Abstract
Along with the advancement of sequencing technology, shotgun metagenomics has been applied for microbial community study. Shotgun metagenomics enables to examine not only taxonomic structure but also functional profile based on the gene repertory. It also allows broad comparative analysis including unknown sequences without annotation. Here we first mention about overview of shotgun metagenomics for microbial community study and then introduce two examples of study that applied different approaches. One is seasonality monitoring based on taxonomic and functional gene composition. This is monthly-bimonthly monitoring of surface water in Sendai Bay, Japan, for about a year. We observed typical seasonality in the taxonomic composition and also found seasonality in the overall functional gene composition. The other is search for red tide marker sequences that applied broad comparative analysis. This search is for sequences that showed different abundance between red tide samples and control samples in Buzen Sea, Japan, using the assembled contigs as the reference. As the candidate of the red tide marker sequences, we obtained 1220 contigs including those without taxonomic annotation. As in these examples, shotgun metagenomic studies provide insights to help understanding marine microbial community.
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References
Altschul SF, Gish W, Miller W et al (1990) Basic local alighnment search tool. J Mol Biol 215(3):403–410
Amin SA, Parker MS, Armbrust EV (2012) Interaction between diatoms and bacteria. Microbiol Mol Biol R 76(3):667–684
Amin SA, Hmelo LR, van Tol HM et al (2015) Interaction and signaling between a cosmopolitan phytoplankton and associated bacteria. Nature 522(4):98–101
Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc B Met 57(1):289–300
Buchan A, LeCleir GR, Gulvik CA et al (2014) Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol 12(10):686–698
Bunse C, Pinhassi J (2017) Marine bacterioplankton seasonal succession dynamics. Trends Microbiol 25(6):494–505
Burke C, Steinberg P, Rusch D et al (2011) Bacterial community assembly based on functional genes rather than species. PNAS 108(34):14288–14293
Cole JJ, Findlay S, Pace ML (1988) Bacterial production in fresh and saltwater ecosystems: a cross-system overview. Mar Ecol Prog Ser 43:1–10
Coutinho FH, Silveira CB, Gregoracci GB et al (2017) Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat Commun 8:15955
Croft MT, Lawrence AD, Raux-Deery E et al (2005) Algae acquire vitamin B12 through a symbiotic relationship with bacteria. Nature 438:90–93
Edwards RA, Rohwer F (2005) Viral metagenomics. Nat Rev Microbiol 3(6):504–510
Fandino LB, Riemann L, Steward GF et al (2001) Variations in bacterial community structure during a dinoflagellate bloom analyzed by DGGE and 16S rDNA sequencing. Aqua Microb Ecol 23:119–130
Fuhrman JA, Cram JA, Needham DM (2015) Marine microbial community dynamics and their ecological interpretation. Nat Rev Microbiol 13(3):133–146
Ganesh S, Parris DJ, DeLong EF et al (2014) Metagenomic analysis of size-fractionated picoplankton in a marine oxygen minimum zone. ISME J 8(1):187–211
Geng H, Belas R (2010) Molecular mechanisms underlying roseobacter-phytoplankton symbioses. Curr Opin Biotech 21:332–338
Giovannoni SJ, Vergin KL (2012) Seasonality in ocean microbial communities. Science 335(6069):671–676
Haggerty JM, Dinsdale EA (2017) Distinct biogeographical patterns of marine bacterial taxonomy and functional genes. Glob Ecol Biogeogr 26(2):177–190
Huson DH, Beier S, Flade I et al (2016) MEGAN community edition-interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput Biol 12(6):e1004957
Imai I, Yamaguchi M, Hori Y (2006) Eutrophication and occurrences of harmful algal blooms in the Seto Inland Sea, Japan. Plankton Benthos Res 1(2):71–84
Kanehisa M, Sato Y, Morishima K (2016) BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol 428(4):726–731
Klindworth A, Pruesse E, Schweer T et al (2013) Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res 41(1):e1
Margulies M, Egholm M, Altman WE et al (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437:376–380
Paerl HW, Dyble J, Moisander PH et al (2003) Microbial indicators of aquatic ecosystem change: current applications to eutrophication studies. FEMS Microbiol Ecol 46(3):233–246
Qin J, Li Y, Cai Z et al (2012) A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490:55–60
Raes J, Letunic I, Yamada T et al (2011) Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data. Mol Syst Biol 7:473
Rappé MS, Giovannoni SJ (2003) The uncultured microbial majority. Annu Rev Microbiol 57:369–394
Riemann L, Steward GF, Azam F (2000) Dynamics of bacterial community composition and activity during a mesocosm diatom bloom. Appl Environ Microbiol 66(2):578–587
Ruvindy R, White RA III, Neilan BA et al (2016) Unravelling core microbial metabolisms in the hypersaline microbial mats of Shark Bay using high-throughput metagenomics. ISME J 10(1):183–196
Segata N, Boernigen D, Tickle TL et al (2013) Computational meta’omics for microbial community studies. Mol Syst Biol 9:666
Sharpton TJ (2014) An introduction to the analysis of shotgun metagenomic data. Front Plant Sci 5:209
Simon C, Daniel R (2011) Metagenomic analyses: past and future trends. Appl Environ Microbiol 77(4):1153–1161
Smith DC, Steward GF, Long RA et al (1995) Bacterial mediation of carbon fluxes during a diatom bloom in a mesocosm. Deep-Sea Res Pt II 42(1):75–97
Smith MW, Allen LZ, Allen AE et al (2013) Contrasting genomic properties of free-living and particle-attached microbial assemblages within a coastal ecosystem. Front Microbiol 4:120
Sohm JA, Ahlgren NA, Thomson ZJ et al (2016) Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J 10(2):333–345
Sunagawa S, Mende DR, Zeller G et al (2013) Metagenomic species profiling using universal phylogenetic marker genes. Nat Methods 10(12):1196–1199
Tada Y, Taniguchi A, Nagao I et al (2011) Differing growth responses of major phylogenetic groups of marine bacteria to natural phytoplankton blooms in the western North Pacific Ocean. Appl Environ Microbiol 77(12):4055–4065
Taniuchi Y, Watanabe T, Kakehi S et al (2017) Seasonal dynamics of the phytoplankton community in Sendai Bay, northern Japan. J Oceanogr 73(1):1–9
Urabe J, Suzuki T, Nishita T et al (2013) Immediate ecological impacts of the 2011 Tohoku earthquake tsunami on intertidal flat communities. PLoS One 8(5):e62779
Ward CS, Yung CM, Davis KM et al (2017) Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J 11(6):1412–1422
West NJ, Obernosterer I, Zemb O et al (2008) Major differences of bacterial diversity and activity inside and outside of a natural iron-fertilized phytoplankton bloom in the southern ocean. Environ Microbiol 10(3):738–756
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Matsumoto, K., Kitamura, N., Ikeo, K. (2019). Shotgun Metagenome Analyses: Seasonality Monitoring in Sendai Bay and Search for Red Tide Marker Sequences. In: Gojobori, T., Wada, T., Kobayashi, T., Mineta, K. (eds) Marine Metagenomics. Springer, Singapore. https://doi.org/10.1007/978-981-13-8134-8_10
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DOI: https://doi.org/10.1007/978-981-13-8134-8_10
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