Pohorecky La Biphasic Action of Ethanol Biobehavioral Reviews

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J Neurochem. Author manuscript; available in PMC 2015 Apr 1.

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Ethanol, non metabolized in brain, significantly reduces encephalon metabolism, probably via specific GABA(A) receptors

Caroline D. Rae

¹Neuroscience Research Commonwealth of australia, and Encephalon Sciences UNSW, Barker St, Randwick, NSW 2031 Australia

Joanne E. Davidson

¹Neuroscience Enquiry Commonwealth of australia, and Brain Sciences UNSW, Barker St, Randwick, NSW 2031 Australia

Anthony D. Maher

¹Neuroscience Research Commonwealth of australia, and Brain Sciences UNSW, Barker St, Randwick, NSW 2031 Australia

Benjamin D. Rowlands

¹Neuroscience Inquiry Australia, and Encephalon Sciences UNSW, Barker St, Randwick, NSW 2031 Australia

Mohammed A. Kashem

^aneNeuroscience Research Commonwealth of australia, and Encephalon Sciences UNSW, Barker St, Randwick, NSW 2031 Commonwealth of australia

Fatima A. Nasrallah

¹Neuroscience Research Commonwealth of australia, and Brain Sciences UNSW, Barker St, Randwick, NSW 2031 Australia

Sundari K. Rallapalli

²Dept of Chemistry, University of Wisconsin, Milwaukee, WI, USA

James Grand Cook

²Dept of Chemistry, University of Wisconsin, Milwaukee, WI, USA

Vladimir J. Balcar

³Bosch Institute and School of Medical Sciences (Subject area of Anatomy and Histology), Sydney Medical Schoolhouse, The University of Sydney, NSW 2006, Australia

Abstract

Ethanol is a known neuromodulatory amanuensis with reported deportment at a range of neurotransmitter receptors. Hither, we used an indirect approach, measuring the effect of alcohol on metabolism of [3-^thirteenC]pyruvate in the adult Republic of guinea sus scrofa brain cortical tissue piece and comparing the outcomes to those from a library of ligands agile in the GABAergic system likewise equally studying the metabolic fate of [i,2-¹³C]ethanol. Ethanol (10, 30 and 60 mM) significantly reduced metabolic flux into all measured isotopomers and reduced all metabolic pool sizes. The metabolic profiles of these three concentrations of ethanol were similar and clustered with that of the α4β3δ positive allosteric modulator DS2 (iv-Chloro-Northward-[2-(2-thienyl)imidazo[ane,2a]-pyridin-three-yl]benzamide). Ethanol at a very low concentration (0.1 mM) produced a metabolic profile which clustered with those from inhibitors of GABA uptake, and ligands showing analogousness for α5, and to a lesser extent, α1-containing GABA(A)R. At that place was no measureable metabolism of [1,ii-¹³C]ethanol with no significant incorporation of ¹³C from [ane,2-^xiiiC]ethanol into whatsoever measured metabolite to a higher place natural abundance, although in that location were measurable furnishings on total metabolite sizes like to those seen with unlabeled ethanol.

The reduction in metabolism seen in the presence of ethanol is therefore probable to be due to its deportment at neurotransmitter receptors, particularly α4β3δ receptors, and not considering ethanol is substituting every bit a substrate or considering of the furnishings of ethanol catabolites acetaldehyde or acetate. We suggest that the stimulatory furnishings of very low concentrations of ethanol are due to release of GABA via GAT1 and the subsequent interaction of this GABA with local α5-containing, and to a lesser extent, α1-containing GABA(A)R.

Keywords: alcohol, metabonomics, NMR spectroscopy, ¹³C metabolism, GABA(A)

Introduction

Ethanol, one of the most normally used (and driveling) drugs in the Western world, is a known sedative and depressant acting on a broad range of targets including several neurotransmitter receptors and ion channels in the fundamental nervous system. Physicochemical characteristics of ethanol molecule assure that information technology readily passes claret brain bulwark (Gratton et al. 1997) and, despite earlier conflicting data (for a review see (Phillips 1981)), contempo evidence favours the view that ethanol tin actually damage and/or selectively lower claret brain barrier via specific mechanisms (Ehrlich et al. 2012, Muneer et al. 2011).

Until virtually two or 3 decades ago, well-nigh of the attempts to explicate pharmacological actions of ethanol were based on interactions between ethanol and the lipid components of biological membranes presumably resulting in non-specific alterations of membrane fluidity (Spanagel 2009). Such explanations were, even so, untenable because the membrane lipids are not significantly perturbed until concentrations of ethanol reach levels about ane to two orders of magnitude greater than those encountered during mild to medium alcohol intoxication in human subjects (Spanagel 2009). Consequently, the membrane lipid theory of ethanol deportment might perhaps assistance to explicate the lethality of very loftier doses of alcohol (leading to concentrations ≫100 mM in situ) merely cannot account for the multifariousness of specific effects of booze on brain and behaviour at concentrations resulting from common alcohol self-administration (typically 5 to 40 mM). More recent studies (for a review see (Spanagel 2009)), though non yet identifying any specific "ethanol receptors", point to several potential targets, mainly neurotransmitter receptors and ion channels, which could selectively respond to low and medium doses of alcohol (review Hodge 2006).

Molecules sensitive to low concentrations of ethanol include glycine receptors (Perkins et al. 2010, Engblom & Akerman 1991), NMDA receptors (Allgaier 2002, Lovinger et al. 1990), L-type Ca²⁺-channels and K-protein coupled inwardly-rectifying potassium channels (GIRK; functionally altered by as low every bit i mM ethanol) (Lewohl et al. 1999, Ikeda et al. 2002).

GABA-A receptors take likewise been considered as potential ethanol targets. Interestingly, the nigh abundant synaptic GABA-A receptors consisting mainly from α1, β2 and γ2 subunits are practically non-responsive to ethanol (Mori et al. 2000) while those containing α4β3δ (and α6 in cerebellum) and thought to be located more often than not extrasynaptically, are near as ethanol-sensitive as NMDA receptors (except for being activated rather than inhibited by ethanol; (Wallner et al. 2006); see as well (Kaur et al. 2009, Lovinger & Homanics 2007)). The GABAergic inhibitory organization can also be influenced by ethanol via boosted mechanisms such as potentiation of GABA release at GABAergic synapses (Roberto et al. 2004, Roberto et al. 2003).

Ethanol has dramatic effects on brain energy metabolism, particularly in terms of D-glucose utilization. Ethanol reduces D-glucose uptake and metabolism (Pawlosky et al. 2010, Volkow et al. 2006) and increases the metabolism of acetate (Wolkow 2013).

We employ a cortical tissue slice system in vitro where metabolism of [3-^thirteenC]pyruvate is used as a marker of drug effects by measuring resultant isotopomer and total metabolite pools following a period of incubation both with and without the drug (Nasrallah et al. 2010b, Rae et al. 2009). This arroyo is particularly suitable for investigating specific effects of alcohol on brain tissue. Information technology circumvents the possible confounding interest of blood brain barrier equally mentioned above (there is neither blood brain barrier nor claret circulation in our model) and eliminates actions of ethanol metabolites as alcohol is non metabolised past brain to whatever significant extent (Mukherji et al. 1975, Xiang & Shen 2011). The resulting metabolic profiles were and then compared with our extensive database describing effects, respectively, of diverse neurotransmitter (GABA) concentrations and activators/inhibitors of particular GABA receptors or transporters by specific drugs. This approach has been used successfully in the by to identify possible sites of action for the political party drug γ-hydroxybutyrate (Nasrallah et al. 2010b), sites which were later on confirmed past others (Absalom et al. 2012). Here, we accept explored the effects of a range of ethanol concentrations (0.1 ≥ ≤ sixty mM) on brain metabolism in vitro.

We take also taken advantage of the encephalon piece every bit a model of brain metabolism gratis of peripheral interference to examine the consequence of whether or not ethanol itself is metabolized in the brain by studying potential incorporation of ^thirteenC from [i,2-¹³C]ethanol past encephalon cortical tissue slices.

Methods

Materials

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

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

Modulation of metabolic activity by ethanol and related ligands

Guinea pig cortical slices were made and prepared every bit described previously (Nasrallah et al. 2010b). To determine the metabolic effects of modulation of metabolism past ethanol, slices were incubated for 1 h with 2 mmol/L sodium [3-^thirteenC]pyruvate (command) and a range of concentrations of ethanol: 0.1, ane.0, 10, 30.0, sixty.0 and 100 mmol/L.

Nosotros studied whether ethanol itself was used as a substrate by slices past incubating slices for ane h with two mM sodium pyruvate (control) and 1.0 and ten mmol/50 [one,2-^thirteenC]ethanol.

We as well studied the effects of various ethanol-related ligands by incubating slices with ii mmol/L sodium [3-¹³C]pyruvate (control) and one.0 and 10 nmol/50 RO 19-4603, 0.i and 1.0 nmol/L RO 15-4516, 2 and 20 μmol/L etomidate, 0.1 and ane.0 μmol/L DS2 or 4 and xl nmol/L QH-2-066.

The number of samples was Northward = iv in all cases.

Training of samples and NMR analysis

On completion of the incubation menstruation, slices were removed from the incubation buffer by rapid filtration and extracted in methanol/chloroform co-ordinate to the method of Le Belle (Le Belle et al. 2002). Extracts were lyophilized, and the pellet retained for protein estimation past the Lowry technique. Lyophilized supernatants were stored at −20 °C until required for acquisition of NMR spectra. This was conducted every bit described previously and included conquering of fully-relaxed ¹H, ^aneH{¹³C-decoupled} and ¹³C{¹H-decoupled spectra. {Nasrallah, 2010 #2593}. In the instance of the experiments with [1,two-^thirteenC]ethanol and QH-2-066, spectra were acquired on a Bruker AVANCE III Hard disk 600 spectrometer fitted with a cryoprobe (TCI) and refrigerated sample changer. ¹H{¹³C-decoupled} spectra were caused using bilev blended pulse decoupling across an effective bandwidth of 48000 Hz during the acquisition fourth dimension, on a 30s duty bicycle, while ^xiiiC{¹H-decoupled} spectra were acquired on a four south duty bike using continuous WALTZ-65 decoupling.

Experimental information (Due north = 4) are given as ways (standard difference). Statistical analysis was done using ANOVA for comparison ligand-treated metabolism at each receptor with control (N = 4, followed, only where statistical significance was indicated by Scheffe F-test, by a nonparametric (Mann-Whitney U) examination (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. Information are shown graphically as modify 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 command.

Pattern recognition of the data

Multivariate statistical assay used the Simca-P+ software parcel (v11.v, Umetrics, Umeå, Sweden). Each dataset for a detail 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 betwixt the high and low concentration metabolites (Wold et al. 1998), to ensure that the ¹³C labeling and steady state pool size concentrations equally contributed to the model.

Hither, we included the information from experiments acquired nether 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 piece activity (Nasrallah et al. 2011) into SIMCA P+ along with data acquired in this piece of work. The data were then subject area to principal components analysis to place mutual patterns of activation betwixt ethanol and other compounds agile in the GABAergic system.

Results

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

The metabolic profiles of the furnishings 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. The statistically significant changes shown were calculated using the total values of metabolic flux or metabolic pools (i.e."raw data" equally opposed to the values of changes relative to controls). The different concentrations of ethanol produced different results, with the lower 2 concentrations (0.i and ane.0 mM) producing profiles distinct from 1 another and distinct from those of x, 30 and lx mM ethanol, which were all broadly similar. The lowest concentration of ethanol (0.1 mM) produced a decrease in net flux into all measured isotopomers apart from Asp C2 and C3, a decrease in the pool size of lactate and Gln simply produced pregnant increases in the total metabolite pools of Glu, GABA, Asp and Ala. Higher concentrations of ethanol produced pregnant decreases in metabolite pools and isotopomer net fluxes.

Relative issue of dissimilar concentrations of exogenous ethanol on cyberspace flux of ¹³C and on full metabolite pool sizes in encephalon cortical tissue slices incubated 1 h with sodium [iii-¹³C]pyruvate

Data are shown as relative to the control mean, with control metabolism centered at zip. Error bars stand for standard deviations. Statistically pregnant changes (calculated on the raw data not the relative change in flux or pool size, see Methods) are indicated past * (P < 0.05, different to control).

Incubation of encephalon cortical tissue slices with sodium pyruvate and [ane,two-¹³C]ethanol

No carbon characterization was detected in ¹H spectra and no label was detected in ^xiiiC{¹H-decoupled} spectra above natural affluence level. There was no prove for double label (due to adjacent ^xiiiC nuclei) in any of the resonances. In addition, no [1,2-¹³C]ethanol was observed in whatever of the spectra. 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 [ane,2-¹³C]ethanol. This extract was then subjected to freeze drying, resuspended in D_iiO and another ¹³C spectrum acquired. As expected, there was no resonance from [1,2-¹³C]ethanol (spectra supplied as supplementary information). From this nosotros concluded that [1,2-¹³C]ethanol was non metabolized by brain cortical slices to any significant extent and that the residual ethanol was removed past lyophilisation.

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. Depression (0.i μM) concentrations of DS2 produced pregnant decreases in net flux into all isotopomers measured, apart from Gln C4 which increased and Asp C2 which was not inverse. The total metabolite pool size of all measured metabolites was also decreased. When the concentration of DS2 was increased to i.0 μM all internet fluxes and metabolite pool sizes were decreased (Fig. 2).

Relative effect of ligands with relevance to ethanol on net flux of ¹³C and on total metabolite pool sizes in brain cortical tissue slices incubated i h with sodium [three-¹³C]pyruvate

Data are shown as relative to the control mean, with command metabolism centered at zilch. Fault confined stand for standard deviations. Statistically meaning changes (calculated on the raw data not the relative change in flux or puddle size, run into Methods) are indicated past * (P < 0.05, different to control) or # (P < 0.05, different to other concentration of same ligand).

The β-subunit selective activator etomidate (ii μM) significantly increased net flux into all Krebs bike related metabolites, had no effect on flux into GABA C2 and decreased net flux into glycolysis byproducts Lac C3 and Ala C3. All metabolite pools were significantly decreased. Increasing the concentration to twenty μM resulted in farther significant increases in net flux into the isotopomers of Glu, Gln and Asp. The full 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 every bit increased in the net fluxes into Ala C3 and GABA C2. All other cyberspace fluxes measured were decreased apart from Lac C3 which was unaffected. Increasing the concentrations to 1.0 nM produced similar metabolite pool increases and increased net flux into Ala C3 but there was no change in GABA C2 in this example.

RO19-4603 ( 1.0 nM) strongly stimulated cyberspace flux into well-nigh isotopomers (Fig. 2), except for Gln C4, which was not afflicted. Increasing the concentration tenfold further increased net fluxes into many isotopomers and besides increased internet flux into Glu C4. Puddle sizes, autonomously from that of lactate, which was decreased, were as well increased (Fig. two). This metabolic pattern is indicative of increased internet flux into the Krebs wheel, with increased pyruvate clearance.

QH-2-066 at four nmol/L had strongly stimulatory furnishings on net flux through 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 too 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 labeling 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 information

Principal components analysis of the data generated a three component model accounting for 82 % of the variance in the information (PC1 = 47%, PC2 = 26% and PC3 = 9%; Q² = 70%, where Qⁱⁱ is the fraction of the full variation in the information which tin can be explained by a component. A rule of pollex is that values of Q² > 50% are considered a good fit (Eriksson et al. 2006). The showtime two (major) components of the model are shown in Fig. 3 with the concentrations of ethanol each shown in red. The loftier (x, xxx and 60 mM) concentrations of ethanol cluster most the bottom left hand corner of the plot forth 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 gene 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)). By dissimilarity the 0.1 mM ethanol cluster (run into inset to Fig. iii) is located closest to information derived from an experiment using both the GABA-T inhibitor vigabatrin (100 μM) and the glutamate receptor agonist five μM AMPA (AV). It is also most to 40 nM zolpidem (z), ten μM isoguvacine (G), 10 μM SGS-742 (S), 5 μM picrotoxin (P), 0.1 nM L655–708 (50) and the GAT1 inhibitor CI966 (C) likewise as an experiment where vigabatrin (100 μM) was incubated with AMPA (5 μM) and the GAT blocker SKF-89976A.

Principal components analysis of ethanol and ligand data shown against metabolic fingerprint information from selected ligands agile at GABA(A), GABA(B) and GABA(C) receptors, 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 information are shown as red circles, DS2 and etomidate as black squares, RO15-4513 and RO19-4603 as blue diamonds, Gabapentin as pink squares, tiagabine as purple squares, diazepam as greenish diamonds, QH-ii-066 equally blue triangles, Zolpidem as orange squares and baclofen as dark-green squares. All other data are represented every bit greyness squares. The large outer ellipse represents the 95% conviction interval (Hotelling score). The inset to the effigy shows an enlargement of the area of the PCA plot immediately surrounding the low (0.1 mM) concentration of ethanol. Key T, ten μM tiagabine; Th, x x μM THIP; G, 10 μM isoguvacine; South, ten μM SGS-742; AV, 100 μM vigabatrin with 5 μM AMPA; z, xl nM zolpidem; C, CI966; 50, L655–708; AVS 100 μM vigabatrin + 5 μM AMPA + 10 μM SKF-89976A; P, picrotoxin.

RO15-4513 at 0.1 nM clusters outside the Hotelling circle (ten,y coordinates, ~ 0,five; Fig. 3) with the only other ligand in the vicinity (−0.v, 5) the ρ-subunit specific antagonist (+)-(S)-4-amino-1-cyclopent-i-enyl(butyl)phosphinic acid (Kumar et al. 2008). At this concentration RO15-4513 should exist reasonable specific for benzodiazepine insensitive GABA(A)R; i.e. α4 and α6-containing GABA(A)R which too take a γ subunit. At ane.0 nM RO15-4513 clusters within the Hotelling circle (−0.five,3) with nearby ligands being the GABA(B)R agonist Baclofen (ten μM), the GABA(B)R adversary phaclofen (100 μM) and the α5-specific changed agonist L655–708.

The other ligand used as an booze antagonist, RO19-4603, clusters in the peak correct hand quadrant of the Hotelling circumvolve (2, 1) at one.0 nM (Fig. three). 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 ten nM shifts the cluster (iii.5, 2) close to the α5-specific agonist QH-ii-066 (4 nmol/50).

Discussion

Ethanol and encephalon metabolism

Ethanol at all concentrations used from 0.one to 60 mM had significant furnishings on brain metabolism. At concentrations >ane.0 mM these effects were fairly uniform and resulted in decreased net flux into the Krebs cycle along with decreased net flux into the glycolytic byproducts lactate and alanine besides 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 merely there is piddling information bachelor at concentrations equivalent to the lowest ones used hither (0.one mM).

Decreased glucose metabolism in the encephalon in the presence of ethanol is a consistently reported finding in the literature (Volkow et al. 1990, Handa et al. 2000, Volkow et al. 2006). The question equally to whether this is caused by substitution of glucose equally a fuel source by ethanol has been dealt with by the finding that ethanol is non significantly metabolized in the brain (Mukherji et al. 1975, Xiang & Shen 2011) although uptake varies regionally (Li et al. 2012). In the brain cortical slice, there is no peripheral metabolism or blood brain barrier to complicate the analysis, showing that here, ethanol is unlikely to act every bit a metabolic fuel and the results are not influenced by significant levels of acetaldehyde or acetate produced from ethanol metabolism elsewhere in the body (Pawlosky et al. 2010). Our results suggest that ethanol at higher concentrations (ten, 30 and lx mM) is producing similar metabolic outcomes and is probable acting at α4β3δ-containing receptors. This conclusion is based on the fact that the metabolic profiles of these concentrations of ethanol cluster with that from one.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 information technology 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 take reported activeness at concentrations ≥ three mM but other authors have reported effects of alcohol at lower (1–three mM) concentrations (Sundstrom-Poromaa et al. 2002). This difference in concentration has been explained every bit being due to the timecourse of exposure to ethanol such that higher concentrations were shown to have a larger effect when ethanol at lower concentrations was not preapplied (Smith & Gong 2007). This effect is also seen here where the concentrations of ethanol were applied for 40 min without pre-exposure; ethanol at concentrations of ten mM and in a higher place showed strong clustering with the metabolic profile generated by DS2, but ethanol at 1 mM or less did not (Fig. 3).

To actually nail the question equally to whether ethanol is serving as a significant substrate in the brain, nosotros performed the opposite experiment, supplying 2 mM pyruvate as substrate and studying the flux of label supplied as [1,two-¹³C]ethanol. No incorporation of ¹³C label into whatever metabolic intermediate was observed following a i h incubation, with no label observed in acetate above natural abundance levels and no detection of carbon-carbon coupling in any sample. While, as expected, ethanol was removed by the freeze-drying process, inspection of the aqueous phase of the extracted tissue revealed [1,ii-¹³C]ethanol but no significant incorporation of label into any other metabolite. While nosotros 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 [iii-¹³C]pyruvate in the presence of unlabeled ethanol (Fig. one).

It can therefore be concluded that the decreased metabolism seen previously in brains exposed to typical concentrations (v–40 mM) of ethanol is most likely due to effects at neurotransmitter receptors, particularly α4β3δ-containing GABA(A)R.

Ethanol effects at GABA receptors

Ethanol has long been known to have biphasic effects (Pohorecky 1977b) with excitatory (stimulatory) effects as well as "relaxing" effects reported at low concentrations. Indeed, the effects of low concentrations of booze take been suggested to underpin its pleasurable and addictive actions (Pohorecky 1977a, Learn et al. 2003). Ethanol at 0.1 mM produced a metabolic profile different to those produced past higher concentrations, with the main departure being increases in the total metabolite pools of Glu, GABA, Asp and Ala (Fig. one). Total internet flux was decreased compared to command, 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 past alcohol.

The metabolic patterns generated by 0.1 mM ethanol clustered with those of a range of other ligands. At that place 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 (v μM), both with and without the GAT1 channel blocker SKF89976A.

2. Drugs which are agonists at GABA(A)R including isoguvacine, THIP (four,5,6,7-Tetrahydroisoxazolo[v,four-c]pyridin-iii-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 non like to those of ethanol, existence located in the bottom right manus quadrant of the PCA plot shown in Fig. 3. However, a number of GAT inhibitors practice show potent similarity to the metabolic profile of 0.1 mM ethanol. Tiagabine is a strong and specific inhibitor of GABA uptake showing 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 than potent at GAT1 than GAT2 or GAT3.

Vigabatrin, which is an irreversible inhibitor of GABA-transaminase (Lippert et al. 1977), increases GABA levels in a dose dependent way (Jung et al. 1977). When incubated with slices, 100 μM vigabatrin increases GABA levels and increases net flux into GABA C2 as well every bit increasing cyberspace flux into Glu C4 while decreasing glutamate/glutamine cycling (decreasing net flux into Gln C4 and Ala C3) (Nasrallah et al. 2011). When the slices are activated in some fashion, such every bit by addition of AMPA, the presence of 400 μM vigabatrin results in significantly decreased net activity in the piece. We interpreted this as resulting from increased inhibition upon slice activation, possibly due to efflux of GABA from GATs (Nasrallah et al. 2011). In the presence of but 100 μM vigabatrin, the consequences of activating slices are much milder but are likely to arise from a like mechanism. Ethanol has been shown to issue in GABA release (Roberto et al. 2004, Criswell et al. 2008) peradventure through a protein-kinase C coupled machinery (Kelm et al. 2010). If we take that ethanol is inducing a localized GABA release and so the resulting metabolic blueprint 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). Information technology is as potent every bit 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, merely at concentrations much lower than that used here (submicromolar vs 10 μM). Picrotoxin is a relatively nonspecific noncompetitive adversary at GABA(A) receptors (Inoue & Akaike 1988) and since it is turning off inhibition, information technology is hard 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, information technology would seem that the action of the ethanol-stimulated released GABA may be at α5βγ receptors and to a lesser extent at α1βγ. Occludent 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, 2000 #4636} when stimulated beneath the IC_l for α5β2γ2 (half-dozen.8 nM) does not cluster in the vicinity of 0.1 mM ethanol (Fig. three) but in another quadrant of the Hotelling circumvolve well-nigh to RO19-4603, Ethanol at 1.0 mM produced a metabolic contour that amassed role way between those at 10 mM+ concentrations at that at 0.1 mM (Fig. iii) and probably represents a composite metabolic upshot. Nearby ligands included gabapentin (200 μM). Gabapentin, a GABA mimetic, has a plethora of pharmacological actions, including at Ca2+ channels (Sills 2006). Notably, information technology has shown utility in reducing alcohol consumption and cravings (Anton et al. 2011, Furieri & Nakamura-Palacios 2007). Other nearby ligands (Fig. 3) include tiagabine (50 μM) a GAT1 inhibitor which has too shown some utility in reducing alcohol consumption (Myrick et al. 2005, Nguyen et al. 2005) although the results take been somewhat mixed (Rimondini et al. 2002, Fehr et al. 2007). Neither gabapentin nor tiagabine (Kastberg et al. 1998) interact straight with ethanol. The merely other nearby ligand, Baclofen (1.0 μM), the typical agonist at GABA(B)R, is likewise 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).

While we have focused in this work on the consequence 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 human activity on other alcohol targets such every bit GIRK, L-type Ca²⁺ channels or glycine receptors. For instance, etomidate interacts with glycine (and several other) receptors simply has no issue on GIRK. Little is known of DS2 or RO19-4603 or RO15-4513 effects on those targets.

In summary, the furnishings of ethanol at concentrations of x mM and to a higher place appear to be mediated via GABA(A) receptors, specifically α4β3δ-containing GABA(A)R. The action of alcohol at these receptors causes a directly 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 office for GABA(A)rho receptors in this effect is also a possibility.

Supplementary Material

supp data

Acknowledgments

The staff 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).

Abbreviations used in text

GIRK	G-protein coupled inwardly rectifying potassium channels
GABA(A)R	GABA-A receptors
GABA(B)R	GABA-B receptors
DS2	iv-Chloro-Northward-[2-(2-thienyl)imidazo[1,2a]-pyridin-3-yl]benzamide
GAT1	GABA transporter 1
NMDA	N-methyl-D-aspartate
THIP	4,5,half-dozen,7-Tetrahydroisoxazolo[v,iv-c]pyridin-3-ol hydrochloride

Footnotes

The authors have no conflict of interest to declare.

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