 |
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.
 |
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.

View larger version (37K):
[in this window]
[in a new window]
|
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).
|
|
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.

View larger version (20K):
[in this window]
[in a new window]
|
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.
|
|

View larger version (20K):
[in this window]
[in a new window]
|
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.
|
|

View larger version (19K):
[in this window]
[in a new window]
|
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.
|
|

View larger version (18K):
[in this window]
[in a new window]
|
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.
|
|
View this table:
[in this window]
[in a new window]
|
Table IV
Comparison of Simulation Values and Fitted Values of Rate Constants from Individual Fitting Based on Model 1 (Fig. 11)
|
|
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.

View larger version (39K):
[in this window]
[in a new window]
|
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.
|
|
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)
|

View larger version (63K):
[in this window]
[in a new window]
|
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.
|
|

View larger version (18K):
[in this window]
[in a new window]
|
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.
|
|
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.

View larger version (35K):
[in this window]
[in a new window]
|
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.
|
|
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.

View larger version (27K):
[in this window]
[in a new window]
|
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.
|
|
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.

View larger version (40K):
[in this window]
[in a new window]
|
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.
|
|
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)
|
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:
|
(12)
|
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:
|
(13)
|
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.

View larger version (37K):
[in this window]
[in a new window]
|
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.
|
|
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.

View larger version (19K):
[in this window]
[in a new window]
|
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).
|
|
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.

View larger version (13K):
[in this window]
[in a new window]
|
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.
|
|
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.

View larger version (43K):
[in this window]
[in a new window]
|
Fig. 15.
The effect of voltage on the single channel currents, mean open and closed times, and Po. (A) Single-channel currents activated by 30 µM ATP were recorded at different membrane potentials. Other conditions were the same as in Fig. 4. All the current traces in
this figure are from the same patch. The currents were idealized for further kinetic analysis after filtering at 5 kHz using a Gaussian digital
filter. (B) Mean open and closed times extracted from the idealized currents as a function of voltage. (C) The probability of being open,
Po, as function of voltage calculated from idealized currents ( ), all-points histograms ( ), and the prediction by Fig. 13, Model 1-4 ( ).
Po from the histogram is slightly larger than that from idealized currents, indicating that some short-lived openings were missed during
idealization. Po predicted by Model 1-4 is close to Po from idealized currents. (D) The closed and open time histograms at different voltages. The solid lines in the histograms are predicted probability density functions based on Model 1-4. Ni/NT has the same meaning as in
Fig. 10 C.
|
|
To examine which rate constants vary with voltage,
we globally fit the data between
80 and
120 mV with
Model 1-4 (Fig. 13). Each rate constant was taken to be
of the form:
|
(14)
|
where kij(0) is the rate constant at 0 mV, kBT is Boltzmann's constant times absolute temperature, and z
ij is
the effective charge (in a lumped parameter model, a
product of the sensing charge and the fraction of the
total electric field felt at the location of the sensor).
However, this model has many parameters and did not
converge [with a detailed balance constraint in loop, there are 30 parameters, including 15 kij(0) and 15 z
ij].
We had to apply further constraints to reduce the number of parameters. Since it is presumed that the ATP
binding site is located in the extracellular loop (Brake
et al., 1994
), it is reasonable to assume that it is outside
the electric field, and therefore the association and dissociation rates are voltage independent. We fixed them to the values obtained by global fitting based on Model
1-4 (Table VII). The likelihood estimator converged,
but with large error limits for kij and z
ij (Table IX).
Therefore, we could not make a firm conclusion regarding the voltage dependence for any individual rate
constant. However, the predicted Po using the mean
values of rate constants does decrease with depolarization and is similar to the Po from idealized currents
(Fig. 15 C). Fig. 15 D shows the open and closed interval histogram and the predicted probability densities
(solid lines) from the rate constants.
We predicted the shape of the whole cell I-V relationship by combining Po from outside-out patches and
the single channel conductance. The whole cell current I is determined by the product of single channel
current, i, the number of channels, n, and the open
probability, Po:
|
(15)
|
Po obtained from histograms can be fit with the Boltzmann equation:
|
(16)
|
where Po(0) is Po at 0 mV, and is equal to 0.11. z
is
equal to 0.34, indicating that a hyperpolarization of 74 mV is needed for an e-fold increase of Po. If we presume that the voltage dependence of Po at different
ATP concentrations is the same, we can use this result,
together with the dose-response curve (Fig. 4 C), to estimate Po(V) of a single channel. Multiplying Po(V) by
the single channel current (Fig. 2) predicts the shape
of the whole-cell I-V relationship. It is close to that predicted by Fig. 13, Model 1-4 (Fig. 3 B).
 |
DISCUSSION |
In this study, we have characterized the single channel
properties of cloned P2X2 receptor ion channels. The
characterization included general gating features, permeation properties, ATP concentration dependence,
effects of pH, and kinetic analysis.
Single Channel Current Behavior and Excess Open
Channel Noise
The typical single channel Na+ current has a chord
conductance of ~30 pS at
100 mV (Fig. 1). The open
channel current shows high frequency, high amplitude
flickering with some apparent full closures. Because of
the difficulty in resolving the fluctuations comprising
this "buzz mode," rather than build a substate model to
characterize the open channel behavior, we characterized it as a single conductance with noise. The standard
deviation of the excess open channel current is ~30%
of the mean. This is much larger than that of the acetylcholine receptor, for example, where the noise is only
2~5% of the mean (Auerbach and Sachs, 1984
; Sigworth, 1985
, 1986
). The excess noise does not arise
from thermal or shot noise nor from the voltage noise
of the amplifier (in the worst case, this is 10
8× the
thermal noise of the channel; Sigworth, 1985
). The
fluctuations appear to represent rapid conformational
changes that modulate the open channel conductance.
Occasionally, we saw relatively long-lived subconductance levels (see Fig. 1 D), but these were too infrequent to be evident in the all-points histograms. We attempted to estimate whether the fluctuations were to a
discrete number of conducting states using a maximum-point likelihood approach. However, we could
not find a consistent set of substate amplitudes between
records from different patches, although it is clear that the flickers do not represent simple band-limited full
closures of the channel. It is possible that there are actually a large number of states better described by a
noise rather than a state model. The presence of these
rapid fluctuations means that attempts to estimate the
unitary channel current with noise analysis are prone
to large, bandwidth-dependent errors.
We do not think that the flickers arise from channel
block by a diffusible agent, a mechanism that is often
seen in other channels. Ca2+-activated K+ channels can
be blocked in a flickery manner by Na+ (Yellen, 1984
),
and cardiac Ca2+ channels are discretely blocked by divalent ions (Lansman et al., 1986
). Since our currents
were equally noisy with (Fig. 1 A) and without (Figs. 4 A
and 5) extracellular divalent ions, we do not believe
that the excess noise comes from the block of divalent
ions. ATP is not a candidate for blocking the channels since the mean amplitude and the excess noise are independent of ATP concentration (Fig. 4 A). The voltage dependence of the excess noise is also not significant, suggesting that the flickers do not involve processes that sense the electric field. The simplest interpretation is that the excess noise arises from conformational transitions of the channel itself.
The general features of the cloned P2X2 receptors we
have discussed are similar to data recorded from native
receptors in rat sensory neurons and PC12 cells (Krishtal et al., 1988
; Nakazawa and Hess, 1993
). In rat dorsal
root ganglion (DRG) cells, single channel currents
flickered much more rapidly than in PC12 cells
so rapidly that the lifetime of both states was almost always
too short to be resolved by the recording system (Bean
et al., 1990
). The mean amplitude of the open state
with 150 mM extracellular Na+ was only 0.9 pA at
130
mV with an equivalent chord conductance of 7 pS and
no obvious substates. In contrast, we observed a mean single channel current of 3.2 pA, much larger than in
the rat DRG cells, with 145 mM extracellular Na+ at
100 mV with an equivalent mean chord conductance
of 32 pS. The true maximum conductance is even
larger since the difference in amplitude of the upper
5% of closed and open distributions is 4.3 pA, ~34%
larger than the mean current.
I-V Relationship
The single channel I-V relationship of the cloned P2X2
receptors exhibited strong inward rectification (Fig. 2).
This result is consistent with the whole cell I-V relationship (Fig. 3) (Brake et al., 1994
; Valera et al., 1994
).
Zhou and Hume (1998)
studied the mechanisms of inward rectification of P2X2 receptors. In their data, both
gating and single channel conductance contributed to the inward rectification. They also reported that inward
rectification did not require intracellular Mg2+ or
polyamines, and was present when the same solution
was used on both sides of the patch. Our data supports
these results. The currents in Fig. 2 were recorded in the
presence of 1 mM extracellular Ca2+ and Mg2+; however, the single channel current I-V relation showed similar inward rectification when currents were recorded
in the absence of divalent cations (data not shown).
Since the mean open and closed times vary with voltage (Fig. 15 B), the opening and closing rate constants
are voltage dependent. Although our kinetic analysis
was unable make a firm assignment of the voltage dependence to particular rate constants, Po did decrease
with depolarization (Fig. 15 C). The predicted whole
cell currents (Fig. 3 B) based on Model 1-4 (Fig. 13)
and single channel I-V curve matched reasonably well
with the data. These results also suggest that both the
instantaneous conductance and voltage-dependent gating contribute to the inward rectification. The dual
mechanisms of inward rectification in this receptor are
similar to the neuronal nicotinic acetylcholine receptor
(Sands and Barish, 1992
). The most important feature
of the voltage dependence of P2X2 kinetics is that it is minor.
Probability of Being Open
ATP is a potent agonist for cloned P2X receptors, except P2X7, and the receptors are highly selective for ATP
over most other adenosine derivatives. Dose-response
studies of whole cell currents reveal a Hill coefficient
larger than 1 (Brake et al., 1994
), suggesting that activation requires more than one agonist. This is reasonable since the channels are composed of multiple homomeric subunits. We studied Po over a wide range of
ATP concentrations with outside-out patches (Fig. 4)
and showed that the Po curve has a Hill coefficient of
2.3, an EC50 of 11.2 µM, and a maximum of 0.61. A Hill
coefficient of 2.3 suggests that there are at least three
binding sites in the receptor. (That the Hill coefficient is only a lower estimate of the stoichiometry is emphasized in Fig. 4 C, where data from a simulation of
Model 1-4 [Fig. 13] with three binding steps could be
well fit with a Hill coefficient of 1.8). Presumably, the
cooperativity arises from the multimeric structure of
the channel. Based on refolding studies of the P2X2 extracellular domain (P2X2-ECD), Kim et al. (1997)
predicted that the naturally assembled form of P2X2 receptors may be tetrameric. Lewis et al. (1995)
found that
coexpression of P2X2 and P2X3 can form a new channel type by subunit heteropolymerization, providing further evidence that the P2X receptors are multimers.
Recent experiments with chemical cross-linking of
P2X1 and P2X3 receptors (Nicke et al., 1998
) indicate
that P2X receptor channels are trimeric. Since these results were obtained from native P2X receptors expressed in Xenopus oocytes, we expect that they are
more representative than the studies on the isolated extracellular domains.
The maximum Po of ~0.6 indicates that the mean
opening rates are slower than the closing rates. Our kinetic analysis based on Model 1-4 (Fig. 13) shows that
the two opening rates k46 and k76 are much slower than
corresponding closing rates k64 and k67. The opening
rate k45 is faster than closing rate k54, while the opening
rate k75 is similar to the closing rate k57, so the overall opening rate is slower than the closing rate.
Affinity of the Pore for Na+
The theory of independent ion passage predicts that
the flux of a permeating ion should increase linearly
with the ion concentration (Hille, 1992
). However,
most channels do not exhibit this behavior due to the
competition for binding sites in the channel. Ion flux saturates when the binding-unbinding steps of permeation become rate limiting. This occurs at high ion concentrations when the rate of ion entry approaches the
rate of unbinding. Conductances in the P2X2 channels
show clear deviations from independence (Fig. 6 A).
When the concentration of extracellular NaCl is raised, the single channel conductance saturates. In our data,
the mean conductance versus Na+ concentration was
well fit by the Michaelis-Menten (MM) equation, with
one binding site, X, in the pore (Scheme II).
The rate constants are, in general, dependent on
membrane potential. We tried to fit our conductance
data with Scheme II, but could not obtain a unique set
of rate constants: Scheme II is over determined because the data does not have enough distinguishing features. If we assume that at high potentials the reverse flux is negligible, only the two forward rates are
necessary and we can obtain solutions. We found that
the equilibrium constant Ks is voltage dependent (Fig.
6 B), with the binding site located ~20% of the way
through the field relative to the extracellular face. It is
interesting to speculate as to where the site may be relative to the primary sequence if one assumes that side
chains form the selectivity filter rather than the backbone carbonyls (Doyle et al., 1998
). In their study of
the ionic pores of P2X2 receptors using the substituted
cysteine accessibility method (SCAM), Rassendren et al.
(1997a)
identified three residues: I328, N333, and
T336 in the M2 domain that were located in the outer vestibule of the pore. Two of these are polar and might
be part of a binding site for Na+. When the channel was
open, D349C could be inhibited only by the small, positively charged MTSEA (2-aminoethyl-methanethiosulfonate), but not by MTSET {[2-(trimethylammonium)
ethyl]methanethiosulfonate} or MTSES [sodium (2-sulfonatoethyl)methanethiosulfonate], implying that D349
is located near the middle of the channel. D349 is a negatively charged amino acid and is conserved among
all seven P2X receptors. It is possible that D349 could
be the site of permeant cation binding and is responsible for ionic selectivity.
Cation Selectivity
Our data shows P2X2 is a nonselective cation channel.
The ionic selectivity based on the conductance is: K+ > Rb+ > Cs+ > Na+ > Li+, Eisenman sequence IV (Hille,
1992
). This sequence is different from free solution
mobility and from the sequence of high field sites. This
suggests the pore may be smaller than the nicotinic acetylcholine receptor with an interior having little charge
in the selectivity filter. This is consistent with the results
from substituted cysteine accessibility method experiments (Rassendren et al., 1997a
) where only I328C,
N333C, T336C, L338C, and D349C in the M2 domain
were accessible to MTS reagents. Only D349 is negatively charged among these residues, and it may not be
part of the selectivity filter.
Based on whole cell currents of P2X2 receptors,
Brake et al. (1994)
reported that the replacement of
extracellular Na+ by K+ did not affect the reversal potential, suggesting that Na+ and K+ have a similar permeability near 0 mV. In our experiments, the currents
carried by Na+ were larger than the currents carried by
K+ at negative potentials. Similar results were reported
for PC12 cells (Nakazawa et al., 1990b
). The origin of
the discrepancy between our results and theirs is
masked by the lack of knowledge of the interplay between permeation and gating in the whole cell current.
Different ionic environments may change the agonist
binding and/or gating. In the nicotinic acetylcholine
receptor (Akk and Auerbach, 1996
), external monovalent ions compete with agonists for binding, changing
the dose-response curves for reasons that have nothing
to do with the permeation process itself. If the kinetics
of ATP binding and/or gating is different for Na+ and
K+ in the extracellular solution, Po will be different,
changing the maximum conductance at a fixed agonist concentration.
The excess open channel noise sequence is the same
as the cation selectivity (i.e., K+
Rb+ > Cs+ > Na+ > Li+ (Table I), indicating that it is proportional to the
single channel current amplitude, as expected if the
noise arises from modulation of the normal flow. If
the excess noise arose completely from simple conformational modulation of the pore, all ions would have
the same relative selectivity. This is true for all alkali ions with the exception of Li+, which had ~20%
smaller relative fluctuations. This suggests a more specific interaction between Li+ and the channel than for
other permeant ions.
pH Potentiation
The sensitivity of cloned P2X2 receptors to ATP was affected by extracellular pH. King reported (King et al.,
1996
) that with acidification, the ATP dose-response
curve of whole cell currents shifted to the left without
altering the maximal response. The effective receptor
affinity for ATP was enhanced 5-10-fold by acidifying
the bath solution (to pH 6.5), but was diminished four- to fivefold in an alkaline solution (pH 8.0). Different
P2X receptors have different sensitivities to pH. Unlike
P2X2 receptors, P2X1, P2X3, and P2X4 receptors decrease their apparent affinity with acidification (Stoop
et al., 1997
). Our studies on outside-out patches
showed that the mean current increased about an order of magnitude when the extracellular pH changed
from 8.3 to 6.8, exhibiting a pKa of ~7.9 (Fig. 8 B). The
Hill coefficient of 2.5 suggests that the channel has at
least three binding sites, which is consistent with the
stoichiometry study by Nicke et al. (1998)
. In related
experiments, extracellular protons potentiated adenosine binding to A2A receptors, and this effect could be
modified by mutagenesis or by chemically altering the
strategic residues (Allende et al., 1993
). In the extracellular loop of P2X2 receptors, there are 9 histidine residues interspersed between 10 cysteine residues, the latter being conserved throughout the P2X1-7 proteins. Both cysteine and histidine residues have been shown
to be important for agonist and antagonist binding at
the A1 receptor, which is pH sensitive (Allende et al.,
1993
). It is reasonable to speculate that these two
amino acids may play a similar role in ATP binding to
P2X2 receptors. Protonation of the histidine residues
may account for the increase in P2X2 current at low
pH, but this seems unlikely because diethylpyrocarbonate, which irreversibly denatures histidyl residues, has
no effect on the magnitude of the currents (Wildman et al., 1998
).
The major effect of pH was on the kinetics of activation. The rate of activation increased as pH decreased
(Fig. 8 C), while the deactivation time constant was independent of pH. This suggests that the closing rates
and the dissociation rates are not affected by protons.
The simplest interpretation of the data is that in acidic
environments, the binding site becomes more positive,
increasing its affinity for ATP. However, since macroscopic kinetics is a function of all of the rate constants,
many of which are not associated with binding, such an
interpretation is not reliable.
The single channel current amplitudes at different
pH were similar, but the excess open channel noise increased when pH was lowered (Table II). Comparing
the single channel currents at different values of pH, as
shown in Fig. 9 A, more brief closings can be seen at
lower pH. The fluctuations caused by protons are
slower than the fluctuations of the intrinsic channel
flicker. While these results suggest that protons served
as blockers, analysis of the power spectra and single
channel kinetics contradict this interpretation. The
blocking and unblocking rates were only weakly dependent on proton concentration (see Eq. 13). Power spectral analysis also showed that the corner frequency decreased with an increase in pH, opposite to the prediction for proton block. It appears that the brief closings
at low pH are due to conformational changes produced
by the titration of several sites.
Kinetics
Preferred model.
We used the maximum interval likelihood method to statistically compare kinetic models
(Vandenberg and Horn, 1984
; Horn, 1987
; Qin et al.,
1996a
). The models were built hierarchically, beginning with one that described the transitions near the
open states (Vandenberg and Bezanilla, 1991
). The
first model we chose (Model 1 or, equivalently, Model
2; Figure 11) had three closed states, C1, C2, and C3,
representing unliganded, monoliganded, and biliganded closed states, and two open states O4 and O5.
Starting with this model, we expanded to Models 1-2, 1-3, and 1-4 (Fig. 13) to account for the ATP titration
data. Comparing Model 1 (Fig. 11) with Model 1-2, and
Model 1-3 with Model 1-4, we found that the opening
and closing rate constants were surprisingly close (see
Tables VI and VII), suggesting that the transitions near
the open state were well defined. This result supports
our strategy of model development. Among the four
models, Model 1-4 had the highest likelihood and AIC
rank, and therefore is our preferred model (Table VIII).
In this model, there are five closed states, C1, C2, C3, C4,
and C7, representing unliganded, monoliganded, biliganded, and triliganded closed states, and two open
states, O5 and O6. The three ATP binding steps require
the channel to be at least a trimer. By constraining the
ATP association rates to be proportional to concentration, reliable rate constants were obtained from global
fitting with this model (Table VII). The predicted probability density functions match reasonably well the
open- and closed-time histograms at all concentrations,
and the predicted Po is close to the experimental data
so that our final preferred model is Model 1-4.
ATP binding sites and cooperativity.
From the results of
the basic model (Fig. 11, Model 1) and its expanded
versions (Fig. 13, Models 1-2, 1-3, and 1-4), we found that association and dissociation rate constants increased as they approached the open states (Table VI
and VII). This means that the subunits are not independent and that the association rate for an incoming
ATP is increased by the presence of bound ATPs. This
trend was consistent among all models examined.
The increase in association rates with consecutive
binding is most surprising when one thinks about the
opposing electrostatic factors. Bound ATP with a
charge of
4 should repel the next incoming ATP
(note: the actual valence of bound ATP is not known
and is probably less than
4). Since the energy is proportional to the product of the charges, if the sites were
independent, the second ATP would be repelled by a
resident ATP with an energy proportional to 4*4 = 16, and the third by 8*4 = 32. We would thus expect the later binding rates to decrease by more than just the
number of available sites. The trend of increasing association rates with occupancy must be caused by conformational changes in the channel that mask the electrostatic contribution. While the increasing rates of dissociation with occupancy fit the predicted electrostatic trend, given the conformational changes associated
with binding, this trend may be coincidental. The binding of ATP causes the remaining unoccupied sites to
open up in such a manner so as to increase the rates of
ATP entry and exit without having a large effect on the
equilibrium affinity of the site (Fig. 14).
If the channel were a tetramer, as predicted by Kim
et al. (1997)
, we might add one closed state to the left
of C1 as in Model 1-5 (Fig. 13). We attempted to fit this
model, but could not obtain unique rates. This may be
because we had insufficient data at very low ATP concentrations, or because the channel really is a trimer
(Nicke et al., 1998
). The predicted Po vs. ATP concentration from Model 1-4 (Fig. 13) fits well the Po, EC50,
and Hill coefficient obtained from the dose-response
curve (Fig. 4 C), supporting the consistency of the
model and the necessity of the last ATP-independent closed state.
Other kinetic models.
There have been only a few studies on the kinetics of P2X receptor ion channels based
on single channel currents (Krishtal et al., 1988
). Kinetic studies using whole cell currents showed that the
rise time of current elicited by ATP was strongly concentration dependent, but the decay time was not (Surprenant, 1996
). This is in accord with the pH experiments discussed above. There is some variability among
reports regarding the rise and fall times. In rat sensory
neurons, using fast solution exchange, the rise time was
~10 ms at saturating ATP and the decay time was ~100 ms (Bean et al., 1990
). In smooth muscle and cloned
P2X1 receptors, the rise time was ~5 ms, while in PC12
cells, cloned P2X2 receptors, and rat superior cervical
ganglion (SCG) cells, the rise time was ~25 ms (Surprenant, 1996
). In rat SCG cells, nodose, and guinea-pig coeliac neurons, the latency to the onset of whole
cell currents was estimated to be ~0.8-4 ms, and the
10-90% rise time at high ATP concentrations ranged
from 5 to 20 ms (Khakh et al., 1995
). Since these experiments were done in the whole cell configuration, the
rate of rise was likely limited by the speed of the solution exchange around the cells. Hess (1993)
reported
that the time resolution for solution exchange around
a whole cell of this size is 2~10 ms under maximal flow
velocities and even slower for lower velocities. With outside-out patches, the rise times can be ~250 µs (Colquhoun et al., 1992
). Moreover, because in the whole
cell experiments there was no marker for the start of
solution exchange, the latency to the onset of the currents could not be estimated accurately.
Preliminary kinetic analysis of single channel currents from rat sensory neurons showed that the distribution of the open times could be approximately fit by
two exponentials with time constants of 0.35 and 3.4 ms
(Krishtal et al., 1988
). The ratio between the fast component amplitude and the slow one varied between patches, with ratios of 47.4:1 to 4.8:1.
Using the rise and fall time constants from whole cell
currents at different concentrations of ATP, and based
on kinetic models of the ACh receptor channel, Bean
(1990)
proposed a linear kinetic model with independent subunits for ATP activation (Fig. 13, Model 9).
The association and dissociation rate constants k+ = 1.2 × 107 M
1s
1 and k
= 4 s
1 were chosen so that the
simulations mimicked the kinetics seen in the bullfrog
sensory neurons. Agreement between the model and the data suggested that the ATP binding sites could be independent.
We applied the independent binding site assumption
to Model 1-2; i.e., k34 = 1/2k23 = 1/3k12 = 1/3k10 = k+,
and k21 = 1/2k32 = 1/3k43 = k
. Although the estimation converged, the likelihood was much lower than
our favored models. We also fit our data with Bean's
(1990) model, which has only one open state (Fig. 13,
Model 9), and again the fits were poor.
In other ligand-gated channels, such as the Ca2+-activated potassium channel and ACh receptors (Magleby
and Song, 1992
; Auerbach et al., 1996
), partially
liganded channels can open. To explore whether these
states were visible in our data, we compared the maximum likelihood of Model 8-1 (Fig. 13) that has partially
liganded openings with Model 1-2 (Fig. 13). Global fitting of Model 8-1 with data from 5, 10, 15, 20, 30, and
50 µM ATP produced a maximum likelihood 664 units
lower than Model 1-2, suggesting the model is e664×
less likely to produce the data. Apparently, P2X2 channels do not open for a significant fraction of time in
partially liganded states.
In summary, Model 1-4 (Fig. 13) can well represent
the channel gating processes. It is adequate to explain
the behavior across concentration and is physically reasonable. Fig. 14 shows the calculated free energy barriers and wells at
120 mV referenced to 1 M ATP. The
energies were calculated from the rate constants assuming an Eyring model. The free energies of all wells decreased with the reaction coordinate. State C4 could go
to either open state O5 or O6 with state O5 being
slightly more stable. O5 and O6 go to the same closed
state C7 that is the most stable in the reaction pathway.
Voltage dependence of Po and rate constants.
Although Po
is not strongly dependent on membrane potential, it
did decrease with depolarization (Fig. 15 C), indicating
that some of the rate constants are voltage dependent.
To reduce the free parameters, we limited the voltage
sensitivity to only the rate constants in the final loop
and ended up with very large errors limits for kij(0) and
z
ij. We have no confidence in the voltage dependence
of any of the individual rate constants. Data from a
wider voltage range will be required to adequately address this question.
Despite the wide error limits, by lumping the kinetics
into an equilibrium model to predict the probabilities
of occupancy, the predicted Po and whole cell currents
calculated from the mean values of kij(0) and z
ij were
consistent with those obtained by idealization (Fig. 15
C) and with experimental data (Fig. 3 B). The probability density functions match reasonably well with duration histograms (Fig. 15 D).
In conclusion, the currently optimal model, Model 1-4 (Fig. 13), can be summarized as follows: (a) the channel proceeds through three ATP binding steps before
opening; (b) the three ATP binding sites are not independent, but positively cooperative; (c) There are two
open states, which connect to a common ATP-independent closed state; (d) activation and deactivation proceed along the same pathway; and (e) channels only
open after being fully liganded.
Original version received 10 December 1998 and accepted version received 9 March 1999.
We thank Drs. David Julius and Tony Brake (University of California, San Francisco) for providing P2X2 DNA, Dr. Annmarie
Surprenant (Glaxo Welcome and University of Sheffield, Sheffield, UK) for providing stably transfected cell lines, Dr. Tao Zeng
(Department of Physiology and Biophysics, State University of New York, Buffalo) for assistance with the LabViewTM programs.
We also thank Drs. Tony Auerbach, Feng Qin, Alan North, and Annmarie Surprenant for many helpful discussions.
1.
|
Akk, G., and
A. Auerbach.
1996.
Inorganic, monovalent cations
compete with agonists for the transmitter binding site of nicotinic acetylcholine receptors.
Biophys. J.
70:
2652-2658
[Abstract].
|
2.
|
Allende, G.,
V. Casado,
J. Mallol,
R. Franco,
C. Lluis, and
E.I. Canela.
1993.
Role of histidine residues in agonist and antagonist
binding sites of A1 adenosine receptor.
J. Neurochem.
60:
1525-1533
[Medline].
|
3.
|
Auerbach, A., and
F. Sachs.
1984.
Single-channel currents from acetylcholine receptors in embryonic chick muscle. Kinetic and conductance properties of gaps within bursts.
Biophys. J.
45:
187-198
[Abstract].
|
4.
|
Auerbach, A.,
W. Sigurdson,
J. Chen, and
G. Akk.
1996.
Voltage dependence of mouse acetylcholine receptor gating: different
charge movements in di-, mono-, and unliganded receptors.
J.
Physiol. (Camb.).
494:
155-170
[Abstract].
|
5.
|
Barry, P.H., and
J.W. Lynch.
1991.
Liquid junction potentials and
small cell effects in patch-clamp analysis. [Published erratum appears in J. Membr. Biol. 1992. 125:286.]
J. Membr. Biol.
121:
101-117
[Medline].
|
6.
|
Bean, B.P..
1990.
ATP-activated channels in rat and bullfrog sensory
neurons: concentration dependence and kinetics.
J. Neurosci.
10:
1-10
[Abstract].
|
7.
|
Bean, B.P..
1992.
Pharmacology and electrophysiology of ATP-activated ion channels.
Trends Pharmacol. Sci.
13:
87-90
[Medline].
|
8.
|
Bean, B.P.,
C.A. Williams, and
P.W. Ceelen.
1990.
ATP-activated
channels in rat and bullfrog sensory neurons: current-voltage relation and single-channel behavior.
J. Neurosci.
10:
11-19
[Abstract].
|
9.
|
Bo, X.,
Y. Zhang,
M. Nassar,
G. Burnstock, and
R. Schoepfer.
1995.
A P2X purinoceptor cDNA conferring a novel pharmacological
profile.
FEBS Lett.
375:
129-133
[Medline].
|
10.
|
Brake, A.J.,
M.J. Wagenbach, and
D. Julius.
1994.
New structural
motif for ligand-gated ion channels defined by an ionotropic
ATP receptor.
Nature.
371:
519-523
[Medline].
|
11.
|
Bretschneider, F.,
M. Klapperstuck,
M. Lohn, and
F. Markwardt.
1995.
Nonselective cationic currents elicited by extracellular ATP
in human B-lymphocytes.
Pflügers Arch.
429:
691-698
[Medline].
|
12.
|
Buell, G.,
C. Lewis,
G. Collo,
R.A. North, and
A. Surprenant.
1996.
An antagonist-insensitive P2X receptor expressed in epithelia
and brain.
EMBO (Eur. Mol. Biol. Organ.) J.
15:
55-62
[Abstract].
|
13.
|
Capiod, T..
1998.
ATP-activated cation currents in single guinea-pig
hepatocytes.
J. Physiol. (Camb.).
507:
795-805
[Abstract/Free Full Text].
|
14.
|
Chen, C.C.,
A.N. Akopian,
L. Sivilotti,
D. Colquhoun,
G. Burnstock, and
J.N. Wood.
1995.
A P2X purinoceptor expressed by a subset
of sensory neurons.
Nature.
377:
428-431
[Medline].
|
15.
|
Chung, S.H.,
J.B. Moore,
L.G. Xia,
L.S. Premkumar, and
P.W. Gage.
1990.
Characterization of single channel currents using digital
signal processing techniques based on Hidden Markov Models.
Philos. Trans. R. Soc. Lond. B Biol. Sci.
329:
265-285
[Medline].
|
16.
|
Collo, G.,
R.A. North,
E. Kawashima,
E. Merlo-Pich,
Neidhart,
A. Surprenant, and
G. Buell.
1996.
Cloning OF P2X5 and P2X6 receptors and the distribution and properties of an extended family of ATP-gated ion channels.
J. Neurosci.
16:
2495-2507
[Abstract].
|
17.
|
Colquhoun, D.,
P. Jonas, and
B. Sakmann.
1992.
Action of brief
pulses of glutamate on AMPA/kainate receptors in patches from
different neurones of rat hippocampal slices.
J. Physiol. (Camb.).
458:
261-287
[Abstract].
|
18.
| Defelice, L. 1981. Introduction to Membrane Noise. Plenum Publishing Corp. New York. 231-329.
|
19.
|
Doyle, D.A.,
J.M. Cabral,
R.A. Pfuetzner,
A. Kuo,
J.M. Gulbis,
S.L. Cohen,
B.T. Chait, and
R. MacKinnon.
1998.
The structure of
the potassium channel: molecular basis of K+ conduction and selectivity.
Science.
280:
69-77
[Abstract/Free Full Text].
|
20.
|
Evans, R.J..
1996.
Single channel properties of ATP-gated cation
channels (P2X receptors) heterologously expressed in Chinese
hamster ovary cells.
Neurosci. Lett.
212:
212-214
[Medline].
|
21.
|
Friel, D.D..
1988.
An ATP-sensitive conductance in single smooth
muscle cells from the rat vas deferens.
J. Physiol. (Camb.).
401:
361-380
[Abstract].
|
22.
|
Hamill, O.P.,
A. Marty,
E. Neher,
B. Sakmann, and
F.J. Sigworth.
1981.
Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches.
Pflügers Arch.
391:
85-100
[Medline].
|
23.
|
Hess, G.P..
1993.
Determination of the chemical mechanism of neurotransmitter receptor-mediated reactions by rapid chemical kinetic techniques.
Biochemistry.
32:
989-1000
[Medline].
|
24.
| Hille, B. 1992. Ionic Channels of Excitable Membranes. 2nd edition. Sinauer Associates Inc., Sunderland, MA. 362-389.
|
25.
|
Horn, R..
1987.
Statistical methods for model discrimination. Applications to gating kinetics and permeation of the acetylcholine receptor channel.
Biophys. J.
51:
255-263
[Abstract].
|
26.
|
Khakh, B.S.,
P.P. Humphrey, and
A. Surprenant.
1995.
Electrophysiological properties of P2X-purinoceptors in rat superior cervical, nodose and guinea-pig coeliac neurones.
J. Physiol. (Camb.).
484:
385-395
[Abstract].
|
27.
|
Kim, M.,
O.J. Yoo, and
S. Choe.
1997.
Molecular assembly of the extracellular domain of P2X2, an ATP-gated ion channel.
Biochem.
Biophys. Res. Commun.
240:
618-622
[Medline].
|
28.
|
King, B.F.,
S.S. Wildman,
L.E. Ziganshina,
J. Pintor, and
G. Burnstock.
1997.
Effects of extracellular pH on agonism and antagonism at a recombinant P2X2 receptor.
Br. J. Pharmacol.
121:
1445-1453
[Abstract].
|
29.
|
King, B.F.,
L.E. Ziganshina,
J. Pintor, and
G. Burnstock.
1996.
Full
sensitivity of P2X2 purinoceptor to ATP revealed by changing extracellular pH.
Br. J. Pharmacol.
117:
1371-1373
[Abstract].
|
30.
|
Krishtal, O.A.,
S.M. Marchenko, and
A.G. Obukhov.
1988.
Cationic
channels activated by extracellular ATP in rat sensory neurons.
Neuroscience.
27:
995-1000
[Medline].
|
31.
|
Lansman, J.B.,
P. Hess, and
R.W. Tsien.
1986.
Blockade of current
through single calcium channels by Cd2+, Mg2+, and Ca2+. Voltage and concentration dependence of calcium entry into the
pore.
J. Gen. Physiol.
88:
321-347
[Abstract].
|
32.
|
Lewis, C.,
S. Neidhart,
C. Holy,
R.A. North,
G. Buell, and
A. Surprenant.
1995.
Coexpression of P2X2 and P2X3 receptor subunits can account for ATP-gated currents in sensory neurons.
Nature.
377:
432-435
[Medline].
|
33.
|
Li, C.,
R.W. Peoples, and
F.F. Weight.
1996.
Acid pH augments excitatory action of ATP on a dissociated mammalian sensory neuron.
Neuroreport.
7:
2151-2154
[Medline].
|
34.
|
Li, C.,
R.W. Peoples, and
F.F. Weight.
1997.
Enhancement of ATP-activated current by protons in dorsal root ganglion neurons.
Pflügers Arch.
433:
446-454
[Medline].
|
35.
|
Magleby, K.L., and
L. Song.
1992.
Dependency plots suggest the kinetic structure of ion channels.
Proc. R. Soc. Lond. B Biol. Sci.
249:
133-142
[Medline].
|
36.
|
McQueen, D.S.,
S.M. Bond,
C. Moores,
I. Chessell,
P.P. Humphrey, and
E. Dowd.
1998.
Activation of P2X receptors for adenosine
triphosphate evokes cardiorespiratory reflexes in anaesthetized
rats.
J. Physiol. (Camb.).
507:
843-855
[Abstract/Free Full Text].
|
37.
|
Nakazawa, K.,
K. Fujimori,
A. Takanaka, and
K. Inoue.
1990a.
ATP-induced current in isolated outer hair cells of guinea pig cochlea.
J. Neurophysiol. (Bethesda).
63:
1068-1074
[Abstract/Free Full Text].
|
38.
|
Nakazawa, K.,
K. Fujimori,
A. Takanaka, and
K. Inoue.
1990b.
An
ATP-activated conductance in pheochromocytoma cells and its
suppression by extracellular calcium.
J. Physiol. (Camb.).
428:
257-272
[Abstract].
|
39.
|
Nakazawa, K., and
P. Hess.
1993.
Block by calcium of ATP-activated
channels in pheochromocytoma cells.
J. Gen. Physiol.
101:
377-392
[Abstract].
|
40.
|
Nakazawa, K.,
K. Inoue,
K. Fujimori, and
A. Takanaka.
1991.
Effects
of ATP antagonists on purinoceptor-operated inward currents in
rat phaeochromocytoma cells.
Pflügers Arch.
418:
214-219
[Medline].
|
41.
|
Nicke, A.,
H.G. Baumert,
J. Rettinger,
A. Eichele,
Lambrecht,
E. Mutschler, and
G. Schmalzing.
1998.
P2X1 and P2X3 receptors
form stable trimers: a novel structural motif of ligand-gated ion
channels.
EMBO (Eur. Mol. Biol. Organ.) J.
17:
3016-3028
[Abstract/Free Full Text].
|
42.
|
North, R.A..
1996.
Families of ion channels with two hydrophobic
segments.
Curr. Opin. Cell Biol.
8:
474-483
[Medline].
|
43.
|
Qin, F.,
A. Auerbach, and
F. Sachs.
1996a.
Estimating single-channel kinetic parameters from idealized patch-clamp data containing missed events.
Biophys. J.
70:
264-280
[Abstract].
|
44.
|
Qin, F.,
A. Auerbach, and
F. Sachs.
1996b.
Idealization of single-channel currents using the segmental K-mean method.
Biophys. J.
72:
A227
.
|
45.
|
Rassendren, F.,
G. Buell,
A. Newbolt,
R.A. North, and
A. Surprenant.
1997a.
Identification of amino acid residues contributing to the pore of a P2X receptor.
EMBO (Eur. Mol. Biol. Organ.) J.
16:
3446-3454
[Abstract/Free Full Text].
|
46.
|
Rassendren, F.,
G.N. Buell,
C. Virginio,
G. Collo,
R.A. North, and
A. Surprenant.
1997b.
The permeabilizing ATP receptor, P2X7.
Cloning and expression of a human cDNA.
J. Biol. Chem.
272:
5482-5486
[Abstract/Free Full Text].
|
47.
| Rudy, B., and L. Iverson. 1992. Methods in Enzymology. Vol. 207. Academic Press, Inc., San Diego, CA. 266-279.
|
48.
|
Sands, S.B., and
M.E. Barish.
1992.
Neuronal nicotinic acetylcholine receptor currents in phaeochromocytoma (PC12) cells: dual
mechanisms of rectification.
J. Physiol. (Camb.).
447:
467-487
[Abstract].
|
49.
|
Seguela, P.,
A. Haghighi,
J.J. Soghomonian, and
E. Cooper.
1996.
A
novel neuronal P2X ATP receptor ion channel with widespread
distribution in the brain.
J. Neurosci.
16:
448-455
[Abstract].
|
50.
|
Sigworth, F.J..
1985.
Open channel noise. I. Noise in acetylcholine
receptor currents suggests conformational fluctuations.
Biophys.
J.
47:
709-720
[Abstract].
|
51.
|
Sigworth, F.J..
1986.
Open channel noise. II. A test for coupling between current fluctuations and conformational transitions in the
acetylcholine receptor.
Biophys. J.
49:
1041-1046
[Abstract].
|
52.
|
Sigworth, F.J., and
S.M. Sine.
1987.
Data transformations for improved display and fitting of single-channel dwell time histograms.
Biophys. J.
52:
1047-1054
[Abstract].
|
53.
|
Sivilotti, L.G.,
D.K. McNeil,
T.M. Lewis,
M.A. Nassar,
R. Schoepfer, and
D. Colquhoun.
1997.
Recombinant nicotinic receptors, expressed in Xenopus oocytes, do not resemble native rat sympathetic ganglion receptors in single-channel behaviour.
J. Physiol.
(Camb.).
500:
123-138
[Abstract].
|
54.
|
Soto, F.,
M. Garcia-Guzman,
J.M. Gomez-Hernandez,
M. Hollmann,
C. Karschin, and
W. Stühmer.
1996.
P2X4: an ATP-activated ionotropic receptor cloned from rat brain.
Proc. Natl. Acad. Sci. USA.
93:
3684-3688
[Abstract/Free Full Text].
|
55.
|
Stoop, R.,
A. Surprenant, and
R.A. North.
1997.
Different sensitivities to pH of ATP-induced currents at four cloned P2X receptors.
J. Neurophysiol.
78:
1837-1840
[Abstract/Free Full Text].
|
56.
|
Surprenant, A..
1996.
Functional properties of native and cloned
P2X receptors.
Ciba Found. Symp.
198:
208-219
[Medline].
|
57.
|
Surprenant, A.,
F. Rassendren,
E. Kawashima,
R.A. North, and
G. Buell.
1996.
The cytolytic P2Z receptor for extracellular ATP
identified as a P2X receptor (P2X7).
Science.
272:
735-738
[Abstract].
|
58.
|
Ugur, M.,
R.M. Drummond,
H. Zou,
P. Sheng,
J.J. Singer, and
J.V. Walsh Jr..
1997.
An ATP-gated cation channel with some P2Z-like
characteristics in gastric smooth muscle cells of toad.
J. Physiol.
(Camb.).
498:
427-442
[Abstract].
|
59.
|
Valera, S.,
N. Hussy,
R.J. Evans,
N. Adami,
R.A. North,
A. Surprenant, and
G. Buell.
1994.
A new class of ligand-gated ion
channel defined by P2X receptor for extracellular ATP.
Nature.
371:
516-519
[Medline].
|
60.
|
Vandenberg, C.A., and
F. Bezanilla.
1991.
Single-channel, macroscopic, and gating currents from sodium channels in the squid
giant axon.
Biophys. J.
60:
1499-1510
[Abstract].
|
61.
|
Vandenberg, C.A., and
R. Horn.
1984.
Inactivation viewed through
single sodium channels.
J. Gen. Physiol.
84:
535-564
[Abstract].
|
62.
|
Walz, W.,
G. Gimpl,
C. Ohlemeyer, and
H. Kettenmann.
1994.
Extracellular ATP-induced currents in astrocytes: involvement of a
cation channel.
J. Neurosci. Res.
38:
12-18
[Medline].
|
63.
|
Wang, C.Z.,
N. Namba,
T. Gonoi,
N. Inagaki, and
S. Seino.
1996.
Cloning and pharmacological characterization of a fourth P2X
receptor subtype widely expressed in brain and peripheral tissues
including various endocrine tissues.
Biochem. Biophys. Res. Commun.
220:
196-202
[Medline].
|
64.
|
Wildman, S.S.,
B.F. King, and
G. Burnstock.
1998.
Zn2+ modulation
of ATP-responses at recombinant P2X2 receptors and its dependence on extracellular pH.
Br. J. Pharmacol.
123:
1214-1220
[Abstract].
|
65.
|
Yellen, G..
1984.
Ionic permeation and blockade in Ca2+-activated
K+ channels of bovine chromaffin cells.
J. Gen. Physiol.
84:
157-186
[Abstract].
|
66.
|
Zhou, Z., and
R.I. Hume.
1998.
Two mechanisms for inward rectification of current flow through the purinoceptor P2X2 class of
ATP-gated channels.
J. Physiol. (Camb.).
507:
353-364
[Abstract/Free Full Text].
|