Current Pharmacogenomics and Personalized Medicine, 2009, 7, 000-000 1

1875-6921/09 $55.00+.00 © 2009 Bentham Science Publishers Ltd.

Expert Review Article

Nutrient-By-Genotype Interactions and Personalized Diet: What Can We Learn From Drosophila and Evolutionary Biology?

D.M. Ruden1,*, P. Rasouli1, L. Wang1 and X. Lu1

1Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48302, USA

Abstract: While human nutrigenomics research and efforts for targeted nutritional interventions intensified over the past

few years, there are fundamental lessons to be learned from Drosophila and evolutionary biology for human nutrient-gene

interactions. With the advent of inexpensive whole genome sequencing, single-nucleotide polymorphisms (SNPs) and

insertions-deletions (INDELs) can now be associated with specific nutrient-by-genotype interactions that affect the

lifespan and health of Drosophila. Because of the increase in statistical power in using inbred Drosophila lines that are

freely available to investigators rather than outbred humans, the SNP and INDEL information should be invaluable to

anyone interested in personal genomes and personalized medicine and nutrition. This preclinical model offers a unique

opportunity to move beyond the artificial barriers among genomics, proteomics and metabolomics to integrate information

from diverse omics biomarker research streams. Drosophila life span also allows an evolutionary biology perspective to

forecast the long term impact of personalized nutritional interventions based on individual genetic make-up, before costly

prospective clinical studies in humans are initiated. The aim of this paper is to present a critical synthesis of the ongoing

work in the field of evolutionary biology in Drosophila models as a complement to human nutrigenomics. In addition,

we present and synthesize several key promises and challenges in extrapolation of data from Drosophila to humans, and

identify specific strategies to optimize this timely confluence of preclinical and clinical nutrigenomics research.

Key Words: Personal Genomes, Drosophila, Whole-genome SNP analysis, Nutrigenomics, Evolutionary biology.

“Unacceptable risk of heart failure.” I think that's what the manual says. The only trip I'll take in space is around the sun

on this satellite right here.” -- Irene (Uma Thurman), from the movie GATTACA (1997).

1. INTRODUCTION

The movie GATTACA is a dark view of how genetics research can have negative impacts on future society. In the dismal future portrayed by the writer-director Andrew Niccol, who also wrote and directed the “The Truman Show” everyone’s genome is sequenced and, consequently, every-one’s life-long health history is predicted from birth. The two main characters, Vincent and Irene, are victims of prejudice by a genetic caste system called “genoism,” since both of them have “owners’ manuals” that predict early death from heart failure. Because of the futility and expense of training short-lived people for long-term projects, they are denied their dream jobs of being astronauts.

The US Senate and House of Representatives recently passed the Genetic Information Nondiscrimination Act (GINA) [1] that offers protection against genetic discrimina-tion and some of the socio-ethical concerns described in GATTACA. While GATTACA and GINA focus on the negative aspects of having access to everyone’s DNA sequence, the “thousand-dollar genome” initiative from the NIH, which proposes to be able to sequence a human genome for under one thousand dollars, emphasizes positive potentials of this knowledge [2]. For example, “personalized

*Address correspondence to this author at the Institute of Environmental Health Sciences, Wayne State University, Detroit, MI 48302, USA; Tel:

313-964-5251; Fax: 313-577-0082; E-mail: douglasr@wayne.edu

medicine”, one of the anticipated outcomes of the thousand-dollar genome, promises to eliminate the need for trial-and-error approaches to drug selection and dosage because, when someone’s entire genome sequence is known, one could con-ceivably predict the efficacy and dose of a drug beforehand [3, 4]. Massively parallel high-throughput DNA sequencing platforms, which are also called “next generation” DNA sequencing technologies, will soon allow the inexpensive sequencing of the entire human genome [5], such that it will soon be a standard laboratory protocol when a person goes to the doctor’s office.

Nutrigenomics, which entails analyzing nutrient-gene interactions on a genome-wide scale, will also be facilitated when a person’s genome sequence becomes available as a standard laboratory protocol. Nutrigenomics is important and timely because improper diets are risk factors for disease, and a person’s risk for disease is often dependent, in part, on his or her genome profile [6-10]. To study diet-gene interactions, whole-genome microarrays and newer “digital gene expression” (DGE) profiles can be used to take advan-tage of the numerous whole-genome sequences that are available [5, 11-13]. These systems can be used to measure the changes in gene expression, alternative splicing, or DNA methylation of every gene in the genome when a particular nutrient is added or removed from the diet. Also, nutrige-nomics approaches can be used to identify markers of aging [14-20], disease predisposition [21-23], and for behavioral genomics [24-26].

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Traditionally, while Drosophila (fruit fly) is primarily known for identifying and characterizing the molecular transduction and signaling pathways, such as the Wingless (Wnt), Hedgehog (Hh), Notch, Transforming Growth Factor Beta (TGF- , Epidermal Growth Factor Receptor (Egfr), Tumor Necrosis Factor Alpha (TNF- ), and many others, the fruit fly is also ideally suited for conducting many types of nutrigenomics studies [27, 28]. Importantly, unlike yeast or C. elegans, Drosophila have fat bodies with adipocytes [28], and thus are more similar to mammals. Drosophila also have conserved metabolic and signaling pathways involved in fat metabolism, insulin signaling, among many others [28-31].

Other advantages of Drosophila for nutrigenomics research are its sophisticated genetics, small genome size, high fecundity, low cost, and short generation time. Mutations are available in over 70% of the genes and deficiencies have been generated that uncover over 90% of the Drosophila genome (www.flybase.org) [32]. Over 70% of human disease genes are conserved in Drosophila [33]. Also, a growing number of insect and other animal species with distinctive ecological niches are being sequenced, as well as numerous isogenic strains within a species, opening the door for both comparative genomics and “adaptive” nutrigenomics. The challenge for the future is to use nutrige-nomics and evolutionary approaches in Drosophila and other model organisms to provide clues to human gene-nutrient interactions [34].

Genome-wide association (GWA) studies in human populations have been only able to map a disappointingly small proportion of genetic variation from many complex diseases. Human GWA studies on nutrition have additional challenges, such as difficulties in controlling the diet, environment, and exercise. Thus, more advanced empirical models, such as Drosophila, must be used to improve our understanding of metabolic disease and nutrigenomics in general. To devise the most appropriate dietary interventions, many nutrient-related genetic disorders, such as phenylke-tonuria [35] can be modeled in Drosophila. Also, dietary interventions to treat diseases such as Parkinson’s Disease (PD) can be modeled in Drosophila [36]. With the advent of inexpensive whole genome sequencing, single-nucleotide polymorphisms (SNPs) and insertions-deletions (INDELs) can now be associated with specific nutrient-by-genotype interactions that affect the lifespan and health of both humans and Drosophila.

In addition to whole-organism nutrigenomic studies, nutrient-gene interactions can also be characterized in any tissue or organ in Drosophila using next generation DNA sequencing technologies. For example, we and several other laboratories are studying the effects of nutrition on the Drosophila heart [37-39]. Other laboratories are studying the effects of nutrition on central nervous system functions [40]. One common theme that is emerging in these organ health studies is that diets that promote longevity, such as reduced insulin signaling, can be bad for the heart and brain [40]. This is an important consideration in designing customized diets in humans that has previously not been adequately appreciated. Because of the increase in statistical power in

using inbred Drosophila lines that are freely available to investigators rather than outbred humans, the SNP, INDEL, and epigenetic information should be invaluable to anyone interested in personal genomes and developing effective protocols in personalized medicine and nutrition for treating organ-specific diseases.

This Drosophila preclinical model offers a unique oppor-tunity to move beyond the artificial barriers among genom-ics, epigenomics, proteomics and metabolomics to integrate information from diverse types of -omics biomarkers. The short Drosophila life span, which is generally less than 3 months, and the recently sequencing of 12 Drosophila species, also allows an evolutionary biology perspective to forecast the long term impact of nutritional interventions based on genetic make-up.. The aim of this paper is to present a critical analysis of the ongoing work in the field of evolutionary biology and genetics in Drosophila models as a complement to human nutrigenomics. In addition, we address the challenges in extrapolation of data from Drosophila to humans and identify specific strategies to optimize this timely confluence of basic science and clinical nutrigenomics research.

2. INSULIN AND TOR SIGNALING IN DROSOPHILA

Obesity in humans often causes insulin resistance, which is a major characteristic of type II (insulin-resistant) diabetes. The insulin and TOR (Target of Rapamycin) signalling pathways are both induced by nutrients and have been particularly productive areas of research in Drosophila. As discussed in this section, Drosophila research in insulin/TOR signaling has made major contributions to understanding these pathways in humans. Under high glucose conditions, Drosophila expresses insulin peptides from a small cluster of medial neurosecretory cells (mNSC) in the brain and act in an endocrine manner by binding to the cell surface Insulin Receptor (InR) [41-46]. Activated InR triggers an intra- cellular signalling cascade consisting of the kinases PI3 kinase (PI3K) and Akt (Fig. 1) (reviewed in [47]). Phos-phorylation by Akt inhibits the nuclear localization of the FOXO transcription factor, and the PTEN tyrosine phospha-tase reverses FOXO phosphorylation. The TOR pathway is a second nutrient signalling pathway that is activated when there are high levels of extracellular amino acids. Most components of the insulin/TOR pathways are highly conserved in metazoans and are often either oncogenes or tumor suppressor genes [48]. The greatest contribution that Drosophila has made to the Insulin/TOR pathways is teasing apart the downstream components of growth, metabolism and homeostasis that are regulated by these pathways (Fig. 1).

Fig. (1) summarizes several key metabolic processes that are regulated by insulin/TOR signalling pathways. The control of ribosome synthesis is tightly regulated by these signalling pathways because translation of mRNAs is required for growth. In Drosophila larvae, starvation reduces insulin/TOR signalling and leads to the inhibition of ribosome synthesis [49]. In a recent paper, Teleman et al. (2008) showed that repression of ribosome-synthesis genes is dependent on the FOXO transcription factor, whereas activa-

Drosophila, Nutrigenomics and Personalized Diet Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 3

tion of these genes is dependent on the Myc transcription factor [50]. Furthermore, these investigators determined that Myc is regulated by FOXO in a tissue-dependent manner [50]. The control of rRNA transcription is a rate-limiting step in cell growth. In Drosophila, TOR mediates the regula-tion of rRNA synthesis via the Pol I transcription factor TIF-1A [51].

Another major function of insulin/TOR signalling is the regulation of translation [52]. One mechanism is via TOR phosphorylation and repression of 4EBP (EIF4E-binding protein), an inhibitor of cap-dependent mRNA translation initiation [52]. Drosophila 4EBP mutants are hypersensitive to nutrient starvation and oxidative stress and have defective fat metabolism [53]. 4EBP is thought to function as a “metabolic break” to ensure that mRNA translation is kept low during nutrient deprivation [53]. Like 4EBP, InR levels are directly induced by FOXO upon nutrient starvation [54]. Interestingly, translation of InR mRNA occurs via an internal ribosome entry site (IRES) [54]. Thus, there is a switch from cap to IRES-dependent translation when nutrients are low.

Autophagy is also regulated by insulin/TOR signalling (Fig. 1). Autophagy is a conserved response to starvation in which cells degrade and recycle their organelles to maintain their nutrient levels. When Drosophila larvae are starved, autophagy is rapidly induced especially in the fat body, an adipose tissue that has taken on the role of fat and liver in insects [55, 56]. Importantly, autophagy requires the down

regulation of insulin/TOR signalling. For example, a key autophagy regulator, Atg1, is mutually inhibitory with TOR signalling [55]. Drosophila that are mutant for autophagy genes are sensitive to starvation stress and are either lethal or show a reduced lifespan [55, 57-60]. This phenotype has also been seen in C. elegans [61].

In a genetic screen for modifiers of TOR function, Hennig et al. (2006) identified a connection between endocy-tosis and TOR signaling [62]. They showed that starvation causes a block in bulk endocytosis in larval tissues, and that this response requires reduced TOR signalling (Fig. 1) [62]. TOR also postranslationally inhibits the amino-acid trans-porter Slimfast (Fig. 1) [62]. They conclude that cell growth, endocytosis, and insulin-signaling are all mutually obligatory (Fig. 1).

Detection of oxygen levels (oxygen sensing) is another way in which insulin/TOR signalling interacts with the environment (Fig. 1). In both Drosophila and mammals, low oxygen inhibits insulin/TOR activity via two hypoxia-induced proteins, Scylla and Charybdis, which are homologs of mammalian REDD1 and REDD2 [63]. The phosphatise Ptp61f is another hypoxia-induced TOR inhibitor in Droso-phila [64]. HIF1-a and HIF1-b are conserved hypoxia-inducible transcription factors that are also regulated by insu-lin/TOR signalling by both protein levels and nuclear local-ization [65, 66]. The significance of this cross regulation is not understood, but it may be a way to regulate homeostasis during sub-optimal conditions, such as starvation and low

Fig. (1). A schematic of Insulin/TOR signalling in Drosophila showing metabolic outputs (all caps) and downstream effectors (ovals).

Arrows and bars indicate positive and negative regulation, respectively.

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oxygen conditions. Discovery of new regulators of insulin production in Drosophila could lead to the discovery of new mechanisms of insulin control in humans.

One of the most often studied “extreme diets” is dietary restriction (DR), which is a reduction in caloric intake by often as much as 50%. DR increases lifespan in Drosophila and many other animals, including humans [67-74]. One could hypothesize that both DR and diminished insulin signalling increase longevity via a common mechanism. After eating, insulin levels increase, and one of the main effects of high insulin levels is to activate glucose transport into peripheral tissues. Conversely, fasting leads to lower insulin levels and the glucose transport pathway is not stimu-lated. However, several papers over the past decade suggest that DR and insulin signalling function, at least in part, via parallel pathways. At optimal conditions for DR, most animals live 20-50% longer than fully-fed animals [75-77]. Similarly, at optimal conditions that reduce insulin signal-ling, such as in InR1 knockout worms and InR-mutant flies, some animals can live up to 2-fold longer than control animals [75-78]. In other words, insulin signalling increases in longevity are generally more efficient than DR increases in longevity.

If DR and insulin signalling-induced longevity function through a common pathway, then combining DR and insulin signalling should not further increase in longevity. However, several studies have shown that DR and insulin signalling-induced longevity are additive, suggesting independent or parallel mechanisms for lifespan extensions. For example, Houthoofd et al. have shown that DR-induced lifespan extension is independent of the insulin signalling pathway in C. elegans [77]. Similarly, dwarf-1 mice are long lived because they have reduced insulin signalling, and caloric restriction has an additive effect on longevity in these mice [79]. In Drosophila, DR has been shown to extend lifespan even in the absence of FOXO, a conserved transcription factor, indicating that DR functions independently to insulin signalling [80].

In transcription profiling experiments, multiple-liver-specific genes are additively up-regulated when long-lived dwarf-1 (reduced insulin signaling) mice are caloric restricted, suggesting that DR is independent of insulin signalling in mammals at the transcriptional level [81]. To help explain the transcriptional- independence of DR and insulin signalling, it was found that forkhead-family transcription factors FOXO and FOXA in C. elegans affect gene expression independently under optimal insulin signal-ling and DR conditions for increasing the lifespan. The only homolog to FOXA in Drosophila is Forkhead (Fkh), but it is not known whether Fkh is involved in DR-induced longev-ity. However, Fkh controls the timing and tissue selectivity of steroid-induced developmental cell death in the larval salivary glands [82], suggesting that Fkh/FOXA might be downstream of TOR signalling (Fig. 1).

There are likely many other mechanisms and pathways to increase lifespan. For example, Bishop and Guarente showed that the red-wine polyphenol resveratrol, which stimulates the activities of Sir2 and other sirtuin HDACS [83-85], is still able to increase longevity in FOXA-mutant worms [86].

However, the pertinant pathway induced by resveratrol remains controversial because Kaeberlein et al. have shown that resveratrol increases longevity in a Sir2-independent manner in yeast [87-89]. Also, the Partridge laboratory recently published a research paper that shows that resvera-trol does not increase the lifespan in flies, and that Sir2-deficiency does not affect lifespan in either flies or worms [90].

Panowski et al. have shown that worms with mutations that block mitochondrial electron transport have longevity increases that are independent of FOXA [91]. However, Bishop and Guarente have shown that drugs that inhibit electron transport, such as antimycin, prevent DR signaling through the Nrf2 pathway [86]. These results suggest that DR-induced longevity is mediated by FOXA in a respiration-independent manner, but through Nrf2 in a respiration-dependent manner. Nevertheless, despite the disputed requirement for respiration in DR-induced longevity, these studies have profound implications on improving health and longevity in humans.

How might one use these Drosophila dietary studies to understand the evolution of metabolism in humans? Humans have evolved to efficiently utilize a variety of foods, but much of human evolution was under extreme dietary condi-tions. Therefore, the plentiful and improper dietary habits of modern humans have lead to an obesity epidemic [92]. The studies in Drosophila suggest that there might be unintended consequences of increased and improper food intake in hu-mans, in addition to obesity, such as a possible decrease in stress resistance. This might help explain why obese humans have increased risks in several other diseases, such as cancer [93-96], cardiovascular disease [97-100], respiratory diseases [101], and Alzheimer’s Disease [102-106]. Similarly, the absence of an effect of so-called “longevity drugs,” such as resveratrol, on effecting the lifespan of Drosophila should cause one to be more cautious about the claims being made about these drugs in humans.

There is a need for additional nutrigenomics studies in Drosophila with regards to insulin/TOR signaling. The in-volvement of genes in the insulin/TOR signaling pathway has been well reviewed in response to DR. However, there are few if any studies in Drosophila regarding other potential

mechanisms of DR such as the effects on glucation. Oudes and colleagues have shown age-dependent accumulation of advanced glycation end-products (AGE) in adult Drosophila, thus suggesting that this would be a productive area of inves-tigation [107]. The affects of nutrition on free radical production would be another promising area of research that is under investigated in Drosophila. The laboratories of Sohal [108-112], Phillips [113-117], and others have studied free radicals and longevity in terms of phase II enzyme level manipulation. Lasko’s laboratory showed in 2005 that starvation and oxidative stress resistance in Drosophila are both mediated through the eIF4E-binding [118], thus linking

insulin/TOR signaling and oxidative stress. Clearly, there is still much to be learning in linking and identifying novel nutrigenomics pathways using the Drosophila model. Drosophila nutrigenomics studies will further help us further understand nutrigenomics mechanisms in humans.

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3. OTHER EXTREME DIETARY STUDIES IN

DROSOPHILA

How might we extrapolate nutrigenomics and nutrition data from Drosophila and other models to humans? As mentioned in the previous section, the extreme diet, DR, increases lifespan in Drosophila and many other animals, including humans [67-74]. Conversely, the effects of saturated fats in diets, that are otherwise isocaloric (identical calorie), have been shown to decrease both mean and maximal lifespan of Drosophila [119-121], and saturated fats are well known to adversely affect health in humans [122-126]. However, the specific mechanism responsible for the deleterious effects of saturated fats is mostly unknown, and research in Drosophila and other models might help address this question. One of the first studies that attempted to make a connection between Drosophila dietary components and longevity found that isocaloric diets consisting of high satu-rated fats (such as palmitic acid) and low carbohydrates will, on average, shorten the lifespan of Drosophila compared with flies fed “control” diets high in carbohydrates and low in saturated fats [119-121]. These early studies, performed in the late 1970s and early 1980s by Driver and colleagues [119-121], did not determine the specific metabolic processes that were negatively affected by the consumption of fat. Instead, they potentially laid the groundwork for further studies.

In a recently published book chapter, we replicated of some of Driver and colleagues’ studies with more modern technology, taking full advantage of the genomic studies that have been conducted since the early days of nutrigenomics [119]. As in the earlier studies, we found that flies fed diets high in palmitic acid (17:0) have a much shorter lifespan than flies fed a control diet [14]. We used the same recipe as Driver and colleagues, with the addition of the mold inhibitors propionic acid and tegosept [14]. As with the diets used by Driver and colleagues, our control diet was under isocaloric conditions with the experimental diets, but the sucrose was isocalorically replaced with some other dietary component. Sucrose is a simple sugar with two hexose rings (one glucose and one fructose), and is therefore easily metabolized to glucose, whereas palmitic acid (17:0) is a long-chain saturated fatty acid found in palm oil and other plants that requires several lipases and other enzymes to convert it to energy sources such as glucose.

In addition to confirming the results published over 25 years ago by Driver and colleagues [119], we found that flies fed the palmitic acid diet had significantly elevated triglyc-eride levels compared with flies fed the control diet [14]. Triglyceride levels were not analyzed in the earlier studies by Driver and colleagues [119]. Furthermore, using tech-niques that were not available 25 years ago, we also did whole-genome microarray studies to determine what gene expression changes occur when flies are fed the high palmitic acid diet and compared these changes to those induced by other diets. We also determined that the palimitic acid diet fed to flies during the larval stages caused as much as a two-fold increase in the duration of the larval period [14]. In a classic paper published over 70 years ago, Beadle, Tatum, and Clancy (1938) showed that malnutrition causes a

similar prolongation in the larval stages of Drosophila [127]. They explained that larvae must reach a “critical weight” before pupation (reviewed in [128])

Other than the high palmitic acid diet, we also analyzed diets that approximate “extreme diets” that humans have used to attempt to lose weight. For our “Atkins-like diet,” we replaced the sucrose in the control diet isocalorically with 95% lean ground beef. For an “Asian-like” soy diet, we replaced the sucrose in the control diet isocalorically with soy tofu [14]. Not unexpectedly, both extreme diets signifi-cantly decreased the lifespan of flies compared with the control diet. We repeated these experiments under more optimal growth conditions (i.e., virgin males and females were measured separately and the flies were kept in less crowded vials rather than cages) and, again, both extreme diets decreased the mean and maximal lifespan by over 50% (D.M.R., unpublished observations).

Why did the three extreme diets reduce the lifespan of flies? The palmitic acid diet reducing longevity is not sur-prising because diets high in saturated fats increases obesity and cardiovascular diseases in humans. However, the high beef (Atkins-like) and high soy (Asian-like) diets have been shown to combat many age-related diseases including cardiovascular disease, stroke, and cancer [129-131], and soy isoflavones have been shown to increase longevity in certain strains of mice [132].

It is possible that too much carbohydrate (sucrose) was displaced in the extreme diets and this is what shortened lifespan. To address this issue, and to further validate the Drosophila model to understand the effects of extreme diets, a range of ratios of beef or soy-to-sucrose should be exam-ined. This will allow investigators to determine whether beef or soy is intrinsically life shortening in Drosophila, or whether the absence of sucrose is itself life shortening. Nevertheless, it causes one to be aware that extreme diets in humans might have short term benefits but have long term decreases in longevity or quality of life.

In addition to nutrigenomics research described above, several other laboratories have recently studied the effects of extreme dietary and environmental conditions on lifespan in Drosophlila. For example, the Partridge laboratory studied the effects of stock maintenance, genotype differences and microbial infection on the ability of Dietary Restriction (DR) to extend life in Drosophila [133]. They found that, while lifespan differed dramatically in the different environmental conditions, the DR effect was nevertheless observed in all of the conditions, and that any mechanistic discoveries made are of potential relevance to other models and in humans [133]. In a related paper, the Partridge laboratory optimize the DR conditions in Drosophila to maximize lifespan [134]. Such optimization is important and often overlooked or deemed impractical and too expensive in DR studies in humans and mammalian models.

The presence of natural variation in response to extreme diets is also very important in both humans and model organ-isms. The Rose laboratory studies the evolution of stress resistance in Drosophila in “experimental evolution” selec-tion experiments. For example, they show that selection for

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increased resistance to starvation and desiccation produces rapid responses with high heritability estimates within popu-lations of Drosophila [135]. Interestingly, they found that direct selection for increased starvation (Rose system “SO” and SB” lines) and desiccation resistance (Rose system “D” lines) results in an increase in longevity in the absence of stress, albeit with a decreased fecundity, decreased pre-adult viability and slower development times [135]. Thy conclude that there appear to be a complex trade-off between resource acquisition during larval stages and adult stress-resistance [135]. Similarly, the Simpson laboratory has shown that starvation resistance is positively correlated with body lipid levels in five Drosophila simulans populations [136].

Twelve Drosophila species have recently been sequenced [137], and numerous other species are in progress, so it might be preferable to focus on species other than Droso-phila melanogaster to study the evolution of adaptation to a specialized or unusual diet. For example, Matzkin and col-leagues have studied “cactophilic” Drosophila, Drosophila mojavensis, which lives in the Sonoran Desert and utilizes four different cactus hosts across its geographical range [138]. D. mojavensis lays eggs in the necrotic tissues of cacti, thereby exposing them to the toxic and unusual com-pounds of rotting cacti. With this system, they have observed differential gene expression associated with cactus host use in genes involved in metabolism and detoxification [138]. While probably not as extensive as Drosophila, humans also have natural variation to dietary conditions. Modelling extreme diets in Drosophila will therefore likely contribute a great deal to human nutrigenomics studies.

4. QTL MAPPING STUDIES OF TRIGLYCERIDES IN

DROSOPHILA

The key question in nutrigenomics, or personal medicine in general, is how one can go from a genome sequence to a phenotypic response to a food or a drug. To help answer this question, several years ago, Mackay and colleagues gener-ated a large collection of recombinant inbred lines (RILs) from divergent Drosophila strains Oregon R (ORE) and Rus-sian 2b (2b) that were isolated on opposite sides of the world. Mackay’s and other laboratories have used these to map numerous quantitative traits [139-145]. Similarly, Curtsinger and colleagues have made other collections of RILs to study the effects of natural variation on longevity in Drosophila [15-20].

Quantitative traits are phenotypes that are not all-or-none, such as those caused by most single-gene disorders, but rather differ in a normal (Gaussian) distribution in a popula-tion, such as blood pressure or triglyceride levels. A quanti-tative trait locus (QTL) is the genome region that causes a significant variation in a quantitative trait, presumably because there is a sequence polymorphism in a gene or genes that underlie the QTL. A disadvantage of the QTL approach using RILs is the lack of heterozygosity, which is less of a problem in population studies. However, the Suto laboratory has successfully used a “round-robin” approach to reintro-duce heterozygosity in experiments to identify QTLs involved in plasma cholesterol and phospholipid levels in mice [146-152]. A “round-robin” approach, in the strictest

definition, is one in which every strain is mated to every other strain. The “round-robin” approach involves analyzing the F2 offspring in paired crosses of three or more RILs (e.g., AxB, BxC, and AxC for three RILs, such as in the Suto studies) [146-152].

Long and Macdonald used a modified “round-robin” approach to generate a “synthetic recombinant” population to study the quantitative genetics of bristle number in Droso-phila [153]. In their modified round-robin approach, they started with 8 isogenic strains and crossed 1x2, 2x3, 3x4, 4x5, 5x6, 6x7, 7x8, and 8x1, where the first number in each cross was a male and the second number was a female (Fig. 2A). Next, they batch mated the G0 offspring of these crosses to each other in two large pools for several generations to generate Gn offspring. In these studies, RILs were not gener-ated, but rather large numbers of flies from each generation were batch mated for several generations to increase the recombination events between strain-specific SNPs and INDELs [153]. They called this a “synthetic recombinant population” because RILs were not generated.

Fig. (2). Set up of the 8-way cross for speed-mapping QTLs. (a)

The round-robin cross. 1-8 represent 8 different sequenced strains

of Drosophila that were isolated from around the world. Males are

represented by the first number and females by the second number

in the cross. The progeny from the round-robin cross (G0 Genera-

tion) are mated in large pools for several generations to generate Gn

flies that have regions of DNA from all 8 parental strains. (b) The

collaborative cross. The progeny of the cross between stains 1 and 2

are mated to the progeny from the strains 3 and 4, etc. Next, the

progeny of the two sets of crosses are mated together to generate

animals that have SNPs from all 8 parental strains. Further genera-

tions can be isolated to increase the recombination between SNPS

and to increase the number of flies analyzed.

Another approach of crossing 8-strains together, which was done in the mouse “collaborative cross” experiment to generate ~1,000 RILs [154], is to cross 1x2, 3x4, 5x6, and 7x8 in the initial cross, 1/2x3/4 and 5/6x7/8 in the second cross, and 1/2/3/4x5/6/7/8 in the third cross to generate a large pool of G0 offspring (Fig. 2B). To ensure that the X and Y chromosomes from each strain are represented equally in the G0 population, it is important to do reciprocal crosses initially. For example, in the initial cross, X1X1 females from strain 1 are mated to X2/Y2 males from strain 2, and vice versa. The first cross will generate X1X2 females and X1/Y2 males, and the reciprocal cross will generate X1X2 females

Drosophila, Nutrigenomics and Personalized Diet Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 7

and X2/Y1 males. Likewise, X3X4 females and X3/Y4 males, and X4/Y3 males in the reciprocal cross, will be generated from the second pair of strains. In the next cross, X1X2

females are mated to both X3/Y4 and X4/Y3 males, and the X3X4 females are mated to both X1/Y2 and X2/Y1 males.

If reciprocal crosses are not done in the collaborative cross, when X1X2 females are mated to X3/Y4 males, the X4

and Y3 chromosomes will be lost. Reciprocal crosses in each generation ensure that each of the 8 parental lines contributes 1/8 of the genome in the G0 flies, with the exception of the 8 Y chromosomes, Y1 through Y8, which would remain un-recombined. The round-robin approach (Fig. 2A) elimi-nates the need for doing reciprocal crosses, but since a large pool of animals are mated together, individual Gn offspring in the round-robin crosses are less likely to have an equal proportion of DNA from all 8 progenitor strains than the offspring in the collaborative cross. Since the main purpose of the collaborative cross is to generate RILs, brother sister matings of individual G0 offspring were done for several more generations to eventually generate Gn isogenic RILs (Fig. 2B) [154].

To begin a QTL mapping study with RILs, one starts with two parental strains that are in the same species, but are widely divergent in DNA sequence, such as Oregon R (ORE) from Oregon, USA, and Russian 2B (2B) from the former Soviet Union. ORE and and 2B have numerous single nucleotide polymorphisms (SNPs) between them. The F1 hybrids between these isogenic strains are also genetically identical because they contain one set of chromosomes from ORE and one set from 2B [141]. In the F2 flies, however, there is a “shuffling of the decks” of the ORE and 2B genomes, and each of the progeny contains a random combination of genetic material from each of the parental lines.

There are two general approaches used in QTL analyses: 1) directly analyzing F2 recombinant individuals, and 2) the more laborious method of generating “recombinant inbred” (RI) lines. In many mouse studies, the first approach has been used, whereby hundreds of F2 mice are individually phenotyped and genotyped [155-159]. For the second approach, Trudy Mackay's laboratory has developed a collection of Drosophila RILs [141] that our laboratory and others have used to identify obesity QTLs [160, 161]. In the BXD and other mouse RILs, as in the Drosophila RILs, at least 20 generations of brother-sister matings were conducted to generate nearly isogenic RILs.

The F2 and RILs are genotyped typically by analyzing hundreds of evenly-spaced single nucleotide polymorphisms (SNPs) that are specific for one parental strain or the other. However, the Drosophila RILs were characterized prior to the completion of the genome sequence, and the cytological locations of the abundant roo transposon were used to characterize each line [162]. QTL analyses in mice have the further advantage that both parental strains in the BXD lines have been sequenced, so potentially millions of SNPs can be

used for very fine-scale characterization of the lines. Many of the mouse and Drosophila RILs are available from stock centers and individual investigators.

We note, rather counter intuitively, that it is not critical for the two parental strains to differ in a particular trait, such as triglyceride levels, before one chooses them for a QTL experiment to identify genes that affect the levels of that trait. In the case of ORE and 2B, for instance, despite the fact that the parental strains have nearly identical triglyceride levels, the F2 recombinant inbred lines display a broad distri-bution of triglyceride levels [160]. The parental strains have nearly the same triglyceride levels because combinations of different “high- and low-activity QTLs” netted nearly the same over-all triglyceride level. When different “high- and low-activity QTLs” are found in each parental line, the appearance of so-called “transgressive recombinants” appear in the segregating F2 population [160, 163].

How does one decide whether to use F2 recombinants or RILs in a QTL experiment? RILs require many additional generations of brother-sister matings, whereas, F2 individuals are generated, by definition, in only two generations. The advantage of using RILs, however, is that many of them already exist, and one could theoretically keep them forever. In principle, on could use RILs for QTL mapping experiments on an unlimited number of projects. In contrast, the F2 lines can only be used once for phenotype and genotype analyses before being discarded. In actual practice, however, it is not known how long RILs will be of practical use because each generation of inbreeding decreases the fitness of most of the lines. Also, the RILs accumulate reces-sive-lethal mutations over time, which further decrease their viability.

Therefore, for the above reasons, new RILs will undoubtedly need to be generated every few years. However, at least in Drosophila, the current advantages of using RILs overcome this potential future inconvenience. Advances in global mapping of single nucleotide polymorphisms (SNPs), such as with “SNP Chips” and “next-generation” DNA sequencing might obviate the need to use RILs and encour-age the further utilization of directly analyzing F2 lines. A “SNP Chip” is a microarray-type platform that contains oli-gos specific for many or all of the SNP differences between two parental strains. Non-microarray platforms have also been developed for global-SNP mapping studies [164-167]. Rapid and inexpensive DNA genome-size “next generation” DNA sequencing technologies will also likely increase the practicality of directly genotyping and phenotyping F2 individuals for QTL mapping studies [168].

The principle of QTL analyses is that quantitative traits can be mapped to large (10-100 Mbp) sub-chromosomal genomic regions by correlating the phenotype in question (such as triglyceride levels in Drosophila) with the genotype. For example, in the simplest possible scenario, assume that there is one allelic variation of a gene that causes flies to have low triglyceride levels, i.e., a “Thin Gene.” Also, in the simplest scenario, if this is the only gene polymorphism that affects triglyceride levels, then F2 individuals that inherit both thin genes will have low triglyceride levels, whereas F2 individuals that are homozygous for the other allelic variation, i.e., the “Fat Gene,” will have high triglyceride levels. Individuals with one “Fat Gene” and one “Thin Gene” will have intermediate triglyceride levels. In our

8 Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 Ruden et al.

studies, there were numerous QTLs that affect triglyceride levels, and epistatic (non-linear) interactions have been identified among several of the loci [160]. However, the basic principle is the same whether there is a “single-affect locus” or whether there are “multiple-affect loci” [141].

Obviously, one cannot study human RILs or even “synthetic recombinant populations” so one is restricted to GWA (genome-wide association) studies. However, with the soon-to-come $1000 genome, thousands or even millions of human genomes will soon be sequenced and used in human nutrigenomics studies. The Drosophila studies that are likely most similar to the future human GWA studies are GWA studies conducted by the Mackay and Langley laboratories. The Mackay laboratory has generated 192 isogenic strains isolated from the Raleigh-Durham area and numerous GWA studies have been performed on these lines by several labora-tories on these “Raleigh lines” [169-185] (referenced are studies since 2005). Mackay and colleagues have proposed to sequence these lines with next-generation DNA sequenc-ing technologies (2008 White Paper; www.flybase.org). The Langley laboratory has proposed a similar large-scale sequencing project to study the migration of Drosophila from Africa to Europe [186]. These ongoing Drosophila studies will be invaluable in developing bioinformatics tools for the future human nutrigenomic studies.

5. GENETICAL GENOMICS RESEARCH IN

DROSOPHILA

In the past few years, “genetical genomics” approaches have been developed that combine QTL analyses with microarray studies and have identified “master modulatory loci” that regulate hundreds of genes in the same tissue [187-189]. “Genetical genomics,” also called “genetics of gene expression,” is the term coined to indicate the process in which the levels of every mRNA is used as a quantitative trait in massive QTL analyses of potentially every molecule

in a tissue [189]. In the Kruglyak laboratory, “genetical genomics” has been extended to the genetics of protein [190] and metabolite levels [191] in Saccharomyces cerevisia. However, few studies have taken advantage of proteomics [192-194] and metabolomics in Drosophila [195-199], and none to our knowledge in the field of nutrigenomics. In a previous review, we described how utilizing high-dimensional genetic and bioinformatics resources allow one to conduct sophisticated studies on the interactions among nutrients and genes [200]. Since then, we used both standard QTL [201] and genetical genomics (Ruden et al., manuscript in preparation) approaches to identify the signalling pathways that are affected by developmental exposure to the developmental neurotoxin lead. Here, we propose that simi-lar techniques can be used to identify signalling pathways involved in the metabolism of particular nutrients.

How might one conduct a combined genetical genom-ics/nutrigenomics experiment? Drosophila recombinant inbred lines (RILs) could be reared under two dietary condi-tions: diet 1, which could be a control diet, and diet 2, which could be an isocaloric diet in which the sucrose is replaced with some other nutrient, such as soy. One could then isolate the mRNA from a particular tissue from each of the RILs, such as the brain or the fat body, and determine relative gene expression levels of all ~14,000 Drosophila genes with gene expression arrays, such as with the Affymetrix Dros2 array, or by digital gene expression, such as with the Solexa GA3 instrument, the ABI SOLID instrument, or the Roche 454 instrument [11]. The relative expression levels of each gene can be used as a quantitative trait and QTL mapping pro-grams, such as R/qtl [202], can be used to identify significant expression QTL (eQTL).

Fig. (3) shows a hypothetical “cis-trans plot” of nutrige-nomics results predicted based on similar studies with lead. In a cis-trans plot, the eQTL location is plotted on the x-axis and the gene location is plotted along the y-axis. The dots

Fig. (3). A cis-trans plot of a hypothetical genetical nutrigenomics experiment in Drosophila. The X-axis is the eQTL location on the X, 2nd

(2L and 2R) and 3rd chromosomes (3L and 3R). The small dot 4th chromosome is not shown. The y-axis is the location of the gene along

the genome in the same order. The lightest-coloured dots are the cis-eQTL that map to the gene itself (within a window of ~5 cM) and the

medium-coloured dots represent the trans-eQTL that map outside of the gene. The black dots represent trans-eQTLs that are all regulated by

a common master modulatory protein (Black stars). Notice in B that the 2L transband is specific for Diet 2 (see text).

Drosophila, Nutrigenomics and Personalized Diet Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 9

along the diagonal represent cis-eQTL because they have different levels of expression that is dependent on the SNPs that are in the gene itself. For example, a SNP in a transcrip-tion factor binding site could cause an increase or a decrease in binding and consequently affect the steady state mRNA level of the gene. In our genetical genomics study and in several other studies, approximately 1/3 of the genes have a significant cis-eQTL. The dots no along the diagonal repre-sent the trans-eQTLs, also called distal-eQTLs, because the genes are regulated by eQTLs that do not map to the gene (Fig. 3). The dots that are not in vertical lines represent trans-eQTLs that are unique for a particular gene, whereas the dots in vertical lines and are thought to represent signal-ling pathways of co-ordinately regulated genes (Fig. 3). Finally, the stars represent master-modulatory loci that regulate the co-ordinately regulated genes in a trans-eQTL pathway, which is also called a transband (Fig. 3).

What we observed in our lead studies, and what we predict here for nutrigenomics studies, is that some trans-bands will be present under both environmental conditions, whereas other transbands will be specific for one condition alone. This is represented by the transband on the X-chromosome being present under both dietary conditions, but the transband on 2L (the left arm of the second chromosome) being specific for the second dietary condition. The stars represent that master modulatory proteins that regulate all of the genes in the transband in both diets (the X star) and only in diet 2 (the 2L star) (Fig. 3).

The master modulatory gene on 2L (Fig. 3) represents a gene that has a genotype-by-environment interaction (GEI). Fig. (4) shows a hypothetical model to explain how the 2L transband genes might be regulated in Diet 2 but not in Diet 1. Diet 2 could contain a particular metabolite (Met) that is not present in Diet 1, and this metabolite binds to the ORE version of the master-modulatory protein (ORE square) but not the 2B version (2B square). Consequently, the 2L transband genes are up-regulated only when the master modulatory gene has the ORE genotype and only in Diet 2 (Fig. 4).

Fig. (5) shows a graphical representation of what is meant by a GEI. The most frequently cited example of a GEI is that people heterozygous for the sickle cell (SS) allele of haemoglobin are resistant to the malarial parasite Plasmo-dium falciparum (Fig. 5A). In the absence of malarial infec-tion, the lifespan of +/+ and SS/+ people is approximately the same (Fig. 5A). However, in the presence of the malarial infection, the lifespan of SS/+ people is much higher than +/+ people (Fig. 5A). An analogy can be made for the GEI of a particular diet on lifespan (Fig. 5B). Diet 1 might have no effect on lifespan when a particular locus has either SNP1 or SNP2, but lifespan might be dramatically decreased in Diet 2 when the locus has the SNP2 genotype (Fig. 5B). The characteristic that a statistician is looking for to identify significant GEI’s is a so-called “line crossing” event, as depicted in both Figs. (5A and 5B) [203].

Techniques similar to genetical genomics have also been developed in Drosophila. For example, the Gibson labora-tory used natural populations of flies to identify “quantitative trait transcripts” (QTT) for nicotine resistance in Drosophila

melanogaster [204]. The QTT approach used by Gibson and colleagues correlates changes in gene expression with effects on lifespan when the Drosophila strains are fed nicotine, but the QTT approach can also be used for nutrigenomic studies [205]. Similar QTT studies in humans would be more diffi-cult, but with the next-generation sequencing technology, nutrient-specific QTTs will likely soon be identified in humans.

Fig. (4). A schematic model for genotype-specific regulation of

genes in the 2L transband in Diet 2 but not in Diet 1. The ORE box

is a transcription factor with the ORE SNPs and the 2B box is the

same transcription factor with the 2B SNPs, which make it unable

to bind to the metabolite that is only present in Diet 2 (Met). Con-

sequently, the genes in the 2L transband are only highly expressed

(thick arrows) when the transcription factor has the ORE genotype.

When the transcription factor has the 2B genotype, the genes in the

transband are expressed at basal levels (thin arrows).

6. SPEED MAPPING QTLS IN NUTRITION RE-SEARCH

Recently, dramatic advances have been made to rapidly map SNPs and INDELs underlying QTLs in Drosophila. “Speed-mapping” QTLs is a technique recently developed by members of Trudy Mackay’s laboratory in an extensive collaboration with members of the Nutrition and Genomics Laboratory at JM-USDA HNRCA (Jene Mayer-USDA Human Nutrition Research Center on Aging) at Tufts University to rapidly identify QTLs involved in longevity [206]. They developed this technique by analyzing F2 DNA from a cross of ORE/2B F1 flies. The SNP differences were characterized by hybridizing the DNA from young and old F2 flies (after 90% of the flies died of old age) to standard

10 Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 Ruden et al.

Affymetrix Dros2 expression arrays. They identified 2,326 SNPs that distinguish ORE and 2B because an Affymetrix probe happened to co-align with a SNP in one of the two strains. The longevity SNPs were identified by looking for statistically enriched SNPs in an F2 population after 90% of the flies died of old age [206].

Using next-generation DNA-sequencing technologies, it is possible to dramatically improve on the speed-mapping technique. Once could re-sequence ORE and 2B and identify the ~500,000 SNP differences between the two strains. The F2 flies could be genotyped by custom SNP-arrays, which are very affordable (a slide with ~2 million SNP probes currently costs less than $1000), or by re-sequencing the pooled F2 flies after 90% of the flies have died of old age. A second improvement of the speed-mapping technique, as discussed above in reference to the mouse collaborative cross, could be to do 8-way crosses and analyze the F3 or F4 flies rather than doing a simple 2-way cross (Figs. 2A and 2B). Additionally, if one were doing a collaborative-like cross, one could use an embryo sorter to set up crosses with hundreds of thousands or even millions of pairs of flies. The COPAS™ (Complex Object Parametric Analyzer and Sorter) Instrument (Union Biometrica, Inc.) can sort through 20 to 150 embryos per sec (eps) based on fluorescence intensity. Paul Schedl’s laboratory has made a Sxl-EGFP stock that expresses EGFP only in female embryos which can be sorted on the COPAS instrument (http://flystocks.bio. indiana.edu/Reports/24105.html).

The time limiting factor in many Drosophila genetics experiments, such as collaborative-like crosses, is collecting enough virgin females to set up crosses, and the practical limit for virgin female collection is usually less than 10,000. With the embryo sorter, one could potentially collect hundreds of thousands or even millions of virgin females that were sorted as EGFP-positive embryos. The embryo sorter only has 94-99% accuracy, so a second round of selection is probably necessary. Since males and females contain different longevity QTLs [206] the embryo sorter can also be used to sort the two populations in the final longevity experiment.

Not all Drosophila nutrigenomics experiments require embryo sorting, such as round-robin crosses (Fig. 2A) and studies comparing gene expression changes in the 12 sequenced Drosophila species genomes, but inexpensive next-generation sequencing of individuals within a species will undoubtedly help identify causative SNPs and INDELs. Other high-throughput techniques can also be used to increase the efficiency of QTL studies, such as 96 well plates for metabolomic studies, and the “wing machine” for measuring polymorphisms in Drosophila wing shape [207]. With these proposed improvements of the speed-mapping QTL technique and inexpensive next-generation sequencing, it should be possible to identify individual SNPs and IN-DELs that are involved in a GEI. Once the causative SNPs and INDELs are identified in Drosophila, then the identi- fication of similar changes in human populations will likely help identify human nutrigenomic GEIs.

7. HIGH THROUGHPUT SCREENS IN DROSOPHILA

S2 CELLS

Drosophila tissue culture cell lines were developed several years ago [208]. These cell lines are widely used because they can be grown at room temperature without added CO2 in standard tissue culture media or using defined media. The S2 Drosophila cells can also be easily treated with double-stranded RNA to knock out targeted genes [209]. Because the Drosophila genome is much smaller and there are fewer copies of most genes compared with mammalian cells, the S2 cell is also an ideal system for con-ducting genome-wide RNA screens for genes that affect growth properties or specific signalling pathways. For exam-ple, Mattila et al. (2008) did RNAi screening for kinases and phosphatases that regulate FOXO transcription factor that is induced under dietary restriction or nutrient depravation conditions [210]. They used high throughput transcriptional assays with FOXO-Regulatory-Element-luciferase reporter genes (FRE-luciferase) and screened for RNAi’s that either increased or decreased luciferase levels. They also used a high throughput automated microscopy screen that measured nuclear localization of FOXO and/or protein stability (Fig. 6). For example, Akt RNAi would be identified as a enhan-

Fig. (5). A line-crossing graph showing the genotype-by-environment interactions (GEI) of malaria and diets. A: the longevity of people

heterozygous for the sickle cell allele of beta globin (SS/+) is not significantly affected. However, people homozygous for the normal allele

of beta globin (+/+) have a decreased lifespan when infected with the malarial parasite. B: A hypothetical GEI with diet. People with SNP2

have a significant decrease in lifespan in Diet 2 but not Diet 1, but people with SNP1 have the same relative lifespan in both diets when cor-

rected for all of the other confounders (see text).

Drosophila, Nutrigenomics and Personalized Diet Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 11

cer of FOXO nuclear localization (Akt inhibits FOXO nuclear localization), whereas PTEN RNAi would be identi-fied as a repressor of FOXO nuclear localization (Fig. 6A). This can be seen microscopically by comparing the co-localization of a nucleus specific dye (DAPI) and a FOXO-specific dye (FITC; Fig. 6B). They identified several enzymes that regulate FOXO transcriptional activity, intra-cellular localization and/or protein stability [210]. These new downstream modulators of FOXO signalling can be used as novel targets for drugs to treat human diseases. Similar comprehensive RNAi screen have been done in human cells [211, 212], but the main advantage of conducting these screens in Drosophila is that there is much less redundancy in the fly genome [213].

Fig. (6). Example of a high throughput RNAi screen for modifiers

of FOXO signalling. A: The insulin receptor (InR) activates a

kinase (PI3K) which in turn activates a second kinase (Akt) which

prevents FOXO nuclear localization. A tyrosine phosphatase

(PTEN) inactivates Akt by reversing the PI3K-mediated phosphory-

lation and activation of Akt. B: Relative luciferase expression

levels in control are set at a level of 1.0. Akt RNAi increases

luciferase expression because Akt phosphorlation of FOXO pre-

vents its nuclear localization. PTEN RNAi decreases luciferase

levels because PTEN removes the inhibitory phosphate on FOXO

(adapted from [210]).

8. CONCLUSIONS AND FUTURE OUTLOOK

Recently, the biotech company Exelixis, Inc., made available to the scientific community a large collection of Drosophila stocks that contain “isogenic” transposon inser-tions and deficiencies that mutate or uncover over half of the genes [32, 214]. “Isogenic” means that they are in exactly the same genetic background, which is important because most quantitative phenotypes, such as triglyceride levels, are exceedingly affected by different genetic backgrounds. These new Drosophila strains will likely have a significant impact in nutrigenomics and other genetic studies by provid-ing new models for virtually every metabolic disease conceivable [27]. In addition, numerous whole-genome

resources are becoming available to Drosophila researchers, such as the Vienna Stock Center collection of over 20,000 UAS-RNAi strains that can knock out every gene product in Drosophila in a tissue specific manner [215]. Also, the Ba-sler laboratory is making a collection of UAS-Gene fly stocks that can be used to over-express eventually every gene in a tissue specific manner. The Basler laboratory developed an optimized transgenesis system for Drosophila using germ-line-specific phiC31 integrases to integrate all of the expression vectors at the same genomic locus, and this should considerably reduce variability caused by insertion site heterogeneity [216].

The ability to knock out or over express any gene of interest in any tissue or organ of interest in Drosophila is important because dietary interventions can be modified to benefit a particular tissue or organ that is undergoing a metabolic crisis. For example, some extreme diets, such as dietary restriction, have been shown to promote longevity in Drosophila, but they also have deleterious effects on the health of the heart [37, 217-222]. This is an important consideration for clinical researchers because generally diets are planned for the health of the whole person rather than for the health of a particular organ. If a patient has a heart condi-tion, then a dietician would want to choose a dietary regimen that optimizes the health of the heart.

Nutrigenomics studies in Drosophila have important implications for our understanding of human biology and disease. All of the insulin/TOR pathway downstream effec-tors (Myc, TIF-IA, 4E-BP, etc.) identified in Drosophila are conserved in humans. Defects in insulin/TOR signalling also contribute to a number of human diseases including diabetes, obesity, and cancer [48]. Generally, PI3K and TOR inhibitors are undergoing clinical trials for these diseases, but by identifying critical downstream components using Drosophila nutrigenomics, it may be possible to develop more targeted therapies with fewer side-effects. The power-ful genetic tools available in Drosophila will be increasingly used to improve human health through nutrigenomics and other approaches. Extreme or “fad” diets are also a major concern with how they affect human health. One suggestion, which we proposed in a previous paper, is to use Drosophila as a “Dietary Ames Test” to test extreme diets in Drosophila and other models before they are haphazardly tested in humans [200].

With the $1000 genome soon to become a reality, we proposed several plausible strategies that can be used to extrapolate data from Drosophila to Human or clinical nutri-genomics research. Undoubtedly, numerous new genetic deficiencies in thousands of metabolic enzymes will be iden-tified that can be modelled in Drosophila. Because of the complex cross-interactions between the numerous metabolic pathways, one could not simply increase the missing compo-nent to the diet or prescribe a drug to treat the deficiency because of possible unintended consequences.

Twentieth century medical practice involved trial and error of doses and types of drugs to treat a condition until an optimal combination was identified. A more practical possibility is to model the metabolic deficiencies in model organisms, such as Drosophila, to devise the optimal dietary

12 Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 Ruden et al.

and pharmaceutical intervention. One could even go further and apply Drosophila evolutionary biology to select for optimal genetic backgrounds that compensate for a particular metabolic deficiency. One could then identify alternative and possibly even unexpected routes to treat any of the hundreds of new metabolic deficiencies that will soon be identified in humans.

ACKNOWLEDGEMENTS

This work was in part supported by the Environmental Health Sciences Center in Molecular and Cellular Toxi- cology with Human Applications Grant P30 ES06639 at Wayne State University, NIH R01 grants (ES012933 and CA105349) to D.M.R., and DK071073 to X.L.

DUALITY/CONFLICT OF INTERESTS

None declared/applicable.

LIST OF ABBREVIATIONS

$1000 = An NIH initiative to sequence the human genome genome for $1000

2B = Russian 2B (a strain of Drosophila from Russia)

4E-BP = Elongation Initiation Factor 4E Binding Protein (a protein downstream of insulin/ TOR signalling)

COPAS = Complex Object Parametric Analyzer and Sorter

DAPI = A fluorescent dye that stains the nucleus

eQTL = Expression Quantitative Trait Locus

FITC = A green fluorescent dye

FOXA = A transcription factor downstream of insulin/ TOR signalling

FOXO = A transcription factor downstream of insulin/ TOR signalling

FRE = FOXO Response Element (the binding site for FOXO)

GINA = Genetic Information Nondiscrimination Act

HDAC = Histone deacetylase

INDEL = Insertion/Deletion

InR = Insulin Receptor

Myc = A transcription factor downstream of insulin/ TOR signalling

ORE = Oregon R (a strain of Drosophila from Oregon)

PI3K = Phosphatidyl Inositol 3 Kinase (a kinase downstream of insulin/TOR signalling).

QTL = Quantitative Trait Locus

RIL = Recombinant Inbred Line

RNAi = Interfering RNA

TIF-IA = Translation initiation factor IA (a protein downstream of insulin/TOR signalling)

TOR = Target of Rapamycin

UAS = Upstream Activating Sequence

SNP = Single Nucleotide Polymorphism

REFERENCES

[1] Monsen RB. The Genetic Information Nondiscrimination Act helps us all. J Ped Nursing 2009; 24: 151-2.

[2] Guttmacher AE, Collins FS. Realizing the promise of genomics in biomedical research. JAMA 2005; 294: 1399-402.

[3] Haselden JN, Nicholls AW. Personalized medicine progresses. Nat Med 2006; 12: 510-1.

[4] Singer E. Personalized medicine prompts push to redesign clinical trials. Nat Med 2005; 11: 462.

[5] Morozova O, Marra MA. Applications of next-generation sequenc-ing technologies in functional genomics. Genomics 2008; 92: 255-64.

[6] Jenkins DJ, Kendall CW, Marchie A, et al. Effects of a dietary portfolio of cholesterol-lowering foods vs lovastatin on serum lipids and C-reactive protein.[see comment]. JAMA 2003; 290: 502-10.

[7] Jenkins DJ, Kendall CW, Marchie A, et al. Too much sugar, too much carbohydrate, or just too much? [comment]. Am J Clin Nutr 2004; 79: 711-2.

[8] Jenkins DJ, Kendall CW, Marchie A, et al. Type 2 diabetes and the vegetarian diet. Am J Clin Nutr 2003; 78: 610S-6S.

[9] Hung T, Sievenpiper JL, Marchie A, et al. Fat versus carbohydrate in insulin resistance, obesity, diabetes and cardiovascular disease. [see comment]. Curr Opin Clin Nutr Metab Care 2003; 6: 165-76.

[10] Jenkins DJ, Kendall CW, Faulkner D, et al. A dietary portfolio approach to cholesterol reduction: combined effects of plant ster-ols, vegetable proteins, and viscous fibers in hypercholesterolemia. Metabolism 2002; 51: 1596-604.

[11] Hanriot L, Keime C, Gay N, et al. A combination of LongSAGE with Solexa sequencing is well suited to explore the depth and the complexity of transcriptome. BMC Genomics 2008; 9: 418.

[12] Marioni JC, Mason CE, Mane SM, et al. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 2008; 18: 1509-17.

[13] t Hoen PA, Ariyurek Y, Thygesen HH, et al. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res 2008; 36: e141.

[14] Ye J, Cui X, Loraine A, et al. Methods for nutrigenomics and lon-gevity studies in Drosophila: effects of diets high in sucrose, palmitic acid, soy, or beef. Methods Mol Biol 2007; 371:111-41.

[15] Curtsinger JW. Sex specificity, life-span QTLs, and statistical power. J Gerontol A Biol Sci Med Sci 2002; 57:B409-14.

[16] Curtsinger JW, Khazaeli AA. Lifespan, QTLs, age-specificity, and pleiotropy in Drosophila. Mech Ageing Dev 2002; 123: 81-93.

[17] Khazaeli AA, Nuzhdin SV, Curtsinger JW. Genetic variation for life span, resistance to paraquat, and spontaneous activity in unse-lected populations of Drosophila melanogaster: implications for transgenic rescue of life span. Mech Ageing Dev 2007; 128: 486-93.

[18] Khazaeli AA, Van Voorhies W, Curtsinger JW. Longevity and metabolism in Drosophila melanogaster: genetic correlations be-tween life span and age-specific metabolic rate in populations arti-ficially selected for long life. Genetics 2005; 169: 231-42.

[19] Nuzhdin SV, Khazaeli AA, Curtsinger JW. Survival analysis of life span quantitative trait loci in Drosophila melanogaster. Genetics 2005; 170: 719-31.

[20] Semenchenko GV, Khazaeli AA, Curtsinger JW, et al. Stress resis-tance declines with age: analysis of data from a survival experiment with Drosophila melanogaster. Biogerontology 2004; 5: 17-30.

[21] Fernandes G. Progress in nutritional immunology. Immunol Res 2008; 40: 244-61.

Drosophila, Nutrigenomics and Personalized Diet Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 13

[22] Stover PJ, Caudill MA. Genetic and epigenetic contributions to human nutrition and health: managing genome-diet interactions. J Am Diet Assoc 2008; 108: 1480-7.

[23] Xacur-Garcia F, Castillo-Quan JI, Hernandez-Escalante VM, et al. Nutritional genomics: an approach to the genome-environment in-teraction. Rev Med Chil 2008; 136: 1460-7.

[24] Olivieri F, Spazzafumo L, Antonicelli R, et al. Combination of biomarkers to predict mortality in elderly patients with myocardial infarction. Mech Ageing Dev 2008; 129: 231-7.

[25] Ordovas J. Diet/genetic interactions and their effects on inflamma-tory markers. Nutr Rev 2007; 65: S203-7.

[26] Mathers JC. Nutritional modulation of ageing: genomic and epige-netic approaches. Mech Ageing Dev 2006; 127: 584-9.

[27] Ruden DM, De Luca M, Garfinkel MD, et al. Drosophila nutrige-nomics can provide clues to human gene-nutrient interactions. Ann Rev Nutrition 2005; 25: 21.1-.4.

[28] Canavoso LE, Jouni ZE, Karnas KJ, et al. Fat metabolism in in-sects. Ann Rev Nutrition 2001; 21: 23-46.

[29] Arrese EL, Canavoso LE, Jouni ZE, et al. Lipid storage and mobi-lization in insects: current status and future directions. Insect Bio-chem Mol Biol 2001; 31: 7-17.

[30] Canavoso LE, Wells MA. Metabolic pathways for diacylglycerol biosynthesis and release in the midgut of larval Manduca sexta. In-sect Biochem Mol Biol 2000; 30: 1173-80.

[31] Canavoso LE, Bertello LE, de Lederkremer RM, et al. Effect of fasting on the composition of the fat body lipid of Dipetalogaster maximus, Triatoma infestans and Panstrongylus megistus (Hemip-tera: Reduviidae). J Comp Physiol B 1998; 168: 549-54.

[32] Parks AL, Cook KR, Belvin M, et al. Systematic generation of high-resolution deletion coverage of the Drosophila melanogaster genome. Nat Genet 2004; 36: 288-92.

[33] Bier E. Drosophila, the golden bug, emerges as a tool for human genetics. Nat Rev Genet 2005; 6: 9-23.

[34] Mehta T, Tanik M, Allison DB. Towards sound epistemological foundations of statistical methods for high-dimensional biology. Nat Genet 2004; 36: 943-7.

[35] Hoeks MP, den Heijer M, Janssen MC. Adult issues in phenylke-tonuria. Neth J Med 2009; 67: 2-7.

[36] Wassef R, Haenold R, Hansel A, et al. Methionine sulfoxide reduc-tase A and a dietary supplement S-methyl-L-cysteine prevent Park-inson's-like symptoms. J Neurosci 2007; 27: 12808-16.

[37] Wessells RJ, Bodmer R. Cardiac aging. Semin Cell Dev Biol 2007; 18: 111-6.

[38] Wessells RJ, Bodmer R. Age-related cardiac deterioration: insights from Drosophila. Front Biosci 2007; 12: 39-48.

[39] Wessells RJ, Fitzgerald E, Cypser JR, et al. Insulin regulation of heart function in aging fruit flies. Nat Genet 2004; 36: 1275-81.

[40] Broughton S, Partridge L. Insulin/IGF-like signalling, the central nervous system and aging. Biochem J 2009; 418: 1-12.

[41] Rulifson EJ, Kim SK, Nusse R. Ablation of insulin-producing neurons in flies: growth and diabetic phenotypes. Science 2002; 296: 1118-20.

[42] Shingleton AW, Das J, Vinicius L, et al. The temporal require-ments for insulin signaling during development in Drosophila. PLoS Biol 2005; 3: e289.

[43] Brogiolo W, Stocker H, Ikeya T, et al. An evolutionarily conserved function of the Drosophila insulin receptor and insulin-like peptides in growth control. Curr Biol 2001; 11: 213-21.

[44] Broughton S, Alic N, Slack C, et al. Reduction of DILP2 in Droso-phila triages a metabolic phenotype from lifespan revealing redun-dancy and compensation among DILPs. PLoS ONE 2008; 3: e3721.

[45] Broughton SJ, Piper MD, Ikeya T, et al. Longer lifespan, altered metabolism, and stress resistance in Drosophila from ablation of cells making insulin-like ligands. Proc Natl Acad Sci USA 2005; 102: 3105-10.

[46] Ikeya T, Galic M, Belawat P, et al. Nutrient-dependent expression of insulin-like peptides from neuroendocrine cells in the CNS con-tributes to growth regulation in Drosophila. Curr Biol 2002; 12: 1293-300.

[47] Oldham S, Hafen E. Insulin/IGF and target of rapamycin signaling: a TOR de force in growth control. Trends Cell Biol 2003; 13: 79-85.

[48] Shaw RJ, Cantley LC. Ras, PI(3)K and mTOR signalling controls tumour cell growth. Nature 2006; 441: 424-30.

[49] Grewal SS. Insulin/TOR signaling in growth and homeostasis: A view from the fly world. Int J Biochem Cell Biol 2009; 41: 1006-10.

[50] Teleman AA, Hietakangas V, Sayadian AC, et al. Nutritional con-trol of protein biosynthetic capacity by insulin via Myc in Droso-phila. Cell Metab 2008; 7: 21-32.

[51] Grewal SS, Evans JR, Edgar BA. Drosophila TIF-IA is required for ribosome synthesis and cell growth and is regulated by the TOR pathway. J Cell Biol 2007; 179: 1105-13.

[52] Hay N, Sonenberg N. Upstream and downstream of mTOR. Genes Dev 2004; 18: 1926-45.

[53] Teleman AA, Chen YW, Cohen SM. 4E-BP functions as a meta-bolic brake used under stress conditions but not during normal growth. Genes Dev 2005; 19: 1844-8.

[54] Marr MT, 2nd, D'Alessio JA, Puig O, et al. IRES-mediated func-tional coupling of transcription and translation amplifies insulin re-ceptor feedback. Genes Dev 2007; 21: 175-83.

[55] Scott RC, Juhasz G, Neufeld TP. Direct induction of autophagy by Atg1 inhibits cell growth and induces apoptotic cell death. Curr Biol 2007; 17: 1-11.

[56] Scott RC, Schuldiner O, Neufeld TP. Role and regulation of starva-tion-induced autophagy in the Drosophila fat body. Dev Cell 2004; 7: 167-78.

[57] Chang YY, Juhasz G, Goraksha-Hicks P, et al. Nutrient-dependent regulation of autophagy through the target of rapamycin pathway. Biochem Soc Trans 2009; 37: 232-6.

[58] Csikos G, Lippai M, Lukacsovich T, et al. A novel role for the Drosophila epsin (lqf): Involvement in autophagy. Autophagy 2009; 5(5) [Epub ahead of print].

[59] Juhasz G, Erdi B, Sass M, et al. Atg7-dependent autophagy pro-motes neuronal health, stress tolerance, and longevity but is dispen-sable for metamorphosis in Drosophila. Genes Dev 2007; 21: 3061-6.

[60] Juhasz G, Neufeld TP. Drosophila Atg7: required for stress resis-tance, longevity and neuronal homeostasis, but not for metamor-phosis. Autophagy 2008; 4: 357-8.

[61] Samokhvalov V, Scott BA, Crowder CM. Autophagy protects against hypoxic injury in C. elegans. Autophagy 2008; 4: 1034-41.

[62] Hennig KM, Colombani J, Neufeld TP. TOR coordinates bulk and targeted endocytosis in the Drosophila melanogaster fat body to regulate cell growth. J Cell Biol 2006; 173: 963-74.

[63] Ellisen LW, Ramsayer KD, Johannessen CM, et al. REDD1, a developmentally regulated transcriptional target of p63 and p53, links p63 to regulation of reactive oxygen species. Mol Cell 2002; 10: 995-1005.

[64] Lee SJ, Feldman R, O'Farrell PH. An RNA interference screen identifies a novel regulator of target of rapamycin that mediates hypoxia suppression of translation in Drosophila S2 cells. Mol Biol Cell 2008; 19: 4051-61.

[65] Centanin L, Dekanty A, Romero N, et al. Cell autonomy of HIF effects in Drosophila: tracheal cells sense hypoxia and induce terminal branch sprouting. Dev Cell 2008; 14: 547-58.

[66] Dekanty A, Lavista-Llanos S, Irisarri M, et al. The insulin-PI3K/TOR pathway induces a HIF-dependent transcriptional response in Drosophila by promoting nuclear localization of HIF-alpha/Sima. J Cell Sci 2005; 118:5431-41.

[67] Rogina B, Helfand SL, Frankel S. Longevity regulation by Droso-phila Rpd3 deacetylase and caloric restriction. Science 2002; 298: 1745.

[68] Tatar M, Bartke A, Antebi A. The endocrine regulation of aging by insulin-like signals. Science 2003; 299: 1346-51.

[69] Mair W, Goymer P, Pletcher SD, et al. Demography of dietary restriction and death in Drosophila. [see comment]. Science 2003; 301: 1731-3.

[70] Wood JG, Rogina B, Lavu S, et al. Sirtuin activators mimic caloric restriction and delay ageing in metazoans. Nature 2004; 430(7000): 686-9.

[71] Magwere T, Chapman T, Partridge L. Sex differences in the effect of dietary restriction on life span and mortality rates in female and male Drosophila melanogaster. J Gerontol A Biol Sci Med Sci 2004; 59: 3-9.

[72] Finch CE, Ruvkun G. The genetics of aging. Annu Rev Genomics Hum Genet 2001; 2: 435-62.

[73] Inoue K, Fei YJ, Huang W, et al. Functional identity of Drosophila melanogaster Indy as a cation-independent, electroneutral trans-

14 Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 Ruden et al.

porter for tricarboxylic acid-cycle intermediates. Biochem J 2002; 367: 313-9.

[74] Pletcher SD, Macdonald SJ, Marguerie R, et al. Genome-wide transcript profiles in aging and calorically restricted Drosophila melanogaster. Curr Biol 2002; 12: 712-23.

[75] Hekimi S, Lakowski B, Barnes TM, et al. Molecular genetics of life span in C. elegans: how much does it teach us? Trends Genet 1998; 14: 14-20.

[76] Lakowski B, Hekimi S. The genetics of caloric restriction in Caenorhabditis elegans. Proc Natl Acad Sci USA 1998; 95: 13091-6.

[77] Houthoofd K, Braeckman BP, Johnson TE, et al. Life extension via dietary restriction is independent of the Ins/IGF-1 signalling path-way in Caenorhabditis elegans. Exp Gerontol 2003; 38: 947-54.

[78] Kenyon C, Chang J, Gensch E, et al. A C. elegans mutant that lives twice as long as wild type. Nature 1993; 366: 461-4.

[79] Shimokawa I, Higami Y, Tsuchiya T, et al. Life span extension by reduction of the growth hormone-insulin-like growth factor-1 axis: relation to caloric restriction. FASEB J 2003; 17: 1108-9.

[80] Min KJ, Yamamoto R, Buch S, et al. Drosophila lifespan control by dietary restriction independent of insulin-like signaling. Aging Cell 2008; 7: 199-206.

[81] Tsuchiya T, Dhahbi JM, Cui X, et al. Additive regulation of he-patic gene expression by dwarfism and caloric restriction. Physiol Genomics 2004; 17: 307-15.

[82] Cao C, Liu Y, Lehmann M. Fork head controls the timing and tissue selectivity of steroid-induced developmental cell death. J Cell Biol 2007; 176: 843-52.

[83] Chen D, Guarente L. SIR2: a potential target for calorie restriction mimetics. Trends Mol Med 2007; 13: 64-71.

[84] Guarente L. Sirtuins as potential targets for metabolic syndrome. Nature 2006; 444: 868-74.

[85] Sinclair DA, Lin SJ, Guarente L. Life-span extension in yeast. Science 2006; 312(5771): 195-7; author reply -7.

[86] Bishop NA, Guarente L. Two neurons mediate diet-restriction-induced longevity in C. elegans. Nature 2007; 447: 545-9.

[87] Kaeberlein M, McDonagh T, Heltweg B, et al. Substrate-specific activation of sirtuins by resveratrol. J Biol Chem 2005; 280: 17038-45.

[88] Kaeberlein M, Powers RW, 3rd. Sir2 and calorie restriction in yeast: a skeptical perspective. Ageing Res Rev 2007; 6: 128-40.

[89] Kaeberlein M, Rabinovitch PS. Medicine: grapes versus gluttony. Nature 2006; 444: 280-1.

[90] Bass TM, Weinkove D, Houthoofd K, et al. Effects of resveratrol on lifespan in Drosophila melanogaster and Caenorhabditis ele-gans. Mech Ageing Dev 2007; 128: 546-52.

[91] Panowski SH, Wolff S, Aguilaniu H, et al. PHA-4/Foxa mediates diet-restriction-induced longevity of C. elegans. Nature 2007; 447: 550-5.

[92] Keith SW, Redden DT, Katzmarzyk PT, et al. Putative contributors to the secular increase in obesity: exploring the roads less traveled. Int J Obes (Lond) 2006; 30: 1585-94.

[93] Coyle YM. Lifestyle, genes, and cancer. Methods Mol Biol 2009; 472: 25-56.

[94] Fleming JB, Gonzalez RJ, Petzel MQ, et al. Influence of obesity on cancer-related outcomes after pancreatectomy to treat pancreatic adenocarcinoma. Arch Surg 2009; 144: 216-21.

[95] Guh DP, Zhang W, Bansback N, et al. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health 2009; 9: 88.

[96] Huxley RR, Ansary-Moghaddam A, Clifton P, et al. The impact of dietary and lifestyle risk factors on risk of colorectal cancer: A quantitative overview of the epidemiological evidence. Int J Cancer 2009; 125: 171-80.

[97] Al Lawati NM, Patel SR, Ayas NT. Epidemiology, risk factors, and consequences of obstructive sleep apnea and short sleep duration. Prog Cardiovasc Dis 2009; 51: 285-93.

[98] Huffman DM, Barzilai N. Role of visceral adipose tissue in aging. Biochim Biophys Acta 2009; Jan 30 [Epub ahead of print].

[99] Pack AI, Gislason T. Obstructive sleep apnea and cardiovascular disease: a perspective and future directions. Prog Cardiovasc Dis 2009; 51: 434-51.

[100] Wang J, Widlansky ME. Lifestyle choices and endothelial function: risk and relevance. Curr Vasc Pharmacol 2009; 7: 209-24.

[101] Murugan AT, Sharma G. Obesity and respiratory diseases. Chron Respir Dis 2008; 5: 233-42.

[102] Craft S. Insulin resistance and Alzheimer's disease pathogenesis: potential mechanisms and implications for treatment. Curr Alzheimer Res 2007; 4: 147-52.

[103] Craft S. The role of metabolic disorders in Alzheimer disease and vascular dementia: two roads converged. Arch Neurol 2009; 66: 300-5.

[104] Kivipelto M, Solomon A. Alzheimer's disease - the ways of preven-tion. J Nutr Health Aging 2008; 12: 89S-94S.

[105] Luchsinger JA, Gustafson DR. Adiposity and Alzheimer's disease. Curr Opin Clin Nutr Metab Care 2009; 12: 15-21.

[106] Milionis HJ, Florentin M, Giannopoulos S. Metabolic syndrome and Alzheimer's disease: a link to a vascular hypothesis? CNS spectrums 2008; 13: 606-13.

[107] Oudes AJ, Herr CM, Olsen Y, et al. Age-dependent accumulation of advanced glycation end-products in adult Drosophila mela-nogaster. Mech Ageing Dev 1998; 100: 221-9.

[108] Legan SK, Rebrin I, Mockett RJ, et al. Overexpression of glucose-6-phosphate dehydrogenase extends the life span of Drosophila melanogaster. J Biol Chem 2008; 283: 32492-9.

[109] Orr WC, Radyuk SN, Prabhudesai L, et al. Overexpression of glutamate-cysteine ligase extends life span in Drosophila melanogaster. J Biol Chem 2005; 280: 37331-8.

[110] Radyuk SN, Michalak K, Klichko VI, et al. Peroxiredoxin 5 confers protection against oxidative stress and apoptosis and also promotes longevity in Drosophila. Biochem J 2009; 419: 437-45.

[111] Radyuk SN, Rebrin I, Luchak JM, et al. The catalytic subunit of Drosophila glutamate-cysteine ligase is a nucleocytoplasmic shuttling protein. J Biol Chem 2009; 284: 2266-74.

[112] Rebrin I, Sohal RS. Pro-oxidant shift in glutathione redox state during aging. Adv Drug Deliv Rev 2008; 60: 1545-52.

[113] Anderson PR, Kirby K, Orr WC, et al. Hydrogen peroxide scavenging rescues frataxin deficiency in a Drosophila model of Friedreich's ataxia. Proc Natl Acad Sci USA 2008; 105: 611-6.

[114] Gelsthorpe ME, Tan Z, Phillips A, et al. Regulation of the Droso-phila melanogaster protein, enhancer of rudimentary, by casein kinase II. Genetics 2006; 174: 265-70.

[115] Kirby K, Jensen LT, Binnington J, et al. Instability of superoxide dismutase 1 of Drosophila in mutants deficient for its cognate copper chaperone. J Biol Chem 2008; 283: 35393-401.

[116] Lind MI, Missirlis F, Melefors O, et al. Of two cytosolic aconitases expressed in Drosophila, only one functions as an iron-regulatory protein. J Biol Chem 2006; 281: 18707-14.

[117] Wicks S, Bain N, Duttaroy A, et al. Hypoxia rescues early mortal-ity conferred by superoxide dismutase deficiency. Free Radic Biol Med 2009; 46: 176-81.

[118] Tettweiler G, Miron M, Jenkins M, et al. Starvation and oxidative stress resistance in Drosophila are mediated through the eIF4E-binding protein, d4E-BP. Genes Dev 2005; 19: 1840-3.

[119] Driver CJI, Cosopodiotis G. The effect of dietary fat on longevity of Drosophila melanogaster. Exp Geront 1979; 14: 95-100.

[120] Driver CJ, Lamb MJ. Metabolic changes in ageing Drosophila melanogaster. Exp Geront 1980; 15:167-75.

[121] Driver CJ, Wallis R, Cosopodiotis G, et al. Is a fat metabolite the major diet dependent accelerator of aging? Exp Geront 1986; 21: 497-507.

[122] Galgani JE, Uauy RD, Aguirre CA, et al. Effect of the dietary fat quality on insulin sensitivity. Br J Nutr 2008; 100: 471-9.

[123] Garbarino J, Sturley SL. Saturated with fat: new perspectives on lipotoxicity. Curr Opin Clin Nutr Metab Care 2009; 12: 110-6.

[124] Joyce T, Wallace AJ, McCarthy SN, et al. Intakes of total fat, saturated, monounsaturated and polyunsaturated fatty acids in Irish children, teenagers and adults. Public Health Nutr 2009; 12: 156-65.

[125] Margioris AN. Fatty acids and postprandial inflammation. Curr Opin Clin Nutr Metab Care 2009; 12: 129-37.

[126] Miller M, Beach V, Sorkin JD, et al. Comparative effects of t hree popular diets on lipids, endothelial function, and C-reactive protein during weight maintenance. J Am Diet Assoc 2009; 109: 713-7.

[127] Beadle G, Tatum E, Clancy C. Food level in relation to rate of development and eye pigmentation in Drosophila melanogaster. Biol Bull 1938; 75: 447-62.

Drosophila, Nutrigenomics and Personalized Diet Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 15

[128] Edgar BA. How flies get their size: genetics meets physiology. Nat Rev Genet 2006; 7: 907-16.

[129] Arora SK, McFarlane SI. The case for low carbohydrate diets in diabetes management. Nutr Metab (Lond) 2005; 2: 16.

[130] Cassidy A, Albertazzi P, Lise Nielsen I, et al. Critical review of health effects of soyabean phyto-oestrogens in post-menopausal women. Proc Nutr Soc 2006; 65: 76-92.

[131] Hooper L, Griffiths E, Abrahams B, et al. Dietetic guidelines: diet in secondary prevention of cardiovascular disease (first update, June 2003). J Hum Nutr Diet 2004; 17: 337-49.

[132] Bartke A, Peluso MR, Moretz N, et al. Effects of Soy-derived diets on plasma and liver lipids, glucose tolerance, and longevity in nor-mal, long-lived and short-lived mice. Horm Metab Res 2004; 36: 550-8.

[133] Grandison RC, Wong R, Bass TM, et al. Effect of a standardised dietary restriction protocol on multiple laboratory strains of Droso-phila melanogaster. PLoS ONE 2009; 4: e4067.

[134] Bass TM, Grandison RC, Wong R, et al. Optimization of dietary restriction protocols in Drosophila. J Gerontol A Biol Sci Med Sci 2007; 62: 1071-81.

[135] Burke MK, Rose MR. Experimental Evolution with Drosophila. Am J Physiol Regul Integr Comp Physiol 2009; 296(6): R1847-54.

[136] Ballard JW, Melvin RG, Simpson SJ. Starvation resistance is positively correlated with body lipid proportion in five wild caught Drosophila simulans populations. J Insect Physiol 2008; 54: 1371-6.

[137] Clark AG, Eisen MB, Smith DR, et al. Evolution of genes and genomes on the Drosophila phylogeny. Nature 2007; 450: 203-18.

[138] Matzkin LM, Watts TD, Bitler BG, et al. Functional genomics of cactus host shifts in Drosophila mojavensis. Mol Ecol 2006; 15: 4635-43.

[139] Mackay TF. The genetic basis of quantitative variation: numbers of sensory bristles of Drosophila melanogaster as a model system. Trends Genet 1995; 11: 464-70.

[140] Mackay TF. The nature of quantitative genetic variation revisited: lessons from Drosophila bristles. Bioessays 1996; 18: 113-21.

[141] Mackay TF. Quantitative trait loci in Drosophila. Nat Rev Genet 2001; 2: 11-20.

[142] Mackay TF. The nature of quantitative genetic variation for Droso-phila longevity. Mech Ageing Dev 2002; 123: 95-104.

[143] Mackay TF, Hackett JB, Lyman RF, et al. Quantitative genetic variation of odor-guided behavior in a natural population of Droso-phila melanogaster. Genetics 1996; 144: 727-35.

[144] Mackay TF, Langley CH. Molecular and phenotypic variation in the achaete-scute region of Drosophila melanogaster. Nature 1990; 348: 64-6.

[145] Mackay TF, Lyman RF. Polygenic mutation in Drosophila mela-nogaster: genotype x environment interaction for spontaneous mu-tations affecting bristle number. Genetica 1998; 103: 199-215.

[146] Suto J. Apolipoprotein gene polymorphisms as cause of cholesterol QTLs in mice. J Vet Med Sci 2005; 67: 583-9.

[147] Suto J. Characterization of Cq3, a quantitative trait locus that con-trols plasma cholesterol and phospholipid levels in mice. J Vet Med Sci 2006; 68: 303-9.

[148] Suto J. Quantitative trait locus analysis of plasma cholesterol levels and body weight by controlling the effects of the Apoa2 allele in mice. J Vet Med Sci 2007; 69: 385-92.

[149] Suto J. Metabolic consequence of congenital asplenia caused by the Dominant hemimelia mutation in mice. J Vet Med Sci 2009; 71: 177-82.

[150] Suto J, Matsuura S, Yamanaka H, et al. Quantitative trait loci that regulate plasma lipid concentration in hereditary obese KK and KK-Ay mice. Biochim Biophys Acta 1999; 1453: 385-95.

[151] Suto J, Sekikawa K. Quantitative trait locus analysis of plasma cholesterol and triglyceride levels in KK x RR F2 mice. Biochem Genet 2003; 41: 325-41.

[152] Suto J, Takahashi Y, Sekikawa K. Quantitative trait locus analysis of plasma cholesterol and triglyceride levels in C57BL/6J x RR F2 mice. Biochem Genet 2004; 42: 347-63.

[153] Macdonald SJ, Long AD. Joint estimates of quantitative trait locus effect and frequency using synthetic recombinant populations of Drosophila melanogaster. Genetics 2007; 176: 1261-81.

[154] Churchill GA, Airey DC, Allayee H, et al. The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet 2004; 36: 1133-7.

[155] Devor M, Gilad A, Arbilly M, et al. Pain1: a neuropathic pain QTL on mouse chromosome 15 in a C3HxC58 backcross. Pain 2005; 116: 289-93.

[156] Cheung CC, Martin IC, Zenger KR, et al. Quantitative trait loci for steady-state platelet count in mice. Mammalian Genome 2004; 15: 784-97.

[157] Kleeberger SR. Genetic aspects of pulmonary responses to inhaled pollutants. Exp Toxicol Pathol 2005; 57(Suppl. 1): 147-53.

[158] Welton AR, Chesler EJ, Sturkie C, et al. Identification of quantita-tive trait loci for susceptibility to mouse adenovirus type 1. J Virol-ogy 2005; 79: 11517-22.

[159] Kim EH, Lee CH, Hyun BH, et al. Quantitative trait Loci for glomerulosclerosis, kidney weight and body weight in the focal glomerulosclerosis mouse model. Exp Anim 2005; 54: 319-25.

[160] De Luca M, Yi N, Allison DB, et al. Mapping quantitative trait loci affecting variation in Drosophila triacylglycerol storage. Obes Res 2005; 13: 1-10.

[161] Clark AG, Keith LE. Variation among extracted lines of Drosophila melanogaster in triacylglycerol and carbohydrate stor-age. Genetics 1988; 119: 595-607.

[162] Nuzhdin SV, Pasyukova EG, Dilda CL, et al. Sex-specific quantita-tive trait loci affecting longevity in Drosophila melanogaster. Proc Natl Acad Sci USA 1997; 94: 9734-9.

[163] Masojc P, Milczarski P. Mapping QTLs for alpha-amylase activity in rye grain. J Appl Genet 2005; 46: 115-23.

[164] Drazinic CM, Ercan-Sencicek AG, Gault LM, et al. Rapid array-based genomic characterization of a subtle structural abnormality: a patient with psychosis and der(18)t(5;18)(p14.1;p11.23). Am J Med Genet A 2005; 134: 282-9.

[165] Tebbutt SJ, He JQ, Burkett KM, et al. Microarray genotyping resource to determine population stratification in genetic associa-tion studies of complex disease. Biotechniques 2004; 37: 977-85.

[166] Tonisson N, Kurg A, Kaasik K, et al. Unravelling genetic data by arrayed primer extension. Clin Chem Lab Med 2000; 38: 165-70.

[167] Du W, Cieplik M, Durstewitz G, et al. Minisequencing on functionalised self-assembled monolayer as a simple approach for single nucleotide polymorphism analysis of cattle. Z Naturforsch [C] 2003; 58: 413-20.

[168] Church GM. Genomes for all. Scientific American 2006; 294: 46-54.

[169] Ayroles JF, Carbone MA, Stone EA, et al. Systems genetics of complex traits in Drosophila melanogaster. Nat Genet 2009; 41: 299-307.

[170] Edwards AC, Rollmann SM, Morgan TJ, et al. Quantitative genomics of aggressive behavior in Drosophila melanogaster. PLoS Genet 2006; 2: e154.

[171] Harbison ST, Carbone MA, Ayroles JF, et al. Co-regulated transcriptional networks contribute to natural genetic variation in Drosophila sleep. Nat Genet 2009; 41: 371-5.

[172] Harbison ST, Chang S, Kamdar KP, et al. Quantitative genomics of starvation stress resistance in Drosophila. Genome Biol 2005; 6: R36.

[173] Hsieh WP, Passador-Gurgel G, Stone EA, et al. Mixture modeling of transcript abundance classes in natural populations. Genome Biol 2007; 8: R98.

[174] Jordan KW, Carbone MA, Yamamoto A, et al. Quantitative genomics of locomotor behavior in Drosophila melanogaster. Genome Biol 2007; 8: R172.

[175] Jordan KW, Morgan TJ, Mackay TF. Quantitative trait loci for locomotor behavior in Drosophila melanogaster. Genetics 2006; 174: 271-84.

[176] Mackay TF, Heinsohn SL, Lyman RF, et al. Genetics and genom-ics of Drosophila mating behavior. Proc Natl Acad Sci USA 2005; 102(Suppl. 1): 6622-9.

[177] Mackay TF, Lyman RF, Lawrence F. Polygenic mutation in Drosophila melanogaster: Mapping spontaneous mutations affect-ing sensory bristle number. Genetics 2005; 170: 1723-35.

[178] Morgan TJ, Mackay TF. Quantitative trait loci for thermotolerance phenotypes in Drosophila melanogaster. Heredity 2006; 96: 232-42.

[179] Morozova TV, Anholt RR, Mackay TF. Phenotypic and transcrip-tional response to selection for alcohol sensitivity in Drosophila melanogaster. Genome Biol 2007; 8: R231.

16 Current Pharmacogenomics and Personalized Medicine, 2009, Vol. 7, No. 3 Ruden et al.

[180] Palsson A, Dodgson J, Dworkin I, et al. Tests for the replication of an association between Egfr and natural variation in Drosophila melanogaster wing morphology. BMC genetics 2005; 6: 44.

[181] Sambandan D, Carbone MA, Anholt RR, et al. Phenotypic plastic-ity and genotype by environment interaction for olfactory behavior in Drosophila melanogaster. Genetics 2008; 179: 1079-88.

[182] Sambandan D, Yamamoto A, Fanara JJ, et al. Dynamic genetic interactions determine odor-guided behavior in Drosophila mela-nogaster. Genetics 2006; 174: 1349-63.

[183] Wang P, Lyman RF, Shabalina SA, et al. Association of polymor-phisms in odorant-binding protein genes with variation in olfactory response to benzaldehyde in Drosophila. Genetics 2007; 177: 1655-65.

[184] Wilson RH, Morgan TJ, Mackay TF. High-resolution mapping of quantitative trait loci affecting increased life span in Drosophila melanogaster. Genetics 2006; 173: 1455-63.

[185] Yamamoto A, Zwarts L, Callaerts P, et al. Neurogenetic networks for startle-induced locomotion in Drosophila melanogaster. Proc Natl Acad Sci USA 2008; 105: 12393-8.

[186] Anderson JA, Gilliland WD, Langley CH. Molecular population genetics and evolution of Drosophila meiosis genes. Genetics 2009; 181: 177-85.

[187] Li H, Lu L, Manly KF, et al. Inferring gene transcriptional modula-tory relations: a genetical genomics approach. Human Mol Genet 2005; 14: 1119-25.

[188] Carlborg O, De Koning DJ, Manly KF, et al. Methodological aspects of the genetic dissection of gene expression. Bioinformatics 2005; 21: 2383-93.

[189] Page GP, Ruden DM. Combining high dimensional biological data to study complex diseases and quantitative traits - Design, Analy-sis, and Interpretation of Results. Boca Raton, FL: Chapman & Hall/CRC, 2005.

[190] Foss EJ, Radulovic D, Shaffer SA, et al. Genetic basis of proteome variation in yeast. Nat Genet 2007; 39: 1369-75.

[191] Zhu J, Zhang B, Smith EN, et al. Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat Genet 2008; 40: 854-61.

[192] Loevenich SN, Brunner E, King NL, et al. The Drosophila melanogaster PeptideAtlas facilitates the use of peptide data for improved fly proteomics and genome annotation. BMC Bioinfor-matics 2009; 10: 59.

[193] Schrimpf SP, Weiss M, Reiter L, et al. Comparative functional analysis of the Caenorhabditis elegans and Drosophila mela-nogaster proteomes. PLoS Biol 2009; 7: e48.

[194] Takemori N, Yamamoto MT. Proteome mapping of the Drosophila melanogaster male reproductive system. Proteomics 2009; 9: 2484-93.

[195] Coquin L, Feala JD, McCulloch AD, et al. Metabolomic and flux-balance analysis of age-related decline of hypoxia tolerance in Drosophila muscle tissue. Mol Syst Biol 2008; 4: 233.

[196] Dow JA. Insights into the Malpighian tubule from functional genomics. J Exp Biol 2009; 212: 435-45.

[197] Feala JD, Coquin L, Paternostro G, et al. Integrating metabolomics and phenomics with systems models of cardiac hypoxia. Prog Biophys Mol Biol 2008; 96: 209-25.

[198] Pedersen KS, Kristensen TN, Loeschcke V, et al. Metabolomic signatures of inbreeding at benign and stressful temperatures in Drosophila melanogaster. Genetics 2008; 180: 1233-43.

[199] Stevenson-Paulik J, Chiou ST, Frederick JP, et al. Inositol phos-phate metabolomics: merging genetic perturbation with modernized radiolabeling methods. Methods 2006; 39: 112-21.

[200] Ruden DM, Lu X. Evolutionary conservation of metabolism explains how Drosophila nutrigenomics can help us understand human nutrigenomics. Genes Nutr 2006; 1: 75-84.

[201] Hirsch HVB, Possidente D, Averill S, et al. Variations at a quanti-tative trait locus (QTL) affect development of behavior in lead-exposed Drosophila melanogaster. Neurotoxicology 2009; 30: 305-11.

[202] Broman KW, Wu H, Sen S, et al. R/qtl: QTL mapping in experi-mental crosses. Bioinformatics 2003; 19: 889-90.

[203] Gibson G. The environmental contribution to gene expression profiles. Nat Rev Genet 2008; 9: 575-81.

[204] Passador-Gurgel G, Hsieh WP, Hunt P, et al. Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster. Nat Genet 2007; 39: 264-8.

[205] Ruden DM. Personalized medicine and quantitative trait tran-scripts. Nat Genet 2007; 39: 144-5.

[206] Lai CQ, Leips J, Zou W, et al. Speed-mapping quantitative trait loci using microarrays. Nat Methods 2007; 4: 839-41.

[207] Carter AJ, Weier TM, Houle D. The effect of inbreeding on fluctu-ating asymmetry of wing veins in two laboratory strains of Droso-phila melanogaster. Heredity 2009; 102(6): 563-72.

[208] Ashburner M. Drosophila: A laboratory Handbook. Cold Spring Harbor: Cold Spring Harbor Laboratory Press, 1989.

[209] Caplen NJ, Fleenor J, Fire A, et al. dsRNA-mediated gene silenc-ing in cultured Drosophila cells: a tissue culture model for the analysis of RNA interference. Gene 2000; 252: 95-105.

[210] Mattila J, Kallijarvi J, Puig O. RNAi screening for kinases and phosphatases identifies FoxO regulators. Proc Natl Acad Sci USA 2008; 105: 14873-8.

[211] Ali N, Karlsson C, Aspling M, et al. Forward RNAi screens in primary human hematopoietic stem/progenitor cells. Blood 2009; 113: 3690-5.

[212] Mullenders J, Fabius AW, Madiredjo M, et al. A large scale shRNA barcode screen identifies the circadian clock component ARNTL as putative regulator of the p53 tumor suppressor pathway. PLoS ONE 2009; 4: e4798.

[213] DasGupta R, Gonsalves FC. High-throughput RNAi screen in Drosophila. Methods Mol Biol 2008; 469: 163-84.

[214] Thibault ST, Singer MA, Miyazaki WY, et al. A complementary transposon tool kit for Drosophila melanogaster using P and pig-gyBac. [see comment]. Nat Genet 2004; 36: 283-7.

[215] Dietzl G, Chen D, Schnorrer F, et al. A genome-wide transgenic RNAi library for conditional gene inactivation in Drosophila. Na-ture 2007; 448: 151-6.

[216] Bischof J, Maeda RK, Hediger M, et al. An optimized transgenesis system for Drosophila using germ-line-specific phiC31 integrases. Proc Natl Acad Sci USA 2007; 104: 3312-7.

[217] Lim HY, Bodmer R, Perrin L. Drosophila aging 2005/06. Exp Gerontol 2006; 41: 1213-6.

[218] Luong N, Davies CR, Wessells RJ, et al. Activated FOXO-mediated insulin resistance is blocked by reduction of TOR activ-ity. Cell Metab 2006; 4: 133-42.

[219] Ocorr K, Akasaka T, Bodmer R. Age-related cardiac disease model of Drosophila. Mech Ageing Dev 2007; 128: 112-6.

[220] Ocorr K, Perrin L, Lim HY, et al. Genetic control of heart function and aging in Drosophila. Trends Cardiovascular Med 2007; 17: 177-82.

[221] Shaw P, Ocorr K, Bodmer R, et al. Drosophila aging 2006/2007. Exp Gerontol 2008; 43: 5-10.

[222] Wessells RJ, Fitzgerald E, Cypser JR, et al. Insulin regulation of heart function in aging fruit flies. Nat Genet 2004; 36: 1275-81.

Received: March 27, 2009 Revised: May 7, 2009 Accepted: May 31, 2009