Skip to main content

Advertisement

Log in

The Role of Diversity in Mediating Microbiota Structural and Functional Differences in Two Sympatric Species of Abalone Under Stressed Withering Syndrome Conditions

  • Invertebrate Microbiology
  • Published:
Microbial Ecology Aims and scope Submit manuscript

Abstract

Withering syndrome (WS) is a gastro-intestinal (GI) infectious disease likely affecting all abalone species worldwide. Structural and functional changes in abalone GI microbiotas under WS-stressed conditions remain poorly investigated. It is unclear if interspecific microbiota differences, such as the presence of certain microbes, their abundance, and functional capabilities, may be involved in the occurrence of this disease. Bacterial microbiotas of healthy Haliotis fulgens and Haliotis corrugata are mainly composed by Tenericutes, Proteobacteria, Fusobacteria, and Spirochaetes. We previously reported species-specific structural and functional profiles of those communities and suggested that they are of consequence to the different susceptibility of each species to WS. Here, we address this question by comparing the structure and function of healthy and dysbiotic microbiota through 454 pyrosequencing and PICRUSt 2, respectively. Our findings suggest that the extent to which WS-stressed conditions may explain structural and functional differences in GI microbiota is contingent on the microbiota diversity itself. Indeed, microbiota differences between stressed and healthy abalone were marginal in the more complex bacterial communities of H. corrugata, in which no significant structural or functional changes were detected. Conversely, significant structural changes were observed in the less complex bacterial microbiota of H. fulgens. Moreover, structural alterations led to a significant downregulation of some metabolic activities conducted by GI bacteria. Accordingly, results suggest that gastro-intestinal bacterial diversity appears to be related with both the health of abalone and the etiology of WS.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of Data and Materials

Raw reads were deposited in the National Center for Biotechnology Information (NCBI; BioProject: PRJNA494699, accession number: SRR7969002).

Code Availability

Not applicable.

References

  1. Moore JD, Byron SN, Marshman BC, Snider JP (2019) An oxytetracycline bath protocol to eliminate the agent of withering syndrome, Candidatus Xenohaliotis californiensis, in captive abalone populations. Aquaculture 503:267–274. https://doi.org/10.1016/j.aquaculture.2019.01.014

    Article  CAS  Google Scholar 

  2. Miner CM, Altstatt JM, Raimondi PT, Minchinton TE (2006) Recruitment failure and shifts in community structure following mass mortality limit recovery prospects of black abalone. Mar Ecol Prog Ser 327:107–117. https://doi.org/10.3354/meps327107

    Article  Google Scholar 

  3. Crosson LM, Friedman CS (2018) Withering syndrome susceptibility of northeastern Pacific abalones: a complex relationship with phylogeny and thermal experience. J Invertebr Pathol 151:91–101. https://doi.org/10.1016/j.jip.2017.11.005

    Article  Google Scholar 

  4. Lee MJ, Lee JJ, Han YC et al (2016) Analysis of microbiota on abalone (Haliotis discus hannai) in South Korea for improved product management. Int J Food Microbiol 234:45–52. https://doi.org/10.1016/j.ijfoodmicro.2016.06.032

    Article  Google Scholar 

  5. Vater A, Byrne BA, Marshman BC et al (2018) Differing responses of red abalone (Haliotis rufescens) and white abalone (H. sorenseni) to infection with phage-associated Candidatus Xenohaliotis californiensis. PeerJ 6:e5104. https://doi.org/10.7717/peerj.5104

    Article  CAS  Google Scholar 

  6. Morales-Bojórquez E, Muciño-Díaz MO, Vélez-Barajas JA (2008) Analysis of the decline of the abalone fishery (Haliotis fulgens and H. corrugata) along the westcentral coast of the Baja California Peninsula. Mexico J Shellfish Res 27:865–870. https://doi.org/10.2983/0730-8000(2008)27[865:AOTDOT]2.0.CO;2

    Article  Google Scholar 

  7. Cáceres-Martínez J, Vásquez-Yeomans R, Flores-Saaib RD (2011) Intracellular prokaryote Xenohaliotis californiensis in abalone Haliotis spp. from Baja California. México Cienc Pesq 19:5–11

    Google Scholar 

  8. Cicala F, Moore JD, Cáceres-Martínez J et al (2017) Multigenetic characterization of ‘Candidatus Xenohaliotis californiensis’. Int J Syst Evol Microbiol 67:42–49. https://doi.org/10.1099/ijsem.0.001563

    Article  CAS  Google Scholar 

  9. Friedman CS, Andree KB, Beauchamp KA et al (2000) ‘Candidatus Xenohaliotis californiensis‘, a newly described pathogen of abalone, Haliotis spp., along the west coast of North America. Int J Syst Evol Microbiol 50:847–855

    Article  CAS  Google Scholar 

  10. Álvarez Tinajero MDC, Cáceres-Martínez J, Gonzáles Avilés JG et al (2002) Histopathological evaluation of the yellow abalone Haliotis corrugata and the blue abalone Haliotis fulgens from Baja California. México J Shellfish Res 21:825–830

    Google Scholar 

  11. Horwitz R, Mouton A, Coyne VE (2016) Characterization of an intracellular bacterium infecting the digestive gland of the South African abalone Haliotis midae. Aquaculture 451:24–32. https://doi.org/10.1016/j.aquaculture.2015.08.024

    Article  Google Scholar 

  12. McFall-Ngai M, Hadfield MG, Bosch TCG et al (2013) Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci U S A 110:3229–3236. https://doi.org/10.1073/pnas.1218525110

    Article  Google Scholar 

  13. Zaneveld JR, McMinds R, Thurber RV (2017) Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat Microbiol 2:1–8. https://doi.org/10.1038/nmicrobiol.2017.121

    Article  CAS  Google Scholar 

  14. Mallott EK, Amato KR (2021) Host specificity of the gut microbiome. Nat Rev Microbiol 19(10):639–653. https://doi.org/10.1038/s41579-021-00562-3

    Article  CAS  Google Scholar 

  15. Libertucci J, Young VB (2019) The role of the microbiota in infectious diseases. Nat Microbiol 4:35–45. https://doi.org/10.1038/s41564-018-0278-4

    Article  CAS  Google Scholar 

  16. Yachi S, Loreau M (1999) Biodiversity and ecosystem productivity in a fluctuating environment: the insurance hypothesis. Proc Natl Acad Sci U S A 96:1463–1468. https://doi.org/10.1073/pnas.96.4.1463

    Article  CAS  Google Scholar 

  17. Larsen OFA, Claassen E (2018) The mechanistic link between health and gut microbiota diversity. Sci Rep 8:6–10. https://doi.org/10.1038/s41598-018-20141-6

    Article  CAS  Google Scholar 

  18. Gobet A, Mest L, Perennou M et al (2018) Seasonal and algal diet-driven patterns of the digestive microbiota of the European abalone Haliotis tuberculata, a generalist marine herbivore. Microbiome 6:60. https://doi.org/10.1186/s40168-018-0430-7

    Article  Google Scholar 

  19. Zaneveld JR, Burkepile DE, Shantz AA et al (2016) Overfishing and nutrient pollution interact with temperature to disrupt coral reefs down to microbial scales. Nat Commun 7:1–12. https://doi.org/10.1038/ncomms11833

    Article  CAS  Google Scholar 

  20. Villasante A, Catalán N, Rojas R et al (2020) Microbiota of the digestive gland of red abalone (Haliotis rufescens) is affected by withering syndrome. Microorganisms 8:1–13. https://doi.org/10.3390/microorganisms8091411

    Article  CAS  Google Scholar 

  21. Cicala F, Cisterna-Celiz JA, Moore JD, Rocha-Olivares A (2018) Structure, dynamics and predicted functional ecology of the gut microbiota of the blue (Haliotis fulgens) and yellow (H . corrugata) abalone from Baja California Sur, Mexico. PeerJ 5:e3233v1. https://doi.org/10.7717/peerj.5830

    Article  CAS  Google Scholar 

  22. Friedman CS (2012) Infection with Xenohaliotis californiensis. Man Diagnostic Tests Aquat Anim 511–523

  23. Ludwig W, Mittenhuber G, Friedrich CG (1993) Transfer of Thiosphaera pantotropha to Paracoccus denitrificans. Int J Syst Bacteriol 43:363–367. https://doi.org/10.1099/00207713-43-2-363

    Article  CAS  Google Scholar 

  24. Ruff-Roberts AL, Kuenen JG, Ward DM (1994) Distribution of cultivated and uncultivated cyanobacteria and Chloroflexus-like bacteria in hot spring microbial mats. Appl Environ Microbiol 60:697–704

    Article  CAS  Google Scholar 

  25. Dowd SE, Callaway TR, Wolcott RD et al (2008) Evaluation of the bacterial diversity in the feces of cattle using 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing (bTEFAP). BMC Microbiol 8:125. https://doi.org/10.1186/1471-2180-8-125

    Article  CAS  Google Scholar 

  26. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461. https://doi.org/10.1093/bioinformatics/btq461

    Article  CAS  Google Scholar 

  27. Bolyen E, Rideout J, Dillon M et al (2019) Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852–857

    Article  CAS  Google Scholar 

  28. Rognes T, Flouri T, Nichols B et al (2016) VSEARCH: a versatile open source tool for metagenomics. PeerJ 4:e2584. https://doi.org/10.7717/peerj.2584

    Article  Google Scholar 

  29. Edgar RC, Haas BJ, Clemente JC et al (2011) UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27:2194–2200. https://doi.org/10.1093/bioinformatics/btr381

    Article  CAS  Google Scholar 

  30. Camacho C, Coulouris G, Avagyan V et al (2009) BLAST+: architecture and applications. BMC Bioinformatics 10:1–9. https://doi.org/10.1186/1471-2105-10-421

    Article  CAS  Google Scholar 

  31. Quast C, Pruesse E, Yilmaz P et al (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41:590–596. https://doi.org/10.1093/nar/gks1219

    Article  CAS  Google Scholar 

  32. Bokulich NA, Subramanian S, Faith JJ et al (2013) Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods 10:57–59. https://doi.org/10.1038/nmeth.2276.Quality-filtering

    Article  CAS  Google Scholar 

  33. Chao A (1984) Nonparametric estimation of the number of classes in a population. Scanadinavian J Stat 11:265–270. https://doi.org/10.1214/aoms/1177729949

    Article  Google Scholar 

  34. Vázquez-Baeza Y, Pirrung M, Gonzalez A, Knight R (2013) EMPeror : a tool for visualizing high-throughput microbial community data. Gigascience 2:1–4

    Article  Google Scholar 

  35. Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46. https://doi.org/10.1080/13645700903062353

    Article  Google Scholar 

  36. Clarke KR, Warwick RM (2001) A further biodiversity index applicable to species lists: variation in taxonomic distinctness. Mar Ecol Prog Ser 216:265–278. https://doi.org/10.3354/meps216265

    Article  Google Scholar 

  37. Heberle H, Meirelles VG, da Silva FR et al (2015) InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics 16:1–7. https://doi.org/10.1186/s12859-015-0611-3

    Article  Google Scholar 

  38. Segata N, Izard J, Waldron L et al (2011) Metagenomic biomarker discovery and explanation. Genome Biol 12:1–18. https://doi.org/10.1186/gb-2011-12-6-r60

    Article  Google Scholar 

  39. Douglas GM, Maffei VJ, Zaneveld J et al (2019) PICRUSt2: an improved and extensible approach for metagenome inference. BioRxiv 1:42. https://doi.org/10.3997/2214-4609.201404048

    Article  Google Scholar 

  40. Langille M, Zaneveld J, Caporaso JG et al (2013) Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31:814–821. https://doi.org/10.1038/nbt.2676

    Article  CAS  Google Scholar 

  41. de Voogd NJ, Cleary DFR, Polónia ARM, Gomes NCM (2015) Bacterial community composition and predicted functional ecology of sponges, sediment and seawater from the thousand islands reef complex, West Java, Indonesia. FEMS Microbiol Ecol 91:1–12. https://doi.org/10.1093/femsec/fiv019

    Article  CAS  Google Scholar 

  42. Parks DH, Tyson GW, Hugenholtz P, Beiko RG (2014) STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30:3123–3124. https://doi.org/10.1093/bioinformatics/btu494

    Article  CAS  Google Scholar 

  43. Moore JD, Juhasz CI, Robbins TT, Ignacio Vilchis L (2009) Green abalone, Haliotis fulgens infected with the agent of withering syndrome do not express disease signs under a temperature regime permissive for red abalone, Haliotis rufescens. Mar Biol 156:2325–2330. https://doi.org/10.1007/s00227-009-1260-8

    Article  Google Scholar 

  44. Harris VC, Haak BW, Boele van Hensbroek M, Wiersinga WJ (2017) The intestinal microbiome in infectious diseases: the clinical relevance of a rapidly emerging field. Open Forum Infect Dis 4:1–8. https://doi.org/10.1093/ofid/ofx144

    Article  CAS  Google Scholar 

  45. Offret C, Jégou C, Mounier J et al (2019) New insights into the haemo- and coelo-microbiota with antimicrobial activities from Echinodermata and Mollusca. J Appl Microbiol 126:1023–1031. https://doi.org/10.1111/jam.14184

    Article  CAS  Google Scholar 

  46. Birhanu AG, Yimer SA, Kalayou S et al (2019) Ample glycosylation in membrane and cell envelope proteins may explain the phenotypic diversity and virulence in the Mycobacterium tuberculosis complex. Sci Rep 9:1–15. https://doi.org/10.1038/s41598-019-39654-9

    Article  CAS  Google Scholar 

  47. Wang Y, Huang JM, Wang SL et al (2016) Genomic characterization of symbiotic mycoplasmas from the stomach of deep-sea isopod bathynomus sp. Environ Microbiol 18:2646–2659. https://doi.org/10.1111/1462-2920.13411

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to Dr. W. Kelley Thomas for providing us with the possibility of running bioinformatics pipelines using the server of the Research Computing Center of the University of New Hampshire and Andrea Lievana-MacTavish for English language editing.

Funding

This study was supported by SAGARPA-CONACYT [grant number 2011-C01-163322], UC Mexus-CONACYT [grant number CN-14–14], and the Fund for Scientific Research and Technological Development of CICESE [grant number FID-2012–01]. The first author received a graduate fellowship from CONACYT to support his Ph.D. research in Marine Ecology at the Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE). sagarpa-conacyt,2011-CO1-163322,Axayácatl Rocha-Olivares,uc mexus-conacyt,CN-14–14,Axayácatl Rocha-Olivares,cicese,Fund for Scientific Research,Axayácatl Rocha-Olivares,Technological Development FID-2012–01,Axayácatl Rocha-Olivares

Author information

Authors and Affiliations

Authors

Contributions

Experimental design: Francesco Cicala, James D. Moore, and Axayácatl Rocha-Olivares. Data collection and preparation: Francesco Cicala. Bioinformatics analysis: Francesco Cicala, José Alejandro Cisterna-Céliz, Marcos Paolinelli, and Joseph Sevigny. Funding acquisition: Axayácatl Rocha-Olivares and James D. Moore. The first draft of the manuscript was written by Francesco Cicala and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Axayácatl Rocha-Olivares.

Ethics declarations

Ethics Approval

Not applicable.

Consent to Participate

Not applicable.

Consent for Publication

Not applicable.

Competing Interests

The authors declare no competing interests.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 368 KB)

Supplementary file2 (XLSX 329 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cicala, F., Cisterna-Céliz, J.A., Paolinelli, M. et al. The Role of Diversity in Mediating Microbiota Structural and Functional Differences in Two Sympatric Species of Abalone Under Stressed Withering Syndrome Conditions. Microb Ecol 85, 277–287 (2023). https://doi.org/10.1007/s00248-022-01970-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00248-022-01970-5

Keywords

Navigation