Abstract:
Here we investigate the effects of oil and the dispersant, Corexit on the black coral, Leiopathes glaberrima. Six genetically distinct colonies of two color morphotypes (red and white) of L. glaberrima were exposed to different concentrations of oil, dispersant, and dispersed oil and dispersant mixtures. Gene expression changes were analyzed after 24 hours of exposure at high chemical concentrations compared to water-only controls.
Suggested Citation:
Iliana Baums. 2016. Stress-response of black corals (Anthipataria) to the exposure of crude oil and dispersant. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/N73T9F5N
Data Parameters and Units:
HealthRatings_2012.csv: Time(hour);Concentration(25= 25ppm, 8.0= 8ppm, 0.8 = ppm, 0 = 0ppm);Healthrating (0 = health, 1 = nearly dead, 2 = compromised, 3 = below average, 4 = nearly healthy, 5 = healthy); Species (L.glaberrima); Treatment (C = Conrole, D = Dispersant, O = Oil, OD = Oil and Dispersant); Leiocolor (white = white coral morph, red = red coral morph); Colony number (1= colony 1, 2 = colony 2, 3 = colony 3, 4 = colony 4)HealthRatings_2013.csv: Time(hour);Concentration(250= 25ppm, 150= 150ppm, 50 = 50ppm,0 = 0ppm);Healthrating (0 = health, 1 = nearly dead, 2 = compromised, 3 = below average, 4 = nearly healthy, 5 = healthy); Species (L.glaberrima); Treatment (C = Conrole, D = Dispersant, O = Oil, OD = Oil and Dispersant); Leiocolor (white = white coral morph, red = red coral morph); Colony number (1= colony 1, 2 = colony 2, 3 = colony 3, 4 = colony 4) RedTrinity250filtered.fasta: header (>comp#_c#_seq#= sequence identifier produce by the program “Trinity”; len=length of sequence in bp; path = path of the sequence traversed by the Trinity component “Butterfly” through nodes in the sequence graph, listing the identifiers of the ordered nodes and the ranges within the reconstructed transcript sequence that correspond to each respective node); sequence of transcript WhiteTrinity250filtered.fasta: header (>comp#_c#_seq#= sequence identifier produce by the program “Trinity”; len=length of sequence in bp; path = path of the sequence traversed by the Trinity component “Butterfly” through nodes in the sequence graph, listing the identifiers of the ordered nodes and the ranges within the reconstructed transcript sequence that correspond to each respective node); sequence of transcript
Methods:
Total RNA was extracted using the modified Trizol extraction protocol from Polato et al., 2011 Mol Ecol and then applied to a Qiagen RNeasy mini spin column (Qiagen, CA). The quality and concentration of total RNA was quantified on an Agilent 2100 Bioanalyzer (Agilent Technologies, CA). RNA samples were pooled by the color phenotype of the colonies (red and white) and treatment. A total of 8 libraries (Control-Red, Control-White, Oil-Red, Oil-White, Dispersant-Red, Dispersant-White, Oil-dispersant-Red, Oil-dispersant-White) were prepared using the TruSeq Stranded mRNA Sample Prep Kit according to the manufacturer's protocol. Size selected library fragments were single-end sequenced for 100 cycles in 2 lanes on an Illumina HiSeq 2500 in RapidRun mode at the Penn State Genomics Core Facility. Transcripts were normalized using Trinity’s in-silico read normalization with targeted maximum coverage of 30. After normalization, transcripts were assembled de novo with the software Trinity with a minimum contig length of 250 base pairs and minimum k-mer coverage of 2. Trimmed transcripts were mapped back to the assembled transcriptome with Bowtie2 with default parameters. Mapped reads were sorted and duplicates were removed using the MarkDuplicates program from the Picard package. Only sequences that mapped to a single reference were used for gene expression analysis. Count data (from the reads that map uniquely to each contig or gene) from the alignment files were obtained with Palumbi’s countxpression.py script (http://sfg.stanford.edu/expression.html). Transcripts with less than 10 counts were filtered out, and a differential expression analysis was run with the DESeq package in R (http://bioconductor.org). For this the read counts were normalized by the effective library size, and variance of the data was estimated. Because the technical replicates of the colors were pooled for sequencing, there were no replicates to compare between color morphotypes, therefore the variance of the data was calculated by treating all samples as replicates of the same condition (function: estimateDispersions, method = blind).
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