American Journal of Molecular Biology, 2011, 1, 87-113 AJMB
doi:10.4236/ajmb.2011.12011 Published Online July 2011 (http://www.SciRP.org/journal/ajmb/).
Published Online July 2011 in SciRes. http://www.scirp.org/journal/AJMB
Effects of genetic and environmental factors and
gene-environment interaction on expression variations of
genes related to stroke in rat brain
Yuande Tan1, Myriam Fornage2
1College of Life Science, Hunan Normal University, Changsha, China;
2The University of Texas Health Science Center at Houston, Brown Foundation Institute of Molecular Medicine, Houston, USA.
Received 15 May 2011; revised 22 June 2011; accepted 1 July 2011.
ABSTRACT
To determine if genetic and environmental (dietary)
factors and gene-environment interaction impact on
the expression variations of genes related to stroke,
we conducted microarray experiments using two ho-
mozygous rat strains SHRSR and SHRSP fed with
high and low dietary salt levels. We obtained expres-
sion data of 8779 genes and performed the ranking
analysis of microarray data. The results show that
the genetic difference for stroke in rat brain has a
strong effect on expression variations of genes. At
false discovery rate (FDR) ≤ 5%, 534 genes were
found to be differentially expressed between the geno-
types resistant and prone to stroke, among which 304
genes were up-regulated in the resistant genotype and
down-regulated in the prone genotype and 230 were
down-regulated in the former and up-regulated in the
latter. In addition, 365 were functional genes for
transcription and translation, receptors (in particular,
neurotransmitter receptor), channels of ions, trans-
portation, metabolism and enzymes, and functional
and structural proteins. Some of these genes are piv-
otal genes that cause stroke. However, dietary salt
levels and GE interaction do not strongly impact on
the expression variations of these genes detected on
arrays.
Keywords: Rat; Ischemia Stroke; Microarray;
Differential Expression; Genotype; Environment Factor;
GE-Interaction
1. INTRODUCTION
Stroke is a major cause of severe disability and the third
leading cause of death in the world. Stroke occurrence is
a complex biological process involving obstruction of
blood flow in a major cerebral vessel which leads to de-
regulation of genes whose expression promotes ischemic
neuronal death and subsequent neurological dysfunction
[1-3]. The development of stroke in an individual is in-
fluenced by a number of cardiovascular risk factors in-
cluding genetic predispositions, hypertension, smoking,
diabetes mellitus [4] as well as by dietary salt. The im-
portance of genetic factor for stroke etiology has been
documented by several rare monogenic diseases such
CADASIL (cerebral autosomal dominant ateriopathy
with subcortical infarcts and leukoencenphalopathy) and
the genetic behavior of some genes for stroke [5-10].
However, the genetic basis of stroke is quite complex
and the genes that are relevant to strokes have continu-
ously being discovered. Some genetic factors including
stroke-prediposing loci (QTLs) have been identified in
the regions on rat chromosomes 1, 3, 4 and 5 [11,12]. In
recent years, single nucleotide polymorphisms (SNPs) as
an important and conservative genetic variations have
widely been used to monitor human genetic diseases.
For instance, G-50T in genes cytochrome P450 2J2
(CYP2J2) [4], G860A in a soluble epoxide hydrolase
(EPHX2) [4], and plasma IL6 and CRP levels [13] were
found to be significantly associated with risk of ischemic
stroke.
Global gene expression profiles are becoming an im-
portant and necessary tool for exploring etiological me-
chanism of stroke. Several microarray experiments
[14-22] have been used to demonstrate gene expression
change in the postischemic rat subjected to ischemic
stroke, hemorrhagic stroke, sham surgeries, hypoxia, and
insulin-induced hypoglycemia. The blood genomic ex-
pression profiles of genes in human stroke have been
obtained from the pilot studies [23,24]. But all these
studies concentrated on gene-expression change in time

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 88
series after stroke while the associations between the
stroke risk factors and expression variations of genes
still remain unclear. Detection of effect of genetic back-
grounds of stroke on differential expressions of genes is
significant for illustrating mechanism of which stroke
occurs, in particular, for finding functional genes par-
ticipating in stroke.
In addition, since dietary factors play an important
role in the onset of stroke in rat [25-28], to investigate if
dietary factors regulate significantly the expressions of
genes may be helpful for understanding stroke etiology.
It is also deserved to ascertain if interaction between
genetic and environmental (dietary) factors significantly
contributes to expression variations of genes. In order to
investigate effects of stroke genetic and dietary factors
and their interactions on expression variations of genes,
we employed a two-by-two design to conduct microar-
ray experiments. Unlike the conventional two-by-two
design that yields frequency data for association between
two factors, here our two-by-two design is utilized to
obtain large-scale continuous data of expression varia-
tions of genes in two ways: genetic and environmental
(dietary) factors. Actually, for the continuous data,
two-by-two design may be viewed as a simple
two-factor design in which the data may be analyzed by
two-way analysis of variance (ANOVA). For example,
Kerr, et al. [29] and Black and Doerge [30] provided
ANOVA models to account for multiple-factor microar-
ray data. In two-by-two design, each factor has only two
levels, and hence, ANOVA analysis is square of conven-
tional t-statistic for each gene. ANOVA analysis of
two-class data therefore is equivalent to the conventional
two-tail t-test. In microarray experiments, however, since
sample sizes are extremely small compared to conven-
tional experiments but number of genes detected is huge,
there would be a lot of chances to generate fudge effects
in t-tests [31,32], that is, some of the t-values would be
falsely inflated by standard errors smaller than 1. Except
for significance analysis of microarray (SAM) [32] and
ranking analysis of microarray (RAM) [31], all existing
methods for t-tests do not consider the fudge effects. But
SAM has a low power to identify genes differentially
expressed, so we here choose RAM for our microarray
data.
In this paper, our focus is on genetic and environ-
mental factors and GE interaction related to stroke pro-
ducing effects on differential expression of genes in rat.
In another paper, we will use ranking analysis of correla-
tion coefficients (RAC) [33], a large-scale correlation
analysis method, to ascertain coexpression or coregula-
tion of these differential expressed genes, furthermore,
classify them into different functional groups, and build
coexpression/coregulation network within groups.
2. MATERIALS AND METHODS
2.1. Animal Model and Two-by-Two
Experimental Design
The experiments were performed on male stroke-resis-
tant SHR/N (CRiv) (SHRSR) and stroke-prone SHR/A3
(Heid) (SHRSP) rats from a breeding colony maintained
by the investigators as previously described [34]. Both
rat strains SHRSR and SHRSP are inbred and homozy-
gous, as a result of brother-sister mating over many gen-
erations. Almost all loci are the same in these two rat
strains except those relevant to stroke. There are distinct
gender differences in the establishment of hypertension
and in stroke mortality rate in the SHRSP. Blood pres-
sure is higher and rises more quickly and the incidence
of stroke mortality is accelerated in males as compared
to females. The cerebral cortex is the predilection site of
cerebrovascular lesions in the SHRSP where rate of
stroke occurrence is over 70%. Age-matched male rats
from each strain (12 SHRSP rats and 12 SHRSR rats)
were fed with a standard rat chow and water ad libitum
until age 8 weeks. Subsequently, animals from each
strain were randomized to one of 2 dietary regimens (N
= 6 in each strain-diet group): a “stroke-permissive diet”
high in sodium (HS) (0.63% potassium, 0.37% sodium)
and 1% NaCl drinking solution; a “stroke-protective
diet” low in sodium and high in potassium (LS) (1.3%
potassium, 0.37% sodium) and regular drinking water.
All animals were housed at 23˚C on a 12-hour light-dark
cycle. The SHRSP rat strain in the HS environment
showed stroke symptoms and died at 12 weeks of age.
The stroke symptoms are defined as severe lethargy, loss
of balance, poor grooming, convulsive rhythmic move-
ment of the forelimbs, immobility, and kangaroo-like
posture [35]. The brain tissues were collected for RNA
extraction and subsequent microarray analysis. The stu-
dy protocols were approved by the Animal Care Com-
mittee of the University of Texas-Houston. Thus, HS-
SHRSPs, LS-SHRSPs, HS-SHRSRs, and LS-SHRSRs
were tabbed by two-by-two tables.
2.2. Brain Tissues Collection and RNA Isolation
The brain was quickly removed, weighted, cut, and then
transferred to an ice cold brain matrix block in two 2
mm coronal slices that was incubated at 37˚C for 30 min
with 2% 2,3,5-triphenyltetrazolium chloride (TTC) in
0.9% normal saline according to a modified protocol
[36]. Then, the tissue slices were transiently immersed in
a phosphate-buffered solution with 10% formalin and
examined. The cortical tissue from the remaining slices
was dissected and total RNA was isolated using the me-
thod of Chomczynski and Sacchi [37], washed in ethanol,
resuspended in RNase-free water, and quantified by

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 89
spectrophotometric determination of optical density at
260 nm.
2.3. Microarray Experiment
Microarray analysis was performed as described by
Lockhart, et al. [38]. Briefly, 10 µg total RNA extracted
from each of the 24 rats was used to synthesize cDNA,
which was then used as a template to generate bioti-
nylated cRNA. cRNA was fragmented and hybridized to
a Test 2 chip to verify quality and quantity of the sam-
ples. Each sample was then hybridized to a RGU34A
array (Affymetrix, Santa Clara, CA) that contains 7779
full length cDNA and 1000 ESTs. After hybridization,
each array was washed and scanned, and fluorescence
values were measured and normalized using the Affy-
metrix Microarray Suite v.5.0 software.
2.4. Statistical Methods
For convenience, inbred animals SHRSR with genotype
resistant to stroke are denoted by G and SHRSP with
genotype prone to stroke by G. The animals with G

and G exposed to the high and low levels of salt are
separately labeled by E and E. Thus animals are
grouped into four groups (GE

, GE

, GE

,
GE

) and each group has n individuals for statistical
analysis [39]. Assume that genome-wide expression data
of N genes are obtained from microarray experiments,
each of which can be summarized by a 2 × 2 table. For
example, the kth 2 × 2 table may be similar to Table 1. It
is worth noting that the data in Table 1 are continuous
variables instead of categorical ones.
Let x be an expression value of gene g, which can be
original or transformed. We here assume a linear model
for x as
g
ijkggigjgij gijk
x
GE e

I (1)
where
g

is the mean of expression values for gene g (g
= 1, 2, …, N),
g
i
G, the effect of the ith genotype aver-
aged over all environmental factor levels,
g
j
E, the ef-
fect of the jth environmental factor level over all geno-
types,
g
ij
I, effect of GE interaction between genotype i
and level j of environmental factor, and
g
ijk
e, the special
expression noise of observation k (k = 1, 2, …, n) in ge-
notype i and at level j of the environmental factor where
both genotypes and environmental factor levels
Table 1. Two-by-two design for studying genetic, environ-
mental, and gene-environment interaction effects on expression
of genes related to rat stroke.
Exposure Genotypes
G
– G+
E–

GE


GE

E+ 
GE


GE

are dichotomous variables with i = 1 for G and i = 2
for G

, j = 1 for E

and j = 2 for E. For such a 2 ×
2 experimental design, the estimates of
g

,
g
i
G,
g
j
E
and
g
ij
I are respectively given by
ˆ()
g
igig
Gxx, (2)
ˆ()
g
jgjg
Exx, (3)
gjgiggijgij EGxx ˆ
ˆ ˆI (4)
where
22
11 1
1
4
n
g
gijk
ki j
x
x
n 
 ,
1
1n
g
ij gijk
k
x
x
n
,
2
11
1
2
n
g
i gijk
jk
x
x
n
 ,
2
11
1
2
n
g
j gijk
ik
x
x
n

Note that g x is an estimate of
g

. Thus, the fol-
lowing three sets of equalities 12
0
gg
GG, 1g E

20 g
E

, and 11122122 0
gg g g

IIII correspond,
respectively, to the three null hypotheses: no genetic
effects, no environmental effects, and no GE interaction
effects on expression variations of gene g. In the case of
microarray data, sample sizes are extremely small but
number of genes detected on arrays is huge, therefore,
there would be a lot of chances to generate a fudge effect
in traditional t-tests [31,32], that is, some of the t-values
would be falsely inflated by standard deviation smaller
than 1 due to 1
gg
d

 where
g
d is difference be-
tween two means and 22
112 2
//
gg g
nn    for
gene g. To remove the fudge effects, we have developed
a modified t-statistic [31]. In the current notation, we
used T-statistics to test for the above three hypotheses.
Appendix A shows that the T-statistic is an extension of
the traditional t-statistic and reduced to the traditional t-
statistics when ()0Cg A

. Associated with the T-statis-
tic, we can perform a ranking analysis of microarray
(RAM) [31]. RAM is based on comparisons between a
set of ranked T statistics and a set of ranked Z values (a
set of ranked estimated null T-statistics) yielded by a
“randomly splitting” approach instead of a “permuta-
tion” approach and a two-simulation strategy for esti-
mating the proportion of genes identified by chance, i.e.,
the false discovery rate (FDR) [31]. RAM is powerful to
identify genes of differential expressions, especially, be-
tween small samples.
3. RESULTS
3.1. Effects of Genetic and Environmental
Factors (Dietary Salt) for Stroke and GE
Interaction on Gene-Expression Variations
Figure 1 shows the observed linear T-Z dots with re-
spect to the contribution of genetic factors for stroke in

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 90
Figure 1 Linear plot of genetic effects on expression variations
of genes. The T- and Z-values were obtained from the real
microarray data set of 8799 genes. The blue linear dots are the
ranked T-Z dots and red linear dots, the ranked Z-Z dots. The
T-Z dots violently deviate from the Z-Z dots at two sides. Two
break lines represent a given pair of thresholds Δ and –Δ.
rat brain to the expression variations of genes. Two tails
rat brain to the expression variations of genes. Two tails
of the linear T-Z dots remarkably deviate from the linear
Z-Z dots (red line) at |Z| > 2, indicating that the genetic
difference between two genotypes with respect to stroke
strongly altered expression regulations of a bulk of genes.
But the environmental factors (salt) and GE interaction
show a quite weak effect on the expression variations of
genes (Figures 2 and 3).
3.2. Genes Differentially Expressed between
Genotypes Resistant and Prone to Stroke
Table 2 offers the numbers of the genes called differen-
tial expressions between genotypes with respect to
stroke in the rat brain at a set of given threshold levels
and controls of false discovery rates (FDR) [31,39]. In
Table 2, we found 534, 375, and 311 cDNAs displaying
differential expressions between genotypes resistant and
prone to stroke in the rat brain at FDR ≤ 5%, 1%, and
0.5%, respectively. Here we chose these 534 cDNAs at
FDR ≤ 5 %. Of which 304 genes were up-regulated and
230 were down-regulated. In addition, 169 cDNAs were
the expressed sequence tags (ESTs) (supplemental Table
2) and 341 of the remainders have been recognized as
different functional genes in the rat brain or cerebral
cortex due to replicates of some cDNAs and were sorted
to several major functional groups: 1) Transcription and
translation regulations, 2) Receptors, 3) Channels of ions,
4) Transporters, 5) Metabolisms and enzymes, and 6)
functional and structural proteins (supplemental Table 1).
Figure 2. Linear plot of environmental (salt) effects on expres-
sion variations of genes. The T- and Z-values were obtained
from the real microarray data set of 8799 genes. The blue lin-
ear dots are the ranked T-Z dots and red linear dots, the ranked
Z-Z dots. The T-Z dots slightly deviate from the Z-Z dots at
two sides. Two break lines represent a given pair of thresholds
Δ and –Δ.
Figure 3. Linear plot of GE interaction effects on expression
variations of genes. The T- and Z-values were obtained from
the real microarray data set of 8799 genes. The blue linear dots
are the ranked T-Z dots and red linear dots, the ranked Z-Z dots.
The T-Z dots slightly deviate from Z-Z dots in two sides. Two
break lines represent a given pair of thresholds Δ and –Δ.
A remarkable genetic effect is that some of genes for
transcript factors, translation factors, receptors, channels,
and transportation show strongly differential expressions
between two genotypes of stroke (Figure 4). Figure 4
shows that there are significantly more genes for tran-
script factors, receptors, and channels in up-regulation
than in down-regulation. But in translation, 14 of the 17

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 91
Table 2. Number of genes whose expressionare impacted by
genetic effects identified by RAM and estimated FDR at a
given threshold.
Threshold Number of
positive genes
Number of false
positive genes
Estimated
FDR
0.274734934 4311 2874 0.667
0.391600220 3053 2034 0.666
0.436597059 2736 1791 0.655
0.444170987 2698 1758 0.652
0.490108147 2433 1549 0.637
0.529096989 2240 1384 0.618
0.576847353 2058 1213 0.589
0.601159520 1967 1131 0.575
0.650760219 1824 979 0.537
0.684624193 1714 883 0.515
0.754544671 1504 704 0.468
0.763511830 1477 685 0.464
0.837328665 1291 532 0.412
0.875781020 1232 472 0.383
0.935709478 1123 382 0.340
0.966811203 1081 343 0.317
1.031635965 973 272 0.280
1.077064276 932 228 0.245
1.174369276 831 154 0.185
1.187233769 813 145 0.178
1.297105088 713 88 0.123
1.357611212 665 69 0.104
1.457567137 585 45 0.077
1.512749689 567 33 0.058
1.572320626 534 24 0.045
1.593312976 531 21 0.040
1.614943053 520 18 0.035
1.637260305 513 15 0.029
1.708927672 478 11 0.023
1.818484265 427 7 0.016
1.849113170 411 6 0.015
1.951549098 375 3 0.008
2.076044015 323 2 0.006
2.124648736 311 1 0.003
2.237727633 282 0 0.000
genes coding for ribosomal proteins (5 small subunits
and 7 large subunits, one P subunit, and one 40 kDa
subunit) were up-regulated in genotype SHRSP and
down-regulated in genotype SHRSR. Two genes coding
for translation factors, i.e., elongin A and protein synthe-
sis initiation factor 4AII were down-regulated in
SHRSHP.
29 genes for receptors were found to be differentially
expressed between these two genotypes with respect to
stroke in our data. The highlight is the genes for recap-
Figure 4. Number of differential expressed genes for transcript
factors, translation factors, receptors, channel, and transporta-
tion. Up-regulations and down-regulations are defined in the
genotype resistant to stroke.
tors that respond to

-aminobutyric acid (GABA), one
of the most important neurotransmitters, in the brain.
GABA-B receptors 1 and 2 are expressed in the nerve
fibers at early development stage. The nerve fibers are
covered by myelin sheath. Stroke, an acute neurological
event leading to death of neural tissue, is involved in
subcortical infarcts and leukoencephalopathy that de-
structs the myelin sheaths. Co-activation of GABA-A
and GABA-B receptors results in neuroprotection during
in vitro ischemia, which is possibly due to the fact that
co-activation of GABA-A and GABA-B receptors could
strongly increase activation of Akt (or protein kinase B)
and inhibit activation of apoptosis signal-regulating ki-
nase 1 (ASK1) by phosphorylation of serine 83 of ASK1
[40]. Therefore, expression variations of GABA receptor
genes in these two genotypes of being resistant and
prone to stroke may be associated with stroke in etiol-
ogy.
Similar situation also happened in expression of the
gene for glutamate receptor, a prominent neurotransmit-
ter receptor. It is interesting that 26 genes for receptors
displayed lower expression levels in SHRSP than in
SHRSR. More interestingly, except for the gene for pro-
tein kinase C-regulated chloride channel, all 17 ionic
channel genes were down-regulated in SHRSP. It is
worth noting that the genes for glutamate transporter and
glutamate/aspartate transporter were differentially ex-
pressed between these two genotypes. The glutamate
transporters might increase the susceptibility of tissue to
the consequences of insults that lead to a collapse of the
electrochemical gradients required for a normal function
[41]. Excitotoxicity may be an important pathophysi-
ological mechanism of which Purkinje cell would be
died after ischemia.
The glutamate transporter can remove glutamate from
the extracellular fluid in the brain, suggesting that glu-
tamate transporters may play a critical role in protecting
Purkinje cells from ischemia-induced damage. Among
10 genes for transporters showing significant change in
expression, 4 genes are responsible for glutamate or glu-

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 92
tamate/aspartate transport. Yamsashita, et al also found
that glutamate/aspartate transporter (GLAST) is abun-
dantly expressed in the cerebellar cortex [42].
Another big genetic effect on gene-expression varia-
tions was also found in those coding for enzymes con-
trolling metabolisms. In our microarray data, 88 genes
found to be differentially expressed between SHRSP and
SHRSR encode enzymes that respectively participate in
phosphate metabolism, oxidization and reduction, en-
ergy metabolism, carbohydrate metabolism, lipid me-
tabolism, amino acid and nucleotide acid metabolism,
sterol metabolism, neurotransmitter, extracellular and
intracellular signaling, and so on. Figure 5 shows that
these 88 genes mostly work in phosphate metabolism,
oxidization and reduction, extracellular and intracellular
signaling, and carbohydrate metabolism. But here our
focus is on a pivotal enzyme, i.e., Casein Kinase II
(CKII) because CKII may play a crucial role in IFN-
[gamma] signaling relevant to change in gene-expression
in macrophage during atherosclerosis [43]. In particular,
nuclear factor-κB (NFκB), a transcription factor, is acti-
vated after cerebral ischemia. NFκB activation leads to
the expressions of many inflammatory genes involved in
the pathogenesis of stroke [44]. NFκB is in general se-
questered in the cytoplasm via interaction with specific
inhibitory proteins (IκBα) [45]. Phosphorylation by CKII
of serine/threonine in the C-terminus influences IκBα
stability [46-50] and promotes deg- radation of IκBα via
calpain [45]. Our results (supple- mental Table 1) show
that the genes for CKII alpha subunit and calpain small
subunit were significantly up-regulated in SHRSP but
down-regulated in SHRSR. This indicates that expres-
sions of CKII and calpain genes are suppressed in
SHRSR so that IκBα is active and stable. The active
IκBα inhibits NFκB. As a result, the expressions of many
inflammatory genes dealing with the pathogenesis of
stroke are closed in SHRSR. Therefore, CKII and cal-
Figure 5. Number of differential expressed genes participating
in metabolisms. Up-regulations and down-regulations of genes
are defined in the genotype resistant to stroke.
pain may be viewed as biomarkers for early diagnosis of
stroke.
In addition to those above, 166 genes for functional
and structural proteins such as binding proteins, mem-
brane proteins, microtubule and skeletal proteins, glyco-
proteins, nervous system-associated protein, cell signal-
associated protein, cell growth and cell division-associ-
ated proteins, immunological system-associated proteins,
cell adhesion proteins, tumor-associated proteins, etc
also were found to have strong expression variations.
Among these genes coding for proteins, the most are
those for nervous system-associated proteins. Most of
the genes for glycoproteins, nervous system-associated
protein, cell signal-associated protein, and cell division-
associated proteins were down-regulated in genotype
SHRSP compared to genotype SHRSR (Figure 6). Here
it is especially worth noting that the genes for cathepsin
S and NaPi-2 were significantly higher in genotype
SHRSP than in genotype SHRSR while the calpastatin
was significantly down-regulated in genotype SHRSP
(supplemental Table 1). Cathepsin S is a prominent pro-
tein, a novel biomarker relevant to atherogenesis [51-53].
It is well known that stroke occurs when a blood clot
forms and blocks blood flow in an artery damaged by
atherosclerosis. Excess cathepsin S would produce a
potential deleterious effect on the arterial wall because
cathepsin S forms a plausible molecular link between
enlarged fat mass and atherosclerosis [53]. Atheroscle-
rosis complicated by plaque rupture or thrombosis could
be a major factor causing potential lethal acute coronary
syndromes and stroke [54]. In addition, type 2 sodium
phosphate cotransporter (NaPi-2) beta has been demon-
strated to be associated with hypertension and obesity
[55-57]. This is why cathepsin S and NaPi-2 were up-
regulated in genotype SHRSP but down-regulated in
SHRSR. But calpastatin is a calpain-specific inhibitor
Figure 6. Number of differential expressed genes for proteins.
Up-regulations and down-regulations of genes are defined in
the genotype resistant to stroke.

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 93
[58]. As mentioned above, calpain aids CKII to degrade
IκBα. Hence, in presence of calpastatin, IκBα has a
higher activity level. The higher active IκBα inhibits
NFκB.
3.3. Genes Differentially Expressed between Two
Salt Levels
The role of environmental factors is an exterior effect in
stroke. Therefore, compared to the genetic effects, as
seen in Figure 2, the environmental effect on expression
variations of genes is very weak. The numbers of genes
differentially expressed between two different dietary
salt levels at a set of given thresholds are listed in Table
3. As expected in Figure 2, merely 22 genes were found
to have differential expressions between two dietary salt
levels: high in sodium (HS) and low in sodium (LS) at
FDR ≤ 5%. Among them, 10 are EST, 9 are functional
genes for connexin 40, carboxyl-terminal PDZ ligand of
neuronal nitric oxide synthase, NF1-B2, prolyl 4-hy-
droxylase alpha subunit, RhoA, syntaxin B, and taurine
transporter, respectively, and 4 are unkown sequences
(supplemental Table 1).
3.4. Genes Differentially Expressed Due to
Interaction between Genotypes and
Environmental Factor (Dietary Salt)
As such, contribution of interaction (GE) between geno-
Table 3. Number of genes whose expression variations were
impacted by environmental factors (dietary salt) identified by
RAM and estimated FDR at a given threshold.
Threshold Number of
positive genes
Number of false
positive genes Estimated FDR
0.23054 4452 2968 0.667
0.307166 774 398 0.514
0.341366 485 292 0.602
0.378962 338 198 0.586
0.386128 301 165 0.548
0.409155 266 145 0.545
0.467169 168 86 0.512
0.478735 157 71 0.452
0.484306 147 65 0.442
0.510608 142 62 0.437
0.56461 114 46 0.404
0.579948 101 35 0.346
0.666147 62 20 0.322
0.686774 57 15 0.263
0.789565 36 9 0.250
0.92422 24 3 0.125
1.07455 22 1 0.045
1.323427 7 0 0
1.480786 4 0 0
1.546755 3 0 0
types and environmental factor (dietary salt) to expres-
sion variations of genes is also pretty small (Figure 3).
The numbers of the genes differential expressed due to
GE interaction at a set of threshold levels were listed in
Table 4. 25 genes were found to have the interesting
change in expression at FDR ≤ 5%. Among them, 11 are
EST, 12 are functional genes coding for synuclein 1,
synaptojanin, flk protein, metabotropic glutamate re-
ceptor 3, neurexin III-alpha, carboxypeptidase D pre-
cursor (Cpd), electrogenic Na+ bicarbonate cotransporter
(NBC), and ET-B endothelin receptor, GST Yc1, respec-
tively, and 3 are unknown sequences (Supplemental Ta-
ble 1). 23 of these 25 genes were down-regulated in the
genotype resistant to stroke and the low salt environment
or in the genotype prone to stroke and the high salt en-
vironment but up-regulated in the genotype resistant to
stroke and the high salt environment or in the genotype
prone to stroke and the low salt environment.
4. DISCUSSION
From Figures 1, 2 and 3, it can be seen that the effects
of these factors for stroke on the expression variations of
genes were well displayed by deviation of the T-distri-
bution from the Z-distribution at two sides. As expected,
the genetic difference between genotypes results in the
significant expression variations of genes related to
stroke (Figure 1). At FDR ≤ 5%, we found 341 func-
tional genes differentially expressed between the geno-
types SHRSP and SHRSR. Unlike the environmentally
Table 4. Number of genes whose expression variations were
impacted by GE identified by RAM and estimated FDR at a
given threshold.
Threshold Number of
positive genes
Number of false
positive genes
Estimated
FDR
0.04302 7487 5729 0.765
0.125184 839 642 0.765
0.176549 511 385 0.753
0.186523 453 303 0.667
0.290752 263 175 0.665
0.390944 160 71 0.444
0.394555 158 58 0.367
0.536208 99 35 0.355
0.597688 77 16 0.208
0.621986 73 13 0.178
0.631139 69 11 0.159
0.685069 55 7 0.127
0.849769 38 4 0.105
0.876529 35 2 0.057
1.015051 25 1 0.040
1.071766 24 1 0.041
1.345698 8 0 0

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 94
differential expressions of the genes, the genetically dif-
ferential expressions of the genes are resulted from
change in structure of these genes or from altering regu-
lation elements of an operon system. Compare to
SHRSP, SHRSR genetically strengthens (up-regulate)
expressions of the genes associated with rat resistant to
stroke and weakens (or down-regulate) expressions of
the genes making rat prone to stroke. Our data show that
these genes work in transcription and translation regula-
tions, ion and molecule transportations including chan-
nels and transporters, metabolisms, nervous system, and
functional and structural proteins. Since stroke, which
mostly occur in the cortical region of the brain, is a
complex neurological event, the genes working for the
nervous system, including receptors, neurotransmissions,
binding proteins, neurons, synapses, etc, were strongly
regulated by some other key genes for stroke. Mutations
would change expressions of these genes working in the
nervous system. For example, as mentioned above, 8
genes for the GABA receptors (GABA-A and -B recep-
tors) showed higher expression levels in SHRSR than in
SHRSP. The GABA-B receptors potentially play an im-
portant role in the inflammatory response and neutro-
phil-dependent ischemia-reperfusion injury such as
stroke [59]. Another example is that the TrkB receptor, a
high-affinity receptor of two neurotrophins (brain-de-
rived neurotrophic factor and neurotrophin 4/5), is im-
portant for neuronal growth and differentiation and regu-
lation of synaptic transmission and for prevention from
neuronal damage after ischemia [60,61]. In our microar-
ray data genes for the full-length (FL) and the truncated
TrKB showed differential expressions between SHRSR
and SHRSP even though their differential expression
direction is just opposite (supplemental Table 1).
In addition, the genetic difference for stroke between
two rat strains also alters expressions of some critical
genes involved in stroke. For instance, CKII plays po-
tentially important roles in specific neural functions and
is significantly associated with change in expressions of
many inflammatory genes involved in stroke. The ex-
pression difference of CKII gene between SHRSR and
SHRSP causes differential expressions of a set of rele-
vant genes.
Fornage, et al. [34] used TagMan assay to measure the
relative expression levels of 7 functional genes encoding
atrial natriuretic peptide (Anp), the neurotrophin recep-
tor protein tyrosine kinase (TrkB, a truncated form), ca-
sein kinase 2 (CKII), complexin 2 (Cplx2), stearoyl CoA
desaturase 2 (Scd2), glycerol-3-phosophate acyltran-
sterase (Gpan), and inositol 1,4,5-triphosphate receptor
(Itpr1). They found that these 7 genes were significantly
differentially expressed between genotypes SHRSR and
SHRSP with p < 0.05. In our current microarray data,
these 7 genes were also found to have significant ex-
pression change between these two genotypes at FDR <
0.3%. Furthermore, our microarray data were well
agreeable with the TaqMan data for the direction of
change in expressions between these two genotypes. In
addition, Tropea, et al. [62] also found that the genes
encoding glutamate receptor (GluR-A) and GABA re-
ceptor had significantly expression change between two
mice treated by respective dark rearing and monocular
deprivation. We performed RAC for these 534 genes
detected to be differentially expressed between two ge-
notypes and the other 481 genes that were not identified
to be differently expressed and found that there were a
lot of strongly positive and negative correlation expres-
sions between these differentially expressed 534 genes,
while all the 481 genes without differential expression
were not significantly correlated in expression variations
(the results will be shown elsewhere).
The differentially expressed genes may be classified
into different functional groups because genes in a func-
tional group possibly have the same or similar expres-
sion pattern. The similar expression pattern may be
measured by correlated expressions, including co-expre-
ssions and co-regulations of gene-expressions. By using
correlation of gene-expressions, one can build clusters or
networks of functional genes related to stroke and find
associations between functional genes and build gene
pathways for a global insight into a pathogenesis of
stroke. These valuable and interesting studies will be
given elsewhere.
The role of dietary factors in occurrence of stroke has
been well documented. For example, when feeding a diet
low in potassium and high in sodium, the SHRSP strain
developed a rapid onset of stroke [25,26], while potas-
sium supplementation remarkably reduced the incidence
of stroke and delayed its onset [63]. In addition, high
dietary potassium intake was significantly associated
with a reduced risk to stroke [64]. However, our current
data did not show that the dietary salt has a strong regu-
lation effect on expression variations of genes in vivo.
But it might play an important role in metabolic and
physiological processes for stroke. We also found that
the interaction between genetic difference and dietary
salt for stroke has a weak contribution to expression
variations of genes.
5. ACKNOWLEDGEMENTS
This research was supported by grants from the U.S. National Institutes
of Health (NS41466 and HL69126) to MF.
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Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 98
Supplemental Materials
Supplemental Table 1: 341 functional genes differential-
ly expressed between two genotypes SHRSR and SHRSP,
8 functional genes differentially expressed between high
salt and low salt and 11 genes differentially expressed
due to GE interaction. Supplemental Table 2: 169, 4, and
3 ESTs differentially expressed due to genetic, environ-
mental, and GE interaction effects, separately. ”
Supplemental Table 1. Functional genes found to have significantly differential expressions by RAM at FDR ≤ 0.05.
Genetic effects for stroke in rat brain
Gene ID Gene notation Prone Resistant T-value
Transcription and translation
Transcript factor and regulators
L42855 RNA polymerase II transcription factor SIII p18 subunit 234.083 –234.083 –5.454
AF031657 Zinc finger protein 94 (Zfp94) –32.325 32.325 3.752
AF052042 Zinc finger protein Y1 (RLZF-Y) 49.258 –49.258 –3.738
U41164 Cys2/His2 zinc finger protein (rKr1) –206.252 206.252 15.78
U67082 KRAB-zinc finger protein KZF-1 –20.306 20.306 4.111
X12744 c-erb-A thyroid hormone receptor –169.979 169.979 3.976
D14046 DNA topoisomerase IIB –20.331 20.331 4.045
AF036959 nuclear serine/threonine protein kinase –58.142 58.142 4.083
M65148 rATF2 –36.535 36.535 4.548
D26307 jun-D gene 241.619 –241.619 –15.401
M84716 putative v-fos transformation effector protein (Fte-1) 235.642 –235.642 –3.684
U61405 aryl hydrocarbon receptor nuclear translocator 2 (ARNT2) –49.4 49.4 4.562
U09228 New England Deaconess E-box binding factor –94.215 94.215 4.752
Translation factors and ribosomal protein
L46816 elongin A –22.565 22.565 4.543
U64705cds protein synthesis initiation factor 4AII –372.002 372.002 4.522
X83747 5S rRNA gene 68.804 –68.804 –7.259
M89646 ribosomal protein S24 84.14 –84.14 –5.805
X06423 ribosomal protein S8 455.608 –455.608 5.442
X57529cds ribosomal protein S18 298.652 298.652 –3.979
X58465 ribosomal protein S5 245.819 –245.819 –4.64
X59375 ribosomal protein S27 37.665 –37.665 –3.574
M17419 ribosomal protein L5 384.702 –384.702 –4.453
X78167 ribosomal protein L15 147.944 –147.944 –3.554
X78327 ribosomal protein L13 264.454 –264.454 –7.126
X62145cds ribosomal protein L8 672.379 –672.379 –8.816
X62166cds ribosomal protein L3 241.604 –241.604 –4.127
X06483cds ribosomal protein L32 332.167 –332.167 –5.067
X55153 ribosomal protein P2 292.629 –292.629 –4.896
D25224 40 kDa ribosomal protein 153.731 –153.731 –3.745
X60212 ASI mammalian equivalent of bacterial large ribosomal subunit protein L22–1367.33 1367.325 13.748
Receptors
AB016160 GABAB receptor 1c –375.954 375.954 4.597

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 99
AB016161UTR#1 GABAB receptor 1d –375.954 375.954 4.597
AF058795 GABA-B receptor gb2 385.231 –385.231 –3.622
L08490cds GABA-A receptor alpha-1 subunit –200.477 200.477 6.777
L08491cds GABA-A receptor alpha-2 subunit –42.858 42.858 3.774
L08493cds GABA-A receptor alpha-4 subunit –59.423 59.423 4.818
L08497cds GABA-A receptor gamma-2 subunit –144.552 144.552 3.686
X15467cds GABA(A) receptor beta-2 subunit –33.492 33.492 4.57
X15468cds GABA(A) receptor beta-3 subunit –39.298 39.298 6.074
L09653 transforming growth factor-b type II receptor –26.788 26.788 3.54
L19112 heparin-binding fibroblast growth factor receptor 2 –35.744 35.744 4.911
D28498 Flt-1 tyrosine kinase receptor –48.652 48.652 3.676
L19341 activin type I receptor –38.242 38.242 4.348
L27487 calcitonin receptor-like receptor (CRLR) –17.877 17.877 4.011
L31622 nicotinic acetylcholine receptor beta 2 subunit –39.371 39.371 4.856
M64699 inositol 145-trisphosphate receptor (IP-3-R) –95.504 95.504 8.663
M77184 parathyroid hormone receptor –34.298 34.298 4.616
S39221 NMDA receptor –89.133 89.133 4.177
U21871 outer mitochondrial membrane receptor rTOM20 205.529 –205.529 –4.44
U38653 olfactory inositol 145-trisphosphate receptor (InsP3R) alternatively spliced
variant –134.683 134.683 10.547
U79031 Alpha-2D adrenergic receptor –14.206 14.206 3.942
AF071014 alpha-1D adrenergic receptor –50.375 50.375 3.708
U87306 transmembrane receptor Unc5H2 –130.031 130.031 4.307
D14908 PACAP receptor –37.054 37.054 3.638
M36418 glutamate receptor (GluR-A) –115.765 115.765 7.397
U11419 glutamate receptor subunit –105.019 105.019 4.847
S68284 S1 progestin receptor form B –33.294 33.294 4.343
S64044 progesterone receptor steroid-binding domain –26.452 26.452 3.971
X97121 NTR2 receptor 294.258 –294.258 –6.335
Channel
M59211 potassium channel Kv3.2b –16.429 16.429 4.404
M27159cds potassium channel-Kv2 –41.379 41.379 7.229
AF091247 potassium channel (KCNQ3) –98.731 98.731 5.995
M81783 K+ channel –63.573 63.573 4.469
M84203 K+ channel protein (KSHIIIA3) –32.598 32.598 4.066
X70662 K+ channel protein beta subunit –93.221 93.221 6.27
U37147 sodium channel beta 2 subunit (SCNB2) gene –56.073 56.073 9.165
Z36944 putative chloride channel –45.958 45.958 3.496
U27558 brain-specific inwardly rectifying K+ channel 1 –60.713 60.713 4.059
Z67744 CLC-7 chloride channel protein –25.602 25.602 3.472
AF048828 voltage dependent anion channel (RVDAC1) –99.319 99.319 5.013
AF049239 voltage-gated sodium channel rPN4 –74.358 74.358 6.928
D38101 rCACN4A L-type voltage-dependent calcium channel alpha 1 subunit –43.992 43.992 4.918

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 100
D17521 protein kinase C-regulated chloride channel 125.692 –125.692 –5.216
L39018 sodium channel protein 6 (SCP6) –104.696 104.696 4.592
AF021923 potassium-dependent sodium-calcium exchanger (NCKX2) –91.338 91.338 5.179
X76724 RCK beta2 –93.646 93.646 4.92
X76452cds ATPase isoform 4 calcium-pumping –65.59 65.59 7.027
Transportation
S59158 glutamate transporter 439.546 –439.546 –10.514
S75687 glutamate/aspartate transporter –195.281 195.281 4.825
X63744 glutamate/aspartate transporter protein 23.71 –23.71 –3.952
S82233 rBSC2 = Na-K-Cl cotransporter homolog –31.594 31.594 4.381
U15098 GluT and GluT-R glutamate transporter –43.744 43.744 5.19
U75395UTR#1 furosemide-sensitive K-Cl cotransporter 31.923 –31.923 –5.004
U89529 fatty acid transport 132.927 –132.927 –4.963
AB015432 LAT1 (L-type amino acid transporter 1) 227.856 –227.856 –8.148
D13962 neuron glucose transporter –130.55 130.55 6.329
M15882 clathrin light chain (LCA1) –122.792 122.792 3.55
Metabolism and enzymes
Phosphate metabolism
M89945 farnesyl diphosphate synthase 96.021 –96.021 –5.884
U28938 protein tyrosine phosphatase D30 41.208 –41.208 –4.588
U36771 glycerol 3-phosphate acyltransferase nuclear gene encoding mitochondrial
protein –24.71 24.71 3.755
U36772 glycerol-3-phosphate acyltransferase nuclear gene encoding mitochondrial
protein –30.313 30.313 4.578
U36773 glycerol-3-phosphate acyltransferase nuclear gene encoding mitochondrial
protein –175.954 175.954 7.066
U55192 inositol polyphosphate 5 phosphatase Ship (SHIP) 45.587 –45.588 –6.486
U73458 protein tyrosine phosphatase (PTPNE6) –25.885 25.885 7.108
X12355 phosphoinositide-specific phospholipase C form-I (PI-PLC I) 165.656 –165.656 –4.498
X94185 RNMKP3 dual specificity phosphatase MKP-3 78.738 –78.738 –5.876
Y12635 vacuolar adenosine triphosphatase subunit B –215.946 215.946 5.968
D14419 PP2A BRa B regulatory subunit of protein phosphatase 2A –36.3 36.3 3.894
D38261 BRgamma B-regulatory subunit of protein phosphatase 2A –104.346 104.346 3.515
M27726 phosphorylase (B-GP1) –66.765 66.765 3.864
M23591#2 catalytic protein phosphatase 2A-beta 228.944 –228.944 –3.916
D83538 230kDa phosphatidylinositol 4-kinase –35.392 35.392 2.266
Oxidization and reduction
S45812 monoamine oxidase A 65.823 –65.823 –4.234
U75927UTR#1 cytochrome oxidase subunit VIIa 41.571 –41.571 –4.632
L48209 cytochrome c oxidase subunit VIII (COX-VIII) 386.258 –386.258 –4.105
D00688 monoamine oxidase A 115.533 –115.533 –4.675
X60328 cytosolic epoxide hydrolase 49.658 –49.658 –5.367
AA686031 NGF-treated similar to NADH-ubiquinone oxidoreductase 75 kDa subunit–34.798 34.798 3.78

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 101
S81448 type I 5 alpha-reductase –52.048 52.048 3.688
M29249cds 3-hydroxy-3-methylglutaryl coenzyme A reductase –22.027 22.027 3.695
U60063 aldehyde dehydrogenase 23.792 –23.792 –3.517
AF001898 aldehyde dehydrogenase (ALDH) 134.763 –134.763 –10
X97772 D-3-phosphoglycerate dehydrogenase 46.502 –46.502 –3.913
U64451 short-branched chain acyl-CoA dehydrogenase precursor –34.629 34.629 3.718
E03428cds peptidylglycin-alpha-amidating monooxygenase –255.258 255.258 4.885
Energy Metabolism
D84450 Na+ K+-ATPase beta-3 subunit 164.085 –164.085 –4.705
D90048exon Na+ K+-ATPase (EC 3.6.1.3) beta2 subunit –67.304 67.304 4.455
D90049exon#1-2 Na+ K+-ATPase (EC 3.6.1.3) alpha2 subunit –387.342 387.342 10.57
M74494 sodium/potassium ATPase alpha-1 subunit truncated isoform –722.437 722.438 8.373
D50696 proteasomal ATPase (S4) 68.333 –68.333 –5.248
X56133 F1-ATPase alpha subunit (EC 3.6.1.34) 124.333 –124.333 –3.66
U15408 plasma membrane Ca2+-ATPase isoform 4 and alternatively spliced varia-
tions –98.173 98.173 7.509
Extracellular and Intracellular signaling
D64045 phosphatidylinositol 3-kinase p85 alpha subunit 10.219 –10.219 –3.738
X53428cds glycogen synthase kinase 3 beta (EC 2.7.1.37) 50.821 –50.821 –5.437
U42627 dual-specificity protein tyrosine phosphatase (rVH6) 64.627 –64.627 –6.088
M25350 cAMP phosphodiesterase (PDE4) –30.869 30.869 5.297
Z36276 GK II cGMP dependent protein kinase II 41.292 –41.292 –5.598
X07286cds protein kinase C alpha –25.513 25.513 4.173
M64300 extracellular signal-related kinase (ERK2) –88.298 88.298 4.187
AF068261 pancreatic serine threonine kinase –37.533 37.533 3.625
AJ000557 RNJAK2 Janus protein tyrosine kinase 2 JAK2 –25.952 25.952 5.331
L15618 casein kinase II alpha subunit (CK2) 111.242 –111.242 –6.057
M96159 adenylyl cyclase type V –44.754 44.754 4.219
Carbohydrate metabolism
U27319exon type I hexokinase (HKI) –129.292 129.292 4.522
J04526 brain hexokinase –383.367 383.367 6.856
M54926 lactate dehydrogenase A 191.99 –191.99 –3.665
D21869 PKF-M (phosphofructokinase-M) –217.973 217.973 6.708
X89383 SNF1-related kinase –47.942 47.942 6.312
D10852 N-acetylglucosaminyltransferase III –50.229 50.229 3.922
D49434 ARSB arylsulfatase B –59.842 59.842 4.292
D89340 dipeptidyl peptidase 60.481 –60.481 –5.147
L27075 ATP-citrate lyase –95.488 95.488 7.533
L02615 cAMP-dependent protein kinase inhibitor (PKI) –47.113 47.113 3.991
D10770 beta isoform of catalytic subunit of cAMP-dependent protein kinase –209.046 209.046 7.777
U75932 cAMP-dependent protein kinase type I regulatory subunit –183.675 183.675 5.152
Lipid metabolism
U08976 Wistar peroxisomal enoyl hydratase-like protein (PXEL) 62.96 –62.96 –6.325

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 102
U67995 stearyl-CoA desaturase 2 –433.358 433.358 4.194
AF036761 stearoyl-CoA desaturase 2 –1419.63 1419.633 12.336
S75730 stearoyl-CoA desaturase 2 SCD2 homolog –367.006 367.006 9.267
X05341 3-oxoacyl-CoA thiolase –87.423 87.423 5.111
E12286cds GM2 activator protein 12.392 –12.392 –3.793
Amino acid and nucleotide acid metabolism
U35774 cytosolic branch chain aminotransferase –749.29 749.29 7.912
L34821 succinate-semialdehyde dehydrogenase (SSADH) –41.54 41.54 4.973
D26073 phosphoribosylpyrophosphate synthetase-associated protein (39kDa) 67.235 –67.235 –4.348
Neurotransmitter
M93257 cathechol-O-methyltransferase 69.431 –69.431 –4.271
X02610 non-neuronal enolase (NNE) (alpha-alpha enolase 2-phospho-D-glycerate
hydrolase EC 4.2.1.11) –451.39 451.39 3.074
M55291 neural receptor protein-tyrosine kinase (trkB) (FL) 304.527 –304.527 –8.829
M55293 neural receptor protein-tyrosine kinase (trkB) (short) –49.388 49.388 5.896
Ster ol metabolism
U17697 lanosterol 14-alpha-demethylase 115.121 –115.121 –4.903
D45252 23-oxidosqualene: lanosterol cyclase –43.848 43.848 3.936
proteinases
U27201 tissue inhibitor of metalloproteinase 3 (TIMP-3) –43.229 43.229 3.814
U38379 gamma-glutamyl hydrolase precursor 32.988 –32.988 –4.052
Protein transfer
X73653 tau protein kinase I 68.044 –68.044 –6.602
U86635 glutathione s-transferase M5 120.1 –120.1 –3.987
AF084205 serine/threonine protein kinase TAO1 –74.75 74.75 3.623
The others
Z48444 disintegrin-metalloprotease –52.638 52.637 6.193
M13707 protein kinase C type I 112.571 –112.571 –4.183
E01789cds Protein kinase C type-II –169.185 169.185 4.561
K03486 protein kinase C type III –295.848 295.848 3.502
E07296cds N-acetylglucosamine transferase-I (brain) –41.748 41.748 4.17
L19998 minoxidil sulfotransferase 125.008 –125.008 –5.916
L13406 calcium/calmodulin-dependent protein kinase II delta subunit (brain) –24.379 24.379 4.142
L05557cds plasma membrane calcium ATPase isoform 2 –43.725 43.725 3.659
D30041 RAC protein kinase beta –50.008 50.008 5.205
M81225 farnesyl transferase alpha subunit 45.469 –45.469 –3.674
Protein
Membrane prot ei ns
X53565 trans-Golgi network integral membrane protein TGN38 –71.646 71.646 8.695
AF102853 membrane-associated guanylate kinase-interacting protein 1 Maguin-1 –61.667 61.667 3.883
M24104 vesicle associated membrane protein(VAMP-1) –169.092 169.092 3.698
D13623 p34 protein 69.16 –69.16 –3.889
AB016425 occludin –35.948 35.948 4.087

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U31367 myelin protein MVP17 225.469 –225.469 –4.242
L18889 calnexin –111.698 111.698 3.926
U27767 reversibly glycosylated polypeptide 4(RGP4) –221.242 221.242 3.772
D28111 myelin-associated oligodendrocytic basic protein (MOBP) 469.748 –469.748 –9.107
Binding proteins
M69055 insulin-like growth factor binding protein (rIGFBP-6) 45.185 –45.185 –4.194
U02096 acid binding protein 61.238 –61.238 –4.051
S83025 TSH receptor suppressor element-binding protein-1 301.702 –301.702 –6.346
S69874 cutaneous fatty acid-binding protein (C-FABP) 122.942 –122.942 –6.337
U39875 EF-hand Ca2+-binding protein p22 –57.144 57.144 5.125
AF090306 retinoblastoma binding protein 99.437 –99.438 –3.724
X13167cds NF-1 like DNA-binding protein –30.185 30.185 4.489
AF053768 brain specific cortactin-binding protein CBP90 –17.725 17.725 3.852
D13125 neural visinin-like Ca2+-binding protein type 2 (NVP-2) –94.375 94.375 5.185
D13309 DNA-binding protein B 160.867 –160.867 –4.92
M12672 guanine nucleotide-binding protein G-i alpha subunit –107.513 107.513 4.01
M14050 immunoglobulin heavy chain binding protein (BiP) 178.44 –178.44 –3.741
L27663 DNA binding protein (Brn-2) 14.6 –14.6 –3.555
D14819 calcium-binding protein P23k beta –89.675 89.675 4.891
L10326 alternatively spliced GTP-binding protein alpha subunit 282.581 –282.581 –7.238
L19698 GTP-binding protein (ral A) 34.673 –34.673 –4.669
M64986 amphoterin 62.135 –62.135 –4.356
L12380 ADP-ribosylation factor 1 –292.769 292.769 4.657
L12382 ADP-ribosylation factor 3 –190.792 190.792 5.941
AB000362 cold inducible RNA binding protein (CIRP) 68.896 –68.896 –3.631
X13933 calmodulin (pRCM1) (a calcium-binding protein) 472.477 –472.477 –3.965
AF019043 dynamin-like protein (DLP1) a large GTP-binding protein –72.504 72.504 5.145
Shock-proteins
S81917 34 kDa DnaJ-hsp40 heat shock-chaperone protein –81.517 81.517 6.626
S75280 heat shock protein precursor –38.175 38.175 3.7
S45392 heat shock protein 90 646.619 –646.619 –3.743
AJ002967 utrophin –50.177 50.177 4.153
Microtubule and s ke l e tal proteins
S74265 high molecular weight microtubule-associated protein(HMW MAP2) –20.917 20.917 4.551
U25264 skeletal muscle selenoprotein W (SelW) 254.646 –254.646 –3.824
J00692 skeletal muscle alpha-actin 77.365 –77.365 –8.101
X53455cds microtubule-associated protein 2 –67.748 67.748 4.63
X66840cds microtubule associated protein 1A (partial) –20.808 20.808 3.475
AF035953 kinesin-related protein KRP4 (KRP4) –36.773 36.773 4.588
D88461 N-WASP –53.617 53.617 4.436
U59241 E-tropomodulin –52.869 52.869 4.021
S77900 myosin regulatory light chain isoform C 30.39 –30.39 –3.746

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 104
AJ000485 cytoplasmic linker proteins CLIP-115 protein 45.527 –45.527 –3.783
X62952 vimentin 75.221 –75.221 –3.977
U15766#1 nonmuscle myosin heavy chain-B fragment II –40.644 40.644 4.194
Glycoproteins
M99485 myelin/oligodendrocyte glycoprotein (MOG) 63.856 –63.856 –3.506
X02002 thy-1 gene for cell-surface glycoprotein –557.267 557.267 20.92
X07648cds amyloidogenic glycoprotein (rAG) –444.925 444.925 4.226
X99337 RNGP55 glycoprotein 55 –267.369 267.369 4.572
X99338 RNGP56 glycoprotein 65 –254.163 254.163 4.228
D10587 85kDa sialoglycoprotein (LGP85) –31.871 31.871 4.611
nervous system-associated protein
Y16563 brain-specific synapse-associated protein –25.227 25.227 4.512
Y08981 synaptonemal complex lateral element protein 9.256 –9.256 –3.786
U56261 SNAP-25a –121.554 121.554 5.507
AB003991 SNAP-25A –236.8 236.8 5.942
AB003992 SNAP-25B –236.8 236.8 5.942
D32249 neurodegeneration associated protein 1 574.427 –574.427 –6.016
U33553 neuroglycan C precursor 384.708 –384.708 –4.548
X16623cds neuraxin –75.504 75.504 7.341
AF060879 neurocan –106.413 106.413 4.138
L10362 synaptic vesicle protein 2B (SV2B) –224.181 224.181 9.267
M64488 synaptotagmin II –11.073 11.073 3.545
L27421 neuronal calcium sensor (NCS-1) –33.075 33.075 3.921
M27812 synapsin Ia –346.852 346.852 3.45
U20105 synaptotagmin VI –21.094 21.094 3.881
U14398 synaptotagmin IV homolog –141.652 141.652 4.313
AF000423 synaptotagmin XI. –157.29 157.29 6.632
AF007836 rab3 effector (RIM) –22.688 22.688 3.762
S65091 cAMP-regulated phosphoprotein 135.383 –135.383 –6.047
S73007 synuclein SYN1 –248.146 248.146 4.063
U39320 cysteine string protein –51.579 51.579 6.221
X77934 RNWAPLP2 (Wistar) amyloid precursor-like protein 2 –168.815 168.815 3.714
Y08355 PKC-zeta-interacting protein –581.092 581.092 5.502
Y13413 Fe65L2 protein –111.915 111.915 4.706
M31176#2 gastrin-releasing peptide 36.546 –36.546 –3.848
AF091834 N-ethylmaleimide sensitive factor NSF (phosphorylation of NSF by PKC)–205.527 205.527 4.191
U01022 Huntington’s disease –27.44 27.44 5.351
U35099 complexin II (related to Huntington disease) –174.902 174.902 7.925
Y17048 caldendrin 133.208 –133.208 –4.055
X78689 RNEHK1 ehk-1 –31.852 31.852 3.744
Cell signal-associated protein
AB011544 TUBBY protein –37.306 37.306 4.655

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AF055065 signal regulatory protein alpha –78.685 78.685 6.927
D44481 CRK-II –40.298 40.298 4.428
AF023621 sortilin –149.46 149.46 7.603
AF081196 calcium and DAG-regulated guanine nucleotide exchange factor II –159.973 159.973 4.234
D14425 calcineurin B –312.738 312.737 7.18
AJ003148 RNAJ3148 GAS-7 protein –109.26 109.26 8.053
U50842 ubiquitin ligase (Nedd4) protein –115.719 115.719 4.823
U49049 chapsyn-110 –21.508 21.508 5.745
X80290 pituitary adenylate cyclase activating peptide –51.6 51.6 5.056
Cell growth and cell divi si on
AF083330 kinesin-like protein KIF3C (KIF3C) –148.24 148.24 8.059
D38629 adenomatosis polyposis coli (APC) protein –51.337 51.338 3.54
L26268 anti-proliferative factor (BTG1) 90.512 –90.512 –6.308
D16308 cyclin D2 127.925 –127.925 –7.945
X62322 epithelin 1 and 2 –0.769 0.769 0.042
Immunosystem-associated proteins
L10336 guanine nucleotide-releasing protein (mss4) –47.744 47.744 5.329
AF060819 ras guanyl releasing protein (rasGRP) –106.413 106.413 4.138
AF036548 response gene to complement 32 (RGC-32) 23.581 –23.581 –3.918
U49062 heat sle antigen CD24 83.394 –83.394 –5.509
X54640 the OX47 antigen 242.831 –242.831 –3.929
M58404 thymosin beta-10 (testis-specific) 193.348 –193.348 –4.213
Cell adhesion
U81037 ankyrin binding cell adhesion molecule (NrCAM) –145.677 145.677 5.063
M88709 cell adhesion-like –186.229 186.229 3.961
U65916 ankyrin membrane binding domain –37.515 37.515 8.653
AB004276 protocadherin 4 –152.015 152.015 4.879
U83230 l-Afadin –20.998 20.998 3.555
S58528 integrin alpha v subunit –20.865 20.865 3.877
Tumor-associated proteins
X12535cds ras-related protein p23 –184.023 184.023 7.176
X13905cds ras-related rab1B protein –102.883 102.883 5.705
L19304 tumor suppressor fragment 2 of 6 –81.4 81.4 7.522
L19306 tumor suppressor fragment 4 of 6 –69.252 69.252 4.743
D89863 M-Ras –56.265 56.265 4.035
AF015911 NAC-1 protein (NAC-1) linked to ovarian cancer recurrence –29.006 29.006 3.794
M91235 VL30 element 52.354 –52.354 –4.13
The other proteins
M81687 core protein (HSPG) –32.6 32.6 3.579
U07619 tissue factor protein 15.644 –15.644 –4.19
U20181 iron-regulatory protein 2 (IRP2) –23.352 23.352 4.003
U37142 brevican core protein 100.385 –100.385 –4.032

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 106
V01543 fragment isolated from the brain and coding for brain specific peptide –86.646 86.646 3.995
X01118 gamma atrial natriuretic peptide precursor (gamma-γANP) 30.504 –30.504 –5.358
E00698cds gamma atrium natriuretic polypeptide gamma-γANP 46.577 –46.577 –7.296
X96394 multidrug resistance protein –38.102 38.102 4.624
U61729 proline rich protein 39.804 –39.804 –4.279
X57405 homologue of Drosophila notch protein –54.158 54.158 3.928
AF053362 death effector domain-containing protein DEFT 26.854 –26.854 –3.77
U77918 spermatogenic cell/sperm-associated Tat-binding protein homolog Sata76.302 –76.302 –5.379
AF020212 DLP1 splice variant 2 (DLP1) –40.938 40.938 5.057
AF087697 dlg 3 –58.725 58.725 3.933
AF095741 MG87 80.017 –80.017 –3.914
V01217 cytoplasmic beta-actin –517.133 517.133 4.073
D00092 70 kd mitochondrial autoantigen –38.969 38.969 3.611
AJ007291 RNO7291 CAP1 gene 208.46 –208.46 –5.285
D30804 proteasome subunit RC6-1 –5.644 5.644 0.172
D45247 proteasome subunit RCX –82.225 82.225 1.542
K01934#2 hepatic product spot 14 34.698 –34.698 –4.442
K02816 unidentified expressed in embryo and tumor –236.781 236.781 4.875
L02915 RATSOM fragment –41.888 41.888 6.031
L03201 cathepsin S 439.281 –439.281 –5.23
L14462 R-esp1 322.681 –322.681 –4.06
L21192 GAP-43 217.45 –217.45 –4.111
AA684963 RPCAU48 134.917 –134.917 –4.781
AB003515 GEF-2 133.3 –133.3 –4.544
AB006451 Tim23 132.988 –132.988 –5.285
AB008908 FHF-4b –20.794 20.794 4.27
AB013454 type 2 sodium phosphate cotransporter NaPi-2 beta 36.871 –36.871 –5.444
M34176 adaptin –141.508 141.508 4.957
M83679 RAB15 –37.377 37.377 4.02
M87634 BF-1 90.588 –90.588 –4.551
S70011 tricarboxylate carrier 44.098 –44.098 –3.671
S75019 turgor protein homolog 42.773 –42.773 –3.833
U15138 LIC-2 dynein light intermediate chain 53/55 382.858 –382.858 –4.316
U47312 R2 cerebellum DDRT-T-PCR LIARCD-3 –70.275 70.275 4.388
U53859 calpain small subunit (css1) 315.635 –315.635 –4.154
Y13591 calpastatin (a calpain-specific inhibitor ) –6.879 6.879 4.014
U94189 Duo –29.715 29.715 6.779
X05300 ribophorin I –63.44 63.44 3.449
X05472cds#3 2.4 kb repeat DNA right terminal region 39.817 –39.817 –5.063
X52140 integrin alpha-1 –22.483 22.483 3.974
X52772cds p65 –164.415 164.415 8.833
X52817cds C1-13 gene product –549.125 549.125 3.517

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 107
X82445 C15 80.196 –80.196 –5.542
X74401 (GDP dissociation inhibitor 2) GDI beta –72.648 72.648 3.635
X74402 (GDP dissociation inhibitor 1) GDI alpha –377.185 377.185 8.079
Z34004 growth hormone-releasing hormone alternate –86.606 86.606 5.154
Environmental effects for stroke in rat brain
Gene ID Gene notation Low salt High salt T-value
M96601 taurine transporter –39.423 39.423 4.317
AF022136 connexin 40 (GJA5) 19.531 –19.531 –4.268
X78949 prolyl 4-hydroxylase alpha subunit 49.721 –49.721 –4.405
M95735 syntaxin B 296.838 –296.838 –4.479
U53505s type II iodothyronine deiodinase 22.735 –22.735 –4.209
D84477 RhoA 86.992 –86.992 –6.09
AB012231 NF1-B2 –87.129 87.129 4.456
AF037071 carboxyl-terminal PDZ ligand of neuronal nitric oxide synthase –145.683 145.683 4.597
Genetic-Environmental interaction for stroke in rat brain
Gene ID Gene notation LSSP, HSSR LSSR, HSSP T-value
S65355 nonselective-type endothelin receptor –38.51 38.51 3.929
D64061 annexin V-binding protein (ABP-7) –148.86 148.86 4.195
U45479 synaptojanin –214.742 214.742 4.301
AF007758 synuclein 1 –233.569 233.569 3.79
X13412cds flk protein –18.106 18.106 3.8
L14851 neurexin III-alpha gene –100.21 100.21 4.531
AF004017 Na+ bicarbonate cotransporter (NBC) –65.935 65.935 4.559
X57764 ET-B endothelin receptor –50.192 50.192 4.692
U62897 carboxypeptidase D precursor (Cpd) –73.869 73.869 4.721
M92076 metabotropic glutamate receptor 3 –185.475 185.475 4.901
X78848cds RNGSTYC1F GST Yc1 154.285 –154.285 –4.334
Supplemental Table 2. ESTs found to have significantly differential expressions by RAM at FDR ≤ 0.05.
Genetic effects for stroke in rat brain
Gene ID Expressed sequence tag Prone Resistant T-value
rc_AA799406 EST188903 173.358 –173.358 –5.893
rc_AA799538 EST189035 –0.892 0.892 0.259
rc_AA799576 EST189073 47.631 –47.631 –3.585
rc_AA799599 EST189096 128.765 –128.765 –4.717
rc_AA799621 EST189118 –25.621 25.621 4.007
rc_AA799721 EST189218 –39.392 39.392 3.459
rc_AA799755 EST189252 6.452 –6.452 –3.823
rc_AA799786 EST189283 –13.406 13.406 3.687
rc_AA800015 EST189512 101.158 –101.158 –3.96
rc_AA800034 EST189531 102.317 –102.317 –3.532
rc_AA800170 EST189667 –70.846 70.846 4.246

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 108
rc_AA800198 EST189695 91.712 –91.713 –3.705
rc_AA800535 EST190032 –36.675 36.675 3.463
rc_AA800784 EST190281 42.215 –42.215 –4.606
rc_AA800908 EST190405 –14.529 14.529 3.573
rc_AA818072 UI-R-A0-ag-b-06-0-UI.s2 81.819 –81.819 –3.96
rc_AA818888 UI-R-A0-av-c-08-0-UI.s1 279.373 –279.373 –3.708
rc_AA849038 EST191800 450.656 –450.656 –4.609
rc_AA849648 EST192415 46.796 –46.796 –4.079
rc_AA849648 EST192415 46.796 –46.796 –4.079
rc_AA849722 EST192489 125.521 –125.521 –3.549
rc_AA852004 EST194773 –311.069 311.069 3.777
rc_AA859372 UI-R-E0-bt-a-03-0-UI.s1 21.508 –21.508 –4.561
rc_AA859520 UI-R-E0-br-b-02-0-UI.s1 84.873 –84.873 –5.748
rc_AA859633 UI-R-E0-bs-h-09-0-UI.s1 –57.373 57.373 5.532
rc_AA859663 UI-R-E0-bs-c-07-0-UI.s1 110.933 –110.933 –4.617
rc_AA859665 UI-R-E0-bs-c-09-0-UI.s1 –59.358 59.358 3.475
rc_AA859688 UI-R-E0-bx-e-09-0-UI.s1 –134.154 134.154 3.65
rc_AA859829 UI-R-E0-cc-f-12-0-UI.s1 –112.177 112.177 4.193
rc_AA859837 UI-R-E0-cc-g-09-0-UI.s1 –288.188 288.188 7.272
rc_AA859837 UI-R-E0-cc-g-09-0-UI.s1 –288.188 288.188 7.272
rc_AA859848 UI-R-E0-cc-h-10-0-UI. 26.071 –26.071 –4.346
rc_AA859877 UI-R-E0-cc-c-04-0-UI.s1 224.473 –224.473 –3.716
rc_AA859990 UI-R-E0-ca-a-08-0-UI.s1 157.642 –157.642 –8.206
rc_AA866237 UI-R-A0-bg-f-12-0-UI.s1 40.165 –40.165 –11.117
rc_AA874856 UI-R-E0-cg-h-11-0-UI.s1 –16.44 16.44 5.08
rc_AA874873 UI-R-E0-ci-d-11-0-UI.s1 –59.525 59.525 3.519
rc_AA874918 UI-R-E0-ck-g-08-0-UI.s1 –23.206 23.206 5.16
rc_AA874934 UI-R-E0-ci-c-05-0-UI.s1 313.988 –313.988 –9.047
rc_AA875054 UI-R-E0-cb-e-04-0-UI.s1 –158.067 158.067 17.55
rc_AA875105 UI-R-E0-cf-h-06-0-UI.s1 –25.24 25.24 3.756
rc_AA875135 UI-R-E0-bu-f-01-0-UI.s2 –22.981 22.981 3.749
rc_AA875225 UI-R-E0-cq-a-06-0-UI.s1 –63.581 63.581 0.51
rc_AA875275 UI-R-E0-ce-c-01-0-UI.s1 –25.106 25.106 5.258
rc_AA875427 UI-R-E0-cs-f-11-0-UI.s1 86.154 –86.154 –5.631
rc_AA875427 UI-R-E0-cs-f-11-0-UI.s1 86.154 –86.154 –5.631
rc_AA875470 UI-R-E0-cp-c-12-0-UI.s1 –183.688 183.688 3.829
rc_AA875506 UI-R-E0-ct-c-05-0-UI.s1 –74.846 74.846 12.647
rc_AA875577 UI-R-E0-cm-c-10-0-UI.s1 99.771 –99.771 –3.737
rc_AA891049 EST194852 84.856 –84.856 –5.772
rc_AA891302 EST195105 16.606 –16.606 –3.758
rc_AA891311 EST195114 15.308 –15.308 –3.581
rc_AA891595 EST195398 –36.196 36.196 5.339

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 109
rc_AA891666 EST195469 104.517 –104.517 –4.073
rc_AA891729 EST195532 500.083 –500.083 –5.682
rc_AA891740 EST195543 –73.5 73.5 4.687
rc_AA891742 EST195545 –38.715 38.715 5.191
rc_AA891785 EST195588 –4.871 4.871 0.285
rc_AA891800 EST195603 –7.783 7.783 0.717
rc_AA891810 EST195613 –125.629 125.629 3.795
rc_AA891818 EST195621 –69.681 69.681 4.168
rc_AA891890 EST195693 –26.223 26.223 4.413
rc_AA891911 EST195714 –23.404 23.404 3.78
rc_AA891920 EST195723 15.777 –15.777 –4.464
rc_AA891949 EST195752 40.694 –40.694 –3.814
rc_AA892012 EST195815 –62.613 62.613 3.945
rc_AA892297 EST196100 50.64 –50.64 –4.875
rc_AA892310 EST196113 –81.129 81.129 3.673
rc_AA892325 EST196128 26.917 –26.917 –3.534
rc_AA892339 EST196142 20.683 –20.683 –3.828
rc_AA892376 EST196179 81.869 –81.869 –3.651
rc_AA892378 EST196181 168.796 –168.796 –6.312
rc_AA892570 EST196373 –108.971 108.971 4.349
rc_AA892582 EST196385 243.565 –243.565 –3.642
rc_AA892798 EST196601 –26.538 26.538 4.344
rc_AA892801 EST196604 –590.127 590.127 5.166
rc_AA892801 EST196604 –590.127 590.127 5.166
rc_AA892813 EST196616 14.998 –14.998 –4.039
rc_AA892851 EST196654 35.777 –35.777 –4.049
rc_AA892895 EST196698 –341.698 341.698 3.614
rc_AA892895 EST196698 –341.698 341.698 3.614
rc_AA892918 EST196721 78.99 –78.99 –3.957
rc_AA893043 EST196846 11.035 –11.035 –4.26
rc_AA893172 EST196975 46.271 –46.271 –7.257
rc_AA893206 EST197009 –37.717 37.717 4.722
rc_AA893857 EST197660 –19.871 19.871 5.195
rc_AA893870 EST197673 34.294 –34.294 –5.568
rc_AA894119 EST197922 –36.665 36.665 3.785
rc_AA894148 EST197951 25.363 –25.363 –3.968
rc_AA894317 EST198120 –198.2 198.2 3.715
rc_AA899106 UI-R-E0-cw-d-04-0-UI.s1 137.733 –137.733 –5.317
rc_AA924925 UI-R-A1-eg-d-06-0-UI.s1 229.085 –229.085 –10.088
rc_AA925495 UI-R-A1-ep-c-04-0-UI.s1 11.731 –11.731 –3.525
rc_AA925506 UI-R-A1-ep-d-03-0-UI.s1 –79.448 79.448 5.077
rc_AA925762 UI-R-A1-ep-g-08-0-UI.s1 33.254 –33.254 –3.873

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 110
rc_AA925887 UI-R-A1-eo-h-06-0-UI.s1 –20.552 20.552 4.13
rc_AA946040 EST201539 139.458 –139.458 –5.187
rc_AA946313 EST201812 119.421 –119.421 –4.632
rc_AA946439 EST201938 –50.002 50.002 8.464
rc_AA956930 UI-R-E1-fl-a-11-0-UI.s1 –29.996 29.996 3.626
rc_AA956941 UI-R-E1-fl-c-10-0-UI.s1 35.215 –35.215 –4.96
rc_AA957777 UI-R-E1-fv-f-08-0-UI.s1 98.269 –98.269 –5.608
rc_AA957961 UI-R-E1-fz-g-08-0-UI.s1 –51.963 51.963 6.959
rc_AA963674 UI-R-E1-gg-h-01-0-UI.s1 –617.158 617.158 3.626
rc_AA963682 UI-R-E1-gg-h-11-0-UI.s1 28.265 –28.265 –5.223
rc_AA996484 UI-R-C0-hi-h-10-0-UI.s1 –65.765 65.765 3.628
rc_AA997367 UI-R-C0-hl-d-02-0-UI.s1 –31.423 31.423 4.105
rc_AA998683 UI-R-C0-ig-h-06-0-UI.s1 –103.6 103.6 1.671
rc_AI008852 EST203303 47.133 –47.133 –5.554
rc_AI012805 EST207256 300.475 –300.475 –6.671
rc_AI013472 EST208147 111.158 –111.158 –6.1
rc_AI013627 EST208302 155.006 –155.006 –4.062
rc_AI014087 EST207642 256.046 –256.046 –4.601
rc_AI029183 UI-R-C0-iv-h-08-0-UI.s1 –39.973 39.973 3.938
rc_AI044508 UI-R-C1-kc-a-07-0-UI.s1 –114.175 114.175 7.203
rc_AI044517 UI-R-C1-kc-b-10-0-UI.s1 –55.152 55.152 6.194
rc_AI044716 UI-R-C1-ki-a-09-0-UI.s1 36.3 –36.3 –3.858
rc_AI058393 UI-R-C1-kx-c-12-0-UI.s1 –123.498 123.498 3.79
rc_AI058601 UI-R-C1-kv-h-10-0-UI.s1 –44.135 44.135 3.681
rc_AI070521 UI-R-Y0-lv-f-09-0-UI.s1 100.425 –100.425 –6.923
rc_AI071507 UI-R-C2-nc-g-02-0-UI.s1 –19.15 19.15 3.735
rc_AI072089 UI-R-C2-nf-d-09-0-UI.s1 42.902 –42.902 –5.489
rc_AI073164 UI-R-Y0-mi-e-03-0-UI.s1 –87.256 87.256 3.521
rc_AI101103 EST210392 –484.602 484.602 4.081
rc_AI103236 EST212525 142.031 –142.031 –4.016
rc_AI104012 EST213301 24.904 –24.904 –3.634
rc_AI104035 EST213324 533.519 –533.519 –5.991
rc_AI104399 EST213688 487.592 –487.592 –3.652
rc_AI104500 EST213789 –31.413 31.413 3.467
rc_AI104513 EST213802 25.035 –25.035 –4.046
rc_AI105076 EST214365 –48.475 48.475 5.096
rc_AI105463 EST214752 –38.646 38.646 3.836
rc_AI137862 UI-R-C0-ik-g-07-0-UI.s1 –205.906 205.906 3.962
rc_AI145044 UI-R-BT0-pt-a-03-0-UI.s1 174.692 –174.692 –4.792
rc_AI145444 UI-R-BT0-pv-c-12-0-UI.s1 –15.123 15.123 4.733
rc_AI145494 UI-R-BT0-qf-f-12-0-UI.s1 –77.698 77.698 6.099
rc_AI170268 EST216194 198.048 –198.048 –5.21

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 111
rc_AI170613 EST216547 193.254 –193.254 –5.073
rc_AI171844 EST217831 269.933 –269.933 –4.5
rc_AI172097 EST218092 34.271 –34.271 –3.602
rc_AI172162 EST218157 262.413 –262.412 –5.996
rc_AI175900 EST219472 –81.635 81.635 4.146
rc_AI176460 EST220045 24.296 –24.296 –4.874
rc_AI177096 EST220703 62.14 –62.14 –4.168
rc_AI179399 EST223101 16.315 –16.315 –3.738
rc_AI228738 EST225433 –394.217 394.217 3.809
rc_AI228850 EST225545 –101.344 101.344 4.271
rc_AI229497 EST226192 144.385 –144.385 –4.771
rc_AI230572 EST227267 12.604 –12.604 –4.771
rc_AI231213 EST227901 126.613 –126.613 –8.349
rc_AI231354 EST228042 –107.173 107.173 6.339
rc_AI231445 EST228133 –79.029 79.029 6.748
rc_AI232096 EST228784 32.133 –32.133 –3.686
rc_AI232194 EST228882 –25.577 25.577 3.554
rc_AI232256 EST228944 –26.481 26.481 3.644
rc_AI236721 EST233283 –13.515 13.515 3.849
rc_AI237576 EST234138 –38.842 38.842 6.849
rx02409 3 sequence [] –22.883 22.883 3.879
rx01268 3 sequence [] –207.946 207.946 7.748
rx04826 3 sequence [] –272.36 272.36 3.438
rx04485 3 sequence [] 38.325 –38.325 –5.887
rx00364 3 sequence [] –148.348 148.348 3.869
rx05007 3 sequence [] –13.146 13.146 3.541
rx04757 3 sequence [] –26.315 26.315 4.264
rx02055 3 sequence [] 33.294 –33.294 –3.614
rx01635 3 sequence [] –31.179 31.179 3.608
rx01030 3 sequence [] 28.467 –28.467 –4.879
rc_H32977 EST108553 85.158 –85.158 –4.815
rc_H33426 EST109414 34.346 –34.346 –4.422
Environmental effects for stroke in rat brain
Gene ID Expressed sequence tag Low salt High salt T-value
rc_AA892817 EST196620 46.233 –46.233 –3.958
rc_AA875263 UI-R-E0-ce-a-08-0-UI.s1 73.427 –73.427 –4.096
rc_AI105374 EST214663 47.902 –47.902 –4.26
rc_AA891998 EST195801 32.26 –32.26 –4.755
rc_AA858621 UI-R-E0-bq-b-10-0-UI.s1 –330.613 330.613 3.971
rc_AA893939 EST197742 –77.875 77.875 4.004
rc_AA799340 EST188837 –215.35 215.35 4.019
rc_AI176456 EST220041 –366.979 366.979 4.028

Y. D. Tan et al. / Advances in Bioscience and Biotechnology 1 (2011) 87-113 112
rc_AA859627 UI-R-E0-bs-h-03-0-UI.s1 –15.579 15.579 4.267
rc_AA893065 EST196868 –246.806 246.806 5.056
rz00769 3 Unknown –29.769 29.769 5.909
rx01287 3 Unknown 122.506 –122.506 –4.01
rx02839 3 Unknown 21.352 –21.352 –4.19
rx01185 3 Unknown –15.431 15.431 4.56
Genetic-Environmental effects for stroke in rat brain
Gene ID Expressed sequence tag LSSP,HSSR LSSR,HSSP T-value
rc_AA859869 UI-R-E0-cc-b-08-0-UI.s1 –81.396 81.396 3.759
rc_AA858621 UI-R-E0-bq-b-10-0-UI.s1 –697.848 697.848 3.977
rc_AA875659 UI-R-E0-ct-h-07-0-UI.s1 –218.225 218.225 3.903
rc_AA800029 EST189526 –239.983 239.983 4.523
rc_AA891069 EST194872 –182.685 182.685 3.97
rc_H31692 EST106007 –93.073 93.073 3.878
rc_AI013194 EST207869 –271.792 271.792 3.885
rc_H31610 EST105814 –319.533 319.533 3.897
rc_AA894321 EST198124 –36.919 36.919 4.272
rc_AA799607 EST189104 –96.388 96.388 3.988
rc_AA893853 EST197656 –67.677 67.677 3.913
rx05078 3 unknown –305.14 305.14 4.368
rx01187 3 unknown –305.14 305.14 3.879
rx04752 3 unknown 23.421 –23.421 –5.32
Appendix A
22
() ()()
() ()
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() () ()
g
gg
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x
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GG
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, (A1)
22
() ()()
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() () ()
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ExEDE
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AE nE nE
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
, (A2)
11 22
211 222g
11 22
()() (I)
(I )(I )
()()
(I )()()
g
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xGE xGED
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(A3)
in the case of two unequal sample variances or
22
() ()()
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()
[()1]()[()1]()11
() () ()2()()
gg g
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





 
(A4)
22
() ()()
()
()
[()1]()[()1]()11
() () ()2()()
gg g
g
g
gggg
g
ggg g
xE xEDE
TE E
nEE nEE
AE nE nEnEnE












 
(A5)

Y. D. Tan et al. / American Journal of Molecular Biology 1 (2011) 87-113 11 3
11 22
112 1122222g
11 221122
()() (I)
(I )
(I )
[()1]()[()1]()11
() ()()2 ()()
gg ggg
g
gggg gggg
g
gg gggggg
xGE xGED
T
nGEGE nGEGE
AI nGEnGEnGEnGE



 




 

(A6)
in the case of two equal sample variances but unequal
sample sizes where ()() ()
g
gg
DGxG xG

, ()
g
DE

() ()
g
g
x
ExE

, 1122 (I )()()
g
gg gg
DxGExGE where
11
()(,)
g
ggggg
GEGE GE
 
 and22 ()(, )
g
ggggg
GEGE GE
 
.
Note that 11 () () () () ()
gggggg
nG nG nEnEnGE

 
22
()
gg
nG En

if there are not missing data in microarray
dataset. For ,
g
gg
CGE

or
g
I
, ()C
g
Ais defined as
1,if ()()1
() 0otherwise
gg
g
DC C
AC
 


 (A7)