 |
INTRODUCTION |
P2X purinoceptors are ligand-gated cation channels
that are activated by extracellular ATP and its analogues. These receptors exist in excitable and nonexcitable cells, including neurons, smooth and cardiac muscles, glands, astrocytes, microglia, and B lymphocytes
(Nakazawa et al., 1990a
; Bean, 1992
; Walz et al., 1994
;
Bretschneider et al., 1995
; Capiod, 1998
; McQueen et al., 1998
). During the past few years, seven P2X purinoceptor subunits (P2X1-P2X7) have been cloned (Brake et
al., 1994
; Valera et al., 1994
; Bo et al., 1995
; Lewis et al.,
1995
; Chen et al., 1995
; Buell et al., 1996
; Seguela et al.,
1996
; Soto et al., 1996
; Surprenant et al., 1996
; Wang
et al., 1996
; Rassendren et al., 1997b
). The P2X family
has a distinctive motif for ligand-gated ion channels, with each subunit containing two hydrophobic transmembrane domains (M1 and M2) joined by a large intervening hydrophilic extracellular loop (Brake et al.,
1994
). The cDNA of each receptor is ~2,000 bp in
length and has a single open reading frame encoding
~400 amino acids. A comparison of the amino acid sequences of the seven members shows an overall similarity of 35-50% (Collo et al., 1996
; North, 1996
; Surprenant et al., 1996
).
Dose-response analyses of the cloned receptors
made with whole cell currents revealed a Hill coefficient larger than 1, suggesting that activation of the
channel involves more than one agonist. This is consistent with experiments on the native receptors in PC12
cells and sensory neurons (Friel, 1988
; Nakazawa et al., 1991
; Bean, 1992
; Ugur et al., 1997
). Studies aimed at
measuring the subunit stoichiometry predict that the
naturally assembled form of P2X receptor channels
contains three subunits (Nicke et al., 1998
).
All of the P2X clones can be expressed in heterologous cells, such as HEK 293 cells and Xenopus oocytes.
ATP is a potent agonist for all cloned P2X receptors,
and the receptors are highly selective for ATP over
most other adenosine derivatives. However, benzyl-ATP is 10-fold more potent than ATP in activating P2X7 receptors (Surprenant et al., 1996
). It is interesting to
point out that
,
-methyl ATP is a poor agonist for the
subtypes that do not show desensitization: P2X2, P2X4,
P2X5, P2X6, and P2X7, but is a potent agonist for P2X1
and P2X3 receptors that do desensitize.
Most studies of cloned P2X receptors have focused
on the primary structure and pharmacology based on
whole cell currents, while only a small amount of work
has been done on the single channel properties. Single
channel currents from P2X1 receptors expressed in Xenopus oocytes were reported to have a mean amplitude of ~2 pA at
140 mV and a chord conductance of 19 pS between
140 and
80 mV (Valera et al., 1994
).
The conductance for P2X1, P2X2, and P2X4 channels
expressed in Chinese hamster ovary cells were ~18, 21, and 9 pS, respectively, at
100 mV with 150 mM extracellular NaCl, but the openings of P2X3 were not resolved (Evans, 1996
).
To provide a firmer basis for further analysis of the
P2X family, we have examined P2X2 receptors at the
single channel level. We have characterized the current-voltage (I-V)1 relationships, cation selectivity of
permeation, ATP sensitivity, proton modulation, and
gating kinetics.
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MATERIALS AND METHODS |
Expression Systems
P2X2 receptors were expressed either in stably transfected human embryonic kidney 293 (HEK 293) cells or in Xenopus oocytes by mRNA injection (Rudy and Iverson, 1992
). Since receptor expression is generally too high to obtain patches with only a single
channel, we decreased the expression of the receptors in Xenopus
oocytes by reducing the amount of mRNA to 25 ng, lowering the
incubation temperature from 17° to 14°C, and shorting the incubation time to 16 h.
For electrophysiological experiments, HEK 293 cells were cultured at 37°C for 1-2 d after passage. The medium for HEK 293 cells contained 90% DMEM/F12, 10% heat inactivated fetal calf serum, and 300 µg/ml geneticin (G418). The media were adjusted to pH 7.35 with NaOH and sterilized by filtration. The incubation medium (ND96) for Xenopus oocytes contained (mM): 96 NaCl, 2 KCl, 1 MgCl2, 1.8 CaCl2, 5 HEPES, titrated to pH 7.5 with
NaOH. All chemicals were purchased from Sigma Chemical Co.
Electrophysiology
We made patch clamp recordings from HEK 293 cells 1-2 d after
passage and from Xenopus oocytes 16 h after injecting mRNA. Single channel currents from outside-out patches and whole cell currents were recorded at room temperature (Hamill et al.,
1981
). Recording pipettes, pulled from borosilicate glass (World
Precision Instruments, Inc.) and coated with Sylgard, had resistances of 10-20 M
.
For recording from HEK 293 cells, the pipette solution contained (mM): 140 NaF, 5 NaCl, 11 EGTA, 10 HEPES, pH 7.4. The
bath solution and control perfusion solutions were the same and
contained (mM): 145 NaCl, 2 KCl, 1 MgCl2, 1 CaCl2, 11 glucose,
10 HEPES, pH 7.4. For Xenopus oocytes, the pipette solution contained 90 mM NaF instead of 140 mM NaF and other components were the same as for HEK 293 cells; the bath and control
perfusion solutions were the same as those used for HEK 293 cells except that they contained 100 instead of 145 mM NaCl.
The patch perfusion solutions were the same as the bath solutions, except for modified divalent and ATP concentrations. Perfusate was driven by an ALA BPS-4 perfusion system (ALA Scientific Instruments). To investigate the cation selectivity of the
channels, we substituted different cations for Na+ ion in the perfusate. To investigate the affinity of Na+ for the channel pore, we
varied the extracellular NaCl concentration without compensation by other ions, while the pipette solution was kept constant.
The resulting change of ionic strength caused the development
of small liquid junction potentials between the bulk solution and
the perfusate. We calculated these potentials according to the
Henderson equation (Barry and Lynch, 1991
). For solution exchanges from 100 to 150, 125, 100, 75, 50, and 25 mM NaCl, the
junction potentials were
2.1,
1.2, 0, 2.5, 3.6, and 7.3 mV, respectively. Because these values are small compared with the
holding potentials, we did not correct the membrane potential when we calculated the chord conductances.
Currents were recorded with a patch clamp amplifier (AXOPATCH 200B; Axon Instruments), and stored on videotape using
a digital data recorder (VR-10A; Instrutech Corp.). The data
were low-pass filtered at 5-20 kHz bandwidth (
3 dB) and digitized at sampling intervals of 0.025-0.1 ms using a LabView data
acquisition program (National Instruments).
Analysis
Amplitudes and excess channel noise.
The mean amplitudes of single channel currents were determined by all-points amplitude
histograms that were fit to a sum of two Gaussian distributions.
Chord conductances were calculated assuming a reversal potential of 0 mV. The excess open channel noise (
ex) was computed
as the root mean square (rms) difference between the variances
of the open channel current and the shut channel current (Sivilotti et al., 1997
).
The probability of the channel being open.
Po was defined as the ratio of open channel area (Ao) to the total area (Ao + Ac) in the
all-points amplitude histogram:
|
(1)
|
This calculation is insensitive to short events. In our initial analysis, we treated the channel as having two amplitude classes, open
and closed. All rapid gating events associated with the open channel were treated as noise. Essentially we defined open as "not closed."
Power spectra.
Power spectra, S(f), were computed using the
fast Fourier transform routine in LabViewTM. We used records of
50~100-ms duration associated with open and closed states. The
power spectrum of the excess noise was obtained by subtracting
the spectrum of the closed state spectrum from that of the open
state. The spectra were fit with the sum of a Lorentzian plus a
constant:
|
(2)
|
The rms noise,
L, from the Lorentzian component is:
|
(3)
|
Thermal and shot noise.
The thermal (or Johnson) and shot
noise contributions were calculated according to Defelice
(1981)
. The power spectra of thermal noise is white; i.e., it does
not vary with frequency. Its value, Sth, is given by:
|
(4)
|
where kBT is Boltzmann's constant times the absolute temperature, and R is the equivalent resistance of the open channel. The
rms noise in a bandwidth f is given by:
|
(5)
|
The power spectrum of shot noise, Ssh, is also white and given by:
|
(6)
|
where q is the charge of an elementary charge carrier, and i is the
single channel current amplitude. The rms amplitude of shot
noise over bandwidth f is:
|
(7)
|
Multiple conductance levels.
We tried to see if there were discrete
subconductance levels making up the open channel "buzz
mode" by using a maximum-point likelihood method (MPL;
) that use the Baum-Welch algorithm
(Chung et al., 1990
). While we did get convergence at four to six
levels, these levels were not consistent among data from different
patches, so at the present time we cannot confidently describe
the substate structure.
Kinetic analysis.
The single channel currents were idealized
with a recursive Viterbi algorithm known as the "segmental
k-means" algorithm (SKM; ; Qin et al.,
1996b
). Idealization is dominated by the amplitude distribution,
and therefore is essentially model independent. For simplicity,
we used a two-state model for idealization: closed
open (C
O). The distributions of closed and open times were displayed as
histograms with log distributed bin widths versus the square root
of the event frequency (Sigworth and Sine, 1987
). The mean
open and closed times were simple averages from the idealized currents.
The rate constants of state models were obtained by using the
maximal interval likelihood method with corrections for missed events (MIL; ; Qin et al., 1996a
). We used two strategies to fit the data: (a) individual fitting (i.e., fitting the
data sets from each experimental condition individually), and
(b) global fitting (i.e., fitting a group of data sets obtained under
different experimental conditions). The first method produces an independent set of rate constants for each condition, but suffers from poor identifiability: a given model may not have unique
rate constants. Global fitting improves identifiability by using a model with fewer parameters. We did both kinds of analysis of the data, and the results were consistent between the two methods of analysis, but global fitting permitted fitting more complicated models. For simplicity, we will emphasize the results of global fitting across ATP concentrations at the same voltage, or
across voltages at the same ATP concentration. When we globally
fit data from different ATP concentrations, we assumed that the
association rates were proportional to concentration [i.e., kij = kij(0)[ATP], where kij(0) is an intrinsic rate constant at the specified voltage], while the other rates were assumed to be independent of ATP. When we performed global fitting on data from different voltages at same ATP concentration, the rates were assumed to be exponential functions of voltage; i.e., kij = kij(0)exp(
z
ijV/kBT), where kij(0) is the apparent rate constant
at 0 mV and the specified ATP concentration, and z
ij is the effective sensing charge.
We used Akaike's asymptotic information criterion (AIC) to
rank different kinetic models (Vandenberg and Horn, 1984
;
Horn, 1987
):
|
(8)
|
where j is the number of the data set. The model with a higher
AIC is considered a better fit.
Simulation.
We used Origin (Microcal Software, Inc.) and Scientist (MicroMath Scientific Software, Inc.) software to simulate
and fit data.
 |
RESULTS |
Basic Features of Single Channel Currents
Fig. 1 A shows a typical single channel current, activated by 1.5 µM ATP at a membrane potential of
100
mV from a stably transfected HEK 293 cell. Channel
openings appeared as flickery bursts with ill-defined
conductance levels. There were a few clear closures and
subconductance levels within a burst, but discrete levels
could not be resolved from the all-points histogram
(see Fig. 1 B). The spread of current levels was reflected by the much larger standard deviation of the
open than the closed component, each of which could
be fit reasonably well with a single Gaussian. In Fig. 1 B,
the mean open current amplitude is 3.2 pA, equivalent to a chord conductance of 32 pS. The standard deviations of the open and closed histograms are 0.95 and
0.24 pA, respectively, so that the excess open channel
noise
ex is 0.92 pA; i.e., 29% of the mean current amplitude. Since the mean is clearly less than the peak
current, we obtained a closer estimate of the maximal open channel current by measuring the mean of extreme values. Comparing the upper 5% of the two distributions, the peak amplitude and conductance were
4.3 pA (Fig. 1 B, arrow) and 43 pS, respectively, a closer
estimate of the maximum ion flux. However, for convenience in discussing later results, unless specified otherwise, "channel current" and "conductance" will refer
to the mean rather than the peak values.

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Fig. 1.
The general features of single channel currents from an outside-out patch of an HEK cell stably transfected with P2X2 receptors. (A) Typical single channel current traces with inward current shown downward. The current was activated by 1.5 µM ATP at 100
mV, in the presence of 1 mM extracellular Mg2+ and Ca2+. The data were low-pass filtered at 5 kHz and digitized at 10 kHz. There are large
fluctuations in the open channel current. A distinct substate can be seen occasionally in some bursts (e.g., second trace, second opening
burst; bottom trace, third opening burst). Due to their short lifetimes, these substates are not evident in the amplitude histogram (B). (B)
The all-points amplitude histogram of single channel currents from A (0.05 pA/bin). The distribution was fit by a sum of two Gaussians
(solid lines) with means of 0 and 3.2 pA. The standard deviation in the open state peak (0.95 pA) is much larger than that of the closed
state peak (0.20 pA). (C) Comparison of the power spectrum of excess open channel fluctuations (solid line) with the expected thermal
(dot line) and shot (short dot line) noises. Note that the amplitude of the open channel fluctuations is much larger than either thermal or
shot noise. The solid line is a fit of the data to a Lorentzian with a cutoff at 264 Hz (dash dot line) plus a constant noted S1 (dash dot dot
line). The plotted spectrum is the average of three different spectra. (D) A higher time-resolution example of a burst at 5 kHz showing the
general absence of easily discernible substates with one possible exception (arrow). The solid line is the mean current (3.2 pA).
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To further characterize the open channel noise, we
followed the procedures of Sigworth (1985
a) for noise
analysis using differential power spectra. We compared
the power spectra of excess open channel fluctuations
with the expected thermal and shot noise (Fig. 1 C).
The open channel spectrum was well fit with a Lorentzian with fc = 264 Hz, equivalent to a relaxation time of
0.62 ms, plus a constant. This noise is much larger than
the expected thermal or shot noise, suggesting that the
fluctuations most likely arise from rapid conformational changes in the channel.
I-V Curve
Fig. 2 A shows the single channel currents recorded
from HEK 293 cells activated by 2 µM ATP at different
holding potentials using outside-out patches with symmetrical Na+. The currents became small and noisy at
positive holding potentials so that the unitary currents
were not discernible. The single channel I-V curve
(Fig. 2 B) exhibited a strong inward rectification similar to whole cell currents recorded under the same
conditions (Fig. 3, A and B). The rapid fluctuation of
current in the "open" state was maintained at all holding potentials.

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Fig. 2.
The current-voltage
relationship of single channel
currents. (A) Single channel currents of an outside-out patch from
HEK 293 cells activated by 2 µM
ATP (1 mM extracellular Mg2+
and Ca2+ at different membrane
potentials, symmetrical Na+ solutions containing 145 mM extracellular NaCl/145 mM intracellular
NaF). The data were filtered at
5 kHz and digitized at 10 kHz. All
of the current traces in this figure
are from the same patch. (B)
Mean I-V relationship of single
channel currents. The error bars
indicate the standard deviation of
the single channel currents from
the all-points histograms. The single channel I-V relationship
shows strong inward rectification
despite exposure to identical Na+
solutions across the patch. The same result was obtained when single channel currents were recorded in the absence of Mg2+ and Ca2+;
therefore, divalent cations are not responsible for the rectification.
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Fig. 3.
The current-voltage relationship of whole cell currents (WCCs). (A) Whole cell currents from an HEK 293 cell at different
holding potentials: 100 to +80 mV at 20-mV intervals. Voltage drops from incomplete series resistance compensation were subtracted
from the membrane potential. The currents were activated by 10 µM ATP in the presence of 1 mM extracellular Ca2+ with symmetrical
Na+ solutions: 145 mM extracellular NaCl/145 mM intracellular NaF. The data were filtered at 2 kHz and digitized at 5 kHz. (B) The
mean WCCs (±SD) ( , n = 5) and predicted WCCs activated by 10 µM ATP. The I-V curve exhibits strong inward rectification, similar to
the single channel currents shown in Fig. 2. The reversal potential was ~0 mV. The predicted WCCs were calculated using Eq. 15 where i
(single channel amplitude) was taken from Fig. 2, and Po from the calculations for Model 1-4 ( ) (Fig. 13), and Eq. 16 ( ) as a function
of voltage. The number of channels was chosen so that the predicted WCC at 60 mV was equal to the experimental data. The predicted
WCCs match reasonably well with the experimental data.
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Fig. 13.
Simplified and expanded versions of Model 1 from
Fig. 11 (1-1, 1-2, 1-3, and 1-4),
and other kinetic models (9 and
8-1) that have been used in the
literature.
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Fig. 11.
All models that converged during maximal likelihood estimation in the initial topology screen using the MSEARCH program. The relative likelihoods and AIC rankings of these models are listed in Table IV.
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Table IV
Comparison of Simulation Values and Fitted Values of Rate Constants from Individual Fitting Based on Model 1 (Fig. 11)
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Dose-Response Curve of Single Channel Currents
We were unsuccessful in obtaining outside-out patches
containing only a single P2X2 channel when the receptor was expressed in HEK 293 cells. However, we were
able to obtain patches with a single channel from
Xenopus oocytes provided we carefully controlled the
amount of mRNA, and the time and temperature of incubation. Fig. 4 A shows the single channel currents
from an outside-out patch activated by different concentrations of ATP. As expected, increasing the concentration of ATP increased Po. The all-points histograms
(Fig. 4 B) show that the average current and excess noise were independent of ATP concentration (i = 3.5 pA
and
ex = 1.6 pA at
120 mV). Thus there is no indication that ATP is blocking the open channel. We calculated the probability of being open at each ATP concentration from the amplitude histograms using ratio
of the open area to the total area (Fig. 4 C). The open probability saturated when the ATP concentration
reached 30 µM. The dose-response curve was fitted by
the Hill equation with a Hill coefficient of 2.3, an EC50
of 11.2 µM, and a maximal open probability of 0.61. The Hill coefficient and EC50 are similar to those obtained from the dose-response curves of whole cell currents of our own data (not shown) and the literature
(Brake et al., 1994
), indicating that there are at least
three subunits in a functional P2X2 receptor ion channel. The data from this patch were very stable and used
later in the comparison of kinetic models.

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Fig. 4.
The effect of ATP concentration on single channel currents. (A) Single channel currents of P2X2 receptors expressed in Xenopus oocytes activated by different concentrations of ATP in the absence of extracellular Ca2+ ( 120 mV). The data were filtered at 20 kHz
and sampled at 40 kHz. All of the current traces in this figure are from the same patch. (B) All-points amplitude histograms of the currents
from A (0.05 pA/bin), with the distributions fit to the sum of two Gaussians. At this bandwidth, the excess channel noise was ~45% of the
mean channel amplitude. (C) ATP dose-response curves. The probability of a channel being open is shown from experimental data ( )
and simulated data generated by Fig. 13, Models 1-2 ( ) and 1-4 ( ), as a function of ATP concentration. Fits of the data sets to the Hill
equation are shown as dotted (experimental data), dash dot (Model 1-2), and solid (Model 1-4) lines. The Hill coefficient = 2.3, EC50 = 11.0 µM, and maximum Po = 0.61 for the experimental data, 1.5, 17.4 µM, and 0.74 for simulated data of Model 1-2, and 1.8, 13.3 µM, and
0.64 for simulated data of Model 1-4. All the experimental data were from same patch. The errors in the experimental Po were estimated
from the errors in the mean and standard deviation estimates reported by Origin when fitting the amplitude histograms to Gaussians.
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Na+ Conduction through the Ion Channel
To investigate the affinity of Na+ for the open channel,
we measured single channel amplitudes at holding potentials of
80,
100,
120,
140 mV for extracellular
NaCl concentrations of 10, 25, 50, 75, 100, 125, and 150 mM. Fig. 5 shows single channel currents activated by
15 µM ATP at different extracellular Na+ concentrations with a holding potential of
120 mV (Xenopus oocyte). The amplitude increased with the concentration
of NaCl but approached saturation at high Na+ levels.
Because the solutions were asymmetric across the
patch, we calculated the conductance with the driving
force as the difference between the holding potential
and the Nernst potential. The single channel chord
conductance,
, calculated this way is plotted as a function of Na+ concentration in Fig. 6 A. The conductance
versus [NaCl] at each potential was well fit with the
Michaelis-Menten equation (Hille, 1992
):
|
(9)
|

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Fig. 5.
The effect of extracellular NaCl concentration on the
single channel current amplitude. Currents activated by 15 µM
ATP were recorded from an outside-out patch from a Xenopus oocyte with different concentrations of NaCl without ionic substitution (different ionic strength) in the absence of Ca2+ at 120 mV.
The data were digitized at 20 kHz and low pass filtered at 10 kHz.
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Fig. 6.
The affinity of Na+ for the channels. (A). The relationship between the mean open channel conductance from Fig. 5 and the
NaCl concentration at different voltages. The error bars are the standard deviations of the excess open channel noise. The solid line is a fit
of Eq. 9. Note that at each concentration the driving force changes because of the change in Nernst potential. We have assumed Ks is dependent only on the holding potential and not the driving force. (B) The dependence of Ks on holding potential. The solid line was fit by
the Boltzmann equation with z = 1, = 0.21, and Ks(0) = 148 mM. A depolarization of 118 mV is required for an e-fold increase of Ks. The
error bars are the parameter fitting errors from A. (C) The maximal conductance as a function of holding potential. The error bars are
the fitting errors from A. The solid line is simply the connection of data. The conductance is approximately linear with the holding potential supporting the simple approximation of Eq. 9.
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yielding Ks and
max at each voltage.
The equilibrium constant, Ks, increased with depolarization (Fig. 6 B). The relationship between Ks and
holding potential can be described by a Boltzmann
equation for a single binding site:
|
(10)
|
where Ks(0) is the dissociation constant at 0 mV,
is the
fractional electrical distance of the site from the extracellular surface, z is the valence of the permeating ion
(1 in this case), and kBT is Boltzmann's constant times absolute temperature (~25 mV at room temperature). The
fitted values of Ks(0) and
were 148 mM and 0.21, respectively, so that a depolarization of 118 mV is required
for an e-fold increase of Ks (Fig. 6 B). The Na+ binding
site appears to be ~20% of the electrical distance from the extracellular surface, and is half saturated when exposed to 148 mM Na+ at 0 mV. The maximal conductance,
max, also increased with the potential as expected
in a nearly linear part of the I-V curve (Fig. 6 C).
The Selectivity between Cations
The P2X2 receptor ion channel is a nonselective cation
channel; however, the conductance is different for different cations. We measured the single channel currents at
120 mV from HEK 293 cells using outside-out
patches with NaF as the intracellular solution, and LiCl,
NaCl, KCl, CsCl, and RbCl as the extracellular solutions
(Fig. 7). From the currents obtained at
120 mV (Table I), the selectivity was K+ > Rb+ > Cs+ > Na+ > Li+.
Although currents carried by the different cations had
the same flickering behavior, the excess open channel
noise,
ex, had a slightly different selectivity K+
Rb+ > Cs+ > Na+ > Li+. The relative noise, defined as
ex/i,
was Rb+
Na+
Cs+
K+ > Li+. The difference in selectivity of the relative noise for Li+ suggests that it can
affect the flickery kinetics. We compared
ex with the
thermal,
th, and shot,
sh, noise (Table I). Again,
th
and
sh were very small compared with
ex, and the ratio (
th +
sh) to
ex ranged from 8 to 14% depending
on the ions. The relative noise caused by the open
channel fluctuations, when corrected for thermal and
shot noise (
2ex
2th
2sh)1/2/i, followed the same
cation sequence as
ex/i.

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Fig. 7.
The effect of different permeant ions on the single
channel currents. Single channel currents from HEK 293 cells at
120 mV activated by 2 µM ATP in the presence of 1 mM Mg2+ and
Ca2+ from an outside-out patch. The data were filtered at 5 kHz and
digitized at 10 kHz.
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Effect of pH
The effect of pH on channel activation.
The effect of extracellular pH on the channel currents is illustrated in
Fig. 8 A. Multiple-channel outside-out currents activated by 2 µM ATP increased ~10-fold when pH was
decreased from 8.3 to 6.8, and saturated with further
decreases in pH (Fig. 8 B). The fluctuations in these
multichannel currents at higher pH appeared to be
dominated by the overlap of independent channels, so
that at pH 8.3, where the mean current is small, single
channel events were visible. At pH 6.3, the current saturated and the frequency of fluctuations increased dramatically, apparently dominated by the open channel
noise. As is clear from the rise time of the currents, the
activation rate decreased with increasing pH, and the
fall time remained constant (Fig. 8 C). The potentiation of channel activity by protons is similar to the effect of increasing the ATP concentration, suggesting
that protons may increase the affinity of the binding
site for ATP. The pKa was ~7.9 and the Hill coefficient
was 2.5, again suggesting that there are more than two
subunits in the channel.

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Fig. 8.
The effect of pH on the affinity of channel for ATP. (A) Multiple-channel currents from an outside-out patch of HEK cells at
120 mV and 2 µM ATP at different values of extracellular pH (0.3 mM extracellular Ca2+). Note the increase in rise time with increasing
pH. The data were low pass filtered at 5 kHz and digitized at 10 kHz. The horizontal bar indicates the duration of ATP application. (B)
The effect of pH on mean patch current. The data were fitted by the Hill equation with a maximum mean current of 25.1 pA, an EC50 of
pH 7.9 (pKa), and a Hill coefficient of 2.5. The error bars are the standard deviation of the data and contain both open channel and gating
noise. (C) The pH dependence of the rise and fall times. The time constants for rising ( ) and falling ( ) phase were obtained from fitting single exponential functions (solid lines) to multiple channel currents.
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Effect of pH on single channel properties.
The results above
and published studies on the effect of pH were based
on whole cell or multi-channel recordings (Li et al., 1996
,
1997
; King et al., 1997
; Stoop et al., 1997
; Wildman et al., 1998
). To explore the possible effect of pH on gating and channel conductance, we examined the effect
of pH on single channels. To obtain single channel activity from the stable cell lines, we exposed the patch to
ATP for long times, so that run down reduced the number of active channels. Fig. 9 A shows these currents recorded at different values of extracellular pH. We measured the single channel amplitude and excess noise
from the all-points amplitude histograms. The mean amplitude of the current was independent of pH, but the excess open channel noise increased with decreasing pH
(Table II). As visible in Fig. 9 A, the frequency of brief closures within open channel bursts appeared to increase
as pH decreased. These interruptions were longer than
the normal fast "flickery" behavior. It has been suggested
that protons may block an open channel (Yellen, 1984
).
To further characterize this phenomenon, we computed
the power spectra of the open channel fluctuations (Fig.
9 B), and fit them with a Lorentzian plus a constant (Eq. 3). The constant represents relaxations occurring at frequencies beyond our resolution.

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Fig. 9.
The effect of extracellular pH on single channel currents. (A) Currents recorded from an outside-out patch of an HEK 293 cell under the same experimental conditions as in Fig. 8 A. The data were low-pass filtered at 10 kHz and digitized at 20 kHz. (B) Differential power spectra of the open channel currents at different values of extracellular pH. The spectra were fit with the sum of a Lorentzian
function plus a constant (solid line). The corner frequencies are indicated by the arrows. Plotted spectra are averages of three separate
data segments.
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The fits are illustrated in Fig. 9 B as solid lines. The
Lorentzian represents a two-state relaxation process
whose characteristic time constant
is related to the
corner frequency, fc, by:
|
(11)
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Diffusional block of the open channel can be described
as a two-state model (Scheme I), where O is the open
state and Cb is the protonated-blocked state.
and
are the blocking and unblocking rate constants. The relaxation time for this two-state process is
related to the rate constants by:
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(12)
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The prediction of proton block is that fc increases linearly with increasing proton concentration. However,
our data show that fc decreased with increasing proton
concentration (Fig. 9 B). To further examine the possibility of proton block, we analyzed bursts kinetically using the maximum likelihood method with a two-state
model. We fit the extracted
's and
's at different pH
to an equation of the form:
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(13)
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where
0 = 224 µM
0.33s
1, n
= 0.33,
0 = 1,493 µM
0.16s
1, and n
= 0.16. Since
and
are not directly
proportional to the proton concentration, a single site
model appears to be inappropriate. We speculate that we
may be titrating several sites that display negative cooperativity. The effect of pH on the open channel is to modify
the conformation of the channel rather than to provide a
simple proton block. Table II summarizes the open channel properties at different pH. Remarkably, the effect
of pH on the mean open channel current is negligible.
Kinetic Analysis with the Maximal-Interval
Likelihood Method
To understand the kinetics of agonist binding and
channel gating, we applied the maximum likelihood
method to data from outside-out patches that were stable over time and ATP concentration (Fig. 4 A). We began by fitting simple noncyclic models using the maximum-likelihood interval analysis and used AIC ranking
to select a preferred model. The analysis was hierarchical in the sense that we fit portions of the reaction
scheme under restricted conditions, and then merged these models to create a full description. The kinetic
description required: (a) the number of closed and
open states, (b) the connections between states, and
(c) the values of the rate constants between the states
and their dependence on concentration and voltage.
Mean open and close times.
The data was idealized into
two classes: open and closed (see Fig. 10 A). We did not
attempt to idealize the data making up the bulk of the
flickery open channel activity since the amplitudes were uncertain, but instead defined open as a single
conductance state possessing a lot of noise.

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Fig. 10.
Idealization and open- and closed-interval duration histograms of single channel currents activated by different ATP concentrations (from the data in Fig. 4 A). (A) Examples of idealized single channel currents activated by 5 and 15 µM ATP. These data were recorded at 40 kHz and filtered at 20 kHz. Before idealization, the data were further filtered at 5 kHz using a Gaussian digital filter. The idealization was performed with the segmental k-means method based on a two-state model (see MATERIALS AND METHODS). (B) The mean
open and closed times of single channel currents as a function of ATP concentration. (C) The open- and closed-time histograms of single
channel currents activated by different concentrations of ATP. The solid lines are the predicted probability density functions for Fig. 13,
Model 1-4, with rate constants determined by global fitting across concentrations (see Table VIII). Ni/NT on the ordinate is the ratio of the
number of events per bin to the total number of events.
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The probability of a channel being open increased
with ATP, as shown in Fig. 4 C. This could result from
an increase in mean open time, a decrease in mean
closed time, or a combination. Fig. 10 B shows the
mean open and closed times calculated from idealized single channel currents, and plotted as a function of
ATP concentration. The mean closed time dramatically
decreased with the increase in ATP and saturated at
30 µM, while the mean open time was not affected by
ATP. The results indicate that ATP controls the rate at
which the channel opens, but not the rate at which it closes.
Duration histograms.
Fig. 10 C shows the open- and
closed-time histograms from idealized single channel
currents induced by different ATP concentrations (Fig.
10 A). The open-time histograms have two peaks and
the closed-time histograms have at least three peaks at
low concentration. When the ATP concentration was
increased, the intermediate and long time constant
peaks of the closed time distribution merged and only
two peaks were visible. These results indicate that the
channel has at least three closed and two open states.
Kinetic model comparison.
We made quantitative comparisons of various kinetic models to determine which
model best described the behavior. The models were
limited to three closed and two open states (of the
same conductance), and at most 10 rate constants.
These constraints proved necessary to obtain unique
solutions during optimization. There are 98 unique
models with that many states. Further constraints were
imposed to simplify analysis. (a) We discarded models
in which the unliganded states were open because we
did not see any spontaneous openings in the absence
of ATP. (b) Following traditional models for other
ligand-gated channels, the closed states were connected so as to represent the binding of ATP. To evaluate the possible topologies, we used the program
MSEARCH () to compare the likelihood of all remaining models. The program evaluates
all topologically unique models having a specified
number of states of each conductance and optimizes
the rate constants for each one. For this stage of the
analysis, we used three data sets obtained at 5, 10, and
15 µM ATP. We calculated the likelihood of each model
by adding the log-likelihoods from each concentration. This is more a test of the topology of the models than a
test of the optimal values of the rate constants since the
rate constants will change over concentration, but the
connectivity won't. Fig. 11 shows the eight kinetic models that converged on all data sets within 100 iterations.
They are listed in the order of AIC rank.
To determine which model was best, we compared
the log(maximum likelihood)s and AIC rankings (Table III). The likelihoods of Models 1, 2, and 3 (Fig. 11)
are the same, but Model 3 has two more parameters
and, hence, a lower AIC rank. Models 1 and 2 have the
same number of parameters, likelihood, and AIC rank,
so we can not tell the difference between them. Model
7 (Fig. 11) has a larger likelihood than Models 1 and 2;
however, its AIC ranking is much lower because of the
increased number of parameters. Model 8 (Fig. 11),
which has a partially liganded open state, has the smallest likelihood and lowest AIC rank. When Models 1 and
2 were compared across concentration, they were indistinguishable and, for simplicity in what follows, we arbitrarily selected Model 1. In both models, state C1 is unliganded, C2 and C3 are liganded, and O4 and O5 are
open. k12 and k23 are the agonist association rates, k21
and k32 are the agonist dissociation rates, k34 and k35 are
the channel opening rates, and k43 and k53 are the
channel closing rates.
The rate constants governing ATP binding and gating were solved by fitting across a range of ATP concentrations. Fig. 12 shows the rates from the model at bottom (from Fig. 11, Model 1) as a function of ATP concentration when the data from each concentration were fit independently. The association rates k12 and k23
showed a strong dependence on ATP concentration in
the 5-20 µM range. However, when the ATP concentration was >20 µM, the rate constants appeared to saturate and the error limits on the parameters increased. A concentration-driven rate should not saturate, but
there are a few explanations. First, there may be a concentration-independent state not contained in the
model. Second, k12 and k23 approach k35 at high ATP
concentration, making k35 rate limiting and rendering the optimizer incapable of properly solving the model.
Third, if k12 and k23 are linearly proportional to concentration, then the intrinsic rate constants of both k12
an k23 are ~2 × 107 M
1s
1, which is approaching the
diffusion limit.

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Fig. 12.
The effect of ATP concentration on the rate constants near the open states. The rate constants are based on kinetic Model 1 (shown at bottom), with each data set fit separately. Missed events were corrected by imposing a dead time of 0.05 ms. k12 and k23 are the
most sensitive to the concentration of ATP, increasing over the range from 5 to 20 µM, and saturating at higher concentrations. The rate
constants are plotted in two panels to avoid overlap (the scales in both plots are identical).
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We tested the first possibility by adding concentration-independent states to the model in Fig. 12, but
that did not prevent the association rates from saturating. We tested the identifiability of the model by
simulating the model across concentrations (SIMU; ) and attempting to extract the
rate constants using maximal interval likelihood. Fitting the simulated data, we found that the estimated
rate constants also saturated (see below) so that the
correct model is not identifiable with data from a single
concentration. As far as the diffusion limit providing a
true saturation, further experiments are required to
test that prediction. However, we currently believe that
the apparent saturation is an artifact caused by the lack
of identifiability of the model at high concentrations.
(Details of the test on the artifactual origin of saturating rates. We simulated data using Model 1 [Fig. 11],
with the rate constants k12 and k23 increasing linearly
with ATP in the 5-50 µM range. The intrinsic rate constants k12(0) and k23(0), obtained from the slope of k12
and k23 versus ATP from 5-30 µM (Fig. 12), were 14 and 22 µM
1s
1, respectively. All other rate constants
were made independent of ATP and set to values averaged across the data sets. We then analyzed the simulated data as if it were experimental data. The recovered rate constants were similar to the values used to
simulate the data for ATP <20 µM. At higher ATP levels, however, the estimated values of k12 saturated and
k21 even decreased. Large error limits also occurred in
k12 and k21 at the high concentrations [Table IV]. Thus,
Model 1 [Fig. 11] cannot uniquely fit data at single high ATP concentration.)
To improve identifiability, we fit the data simultaneously across all concentrations. Such global fitting
makes the likelihood surface steeper (Qin et al.,
1996a
). We assumed that the association rate constants
were proportional to the ATP concentration, and the
other rate constants were independent of ATP (see MATERIALS AND METHODS). This time, the rate constants
derived from global fitting of simulated data were very
close to the values used for simulation (Table V). The
results of global fitting to the experimental data are
listed in Table VI. It is worth noting that the second ATP association rate constant, k23(0), is larger than the
first, k12(0). This result shows that the binding sites are
not independent, but that binding to one site modifies
binding to the other. With independent sites, the association rate should decrease as the number of free sites
decreases. The conclusion is quite model independent;
for every model we tested, the association rates increased with proximity to the open state (see below).
Model simplification and expansion.
As ATP concentration increased, the three peaks in the closed time duration histograms became two (Fig. 10 C), suggesting that at high ATP concentration, Model 1 (Fig. 11) could be
simplified by removal of state C1 (Fig. 13, Model 1-1).
When we fit the kinetics of high concentrations of ATP
by Model 1 and Model 1-1, the likelihoods were equal.
Thus, at high ATP, k12 gets so fast that C1 is rarely occupied and Model 1-1 is sufficient to describe the kinetics. However, a large difference in maximum likelihoods
arose when we fitted Models 1 and 1-1 to the data at low
concentrations of ATP. Model 1-1 can well describe
the kinetics of single channel currents of high ATP, but
not low.
Our model has only two binding steps. The fact that
the Po curve has a Hill coefficient of 2.3 suggests that
there are at least three binding sites in the P2X2 channel. Since it is a homomer, this implies that three or
more subunits are needed to form the channel. A more
realistic model should have at least one additional partially liganded closed state (Fig. 13, Model 1-2).
The rate constants from Model 1-2 (Fig. 13) are
shown in Table VI. Again in this model, the first ATP
binding step speeds up the second one. The transition
rates near the open state are similar between Model 1 (Fig. 11) and Model 1-2. While Model 1-2 has two more
free parameters than Model 1, it has 5.4 units higher
likelihood so that Model 1-2 is preferred (see Table
VIII). The predicted Po as a function of ATP concentration is plotted in Fig. 4 C (
) and fit with the Hill equation with a Hill coefficient of 1.5, an EC50 of 17.4 µM,
and maximal Po of 0.74. However, compared with the
experimental data, the EC50 and maximal Po are too
large and the Hill coefficient too small. These discrepancies can be reduced by connecting an ATP-independent closed state to the open states. Additional evidence for this closed state comes from the closed time
histogram that has two components at saturating ATP (Fig. 10 C). Adding a closed state to the right of the
open states in Model 1-2 produces Model 1-4 (Fig. 13).
This modification corrects the prediction of the dose-
response curve. Similarly, adding one more closed state
to Model 1 produces Model 1-3 (Fig. 13).
Constraining Models 1-3 and 1-4 (Fig. 13) with detailed balance in the loops, and globally fitting the data
from 5 to 50 µM ATP, we obtained rate constants with
small error limits (Table VII). The relative likelihoods
and the AIC ranking of Model 1 (Fig. 11) and its expanded versions, Models 1-2, 1-3, and 1-4 (Fig. 13) are
listed in Table VIII. Models 1-3 and 1-4, which contain
loops, have much higher likelihoods than Model 1 or 1-2. Model 1-4 has the highest AIC rank, and therefore is
the preferred model. The rate constants are listed in
Table VI and VII. The transition rates near the open
states for Models 1 and 1-2, and Models 1-3 and 1-4 are
very similar, supporting the hierarchical approach. The
predicted probability densities for the open and closed
lifetimes of Model 1-4 are shown in Fig. 10 C and match
the histograms reasonably well. Again, we found that
the ATP association rate constants increased with proximity to the open states: k12(0) < k23(0) < k34(0). This
is opposite to what would be expected from independent subunits. Each binding step makes the next faster.
This cooperativity of binding appears model independent since all models tested had the same trend. From
the Eyring model for the rates, the energy landscape
for the whole reaction is shown in Fig. 14.

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Fig. 14.
Representation of
the channel activation pathway
in terms of energy barriers and
wells based on Model 1-4 (Fig.
13). The free energy landscape
of the reaction scheme calculated from the transition rates.
The relation between kij and free
energy is defined by the Eyring
equation:
(kijs in this case are the rate constants at 120 mV). is the
transmission coefficient (assumed to be 1; Hille, 1992 ), kB is
Boltzmann's constant, h is
Planck's constant, and T is the
absolute temperature. At 20°C,
kBT/h equals 6.11 × 1012 s 1. Gij
is the free energy at the top of
the barrier between states i and j, and Gi is the free energy of state i. The free energies are arbitrarily referenced to a solution of 1 M ATP.
The use of kBT/h as the preexponential term of the rates is undoubtedly far off for a macromolecule. However, it is a maximum estimate
that will cause the energy barriers to also be maximal estimates. The relationship of the well (state) energies, however, is much more likely
to be correct since these energies are determined by ratios of rate constants where the preexponential terms will tend to cancel.
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The kinetic model fits the single channel data quite
well. In Fig. 4 C, the predicted Po (
) from Model 1-4 (Fig. 13) and its fit to the Hill equation (solid line) are
plotted as a function of ATP. The maximal Po (0.64)
and EC50 (13.3 µM) are close to those of the experimental data, although the Hill coefficient (1.8) is
slightly smaller. These values are much closer to experimental data than that from Model 1-2 (Fig. 13), again
suggesting that Model 1-4 is better than Model 1-2.
The dependence of Po and rate constants on membrane potential.
We next tried to determine whether the rate
constants were dependent on membrane potential using Model 1-4 (Fig. 13). Fig. 15 A shows the single channel currents activated by 30 µM ATP at voltages from
120 to
80 mV. Fig. 15 B shows the voltage dependence of the mean open and closed times obtained
from idealized currents, and Fig. 15 C shows Po as a
function of voltage. The mean open time decreased
with depolarization, while the mean closed time increased. The closing and opening rates are both voltage dependent, and the overall effect is to reduce the
open probability with depolarization. Po values calculated from the all-points histogram were slightly larger
than those calculated from the idealized currents, suggesting that some short lived events were missed, but
the trend was the same; i.e., Po decreased with depolarization.