1887

Abstract

A novel mesophilic, hydrogen-, and sulfur-oxidizing bacterium, designated strain ST-419, was isolated from a deep-sea hydrothermal vent plume on the Carlsberg Ridge of the Northwestern Indian Ocean. The isolate was a Gram-staining-negative, non-motile and coccoid to oval-shaped bacterium. Growth was observed at 4–50 °C (optimum 37 °C), pH 5.0–8.6 (optimum pH 6.0) and 1.0–5.0 % (w/v) NaCl (optimum 3.0 %). ST-419 could grow chemlithoautotrophically with molecular hydrogen, sulfide, elemental sulfur and thiosulfate as energy sources. Molecular oxygen, nitrate and elemental sulfur could be used as electron acceptors. The predominant fatty acids were Cω7, Cω7 and C. The major polar lipids were phosphatidylethanolamine, diphosphatidylglycerol and phosphatidylglycerol. The respiratory quinone was menaquinone MK-6 and the G+C content of the genomic DNA was 42.4 mol%. Phylogenetic analysis based on 16S rRNA gene sequences revealed that ST-419 represented a member of genus and was most closely related to 1812E, with 97.6 % sequence similarity. The average nucleotide identity (ANI) and digital DNA–DNA hybridization (dDDH) values between ST-419 and 1812E were 74.6 and 19.6 %, respectively. The combined genotypic and phenotypic data indicate that ST-419 represents a novel species within the genus , for which the name sp. nov. is proposed. The type strain is ST-419 (=MCCC 1A17954=KCTC 25164).

Funding
This study was supported by the:
  • China Ocean Mineral Resources R & D Association (Award No. DY135-S2-01)
    • Principle Award Recipient: NotApplicable
  • China Ocean Mineral Resources Research and Development Association (Award No. DY135-B2-01)
    • Principle Award Recipient: NotApplicable
  • National Natural Science Foundation of China (Award No.91951201)
    • Principle Award Recipient: NotApplicable
  • National Natural Science Foundation of China (Award No. 41672333)
    • Principle Award Recipient: NotApplicable
  • National Key R&D Program of China (Award 2018YFC0310701)
    • Principle Award Recipient: NotApplicable
Loading

Article metrics loading...

/content/journal/ijsem/10.1099/ijsem.0.004748
2021-03-18
2022-01-19
Loading full text...

Full text loading...

References

  1. Dick GJ. The microbiomes of deep-sea hydrothermal vents: distributed globally, shaped locally. Nat Rev Microbiol 2019; 17:271–283 [View Article][PubMed]
    [Google Scholar]
  2. Mccollom TM, Shock EL. Geochemical constraints on chemolithoautotrophic metabolism by microorganisms in seafloor hydrothermal systems. Geochim Cosmochim Acta 1997; 61:4375–4391 [View Article][PubMed]
    [Google Scholar]
  3. Nakagawa S, Takai K. Deep-sea vent chemoautotrophs: diversity, biochemistry and ecological significance. FEMS Microbiol Ecol 2008; 65:1–14 [View Article]
    [Google Scholar]
  4. Meier DV, Pjevac P, Bach W, Hourdez S, Girguis PR et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. Isme J 2017; 11:1545–1558 [View Article]
    [Google Scholar]
  5. McNichol J, Stryhanyuk H, Sylva SP, Thomas F, Musat N et al. Primary productivity below the seafloor at deep-sea hot springs. Proc Natl Acad Sci U S A 2018; 115:6756–6761 [View Article][PubMed]
    [Google Scholar]
  6. Mengis N, Partanen A -I, Jalbert J, Matthews HD, Mengis N. 1.5 °C carbon budget dependent on carbon cycle uncertainty and future non-CO2 forcing. Sci Rep 2018; 8:5831 [View Article][PubMed]
    [Google Scholar]
  7. Urakawa H, Dubilier N, Fujiwara Y, Cunningham DE, Kojima S et al. Hydrothermal vent gastropods from the same family (Provannidae) harbour epsilon- and gamma-proteobacterial endosymbionts. Environ Microbiol 2005; 7:750–754 [View Article][PubMed]
    [Google Scholar]
  8. Campbell BJ, Engel AS, Porter ML, Takai K. The versatile ε-proteobacteria: key players in sulphidic habitats. Nat Rev Microbiol 2006; 4:458–468 [View Article][PubMed]
    [Google Scholar]
  9. Nakagawa S, Takai K. Deep-Sea vent chemoautotrophs: diversity, biochemistry and ecological significance. FEMS Microbiol Ecol 2008; 65:1–14 [View Article][PubMed]
    [Google Scholar]
  10. Meier DV, Pjevac P, Bach W, Hourdez S, Girguis PR et al. Niche partitioning of diverse sulfur-oxidizing bacteria at hydrothermal vents. Isme J 2017; 11:1545–1558 [View Article][PubMed]
    [Google Scholar]
  11. Inagaki F, Takai K, Nealson KH, Horikoshi K. Sulfurovum lithotrophicum gen. nov., sp. nov., a novel sulfur-oxidizing chemolithoautotroph within the ε-Proteobacteria isolated from Okinawa trough hydrothermal sediments. Int J Syst Evol Microbiol 2004; 54:1477–1482 [View Article][PubMed]
    [Google Scholar]
  12. Mino S, Kudo H, Arai T, Sawabe T, Takai K et al. Sulfurovum aggregans sp. nov., a hydrogen-oxidizing, thiosulfate-reducing chemolithoautotroph within the Epsilonproteobacteria isolated from a deep-sea hydrothermal vent chimney, and an emended description of the genus Sulfurovum . Int J Syst Evol Microbiol 2014; 64:3195–3201 [View Article][PubMed]
    [Google Scholar]
  13. Giovannelli D, Chung M, Staley J, Starovoytov V, Le Bris N et al. Sulfurovum riftiae sp. nov., a mesophilic, thiosulfate-oxidizing, nitrate-reducing chemolithoautotrophic epsilonproteobacterium isolated from the tube of the deep-sea hydrothermal vent polychaete Riftia pachyptila . Int J Syst Evol Microbiol 2016; 66:2697–2701 [View Article][PubMed]
    [Google Scholar]
  14. Mori K, Yamaguchi K, Hanada S. Sulfurovum denitrificans sp. nov., an obligately chemolithoautotrophic sulfur-oxidizing epsilonproteobacterium isolated from a hydrothermal field. Int J Syst Evol Microbiol 2018; 68:2183–2187 [View Article][PubMed]
    [Google Scholar]
  15. Takai K, Inagaki F, Nakagawa S, Hirayama H, Nunoura T et al. Isolation and phylogenetic diversity of members of previously uncultivated ε-Proteobacteria in deep-sea hydrothermal fields. FEMS Microbiol Lett 2003; 218:167–174 [View Article][PubMed]
    [Google Scholar]
  16. Jiang L, Lyu J, Shao Z. Sulfur metabolism of Hydrogenovibrio thermophilus strain S5 and its adaptations to deep-sea hydrothermal vent environment. Front Microbiol 2017; 8:2513 [View Article][PubMed]
    [Google Scholar]
  17. Baross JA. Isolation, growth, and maintenance ofhyperthermophiles. In Robb FT, Place AR. (editors) Archaea, a Laboratory Manual. Thermophiles Cold Spring Harbor, NY: Cold Spring Harbor Laboratory; 1995 pp 15–23
    [Google Scholar]
  18. Takai K, Horikoshi K. Thermosipho japonicus sp. nov., an extremely thermophilic bacterium isolated from a deep-sea hydrothermal vent in Japan. Extremophiles 2000; 4:9–17 [View Article][PubMed]
    [Google Scholar]
  19. Minnikin DE, O'Donnell AG, Goodfellow M, Alderson G, Athalye M et al. An integrated procedure for the extraction of bacterial isoprenoid quinones and polar lipids. J Microbiol Methods 1984; 2:233–241 [View Article]
    [Google Scholar]
  20. Tindall BJ. Lipid composition of Halobacterium lacusprofundi . FEMS Microbiol Lett 1990a; 66:199–202 [View Article]
    [Google Scholar]
  21. Tindall BJ. A comparative study of the lipid composition of Halobacterium saccharovorum from various sources. Syst Appl Microbiol 1990b; 13:128–130 [View Article]
    [Google Scholar]
  22. Garrity GM, Bell JA, Lilburn T. Class V. Epsilonproteobacteria class. nov. In Bergey’s Manual Syst Bacteriol Springer; 2005 pp 1145–1194
    [Google Scholar]
  23. Jiang LJ, Zheng YP, Peng XT, Zhou HY, Zhang CL. Vertical distribution and diversity of sulfate-reducing prokaryotes in the Pearl River estuarine sediments, southern China. Fems Microbiol Ecol 2010; 70:93–106
    [Google Scholar]
  24. Lane DJ. 16S/23S rRNA sequencing. In Stackbrandt E, Goodfellow M. (editors) In Nucleic Acid Techniques in Bacterial Systematics New York: Wiley; 1991 pp 115–176
    [Google Scholar]
  25. Yoon S-H, Ha S-M, Kwon S, Lim J, Kim Y et al. Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int J Syst Evol Microbiol 2017; 67:1613–1617 [View Article][PubMed]
    [Google Scholar]
  26. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. mega6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol 2013; 30:2725–2729 [View Article][PubMed]
    [Google Scholar]
  27. Saitou N, Nei M. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 1987; 4:406–425 [View Article][PubMed]
    [Google Scholar]
  28. Felsenstein J. Evolutionary trees from DNA sequences: a maximum likelihood approach. J Mol Evol 1981; 17:368–376 [View Article][PubMed]
    [Google Scholar]
  29. Rzhetsky A, Nei M. Statistical properties of the ordinary least-squares, generalized least-squares, and minimum-evolution methods of phylogenetic inference. J Mol Evol 1992; 35:367–375 [View Article][PubMed]
    [Google Scholar]
  30. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article][PubMed]
    [Google Scholar]
  31. Lowe TM, Chan PP. tRNAscan-SE on-line: integrating search and context for analysis of transfer RNA genes. Nucleic Acids Res 2016; 44:W54–W57 [View Article]
    [Google Scholar]
  32. Lagesen K, Hallin P, Rødland EA, Staerfeldt HH, Rognes T et al. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res 2007; 35:3100–3108 [View Article][PubMed]
    [Google Scholar]
  33. Delcher AL, Bratke KA, Powers EC, Salzberg SL. Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics 2007; 23:673–679 [View Article][PubMed]
    [Google Scholar]
  34. Na S-I, Kim YO, Yoon S-H, Ha S-M, Baek I et al. UBCG: up-to-date bacterial core gene set and pipeline for phylogenomic tree reconstruction. J Microbiol 2018; 56:280–285 [View Article][PubMed]
    [Google Scholar]
  35. Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 2014; 30:1312–1313 [View Article][PubMed]
    [Google Scholar]
  36. Chun J, Oren A, Ventosa A, Christensen H, Arahal DR et al. Proposed minimal standards for the use of genome data for the taxonomy of prokaryotes. Int J Syst Evol Microbiol 2018; 68:461–466 [View Article][PubMed]
    [Google Scholar]
  37. Yoon SH, Ha S-M, Lim J, Kwon S, Chun J. A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie van Leeuwenhoek 2017; 110:1281–1286 [View Article][PubMed]
    [Google Scholar]
  38. Meier-Kolthoff JP, Auch AF, Klenk HP, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics 2013; 14:60 [View Article][PubMed]
    [Google Scholar]
  39. Rodriguez-R LM, Konstantinidis KT. The enveomics collection: a toolbox for specialized analyses of microbial genomes and metagenomes. Peer J Preprints 2016
    [Google Scholar]
  40. Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep 2015; 5:8365 [View Article][PubMed]
    [Google Scholar]
  41. Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ et al. The seed and the rapid annotation of microbial genomes using subsystems technology (RAST). Nucleic Acids Res 2014; 42:D206–D214 [View Article][PubMed]
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journal/ijsem/10.1099/ijsem.0.004748
Loading
/content/journal/ijsem/10.1099/ijsem.0.004748
Loading

Data & Media loading...

Supplements

Supplementary material 1

PDF

Most cited this month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error