Sperm Methylation Profiles Reveal
Features of Epigenetic Inheritance
and Evolution in Primates
Antoine Molaro,1,3Emily Hodges,1,3Fang Fang,2Qiang Song,2W. Richard McCombie,1Gregory J. Hannon,1,*
and Andrew D. Smith2,*
1Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
2Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
3These authors contributed equally to this work
*Correspondence: firstname.lastname@example.org (G.J.H.), email@example.com (A.D.S.)
During germ cell and preimplantation development,
mammalian cells undergo nearly complete reprog-
ramming of DNA methylation patterns. We profiled
the methylomes of human and chimp sperm as
a basis for comparison to methylation patterns of
ESCs. Although the majority of promoters escape
methylation in both ESCs and sperm, the corre-
sponding hypomethylated regions show substantial
structural differences. Repeat elements are heavily
methylated in both germ and somatic cells; however,
retrotransposons from several subfamilies evade
methylation more effectively during male germ cell
development, whereas other subfamilies show the
opposite trend. Comparing methylomes of human
and chimp sperm revealed a subset of differentially
methylated promoters and strikingly divergent meth-
ylation in retrotransposon subfamilies, with an evolu-
tionary impact that is apparent in the underlying
genomic sequence. Thus, the features that deter-
mine DNA methylation patterns differ between male
germ cells and somatic cells, and elements of these
features have diverged between humans and chim-
and viability of offspring (Bestor, 1998; Bourc’his and Bestor,
2004; Li et al., 1992; Okano et al., 1999; Walsh et al., 1998).
DNA methylation in germ cells is required for successful meiosis
(Bourc’his and Bestor, 2004), and blastocysts derived from
embryonic stem cells (ESCs) lacking DNA methyltransferases
(DNMTs) cannot survive past approximately 10 days of develop-
ment (Li et al., 1992).
Mammalian germ cells are derived from somatic cells, rather
than being set-aside during the first zygotic cleavages. During
germ cell development, the genome undergoes a wave of nearly
complete demethylation and remethylation (Popp et al., 2010;
Walsh et al., 1998). This reprogramming event correlates with
re-establishment of totipotency and with the creation of sex-
specific methylation patterns at imprinted loci (reviewed by
Sasaki and Matsui, 2008). Germ cell methylation patterns are
ming that occurs during preimplantation development. Post-
fertilization, DNA methylation levels reach a nadir around the
eight-cell stage, after which methylation is rewritten, attaining
its somatic level by the blastocyst stage (Mayer et al., 2000).
Because this is completed prior to the establishment of the inner
cell mass from which cultured ESCs are derived, one can view
ESCs and mature germ cells as the terminal products of the
two landmark epigenetic reprogramming events in mammals.
Mobile genetic elements constitute roughly half of most mam-
relies critically on DNA methylation and is essential for the
maintenance of genomic stability in the long term and of germ
2004; Okano et al., 1999; Walsh et al., 1998). At least in part,
silencing of repeated DNA depends upon an abundant class of
PIWI-associated small RNAs, called piRNAs (reviewed in Aravin
and Hannon, 2008). In the absence of this pathway, methylation
is lost on at least some element copies, transposons are dere-
pressed, and germ cell development is arrested in meiosis.
CpG dinucleotides are underrepresented in mammalian
genomes, most likely because a higher rate of spontaneous
deamination of methylated cytosines exerts evolutionary pres-
sure for CpG depletion by frequent CpG-to-TpG transitions
(Duncan and Miller, 1980; Ehrlich et al., 1990). Mammalian
genomes contain areas of relatively high CpG density, called
‘‘CpG islands’’ (CGIs) (Gardiner-Garden and Frommer, 1987),
which have avoided CpG depletion over evolutionary time.
CGIs are frequently observed at promoters and in some cases
have been shown to exert regulatory effects. Thus, selection
against CpG depletion may reflect the importance of specific
CpG dinucleotides as sequence-based binding sites or simply
the requirement for a certain regional density of CpGs. As an
alternative, the existence of CGIs may simply be an artifact of
longstanding hypomethylation of these regions, and consequent
Cell 146, 1029–1041, September 16, 2011 ª2011 Elsevier Inc. 1029
relief from CpG erosion, in mammalian germ cells. Under this
hypo-deamination model, selective pressure is independent of
CpG density, per se, and CGIs may instead be a secondary
consequence of protection from methylation at specific sites
combined with prevalent methylation elsewhere in the genome
(Cooper and Krawczak, 1989; Duncan and Miller, 1980; Ehrlich
et al., 1990).
Studies encompassing evolutionarily distant species have
shown that broad features of the epigenome, such as the high
methylation levels of gene bodies and repeats, are deeply
conserved (Zemach et al., 2010). In closely related species,
however, fine-scale analysis of DNA methylation state reveals
variation. The chimpanzee and human genomes share more
than 95% sequence homology but display regions of differential
methylation (Enard et al., 2004). Through focused studies, we
have gained glimpses into the characteristics of the methylome
and the evolutionary pressures that shape it. We wished to
enable genome-wide comparisons of DNA methylation states
in closely related species and to examine possible differences
between the two major waves of epigenetic remodeling that
occur during the mammalian life cycle. We therefore produced
full-genome, single-CpG resolution DNA methylation profiles in
human and chimp sperm and compared these with methylation
maps from human ESCs (Laurent et al., 2010).
Methylomes of Mature Male Germ Cells in Human
We conducted genome-wide shotgun bisulfite sequencing of
spermDNAsamples isolated fromtwohumanandchimp donors
(see Extended Experimental Procedures for details). Basic data
analysis was conducted using a custom pipeline. We were
able to determine methylation status for 96% of genomic
CpGs in the human and chimp samples from a total of 28 million
and 27 million CpGs, respectively (Table 1). Read coverage for
CpGs on autosomes averaged 163 in human with an overall
methylation level of ?70% for all CpG sites. For chimp we
sequenced to an average coverage of nearly 143 and observed
an average methylation level of ?67%. We did not observe
significant methylation at non-CpG sites in either dataset. For
bisulfite dataset from human ESCs (Laurent et al., 2010). This
dataset was comparable to our own, with 93% of CpG
dinucleotides covered and an average depth of 143 on CpGs
We identified contiguous domains of low methylation, termed
hypomethylated regions or HMRs, in a manner independent of
genomic annotations such as CGIs and promoters. Because
obvious on browser plots as valleys in which methylation drop-
ped to very low levels. To call HMRs in a statistically principled
manner, we designed a novel computational approach, based
on a two-state hidden Markov model with Beta-Binomial emis-
sion distributions (see Extended Experimental Procedures).
This algorithm identified ?79k HMRs in human sperm and
?70k HMRs in chimp sperm. Only ?44.5k HMRs were identified
using the human ESC dataset, despite similar sequence
coverage and overall methylation level (Laurent et al., 2010;
see Table 1 and Table S1A available online). The sizes of
HMRs also differed between germ and ESCs. In both chimp
and human sperm, the mean size of HMRs was ?1.8 kb, and
the median was ?1.3 kb. In ESCs, HMRs showed a mean size
of genomic annotation (see Table S1B).
Global Comparisons among Primate Sperm Methylomes
and with Human ESCs
Average methylation levels differed by a small amount among
the human donors (donor 1: 72%; donor 2: 67%) but were
more similar among chimp donors (donors 1 and 2: 67%). The
methylation status of individual CpGs of HMRs correlated very
highly between individuals, with divergence being higher in
repeats as compared to promoters (Figures 1A and 1B). High
interindividual correlations at the CpG and the HMR levels imply
that our datasets permit accurate calling of CpG methylation
We also compared methylation between species at an indi-
vidual nucleotide level (see Extended Experimental Procedures
for details). As expected, the correlations between human and
chimp sperm methylation are high, but the correlation remains
generally highest within species.
Table 1. Shotgun Bisulfite Sequencing of Human and Chimp Sperm Methylomes
SpeciesSample Mapped DistinctMismatches BS ConversionMethylation CpG CoverageCpGs Covered
Humansperm (1) 609,127,589 388,835,058 1.580.992 0.7248.8 0.96
sperm (2)588,920,777 316,860,2451.84 0.9830.674 7.30.94
sperm (both)1,198,048,366 705,695,303 1.700.9880.701 16.1 0.96
ESCs 940,731,922 366,844,2120.64 0.9880.66314.10.93
Chimpsperm (1) 459,258,834255,193,4931.87 0.985 0.6656.2 0.95
sperm (2)520,905,232 327,796,6141.70 0.9840.672 7.40.94
sperm (both)980,164,066582,990,1071.780.985 0.66913.6 0.96
Mapped: reads mapping optimally to a single location in the reference genome. Distinct: number of genomic locations to which a read maps; when
multiple reads map to the same position, one with the best mapping score was selected at random, and all others discarded. Mismatches: average
number of mismatches for the reads indicated in the distinct fragments column. Bisulfite (BS) conversion rate was calculated at non-CpG cytosines.
Methylation: proportion of Cs in reads mapping over CpG dinucleotides.
1030 Cell 146, 1029–1041, September 16, 2011 ª2011 Elsevier Inc.
by an increased sequence divergence even at non-CpG dinucle-
otides. One interpretation is that most species-specific HMRs
have arisen newly along one lineage with these novel functional
elements showing signs of recentadaptation. On the other hand,
if this accelerated sequence change were more a reflection of
relaxed selective pressure, we would expect species-specific
HMRs to more frequently result from loss of functional elements
along the opposite lineage. Resolution of these questions can
only come from a broadening to many more species of the
studies reported herein.
Detailed methods can be found in the Extended Experimental Procedures.
Two anonymous human donors were used and data pooled after sequencing.
Two chimp donors were used. Semen was collected at the New Iberia
Research Center (New Liberia, LA) or the Southwest National Primate
Research Center (San Antonio, TX, USA). Coagulated semen was separated
from the liquid phase manually. Both human and chimp samples were diluted
(1:1) in HBS buffer (0.01M HEPES, ph 7.4; 150 mM NaCl) and passed though
a silica-based gradient, SpermFilter (Cryobiosystems), by centrifugation
(according to manufacturer’s instructions).
DNA from ?100 million cells was extracted and sheared to a size of ?150–200
ntby sonication.Double-strandedDNAfragments wereend repaired,A-tailed,
and ligated to methylated Illumina adaptors. Ligated fragments were bisulfite
converted using the EZ-DNA Methylation-Gold Kit (Zymo research). Following
PCR enrichment, fragments of 340 to 360 bp were size selected and
Reads were mapped with RMAPBS (Smith et al., 2009). The accuracy of our
mapping method is discussed in the Extended Experimental Procedures.
Mapped reads were used to infer the methylation frequency at each CpG
dinucleotide. These frequencies, along with the number of reads contributing
to each frequency estimate, were supplied to a segmentation algorithm used
the liftOver tool available through the UCSC Genome Browser. Sequence
conservation between human, chimp, and was measured based on MULTIZ
44-way vertebrate alignments, also available through the UCSC Genome
Browser. Complete details of all computational methods are provided in the
Extended Experimental Procedures.
Data analyzed herein have been deposited in GEO with accession GSE30340.
Supplemental Information includes Extended Experimental Procedures,
four figures, and five tables and can be found with this article online at
We thank Michelle Rooks, Pramod Thekkat, and Colin Malone for help with
experimental procedures and Assaf Gordon, Luigi Manna, and the CSHL
and USC High Performance Computing Centers for computational support.
We thank Babette Fontenot (New Iberia Research Center) and Jerilyn Pecotte
(Southwest National Primate Center) for help with chimp sperm collection. We
thank Sergey Nuzhdin, Ed Green, Peter Calabrese, Maren Friesen, Magnus
Norborg, and Marie-Stanislas Remigereau for helpful discussions. This work
was supported in part by grants from the NIH (R01HG005238 and
1RC2HD064459) and by a kind gift from Kathryn W. Davis.
Received: December 16, 2010
Revised: May 9, 2011
Accepted: August 10, 2011
Published: September 15, 2011
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