| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
Fishberg Department of Neuroscience, Mount Sinai School of Medicine, New York, New York
Submitted 10 May 2005; accepted in final form 6 June 2005
 |
ABSTRACT |
|---|
|
|
|
INTRODUCTION |
|---|
|
The ARC muscle is one of the muscles of the buccal mass, a complex structure that produces rhythmic, cyclical movements of the animal's food-grasping organ, the radula, in several types of consummatory feeding behavior such as biting, swallowing, and rejection of unsuitable food (Kupfermann 1974
). The structure of the ARC system is summarized in Fig. 1. The muscle is innervated by two motor neurons, B15 and B16, which, when they fire, release their classical transmitter acetylcholine (ACh) to depolarize and contract the muscle (Cohen et al. 1978; Fig. 1, left). In addition, however, the two motor neurons release several families of modulatory peptide cotransmitters that shape the basal ACh-induced contractions (Fig. 1, right; for review and complete references see Brezina et al. 2003a, 2005; Hooper et al. 1999; Kupfermann et al. 1997; Weiss et al. 1993). Most important are the small cardioactive peptides (SCPs), released from motor neuron B15 (e.g., Cropper et al. 1987a; Lloyd et al. 1984; Vilim et al. 1996b), and the myomodulins (MMs), released from motor neuron B16 (Brezina et al. 1995; Cropper et al. 1987b, 1991; Vilim et al. 2000). The SCPs and MMs shape contractions through three main effects on the muscle: 1) they potentiate the contractions by enhancing the muscle's depolarization-activated Ca current that supplies Ca2+ essential for contraction (e.g., Brezina and Weiss 1995; Brezina et al. 1994a, 1995); 2) they depress the contractions by activating in the muscle a K current that opposes the ACh-induced depolarization, activation of the Ca current, and Ca2+ influx (Brezina et al. 1994b, 1995; Orekhova et al. 2003); and 3) they increase the relaxation rate of the contractions probably by modulating the muscle's contractile machinery (Heierhorst et al. 1994, 1995; Probst et al. 1994). The balance of the competing potentiating and depressing effects determines the net modulation of contraction size that, together with the modulation of the relaxation rate, then produces the final shape of the contraction. Similar networks of modulatory effects shape the contractions of the other muscles of the buccal mass (e.g., Church et al. 1993; Evans et al. 1999; Hurwitz et al. 2000; Keating and Lloyd 1999).
|
, 2000b, 2003a,b, 2005).
There is, however, a serious potential difficulty with this scenario. Vilim et al. (1996a) found that the release of the modulatory peptide cotransmitters from the motor neurons is extremely sensitive to temperature. Figure 2 replots the data of Vilim et al. (1996a) for the release of the SCPs from the ARC motor neuron B15, measured by direct radioimmunoassay of the amount of released peptide (each point is the mean ± SE, smaller than the mean symbol size, of four preparations). Also plotted is the simultaneous release of the buccalins, peptides of another family that are costored in the same dense-core vesicles with the SCPs in B15 (as with the MMs in B16; "Buc" in Fig. 1) and coreleased by the same release process (Vilim et al. 1996b, 2000). Release of both peptide families declines dramatically with increasing temperature: an increase of 10°C reduces release approximately 20-fold (see Fig. 2 legend).
|
). [Other Aplysia species, such as A. fasciata in the Mediterranean Sea or A. dactylomela in the Caribbean, live and feed at up to at least 29°C (Carefoot 1987; Gev et al. 1984), but the modulatory tuning of the feeding musculature has not been studied in these species.] Aplysia are, of course, poikilotherms that cannot maintain an internal temperature that is appreciably different from that of the surrounding water. According to Fig. 2, there is little release of the peptide modulators at, say, 25°C.1 Yet Aplysia live and feed at this temperature. Does this mean that the modulatory tuning is, after all, not important for functional, efficient feeding? In this paper we provide a resolution of this difficulty. Even though the release of the modulators declines dramatically with increasing temperature, the overall strength of the modulatory tuning does not decline. This is because the modulatory system incorporates mechanisms, individual effects with the opposite temperature dependency as well as more general mechanisms that are inherent in the network structure of the system, that maintain its output throughout the entire temperature range.
|
|
METHODS |
|---|
|
PREPARATION.
The preparation was the standard preparation used for recording motor neuron-elicited contractions of the ARC muscle (Cohen et al. 1978; Weiss et al. 1978; for recent use see, e.g., Horn et al. 2004; Orekhova et al. 2003). Briefly, the preparation consisted of the bilateral buccal ganglia, the two ARC muscles, and the connecting buccal nerves 3 through which the buccal motor neurons B15 and B16 innervate the ARC muscle. The cerebral ganglion, connected to the buccal ganglia by the cerebral–buccal connectives, was also retained. The buccal ganglia (but not the cerebral ganglion) were desheathed. One ARC muscle was pinned out in a separate subchamber and connected to an isotonic transducer (Model 60–3000, Harvard Apparatus, Holliston, MA) to measure the length of the muscle with a light counterbalancing load. The ipsilateral motor neurons B15 and B16 were impaled with standard intracellular microelectrodes and connected to an intracellular amplifier (Axoclamp 2A/B, Axon Instruments, Union City, CA). Under the control of external timers/stimulators (Pulsemaster A300, World Precision Instruments, Sarasota, FL, and Grass S48/S88, Astro-Med, West Warwick, RI), the motor neurons were stimulated with brief current injections to fire spikes in the desired pattern. All signals were sampled and recorded simultaneously by a computer using Digidata 1322A data-acquisition hardware and pCLAMP 8/9 software (Axon Instruments). When the experiment on the first ARC muscle was finished, the ARC muscle on the opposite side of the preparation was also used, whenever possible, for an independent experiment or a matched pair (see RESULTS).
EXPERIMENTAL DESIGN. Three basic experimental paradigms were used.
1) Single motor neuron self-modulation. Only one of the two ARC motor neurons, either B15 or B16, was fired in a relatively intense bursting pattern, generally at 10–15 Hz for 2 s every 5 s for 3 min, calculated to release the peptide modulators (see Fig. 3).
|
; Cropper et al. 1990c; Vilim et al. 1996a). The modulator-releasing neuron was fired for 4 s every 10 s for 60 s, B15 at 20 Hz, and B16 at 20 or 25 Hz.
|
7 ml) steadily at 1.25 ml/min, while the reversal of the effects was followed with continuing monitoring contractions (Fig. 9C). When the effects had reversed completely (after 
30 min but sometimes up to 1 h of wash), the application of SCPB was repeated at a different temperature. For additional details see RESULTS and figure legends.
|
|
MEASUREMENT OF CONTRACTION PARAMETERS. The peak amplitude of each contraction was measured relative to the baseline muscle length at the beginning of the motor neuron burst that elicited the contraction, or, in the case of closely spaced bursts that did not allow the muscle to relax fully to the baseline (Fig. 3), the baseline length at the beginning of the first burst. The relaxation rate of each contraction was then evaluated, with a custom-written program in Mathematica (Wolfram Research, Champaign, IL), by fitting a single exponential to the relaxation phase of the contraction from 90 to 33% of the peak amplitude (see Fig. 1, bottom right), or, with the closely spaced contractions, to the beginning of the next motor neuron burst if that came earlier. Over this upper middle portion of the relaxation phase, most contractions were in fact well fitted by single exponentials. The relaxation rate was then the reciprocal of the time constant of the exponential.
Modeling
The dynamic model of the B15/B16–ARC neuromuscular system and its modulation was taken from Brezina et al. (2003a,b) and analytically solved or numerically integrated in Mathematica. Specific changes in parameters, dictated by the experimental results, were made as described in the figure legends. The relaxation rate of the simulated contractions was measured in the same way as that of the real contractions.
|
|
RESULTS |
|---|
|
In the B15/B16–ARC neuromuscular system in vitro, the simplest way to observe the effects of the released modulators is with the single motor neuron self-modulation paradigm, in which just one motor neuron is stimulated both to release the modulators and to elicit the contraction that they modulate. Figure 3 shows a representative experiment of this kind. Motor neuron B16 was stimulated to fire in a repetitive bursting pattern—similar to that which it in fact fires in the intact feeding animal (Cropper et al. 1990a; Horn et al. 2004; see Fig. 12)—that elicited a sequence of contractions. These contractions progressively changed in shape, in amplitude (continuous waveform; axis at left) and in relaxation rate (large black points; axis at right). Previous studies have strongly suggested that much of the change in amplitude and especially relaxation rate arises from the progressive development of the effects of the modulators that such a pattern of firing also releases (e.g., Brezina et al. 2003b; Whim and Lloyd 1990).
|
We did not analyze these results in any detail because, although the single motor neuron paradigm is simple, its results are difficult to interpret. As the motor neuron firing continues, all of the system components shown in Fig. 1—modulator release, modulator concentrations, the various modulator effects, as well as processes that shape the basal contraction such as presynaptic facilitation and potentiation of ACh release (Cohen et al. 1978) and postsynaptic summation of the contraction waveform itself—continue to evolve at different rates (Brezina et al. 2003a), in continuing interaction with each other. Each feature of the contraction shape thus has multiple interpretations.
Two motor neuron cross-modulation is not weaker at higher temperature
More readily interpretable are the results of the two motor neuron cross-modulation paradigm, first introduced in this system by Whim and Lloyd (1990). One motor neuron is stimulated to fire brief bursts at regular infrequent intervals, which release little or no modulator themselves (Brezina et al. 2003b; Cropper et al. 1990c; Vilim et al. 1996a) but elicit regular "monitoring" contractions. The other motor neuron is then fired in a relatively short, intense bursting pattern to release the modulators. This pattern inevitably elicits a contraction, too, but afterward the contraction rapidly relaxes while the effects of the released modulators, some of which are very long lasting (Brezina et al. 2003a; see following text), persist to modulate the continuing monitoring contractions.2 Figure 4A1 shows a representative experiment. After the modulator-releasing firing of motor neuron B16, the monitoring contractions elicited by motor neuron B15 were increased in both amplitude (continuous waveform; axis at left) and relaxation rate (large black points; axis at right), and remained so for many minutes. From what is known about the system (see INTRODUCTION), both effects were presumably caused by the MMs released from motor neuron B16. When, in other experiments, the roles of the monitoring and modulator-releasing motor neurons were reversed, similar increases in contraction amplitude and relaxation rate were observed, in that case presumably caused by the SCPs released from motor neuron B15. Previous work has shown that the SCPs and MMs increase the relaxation rate in an identical fashion and have identical potentiating effects on contraction amplitude (Fig. 1; Brezina et al. 1996, 2003a).
Figure 4, A and B shows the same experiment repeated, in the two ARC muscles of the same preparation, at 15 and 25°C. Figure 4, A2 and B2 compares in detail three contractions expanded from A1 and B1, in particular a control contraction before the modulator-releasing firing (contraction 1) with the modulated contraction about 2 min after the end of the modulator-releasing firing (contraction 2), at each temperature. Clearly, the observed modulation was not weaker at the higher temperature. The modulation of the relaxation rate, especially, was significantly stronger.
Quantification of the modulation of relaxation rate
In our analysis of the results of the two motor neuron cross-modulation experiments, we focused primarily on the modulation of the relaxation rate and only secondarily on that of contraction amplitude. Even in the cross-modulation paradigm, the modulation of contraction amplitude is still a composite process with multiple interpretations. On the potentiating effect of the SCPs and MMs, mediated by enhancement of Ca current in the muscle, is superimposed a simultaneous depressing effect, mediated by activation of a K current (INTRODUCTION, Fig. 1). Furthermore, together with the SCPs and MMs both motor neurons B15 and B16 corelease the buccalins, which depress contractions (Cropper et al. 1988, 1990b; Orekhova et al. 2003; Vilim et al. 1994). Thus no net change in contraction amplitude, for example, could mean no modulator release, or conversely robust modulator release with the potentiating effect of the released SCPs, or MMs, balanced by an equally strong depressing effect of the SCPs or MMs, or of the coreleased buccalins.3 To fully understand how the modulation of contraction amplitude is affected by temperature, these various effects would have to be studied in isolation, each with its own paradigm in a different type of reduced preparation (see, e.g., Brezina et al. 1994a,b, 1995, 2003a; Orekhova et al. 2003).
In contrast, the modulation of the relaxation rate is, as far as is known, a unitary process, which furthermore is activated, in an identical fashion, just by the SCPs and MMs: the buccalins do not modulate the relaxation rate (Cropper et al. 1988; Vilim et al. 1994). Analysis of the relaxation rate, however, has its own difficulty. In different preparations, the initial, "unmodulated" relaxation rate of the ARC muscle—the control relaxation rate against which the modulation is then evaluated—can be very different (Brezina et al. 1995; see following text and Figs. 5 and 8). We attempted to manipulate the control relaxation rate by altering the load on the muscle, with limited success. Where possible we also made matched pairs of measurements, at 15 and 25°C, in the two muscles of the same preparation or successively in the same muscle. This reduced the variability somewhat, but considerable variability still remained.
|
|
First, it can be seen that the modulator-releasing firing always brought about an increase—never any significant decrease—in the relaxation rate. Essentially all points in Fig. 5A lie above the 0% diagonal line; the lines joining the corresponding control and modulated values in Fig. 5B all slant upward. In individual experiments the increase could be small (e.g., experiment a in Fig. 5A, lying close to the 0% diagonal line) or large (experiment b, with >3,000% increase in the relaxation rate). The mean increase, however, was statistically highly significant at both 15 and 25°C (see Fig. 5 legend).
Previous work has suggested that a major factor that determines the size of the increase in relaxation rate in this kind of experiment is the control relaxation rate that the modulation acts on (Brezina et al. 1995). If the control relaxation rate is small, the absolute increase (the change in units of s–1), and still more the relative increase (the change in %), is large. If the control relaxation rate is already large, the absolute and the relative increases are small. In either case, the modulation tends to bring the relaxation rate to about the same absolute level. A natural explanation is that there is some upper limit, some maximal rate, beyond which the muscle cannot be made to relax any faster by the modulation under those conditions. To demonstrate this again here, we performed six experiments with the two motor neuron cross-modulation paradigm, firing motor neuron B15 to release the SCPs, at 15°C, but in the presence of a saturating concentration of one of the SCPs, 10–5 M SCPB (see Brezina et al. 1996; Lloyd et al. 1984). These experiments are shown by the open blue circles in Fig. 5, A and B. The exogenous SCP increased—essentially tripled—the relaxation rate to >1.5 s–1, whereupon the modulator-releasing firing had little further effect. All of these experiments lie close to the 0% diagonal line in Fig. 5A; there was no statistically significant difference between their control and modulated means in Fig. 5B (see legend). We can reasonably take the mean modulated relaxation rate in these experiments, about 1.8 s–1, to be the presumed maximal relaxation rate at 15°C. This upper limit is indicated by the horizontal dashed line across Fig. 5, A and B. Note that all of the other experiments at 15°C fall below the line.
Consider now the experiments at 25°C (red symbols) in Fig. 5, A and B. Clearly, these often had modulated relaxation rates considerably larger than any experiment at 15°C, exceeding by a substantial margin the limit just discussed. The green lines in Fig. 5A indicate matched pairs of experiments, at 15 and 25°C, in the two muscles of the same preparation. In some cases at least, the two muscles had similar control relaxation rates—for example, the pair c–d—and in each of these cases the modulated relaxation rate was larger at 25 than at 15°C. Finally, although in Fig. 5B the mean control relaxation rate was somewhat larger at 25 than at 15°C (see also Figs. 7–9), statistical analysis (see legend) showed that the difference was not statistically significant. The mean modulated relaxation rate, however, was substantially larger at 25 than at 15°C, and this difference was highly significant.
It thus appears that in the two motor neuron cross-modulation experiments the modulation of the relaxation rate was not only not weaker at the higher temperature, but on the contrary was significantly stronger.
The modulation of relaxation rate reverses more rapidly at higher temperature
In Fig. 4 it appears that, although the modulation of the relaxation rate was stronger at 25 than at 15°C, it reversed more rapidly after the end of the modulator-releasing firing (as, apparently, did the modulation of contraction amplitude). The group data in Fig. 6, averaging together the time courses of the modulation of the relaxation rate in all of the experiments (without exogenous SCP) in Fig. 5, bear this out. The faster reversal of the modulation at 25 than at 15°C was statistically highly significant (see Fig. 6 legend).
|
So far, with both of our motor neuron stimulation paradigms, we have studied the modulation, that of the relaxation rate in particular, as one overall input–output process, Motor neuron firing 
Relaxation rate modulation, and determined what we will call its cumulative temperature dependency. The overall process is composed of a sequence of more elementary steps: Motor neuron firing Modulator release Modulator concentration Relaxation rate modulation (Fig. 1). Each of these steps will have its own intrinsic temperature dependency, which will then combine with that of the other steps to give the cumulative temperature dependency of the overall process. To understand the origin of the cumulative temperature dependency, we must therefore know the intrinsic temperature dependency of the individual steps. We already know that of the first step, Motor neuron firing Modulator release (Fig. 2). What about the intrinsic temperature dependency of the subsequent steps, in particular the last step, Modulator concentration Relaxation rate modulation, the actual modulatory effect itself?4
|
|
On contraction amplitude, previous work has shown that the potentiating (Ca-current) and depressing (K-current) effects of the modulators combine asymmetrically so as to give net potentiation at low modulator concentrations and net depression at high concentrations (see Brezina et al. 1995, 1996, 2003a). It appears from Fig. 7, A and B, that both the net potentiation at low SCPB concentrations and the net depression at high SCPB concentrations—and therefore presumably, both of the underlying potentiating and depressing effects—were larger at 25 than at 15°C. Indeed, high SCPB concentrations often produced at 25°C a very substantial net depression (Fig. 7A), such as is only rarely seen at lower temperatures with the SCPs—although it is seen with the MMs, which activate a much larger amplitude of the K current in the muscle (Brezina et al. 1995, 1996).5
The modulation of the relaxation rate, too, was larger at 25 than at 15°C (Fig. 7C). Indeed, it was much larger: as Fig. 7C shows, it was necessary to scale the 15°C dose–response relation up approximately fivefold before it reproduced, quite well at all concentrations, the magnitude of the dose–response relation obtained at 25°C.6
In another series of these experiments, we applied just a single steady concentration of the modulator, 10–7 M SCPB, and followed systematically the time course of the development of the modulation, and in some experiments also that of its reversal on washout of the SCPB, at both 15 and 25°C. From these experiments, Fig. 8 first presents, in the same way as did Fig. 5, the magnitude of the fully developed modulation of the relaxation rate. Figure 8A plots the modulated relaxation rate against the control relaxation rate in each experiment at 15°C (blue circles) and 25°C (red circles). The green lines join matched pairs of measurements. (In this series of experiments the matched measurements were made not just in the same preparation, but in the very same muscle with two applications of SCPB separated by wash.) Figure 8B then plots the same measurements, and their means, for statistical comparison. Again, the modulated relaxation rate was always much larger at 25 than at 15°C. At 15°C the modulated relaxation rate was smaller, but at 25°C considerably larger, than the presumed maximal relaxation rate attainable at 15°C (horizontal dashed line, taken from Fig. 5). In Fig. 8B, the difference between the mean modulated relaxation rates at the two temperatures was statistically highly significant (see Fig. 8 legend). (In this series of experiments, unlike in Fig. 5, the difference between the mean control relaxation rates—larger at 25 than at 15°C—was also significant.)
Figure 9 summarizes the time course of the modulation of the relaxation rate in these experiments. Figure 9A shows simply the averaged absolute relaxation rates at each time point during the development of the modulation in all of the experiments at 15°C (blue) and at 25°C (red). Figure 9B shows the same data first normalized within each experiment, as in Fig. 6, to discard magnitude differences and allow comparison just of the time course shapes at the two temperatures. Figure 9C shows, similarly normalized, the time courses of the reversal of the modulation on washout of the SCPB. To characterize the time courses quantitatively, we fitted the experimental data with best single-exponential fits (solid black curves) as follows. First, statistical analysis (see Fig. 9 legend) showed that the normalized time courses of the development of the modulation in Fig. 9B were not significantly different at 15 and 25°C. We therefore fitted all of the data in Fig. 9B simultaneously with the same exponential, with time constant 
= 179 s. With this time constant, fitting the unnormalized data in Fig. 9A then yielded the magnitudes of the fully developed modulation at 15 and 25°C. In this series of experiments, the modulation was about 3.2-fold larger at 25 than at 15°C. (This result was already implicit in Fig. 8, which used the same data.) Finally, statistical analysis—and just simple inspection—showed that the time courses of the reversal of the modulation in Fig. 9C were very significantly different at 15 and 25°C. We therefore fitted the data in Fig. 9C, over the range of times shown, with two different exponentials at the two temperatures, yielding = 3,995 s at 15°C and = 462 s at 25°C. Thus the modulation of the relaxation rate by the exogenous modulator was not only intrinsically larger at the higher temperature, but, just like the modulation by the endogenously released modulator in Figs. 4 and 6, it reversed more rapidly when the modulator was removed.
Reproduction of the observed temperature dependency in a mathematical model of the system
A dynamic model of the B15/B16–ARC neuromuscular system, including the basal ACh-induced contraction and the major components of the modulation—all of the variables X(t) in Fig. 1—was previously constructed from a variety of experimental data by Brezina et al. (2003a,b). We now modified the model to incorporate into it temperature dependency for the two steps that we have focused on in this paper. From the experimental data in Fig. 2, we modeled the intrinsic temperature dependency of the release of the modulators (for details see Fig. 2 legend). The continuous curves in Fig. 2 show the modeled release, decreasing, like the experimentally measured release, about 20-fold for a 10°C increase in temperature. From the experimental data in Figs. 7– 9, we then modeled the intrinsic temperature dependency of the modulation of the relaxation rate (for details see Fig. 10 legend). Figure 7C suggests that the magnitude of the modulation might increase about fivefold, whereas Fig. 9A suggests about 3.2-fold, for a 10°C increase in temperature. Conservatively, we modeled it as increasing threefold. We also modeled a fivefold increase in the rate constants of the modulation, so that the reversal of the modulation, in particular, would proceed faster at higher tem-perature as observed in Fig. 9C. Again, the fivefold increase was conservative: Fig. 9C suggests a greater than eightfold increase.
With the incorporation of the intrinsic temperature dependencies of these two steps, the model was then well able to reproduce the cumulative temperature dependency that we observed, in particular, in our two motor neuron cross-modulation experiments. Figure 10 shows a simulation of an experiment like that in Fig. 4. After the firing of the modulator-releasing motor neuron, the modeled relaxation rate in Fig. 10, just like the experimentally measured rate in Fig. 4, increases considerably more at 25 than at 15°C, but then falls back to the basal rate much faster.
The modulation of contraction amplitude in the simulation in Fig. 10—not only the net modulation but each of its component effects, which can be inspected in the model—is smaller at 25 than at 15°C, whereas in the real experiment in Fig. 4 it is not smaller, and may even be somewhat larger. Because we did not characterize separately the intrinsic temperature dependencies of each of the component effects, we could not incorporate them into the model as we did that of the modulation of the relaxation rate, but presumably the modeling of such increases of the effects with temperature as were observed in Fig. 7, A and B, would largely correct the deficiency seen in Fig. 10. Even without this correction, nevertheless, it is remarkable in Fig. 10 that the modulation of contraction amplitude is not grossly incorrect. Even though the modulator release decreases 20-fold as the temperature increases from 15 to 25°C, the modulation of contraction amplitude decreasesonly moderately. This is because the system incorporates a second type of temperature-compensating mechanism, quite distinct from the intrinsic temperature dependencies of the various steps, as the following analysis of the model reveals.
Second, structural mechanism of temperature compensation: saturating curvature of dose–response relations
For consistency we will discuss the second mechanism as it operates, once again, in the particular case of the modulation of the relaxation rate, but from its principles it will be seen that the mechanism will operate similarly for all of the modulatory effects, all those that contribute to the modulation of contraction amplitude as well. In the case of the relaxation rate, that there must be an additional mechanism becomes clear when we consider the magnitudes of the intrinsic temperature dependencies that we modeled above. Modulator release decreases 20-fold for a 10°C increase in temperature, whereas the modulation of the relaxation rate increases, intrinsically, only threefold. Cumulatively, when the modulation is caused by the released modulator, we should therefore expect the modulation still to decrease, to a first approximation, approximately 20/threefold—more than sixfold—when the temperature is raised from 15 to 25°C. Instead, we see in Fig. 10 that the modulation does not decrease, and even increases. An additional temperature-compensating mechanism must therefore be operating in the model and, presumably, in the real system. Furthermore, it is not a mechanism that we ourselves have explicitly incorporated into the model. It must be implicit in the structure of the model network—present, for example, even in the previous model of Brezina et al. (2003a,b), which had no explicit temperature dependency at all.
To demonstrate the second mechanism more formally, in Fig. 11A we have analytically solved the model equations to plot the cumulative steady-state temperature dependency from the level of motor neuron firing to each of the subsequent levels in the sequence leading to the modulation of the relaxation rate. In other words, for the same representative pattern of motor neuron firing throughout (see Fig. 11 legend), Fig. 11A plots the temperature dependency of modulator release (i.e., of the step Motor neuron firing Modulator release; light gray curve), modulator concentration (Motor neuron firing Modulator concentration; light gray curve again, identical to that for modulator release), and finally the modulation of the relaxation rate (Motor neuron firing Relaxation rate modulation), either without (dark gray curve) or with (black curve) the incorporation of the intrinsic temperature dependency of the step Modulator concentration Relaxation rate modulation that we modeled above. The light gray curve of the temperature dependency of modulator release is simply the curve already seen in Fig. 2, along which modulator release decreases 20-fold for a 10°C increase in temperature. Because modulator concentration is, in this model, simply a linear reflection of modulator release (see Eq. 8 in Fig. 11 legend), its cumulative temperature dependency is identical. However, the next level of the model, the modulation of the relaxation rate, does not at all follow this dramatic decrease with temperature. Even without any intrinsic temperature dependency of its own (dark gray curve), the modulation of the relaxation rate decreases only very moderately. The incorporation of the intrinsic temperaturedependency of the modulatory effect then converts the moderate decrease into a positive increase (black curve). Foldwise, however, it is the second mechanism, revealed in the large difference between the cumulative temperature dependency of the modulator concentration and that of the modulation of the relaxation rate, even without its own intrinsic temperature dependency, that is most significant under these circumstances.
What precisely is the nature of this second mechanism? Consider Fig. 11B, which shows simply the intrinsic steady-state dose–response relation of the modulatory effect, the step Modulator concentration Relaxation rate modulation. This quasi-hyperbolic relation is everywhere curved concave-down.7 Consequently any change in the input modulator concentration will result in a smaller relative change in the output relaxation rate modulation. For example, the point labeled "Actual at 15°C" is the steady-state point reached with the motor neuron firing used in Fig. 11A at 15°C. When the temperature is now increased to 25°C, there is a 20-fold decrease in modulator concentration and, if the modulation of the relaxation rate simply reflected the modulator concentration linearly, we would also expect a 20-fold decrease in the modulation of the relaxation rate, to the point labeled "Expected at 25°C with linear dose–response relation." Instead, along the curved dose–response relation, the modulation of the relaxation rate decreases much less, to the point labeled "Actual at 25°C."
The inset of Fig. 11A extends the main plot to higher temperatures. Even at much higher temperatures, together the two temperature-compensating mechanisms operating in the system—the "first" mechanism of compensating intrinsic temperature dependency and the second, structural mechanism—do a remarkably good job of maintaining the overall output, the modulation of the relaxation rate. From 15 to about 27°C, as already mentioned, the modulation (black curve) actually increases. Above 27°C it begins to decrease, but even at 40°C—where modulator release and concentration have decreased 1,745-fold relative to 15°C—it still has not decreased below that at 15°C. Only above 40°C does this happen.
Temperature compensation during realistic feeding behavior
The power of the second, structural mechanism will decrease as the motor neuron firing frequency and thus modulator release and modulator concentration decrease, because the system will be operating less along the saturated, horizontal limb and more along the vertical—although still concave-down—limb of the dose–response relation in Fig. 11B. The firing frequencies of the motor neurons B15 and B16 recorded with chronically implanted electrodes in intact, feeding animals (Cropper et al. 1990a,c; Horn et al. 2004) are lower than the frequency used in Fig. 11A, and the released modulators probably reach concentrations only of the order of 100 nM (Brezina et al. 2003a). Furthermore, the analysis in Fig. 11 was done with a constant, regular motor neuron firing pattern in the steady state, whereas the firing patterns recorded in vivo are extremely variable and irregular (Horn et al. 2004; Zhurov et al. 2004) and the modulated neuromuscular system generally operates far from the steady state (Brezina et al. 2003a,b, 2005). Is the temperature compensation still effective under these circumstances?
To answer this question, we ran our model with the motor neuron firing patterns recorded in vivo. Specifically, we used the firing patterns of the motor neurons B15 and B16 from an entire approximately 2.5-h-long meal, consisting of 749 cycles of the feeding behavior, recorded in an intact, freely feeding animal by Horn et al. (2004) and analyzed by Brezina et al. (2005). Figure 12 shows the results. The two traces at the top of each of A and B are the 2.5-h-long firing patterns of the two motor neurons, the inputs to the model. The traces below then show the resulting trajectories of the most relevant downstream variables of the model, that is, the concentrations of SCP and MM, the modulation of the relaxation rate, and the final modulated contraction amplitude, at 15°C (A) and at 25°C (B). Clearly, increasing the temperature from 15 to 25°C decreased the modulator concentrations so as to make them essentially invisible on the scale used in Fig. 12. Yet the modulation of the relaxation rate decreased hardly at all, and neither did the modulated contraction amplitude. (Note again that the model incorporated no intrinsic temperature dependency for any component of the modulation of contraction amplitude; with such incorporation, the modulated contraction amplitude would probably have been as large at 25 as at 15°C or even larger.) Figure 12C summarizes measurements, comparable to those made in Figs. 3, 4, and 10, of the absolute relaxation rate of the simulated contraction in each of the 749 cycles of the meal. This final, most realistic measure of the relaxation rate was likewise well maintained at 25°C at a high level (red) not appreciably different from that at 15°C (blue). Furthermore, this maintenance was the result of the modulation: when the modulation of the relaxation rate was disabled in the model, the absolute relaxation rate decreased severalfold, at both 15 and 25°C (empty circles).8 It thus appeared that the temperature-compensating mechanisms operating in the model, and presumably in the real system, are indeed very effective even with the frequencies and patterns in which the motor neurons actually fire in real behavior.
|
|
DISCUSSION |
|---|
|
The basic experimental result of this report is that, although modulator release from the ARC motor neurons decreases dramatically with increasing temperature (Fig. 2), the modulation of ARC muscle contraction amplitude and relaxation rate brought about by the released modulators does not decrease at all, and may even increase (Figs. 3–5).
The apparent paradox of this result requires us to reexamine our assumptions. Two potential straightforward explanations must be considered. First, it could be that, after all, modulator release does not decrease with increasing temperature. The data in Fig. 2 are sparse. However, they are part of an extensive, solid body of work by Vilim et al. (1996a,b, 2000) in which modulator release was directly measured by radioimmunoassay rather than merely inferred from the subsequent effects of the released modulators. Furthermore, one of the early effects of the released modulators, elevation of cyclic AMP (cAMP) in the ARC muscle, has likewise been reported to decrease with increasing temperature (Whim and Lloyd 1990; see further below).
Second, the possibility has to be considered that the observed changes in contraction amplitude and relaxation rate are not really a result of the released modulators. A very extensive body of evidence (reviewed by Brezina et al. 2003a; Weiss et al. 1993; see INTRODUCTION) argues, however, that they are. The modulatory peptide genes are expressed by the motor neurons; the peptides are processed and are contained in dense-core vesicles in the motor neuron terminals within the ARC muscle; the modulators are released from these terminals onto the muscle fibers with physiological frequencies and patterns of firing of the motor neurons; exogenous application of the modulators, at the concentrations likely be achieved by the motor neuron firing, mimics the changes in contraction shape observed with the motor neuron firing. It has not yet been possible to carry out in a completely satisfactory manner the final demonstration, that blocking one of the earlier steps in this sequence—release of the modulators, ideally—also blocks the observed changes in contraction shape, because of the lack of specific pharmacological or other tools. (Indeed, our initial motivation for studying temperature dependency was to develop such a tool, in the expectation that, if increased temperature so dramatically decreases the release of the modulators, it should of course decrease all of their effects too, indeed for practical purposes eliminate them to reveal just the basal, unmodulated contractions of the muscle. Evidently, this simple logic can fail.) However, the gap has to a large extent been bridged by modeling of the sort that we have continued in this paper, which has previously shown that, when the intrinsic characteristics of the individual steps in the modulatory sequence are modeled individually, joined together, and activated by the patterns of motor neuron firing that are used in experiments or recorded in vivo, the cumulative changes in contraction shape predicted by the model largely reproduce those that are actually observed (Brezina et al. 2003a,b, 2005). As a last point, we note that it would be difficult to explain how the two motor neuron cross-paradigm in Fig. 4 works if it is not through the release from each motor neuron of some modulatory substance that has long-lasting, general effects on contractions, even those elicited by the other motor neuron.
We believe therefore that we can rule out these two essentially artifactual explanations for our results. We can rule them out all the more easily because we have in this paper developed what we believe is the correct resolution of the apparent paradox. Experimentally, we have found that the final modulatory effects have very considerable intrinsic temperature dependencies of their own, opposite to that of the modulator release (Figs. 7– 9), so that as the modulator release decreases with increasing temperature, the modulatory effects intrinsically increase. At the same time, our modeling has revealed another, very different mechanism of temperature compensation that is built into the structure of the system. Because of the curvature of the intrinsic dose–response relations of the modulatory effects, a given decrease in modulator release and concentration decreases the modulatory effects much less (Fig. 11). The model incorporating both of these intrinsic mechanisms of temperature compensation is well able to reproduce the cumulative temperature dependency of the modulation that is brought about, through the entire sequence of modulatory steps, by the motor neuron firing (Fig. 10). That is, even though one of the steps in the sequence—the release of the modulators—undergoes a dramatic decrease as the temperature increases, the final modulation that is observed does not decrease at all. Even though there are large intrinsic temperature dependencies within the sequence, the cumulative final temperature dependency is small.
We have focused here principally on the modulation of the relaxation rate and neglected that of contraction amplitude, largely for technical reasons: the modulation of the relaxation rate is, to all appearances, a single effect whereas that of contraction amplitude is distinctly composite. However, it is likely that the modulation of contraction amplitude is temperature compensated by the same two mechanisms. There are indications in our data that the opposing effects that combine to give the net modulation of contraction amplitude—in particular, the enhancement of Ca current that potentiates contraction amplitude and the activation of K current that depresses it—each have their own intrinsic temperature dependency, increasing, like the modulation of the relaxation rate, with increasing temperature (Fig. 7B). The Ca- and K-current effects' intrinsic dose–response relations, too, are shaped like that of the modulation of the relaxation rate (Brezina et al. 1996, 2003a).
Clearly, each of the "elementary" steps into which we have analytically decomposed the modulatory sequence here is itself a black box containing lower-level processes, each with its own intrinsic temperature dependency. This is implied already by the fact that the rate of one of our "elementary" processes, that is, the modulator release, decreases with increasing temperature. On thermodynamic grounds, we should expect all truly elementary reaction rates to increase with temperature. The temperature dependency of the modulator release is therefore presumably the resultant of the temperature dependencies of opposing lower-level processes, each increasing with temperature but with the "backward" rate—itself probably the resultant of still lower-level processes—increasing more steeply than the "forward" rate. A similar explanation can be given for our attribution of temperature dependency to the magnitude of the modulation (Figs. 7C and 9A; Fig. 10 legend), which is not a rate at all but presumably is the resultant of the rates of lower-level processes.
For the modulation of the relaxation rate, some of the lower-level detail can be sketched in. The step Modulator concentration Relaxation rate modulation probably proceeds, in its early stages, through a conventional sequence of binding of the modulators, the SCPs and MMs, to their respective receptors in the membrane of the ARC muscle fibers, activation of a G protein such as Gs, activation of adenylyl cyclase, and elevation of cAMP in the muscle fibers (Brezina et al. 1994a; Hooper et al. 1994b; Lloyd et al. 1984; Whim and Lloyd 1989, 1990). Whim and Lloyd (1990) reported that, when a fixed concentration of SCP is exogenously applied, the resulting elevation of cAMP is identical at 16 and 22°C. If so, then this entire early sequence of events, up to the elevation of cAMP, has no intrinsic temperature dependency and cannot be the locus of the temperature dependency that we have discovered here in Figs. 7–9. cAMP then activates cAMP-dependent protein kinase (PKA; Hooper et al. 1994a,b), which then phosphorylates the giant muscle protein twitchin (Heierhorst et al. 1994, 1995, 1996; Probst et al. 1994; see also Siegman et al. 1997, 1998; Yamada et al. 2001). The phosphorylation/dephosphorylation state of twitchin in the ARC muscle has been implicated in the control of the relaxation rate (Probst et al. 1994), although the precise mechanism remains unclear. Twitchin is a structural protein in the muscle, but it is also a kinase that, under the control of signals such as the cAMP-dependent phosphorylation as well as Ca2+ acting through Ca2+-binding proteins such as calmodulin and S100 (Heierhorst et al. 1996), will phosphorylate, directly or indirectly by phosphorylating other kinases, other muscle proteins. With regard to the temperature dependency, the activity of some molluscan PKAs has been reported to be temperature dependent (e.g., Bardales et al. 2004), but probably much of the temperature dependency that we have discovered will be found to originate somewhere within the late, complex steps involving twitchin. Certainly the very slow reversal of the modulation of the relaxation rate, which is strongly temperature dependent (Fig. 9C), is not likely to be rate limited by any such early process as a decay in free Ca2+ concentration, cAMP level, or even PKA activity, all of which decay much faster (Probst et al. 1994; Whim and Lloyd 1990; see Brezina et al. 2003a), but may well reflect a subsequent slow process such as dephosphorylation.
Temperature affects all biological processes, and a full appreciation of how the B15/B16–ARC neuromuscular system functions throughout the temperature range that the animal may encounter will require knowledge of the temperature dependency also of all of the other important components of the system. For example, how does temperature affect the shape of the basal, unmodulated contractions? [There are indications that increasing temperature decreases their amplitude (Vilim et al. 1996a) and perhaps increases their relaxation rate (Figs. 7C, 8, and 9A here).] How does temperature affect the firing frequencies and patterns of the motor neurons? Nothing is known furthermore about how the animal may acclimatize to different temperatures in the wild. Nevertheless, we believe that our results here have demonstrated, in basic outline at least, how the combination of the temperature-compensating mechanisms that are built into the structure of the system can maintain its output—the strength of the modulatory tuning and, as a consequence, the modulated contraction shape—relatively constant despite large changes in temperature. In our simulation with realistic motor neuron firing patterns (Fig. 12), the overall strength of the modulatory tuning varies hardly at all from 15 to 25°C and perhaps even higher (Fig. 11A, inset). Because the different dimensions of the contraction shape—amplitude and relaxation rate, for instance—are the result of different processes with somewhat different cumulative temperature dependency, the shape will not remain exactly identical at all temperatures, and the extent of its change will furthermore depend on the motor neuron firing frequency and pattern. However, these small residual changes in contraction shape are unlikely to matter functionally. In this neuromuscular system, the real motor neuron firing patterns and resulting contraction shapes do not appear to be precisely tailored to the specific behavioral demands in any particular cycle of the feeding behavior. Rather, they exhibit large quasi-random variability—much larger than the residual changes with temperature—from one cycle to the next (as can be seen in Fig. 12) so as to diversify, it is conjectured, the feeding movements in the face of environmental uncertainty (Brezina et al. 2005; Horn et al. 2004).
Temperature compensation in other systems
Because temperature affects all biological reactions, all animals that are active over a range of temperatures, unless they regulate their internal temperature, face this same problem with respect to each of their physiological systems, large and small: how to make the overall output of the system built from individual temperature-sensitive reactions sufficiently temperature insensitive to maintain function throughout the temperature range. A well-known example is that of circadian oscillators, which cannot allow the period of the clock to change if the temperature changes (see, e.g., Roenneberg and Merrow 1999). The fact that temperature affects all reactions, and furthermore has—unlike, say, a neuromodulator—universal access to them all, at the same time provides plenty of material from which temperature compensation can be built. In the systems that have been investigated, it is interesting to note the extreme heterogeneity of the types of processes whose intrinsic temperature dependencies complement each other to maintain the overall output. On different time scales, the temperature dependencies of gene expression, biochemical and signal-transduction pathways, biophysical processes in neurons, synaptic physiology, muscle contractility, neuronal firing patterns and higher-order control strategies of the nervous system, and behavior can all play complementary roles (e.g., Feder and Hofmann 1999; Heitler and Edwards 1998; Montgomery and Macdonald 1990; Neumeister et al. 2000; Podrabsky and Somero 2004; Pörtner 2002; Rall and Woledge 1990; Rome 1990). In addition to this relatively self-evident mechanism of complementation of intrinsic temperature dependencies, it is recognized (e.g., Montgomery and Macdonald 1990; see following text) that the way in which the processes are coupled together—the structure of the network—also plays a major role. Temperature-compensatory mechanisms are thus of two types, an instance of each of which we have analyzed in this paper.
General lessons
Changing temperature is just one kind of stress that biological networks must resist (Roenneberg and Merrow 1999), and the mechanisms of temperature compensation have much in common with the general mechanisms that have been identified in many networks that make their output robust against dynamic noise, phenotypic parameter variation, and environmental perturbations of all kinds. Both the intrinsic characteristics of the processes within the network and the dynamic nonlinear properties that emerge from the way in which the processes are connected together are important. For example, recent work has particularly emphasized the role of negative feedback in the stabilization of circadian oscillators (e.g., Cheng et al. 2001; Gonze et al. 2002; Smolen et al. 2001; Vilar et al. 2002). However, as we have shown here, even in the absence of feedback merely the typical saturating shapes of the dose–response relations in the network exert a stabilizing influence, and this influence will grow as more such steps are connected in sequential cascades (see, e.g., Kenakin 1997).
At the same time, the dynamic interconnectedness of such networks makes it difficult to use simple linear logic to predict the outcome of their perturbation. Fox and Lloyd (2001) and Koh and Weiss (2005), citing the fact that modulator release decreases dramatically with increasing temperature in the ARC system, used temperature to try to separate, in another buccal neuromuscular system and in the CNS of Aplysia, the subsequent effects of modulators released there from basal, modulator-independent effects. Effects that likewise decreased with increasing temperature were identified as potential effects of the released modulators; effects that did not decrease were not. We, too, initially sought to use this logic in the ARC system itself. There, however, the logic clearly fails: by it, we would have had to conclude that the effects on contraction amplitude and relaxation rate, very well established as caused by the modulators by numerous other lines of evidence, were not modulator dependent. Instead, our studies have led us to discover the temperature-compensating mechanisms that we have described in this paper. In view of the existence of such mechanisms, the use of temperature to separate modulator-dependent from modulator-independent processes requires reconsideration.
|
|
GRANTS |
|---|
|
|
|
ACKNOWLEDGMENTS |
|---|
|
|
|
FOOTNOTES |
|---|
1 As Fig. 2 implies, release of the peptide modulators has not been directly measured at temperatures >19°C. However, measurements of downstream effects of the released modulators such as the SCP-induced elevation of cAMP in the ARC muscle (Fig. 1) strongly suggest that the release continues to decrease up to at least 22°C (e.g., Whim and Lloyd 1990), although one lesson of our work here is that such indirect evidence must be treated with caution because the downstream effects are by no means a straightforward, linear readout of the release (see DISCUSSION). 
2 The persisting modulation is not simply a long-lasting consequence of the (often quite large) contraction elicited by the modulator-releasing firing. This was shown by Whim and Lloyd (1990), who reversibly applied hexamethonium, a blocker of the ACh receptors in the muscle that mediate the contraction, during the period of the modulator-releasing firing. This completely blocked the contraction elicited by the modulator-releasing firing, but when the hexamethonium was then washed out to bring back the monitoring contractions elicited by the other motor neuron, these were found to be modulated as usual.
3 Indeed, although usually the modulator-releasing firing increased the amplitude of the monitoring contractions as in Fig. 4, occasionally, when the modulator-releasing motor neuron was B16, the amplitude was at first decreased, for the first one or two monitoring contractions after the modulator-releasing firing, before increasing in the usual way. Based on what is known quantitatively about the modulatory effects and their interaction (Brezina et al. 1995, 1996, 2003a), this was almost certainly a sign, not of weak modulation, but of strong modulation. The potentiating (Ca-current) and depressing (K-current) effects combine asymmetrically so as to give net potentiation at low modulator concentrations and net depression at high concentrations (see, e.g., Fig. 7, A and B). The depressing effect has fast intrinsic dynamics, and so will reverse rapidly after the modulator-releasing firing as the released modulator concentration falls, whereas the potentiating effect, with slow dynamics, will persist. Finally, the depressing effect is activated particularly strongly by the MMs, released from motor neuron B16. Thus, very likely, the transient net depression occasionally observed after the modulator-releasing firing of motor neuron B16 reflected exceptionally strong release of the MMs and activation of the depressing (K-current) effect. By extension, the depressing effect probably contributed to the net modulation of contraction amplitude much more generally, even when it was hidden in the usual net potentiation.
4 Clearly, each of the "elementary" steps mentioned here can be broken down further and, in particular, the last step Modulator concentration Relaxation rate modulation can be broken down to Modulator concentration Muscle cAMP level Relaxation rate modulation (Fig. 1). Nevertheless, we study here the Modulator concentration Relaxation rate modulation step as a unit, primarily because we can easily manipulate the input to this step, modulator concentration, but not so easily the muscle cAMP level. For the same reason, also, the muscle cAMP level is not an explicit part of our model of the system (see Brezina et al. 2003a) with which we seek to reproduce our experimental results in Figs. 10 and 11.
5 Potentially, the changes in contraction amplitude were a problem for the interpretation of the modulation of the relaxation rate common because the relaxation rate depends to some degree on the amplitude: small contractions tend to relax faster than large contractions (Brezina et al. 2003b). However, in these and indeed in most experiments in this paper, the changes in amplitude were relatively small (the magnitudes in Figs. 4 and 7, A and B, are typical), considerably smaller than the severalfold potentiation, or complete suppression of contractions, that can occur (see Brezina et al. 1996). In some of the experiments in Fig. 9, we explicitly tested to what extent the changes in contraction amplitude could account for the changes in relaxation rate, by adjusting, once the modulation had developed, the firing frequency of the contraction-eliciting motor neuron so as to return the amplitude of the modulated contraction back to the control amplitude. The change in relaxation rate nevertheless remained very substantial. Although there was indeed an inverse relationship between contraction amplitude and relaxation rate, its slope was relatively shallow, so that the changes in contraction amplitude could account for only a small part of the observed changes in relaxation rate. We therefore made no special attempt to allow for the changing contraction amplitude in our analysis of the modulation of the relaxation rate in this paper.
6 Our treatment of the relaxation-rate data in Figs. 7–9 is based on the simplest conceptual model—then formalized in our actual model in Figs. 10–12—of the effect of temperature: that it is only the modulation that is temperature dependent. Effects of temperature on the basal relaxation rate are neglected. With the data in Fig. 5 this treatment appears to be adequate, but the data in Figs. 7–9 suggest that the basal relaxation rate, too, has a significant temperature dependency. A completely realistic model would need to postulate a separate temperature dependency for each process; our model here lumps the temperature dependency of the basal relaxation rate into that of the modulation. Functionally (e.g., in Fig. 12), the result will be similar in either case: because the modulation is coupled, through the firing of the motor neurons, to the basal contraction, the final modulated relaxation rate will have a similar cumulative temperature dependency no matter how the cumulative temperature dependency has been decomposed into the intrinsic temperature dependencies of the processes within the system.
7 The dose–response relation (Eqs. 9 and 10 in Fig. 11 legend) is strictly hyperbolic only if the Hill coefficient h, here hR, is equal to 1; here hR = 0.7. The concave-down curvature of the dose–response relation, and thus the consequences discussed here, holds for h 
1; for h > 1, it holds only over part of the domain of the dose–response relation.
8 Even without the modulation of the relaxation rate, the relaxation rates were slightly different at 15 and 25°C in Fig. 12C because the modulation of contraction amplitude, still present, also altered to some extent the relaxation rate, in the model as in reality (see footnote 5).
Address for reprint requests and other correspondence: V. Brezina, Department of Neuroscience, Box 1218, Mt. Sinai School of Medicine, 1 Gustave L. Levy Place, New York, NY 10029 (E-mail Vladimir.Brezina{at}mssm.edu)
|
|
REFERENCES |
|---|
|
Brezina V, Bank B, Cropper EC, Rosen S, Vilim FS, Kupfermann I, and Weiss KR. Nine members of the myomodulin family of peptide cotransmitters at the B16–ARC neuromuscular junction of Aplysia. J Neurophysiol 74: 54–72, 1995.
Brezina V, Church PJ, and Weiss KR. Temporal pattern dependence of neuronal peptide transmitter release: models and experiments. J Neurosci 20: 6760–6772, 2000a.
Brezina V, Evans CG, and Weiss KR. Enhancement of Ca current in the accessory radula closer muscle of Aplysia californica by neuromodulators that potentiate its contractions. J Neurosci 14: 4393–4411, 1994a.[Abstract]
Brezina V, Evans CG, and Weiss KR. Activation of K current in the accessory radula closer muscle of Aplysia californica by neuromodulators that depress its contractions. J Neurosci 14: 4412–4432, 1994b.[Abstract]
Brezina V, Horn CC, and Weiss KR. Modeling neuromuscular modulation in Aplysia. III. Interaction of central motor commands and peripheral modulatory state for optimal behavior. J Neurophysiol 93: 1523–1556, 2005.
Brezina V, Orekhova IV, and Weiss KR. Functional uncoupling of linked neurotransmitter effects by combinatorial convergence. Science 273: 806–810, 1996.[Abstract]
Brezina V, Orekhova IV, and Weiss KR. Optimization of rhythmic behaviors by modulation of the neuromuscular transform. J Neurophysiol 83: 260–279, 2000b.
Brezina V, Orekhova IV, and Weiss KR. Neuromuscular modulation in Aplysia. I. Dynamic model. J Neurophysiol 90: 2592–2612, 2003a.
Brezina V, Orekhova IV, and Weiss KR. Neuromuscular modulation in Aplysia. II. Modulation of the neuromuscular transform in behavior. J Neurophysiol 90: 2613–2628, 2003b.
Brezina V and Weiss KR. Ca2+-activated K current in the ARC muscle of Aplysia. J Neurophysiol 73: 993–1004, 1995.
Carefoot TH. Aplysia: its biology and ecology. Oceanogr Mar Biol Ann Rev 25: 167–284, 1987.
Cheng P, Yang Y, and Liu Y. Interlocked feedback loops contribute to the robustness of the Neurospora circadian clock. Proc Natl Acad Sci USA 98: 7408–7413, 2001.
Church PJ, Whim MD, and Lloyd PE. Modulation of neuromuscular transmission by conventional and peptide transmitters released from excitatory and inhibitory motor neurons in Aplysia. J Neurosci 13: 2790–2800, 1993.[Abstract]
Cohen JL, Weiss KR, and Kupfermann I. Motor control of buccal muscles in Aplysia. J Neurophysiol 41: 157–180, 1978.
Cropper EC, Kupfermann I, and Weiss KR. Differential firing patterns of the peptide-containing cholinergic motor neurons B15 and B16 during feeding behavior in Aplysia. Brain Res 522: 176–179, 1990a.[CrossRef][Web of Science][Medline]
Cropper EC, Lloyd PE, Reed W, Tenenbaum R, Kupfermann I, and Weiss KR. Multiple neuropeptides in cholinergic motor neurons of Aplysia: evidence for modulation intrinsic to the motor circuit. Proc Natl Acad Sci USA 84: 3486–3490, 1987a.
Cropper EC, Miller MW, Tenenbaum R, Kolks MAG, Kupfermann I, and Weiss KR. Structure and action of buccalin: a modulatory neuropeptide localized to an identified small cardioactive peptide-containing cholinergic motor neuron of Aplysia californica. Proc Natl Acad Sci USA 85: 6177–6181, 1988.
Cropper EC, Miller MW, Vilim FS, Tenenbaum R, Kupfermann I, and Weiss KR. Buccalin is present in the cholinergic motor neuron B16 of Aplysia and it depresses accessory radula closer muscle contractions evoked by stimulation of B16. Brain Res 512: 175–179, 1990b.[CrossRef][Web of Science][Medline]
Cropper EC, Price D, Tenenbaum R, Kupfermann I, and Weiss KR. Release of peptide cotransmitters from a cholinergic motor neuron under physiological conditions. Proc Natl Acad Sci USA 87: 933–937, 1990c.
Cropper EC, Tenenbaum R, Kolks MAG, Kupfermann I, and Weiss KR. Myomodulin: a bioactive neuropeptide present in an identified cholinergic buccal motor neuron of Aplysia. Proc Natl Acad Sci USA 84: 5483–5486, 1987b.
Cropper EC, Vilim FS, Alevizos A, Tenenbaum R, Kolks MAG, Rosen S, Kupfermann I, and Weiss KR. Structure, bioactivity, and cellular localization of myomodulin B: a novel Aplysia peptide. Peptides 12: 683–690, 1991.[CrossRef][Web of Science][Medline]
Evans CG, Vilim FS, Harish O, Kupfermann I, Weiss KR, and Cropper EC. Modulation of radula opener muscles in Aplysia. J Neurophysiol 82: 1339–1351, 1999.
Feder ME and Hofmann GE. Heat-shock proteins, molecular chaperones, and the stress response: evolutionary and ecological physiology. Annu Rev Physiol 61: 243–282, 1999.[CrossRef][Web of Science][Medline]
Fox LE and Lloyd PE. Evidence that post-tetanic potentiation is mediated by neuropeptide release in Aplysia. J Neurophysiol 86: 2845–2855, 2001.
Gev S, Achituv Y, and Susswein AJ. Seasonal determinants of the life cycle in two species of Aplysia found in shallow waters along the Mediterranean coasts of Israel. J Exp Mar Biol Ecol 74: 67–73, 1984.[CrossRef]
Gomez D, Halloy J, and Goldbeter A. Robustness of circadian rhythms with respect to molecular noise. Proc Natl Acad Sci USA 99: 673–678, 2002.
Heierhorst J, Kobe B, Feil SC, Parker MW, Benian BM, Weiss KR, and Kemp BE. Ca2+/S100 regulation of giant protein kinases. Nature 380: 636–639, 1996.[CrossRef][Medline]
Heierhorst J, Probst WC, Kohanski RA, Buku A, and Weiss KR. Phosphorylation of myosin regulatory light chains by the molluscan twitchin kinase. Eur J Biochem 233: 426–431, 1995.[Web of Science][Medline]
Heierhorst J, Probst WC, Vilim FS, Buku A, and Weiss KR. Autophosphorylation of molluscan twitchin and interaction of its kinase domain with calcium/calmodulin. J Biol Chem 269: 21086–21093, 1994.
Heitler WJ and Edwards DH. Effect of temperature on a voltage-sensitive electrical synapse in crayfish. J Exp Biol 201: 503–513, 1998.
Hooper SL, Brezina V, Cropper EC, and Weiss KR. Flexibility of muscle control by modulation of muscle properties. In: Beyond Neurotransmission: Neuromodulation and Its Importance for Information Processing, edited by Katz PS. Oxford, UK: Oxford Univ. Press, 1999, p. 241–274.
Hooper SL, Probst WC, Cropper EC, Kupfermann I, and Weiss KR. SCP application or B15 stimulation activates cAPK in the ARC muscle of Aplysia. Brain Res 657: 337–341, 1994a.[CrossRef][Web of Science][Medline]
Hooper SL, Probst WC, Cropper EC, Kupfermann I, and Weiss KR. Myomodulin application increases cAMP and activates cAMP-dependent protein kinase in the accessory radula closer muscle of Aplysia. Neurosci Lett 179: 167–170, 1994b.[CrossRef][Web of Science][Medline]
Horn CC, Zhurov Y, Orekhova IV, Proekt A, Kupfermann I, Weiss KR, and Brezina V. Cycle-to-cycle variability of neuromuscular activity in Aplysia feeding behavior. J Neurophysiol 92: 157–180, 2004.
Hurwitz I, Cropper EC, Vilim FS, Alexeeva V, Susswein AJ, Kupfermann I, and Weiss KR. Serotonergic and peptidergic modulation of the buccal mass protractor muscle (I2) in Aplysia. J Neurophysiol 84: 2810–2820, 2000.
Keating C and Lloyd PE. Differential modulation of motor neurons that innervate the same muscle but use different excitatory transmitters in Aplysia. J Neurophysiol 82: 1759–1767, 1999.
Kenakin T. Pharmacologic Analysis of Drug-Receptor Interaction (3rd ed). Philadelphia, PA: Lippincott–Raven, 1997.
Koh H-Y and Weiss KR. Peptidergic contribution to posttetanic potentiation at a central synapse of Aplysia. J Neurophysiol 94: 1281–1286, 2005.
Kupfermann I. Feeding behavior in Aplysia: a simple system for the study of motivation. Behav Biol 10: 1–26, 1974.[CrossRef][Web of Science][Medline]
Kupfermann I, Brezina V, Cropper EC, Deodhar D, Probst WC, Rosen SC, Vilim FS, and Weiss KR. Reconfiguration of the peripheral plant during various forms of feeding behaviors in the mollusc Aplysia. In: Neurons, Networks, and Motor Behavior, edited by Stein PSG, Grillner S, Selverston AI, and Stuart DG. Cambridge, MA: MIT Press, 1997, p. 217–222.
Kupfermann I and Carew TJ. Behavior patterns of Aplysia californica in its natural environment. Behav Biol 12: 317–337, 1974.[CrossRef][Web of Science][Medline]
Lloyd PE, Kupfermann I, and Weiss KR. Evidence for parallel actions of a molluscan neuropeptide and serotonin in mediating arousal in Aplysia. Proc Natl Acad Sci USA 81: 2934–2937, 1984.
Montgomery JC and Macdonald JA. Effects of temperature on nervous system: implications for behavioral performance. Am J Physiol Regul Integr Comp Physiol 259: R191–R196, 1990.
Neumeister H, Ripley B, Preuss T, and Gilly WF. Effects of temperature on escape jetting in the squid Loligo opalescens. J Exp Biol 203: 547–557, 2000.[Abstract]
Orekhova IV, Alexeeva V, Church PJ, Weiss KR, and Brezina V. Multiple presynaptic and postsynaptic sites of inhibitory modulation by myomodulin at ARC neuromuscular junctions of Aplysia. J Neurophysiol 89: 1488–1502, 2003.
Podrabsky JE and Somero GN. Changes in gene expression associated with acclimation to constant temperatures and fluctuating daily temperatures in an annual killifish Austrofundulus limnaeus. J Exp Biol 207: 2237–2254, 2004.
Pörtner HO. Physiological basis of temperature-dependent biogeography: trade-offs in muscle design and performance in polar ectotherms. J Exp Biol 205: 2217–2230, 2002.
Probst WC, Cropper EC, Heierhorst J, Hooper SL, Jaffe H, Vilim F, Beushausen S, Kupfermann I, and Weiss KR. cAMP-dependent phosphorylation of Aplysia twitchin may mediate modulation of muscle contractions by neuropeptide cotransmitters. Proc Natl Acad Sci USA 91: 8487–8491, 1994.
Rall JA and Woledge RC. Influence of temperature on mechanics and energetics of muscle contraction. Am J Physiol Regul Integr Comp Physiol 259: R197–R203, 1990.
Roenneberg T and Merrow M. Circadian systems and metabolism. J Biol Rhythms 14: 449–459, 1999.
Rome LC. Influence of temperature on muscle recruitment and muscle function in vivo. Am J Physiol Regul Integr Comp Physiol 259: R210–R222, 1990.
Siegman MJ, Funabara D, Kinoshita S, Watabe S, Hartshorne DJ, and Butler TM. Phosphorylation of a twitchin-related protein controls catch and calcium sensitivity of force production in invertebrate smooth muscle. Proc Natl Acad Sci USA 95: 5383–5388, 1998.
Siegman MJ, Mooers SU, Li C, Narayan S, Trinkle-Mulcahy L, Watabe S, Hartshorne DJ, and Butler TM. Phosphorylation of a high molecular weight (
600 kDa) protein regulates catch in invertebrate smooth muscle. J Muscle Res Cell Motil 18: 655–670, 1997.[CrossRef][Web of Science][Medline]
Smolen P, Baxter DA, and Byrne JH. Modeling circadian oscillations with interlocking positive and negative feedback loops. J Neurosci 21: 6644–6656, 2001.
Vilar JMG, Kueh HY, Barkai N, and Leibler S. Mechanisms of noise-resistance in genetic oscillators. Proc Natl Acad Sci USA 99: 5988–5992, 2002.
Vilim FS, Cropper EC, Price DA, Kupfermann I, and Weiss KR. Release of peptide cotransmitters in Aplysia: regulation and functional implications. J Neurosci 16: 8105–8114, 1996a.
Vilim FS, Cropper EC, Price DA, Kupfermann I, and Weiss KR. Peptide cotransmitter release from motorneuron B16 in Aplysia californica: costorage, corelease, and functional implications. J Neurosci 20: 2036–2042, 2000.
Vilim FS, Cropper EC, Rosen SC, Tenenbaum R, Kupfermann I, and Weiss KR. Structure, localization, and action of buccalin B: a bioactive peptide from Aplysia. Peptides 15: 959–969, 1994.[CrossRef][Web of Science][Medline]
Vilim FS, Price DA, Lesser W, Kupfermann I, and Weiss KR. Costorage and corelease of modulatory peptide cotransmitters with partially antagonistic actions on the accessory radula closer muscle of Aplysia californica. J Neurosci 16: 8092–8104, 1996b.
Weiss KR, Brezina V, Cropper EC, Heierhorst J, Hooper SL, Probst WC, Rosen SC, Vilim FS, and Kupfermann I. Physiology and biochemistry of peptidergic cotransmission in Aplysia. J Physiol (Paris) 87: 141–151, 1993.
Weiss KR, Cohen JL, and Kupfermann I. Modulatory control of buccal musculature by a serotonergic neuron (metacerebral cell) in Aplysia. J Neurophysiol 41: 181–203, 1978.
Whim MD and Lloyd PE. Frequency-dependent release of peptide cotransmitters from identified cholinergic motor neurons in Aplysia. Proc Natl Acad Sci USA 86: 9034–9038, 1989.
Whim MD and Lloyd PE. Neuropeptide cotransmitters released from an identified cholinergic motor neuron modulate neuromuscular efficacy in Aplysia. J Neurosci 10: 3313–3322, 1990.[Abstract]
Yamada A, Yoshio M, Kojima H, and Oiwa K. An in vitro assay reveals essential protein components for the "catch" state of invertebrate smooth muscle. Proc Natl Acad Sci USA 98: 6635–6640, 2001.
Zhurov Y, Weiss KR, and Brezina V. Intraburst variability of motor neuron spike timing shapes contractions of the ARC muscle of Aplysia. Abstract 537.5, 2004 Abstract Viewer. Washington, DC: Society for Neuroscience, online, 2004.
This article has been cited by other articles:
|
|
 |
|
  I. Hurwitz, A. Harel, S. Markowitz, O. Noy, and A. J. Susswein Control of Feeding in Aplysia With Ad Libitum Access to Food: Presence of Food Increases the Intervals Between Feeding Bouts J Neurophysiol, January 1, 2006; 95(1): 106 - 118. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. T. Birmingham Simple Mechanism for Stabilizing Motor Output. Focus on "Temperature Compensation of Neuromuscular Modulation in Aplysia" J Neurophysiol, November 1, 2005; 94(5): 2997 - 2998. [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Visit Other APS Journals Online |
TOP
ABSTRACT
INTRODUCTION
= 
f
kp+/kp– and 
= D1–y is the pattern dependency of the release, D = dintra/(dintra + dinter) is the duty cycle of the firing pattern, and 
1/20), S0,SCP = 628 fmol, and S0,Buc = 245 fmol, and the curves shown. [Value of S0,SCP found here is 16% larger, and the value of S0,Buc is 24% larger, than the consensus values of 541 and 198 fmol that were determined by Brezina et al. (2000a), presumably again because the different preparations used released different absolute amounts of peptides.]
R (see Eq. 1h of Brezina et al. 2003b; here 
is the concentration of SCP, CSCP, modified by the Hill coefficient hR = 0.7, kR+ and kR– are rate constants, and t is time. After a step at t = 0 to a new, maintained CSCP(t 
kR–/kR+, and time constant 
of Brezina et al. (2003a) must be preserved at all temperatures. This restricts the choice of kR+ and kR– and requires both to have the same temperature coefficient, Q10,kR. When CSCP(t 
as well as, through the relationship KR = kR–/kR+ = 3.64 x 10–6 
, kR–(15°C) = 5.55 x 10–4 s–1 (so that, still, KR = 3.64 x 10–6 
. 
