16S rRNA gene amplicon dataset of prokaryotic communities from a subantarctic marine ecosystem: Ushuaia Bay and surrounding waters

Abstract

We provide a 16S rRNA gene dataset of prokaryotic assemblages of a subantarctic marine ecosystem. Samples were collected at 2 stations (one near Ushuaia Bay and the other close to Bridges islands in the Beagle Channel). At each station, 2 depths (subsurface and bottom waters) were sampled in february, march, may and september during 2018, giving a total of 10 samples. 16S rRNA gene amplicon sequencing (V4 hypervariable region) was performed with the Illumina MiSeq platform. A total of 1116 amplicon sequence variants (ASVs) were recovered from the dataset. The sequences were taxonomically assigned to Alphaproteobacteria (23 ± 2%, mean ± standard error), Gammaproteobacteria (17 ± 1.5%), Flavobacteriia (8 ± 2%), Deltaproteobacteria (3.7 ± 0.5%), Acidimicrobiia (1.7 ± 0.1%), Planctomycetia (1.9 ± 0.4%), and AB16 group (1.7 ± 0.3%). Sequences affiliated with Archaea were abundant, reaching one third of analyzed sequences, mainly Thaumarchaeota (22 ± 3%), and Thermoplasmata (10 ± 1%). Together, sequences assigned to all these groups accounted for more than 90% of the sequences. This dataset constitutes a valuable resource for future scientific research aiming to unveil the role of these communities in ecosystem services such as carbon and nutrient cycling, and pollutants degradation. This will turn into benefits for future environmental monitoring and preservation actions, considering the tangible heritage of Ushuaia Bay and surrounding waters.

Full text

Data in Brief 32 (2020) 106 171
Contents lists available at ScienceDirect
Data in Brief
journal homepage: www.elsevier.com/locate/dib
Data Article
16S rRNA gene amplicon dataset of
prokaryotic communities from a subantarctic
marine ecosystem: Ushuaia Bay and
surrounding waters
Clara Natalia Rodríguez-Flórez
a
, Andrea Malits
a
, Mariana Lozada
b , ∗
a
Laboratorio de Oceanografía Biológica (CADIC-CONICET), Ushuaia, Argentina
b
Laboratorio de Microbiología Ambiental (IBIOMAR-CONICET), Puerto Madryn, Argentina
a r t i c l e i n f o
Article history:
Received 19 June 2020
Revised 27 July 2020
Accepted 6 August 2020
Available online 12 Au gust 2020
Keywo rds:
Bacteria
Archaea
Seawater
Subantarctic
16S rRNA
Gene amplicon sequencing
a b s t r a c t
We provide a 16S rRNA gene dataset of prokaryotic assem-
blages of a subantarctic marine ecosystem. Samples were col-
lected at 2 stations (one near Ushuaia Bay and the other
close to Bridges islands in the Beagle Channel). At each sta-
tion, 2 depths (subsurface and bottom waters) were sam-
pled in february, march, may and september during 2018,
giving a total of 10 samples. 16 S rRNA gene amplicon se-
quencing (V4 hypervariable region) was performed with the
Illumina MiSeq platform. A total of 1116 amplicon sequence
variants (ASVs) were recovered from the dataset. The se-
quences were taxonomically assigned to Alphaproteobacte-
ria (23 ±2%, mean ±standard error), Gammaproteobacte-
ria (17 ±1.5% ) , Flavobacteriia (8 ±2%), Deltaproteobacteria
(3.7 ±0.5%), Acidimicrobiia (1.7 ±0.1%), Planctomycetia (1.9
±0.4%), and AB16 group (1.7 ±0.3%). Sequences affiliated
with Archaea were abundant, reaching one third of analyzed
sequences, mainly Thaumarchaeota (22 ±3%), and Thermo-
plasmata (10 ±1%). Together, sequences assigned to all these
groups accounted for more than 90% of the sequences. This
dataset constitutes a valuable resource for future scientific
research aiming to unveil the role of these communities in
ecosystem services such as carbon and nutrient cycling, and
pollutants degradation. This will turn into benefits for future
∗Corresponding author.
E-mail address: lozada@cenpat-conicet.gob.ar (M. Lozada).
https://doi.org/10.1016/j.dib.2020.106171
2352-3409/© 2020 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.
( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

2 C.N. Rodríguez-Flórez, A. Malits and M. Lozada / Data in Brief 32 (2020) 10617 1
environmental monitoring and preservation actions, consid-
ering the tangible heritage of Ushuaia Bay and surrounding
waters.
©2020 Published by Elsevier Inc.
This is an open access article under the CC BY-NC-ND
license. ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )
Specifications Tabl e
Subject Environmental Science (General)
Specific subject area Marine microbial diversity and ecology
Type of data fastq file
How data were acquired
16S rRNA ge ne amplicon sequencing
Instruments: Illumina MiSeq platform, QIIME2 software
Data format Raw
Filtered
Analyzed
Parameters for data collection Conditions considered for data collection were: seawater samples from subsurface
and bottom, at two stations: one in Ushuaia Bay and the other in surrounding
waters. Subsurface waters: -20m. Bottom waters: ∼-120m (Ushuaia Bay station)
and ∼-148m (surrounding waters station).
Description of data collection Seawater samples were collected from a boat in Niskin bottles, prefiltered and
passed through a Sterivex
filter (0.2μm). Metagenomic DNA extraction was
performed from filters, and after PCR amplification of 16S rRNA gene V4
hypervariable region, amplicon sequencing was done on Illumina MiSeq platform.
Preprocessing and taxonomic profiling was performed in QIIME2.
Data source location City/Region: Ushuaia/Tierra del Fuego
Country: Argentina
Latitude and longitude:
Ushuaia bay station: 54 °48.728
´
S, 68 °14.388 W, close to Ushuaia city (Tierra del
Fuego, Argentina). Surrounding waters station: 54 °52.939
´
S, 68 °10.927
´
W, close to
Bridges islands and near kelp forest, in the Beagle Channel.
Maritime traffic: the sampling stations are located along a sea route. Possible
environmental impact. High nutrient and organic matter load to Ushuaia Bay
through glacial runoff and urban and industrial activities.
Data accessibility The sequence data from this work is available at the NCBI Genbank Sequence Read
Archive (SRA) as Bioproject ID: PRJNA622742 and SRA accession no. PRJNA637656.
Repository name: Genbank Sequence Read
Archive
Data identification number:
PRJNA637656 (Bioproject)
SRR11941686-SRR11941695 (Runs)
Direct URL to data:
Bioproject:
https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA637656&o=acc _ s%3Aa
Runs:
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941686
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941687
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941688
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941689
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941690
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941691
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941692
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941693
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941694
https://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR11941695

C.N. Rodríguez-Flórez, A. Malits and M. Lozada / Data in Brief 32 (2020) 10617 1 3
Value of the Data
• This is the first checklist of amplicon sequence variants and their taxonomic classification
from Ushuaia Bay and surrounding waters in the Beagle Channel, a subantarctic marine en-
vironment exposed to anthropogenic impact and suffering rapid shifts due to climate change.
• This first 16S rRNA gene profiling of prokaryotic assemblages of a subantarctic marine ecosys-
tem is a valuable resource for the scientific community as well as for institutions performing
environmental monitoring and preservation actions.
• This dataset is useful for upcoming scientific research covering the role of these communities
in ecosystem services.
1. Data description
The raw sequencing dataset contained 550,960 sequences from a total of 10 samples. Af-
ter preprocessing, the dataset contained 302,986 sequences, 150 bp on average. A total of
1116 amplicon sequence variants (ASVs) were recovered from this environment. Of the
total analyzed sequences, 68 ±3 % corresponded to Bacteria (mean ±standard error)
and 32 ±3 % to Archaea. The major phyla (out of 24, B__: Bacteria and A__: Archaea)
were : B__ Proteobacteria (45 ±2 %), A__ Crenarchaeota (22 ±3%), A__ Euryarchaeota 10 ±1%,
B__ Bacteroidetes 9 ±2% and B__ Planctomycetes 4 ±0.6%. The most abundant classes (out of
40) were : B__ Alphaproteobacteria 23 ±2%, A__ Thaumarchaeota 2 ±3%, B__ Gammaproteobacteria
17 ±1%, A__ Thermoplasmata 10 ±1%, B__ Flavobacteriia 8 ±2%, B__Deltaproteobacteria 4 ±0.5%,
B__Acidimicrobiia 2 ±0.1%, B__Planctomycetia 2 ±0.4%, B__AB16 1.71 ±0.2%. The most repre-
sentative orders (out of 60) were: A__Cenarchaeales 22 ±3%, B__Oceanospirillales 10 ±0.7%,
A__E2 10 ±1%, B__Rhodobacterales 9 ±2%, B__Rickettsiales 9 ±1%, and B__Flavobacteriales 8
±2%. The major families (out of 78) were: A__ Cenarchaeaceae 22 ±3%, A__ Marine group II 9
±1%, B__ Rhodobacteraceae 9 ±2%, B__ Pelagibacteraceae 9 ±1%, B__ Flavobacteriaceae 5 ±1%,
B__ Halomonadaceae 4 ±0.6%, B__ Alteromonadaceae 1.8 ±0.5% and B__ Nitrospinaceae 1.8 ±0.3%
( Fig. 1 ).
The major genera (out of 82) were : A__ Nitrosopumilus 22 ±3%, B__ Pelagibacter 7 ±1%,
B__ Candidatus Portiera 4 ±0.6%, B__ Sulfitobacter 4 ±1%, 1. 7 ±0.3%, B__ Colwellia 1.4 ±0.7%,
B__ Polaribacter 1.3 ±0.4% and B__ Glaciecola 0.9 ±0.5%.
Supplementary File 1. Bioinformatic script used to preprocess the raw sequences. QIIME2 v
2019.1 ( https://qiime2.org/ ) was used to process the data.
2. Experimental design, materials and methods
Seawater was collected in Niskin bottles (5L) and prefiltered by a mesh of 213 μm after which
each water sample ( ∼4L) was concentrated in a Sterivex filter unit of 0.22 μm (Millipore). The
Sterivex units were stored without excess liquid into sterile sampling bags (Microclar) at -20 °C,
until cell lysis and nucleic acid extraction. The sampling took place during February (2 samples),
March (1), May (4) and September (3) 2018.
Cell lysis and nucleic acid extraction were carried out following the protocol modified
from Somerville and collaborators [1] . DNA quantification was done using Lambda Phage DNA
(Promega). Samples were sequenced at INDEAR (Argentina). Quantification of initial DNA sam-
ples was performed using the Quant-iTTM PicoGreen® DNA Assay Kit (Invitrogen). Amplicons of
V4 region were obtained by using primers 515F (GTGCCAGCMGCCGCGGTA) [2] and 806R (GGAC-
TACNVGGGT W TCTAAT) [3] . A second round of PCR was performed with standard Illumina bar-
codes and adapters. Libraries were pooled at the same concentration and 1ul of the pool was
run in the 2100 Bioanalyzer (Agilent Technologies) using the DNA 12000 chip Kit. The library
pool was quantified using DeNovix. Libraries were sequenced on an Illumina MiSeq platform.

4 C.N. Rodríguez-Flórez, A. Malits and M. Lozada / Data in Brief 32 (2020) 10617 1
Fig. 1. Percent abundance of major (a) classes and (b) families, identified in prokaryotic communities from Ushuaia Bay
and surrounding waters in the Beagle Channel. Only classes and families with more than 1% relative abundance are
shown. A_: Archaea. B_: Bacteria.

C.N. Rodríguez-Flórez, A. Malits and M. Lozada / Data in Brief 32 (2020) 10617 1 5
The generated reads were preprocessed with the QIIME2 package [4] . Briefly, demultiplexed
paired-end reads were trimmed to 150 bp, merged and the resulting sequences were denoised
in Deblur [5] in QIIME2 environment, in order to identify amplicon sequence variants (ASV).
ASVs were taxonomically classified in QIIME2 using Greengenes classifier ( https://data.qiime2.
org/2019.1/common/gg- 13 - 8- 99- 515- 806- nb- classifier.qza ). The resulting feature table and tax-
onomic assignments were analyzed in QIIME2 and phyloseq [6] .
Supplementary File 1: Bioinformatic script used in QIIME2.
Ethics statement
The work did not involve the use of human subjects or animal experiments.
Declaration of Competing Interest
The authors declare that they have no competing financial interests or personal relationships
which have, or could be perceived to have, influenced the work reported in this article.
Acknowledgments
AM and ML are members of the Argentinean National Research Council (CONICET). CNRF is
a doctoral fellow (CONICET program). This work was supported by ANPCyT (Grants PICT-2015-
0384 and PICT-2018-0903 ).
Supplementary materials
Supplementary material associated with this article can be found, in the online version, at
doi:10.1016/j.dib.2020.106171 .
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