Genome-wide DNA methylation of the liver reveals delayed effects of
early-life exposure to 17-α-ethinylestradiol in the self-fertilizing
mangrove rivulus
Anne-Sophie Voisina, Victoria Suarez
Ulloaa, Peter Stockwellb, Aniruddha
Chatterjeec, Frédéric Silvestrea*
aLaboratory of Evolutionary and Adaptive Physiology -
Institute of Life, Earth and Environment - University of Namur, 61 Rue
de Bruxelles, B5000 Namur, Belgium
b Department of Biochemistry, University of Otago, 710
Cumberland Street, Dunedin 9016, New Zealand
c Department of Pathology, Dunedin School of Medicine,
University of Otago, 270 Great King Street, Dunedin 9054, New Zealand
*Corresponding author: frederic.silvestre@unamur.be
Dr Aniruddha Chatterjee and Dr Frédéric Silvestre contributed equally as
senior scientists
Running title: Effects of EE2 on the methylome of rivulus
Number of words: 7618
Abstract
Organisms exposed to endocrine disruptors in early life can show altered
phenotype later in adulthood. Although the mechanisms underlying these
long-term effects remain poorly understood, an increasing body of
evidence points toward the potential role of epigenetic processes. In
the present study, we exposed hatchlings of an isogenic lineage of the
self-fertilizing fish mangrove rivulus for 28 days to 4 and 120 ng/L of
17-α-ethinylestradiol. After a recovery period of 140 days, reduced
representation bisulfite sequencing (RRBS) was performed on the liver in
order to assess the hepatic genome-wide methylation landscape. Across
all treatment comparisons, a total of 146 differentially methylated
fragments (DMFs) were reported, mostly for the group exposed to 4 ng/L,
suggesting a non-monotonic effect of EE2 exposure. Gene ontology
analysis revealed networks involved in lipid metabolism, cellular
processes, connective tissue function, molecular transport and
inflammation. The highest effect was reported for nipped-B-like
protein B (NIPBL) promoter region after exposure to 4 ng/L EE2 (+
21.9%), suggesting that NIPBL could be an important regulator for
long-term effects of EE2. Our results also suggest a significant role of
DNA methylation in intergenic regions and potentially in transposable
elements. These results support the ability of early exposure to
endocrine disruptors of inducing epigenetic alterations during
adulthood, providing plausible mechanistic explanations for long-term
phenotypic alteration. Additionally, this work demonstrates the
usefulness of isogenic lineages of the self-fertilizing mangrove rivulus
to better understand the biological significance of long-term
alterations of DNA methylation by diminishing the confounding factor of
genetic variability.
Keywords : mangrove rivulus, 17-α-ethinylestradiol, delayed
effects, DNA methylation, RRBS, nipped-B-like protein B, Transposable
Elements
List of abbreviations : DMF, differentially methylated fragment;
EDC, endocrine disrupting chemical; EE2, 17-α-ethinylestradiol; NIPBL,
nipped-B-like protein B; RRBS, reduced representation bisulfite
sequencing; RR genome; reduced representative genome; TE, Transposable
element; WGBS, whole genome bisulfite sequencing
Introduction
Early-life exposure to environmental stressors, encountered during the
sensitive period of embryogenesis or in juveniles, can be critical in
shaping the long-term control of tissue physiology and homeostasis.
Although this paradigm, referred to as the Developmental Origins of
Health and Disease (DOHaD) (Barker, 2004), is now widely recognized, the
molecular mechanisms by which early exposures influence the propensity
of disease and phenotype later in life remain elusive. Identifying these
mechanisms has become extremely important to understand the long-term
effects of toxicants in human and wildlife and ensure the proper risk
assessment of xenobiotic exposure (Brockmeier et al., 2017).
Nowadays, endocrine disrupting chemicals (EDCs) – xenobiotics able to
interfere with the proper functioning of the endocrine system – are
ubiquitous in the environment and our everyday life. The timely release
and tightly regulated concentrations of hormones in early-life is
crucial for the proper development of the organism, including the
reproductive, nervous, and immune systems. Therefore, early-life
exposure to EDCs can have dramatic consequences on homeostasis and
physiology. In fact, mounting epidemiological evidence link exposures to
EDCs to the increased incidence of metabolic diseases, immune diseases,
neurological disorders, cancer and alteration of fertility in humans
(Braun, 2017; Heindel, Newbold, & Schug, 2015; Kajta & Wójtowicz,
2013; Kuo et al., 2012). Of the many EDCs present in the aquatic
environment, 17-α-ethinylestradiol (EE2) - a synthetic derivative of
estradiol used in oral contraceptives - is of particular concern due to
its high potency and resistance to degradation (Clouzot et al., 2008).
In treated seawage, EE2 is often the major compound with estrogenic
activity and its impact on aquatic wildlife has been deeply investigated
(reviewed in (Matthiessen, Wheeler, & Weltje, 2018). In aquatic
species, field studies and laboratory experiments show that exposure to
EE2 affects fecundity, fertility, reproductive behavior and induces
intersex, endangering natural populations (Aris, Shamsuddin, &
Praveena, 2014).
Potential long-term and persistent effects of early-life EDC exposure
involve the stable alteration of gene expression. This is possible
through epigenetic regulation, involving histone modifications,
non-coding RNAs, and DNA methylation. The latter refers to the transfer
of a methyl group from a methyl-donor, S-adenosyl-methionine (SAM), to
the fifth position of cytosines in the context of CpG dinucleotides,
forming a 5-methyl-cytosine (5mC) (Feng et al., 2010, #41462; Law &
Jacobsen, 2010; Lister & Ecker, 2009). This process is catalyzed by two
families of DNA methyltransferases (DNMTs): whereas DNMT3 enzymes are
responsible for de novo methylation during development and
differentiation, the DNMT1 family is maintaining methylation through
each cell division by copying hemimethylated DNA, making DNA methylation
a stable and heritable modification (de Mendoza, Lister, & Bogdanovic,
2020; Moore, Le, & Fan, 2013). DNA methylation regulates gene
expression by altering chromatin state and accessibility to CpG sites.
For instance, methylation occurring at promoter regions can prevent the
binding of transcription factors, resulting in gene silencing. Yet, the
relationship between methylation and gene expression is more complex, as
methylation also occurs at intragenic sites, enhancers or suppressor
elements (Edwards et al., 2017; Moore et al., 2013). DNA methylation is
dynamically regulated throughout life: during gametogenesis and
embryogenesis, two waves of demethylation/remethylation deeply reprogram
the methylome to produce a totipotent zygote (Edwards et al., 2017; Guo
et al., 2014; Smith et al., 2014). Then, DNA methylation plays an
important role throughout development as it directs cellular
differentiation after reprogramming. During early development and
juvenile stages, DNAme is thus thought to be particularly sensitive to
environmental factors (Dorts et al., 2016).
An increasing number of studies highlight the potential roles of DNA
methylation in mediating long-term effects of sub-toxic developmental
exposure to xenobiotics and roles in the etiology of diseases including
cancer, obesity, cardiovascular disease, diabetes, hypertension, and
neurodegenerative disorders (Eid & Zawia, 2016; Gore et al., 2011;
Goyal, Limesand, & Goyal, 2019; Stel & Legler, 2015; Wadhwa et al.,
2009). Indeed, DNA methylation could act as a long-term memory of past
exposures mediating stable changes in gene expression (Barouki et al.,
2018; Mirbahai & Chipman, 2014). In ecotoxicology, the role of
epigenomics is receiving more and more attention to explain the
long-term delayed and potential transgenerational effects of xenobiotics
(Brander, Biales, & Connon, 2017; Vandegehuchte & Janssen, 2011;
Vandegehuchte & Janssen, 2014). In particular, accumulating evidence
indicates that hormones and endocrine disrupting compounds can alter the
epigenome (Stel & Legler, 2015; Walker, 2016; Zhang & Ho, 2011). For
instance, the regulation of DNMT transcription by ESR1 (an estrogen
receptor that acts as a transcription factor) may represent one of the
possible mechanisms by which hormones influence methylation (Shi et al.,
2012). Other potential mechanisms may involve the reduction of SAM
availability (Lee, Jacobs, & Porta, 2009), the alteration of histone
activity as a result of membrane receptor estrogenic signaling (Anderson
et al., 2012; Casati et al., 2015), the expression of miRNAs, or direct
interactions between estrogen receptors and enzymes involved in the
methylation machinery, such as thymine-DNA glycosylase (TDG) (Liu et
al., 2016).
So far, relatively few studies have examined the effects of endocrine
disruptors on DNA methylation in aquatic species. Using bisulfite
conversion and pyrosequencing, Strömqvist et al. (Strömqvist, Tooke, &
Brunström, 2010) have shown decreased methylation levels of three CpG
sites located in the 5’ flanking region of the vitellogenin Igene, a known biomarker of estrogenic exposure, in the liver of male and
female zebrafish, following a 14-day exposure to 100 ng/L EE2. Using
Methylated DNA Immunoprecipitation (MeDIP) and high-throughput
sequencing, followed by validation using bisulfite sequencing PCR (BSP)
and RT-PCR, Mirbahai et al. (Mirbahai et al., 2011) investigated the
whole-genome methylation and gene expression in tumors in the liver of
the Common dab (Limanda limanda ), sampled in various sites in
English rivers. Genes involved in pathways related to cancer, including
apoptosis, wnt/β-catenin signaling and genomic and non-genomic estrogen
responses, were altered both in methylation and transcription. In that
case the exact mixture of environmental contaminants causing the liver
tumors was not identified but the molecular responses point towards
estrogenic disruption. On zebrafish early life stages, Falisse et al.
(Falisse et al., 2018) showed that exposure to the antibacterial agent
and EDC triclosan modified the methylome after 7 days exposure, as well
as the expression of related genes, using Reduced Representation
Bisulfite Sequencing (RRBS).
The identification of environmentally-induced alterations in the
epigenome ideally requires the use of individuals with no existing or
very low genetic variability to rule out the confounding factors of the
genotype on the observed phenotype (Heard & Martienssen, 2014). Such a
model species is provided by the mangrove rivulus, Kryptolebias
marmoratus , one of the only known self-fertilizing vertebrates (the
other one being its sister species, K. hermaphroditus ). Although
males and hermaphrodites are present in natural populations, most
reproduction occurs by self-fertilization of hermaphrodites. Exclusive
selfing of hermaphrodites during several generations results in highly
homozygous and virtually “clonal” lineages that can occur naturally or
be bred in a laboratory setting (Avise & Tatarenkov, 2015; Mackiewicz
et al., 2006). A naturally clonal and sexually-reproducing vertebrate
species allows the study of epigenetic mechanisms ruling out the
confounding factor of genetic variability, which is especially useful in
ecotoxicology experiments in the sublethal range and/or dealing with
delayed effects where non-dramatic molecular changes are expected.
Little is known about the epigenetic mechanisms in K. marmoratusbut it has been reported that the sex-ratio can be modulated by
temperature-sensitive DNA methylation (Ellison et al., 2015). Recent
studies described the DNA methylation reprogramming event during
embryogenesis (Fellous et al., 2018), as well as the time course
expression of enzymes involved in epigenetics during its development
(Fellous, Earley, & Silvestre, 2019a; Fellous, Earley, & Silvestre,
2019b). Another study has shown that parasite load can modify DNA
methylation (Berbel-Filho et al., 2019a), while these authors also found
a higher differentiation of the methylation landscape between different
genotypes than between environments (Berbel-Filho et al., 2019b).
In previous studies, we successfully used the mangrove rivulus to assess
the delayed effects of BMAA (beta-N-Methylamino-L-alanine) (Carion et
al., 2020) and 17-α-ethinylestradiol (EE2) (Voisin et al., 2016; Voisin,
Kültz, & Silvestre, 2018) in adults after an early-life stage exposure.
Hatchlings exposed to 4 and 120 ng/L EE2 for 28 days, and allowed to
recover for 140 additional days in clean water, reported delayed and
persistent effects of EE2 on growth, reproduction and steroid levels as
well as liver, ovotestis and brain molecular phenotypes assessed by
label-free quantitative proteomics. Effects of EE2 were tissue and
dose-dependent, with most effects occurring at the environmentally
relevant concentration (4 ng/L) in brain and liver, and at the higher
concentration in the gonads. Among the three studied organs, the liver
showed the most sensitive response to EE2. In this organ, EE2 affected
known estrogen-responsive pathways such as lipid, fatty acid and steroid
metabolism, apolipoproteins, innate immune system and inflammation.
Interestingly, several proteins involved in SAM metabolism were affected
in the liver, providing further indications of the potential effect of
EE2 on DNA methylation. The liver serves an essential and conserved role
for metabolic homeostasis in all vertebrates, and reproduction in the
case of oviparous species. Several studies have shown that the liver is
an important target of endocrine disruption in mammals (Foulds et al.,
2017) and fish (De Wit et al., 2008; Feswick, Munkittrick, & Martyniuk,
2017; Humble et al., 2013; Mirbahai et al., 2011). Using a RRBS
approach, the present study aimed at characterising the hepatic
methylation landscape in adults and investigating the genome-wide
changes in DNA methylation in the liver of 168 dph adults that were
exposed to EE2 as hatchlings to test the hypothesis of the potential
involvement of DNA methylation in the delayed effects of EDCs.
Materials and methods
2.1. Experimental fish, EE2 exposure and sample collection
Individuals used in this study were from the same experiment reported in
Voisin et al. (Voisin et al., 2016), which also described the generation
of breeding stock population and collection of eggs. Upon hatching,
mangrove rivulus were transferred to 300 mL glass jars filled with 100
mlL of 25 ppt reconstituted salt water (Instant Ocean™ sea salt), and
the corresponding treatment: vehicle control (0.000012 % ethanol), 4
ng/L and 120 ng/L 17-α-ethinylestradiol (EE2) (Sigma-Aldrich E4876-1G).
Fish were raised at a temperature of 26°C and a 12:12 h photoperiod.
Detailed preparation of EE2 solutions and the measurement of actual
exposure concentrations by ELISA are reported in Voisin et al. (Voisin
et al., 2016). Average measured concentration of the nominal 120 ng/L
exposure solution was 132.3 ± 14.7 ng/L. Since 4 ng/L is situated below
the detection limit (20 ng/L), we refer to the measure of the stock
solution, which was 10.15 ± 0.15 µg/L for the nominal 10 µg/L stock
solution. Throughout the paper, we will refer to the nominal
concentrations of 4 and 120 ng/L. Fish were exposed for a period of 28
days with a 100 % water renewal 3 times a week. At 28 dph, fish were
transferred to individual 1.2 L plastic containers filled with 400 mL of
25 ppt clean salt water and reared until 168 dph. Rivulus were fed 1 mL
of concentrated newly hatched brine shrimp (Artemia nauplii)
until 56 dph, 2 mL starting at 56 dph and 4 mL starting at 91 dph. At
168 dph, fish were sacrificed by a rapid transfer to 4°C 25 ppt salt
water and sectioning the spinal cord. For each experimental condition,
livers from five individuals were dissected out at 168 dph, snap-frozen
in liquid nitrogen and stored at -80°C until DNA extraction. All rivulus
husbandry and experimental procedures were performed in accordance with
the Belgian animal protection standards and were approved by the
University of Namur Local Research Ethics Committee (UN KE14/230). The
agreement number of the laboratory for fish experiments is the
LA1900048.
2.2. DNA extraction, RRBS library preparation and post-processing
Genomic DNA from individual livers was extracted using the NucleoSpin
Tissue XS kit (Macherey-Nagel, Germany) following the manufacturer’s
protocol. DNA concentration and quality were assessed using the NanoDrop
8000 spectrophotometer (Thermo Scientific, Waltham, MA) and 1% agarose
gel electrophoresis. A total of 15 RRBS libraries (5 for each
experimental condition) were prepared following established protocols
(Chatterjee et al., 2013; Chatterjee et al., 2014). In brief, genomic
DNA was digested with MSPI (New England Biolabs, Ipswich, MA) followed
by end repair, addition of 3′ A overhangs and addition of methylated
adapters (Illumina, San Diego, CA) to the digested fragments. Following
adapter ligation, size selection was performed by cutting 40–220 bp DNA
fragments (pre-ligation size) from a 3% (w/v) NuSieve GTG agarose gel
(Lonza, Basel, Switzerland). Subsequently, libraries were bisulfite
converted using the EZ DNA methylation kit (Zymo Research, Irvine, CA)
with an extended incubation time of 18–20 h. Bisulfite converted
libraries were amplified by PCR and sequenced (100-bp single ended
reads) by New Zealand Genomics Limited (University of Otago, New
Zealand) on an Illumina HiSeq2000 sequencer. Sequences were obtained in
FASTQ format.
The quality of sequenced reads was performed using the FastQC
application (Babraham Institute, Cambridge, UK). Adaptor sequences were
removed from the reads using the cleanadaptors program of the
DMAP package (Chatterjee et al., 2012b). Single-ended bisulfite reads
were aligned against the Kryptolebias marmoratus assembly
(GCF_001649575.1_ASM164957v1) using the Bismark software (Krueger &
Andrews, 2011). Proximal genes and genomic positions of the fragments
were identified using the identgeneloc program from the DMAP
package (Stockwell et al., 2014). Fragments were linked to the nearest
protein-coding gene and the genomic position was attributed as follows:
internal (within the gene body), promoter (0-5 kb upstream of the
transcription start site), upstream (5-10 kb upstream of the TSS) and
intergenic (>10 kb). The annotation of intergenic fragments
or fragments located upstream of genes (5-10 kb) and in intergenic
regions (>10 kb) is provided as an indication and should be
considered with caution as methylation of these regions may not have any
influence on the corresponding gene expression. Fragments with a
methylation level ≥ 80% are qualified as highly methylated, while those
with a methylation level ≤ 20% are qualified as lowly methylated.
2.3. Analysis of differential DNA methylation
Base resolution differences in DNA methylation following developmental
exposure to EE2 were assessed based on MSPI fragments (40-220 bp range)
as a unit of analysis. To filter the fragments suitable for differential
methylation analysis, we selected the fragments with ≥ 2 CpG sites, ≥10
reads per fragment (CpG10) and present in at least 3 of 5 individuals
for each experimental condition. These fragments are referred to as
“analyzed fragments”. The 102,018 analyzed fragments form our reduced
representative genome (RR genome) corresponds to a total of 14,679
unique RefSeq identifiers, as more than one fragment often maps to a
same gene. Differential methylation analyzes were performed as
previously described using the diffmeth program from DMAP (Chatterjee et
al., 2012; 2017a; 2017b; Stockwell et al., 2014) between each
experimental group, resulting in three group comparisons: Ctl versus 4
ng/L EE2 exposed individuals, Ctl versus 120 ng/L and 4 versus 120 ng/L.
Determination of significantly differentially methylated fragments
(DMFs) was based on ≥ 10% differential methylation between the groups
and P-value <0.01, and are qualified as hyper- or
hypomethylated fragments.
RefSeq identifiers were retrieved using BLAST for all analyzed DMFs. To
establish a reference gene list for the network analysis, a list
including all DMFs was filtered to include only gene body and promoter
genomic locations and contained 68,762 fragments mapped to rivulus
RefSeq and human RefSeq identifiers. As more than one fragment often
mapped to a same gene, this list corresponded to 10,440 unique human
RefSeq identifiers. The list, containing P-values and methylation
difference for each of the three treatment comparisons was uploaded in
the Ingenuity Pathway Analysis software (IPA, QIAGEN,
www.qiagen.com/ingenuity). To gain an overall view of the pathways and
functions associated with promoter and gene body DMFs, as well as the
connections between differentially abundant proteins, we performed a
network analysis for each group comparison.
3. Results
3.1. Characterization of the hepatic DNA methylation landscape inK. marmoratus
Using RRBS, we generated 15 K. marmoratus liver methylomes with
an average of 29.5 million sequenced reads (between 19.7 and 40 million
reads) (Table 1, Supplementary Table 1). Unique alignment efficiency of
these libraries ranged from 40.3 % to 46.8 % (except the library 23-4
with 28.5%, which was discarded in further analyses), with an average
of 44.4 %. This corresponds to a total of 102 018 fragments suited for
the analysis after MSPI digestion of genomic DNA and 40–220 bp
size-selection (Figure 1A). The total size of the RR genome was 9.37 Mb,
which accounted for 1.4% of the published full genome
(GCF_001649575.1_ASM164957v1) (Rhee et al., 2017). Within this reduced
genome, we counted 701 862 CpG dinucleotides, representing 6.1% of the
11.5 million CpG in the reference genome. Consequently, the enrichment
factor in CpGs of the RR genome was 4.3-fold. Of all the analyzed
fragments, the majority was located within gene bodies (47%), followed
by intergenic regions (30%), promoter (16%) and upstream regions (7%)
(Figure 1B). The distribution of CpGs followed exactly the same pattern
as the fragments (data not shown). We consider this dataset of about 6%
of all genomic CpGs, albeit not uniformly distributed across the genome,
as a representative sample that permits the investigation of changes in
DNA methylation in liver due to exposure to toxicants. Global CpG
methylation levels in liver tissue ranged from 63.5% to 66.4% in all
samples. On the contrary, methylation level of non-CpG cytosines
remained low between 1.7 and 2.4%.
For the following description, we took only the control group into
consideration, in order to avoid any possible effect of the EE2
treatment. The average DNA methylation level of all MSPI fragments in
mangrove rivulus followed a bimodal distribution with most fragments
below 20% of methylation (20% of all fragments - considered as lowly
methylated) or above 80% (67% of all fragments - considered as highly
methylated) (Figure 1C). The level of DNA methylation was the lowest in
fragments located in promoter regions (mean=42.3%; median=12.8%)
compared to upstream (mean=66.2%; median=88.4%), intergenic
(mean=72.6%; median=89.1%) and gene body (mean=78.0%; median=91.5%).
Figure 1D depicts the distribution and spread (25-75 quantiles) of DNA
methylation level for fragments within each region. We can observe that
the methylation range was the highest for fragments located in the
promoter region, showing that even if the majority of these fragments
were lowly methylated (52%), a considerable part was highly methylated
(38%). Despite the fact that fragments in promoter regions represented
only 16% of the total analyzed fragments, they accounted for 42% of
all lowly methylated fragments (Figure 2A), compared to fragments
located in gene bodies (27%), intergenic (23%) and upstream (8%)
regions. In contrast, only 9% of all highly methylated fragments were
located in the promoter regions, compared to 53% in gene bodies, 31%
in intergenic regions and 7% in upstream regions (Figure 2B).
Supplementary figure 1 depicts the coefficient of variability of the
methylation level within each genomic region, for lowly methylated
fragments on the one hand, and for highly methylated fragments on the
other hand. Highly methylated fragments presented a low level of
variability (< 3%), in opposition to lowly methylated
fragments for which the coefficient of variability was higher than 33%.
This pattern was the same for all the fragments, independently of the
genomic region.
3.2. Changes in DNA methylation following developmental exposure to EE2
Global DNA methylation levels of rivulus liver averaged 65.1 ± 1.0% in
Ctl samples, 66.0 ± 0.2% in 4 ng/L and 65.5 ± 0.6% in 120 ng/L exposed
individuals and were not significantly different (Anova on arcsin
squareroot transformed values). Compared to the Ctl group, 58 DMFs were
reported for the low EE2 concentration of 4 ng/L, 33 for the higher
concentration of 120 ng/L, while 62 DMFs were significant between 120
and 4 ng/L groups. The majority of DMFs had a higher methylation level
at 4 ng/L compared to Ctl and 120 ng/L groups, and at 120 ng/L compared
to Ctl (Table 2, Figure 3A). Figure 3B depicts the DMF location within
the RR genome and showed a similar pattern as the general distribution
of the analyzed fragments (Figure 1B) with the DMFs located in the order
gene body > intergenic > promoter
> upstream regions. None of the significant DMF was common
to all three group comparisons (Figure 3C). However, 2 DMFs were
significant at both EE2 treatments compared to Ctl: one located in an
intergenic region, in proximity of the liprin-alpha-4 (PPFIA4)
gene, and one located in the gene body of the ATP-binding cassette
sub-family A member 1 gene (ABCA1) (Figure 3D). In addition, there were
3 common DMFs to 4 ng/L vs Ctl and 4 ng/L vs 120 ng/L comparisons, of
which two were found within gene bodies:calcium/calmodulin-dependent protein kinase type 1D (CAMK1D),testis-specific serine/threonine-protein kinase (TSSK1B) and one
in the intergenic region in proximity of the lymphoid-restricted
membrane protein-like gene (LRMP). Finally, one fragment was
significantly differentially methylated in both 120 vs Ctl and 120 vs 4
ng/L, located in the intergenic region in proximity to the PTK2B
(protein-tyrosine kinase 2 ) gene. Tables 3, 4 and 5 report the
DMFs for the three comparisons: 4 ng/L vs Ctl, 120 ng/L vs Ctl, and 4
ng/L vs 120 ng/L, respectively. Among all group comparisons, the maximum
effect size (difference in mean methylation) observed for
hypermethylation was 21.9 % (promoter region of nipped-B-like
protein B , NIPBL) and -19.0% for hypomethylation (intergenic region,eukaryotic translation initiation factor 4 gamma 3 EIF4G3). Both
DMFs were significant between 4 ng/L and Ctl groups. It is also
important to report the fact that very few DMFs were classified as
hypomethylated fragments. When comparing exposed groups to Ctl, only the
NIPBL was hypomethylated in Ctl at 19.6% and increased to 41.5% after
exposure to 4 ng/L EE2.
3.3. Networks associated with gene body and promoter DMFs
A network analysis was performed with the IPA software using genes
corresponding to DMFs located in gene body and promoter regions. Only
networks with the highest number of associated DMFs are reported (Table
6). The highest network scores were found in the 4 ng/L vs Ctl
comparison: the first network involved 16 molecules, with functions
related to organismal abnormalities, cell morphology and nervous system
function and is represented in Figure 4. The second network was composed
of 15 molecules and was related to gene expression, drug metabolism and
lipid metabolism. In 120 ng/L exposed individuals compared to Ctl, only
one network with more than 9 molecules was established in IPA, related
to lipid metabolism, molecular transport and small molecule
biochemistry. Finally, four networks were created when comparing 120
ng/L to 4 ng/L exposed individuals, endorsing the differences in the
response at these two concentrations. The first network was related to
antigen presenting, cell-to-cell signalling and the inflammatory
response. The 2nd and 3rd networks
were mostly associated with cellular processes (e.g. cellular
organization and morphology, cell-to-cell signalling) and the
4th network was linked to cellular death and survival
and connective tissue function.
4. Discussion
4.1. Characterization of the mangrove rivulus RR genome
Cytosine methylation is a predominant epigenetic regulation of gene
expression. It occurs mainly on CpG dinucleotides and is classically
associated with gene silencing when it occurs at CpG rich promoter
regions (Bock et al., 2012). Whole genome bisulfite sequencing (WGBS)
provides global genome coverage of DNA methylation at single-base
resolution, and is consequently referred as the ‘gold standard’ method.
Despite its benefits, including a high coverage of CpGs
(>90% in human) and an unbiased representation, it remains
expensive and difficult to use with large number of samples, mostly
because the detection of changes in methylation between samples demands
high sequencing depth (Wreczycka et al., 2017). In the present study, we
opted for reduced representation bisulfite sequencing (RRBS), an
approach that also resolves single-base DNA methylation, combining the
use of restriction enzymes and bisulfite sequencing. Unlike WGBS, RRBS
can only sequence rich-CpG regions, and has limited coverage of the
genome (Meissner et al., 2005). However, the fact that it enriches the
analyzed fragments with a high density of CpGs makes this technique
cost-effective and allows to work on a higher number of samples with a
high sequencing depth (Chatterjee et al., 2012a). Here we reported the
DNA methylation pattern of mangrove rivulus liver using RRBS. This
species shows a very peculiar reproduction strategy as it is the only
vertebrate which alternates between cross-fertilization and selfing, the
latter being the most common. It results in several lineages of isogenic
individuals, and epigenetic mechanisms have been proposed to explain the
occurrence of phenotypic variability despite a low level of genetic
diversity (Fellous et al., 2018). We reported a RR genome representing
1.4% of the whole genome, which was comparable to what was obtained in
mouse (1.4%) or in zebrafish (2.2%) brain (Chatterjee et al., 2013;
Smith et al., 2009). The percentage of CpG covered in the RR genome
reached 6.1% of the total number of genomic CpGs, which was similar to
mouse and zebrafish (7.0% and 5.3%, respectively, (Chatterjee et al.,
2013)), but higher than what other studies reported for diverse fish
species such as stickleback [1% (Metzger & Schulte, 2017)],
Atlantic salmon [2.75% (Uren Webster et al., 2018)], rainbow trout
[<1% (Baerwald et al., 2016)] and guppies [1.5-2% (Hu
et al., 2018)]. In mangrove rivulus brain, Berbel-Filho et al.
(Berbel-Filho et al., 2019b) also reported a lower CpG coverage at
1.2%. This is surprising considering that different tissues of the same
species usually show the same methylation profile (Zhang, Hoshida, &
Sadler, 2016), and methodological aspects can not be rejected to explain
this discrepancy. Our data indicated a 4.3-fold enrichment in CpG sites,
which is higher than the enrichment obtained in zebrafish brain
(2.4-fold) and a bit lower to the one obtained in mouse brain (5-fold).
The distribution of the analyzed fragments across the RR genome showed
16% located in promoter region. The analyzed fragments in this region
are also low in zebrafish liver at 12%, and even lower in Atlantic
salmon (4%) (Uren Webster et al., 2018). On the contrary, this
percentage is higher in mammal species such as mouse liver (52%) (Zhang
et al., 2016). This proportion of analyzed fragments located in promoter
region can explain the observed difference of the mean global
methylation level reported in fish species and in mammals, the latter
having a lower value (28% in mouse vs 65% in the mangrove rivulus,
70% in zebrafish, 75% in Atlantic salmon). The promoter regions being
less methylated on average than other regions in both fish and mammals,
a higher proportion of fragments from this region decreases the average
global methylation as observed in mouse RR genome. These differences
between mammals and fish can also be reflected in the proportion of
highly and lowly methylated fragments. If the distribution of
methylation is bimodal in all reported vertebrates (fragments being
either highly or lowly methylated), a higher proportion of fragments
present a methylation level higher than 80% in fish than in mammals
(67% of fragments in our study) (Zhang et al., 2016).
Regarding the mangrove rivulus, even if the majority of promoter
fragments were lowly methylated, a significant proportion were highly
methylated (38%), which indicates an elevated range of the methylation
level in promoter regions, and consequently a regulatory activity of
gene expression. In contrast, other genomic regions presented a narrower
range in methylation levels, with the majority of fragments being highly
methylated. When compared to zebrafish, an interesting difference
appeared regarding the proportion of analyzed fragments located in gene
bodies. While it reached 47% in mangrove rivulus, zebrafish has only
25% of CpGs in introns and exons. Conversely, the proportion of
analyzed intergenic fragments is higher in zebrafish (63%) than in
mangrove rivulus (37% including intergenic and upstream regions). A
possible explanation could reside in the higher proportion of
transposable elements (TEs) in zebrafish genome. There is increasing
evidence that TEs are important targets of DNA methylation to silence
their expression and consequently show a high level of CpG methylation.
The zebrafish genome is rich in TEs as 52% of its genomic sequence is
composed of the different classes of TEs (Chalopin et al., 2015; Shao,
Han, & Peng, 2019), compared to 27.3% in the mangrove rivulus (Rhee et
al., 2017). This difference could explain the relatively lower
proportion of analyzed fragments located in intergenic regions in the
mangrove rivulus compared to zebrafish. When considering other fish
species, the part of TEs in genomes is very variable and values as low
as 5% have been reported in Tetraodon nigroviridis (Shao et al.,
2019). This variability of TE amount is explained by the large range of
genome size among fish, and Chalopin et al. (Chalopin et al., 2015)
reported a positive correlation between TE content and genome size in
teleosts species, which also applies to the mangrove rivulus. Contrary
to what was advanced by Zhang et al. (Zhang et al., 2016), it is
therefore unlikely that the pattern of high global methylation observed
in fish RR genomes is the result of an enrichment in TEs. Nevertheless,
the role of DNA methylation in TEs should be further investigated,
including a precise look at the different types of TEs. For example, the
rolling-circle transposons, known as Helitrons, are particularly
abundant in the mangrove rivulus genome (0.65%) compared to other fish
species including its phylogenetic relatives [Japanese medaka (0.03%)
and Turquoise killifish (0.06%)]. From all studied fish species, only
zebrafish showed a higher level of Helitrons at 1.5% (Shao et al.,
2019). It is known that TEs in general, and the Helitron subfamily in
particular, are mediator of host genes regulation and contribute to
genome evolution and adaptation (Volff, 2005). It has been suggested
that the mobilization of TEs and the changes in epigenetic pattern could
be important in the process of organism’s rapid adaptation to
environmental changes (Lerat et al., 2019). The exact role of these
elements and their methylation level in the mangrove rivulus is a
promising perspective to investigate how this species adapt and evolve
using a mixed-mating reproduction system and consequently a low genetic
diversity within lineages.
4.2. Effects of EE2 on the liver methylome
Growing evidence suggest that EDCs can modify DNA methylation and
consequently gene expression pattern. Recently, experiments on zebrafish
embryos clearly established a link between exposure to bisphenol A, DNA
methylation and behavioral impairments (Olsvik et al., 2019). Data also
showed that EE2 can modify the methylation status of target genes
(Strömqvist et al., 2010). In a previous study, we showed that the liver
proteome of adult mangrove rivulus was impaired by exposure to EE2
during early life stages (ELS) (Voisin et al., 2018). It has been
hypothesized that exposure to EE2 during ELS can impair DNA methylation
and induce long term consequences later in life as observed on the
proteome and on the phenotype (Voisin et al., 2016). To test this
hypothesis, we aimed at detecting potential long-term changes in the
hepatic DNA methylation of 168 dph adults following a 28-day early-life
exposure of mangrove rivulus hatchlings. We obtained a total of 146 DMFs
among all group comparisons, from which a majority were observed between
the two EE2 treatments and between 4 ng/L exposed individuals and Ctl.
The larger observed impacts of the lowest EE2 concentration on DNA
methylation is in accordance with a non-monotonic dose-response
relationship often reported for exposure to endocrine disruptors.
Hormones are effective at very low concentration, having a high affinity
for their receptors, and so are the endocrine disrupting chemicals
(Vandenberg et al., 2012). The present results are reinforced by a
similar response detected in brain and liver protein expression profiles
of the mangrove rivulus exposed in the same conditions to EE2 (Voisin et
al., 2018). To our knowledge, only one other study investigated the
long-term effects of early-life exposure to chemicals on fish methylome
(Kamstra et al., 2017). This study investigated the effects of two
compounds, mono(2-ethylhexyl) phthalate (MEHP, 30 µM) and 5-azacytidine
(5AC, 10 µM, an inhibitor of DNMT1) in zebrafish. They found that most
methylation changes occurred at conserved non-genic regions with
cis-regulatory functions (i.e. enhancers, silencers, transcription
factor binding sites).
Among the differentially methylated fragments (DMFs) found in adult
mangrove rivulus liver following early-life exposure to EE2, none was
significantly affected in all three group comparisons (Ctl vs 4 ng/L;
Ctl vs 120 ng/L; 4 vs 120 ng/L). Six DMFs were significant in two out of
three comparisons, of which three were located in gene bodies. A DMF
corresponding to ATP-binding cassette sub-family A member 1(ABCA1) was hypermethylated at both 4 ng/L and 120 ng/L compared to
Ctl. ABCA1 is a known estrogen-responsive gene (Srivastava,
2002). It was included in networks in both comparisons and appeared to
play a central role in the response to EE2, showing interactions with
the estrogen receptor and several other molecules (Figure 4). It is a
major regulator of cholesterol and phospholipid homeostasis by
regulating efflux of cholesterol and phospholipids outside of the cell,
that are then taken up by apolipoproteins A1 and E to form high density
lipoprotein (HDL) molecules. Namely, estradiol exposure of smooth muscle
cells has been shown to enhance cholesterol efflux to APOA1 and HDL,
which was associated to ABCA1 overexpression via ESR2 and liver X
receptor (LXR) activation (Wang et al., 2014). We suggest that the
alteration of methylation level of ABCA1 gene might be related to the
altered abundances of liver apolipoproteins and other downstream
proteins involved in lipid metabolism previously reported (Voisin et
al., 2018). The fact that both concentrations of EE2 impacted this gene
methylation level indicates that this mechanism might be a general mode
of action of EE2, and could induce effects at environmental
concentrations as low as 4 ng/L.
Two DMFs were significantly hypermethylated at 4 ng/L in comparison to
both Ctl and 120 ng/L treatments: the calcium/calmodulin-dependent
protein kinase type 1D (CAMK1D), and the testis-specific
serine/threonine-protein kinase (TSSK1B). Both DMFs were included in
the first network of 4 ng/L vs Ctl and 120 vs 4 ng/L comparisons. CAMK1D
is a protein kinase that participates in the calcium-regulated
calmodulin-dependent kinase cascade, regulating calcium-mediated
granulocyte function and respiratory burst and activating the
transcription factor CREB1. According to Figure 4, CAMK1D interacts not
only with calmodulin, but also with the amyloid precursor APP, which can
be induced by estrogens through extracellular-regulated kinase 1 and 2
(ERK1/2) phosphorylation (Zhang et al., 2005). Finally, TSSK1B is
essential for spermatid development and spermatogenesis. While TSSK1B is
likely not expressed in the liver, it can be possible that an epigenetic
alteration of this gene could be found in the ovotestis following EE2
exposure, with potential consequences on reproduction. Although no study
to date has linked EE2 and TSSK1B, a study in rats has shown that
bisphenol A exposure decreases the expression of TSSK1B mRNA (Marmugi et
al., 2012).
We must nevertheless be careful when interpreting the changes of
methylation in these 3 DMFs. First, the observed differences in
methylation, if significant, ranged between 10.3 and 14.6%. Further
studies should determine whether these differences have a biological
impact on gene expression. However, the individuals used in the present
study being issued from the same isogenic lineage, the confounding
factor of genetic variability is reduced, which may help to reveal more
subtle difference in DNA methylation level, and stress the usefulness of
this species in epigenetic studies. Moreover, these 3 DMFs occurred in
gene bodies, as did the majority of DMFs in this study (and MSPI
fragments). While methylation of DNA at promoters is well known to
suppress transcription, the role of DNA methylation in gene bodies is
more difficult to interpret (Maunakea et al., 2010). Gene body
methylation is conserved among plants and animals, with a general
preference for exons (Feng et al., 2010). Potential roles include the
stimulation or inhibition of transcription elongation (Yang et al.,
2014) and the regulation of splicing (Jones, 2012). In mammals, most
genes possess alternative transcription start sites that can be located
within the gene bodies, complicating the link between methylation and
expression (Jones, 2012). Nevertheless, it is now recognized that gene
body methylation is positively correlated with transcriptional activity
in most species (de Mendoza et al., 2020) and we should not disregard
the possible implication of these observed changes in methylation after
exposure to EE2.
Beside significant changes of DNA methylation within gene bodies, we
also reported several DMFs belonging to intergenic regions. Interpreting
alterations in DNA methylation in these regions is challenging as the
gene annotation may not directly apply. Intergenic regions are typically
highly methylated in all vertebrates and bear a conserved role of
maintaining genome stability, by preventing the transposition of
repetitive TEs and silencing cryptic promoters and cryptic splice sites
(Zemach et al., 2010). Moreover, TEs possess strong promoters that may
be able to transcribe neighboring genes (transcriptional interference).
Finally, a compact chromatin state in these regions can prevent
recombination. Most (>90%) methylcytosines occur in
transposons, and low methylation of these regions could reduce genomic
stability (Baccarelli & Bollati, 2009). As above mentioned, the
diversity and composition of TEs in the mangrove rivulus could
contribute to the genetic recombination and evolutionary adaptation in
this self-fertilizing species (Rhee et al., 2017). To gain further
insights into the role of intergenic methylation, future research should
investigate the overlapping of DMFs with potential TEs.
In addition, we pointed out the very low proportion of DMFs classified
as lowly methylated fragment (< 20% methylation). Thenipped-B-like protein B (NIPBL) is the exception as it showed
19.6% methylation in Ctl but increased to 41.5% in fish exposed to 4
ng/L EE2. It is remarkable that this fragment is also the one showing
the highest effect size of our study (+ 21.9%) and is located in the
promoter region. Few more lowly methylated fragments were significant
when comparing the 2 treatments, but all others were either highly
methylated or showed an intermediary methylation level. Two explanations
can be advanced. First, the lowly methylated fragments were about 1/3
less numerous than the highly methylated ones in the RR genome as
reported in Figure 2. This is related to the proportionally low
occurrence of fragments located in the promoter region, which have the
lowest methylation level. Second, we also showed that lowly methylated
fragments have a 10-fold higher coefficient of variability (33% vs 3%
in highly methylated fragments). It results that the statistical power
to detect changes of lowly methylated fragments is lower and that we can
consequently report only DMFs with a high effect size, such as NIPBL. To
overstep this limitation, one should include a higher number of
replicates in future RRBS analyses. It is even more important for this
kind of studies for which effects are searched long after the end of the
exposure, which decrease the probability to observe high effect sizes.
Nevertheless, our study is the first to report the effect of an
endocrine disruptor exposure on the methylation level of NIPBL promoter
region. This effect, which was significant while the exposure had
stopped 140 days earlier, could have serious long-term consequences on
adult fish. NIPBL is associated to cohesin in order to mediate chromatin
cohesion, important for mitosis. This complex plays an important role in
histone acetylation, chromosome architecture and therefore in regulation
of gene expression (Gao et al., 2019; Zhu & Wang, 2019). In human, a
mutation of NIPBL is associated to the Cornelia de Lange syndrome, a
developmental disorder (Newkirk et al., 2017). In zebrafish, the zygotic
genome activation is facilitated by cohesin and was a key for the
embryogenesis (Meier et al., 2018). Intriguingly, the cohesin complex
has been proposed to modulate estrogen-dependent gene transcription and
a link has been established with breast cancer (Antony et al., 2015).
Our results suggest that NIPBL hypermethylation could be a mode of
action of EE2 exposure and could potentially modify gene regulation.
Further studies should investigate the long-term links between the
change in NIPBL methylation level and the overall changes in gene
expression in adults, long after the exposure to EE2.
Little correspondence was found between the proteome previously
published (Voisin et al., 2018) and the epigenome (Supplementary Figure
2). Although the number of identified genes was about 10 x greater in
the methylome analysis, only about half of the total identified proteins
in the liver was found in the methylome analysis. Similarly, we could
retrieve the methylation level of fragments belonging to approximately
half (52) of the differentially abundant proteins. None of these
differentially abundant proteins could be related to a change in DNA
methylation level of the promotor region. Conversely, only five of the
significantly DMFs were also identified, but not significant, in the
proteomic analysis in liver: G1/S-specific cyclin-D2-like (CCND2,
promoter region, significant between 4 ng/L and Ctl),teneurin-1-like (TENM1, gene body, significant between 120 ng/L
and Ctl), protein NDRG1-like (NDRG1, promoter, significant
between 120 ng/L and Ctl), fibrinogen alpha chain (NGA,
intergenic, significant between 120 ng/L and Ctl), anddeath-associated protein 1 (DAP, gene body, significant between 4
and 120 ng/L). Comparison between DNA methylation and protein expression
pattern is rare but is fully justified. The proteome is the main
functional product of gene expression and is closely related to the
molecular phenotype (Silvestre, Gillardin, & Dorts, 2012). As such, the
proteomic level can generate new hypothesis on the modes of action of
xenobiotics and on the adaptive responses of organisms (Diz,
Martínez-Fernández, & Rolán-Alvarez, 2012). Epigenetics being at the
interface between the genotype and the environment, a more systematic
link with the proteome would improve the understanding of the adverse
outcome pathways (AOPs) and the outcomes in terms of adaptation to the
environment. However, it is challenging to correlate these two levels,
because of technical variability, and because differentially abundant
proteins are not necessarily regulated by differential methylation, but
rather the end product of upstream regulators, themselves differentially
methylated. Also, protein abundance results not only from transcription,
which can be influenced by DNA methylation, but also from mRNA
stability, alternative splicing, translation, protein turnover and
post-translational modifications. Nevertheless, comparison of proteome
and methylation data can be eased by the analysis of gene ontology. Some
biological processes were identified in both datasets, such as lipid
metabolism, inflammation, connective tissue development and molecular
transport, which makes their long-term impairment plausible mechanisms
of EE2 toxicity.
4.3. Conclusions
In conclusion, our study provides further evidence on the capacity of
EDCs to alter the methylome and shows that these changes can be apparent
several months after the exposure, supporting the hypothesis of possible
long-term modulation of gene expression through epigenetics. It also
confirms the non-monotonic response to EDCs as most significant effects
were observed at the environmental concentration of 4 ng/L. The DMFs
belonging to promoter and gene bodies revealed networks involved in
several biological functions potentially regulated by estrogens,
including lipid metabolism, cellular processes (death and survival),
connective tissue function, molecular transport and inflammation, many
of which were also identified in a previous proteomic analysis.
Importantly, we reported that methylation of nipped-B-like protein
B (NIPBL) might be an important mode of action of EE2 and, as involved
in chromosome organization and gene expression, could have long term
impact on gene regulation and on organism’s general functions. To gain a
more comprehensive view of the significance of DMFs in promoter, gene
body, upstream and intergenic regions, future studies should investigate
the overlapping of the DMFs with potential enhancers, insulators,
transcription start sites, TEs, estrogen-responsive elements and other
transcription factor binding sites that may interact with estrogen
receptors. Among them, TEs, and more particularly helitrons, are
potentially important targets of environmental stressors that could be
involved in the response of organisms to environmental changes. Despite
its limitations, RRBS has proved to be an efficient method to
investigate effects of EE2 on DNA methylation in the mangrove rivulus.
This self-fertilizing fish should be further considered as a model
species to investigate the interplay between environmental stressors,
epigenetics and adaptation. The possibility to naturally discard the
confounding factor of genetic variability makes this species a top model
to better understand the roles of epigenetics in the long-term response
to environmental xenobiotics and in the developmental origin of health
and diseases.
5. Acknowledgements
We thank the Department of Pathology at the University of Otago (New
Zealand) for providing facilities for RRBS library preparation. Special
thanks to Prof Mike Eccles, Jackie Ludgate and Anna Leichter for their
advice on the workflow and sequencing. This study was supported by the
FRS-FNRS (Belgian National Fund for Scientific Research) grant
N°T.0174.14 (Epigenetics in the mangrove rivulus), including a PhD
fellowship to A.-S. Voisin and a post-doc fellowship to Victoria
Ulloa-Suarez. Travel support was provided by a credit for short stay
abroad by FRS-FNRS (2017/V 3/5/079 – IB/MF). We are also thankful to
Ivan Blanco for his help in data post-processing.
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7. Data Accessibility Statement
All the data generated by the RRBS workflow will be submitted to Dryad
repository and will be publicly accessible. The BAM files after Bismark
alignment and the excel sheet containing the values for all analyzed
fragments have already been submitted and are available at this link:https://datadryad.org/stash/share/k7Lq6mHbxBMF5BHdk8cOp1EFGSGVK3tfJoBx4tPYnzc.
The DOI is: https://doi.org/10.5061/dryad.v41ns1rsv
The Fastq files will be submitted when a decision will have been given
for the acceptance of the manuscript.
8. Author Contributions
Anne-Sophie Voisin: investigation, manuscript writing and editing,
experimental design, data acquisition and analysis
Victoria Suarez Ulloa: data analysis, manuscript editing
Peter Stockwell: bioinformatic data analyses
Aniruddha Chatterjee: supervision, methodology, RRBS analyses,
manuscript editing
Frédéric Silvestre: investigation, supervision, funding acquisition,
manuscript writing and editing, experimental design, submission
9. Tables