signal. 35, 1376–1392 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  17. Wein, T. et al. CARD domains mediate anti-phage defence in bacterial gasdermin systems. Nature 639, 727–734 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  18. Layden, M. J., Rentzsch, F. & Röttinger, E. The rise of the starlet sea anemone Nematostella vectensis as a model system to investigate development and regeneration. Wiley Interdiscip. Rev. Dev. Biol. 5, 408–428 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  19. Technau, U. & Steele, R. E. Evolutionary crossroads in developmental biology: Cnidaria. Development 138, 1447–1458 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  20. Al-Shaer, L., Havrilak, J. & Layden, M. J. in Handbook of Marine Model Organisms in Experimental Biology (eds Boutet, A. & Schierwater, B.) Ch. 7 (CRC, 2021).

  21. Ikmi, A., McKinney, S. A., Delventhal, K. M. & Gibson, M. C. TALEN and CRISPR/Cas9-mediated genome editing in the early-branching metazoan Nematostella vectensis. Nat. Commun. 5, 5486 (2014).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  22. Fraune, S., Forêt, S. & Reitzel, A. M. Using Nematostella vectensis to study the interactions between genome, epigenome, and bacteria in a changing environment. Front. Mar. Sci. 3, 148 (2016).

    Article 

    Google Scholar 

  23. Lewandowska, M., Sharoni, T., Admoni, Y., Aharoni, R. & Moran, Y. Functional characterization of the cnidarian antiviral immune response reveals ancestral complexity. Mol. Biol. Evol. 38, 4546–4561 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  24. Kozlovski, I. et al. Induction of apoptosis by double-stranded RNA was present in the last common ancestor of cnidarian and bilaterian animals. PLoS Pathog. 20, e1012320 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  25. Margolis, S. R. et al. The cyclic dinucleotide 2′3′-cGAMP induces a broad antibacterial and antiviral response in the sea anemone Nematostella vectensis. Proc. Natl Acad. Sci. USA 118, e2109022118 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  26. Goubau, D., Deddouche, S. & Reis e Sousa, C. Cytosolic sensing of viruses. Immunity 38, 855–869 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  27. Shaner, N. C. et al. Improved monomeric red, orange and yellow fluorescent proteins derived from Discosoma sp. red fluorescent protein. Nat. Biotechnol. 22, 1567–1572 (2004).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  28. Kim, J. H. et al. High cleavage efficiency of a 2A peptide derived from porcine teschovirus-1 in human cell lines, zebrafish and mice. PLoS ONE 6, e18556 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  29. Li, K. et al. Insights into the structure and RNA-binding specificity of Caenorhabditis elegans Dicer-related helicase 3 (DRH-3). Nucleic Acids Res. 49, 9978–9991 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  30. Batachari, L. E., Dai, A. Y. & Troemel, E. R. Caenorhabditis elegans RIG-I-like receptor DRH-1 signals via CARDs to activate antiviral immunity in intestinal cells. Proc. Natl Acad. Sci. USA 121, e2402126121 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  31. Kozlovski, I. et al. Functional characterization of specialized immune cells in a cnidarian reveals an ancestral antiviral program. Nat. Commun. 17, 3502 (2026).

    Google Scholar 

  32. Sharoni, T. et al. Heat stress drives rapid viral and antiviral innate immunity activation in Hexacorallia. Mol. Ecol. 34, e70098 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  33. Koonin, E. V. & Aravind, L. Origin and evolution of eukaryotic apoptosis: the bacterial connection. Cell Death Differ. 9, 394–404 (2002).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  34. Lewandowska, M., Hazan, Y. & Moran, Y. Initial virome characterization of the common cnidarian lab model Nematostella vectensis. Viruses 12, 218 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  35. McFadden, C. S. et al. Phylogenomics, origin, and diversification of Anthozoans (phylum Cnidaria). Syst. Biol. 70, 635–647 (2021).

    Article 
    PubMed 

    Google Scholar 

  36. Irimia, M. et al. Extensive conservation of ancient microsynteny across metazoans due to cis-regulatory constraints. Genome Res. 22, 2356–2367 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  37. Schüler, A. & Bornberg-Bauer, E. Evolution of protein domain repeats in metazoa. Mol. Biol. Evol. 33, 3170–3182 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  38. Gack, M. U. et al. TRIM25 RING-finger E3 ubiquitin ligase is essential for RIG-I-mediated antiviral activity. Nature 446, 916–920 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  39. Kouwaki, T., Nishimura, T., Wang, G., Nakagawa, R. & Oshiumi, H. K63-linked polyubiquitination of LGP2 by Riplet regulates RIG-I-dependent innate immune response. EMBO Rep. 24, e54844 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  40. Peisley, A., Wu, B., Xu, H., Chen, Z. J. & Hur, S. Structural basis for ubiquitin-mediated antiviral signal activation by RIG-I. Nature 509, 110–114 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  41. Wu, B. et al. Molecular imprinting as a signal-activation mechanism of the viral RNA sensor RIG-I. Mol. Cell 55, 511–523 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  42. Fitzgerald, M. E., Rawling, D. C., Vela, A. & Pyle, A. M. An evolving arsenal: viral RNA detection by RIG-I-like receptors. Curr. Opin. Microbiol. 20, 76–81 (2014).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  43. Alsemarz, A., Lasko, P. & Fagotto, F. Limited significance of the in situ proximity ligation assay. Preprint at bioRxiv https://doi.org/10.1101/411355 (2018).

  44. Onomoto, K., Onoguchi, K. & Yoneyama, M. Regulation of RIG-I-like receptor-mediated signaling: interaction between host and viral factors. Cell. Mol. Immunol. 18, 539–555 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  45. Pett, W. et al. The role of homology and orthology in the phylogenomic analysis of metazoan gene content. Mol. Biol. Evol. 36, 643–649 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  46. Nestor, B. J., Bayer, P. E., Fernandez, C. G. T., Edwards, D. & Finnegan, P. M. Approaches to increase the validity of gene family identification using manual homology search tools. Genetica 151, 325–338 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  47. Bernheim, A., Cury, J. & Poirier, E. Z. The immune modules conserved across the tree of life: towards a definition of ancestral immunity. PLoS Biol. 22, e3002717 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  48. Wein, T. & Sorek, R. Bacterial origins of human cell-autonomous innate immune mechanisms. Nat. Rev. Immunol. 22, 629–638 (2022).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  49. Zipple, M. N., Vogt, C. C. & Sheehan, M. J. Re-wilding model organisms: opportunities to test causal mechanisms in social determinants of health and aging. Neurosci. Biobehav. Rev. 152, 105238 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  50. Oyesola, O. et al. Genetic and environmental interactions contribute to immune variation in rewilded mice. Nat. Immunol. 25, 1270–1282 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  51. Barber, G. N. Host defense, viruses and apoptosis. Cell Death Differ. 8, 113–126 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  52. Orzalli, M. H. & Kagan, J. C. Apoptosis and necroptosis as host defense strategies to prevent viral infection. Trends Cell Biol. 27, 800–809 (2017).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  53. Genikhovich, G. & Technau, U. Induction of spawning in the starlet sea anemone Nematostella vectensis, in vitro fertilization of gametes, and dejellying of zygotes. Cold Spring Harb. Protoc. 2009, pdb.prot5281 (2009).

    Article 
    PubMed 

    Google Scholar 

  54. Hand, C. & Uhlinger, K. R. The culture, sexual and asexual reproduction, and growth of the sea anemone nematostella vectensis. Biol. Bull. 182, 169–176 (1992).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  55. Concordet, J.-P. & Haeussler, M. CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46, W242–W245 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  56. Stefanik, D. J., Friedman, L. E. & Finnerty, J. R. Collecting, rearing, spawning and inducing regeneration of the starlet sea anemone, Nematostella vectensis. Nat. Protoc. 8, 916–923 (2013).

    Article 
    PubMed 

    Google Scholar 

  57. Karabulut, A., He, S., Chen, C.-Y., McKinney, S. A. & Gibson, M. C. Electroporation of short hairpin RNAs for rapid and efficient gene knockdown in the starlet sea anemone, Nematostella vectensis. Dev. Biol. 448, 7–15 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  58. Admoni, Y., Kozlovski, I., Lewandowska, M. & Moran, Y. TATA binding protein (TBP) promoter drives ubiquitous expression of marker transgene in the adult sea anemone Nematostella vectensis. Genes 11, 1081 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  59. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  60. Aguilar-Camacho, J. M. et al. Functional analysis in a model sea anemone reveals phylogenetic complexity and a role in cnidocyte discharge of DEG/ENaC ion channels. Commun. Biol. 6, 17 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  61. Wang, H., He, M., Willard, B. & Wu, Q. Cross-linking, immunoprecipitation and proteomic analysis to identify interacting proteins in cultured cells. Bio-Protocol 9, e3258 (2019).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  62. Hu, M. et al. Lineage dynamics of the endosymbiotic cell type in the soft coral Xenia. Nature 582, 534–538 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  63. Liew, Y. J., Aranda, M. & Voolstra, C. R. Reefgenomics.Org – a repository for marine genomics data. Database 2016, baw152 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  64. Zimmermann, B. et al. Topological structures and syntenic conservation in sea anemone genomes. Nat. Commun. 14, 8270 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  65. Tamura, K., Stecher, G. & Kumar, S. MEGA11: molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  66. Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  67. Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  68. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinforma. Oxf. Engl. 30, 2114–2120 (2014).

    Article 
    CAS 

    Google Scholar 

  69. Putnam, N. H. et al. Sea anemone genome reveals ancestral eumetazoan gene repertoire and genomic organization. Science 317, 86–94 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  70. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinforma. Oxf. Engl. 29, 15–21 (2013).

    Article 
    CAS 

    Google Scholar 

  71. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinforma. Oxf. Engl. 30, 923–930 (2014).

    Article 
    CAS 

    Google Scholar 

  72. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  73. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinforma. Oxf. Engl. 26, 139–140 (2010).

    Article 
    CAS 

    Google Scholar 

  74. Chen, H. & Boutros, P. C. VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinf. 12, 35 (2011).

    Article 

    Google Scholar 

  75. Wu, T. et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innov. Camb. Mass 2, 100141 (2021).

    CAS 

    Google Scholar 

  76. Binns, D. et al. QuickGO: a web-based tool for gene ontology searching. Bioinforma. Oxf. Engl. 25, 3045–3046 (2009).

    Article 
    CAS 

    Google Scholar 

  77. Frickey, T. & Lupas, A. CLANS: a Java application for visualizing protein families based on pairwise similarity. Bioinforma. Oxf. Engl. 20, 3702–3704 (2004).

    Article 
    CAS 

    Google Scholar 

  78. Kassambara, A. & Mundt, F. factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 2.0.0. CRAN https://CRAN.R-project.org/package=factoextra (2026).

  79. Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  80. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  81. Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2021).

  82. Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  83. Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  84. Andrews, S. FastQC: a quality control tool for high throughput sequence data. Babraham Bioinformatics https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).

  85. Fletcher, C. & Pereira da Conceicoa, L. The genome sequence of the starlet sea anemone, Nematostella vectensis (Stephenson, 1935). Wellcome Open Res. 8, 79 (2023).

  86. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  87. Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience 10, giab008 (2021).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  88. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  89. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  90. Nawrocki, E. P. et al. Rfam 12.0: updates to the RNA families database. Nucleic Acids Res. 43, D130–D137 (2015).

    Article 
    CAS 
    PubMed 

    Google Scholar 

  91. Parks, D. H. et al. GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy. Nucleic Acids Res. 50, D785–D794 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  92. Bushmanova, E., Antipov, D., Lapidus, A. & Prjibelski, A. D. rnaSPAdes: a de novo transcriptome assembler and its application to RNA-seq data. GigaScience 8, giz100 (2019).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

Download references

Acknowledgements

We would like to thank M. Bronstein and A. Turjeman for their help with sequencing. We also thank T. Hagai for helpful discussions about the results and their interpretation.

Funding

Y.M. discloses support for the research of this work from the European Research Council (grant no. 863809), Y.M. and A.M.R. disclose support for the research of this work from US–Israel Binational Science Foundation joint Grant with the National Science Foundation (grant no. 2020669).

Author information

Authors and Affiliations

Authors

Contributions

T.S. and Y.M. conceived the study. D.A., S.B., H.-J.K., T.S., R.A., Y.M. and A.M.R. designed the experiments. T.S., D.A. and H.-J.K. performed the microinjections. T.S. and H.-J.K. generated the CRISPR–Cas9 mutant lines and maintained the animals for experiments. R.A. performed the western blotting and immunoprecipitation experiments. T.S. conducted the viral load assays under laboratory conditions. S.B., H.-J.K. and H.J. conducted the mesocosm assays for viral load analysis. D.A. conducted the PLA assays. A.J.-B. performed the AlphaFold2 prediction of the CARD domains, the scRNA-seq correlation analysis and the CARD phylogenetic and clustering analyses. J.M.S. analysed the genomic organization of CARDIB and RLRb in anthozoa. T.S. and M.L. conducted the quantification of gene expression. T.S. and A.J.-B. conducted the bulk RNA-seq analysis. H.K. conducted the apoptotic assays. All authors contributed to revising the manuscript and approved the final version.

Corresponding authors

Correspondence to
Ton Sharoni or Yehu Moran.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Enzo Poirier, Irene Salinas and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Basal expression of immune-related proteins in CARDIB KO adult animals and CARDIB over-expression after 28 hrs of mRNA injection.

a, Western blot images showing the basal expression of six immune-related proteins and GAPDH as loading control, in wild-type adult animals compared to CARDIB mutants of two different CRISPR KO strains. Protein sizes in KD are indicated. All western blot results are based on at least four independent biological repeats for each mutant line and wild type. b, Western blot images showing the expression of six immune-related proteins, GAPDH as a loading control, and CARDIB in CARDIB mRNA-injected animals compared to mCherry mRNA-injected animals as a control at 28 hrs post injection. Protein sizes in KD are indicated. All western blot results are based on at least four independent biological repeats of CARDIB mRNA-injected animals against control mRNA-injected animals.

Extended Data Fig. 2 Functional and genetic characterization of viral communities following mesocosm exposure.

Viral communities associated with wild-type and immune mutant N. vectensis lines (RLRa, RLRb, and CARDIB) following 96 h of exposure to natural estuarine water in a mesocosm experiment (T96). a, Genetic composition of viral communities based on genome type (dsDNA, dsRNA, ssDNA, ssRNA, and unclassified viruses). Relative abundances (left) and absolute counts (right) are shown for each genotype. Viral genome classifications were assigned using the ICTV Virus Metadata Resource (VMR). b, Functional annotation of viral open reading frames using the Virus Orthologous Groups (VOG) database. Bars represent the relative abundance (left) and total counts (right) of VOG functional categories, including viral replication, structure, and host–virus interaction functions. c, Functional annotation of viral genes using KEGG Orthology (KO). Bars depict the relative abundance (left) and total counts (right) of KEGG functional categories associated with viral and host-related biological processes.

Extended Data Fig. 3 Schematic representation of CRISPR/Cas9-generated knockout alleles in N. vectensis.

Schematic depictions of the wild-type (WT) loci and CRISPR/Cas9-generated knockout alleles for RLRa a, RLRb b, and CARDIB c. For RLRa, two independent knockout lines are shown, carrying a 4-nucleotide deletion (Δ4 bp) or a 2-nucleotide deletion at the guide RNA target site. For RLRb, a single knockout allele harboring two distinct CRISPR-induced modifications on the same allele is shown, consisting of a 5-nucleotide insertion and a large 676-nucleotide deletion. For CARDIB, two independent knockout lines are shown, one carrying a 5-nucleotide deletion and the other carrying a 22-nucleotide insertion at the target site. Exons are represented as boxes and introns as lines; the scissors symbol marks the Cas9 cleavage site.

Extended Data Fig. 4 The sequence and the design of the eGFP Nve gBlock. C-terminal CARDIB-FLAGx3-eGFP Nve gBlock, CARDIB-mCherry Nve gBlock and mCherry gBlock.

Complete sequence of the synthetic gBlock template encoding the C-terminal CARDIB-3xFLAG-eGFP construct. From 5′ to 3′, the sequence includes a T7 promoter for in vitro transcription, an ef1a Kozak consensus sequence to enhance translation initiation, the Nematostella vectensis CARDIB coding region, a P2A self-cleaving peptide sequence enabling bicistronic expression, mCherry as a fluorescent reporter, a 3xFLAG epitope tag, eGFP, a complexity-fix sequence to improve cloning and transcription stability, and a 3′ untranslated region (3′ UTR) to support transcript stability. The entire sequence shown represents the transcriptional template used for mRNA synthesis.

Extended Data Fig. 5 Co-immunoprecipitation of CARDIB and RLRb measured by western blot.

Western blot image showing CARDIB presence, after Co-Immunoprecipitation of RLRb with CARDIB from N. vectensis 24-h embryos as described in the materials and methods section. Membrane was incubated with αCARDIB antibody as primary antibody and Peroxidase conjugated antibody against rabbit as secondary antibody and finally detected with a CCD camera of the Odyssey Fc imaging system (Li-COR Biosciences) using Clarity™ ECL and Clarity™ max kits (Biorad) as described in detail. Co-IP Sample name and biological repeat number are indicated above the blot image. The upper numbers below the blot image show the serial numbers of the bands on the blot. The lower numbers below the blot image indicate band intensities as were measured by the Image Studio software (Li-COR Biosciences) and used for the fold change calculations between RLRb and NeNaC2 samples in each repeat. CoIP experiment was done on four independent biological repeats.

Extended Data Fig. 6 Co-immunoprecipitation of CARDIB and RLRb measured by western blot.

c Zygotes were injected with shRNA against each of the target genes in addition to poly (I:C), and the gene expression was tested at the protein level. Scramble shRNA with poly (I:C) injected as a control. a, extracted protein from planulae that were injected with shRNA with poly (I:C) against CARDIB and scramble shRNA with poly (I:C) as a control. b, extracted protein from planulae that were injected with shRNA with poly (I:C) against GBP and scramble shRNA with poly (I:C) as a control. c, extracted protein from planulae that were injected with shRNA with poly (I:C) against OAS and scramble shRNA with poly (I:C) as a control. For the normalization of the amount of protein in the western blot, GAPDH was used for the samples of αCARDIB and αGBP, for the samples of αOAS, AGO1 was used for the normalization of the amount of protein to avoid size similarity (which was shown not to respond to poly (I:C) or be involved in the N. vectensis immunity1) used for the normalization. All antibodies validations were done on at least three independent biological repeats. Additional information on the shRNA sequences is provided in Supplementary File 1, Table 23.

Extended Data Fig. 7 Phylogenetic relationship of metazoan RLRs.

Maximum likelihood consensus phylogenetic tree of representative RLR sequences, numbers in parentheses are SH-aLRT support (%) / aBayes support / ultrafast bootstrap support (%). Amu, Acropora muricata; Aqu, Amphimedon queenslandica; Bbe, Branchiostoma belcheri; Cte, Capitella teleta; Cgi, Crassostrea gigas; Cel, Caenorhabditis elegans; Dre, Danio rerio; Edia, Exaiptasia diaphana; Hsa, Homo sapiens; Lan, Lingula anatina; Mli, Macrostomum lignano; Nve, Nematostella vectensis; Pda, Pocillopora damicronis; Spi, Stylophora pistillata; Xsp, Xenia spp; Xtr, Xenopus tropicalis. anatina; Mli, Macrostomum lignano; Nve, Nematostella vectensis; Pda, Pocillopora damicronis; Spi, Stylophora pistillata; Xsp, Xenia spp; Xtr, Xenopus tropicalis.

Extended Data Fig. 8 Mesocosm Field Design.

a, Cartoon depiction of the mesocosm samples used in this study. Each Mesocosm (plastic bin) is a replicate. Animal container placements were randomized in each replicate. b, The five mesocosm replicates in the field at Belle W. Baruch Marine Field Laboratory (Georgetown, South Carolina, US). c, A close-up image of the inside of an animal container. Each animal container contained one N. vectensis strain and began with six individuals to be sampled at different time points. d, An above view of the inside of one mesocosm. e, A side view of the mesocosms to demonstrate the water line. Natural estuary water was added to submerge half of the animal containers to prevent animals from escaping. Panel a created in BioRender; Reitzel, A. https://biorender.com/zot2t2l (2026).

Extended Data Fig. 9 Heatmap of T96 Mesocosm N. vectensis immune gene expression.

Gene expression of the same 56 immune-related genes was interrogated in Fig. 4g. The heatmap shows expression of N.vectensis KOs at the T96 timepoint (96 h after mesocosm exposure). Each row represents a gene in the 56 immune-related gene set, with yellow indicating high expression and dark blue indicating low expression.

Extended Data Fig. 10 Bioinformatic pipeline to assess viral read counts.

N. vectensis samples were collected at two timepoints during the mesocosm experiment: the initial timepoint (T0), prior to exposure, and after 96 h of exposure to natural estuary water (T96). Four strains were examined with 3-4 biological replicates each, yielding a total of 31 samples (that is, four replicates for each T0 and T96 strain, except for CARDIB T96, which had three replicates). RNA was extracted from each sample and used to generate RNA libraries for total RNA sequencing. In the bioinformatics pipeline, the first step after adapter trimming and quality control (FastQC) was host read removal. Trimmed reads were iteratively aligned to the N. vectensis genome until no reads mapped, leaving viral and microbial reads. An rRNA filtration step was then performed by aligning these unmapped reads to an rRNA database to remove prokaryotic and eukaryotic rRNA sequences. The resulting rRNA-filtered dataset was designated as ’presumably viral reads.’. These reads were used to assemble transcriptomes. Two assemblies were generated: the Total-T0 assembly, constructed from all 16 T0 samples, and the Total-T96 assembly, constructed from all 15 T96 samples. Viral reads from each sample were then mapped back to the appropriate assembly, and normalization factors were calculated. Statistical analyses were subsequently performed. Figure created in BioRender; Reitzel, A. https://biorender.com/co5tt71 (2026).

Supplementary information

Reporting Summary (download PDF )

Peer Review File (download PDF )

Supplementary File 2 (download TXT )

Additional supporting information.

Supplementary File 3 (download TXT )

Additional supporting information.

Supplementary File 1 (download XLSX )

Supplementary data and supporting information.

Source data

Source Data Figs. 1 and 4 (download ZIP )

Unprocessed western blots.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article

Sharoni, T., Jaimes-Becerra, A., Birch, S. et al. An ancient anthozoan protein reveals an alternative evolutionary path of antiviral signalling.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-03112-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • DOI: https://doi.org/10.1038/s41559-026-03112-3


Source: Ecology - nature.com

Distinguishing leaf scorching from senescence under climate extremes

The continuous global greening under climate change