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Keywords:

  • Candidate genes;
  • ethanol withdrawal;
  • quantitative trait locus

Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments

We recently mapped quantitative trait loci (QTLs) with large effects on predisposition to physical dependence and associated withdrawal severity following chronic and acute alcohol exposure (Alcdp1/Alcw1) to a 1.1-Mb interval of mouse chromosome 1 syntenic with human chromosome 1q23.2-23.3. Here, we provide a detailed analysis of the genes within this interval and show that it contains 40 coding genes, 17 of which show validated genotype-dependent transcript expression and/or non-synonymous coding sequence variation that may underlie the influence of Alcdp1/Alcw1 on ethanol dependence and associated withdrawal. These high priority candidates are involved in diverse cellular functions including intracellular trafficking, oxidative homeostasis, mitochondrial respiration, and extracellular matrix dynamics, and indicate both established and novel aspects of the neurobiological response to ethanol. This work represents a substantial advancement toward identification of the gene(s) that underlies the phenotypic effects of Alcdp1/Alcw1. Additionally, a multitude of QTLs for a variety of complex traits, including diverse behavioral responses to ethanol, have been mapped in the vicinity of Alcdp1/Alcw1, and as many as four QTLs on human chromosome 1q have been implicated in human mapping studies for alcoholism and associated endophenotypes. Thus, our results will be primary to further efforts to identify genes involved in a wide variety of behavioral responses to alcohol and may directly facilitate progress in human alcoholism genetics.

The genetic determinants of alcoholism are largely unknown, significantly hindering effective treatment and prevention. Only a few genes have consistently showed a role in alcoholism or associated endophenotypes in human studies and can thus be nominated as candidate vulnerability genes. These include isoforms of the alcohol metabolizing enzymes ADH1, ADH4, and ALDH2, and neurotransmitter receptor subunits GABRB1, GABRA2, and CHRM2 (reviewed in Dick & Foroud 2003; Edenberg & Foroud 2006). However, identification of these genes relied heavily on a priori knowledge of physiological responses to ethanol and has not wholly explained the complex genetic susceptibility to alcoholism. Thus, unbiased and systematic approaches to gene discovery are critical if novel genes and mechanisms underlying alcohol dependence are to be discovered and translated to improved treatment and prevention.

No animal model can duplicate alcoholism in humans, but robust animal models for specific traits, including alcohol withdrawal, are valuable genetic resources. Detection of quantitative trait loci (QTLs), chromosomal regions at which allelic variation affects a complex (quantitative) trait, in these models is fundamental to an unbiased genome-wide search for candidate genes. In the accompanying paper (Kozell et al. 2008) we mapped QTLs (Alcdp1 and Alcw1) affecting alcohol physical dependence and associated withdrawal following chronic and acute alcohol exposure to a minimal 1.1 Mb interval of mouse chromosome 1 syntenic with human chromosome 1q23.2-23.3. Quantitative trait loci affecting alcohol-conditioned aversion (Risinger & Cunningham 1998), alcohol preference drinking (Tarantino et al. 1998) and alcohol-induced locomotor activation (Demarest et al. 1999) and hypothermia (Crabbe et al. 1994) are also detected on distal chromosome 1. The possibility that Alcdp1/Alcw1 plays an important role in such diverse responses to alcohol makes it an important target. Moreover, at least four studies have identified markers on human chromosome 1q associated with alcoholism or associated endophenotypes (Aragaki et al. 1999; Dick et al. 2002; Guerrini et al. 2005; Turecki et al. 1999) and, while still only localized to large regions, carry the potential to overlap with sytenic stretches on distal mouse chromosome 1. Therefore, detailed analyses of Alcdp1/Alcw1 candidate genes may inform developing models for genetic influences on human alcoholism.

Identification of the specific genes underlying QTLs (quantitative trait genes, QTGs) is a major challenge in the translation of preclinical research (Flint et al. 2005). Successful strategies have most often relied upon evidence from several sources, and many have used congenic strains to their advantage (Belknap et al. 2001; Cicila et al. 2001; Liang et al. 2007; Shirley et al. 2004). To date, few QTGs have been identified for the thousands of detected QTLs (Flint et al. 2005), and only one QTG (Mpdz) has been convincingly determined to influence alcohol withdrawal (Shirley et al. 2004). A comprehensive understanding of genetic variation in humans and informative animal models is crucial to establish relationships between genotype and biological function (Collins et al. 2003). Here, we identify a list of high-priority QTG candidates for Alcdp1/Alcw1 using an integrative and unbiased strategy to systematically assess the molecular genetic variation within this interval.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments

Animals

All animals used were bred in our colony at the Portland VA Medical Center. C57BL/6J (B6) and DBA/2J (D2) inbred strain breeders were purchased from the Jackson Laboratory (Bar Harbor, ME, USA). Breeder stock for two chromosome 1 congenic strains (R8 and R4) were provided by Drs John Hofstetter and Aimee Mayeda at the Indianapolis VA Medical Center. Detailed descriptions and capture of the increased ethanol withdrawal severity phenotype by R4 and R8 congenics can be found in the companion paper, Kozell et al. (2008). Mice were group-housed 2–5 per cage by strain and sex. Mouse chow (Purina #5001) and water were available ad libitum, and lights were on from 0600 h to 1800 h in a colony room maintained at 22.0 ± 1.0°C. All procedures were approved by the Oregon Health and Science University (OHSU) and VA Medical Center Care and Use Committees in accordance with U.S. Department of Agriculture and U.S. Public Health Service guidelines.

Candidate genes and brain expression analyses

The Ensembl (www.ensembl.org, NCBI Build 36) and UCSC Genome Browser (www.genome.ucsc.edu, mm8) databases were used to identify as many known and predicted genes as possible within the 1.1-Mb Alcdp1/Alcw1 interval. Ensembl mouse transcript and NCBI RefSeq sequences were used as consensus sequences in subsequent gene and probe set alignments.

The Unigene (NCBI, www.ncbi.nlm.nih.gov/entrez/query) and Allen Brain Atlas (ABA, www.brain-map.org) databases were searched to obtain brain expression information for each known and predicted gene within Alcdp1/Alcw1. Additionally, genes for which public information was absent, minimal or conflicting were screened with transcript-specific polymerase chain reaction (PCR) on a panel of mouse brain cDNAs (whole brain, brainstem, cerebellum, cortex, frontal cortex, hippocampus, hypothalamus, medulla, pons and thalamus) as in previous work (Shirley et al. 2004). Unigene is the largest organized resource for mouse transcriptome information, including source cDNA. All genes were queried by mouse genome informatics (MGI) accession number and/or gene ID and considered expressed only if ‘brain’ or any brain region was listed as a cDNA source. Annotations of ‘whole head/neck,’‘eye,’ or ‘retina’ were not considered evidence of brain expression. ABA is a database of genome-wide in situ hybridization (ISH) mouse brain mRNA expression data superimposed on a detailed and interactive anatomical atlas. All Alcdp1/Alcw1 genes were queried by gene ID and/or MGI accession number and analyzed individually in both two-dimensional slice and three-dimensional BrainExplorer modes. Expression patterns and densities were visually assessed for each of the major anatomical divisions curated by ABA and a qualitative expression rating assigned for a region based on non-isotopic ISH signal as detected and annotated by the database. Details of the methods used to derive relative expression level and density annotations can be found at www.brain-map.org/pdf/InformaticsDataProcessing.pdf.

Microarray expression analyses

Naïve R4 chromosome 1 congenic and B6 background strain mice (males 60–90 days of age) were euthanized by cervical dislocation and the whole brain rapidly dissected and flash frozen in liquid nitrogen. The genetic composition of the R4 strain is >99.5% B6, except for an introgressed 5-Mb donor interval (172.97–177.97 Mb) spanning Alcdp1/Alcw1 derived from the D2 strain (Kozell et al. 2008). Total RNA was isolated from pseudo-randomly chosen left or right hemispheres using TRIzol® reagent (Invitrogen, Carlsbad, CA, USA) in a one-step guanidine isothiocyanate procedure as in previous work (Daniels & Buck 2002). The extracted RNA was purified using the RNeasy kit (Qiagen, Valencia, CA, USA) and evaluated by ultraviolet spectroscopy for purity and concentration. Samples containing 10 μg of total RNA were sent to the OHSU Gene Microarray Shared Resource facility for further quality control and microarray analysis. After labeling, individual RNA samples (n = 6 per strain) were hybridized to Affymetrix MOE430A chips as specified by Affymetrix and detailed at www.ohsu.edu/gmsr/amc/services/serv_labeling.htm.

Position-dependent nearest neighbor (PDNN) analysis (Zhang et al. 2003) using default settings was implemented using PerfectMatch software (Li Zhang, v2.1, http://odin.mdacc.tmc.edu/~zhangli/ PerfectMatch/). Quantile normalization was performed with PDNN to enhance low-level analysis; this method incorporates hybridization efficiencies of individual probes and does not include mismatch probe data in the analysis.

Quantitative real-time PCR expression analyses

Naïve R8 congenic and B6 background strain mice (males 60–90 days of age) were euthanized by cervical dislocation and whole brains removed immediately by rapid dissection. The genetic composition of the R8 strain is >99.5% B6, except for a small interval donated from the D2 strain that entirely captures the Alcdp1/Alcw1 effect on withdrawal severity (Kozell et al. 2008). The minimal R8 introgressed interval is 1.1 Mb (172.97–174.06 Mb). The maximal interval includes a proximal 0.6-Mb boundary region (172.37–172.97 Mb) currently undifferentiated between B6 and D2 because of strong identity by descent (data not shown) and thus not likely to contain positional QTG candidates (Mehrabian et al. 2005). The left or right hemisphere of individual animals (n = 10 to 12 per strain) was chosen pseudo-randomly and placed in 500 μl RNAlater (Qiagen) for storage at 4°C until RNA extraction. Total RNA from half-brains was isolated with RNeasy (Qiagen) and mRNA purified with Oligotex (Qiagen). Prior to first-strand cDNA synthesis with High Capacity cDNA Archive Kit (ABI, Foster City, CA, USA), aliquots of mRNA were treated with DNase (Promega, Madison, WI, USA) at 37°C for 30 min to eliminate potential contaminating genomic DNA.

Quantitative real-time polymerase chain reaction (qRT-PCR) was used to confirm promising expression results obtained by microarray, as well as to assess those genes within the QTL interval that were not represented on the MOE430A chip. Relative expression was measured using validated gene-specific TaqMan assays (ABI), most of which span an intron as an additional control against contaminating genomic DNA. Reactions (20 μl) were performed in an ABI Prism7500 thermal cycler (ABI) using Two-Step PCR Master Mix. Threshold values (Ct) for target (candidate) gene expression levels were determined automatically by the standard TaqMan software package and normalized to reference gene (mouse Hmbs, Mm00660262_g1) expression. The comparative (ΔΔCt) method (Livak & Schmittgen 2001) was used for relative quantification, which powerfully corrects for run-to-run variability (e.g. pipetting errors, cDNA concentration or quality differences) by normalization of sample target and reference gene expression to expression levels of a calibrator sample (n = 6 B6 and n = 6 R8 mRNA aliquots pooled for synthesis of a single cDNA) included on each run.

Single nucleotide polymorphism annotation

We compiled several public single nucleotide polymorphism (SNP) data sets to annotate all known SNPs within the 1.1 Mb Alcdp1/Alcw1 interval between the B6 and D2 progenitor strains as recently described (Walter et al. 2007). This compilation includes D2 strain sequence data from the National Institute of Environmental Health Sciences/Perlegen Mouse Resequencing Project, the Mouse Phenome Database SNP Tool (http://www.jax.org/phenome/snp.html) and the Sanger resequencing SNPs from Ensembl. Direct sequencing of selected exons was conducted in-house when in silico D2 data were absent, of low quality or based on single reads. Sequence-specific primers were designed to amplify cDNA products of 0.1–1.0 kb based on consensus transcripts, and where possible, to span an intron for control against genomic contamination. Polymerase chain reaction products were analyzed on ethidium bromide-stained 2% agarose gels, and selected bands excised, purified (QiaQuick; Qiagen) and submitted to the OHSU sequencing core facility. Details of the sequencing protocol used can be found at http://www.ohsu.edu/research/core/dseq/. Sequencing was performed on both strands for all products and raw reads reassembled in-house using sequencher (v4.5, Gene Codes, Ann Arbor, MI, USA) DNA analysis software. The sequences have been deposited in dbSNP.

All probes and target sequences were aligned with SNP annotation to assess the potential impact of allelic differences on gene expression results. Affymetrix probe set target sequences and locations of individual probes were obtained at www.affymetrix.com (R20, July 2006). As this information for TaqMan assays is proprietary, amplicon sequences were approximated from ABI (www2.appliedbiosystems.com) by assuming that the provided base number coordinate approximately corresponds to the amplicon’s center nucleotide and taking into account the provided amplicon length. All probe sets spanning a SNP(s) were masked from the microarray analyses using a masking algorithm developed in our laboratory (Walter et al. 2007). Alternative gene-specific TaqMan assays were used when B6/D2 SNPs were detected in the initial probe’s target sequence. Only expression results obtained using probes free of known SNPs are reported.

Data analysis. All Affymetrix microarray (post low-level analysis with PDNN) and qRT-PCR data were analyzed for strain-dependent expression (B6 vs. R4 or R8, respectively) using a two-tailed (Student‘s) t-test implemented by Excel (Microsoft Office, 2003). Significance level for expression differences was set at α < 0.05 to increase the probability that the maximum of potentially true genotype-driven variation would be detected.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments

Identification of candidate genes within the 1.1-Mb Alcdp1/Alcw1 interval

Using mouse genome databases (Ensembl, UCSC) we identified as many known and predicted transcripts as possible within the 1.1-Mb QTL interval. As shown in Table 1, the minimal 1.1-Mb QTL contains a total of 37 known and three novel (predicted) protein-coding genes, as well as three non-coding RNAs and one pseudogene. These results distinguish the Alcdp1/Alcw interval as remarkably gene dense (one known or predicted gene every 27.5 kb) compared with estimates of the draft mouse genome (one per 113.6 kb; Waterston et al. 2002).

Table 1.  Genes within the 1.1 Mb Alcdp1/Alcw1 interval and evidence for brain expression
Gene symbolDescriptionEntrez gene IDUni gene*ABA regional expression
CTXOLFHIPRHPAMYSTdSTvLSXPALTHHYMBPMYCB
  • CTX, cerebral cortex; OLF, olfactory areas; HIP, hippocampal region; RHP, retrohippocampal region; AMY, amygdalar nuclei; STd, dorsal striatum; STv, ventral striatum; LSX, lateral septal complex; PAL, pallidum; TH, thalamus; HY, hypothalamus; MB, midbrain; P, pons; MY, medulla; CB, cerebellum. The Ensembl and UCSC public genome databases were used to identify 37 known, 3 predicted, 1 pseudogene and 3 non-coding RNA genes as all candidate genes within the 1.1-Mb QTL interval. The Unigene and ABA databases contain evidence of brain expression for 33 of these (bold face type), which represent the primary candidates for Alcdp1/Alcw1 evaluated in subsequent studies.

  • *

    Evidence of brain expression in Unigene: (+) ‘brain’ or brain region listed as cDNA source; (−) ‘brain’ or brain region not listed as cDNA source; (nd) no data available.

  • Evidence of regional brain expression in ABA: (−) no signal; (−/+) minimal signal, indistinguishable from artifact or extremely restricted expression; (+) moderate signal, significant expression present; (++) strong signal, dense and/or high-level expression present; (nd) no data available.

  • Not detected by PCR on panel of mouse brain region cDNAs.

Fcgr3Fc receptor, IgG, low affinity III14131+−/+−/+−/+−/+−/+−/+−/+−/+-/+−/+−/+
1700009P17RikRIKEN cDNA 1700009P17 gene75472+−/++−/+−/+−/++++−/+−/++−/+++
SdhcSuccinate dehydrogenase complex, subunit C66052++++++++++++++++
MpzMyelin protein zero17528++++++−/+−/+++
Pcp4l1Purkinje cell protein 4-like 166425+++++++++++++++++++++++++++++++
Nr1i3Nuclear receptor subfamily 1, group I, member 312355+++++++++−/++++++
Tomm40lTranslocase of outer mitochondrial membrane 40 homolog-like (yeast)641376+nd
Apoa2Apolipoprotein A-II11807+−/+−/+−/+−/+
Fcer1gFc receptor, IgE, high affinity I, gamma polypeptide14127+nd
Ndufs2NADH dehydrogenase (ubiquinone) Fe-S protein 2226646++++++++++++++++++++++++++++
Adamts4A disintegrin-like and metallopeptidase (reprolysin type) with thrombospondin type 1 motif, 4240913+++++++++++++++++
B4galt3UDP-Gal:betaGlcNAc beta 1,4-galactosyltransferase, polypeptide 357370++++++++++++++++++++++
PpoxProtoporphyrinogen oxidase19044++++++++−/+++++++
Usp21Ubiquitin specific peptidase 2130941++++++++++++++++++++++++++
Ufc1Ubiquitin-fold modifier conjugating enzyme 166155++++++−/++++++++++++
novelENSMUSG00000079137 ndnd
DeddDeath effector domain-containing21945++++++++++++++++++++++++++++++
Nit1Nitrilase 127045++++−/++++
Pfdn2Prefoldin 218637++++++++++++++++++++
Klhdc9Kelch domain containing 9, 1190002J23Rik68874++++++++++++++
Pvrl4Poliovirus receptor-related 471740+++++++++++++++++++
Arhgap30Rho GTPase activating protein 30226652
Usf1Upstream transcription factor 122278+++++++++++++++++++++
F11rF11 receptor16456++++−/+−/+−/+−/+++−/+
snRNAU5 ndnd
Refbp2RNA and export factor binding protein 256009+nd
NovelENSMUSG00000038218 ndnd
Itln1Intelectin 116429nd
Cd244CD244 natural killer cell receptor 2B418106nd
Ly9Lymphocyte antigen 917085
Slamf7SLAM family member 775345++++++++++−/+++−/+
Cd48CD48 antigen12506++++
novelENSMUSG00000059058, pseudogene ndnd
snRNAU6 ndnd
Slamf1SLAM family member 127218−/+
Cd84CD84 antigen12523+
Q3UP30Novel, ENSMUSG00000073492 ndnd
MiRNAmmu-mir-680-3 ndnd
Slamf6SLAM family member 630925++−/++−/+−/++−/+−/+++
LtapLoop tail-associated protein 293840+−/++−/+−/+−/+−/+−/+−/+−/+−/++
Nhlh1Nescient helix loop helix 118071+−/++++−/+−/+−/+−/+−/+
NcstnNicastrin59287+++++++++−/+−/++++−/++−/+
CopaCoatomer protein complex subunit alpha12847++++++++++++++++++++++
Pex19Peroxisome biogenesis factor 1919298+++++++−/++−/+−/+−/++−/+−/++

We used evidence for brain expression as an initial filter in prioritizing potential candidate genes because convulsions are centrally mediated, and handling-induced convulsions are an index of alcohol withdrawal severity for which pharmacokinetic factors are not crucially important (Crabbe 1983; Metten & Crabbe 1994). Using the Unigene and ABA databases, as well as a PCR-based screen on a panel of mouse brain cDNAs, including whole brain and nine specific regions (data not shown), we confirmed expression in mouse brain for 33 of the 40 coding genes mapped within the Alcdp1/Alcw1 interval and established these as primary candidates (Table 1). Additionally, while absolute quantitation of expression levels is currently not possible with ABA, we used this resource to derive qualitative pattern and relative expression density information for primary candidates in the major brain regional subdivisions as a first step toward resolving cellular substrates of Alcdp1/Alcw1-influenced withdrawal (Table 1).

Microarray analyses

The Affymetrix database (http://www.affymetrix.com/) was queried using the basic local alignment search tool search to determine the microarray probe set(s) representing each of the confirmed candidate genes. To ensure that all brain regions potentially relevant to the behavioral expression of withdrawal were included in the analyses, and to increase power through biological replication, arrays were run on whole-brain samples obtained from individual animals of the chromosome 1 congenic (R4) and B6 background strains (n = 6 per strain). Comparison of gene expression between the R4 congenic and B6 background strains affords substantially increased confidence that expression differences detected for transcripts physically mapped within Alcdp1/Alcw1 are cis-mediated, as would be expected for a true QTG. In the present studies, we decided to nominate candidates based on inherent genotype-dependent expression for three reasons: (1) Many more genes are differentially expressed between ethanol-naïve B6 and D2 mice than after a single injection of ethanol (Daniels & Buck 2002; Kerns et al. 2005), (2) most of the ethanol-induced B6/D2 expression differences are already apparent before ethanol exposure (Kerns et al. 2005) and (3) the 4 to 7-h time frame post-ethanol injection is shorter than that generally thought to be required for the majority of gene expression changes to show the corresponding changes in protein critical for behavioral expression of differential withdrawal severity, increasing the likelihood that the relevant gene expression disparity(s) is pre-existing.

Thirty-one of the 33 primary candidates were represented by at least one high-quality probe set on the Affymetrix MOE430A chip, of which 11 (represented by 12 probe sets) showed evidence of inherent genotype-dependent brain expression (Fig. 1). Nine out of 11 (82%) genes showed evidence of lower expression in R4 congenic vs. B6 background strain mice, while only two genes (Ndufs2, Sdhc) were apparently more highly expressed in congenic than background strain animals. None of the detected expression differences approached 50%, highlighting the importance of performing sensitive low-level analyses and potentially accepting less-stringent false-positive or false discovery rates and performing confirmation analyses (e.g. qRT-PCR) in initial microarray assessments of genome-wide brain expression (Allison et al. 2006).

image

Figure 1. Alcdp1/Alcw1 candidate genes with microarray evidence for differential expression between congenic (R4) and background strain (B6) mice. Genome-wide expression analyses were performed on R4 chromosome 1 congenic and background strain (B6) whole-brain RNA with Affymetrix MOE430A chips (n = 6 per strain). Thirty-one brain-expressed genes within the 1.1-Mb QTL interval were represented by at least one high quality probe-set; 11 of these displayed differential expression between strains (P ≤ 0.03). Bars shown in light gray denote B6 > R4 expression, and bars shown in dark gray denote R4 > B6 expression. Nit1 was represented by two probe sets both showing differential expression.

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Confirmatory candidate gene expression analyses

Gene- and transcript-specific qRT-PCR was performed for confirmation testing of genotype-dependent expression of the 11 microarray-implicated candidates, as well as the three candidates not represented on the Affymetrix MOE430A chip. Subsequent to microarray analyses, we identified a small donor segment congenic (R8) with a minimal 1.1-Mb donor interval, which entirely captured the Alcdp1/Alcw1 effect on alcohol withdrawal following chronic and acute alcohol exposure at a magnitude equivalent to R4 (Kozell et al. 2008). Therefore, qRT-PCR studies compared gene expression between R8 congenic and B6 background strain animals, even further increasing confidence that all detected differences are because of allelic variation contained within the Alcdp1/Alcw1 interval. Because microarray results indicated no evidence for genotype-dependent expression for any gene within the 0.6-Mb proximal R8 boundary region and we have detected no B6/D2 allelic variation to date (i.e. the region is highly identical by descent; data not shown), these genes were not assessed further.

Relative quantitation analyses confirmed genotype-dependent brain expression for 8 of the 11 microarray-implicated candidates (Sdhc, Apoa2, Ndufs2, Adamts4, Ppox, Ufc1, Ncstn and Copa; P < 0.02), as well as for two of the three candidates not represented on the MOE430A array (Klhdc9, Refbp2(b); P < 0.0007; Table 2). This is an unusually high number of genes for such a small region to show baseline strain-specific expression, particularly because they are structurally unrelated, and corroborates earlier findings of disproportionately frequent basal expression differences between the B6 and D2 strains for genes in this region of chromosome 1, even after correction for the increased gene density (Kerns et al., 2005). The direction of effect was consistent in all cases (B6 > congenic: Apoa2, Adamts4, Ppox, Ufc1, Ncstn, Copa; congenic > B6: Sdhc, Ndufs2), and while there was an increase in magnitude when compared with the microarray (Fig. 1), only one candidate (Apoa2) showed a between-strains difference greater than twofold. Interestingly, recent independent genome-wide expression data sets for two populations (BXD, B6D2F2) used to initially map the Alcw1/Alcp1 QTL to distal chromosome 1 indicate cis-regulation (i.e. genotype at markers within the R8 interval is significantly associated with expression level) for six of the eight candidates implicated by microarray and subsequently confirmed by qRT-PCR to exhibit genotype-dependent expression (Sdhc, Apoa2, Ndufs2, Adamts4, Ufc1, Copa; www.genenetwork.org/).

Table 2.  qRT-PCR confirms genotype-dependent expression between R8 chromosome 1 congenic and background (B6) strains for 10 Alcdp1/Alcw1 candidates
Gene symbolTaqman probe IDR8/B6
  1. Bold face type indicates genes for which expression in R8 is statistically different than B6 as detected by the listed TaqMan assay and described in terms of percentage B6 level: *P < 0.00005, **P < 0.0005, ***P ≤ 0.016, ****P < 0.001. nd, not determined, expression levels beneath the threshold of reliable detection (mean Ct ≥ 33), or only available probe contains B6/D2 SNP(s).

1700009P17RikMm00512620_m10.98
SdhcMm00481172_m11.49*
Tomm40lMm01290711_g11.06
Apoa2Mm01290539_g10.01*
Ndufs2Mm00467605_g11.58**
Adamts4Mm00556068_m10.73*
PpoxMm00447232_m10.59***
Ufc1Mm00502923_m10.83***
Nit1Mm00449588_m11.02
Klhdc9Mm01295280_g10.75**
Refbp2(a)Mm00727241_s1Nd
Refbp2(b)Mm01309506_s10.62****
Slamf7Mm00513807_m1Nd
NcstnMm01293326_g10.73****
CopaMm01283371_m10.60*

qRT-PCR did not confirm genotype-dependent expression of three genes implicated by microarray (Tomm40l, Nit1 and Slamf7; P > 0.4), all of which showed initial evidence of lower expression in R4 congenic compared with the B6 background strain. Because Affymetrix probes are designed to match the reference B6 strain, this bias may have been caused by SNPs in the target sequences resulting in spuriously low hybridization signals for non-B6 alleles. We recently showed that at least 16% of the probe sets on the MOE430A microarray harbor SNPs between the B6 and donor (D2) strains and that application of a SNP mask based on public database D2 sequence mitigates potentially false expression differences detected by microarray (Walter et al. 2007). We applied this SNP mask to assess the impact of known SNPs between the congenic and B6 background strains on our microarray results and observed no alteration in detected expression differences for genes within the Alcdp1/Alcw1 interval (data not shown). However, it should be kept in mind that the public D2 sequence is not yet complete and cryptic SNPs may still exist between the two strains that have the potential to impact hybridization-based methods of assessing gene expression.

Candidate gene coding sequence variation

We began by utilizing public databases to systematically assess in silico which genes within the 1.1 Mb Alcdp1/Alcw1 interval contain non-synonymous coding SNPs (i.e., that result in changes of predicted protein sequences) between the B6 and D2 progenitor strains. To date, high quality D2 sequence provides evidence for nine such genes (Fcgr3, Ndufs2, Adamts4, Nit1, Klhdc9, Slamf7, CD48, CD84, Pex19; Table 3), the majority of which did not display genotype-dependent expression and are thus additional QTG candidates. No B6/D2 coding polymorphism has been detected for any gene within the 0.6 Mb boundary region (data not shown).

Table 3.  High priority candidate genes for Alcdp1/Alcw1 based on non-synonymous B6/D2 coding sequence polymorphism
Gene symbolB6/D2 amino acid differencePublic SNP identifier
  • Predicted amino acid differences because of coding B6/D2 SNPs obtained through public databases and by direct in-house sequencing.

  • *

    Novel B6/D2 SNP identified by direct sequencing. Public identifier is pending assignment in dbSNP.

  • Novel SNP identified by direct sequencing. Identifier previously assigned for allelic difference between reference (B6) and strain(s) other than D2.

Fcgr3Trp217Glyrs8242800
Ser222Argrs8242799
Ser244Alars4222823
SdhcPhe4Leurs31116911
Ser10GlyENSMUSSNP5307558
Leu253Valrs31554163
Ndufs2Arg8Glyrs31541214
Adamts4Gln30Argrs31538642
Phe574TyrENSMUSSNP6311765
Usp21Gly461Val*
Asp463His*
Asp500Ser*
Nit1Thr21Ilers31552464
Klhdc9Thr6Prors31549115
Glu115GlyENSMUSSNP4475076
Refbp2Asn18Serrs31537262
Arg37SerENSMUSSNP4909206
Thr151Alars31537260
Slamf7Met248Thrrs31080132
Gly253Argrs32013887
Cd48Ile4Argrs31533394
Asn90Asprs3718911
Cd84Val27Metrs31528577
NcstnSer21Phers31580400
Thr678IleENSMUSSNP4494825
Val680IleENSMUSSNP2251306
CopaAsn984SerENSMUSSNP1061247
Pex19Pro55Serrs32297387

Additionally, we performed direct sequencing of the D2 strain for selected exons in genes for which there was no in silico evidence of non-synonymous SNPs as a result of the lack of coverage, or where evidence was based entirely on single or low-quality reads. This resulted in the addition of five genes verified to contain previously unannotated non-synonymous SNPs (Sdhc, Usp21, Refbp2, Ncstn, Copa; Table 3), thus nominating a total of 14 genes in the QTL interval as high-priority candidates on the basis of predicted amino acid differences. These experiments provide a sound and unbiased evaluation of all genes within Alcdp1/Alcw1 for potential QTG candidacy based on either the expression or sequence variation criteria. For genes already nominated as candidates based upon genotype-dependent expression or sequence, 100% coverage of D2 coding sequence was not necessarily sought. More complete D2 coverage will likely require bacterial artificial chromosome sequencing in future work to fully interrogate potential mechanisms by which the causal quantitative trait nucleotide(s) influences withdrawal.

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments

Quantitative trait loci for ethanol physical dependence and associated withdrawal are detected on distal mouse chromosome 1 in several mapping populations derived from the B6 and D2 progenitor strains and underlie ∼25% of the genetic variability displayed in acute (Alcw1) and chronic (Alcdp1) withdrawal severity (Buck et al. 1997, 2002). In the accompanying paper (Kozell et al. 2008), we mapped Alcw1 and Alcdp1 to the same 1.1 Mb minimal interval. Here, we report a detailed molecular analysis identifying 17 genes as promising candidates to underlie these QTLs’ effects on alcohol withdrawal. Seven of these display non-synonymous B6/D2 coding sequence variation, three show confirmed genotype-dependent brain expression between congenic and background strains, and the remaining seven exhibit both features. These types of genetic variation account for most established QTGs (Flint et al. 2005), indicating that their thorough assessment is a key component of candidate QTG prioritization.

This work represents a substantial advance toward identification of a QTG(s) involved in ethanol dependence/withdrawal. Although there is considerable evidence that vulnerability to withdrawal from a variety of sedative-hypnotics has some genetic factors in common, Alcdp1/Alcw1 apparently does not influence barbiturate withdrawal (L. Kozell, N. Walter, K. Wickman, K. Buck submitted). This is the first evidence for an alcohol withdrawal QTL that does not generalize to barbiturates and is a crucial clue as to the identity of the Alcdp1/Alcw1 QTG(s). Interpretation of the present results and future tests of promising candidates for their influence on withdrawal will likely be informed by probing for effects specific to ethanol and/or not influenced by barbiturates.

Copa is a high-priority candidate based on genotype-dependent expression and amino acid sequence. It encodes the largest (alpha-COP) of the seven-subunit coatomer protein complex, COPI, that mediates the essential retrieval pathway in early vesicular transport (reviewed by Bethune et al. 2006). Specific interaction of COPI with subunits of various multimeric transmembrane proteins involved in both inhibitory and excitatory neurotransmission (Brock et al. 2005; Keller et al. 2001; Margeta-Mitrovic et al. 2000; O’Kelly et al. 2002; Vivithanaporn et al. 2006; Yuan et al. 2003) is proposed to be a key trafficking checkpoint ensuring that only properly oligomerized complexes proceed through the secretory pathway for plasma membrane expression. Ethanol has direct effects on COP-mediated vesicular trafficking of sphingolipids (Pascual et al. 2003), nascent glycoproteins and glycolipids (Minana et al. 2000) and decreases the expression of another coatomer protein, beta-COP (Tomas et al. 2005). Intriguingly, the first 25 amino acids of the mammalian Copa gene product are cleaved post-translationally to form xenin (Chow & Quek 1997), a peptide that binds neurotensin receptors (Kinkead & Nemeroff 2006). Xenin exhibits hypothalamic expression (Hamscher et al. 1995) and function consistent with neurotensin system activation (Alexiou et al. 1998), but little is known about its potential expression in other brain regions and broader neurobiological role. Because central neurotensinogenic function has been genetically and pharmacologically linked to hypnotic sensitivity to ethanol (Erwin et al. 2001a; Radcliffe et al. 2006), and specifically not to pentobarbital (Erwin et al. 1987, 2001b), Copa is a particularly promising Alcdp1/Alcw1 candidate. Interestingly, Copa mRNA expression (ABA; Table 1) is found in all major brain regions known to express neurotensin (Smits et al. 2004) and its’ receptors (Elde et al. 1990; Mazella et al. 1996).

Our analyses also implicate oxidative stress and mitochondrial function as factors that potentially contribute to ethanol withdrawal. Ethanol exposure introduces intense oxidative stress in vitro and in vivo largely mediated by its effects on mitochondria (Bailey 2003; Sun & Sun 2001) and contrasts with evidence suggesting a neutral or antioxidative effect of pentobarbital exposure in brain (Almaas et al. 2000; Smith et al. 1980; Ueda et al. 2007). While there has been little investigation, evidence from rat models suggests that ethanol withdrawal is accompanied by increases in reactive oxygen species that correlate with the severity of withdrawal behavior (Dahchour et al. 2005; Vallett et al. 1997). Moreover, D2 mice maintain significantly higher brain levels of some oxidative stress markers than B6 mice (Rebrin et al. 2007), making this an attractive hypothesis for Alcdp1/Alcw1 candidate analyses.

Three Alcdp1/Alcw1 candidates (Sdhc, Ndufs2 and Ppox) encode mitochondrial proteins involved in oxidative stress pathways. Two code for integral subunits of respiratory complexes I (NDUFS2) and II (SDHC), the first enzymes of the mitochondrial electron transport chain (ETC). Proper and efficient ETC function is particularly critical to cells with high metabolic demand (e.g. central nervous system), as this activity generates the chemical gradient from which most cellular energy is produced. The third, protoporphyrin oxidase (PPOX), catalyzes the penultimate step of heme biosynthesis, the prosthetic group required for the cytochrome function central to ETC activity and other cellular processes. NDUFS2 is crucial to the assembly and ubiquinone-reducing catalytic capacity of Complex I (Kashani-Poor et al. 2001) and is highly conserved across prokaryotic and eukaryotic species. SDHC is the larger of two membrane-anchoring subunits and is required for proper functional assembly of Complex II (Sun et al. 2005), the point at which the Krebs Cycle and ETC converge to couple substrate metabolism to ATP-generating oxidative phosphorylation. Mutation in the Caenorhabditis elegans Ndufs2 homolog (gas-1) creates hypersensitivity to ethanol and volatile anesthetics (Kayser et al. 2001; Morgan & Sedensky 1995) and increased oxidative stress (Kayser et al. 1999, 2003), while mutations in human NDUFS2 (Ugalde et al. 2004) and PPOX (Gonzalez-Arriaza & Bostwick 2003) are causal for inherited diseases that include seizures as a prominent symptom. Lastly, expression changes for a number of oxidative stress and mitochondrial genes are hallmarks of the human alcoholic brain (Flatscher-Bader et al. 2006; Liu et al. 2006).

Other candidates showed by our analyses suggest both previously indicated and entirely novel aspects of neurobiology as potentially related to alcohol responses. Adamts4 codes for aggrecanase-1, a glutamyl endopeptidase that functions largely within the extracellular matrix, is widely expressed in mammalian brain (Held-Feindt et al. 2006; Yuan et al. 2002) and plays an important role in regenerative neural plasticity through its actions on the synapse-stabilizing proteoglycan brevican (Mayer et al. 2005; Yuan et al. 2002). Interestingly, Adamts4 null mutants display a strong neurological phenotype including increased sensitivity to pharmacologically-induced seizures (www.jaxmice.jax.org/strain). Usp21 and Ufc1 encode proteins involved in ubiquitin pathways, a complex protein degradation regulatory system implicated in alcoholism by human microarray studies (Flatscher-Bader et al. 2006; Liu et al. 2006). Ncstn codes for the largest subunit of gamma-secretase, an intracellular protease complex required for processing of several type I integral membrane brain proteins, including amyloid precursor protein (De Strooper et al. 1998) and Notch (De Strooper et al. 1999). The 100-fold genotype-dependent difference in Apoa2 expression is intriguing, considering that even subtle gene expression changes in brain can be functionally important. While the Apoa2 product, apolipoprotein A-II, is known almost entirely for its role in peripheral cholesterol metabolism and expression thought to be restricted to liver and intestine, protein expression was recently detected in mouse (Kislinger et al. 2006) and human (B. Balgley 2007, www.humanproteinpedia.org) brain. Other apolipoproteins have primary roles in healthy and diseased brain states (Herz & Chen 2006; Thomas et al. 2003), so Apoa2 may have an as-yet unrecognized brain function that contributes to ethanol withdrawal. Lastly, several genes encoding proteins for immune function (Fcgr3, CD48, CD84, Slamf7), RNA-binding (Refbp2), and peroxisome genesis (Pex19), and one currently uncharacterized (Klhdc9), were identified as candidates that warrant further exploration.

These studies contribute significantly to progress in understanding the genetic determination of alcohol withdrawal and other behaviors, but there are some limitations. First, our genotype-dependent expression results are based on a survey of whole brain that, while an important first step, will need to be expanded in future studies assessing discrete brain regions and/or cell populations. New public resources containing genome-wide regional expression information (ABA and others), however, can potentially be useful in guiding these studies as the cellular resolution of Alcdp1/Alcw1-influenced withdrawal is currently unknown. Additionally, candidates were nominated based on comparisons of gene expression in naïve animals because several lines of evidence indicate that inherent genetic variation is primary to differential withdrawal vulnerability: many more genes are differentially expressed in naïve B6 and D2 animals than after acute ethanol exposure and most ethanol-induced differences are already present at baseline (Daniels & Buck 2002; Kerns et al. 2005), and lines selected to differ in chronic or acute withdrawal severity, but never exposed to alcohol, also identify QTLs in this region of chromosome 1 (Bergeson et al. 2003; Buck et al. 1997). No direct relationships have yet been established to delineate how acute ethanol-induced gene expression changes translate through protein levels to behavior. While such evidence might increase the value of potentially assessing gene expression during withdrawal in future studies, it is worth noting that no genes within Alcdp1/Alcw1 have shown ethanol-induced B6/D2 differential expression (Daniels & Buck 2002; Kerns et al. 2005). Finally, it is possible that the genetic factor(s) underlying Alcdp1/Alcw1 is not a protein-coding gene. We identified three non-coding RNAs within the QTL interval; as reliable quantitative techniques for their assessment are still evolving (Clancy et al. 2007), these genes were not evaluated here. Although there is currently no evidence to suggest a role for non-coding RNAs in alcohol withdrawal, nor has any function been described for those mapped to Alcdp1/Alcw1, these entities remain of interest for future studies along with other genomic (non-protein-coding) elements to determine whether they play a role in withdrawal and/or other QTLs on distal chromosome 1.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments
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Acknowledgments

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. References
  7. Acknowledgments

The authors gratefully acknowledge Nikki Walter and Drs John Belknap, Shannon McWeeney, Robert Hitzemann, Renee Shirley and Laura Kozell for extremely helpful discussions on this project and Drs Aimee Mayeda and John Hofstetter for providing congenic breeder stock. We also thank Dr Heather Hood for spearheading the initial microarray work, and Shyla Myrick, Laurie Tull and Dimitri Boss for excellent technical assistance. This work was supported by a VA Merit Award and by NIH grants AA011114, AA10760, AA017342, AA007468 and DA05228.