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1 Department of Pharmacology, University College London, Gower Street, London WC1E 6BT, UK2 Department of Pharmacology, The Danish University of Pharmaceutical Sciences, Universitetsparken 2, 2100 Copenhagen Ø, Denmark3 Department of Molecular Pharmacology, H. Lundbeck A/S, Otilliavej 8, 2500 Valby, Copenhagen, Denmark4 Department of Medicinal Chemistry, The Danish University of Pharmaceutical Sciences, Universitetsparken 2, 2100 Copenhagen Ø, Denmark
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Abstract |
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1ß2
2S GABAA receptors expressed in human embryonic kidney cells using the following series of receptor agonists: GABA, isoguvacine, 4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridin-3-ol (THIP), isonipecotic acid, piperidine-4-sulphonic acid (P4S), imidazole-4-acetic acid (IAA), 5-(4-piperidyl)-3-isothiazolol (thio-4-PIOL) and 5-(4-piperidyl)-3-isoxazolol (4-PIOL). Whole-cell concentration–response curves enabled the agonists to be categorized into four classes based upon their maximum responses. Single channel analyses revealed that the channel conductance of 25–27 pS was unaffected by the agonists. However, two open states were resolved from the open period distributions with mean open times reduced 5-fold by the weakest partial agonists. Using saturating agonist concentrations, estimates of the channel shutting rate, , ranged from 200 to 600 s–1. The shut period distributions were described by three or four components and for the weakest partial agonists, the interburst shut periods increased whilst the mean burst durations and longest burst lengths were reduced relative to the full agonists. From the burst analyses, the opening rates for channel activation, ß, and the total dissociation rates, k–1, for the agonists leaving the receptor were estimated. The agonist efficacies were larger for the full agonists (E
7–9) compared to the weak partial agonists (0.4–0.6). Overall, changes in agonist efficacy largely determined the different agonist profiles with contributions from the agonist affinities and the degree of receptor desensitization. From this we conclude that GABAA receptor activation does not occur in a switch-like manner since the agonist recognition sites are flexible, accommodating diverse agonist structures which differentially influence the opening and shutting rates of the ion channel.
(Received 8 September 2003;
accepted after revision 25 February 2004;
first published online 27 February 2004)
Corresponding author T. G. Smart: Department of Pharmacology, University College London, Gower Street, London WC1E 6BT, UK. Email: t.smart{at}ucl.ac.uk
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Introduction |
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-aminobutyric acid type A (GABAA) receptor family is a major group of proteins in the central nervous system (CNS) whose main function is to enable neurones to swiftly and reproducibly control their excitability (Moss & Smart, 2001). This key role is reflected by the widespread distribution of these receptors throughout the brain and by their importance as targets for therapeutic agents (Sieghart, 1995; Sieghart & Sperk, 2002). Exogenous ligands can interact with GABAA receptors at various loci, for example by binding at one of many different sites on the external domain of the receptor to allosterically modify receptor function, by directly binding to residues within the ion channel lumen, or by competing with the natural transmitter, GABA, for its recognition site (Sieghart, 1995; Rabow et al. 1996; Korpi et al. 2002). Although the binding sites for many of the allosteric ligands that can affect GABAA receptor function are mostly unresolved, the molecular determinants that form the boundaries and key interacting surfaces for the GABA binding site are increasingly being unravelled (Sigel et al. 1992; Amin & Weiss, 1993; Boileau et al. 1999; Wagner & Czajkowski, 2001; Boileau et al. 2002). Similarly, those external domains of the GABAA receptor that are instrumental in linking the binding of GABA to the receptor with the activation of the ion channel are, at least in outline, becoming clearer (Horenstein et al. 2001; Scheller & Forman, 2002; Kash et al. 2003).
However, what is unclear is the impact the agonist molecule has on the activation, at the single channel level, of a largely homogeneous GABAA receptor population. In particular, how does receptor activation correspond to variations in agonist affinity and efficacy? This question forms the basis of the present study, which examines the effects of GABA, and a range of GABAA receptor agonists with variable affinities and efficacies, to deduce which kinetic parameters are changing at the single channel level to affect overall activation of the GABAA receptor.
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Methods |
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The human GABAA receptor 1 and ß2 subunit cDNAs were cloned into the pCDM8 vector, and the 2 subunit cDNA was cloned into the pcDNAI/Amp vector as previously described (Hadingham et al. 1993a, b).
Cell culture and electroporation
Human embryonic kidney (HEK) cells were cultured as previously described (Wooltorton et al. 1997). HEK cells were plated onto poly L-lysine coated glass coverslips and transfected using a calcium phosphate protocol. DNA for the GABAA receptor subunits and enhanced green fluorescent protein (pEGFP-C1) were present in equal amounts (0.5 µg each per culture dish). The DNA solutions were mixed with 340 mM CaCl2 before the precipitate was formed by gently mixing the DNA–CaCl2 solution with an equal volume of double-strength Hanks' balanced salt solution (280 mM NaCl, 2.8 mM Na2HPO4, 50 mM Hepes; pH 7.2) followed by a 20 min incubation at room temperature. The DNA–calcium phosphate suspension was added to the HEK cells with the transfection proceeding overnight. Cells were used for electrophysiological recording 18–72 h after transfection.
Patch clamp electrophysiology
GABA-activated membrane currents and single channel currents were recorded using whole-cell or outside-out patch clamp techniques from single HEK cells with a List EPC7 amplifier. Patch pipettes (resistance 3–5 M
for whole-cell and 10–15 M for single channels) were filled with a solution containing (mM): 120 KCl, 1 MgCl2, 11 EGTA, 30 KOH, 10 Hepes, 1 CaCl2, and 2 adenosine triphosphate; pH 7.11. The cells were continuously perfused with Krebs solution containing (mM): 140 NaCl, 4.7 KCl, 1.2 MgCl2, 2.52 CaCl2, 11 glucose and 5 Hepes; pH 7.4. Membrane currents were filtered at 2 kHz (single channels) or 5 kHz (whole-cell currents; –3dB, 8 pole Bessel, 48 dB octave–1) and recorded on a DTR-1201 digital tape-recorder prior to A/D conversion via a Digidata 1320A (Axon Instuments) and final off-line analysis with a PC Pentium III processor (Viglen/Dell). Individual single channel currents were analysed with Strathclyde electrophysiology software (John Dempster, WinEDR ver 2.3.8). Any change exceeding 10% in the membrane conductance and/or series resistance resulted in cessation of the recording. For the whole-cell recordings, series resistance compensation to approximately 50% was achieved.
Analysis of whole-cell current data: receptor model
The amplitudes of membrane currents activated by GABA and the analogues (I) were determined at –50 mV holding potential. The GABA/analogue concentration–response relationships were constructed by measuring the GABA/analogue currents, which were normalized to the response induced by a maximal, saturating concentration of GABA (3000 µM) in control Krebs solution (Imax) and subsequently fitted initially with the Hill equation:
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| (1) |
To provide a physically plausible mechanism to describe the whole-cell currents activated by GABA and the analogues, the following linear receptor model, based upon the scheme of Del Castillo & Katz (1957), was devised:
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where A represents the GABAA receptor agonist and R the receptor, with forward and backward rate constants for binding and unbinding of k1 and k–1, and ß and are rate constants for channel opening and shutting with active conducting forms of the receptor represented by AR* and A2R*. The receptor can also access desensitized states depicted by D, with entry and exit rate constants of 
1 and –1. Given the rarity of what might be considered as monoliganded receptor openings (see Results and Discussion), the AR* state was discounted from further analysis and consideration. Under such conditions (ignoring AR*), the function that describes this receptor model is:
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| (2) |
) and D represents the desensitization conformation constant (1/–1). Note for this form of the state function, in order to reduce the number of determined variables, the agonists are assumed to bind independently to equivalent binding sites on the receptor with equal rates of association and dissociation. Allowing cooperative agonist binding to the receptor does not alter the conclusions of this study (data not shown). The general case of eqn (2), describing ligand binding to multiple sites on the receptor, which includes a single desensitized state, was used to fit the GABA/analogue concentration–response relationships. This general form is given by:
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, PopenPopen,max, which is given by E/(1 +E+D). For the non-linear least squares data fitting procedure, E was accepted from the single channel measurements and deduced to be unaffected by desensitization (see Microscopic rate constants, below). These values were therefore fixed allowing the other parameter variables to free-run during fitting. The estimates of K and D that are derived in this manner will, however, depend on the accurate description of the whole-cell currents and these can be affected by fast desensitization. To obviate this, agonists can be rapidly applied to ensure near-accurate peak currents are obtained, but despite even the fastest application methods, one cannot completely discount the impact of even faster desensitization on the membrane current profiles. In our measurements, rapid desensitization could affect the estimates of K and D for the full agonists, but will be less important for the partial agonists where rates of desensitization are apparently much slower.
To assess the accuracy of the determined K and D values from the concentration–response curves, we attempted to reproduce the agonist-activated current profiles using two simulation methods. The first relied on numerically integrating the differential rate equations for the receptor model using a Runge-Kutta routine (ModelMaker ver. 3), and largely adopting the single channel estimates of ß and . The values of k1, k–1, 1 and –1 were empirically adjusted until the theoretical currents were similar to their experimental counterparts. The robustness of this approach was assessed for the agonists, GABA, THIP, P4S and 4-PIOL, which are representatives of agonists in each of the groups I–IV (see Results). The second relied on a Q matrix simulation of the receptor model using the program ‘Scalcs’ (available from D. Colquhoun, http://www.ucl.ac.uk/pharmacology/dc.html). Both approaches allowed similar estimates of rate constants for agonist association and dissociation, as well as entry into and exit from desensitization. The derived constants of K and D corresponded well with those obtained using eqn (3).
Single-channel analysis
Single GABA channel currents were recorded in excised outside-out membrane patches, at –70 mV holding potential, on the condition that there appeared to be only one active channel, or the number of multiple channel openings never exceeded 2% of all detected openings. Stored prefiltered channel data were digitized at 20 kHz before analysis and a fixed time resolution based on the dead time of the system was set at 89 µs. The analysis of the single channel current amplitudes was performed by fitting Gaussian components to the amplitude distributions that defined the mean current, standard deviation and the total area of the component using a non-linear least-squares routine. The single-channel conductance was calculated from the mean unitary current, determined from the Gaussian curve fits, and the difference between the patch potential and GABA response reversal potential. Individual open and shut durations were measured using a 50% threshold cursor applied to the main single-channel current amplitude in each patch. The transition detection of open and shut events was then used to form an idealized record of the digitized data. The duration of events that were included in the analysis was not less than 200 µs (set at 1.3 times the filter rise time) before fitting the dwell time histograms. Frequency distributions were constructed from the measured individual open and shut durations and analysed by fitting a mixture of exponentials, defined in the function y(t) as:
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i represents the corresponding time constant. Using a Levenberg-Marquardt non-linear least-squares routine the area representing the individual exponential components, the relative time constants and standard errors of these parameters were determined. An F test was used to determine the optimal number of exponential components required to fit each individual dwell time histogram. The burst length analyses relied on determining a critical shut time (crit); thus any series of open and shut periods where the shut periods do not exceed crit were deemed to be contained in a single burst event (Colquhoun & Sakmann, 1985). For most of the agonists the critical shut time was determined between the shut time constants, C2 and C3 (see Results for rationale) from:
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crit values for these compounds were calculated as three times the shortest intraburst closure (C1). Inspection of the data (including burst lengths and number of openings per burst) suggested this to be a satisfactory margin to accurately reflect the activity of these weak partial agonists (see Discussion for further explanation). A correction for missed events was performed mostly according to the methods of Colquhoun & Sakmann (1985). Essentially, we considered that most open events were successfully detected and so concentrated on the likelihood that very short closures, particularly in the long bursts, were possibly undetected and the consequences this may have on the data analysis. The mean duration of closures within bursts was calculated from the total shut time within bursts (< tcrit)/total number of shut events with durations less than the defined tcrit for each patch. The true mean number of shut periods that occur during a burst (nbs) was estimated as the total number of shut events (with durations < tcrit)/the total number of bursts (Colquhoun & Sakmann, 1985) and subsequently applied to eqn (7). Number of active channels in a patch
One major problem when analysing single channels is the determination of the number of active channels in the patch (Colquhoun & Hawkes, 1990). When this is greater than 1, it will affect the accurate determination of long shut periods that arise between bursts of channel activity. To estimate the number of active channels per patch, we assessed the extent of simultaneous channel activation by agonists. Where multiple channel activation accounted for more than 2% of the total currents measured in one recording, the patch was rejected. Alternatively, we also determined the open probabilities (PO) between patches that possessed, ostensibly, only one active channel and those that clearly had two or more channels. In these examples, the PO values were not markedly dissimilar for the ‘one channel patches’ whereas those patches displaying multiple channel activation produced considerable variation in the PO determinations.
Microscopic rate constants
The microscopic rate constants, , ß and k–1 in the linear receptor model, were calculated from the burst analyses. Estimates of the brief shut periods within a burst and the number of openings per burst were used to derive approximations for the channel opening rate constant, ß (Colquhoun & Sakmann, 1985; Hatton et al. 2003). In essence, by adopting a scheme such as the receptor model, the brief shut periods of the channel during a burst are assumed to reflect a predominant residence in the A2R state. The mean length of these shut periods (BS, typified by C1, see Results) will therefore be given by:
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1 has no role in determining ß. Moreover, the desensitized periods of channel activity were essentially removed from the single channel analysis when considering only the kinetic properties of discrete burst events by disregarding the long shut periods to prevent any corruption. This procedure can be validated by modelling fits to single channel data including desensitized states, or by simply excising the individual bursts for kinetic analyses. The fitted rate constants are quite similar when compared for either analytical approach (see Colquhoun et al. 2003). The shutting rate of the ion channel, , was estimated from the reciprocal of the longest open time constant (O2) determined from the open period distributions compiled after receptor activation by the highest concentrations of the agonists. Similar results were also obtained for the determination of by using the longest open time within bursts. Drugs and solutions
Drugs and solutions were rapidly applied to the HEK cells using a modified Y-tube positioned approximately 300 µm from the recorded cell. The response rise times were within 20–30 ms (Wooltorton et al. 1997). All drugs were dissolved in external Krebs solution, and if required, readjusted to pH 7.4 with 1 M NaOH. All drugs were synthesised in our laboratory (BF and PK-L), except for isoguvacine and IAA (Sigma).
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Results |
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To assess the effects of activating GABAA receptors by different agonists, 1ß22S subunit-containing receptors were expressed in HEK cells. Whole-cell currents were recorded following the rapid application of the agonists over the concentration range 0.1 µM to 3 mM. The Hill equation was used to fit agonist concentration–response curves providing estimates of the agonist EC50 values (see Methods). Overall, eight ligands were selected for study, based upon variations in their potencies and maximal responses when activating GABAA receptors (Kristiansen et al. 1991; Ebert et al. 1994, 2001). These included GABA, isoguvacine, 4,5,6,7-tetrahydroisoxazolo [5,4-c]pyridin-3-ol (THIP), isonipecotic acid, piperidine-4-sulphonic acid (P4S), imidazole-4-acetic acid (IAA), 5-(4-piperidyl)-3-isothiazolol (thio-4-PIOL) and 5-(4-piperidyl)-3-isoxazolol (4-PIOL) (Fig. 1).
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The proposed receptor model (see Methods), based on the sequential binding of agonist to the receptor, could also account for the whole-cell agonist-activated currents on the 1ß22S receptor providing estimates of the dissociation constant, K, the gating constant, E, and the desensitization constant, D (data not shown). However, initial attempts to fit the whole-cell data with eqn (3) showed that with four variables many acceptable fits to the dose–response curve data could be achieved but with some variation in the values of D, E and K. This is not surprising since variations in agonist efficacy can produce not only changes in the maximum response but also lateral shifts in the whole-cell concentration–response curve without any change in the dissociation constant being required. The effect that a change in E has on the concentration–response curve will depend, to some extent, on the initial value of E (Colquhoun, 1998). For example, concentration–response curves for agonists evoking channel opening with high values of E (1000 or 10000) will only shift laterally (mimicking competitive-type inhibition) if E is reduced arbitrarily tenfold (to 100 or 1000). However, a similar tenfold reduction in E for a less efficacious agonist (say from E= 10 or 25 to 1 or 2.5) will produce a lateral shift with a clear reduction in the maximum response.
It is therefore probable that any alterations to the whole-cell currents, under conditions where receptor number is likely to stay constant, will reflect one or more changes to the channel open probability and the single channel current. To deduce how these parameters varied with the different agonist activation profiles, single channel studies were performed using outside-out patches. Furthermore, the single channel data were used to estimate the microscopic rate constants, , ß and k–1, for each agonist, which provided more accurate estimates of E for the receptor model fits to the agonist concentration–response curve data. The values of E were then constrained during the fitting procedure for the dose–response data to yield estimates of K and D (see Methods for limitations). This approach can then provide values of k–1, k1, ß and that are consistent with both the single channel and dose–response curve data.
Single channel studies: consistency of the channel conductance
To analyse the properties of the single channels activated by the different agonists, three concentrations were selected from the respective concentration–response curves reflecting the different agonist potencies and maximal currents that were activated. The selected concentrations encompassed: an above threshold concentration designated as ECmin (near EC10); a mid-range concentration defined as ECmid (near the EC50); and a saturating, ‘maximal’ concentration, ECmax (giving a response close to 100%; see Table 1 for the actual concentrations used for each agonist). For GABA, these concentrations were 1, 10 and 1000 µM. Using steady-state recording of single GABA channel currents from outside-out patches revealed openings to mostly one conductance level, referred to as the main state, at 26.7 ± 0.4 pS (n= 23; Fig. 3A). Although an occasional, less predominant lower conductance level may be observed at 13 ± 2 pS, the frequencies of the main and low conductance states were approximately >98% and <2% in all patches. The main conductance state was constant for channel currents activated by the range of agonists at all the concentrations used (Fig. 3B, mean for all agonists, at all concentrations, was 26.2 pS), and it was also independent of the patch holding potential over the range –80 mV to 40 mV (data shown for three examples in Fig. 3C).
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Properties of the open states activated by the GABAA receptor agonists
The open periods of the GABA ion channels were analysed at the three selected concentrations and the resulting distributions of all open periods were fitted mostly with two exponential probability density functions. Some of the open period frequency distributions were dominated by the short open period component at the lower GABA concentrations (ECmin), changing to an equal contribution from both components at the ECmid concentration, before revealing an increasing contribution from the longer open period component at the higher GABA concentrations (ECmax, Fig. 4A). However, in many other patches, the frequencies of short and longer open periods were quite similar (Fig. 4D). Surprisingly, there was little change in the mean open times (O,mean) with increasing GABA concentrations (Fig. 4B). This was reflected by the lack of change to the underlying time constants (O1 and O2, Fig. 4C). Although by comparing openings at different GABA concentrations it appeared that higher concentrations were associated with a greater frequency of the longer open periods, this was not a significant change when examined over all the patches (Fig. 4D). Thus raising the GABA concentration did not cause a fundamental change in the length of the openings and only in some patches were the relative frequencies of their occurrence affected such that at high GABA concentrations, the longer open periods appeared more frequent.
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O1, was largely invariant between the different agonists; however, the long open time constant, O2, was reduced by over 50% when comparing the full agonists, such as GABA and isoguvacine, with the weak partial agonists, thio-4-PIOL and 4-PIOL (Fig. 5C).
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Single channel shut times and the GABAA receptor agonists
The shut periods provided information on the time the channel was dormant in shut and desensitized states and could also be used to define the bursting behaviour of the channels after activation by the different agonists. For GABA-activated channels, all patches required three exponential components to fit the distributions of all shut periods for the ECmin, ECmid and ECmax concentrations (Fig. 6A). A fourth shut time was also resolved occasionally for ECmid concentrations (2/8 patches) and more frequently for the ECmax concentration (14/21 patches), but it was never detected at the ECmin. As far as possible, patches were selected for shut time analyses where multiples of single channel currents were not observed; however, we cannot be absolutely sure that each patch contained only one active channel, and also at low concentrations of GABA, the number of shut events was usually quite small in contrast to the numbers evident with higher concentrations. As the GABA concentration increased, the mean shut time was slightly reduced (Fig. 6B). The underlying shut period frequency distributions for GABA possessed characteristic properties with no concentration dependence evident for the shorter shut periods defined by C1 and C2 (Fig. 6C), whilst for the longer shut periods, described by C3 and C4, both were concentration dependent with C3 decreasing with higher GABA concentrations and C4 only appearing at the higher agonist concentrations (Fig. 6C). The areas for the shortest shut period components (AC1 and AC2) were independent of GABA concentration whilst the area for the component described by C3 (AC3) was slightly reduced at high GABA concentrations probably due to the appearance of C4 (Fig. 6D).
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C1 and C2, were mostly unchanged when compared to the values obtained for GABA, except for the weakest agonists thio-4-PIOL and 4-PIOL, where C2 was increased (Fig. 7C). Similarly, C3 was increased for the same two partial agonists whilst in contrast, C4 appeared to decrease for the full agonists exhibiting lower potencies than GABA, eventually becoming unresolved for the weaker agonists (Fig. 7D). Overall, as the agonist profile changed from full to weak partial, only one longer shut period time constant, C3, appeared to change, which increased for the weaker agonists. The lack of a resolvable C4 for the partial agonists may explain part of the increase in C3. It further suggests that the weaker agonists are spending more time in longer shut states, a feature reflected in the reduced whole-cell current amplitudes.
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C3 but also by increments in the areas describing the C3 component with corresponding reductions in the area for the shortest shut times, AC1. These changes would account for the greater mean shut times measured for the weaker agonists compared to the full agonists at each concentration (Fig. 7B). Burst structures for the GABAA receptor agonist-activated ion channels
To define bursts of channel activity a critical shut time (tcrit) was determined that signalled the cessation of individual bursts. We would expect the shut times between individual bursts of channel activity to be dependent upon agonist concentration but this may be complex. As agonist concentration increases, the interburst shut times should become smaller because channel opening becomes more frequent; however, at high agonist concentrations, when entry into desensitized states becomes more likely, these shut times may again lengthen. Thus if the two processes overlap, as seems likely, then the concentration dependence becomes obscure. In addition, long shut times will also depend upon the number of active channels in the isolated patch. The critical shut time was therefore determined between the shut time constants C2 and C3 since these were common to channels activated by all of the agonists and because C1 and for the most part C2 were independent of agonist concentration. This suggested that they described shut periods occurring during a single burst of channel activity, defined as the continued activation of a single receptor by retained agonist molecules until their dissociation. In contrast, C3 was clearly concentration dependent in accord with a time constant describing shut periods that occur between individual bursts. The difference between C3 and C4 was not used to determine crit since C4 only appeared for high efficacy agonists in the highest concentrations and could not be reliably detected in all patches.
Examination of GABA channel activity revealed that the bursts had a complex structure. Even at low GABA concentrations (ECmin) rarely were bursts associated with single open events, instead presenting with multiple open and shut periods (Fig. 8A–C). The burst length distributions, at each GABA concentration, required multiexponential component fits. For 1 and 10 µM GABA, two exponentials were sufficient, but at 1 mM GABA, three exponential components were necessary in most patches (18/21, Fig. 9A). The third burst time constant (B3) was never detected at lower concentrations of GABA. The mean burst length increased clearly with the GABA concentration (Fig. 9B) but the burst length distributions revealed that there was little change in the underlying burst length time constants, B1 and B2 (Fig. 9C). However, the relative areas of each of the burst components displayed some dependence on the GABA concentration. The area describing the shortest bursts (AB1) was reduced, whereas the relative area for the long bursts (AB2) was increased at 10 µM GABA. Eventually at the highest GABA concentration, a new longer burst component appeared for GABA (AB3) which caused AB2 to decrease (Fig. 9D).
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B1 and B2, were unaffected by the agonist concentration for isoguvacine and THIP, and also for the weak partial agonists IAA, thio-4-PIOL and 4-PIOL (Fig. 11C). However, for those agonists of intermediate profile between the full and weaker partial agonists, e.g. isonipecotic acid and P4S, where B3 was not detected, the values of B2 were increased with the agonist concentration (Fig. 11C). Thus in changing the agonist activity profile from full to partial, the longest burst lengths, described variously by either B3 or B2, were progressively reduced (Fig. 11C), whilst B1 remained unaltered.
A comparison of the relative areas for the burst components also revealed that for the partial agonists compared to the full agonists, the areas for B1 were greater. For the full agonists, the relative areas for the B2 components were generally larger compared to the partial agonists at ECmin and ECmid, and at the high concentrations of the full agonists, B3 appeared as a component at the expense of the area for B2 (Fig. 11D–F). Taken together, with regard to the full agonists compared to the partial agonists, more bursts were occurring with longer burst lengths, and this change was accentuated by raising the agonist concentration.
The number of openings per burst
To gain further insight into the structure of the bursts induced by the GABAA receptor agonists, the number of individual open periods that occurred in each burst was quantified for each agonist. The number of openings per burst was dependent on the agonist concentration with more openings evident at high agonist concentrations (Fig. 12). Moreover, more openings per burst occurred with the full agonists (6.4 ± 0.7 openings for 1000 µM GABA) compared to the partial agonists (1.3 ± 0.05 openings for 3000 µM thio-4-PIOL). The number of openings per burst was strongly correlated with the relative maximal whole-cell currents activated by the GABAA receptor agonists (Fig. 12 inset).
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Given that the mean burst lengths and the number of openings per burst appeared to be dependent not only on the agonist concentration but on the type of agonist, the open periods that occur within individual bursts were analysed. In accord with the analyses for all of the open periods, those open periods within a burst were described by two exponential densities with time constants BO1 and BO2. The time constant for the short open period component was independent of concentration and the type of agonist used; however, the long open period time constant was reduced as the agonist profile changed from full to partial (Fig. 13A). The relative areas for short and long open periods within bursts varied depending upon the type of agonist activating the channels with the area for the short burst component, ABO1, increasing for the partial agonists whilst ABO2 was reduced (Fig. 13B). However, for the full agonists, the areas of the two components remained largely unaltered.
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Discussion |
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Macroscopic current properties of full and partial agonists
On the basis of the concentration–response curves, the agonist profiles varied from the full agonists of GABA and isoguvacine, which differed in potency only, to the weakest partial agonists, thio-4-PIOL and 4-PIOL, which caused minimal activation of the receptor. The maximum responses evoked by all the agonists showed strong correlations with the individual channel open probabilities and the number of channel openings per burst. Overall, these results suggested that the interaction of the agonists with a single GABAA receptor subtype induced the receptor to preferentially adopt different combinations of open, shut and desensitized states.
It is important to note that the shifts in the agonist concentration–response curves may be caused by changes in affinity, but this would not depress the dose–response maxima. By contrast, changes in agonist efficacy can induce curve shifts and alterations to the curve maxima and the latter would also be affected by the extent of desensitization. Thus, by obtaining estimates of E, K and D, the agonist concentration–response data may be described by theoretical curves, assuming the designated receptor model describing the data is reasonably accurate. However, even without further analysis we can conclude from a comparison of the dose–response curves that if a reduction in E alone explains the relative curve displacement and maximum response for a partial agonist, then the value of E must be low (<10). Otherwise, reductions in efficacy would simply cause a near parallel displacement of the curve and virtually no reduction in the maximum response. The second conclusion to be drawn is that a change in E alone will not account for the P4S curve since the maximum is depressed but the curve is also displaced leftwards in comparison to other more efficacious agonists, e.g. THIP and isonipecotic acid.
Agonist-induced differences at the single channel level: conductance
The single channel conductance will influence the size of the agonist responses. However, in our study the vast majority of single channel open events proceeded to a conductance level of 25–27 pS, in accord with previous determinations for recombinant ß subunit-containing and many neuronal GABAA receptors (Jackson et al. 1982; Hamill et al. 1983; Mathers, 1985; Weiss et al. 1988; MacDonald et al. 1989; Smart, 1992; Angelotti & MacDonald, 1993). This conductance level was unaffected by the agonist or by variations in agonist concentrations, indicating that the channel conductance for the human 1ß22S isoform expressed in HEK cells is a constant parameter (cf. Eghbali et al. 1997, 2000). Earlier fluctuation analyses of GABA agonist-induced currents in spinal neurones also concluded that the channel conductance was unaltered by THIP, isoguvacine, IAA and P4S (Barker & Mathers, 1981), or by 4-PIOL (Kristiansen et al. 1995), when compared to GABA. However, in chick neurones, isoguvacine (and muscimol) preferentially activated subconductance states (Mistry & Hablitz, 1990), but this was not apparent in our experiments. Thus, the main changes to agonist affinity and efficacy must be derived from the kinetic properties of ion channel gating.
Open periods and the shutting rate of the channel
Analysis of the channel open times implied that two independent open states for the receptor could exist. These states may relate to the two agonist binding sites thought to reside at specific –ß subunit interfaces in the GABAA receptor (Sigel et al. 1992; Amin & Weiss, 1993; Smith & Olsen, 1994; Tretter et al. 1997; Boileau et al. 1999; Hartvig et al. 2000; Baumann et al. 2001; Wagner & Czajkowski, 2001). Although the open time constants were independent of the agonist concentrations, the lower frequency and shortening of the ‘longest’ openings accounted for the reduced mean open times measured with the weaker agonists. This independence from the agonist concentration suggested that the two open states must be fully (di-) liganded and that monoliganded openings are relatively rare, supporting the receptor model proposed to explain the whole-cell current data. Such a model also predicted that at high agonist concentrations, the receptor would reside in a diliganded state, whereupon the longest openings (associated with O2) would be observed. In this circumstance, the reciprocal of O2 could provide an estimate of the ion channel shutting rate, . For GABA and the other agonists, estimates of ranged from 200 s–1 to 600 s–1 (Table 2), gradually increasing as the agonist profile changed from full to partial. These values proved similar to estimates used in other receptor models for GABAA receptors (Twyman et al. 1990; Jones & Westbrook, 1995; Burkat et al. 2001). It is an interesting and possibly unexpected finding that the shutting rate is not a constant for the ion channel protein. The three-fold variation in suggests that the channel is not simply closed in a switch-like manner, but is apparently sensitive to how it was activated by a particular agonist. This would suggest that even if a common sequence of conformational changes leads to channel activation (Kash et al. 2003; Miyazawa et al. 2003) this process is more flexible than realized hitherto.
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The shortest shut times were independent of agonist concentration whilst one of the longer shut times, C3, was reduced with increasing concentration. This behaviour was expected, if the shortest shut times reflected closures within single bursts and the longer shut times represented shut periods between bursts. The longest shut time constant, C4, was only resolved with the highest concentrations of relatively high efficacy agonists and may represent entry of the receptor into desensitized states. Although desensitization was seemingly absent for the weaker agonists at saturating concentrations and that C4 was not resolved in their respective shut time distributions, we cannot exclude the possibility that weak agonists can drive the receptor into desensitized states which may be represented, in part, by C3. The weaker agonists also reduced the number of short shut periods whilst increasing the number of longer shut periods between bursts thus causing the channel to open less frequently. The effect on the short shut times was surprising and suggested that within single bursts, the channel was not entering into as many short shut states. This may reflect the reduced burst length activated by these weak agonists and a reduction in the number of openings per burst leading to a reduction in the number of short shut periods. In addition, C2 was increased for the weakest partial agonists (thio-4-PIOL and 4-PIOL) suggesting that this component was probably associated with shut periods between bursts. This was supported by estimating the number of openings per burst for thio-4-PIOL and 4-PIOL, which was close to one, indicating that the majority of bursts consisted of single openings with only 1 in 5–10 bursts containing shut periods. Taken together, the shut period analyses clearly indicated that the partial agonists induced a higher mean shut time for the GABAA receptor compared to the full agonists.
The frequencies of the burst durations were largely independent of the agonist concentration; however, by changing the agonist profile from full to partial more short bursts occurred at the expense of the long bursts which most likely contributed to the reported small whole-cell currents activated by 4-PIOL in olfactory bulb neurones (Kristiansen et al. 1995). Whilst the open periods within bursts were independent of agonist concentration, for the weak partial agonists, not only were the longer open periods reduced, but their frequency was substantially reduced. Overall, for the partial agonists, bursts appeared less frequently with generally shorter durations, containing fewer numbers of channel openings and those openings tended to be briefer.
Channel opening rate and determining agonist efficacy
To determine the agonist efficacies, values for the opening and shutting rates of the ion channel were required. The opening rate constant, ß, was determined from the (shortest) shut periods within a burst and the number of shut periods per burst (see Methods). Values for ß ranged from 1700 s
