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Methylation and gene expression data from: Differential DNA methylation across environments has no effect on gene expression in the eastern oyster

Citation

Marquez Johnson, Kevin (2021), Methylation and gene expression data from: Differential DNA methylation across environments has no effect on gene expression in the eastern oyster, Dryad, Dataset, https://doi.org/10.5061/dryad.q573n5tk4

Abstract

1. It has been hypothesized that environmentally induced changes to gene body methylation could facilitate adaptive transgenerational responses to changing environments.

2. We compared patterns of global gene expression (Tag-seq) and gene body methylation (reduced representation bisulfite sequencing) in 80 eastern oysters (Crassostrea virginica) from six full-sib families, common gardened for 14 months at two sites in the northern Gulf of Mexico that differed in mean salinity.

3. At the time of sampling, oysters from the two sites differed in mass by 60% and in parasite loads by nearly two orders of magnitude. They also differentially expressed 35% of measured transcripts. However, we observed differential methylation at only 1.4% of potentially methylated loci in comparisons between individuals from these different environments, and little correspondence between differential methylation and differential gene expression.

4. Instead, methylation patterns were largely driven by genetic differences among families, with a PERMANOVA analysis indicating nearly a two orders of magnitude greater number of genes differentially methylated between families than between environments.

5. An analysis of CpG observed/expected values (CpG O/E ) across the C. virginica genome showed a distinct bimodal distribution, with genes from the first cluster showing the lower CpG O/E values, greater methylation, and higher, and more stable gene expression, while genes from the second cluster showed lower methylation, and lower and more variable gene expression.

6. Taken together, the differential methylation results suggest that only a small portion of the C. virginica genome is affected by environmentally induced changes in methylation. At this point, there is little evidence to suggest that environmentally induced m­­­ethylation states would play a leading role in regulating gene expression responses to new environments.

Methods

In May 2016, adult oysters (C. virginica) were collected by dredging from Sister Lake, LA (29˚14’57” N, 90˚55’16” W). These oysters were transported to the Louisiana Department of Wildlife and Fisheries Michael C. Voisin Oyster Hatchery in Grand Isle, LA (29°14'20.3" N, 90°00'11.2" W) and placed into off-bottom mesh cages for long-term acclimation. In October 2016, after six months of acclimation, the oysters were spawned at the MCV oyster hatchery using 3 males and 2 females. Oyster spat were reared in an upwelling system, individually tagged, and outplanted in one of three adjustable long-line mesh bags at both the Grand Isle Hatchery farm and near the Louisiana Universities Marine Consortium (LUMCON) (29°15'12.6" N, 90°39'45.9" W) on February 20th, 2017. Because larvae from all six families were combined for culturing, parentage assignments were unknown at the time of outplant, and as such families were unequally outplanted between sites. Oysters within each bag were monitored for mortality and cleaned of epibionts approximately every 3 months over a 14-month period. On April 24th, 2018, after 14-months at the two outplant sites, 40 individuals were haphazardly chosen from each site. Shell height of each individual was measured from shell umbo to distal edge using a digital caliper (ABS Coolant Proof Calipers, Mituyoto Corporation, Japan). Approximately 1 cm2 piece of gill tissue was sampled in the field from each individual and preserved with either Invitrogen RNAlater (gene expression) or 95% ethanol (DNA methylation). The remaining whole animal was placed in a pre-weighed 50 ml test tube and used to measure wet meat weight and Perkinsus marinus infection intensities.

Gene expression

Total RNA was extracted using a E.Z.N.A.® Total RNA Kit I (Omega BIO-TEK Inc., Norcross, GA, USA) following the manufacturer's instructions. The yield and quantity were initially assessed using a NanoDrop 2000 spectrophotometer. Total RNA extracted from the 80 individuals was sent to the University of Texas at Austin’s Genomic Sequencing and Analysis Facility where RNA quality control was confirmed using a 2100 Agilent Bioanalyzer on a Eukaryote Total RNA Nano chip and libraries were produced using the Tag-Sequencing approach (Meyer, Aglyamova, & Matz, 2011). The resulting 80 libraries were sequenced on two lanes of an Illumina HiSeq 2500 platform, with 100 base pair single-end reads.

Sequencing reads were trimmed of adapter sequences using Trimmomatic (version 0.39) (Bolger, Lohse, & Usadel, 2014) and base pairs with quality scores below 30 were removed (Table S2). The trimmed reads were mapped to the C. virginica reference genome (Gómez-Chiarri, Warren, Guo, & Proestou, 2015) with known haplotigs removed (https://github.com/jpuritz/OysterGenomeProject/tree/master/Haplotig_Masked_Genome) using the single pass option for STAR RNA-seq aligner (version 2.6.0a) (Dobin et al., 2013). Reads were mapped to gene features with the options (--quantMode GeneCounts --outFilterScoreMinOverLread 0.50 --outFilterMatchNminOverLread 0.50) specified to adjust for poly-A tail contamination. A count matrix was generated from the ReadsPerGene.out.tab output from STAR.

Usage Notes

The R scripts files will provide data analysis steps needed for reproducing the results.

Funding

National Science Foundation, Award: 1731710

Louisiana Sea Grant, Award: NA14OAR4170099

Alfred P. Sloan Foundation

National Science Foundation, Award: 1711319

National Science Foundation, Award: 00001414