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www.elsevier.com/locate/mechagedev
Mechanisms of Ageing and Development 128 (2007) 149–160
Application of advanced technologies in ageing research
Ching-Aeng Lim a,*, Huck-Hui Ng a,b
a Gene Regulation Laboratory, Genome Institute of Singapore, Singapore 138672b Department of Biological Sciences, National University of Singapore, Singapore 117543
Available online 17 November 2006
Summary
Several technologies that emerged in the post-genomic era have been particularly useful in dissecting the molecular mechanisms of complex
biological processes through the systems approach. Here, we review how three of these technologies, namely transcriptional profiling, large-scale
RNA interference (RNAi) and genome-wide location analysis of protein-DNA interactions, have been used in the study of ageing in metazoans. We
also highlight recent developments of these three technologies and how these developments are applicable to ageing research.
# 2006 Elsevier Ireland Ltd. All rights reserved.
1. Introduction
Ageing is the biological process characterized by the
progressive and irreversible loss of physiological function
accompanied by increasing mortality with advancing age
(Sohal and Weindruch, 1996). It is a complex physiological
phenomenon associated with a multitude of biological changes
at the molecular level, which is eventually manifested at the
tissue and organism levels for metazoans (Kriete et al., 2006).
Understanding the molecular mechanism behind the ageing
process is therefore, key to the development of therapeutics
which prolong human lifespan, and more importantly, to reduce
morbidity among the elderly.
The complexity of the ageing process poses a major
challenge for experimental interrogation in metazoan models.
As a means of studying complex biological processes,
biologists traditionally resort to the reductionist experimental
approach, which breaks the problem into simpler functional
units that are experimentally more tractable. However, by
studying biological pathways in isolation, one may not fully
appreciate the complex interplay that occurs between
individual pathways within the system (Ahn et al., 2006).
An alternative method for studying complex processes, such as
* Corresponding author at: Genome Institute of Singapore, 60 Biopolis Street
#02-01, Genome Building, Singapore 138672. Tel.: +65 6478 8145;
fax: +65 6478 9004.
E-mail addresses: [email protected] (C.-A. Lim),
[email protected] (H.-H. Ng).
0047-6374/$ – see front matter # 2006 Elsevier Ireland Ltd. All rights reserved.
doi:10.1016/j.mad.2006.11.024
ageing, is through a systems approach (Ideker et al., 2001). In
the systems approach, the behaviors of all the elements in a
biological system are investigated simultaneously, providing a
bird’s eye view of the molecular landscape. By doing so, one
can better understand how different elements interact with each
other to generate a particular biological outcome. The systems
approach, however, requires experimental tools that can survey
all the elements of the biological system in a high-throughput
fashion. With the completion of key animal genome sequencing
projects in recent years, such as nematode (The Caenorhabditis
elegans Sequencing Consortium, 1998), fly (Adams et al.,
2000), mouse (Waterston et al., 2002) and human (IHGS, 2004;
Lander et al., 2001; Venter et al., 2001), several technologies
have been developed that allow us to examine biological
phenomena on a genome-wide scale.
Certain high-throughput technologies that have emerged in
the post-genomic era are now recognized as powerful discovery
tools in biology (Ideker et al., 2001). Examples include
transcriptional profiling and proteomics, which allow for global
gene expression profiling; high-throughput RNA interference
(RNAi), which enables systematic perturbation of biological
systems; as well as genome-wide analysis of protein-DNA
interactions, which is useful for the study of transcription
networks. The use of these high-throughput tools offers a
distinct advantage over traditional reductionist approaches-the
ability to study biological processes in an unbiased manner.
These discovery tools thus, provide novel means to generating
hypotheses or models about the molecular mechanisms
underlying complex biological phenomena.
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160150
In this review, we will highlight developments in transcrip-
tional profiling, RNA interference and protein-DNA location
analysis, because they represent powerful systems approach
tools that can make significant contributions to the study of
ageing. How these tools have already been implemented in the
field of ageing will also be summarized.
Finally, we will discuss how these novel experimental
approaches can be further exploited in the field of ageing
research.
2. Transcription profiling
Transcriptional profiling refers to the simultaneous analysis
of gene expression of all the transcripts encoded by the genome.
Such global profiling of gene expression has been proven to be
useful in the analysis of complex biological processes such as
metabolism in yeast (DeRisi et al., 1997), cell cycle regulation
in dividing fibroblasts (Cho et al., 2001) and embryo develop-
ment (Sharov et al., 2003). Recognized as a powerful discovery
tool, it is therefore, not surprising that transcriptional profiling
has also made significant inroads into the field of ageing (Park
and Prolla, 2005; Weindruch et al., 2002).
Currently, there exist three techniques that are widely used for
transcriptional profiling. Two of them, serial analysis of gene
expression (SAGE) and massively parallel signature sequencing
(MPSS), involves the sequencing of transcripts. DNA micro-
arrays represent the third tool for transcriptional profiling, which
relies on the specific hybridization of complementary nucleotide
sequences (Ruan et al., 2004).
In SAGE, 14 bp or 21 bp tags derived from transcripts are
concatenated and sequenced. The sequenced tags are then
computationally assigned to a particular gene based on sequence
identity (Velculescu et al., 1995). Due to the random sampling of
transcripts during the molecular cloning of sequence tags, the
number of sequenced tags associated with a particular gene (gene
tag) is reflective of transcript abundance. The greater the gene tag
count, the more highly expressed that gene is. Thus far, SAGE
has been successfully employed to identify age-related changes
in gene expression of mouse cerebellum (Popesco et al., 2004), as
well as to identify differentially expressed genes between control
and long-lived C. elegans daf-2 mutants (Halaschek-Wiener
et al., 2005).
Another sequencing-based transcriptional profiling method
developed by Brenner and colleagues is MPSS. Unlike SAGE,
it uses bead-based in situ cloning and a restriction-hybridiza-
tion-ligation sequencing method that can assess up to 1 million
transcripts per assay (Brenner et al., 2000). One major
advantage that MPSS has over SAGE is that it is highly
parallel; capable of recording millions of tag-sequencing events
in a single run. Despite its high-throughput capability, there are
still no reports of the use of MPSS to study ageing. This is
probably because MPSS is still more costly and technically
more demanding as compared to microarray-based transcrip-
tional profiling technologies.
DNA microarrays are now commonly used for transcrip-
tional profiling. These devices provide a platform for sequence-
specific nucleic acid hybridization analysis to be performed in
microminiturized highly-parallel formats (Heller, 2002). The
two main classes of microarrays used for transcriptional
profiling are cDNA and oligonucleotide microarrays. In cDNA
microarrays, cDNA clones derived from the reverse transcrip-
tion of mRNA transcripts are spotted onto a solid substrate such
as glass (Schena et al., 1995). The immobilized cDNA thus
function as probes, which detect the presence of complemen-
tary nucleic acid sequences via sequence-specific hybridiza-
tion. In contrast, the probes used in oligonucleotide microarrays
consist of oligonucleotides that are usually synthesized in situ
on the microarray surface. Since oligonucleotide probes can be
designed to be highly specific towards its intended target gene,
oligonucleotide microarrays are now the platform of choice for
transcriptional profiling.
One important application of transcriptional profiling is the
discovery of molecular biomarkers associated with the ageing
process. Ageing biomarkers are genes that are differentially
expressed as the organism ages. Collectively, these biomarkers
provide a signature that defines the aged phenotype, and may be
used as a molecular ‘ruler’ to determine the extent of ageing.
Although microarrays have been used to profile age-related
transcriptional changes in worms and flies, it is particularly
useful for the study of ageing in longer-lived mammals where
the use of survival curves is not practical (Weindruch et al.,
2002). Numerous groups have already used DNA microarrays
to identify ageing biomarkers in tissues of different mammalian
species. So far, transcriptional profiling has been carried out in
skeletal muscle derived from mice, monkeys and humans of
different ages (Kayo et al., 2001; Lee et al., 1999; Welle et al.,
2004; Welle et al., 2003). Transcriptional profiling of ageing
mouse cardiac muscle (Lee et al., 2002), mouse brain (Lee
et al., 2000), mouse liver (Cao et al., 2001), human fibroblast
(Ly et al., 2000), human brain (Lu et al., 2004) and human
kidney (Rodwell et al., 2004) have also been conducted.
Although these studies were able to identify numerous genes,
which were up- or down-regulated as a function of chrono-
logical age, such an experimental approach may not be ideal
because individuals age at different rates (Butler et al., 2004).
This prompted investigators to begin identifying biomarkers,
which are associated with physiological age rather than
chronological age. Rodwell and colleagues assigned physio-
logical ages to kidney tissues based on morphological
examination of histological stains and used transcriptional
profiling to identify genes that were differentially expressed as
a function of physiological age. With this approach, they
observed that genes encoding extracellular matrix proteins and
ribosomal proteins are significantly up-regulated in kidneys
that are physiologically old (Rodwell et al., 2004). Recently,
molecular markers that reflect the physiological age of human
skeletal muscle were also identified (Zahn et al., 2006). Using
the ratio of the diameters of type I and type II muscle fibres to
determine the physiological age of the muscle tissues, Zahn and
colleagues identified 250 genes that are age-regulated. More
importantly, by comparing the transcriptional profiles of ageing
in skeletal muscle, kidney and brain, Zahn et al. discovered a
common signature of ageing in diverse human tissue. This
molecular signature is represented by an up-regulation of genes
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160 151
encoding extracellular matrix and ribosomal proteins, as well as
genes involved in cell growth and complement activation; and
the down-regulation of genes involved in chloride transport and
the electron transport chain. A further comparison of ageing
biomarkers identified in humans, mouse and fly revealed that
the down-regulation of genes encoding components of the
electron transport chain is strikingly conserved across species.
The transcriptional profiling experiments described thus far,
make use of DNA microarrays that only probe for exons of
annotated and/or characterized transcripts. However, genome
tiling arrays that can probe entire non-repetitive genomic
sequences from both strands of the mouse and human genome
at high resolution are now available. With these high-
density arrays, it is now possible to comprehensively catalogue
all expressed transcripts in a completely unbiased manner
(Bertone et al., 2004; Cheng et al., 2005). These arrays
therefore, allow for the identification of unannotated alter-
natively spliced transcripts, novel protein-coding transcripts
and even non-protein-coding RNA transcripts that are
differentially-expressed with age, thereby extending the current
list of ageing biomarkers.
Once the molecular markers associated with ageing are
identified, one can easily assay for interventions that regulate
the ageing process. These interventions can be nutritional,
pharmaceutical or genetic in nature. Besides identifying
interventions that can accelerate or decelerate ageing, trans-
criptional profiling also allows for the molecular interrogation
of interventions already known to exert ageing-regulatory
effects at the phenotypic level. One example of such an
intervention is caloric restriction (CR). CR is the only
intervention conclusively shown to exert anti-ageing effects
across the phylogenetic spectrum and probably humans (Dirks
and Leeuwenburgh, 2006). Although CR is observed to prolong
lifespan and maintain health in various organisms, the
underlying molecular mechanism remains largely unknown
(Masoro, 2005). Transcriptional profiling, however, has
managed to shed some light on the age-retarding effects of
CR. Prolla and colleagues, for instance, observed that CR was
able to prevent the age-dependent up-regulation of stress
response genes and down-regulation of biosynthetic genes in
murine skeletal muscle, thus, suggesting that CR may slow
down ageing by causing a metabolic shift towards increased
protein turnover and decreased macromolecular damage (Lee
et al., 1999). Another study attributed the anti-ageing effects of
CR to the increased ability of rat skeletal muscle to scavenge
reactive oxygen species (Sreekumar et al., 2002). Transcrip-
tional profiling has also been especially useful for examining
the temporal effects of CR. Using mouse liver as a model, Cao
and colleagues demonstrated that in young mice, CR-specific
changes in gene expression was a subset of those found in old
mice. This, together with the observation that rats which
underwent CR only in their first year outlived rats that were on
CR after their first year, suggests that CR produces a change in
gene expression profile early in life and this change may be key
to extended longevity (Cao et al., 2001). Another interesting
observation made by Cao and colleagues was that a short-term
CR of just four weeks is sufficient to shift the transcriptional
profile of old mice towards that of mice undergoing long-term
CR. This is a significant finding because it suggests that CR
may be able to exert anti-ageing effects within a short span of
time.
Transcriptional profiling can also be used to screen for
interventions that exert anti-ageing effects. Since CR has been
shown to exert strong anti-ageing effects, there have been
attempts to identify pharmaceuticals that can mimic the
transcriptional effects of CR. An example of a small molecule
that may function as a CR mimetic in worms and flies is
resveratrol (Wood et al., 2004). Resveratrol was suspected to
be a CR mimetic because of its ability to activate Sir2-like
proteins (Howitz et al., 2003) and the requirement of SIR2 for
lifespan extension by CR in budding yeast (Lin et al., 2000).
Transcriptional profiling later revealed that resveratrol might
exert its CR-like effects in worms by up-regulating the
expression of endoplasmic reticulum stress response genes
(Viswanathan et al., 2005). The identification of other CR
mimetics such as resveratrol will be highly beneficial since
these drugs can help reduce morbidity among the elderly and
yet be able to circumvent the feeling of hunger associated with
CR (Ingram et al., 2006). Transcriptional profiling using
microarrays provides a convenient and efficient method for
assaying and identifying CR mimetics (Spindler, 2006).
Candidate CR mimetics are identified based on drugs that
give a gene expression profile similar to that generated by CR.
This use of surrogate biomarkers to assay for potential
longevity therapeutics is attractive because it does not require
the knowledge of the molecular mechanisms underlying
ageing. Through the use of surrogate transcriptional profiling
assays, Spindler and colleagues identified the glucoregulatory
drug, metformin, as a potential CR mimetic (Dhahbi et al.,
2005). They observed that eight weeks of metformin treatment
was superior to eight weeks of CR at reproducing long term-
CR-like gene expression changes. Their results were consistent
with observations that metformin can reduce cancer incidence
in both mice and humans (Anisimov et al., 2003; Evans et al.,
2005). Recently, Golub and colleagues proposed the initiation
of a large-scale Connectivity Map project (Lamb et al., 2006).
The Connectivity Map uses platform-independent pattern-
matching software to identify connections between test and
reference gene expression profiles. A reference database could
comprise of gene expression profiles of cellular responses to
FDA-approved drugs. Not only was such a Connectivity Map
demonstrated to rediscover drugs known to modulate the
estrogen nuclear receptor pathway, it was also able to identify
potential new therapeutics to overcome dexamethasone
resistance in acute lymphoblastic leukemia. If such a project
is realized, it will serve as a valuable resource for the
identification of CR mimetics among drugs that have already
been approved for human administration.
DNA microarrays have also been used to study genetic
disorders known collectively as progerias. Progeria is character-
ized by the early onset of symptoms that are typically manifested
only much later in life. Werner syndrome (WS) and Hutchinson-
Gilford progeria syndrome (HGPS) are two examples of
progeria. The transcriptional profiling of tissues from individuals
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160152
diagnosed with progeria can help in the identification of the
molecular mechanisms underlying progeria and hopefully lead to
the development of therapeutics that can prevent or reduce the
severity of the disease. Understanding the molecular basis of
progeria may also provide important clues to the mechanism of
normal ageing (Prolla, 2005). Using cDNA microarrays, Bohr
and colleagues profiled fibroblast cell lines derived from young,
old and WS-stricken individuals and observed that 91% of
annotated genes displayed similar expression changes in WS as
compared with normal ageing. WS thus, appears to be a result of
an acceleration of the normal ageing process (Kyng et al., 2003).
Bohr and colleagues later reported that fibroblast derived from
WS and old individuals displayed aberrant transcriptional
responses to DNA-damaging agents. These results point to the
possibility that both normal ageing and WS may be a
consequence of the impaired ability of cells to repair DNA in
response to genotoxic insults (Kyng et al., 2005). In contrast to
WS, transcriptional profiling of fibroblasts from individuals
afflicted with HGPS showed the lack of similarity between the
gene expression profiles of HGPS tissue and tissues in ageing
mice (Csoka et al., 2004). This suggests that the molecular events
underlying HGPS and ageing are separate and distinct despite the
phenotypic similarities of these two biological phenomena.
3. RNA interference
Transcriptional profiling, as useful as it may be for analysing
gene expression changes associated with a biological process, is
unable to define the functional contribution of a particular gene
to that process. Data from global gene expression analysis can
at best, hint at the functional role of a particular gene or a cluster
of genes based on observed correlations. The identification of
gene products that contribute functionally to biological
phenomena involves the use of molecular tools that enable
us to systematically perturb specific genetic components in the
system.
A common strategy used to determine the role of a particular
gene in a biological process involves the removal or
inactivation of that gene and observing its consequence on
the system. Traditionally, such loss-of-function genetic screens
involve the random mutagenesis of genomic DNA followed by
the identification of mutant phenotypes that emerge. Such
forward genetic screens are time-consuming, however, since it
requires the experimenter to map the genetic lesion that
contributes to the abnormal phenotype. This can be circum-
vented through the targeted disruption of a desired genetic locus
with reverse genetic approaches. However, classical reverse
genetic approaches, which involve the generation of knockout
cell lines or organisms, are technically challenging, costly and
time-consuming (Rajewsky et al., 1996). It is for these reasons
that classical reverse genetic approaches have not been adopted
for large-scale functional genetic screens except for genetically
amenable organisms such as yeast (Giaever et al., 2002).
A recent addition to the reverse genetics toolbox, however,
has revolutionized the way biologists carry out functional
genetic studies. Known as RNA interference, it is an evolution-
ary conserved phenomenon whereby the introduction of
double-stranded RNA (dsRNA) corresponding to a particular
mRNA results in the specific and rapid degradation of that
mRNA in cells. Since its discovery in C. elegans, RNAi has
been shown to operate in organisms ranging from yeast to
mammals (Sen and Blau, 2006). In order to use RNAi as a tool
for gene silencing, techniques had to be developed for the
convenient and effective delivery of dsRNA into cells. Initially,
it was found that RNAi could be triggered in C. elegans simply
by soaking the worms in a solution of dsRNA or by feeding
worms with E. coli that expresses the dsRNA (Tabara et al.,
1998; Timmons and Fire, 1998). Either method leads to RNAi
throughout almost the entire worm, which allows biologists to
study the effect of gene silencing on the physiology of the entire
organism. The ability of RNAi to silence, or knockdown, any
gene in a whole adult organism makes it well-suited for the
study of ageing in C. elegans. Numerous groups have already
exploited this property of RNAi to identify genes, which when
silenced, extend or shorten the lifespan of worms. Initially, the
genes that were selected to be silenced are usually those that
belong to biological pathways already known to regulate
longevity. This included genes that play a role in mitochondrial
respiration, oxidative homeostasis and the insulin/IGF-1
pathway (Deocaris et al., 2004). Such reductionist approaches,
although useful at dissecting the molecular mechanisms of
known longevity pathways, are not ideal for identifying novel
regulators of ageing. A more suitable approach is one that
allows for the systematic knockdown of every gene encoded by
the C. elegans genome.
Ahringer and colleagues were the first to carry out a syste-
matic functional analysis on C. elegans at the genome-wide scale
using RNAi (Kamath et al., 2003). The genome-wide RNAi
library was created by the cloning of PCR-amplified genomic
fragments into vectors, which when transformed into E. coli,
generated dsRNA that targeted approximately, 86% of the 19,427
predicted genes of C. elegans. Functional analysis of the worms
fed with this bacterial library revealed mutant phenotypes for
1722 genes, two-thirds of which have not been previously
associated with any phenotype. Following the success of the first
functional genomics screen in C. elegans, Vidal and colleagues
announced the creation of another genome-wide RNAi library
based on cDNA sequences derived from a C. elegans ORFeome-
library (Rual et al., 2004). The development of these genome-
wide RNAi libraries enables reverse genetic studies to be carried
out in a cost-and time-efficient manner. One important advantage
of systematic reverse genetic screens is the ability to test the
function of every gene encoded by the genome in a finite number
of samples. Traditional forward genetic screens, on the other
hand, require a collection of random mutants that is many times
the total number of genes in the organism in order to cover most
of the genome.
The availability of genome-wide RNAi libraries for C.
elegans is a boon for researchers in the field of ageing. With
genome-wide high-throughput RNAi, researchers have the
unprecedented ability to systematically identify all of the genes
that can affect the lifespan of the worm. Recently, two groups
reported the use of the RNAi library generated by Ahringer’s
lab to screen for genes that regulate longevity in C. elegans.
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160 153
Through RNAi screening, Lee and colleagues identified 89
genes, which when silenced, extended the lifespan of worms
(Hamilton et al., 2005). Besides rediscovering known longevity
genes such as age-1 and akt-1, most other genes identified in the
screen were previously unknown to be involved in the regulation
of lifespan. Further, analysis revealed that a large proportion of
the identified genes affect lifespan by acting upstream of daf-16,
a known longevity determinant. Another group, led by Kenyon,
independently carried out a similar screen for the identification of
longevity genes (Hansen et al., 2005). They identified 29 genes
that reproducibly extended the lifespan of C. elegans when they
were knocked-down. One of the genes identified was daf-2, a
known longevity gene. All but one of the novel longevity genes
identified by Kenyon’s group are associated with known
longevity pathways. Eight of the genes identified were also
found to require daf-16 to extend lifespan. By identifying
numerous longevity genes belonging to known as well as novel
pathways, the screens performed by these two groups represent
significant milestones in ageing research and highlights the
usefulness of genome-wide RNAi methods in probing the
functional components of the ageing process.
The success of using RNAi to carry out functional genomic
screens in C. elegans has spurred interest in developing tools
that enable such genome-wide functional screens to be
performed in more complex organisms such as mammals.
Mammalian cells, unlike C. elegans, do not have the ability to
propagate the RNAi-inducing dsRNA. Thus, the effect of
dsRNA on mammalian cells is transient, making it unsuitable
for depleting stable proteins and for studying long-term effects
of gene silencing. This problem was subsequently resolved
with the development of DNA vector-based technology that
drives the expression of short hairpin RNA (shRNA) in
mammalian cells (Miyagishi and Taira, 2002; Sui et al., 2002;
Yu et al., 2002). This was soon followed by the development of
viral vectors that drive the expression of shRNA (Abbas-Terki
et al., 2002). Lentiviral-based shRNA vectors are favored for a
couple of reasons. Firstly, lentiviruses have the ability to
transduce both dividing as well as non-dividing cells with high
efficiency. This makes it particularly useful for carrying out
RNAi in primary cells and difficult to transfect cell lines. The
other advantage of lentiviral-based shRNA is that the silencing
cassette is integrated into the genome, thus, providing a
convenient method for generating a stable cell line that exhibits
constitutive gene suppression.
As technology to effectively trigger RNAi in mammalian
cells matured, methods were also devised to perform large-
scale screens of gene function analogous to those carried out in
C. elegans. The first reported use of large-scale RNAi in
mammalian cells involved the systematic knockdown of 510
genes in HeLa cells using siRNA duplexes (Aza-Blanc et al.,
2003). This was followed by a more ambitious study, whereby
23,742 shRNA-expressing retro-viral vectors targeting 7914
human genes was constructed and used to screen for genes that
are involved in p53-mediated cell cycle arrest (Berns et al.,
2004). To facilitate high-throughput genome-scale RNAi
screening in mammalian systems, two groups have recently
created lentiviral-shRNA libraries that target almost every
gene encoded by the mouse and human genomes. The RNAi
library generated by The RNAi Consortium (TRC) contains
shRNA that targets more than 14,000 human genes and more
than 12,000 mouse genes, with an average of 5 shRNAs per
target gene (Moffat et al., 2006; Root et al., 2006). Hannon and
colleagues have generated a more comprehensive RNAi library
(Hannon–Elledge library) targeting more than 32,000 and
30,000 human and mouse genes, respectively (Chang et al.,
2006; Silva et al., 2005). Despite having these RNAi libraries
in hand, performing functional genomic screens in mammalian
systems still poses a significant challenge. The limitation now
shifts to technologies that enable the high-throughput screen-
ing of knockdown phenotypes. High content screening (HCS)
technologies are now being developed to automate the
screening process (Carpenter and Sabatini, 2004). Currently,
the most commonly employed HCS methods involve auto-
mated microscopy imaging systems that detects fluorescent
signals or recognizes cellular morphology. HCS technology
has recently been successfully implemented in a small scale
RNAi screen of 49 genes in HeLa cells. Using time-lapse
fluorescence microscopy, Ellenberg and colleagues were able
to classify cell division-associated phenotypes resulting from
the knockdown (Neumann et al., 2006).
As described, large-scale RNAi screening in C. elegans have
identified numerous genes that can extend the lifespan of
worms. Since orthologous counterparts of these genes may
exist in mammals, it would be of great interest to be able to
examine the functional contribution of these orthologous genes
to the regulation of mammalian lifespan. RNAi of mammalian
cells in culture, although useful for the study of age-related
changes at the cellular level, is unable to assess the effect of
gene depletion on organism level traits such as lifespan.
Traditionally, the only way to perform targeted loss-of-function
genetic studies in whole mammalian organisms is by generating
knockout mice through homologous recombination, a techni-
que that is tedious and costly. A more promising approach
involves the generation of transgenic mice expressing RNAi-
inducing shRNA.
Recently, two groups successfully generated transgenic mice
that are deficient in the intended target genes by introducing
shRNA-expressing constructs into mice as transgenes via
conventional pronuclear injection (Peng et al., 2006; Xia
et al., 2006). Thus, transgenic RNAi technology offers a more
convenient method of testing the role of candidate longevity
genes in mammals. Besides using transgenic RNAi technology to
assess the function of a few suspected candidate genes, Zhang
and colleagues successfully adopted this approach in a small-
scale screen involving 15 genes and managed to identify a gene
that contributed to kidney development (Peng et al., 2006). This
pilot study may be a harbinger of RNAi-based functional
genomic screens for genes that regulate ageing and longevity in
mammals.
4. Analysis of protein-DNA interactions
As highlighted earlier, transcriptional profiling of mamma-
lian systems identified numerous genes that displayed changes
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160154
in expression as a function of physiological as well as chro-
nological age. Global scale changes in transcriptional states
were similarly observed in other model organisms such as
worm (Lund et al., 2002; Murphy et al., 2003; Viswanathan
et al., 2005) and fly (Girardot et al., 2006; Kim et al., 2005b;
Pletcher et al., 2002). These findings underscore the importance
of studying ageing-associated transcriptional regulation if one
wishes to dissect the molecular mechanisms underlying the
ageing process. The study of transcriptional regulation involves
the identification of the transcriptional regulators that effect the
transcriptional changes followed by the identification of the
targets of these transcriptional regulators. Transcriptional
regulators that regulate age-related processes may be identified
through transcriptional profiling strategies and/or functional
genetic screens. An example of a transcription regulator that
functions as a key regulator of longevity is DAF-16, a forkhead
transcription factor that is negatively regulated by the DAF-2
insulin receptor pathway (Larsen et al., 1995; Ogg et al., 1997).
One approach to identify the genes that are regulated by a
particular transcriptional regulator involves the perturbation of
the transcriptional regulator in question and examining the
changes in the transcriptional profile as a result of that
perturbation. This approach was adopted by Thomas and
colleagues to identify the downstream targets of DAF-16 in C.
elegans. Using cDNA microarrays, they looked for genes that
are differentially expressed between daf-2 (DAF-16 active
state) and daf-16;daf-2 (DAF-16 inactive state) mutant strains
(McElwee et al., 2003; McElwee et al., 2004). Kenyon and
colleagues carried out a similar analysis, except that RNAi was
used to generate the daf-2- and daf-2;daf-16-deficient strains
of C. elegans (Murphy et al., 2003). Both groups observed the
up-regulation of stress response and metabolic genes when
DAF-16 is active. These genes thus, represent downstream
targets of DAF-16 that probably play a role in prolonging the
lifespan of C. elegans. Such perturbation-based approaches,
however, cannot distinguish between direct and indirect targets
of DAF-16.
A method currently employed to identify the endogenous or
in vivo direct targets of a transcriptional regulator is chromatin
immunoprecipitation (ChIP). The ChIP procedure is a deri-
vative of an earlier protocol developed to interrogate in vivo
protein-DNA interactions (Solomon et al., 1988; Solomon and
Varshavsky, 1985). ChIP involves the treatment of living cells
with formaldehyde, a procedure that crosslinks DNA to
proteins that are in physical contact with it. This procedure
preserves the endogenous DNA-protein interactions. Chro-
matin is then fragmented either by physical or enzymatic
means. DNA fragments associated with a particular protein are
then isolated by immunoaffinity purification using a specific
antibody against the protein. The ChIP-enriched DNA is then
identified by various molecular techniques and is amenable to
techniques that allow for the identification of target sequences
on the genome-wide scale. Besides identifying the binding
sites of transcriptional regulators, ChIP can also be employed
to map histone modifications in the genome (Roh et al., 2004).
This may have important applications in ageing research
especially since links between histone modifications and the
ageing phenotype have been demonstrated (Bandyopadhyay
and Medrano, 2003; Chang and Min, 2002; Mostoslavsky
et al., 2006; Sarg et al., 2002; Scaffidi and Misteli, 2006;
Shumaker et al., 2006).
It is now possible to perform large-scale sequencing of
ChIP-enriched genomic fragments due to dramatic improve-
ments in DNA sequencing technology that allows for quick and
accurate sequencing at ever decreasing costs (Margulies et al.,
2005; Metzker, 2005). Once the immunoprecipitated DNA is
sequenced, its genomic location can be determined compu-
tationally by scanning through genome sequence databases.
The first reported use of sequencing technology to map protein-
interacting regions leveraged on the SAGE cloning and
sequencing method (described earlier) to identify the
distribution of hyperacetylated histones in the yeast genome
(Roh et al., 2004). Other variations of the ChIP-SAGE
procedure were subsequently used to probe protein-DNA
interactions in mammalian cells. For instance, ChIP-SACO
was employed by Goodman and colleagues to map the
genome-wide binding sites of CREB in a cultured rat cell line
(Impey et al., 2004). Another variation of ChIP-SAGE known
as sequence tag analysis of genomic enrichment (STAGE) was
also developed by Iyer and colleagues to map the binding sites
of E2F4 in human fibroblasts (Kim et al., 2005a). One inherent
disadvantage of the SAGE-based methods, however, is that it
suffers from the ambiguity of mapping monotags to the
genome. ChIP-PET, a technique developed by Ruan and
colleagues, was therefore, developed to circumvent the
problems associated with ChIP-SAGE. In contrast to ChIP-
SAGE, ChIP-PETextracts two 18 bp tags from the 50 end and 30
end of each enriched genomic fragment (Fig. 1). These tags are
known as paired-end ditags (PETs). By sequencing PETs, not
only can ChIP-PET unambiguously assign enriched genomic
fragments to a specific region in the genome, it is also able to
define the exact length of each enriched genomic fragment.
Due to the random sampling of DNA fragments during PET
cloning and sequencing, genomic fragments that are enriched
by ChIP are more likely to be sequenced and mapped.
Therefore, protein-DNA interaction sites are represented by
genomic loci that contain multiple overlapping PETs, with
overlap regions demarcating the protein binding site. The
ChIP-PET approach has so far been employed to map the
binding sites of p53 in a colorectal cancer cell line, as well as
Oct4 and Nanog in mouse embryonic stem cells (Loh et al.,
2006; Wei et al., 2006). The genome-wide binding sites of
SRC-3 in breast cancer cells and the localization of acetylated-
and methylated histone H3 has also been mapped using other
variations of ChIP-sequencing techniques (Labhart et al.,
2005; Liang et al., 2004).
Recently, Tissenbaum and colleagues have employed
cloning and sequencing technology to map the direct targets
of DAF-16 in C. elegans (Oh et al., 2006). The activation of
DAF-16 is known to extend the lifespan of C. elegans. Thus,
identifying the direct in vivo targets of DAF-16 will help to
elucidate the longevity-regulating effect of this transcription
factor. With the ChIP-sequencing approach, 103 putative DAF-
16 target genes were identified. To assess the contribution of
Fig. 1. Schematic diagram of the ChIP-PET procedure. Chromatin immunoprecipitation - paired-end ditag (ChIP-PET) technology is a method that can be used in the
genome-wide location analysis of protein-DNA interactions. Cells of interest are first treated with formaldehyde to covalently crosslinks and preserves endogenous
DNA-protein interactions. The crosslinked chromatin is then fragmented either by enzymatic or physical means to an average size of about 500 bp. Using antibody-
coupled beads, ChIP is then carried out to enrich chromatin fragments that are bound by the protein of interest. The ChIP-enriched chromatin fragments are then
decrosslinked and subjected to proteinase digestion to liberate free ChIP-enriched DNA. To identify the ChIP-enriched DNA sequences, the PET cloning and
sequencing method is employed. Briefly, the enriched DNA fragments are blunt-ended before being cloned into a primary vector library that appends the cloned
fragments with MmeI restriction sites. Using MmeI, 18 bp tags from each end of the cloned DNA fragments are generated. These tags are then re-ligated to generate
paired-end ditags (PETs). These PETs are released from the primary vector library by restriction digestion before being concatenated and re-cloned into a PET
sequencing library. The PETs are subsequently sequenced and the sequences obtained are computationally mapped onto the genome. Genomic regions that are bound
by the protein of interest are indicated by overlap regions within PET clusters.
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160 155
these target genes to DAF-16 function, 33 randomly selected
putative DAF-16 target genes were inactivated by RNAi or by
using hypomorphic alleles. The inactivation of three genes
(egl-10, lin-2, sca-1) resulted in the extension of lifespan, while
the inactivation of four other genes (C01B7.1, F42G10.1, Idb-1,
zfp-1) was observed to shorten the lifespan of C. elegans. Since
these putative target genes were identified through the
sequencing of only 320 ChIP-enriched DNA-derived clones,
a larger scale analysis using the high-throughput tag sequencing
approaches discussed above should result in the construction of
a more comprehensive regulatory network controlled by DAF-
16. It is also of great interest to extend these studies to identify
the targets of DAF-16 orthologues (FOXO) in mammalian
systems because the DAF-2/DAF-16 signalling pathway is
evolutionary conserved and the down-regulation of this pathway
prolongs lifespan in both worms and mammals. The mapping of
SIR-2.1 binding sites in C. elegans may also reveal novel insights
into lifespan regulation since SIR-2.1 was recently shown to bind
DAF-16 and extend lifespan in a stress-dependent pathway
(Berdichevsky et al., 2006).
ChIP-enriched DNA can also be profiled using oligonu-
cleotide microarrays, a procedure commonly referred to as
ChIP-chip. These microarrays are similar to those used for
transcriptional profiling, except that the oligonucleotides on
the microarray are designed to probe genomic sequences
instead of transcript sequences. Early microarrays designed
to analyse protein-DNA interactions of mammalian systems
only probed selected regions of the mammalian genome. This
included core promoter microarrays that probe genomic
sequences flanking the transcription start sites of genes
(Balciunaite et al., 2005; Scacheri et al., 2006; Schreiber et al.,
2006); CpG microarrays that analyses genomic CpG islands,
which includes most core promoter regions and other regu-
latory regions (Paris et al., 2004; Wells et al., 2003); ENCODE
regions microarrays that probe the 44 ENCODE regions in the
human genome (Bieda et al., 2006; Kim et al., 2005c); and
tiling microarrays that probed the entire non-repeat regions of
chromosomes 21 and 22 (Cawley et al., 2004; Martone et al.,
2003). Over the years, advancements in microarray fabrication
technology have enabled the manufacture of oligonucleotide
microarrays with much greater probe densities, thus, making it
increasingly feasible to carry out genome-wide ChIP analysis
using microarrays. In fact, Ren and colleagues went ahead to
demonstrate the feasibility of using ChIP-chip to carry out
genome-wide location analysis of protein-DNA interactions in
mammalian systems. Using a series of microarrays containing
roughly 14.5 million 50-mer probes that covered all non-repeat
regions of human DNA at 100 bp resolution, they generated a
genome-wide map of active promoters in human fibroblasts by
determining the binding sites of transcriptional pre-initiation
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160156
complexes (Kim et al., 2005d). The availability of genome-
wide ChIP-chip arrays finally puts genome-wide location
analysis of protein-DNA interactions within reach of many
labs that cannot afford or do not have the expertise to perform
ChIP-sequencing experiments.
As tools to analyze genome-wide protein-DNA interaction
sites continue to mature and become more accessible, this
should further catalyze the study of other transcription factors
that have been shown to regulate lifespan in C. elegans.
Examples of such transcription factors include C01B7.1,
zfp-1 (Oh et al., 2006); mdl-1 (Murphy et al., 2003); and
C13C4.2, T01B10.4, ZC64.3 (Hamilton et al., 2005). With
these tools, it will be possible to comprehensively identify
the direct target genes of these longevity-regulating trans-
cription factors, and this will help in the construction of the
transcriptional regulatory network underlying the ageing
process.
5. Concluding remarks
The post-genomic era has ushered the development of
several high-throughput technologies that enable the systems-
level analysis of complex biological processes. These tools now
provide researchers with the unprecedented opportunity to
unravel the molecular mechanisms of ageing in a comprehen-
sive, yet time- and cost-efficient manner. So far, systems
approach tools, such as genome-wide transcriptional profiling,
RNAi and location analysis of protein-DNA interactions, have
Fig. 2. Integration of advanced technologies to uncover the ageing regulatory networ
in this review provide the unprecedented opportunity to study the ageing process at
these three technologies in the study of ageing and also highlights how these techno
ageing phenomenon.
been used to catalogue key regulators of ageing. This is easily
done using readily available computational tools that are able to
perform standard statistical analyses with the large datasets
generated by these high-throughput analysis tools. Such low
level computational analyses, however, fail to harness the full
potential of the systems approach. If applied properly, systems
approach tools can be used to interrogate complex molecular
events and map out the regulatory circuitry underlying a
biological phenomenon. The construction of such molecular
networks is useful because it may expose unexpected conclu-
sions and novel insights. The higher order analysis of trans-
criptional profiling data, for example, has recently been used to
identify a novel negative regulator of the innate immune response
(Gilchrist et al., 2006). Data from complementary tools, such as
transcriptional profiling and genome-wide transcription factor
binding site analysis, can also be integrated to construct
regulatory networks (Fig. 2). Such an approach has already
been implemented to assemble the transcriptional regulatory
network controlling myogenic differentiation, for instance (Blais
et al., 2005). The use of systems approach methods should
ultimately lead to the development of mechanistic models that
explain the ageing process. With such mechanistic models in
hand, it will then be possible to computationally simulate the
ageing process and to carry out in silico predictions of molecular
interventions that can potentially inhibit the ageing process. The
predictive power of the systems biology approach may therefore,
be useful for the search of therapeutics that can reduce the
ravaging effects of ageing.
k. The three technologies that have emerged in the post-genomic era as discussed
the systems level. The schematic diagram here summarizes the applicability of
logies can be used in concert to uncover the regulatory network underlying the
C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160 157
Acknowledgements
We acknowledged funding by the Agency of Science,
Technology and Research (A*STAR) of Singapore. Ching-
Aeng Lim is supported by the A*STAR scholarship. We are
grateful to Norbert Lehming and Edwin Cheung for critical
comments on the manuscript.
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