Epigenomic profiling indicates a role for DNA
methylation in early postnatal liver development
Robert A. Waterland1,2,?, Richard Kellermayer1, Marie-Therese Rached1, Nina Tatevian3,
Marcus V. Gomes1, Jiexin Zhang4, Li Zhang4, Abrita Chakravarty5, Wei Zhu6,
Eleonora Laritsky1, Wenjuan Zhang1, Xiaodan Wang6and Lanlan Shen6
1Department of Pediatrics and2Department of Molecular and Human Genetics, Baylor College of Medicine, USDA
Children’s Nutrition Research Center, 1100 Bates St., Ste. 5080, Houston, TX 77030, USA,3Department of Pathology
and Laboratory Medicine, The University of Texas Health Science Center, Houston, TX, USA,4Department of
Biostatistics and Applied Biomathematics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA,
5Department of Computer Science, Duke University, Durham, NC, USA and6Department of Leukemia, The University
of Texas M.D. Anderson Cancer Center, Houston, TX, USA
Received March 4, 2009; Revised April 24, 2009; Accepted May 18, 2009
The question of whether DNA methylation contributes to the stabilization of gene expression patterns in dif-
ferentiated mammalian tissues remains controversial. Using genome-wide methylation profiling, we screened
3757 gene promoters for changes in methylation during postnatal liver development to test the hypothesis
that developmental changes in methylation and expression are temporally correlated. We identified 31
genes that gained methylation and 111 that lost methylation from embryonic day 17.5 to postnatal day 21.
Promoters undergoing methylation changes in postnatal liver tended not to be associated with CpG islands.
At most genes studied, developmental changes in promoter methylation were associated with expression
changes, suggesting both that transcriptional inactivity attracts de novo methylation, and that transcriptional
activity can override DNA methylation and successively induce developmental hypomethylation. These
in vivo data clearly indicate a role for DNA methylation in mammalian differentiation, and provide the
novel insight that critical windows in mammalian developmental epigenetics extend well beyond early
Methylation of cytosines within CpG dinucleotides is an epi-
genetic mechanism critical for mammalian development,
serving an important role in genomic imprinting, X-
chromosome inactivation and silencing of retrotransposons
(1,2). Due to the mitotic stability of locus-specific CpG
methylation patterns, it has long been postulated that DNA
methylation additionally serves the broader developmental
function of stabilizing gene expression patterns in differen-
tiated mammalian tissues (3,4). Early studies failed to detect
correlations between tissue-specific gene expression and
CpG methylation, causing some to reject this conjecture (5).
Single-gene studies (6) and recent genome-wide analyses of
DNA methylation (7–9) have, however, identified numerous
genes with tissue-specific expression and hypomethylation,
reinvigorating the hypothesis. A key unaddressed issue that
would elucidate the specific role of DNA methylation in
differentiation is the developmental dynamics of gene-specific
changes in methylation and expression. Limited data in trans-
genic mice suggest that changes in transcription are succeeded
by methylation changes which stabilize the transcriptional
state (10), but in vivo data on normal mammalian development
If DNA methylation successively stabilizes gene expression
statesduring mammalian differentiation,
changes in methylation and expression should be temporally
correlated. Except for studies of genomically imprinted
genes (11), retrotransposons (12,13) and X-chromosome inac-
tivation (14), few such temporal correlations have been
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Human Molecular Genetics, 2009, Vol. 18, No. 16
Advance Access published on May 20, 2009
epigenetics have largely focused on narrow windows of early
embryonic and germline development (2). The interpretation
of DNA methylation changes in somatic tissues during fetal
development is complicated because maturation of tissue rudi-
ments is often associated with dramatic changes in the relative
proportions of cellular subtypes. Recognizing this fact, we set
out to investigate the developmental dynamics of gene-specific
DNA methylation and expression in the mouse liver during
late fetal and early postnatal life, a period during which the
organ is undergoing extensive functional maturation (15).
The endodermal hepatoblasts that comprise the fetal liver
primordium have the potential to develop into either hepato-
cytes or bile duct cells. In addition to hepatoblasts, the fetal
mouse liver is transiently home to hematopoietic cells,
which migrate to the liver around embryonic day 11 (E11),
and emigrate to the bone marrow around the time of birth
(16). Despite this added complexity, the mouse liver is an
attractive model in which to study developmental epigenetics
because the organ is fully differentiated by postnatal day 21
(P21) (17) and is relatively homogenous, consisting predomi-
nantly of hepatocytes (70% by cell number) together with
endothelial, stellate and kupfer cells (18).
Since tissue-specific gene expression is often achieved
without tissue-specific differences in promoter DNA methyl-
ation (5), we first screened for genes undergoing methylation
changes, then assessed these for expression changes. Human
studies have demonstrated that combining methylated CpG
island (CGI) amplification (19) with microarray hybridization
(MCAM) provides a sensitive and reliable tool for genome-
wide methylation profiling (8). Application of this technique
in the mouse has not been reported. In this study, we used
MCAM to identify genes undergoing methylation changes
during late fetal to early postnatal mouse liver development,
expression in a subset of these. Our data show that MCAM
is capable of identifying even subtle developmental changes
in methylation in normal tissues. In almost every case exam-
ined, developmental changes in CpG methylation were associ-
ated with transcriptional changes in the postnatal liver. These
changes were not attributable to the emigration of hematopoie-
tic cells from the liver. Taken together, our findings support
the hypothesis that CpG methylation serves to stabilize
gene-specific transcriptional states during postnatal liver
Identification of DNA methylation changes in postnatal
We used MCAM (8) for genome-wide methylation analysis. In
this method, genomic DNA is digested first with the
methylation-sensitive restriction endonuclease SmaI (leaving
blunt ends); methylated SmaI sites remain as templates for
subsequent digestion with the methylation-insensitive isoschi-
zomer XmaI (which leaves a 4-nucleotide overhang). Ligation
of adapters to these ‘sticky ends’, followed by whole-genome
PCR, results in preferential amplification of SmaI/XmaI
genomic intervals methylated at both ends. For each cohybri-
dization, two such whole-genome PCR products were labeled
differentially with Cy3 or Cy5, and cohybridized to mouse
proximal promoter microarrays. The arrays contained 45–
60mer oligonucleotide probes covering from
+0.3 kb relative to the transcription start sites of 17 000
mouse transcripts defined by RefSeq (20). Bioinformatic
analysis predicted that 8611 probes corresponding to 3757
unique genes on the array are potentially informative, assum-
ing a maximal SmaI/XmaI amplicon size of 2 kb. We also
annotated all the SmaI/XmaI intervals relative to CGIs and
repetitive sequences. The approach yields excellent coverage
of promoter-region CGIs; of the 3757 gene promoters
flanked by SmaI/XmaI restriction sites, both restriction sites
were within CGIs in 52% (1961), one restriction site was
within a CGI in 35% (1324) and only 13% (472) were not
associated with CGIs.
Many X-linked promoter CGIs are hypermethylated specifi-
cally on the inactive X chromosome in female mammals (14).
Therefore, as an initial validation of MCAM in the mouse, we
compared genomic methylation patterns of males and females.
Genomic DNA was isolated from male and female C57BL6/J
mouse liver at E17.5, and a female versus male MCAM cohy-
bridization was performed. Signal intensity at autosomal
probes showed excellent agreement between the male and
female, but most probes on the X chromosome yielded 50–
100-fold higher signal in the female relative to the male
(Fig. 1A). Since only one of the two X chromosomes in
each female cell undergoes promoter hypermethylation, and
promoters on the single X chromosome in males are hypo-
methylated, this experiment demonstrates that even an absol-
ute methylation difference of only 50% (female versus male)
yields a robust MCAM signal.
To detect methylation changes occurring during normal
development, we isolated genomic DNA from C57BL6/J
mice at E17.5 and P21 and performed two independent P21
versus E17.5 MCAM cohybridizations. Focusing on genes
that showed concordant methylation changes in both cohybri-
dizations, we identified 31 genes that gained methylation and
111 that lost methylation from E17.5 to P21 (Supplementary
Material, Table S1). We used quantitative bisulfite sequencing
(Fig. 1B) to verify the methylation changes in 13 of the
MCAM hits. Overall, P21:E17.5 methylation ratios measured
by bisulfite sequencing were highly correlated with MCAM
P21:E17.5 signal ratio (R2= 0.76), and the linear relationship
between bisulfite and MCAM values did not differ signifi-
cantly from the line of identity (Fig. 1C). These data clearly
demonstrate that MCAM provides a reliable method by
which to screen for DNA methylation changes during
normal development. Further, by extending our bisulfite
sequencing studies to a larger number of E17.5 and P21
mice (n =5 at each age), we demonstrated that our approach
of screening for concordant methylation changes in two inde-
pendent P21 versus E17.5 MCAM cohybridizations effec-
tively identifies developmental changes (Fig. 1B), rather
than coincidental concordance of pairwise interindividual
Since MCAM is based upon SmaI/XmaI digestion, it was
important to determine if the developmental changes we
detected are limited to the informative SmaI/XmaI sites, or
in fact reflect methylation changes occurring over broad
genomic regions. We performed a detailed methylation
Human Molecular Genetics, 2009, Vol. 18, No. 163027
analysis on three genes that lost methylation (Azgp1, Fcgrt and
Phyhd1) and three that gained methylation from E17.5 to P21
(Def6, Lingo4 and Nrbp2). Genes were selected on the basis of
robust (.3-fold) methylation changes by MCAM. In most
cases, both informative SmaI/XmaI sites (separated by hun-
dreds of base pairs) and respective neighboring CpG sites
showed concordant changes in methylation (Fig. 2). These
data also illustrate that MCAM is capable of detecting even
small methylation changes. For example, nearly the entire
MCAM signal at Lingo4 is due to an increase from just 5 to
30% at one SmaI/XmaI site (Fig. 2E).
Quantitative sequencing of post-bisulfite PCR products,
while highly sensitive, provides no information on the distri-
bution of CpG methylation on individual DNA molecules.
This could be very important if, for example, during this
developmental period a cell type with a methylated promoter
is being replaced by one with an unmethylated promoter, or
vice versa. We therefore performed bisulfite cloning and
sequencing at the same six genes studied by quantitative bisul-
fite sequencing. Given that some of the informative SmaI/
XmaI-containing regions previously evaluated were several
hundred base pairs from the transcription start site we tried,
when possible, to center the assays at the transcription start
site of the genes. In every case, the clonal sequencing
(Fig. 3) showed methylation changes that were consistent
with the quantitative sequencing results. For example, while
our quantitative assays at Def6 and Lingo4 were located hun-
dreds of base pairs from the transcription start sites (Fig. 2D
and E), the clonal assays closer to the transcription start
sites (Fig. 3D and E) yielded similar results. Most importantly,
rather than a homogeneous pattern of methylation that might
be consistent with differential promoter methylation in differ-
ent cell types at E17.5 and P21, clonal methylation was hetero-
geneous at all genes tested (Fig. 3).
Genomic characteristics of regions undergoing
The MCAM approach used in this study is biased toward CGIs
fortwo reasons. First, we usedaproximalpromotermicroarray,
and over half of all promoters in the mouse genome are associ-
ated with CGIs (21). Second, the SmaI/XmaI restriction site
(CCCGGG) isoverrepresented inCGIs.Hence, itis notsurpris-
ing that almost all (87%) of the potentially informative SmaI/
XmaI intervals represented on the microarray are associated
with CGIs. Compared with all the potentially informative inter-
vals, however, genomic regions undergoing gain or loss of
methylation during postnatal liver development tended not to
be associated with CGIs (P , 1 ? 10210for both compari-
sons); this CGI underrepresentation was strongest among
genes that gained methylation (Table 1). Since flanking repeti-
tive elements have been implicated in the de novo methylation
undergoing developmental changes in methylation were excep-
tional in their proximity to repetitive elements. Overall, 14% of
the 3757 potentially informative Sma1/XmaI intervals were
associated with repetitive elements, the same proportion as in
the gene sets that gained or lost methylation. We examined
the distributions of LINE, SINE and LTR retrotransposons,
and DNA transposons in 6 kb windows centered on the
Figure 1. Validation of MCAM in the mouse. (A) Scatter plot of normalized
signal intensity for a female versus male MCAM cohybridization. Probes not
on the X chromosome show close agreement in male and female genomic
DNA, whereas those on the X chromosome yield 50–100-fold higher signal in
the female than in the male. (B) Manual bisulfite sequencing results at Fcgrt,
comparing five E17.5 and five P21 mice. The arrows indicate CpG sites; the
upper CpG site is the 50end of the SmaI/XmaI interval that yielded a ‘hit’ in
the MCAM assay. (C) Scatter plot of P21:E17.5 methylation ratio obtained by
bisulfite sequencing versus P21:E17.5 MCAM ratio. In addition to the 13
MCAM hits, three genes that did not meet the final criteria used to identify
hits (Mcm2, Gzma and Tex19) are shown. Since two P21 versus E17.5 MCAM
cohybridizations were performed, there are two data points for each gene vali-
dated. The points do not depart significantly from the line of identity (shown).
3028Human Molecular Genetics, 2009, Vol. 18, No. 16
transcription start sites of genes undergoing methylation
changes, compared with those of 2562 genes that did not
change methylation (Supplementary Material, Fig. S1). The
only significant finding was that, compared with the reference
set, genes that lost methylation contained fewer LTR retrotran-
sposons downstream of the transcription start site (P= 0.0018).
Since specific sequence motifs have been associated with
tissue-specific methylation at promoter CGIs (8), we used a
ment and search tool (MAST) (23) to determine if the develop-
mental methylation changes were associated with specific
sequence motifs. We analyzed 2 kb sequences centered on the
transcription start sites of genes undergoing methylation
changes, compared with those of 2562 genes that did not
change methylation. The top 20 significantly enriched motifs in
each group were identified by MEME, and MAST was then
used to identify motifs significantly enriched among the genes
that changed methylation, compared with the reference group.
Both the gene regions that gained methylation and those that
lost methylation were significantly enriched in distinct sequence
the group of promoters that gained methylation, although this
group includes only 31 genes. There was no overlap between
motifs enriched in the ‘gained methylation’ group and those
enriched in the ‘lost methylation’ group (Table 2).
Previous studies (24) indicate that polycomb-mediated tri-
methylation of histone H3 lysine 27 (H3K27me3) in embryo-
nic stem cells may designate gene promoters for DNA
methylation during later differentiation. We therefore tested
whether genes that gained methylation during postnatal liver
development are enriched in known polycomb targets. From
three recent studies (24–26), we identified a consensus set
of 1002 polycomb targets in mouse stem cells. Including
only those genes present in both the list of polycomb targets
and the list of genes potentially informative by MCAM, we
then determined the proportion of polycomb targets among
Figure 2. Detailed analysis of DNA methylation. The upper section of each panel displays the gene region studied by bisulfite sequencing. Vertical lines indicate
CpG sites, and downward arrows indicate the boundaries of each informative SmaI/XmaI interval. The lower section of each panel shows percent methylation
(Mean+SEM) versus CpG site location relative to the transcription start site (n= 5 mice per age). Shaded data points indicate the SmaI/XmaI sites that were
informative by MCAM. (A) Azgp1. (B) Fcgrt. (C) Phyhd1. (D) Def6. (E) Lingo4. (F) Nrbp2.
Human Molecular Genetics, 2009, Vol. 18, No. 163029
genes that gained, lost or did not change methylation (Sup-
plementary Material, Fig. S2). Although not statistically sig-
nificant, we did find a trend in the expected direction; 25.0%
of the genes that gained methylation are polycomb targets,
versus only 14.9% in the reference group.
Morphometric analysis of hematopoietic cell prevalence in
The fetal liver is temporarily home to a sizable population of
hematopoietic cells; we therefore wished to determine if the
methylation changes we found might be attributable to their
emigration from the liver during late fetal development. We
performed a histological evaluation to assess the prevalence
of hematopoietic cells in the mouse liver from E17.5 to P21
(Fig. 4). At E17.5, hematopoietic cells were the major cell
population, but by P5 their emigration from the liver was
essentially complete (Fig. 4B).
Temporal analyses of DNA methylation and gene
Having identified a set of gene promoters undergoing changes
in DNA methylation from E17.5 to P21, we used real time
RT–PCR to assess associated changes in gene expression.
We focused on the same six genes evaluated in the detailed
methylation analyses. To assess developmental dynamics of
DNA methylation and gene expression, we performed quanti-
tative measurements of both at E17.5, P0, P5, P10 and P21
(Fig. 5). All three genes that lost methylation showed coordi-
nate increases in expression (Fig. 5A–C). In contrast, develop-
mental increases in DNA methylation were less consistently
related to expression changes. At Def6, we found the
anticipated decrease in expression (Fig. 5D). At Lingo4,
however, expression increased coordinately with methylation
from E17.5 to P21 (Fig. 5E). Notably, at both Def6 and
Lingo4 expression changed most rapidly (from P0 to P5),
directly before the most rapid increase in methylation (from
P5 to P10). At Nrbp2, methylation and expression appeared
to be uncoupled; the transient peak in Nrbp2 expression at
P0 preceded the rise in methylation from P5 to P21
(Fig. 5F). To examine whether further methylation changes
occur in these genes, we measured DNA methylation in
P120 mouse liver. Overall, methylation was fairly stable
after P21 (Supplementary Material, Fig. S3); the largest post-
weaning methylation change was at Nrbp2, which increased
from 37 to 55%.
Figure 3. Clonal bisulfite sequencing results. Each panel compares bisulfite sequencing results for E17.5 and P21. The position relative to the transcription start
site is indicated. Each row of circles represents a single clone; open circles represent unmethylated cytosines, and filled circles indicate methylation. In all cases,
methylation patterns of individual clones were heterogeneous, and the temporal methylation changes detected in the quantitative assays were corroborated. (A)
Azgp1. (B) Fcgrt. (C) Phyhd1. (D) Def6. (E) Lingo4. (F) Nrbp2.
3030 Human Molecular Genetics, 2009, Vol. 18, No. 16
Bioinformatic analyses of gene expression and gene
If developmental changes in DNA methylation function to
stabilize epigenetic states in differentiated tissues, then DNA
methylation changes from E17.5 to P21 should generally
predict expression changes from late fetal to adult liver. We
queried a public gene expression database (www.mouseatlas.
org) to assess hepatic expression changes from E18 to P84
among genes that gained or lost DNA methylation during
early postnatal development. Additionally, we included a
group of 2562 genes in which methylation did not change
from E17.5 to P21. Genes not mapping to any expressed
sequence tags at either time point were excluded. Compared
with the other two classes, genes that lost methylation from
E17.5 to P21 showed a greater tendency for expression to
increase from late fetal to adult liver (Fig. 6A). Performing
the same analysis in tissues of ectodermal (brain) and meso-
(Fig. 6A), indicating that the developmental increases in
expression in this group of genes are specific to liver. We
also tested whether the methylation changes we identified
might contribute to the maintenance of tissue-specific gene
expression. Using data downloaded from a mouse gene
expression in liver as a Z score relative to expression in 36
other tissues (see Materials and Methods). Compared with
genes that gained methylation from E17.5 to P21, those that
lost methylation during this period were expressed at higher
levels in adult liver relative to other tissues (Fig. 6B).
no group differences
Table 2. Sequence motifs enriched near the transcription start sites of genes that gained or lost methylation from E17.5 to
Table 1. Association of methylation changes with CGIs
All genes analyzed
Both SmaI/XmaI sites in CGI
One SmaI/XmaI site in CGI
Neither SmaI/XmaI site in CGI
Human Molecular Genetics, 2009, Vol. 18, No. 16 3031
Together, these data support the hypothesis that developmen-
tal changes in DNA methylation participate in the regulation
of locus-specific transcriptional competence in differentiated
We also performed a gene ontology (GO) analysis (http://
babelomics.bioinfo.cipf.es/) to determine if gene regions
undergoing postnatal methylation change in liver are associ-
ated with particular biological processes, molecular functions
or cellular components. The reference group consisted of
3000 genes that met our criteria for hybridization signal but
for which methylation was unchanged from E17.5 to P21.
There were no significant GO terms among the 111 genes
that lost methylation from E17.5 to P21. Among the 31
genes that gained methylation, one GO cellular component
term (voltage-gated sodium channel complex) was signifi-
cantly over-represented (P =0.005). This result, however, is
attributed to only two genes (Scn2b and Scn4b) among those
that gained methylation (Supplementary Material, Table S1).
DNA methylation and gene expression during postnatal
The hypothesis that gene-specific patterns of DNA methyl-
ation participate in the maintenance of tissue-specific gene
expression in mammals, first proposed over 30 years ago
(3,4), remains controversial (1,27). One outstanding issue is
the elaboration of ontogenic periods during which DNA
methylation is in flux. A widely held model is that develop-
mental changes in DNA methylation occur principally in the
preimplantation embryo and during primordial germ cell
development (1,2,10). This view, however, is irreconcilable
with the increasing number of reports demonstrating tissue-
specific patterns of DNA methylation and associated gene
expression (6,7,9,28). If diverse adult tissues display distinct
patterns of epigenetic regulation and DNA methylation,
clearly these must be established after gastrulation. But cur-
rently, little is known about the role of epigenetic mechanisms
in late fetal and early postnatal development.
To our knowledge, ours is the first study to quantitatively
assess temporal relationships between DNA methylation and
gene expression during normal mammalian development.
We show for the first time that during postnatal life develop-
mental changes in DNA methylation—both increases and
decreases—are associated with functional changes in hepatic
gene expression. Given that developmental changes in gene
expression can be regulated by many mechanisms, this study
took the novel approach of first screening for methylation
changes, and then determining if these are associated with
expression changes. In all three genes studied that lost methyl-
ation from E17.5 to P21, transcript levels were inversely cor-
related with DNA methylation (Fig. 5A–C). Among the
studied genes that gained methylation from E17.5 to P21,
the association between methylation and expression was
more variable. At Def6 (Fig. 5D), down-regulation of tran-
scription was correlated with and largely preceded hyper-
suggesting that transcriptional activity prevents de novo
methylation. The direct relationship between developmental
changes in DNA methylation and expression at Lingo4
(Fig. 5E) was unexpected. Lingo4 transcriptional activation
largely preceded methylation changes. While it is generally
assumed that DNA methylation correlates with transcriptional
Figure 4. Dynamics of hematopoietic cell migration from the liver. (A) Repre-
sentative high power fields of hematoxylin–eosin stained liver from E17.5, P5
and P21 mice. Note the rapid disappearance of hematopoietic cells between
E17.5 and P5 (for description see Materials and Methods) including megakar-
yocytes (dark arrows), and the coordinate emergence of hepatocytes. Hepato-
cytes were the dominant cell type by P5. A single megakaryocyte was detected
in one of the P21 specimens (shown). (B) Time course showing percentage
hematopoietic cells versus age. Each point indicates the mean and SD of
livers from 3–5 mice. Hematopoietic cells were the dominant cell type at
E17.5, but almost completely gone by P5.
3032 Human Molecular Genetics, 2009, Vol. 18, No. 16
silencing, direct correlations between gene expression and
methylation in non-promoter regions have been reported
(30,31). We are not aware of any previous reports of promoter
methylation correlating directly with transcriptional activity.
Since DNA methylation can have either an attractive or repul-
sive effect on methylation-sensitive DNA binding proteins
(10), however, there is no a priori reason to expect that promo-
ter methylation must universally facilitate transcriptional
The DNA methylation changes we identified in the post-
natal liver tended to occur at non-CGI promoters (Table 1).
A previous epigenomic analysis using restriction landmark
genome scanning (RLGS) in mice (9) reported that two-third
of ‘tissue differentially methylated regions’ are within CGIs.
This conclusion was perhaps skewed by the innate bias of
RLGS toward CGIs. Indeed, a more recent RLGS study in
mice (32) reported that genomic regions showing tissue-
specific methylation are only rarely associated with CGIs, con-
sistent with our findings.
The functional significance of DNA methylation at
CpG-poor promoters is controversial. Our temporal analyses
(Fig. 5) showed strong correlations between methylation and
expression in five of six genes examined, and our bioinfor-
matic analyses (Fig. 6) showed that, overall, the methylation
changes we identified are associated with developmental
changes in and tissue-specific expression. These results
appear to contradict an earlier analysis using methylated
DNA immunoprecipitation (MeDIP) in primary human fibro-
blasts, which concluded that CpG-poor promoters are predo-
minantly hypermethylated, and that ‘repression by DNA
Figure 5. Temporal analysis of DNA methylation and expression. Each panel displays both mRNA expression and percentage DNA methylation versus age.
Transcript levels are expressed relative to those at P21 or E17.5 (whichever is higher) using the 22DDCtmethod. Points represent mean+SEM of n= 5
mice per age for both expression and methylation. (A) Azgp1. (B) Fcgrt. (C) Phyhd1. (D) Def6. (E) Lingo4. (F) Nrbp2. At Azgp1, Fcgrt, Phyhd1 and Def6
expression is inversely correlated with methylation. At Lingo4 expression and methylation are directly correlated, while at Nrbp2 developmental changes in
expression and methylation appear uncoupled.
Human Molecular Genetics, 2009, Vol. 18, No. 163033
methylation requires high CpG density’ (33). More recently,
Rakyan et al. (34) used MeDIP to compare genome-wide
methylation patterns across seven human tissues, and found
a significant negative correlation between tissue-specific pro-
moter methylation and gene expression, even at CpG-poor
promoters. Our findings are clearly consistent with those of
Rakyan et al.
We computationally identified several sequence motifs sig-
nificantly enriched in promoter regions that gained or lost
DNA methylationduring postnatal
(Table 2). Interestingly, two of the motifs most highly
enriched among genes that gained methylation (rows 3 and
4 in Table 2) are potential binding sites for the tran-
scription factor Sp1 (consensus
CCCCNCCCCNCCCC). This observation seems somewhat
inconsistent with previous reports that Sp1 sites protect
CGIs from methylation during embryogenesis (35). In any
event, the sequence motifs identified here may be useful in
future transgenic experiments aiming to characterize cis- and
trans-regulatory mechanisms involved in the developmental
establishment of DNA methylation.
The timingof ourstudyoverlaps with the migrationof hema-
topoietic cells from the liver. We originally selected E17.5 as
the baseline time point based on a study (36) indicating that
hematopoietic cells are no longer a major population in the
mouse liver at this time. When we attempted to verify this,
however, we obtained results more consistent with earlier
studies (17) indicating that hematopoietic cells comprise a
major proportion of mouse liver cells at E17.5 (Fig. 4). There-
fore, some of the methylation changes detected by MCAM may
to P21. But clearly this is not the entire story. In all six of the
continued after P5 (Fig. 5), by which time the hematopoietic
migration was nearly complete (Fig. 4). Further, our clonal
bisulfite sequencing studies (Fig. 3) show no evidence of clone-
type containing a methylated promoter is being replaced by one
with an unmethylated promoter. Hence, in many cases the
methylation changes identified here are likely to reflect devel-
opmental maturation of epigenetic regulation in hepatic cells
during postnatal development.
Owing perhaps to the relatively small number of genes
identified, GO analysis did not detect associations among
gene methylation changes and specific developmental path-
ways. Nonetheless, our results do highlight several genes at
which epigenetic changes may be important to liver develop-
ment. Jarid2 (37), Bmp4 (38) and Onecut1 (39) encode
important transcriptional regulators critical to early liver
development. All of these genes were found to gain methyl-
ation in the postnatal liver, suggesting they may be epigeneti-
cally down-regulated following the completion of hepatic
morphogenesis. Conversely, among the genes found to lose
methylation postnatally were Fdps, which encodes an
enzyme involved in cholesterol biosynthesis, and Trf, which
encodes the iron transporter transferrin (40). Both are highly
expressed in adult liver; their loss of methylation postnatally
is therefore consistent with hepatic functional maturation.
to the developmental origins hypothesis, which proposes that
early environmental influences on development cause perma-
nent changes in structure and function that affect adult metab-
olism and disease risk. Myriad human epidemiologic data
indicate that environmental factors affecting fetal and early
Figure 6. Association of gene expression with postnatal methylation changes. (A) Box plots of P84:E18 expression ratio in liver, brain and spleen of genes that
lost or gained methylation from E17.5 to P21, compared with those in which methylation did not change. Each box plot depicts the median (thick line), 25th–
75th percentiles (box) and 5th–95th percentiles of the distribution (whiskers). Among genes that lost methylation in liver from E17.5 to P21, the expression
increase from E18 to P84 is greater than that of the reference group, specifically in liver. (B) Distribution of expression Z score in liver versus 36 other
tissues among genes that either gained or lost methylation from E17.5 to P21. Genes that lost methylation during early postnatal development are generally
expressed at higher levels in liver.
3034 Human Molecular Genetics, 2009, Vol. 18, No. 16
postnatal growth play a role in such ‘developmental program-
ming’ (41), and epigenetic mechanisms are increasingly impli-
cated in these effects (42). Since mammalian epigenetic
regulation is most labile to environment when DNA methyl-
ation is undergoing developmental establishment or maturation
(43,44), elucidating a potential epigenetic basis for develop-
mental programming will require an understanding of epige-
netic changes in diverse tissues throughout mammalian
development (not just in the early embryo).
Sensitivity and coverage of MCAM
Our validation data indicate that MCAM has excellent speci-
ficity and can detect even small methylation differences.
Also, although MCAM is based upon SmaI/XmaI restriction
ally, effectively increasing the method’s genomic coverage.
Current genome-wide methylation analysis methods can be
divided into antibody-based methods such as MeDIP (33), and
methylation-sensitive restriction enzyme-based approaches
(such as MCAM) [A nascent method, whole-genome bisulfite
shotgun sequencing (45), has yet to be applied to an entire
mammalian genome]. Although theoretically capable of pro-
vidingbetter coverage than
approaches, MeDIP works best in genomic regions of fairly
high CpG density (33). Further, even in regions of high CpG
density the method appears to lack sensitivity, and apparently
detects only full-scale methylation differences (i.e. 100 versus
0%) (33). This lack of sensitivity was also documented in a
recent study comparing various genome-wide methylation
analysis methods (46).
Methylation-sensitive restriction enzyme-based approaches
include RLGS (9), microarray hybridization of labeled NotI
fragments (7), and the HELP assay (47,48), which is based
upon digestion of genomic DNA with the methylation-
sensitive restriction enzyme HpaII, followed by ligation-
mediated PCR and microarray hybridization. A recent study
comparing various genome-wide methylation assays (46)
demonstrated that the HELP assay suffers from low speci-
ficity. This is likely attributable to incomplete digestion of
unmethylated restriction sites causing false positive hits, a
problem that generally plagues digestion-based approaches.
In MCAM, serial digestion of each sample with methylation
sensitive and insensitive isoschizomers effectively eliminates
this problem (8). Only SmaI/XmaI sites that are (i) uncleaved
by SmaI and (ii) subsequently cleaved by XmaI serve as tem-
plate for amplification; failure to digest cannot be misinter-
preted as a methylation signal. This unique characteristic
likely explains the high specificity of MCAM (Fig. 1C).
Additionally, MCAM is sufficiently sensitive to detect even
small methylation changes (Fig. 2). In the most striking
example (Fig. 2E), we detected developmental hypermethyla-
tion at Lingo4, in which essentially the entire MCAM signal
was derived from a methylation increase at the 30SmaI/
XmaI site from 5 to 30%.
A general concern regarding restriction enzyme-based
approaches for methylation profiling is their limited genomic
coverage. Of the ?21 000 000 CpG sites in the mouse
genome (45), only ?75 000 (0.4%) are within SmaI/XmaI
sites that are potentially informative by MCAM. But it is
misleading to say that MCAM only ‘covers’ 0.4% of the
genome. Rather, since variation in DNA methylation usually
occurs regionally (33), MCAM actually enables the identifi-
cation of broad genomic regions of differential DNA methyl-
ation. Indeed, although the SmaI/XmaI intervals yielding
MCAM ‘hits’ were several hundred base pair long, in almost
every case we found concordant methylation changes at both
ends, as well as at CpG sites adjacent to the informative
SmaI/XmaI sites (Fig. 2). Further, even when the informative
SmaI/XmaI sites were several hundred base pair away (as in
Def6 and Lingo4), our clonal bisulfite sequencing studies
showed similar methylation changes near the transcription
start site (Fig. 3D and E). Clearly, MCAM generally identified
not single CpG sites, but rather broad genomic regions
undergoing methylation changes. Of the 48 657 potentially
informative SmaI/XmaI intervals in the mouse genome, the
proximal promoter array we used included probes within
only 3757. Having demonstrated the utility of MCAM at pro-
moter regions, custom arrays designed specifically for MCAM
will include only probes within potentially informative SmaI/
XmaI intervals, dramatically improving the depth and genomic
coverage of the method.
Methylated CGI amplification (MCA) was originally devel-
oped to screen for aberrant CGI hypermethylation in cancer
(19). When coupled with microarray hybridization, the
method was dubbed ‘MCAM’ (8). Our data, however,
clearly demonstrate that the genomic coverage of the
method extends beyond CGIs: most of the developmental
changes we identified are not at CGIs (Table 1). Hence, we
suggest that future studies refer to the method as ‘Methylation-
Using a DNA methylation microarray approach, we have
identified promoter regions in the mouse genome at which
DNA methylation is undergoing developmental maturation
in the early postnatal period. Our characterization of the tem-
poral relationships between developmental changes in DNA
methylation and expression in vivo provides support for the
hypothesis that DNA methylation functions during mamma-
lian differentiation to maintain the silence of genes whose
activity is not required in specific tissues. Importantly,
however, our data also show that promoter methylation does
not irrevocably prevent transcriptional activation; even
during postnatal development, promoter demethylation was
Lastly, our successful application of MCAM to detect subtle
DNA methylation changes during normal development under-
scores the broad potential applicability of the method.
MATERIALS AND METHODS
Animals and tissue collection
C57BL6/J mice (Jackson Laboratories, Bar Harbor, ME) were
used. For collection of fetal liver samples, timing of coitus was
determined by observation of vaginal plugs; the morning a
plug was observed was considered 0.5 days post coitum. At
17.5 days post coitum (E17.5), pregnant females were killed
by CO2asphyxiation, and fetal liver collected. Fetal sex was
determined by PCR for Sry. P21 liver was collected directly
Human Molecular Genetics, 2009, Vol. 18, No. 163035
after removing mice from their dams in the morning. All appli-
cable institutional and governmental regulations concerning
the ethical use of animals were followed during this research.
The protocol was approved by the Institutional Animal Care
and Use Committee of Baylor College of Medicine.
Methylated CpG island amplification and microarray
MCAM was performed as previously described (8), with the
following modifications. During the ligation step, adaptors
appropriate for mouse genomic DNA were used: RXMA24:
50-AGCACTCTCCAGCCTCTCACCGAC-30, and RXMA12:
50-CCGGGTCGGTGA-30. Following the amplification step,
E17.5 and P21 MCA products were labeled with Cy3 and
Cy5, respectively. Mouse proximal promoter microarrays
were obtained from Agilent Technologies (Santa Clara, CA).
Computational identification of potentially informative SmaI/
XmaI genomic intervals, and annotation relative to CGIs and
repetitive regions were performed as previously described,
using the March 2005 (mm6) mouse genome assembly. Two
P21 versus E17.5 cohybridizations were performed, and
[based upon our earlier study in humans (8)] a SmaI/XmaI
interval was considered a ‘hit’ if the signal ratios and intensi-
ties of all probes within the interval met the following criteria
in both cohybridizations: (i) median signal ratio .2 or ,0.5,
(ii) median upper signal intensity .1000 and (iii) median
P-value log ratio ,0.0001. The data discussed in this publi-
cation have been deposited in NCBI’s Gene Expression
Omnibus (49) and are accessible through GEO Series acces-
sion number GSE15634 (http://www.ncbi.nlm.nih.gov/geo/
DNA methylation analysis methods
Bisulfite modification of genomic DNA was performed as pre-
viously described (50). Validation of MCAM hits was per-
formed by direct manual sequencing and phosphorimager
quantitation (50) (n= 11), or by bisulfite pyrosequencing
(51) (n =5). We compared both methods on a subset of
samples and obtained identical results (data not shown). A
list of genes validated, including primers used, is provided
in Supplementary Material, Table S2. Whenever possible,
we obtained bisulfite sequencing data on both SmaI/XmaI
sites for each SmaI/XmaI interval. The temporal analyses of
DNA methylation (Fig. 5) are based on % methylation
values averaged over the following CpG sites (relative to tran-
scription start site): Azgp1 -17, -7; Fcgrt -155, -146; Phyhd1
19, 21; Def6 244, 262; Lingo4 -234, -226; Nrbp2 -557, -552,
-549, -542, -534. Bisulfite cloning and sequencing were per-
formed for six genes to analyze clonal methylation. We
cloned the bisulfite-PCR products into the TA vector
pCR2.1 (Invitrogen), then extracted and sequenced plasmid
DNA from the resulting clones, using the sequencing primer
provided by the manufacturer. PCR primer sequences are
described in Supplementary Material, Table S2.
Histological evaluation of liver
Flash frozen pieces of liver were fixed in 10% formaldehyde
for more than 24 h, paraffin embedded, sectioned and hema-
toxylin–eosin stained according to standard pathological pro-
cedures. Hematopoietic cells were identified by dense pink
cytoplasm and round dark nuclei in erythroid precursors and
larger size of nuclei and lesser cytoplasm in granulocytic pre-
cursor cells. Megakaryocytes were easily recognizable, large
cells with pink cytoplasm and large, often multilobulated
nuclei. The percentage of hematopoetic cells was estimated
in 10 high power fields of the sections from each animal
from E17.5, P0, P5, P10, P21 (3–5 animals per age).
Gene expression measurements
Total RNA was isolated (RNA Stat-60, Tel-Test, Inc., Friends-
wood, TX), and reverse transcribed using random priming
(M-MLV reverse transcriptase, Promega, Madison, WI).
No-RT negative controls were included in all assays. For Def6
and Lingo4, real-time RT–PCR was performed using Syber
Green PCR Master Mix (Applied Biosystems, Foster City,
CA) according to manufacturer’s instructions. Gene-specific
cDNA was amplified using primers spanning an intron, and all
RT–PCR products were examined by agarose gel electrophor-
esis to confirm single bands of the correct size. Bands resulting
The primers used are provided in Supplementary Material,
Table S3. For the other genes the following TaqMan gene
expression assays (Applied Biosystems) were used, according
to manufacturer’s instructions: Azgp1 (Mm00516330_m1),
Fcgrt (Mm01205449_g1), Phyhd1 (Mm00549288_m1), Nrbp2
Expression changes were quantitated relative to b-actin as an
endogenous control, using the 22DDCtmethod.
To analyze the distribution of repetitive elements in the vicin-
ity of genes of interest, we downloaded the gene annotation
file (refGene.txt.gz) and RepeatMasker files (chr_rmsk.txt.gz)
from the UCSC genome browser (mm9, NCBI Build 37). The
distances of repetitive element midpoints from relevant tran-
scription start sites were calculated. For genes with multiple
transcription start sites, the one nearest the informative SmaI
interval was used. To search for sequence motifs enriched in
the different gene groups, we used MEME and MAST as
described previously (8). We used Fatigo functional enrich-
ment analysis (http://babelomics.bioinfo.cipf.es/) (52) to ident-
ify GOclassifications significantly over- or underrepresented
among our gene lists; cited P-values were adjusted for mul-
tiple testing. To globally assess developmental changes in
expression among the genes identified by MCAM, we down-
loaded SAGE data from the Mouse Atlas of Gene Expression
(http://www.mouseatlas.org) for E18 and P84 C57BL6 mouse
liver, brain and spleen. We used the Mouse Atlas Discovery-
Space platform to map expressed tags to genes, then cross-
referenced these expression data to the gene lists from our
MCAM experiment. To globally assess gene expression in
liver relative to other tissues, we downloaded mouse
symatlas.gnf.org/SymAtlas/). For each gene identified as
undergoing postnatal methylation change in liver, a Z score
was calculated for expression in liver relative to other
3036Human Molecular Genetics, 2009, Vol. 18, No. 16
tissues: Z=((liver expression)2(average expression in other
tissues))/(SD of expression in other tissues). Tissues included
were brain, pituitary, eye, lymph node, trachea, uterus, ovary,
adipose tissue, adrenal gland, bladder, epidermis, digits, snout,
tongue, medial olfactory epithelium, prostate, vomeralnasal
organ, lung, stomach, large intestine, bone marrow, bone,
spleen, thymus, brown fat, heart, skeletal muscle, placenta,
mammary gland, kidney, small intestine, salivary gland,
thyroid, pancreas and testis.
Chi-square tests were used to perform group comparisons of
the proportions of SmaI/XmaI intervals with no, 1, or both
SmaI/XmaI sites within a CGI (Table 1). To analyze group
differences in developmental expression changes (Fig. 6A)
and liver-specific expression (Fig. 6B), values were log trans-
formed to improve normality, then two-tailed, unpaired t-tests
Whereas MCA is molecule-specific (both SmaI/XmaI sites
on a specific DNA molecule must be methylated for the
SmaI/XmaI interval to be amplified), our quantitative bisulfite
sequencing methods provide no molecule-specific infor-
mation. We therefore created an algorithm to compare methyl-
hybridization ratios obtained by MCAM. If methylation at
the two sites on a single allele is uncorrelated, the product
of fractional methylation at the two sites should compare
best with the MCAM result. If they are highly correlated,
the average methylation should compare best with the
MCAM result. We assumed partial correlation, calculating
the mean of the average and the product of methylation at
the two SmaI/XmaI sites to obtain the final percent methylation
ratios that were compared with the MCAM ratios in
For A ¼ fractional methylation at 5’ SmaI=XmaI site
and B ¼ fractional methylation at 3’ SmaI=XmaI site;
Combined % methylation ¼ 100 ? ½ðA þ BÞ=2 þ ðABÞ?=2:
Supplementary Material is available at HMG online.
We wish to acknowledge Adam Gillum for his assistance in
creating the figures.
Conflict of Interest statement. None declared.
This work was supported by National Institutes of Health
[5K01DK070007 to R.A.W.], the March of Dimes Birth
Defects Foundation [#5-FY05-47 to R.A.W.] and the United
States Department of Agriculture [CRIS #6250-51000-049 to
R.A.W.]. L.S. is a Sidney Kimmel Foundation Scholar.
1. Jaenisch, R. and Bird, A. (2003) Epigenetic regulation of gene expression:
how the genome integrates intrinsic and environmental signals. Nat.
Genet., 33 (Suppl.), 245–254.
2. Reik, W. (2007) Stability and flexibility of epigenetic gene regulation in
mammalian development. Nature, 447, 425–432.
3. Holliday, R. and Pugh, J.E. (1975) DNA modification mechanisms and
gene activity during development. Science, 187, 226–232.
4. Riggs, A.D. (1975) X inactivation, differentiation, and DNA methylation.
Cytogenet. Cell. Genet., 14, 9–25.
5. Walsh, C.P. and Bestor, T.H. (1999) Cytosine methylation and
mammalian development. Genes Dev., 13, 26–34.
6. Ehrlich, M. (2003) Expression of various genes is controlled by DNA
methylation during mammalian development. J. Cell. Biochem., 88,
7. Ching, T.T., Maunakea, A.K., Jun, P., Hong, C., Zardo, G., Pinkel, D.,
Albertson, D.G., Fridlyand, J., Mao, J.H., Shchors, K. et al. (2005)
Epigenome analyses using BAC microarrays identify evolutionary
conservation of tissue-specific methylation of SHANK3. Nat. Genet., 37,
8. Shen, L., Kondo, Y., Guo, Y., Zhang, J., Zhang, L., Ahmed, S., Shu, J.,
Chen, X., Waterland, R.A. and Issa, J.P. (2007) Genome-wide profiling of
DNA methylation reveals a class of normally methylated CpG island
promoters. PLoS Genet., 3, 2023–2036.
9. Song, F., Smith, J.F., Kimura, M.T., Morrow, A.D., Matsuyama, T.,
Nagase, H. and Held, W.A. (2005) Association of tissue-specific
differentially methylated regions (TDMs) with differential gene
expression. Proc. Natl. Acad. Sci. USA, 102, 3336–3341.
10. Bird, A. (2002) DNA methylation patterns and epigenetic memory. Genes
Dev., 16, 6–21.
11. Reik, W., Dean, W. and Walter, J. (2001) Epigenetic reprogramming in
mammalian development. Science, 293, 1089–1093.
12. Lane, N., Dean, W., Erhardt, S., Hajkova, P., Surani, A., Walter, J. and
Reik, W. (2003) Resistance of IAPs to methylation reprogramming may
provide a mechanism for epigenetic inheritance in the mouse. Genesis, 35,
13. Walsh, C.P., Chaillet, J.R. and Bestor, T.H. (1998) Transcription of IAP
endogenous retroviruses is constrained by cytosine methylation. Nat.
Genet., 20, 116–117.
14. Heard, E. (2004) Recent advances in X-chromosome inactivation. Curr.
Opin. Cell Biol., 16, 247–255.
15. Greengard, O. (1969) Enzymic differentiation in mammalian liver.
Science, 163, 891–895.
16. Lemaigre, F. and Zaret, K.S. (2004) Liver development update: new
embryo models, cell lineage control, and morphogenesis. Curr. Opin.
Genet. Dev., 14, 582–590.
17. Herbst, R.S. and Babiss, L.E. (1990) Fisher, P.B. (eds), Mechanisms of
Differentiation.CRC Press, Boca Raton, Vol. 1, pp. 15–45.
18. Gao, B., Jeong, W.I. and Tian, Z. (2008) Liver: an organ with
predominant innate immunity. Hepatology, 47, 729–736.
19. Toyota, M., Ho, C., Ahuja, N., Jair, K.W., Li, Q., Ohe-Toyota, M., Baylin,
S.B. and Issa, J.P. (1999) Identification of differentially methylated
sequences in colorectal cancer by methylated CpG island amplification.
Cancer Res., 59, 2307–2312.
20. Pruitt, K.D., Tatusova, T. and Maglott, D.R. (2007) NCBI reference
sequences (RefSeq): a curated non-redundant sequence database of
genomes, transcripts and proteins. Nucleic Acids Res., 35, D61–D65.
21. Antequera, F. (2003) Structure, function and evolution of CpG island
promoters. Cell. Mol. Life Sci., 60, 1647–1658.
22. Bailey, T.L., Williams, N., Misleh, C. and Li, W.W. (2006) MEME:
discovering and analyzing DNA and protein sequence motifs. Nucleic
Acids Res., 34, W369–W373.
23. Bailey, T.L. and Gribskov, M. (1998) Combining evidence using
P-values: application to sequence homology searches. Bioinformatics, 14,
24. Mohn, F., Weber, M., Rebhan, M., Roloff, T.C., Richter, J., Stadler, M.B.,
Bibel, M. and Schubeler, D. (2008) Lineage-specific polycomb targets and
de novo DNA methylation define restriction and potential of neuronal
progenitors. Mol. Cell, 30, 755–766.
25. Boyer, L.A., Plath, K., Zeitlinger, J., Brambrink, T., Medeiros, L.A., Lee,
T.I., Levine, S.S., Wernig, M., Tajonar, A., Ray, M.K. et al. (2006)
Human Molecular Genetics, 2009, Vol. 18, No. 163037
Polycomb complexes repress developmental regulators in murine
embryonic stem cells. Nature, 441, 349–353.
26. Mikkelsen, T.S., Ku, M., Jaffe, D.B., Issac, B., Lieberman, E.,
Giannoukos, G., Alvarez, P., Brockman, W., Kim, T.K., Koche, R.P. et al.
(2007) Genome-wide maps of chromatin state in pluripotent and
lineage-committed cells. Nature, 448, 553–560.
27. Suzuki, M.M. and Bird, A. (2008) DNA methylation landscapes:
provocative insights from epigenomics. Nat. Rev. Genet., 9, 465–476.
28. Futscher, B.W., Oshiro, M.M., Wozniak, R.J., Holtan, N., Hanigan, C.L.,
Duan, H. and Domann, F.E. (2002) Role for DNA methylation in the
control of cell type specific maspin expression. Nat. Genet., 31, 175–179.
29. Brandeis, M., Frank, D., Keshet, I., Siegfried, Z., Mendelsohn, M.,
Nemes, A., Temper, V., Razin, A. and Cedar, H. (1994) Sp1 elements
protect a CpG island from de novo methylation. Nature, 371, 435–438.
30. Hu, M., Yao, J., Cai, L., Bachman, K.E., van den Brule, F., Velculescu, V.
and Polyak, K. (2005) Distinct epigenetic changes in the stromal cells of
breast cancers. Nat. Genet., 37, 899–905.
31. Ladd-Acosta, C., Pevsner, J., Sabunciyan, S., Yolken, R.H., Webster,
M.J., Dinkins, T., Callinan, P.A., Fan, J.B., Potash, J.B. and Feinberg,
A.P. (2007) DNA methylation signatures within the human brain.
Am. J. Hum. Genet., 81, 1304–1315.
32. Sakamoto, H., Suzuki, M., Abe, T., Hosoyama, T., Himeno, E., Tanaka,
S., Greally, J.M., Hattori, N., Yagi, S. and Shiota, K. (2007) Cell
type-specific methylation profiles occurring disproportionately in
CpG-less regions that delineate developmental similarity. Genes Cells, 12,
33. Weber, M., Hellmann, I., Stadler, M.B., Ramos, L., Paabo, S., Rebhan, M.
and Schubeler, D. (2007) Distribution, silencing potential and
evolutionary impact of promoter DNA methylation in the human genome.
Nat. Genet., 39, 457–466.
34. Rakyan, V.K., Down, T.A., Thorne, N.P., Flicek, P., Kulesha, E., Graf, S.,
Tomazou, E.M., Backdahl, L., Johnson, N., Herberth, M. et al. (2008) An
integrated resource for genome-wide identification and analysis of human
tissue-specific differentially methylated regions (tDMRs). Genome Res.,
35. Macleod, D., Charlton, J., Mullins, J. and Bird, A.P. (1994) Sp1 sites in
the mouse aprt gene promoter are required to prevent methylation of the
CpG island. Genes Dev., 8, 2282–2292.
36. Chagraoui, J., Lepage-Noll, A., Anjo, A., Uzan, G. and Charbord, P.
(2003) Fetal liver stroma consists of cells in epithelial-to-mesenchymal
transition. Blood, 101, 2973–2982.
37. Takeuchi, T., Watanabe, Y., Takano-Shimizu, T. and Kondo, S. (2006)
Roles of jumonji and jumonji family genes in chromatin regulation and
development. Dev. Dyn., 235, 2449–2459.
38. Rossi, J.M., Dunn, N.R., Hogan, B.L. and Zaret, K.S. (2001) Distinct
mesodermal signals, including BMPs from the septum transversum
mesenchyme, are required in combination for hepatogenesis from the
endoderm. Genes Dev., 15, 1998–2009.
39. Margagliotti, S., Clotman, F., Pierreux, C.E., Beaudry, J.B., Jacquemin,
P., Rousseau, G.G. and Lemaigre, F.P. (2007) The Onecut transcription
factors HNF-6/OC-1 and OC-2 regulate early liver expansion by
controlling hepatoblast migration. Dev. Biol., 311, 579–589.
40. Kasik, J.W. and Rice, E.J. (1993) Transferrin gene expression in maternal
liver, fetal liver and placenta during pregnancy in the mouse. Placenta, 14,
41. Gluckman, P.D. and Hanson, M.A. (2004) Living with the past: evolution,
development, and patterns of disease. Science, 305, 1733–1736.
42. Jirtle, R.L. and Skinner, M.K. (2007) Environmental epigenomics and
disease susceptibility. Nat. Rev. Genet., 8, 253–262.
43. Waterland, R.A. and Jirtle, R.L. (2003) Transposable elements: targets for
early nutritional effects on epigenetic gene regulation. Mol. Cell. Biol., 23,
44. Weaver, I.C., Cervoni, N., Champagne, F.A., D’Alessio, A.C., Sharma, S.,
Seckl, J.R., Dymov, S., Szyf, M. and Meaney, M.J. (2004) Epigenetic
programming by maternal behavior. Nat. Neurosci., 7, 847–854.
45. Meissner, A., Mikkelsen, T.S., Gu, H., Wernig, M., Hanna, J.,
Sivachenko, A., Zhang, X., Bernstein, B.E., Nusbaum, C., Jaffe, D.B.
et al. (2008) Genome-scale DNA methylation maps of pluripotent and
differentiated cells. Nature.
46. Irizarry, R.A., Ladd-Acosta, C., Carvalho, B., Wu, H., Brandenburg, S.A.,
Jeddeloh, J.A., Wen, B. and Feinberg, A.P. (2008) Comprehensive
high-throughput arrays for relative methylation (CHARM). Genome Res.,
47. Khulan, B., Thompson, R.F., Ye, K., Fazzari, M.J., Suzuki, M., Stasiek,
E., Figueroa, M.E., Glass, J.L., Chen, Q., Montagna, C. et al. (2006)
Comparative isoschizomer profiling of cytosine methylation: the HELP
assay. Genome Res., 16, 1046–1055.
48. Mill, J., Tang, T., Kaminsky, Z., Khare, T., Yazdanpanah, S., Bouchard,
L., Jia, P., Assadzadeh, A., Flanagan, J., Schumacher, A. et al. (2008)
Epigenomic profiling reveals DNA-methylation changes associated with
major psychosis. Am. J. Hum. Genet., 82, 696–711.
49. Edgar, R., Domrachev, M. and Lash, A.E. (2002) Gene Expression
Omnibus: NCBI gene expression and hybridization array data repository.
Nucleic Acids Res., 30, 207–210.
50. Waterland, R.A., Lin, J.R., Smith, C.A. and Jirtle, R.L. (2006)
Post-weaning diet affects genomic imprinting at the insulin-like growth
factor 2 (Igf2) locus. Hum. Mol. Genet., 15, 705–716.
51. Shen, L., Guo, Y., Chen, X., Ahmed, S. and Issa, J.P. (2007) Optimizing
annealing temperature overcomes bias in bisulfite PCR methylation
analysis. Biotechniques, 42, 48, 50, 52. passim.
52. Al-Shahrour, F., Minguez, P., Tarraga, J., Montaner, D., Alloza, E.,
Vaquerizas, J.M., Conde, L., Blaschke, C., Vera, J. and Dopazo, J. (2006)
BABELOMICS: a systems biology perspective in the functional
annotation of genome-scale experiments. Nucleic Acids Res., 34, W472–
3038 Human Molecular Genetics, 2009, Vol. 18, No. 16