Abstract
DNA methylation undergoes dramatic age-related changes, first described
more than four decades ago. Loss of DNA methylation within partially
methylated domains (PMDs), late-replicating regions of the genome
attached to the nuclear lamina, advances with age in normal tissues,
and is further exacerbated in cancer. We present here experimental
evidence that this DNA hypomethylation is directly driven by
proliferation-associated DNA replication. Within PMDs, loss of DNA
methylation at low-density CpGs in A:T-rich immediate context (PMD
solo-WCGWs) tracks cumulative population doublings in primary cell
culture. Cell cycle deceleration results in a proportional decrease in
the rate of DNA hypomethylation. Blocking DNA replication via Mitomycin
C treatment halts methylation loss. Loss of methylation continues
unabated after TERT immortalization until finally reaching a severely
hypomethylated equilibrium. Ambient oxygen culture conditions increases
the rate of methylation loss compared to low-oxygen conditions,
suggesting that some methylation loss may occur during unscheduled,
oxidative damage repair-associated DNA synthesis. Finally, we present
and validate a model to estimate the relative cumulative replicative
histories of human cells, which we call “RepliTali” (Replication Times
Accumulated in Lifetime).
Subject terms: DNA methylation, DNA replication, DNA synthesis
__________________________________________________________________
DNA methylation loss has been observed in aging tissues and cancers for
decades. Researchers from Van Andel Institute have now provided
experimental evidence that this process is directly driven by cell
division.
Introduction
Age-associated DNA hypomethylation^[30]1–[31]4 is associated with
several intertwined spatio-temporal features. DNA methylation loss
occurs primarily within PMDs, which largely coincide with late
replication timing domains^[32]5–[33]11, are enriched in higher order
chromatin compartment B^[34]12, and tend to be associated with the
nuclear lamina^[35]7. Cancer-associated DNA methylation
loss^[36]6,[37]7,[38]13 is accompanied by changes in replication timing
and 3D genome organization^[39]14. Replicative senescence alters 3D
genome compartmentalization^[40]15–[41]17. Replication timing, altered
in both cancer and aging-associated diseases including
progeria^[42]18–[43]20, is purported to maintain the
epigenome^[44]21,[45]22, although this relationship may be
bidirectional^[46]23.
Epigenetic ‘clocks’—models trained upon large DNA methylation datasets
to predict either chronological age^[47]24–[48]26 or features of
biological aging^[49]27,[50]28—have emerged as powerful tools in aging
research in recent years, facilitated by the affordability of DNA
methylation microarrays and the subsequent availability of increasingly
large publicly available datasets. DNA methylation clocks have far
outperformed other metrics of biological age, such as telomere length
and transcriptional signatures. Although much focus is on the
epigenetic age acceleration that is observed with a multitude of
diseases^[51]28,[52]29, and the slowing or reversal of epigenetic
age^[53]30, recent clock iterations have the intriguing ability to
estimate chronological age across mammalian species^[54]31,[55]32,
likely detecting conserved features of aging. Although there have been
recent attempts to retroactively classify underlying clock
mechanisms^[56]33, a major limitation to the interpretation of clock
results is the lack of understanding of what drives the methylation
behaviors of each clock’s CpGs. Whether the age-associated changes in
DNA methylation actively contribute to aging, or are merely passenger
events, remains largely unknown.
By their nature, chronological methylation clocks are not mitotic
clocks. The various tissues within an organism have the same
chronological age, but are comprised of cell types with different
proliferation rates and replicative histories^[57]34. DNA methylation
clocks calibrated to organismal age therefore need to be impervious to
cell type composition differences. This eliminates DNA methylation
changes that directly reflect ongoing or past cell division from most
epigenetic clocks trained to chronological age using multiple tissues.
The process of cell division requires the passage of chronological
time, but the two can be unlinked since time can pass without cell
division, such as in post-mitotic cells.
It is important to distinguish between replicative history and
proliferation rate. Replicative history refers to the cumulative number
of cell divisions within a single cell’s lineage. Proliferation rate
refers to the number of divisions per unit of time, usually as a
current, ongoing measure. In the greater context of biological aging,
three of nine ‘Hallmarks of Aging’ are attributable, in great part, to
cumulative cell divisions: telomere attrition, stem cell exhaustion,
and cellular senescence^[58]5. Therefore, replicative history is
closely tied to biological age and thus an important feature to measure
independent of biological measures of chronological time. In light of
this, the term ‘clock’ is a misnomer for estimates of cumulative cell
divisions. A ‘counter’, ‘enumerator’ or ‘tally’ would more accurately
capture the nature of cell division. However, the term ‘epigenetic
mitotic clock’ has become cemented into the existing literature for
proposed DNA methylation-based measures of cell division^[59]33,[60]35.
We have previously identified a hypomethylation-prone sequence
signature, PMD solo-WCGW, representing PMD CpG dinucleotides
immediately flanked by an adenine or thymine (‘W’) and located at least
35 bp away from the nearest CpG (‘solo’)^[61]36 (Fig. [62]1b). PMD
solo-WCGW hypomethylation appears to correspond to the approximate
replicative history of various tissue types and malignancies, and we
hypothesized that this could be attributed to incomplete maintenance
methylation at each cell division^[63]6. Subsequent analyses by other
groups confirmed that methylation at PMD solo-WCGWs is indeed
maintained poorly relative to other sequence contexts^[64]37. However,
there has been little direct experimental evidence to establish a
causal or mechanistic link between replicative history and PMD
hypomethylation, and this interpretation has been challenged by others
in the field^[65]38,[66]39.
Fig. 1. Methylation loss at PMD solo-WCGWs is driven by
proliferation-associated DNA replication.
[67]Fig. 1
[68]Open in a new tab
a Schematic illustration of primary cells cultured through replicative
senescence. b Illustration of immediate (≤35 bp) CpG contexts
investigated in this study. c Fractional methylation change per
population doubling (PD) for neonatal foreskin fibroblast AG21839 at
select CpG contexts, within and outside of common partially methylated
domain (PMD) boundaries. HMD: highly methylated domains. CpGs within
CGIs were excluded. d Median fractional methylation of multiple primary
cells derived from unique donors and tissues (n = 7) plotted against
PDs achieved during this study. e DNA methylation heatmap of PMD
solo-WCGWs during primary cell culture of neonatal foreskin fibroblast
AG21859. Heatmap is separated by parallel subculture, with samples
ordered from passage 1 through replicative senescence. f–h Primary
fetal skin fibroblast (AG06561) grown in media containing different %
v/v fetal bovine serum loses PMD solo-WCGW methylation as a function of
proliferative rate. i–k Primary cells (n = 3) transiently treated with
DNA crosslinking agent Mitomycin C for 3 h, followed by drug-free
culture for 25 days, resulting in the inhibition of DNA synthesis and
subsequent growth arrest (i), have stable PMD solo-WCGW methylation.
PMD solo-WCGW methylation is tightly correlated to PDs (j),
independently of time (k). Solid lines depict linear regression with
gray shading depicting 95% confidence interval; statistical analyses
are two-sided. Statistical comparisons for panels h and k were
performed using mixed effects modeling; p-values adjusted (Tukey) for
multiple comparisons presented in panel h.
Here, we show experimental evidence that hypomethylation within PMDs is
driven by cell division. Erosion of PMD methylation at the most
hypomethylation-prone sequence context, solo-WCGW, occurs progressively
in each cell type studied and continues after immortalization until
equilibrium is reached at a very low DNA methylation level. We further
characterize the roles of gene expression and replication timing in PMD
methylation maintenance. Finally, we present a model, RepliTali,
trained on cultured primary cells, to infer replicative history.
Results
Context-dependent methylation change in response to cell divisions
We used serial primary human cell cultures to closely track the in
vitro replication of cell populations. Primary human cells (n = 7,
Supplementary Data [69]1) were obtained from the NIA Aging Cell Culture
Repository Apparently Healthy Collection, at the Coriell Institute for
Medical Research, and cultured under recommended conditions with
multiple parallel subcultures originating from the same initiating
cells (Fig. [70]1a) through replicative senescence, tracking cumulative
cell divisions (population doublings, PDs) at each passage (methods).
At each passaging, a fraction of cells was retained for DNA methylation
analysis using the Infinium MethylationEPIC array (Illumina).
Analysis of DNA methylation revealed divergent behavior between non-CGI
CpGs within different contexts: CpGs located in PMDs progressively lost
methylation, and CpGs located outside PMD boundaries experienced either
a slight gain of methylation if they were located near other CpGs
(‘social’), or a slight loss of methylation if they were isolated
‘solo’ CpGs (Fig. [71]1c). For CpGs within PMDs, the rate of
hypomethylation appears influenced by immediate context, again with
‘solo’ CpGs losing methylation more rapidly than ‘social’ CpGs, and
specifically with solo-WCGWs experiencing the most dramatic methylation
loss, which is consistent with previous cross-sectional static
characterizations in tissues.
We investigated whether PD-dependent PMD solo-WCGW hypomethylation
occurs in different cellular contexts. We observed that across a range
of primary human cell types from different developmental stages, the
median methylation of PMD solo-WCGWs is tightly anticorrelated with PDs
(Fig. [72]1d). The starting median methylation varies across the
primary cells, suggesting that the tissues from which these cells were
derived have distinct replicative histories—an observation consistent
with the variation in donor age and source tissue. In addition, the
rates of global PMD solo-WCGW methylation loss vary between cell types,
perhaps reflecting different landscapes of CpG behavior. The pattern of
methylation loss at individual PMD solo-WCGWs was reproducible between
biological replicates (Fig. [73]1e).
PMD solo-WCGW methylation loss is driven by proliferation-associated DNA
replication
Elapsed time is linearly correlated with PDs until near-senescence for
each primary cell culture with a constant rate of cell division
(Supplementary Fig. [74]1a). As a result, methylation at PMD solo-WCGWs
also correlates strongly with time (Supplementary Fig. [75]1b).
Therefore, the serial passage by itself cannot distinguish between
time-dependent loss of DNA methylation versus hypomethylation driven by
cell division. To determine whether PMD solo-WCGW methylation loss is
driven by cell division, or merely ensues with the passage of time, we
cultured primary human fibroblasts with media containing decreasing
concentrations of fetal bovine serum to impose different proliferation
rates. We found that decreased rates of cell division by serum
deprivation caused a dose-dependent reduction in DNA methylation loss,
consistent with proliferation-associated loss of PMD solo-WCGW
methylation (Fig. [76]1f–h). We have previously hypothesized that PMD
solo-WCGW methylation loss is driven by incomplete maintenance
methylation. Evidence from other groups has found that the solo-WCGW
context is maintained inefficiently, although replication-uncoupled
methylation was able to compensate somewhat, at least for a single cell
cycle^[77]37. To test whether methylation loss is indeed driven by
proliferation-associated DNA synthesis, we transiently treated several
primary cells (n = 3) for 3 h either with mitomycin C (MMC), a DNA
replication inhibitor that can achieve full permanent cell cycle
arrest, or with vehicle control, and maintained the cells for several
weeks free of drug (Fig. [78]1i–k). Two of three primary cells did not
lose significant PMD solo-WCGW methylation upon DNA synthesis arrest
via MMC (one-sided t-test of logit-transformed beta values: AG11182:
p-val 0.28, AG11546: p-val 0.60, AG16146: p-val 1.2e−4). Interestingly,
MMC-treated adult fibroblast AG16146 did lose a statistically
significant amount of methylation at PMD solo-WCGWs, albeit roughly 5x
less than the control condition, indicating that these cells may have
somewhat higher tolerance for MMC (Fig. [79]1i, k). Untreated, freely
proliferating cells all experienced significant methylation loss (p-val
<2.2e−16 for each cell) albeit at different levels (change in
fractional methylation from pre-treatment 0.048 AG16146, 0.028 AG11182,
0.03 AG11546), again suggesting that these primary cells may have
unequal susceptibility to MMC. Despite this, these experiments clearly
show that PMD solo-WCGW methylation is lost as a function of cellular
proliferation. Importantly, MMC treatment may have effects beyond the
blockade of DNA synthesis. However, our results, plus previous
mechanistic studies^[80]37, strongly indicate that progressive
methylation loss at PMD solo-WCGWs is caused directly by a failure of
maintenance re-methylation.
Taken together, these results present experimental evidence of a direct
causal relationship between proliferation-associated DNA synthesis and
PMD solo-WCGW hypomethylation.
Factors driving CpG methylation trajectories in primary and immortalized
cells
We investigated factors that could influence the varied rates of
methylation loss among CpGs and between primary cell types. Despite the
similar profiles of median PMD solo-WCGW methylation loss, we observed
subtle cell-type differences at individual CpGs (Supplementary
Fig. [81]2). To explore the possibility that differential expression of
maintenance methylation machinery, de novo methyltransferases, or TET
enzymes may underpin cell type differences and/or the overall
methylation loss, we conducted time-series RNA-seq of our cultured
primary cells. PCNA-normalized expression patterns were inconsistent
between primary cells and did not clearly accompany the progressive
methylation loss we observed in all cultured primary cells
(Supplementary Figs. [82]3, [83]4).
PMD solo-WCGWs were grouped into major categories (Fig. [84]2a,
Supplementary Fig. [85]5); those that remained stably methylated
through replicative senescence, those that displayed variable
methylation (>10% change), and those that were stably unmethylated.
Primary cells from chronologically older individuals displayed a
smaller stably methylated group, and larger stably unmethylated group
(Supplementary Fig. [86]5). The variably methylated group was the
largest for most primary cell types, and was comprised overwhelmingly
of CpGs that lost methylation, although a minor subset gained
methylation. We further split the variably methylated group for primary
fibroblast AG06561 into quartiles of initial methylation levels to
visualize the consistency of methylation loss across a spectrum of
starting methylation (Fig. [87]2a, dark right panels).
Fig. 2. Meaningful groupwise PMD solo-WCGW behaviors.
[88]Fig. 2
[89]Open in a new tab
a PMD solo-WCGWs were separated into major categories: (from top)
stably hypermethylated, variable, and stably hypomethylated.
Representative primary cell AG06561 (fetal skin fibroblast) is
depicted. Left, methylation heatmap of CpGs (rows) within each
category. Samples (columns) are ordered by advancing population
doublings (PDs). Right, density plot of probes within each major
category, with the variable group further split into quartiles by
starting methylation. b Median PMD solo-WCGW methylation for
TERT-immortalized and control primary fibroblasts through replicative
senescence for control fibroblasts (pink-shaded region) and through
late PDs for immortalized fibroblasts (blue-shaded region). c
Redistribution of PMD solo-WCGWs at early, non-immortalized passage,
late, non-immortalized passage, and late, TERT-immortalized passage. d
PMD solo-WCGWs in TERT-immortalized fibroblasts were grouped by same
paradigm in panel a. Locus overlap enrichment analysis was performed on
each group, with all PMD solo-WCGWs on array as background. e PMD
solo-WCGW methylation change per population doubling (PD) for neonatal
foreskin fibroblast 2 (AG21859) binned into quintiles based on ENCODE
replication timing WA scores from BJ fibroblasts. f Methylation change
per PD binned into expression quintiles of CpG-associated genes
(primary RNA-seq data, AG21859). g Fibroblast gene expression for
differentially expressed genes ADAMTS2 and CARD11. h Fibroblast DNA
methylation heatmaps for PMD solo-WCGWs associated with differentially
expressed genes ADAMTS2 (left) and CARD11 (right). Samples (rows) are
arranged from early PD to late PD. i PMD solo-WCGW methylation change
per PD for adult vascular endothelial cell (AG11182) binned into
quintiles based on ENCODE replication timing WA scores from HUVECs. j
Methylation change per PD binned into expression quintiles of
CpG-associated genes (primary RNA-seq data, AG11182). k Endothelial
cell gene expression for differentially expressed genes ADAMTS2 and
CARD11. l Endothelial cell DNA methylation heatmaps for PMD solo-WCGWs
associated with differentially expressed genes ADAMTS2 and CARD11.
Boxplots in panels e–g and i–k depict data quartiles; center bar
depicts median value. Statistical comparisons for panels e, f, I, j by
two-sided Kruskal–Wallis test.
To test whether there is a meaningful threshold of replicative history
at which PMD solo-WCGW methylation stabilizes, primary fibroblasts
(AG06561) were immortalized with a lentiviral construct carrying
telomerase reverse transcriptase (TERT). DNA methylation was profiled
at multiple passages following selection for both immortalized and
control vector cells.
Immortalized cells achieved drastically higher PDs than did control
cells. We terminated the experiment after more than 150 PDs. At the
last passage in this experiment, the immortalized cells remained highly
proliferative (Supplementary Fig. [90]6). DNA methylation analysis
indeed revealed a threshold at which PMD solo-WCGW methylation
stabilized (Fig. [91]2b), ~40 PDs following replicative senescence of
control cells. Although by the end of the experiment most CpGs had
dropped to low levels of methylation, a small minority remained stably
methylated (Fig. [92]2c).
We further investigated the distribution of residual methylation in
high-PD TERT-immortalized cells. We used the methylation state to group
CpGs into high, intermediate, and low methylation for early passage,
late passage, and late TERT-immortalized cells (Fig. [93]2c). We
identified CpGs that were stably methylated or stably unmethylated
throughout, and split the remaining variably methylated CpGs into
quartiles of terminal methylation values (Fig. [94]2c). The genomic
coordinates of CpGs in each group were analyzed for enrichment of
chromatin marks, genomic features, DNA binding proteins, and other
characteristics that may explain their behavior (Fig. [95]2d,
Supplementary Data [96]2). CpGs that were still highly methylated after
extended post-immortalization culture had significant overlap with
genomic features related to actively transcribed gene bodies. Among the
top enriched overlapping features was H3K36me3, which is known to
recruit de novo methyltransferase DNMT3B to transcribed gene
bodies^[97]40. CpGs that had achieved low terminal methylation
overlapped significantly with features bound by CTCF/cohesin complex
members. The loss of methylation at sites bound by CTCF/cohesin complex
members in severely hypomethylated immortalized cells is intriguing,
given both the role of CTCF in maintaining chromosomal
stability^[98]41,[99]42, and the well-established link between DNA
hypomethylation and chromosomal instability in cancer^[100]43–[101]45.
We also observed an enrichment for hypomethylation at sites bound by
c-Fos. We have previously shown that the AP-1 binding motif is
overrepresented in genomic regions prone to hypomethylation in
colorectal cancer^[102]6. We propose that DNA hypomethylation continues
unabated upon TERT immortalization until finally reaching a severely
hypomethylated equilibrium, in which compensatory de novo methylation
offsets further demethylation. We cannot rule out that the observed
methylation stabilization in late-culture TERT-immortalized cells is
caused by selection against cells undergoing further loss of
methylation, but we did not observe a slowing of proliferation rate,
nor an increase in cell death in immortalized cells with stabilized
methylation.
Strong selective pressures are present during cell culture. However, it
seems unlikely that such pressures would produce such consistent and
reproducible methylation changes at specific sequence contexts
throughout the genome, tracking population doublings in multiple cell
types. Others have reported that single memory T cells sorted from the
same bulk input and clonally expanded into separate colonies all
experienced PMD hypomethylation^[103]46.
While we did not find evidence of altered de novo methyltransferase,
TET enzyme, or maintenance methylation machinery expression, our
analysis cannot rule out the possibility of a mislocalization event of
these factors in near-senescence cells leading to methylation loss, as
suggested by others^[104]7. However, our evidence, as well as past
static characterizations of PMD solo-WCGWs in vivo^[105]36 and
mechanistic findings that methylation at the solo-WCGW sequence context
is maintained relatively inefficiently^[106]37, indicates that the
overwhelming majority of methylation loss occurs in actively
proliferating cells and continues beyond replicative senescence, until
an equilibrium is reached at a low stable level of DNA methylation,
likely reflecting compensatory de novo methylation offsetting further
loss of DNA methylation.
Replication timing and gene expression
To leverage our high-resolution methylation data into a more complete
mechanistic understanding of PMD solo-WCGW dynamics, we regressed
methylation to PDs at individual CpGs and compared the rate of
methylation change to public replication timing annotations and primary
gene expression data.
The short time window for maintenance re-methylation in
late-replicating regions is thought to contribute to hypomethylation at
PMDs^[107]13. However, recent mechanistic studies indicate that
maintenance methylation continues beyond S phase, uncoupled from the
replication fork^[108]37. Although replication-uncoupled methylation
mostly compensates for incomplete replication-coupled methylation
following a single cell cycle^[109]37, its efficiency appears strongly
influenced by neighboring CpG content, and the cumulative effect over
many cell divisions has not been studied. PMD solo-WCGWs located in the
regions replicating the latest lost methylation faster compared to
those in earlier-replicating regions (Fig. [110]2e, i). This
relationship suggests that PMD methylation loss is indeed driven by
poor maintenance methylation, likely because of poor
replication-coupled maintenance and subsequent failure of
replication-uncoupled methylation. Other features co-occurring with
late replication such as chromatin inaccessibility may further explain
this relationship.
Enrichment analysis of CpGs that retained methylation at high PDs in
TERT-immortalized fibroblasts (Fig. [111]2d) suggested that active
transcription protects against replication-associated methylation loss.
Although PMDs are relatively gene-poor^[112]47, there are several
thousand gene-associated PMD solo-WCGWs on the EPIC array. Methylation
change per PD was compared to expression level of associated genes.
Indeed, high gene expression was protective against methylation loss
(Fig. [113]2f, j). This relationship was cell-type-specific; genes with
differential expression between fibroblast AG21859 and endothelial cell
AG11182 displayed alternate methylation at associated PMD solo-WCGWs
(Fig. [114]2g, h, k, l). Genes with similar expression levels displayed
similar methylation (Supplementary Fig. [115]7). We also examined
whether the presence of H3K36me3 influenced the rate of methylation
loss (Supplementary Fig. [116]8). Although there were few array PMD
solo-WCGWs overlapping public annotations of this histone mark, its
presence was significantly associated with reduced methylation loss for
both cell types.
Methylation loss during scheduled and unscheduled DNA synthesis
Culture characteristics are arguably non-physiologic; one with
particular relevance to longevity research is oxygen exposure^[117]48.
Chronic exposure to either high oxygen or reactive oxygen species
results in premature aging phenotypes^[118]49,[119]50. Primary cells
grown in hypoxic chambers achieve more PDs^[120]51–[121]53. To
determine whether oxygen partial pressure affects PMD solo-WCGW
dynamics in cultured cells, we serially cultured primary fibroblasts
(AG21859) under ambient (~20%) and low oxygen (3%) conditions
(Fig. [122]3a), then performed DNA methylation profiling and
RNA-sequencing across the series.
Fig. 3. Low culture oxygen slows PMD solo-WCGW methylation loss.
[123]Fig. 3
[124]Open in a new tab
a Schematic of tandem hypoxic/ambient oxygen primary cell culture. b
Median PMD solo-WCGW methylation plotted against population doublings
(PDs) for both culture oxygen conditions. Solid lines depict linear
regression with gray shading depicting 95% confidence interval.
Statistical comparison of slopes by one-way ANOVA (F = 5.3, two-sided
comparison). c DNA methylation heatmap of PMD solo-WCGWs for both
culture oxygen conditions. d Volcano plot of differentially expressed
genes between low oxygen and ambient oxygen culture. e Pathway
enrichment analysis results.
Primary cells grown under low oxygen conditions indeed achieved more
PDs before replicative senescence than those grown under ambient oxygen
conditions (Supplementary Fig. [125]9). Interestingly, median PMD
solo-WCGW methylation loss was slowed under low oxygen culture
(Fig. [126]3b). Individual CpGs behaved similarly across PDs between
conditions (Fig. [127]3c), suggesting that cells grown in low oxygen
conditions simply lose methylation more slowly (Supplementary
Fig. [128]10).
Gene expression analysis between cells grown under both conditions
revealed 641 genes significantly upregulated and 373 genes
significantly downregulated under low oxygen culture (Fig. [129]3d,
Supplementary Data [130]3). Top-upregulated genes in the low oxygen
condition included many well-known hypoxia markers, such as carbonic
anhydrase 9 (CA9) and adenylate kinase 4 (AK4), validating the
experimental system and accompanying gene expression analysis. Top hits
from differential pathway analysis included multiple metabolic
pathways, pro-inflammatory pathways activated under low oxygen culture,
and reactive oxygen species pathway activated under ambient oxygen
culture (Fig. [131]3e).
While the bulk of DNA synthesis and accompanying DNA methylation
maintenance occurs during the cell cycle^[132]54,[133]55, a smaller
amount also occurs during unscheduled DNA synthesis (UDS)^[134]56.
UDS-coupled methylation maintenance efficiency is also sensitive to
CpGs context^[135]57. We hypothesize that the accelerated methylation
loss at PMD solo-WCGWs cultured in ambient oxygen is caused by
incomplete methylation maintenance accompanying UDS as a consequence of
increased oxidative damage^[136]58. This presents a minor caveat to
using PMD solo-WCGW methylation as a proxy for replicative history.
Conversely, the measure might also be useful to sensitively track
cumulative oxidant/DNA damaging agent exposure in slowly proliferating
cells or tissues.
RepliTali: modeling estimates of cumulative cell divisions
While median PMD solo-WCGW methylation correlates strongly with cell
divisions in culture through standard replicative lifespans, we
developed a more refined metric, which we named ‘RepliTali’ (for
Replication Times Accumulated in Lifetime) to estimate relative
replicative histories of human cells and tissues. PMD solo-WCGWs
experience dramatic replication-associated methylation loss and are
therefore depleted of methylation with relatively few cell divisions.
To access a wider dynamic range of replication-associated methylation
loss, we expanded the pool of eligible model CpGs to those in all
sequence contexts within common PMDs. Since the total number of cell
divisions prior to establishment of primary cell culture in our system
is unknown, we envision this tool to be useful as a relative measure as
opposed to an absolute benchmark for mitotic history. To adjust for
variations in the in vivo replicative histories of the primary cells,
we trained RepliTali upon normalized PDs using elastic net regression
(Fig. [137]4a, Supplementary Data [138]4).
Fig. 4. Construction and performance of RepliTali.
[139]Fig. 4
[140]Open in a new tab
a Performance of RepliTali on randomized training (n = 122) and test
(n = 60) sets. Population doublings (PDs) were normalized using a model
trained on chronologically youngest cell AG06561 to correct the
starting passage PD, with the change in actual PD added to this value
for subsequent datapoints. Solid lines depict linear regression with
gray shading depicting 95% confidence interval. b Performance of
epiTOC2, a hypermethylation-based mitotic clock, on primary cell
culture DNA methylation data. c Model performance on primary
fibroblasts (AG06561) grown with different concentrations (%v/v) of
media serum to achieve different proliferation rates. d Mitomycin C
(MMC) treated primary cells (n = 3) derived from unique donors and
tissues. e Performance of RepliTali on external methylation dataset of
cultured fibroblasts (n = 6 unique donors). Solid lines depict linear
regression with gray shading depicting 95% confidence interval;
statistical analyses are two-sided.
Comparing RepliTali performance to other models
We applied other published DNA methylation-based ‘mitotic
clocks’^[141]38,[142]59–[143]61 to our primary cell data (Fig. [144]4b,
Supplementary Fig. [145]11a). Performance of these models was not as
tight and appeared highly cell-type-specific. Interestingly,
hypermethylation-based clocks epiTOC2, pcgtAge, and MiAge were
vulnerable to cell type differences, whereas epiCMIT^[146]61, a clock
that selects the higher estimated mitotic age from either a set of CpGs
that gains or a set that loses methylation, performed remarkably well
on all cultured cell types. This is particularly interesting as epiCMIT
was created exclusively from hematopoietic cell DNA methylation data.
Since the published ‘mitotic clocks’ were not trained on measured cell
divisions, but rather on comparisons between different timepoints, it
is important to investigate the extent to which RepliTali and these
other clocks are reflecting time versus cell division. Our
cell-cycle-attenuated and -arrested cell cultures are the best way to
disentangle these two factors. Although RepliTali was trained on
methylation data from primary cells cultured under standard conditions,
it performed very well on growth-attenuated (Fig. [147]4c) and
-arrested (Fig. [148]4d) primary cells, successfully distinguishing
between divisions and time. Other existing ‘mitotic clocks’ performed
inconsistently, with cell type-dependent performance again observed for
hypermethylation-based clocks (Fig. [149]4c, d, Supplementary
Fig. [150]11b, c).
Validating RepliTali on external datasets
We tested the performance of RepliTali and other clocks on a recent DNA
methylation dataset of serially cultured primary fibroblasts^[151]62
(Supplementary Data [152]5). RepliTali performed strongly across all
fibroblasts, correlating strongly with PDs under standard culture
conditions (Fig. [153]4e). It is noteworthy that RepliTali produced a
higher estimate for some cells; RepliTali estimates total proliferative
history, which for primary cells begins in vivo, long before cell
cultures have been established. We also observed differences between
the slopes of RepliTali-estimated PDs, suggesting that RepliTali may be
best suited to compare relative proliferative histories within a given
cellular lineage. As the model was trained upon homogeneous primary
cell cultures, it will likely perform best on pure or sorted cell
populations, as opposed to the heterogeneous cell composition present
in primary tissues. For cells growth-arrested via long-term contact
inhibition, RepliTali was very stable. Median PMD solo-WCGW methylation
also performed well on this external dataset (Supplementary
Fig. [154]12), supporting its use as a measure of replicative history,
perhaps on non-EPIC array methylation datasets. Other mitotic clocks
had varied performance, appearing sensitive to variations between
fibroblasts (Supplementary Fig. [155]13). Again, epiCMIT outperformed
exclusively hypermethylation-based clocks. Finally, we applied
RepliTali and other mitotic clocks to several cell lines that have been
extensively profiled (Supplementary Fig. [156]14). All clocks estimated
colon adenocarcinoma-derived cell lines SW480 and HCT15 as having
extremely high replicative histories. Curiously, the three
hypermethylation-based clocks estimated that IMR90, a cell line
initially derived from fetal lung fibroblasts that has been extensively
cultured, had a replicative history comparable to low-passage primary
skin fibroblast AG06561, whereas RepliTali and epiCMIT estimated higher
values.
Whereas RepliTali was calibrated on actual, observed PDs accumulated in
culture, other mitotic clocks were created using cancer
data^[157]60,[158]61 or normal aging blood^[159]38,[160]59 data, with
the assumption that malignant or aged tissues have experienced more
cell divisions than non-malignant tissue. However, it is possible that
CpGs prone to DNA methylation events co-occurring with, but not
directly attributable to increased mitotic history in cancer and aging
have been selected into these models. In addition, past mitotic clocks
were developed using the Infinium HumanMethylation450 (450 K) array.
This may explain why PMD solo-WCGWs have not yet been selected en masse
as a tool for estimating mitotic age; they are severely
underrepresented on this platform. Approximately 11% of genomic CpGs
are PMD solo-WCGWs^[161]36, yet they comprise only 1.5% of 450 K array
CpGs. The relatively few (n = 6214) on the 450 K array were likely
included because they overlap an enhancer or other gene regulatory
feature, and thus often do not display the characteristic behavior of
PMD solo-WCGWs. PMD solo-WCGWs represent approximately 27% of the
probes in epiCMIT’s hypomethylation probeset, vastly exceeding the 1.5%
represented on the 450 K array. EpiCMIT had arguably stronger
performance on our data and on the external dataset than the
hypermethylation-based mitotic clocks. By comparison, while PMD
solo-WCGWs comprise 18 of 87 CpGs in RepliTali, their mean coefficient
weight was −3.35, versus a mean coefficient weight of −0.73 of all
RepliTali CpGs, indicating that PMD solo-WCGWs contribute heavily to
the model. In addition, RepliTali CpGs recapitulated the progressive
methylation loss behavior of PMDs at large (Supplementary
Fig. [162]15).
Epigenetic clocks, representing models based on the methylation status
at typically dozens to hundreds of CpGs, have become ubiquitous.
Despite the astounding power of these models to predict features
associated with biological aging—and its reversal^[163]30—the
biological underpinnings of the CpGs that make these clocks ‘tick’ are
often poorly understood. RepliTali is a DNA methylation-based estimator
of replicative history. Among methylation ‘clocks’ it is unique both in
its construction—finely tuned upon serially passaged primary cells—and
in our understanding of its driving mechanisms. RepliTali outperforms
other models both on our own data and on an extensive external dataset.
A challenge of developing a methylation clock to track mitoses is the
highly variable rates of cell divisions between tissues^[164]34.
However, the ability to dissect replicative history from other aspects
of biological aging (perhaps simultaneously measured by another
methylation clock) will aid our understanding of the aging process and
inform therapies that seek to slow or reverse it.
Methods
Primary cell culture
All primary cells were obtained from the NIA Aging Cell Culture
Repository at the Coriell Institute for Medical Research and cultured
under recommended conditions. Fetal skin fibroblast AG06561 was
maintained in Eagle’s MEM with Earle’s salts and non-essential amino
acids (Gibco 11140-050) with 15% v/v fetal bovine serum. Neonatal
foreskin fibroblasts AG21859 and AG21839 were maintained in Ham’s
F12/DMEM 1:1 media supplemented with 10% v/v fetal bovine serum.
Neonatal foreskin keratinocyte AG21837 was maintained in serum-free
human epidermal keratinocyte media (MilliporeSigma SCMK001) on collagen
IV-coated dishes (Corning 354453). Adult skin fibroblast AG16146 was
maintained in Eagle’s MEM with Earle’s salts with 10% v/v fetal bovine
serum. Vascular endothelial cell AG11182 was maintained in Medium 199
with 1X GlutaMAX (ThermoFisher 35050061), 0.02 mg/ml endothelial cell
growth supplement (Corning 354006), 0.05 mg/ml sodium heparin (Alfa
Aesar A16198MD) and 15% v/v fetal bovine serum on plates pre-coated
with gelatin (MilliporeSigma ES006B). Vascular smooth muscle cell
AG11546 was maintained under the same conditions as AG11182 with the
exception of 10% v/v fetal bovine serum. All primary cells were
maintained at 37 °C and 5% CO[2] with ambient O[2] unless otherwise
noted. Media was changed at minimum three times per week.
Triplicate cultures derived from the same parent plate or vial obtained
from Coriell were maintained in parallel through replicative
senescence, which was defined in this study as drastically slowed
growth (inability to reach near-confluence at 14 days after previous
passage) or viable fraction of cells falling below 60%.
Passaging occurred as cells became ~90% confluent. At each passage, one
fraction of cells was pelleted and frozen for future nucleic acid
extraction. Another fraction was kept in suspension at room temperature
and counted on an automated hemocytometer (BioRad TC20) in duplicate.
Viability was determined by trypan blue dye exclusion.
Cumulative cell divisions in culture (population doublings, PDs) were
determined using the following equation:
[MATH: PD=3.32log10(cellyield)−log10(viablecellinoculum)+X,withXbeingthePDoftheinoculum :MATH]
1
Mitomycin C treatment
Primary cells AG11182, AG11546, and AG16146 were reintroduced into
culture from cryopreserved early-passage cells. Duplicate subcultures
were derived from the initial recovered plate for treatment and control
conditions. Cells were treated with DNA intercalating agent Mitomycin C
(MMC, Alfa Aesar J63193MA) reconstituted in DMSO at a final
concentration of 10 μg/ml. An equal volume of DMSO was added to control
subcultures. Both conditions were incubated for 3 h at 37 °C before
media containing MMC or vehicle was removed, cells rinsed with PBS, and
basal media replaced. Control cells were passaged normally, and
growth-arrested, MMC-treated cells were collected on days 18 and 25.
Primary cell growth slowing
Primary fibroblast AG06561 was reintroduced into culture from
cryopreserved early-passage cells. Four parallel cultures were
established and were maintained in media containing 15%, 5%, 1%, and
0.5% v/v fetal bovine serum to encourage different rates of
proliferation. At each passaging a fraction of cells was retained for
DNA methylation analysis.
TERT-immortalization
Low-PD primary fibroblasts (AG06561) were transduced with purified
lentiviral particles containing expression vectors encoding human
Telomerase Reverse Transcriptase (TERT) and hygromycin resistance
marker (AMSBIO LVP1131-Hygro-PBS), or hygromycin resistance marker
alone (control, AMSBIO EF1a-Null-Hygro). Following selection with
250 μg/ml hygromycin B, cells were serially cultured either through
replicative senescence (control) or in perpetuity (TERT). At the time
of analysis, nearly a full year after transduction, the immortalized
cells remained highly proliferative.
Low oxygen cell culture
Low-PD primary fibroblasts (AG21859) were cultured in triplicate in
either a standard incubator (Panasonic MCO-19AICUVPA) with ambient
O[2], or dual-gas CO[2]/N[2] incubator (PHCbi MCO-170M-PA) at 3% O[2],
through replicative senescence.
Methylation analysis by microarray
Frozen cell pellets were thawed and lysed using QIAshredder spin
columns (Qiagen 79656). Genomic DNA was extracted from each sample
using the AllPrep DNA/RNA Mini Kit (Qiagen 80204), then stored at
−80 °C before analysis. DNA was quantified by Qubit fluorimetry (Life
Technologies). Approximately 500 ng of genomic DNA was bisulfite
converted using the Zymo EZ DNA methylation kit (Zymo Research D5004)
then hybridized overnight on an Infinium MethylationEPIC BeadChip
(Illumina), in which the genomic DNA molecules anneal to locus-specific
DNA oligomers linked to individual bead types. Raw signal intensities
were exported as.idat files, which were processed using the R package
SeSAMe^[165]63,[166]64. Of 386 DNA methylation samples run, 14 failed
quality control and were excluded from further analysis, producing a
final analytical sample count of 372. All DNA methylation data can be
accessed through the Gene Expression Omnibus (GEO) accession
[167]GSE197512.
Statistical analysis
Analysis was performed in R software (version 4.1.1). For comparisons
of effect of MMC growth arrest and serum-dependent growth slowing on
PMD solo-WCGW methylation, mixed-effects modeling (R package ‘lme4’)
was performed, using logit-transformed (m) methylation values. Multiple
comparisons were performed via Tukey contrasts. For comparison of the
effect of culture oxygen condition on rate of PMD solo-WCGW methylation
models were compared via ANOVA, again using logit-transformed (m)
methylation values.
LOLA
Genomic coordinates (hg19) of PMD solo-WCGW probes of interest were
subject to Locus Overlap Enrichment Analysis (LOLA) using R package
‘LOLA’^[168]65 and LOLACore (hg19) region set database, available here:
[169]https://databio.org/regiondb.
Coordinates of all PMD solo-WCGW probes on the InfiniumEPIC Methylation
array were used as background for enrichment analysis.
RNA-seq
RNA was isolated from frozen cell pellets using the AllPrep DNA/RNA
Mini Kit (Qiagen 80204), then stored at −80 °C before analysis. RNA
Libraries were prepared from 100 ng of total RNA with the KAPA Stranded
mRNA-Seq Kit (Kapa Biosystems KK8401). Indexed libraries were then
pooled and 2 × 50 bp, paired-end sequencing was performed on an
Illumina NovaSeq 6000 sequencer to a minimum read depth of 30 M
reads/library. Demultiplexing was performed using Bcl2fastq (v1.9.0).
Differential expression analysis was performed with standard edgeR and
DESeq2 workflow. Senescence timepoints were excluded from differential
expression and pathway enrichment analysis for oxygen culture condition
experiment. Scripts for RNA-seq analytical workflow, including
downstream analysis in R, are available here:
[170]https://github.com/vari-bbc/rnaseq_workflow.
RepliTali construction
Starting PD values of primary cells were normalized using an elastic
net regression model with alpha parameter = 0.5 (R package ‘glmnet’)
trained on the chronologically youngest primary cell, fetal skin
fibroblast AG06561. Samples from all cells were randomized into
training (n = 122) and test (n = 60) sets; normalized PDs were used to
construct the final ‘RepliTali’. RepliTali is constructed using array
CpGs within common PMD boundaries. Coefficients are presented in
Supplementary Data [171]4.
Mitotic clock comparisons
EpiTOC estimates were obtained using the R script available at:
[172]https://zenodo.org/record/2632938#.YdWva5DMKrc. Script was run
separately on each primary cell culture, per the author’s
specifications. Of note, SeSAMe methylation array processing is more
stringent than Minfi, hence the suggestion of specifying p-val = 0.1
for SeSAMe processing. Care must be taken to evaluate clock CpG
dropouts. MiAge estimates were calculated with materials deposited
here: [173]http://www.columbia.edu/~sw2206/softwares.htm. epiCMIT
estimates were calculated as described in
[174]https://duran-ferrerm.github.io/Pan-B-cell-methylome/Estimate.epiC
MIT.html.
Replication timing
Replication timing data from BJ foreskin fibroblasts and HUVECs was
generated by the University of Washington and maintained by ENCODE.
Files are available here:
[175]http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeUwRepliS
eq. Replication timing weighted average (WA) scores were calculated as
previously specified^[176]66:
WA = (0.917*G1b)+ (0.750*S1) + (0.583*S2) + (0.417*S3) + (0.250*S4) + (
0*G2).
H3K36me3
Histone ChIP-seq data from neonatal foreskin fibroblasts was generated
by Joseph Costello’s lab at UCSF/Roadmap Epigenomics Project. Histone
ChIP-seq data from HUVECs was generated by the University of
Washington/ENCODE project. Neonatal foreskin fibroblast:
ENCSR889OUV|[177]GSM817238. HUVEC: ENCSR000DVM|[178]GSM945233.
DNA methylation data
Infinium MethylationEPIC array data from serially passaged human
fibroblasts was generated by Martin Picard’s lab at Colombia University
(Cellular Lifespan Study 1.0^[179]62, [180]GSE179847). Raw idats were
reprocessed as above.
Reporting summary
Further information on research design is available in the [181]Nature
Research Reporting Summary linked to this article.
Supplementary information
[182]Supplementary Information^ (6MB, pdf)
[183]Peer Review File^ (3.9MB, pdf)
[184]41467_2022_34268_MOESM3_ESM.pdf^ (4.7KB, pdf)
Description of additional Supplementary File
[185]Supplementary Dataset 1^ (16.1KB, docx)
[186]Supplementary Dataset 2^ (1.2MB, xlsx)
[187]Supplementary Dataset 3^ (6.5MB, xls)
[188]Supplementary Dataset 4^ (54KB, xls)
[189]Supplementary Dataset 5^ (15.7KB, docx)
[190]Reporting Summary^ (329.6KB, pdf)
Acknowledgements