Germline DNA methylation in six species of reef corals: patterns and potential roles in response to environmental change
DNA methylation is an epigenetic mark that plays an inadequately understood role in gene regulation, particularly in non-model species. Because it can be influenced by the environment and potentially transferred to subsequent generations, DNA methylation may contribute to the ability of organisms to acclimatize and adapt to environmental change. We evaluated the distribution of gene body methylation in reef-building corals, a group of organisms facing significant environmental challenges. Gene body methylation in six species of corals was inferred from in silico transcriptome analysis of CpG O/E, an estimate of germline DNA methylation that is highly correlated with patterns of methylation enrichment. Consistent with what has been documented in most other invertebrates, all corals exhibited bimodal distributions of germline methylation suggestive of distinct fractions of genes with high and low levels of methylation. The hypermethylated fractions were enriched with genes with housekeeping functions, while genes with inducible functions were highly represented in the hypomethylated fractions. In three of the coral species, we found that genes differentially expressed in response to thermal stress and ocean acidification exhibited significantly lower levels of methylation. These results support a link between gene body hypomethylation and transcriptional plasticity that may point to a role of DNA methylation in the response of corals to environmental change.
As human influence on the planet expands, many organisms must acclimatize and adapt to rapid environmental change. Phenotypic plasticity facilitates a more rapid response to environmental change than is possible through natural selection, and will likely be critical to the persistence of many species (Charmantier ), (Chevin 2010). Phenotypic change often involves modifications in gene expression. Epigenetic mechanisms, involving alterations to the genome that do not affect the underlying DNA sequence, are increasingly recognized as some of the principal mediators of gene expression (Duncan 2014). The most researched and best understood epigenetic process is DNA methylation, which most commonly involves the addition of a methyl group to a cytosine in a CpG dinucleotide pair. The role of DNA methylation is best understood in mammals, where methylation in promoter regions has a repressive effect on gene expression (Jones 2001). In plants and invertebrates, methylation of gene bodies prevails, and is thought to be the ancestral pattern (Zemach 2010). Gene body methylation appears to have a range of functions, including regulating alternative splicing, repressing intragenic promoter activity, and reducing the efficiency of transcriptional elongation (Duncan 2014). Methylation of gene bodies also varies according to gene function, and studies on invertebrates indicate that highly conserved genes with housekeeping functions tend to be more heavily methylated than those with inducible functions (Roberts 2012), (Sarda 2012), (Dixon 2014), (Gavery 2014). This has led to speculation that gene body methylation may promote predictable expression of essential genes for basic biological processes, while an absence of methylation could allow for stochastic transcriptional opportunities in genes involved in phenotypic plasticity (Roberts 2012), (Dixon 2014), (Gavery 2014).
Direct relationships between DNA methylation and phenotypic plasticity are increasingly being established. Some examples include caste structure in honeybees and ants (Kucharski 2008), (Bonasio ), expression of the agouti gene in mice ((Wolff )), and the influence of prenatal maternal mood on newborn stress levels in humans (Oberlander ). In many cases, changes in methylation patterns can be attributed to external cues such as temperature, stress, or nutrition. A prime example is the honeybee Apis mellifera, where larval consumption of royal jelly induces changes in methylation that ultimately determine the developmental fate of an individual into a queen or a worker (Kucharski 2008). Thus, DNA methylation has been established as a key link between environment and phenotype.
Reef-building corals, the organisms that form the trophic and structural foundation of coral reef ecosystems, are known to display a significant degree of phenotypic plasticity (Todd ), (Forsman 2009), (Granados-Cifuentes ). As long-lived, sessile organisms, corals are thought to be particularly reliant on phenotypic plasticity to cope with environmental heterogeneity, because they must be able to withstand whatever nature imposes on them over long periods of time (Bruno 1997). As phenotypically flexible as they may be, corals’ longevity and immobility may also contribute to their vulnerability in a changing environment. Reef corals worldwide are experiencing severe declines due to a variety of anthropogenic effects, including climate change, ocean acidification, and a host of local stressors (Hoegh-Guldberg 2007). This has raised doubt concerning the ability of corals to survive coming decades. Yet there are also signs that, at least in some cases, corals possess sufficient resiliency to overcome their numerous challenges (Palumbi 2014). Recent studies on gene expression variation, for example, support the view that phenotypic plasticity in corals is robust and may provide resilience in the face of ocean warming (Barshis 2013), (Granados-Cifuentes ), (Palumbi 2014). However, the underlying basis of gene expression variation, and indeed phenotypic plasticity, remain largely unknown.
Evaluation of epigenetic processes therefore represents a logical next step in understanding coral gene expression and phenotypic variation. While recent annotation of the Acropora digitifera genome revealed a broad repertoire of genes involved in DNA methylation and other epigenetic processes (Dunlap 2013), to date, only one study has investigated possible roles of epigenetic processes in corals (Dixon 2014). Germline DNA methylation patterns in the transcriptome of Acropora millepora corroborated findings reported in studies of other invertebrate species (Dixon 2014). Most interestingly, genes that were differentially expressed in response to a common garden transplantation experiment were among the genes exhibiting lower levels of germline methylation (Dixon 2014). This finding further supports studies on invertebrates showing that hypomethylated genes tend to be those with inducible functions (Gavery 2010), (Sarda 2012).
Coral gene expression studies continue to expand, providing rich datasets to further probe the relationship between DNA methylation and gene function. In this study, we performed a comprehensive evaluation of germline methylation patterns in reef corals by examining the transcriptomes of six scleractinian coral species. Germline methylation levels in these data were inferred based on the hypermutability of methylated cytosines, which leads to a reduction in CpG dinucleotides over evolutionary time (Sved ). These data were then matched with gene ontology information, permitting evaluation of methylation patterns associated with broad categories of biological processes. Lastly, in three of the six species, we evaluated germline methylation patterns in genes involved in response to thermal stress and ocean acidification.
Transcriptome data sources
The transcriptomes of six scleractinian coral species were evaluated to determine germline methylation patterns in relation to gene function and activity. Species examined included Acropora hyacinthus, A. millepora, A. palmata, Pocillopora damicornis, Porites astreoides, and Stylophora pistillata (Table 1). These transcriptomes reflect a range of life history stages. Some transcriptomes were developed from life history stages that had not yet been infected with symbiotic dinoflagellates (Symbiodinium spp.), while others used bioinformatic techniques to filter out putative Symbiodinium sequences. However, two of the transcriptomes (P. damicornis and S. pistillata) were developed from adult corals and did not remove putative symbiont sequences. We therefore applied a filtering step to these transcriptomes by comparing them to Symbiodinium clade A and B transcriptomes from (Bayer 2012) using blastn (version 2.2.29). An evalue threshold of of 10-5 was used for these queries, and all matched sequences were removed from further analyses. Details of blastn query and filtering are provided (https://github.com/jldimond/Coral-CpG-ratio-MS)
Table 1. Transcriptomes used in this study
|Organism||Life history stage||Method||No. Contigs||Reference|
|Acropora hyacinthus||Adult||Illumina||33,496||Barshis et al. (2013)(Barshis 2013)|
|Acropora millepora||Embryo to adult||454 & Illumina||52,963||Moya et al. (2012)(Moya 2012)|
|Acropora palmata||Larval||454||88,020||Polato et al. (2011)(Polato 2011)|
|Pocillopora damicornis||Adult||Illumina||72,890||Vidal-Dupiol et al. (2013)(Vidal-Dupiol 2013)|
|Porites astreoides||Adult||454||30,740||Kenkel et al. (2013)(Kenkel 2013)|
|Stylophora pistillata||Adult||454||15,052||Karako-Lampert et al. (2014)(Karako-Lampert 2014)|
$$ Illumina is Company - 454 is platform, several types of Illumina platforms.
Differentially expressed gene datasets
In addition to analyzing whole transcriptomes, we also examined genes differentially expressed in response to environmental stressors for the three acroporid species. For A. hyacinthus and A. millepora these gene sets were derived from the same studies that developed the reference transcriptomes ((Barshis 2013), (Moya 2012)), and for A. palmata differentially expressed genes sets were reported in (Polato 2013)(Polato Nicholas R.; Baums 2013) (Table 2).
Table 2. Differentially expressed gene sets examined
|Organism||Life history stage||Method||No. Contigs||Environmental factor||Reference|
|Acropora hyacinthus||Adult||sequencing||484||Thermal stress||Barshis et al. (2013)(Barshis 2013)|
|Acropora millepora||Juvenile||sequencing||234||Ocean acidification||Moya et al. (2012)(Moya 2012)|
|Acropora palmata||Larval||microarray||2002||Thermal stress||Polato et al. (2013)(Polato 2013)(Polato Nicholas R.; Baums 2013)|
To maintain consistency in comparing datasets, all transcriptomes and differentially expressed gene sets were compared to the UniProt/Swiss-Prot protein database (version 2/17/2015) using Blastx (version 2.2.29) with an evalue threshold of 10-5.
To further annotate genes with functional categories, corresponding Gene Ontology Slim (GOSlim) biological process were identified. Details of annotation are provided in jupyter notebooks in accompanying repository (https://github.com/jldimond/Coral-CpG-ratio-MS).
Predicted germline methylation
Germline methylation levels were inferred based on the hypermutability of methylated cytosines, which tend towards conversion to thymines over evolutionary time. This results in a reduction in CpG dinucleotides, meaning that heavily methylated genomic regions are associated with reduced numbers of CpGs. Thus, methylation patterns that have been inherited through the germline over evolutionary time can be estimated using the ratio of observed to expected CpG, known as CpG O/E. Germline DNA methylation estimated by analysis of CpG O/E is highly correlated with direct assays of methylation ((Suzuki 2007), (Sarda 2012), (Gavery 2013)). CpG O/E was defined as:
CpG O/E = (number of CpG / number of C x number of G) x (l2/l-1)
where l is the number of nucleotides in the contig.
Only annotated sequences were used for calculation of CpG O/E to increase the likelihood that sequences were oriented in the 5' to 3' direction. For subsequent analyses, we set minimum and maximum limits for CpG O/E at 0.001 and 1.5, respectively, to exclude outliers. Details of germline methylation prediction methods are provided in jupyter notebooks (https://github.com/jldimond/Coral-CpG-ratio-MS/)
Transcriptome CpG O/E patterns were fitted with the normalmixEM function in the mixtools package in the R statistical platform. Mixture models were evaluated against the null single component model by comparison of log-likelihood statistics. High and low CpG O/E components were delineated in mixture models using the intersection point of component density curves. For each GOSlim term, enrichment in the high and low CpG O/E components identified in mixture models was evaluated with Fisher's exact test. Whole transcriptome and differentially expressed gene CpG O/E distributions were compared with the Kolmogorov–Smirnov test. To compare representation of GOSlim terms in the differentially expressed genes relative to whole transcriptomes, the relative abundance of each GOSlim term in each dataset was calculated as a percentage, and these values for whole transcriptomes were subtracted from those for differentially expressed genes. Details and specific code used are available (https://github.com/jldimond/Coral-CpG-ratio-MS/).
For P. damicornis and S. pistillata transcriptomes, 2892 and 138 putative Symbiodinium sequences were removed from the transcriptomes, respectively. Comparisons of the six coral transcriptomes to the UniProt/Swiss-Prot database resulted in annotation of 26% to 47% of the contigs in each transcriptome (Table 3).
Table 3. Transcriptome annotation results.
|Organism||No. Contigs||Contigs Annotated (UniProt/Swiss-Prot)||Contigs Annotated (GOslim)|
Predicted Gene Methylation
Whole transcriptome patterns of predicted germline DNA methylation were similar for all coral species, suggesting bimodal distributions of CpG O/E with relatively large high-CpG O/E components (Figure 1). This was confirmed by mixture model analyses indicating that a 2-component Gaussian model provided better fit than a single-component model in all cases (Table 4). While there was some variability in models between species, all models were characterized by a low-CpG O/E component with a mean of 0.24 to 0.38 and weighted at 14-28% of the distribution. Conversely, high-CpG O/E components had means of 0.69 to 0.75 and weights of 72-86%.
#####Figure 1. Transcriptome-wide CpG O/E in the six coral species. The component density curves of two-component mixture models are superimposed over histograms and density curves.
Table 4. Results of mixture model analyses of CpG O/E in the six coral transcriptomes. Model statistics are reported for a 2-component mixture model, which provided better fit than a single component model.
|Organism||lambda||mu||sigma||log-likelihood (k = 1)||log-likelihood (k = 2)|
|Acropora hyacinthus||0.14, 0.86||0.30, 0.75||0.11, 0.26||-1950||-1771|
|Acropora millepora||0.17, 0.83||0.38, 0.74||0.11, 0.20||1023||1307|
|Acropora palmata||0.27, 0.73||0.35, 0.73||0.12, 0.22||-2664||-1824|
|Pocillopora damicornis||0.22, 0.78||0.24, 0.69||0.10, 0.25||-3324||-2464|
|Porites astreoides||0.26, 0.74||0.34, 0.73||0.12, 0.23||-1450||-1115|
|Stylophora pistillata||0.28, 0.72||0.33, 0.72||0.11, 0.23||-660||-413|
Predicted Gene Methylation
When CpG O/E was evaluated according to gene function, we observed consistent patterns among species in relation to broad classes of biological processes. For all six species, the top four biological processes with the highest mean CpG O/E were cell-cell signalling, cell adhesion, signal transduction, and developmental processes (Figure 2). Low-ranked biological processes with the lowest mean CpG O/E were more variable between species. However, DNA metabolism was consistently ranked lowest, while protein metabolism and other metabolic processes were also typically among the lowest categories in terms of CpG O/E. Relatively high and low ranked biological processes were also more likely to be significantly enriched in the high and low CpG O/E components identified in the mixture model.
#####Figure 2. Patterns of CpG O/E in relation to gene function in the six coral species (mean +/- SE).
In Acropora hyacinthus, A. millepora, and A. palmata, genes expressed differentially in response to environmental stress showed distinct CpG O/E distributions from those of the whole transcriptomes (Figure 3, upper panel). In all cases, mean CpG O/E of differentially expressed genes (differentially expressed genes) was higher than that of the whole transcriptome. This was especially true for differentially expressed genes in response to thermal stress in A. hyacinthus and A. palmata (both p < 0.001), but CpG O/E distributions of ocean acidification differentially expressed genes and the whole transcriptome of A. millepora were also significantly different (p = 0.005).
#####Figure 3. Comparison of transcriptome-wide and differentially expressed gene CpG O/E in the acroporid corals. Upper panel: density curves of whole transcriptomes and differentially expressed genes. Lower panel: relative expression of different gene classes in differentially expressed genes as a percentage of whole transcriptome.
We also evaluated the contribution of different biological processes (GOslim terms) to differentially expressed gene profiles by comparing their abundances in differentially expressed genes relative to whole transcriptomes (Figure 3, lower panel). In A. hyacinthus, differentially expressed genes were overrepresented by biological processes associated with the high-CpG O/E components identified in the mixture model, and underrepresented by biological processes associated with the low CpG O/E component. In A. millepora, differentially expressed genes were characterized by a striking increase in transport processes, which were not significantly enriched in either the high or low CpG O/E components of the mixture model. To a lesser extent, cell adhesion and developmental processes were overrepresented in differentially expressed genes, and these processes were enriched in the high CpG O/E component. Biological processes underrepresented in differentially expressed genes tended to be associated with the low CpG O/E component. Finally, in A. palmata, a mixture of processes enriched in both the high and low CpG O/E components were overrepresented in the differentially expressed genes. Processes underrepresented in differentially expressed genes were largely associated with the low CpG O/E component.
Research on DNA methylation to date has revealed that its extent and function varies considerably among organisms. While several studies have compared DNA methylation patterns between distantly related taxa ((Tweedie 1997), (Zemach 2010), (Sarda 2012)), our analysis focused on a single taxonomic group, the reef-building scleractinian corals. Within this group, we took a comparative approach of six species to provide a comprehensive evaluation of germline methylation patterns. In three of these species, representing three independent studies, we found that genes expressed differentially in response to environmental stressors exhibited significantly lower levels of methylation. This work adds to a small but growing body of evidence supporting an association between hypomethylation and gene expression plasticity ((Roberts 2012), (Dixon 2014)).
As in most other invertebrate taxa surveyed ((Gavery 2010), (Sarda 2012)), we observed patterns of CpG O/E that were indicative of bimodal distributions in all of the coral transcriptomes. All distributions were dominated by a relatively high CpG O/E fraction, suggesting that the majority of genes in reef corals experience relatively low levels of methylation. A similar pattern was observed in a genome-wide analysis of the sea anemone Nematostella vectensis ((Zemach 2010), (Sarda 2012)), and in an analysis of exons in A. millepora (Dixon 2014). In contrast, CpG O/E profiles in other invertebrates, such as the oyster Crassostrea gigas and the sea squirt Ciona intestinalis, suggest larger low-CpG O/E fractions ((Gavery 2010), (Sarda 2012)). This is consistent with higher genome-wide levels of CpG methylation in C. intestinalis and C. gigas than in N. vectensis (21.6%, 15%, and 9.4%, respectively; (Zemach 2010), (Olson 2014)). Levels of DNA methylation broadly reflect evolutionary relationships ((Tweedie 1997), (Zemach 2010)), so it could be speculated that coral methylation is similar to that of N. vectensis. However, phylogenies derived from methylation patterns may differ considerably from those derived from protein sequences, suggesting lineage-specific changes in methylation (Sarda 2012). Lineage-specific changes in methylation could reflect differences in life history strategies (Sarda 2012).
Across reef coral species, ranking of biological processes according to mean CpG O/E was largely consistent, corroborating a trend reported in other studies of invertebrate gene body methylation ((Gavery 2010), (Sarda 2012), (Dixon 2014)). With little variation between coral species, the biological processes enriched in high CpG O/E values (predicted to have low CpG DNA methylation) reflect genes involved in inducible functions, while processes associated with the low CpG O/E values reflect genes for essential housekeeping functions. Housekeeping genes are ubiquitously expressed across tissue types, and tend to be evolutionarily conserved. Indeed, higher levels of germline DNA methylation are associated with gene orthology among invertebrate taxa ((Sarda 2012), (Park 2011), (Suzuki 2007)), suggesting a protective effect that contrasts with the tendency for methylated cytosines to experience higher mutation rates than nonmethylated nucleotides. However, it may be that DNA methylation has an overall protective influence despite the mutagenic effect on CpGs, or that CpG mutations have largely run their course over time in heavily methylated genes, with fewer methylated CpGs left for substitutions (Park 2011). One hypothesis for the role of gene body methylation is that it may facilitate consistent expression of ubiquitously expressed core genes needed for essential biological functions ((Bird 1995), (Roberts 2012), (Gavery 2014)). While there have been some reports of negative associations between gene body methylation and gene expression in invertebrates ((Suzuki 2007), Riviere et al. 2013), positive or bell-shaped relationships have been reported in several taxa, including corals ((Zemach 2010), (Gavery 2013), (Dixon 2014), (Jjingo 2012)). In A. millepora, Dixon et al. (Dixon 2014) reported that the most highly expressed genes in a reciprocal transplant experiment were those exhibiting higher levels of gene body methylation. These genes were enriched for housekeeping functions. A similar phenomenon of increased expression among hypermethylated genes was reported for C. gigas, in addition to an inverse relationship between DNA methylation and variation in expression between tissues (Gavery 2013). High transcript abundances among highly methylated genes may reflect their widespread expression across cell and tissue types ((Gavery 2013), (Dixon 2014)).
In contrast to hypermethylation and consistent expression, our finding of relatively low methylation among differentially expressed genes in response to thermal stress and ocean acidification in the three acroporid corals supports the hypothesis that hypomethylation is associated with transcriptional plasticity (Roberts 2012). Sparse methylation of gene bodies, and the tendency for genes involved in inducible functions to have lower methylation levels, is thought to leave these genes open to a greater variety of transcriptional opportunities (Roberts 2012). Potential sources of transcriptional variation could include access to alternative start sites, increased sequence mutations, exon skipping, and transient methylation (Roberts 2012). Such transcriptional variation could contribute to phenotypic plasticity, and it might be particularly beneficial among species that experience variable environments that require constant tuning of gene expression, such as corals and other sessile organisms. Dixon et al. (Dixon 2014) found support for this theory in A. millepora, showing that genes differentially expressed in response to transplantation to new environments were significantly enriched in the low methylation (high CpG O/E) component.
In our analysis, lower methylation among differentially expressed genes was at least partially attributable to increased representation of genes involved in inducible functions relative to those with housekeeping functions. This was especially true in A. hyacinthus. While unsurprising, this highlights an important caveat to our analysis; low methylation among differentially expressed genes could simply reflect the fact that environmental stressors elicit higher expression of inducible genes relative to housekeeping genes, and that these genes simply happen to exhibit different methylation patterns. Furthermore, it remains unclear whether gene body DNA methylation actively regulates genes, as opposed to the alternative hypothesis that it is simply a byproduct of an open chromatin state (Jjingo 2012). For example, gene body methylation may reflect exposure of DNA to DNA methyltransferases (DNMTs) as a consequence of unpacked chromatin during transcription (Jjingo 2012). Further studies will be needed to evaluate causative relationships between DNA methylation and transcriptional activity. Additionally, despite the utility of CpG O/E to infer germline methylation patterns, experimental work on transient methylation in somatic cells and how it might influence transcription is needed. However, experimental studies on social insects have already provided compelling evidence for links between differential DNA methylation, transcription, and phenotypic plasticity ((Kucharski 2008), (Elango 2009)). Studies of DNA methylation in plants have gone even further, having already incorporated epigenetics into ecological research (Bossdorf 2008).
There is a great deal of interest in the adaptive potential of corals in the face of continued environmental change. Perhaps the most intriguing aspect of DNA methylation is that its effects may extend beyond an individual organism’s lifetime and be transferred to successive generations. Although the epigenomes of some organisms, such as mammals, are extensively reprogrammed during meiosis and embryogenesis, in some cases DNA methylation can be passed on to offspring (Duncan 2014). This is especially true in plants ((Hauser 2011), (Heard 2014)), but there is also evidence for inheritance of DNA methylation in oysters (Olson 2014a). Transgenerational epigenetic inheritance is a potentially transformative biological concept, but its extent and significance is only just beginning to be understood, and remains largely unstudied in the vast majority of organisms ((Grossniklaus 2013), (Heard 2014)).
Our results illustrate patterns of gene body DNA methylation that are similar across coral species and largely consistent with what has been found in other invertebrates. This work serves as a basis with which to further characterize and compare DNA methylation in corals and related taxa. Within the context of environmental challenges faced by corals, our analysis found broad support for an inverse relationship between gene body methylation and differential gene expression. This suggests that the potential role of DNA methylation in the response of corals to environmental change warrants further investigation.
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