Gigantic genomes of salamanders indicate body temperature, not genome size, is the driver of global methylation and 5-methylcytosine deamination in vertebrates
Adams, Alexander; Denton, Robert Daniel; Mueller, Rachel Lockridge (2022), Gigantic genomes of salamanders indicate body temperature, not genome size, is the driver of global methylation and 5-methylcytosine deamination in vertebrates, Dryad, Dataset, https://doi.org/10.5061/dryad.fqz612jtz
Transposable elements (TEs) are sequences that replicate and move throughout genomes, and they can be silenced through methylation of cytosines at CpG dinucelotides. TE abundance contributes to genome size, but TE silencing variation across genomes of different sizes remains underexplored. Salamanders include most of the largest C-values -- 9 to 120 Gb. We measured CpG methylation levels in salamanders with genomes ranging from 2N = ~58 Gb to 4N = ~116 Gb. We compared these levels to results from endo- and ectothermic vertebrates with more typical genomes. Salamander methylation levels are ~90%, higher than all endotherms. However, salamander methylation does not differ from other ectotherms, despite a ~100-fold difference in nuclear DNA content. Because methylation affects the nucleotide compositional landscape through 5-methylcytosine deamination to thymine, we quantified salamander CpG dinucleotide levels and compared them to other vertebrates. Salamanders and other ectotherms have comparable CpG levels, and ectotherm levels are higher than endotherms. These data show no shift in global methylation at the base of salamanders, despite a dramatic increase in TE load and genome size. This result is reconcilable with previous studies by considering endothermy and ectothermy, which may be more important drivers of methylation in vertebrates than genome size.
Luminometric Methylation Assay and Data Standardization
To measure DNA methylation levels, we used the luminometric methylation assay (LUMA), a form of pyrosequencing that targets CpG dinucleotides capable of methylation (Karimi et al. 2006). LUMA runs were carried out by the sequencing facility EpigenDx (Hopkinton, MA, USA) using a PyroMark MD system from Qiagen. Duplicate LUMA runs were done for each DNA sample. All assays included four Lambda DNA standards with methylation percentages of 0, 50, 60, and 100% as internal controls. To account for any differences across assay runs, we calibrated against the internal controls using inverse regression calibration (Ott and Longnecker, 2015). Random effects were added per subject and per combination of subject and tissue type to limit batch effects and account for pseudoreplication of duplicate runs.
Comparative Analyses of Global Methylation
First, we tested for differences in methylation levels among the A. mexicanum tissues using a mixed model ANOVA, with tissue as a fixed effect factor and individual and duplicate runs as random effect factors, followed by a Tukey HSD to test for significance between tissues. Next, we tested for differences in methylation levels among the polyploid unisexual salamander biotypes A. laterale (LJJ) and A. laterale (LJJJ) and the four diploid species A. jeffersonianum (JJ), A. mexicanum, P. cinereus, and N. beyeri using a mixed model ANOVA, with species as a fixed effect factor and individual and duplicate runs as random effect factors. We then performed a Tukey HSD to test for significance between species/biotype. Finally, we tested whether methylation levels vary across vertebrates as a function of salamander vs. non-salamander (i.e. genome size 29 Gb vs. genome size 6.4 Gb), ploidy (i.e. diploid vs. polyploid), and body temperature regulation (i.e. endothermy vs. ectothermy). We assigned species to the appropriate subgroup(s) and tested for variation between subgroups using linear regression contrasts. We note that we are combining our data with previously published results, but are unable to incorporate a study effect because of small sample size and dependency in the categorical variables (i.e. our study is the only one to include salamanders). We carried out all analyses in R Studio (RStudio Team 2021; R Core Team 2021) using R packages emmeans (Lenth 2021), parameters (Lüdecke et al. 2020), and lme4 (Bates et al. 2015). We used the ggpubr package (Kassambara 2020) to visualize the results.
NSF, Award: 1911585
NSF, Award: 2045704
NIH, Award: P40-OD019794