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.
- 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.
- 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.