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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Alcohol. Author manuscript; available in PMC Sep 12, 2007.
Published in final edited form as:
PMCID: PMC1976291
NIHMSID: NIHMS26551

Functional gene expression differences between inbred alcohol-preferring and —non-prerats in five brain regions

Abstract

The objective of this study was to determine if there are innate differences in gene expression in selected CNS regions between inbred alcohol-preferring (iP) and —non-preferring (iNP) rats. Gene expression was determined in the nucleus accumbens (ACB), amygdala (AMYG), frontal cortex (FC), caudate-putamen (CPU), and hippocampus (HIPP) of alcohol-naïve adult male iP and iNP rats, using Affymetrix Rat Genome U34A microarrays (n = 6/strain). Using Linear Modeling for Microarray Analysis with a false discovery rate threshold of 0.1, there were 16 genes with differential expression in the ACB, 54 in the AMYG, 8 in the FC, 24 in the CPU, and 21 in the HIPP. When examining the main effect of strain across regions, 296 genes were differentially expressed. Although the relatively small number of genes found significant within individual regions precluded a powerful analysis for over-represented Gene Ontology categories, the much larger list resulting from the main effect of strain analysis produced 17 over-represented categories (P <.05), including axon guidance, gliogenesis, negative regulation of programmed cell death, regulation of programmed cell death, regulation of synapse structure function, and transmission of nerve impulse. Co-citation analysis and graphing of significant genes revealed a network involved in the neuropeptide Y (NPY) transmitter system. Correlation of all significant genes with those located within previously established rat alcohol QTLs revealed that of the total of 313 significant genes, 71 are located within such QTLs. The many regional and overall gene expression differences between the iP and iNP rat lines may contribute to the divergent alcohol drinking phenotypes of these rats.

Keywords: Alcohol-preferring P rat, Alcohol—non-preferring NP rat, Microarrays, Gene expression, Nucleus accumbens, Amygdala, Frontal cortex, Hippocampus, Caudate-putamen

1. Introduction

Alcoholism and alcohol abuse are complex disorders that result from a combination of genetic and environmental factors. Selective breeding strategies for ethanol preference have yielded divergent rat lines that possess different frequencies of genes that impact ethanol preference, whereas the frequency of trait-irrelevant genes remains randomly distributed (Lumeng et al., 1977). The alcohol-preferring (P) and alcohol—non-preferring (NP) rat lines were established from a randomly bred, closed colony of Wistar rats using free-choice access to 10% (vol/vol) ethanol and water (Lumeng et al., 1977). P rats meet the proposed criteria (Cicero, 1979) for an animal model of alcoholism (reviewed in McBride & Li, 1998; Murphy et al., 2002). In brief, the P line of rats (1) consumes in excess of 5 g ethanol/kg body weight/day, attaining blood alcohol concentrations in the range of 50-200 mg%; (2) works to obtain ethanol when food and water are freely available; (3) consumes ethanol for its pharmacological effects, and not solely for caloric value nor taste or odor properties; (4) develops functional and metabolic tolerance; (5) develops physical dependence; and (6) demonstrates robust relapse ethanol drinking after a period of abstinence. On the other hand, NP rats consume less than 1 g ethanol/kg/day and do not attain measurable blood alcohol concentrations under free-choice conditions. Compared to NP rats, P rats are more sensitive to the low-dose stimulating effects of ethanol (Rodd et al., 2004; Waller et al., 1986), less sensitive to the high-dose motor impairing effects of ethanol (Lumeng et al., 1982), and develop acute tolerance more rapidly (Waller et al., 1983).

Innate differences in neurotransmitter and receptor systems in several brain regions have been reported between the selectively bred P and NP rat lines (reviewed in McBride & Li, 1998; Murphy et al., 2002). P rats have reduced serotonin (5-HT) and dopamine (DA) innervations (Zhou et al., 1991, 1994a, 1994b, 1995), as well as differences in 5-HT (McBride et al., 1993a, 1994, 1997; Wong et al., 1993), DA (McBride et al., 1993b), and opioid (McBride et al., 1998; Strother et al., 2001) receptors. Furthermore, neuropeptide Y (NPY) (Ehlers et al., 1998), corticotropin-releasing factor (Ehlers et al., 1992), neurotensin (Ehlers et al., 1999), substance P, and neurokinin levels (Slawecki et al., 2001) are all significantly lower in CNS regions of P compared to NP rats. Additionally, higher functional neuronal activity has been found in numerous brain regions of the P rat compared to the NP rat (Smith et al, 2001; Strother et al., 2005).

Witzmann et al. (2003) examined differences in protein levels in the hippocampus (HIPP) and nucleus accumbens (ACB) of alcohol-naïve inbred-P (iP) and inbred-NP (iNP) rats, and found that almost all of the proteins that differed were lower in the iP rats compared to the iNP rats. Those proteins that could be identified were involved in many key aspects of neuronal function such as metabolism, cell signaling, and protein transport, which may suggest that there are basic differences in synaptic transmission mechanisms between the two rat strains (Witzmann et al., 2003). Edenberg et al. (2005) compared gene expression differences in the HIPP of two different strains of iP and iNP rats, when microarray analyses were conducted several months apart. The results indicated excellent repeatability of the assay. Genes involved in cell growth and adhesion, cellular stress reduction and antioxidation, protein trafficking, cellular signaling pathways, and synaptic function were differentially expressed in the HIPP (Edenberg et al., 2005). Worst et al. (2005) reported on the transcriptome analysis in the anterior cerebral cortex of alcohol-naïve Alko, alcohol (AA) and Alko, nonalcohol (ANA) rats, and found differences in mRNA levels between the AA and ANA rats that could alter transmitter release (e.g., vesicle-associated membrane protein 2, syntaxin 1, syntaxin binding protein). Kerns et al. (2005) examined gene expression differences in response to acute ethanol in the ACB, prefrontal cortex, and ventral tegmental area of C57BL/6J and DBA/2J mice, which have high and low alcohol drinking characteristics, respectively. Ethanol-regulated genes were region specific and involved in glucocorticoid signaling, neurogenesis, myelination, neuropeptide signaling, and retinoic acid signaling. Gene expression profiles were also reported for whole brain of inbred long-sleep and inbred short-sleep mice (Xu et al., 2001). A total of 41 genes or expressed sequence tags (ESTs) displayed significant differences between these inbred strains of mice. Expression of genes encoding tyrosine protein kinase and ubiquitin carboxyl terminal hydrolase was higher in the brain of inbred long-sleep compared to short-sleep mice. In a comprehensive transcriptome meta-analysis of different mice strains, Mulligan et al. (2006) identified several cis-regulated candidate genes for an alcohol preference quantitative trait loci (QTL) on chromosome 9.

Portions of the present data have been used for comparison analyses in two recently published studies, although none of the present data have been presented in duplicate form. In one study, the present HIPP data were used with other HIPP data to evaluate the reliability of the microarray analysis, when assays were conducted months apart (Edenberg et al., 2005). In the second study (Rodd et al., 2006), the data were used as part of a convergent functional genomics approach to identify common genes across different experimental approaches and between human and animal findings. In that study, however, the iP—iNP data were not analyzed using rigorous statistical criteria (for a gene to be considered significant, an false discovery rate (FDR)-uncorrected P <.05 was considered sufficient), and the results were presented only in a summarized format, which were then integrated with information from other studies. As the P and NP lines are well established animal models in the alcohol field, we believe it is important that the present findings, derived using rigorous region-by-region analyses, are presented because they yield a much more complete and statistically reliable picture of the genetic factors involved in the high and low alcohol drinking behavior in these rat lines.

The objective of the present study was to determine if there are innate differences between inbred P and NP rats in the expression of functionally relevant genes in selected brain regions. The current study focuses on five distinct brain regions: the ACB, caudate-putamen (CPU), amygdala (AMYG), HIPP, and frontal cortex (FC). These regions were selected based on their inclusion in the mesolimbic and mesocortical systems, both of which are critically important in the initiation and maintenance of goal directed and reward mediated behaviors (reviewed in Bonci et al., 2003; and Maldonado, 2003).

2. Materials and methods

2.1. Animals and RNA preparation

Inbred adult male rats, 90-100 days old, from the iP-5C and iNP-1 strains were used in these experiments. Inbreeding by brother—sister mating was initiated after the S30 generation of mass selection and was in the F37 generation at the start of these experiments. It should be noted that the iP and iNP rats have not been characterized to the extent to which the parent selected lines have been studied. However, preliminary studies indicate that alcohol intake (Bell et al., 2004), and differences in sweet preference, anxiety, spontaneous motor activity, and the development of rapid tolerance (Stewart et al., 2004) are similar to the parent lines.

Animals were received in our facilities 3 weeks prior to the experiment. Rats were double housed on a 12:12-h light dark cycle with lights on at 0700 h. Rats had water and rat chow ad libitum. Animals were habituated to handling and to the guillotine daily between 0900 and 1000 h for 10 days prior to sacrifice. The animals used in these experiments were maintained in facilities fully accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care. All research protocols were approved by the Institutional Animal Care and Use Committee and are in accordance with the guidelines of the Institutional Animal Care and Use Committee of the National Institute on Drug Abuse, NIH, and the Guide for the Care and Use of Laboratory Animals (Institute of Laboratory Animal Resources, Commission on Life Sciences, National Research Council 1996).

Animals were sacrificed by decapitation between 0900 and 1000 h over consecutive days, with equal numbers of animals from each strain sacrificed each day. This minimized differences in time of sacrifice and dissection, and maintained the experimental balance across the two strains. The head was immediately placed in a cold box maintained at -15°C, where the brain was rapidly removed and placed on a glass plate for dissection. All equipment used to obtain tissue was treated with RNAse Zap (Ambion, Inc. Austin, TX) to prevent RNA degradation. The ACB, CPU, FC, AMYG, and HIPP were dissected according to the coordinates of Paxinos and Watson (1998). Briefly, the ACB, CPU, and FC were dissected from a 2 mm section generated by a coronal cut at 2 mm anterior to the optic chiasm (Bregma 1.70 mm) and a coronal cut at the optic chiasm (Bregma -0.26 mm). The AMYG was dissected by a cut at the lateral borders of the lateral hypothalamus (Bregma -2.12 mm) and ventral of the rhinal fissure, with cortical tissue then trimmed at the lateral edges of the dissected slice. The entire HIPP was dissected from the remaining brain by a midline incision between the hemispheres and gently rolling the HIPP out of the cerebral cortex. We have previously demonstrated the consistency of dissection of discrete brain regions in our laboratory (Liang et al., 2003; Witzmann et al., 2003). Dissected tissues were immediately homogenized in Trizol reagent (Invitrogen, Carlsbad, CA) and processed according to the manufacturer’s protocol, but with twice the suggested ratio of Trizol to tissue. Ethanol precipitated RNA was further purified through RNeasy® columns (Qiagen, Valencia, CA) according to the manufacturer’s protocol. The yield, concentration, and purity of the RNA were excellent, and were determined by running a spectrum from 210 to 350 nm and analyzing the ratio of large and small ribosomal bands using an Agilent Bioanalyzer.

2.2. Labeling and hybridization

RNA from each individual rat was labeled and analyzed separately on an Affymetrix Rat Genome U34A microarray. Starting with 10 μg of total RNA from each animal, first-and second-strand cDNA synthesis was carried out according to the standard protocol (Affymetrix: GeneChip® Expression Analysis Technical Manual. Santa Clara, CA: Affymetrix; 2001). Biotinylated cRNA was synthesized in vitro from the double-stranded cDNA using the ENZO Bio-Array High Yield RNA Transcript Labeling Kit (ENZO Diagnostics, Inc., Farmingdale, NY) according to the Affymetrix protocol. Fragmented, biotinylated cRNA (15 μg) was mixed into 300 μl of hybridization cocktail, of which 200 μl were used for each hybridization. Hybridization was for 17 h at 42°C. Washing, staining, and scanning were carried out according to the standard protocol.

To minimize potential systematic errors, all stages of the experiment were balanced across phenotypes. That is, equal numbers of iP and iNP animals were sacrificed each day, and equal numbers of RNA preparations from iP and iNP animals were processed through the labeling, hybridization, washing, and scanning protocols on each day, in different alternating orders. Whenever possible, common premixes of reagents were used to minimize effects due to differences in reagent preparation.

2.3. Data and neuroinformatic analyses

Each GeneChip® was scanned using an Affymetrix Model 3000 scanner and underwent image analysis using Affymetrix GCOS software. Microarray data are available from the National Center for Biotechnology Information’s Gene Expression Omnibus, http://www.ncbi.nlm.nih.gov/geo/, (Barrett et al., 2005; Edgar et al., 2002) under series accession no. GSE4494 (GSM100999... GSM101057). Raw cell files were then imported into the statistical programming environment R (R Development Core Team, 2006) for further analysis with tools available from the Bioconductor Project (Gentleman et al., 2004), themselves further expanded by the authors using the R language. Expression data from 59 arrays were normalized and converted to log2 as a set using the Robust Multi-chip Average (RMA) method (Irizarry et al., 2003) implemented in the Bioconductor package RMA. As a standardization step to facilitate later comparisons with other experiments, expression levels were scaled such that the mean expression of all arrays was log2(1000). To increase power and decrease the FDR (see below), probe sets were subjected to further filtering steps. As we were primarily concerned with identifying genes that could be subjected to further bioinformatic analysis, all probe sets currently annotated by Affymetrix as “expressed sequence tags” or whose gene names contain the words “riken,” “predicted,” or “similar to” were filtered out. We next filtered out probe sets with a very low likelihood of actual expression in our samples, accomplished with the Bioconductor package “genefilter.” Probe sets that did not have at least 25% of samples with normalized, scaled expression greater than 64 were filtered out. Finally, we wished to filter out, in an unbiased manner, probe sets with very low variability across samples. This was accomplished by constructing a distribution of the interquartile range of all probe sets from all samples of both groups and then filtering out those probe sets that fell in the lowest 25% of this distribution using the Bioconductor package genefilter according to the method described by Scholtens and von Heydebreck (2005). Linear modeling to calculate gene-wise P values was performed using the package Linear Modeling for Microarray Analysis (LIMMA) (Smyth, 2004) P values were then corrected for FDR by the method of Benjamini & Hochberg (1995). Probe sets were considered to be differentially expressed if the FDR adjusted P value was P <.10 (FDR 10%). Using this method, the data were analyzed two ways. In the first analysis, each individual brain region was analyzed separately, using only strain as a factor in the linear model. In the second analysis, the five brain regions tested were combined in the linear model, using factors of region and strain, and the main effect of strain was examined. This analysis will be referred to as the “overall analysis.”

Testing for over-representation of Gene Ontology (GO) (Ashburner et al., 2000; Harris et al., 2004) biologic process categories was performed using the Bioconductor package GOstats (Gentleman, 2004). Briefly, for each gene set tested, a list of unique Entrez-Gene identifiers was constructed. This list was then compared to the list of all known Entrez-Gene identifiers that are represented on the Affymetrix chipset RGU34A. Identification of over-represented GO categories was then accomplished within GOstats using the hypergeometric distribution. To filter out uninteresting categories, those categories with less than 9 or greater than 300 genes represented on the chipset were excluded from the analysis, as were categories with less than five significant genes. Categories were called significant for P <.05.

Co-citation analysis was accomplished within R using a modification of the package “MedlineR” (Lin et al., 2004). Gene aliases were obtained from the Rat Genome Database, http://rgd.mcw.edu/. A list of all PubMed, http://www.pubmedcentral.nih.gov/, occurrences of genes and their aliases in abstracts and keywords was constructed for each gene, from which a co-citation matrix was constructed. The Bioconductor packages “graph” (Gentleman et al., 2004) and “Rgraphviz” (Gentry, 2006) were used to graphically display networks of co-citated genes.

2.4. Quantitative real-time PCR

Real-time (RT) polymerase chain reaction (PCR) was carried out using SybrGreen chemistry and the ABI Prism 7700 Sequence Detection System (Applied Biosystems). The amplification primers were designed using Vector NTI software. Total RNA, isolated for the microarray analyses, was used for these analyses. Following reverse transcription of the RNA (TaqMan Reverse Transcription Reagents, Applied Biosystems), an aliquot of each reverse transcription reaction was amplified in triplicate. This reaction was repeated to generate six values for each test group. Two control reactions were run for each RNA preparation: (1) a reverse transcription and PCR reaction with no added RNA to control for contamination of the reagents and (2) a PCR reaction without the reverse transcription reaction in the presence of RNA to detect DNA contamination of the RNA preparation. To correct for sample-to-sample variation, an endogenous control, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was amplified with the target and served as an internal reference to normalize the data. The average GAPDH Ct values for iP and iNP were the same in each brain region tested, making this an appropriate control gene to normalize the expression of the candidate genes of interest. Relative quantification of data from the ABI Prism 7700 Sequence Detection System was performed using the standard curve method (Applied Biosystems, User Bulletin #2; http://www.appliedbiosystems.com). Quantitative real-time polymerase-chain-reaction (qRT-PCR) measurements were conducted on genes to verify differences observed with microarray hybridization. Genes were selected on the basis of significant differential expression in at least one brain region and reasonable fold changes. When this list was compiled, initial analysis had been completed using MAS5 background correction; after the RMA algorithm had been substituted for MAS5, mRNA samples were no longer of sufficient quality to select a new list.

3. Results

Principal component analysis, using all probe sets that passed through the filters described in Methods, indicated that regional differences in gene expression were greater than strain differences, as illustrated by a biplot of the first and second principal components (Fig. 1). The clusters representing arrays from the ACB, CPU, and FC are tightly grouped, with the exception of one outlier in each region. Those from the HIPP give a fairly good grouping, but not as tight as the other three regions; the AMYG gives the least grouping of data, with one outlier that groups with the HIPP cluster. As can be seen from the figure, there is no clear pattern of separation of the iP and iNP lines within brain region. Visualization of the third and fourth principal components (not shown) also did not resolve line differences within regions.

Fig. 1
Principal components plot of all 59 arrays. Only processed, filtered data were used (see Methods for details) in the computation. Principal component 1 is plotted on the horizontal axis and principal component 2 is plotted on the vertical axis. Clusters ...

We used LIMMA (Smyth, 2004) on RMA preprocessed (Irizarry et al., 2003), log2 transformed, filtered data (see Methods for details), to determine differences between the inbred strains. The FDR, as calculated by the method of Benjamini and Hochberg (1995), was set at ≤0.10. In an analysis of individual brain regions (n = 6 iNP, 6 iP in all regions except the FC, in which data were missing from one iP animal), significant differential gene expression was found in the five brain regions (Tables (Tables11 and and2).2). The number of differences between the two inbred lines in each of the five individual regions ranged from 8 to 63, with the order of number of differences being AMYG > CPU > HIPP > ACB > FC (Table 1). Across regions, the total number of genes that demonstrated differential expression ranged from 8 to 54; the number of genes that were located within established alcohol QTLs (Bice et al., 1998; Carr et al., 1998, 2003; Foroud et al., 2002, 2003; Radcliffe et al., 2004; Terenina-Rigaldie et al., 2003) ranged from 1 to 16. Table 2 lists the significant genes that were different within each region along with individual fold changes and gene symbols. Table 3 contains detailed expression levels and standard deviations for all the regionally significant probe sets.

Table 1
Summary of biostatistical and bioinformatic analyses for individual regions and for the combined regions examining the main effect of straina
Table 2
Regionally significant genes (FDR < 0.10)
Table 3
Detailed expression levels and S.D.s for all regionally significant probe sets

Over-represented GO biologic process categories (P <.05) were statistically sought using the Bioconductor package GOstats (Gentleman et al., 2004). Briefly, GO categories of significant genes were tested for over-representation using the hypergeometric distribution. An analysis of each of the five individual brain regions did not reveal any over-represented GO biologic process categories, perhaps because there were too few differentially expressed genes in each region.

An overall analysis of all brain regions, which used a linear model with strain and brain region as factors, demonstrated significant differential expression of 296 genes (351 probe sets) (FDR ≤0.10). Of these, 173 genes (198 probe sets) demonstrated higher and 123 genes (153 probe sets) lower expression levels in the iP rats than in the iNP rats (Table 4). Although almost all of the genes identified as being differentially expressed within the individual regions were also identified in the overall analysis, the latter method detected many that fell below the threshold of statistical detection for individual regions, most likely because of the increased power gained by including a much larger number of arrays in the linear model (Table 5). There were 53 genes (71 probe sets) found significant in at least one individual region and in the overall analysis; 241 genes (280 probe sets) were found significant only in the overall analysis. Additionally, there were 15 genes (17 probe sets) found significant in the AMYG that were not significant in the overall analysis, one of which was also significant in the ACB.

Table 4
Complete list of probe sets that were significantly different between iP and iNP rats in the overall analysis
Table 5
Significant fold differences in combined region analysis and in individual region analyses

Analysis for over-represented GO categories in the set of significant genes found with the overall analysis indicated that 17 GO categories were over-represented (P <.05). Six of the most neurobiologically interesting categories are axon guidance, gliogenesis, negative regulation of programmed cell death, regulation of programmed cell death, regulation of synapse structure function, and transmission of nerve impulse (Table 6); the full list can be viewed in Table 7. More genes demonstrate increased expression in the iP versus iNP rat comparison in the categories of regulation of cell death, negative regulation of cell death, regulation of synapse structure and function, and gliogenesis; whereas, more genes demonstrate decreased expression in the iP versus iNP in the category of axon guidance. Equal numbers of genes with increased and decreased expression in the iP versus iNP are present in the category of transmission of nerve impulse.

Table 6
Biologically interesting, nonredundant, significantly over-represented GO biologic process categoriesa
Table 7
All significant Gene Ontology (GO) biologic process categories

For the purposes of further discussion, the authors found it desirable to use a gene classification schema that would include genes found significant in individual regions and that would include functional neuroinformatic categories such as neuroplasticity. Such customized, neuroinformatic classifications of genes found significant in any of the individual regions, or in the combined analysis, were obtained by first assigning all genes to their appropriate GO biologic process categories, regardless of statistical significance of the category. Of the categories obtained, further selection was made based on the number of genes within category and degree of neuroinformatic interest. The resultant GO categories were synaptic transmission, regulation of action potential, positive regulation of transport, potassium ion transport, chloride transport, metal ion homeostasis (collapsed to form “neurotransmission,” Table 8); apoptosis, axon guidance, axonogenesis, cell migration, central nervous system development, cytoskeleton organization and biogenesis, gliogenesis, myelination, negative regulation of apoptosis, neuron differentiation, positive regulation of apoptosis, regulation of apoptosis, regulation of axonogenesis, regulation of neurogenesis, regulation of programmed cell death, and regulation of synapse structure and function (collapsed to form “neuroplasticity,” Table 9); and small GTPase mediated signal transduction, negative regulation of protein kinase activity, negative regulation of enzyme activity, transmembrane receptor protein tyrosine kinase signaling pathway (collapsed to form “intracellular messaging,” Table 10). Finally, the lists of genes thus classified were supplemented with manual curation based on a search of the literature. One last category was obtained by classifying genes as transcription factors based on results obtained with Genomatix Suite (Genomatix, Munich, Germany; Table 11).

Table 8
Neurotransmission-related significant genesa
Table 9
Neuroplasticity-related significant genesa
Table 10
Intracellular messaging-related significant genesa
Table 11
Transcription-related significant genesa

3.1. qRT-PCR confirmation

qRT-PCR measurements were conducted on genes to verify differences observed with microarray hybridization. Genes were selected on the basis of significant differential expression in at least one brain region and reasonable fold changes (Table 12). GSTa4 and Scn1a are not differentially expressed using the RMA analysis presented here, but were with an earlier analysis using MAS5 (unpublished observation). The direction of the expression differences using qRT-PCR was the same as in the microarray data for all genes and regions tested. For the Akap11 gene, the magnitude of the change of the qRT-PCR data for all five regions was far greater than that observed in the microarray data (fold change in CPU was 785, and in HIPP 1,900), which probably reflects technical differences between the two assays. Expression levels of Akap11 in iNP were near back-ground levels in the array experiment; qRT-PCR assays allow one to measure much lower levels of RNA with more accuracy in that range (background is much lower), which allows one to better estimate the fold change.

Table 12
Quantitative RT-PCR confirmation of microarray fold change (iP vs. iNP) data

4. Discussion

The major findings of this study were that (1) there was a greater degree of between-region differential expression than within-region, between-strain differential expression (Fig. 1); (2) there was within-region, between-strain differential expression in all five regions examined, with many of these genes classifiable as being related to neurotransmission (Table 8), neuroplasticity (Table 9), intracellular messaging (Table 10), and regulation of transcription (Table 11), with the AMYG as the region demonstrating the greatest number of genes differentially expressed; (3) considerable statistical power could be obtained by minimizing the effect of variance by examining the main effect of strain in a linear model that contained all five brain regions and that with this approach over 300 genes demonstrated differential expression; and (4) in this main effect of strain model, the significant genes represented 17 over-represented GO categories, for example, axon guidance, gliogenesis, negative regulation of programmed cell death, regulation of programmed cell death, regulation of synapse structure function, and transmission of nerve impulse (Table 6).

Inbred P and NP rats were used in the present study in an attempt to reduce the biological variability in the gene expression data. However, there are two potential weaknesses with this approach, in that the particular inbred strains used in the present study may not necessarily have the same characteristics as the parent foundation lines of selectively bred P and NP rats; and, in that inbreeding randomly fixes genes that are not relevant to the phenotype of alcohol preference, which can result in some irrelevant differences in gene expression levels. Although preliminary findings indicate that similar differences are found for the inbred strains and parent lines in sweet preference, anxiety, spontaneous motor activity, and the development of rapid tolerance (Stewart et al., 2004), as well as alcohol drinking (Bell et al., 2004), extrapolating the results to the selectively bred lines should be done cautiously.

4.1. Within-region analysis

Principal components analysis (Fig. 1) revealed that the first two principal components resolve the differences between arrays based on brain region, but not on strain, indicating that most of the differences in gene expression between arrays in the present experiment are accounted for by brain region and not by strain. This finding is in agreement with observations made by others that regional differences in gene expression in the adult mouse brain are greatly influenced by embryological origins (Zapala et al., 2005) and that, across a range of behavioral phenotypes, the number of between-brain region differences of expression can be greater than within-region differences (Nadler et al., 2006; Pavlidis & Noble, 2001).

The AMYG had the greatest number of differences in gene expression between the iP and iNP strains (Tables (Tables11 and and2),2), which may be a result of the multiple nuclei within this region that mediate different behavioral functions. Of the 54 differentially expressed genes in this region, the biologic process most represented is neuroplasticity, in which 8 genes have been implicated (Table 9). Co-citation analysis indicated a relationship between two pairs of these genes. Fgfr1 and Pak2 both show lower expression in the iP rat than iNP rat, and are involved in the Ras/ERK/PAK2 pathway that governs neuronal differentiation (Shin et al., 2002). Neuritin and attractin, both of which show higher expression in the iP than iNP rat, have been identified as part of a cluster of genes that are up-regulated during the recovery phase of spinal cord injury (Di Giovanni et al., 2004). A 36% reduction in expression of Phgdh in the AMYG of the iP rat compared to the iNP rat has the potential to alter cell survival, neuritogenesis, and voltage propagation (Furuya et al., 2000) in this region. The expression of sodium channel, voltage-gated, type III, alpha polypeptide (Scn3a) was significantly lower in the AMYG of the iP than iNP line, and was generally lower in the other regions of the iP rats. Widespread differences in the expression of Scn3a between the iP and iNP lines could contribute to differences in the overall neuronal excitability in these animals (Hains et al., 2003) and potentially alter responses of the two lines to alcohol.

Thirteen of the 54 differentially expressed genes in the AMYG were located within established alcohol QTLs (Bice et al., 1998; Carr et al., 1998, 2003; Foroud et al., 2002, 2003; Radcliffe et al., 2004; Terenina-Rigaldie et al., 2003) (Table 5). The pathway involving caspase 3 (lower expression in iP rats) and interleukin-1 receptor-associated kinase 2 (higher expression in iP rats) is involved in the age-related decline in function of hippocampal cells (Lynch & Lynch, 2002). Amphoretin-induced gene and ORF (Amigo), higher in the AMYG of the iP than iNP rat, is a transmembrane protein that modulates neurite out-growth (Kuja-Panula et al., 2003), and thus has the potential to mediate neuroplastic responses to alcohol.

The CPU demonstrated the second highest number of differentially expressed genes (Table 1). Src homology 2 domain-containing transforming protein C3 (Shc3) had lower expression in the iP than in the iNP. Although classified by GO as having a function in neurotransmission (Table 6), Shc3 is perhaps more appropriately thought of as playing a role in the regulation of neuronal development and synaptic function (Liu & Meakin, 2002). In the CPU, 8 of the 24 differentially expressed genes were located within established alcohol QTLs (Bice et al., 1998; Carr et al., 1998, 2003; Foroud et al., 2002, 2003; Radcliffe et al., 2004; Terenina-Rigaldie et al., 2003) (Table 5). Among these 8 genes, several are involved in protein formation, that is, ribosomal protein S2 heat shock protein 2, and zinc finger protein 386 (Kruppel-like). Some of the differences in the CPU between the iP and iNP rats may reflect differences in response to the low-dose stimulating (Rodd et al., 2004; Waller et al., 1986) and high-dose motor impairing (Lumeng et al., 1982) effects of alcohol exhibited by the parent lines.

The HIPP demonstrated significant differential expression of 21 genes (Table 1). Slit homolog 1 (Drosophila), higher in the iP line, has been implicated in processes related to neuroplasticity (Table 9), specifically in axon guidance (Chang et al., 2006; Long et al., 2004; Thompson et al., 2006). One of the 21 differentially expressed genes in the HIPP, ribosomal protein S2, was located within established alcohol QTLs (Bice et al., 1998; Carr et al., 1998, 2003; Foroud et al., 2002, 2003; Radcliffe et al., 2004; Terenina-Rigaldie et al., 2003) (Table 5). Some of these differences in the HIPP between iP and iNP rats may be associated with the differences in the development and/or persistence of tolerance to the motor impairing effects of alcohol observed for the parent lines (Bell et al., 2001; Gatto et al., 1987a, 1987b; Waller et al., 1983).

In a previous study that jointly analyzed the HIPP data here and data from different inbred iP and iNP strains, many more differences were observed in the HIPP between iP and iNP rats, most likely due to the higher power of that study (a total of 10 arrays used for each group) (Edenberg et al., 2005). Nine of the 21 genes reported here (indicated in Table 2 with footnote “b”) were found in the previous study; the absence of a greater overlap is likely due to differences in the study design discussed above, differences in the preprocessing of data (MAS5 was used in the previous study, whereas RMA was used in the present study), and the unavoidable fact that the true FDR of the two studies, while estimated to be low, was almost certainly not zero.

The ACB demonstrated significant differential expression of 16 genes (Table 1). Catalase, expressed higher in the iP line, is the major enzyme metabolizing ethanol to acetaldehyde in the CNS (Aragon et al., 1991, 1992; Cohen et al., 1980; Zimatkin and Lindros, 1996). Gill et al. (1996) have also reported higher catalase activity in the CNS of P rats compared to NP rats. Two of the 15 differentially expressed genes in the ACB were located within established alcohol QTLs (Bice et al., 1998; Carr et al., 1998, 2003; Foroud et al., 2002, 2003; Radcliffe et al., 2004; Terenina-Rigaldie et al., 2003), that is, ribosomal protein S2 and proteasome (prosome, macropain) subunit, alpha type 5 (Table 5). Some of the differences in the ACB could be related to the disparate alcohol drinking characteristics of the iP and iNP strains.

The FC demonstrated significant differential expression of eight genes (Table 1). The one gene that is differentially expressed in the FC but in no other region is glial fibrillary acidic protein (Gfap). This gene is abundantly expressed in glial cells of the central nervous system and can be used as a marker for glial cell density and health. It is decreased in the FC of human adult alcoholics and more so in alcoholics with depressive symptoms (Miguel-Hidalgo et al., 2002). Humans with a history of chronic alcoholism have been found to have substantial glial pathology in the FC irrespective of current alcohol use or liver pathology (Cullen & Halliday, 1994). It is possible that higher Gfap expression in the FC of the iP line confers protective benefits. The FC has only two significant genes that map to established alcohol QTLs (Bice et al., 1998; Carr et al., 1998, 2003; Foroud et al., 2002, 2003; Radcliffe et al., 2004; Terenina-Rigaldie et al., 2003) (Table 5), ribosomal protein S2 and proteasome (prosome, macropain) subunit, alpha type 5.

4.2. Overall strain differences

Within the 296 genes found significant in the overall analysis (Table 4), 17 GO categories were over-represented (P <.05, Table 7). Six categories associated with neuronal function included axon guidance, gliogenesis, negative regulation of programmed cell death, regulation of programmed cell death, regulation of synapse structure function, and transmission of nerve impulse (Table 6).

To guide determination of relationships between and within genes of the significant GO categories listed in Tables Tables88--11,11, a co-citation network graph was constructed. Of the 63 genes present in the combined lists (Tables (Tables88--11),11), 24 genes are present in a co-citation graph with a threshold for inclusion of four co-citations per link (Fig. 2). The expression of Cdkn1a and Bcl2l1 was lower in the iP than iNP strain; both are involved in regulation of apoptosis, suggesting the reduced expression of these genes may have a positive impact on neuronal function. Ntf3 and Ntf5 code for proteins classified as neurotrophic factors, that is, they function to promote both neuroplasticity and neuro-protection (Filus & Rybakowski, 2005). There is evidence that these two neurotrophic factors might be involved in maintaining functional tolerance to ethanol (Szabo & Hoffman, 1995).

Fig. 2
Co-citation network graph constructed with modified BioConductor software and manual curation (see Methods for details). Grey filled circles denote genes that have decreased expression in the inbred alcohol-preferring rat, unfilled circles denote genes ...

Cntf codes for a neurotrophic factor; however, unlike the Ntfs, this factor has been shown to play a positive role in myelinogenesis (Stankoff et al., 2002). Cntf and Ntf3 have both been shown to play a positive role in psychostimulant-induced behavioral sensitization via their participation in DA-mediated neuronal plasticity (Pierce & Bari, 2001). The higher expression of both of these factors in the iP than iNP strain could be a factor contributing to the reinforcing properties of ethanol observed for the P but not NP line (Gatto et al., 1994). The differences in expression of Ntf and Cntf between iP and iNP rats might indicate that the neuronal system in general might be more geared toward undergoing positive neuronal changes in response to environmental manipulations.

The lower expression of Npy in the iP than iNP strain may be the result of the combined effects of higher expression of Cnft and lower expression of Ntf5 in the iP rats. Cnft has been shown to decrease the expression of Npy (Rao et al., 1992) and the Cnft analogue CNTF (A×15) has been shown to exert a delayed effect in suppressing the expression of Npy in calorie restricted, diet-induced, obese rats (Bluher et al., 2004). Ntf5, but not Ntf3 has been shown to stimulate the expression of Npy in the rat neocortex (Wirth et al., 1998). The findings indicating lower expression of Npy in the iP than iNP rats are in general agreement with reports on tissue levels of NPY reported for the selectively bred P and NP lines (Ehlers et al., 1998; Hwang et al., 2004). A brief survey of other genes connected to Npy reveals that the products of Reln and Npy5r also interact with Npy. Reln, secreted by GABAergic interneurons, is involved in the control of neuronal migration and Npy immunopositive cells have Reln receptors (Pesold et al., 1999). A decrease in the expression of Reln in the iP line is consistent with the decreased expression of Npy in this line. The higher expression of Npy5r in the iP than iNP strain may reflect a compensatory response of having lower expression levels of Npy. The Npy5r antagonist L-152,804 has recently been shown to reduce ethanol self-administration and reinforcement in the iP rat (Schroeder et al., 2003, 2005). The higher expression of Npy5r may be a factor contributing to the high alcohol drinking behavior of the iP rats.

A review of Table 8 indicates that the metabotropic glutamate, glycine, and GABA receptors may also have roles in contributing to the disparate alcohol drinking behaviors of the iP and iNP rats. Table 9 lists many genes that may mediate differences in neuronal development and neuroplasticity between the iP and iNP strains, for example, neurexin 3 is an axonal receptor of neuroligan and this complex can control excitatory and inhibitory synapse formation (Dean et al., 2003; Hussain & Sheng, 2005). Table 10 lists many genes that are different between the strains and may play pivotal roles in intracellular messaging, for example, the expression of Prkce (protein kinase C, epsilon) is almost 30% lower in the iP than iNP strain. This relationship between alcohol drinking and Prkce levels is opposite to that found in Prkce knockout mice, which demonstrated decreased preference for ethanol and increased sensitivity to the behavioral effects of ethanol (Choi et al., 2002; Hodge et al., 1999). These apparently contradictory findings between the inbred rat strains and the mutant mice may be due to species differences and/or to compensatory changes in the knockout mouse. Finally, Table 11 lists transcription factors that were different between the iP and iNP line in the overall analysis. Too little is known about the role of transcription factors in the development of specific neural structures and cell types to speculate on the effects of these differences.

Among the top 20 or so candidates identified in the convergent functional genomics analysis of Rodd et al. (2006), there were seven genes (Fn1, Syn2, Agt, Lyz, Nrn1, Aldh1a1, and Prkce) that were also identified in the overall analysis in the present study as being significantly different between iP and iNP rats (Table 4). These genes should be considered as potential candidates contributing to high alcohol drinking behavior.

In summary, through careful analysis of differential expression of the genome of the iP and iNP rat strains, we have seen that the biggest difference is not between lines, but between regions. We have also seen that, at least in the alcoholnaïve state, the AMYG demonstrates the greatest number of differences in gene expression of the five regions examined, although all five regions demonstrate at least some genes with significant differential expression (FDR < 0.10). Taken together, the individual region and combined region analyses indicate that the expressions of genes involved in biologic networks of neurotransmitters, intracellular messengers, neuroplastins, neurotrophins, and transcription factors may all contribute to behavioral and neurobiological differences between the iP and iNP rats and their parent stocks. A network involving Npy and related genes has the most supporting evidence from both the data of this experiment and a co-citation bioinformatic analysis.

Acknowledgments

This study was supported in part by AA07611, AA07462, AA13521 [INIA], and AA16652 [INIA]. Microarrays were analyzed using the facilities of the Center for Medical Genomics at Indiana University School of Medicine, which is supported in part by the Indiana Genomics Initiative (INGEN®) of Indiana University. INGEN is supported in part by the Lilly Endowment Inc.

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