Advertisement

Antonie van Leeuwenhoek

, Volume 111, Issue 7, pp 1117–1129 | Cite as

The impact of depuration on mussel hepatopancreas bacteriome composition and predicted metagenome

  • J. A. Rubiolo
  • A. Lozano-Leon
  • R. Rodriguez-Souto
  • N. Fol Rodríguez
  • M. R. Vieytes
  • L. M. Botana
Original Paper

Abstract

Due to the rapid elimination of bacteria through normal behaviour of filter feeding and excretion, the decontamination of hazardous contaminating bacteria from shellfish is performed by depuration. This process, under conditions that maximize shellfish filtering activity, is a useful method to eliminate microorganisms from bivalves. The microbiota composition in bivalves reflects that of the environment of harvesting waters, so quite different bacteriomes would be expected in shellfish collected in different locations. Bacterial accumulation within molluscan shellfish occurs primarily in the hepatopancreas. In order to assess the effect of the depuration process on these different bacteriomes, in this work we used 16S RNA pyrosequencing and metagenome prediction to assess the impact of 15 h of depuration on the whole hepatopancreas bacteriome of mussels collected in three different locations.

Keywords

Mussel depuration 16S RNA sequencing Taxonomic profiling Metagenome prediction 

Notes

Funding

The research leading to these results has received funding from the following FEDER cofunded-grants. From CDTI and Technological Funds, supported by Ministerio de Economía y Competitividad, AGL2012-40185-CO2-01, AGL2014-58210-R, andConsellería de Cultura, Educación e Ordenación Universitaria, GRC2013-016, and through Axencia Galega de Innovación, Spain, ITC-20133020 SINTOX. From CDTI under ISIP Programme, Spain, IDI-20130304 APTAFOOD. From the European Union’s Seventh Framework Programme managed by REA—Research Executive Agency (FP7/2007-2013) under grant agreement 312184 PHARMASEA.

Conflict of interests

The authors declare that they have no conflict of interest.

Supplementary material

10482_2018_1015_MOESM1_ESM.pdf (69 kb)
Significant differences at the phyla level observed in D and ND samples (main graph, green bars). Graph insert shows the differences observed in the tenericutes and proteobacteria phyla, purple and pink bars respectively. [**p < 0.01, *p < 0.05, n = 3 (ND), n = 2 (D)]. Supplementary material 1 (PDF 68 kb)
10482_2018_1015_MOESM2_ESM.pdf (1.6 mb)
Networks showing OTU interactions between all rarified samples from ND (blue dots) and D samples (red dots). OTUs are represented by white dots and were grouped according on the microbiomes they were found. The lines radiating from each sample link them to their microbiome. Supplementary material 2 (PDF 1610 kb)
10482_2018_1015_MOESM3_ESM.pdf (768 kb)
a Heatmap of core OTU frequencies for all samples analyzed clustered according to the phylogenetic tree and sample condition. b Frequencies of core phyla observed before and after depuration [**p < 0.01, *p < 0.05, n = 3 (ND), n = 2 (D)]. Supplementary material 3 (PDF 768 kb)
10482_2018_1015_MOESM4_ESM.pdf (19 kb)
Supplementary material 4 (PDF 18 kb)
10482_2018_1015_MOESM5_ESM.pdf (51 kb)
Supplementary material 5 (PDF 51 kb)

References

  1. Altschul SF, Gish W, Miller W et al (1990) Basic local alignment search tool. J Mol Biol 215:403–410.  https://doi.org/10.1016/S0022-2836(05)80360-2 CrossRefPubMedGoogle Scholar
  2. Álvarez-Vázquez MA, Bendicho C, Prego R (2014) Ultrasonic slurry sampling combined with total reflection X-ray spectrometry for multi-elemental analysis of coastal sediments in a ria system. Microchem J 112:172–180.  https://doi.org/10.1016/j.microc.2013.09.026 CrossRefGoogle Scholar
  3. Anonymous (2004) Directive of the European Parlament and of the CouncilGoogle Scholar
  4. Anonymous (2006) Directive of the European Parlament and of the Council of 12th of December 2006 Laying Down Harvest Water QualityGoogle Scholar
  5. Azevedo C (1993) Occurrence of an unusual branchial mycoplasma-like infection in cockle Cerastoderma edule (Moliusca, Bivalvia). Dis Aquat Organ 16:55–59.  https://doi.org/10.3354/dao016055 CrossRefGoogle Scholar
  6. Boyle PJ, Maki JS, Mitchell R (1987) Mollicute identified in novel association with aquatic invertebrate. Curr Microbiol 15:85–89.  https://doi.org/10.1007/BF01589367 CrossRefGoogle Scholar
  7. Caporaso JG, Bittinger K, Bushman FD et al (2010a) PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26:266–267.  https://doi.org/10.1093/bioinformatics/btp636 CrossRefPubMedGoogle Scholar
  8. Caporaso JG, Kuczynski J, Stombaugh J et al (2010b) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336.  https://doi.org/10.1038/nmeth.f.303 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Cleary DFR, Becking LE, Polónia ARM et al (2015) Composition and predicted functional ecology of mussel-associated bacteria in Indonesian marine lakes. Antonie van Leeuwenhoek Int J Gen Mol Microbiol 107:821–834.  https://doi.org/10.1007/s10482-014-0375-1 CrossRefGoogle Scholar
  10. Craft JA, Gilbert JA, Temperton B et al (2010) Pyrosequencing of Mytilus galloprovincialis cDNAs: tissue-specific expression patterns. PLoS ONE.  https://doi.org/10.1371/journal.pone.0008875 PubMedPubMedCentralCrossRefGoogle Scholar
  11. Demain A, Fang A (2000) The natural functions of secondary metabolites. Adv Biochem Eng Biotechnol 69:222.  https://doi.org/10.1016/S0300-9084(79)80192-3 CrossRefGoogle Scholar
  12. DeSantis TZ, Hugenholtz P, Larsen N et al (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol 72:5069–5072.  https://doi.org/10.1128/AEM.03006-05 CrossRefPubMedPubMedCentralGoogle Scholar
  13. Diario Oficial de la Unión Europea (2007) REGLAMENTO (CE) No 1441/2007 DE LA COMISIÓN de 5 de diciembre de 2007 que modifica el Reglamento (CE) no 2073/2005 relativo a los criterios microbiológicos aplicables a los productos alimenticiosGoogle Scholar
  14. Dore WJ, Farthing J, Laing I (2003) Depuration conditions for great scallops (Pecten maximus). J Shellfish Res 22:409–414Google Scholar
  15. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461.  https://doi.org/10.1093/bioinformatics/btq461 CrossRefPubMedGoogle Scholar
  16. Giovannoni SJ, Tripp HJ, Givan S et al (2005) Genome streamlining in a cosmopolitan oceanic bacterium. Science 309:1242–1245.  https://doi.org/10.1126/science.1114057 CrossRefPubMedGoogle Scholar
  17. Green TJ, Barnes AC (2010) Bacterial diversity of the digestive gland of Sydney rock oysters, Saccostrea glomerata infected with the paramyxean parasite, Marteilia sydneyi. J Appl Microbiol 109:613–622.  https://doi.org/10.1111/j.1365-2672.2010.04687.x PubMedCrossRefGoogle Scholar
  18. Guerlet E, Ledy K, Giambérini L (2006) Field application of a set of cellular biomarkers in the digestive gland of the freshwater snail Radix peregra (Gastropoda, Pulmonata). Aquat Toxicol 77:19–32.  https://doi.org/10.1016/j.aquatox.2005.10.012 CrossRefPubMedGoogle Scholar
  19. Harshbarger JC, Chang SC (1977) Chlamydiae (with phages), mycoplasmas, and richettsiae in Chesapeake Bay bivalves. Science 196:666–668.  https://doi.org/10.1126/science.193184 CrossRefPubMedGoogle Scholar
  20. Holben WE, Williams P, Saarinen M et al (2002) Phylogenetic analysis of intestinal microflora indicates a novel Mycoplasma phylotype in farmed and wild salmon. Microb Ecol 44:175–185.  https://doi.org/10.1007/s00248-002-1011-6 CrossRefPubMedGoogle Scholar
  21. Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9:90–95.  https://doi.org/10.1109/MCSE.2007.55 CrossRefGoogle Scholar
  22. Huss H (1994) Assurance of seafood quality. FAO Fish Tech Pap 334:1–169Google Scholar
  23. Kellogg CA, Lisle JT, Galkiewicz JP (2009) Culture-independent characterization of bacterial communities associated with the cold-water coral Lophelia pertusa in the northeastern Gulf of Mexico. Appl Environ Microbiol 75:2294–2303.  https://doi.org/10.1128/AEM.02357-08 CrossRefPubMedPubMedCentralGoogle Scholar
  24. Kraak MHS, Scholten MCT, Peeters WHM, de Kock WC (1991) Biomonitoring of heavy metals in the Western European Rivers Rhine and Meuse using the freshwater mussel Dreissena polymorpha. Environ Pollut 74:101–114.  https://doi.org/10.1016/0269-7491(91)90107-8 CrossRefPubMedGoogle Scholar
  25. Krol RM, Hawkins WE, Overstreet RM (1991) Rickettsial and mollicute infections in hepatopancreatic cells of cultured Pacific white shrimp (Penaeus vannamei). J Invertebr Pathol 57:362–370.  https://doi.org/10.1016/0022-2011(91)90140-L CrossRefPubMedGoogle Scholar
  26. 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 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Lee RJ, Younger AD (2002) Developing microbiological risk assessment for shellfish purification. Int Biodeterior Biodegrad 50(3):177–183CrossRefGoogle Scholar
  28. Lees D (2000) Viruses and bivalve shellfish. Int J Food Microbiol 59:81–116CrossRefPubMedGoogle Scholar
  29. Maidak BL, Cole JR, Lilburn TG et al (2001) The RDP-II (Ribosomal Database Project). Nucleic Acids Res 29:173–174.  https://doi.org/10.1093/nar/29.1.173 CrossRefPubMedPubMedCentralGoogle Scholar
  30. McKinney W (2010) Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference. pp 51–56Google Scholar
  31. Mcmahon RF (1996) The Physiological Ecology of the Zebra Mussel, Dreissena polymorpha in North America and Europe. Am Zool 36:339–363.  https://doi.org/10.1093/icb/36.3.339 CrossRefGoogle Scholar
  32. Oliphant TE (2007) Python for scientific computing. Comput Sci Eng 9:10–20.  https://doi.org/10.1109/MCSE.2007.58 CrossRefGoogle Scholar
  33. Paillard C, Le Roux F, Borrego JJ (2004) Bacterial disease in marine bivalves, a review of recent studies: trends and evolution. Aquat Living Resour 17:477–498.  https://doi.org/10.1051/alr:2004054 CrossRefGoogle Scholar
  34. Parks DH, Beiko RG (2010) Identifying biologically relevant differences between metagenomic communities. Bioinformatics 26:715–721.  https://doi.org/10.1093/bioinformatics/btq041 CrossRefPubMedGoogle Scholar
  35. Pfister CA, Meyer F, Antonopoulos DA (2010) Metagenomic profiling of a microbial assemblage associated with the california mussel: a node in networks of carbon and nitrogen cycling. PLoS ONE.  https://doi.org/10.1371/journal.pone.0010518 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Prego R, Cobelo-García A, Santos-Echeandía J, de Castro M, Ospina-Alvarez N, García-Pérez M (2010) Estuary-ria exchange of cadmium, lead and zinc in the coastal system of the Ria of Vigo (NW Iberian Peninsula). Sci Mar 74:77–87.  https://doi.org/10.3989/scimar.2010.74s1077 CrossRefGoogle Scholar
  37. 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.  https://doi.org/10.1093/nar/gks1219 PubMedCentralCrossRefPubMedGoogle Scholar
  38. Razin S, Yogev D, Naot Y (1998) Molecular biology and pathogenicity of mycoplasmas. Microbiol Mol Biol Rev 62:1094–1156.PubMedPubMedCentralGoogle Scholar
  39. Richards GP (1991) Shellfish depuration. In: Ward DR, Hackney CR (eds) Microbiology of marine food products. Van Nostrand Reinhold, New York, pp 395–428CrossRefGoogle Scholar
  40. Richards GP (2001) Title : enteric virus contamination of shellfish: intervention strategies. J Shellfish Res 20:1241–1243Google Scholar
  41. Roderick GE, Schneider KR (1994) Depuration and relaying of molluscan shellfish. In: Hackney CR, Pierson MD (eds) Environment indicators and shellfish safety. Chapman & Hall, New YorkGoogle Scholar
  42. Tanaka R, Ootsubo M, Sawabe T et al (2004) Biodiversity and in situ abundance of gut microflora of abalone (Haliotis discus hannai) determined by culture-independent techniques. Aquaculture 241:453–463.  https://doi.org/10.1016/j.aquaculture.2004.08.032 CrossRefGoogle Scholar
  43. Tringe SG, Hugenholtz P (2008) A renaissance for the pioneering 16S rRNA gene. Curr Opin Microbiol 11:442–446.  https://doi.org/10.1016/j.mib.2008.09.011 CrossRefPubMedGoogle Scholar
  44. Vezzulli L, Stagnaro L, Grande C, Tassistro G, Canesi L, Pruzzo C (2018) Comparative 16SrDNA Gene-Based Microbiota Profiles of the Pacific Oyster (Crassostrea gigas) and the Mediterranean Mussel (Mytilus galloprovincialis) from a Shellfish Farm (Ligurian Sea, Italy). Microb Ecol 75(2):495–504.  https://doi.org/10.1007/s00248-017-1051-6 CrossRefPubMedGoogle Scholar
  45. Viollier PH, Shapiro L (2004) Spatial complexity of mechanisms controlling a bacterial cell cycle. Curr Opin Microbiol 7:572–578CrossRefPubMedGoogle Scholar
  46. Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73:5261–5267.  https://doi.org/10.1128/AEM.00062-07 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Winters AD, Marsh TL, Faisal M (2011) Heterogeneity of bacterial communities within the zebra mussel (Dreissena polymorpha) in the Laurentian Great Lakes Basin. J Great Lakes Res 37:318–324.  https://doi.org/10.1016/j.jglr.2011.01.010 CrossRefGoogle Scholar
  48. Zhou Y, Call D, Broschat S (2013) Whole-proteome analysis of twelve species of alphaproteobacteria links four pathogens. Pathogens 2:627–635.  https://doi.org/10.3390/pathogens2040627 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • J. A. Rubiolo
    • 1
  • A. Lozano-Leon
    • 2
  • R. Rodriguez-Souto
    • 2
  • N. Fol Rodríguez
    • 3
  • M. R. Vieytes
    • 3
  • L. M. Botana
    • 4
  1. 1.Departamento de Zoología, Genética y Antropología FísicaUniversidad de Santiago de CompostelaLugoSpain
  2. 2.Institute of Applied Microbiology ASMECRUZBueuSpain
  3. 3.Departamento de Fisiología, Facultad de VeterinariaUniversidad de Santiago de CompostelaLugoSpain
  4. 4.Departamento de Farmacología, Facultad de VeterinariaUniversidad de Santiago de CompostelaLugoSpain

Personalised recommendations