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Vollum Institute, Oregon Health Sciences University, Portland, Oregon 97201
Correspondence: Address reprint requests to Victor A. Derkach, Vollum Institute L-474, Oregon Health Sciences University, 3181 S.W. Sam Jackson Park Rd., Portland, OR 97201. Tel.: 503-494-6904, Fax: 503-494-4534, E-mail: derkachv{at}ohsu.edu.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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; Sigworth, 1980, Traynelis et al., 1993) and single-channel analyses (Hamill et al., 1981; Colquhoun and Hawkes, 1995; Colquhoun and Sigworth, 1995). These insightful approaches have, however, some limitations. Reliable determination of the number of functional channels by variance analysis is critically dependent on a high open probability of the channels involved. This condition dictates high or saturating agonist concentrations, if ligand-gated ion channels are of interest. In contrast, correct measurements of single-channel currents require recordings from either one functional channel (this might be difficult to prove; see Horn, 1991; also see Colquhoun and Hawkes, 1995) or from a population of channels with a low open probability, to observe well-separated single-channel events. The latter is usually accomplished by using low agonist concentrations. However, determination of the number of functional channels becomes challenging at low open probability (Horn, 1991; Colquhoun and Hawkes, 1995), and channel properties can also depend on the concentration of agonist used, if the property is a function of receptor occupancy by agonist (Rosenmund et al., 1998; Smith et al., 2000; Smith and Howe, 2000). Thus, variance and singe-channel analyses may not necessarily speak for the same values because of different experimental requirements. Therefore, it would be beneficial to develop techniques that measure channel properties under the same experimental conditions as, for example, variance analysis does, but independently. This would allow a cross-examination of a particular molecular mechanism by such techniques, and thus a firm establishment of the mechanism.
Here I propose such an approach, silence analysis, and apply it to an important problem of modern neuroscience—the regulation of AMPA-type glutamate receptors by CaM-KII. This kinase is crucial for the enhanced strength of glutamate synapses during long-term potentiation (LTP; see Malinow et al., 1989; Silva et al., 1992; Giese et al., 1998), a cellular model for memory and learning. The activity of CaM-KII increases upon induction of LTP (Fukunaga et al., 1993, Barria et al., 1997a) and one of major substrates for this kinase in the postsynaptic density is the GluR1 subunit of AMPA receptors (McGlade-McCulloh et al., 1993; Barria et al., 1997a; Lee et al., 2000; Yoshimura et al., 2000) phosphorylated at Ser831 on the C-terminus. (Barria et al., 1997b; Mammen et al., 1997; Lee et al., 2000; Huang et al., 2001). This phosphorylation results in increased channel conductance of GluR1 AMPA receptor (Derkach et al., 1999), and is reminiscent of the increased conductance of postsynaptic AMPA receptors during LTP (Benke et al., 1998) or upon elevation of CaM-KII activity in CA1 pyramidal neurons (Poncer et al., 2002). These data on the regulation of AMPA receptors by CaM-KII have been previously obtained through variance and single-channel analyses, which were performed, however, under different experimental conditions. In the present study, the receptor regulation is tested independently and nonambiguously: by applying silence analysis to the GluR1 AMPA receptor mutated at the CaM-KII site, and by concurrent application of silence and variance analyses to the same sets of data.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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). All recordings were performed in outside-out patches. Patch pipettes were filled with the following solution (in mM): 160 CsCl, 2 MgCl2, 4 Na-ATP, 1 EGTA, 10 HEPES, pH 7.3. Extracellular solution contained (in mM): 165 NaCl, 2.5 KCl, 1 MgCl2, 2 CaCl2, 5 HEPES, pH 7.3. Fast application of glutamate to outside-out patches was performed by piezo-driven application system or by puffing. Glutamate at 10 mM was applied in pulses of 40–50 ms with 5-s intervals between pulses. Currents were recorded at room temperature and membrane potentials -80 mV to -140 mV using an Axopatch 1B amplifier (Axon Instruments, Union City, CA). Frequency bandwidth and sampling rate were 1–2 kHz and 5–10 kHz, respectively (pCLAMP6 software, Axon Instruments). Statistics on channel parameters are presented as mean ± SE. Difference in values of a parameter was considered significant if p < 0.05, as evaluated by two-tailed Student's t-test.
Analyses
A total of 37–99 trials were performed on individual patches to collect data for concurrent application of silence or nonstationary variance analyses. For both techniques, experimental data were fitted by theoretical functions using Chi-square minimization procedure (KaleidaGraph, Synergy Software, Reading, PA). The relationship between the variance, var, of macroscopic current fluctuations and the mean current, I, was fitted by the function (Sigworth, 1980),
 | (1) |
 | (2) |
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For silence analysis (described in the text, Fig. 1), the threshold for detection of single-channel openings was set at 3
of background noise to minimize its contribution to the estimated silence factor. The actual contribution of false events related to the noise was measured in the absence of glutamate applications using deflections of noise opposite in polarity to the channel activity. It was less than 1% of the total number of detected events, and its contribution to the silent factor was regarded as negligible.
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; Banke et al., 2000; see also Fig. 2 A) and Axograph 4.2 software (Axon Instruments). The model was run at 10 kHz sampling and filtration bandwidth 2 kHz resembling our experimental conditions. Parameters of the model were adjusted to reproduce properties of the flip version of GluR1 receptor, because this receptor was examined in our experiments (Mosbacher et al., 1994; Partin et al., 1996; Derkach et al., 1999; Banke et al., 2000); including: activation and inactivation kinetics, kinetics of desensitization and recovery, steady-state desensitization, agonist dependence of desensitization kinetics, apparent affinity to glutamate, and the peak open probability of channels (Table 1). The model was run then in a stochastic mode. Conditions of low (0.128) and high (0.58) open probabilities were achieved at 0.3 mM and 10 mM glutamate, respectively. For each set of channel parameters, 100 trials were collected for a single analysis (run), and 16–20 of such runs were generated for each set of experimental conditions to verify accuracy and stability of the analysis. Estimates of each parameter are presented as mean ± SE (Fig. 3) and the accuracy of the analyses was evaluated by an estimation error calculated as a relative deviation of estimated parameter from its model value. Because in a real experiment the threshold for detection of channel openings/closings can vary in regard to the amplitude of single-channel currents, silence analysis was performed at different thresholds of detection for each run (set at 0.2, 0.5, and 0.8 of single-channel current amplitude and shown by different symbols on Fig. 3) and for every set of experimental conditions, to see how this may affect the analysis outcome.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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; Colquhoun and Hawkes, 1995). The problem is that channel openings are not explicit on the actual number of channels (N) contributing to the activity. Another approach would be to look at the closed states of the channels. Indeed, at moments of time when channels are simultaneously closed their contributing number becomes explicit and should be identified as "all channels." These moments in channel activity can be directly measured and treated quantitatively as a probability for all channels being simultaneously silent (closed), or a silent factor S. This factor is an extreme condition for the above equation when m = 0, so as
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This equation can be also expressed as a function of macroscopic current I after substitution for Po from Eq. 2:
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Two channel parameters, i and N, can be found then upon fitting S values by Eq. 6, and Po can be calculated using Eq. 2. Note, the initial slope of S factor carries information on single-channel current.
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Critically, Eq. 6 cannot be obtained by a combination of Eqs. 1 and 2, indicating silence analysis is independent of variance analysis.
Measuring S factor and channel parameters
For a population of channels, their probability to be simultaneously silent at a particular moment of time was calculated as a relative frequency of finding all of the channels closed. An example of calculations of S factor for actual channel activity is shown on Fig. 1. Activity of GluR1 AMPA receptors was evoked in an outside-out patch by repetitive applications of 10 mM glutamate (Fig. 1 A). In each trial, for those periods of time when all channels were simultaneously closed (no events above the threshold of detection), the value of S(t) was assigned 1 (successful event) and 0 otherwise (failure) (Fig. 1 B, upper traces in each pair). S factor was calculated then as a weighted value of S(t), upon averaging of S(t) for each time point across multiple trials (Fig. 1 C, upper trace). Channel properties were found upon fitting the relationship between S factor and the macroscopic current by Eq. 6 with i and N as free parameters (Fig. 1 D, solid line). Po was calculated then from Eq. 2.
Thus, all three values, i, N, and Po can be determined without knowledge of the power of fluctuations of macroscopic current, which would be required for variance analysis. That suggests silence analysis as a new and alternative tool for evaluation of channel properties.
Accuracy and limitations
Due to its nature, silence analysis puts certain requirements on the experimental situation. The approach assumes S factor is a measurable value, and that requires that during a macroscopic response all channels in a population will be temporary and simultaneously closed. As Eq. 5 states, such events in channel activity can be infrequent, if either the open probability or the number of functional channels is high. This may affect the accuracy of the silence factor calculations and thus measurements of channel parameters. Therefore, I tested silence analysis in more detail, under variable and controllable conditions provided by modeling experiments (Figs. 2 and 3, see Experimental Procedures). I was particularly interested to see how well the approach performs at low versus high open probability and with a variable number of channels in a population. Deviation of each estimated parameter from its modeling value was calculated as an estimation error. The method was particularly precise in measuring single-channel current (the error was between 1% and 8%) independently of experimental conditions (Fig. 3, A and B). Calculations of N and Po were also close to the model (the error ranged from 1% to 28%) as long as open probability was low or channel population was small (10 channels or below, Fig. 3, A–C). However, for a high open probability (0.6) and increased population of channels (more than 10), the error became significant (49% for N and 125–170% for Po, Fig. 3, B and C).
Overall, the approach was quite sensitive for the evaluation of channel conductance in a broad range of conditions, being more accurate and persistent than nonstationary variance analysis at low Po and small channel population (Fig. 3). It was also reliable for measuring of N and Po, if the number of channels in a population was small (
10 or less); whereas variance analysis became more accurate at a high Po and bigger channel population (Fig. 3). Therefore, to measure all three properties using both approaches simultaneously, I followed the requirement of small channel population by restricting the number of channels in my patch experiments to fewer than 10–12. This reduced the number of suitable patches to 13 from the total of 49 analyzed.
Testing S831 mutants
I chose two mutants of the GluR1 receptor with either aspartate or alanine at position S831, the residue phosphorylated by CaM-KII during LTP in the CA1 region of hippocampus (Barria et al., 1997a; Lee et al., 2000; Huang et al., 2001). These mutations functionally mimic the receptor either phosphorylated by CaM-KII or dephosphorylated at S831, respectively (Barria et al., 1997b; Mammen et al., 1997; Derkach et al., 1999) and maximized the functional homogeneity of the receptor population in regard to the phosphorylation state of S831.
Silence analysis applied to the D831 mutant (Fig. 1 and Fig. 4 A) revealed that the single-channel conductance ranged from 20 pS to 31.8 pS, having an average value of 24.3 ± 1.4 pS (n = 7, Fig. 4 A). In contrast, single-channel conductance of the A831 mutant was significantly lower, having an average value of 12.8 ± 0.8 pS (p < 0.01, variations from 8.9 pS to 15.3 pS, Fig. 4 A). Channel open probabilities varied broadly for both mutants (Fig. 4 B), from 0.36 to 0.87 for the S831D mutant with an average value of 0.55 ± 0.07 (n = 7; coefficient of variation, CV = 0.31), and from 0.35 to 0.71 for the S831A mutant, with an average value of 0.54 ± 0.06 (n = 6, CV = 0.28, Fig. 4 B) and were not distinguishable (p = 0.42).
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, Po, and N, as correlation coefficients were 0.92, 0.88, and 0.95 for , Po, and N, respectively (Fig. 6). Ratios of values estimated by silence analysis to those by variance analysis were 1.01 ± 0.04, 0.96 ± 0.1 and 1.07 ± 0.04 for , Po, and N, respectively (n = 13 for each parameter).
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DISCUSSION |
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Although the present study demonstrates an application of silence analysis to nonstationary channel activity, there are no principal limitations to use it in equilibrium, for steady-state channel activity. The universal requirement would be the same—small channel population.
AMPA receptor regulation by CaM-KII
This study presents new evidences for the mechanism of AMPA receptor regulation by CaM-KII. First, silence analysis independently shows that a negative charge at S831 of GluR1 receptor is critical for the enhanced efficiency of channel as a charge carrier, because of increased channel conductance. Second, the mechanism was scrutinized by simultaneous application of silence and variance analyses to the same sets of data. This eliminates the ambiguity of different experimental conditions previously used for variance and single-channel analyses to examine the regulation of the GluR1 AMPA receptor by CaM-KII (Derkach et al., 1999). Because both approaches agree on the mechanism, this further strengthens the conclusion of how the AMPA receptor is regulated by CaM-KII mediated phosphorylation.
Results of this study strongly support the recent finding on the regulation of postsynaptic AMPA receptors by CaM-KII. Overexpression of constitutively active form of the kinase in CA1 pyramidal neurons of hippocampus resulted in increased single-channel conductance of synaptic AMPA receptors (assayed by variance analysis) to be apparently a major contributing mechanism to the enhanced synaptic strength, and this was accompanied by increased phosphorylation of the S831 (Poncer et al., 2002). These observations are consistent with the increased channel conductance of synaptic AMPA receptors during LTP expression (Benke et al., 1998; Luthi et al., 1999). Silence analysis provides new and independent evidences for the molecular mechanism of AMPA receptor regulation by CaM-KII, and further strengthens the idea of how calcium-dependent phosphorylation of AMPA receptors can contribute to the plasticity at central glutamatergic synapses (Lisman et al., 1997; Malenka and Nicoll, 1999; Soderling and Derkach, 2000).
AMPA channel multiconductance and synaptic strength
It becomes increasingly apparent that AMPA receptors can adopt multiple conductance states. This is true for native (Jahr and Stevens, 1987; Cull-Candy and Usowicz, 1987; Banke et al., 2000; Smith et al., 2000; Smith and Howe, 2000) and recombinant receptors (Swanson et al., 1997; Rosenmund et al., 1998; Derkach et al., 1999; Banke et al., 2000). The functional significance for such channel multiconductance was unclear until recently. It was found that 1), LTP in the CA1 region of hippocampus is accompanied by an increased conductance of postsynaptic AMPA receptors in the majority of potentiated synapses (Benke et al., 1998); and 2), the conductance of AMPA receptors can be increased by increasing the contribution of high conductance states to the channel activity. The latter can be achieved in two different ways, however—either by increasing the receptor occupancy by agonist (Rosenmund et al., 1998; Smith et al., 2000; Smith and Howe, 2000) or through receptor phosphorylation by CaM-KII (Derkach et al., 1999, this study). These observations bring in focus two critical questions: whether postsynaptic AMPA receptors in CA1 synapses are saturated upon glutamate release (reviewed in Bergles et al., 1999; Clements, 1996; see also Liu et al., 1999; McAllister and Stevens, 2000) and what is the phosphorylation status of postsynaptic AMPA receptors before and after LTP induction (reviewed in Swope et al., 1999; Soderling and Derkach, 2000; see also Lee et al., 2000; Huang et al., 2001). Although other mechanisms for the regulation of synaptic strength should be also considered (Malinow et al., 2000; Luscher et al., 1999; Sheng and Lee, 2001; Huber et al., 2000; Liu and Cull-Candy, 2000; Linden, 2001), the data presently collected in the field strongly indicate that the increase in AMPA receptor conductance mediated by CaM-KII is one of the major contributors to LTP in CA1 hippocampal synapses (Lisman et al., 1997; Malenka and Nicoll, 1999; Lisman et al., 2002; Poncer et al., 2002). Interestingly, this regulation of synaptic strength is specific for a particular form of plasticity because AMPA receptors are selectively phosphorylated at S831 during LTP but dephosphorylated at S845 during LTD (Lee et al., 2000; Huang et al., 2001) and this is accompanied by increased channel conductance during LTP (Benke et al., 1998), but not LTD (Luthi et al., 1999). One remaining question is, what is the relative contribution of the modified channel properties versus incorporation of new receptors for the enhanced synaptic strength?
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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I thank Drs. Craig Jahr, Jeff Diamond, Matt Jones, Laurence Trussell, Thomas Soderling, and Coleen Atkins for insightful discussions and critical reading.
This work has been supported by a grant from the Medical Research Foundation.
Submitted on June 13, 2002; accepted for publication November 7, 2002.
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