understanding your inhibitions: effects of gaba and gaba a receptor modulation on brain cortical...

,

*Prince of Wales Medical Research Institute, and Brain Sciences UNSW, Randwick, New South Wales, Australia

�Department of Biochemistry, The University of Cambridge, Cambridge, UK

�Bosch Institute and School of Medical Sciences, The University of Sydney, New South Wales, Australia

§School of Chemistry, The University of New South Wales, Sydney, New South Wales, Australia

The major inhibitory neurotransmitter in the brain, GABAacts on two major receptor types; GABAA receptors(including the ionotropic q subunit-containing ‘GABAC’receptors) and the metabotropic GABAB receptors. TheGABAA receptor is a heteromeric pentamer composed from arepertoire of at least 19 subunits, classified into groups by thedegree of sequence homology; 6a, 4b, 3c, 1d, 3q, 1e, and 1psubunits, many of which have splice variants. The receptorfunctions as a ligand-gated chloride channel regulated byGABA binding at a specific site (Olsen and Tobin 1990) andalso has distinct binding sites for allosteric modulators suchas benzodiazepines, neurosteroids, and barbiturates (Sieghart1989; Macdonald and Olsen 1994). Subunit compositionseems to be the key determinant of the pharmacologicalcharacteristics of GABAA receptors (Hevers and Luddens1998). However, there are not any readily accessible drugsthat display sufficient overall selectivity for GABAA recep-tors (Barnard et al. 1998; Gibbs and Johnston 2005).

In this work we focused on ligands which are active at theGABAA receptor, but have not considered those known to be

preferentially active at q subunits (i.e. GABAC receptors). Inaddition to exogenous GABA, we studied a range of ligandsactive at the GABA binding site, as well as selected allostericmodulators. We used the total metabolite pool size of keymetabolites and the metabolic fate of 13C added as[3-13C]pyruvate following 1 h of incubation in the absence(control) or presence of ligand to generate a metabolic profilespecific for each concentration of ligand. These data werethen subjected to multivariate statistical analysis resulting inan objective definition of the metabolic fingerprint generatedby each ligand.

Received May 23, 2008; revised manuscript received September 27,2008; accepted September 30, 2008.Address correspondence and reprint requests to Caroline Rae, Prince

of Wales Medical Research Institute, Barker St, Randwick, NSW 2031,Australia. E-mail: [email protected] used: PCA, principal components analysis; PLS-DA,

partial least squares discriminant analysis; THIP, 4,5,6,7-tetrahydrois-oxazolo[5,4-c]pyridine-3-ol hydrochloride.

Abstract

A targeted neuropharmacological, 1H/13C NMR spectroscopy

and multivariate statistical approach was used to examine the

effects of exogenous GABA and ligands at the GABAA

receptor family on brain metabolism in the Guinea pig cortical

tissue slice. All ligands at GABAA receptors generated meta-

bolic patterns which were distinct from one another with the

major variance in the data arising because of metabolic work

(shown by net flux into Krebs cycle byproducts and increased

metabolic pool sizes). Three major clusters of metabolic sig-

natures were identified which corresponded to: (i) activity at

phasic (synaptic) GABAA receptors, dominated by a1-con-

taining receptors and responsive to GABA at 10 lmol/L; (ii)

activity at perisynaptic receptors, dominated by response to

high (40 lmol/L) GABA and the superagonist 4,5,6,7-tetra-

hydroisoxazolo[5,4-c]pyridine-3-ol hydrochloride, and C,

activity at extrasynaptic receptors, dominated by response to

low (0.1–1.0 lmol/L) GABA, zolpidem (400 nmol/L) and the

non-specific allosteric modulator RO19-4603 (1 nmol/L).

These results highlight the utility of a different but robust ap-

proach to study of the GABAergic system using metabolic

systems analysis.

Keywords: 13C nuclear magnetic resonance spectroscopy,

inhibitory activity, metabolomics, principal components anal-

ysis, c-aminobutyric acid.

J. Neurochem. (2009) 108, 57–71.

JOURNAL OF NEUROCHEMISTRY | 2009 | 108 | 57–71 doi: 10.1111/j.1471-4159.2008.05742.x

� 2008 The AuthorsJournal Compilation � 2008 International Society for Neurochemistry, J. Neurochem. (2009) 108, 57–71 57

Materials and methods

MaterialsGuinea pigs (Dunkin-Hartley), weighing 400–800 g, were fed

ad libitum on standard Guinea pig/rabbit pellets, with fresh cabbage

leaves and lucerne hay roughage. Animals were maintained on a

12 h light/dark cycle. All experiments were conducted in accor-

dance with the guidelines of the National Health and Medical

Research Council of Australia and were approved by the institu-

tional (UNSW) Animal Care Ethics Committee.

Sodium [3-13C]pyruvate and sodium [13C]formate were pur-

chased from Cambridge Isotope Laboratories Inc. (Andover, MA,

USA). GABA; muscimol (5-aminoethyl-3-hydroxyisoxazole); iso-

guvacine (1,2,3,6-tetrahydro-4-pyridinecarboxylic acid hydrochlo-

ride); 4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridine-3-ol hydrochloride

(THIP); ())- bicuculline ([R-(R*,S*)]-5-(6,8-dihydro-8-oxofuro[3,4-e]-1,3-benzodioxol-6-yl)-5,6,7,8-tetrahydro-6,6-dimethyl-1,3-dioxolo

[4,5-g]isoquinolinium chloride); gabazine (SR 95531, 6-imino-3-(4-

methoxyphenyl)-1(6H)-pyridazinebutanoic acid hydrobromide);

picrotoxin (1 : 1 mixture of picrotin and picrotoxinin); zolpidem

(N,N,6-trimethyl-2-(4-methylphenyl)imidazo[1,2-a]pyridine e-3-

acetamide); flumazenil (8-fluoro-5,6-dihydro-5-methyl-6-oxo-4H-

imidazo[1,5-a][1,4]benzodiazepine-3-carboxylic acid, ethyl ester);

RO19-4603 (5,6-dihydro-5-methyl-6-oxo-4H-imidazo[1,5-a]thie-

no[2,3-f][1,4]diazepine-3-carboxylic acid 1,1-dimethylethyl ester);

diazepam (7-chloro-1,3-dihydro-5-phenyl-2H-1,4-benzodiazepine-

2-one); and L-655,708 (11,12,13,13a-tetrahydro-7-methoxy-9-oxo-

9H-imidazo[1,5-a]pyrrolo[2,1-c][1,4]benzodiazepine-1-carboxylic

acid, ethyl ester) were purchased from Tocris Bioscience (Bristol,

UK). All other reagents were of analytical reagent grade. The

modulator midazolam hydrochloride (8-chloro-6-(2-fluoro-phenyl)-

1-methyl-4H-imidazo[1,5-a][1,4]benzodiazepine was purchased

from Roche (Dee Why, NSW, Australia).

Preparation of brain cortical tissue slicesFollowing cervical dislocation, guinea pig brains were removed

from the cranial vault and 350 lm cortical slices, dissected in the

paraxial plane, were obtained using a McI1wain tissue chopper. The

slices were then washed three times in a modified Krebs-Henseleit

buffer (124 mmol/L NaCl, 5 mmol/L KCl, 1.2 mmol/L KH2PO4,

1.2 mmol/L CaCl2, 1.2 mmol/L MgSO4, and 26 mmol/L NaHCO3

(Badar-Goffer et al. 1990), resuspended for 1 h in fresh buffer

containing 10 mmol/L unlabeled glucose and gassed with 95% O2/

5% CO2 in a shaking water bath, maintained at 37�C, to allow

metabolic recovery (McIlwain and Bachelard 1985). Slices were

then washed three times in glucose-free buffer and resuspended in

fresh buffer with the substrate of choice.

Modulation of GABAA receptor activityTo determine the metabolic effects of modulation of GABAA

receptors on brain activity, slices were incubated with 2 mmol/L

sodium [3-13C]pyruvate (control) and, in the case of ligand treatment

groups, with one of two concentrations of the ligand. We used

2 mmol/L sodium [3-13C]pyruvate as the 13C substrate as this

compound shows increased label penetration compared with

[1-13C]glucose (Rae et al. 2005, 2006; Broer et al. 2007; Nasrallahet al. 2007). The rate of label incorporation after 1 h is assumed to be

linear in cortical slices (Griffin 1997) and to reflect initial velocity.

Specific ligands were used to target the GABAA receptor.

Concentrations of 0.1, 1.0, 4.0, 10.0, and 40.0 lmol/L GABA were

used [endogenous ligand at GABA receptors (Curtis et al. 1970)].Other ligands used were the GABAA agonists, muscimol [0.01, 0.1,

10, and 50 lmol/L classical potent agonist at the post-synaptic

GABAA receptor (Johnston et al. 1968)], THIP [10 and 100 lmol/

L; potent partial agonist at GABAA receptors (Krogsgaard-Larsen

et al. 1977), superagonist at d-containing GABAA receptors (Brown

et al. 2002)], isoguvacine [10 and 100 lmol/L; agonist at GABAA

receptors (Mortensen et al. 2003)] and the GABAA antagonists,

bicuculline [0.5 and 50 lmol/L; classical, potent, and specific

GABAA antagonist (Curtis et al. 1970)], picrotoxin (5 and 50 lmol/

L potent GABAA channel blocker and non-competitive antagonist

(Takeuchi and Takeuchi 1969) and gabazine [10 and 100 lmol/L

competitive antagonists at GABAA receptors, (Wermuth et al.1987)]. The GABAA receptor modulators included zolpidem [40

and 400 nmol/L; a positive allosteric modulator with preferential

affinity for a1 containing GABAA receptors, (Puia et al. 1991)],RO19-4603 [1 and 10 nmol/L; negative allosteric modulator

(Chebib and Johnston 2000)]; diazepam [0.5 and 5 lmol/L, positive

allosteric modulator (Chebib and Johnston 2000)]; flumazenil [2 and

20 nmol/L; neutralizing allosteric modulator at GABAA (Chebib

and Johnston 2000)]; midazolam [0.1 and 20 lmol/L, benzodiaz-

epine agonist at GABAA receptors (Wang et al. 2003)]; and L-

655,708 [0.1 and 1 nmol/L, potent benzodiazepine agonist selective

for a5 containing GABAA receptors (Quirk et al. 1996)].The concentrations of ligands were chosen to reflect the affinity

of the individual drugs for the GABAA receptor and to separate

other possible non-specific activities. Typically, the concentrations

studied were around the KM or Kd (whichever published values were

available) and 10 times this amount, although this was subject to

variation depending on the various subtype affinities of each ligand.

Each experiment (n = 4) was performed with a control incubation

and two concentrations of ligand. Slices were then incubated for 1 h

with [3-13C]pyruvate and the experiment was stopped as indicated.

Preparation of samples and NMR analysisFollowing a 1 h incubation period, slices were removed from the

incubation buffer by rapid filtration and extracted in methanol/

chloroform according to the method of 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 NMR analysis. Samples were

resuspended in 0.65 mL D2O containing 2 mmol/L sodium [13C]for-

mate as an internal intensity and chemical shift reference (13C d171.8). Fully relaxed 1H and 1H[13C-decoupled] spectra (see Fig. S1)

(total cycle of 30 s, comprising 24 s relaxation delay, 4 s water

suppression and �2 s acquisition time), WURST-40 (Kupce and

Freeman 1995) with a 112-step phase cycle (Skinner and Bendall

1997), decoupling during acquisition were obtained at 600.13 MHz

on a Bruker DRX-600 spectrometer (Bruker Biospin, Karlsruhe,

Germany) with a 5 mm dual 1H/13C probe, followed by 13C [1H-

decoupled] spectra (see Fig. S2) (typically 3000–5000 transients,

cycle of 4 s comprising 2 s relaxation delay and �2 s acquisition

time, continuous WALTZ-16 decoupling, 131072 data points).

Assignments were made as described previously (Rae et al. 2000).13C [1H-Decoupled] spectra were transformed using 3 Hz

exponential line-broadening and peak areas were determined by

Journal Compilation � 2008 International Society for Neurochemistry, J. Neurochem. (2009) 108, 57–71� 2008 The Authors

58 | F. A. Nasrallah et al.

https://www.researchgate.net/publication/11519607_A_comparison_of_cell_tissue_extraction_techniques_using_high-resolution_1H_NMR_spectroscopy?el=1_x_8&enrichId=rgreq-9b574c5a-2938-48a1-813b-6224eed14267&enrichSource=Y292ZXJQYWdlOzIzNDc4NjM2O0FTOjEwMzA1MzcwNTM1MTE3MEAxNDAxNTgxMzI2Mjc3

https://www.researchgate.net/publication/12535668_GABA-Activated_Ligand_Gated_Ion_Channels_Medicinal_Chemistry_and_Molecular_Biology?el=1_x_8&enrichId=rgreq-9b574c5a-2938-48a1-813b-6224eed14267&enrichSource=Y292ZXJQYWdlOzIzNDc4NjM2O0FTOjEwMzA1MzcwNTM1MTE3MEAxNDAxNTgxMzI2Mjc3

integration using standard Bruker software (TOPSPIN, Version 1.3;

Bruker Biospin) following baseline correction. Peak areas were

adjusted for nuclear Overhauser effect, saturation and natural

abundance effects and quantified by reference to the area of the

internal standard resonance of [13C]formate. After adjustment for

protein levels, peak areas were used as measures of ‘net flux’ of 13C

into that isotopomer (lmol/L/100 mg protein/h). Glu C3 was not

quantified because of possible resonance overlap with residual

pyruvate dimer, as described previously (Rae et al. 2000). Metab-

olite pool sizes (lactate, alanine, GABA, glutamate, glutamine, and

aspartate) were determined by integration of resonances in fully

relaxed 600 MHz 1H[13C-decoupled] spectra using [13C]formate as

the internal intensity reference (Rae et al. 2000). An estimate of

anaplerosis (the amount of metabolism of pyruvate via pyruvate

carboxylase) is calculated from the ratio of Asp C3 to Asp C2 [(Asp

C3 ) Asp C2)/Asp C2; where Asp C2 and C3 are produced in

equimolar amounts via pyruvate dehydrogenase and the Krebs cycle

because of label position scrambling in the symmetric molecules

succinate and fumarate, but pyruvate carboxylase activity exclu-

sively produces Asp C3].

Experimental data (n = 4) are given as mean (SD). Univariate

statistical analysis was done using ANOVA for comparing ligand-

treated metabolism at each receptor with its control (n = 4),

followed, only where statistical significance was indicated by

Scheffe F-test, by a non-parametric (Mann–Whitney U-test) test

(Statview Student).

Pattern recognition of the dataMultivariate pattern recognition and data reduction tools are capable

of taking into account several predictive variables simultaneously.

They are especially useful for analysis of the type of metabolic data

presented here as they can objectively distil the major response

variables to a few controlled factors, termed latent variables (Wold

1994). Multivariate statistical analysis was performed within the

Simca P+ software package (version 11, Umetrics, Umea, 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 was scaled by univariate

scaling to standardise variance between the high concentration and

low concentration metabolites, and ensure that the 13C labeling and

steady-state pool size concentrations equally contributed to the

model.

All datasets were initially examined by principal components

analysis (PCA) to define clusterings and dominant trends in the

dataset. The scores plots for these analyses were used to examine

clusterings, while the loading plots were used to determine which

metabolites were responsible for the clusterings. For the loading

plots only metabolites that significantly contributed to an individual

principal component (PC), as determined by a jack knifing

procedure, were deemed to have changed. For each PC the variance

explained by that component (R2) was reported as a percentage.

Where particular trends were investigated partial least squares

discriminant analysis (PLS-DA) was used for supervised clustering.

This is a regression extension of PCA whereby the separation of

particular groups are maximised by regressing the PCs against a

dummy matrix representing class membership. Only models where

the goodness of fit (Q2) was > 40% were deemed to be statistically

robust and all models in this work had Q2 > 55%.

Results

In general, net flux into Glu C4 and C2 and Asp C2 and C3 isindicative of net flux through the Krebs cycle. Net flux intoGln C4 may be indicative of glutamate/glutamine cyclingand is often accompanied by alterations in net flux into AlaC3 (Rae et al. 2003; Broer et al. 2007). Net flux into Lac C3in these experiments is largely indicative of pyruvateclearance rates.

GABAA agonists – GABAThe effect of a range of concentrations of GABA on braincortical metabolism is shown in Fig. 1. There were cleardifferences between the metabolic profiles produced by eachconcentration of GABA, with a metabolic switch fromincreased net flux into the glutamate and aspartate isotopo-mers to decreased net flux occurring between the two lowestand three higher GABA concentrations. The total metabolitepool size of GABA increased as the amount of added GABAincreases, as would be expected.

Principal components analysis of effects of exogenousGABAThe concentration of the total GABA pool size was excludedfrom all analyses where the GABA dosed groups wereexamined as the effect of the added GABA on this variableper se would outweigh any variation because of metabolicchanges. Examining all the GABA dosed groups together, athree PC model was formed representing 47%, 26%, and14% of the variance in the data, respectively (Fig. 2). Thefive different concentrations of GABA clustered into distinctgroups (Fig. 2) with the lower concentrations (0.1 and1.0 lmol/L) separating from the mid-range concentrations(4.0 and 10.0 lmol/L) along PC1. Inspection of loadings ofvariable contributing to PC1 (inset to Fig. 2) revealed this PCto be weighted most significantly by increases in net flux intoGlu C2 and C4, GABA C2, Gln C4 and Asp C2 and C3 anddecreases in net flux into lactate C3. The highest concentra-tion of GABA (40 lmol/L) separated from all otherconcentrations along PC2 (Fig. 2), which representedincreases in the total pool size of lactate, glutamate, aspartateand alanine and decreases in net flux into GABA C2 and thetotal pool size of glutamine (inset to Fig. 2). The third PC(not shown) served mostly to separate 4.0 lmol/L GABAfrom 10 lmol/L GABA and was loaded most significantlyby decreased net flux into Ala C3 and Gln C4, decreasedtotal pool sizes of lactate, glutamate and alanine andincreased net flux into Glu C4.

Metabolic effects of GABAA receptor ligandsThemetabolic profiles generated byGABAA receptor agonistsand antagonists are shown in Fig. 3. In the case of muscimol,data was compared only to the control experiment.

� 2008 The AuthorsJournal Compilation � 2008 International Society for Neurochemistry, J. Neurochem. (2009) 108, 57–71

GABA and metabolism | 59

In addition to the changes shown in Fig. 3, 10 lmol/Lmuscimol resulted in a significant decrease in the aspartateanaplerotic ratio [p < 0.03; control 0.15 (0.02); 10 lmol/L

0.12 (0.01)]. Gabazine (100 lmol/L) produced an increase(p = 0.04) in the anaplerotic ratio from 0.16 ± 0.01 (control)to 0.21 ± 0.04 (100 lmol/L). No significant changes in the

Fig. 1 Relative effect of exogenous GABA

on net flux of 13C 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 to

zero. Error bars represent SDs. Statistically

significant changes (calculated on the raw

data not the relative change in flux or pool

size) are indicated by *p < 0.05 (different to

control).



aspartate anaplerotic ratio were found for all other GABAA

ligands.

PCA of GABAA ligand effects on metabolismThe six ligands were separated from one another (Fig. 4)using a three compartment PCA model of this groupaccounting for 72% of the variation in the dataset (46%,18%, and 14%, respectively). The only ligands not clearlyseparated by the first two PC were 100 lmol/L THIP,100 lmol/L isoguvacine, and 5 lmol/L bicuculline. PC1represented relative increases in glutamate and aspartatemetabolism, with increased net flux into Glu C2 and C4 andAsp C2 and C3 as well as increased total pool size of

glutamate and aspartate in the 100 lmol/LTHIP, 100 lmol/Lisoguvacine, and 5 lmol/L bicuculline group. Increased netflux into GABA C2 and decreased net flux into lactate C3also contributed to PC1 (Fig. 4). PC2 represented increasesin total pool sizes of lactate, glutamate, GABA, aspartate andalanine and increased net flux into Ala C3, but decreased netflux into Glu C2 and C4 and Asp C2 and C3. This PC mostseparated the gabazine groups from the muscimol groups.The PC3 which represented increased net flux into GABAC2, lactate C3, Gln C4 and Ala C3 (Fig. 4) separated100 lmol/L isoguvacine (which was loaded negatively byPC3) and allowed resolution of 100 lmol/L THIP and0.5 lmol/L bicuculline along PC2.

PC

2

PC 1

4

2

0

–4

–2

0

-2 2 4 6

0.1 µM GABA

1.0 µM GABA

4.0 µM GABA

10 µM GABA

40 µM GABA

-4 -6

Glu

C2

Glu

C4

GA

BA

C2

Lac

tate

C3

Gln

C4

Asp

C2

Asp

C3

Ala

C3

To

tal l

acta

te

To

tal G

lu

To

tal A

sp

To

tal G

ln

To

tal A

la

PC

1

0.0

0.1

0.2

0.3

0.4

-0.1

-0.2

-0.3

-0.4

(Asp

C3-

C2)

/Asp

C2

Glu

C2

Glu

C4

GA

BA

C2

Lac

tate

C3

Gln

C4

Asp

C2

Asp

C3

(Asp

C3-

C2)

/Asp

C2

Ala

C3

To

tal l

acta

te

To

tal G

lu

To

tal A

sp

To

tal G

ln

To

tal A

la

PC

2

0.0

0.2

0.4

-0.2

-0.4

Fig. 2 Principal components analysis of labeling and total metabolite

concentrations for exogenous GABA. The figure shows the first two

principal components of the analysis and demonstrates separation of

metabolic profiles according to the amount of Krebs cycle activity

(increases with positive loading in PC1, shown in inset). The large

outer ellipse represents the 95% confidence interval (Hotellings

score). The shaded ellipses define subset groupings to make the fig-

ure more readable and are not representative of any statistical test.

The smaller inset graphs show the relative contribution of each vari-

able to each principal component. The loading shown is the coefficient

each variable contributes to the model (these would all be the same if

the contributions were equal, because of unit variance scaling of the

data). The error bars represent SDs of the loadings determined by a

jack-knife routine.



Fig. 3 Relative effect of agonists and antagonists at GABAA on net

flux of 13C 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 SDs. Statistically significant changes

(calculated on the raw data not the relative change in flux or

pool size) are indicated by *p < 0.05 (different to control) or

#p < 0.05 (different to the other concentration of ligand used).

Data are depicted in this fashion in order to facilitate pattern visu-

alization.



Partial least squares analysis of effects of GABAA ligandsand exogenous GABAA three component PLS-DA model was built following theanalysis of the combined GABA concentrations and GABAA

receptor dataset to determine whether the effects of ligandsthat act at GABAA receptor were different to the metaboliceffects of the various concentrations of GABA. The threelower concentrations of GABA (0.1, 1.0, and 4.0 lmol/L)were clearly separated from the ligands that act at GABAA

by the first two components of this model (Fig. 5) whichaccounted for 45% and 15% of the variance in the data,respectively (Q2 = 64%), while the higher concentrations ofGABA (10 and 40 lmol/L) clustered with gabazine(10 lmol/L) and THIP (100 lmol/L), respectively. The firstPC represented increases in labelling at Glu C2, Glu C4,GABA C2, and Asp C3 and the total concentration of

aspartate, while the second PC represented increases inGABA C2, Lac C3 and Ala C3, and a decrease in the totalpool size of glutamate.

GABAA allosteric modulatorsMetabolic profiles resulting from experiments with GABAA

receptor allosteric modulators are shown in Fig. 6.

PCA of allosteric modulators, GABAA ligands andexogenous GABAData from the experiments using the GABAA modulatorswere subjected to PCA (Fig. 7a). All allosteric modulatorswere separated from one another by two PCs accounting for40% and 35% of the variance, respectively. PC1 representedrelative increases in net flux into Glu C2 and C4, GABA C2,Gln C4, Asp C2 and C3 and increases in the total metabolite

Fig. 4 Principal components analysis of labeling and total metabo-

lite concentrations for GABAA ligands. The figure shows the first

three principal components of the analysis, plus the loadings scores

for each variable for each principal component. The large outer

ellipse represents 95% confidence interval (Hotellings score). The

shaded ellipses define subset groupings to make the figure more

readable and are not representative of any statistical test. The

smaller inset graphs show the relative contribution of each variable

to each principal component. The loadings shown are the

coefficients of each variable contributing to the model. The error

bars represent SDs of the loadings determined by a jack-knife

routine.



pools of Glu, GABA, Asp and Gln. PC2 representeddecreases in net flux into Glu C2, GABA C2, Asp C2 andC3 and decreased Asp anaplerotic ratio and increases in netflux into Lac C3, Gln C4, and Ala C3 as well as increasedmetabolite pools of lactate, Glu, GABA, Asp, Gln, and Ala.

The a5 subunit-specific ligand L-655,708 separated intothe top left hand quadrant of the Hotelling ellipse at bothconcentrations, consistent with it having a specific and, inthis case, unique site of action. Zolpidem at 40 nmol/Lwould be expected to target a1-containing receptorsspecifically, but be less selective (including a2 and a3)at 400 nmol/L. This was reflected in the separation of thetwo concentrations of zolpidem into separate halves of theplot, with the majority of the separation along PC1.Midazolam, a non-specific benzodiazepine was used at twoquite different concentrations (0.1 and 20 lmol/L) andthese were separated from one another, but not to the sameextent as, for example, the two concentrations of zolpidem.This was also seen with the other non-specific benzodiaz-

epine diazepam which was, however, separated by thePCA to the opposite quadrant to midazolam. The other twonon-specific effectors RO19-4603 and flumazenil wereseparated from one another along PC2, while the individ-ual concentrations of each of these drugs separatedsignificantly along PC1.

Next, the data from experiments with exogenous GABAwas introduced into the model and the total metabolite poolof GABA was removed from the analysis. These data werethen subjected to PCA (Fig. 7b). The data were described bytwo PC (40% and 29% of the variance). PC1 was loaded bymetabolic changes typical of increased metabolic work, withincreased net flux into Glu C2 and C4, Asp C2 and C3,GABA C2 and Gln C4, increased Asp anaplerotic ratio anddecreased net flux into Ala C3 and Lac C3, as well asincreased total pool sizes of Glu, Gln and Asp and decreasedlactate and Ala. PC2 was dominated by increased net fluxinto Lactate C3, Gln C4 and Ala C3, as well as increasedtotal pool sizes of lactate, Glu, Asp, Gln, and Ala.

2

0

0

PLS

-DA

2-2

-1-2-4 6-3-6 2 3-5 1 4 5

PLS-DA 1

GABA 0.1 µM

GABA 1.0 µM

GABA 4.0 µM

GABA 10 µM

GABA 40 µMTHIP 100 µM

Muscimol 0.1 µM

Gabazine 10 µMMuscimol 0.01 µM

Gabazine 100 µM

Glu

C2

GA

BA

C2

Lac

tate

C3

Gln

C4

Asp

C2

Asp

C3

Ala

C3

To

tal l

acta

te

To

tal G

lu

Glu

C4

To

tal A

sp

To

tal G

ln

To

tal A

la

0.4

0.2

0.0

-0.2

$M2.

DA

1

$M2.

DA

2

Glu

C2

GA

BA

C2

Lac

tate

C3

Gln

C4

Asp

C2

Asp

C3

(Asp

C3-

Asp

C2)

/Asp

C2

Ala

C3

To

tal l

acta

te

To

tal G

lu

Glu

C4

To

tal A

sp

To

tal G

ln

To

tal A

la

0.5

0.0

-0.5

$M2.

DA

1

$M2.

DA

2

(Asp

C3-

Asp

C2)

/Asp

C2

Fig. 5 Partial least squares discriminant analysis (PLS-DA) of label-

ling and total metabolite concentrations for GABAA ligands and

exogenous GABA. The figure shows clear separation of the lower

three concentrations of GABA from the GABAA ligands. Note that the

total metabolite pool of GABA has been removed from this analysis.

The outer ellipse represents the 95% confidence interval (Hotellings

score). The shaded ellipses define subset groupings to make the fig-

ure more readable and are not representative of any statistical test.



In this PCA, the five concentrations of GABA wereseparated from each other along PC1. The non-specificnegative allosteric modulator RO19-4603 at 0.1 nmol/Lclustered near the two lowest concentrations of GABA (0.1and 1.0 lmol/L), while 40 nmol/L zolpidem clustered near10 lmol/L GABA. Midazolam, flumazenil, and diazepamwere separated from all concentrations of GABA by PC2, aswas 400 nmol/L zolpidem.

Thirdly, the data from experiments conducted with thethree GABAA agonists, muscimol, isoguvacine and THIPwere introduced into the model along with data from theallosteric modulators and subjected to PCA. The dataproduced four PCs of which the first two are shown inFig. 7(c) (47% and 27% of the variance in the data,respectively). Again, PC1 was descriptive of metabolicwork, with increased net flux into Glu C2 and C4, Gln C4,

Fig. 6 Relative effect of allosteric modulators at GABAA receptors on

net flux of 13C 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) are indicated by *p < 0.05 (different to control) or #p < 0.05

(different to the other concentration of ligand used).



Asp C2 and C3 and GABA C2, as well as increasedmetabolic pools of Glu, GABA, Asp, and Gln. PC2 wasdominated by increased net flux into lactate C3 and Ala C3,with decreased net flux into Glu C2 and C4, Gln C4 and AspC2 and C3, while all metabolite pools were increased in thisPC, with Asp, Glu, and GABA having the largest loadings.

The two lowest concentrations of muscimol (10 and100 nmol/L) were clearly separated from all other drugs by anegative score in PC1. The d-subunit preferring agonist THIP(10 lmol/L) clustered with the a5-specific ligand 0.1 nmol/L

L-655,708, while flumazenil, diazepam and RO19-4603 and400 nmol/L zolpidem were strongly separated from allGABAA agonists, with these modulators loading positivelyon PC1. Midazolam (20 lmol/L) clustered near 50 lmol/Lmuscimol, while the lower dose of midazolam (0.1 lmol/L)clustered proximate to 100 lmol/L isoguvacine.

OverviewFinally, we have considered all the above analyses and havefurther grouped the data based on similarities in metabolicprofiles (Table 1). This enabled us to generate a final modelwhere the data were classified as belonging to one of threegroups:

• low GABA (data from ligands in rows 1 and 2 ofTable 1), which likely represents the metabolic effect at highaffinity, extrasynaptic GABA receptors,

• synaptic levels of GABA (10 lmol/L) such as would beexpected to activate mainstream GABAergic synapses (datafrom ligands in row 4 of Table 1), and

• high levels of GABA (40 lmol/L) as might be expectedto be found in the case of synaptic spillover, activating bothsynaptic and perisynaptic receptors (data from ligands listedin row 5 of Table 1).

Data from these three groups were loaded into SIMCA P+where PLS-DAwas used to determine whether the data couldbe separated on the basis of their group membership. Thisgenerated a three component model accounting for 44%,18%, and 13% of the variance (R2) in the data, respectively(Q2 = 72%). The first two components are shown in Fig. 8,which illustrates succinctly how this metabolic approach canbe used to identify metabolic patterns common to thelocation of receptors.

Discussion

Exogenous GABAThe metabolic sequelae of addition of GABA to corticalslices (Figs 1 and 2) clearly divided into two distinct

(a)

(b)

(c)

Fig. 7 Principal components analysis of GABAA modulators. The fig-

ure shows three separate analyses. (a) Shows PCA of the GABAA

modulators by themselves. They are easily separated from one an-

other by two principal components, which accounted for 40% and 35%

of the variance in the data, respectively. (b) PCA of GABAA modula-

tors and exogenous GABA. The metabolite pool of GABA was not

included as a variable in this analysis. The different modulators and

each concentration of GABA were separated from one another by two

principal components which accounted for 40% and 29% of the vari-

ance in the data, respectively. (c) PCA of GABAA modulators and the

three GABAA agonists. Each drug was separated from all others by

four principal components of which only the two major are shown,

accounting for 47% and 27% of the total variance, respectively. The

large outer ellipse represents 95% confidence interval (Hotelling

score).

Table 1 Ligands showing similar metabolic profiles to exogenous

GABA

GABA (lmol/L) GABAA Effector

0.1 – 1 nmol/L RO19-4603

400 nmol/L zolpidem

1.0 – 1 nmol/L RO19-4603

0.1 lM diazepam

4.0 – –

10 10 lmol/L gabazine 40 nmol/L zolpidem

0.1 lmol/L midazolam

40 100 lmol/L THIP (near) 0.1 nmol/L L655,708

THIP, 4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridine-3-ol hydrochloride.



responses, characterized by either increased or decreased netflux into the Krebs cycle byproducts Glu C2 and C4 and AspC2 and C3 as well as Gln C4. This increased relative fluxinto Gln C4 compared with control conditions was ametabolic feature which was not seen following addition ofagonists or antagonists at GABAA receptors, where the netflux into Gln C4 is always the same or decreased comparedto control (Fig. 3) but it was a feature of some metabolicprofiles after addition of allosteric modulators at GABAA

(Fig. 6) which act to enhance the action of GABA. That lowconcentrations of exogenous GABA are producing an effectdistinct from that induced by classic GABAA agonists is alsoreinforced by lack of clustering of these concentrations ofGABA with metabolic fingerprints produced by GABAA

receptor ligands (e.g. Fig. 5), clustering at the far right of theHotelling ellipse, and at the opposite end to muscimol, theclassical GABAA agonist.

Taken together, the results suggest that this effect arosebecause of action of low concentrations of GABA atparticular receptors. It is known that different GABAreceptors respond differently to GABA depending on theirsubunit composition, with published affinities for GABAvarying from low micromolar (0.01 lmol/L) to millimolar(Saxena and Macdonald 1996; Farrant and Kaila 2007). Theconcentrations of GABA used in this experiment (0.1 and1.0 lmol/L) are in the same range as the levels of GABAreported to exist ambiently in the extracellular space in slices(Dekoninck and Mody 1994).

It is interesting that addition of low concentrations ofGABA (0.1 and 1.0 lmol/L) has resulted in increased netflux of label from [3-13C]pyruvate into the Krebs cycleintermediates Glu C2 and C4 and Asp C2 and C3 (Fig. 1). Inthe cortical tissue slice under resting conditions much of theactivity (�50%) reflects spontaneous NMDA receptor cur-rents (Rae et al. 2006) and, by comparison to an intact brain,even a deeply anaesthetised one, the metabolic rate is slow(Griffin 1997). Under these circumstances the circuits thatthe high affinity GABA receptors are modulating are likely tobe relatively quiescent. Increased activity at these receptorswould result in hyperpolarisation, itself an energy requiringprocess because of Na+/K+ pumping. Because these circuitsare not actively driving other circuits, the net sum outcomemay simply reflect this hyperpolarisation-induced activityrather than a decrease in total activity as might be seen werecircuits being actively driven (Tagamets and Horwitz 2001;Nasrallah et al. 2007).

GABAA agonistsMuscimol and isoguvacine are broad spectrum GABAA

agonists which do not show specificity for any particularsubtype. Muscimol is also a partial agonist (e.g. Kd = 1.4lmol/L at q2-containing receptors) at GABAC receptors(Chang et al. 2000) and a weak GABA uptake inhibitor(Johnston 1971). At the low concentrations employedmuscimol acts as a broad and potent agonist at GABAA

[IC50 6 nM (Johnston et al. 1968)] and the metabolicoutcomes reflect this with the two lower concentrations ofmuscimol showing decreases in relative metabolic flux intoall isotopomers measured, as well as decreased pool sizes(Fig. 3). In practice in vivo, this sort of metabolic response isunlikely; GABA itself is unlikely to simultaneously activatethe full spectrum of GABAA receptors because of thedifferent affinities it has at different receptor subtypes.Isoguvacine, a full agonist at GABAA (IC50 = 5.6 lM), isless potent than muscimol. At low (10 lmol/L) concentra-tions it had a neutral loading on PC1 (Fig. 4), with milddecreases in net flux into Krebs cycle intermediates relativeto control (Fig. 3). It clusters proximate to 10 lmol/L GABA(Fig. 5) suggesting its net effect is to act at a similar subclassof receptors as this concentration of GABA. Isoguvacine hasbeen reported to inhibit GABA uptake (White and Snodgrass1983) which may be another explanation for a GABA-likeeffect of this ligand.

THIP displays some subtype specificity unlike muscimoland isoguvacine. THIP is a partial agonist at a1 or a3containing receptors and is a full agonist at a5 receptors(Ebert et al. 1994). This latter subtype affinity is illustrated inFig. 7(c) where 10 lmol/L THIP clustered with 0.1 nmol/LL-655,708, a modulator which is highly specific for a5-containing receptors (Quirk et al. 1996). THIP is also anefficient activator for d subunit-containing receptors, beingmore potent (a superagonist) at these receptors than c2-

10 µmol/L GABA

PLS-DA [1]

PL

S-D

A [

2]

–2

–2

–4–6 2

2

4 6

High (40 µmol/L) GABA

Low (0.1 - 1.0µmol/L) GABA

0

Fig. 8 Partial least squares discriminant analysis (PLS-DA) of

labeling and total metabolite concentrations classified according to

Table 1. The figure shows clear separation of three metabolic clus-

ters into those giving a metabolic effect similar to low (0.1–1.0 lmol/

L) GABA (extrasynaptic or high affinity receptors), 10 lmol/L GABA

(synaptic receptors) and high GABA (40 lmol/L) representative of

peri-synaptic receptors. The ligands are separated by a three com-

ponent model accounting for 44%, 18%, and 13% of the variance

(R2) in the data, respectively (Q2 = 72%). Blue diamonds are data

from ligands in rows 1 and 2 of Table 1, Black squares from row 4

and red diamonds from row 5. Note that the total metabolite pool of

GABA has been removed from this analysis. The outer ellipse rep-

resents the 95% confidence interval (Hotellings score). The shaded

ellipses define subset groupings to make the figure more readable

and are not representative of any statistical test.



containing receptors (Adkins et al. 2001). GABAA receptorscontaining the d subunit have been shown to be locatedextrasynaptically and THIP, acting on mouse neocortex,exerts an overall depression of inhibitory activity and apronounced increase in excitatory activity (EC50 = 44 lM;Drasbek and Jensen 2006). This is illustrated in Fig. 4 whereTHIP loaded more positively on PC1 than the other GABAA

agonists. Reference to Fig. 5 where 100 lmol/L THIPclustered with 40 lmol/L exogenous GABA, suggests thatTHIP is activating the same subclass of receptors at thisconcentration.

In summary, the effects of agonists are highly dependenton the concentrations used and also on ambient GABAconcentrations. At different concentrations, different receptorpopulations may contribute to the net outcome and differentneuronal networks may be affected.

GABAA antagonistsBicuculline is classically the defining GABAA antagonistwith a reported Kb of 1.1 lmol/L (Woodward et al. 1993).However, bicuculline may not always reliably discriminatebetween GABA and glycine receptors (Li and Slaughter2007) and inhibits acetylcholinesterase at concentrations[IC50 43 lmol/L (Svenneby and Roberts 1973; Olsen andTobin 1990)] similar to those used to antagonize GABAA

receptors. Picrotoxin, a broad spectrum GABAA ligand(Sigel et al. 1990), is also an antagonist at glycine receptors(Schmieden et al. 1989), GABAC receptors (Zhang et al.1995), 5HT3A receptors (Dibas et al. 2002) and has blockingactivity at glutamate gated chloride channels (Etter et al.1999). Gabazine inhibits monoamine oxidase A (Luque et al.1994) and glycine receptors (IC50 66.2 lmol/L; (Dieudonne1995) as well as acting as an antagonist at GABAC receptors[Kb = 35 lmol/L, (Woodward et al. 1993)].

Somewhat surprisingly, bicuculline does not induce aparticularly large metabolic load; net flux into Krebs cycleintermediates Glu C2 and C4 and Asp C2 and C3 (Fig. 3)was increased only mildly by low concentrations ofbicuculline (0.5 lmol/L) and either reduced or not signifi-cantly altered by higher (50 lmol/L) concentrations ofbicuculline (Fig. 3). Bicuculline showed the highest loadingson PC1 (Fig. 4); both picrotoxin and gabazine showedneutral or slightly negative loadings (Fig. 4). This relativelymild metabolic response seems a feature of antagonistactivity at both GABAA and GABAB receptors (Nasrallahet al. 2007).

The second surprising observation about the metabolicresponse to antagonists at GABAA is the clustering ofresponses of antagonists and agonists (Fig. 4). We mighthave reasonably expected a scores plot showing agonistsclustering on one side of the PCA model and antagonists onthe other. On first inspection, this seems to be the case, withthe agonist muscimol and the antagonist bicuculline onopposite sides of the model (Fig. 4). However, the agonist

THIP (100 lmol/L) is near bicuculline and the agonistisoguvacine (100 lmol/L) also clusters very closely to thelower dose of bicuculline, suggesting that these agonistsimpose a metabolic workload on cortical tissue slicesequivalent to bicuculline. This is similar to the situationfound with GABAB agonists and antagonists and can beexplained by each ligand producing a similar net sumoutcome from additive excitatory and inhibitory effects(Nasrallah et al. 2007).

Allosteric modulatorsThe allosteric modulators which alter the efficacy of GABA(Skerritt and Johnston 1983) are specific for GABAA

receptors, with some showing selectivity for particularsubunit combinations. Their modulatory activity is complex.Diazepam, for example, modulates two distinct activities in aconcentration-dependent manner (Walters et al. 2000), asdoes midazolam (Wang et al. 2003), with the nanomolar butnot the millimolar effects of each abolished by flumazenil(Hunkeler et al. 1981). The range of possible effects isillustrated well in Fig. 7(a), where all ligands modulatedistinct metabolic outcomes. Flumazenil, a potent, but weakpositive allosteric modulator (Chan and Farb 1985; Downinget al. 2005) has the most distinct effects; indeed at 2 nmol/Lflumazenil induces the largest metabolic workload (increasednet flux into the Krebs cycle byproducts) of any drug used inthis study (Fig. 6).

To first discuss those modulators whose actions are morespecific, the effect produced by the positive modulatorzolpidem (40 nmol/L) is most similar to that of the classicGABAA agonist muscimol and 10 lmol/L GABA. At thisconcentration zolpidem is specific for a1-containing recep-tors which are the major subtype (�60%) of GABAA

receptors in the brain (Mohler 2006) and which thereforedominate the metabolic response in this instance. Zolpidem ispotentiating the effect of GABA and this is reflected in theclose similarity of the metabolic profiles (Table 1, Fig. 7b).At higher (400 nmol/L) concentrations zolpidem produces ametabolic profile similar to that of 0.1 lM GABA, suggest-ing it potentiates the effect of ambient GABA at extra-synaptic GABAA receptors.

The 1,4-diazepinone RO19-4603 at 1 nmol/L also pro-duces a metabolic profile which clusters near that of 0.1 and1.0 lmol/L GABA groups (Fig. 7b, Table 1). RO19-4603shows affinity for so-called diazepine-insensitive receptors[a4- and a6-containing receptors (Mohler 2006)], showing10-fold lower sensitivity for diazepine-insensitive (Ki = 2.6nmol/L) over diazepine-sensitive receptors (Ki = 0.2 nmol/L) (Wong and Skolnick 1992). At 1 nmol/L much of themetabolic outcome would be due to activity at diazepine-sensitive receptors, whereas at 10 nmol/L RO19-4603 wouldbe strongly active at both types of receptor. RO19-4603 is anegative allosteric modulator with inverse agonist activity atdiazepine-insensitive receptors (Wong and Skolnick 1992).



This is reflected in the location of the cluster of 10 nmol/LRO19-4603 data with high positive scores on both PC1and PC2 (Fig. 7a) unlike any other benzodiazepine in thisstudy.

The a5-selective ligand L-655,708 also produces a uniquemetabolic profile, clustering in the top left hand quadrant ofthe PCA model (Fig. 7). It produces a profile similar to thatof 40 lmol/L GABA and 10 lmol/L THIP (Table 1, Fig. 7).The a5 subunit comprises < 5% of all GABA receptors(Mohler 2006) which may explain why the relatively weaknet metabolic response to this ligand (Fig. 6). L-655,708 is aweak negative allosteric modulator with cognition enhancingproperties (Atack et al. 2006).

The benzodiazepines midazolam and diazepam are clini-cally similar although midazolam acts faster and with shorterduration (Nugent et al. 1982; Buhrer et al. 1990). Midazo-lam is �6· more potent than diazepam but the two ligandsshow similarities in their binding profiles (Graham et al.1996). Metabolically, they produce significantly differentoutcomes (Fig. 6), separating along both PC1 and PC2(Fig. 7a). Figure 7(c) suggests that the metabolic profilesproduced by midazolam are more similar to those of theGABAA agonists muscimol and isoguvacine than thoseproduced by diazepam.

Flumazenil (RO15-1788) is variously classified as a weak,but potent positive allosteric modulator or as a pureantagonist of the benzodiazepine site. At best, it only has avery weak effect on the action of GABA at the receptor. It istherefore interesting to see the large metabolic outcome ofaddition of 2 nmol/L flumazenil (Fig. 6). Flumazenil is usedcommonly in positron emission tomography studies as aGABAergic ligand where it is commonly held to be devoidof pharmacological activity, or in clinical settings where it isused to reverse over-sedation. There is evidence that the drugpossesses effects of its own at quite low concentrations,including inverse agonist activity (File and Pellow 1986; Fileet al. 1986).

SummaryTable 1 shows the major clusterings of metabolic data fromthis study. Three distinct metabolic clusters emerged, form-ing around low (0.1 and 1.0 lmol/L) GABA, 10 lmol/LGABA, and 40 lmol/L GABA as seen in Fig. 8, illustratingthat different affinity GABAA receptors can be separatedfrom one another on the basis of the metabolic profilesgenerated by their modulation. Given that it is difficult toclearly distinguish GABAA receptors from one another basedon their cellular location using pharmacological or in situhybridisation means, this metabolic approach may prove tobe a useful way in which objectively to identify differenttypes of inhibitory activity, such as phasic, tonic and thatwhich occurs as a result of synaptic GABA spillover. Finally,it is also apparent that a ‘one size fits all’ approach to studyof GABAergic activity is not appropriate; it is possible for

agonists and antagonists at GABAA both to give increased ordecreased metabolic outcomes.

Acknowledgements

This work was supported by UNSW (Goldstar), the Australian

NHMRC and NewSouth Global. The authors are grateful to

Dr James Hook and Ms Adele Shasta of the UNSW Analytical

Centre for expert technical support.

Supporting information

Additional Supporting Information may be found in the online

version of this article:

Fig. S1 Low frequency section of [13C-decoupled]1H NMR

spectrum of an aqueous extract of Guinea pig brain cortical tissue

slices following 1 h of incubation with 2 mmol/L [3-13C]pyruvate

and 10 lmol/L muscimol.

Fig. S2 Low frequency section of [1H-decoupled]13C NMR

spectrum of an aqueous extract of Guinea pig brain cortical tissue

slices following 1 h of incubation with 2 mmol/L [3-13C]pyruvate

and 10 lmol/L muscimol.

Please note: Wiley-Blackwell are not responsible for the content

or functionality of any supporting materials supplied by the authors.

Any queries (other than missing material) should be directed to the

corresponding author for the article.

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