The α1β2γ2 and α3β3γ2 are two isoforms of γ-aminobutyric acid type A (GABAA) receptor that are widely distributed in the brain. Both are found at synapses, for example in the thalamus, where they mediate distinctly different inhibitory postsynaptic current profiles, particularly with respect to decay time. The two isoforms were expressed in HEK293 cells, and single-channel activity was recorded from outside-out patches. The kinetic characteristics of both isoforms were investigated by analyzing single-channel currents over a wide range of GABA concentrations. α1β2γ2 channels exhibited briefer active periods than α3β3γ2 channels over the entire range of agonist concentrations and had lower intraburst open probabilities at subsaturating concentrations. Activation mechanisms were constructed by fitting postulated schemes to data recorded at saturating and subsaturating GABA concentrations simultaneously. Reaction mechanisms were ranked according to log-likelihood values and how accurately they simulated ensemble currents. The highest ranked mechanism for both channels consisted of two sequential binding steps, followed by three conducting and three nonconducting configurations. The equilibrium dissociation constant for GABA at α3β3γ2 channels was ∼2.6 µM compared with ∼19 µM for α1β2γ2 channels, suggesting that GABA binds to the α3β3γ2 channels with higher affinity. A notable feature of the mechanism was that two consecutive doubly liganded shut states preceded all three open configurations. The lifetime of the third shut state was briefer for the α3β3γ2 channels. The longer active periods, higher affinity, and preference for conducting states are consistent with the slower decay of inhibitory currents at synapses that contain α3β3γ2 channels. The reaction mechanism we describe here may also be appropriate for the analysis of other types of GABAA receptors and provides a framework for rational investigation of the kinetic effects of a variety of therapeutic agents that activate or modulate GABAA receptors and hence influence synaptic and extrasynaptic inhibition in the central nervous system.

γ-aminobutyric acid type A (GABAA) receptors are a group of closely related yet biophysically and pharmacologically diverse receptors that belong to a larger family of ligand-activated ion channel receptors. All GABAA receptors are comprised of pentameric combinations of homologous subunits, and all subunits are members of a large gene family. In eukaryotes, members of this family are characterized by a functionally important loop in the extracellular domain of each subunit (“cys-loop”) formed by a cysteine–cysteine bond. GABAA receptors are found in nature as distinct assemblies from a total of 16 known subunits (19 if you include the ρ subunits; Olsen and Sieghart, 2008). The functional role of GABAA receptors depends on their location, which itself depends to a large extent on subunit composition. Synaptic GABAA receptors mediate phasic inhibition and play a role in timing and synchronization, and these require a γ subunit to be trafficked to the synapse. In contrast, the δ subunit has been found only in extrasynaptic receptors. These GABAA receptors are tonically activated by ambient GABA in the perisynaptic space and are presumed to contribute to the control of basal excitability level (Mody and Pearce, 2004; Farrant and Nusser, 2005).

The most abundant and widely distributed synaptic GABAA receptor contains α1, β2, and γ2 subunits at a ratio of 2:2:1 (2α122). Another synaptic GABAA receptor, which is less abundant, is comprised of α3, β3, and γ2 subunits. These two GABAA receptor subtypes are often located in the same neuroanatomical regions, such as the cortex and thalamus, but are confined to different nuclei, tissue layers, or cell types (Sieghart and Sperk, 2002). For instance, in the thalamus, the GABAA receptor found at the inhibitory synapses on thalamocortical neurons in the ventrobasal nucleus (VB) is the α1β2γ2 isoform, whereas the GABAA receptor found at the reciprocal inhibitory synapses between neurons in the reticular nucleus (RTN) is of the α3β3γ2 subtype. These two receptors have been carefully studied in expression systems and in brain slice preparations, where it has been reported that inhibitory postsynaptic currents (IPSCs) recorded from RTN decay much more slowly than IPSCs recorded from VB (Mozrzymas et al., 2007; Schofield and Huguenard, 2007; Jia et al., 2009). This observation strongly suggests that subunit composition is a possible determinant of the time course of synaptic currents, which in turn implies that subunit composition confers distinct kinetic properties on individual channels.

A considerable body of literature is now available on the relationship between structure and function of ligand-gated ion channels at the molecular level. The most thoroughly examined and understood of these channels are those belonging to the cys-loop family, and of these, the muscle nicotinic acetylcholine (nACh) receptor has been investigated most assiduously. One especially productive approach to investigating ion channels is to record single-channel currents, postulate reaction schemes for channel activation, and fit those schemes to the idealized data by maximum likelihood methods (Colquhoun and Hawkes, 1995; Qin et al., 1996; Colquhoun et al., 2003). Using this methodology, single-channel studies have yielded fine-grained structural information concerning interacting domains (Lee et al., 2009) and detected concerted microdomain movements involved in the transduction of agonist binding to channel opening (Grosman et al., 2000; Auerbach, 2005; Purohit et al., 2007). Similar studies that are compatible with the notion of discrete movements along the reaction pathway have revealed previously undiscovered functional states in the context of refined gating schemes that describe channel activation in both nACh and glycine receptors (Burzomato et al., 2004; Lape et al., 2008; Mukhtasimova et al., 2009).

Even though GABAA receptors are the main mediators of neuronal inhibition in the brain, fewer attempts have been made to apply a rigorous analytical treatment, with the notable exception of a study on α1β1γ2S GABAA receptors (Lema and Auerbach, 2006). Several gating mechanisms have been proposed for GABAA receptor activation, and these describe the properties of ensemble (Jones and Westbrook, 1995) and single-channel (Steinbach and Akk, 2001) currents well, but a universally accepted mechanism that can be broadly applied to GABAA receptors is unavailable. Such a mechanism would be of use in describing and reconciling a broad spectrum of data on a variety of drugs that interact with GABAA receptors—from agonists, including “partial agonism” as recently elucidated for nACh and glycine receptors (Lape et al., 2008), to channel modulation by agents such as benzodiazepines and neurosteroids, and activation by anaesthetics (Krasowski and Harrison, 1999).

Here, we have developed and evaluated several reaction schemes that describe GABAA receptor activation, one of which we began to describe in a previous study (Keramidas and Harrison, 2008). The evaluation of these kinetic schemes was done initially on the basis of the log-likelihood (LL) values, which indicate the accuracy with which a postulated scheme fits single-channel records. The general applicability of the schemes was tested by fitting them to single-channel data obtained from two isoforms of GABAA receptor, the α1β2γ2S and α3β3γ2S channels. The best of these schemes were then used to simulate multichannel currents, and these simulations were compared with real macropatch currents in response to single and paired 1-ms pulses of agonist, providing an additional means of distinguishing schemes.

The mechanism that best satisfied our selection criteria consists of two sequential binding steps for GABA, leading to three shut and three open states and is similar in some ways to a mechanism proposed for α1β1γ2S GABAA receptors (Lema and Auerbach, 2006). The best two mechanisms evaluated both involve a fully liganded shut channel transitioning to a second fully liganded shut state before entering any conducting configurations. The existence of preconducting states has recently been described in other cys-loop receptors (Burzomato et al., 2004; Lape et al., 2008; Mukhtasimova et al., 2009) and may be a common feature of the activation mechanism for the entire receptor family.

Expression of GABAARs in HEK293 cells

HEK293 cells were seeded onto poly-d-lysine–coated glass coverslips and transfected with cDNA encoding the human α1, β2, and γ2S or α3, β3, and γ2S subunits at a plasmid ratio of 1:1:4 (α:β:γ) using the calcium phosphate coprecipitation method. The cells were cotransfected with the CD4 surface antigen, which acted as a transfection marker. Transfected cells were washed after 24 h of exposure to the cDNA precipitate and used for patch clamp recordings 48–72 h later.

Electrophysiology and solutions

GABA-gated currents were recorded at room temperature (21 ± 1°C) using the excised outside-out patch configuration of the patch clamp technique. All patches were voltage clamped at a potential of −70 mV. Single-channel and macropatch currents were recorded using an Axopatch 200B amplifier (Axon Instruments), filtered with a four-pole Bessel filter that is built into the amplifier at a −3-dB value of 5 or 10 kHz, and acquired at a sampling frequency of 20 kHz (macropatch recordings) or 50 kHz (single-channel recordings). The standard extracellular solution contained (in mM): 140 NaCl, 5 KCl, 2 CaCl2, 1 MgCl2, 10 d-glucose, and 10 HEPES, and titrated to a pH of 7.4 with NaOH. The standard intracellular solution contained (in mM): 80 NaCl, 60 CsCl, 3 KCl, 2 K2ATP, 4 MgCl2, 1 CaCl2, 5 EGTA, 10 d-glucose, and 10 HEPES, with pH adjusted to 7.4 with NaOH. Patch pipettes were made of standard wall borosilicate glass and had resistances of 6–15 MΩ when filled with intracellular solution. Pipettes used for single-channel recordings were also coated with a silicon polymer (Sylgard 184; Corning) and fire-polished to improve signal resolution. Single-channel (equilibrium) recordings were made in the continuous presence of agonist, which was dissolved at the desired concentration in the extracellular solution. Macropatch (nonequilibrium) recordings were done by positioning the patches near the solution interface of a double lumen glass tube mounted on a piezo-electric translator. Currents were elicited by exposing the patches to 5 mM GABA for either 1 ms or a pair of 1-ms pulses separated by 24 ms.

Data analysis

Plots of agonist-dependent variables (cluster or burst length and intraburst open probability, PO) were fitted to a modified Hill equation of the form:

Y=Ymax[A]n[A]n+EC50n+y0,
(1)

where [A] is the agonist concentration, EC50 is the concentration of agonist that produces half of the maximal response, n is the Hill coefficient (slope at EC50), Y is the response to [A], Ymax is the maximal response, and yO is the lower limit of the response as [A] approaches 0.

Single-channel current amplitude was determined by generating amplitude histograms for selected segments of record and fitting the histograms to Gaussian curves. Single-channel conductance was calculated using the following expression:

γ=i(VmVrev),
(2)

where γ is the single-channel conductance, i is the single-channel current amplitude, Vm is the transmembrane potential, and Vrev is the potential at which current reverses direction (reversal potential). The values of Vrev were obtained from i-V measurements, covering a range of voltages between −70 and +70 mV. Liquid junction potential offsets were accounted for (using ion activities) in the conductance calculation. In addition, an estimate of current rectification (rectification index [RI]) over the experimental voltage range was determined by taking the ratio of current amplitude at −60 and +60 mV.

The activation rate (10–90% rise time) for macropatch currents was determined by fitting the following equation to middle 80% of the onset phase of currents.

I(t)=Imax(1etτ),
(3)

where I(t) is the current at time t, Imax is the maximum current, and τ is the time constant. The deactivation phase of macropatch currents was fitted with two standard exponential equations.

Single-channel kinetic analysis and modeling were done using QuB software (http://www.qub.buffalo.edu). A saturating concentration of 5 mM of agonist was initially used to isolate the gating steps from agonist binding. At this concentration, agonist binding preequilibrates, and the resulting activity arises from fully liganded channels. Periods of continuous activity were selected initially by eye and extracted to separate files within QuB for further analysis. These eye-selected segments were discrete and separated from each other by quiescent periods lasting at least 100 ms. Isolated brief openings that sometimes occurred between clusters and multiple openings within active periods were excluded during cluster selection and not included in any analysis (Fig. 3). The segmented k-means algorithm was used for idealizing the data, and the first and last shutting events of discrete active periods were dropped from the idealized data. The segments were then divided into clusters of activity by applying a critical shut time (tcrit) that marked the end of a cluster (Colquhoun and Hawkes, 1995). tcrit values were determined for each patch by generating shut-time dwell histograms of the idealized eye-selected segments of data. Some eye-selected segments produced several tcrit values, and the longest one (between the two longest components) was chosen and used to divide the eye-selected segments. This retained only three shut components in the distributions. For consistency, eye-selected segments that produced more shut components (as was the case with some selections in Fig. 3) with the initial fit were divided by a tcrit that retained the shortest three components. These preliminary fits were needed solely to determine tcrit values for the purpose of dividing eye-selected segments into shorter clusters of activity and were not used for further analysis. The tcrit values at 5 mM GABA ranged between 8–20 ms for the α1β2γ2S GABAA receptors and 9–25 ms for the α3β3γ2S GABAA receptors. The system dead time was 40 µs. The resulting clusters were analyzed for mean duration, open probability (intraburst PO), and open and shut durations. The number of individual exponential components in each dwell class (shut or open) resulting from the final fit was taken as the minimum number of states in any underlying reaction scheme.

Subsaturating concentrations of GABA (200, 20, and 2 µM) were used to obtain information for agonist binding steps and were analyzed in the same way as the data obtained at 5 mM GABA. 200 µM GABA also produced clusters of activity at both channel isoforms. The tcrit values were chosen by the same methods as described for the 5-mM activity. At this concentration, the tcrit values were 19–31 ms for the α1β2γ2S channels and 11–30 ms for the α3β3γ2S channels. At 20 and 2 µM GABA, channel activity occurred as shorter, scattered bursts, and it was generally more difficult to isolate segments of activity that appeared to arise from a single channel, especially for the α1β2γ2S isoform. In this case, we selected segments lasting several seconds that did not contain any overlapping openings and constructed shut histograms for these. A tcrit of 35 ms applied to this activity retained the three fastest components and a fourth that accounted for the binding steps for GABA. We also used a tcrit of 100 ms, retrospectively, to some 2 µM of data to determine if our original choice of 35 ms inadvertently excised some longer components associated with ligand binding.

Global fitting of data across concentrations was done by postulating a scheme that contained the number of fully liganded states (gating states) determined from the analysis of data at 5 mM GABA and adding two sequential binding steps to each of the doubly liganded shut states for evaluation. Schemes were fitted simultaneously to sets of data, with each set including data recorded at 5 mM, 200 µM, and 20 µM for α1β2γ2S channels and 5 mM, 200 µM, and 2 µM for α3β3γ2S channels. Data recorded at 2 µM for the α1β2γ2S channels were not included in the initial global fitting analysis because this concentration induced mostly very short, sparse activity with too few complex bursts. However, the two patches that did yield sufficient complex activity were analyzed separately, included in separate datasets, and analyzed as described above to verify the agonist dissociation constant obtained from the initial global fitting.

Generating dwell histograms and fitting idealized data to schemes were done by the method of maximum likelihood. The dwell histograms were fitted with mixtures of probability density functions (Colquhoun and Hawkes, 1995; Lema and Auerbach, 2006). Each set of data produced a LL value, and the sums of LL values (ΣLL) were compared across all postulated schemes.

Single-channel currents in the presence of high concentrations of GABA often exhibit modal activity based on intraburst PO (Lema and Auerbach, 2006; Keramidas and Harrison, 2008). In α1β2γ2S GABAA receptors, the two prevalent gating modes are characterized by POs of ∼0.7 and ∼0.9. Both of these modes were evident in α1β2γ2S and α3β3γ2S single-channel records but were not treated separately in the analysis (Fig. 3). PO values stated in the text represent the mean of high- and mid-PO activity.

The derived schemes along with the mean rate constants from the single-channel analysis were used to generate simulations of macropatch currents in response to a single or a pair of 1-ms applications of agonist. These simulations were also done in QuB with the number of channels contributing to the currents set to 1,000. The simulated single-pulse currents were then analyzed for activation rate (10–90% rise time) and deactivation by fitting single- or double-exponential equations, respectively, to the currents as described above for the real currents. For the paired-pulse simulations, the percentage change in the second peak relative to the first was measured and compared with that of the real paired-pulse currents for the α1β2γ2S channels that we recorded or the α3β3γ2S channels, which we obtained from the literature. Deactivation time constants for single-pulse responses of the α3β3γ2S channels were also obtained from the literature.

Four of the schemes that were tested against the data (among others) are the following:

(SCHEME 1)
(SCHEME 2)
(SCHEME 3)
(SCHEME 4)

R represents the receptor, A represents the agonist, and AR and A2R are the mono- and diliganded receptor in the closed state, respectively. The state number is denoted by a superscript on the right of the state, and an asterisk denotes an open, conducting channel. The symbols on either side of the double arrows are the microscopic rate constants governing the transitions to and from each state.

Scheme 1 is based on a gating scheme that was derived for α1β2γ2S GABAA receptors expressed in outside-out patches in response to 10 mM GABA (Keramidas and Harrison, 2008). Scheme 2 was the highest ranking scheme obtained from comparable analysis of α1β1γ2S GABAA receptors also expressed in HEK293 cells, but in the cell-attached configuration (Lema and Auerbach, 2006), and Scheme 3 is based on a mechanism that was derived from single-channel analysis of the muscle-type nACh receptor (Lape et al., 2008). Microscopic reversibility was applied to the loop part of Scheme 3. Scheme 4 was one of the top-ranking schemes in terms of LL values and the simulation of macropatch currents. In total, 20 distinct schemes were tested. Data are stated as mean ± SEM for (n) separate experiments (recorded patches or datasets).

Single-channel activity and amplitude

Single-channel currents were initially recorded from excised outside-out membrane patches expressing either α1β2γ2S or α3β3γ2S GABAA receptors in the presence of 5 mM GABA. Both α1β2γ2S and α3β3γ2S GABAA receptors exhibited visibly similar single-channel activity, examples of which are shown in Fig. 1. Active periods occurred as discrete clusters of openings and shuttings, separated by quiescent periods lasting from generally ∼15 ms to several minutes (see also Fig. 3). Two current amplitudes were evident in most patches. The predominant level was just under −2 pA at a holding potential of −70 mV (Fig. 1, A and C, bottom record), but the channels also opened to a smaller level of about −1.2 pA. For both channel types, the smaller amplitude occurred either as brief transitions from the larger amplitude openings, as shown in Fig. 1 A for α1β2γ2S GABAA receptors, or as separate active periods, as illustrated by the cluster of activity in Fig. 1 C (top record) for α3β3γ2S GABAA receptors.

To obtain estimates of single-channel amplitudes at −70 mV, amplitude histograms were plotted and fitted with Gaussian curves for both channel types. Examples of these plots are shown in Fig. 1 (B and D) for the accompanying recordings. For α1β2γ2S GABAA receptors, the mean amplitude of the most prevalent openings was −1.81 ± 0.05 pA (n = 7), whereas the smaller, rarer activity was −1.11 ± 0.04 pA (n = 6). For the α3β3γ2S GABAA receptors, the more common, larger openings had a mean amplitude of −1.66 ± 0.03 pA (n = 5) and the smaller currents were −1.02 ± 0.05 pA (n = 8). The measurements for the larger openings are similar to previously published values for α1β2γ2S GABAA receptors (Keramidas and Harrison, 2008) and α3β2γ2S GABAA receptors (Barberis et al., 2007). It should be noted that the smaller openings are not likely mediated by binary αβ channels, as these do not exhibit long clusters of activity (Wagner et al., 2004) nor are they likely to appear as transitions within clusters of the larger amplitude activity.

Current–voltage relationships

For the purpose of obtaining estimates of single-channel conductance and investigating current rectification, single-channel currents were recorded over a voltage range of −70 to +70 mV for each patch. The larger current amplitudes were plotted as a function of membrane potential for each channel type. The group plots, accompanied by an example of an experiment from a single patch, are shown for α1β2γ2S GABAA receptors in Fig. 2 A and for α3β3γ2S GABAA receptors in Fig. 2 B. From these plots, the reversal potential was read and the conductance for each channel was computed using Eq. 2, after correcting for liquid junction potentials. The reversal potential for α1β2γ2S GABAA receptors was 0.9 ± 0.2 mV (n = 7) and for α3β3γ2S GABAA receptors was 0.9 ± 0.3 mV (n = 5). The reversal potential of the smaller current amplitude was assumed to be the same as the corresponding larger amplitude for the same channel type. As can be seen in Fig. 2 and Table I, the main and lower conductance values of α1β2γ2S GABAA receptors were 24.5 ± 0.7 pS (n = 7) and 15.1 ± 0.5 pS (n = 6), respectively, and for α3β3γ2S GABAA receptors were 22.7 ± 0.5 pS (n = 5) and 14.3 ± 0.7 pS (n = 8), respectively. There was also little evidence of current rectification in either channel, with the RI for α1β2γ2S channels being 1.21 ± 0.02 (n = 7) and that for α3β3γ2S channels being 1.15 ± 0.03 (n = 5; Table I).

Discrete segments of activity of the low conductance type, as is shown for the α3β3γ2S channels in the top trace of Fig. 1 C and for α1β2γ2S channels in Fig. 3 A (e), were quite rare. Out of 18 patches expressing α1β2γ2S channels at 5 mM or 200 µM GABA, only four exhibited low conductance clusters that were >1-s long (a combined total of ∼1,000 events). The α3β3γ2S channels showed a similar reluctance to activate to the low conductance openings. From 33 patches at the higher GABA concentrations, only five low conductance clusters longer than 1 s were observed (a combined total of ∼1,500 events). The remainder of patches for both channel isoforms exhibited much shorter (∼10–50 ms), sporadic periods of low conductance activity that were discrete. Low conductance openings that occurred as brief transitions from the high to the low conductance, as illustrated in Fig. 1 A, were more frequently seen but also very short lived. Due to the paucity of low conductance data, we decided to analyze further only the high conductance currents. In our attempts to derive activation mechanisms, we were careful to reject low conductance periods that were either discrete or constituted more than ∼2–3% of the overall length the high conductance active periods by scanning the records at high resolution.

Burst length and intraburst open probability

The next set of experiments was aimed at characterizing the kinetic differences between α1β2γ2S and α3β3γ2S GABAA receptors. To this end, single-channel currents were recorded over a wide range of GABA concentrations, and the duration and intraburst open probabilities were measured and plotted as a function of GABA concentration. For α1β2γ2S GABAA receptors, the agonist concentrations used were 5 mM, 200 µM, 20 µM, and 2 µM, whereas 5 mM, 200 µM, and 2 µM GABA were used for α3β3γ2S GABAA receptors.

Discrete segments of data lasting from hundreds of milliseconds to several seconds were selected (Fig. 3) for subdivision into active periods (clusters or bursts). These were determined by applying a critical shut time (tcrit), which separated one active period from the next (Colquhoun and Hawkes, 1995). A tcrit was determined for each patch and generally ranged between 8 and 35 ms for both channel types at the concentrations tested, although the mean tcrit values at 5 mM GABA were a little lower, being ∼10 and ∼12 ms for the α1β2γ2S and α3β3γ2S channels, respectively. With decreasing GABA concentrations, there was a corresponding decrease in cluster length and intraburst PO.

Although cluster length decreased with decreasing GABA concentration, the clusters exhibited by α3β3γ2S GABAA receptors were consistently longer than those of α1β2γ2S GABAA receptors at the same concentration, as shown in Fig. 4 A. Exposing the α1β2γ2S channels to 5 mM GABA elicited clusters that were 114 ± 11–ms (n = 7) long, which is similar to previously published measurements (Keramidas and Harrison, 2008). In contrast, 5 mM GABA elicited substantially longer clusters at α3β3γ2S channels, being 196 ± 16 ms (n = 16). The mean cluster length in response to 200 µM GABA of α1β2γ2S GABAA receptors was 83.5 ± 13.3 ms (n = 11) and for α3β3γ2S GABAA receptors was 162 ± 22 ms (n = 17). 20 µM GABA elicited 45.5 ± 5.0 ms (n = 4) bursts at α1β2γ2S GABAA receptors, and 2 µM GABA elicited 17.8± 3.0 ms (n = 5) and 51.5 ± 2.6 ms (n = 9) bursts at α1β2γ2S and α3β3γ2S GABAA receptors, respectively (see also Figs. 5 and 6). Intraburst PO was also greater at α3β3γ2S GABAA receptors, but only for GABA concentrations below 5 mM (Fig. 4 B). For the α1β2γ2S channels, the PO values were: 0.81 ± 0.01 (n = 7; 5 mM GABA), 0.61 ± 0.04 (n = 11; 200 µM GABA), 0.33 ± 0.06 (n = 4; 20 µM GABA), and 0.15 ± 0.01 (n = 5; 2 µM GABA). For α3β3γ2S channels, the PO values were: 0.77 ± 0.02 (n = 16; 5 mM GABA), 0.69 ± 0.02 (n = 17; 200 µM GABA), and 0.45 ± 0.01 (n = 9; 2 µM GABA). Both channels exhibited modal gating with high-, mid-, and occasional low-PO activity present in individual patches. At 5 mM GABA (n = 3 patches) and 200 µM GABA (n = 3 patches), the mid-mode activity was the most frequent and accounted for 80–90% of the activity for both channels. Modal switching was also evident when inspecting long segments of data (Fig. 3, purple). We excluded low-PO clusters and conflated the high- and mid-PO in our analysis.

Analysis of open and shut durations within active periods

The analysis of clusters and bursts of activity was extended to include measurements of dwell-time distributions across GABA concentrations for both channels. Fig. 5 shows typical recordings obtained from patches expressing α1β2γ2S GABAA receptors. As mentioned above, as GABA concentration was lowered, active periods were abbreviated and the intraburst open probability also decreased. On the right of each record are the open and shut dwell histograms for clusters (5 mM and 200 µM) and bursts (≤20 µM), which are fitted with mixtures of probability density functions. The time constants and fractions for each component are summarized in Table II. Three exponential components were required to adequately describe each dwell class (shut or open) at 5 mM and 200 µM GABA. Although the open time constants and fractions did not vary much between these two concentrations, the latter two shut-time constants increased with 200 µM GABA. Applying 20 µM GABA resulted in a further increase in the latter two shut-time constants and the introduction of a fourth, without any substantial change in the open component parameters (Table II and Fig. 5).

The α3β3γ2S GABAA receptors were similarly analyzed. Fig. 6 shows examples of single-channel activity of α3β3γ2S channels at 5 mM, 200 µM, and 2 µM GABA along with corresponding dwell histograms. Channel activity was also best described with three shut and three open components, with the first two shut components remaining little changed across concentrations. At 2 µM GABA, the third shut component increased and a fourth was also required to fit the histograms adequately. The second and third open components decreased with decreasing concentrations in contrast to those describing the α1β2γ2S channels (Table III). The open and shut dwell-time constants for the α3β3γ2S channels were generally higher than the corresponding values for the α1β2γ2S channels.

Fitting single-channel data to postulated kinetic schemes

The dwell-time distribution analysis provided the basis for building kinetics schemes that describe the activation of α1β2γ2S and α3β3γ2S GABAA receptors by GABA. The number of components in the dwell-time distributions at saturating GABA concentrations (5 mM) was taken to represent the minimum number of diliganded states in the postulated kinetic schemes. Records obtained at subsaturating agonist concentrations, where the binding of agonist molecules becomes increasingly significant in the record with decreasing concentration, were exploited for obtaining information about agonist binding steps. Several schemes comprised of three shut and three open states were first tested for adequacy of fit to clusters of activity induced by 5 mM GABA. The states in each scheme were connected in various combinations and tested for goodness of fit to five to seven patches expressing either α1β2γ2S or α3β3γ2S channels. Schemes were evaluated by summing the individual LL values for each dataset (for each channel type) and comparing the sum of LLs (ΣLL) from each scheme. We chose to focus on the best three of these schemes for further analysis. To these, we added a fourth (Scheme 3) for evaluation. To extend and reevaluate the schemes tested at 5 mM GABA, binding steps were included and fitted to data recorded at less than saturating GABA concentrations in conjunction with data obtained at 5 mM GABA. Each of the two binding steps was assumed to occur equivalently and independently. The binding steps were arranged in series and connected to either one of the three shut states (A2R1, A2R2, or A2R3) for global fitting. Mechanisms were fitted to four datasets, each set containing records at three GABA concentrations, for both channels. One 5-mM and one 20-µM record were used twice in the α1β2γ2S channel analysis, and one 2-µM record was used twice for the α3β3γ2S channel analysis. As before, LL values were used to rank schemes. These values are shown in Table IV for the best three schemes (Schemes 1, 2, and 4) and Scheme 3, which produced either low LL values or failed to successfully fit the data. In cases where Scheme 3 did not fit the datasets, the fitting algorithm failed to converge. For a single dataset of α1β2γ2S channel activity, the fit was successful, and we tried to refit the other three datasets using the rate constants computed from that fit, but this also either failed (two cases) or produced an LL value that was <15,000, along with visibly poor fits of the corresponding dwell histograms (one case). For the α3β3γ2S channels, all attempts to fit Scheme 3 resulted in nonconvergence of the fitting algorithm.

Table IV also shows the mean Kd values for each scheme. For the α1β2γ2S receptors, Scheme 1 yielded higher LL values for three out of four datasets, and Scheme 4 was the leading scheme for the fourth dataset. Scheme 2 produced competitive LL values but always lagged behind Schemes 1 and 4. Overall, Scheme 1 produced the highest ΣLL, but it was only marginally higher than that for Scheme 4. There was a greater difference in the Kd values between schemes, with Scheme 1 producing the lowest Kd (highest affinity). The affinity values for the α3β3γ2S receptors were very similar for Schemes 1, 2, and 4 and substantially lower than those for the α1β2γ2S receptors. Again, Scheme 1 emerged with the highest ΣLL, closely followed by Scheme 4.

Our analysis on the basis of LL fitting suggests that Scheme 1 describes the data most accurately, with Scheme 4 being the nearest competitor. The global fitting yielded state transition rate constants for Scheme 1 for the α1β2γ2S channels. These were: k+1 = (17.0 ± 3.6) × 106 M−1s−1; k−1 = 330 ± 56 s−1; ϕ+1 = 521 ± 49 s−1; ϕ−1 = 1,362 ± 149 s−1; β1 = 1,684 ± 155 s−1; α1 = 340 ± 67 s−1; β2 = 1,660 ± 357 s−1; α2 = 1,986 ± 188 s−1; β3 = 223 ± 98 s−1; α3 = 205 ± 42 s−1; σ+1 = 1,216 ± 134 s−1; and σ−1 = 153 ± 36 s−1 (n = 4; Fig. 7 A). Similarly, for the α3β3γ2S channels, the rate constants for Scheme 1 were: k+1 = (51.0 ± 0.6) × 106 M−1s−1; k−1 = 134 ± 25 s−1; ϕ+1 = 396 ± 34 s−1; ϕ−1 = 1,275 ± 143 s−1; β1 = 3,003 ± 274 s−1; α1 = 895 ± 228 s−1; β2 = 742 ± 28 s−1; α2 = 192 ± 51 s−1; β3 = 534 ± 149 s−1; α3 = 645 ± 55 s−1; σ+1 = 1,940 ± 243 s−1; and σ−1 = 2,509 ± 118 s−1 (n = 4; Fig. 7 B).

To facilitate a comparison between key kinetic differences between α1β2γ2S and α3β3γ2S GABAA receptor activation, the equilibrium constants for the transition of each state was calculated and tabulated (Table V). Most equilibrium constants were either not distinguishable (Φ) or differed by only two- to fivefold (E1, E2, and E3). The equilibrium constants that varied most between the two channels were the dissociation equilibrium constant, Kd, and the transition constant, Σ. These were, respectively, over sevenfold and 10-fold higher for the α1β2γ2S channels than for the α1β2γ2S channels.

We did not include single-channel data from α1β2γ2S GABAA receptors in the presence of 2 µM GABA in our main datasets for global fitting. This was because most records at this concentration did not produce sufficient bursts of activity, and it was generally difficult to confidently select segments of data that were generated by a single channel. At this concentration, however, binding steps would be even more prominent in the record than at 20 µM GABA and potentially provide the most information regarding Kd. Nevertheless, we endeavored to check our estimate of Kd for the α1β2γ2S channels. Two relatively long-lasting patches at 2 µM GABA gave a sufficient number of short bursts of activity (total of ∼3,000 and ∼1,950 events), which were analyzed separately and in combination with data recorded at 20 and 200 µM GABA for global fitting. We first tested if our choice of tcrit affected the computed value of Kd. A tcrit of 35 ms applied to the 2-µM GABA data yielded the same Kds as a tcrit of 100 ms for both patches, validating our original choice of tcrit. Second, we tested if Kd was dead-time sensitive with global fitting analysis. A dead time of 40 µs produced Kds of 17.8 µM (dataset 1) and 29.2 µM (dataset 2). Increasing the dead time to 70 µs yielded Kds of 17.4 and 30.6 µM, respectively, suggesting that the Kd estimates were not dead-time sensitive to an appreciable extent.

Macropatch current simulations

It has been suggested that nonequilibrium data can help discriminate between statistically similar mechanistic schemes that were derived using equilibrium data (Kienker, 1989). As an additional test for our schemes, we either measured salient kinetic parameters from macropatch currents we recorded or obtained them from the literature (Table VI). Currents were evoked in patches expressing either α1β2γ2S or α3β3γ2S channels by rapid application of saturating concentrations of agonist. The 10–90% rise time and the deactivation time constants were determined for single-pulse responses, and the relative difference between peaks was determined for paired-pulse responses. Schemes 1, 2, and 4, along with mean rate constants, were used to simulate macropatch currents, also in response to a single or pairs of ultrafast agonist applications. These are shown in Fig. 8. As can be seen from comparisons of real and simulated current parameters, Schemes 1 and 4 recapitulated ensemble currents most accurately. Scheme 1 reproduced single-pulse deactivation kinetics and paired-pulse data best for the α1β2γ2S channels. The simulated rise time was somewhat high and was improved by increasing ϕ+1 to 1,250 s−1, but at the expense of the other parameters. Scheme 4 produced a slow activation rate and inadequate deactivation and paired-pulse values. For the α3β3γ2S channels, Schemes 1 and 4 were more difficult to distinguish. Both produced activation rates and paired-pulse peaks that were similar to the real currents. The literature produced a wide range of deactivation parameters, but our simulated currents were most similar to IPSCs and excised patch currents recorded by Schofield and Huguenard (2007).

This study was undertaken with two complementary aims. We first wished to characterize the α1β1γ2S and α3β3γ2S GABAA receptors by measuring single-channel properties, such as i-V relationships, conductance, cluster duration, and intraburst PO so as to better understand the differences in IPSCs mediated by the two channels. Second, we also wished to derive a plausible activation mechanism for the GABAA receptor that was more comprehensive than our previous attempt (Keramidas and Harrison, 2008) by including agonist-binding steps and by applying a full mechanism to two channel isoforms. This also enabled us to express the observed differences between the two channels within the framework of a mechanism and rate constants.

Single-channel conductance and i-V relationships

The first series of experiments was undertaken to yield information about some straightforward single-channel properties of α1β2γ2S and α3β3γ2S GABAA receptors. The two channels were similar in several respects. i-V relationships for both channels were comparable with little evidence for current rectification in either. Both channels exhibited a predominant high conductance (∼23–25 pS at −70 mV), which was only slightly higher in α1β2γ2S GABAA receptors, and a less frequent small conductance of ∼14–15 pS. Active periods could be characterized in three ways in relation to conductance. They were either exclusive of the high (>80% of the activity) or low conductance, or they contained transitions from one to the other. The most observable case of transitions was the large conductance transitioning to the smaller for brief sojourns of ∼10–50 ms. The mechanism of multiple conductance states in ion channels is somewhat of an enigma, but there is now good evidence that in cys-loop channels it is regulated by the intracellular domain (Kelley et al., 2003; Hales et al., 2006; Carland et al., 2009). It is conceivable that the channels could achieve multiple conductance states by dynamically regulating the orientation of residues lining the intracellular conductance portals or the shape of the portals themselves.

Divergent properties of active periods of α1β2γ2S and α3β3γ2S GABAA receptors

We made measurements of the durations and open probabilities of bursts or clusters (intraburst POs). This analysis exposed significant differences between the two channel isoforms. The cluster/burst lengths of α1β2γ2S GABAA receptors were consistently shorter at a given GABA concentration than the corresponding cluster/burst lengths for α3β3γ2S GABAA receptors. At 5 mM GABA, which is close to the concentration reached at the synaptic cleft upon mass vesicular release of GABA (Mody et al., 1994), the cluster length of the α1β2γ2S channels was ∼100 ms, approximately half that of α3β3γ2S channels. Lema and Auerbach (2006) report cluster durations for the α1β1γ2S isoform in the presence of 5 mM GABA to be ∼5–10 s, not the ∼100–200-ms cluster lengths estimated in this study for the α1β2γ2S and α3β3γ2S channels. The likely reason for the disparity is the consistently shorter tcrit values used in this study to isolate clusters. At 5 mM GABA, our clusters were defined by tcrit values that ranged between 8 and 20 ms, with a mean of ∼10 ms. In the Lema and Auerbach (2006) study, tcrit values were ≥15 ms and up to ∼60 ms.

At very low GABA concentrations (2 µM), which is the concentration reached at the synaptic cleft near the end of a synaptic event, the duration of bursts of α1β2γ2S channel activity was ∼18 ms compared with ∼52 ms for the α3β3γ2S channels. Indeed, the mean burst duration elicited by 20 µM GABA at α1β2γ2S channels (∼46 ms) was closer to, but still shorter than, that evoked by 2 µM GABA at α3β3γ2S channels, an order of magnitude difference in GABA. This property alone could to a significant degree account for the slower deactivation time course of IPSCs recorded in RTN (Schofield and Huguenard, 2007; Jia et al., 2009), as has been clearly shown for the deactivation phase of ensemble currents mediated by NMDA channels (Wyllie et al., 1998).

Another marked difference between the two channels was the divergent intraburst PO as a function of decreasing GABA concentration. At 5 mM GABA, the PO estimates were very similar for both channels, being 0.81 and 0.77. However, at 2 µM GABA, the difference in POs was substantial. For the α1β2γ2S channels, the PO was 0.16, whereas for the α3β3γ2S channels, it was 0.45. This relatively high PO of the later channels may also contribute to a slower rate of IPSC decay at RTN synapses, as GABA is eliminated within the synaptic cleft. But it should be acknowledged that other factors may also play a role because the rate of active GABA clearance at the synaptic cleft is itself dependent on uptake mechanisms, as passive clearance is dependent on synaptic architecture, both of which could differ between VB and RTN synapses.

Global fitting of mechanisms across GABA concentrations

The second aim was achieved by global fitting of single-channel data recorded over a wide range of GABA concentrations, as has been done previously for glycine and muscle nACh receptors (Burzomato et al., 2004; Lape et al., 2008) and another subtype of GABAA receptor (Lema and Auerbach, 2006). Saturating GABA (5 mM) provided data for the gating portion of the mechanism and an initial means of reducing the number of potential schemes. The agonist-binding steps were determined with the addition of data recorded at below-saturating concentrations (200 µM and 20 or 2 µM).

Generating dwell histograms for the high, intermediate, and low concentration data revealed a general pattern in the time constants of the individual exponential components. Three open components and three or four shut components were required to adequately fit the data from both channels, in agreement with other studies on GABAA receptors (Steinbach and Akk, 2001; Lema and Auerbach, 2006). Dwell-time distribution components (time constants and fractions) collectively determine the rate constants for transitions between channel states in postulated mechanisms, but in a complex and mechanism-dependent manner (Colquhoun and Hawkes, 1995; Shelley and Magleby, 2008). To understand better how the differences in burst properties between the two channels are achieved, we need to refer to the fitted schemes. Our mechanisms are consistent with the observed longer active periods in α3β3γ2S receptors. 20 mechanisms were evaluated, including the best scheme obtained from cell-attached patch recordings of α1β1γ2S GABAA receptors (Scheme 2; Lema and Auerbach, 2006) and a scheme proposed for the muscle nACh receptor (Lape et al., 2008) that we modified to included three conducting states (Scheme 3). Other schemes that included loops were not considered. Of the schemes we did test, the one that fit the data most consistently and with the highest ΣLL values was Scheme 1. Scheme 2 also fit the datasets adequately, whereas Scheme 3 generally failed to properly fit most of the datasets. Scheme 4 was statistically very similar to Scheme 1. This scheme features two sequential open states and an intermediate shut state preceding the third open state.

Simulations of macropatch currents

So as not to be exclusively reliant on LL fitting of single-channel data, we simulated macropatch currents using Schemes 1, 2, and 4 along with optimized rate constants as an independent means of ranking schemes. Simulations of single- and double-pulse currents were then compared with real currents for both channel isoforms. Consistent with the single-channel burst analysis, Scheme 1 generated currents that most closely resembled real currents in terms of activation, deactivation, and paired-pulse peak measurements for both channels. None of the schemes reproduced all the measured macropatch parameters for both channels. For instance, Scheme 1 reproduced the deactivation time constants and paired-pulse peak values well for the α1β2γ2S channels, but it gave an activation rate that was 1.6 times slower than the measured rate. However, we do not regard this shortcoming as a major drawback of the scheme. Nevertheless, in an attempt to reduce the activation rate, we manually varied some of the rate constants and found that increasing ϕ+1 had the desired effect, but this perturbed the other values. For the α3β3γ2S channels, Scheme 1 successfully reproduced the activation rate and the paired-pulse peak currents. The deactivation time constants were also in reasonable agreement with the real data, but the fractions were less so. It was also difficult to find consistent deactivation data in the literature for this channel.

The simulations provided good corroborative evidence that Scheme 1 is the overall best mechanism for channel activation. The result that single-channel analysis and macropatch data both favor Scheme 1, albeit only marginally and for two kinetically different GABAA receptors, suggest that this scheme is a sufficient general mechanism that could form the basis for further GABAA receptor kinetic studies.

Interpretations and caveats

In our efforts to develop an activation mechanism for the GABAA receptor, we simplified the analysis by omitting low conductance activity, even though it is clearly present in both channel isoforms, either on its own or as sojourns from the high conductance openings. Indeed, GABAA receptor single-channel sublevels have been known for considerable time (Hamill et al., 1983). As we removed all (or most) of the low conductance activity, our schemes describe only activation to high conductance activity, and in this regard our mechanism(s) can only be partial. We know of no modeling studies of cys-loop receptors that explicitly incorporate subconductance levels in the analysis. We hypothesize that the low conductance activity represents a distinct class of protein conformations, but we cannot here distinguish between intrinsic regulation of low conductance states, as might occur by dynamic changes in conformation of the intracellular conductance portals and extrinsic effects, such as channel block by ions in solution. This indeed applies to any state in our mechanism. The notion that channel activation occurs as a “conformational wave” in the muscle nACh receptor (Grosman et al., 2000; Purohit et al., 2007) is consistent with classes of functional (open or shut or conductance) states in cys-loop channels being intrinsic properties of the channels. However, it has been demonstrated convincingly that fast-channel block in the muscle-type nACh receptor by organic cations effectively diminished channel conductance (Akk and Steinbach, 2003; Lape et al., 2008, 2009). These studies also show that channel block does not necessarily have to occur from open states, and that shut channels can trap the blocking agent, producing discrete shut states. It is not clear from this study whether the states (open, shut, or conductance) arise due to intrinsic properties of the channel or are induced extrinsically. For instance, our investigations found that one of the primary kinetic differences between the two GABAA receptor isoforms was the transition between an open state (A2R3*) and a shut state (A2R3), and we infer that the A2R3 state is essentially similar in both channel isoforms but shorter lived in the α3β3γ2S channels. However, we cannot distinguish between A2R3 being intrinsically less stable or preferentially destabilized in the α3β3γ2S channels by solutes (e.g., Mg2+) or if A2R3* is simply blocked by ions in the α1β2γ2S channels, manifesting as A2R3. The similarity in the i-Vs of both channel isoforms leads us, however, to strongly favor intrinsic instability of the third shut state in the α3β3γ2S channels on the basis of subunit differences rather than a supposed modulation by ions.

Although our analysis favored Scheme 1 over Schemes 4 and 2, it should be pointed out that there are some common features between the three schemes. All have three open and three shut diliganded states and connect the first diliganded shut state to a second before channel opening occurs. The entry into preconducting states after agonist binding is also a critical feature of Scheme 3, the original form of which was applied to the muscle nACh receptor and elucidates the mechanism of partial agonism (Lape et al., 2008). Liganded preconducting states, coined “flipped” or “primed,” have been demonstrated in several cys-loop receptors, including glycine receptors (Burzomato et al., 2004; Lape et al., 2008), muscle ACh receptors (Lape et al., 2008; Mukhtasimova et al., 2009), and GABAA receptors (Lema and Auerbach, 2006; Keramidas and Harrison, 2008). Preconducting states have also been identified in other ligand-gated ion channels that are not closely related to the cys-loop family. Using partial agonists as substitutes for either glycine or glutamate at NMDA receptors composed of NR1 and NR2B subunits revealed subunit-specific conformational transitions involving either the NR1 or NR2B before channel opening (Banke and Traynelis, 2003). Kinetic studies of P2X2-mediated macroscopic currents have provided evidence of a lag in time between ligand binding and the detection of current. These data were modeled accurately with schemes that included fully liganded shut states that preceded the opening of the channel (Moffatt and Hume, 2007).

The main difference between Schemes 1, 2, and 4 in this study is the connectivity of the fully liganded states. With regard to Schemes 1 and 2, if we assume that both schemes accurately account for the respective data they are applied to, we can only speculate as to why the schemes are different, even though they both fit GABAA receptor single-channel data recorded across high and low GABA concentrations using essentially the same analysis method. Scheme 2 was determined for data obtained in the cell-attached configuration, where the channels are exposed to intracellular solutes, including ions that affect channel kinetics, such as Ca2+ and Cl (Fucile et al., 2000; Pitt et al., 2008). Pitt et al. (2008) show that low intracellular Cl, as is present in intact cells, slows ensemble current deactivation and alters the rate constants and potentially the states in the underlying scheme in glycine receptors, which are close Cl-selective cousins of GABAA receptors. It has also been shown that kinetics of GABAA receptors expressed in HEK293 cells are substantially affected by the extent of phosphorylation by protein kinase A (Hinkle and Macdonald, 2003). It is conceivable that patch excision alters the constitutive phosphorylation state of the channels, impacting on their kinetic behavior.

To facilitate a comparison of the activation properties of the two channels, the equilibrium constants (ratio of forward and backward rate constants) were computed (Table V). From these numbers it can be deduced that the tendency toward the conducting states (A2Rn*) between the two channels differs (E1, E2, and E3) by only two- to fivefold. Notably, the equilibrium constant for the diliganded preconducting state (Φ, or the “flipping” or “priming” constant) is essentially the same for both channels (∼0.3–0.4) and not very different to that determined for α1β1γ2S GABAA receptors (Lema and Auerbach, 2006). It would be informative to investigate if other agonists, such as muscimol and piperidine-4 sulphonic acid (P4S) affect Φ, as might be predicted by the Lape et al. (2008) study. The most evident differences between the channels is the GABA-binding reaction (Kd) and the transition between the open and shut states, A2R3* and A2R3 (Σ). Lema and Auerbach (2006) report a Kd value of 91 µM, which is dissimilar to the values determined in this study. According to our estimates, GABA binds to the α3β3γ2S GABAA receptor (Kd = 2.62) with an over sevenfold higher affinity for GABA than α1β2γ2S GABAA receptors (Kd = 19.4). We suggest that this would tend to favor the right-hand or gating part of mechanism and consequently lengthen the active periods. Moreover, Σ is about an order of magnitude smaller for the α3β3γ2S receptors (A2R3 has shorter lifetime), which would also account for the higher PO of these channels at most GABA concentrations tested. Collectively, these kinetic effects are consistent with observed differences in deactivation of IPSCs of RTN synapses compared with those of VB. Our estimates of Kd obtained from Scheme 1 for the α1β2γ2S channels are relatively low compared with that found by Lema and Auerbach (2006) for the closely related α1β1γ2S receptor. Schemes 2 and 4 yielded Kds that were more comparable, being 132 and 96 µM, respectively. We can offer several plausible reasons for the discrepancy in Kds. First, it is apparent from our analysis that the estimate of Kd is mechanism dependent, and that the α1β2γ2S the α1β1γ2S channels were described by different schemes. If we compare Kds computed from the same scheme (Scheme 2), the difference in affinity between the α1β2γ2S and α1β1γ2S channels is reduced from 4.7- to 1.1-fold. Another potential reason for the discrepant Kds between the α1β2γ2S and α1β1γ2S channels is the choice of tcrit values between this study and that of Lema and Auerbach (2006). It is conceivable that relatively short tcrit values used in this study, especially for the data at 20 and 2 µM GABA, led to inadvertently omitting some long binding events that are longer than tcrit. This would result in underestimating Kd. However, when we varied tcrit from 35 to 100 ms in two patches recorded at 2 µM GABA, we found no difference in the computed Kd for either patch. It should also be emphasized that the different Kds may reflect a real difference in affinity between the α1β2γ2S and α1β1γ2S channels, conferred by different amino acid sequences at critical binding segments. The β subunit contributes three components to the GABA-binding pocket. One of these is the crucial loop 10-β sheet 10-segment or “loop C” (Kash et al., 2004). An alignment of these segments reveals a difference in primary sequence at two positions between the β2 and β1 subunits.

The complication of affinity estimates obtained via different mechanisms is avoided with respect to the different Kds between the α1β2γ2S and α3β3γ2S channels, which we hypothesize reflect different binding site affinities. Alignments of homologous segments that form the GABA-binding pocket between the β–α subunit interfaces reveal a total of 11 amino acids that differ between the two channels. Ultimately, site-directed mutations and a reanalysis using the same mechanism will aid in verifying the Kds and determining the underlying factors for the apparent affinity differences between the α1β1γ2S, α1β2γ2S, and α3β3γ2S channels.

Conclusion

We have constructed and tested a potential reaction mechanism that provides an adequate description of the kinetic behavior of two major subtypes of the GABAA receptor and explains why inhibitory synaptic currents are much longer lasting at synapses containing one subtype than the other. The model incorporates preconducting states that are apparently a conserved feature of the family of cys-loop receptors. Future efforts will be directed toward the application of this mechanism to additional GABAA receptor subtypes and evaluation of the effects of a variety of pharmacological challenges, including agonists, partial agonists, and modulators, such as benzodiazepines and general anaesthetics.

This work was supported by National Institutes of Health (grant AA016393).

Angus C. Nairn served as editor.

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Abbreviations used in this paper:
GABAA

γ-aminobutyric acid type A

IPSC

inhibitory postsynaptic current

LL

log-likelihood

nACh

nicotinic acetylcholine

RI

rectification index

RTN

reticular nucleus

VB

ventrobasal nucleus

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