Jumat, 08 Juli 2016

Nutrigenomics and Health

source: shcs-dev.ucdavis.edu

Nutrigenomics represents the dynamic interface between nutrition and
genomic-regulated processes [18, 19]. A set of fundamental principles exist
which underpins the nutrigenomics concept. The first is that the genotype can
influence the ability of a food component to influence cellular processes associated
with health and/or disease. Second, numerous dietary components are
capable of influencing, singly or in combination, the gene expression patterns
involved in multiple cellular processes. Third, the observed cellular response is
dependent on the amount and duration of exposure to a specific or blend of
food components. Finally, the ability of a bioactive food component to influence
cellular processes will depend, in some cases, on the stage of the life cycle.
Collectively, nutrigenomics embodies the interrelationships occurring among
variation in DNA base sequences, epigenetics events and transcriptomics. Such
interactions may influence not only the magnitude, but sometimes the direction,
of the response to specific bioactive food components [6, 19–22]. Inappropriate
dietary habits may tip the scale from a healthy condition to a state of disease
progression. Thus, appropriate dietary intake of food components is fundamental
to regulating normal physiological processes, as well as the squelching of
potential pathologic conditions. The scientific literature already provides evidence
that the response to food components can vary from tissue to tissue, as
well as a function of the time and duration of intervention [23–25]. Undeniably,
the capturing of this genomic-diet information is critical to the identification of those individuals who will benefit from intervention strategies and those who
might be placed at risk because of a dietary change. The incorporation of this
information will allow for nutritional preemption strategies which utilize foods
or their components to enhance normal processes and/or to retard or reverse
cellular events that lead to aberrant conditions including those associated with
chronic disease.
Figure: The influence of nutrigenetics, epigenetics, transcriptomics, proteomics, and
metabolomics on the phenotypic response to food components.

The 30,000 genes in the human genome are responsible for more than
100,000 functionally distinct proteins, and likely 3–5 times that number of
small-molecular-weight cellular constituents (such as metabolic intermediates,
hormones and other signaling molecules, and secondary metabolites) which
collectively can enhance or suppress a number of physiological processes.
Understanding how foods and their components influence each step in the cascade
of events leading to a phenotype (fig. 3) is a daunting task, but holds great
promise in helping improve the quality of life and reduce the risk of diseases.

Health Implication of Individual Genomic Variability

source: www.seven-health.com/

The human genome is a complicated blueprint of information. While all
DNA has four relatively simple bases (adenine, guanine, cytosine, and thymidine),
their sequence can have a pronounced effect on what ultimately evolves.
The nearly 3 billion base pairs (3.2 Gb) in the human genome constitute what is
sometimes affectionately called the ‘genome encyclopedia’. If a gene is analogous
to a word, then a chromosome must be a chapter and the genome the whole
book. Similar to a word, a gene may have a single or multiple meanings, and can
be influenced by the context in which it is expressed. Like a chapter, a chromosome
is a large collection of genes organized into a linear string of information.
The complete set of chapters is necessary to form the ‘book’ of information that
comprises the genetic blueprint of each and every organism.
The size of this blueprint is illustrated by assuming each DNA basis creates
a series of words each of which contain 5 characters. Thus, about 600 million
words could be generated from the human genome. If these words were
compiled to an average of 12 words per line then an equivalent of about 50,000
text lines would be generated. Since an average page only has about 70 lines,
this would mean the human genome would contribute about 700,000 pages. If
these pages were assembled into an encyclopedia with 1,000 pages in each volume,
there would be about 700 volumes for late-night reading and enjoyment!
Even this analogy is overly simplistic, since it does not take into consideration
genetics, epigenetics, proteomics and metabolomics variations that occur
within and among individuals. Human genetic predictions are exceedingly
complicated by the presence of comparatively long and variable intron sequences
[13]. These intron sequences (noncoding DNA regions) interrupt the sequences
containing instructions for making a protein (exons). The panoramic views of
the human genome have already begun to reveal a wealth of information and
some early surprises. While much remains to be deciphered in this vast information
source, several fundamental principles have emerged. It is safe to conclude that the more we learn about the human genome, the more there will be to learn.
Interestingly, the coding region of a gene (exons) which is the portion of DNA
that is transcribed into mRNA and translated into proteins only constitutes
about 1.5% of the human genome [14]. Furthermore, transcription units, consisting
of exons, introns, and the regulatory region, constitute about 20% of the
entire human genome. One must wonder if the remainder is simply a filler or
has a yet to be defined role. Evidence already exists that multiple gene messages
can be derived from a single stretch of DNA based on alternative uses of
promoters, exons, and termination sites. Adding to these overlapping transcription
units, somatic recombination events and the existence of highly similar
gene families and pseudogenes make it difficult to identify and categorize
genes. Regardless, the fundamental premise of genomics is that DNA reading
results in the formation of messenger RNA which then codes from proteins
which ultimately bring about changes in small-molecular-weight compounds
and in cellular processes (fig. 2).

It is already known that human genome variability can arise for several
reasons including single nucleotide changes (polymorphisms), deletions, insertions,
and translocations. Translocations and gross deletions are important
causes of both cancer and inherited disease. Such gene rearrangements are nonrandomly
distributed in the human genome as a consequence of selection for
growth advantage and/or the inherent potential of some DNA sequences to be
frequently involved in breakage and recombination. Alu insertional elements,
the most abundant class of short interspersed nucleotide elements in humans,
are dimeric sequences approximately 300 bp in length derived from the 7SL
RNA gene. About 500,000 to 1 106 Alu units are dispersed throughout the human haploid genome primarily in AT-rich neighborhoods located within
larger GC dense chromosomal regions. These sequences contain a bipartite
RNA polymerase III promoter, a central poly-A tract, a 3 poly-A tail, numerous
CpG islands and are bracketed by short direct repeats. Such insertions are
associated with a number of disease states [15].
Restriction fragment length polymorphisms, short tandem repeats, and
variable-number tandem repeats are also present in the genome. Intraspecies
variation in the length of DNA fragments generated by the action of restriction
enzymes or caused by mutations that alter the sites at which these enzymes act
can change the length, number, or production of fragments. Restriction fragment
length polymorphism is a term used in two related contexts: as a characteristic
of DNA molecules (arising from their differing nucleotide sequences)
by which they may be distinguished, and as the laboratory technique which uses
this characteristic to compare DNA molecules. The short tandem repeats are
tandemly repeated DNA sequences of a pattern of length from 2 to 10 bp [for
example (CA)n(TG)n in a genomics region] and the total size is lower than
100 bp. Repeated sequences represent a large part of eukaryotic genomes.
Single nucleotide polymorphisms (SNPs) are the most common DNA
sequence variation. They occur when a single nucleotide in the genome is
altered. A variation in the incidence must occur in at least 1% of the population
to be considered an SNP. Huntington’s disease, cystic fibrosis, and muscular
dystrophy are examples of diseases which are linked to a single gene polymorphism
[16]. While we have known about the genetics of these diseases for a
number of years, reliable and effective therapies have remained largely elusive.
Cancer and possibly several other chronic diseases are likely a result of multiple
genetic shifts and thus present an even more daunting task for understanding
the disease but are important for developing strategies for prevention and/or

Figure : Relationship between dietary components and genomic regulation.

Fortunately, the majority of SNPs do not appear to cause disease; however,
they may assist in determining the likelihood that a particular abnormality may
occur [17]. Nevertheless, some SNPs have been linked to an increased disease
risk. For example, a gene associated with Alzheimer’s disease is apolipoprotein
E. This gene can contain two SNPs which may result in three possible alleles:
E2, E3, and E4. Each allele differs by one DNA base, and the protein product of
each gene differs by one amino acid. Typically an individual inherits one maternal
and one paternal copy of a gene. Research has shown that an individual who
inherits at least one E4 allele has a greater risk of developing Alzheimer’s disease,
presumably as a result of the one amino acid substation in the E4 protein
which influences its structure and function. Inheriting the E2 allele, on the other
hand, appears to protect against Alzheimer’s disease. Of course, SNPs are not
absolute since those inheriting two E4 alleles do not always develop Alzheimer’s disease [18].

Thus, other factors or events, including the environment (diet),
may affect the disease risk. It is certainly possible that either internal or external
stressors set the stage for when bioactive food components are most effective.
Thus, expanded knowledge about genetic and environmental interactions is
fundamental to unraveling global variation in the incidence and/or severity of
several disease states. Evidence is already surfacing that genetic variation can
influence the propensity for the initiating event, the progression to a clinical
disease state, and the trajectory of several diseases. For example, the interleukin
1 family of cytokines has a critical role in mediating inflammation, which is
considered a factor in many chronic diseases, including coronary artery disease,
rheumatoid arthritis and cancer. Recent research has identified several
sequence variations in the regulatory DNA of the genes coding for important
members of the interleukin 1 family, and these variations are associated with
differential effects on the inflammatory response [17]. While inconclusive, evidence
is beginning to surface that the physiological relevance of such genetic
variation can be modified by the foods that are consumed.

Controversies Involving Nutrition and Health

source: www.listtoday.org/

Several recent meta-analyses illustrate the mixed messages that can surface
about what role, if any, diet has in health promotion [3–6]. While these
findings frequently reflect disagreements in interpretation among scientists,
they also lead to confusion among consumers and can erode the trust that they
have for the scientific enterprise. Thus, greater attention needs to be given to
the totality of the information rather than to individual studies in defining the
health significance of the diet. It would be truly disappointing if a simple summation
of evidence-based nutrition studies became the ‘gold standard’ since the
majority of case-control and cohort studies are not simple repeats of previous
undertaking and thus vary enormously in experimental design, tested populations,
and outcome measures; all of this can surely influence overall interpretations.
Unfortunately, conclusions based on a meta-analysis may even depend on
the method the authors used to select trials for inclusion in the analysis. Thus,
summaries of evidence which do not consider the biological response, plausibility
and consequences are doomed to create more confusion than they resolve.
What is increasingly clear is that inadequate long-term intervention studies
exist for making definitive conclusions about who will benefit and who might
be placed at risk by dietary change. Indisputably, well-controlled long-term
intervention studies which incorporate the newest technologies hold the greatest
promise for unraveling the complex interplay between diet and health.
Future clinical studies must incorporate genomics in the study design, and not
just use it as an analytic approach to confounders to data interpretation.
Considerable preclinical evidence linking diet to health outcomes centers
on the response to a single bioactive component as a modulator of a key cellular
process or series of critical processes [7, 8]. Both in vitro and in vivo studies
suggest that multiple targets are likely responsible for the phenotypic response
to foods and/or dietary supplements [8, 9]. These targets may be involved in cell
division, inflammation, apoptosis, compound bioactivation or a host of other
biological processes which influence the phenotype. Focusing on a process
which can be modified by one or more bioactive food components will help
with a systematic approach to understanding the role of diet in health promotion.
However, in some cases, research suggests that whole foods may offer
advantages over isolated components, possibly indicating that multiple food
components or multiple targets are needed to bring about a desired effect.
Nutrient-nutrient, as well as nutrient-drug interactions can be significant determinants
of the overall phenotypic response. For example, the ability of n–3 fatty
acids to increase the sensitivity to anthracyclines is dependent on vitamin E
intake [10] or the benefits of calcium are generally dependent on the intake of
vitamin D [11]. While not as frequently examined, negative interactions among
food components or nutrient-drug interactions are also possible and such lines
of investigation deserve added attention to assist with the identification of
potentially vulnerable individuals.

Since the quantity of exposure can markedly influence the outcome, it is
imperative that nutrition studies use physiologically relevant concentrations and
consider the totality of the diet as a factor influencing the overall response
[7, 8]. Sadly, multiple exposures in humans are hampered by the availability of
definitive biomarkers that reflect a long-term health outcome. Finally, it is
worth noting that studies which examine the impact of dietary change through
more than one phase of life are exceedingly rare, yet are desperately needed if sense is to be made out of the diet-health conundrum and the opportune time for
intervention to bring about a desired change. A narrow and simplistic view of
dietary patterns is obviously problematic and may contribute to misconceptions
and thus the confusion that exist today about the importance of functional foods
and health promotion and disease prevention.
Clearly, predicative biomarkers which can evaluate long-term consequences
are fundamental to resolving dietary issues which cannot be addressed
for practical or ethical reasons. At present, few validated biomarkers are available
for assessing the impact of diet in health promotion. Similar to environmental
toxicity research, it is likely that at least 3 different types of biomarkers
will be needed to assess the impact of diet (fig. 1) [12]. Foremost among these
is the need to accurately identify exposures to foods and their components.
Obviously, if the effective concentration does not reach the target tissue, there is
little hope that it will be effective in bringing about a desired effect. Likewise,
sensitive and reliable biomarkers for identifying the impact of bioactive food
components are in short supply. These ‘effect’ biomarkers should provide sensitive
and time-/dose-dependent information about how the food component(s)
modifies/modify one or more specific cellular processes [6–8]. Assuming this/
these molecular target(s) can be analyzed in the affected or surrogate tissue, it
can ideally be an effective biomarker for assessing the response to physiologically
relevant exposures to foods or their components. Finally, it is clear that we need to assess susceptibility factors including genetic and epigenetic markers
which can reflect an individual’s responsiveness to the biological response to a
food, food patterns or dietary supplements. The use of such information may
assist with improving the usefulness of standard 24-hour recall and food frequency
questionnaires by developing predictive models that take into account
genetic factors influencing absorption, metabolism and excretion. These susceptibility
biomarkers (fig. 1) will again provide important clues about responders,
both positive and negative, in the molecular target to diet-induced phenotypic

Minggu, 03 Juli 2016

Phytochemicals and Gene Expression

Source: www.blogs.oregonstate.edu


The practice of self-medicating with botanical products is probably as old as humankind
itself. Depicted in literature and film, we are well-familiar with the herbal remedies
prepared and administered by the healers of ancient Egypt, medieval physicians, and
American Indian cultures. Only in the past century has modern chemistry produced a
distinction: a choice between synthetically derived drugs vs. natural products. With the
great advances made by the pharmaceutical industry, our modern Western culture has
distanced itself from the notion that phytochemicals may have specific medicinal actions.
Most synthetically derived medicines are considered to have a specific target: as an
enzyme inhibitor (e.g., cholesterol-lowering statins), receptor agonists, or receptor
antagonists (the antidiabetic drug rosiglitazone is an agonist of the peroxisome
proliferator activated receptor). Synthetic drugs are generally considered to have high
specificity; hallmark examples may be action on specific serotonin receptor subtypes or
the selective estrogen receptor (ER) modulators (SERMs) that act more potently on ERα
or ERβ

Understanding the specific effects of phytochemicals represents distinct challenges.
Most often, commercially available products represent solvent-soluble extracts prepared
from a botanical. These products usually contain mixtures of a wide number of
compounds. The exact number and relative abundance of these products often varies
from preparation to preparation. Growing conditions and the geographical source of a
botanical may ultimately affect the potency of the extract. Evaluating the effect of a
mixture on a biological system—whether a cell or animal model—becomes a more
difficult task compared to evaluating the effect of a single compound. Different
compounds present in the extract may have distinct effects, and may even interact
negatively or positively. This interaction may vary in significance depending on the
relative abundance of the different compounds in the extract.
To evaluate the effect of phytochemicals on gene expression, one may use both cell
and animal models. Further, if a specific compound is presumed to be the bioactive
compound present in a botanical extract, that compound can be studied alone, as a
purified compound, in parallel with studies evaluating a mixture containing the putative
active compound. One would expect to see the same effect on the model system, if the
putative compound is indeed the active factor.

If a candidate phytochemical is being evaluated as a potential regulator of gene
expression, one might predict specific sites where gene regulation would be affected. For
example, a phytochemical acting as a ligand for a specific nuclear receptor might enhance
the trancription rate of a specific gene. Alternatively, a phytochemical might repress gene
transcription if it interferes with coactivator recruitment. Phytochemicals acting as
agonists or antagonists of kinases or phosphatases involved in a signal transduction
pathway may ultimately affect the expression of a gene or sets of genes regulated by that
specific pathway.

A wide number of research reports have detailed the interaction between various
phytochemicals. Rather than attempt to provide an encyclopedic collection of those
reports, we have chosen a number of examples where phytochemicals have been shown
to affect gene expression in different model systems.


St. John’s wort is used to treat mild to moderate depression and anxiety (1). It is
composed of numerous constituents, although its active component remains speculative.
One component, hyperforin, inhibits synaptic uptake of various neurotransmitters such as
serotonin and dopamine in vitro, although in vivo studies suggest other potential modes
of action not fully dependent on hyperforin (1). St. John’s wort serves a wide array of
therapeutic uses, not only historically, but also currently in both the United States and
Europe (2). Recent reports have suggested that St. John’s wort decreases the efficacy of
medications metabolized by cytochrome P450 3A4 (CYP3A4), a member of the
cytochrome P450 monooxygenase family (CYP) (Table 1) (3). The CYP family of
enzymes is responsible for clearing the majority of prescription drugs and ingested
pollutants (4). These enzymes are located in hepatocytes and intestinal cells and are
capable of metabolizing many endogenous compounds along with exogenous drugs and
pollutants (5). Several CYP families exist, although CYP1, CYP2, and CYP3 metabolize
most xenobiotic substrates (5). Moore et al. investigated the effect of St. John’s wort on
the human pregnane X receptor (PXR), a nuclear receptor responsible for regulating
CYP3A4 transcription (Figure 1) (3). They found that three different commercial brands
of St. John’s wort extract activated PXR. Of the components tested, hyperforin induced
PXR at half-maximal effective concentration (EC50) of 23 nM. Importantly, this
concentration is well below the 200 nM level observed in plasma of individuals using St.
John’s wort on a regular basis. Hyperforin was also found to directly bind PXR, as
confirmed by competition binding assays. Finally, Moore et al. found that the St. John’s
wort extract and hyperforin induced the expression of CYP3A4, validating the premise
that this botanical could interfere with and increase the metabolism of drugs metabolized
by CYP3A4 (5). Wentworth et al. also found that St. John’s wort and hyperforin
activated the ligand-binding domain of the steroid X receptor (SXR), known as the
human PXR (6). This occurs via the activation function site 2 (AF-2) of S×R. Steroid
receptor coactivator-1 is a coactivator recruited by various nuclear receptors including
S×R. St. John’s wort and hyperforin mediated the SXR-SRC1 association, further
confirming of activation of CYP3A transcription by this phytochemical (6). Hyperforin
has recently been confirmed by crystal structure analysis to bind to the ligand-binding
domain of PXR (7). St. John’s wort has also been implicated to increase the expression of
CYP1A2, the second most abundant CYP accounting for over 10% of human hepatic
CYP content (4). However, a human in vivo study did not find evidence of increased
CYP1A2 activity after two weeks of St. John’s wort intake (300 mg three times per day),
although CYP3A4 activity was significantly induced in the intestinal wall (8). These
studies concluded that St. John’s wort increases the metabolism of certain medications by
increasing the expression of CYP3A4 via hyperforin binding and activating human PXR.


Guggulsterone is the active, lipid-lowering fraction of gugulipid, a gum resin extract from
the Commiphora mukul tree used in India for thousands of years to treat hyperlipidemia
(9). Numerous animal studies and clinical studies have been conducted since the 1960s
when the initial scientific studies were begun on the hypolipidemic effect of the gum
resin and its extracts. Most of the clinical studies found that gugulipid or guggulsterone
reduced serum cholesterol levels by an average of 30% (9). Upon further investigation,
Urizar et al. proposed that the lipid-lowering mechanism of this gum resin occurs via the
antagonistic activity of guggulsterone for the farnesoid X receptor (FXR) (10). FXR
heterodimerizes with the retinoid X receptor (RXR) upon ligand binding (11) and is
known as the “bile acid sensor” because it is responsible for repressing bile acid synthesis
via transcription of ileal bile acid-binding protein (I-BABP). Ligands of the nuclear
receptor FXR include bile acids such as chenodeocycholic acid (CDCA). Activation of
FXR also increases bile acid recirculation due to elevated bile acid concentrations within
the cell (11). In the recent study by Urizar et al., the Z-guggulsterone isomer had no
effect on FXR alone, although the E- and Z-guggulsterone isomers were able to inhibit
CDCA activation of FXR along with FXR-regulated genes (10). The isomers were also
able to inhibit CDCA activation of small heterodimeric partner (SHP). Small heterodimeric
partner is a nuclear receptor that heterodimerizes with other nuclear receptor
complexes such as the active FXR-RXR complex, although it does not have a DNAbinding
motif, as do most other nuclear receptors (11). Guggulsterone inhibited
transactivation of FXR-RXR and not DNA-binding of these complexes, indicating that
the isomers were exerting an inhibitory effect via the ligand-binding domain of the FXR (10). This effect was confirmed by using a fluorescence resonance energy transfer (FRET)-based coactivator
binding assay, in which guggulsterone was found to directly compete with CDCA for the
ligand-binding domain, inhibiting the recruitment of a necessary coactivator, SRC-1.
Finally, FXR-null mice did not respond to the cholesterol-lowering effect of
guggulsterone seen in the wild-type mice, indicating that FXR is necessary for the
hypolipidemic effect of guggulsterone (10). Wu et al. also found that guggulsterone had
an antagonistic activity of FXR in the presence of FXR activators and was able to
decrease gene expression of FXR-regulated genes (12). More recently, a study found that
guggulsterone induced the expression of the FXR-regulated bile salt export pump gene
(BSEP) in vitro in the presence of two different FXR ligands, CDCA and GW4064 (13).
This induction was also evident in rats fed a diet containing either 2.8 or 5.6%
guggulsterone, in which both BSEP and SHP mRNA were elevated compared to the
control-fed rats. However, mRNA levels of other FXR-regulated genes tested—such as
cholesterol 7α-hydroxylase (CYP7α1), sterol 12α-hydroxylase (CYP8b1), and I-BABP—
were unaffected. In this study, guggulsterone blocked coactivator recruitment of p120
and PBP as well as SRC-1, consistent with the prior report (13). These results indicate
that guggulsterone may have selective antagonistic activity on required coactivator
recruitment for FXR-mediated transcription, but also agonist-enhancing activity on selective FXR-regulated genes.


4.1. Genistein and PPARs
Soy isoflavones are phytochemicals often termed “phytoestrogens” due to the estrogenic
properties of these botanically derived products (14). Soy isoflavones have been credited
to have antiatherosclerotic, antidiabetic, and anticarginogenic properties, although the
specific physiological and cellular mechanisms affected by isoflavones are an area of
controversy and debate (15–17). Recent studies found that soy isoflavones were able to
activate two isoforms of the peroxisome-proliferator-activated receptors (PPARα and
PPARγ), proposing a novel way in which the isoflavones may be exerting their
antiatherosclerotic and antidiabetic properties (Figure 2) (18,19). The PPARs are nuclear
receptors involved in cellular lipid homeostasis (20). They have a promiscuous ligandbinding
domain able to bind a variety of lipophilic ligands, resulting in receptor
activation. Activation of PPARα results in increased expression of genes involved in fatty
acid catabolism, whereas activation of PPARγ results in increased expression of genes involved in cellular differentiation and insulin sensitization (20). Dang et al. found that
the soy isoflavone genistein was able to activate PPARγ in a dose-dependent manner, and
genistein also increased the expression of PPARγ-regulated genes and adipogenesis in
KS483 cells at a dose of 25μM (19). Genistein interacts directly with the nuclear
receptor, as verified by a membrane-bound PPARγ binding assay. However, a lower dose
of genistein (1 μM) actually had an inhibitory effect on PPARγ-regulated genes as well as
on adipogenesis. This is most likely due to the ability of low concentrations of genistein
to activate estrogen receptor-mediated activity, resulting in a decrease of PPARγ
activation (19). Mezei et al. found that both genistein and the soy isoflavone daidzein
were able to activate PPARγ-mediated transcription (18). Furthermore, female obese
Zucker rats fed a highisoflavone-containing soy diet had significantly improved glucose
tolerance compared to casein and low-isoflavone-containing diets, consistent with effects
of PPARγ activation. Genistein and daidzein were also able to activate PPARα-mediated
transcription. Both male and female obese Zucker rats fed a high-isoflavone-containing
diet had reduced liver cholesterol, liver triglycerides, and total liver weight, consistent
with effects of PPARα activation (18). Harmon and Harp found an opposing effect of
genistein on PPARγ (21). In this study, genistein was found to inhibit PPARγ expression
as well as adipogenesis in adipocytes, a well-characterized consequence of PPARγ
activation. However, these inconsistent effects may be due to the elevated concentration
used in this study. Other studies found that a genistein concentration of 50 μM was
enough to induce apoptosis in certain culture models such as colon carcinoma cell lines,
whereas Harmon et al. used a genistein concentration of 100 μM (22, 23). Genistein and
daidzein are not the only phytochemicals with PPAR-activating ability. Takahashi et al.
discovered that farnesol and geranylgeraniol, two common fruit and herb isoprenols, are
able to activate both PPARα and PPARγ along with several PPAR-regulated genes (24).
Therefore, these studies give new insight on the mechanism by which soy isoflavones and
other botanicals exert their favorable consequences.

4.2. Isoflavones and Estrogen Receptors
It is estimated that 80% of women over the age of 45 use some type of non-prescription
therapy to manage menopause symptoms, ranging from the consumption of soy or
Genomics and proteomics in nutrition 252
evening primrose oil to acupuncture (25). Of these therapies, the use of isoflavones as an
alternative to hormone replacement therapy may be an attractive alternative to classical
“hormone replacement therapy” (HRT) for many postmenopausal women, although their
potential side effects and long-term health implications are still not fully understood (26).
One of the soy isoflavones, genistein, has been shown in studies to have estrogenic
activity (27, 28). Because some postmenopausal women with estrogen-dependent breast
tumors may be consuming genistein as an alternative to HRT, Ju et al. studied the effect
of genistein on estrogen-dependent breast cancer growth (29). In this study, mice with
estrogen-dependent tumors had a significant, dose-dependent increase in tumor
presenelin-2 (pS2) mRNA levels when provided dietary genistein. The level of induction
seen with the higher genistein doses was similar to the induction produced by the
subcutaneous 17β-estradiol pellet. The pS2 gene is an estrogen-responsive gene and
indicative of estrogen-dependent growth. The ability of genistein to induce estrogendependent
growth in these tumors was also observed in tumor size and proliferation; both
were significantly increased with genistein ingestion or 17β-estradiol supplementation
(29). Therefore, the results of this study indicate that genistein consumption may promote
the growth of certain estrogen-dependent breast tumors. Another phytochemical with
known estrogenic activity is resveratrol, a polyphenolic compound in grapes and wine
(30). Resveratrol has also been attributed to have cancer-preventative properties in colon
cancer cell lines (31, 32). However, the effect of resveratrol on breast cancer growth is
controversial, especially with respect to estrogen-dependent tumors (33). Levenson et al.
found that resveratrol was able to induce gene expression of the estrogen-responsive gene
tumor growth factor a (TGFα) in a dose-dependent manner in breast cancer cells
expressing wild-type estrogen receptor (33). Higher doses of resveratrol were needed to
mimic this effect in breast cancer cells expressing a mutant form of the estrogen receptor.
However, resveratrol did not further stimulate TGFα expression when 17β-estradiol was
present in its optimal concentration. Resveratrol inhibited the growth of the breast cancer
cells regardless of the presence of the estrogen receptor or the antiestrogen ICI, indicating
that growth inhibition by resveratrol is, at least in part, estrogen receptor-independent.
Estrogen receptor protein levels were analyzed in both wild-type and mutant estrogen
receptor-expressing cells. Both 17 β-estradiol and resveratrol decreased the wild-type
estrogen receptor levels. Finally, resveratrol and 17(β-estradiol both increased the protein
levels of p21cip/WAF1, a cyclin-dependent kinase inhibitor, although this increase
appears to be an estrogen-mediated effect (33). Both resveratrol and genistein have
estrogenic effects and are able to regulate many estrogen receptor-mediated genes. This
activity may explain some of the beneficial effects of these phytochemicals, but also
warrants further investigation due to possible harmful side effects.

4.3. Genistein and Gene Expression Patterns
Recent studies utilizing microarray technology reveal that genistein affects the regulation
of many genes, including those involved in reproductive development and prostate cancer
(34–36). Naciff et al. found that genistein had a gene expression profile similar to an
estrogen (17 α-Ethynyl estradiol) and a weak estrogenic chemical (bisphenol A) in the
developing uterus and ovary of the rat (34). Genes involved in cell growth (growth
hormone receptor), differentiation (progesterone receptor), stress response (glutathione transferase M5), and apoptosis (interleukin 4 receptor) were regulated similarly by all
three compounds. RT-PCR confirmed some of these results, such as increased expression
of the progesterone receptor by 17 α-Ethynyl estradiol, bisphenol A, and genistein (34). It
is important to note that although the three compounds have a similar gene expression
profile in the developing reproductive system, the gene expression profiles of 17 α-
Ethynyl estradiol and bisphenol A were more similar to each other than to genistein. This
may be due to the mainly “estrogenic” activity of these compounds, whereas genistein
has other known activities, such as tyrosine kinase and topoisomerase-II inhibition along
with activity as a PPAR agonist profiled earlier (34). Two recent studies also analyzed
the gene expression profile of genistein using a human prostate cancer cell line (35, 36).
Li and Sarkar found that genistein downregulated genes involved in angiogenesis, such as
vascular endothelial growth factor and its receptor, and upregulated genes inhibiting
angiogenesis, such as connective tissue growth factor and connective tissue activation
peptide (35, 36). Furthermore, genistein also downregulated genes necessary for tumor
cell invasion and metastasis (MMP-9/type IV collagenase, urokinase plasminogen
activator, and urokinase plasminogen activator receptor). These results indicate that
genistein may inhibit tumor metastasis and growth. Another study by Li and Sarkar found
that genistein caused a difference in expression profiles of genes involved in cell cycle
control, apoptosis, and cell signaling (36). Genistein downregulated cell cycle promoter
genes such as cyclin A and cyclin B and induced genes that inhibit cell cycle progression,
such as cyclin G2 in human prostate cancer cultured cells. Genes involved in the
inhibition of apoptosis (survivin) and genes involved in cell growth (pescadillo) were also
downregulated in genistein-treated cells (36, 37). Genistein also downregulated signaling
genes such as NF-κB-inducing kinase and MAP kinase kinase potentially resulting in
decreased cell proliferation (36). Therefore, microarray analysis revealed that genistein is
able to affect many genes involved in biological processes such as cell growth, cell cycle
control, differentiation, stress response, angiogenesis, tumor cell invasion, metastasis,
signaling, and apoptosis.


Soy consumption may have many advantageous outcomes, such as an improved
management of blood lipids and a decreased risk of cancer (38–40). In one rodent study,
Tovar-Palacio et al. found that soy-fed gerbils had significantly reduced levels of
circulating apolipoprotein B and significantly increased circulating levels of
apolipoprotein A-I after a 28-day feeding study (41). However, apolipoprotein A-I gene
expression was significantly reduced in gerbils fed a soy diet containing various amounts
of isoflavones. This reduction was not reflected in the circulating protein content. This
discrepancy may be a result of decreased circulating lipoprotein turnover or a
downregulation of apolipoprotein A-I synthesis due to its elevated level in circulation in
the soy-fed animals. It is important to note that the mRNA levels of apolipoprotein E an
apolipoprotein also synthesized in the liver and similar in abundance to apolipoprotein A,
remained unchanged (41). Two other genes also affected by soy consumption in gerbils
are phosphoribosylpyrophosphate synthetase-associated protein (PAP) and a member of
the cytochrome P450 2A family (CYP2A) (42). In a study by Mezei et al., gerbils fed a soy diet with increasing levels of isoflavones had a dose-dependent increase in both PAP
and CYP2A gene expression. PAP is a protein that negatively regulates
phosphoribosylpyrophosphate synthetase (PRPP-synthetase) activity, an enzyme
involved in nucleotide synthesis. Therefore, soy may be able to decrease nucleotide
synthesis and cell proliferation via PAP regulation. CYP2A belongs to a family of
enzymes used to metabolize endogenous and exogenous toxins and other xenobiotics
(43). Therefore, upregulation of this CYP might decrease mutagenic threat to the cell.
Ronis et al. found an induction in CYP3A protein levels in dexamethasone-treated (DEXtreated),
soy-fed rats relative to DEX-treated, casein-fed rats (44). This induction in
protein level appears to be due in part to the increased expression of CYP3A2 mRNA.
CYP3Al, CYP3A9, and CYP3A18 did not have increased mRNA levels in DEX-treated, soy-fed rats compared to the DEX-treated, casein-fed rats. Neither
CYP2B1 protein levels nor mRNA levels were different between these soy-fed and
casein-fed rats. However, the enzymatic activity of CYP2B1 was greater in the DEXtreated,
soy-fed rats. This effect was also seen in CYP3A enzymatic activity except when
CYP3A18-specific lithocholic acid was used as substrate (44). Therefore, soy had other
stimulatory effects on CYP enzymes besides increased gene and subsequent protein
expression. A second study by Ronis et al. focused on CYP1 A induction and activity
resulting from a casein, whey, or soy protein source in 3methylcholanthrene- (3-MC) or
isosafrole- (ISO) induced rats (45). 3-MC is an environmental carcinogen that induces
CYP1A expression via the aryl-hydrocarbon receptor (AhR) located in the promoter of
CYP1A genes, and ISO is a common phytochemical component of foodstuffs that
induces CYP1A in an AhR-independent manner (45). 3-MC induced CYP1A1 gene
expression while protein levels were significantly reduced in soy-fed rats compared to
casein-fed rats. CYP1A2 mRNA levels were also significantly reduced in soy-fed rats
compared to casein-fed rats, although protein levels were comparable. Finally, the
enzymatic activities of both CYP1A1 and 1A2 were lower in the soy-fed group (45).
Consistent with the results just noted, AhR expression was 50% lower in soy-fed rats, and
AhR expression was highly correlated to 3-MC-induced CYP1A1 expression (45). The
selective regulation of soy on CYP expression and activity may account for some of the
anticargenogenic activities attributed to soy.


Flavonoids are polyphenolic phytochemicals with antioxidative, anticarginogenic, and
estrogenic activity that exert their effects through various biological processes including
signaling cascades (46–48). Frigo et al. found that the flavonoids apigenin, flavone, and
chalcone induced activator protein-1 (AP-1) activation in two estrogen-unresponsive cell
lines (46). AP-1 is a transcription factor that is a target for multiple signaling cascades.
Chalcone was the only flavonoid that induced all of the transcription factors tested (AP-1,
Elk-1, c-Jun, and CHOP). The flavonoids kaempferide and apigenin inhibited PMAinduced
Elk-1 and c-Jun activity, decreasing cellular proliferation signaling important in
tumor prevention (46). Genistein is another flavonoid that can moderate its biological
effects such as cell-cycle arrest through intracellular signaling pathways (47). Frey and
Singletary showed that genistein inhibited the growth of immortalized human breast
cancer cells, as seen by DNA synthesis arrest (47). In this study, genistein was able to
cause phosphorylation of the p38 mitogen-activated protein kinase (p38 MAPK) in a
dose- and time-dependent manner and increase its activity. This ultimately caused the
downregulation of Cdc25C, a cell-cycle promoter protein. Genistein also inactivated
ERK1/ERK/2 and had no effect on SAPK/JNK activity, indicating that this isoflavone
has a selective action on MAPK signaling pathways that may be dependent on cell type
(47). Another flavonoid that affects signaling cascades is naringenin, a flavonoid found in
grapefruit (48). Harmon and Patel found that this flavonoid inhibited insulin-mediated
glucose uptake in adipocytes (48). Naringenin arrested Akt activation, but had no effect
on the insulin receptor (IRβ), insulin receptor substrate-1 and -2 (IRS-1, IRS-2), or
phosphoinositide 3-kinase (PI3K) phosphorylation status. Although naringenin did not
affect the phosphorylation state of PI3K, it did inhibit the activity of PI3K, resulting in
the observed decrease in Akt phosphorylation (48). In conclusion, flavonoids such as
chalcone, genistein, and naringenin are able to mediate biological processes such as cellcycle
arrest or altered gene transcription via intracellular signaling cascades.


One of the most exciting advances in the field of regulation of gene expression by dietary
constituents is the explosion of information becoming available regarding nuclear
receptors such as PPAR, FXR, PXR, AhR, and many others. A particularly important
aspect that must be considered is that many nuclear receptors are “promiscuous”
receptors, having the ability to bind many different endogenous and exogenous ligands.
Thus, is becomes immediately apparent that many phytochemicals have such lipophilic
Genomics and proteomics in nutrition 258
properties that they may make excellent ligands for one or more of these nuclear
receptors. In previous sections, we have discussed some of the recent examples just
beginning to show how phytochemicals interact with these promiscuous receptors. One of
the exciting challenges of future research will be to identify further phytochemical
ligands for these receptors and to study phytochemical/phytochemical and
drug/phytochemical interactions. The ability to point out both negative interactions and
potentially promising positive interactions between drugs and phytochemicals may
provide great practical health benefits to the consumer.

Author: Orsolya Mezei and Neil F.Shay
Department of Biological Sciences, University of Notre Dame,
Notre Dame, Indiana, U.S.A.

Genomic Approaches to Understanding Vitamin D Action

Source: www,acemaxs31.com

The molecular actions of vitamin D metabolites have been studied extensively over the
past 30 years. This has led researchers to recognize roles for vitamin D nutriture and
vitamin D metabolite action in a variety of physiological systems, e.g., calcium
homeostasis, immune function, and the control of cell proliferation, differentiation, and
apoptosis resulting in the prevention of various cancers. The following review is intended
to summarize our understanding of the molecular actions of vitamin D, to review the
limited approaches taken to date using genomic approaches to study vitamin D action,
and to identify issues that may benefit from a genomic approach to vitamin D action.

Vitamin D is a conditionally required nutrient.UV light-stimulated skin conversion of 7-
dehydrocholesterol to vitamin D can meet the physiological needs of most individuals.
However, low vitamin D status is a common condition during the winter months in
people who live in the Northern United States, Northern Europe, and in Canada, in
people who limit their sun exposure by wearing protective clothing and sunscreen, and in
the elderly (1). High vitamin D status has been associated with protection from
osteoporosis, through its traditional effects on calcium homeostasis (2), and protection
from cancer, due to its ability to suppress cellular proliferation, promote differentiation,
and activate apoptosis (3). These later features of vitamin D biology may also account for
the anti-inflammatory and immunoregulatory actions of vitamin D (4). Recent studies
suggest that current recommendations for vitamin D intake (400–600 IU per day) are not
sufficient to protect bone health, a classic role for vitamin D in the optimization of human
health (1,5,6).

2.1. Metabolism of Vitamin D
Vitamin D, whether from the diet or produced in skin, is hydroxylated in the liver to form
25 hydroxyvitamin D3 (25-OH D) (7), a marker of vitamin D status (8,9). The biological
actions of vitamin D require further activation of 25-OH D to 1α, 25 dihydroxyvitamin
D3 (1, 25(OH)2 D) by a la hydroxylase before the hormone is biologically active (10).
Alterations in renal la hydroxylase activity are responsible for changes in circulating 1,
25(OH)2 D levels associated with variations in dietary calcium intake (i.e., low calcium
intake increases renal lα hydroxylase activity through elevated parathyroid hormone
production). However, extra-renal lα hydroxlase has been documented in a variety of
tissues, including skin, prostate epithelial cells, colonocytes, and mammary epithelial
cells. Thus, 1, 25(OH)2D, which has traditionally been viewed as an endocrine hormone,
may also function as an autocrine- or paracrine-signaling molecule.

Vitamin D compounds can also be modified by the actions of cytochrome P-450
family member, 24-hydroxylase (CYP24). When 25-OH D is the substrate, 24, 25(OH)2
D results. This vitamin D metabolite has been implicated in chondrocyte biology and in
bone-fracture repair (11,12) Twenty-four hydroxylation of 1, 25(OH)2 D is the first step
in the metabolic degradation of the active hormone. CYP24 gene transcription and
activation is strongly activated by 1, 25(OH)2 D (13). Thus, CYP24 induction can be
viewed as a feedback mechanism to control the biological actions of 1, 25(OH)2D.

2.2. Vitamin D Mediated Gene Transcription
Classically, 1, 25(OH)2D alters cell biology by activating the nuclear vitamin D receptor
(nVDR), a member of the steroid hormone receptor superfamily, leading to the induction
of gene transcription (10). The nVDR is expressed in a wide variety of cell types, from
those that are involved in whole body calcium metabolism, i.e. enterocytes, renal tubule
epithelial cells, and osteoblasts, to nontraditional vitamin D target tissues, e.g. immune
cells, epithelial cells (mammary, prostate, colon, lung), pancreatic (β cells, and
adipocytes (14). The biological actions of 1, 25(OH)2 D depend upon the presence and
level of the nVDR. For example, vitamin D-mediated calcium absorption is increased in
nVDR-overexpressing Caco-2 cells (15) and lower in nVDR null mice (16,17) while
nVDR level is an important determinant of the growth inhibition in response to 1,
25(OH)2D in prostate cancer cells (18–21).

The steps leading to vitamin D-mediated gene transcription are summarized in Fig. 1.
Ligand binding promotes heterodimerization of the nVDR with the retinoid X receptor
(RXR) and is required for migration of the RXR-nVDR-ligand complex from the
cytoplasm to the nucleus (22–25) where it then regulates gene transcription by interacting
with specific vitamin D response elements (VDRE) in the promoters of vitamin Dresponsive
genes (14). Although the consensus is that only a direct repeat with a 3 base
spacing (DR3)-type VDRE is functional in vivo (14,26), Makishima et al., (27) recently
found that both 1, 25(OH)2 D and lithocholic acid bind to the nVDR and induces CYP3A
gene transcription through a nontraditional ER6 (everted repeat with a 6 base spacing)
element. This suggests that the promoter elements conferring molecular regulation of
gene expression by 1, 25 (OH)2 D may be more diverse than researchers have
traditionally considered.

FIGURE 1 Steps required for activation of gene transcription by 1, 25(OH)2 D.

Access to VDREs in their chromosomal context may be limited (28) and may require
the release of constraints imposed by chromosomal structure through phosphorylation of
histone H3, acetylation of histones H3 and H4, and SWI/SNF complex-mediated
phosphorylation events (29–31). Protein-protein interactions mediated by the nVDR are
critical for chromosomal unwinding. The nVDR-RXR dimer recruits a complex with
histone acetyl transferase (HAT) activity (e.g., CBP/p300, SRC-1 (32,33)) and the
BAF57 subunit of mammalian SWI/SNF directly interacts with p160 family members
like SRC-1 as well as steroid hormone receptors (34). After chromosomal unwinding, the
nVDR-RXR dimer recruits the mediator D complex (DRIP) and utilizes it to recruit and
activate the basal transcription unit containing RNA polymerase II (35,36). It is known
that the composition of the mediator complex can vary depending upon the anchoring
transcription factor (31). Thus, mediator D complex contains 16 proteins, only 14 of
which are a part of the 18 protein mediator T/S complex involved in thyroid hormone
receptor gene transcription. Further examination of coactivator complexes associated
with nVDR-mediated gene transcription may be warranted. Preliminary evidence from
kerotinocytes indicates that the major anchoring protein in the mediator complex,
DRIP205, is replaced by the the steroid receptor coactivator (SRC) family members
SRC-2 and SRC-3 in differentiated cells (37). Several smaller members of the mediator
complex were still present in the complex. This suggests that there may be cell stagespecific
(and perhaps cell type-specific) differences in the coactivator complexes that
drive vitamin D-mediated gene expression.

2.3. Rapid Actions of 1, 25(OH)2 D
There is now compelling evidence for the existence of 1, 25(OH)2D-inducible signal
transduction pathways within various cell types (38) that includes the rapid (within
seconds and minutes) activation of phospholipase C (PLC), protein kinase C (PKC), and
the MAP kinases JNK and ERK (39–42). Figure 2 summarizes the pathways that have
been shown to be activated through rapid 1, 25(OH)2 D-mediated signaling. While
activation of these pathways by 1, 25(OH)2 D is now generally accepted, it is not clear
whether these actions require the activation of a unique membrane vitamin D receptor, as
suggested by Nemere et al. (43), or whether these rapid actions reflect a unique,
nonnuclear function of the traditional nVDR. Using cells isolated from mice expressing a
mutant nVDR lacking a DNA binding domain, Erben et al. (44) found that rapid calcium
signaling was dependent upon a functioning nVDR. This hypothesis is also supported by
recent work in myocytes, where nVDR binds to, and is a target for src kinase (45) and in
the enterocyte-like cell line, Caco-2, where 1, 25(OH)2 D binding to nVDR induces an
interaction between a ser/thr phosphatase that results in cell cycle arrest (46). In contrast,
Wali et al. (47) found that in osteoblasts from nVDR null mice, rapid increases in
calcium fluxes and PKC translocation did not require the presence of the nVDR.

2.4. Do Rapid and Nuclear Signaling Pathways Interact?
Several studies support the hypothesis that signal transduction pathways are important
regulators of nVDR-mediated gene expression. For example, suppression of PKC activity
with staurosporine or H7 inhibited 1, 25(OH)2 D-regulated 25-hydroxyvitamin D 24-
hydroxylase (CYP24) gene expression in proliferating, small intestine crypt-like, rat IEC-
6 cells (48) and activation of PKC with phorbol esters enhanced 1, 25(OH)2 D-regulated
CYP24 gene transcription in IEC-6 and IEC-18 cells (49). Similar findings have been
observed for 1, 25(OH)2 D-mediated osteocalcin gene expression in the osteoblast-like
ROS 17/2.8 cell (50), CYP24 gene induction in COS-1 cells (51), c-myc activation in
proliferating skeletal muscle (52) and CYP3A4 gene regulation in proliferating Caco-2
cells (53). Specific cross-talk between rapid, membrane initiated vitamin D actions and
nVDR-mediated genomic actions are supported by the observation that an antagonist of
the nongenomic pathway, 1β, 25(OH)2 D, blocks 1α, 25(OH)2 D-mediated osteocalcin
gene transcription in osteoblasts (54).


The application of genomic technology to the study of vitamin D action has been
relatively limited to date. Like most of the work that has been conducted using arrays, the
full power of the technology has not been applied. This is because until very recently,
arrays capable of profiling the entire transcriptome of 30,000–40,000 transcripts did not
exist.The studies that do exist have examined gene expression profiles in both classical
(e.g., bone, kidney, intestine, Caco-2, ROS/17/2.8) cells and nonclassical (e.g., HL-60,
squamous cell carcinoma, B) cells and used a variety of platforms (e.g., filters, spotted
Genomics and proteomics in nutrition 218
cDNA arrays, Affymetrix Genechips), sometimes with a limited number of highly
focused transcripts (e.g., 406 transcripts related to human hematology), and rarely with a
significant transcript target overlap with other platforms. This lack of consistency makes
it very hard to compare the results of one experiment to the next. However, even with this
caveat, the few studies available have been very promising. This section will review the
available array studies on vitamin D action.
4.1. Preliminary Reports in Classical Vitamin D Target Tissues
Surprisingly, a genomic examination of classical vitamin D target tissues is not yet
available as a peer reviewed report. However, several preliminary reports are available,
although caution should be used when interpreting these reports due to the lack of
experimental description (e.g., replicates, validation, statistical analysis, number of genes
represented on array that are present). Henry et al. (63) used the Affymetrix U74B
Genechip (12,000 targets; 6000 named genes) to compare the response of nVDR null
mice and wild-type mice to a single i.p. injection with 1, 25(OH)2 D (250 ng, 8 h). Using
a two-fold cut off, they identified 43 bone transcripts, 20 intestinal transcripts, and 98
kidney transcripts as 1, 25 (OH)2 D regulated in wild-type, but not in nVDR null, mice.
Peng et al. (64) injected vitamin D depleted mice with 1, 25(OH)2 D three times over 48 h
(30 ng per injection at 48, 24, and 6 h prior to the end of the experiment) and examined
the induction of renal transcripts using the U74B chip. They found only 57 genes
increased by 50% or greater and they confirmed vitamin D regulation of two of them,
C/EBP β and FK506. C/EBP β was subsequently shown to be involved in the regulation
of another 1, 25(OH)2 D-inducible gene, CYP24.
Megalin is a protein involved in the renal reabsorption of fat soluble vitamins like
vitamin D; as such, the megalin null mouse is somewhat equivalent to a vitamin D
depleted animal (plasma 1, 25(OH)2 D and 25-OH D are 60% lower in these mice). When
Hilpert et al. (65) examined renal gene expression in megalin knockout mice using the
Affymetrix MullK B chip (6,000 known transcripts), they found that the level of only six
transcripts fell and 13 transcripts increased. Finally, Wood et al. (66) examined gene
expression in the enterocyte-like Caco-2 cells after treatment with 100 nM 1, 25(OH)2 D
for 24 h using the Affymetrix U95A chip (12,000 targets). Using a two-fold cut off, 25
genes were upregulated (including amphiregulin, alkaline phosphatase, carbonic
anhydrase XII, and CYP 24) and five genes were downregulated (including dihydrofolate
reductase and a Ras-like protein). While these preliminary reports are interesting, the
genomic analysis of classical vitamin D target tissues clearly requires additional

4.2. Nonclassical Cells
1, 25(OH)2 D action has been studied in a number of nonclassical cell systems due to its
ability to initiate growth arrest and differentiation—characteristics that may be useful for
the prevention and treatment of cancer. A short report by Savli et al. (67) used the Atlas
hematology spotted filter array (406 genes) to examined the impact of 1, 25(OH)2
treatment (5nM, 24 or 72 h) on HL-60 leukemia cell gene expression. At 24 h 7 transcript
levels were upregulated and 25 transcript levels were downregulated. Twelve of these
Genomic approaches to understanding vitamin D action 219
transcripts were also downregulated at 72 h, including c-myc and 3 other oncogenes,
providing a glimpse into the mechanisms of chemop-revention by 1, 25(OH)2 D.
The most extensive genomic profiling of vitamin D action has been reported in
squamous cell carcinoma cell lines (68, 69). Akutsu et al. (68) found that 24 h of
treatment with 100 nM EB 1089 (a 1, 25(OH)2 D analog that is resistant to 24-
hydroxylation) increased 38 transcript levels (1.5-fold cut off) based on a combined
screening with an Atlas spotted cDNA filter array (588 genes) and a Research Genetics
GF211 spotted cDNA filter array (4000 named genes). This is likely a conservative
estimate due to problems the authors encountered with filter-to-filter, and hybridization
variability (a common problem with spotted filter arrays). Still, this analysis identified
up-regulation of several interesting transcripts that were validated by Northern blot
analysis: gadd45α, a p53 target gene that is involved in DNA repair, components of
various signal transduction pathways like the growth factor amphiregulin and
transcription factors AP-4, STAT3, and fra-1, and cell adhesion proteins like integrin
α7B. Six transcripts continued to be regulated in subsequent experiments even in the
presence of cycloheximide (e.g., p21, amphiregulin, VEGF, fra-1, gadd45α, and integrin
α7B). The mode of vitamin D regulation was not explored.
Lin et al. (69) conducted a time course of response to 100 nM 1, 25(OH)2 D and
EB1089 in squamous cell carcinoma cells (SCC25). Using the Affymetrix FL array and a
2.5-fold cut-off, 152 genes were identified as vitamin D regulated (89 up, 63 down).
Where overlap occurred, the results from Akutsu et al. (68) were validated and a number
of expected changes in transcripts previously shown to be vitamin D regulated were also
seen (e.g., CYP24, osteopontin, TGF β, PTHrp, CD14, VDUP1, carbonic anhy-drase II).
Clustering was done based upon the pattern of expression or the functional classification
of the transcripts. Figure 5 shows the diversity of the vitamin D responses in these cells.
Even within the category of genes with documented, functional VDREs, there was
heterogeneity in the response. For example, the CYP24 transcript level was rapidly
increased (significantly increased in 1 h) and was placed in group 1 (U1) while
osteopontin transcript levels increase more slowly (maximum expression at 12 h). This
suggests that similar DR3-type VDREs are differentially regulated depending upon the
promoter context, a finding that is consistent with studies by Toell (26). Another
interesting finding from this study is that the vitamin D-induced responses were much
more diverse than one might have previously predicted. For example, a number of
transcripts encoding for proteins involved in the protection from oxidative stress were
gradually up-regulated by EB1089 (falling into class U4 and U5); these include glucose 6
phosphate dehydrogenase (NADPH generation), glutathione peroxidase, and
selenoprotein P. In addition, the thioredoxin reductase transcript was increased by 1 h
after treatment with a peak induction by 6 h. Rapid suppression of a transcripts for a
variety of signaling peptides (e.g., PTHrp, galanin) and induction of intracellular cell
signaling proteins (e.g., cox-2, PI3K p85 subunit) was also seen after treatment. It is not
clear which of these responses is primary; none of these genes has previously been shown
to be vitamin D regulated or contain a functional VDRE. However, since 1, 25(OH)2 D
promotes cellular differentiation, the up regulation of some transcripts may represent a
vitamin D-induced shift to a more differentiated phenotype. Regardless, these data
suggest that the traditional approach of examining cell cycle proteins alone provides only
a limited

Author: James C.Fleet
Purdue University, West Lafayette, Indiana, U.S.A.

Regulation of Fat Synthesis and Adipogenesis


Adipocytes are highly specialized cells that play a crucial role in the energy balance of
most vertebrates. Adipocytes convert excess energy to triacylglycerol and deposit it
during feeding in preparation for periods of food deprivation when energy intake is low.
Adipocytes may become enlarged by increased fat storage. Moreover, precursor cells
present in the stromal vascular fraction of adipose tissue can differentiate into adipocytes
even in mature animals.These two processes, fat synthesis and adipogenesis, are under
tight hormonal and nutritional control. In this review, we have summarized our work on
the regulation of fat synthesis. We have focused specifically on the transcriptional
activation of the fatty acid synthase (FAS) gene and on the inhibitory role of two
secretory factors, preadipocyte-specific preadipocyte factor-1 (Pref-1) and adipose tissuespecific
adipocyte differentiation-specific factor (ADSF), in adipose differentiation.


2.1. Nutritional and Hormonal Regulation of Lipogenic Enzymes
Fatty acid and triacylglycerol synthesis is regulated in response to the
nutritional/hormonal state in animals. Subjecting rodents to fasting causes a decrease in
lipogenesis; when fasted animals are subsequently refed a diet high in carbohydrate and
low in fat, there is a prompt and drastic rise in the production of fatty acids and
triacylglycerol to levels well above those observed in normally fed rats. Under lipogenic
conditions, excess glucose is converted to acetyl-CoA, which is used for the synthesis of
long-chain fatty acids. By the action of its seven active sites, fatty and synthase (FAS)
catalyzes all of the reaction steps in the conversion of acetyl-CoA and malonyl-CoA to
palmitate. The fatty acids produced are then used for esterification of glycerol-3-
phosphate to generate triacylglycerol. Mitochondrial glycerol-3-phosphate
acyltransferase (GPAT) catalyzes the first committed step in glycerophospholipid
biosynthesis by catalyzing acylation of glycerol-3-phosphate using fatty acyl-CoA to
generate l-acylglycerol-3-phosphate. The concentrations of many of the key enzymes in
this pathway, including FAS and mitochondrial GPAT, are decreased during fasting and
subsequently “superinduced” during the refeeding period. Induction of these enzymes is
highly coordinated and these inducible genes may be regulated via common mechanisms

It is generally accepted that insulin in the circulation, along with glucose, is elevated
during feeding of a high carbohydrate diet and induces enzymes involved in fatty acid
and triacylglycerol synthesis. Glucagon, on the other hand, is elevated during starvation
and suppresses activities of enzymes in fatty acid and fat synthesis by increasing
intracellular cyclic adenosine monophosphate (cAMP). In our early studies, we showed
that transcription of the FAS and mitochondrial GPAT genes increased when previously
fasted mice were refed a high carbohydrate diet (2, 3). There was no detectable
transcription of FAS or mitochondrial GPAT genes in fasted or fasted-refed
streptozotocin-diabetic mice, indicating that insulin is required for transcriptional
induction by fasting/refeeding. Administration of cAMP at the start of feeding in normal
mice prevented an increase in the transcription of these genes by feeding. Furthermore,
there was a rapid and marked increase in the transcription rates of the FAS and GPAT
genes when insulin was given to diabetic mice (2, 3). Overall, these genes are regulated at
the transcriptional level by nutritional and hormonal stimuli. The molecular mechanisms
underlying transcriptional regulation of these genes need to be elucidated.

2.2. Regulation of Fatty Acid Synthase Gene Transcription
To study the molecular mechanisms by which lipogenic enzymes such as FAS and
GPATare regulated, we employed 3T3-L1 adipocytes in culture. These cells provide a
good model system for studying lipogenic gene transcription since these genes are highly
induced during the differentiation process and are sensitive to hormones. In these cells
the regulation of FAS and GPAT mimics regulation in vivo (2). We identified an E-box
motif (5′-CATGTG-3′) at position-65 that is a binding site for upstream stimulating factor
(USF) (4), a ubiquitous member of the bHLH leucine zipper family of transcription
factors implicated in glucose control of L-type pyruvate kinase gene transcription (5).
Both USF-1 and USF-2 occupy the-65 complex (4); dominant negative mutants of USF-1
and USF-2 inhibited insulin stimulation of the FAS promoter (6), demonstrating that
these proteins are required for insulin stimulation of FAS gene transcription. We also
found that insulin regulation of the FAS promoter occurs via the PI3-kinase/Akt pathway

Another transcription factor found to play a key role in FAS gene transcription is
sterol regulatory element binding protein-1 (SREBP-1). This protein recognizes a sterol
regulatory element (SRE) (5′-TCACNCCAC-3′) sequence (8–12), but can also bind to Eboxes
due to the presence of an atypical tyrosine residue in the DNA-binding domain
(13). A major role for SREBP in transcriptional regulation of FAS was first suggested
when Goldstein and Brown demonstrated that overexpression of the truncated active
form of SREBP-1 in liver causes a large accumulation of triacylglycerol and the
induction of a battery of lipogenic genes including FAS and mitochondrial GPAT (14).
Others have shown that induction of FAS and other lipogenic enzymes by
fasting/refeeding is severely impaired in SREBP-1 knockout mice (15). It has also been
shown that SREBP-1c, one of two isoforms of SREBP-1, is highly induced by refeeding
a carbohydrate-enriched diet and that SREBP can transactivate the FAS promoter by binding to the -65 E-box (16). However, as described earlier, our in vitro data strongly
indicated to us that the critical factor functioning through the-65 E-box was USF, not
SREBP (Fig. 1A). In addition, we identified a canonical SRE, at150 (5′-ATCACCCCAC-
3′) in the FAS promoter suggesting a potential role of SREBP in regulation of the FAS
gene (17). To determine whether SREBP functions through this site, we cotransfected a
truncated active form of SREBP-1a into 3T3-L1 cells along with various FAS-LUC
reporter plasmids (17)FAS-luciferase(FASLUC).

InductionoftheFASreportergenebySREBP in vitro was reduced when sequences
between −136 and −19 were deleted (not shown), suggesting the presence of a binding
site for SREBP in this region, probably the −65 E-Box. However, when binding of
SREBP to the −65 E-box was prevented by mutation, deletion of sequences between
−184 and −136 abolished transactivation by SREBP-1 (Fig. 1B), indicating the presence
of an SREBP-responsive element in this region. In vitro, SREBP may activate and
increase FAS promoter activity if any single putative SREBP binding site is present,
regardless of its true physiological relevance. In support of this, only when both the −150
SRE and −65 E-box were mutated was trans-activation of the FAS promoter prevented by
SREBP in vitro (Fig. 1C).
FIGURE 1 Constructs of FAS promoter and localization of the FAS promoter region mediating FAS transactivation by SREBP-1a. (A) Schematic of putative USF and SREBP binding site in the FAS promoter. The diagram represents the proximal 444 bp of the FAS promoter. Genomics and proteomics in nutrition 80(B) Localization of the FAS promoter region mediating FAS transactivation
by SREBP-1a. Five micrgrams of −2100, −2100 (−65), −444 (−65), − 184, and −136 (−65) FAS-LUC plasmids were cotransfected with 25 ng of an expression vector for SREBP- 1a into 3T3-L1 fibroblasts. The values represent the mean± standard deviation. (C) Role of the −150 SRE in
activation of the FAS promoter by SREBP Five micrograms of each of the indicated constructs containing −444 bp of the 5′-flanking sequence of the FAS gene bearing mutations at the indicated positions were cotransfected with 25 ng of an expression vector for SREBP-1a into 3T3-L1 fibroblasts.The values represent the mean ± standard deviation.

To examine regulation of the FAS promoter in a physiological context in vivo, we
generated transgenic mice carrying the 2.1-kb 5′-flanking promoter region of the rat FAS
gene fused to the chloramphenicol acetyltransferase (CAT) reporter gene (18). The
transgene was expressed strongly only in lipo-genic tissues, liver, and white adipose
tissue, and was drastically induced by feeding and insulin. Overall, the studies from these
transgenic mice demonstrated that the first 2.1-kb 5′-flanking sequence of the FAS gene
is sufficient for tissue-specific and hormonal/nutritional regulation. To further define the
FAS promoter sequences required for transcriptional activation by nutrients and
hormones in vivo, we generated several additional lines of transgenic mice (19), each
carrying different 5′-deletion constructs: −644, −444, −278, and −131 FAS-CAT (Fig.
2A). As shown in Fig. 2B, both the −644 and −444 constructs behaved in a manner
similar to that in the −2.1 kb transgenic mice, indicating that the region between −444
and −2.1 kb does not contain any sequences necessary for activation of FAS by fasting/
refeeding. However, when the same experiment was conducted on −278 FAS-CAT
transgenic mice, the induction of CAT, although detectable, was severely decreased. This
indicates that the region between −444 and −278 contains one or more elements required
for transcriptional activation of FAS. Furthermore, no CAT expression was detectable in
fasted/refed −131 FAS-CAT transgenic mice, indicating that the region between −278
and −131 contains additional element(s) required for basal transcription of FAS. Similar
results were obtained upon insulin administration to streptozotocin diabetic transgenic
mice. We concluded that two major regions of the FAS promoter are required for
transcriptional activation by refeeding/insulin: one between −278 and −131, which we

showed to be required for low-level activation of the promoter, and a second region
between −278 and −444, required for maximal “superinduction” of the gene. We also
showed through gel shift assays that a second E-box present at position −332 is a binding
site for USF-1 in vitro, and that this site may play an important role for the high-level
induction of FAS that is observed with feeding/insulin (19).
To determine the regions in the FAS promoter through which SREBP-1 functions in
vivo, we employed mice transgenic for the truncated, nuclear-active form of human
SREBP-1a (amino acids 1–460) under the control of the PEPCK promoter (14, 17). In
these mice, the transgenic SREBP-1a is induced by fasting and repressed by refeeding,
whereas endogenous

SREBP-1c is absent in the fasted state but induced by refeeding (17). The level of hepatic
FAS mRNA was found to be high whether these animals were fasted or refed (Fig. 2B).
We mated our five 5′-deletion FAS-CAT transgenic mice to SREBP transgenic mice and
subjected them to fasting/refeeding. As with the single FAS-CAT transgenic mice
described earlier, the −644 and −444 kb double transgenic mice both showed high CAT
expression in the refed state (Fig. 2B). Notably, CAT expression was also high in the
fasted state in all three constructs, indicating that the region between −2100 and −444
does not contain the putative site(s) for binding and function of SREBP-1. However, in
the −278 FAS-CAT transgenic mice, CAT induction was reduced whether the animals
were fasted or refed, and was completely absent in the −131 FAS-CAT double transgenic
mice, suggesting that the region between −131 and −278 contains at least one element
responsible for induction of FAS by SREBP. An increase in SREBP and its binding to the
− 150 SRE may be the major limiting mechanism for activation of FAS gene
transcription by fasting/refeeding in vivo.

2.3. Fatty Acid Synthase Promoter Occupancy and Function of USF and SREBP In Vivo
Our in vitro experiments clearly established the importance of the cis-acting elements in
the proximal FAS promoter required for insulin regulation in vitro. A critical remaining
question was whether the −150 SRE and −65 E-box each were required elements in vivo;
in our in vitro experiments, mutation of the −65 E-box in the context of the largest
−2.1kb promoter prevented induction by insulin (6), but had no effect on activation by
cotransfected SREBP-1 (17). To address these questions, we introduced mutations into
the −150 SRE and −65 E-box in the context of the −444 FAS-CAT transgenic construct
(20). We chose the −444 promoter fragment for these experiments as it was the shortest
5′-deletion construct that conferred maximal expression of CAT. As shown in Fig. 3A, no
expression of CAT was detected in three transgenic lines carrying a mutation at the −150
SRE, indicating that this element indeed is required for induction in vivo. Similar results
were obtained when the −444 (−65 mut) mice were fasted and refed: no significant
expression of CAT was detected, whereas the control −444 FAS-CAT mice showed a
strong induction by refeeding, and endogenous FAS expression was high in all mice (Fig.
3B). Moreover, CAT expression was still not detected when −444 (−150 mut) FASCAT/
PEPCK-SREBP-1 double transgenic mice were fasted or refed, strongly suggesting
that SREBP-1 functions directly through the −150 SRE in vivo. We concluded that both
the −150 SRE and −65 E-boxes are required for induction by fasting/refeeding.

Author: Kee-Hong Kim, Michael J.Griffin, Josep A. Villena, and Hei Sook Sul
Department of Nutritional Sciences and Toxicology, University of
California, Berkeley, California, U.S.A.

The Human Sweet Tooth and Its Relationship to Obesity

source: http://3.bp.blogspot.com/


The term “sweet tooth” has been used widely in both popular culture and in the scientific
literature. But what is meant by the term sweet tooth and how do we measure it? When
we say that a person has a sweet tooth, we may be thinking of a person who usually
prefers to eat a sweet food or beverage rather than one that is savory or salty. Or we
might assume that the sweeter a food or beverage is, the more someone with a sweet
tooth will prefer it. Because to like sweet foods is seen as a prerequisite to eating too
much, the study of the human sweet tooth has usually been undertaken with the goal of
understanding how the perception of or preference for sweet foods contributes to
overeating and obesity. But the underlying assumptions of this hypothesis—that
increased perception of and preference and desire for sugar leads to increased intake of
sweet food and drinks—is rarely directly tested.
This review is divided into two sections. In the first section, we assess the ways in
which human behavior toward sweets is measured, and the factors that influence it. In the
second section, we examine the relationship between the preference for sweet foods, their
intake, and the effect on obesity.

2.1. Sensation, Behavior, or Desire?
Sweet is one of the five primary taste qualities, and there are several measures of human
perception of sweetness. The lowest concentration at which someone can detect sugar or
recognize its sweet quality can be measured. The terms for these measures are detection
and recognition thresholds; the detection threshold usually occurs at lower concentrations
than recognition because subjects can tell that there is something in a solution before they
can identify its quality (1). A second measure of sweetness is how intense abovethreshold
concentrations of sweetness are perceived to be. For instance, some people may
find the sweetness of a commercially available carbonated beverage to be “very strong”
but another person might find it to be “weak”. This concept is referred to as perceived
intensity. The next measure is “liking”—defined as the degree to which the person
perceives it as acceptable and desirable when presented with a single stimulus. This
measure is sometimes also referred to as “acceptability” Sometimes people have a choice
among stimuli and choose the one that is the most acceptable or desirable. These types of
measures are referred to as “preference”. When the degree of the desire to eat a sweet
food or drink is measured, this is referred to as craving. A final and important measure of
human behavior toward sweetness is the amount of sugar someone eats when offered a
choice of foods or drinks, either in the laboratory or in their daily lives.
There is no agreement within the experimental literature upon a definition of sweet
tooth. Sometimes it is assessed using measures of liking or preference (2–9), sometimes
by measures of food intake or food selection, and sometimes by measures of the
motivation to eat sweet foods (10, 11). Most laboratory measures designed to assess
sweet tooth use preference measures rather than measures of food intake or desire and
motivation. Food intake and food selection outside of the laboratory are hard to measure
accurately because of the disinclination of subjects to correctly report the food they eat.
Therefore, proxy indices of sugar intake—such as the number of dental caries or the
amount of oral bacteria per subject—are sometimes substituted as measures to
circumvent report bias (12). Also, asking specific questions about sugar usage, for
instance on cereal or in coffee, may elicit accurate responses regarding sugar intake and
preference (4, 13).
Perhaps one reason that preference is most often measured in human studies is because
these methods detect reliable individual differences among subjects (14). Preference
measures are also desirable because people can be classified into categories. For instance,
some investigators have identified two different response patterns to sucrose solutions, a
type I response whereby subjects increase in the liking for sucrose up to a middle range
of concentration, followed by a breakpoint after which preference decreases with
increasing concentration. This pattern is referred to as an inverted-U shape. The type II
response is characterized by increased liking as the concentration increases, but levels off
(15). Other investigators have reported similar patterns among subjects (5).
Although laboratory measures of sweet preference are commonly used, they may not
predict the preference for other sweeteners (16) or the preference for sweet foods or
beverages. Investigators have tried to bridge the gap between preference measures for
laboratory stimuli and preference measures for real-world foods and drinks by using
The human sweet tooth and its relationship to obesity 45
mixtures of sugar and milk (17, 18) or by adding sugar to simple beverages or foods (9).
Finally, some investigators have compared sweet preference measures inside the
laboratory to self-reported behaviors outside of the laboratory (8, 13).
Human behavior toward sweet may be affected by the degree to which the subjects
can perceive the stimuli. There are individual differences in the detection or recognition
thresholds for sweetness (19, 20), and although rare, there are people who do not perceive
a sweet taste from sucrose (21). Therefore, when measuring preference for sucrose at low
concentrations, it is important to consider that some people will not be able to perceive
the stimulus as well as other people. Thus far in human studies, sweet detection threshold
does not predict either how intense higher concentrations are perceived or how much they
are liked (22–25). Although in mice there is a relationship between peripheral sensitivity
and intake of sweeteners (26), this relationship in humans is unclear, and more focused
study is needed.

2.2. Stable and Variable Aspects of Sweet Taste Perception
Individual differences in the response to sweet are present at birth, with some infants
responding more positively than others to the taste of sucrose (27), and these individual
differences persist as children become young adults (14). However, the same people
measured on two occasions, weeks or months apart, have similar but not identical sweet
preference, suggesting that sweet preference changes over the short term (3, 7, 28).
There are effects of race and sex on sweet preference. Americans of African descent
prefer higher concentrations and Pima Indians prefer lower concentrations of sugar
compared with those of European ancestry (7, 13, 29–33). However, race differences in
sweet preference may be specific to types of foods. For instance,Taiwanese students rate
sucrose solutions as more pleasant but sweetened cookies as less pleasant compared with
students of European descent (34). Studies of sex differences suggest that male and
female infants do not differ in sweet preference (29) but that older boys and men prefer
higher concentrations of sweets compared with women (7, 11, 31, 35, 36). Although men
prefer high concentrations of sweet in their food and drink, studies of food craving in
men show they experience less desire to eat sweet foods compared with women (37, 38).
Sex differences in food craving may be population-specific, however, since women in
Egypt did not show elevations in sweet food craving compared with men (38). Week-toweek
variations in sex hormone concentrations in women predict changes in sucrose
threshold (39) but with equivocal effects on sucrose preference (40, 41), and it is not
clear to what extent sex hormones account for sex differences in human behavior toward
In addition to race and sex, age is also a reliable predictor of sweet preference.
Children prefer more highly sweetened solutions compared with adults (31, 35, 42) but
see (36). Children may also have lower detection thresholds (23) and lower perceived
intensity at high-sucrose concentrations (43) compared with adults, but not all studies
agree (35, 36, 44). Younger people also eat more sugar than do older people (45). Dietary
experience alters sweet preference in children; for instance, children fed sweet water like
it more than children not fed sweet water (29). Children are less afraid of sugar than other
nutrients and even neophobic children will accept sweets (46). Sweet craving changes
Genomics and proteomics in nutrition 46
over the life span, and older women report less craving for sweet food compared with
younger women (47).
An immediate but short-lived reduction in the preference for sweet-tasting solutions
can be produced by ingesting a sweet solution (48). The reduction of sugar preference
immediately after the ingestion of sweet solutions may extrapolate to situations outside
the laboratory, such as after a meal. This effect, when measured in the laboratory, is more
pronounced in people who are chronic dieters (49, 50), is not observed in obese subjects
(51), and is influenced by the menstrual cycle (52, 53).

2.3. Genes and Genetics
Because family and twin studies have shown modest heritability for sweet intake, sweet
perception or preference may be partially due to genetic variation (54). Most studies of
sweet preference use sugar or carbohydrate intake as a measure of preference and as
measures of food intake collected through diaries. Family and twin studies using other
measures of sweet perception and preference are needed to assess more specifically the
degree to which these phenotypes are heritable. In considering how and where genetic
differences may influence the human behavior toward sweetness, we now discuss recent
advances in our understanding of sweet taste biology.
The initial events in the perception of sweet taste occur in taste receptor cells in the
tongue, which are found clustered in taste buds in taste papillae. The perception of
sweetness intensity is related to the number of papillae (55). The number of taste papillae
and taste buds varies widely in humans, and these differences among people may be due
to alleles in genes that develop and maintain sensory cells. For at least one genetic
disorder (familial dysautonomia), mutations in a single gene (IKBKAP) (56, 57) are
associated with few or no taste papillae and taste buds (58). It is possible that less
harmful alleles of this gene may influence the density of taste buds in otherwise healthy
Inside the taste papillae, taste receptor cells produce proteins that participate in sweet
taste transduction, and some of these proteins are inserted into the cell membrane to form
taste receptors. Two proteins twist together to create a sweet receptor (Fig. 1) (59, 60).
The names of these proteins are T1R2 and T1R3, for taste receptor family 1, proteins 2
and 3, and the names of the associated genes for these proteins are Tas1r2 and Tas1r3. If
T1R3 pairs with the first member of this family,T1R1, the receptor is sensitive to umami,
the taste quality of monosodium glutamate and an important flavor principle of Asian
These sweet and umami receptor genes were discovered through mapping experiments
in mice. Inbred mouse strains differ in their intake of saccharin, and the results of
breeding experiments suggested that an allele of a single gene was partially responsible
for these differences (61). Through positional cloning approaches, this gene was identified and found to be the gene Tas1r3 (60, 62–66). An important advance in our understanding of the behavior of animals toward sweetness was the observation that small changes in the DNA sequence of the
mouse Tas1r3 gene lead to large differences in the consumption of sweetener (67). This
reduction of sweetener preference by mice with certain Tas1r3 alleles is probably due to
their reduced ability to perceive the intensity of the sweeteners. Recordings of their
peripheral taste nerves suggest that mice with the low-preference Tas1r3 alleles exhibit
lower nerve firing in response to saccharin (26). Furthermore, when the Tas1r3 gene is
eliminated by genetic engineering in mice, the peripheral nerve firing is reduced in
response to sweeteners (68).

FIGURE 1 Representation of a human taste bud and taste receptors cells. T1R2 and T1R3 co-localize (and probably dimerize) to create a receptor for sweet stimuli. The receptors are embedded in the apical membrane of the taste receptor cell and stimulate G proteins to initiate a transduction signal inside the cell. Genetic variation in theTas1r3 gene (which codes for T1R3 protein) accounts for differences in sweet intake of mice.

he pairing of T1R2 and T1R3 does not constitute the only receptor for all sweeteners,
however. When the Tas1r3 gene is knocked out in mice, their ability to detect glucose
and maltose is unaffected compared with mice with a normal Tas1r3 gene (68).
Furthermore, the ability to detect other sugars and high-intensity sweeteners is reduced in
Tas1r3 knockout mice, but not absent.Therefore, other receptors or mechanisms exist that
signal sweetness in mice, for instance, the remaining partner (T1R2) could act as a taste
receptor by itself (69).
If DNA sequence variants have a large effect on the intake of saccharin and other
sweeteners in mice, then this may also be true in humans. There is a human counterpart to
each of the mouse sweet receptor genes (TAS1R1, TAS1R2, and TAS1R3*) (70). Because
the peripheral neural responses of humans to sugars predict their verbal reports about the
taste of sugars (71), peripheral differences in taste sensitivity may be an important
component of the human behavior toward sweetness. There is more variation than
appreciated in human perception of sweeteners, and one investigator has even suggested
that there is a “different receptor site for each subject” (72) or, in other words, each
person may perceive sugars slightly differently. Although the differences in the ability to
perceive sweet stimuli has been thought to be of little consequence in human sweet intake
and preference, the relationship in mice may stimulate further study of this topic.
Sweet preference may be influenced by genetic variants in the sensory system in
humans as it is in mice. However, the appreciation of sweet and the pleasure that it brings
to some people may be due to differences in the degree to which they have learned about
its rewarding properties. The genes and genetics involved in the perception of the
pleasure associated with sugar are not known, but several observations provide clues
about which mechanisms may be involved. Sweet preference is increased in opioid
addicts compared with healthy subjects (73), and the opioid antagonist naloxone reduces
the pleasantness of sucrose (74). Studies suggest that the rewarding aspects of alcohol
and sweeteners may also share brain pathways, because alcoholic subjects and their
family members may prefer sweeter solutions compared with nonalcoholic subjects (6,
75). Therefore, the investigation of genes that participate in the shared brain pathways
responsible for the pleasurable effects of drugs and sweeteners is warranted.

People assume that because increases in sugar consumption in the human diet are
associated with a proportional rise in obesity, eating sugar and foods that are sweet is the
cause. More specifically, people often hypothesize that if someone has a sweet tooth, it
will cause the person to eat sweet food in excess of his or her caloric needs and
consequently gain weight. In other words, the sweet tooth is the cause and obesity is the
effect. However, an alternative hypothesis is that obesity, per se, may change sweet
* The protein name for each of the three receptors has the same name in mice and humans (T1R1,
T1R2, and T1R3). However, the gene names in the mice (Taslr1, Taslr2, and Taslr3) are lowercase
and italic whereas the human gene symbols are in uppercase and italic: TAS1R1, TAS1R2, TAS1R3.
The human sweet tooth and its relationship to obesity 49
and that metabolism and taste may participate in a feedback loop. Pathways that could
influence sweet preferences and contribute to these loops are shown in Fig. 2.
3.1. Do Obese People Have Different Behavior Toward Sweet Food
than Lean People?
Most studies have compared lean and obese subjects for the preference or liking of sweet
stimuli, usually sucrose solutions, or have compared lean and obese subjects for their
intake of sweet foods in the laboratory. These studies have produced mixed results: In
some studies, lean people prefer sweet food or drinks more than do obese people (76–80),
and in one study the reverse was observed (36). However, the most common observation
is that there is no difference in sweet preference between lean and obese people (2, 31,
81– 87). Outside of the laboratory, when food intake is measured in situations where
people choose their own meals, most studies demonstrate that lean subjects eat more of
their calories as sugar compared with obese subjects (88).
Based upon these data, it would appear that there is little evidence that obese people
prefer sweets or eat more sweet food and drink compared with lean people. However,
there are three points that are important to consider before drawing this conclusion. First,
because subjects can and do restrain their intake of foods, especially sweets, when they
are dieting or trying to avoid gaining weight, food intake outside the laboratory may not
correspond with sweet preference (i.e., subjects may choose to not eat their most
preferred foods.) Second, food intake as reported by subjects can be biased, and when
proxy measures of sweet intake such as oral bacteria associated with sucrose
consumption are measured, obese women have higher indices of sweet consumption
compared with lean women (89). Third, none of these studies measures people before
they become obese and therefore does not directly test the hypothesis that a subject’s
behavior toward sweet food and drink is a factor in the development of obesity.
Once someone becomes obese, the preference for sweet may change because of a shift
in the homeostatic mechanisms and feedback loops that regulate hunger and satiety (Fig.
2). To try to understand the behavior of the obese subject in the absence of obesity,
investigators have studied formerly obese people who have reduced their weight and are
no longer obese. These subjects demonstrate a heightened preference for sugar when it is
mixed with high concentrations of fat (18). In another study, diabetic patients measured
during weight loss preferred lower concentrations of sweetness compared to the
preferences before weight loss (90). It is unclear what effect weight loss alone has on
sweet preference, and whether changes in preference after weight loss reflect the
preferences subjects had prior to
becoming obese. Lean people, who restrict their food intake, however, such as ballerinas
and patients with anorexia nervosa, vary in their sweet preference (91–93). There is no
consistent change in sweet preference when people restrict their food intake, regardless of
their starting weight.

3.2. Metabolic Effects of Sugar
For diets with the same caloric content, the macronutrient composition affects the balance
of nutrients stored or burned for energy.When excess calories are eaten as sugar, then
insulin secretion and other endocrine changes convert the excess calories to glycogen and
the body may also increase its overall metabolism temporarily to burn the excess calories.
This process of glycogen storage and increased carbohydrate oxidation avoids the
comparatively costly conversion of carbohydrate to stored lipids. Excess dietary fat,
however, is stored as triglyceride in adipose tissue and is less readily oxidized compared
with glycogen (94).
Extrapolating from this observation, humans who consume calories from sugar should
be leaner than those who consume an equivalent number of calories from dietary fat (88).
In fact, in a rodent study, substituting sucrose for other macronutrients led to a higher rate
The human sweet tooth and its relationship to obesity 51
of metabolism, a lower overall caloric intake, and less body fat compared with a
comparable diet without sugar (95). Consistent with this hypothesis, human patients who
ate a higher proportion of their calories as sugar lost more weight after gastric surgery
compared with those who ate less sugar (96). However, when subjects are asked to add
sugared drinks to their diet, they gain weight (97). In other words, when liquid sugar is
added to the diet, there is poor caloric compensation and subjects gain weight, but when
sugar is added as a solid food (jelly beans), then subjects appear to compensate for the
added calories and do not gain weight (98). The metabolic consequences of eating sugar
would encourage leanness rather than obesity if sugar is replacing calories from other
sources, but not if sugar is added to an already adequate diet. The composition of the
calories (liquid or solid) from sugar might be important in determining whether subjects
will reduce their calories from other sources.

3.3. The Pleasure of Sweet
Sugar is a fuel that provides calories, but it is also a pleasure that is rewarding in the
absence of any other benefit. The pleasure of sweetness soothes crying infants (99–104).
The effects of sugar are partially due to its taste because, although oral sucrose reduces
pain in babies, sugar placed directly into the stomach does not (105). Sugar is soothing to
adults as well as babies. Investigators examined the intake of sweet foods in women and
noted a higher intake of sweets both during the menstrual cycle and in those with more
incidences of psychiatric problems (12). Sweets may alleviate depression and
premenstrual symptoms, and provide relief from the cravings for other drugs because
sweet taste releases opiates into the blood, at least in rodents (106). Human babies
exposed to the distress of cocaine withdrawal suck sweet pacifiers more than do babies
without prior cocaine exposure (107). In addition to the release of opiates, the ability of
sugar to bring pleasure is caused by changes in the neural circuits in specific brain areas
(108, 109). People may differ in their ability to perceive pleasure from sucrose because of
individual differences in these neural circuits. People who derive a greater than average
pleasure from sucrose and who have a greater than average amount of distress may gain
weight if they eat sugar to soothe themselves and do not reduce calories from other

3.4. Insulin and Leptin
Sweetness in food and drink provides a signal of the number of calories available in the
form of readily digested sugar. Therefore we might expect that sweet taste sensitivity
would change in the face of the metabolic need for glucose. This has proved to be the
case. When metabolic changes occur that reduce glucose availability, such as increases in
plasma insulin concentration, then sweet preference increases (110–112). A similar
response is seen in diabetic animals with high levels of plasma glucose but limited ability
to utilize it because of insulin resistance. This effect, however, may only occur during
dire metabolic states, because moderate levels of hunger (and the concomitant metabolic
consequences of normal food deprivation) do not influence the preference for sweet
solutions (113).

Genomics and proteomics in nutrition 52
In addition to hormones such as insulin that regulate immediate glucose availability,
other hormones regulate long-term energy stores. Investigators have proposed that the
body has a regulatory mechanism that maintains weight at or near a set point, and that
obesity ensues either because people have a high set point or because the set point is
overridden by increased caloric consumption (114, 115). A fall below set point increases
appetite and may increase the preference for energy-dense foods such as sweets and fats
(116, 117). One hormone proposed to provide this signal of long-term energy stores is
leptin. Leptin is secreted by adipose tissue and acts as a signal to the brain to indicate
high or low energy reserves. Receptors for leptin are located in the brain as well as in
other peripheral tissues (118).
Mice with mutations of the leptin receptor have a higher behavioral and neural
response to sugars compared with littermates without mutations, which suggests that
leptin might suppress the peripheral sweet taste system (119). Evidence in support of this
hypothesis comes from the observation that leptin receptors are present on taste receptor
cells in mice, and the administration of exogenous leptin acts directly to suppress the
neuronal activation to sweet—but not salty, sour, or bitter—stimuli (120). Obese mice
that lack a functional leptin receptor (db/db) do not reduce their consumption of sweet
solutions after leptin administration, but their lean littermates, which have normal leptin
receptors, do reduce their consumption (121).
Although exogenous administration of leptin reduces the neural response to sweet in
mice with a functioning leptin receptor, insulin resistance, inability to utilize plasma
glucose, and leptin resistance induced by prolonged obesity or diabetes may override the
normal ability of leptin to reduce the cellular response to sweet taste (122). In obese and
diabetic animals, the increase in plasma leptin concentration does not appear to have an
effect on the neural response to sweet.
To extrapolate from these studies in rodents to human behavior should be approached
cautiously. The only study performed on humans to date found that the plasma leptin
concentration of obese women was not correlated with sucrose preference (123).
However, as demonstrated earlier, in humans, indices of the perceived intensity of a
sucrose concentration do not necessarily correspond to how much that sweet
concentration will be liked. Thus, future studies in humans may examine how the
perceived intensity of a sucrose solution correlates with plasma leptin concentration, and
if leptin is shown to have a direct effect on human taste receptor cell function, then
manipulation of plasma leptin concentrations and the measure of sucrose perception
would be a logical next step for human studies.

3.5. Digestion
Some people are born with an impaired ability to digest specific sugars, such as lactose or
fructose. As a consequence of their inability to digest the sugar, they often do not wish to
eat it and find it repugnant (124, 125). Similarly, there may be cases in which sugar is
more easily digested than other nutrients and therefore is more desired. One such
example of this situation is the high sugar intake of patients with Crohn’s disease (126).
One hypothesis is that sweet preferences and aversions may be learned responses that
depend upon the punishing or rewarding properties of sugar ingestion. In healthy people,
the ability to digest sugars varies from person to person, and this normal variation may
The human sweet tooth and its relationship to obesity 53
affect sweet preference through learning. Differences in the degree of digestive tolerance
for sugars are correlated with geography and genotype. For instance, there are
geographical differences in the ability to digest lactose that reflect the degree of dairy
farming in a region. Therefore, differences in the efficacy of digestive enzymes by
geography and traditional diet may partially account for racial differences in the
preference for sugar (127). Studies designed to assay differences in the digestion of sugar
and its impact on the human sweet tooth in otherwise healthy subjects might prove

Understanding human behavior toward sweetness and its influence on body weight
requires further study. Longitudinal studies of people before they become obese are
needed to assess the effects of sweet preference on body weight. Experimental results in
mice have taught us two things: Sweet tooth is partially explained by differences in the
DNA sequence of taste receptor genes, and the hormone leptin has a direct effect on taste
receptor cells. Changes in sweet preference may be part of the homeostatic mechanism
that regulates body weight in humans and is worthy of further study.

Author: Amanda H.McDaniel and Danielle R.Reed
Monell Chemical Senses Center, Philadelphia, Pennsylvania, U.S.A.