Contributors | Affiliation | Role |
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Orcutt, Beth N. | Bigelow Laboratory for Ocean Sciences | Principal Investigator |
D'Angelo, Timothy | Bigelow Laboratory for Ocean Sciences | Co-Principal Investigator |
Rauch, Shannon | Woods Hole Oceanographic Institution (WHOI BCO-DMO) | BCO-DMO Data Manager |
Metadata for sequence datasets used in ocean crust microbiome survey.
This metadata table and supporting PDF document describe data analysis performed for a review chapter to be published in an edited book:
Authors: Beth N. Orcutt, Timothy D'Angelo, Sean P. Jungbluth, Julie A. Huber, Jason B. Sylvan
Chapter Title: Microbial Life in Oceanic Crust
Book title: The Microbiology of the Deep-Sea
Editors: Donato Giovannelli, Costantino Vetriani
Publisher: Springer International Publishing AG
Analysis of publicly available 16S rRNA gene sequence datasets for taxonomic profiling
To summarize crustal bacterial and archaeal taxa for this review, we synthesized publicly-available sequence datasets of the 16S rRNA gene in environmental DNA extracted from seafloor and subseafloor basalts generated using 454, Illumina and Ion Torrent amplicon platforms. These include seafloor basalts from the Dorado Outcrop (Lee et al., 2015) and the Lō'ihi Seamount (Jacobsen Meyers et al., 2014) in the Pacific Ocean and subseafloor basalts from North Pond on the western flank of the Mid-Atlantic Ridge (Jørgensen & Zhao, 2016) and the Juan de Fuca Ridge flank in the northeastern Pacific Ocean (LaBonté et al., 2017). Datasets from rock colonization experiments conducted in the subseafloor at the Juan de Fuca Ridge flank site (Smith et al., 2016; Ramírez et al., 2019) were also included, as well as microbial community surveys of the subseafloor crustal fluids from the anoxic Juan de Fuca site (Jungbluth et al., 2016) and the oxic North Pond site (Tully et al., 2017; Meyer et al., 2016). For comparison, we included select reference datasets from oxic (Reese et al., 2018; Zinke et al., 2018) and anoxic sediment (LaBonté et al., 2017) and the overlying bottom seawater (Lee et al., 2015) from these same study sites.
Raw sequence data from the reviewed studies were downloaded from the NCBI Short Read Archive. Sequencing reads generated using Illumina and Ion Torrent platforms were quality filtered and processed to unique Amplicon Sequence Variants (ASVs) using DADA2 (Callahan et al, 2016), with taxonomy determined by the naïve Bayesian classifier in DADA2 using a training set from the SILVA v132 database (Quast et al., 2013; Yilmaz et al., 2014; Glöckner et al., 2017). For the 454 GS-FLX sequence datasets, operational taxonomic units (OTUs) constructed with 97% or greater sequence similarity in the original analyses were reprocessed in mothur V.1.37.6 (Schloss et al., 2009) against the same SILVA database. All short read datasets were merged and summarized to the relative abundance at phylum resolution (or to class level for Proteobacteria phyla) using Phyloseq v1.24.0 (McMurdie & Holmes, 2013). Figures were produced using ggplot2 R package version 2.2.1 (Wickham, 2016) in RStudio (RStudio Team, 2017). Taxonomic grouping in each sample separated taxa into common (>5% abundance in at least one sample) versus rare (never more than 5% in any sample). Supplemental Figure S1 shows the breakdown of Gammaproteobacteria families in the samples presented in Figure 4 of the main text, and Supplemental Figure S2 highlights the abundance of rare taxa (never >5% abundance in any sample). The Bray-Curtis distances between samples was calculated using the same dataset described above, summarized to relative abundance at the Family taxonomic level using Phyloseq and the Vegan package (Oksanen et al., 2018). A Non-Metric Multidimensional Scaling (NMDS) ordination was produced from this distance matrix. It should be noted that common rules for beta diversity comparisons, such as common library preparation/sequencing protocols and library-size normalization, were not performed in this analysis due to the diversity of the datasets being considered and the resulting NMDS ordination having high-stress (>20%). Therefore, the results should be viewed as broadly qualitative and not quantitative.
All data processing steps and markdown files are available via github: https://github.com/orcuttlab/ocean-crust-micro
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microbiome_amplicon_metadata.csv (Comma Separated Values (.csv), 22.19 KB) MD5:c94774915e2b3cd6811678f02be8ae2a Primary data file for dataset ID 789136 |
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Supplemental material for "Microbial life in Oceanic Crust" book chapter filename: Orcutt_OceanCrustMBIOchapter_Supplemental_v5.pdf (Portable Document Format (.pdf), 428.30 KB) MD5:69374d66fb24425a9550717e7520b420 Supplemental material for "Microbial life in Oceanic Crust" book chapter by Orcutt et al. |
Parameter | Description | Units |
Plot_Order | Numerical order on the "Sample" Axis of invididual samples in Figure 4 of the main text. Values: integers from 1 to 120 for samples included in plot; none, samples from blank DNA extractions used for comparison; not-in-plot_used-in-NMDS, additional sediment comparison samples not included in plots but used in NMDS analysis | unitless |
Sample_Name | Unique name of the sample used in the plot | unitless |
SRA_Run | Unique Seqence Read Archive (SRA) Accession Number to download fastq-formatted file of sequence data for the Sample_Name from the NCBI Archive | unitless |
SRA_LibraryName | The unique library name given to the Sample_Name by the authors as listed on the NCBI archive | unitless |
Study_Nickname | Short hand code referencing the first author and location of a given study | unitless |
Sample_Type | Environmental type that the sample was collected from. Values: Basalt, Seafloor or subseafloor basalt core samle; FLOCS, mineral colonization experiment from an in situ sytem; Fluids, subsurface crustal fluids collected from a subseafloor observatory; Sediment, sediment core samples; SW, bottom seawater near field sites; blank, DNA extraction blank | unitless |
Temp | Description of the temperature of the sampling environment. Values: cool, <10 degrees C; warm, >10 degrees C; na, not applicable | unitless |
Location | Descriptive name of field site where Sample_Name originated. Values: NorthPond; Dorado; Loihi; JuanDeFuca | unitless |
Depth | Descriptive category of the relative position of the Sample_Name in the environment. Values: seafloor, collected from the seafloor; subsurface, below the seafloor; none, not applicable | unitless |
Sequencer_Type | Sequencing platform used to sequence extracted DNA from the Sample_Name. Values: IonTorrent; Illumina; 454 | unitless |
region16S | Variable region(s) of the 16S rRNA gene that was sequenced from the extracted DNA from the Sample_Name, as desxcribed in the primary literature. Values: V4; V6; V4-V6; V1-V3 | unitless |
Primers | Primer set used to amplify the 16S rRNA variable region(s) from the DNA prior to sequencing of the Sample_Name, as described in the primary literature. Values: 519F-805R; 515F-806R; 967F-1046R; 518F-1064R; 28F-388R; 27F-518R | unitless |
DNAextraction | Short-hand name for protocol used for extracting DNA from the sample, as described in the primary literature. Values: MPBiomedicalsFastDNA; CTABPhenolChloroform; TCEPPhenolChloroform; MoBioPowerSoil; EnzymePhenolChloroform; SDSPhenolChloroform | unitless |
DOI | Digital Object Identitfyer information for publications that describe the original study for the data used here | unitless |
SRA_Study | Sequence Read Archive Identifier number for finding original datafiles on the NCBI Archive | unitless |
NSF Award Abstract:
The marine deep biosphere is the habitat for life existing under the sea floor. The zone has remarkably low energy sources creating a paradox of how life can persist there. Resolving this energy paradox is a grand challenge in deep biosphere research. The Juan de Fuca Ridge flank off the coast of Washington, USA, is an accessible, low energy environment making it an attractive location for addressing this challenge. A series of experiments will be conducted on the seafloor at the Juan de Fuca Ridge flank, using established subseafloor observatories that access the crustal deep biosphere, to provide the first direct in situ measurement of microbial activity in the crustal subsurface. This project will provide essential information about the ability of life to survive under conditions that we are not able to replicate in the laboratory, but that are increasingly important for understanding microbial community interaction in the environment. This information can then be used in models of global microbial activity for estimating the impact of this biosphere on elemental cycling, transforming our understanding of microbial processes within this vast subseafloor habitat. To communicate these discoveries to the public, the project will include a ship-to-shore outreach program during the cruise. In addition public lectures will be presented, and an interactive display of deep-sea video footage will be set up for the annual public Open House at the Bigelow Laboratory for Ocean Sciences in Maine. Diverse undergraduate students and a postdoctoral researcher will be recruited to participate in the research and public outreach activities.
This project proposes to leverage existing subsurface infrastructure on the eastern flank of the Juan de Fuca Ridge with advances in single-cell based molecular and geochemical approaches to make fundamental new discoveries about the activity of life in the deep crustal biosphere. During a two-week research cruise, the research team will incubate crustal fluids in situ and in the laboratory with labeled substrates for tracking single-cell activity, coupled with radioisotope tracer activity and potentiostat measurements, with the objective of determining in situ and potential rates of activity and cellular physiology. The research will also identify which metabolisms active microorganisms utilize under in situ and laboratory conditions, the rates of these processes, and the microorganisms involved. The results are expected to provide explicit hypothesis testing of microbial activity and in situ microbial growth rates from the crustal deep biosphere to transform understanding of microbial activity in the crustal deep biosphere and generate critical information about the ability of life to survive under low energy conditions.
Funding Source | Award |
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NSF Division of Ocean Sciences (NSF OCE) |