DOI: 10.5061/dryad.fqz612jtz
title: Gigantic genomes of salamanders indicate body temperature, not genome size, is the driver of global methylation and 5-methylcytosine deamination in vertebrates

description: 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.
description: <strong>Luminometric Methylation Assay and Data
 Standardization </strong> 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. <strong>Comparative Analyses of Global
 Methylation </strong> First, we tested for
 differences in methylation levels among the <em>A.
 mexicanum</em> 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 <em>A. laterale</em> (LJJ) and
 <em>A. laterale</em> (LJJJ) and the four diploid
 species<em> A. jeffersonianum</em> (JJ), <em>A.
 mexicanum</em>, <em>P. cinereus</em>, and <em>N.
 beyeri</em> 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.