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Human Molecular Genetics Advance Access originally published online on October 10, 2007
Human Molecular Genetics 2008 17(1):38-51; doi:10.1093/hmg/ddm283
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© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Smoking and alcoholism target genes associated with plasticity and glutamate transmission in the human ventral tegmental area

T. Flatscher-Bader*, N. Zuvela, N. Landis and P.A. Wilce

Department of Biochemistry, School of Molecular and Microbial Sciences, Alcohol Research Unit, The University of Queensland, St Lucia, Queensland 4073, Australia

* To whom correspondence should be addressed. Tel: +61 732142228; Fax: +61 732142480; Email: t.flatscher-bader{at}uq.edu.au

Received August 13, 2007; Revised August 13, 2007; Accepted September 14, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Drugs of abuse including nicotine and alcohol elicit their effect by stimulating the mesocorticolimbic dopaminergic system. There is a high incidence of nicotine dependence in alcoholics. To date only limited data is available on the molecular mechanism underlying the action of alcohol and nicotine in the human brain. This study utilized gene expression screening to identify genes sensitive to chronic alcohol abuse within the ventral tegmental area (VTA) of the human brain. Alcohol-responsive genes encoded proteins primarily involved in structural plasticity and neurotransmitter transport and release. In particular, genes involved with brain-derived neurotrophic factor signalling and glutamatergic transmission were found to be affected. The possibility that glutamate transport was a target of chronic alcohol and/or tobacco abuse was further investigated in an extended case set by measurement of mRNA and protein expression. Expression levels of vesicular glutamate transporters SLC17A6 and SLC17A7 were robustly induced by smoking, an effect that was reduced by alcohol co-exposure. Glutamatergic transmission is vital for the control of the VTA and may also be critical to the weighting of novelty and importance of a stimulus, an essential output of this brain region. We conclude that enduring plasticity within the VTA may be a major molecular mechanism for the maintenance of smoking addiction and that alcohol, nicotine and co-abuse have distinct impacts on glutamatergic transmission with important implications for the control of this core mesolimbic structure.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Activation of the mesocorticolimbic dopaminergic system (MDS) by drugs of abuse, including alcohol and nicotine, is thought to mediate their pleasurable, rewarding and addictive properties. The MDS consists of dopaminergic neurons arising in the ventral tegmental area (VTA) of the midbrain and projecting to the prefrontal cortex (PFC), and the nucleus accumbens (NAC) and extending to the amygdala and hippocampus (1,2). The VTA extends inhibitory GABAergic neurons to the tegmental pedunculopontine nucleus (3), which in turn provides excitatory cholinergic afferent modulation of VTA dopaminergic neurons (4,5). The VTA also receives excitatory glutamatergic input from the PFC and the brainstem laterodorsal/pedunculopontine tegmentum (6–10). Within the VTA these glutamatergic afferent neurons synapse onto dopaminergic cells which project back to the PFC and NAC, and onto GABAergic cells projecting to the NAC (11), thus providing multiple levels of glutamatergic control over the system.

Co-morbidity of alcohol and nicotine dependence is well documented. Over 80% of alcohol-dependent individuals are smokers (12–14). Smokers consume twice as much alcohol as non-smokers (15), and alcoholism is over 10 times more common among smokers than non-smokers (14). Individuals seeking treatment for alcohol abuse are frequently active smokers (16) reflecting the strong positive correlation between the severity of nicotine and alcohol dependence (17–19).

Nicotine activates the MDS directly via cholinergic afferents or indirectly by potentiating glutamatergic afferents through presynaptic nicotinic acetylcholine receptors (nAChRs) (18,20–22). Ethanol may activate the system through several mechanisms including modulating glutamatergic, GABAergic or cholinergic regulation of VTA output (18,23–27). The net effect of both nicotine and alcohol exposure is increased extracellular dopamine concentrations in the NAC (28,29) and the development of a compensatory hypofunction in the long term (30,31).

Recently, drug-related changes in the transcriptome of the human MDS have been explored. In the NAC of cocaine abusers, changes in myelination genes were evident (32) but in heroin abusers, changes in expression of genes encoding presynaptic proteins were prominent (33). Similarly, an initial study, which compared the gene expression profile of the NAC and PFC of alcoholic smokers (34) reported that alcoholism-sensitive genes in the NAC encoded proteins involved in cell morphology and vesicle formation. These data suggest a persistent structural and functional change in drug-sensitive cells, may be the end point of drug exposure. In support of this concept, Zhou et al. (35) identified lower spine density and a perturbation of spine morphology in the NAC of alcohol-treated rodents. Further, sensitization to nicotine resulted in prolonged spine length and increased spine density in the NAC of rats (36).

There are no previous studies of the influence of alcoholism and smoking on the transcriptome of the human VTA. The current study compared the expression profile of the human VTA in cases with and without chronic alcohol abuse and defined drug-responsive sets of functionally related genes. These included genes encoding proteins concerned with neuronal outgrowth and synaptic function. A subgroup of these genes are involved in glutamate transmission and contained in this group, was the gene for the solute carrier family 17 (sodium-dependent inorganic phosphate cotransporter), member 7 (SLC17A7). This transporter protein is located in the presynaptic neuron and functions in packaging glutamate into vesicles (37–40). Also included was the gene for the neuronal and astrocytic solute carrier family 1 (high affinity glutamate transporter), member 2 (SLC1A2) which removes glutamate from the synapse and terminates neurotransmission (41–43).

Given the important role of glutamatergic transmission in regulating VTA output and the data from a previous study that revealed smoking to be either the sole cause of change in gene expression or to enhance alcohol-associated differential gene expression (44), we examined the RNA and protein expression of these two, and other related genes, in an expanded case set of chronic alcohol abusers and controls with and without smoking co-morbidity. In this follow-up study, we included the solute carrier family 1 (glial high affinity glutamate transporter), member 3 (SLC1A3) which has been shown to be alcohol-sensitive in the PFC (44) and the second isoform of the vesicular glutamate transporter, solute carrier family 17 (sodium-dependent inorganic phosphate cotransporter), member 6 (SLC17A6).

Marked changes in mRNA and protein expression of SLC17A6 and SLC17A7 were observed in response to smoking. These data and the changes revealed by the analysis of expression profiles are indicative of adaptive changes in neuronal structure and function in response to drug exposure in the VTA, the core region of the MDS.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Microarrays
Six thousand one hundred and fifty six reliably expressed data elements were detected after initial filtering. Principal component (PC) analysis based on this gene set revealed that the first PC accounted for 61.6% of the total variance between the arrays. Within the PC1 profile of all cases a maximum log transformed difference of –0.52 was observed in relative gene expression. The second and third PC accounted for an additional 19.6% of the total variance. Unsupervised hierarchical clustering based on the gene sets of all reliably expressed genes, and those correlating significantly with PC1, 2 or 3, respectively, failed to differentiate any pathology.

Six cases without a history of excessive drinking were compared with six cases characterized by chronic alcohol abuse. This comparison using ‘Analysis A’ revealed 254 clones as differentially expressed in the VTA, 200 were characterized by a Genbank accession number, of these 101 were up-regulated and 98 were down-regulated. Unsupervised hierarchical clustering based on all characterized, differentially expressed genes (Fig. 1) separated the two case groups (with the exception of one control case which clustered with the chronic alcohol abusers).


Figure 1
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  		Figure 1. Analysis of gene expression profiles of alcoholic (n = 6) and control (n = 6) cases by a Student's t-test (P ≤ 0.05) identified alcohol-sensitive genes. Subsequently, control (pale blue letters and branches) and alcoholic cases (crimson red letters and branches) were subjected to unsupervised hierarchical gene clustering based on all differentially expressed genes. Expression level is coded as a colour scale where blue bars represent lowest, yellow medium and red bars highest expression, dark red and blue shades denote stronger changes compared with light colours.

 
Database for Annotation, Visualization and Integrated Discovery (DAVID) software recognized 178 of the Genbank accession numbers, and 95 of these participated in clustering. Five functional clusters containing at least one significant annotation term (P ≤ 0.05) were identified (Table 1 – A). Comparing the pathology groups based on ‘Analysis B’ revealed 84 differentially expressed genes. Seventy six of these genes were recognized by DAVID software and 47 of these were clustered. Three clusters were identified (Table 1 – B). A complete list of differentially expressed genes derived from both analyses is accessible at <http://smms.uq.edu.au/dloads/wilce/vta.xls>.


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  		Table 1. Functional themes of alcoholism-sensitive genes in the human VTA

 
Strikingly, in the lenient analysis, neuronal plasticity was revealed as a functional theme in three of these modules. Two of the modules were associated with synaptic transmission and contained three and five significantly enriched annotation terms. Genes in the third module were tied together on the basis of six significantly enriched Gene Ontology (GO) terms pertaining to neuronal/cell morphology. Within these functional modules, the GO annotation terms ‘cell–cell signalling’ (P = 8E–04, 11 genes), ‘neuron projection’ (P = 5E–03, four genes), ‘dendrite’ (P = 7E–03, three genes), ‘synaptic transmission’ (P = 7E–03, seven genes) and ‘transmission of a nerve impulse’ (P = 8E–03, seven genes) were markedly enriched. Additional modules contained genes concerned with vascular function and cell division (three and one significantly over-represented annotation terms respectively). The stringent analysis resulted in three distinct functional themes. The major theme once more was associated with neuron function and contained seven significantly enriched GO terms including ‘cell–cell signalling’ (P = 1E–03, seven genes) and ‘synaptic transmission’ (P = 3E–02, four genes). Genes with the GO term ‘cell–cell signalling’ also clustered as an independent functional theme (P = 1E–03, seven genes). The third module was associated with alcohol (P = 3E–02, four genes) and glucose metabolism. (P = 4E–02, three genes).

The list of genes involved in neuronal morphogenesis contained the genes for the receptor for brain-derived neurotrophic factor (BDNF) (neurotrophic tyrosine kinase, receptor, type 2, NTKR2) and the presynaptic-protein encoding genes; neurexin 1 and synaptopodin. Further, mRNA levels of a group of genes concerned with glutamate neurochemistry were identified as alcohol-sensitive. This group included the genes encoding the vesicular glutamate transporter SLC17A7 and the high affinity glutamate transporter SLC1A2. In view of the potential implication of the glutamate transporters in the control of the VTA output (7–10), and previous data emphasizing the effects of smoking on gene expression in the PFC (44), we investigated the expression of these genes at the mRNA and the protein level in the VTA of an extended case set which included four case groups: long-term alcohol abusers and controls, with and without smoking co-morbidity. We also included SLC17A6, an isoform of SLC17A7, which was not represented on the array but may be predominantly expressed in the VTA (40,45,46) and SLC1A3, as the gene encoding this glutamate transporter was previously revealed to be alcohol-sensitive in the human PFC (44).

Real-time PCR
Age, post-mortem interval (PMI), agonal state factor (ASF) and pH had no influence on the relative expression of candidate genes in the majority of the case groups. When all cases were considered together, pH correlated with relative mRNA levels of SLC1A2 (r: –0.12, P = 7E–04) and age impacted on relative SLC1A3 transcript levels (r: 0.46, P = 3E–02). Further, pH influenced mRNA expression levels of SLC1A3 (r: –0.76, P = 5E–02) in the group of alcoholic smoker, whilst PMI correlated with expression levels of SLC1A2 (r: 0.82, P = 2E–02) in the non-alcoholic smokers. Finally, ASF correlated with SLC17A7 transcript levels in the control group (r: 0.92, P = 2E–03). There was no significant influence of age, PMI or pH on SLC17A6 or SLC17A7 levels in any of the case groups or when all cases were considered together (Table 2).


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  		Table 2. Pearson correlation of selected candidate genes to age, PMI, ASF and pH

 
The relative expression levels of the glutamate transporters SLC1A2, SLC1A3, SLC17A6 and SLC17A7 in the four case groups are shown in Figure 2.


Figure 2
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  		Figure 2. Relative expression of the solute carrier family 1, member 2 and 3 (SLC1A2 and 3) and the solute carrier family 17, member 6 and 7 (SLC17A6 and 7) to glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was established in individual cases of non-alcoholic non-smokers (C; n = 6), alcoholic non-smokers (A; n = 5), non-alcoholic smokers (S; n = 7) and alcoholic smokers (AS; n = 6). Bars show the average relative expression within each case group, error bars indicate SEM. A two-way ANOVA with Bonferroni post hoc analysis revealed significant changes (*P ≤ 0.05, **P ≤ 0.01; light brackets indicate significant changes to gene expression in response to alcoholism or smoking; dark brackets denote significant differentially expressed case groups).

 
No significant changes were evident in the expression of SLC1A3 in any of the treatment groups. Within the control group, average relative SLC17A6 transcript number was 35 times higher than that of SLC17A7, supporting previous observations in animal models of a predominant expression of SLC17A6 in this brain area (40,45,46).

Two-way analysis of variance (ANOVA) of log transformed values revealed significant changes in the expression levels of SLC1A2, SLC17A6 and SLC17A7 (P = 2E–02, 2E–08 and 3E–04, respectively) in response to chronic smoking and further, the impact of chronic smoking on SLC1A2, SLC17A6 and SLC17A7 expression interacted with that of alcohol abuse (P = 2E–02, 5E–03 and 9E–03, respectively). Bonferroni post hoc analyses identified a significant increase in SLC1A2 mRNA levels in the group of chronic smokers with and without alcohol abuse compared with the non-smoking alcoholics (3.99, P = 6E–03 and 2.94, P = 6E–03, respectively). There was a striking induction of SLC17A6 expression in the group of non-alcoholic smokers compared with the control group (27.91, P = 2E–07) as well as to the smoking (3.08, P = 3E–02) and non-smoking (13.27, P = 3E–06) chronic alcoholics. Additionally, SLC17A6 transcript levels were significantly elevated in the group with alcohol and nicotine co-abuse compared with the control group (9.06, P = 1E–04) and the non-smoking alcohol abusers (4.31, P = 3E–03). Further, SLC17A7 transcription was highly induced in the heavy smokers compared with the controls (18.64, P = 5E–03), the non-smoking and the smoking chronic alcoholics (15.16, P = 2E–03 and 9.97, P = 6E–04, respectively).

Western blots
To confirm differential protein expression of the target genes, we compared the protein level of SLC1A2 in the four case groups (C, A, S and AS) and that of SLC17A6 and SLC17A7 in two of the case groups (C and S, Fig. 3). No significant changes were observed in protein expression of SLC1A2 after adjustment for actin beta (ACTB) expression (data not shown). SLC17A6 and SLC17A7 protein expression was revealed as an immunoreactive band of approximately 60 kDa in four of the six smoking cases but in none of the controls. The size of the observed bands is in accordance with recent studies on the human putamen (47).


Figure 3
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  		Figure 3. On western blots, solute carrier family 17, member 6 (A) and 7 (B) immunoreactive bands were evident in four of six synaptosomal fractions of individual smokers (1–6), while fractions of six non smokers (7–12) were devoid of signal. The band size was calculated to be approximately 60 kDA in each case.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 FUNDING
 REFERENCES
 
Candidate genes revealed by microarray gene expression profiling: involvement of plasticity
The first PC based on the gene expression profiles of all cases used for transcriptome screening accounted for the majority of variance between these profiles. The small maximum change in expression within the PC1 profile highlights that the expression of the majority of genes was similar between the cases and that there was no technical bias in the data set. Furthermore, unsupervised clustering using all reliably expressed genes or genes correlating significantly with one of the three PCs also failed to cluster cases with chronic alcohol abuse. This suggests that there are no confounding variables with sufficient influence to distinguish cases with chronic alcohol abuse from controls. However, unsupervised hierarchical clustering of cases based on a set of genes identified as sensitive to chronic alcohol abuse identified five of the six controls. This clearly points to a significant impact of long-term alcohol abuse on VTA gene expression in the human. This finding is consistent with the notion that chronic drug abuse and the development of dependence, in response to repeated abuse, is associated with semi-persistent neuroadaptive changes within core regions of the MDS (48).

Functional gene clustering by the web-based software DAVID, (http://david.abcc.ncifcrf.gov/) assigned enrichment scores and allowed objective grouping of the genes. The software revealed a cluster of genes concerned with vascular function in the gene set derived from lenient analysis. There is a possibility of this group being identified due to the inclusion of false positives. However, the finding correlates strikingly with the influence of alcohol with or without nicotine co-abuse on vascular tissue (49). Further, a group of genes concerned with alcohol and glucose metabolism was unique to the data set obtained by stringent analysis. These changes may be indicative of repeatedly elevated alcohol levels within the central nervous system of long-term alcohol abusers. This cluster may have gone undetected in the lenient analysis because of the presence of additional genes with metabolic function. The highly significant enrichment of a number of genes concerned with neurotransmission and neurophysiology was a striking observation in both gene sets. These alterations strongly suggest the development of enduring alterations in neuronal plasticity within the VTA of chronic alcohol abusers. In summary the results suggest that an analysis at two stringency levels may be beneficial and complementary in the detection of biological themes relevant to the pathology.

Within the functional gene clusters concerned with neuronal function and plasticity, differential expression of NTRK2 was evident. NTRK2 is activated by BDNF during neuronal remodelling (50,51). This signalling pathway plays a critical role in activity-dependent synaptic plasticity, enhances docking of glutamatergic vesicles and has been suggested as a mediator of hippocampal-dependent learning and memory (52–54). In response to prolonged nicotine exposure, expression of NTRK2 changes into a region-specific pattern within the MDS of rats (55) and BDNF-mediated NTRK2 signalling in the dorsal striatum of mice modulates ethanol intake (56). Interestingly, within the VTA of rats, elevated BDNF levels, resulting from cocaine withdrawal, stimulated an increase in glutamate release and long-term potentiation of excitatory inputs to dopaminergic neurons. The net result was synaptic sensitization (57).

A number of genes involved in neurite outgrowth were revealed as sensitive to chronic alcohol abuse in the human VTA. These included synaptopodin, a gene involved in spine remodelling (58,59) and neurexin 1, which is crucially important in the formation of excitatory and inhibitory synapses (60,61). Changes in cell architecture in response to drug exposure have been observed in the NAC of animal models, where repeated nicotine or alcohol treatment resulted in alterations in spine morphology (35,36). Similarly, repeated cocaine and amphetamine altered the number of dendritic branch points and spines of both medium spiny neurons in the NAC and pyramidal neurons in the medial PFC (62–64). Chronic morphine exposure leads to structural alterations of VTA dopaminergic neurons (65,66).

The gene targets revealed by the transcriptome screening reported in this study suggest drug-induced structural plasticity within the neurocircuitry of the VTA and, as indicated by animal studies of this and other regions of the MDS, persistent structural changes may be an endpoint of alcohol abuse-associated adaptations in the human VTA.

Impact of smoking and alcohol abuse on glutamate transport in the VTA
A functional cluster of genes with a crucial role in neurotransmission was revealed as sensitive to long-term alcohol abuse in the human VTA. Striking were the changes to the RNA expression of the glutamate transporters, SLC1A2, and SLC17A6 and SLC17A7.

SLC1A2 modulates the speed of glutamate clearance from the synaptic cleft (43) and contributes to plasticity within the hippocampus (43). SLC17A7 is crucial for the packaging of glutamate into presynaptic vesicles, controls vesicular pool availability and regulates quantal release of glutamate at the excitatory synapse (67,68). Both SLC1A2 and SLC17A7 transcript levels were altered in schizophrenic patients (69,70) and SLC17A7 mRNA levels were modified in cases of Parkinson disease (47). Protein expression of SLC17A7 is influenced by the application of psychotropic medications in the human hippocampus and PFC (71) while in the rat, alcohol treatment alters SLC1A2 protein levels in cortex (72). These reports emphasize the connection of the glutamate transporters with neural plasticity and implicate differential expression in neuroadaptive modifications.

The investigation of possible confounding factors is important in the interpretation of gene expression data. Thus, age may contribute to changes to specific genes. While PMI was found to have a minor impact on transcript integrity, increased or prolonged agonal state may lead to brain acidiosis, which in turn may increase RNA degradation (73,74).

No differential expression of SLC1A3 mRNA levels was evident. Age (r: 0.46, P = 3E–02) and pH (r: –0.76, P = 5E–02) correlated with modest significance with SLC1A3 transcription, indicating that unlike in the human PFC (44), this transporter is not sensitive to heavy smoking or chronic alcohol abuse in the VTA. An influence of heavy smoking on SLC1A2 mRNA expression was established by two-way ANOVA but this was not evident at the protein level. PMI impacted positively (r: 0.82, P = 2E–02) and pH only slightly negatively (r: –0.12, P = 7E–04) on SLC1A2 mRNA levels. These variables or the small net change in expression associated with chronic smoking or the sensitivity of western blots could account for the discrepancy between the RNA and protein analyses in this study.

There was no correlation between PMI or age and expression of SLC17A6 or SLC17A7 either considering all cases together or the pathology groups individually. ASF correlated with SLC17A7 transcript levels in the control group (r: 0.92, P = 2E–03). However, cases within this group had a low agonal state (average ASF: 0.80, standard error of the mean – SEM: 0.58). Further, the control group did not differ significantly in ASF or pH from any other group. Moreover, there was no correlation between pH and SLC17A6 or SLC17A7 transcript levels. Therefore, there was no evidence of any influence of pH on SLC17A6 or SLC17A7 expression in any of the case groupings. This data emphasizes a lack of input of contributing factors to the pronounced changes in mRNA and protein expression of these two genes.

The relative levels of mRNA in control cases indicate that SLC17A6 transcripts predominate in the human VTA supporting a complementary expression of the vesicular glutamate transporters (40,45,46). In the VTA of rats, SLC17A6 mRNA expressing neurons were revealed by in situ hybridization while SLC17A7 expression remained below the limit of detection (75,76). In the current study, SLC17A7 mRNA was detected at low levels in nine of 10 control or alcoholic cases indicating a sparse expression of the transporter within the human VTA. These data provide the first evidence of SLC17A7 mRNA, albeit at low levels, within this brain region. Further, the stimulation of expression by chronic smoking implies an uncharacterized type of nicotine-modulated excitatory neuron in the VTA.

Glutamatergic projection neurons originating in the cortex express SLC17A7 (45,46,77), while those located in the brainstem utilize SLC17A6 (40,45,46). Both brain regions project to the VTA, where SLC17A6 and SLC17A7 protein was detected in the rat and human (21,46,47). Once again this is in agreement with the current study, where protein of both transporters was evident in the VTA of smoking cases.

Real-time PCR analysis revealed a significant induction of both, SLC17A6 and SLC17A7, in response to smoking which was ameliorated by alcohol consumption, partially for SLC17A6 and almost completely for SLC17A7. Additionally, there was a robust interaction effect of the combined impact of the two drugs. It is clearly important to delineate the action of alcohol and nicotine particularly in brain regions crucially involved with drug-related learning when the overall effects of alcoholism are considered. These data demonstrate that specific alterations to neuronal plasticity revealed in the VTA of chronic alcoholics with and without smoking co-morbidity are distinct in long-term alcohol abuse, heavy smoking and co-abuse.

Implications of increased vesicular glutamate transporter levels in the VTA of the chronic smoker
Glutamatergic control of dopamine release: a role in drug-related learning and memory
nAChRs containing the {alpha}7 or the β2-subunit within the VTA mediate nicotine's rewarding and motivational properties (78–81). nAChRs on VTA dopaminergic and GABAergic neurons contain the β2 subunits and are initially activated by smoking but desensitize quickly (82–84). The VTA is under multiple levels of glutamatergic control from the PFC and the brainstem (7,9–11). Presynaptic {alpha}7nAChRs are located in glutamatergic afferents synapsing on dopaminergic neurons in the VTA with similar levels of {alpha}7nAChRs receptors present on SLC17A6- and SLC17A7-positive boutons (6,7,10,21). Differential protein expression of the two vesicular glutamate transporters revealed in this study highlights that glutamatergic drive from both, PFC and brainstem (40,45,46,77), is affected by the impact of chronic smoking.

Low nicotine concentrations lingering after initial nicotine exposure are sufficient to activate {alpha}7nAChRs and prolong glutamatergic neurotransmission as these receptors are resistant to desensitization (20,82,84). Activation allows Ca2+ and Na2+ influx, depolarizing the membrane (85,86) and elevating frequency and amplitude of action potentials (87). By shifting neuronal activity from tonic to burst firing, neurotransmitter release is induced (87). Interestingly, repeated activation of presynaptic {alpha}7nAChRs by nicotine in synaptosomes resulted in an increase in the ready releasable pool of synaptic vesicles, fusion of these vesicles and exocytosis with a net rise of neurotransmitter release (88). Considering the role of SLC17A6 and SLC17A7 in controlling quantal glutamate release (67,68), the marked elevation of protein levels in the VTA of chronic smokers may reflect a rise in the number, and/or turnover of vesicular cycling in these synapses. Importantly, a concomitant further potentiation of glutamatergic transmission in response to continued smoking may persistently enhance the activation of the dopaminergic target cells within this brain region. The observed changes to vesicular glutamate transporter expression complement electrophysiological demonstrations of nicotine-induced enhancement of excitatory transmission in the VTA via pre-synaptic {alpha}7-containing nAChRs (20,82,89). The results of this study lend strong support to the notion of a glutamate-mediated LTP induction and maintenance at these synapses of smokers. This effect is characteristic of drug-related learning and memory and sensitization (20,82,89).

A hypothesis of chronic reinforcement via modulation of ‘stimulus weighting’ by the VTA
In culture, dopaminergic neurons co-release glutamate (90,91). In the VTA, many dopaminergic neurons, express SLC17A6 mRNA (75,76,90) and have SLC17A6 protein-positive varicosities. These neurons are situated in subnuclei projecting to other limbic areas and the PFC (76,92). Induction of SLC17A6 mRNA in the smokers, as revealed in the current study, suggests nicotinic action on these neurons. β2nAChRs are present at almost all dopaminergic synapses (93) while presynaptic {alpha}7nAChRs were reported in ~45% of dopaminergic neurons within the VTA (21) and {alpha}7-mediated nicotinic responses in midbrain dopaminergic neurons have been demonstrated (83,88). A co-localization of {alpha}7nAChRs within dopamine and glutamate co-releasing synapses will have consequences for VTA output function. Lavin et al. (94) suggest that short bursts of glutamate co-release by midbrain dopaminergic neurons weights the novelty and quality of a stimulus. Therefore, an over-representation of glutamatergic and dopaminergic vesicles in the terminal endfields as implicated by this study, may increase glutamate/dopamine signalling in response to smoking and over-emphasize salience of this experience. This mechanism may represent a potent reinforcing effect to commence smoking and therefore contribute to the addictive properties of nicotine.

Nicotine and alcohol interaction on SLC17A6 and SLC17A7 expression: a potential shift in reinforcing mechanisms
Excitatory neurotransmission may be a common target of alcohol and nicotine (95,96). In the current study, effects observed of the two drugs on the expression of vesicular glutamate transporters emphasize a complex interaction of alcohol and nicotine within core regions of the MDS. Alcohol per se did not alter SLC17A6 or SLC17A7 mRNA expression, which may indicate that persistent changes in response to chronic alcohol abuse do not include cycling of glutamatergic vesicles within neurons of the VTA. Strikingly, alcohol co-abuse successfully reversed the robust increase of SLC17A7 mRNA level by nicotine to control levels. It also reduced induction of SLC17A6 by two-thirds. However, a significant elevation of SLC17A6 expression remained in smoking alcoholics compared with non-smoking controls. Alcohol inhibits {alpha}7nAChRs function in vitro (97). Dampened induction of the transporter expression evident in co-morbid cases supports a similar molecular action in the VTA, where alcohol may counteract nicotinic signalling through presynaptic {alpha}7nAChRs in SLC17A6 and SLC17A7 containing neurons. On the other hand, alcohol potentiates β2nAChRs-mediated currents in vitro (97). In context with only partial inhibition of nicotinic SLC17A6 induction by alcohol co-abuse, this may indicate a net synergistic action of the two drugs on nAChRs mediated glutamatergic signalling. Therefore, the complex interaction of nicotine and alcohol mediated effects on the expression of SLC17A6 and SLC17A7 in the VTA may contribute to the robust co-morbidity of alcohol and nicotine abuse (13,14,18,19).

In summary, this study provides the first large-scale identification of target genes for long-term alcohol and tobacco abuse in the human VTA. It reveals gene sets concerned with neuronal plasticity are sensitive to chronic alcohol abuse. Elevated levels of SLC17A6 and SLC17A7 were observed in chronic smokers and strongly point to an increase in vesicular glutamate packaging in response to nicotinic receptor activation. The physiological consequences may be changes to afferent glutamatergic control, VTA plasticity and output function in the context with chronic reinforcement, drug-related learning and memory.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
FUNDING
 REFERENCES
 
Case selection
Approval from the Central Sydney Area Health Service (Protocol No. X03-0117), The Ethics Committee of the University of Sydney (Ref No. 95/2/7) and The Medical Ethics Review Committee of The University of Queensland was obtained for the use of post-mortem brain samples in this study. Frozen tissue was obtained from the Australian Brain Donor Programs of the NSW Tissue Resource Centre and stored at –80°C.

A full medical history of all patients was available. All cases had full autopsy and were examined for extant neuropathology. There was no evidence of any degenerative changes to the cellebellar granule cells and no confounding neurological or psychiatric disorders were diagnosed in any of the cases used for this study. All alcoholic cases complied with the definition by the Diagnostic and Statistical Manual for Mental Disorders (DSM IV) of the American Psychiatric Association for ‘substance abuse alcohol’. They had a history of chronic excessive drinking (≥80 g of ethanol/day or diagnosed as long-term heavy drinker) and in most instances this behaviour persisted throughout adult life (>10 years). Smokers were not formally assessed for long-term nicotine abuse but classified as tobacco abusers according to a record of current heavy smoking at time of death.

In an initial transcriptome study, cases were classified as alcoholics (≥80 g of ethanol/day or diagnosed as long-term heavy drinker) or non-alcoholic controls (≤10 g of ethanol/day). Clinical details of these cases are displayed in Table 3.


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  		Table 3. Clinical details and characteristics of cases used in micorarray gene expression profiling

 
In the subsequent, expanded studies, cases were classified into four groups: non-alcoholic non-smoker (C: ≤10 g of ethanol/day and non-smoker), alcoholic non-smoker (A: ≥80 g ethanol/day or diagnosed as long-term heavy drinker and non-smoker) non-alcoholic smoker (S: ≤10 g of ethanol/day or non-drinker and ≥10 cigarettes/day or diagnosed as heavy smoker), or alcoholic smoker (AS: ≥80 g ethanol and ≥10 cigarettes/day or diagnosed as long-term heavy drinker and heavy smoker). The background and clinical data of this case set is shown in Table 4.


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  		Table 4. Clinical details and characteristics of cases selected for TaqMan® real-time PCR

 
Cases groups were for each study compared in age, PMI, ASF and pH using Student's t-test and one-way ANOVA (Table 5).


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  		Table 5. Comparison of cohorts

 
There were no significant differences in age, PMI and pH between the cohorts used in this study. Within the case groups used for gene expression studies, ASF was significantly (P<0.05) higher in the group of alcoholics compared with non-alcoholics and in the group of smokers without alcohol abuse compared with non-smoking alcoholics. A higher agonal state at death lowers brain pH, which in turn may decrease RNA integrity (73,74). However, the observed agonal states within these cohorts were relatively low considering the agonal factor scale and not strong enough to evoke a significant difference in pH between the case groups.

RNA extraction and synthesis of amplified (a) RNA
Total cellular RNA was extracted and amplified as described in detail elsewhere (34). Integrity of total RNA and amplified RNA (aRNA) was monitored with the Agilent Bioanalyser (Agilent, Forest Hill, Australia). Only cases that produced RNA samples with distinct 18S and 28S absorbance peaks and aRNA with consistent size profiles were allowed to proceed. Moreover, northern hybridization was performed on each aRNA sample using a P32-labelled GAPDH (glyceraldehydes-3-phosphate dehydrogenase) probe as described in detail elsewhere (34). Only samples with a single, non-diffused GADPH signal were selected for further study.

Microarray hybridization
Microarray hybridization was performed on human single-spotted 19k (version 6) cDNA arrays (University Health Network Microarray Centre, Ontario Cancer Institute) as previously described in detail (34) with the following minor amendments. Input aRNA was increased to 10 µg and a universal reference sample was prepared by pooling aRNA from various brain regions. During reverse transcription of aRNA individual samples were labelled with Cyanine-3-dCTP incorporation, while the pooled reference sample was labelled with Cyanine-5-dCTP. Each individual sample was hybridized together with the universal reference to one microarray slide.

Microarray analysis
Slides were immediately scanned on a Genetic Microsystem scanner using GMS scan array software (Genetic Microsystem Inc., Woburn, MA, USA) at a resolution of 10 nm. Spots were detected in the scanned images by auto-segmentation using the standard setting of the IMAGEne 6.1 (Biodiscovery, El Segundo, CA, USA) software. Data normalization and analysis was completed with GeneSpring 6.2 software (Silicon Genetics, Redwood City, CA, USA). A per-spot and per-chip intensity-dependent LOWESS fit normalization was carried out. Genes other than those that were marked as ‘present’ in at least half of the hybridizations and not marked ‘absent’ in at least half the samples were filtered out. The cross-gene error model was used to quantify the average constant error across the spots for each pathology group and genes with a raw control signal less than this were removed from analysis. The individual resulting expression profiles containing all reliably expressed genes were investigated for similarity and differences in features by PC Analysis. Genes correlating significantly (P < 0.05) with the first, second or third PC were filtered out. All cases were subjected to unsupervised hierarchical clustering using Pearson Correlation based on each of the resulting gene sets.

Arrays performed on cases with identical pathology were then treated as biological replicates. Using all reliably expressed data elements the arrays were subjected to two analyses of different stringency level in the applied criteria. In a first, more lenient analysis (Analysis A) no multiple testing correction was applied. Stringent filtering may lead to the exclusion of a number of true differentially expressed genes and a concurrent loss of functional gene clusters revealing the biology underlying the pathology. In this first analysis, genes were filtered for ‘confidence of expression’ (GeneSpring 6.2; Student's t-test, P < 0.05 in one of the two case groups). This step served to reduce false positive genes. Within the resulting gene list a Student's t-test was employed to detect differentially expressed elements (P ≤ 0.05) by comparing the mean expression of each gene from alcoholic cases (n = 6) with that of controls (n = 6). To minimize false positive results further, genes with a log-transformed fold change of ≤–0.30 or ≥0.18 were filtered out. In a second analysis of higher stringency (Analysis B) Benjamini Hochberg Multiple Testing Correction was included at both filter stages. This reduced the false positives to ≤5% at each stage.

All differentially expressed genes of ‘Analysis A’ were utilized for unsupervised hierarchical clustering of the cases according to similarity in gene expression. The gene sets of ‘Analysis A’ and ‘B’ were annotated and grouped separately into functional enrichment modules using the DAVID software (http://david.abcc.ncifcrf.gov/). This program identifies general categories (GO terms, keywords, etc.) present in a list of target genes (98,99). In a first step, DAVID groups genes with the same GO term in a specific data set allowing each gene to appear in as many groups as there are GO terms associated with it. In the second step, DAVID compares the prevalence of genes with one GO term to the total expected number of genes with this term on the array (99). Importantly, within this process, DAVID scores the enrichment of each GO term based on kappa statistics (100). Additionally, DAVID utilizes fuzzy heuristic partitioning to tie genes together as a function of similarity in annotation terms revealing biological themes within the data set (99). A kappa similarity threshold value and a multiple linkage threshold value of ≥0.5 respectively were applied for this functional analysis; group size was limited to a minimum member number of three. The microarray data is publicly accessible in MIAMI format at GEO database (http://www.ncbi.nlm.nih.gov/geo/).

Real-time PCR
mRNA levels of target genes of individual cases (C: n = 5, A: n = 5, S: n = 7 and AS: n = 6) were assayed individually. Total RNA was reverse transcribed to cDNA and differential expression of transcripts was assessed by semi-quantitative TaqMan® real-time PCR (Applied Biosystems, Scoresby, Victoria, Australia). Primers and Taqman probes to the genes SLC1A2, SLC1A3, SLC17A6 and SLC17A7 and to GAPDH, were acquired from Applied Biosystems and all reactions were performed using the TaqMan Inventoried Assay on Demand Kit (Applied Biosystems) on the ABI Model 7000 Sequence Detection System (Applied Biosystems) utilizing the following cycling conditions: step 1, 50°C for 2 min; step 2, 95°C for 10 min; step 3, 95°C for 15 s; step 4 60°C for 60 s. Steps 3 and 4 were repeated for 40 cycles. The sequences for the forward and reverse primers are in commercial confidence; however, the regions of the Taqman probes’ alignment to target genes are shown in Table 6.


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  		Table 6. Alignment regions of TaqMan® probes

 
High efficiency of amplification was confirmed for each target gene over a dilution series of cDNA. Each sample was assayed in triplicate and for each gene a control reaction without template was performed with every assay. Expression levels relative to GAPDH were calculated according to Pfaffl (101) and normalized to a standard sample assayed on each plate.

Real-time PCR analysis
Any influence of age, PMI, ASF and pH on relative expression was examined using Pearson Correlation. Statistica software version 7.1 (StatSoft, Tulsa, OK, USA) was used for statistical analysis. The effect of alcohol abuse and smoking was revealed by two-way ANOVA followed by Bonferroni post hoc multiple comparison analysis to establish the significance of changes between individual case groups.

Preparation of cell membrane and synaptosomal protein fractions
VTA membrane fractions were prepared to quantify the protein expression of SLC1A2 in individual cases of each pathology group (C: n = 4, A: n = 5, S: n = 4 and AS: n = 5). Similarly, protein levels of SLC17A6 and SLC17A7 were established in VTA synaptosomal fractions of individual control (C: n = 6) and non-alcoholic smoking cases (S: n = 6). Briefly, 20 mg of frozen tissue was homogenized in 400 ml of ice-cold extraction buffer (mannitol 210 mM, sucrose 60 mM, KCl 10 mM, HEPES-KOH 10 mM, pH 7.4) containing complete mini protease inhibitor cocktail (Roche Diagnostics, Castle Hill, NSW, Australia) and nuclei were sedimented at 800g for 5 min. The supernatant was centrifuged at 10 000g for 10 min at 4°C. The pellet was resuspended in 1.5 ml ice cold extraction buffer and homogenized prior to addition of 1.5 ml of cold 24%(v/v) Percoll® in extraction buffer. Following centrifugation at 31 000g for 5 min at 4°C, the upper layer containing the cell membranes was removed. Digitonin (25µl of 50 mg/ml solution) was added to the lower layer. After a 10 min incubation on ice, the extract was layered over 3.5 ml 19%(v/v) Percoll and 3.5 ml 40%(v/v) Percoll and centrifuged at 31 000g for 10 min at 4°C. The resultant upper layer, containing the synaptosomal proteins, was stored at –80°C until use. Protein concentration was established by bicinchoninic acid assay (102).

Western blots
Ten microgram of membrane or synaptosomal proteins respectively were separated on a 10%(w/v) denaturing SDS polyacrylamide gel at 100 V for 45 min and transferred to a nitrocellulose membrane (Pall Life Sciences, Cheltenham, Victoria, Australia). The membrane was blocked with 1.5%(w/v) fish gelatin extract, 0.02%(w/v) sodium azide in PBS for 1 h. Membranes with synaptosomal fractions were exposed to polyclonal guinea pig anti-human SLC17A7 (Chemicon, Boronia, Victoria, Australia, 1:2000) or polyclonal guinea pig anti-human SLC17A6 (Chemicon, Boronia, Victoria, Australia, 1:1000) antibody in 1.5%(w/v) fish gelatin extract, 0.02%(w/v) sodium azide in PBS at 4°C for 4 days. The membrane containing membrane protein was exposed to polyclonal rabbit anti-human SLC1A2 antibody (Sapphire Biosciences, Redfern, Sydney, Australia, 1:1000) in 1.5%(w/v) fish gelatin extract, 0.02%(w/v) sodium azide in PBS at 4°C over night. For signal normalization the hybridization mix was also supplemented with polyclonal mouse ACTB antibody (Sigma, Sydney, Australia, 1:3000) for 24 h at 4°C. After thorough washing the membranes were incubated for 1 h with anti-guinea pig or anti-rabbit IgG coupled to the fluorophore IRDye 680 (Molecular Probes, Invitrogen, Mulgrave, Victoria, Australia; 1:10 000) in 1.5%(w/v) fish gelatin extract, 0.02%(w/v) sodium azide in PBS. For detection of ACTB immunofluorescence anti-mouse IgG coupled to IRDye 700 (Molecular Probes, Invitrogen, Mulgrave, Victoria, Australia; 1:10 000) was added to the hybridization mix and incubated with the membrane for 1 h at 4°C. The proteins were then visualized by excitation of the fluorophores and scanning of the signal with the Odyssey System (Li-Cor® Biosciences, John Morris Scientific, Chatswood, Victoria, Australia). Likewise, the optical density of signal was established with the Odyssey system.


   FUNDING
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
REFERENCES
 
The authors thank the Australian Association of Brewers Research Foundation for financial support.

Conflict of Interest statement. None declared.


   ACKNOWLEDGEMENTS
 
Tissues were received from the Australian Brain Donor Programs NSW. Tissue Resource Centre which is supported by The University of Sydney, National Health and Medical Research Council of Australia, Neuroscience Institute of Schizophrenia and Allied Disorders, National Institute of Alcohol Abuse and Alcoholism and NSW Department of Health.


   REFERENCES
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 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 FUNDING
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