12
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 138672 b 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 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. www.elsevier.com/locate/mechagedev Mechanisms of Ageing and Development 128 (2007) 149–160 * 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

Application of advanced technologies in ageing research

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Page 1: Application of advanced technologies in ageing research

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.

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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

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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

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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.

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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

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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

Page 7: Application of advanced technologies in ageing research

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

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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

Page 9: Application of advanced technologies in ageing research

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.

References

Abbas-Terki, T., Blanco-Bose, W., Deglon, N., Pralong, W., Aebischer, P., 2002.

Lentiviral-mediated RNA interference. Hum. Gene Ther. 13, 2197–2201.

Adams, M.D., Celniker, S.E., Holt, R.A., Evans, C.A., Gocayne, J.D., Amana-

tides, P.G., Scherer, S.E., Li, P.W., Hoskins, R.A., Galle, R.F., George, R.A.,

Lewis, S.E., Richards, S., Ashburner, M., Henderson, S.N., Sutton, G.G.,

Wortman, J.R., Yandell, M.D., Zhang, Q., Chen, L.X., Brandon, R.C.,

Rogers, Y.H., Blazej, R.G., Champe, M., Pfeiffer, B.D., Wan, K.H., Doyle,

C., Baxter, E.G., Helt, G., Nelson, C.R., Gabor, G.L., Abril, J.F., Agbayani,

A., An, H.J., Andrews-Pfannkoch, C., Baldwin, D., Ballew, R.M., Basu, A.,

Baxendale, J., Bayraktaroglu, L., Beasley, E.M., Beeson, K.Y., Benos, P.V.,

Berman, B.P., Bhandari, D., Bolshakov, S., Borkova, D., Botchan, M.R.,

Bouck, J., Brokstein, P., Brottier, P., Burtis, K.C., Busam, D.A., Butler, H.,

Cadieu, E., Center, A., Chandra, I., Cherry, J.M., Cawley, S., Dahlke, C.,

Davenport, L.B., Davies, P., de Pablos, B., Delcher, A., Deng, Z., Mays,

A.D., Dew, I., Dietz, S.M., Dodson, K., Doup, L.E., Downes, M., Dugan-

Rocha, S., Dunkov, B.C., Dunn, P., Durbin, K.J., Evangelista, C.C., Ferraz,

C., Ferriera, S., Fleischmann, W., Fosler, C., Gabrielian, A.E., Garg, N.S.,

Gelbart, W.M., Glasser, K., Glodek, A., Gong, F., Gorrell, J.H., Gu, Z.,

Guan, P., Harris, M., Harris, N.L., Harvey, D., Heiman, T.J., Hernandez,

J.R., Houck, J., Hostin, D., Houston, K.A., Howland, T.J., Wei, M.H.,

Ibegwam, C., et al., 2000. The genome sequence of Drosophila melano-

gaster. Science 287, 2185–2195.

Ahn, A.C., Tewari, M., Poon, C.S., Phillips, R.S., 2006. The limits of reduc-

tionism in medicine: could systems biology offer an alternative? PLoS

Med. 3, e208.

Anisimov, V.N., Semenchenko, A.V., Yashin, A.I., 2003. Insulin and longevity:

antidiabetic biguanides as geroprotectors. Biogerontology 4, 297–307.

Aza-Blanc, P., Cooper, C.L., Wagner, K., Batalov, S., Deveraux, Q.L., Cooke,

M.P., 2003. Identification of modulators of TRAIL-induced apoptosis via

RNAi-based phenotypic screening. Mol. Cell 12, 627–637.

Balciunaite, E., Spektor, A., Lents, N.H., Cam, H., Te Riele, H., Scime, A.,

Rudnicki, M.A., Young, R., Dynlacht, B.D., 2005. Pocket protein com-

plexes are recruited to distinct targets in quiescent and proliferating cells.

Mol. Cell Biol. 25, 8166–8178.

Bandyopadhyay, D., Medrano, E.E., 2003. The emerging role of epigenetics in

cellular and organismal aging. Exp. Gerontol. 38, 1299–1307.

Berdichevsky, A., Viswanathan, M., Horvitz, H.R., Guarente, L., 2006. C.

elegans SIR-2.1 interacts with 14-3-3 proteins to activate DAF-16 and

extend life span. Cell 125, 1165–1177.

Berns, K., Hijmans, E.M., Mullenders, J., Brummelkamp, T.R., Velds, A.,

Heimerikx, M., Kerkhoven, R.M., Madiredjo, M., Nijkamp, W., Weigelt, B.,

Agami, R., Ge, W., Cavet, G., Linsley, P.S., Beijersbergen, R.L., Bernards,

R., 2004. A large-scale RNAi screen in human cells identifies new compo-

nents of the p53 pathway. Nature 428, 431–437.

Bertone, P., Stolc, V., Royce, T.E., Rozowsky, J.S., Urban, A.E., Zhu, X., Rinn,

J.L., Tongprasit, W., Samanta, M., Weissman, S., Gerstein, M., Snyder, M.,

2004. Global identification of human transcribed sequences with genome

tiling arrays. Science 306, 2242–2246.

Bieda, M., Xu, X., Singer, M.A., Green, R., Farnham, P.J., 2006. Unbiased

location analysis of E2F1-binding sites suggests a widespread role for E2F1

in the human genome. Genome Res. 16, 595–605.

Blais, A., Tsikitis, M., Acosta-Alvear, D., Sharan, R., Kluger, Y., Dynlacht,

B.D., 2005. An initial blueprint for myogenic differentiation. Genes Dev.

19, 553–569.

Brenner, S., Johnson, M., Bridgham, J., Golda, G., Lloyd, D.H., Johnson, D.,

Luo, S., McCurdy, S., Foy, M., Ewan, M., Roth, R., George, D., Eletr, S.,

Albrecht, G., Vermaas, E., Williams, S.R., Moon, K., Burcham, T., Pallas,

M., DuBridge, R.B., Kirchner, J., Fearon, K., Mao, J., Corcoran, K., 2000.

Gene expression analysis by massively parallel signature sequencing

(MPSS) on microbead arrays. Nat. Biotechnol. 18, 630–634.

Butler, R.N., Sprott, R., Warner, H., Bland, J., Feuers, R., Forster, M., Fillit, H.,

Harman, S.M., Hewitt, M., Hyman, M., Johnson, K., Kligman, E.,

McClearn, G., Nelson, J., Richardson, A., Sonntag, W., Weindruch, R.,

Wolf, N., 2004. Biomarkers of aging: from primitive organisms to humans.

J. Gerontol. A Biol. Sci. Med. Sci. 59, B560–B567.

Cao, S.X., Dhahbi, J.M., Mote, P.L., Spindler, S.R., 2001. Genomic profiling of

short- and long-term caloric restriction effects in the liver of aging mice.

Proc. Natl. Acad. Sci. USA 98, 10630–10635.

Carpenter, A.E., Sabatini, D.M., 2004. Systematic genome-wide screens of

gene function. Nat. Rev. Genet. 5, 11–22.

Cawley, S., Bekiranov, S., Ng, H.H., Kapranov, P., Sekinger, E.A., Kampa, D.,

Piccolboni, A., Sementchenko, V., Cheng, J., Williams, A.J., Wheeler, R.,

Wong, B., Drenkow, J., Yamanaka, M., Patel, S., Brubaker, S., Tammana,

H., Helt, G., Struhl, K., Gingeras, T.R., 2004. Unbiased mapping of

transcription factor binding sites along human chromosomes 21 and 22

points to widespread regulation of noncoding RNAs. Cell 116, 499–509.

Chang, K., Elledge, S.J., Hannon, G.J., 2006. Lessons from nature: microRNA-

based shRNA libraries. Nat. Methods 3, 707–714.

Chang, K.T., Min, K.T., 2002. Regulation of lifespan by histone deacetylase.

Ageing Res. Rev. 1, 313–326.

Cheng, J., Kapranov, P., Drenkow, J., Dike, S., Brubaker, S., Patel, S., Long, J.,

Stern, D., Tammana, H., Helt, G., Sementchenko, V., Piccolboni, A.,

Bekiranov, S., Bailey, D.K., Ganesh, M., Ghosh, S., Bell, I., Gerhard,

D.S., Gingeras, T.R., 2005. Transcriptional maps of 10 human chromo-

somes at 5-nucleotide resolution. Science 308, 1149–1154.

Cho, R.J., Huang, M., Campbell, M.J., Dong, H., Steinmetz, L., Sapinoso, L.,

Hampton, G., Elledge, S.J., Davis, R.W., Lockhart, D.J., 2001. Transcrip-

tional regulation and function during the human cell cycle. Nat. Genet. 27,

48–54.

Consortium, T.C.e.S., 1998. Genome sequence of the nematode C. elegans: a

platform for investigating biology. Science 282, 2012–2018.

Csoka, A.B., English, S.B., Simkevich, C.P., Ginzinger, D.G., Butte, A.J.,

Schatten, G.P., Rothman, F.G., Sedivy, J.M., 2004. Genome-scale expres-

sion profiling of Hutchinson-Gilford progeria syndrome reveals widespread

transcriptional misregulation leading to mesodermal/mesenchymal defects

and accelerated atherosclerosis. Aging Cell 3, 235–243.

Deocaris, C.C., Kaul, S.C., Taira, K., Wadhwa, R., 2004. Emerging technol-

ogies: trendy RNA tools for aging research. J. Gerontol. A Biol. Sci. Med.

Sci. 59, 771–783.

DeRisi, J.L., Iyer, V.R., Brown, P.O., 1997. Exploring the metabolic and genetic

control of gene expression on a genomic scale. Science 278, 680–686.

Dhahbi, J.M., Mote, P.L., Fahy, G.M., Spindler, S.R., 2005. Identification of

potential caloric restriction mimetics by microarray profiling. Physiol.

Genomics 23, 343–350.

Dirks, A.J., Leeuwenburgh, C., 2006. Caloric restriction in humans: potential

pitfalls and health concerns. Mech. Ageing Dev. 127, 1–7.

Evans, J.M., Donnelly, L.A., Emslie-Smith, A.M., Alessi, D.R., Morris, A.D.,

2005. Metformin and reduced risk of cancer in diabetic patients. Bmj 330,

1304–1305.

Giaever, G., Chu, A.M., Ni, L., Connelly, C., Riles, L., Veronneau, S., Dow, S.,

Lucau-Danila, A., Anderson, K., Andre, B., Arkin, A.P., Astromoff, A., El-

Bakkoury, M., Bangham, R., Benito, R., Brachat, S., Campanaro, S.,

Curtiss, M., Davis, K., Deutschbauer, A., Entian, K.D., Flaherty, P., Foury,

F., Garfinkel, D.J., Gerstein, M., Gotte, D., Guldener, U., Hegemann, J.H.,

Hempel, S., Herman, Z., Jaramillo, D.F., Kelly, D.E., Kelly, S.L., Kotter, P.,

LaBonte, D., Lamb, D.C., Lan, N., Liang, H., Liao, H., Liu, L., Luo, C.,

Lussier, M., Mao, R., Menard, P., Ooi, S.L., Revuelta, J.L., Roberts, C.J.,

Rose, M., Ross-Macdonald, P., Scherens, B., Schimmack, G., Shafer, B.,

Shoemaker, D.D., Sookhai-Mahadeo, S., Storms, R.K., Strathern, J.N.,

Valle, G., Voet, M., Volckaert, G., Wang, C.Y., Ward, T.R., Wilhelmy, J.,

Winzeler, E.A., Yang, Y., Yen, G., Youngman, E., Yu, K., Bussey, H., Boeke,

J.D., Snyder, M., Philippsen, P., Davis, R.W., Johnston, M., 2002. Func-

tional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–

391.

Page 10: Application of advanced technologies in ageing research

C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160158

Gilchrist, M., Thorsson, V., Li, B., Rust, A.G., Korb, M., Kennedy, K., Hai, T.,

Bolouri, H., Aderem, A., 2006. Systems biology approaches identify ATF3

as a negative regulator of Toll-like receptor 4. Nature 441, 173–178.

Girardot, F., Lasbleiz, C., Monnier, V., Tricoire, H., 2006. Specific age-related

signatures in Drosophila body parts transcriptome. BMC Genomics 7, 69.

Halaschek-Wiener, J., Khattra, J.S., McKay, S., Pouzyrev, A., Stott, J.M., Yang,

G.S., Holt, R.A., Jones, S.J., Marra, M.A., Brooks-Wilson, A.R., Riddle,

D.L., 2005. Analysis of long-lived C. elegans daf-2 mutants using serial

analysis of gene expression. Genome Res. 15, 603–615.

Hamilton, B., Dong, Y., Shindo, M., Liu, W., Odell, I., Ruvkun, G., Lee, S.S.,

2005. A systematic RNAi screen for longevity genes in C. elegans. Genes

Dev. 19, 1544–1555.

Hansen, M., Hsu, A.L., Dillin, A., Kenyon, C., 2005. New genes tied to

endocrine, metabolic, and dietary regulation of lifespan from a Caenor-

habditis elegans genomic RNAi screen. PLoS Genet. 1, 119–128.

Heller, M.J., 2002. DNA microarray technology: devices, systems, and applica-

tions. Annu. Rev. Biomed. Eng. 4, 129–153.

Howitz, K.T., Bitterman, K.J., Cohen, H.Y., Lamming, D.W., Lavu, S., Wood,

J.G., Zipkin, R.E., Chung, P., Kisielewski, A., Zhang, L.L., Scherer, B.,

Sinclair, D.A., 2003. Small molecule activators of sirtuins extend Sacchar-

omyces cerevisiae lifespan. Nature 425, 191–196.

Ideker, T., Galitski, T., Hood, L., 2001. A new approach to decoding life:

systems biology. Annu. Rev. Genomics Hum. Genet. 2, 343–372.

IHGS, C., 2004. Finishing the euchromatic sequence of the human genome.

Nature 431, 931–945.

Impey, S., McCorkle, S.R., Cha-Molstad, H., Dwyer, J.M., Yochum, G.S., Boss,

J.M., McWeeney, S., Dunn, J.J., Mandel, G., Goodman, R.H., 2004.

Defining the CREB regulon: a genome-wide analysis of transcription factor

regulatory regions. Cell 119, 1041–1054.

Ingram, D.K., Zhu, M., Mamczarz, J., Zou, S., Lane, M.A., Roth, G.S., deCabo,

R., 2006. Calorie restriction mimetics: an emerging research field. Aging

Cell 5, 97–108.

Kamath, R.S., Fraser, A.G., Dong, Y., Poulin, G., Durbin, R., Gotta, M.,

Kanapin, A., Le Bot, N., Moreno, S., Sohrmann, M., Welchman, D.P.,

Zipperlen, P., Ahringer, J., 2003. Systematic functional analysis of the

Caenorhabditis elegans genome using RNAi. Nature 421, 231–237.

Kayo, T., Allison, D.B., Weindruch, R., Prolla, T.A., 2001. Influences of aging

and caloric restriction on the transcriptional profile of skeletal muscle from

rhesus monkeys. Proc. Natl. Acad. Sci. USA 98, 5093–5098.

Kim, J., Bhinge, A.A., Morgan, X.C., Iyer, V.R., 2005a. Mapping DNA-protein

interactions in large genomes by sequence tag analysis of genomic enrich-

ment. Nat. Methods 2, 47–53.

Kim, S.N., Rhee, J.H., Song, Y.H., Park, D.Y., Hwang, M., Lee, S.L., Kim, J.E.,

Gim, B.S., Yoon, J.H., Kim, Y.J., Kim-Ha, J., 2005b. Age-dependent

changes of gene expression in the Drosophila head. Neurobiol. Aging

26, 1083–1091.

Kim, T.H., Barrera, L.O., Qu, C., Van Calcar, S., Trinklein, N.D., Cooper, S.J.,

Luna, R.M., Glass, C.K., Rosenfeld, M.G., Myers, R.M., Ren, B., 2005c.

Direct isolation and identification of promoters in the human genome.

Genome Res. 15, 830–839.

Kim, T.H., Barrera, L.O., Zheng, M., Qu, C., Singer, M.A., Richmond, T.A.,

Wu, Y., Green, R.D., Ren, B., 2005d. A high-resolution map of active

promoters in the human genome. Nature 436, 876–880.

Kriete, A., Sokhansanj, B.A., Coppock, D.L., West, G.B., 2006. Systems

approaches to the networks of aging. Ageing Res. Rev..

Kyng, K.J., May, A., Kolvraa, S., Bohr, V.A., 2003. Gene expression profiling in

Werner syndrome closely resembles that of normal aging. Proc. Natl. Acad.

Sci. USA 100, 12259–12264.

Kyng, K.J., May, A., Stevnsner, T., Becker, K.G., Kolvra, S., Bohr, V.A., 2005.

Gene expression responses to DNA damage are altered in human aging and

in Werner syndrome. Oncogene 24, 5026–5042.

Labhart, P., Karmakar, S., Salicru, E.M., Egan, B.S., Alexiadis, V., O’Malley,

B.W., Smith, C.L., 2005. Identification of target genes in breast cancer cells

directly regulated by the SRC-3/AIB1 coactivator. Proc. Natl. Acad. Sci.

USA 102, 1339–1344.

Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J.,

Lerner, J., Brunet, J.P., Subramanian, A., Ross, K.N., Reich, M., Hierony-

mus, H., Wei, G., Armstrong, S.A., Haggarty, S.J., Clemons, P.A., Wei, R.,

Carr, S.A., Lander, E.S., Golub, T.R., 2006. The Connectivity Map: using

gene-expression signatures to connect small molecules, genes, and disease.

Science 313, 1929–1935.

Lander, E.S., Linton, L.M., Birren, B., Nusbaum, C., Zody, M.C., Baldwin, J.,

Devon, K., Dewar, K., Doyle, M., FitzHugh, W., Funke, R., Gage, D.,

Harris, K., Heaford, A., Howland, J., Kann, L., Lehoczky, J., LeVine, R.,

McEwan, P., McKernan, K., Meldrim, J., Mesirov, J.P., Miranda, C., Morris,

W., Naylor, J., Raymond, C., Rosetti, M., Santos, R., Sheridan, A., Sougnez,

C., Stange-Thomann, N., Stojanovic, N., Subramanian, A., Wyman, D.,

Rogers, J., Sulston, J., Ainscough, R., Beck, S., Bentley, D., Burton, J., Clee,

C., Carter, N., Coulson, A., Deadman, R., Deloukas, P., Dunham, A.,

Dunham, I., Durbin, R., French, L., Grafham, D., Gregory, S., Hubbard,

T., Humphray, S., Hunt, A., Jones, M., Lloyd, C., McMurray, A., Matthews,

L., Mercer, S., Milne, S., Mullikin, J.C., Mungall, A., Plumb, R., Ross, M.,

Shownkeen, R., Sims, S., Waterston, R.H., Wilson, R.K., Hillier, L.W.,

McPherson, J.D., Marra, M.A., Mardis, E.R., Fulton, L.A., Chinwalla, A.T.,

Pepin, K.H., Gish, W.R., Chissoe, S.L., Wendl, M.C., Delehaunty, K.D.,

Miner, T.L., Delehaunty, A., Kramer, J.B., Cook, L.L., Fulton, R.S.,

Johnson, D.L., Minx, P.J., Clifton, S.W., Hawkins, T., Branscomb, E.,

Predki, P., Richardson, P., Wenning, S., Slezak, T., Doggett, N., Cheng,

J.F., Olsen, A., Lucas, S., Elkin, C., Uberbacher, E., Frazier, M., et al., 2001.

Initial sequencing and analysis of the human genome. Nature 409, 860–921.

Larsen, P.L., Albert, P.S., Riddle, D.L., 1995. Genes that regulate both devel-

opment and longevity in Caenorhabditis elegans. Genetics 139, 1567–1583.

Lee, C.K., Allison, D.B., Brand, J., Weindruch, R., Prolla, T.A., 2002. Tran-

scriptional profiles associated with aging and middle age-onset caloric

restriction in mouse hearts. Proc. Natl. Acad. Sci. USA 99, 14988–14993.

Lee, C.K., Klopp, R.G., Weindruch, R., Prolla, T.A., 1999. Gene expression

profile of aging and its retardation by caloric restriction. Science 285, 1390–

1393.

Lee, C.K., Weindruch, R., Prolla, T.A., 2000. Gene-expression profile of the

ageing brain in mice. Nat. Genet. 25, 294–297.

Liang, G., Lin, J.C., Wei, V., Yoo, C., Cheng, J.C., Nguyen, C.T., Weisenberger,

D.J., Egger, G., Takai, D., Gonzales, F.A., Jones, P.A., 2004. Distinct

localization of histone H3 acetylation and H3-K4 methylation to the

transcription start sites in the human genome. Proc. Natl. Acad. Sci.

USA 101, 7357–7362.

Lin, S.J., Defossez, P.A., Guarente, L., 2000. Requirement of NAD and SIR2 for

life-span extension by calorie restriction in Saccharomyces cerevisiae.

Science 289, 2126–2128.

Loh, Y.H., Wu, Q., Chew, J.L., Vega, V.B., Zhang, W., Chen, X., Bourque, G.,

George, J., Leong, B., Liu, J., Wong, K.Y., Sung, K.W., Lee, C.W., Zhao,

X.D., Chiu, K.P., Lipovich, L., Kuznetsov, V.A., Robson, P., Stanton, L.W.,

Wei, C.L., Ruan, Y., Lim, B., Ng, H.H., 2006. The Oct4 and Nanog

transcription network regulates pluripotency in mouse embryonic stem

cells. Nat. Genet. 38, 431–440.

Lu, T., Pan, Y., Kao, S.Y., Li, C., Kohane, I., Chan, J., Yankner, B.A., 2004. Gene

regulation and DNA damage in the ageing human brain. Nature 429, 883–891.

Lund, J., Tedesco, P., Duke, K., Wang, J., Kim, S.K., Johnson, T.E., 2002.

Transcriptional profile of aging in C. elegans. Curr. Biol. 12, 1566–1573.

Ly, D.H., Lockhart, D.J., Lerner, R.A., Schultz, P.G., 2000. Mitotic misregula-

tion and human aging. Science 287, 2486–2492.

Margulies, M., Egholm, M., Altman, W.E., Attiya, S., Bader, J.S., Bemben,

L.A., Berka, J., Braverman, M.S., Chen, Y.J., Chen, Z., Dewell, S.B., Du, L.,

Fierro, J.M., Gomes, X.V., Godwin, B.C., He, W., Helgesen, S., Ho, C.H.,

Irzyk, G.P., Jando, S.C., Alenquer, M.L., Jarvie, T.P., Jirage, K.B., Kim, J.B.,

Knight, J.R., Lanza, J.R., Leamon, J.H., Lefkowitz, S.M., Lei, M., Li, J.,

Lohman, K.L., Lu, H., Makhijani, V.B., McDade, K.E., McKenna, M.P.,

Myers, E.W., Nickerson, E., Nobile, J.R., Plant, R., Puc, B.P., Ronan, M.T.,

Roth, G.T., Sarkis, G.J., Simons, J.F., Simpson, J.W., Srinivasan, M.,

Tartaro, K.R., Tomasz, A., Vogt, K.A., Volkmer, G.A., Wang, S.H., Wang,

Y., Weiner, M.P., Yu, P., Begley, R.F., Rothberg, J.M., 2005. Genome

sequencing in microfabricated high-density picolitre reactors. Nature

437, 376–380.

Martone, R., Euskirchen, G., Bertone, P., Hartman, S., Royce, T.E., Luscombe,

N.M., Rinn, J.L., Nelson, F.K., Miller, P., Gerstein, M., Weissman, S.,

Snyder, M., 2003. Distribution of NF-kappaB-binding sites across human

chromosome 22. Proc. Natl. Acad. Sci. USA 100, 12247–12252.

Page 11: Application of advanced technologies in ageing research

C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160 159

Masoro, E.J., 2005. Overview of caloric restriction and ageing. Mech. Ageing

Dev. 126, 913–922.

McElwee, J., Bubb, K., Thomas, J.H., 2003. Transcriptional outputs of the

Caenorhabditis elegans forkhead protein DAF-16. Aging Cell 2, 111–121.

McElwee, J.J., Schuster, E., Blanc, E., Thomas, J.H., Gems, D., 2004. Shared

transcriptional signature in Caenorhabditis elegans Dauer larvae and long-

lived daf-2 mutants implicates detoxification system in longevity assurance.

J. Biol. Chem. 279, 44533–44543.

Metzker, M.L., 2005. Emerging technologies in DNA sequencing. Genome Res.

15, 1767–1776.

Miyagishi, M., Taira, K., 2002. U6 promoter-driven siRNAs with four uridine 30

overhangs efficiently suppress targeted gene expression in mammalian

cells. Nat. Biotechnol. 20, 497–500.

Moffat, J., Grueneberg, D.A., Yang, X., Kim, S.Y., Kloepfer, A.M., Hinkle, G.,

Piqani, B., Eisenhaure, T.M., Luo, B., Grenier, J.K., Carpenter, A.E., Foo,

S.Y., Stewart, S.A., Stockwell, B.R., Hacohen, N., Hahn, W.C., Lander,

E.S., Sabatini, D.M., Root, D.E., 2006. A lentiviral RNAi library for human

and mouse genes applied to an arrayed viral high-content screen. Cell 124,

1283–1298.

Mostoslavsky, R., Chua, K.F., Lombard, D.B., Pang, W.W., Fischer, M.R.,

Gellon, L., Liu, P., Mostoslavsky, G., Franco, S., Murphy, M.M., Mills,

K.D., Patel, P., Hsu, J.T., Hong, A.L., Ford, E., Cheng, H.L., Kennedy, C.,

Nunez, N., Bronson, R., Frendewey, D., Auerbach, W., Valenzuela, D.,

Karow, M., Hottiger, M.O., Hursting, S., Barrett, J.C., Guarente, L.,

Mulligan, R., Demple, B., Yancopoulos, G.D., Alt, F.W., 2006. Genomic

instability and aging-like phenotype in the absence of mammalian SIRT6.

Cell 124, 315–329.

Murphy, C.T., McCarroll, S.A., Bargmann, C.I., Fraser, A., Kamath, R.S.,

Ahringer, J., Li, H., Kenyon, C., 2003. Genes that act downstream of DAF-

16 to influence the lifespan of Caenorhabditis elegans. Nature 424, 277–

283.

Neumann, B., Held, M., Liebel, U., Erfle, H., Rogers, P., Pepperkok, R.,

Ellenberg, J., 2006. High-throughput RNAi screening by time-lapse ima-

ging of live human cells. Nat. Methods 3, 385–390.

Ogg, S., Paradis, S., Gottlieb, S., Patterson, G.I., Lee, L., Tissenbaum, H.A.,

Ruvkun, G., 1997. The Fork head transcription factor DAF-16 transduces

insulin-like metabolic and longevity signals in C. elegans. Nature 389, 994–

999.

Oh, S.W., Mukhopadhyay, A., Dixit, B.L., Raha, T., Green, M.R., Tissenbaum,

H.A., 2006. Identification of direct DAF-16 targets controlling longevity,

metabolism and diapause by chromatin immunoprecipitation. Nat. Genet.

38, 251–257.

Paris, J., Virtanen, C., Lu, Z., Takahashi, M., 2004. Identification of MEF2-

regulated genes during muscle differentiation. Physiol. Genomics 20, 143–

151.

Park, S.K., Prolla, T.A., 2005. Gene expression profiling studies of aging in

cardiac and skeletal muscles. Cardiovasc. Res. 66, 205–212.

Peng, S., York, J.P., Zhang, P., 2006. A transgenic approach for RNA inter-

ference-based genetic screening in mice. Proc. Natl. Acad. Sci. USA 103,

2252–2256.

Pletcher, S.D., Macdonald, S.J., Marguerie, R., Certa, U., Stearns, S.C.,

Goldstein, D.B., Partridge, L., 2002. Genome-wide transcript profiles in

aging and calorically restricted Drosophila melanogaster. Curr. Biol. 12,

712–723.

Popesco, M.C., Frostholm, A., Rejniak, K., Rotter, A., 2004. Digital transcrip-

tome analysis in the aging cerebellum. Ann. N.Y. Acad. Sci. 1019, 58–63.

Prolla, T.A., 2005. Multiple roads to the aging phenotype: insights from the

molecular dissection of progerias through DNA microarray analysis. Mech.

Ageing Dev. 126, 461–465.

Rajewsky, K., Gu, H., Kuhn, R., Betz, U.A., Muller, W., Roes, J., Schwenk, F.,

1996. Conditional gene targeting. J. Clin. Invest 98, 600–603.

Rodwell, G.E., Sonu, R., Zahn, J.M., Lund, J., Wilhelmy, J., Wang, L., Xiao, W.,

Mindrinos, M., Crane, E., Segal, E., Myers, B.D., Brooks, J.D., Davis, R.W.,

Higgins, J., Owen, A.B., Kim, S.K., 2004. A transcriptional profile of aging

in the human kidney. PLoS Biol. 2, e427.

Roh, T.Y., Ngau, W.C., Cui, K., Landsman, D., Zhao, K., 2004. High-resolution

genome-wide mapping of histone modifications. Nat. Biotechnol. 22, 1013–

1016.

Root, D.E., Hacohen, N., Hahn, W.C., Lander, E.S., Sabatini, D.M., 2006.

Genome-scale loss-of-function screening with a lentiviral RNAi library.

Nat. Methods 3, 715–719.

Rual, J.F., Ceron, J., Koreth, J., Hao, T., Nicot, A.S., Hirozane-Kishikawa, T.,

Vandenhaute, J., Orkin, S.H., Hill, D.E., van den Heuvel, S., Vidal, M.,

2004. Toward improving Caenorhabditis elegans phenome mapping with

an ORFeome-based RNAi library. Genome Res. 14, 2162–2168.

Ruan, Y., Le Ber, P., Ng, H.H., Liu, E.T., 2004. Interrogating the transcriptome.

Trends Biotechnol. 22, 23–30.

Sarg, B., Koutzamani, E., Helliger, W., Rundquist, I., Lindner, H.H., 2002.

Postsynthetic trimethylation of histone H4 at lysine 20 in mammalian

tissues is associated with aging. J. Biol. Chem. 277, 39195–39201.

Scacheri, P.C., Davis, S., Odom, D.T., Crawford, G.E., Perkins, S., Halawi,

M.J., Agarwal, S.K., Marx, S.J., Spiegel, A.M., Meltzer, P.S., Collins, F.S.,

2006. Genome-wide analysis of menin binding provides insights into MEN1

tumorigenesis. PLoS Genet. 2, e51.

Scaffidi, P., Misteli, T., 2006. Lamin A-dependent nuclear defects in human

aging. Science 312, 1059–1063.

Schena, M., Shalon, D., Davis, R.W., Brown, P.O., 1995. Quantitative mon-

itoring of gene expression patterns with a complementary DNA microarray.

Science 270, 467–470.

Schreiber, J., Jenner, R.G., Murray, H.L., Gerber, G.K., Gifford, D.K., Young,

R.A., 2006. Coordinated binding of NF-kappaB family members in the

response of human cells to lipopolysaccharide. Proc. Natl. Acad. Sci. USA

103, 5899–5904.

Sen, G.L., Blau, H.M., 2006. A brief history of RNAi: the silence of the genes.

Faseb. J. 20, 1293–1299.

Sharov, A.A., Piao, Y., Matoba, R., Dudekula, D.B., Qian, Y., VanBuren, V.,

Falco, G., Martin, P.R., Stagg, C.A., Bassey, U.C., Wang, Y., Carter, M.G.,

Hamatani, T., Aiba, K., Akutsu, H., Sharova, L., Tanaka, T.S., Kimber,

W.L., Yoshikawa, T., Jaradat, S.A., Pantano, S., Nagaraja, R., Boheler, K.R.,

Taub, D., Hodes, R.J., Longo, D.L., Schlessinger, D., Keller, J., Klotz, E.,

Kelsoe, G., Umezawa, A., Vescovi, A.L., Rossant, J., Kunath, T., Hogan,

B.L., Curci, A., D’Urso, M., Kelso, J., Hide, W., Ko, M.S., 2003. Tran-

scriptome analysis of mouse stem cells and early embryos. PLoS Biol. 1,

E74.

Shumaker, D.K., Dechat, T., Kohlmaier, A., Adam, S.A., Bozovsky, M.R.,

Erdos, M.R., Eriksson, M., Goldman, A.E., Khuon, S., Collins, F.S.,

Jenuwein, T., Goldman, R.D., 2006. Mutant nuclear lamin A leads to

progressive alterations of epigenetic control in premature aging. Proc. Natl.

Acad. Sci. USA 103, 8703–8708.

Silva, J.M., Li, M.Z., Chang, K., Ge, W., Golding, M.C., Rickles, R.J., Siolas,

D., Hu, G., Paddison, P.J., Schlabach, M.R., Sheth, N., Bradshaw, J.,

Burchard, J., Kulkarni, A., Cavet, G., Sachidanandam, R., McCombie,

W.R., Cleary, M.A., Elledge, S.J., Hannon, G.J., 2005. Second-generation

shRNA libraries covering the mouse and human genomes. Nat. Genet. 37,

1281–1288.

Sohal, R.S., Weindruch, R., 1996. Oxidative stress, caloric restriction, and

aging. Science 273, 59–63.

Solomon, M.J., Larsen, P.L., Varshavsky, A., 1988. Mapping protein-DNA

interactions in vivo with formaldehyde: evidence that histone H4 is retained

on a highly transcribed gene. Cell 53, 937–947.

Solomon, M.J., Varshavsky, A., 1985. Formaldehyde-mediated DNA-protein

crosslinking: a probe for in vivo chromatin structures. Proc. Natl. Acad. Sci.

USA 82, 6470–6474.

Spindler, S.R., 2006. Use of microarray biomarkers to identify longevity

therapeutics. Aging Cell 5, 39–50.

Sreekumar, R., Unnikrishnan, J., Fu, A., Nygren, J., Short, K.R., Schimke, J.,

Barazzoni, R., Nair, K.S., 2002. Effects of caloric restriction on mitochon-

drial function and gene transcripts in rat muscle. Am. J. Physiol. Endocrinol.

Metab. 283, E38–E43.

Sui, G., Soohoo, C., Affar el, B., Gay, F., Shi, Y., Forrester, W.C., 2002. A DNA

vector-based RNAi technology to suppress gene expression in mammalian

cells. Proc. Natl. Acad. Sci. USA 99, 5515–5520.

Tabara, H., Grishok, A., Mello, C.C., 1998. RNAi in C. elegans: soaking in the

genome sequence. Science 282, 430–431.

Timmons, L., Fire, A., 1998. Specific interference by ingested dsRNA. Nature

395, 854.

Page 12: Application of advanced technologies in ageing research

C.-A. Lim, H.-H. Ng / Mechanisms of Ageing and Development 128 (2007) 149–160160

Velculescu, V.E., Zhang, L., Vogelstein, B., Kinzler, K.W., 1995. Serial analysis

of gene expression. Science 270, 484–487.

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., Mural, R.J., Sutton, G.G.,

Smith, H.O., Yandell, M., Evans, C.A., Holt, R.A., Gocayne, J.D., Amana-

tides, P., Ballew, R.M., Huson, D.H., Wortman, J.R., Zhang, Q., Kodira,

C.D., Zheng, X.H., Chen, L., Skupski, M., Subramanian, G., Thomas, P.D.,

Zhang, J., Gabor Miklos, G.L., Nelson, C., Broder, S., Clark, A.G., Nadeau,

J., McKusick, V.A., Zinder, N., Levine, A.J., Roberts, R.J., Simon, M.,

Slayman, C., Hunkapiller, M., Bolanos, R., Delcher, A., Dew, I., Fasulo, D.,

Flanigan, M., Florea, L., Halpern, A., Hannenhalli, S., Kravitz, S., Levy, S.,

Mobarry, C., Reinert, K., Remington, K., Abu-Threideh, J., Beasley, E.,

Biddick, K., Bonazzi, V., Brandon, R., Cargill, M., Chandramouliswaran, I.,

Charlab, R., Chaturvedi, K., Deng, Z., Di Francesco, V., Dunn, P., Eilbeck,

K., Evangelista, C., Gabrielian, A.E., Gan, W., Ge, W., Gong, F., Gu, Z.,

Guan, P., Heiman, T.J., Higgins, M.E., Ji, R.R., Ke, Z., Ketchum, K.A., Lai,

Z., Lei, Y., Li, Z., Li, J., Liang, Y., Lin, X., Lu, F., Merkulov, G.V., Milshina,

N., Moore, H.M., Naik, A.K., Narayan, V.A., Neelam, B., Nusskern, D.,

Rusch, D.B., Salzberg, S., Shao, W., Shue, B., Sun, J., Wang, Z., Wang, A.,

Wang, X., Wang, J., Wei, M., Wides, R., Xiao, C., Yan, C., et al., 2001. The

sequence of the human genome. Science 291, 1304–1351.

Viswanathan, M., Kim, S.K., Berdichevsky, A., Guarente, L., 2005. A role for

SIR-2.1 regulation of ER stress response genes in determining C. elegans

life span. Dev. Cell 9, 605–615.

Waterston, R.H., Lindblad-Toh, K., Birney, E., Rogers, J., Abril, J.F., Agarwal,

P., Agarwala, R., Ainscough, R., Alexandersson, M., An, P., Antonarakis,

S.E., Attwood, J., Baertsch, R., Bailey, J., Barlow, K., Beck, S., Berry, E.,

Birren, B., Bloom, T., Bork, P., Botcherby, M., Bray, N., Brent, M.R.,

Brown, D.G., Brown, S.D., Bult, C., Burton, J., Butler, J., Campbell, R.D.,

Carninci, P., Cawley, S., Chiaromonte, F., Chinwalla, A.T., Church, D.M.,

Clamp, M., Clee, C., Collins, F.S., Cook, L.L., Copley, R.R., Coulson, A.,

Couronne, O., Cuff, J., Curwen, V., Cutts, T., Daly, M., David, R., Davies,

J., Delehaunty, K.D., Deri, J., Dermitzakis, E.T., Dewey, C., Dickens, N.J.,

Diekhans, M., Dodge, S., Dubchak, I., Dunn, D.M., Eddy, S.R., Elnitski,

L., Emes, R.D., Eswara, P., Eyras, E., Felsenfeld, A., Fewell, G.A., Flicek,

P., Foley, K., Frankel, W.N., Fulton, L.A., Fulton, R.S., Furey, T.S., Gage,

D., Gibbs, R.A., Glusman, G., Gnerre, S., Goldman, N., Goodstadt, L.,

Grafham, D., Graves, T.A., Green, E.D., Gregory, S., Guigo, R., Guyer, M.,

Hardison, R.C., Haussler, D., Hayashizaki, Y., Hillier, L.W., Hinrichs, A.,

Hlavina, W., Holzer, T., Hsu, F., Hua, A., Hubbard, T., Hunt, A., Jackson, I.,

Jaffe, D.B., Johnson, L.S., Jones, M., Jones, T.A., Joy, A., Kamal, M.,

Karlsson, E.K., et al., 2002. Initial sequencing and comparative analysis of

the mouse genome. Nature 420, 520–562.

Wei, C.L., Wu, Q., Vega, V.B., Chiu, K.P., Ng, P., Zhang, T., Shahab, A., Yong,

H.C., Fu, Y., Weng, Z., Liu, J., Zhao, X.D., Chew, J.L., Lee, Y.L., Kuznetsov,

V.A., Sung, W.K., Miller, L.D., Lim, B., Liu, E.T., Yu, Q., Ng, H.H., Ruan,

Y., 2006. A global map of p53 transcription-factor binding sites in the

human genome. Cell 124, 207–219.

Weindruch, R., Kayo, T., Lee, C.K., Prolla, T.A., 2002. Gene expression

profiling of aging using DNA microarrays. Mech. Ageing Dev. 123,

177–193.

Welle, S., Brooks, A.I., Delehanty, J.M., Needler, N., Bhatt, K., Shah, B.,

Thornton, C.A., 2004. Skeletal muscle gene expression profiles in 20–29

year old and 65–71 year old women. Exp. Gerontol. 39, 369–377.

Welle, S., Brooks, A.I., Delehanty, J.M., Needler, N., Thornton, C.A., 2003.

Gene expression profile of aging in human muscle. Physiol. Genomics 14,

149–159.

Wells, J., Yan, P.S., Cechvala, M., Huang, T., Farnham, P.J., 2003. Identification

of novel pRb binding sites using CpG microarrays suggests that E2F recruits

pRb to specific genomic sites during S phase. Oncogene 22, 1445–1460.

Wood, J.G., Rogina, B., Lavu, S., Howitz, K., Helfand, S.L., Tatar, M., Sinclair,

D., 2004. Sirtuin activators mimic caloric restriction and delay ageing in

metazoans. Nature 430, 686–689.

Xia, X.G., Zhou, H., Samper, E., Melov, S., Xu, Z., 2006. Pol II-expressed

shRNA knocks down Sod2 gene expression and causes phenotypes of the

gene knockout in mice. PLoS Genet. 2, e10.

Yu, J.Y., DeRuiter, S.L., Turner, D.L., 2002. RNA interference by expression of

short-interfering RNAs and hairpin RNAs in mammalian cells. Proc. Natl.

Acad. Sci. USA 99, 6047–6052.

Zahn, J.M., Sonu, R., Vogel, H., Crane, E., Mazan-Mamczarz, K., Rabkin, R.,

Davis, R.W., Becker, K.G., Owen, A.B., Kim, S.K., 2006. Transcriptional

profiling of aging in human muscle reveals a common aging signature. PLoS

Genet. 2, e115.