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Synaptic GABAA receptors (GABAARs) mediate most of the inhibitory neurotransmission in the brain. The majority of these receptors are comprised of α1, β2, and γ2 subunits. The amygdala, a structure involved in processing emotional stimuli, expresses α2 and γ1 subunits at high levels. The effect of these subunits on GABAAR-mediated synaptic transmission is not known. Understanding the influence of these subunits on GABAAR-mediated synaptic currents may help in identifying the roles and locations of amygdala synapses that contain these subunits. Here, we describe the biophysical and synaptic properties of pure populations of α1β2γ2, α2β2γ2, α1β2γ1 and α2β2γ1 GABAARs. Their synaptic properties were examined in engineered synapses, whereas their kinetic properties were studied using rapid agonist application, and single channel recordings. All macropatch currents activated rapidly (<1 ms) and deactivated as a function of the α-subunit, with α2-containing GABAARs consistently deactivating ~10-fold more slowly. Single channel analysis revealed that the slower current decay of α2-containing GABAARs was due to longer burst durations at low GABA concentrations, corresponding to a ~4-fold higher affinity for GABA. Synaptic currents revealed a different pattern of activation and deactivation to that of macropatch data. The inclusion of α2 and γ1 subunits slowed both the activation and deactivation rates, suggesting that receptors containing these subunits cluster more diffusely at synapses. Switching the intracellular domains of the γ2 and γ1 subunits substantiated this inference. Because this region determines post-synaptic localization, we hypothesize that GABAARs containing γ1 and γ2 use different mechanisms for synaptic clustering.
GABAA receptor (GABAAR)3 channels mediate the majority of inhibitory neurotransmissions in the mammalian brain. These receptors are pentamers assembled from a large family of subunits, of which 19 members have so far been identified (1). Receptors targeted to the synaptic compartment are composed of two α, two β, and a single γ subunit, with the most highly expressed and best studied being the α1β2γ2 GABAARs. However, GABAARs that contain other subunits are also expressed in the brain (2).
The kinetics of inhibitory post-synaptic currents (IPSCs) at GABAergic synapses are determined by the biophysical properties of postsynaptic receptors (3, 4), and how they are clustered at the postsynaptic membrane (5, 6). The α subunit is a key determinant of the functional properties of GABAARs (7, 8), and as such has a prominent role in setting the kinetics of IPSCs (3, 4, 9). The factors that regulate the synaptic clustering of GABAARs are still being unraveled, but recent studies have shown that it involves complex, subunit-specific interactions with scaffolding proteins such as gephyrin (10,–13), collybistin (14), and dystrophin (15).
The amygdala is a temporal lobe structure that plays a key role in processing fear, and amygdala dysfunction is associated with anxiety-related disorders such as generalized anxiety, depression, and post-traumatic stress. These disorders are commonly managed using benzodiazepines, which produce their therapeutic actions by enhancing the action of GABA at GABAARs containing γ2 subunits (16, 17). However, as benzodiazepines act indiscriminately on GABAARs expressed throughout the brain, their therapeutic activity is compromised by side effects such as sedation and tolerance.
Whereas the α1 and γ2 subunits are expressed throughout the central nervous system, the α2 and γ1 subunits have a restricted distribution, being prominent in brain structures such as the amygdala, forebrain, cerebellum, and hypothalamus (α2), and amygdala, pallidum, and substantia nigra (γ1) (2, 18, 19). The properties of receptors containing α1 and γ2 subunits, and their impact on synaptic currents have been extensively studied (4, 9, 20). In contrast, apart from limited information about their pharmacological profile (18, 19, 21), almost nothing is known about the impact of γ1-containing GABAARs on inhibitory synaptic transmission.
Here we describe the kinetic and synaptic properties of GABAARs containing α2 and γ1 subunits and compare them to those containing α1 and γ2 subunits. By providing new insights into the functional properties of α2- and γ1-containing GABAARs, our study facilitates investigations into whether these GABAARs contribute to synaptic currents in brain regions that mediate anxiety-related disorders such as fear, depression, and post-traumatic stress.
Human α1 (pCIS2), α2 (pCIS2 or pcDNA3.1), β2 (pcDNA3.1+ or pcDNA3.1Zeo), γ1 (pcDNA3.1+), and γ2L (pcNDA3.1+) subunits were transfected in a subunit plasmid ratio of 1α:1β:3γ (total DNA was 0.2–2.0 μg), into HEK293 cells using Ca2+ phosphate-DNA coprecipitation. This transfection ratio ensured the incorporation of the γ subunit into the receptors. GABAARs comprised only of α and β subunits were produced by transfecting these subunits at a plasmid ratio of 1:1. Cotransfecting the neuroligin splice variant neuroligin 2A (with HA tag), which was obtained from Addgene (USA) (22), facilitated the formation of heterosynapses. Enhanced GFP and CD4 were also transfected and acted as expression markers. Interchanging the intracellular domain (ID) and fourth transmembrane domain (TM4) domain of one γ subunit isoform with the other produced two γ subunit chimeras, which were transfected with α2 and β2 subunits. The two γ subunit chimeras were: 1) the γ2L-γ1, which expresses the γ2L subunit sequence from the N terminus up to the end of TM3 (up to Leu-317) and the ID and TM4 of the γ1 subunit sequence (from His-320), and 2) the γ1-γ2L, which contains the γ1 sequence from the N terminus to the end of TM3 (up to Leu-319) and the ID and TM4 of the γ2L sequence (from His-318). In a separate set of transfections we co-transfected the α2-containing GABAARs along with rat gephyrin (with and without an N terminus GFP tag), and the human collybistin homologue, hPEM.
Primary neuronal cultures were prepared using standard protocols (23). The cortices of E18 rat embryos were triturated and plated at ~80,000 cells per 18-mm poly-d-lysine-coated coverslip in DMEM with 10% fetal bovine serum. After 24 h the entire medium was replaced with Neurobasal medium including 2% B27 and 1% GlutaMAX supplements; a second feed after 1 week replaced half of this medium. Neurons were grown for 3 to 5 weeks in vitro and the heterosynapse co-cultures were prepared by directly introducing transfected HEK293 cells onto the primary neuronal cultures. Recordings of synaptic currents were done 1–3 days later.
Coverslips with cells were fixed for 5–10 min in 4% paraformaldehyde in phosphate-buffered saline, then blocked and permeabilized in 3% bovine serum albumin with saponin (0.05%) for 30 min. HA-tagged neuroligin 2A was labeled with rabbit anti-HA (Santa Cruz, 1/100) and GABAergic terminals were labeled for the GABA synthesizing enzyme GAD65 (mouse anti-GAD65, Chemicon/Millipore, 1/10,000). Primary antibodies were added to blocking solution overnight at room temperature, the cells were washed and secondary antibodies were applied at 1/500 for 30 min. Coverslips were mounted using DAKO fluorescent mounting medium and imaged on upright fluorescent and confocal microscopes.
All experiments were performed at room temperature in either the whole cell or outside-out patch configuration of the patch clamp technique, at a holding potential of −70 mV. The intracellular solution was composed of (in mm): 145 CsCl, 2 CaCl2, 2 MgCl2, 10 HEPES, and 10 EGTA, adjusted to pH 7.4 with CsOH. Cells and patches were continuously perfused with extracellular solution made up of (in mm): 140 NaCl, 5 KCl, 2 CaCl2, 1 MgCl2, 10 HEPES, and 10 d-glucose, adjusted to pH 7.4 with NaOH. The liquid junction potential between the intra- and extracellular solutions was calculated to be 4.0 mV (24). A double-barreled glass tube was mounted onto a piezo-electric translator (Siskiyou) to achieve rapid solution exchange (<1 ms) over outside-out patches by lateral movement of the glass tube. Synaptic currents were filtered (−3 dB, 4-pole Bessel) at 4 kHz and sampled at 10 kHz, whereas the macropatch recordings were filtered at 10 kHz and sampled at 30 kHz. Synaptic and macropatch data were recorded using a Multiclamp 700B amplifier and pClamp 9 software. Single channel currents were recorded using an Axopatch 200B amplifier, pClamp 10 software, filtered at 10 kHz and sampled at 50 kHz. Current traces were filtered off-line at 5 kHz for making figures.
Stock solutions of flunitrazepam and diazepam were kept frozen and diluted to the desired concentration in extracellular solution on the day of recording. Typically, at least 3 min of spontaneous activity was recorded before and during drug application. To preserve network activity for spontaneous recordings, the drug solution was targeted to the recorded cell, whereas the extracellular solution was washed over the surrounding area. Drug washout was obtained in about half of the cells recorded, and was averaged with the baseline data to minimize time-dependent effects.
Data are presented as mean ± S.E. Exponential equations were fit to the rising phase (10–90%) and current decay (weighted double- or mono-exponentials) of macropatch and synaptic currents as previously described (7) using Axograph X. Each current from a recorded cell or patch was analyzed separately and then averaged for that record. These averages were then pooled into data sets, from which means were calculated. Currents containing double events or artifacts in current rise and decay were manually excluded. Current-voltage (I-V) experiments were done by measuring single channel current amplitude at the corresponding voltage, for voltages of (in mV): ±70, ±35, ±15, and 0. The current reversal potential was read directly from the I-V plots.
Single channel kinetic analysis was done using QuB software. Current records were idealized at a cut-off resolution of 70 μs. The idealized records were then divided into discrete, single channel active periods by applying a tcrit shut duration. Tcrit values were determined for each patch and selected so as to retain the three briefest shut components (common to all records) in the dwell distributions as previously outlined (7, 25). Clusters (3 mm GABA) and bursts (2 μm GABA) of activity were accepted for deriving an activation mechanism if they contained >10 or 3 events, respectively (for estimating the mean burst duration at 2 μm GABA, bursts that contained ≥2 events were also included). This resulted in open dwell distributions that were also composed of three components, when fitted using the “star” function in QuB. Three shut and three open components were taken to represent the minimum number of corresponding states for constructing activation schemes. Mechanisms were then postulated and used to generate fits to the dwell distributions by maximum likelihood fitting (26, 27). The procedure optimized the rate constants and produced a goodness of fit value (log likelihood) that was used to evaluate the schemes. Data obtained at 3 mm GABA were first analyzed for determining the best consensus scheme for all four GABAARs. The rate constants thus obtained were averaged across records for each GABAAR. To estimate the rate constants for the binding (k+1) and unbinding (k−1) of GABA, the averaged rate constants for activation at 3 mm GABA were fixed. Binding steps were then appended to the first shut state in the scheme(s) (A2R1), and the scheme was re-fitted to data sets that included low (2 μm) data, allowing k+1 and k−1 to vary freely in the fitting. Combining several records at 2 μm GABA was required to increase the number of total events for that concentration. These were then combined with data obtained at 3 mm GABA to produce a data set for simultaneous fitting to the mechanism. The binding affinity (Kd = k−1/k+1) was then calculated for each data set and averaged for each GABAAR. Macropatch simulations were generated by the finalized mechanism (with all rate constants). The “dose-response” function in QuB was used to simulate macropatch currents, after setting the number of channels to 1000 and the Kd values of α1- and α2-containing GABAARs to 25 and 100 μm, respectively. Exponential fitting to the rise and decay phases of the simulated currents was done in QuB or pClamp 10 (Clampfit).
On the basis of conductance and kinetic properties, GABAARs comprising of α, β, and γ subunits are clearly distinguishable on the single channel level from those composed of α and β subunits. αβγ receptors activate with a predominant unitary conductance of ~26 pS (at −70 mV) and exhibit complex bursting behavior with relatively long burst durations. In contrast, αβ receptors under similar recording conditions have a conductance of ~15 pS and exhibit simple, relatively short periods of activity (28, 29). We wished to investigate the presence of GABAARs comprised only of α and β subunits in our standard αβγ receptor transfections to determine whether our transfections produced pure populations of αβγ receptors. To facilitate the identification of αβ receptors we transfected α1 with β2 or α2 with β2 at an α:β plasmid ratio of 1:1, and recorded the resulting single channel activity. αβ receptors comprised of α1 and β2 subunits opened to 1.0 pA (γ = 12.7 pS, n = 7 pooled), whereas α2β2 receptors opened to a mean amplitude of 1.1 pA (γ = 14.0 pS, n = 8 pooled). No activations were observed that exceeded these levels (Fig. 1A). We then looked for these αβ receptor activations in patches excised from cells transfected with an α:β:γ plasmid ratio of 1:1:3. To obtain an estimate of the incidence of αβ (~1 pA) versus αβγ (~2 pA) receptor activity we conducted a count of discrete (well separated) single channel activations mediated by both receptor types. Activations (burst or clusters) that were due to a single receptor were determined as outlined under “Experimental Procedures.” Counting the relative numbers of well separated periods of activity minimized the false positive detection of αβ receptor activity, as it is well known that αβγ channels can transition to sublevels within activations (7). The appearance of αβ channel activations in all four αβγ receptor transfections was minimal. Transfections that included α2, β2, and γ2L subunits exhibited α2β2 receptor activations that constituted 10 ± 2% (n = 3) of the total activity, whereas those that included α2, β2, and γ1 subunits produced α2β2 receptor activations that were only 12 ± 3% (n = 5) of the total activity (Fig. 1B). In patches expressing α1, β2, and γ2L subunits the incidence of α1β2 receptor-mediated activity was 11 ± 2% (n = 4) of the total measured. Similarly, when expressing α1, β2, and γ1 subunits, 6 ± 1% (n = 3) of the activations were of the α1β2 phenotype (Fig. 1C). Hence, our standard transfection ratio produced mainly signature αβγ channel activations, ranging from 88 to 94% of the total number. This result is consistent with a study that deduced that αβγ receptors are the almost exclusively preferred assembly, even with a transfection ratio of 1:1:1 (28).
To understand the impact of γ (γ1 and γ2L) and α (α1 and α2) subunits on the intrinsic properties of GABAARs we recorded ensemble currents from outside-out patches excised from HEK293 cells expressing α1β2γ2L, α1β2γ1, α2β2γ1, or α2β2γ2L GABAARs in response to brief (<1 ms, Fig. 2, A and B) saturating GABA (3 mm). Receptors containing α1 subunits activated relatively rapidly as compared with those containing α2 subunits. α1β2γ2L and α1β2γ1 GABAARs activated with 10–90% rise times of 0.49 ± 0.05 ms (n = 10) and 0.30 ± 0.04 ms (n = 6, Fig. 2, C and D), respectively, whereas, α2β2γ2L and α2β2γ1 GABAARs activated with rise times of 0.53 ± 0.10 ms (n = 7) and 0.58 ± 0.07 ms (n = 9, Fig. 2, C and D), respectively. A two-way ANOVA revealed a correlation between rise time and the α subunit (p = 0.02), but not the γ subunit isoform. The deactivation phase of the currents was also substantially slower for GABAARs containing the α2 subunit (Fig. 2, C and E). The weighted deactivation time constants for α1β2γ2L and α1β2γ1 GABAARs were 5.9 ± 0.5 (n = 10) and 9.1 ± 0.9 ms (n = 6), respectively. The presence of the α2 subunit dramatically slowed current decay with the mean decay time constant of α2β2γ2L GABAARs, being 44.9 ± 3.9 ms (n = 7), and that of α2β2γ1 GABAAR-mediated currents being 33.4 ± 4.2 ms (n = 9). Again, a two-way ANOVA revealed a highly significant correlation between the α subunit and current decay (p < 0.0001), but not the γ subunit isoform. These results confirm previous results showing that α1β2γ2 GABAARs (30, 31) display significantly faster activation and deactivation kinetics, as compared with those containing α2 subunits (8, 32). Thus, whereas the α subunit isoform has a profound affect on ensemble current kinetics, mainly by slowing current deactivation, replacing γ2L subunits with γ1 has no effect on the kinetics of expressed receptors.
We next asked how the α2 subunit enables the current to persist as the GABA concentration drops to zero. Single channel currents were recorded at saturating (3 mm) and low (2 μm) concentrations of GABA, which mimic the concentration profile at the onset and near the end of a synaptic event, respectively. The initial analysis focused on the durations of discrete activations (bursts and clusters of bursts) that define the activity of a single ion channel, the open state occupancy within activations (Po) and current voltage (I-V) relationships. All four GABAARs exhibited single channel currents that were ~2 pA in amplitude at −70 mV and had I-V with mild inward rectification (Fig. 3). Single channel conductance were calculated at −70 mV after correcting the driving force for reversal (4.5–5.0 mV) and liquid junction (4.0 mV) potentials. The calculations yielded conductance values of 26.6 (α1β2γ2L), 26.9 (α1β2γ1), 25.7 (α2β2γ2L), and 26.7 pS (α2β2γ1). All receptors showed at least 2 gating modes, which were equally prevalent among the receptors. This phenomenon has been observed in other GABAARs (25, 30), but as we were ultimately interested in determining the factors that slowed the deactivation phase of α2-containing receptors, the different modes of activity for each GABAAR were pooled for further analysis. Table 1 summarizes the durations of the activations and the Po values for the four channel types. At 3 mm GABA, the mean durations of clusters of activity ranged between 148 and 206 ms, with a small, but non-significant trend toward longer activations for GABAARs harboring the α2 subunit. The same rank order of, α1β2γ2L < α1β2γ1 < α2β2γ1 < α2β2γ2L was observed for mean burst durations elicited by 2 μm GABA, but the differences here were more dramatic. Burst durations for α1-containing GABAARs ranged between 23 and 27 ms (Fig. 3, A and B). This was ~3–4-fold briefer than those for α2β2γ2L receptors that activated for a mean duration of 99 ms (Fig. 3C), whereas bursts of activity mediated by α2β2γ1 receptors were of intermediate durations, being 56 ms (Fig. 3D, Table 1). The time spent in conducting configurations was similar for all four receptors, especially at 3 mm GABA, yielding Po values of ~0.6–0.7. At 2 μm GABA, the Po values mirrored the rank order of burst durations, but the absolute differences were smaller. It is notable, however, that the Po value for the α2β2γ1 and α2β2γ2L receptors at 2 μm GABA were indistinguishable from those of α1β2γ2L and α1β2γ1 receptors at 3 mm GABA, suggesting that α2-containing GABAARs dwell in conducting states for longer intervals. Overall, the most noteworthy difference between the receptor types was the mean duration of bursts elicited by 2 μm GABA. This likely underlies the longer deactivation times for receptors harboring the α2 subunit. In support of this inference, synaptic currents mediated by other ligand-gated ion channels have also been shown to deactivate as a function of the durations of single channel bursts of activity (33,–35).
We then proceeded to analyze the open and shut dwell time distributions for the purpose of deriving a consensus mechanism for channel activation. A mechanism that accounted for the salient properties of agonist affinity and gating kinetics would allow us to determine the underlying kinetic factors that give rise to the differential ensemble and single channel currents between the four GABAARs, within the same quantitative framework. This would facilitate a direct comparison between receptors. We commenced this analysis by plotting shut dwell histograms to activations elicited by 3 mm and 2 μm GABA. These histograms were then fitted to mixtures of exponentials to determine the minimum number of individual components that were apparent across patches and at both concentrations of GABA, and the tcrit values required to preserve them. Clusters and bursts of activity divided by this method yielded shut and open dwell histograms with three components each, as shown in Fig. 4A. This was consistent across all four receptor types suggesting that, in kinetic terms, they were all broadly similar.
We first considered clusters of activity at saturating (3 mm) GABA because this ensures binding site saturation, allowing us to omit the binding steps in the initial analysis. The number of components in the shut and open histograms was taken to represent the minimum number of functional states in the underlying activation mechanism. Mechanisms with three shut and open states were connected in various schemes and used to fit the dwell histograms to mixtures of exponentials by maximum likelihood fitting (26, 27). The fitting method uses the (apparent) open and shut dwell distributions to compute the likelihood that the data are represented by a postulated sequence of open and shut times. The free parameters to be fitted, for each postulated mechanism, are the rate constants governing the transitions between states, which are optimized to maximize the probability of observing the data. Mechanisms that best described the activity included schemes that were linear with some branching and schemes containing looped connections (Fig. 4, B and D). The schemes were then evaluated and ranked on the basis of a goodness of fit measure (log likelihood) and how accurately the schemes recapitulated the time constants and fractions of the initial “star” fit of the data. The three linear-branched schemes (Fig. 4, B and D, Schemes 1–3) that generated the best fits to the data and the single best, looped scheme (Scheme 4 as shown in Fig. 4). Similar linear-branched schemes have previously been reported for GABAAR activation (7, 25, 36). Scheme 3 has previously been reported as an activation mechanism for α1β2γ2S and α3β3γ2S GABAARs (7). This scheme also fit the activity for γ2L-containing GABAARs. However, we found that Scheme 1 produced higher log likelihood values for γ1-containing channels and was competitive with Scheme 3 for γ2L-containing channels. Summing the likelihood (ΣLL) values for each scheme over all four GABAARs revealed Scheme 1 as the best overall arrangement. Schemes that contained loops did not generally fit the data as well as linear-branched schemes, but Scheme 4 (Fig. 4D) adequately described most of the data, even though it was not as competitive as Schemes 1–3. On the basis of the ΣLL and most accurate reproduction of individual components, in terms of time constants and fractions of the dwell distribution, Scheme 1 was chosen as the consensus mechanism for further analysis of rate constants for GABA activation. Rate constants were computed for each patch, averaged for each receptor subtype (Table 2), and the equilibrium constant for each state transition was determined (Table 3). Equilibrium constants were broadly similar across receptor types. One consistent difference was the constant between the first and second shut states, A2R1 and A2R2 (Φ). GABAARs expressing the γ2 subunit had Φ constants that were >1, whereas those for γ1-containing receptors were <1. Φ was subunit specific, suggesting that the γ subunit is not only involved in the activation process, but its contribution to activation is γ isoform dependent. The mean lifetime of A2R2* was also prolonged by the presence of the α2 subunit, consistent with the higher Po values for these channels. However, none of the equilibrium constants differed to an extent that would adequately account for the longer burst durations for α2-containing receptors at 2 μm GABA.
Bursts of activity at 2 μm GABA were used to estimate the rate constants for GABA binding. Sequential, identical binding steps were appended to A2R1 (red arrows in Fig. 4) and fitted to dwell time histograms derived from data obtained at high and low GABA, which constituted a single data set. The rate constants for the transitions downstream of the binding steps were fixed to the mean values obtained at 3 mm GABA for each receptor subtype (Table 2), allowing only the GABA association and dissociation rate constants to vary during the fitting. More consistent binding rate constants were obtained when data from multiple patches exposed to 2 μm GABA were combined. Three or more data sets were used for each GABAAR, and mean values for GABA binding affinity (Kd) were obtained (Table 3). This analysis revealed clear differences in affinity that closely correlated with the α subunit isoform, but not the γ isoform, and is consistent with the lack of involvement of γ subunits in GABA binding. For α1-containing receptors the GABA association rate constants (k+1) varied between 2.2 × 106 and 3.6 × 106 m−1 s−1 and the dissociation rate constant (k−1) varied between 350 and 450 s−1, yielding a mean Kd of ~100 μm for both receptors. In contrast, α2-containing receptors had a 3–4-fold greater affinity for GABA. The k+1 values estimated for these two GABAARs ranged between 4.0 and 4.5 × 106 m−1 s−1, whereas the k−1 values varied between 75 and 130 s−1, producing mean Kd values of ~25–30 μm for GABA.
As an independent (and non-equilibrium) test for Scheme 1 as a suitable consensus mechanism for activation of multiple types of GABAARs, we used this scheme with the respective mean rate constants for gating for the four channels, and Kd values of 100 and 25 μm for α1- and α2-containing GABAARs, respectively, to generate simulated macropatch ensemble currents (Fig. 4C). The simulated ensemble currents all activated rapidly (~1 ms), being only marginally slower than the measured macropatch currents (Fig. 2). For α1-containing GABAARs the simulated ensemble currents were similar, but not identical. The deactivation phase of these currents, fitted to two exponential equations, produced single weighted time constants of ~10 ms, which was also close to the measured values of ~6–9 ms. Similarly, Scheme 1 produced simulated ensemble currents that activated with 10–90% rise times of ~1 ms for both α2-containing receptors and deactivation time constants of ~40 ms for α2β2γ1 GABAARs (measured ~33 ms) and ~50 ms for α2β2γ2L GABAARs (measured ~45 ms). These estimates corresponded closely with the measurements from experimental currents (Fig. 2), again validating Scheme 1 as an accurate general descriptor of both single channel activations and macropatch currents for the four synaptic GABAARs considered here.
We have shown that α1β2γ2L, α1β2γ1, α2β2γ1, and α2β2γ2L GABAARs can be described by a single kinetic mechanism with the key difference being that receptors containing α2 subunits have a significantly higher affinity for GABA, resulting in slower current deactivation times. In contrast, the γ subunit has little or no impact on the kinetics of ensemble currents. We therefore predicted that the kinetics of synaptic currents mediated by these receptors would be dominated by the α subunit. This prediction was tested in engineered heterosynapses formed between HEK293 cells and cultured cortical neurons, enabling us to examine the properties of synaptic currents mediated by populations of GABAARs of defined subunit composition. Importantly, synaptic currents at these engineered synapses should not be affected by errors due to voltage clamp or electronic distortions commonly present when recording synaptic currents from neurons. Mature cortical neurons readily formed GABAergic synaptic contacts on HEK293 cells transfected with the desired GABAAR. The synapses were observable as GAD65-positive contacts on the surface of the HEK293 cells (Fig. 5A). Higher resolution confocal images of cells where neuroligin 2A had been labeled to represent the postsynaptic density showed a close correspondence between neuroligin 2A and GAD-65 positive synaptic contacts confirming assembly of GABAergic synapses on HEK293 cells (Fig. 5B).
Whole cell recordings from transfected HEK293 cells in co-culture with cortical neurons exhibited spontaneous synaptic currents of variable amplitude that ranged between ~20 and 200 pA for all four receptor types (Fig. 5C). IPSCs mediated by the well characterized α1β2γ2L GABAARs activated rapidly, with mean 10–90% rise times of 1.2 ± 0.2 ms and decayed with a mean time constant of 4.0 ± 0.8 ms (n = 3 cells). These values are similar to rise time and offset time constants for the same receptors expressed in macropatches (Fig. 2). Moreover, they are similar to previously reported recordings of synaptic currents at synapses expressing α1β2γ2 GABAARs (4, 9), including studies on neuronal types that are not susceptible the distorting effects of cable filtering (20). Together, these results show that synapses that form in co-cultures faithfully recapitulate functional synapses.
As compared with those mediated by α1β2γ2L receptors, synaptic currents mediated by the other three GABAARs showed markedly different activation and deactivation profiles (Fig. 5, D and E). The rise times for these synaptic currents were all slower than their respective activation rates in macropatches. α1β2γ1 and α2β2γ2L receptor synaptic currents had mean 10–90% rise times of 4.0 ± 0.7 (n = 4) and 4.0 ± 0.5 ms (n = 7), respectively. The rise time of the α2β2γ1 receptor-mediated currents was exceptionally slow, being 8.2 ± 1.1 ms (n = 5). A two-way ANOVA revealed that both α and γ subunit isoforms had a significant effect on current activation (p < 0.001). Similarly, as compared with macropatches, the deactivation of IPSCs mediated by α1β2γ1 and α2β2γ1 GABAARs were substantially slower (Fig. 4E), with mean time constants of 19.8 ± 3.0 (n = 4) and 67.1 ± 7.6 ms (n = 7), respectively. The α2β2γ2L GABAAR generated IPSCs that deactivated with an intermediate time constant (38.7 ± 3.0 ms, n = 7). Here too, a two-way ANOVA test indicated that both α and γ subunit isoforms had a significant effect on current deactivation (p < 0.001). Synaptic currents mediated by α2-containing receptors had the slowest decay time constants, but this could only partially be explained by the macropatch and single channel data. These data suggest that α2 and γ2L subunits play distinct roles in determining the kinetics of GABAAR-mediated IPSCs. Receptors incorporating the α2 subunit mediate currents with slower activation and deactivation kinetics, whereas the presence of the γ2L subunit tended to accelerate both activation and deactivation. The antagonistic effect between α2 and γ2L is best illustrated in α2β2γ2L GABAARs, whose currents activated more slowly than macropatch currents, but deactivated at about the same rate.
In contrast, the slowing of current decay for GABAARs incorporating the γ1 subunit cannot be attributed to this subunits' contribution to the intrinsic properties of the receptors, as both macropatch and simulated ensemble currents for γ1-containing GABAARs had rapid onsets and decays (Figs. 2 and and4).4). Clearly, then, factors other than the intrinsic kinetic properties of the receptors are responsible for the slower kinetics of the synaptic currents mediated by receptors expressing γ1 subunits. One revealing observation was the reciprocal deactivation pattern for macropatch versus synaptic currents between α2β2γ1 and α2β2γ2L GABAARs. The deactivation rate for α2β2γ1 receptors was marginally faster than α2β2γ2L receptors in macropatch currents but synaptic currents mediated by α2β2γ1 GABAARs were significantly slower than those mediated by α2β2γ2L GABAARs, suggesting the γ subunit has a prominent effect on synaptic current kinetics. One possible explanation is that as with the α subunit (37, 38), the γ subunit isoform may also affect receptor clustering at synapses. GABAARs that are only loosely clustered at synapses would exhibit slow deactivation kinetics due to slower changes in GABA concentration, whereas GABAARs that were more tightly concentrated post-synaptically would give rise to faster current kinetics. Synaptic currents with the slowest kinetics were those generated by α2β2γ1 GABAARs, likely because of a combination of the α2 subunit on mean burst duration and the “de-clustering” effect of both α2 and γ1 subunits.
The analysis of αβ receptors in our transfections suggests that, due to their small conductance (~13–14 pS) and infrequent activation (~10% of total), their presence would not make a substantial impact on ensemble currents (macropatch and synaptic) that included the γ subunit. Nevertheless, we also recorded currents in co-cultures transfected only with α1 and β2 or α2 and β2 subunits to examine if αβ receptors can assemble at synapses. Pure populations of αβ receptors exhibited synaptic currents with rise and decay kinetics that were broadly similar to those of αβγ receptors. α1β2 receptors produced 10–90% rise times of 3.0 ± 0.1 ms and decayed with a mean time constant of 11.0 ± 1.1 ms (n = 3). These values were intermediate between those mediated by α1β2γ2L and α1β2γ1 receptors, and an ANOVA test showed no significant difference (p > 0.05) between α1β2 receptors and either of their γ-containing counterparts. α2β2 receptors produced mean rise and decay times of 10.5 ± 1.9 and 72.0 ± 15.4 ms, respectively (n = 4). As revealed by an ANOVA test, α2β2-mediated synaptic currents were only significantly slower in rise and decay times (p < 0.05 for both) to the corresponding measurements of α2β2γ2L-mediated currents. This result is consistent with the γ2L subunit having a clustering effect on receptors, whereas the incorporation of the α2 subunit tended to de-cluster the receptors to produce slower activation rates. The slow decay times in α2β2-mediated currents are also consistent with α2-containing receptors having a higher affinity for GABA. These data demonstrate that αβ receptors can assemble at synaptic sites, as has been demonstrated for α2β3 and α6β3 receptors (38). However, as in our transfections, αβ receptors only constitute about 10% of the overall activity (Fig. 1), their impact on the kinetics of synaptic currents will be minimal.
Because γ1-containing receptors have been reported to be less sensitive to benzodiazepine drugs (18, 19, 21), we compared the actions of flunitrazepam and diazepam on IPSCs from cells expressing either α2β2γ1 or α2β2γ2L receptors. As shown in Fig. 6A, application of diazepam (1 μm) did not affect α2β2γ1-mediated IPSC decay times (114 ± 7% of control, n = 4) but significantly slowed the decay times of currents from α2β2γ2L-expressing cells (171 ± 20% control decay; p = 0.02, n = 4). Diazepam had no effect on the amplitude of IPSCs (α2β2γ1: 107 ± 13%; α2β2γ2L: 141 ± 15% of control). Flunitrazepam (100 nm) also had no effect on the decay of synaptic currents in α2β2γ1-expressing cells (109 ± 7% of control, n = 5) but increased the mean decay time for α2β2γ2L-expressing cells to 212 ± 6% of control (p < 0.0001, unpaired t test, n = 4, Fig. 6). The peak current amplitude trended in a similar way, but was not significantly different between the two receptor types (α2β2γ1, 98 ± 16%; α2β2γ2L, 182 ± 38% of control).
In α subunits, the intracellular domain between TM3 and TM4 has been shown to play a role in clustering GABAARs to the synapse via interactions with gephyrin (37, 39). An association between gephyrin and the γ2 subunit was suggested to contribute to synaptic targeting of GABAARs (10). Although this has not been confirmed by other studies (14), it remains possible that gephyrin and γ2 interact in mammalian systems, as has been recently shown for the β subunit (13). Interactions with other proteins must mediate gephyrin-independent clustering (5), and the γ subunit could also contribute to these interactions. We tested whether gephyrin and collybistin affected the kinetics of synaptic currents by co-expressing both of these proteins along with either α2β2γ1 or α2β2γ2L receptors. The rise and decay times for the α2β2γ1 and α2β2γ2L receptors in combination with these proteins were, respectively, 7.6 ± 0.6 (n = 7) and 4.7 ± 0.4 ms (n = 4) and 52.9 ± 3.9 and 41.0 ± 2.2 ms. t tests showed that gephyrin and collybistin expression had no significant effect on synaptic current rise times (p > 0.1 for both receptors) or decay times (p > 0.1; for both receptors). These results demonstrate that gephyrin (and collybistin) have little effect on GABAAR-mediated synaptic currents, as has been suggested by some studies (38, 40). Alternatively, because HEK293 cells endogenously express gephyrin (38), recombinantly expressed gephyrin may have no additional effect on current kinetics.
The IDs of the γ1 and γ2L show considerable sequence divergence and their TM4 domains vary at sites that correspond to those shown to be essential for γ2-mediated receptor clustering in cultured neurons (12). Given these observations, we tested the possibility that the γ subunit isoform was also affecting synaptic clustering, by making chimeras of the γ1 and γ2L subunits that replace the ID and TM4 of one isoform with that of the other. This produced two γ-chimeric subunits, γ2L-γ1 and γ1-γ2L (Fig. 7A), which were then cotransfected with α2 and β2 subunits. These transfections also produced robust spontaneous synaptic activity, of comparable frequency and amplitude to the wild-type receptors. Synaptic currents mediated by the α2β2γ1-γ2L GABAARs activated with a mean 10–90% rise time of 4.4 ± 0.5 ms (n = 5) and deactivated with a mean weighted time constant of 38.2 ± 2.4 ms (Fig. 7B). This current profile was indistinguishable from that of the wild-type α2β2γ2L receptors (Fig. 7C). Similarly, the α2β2γ2L-γ1 receptors exhibited activation and deactivation rates of 7.4 ± 1.1 and 53.5 ± 7.2 ms (n = 5), respectively, and these too were similar to wild-type α2β2γ1 GABAARs (Fig. 7, B and C). A two-way ANOVA confirmed that the ID plus the TM4 region had a significant effect on activation and deactivation rates (p < 0.001 for both), whereas the extracellular domain and TM1–3 did not (p > 0.1 for both). These observations show that the γ subunit isoform is a major contributor to the kinetic profile of synaptic currents and the ID and TM4 likely mediates this effect.
In this study we have shown that the presence of the α2 subunit slows the deactivation phase of the IPSC by increasing the receptors' affinity for GABA, whereas inclusion of the α2 and γ1 subunits slows both the activation and deactivation phases of the IPSC by conferring loose clustering properties to the receptors. The presence of the γ1 subunit results in IPSCs with markedly slower activation and deactivation phases, and the kinetics of chimeras of γ1 and γ2 subunits are in agreement with this proposal. Together, these data suggest that GABAARs containing γ1 and γ2 subunits use different mechanisms for synaptic clustering.
We first determined the kinetic properties of four subtypes of GABAARs that vary in their α (α1 or α2) and/or γ (γ1 or γ2L) subunit isoform, whereas keeping the β subunit constant. Brief (<1 ms) GABA application onto macropatches elicited currents that mimic those at synapses, but are unaffected by factors that are not related to the inherent properties of the receptors. The receptor kinetic properties were further investigated on a single channel level, and within the framework of a single activation mechanism, facilitating a correlation between subunit isoform, GABA affinity, and the efficacy with which GABA activated the receptors (41). Macropatch currents mediated by all four GABAARs activated with sub-millisecond rates, with α2-containing receptors activating marginally more slowly. The inclusion of the α2 subunit also slowed current deactivation by almost an order of magnitude.
An analysis of the discrete activations (clusters and bursts) showed that the durations of these activations was α subunit dependent. At a low GABA concentration, α1-containing receptors activated for mean durations of 23–27 ms, whereas the presence of α2 subunits lengthened the bursts to 60–100 ms. Single channel data were also used to derive an activation mechanism that accurately described the single channel and macropatch data of all four GABAARs. This scheme comprised two sequential, equivalent binding steps for GABA followed by three shut and three open functional states (Fig. 4B). Given similar models have previously been applied to other isoforms of GABAARs (7, 25), our activation scheme may be generally applicable to other synaptic GABAAR stoichiometries. This consensus mechanism suggests that the essential contribution made by the α2 subunit is to enhance the GABA binding affinity 3–4-fold, thereby increasing the durations of bursts. A similar result was observed for α3-containing GABAARs (7). The discrepancy in ligand affinity between α1- and α2-containg GABAARs is compatible with the significant primary sequence divergence at the GABA binding domains of these two subunits. A common feature of all schemes that were tested here, and indeed for mechanisms derived for other pentameric ligand-gated ion channels (42,–46) is the presence of at least one shut-to-shut state transition immediately following the binding reaction steps. The equilibrium constant describing the transition between these two shut states was denoted as Φ and it is intriguing that macropatch and single channel analysis failed to detect any kinetic parameter that could be attributed to the γ subunit isoform other than Φ. This constant was <1 only if the receptor expressed the γ1 isoform and may pertain to GABAAR modulation by benzodiazepines, which a recent study has shown to manifest as an enhancement of Φ in γ2-containing GABAARs (47). Our data are consistent with the notion that Φ is γ isoform dependent, the lower value of Φ for γ1-containing receptors might suggest a reduced capacity for enhancement by benzodiazepine modulators.
Transfecting HEK293 cells with GABAAR subunits together with neuroligin 2A, and co-culturing these on a bed of neurons induces the formation of functional synapses between neurons and HEK293 cells (48), demonstrating that all of the essential pre- and post-synaptic elements are present in the artificial system, including neurexin, which is endogenously expressed in neurons, and gephyrin, which is present in HEK293 cells (38).
At these synapses notable pharmacological differences were observed between γ1-containing and γ2L-containing GABAARs. Experiments using flunitrazepam and diazepam demonstrate that benzodiazepines are ineffective at enhancing synaptic currents mediated by γ1-containing GABAARs. This result is consistent with whole cell peak current measurements of γ1-containing GABAARs (18), and key differences in the amino acid sequence between γ2L and γ1 that have been shown to affect the potency with which benzodiazepine-site ligands modulate currents (49,–52).
In addition, our results show that the α2 and γ1 subunits have de-clustering effects when expressed at synapses. Using chimeric constructs we show that the ID (plus TM4) is responsible for this difference in the γ-subunit. The ID and TM4 of GABA receptor subunits is crucial for clustering of receptors at post-synaptic sites (12), and our results suggest that, at these engineered synapses, γ1- and γ2L-containing GABAARs have different synaptic kinetics because of differences in their clustering properties. Thus, at neuronal synapses in situ, it is possible that GABA receptors containing γ1 and γ2 subunits may also be differentially targeted (18, 53). Subunit-specific clustering mechanisms have already been noted for α subunits in neurons. For example, dystrophin is currently thought to be involved in anchoring dendritic clusters of α1 in specific cortical layers (15), and radaxin has been shown to selectively anchor α5 subunits (54). Differential clustering properties have also been demonstrated for α1 and α2 subunits, such as the lower affinity of the α2 subunit for gephyrin (37) and the recruitment of α2, but not α1 subunits to the axon initial segment (55).
Postsynaptic GABAARs are dynamic, diffusing in and out of the synaptic active zone, which is ~200–300 nm in diameter (56, 57), with a diffusion coefficient that ranges from 0.01 to 0.05 μm2 s−1 (56). Quantum dot and Immunogold-labeled GABAARs show sub-micrometer separations between GABAARs that appose the presynaptic density and those that are perisynaptic (57, 58), whereas extrasynaptic GABAARs, such as those containing the δ subunit, are generally located hundreds of nanometers to several micrometers further (38, 57). These observations are consistent with a concentration gradient of receptors that is an inverse function of receptor diffusion mobility. We interpret our data as being consistent with a differential, γ-isoform-dependent gradient of receptors, rather than mutually exclusive zones delineating synaptic receptors from those beyond the synaptic perimeter. The slower rise and decay times for γ1-containing GABAARs suggest that these receptors are more mobile and at a higher density outside the synapse than γ2L-containing receptors, whereas the converse would apply for γ2L-containing receptors. Within this context we refer to γ2L-containing GABAARs as being more tightly clustered at synapses where a higher proportion are perfused with high GABA prior to significant GABA diffusion.
Our findings evince key factors that determine the profile of synaptic currents mediated by GABAARs containing α1, α2, γ1, and γ2L subunits, and provide a solid basis for future studies to establish whether GABAARs containing α2 and γ1 subunits contribute to GABAergic synapses in key brain regions that mediate fear and anxiety (59).
We thank Dr. Robert Harvey for the kind gifts of plasmids encoding collybistin, gephyrin, GFP-gephyrin, and β2-pcDNA3.1Zeo.
*This work was supported in part by Australian Research Council Grant ID-DP120104373.
3The abbreviations used are: