An epigenetic clock controls aging

Abstract

We are accustomed to treating aging as a set of things that go wrong with the body. But for more than twenty years, there has been accumulating evidence that much of the process takes place under genetic control. We have seen that signaling chemistry can make dramatic differences in life span, and that single molecules can significantly affect longevity. We are frequently confronted with puzzling choices the body makes which benefit neither present health nor fertility nor long-term survival. If we permit ourselves a shift of reference frame and regard aging as a programmed biological function like growth and development, then these observations fall into place and make sense. This perspective suggests that aging proceeds under control of a master clock, or several redundant clocks. If this is so, we may learn to reset the clocks with biochemical interventions and make an old body behave like a young body, including repair of many of the modes of damage that we are accustomed to regard as independent symptoms of the senescent phenotype, and for which we have assumed that the body has no remedy.

Full text

OPINION ARTICLE
An epigenetic clock controls aging
Josh Mitteldorf
Received: 25 December 2014 / Accepted: 7 October 2015
Springer Science+Business Media Dordrecht 2015
Abstract We are accustomed to treating aging as a
set of things that go wrong with the body. But for more
than twenty years, there has been accumulating
evidence that much of the process takes place under
genetic control. We have seen that signaling chemistry
can make dramatic differences in life span, and that
single molecules can significantly affect longevity.
We are frequently confronted with puzzling choices
the body makes which benefit neither present health
nor fertility nor long-term survival. If we permit
ourselves a shift of reference frame and regard aging
as a programmed biological function like growth and
development, then these observations fall into place
and make sense. This perspective suggests that aging
proceeds under control of a master clock, or several
redundant clocks. If this is so, we may learn to reset the
clocks with biochemical interventions and make an
old body behave like a young body, including repair of
many of the modes of damage that we are accustomed
to regard as independent symptoms of the senescent
phenotype, and for which we have assumed that the
body has no remedy.
Keywords Senescence Programmed aging 
Epigenetic Evolution Life history Gene expression
Introduction
Reasons to believe that aging derives
from a genetic program
Since the pioneering work of Medawar (1952) and
Williams (1957), it has become customary to under-
stand the phenotypes of aging as failures of home-
ostasis in the body. Where the body has clearly made
metabolic choices that hasten its demise, we look for
tradeoffs and hidden benefits. Sometimes the tradeoffs
and benefits are obvious, but when we cannot find
them, we assume nevertheless that they must exist.
But a number of trends in recent decades suggest
that aging is an independent adaptation, and that
destruction of the body is proceeding under full
control of the genome.
•The genetic basis for aging is conserved across
such great spans of evolutionary distance (Guar-
ente and Kenyon 2000; Kenyon 2001) as to render
pleiotropy an implausible explanation, and muta-
tional load an irrelevance.
•Animals are able to extend life span under some
conditions of hardship and environmental chal-
lenge, including caloric restriction (Calabrese and
Baldwin 1998; Calabrese 2005; Masoro 2005,
2007). Frequently the life extension comes at
minimal cost in fertility (Flatt 2009), especially for
males (Weindruch and Walford 1988; Masoro
2003). The ability of the body to extend life span
J. Mitteldorf (&)
Department of EAPS, MIT, Cambridge, MA, USA
e-mail: josh@mathforum.org
123
Biogerontology
DOI 10.1007/s10522-015-9617-5

under stress hints that, in the absence of stress, the
body harbors a latent capacity for longer life that is
not activated because of genetic programming
(Mitteldorf 2001).
•Evidence for pleiotropy of known aging genes is
weak (Kirkwood 2005; Blagosklonny 2010). In
fact, pleiotropic benefits have only been discov-
ered for a small proportion of genes that shorten
life span (Curtsinger et al. 1995; Stearns 2000),
and several examples are documented in which a
wild-type allele shortens life span and also lowers
fertility (Spitze 1991; Bronikowski and Promislow
2005; Hanson and Hakimi 2008).
•The high fitness cost of aging in the wild (Ricklefs
1998; Bonduriansky and Brassil 2002; Nussey
et al. 2012; Jones et al. 2014) is inconsistent with
the once-dominant Mutation Accumulation the-
ory, and steepens the challenge to the pleiotropic
theories as well.
•The existence of programmed aging in protists, in the
form of cellular senescence (Clark 1999,2004) defies
classical evolutionary theory. The association of
cellular senescence with increased mortality in
humans (Cawthon et al. 2003; Fitzpatrick et al.
2007; Kimura et al. 2008)isprima facie evidence for
a form of programmed death. The classical explana-
tion is that cellular senescence offers protection
against cancer (Sager 1991;Campisi2013;de
Magalhaes 2013). However, this has become increas-
ingly untenable as a clear association has emerged
between short telomeres and higher incidence of
cancer (Rode et al. 2015). Considering also that many
species not susceptible to cancer nevertheless are
subject to mortality increase from cellular senescence
(Mitteldorf 2013), we must regard cell senescence as
a form of programmed aging that has persisted since
the Cambrian explosion.
•Apoptosis in yeast cells under stress has been
documented as an altruistic aging program (Fab-
rizio et al. 2004). Apoptosis continues to play a
role in senescence of multicelled organisms
including humans (Behl 2000; Marzetti and
Leeuwenburgh 2006).
•An inverse association between fertility and life
span is predicted by the Disposable Soma (Kirk-
wood 1977) and other theories of metabolic trade-
off. But this correlation is observed neither in
animals (Ricklefs and Cadena 2007) nor in humans
(Gavrilova et al. 2004; Mitteldorf 2010a,b).
These and other arguments for aging as a genetic
program are reviewed in (Bredesen 2004; Mitteldorf
2004; de Magalha
˜es and Church 2005; Mitteldorf
2010a,b; de Magalha
˜es 2012; Goldsmith 2013;
Pepper et al. 2013; Mitteldorf 2016).
Programmed aging is inconsistent
with the standard model of evolutionary genetics
Historically, the idea that aging could be an evolu-
tionary adaptation has been a non-starter because
aging is the opposite of fitness, defined as individual
reproductive success. If we believe that natural
selection is in the business of maximizing some
measure of reproductive potential (e.g. ror R
o
), then
evolution must always be toward higher fertilities and
longer life spans, to the extent these are not in conflict.
The quantitative formulation on which this thinking
is founded is not Darwin’s, though it is called neo-
Darwinism. It was developed in the early twentieth
Century by Sharpe and Lotka (1911) and formalized
into a fully axiomatic mathematical system by Fisher
(1930). Most theoretical evolutionists take it as gospel,
but it is demonstrably false. There have been many
counter-examples, in which a variety that has shorter
life span, lower fertility, and worse survivability out-
competes a variety that is better in all these respects.
An example of guppies in the river pools of Trinidad
was documented by Reznick (Bryant and Reznick
2004). My favorite example is the Rocky Mountain
Locust, a super-competitor that swarmed to cover
hundreds of thousands of square kilometers of sky in
the American mid-west a hundred years ago, denuded
vast regions of every green leaf and blade of grass in its
heyday. Although individually a super-competed,
collectively, the Rocky Mountain Locust was a
circular firing squad that drove itself into extinction
within a few years. There are no surviving specimens
today (Yoon 2002).
My contribution to this field has been a theoretical
suggestion about why individual success in the
Darwinian competition inevitably leads to a tragedy
of the commons (Hardin 1968) that brings down the
entire ecosystem. Natural selection at the ecosystem
level is an efficient force in direct opposition to
individual selection for reproductive profligacy. This
changes everything we thought we knew about what
constitutes fitness. In particular, it opens a window for
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selection of aging as an adaptation. What follows is a
line of reasoning adapted from (Mitteldorf 2006).
Intuitively, we know that populations are subject to
environmental feedback. If a population is lower than
the carrying capacity, births exceed deaths, and the
population grows. If a population is greater than the
carrying capacity, deaths exceed births, and the
population falls. We may expect a population out of
equilibrium to smoothly approach its carrying capacity
(Figs. 1,2).
But every species is dependent on an ecosystem.
The fox is dependent on the rabbit, and the rabbit
depends on the grass. Crucially, the rate at which the
grass grows is fixed by sunlight, and it cannot grow
faster. But other species, higher up the food chain,
evolve higher and higher growth rates according to
Lotka’s model. Increasing individual reproductive
success has the collective effect of increasing the
population growth rate, or exponential rate of increase.
Rabbits may evolve to grow their population faster
than the grass can keep up. For most species that are not
at the bottom trophic level, it is not difficult to evolve a
faster reproductive rate, because there is a reservoir of
food to be tapped. Tapping the food supply more
deeply permits a faster growth rate in the predator, but
the depleted prey population makes this a losing
proposition, not just in the long run but even in a single
generation. If collectively we abuse the ecosystem on
which we depend, our children will starve.
This dynamic is reflected in the mathematics of the
logistic equation. The plots in Figs. 1and 2were based
on an exponential growth rate for the rabbits that is small
compared to the grass. The ratio of the rabbits’ growth
rate to the growth rate of the grass is called the ‘‘chaos
parameter’’, for reasons we may see in Figs. 3,4,5.
When the rabbits grow at twice the rate of the grass, we
see some oscillation about the carrying capacity.
When the ratio increases to 2.5, this oscillation
becomes more severe.
For ratios greater than about 2.59, the pattern ceases
to be periodic, and fluctuations become increasingly
wide and irregular.
Fig. 1 Logistic solution for
population approaching
steady state from above
Fig. 2 Logistic solution for
population approaching
steady state from below
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For values greater than 3, the population drops to
zero and, of course, cannot recover. The number 3 is a
line in the sand only for mathematics. But there’s no
physiologic reason why reproduction rates can’t get as
high as 3 or 4 or 10 or 20. In fact, the reason that
animals have evolved birth rates that are less than they
are physically capable of and death rates that are
higher than necessary is to avoid population chaos.
Demographic chaos is a fast track to extinction.
Population dynamics is a very rapid and powerful
force of Darwinian selection. For insect species, it can
destroy a population in one season. With growth rates
Fig. 3 Logistic growth for
chaos parameter = 2
Fig. 4 Logistic growth for
chaos parameter = 2.5
Fig. 5 Logistic growth for
chaos parameter = 2.9
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typical of mammals, it can wipe out an entire
population in a single individual’s lifetime. Such an
event was documented in recent history, when rein-
deer were introduced to St Matthews Island in the
Bering Sea in 1944. The reindeer were evolved for an
environment where they were hunted by wolves, but St
Matthews had no natural predators. The reindeer
population grew exponentially for 18 years, then died
off in a single winter (Klein 1968).
Population dynamics is a powerful evolutionary
force at the group level, and it is working in direct
opposition to individual selection for faster reproduc-
tion. This is why the 100-year old idea that natural
selection maximizes individual reproductive success
is wrong.
A window for evolution of aging
With the evolutionary imperative to maximize repro-
duction effective blunted, there is an opportunity for
aging to evolve as an adaptation. Aging may evolve
‘‘opportunistically’’ for reasons that have nothing to do
with population dynamics. Aging offers a selective
advantage for population turnover (Martins 2011),
contributes to evolvability (Mitteldorf and Martins
2014), and helps diversify the population to protect
against infectious epidemics (Mitteldorf and Pepper
2009).
In addition to these ‘‘opportunistic’’ pathways,
aging may evolve because of its direct benefit for
preventing demographic extinctions. Aging stabilizes
population dynamics by leveling out the death rate
when the grass is thick and when the grass is sparse.
Without aging, the entire adult population is uniformly
strong, and deaths tend to be highly clustered in times
of crowding, epidemics, and famine. Aging substitutes
a steady stream of individual deaths, and protects
against the overcrowding that leads to population
collapse (Mitteldorf and Goodnight 2012).
Biological clocks for aging
Once the theoretical objections to programmed aging
are answered and the empirical evidence is considered,
the programmed aspects of aging come into focus. If
aging proceeds under genetic control, it is probable
that the body keeps a reference clock, or perhaps
several redundant clocks to insure the body does not
escape destruction, and to protect against evolutionary
reversion to a non-aging phenotype.
Two biological clocks have been studied and
documented in humans, the first based on telomere
shortening and cell senescence; the second based on
involution of the thymus. Telomere attrition has an
ancient history as the only mode of senescence in one-
celled ciliates (Clark 1999,2004). In humans, telom-
ere length of leukocytes is now a commercially
available lab test. Since stem cells are replicating
through a lifetime and very little telomeraseis exp
ressed after the embryo stage, telomere length declines
over a lifetime. For decades, this was known, but there
was no indication whether telomere shortening had
implications for aging mortality. Then in 2003,
(Cawthon et al. 2003) used historic blood samples
from a Salt Lake City hospital to demonstrate that
telomere length is a strong predictor of age-adjusted
mortality. The finding has been replicated in humans
(Bischoff et al. 2005; Harris et al. 2006; Brouilette
et al. 2007; Fitzpatrick et al. 2007; Kimura et al. 2008;
Fyhrquist et al. 2011; Ma et al. 2011; Strandberg et al.
2011; Willeit et al. 2011) and also in mammals
(Bru
¨mmendorf et al. 2002; McKevitt et al. 2002) and
birds (Pauliny et al. 2006). There are two plausible
mechanisms by which cellular senescence contributes
to aging. First, the population of stem cells that
replenish muscle, skin and immune cells is depleted by
senescence, contributing to aging of these tissues.
Second, cells with short telomeres are a source of
inflammatory cytokines, with system-wide conse-
quences far out of proportion to the number of affected
cells (Baker et al. 2011).
The thymus is a gland in which T-cells are trained
to distinguish self from invader. With age the thymus
shrinks in size and becomes less functional, with
consequences for the competence of the immune
system as a whole. At advanced ages, T cells are prone
to errors of Type 1 and Type 2. Type 1 errors allow
invading microbes to escape undetected, with the
result that the elderly have a heavier burden of
infectious disease. In type 2 errors, healthy indigenous
tissues are attacked in an autoimmune response,
typical of arthritis and other diseases of old age.
Reasons to believe there is a third clock
Williams (1957) first articulated theoretical reasons to
believe that aging should have evolved to be
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controlled by multiple independent factors, and his
reasoning remains valid today.
Some animals suffer senescence even though their
telomeres don’t shorten with age. And some modes of
aging in humans seem unlikely to be related to
telomeres and immune function. For example, the
brain ages though neuronal growth is not directly
limited by short telomere or the state of the thymus.
A broader reason for suspecting the existence of a
third, epigenetic aging clock comes from thinking
about developmental biology. In growth, development
and puberty, timing is exquisitely sensitive. It is
widely believed that development is under control of
gene expression, i.e., epigenetic programming. What
clock tells the body when to secrete growth factors and
when to stop, or when to initiate puberty with
gonadotropic hormones? This remains an unanswered
question in developmental biology. Ebling (2005)In
recent years, it has become clear that gene expression
is a strong function of age (de Magalha
˜es et al. 2009;
Zykovich et al. 2014). The fact that a separate clock
has never been identified suggests that gene expression
itself might be a clock. Time can be measured using a
feedback loop, and gene expression provides such a
loop:
•Epigenetic state of cells controls gene expression
(including circulating hormones).
•Circulating hormones feed back to continually
reprogram the epigenetic state of cells.
The existence of an epigenetic aging clock was
independently suggested by Rando and Chang (2012),
de Magalha
˜es (2012), and Johnson et al. (2012).
Parabiosis and factors in the blood
Parabiosis is the surgical joining of two bodies so that
they share a common circulatory flow. Heterochronic
parabiosis is the joining of a young and old animal.
Experiments were done with mice beginning in the
nineteenth century, and in the 1950s, the field was
renewed by the same Clive McCay et al. (1957) who
discovered the life extension potential of caloric
restriction in the 1930s.
The current wave of parabiosis experiments with
mice grew from Rando’s Stanford University
laboratory, and several of his students in the early
2000s. The Conboys, Villeda, Wagers, and Wyss-
Coray today conduct parabiosis experiments in their
own labs. The first promising experiment in this
new wave was published in 2005 (Conboy et al.
2005), in which it was reported that impaired
muscle and skin healing in an old mouse was
rescued by exposure to blood from a young mouse.
It was not the red or white blood cells that offered
the benefit, but protein and RNA factors dissolved
in the plasma. Intriguingly, gene expression was
found to be broadly impacted, reverting to a more
youthful profile.
These experiments established the possibility that
old tissue could be rejuvenated by a young signaling
environment. The next steps were to transfuse blood
plasma from young to old mice, and to identify
specific factors in the blood that are responsible for
rejuvenation. Mayack et al. (2010) found that
haematopoietic stem cells could be rejuvenated by a
youthful profile of blood factors.
This work is very much in progress. Recently, a
number of results have been published that make
the epigenetic clock model look more plausible.
Villeda (Bouchard and Villeda 2014) reports that
infusion of young blood plasma reverses nerve
damage, improves cognitive function in mouse
model of alzheimer’s disease. It has already
become clear that there are both anti-aging factors
that are under expressed and also pro-aging factors
that are overexpressed in old mice. Among the
anti-aging factors identified are GDF11, which
promotes nerve and muscle growth (Katsimpardi
et al. 2014) and oxytocin, which is necessary for
muscle maintenance (Elabd et al. 2014). Among the
pro-aging factors identified are TGFband NfjB,
which promote inflammation (Conboy et al. 2005),
and FSH which is associated with weight gain,
osteoporosis, and some cancers (Merry and Hole-
han 1981; Bowles 1998).
Using algorithmic searches based on statistics
alone, Horvath (Horvath 2013) has found combi-
nations of DNA methylation sites that change so
consistently with age that an accurate measure of
functional age can be constructed. There is a great
deal of consistency among different tissues and
different donors. (Jones et al. 2015) makes a first
pass at partitioning the difference in methylation
patterns between stochastic change, which may be
regarded as dysregulation, and consistent patterns,
which may be regarded as programmed aging.
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Future directions
It is a fact that gene expression changes with age, and a
reasonable hypothesis that gene expression controls
some aging phenotypes. There is reason to hope that
restoring the body to a youthful state of gene
expression will rejuvenate the repair and growth
faculties, stimulating the body to repair years of
accumulated damage. We have seen that a few
powerful transcription factors are capable of repro-
gramming the epigenetic state of chromatin, and this
suggests a promising path for aging research.
For future medical applications, the existence of an
epigenetic aging clock will do us little good if it is
essentially complex, and must be re-programmed, one
site at a time, with the epigenetic markers character-
istic of youth.
But if we are fortunate, then some manageable
number of circulating hormones and other blood
factors will be discovered that can signal the body to
return epigenetic programming to a more youthful
state. If only because the prize is potentially so large,
this possibility is a worthy focus for intensive research
in the near future.
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