University of Groningen
A system wide view of replicative aging in budding yeast
Janssens, Georges Eric
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2016
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Janssens, G. E. (2016). A system wide view of replicative aging in budding yeast: Protein biogenesis
machinery as a driver of the aging process; molecular and cellular properties associated to longevity in
single cells; and the relevance of aging in yeast to aging in humans [Groningen]: University of Groningen
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Chapter 1
Introduction
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What is aging? Arguably, the question has captured the fascination of humankind for all of history. The concept is one intimately linked to the passage of time,
and therefore goes hand in hand with the quote of philosopher Augustine of Hippo
dating back to the end of the fourth century: “What then is time? If no one asks me,
I know: if I wish to explain it to one that asketh, I know not”.
When consulting academic reviews on the biology of aging, many experts will begin
their introduction by a general sentence that states the definition of aging, and it is
commonly written that it is the result of damage accumulating over the lifetime of the
organism [1–4]. While this idea is inspired by the wear-and-tear of the tools, devices,
and machines that we as humans have built, it may not serve to fully capture the
depreciation of function that afflicts living, growing, biological organisms over time.
Recently, evidence has been amounting that some of the decrepitude of aging may
be the result of developmental pathways, which, while serving essential function
to bring the organism into existence, later play their part in its demise (see [5] for a
review).
Whichever the cause, damage or development, the manifestations are quite clear.
Wrinkles form, resulting from age-related changes in elastin and collagen [6,7]. Hair
greys, whereby all humans have at least some grey hair by the age of 60 [8]. Up
to ages 60 - 80, body mass increases and steadily decreases thereafter [9]. While
these may be seen as aesthetic inconveniences of aging, and may drive some of
the general public’s attention on the subject, the pathological manifestations of the
process form much clearer ailments to societies, families, and individuals. Aging is
a risk factor for disease [10], which is why the elderly population is much more likely
to be in the top-spending percentile for healthcare [11]. With the elderly percentage
of the population expected to continue to grow, this will be placing even greater economic burden on societies in the time to come [12]. Diseases that accompany aging,
such as Alzheimer’s and cancer, increase steadily with age [13,14]. Meanwhile, eyesight decreases, muscle mass wastes away, and the list of unpleasant side effects
of aging goes on (see [15], the Digital Ageing Atlas, for an interactive documentation
of aging related changes in the body). Indeed, the process of aging is a clear focus
for which intense biomedical attention should be given, for both economic and social
reasons.
Unfortunately, while the physiological symptoms are so apparent, the molecular
changes that parallel these are less so. For example, one transcriptome study of
human brain coming from individuals ranging in age from 65 to 100 years reported
that less than 1% of transcripts have significantly altered expression with age (54
out of 14,078 unique transcripts measured [16]). While this may be due to the fact
that they used mixed-tissue samples of the brain and they started with an already
predominantly old reference sample set aged ~65 years, the same study also looked
specifically at blood lymphocytes in a wider range of sample ages and found similar results. Using isolated lymphocytes from individuals spanning 15 to 95 years, it
was found that only about 6% of transcripts have significantly altered expression
(1060 out of roughly 18,500 unique transcripts measured [16]). In another study in
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A system wide view of replicative aging in budding yeast
ages ranging from 27 to 92 years, using a model that took into account tissue type
(cortex and medulla), and gender of the samples, the aging kidney was reported to
have about 2% of transcripts having significant changes (985 out of 44,928 unique
transcripts measured [17]). Similarly, in another study that looked at skeletal muscle
samples (either arm, leg, or abdominal wall) from patients with ages spanning 16
to 89 years, it was found that less than 1% of detectable transcripts significantly
changed in expression (250 out of 31,948 unique transcripts measured [18]). While
standardization for what is considered a significant change is important when comparing results between studies, the four studies mentioned above draw similar conclusions; few transcriptional changes are detected in aging tissue samples.
When assessing these changes for biological insight, we see evidence for both damage and developmental based theories of aging. Highlighting signs of damage, in
the studies above which looked at the kidneys [17] and lymphocytes [16], signatures
reflecting inflammation and immune activity were found to occur with aging. This is
similar to what a meta-analysis of aging human, mouse, and rat tissue datasets has
found [19], and could be interpreted as the organism eliciting a response to deal
with damaged cells. Similarly, from a separate study on the brain (the frontal cortex)
having samples from patients ranging in age from 26 to 106 years, it was found that
aging was accompanied by an induction of stress response, antioxidant, and DNA
repair genes, which suggests that DNA damage is playing a role in the aging brain
[20].
On the other hand, highlighting signs of development in aging, in the study which
focused on human muscle, a main finding was that an increased expression in pathways of cellular growth and cytosolic ribosomes occurs with aging, suggesting that
development and growth are more implicated in muscle aging than damage [18]. In
line with this, in another independent and extensive study of the transcriptome of the
aging brain (prefrontal cortex), with ages spanning fetal development to 80 years, it
was found that the expression patterns that occur in development are found again
in the aging brain. This strongly suggests that pathways that were once active in
development are reactivated and present during the aging process of the brain [21].
Considering these transcriptome studies in different human tissues we thus see that
the changes occurring with age are limited in their numbers, though implicate a variety of processes where both damage and developmental pathways may play a role.
The question ‘what is aging’ can also be answered by a definition of mortality, namely, when tracking a starting population of any species, we will inevitably see a decrease in its numbers over time. As is often the case in nature some of these deaths
will be due to causes other than aging, for example predation, infant mortality, or
communicable diseases, and will distort the population’s lifespan curve away from
one that reflects aging. Indeed, decrease in disease incidence is thought to explain
much of the jump in survival probability of the human population that happened from
the 1800s to present day [12]. In these settings when humans are in a relatively
pathogen free environment, a ‘sigmoidal’ survival distribution occurs (Figure 1A).
That is to say, a generally death free period early in age is enjoyed by all members
of the population, before a sharp decline in survival ensues due to aging related
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deaths, and finally a ‘tail’ exists, whereby the ‘lucky few’ remain alive for some time
more. Though this curve may be different throughout the various taxa of life [22], it
occurs in modern human populations and for populations of laboratory organisms
such as yeast, worms, flies, and mice, when kept in protected environments. Making
use of this characteristic survival curve has allowed researchers to compare the
effects of individual genes and environmental changes on aging, which has been
revealed to be a malleable process. Many of the findings have proven themselves to
be conserved between species [1]. Indeed, it is well accepted that much of what we
know regarding genetics and molecular pathways of aging in humans stems from
seminal work on these model organisms
In line with the transcriptome studies described above, the work of this thesis assumes that the cause of aging can be inferred from observations of molecular changes that occur throughout the process. In this sense, ‘omics’ approaches producing
genome wide measurements are appropriate tools to use [23]. Nonetheless, while
the ‘omics’ studies above have offered important insight into the changes human
tissues undergo with aging, they have not yet provided an understanding of aging at
the organism level. Arguably, this view of aging will need to be provided by a model
organism, which can overcome caveats that exist when working with humans. Specifically, the following is required:
• A protected environment, to have no external causes of mortality (i.e. consider the lifespans of early versus modern humans, and that early humans had
mortality for reasons other than those that are due to aging [12])
• A constant environment, to have no external influences on the organism that
are differential in time (this ensures that all measured changes result from the
internal influence of aging in the population)
• A homogeneous genetic background (this is to reduce natural variability between individuals, which otherwise produces noise at the population level)
• A naturally short lifespan. This allows the experiment to be performed in a
reasonable amount of time, and offers the possibility to generate data that is
longitudinal (i.e. all individuals are followed throughout their lifespans in the
same experiment), rather than cross-sectional (i.e. a single measurement is
made in individuals with diverse ages).
• Limited biological complexity. In the extreme case: a species consisting of
only a single cell allows measurements to be directly interpreted at the system
level of the organism.
These points all serve to build confidence that what is measured in the study are
aging related changes and not artifacts from other sources, and allow the integration
of all changes over time into a system-level picture of the aging process. Considering these points, perhaps the best organism to apply this experimental strategy to
is the well characterized single celled eukaryote Saccharomyces cerevisiae. The
limited divisions a single such yeast cell undergoes is termed its replicative lifespan
(RLS), and at the population level this resembles the sigmoidal shaped lifespan
curve that occurs in humans as well (Figure 1B). This process of aging results in a
median lifespan of about 25 divisions for the population, and lasts slightly more than
2 days, a relatively short period. Furthermore, its genes are well characterized for
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A system wide view of replicative aging in budding yeast
their function, and much is even known on the influence of certain genes on lifespan
[24]. Following this, the major work of this thesis applies a system-level approach
to understand the process of aging using the replicative lifespan of budding yeast.
To do so, we collect data with large scale ‘omics’ measurements (transcriptome and
proteome) on a population of budding yeast cells, with time points spanning the aging process (Figure 1C).
The results of this large-scale study are presented in chapter 2 of this thesis. Here,
we identified that:
1) Aging yeast undergo an uncoupling of their proteome and transcriptome
2) This uncoupling occurs especially through an over-abundance of translation
machinery related proteins
3) Translation machinery genes are a causal force, shaping the behavior of
other molecular changes occurring with aging, and are therefore a major contributor to the aging phenotype
4) Signs of cellular dysfunction accompany these changes, most notably in the
altered stoichiometry of certain protein complexes.
In chapter 3 of the thesis we present the substantial amount of methods development
and supporting analyses that were required to attain the system level understanding presented in chapter 2. This includes a novel culturing method to keep large
cohorts of aging yeast cells in a constant environment, a mathematical ‘un-mixing’
tool to attain the profile of the aging cohort separate from the budding cells (young
offspring) they produce while aging, and normalization techniques to attain the molecular aging profile free of artifacts from culturing or sample processing. Chapter 3
is presented as supplemental notes and figures to chapter 2.
Chapter 4 follows from our understanding of aging developed in chapter 2, which
essentially claimed aging to result from an over-activity of ‘cellular growth’ pathways
within the cell (i.e. translation machinery and its regulatory systems). Here we ask; if
the protein biogenesis machinery is indeed a central force in aging, can we dissect
the lifespan curve of a yeast population to see if factors related to ‘cellular growth’
correlate to individual lifespans? Following the lifespans of single yeast cells using two recently developed microfluidic platforms [25,26], we found that indeed, the
change in size (reflecting pathways of ‘cellular growth’) that cells undergo negatively
correlates to the lifespan the cells attain. Additionally, we found this same negative
correlation to exist regarding the cell’s division time. When assessing the correlation
of concentration of a ribosomal subunit (Rpl13A) to lifespan, we found a negative
correlation to exist, as expected from the known role of protein biogenesis machinery
in aging [27–31], albeit only at older ages. Younger ages proved to have a positive
correlation of Rpl13a to lifespan, closely linked however to the cell’s changes in size.
Taking these findings together, Chapter 4 provides evidence that cell-to-cell variation
in lifespan is explained by differences in the settings of genes and proteins relevant
for protein synthesis and pathways of ‘cellular growth’. Additionally, the methods for
use of the microfluidic platform presented in [25], which were optimized in the lab
[32,33], are placed as an addendum in chapter 4.
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In chapter 5, the discussion of the thesis, we assess both our own findings and
published work in literature within the context of aging in higher organisms. To do
so, we look at the recently published ‘hallmarks of aging’ in humans [34], and highlight examples of where yeast succumb to aging through similar routes as higher
organisms. We place our previous findings within the greater framework of how the
scientific community sees aging, and in doing so, highlight the relevance of our own
work to the study of aging in humans.
Figure 1: Lifespan curves reflect aging-related survival probabilities. (A) Survival probability of
humans generated from the age-of-death entries of the 2,596,861 deaths that occurred in 2013 in the USA.
Ages over 100 are binned together as ‘100+’, since the data accessed in the Centers for Disease Control and
Prevention CDC WONDER Online Database [35], release 2015, is compiled as such. (B) Survival probability of
yeast generated from the 18,589 single yeast replicative lifespans (RLS) as shown in the meta analysis data
from [36]. For comparison to panel ‘A’ the x-axis is truncated to age 50 although maximal lifespan is 79. (C)
Theoretical lifespan curve (generally applicable to humans, yeast, and other common laboratory organisms
used in aging studies) and illustration of time points (red arrows) that would allow for ‘omics’ data to provide
a view on the molecular events that result in the aging process.
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