URLhttps://www.bco-dmo.org/dataset/687844
Download URLhttps://www.bco-dmo.org/dataset/687844/data/download
Media Typetext/tab-separated-values
CreatedApril 18, 2017
ModifiedOctober 12, 2018
StateFinal no updates expected
Brief DescriptionMicrobial 16S rRNA sequence data from East Pacific Rise biofilms

Acquisition Description

Basalt panels were deployed and recovered by DSV Alvin at TicaVent on the EPR (950.4 N, 10417.5 W: 2513-m depth) during R/V Atlantis research cruises AT11-07 (2004/02/03 –2004/02/19), AT11-10 (2004/04/07–2004/04/24), AT11-20 (2004/11/11–2004/11/24). Basalt panels were deployed either at a diffuse-flow vent site (Experiment site1: ∼15◦C) or a nearby, non-vent site (Control site: ∼1.8◦C). Experiment site1 was located within a thriving patch of tubeworms (Riftia pachyptila) and mussels (Bathymodiolus thermophilus). The Control site was located 2.5- m from the experimental site, ∼1/2-m from mussels marking the edge of the colonized vent. These panels are considered ‘pre-eruption’ samples because they were collected 19–28 months before the 2005/2006 EPR eruption. For the purpose of comparison with our experimental results, one post-eruption native basalt biofilm sample (hereafter referred to as ‘native Basalt’) was collected from Tamtown (950.3 N, 10417.4 W: 2503-m depth; ∼0.58 km from TicaVent), during the RESET06 cruise (AT15-06, 2006/06/25-2006/07/01). [official dates: AT15-06 (2006/06/18-2006/07/07)]

Basalt panels (10.2 x 10.2 x 2.5 cm) were constructed of basalt collected in the area of 9oN on the EPR (Supplementary Figure 1D). Before deployment, all panels were sealed in aluminum foil and autoclaved. Each panel was deployed from and recovered into an ethanol-wiped biobox mounted on Alvin’s basket, filled prior to the dive with either 0.2-µm filtered seawater or double-distilled water to prevent contamination with surface seawater (as were the Basalt and Trap samples). All subsequent handling of the basalt panels was with sterilized gloves or tools. Control and experimental basalt panels were exposed for 5 time intervals: 4-days, 9-days, 13-days, 76-days, and 9-mos [283-days (control) and 293-days (experimental)]. One panel was lost (76-day, control), leaving a total of 9 panels for analysis, in addition to the native Basalt and Trap samples.

Environmental DNA was extracted from the basalt panel surfaces using a large-volume CTAB (hexadecyltrimethylammonium bromide) extraction. Individual basalt panels were placed into warmed (55oC) DNA extraction buffer, composed of 100 mM TRIS-HCl (pH 8.0), 20 mM EDTA (pH 8.0), 1.4 M NaCl and 2% CTAB, with mercaptoethanol added to 0.2% via syringe filter. Filter-sterilized Proteinase K solution and sodium dodecyl sulfate (SDS) solution were added to final concentrations of 0.1 mg/mL and 0.65% respectively. Covered by this extraction solution, each block was agitated on a shaker table for 2 hrs at 55oC. Biofilm removal was confirmed by inspection of the extracted blocks under a dissecting microscope. DNA was extracted from this solution with an equivalent volume of phenol:chloroform:isoamyl alcohol (25:24:1, pH 8.0), followed by an equivalent volume of chloroform:isoamyl alcohol (24:1). To precipitate the DNA, 0.1 volume of 3M sodium acetate and 2.5 volumes of cold 100% ethanol were added, then placed into -20oC for 3 hours before centrifuging at 10,000 x g for 15 min. After decanting the supernatant, the pellets were covered with cold 70% ethanol and centrifuged at 16,000 x g for 5 min. The ethanol was pipetted off and the pellets were dried. Isolated DNA was resuspended in 50-200 µl sterile water and kept at -80oC until use.  Environmental DNA from Trap and native Basalt samples was extracted with using the Ultraclean Soil DNA extraction kit (MoBio laboratories).

The 16S rRNA region of environmental DNA was amplified (27F, 1492R primers) and replicates of 10 PCR amplifications (15 cycles each) were combined, precipitated using a QIAquick PCR purification kit (Qiagen), resuspended in 35 µl of sterile water and purified using the QIAquick gel extraction kit (Qiagen). Replicate PCR reactions were combined in order to minimize PCR bias (Polz and Cavanaugh, 1998) and only 15 cycles were used to decrease the formation of chimeric sequences and Taq error. The combined products were then reamplified with five additional PCR cycles to minimize the formation of heteroduplex molecules (Thompson et al., 2002) and purified using a QIAquick gel extraction kit (Qiagen).

Purified PCR products of the 16S rRNA gene were cloned with the Strataclone PCR cloning kit (Stratagene) for sequencing. Nearly complete, double-stranded sequences of 16S rRNA genes (~1,475 bp) were sequenced on a 96-capillary 3730xl DNA analyzer (Applied Biosystems) with primers M13F and M13R. For all libraries, single strand sequences were grouped by 97% similarity with the program Sequencher (version 4.1.2, Applied Biosystems) and one representative of each group was selected to sequence both forward and reverse strands. These groupings were designated operational taxonomic units (OTUs). Sequences were tested for the presence of chimeras with the program Mallard (Ashelford et al., 2006) and with the Bellerophon server (Huber et al., 2004). The ARB software package was used to analyze sequence data and construct trees (version 2.5b; O. Strunk and W. Ludwig, Technische Universitat Munchen, Munich, Germany).

Primers 517F and 806R were used to target the V4 region of the 16S rRNA gene (Caporaso et al., 2010).  For multiplex sequencing, 8 forward primers were synthesized (Eurofin), each with a different tag. PCR reactions (50 µl volume) contained 250 nM of each of forward and reverse primers, 5 ng of template DNA and were performed using Picomaxx taq polymerase (Invitrogen, Carlsbad, CA) using the thermal profile 95°C for 2 min followed by 25 cycles of denaturation at 94°C for 15 s, primer annealing at 61°C and extension at 72°C for 45 s, with final extension of 72°C for 3 min. Amplicons were sequenced at EnGenCore (University of South Carolina) using 454 FLX chemistry. Raw sequences were processed with QIIME 1.8.0 (Caporaso et al., 2010). Sequences were excluded from analysis if they had a mean quality score < 25, were either < 200 or > 1000 bp in length, contained ambiguous nucleotides or had any mismatches in the forward and reverse primers. The sequences were assigned to individual samples by their adapter tags and the 16S rRNA primers were removed prior to analysis. The resulting data sets contained 254 bp of the bacterial V4 region. Potential chimeras were identified and removed using ChimeraSlayer. Trimmed sequences were then classified with RDP classifier. Operational taxonomic units (OTUs) were clustered at 97% similarity based on a distance matrix generated with the program QIIME.

Phylogenetic trees were constructed from aligned clone sequences and closely related environmental clones and cultures using the ARB software package (version 5.5; O. Strunk and W. Ludwig, Technische Universität Munchen, Munich, Germany). Tag sequences found at high abundances (>50 tags detected per sample), as well as those that were identical to a clone sequence or those that represent a unique lineage were inserted into bootstrapped trees using parsimony insertion tool. Alpha and beta diversity estimates were calculated using QIIME. After trimming each sample to an equal number of tags, bacterial diversity was estimated with N obs (observed richness), Chao1 (nonparametric richness estimator), nonparametic (np) Shannon diversity index, Simpson Evenness and Equitability. Structure of the pyrosequenced microbial communities was compared with two different methods. Principal coordinate analysis (PCoA) maps the samples on a set of orthogonal axes in order to explain the maximum amount of variation by the first coordinate and the second largest amount of variation by the second coordinate based on weighted UniFrac values (Lozupone et al., 2010). The UPGMA (unweighted pair group method with arithmetic mean) distance tree is based on a hierarchical clustering in which topological relationships are identified in order of similarity. The robustness of the UPGMA clusters was tested with jackknife analysis, based on 100 randomized subsamples.

Processing Description

The ARB software package was used to analyze sequence data and construct trees (version 2.5b; O. Strunk and W. Ludwig, Technische Universitat München, Munich, Germany).

The raw tag sequences were processed with QIIME 1.8.0 (Caporaso et al., 2010). Sequences were excluded from analysis if they had a mean quality score < 25, were either < 200 or > 1000 bp in length, contained ambiguous nucleotides or had any mismatches in the forward and reverse primers. The sequences were assigned to individual samples by their adapter tags and the 16S rRNA primers were removed prior to analysis. The resulting data sets contained 254 bp of the bacterial V4 region.  Potential chimeras were identified and removed using ChimeraSlayer.  Trimmed sequences were then classified with RDP classifier. Operational taxonomic units (OTUs) were clustered at 97% similarity based on a distance matrix generated with the program QIIME.

Phylogenetic trees were constructed from aligned clone sequences and closely related environmental clones and cultures using the ARB software package (version 5.5; O. Strunk and W. Ludwig, Technische Universität Munchen, Munich, Germany). Tag sequences found at high abundances (>50 tags detected per sample), as well as those that were identical to a clone sequence or those that represent a unique lineage were inserted into bootstrapped trees using parsimony insertion tool. Alpha and beta diversity estimates were calculated using QIIME. After trimming each sample to an equal number of tags, bacterial diversity was estimated with N obs (observed richness), Chao1 (nonparametric richness estimator), nonparametic (np) Shannon diversity index, Simpson Evenness and Equitability. Structure of the pyrosequenced microbial communities was compared with two different methods. Principal coordinate analysis (PCoA) maps the samples on a set of orthogonal axes in order to explain the maximum amount of variation by the first coordinate and the second largest amount of variation by the second coordinate based on weighted UniFrac values (Lozupone et al., 2010). The UPGMA (unweighted pair group method with arithmetic mean) distance tree is based on a hierarchical clustering in which topological relationships are identified in order of similarity. The robustness of the UPGMA clusters was tested with jackknife analysis, based on 100 randomized subsamples.

BCO-DMO Processing Notes:
- added conventional header with dataset name, PI name, version date
- modified parameter names to conform with BCO-DMO naming conventions
- changed n/a (not available) to nd (no data)
- converted latitude and longitude to decimal degrees
- reformatted date from dd-Mon-yy to yyyy-mm-dd
- created links to NCBI accession pages

Instruments

Capillary 3730xl DNA analyzer (Applied Biosystems) and 454 FLX Technology [Automated DNA Sequencer]
Details

General term for a laboratory instrument used for deciphering the order of bases in a strand of DNA. Sanger sequencers detect fluorescence from different dyes that are used to identify the A, C, G, and T extension reactions. Contemporary or Pyrosequencer methods are based on detecting the activity of DNA polymerase (a DNA synthesizing enzyme) with another chemoluminescent enzyme. Essentially, the method allows sequencing of a single strand of DNA by synthesizing the complementary strand along it, one base pair at a time, and detecting which base was actually added at each step.

Parameters

sample_descrip [sample_descrip]
Details
sample_descrip
sample description: either 16S tags or clones
text description of sample collected
cruise_id [cruise_id]
Details
cruise_id
cruise identification
cruise designation; name
date_deployed [date]
Details
date_deployed
date that panels were deployed

date; generally reported in GMT as YYYYMMDD (year; month; day); also as MMDD (month; day); EqPac dates are local Hawaii time. ISO_Date format is YYYY-MM-DD (http://www.iso.org/iso/home/standards/iso8601.htm)

date_recovered [date_re]
Details
date_recovered
date that panels were recovered
date/time instrument/mooring recovered; as MMDDHHmm
lat [latitude]
Details
lat
latitude; north is positive

latitude, in decimal degrees, North is positive, negative denotes South; Reported in some datasets as degrees, minutes

lon [longitude]
Details
lon
longitude; east is positive

longitude, in decimal degrees, East is positive, negative denotes West; Reported in some datsets as degrees, minutes

depth_w [depth_w]
Details
depth_w
depth of water

water depth, in meters

site [site]
Details
site
sampling site
Sampling site identification.
treatment [treatment]
Details
treatment

treatment type: experimental, control, or natural basalt (see methodology description for details)

Experimental conditions applied to experimental units.  In comparative experiments, members of the complementary group, the control group, receive either no treatment or a standard treatment.

panel_id [sample]
Details
panel_id
settling panel identifier

unique sample identification or number; any combination of alpha numeric characters; precise definition is file dependent

BioProject [accession number]
Details
BioProject
NCBI BioProject accession number
Database identifier assigned by repository and linked to GenBank or other repository.
BioProject_link [external_link]
Details
BioProject_link
link to NCBI BioProject accession page

Link to an external data entry.

SRA_experiment [accession number]
Details
SRA_experiment
SRA experiment accession number
Database identifier assigned by repository and linked to GenBank or other repository.
SRA_expt_link [external_link]
Details
SRA_expt_link
link to SRA experiment accession page

Link to an external data entry.

NCBI_accession [accession number]
Details
NCBI_accession
NCBI sequence accession number
Database identifier assigned by repository and linked to GenBank or other repository.
accession_link [external_link]
Details
accession_link
link to sequence accession page

Link to an external data entry.

Dataset Maintainers

NameAffiliationContact
Stefan M. SievertWoods Hole Oceanographic Institution (WHOI)
Nancy CopleyWoods Hole Oceanographic Institution (WHOI BCO-DMO)

BCO-DMO Project Info

Project TitleAn Integrated Study of Energy Metabolism, Carbon Fixation, and Colonization Mechanisms in Chemosynthetic Microbial Communities at Deep-Sea Vents
AcronymMicrobial Communities at Deep-Sea Vents
URLhttps://www.bco-dmo.org/project/2216
CreatedJune 11, 2012
ModifiedJune 11, 2012
Project Description

Deep-sea hydrothermal vents, first discovered in 1977, are poster child ecosystems where microbial chemosynthesis rather than photosynthesis is the primary source of organic carbon. Significant gaps remain in our understanding of the underlying microbiology and biogeochemistry of these fascinating ecosystems. Missing are the identification of specific microorganisms mediating critical reactions in various geothermal systems, metabolic pathways used by the microbes, rates of the catalyzed reactions, amounts of organic carbon being produced, and the larger role of these ecosystems in global biogeochemical cycles. To fill these gaps, the investigators will conduct a 3-year interdisciplinary, international hypothesis-driven research program to understand microbial processes and their quantitative importance at deep-sea vents. Specifically, the investigators will address the following objectives: 1. Determine key relationships between the taxonomic, genetic and functional diversity, as well as the mechanisms of energy and carbon transfer, in deep-sea hydrothermal vent microbial communities. 2. Identify the predominant metabolic pathways and thus the main energy sources driving chemoautotrophic production in high and low temperature diffuse flow vents. 3. Determine energy conservation efficiency and rates of aerobic and anaerobic chemosynthetic primary productivity in high and low temperature diffuse flow vents. 4. Determine gene expression patterns in diffuse-flow vent microbial communities during attachment to substrates and the development of biofilms.

Integration: To address these objectives and to characterize the complexity of microbially-catalyzed processes at deep-sea vents at a qualitatively new level, we will pursue an integrated approach that couples an assessment of taxonomic diversity using cultivation-dependent and -independent approaches with methodologies that address genetic diversity, including a) metagenomics (genetic potential and diversity of community), b) single cell genomics (genetic potential and diversity of uncultured single cells), c) meta-transcriptomics and -proteomics (identification and function of active community members, realized potential of the community). To assess function and response to the environment, these approaches will be combined with 1) measurement of in situ rates of chemoautotrophic production, 2) geochemical characterization of microbial habitats, and 3) shipboard incubations under simulated in situ conditions (hypothesis testing under controlled physicochemical conditions). Network approaches and mathematical simulation will be used to reconstruct the metabolic network of the natural communities. A 3-day long project meeting towards the end of the second year will take place in Woods Hole. This Data Integration and Synthesis meeting will allow for progress reports and presentations from each PI, postdoc, and/or student, with the aim of synthesizing data generated to facilitate the preparation of manuscripts.

Intellectual Merit. Combining the community expression profile with diversity and metagenomic analyses as well as process and habitat characterization will be unique to hydrothermal vent microbiology. The approach will provide new insights into the functioning of deep-sea vent microbial communities and the constraints regulating the interactions between the microbes and their abiotic and biotic environment, ultimately enabling us to put these systems into a quantitative framework and thus a larger global context.

Broader Impacts. This is an interdisciplinary and collaborative effort between 4 US and 4 foreign institutions, creating unique opportunities for networking and fostering international collaborations. This will also benefit the involved students (2 graduate, several undergraduate) and 2 postdoctoral associates. This project will directly contribute to many educational and public outreach activities of the involved PIs, including the WHOI Dive & Discover program; single cell genomics workshops and Cafe Scientifique (Bigelow); REU (WHOI, Bigelow, CIW); COSEE and RIOS (Rutgers), and others. The proposed research fits with the focus of a number of multidisciplinary and international initiatives, in which PIs are active members (SCOR working group on Hydrothermal energy and the ocean carbon cycle, http://www.scorint. org/Working_Groups/wg135.htm; Deep Carbon Observatory at CIW, https://dco.gl.ciw.edu/; Global Biogeochemical Flux (GBF) component of the Ocean Observatories Initiative (OOI), http://www.whoi.edu/GBF-OOI/page.do?pid=41475)

Project Maintainers
NameAffiliationRoleContact
Stefan M. SievertWoods Hole Oceanographic Institution (WHOI)Lead Principal Investigator
Costantino VetrianiRutgers UniversityPrincipal Investigator
Dionysis I. FoustoukosCarnegie Institution for Science (CIS)Principal Investigator
Ramunas StepanauskasBigelow Laboratory for Ocean SciencesPrincipal Investigator
Craig TaylorWoods Hole Oceanographic Institution (WHOI)Co-Principal Investigator
Jeffrey S. SeewaldWoods Hole Oceanographic Institution (WHOI)Co-Principal Investigator
Nadine Le BrisLaboratoire d'Écogéochimie des Environnements Benthiques (LECOB)International Collaborator
Niculina MusatMax Planck Institute for Marine Microbiology (MPI)International Collaborator
Thomas SchwederUniversity of GreifswaldInternational Collaborator
Fengping WangShanghai Jiao Tong University (SJTU)International Collaborator
Menu