SEARCH

SEARCH BY CITATION

Keywords:

  • 13C metabolism;
  • alcohol;
  • GABA(A);
  • metabonomics;
  • NMR spectroscopy

Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information
Thumbnail image of graphical abstract

Ethanol is a known neuromodulatory agent with reported actions at a range of neurotransmitter receptors. Here, we measured the effect of alcohol on metabolism of [3-13C]pyruvate in the adult Guinea pig brain cortical tissue slice and compared the outcomes to those from a library of ligands active in the GABAergic system as well as studying the metabolic fate of [1,2-13C]ethanol. Analyses of metabolic profile clusters suggest that the significant reductions in metabolism induced by ethanol (10, 30 and 60 mM) are via action at neurotransmitter receptors, particularly α4β3δ receptors, whereas very low concentrations of ethanol may produce metabolic responses owing to release of GABA via GABA transporter 1 (GAT1) and the subsequent interaction of this GABA with local α5- or α1-containing GABA(A)R. There was no measureable metabolism of [1,2-13C]ethanol with no significant incorporation of 13C from [1,2-13C]ethanol into any measured metabolite above natural abundance, although there were measurable effects on total metabolite sizes similar to those seen with unlabelled ethanol.

Ethanol has many documented effects on brain neurochemistry including significantly decreasing glucose consumption. Here, we used multinuclear NMR spectroscopy and metabolomic neuropharmacology to examine alcohol effects on metabolism in cortical brain tissue slices. [1,2-13C]Ethanol is not metabolized by brain, but very low ethanol [0.1 mM] has significant effects on brain metabolism. Higher ethanol [1.0–60 mM] likely decreases metabolism through action at α4β3δ-containing GABA(A) receptors.

Abbreviations used
DS2

4-Chloro-N-[2-(2-thienyl)imidazo[1,2a]-pyridin-3-yl]benzamide

GABA(A)R

GABA-A receptors

GABA(B)R

GABA-B receptors

GAT1

GABA transporter 1

GIRK

G-protein coupled inwardly rectifying potassium channels

NMDA

N-methyl-D-aspartate

PCA

principal component analysis

THIP

4,5,6,7-Tetrahydroisoxazolo[5,4-c]pyridin-3-ol hydrochloride

Ethanol, one of the most commonly used (and abused) drugs in the Western world, is a known sedative and depressant acting on a wide range of targets including several neurotransmitter receptors and ion channels in the central nervous system. Ethanol readily enters the brain (Gratton et al. 1997) and recent evidence suggests that it can actually damage and/or selectively lower the blood–brain barrier via specific mechanisms (Muneer et al. 2011; Ehrlich et al. 2012).

Although ethanol interacts with lipid components of biological membranes (Spanagel 2009), the membrane lipid theory of ethanol actions may only explain the lethality of very high doses of alcohol (concentrations >> 100 mM) but cannot account for the specific effects of alcohol on brain and behaviour at concentrations resulting from common alcohol self-administration (typically 5–40 mM). Molecules sensitive to low concentrations of ethanol include glycine receptors (Engblom and Akerman 1991; Perkins et al. 2010), NMDA receptors (Lovinger et al. 1990; Allgaier 2002), L-type Ca2+-channels and G-protein coupled inwardly rectifying potassium channels (GIRK; functionally altered by as low as 1 mM ethanol) (Lewohl et al. 1999; Ikeda et al. 2002).

GABA-A receptors have also been considered as potential ethanol targets. The most abundant synaptic GABA-A receptors consisting mainly from α1, β2 and γ2 subunits are practically non-responsive to ethanol (Mori et al. 2000), whereas those containing α4β3δ (and α6 in cerebellum) and thought to be located mostly extrasynaptically, are about as ethanol-sensitive as NMDA receptors [except for being activated rather than inhibited by ethanol; (Wallner et al. 2006); see also (Lovinger and Homanics 2007; Kaur et al. 2009)]. The GABAergic inhibitory system can also be influenced by ethanol via potentiation of GABA release at GABAergic synapses (Roberto et al. 2003, 2004).

Ethanol reduces D-glucose uptake and metabolism (Volkow et al. 2006; Pawlosky et al. 2010) and increases the metabolism of acetate (Volkow et al. 2013).

We use a cortical tissue slice system in vitro where metabolism of [3-13C]pyruvate is used as a marker of drug effects by measuring resultant 13C-incorporation and total metabolite pools following a period of incubation either with or without the drug (Rae et al. 2009; Nasrallah et al. 2010b). This approach is particularly suitable for investigating specific effects of alcohol on brain tissue. It circumvents the possible confounding involvement of blood–brain barrier as mentioned above (there is neither blood–brain barrier nor blood circulation in our model) and eliminates actions of ethanol metabolites as alcohol is not metabolized by brain to any significant extent (Mukherji et al. 1975; Xiang and Shen 2011). The resulting metabolic profiles were then compared with our extensive database describing effects, respectively, of various neurotransmitter (GABA) concentrations and activators/inhibitors of particular GABA receptors or transporters by specific drugs. This approach has been used successfully in the past to identify possible sites of action for the party drug γ-hydroxybutyrate (Nasrallah et al. 2010b), sites which were subsequently confirmed by others (Absalom et al. 2012). Here, we have explored the effects of a range of ethanol concentrations (0.1 ≥ ≤ 60 mM) on brain metabolism in vitro.

We have also taken advantage of the brain slice as a model of brain metabolism free of peripheral interference to examine the issue of whether or not ethanol itself is metabolized in the brain by studying potential incorporation of 13C from [1,2-13C]ethanol by brain cortical tissue slices.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Materials

Female Guinea pigs (Dunkin–Hartley), weighing 400–800 g, were fed ad libitum on standard Guinea pig/rabbit pellets, with fresh carrots and lucerne hay roughage. Animals, housed in floor pens, were maintained on a 12 h light/dark cycle. All experiments were conducted in accordance with the guidelines of the National Health and Medical Research Council of Australia and were approved by the institutional (UNSW) Animal Care Ethics Committee.

Sodium [3-13C]pyruvate, sodium [13C]formate and [1,2-13C]ethanol were purchased from Cambridge Isotope Laboratories Inc (Andover, MA, USA). 4-Chloro-N-[2-(2-thienyl)imidazo[1,2a]-pyridin-3-yl]benzamide (DS2, positive allosteric modulator of δ subunit-containing GABA(A) receptors; Wafford et al. 2009), (R)-1-(1-Phenylethyl)-1H-imidazole-5-carboxylic acid ethyl ester (etomidate; interacts with β2 and β3-containing subunits of GABA(A) receptors; Uchida et al. 1995; Sanna et al. 1997), 8-Azido-5,6-dihydro-5-methyl-6-oxo-4H-imidazo[1,5-a][1,4]benzodiazepine-3-carboxylic acid ethyl ester (RO-15-4513, highly active benzodiazepine ligand, antagonises effects of ethanol; Harris and Lal 1988) and 5,6-Dihydro-5-methyl-6-oxo-4H-imidazo[1,5a]thieno[2,3-f][1,4]diazepine-3-carboxylic acid 1,1-dimethylethyl ester (RO 19-4603; benzodiazepine inverse agonist, antagonises locomotor effects of ethanol (Suzdak et al. 1988) were purchased from Tocris Cookson (Bristol, UK). 7-Ethynyl-1-methyl-5-phenyl-1,3-dihydro-2H-1,4-benzodiazepin-2-one (QH-ii-066; α5-selective agonist; Huang et al. 2000) was custom synthesized as described previously (Huang et al. 1996). Ethanol (HPLC Grade) was obtained from Merck (Merck Australia, Kilsyth, Vic, Australia).

Modulation of metabolic activity by ethanol and related ligands

Guinea pig cortical slices were made and prepared as described previously (Nasrallah et al. 2010b). To determine the metabolic effects of modulation of metabolism by ethanol, slices were incubated for 1 h with 2 mmol/L sodium [3-13C]pyruvate (control) and a range of concentrations of ethanol: 0.1, 1.0, 10, 30.0, 60.0 and 100 mmol/L.

We studied whether ethanol itself was used as a substrate by slices by incubating slices for 1 h with 2 mM sodium pyruvate (control) and 1.0 and 10 mmol/L [1,2-13C]ethanol.

We also studied the effects of various ethanol-related ligands by incubating slices with 2 mmol/L sodium [3-13C]pyruvate (control) and 1.0 and 10 nmol/L RO 19-4603, 0.1 and 1.0 nmol/L RO 15-4516, 2 and 20 μmol/L etomidate, 0.1 and 1.0 μmol/L DS2 or 4 and 40 nmol/L QH-ii-066. The number of samples was n = 4 in all cases.

Preparation of samples and NMR analysis

On completion of the incubation period, slices were removed from the incubation buffer by rapid filtration and extracted in methanol/chloroform according to the method of Le Belle (Le Belle et al. 2002). Extracts were lyophilized, and the pellet retained for protein estimation by the Lowry technique. Lyophilized supernatants were stored at −20°C until required for acquisition of NMR spectra. This was conducted as described previously and included acquisition of fully relaxed 1H, 1H{13C-decoupled} and 13C{1H-decoupled spectra} (Nasrallah et al. 2010b). The 1H spectra were used to account for unlabelled formate, whereas the 1H{13C-decoupled} spectra were used to quantify total metabolic pool sizes. In the case of the experiments with [1,2-13C]ethanol and QH-ii-066, spectra were acquired on a Bruker AVANCE III HD 600 spectrometer fitted with a cryoprobe (TCI) and refrigerated sample changer. 1H{13C-decoupled} spectra were acquired using bilev composite pulse decoupling across an effective bandwidth of 48 000 Hz during the acquisition time, on a 30 s duty cycle, whereas 13C{1H-decoupled} spectra were acquired on a 4 s duty cycle using continuous WALTZ-65 decoupling.

Experimental data (n = 4) are given as means (SD). Statistical analysis was done using anova for comparing ligand-treated metabolism at each receptor with control [n = 4, followed, only where statistical significance was indicated by Scheffe F-test, by a non-parametric (Mann–Whitney U) test (Statview Student)]. The statistical analysis was performed on the raw experimental values, not the relative differences as shown in the graph. Significance was assumed at α = 0.05. Data are shown graphically as change in each variable relative to the mean of that variable in the control experiment in order more clearly to show the metabolic ‘pattern’ generated by the ligand relative to control.

Pattern recognition of the data

Multivariate statistical analysis used the Simca-P+ software package (v11.5, Umetrics, Umeå, Sweden). Each dataset for a particular manipulation was imported as the relative change from the average value obtained from the control group for that particular experiment. Data were univariance scaled to standardize variance between the high and low concentration metabolites (Wold et al. 1998), to ensure that the 13C-labelling and steady-state pool size concentrations equally contributed to the model.

Here, we included the data from experiments acquired under identical conditions using a range of ligands at GABA(A)R (Rae et al. 2009), GABA(B)R (Nasrallah et al. 2007) and GABA(C)R (Nasrallah et al. 2010b), inhibitors of GABA uptake (GAT inhibitors) (Nasrallah et al. 2010a), exogenous GABA (Nasrallah et al. 2009) and experiments where the GABA-transaminase inhibitor vigabatrin was also incubated with activators of slice activity (Nasrallah et al. 2011) into SIMCA P+ along with data acquired in this work. The data were then subject to principal components analysis to identify common patterns of activation between ethanol and other compounds active in the GABAergic system.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Effects of ethanol on metabolism of [3-13C]pyruvate

The metabolic profiles of the effects of each concentration of ethanol on metabolism are shown in Fig. 1. The figure shows the change in concentration for each variable relative to the control average for that variable. Statistical significance was evaluated using the total values of metabolic flux or metabolic pools (i.e. ‘raw data’ as opposed to the values of changes relative to controls). The different concentrations of ethanol produced different results, with the lower two concentrations (0.1 and 1.0 mM) producing profiles distinct from one another and distinct from those of 10, 30 and 60 mM ethanol, which were all broadly similar. The lowest concentration of ethanol (0.1 mM) produced a decrease in label in all measured 13C-metabolites apart from Asp C2 and C3, a decrease in the pool size of lactate and Gln but produced significant increases in the total metabolite pools of Glu, GABA, Asp and Ala. Higher concentrations of ethanol produced significant decreases in metabolite pools and 13C-labelling.

image

Figure 1. Relative effect of different concentrations of exogenous ethanol on 13C incorporation and on total metabolite pool sizes in brain cortical tissue slices incubated 1 h with sodium [3-13C]pyruvate. Data are shown as relative to the control mean, with control metabolism centred at zero. Error bars represent standard deviations. Statistically significant changes (calculated on the raw data not the relative change in flux or pool size, see 'Methods') are indicated by *(p < 0.05, different to control).

Download figure to PowerPoint

Incubation of brain cortical tissue slices with sodium pyruvate and [1,2-13C]ethanol

No carbon couplings were detected in 1H spectra, with no significant difference between 1H spectra acquired with and without 13C-decoupling. No label was detected in 13C{1H-decoupled} spectra above natural abundance level (Figure S1). There was no evidence for double label (due to adjacent 13C nuclei) in any of the resonances.

No [1,2-13C]ethanol was observed in any of the spectra, including spectra from samples to which [1,2-13C]ethanol had been added. To determine whether this was because of loss of ethanol in the freeze-drying process, we ran spectra of the extract prior to the freeze-drying process and observed the expected peaks from [1,2-13C]ethanol. This extract was then subjected to freeze drying, resuspended in D2O and another 13C spectrum acquired. As expected, there were no resonances from [1,2-13C]ethanol. From this we concluded that the residual ethanol was removed by lyophilization. It is also unlikely that any [1,2-13C]acetaldehyde would have survived the lyophilization process as it has a much lower boiling point than ethanol.

Effects of related ligands

The metabolic profiles of the effects of DS2, a positive allosteric modulator of δ-subunit-containing GABA(A) receptors, are shown in Fig. 2. Low (0.1 μM) concentrations of DS2 produced significant decreases in 13C incorporation into all 13C-metabolites measured, apart from Gln C4 which increased and Asp C2 which was not changed. The total metabolite pool size of all measured metabolites was also decreased. When the concentration of DS2 was increased to 1.0 μM all 13C incorporation and metabolite pool sizes were decreased (Fig. 2).

image

Figure 2. Relative effect of ligands with relevance to ethanol on 13C incorporation and on total metabolite pool sizes in brain cortical tissue slices incubated 1 h with sodium [3-13C]pyruvate. Data are shown as relative to the control mean, with control metabolism centred at zero. Error bars represent standard deviations. Statistically significant changes (calculated on the raw data not the relative change in flux or pool size, see 'Methods') are indicated by *(p < 0.05, different to control) or #(p < 0.05, different to other concentration of same ligand).

Download figure to PowerPoint

The β-subunit selective activator etomidate (2 μM) significantly increased 13C incorporation into all Krebs cycle-related metabolites, had no effect on 13C incorporation into GABA C2 and decreased 13C incorporation into glycolysis byproducts Lac C3 and Ala C3. All metabolite pools were significantly decreased. Increasing the concentration to 20 μM resulted in further significant increases in 13C incorporation into the 13C-metabolites of Glu, Gln and Asp. The total metabolic pool of Asp was also significantly increased, as was Glu (Fig. 2).

RO15-4513 at 0.1 nM resulted in significant increases in the pool sizes of all metabolites measured, as well as increased the 13C incorporation into Ala C3 and GABA C2. All other 13C incorporation measured were decreased apart from Lac C3 which was unaffected. Increasing the concentrations to 1.0 nM produced similar metabolite pool increases and increased 13C incorporation into Ala C3, but there was no change in GABA C2 in this case.

RO19-4603 (1.0 nM) strongly stimulated 13C incorporation into most 13C-metabolites (Fig. 2), except for Gln C4, which was not affected. Increasing the concentration tenfold further increased 13C incorporation into many 13C-metabolites and also increased 13C incorporation into Glu C4. Pool sizes, apart from that of lactate, which was decreased, were also increased (Fig. 2). This metabolic pattern is indicative of increased 13C incorporation into the Krebs cycle, with increased pyruvate clearance.

QH-ii-066 at 4 nmol/L had strongly stimulatory effects on incorporation of 13C into the Krebs cycle with increased incorporation of 13C into Glu C2, C4, Asp C2 and C3, along with relative decreases in incorporation into Lactate C3. The incorporation of 13C into Gln C4 was also increased. The pool sizes of Glu, GABA, Asp and Gln were significantly increased. These changes in 13C incorporation were amplified with 40 nmol/L QH-ii-066, with increased labelling of GABA C2 and further decreases in labelling of Lactate C3 and Ala C3. The metabolic pools of lactate, Glu, GABA, Asp and Ala were significantly reduced compared to control (Fig. 2).

Principal components analysis of the data

Principal components analysis of the data generated a three component model accounting for 82% of the variance in the data (PC1 = 47%, PC2 = 26% and PC3 = 9%; Q2 = 70%, where Q2 is the fraction of the total variation in the data which can be explained by a component). A rule of thumb is that values of Q2 > 50% are considered a good fit (Eriksson et al. 2006). The first two (major) components of the model are shown in Fig. 3 with the concentrations of ethanol each shown in red. The high (10, 30 and 60 mM) concentrations of ethanol cluster near the bottom left hand corner of the plot along with 1.0 μM DS2. The 1.0 mM cluster of ethanol is weighted less heavily on PC1 and PC2 and lies in closer proximity to a cluster of compounds whose common factor is their activity of ‘mainstream’ GABAergic synapses. The cluster includes 1.0 μM Baclofen (GABA(B)R agonist, green squares in Fig. 3), 200 μM Gabapentin (pink squares; Sills 2006) and 50 μM tiagabine (purple squares, inhibitor at GAT1; Borden et al. 1994). In contrast, the 0.1 mM ethanol cluster (see inset to Fig. 3) is located closest to data derived from an experiment using both the GABA-T inhibitor vigabatrin (100 μM) and the glutamate receptor agonist 5 μM AMPA (AV). It is also near to 40 nM zolpidem (z), 10 μM isoguvacine (iG), 10 μM SGS-742 (S), 5 μM picrotoxin (P), 0.1 nM L655-708 (L) and the GAT1 inhibitor CI966 (C) as well as an experiment where vigabatrin (100 μM) was incubated with AMPA (5 μM) and the GAT blocker SKF-89976A.

image

Figure 3. Principal components analysis of ethanol and ligand data shown against metabolic fingerprint data from selected ligands active at GABA(A), GABA(B) and GABA(C) receptors, GABA transporter (GAT) inhibitors and exogenous GABA. These data generated a three component model accounting for 82% of the variance in the data (47, 26 and 9% respectively), the major two components of which are shown in this diagram. Ethanol data are shown as red circles, 4-Chloro-N-[2-(2-thienyl)imidazo[1,2a]-pyridin-3-yl]benzamide (DS2) and etomidate as black squares, RO15-4513 and RO19-4603 as blue diamonds, Gabapentin as pink squares, tiagabine as purple squares, diazepam as green diamonds, QH-ii-066 as blue triangles, Zolpidem as orange squares and baclofen as green squares. All other data are represented as grey squares. The large outer ellipse represents the 95% confidence interval (Hotelling score). The inset to the figure shows an enlargement of the area of the principal component analysis (PCA) plot immediately surrounding the low (0.1 mM) concentration of ethanol. Key T, 10 μM tiagabine; Th, 10 μM 4,5,6,7-Tetrahydroisoxazolo[5,4-c]pyridin-3-ol hydrochloride (THIP); iG, 10 μM isoguvacine; S, 10 μM SGS-742; AV, 100 μM vigabatrin with 5 μM AMPA; z, 40 nM zolpidem; C, CI966; L, L655-708; AVS 100 μM vigabatrin + 5 μM AMPA + 10 μM SKF-89976A; P, picrotoxin.

Download figure to PowerPoint

RO15-4513 at 0.1 nM clusters outside the Hotelling circle (x,y coordinates, ~ 0,5; Fig. 3) with the only other ligand in the vicinity (−0.5, 5) the ρ-subunit specific antagonist (+)-(S)-4-amino-1-cyclopent-1-enyl(butyl)phosphinic acid (Kumar et al. 2008). At this concentration RO15-4513 should be reasonably specific for benzodiazepine insensitive GABA(A)R; that is α4 and α6-containing GABA(A)R which also have a γ subunit. At 1.0 nM RO15-4513 clusters within the Hotelling circle (−0.5, 3) with nearby ligands being the GABA(B)R agonist Baclofen (10 μM), the GABA(B)R antagonist phaclofen (100 μM) and the α5-specific inverse agonist L655-708.

The other ligand used as an alcohol antagonist, RO19-4603, clusters in the top right hand quadrant of the Hotelling circle (2, 1) at 1.0 nM (Fig. 3). Nearby ligands include GABA (1.0 μM), γ-hydroxybutyrate (1 μM) and the potent GABA(B)R agonist SKF-97541 (0.2 μM). Increasing the concentration of RO19-4603 to 10 nM shifts the cluster (3.5, 2) close to the α5-specific agonist QH-ii-066 (4 nmol/L).

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Ethanol and brain metabolism

Ethanol at all concentrations used from 0.1 to 60 mM had significant effects on brain metabolism. At concentrations > 1.0 mM these effects were fairly uniform and resulted in decreased 13C incorporation into the Krebs cycle along with decreased 13C incorporation into the glycolytic byproducts lactate and alanine as well as decreased total metabolite pool sizes. Ethanol has previously been reported to decrease GABA (Gomez et al. 2012) and aspartate levels (Biller et al. 2009) when administered acutely, but there is little data available at concentrations equivalent to the lowest ones used here (0.1 mM).

Decreased glucose metabolism in the brain in the presence of ethanol is a consistently reported finding in the literature (Volkow et al. 1990, 2006; Handa et al. 2000) with decreases of up to 30% reported (Volkow et al. 2006) similar to the relative decreases reported here (Fig. 1). The question as to whether this is caused by substitution of glucose as a fuel source by ethanol has been dealt with by the finding that ethanol is not significantly metabolized in the brain (Mukherji et al. 1975; Xiang and Shen 2011) although uptake varies regionally (Li et al. 2012). Ethanol has been shown to be metabolized in brain homogenates by catalase, a H2O2-dependent enzyme found in peroxisomes (Aragon et al. 1992). Catalase activity in the brain is around 1% of that found in the liver leading to the calculation that brain metabolism of ethanol is likely to contribute from 0.03 to 0.06% of whole body ethanol metabolism (Cohen et al. 1980). Looked at another way, in the presence of 100 mM ethanol, brain catalase produces around 40 nmol/g/h of acetaldehyde; this when fully stimulated by H2O2 production from glucose oxidase (Aragon et al. 1992). At the higher end of estimates, others have suggested that ethanol contributes up to 12% of total astroglial metabolism (Wang et al. 2013b); with estimates for the contribution of astroglial metabolism estimated using acetate as around 17% of total brain metabolism, this arrives at an estimate of around 2% of total brain metabolism being supplied by ethanol.

Previous studies of brain metabolism under ethanol have posited that brain may be using the increased level of acetate produced by liver from ethanol as an alternative fuel to glucose (Volkow et al. 2013). The effect of infusion of high levels of acetate has been studied and there are significant effects of the infusion on brain metabolism; this has been suggested to be due to changes in the mitochondrial transition pore due to the low energy effects of acetate (Pawlosky et al. 2010). Other possible effects of acetate, such as altered acetylation of metabolic enzymes also cannot be ruled out (Zhao et al. 2010; Soliman and Rosenberger 2011). Certainly increasing blood plasma levels of acetate does result in increased brain acetate uptake of acetate (Volkow et al. 2013), but whether this acetate is actually metabolized beyond what might be expected to due to the increased acetate levels is still a matter of conjecture. Rats subjected to chronic ethanol exposure have been shown to metabolize more ethanol-derived acetate than naïve rats (Wang et al. 2013a). Using the same methodology as in this study, it has been found that acetate is mainly oxidized in an astrocytic compartment and its ability to substitute for glucose would thus be limited (Badar-Goffer et al. 1990; Rae et al. 2012).

In the brain cortical slice, there is no peripheral metabolism or blood–brain barrier to complicate the analysis, so the results are not influenced by significant levels of acetaldehyde or acetate produced from ethanol metabolism elsewhere in the body (Pawlosky et al. 2010). Guinea pig brain catalase activity is around twice the levels reported in the rat (Eide and Syversen 1982) so ethanol could contribute at most around 4% of total brain metabolism under ideal conditions. This contribution is much less than the relative decrease in metabolism seen in brain slices in the presence of alcohol (Fig. 1).

To directly address the question as to whether ethanol is serving as a significant substrate in the brain, we supplied slices with [1,2-13C]ethanol in the presence of 2 mM unlabelled pyruvate, this latter substrate supplied to support respiration. This experiment was essentially the reverse of the experiment where ethanol effects on the metabolism of [3-13C]pyruvate was studied. If ethanol were acting as a substrate, label should have been incorporated under these conditions into acetate, and thence into glutamate, glutamine and GABA. No incorporation of 13C label into any metabolic intermediate was observed following a 1 h incubation, with no label observed in acetate above natural abundance levels and no detection of carbon–carbon coupling in any sample. However, as expected, ethanol was removed by the freeze-drying process, inspection of the aqueous phase of the extracted tissue prior to freeze drying revealed [1,2-13C]ethanol, but no significant incorporation of label into any other metabolite. Although we cannot rule out metabolism of ethanol at levels below the detection limit of NMR spectroscopy, we can categorically say that ethanol metabolism is not responsible for the significant decrease in label incorporation that is seen from [3-13C]pyruvate in the presence of unlabelled ethanol (Fig. 1).

So why is metabolism of [3-13C]pyruvate decreased by ethanol?

Ethanol at higher concentrations (10, 30 and 60 mM) is producing similar metabolic outcomes to those reported previously (Volkow et al. 1990, 2006; Handa et al. 2000). Rather than decreasing metabolism by competing as a substrate, we suggest that it is likely producing these effects by action at α4β3δ-containing receptors. This conclusion is based on the fact that the metabolic profiles of these concentrations of ethanol cluster with that from 1.0 μM DS2 (Fig. 3), which is a positive allosteric modulator showing specificity for α4β3δ-containing receptors (Wafford et al. 2009). These concentrations of alcohol show the same weighting on PC2 as etomidate, which is specific for β-containing receptors, although it has slightly higher affinity for β3 than β4 and less specificity for δ vs γ (Sanna et al. 1997). This is in keeping with reports from other laboratories that α4β3δ receptors are sensitive to alcohol (Sundstrom-Poromaa et al. 2002; Wallner et al. 2003). These authors have reported activity at concentrations ≥ 3 mM but other authors have reported effects of alcohol at lower (1–3 mM) concentrations (Sundstrom-Poromaa et al. 2002). This difference in concentration has been explained as being due to the time course of exposure to ethanol such that higher concentrations were shown to have a larger effect when ethanol at lower concentrations was not pre-applied (Smith and Gong 2007). This effect is also seen here where the concentrations of ethanol were applied for 60 min without pre-exposure; ethanol at concentrations of 10 mM and above showed strong clustering with the metabolic profile generated by DS2, but ethanol at 1 mM or less did not (Fig. 3).

In view of α4β3δ GABA-A receptors being mainly associated with tonic GABAergic inhibition which is primarily responsible for the regulation of the overall neuronal activity (Farrant and Nusser 2005) their activation (i.e. increased tonic inhibition resulting in a major decrease in neuronal activity) is consistent with the observed reduction in energy metabolism. Tonic inhibition has been shown to create a threefold larger mean inhibitory conductance than GABA released synaptically (Hamann et al. 2002) which in turn may lead to the relatively large decreases in metabolic activity observed here (Nasrallah et al. 2007).

It can therefore be concluded that the decreased metabolism seen previously in brains exposed to typical concentrations (5–40 mM) of ethanol is most likely due to effects at neurotransmitter receptors, particularly α4β3δ-containing GABA(A)R. We have previously been able to identify using this footprint, probable sites of action of drugs, such as γ-hydroxybutyrate (Nasrallah et al. 2010b), which were subsequently confirmed by others (Absalom et al. 2012). Although the observed similarity of data produced by certain concentrations of ethanol and receptor-specific drugs does not, in principle, necessarily imply identity (or even similarity) of their actions at cellular and molecular levels it nevertheless suggests that their respective mechanisms of action may be similar enough to warrant further investigation by alternative methodologies.

Ethanol effects at GABA receptors

Ethanol has long been known to have biphasic effects (Pohorecky 1977a) with excitatory (stimulatory) effects as well as ‘relaxing’ effects reported at low concentrations. Indeed, the effects of low concentrations of alcohol have been suggested to underpin its pleasurable and addictive actions (Pohorecky 1977b; Learn et al. 2003). Ethanol at 0.1 mM produced a metabolic profile different to those produced by higher concentrations, with the main difference being increases in the total metabolite pools of Glu, GABA, Asp and Ala (Fig. 1). Total 13C incorporation was decreased compared to control, indicating that overall metabolism was still depressed, even with this low concentration of ethanol. The small increase in metabolic pool size, however, suggests that a metabolic pool was activated by alcohol.

The metabolic patterns generated by 0.1 mM ethanol clustered with those of a range of other ligands. There are two major classes of drugs in this cluster.

  1. The cluster of related metabolic profiles includes those from the GABA transporter blockers CI-966 and tiagabine, experiments combining the GABA-T inhibitor vigabatrin (100 μM) with the glutamate receptor agonist AMPA (5 μM), both with and without the GAT1 channel blocker SKF89976A.
  2. Drugs which are agonists at GABA(A)R including isoguvacine, THIP (4,5,6,7-Tetrahydroisoxazolo[5,4-c]pyridin-3-ol hydrochloride) and zolpidem plus inverse agonists or antagonists L655-708 and picrotoxin.

We have previously examined the effects of inhibition of GABA uptake on metabolism (Nasrallah et al. 2010a). In general, inhibition of the GAT1 transporters produces metabolic profiles which are not similar to those of ethanol, being located in the bottom right hand quadrant of the principal component analysis (PCA) plot shown in Fig. 3. However, a number of GAT inhibitors do show strong similarity to the metabolic profile of 0.1 mM ethanol. Tiagabine at low micromolar concentrations would be a very potent and specific inhibitor of GABA uptake, having more than 300 fold specificity for GAT1 over GAT2/3 (Borden et al. 1994). Its efficacy in reducing ethanol-related reward has been studied, with mixed results (Rimondini et al. 2002; Nguyen et al. 2005; Fehr et al. 2007). CI-966 is also a specific GAT1 blocker, being around 200 times more potent at GAT1 than GAT2 or GAT3 (Borden et al. 1994).

Vigabatrin, which is an irreversible inhibitor of GABA-transaminase (Lippert et al. 1977), increases GABA levels in a dose-dependent manner (Jung et al. 1977). When incubated with slices, 100 μM vigabatrin increases GABA levels and increases 13C incorporation into GABA C2 as well as increasing 13C incorporation into Glu C4 while decreasing glutamate/glutamine cycling (decreasing 13C incorporation into Gln C4 and Ala C3; Nasrallah et al. 2011). When the slices are activated in some fashion, such as by addition of AMPA, the presence of 400 μM vigabatrin results in significantly decreased net activity in the slice. We interpreted this as resulting from increased inhibition upon slice activation, possibly owing to efflux of GABA mediated by GATs (Nasrallah et al. 2011). In the presence of only 100 μM vigabatrin, the consequences of activating slices are much milder but are likely to arise from a similar mechanism.

Ethanol has been shown to result in GABA release (Roberto et al. 2004; Criswell et al. 2008) possibly through a protein-kinase C-coupled mechanism (Kelm et al. 2010). If we accept that ethanol is inducing a localized GABA release then the resulting metabolic pattern is likely due to its action at associated nearby GABA receptors.

Isoguvacine is an agonist at GABA(A) and is also active at ρ1-containing GABA(A)R (Kusama et al. 1993). It is as potent as GABA at α5β1-containing receptors. L655-708 is an inverse agonist, selective for α5-containing GABA(A) receptors where it binds to the benzodiazepine binding site, located between the α and γ subunits of αγ-containing receptor subtypes (Quirk et al. 1996). THIP displays a ‘feeble’ affinity for ρ1-containing GABA(A)R but is potent at α5β3γ2 (Ebert et al. 1994). The affinity of THIP for receptors is dramatically increased in the presence of a δ- subunit, but at concentrations much lower than that used here (submicromolar vs 10 μM). Picrotoxin is a relatively nonspecific non-competitive antagonist at GABA(A) receptors (Inoue and Akaike 1988) and as it is turning off inhibition, it is difficult to draw conclusions about its mode of action. Zolpidem binds to the benzodiazepine site but shows specificity for α1-containing receptors (Puia et al. 1991).

Taken together, it would seem that the action of the ethanol-stimulated released GABA may be at α5βγ receptors and to a lesser extent at α1βγ. Blockade of these receptors has been reported to attenuate the abuse of ethanol in squirrel monkeys (Platt et al. 2005). However, an agonist at these receptors with reported specificity, QH-ii-066 (Huang et al. 2000) when stimulated below the IC50 for α5β2γ2 (6.8 nM) does not cluster in the vicinity of 0.1 mM ethanol (Fig. 3), but in another quadrant of the Hotelling circle near to RO19-4603.

Ethanol at 1.0 mM produced a metabolic profile that clustered part way between those at ≥ 10 mM concentrations at that at 0.1 mM (Fig. 3) and probably represents a composite metabolic outcome. Nearby ligands included gabapentin (200 μM). Gabapentin, a GABA mimetic, has a plethora of pharmacological actions, including at Ca2+ channels (Sills 2006). Notably, it has shown utility in reducing alcohol consumption and cravings (Furieri and Nakamura-Palacios 2007; Anton et al. 2011). Other nearby ligands (Fig. 3) include tiagabine (50 μM) a GAT1 inhibitor which has also shown some utility in reducing alcohol consumption (Myrick et al. 2005; Nguyen et al. 2005) although the results have been somewhat mixed (Rimondini et al. 2002; Fehr et al. 2007). Neither gabapentin nor tiagabine (Kastberg et al. 1998) interact directly with ethanol. The only other nearby ligand, Baclofen (1.0 μM), the typical agonist at GABA(B)R, may also be of utility in alcohol dependence (Bucknam 2007) where it has been suggested to ‘substitute’ for alcohol, although it has little impact on alcohol's motivational effects (Maccioni et al. 2008).

However, we have focused in this work on the effect of alcohol in the GABAergic system, an important caveat is that some of the drugs to which the effects of alcohol were compared may also act on other alcohol targets such as GIRK, L-type Ca2+ channels or glycine receptors. For example, etomidate interacts with glycine (and several other) receptors but has no effect on GIRK. Little is known of DS2 or RO19-4603 or RO15-4513 effects on those targets.

In summary, the effects of ethanol at concentrations of 10 mM and above appear to be mediated via GABA(A) receptors, specifically α4β3δ-containing GABA(A)R. The action of alcohol at these receptors causes a direct reduction in metabolic activity which can be attributed solely to the actions of ethanol, not acetaldehyde or acetate, nor to substrate substitution by ethanol. Very low concentrations of ethanol (0.1 mM) generate a metabolic similar to that of low concentrations of GABA released via GAT reversal. This GABA may act at GABA receptors such as α5- (or to a lesser extent) α1-containing βγ GABA(A)R. A role for GABA(A)rho receptors in this effect is also a possibility.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

The staff members of the UNSW Analytical Centre are thanked for expert technical support. This work was supported by the Australian National Health and Medical Research Council (Grant 568767 to CR and VJB, and Fellowship to CR).

The authors have no conflict of interest to declare and followed the ARRIVE guidelines in the preparation of this manuscript.

References

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information

Supporting Information

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  8. Supporting Information
FilenameFormatSizeDescription
jnc12634-sup-0001-FigS1.pdfapplication/PDF291KFigure S1. {1H-decoupled}13C NMR spectra of lyophilized extracts from experiment incubating cortical tissue slices with A: 2 mM sodium pyruvate (control) or B: 2 mM sodium pyruvate and 1.0 mM [1,2-13C]ethanol, or C: 2 mM sodium pyruvate and 10 mM [1,2-13C]ethanol.

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.