Archives of Microbiology

, Volume 197, Issue 2, pp 165–179 | Cite as

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

  • Nelson Alves Junior
  • Pedro Milet Meirelles
  • Eidy de Oliveira Santos
  • Bas Dutilh
  • Genivaldo G. Z. Silva
  • Rodolfo Paranhos
  • Anderson S. Cabral
  • Carlos Rezende
  • Tetsuya Iida
  • Rodrigo L. de Moura
  • Ricardo Henrique Kruger
  • Renato C. Pereira
  • Rogério Valle
  • Tomoo Sawabe
  • Cristiane Thompson
  • Fabiano Thompson
Original Paper


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.


Metagenomics South Atlantic Ocean Microbial diversity Water mass 



Southwestern Atlantic Ocean


Dissolved organic carbon


Tropical waters


Mediterranean deep chlorophyll maximum


Sub-superficial chlorophyll maximum


South Atlantic central water


Antarctic intermediate water


Upper circumpolar deep water







Org N

Organic carbon

Total N

Total nitrogen

Org P

Organic phosphorous

Ortho P





High nucleic acid


Low nucleic acid


Virus-to-bacterium ratio



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.

Supplementary material

203_2014_1035_MOESM1_ESM.tif (7.8 mb)
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 (1.3 mb)
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 (1.3 mb)
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 (5.5 mb)
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 (116 kb)
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 (150 kb)
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 (746 kb)
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 (46 kb)
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 (30 kb)
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 (170 kb)
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)
203_2014_1035_MOESM11_ESM.xlsx (28 kb)
Table S4 Metagenomes information. Detailed information about the 137 metagenomes used in this study. (XLSX 28 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Nelson Alves Junior
    • 1
  • Pedro Milet Meirelles
    • 1
  • Eidy de Oliveira Santos
    • 1
    • 2
  • Bas Dutilh
    • 1
  • Genivaldo G. Z. Silva
    • 3
  • Rodolfo Paranhos
    • 1
  • Anderson S. Cabral
    • 1
  • Carlos Rezende
    • 4
  • Tetsuya Iida
    • 5
  • Rodrigo L. de Moura
    • 1
  • Ricardo Henrique Kruger
    • 6
  • Renato C. Pereira
    • 7
  • Rogério Valle
    • 8
  • Tomoo Sawabe
    • 9
  • Cristiane Thompson
    • 1
  • Fabiano Thompson
    • 1
    • 8
  1. 1.Institute of BiologyFederal University of Rio de Janeiro (UFRJ)Rio de JaneiroBrazil
  2. 2.INMETRORio de JaneiroBrazil
  3. 3.Computational Science Research CenterSan Diego State UniversitySan DiegoUSA
  4. 4.Laboratory of Environmental SciencesUENFCamposBrazil
  5. 5.Osaka UniversityOsakaJapan
  6. 6.Laboratory of Enzymology, Department of cellular Biology, Institute of BiologyUniversity of Brasília (UnB)BrasíliaBrazil
  7. 7.Institute of BiologyUFFNiteróiBrazil
  8. 8.SAGE-COPPERio de JaneiroBrazil
  9. 9.Laboratory of MicrobiologyHokkaido UniversityHakodateJapan

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