contrasting acute behavioral responses to ethanol. We used oligonucleotide microarrays and bioinformatics methods to characterize
patterns of gene expression in three brain regions of the mesolimbic reward pathway of these strains. Expression profiling included
for microarray analysis, we identified 788 genes differentially expressed in control DBA/2J versus C57BL/6J mice and 307 ethanol-
regulated genes in the nucleus accumbens, prefrontal cortex, and ventral tegmental area. There were strikingly divergent patterns of
ethanol-responsive gene expression in the two strains. Ethanol-responsive genes also showed clustering at discrete chromosomal re-
of discrete functional groups and pathways was brain region specific: glucocorticoid signaling, neurogenesis, and myelination in the
signaling in the ventral tegmental area. Bioinformatics analysis identified several potential candidate genes for quantitative trait loci
linked to ethanol behaviors, further supporting a role for expression profiling in identifying genes for complex traits. Brain region-
Acute behavioral responses to ethanol have predictive value re-
garding risk for long-term ethanol drinking behavior in humans
(Schuckit, 1994) and animal models (Metten et al., 1998). Acute
ethanol-mediated brain signaling events may have a role in the
genesis of long-lasting behaviors such as dependence, sensitiza-
tion, and craving. Drug-induced changes in gene expression are
proposed as critical molecular adaptations leading to addiction
with repeated drug exposure (Nestler and Aghajanian, 1997).
Characterization of acute ethanol effects on brain gene expres-
sion could thus provide insight into mechanisms of rewarding
properties or other acute behavioral responses to ethanol, as well
as the neurobiology of long-term behaviors such as addiction.
Rodent model systems differing in ethanol-related pheno-
types have been used extensively to study the behavioral genetics
of ethanol action (Crabbe et al., 1999). The inbred mouse strains
DBA/2J (D2) and C57BL/6 (B6) differ markedly and inversely in
a number of ethanol behaviors. D2 mice show larger locomotor
al., 1994; Metten et al., 1998). We hypothesize that differences in
either specific basal gene expression or acute ethanol-evoked
changes in gene expression could be important determinants of
divergent behavioral responses to ethanol between D2 and B6
Expression profiling with DNA microarrays has been used to
been used to study ethanol-responsive genes in neural cell cul-
This work was supported by Grants AA13678 (M.F.M.), AA11853 (J.M.S.), AA13499 (R.W.W.), and AA13513
(R.W.W.) from the National Institute on Alcohol Abuse and Alcoholism and by funds provided by the State of
TheJournalofNeuroscience,March2,2005 • 25(9):2255–2266 • 2255
tures (Thibault et al., 2000) and genes differentially expressed in
the prefrontal cortex and amygdala in response to chronic etha-
motor cortex of human alcoholics (Lewohl et al., 2000). Expres-
pression in the D2 and B6 hippocampus during acute and
chronic ethanol withdrawal (Daniels and Buck, 2002) or the
whole brain after an anesthetic dose of ethanol (Treadwell and
et al. (2001) also reported on expression profiling comparison of
untreated ILS and ISS mice, strains originally selected for differ-
ences in ethanol-induced sleep time.
The nucleus accumbens (NAC), prefrontal cortex (PFC), and
ventral tegmental area (VTA) are major components of the me-
other drugs of abuse (Koob, 1992). To date, no comprehensive
study of gene expression patterns across the mesolimbocortical
acute ethanol. We therefore used oligonucleotide microarrays
and bioinformatic tools to characterize expression networks in
mice. Our results suggest several discrete mechanisms that may
Administration of acute ethanol and animal microdissection. All animals
were treated according to protocols for animal care established by Vir-
ginia Commonwealth University and the National Institutes of Health.
Adult male B6 or D2 mice (79–95 d old; The Jackson Laboratory, Bar
rodent chow (catalog #7912; Harlan Teklad, Madison, WI) and water in
a 12 h dark/light cycle. All injections were intraperitoneal. The Institu-
tional Animal Care and Use Committees of Virginia Commonwealth
University, the University of California, San Francisco, and the Ernest
Mice were given a saline injection once daily for 5 d to habituate them
to the injection process. On day 6, mice received either an injection of
saline or 20% ethanol in saline at doses mentioned in the text. Experi-
ments were performed in triplicate with five mice per treatment group,
except for an ethanol dose–response study (see Fig. 5) done with dupli-
dislocation. Mouse brains were extracted and chilled for 1 min in phos-
phate buffer on ice before microdissection. Dissections were completed
5–10 min from the time of death. The mouse brain micropunch dissec-
tion method training, dissecting block, and tools were provided by V.
Gene Erwin (University of Colorado, Boulder, CO). Briefly, with the
optic chiasm. The rostral section was placed with the caudal face up, and
the PFC was isolated by slicing a pie-shaped wedge overlapping the in-
terhemispheric fissure from the dorsal cortical surface extending to the
corpus callosum. The NAC was dissected with a 1.5 mm micropunch
tweezers, the caudal portion of the brain was placed dorsal side-up. A
vertical slice was made rostral to the cerebellum, between the superior
and inferior colliculi, and, after discarding the cerebellum, a slice was
made at a 45° angle from the dorsal caudal end down toward the ventral
and a transverse cut was made to separate the VTA from the dorsal
ately with liquid nitrogen, and stored at ?80°C until isolation of total
reagent (Tel-Test, Friendswood, TX) using a Tekmar homogenizer, and
tration was determined by absorbance at 260 nm, and RNA quality was
analyzed by agarose gel electrophoresis and 260:280 nm absorbance ra-
tios. Total RNA (7 ?g) derived from each pool was reverse transcribed
BioArray high-yield RNA-transcript labeling kit (ENZO Diagnostics,
Farmingdale, NY) according to the instructions of the manufacturer,
purified using an RNAeasy Mini kit (Qiagen, Mountain View, CA), and
quantified by absorbance at 260 nm.
Microarray hybridization and scanning. Each treatment group or rep-
licate was hybridized to an individual microarray for each of the three
brain regions studied (n ? 36 total microarrays for experiment 1). La-
rine GeneChip U74Av2; Affymetrix, Santa Clara, CA) that contain
?12,000 named genes and expressed sequence tags. Array hybridization
and scanning were performed exactly according to the protocol of the
manufacturer and as described previously (Thibault et al., 2000). Arrays
were then washed, stained with streptavidin–phycoerythrin (Molecular
Probes, Eugene, OR), and scanned according to standard protocols sup-
plied by the manufacturer (Affymetrix).
Microarray data analysis. Microarray data were initially processed us-
ing Microarray Suite software (MAS; Affymetrix) version 4.0 or 5.0. Ar-
rays were normalized to a median total hybridization intensity (target
average intensity, 190). Array quality was assessed by accepting only
arrays with a scaling factor of ?2.3 and a 3?-5?-actin ratio of ?2 and by
examining chip validity and linearity of intensity values, according to
MAS guidelines. Arrays determined to be acceptable were further ana-
lyzed in three steps to identify genes with altered expression patterns.
First, the S-score algorithm, developed in this laboratory for analysis of
Affymetrix oligonucleotide arrays (Zhang et al., 2002), was applied to
compare hybridization signals between two arrays from different treat-
ment samples. S-score results are independent of the initial analysis al-
gorithm used (MAS 4.0 or MAS 5.0), have a normal distribution with
mean of 0 and SD of 1, and are correlated with the fold change. An
S-score of 2 corresponds to a p ? 0.0455, uncorrected for biological
variability or multiple comparisons. Three types of comparisons were
made: (1) to study ethanol responses, S-scores were calculated for
ethanol-treated samples versus saline control samples within each brain
region and mouse strain; (2) to examine strain expression differences
between D2 and B6 mice, S-scores were calculated for B6 saline control
samples versus D2 saline control samples; and (3) control S-scores were
calculated between biological replicates of the same saline control
groups. S-scores were calculated within replicates.
To reduce the contribution of biological or technical noise, S-scores
regions. We found that this general approach, which has been applied
previously to microarray data (Hughes et al., 2000), reduces variance
across experimental replicates, although it does result in a more conser-
vative estimate of expression changes. Genes with consistently low ex-
pression values were filtered to eliminate genes with MAS 4.0 average
(SAM), a rank-based permutation method, was used to identify genes
with S-scores significantly different from 0 (Tusher et al., 2001). This
Ethanol-regulated genes were identified by performing two-class SAM
on ethanol versus saline S-scores (e.g., D2 NAC E1 vs D2 NAC S1) and
saline versus saline S-scores (e.g., D2 NAC S1 vs D2 NAC S2). Genes
differentially expressed (basal) between mice strains were identified by
PFC S1 vs D2 PFC S1). This basal gene list was further filtered for an
average S-score of ?1.5 or ?1.5 or less (composite significance, p ?
0.01). All SAM analyses used a false discovery rate of ?10% to avoid
eliminating genes that may be biologically important and could assist in
interpretation of expression patterns in multivariate studies. In virtually
significance of p ? 0.01 (uncorrected for multiple testing). Qualitatively
similar results were obtained with multiple other analysis methods as
2256 • J.Neurosci.,March2,2005 • 25(9):2255–2266Kernsetal.•BrainRegionEthanolExpressionNetworks
related genes. We found that these genes also had basal expres-
sion across the BXD RI lines strongly linked to a Chr 1 site near
the Creb1 transcription factor; (2) Creb1, in turn, showed an
expression pattern across the BXD RI lines that was significantly
correlated with the PC1 factor derived from myelin expression
profiles (Fig. 8E); (3) many of the myelin genes and c-Myc-
related signaling molecules (c-Myc, Nrd1, and Bin1) have known
from BiblioSphere analysis); and (4) acute ethanol is known to
activate cAMP signaling and CREB in multiple cell types and
rodent brain, whereas chronic ethanol exposure has been shown
to downregulate CREB signaling (Diamond and Gordon, 1997;
Yang et al., 1998; Asher et al., 2002).
Coordinate upregulation of myelin-related genes in D2 PFC
with acute ethanol contrasts with downregulation of multiple
myelin-related genes observed in our previous microarray stud-
ies on postmortem frontal cortex from human alcoholics (Le-
wohl et al., 2000). This suggests that acute ethanol may cause
direct effects on myelin-related signaling mechanisms subse-
quently downregulated with chronic exposure in alcoholism.
CREB may be an excellent candidate for a proximal mechanism
mediating ethanol effects on myelin-related gene expression,
with activation by acute ethanol and downregulation by chronic
exposure. Correlation of myelin gene-derived PC1 with dopa-
mine transporter expression in the frontal cortex (Fig. 8) is fur-
ther suggestive of a role for dopamine signaling in the myelin
responses. It is unknown whether ethanol-induced alterations in
myelin gene expression may have short- or long-term behavior
consequences. However, clearly, dysfunction of myelin occurs in
alcoholism (Kril and Harper, 1989).
WebQTL analysis also indicated a strong Chr 1 cis-QTL for
Pam expression across the BXD lines (Fig. 8A). Pam is known to
modulate functional activity of multiple neuropeptides, includ-
ing NPY and PACAP, both of which influence ethanol behaviors
(Moore et al., 1998; Thiele et al., 1998). The significant correla-
tion of basal Pam expression with ethanol sensitization (Fig. 8B)
The results described above illustrate striking divergent regu-
lation of gene expression by ethanol in D2 and B6 mice. Brain
region-specific changes in signaling and neuronal plasticity may
be critical components in the development of lasting behavioral
phenotypes, such as dependence, sensitization, and craving. Su-
perimposing our results on biochemical and genetic data has
identified a testable set of hypotheses regarding genes related to
ethanol behavioral QTLs and signaling events evoked by acute
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