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Volume 17, Number 17,
Issue of September 1, 1997
pp. 6597-6610
Copyright ©1997 Society for Neuroscience
Quantitative Single-Cell-Reverse Transcription-PCR Demonstrates
That A-Current Magnitude Varies as a Linear Function of
shal Gene Expression in Identified Stomatogastric
Neurons
Deborah J. Baro1,
Robert M. Levini1,
Marshall
T. Kim1,
Allan R. Willms2,
Cathy Cole Lanning1,
Hilda E. Rodriguez1, and
Ronald M. Harris-Warrick1
1 Section of Neurobiology and Behavior and
2 Center for Applied Mathematics, Cornell University,
Ithaca, New York 14850
ABSTRACT
- INTRODUCTION
- MATERIALS AND METHODS
- RESULTS
- DISCUSSION
- FOOTNOTES
- REFERENCES
ABSTRACT 
Different Shaker family 
-subunit genes generate
distinct voltage-dependent K+ currents when
expressed in heterologous expression systems. Thus it generally is
believed that diverse neuronal K+ current phenotypes
arise, in part, from differences in Shaker family gene
expression among neurons. It is difficult to evaluate the extent to
which differential Shaker family gene expression contributes to endogenous K+ current diversity,
because the specific Shaker family gene or genes
responsible for a given K+ current are still unknown
for nearly all adult neurons. In this paper we explore the role of
differential Shaker family gene expression in creating
transient K+ current (IA)
diversity in the 14-neuron pyloric network of the spiny lobster,
Panulirus interruptus. We used two-electrode voltage clamp to characterize the somatic IA in each
of the six different cell types of the pyloric network. The size,
voltage-dependent properties, and kinetic properties of the somatic
IA vary significantly among pyloric neurons
such that the somatic IA is unique in each pyloric cell type. Comparing these currents with the
IAs obtained from oocytes injected with
Panulirus shaker and shal cRNA (lobster Ishaker and lobster
Ishal, respectively) reveals that
the pyloric cell IAs more closely resemble
lobster Ishal than lobster
Ishaker. Using a novel,
quantitative single-cell-reverse transcription-PCR method to count the
number of shal transcripts in individual identified
pyloric neurons, we found that the size of the somatic
IA varies linearly with the number of
endogenous shal transcripts. These data suggest that the
shal gene contributes substantially to the peak somatic
IA in all neurons of the pyloric network.
Key words:
quantitative;
single-cell-RT-PCR;
stomatogastric;
transient potassium current;
Shaker family;
potassium
channel;
gene regulation;
Kv;
transcriptional control;
pyloric network;
shal;
identified neuron;
noncompetitive PCR;
invertebrate
INTRODUCTION
The components of an electrically
excitable system, be it a heart or a cortical circuit, possess unique
electrophysiological phenotypes that are required for the proper
performance of that system. In many instances, differences in the
amount and/or properties of the transient K+ current
(IA) help to establish these essential
cell-specific phenotypes (Connor, 1975
; Cassell and McLachlan, 1986;
Cassell et al., 1986; Premack et al., 1989; Serrano and Getting, 1989; Hamill et al., 1991; Furakawa et al., 1992; Tierney and Harris-Warrick, 1992; Liu et al., 1993; Banks et al., 1996; Massengill et al., 1997).
The functional consequences of IA heterogeneity
are evident in the pyloric central pattern generator.
The 14-neuron pyloric network, located in the stomatogastric ganglion
of the spiny lobster, Panulirus interruptus, is a model system for neural circuits that generate rhythmic, cyclic movements like locomotion, respiration, and mastication (Selverston and Moulins,
1987; Harris-Warrick et al., 1992; Simmers et al., 1995; Marder and
Calabrese, 1996). In these types of systems, muscles must contract in
proper succession to perform a motor task correctly. The order and
timing of muscle contraction depend on when the different pyloric
network neurons fire bursts of action potentials. The burst phase of
the various pyloric neurons is partially determined by the amount and
specific properties of the IA present in each cell. For example, during an ongoing motor pattern the lateral pyloric
(LP) and pyloric constrictor (PY) neurons are simultaneously released
from synaptic inhibition and display postinhibitory rebound. The LP
rebounds faster and fires first, partly because it has a smaller
IA at any given physiological voltage (Hartline,
1979; Graubard and Hartline, 1991; Hartline and Graubard, 1992; Tierney and Harris-Warrick, 1992; Harris-Warrick et al., 1995a,b). Thus, cell-specific differences in the IA strongly
influence the order and timing of neuronal firing and muscle
contraction.
How is IA heterogeneity established in this
system? Constitutive differences in post-translational modifications
could generate cell-specific differences in the
IA, because the
IAs in pyloric neurons can be differentially
altered by the same neuromodulator. For instance, dopamine shifts the
voltages of the somatic IAs of half activation
in the depolarizing direction in the LP and PY cells (Harris-Warrick et
al., 1995a,b) and in the hyperpolarizing direction in the pyloric
dilator (PD) cell (Levini et al., 1996; P. Kloppenburg, unpublished
data). On the other hand, differential gene expression also might
produce IA heterogeneity.
In arthropods, A-channel -subunits are encoded by two
Shaker family genes, shaker and shal
(Salkoff et al., 1992; M. Kim et al., 1995, 1996; Tsunoda and Salkoff,
1995a,b; Baro et al., 1996a) (also see Results). A single multimeric
A-channel contains either shaker or shal -subunits, but never a
combination of the two (Covarrubias et al., 1991; Li et al., 1992;
Sheng et al., 1993; Wang et al., 1993; Deal et al., 1994; Lee et al.,
1994; Shen et al., 1995; Xu et al., 1995). In addition to -subunits, arthropod A-channels may contain 
-subunits, 
-subunits, and/or other auxiliary proteins (Zhong and Wu, 1993; Chouinard et al., 1995;
Jegla and Salkoff, 1997; Tejedor et al., 1997). For the purposes of
this paper, we will define an A-channel by the type of
Shaker family -subunit it possesses. Because all pyloric
neurons express both the shaker and shal genes
(Baro et al., 1996b) (also see Results), we previously hypothesized
that varying mixtures of shaker and shal channels carry the somatic
IA in each cell type. Differences in the somatic
IA between cell types could be obtained by
varying the fraction of shaker versus shal A-channels.
Like most adult systems, the lobster pyloric network is genetically
intractable, so it is difficult to judge the extent to which
differences in Shaker family gene expression contribute to
IA heterogeneity. Voltage-clamp studies
presented in this paper indicate that the six different pyloric
IAs more closely resemble lobster
Ishal than lobster
Ishaker. To explicate this finding,
we developed a quantitative, single-cell-reverse transcription PCR
(SC-RT-PCR) method to count the number of shal transcripts
in single, identified pyloric neurons. Using this method in conjunction
with standard electrophysiological studies, we discovered a strictly
linear relationship between shal transcript number and the size of the
somatic IA in all pyloric neurons. After
considering all of our data, we believe that our earlier hypothesis was
incorrect. Large variations in the ratio of somatic shaker to shal
channels are not responsible for somatic IA
heterogeneity in the pyloric network.
MATERIALS AND METHODS
Electrophysiology
Pyloric neurons. The protocol used to study pyloric
cell IAs using two-electrode voltage clamp has
been described in detail by Harris-Warrick et al. (1995a,b). Briefly, a
stomatogastric ganglion with the appropriate motor nerves and the
associated commissural and esophageal ganglia was dissected from the
animal (Selverston et al., 1976) and pinned in a dish. The preparation was perfused continually at 16°C with lobster saline containing (in
mM): 479 NaCl, 12.8 KCl, 13.7 CaCl2, 3.9 Na2SO4, 10 MgSO4, 2 glucose, and 11.1 Tris, pH 7.35. Pyloric cells were identified electrophysiologically, using standard intracellular and extracellular recording techniques. IAs were characterized
with a two-electrode voltage clamp. The following drugs were present in
the saline to isolate the IA and block
synaptic transmission: 0.05 mM picrotoxin, 20 mM TEA, 10
7 M TTX, 5 mM Cs+, and 0.2 mM
Cd2+. Activation curves were generated by holding
each cell at a potential at which the IA largely
is inactivated and stepping to depolarized potentials to activate
leak-subtracted non-IAs. These
non-IA records were digitally subtracted from
current traces in which the depolarization was preceded by a 200 msec
hyperpolarizing prestep to remove resting inactivation of
IA. The resulting subtracted current could be abolished by 4 mM 4-AP and represents pure
IA. The inactivation data were generated by
varying the amplitude of the prestep while stepping to a fixed,
depolarized potential near full activation. In both cases the
voltage-dependent peak currents were converted to conductance by using
ERev = 
86 mV (Eisen and Marder, 1982). The
average ERev was determined for each of the six
pyloric cell types using tail current measurements of the
IA. Tail currents were obtained by a series of
hyperpolarizing steps after a 6 msec depolarization to +20 mV (preceded
by a hyperpolarizing prepulse) to activate the
IA. Non-IAs were
digitally subtracted, as previously described. We found that the
average ERev did not vary among the six pyloric
cell types. Peak conductance was plotted versus the step potential for
activation data or the prestep potential for inactivation data. The
Boltzmann equation used for fitting was of the form:
|
(1)
|
where Gmax is the maximal conductance,
VA is the voltage of half-maximal activation,
s is the slope factor, and n = 3 for activation and n = 1 for inactivation. The inactivation
kinetics were fit with two exponentials, using the least-squares
minimization procedure of pClamp (Axon Instruments, Foster City, CA).
The current as a function of time (t) corresponds to the
equation:
|
(2)
|
where 
f and
s represent the time constants of
inactivation, and the amplitude of each time constant, If and
Is, represents the relative
contribution of each component to the peak. The time constants of
activation (a) were estimated by fitting the
entire waveform (as seen in Fig. 2) to Equation 2, using three
exponentials, where f,
s,
If, and
Is were fixed to the values obtained
previously from the inactivation fits to that waveform, and
a and Ia
were allowed to vary. All time constants were determined for a
depolarizing step to +20 mV (PD, PY, LP, and VD) or +25 mV (AB and
IC).
Fig. 2.
The family of IAs in
the pyloric network and the lobster shal and
shaker currents. The six pyloric cell types and the
number of cells in each cell type are PD, pyloric
dilator (2); LP, lateral pyloric (1); PY,
pyloric constrictor (8); AB, anterior burster (1);
IC, inferior cardiac (1); and VD,
ventricular dilator (1). The top panel for each cell
type illustrates the IA waveform and amplitude activated by a depolarizing voltage step to +20 mV
(PD, LP, PY, and VD) or +25 mV
(AB and IC). The A-conductances activated at these voltages experience a nearly identical driving force and are
>96% activated in PD, PY,
IC, and VD and 72 and 82% activated in
LP and AB, respectively. Lobster
Ishal and lobster Ishaker are voltage-clamp
recordings of Xenopus oocytes injected with either
lobster shal or lobster shaker RNA. The
bottom panel for each cell is the peak
conductance/voltage relationship for activation (filled
squares) and inactivation (filled
circles) of the IA. The activation
and inactivation curves are least-squares best fits to third- and
first-order Boltzmann equations, respectively. Each set of
points is the average ± SEM from 5 (PD, AB,
IC, VD), 7 (LP, PY), or 17 (lobster
Ishaker) cells. The lobster Ishal curves were taken from Baro
et al. (1996a). The steady-state IA is the
small window representing the subset of the area under both the
activation and inactivation curves.
[View Larger Version of this Image (30K GIF file)]
The average cellular input capacitance for each of the six pyloric cell
types was determined as previously described by Serrano and Getting
(1989).
Xenopus oocytes. Two-electrode voltage clamp was used
to study the shaker-evoked IA 2-4 d
after injecting an oocyte with shaker RNA [clone K17(I); M. Kim, D. Baro, C. Lanning, M. Doshi, J. Farnham, H. Moskowitz, J. Peck,
B. Olivera, and R. Harris-Warrick, unpublished data]. Harvesting,
injections, and maintenance of oocytes were as previously described
(Baro et al., 1996a). Shaker currents (lobster
Ishaker) were elicited by
depolarizing steps from a holding potential of 70 mV. Protocols and
equations for determining the voltage dependence and inactivation
kinetics of lobster Ishaker were as
described in Baro (1996a), except that a minimum of three exponentials
was required to fit the lobster Ishaker inactivation kinetics. A
similar characterization of lobster
Ishal appeared in Baro et al.
(1996a).
Derivation of the correction factor for IA
Gmax
We have modeled the IA as the sum of a
current passing through two A-channels that differ only in their rates
of inactivation (Harris-Warrick, 1995a,b; Willms, 1997). The peak
conductance, 
A, is given
by:
|
(3)
|
where V is the voltage, Erev
is the reversal potential, p is a positive integer,
f and
s are the maximal conductances
of the populations of fast and slowly inactivating channels,
respectively, m is the activation variable, and
hf and
hs are the inactivation variables for
the fast and slow channels, respectively. Thus, the peak conductance is
determined by both the activation and inactivation variables.
Because of inactivation during the rising phase of the current, the
peak conductance for an IA is always less than
the true maximal conductance (Fig. 1). We
will define the true maximal conductance as the conductance obtained
when all of the A-channels are open, before any inactivation occurs. An
estimate of the true maximal conductance (called the corrected
Gmax) can be obtained by multiplying the
measured Gmax by a correction factor that has been derived by Willms (1997). This correction factor (CF) represents the ratio of the true maximal conductance to the measured peak maximal
conductance and is given by:
|
(4)
|
where:
and
are the fractions of the current that inactivate with the fast
and slow time constants,
and
are the ratios of the inactivation time constants to the
activation time constant, and the effective time ratio is given by:
Fig. 1.
Theoretical conductance traces for a simulated
voltage-clamp experiment starting from a strongly hyperpolarized state
(fully deinactivated) and stepping to a strongly depolarized state
(fully activated). The time constants of inactivation and the fraction of fast and slow channels were derived from Table 1, using the parameters for the PD cell (A) or the VD
cell (B). In both cases the activation
time constant was 1.5 msec. Time courses for activation and
inactivation are displayed also. The scale on the
left ordinate is for the conductance (solid
line), whereas the scale on the right
ordinate is for the dimensionless activation and inactivation variables (dashed lines). The top
inactivation curve is the sum of the two lower inactivation curves for
the fast and slow channels. Note that the ratio of the peak conductance
(Gpeak) to the true maximal
conductance (true Gmax) is ~85%
for the PD cell and 43% for the VD cell.
[View Larger Version of this Image (17K GIF file)]
When the relative number of A-channels in neurons with markedly
different IA inactivation rates is assessed, it
is more appropriate to use the corrected
Gmax, rather than the measured
Gmax, because the corrected
Gmax accounts for differences in
IA inactivation kinetics, which the measured
Gmax does not. Simulated conductance traces
based on our kinetic measurements of the PD and VD
IAs are displayed in Figure 1 along with the
time courses for activation and inactivation. The PD peak conductance
(Fig. 1A) is much closer to the true
Gmax than the VD peak conductance (Fig.
1B), because the VD IA inactivates
much more rapidly than the PD IA (Table 1; see
Results). When multiplied by the correction factor, the peak
conductances of both the PD and VD IAs more
closely approximate the true maximal conductance (Willms, 1997).
Table 1.
Properties of IAs
| Cell type (number/type) |
Inact fast
(msec)a |
Inact slow (msec)a |
Inact
slow2 (msec)a,9
|
% peak IA
(fast)a |
% peak IA
(slow)a |
(%) peak IA
(slow2)a,9
|
|
| PD (2) |
255,6
± 3 |
1066 ± 11 |
NA |
0.36
± 0.01 |
0.64 ± 0.01 |
NA
|
|
n = 5 |
n = 5 |
|
n = 5 |
n = 5 |
|
| LP
(1) |
275,6 ± 2 |
1066
± 11 |
NA |
0.35 ± 0.05 |
0.65 ± 0.04 |
NA
|
|
n = 7 |
n = 7 |
|
n = 7 |
n = 7 |
|
| PY
(8) |
255,6 ± 3 |
1135,6
± 24 |
NA |
0.39 ± 0.04 |
0.61 ± 0.04 |
NA
|
|
n = 7 |
n = 7 |
|
n = 7 |
n = 7 |
|
| AB
(1) |
162,3,4,6,7 ± 1 |
754,6,7
± 12 |
NA |
0.44 ± 0.05 |
0.56 ± 0.05 |
NA
|
|
n = 5 |
n = 5 |
|
n = 5 |
n = 5 |
|
| VD
(1) |
32,3,4,5,7 ± 0.4 |
142,3,4,5,7
± 3 |
NA |
0.45 ± 0.05 |
0.55 ± 0.05 |
NA
|
|
n = 5 |
n = 5 |
|
n = 5 |
n = 5 |
|
| IC
(1) |
295,6 ± 2 |
1365,6
± 15 |
NA |
0.34 ± 0.08 |
0.66 ± 0.08 |
NA
|
|
n = 5 |
n = 5 |
|
n = 5 |
n = 5 |
|
| Lobster1
Ishal |
312,4,5,6
± 1 |
2202,3,4,5,6,7
± 7 |
NA |
0.782,3,4,5,6,7
± 0.01 |
0.222,3,4,5,6,7 ± 0.01 |
NA
|
|
n = 16 |
n = 16 |
|
n = 16 |
n = 16 |
|
| Lobster10
Ishaker |
132,3,4,5,6,7,8
± 0.3 |
5352,3,4,5,6,7,8
± 26 |
1834
± 51 |
0.502,3,4,5,6,7,8
± 0.09 |
0.12,3,4,5,6,7,8
± 0.8 |
0.25 ± 0.9
|
| n = 16 |
n = 16 |
n = 16 |
n = 16 |
n = 16 |
n = 16 |
| 423,5,7
± 1 |
153 ± 0.7 |
675,7
± 1 |
6 ± 0.3 |
3.55 ± 0.11
|
| n = 5 |
n = 5 |
n = 5 |
n = 5 |
n = 5
|
| 332,4,6,7 ± 1.5 |
252,4,5,6,7
± 1.4 |
636,7 ± 1.4 |
8 ± 2.9 |
2.79
± 0.41 |
| n = 8 |
n = 7 |
n = 3 |
n = 3 |
n = 7
|
| 403,5,6,7 ± 1.5 |
143,5
± 0.4 |
636,7 ± 2.7 |
7 ± 0.9 |
2.09
± 0.27 |
| n = 8 |
n = 7 |
n = 6 |
n = 6 |
n = 7
|
| 332,4,6,7 ± 2 |
153,4
± 2 |
602,6 ± 1.4 |
6 ± 0.5 |
1.27
± 0.27 |
| n = 5 |
n = 5 |
n = 5 |
n = 5 |
n = 7
|
| 453,4,5,7 ± 2.2 |
143
± 1.6 |
713,4,5,7 ± 2 |
7 ± 0.5 |
0.44
± 0.05 |
| n = 5 |
n = 5 |
n = 5 |
n = 5 |
n = 5
|
| 362,3,4,5,6 ± 1.2 |
143
± 1.4 |
572,3,4,6 ± 1.2 |
7 ± 0.6 |
0.89
± 0.08 |
| n = 5 |
n = 5 |
n = 5 |
n = 5 |
n = 5
|
| 403,5,6,7 ± 0.4 |
153
± 0.4 |
712,3,4,5,7 ± 0.7 |
5
± 0.2 |
NA |
| n = 16 |
n = 16 |
n = 16 |
n = 16 |
| 462,3,4,5,7,8 ± 0.7 |
142,3,5,8
± 0.3 |
442,3,4,5,6,7,8
± 0.4 |
32,3,4,5,6,7,8 ± 0.1 |
NA
|
| n = 16 |
n = 16 |
n = 16 |
n = 16 |
|
|
Values indicate averages ± SEM.
1A description of how these parameters were obtained can be
found in Baro et al. (1996a).
Significantly different (p < 0.05) from
2PD, 3LP, 4PY, 5AB,
6VD, 7IC,
8Ishal.
9Significant differences not determined.
1015% of the current was noninactivating.
a
Obtained from Equation 2 in Materials and
Methods.
b
Obtained from Equation 1 in Materials and
Methods.
|
|
Quantitative SC-RT-PCR
Pyloric neurons were identified electrophysiologically, the
glial caps were removed, and single neurons were isolated physically and used in shal RT-PCRs, as previously described (Baro et
al., 1996b), with the following modifications. The -tubulin primers were excluded and an RNA standard was added to the RT master mix (see
below). 32P end-labeled primers (Baro et al., 1996b) were
added to the PCR master mix (105 cpm/90 µl of mix)
and the [MgCl2] was 1.5 mM; the PCR cycle was 1× at 95°C for 5 min; 25× at 94°C for 1 min, 
68°C for 1 min, 72°C for 30 sec; and 5-10× at 94°C for 1.5 min, 68°C for 1 min, 72°C for 30 sec + 10 sec extension/cycle. The
completed SC-RT-PCRs were electrophoresed on a 10% polyacrylamide gel.
The gel was dried, and the PCR products were imaged with a
PhosphorImager (Molecular Dynamics, Sunnyvale, CA) and stored on a Dell
Dimension XPS 450V computer. The digitized 32P signals were
quantitated with ImageQuant software (version 3.3, Molecular Dynamics).
The bands usually were positioned in the center of boxes (but see
Results) for which the dimensions did not vary, and the relative amount
of 32P within each box was calculated automatically using a
volume integration procedure.
The RNA standard was made by deleting a 45 bp segment (nucleotides
1282-1326) from the shal cDNA clone K/S10 (Baro et al., 1996a), using a modified, nested deletion method (Henikoff, 1987) in
which the deletion extended bidirectionally from a BspEI
restriction enzyme site. The deleted shal clone
(
shal) was linearized with HindIII in a
standard restriction digest (Sambrook et al., 1989). The linearized
shal clone then served as a template in a transcription reaction using T3 RNA polymerase and a Ribomax kit (Promega, Madison, WI). The transcripts were DNased (Life Technologies, Gaithersburg, MD),
a small amount of 32P-dCTP was added, and free nucleotides
were removed with a Nuctrap column (Stratagene, La Jolla, CA).
Fractions containing no radioactivity were phenol/CHCl3
extracted immediately, ethanol precipitated, and resuspended in
dH2O. The concentration of the RNA standard was determined
with a spectrophotometer. The concentration of the RNA standard was
~109-fold greater than the final concentration in
a SC-RT-PCR. Cloned DNA and RNase contamination were detected by using
small aliquots of the concentrated RNA standard as the template in a
PCR or in an overnight incubation in 1× superscript buffer at 37°C,
followed by denaturing gel electrophoresis. An RNA standard was used
only if both DNA and RNase were absent and the RNA appeared as a
discrete band of the appropriate size. The DNA- and RNase-free
concentrated RNA standard was stored at 70°C in 5 µl aliquots in
siliconized tubes for up to 1 year. One aliquot was used per experiment
and then discarded. At the time of the experiment an aliquot of the RNA
standard was diluted with dH2O, using siliconized tubes to prevent the RNA from sticking. Carrier RNA (MS2, Boehringer Mannheim, Indianapolis, IN) also was added during the dilution series (final MS2:
RNA standard = 106, w/w). The diluted RNA
standard was heated to 95°C for 5 min and quick-frozen on dry ice.
The RNA standard was thawed, spun, and added to the RT master mix
(which was stored immediately on ice) right before aliquotting the mix
into the tubes containing the cells. Three different preparations of
the shal RNA standard were used in the quantitative
SC-RT-PCR experiments described in this paper. All three preparations
gave the same results.
RESULTS
IA is unique in each pyloric cell type
The 14 neurons of the pyloric network fall into six identified
cell types (Fig. 2). Each cell type
possesses a unique, unambiguous electrophysiological phenotype
(Hartline and Graubard, 1992). To determine the extent of
IA heterogeneity in this network, we characterized the IA in each cell type with
two-electrode voltage clamp from the cell soma. Using this method,
Hartline et al. (1993) demonstrated that the maximal amplitude,
activation threshold, voltage dependence, and inactivation kinetics of
the IA were the same in an intact pyloric neuron
as in a ligated soma. Thus, the IAs we measure
from these intact neurons primarily reflect channels in the soma and
initial length of the monopolar neurite, with little contribution from
the current in unclamped distal neurites. We will refer to this current
as the somatic IA.
Figure 2 demonstrates that the somatic IA in
each cell type is unique under the same recording conditions.
The upper panels show the somatic IAs obtained
by depolarizing pyloric cells to nearly the same membrane potential
(+20 or +25 mV). These traces demonstrate that at a given membrane
potential both the size and the inactivation kinetics of
IA vary significantly between cell types. The
peak amplitudes at these voltages vary by up to sevenfold. The
IA inactivation was fit by the sum of two
exponentials. The IAs in the VD and AB cells
inactivate much more rapidly relative to the other four cell types
(Fig. 2, Table 1). This is attributable to two factors: (1) the time constants of inactivation
(fast and slow) are up to 10-fold
faster in these cells, and (2) a greater fraction of the channels
inactivates with the fast, relative to the slow, time constant (Table
1). The lower panels in Figure 2 display the voltage dependence of the
IAs. The activation and inactivation curves are
shifted in different cell types, with the V1/2s
for activation and inactivation varying by up to 14 mV (Table 1).
Consequently, the steady-state "window" IA
is active over a different voltage range in different cells (Fig. 2).
Finally, the maximal conductance (Gmax),
obtained from Boltzmann fits to the peak conductance/voltage relation,
varies between cell types by a factor of eight (Table 1). All of these
data indicate that the properties of the somatic
IA are distinct in each cell type under the same
recording conditions. Because synaptic input is blocked by
Cd2+ and picrotoxin and neuromodulators are not
present in the bath, intrinsic differences in the baseline currents
must be responsible for the observed IA
heterogeneity.
IA density varies significantly among
pyloric neurons
Cell-specific phenotypes can be brought about by changing the
biophysical properties and/or the total amount of the
IA in a given cell type. Table 1 demonstrates
that pyloric neurons differentially regulate the properties of the
somatic IA. Next, we set out to determine
whether the somatic IA density also varies among
cell types or whether the different current amplitudes seen in Figure 2
merely reflect the different sizes of pyloric neurons. To obtain the
somatic IA densities, we needed a measure of the size of both the soma and the maximal somatic IA
for each cell type. We estimated the average soma surface area for each
cell type, using input capacitance as a gauge (Table
2). The average input capacitance for
each cell type indicates that the sizes of pyloric somata vary
considerably. If somatic IA density is constant,
then the maximum size of the somatic IA should
be positively correlated with soma size. Conversely, if the six pyloric
cell types differentially regulate somatic IA
density, then the maximum size of the somatic IA
should vary in a manner that is independent of soma size. The
Gmax, calculated from peak current
measurements (Table 1), is used often as a measure of the size of the
IA in a cell. If we normalize the
Gmax for soma size (average
Gmax/average capacitance), somatic
IA density varies by a factor of 6.9 (Table 2).
Table 2.
Somatic IA and shal transcript
density in pyloric cells
| Cell type
(number/type) |
Input capacitance
(nF) |
IA density8
(µS/nF) |
Corrected Gmax1
(µS) |
Corrected IA density9
(µS/nF) |
shal transcript density10
(transcripts/nF) |
|
| PD (2) |
1.24,5,6,7
± 0.07 |
2.96 |
3.983,4,5,6,7
± 0.12 |
3.32 |
2200 |
|
n = 10 |
|
n = 5 |
| LP (1) |
1.474,5,6
± 0.2 |
1.9 |
3.052,5,6,7 ± 0.47 |
2.07 |
1544
|
|
n = 7 |
|
n = 7 |
| PY (8) |
0.92,3
± 0.08 |
2.32 |
2.392,6,7 ± 0.34 |
2.66 |
1878
|
|
n = 10 |
|
n = 7 |
| AB (1) |
0.662,3,6
± 0.08 |
1.92 |
1.572,3 ± 0.28 |
2.38 |
1742
|
|
n = 3 |
|
n = 5 |
| VD (1) |
1.022,3,5
± 0.03 |
0.43 |
1.002,3,4 ± 0.14 |
0.98 |
1049
|
|
n = 5 |
|
n = 5 |
| IC (1) |
0.872
± 0.14 |
1.02 |
0.982,3,4 ± 0.09 |
1.13 |
1092
|
|
n = 3 |
|
n = 5 |
|
Values indicate averages ± SEM.
1The corrected Gmax = Gmax × correction factor (see Materials and
Methods).
Significantly different (p < 0.05) from
2PD, 3LP, 4PY, 5AB,
6VD, 7IC.
8IA density = average
Gmax  average capacitance.
9Corrected IA density = average
corrected Gmax average capacitance (see
Materials and Methods).
10shal transcript density = average number
of shal transcripts average capacitance.
|
|
The Gmax values in Table 1 were derived from
peak current measurements and thus underestimate the true maximum size
of the IA in a cell, because not all of the
channels are open during the peak current because of channel
inactivation during the rising phase of the current (Fig. 1, Materials
and Methods). This is not a problem when neurons are compared with
similar rates of IA inactivation; however, if
the IA in one cell inactivates much more rapidly
than the others, as is the case with VD, the underestimate is
disproportionately greater for that cell (Fig. 1). To compare more
accurately the maximum size of the IA among cell
types, we multiplied the measured Gmax by a
correction factor (Willms, 1997) that represents the ratio of the
maximal conductance before any inactivation occurs to the measured
conductance at the peak current (see Materials and Methods). The
resulting value, which we will term the corrected
Gmax, is shown in Table 2. The effect of
the correction factor can be seen in Figure
3. In most cases the average corrected
Gmax is not significantly different from the
average measured Gmax. However, the average
corrected Gmax for the rapidly inactivating VD
cell is more than twice the average measured
Gmax.
Fig. 3.
The effect of the correction factor varies among
pyloric cell types. The measured average
Gmax (filled diamonds)
and the corrected average Gmax (open
squares) are plotted for each of the six pyloric cell types.
Error bars indicate the SEM when it is larger than the symbols. Note
that the corrected Gmax is approximately
twice the measured Gmax in the
VD cell, whereas the corrected and measured Gmax do not vary greatly in the other cell
types.
[View Larger Version of this Image (11K GIF file)]
Using the average corrected Gmax as the measure
of the maximum size of the somatic IA in each
cell type and normalizing for cell size (average corrected
Gmax/average capacitance), we find that
the corrected somatic IA density varies between
cell types by up to a factor of 3.4 (Table 2). Therefore, with either
the corrected or uncorrected Gmax, the
size of the IA does not simply increase or
decrease with pyloric cell size. This finding is consistent with the
idea that unique electrophysiological phenotypes are established by
varying both the properties and the density of A-channels in a
cell.
Comparison of the pyloric cell IAs to
lobster Ishal and lobster
Ishaker
Neurons could alter the properties and the amount of
IA by differentially regulating A-channel gene
expression. Like their Drosophila homologs, the
Panulirus shaker and shal genes both encode
-subunits for rapidly inactivating A-type channels, although with
somewhat different properties than for the Drosophila
channels (Fig. 2, Table 1; M. Kim et al., 1995, 1996; Baro et al.,
1996a). We compared the IAs obtained from
overexpressing shaker and shal cRNA in
Xenopus oocytes (lobster
Ishaker and lobster Ishal) with the six pyloric
IAs (Fig. 2, Table 1). We discovered that the
variations in pyloric IAs were not consistent with the idea that distinct pyloric IAs result
from different mixtures of shaker and shal A-channels. Instead, we
found that the pyloric cell IAs qualitatively
resemble lobster Ishal more than
lobster Ishaker; however, no pyloric
IA was identical in all parameters to lobster
Ishal.
The voltage dependence of the six pyloric IAs
was quite variable but generally resembled lobster
Ishal more than lobster
Ishaker. The voltages of half
activation (V1/2act) for pyloric cell
IAs range from 33 to 45 mV. The lobster
Ishal V1/2act
is approximately in the middle of this range (40 mV), whereas the
lobster Ishaker
V1/2act lies below the lower limit of this range
(46 mV). The slopes of the activation curves are similar for all
IAs except the LP. The pyloric
IA voltages of half inactivation
(V1/2 inact) range from 71 to 57 mV. The lobster Ishal
V1/2inact (71 mV) is identical to the VD
IA and marks the lower bound of the range. In
contrast, the lobster Ishaker
V1/2inact (44 mV) is significantly more depolarized, and the slope of the inactivation curve is significantly steeper than any of the six pyloric IAs. The
pyloric IA voltages of half activation and
inactivation are not identical to either lobster
Ishal or lobster
Ishaker, nor do they vary in a manner
that would suggest the pyloric IA is a
mixture of lobster Ishaker and
lobster Ishal. For example, the
V1/2act of the VD IA
current is more similar to lobster
Ishaker, whereas its
V1/2inact is identical to lobster
Ishal.
The inactivation kinetics for all six pyloric
IAs are also more similar to lobster
Ishal than lobster
Ishaker or a mixture of the two
channel types. First, lobster Ishal was fit with a double exponential relation, like all six pyloric IAs, whereas lobster
Ishaker could be fit only with a third-order equation. Second, lobster
Ishaker contains a large
noninactivating component that is not present in the six pyloric
IAs or lobster
Ishal (Fig. 2, Table 1). The fast
time constants of inactivation (fast) for the PD,
PY, LP, and IC IAs are very similar to each
other and to lobster Ishal, but they
are significantly slower than lobster
Ishaker. The slow time constants
(slow) of these pyloric neurons are approximately two times faster than lobster Ishal,
but 5-17 times faster than lobster
Ishaker (Table 1). The time constants
of inactivation for the AB and VD IAs are
significantly different from both lobster
Ishal and lobster
Ishaker (Fig. 2, Table 1).
A comparison of the eight different IAs shown in
Figure 2 and Table 1 is not sufficient to ascertain which A-channels
carry the pyloric IAs. However, the overall
similarity of the neuronal IAs to lobster
Ishal suggested that shal
may be an important contributor to the pyloric cell
IAs. Therefore, we developed a method to
quantitate shal gene expression in single identified neurons, using noncompetitive RT-PCR (Ferre, 1992; Foley et al., 1993;
Gause and Adamovicz, 1994; Sucher and Deitcher, 1995).
Quantitating shal gene expression in single
identified neurons
In our method, RNA from a single cell is reverse-transcribed and
amplified along with 103 shal RNA
standard molecules in an RT-PCR containing 32P-labeled
Panulirus shal-specific primers. The shal RNA
standard is identical to the endogenous shal transcript,
except that it lacks the distal-most portions of the 5
and 3
untranslated regions and it contains a very small deletion in the
region between the two PCR primers. This minor deletion allows the
separation of the cellular shal and the standard
shal RT-PCR products on the basis of size. The number of
cellular transcripts is determined by normalizing the cellular
shal RT-PCR product against the standard shal
RT-PCR product.
The results of a typical experiment are shown in Figure
4. Neurons were identified
electrophysiologically. Glial caps were removed because the
shal gene is expressed in glial cells (Baro et al., 1996b),
and individual neurons were physically isolated and used in RT-PCRs
containing 103 shal RNA standard
molecules. The RT-PCR products were size-separated, using
polyacrylamide gel electrophoresis, and phosphorimaged. The upper band
in each lane represents the product of the endogenous shal
transcripts present in a single cell. The lower band represents the
product of the 1000 shal RNA standard molecules. The
number of shal transcripts in each cell was calculated
from:
|
(5)
|
where X is the relative amplification efficiency per
cycle of a shal to a shal DNA template, and
n is the number of cycles in the PCR.
Fig. 4.
Results from a typical SC-RT-PCR experiment. Each
lane represents one SC-RT-PCR. The template in each
SC-RT-PCR was cloned shal DNA (+), 1000 shal RNA standard molecules
(B), or 1000 shal RNA
standard molecules plus a single identified neuron lacking a glial cap
(PY, LP, PD, PD*, and VD). Data from the
cell PD* were not used because of obvious RNase
degradation (see Results).
[View Larger Version of this Image (37K GIF file)]
Shorter DNA molecules often are amplified more efficiently than longer
molecules in a PCR. To determine whether the 262 bp shal
PCR product was amplified more efficiently than the 307 bp
shal PCR product, we added equal numbers of shal
and shal DNA templates to the same PCR (Fig.
5). The PCR products were electrophoresed
and phosphorimaged, and the digitized 32P signals were
quantitated as described in Materials and Methods. The amplification
efficiency per cycle of a shal relative to a
shal DNA template was determined from the following
equation: X = (cpm shal/cpm
shal)1/n, where X
and n are described above. We found that shal
DNA molecules are amplified on average 1.029 ± 0.002 (n = 103) times more efficiently than an equivalent
number of shal DNA molecules per PCR cycle. So, for a
30-cycle PCR, Xn = (1.029)30 = 2.4.
Fig. 5.
The relative amplification efficiency of
shal over shal. Six representative
PCRs are shown. Equal numbers of shal and
shal DNA molecules were added to each PCR. PCRs were
performed for (A) 20, (B) 25, or
(C) 30 cycles. The PCR products were
electrophoresed and phosphorimaged, and the digitized 32P
signals were quantitated. The amplification efficiency per cycle of
a shal relative to a shal DNA template
was determined from the following equation: X = (cpm shal/cpm
shal)1/n, where
X is the relative amplification efficiency per cycle and n is the number of cycles.
[View Larger Version of this Image (36K GIF file)]
To ensure that our measurements of the relative amplification
efficiency were accurate, we used several different DNA template preparations, and we varied the number of starting molecules and PCR
cycles within the linear range of amplification (see below); otherwise,
the conditions of the PCR were identical to the quantitative SC-RT-PCR.
In those experiments with a large number of template molecules and PCR
cycles, the shal and shal products tended to bleed together along the edges of the lane (Fig. 5C).
Because the bands "smile" (Figs. 4, 5, 6), a smeared/streaked signal
along the edge of the lane belongs to the band just below the
smear/streak. Thus, in the few cases in which bleeding occurred, the
phosphorimager measuring boxes (see Materials and Methods) were
positioned so that the smear/streak between the bands went with the
lower band. The average amplification efficiency of shal
relative to shal did not vary significantly with the number
of starting molecules or PCR cycles.
Fig. 6.
Determining the linear range of amplification.
A, Fifteen shal RT-PCRs were performed
for 35 cycles. The templates in each of three RT-PCRs were 5 × 104, 104, 103,
102, or 50 shal RNA molecules. Ten
microliters of the completed RT-PCRs were run on each of three gels
(only one gel is shown) and phosphorimaged. The average incorporated
counts per minute in the nine resulting bands were determined for each
of the five templates. B, The experiment in
A was repeated three times, and the average incorporated
counts per minute for the five templates were determined. The average
incorporated counts per minute were plotted against the number of
starting molecules on a log/log scale. The error bars represent the
SEM. The line represents a linear regression to the
first four data points (from 50 to 104
molecules).
[View Larger Version of this Image (31K GIF file)]
RNase is the bane of the quantitative SC-RT-PCR experiments. If
an RNase is introduced when the cellular transcripts and the RNA
standard are both present, they should be degraded equally, and the
ratio of the signals will not change, just their intensity. However, if
an RNase acts preferentially on either the endogenous transcript or the
standard, there will be errors in our measurement. To detect and
control for trace RNase contamination, we carried at least two blanks
per experiment (RT-PCRs containing 1000 shal RNA standard
molecules but no cell; Fig. 4). We used the data from an experiment
only if the counts per minute in the standard bands of the blanks
varied by less than a factor of 2. We used the data from an individual
cell within an experiment only if the counts per minute in the standard
band of that SC-RT-PCR were within or above the range of the blanks.
For example, in Figure 4 the starred PD cell failed this criterion, so
the data from this cell were not used.
Demonstrating that input is proportional to output in our
SC-RT-PCRs
For our SC-RT-PCR method to be quantitative, we have to
demonstrate that input is proportional to output. In a typical PCR the
product increases exponentially with cycle number until eventually a
plateau is reached. The PCR product is proportional to the number of
starting molecules only if the PCR remains within the exponential phase
(for review, see Ferre, 1992; Foley et al., 1993; Gause and Adamovicz,
1994). Several factors determine when the plateau is reached, including
the number of starting molecules: everything else being equal, the
larger the number of starting molecules, the sooner the PCR enters the
plateau phase. For a given cycle number the linear range of
amplification is defined as the range of starting template molecules
over which the PCR remains within the exponential phase (for review,
see Ferre, 1992). We determined the linear range of amplification for a
35 cycle RT-PCR under our quantitative SC-RT-PCR conditions (Fig.
6). RT-PCRs containing 50-50,000
shal RNA molecules were performed for 35 cycles, and the
amount of 32P incorporated into the shal
RT-PCR product was quantitated (Fig. 6A). Figure
6B shows the relationship between the number of
starting molecules and the amount of product. Each data point
represents the average of nine different RT-PCR experiments. As Figure
6B demonstrates, the log of the product increases
linearly as a function of the log of the starting template over the
range from 50 to at least 10,000 shal RNA molecules. The
data point at 50,000 molecules is slightly below the line. This
suggests that an RT-PCR containing 50,000 shal starting
molecules enters the beginning stages of the plateau phase by 35 cycles
and input may no longer be proportional to output. However, when the
RT-PCR contains fewer starting molecules, and in particular
<104, input is still proportional to output after
35 cycles. Thus, the linear range of amplification for a 35 cycle
RT-PCR under the present SC-RT-PCR conditions includes at least
50-10,000 shal RNA template molecules. Preliminary
experiments indicated that the number of endogenous shal
transcripts in a pyloric neuron never exceeded 4000. Because we add
1000 shal RNA standard molecules to a SC-RT-PCR, each
reaction has between 1000 and 5000 starting molecules, which is well
within the linear range of amplification for a 35 cycle SC-RT-PCR (Fig.
6B). In some experiments we reduced the SC-RT-PCR
cycle number to 30, and this did not change our results. This is what
we would predict, because the upper limit of the linear range of
amplification increases with decreasing cycle number. We should point
out that the level of nonspecific RNA does not change significantly
when a cell is added to the RT-PCR, because we include 20 ng of carrier
RNA in each RT-PCR and a neuron most likely contributes <100 pg of
nonspecific RNA to a reaction. Thus, adding a cell to the RT-PCR will
not affect the linear range of amplification [see Gause and Adamovicz
(1994) for a discussion of this point].
shal transcript number varies significantly among
cell types
We performed a number of SC-RT-PCR experiments to determine the
average number of shal transcripts in each pyloric cell
type. The mean number of shal transcripts is plotted for
each cell type in Figure 7. There are
several points to be made. First, all pyloric cells express
shal. Second, the number of shal transcripts
within a given cell type was consistent between individuals. Third, we observed significant differences in the average number of
shal transcripts among cell types, with shal
transcript levels varying by a factor of 2.8. Fourth, there is no
positive correlation between the average number of shal
transcripts and the average input capacitance for a given cell type, as
seen by our calculations of the shal transcript density,
which varied from cell type to cell type (Table 2). Thus, pyloric cells
differentially regulate shal gene expression at the level of
the transcript. Pyloric neurons may differentially modulate transcript
levels by varying rates of transcription, transcript processing, and/or
transcript turnover. The fact that transcript levels are regulated does
not exclude additional translational and post-translational regulation
of shal gene expression in pyloric neurons as well.
Fig. 7.
The average number of shal
transcripts varies significantly among pyloric cell types. The average
number of shal transcripts is plotted for each cell
type; the error bars represent the SEM. The number of cells examined in
each cell type was PD, 9; LP, 6;
PY, 14; AB, 4; VD, 9; and
IC, 6. Asterisks represent significant difference (p < 0.05): PY (*); AB, VD, IC
(**); PD, LP (***); PD (****).
[View Larger Version of this Image (16K GIF file)]
The maximum size of the somatic IA varies
as a linear function of shal gene expression
If shal underlies a major fraction of the somatic
IA in pyloric neurons, then it might be possible
to correlate the maximum size of the somatic IA
with the number of shal transcripts in a given pyloric cell
type. Plotting the mean number of shal transcripts in each
cell type versus the average measured Gmax
reveals a remarkably strong positive correlation (Fig.
8A). A linear
regression fit to these data has an R2
value of 0.95, demonstrating that the maximum size of the somatic IA in each cell type varies as a linear function
of shal transcript levels (p < 0.001). The VD data point is significantly below the line in Figure
8A. We suggest this is attributable to an
underestimate of the VD Gmax calculated from
peak current measurements because of the more rapid inactivation of the
VD IA relative to other pyloric neurons (see
Fig. 1). As described above, we can compensate for this underestimate
by plotting the corrected Gmax versus
shal transcript number for each cell type (Fig.
8B). In this case the VD more closely approximates
the line so that R2 becomes 0.98 and
p < 0.0002.
Fig. 8.
The maximum size of the somatic
IA varies as a function of
shal gene expression. The average uncorrected
(A) or corrected (B) Gmax was
plotted against the mean number of shal transcripts for
each of the six pyloric cell types. The lines represent
linear regressions of the data points. The error bars on each data
point represent the SE. The numbers in
parentheses represent the number of cells used to
measure either the Gmax or the number of
shal transcripts.
[View Larger Version of this Image (19K GIF file)]
The simplest interpretation of our data is that shal is an -subunit
for the majority of somatic A-channels in all 14 neurons of the pyloric
network. Research on flies and mammals has shown that
K+ channel -subunits from the shaker and shal
subfamilies cannot coassemble to form a heteromeric channel, and one
never finds an A-channel composed of shaker and shal -subunits
(Covarrubias et al., 1991; Li et al., 1992; Sheng et al., 1993; Wang et
al., 1993; Deal et al., 1994; Lee et al., 1994; Shen et al., 1995; Xu
et al., 1995) (but see Shahidullah et al., 1996). Subfamily-specific assembly is mediated via the NAB domain in the N-terminal regions of
K+ channel subunits (Xu et al., 1995). NAB domains
are conserved in a subfamily-specific manner. The amino acid identity
among different NAB regions within a subfamily is generally >70%, but between subfamilies NAB identity drops to ~30% (Xu et al., 1995). Because the NAB domains of Panulirus shaker and shal are 94 and 97% identical to their Drosophila homologs,
respectively, we believe that Panulirus shaker and shal
-subunits do not form heterotetramers. Thus, if we consider only
Shaker family -subunit genes for the moment, three
possibilities exist: (1) the somatic IAs are
carried by shaker channels alone; (2) the somatic
IAs are carried by two different populations of
A-channels, one containing shal -subunits and the other containing
shaker -subunits; (3) the somatic IAs are
carried by shal channels alone. Because the size of the somatic IA varies as a linear function of
shal transcript number with p < 0.001, and
pyloric somatic IAs qualitatively resemble
lobster Ishal but not lobster
Ishaker, we can rule out the first
possibility. With regard to the second possibility, the extremely high
R2 value for the
shal-IA correlation (Fig. 8)
suggests that any significant contribution to the somatic
IA from the shaker gene must either
(1) remain fairly constant among cell types or (2) vary among cell
types in a manner that is essentially identical to shal. If,
on the one hand, the shaker gene produced a significant, constant number of somatic A-channels in every cell type, then there
should be a sizable IA even when shal
transcripts are absent. In other words, when x is zero in
Figure 8, the y-intercept should be positive. Because the
extrapolated y-intercept in Figure 8 is negative, we can
discard this possibility. If, on the other hand, the ratio of somatic
shaker to shal channels is constant among the six different cell types,
then shaker and shal gene expression must be
completely coregulated in these six different cell types. However,
strict coregulation of shaker and shal A-channel gene expression has not been described in previous studies in other
systems (Roberds and Tamkun, 1991; Kues and Wunder, 1992; Lesage et
al., 1992; Sheng et al., 1992; Tsaur et al., 1992; Dixon and McKinnon,
1994, 1996; Maletic-Savatic et al., 1995; Brahmajothi et al., 1996;
Serôdio et al., 1996) (for review, see Chandy and Gutman, 1995).
Because we have not yet quantified shaker expression in pyloric
neurons, we cannot reject the possibility of coregulation categorically. Nevertheless, because the pyloric somatic
IAs resemble lobster
Ishal more than lobster
Ishaker, we suggest that the third
possibility is the simplest and most likely: in pyloric neurons the
shal gene encodes most or all of the Shaker family -subunits for somatic A-channels. This point eventually could
be confirmed by demonstrating a causal relationship between shal and IA, using
shaker and shal knock-out techniques that use expression of antisense oligonucleotides (Chung et al., 1995) or
dominant-negative mutations (Ribera, 1996).
The previous argument involved Shaker family -subunits
only. This argument did not consider the formation of heterotetramers between Shaker family -subunits and other proteins.
Drosophila mutant analysis indicates that Shaker
family proteins might form heterotetramers with non-Shaker
family K+ channel proteins such as EAG (Warmke et
al., 1991; Zhong et al., 1991, 1993; Warmke and Ganetzky, 1994), and
shaker and EAG have been shown to form heterotetramers in an oocyte
expression system (Chen et al., 1996). Similarly, heterotetramers can
form between shal and subunits (Jegla and Salkoff, 1997). Our
data do not rule out the possibility that some fraction, or even all,
of the somatic A-channels are heterotetramers between -subunits and EAG, -subunits, or other as yet unidentified subunits.
DISCUSSION
IA diversity in the 14-neuron pyloric
networ
