Seasonal changes in the abundance of bacterial genes related to dimethylsulfoniopropionate catabolism in seawater from Ofunato Bay revealed by metagenomic analysis

Toshiaki Kudo, Atsushi Kobiyama, Jonaira Rashid, Shaheed Reza, Yuichiro Yamada, Yuri Ikeda, Daisuke Ikeda, Nanami Mizusawa, Kazuho Ikeo, Shigeru Sato, Takehiko Ogata, Mitsuru Jimbo, Shinnosuke Kaga, Shiho Watanabe, Kimiaki Naiki, Yoshimasa Kaga, Satoshi Segawa, Katsuhiko Mineta, Vladimir B. Bajic, Takashi GojoboriShugo Watabe

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Ofunato Bay is located in the northeastern Pacific Ocean area of Japan, and it has the highest biodiversity of marine organisms in the world, primarily due to tidal influences from the cold Oyashio and warm Kuroshio currents. Our previous results from performing shotgun metagenomics indicated that Candidatus Pelagibacter ubique and Planktomarina temperata were the dominant bacteria (Reza et al., 2018a, 2018b). These bacteria are reportedly able to catabolize dimethylsulfoniopropionate (DMSP) produced from phytoplankton into dimethyl sulfide (DMS) or methanethiol (MeSH). This study was focused on seasonal changes in the abundances of bacterial genes (dddP, dmdA) related to DMSP catabolism in the seawater of Ofunato Bay by BLAST+ analysis using shotgun metagenomic datasets. We found seasonal changes among the Candidatus Pelagibacter ubique strains, including those of the HTCC1062 type and the Red Sea type. A good correlation was observed between the chlorophyll a concentrations and the abundances of the catabolic genes, suggesting that the bacteria directly interact with phytoplankton in the marine material cycle system and play important roles in producing DMS and MeSH from DMSP as signaling molecules for the possible formation of the scent of the tidewater or as fish attractants.
Original languageEnglish (US)
Pages (from-to)174-184
Number of pages11
JournalGene
Volume665
DOIs
StatePublished - Apr 26 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): URF/1/1976
Acknowledgements: We thank Dr. Toshiya Iida and Dr. Masahiro Yuki of RIKEN for their helpful advice regarding the bioinformatic analysis. This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No URF/1/1976.

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