Recurrent networks of submucous neurons controlling intestinal secretion: a modeling study

Jordan D. Chambers,1 Joel C. Bornstein,1 Henrik Sjövall,2 and Evan A. Thomas1,3

1Department of Physiology, University of Melbourne, Parkville, Victoria, Australia; 2Department of Internal Medicine, Sahlgrenska University Hospital, Göteborg, Sweden; 3Howard Florey Institute, Parkville, Victoria, Australia

Submitted 29 October 2004 ; accepted in final form 23 December 2004


    ABSTRACT
 TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
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 REFERENCES
 
Secretomotor neurons, immunoreactive for vasoactive intestinal peptide (VIP), are important in controlling chloride secretion in the small intestine. These neurons form functional synapses with other submucosal VIP neurons and transmit via slow excitatory postsynaptic potentials (EPSPs). Thus they form a recurrent network with positive feedback. Intrinsic sensory neurons within the submucosa are also likely to form recurrent networks with positive feedback, provide substantial output to VIP neurons, and receive input from VIP neurons. If positive feedback within recurrent networks is sufficiently large, then neurons in the network respond to even small stimuli by firing at their maximum possible rate, even after the stimulus is removed. However, it is not clear whether such a mechanism operates within the recurrent networks of submucous neurons. We investigated this question by performing computer simulations of realistic models of VIP and intrinsic sensory neuron networks. In the expected range of electrophysiological properties, we found that activity in the VIP neuron network decayed slowly after cessation of a stimulus, indicating that positive feedback is not strong enough to support the uncontrolled firing state. The addition of intrinsic sensory neurons produced a low stable firing rate consistent with the common finding that basal secretory activity is, in part, neurogenic. Changing electrophysiological properties enables these recurrent networks to support the uncontrolled firing state, which may have implications with hypersecretion in the presence of enterotoxins such as cholera-toxin.

neural networks; recurrent excitation; computational modeling; enteric nervous system; submucous plexus; hypersecretion


THE ENTERIC NERVOUS SYSTEM (ENS) is capable of modulating chloride secretion in the small intestine (12). It contains two major ganglionated plexuses, myenteric and submucous (17). Neurons with cell bodies in the submucous plexus appear to be the final arm in neurally evoked secretion because they provide the majority of projections to the intestinal mucosa (27) and neurally evoked secretion requires the presence of the submucous plexus (8) and does not require the myenteric plexus (8, 13, 26).

Vasoactive intestinal peptide (VIP) neurons, which make up almost one-half the neurons in the submucous plexus (19), are the most likely candidates for the final secretomotor neuron population involved in neurogenic secretory diarrhea (9, 28, 35). In addition to projecting to the mucosa, the axons of VIP neurons can ramify through the submucous plexus for up to 3.5 mm (41) and project preferentially in a circumferential direction. When passing through adjacent ganglia, the VIP neurons have axonal varicosities and occasional varicose collaterals (14, 38). Furthermore, dual impalement experiments have shown that stimulating one VIP neuron will cause a slow excitatory postsynaptic potential (EPSP) in a nearby VIP neuron (38). This structural and electrophysiological evidence indicates that the population of VIP neurons form a recurrent network and that neurons in this network are likely to be very excitable because of this positive feedback (44).

Neurons with Dogiel type II morphology and large afterhyperpolarizing potentials (AHPs) in the submucous plexus are almost certainly intrinsic primary afferent (sensory) neurons (28), and they are also organized in recurrent networks. They have projections that can travel through the submucous plexus for up to 3 mm, have a preference for circumferential projection (41), and provide a large number of varicosities to their own and neighboring ganglia (14, 38). Myenteric sensory neurons transmit to each other by slow EPSPs (29), so it is likely that submucous sensory neurons do also. Unlike VIP neurons, however, sensory neurons have a prominent source of inhibition in the AHP (1, 4, 14, 20, 42), which causes strong accommodation in action potential firing. In the myenteric plexus, the slow EPSP suppresses the AHP (22), and it is likely the same thing occurs in the submucosa. In the myenteric plexus, the degree of suppression of the AHP very strongly influences the excitability of the sensory neuron network (44, 46).

The ENS also plays a major part in the hypersecretion induced by luminal secretagogues in the small intestine (12, 15, 16, 31). According to the prevailing hypothesis, the signal transduction between large luminal toxins like cholera toxin and the ENS is conveyed by the release of serotonin from enterochromaffin cells, which in turn leads to activation of the ENS via 5-hydroxytryptamine type 3 receptors (36, 48, 49), a nicotinic transmission step that may involve sensory neurons, interneurons, and/or secretomotor neurons (10, 11), and a final VIP-mediated secretomotor mechanism (9, 28, 35). This simple model disregards the network behavior of VIP neurons and/or sensory neurons and requires the ongoing release of serotonin to continuously activate the ENS. Currently there is a disagreement in the literature as to whether continued activation of the ENS is required for hypersecretion in the presence of luminal secretagogues (16, 31).

The present study investigated quantitatively how the recurrent networks in the submucous plexus respond to stimuli and maintain control of firing. It also examined whether sustained sensory input is required for overactivity or whether other conditions must be met for this to happen. This was done using a realistic computer model based on physiological and neuroanatomical data. The simulated neural circuit we studied incorporates the neurons in preparations of mucosa/submucosa used for the study of noncholinergic neurogenic secretion in Ussing chambers. In the Ussing chamber preparation, a segment of intestine was cut open and stripped of the circular and longitudinal muscle layers, which also removes the myenteric plexus and other exogenous input, leaving only the mucosa and submucous plexus. The resulting flat sheet is then mounted in Ussing flux chambers, which allow simultaneous recording of transmucosal current and ion fluxes resulting from electrogenic chloride transport across the mucosal epithelium. Firing in the network of VIP neurons in our model is a surrogate for the component of electrogenic chloride secretion that is not sensitive to muscarinic blockade in Ussing chamber studies (8, 13, 26).


    MATERIALS AND METHODS
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 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
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Single Neuron Model

The details of the model used to simulate individual neurons have been previously published (2, 6, 4446) and so are only summarized here. The model neurons are similar to "leaky integrate and fire" neurons (21), but the membrane potential, which is described by the usual equivalent circuit equation, is calculated from the underlying conductance changes and driving voltages rather than directly from currents. The conductances used in this model include those responsible for slow EPSPs, AHPs, action potentials, proximal process potentials (PPPs), and fast EPSPs.

Synaptic Transmission Model

Fast EPSPs were modeled by a stereotyped conductance change with a time course given by an {alpha}-function (44, 46). Similarly, PPPs were modeled with a stereotyped conductance change with a more rapid time course also given by an {alpha}-function (44, 46). The PPPs were large enough to evoke an action potential in a neuron at rest, but not if the membrane was hyperpolarized (e.g., by an AHP).

Slow EPSPs were modeled by a set of equations chosen to reproduce the experimentally observed nonlinear stimulus response relationship (2), and parameters of the equations were systematically varied to reproduce EPSPs with time courses within the physiological range (8–120 s, Table 1) (44, 46). Synaptic strength is a variable that represents receptor occupation, receptor-second messenger coupling, and formation of second messengers (2). Increasing the synaptic strength, while holding other parameters constant, increases the rate of rise of the slow EPSP, increases the membrane potential change for submaximal events and increases the duration of a slow EPSP but does not alter the maximum depolarization that can be achieved. Synaptic strength was varied (Table 1) to give slow EPSPs, in response to five stimuli at 1 Hz, with amplitudes between 1 mV and the maximum slow EPSP amplitude and durations between 5 and 120 s.


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Table 1. Parameter values used in network simulations

 
AHP

Sensory neurons display a prominent AHP after the action potential, which is in turn suppressed by the slow EPSP (22). The AHP and its suppression by the slow EPSP was included in the model as previously described (44, 46).

Network Construction

A novel algorithm was developed to produce anatomically realistic networks of submucosal neurons. Ganglia were randomly assigned a position on a two-dimensional plane so that no ganglia were further than 700 µm apart (50) and no closer than 200 µm, because a single ganglion can be up to 200 µm in length (14). Internodal strands between ganglia were created so that each ganglion had between three and six internodal strands connecting it to ganglia within 700 µm. Each ganglion was filled with 8.15 ± 4.62 (all numbers are expressed as means ± SD) neurons (50). The combination of ganglia distribution and cell distribution within each ganglia gave an overall cell density that matched the observed 129.9 ± 16 neurons/mm2 (41). Each neuron was assigned as a sensory, VIP, cholinergic-secretomotor, or vasodilator neuron, while keeping the overall proportion of each class of neuron in close accordance with reported values (18, 41).

Pathways and synapses were created for the VIP neurons and sensory neurons by first assigning a projection length following the normally distributed patterns reported in the literature (41) and selecting a ganglion close to this distance from the cell body. The procedure for selecting the ganglion was set so that the projection length was within the maximum projection distances for the circumferential, oral, and anal directions (41). The algorithm introduced a bias so that neurons projected more circumferentially than longitudinally and slightly more anally than orally, in accordance with observations (41).

An axon, traveling along the internodal strands from the source neuron to the target ganglion, was created. The algorithm tried to match the reported values for the number of ganglia traversed (14, 38). However, it was not possible to closely match these reported values, while also keeping in close accordance with reported values for projection lengths and ganglia positions. Slightly increasing the number of ganglia traversed above the reported values enabled neurons to form either direct or indirect projections to their terminations that matched observations (3). Because it is hard to follow the dye-filled axons in the submucous plexus (3) and the number of ganglia traversed has only been reported twice (14, 38), it is quite possible that the number of ganglia traversed has been underestimated to date.

Making the assumption that varicose structures observed on axons are synaptic connections, each VIP neuron was set to make 0.5–0.75 synapses per ganglion traversed to match the reported value of 0.5 ± 0.15 varicosities per ganglion traversed for these neurons. Sensory neurons were set to make 11.0–12.0 per ganglion traversed to match the published value of 11.64 ± 2.04 varicosities per ganglion traversed (38).

Simulations

Networks representing a 5 x 5-mm (length by circumference) section of the submucous plexus were used in simulations of network activity for VIP neurons or VIP and sensory neurons. Networks were connected on the circumferential side, mimicking an intact tube. The 5 x 5-mm networks of VIP neurons contained 1,000–1,200 neurons and networks of VIP neurons and sensory neurons contained 1,200–1,500 neurons. A previous study simulating the myenteric plexus using similar models found that networks of 5 x 5 mm are large enough to avoid size-dependent artifacts and small enough to avoid excessive run times (46). Network activity is reported as action potential rate average over a class of neurons.

Several different stimulus protocols were used. For networks containing only VIP neurons, fast, slow, or both types of EPSPs were imposed on these neurons. Networks containing sensory neurons and VIP neurons were stimulated via PPPs applied to the sensory neurons (47). These inputs were randomly applied to the appropriate neurons but with a constant average frequency.

The following parameters were systematically varied as part of the study: maximum slow EPSP amplitude, synaptic strength, slow EPSP duration, and the relative size of the AHP after suppression by the slow EPSP ({rho}) (Table 1). The first three parameters were varied for both VIP and sensory neurons, as appropriate. To a lesser extent the network structure was also varied.

Simulations were undertaken in software developed for this purpose and written in the C and Python programming languages. The differential equations describing neuron dynamics were integrated by using a third order, explicit, adaptive, Runge-Kutta method (39). Software is freely available by contacting the authors.


    RESULTS
 TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
Modeling in Physiological Range

Behavior of isolated VIP neuron networks. We numerically simulated large networks of VIP neurons. Random trains of exogenous EPSPs with constant mean frequency were played into neurons initially at rest. This input is the equivalent for the myenteric plexus to submucous plexus pathways that play a major role in the regulation of the submucous neural circuits (5, 33). The type of EPSP input was varied systematically, as was the maximum amplitude of the slow EPSP (range, 10–25 mV).

Activity in the network reached a stable firing rate after 3–20 s of simulated time (1–8 h on an Intel Pentium-4 2.0 GHz processor). Increasing the frequency of the input caused an increase in stable firing rate (Fig. 1A). The average neuron firing rate in the network (~10 Hz) was well below the maximum firing rate of these neurons in response to a current injection of ~20 Hz.



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Fig. 1. Activity in networks of vasoactive intestinal peptide (VIP) neurons produced by ongoing synaptic input. Constant frequency random input of slow excitatory postsynaptic potentials (EPSPs), fast EPSPs, or both, were played into networks of VIP neurons, initially at rest. Network activity is reported as action potential rate per neuron, averaged over the network. A: network response as a function of time after the start of stimulus for an input of combined fast and slow EPSPs. Each curve is the response to inputs generated at different mean frequencies. Shortly after the stimulus, networks settle on a stable constant firing rate. B: stimulus response curves for network firing in which the stimulus is the frequency of the input and the response is the frequency of the output. These are plotted for each of the possible combinations of slow and fast EPSP input.

 
The type of input into the VIP neurons strongly affected the stable firing rate reached. Combined fast and slow EPSP input always produced the highest stable firing rate (Fig. 1B). Fast EPSP input alone was sufficient to drive the network to a high stable firing rate, but a higher input frequency was required than with combined input. For low input frequencies, slow EPSP input was more efficient in driving the VIP network than exclusively fast EPSP input, as long as the slow EPSPs could evoke action potentials in the VIP neurons. However, at higher input frequencies, fast EPSP inputs achieved higher stable firing rates than those reached during slow EPSP input.

Once the network activity reached a stable firing rate, the input into the networks was removed and the resulting changes in network activity were observed. Three qualitative types of network activity were observed after removal of the input: uncontrolled, long decay, and quiescent (Fig. 2A). With uncontrolled behavior, activity settled to a high stable firing rate independent of the input frequency (Fig. 2B). The second type was called long decay, because the network activity decreased to zero or a low stable firing rate over a period of time longer than durations of individual slow EPSPs. Quiescent behavior was a rapid return to rest within a time period shorter than the duration of the slow EPSP.



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Fig. 2. Three types of network activity after removal of the input stimulus. Random inputs of fast and slow EPSPs at 3 Hz mean frequency were played into networks of VIP neurons initially at rest and then the input was removed after 10 s. Network activity is reported as action potential rate per neuron averaged over the network. A: curves show the three types of network activity observed: uncontrolled, long decay, and quiescent. These types of network activity were induced by different mean maximum slow EPSP amplitudes (10, 15, and 20 mV, respectively). B: these curves were generated by playing fast and slow EPSPs at different mean frequencies into networks displaying uncontrolled firing. After the input stimulus is removed, the network activity converges to the same firing rate, indicating that information about the stimulus strength has been lost.

 
We classified the maximum amplitude of the slow EPSPs in VIP cells into three regimes: 1) subthreshold, when the mean maximum amplitude was below the mean threshold for action potential generation; 2) threshold; and 3) suprathreshold. When the mean maximum amplitude of slow EPSP was subthreshold only quiescent behavior was observed. In the threshold regime both quiescent and long decaying behaviors were observed, but not uncontrolled firing. In the suprathreshold regime all three qualitative behaviors were observed. When the maximum depolarization of slow EPSPs was threshold or suprathreshold and other slow EPSP properties were set in the physiological range for a VIP neuron to VIP neurons slow EPSP (slow EPSP duration between 10 and 17 s and synaptic strength between 20 and 40, which gave slow EPSP amplitudes of 6–10 mV in response to a stimulus of 2 Hz for 10 s), the long decay was observed. When the mean maximum depolarization of slow EPSPs was threshold, the physiological range was produced by a synaptic strength of 30–40 (giving slow EPSP amplitudes of 7.5–10 mV) and a slow EPSP duration of 12–17 s. When the mean maximum depolarization of slow EPSPs was suprathreshold, the physiological range was produced by a synaptic strength of 20–30 (giving slow EPSP amplitudes of 6–10 mV) and a slow EPSP duration of 10–17 s.

Interaction between VIP neuron and sensory neuron networks. The sensory neurons display a large number of varicose structures around all neuron types in the submucous plexus (14, 38) and, in this analysis, we assumed that sensory neurons can transmit to each other via slow EPSPs in analogy with equivalent neurons in the myenteric plexus (29). We also assumed there are connections from VIP neurons to sensory neurons that induce slow EPSPs in their targets (38). We performed numerical simulations of the combined networks stimulated by playing PPPs into the sensory neurons. This simulated the effect of a sensory stimulus lasting several seconds, equivalent to a sustained distension or chemical activation of the sensory neurons either by a nutrient or a toxin.

In this case, we varied the nature of transmission between the two populations of neurons and the maximum amplitudes of slow EPSPs in VIP neurons only. The maximum amplitudes of slow EPSPs in sensory neurons were held constant at threshold. We also varied the amount by which a slow EPSP suppressed the AHP in sensory neurons. This has been shown to be an important regulator of excitability in myenteric networks (44, 46).

When the AHP was not suppressed by the slow EPSP in sensory neurons, activity in these neurons quickly returned to rest after removal of the input stimulus (Fig. 3A). In this case, the VIP neurons behaved just as they did when the sensory neurons were not present and no stimulus was present (see above). However, when the slow EPSP was able to suppress the AHP in sensory neurons, the interactions between the two recurrent networks changed the behavior of the VIP neurons after the stimulus was removed.



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Fig. 3. Activity in interconnected networks of sensory neurons and VIP neurons under various conditions. A random constant frequency input of proximal process potentials (PPPs) was played into the sensory neurons in a network of sensory neurons and VIP neurons initially at rest. Network activity is reported as action potential rate in the indicated class of neuron, average over all neurons in that class. A: activity in the sensory neurons when there was no interaction between the afterhyperpolarizing potential (AHP) and slow EPSP in these neurons. The activity quickly returned to rest after the stimulus was removed. B: activity in sensory neurons when slow EPSP suppressed the AHP to 10% of its resting value. The feedback curve shows activity in sensory neurons when there is slow EPSP transmission from VIP neurons to sensory neurons. The no feedback curve shows activity in sensory neurons when there is no transmission from VIP neurons to sensory neurons. The sensory neurons reach a low stable firing rate under both conditions, but the activity is higher when there is slow EPSP transmission from VIP neurons to sensory neurons. C: activity in sensory neurons when the slow EPSP suppressed the AHP to 10% of its resting value and transmission between sensory neurons was blocked. After removal of the input, sensory neurons reached a low stable firing rate that is a result of transmission from VIP neurons. D: activity in VIP neurons when the sensory neurons reach a low stable firing rate. After removal of the input, the activity in VIP neurons decayed, for a period of time longer than the slow EPSP duration (17 s for this figure) to a low stable firing rate.

 
When the AHP was suppressed to 10% of its resting value by the slow EPSPs in sensory neurons, activity in these neurons did not return to rest when the stimulus ceased, but reached a low stable firing rate regardless of the level of drive to the sensory neurons from VIP neurons (Fig. 3B). However, if transmission between sensory neurons was blocked, the sensory neurons still reached a low stable firing rate. This activity resulted from the VIP neurons activating the sensory neurons. Therefore, the feedback from VIP neurons to sensory neurons resulted in a low stable firing rate for both populations of neurons (Fig. 3C).

After cessation of the input into the sensory neurons, firing of VIP neurons returned to a stable nonzero rate over a substantially longer time than the duration of slow EPSPs in these neurons (Fig. 3D). This was reminiscent of the long decay seen in isolated VIP neuron networks. Ongoing sensory neuron activity or the interaction between the two populations of neurons drove ongoing activity in the VIP neurons for a network that would otherwise return to rest in the absence of input.

We tested how the combined networks responded to a second stimulus once both populations of neurons had reached a low stable firing rate (Fig. 4). This is the equivalent of initiating a second stimulus period in an Ussing chamber experiment. The response of the network during the second stimulus was dependent on the stimulus frequency. This graded firing occurred in both populations of neurons, and there was an amplification of activity from sensory neurons to VIP neurons. Once the second stimulus was withdrawn, the networks returned to the same low stable firing rate. This dependence on the input stimulus frequency and the return to the same low stable firing rate was observed whether or not there was transmission between sensory neurons; whether the transmission from sensory neurons to VIP neurons was via fast or slow EPSPs, or both; and for threshold or suprathreshold slow EPSP maximum depolarizations in VIP neurons.



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Fig. 4. Response of interconnected networks of VIP and sensory neurons to a second stimulus period. Networks of sensory neurons and VIP neurons initially at rest were stimulated by PPPs played into the sensory neurons at a frequency of 3 Hz. The stimulus was removed after 10 s and the networks were allowed to reach a low stable firing rate. A second input stimulus was played into the sensory neurons after 40 s at different input frequencies. These curves show the activity in sensory neurons or VIP neurons as a function of time. During the second stimulus, activity is dependent on the input stimulus frequency. After removal of the second stimulus, the activity returns to the same low stable firing rate, indicating that networks in the low stable firing rate are still able to transduce stimuli.

 
Modeling Overactivity in VIP Neurons

Behavior of isolated VIP networks. We systematically varied a number of neuron and network properties and determined their effects on network firing during combined fast and slow EPSP input. Varying the maximum slow EPSP amplitude from 10 to 25 mV could induce dramatic changes in the stable firing rates reached during the input (Fig. 5A). Increasing the synaptic strength at synapses between VIP neurons from 10 to 50 (Fig. 5B), increasing the slow EPSP duration from 10 to 120 s (Fig. 5C), or varying the network structure by increasing the number of synapses per ganglion traversed from 0.5 to 0.7 (Fig. 6) only produced small changes in the stable firing rate during fast and slow EPSP input.



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Fig. 5. Effect of varying the properties of slow EPSPs in VIP neurons on network activity. Constant frequency random input of slow and fast EPSPs at 3 Hz mean frequency were played into networks of VIP neurons initially at rest. Each curve shows the network response as a function of time for different mean maximum slow EPSP amplitudes (A); different synaptic strengths between VIP neurons (B); and different slow EPSP durations (C).

 


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Fig. 6. Effect of varying number of synapses on activity in networks of VIP neurons. Random input of fast and slow EPSPs at a constant frequency of 3 Hz were played into networks of VIP neurons initially at rest. Each curve is the network response as a function of time for networks differing in the number of synapses per ganglion traversed. The activity during the stimulus was very similar, but residual firing clearly was increased when the number of synapses increased.

 
However, varying neuron and network properties could change the network activity observed after cessation of the fast and slow EPSP input. Increasing the synaptic strengths of the slow EPSPs increases the rise time, increases the duration, and increases the likelihood that the postsynaptic response will reach the maximum depolarization but does not change the maximum depolarization. The type of network activity observed when the mean maximum amplitude of slow EPSPs was set at threshold or suprathreshold depended on the synaptic strength and slow EPSP duration. At suprathreshold, increasing the synaptic strength and/or increasing the slow EPSP duration could change the network activity from quiescent to long decay to uncontrolled (Table 2). Varying the network structure by increasing the number of synapses per ganglion traversed from 0.5 to 0.7 changed the stable firing rate after cessation of the fast and slow EPSP input (Fig. 6).


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Table 2. Type of network activity observed while varying the synaptic strength and slow EPSP duration

 
VIP neuron networks interacting with sensory neuron networks. The amount a slow EPSP suppressed the AHP in sensory neurons has been shown to be an important regulator of excitability in myenteric networks (46). When the activity in sensory neurons was kept low (by allowing a large residual AHP in the presence of a slow EPSP), there was a small amount of residual activity in the VIP neurons, regardless of the type of transmission from sensory neurons to VIP neurons (Fig. 8A). When the mean maximum amplitude of slow EPSPs in VIP neurons was set to threshold, increasing the activity in sensory neurons (by decreasing the size of the residual AHP during a slow EPSP) increased activity in VIP neurons. The amount of increase was dependent on the type of transmission from sensory neurons to VIP neurons (Fig. 7A). Under these conditions, combined fast and slow EPSP transmission from sensory neurons to VIP neurons caused a high firing rate in VIP neurons, as did fast EPSP transmission alone, but the firing rate was lower in the VIP neurons when transmission from sensory neurons was only via slow EPSPs. This is consistent with the results of playing EPSP input directly into VIP neurons (Fig. 1B).



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Fig. 7. Activity in VIP neurons as a function of activity in sensory neurons. Networks of sensory neurons and VIP neurons were initially at rest. Random inputs of PPPs were played into the sensory neurons at 3 Hz to stimulate the networks. The input stimulus was removed and the networks reached stable firing rates reported as action potential rate per neuron averaged over the particular class of neuron. These curves are the response of VIP neurons as a function of the activity in sensory neurons for different combinations of fast and slow EPSP transmission from sensory neurons to VIP neurons. The activity in sensory neurons was altered by changing the amount a slow EPSP suppressed the AHP in sensory neurons. A: curves showing response in VIP neurons when the mean maximum slow EPSP amplitude in VIP neurons was set at threshold. B: curves showing response in VIP neurons when the mean maximum slow EPSP amplitude in VIP neurons was suprathreshold.

 
Increasing the maximum slow EPSP amplitude in VIP neurons from threshold to suprathreshold increased the stable firing rates in VIP neurons (particularly when there was a small residual AHP in the presence of a slow EPSP in sensory neurons); compare Fig. 7, A and B. It also decreased the differences in firing of VIP neurons seen when transmission from sensory neurons was only via slow EPSPs vs. transmission via fast EPSPs (compare Fig. 7, A and B). However, when sensory neuron activity was high fast EPSP, transmission from sensory neurons to VIP neurons was still required to drive the VIP neurons to firing at over 10 Hz per neuron (Fig. 7B).

Figure 3B indicates that increased activity in VIP neurons leads to increased activity for sensory neurons, and Fig. 7A shows that increased activity in VIP neurons (by changing the type of transmission from sensory neurons to VIP neurons) results in increased activity in sensory neurons for the same degree of suppression of the AHP. When transmission between sensory neurons is blocked, the VIP neurons still drive activity in the sensory neurons (Fig. 3C). When the AHP was largely unsuppressed, there was little activity in the VIP neurons. Reducing the AHP (by increasing the degree of suppression) enabled the feedback of fast EPSPs to drive the VIP neurons to a moderate stable firing rate when the slow EPSP amplitude in VIP neurons was threshold. Increasing the maximum slow EPSP amplitude in VIP neurons to suprathreshold caused the VIP neurons to reach a high stable firing rate. In this analysis, when the slow EPSP amplitude in VIP neurons was set at threshold or suprathreshold, the synaptic strength and slow EPSP duration were set at values that would have resulted in a return to rest, if the VIP neurons were not receiving input from sensory neurons. Thus the high stable firing rates in VIP neurons depended on the ability of slow EPSPs in sensory neurons to suppress their characteristic AHPs and on transmission from sensory neurons to VIP neurons including a component mediated by fast EPSPs.


    DISCUSSION
 TOP
 ABSTRACT
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 GRANTS
 REFERENCES
 
We used computer simulation to investigate the function of positive feedback within the submucous plexus in intestinal chloride secretion. Extrinsic innervation and the myenteric plexus were not included, and therefore our models represent Ussing chamber preparations. The absence of other innervation meant that there was no inhibitory synaptic input in our models (5, 32, 40, 43). In addition to studying the recurrent networks with synaptic potentials in the normal physiological range, we studied the conditions that cause overactivity and uncontrolled activity in the VIP neurons. We have defined uncontrolled firing as the firing rate not returning to a low or zero firing rate on cessation of a stimulus. The consequences of this are that the secretory apparatus will be driven at a much higher rate than normal and such networks are not able to mediate normal physiological reflexes (44, 46). Our model showed that in the normal physiological situation, submucous networks fire at low rates in the absence of input without additional inhibitory mechanisms but are easily pushed into uncontrolled firing by intrinsic changes only or in conjunction with exogenous input (e.g., from the myenteric plexus).

Modeling in the Physiological Range

We studied the properties of isolated VIP neuron networks and their response to excitatory stimuli, which would represent input from either submucous sensory neurons or the myenteric plexus (5, 33). We varied the maximum depolarization of the slow EPSP and the type of input into VIP neurons (fast and/or slow EPSPs). When a stimulus was applied to the VIP neurons, the stimulus strength and transmission type largely determined the response for all mean maximum amplitudes of slow EPSPs. This graded response indicates that the VIP neurons are able to encode useful information without inhibition to control firing.

There were three qualitative types of network behavior after removal of the input stimulus to the VIP neurons, which indicates the significance of the positive feedback between VIP neurons. The three states are 1) quiescent state in which activity returned very rapidly to zero after removal of the stimulus, 2) long decay in which activity returned to a low or zero firing rate over a period of time longer than the slow EPSP duration, and 3) uncontrolled in which activity settled on a high firing rate. In quiescent networks, the positive feedback was insignificant in determining the activity for these neurons. The long decay state occurred because the VIP neurons almost, but not quite, had enough positive feedback to sustain network activity in the absence of any input. Finally, in uncontrolled networks, the positive feedback was sufficient to maintain activity for the population of VIP neurons.

Of these three different states, the long decay occurred when the slow EPSP parameters were in the previously described physiological range for a slow EPSP evoked in a VIP neuron by intracellular stimulation of another VIP neuron (38). This decay provides a possible explanation for the long decay in chloride secretion across the mucosa after electrical field stimulation (13, 26). This decay in chloride secretion is TTX-sensitive and has cholinergic and noncholinergic components (13, 26). We predict that blocking transmission between VIP neurons will abolish or decrease the noncholinergic component of the long decay in chloride secretion.

A major aim of the study was to perform simulations on the complex VIP and sensory neuron network. The sensory neuron network probably has recurrent positive feedback, and both the VIP and sensory neuron network are cross connected by excitatory connections (37, 38). Sensory neurons have a prominent AHP (1, 4, 14, 20, 42), which probably interacts with the slow EPSP to regulate firing in this network (44, 46). Physiologically, the AHP (in myenteric neurons) is suppressed by ~90% in the presence of a slow EPSP (Johnson PJ and Bornstein JC, unpublished observations). When this was simulated in network of submucosal neurons, sensory neurons alone in the network reached a low stable firing rate. This is different from a previous study with myenteric sensory neurons (46) because of the variance in electrophysiological parameters of the sensory neurons and the structure of the sensory neuron network (see MATERIALS AND METHODS). This low stable firing rate in sensory neurons results in the VIP neurons being driven at low stable firing rates under conditions that would allow the VIP neurons to return to rest on their own (either quiescent or long decay, see above). Even when transmission between sensory neurons was blocked, the combined networks of sensory neurons and VIP neurons were able to reach a low stable firing rate because of the positive feedback between the two different populations of neurons.

Regardless of whether the low stable firing rate was due to the positive feedback within the sensory neurons or between the VIP neurons and sensory neurons, it was very robust and the networks could still encode useful information. Similarly, the low stable firing rate showed similar characteristics when the transmission from sensory neurons to VIP neurons was via fast or slow EPSPs or both (although the magnitude of the response changed as it did when stimulating the VIP neurons with similar exogenous synaptic inputs). This is important because there may be two subsets of submucosal sensory neurons distinguished by the presence of substance P or calcitonin gene-related peptide (CGRP) (20, 28, 30, 37). The sensory neurons with substance P, and not CGRP, are unlikely to transmit slow EPSPs to VIP neurons because VIP neurons do not have neurokinin receptors (34). It also means that addition of fast or slow EPSP input from other submucosal neurons should not affect the ability of these neurons to encode useful information, but only affect the magnitude and duration of the response.

The low stable firing rates in both populations of neurons are likely to be the basis of the observed tonic activity of submucosal neurons seen in Ussing chambers (7, 26). The tonic activity observed in Ussing chambers is TTX-sensitive and appears to be atropine-insensitive (7, 26). It is also depressed by norepinephrine and somatostatin, both of which hyperpolarize VIP neurons (40, 43) via mechanisms requiring neuronal activity (25). We predict that basal secretion will continue when sensory input is blocked. Furthermore, according to our model, tonic or spontaneous activity may arise from the sensory neurons or because of an interaction between the sensory neurons and VIP neurons. These two possibilities can be distinguished by examining the effect on basal electrogenic secretion of blocking transmission between sensory neurons and comparing this to blocking transmission from VIP to sensory neurons. Of course, testing these predictions will require identification of suitable antagonists specific to these types of synapses.

Whereas we predict basal secretion will continue in the absence of sensory input, inhibitory input onto the VIP neurons from sympathetic or myenteric ganglia may abolish this basal secretion. The effect of inhibitory input will depend on the size and duration of the inhibition, whether the tonic or spontaneous activity arises from the sensory neurons or from an interaction between the sensory neurons and VIP neurons and the presence of any sensory input or input from other neurons. Such variations mean that this basal secretion may or may not be observed in vivo.

Modeling Overactivity in the VIP Neurons

It is almost certain that the hypersecretion is due to overactivity in VIP neurons rather than cholinergic secretomotor neurons (9, 28, 35). Therefore, we investigated the conditions that produced overactivity in VIP neurons.

A high frequency input stimulus readily produced overactivity in the VIP neurons for isolated VIP neuron networks and for VIP and sensory neuron networks. When applying an exogenous stimulus to the VIP neurons in our model, fast EPSP transmission onto these neurons was required to achieve high rates over 60% of the maximum for these neurons. Similarly, when PPPs were played into the sensory neurons, fast EPSP transmission from sensory neurons to VIP neurons was required to achieve high firing rates. These results are in accordance with the observation that administration of hexamethonium prevents manifestation of cholera-induced hypersecretion (11) and indicates that hexamethonium is acting, at least in part, to block fast EPSP transmission onto the VIP neurons.

However, under these conditions, once the high frequency input stimulus is removed, the activity in VIP neurons quickly returned to low or zero firing rates. If Farthing (16) is correct in suggesting that the ENS does not require constant sensory drive during hypersecretion, then overactivity in the VIP neurons would continue in the absence of any input stimulus. Whereas it has been reported that preparations devoid of the myenteric plexus are unable to produce a hypersecretion response to cholera toxin (24), the role of the myenteric plexus may well be to amplify and spread the activity arising in the submucous plexus.

VIP neuron networks switched from the long decay type to the uncontrolled type by increasing the slow EPSP duration or increasing the synaptic strength between VIP neurons. However, the VIP neurons had two requirements for uncontrolled firing. The slow EPSPs in the VIP neurons needed to either have greater than normal amplitudes or greater than normal durations, and the VIP neurons need to make ~0.75 synapses per ganglion traversed (or 3–4 synapses per VIP neuron, which is at the highest end of the reported range). The former requirement therefore leads to the prediction that, if continuous restimulation of the enteric neural circuits is not required, then toxins evoking such can directly affect the properties of the VIP neurons as reported by Jiang et al. (23). The latter suggests that hypersecretion would only occur under certain neuroanatomical conditions, which may explain segmental and species differences in the neurogenic component of cholera secretion.

The AHP may play a role in controlling activity in the submucous plexus. Increasing the amount by which the slow EPSP suppresses the AHP increases the low stable firing rate in both populations of neurons. Therefore, an alternative hypothesis for ongoing high firing within the VIP network is that the toxin can modulate the suppression of the AHP by slow EPSPs in the sensory neurons either via a direct effect on the sensory neurons themselves, or perhaps via an action of serotonin on a receptor other than the 5-HT3 receptor. For the change in AHP suppression to increase the activity in VIP neurons, one of two conditions had to be satisfied: transmission from sensory neurons to VIP neurons must be fast or the slow EPSPs in VIP neurons must be able to evoke action potentials. Both of these conditions are likely to be met under normal physiological circumstances making the suppression of the AHP a critical issue for further physiological experimentation.

In summary, our modeling study shows that positive feedback within submucous neuron networks provides an explanation for the observed decaying secretion response elicited by electrical stimuli and tonic activity leading to a basal secretion rate in the small intestine. The firing rate of the combined network in response to a given stimulus depends strongly on the amplitude of the slow EPSPs in VIP neurons and the interaction between the AHP and slow EPSPs in sensory neurons, but fast transmission onto VIP neurons also has an important role. Under certain conditions, the combined network can go into uncontrolled firing that persists even if the initial stimulus is removed. We propose that this is, in part, the mechanism behind the neurogenic component of cholera secretion in the small intestine in vivo.


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 ABSTRACT
 MATERIALS AND METHODS
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This study was supported by Australian Research Council Grant DP021004, Australia National Health and Medical Research Council (NHMRC) Grant 251504, Swedish Medical Research Council Grant 8288, and a Victorian Partnership for Advanced Computing Expertise grant. E. A. Thomas was partly supported by an NHMRC Peter Doherty Fellowship.


    FOOTNOTES
 

Address for reprint requests and other correspondence: J. D. Chambers, Dept. of Physiology, Univ. of Melbourne, Parkville Vic 3010, Australia (E-mail: jordanc{at}unimelb.edu.au)

The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.


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 TOP
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 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
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 REFERENCES
 

  1. Bertrand PP, Kunze WA, Bornstein JC, and Furness JB. Electrical mapping of the projections of intrinsic primary afferent neurones to the mucosa of the guinea-pig small intestine. Neurogastroenterol Motil 10: 533–541, 1998.[CrossRef][ISI][Medline]
  2. Bertrand PP, Thomas EA, Kunze WA, and Bornstein JC. A simple mathematical model of second-messenger mediated slow excitatory postsynaptic potentials. J Comput Neurosci 8: 127–142, 2000.[CrossRef][ISI][Medline]
  3. Bornstein JC, Costa M, and Furness JB. Synaptic inputs to immunohistochemically identified neurones in the submucous plexus of the guinea-pig small intestine. J Physiol 381: 465–482, 1986.[Abstract]
  4. Bornstein JC, Furness JB, and Costa M. An electrophysiological comparison of substance P-immunoreactive neurons with other neurons in the guinea-pig submucous plexus. J Auton Nerv Syst 26: 113–120, 1989.[CrossRef][ISI][Medline]
  5. Bornstein JC, Furness JB, and Costa M. Sources of excitatory synaptic inputs to neurochemically identified submucous neurons of guinea-pig small intestine. J Auton Nerv Syst 18: 83–91, 1987.[CrossRef][ISI][Medline]
  6. Bornstein JC, Furness JB, Kelly HF, Bywater RA, Neild TO, and Bertrand PP. Computer simulation of the enteric neural circuits mediating an ascending reflex: roles of fast and slow excitatory outputs of sensory neurons. J Auton Nerv Syst 64: 143–157, 1997.[CrossRef][ISI][Medline]
  7. Carey HV and Cooke HJ. Tonic activity of submucosal neurons influences basal ion transport. Life Sci 44: 1083–1088, 1989.[CrossRef][ISI][Medline]
  8. Carey HV, Cooke HJ, and Zafirova M. Mucosal responses evoked by stimulation of ganglion cell somas in the submucosal plexus of the guinea-pig ileum. J Physiol 364: 69–79, 1985.[Abstract]
  9. Cassuto J, Fahrenkrug J, Jodal M, Tuttle R, and Lundgren O. Release of vasoactive intestinal polypeptide from the cat small intestine exposed to cholera toxin. Gut 22: 958–963, 1981.[Abstract]
  10. Cassuto J, Jodal M, and Lundgren O. The effect of nicotinic and muscarinic receptor blockade on cholera toxin induced intestinal secretion in rats and cats. Acta Physiol Scand 114: 573–577, 1982.[ISI][Medline]
  11. Cassuto J, Siewert A, Jodal M, and Lundgren O. The involvement of intramural nerves in cholera toxin induced intestinal secretion. Acta Physiol Scand 117: 195–202, 1983.[ISI][Medline]
  12. Cooke HJ. "Enteric tears": chloride secretion and its neural regulation. News Physiol Sci 13: 269–274, 1998.[ISI][Medline]
  13. Cooke HJ, Shonnard K, and Wood JD. Effects of neuronal stimulation on mucosal transport in guinea pig ileum. Am J Physiol Gastrointest Liver Physiol 245: G290–G296, 1983.[Abstract/Free Full Text]
  14. Evans RJ, Jiang MM, and Surprenant A. Morphological properties and projections of electrophysiologically characterized neurons in the guinea-pig submucosal plexus. Neuroscience 59: 1093–1110, 1994.[CrossRef][ISI][Medline]
  15. Farthing MJ. Diarrhoea: a significant worldwide problem. Int J Antimicrob Agents 14: 65–69, 2000.[CrossRef][ISI][Medline]
  16. Farthing MJ. Novel targets for the control of secretory diarrhoea. Gut 50, Suppl 3: III15–III18, 2002.[Medline]
  17. Furness JB and Costa M. Types of nerves in the enteric nervous system. Neuroscience 5: 1–20, 1980.[CrossRef][ISI][Medline]
  18. Furness JB, Costa M, Gibbins IL, Llewellyn-Smith IJ, and Oliver JR. Neurochemically similar myenteric and submucous neurons directly traced to the mucosa of the small intestine. Cell Tissue Res 241: 155–163, 1985.[CrossRef][ISI][Medline]
  19. Furness JB, Costa M, and Keast JR. Choline acetyltransferase and peptide immunoreactivity of submucous neurons in the small intestine of the guinea-pig. Cell Tissue Res 237: 329–336, 1984.[CrossRef][ISI][Medline]
  20. Furness JB, Kunze WA, Bertrand PP, Clerc N, and Bornstein JC. Intrinsic primary afferent neurons of the intestine. Prog Neurobiol 54: 1–18, 1998.[CrossRef][ISI][Medline]
  21. Gabbiani F and Koch C. Methods in Neuronal Modeling: from Ions to Networks (2nd ed.). Cambridge, MA: MIT Press, 1998.
  22. Grafe P, Mayer CJ, and Wood JD. Synaptic modulation of calcium-dependent potassium conductance in myenteric neurones in the guinea-pig. J Physiol 305: 235–248, 1980.[Abstract]
  23. Jiang MM, Kirchgessner A, Gershon MD, and Surprenant A. Cholera toxin-sensitive neurons in guinea pig submucosal plexus. Am J Physiol Gastrointest Liver Physiol 264: G86–G94, 1993.[Abstract/Free Full Text]
  24. Jodal M, Holmgren S, Lundgren O, and Sjoqvist A. Involvement of the myenteric plexus in the cholera toxin-induced net fluid secretion in the rat small intestine. Gastroenterology 105: 1286–1293, 1993.[ISI][Medline]
  25. Keast JR, Furness JB, and Costa M. Effects of noradrenaline and somatostatin on basal and stimulated mucosal ion transport in the guinea-pig small intestine. Naunyn Schmiedebergs Arch Pharmacol 333: 393–399, 1986.[CrossRef][ISI][Medline]
  26. Keast JR, Furness JB, and Costa M. Investigations of nerve populations influencing ion transport that can be stimulated electrically, by serotonin and by a nicotinic agonist. Naunyn Schmiedebergs Arch Pharmacol 331: 260–266, 1985.[CrossRef][ISI][Medline]
  27. Keast JR, Furness JB, and Costa M. Origins of peptide and norepinephrine nerves in the mucosa of the guinea pig small intestine. Gastroenterology 86: 637–644, 1984.[ISI][Medline]
  28. Kirchgessner AL, Tamir H, and Gershon MD. Identification and stimulation by serotonin of intrinsic sensory neurons of the submucosal plexus of the guinea pig gut: activity-induced expression of Fos immunoreactivity. J Neurosci 12: 235–248, 1992.[Abstract]
  29. Kunze WA, Furness JB, and Bornstein JC. Simultaneous intracellular recordings from enteric neurons reveal that myenteric AH neurons transmit via slow excitatory postsynaptic potentials. Neuroscience 55: 685–694, 1993.[CrossRef][ISI][Medline]
  30. Liu MT, Rothstein JD, Gershon MD, and Kirchgessner AL. Glutamatergic enteric neurons. J Neurosci 17: 4764–4784, 1997.[Abstract/Free Full Text]
  31. Lundgren O. Enteric nerves and diarrhoea. Pharmacol Toxicol 90: 109–120, 2002.[CrossRef][ISI][Medline]
  32. Mihara S, Katayama Y, and Nishi S. Slow postsynaptic potentials in neurones of submucous plexus of guinea-pig caecum and their mimicry by noradrenaline and various peptides. Neuroscience 16: 1057–1068, 1985.[CrossRef][ISI][Medline]
  33. Moore BA and Vanner S. Properties of synaptic inputs from myenteric neurons innervating submucosal S neurons in guinea pig ileum. Am J Physiol Gastrointest Liver Physiol 278: G273–G280, 2000.[Abstract/Free Full Text]
  34. Moore BA, Vanner S, Bunnett NW, and Sharkey KA. Characterization of neurokinin-1 receptors in the submucosal plexus of guinea pig ileum. Am J Physiol Gastrointest Liver Physiol 273: G670–G678, 1997.[Abstract/Free Full Text]
  35. Mourad FH and Nassar CF. Effect of vasoactive intestinal polypeptide (VIP) antagonism on rat jejunal fluid and electrolyte secretion induced by cholera and Escherichia coli enterotoxins. Gut 47: 382–386, 2000.[Abstract/Free Full Text]
  36. Mourad FH, O'Donnell LJ, Dias JA, Ogutu E, Andre EA, Turvill JL, and Farthing MJ. Role of 5-hydroxytryptamine type 3 receptors in rat intestinal fluid and electrolyte secretion induced by cholera and Escherichia coli enterotoxins. Gut 37: 340–345, 1995.[Abstract]
  37. Pan H and Gershon MD. Activation of intrinsic afferent pathways in submucosal ganglia of the guinea pig small intestine. J Neurosci 20: 3295–3309, 2000.[Abstract/Free Full Text]
  38. Reed DE and Vanner SJ. Converging and diverging cholinergic inputs from submucosal neurons amplify activity of secretomotor neurons in guinea-pig ileal submucosa. Neuroscience 107: 685–696, 2001.[CrossRef][ISI][Medline]
  39. Shampine LF. Numerical Solution of Ordinary Differential Equations. New York: Chapman and Hall, 1994.
  40. Shen KZ and Surprenant A. Somatostatin-mediated inhibitory postsynaptic potential in sympathetically denervated guinea-pig submucosal neurones. J Physiol 470: 619–635, 1993.[Abstract]
  41. Song ZM, Brookes SJ, Steele PA, and Costa M. Projections and pathways of submucous neurons to the mucosa of the guinea-pig small intestine. Cell Tissue Res 269: 87–98, 1992.[CrossRef][ISI][Medline]
  42. Surprenant A. Two types of neurones lacking synaptic input in the submucous plexus of guinea-pig small intestine. J Physiol 351: 363–378, 1984.[Abstract]
  43. Surprenant A and North RA. Mechanism of synaptic inhibition by noradrenaline acting at alpha 2-adrenoceptors. Proc R Soc Lond B Biol Sci 234: 85–114, 1988.[ISI][Medline]
  44. Thomas EA, Bertrand PP, and Bornstein JC. A computer simulation of recurrent, excitatory networks of sensory neurons of the gut in guinea-pig. Neurosci Lett 287: 137–140, 2000.[CrossRef][ISI][Medline]
  45. Thomas EA, Bertrand PP, and Bornstein JC. Genesis and role of coordinated firing in a feedforward network: a model study of the enteric nervous system. Neuroscience 93: 1525–1537, 1999.[CrossRef][ISI][Medline]
  46. Thomas EA and Bornstein JC. Inhibitory cotransmission or after-hyperpolarizing potentials can regulate firing in recurrent networks with excitatory metabotropic transmission. Neuroscience 120: 333–351, 2003.[CrossRef][ISI][Medline]
  47. Thomas EA, Sjovall H, and Bornstein JC. Computational model of the migrating motor complex of the small intestine. Am J Physiol Gastrointest Liver Physiol 286: G564–G572, 2004.[Abstract/Free Full Text]
  48. Turvill JL, Connor P, and Farthing MJ. The inhibition of cholera toxin-induced 5-HT release by the 5-HT3 receptor antagonist, granisetron, in the rat. Br J Pharmacol 130: 1031–1036, 2000.[CrossRef][ISI][Medline]
  49. Turvill JL, Mourad FH, and Farthing MJ. Crucial role for 5-HT in cholera toxin but not Escherichia coli heat-labile enterotoxin-intestinal secretion in rats. Gastroenterology 115: 883–890, 1998.[ISI][Medline]
  50. Wilson AJ, Furness JB, and Costa M. The fine structure of the submucous plexus of the guinea-pig ileum. I. The ganglia, neurons, Schwann cells and neuropil. J Neurocytol 10: 759–784, 1981.[CrossRef][ISI][Medline]