Computational protocol: Glutathione S Transferase Regulation in Calanus finmarchicus Feeding on the Toxic Dinoflagellate Alexandrium fundyense

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[…] Total RNA was extracted from whole C. finmarchicus adult females (n = 1) using the QIAGEN RNeasy Mini Kit (QIAGEN Inc., Valencia, CA, USA), in conjunction with a Qiashredder column (QIAGEN Inc.), following the instructions of the manufacturer, with a final elution volume of 30 μL. RNA concentration and quality were checked using an Agilent Model 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA). First-strand synthesis was performed using the QuantiTect Reverse Transcription Kit (Qiagen, Valencia, CA, USA), which is optimized for RT-qPCR down-stream applications. 1 μg of total RNA was reverse transcribed to cDNA, following the manufacturer’s instructions. The resulting cDNA was used as template for conventional PCR and RT-qPCR.Relative expression was measured for three target GSTs (Calfi-Delta I, Calfi-Sigma VI and Calfi-mGST3 III) and three candidate reference genes (actin, 16S and elongation factor 1α) from a total of 36 cDNA libraries (3 replicates × 3 treatments × 4 time points). Primers were designed based on sequences obtained from the C. finmarchicus de novo assembly [] using the primer design tools in Primer3 within the software Geneious (V6.1). Specific primers were designed to target the conserved domain for each gene of interest that was identified using the online program SMART (http://smart.embl-heidelberg.de) []. For all genes the amplicon size was less than 170 bp. The list of genes, forward and reverse primers, amplicon lengths and oligo efficiencies (E) are listed in . Secondary structure and primer accuracy were evaluated using the OligoAnalyzer Tool available in IDT (http://www.idtdna.com) and in silico PCR (Bioinfx http://bioinfx.net/cgi-bin/pcr). Each primer set was first tested with traditional PCR to optimize the temperature range (55–63°C) and primer concentrations (100–400 nM). PCR amplifications were performed in 25 μL reaction volumes using 2.5 μL 10× PCR Buffer minus Mg2+, 0.75 mM MgCl2, 0.75 μM of each primer, 0.5 mM of each dNTP, Invitrogen Taq polymerase (recombinant) at 0.25 units μL−1, 3 μL of template DNA and 16.5 μL of deionized water. Reaction conditions included denaturing at 95°C for 30 s, followed by 40 cycles of denaturation at 95°C for 30 s, the primer specific annealing temperature (between 55–58°C) for 30 s, extension at 72°C for 1 min, then the final extension step at 72°C for 4 min. Only primers that generated a single strong band on a 1.5% agarose gel with no bands visible in the no-template control (NTC) were further considered for the RT-qPCR analysis. [...] RT-qPCR experiments were performed to measure relative expression of the three target and three reference genes using the cDNA previously generated (see above). These experiments were performed on a LightCycler 96 System (Roche) thermal cycler in a final volume of 25 μL containing the following: 12.5 μL of Fast Start SYBR Green Master Mix (Roche), 2 μL of cDNA template and 1 μL of each oligo (final concentration 400 nM). The RT-qPCR thermal profile included pre-incubation at 95°C for 10 min followed by 50 cycles of 95°C for 30 s, primer specific annealing temperature for 1 min and 72°C for 30 s. Melt curve analyses were checked for the presence of a single peak in order to confirm amplification of a single product and the absence of primer dimers. The optimal quantity of template was assessed using serial dilutions of a control for each gene ranging from 1:1 to 1:10000; the lowest template concentration (1/10000) was acceptable and used for sample analysis. In addition to the three biological replicates for each treatment and time point, each RT-qPCR reaction was carried out in triplicate to capture intra-assay variability. Each assay included three no-template controls (NTC) for each primer pair. Efficiencies were calculated for each gene based on a five-point standard curve using the Cycle Threshold (Ct) value versus the logarithm of each dilution factor and the equation [E = 10−1/slope]. All experiments and analyses were conducted following the MIQE guidelines and checklist [,].Two different algorithms were utilized to identify the best reference gene in our experimental design: BestKeeper [] and NormFinder []. Relative expression was determined for each biological replicate and each target gene using the REST tool, which calculates relative expression as the expression ratio (fold change) between Cq values of a target gene versus Cq values for the reference genes []. To assess relative expression for the tested genes (GSTs), we firstly determined the best reference gene from the three genes: elongation factor 1α (EFA), 16S and actin. The expression of the GSTs was then normalized and quantified in Log2 (experimental/control) as described in Pfaffl et al., []. The 1 x-fold expression level was therefore chosen as the threshold for significance of target genes. In one case (EFA, day 0.5, June experiment), one of the three biological replicates was removed as an outlier because the standard deviation (SD) of the mean relative expression of its technical replicates was >1. [...] During the July experiment, high-throughput sequencing was performed on C. finmarchicus females on days 2 and 5 of the experiment. A total of 18 RNA-Seq libraries (3 replicates × 3 treatments × 2 time points) were sequenced []. RNA-Seq reads were quality filtered (FASTX Toolkit, version 0.013; http://hannonlab.cshl.edu/fastx_toolkit/) by trimming the first nine and the last 29 bases, and followed by the elimination of low quality reads (cutoff “Phred” score = 20) as well as Illumina adapters. This resulted in the removal of an average of 34% of reads []; reads were then mapped to the C. finmarchicus reference transcriptome (96,090 contigs) [] using the software Bowtie (version, 2.0.6) with a 2-nucleotide mismatch tolerance []. Identification of significant differences in expression in GST genes was performed using the BioConductor package edgeR []. As implemented by edgeR, each library was normalized using the Trimmed mean of M values (TMM) to reduce the differences between library size. Libraries were also normalized using the RPKM method (reads per kilobase of the transcript per million mapped reads); briefly for each gene, the summarized counts were divided by the length of the transcript and the total number of mapped reads in each library using a custom script written in Perl (www.perl.org). Differentially expressed GSTs were statistically identified using the Exact test, implemented by edgeR (parallel to Fisher’s Exact test), based on pairwise comparisons between the control and experimental treatments: CONTROL vs LD and CONTROL vs HD for each time point. In addition, controls at 2 and 5 days were compared statistically to determine whether GST expression changed during the experimental incubation. Transcripts were identified as differentially expressed using the Exact test (p<0.05) and a multiple comparison correction with Benjamini-Hochberg method (false discovery rate <5%) implemented by edgeR []. Expression rate was quantified in units of Log2 fold (experimental/control) where a value of 0 represents equal expression between the experimental condition and control. […]

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