text:
Epigenetic polymorphism and the stochastic formation of differentially methylated regions in normal and cancerous tissues.
ABSTRACT DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and reprogram differentially methylated regions remain enigmatic. Here, we show that methylation patterns within populations of cells from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300 generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average. The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to methylation accumulation are correlated with changes in histone modification and CTCF occupancy. Characterizing epigenomic polymorphism within cell populations is therefore critical to understanding methylation dynamics in normal and cancer cells.
- N Ari Wijetunga, Fabien Delahaye, Yong M Zhao, Aaron Golden, Jessica C Mar, Francine H Einstein, John M Greally[Show abstract] [Hide abstract]
ABSTRACT: The mechanism and significance of epigenetic variability in the same cell type between healthy individuals are not clear. Here we purify human CD34+ haematopoietic stem and progenitor cells (HSPCs) from different individuals and find that there is increased variability of DNA methylation at loci with properties of promoters and enhancers. The variability is especially enriched at candidate enhancers near genes transitioning between silent and expressed states, and encoding proteins with leukocyte differentiation properties. Our findings of increased variability at loci with intermediate DNA methylation values, at candidate 'poised' enhancers and at genes involved in HSPC lineage commitment suggest that CD34+ cell subtype heterogeneity between individuals is a major mechanism for the variability observed. Epigenomic studies performed on cell populations, even when purified, are testing collections of epigenomes, or meta-epigenomes. Our findings show that meta-epigenomic approaches to data analysis can provide insights into cell subpopulation structure.Nature Communications 10/2014; 5:5195. · 10.74 Impact Factor - SourceAvailable from: Sheng LiSheng Li, Francine Garrett-Bakelman, Alexander E Perl, Selina M Luger, Chao Zhang, Bik L To, Ian D Lewis, Anna L Brown, Richard J D Andrea, M Ross, Ross Levine, Martin Carroll, Ari Melnick, Christopher E Mason[Show abstract] [Hide abstract]
ABSTRACT: We describe methclone, a novel method to identify epigenetic loci that harbor large changes in the clonality of their epialleles (epigenetic alleles). methclone efficiently analyzes genome-wide DNA methylation sequencing data. We quantify the changes using a composition entropy difference calculation and also introduce a new measure of global clonality shift, loci with epiallele shift per million loci covered, which enables comparisons between different samples to gauge overall epiallelic dynamics. Finally, we demonstrate the utility of methclone in capturing functional epiallele shifts in leukemia patients from diagnosis to relapse. methclone is open-source and freely available at https://code.google.com/p/methclone.Genome biology. 09/2014; 15(9):472. - Dan A Landau, Kendell Clement, Michael J Ziller, Patrick Boyle, Jean Fan, Hongcang Gu, Kristen Stevenson, Carrie Sougnez, Lili Wang, Shuqiang Li, [......], Stacey Gabriel, Andreas Gnirke, Eric S Lander, Jennifer R Brown, Donna Neuberg, Peter V Kharchenko, Nir Hacohen, Gad Getz, Alexander Meissner, Catherine J Wu[Show abstract] [Hide abstract]
ABSTRACT: Intratumoral heterogeneity plays a critical role in tumor evolution. To define the contribution of DNA methylation to heterogeneity within tumors, we performed genome-scale bisulfite sequencing of 104 primary chronic lymphocytic leukemias (CLLs). Compared with 26 normal B cell samples, CLLs consistently displayed higher intrasample variability of DNA methylation patterns across the genome, which appears to arise from stochastically disordered methylation in malignant cells. Transcriptome analysis of bulk and single CLL cells revealed that methylation disorder was linked to low-level expression. Disordered methylation was further associated with adverse clinical outcome. We therefore propose that disordered methylation plays a similar role to that of genetic instability, enhancing the ability of cancer cells to search for superior evolutionary trajectories. Copyright © 2014 Elsevier Inc. All rights reserved.Cancer Cell 12/2014; 26(6):813-25. · 23.89 Impact Factor
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Nature GeNetics VOLUME 44 | NUMBER 11 | NOVEMBER 2012
The programming and reprogramming of DNA methylation patterns
during germline development1,2, embryogenesis3 and somatic differ
entiation4–6 are fundamental paradigms in epigenetics. The centrality
of DNA methylation to epigenetics stems from its covalent association
with the genome and its persistence across cell divisions due to house
keeping DNA methyltransferase activity7,8. It is therefore tempting to
theorize that DNA methylation is a modification that occurs deter
ministically at strategic steps during developmental differentiation
and is then maintained until further change is required. Many years
of research, including extensive profiling of methylation patterns at
high resolution9–12, have identified numerous regions that on average
are differentially methylated between tissues but have provided little
evidence of the dynamics of the process generating these differences.
Whether this process changes one methylation pattern to another
in a scheduled and regulated fashion integral to the developmental
program or is instead a noisy and stochastic process occurring inde
pendently and in parallel at different sites remains debated.
In contrast to normal epigenomes, cancer epigenomes form
throughout the course of an inherently stochastic microevolution
that pushes successful clones toward rapid growth and population
takeover13. Methylation patterns can change during this process,
either by a mechanism that works in multiple lineages parallel to
carcinogenic selection or by fewer, singular events that spread in the
population as hitchhikers or drivers of selective sweeps. Numerous
examples of cancerrelated hypermethylated regions14,15 and genomic
blocks undergoing hypomethylation16,17 have been discovered over
the last several years. The process driving methylation changes in
these regions and its commonalities with the process driving methyla
tion changes in normal tissues are not truly understood. Important
challenges in the field are to characterize the similarities and dif
ferences between the mechanisms generating cancerrelated and
normal methylation changes, to determine whether increased cancer
methylome stochasticity underlies the wellestablished heterogeneity
of cancer epigenomes16–19 and to link epigenetic aberration to other
aspects of carcinogenesis.
To explore the dynamics of methylation change in normal and
cancer epigenomes, we employ here the notion of epigenetic poly
morphism or 'epipolymorphism'20. In our proposed framework, the
methylation state of a given locus (a group of adjacent CpG sites) in
a cell population is defined by a mixture of patterns (epialleles) with
variable frequencies. Observed changes in average methylation levels
can then be interpreted according to epiallelic diversity, distinguish
ing, for example, a regulated increase in the frequency of a specific
epiallele from multiple stochastic changes in the frequencies of many
epialleles19. By reanalyzing bisulfite sequencing data at the read level,
we show that somatic methylomes are highly polymorphic, exhibiting
a spectrum of heterogeneous methylation states that range from a
complete lack of methylation to full methylation. Moreover, reanalysis
Epigenetic polymorphism and the stochastic formation
of differentially methylated regions in normal and
cancerous tissues
Gilad Landan1–3, Netta Mendelson Cohen1, Zohar Mukamel1,2, Amir Bar1, Alina Molchadsky3, Ran Brosh3,
Shirley Horn-Saban4, Daniela Amann Zalcenstein4, Naomi Goldfinger3, Adi Zundelevich5, Einav Nili Gal-Yam5,
Varda Rotter3 & Amos Tanay1,2
DNA methylation has been comprehensively profiled in normal and cancer cells, but the dynamics that form, maintain and
reprogram differentially methylated regions remain enigmatic. Here, we show that methylation patterns within populations of cells
from individual somatic tissues are heterogeneous and polymorphic. Using in vitro evolution of immortalized fibroblasts for over 300
generations, we track the dynamics of polymorphic methylation at regions developing significant differential methylation on average.
The data indicate that changes in population-averaged methylation occur through a stochastic process that generates a stream
of local and uncorrelated methylation aberrations. Despite the stochastic nature of the process, nearly deterministic epigenetic
remodeling emerges on average at loci that lose or gain resistance to methylation accumulation. Changes in the susceptibility to
methylation accumulation are correlated with changes in histone modification and CTCF occupancy. Characterizing epigenomic
polymorphism within cell populations is therefore critical to understanding methylation dynamics in normal and cancer cells.
1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. 2Department of Biological Regulation, Weizmann
Institute of Science, Rehovot, Israel. 3Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel. 4Department of Biological Services,
Weizmann Institute of Science, Rehovot, Israel. 5Cancer Research Center, Sheba Medical Center, Tel-Ha'Shomer, Israel. Correspondence should be address to
A.T. (email@example.com).
Received 15 March; accepted 20 September; published online 14 October 2012; doi:10.1038/ng.2442
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.
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VOLUME 44 | NUMBER 11 | NOVEMBER 2012 Nature GeNetics
of tumor bisulfite sequences suggests that cancerrelated methylation
also exhibits high levels of polymorphism, despite the population's
presumed recent clonal origin.
To further explore the a priori discrepancy between highly poly
morphic methylomes and the consistent and reproducible emergence
of differentially methylated regions on average, we developed a simple
in vitro evolutionary model and tracked methylation changes in cul
ture at high temporal resolution for over 300 generations. Genome
wide analysis of average methylation shows that numerous regions
in the system developed differential methylation in a nearly deter
ministic fashion. Intrapopulation deep bisulfite analysis shows that,
despite the consistent populationaveraged changes, the methylomes
of the populations were highly polymorphic throughout the evolu
tionary process. An increase in the average methylation of a locus was
observed when regions that were resistant to the stochastic process of
methylation became susceptible in some or all of the cells in the popu
lation. We hypothesize that resistance mechanisms mask DNA from
otherwise pervasive and locally random events of methylation aber
ration and that gain or loss of such resistance may be instrumental in
the emergence of tissue and cancerspecific epigenomic patterns.
Somatic DNA methylation is highly polymorphic
Uniform, bimodal or completely random DNA methylation patterns
within a population represent outcomes of markedly different epigenetic
mechanisms but may result in identical average methylation profiles
(Fig. 1a). To assess the degree of intrapopulation heterogeneity in
somatic cell populations, we reanalyzed reduced representation bisulfite
sequencing (RRBS) data obtained on a panel of Encyclopedia of DNA
Elements (ENCODE)profiled tissues21,22 (Supplementary Table 1).
We identified groups of four adjacent CpGs that were frequently cov
ered by the same read. For each locus of 4 CpGs, we counted how
many times each of the 16 possible methylation states was observed,
obtaining statistics on the distribution of DNA methylation patterns at
thousands of loci (most of which were in regions of high CpG content).
We defined the epipolymorphism of a locus in the cell population as the
probability that two epialleles randomly sampled from the locus differ
from each other and reconstructed the distribution of epipolymor
phisms as a function of average methylation. We consistently identified
levels of epipolymorphism that approached the theoretical maxima
(Fig. 1b and Supplementary Fig. 1), indicating that, with the exception
of fully protected and fully methylated loci (average methylation of 0
and 1, respectively), the methylation state of individual cells is highly
heterogeneous within tissues. Such epipolymorphism is unlikely to
be a direct consequence of cell type heterogeneity within the tissue
sample, as we observed similar trends in different tissue types and sam
ples (differing in purity) and because coherent populations of cell lines
exhibit equivalent median epipolymorphism (Supplementary Fig. 2).
In contrast, H1 embryonic stem cells (ESCs) and testicular cells showed
reduced levels of epipolymorphism, suggesting that these cells are able
to establish or maintain a more coherent methylation state than those
observed in differentiated populations.
To better understand the patterns of methylation underlying the
above observations, we stratified loci according to the frequency of
fully methylated (all four CpGs methylated) and completely pro
tected (all four CpGs unmethylated) epialleles (Fig. 1c). These distri
butions indicated enrichment in testicular and H1 ESC populations
for loci dominated by completely methylated or protected epialleles,
as well as for loci at which these two archetypes coexist. Such patterns
may represent monoallelic methylation or some bimodal population
structure, and the associated loci showed lower than average epipoly
morphism (Supplementary Fig. 3, redblue color coding). Moreover,
bimodal methylation was much less abundant in somatic popula
tions than it was in H1 ESCs or testicular cells (Supplementary
Fig. 3, yellowpurple color coding). Further quantification of the
fraction of profiled loci in which methylation was neither uniform
Frequency of fully methylated epialleles
Epipolymorphism ≈ 0.844
Epipolymorphism = 0
Epipolymorphism = 0.375
25%25%25% 25%
Breast, 21
Left ventricle, 26
Uterus, 44
Leukocytes, 54
Brain, 66
Pericardium, 70
Skeletal muscle, 71
Pancreas, 71
Skin, 83
Skeletal muscle, 83
Lung, 83
Liver, 83
Kidney, 83
Adrenal gland, 88
–2 –1 0 1 2 3
Standard deviations
from tissue average
H1 ESCs
TestisAdrenal glandSkin
n = 31,608n = 32,596n = 35,375
Median of 16 tissues
(in duplicate)
Four-CpG locus
Bimodal methylation
Methylation (%)
0 20 40 60 80100
Methylation (%)
0 20 40 60 80100
Methylation (%)
0 20 40 60 80100
H1 ESCs
Methylation (%)
0 20 40 60 80100
Left ventricleBrainPancreas
n = 31,190n = 42,762n = 38,975n = 28,406
Methylation (%)
0 20 40 60 80100
Methylation (%)
0 20 40 60 80100
Methylation (%)
0 20 40 60 80100
– Density of points +
H1 ESCs
Frequency of fully protected epialleles
Adrenal gland
00.2 0.4 0.6 0.8 1.000.2 0.4 0.6 0.8 1.0
Figure 1 Epipolymorphism. (a) Schematic of the distributions of
methylation profiles (epialleles) at loci with similar average methylation
but different population structure. Empty and filled circles represent
unmethylated and methylated CpGs, respectively. A locus is defined as a
group of adjacent CpGs (indicated by blue shading). The epipolymorphism
of a locus is defined as the probability that two randomly sampled DNA
molecules differ in their methylation pattern. (b) Epipolymorphism
at loci consisting of four adjacent CpGs as a function of the average
methylation of the locus (n, number loci analyzed). The maximal
epipolymorphism trend was computed assuming methylation profiles
generated independently at each CpG. The bimodal polymorphism trend
was computed assuming a combination of fully methylated and fully
unmethylated alleles, resulting in average locus methylation. (c) Scatter
plots showing the internal organization of individual loci. Each data point
represents a locus. Loci on or near the diagonal represent sites with
bimodal methylation (some epialleles protected, others fully methylated,
see H1 ESC inset for the distribution at one such locus) that may be
associated with a monoallelic state. Loci near the bottom left represent
sites with disordered states characterized by intermediate methylation
content and high epipolymorphism (adrenal gland inset and supplementary
Fig. 3). (d) Fraction of loci with a disordered methylation state for the
16 tissues, shown in terms of standard deviation from the average
methylation in the tissue, ordered by donor chronological age (indicated by
numbers to the right of somatic tissues). Each bar represents results from
one of two duplicate experiments.
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.
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Nature GeNetics VOLUME 44 | NUMBER 11 | NOVEMBER 2012
nor bimodal (indicated by the black borders in Fig. 1c) associated
somatic tissues with a clear increase in the frequency of disordered
methylation states compared to H1 ESCs and testis in a manner that
was correlated with age (Spearman ρ = 0.57, P < 2 × 10−3; Fig. 1d).
Taken together, these results suggest that methylation patterns within
germline and pluripotent populations accumulate substantial noise
during somatic development, giving rise to a large variety of epi
alleles and eroding epigenomic organization in unmethylated, fully
methylated and bimodally methylated loci.
Differentially methylated loci are epipolymorphic
Differentially methylated regions (DMRs) are observed extensively in
multiple tissues and cell types, gaining or losing average methylation
in clusters of adjacent CpGs5,6,23,24. We detected a high degree of
epipolymorphism at loci that show differential average methylation
compared to H1 ESCs (Supplementary Figs. 4 and 5), suggesting that
differential methylation is not associated with takeover of specific
methylation patterns. We next reanalyzed bisulfite sequencing pad
lock probe (BSPP) data assaying colorectal normaltumor pairs over
a panel of putative DMRs14 (Fig. 2a). The overall epipolymorphism
in normal and tumor cells was comparable, as was that observed at
loci showing decreased levels of tumor methylation (at least a 20%
decrease in methylation relative to matched normal colonic mucosa;
Fig. 2a, green). In contrast, loci showing increased average meth
ylation in tumors (at least a 20% increase in methylation relative to
matched normal colonic mucosa; Fig. 2a, purple) showed lower epi
polymorphism than control loci (Fig. 2b). Further analysis showed
that the distribution of epialleles at hypermethylated loci is distinctly
bimodal. Hypermethylated loci with high average methylation
(60–70%) were fully protected or fully methylated in a much
larger fraction of the population than stably methylated loci with
comparable average methylation (Fig. 2c). Notably, recalculat
ing epipolymorphism and average methylation without con
sidering fully unmethylated epialleles showed that unprotected
epialleles are equally polymorphic at hypermethylated and sta
bly methylated loci (Fig. 2d), indicating that the difference in
polymorphism between these loci can be attributed to the exist
ence of a completely unmethylated subpopulation rather than the
existence of a more organized methylated state. We note that, as
this effect was observed for hyper but not hypomethylated loci,
it cannot be explained by the impurity of the tumor samples (which
must impose heterogeneity on all differentially methylated loci).
In vitro evolution of fibroblast epigenomes
Although the data presented above provide an indication of processes
creating and maintaining polymorphic methylomes, it is not possible
to infer the dynamics of such processes using static measurements
alone. To enable longterm tracking of the distribution of methylation
patterns at loci that develop differential methylation on average, we
followed two lines (termed A and B) of immortalized fibroblasts in
culture for over 300 generations25. Each of these lines represents a
microevolutionary experiment, with cells growing in a stable (although
artificial) environment under weak selective constraint (weekly replat
ing) and a very large effective population size (100,000 cells at each
replating). We branched and sampled passages (Fig. 3a) and measured
changes in gene expression (Fig. 3b and Supplementary Fig. 6a,b) and
growth rate (Supplementary Fig. 6c) along the evolution of the two
lines. We used methylated DNA immunoprecipitation–sequencing
(MeDIPseq)26,27 (Supplementary Fig. 7a–c) to globally profile aver
age DNA methylation at six time points in line A and at a late passage
in line B. Both lines developed increased average 5methylcytosine
(5mC) enrichment at regions with high CpG content, which were
originally devoid of methylation (Fig. 3c)28,29. Further unsupervised
analysis (Supplementary Fig. 8a–c) identified a large group of regions
that gained DNA methylation specifically and consistently in the two
lines, as well as regions that lost DNA methylation and some that
maintained a stable level. Temporal gene expression profiles of puta
tive epigenetic regulators suggested that most of these factors were
stably expressed throughout the experiment30–33 (Supplementary
Fig. 9a). The in vitro evolutionary system thereby provided us with
an extensive set of average epigenomic changes that could be further
studied at high temporal, genomic and intrapopulation resolution.
Genomic characterization of average hyper- and hypomethylation
Analysis of methylation around transcription start sites (TSSs) with
high CpG content (from −350 to +50 bp relative to the TSS; CpG
211332 Tumor
Hypermethylated Stably methylated
01234 Methylated
CpGs (n)
Methylation (%)
020 40 60 80 100
Methylation (%)
020 40 60 80 100
Methylation (%)
020 40 60 80 100
Stable methylation median
Stably methylated locus
Hypomethylated locus
Hypomethylation median
Hypermethylated locus
Hypermethylation median
Matched normal
Bimodal methylation
Tumor 1Tumor 2Tumor 3
Figure 2 Differentially methylated loci are
epipolymorphic. (a) Epipolymorphism distributions
(stratified by average methylation) highlighting
loci showing increased (purple) and decreased
(green) average methylation in colorectal cancer
tumors compared to matched normal colonic
mucosa16. (b) Epipolymorphism distributions are
shown for stably methylated and hypermethylated
loci with 60–70% average methylation in tumors.
*P < 3 × 10−6, D > 0.31. (c) Methylation content
distribution, representing the frequency of each
level of methylation content (for example,
a methylation content of 1 represents the fraction
of sampled epialleles with exactly 1 methylated
CpG). Shown are distributions of these frequencies
at loci with 60–70% average tumor methylation,
separately analyzing loci with increased tumor
methylation and loci with stable methylation
between normal-tumor pairs. (d) Epiallele
populations were deconvoluted by removing all
fully unmethylated epialleles from the data, and
average methylation and epipolymorphism were
recomputed within the remaining subpopulations.
In b–d, the median, upper and lower quartiles and
95% confidence intervals are shown.
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.
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VOLUME 44 | NUMBER 11 | NOVEMBER 2012 Nature GeNetics
content of ≥5%; high potential for methylation, as indicated by m.SssI
methylated control) showed that, whereas constitutively active genes
(n = 1,916) were resistant to hypermethylation (Fig. 3d), most con
stitutively silent genes (n = 505) developed substantial methylation
levels over time (Fig. 3e). This phenomenon was observed in both
lines A and B. Downregulated genes (with at least twofold repression
during the evolution of line A, n = 66) showed the same behavior
(Fig. 3f). Promoter hypermethylation was therefore observed to be
a global, sweeping phenomenon that engaged the majority of silent
promoters (whether de novo or constitutive; Supplementary Fig. 10).
Although the negative association between TSS methylation and gene
expression is clear, increased methylation was usually not coupled
with de novo repression and is likely to have occurred in the majority
of cases only after initial repression was established. Hypomethylated
regions, on the other hand, were enriched in long terminal repeat
(LTR) elements, and reduction in the methylation of long interspersed
nuclear element 1 (LINE1) and αsatellite elements in late passages
was demonstrated by deep bisulfite sequencing (Fig. 3g). Reduction
of methylation in large genomic blocks, as previously reported9,16,17,
was marginally detected in MeDIPseq data, which is of limited accu
racy in regions with low CpG content (data not shown). The dynamic
patterns of hyper and hypomethylation thus reflect a global process
of epigenomic aberration that broadly but indirectly correlates with
genome function at promoters and repetitive elements17,34,35.
Hypermethylation occurs gradually
On the basis of the global observations of epigenomic changes in the
evolving lines, we next developed ultradeep bisulfite profiling using
shortread sequencing36 (Fig. 4a and Online Methods) to accurately
quantify the methylation kinetics and population dynamics at select
regions (45 cancerrelated CpG islands37, further classified using the
MeDIPseq data; Supplementary Table 2). We focused on regions
with high CpG density that were prone to hypermethylation, thereby
following up the observation of high epipolymorphism at differentially
methylated loci. Sequencing of samples at eight time points from
lines A and B generated a total of 8.9 × 107 mappable reads, ensur
ing a median coverage above 10,000× per CpG (Supplementary
Fig. 11a). This analysis resulted in a reproducible and accurate
estimation of the methylation levels for single CpGs (Supplementary
Fig. 11b), as well as intrapopulation distributions for groups of
adjacent CpGs. The derived kinetics of all profiled CpGs (organ
ized in clusters in Fig. 4b) reflect a gradual process. According
to the data, the hypermethylation rate (gain in the percentage of
methylated sites divided by the number of generations between
samples; Supplementary Fig. 11c) did not globally increase
throughout the experiment (median of 0.15–0.23% per generation).
The notable differences between CpGs were not in the rate of hyper
methylation but in the time of initiation. Whereas some CpGs
remained resistant to methylation throughout the evolution of lines A
and B (cluster 1, n = 430), distinct groups were not resistant (clusters
8–11, n = 269) or were resistant until intermediate (clusters 4, 6 and
7, n = 451) or late (clusters 2 and 3, n = 416) passages.
Emergence of spatially specific methylation patterns
The two evolving fibroblast lines showed high epipolymorphism,
even as their methylation levels changed markedly, consistent with
the observations on the ENCODE tissue panel (Fig. 4c) and sug
gesting that methylation accumulates through a flux of local events
(Supplementary Fig. 12). Detailed analysis showed that the initial
patterns of methylation at specific foci were established very early
and then dynamically changed and spread. For example, in the
region within PBX1 (Fig. 5a), we observed 25–30% methylation at
CpGs 7–9 at the beginning of the process (passage 19). This focus
of methylation gradually intensified, while complete or almost
Figure 3 Extensive average differential
methylation during fibroblast evolution in vitro.
(a) Schematics of the microevolving cell lines.
Immortalization (by hTERT) was followed by
the prolonged in vitro evolution of two lines.
The number of passages (in circles) and the
number of generations at each branch point and
sampling point are indicated. (b) Genome-wide
comparison of gene expression between cells
at early and late passage in line A. Shown are
normalized log2 expression values, and genes
with at least twofold induction or repression are
marked in red or green, respectively. Further
analysis of gene expression, genomic content
and growth rates is shown in supplementary
Figure 6a–c. (c) Distributions of DNA
methylation differences between primary cells
(passage 19) and sampled time points along the
evolution of lines A and B. The graphs depict
quantiles (upper and lower 5th, 10th, 25th and
50th, represented by light blue to black) of the
change in normalized 5mC relative to
passage 19, stratified by local CpG content.
(d–f) Distributions of normalized 5mC levels
at TSSs with high CpG content (CpG density of
≥5%) at different passages in lines A and B
(and m.SssI control) for genes with
constitutively high expression (top
20 percentiles at each time point (d),
constitutively low expression (bottom 20 percentiles (e) and at least twofold downregulation between passages 19 and A95 (f). Box plots show the
median, upper and lower quartiles and 95% confidence intervals; n indicates the number of genes in each category. (g) Deep bisulfite sequencing–
based average methylation levels for CpGs within L1 (blue) and α−satellite (red) repetitive elements, compared between an early and late passage.
n = 505
Silent TSSs
n = 66
Downregulated line A
n = 1,913
Normalized 5mC levels
Active TSSs
30 generations
Line A
Line B
at passage 19
at passage A95
A39 versus 19 A50 versus 19A58 versus 19A67 versus 19A95 versus 19
CpG (%)CpG (%)
Difference in 5mC
0 10 010 010 010 010
CpG (%)CpG (%)CpG (%)
at passage A95 (%)
B96 versus 19
CpG (%)
206080 100040
Methylation at passage 19 (%)
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.
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Nature GeNetics VOLUME 44 | NUMBER 11 | NOVEMBER 2012
complete resistance was maintained at CpGs 15–51. In later pas
sages, this resistance was lost in a subset of the population, and CpGs
15–51 showed an increase in methylation to ~40%. A similar trend
was observed in the region upstream of CCND1 (Fig. 5b). Analysis
of the distribution of methylation inside the evolving populations
using genetic linkage–like diagrams (Fig. 5c,d) showed that the
intrapopulation correlation between the methylation states of pairs
of CpGs was generally very low. This lack of correlation suggests
that methylation dynamics are typically independent for different
CpGs, making the methylation state of one CpG (whether high
or low) uninformative on the methylation state of nearby CpGs.
Higher correlation between CpGs was observed when some cells in
the population developed susceptibility to methylation at previously
resistant domains (as in PBX1). As observed for hypermethylated
loci in colorectal cancer, this increased correlation (and associated
reduction in epipolymorphism) was a consequence of the existence
of two distinct subpopulations, one gaining methylation and the
other maintaining resistance (Supplementary Fig. 13a,b).
Notably, spatial specificity was quite reproducible in the two inde
pendently evolved lines (Supplementary Fig. 14) and at additional
regions profiled at high resolution (Supplementary Fig. 15 and the
Tanay group website; see URLs). Although methylation accumulation
affected many regions over the time course of the experiment, complete
resistance was observed at specific regions, whereas almost complete
maintenance of methylation was observed at others (Supplementary
Fig. 16). In contrast, two regions that showed an initial bimodal or
monoallelic methylation structure (the imprinted SNRPN region and
the Xlinked GPC3 region) exhibited continuous deterioration of epi
allelic organization and convergence to a distorted state in which the
upstream half of the region was methylated and the downstream half
was unmethylated (Supplementary Fig. 17). In summary, initially
organized epigenomic structure (unmethylated regions, monoallelic
organization) was perturbed over time through a locally stochastic
process of methylation aberration. As suggested by the analysis of
methylation in different tissues, specific patterns of average differential
methylation emerged when certain loci that were resistant to methyla
tion became exposed to it.
Changes in 5mC, histone marks and CTCF are anticorrelated
The evolving fibroblast lines allowed concomitant study of the
microevolution of DNA methylation and the genomic occupancy
of a factor of interest, thereby interrogating the dynamic relation
ship between the two. Chromatin immunoprecipitation combined
with sequencing (ChIPseq) profiling of trimethylation of histone
H3 on lysine 4 (H3K4me3) in evolving cells at early and late pas
sages (Fig. 6a) confirmed the expected lack of DNA methylation
at H3K4me3marked sites (consisting mainly of active promoters),
indicating that loss of H3K4me3 was associated with methylation
gain (KolmogorovSmirnov D = 0.39, P << 1 × 10−15). Further ChIP
seq analysis of the CTCF genomic insulator protein in the evolved
cells showed a marked correlation between hypermethylation and
loss of CTCF binding. CTCFbinding sites that lost CTCF occupancy
compared to H1 ESCs22 were significantly more likely to gain DNA
methylation in late passages than sites with a stable CTCF presence
(KolmogorovSmirnov D = 0.34, P < < 1 × 10−15; Fig. 6b). Notably, the
tendency to hypermethylate in later passages was strong even when
considering only CTCFbinding loss that occurred by passage 39,
before substantial hypermethylation had taken place (Kolmogorov
Smirnov D = 0.24, P < < 1 × 10−15), suggesting that, at least in some
cases, the loss of CTCF binding occurred earlier than DNA hyper
methylation. We next generated a temporal profile of the enrichment
patterns of trimethylation of histone H3 at lysine 27 (H3K27me3),
which was previously implicated in positive38,39 and negative40,41
regulation of de novo DNA methylation. We observed accumulation
of DNA methylation in regions losing H3K27me3 (Kolmogorov
Smirnov D = 0.21, P < 1 × 10−12; Fig. 6c) and used an unsupervised
clustering approach to identify groups of genomic regions with con
sistent H3K27me3 kinetics (Supplementary Fig. 18). Hundreds of
regions showing temporally decreasing (clusters 2–5) and increasing
(clusters 6 and 7) H3K27me3 levels were identified, and further
analysis showed that these regions exhibited reciprocal DNA methy
lation kinetics. In conclusion, H3K4me3, H3K27me3 and CTCF
represent chromatinassociated factors whose dynamic presence is
anticorrelated on average with DNA methylation accumulation.
Initial exclusion of H3K27me3 and 5mC is perturbed
Whereas the temporal anticorrelation between populationaveraged
H3K27me3 levels and DNA methylation is clear, many regions showed
concurrent enrichment for both marks during the evolution of lines A
and B (Supplementary Fig. 19). To directly test whether H3K27me3
and DNA methylation coexisted on the same chromosomal template,
Methylation (%)
20040 60 80100
Methylation (%)
20040 60 80100
Methylation (%)
20040 60 80100
Median of
16 tissues in
Four-CpG locus
Bimodal methylation
Methylation (%)
20 40 60 801000
20040 60 8010020040 60 8010020040 60 80100
20040 60 80100
1939 5058679569 9619
of line B
of line A
1939 50586795699619
of line B
of line A
Bisulfte conversion + multiplex PCR
200–500 bp
Amplicon 1
Amplicon 2
Amplicon N
Amplicon 3…
Figure 4 Hypermethylation is a parallel, stochastic and gradual evolutionary
process. (a) Schematic of the deep bisulfite sequencing protocol.
An adaptation of the standard bisulfite sequencing protocol for use with
short-read sequencing machines, which cost effectively allows very high
coverage (over 10,000×; supplementary Fig. 11a) of single CpGs and
groups of neighboring CpGs within PCR-amplified regions of 300–550 bp.
We interrogated 45 amplicons within cancer-related CpG islands.
(b) Methylation kinetics of all CpGs profiled at high resolution, organized
into clusters (1–12). A methylation level of 1 corresponds to 100%
methylation. For each cluster, we show the methylation profiles of individual
CpGs (pink) and the median cluster methylation trend (red), throughout line
A (left) and line B (right). (c) Epipolymorphism trends (as in Figs. 1 and 2)
for non-overlapping groups of four CpGs that were part of the deep bisulfite
sequencing experiment. An increase in average methylation (x axis) at
different passages is accompanied by an increase in epipolymorphism (y axis).
npg npg
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.
Page 6
VOLUME 44 | NUMBER 11 | NOVEMBER 2012 Nature GeNetics
we developed a combined ChIP–deep bisulfite sequencing assay and
applied it at early (passage 19) and late (passage A95) evolutionary
stages on a panel of genomic regions undergoing H3K27me3 loss and
DNA hypermethylation. We also profiled a set of imprinting control
regions (ICRs) that showed evidence for the presence of H3K27me3
modified histones (Supplementary Table 3). Comparison of methy
lation levels in H3K27me3bound and input DNA showed exclusion of
the two marks in earlypassage cells at ICRs (H19 DMR, DLK-DIO3 IG
DMR and GNAS XL, more weakly at MEST and KCNQ1DN; Fig. 6d).
In contrast, no difference in the DNA methylation of generic CpG
islands was detected between H3K27me3bound and unbound DNA,
even when methylation increased to high levels in passage 95 (con
sidering only loci with H3K27me3 enrichment in the top 10 genomic
percentiles). Additional analysis of partially methylated regions at
an intermediate evolutionary stage (passage 58; Supplementary
Fig. 19b) confirmed the lack of direct exclusion between H3K27me3
and 5mC in regions undergoing gradual hypermethylation. Detailed
highresolution analysis of the distribution of DNA methylation in
ICRs and CpG islands (Fig. 6e,f) further showed the differences
between the two classes. At the GNAS XL and H19 DMRs, methylation
was uniformly distributed and strongly correlated inside the region,
with H3K27me3 linked to the unmethylated epiallele. In contrast, the
RUNX3 region showed a complex, evolving methylation pattern that
was precisely recapitulated in H3K27me3enriched DNA. In summary,
whereas the preestablished allelespecific methylation patterns at
imprinted loci were specifically observed in the allele not marked with
H3K27me3, CpG islands that were not associated with imprinting did
not show evidence of this phenomenon. Direct mechanistic exclusion
between DNA methylation and H3K27me3 is therefore not supported,
suggesting that these two modifications contribute independently to
the complex process of epigenomic remodeling.
We have reanalyzed bisulfite sequencing data to systematically quantify
the heterogeneity of DNA methylation patterns in cell populations
from different sources, including tissues and cell lines with normal and
cancerous backgrounds. The results substantiate a wellappreciated
phenomenon—two methylation patterns of randomly sampled DNA
molecules from the same cell population are very seldom identical to
each other, even if one restricts analysis to small groups of spatially
coupled CpGs (four adjacent CpGs, for example, as in our reanalysis).
The only exceptions we observed to this general rule were in the testis
and H1 ESC populations, suggesting that the specific developmen
tal states represented by these tissues maintain (or inherit) a more
coherent and homogenous methylome. We developed an in vitro
model to temporally track the emergence and maintenance of such
epipolymorphism in populations of somatically evolving fibroblasts.
As observed in the tissue samples, the in vitro populations exhibited
Chr. 1: 162,812,066–162,812,615
1.8 kb 32.3 kb
Chr. 1: 25,130,851–25,131,313
Chr. 11: 17,697,769–17,698,203
100 bp
4.5 kb
Chr. 11: 69,160,261–69,160,818
Chr. 1: 162,812,066–162,812,615
27 kb
Pairwise correlation
Percent methylated
12 3 45
Chr. 11: 69,160,261–69,160,818
85% 5%
5% 45%
High correlation
Low correlation
Figure 5 Spatial constraints canalize hypermethylation patterns. (a,b) Average methylation in line A of the regions near PBX1 (a) and CCND1 (b) at
different passages. CpGs are depicted as circles and are drawn to genomic scale. Color is used to indicate methylation level. Vertical arrows indicate
foci of initial methylation, and horizontal arrows highlight its spatial expansion. (c) Schematics of methylation linkage using five hypothetical CpGs.
The average methylation percentage for each CpG is shown at both genomic (circles) and uniform (bars) scales. In the triangle below the bars, color
indicates the correlation (cor) between the methylation states of pairs of CpGs. Also shown are two examples of average methylation patterns and two
contrasting epipolymorphism scenarios that could underlie each of them. Whether or not the average methylation levels are similar, the distribution of
methylation patterns may generate significant correlation between the CpGs (high correlation) or suggest they are completely unlinked (low correlation).
(d) Average methylation profiles and methylation linkage diagrams for four regions at different passages, indicating correlations for all pairs of CpGs
with coverage greater than 100. (Data for line B are given in supplementary Fig. 14, and additional examples are provided in supplementary Fig. 15.)
Schematics of the genomic loci are shown below, representing profiled regions as floating gray rectangles.
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.
Page 7
Nature GeNetics VOLUME 44 | NUMBER 11 | NOVEMBER 2012
highly polymorphic methylomes, irrespective of the times spent in cul
ture. Characterization of epigenomic variation within somatic popula
tions has the potential to link the epigenomic state with underlying
developmental dynamics (Supplementary Fig. 20), thereby greatly
enhancing the impact of emerging epigenomic surveys42,43.
Recent studies of tissue and cancerspecific DMRs can now be
understood in the context of polymorphic methylation states. We
show that regions that are differentially methylated are, in general, as
epipolymorphic as loci with stable methylation, suggesting that their
emergence and/or maintenance involve a strong stochastic compo
nent. At loci that gained methylation in tumors compared to matched
normal colonic mucosa, we observed a subpopulation with high meth
ylation levels and high epipolymorphism, coexisting with a distinct
subpopulation that remained completely resistant to methylation. This
finding suggests that once such resistance or protection is lifted, methy
lation accumulates without any particular order. Using the in vitro
system, we tracked methylation dynamics by a combination of global
methylation profiling and deep bisulfite sequencing of specific regions,
showing that changes in average methylation are a consequence of a
stochastic and highly parallel process involving local perturbations.
Detailed analysis of methylation patterns within the evolving popula
tions shows how specific patterns of average methylation emerge as
methylation accumulates at groups of contiguous CpGs while adja
cent groups remain resistant. Furthermore, as observed for hyper
methylated loci in colorectal cancer, resistance patterns sometimes
subdivide the population (with respect to a specific locus) into low
and high methylation fractions, in a manner that changes with time.
The dynamics leading to loss of average methylation (for example, in
repetitive elements or large genomic blocks16,17) need to be further
explored at high resolution but may involve similar considerations.
Multiple lines of evidence suggest that certain chromatinmodifying
complexes and structural constituents of nucleosomal organiza
tion are positively correlated with average DNA methylation levels,
whereas others are negatively correlated. Previous studies in ESCs,
differentiation models, cancer cell lines and tumors have used popu
lationaveraged measurements to show that H3K4me3, H3K27me3
and CTCF binding are all spatially and temporally anticorrelated with
DNA methylation in regions with high CpG content26,44–48. In the
in vitro system studied here, we recapitulate these reciprocal relation
ships. Despite these extensive correlations, the underlying mecha
nisms remain unclear. Through a combination of ChIP and deep
bisulfite sequencing, we show that chromatin with the H3K27me3
modification is initially hypomethylated at regions associated with
regulation of genomic imprinting but not at classical CpG islands
(neither before nor after developing hypermethylation). This result
suggests that, whereas the marks are likely to be mutually exclusive in
much of the germline, as recently shown49,50, they are not physically
separated at the singlemolecule level in somatic cells.
The stochastic and gradual dynamics of methylation aberration
may have several possible interpretations. One model considers the
accumulation of methylation to be an asymmetric process that begins
with a clean state and exposes unprotected genomic sites to a stochastic
flux of de novo methylation. Cell type–specific average methylation pat
terns can then emerge when tissuespecific chromatin configurations
render loci differentially resistant to methylation. The stochastic nature
of the process, with a relatively wellorganized germline as its starting
point, may result in a progressive increase in methylation heterogeneity
during development. A variation on this model posits that average
methylation is determined by the balance between methylation and
demethylation at a given locus. According to this scenario, resistance
to methylation may be achieved in regions that purge aberrant methy
lation more efficiently51, and increases in average methylation result
from local shifts in the balance between methylation gain and loss. This
equilibrium model may also explain cases of slow decrease in average
DNA methylation in regions that develop a more resistant chromatin
context. Further highresolution exploration of epipolymorphism will
lead to significant advances in the understanding of the functional role
of normal epigenetic programs and the consequences of their aber
ration, providing a muchneeded perspective into the emergence of
heterogeneous cell populations in normal tissues and tumors.
URLs. Deep bisulfite sequencing data, http://compgenomics.
weizmann.ac.il/tanay/?page_id=99; CpG island classifications, http://
compgenomics.weizmann.ac.il/tanay/?page_id=196; dChip, http://
Methods and any associated references are available in the online
version of the paper.
Accession codes. RRBS data are available at the Gene Expression
Omnibus (GEO) under accession GSE27584. Samples used included
BC_Adrenal_Gland_H12803N, BC_Brain_H11058N, BC_Breast_
0203015, BC_Kidney_0111002, BC_Left_Ventricle_N41, BC_
Leukocyte_UHN00204, BC_Liver_0111002, BC_Lung_0111002,
RUNX3 passage 19 RUNX3 passage A58RUNX3 passage A95
K27 ChIP
010025 50 75
00.50 0.25
pairs of CpGs
H19 DMR passage 19
GNAS XL passage 19
H19 DMR passage 19GNAS XL passage 19
K27 ChIP
Difference in 5mC (A95-19)
Stable H3K4me3
Loss of H3K4me3
Stable CTCF binding
Loss of CTCF binding
Difference in 5mC (A95-19)
–5 15
Stable H3K27me3
Loss of H3K27me3
Difference in 5mC (A95-19)
–5 15
Passage 19
DLK-DIO3 intergenic
020 40 60 80 100
Percent 5mC (input)
Percent 5mC
(H3K27me3 ChIP)
Percent 5mC (input)
Percent 5mC
(H3K27me3 ChIP)
020 40 60 80 100
Passage A95
Figure 6 H3K4me3, CTCF binding and H3K27me3 are anticorrelated
with DNA methylation kinetics. (a) Distributions of methylation differences
at sites with stable H3K4me3 enrichment and sites that lost H3K4me3.
(b) Distributions of methylation differences at CTCF-binding sites that
maintained or lost CTCF binding relative to H1 ESCs22. (c) Distributions
of methylation difference at sites with stable H3K27me3 enrichment and
sites with decreased H3K27me3. (d) H3K27me3 ChIP–deep bisulfite
sequencing in fibroblasts from early and late passages. Shown are the
average methylation levels (over all CpGs in a given region) for all assayed
regions in input DNA (x axis) and H3K27me3-enriched DNA (y axis).
Imprinting control regions are labeled for passage 19 (additional data
appear in supplementary Fig. 19b). (e) High-resolution methylation profiles
and methylation linkage diagrams for two imprinting control regions (H19
DMR and GNAS XL), derived from input DNA (top) and H3K27me3
ChIP (bottom). (f) High-resolution methylation profiles and methylation
linkage diagrams for the RUNX3 region at three passages.
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.
Page 8
VOLUME 44 | NUMBER 11 | NOVEMBER 2012 Nature GeNetics
BC_Pancreas_H12817N, BC_Pericardium_H12529N, BC_Placenta_
UHN00189, BC_Skeletal_Muscle_0111002, BC_Skeletal_Muscle_
H12817N, BC_Skin_0111002, BC_Testis_N30, BC_Uterus_BN0765,
H1hESC, Caco2, HSMM, Hepatocytes, IMR90, K562, LNCaP,
Melano, Osteobl and PrEC. BSPP data are available at the SRA under
accession SRA036589. MeDIPseq, ChIPseq and gene expression
array data are available at GEO under accession GSE41050. Deep
bisulfite data are available from the Tanay group website (see URLs).
Note: Supplementary information is available in the online version of the paper.
We thank the members of the Tanay group for discussions. Research in the
Tanay laboratory was supported by grants from the Israel Science Foundation
(grant 1372/08), the European Commission Framework Programme 7 Network
of Excellence EPIGENESYS and the BLUEPRINT European Union consortium.
Research in the Rotter laboratory was supported by a Center of Excellence grant
from the Flight Attendant Medical Research Institute (FAMRI). E.N.G.Y. is a
fellow of the Talpiot Medical Leadership Program at the Sheba Medical Center.
AUTHoR CoNTRIBUTIoNS
G.L., E.N.G.Y., V.R. and A.T. designed the experiments. G.L. and Z.M. performed
bisulfite and immunoprecipitation experiments with help from E.N.G.Y. and A.Z.
G.L., N.M.C., A.B. and A.T. analyzed the data. G.L. extracted RNA and conducted
tissue culture with help from A.M., R.B. and N.G. S.H.S. and D.A.Z. performed
highthroughput sequencing and assisted with protocol adaptations. G.L. and A.T.
wrote the manuscript.
CoMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests.
Published online at http://www.nature.com/doifinder/10.1038/ng.2442.
Reprints and permissions information is available online at http://www.nature.com/
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Nature GeNetics
Analysis of RRBS data. RRBS data were downloaded from the ENCODE USCS
genome browser. We used raw sequence data from 16 tissues in duplicate and
additional cell line data for the control (Supplementary Table 1). Sequences
were mapped to the bisulfiteconverted human genome (NCBI hg18) using
Bismark52, after trimming the first four nucleotides, using the following
parameters: −directional–phred64–quals −n 1 −l 20 −e 140. Individual CpG
methylation was called for each read, and the first four CpGs were retained.
We discarded CpG sets with low coverage (<40×) or sufficient coverage in
fewer than five tissues. We note that the sets of four CpGs were estimated from
36bp tags, making them densely clustered and increasing the chance that
they share genomic context and resistance mechanisms. Longer tags are likely
to generate more cases with larger distances between adjacent CpGs, which
may be under distinctly different control mechanisms (for example, the first
CpG may be methylated, whereas the others are not), resulting in more cases
with intermediate average methylation (for example, 1 out of 4) with low epi
polymorphism (down to 0). Note that few such cases were already observed.
Analysis of BSPP data. BSPP data were obtained from the Sequence Read
Archive (SRA; SRA036589). The data included matched normaltumor pairs
from three individuals with colorectal cancer. Data were converted to FASTQ
format using the SRA Toolkit and mapped to the hg18 reference genome
using Bismark. Sets of four CpGs were obtained as above, requiring no over
lap between sets.
Methylation average and epipolymorphism. The methylation content of
a bisulfite read of four CpGs is defined as the fraction of methylated CpGs
(0, 0.25, 0.5, 0.75 or 1). We defined the average methylation of a fourCpG
locus as the average methylation content of all reads mapped to that locus.
The distribution of methylation content for each locus can be studied in detail
(Fig. 2c). Hyper and hypomethylated loci in tissues were extracted by compar
ing tissue methylation to a baseline defined by the H1 ESC duplicate average
(requiring ≥20% increase or decrease in average methylation, respectively).
Hyper and hypomethylated loci in cancer were defined as loci for which the
difference in average methylation between a tumor sample and its matched
normal sample was ≥20%. Stably methylated loci were defined as those for
which the change in methylation was <20%.
Epipolymorphism was calculated as ppoly = 1 − Σ(pi2), where pi is the fre
quency of epiallele i in the population (with 16 potential epialleles representing
all possible methylation states of the set of four CpGs). Median polymorphism
trends were calculated for each tissue using binned average methylation.
The median trend for all tissues was computed using the median of epipoly
morphism averages in each average methylation bin across the 32 analyzed tissue
samples. Maximum polymorphism was modeled as 1 – (1 – 2(m(1 – m)))4,
where m is the average methylation. Bimodal epipolymorphism, defined as
the epipolymorphism obtained where a locus comprised only fully methyl
ated and fully unmethylated alleles (Fig. 1a), was modeled as 2m(1 –m). To
analyze the subpopulation of unprotected epialleles (Fig. 2d), we recomputed
the methylation average and epipolymorphism while only using data for the
15 unprotected epialleles.
Frequency of disordered epialleles. Disordered loci (loci within the black
border in Fig. 1c and Supplementary Fig. 3) were defined as those that
were somewhat but not fully methylated (10% < fraction fully methylated
alleles < 50%), somewhat but not fully unmethylated (10% < fraction fully
unmethylated alleles < 50%) and not highly bimodal (fully methylated
+ fully unmethylated < 80%). These loci represent those with high epipoly
morphism (scatter plots in Supplementary Fig. 3). To quantify and visualize
the frequency of disordered epialleles in each of the 32 tissue samples, we
converted the percentage of disordered epialleles in each sample to a Z score
representing the number of standard deviations of each sample from the
mean of the 32 samples.
Cell culture. WI38 primary human embryonic lung fibroblasts were grown
in MEM supplemented with 10% FCS, 1 mM sodium pyruvate, 2 mM
lglutamine and antibiotics (Biological Industries), as previously described25.
Amphotropic Phoenix retrovirusproducing cells were grown in DMEM
supplemented with 10% FCS, 2 mM lglutamine and antibiotics. Cells were
split weekly by incubation with trypsin and were replated into a new plate at
a cell density of 1,275 cells/cm2. Retroviral infection with viruses encoding
human TERT (coding for telomerase reverse transcriptase) was performed as
previously described25.
RNA preparation and microarray processing and analysis. RNA was
extracted with TRI reagent (Molecular Research Center), according to the
manufacturer's instructions, in duplicate for each sample. Processing and
analysis were performed as previously described53. Briefly, purified RNA was
reverse transcribed, amplified and labeled with the Affymetrix GeneChip
WholeTranscript Sense Target Labeling kit. Labeled cDNA was analyzed
using Affymetrix human Gene ST 1.0 microarrays, according to the manu
facturer's instructions. Microarrays were scanned using the GeneChip
scanner 3000 7G.
Sequence analysis and classes of CpG islands. Genomic CpG content was
estimated by computing the linearly weighted number of CpGs within a win
dow of 800 bp. CpGrich regions were defined on the basis of CpG content
alone. Two classes of CpGrich elements were used on the basis of evolu
tionary characterization and are available from the Tanay group website
(see URLs)28. The first class includes CpGrich regions that exhibit low levels
of CtoT deamination in primates and correspond to the classical definition
of CpG islands (denoted as hypodeaminated CpG islands). The second class
includes CpGrich regions that are observed in sequences that are subject to
an asymmetric evolutionary process enriching for G or C nucleotides (a pro
cess attributed to biased gene conversion). This class is typically constitutively
methylated and is useful for normalization purposes.
Gene expression analysis. Microarray data were log2 transformed and normali
zed using dChip modelbased expression (5th percentile; one of the duplicate
arrays for passage 19 serving as baseline), and data from duplicates were aver
aged for each sample. For clustering purposes, data were further normalized
by meancentering each sample and then meancentering each gene. kmeans
clustering (Supplementary Fig. 6) was performed in R54 using the Euclidean
distance metric and k = 10. Downregulated genes were defined as those with
at least twofold downregulation in line A. Constitutively active genes were
defined as those in the top 20% of expressed genes at all time points, and
constitutively silent genes were defined as those in the bottom 20% at all
time points.
MeDIP-seq processing. MeDIPseq reads were mapped to the genome (hg18)
using the Illumina pipeline. Uniquely mappable reads were extended to 180 bp,
and raw coverage profiles for genomic bins of 50 bp were obtained. Coverage
trends were then generated for each condition by computing the distribution
of MeDIPseq coverage in groups of loci with a fixed range of CpG content
(calculated in 800bp linearly weighted windows, using bins of 0.5% CpG
content). Coverage distributions were computed while excluding regions that
were not uniquely mappable and those that were identified as hypodeaminated
CpG islands. The 75th quantile of the coverage distributions for each condi
tion was defined as that condition's master trend (Supplementary Fig. 7a).
Data from condition A95 were used as a reference master trend. Normalized
methylation profiles were generated by multiplying raw coverage values for a
50bp genomic bin by the ratio between the condition and reference master
trends at the appropriate CpG content bin (Supplementary Fig. 7b).
ChIP-seq processing. Mapping and generation of raw coverage profiles
from ChIPseq reads were performed as for MeDIPseq. Genomic regions
that were not uniquely mappable were excluded from further analysis.
To enable robust comparison of H3K27me3 modification under different con
ditions, we smoothed the coverage profiles using 1,000bp linearly weighted
windows and computed the distribution of smoothed coverage across the
entire genome. The normalized H3K27me3 level was then defined as −log2
(1 – percentile(smoothed coverage)). This normalization corrected for variable
sequencing depth and accounted for some of the unavoidable differences in
ChIP efficiency between conditions. H3K4me3 and CTCF data are inherently
less broad than those for H3K27me3 (consisting of defined peaks), simplifying
© 2012 Nature America, Inc. All rights reserved. © 2012 Nature America, Inc. All rights reserved.
Page 10
Nature GeNetics
comparison between conditions. Therefore, 250bp smoothed H3K4me3 and
raw CTCF data were simply scaled by total reads to adjust for variable sequenc
ing depth. Stable H3K4me3 marks (Fig. 6a) were defined as sites in the top 3%
of those enriched in cells from passages 19 and A95 with less than 5% change;
loss of H3K4me3 was defined as sites in the top 3% in passage 19 and the bot
tom 80% in passage A95. Stable CTCF occupancy (Fig. 6b) was defined as the
bottom 10% of sites showing decreased binding between H1 ESCs and passage
A95; loss of CTCF was defined as the top 33% of sites showing decreased
binding. Stable H3K27me3 marks (Fig. 6c) were defined as sites in the top
3% of those enriched in cells from passages 19, A39 and A95 with less than
5% change; loss of H3K27me3 was defined as sites in the top 3% in passages
19 and A39 and the bottom 80% in passage A95.
TSS analysis. We defined the TSS region as the sequence between −350 and
+50 bp relative to the start site. Linearly weighted averagenormalized MeDIP
seq coverage was used as an indication of TSS methylation.
Repeat element bisulfite analysis. We screened 100bp deep bisulfite sequenc
ing reads for the presence of a precise prefix with the LINE1 or αsatellite
primer sequences (Supplementary Table 3). Read suffixes were extracted, and
the numbers of CpGs and TpGs in forward primer suffixes (CpGs and CpAs
in reverse primer suffixes) were counted. All CpGs covered by over 100 reads
were considered, and the ratio between CpGs and TpGs and/or CpAs was used
as an indication of the level of methylation at the locus. This approach could
potentially be biased by polymorphic CpG and/or TpG repeat sequences, as
not all TpGs necessarily represent bisulfiteconverted unmethylated CpGs,
resulting in underestimation of the methylation level. However, this bias is
unlikely to affect the comparison between early and late passages, which was
the sole purpose of this analysis.
Deep bisulfite analysis. We pooled all partial methylation profiles cover
ing a given amplicon and extracted three statistics: the average singleCpG
methylation, the distribution of methylation patterns for contiguous sets of
four/six CpGs and the methylation correlation between pairs of CpGs (com
puted for all pairs that were covered by at least 100 reads). We compared statis
tics between replicate experiments to verify that stochastic amplification biases
had a limited effect on the data (Supplementary Fig. 11b). For ChIP–deep
bisulfite sequencing, analysis was performed separately for K27me3bound and
input samples. Epipolymorphism was calculated as for RRBS and BSPP data.
To characterize singleCpG kinetics, we clustered replicateaveraged temporal
methylation profiles for single CpGs using a standard kmeans algorithm.
To generate methylation linkage diagrams, we employed standard correla
tion analysis of the methylation profiles of each pair of CpGs (in a manner
analogous to the computation of genetic linkage between SNPs). Specifically,
we counted the number of reads with common methylation (n11), common
lack of methylation (n00) and different methylation states (n01 and n10) for
each pair of CpGs. We ignored reads in which data for one of the methyla
tion states were not available. We then computed the average methylation
for each CpG (m1 = (n11 + n10)/N, m2 = (n11 + n01)/N, where N is the total
number of reads) and the variance of methylation for each CpG (v1 = m1 ×
(1 – m1), v2 = m2 × (1 – m2)). The correlation was then defined as (n11/N – m1
× m2)/sqrt(v1 × v2). Note that correlation values are subject to considerable
noise when coverage is low or when m1 or m2 are close to 0 or 1. Methylation
linkage data are therefore interpreted over groups of spatially coupled CpGs
rather than for specific pairs.
Details of the protocols for MeDIPseq, ChIPseq, deep bisulfite sequenc
ing and ChIP–deep bisulfite sequencing, as well as algorithmic details
for deep bisulfite mapping and pattern clustering, can be found in the
Supplementary Note.
52. Krueger, F. & Andrews, S.R. Bismark: a flexible aligner and methylation caller for
Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).
53. Pencovich, N., Jaschek, R., Tanay, A. & Groner, Y. Dynamic combinatorial interactions
of RUNX1 and cooperating partners regulates megakaryocytic differentiation in cell
line models. Blood 117, e1–e14 (2011).
54. R Development Core Team. R: A Language and Environment for Statistical Computing
(R Foundation for Statistical Computing, Vienna, 2009).
npg npg
© 2012 Nature America, Inc. All rights reserved.© 2012 Nature America, Inc. All rights reserved.


text:
Blue Mountains, Sydney Australia - Sunday, 1 March - 5:51pm
Stay connected with Fresh Air Daily »
Family Accommodation
Looking for child friendly accommodation in the Blue Mountains? Here is a list of family accommodation venues in the Blue Mountains region that happily accommodate children of all ages.
Family Accommodation Menu:
See also:
Have You Visited
Segway Blue Mountains
Segway Blue Mountains
Blue Mountains Mystery Tours
Ghost tours and sightseeing tours
Melaleuca Mountain Chalets
Luxury self contained accommodation Oberon
Jenolan Caves
Dinner at Chisolms - Edwardian ambiance, upstairs in Caves House.
Eagle View Escape
Breathtaking views and experiences
Lilianfels Blue Mountains
Warm personalised service
Blue Mountains Information
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- Transport
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subset: commoncrawl


text:
(1 year ago)
My daughter has attended this school from kindergarten through 5th grade and I have been EXTREMELY satisfied with all of her teachers. MACS teachers make learning engaging and staff are willing to do whatever it takes to help struggling students. MACS involves the parents and the community in education. This school has great school lunches helpful staff and teachers that are always willing to talk to parents about any questions or concerns they may have. I will miss MACS next year as my daughter advances to middle school and I would highly recommend it to another parent.


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I found this fake pear at Hobby Lobby with a bite in it... (imgur.com)
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[–]CornFedHonky 1 point2 points3 points 1 year ago (0 children)
lol thanks. I've since been informed that I also religiously get high and pass out ... so that's probably how I missed it.
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Gator Creek Films Plus
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Life Care Physician Services, LLC, a subsidiary of Life Care Centers of America, has placed Amanda Winters, D.O., as on-site physician at Life Care Center of Hixson.
Dr. Winters was hired as part of Life Care Centers of America's initiative to place physicians in each of its buildings in Tennessee.
"Some of my earliest memories are of visits to nursing homes with my father, Dr. David Winters, a medical director at several facilities," said Dr. Winters. "He would take us children and let us visit with residents while he did his work. Since completing my medical education, I have enjoyed moonlighting at the nursing facilities with my dad over the past two years. I have come to believe that it is the right fit for me, and I am excited for this ministry to be my career."
Dr. Winters will work with the Life Care Center of Hixson's interdisciplinary team, facility medical director and other attending physicians in caring for each patient. Her presence in the nursing home will enhance physician accessibility to patients and families. She will aid in hospital transitions and pharmacy communications, and being on-site will allow her to personally respond to medical changes or emergencies in a timely manner.
Dr. Winters most recently worked as a physician in internal medicine for Michael Goodman, M.D., P.C., in Chattanooga. She has also assisted her father in serving patients at Life Care Center of Hixson, Life Care Center of Collegedale and Life Care Center of East Ridge.
Board certified in internal medicine, Dr. Winters is a member of the American Osteopathic Association, the American College of Physicians and the American College of Osteopathic Internists.
Dr. Winters earned her doctor of osteopathy degree from Pikeville College of Osteopathic Medicine in Pikeville, Ky. She earned her bachelor's degree in biology from Southern Adventist University in Collegedale.
She currently resides in Chattanooga.
February 26, 2015
February 23, 2015
Winter weather across the region is hindering blood donors from volunteering to give blood donations at Blood Assurance. Today, the donor center located inside the Baroness Erlanger campus will ... (click for more)
Representatives from Children's Hospital Foundation accepted a $10,000 check from BlueCross BlueShield of Tennessee for proceeds raised during the 2014 BlueCross Riverbend Run & ... (click for more)
Tennessee Oncology, Bradley County's only cancer center, relocated Monday in Cleveland to a facility near SkyRidge Medical Center at 2390 N. Ocoee St. "We are excited to offer Cleveland ... (click for more)
Winter weather across the region is hindering blood donors from volunteering to give blood donations at Blood Assurance. Today, the donor center located inside the Baroness Erlanger campus will be closed. Johnson City and Abingdon locations open at 9 a.m. Bristol and Kingsport donor centers open at 10 a.m. All other Blood Assurance donor centers will open at noon. Because ... (click for more)
Representatives from Children's Hospital Foundation accepted a $10,000 check from BlueCross BlueShield of Tennessee for proceeds raised during the 2014 BlueCross Riverbend Run & Walk . "We are so grateful for our partnership with BlueCross and the generosity of so many people who participated in last year's event," said Julie Taylor, Erlanger's chief ... (click for more)
The Federal Communications Commission voted 3-2 on Thursday to allow EPB to expand its broadband offerings to neighboring communities. Chairman Tom Wheeler and two other Democrats were in favor, while the two Republican members said the agency was acting illegally in overriding state law. EPB's phone, Internet and TV offerings have been far more successful than expected and ... (click for more)
Due to remaining snow on some of the roadways and the potential for black ice overnight and into the morning, Hamilton County Schools will be closed on Friday . School Age Child Care will also be closed. Employees who accrue vacation will report by 10 a.m. and maintenance employees will report on regular schedule. All classes are being ... (click for more)
Newly elected Senator Lee Harris (D-Memphis) and I would seem like total polar opposites politically. He's from the big city of Memphis; I'm from a small town in rural Tennessee; He's a Democrat, I'm a Republican; He's black, I'm white; He's in the Senate, I'm in the House. Unfortunately, many would draw a seemingly logical conclusion that we don't like each other-- that we would ... (click for more)
Roger Dean Kiser is something of a legend. Aside from being one of the best story-tellers I know of – I've read his works for years – the fact he came upon it honest, like Chattanooga's Roger Allan Wade, makes him kind of special. Again, I've followed him for a long time. By the time he was four, he had been abandoned by parents and grandparents alike. The state of Florida placed ... (click for more)


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How many and what kinds to plant in Fort Worth?
We are building a new home in Fort Worth and will be closing on it in February (hopefully!). We'll have a standard lot, and I believe it's 55 feet wide and will be sloped at a negative grade in the back, but will level toward the house, of course.
I've been thinking about planting some fruit trees in the backyard. The one I am sure on is a FIG tree as I love them and they're so hard to find at the store... My grandma had a few on her property growing up and they're such a delicious treat.
The others I am considering are pear, persimmon, pomegranate... I know that each variety is different, but I'd like to be able to get self-fruiting varieties for whatever I choose. How many are too many? What kind of a layout should I consider? How far from the house and how far from the fence do I need to plant?
I would love some tips and advice. :) Thanks so much! I'm very eager and excited.
I live in Wisconsin, but I have relatives in Fort Worth, so I know something about the weather & climate. There is a pretty good chance of late frost, after flowers emerge in the spring, so peaches are iffy, at best. Pecans thrive in Fort Worth. I know from experience that blueberry shrubs can survive a late frost, and still produce a good harvest in the summer.
If you have a spot with 7 or more hours of sun, I�d plant a Celeste fig. They like a fair amount of water, & you�ll need to use netting to keep the birds away, but they�re really easy to grow.
Persimmons do well, are 0 maintenance, & are self fertile; so you only need 1. They need 6+ hours of sun. I�d plant a Fuyu for an eat while hard type; for a eat while soft type, my favorites are 1: giombo, 2: eureka, 3: hichaya.
For 5+ hours of sun, I�d go with thornless blackberries.
A Keiffer pear would do well, I really like them, but many people don�t. Im getting mixed results so far with my Asian pears. Hosui & 20th century are usually recommended.
I really like my tomcot apricot; I have to spray once or twice; its self fertile, one will do
Peaches are doable, but are a lot of work. Apples are a challenge, but are also doable. Jujube�s do well but they have an invasive root system, I�d keep them 30-40 ft away from flowerbeds & foundations. My persimmons have never been as good as store bought ones; but you have different soil so they might work better for you; sweet &desertnyi, show the most promise for me; texas a&m recommends wonderfull; eversweet does well but seems bland to me; Poms are a pretty bush in any event
My favorite nursery is Womack�s. Doan's in Irving is pretty good too. Beware of nurseries that don�t tell you the rootstock, those plants often are on rootstocks that don�t do well in Texas.
Some interesting reads can be found by googling "neil sperry tamu radio fruit north texas", and "dallas fruit grower".
If you haven�t bought by next fall, you might want to visit a pick your own place in Mexia. Lightsey�s farm; you could taste what you�ll eventually be harvesting in 3-4 years; they have most everything I�ve mentioned


text:
Dr. Kariampuzha works in Tyler, TX and specializes in Endocrinology, Diabetes & Metabolism and Internal Medicine. Dr. Kariampuzha is affiliated with Mother Francis Hospital. Dr. Kariampuzha speaks English and Spanish.
Gender: Not Available
Languages Spoken: English, Spanish
Endocrinology, Diabetes & Metabolism, Internal Medicine
Medical School: Not Available
619 S Fleishel Ave Ste 327
Tyler, TX 75701
(903) 510-1173


text:
Basix Medicine Cabinet in Frameless Bevel Finish (Large)
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Adjustable glass shelves. No. of shelves:. Small: 2. Medium: 3. Large: 3. Extra Large: 3. No unsightly hinges. Single door recessed. 130°+ Door swing opening
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Small: 14 in. W x 18 in. H. Medium: 14 in. W x 24 in. H. Large: 18 in. W x 24 in. H. Extra Large: 22 in. W x 28 in. H. Option surface mount kit:. Small:
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Kloster Basilika Birnau
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