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Microbial community diversity and physical–chemical features of the Southwestern Atlantic Ocean

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Abstract

Microbial oceanography studies have demonstrated the central role of microbes in functioning and nutrient cycling of the global ocean. Most of these former studies including at Southwestern Atlantic Ocean (SAO) focused on surface seawater and benthic organisms (e.g., coral reefs and sponges). This is the first metagenomic study of the SAO. The SAO harbors a great microbial diversity and marine life (e.g., coral reefs and rhodolith beds). The aim of this study was to characterize the microbial community diversity of the SAO along the depth continuum and different water masses by means of metagenomic, physical–chemical and biological analyses. The microbial community abundance and diversity appear to be strongly influenced by the temperature, dissolved organic carbon, and depth, and three groups were defined [1. surface waters; 2. sub-superficial chlorophyll maximum (SCM) (48–82 m) and 3. deep waters (236–1,200 m)] according to the microbial composition. The microbial communities of deep water masses [South Atlantic Central water, Antarctic Intermediate water and Upper Circumpolar Deep water] are highly similar. Of the 421,418 predicted genes for SAO metagenomes, 36.7 % had no homologous hits against 17,451,486 sequences from the North Atlantic, South Atlantic, North Pacific, South Pacific and Indian Oceans. From these unique genes from the SAO, only 6.64 % had hits against the NCBI non-redundant protein database. SAO microbial communities share genes with the global ocean in at least 70 cellular functions; however, more than a third of predicted SAO genes represent a unique gene pool in global ocean. This study was the first attempt to characterize the taxonomic and functional community diversity of different water masses at SAO and compare it with the microbial community diversity of the global ocean, and SAO had a significant portion of endemic gene diversity. Microbial communities of deep water masses (236–1,200 m) are highly similar, suggesting that these water masses have very similar microbiological attributes, despite the common knowledge that water masses determine prokaryotic community and are barriers to microbial dispersal. The present study also shows that SCM is a clearly differentiated layer within Tropical waters with higher abundance of phototrophic microbes and microbial diversity.

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Abbreviations

SAO:

Southwestern Atlantic Ocean

DOC:

Dissolved organic carbon

TW:

Tropical waters

DCM:

Mediterranean deep chlorophyll maximum

SCM:

Sub-superficial chlorophyll maximum

SACW:

South Atlantic central water

AAIW:

Antarctic intermediate water

UCDW:

Upper circumpolar deep water

Lat:

Latitude

Lon:

Longitude

Sal:

Salinity

Org N:

Organic carbon

Total N:

Total nitrogen

Org P:

Organic phosphorous

Ortho P:

Orthophosphate

N/P:

Nitrogen/phosphorous

HNA:

High nucleic acid

LNA:

Low nucleic acid

VBR:

Virus-to-bacterium ratio

References

  • Agogué H, Lamy D, Neal PR, Sogin ML, Herndl GJ (2011) Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol Ecol 20:258–274

  • Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    Article  CAS  PubMed  Google Scholar 

  • Amano-Sato C, Akiyama S, Uchida M, Shimada K, Utsumi M (2013) Archaeal distribution and abundance in water masses of the Arctic Ocean, Pacific sector. Aquat Microb Ecol 69:101–112

    Article  Google Scholar 

  • Andrade L, Gonzalez a. M, Araujo FV, Paranhos R (2003) Flow cytometry assessment of bacterioplankton in tropical marine environments. J Microbiol Methods 55:841–850

  • Andrade L, Gonzalez AM, Valentin JL, Paranhos R (2004) Bacterial abundance and production in the Southwest Atlantic Ocean. Hydrobiologia 511:103–111

  • Andrade L, Gonzalez AM, Rezende CE, Suzuki M, Valentin JL, Paranhos R (2007) Distribution of HNA and LNA bacterial groups in the Southwest Atlantic Ocean. Brazilian J Microbiol 38:330–336

  • Arístegui J, Gasol J, Duarte C, Herndl G (2009) Microbial oceanography of the dark ocean’s pelagic realm. Limnol Ocean 54:1501–1529

    Article  Google Scholar 

  • Barber RIT (1968) Dissolved organic carbon from deep waters resists microbial oxidation. Nature 220:274–275

    Article  CAS  PubMed  Google Scholar 

  • Borcard D, Gillet F, Legendre P (2011) Numerical ecology with R. Springer, New York, p 306

    Book  Google Scholar 

  • Bowler C, Karl DM, Colwell RR (2009) Microbial oceanography in a sea of opportunity. Nature 459:180–184

  • Breitbart M, Salamon P, Andresen B, Mahaffy JM, Segall AM, Mead D, Azam F, Rohwer F (2002) Genomic analysis of uncultured marine viral communities. Proc Natl Acad Sci USA 99:14250–14255

  • Bruce T, Meirelles PM, Garcia G, Paranhos R, Rezende CE, de Moura RL, Filho R-F, Coni EOC, Vasconcelos AT, Amado Filho G, Hatay M, Schmieder R, Edwards R, Dinsdale E, Thompson FL (2012) Abrolhos bank reef health evaluated by means of water quality, microbial diversity, benthic cover, and fish biomass data. PLoS One 7:e36687

  • Cavalcanti GS, Gregoracci GB, Longo LDL, Bastos AC, Ferreira CM, Francini-Filho RB, Paranhos R, Ghisolfi RD, Krüger R, Güth AZ, Sumida PYG, Bruce T, Maia-Neto O, de O. Santos E, Iida T, Moura RL, Amado-Filho GM, Thompson FL (2013) Sinkhole-like structures as bioproductivity hotspots in the Abrolhos Bank. Cont Shelf Res 8(1):52–62. doi:10.1038/ismej.2013.133

  • Clasen JL, Brigden SM, Payet JP, Suttle CA (2008) Evidence that viral abundance across oceans and lakes is driven by different biological factors. Freshw Biol 53:1090–1100

    Article  CAS  Google Scholar 

  • DeLong EF (1992) Archaea in coastal marine environments. Proc Natl Acad Sci USA 89:5685–5689

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • DeLong EF (2009) The microbial ocean from genomes to biomes. Nature 459:200–206

    Article  CAS  PubMed  Google Scholar 

  • DeLong EF, Preston CM, Mincer T, Rich V, Hallam SJ, Frigaard N-U, Martinez A, Sullivan MB, Edwards R, Brito BR, Chisholm SW, Karl DM (2006) Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311:496–503

    Article  CAS  PubMed  Google Scholar 

  • Estrada M, Marrase C, Latasa M (1993) Variability of deep chlorophyll maximum characteristics in the Northwestern Mediterranean. Mar Ecol 92:289–300

  • Foloni NH (2010) Water masses on Campos Basin. University of São Paulo, 120 pp

  • Francini-Filho RB, Coni EOC, Meirelles PM, Amado-Filho GM, Thompson FL, Pereira-Filho GH, Bastos AC, Abrantes DP, Ferreira CM, Gibran FZ, Güth AZ, Sumida PYG, Oliveira NL, Kaufman L, Minte-Vera CV, Moura RL (2013) Dynamics of coral reef benthic assemblages of the Abrolhos Bank, eastern Brazil: inferences on natural and anthropogenic drivers. PLoS ONE 8:e54260

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Frank JA, Lorimer D, Youle M, Witte P, Craig T, Abendroth J, Rohwer F, Edwards RA, Segall AM, Burgin AB (2013) Structure and function of a cyanophage-encoded peptide deformylase. ISME J 7:1150–1160

  • Fuhrman JA, McCallum K, Davis AA (1992). Novel major archaebacterial group from marine plankton. Nature 356:148–149

  • Fuhrman JA, Liang X, Noble RT (2005) Rapid detection of enteroviruses in small volumes of natural waters by real-time quantitative reverse transcriptase PCR rapid detection of enteroviruses in small volumes of natural waters by real-time quantitative reverse transcriptase PCR. Appl Environ Microbiol 71:4523–4530

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Ghai R, Martin-Cuadrado A-B, Molto AG, Heredia IG, Cabrera R, Martin J, Verdú M, Deschamps P, Moreira D, López-García P, Mira A, Rodriguez-Valera F (2010) Metagenome of the Mediterranean deep chlorophyll maximum studied by direct and fosmid library 454 pyrosequencing. ISME J 4:1154–1166

  • Grasshoff, Klaus, Kremling K, Ehrhardt M (Eds) (1999) Methods of seawater analysis, 3rd ed. Wiley-VCH Verlag GmbH, pp 1–600

  • Hagberg AA, Schult DA, Swart PJ (2008) Exploring network structure, dynamics, and function using NetworkX. In: Varoquaux G, Vaught T, Millman J (eds) Proceedings of 7th Python science conference. Pasadena, CA, USA, pp 11–15

    Google Scholar 

  • Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 90–95

  • Ivars-Martinez E, Martin-Cuadrado A-B, D’Auria G, Mira A, Ferriera S, Johnson J, Friedman R, Rodriguez-Valera F (2008) Comparative genomics of two ecotypes of the marine planktonic copiotroph Alteromonas macleodii suggests alternative lifestyles associated with different kinds of particulate organic matter. ISME J 2:1194–1212

    Article  CAS  PubMed  Google Scholar 

  • Jannasch H, Eimhjellen K, Wirsen CO (1971) Farmanfarmalan a: microbial degradation of organic matter in the deep sea. Science (80) 171:672–675

  • Jiao N, Herndl GJ, Hansell D a, Benner R, Kattner G, Wilhelm SW, Kirchman DL, Weinbauer MG, Luo T, Chen F, Azam F (2010) Microbial production of recalcitrant dissolved organic matter: long-term carbon storage in the global ocean. Nat Rev Microbiol 8:593–599

  • Karl DM (2007) Microbial oceanography: paradigms, processes and promise. Nat Rev Micro 5:759–769

    Article  CAS  Google Scholar 

  • Karner MB, DeLong EF, Karl DM (2001) Archaeal dominance in the mesopelagic zone of the Pacific Ocean. Nature 409:507–510

    Article  CAS  PubMed  Google Scholar 

  • Lindell D, Sullivan MB, Johnson ZI, Tolonen AC, Rohwer F, Chisholm SW (2004) Transfer of photosynthesis genes to and from Prochlorococcus viruses. Proc Natl Acad Sci USA 101:11013–11018

  • López-Pérez M, Gonzaga A, Martin-Cuadrado A-B, Onyshchenko O, Ghavidel A, Ghai R, Rodriguez-Valera F (2012) Genomes of surface isolates of Alteromonas macleodii: the life of a widespread marine opportunistic copiotroph. Sci Rep 2:696

    Article  PubMed Central  PubMed  Google Scholar 

  • Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman MS, Chen Y, Chen Z, Dewell B, Du L, Fierro JM, Gomes XV, Goodwin BC, He W, Helgesen S, Ho CH, Irzyk GP, Jando SC, Maria LI, Jarvie TP, Jirage KB, Kim J, Knight JR, Lanza R, Leamon JH, Lefkowitz SM, Lei M, Li J et al (2006) NIH Public Access. Nature 437:376–380

    Google Scholar 

  • Martin-Cuadrado A-B, Ghai R, Gonzaga A, Rodriguez-Valera F (2009) CO dehydrogenase genes found in metagenomic fosmid clones from the deep mediterranean sea. Appl Environ Microbiol 75:7436–7444

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Mémery L, Arhan M, Alvarez-Salgado XA, Messias M-J, Mercier H, Castro CG, Rios AF (2000) The water masses along the western boundary of the South and equatorial Atlantic. Prog Oceanogr 47:69–98

    Article  Google Scholar 

  • Meyer F, Paarmann D, D’Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA (2008) The metagenomics RAST server—a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinf 9:386

  • Morris RM, Rappe MS, Vergin KL, Siebold WA, Carlson CA, Giovannoni SJ (2004) SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420(6917):806–810

  • Oksanen J, Kindt R, O’Hara B (2005) Vegan: R functions for vegetation ecologists. http//cc.oulu.fi/jarioksa/softhelp/vegan.html

  • Orcutt BN, Sylvan JB, Knab NJ, Edwards KJ (2011) Microbial ecology of the dark ocean above, at, and below the seafloor. Microbiol Mol Biol Rev 75:361–422

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Parada V, Sintes E, van Aken HM, Weinbauer MG, Herndl GJ (2007) Viral abundance, decay, and diversity in the meso- and bathypelagic waters of the north atlantic. Appl Environ Microbiol 73:4429–4438

  • Paradis E, Claude J, Strimmer K (2004) APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20:289–290

    Article  CAS  PubMed  Google Scholar 

  • Pedrosa P, Paranhos R, Suzuki MS, Andrade L, Carlos I, Silveira A, Campos A, Schmidt K, Falcão AP, Lavrado HP, Rezende CE (2006) Hidroquímica de massas d’ água oceânicas em regiões da margem continental Brasileira, Bacia de Campos, Estado do Rio de Janeiro, Brasil. Geochemica Bras 20:101–119

  • R: a language and environment for statistical computing. http://www.r-project.org/

  • Rezende C, Andrade L, Suzuki MS, Faro BCMTF, Gonzalez ASM, Paranhos R (2006) Hidroquímica. In: Valentin JL (ed) Caracter hidrobiológicas da região Cent da Zo Econômica Exclus Bras (Salvador, BA, ao Cabo São Tomé, RJ). Volume 60. Ideal Gráfica e Editora, Brasília, pp 31–60

  • Rezende CE, Pfeiffer WC, Martinelli LA, Tsamakis E, Hedges JI, Keil RG (2010) Lignin phenols used to infer organic matter sources to Sepetiba Bay RJ, Brasil. Estuar Coast Shelf Sci 87:479–486

    Article  CAS  Google Scholar 

  • Rho M, Tang H, Ye Y (2010) FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res 38:e191

    Article  PubMed Central  PubMed  Google Scholar 

  • Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson S, Yooseph S, Wu D, Eisen JA, Hoffman JM, Remington K, Beeson K, Tran B, Smith H, Baden-Tillson H, Stewart C, Thorpe J, Freeman J, Andrews-Pfannkoch C, Venter JE, Li K, Kravitz S, Heidelberg JF, Utterback T, Rogers Y-H, Falcón LI, Souza V, Bonilla-Rosso G, Eguiarte LE, Karl DM, Sathyendranath S et al (2007) The Sorcerer II global ocean sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol 5:e77

  • Schmieder R, Edwards R (2011a) Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PLoS ONE 6:e17288

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Schmieder R, Edwards R (2011b) Quality control and preprocessing of metagenomic datasets. Bioinformatics 27:863–864

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Servain J, Busalacchi AJ, McPhaden MJ, Moura AD, Reverdin G, Vianna M, Zebiak SE (1998) A pilot research moored array in the tropical Atlantic (PIRATA). Bull Am Meteorol Soc 79:2019–2031

  • Stramma L, England M (1999) On the water masses and mean circulation of the South Atlantic Ocean. J Geophys Res 104:20863–20883

  • Swan BK, Martinez-Garcia M, Preston CM, Sczyrba A, Woyke T, Lamy D, Reinthaler T, Poulton NJ, Masland EDP, Gomez ML, Sieracki ME, DeLong EF, Herndl GJ, Stepanauskas R (2011) Potential for chemolithoautotrophy among ubiquitous bacteria lineages in the dark ocean. Science 333:1296–1300

    Article  CAS  PubMed  Google Scholar 

  • The Matplotlib Basemap Toolkit user’s guide (2014)

  • Tomczak M Jr (1981) A multi-parameter extension of temperature/salinity diagram techniques for the analysis of non-isopycnal mixing. Prog Oceanogr 10:147–171

    Article  Google Scholar 

  • Trindade-Silva AE, Rua C, Silva GGZ, Dutilh BE, Moreira APB, Edwards RA, Hajdu E, Lobo-Hajdu G, Vasconcelos AT, Berlinck RGS, Thompson FL (2012) Taxonomic and functional microbial signatures of the endemic marine sponge Arenosclera brasiliensis. PLoS ONE 7:e39905

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden-Tillson H, Pfannkoch C, Rogers Y-H, Smith HO (2004) Environmental genome shotgun sequencing of the Sargasso Sea. Science 304:66–74

    Article  CAS  PubMed  Google Scholar 

  • Weinbauer MG, Brettar I, Ho MG (2003) Lysogeny and virus-induced mortality of bacterioplankton in surface, deep, and anoxic marine waters. Limnol Ocean 48(4):1457–1465. doi:10.4319/lo.2003.48.4.1457

  • Wickham H (2007) Reshaping data with the reshape package. J Stat Softw 21:1–20

    Google Scholar 

  • Wickham H (2009) ggplot2: elegant graphics for data analysis. Springer Publishing Company, Incorporated

    Book  Google Scholar 

  • Zubkov MV, Sleigh MA, Burkill PH, Leakey RJG (2000) Picoplankton community structure on the Atlantic Meridional Transect: a comparison between seasons. Prog Oceanogr 45:369–386

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Acknowledgments

We thank the PIRATA project and the Seward Johnson crew for handling the CTD data, CENPES-PETROBRAS for providing sampling and analysis at Bacia de Campos study. We thank CNPq, CAPES, FAPERJ for funding. The present study is part of the PhD thesis of NAJ and PMM.

Conflict of interest

The authors would like to declare that they have no financial or non-financial competing interests in the publication of this manuscript.

Author information

Authors and Affiliations

  1. Institute of Biology, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil

    Nelson Alves Junior, Pedro Milet Meirelles, Eidy de Oliveira Santos, Bas Dutilh, Rodolfo Paranhos, Anderson S. Cabral, Rodrigo L. de Moura, Cristiane Thompson & Fabiano Thompson

  2. INMETRO, Rio de Janeiro, Brazil

    Eidy de Oliveira Santos

  3. Computational Science Research Center, San Diego State University, San Diego, CA, USA

    Genivaldo G. Z. Silva

  4. Laboratory of Environmental Sciences, UENF, Campos, Brazil

    Carlos Rezende

  5. Osaka University, Osaka, Japan

    Tetsuya Iida

  6. Laboratory of Enzymology, Department of cellular Biology, Institute of Biology, University of Brasília (UnB), Brasília, Brazil

    Ricardo Henrique Kruger

  7. Institute of Biology, UFF, Niterói, Brazil

    Renato C. Pereira

  8. SAGE-COPPE, Rio de Janeiro, Brazil

    Rogério Valle & Fabiano Thompson

  9. Laboratory of Microbiology, Hokkaido University, Hakodate, Japan

    Tomoo Sawabe

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Corresponding author

Correspondence to Fabiano Thompson.

Additional information

Communicated by Erko Stackebrandt.

Nelson Alves Junior and Pedro Milet Meirelles have contributed equally to this work.

Electronic supplementary material

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203_2014_1035_MOESM1_ESM.tif

Figure S1 Pairwise relationships between environmental variables and bacterial counts, diversity and evenness using Pearson’s r correlations. On the upper diagonal, positive correlations are in blue, negative in red, the numbers are the Pearson’s r coefficients and the asterisks the significance levels (. - p = < 0.1; * - p = < 0.05; ** - p = < 0.01; *** - p = < 0.001). On the lower diagonal scatter plots showing the relationships between the variables with the best fit line. Diagonals, frequencies histograms of the variables values. (TIFF 8013 kb)

203_2014_1035_MOESM2_ESM.tif

Figure S2 Relationship between the different samples. A – Core metagenome based in consecutive blast and KEGG Orthology. The samples were ordered according to the homology between the samples of the same group. B – Venn diagram of the number of shared functions between the depths based on KEGG Orthology. C- Network presenting the percentage of homologous proteins between the different samples. ~ 85 % of all correlations are presented in the figure. (TIFF 1364 kb)

203_2014_1035_MOESM3_ESM.tif

Figure S3 Taxonomic diversity of the metagenomes corresponding to eukaryotic (A) and archaeal phyla (B). The classification was based in Genbank Database and the standard error for the Surface, SCM, and Deep was calculated based on 9, 4 and 15 metagenomes from each group, respectively. (TIFF 1369 kb)

203_2014_1035_MOESM4_ESM.tif

Figure S4 Bacterial classes abundance distribution according to the water mass. Total error was calculated based in 2 replicates (except for SCM that has 4 metagenomes sequenced) (TIFF 5590 kb)

203_2014_1035_MOESM5_ESM.png

Figure S5 Richness, diversity and evenness estimation of the different depths. The index was calculated based on family-level taxonomic (panels a, b c) and Subsystem level 3 classification (panel d, e and f) by MG-RAST. (PNG 116 kb)

203_2014_1035_MOESM6_ESM.png

Figure S6 Richness, diversity and evenness estimation of the different water masses. The index was calculated based on family-level taxonomic (panels a, b c) and Subsystem level 3 classification (panel d, e and f) by MG-RAST. (PNG 150 kb)

203_2014_1035_MOESM7_ESM.tif

Figure S7 Relative abundance of each core function. The abundance was estimated based on classification of each metagenome from different oceans by MG-RAST. (TIFF 746 kb)

203_2014_1035_MOESM8_ESM.xls

Table S1 General features of public metagenomes. The table includes information and taxonomical and functional annotation of the sequences. (XLS 45 kb)

203_2014_1035_MOESM9_ESM.xls

Table S2 Most abundant functions of the SAO metagenomes. The most abundant function of each metagenome is presented. (XLS 30 kb)

203_2014_1035_MOESM10_ESM.xls

Table S3 SAO core functions. 446 sequences remained after consecutive BLASTP against all of the SAO metagenomes. The functions were assigned by blast against the nr database. (XLS 170 kb)

Table S4 Metagenomes information. Detailed information about the 137 metagenomes used in this study. (XLSX 28 kb)

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Alves Junior, N., Meirelles, P.M., de Oliveira Santos, E. et al. Microbial community diversity and physical–chemical features of the Southwestern Atlantic Ocean. Arch Microbiol 197, 165–179 (2015). https://doi.org/10.1007/s00203-014-1035-6

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