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First published online April 20, 2007
Journal of Experimental Biology 210, 1607-1612 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.004887
Review Article |
Common aging pathways in worms, flies, mice and humans
Department of Developmental Biology and Genetics, Stanford University Medical Center, Stanford, CA 94305-5329, USA
e-mail: kim{at}cmgm.stanford.edu
Accepted 8 March 2007
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Summary |
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 TOP Summary IntroductionAging - a universal...Three categories of agingReferences |
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Key words: age regulation, gene expression profile, electron transport chain, gene set enrichment analysis, aging, evolution, genomics
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Introduction |
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TOPSummaryIntroductionAging - a universal...Three categories of agingReferences |
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).
Nearly all organisms age, and yet lifespan can be very different between
species. For example, among major model organisms, the worm Caenorhabditis
elegans lives for 2 weeks, the fly Drosophila melanogaster lives
for 2 months, the mouse Mus musculus lives for 2 years and humans
lives for 
80 years. A great deal might be learned by comparing the aging
process in different species, revealing why humans age so slowly compared with
worms. For example, one could ask whether human cells are exceptionally well
protected against mitochondrial oxidative damage, DNA damage or telomerase
shortening compared with worm cells. Several recent papers have compiled gene
expression profiles for aging from multiple species and then compared them to
each other to distinguish aspects of aging that are species specific and those
that are shared.
There is a rich set of literature on using DNA microarray experiments to
profile gene expression differences for aging in worms, flies, mice and humans
(Kim, in press).
Transcriptional profiles for aging contain quantitative data on age-related
changes in expression for a large fraction of the genome. However, relatively
few studies have integrated aging transcriptional profiles from different
studies in a systematic way to find similarities and differences in aging
among different species. Genes that show age-related transcriptional
differences in multiple species are exceptionally interesting as biomarkers
for age. Their age-related decline scales with lifespan, such that age-related
changes occur relatively quickly in short-lived animals but slowly in
long-lived ones. By contrast, genes that show age regulation in mice but not
humans may help identify pathways and mechanisms that account for much longer
lifespan in humans.
Early work by McCarroll and colleagues compared transcriptional changes in
flies and worms (McCarroll et al.,
2004). This work introduced the concept of treating quantitative
changes in gene expression as a molecular phenotype and then comparing
different expression profiles to each other to reveal overlaps in expression
changes between two experiments. One of the results from this early work was
an apparent similarity between aging in flies and worms. However, there were
statistical flaws used in the calculation, and it is unclear whether the
overlap between flies and worms was statistically significant
(Melov and Hubbard, 2004).
Furthermore, the work did not really measure aging, as the greatest changes in
gene expression occurred in young adulthood and not in old age
(McCarroll et al., 2004).
Nevertheless, this paper introduced key concepts about using gene expression
profiles as molecular phenotypes and set the stage for later papers to
generate expression data on aging and statistical methods to analyze the
data.
Fraser and colleagues compared the effects of aging on gene expression in
the brains of humans and chimpanzees
(Fraser et al., 2005). They
analyzed previously published data on aging in the human brain, including five
different areas of the cerebral cortex
(Evans et al., 2003;
Khaitovich et al., 2004;
Lu et al., 2004). All five
areas of the cortex showed similar patterns of age-related expression changes.
Next, they measured changes in expression in the brain cortex as a function of
age in chimpanzees. They found no correlation between age-related changes in
the cortex of humans and chimpanzees, indicating that aging in humans and
chimpanzees is very different.
A series of recent papers has compared age-related expression profiles in
worms, mice, flies and humans. For worms and flies, DNA microarrays have been
used on whole animals over the entire lifespan to profile transcriptional
changes of aging (Landis et al.,
2004; Lund et al.,
2002; Pletcher et al.,
2002). For mice, Zahn et al. used data from AGEMAP, which is a
large database of expression changes as a function of age in 16 mouse tissues
(J. Zahn, unpublished data). For humans, Zahn et al. measured age-related
transcriptional changes in muscle and compared them with aging changes in the
kidney and the brain (Lu et al.,
2004; Rodwell et al.,
2004; Zahn et al.,
2006).
To compare transcriptional profiles between these four species, Zahn et al.
developed new statistical methods for gene set enrichment analysis and
empirical meta-analysis (Zahn et al.,
2006). These new statistical methods were needed to overcome a
pervasive methodological challenge in genomics studies called multiple
hypothesis testing. The main strength of DNA microarrays – simultaneous
readings of expression from thousands or tens of thousands of genes – is
also a major statistical hurdle because the thousands of gene expression
measurements make it possible for random events to occur that are very rare
(i.e. the problem of multiple hypothesis testing). In a standard experiment
testing only one gene, a P value of 0.05 is convincing because there
is only a 1 in 19 chance that the result could occur by chance. However, with
1000 genes, a P value threshold of 0.05 is not convincing because
about 50 of the 1000 genes can meet this threshold by chance. Hence, DNA
microarray experiments nearly always use more stringent P values
(e.g. <0.001) in order to screen out most of the data that can occur by
chance.
One approach to overcome the statistical hurdles posed by multiple
hypothesis testing is to use gene set enrichment analysis
(Subramanian et al., 2005;
Zahn et al., 2006). To use
this method, one first categorizes all of the genes into pathways or gene
sets; typically, one uses the gene sets defined by the Gene Ontology
consortium
(http://www.geneontology.org/),
such as the set of ribosomal genes or the set of genes that encode components
of the electron transport chain. Then, one looks at the expression of every
gene in the gene set to see if there is an overall trend in expression. Do
most of the genes increase or decrease with age? This method is both powerful
and sensitive because: (1) there are only about 600 gene sets
versus 20 000 genes in the genome, which reduces the problem of
multiple hypothesis testing and (2) small changes in expression in many genes
in a pathway can become statistically significant when considered as a group.
For example, there are 95 genes that encode components of the electron
transport pathway. Using gene set enrichment analysis, Zahn et al. showed that
there is an overall trend of decreasing expression with age for this set of
electron transport genes in worms, flies, mice and humans
(Zahn et al., 2006), even
though only a small number of these genes show very strong age-related changes
by themselves (Fig. 1).
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Empirical meta-analysis is a method to combine gene expression results from
different experiments (Zahn et al.,
2006). Zahn et al. compiled data on age-related changes in
expression from worms, flies, mouse kidneys and human brain, kidney and
muscle. The experimental designs used in each of these studies were quite
different. The human study used patients with varying ages whereas the worm,
fly and mouse studies used staged animals at discrete ages. The human and fly
experiments used Affymetrix GeneChips (Santa Clara, CA, USA), the mouse
experiment used spotted cDNA filters, and the worm experiment used DNA
microarrays. Each gene expression study used different numbers of samples
(e.g. 26 worm samples, 40 mouse samples, 81 human muscle samples). Despite
these differences in experimental design, Zahn et al. were able to use
empirical meta-analysis to search for gene sets that showed a general increase
or decrease in expression with age in different tissues and species. From each
experiment, one can calculate a P value that a gene or gene set
changes expression with age. Empirical meta-analysis combines the P
values from each individual experiment and then calculates an overall
P value for an age-related trend in multiple experiments.
Only one gene group, the electron transport chain gene set, was similarly
age regulated in worms, flies, mice and humans
(Fig. 1)
(Zahn et al., 2006). The
overall level of expression in this set of genes decreased about twofold in
old age for all four species. The electron transport chain genes are located
in the nuclear DNA and encode components of a mitochondrial enzyme complex
that is the primary source of generation of free radicals in the cell. Free
radicals are highly reactive side-products that non-specifically damage cell
components such as proteins and DNA. Oxidative damage from the mitochondrial
free radicals may accumulate with time and thereby decrease overall cell
function and ultimately limit organismal lifespan
(Golden et al., 2002). The
lifespan of worms and humans differ by 2000-fold (2 weeks versus
80 years), and the slope of age-related changes in expression for this pathway
scales with lifespan such that old worms and old humans showed a similar
overall decrease in expression levels. Because this genetic pathway showed
similar age regulation in diverse species, it may be an exceptionally good
biomarker of age.
Do changes in the electron transport chain genes in old age promote
longevity or hasten senescence? In mammals, the functional significance of
age-related changes in this pathway is unclear. However, in C.
elegans, reduction of gene activity of the electron transport chain genes
by RNAi has a strong effect on extending lifespan
(Lee et al., 2003). This
observation suggests that decreased expression of genes in the electron
transport chain pathway in old age may help prolong life in worms, and
possibly other species as well.
Genes that encode proteins in the lysosome show a common increasing trend in expression in humans, mice and flies but not worms (J. Zahn, unpublished data). The lysosome is responsible for degeneration of cell surface receptors, and increased expression of lysosomal genes may mark increased receptor turnover in old age.
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Aging – a universal process that falls outside the force of natural selection |
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TOPSummaryIntroductionAging - a universal...Three categories of agingReferences |
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;
Golden et al., 2002;
Kenyon, 2005;
Kirkwood, 2005;
Kirkwood and Austad, 2000;
Martin, 2002).
A key consideration is that old animals do not normally constitute a
significant fraction of the population in the wild. Wild populations die from
predation and disease, and thus the principle determinant of natural longevity
is extrinsic mortality (Fig. 2)
(Kirkwood and Austad, 2000).
For example, mice live 2–3 years under laboratory conditions, but 90% of
mice die in their first year in the wild
(Phelan and Austad, 1989).
During human evolutionary history, the effective human population size was a
mere 10 000 individuals, and life expectancy was around 20 years. Human life
expectancy increased to 28 years in ancient Greece and Rome and to 46 years by
the year 1900 (Martin, 2002).
Currently, life expectancy is 77 years in North America. Thus, only in
modern human history do old individuals constitute a significant fraction of
human society. In ancient times, most individuals died before the onset of
white hair, wrinkled skin, muscle deterioration, loss of joint fluidity and
other signs of old age. The effects of aging such as these are processes
observed only under laboratory conditions (for animals) or in modern society
(for humans).
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). Mutations with late-acting phenotypes accumulate in the population over evolutionary time, and the cumulative effect of all of these late-acting mutations causes organismal senescence and limits lifespan.
Evolution would select for aging only insofar as that aging must occur more
slowly than extrinsic mortality (i.e. one year for mice and 20 years for
humans). For animals that lack predators, such as humans, bats and birds, the
aging process is relatively slow because these animals survive many years in
the wild. For animals that die rapidly from predation, such as worms, flies
and mice, the aging process is correspondingly rapid as there is no
evolutionary pressure to select for long life. In a classic experiment on the
evolution of aging, Austad assessed the effect on aging due to a rapid decline
in predation (Austad, 1993). He
found that mainland opossums have a high rate of predation from carnivores. He
found a small section of land that had become isolated from the mainland by a
river, and which also happened to lack predators of the opossum. The opossums
on the island had a significantly longer lifespan than those on the mainland,
indicating rapid evolution of a longer lifespan. This result suggests that the
lack of predation of island opossums made it possible for those individuals
with slower rates of aging to come under natural selection and for the
population to then evolve a longer lifespan. By analogy to the opossum
example, the human genome is capable of supporting a longer lifespan than the
mouse genome because there is less death from predation and disease in
primitive humans than in mice.
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Three categories of aging |
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TOPSummaryIntroductionAging - a universal...Three categories of agingReferences |
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), DNA damage
(Hasty et al., 2003), telomere
shortening (Shay and Wright,
2001), protein glycation
(Schmidt et al., 1994) and
transcriptional noise (Bahar et al.,
2006), which are all mechanisms that could lead to cellular
degradation and dysfunction in old age.
|
;
Sulston et al., 1983).
Telomere shortening may have a stronger effect on lifespan of humans than mice
because mice have much longer telomeres than humans
(Hemann et al., 2000). It
could be that mutations that have accumulated in mice and that lead to rapid
decline in three years may be entirely different from those that have
accumulated in humans and that result in slow decline over 80 years. For
mechanisms that fall in this category, genetic causes of aging are likely to
be specific for each species.
The second category of aging mechanism involves pathways that are linked to
a process that occurs during development or young adulthood. Some genetic
pathways may exhibit antagonistic pleiotropy, which means that the same
pathway is beneficial in young adulthood but detrimental in old age
(Williams, 1957). For example,
cell senescence is a mechanism to limit cell division that may be beneficial
early in life to help prevent cancer but may be detrimental late in life
because it limits cell divisions and eventually prevents cellular regeneration
in aging tissues (Campisi,
2005). Caloric restriction falls into this category of aging
because it can extend life in old age but is clearly linked to beneficial
effects in young adults. Caloric restriction extends lifespan in yeast, worms,
flies, mice and probably primates (Kenyon,
2005). All animals face times of starvation and drought, and a
common strategy to improve fitness during harsh times is to extend lifespan
and fertility in order to propagate the species when plentiful times
return.
Because cell senescence and famine survival are key processes affecting young animals, they are under strong selective pressure and are evolutionarily conserved. The effects of cell senescence and caloric restriction on aging in late life may occur repeatedly for different animals, not because their effects on aging are conserved but rather because these aging mechanisms are tightly linked to functions in young animals.
Caloric restriction appears to be under control of the insulin-like growth
factor signaling pathway (Guarente and
Kenyon, 2000). Like caloric restriction, this signaling pathway
has a role in famine survival and lifespan extension in many species.
Specifically, mutations in the insulin-like signaling pathway have been shown
to increase lifespan in worms, flies and possibly mice
(Guarente and Kenyon,
2000).
The third category of aging includes external mechanisms that are
unavoidably associated with old age. One example might be infection and
inflammation that occurs in old age
(Franceschi et al., 2000). As
any animal grows old and begins to show physiological decline, there is an
increased opportunity for pathogenic invasion. Thus, pathogenic invasion from
external sources may occur in all species as they grow old, not because it is
evolutionarily conserved but rather because it is an inescapable condition of
old age.
What do the transcriptional profiles of aging in multiple species inform us
about these three categories of aging? First, the vast majority of age-related
transcriptional changes are different between different species
(Fraser et al., 2005;
Zahn et al., 2006). This
observation suggests that most of the aging process falls under the first
category – species-specific degeneration of cellular and metabolic
pathways in old age.
Second, age regulation of only one pathway, the electron transport pathway,
was observed in worms, flies, mice and humans
(Zahn et al., 2006). The
mechanisms responsible for decreased expression of the electron transport
chain in old age are not known. It is unlikely that this pathway is commonly
age regulated because of evolutionary selective pressure per se.
Rather, common age regulation is likely the result of an unavoidable
consequence of old age (aging category three). One possibility is that
cellular damage from mitochondria in old age is unavoidable, which would
suggest that oxidative damage may be a ubiquitous source of damage that limits
lifespan in all animals. However, this possibility seems unlikely because
mitochondrial function is highly conserved in all metazoans, and it seems
unlikely that mitochondria in mice would cause extensive oxidative damage that
severely limits life span (2 years) whereas mitochondria in humans would cause
minimal damage and permit a long lifespan (80 years). Second, it is not
obvious why mitochondria in lower animals would not function as efficiently as
human mitochondria, and hence permit lifespans approaching that of humans.
Another possibility is that expression of the electron transport chain scales
with overall metabolic activity of the cell and that aging lowers cellular
metabolic activity in all animals.
In summary, comparison of aging transcriptional profiles in worms, flies, mice and humans provides a quantitative, global view of the overall relatedness of the aging process across different species. These results provide a view of the relative proportion of the aging process that is specific to humans (private) rather than shared across animals (public). The vast majority of age-related transcriptional changes are private to humans, and these are likely the result of cell degeneration pathways that are species specific (aging category one) (Fig. 3). The emerging view from the genomics experiments is that the aging process is quite different in mice and humans, emphasizing the need for research using human samples to uncover aging mechanisms relevant to human longevity. A small amount of age-related changes in expression are public across species. These public aging pathways may be linked to functions in young adults (aging category two) or may be unavoidable consequences of growing old (aging category three). Identification of these public pathways is key because they highlight specific aging pathways that can be dissected apart in model organisms to elucidate general principles of aging.
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Acknowledgments |
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Footnotes |
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References |
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TOPSummaryIntroductionAging - a universal...Three categories of agingReferences |
|---|
Austad, S. N. (1993). Retarded senescence in an insular-population of opossums. J. Zool. 152,695 -708.
Bahar, R., Hartmann, C. H., Rodriguez, K. A., Denny, A. D., Busuttil, R. A., Dolle, M. E., Calder, R. B., Chisholm, G. B., Pollock, B. H., Klein, C. A. et al. (2006). Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature 441,1011 -1014.[CrossRef][Medline]
Campisi, J. (2005). Aging, tumor suppression and cancer: high wire-act! Mech. Ageing Dev. 126, 51-58.[CrossRef][Medline]
Evans, S. J., Choudary, P. V., Vawter, M. P., Li, J., Meador-Woodruff, J. H., Lopez, J. F., Burke, S. M., Thompson, R. C., Myers, R. M., Jones, E. G. et al. (2003). DNA microarray analysis of functionally discrete human brain regions reveals divergent transcriptional profiles. Neurobiol. Dis. 14,240 -250.[CrossRef][Medline]
Franceschi, C., Bonafe, M., Valensin, S., Olivieri, F., De Luca, M., Ottaviani, E. and De Benedictis, G. (2000). Inflamm-aging. An evolutionary perspective on immunosenescence. Ann. N. Y. Acad. Sci. 908,244 -254.[CrossRef][Medline]
Fraser, H. B., Khaitovich, P., Plotkin, J. B., Paabo, S. and Eisen, M. B. (2005). Aging and gene expression in the primate brain. PLoS Biol. 3,e274 .[CrossRef][Medline]
Golden, T. R., Hinerfeld, D. A. and Melov, S. (2002). Oxidative stress and aging: beyond correlation. Aging Cell 1,117 -123.[CrossRef][Medline]
Guarente, L. and Kenyon, C. (2000). Genetic pathways that regulate ageing in model organisms. Nature 408,255 -262.[CrossRef][Medline]
Harmon, D. (1972). The biologic clock: the mitochondria? J. Am. Geriatr. Soc. 20,145 -147.[Medline]
Hasty, P., Campisi, J., Hoeijmakers, J., van Steeg, H. and Vijg,
J. (2003). Aging and genome maintenance: lessons from the
mouse? Science 299,1355
-1359.
Hemann, M. T., Hackett, J., IJpma, A. and Greider, C. W. (2000). Telomere length, telomere-binding proteins, and DNA damage signaling. Cold Spring Harb. Symp. Quant. Biol. 65,275 -279.[CrossRef][Medline]
Kenyon, C. (2005). The plasticity of aging: insights from long-lived mutants. Cell 120,449 -460.[CrossRef][Medline]
Khaitovich, P., Muetzel, B., She, X., Lachmann, M., Hellmann,
I., Dietzsch, J., Steigele, S., Do, H. H., Weiss, G., Enard, W. et al.
(2004). Regional patterns of gene expression in human and
chimpanzee brains. Genome Res.
14,1462
-1473.
Kim, S. K. (in press). Genome-wide views of aging gene networks. In Molecular Biology of Aging (ed. D. W. L. Guarente and L. Partridge). Cold Spring Harbor: Cold Spring Harbor Laboratory Press.
Kirkwood, T. B. (2005). Understanding the odd science of aging. Cell 120,437 -447.[CrossRef][Medline]
Kirkwood, T. B. and Austad, S. N. (2000). Why do we age? Nature 408,233 -238.[CrossRef][Medline]
Landis, G. N., Abdueva, D., Skvortsov, D., Yang, J., Rabin, B.
E., Carrick, J., Tavare, S. and Tower, J. (2004). Similar
gene expression patterns characterize aging and oxidative stress in
Drosophila melanogaster. Proc. Natl. Acad. Sci. USA
101,7663
-7668.
Lee, S. S., Lee, R. Y., Fraser, A. G., Kamath, R. S., Ahringer, J. and Ruvkun, G. (2003). A systematic RNAi screen identifies a critical role for mitochondria in C. elegans longevity. Nat. Genet. 33,40 -48.[CrossRef][Medline]
Lu, T., Pan, Y., Kao, S. Y., Li, C., Kohane, I., Chan, J. and Yankner, B. A. (2004). Gene regulation and DNA damage in the ageing human brain. Nature 429,883 -891.[CrossRef][Medline]
Lund, J., Tedesco, P., Duke, K., Wang, J., Kim, S. K. and Johnson, T. E. (2002). Transcriptional profile of aging in C. elegans. Curr. Biol. 12,1566 -1573.[CrossRef][Medline]
Martin, G. M. (2002). Gene action in the aging brain: an evolutionary biological perspective. Neurobiol. Aging 23,647 -654.[CrossRef][Medline]
McCarroll, S. A., Murphy, C. T., Zou, S., Pletcher, S. D., Chin, C. S., Jan, Y. N., Kenyon, C., Bargmann, C. I. and Li, H. (2004). Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat. Genet. 36,197 -204.[CrossRef][Medline]
Medawar, P. B. (1952). An Unsolved Problem of Biology. London: H. K. Lewis.
Melov, S. and Hubbard, A. (2004). Microarrays
as a tool to investigate the biology of aging: a retrospective and a look to
the future. Sci. Aging Knowledge Environ.
2004, re7.
Park, S. K. and Prolla, T. A. (2005). Lessons learned from gene expression profile studies of aging and caloric restriction. Ageing Res. Rev. 4,55 -65.[CrossRef][Medline]
Phelan, J. P. and Austad, S. N. (1989). Natural selection, dietary restriction, and extended longevity. Growth Dev. Aging 53,4 -6.[Medline]
Pletcher, S. D., Macdonald, S. J., Marguerie, R., Certa, U., Stearns, S. C., Goldstein, D. B. and Partridge, L. (2002). Genome-wide transcript profiles in aging and calorically restricted Drosophila melanogaster. Curr. Biol. 12,712 -723.[CrossRef][Medline]
Rodwell, G. E., Sonu, R., Zahn, J. M., Lund, J., Wilhelmy, J., Wang, L., Xiao, W., Mindrinos, M., Crane, E., Segal, E. et al. (2004). A transcriptional profile of aging in the human kidney. PLoS Biol. 2,e427 .[CrossRef][Medline]
Schmidt, A. M., Hori, O., Brett, J., Yan, S. D., Wautier, J. L.
and Stern, D. (1994). Cellular receptors for advanced
glycation end products. Implications for induction of oxidant stress and
cellular dysfunction in the pathogenesis of vascular lesions.
Arterioscler. Thromb.
14,1521
-1528.
Shay, J. W. and Wright, W. E. (2001). Telomeres and telomerase: implications for cancer and aging. Radiat. Res. 155,188 -193.[CrossRef][Medline]
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S.,
Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R.,
Lander, E. S. et al. (2005). Gene set enrichment analysis: a
knowledge-based approach for interpreting genome-wide expression profiles.
Proc. Natl. Acad. Sci. USA
102,15545
-15550.
Sulston, J. E. and Horvitz, H. R. (1977). Post-embryonic cell lineages of the nematode, Caenorhabditis elegans.Dev. Biol. 56,110 -156.[CrossRef][Medline]
Sulston, J. E., Schierenberg, E., White, J. G. and Thomson, J. N. (1983). The embryonic cell lineage of the nematode Caenorhabditis elegans. Dev. Biol. 100,64 -119.[CrossRef][Medline]
Williams, G. C. (1957). Pleiotropy, natural selection and the evolution of senescence. Evolution 11,398 -411.[CrossRef]
Zahn, J. M., Sonu, R., Vogel, H., Crane, E., Mazan-Mamczarz, K., Rabkin, R., Davis, R. W., Becker, K. G., Owen, A. B. and Kim, S. K. (2006). Transcriptional profiling of aging in human muscle reveals a common aging signature. PLoS Genet. 2, e115.[CrossRef][Medline]
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