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Coral

Coral is a marine invertebrate animal that belongs to the phylum Cnidaria.
These organisms typically live in warm, shallow waters and form colonies that can create large, diverse ecosystems known as coral reefs.
Corals come in a variety of shapes, sizes, and colors, and play a crucial role in the overall health and biodiversity of marine environments.
They are sensitive to environmental changes, making them important indicators of ecosystem health.
The study of corals and coral reefs is a critical area of research, as these unique habitats face numerous threats from climate change, pollution, and human activity.
Reserching optimal protocols and methods for coral studies is esential to understanding and protecting these fragile, yet vital, marine ecosystems.

Most cited protocols related to «Coral»

1
Validating PICRUSt's Metagenome Prediction Accuracy
Cited 1640 times
Several microbiome studies that included both 16S sequencing and WGS metagenome sequencing for the same samples were used to test the accuracy of PICRUSt. These included 530 paired human microbiome samples22 (link), 39 paired mammal gut samples24 (link), 14 paired soil samples34 (link), 10 paired hypersaline microbial mats23 (link), 24 (link) and two even/staggered synthetic mock communities from the HMP33 (link). We additionally used PICRUSt to make predictions on three 16S-only microbiome studies, specifically 6,431 HMP samples (http://hmpdacc.org/HMQCP), 993 vaginal time course samples43 and 335 coral mucus samples(http://www.microbio.me/qiime/; Study ID 1854).
For 16S data, PICRUSt-compatible OTU tables were constructed using the closed-reference OTU picking protocol in QIIME 1.5.0-dev (pick_reference_otus_through_otu_table.py) against Greengenes+IMG using ‘uclust’48 (link). For paired metagenomes, WGS reads were annotated to KOs using v0.98 of HUMAnN30 (link). Expected KO counts for the HMP mock communities were obtained by multiplying the mixing proportions of community members by the annotated KO counts of their respective reference genomes in IMG. PICRUSt was used to predict the metagenomes using the 16S-based OTU tables, and predictions were compared to the annotated WGS metagenome across all KOs using Spearman rank correlation. In addition, KOs were mapped to KEGG Module abundances, following the conjugative normal form as implemented in HUMAnN script “pathab.py” for the HMP and vaginal datasets to compare modules and pathways. Bray-Curtis distances (for Beta-diversity comparison between OTU or PICRUSt KO abundances across samples) were calculated using as implemented in the QIIME “beta_diversity.py” script. The PCA plot and identification of KEGG modules with significant mean proportion differences for both the HMP and vaginal datasets was created using STAMP v2.036 (link).
The Nearest Sequenced Taxon Index (NSTI) was developed as an evaluation measure describing the novelty of organisms within an OTU table with respect to previously sequenced genomes. For every OTU in a sample, the sum of branch lengths between that OTU in the Greengenes tree to the nearest tip in the tree with a sequenced genome is weighted by the relative abundance of that OTU. All OTU scores are then summed to give a single NSTI value per microbial community sample. PICRUSt calculates NSTI values for every sample in the given OTU table, and we compared NSTI scores and PICRUSt accuracies for all of the metagenome validation datasets.
In the metagenome rarefaction analysis (Fig. 4), a given number of counts were randomly selected from either the collection of microbial OTUs for each sample (i.e. the 16S rRNA OTU table) or the collection of sequenced genes in that sample using the multiple_rarefactions.py script in QIIME 1.5.0-dev29 (link). To estimate the number of raw reads at which PICRUSt outperforms metagenomic sequencing the annotated shotgun reads were transformed to total sequenced reads by dividing by the mean annotation rates from the original manuscript (17.3%), while 16S rRNA reads were transformed using the success rate for closed-reference OTU picking at a 97% 16S rRNA identity threshold (68.9%). Both the subsampled metagenome and the PICRUSt predictions from the subsampled OTU table were compared for accuracy using Spearman rank correlation versus the non-subsampled metagenome.
Langille M.G., Zaneveld J., Caporaso J.G., McDonald D., Knights D., Reyes J.A., Clemente J.C., Burkepile D.E., Vega Thurber R.L., Knight R., Beiko R.G, & Huttenhower C. (2013). Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature biotechnology, 31(9), 814-821.
Publication 2013
Coral Genes Genome Human Microbiome Mammals Metagenome Microbial Community Microbiome Mucus RNA, Ribosomal, 16S Trees Vagina
2
Metagenome Grouping by Environment
Cited 139 times
We retrieved from the online supplemental material of [69 (link)] the 80 available metagenomes (42 viromes, 38 microbiomes). We identified three environments containing at least seven samples and grouped them into coral, hyper-saline, and marine subclasses; the fourth subclass, other, groups all environments with few samples.
Segata N., Izard J., Waldron L., Gevers D., Miropolsky L., Garrett W.S, & Huttenhower C. (2011). Metagenomic biomarker discovery and explanation. Genome Biology, 12(6), R60.
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Publication 2011
Coral Marines Metagenome Microbiome Saline Solution Virome
3
Interactive Coral Visualization Tool
Cited 38 times

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Publication 2018
cDNA Library Cloning Vectors Coral Trees
4
Global Microbiome Sampling Protocol
Cited 38 times
The global community of microbial ecologists was invited to submit samples for microbiome analysis, and samples were accepted for DNA extraction and sequencing provided that scientific justification and high-quality sample metadata were provided before sample submission. Standardized sampling procedures for each sample type were used by contributing investigators. Samples were collected fresh and, where possible, immediately frozen in liquid nitrogen and stored at −80 °C. Detailed sampling protocols are described in publications of the individual studies (Supplementary Table 1). Bulk samples (e.g., soil, sediment, feces) and fractionated bulk samples (e.g., sponge coral surface tissue, centrifuged turbid water) were taken using microcentrifuge tubes. Swabs (BD SWUBE dual cotton swabs or similar) were used for biofilm or surface samples. Filters (Sterivex cartridges, 0.2 μm, Millipore) were used for water samples. Samples were sent to laboratories in the United States for DNA extraction and sequencing: water samples to Argonne National Laboratory, soil samples to Lawrence Berkeley National Laboratory (pre-2014) or Pacific Northwest National Laboratory (2014 onward), and fecal and other samples to the University of Colorado Boulder (pre-2015) or the University of California San Diego (2015 onward).
Thompson L.R., Sanders J.G., McDonald D., Amir A., Ladau J., Locey K.J., Prill R.J., Tripathi A., Gibbons S.M., Ackermann G., Navas-Molina J.A., Janssen S., Kopylova E., Vázquez-Baeza Y., González A., Morton J.T., Mirarab S., Zech Xu Z., Jiang L., Haroon M.F., Kanbar J., Zhu Q., Jin Song S., Kosciolek T., Bokulich N.A., Lefler J., Brislawn C.J., Humphrey G., Owens S.M., Hampton-Marcell J., Berg-Lyons D., McKenzie V., Fierer N., Fuhrman J.A., Clauset A., Stevens R.L., Shade A., Pollard K.S., Goodwin K.D., Jansson J.K., Gilbert J.A, & Knight R. (2017). A communal catalogue reveals Earth’s multiscale microbial diversity. Nature, 551(7681), 457-463.
Publication 2017
Biofilms Coral Dietary Fiber Feces Freezing Gossypium Microbial Community Microbiome Nitrogen Porifera Tissues
5
Standardized Microbiome Sampling Protocol
Cited 37 times
The global community of microbial ecologists was invited to submit samples for microbiome analysis, and samples were accepted for DNA extraction and sequencing provided that scientific justification and high-quality sample metadata were provided before sample submission. Standardized sampling procedures for each sample type were used by contributing investigators. Samples were collected fresh and, where possible, immediately frozen in liquid nitrogen and stored at –80 °C. Detailed sampling protocols are described in publications of the individual studies (Supplementary Table 1). Bulk samples (for example, soil, sediment, faeces) and fractionated bulk samples (for example, sponge coral surface tissue, centrifuged turbid water) were taken using microcentrifuge tubes. Swabs (BD SWUBE dual cotton swabs or similar) were used for biofilm or surface samples. Filters (Sterivex cartridges, 0.2 μm, Millipore) were used for water samples. Samples were sent to laboratories in the United States for DNA extraction and sequencing: water samples to Argonne National Laboratory, soil samples to Lawrence Berkeley National Laboratory (pre-2014) or Pacific Northwest National Laboratory (2014 onward), and faecal and other samples to the University of Colorado Boulder (pre-2015) or the University of California San Diego (2015 onward).
Thompson L.R., Sanders J.G., McDonald D., Amir A., Ladau J., Locey K.J., Prill R.J., Tripathi A., Gibbons S.M., Ackermann G., Navas-Molina J.A., Janssen S., Kopylova E., Vázquez-Baeza Y., González A., Morton J.T., Mirarab S., Zech Xu Z., Jiang L., Haroon M.F., Kanbar J., Zhu Q., Jin Song S., Kosciolek T., Bokulich N.A., Lefler J., Brislawn C.J., Humphrey G., Owens S.M., Hampton-Marcell J., Berg-Lyons D., McKenzie V., Fierer N., Fuhrman J.A., Clauset A., Stevens R.L., Shade A., Pollard K.S., Goodwin K.D., Jansson J.K., Gilbert J.A, & Knight R. (2017). A communal catalogue reveals Earth’s multiscale microbial diversity. Nature, 551(7681), 457-463.
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Publication 2017
Biofilms Coral Dietary Fiber Feces Freezing Gossypium Microbial Community Microbiome Nitrogen Porifera Tissues

Most recents protocols related to «Coral»

1
Inferring Functional Potential of 16S rRNA in Coral Disease
To infer the functional potential of 16S rRNA gene data among AH, DU, and DL, the program Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) was used in QIIME2-2021.11 [82 (link)]. Only SCTLD-susceptible corals were evaluated and only ASVs that were present in at least 100 samples were selected. The picrust2 full-pipeline was used with the hidden state set to “mp” and the placement tool to place sequences into a tree set to “epa-ng.” The outputs were predicted metagenomes for Kyoto Encyclopedia of Genes and Genomes (KEGG [83 (link)]) orthologs and MetaCyc pathway [84 (link)] abundances. To assess the differential abundance of these outputs among disease states, the R package Maaslin2 was utilized [85 ]. For both KEGG and MetaCyc tests, data were log-transformed, a random effect was set to coral species, and the data were subsequently analyzed with a linear model. In the KEGG assessment, the minimum abundance = 0.05 and the minimum prevalence = 0.1. There were no minimums set for the MetaCyc test due to the lower number of pathways found in MetaCyc. The top 10 predicted pathways were selected based on values with the lowest Padj and effect sizes < −0.5 and >0.5. The top pathways were manually annotated on KEGG and MetaCyc websites.
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
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Publication 2023
asunaprevir Coral Genes Genome Metagenome Reconstructive Surgical Procedures Ribosomal RNA Genes Trees
2
Microbial Survey of SCTLD Outbreak in Corals
To acquire small subunit (SSU) 16S rRNA datasets for this meta-analysis, an email was sent on July 14, 2020, and July 23, 2020, to the hosts of the coral-list listserv and the SCTLD Disease Advisory Committee (DAC) email list, respectively, requesting scientists to share unpublished SCTLD-associated microbiome datasets. In addition, to allow for comparisons of microbiomes between a past Caribbean coral disease to the novel SCTLD outbreak, a rapid tissue loss (RTL) disease study in Acropora palmata (APAL) and Acropora cervicornis (ACER) comprising apparently healthy (AH) samples, inoculated AH samples, and inoculated diseased samples [61 ], also was included in some analyses. This particular study was selected because Acropora spp. reportedly are not susceptible to SCTLD and the study used V4 primers [3 ]. In total, 17 studies were analyzed, 16 from SCTLD and one from an Acropora spp. RTL study (Supplementary Table 1).
Study authors were requested to complete a preformatted metadata file to facilitate comparisons of data across studies. Requested metadata included sample handling information (e.g., source laboratory, and sample collector) and ecological information (e.g., source reef name, coordinates, zone, water temperature, and coral colony measurements). SCTLD zones included vulnerable (i.e., locations where the disease had not been observed/reported), endemic (i.e., locations where the initial outbreak of the disease had moved through and no or few active lesions were observed on colonies), and epidemic (i.e., locations where the outbreak was active and prevalent across colonies of multiple species). Invasion zone sites, where the disease was newly arrived but not yet prevalent, were grouped within the epidemic zone for consistency across studies and simplicity of analysis. Some metadata required standardization of units and not all metadata were available across all studies. However, in all field-collected samples, all sampling dates and site information were available, enabling the completion of SCTLD disease zone metadata for Florida studies by referencing the Coral Reef Evaluation and Monitoring Project, Disturbance Response Monitoring, and SCTLD boundary reconnaissance databases. For USVI, zones were assigned based on the USVI Department of Planning and Natural Resources SCTLD database (https://dpnr.vi.gov/czm/sctld/).
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
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Publication 2023
Caribbean People Coral Coral Reefs Epidemics Microbiome Oligonucleotide Primers Protein Subunits RNA, Ribosomal, 16S Specimen Collection Tissues
3
Microbiome Composition Analysis with ANCOM-BC
The program Analysis of Compositions of Microbiomes with Bias Correction (ANCOM_BC) was used to identify differentially abundant microbial taxa [75 ]. ANCOM_BC was used with the global test option and the results were considered significant if the false discovery rate adjusted p-value (Padj) was <0.001 and if the W statistic was >90. Field-sourced AH samples were tested for differential abundance among zones (vulnerable, endemic, and epidemic), and SCTLD-susceptible coral samples (without Acropora spp.) were evaluated for differences in disease state (AH, DU, and DL). For SCTLD-susceptible corals, the data were parsed by the three coral compartments (mucus, tissue slurry, and tissue slurry skeleton). ANCOM_BC analyses were run for each compartment due to the relatively low sample size of tissue slurry skeleton samples compared to the two other compartment types. The taxa were further evaluated if they had a log-fold change between −1.5< and >1.5. The ASVs that were significantly enriched were used to identify the relative abundance of the ASVs across sample types and zones. In addition, those enriched only in either DU or DL were used to identify the presence or absence of each ASV in coral species and study per biome. The same ANCOM_BC analysis was repeated without MCAV and OFAV to evaluate if the two dominant coral species in our meta-analysis were driving the enriched bacteria.
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
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Publication 2023
Bacteria Biome Coral Epidemics Microbiome Mucus Skeleton Tissues
4
Evaluating Coral Diversity Across Zones
Shannon diversity metrics were generated with the phyloseq function rarefy_even_depth with option replace = TRUE, and a minimum sequence depth for a sample of 1000. Prior to rarefaction, taxa with a sum of zero across the subsetted data were removed. Two sets of alpha-diversity analyses were run: [1 (link)] evaluated differences across the three zones (vulnerable, endemic, and epidemic) in field-sourced apparently healthy (AH) corals, and [2 ] evaluated differences across disease states (AH, unaffected tissue [DU], and lesion tissue [DL] on a diseased colony) in SCTLD-susceptible corals (i.e., without Acropora spp.). Significance was tested with linear mixed models with the R packages lme4 v1.1.21 [68 ], and emmeans v1.4.3.1 [69 (link)], and Tukey’s HSD was used for pairwise comparisons. For zones and disease states, coral species was used as a random effect.
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
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Publication 2023
Coral Epidemics Tissues
5
Microbial Community Dynamics in Coral Ecology
The data were imported into R v4.0.5 and converted into a phyloseq object [70 ]. ASVs were removed if they were present less than four times in 20% of the samples. The filtered count table was transformed using centered log-ratio (CLR) with the package microbiome [71 ]. Beta-diversity was analyzed with the package VEGAN 2.5.4 [72 (link)] and the filtered CLR-transformed table. The function vegdist was used to generate dissimilarity indices with a Euclidean distance. To identify significant differences among groups, a Permutational Multivariate Analysis of Variance (PERMANOVA) was used with the function adonis2 with 999 permutations, using a Euclidean distance. The function betadisper was used to calculate group dispersion, which was then tested for significance with the function Permutest.
Differences in beta-diversity for field samples were evaluated in apparently healthy (AH) corals across three zones (vulnerable, endemic, and epidemic). In addition, pairwise group comparison was assessed from betadisper output using the Tukey’s HSD function. The PERMANOVA output was also tested for pairwise comparisons with the function pairwise.adonis and adjusted with a Bonferroni correction [73 ]. Furthermore, all samples (including Acropora spp., sediment, and seawater) were also evaluated for beta-diversity differences in primers, year of collection, biome (field and aquaria), studies, coral species, and sample type (seawater, mucus, tissue slurry, tissue slurry and skeleton, and sediment). These factors were also correlated to principal components (PCs) using the R package PCAtools 2.5.15, and the functions pca and eigencorplot were used to remove the lowest 10% of the variance and to correlate the data and test for significance, respectively.
SCTLD-susceptible coral samples (i.e., without Acropora spp., sediment, and seawater) were also evaluated for beta-diversity. Both biomes (aquaria or field) were examined together and also separately. The matrices were generated with QIIME2-2021.11 with the plugin DEICODE, which runs a robust Aitchison Distance—a method that is not influenced by zeros in the data [74 ]. Pairwise comparisons of dispersion and differences in microbial composition between groups were evaluated using the QIIME2-2021.11 diversity beta-group-significance function using either the permdisp or PERMANOVA method, respectively. DEICODE was also applied to the data without the two most prevalent corals species, Orbicella faveolata (OFAV) and Montastraea cavernosa (MCAV), to see if the same pattern was evident in disease states with and without these coral species.
Rosales S.M., Huebner L.K., Evans J.S., Apprill A., Baker A.C., Becker C.C., Bellantuono A.J., Brandt M.E., Clark A.S., del Campo J., Dennison C.E., Eaton K.R., Huntley N.E., Kellogg C.A., Medina M., Meyer J.L., Muller E.M., Rodriguez-Lanetty M., Salerno J.L., Schill W.B., Shilling E.N., Stewart J.M, & Voss J.D. (2023). A meta-analysis of the stony coral tissue loss disease microbiome finds key bacteria in unaffected and lesion tissue in diseased colonies. ISME Communications, 3, 19.
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Publication 2023
Adonis asunaprevir Biome Coral Epidemics Microbiome Mucus Oligonucleotide Primers Skeleton Tissues Vegan

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More about "Coral"

Coral, a captivating marine invertebrate belonging to the phylum Cnidaria, has long been a subject of fascination for scientists and nature enthusiasts alike.
These remarkable organisms, typically found in warm, shallow waters, form intricate colonies that create the breathtaking ecosystems known as coral reefs.
Corals come in a dazzling array of shapes, sizes, and vibrant colors, each species playing a crucial role in the overall health and biodiversity of marine environments.
These delicate creatures are highly sensitive to environmental changes, making them invaluable indicators of ecosystem well-being.
The study of corals and their habitats is a critical area of research, as coral reefs face numerous threats from climate change, pollution, and human activity.
Researchers utilize a variety of tools and techniques to understand and protect these fragile, yet vital, marine ecosystems.
One such tool is the Diving-PAM fluorometer, which allows scientists to measure the photosynthetic activity of corals, providing insights into their health and stress levels.
The MiSeq platform, a powerful DNA sequencing technology, is also employed to analyze the microbial communities associated with corals, shedding light on their complex interactions.
The DNeasy Blood and Tissue Kit and the RNAlater solution are commonly used to extract and preserve DNA and RNA samples from coral specimens, enabling in-depth genetic analysis.
The NanoDrop 2000 spectrophotometer is another essential tool, allowing researchers to quantify and assess the purity of nucleic acid extracts.
Additionally, the use of DMSO (Dimethyl Sulfoxide) and TRIzol reagent helps in the effective preservation and extraction of coral RNA, crucial for understanding gene expression and transcriptional profiles.
By leveraging these advanced techniques and technologies, researchers are better equipped to unravel the mysteries of coral biology, ecology, and resilience, ultimately paving the way for more effective conservation and management strategies.