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Population-level prokaryotic community structures associated with ferromanganese nodules in the Clarion-Clipperton Zone (Pacific Ocean) revealed by 16S rRNA gene amplicon sequencing.

Kento TominagaHiroaki TakebeChisato MurakamiAkira TsuneTakahiko OkamuraTakuji IkegamiYosuke OnishiRyoma KamikawaTakashi Yoshida
Published in: Environmental microbiology reports (2023)
Although deep-sea ferromanganese nodules are a potential resource for exploitation, their formation mechanisms remain unclear. Several nodule-associated prokaryotic species have been identified by amplicon sequencing of 16S rRNA genes and are assumed to contribute to nodule formation. However, the recent development of amplicon sequence variant (ASV)-level monitoring revealed that closely related prokaryotic populations within an operational taxonomic unit often exhibit distinct ecological properties. Thus, conventional species-level monitoring might have overlooked nodule-specific populations when distinct populations of the same species were present in surrounding environments. Herein, we examined the prokaryotic community diversity of nodules and surrounding environments at the Clarion-Clipperton Zone in Japanese licensed areas by 16S rRNA gene amplicon sequencing with ASV-level resolution for three cruises from 2017 to 2019. Prokaryotic community composition and diversity were distinct by habitat type: nodule, nodule-surface mud, sediment, bottom water and water column. Most ASVs (~80%) were habitat-specific. We identified 178 nodule-associated ASVs and 41 ASVs associated with nodule-surface mud via linear discriminant effect size analysis. Moreover, several ASVs, such as members of SAR324 and Woeseia, were highly specific to nodules. These nodule-specific ASVs are promising targets for future investigation of the nodule formation process.
Keyphrases
  • healthcare
  • mental health
  • single cell
  • climate change
  • genome wide
  • genetic diversity
  • risk assessment
  • copy number
  • dna methylation
  • mass spectrometry
  • transcription factor
  • data analysis