Vibrio pectenicida strain FHCF-3 is a causative agent of sea star wasting disease

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Data availability

Metatranscriptomic and 16S rRNA gene sequence datasets are archived in the NCBI Short Read Archive (BioProject no. PRJNA1195080). The whole-genome of V. pectenicida strain FHCF-3 is available from the NCBI GenBank Repository (accession no. JBLZMR000000000), with raw sequence reads archived in the NCBI Short Read Archive (BioProject no. PRJNA1232168). The complete 16S rRNA gene sequences of V. pectenicida strain FHCF-3 are deposited in the NCBI GenBank Repository (accessions PQ700178 and PQ763222PQ763229). Source data are provided with this paper.

Code availability

Code generated in this study is available via Dryad at https://doi.org/10.5061/dryad.5mkkwh7g9 (ref. 41).

References

  1. Eisenlord, M. E. et al. Ochre star mortality during the 2014 wasting disease epizootic: role of population size structure and temperature. Philos. Trans. R. Soc. B 371, 20150212 (2016).

    Article  Google Scholar 

  2. Hewson, I. et al. Densovirus associated with sea-star wasting disease and mass mortality. Proc. Natl Acad. Sci. USA 111, 17278–17283 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Dawson, M. N. et al. A decade of death and other dynamics: deepening perspectives on the diversity and distribution of sea stars and wasting. Biol. Bull. 244, 143–163 (2023).

    Article  PubMed  Google Scholar 

  4. Harvell, C. D. et al. Disease epidemic and a marine heat wave are associated with the continental-scale collapse of a pivotal predator (Pycnopodia helianthoides). Sci. Adv. 5, eaau7042 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hamilton, S. L. et al. Disease-driven mass mortality event leads to widespread extirpation and variable recovery potential of a marine predator across the eastern Pacific. Proc. R. Soc. B 288, 20211195 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Gravem, S. A. et al. Sunflower Sea Star (Pycnopodia helianthoides) (IUCN Red List of Threatened Species, 2021).

  7. Lowry, D. et al. Endangered Species Act Status Review Report: Sunflower Sea Star (Pycnopodia helianthoides) (National Marine Fisheries Service, Office of Protected Resources, 2022).

  8. Galloway, A. W. E. et al. Sunflower star predation on urchins can facilitate kelp forest recovery. Proc. R. Soc. B 290, 20221897 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Heady, W. N. et al. Roadmap to Recovery for the Sunflower Sea Star (Pycnopodia helianthoides) along the West Coast of North America (The Nature Conservancy, 2022).

  10. Hewson, I. et al. Investigating the complex association between viral ecology, environment, and Northeast Pacific sea star wasting. Front. Mar. Sci. 5, 77 (2018).

    Article  Google Scholar 

  11. Hewson, I., Johnson, M. R. & Reyes-Chavez, B. Lessons learned from the sea star wasting disease investigation. Ann. Rev. Mar. Sci. 17, 2.1–2.23 (2025).

    Article  Google Scholar 

  12. Jackson, E. W., Pepe-Ranney, C., Johnson, M. R., Distel, D. L. & Hewson, I. A highly prevalent and pervasive densovirus discovered among sea stars from the North American Atlantic Coast. Appl. Environ. Microbiol. 86, e02723-19 (2020).

  13. Jackson, E. W. et al. Diversity of sea star-associated densoviruses and transcribed endogenous viral elements of densovirus origin. J. Virol. 95, e01594-20 (2021).

  14. Lloyd, M. M. & Pespeni, M. H. Microbiome shifts with onset and progression of sea star wasting disease revealed through time course sampling. Sci. Rep. 8, 16476 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Aquino, C. A. et al. Evidence that microorganisms at the animal–water interface drive sea star wasting disease. Front. Microbiol. 11, 610009 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Schiebelhut, L. M., DeBiasse, M. B., Gabriel, L., Hoff, K. J. & Dawson, M. N. A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides. J. Hered. 115, 86–93 (2023).

    Article  PubMed Central  Google Scholar 

  17. Buchfink, B., Reuter, K. & Drost, H.-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat. Methods 18, 366–368 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Bolyen, E. et al. Reproducible, interactive, scalable, and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. McDonald, D. et al. Greengenes2 unifies microbial data in a single reference tree. Nat. Biotechnol. 42, 715–718 (2024).

    Article  CAS  PubMed  Google Scholar 

  21. Lin, H. & Das Peddada, S. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11, 3514 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Baker-Austin, C., Trinanes, J., Gonzalez-Escalona, N. & Martinez-Urtaza, J. Non-cholera Vibrios: the microbial barometer of climate change. Trends Microbiol. 25, 76–84 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Gehman, A.-L. M. et al. Fjord oceanographic dynamics provide refuge for critically endangered Pycnopodia helianthoides. Proc. R. Soc. B 292, 20242770 (2025).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Menge, B. A. et al. Sea star wasting disease in the keystone predator Pisaster ochraceus in Oregon: insights into differential population impacts, recovery, predation rate, and temperature effects from long-term research. PLoS ONE 11, e0153994 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Suttle, C. A., Chen, F. & Chan, A. M. in International Marine Biotechnology Conference IMBC-91: Short Communications of the Invited Lectures (ed. Nash, C. C.) 153–163 (W. Brown,1992).

  26. Suttle, C. A. & Chen, F. Mechanisms and rates of decay of marine viruses in seawater. Appl. Environ. Microbiol. 58, 3721–3729 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Middleboe, M., Chan, A. M. & Bertelsen, S. K. in Manual of Aquatic Viral Ecology (eds Wilhelm, S. W. et al.) 118–133 (ASLO, 2010).

  28. Zhong, K. X. et al. Draft genome sequence of Vibrio pectenicida strain FHCF-3, a causative agent of sea star wasting disease in the sunflower sea star (Pycnopodia helianthoides), reveals the genetic potential to produce aerolysin-like toxins. Microbiol. Resour. Announc. (in the press).

  29. Lambert, C., Nicolas, J. L., Cilia, V. & Corre, S. Vibrio pectenicida sp. nov., a pathogen of scallop (Pecten maximus) larvae. Int. J. Syst. Bacteriol. 48, 481–487 (1998).

    Article  PubMed  Google Scholar 

  30. McCracken, A. R. et al. Microbial dysbiosis precedes signs of sea star wasting disease in wild populations of Pycnopodia helianthoides. Front. Mar. Sci. 10, 1130912 (2023).

    Article  Google Scholar 

  31. Nicolas, J. L., Corre, S., Gauthier, G., Robert, R. & Ansquer, D. Bacterial problems associated with scallop Pecten maximus larval culture. Dis. Aquat. Organ. 27, 67–76 (1996).

    Article  Google Scholar 

  32. Lambert, C. & Nicolas, J. L. Specific inhibition of chemiluminescent activity by pathogenic Vibrios in hemocytes of two marine bivalves: Pecten maximus and Crassostrea gigas. J. Invertebr. Pathol. 71, 53–63 (1998).

    Article  CAS  PubMed  Google Scholar 

  33. Sandlund, N., Torkildsen, L., Magnesen, T., Mortensen, S. & Bergh, Ø. Immunohistochemistry of great scallop Pecten maximus larvae experimentally challenged with pathogenic bacteria. Dis. Aquat. Organ. 69, 163–173 (2006).

    Article  PubMed  Google Scholar 

  34. Kesarcodi-Watson, A., Miner, P., Nicolas, J. L. & Robert, R. Protective effect of four potential probiotics against pathogen-challenge of the larvae of three bivalves: Pacific oyster (Crassostrea gigas), flat oyster (Ostrea edulis) and scallop (Pecten maximus). Aquaculture 344349, 29–34 (2012).

    Article  Google Scholar 

  35. Kesarcodi-Watson, A., Miner, P., Nicolas, J. L., Asmani, K. & Robert, R. Pathogenic threats and probiotic use in larviculture of the scallop, Pecten maximus. Aquac. Res. 47, 1221–1230 (2016).

    Article  Google Scholar 

  36. Lambert, C., Nicolas, J. L. & Bultel, V. Toxicity to bivalve hemocytes of pathogenic Vibrio cytoplasmic extract. J. Invertebr. Pathol. 77, 165–172 (2001).

    Article  CAS  PubMed  Google Scholar 

  37. Kehlet-Delgado, H., Häse, C. C. & Mueller, R. S. Comparative genomic analysis of Vibrios yields insights into genes associated with virulence towards C. gigas larvae. BMC Genom. 21, 599 (2020).

    Article  CAS  Google Scholar 

  38. GBIF Occurrence Download (GBIF, accessed 5 May 2025); https://doi.org/10.15468/dl.ast4b7

  39. Kanungo, K. in Invertebrate Blood (ed. Cheng, T. C.) 7–39 (Springer, 1984).

  40. Hodin, J., Pearson-Lund, A., Anteau, F. P., Kitaeff, P. & Cefalu, S. Progress toward complete life cycle culturing of the endangered sunflower star, Pycnopodia helianthoides. Biol. Bull. 241, 3 (2021).

    Article  Google Scholar 

  41. Prentice, M. B. et al. Vibrio pectenicida strain FHC F-3 is a causative agent of sea star wasting disease. Dryad https://doi.org/10.5061/dryad.5mkkwh7g9 (2025).

  42. Montecino-Latorre, D. et al. Devastating transboundary impacts of sea star wasting disease on subtidal asteroids. PLoS ONE 11, e0163190 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  43. Fuess, L. E. et al. Up in arms: immune and nervous system response to sea star wasting disease. PLoS ONE 10, e0133053-16 (2015).

  44. Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).

    Article  Google Scholar 

  45. Lüdecke, D. sjPlot: Data visualization for statistics in social science. R package version 2.8.17 https://CRAN.R-project.org/package=sjPlot (2024).

  46. Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data (2010); www.bioinformatics.babraham.ac.uk/projects/fastqc

  49. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  51. Danecek et al. Twelve years of SAMools and BCFtools. Gigascience 10, gia008 (2021).

    Article  Google Scholar 

  52. 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 

  53. Huson, D. H. et al. MEGAN Community Edition: interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2024).

  55. Oksanen, J. et al. Vegan: Community ecology package. R package version 2.7-0 https://github.com/vegandevs/vegan (2024).

  56. Wickham, H. ggplot2: Elegant graphics for data analysis. R package version 3.5.1 https://ggplot2.tidyverse.org (2016).

  57. Hester, J. & Bryan, J. glue: Interpreted string literals. R package version 1.8.0 https://glue.tidyverse.org (2024).

  58. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10 (2011).

    Article  Google Scholar 

  59. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Davis, N. M., Proctor, D., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Wick, R. Porechop: adapter trimmer for Oxford Nanopore reads, version 0.2. GitHub https://github.com/rrwick/Porechop (2018).

  62. Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Dong, X. & Strous, M. An integrated pipeline for annotation and visualization of metagenomic contigs. Front. Genet. 10, 999 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. 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 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Steenwyk, J. L., Buida, T. J. III, Li, Y., Shen, X.-X. & Rokas, A. ClipKIT: a multiple sequence alignment trimming software for accurate phylogenetic inference. PLoS Biol. 18, e3001007 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. 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 

  69. Yoon, S.-H., Ha, S.-M., Lim, J. M., Kwon, S. J. & Chun, J. A large-scale evaluation of algorithms to calculate average nucleotide identity. Anton. Leeuw. 110, 1281–1286 (2017).

    Article  CAS  Google Scholar 

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Acknowledgements

B. Blake and N. Siu helped to acquire permits for this work. K. Bachen, H. Carson, K. Collins, D. Currie-Olsen, T. Frierson, T. Froese, J. Kocian, E. Loose, Z. Monteith, O. Pontier, G. Sadlier-Brown, A. Schmill, K. Sowul, B. Stevick and D. VanMaanen assisted with field collections. F. Curliss, A. Kalytiak-Davis, C. Schwab and V. Valdez supported sea star transfers from and sample collections at Friday Harbor Laboratories. D. Rogers provided local shellfish to feed experimental stars. J. Beal, C. Grady, J. Gregg, A. MacKenzie and W. Richards provided facilities and logistical support at the USGS Marrowstone Marine Field Station and H. Kuttenkeuler, K. Rolheiser and M. Winningham aided experiment monitoring and sampling. Y. Gouin and A. Nimmon assisted with entering and collating data. C. L. J. Huang assisted with preparation of culture media. C. Burge, C. Conway, J. Hansen, A. Hawthorn, J. Lovy, A. Roberts and Q. Yang provided advice. Graphic illustrations are credited to M. Minck. We are grateful for the support from E. Peterson, C. Munck, N. Eddy and J. Wilson. Funding was provided by The Nature Conservancy of California (C.D.H. and A.-L.M.G.), the Tula Foundation (A.-L.M.G. and C.A.S.), the Natural Sciences and Engineering Research Council of Canada Discovery grant no. RPGIN-2020-06515 (C.A.S.), the Canadian Foundation for Innovation and British Columbia Knowledge Development Fund Infrastructure award no. 25412 (C.A.S.), the University of British Columbia, the USGS Biological Threats Research Program, Ecosystems Mission Area (P.K.H.) and the Quantitative and Evolutionary STEM Traineeship NRT-1735316 (A.M.). Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the US government.

Author information

Author notes

  1. Katherine M. Davis

    Present address: Washington Department of Fish and Wildlife, Port Townsend, WA, USA

Authors and Affiliations

  1. Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia, Vancouver, British Columbia, Canada

    Melanie B. Prentice, Amy M. Chan, Katherine M. Davis, Jan F. Finke, Kevin X. Zhong & Curtis A. Suttle

  2. Hakai Institute, Campbell River, British Colombia, Canada

    Melanie B. Prentice, Katherine M. Davis, Jan F. Finke, Colleen T. E. Kellogg, Rute B. G. Clemente-Carvalho, Carolyn Prentice & Alyssa-Lois M. Gehman

  3. School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USA

    Grace A. Crandall

  4. US Geological Survey, Western Fisheries Research Center, Marrowstone Marine Field Station, Nordland, WA, USA

    Paul K. Hershberger

  5. Friday Harbor Laboratories, University of Washington, Friday Harbor, WA, USA

    Jason Hodin & C. Drew Harvell

  6. Department of Biology, University of Vermont, Burlington, VT, USA

    Andrew McCracken

  7. Institute for the Oceans and Fisheries, The University of British Columbia, Vancouver, British Columbia, Canada

    Colleen T. E. Kellogg, Curtis A. Suttle & Alyssa-Lois M. Gehman

  8. Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA

    C. Drew Harvell

  9. Department of Microbiology & Immunology, The University of British Columbia, Vancouver, British Columbia, Canada

    Curtis A. Suttle

  10. Department of Botany, The University of British Columbia, Vancouver, British Columbia, Canada

    Curtis A. Suttle

Authors

  1. Melanie B. Prentice
  2. Grace A. Crandall
  3. Amy M. Chan
  4. Katherine M. Davis
  5. Paul K. Hershberger
  6. Jan F. Finke
  7. Jason Hodin
  8. Andrew McCracken
  9. Colleen T. E. Kellogg
  10. Rute B. G. Clemente-Carvalho
  11. Carolyn Prentice
  12. Kevin X. Zhong
  13. C. Drew Harvell
  14. Curtis A. Suttle
  15. Alyssa-Lois M. Gehman

Contributions

Conceptualization: M.B.P., G.A.C., A.M.C., K.M.D., P.K.H., J.F.F., C.D.H., C.A.S., A.-L.M.G. Methodology: M.B.P., G.A.C., A.M.C., K.M.D., P.K.H., C.T.E.K., C.D.H., C.A.S., A.-L.M.G. Formal analysis: M.B.P., A.M.C., K.X.Z., A.-L.M.G. Investigation: M.B.P., G.A.C., A.M.C., K.M.D., A.M., R.B.G.C.-C., C.P., A.-L.M.G. Resources: M.B.P., G.A.C., K.M.D., P.K.H., J.H., A.M., C.A.S., A.-L.M.G. Data curation: M.B.P., G.A.C., A.M.C., K.M.D., C.P., A.-L.M.G. Writing—original draft: M.B.P., A.-L.M.G. Writing—review and editing: M.B.P., G.A.C., A.M.C., K.M.D., P.K.H., J.F.F., J.H., A.M., C.T.E.K., R.B.G.C.-C., C.P., K.X.Z., C.D.H., C.A.S., A.-L.M.G. Visualization: M.B.P., K.X.Z., A.-L.M.G. Supervision: A.M.C., C.D.H., C.A.S., A.-L.M.G. Funding acquisition: A.M.C., C.D.H., C.A.S., A.-L.M.G.

Corresponding authors

Correspondence to Melanie B. Prentice or Alyssa-Lois M. Gehman.

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

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Prentice, M.B., Crandall, G.A., Chan, A.M. et al. Vibrio pectenicida strain FHCF-3 is a causative agent of sea star wasting disease. Nat Ecol Evol 9, 1739–1751 (2025). https://doi.org/10.1038/s41559-025-02797-2

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