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Previous Article | Next Article
Volume 17, Number 17, Issue of September 1, 1997 pp. 6597-6610
Copyright ©1997 Society for NeuroscienceQuantitative 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
Different Shaker family
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-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.
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
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
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
In arthropods, A-channel
-subunits,
-subunits, and/or other auxiliary proteins (Zhong and Wu, 1993
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. (1995a7 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
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:
(1) where
(2) f and
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)
[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) 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
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 A, is given by:
where V is the voltage, Erev is the reversal potential, p is a positive integer,
(3) f and
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) where:
(4) 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)]
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
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 Inact Inact % peak IA ( % peak IA ( (%) peak IA ( 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 = 16 153 ± 0.7 6 ± 0.3 3.55 ± 0.11 n = 5 n = 5 n = 5 n = 5 n = 5 252,4,5,6,7 ± 1.4 8 ± 2.9 2.79 ± 0.41 n = 8 n = 7 n = 3 n = 3 n = 7 143,5 ± 0.4 7 ± 0.9 2.09 ± 0.27 n = 8 n = 7 n = 6 n = 6 n = 7 153,4 ± 2 6 ± 0.5 1.27 ± 0.27 n = 5 n = 5 n = 5 n = 5 n = 7 143 ± 1.6 7 ± 0.5 0.44 ± 0.05 n = 5 n = 5 n = 5 n = 5 n = 5 143 ± 1.4 7 ± 0.6 0.89 ± 0.08 n = 5 n = 5 n = 5 n = 5 n = 5 153 ± 0.4 5 ± 0.2 NA n = 16 n = 16 n = 16 n = 16 142,3,5,8 ± 0.3 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) 68°C for 1 min,
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 shal) was linearized with HindIII in a standard restriction digest (Sambrook et al., 1989
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
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 ( 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
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 
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
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
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 (
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 Quantitating shal gene expression in single identified neurons
In our method, RNA from a single cell is reverse-transcribed and amplified along with 103and 3
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 where X is the relative amplification efficiency per cycle of a
(5) 
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
[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 
Fig. 5. The relative amplification efficiency of
[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 
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
[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 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, 1992shal 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
The previous argument involved Shaker family
DISCUSSION
IA diversity in the 14-neuron pyloric network of the spiny lobster, Panulirus interruptus, is established by varying the density and/or the properties of A-channels between cells. Arthropod A-channels are multimeric proteins containing Shaker familySomatic IA heterogeneity in the pyloric network
Our results suggest that although lobster shal
Qualitative and quantitative differences in Shaker family
Other mechanisms also could contribute to pyloric IA diversity. For instance, it has been shown recently that the Shaker gene family encodes both classical The VD IA
The VD IA is significantly different from the other pyloric neurons: it is much smaller, more rapidly inactivating, and both the activation and inactivation curves are more hyperpolarized (Fig. 2, Tables 1, 2). With uncorrected Gmax values, the VD data point does not lie on or near the line relating IA Gmax to shal transcript number, as do the data points from all the other pyloric neurons (Fig. 8A). There are at least three possible interpretations of these data, and they are not mutually exclusive. The first interpretation is that shaker contributes more substantially to the VD IA than to other pyloric IAs. The second interpretation is that most VD somatic A-channels contain shalIf shal underlies the somatic IA, where are the shaker A-channels?
We have shown with nonquantitative methods that shaker is expressed in all pyloric neurons and that shaker produces A-channels (Fig. 2; Baro et al., 1996b
Electrophysiological analyses of Drosophila mutants indicate that shal encodes the somatic IA in most embryonic neurons (Tsunoda and Salkoff, 1995a
Note that our proposal does not imply that shal channels are localized only to the soma and therefore are excluded from the distal regions of the neuropil. Shal channels may be distributed over the entire surface of a pyloric neuron, in which case they certainly can affect neuronal activity and firing patterns. We have shown previously that dopamine modulation of the measured somatic IA can explain quantitatively the alterations in the postinhibitory rebound characteristics of the LP and PY neurons (Harris-Warrick et al., 1995a shal transcripts and the somatic IA
It was somewhat surprising to find such a simple relationship between shal transcript number and the IA Gmax. Any number of phenomena could have masked this relationship. If there were large cell-specific differences in the translational regulation of shal such that some cells produced 103 functional proteins per transcript whereas others produced one, then the size of IA would not correlate with the number of shal transcripts. Similarly, if two cells had the same number of shal transcripts and proteins, but the first cell concentrated all of its shal channels in the soma whereas the second localized them to the unclamped regions of the neuropil, we would detect an IA in the first cell, no IA in the second cell, and no positive correlation between transcript number and IA magnitude. By the same token, if the average conductance of somatic A-channels varied significantly among pyloric neurons, then the R2 values in Figure 8 would be reduced dramatically even if shal encoded the
The fact that the x-intercept in Figure 8 is not zero suggests that there is a sizable pool of unused shal RNA in these cells. Some portions of the transcripts we measure almost certainly are processed incompletely or incorrectly (Kramer, 1996 Conclusion
The most parsimonious explanation of the linear relationship between shal gene expression and IA Gmax is that shal encodes the Shaker family
FOOTNOTES
Received March 4, 1997; revised May 28, 1997; accepted June 17, 1997.
This work was supported by the Human Frontier Science program; National Institutes of Health Grants NS25915, NS35631, and NS17323; Office of Naval Research Grant N00014-95-1-0292 to R.H.-W.; and a Hughes undergraduate fellowship to H.E.R. We thank our anonymous reviewers, Ole Kiehn, Scott Hooper, Thomas Podleski, Jack Peck, Jenifer Levini, Amir Ayali, Ronald Hoy, Bruce Johnson, and David Deitcher for helpful comments on this manuscript.
Correspondence should be addressed to Dr. Deborah J. Baro, Section for Neurobiology and Behavior, Cornell University, Seeley G. Mudd Hall, Ithaca, NY 14853.
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