OTU molecular abundances for coral Symbiodinium, Belize Mesoamerican Barrier Reef System (MBRS), 2014-2015 (NCEI Accession 0292212)
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title: OTU molecular abundances for coral Symbiodinium, Belize Mesoamerican Barrier Reef System (MBRS), 2014-2015 (NCEI Accession 0292212)
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abstract: This dataset contains biological and survey - biological data collected from 2014-11-01 to 2015-10-31. These data include relative abundance, species, and taxon_code. The instruments used to collect these data include Automated DNA Sequencer, PCR Thermal Cycler, and Spectrophotometer. These data were collected by Karl D. Castillo of University of North Carolina at Chapel Hill as part of the "Investigating the influence of thermal history on coral growth response to recent and predicted end-of-century ocean warming across a cascade of ecological scales (Thermal History and Coral Growth)" project. The Biological and Chemical Oceanography Data Management Office (BCO-DMO) submitted these data to NCEI on 2019-12-09. The following is the text of the dataset description provided by BCO-DMO: Symbiodinium OTU Dataset Description: This dataset contains relative abundance (counts?) of operational taxonomic units (OTUs) from Sumbiodinium samples collected from three coral species (S. siderea, S. radians, and P. strigosa) at nine sites across four latitudes along the Belize MBRS in 2014 and 2015. These sites were previously characterized into three thermally distinct regimes (lowTP, modTP, highTP) and exhibited variations in coral species diversity and richness.
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title: Castillo, K. D. (2018) OTU molecular abundances for coral Symbiodinium, Belize Mesoamerican Barrier Reef System (MBRS), 2014-2015. Biological and Chemical Oceanography Data Management Office (BCO-DMO). Dataset version 2018-04-16. https://doi.org/10.1575/1912/bco-dmo.734674.1
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beginPosition: 2014-11-01
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supplementalInformation: Acquisition Description: From Baumann et al (2017): DNA Extraction Coral holobiont (coral, algae, and microbiome) DNA was isolated from each sample following a modified phenol-chloroform [86,87,88] method described in detail by Davies et al. [87]. Briefly, DNA was isolated by immersing the tissue in digest buffer (100 mM NaCL, 10 mM Tris-Cl pH 8.0, 25 mM EDTA pH 9.0, 0.5% SDS, 0.1 mg ml-1 Proteinase K, and 1 µg ml-1 RNaseA) for 1 h at 42 °C followed by a standard phenol-chloroform extraction. Extracted DNA was confirmed on an agarose gel and quantified using a Nanodrop 2000 Spectrophotometer (Thermo Scientific). PCR Amplification and Metabarcoding The ITS-2 region (350 bp) was targeted and amplified in each sample using custom primers that incorporated Symbiodinium specific ITS-2-dino-forward and its2rev2-reverse regions [65, 73, 89]. Each primer was constructed with a universal linker, which allowed for the downstream incorporation of Illumina specific adapters and barcodes during the second PCR as well as four degenerative bases whose function was to increase the complexity of library composition. The forward primer was 5'-GTCTCGTCGGCTCGG + AGATGTGTATAAGAGACAG+ NNNN + CCTCCGCTTACTTATATGCTT-3', where the underlined bases are the 5'-universal linker, italicized bases indicate spacer sequences, Ns denote degenerative bases, and the bold bases are the ITS-2-dino. The reverse primer was 5'-TCGTCGGCAGCGTCA + AGATGTGTATAAGAGACAG + NNNN + GTGAATTGCAGAACTCGTG-3'. Each 20 µL PCR reaction contained 5-100 ng DNA template, 12.4 µL Milli-Q H2O, 0.2 µM dNTPs, 1 µM forward and 1 µM reverse primers, 1× Extaq buffer, and 0.5 U (units) Extaqpolymerase (Takara Biotechnology). PCR cycles were run for all samples using the following PCR profile: 95 °C for 5 min, 95 °C for 40 s, 59 °C for 2 min, 72 °C for 1 min per cycle and a final elongation step of 72 °C for 7 min. The optimal number of PCR cycles for each sample was determined from visualization of a faint band on a 2% agarose gel (usually between 22 and 28 cycles) as per Quigley et al. [65]. PCR products were cleaned using GeneJET PCR purification kits (Fermentas Life Sciences), and then a second PCR reaction was performed to incorporate custom barcode-primer sequences [65] modified for Illumina Miseq as in Klepac et al. [90]. Custom barcode primer sequences included 5'-Illumina adaptor + 6 bp barcode sequence + one of two universal linkers-3' (e.g., 5'-CAAGCAGAAGACGGCATACGAGAT + GTATAG + GTCTCGTGGGCTCGG-3', or 5'-AATGATACGGCGACCACCGAGATCTACAC + AGTCAA + TCGTCGGCAGCGTC-3'). Following barcoding, PCR samples were visualized on a 2% agarose gel and pooled based on band intensity (to ensure equal contributions of each sample in the pool). The resulting pool was run on a 1% SYBR Green (Invitrogen) stained gel for 60 min at 90 V and 120 mA. The target band was excised, soaked in 30 µL of Milli-Q water overnight at 4 °C, and the supernatant was submitted for sequencing to the University of North Carolina at Chapel Hill High Throughput Sequencing Facility across two lanes of Illumina MiSeq (one 2 × 250, one 2 × 300). The two lanes produced similar mapping efficiencies (73 and 73%, respectively; Table S3). Bioinformatic Pipeline The bioinformatic pipeline used here builds upon previous work by Quigley et al. [65] and Green et al. [73]. Raw sequences were renamed to retain sample information, and then all forward (R1) and reverse (R2) sequences were concatenated into two files, which were processed using CD-HIT-OTU [91]. CD-HIT-OTU clusters concatenated reads into identical groups at 100% similarity for identification of operational taxonomic units (OTUs). Each sample was then mapped back to the resulting reference OTUs, and an abundance count for each sample across all OTUs was produced. A BLASTn search of each reference OTU was then run against the GenBank (NCBI) nucleotide reference collection using the representative sequence from each OTU to identify which Symbiodinium lineage was represented by each OTU (Table S2). The phylogeny of representative sequences of each distinct Symbiodinium OTU was constructed using the PhyML tool [92, 93] within Geneious version 10.0.5 (http://geneious.com) [94]. PhyML was run using the GTR+I model (chosen based on delta AIC values produced from jModelTest [92, 95]) to determine the maximum likelihood tree. The TreeDyn tool in Phylogeny.fr was used to view the tree (Fig. 2) [96, 97, 98]. The reference sequences included in the phylogeny were accessed from GenBank (Table S6). These data are reported in: Baumann, J.H., Davies, S.W., Aichelman, H.E. and Castillo, K. D. (2017) Coral Symbiodinium Community Composition Across the Belize Mesoamerican Barrier Reef System is Influenced by Host Species and Thermal Variability. Microb Ecol. https://doi.org/10.1007/s00248-017-1096-6. Methodology References: 65. Quigley KM, Davies SW, Kenkel CD, Willis BL, Matz MV, Bay LK (2014) Deep-sequencing method for quantifying background abundances of Symbiodinium types: exploring the rare Symbiodinium biosphere in reef-building corals. PLoS One 9:e94297 73. Green EA, Davies SW, Matz MV, Medina M (2014) Quantifying cryptic Symbiodinium diversity within Orbicella faveolata and Orbicella franksi at the Flower Garden Banks, Gulf of Mexico. PeerJ 2:e386 86. Aronson RB, Precht WF, Toscano MA, Koltes KH (2002) The 1998 bleaching event and its aftermath on a coral reef in Belize. Marine Biology xxx 87. Davies SW, Rahman M, Meyer E, Green EA, Buschiazzo E, Medina M, Matz MV (2013) Novel polymorphic microsatellite markers for population genetics of the endangered Caribbean star coral, Montastraea faveolata. Mar Biodivers 43:167-172 88. Chomczynski P, Sacchi N (2006) The single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction: twenty-something years on. Nat Protoc 1:581-585 89. Stat M, Loh WKH, Hoegh-Guldberg O, Carter DA (2009) Stability of coral-endosymbiont associations during and after a thermal stress event in the southern Great Barrier Reef. Coral Reefs 28:709-713 90. Klepac CN, Beal J, Kenkel CD, Sproles A, Polinski JM, Williams MA, Matz MV, Voss JD (2015) Seasonal stability of coral-Symbiodinium associations in the subtropical coral habitat of St. Lucie Reef, Florida. Mar Ecol Prog Ser 532:137-151 91. Li W, Fu L, Niu B, Wu S, Wooley J (2012) Ultrafast clustering algorithms for metagenomic sequence analysis. Briefings in bioinformatics: bbs035 92. Guindon S, Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52:696-704 93. Guindon S, Dufayard J-F, Lefort V, Anisimova M, Hordijk W, Gascuel O (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst Biol 59:307-321 94. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, Buxton S, Cooper A, Markowitz S, Duran C (2012) Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics 28:1647-1649 95. Darriba D, Taboada GL, Doallo R, Posada D (2012) jModelTest 2: more models, new heuristics and parallel computing. Nat Methods 9:772-772 96. Dereeper A, Guignon V, Blanc G, Audic S, Buffet S, Chevenet F, Dufayard J-F, Guindon S, Lefort V, Lescot M (2008) Phylogeny.fr: robust phylogenetic analysis for the non-specialist. Nucleic Acids Res. 36:W465-W469 97. Dereeper A, Audic S, Claverie J-M, Blanc G (2010) BLAST-EXPLORER helps you building datasets for phylogenetic analysis. BMC Evol. Biol. 10:8 98. Chevenet F, Brun C, Bañuls A-L, Jacq B, Christen R (2006) TreeDyn: towards dynamic graphics and annotations for analyses of trees. BMC bioinformatics 7:439
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description: thermal cycler, thermocycler, PCR machine, DNA amplifier The thermal cycler (also known as a thermocycler, PCR machine or DNA amplifier) is a laboratory apparatus most commonly used to amplify segments of DNA via the polymerase chain reaction (PCR). https://en.wikipedia.org/wiki/Thermal_cycler, 2017-05-02.
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description: spectrophotometer, optical spectrometer, spectrograph or spectroscope An optical spectrometer (spectrophotometer, spectrograph or spectroscope) is an instrument used to measure properties of light over a specific portion of the electromagnetic spectrum, typically used in spectroscopic analysis to identify materials. Butler, L. R. P.; Laqua, K. (1995). "Nomenclature, symbols, units and their usage in spectrochemical analysis-IX. Instrumentation for the spectral dispersion and isolation of optical radiation (IUPAC Recommendations 1995)". Pure Appl. Chem. IUPAC. 67 (10): 1725–1744. doi:10.1351/pac199567101725. A spectrometer is the general term for describing a combination of spectral apparatus with one or more detectors to measure the intensity of one or more spectral bands."
