Current Pharmaceutical Biotechnology, 2006, 7, 000-000
1389-2010/06 $50.00+.00 © 2006 Bentham Science Publishers Ltd.
High-Throughput Functional Genomic Methods to Analyze the Effects of
László G. Puskás*, Dalma Ménesi, Liliána Z. Fehér, Klára Kitajka
Laboratory for Functional Genomics, Biological Research Center of the Hungarian Academy of Sciences, Temesvari
krt. 62. Szeged, H-6726, Hungary
Abstract: The applications of ’omics’ (genomics, transcriptomics, proteomics and metabolomics) technologies in
nutritional studies have opened new possibilities to understand the effects and the action of different diets both in healthy
and diseased states and help to define personalized diets and to develop new drugs that revert or prevent the negative
dietary effects. Several single nucleotide polymorphisms have already been investigated for potential gene-diet
interactions in the response to different lipid diets. It is also well-known that besides the known cellular effects of lipid nu-
trition, dietary lipids influence gene expression in a tissue, concentration and age-dependent manner. Protein expression
and post-translational changes due to different diets have been reported as well. To understand the molecular basis of the
effects and roles of dietary lipids high-throughput functional genomic methods such as DNA- or protein microarrays,
high-throughput NMR and mass spectrometry are needed to assess the changes in a global way at the genome, at the
transcriptome, at the proteome and at the metabolome level. The present review will focus on different high-throughput
technologies from the aspects of assessing the effects of dietary fatty acids including cholesterol and polyunsaturated fatty
acids. Several genes were identified that exhibited altered expression in response to fish-oil treatment of human lung
cancer cells, including protein kinase C, natriuretic peptide receptor-A, PKNbeta, interleukin-1 receptor associated kinase-
1 (IRAK-1) and diacylglycerol kinase genes by using high-throughput quantitative real-time PCR. Other results will also
be mentioned obtained from cholesterol and polyunsaturated fatty acid fed animals by using DNA- and protein
mics and metabolomics technologies in nutritional studies
have opened new possibilities to understand the effects and
the action of different diets both in healthy and diseased
The applications of genomics, transcriptomics, proteo-
saturated fatty acids and cholesterol has impact on the risk of
heart disease, hypertension, Alzheimer’s disease, cancer,
metabolic syndromes, psychiatric diseases and these lipids
can influence the immune system and are required for
normal fetal brain and visual development [4-11].
Dietary intake of saturated, unsaturated and polyun-
put technologies from the aspects of assessing the effects of
dietary fatty acids including cholesterol and polyunsaturated
fatty acids (PUFA).
The present review will focus on different high-through-
interactions between single nucleotide polymorphisms
(SNPs) in the coding and non-coding regions of various
genes and the metabolic response to different diets revealed
several SNP markers. Several loci have already been investi-
gated for potential gene-diet interactions in the response to
dietary interventions [1, 2, 12-14]. Identifying these SNPs
can be applied to predict and optimize nutrition intake at the
A significant number of human studies focusing on the
*Address correspondence to this author at the Laboratory for Functional
Genomics, Biological Research Center of the Hungarian Academy of
Sciences, Temesvari krt. 62. Szeged, H-6726, Hungary;
(changing metabolism, eicosanoid make-up, membrane flu-
idity, microdomain formation and stability, etc.), it is now
evident that dietary lipids influence gene expression directly
or indirectly through modulating the expression of different
transcription factors [4, 15-17]. Genomic technologies such
as cDNA or oligonucleotide microarrays and quantitative
real-time PCR (QRT–PCR) are quickly becoming standard
methodologies in functional molecular biology including
those that intend to reveal the action of different nutritions at
the gene activity level [4, 15].
To widen our scope for studying a biological system at
the proteome level, antibody or protein microarrays could
serve us as an outstanding technology to follow the expre-
ssion and post-translational modifications of hundreds or
even thousands of proteins in a parallel way.
This review will address the different platforms that are
available for gene expression, single nucleotide variation,
protein expression and modification analysis in nutrigenom-
Furthermore, the review will discuss the limitations of
applying gene expression data solely in nutritional investiga-
tions and, finally, propose an integrated approach that is
currently being suggested by several recent publications and
research groups. This approach integrates genomics, proteo-
mics and metabolite screening assays (metabonomics) and is
referred to as a „systems biology” approach [18, 19]. Sys-
tems biology provides comprehensive data sets that give us
deep insight into the positive and negative effects of different
Besides the known cellular effects of lipid nutrition
2 Current Pharmaceutical Biotechnology, 2006, Vol. 7, No. 6 Puskás et al.
nutritions and will help us to define personalized diets for
disease prevention and treatment.
RESULTS AND DISCUSSION
1. High-Throughput SNP Analysis
tend to be present in or close proximity to genetic regions
that are implicated in different complex diseases or could
modulate the effects of certain diets. SNPs are transferred
from generation to generation and therefore can be used as
biomarkers. The analysis of specific SNPs is of great value
for nutritional point of view [1, 2, 12-14, 20].
Ordovas et al. found a significant gene-diet interaction
associated with the APOA1 gene promoter G-A SNP. In
women carriers of the A allele, higher PUFA intakes were
associated with higher HDL-cholesterol concentrations,
whereas the opposite effect was observed in G/G women
. This was an important example how interactions
between environmental factors and the genetic background
influence our response to a certain diet. Preliminary studies
on the interactions between different types of diets,
cardiovascular risk factors and SNPs have identified several
specific polymorphic forms of genes (apoE, apoB, apoC3,
apoA4, mttp, fabp2, cetp and lipc genes), where the genotype
influenced the level of LDL-cholesterol and triacylglycerols
. Other candidate genes have been also suspected to be
definitive in the diet-cardiovascular risk connection: those
that are involved in intracellular lipid metabolism (eg,
iPPARs, CYP7A1) .
Low- or medium-throughput PCR-based SNP screening
methods utilize multiplex techniques such as the TaqMan
protocol , however until recently high-throughput SNP
screening was not possible by using this approach. The
OpenArrayTM system (BioTrove, Woburn, MA) has been
established for high-throughput SNP screening applying the
TaqMan protocol with a microarray-like QRT-PCR plat-
form. The OpenArray system is composed of a standard
micorarray slide-sized array containing 3,072 through-holes,
where in each hole a TaqMan-based PCR can run in a 33 nl
reaction volume. The OpenArray plates preloaded with
custom SNP assays are further arranged in 48 sub-arrays of
64 holes. In one run three plates can be read corresponding
to more than 9000 SNP assays. Custom plates can be defined
allwing the screening of 64 SNP markers over 144 samples.
Single nucleotide variations in DNA sequences, SNPs,
throughput flexible format for large scale nutrigenomic
studies. We have already selected 64 SNPs related to the
metabolism, transcriptional regulation, intracellular and
extracellular transport of lipids for starting a population
Methodologies that can analyze tens of thousands of
SNPs are potentially important, because one does not have to
predict which SNPs could be useful markers for a given
metabolic status, but it is possible to scan large regions of the
genome. Large scale identification of SNPs can be achieved
by using high-density DNA microarrays, or bead-arrays
(GoldenGate genotyping platform - Illumina, San Diego,
CA) [22, 23] that are hybridization based. SNP screening can
be done based on enzymatic reactions (strand extension by
This technology allows SNP genotyping in a high-
polymerization - the GenomeLabTM SNPstream system
Beckman Coulter - or by ligation) . One example for this
high-throughput approach is a huge study analyzing 1200
patients at the National Institutes of Health (NIH) for
cardiovascular risks (Harvard Medical School – Partners
Healthcare Center for Genetics and Genomics; URL: http://
2. High-Throughput Gene Expression Analysis
2.1. Quantitative Real-Time PCR Approaches
absolute quantification of mRNA, reverse transcription
coupled to the polymerase chain reaction (RT-PCR) is
becoming the technique of choice to detect low amounts of
mRNA copies, because of the exponential increase of the
template during thermal cycling. Moreover, the application
of fluorescence techniques with specific instrumentation
capable of combining amplification, real-time detection and
quantification, has led to the development of quantitative
real-time PCR (QRT-PCR) methodologies .
While there are several methods for the relative and
able to detect the amplified products with similar sensiti-
vities: fluorogenic probes and DNA binding dyes.
The most common fluoregenic probes are the TaqMan
probes, where a fluorescent quenching is diminished when a
specific polymerization occurs increasing the fluorescent
signals cycle by cycle in an exponential manner. There are
commercially available medium-throughput TaqMan-based
assays, the TaqMan low-density arrays that are customized
384-well micro fluidic cards (Applied Biosystems, Foster
City, CA). In this platform 384 parallel PCR can be run.
Although the reaction volumes are only 2 μl, the cost of the
cards are still high, and higher throughput is labour and cost
intensive and difficult to achieve.
SYBR Green dye is a minor groove DNA binder; in its
unbound state it has relatively low fluorescence and it
increases significantly when bound to DNA. As the amount
of DNA in the PCR increases, the amount of fluorescence
from the dye increases proportionally . The incorpora-
tion of SYBR Green into real-time RT-PCR allows the
detection of any double-stranded DNA generated during
PCR. This provides great flexibility since no target specific
probes are required; however, both desired and undesired
products can generate a signal. The problem can be
overcome by melting curve analyis of the products and
optimization of the reaction. This approach was used to
assess the effects of dietary cholesterol and the combination
of cholesterol and PUFAs on the modulation of gene
expression in the brain and in the eye in mice . At the
transcription level specific changes could be detected in both
tissues among transcription factor genes coding for sterol-
regulatory element binding proteins, retinoid X receptors and
peroxisome proliferator-activated receptors, different fatty
acid binding protein genes and genes involved in inflamm-
Currently, there are two different types of reagents avail-
has been developed by using the SYBR Green chemistry and
the BioTrove platform. Human non-small cell lung cancer
cell line, A549 was treated with fish oil (40 μg/ml; 33%
High-throughput QRT-PCR for gene expression profiling
High-Throughput Functional Genomic Methods Current Pharmaceutical Biotechnology, 2006, Vol. 7, No. 6 3
EPA, eicosapentaenoic acid (20:5 n-3), 23% DHA, doco-
sahexaenoic acid (22:6 n-3) for 20 h and the expression of
508 kinase coding genes were analysed by using OpenArray
kinase plates (BioTrove). Tight distribution among the
relative mRNA abundance can be seen in Fig. (1). Because
of the relatively low concentration of fish oil only a few
genes exhibited changes due to the treatment (see green and
red bars for down-regulated and up-regulated genes, res-
pectively). The list of the genes that exhibited significant
changes (over +1 and below -1 log2 values) can be seen in
Fig. (1). Relative gene expression changes in human lung cells treated with fish oil by using the BioTrove OpenArray platform. Induced
genes can be seen as red bars (over +1 log2 values). Repressed genes can be seen as green bars (below -1 log2 values).
Table 1. Twelve Genes Exhibiting Relative Gene Expression Changes in Response to Fish Oil Treatment in Human Non-Small
Cell Lung Cancer Cell Line
Assay. Gene Name
ROR2, receptor tyrosine kinase-like orphan receptor 2
PCTK1, PCTAIRE protein kinase 1
FGR, Gardner-Rasheed feline sarcoma viral (v-fgr) oncogene homo
PKNB, protein kinase PKNbeta
TYK2, tyrosine kinase 2
NM_001569 IRAK1, interleukin-1 receptor-associated kinase 1 -1,10
NM_016203 PRKAG2, protein kinase, AMP-activated -1,22
NM_016151 PSK, prostate derived STE20-like kinase PSK -1,45
NM_003942 RPS6KA4, ribosomal protein S6 kinase -1,58
NM_002744 PRKCZ, protein kinase C zeta -1,80
NM_000906 NPR1, natriuretic peptide receptor A/guanylate cyclase A -1,95
NM_003646 DGKZ, diacylglycerol kinase zeta -2,89
Log 2 fold relative gene expression
Kinase-coding genes Kinase-coding genes
Log 2 fold relative gene expression
2 Current Pharmaceutical Biotechnology, 2006, Vol. 7, No. 6 Puskás et al.
oil treatment were 7 repressed and 5 up-regulated genes.
Protein kinase C zeta was repressed maybe due to the
alteration of lipid rafts by PUFA as it was published in the
case of PKC theta . Activation of natriuretic peptide
receptor-A (NPR1) plays a pivotal role in maintaining blood
pressure and cardiovascular homeostasis . We found that
NPR1 was down-regulated in our experiment. This result is
in good concordance with the observations of Awazu et al.
who found that natriuretic peptide receptor A density
decreased upon dietary fatty acids in rat kidney . While
diacylglycerol kinase activity was shown to be stimulated by
arachidonic acid (AA)  and DHA  we observed
down-regulation of its expression in human lung cancer cell
line. Among the up-regulated genes PKNbeta is worth
mentioning. PKNbeta is activated by AA and is expressed in
cancer cells . Further detailed gene expression analysis
of PUFA treated samples will uncover early PUFA-regulated
genes that could be responsible for the early events and that
are directly interact with the genes.
Among the genes exhibiting changes in response to fish
comprehensive and large-scale QRT-PCR studies will be
possible in genomics in general and in the field of nutrigeno-
2.2. DNA-Microarray Technologies
By using the BioTrove OpenArray platform a more
possible by using DNA microarray technologies. The princi-
ple advantage of microarray technologies compared to
traditional methods is that instead of conducting experiments
based on results from one or a few genes, microarrays allow
for the simultaneous interrogation of thousands or tens of
thousands of genes. Two dominant platforms for the
construction of high-density microarrays have emerged:
cDNA and oligonucleotide microarrays and GeneChips [34-
36]. The first involves robotic spotting of DNA molecules
(synthetic oligonucleotides or PCR fragments derived from
EST clone collections) onto a chemically activated glass
slide. The second involves direct in situ synthesis of sets of
gene-specific oligonucleotides on a silicon wafer by an
eloquent derivative of the photolithography process. In both
approaches the expression of complete genomes can be
monitored simultaneously and the expression profiles in
different samples compared . Recent applications of this
technique in nutrigenomics revealed that dietary PUFA can
alter brain gene expression in an age and brain region-
specific manner [4, 38-40]. Although the exact mechanism
of action of lipids on gene expression modulation is still far
from being fully understood we have previously showed that
diverse family of genes are regulated in the brain. The
altered genes included those involved in synaptic plasticity,
cytoskeleton, signal transduction, ion channel formation,
energy metabolism, raft formation and transcription regula-
tions [4, 15]. Different dietary lipids exert different effects at
the level of transcription. A proper ratio of n-6 to n-3 PUFA
might also be important in positively influencing cognitive
functions as it was analyzed by DNA microarrays .
The recently available data highlight the complexity in
studying the transcriptional effects of PUFA supplementa-
tion or depletion on different cell lines and tissues [4, 17, 41-
47]. Therefore more microarray experiments and data are
High-throughput analysis of gene expression is now
needed to fully describe the effects of dietary lipids on the
transcriptome and to reveal complex networks of genes that
3. High-Throughput Proteome Profiling with Protein
processing of the precursor protein and/or covalent modi-
fications such as phosphorylation by kinases/phosphatases or
glycosylation, lipid modifications. To obtain detailed infor-
mation about a biological system, besides gene expression
results, data on expression and post-translational modifica-
tion of many proteins are required. Protein microarrays are
miniaturized and parallelized assay systems that have the
potential to replace state-of-the-art singleplex analysis
systems. Protein microarray platform is an outstanding
method to follow the expression and post-translational modi-
fications of hundreds or even thousands of proteins in a
parallel way [48, 49].
Protein activity is often dependent on posttranslational
showing a high affinity to their target (like specific mono-
clonal or polyclonal antibodies) are prerequisites for the
preparation of protein microarrays. Fluorescently labeled
proteins can bind to their specific partners and as in case of
DNA-microarrays fluorescent signals correlate to the protein
of interest. By scanning the protein microarray a large num-
ber of binding events are detected in parallel. Modification-
specific antibody (e.g. phosphorylation at a specific amino
acid position) can determine the amount of a post-
translationally modified protein in a biological sample.
Large numbers of highly specific capture molecules
(PanoramaTM Ab Microarray Cell Signalling kit [CSAA1]
Sigma, St. Louis, MD, USA) in nutrition research we could
detect changes in the expresion level of several proteins
involved in the signal transduction pathway in the brain in
response to high cholesterol diet [unpublished results]. We
found that in an apoB transgenic mouse brain after a 16
week high-cholesterol diet the expression of proteins
involved in signal transduction (PKC beta and gamma, and
phospholipase C gamma) was down-regulated.
Cholesterol diet-induced protein expression analysis by
using the Panorama protein micorarray platform revealed
that not only the protein expression changed, but a different
phosphorylation pattern could be detected as well [unpub-
lished results]. This result is of great interest in nutrition
research as it became obvious that not only the expression
level changed in response to dietary lipids but specific post-
translational modifications also occur.
By using a commercially available protein microarray
how dietary lipids exert their effects, because they focus on
only a few genes or biomarkers. It turned out that dietary
lipids not only influence the biophysical state of cell
membranes but via direct and indirect routes they also act on
multiple pathways including signalling, gene and protein
activities. Therefore, to understand the molecular basis of the
effects and roles of dietary lipids in the central nervous
system and in other tissues we need global screening tec-
Traditional approaches cannot give us detailed picture on
High-Throughput Functional Genomic Methods Current Pharmaceutical Biotechnology, 2006, Vol. 7, No. 6 3
hniques such as DNA- or protein microarrays, high-through-
put NMR and mass spectrometry to assess the changes in a
global way at the genome, at the transcriptome, at the
proteome and at the metabolome level [2, 50, 51]. The
systems biology approach uses all genomic information, all
gene and protein expression information and all metabolites
to describe the complex effects of nutrients in the most
comprehensive way, based on the networks of biological
regulation. The addition of new screening methods (fluore-
scence, nuclear-magnetic resonance, affinity chromatogra-
phy, surface plasmon resonance, small molecule and novel
microarray platforms) to the existing ones [51, 52] will
further increase the application of these high-throughput
technologies in nutrigenomics and its related fields and
provide integrated data for systems biology investigations.
Asbóth grant (National Research and Technology Office,
Hungary NKTH: XTTPSRT1). BioTrove OpenArray system
was run under the agreement between HAS, BRC and
Avidin Ltd. Szeged, Hungary.
This work was supported by the grant from the Oszkár
Ferguson, L.R. (2006) Mol. Diag. Ther., 10, 101-108.
Rist, M.J.; Wenzel, U. and Daniel, H. (2006) Trends Biotechnol.,
Narce, M.; Frenoux, J.M.; Dardel, V.; Foucher, C.; Germain, S.;
Delachambre, M.C. and Poisson, J.P. (1997) Biochimie, 79, 135-
Kitajka, K.; Sinclair, A.J.; Weisinger, R.S.; Weisinger, H.S.;
Mathai, M.; Jayasooriya, A.P.; Halver, J.E. and Puskas, L.G.
(2004) Proc. Natl. Acad. Sci. USA., 101, 10931-10936.
Crawford, M.A.; Golfetto, I.; Ghebremeskel, K.; Min, Y.;
Moodley, T.; Poston, L.; Phylactos, A.; Cunnane, S. and Schmidt,
W. (2003) Lipids, 38, 303-315.
Attar-Bashi, N.M.; Frauman, A.G. and Sinclair, A.J. (2004) J.
Urol., 171, 1402-1407.
Lapillonne, A.; Clarke, S.D. and Heird, W.C. (2004) Curr. Opin.
Clin. Nutr. Metab. Care, 7, 151-156.
Diau, G.Y.; Hsieh, A.T.; Sarkadi-Nagy, E.A.; Wijendran, V.;
Nathanielsz, P.W. and Brenna, J.T. (2005) BMC Med., 23, 3-11.
Mitchell, D.C.; Gawrisch, K.; Litman, B.J. and Salem, N. Jr. (1998)
Biochem. Soc. Trans., 26, 365-370.
Das, U. N. (2000) Prostaglandins Leukot. Essent. Fatty Acids,
Das, U. N. (2004) Med Sci Monit., 10(5), HY19-25.
Ordovas, J.M.; Corella D.; Cupples, L.A.; Demissie, S.; Kelleher,
A.; Coltell, O.; Wilson, P.W.F.; Schaefer, E.J. and Tucker, K.
(2002) Am. J. Clin. Nutr., 75, 38–46.
Vincent, S.; Planells, R.; Defoort, C.; Bernard, M.C.; Gerber,
M.; Prudhomme, J.; Vague, P. and Lairon, D. (2002) Proc. Nutr.
Soc., 61, 427-434.
Ordovas, J.M. (1999) The Proc. Nutr. Soc., 58, 171–87.
Kitajka, K.; Puskas, L.G.; Zvara, A.; Hackler, L. Jr.; Barcelo-
Coblijn, G.; Yeo, Y.K. and Farkas, T. (2002) Proc. Natl. Acad. Sci.
USA., 99(5), 2619-2624.
Jump, D.B.; Clarke, S.D.; Thelen, A.; Liimatta, M.; Ren, B. and
Badin, M. (1996) Prog. Lipid. Res., 35, 227–41.
Berger, A.; Mutch, D.M.; German, J.B. and Roberts, M.A. (2002)
Lipids Health Dis., 1, 2.
Bonneau, R.; Baliga, N.S.; Deutsch, E.W.; Shannon, P. and Hood,
L. (2004) Genome Biol., 5, 1–15.
Weston, A.D. and Hood, L. (2004) J. Proteomic Res., 3, 179–196.
Ordovas, J.M. and Corella, D. (2005) Curr. Cardiol. Rep., 7(6),
 Whitcombe, D.; Brownie, J.; Gillard, H.L.; McKechnie, D.;
Theaker, J.; Newton, C.R. and Little, S. (1998) Clin. Chem., 44 (5),
Steemers, F.J. and Gunderson, K.L. (2005) Pharmacogenomics,
Wang, D.G.; Fan, J.B.; Siao, C.J.; Berno, A.; Young, P; Sapolsky,
R.; Ghandour, G.; Perkins, N; Winchester, E.; Spencer, J.;
Kruglyak, L.; Stein, L.; Hsie, L.; Topaloglou, T.; Hubbell, E.;
Robinson, E.; Mittmann, M.; Morris, M.S.; Shen, N.; Kilburn, D.;
Rioux, J.; Nusbaum, C.; Rozen, S.; Hudson, T.J.; Lipshutz, R.;
Chee, M. and Lander, E.S. (1998) Science, 280, 1077-1082.
Budowle, B. (2004) Forensic Sci. Int., 146 (Suppl), S139-142.
Heid, C.A.; Stevens, J.; Livak, K.J. and Williams, M. (1996)
Genome Res., 6, 986–994.
Wittwer, C.T.; Herrmann, M.G.; Moss, A.A. and Rasmussen, R.P.
(1997) Biotechniques, 22, 130–138.
Puskas, L.G.; Bereczki, E.; Santha, M.; Vigh, L.; Csanadi, G.;
Spener, F.; Ferdinandy, P.; Onochy, A. and Kitajka, K. (2004)
Biochimie, 86(11), 817-824.
Fan, Y.Y.; Ly, L.H.; Barhoumi, R.; McMurray, D.N. and Chapkin,
R.S. (2004) J. Immunol., 173(10), 6151-6160.
Kumar, P.; Arise, K.K. and Pandey, K.N. (2006) Peptides, 27(7),
Awazu, M.; Yared, A.; Swift, L.L.; Hoover, R.L. and Ichikawa, I.
(1992) Kidney Int., 42(2), 265-271.
Rao; K.V.; Vaidyanathan, V.V. and Sastry, P.S. (1994) J.
Neurochem., 63(4), 1454-1459.
Vaidyanathan, V.V.; Rao, K.V. and Sastry, P.S. (1994) Neurosci.
Lett., 179(1-2), 171-174.
Oishi, K.; Mukai, H.; Shibata, H.; Takahashi, M. and Ona, Y.
(1999) Biochem. Biophys. Res. Commun., 261(3), 808-814.
Csako, G. (2006) Clinica Chimica Acta, 363, 6-31.
Chee, M.; Yang, R.; Hubbell, E.; Berno A.; Huang, X.C.; Stern, D.;
Winkler, J.; Lockhart, D.J.; Morris, M.S. and Fodor, S.P (1996)
Science, 274, 610-614.
Jain, K.K. (2004) Curr. Opin. Drug Discov. Devel., 7(3), 285-289.
Bowtell, D.D. (1999) Nat. Genet., 21(1Suppl), 25-32.
Barcelo-Coblijn, G.; Kitajka, K.; Puskas, L.G.; Hogyes, E.; Zvara,
A; Hackler, L. Jr. and Farkas, T. (2003) Biochim. Biophys. Acta.,
Puskas, L.G.; Kitajka, K.; Nyakas, C.; Barcelo-Coblijn, G. and
Farkas, T. (2003) Proc. Natl. Acad. Sci. USA., 100(4), 1580-1585.
Barcelo-Coblijn, G.; Hogyes, E.; Kitajka, K.; Puskas, L.G.; Zvara,
A.; Hackler, L. Jr.; Nyakas, C.; Penke, Z. and Farkas, T. (2003)
Proc. Natl. Acad. Sci. USA., 100(20), 11321-11326.
Puskas, L.G.; Nagy, Z.B.; Giricz, Z.; Onody, A.; Csonka, C.;
Kitajka, K.; Hackler, L. Jr.; Zvara, A. and Ferdinandy, P. (2004)
FEBS Lett. 562(1-3), 99-104.
Berger, A.; Roberts, M.A. and Hoff, B. (2006) Lipids Health Dis.,
House, R.L.; Cassady, J.P.; Eisen, E.J.; Eling, T.E.; Collins, J.B.;
Grissom, S.F. and Odle, J. (2005) Physiol. Genomics., 21(3), 351-
Narayanan, B.A.; Narayanan, N.K. and Reddy, B.S. (2001) Int. J.
Oncol., 19(6), 1255-1262.
Kuperstein, F.; Yakubov, E.; Dinerman, P.; Gil, S.; Eylam, R.;
Salem, N. Jr. and Yavin, E. (2005) J. Neurochem., 95(6), 1550-
Jayasooriya, A.P.; Ackland, M.L.; Mathai, M.L.; Sinclair, A.J.;
Weisinger, H.S.; Weisinger, R.S.; Halver, J.E.; Kitajka, K. and
Puskas, L.G. (2005) Proc. Natl. Acad. Sci. USA., 102(20), 7133-
Middleton, F. A.; Ramos, E. J.; Xu, Y.; Diab, H.; Zhao, X.; Das, U.
N. and Meguid, M. (2004) Nutrition, 20(1),14-25.
Seong, S.Y. and Choi, C.Y. (2003) Proteomics, 3(11), 2176-2189.
Stoll, D.; Templin, M.F.; Bachmann, J. and Joos, T.O. (2005) Curr.
Opin. Drug Discov. Devel., 8(2), 239-252.
Hardimann, G. (2004) Pharmacogenomics, 5, 487–502.
Liu, B.; Li, S. and Hu, J. (2004) Am. J. Pharmacogenomics, 4(4),
Darvas, F.; Dorman, G.; Krajcsi, P.; Puskas, L.G.; Kovari, Z.;
Lorincz, Z. and Urge, L. (2004) Curr. Med. Chem., 11(23), 3119-