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  • Temperature influence on DNA methylation in oysters


    • DNA Methylation
    • Function in bivalves - MERV
    • Relationship with expression


    Adult oysters n=3 (191.93 g+/- 56.45) (Mean +/- SD) were anesthetized by overnight holding in tanks (6L) with MgCl2 (50g/l). The following day, mantle tissue was taken and placed at -80C and oysters placed back into seawater. After 7 days, oysters were subjected to a 40C, 1 hour acute heat shock. Tissue samples were taken immediately following heat shock exposure and placed at -80C.


    Genomic DNA was isolated using DNAzol (Molecular Research Center) from mantle tissue taken prior to and post heat shock (three oysters). Methylation enrichment was performed using the MethylMiner Kit (Invitrogen) following the manufacturer’s instructions. Specifically, DNA was sheared by sonication on a Covaris S2 (Covaris) (parameters: 10 cycles at 60 seconds each, duty cycle of 10%, intensity of 5, 100 cycles/burst). Sheared DNA was used as input DNA and incubated with MBD-Biotin Protein coupled to M-280 Streptavidin Dynabeads following the manufacturer’s instructions (MethylMiner (Invitrogen)). Enriched, methylated DNA was eluted from the bead complex with 1M NaCl and purified by ethanol precipitation. DNA was further purified using PCR purification columns (Qiagen) prior to labeling.

    A custom DNA tiling array containing 697,753 probes covering 9158 full-length C. gigas genes including 2kb upstream of the start site was used. Probes were designed using an interval size of 100bp and a window size of 25bp.

    Samples were labeled using the NimbleGen Dual-Color DNA Labeling Kit and the arrays were processed according to the manufacturer’s recommendations (Roche NimbleGen, Madison, Wisconsin) and imaged at 5um using a GenePix 4000B microarray scanner (Molecular Devices, Sunnyvale, CA).

    Fluorescent intensities from the Cy3 (input) and Cy5 (IP) channels were extracted from each array using DEVA 1.2.1 (Roche NimbleGen; Madison, WI) and the output was processed using the Bioconductor package, Ringo (Toedling 2007). Control probes were discarded and the data from each array was initially normalized using the Tukey biweight mean method. Paired pre- and post-heat shock log2(IP/Input) values were adjusted using a linear regression fit (setting m = 1 and b = 0) to compensate for variation in data compression between arrays. To determine differential methylation, the input channels from the paired datasets were loess normalized and the results were used to calculate a threshold value, T, equivalent to 3 s.d from the mean. Applying this back to each pairwise comparison of pre- and post-heat shock log2(IP/Input) ratios, differential methylation was assigned to a given probe when the absolute value of the paired ratio exceeded T. For our studies, we concentrated on runs of at least 3 adjacent probes identified as differentially hyper- or hypo-methylated and localized to a gene body (plus 1000bp upstream). The R programing language [2] was used for data processing, including the generation of bedGraph and GFF tracks for visualization in IGV (Robinson 2011).


    Total RNA was extracted using Tri-Reagent per manufacturer’s instruction. Potential DNA carry-over was removed from extracted RNA using the Turbo DNA-free treatment according to the manufacturer's instructions (Ambion). Messenger RNA (mRNA) enrichment, library preparation, and sequencing was performed by GENEWIZ. Poly(A) enrichment was performed using NEBNext® Poly(A) mRNA Magnetic Isolation Module (New England Biolabs). Illumina sequencing libraries were prepared with the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (New England Biolabs) and each library was uniquely barcoded with the NEBNext® Multiplex Oligos for Illumina® (Index Primers Set 1) (New England Biolabs). All six libraries were multiplexed and run on a single lane on a HiSeq2500 (Illumina), with a read length of 100bp (single end).

    RNA-seq analysis was carried out using the TopHat2 suite in the iPlant Collaborative Discovery Environment. Specifically TopHat2-SE was used to align the six libraries (3 individuals, pre and post heat shock) to the Crassostrea gigas genome.

    Below find the specific procedure presented in a tutorial-based method.

    Task 1: Align read data to Crassostrea gigas genome.

    Tophat is a specialized alignment software for RNA-seq reads that is aware of splice junctions when aligning to a reference assembly.

    1) Click Apps from DE workspace and select TopHat2-SE. Use search bar.


    2) Under 'Analysis Name' leave as defaults or make desired changes.

    3) Under Input data for FASTQ files add six fastq.gz files located in paper-Cg-temp-methylation/raw/ with prefixes 2M, 2M-HS, 4M, 4M-HS, 6M, 6M-HS.

    4) Under Reference Genome for 'Provide a reference genome file in FASTA format' select paper-Cg-temp-methylation/genome/Crassostrea_gigas.GCA_000297895.1.24.dna_sm.toplevel.fa

    5) For Reference Annotations add the GTF file paper-Cg-temp-methylation/genome/Crassostrea_gigas.GCA_000297895.1.24.gtf

    6) Click Launch Analyses and monitor the status of you job (this takes ~10 hours)

    Task 2: Assemble transcripts using Cufflinks2

    Cufflinks assembles RNA-Seq alignments into a parsimonious set of transcripts, then estimates the relative abundances of these transcripts based on how many reads support each one, taking into account biases in library preparation protocols. A detailed manual can be found at

    1) Open Cufflinks2

    2) For Input Data add the six bam files from the bam subdirectory of the TopHat2 output (/paper-Cg-temp-methylation/analyses/TopHat2-SE_analysis_heat-b-2014-12-19-15-09-00.4/bam/)

    3) Under Reference Sequence use custom option select paper-Cg-temp-methylation/genome/Crassostrea_gigas.GCA_000297895.1.24.dna_sm.toplevel.fa

    4) For Reference Annoations add the GTF file paper-Cg-temp-methylation/genome/Crassostrea_gigas.GCA_000297895.1.24.gtf

    5) Click Launch Analyses and monitor the status of you job (This takes about 2 hours).

    Task 3: Merge all Cufflinks transcripts into a single transcriptome annotation file using Cuffmerge2

    Cuffmerge merges together several Cufflinks assemblies. It handles also handles running Cuffcompare. The main purpose of this application is to make it easier to make an assembly GTF file suitable for use with Cuffdiff. A merged, empirical annotation file will be more accurate than using the standard reference annotation, as the expression of rare or novel genes and alternative splicing isoforms seen in this experiment will be better reflected in the empirical transcriptome assemblies.

    1) Open the Cuffmerge2 app. Under 'Input Data', browse to the results of the Cufflinks analyses (above) and add the 6 gtf files located in the gtf subdirectory (/paper-Cg-temp-methylation/analyses/Cufflinks2_analysis_heat-2014-12-20-15-57-48.9/gtf/).

    2) For Reference Annotations add the GTF file paper-Cg-temp-methylation/genome/Crassostrea_gigas.GCA_000297895.1.24.gtf

    3) Under Reference Sequence use custom option select paper-Cg-temp-methylation/genome/Crassostrea_gigas.GCA_000297895.1.24.dna_sm.toplevel.fa

    4) Click Launch Analyses and monitor the status of you job.

    Task 4: Compare expression analysis using CuffDiff2

    Cuffdiff is a program that uses the Cufflinks transcript quantification engine to calculate gene and transcript expression levels in more than one condition and test them for significant differences.

    1) Open Cuffdiff2. For 'Input Data' Sample 1 Name enter Pre and add three bam file from Tophat analysis (paper-Cg-temp-methylation/analyses/TopHat2-SE_analysis_heat-b-2014-12-19-15-09-00.4/bam/).


    For Sample 2 enter Post and add other three bam files ...


    2) Next, open the Reference Annotations section and add a custom reference annotation file using the merged_with_ref_ids.gtf file from the Cuffmerge output folder (paper-Cg-temp-methylation/analyses/Cuffmerge2_heat-2014-12-20-19-14-34.8/cuffmerge_out/merged_with_ref_ids.gtf).

    3) Click Launch Analyses and monitor the status of you job.

    4) Upon completion several outputs will be produced (see Results section).

    directory: cuffdiff_out

    directory: graphs

    directory: sorted_data