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Sigh and Eupnea Rhythmogenesis Involve Distinct Interconnected Subpopulations: A Combined Computational and Experimental Study(1,2,3).

Toporikova N, Chevalier M, Thoby-Brisson M - eNeuro (2015)

Bottom Line: Based on recent in vitro data obtained in the mouse embryo, we have built a computational model consisting of two compartments, interconnected through appropriate synapses.The model reproduces basic features of simultaneous sigh and eupnea generation (two types of bursts differing in terms of shape, amplitude, and frequency of occurrence) and mimics the effect of blocking glycinergic synapses.Furthermore, we used this model to make predictions that were subsequently tested on the isolated preBötC in mouse brainstem slice preparations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, Washington and Lee University , Lexington, Virginia 24450.

ABSTRACT
Neural networks control complex motor outputs by generating several rhythmic neuronal activities, often with different time scales. One example of such a network is the pre-Bötzinger complex respiratory network (preBötC) that can simultaneously generate fast, small-amplitude, monophasic eupneic breaths together with slow, high-amplitude, biphasic augmented breaths (sighs). However, the underlying rhythmogenic mechanisms for this bimodal discharge pattern remain unclear, leaving two possible explanations: the existence of either reconfiguring processes within the same network or two distinct subnetworks. Based on recent in vitro data obtained in the mouse embryo, we have built a computational model consisting of two compartments, interconnected through appropriate synapses. One compartment generates sighs and the other produces eupneic bursts. The model reproduces basic features of simultaneous sigh and eupnea generation (two types of bursts differing in terms of shape, amplitude, and frequency of occurrence) and mimics the effect of blocking glycinergic synapses. Furthermore, we used this model to make predictions that were subsequently tested on the isolated preBötC in mouse brainstem slice preparations. Through a combination of in vitro and in silico approaches we find that (1) sigh events are less sensitive to network excitability than eupneic activity, (2) calcium-dependent mechanisms and the Ih current play a prominent role in sigh generation, and (3) specific parameters of Ih activation set the low sensitivity to excitability in the sigh neuronal subset. Altogether, our results strongly support the hypothesis that distinct subpopulations within the preBötC network are responsible for sigh and eupnea rhythmogenesis.

No MeSH data available.


Related in: MedlinePlus

Eupneic activity is more sensitive to [K+]o than sigh activity. A, Voltage traces of in silico experiments where VK, which is directly correlated to [K+]o was progressively increased (from top to bottom). B, Bar plots representing the mean frequency for eupnea (left, unfilled bars) and sigh bursts (right, shaded bars) for different VK. C, Extracellular recordings of spontaneous preBötC activity from slice preparation bathed in aCSF containing 4, 6, 8, and 10 mM of [K+]o (respectively, from top to bottom). D, Histograms of mean (+SEM) eupneic (unfilled bars) and sigh (shaded bars) burst frequencies for different [K+]o. Sigh bursts were generated at a relatively constant frequency whereas the eupnea burst frequency increased with increasing [K+]o both in silico and in vitro. *p < 0.05. Orange stars indicate sigh events.
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Figure 5: Eupneic activity is more sensitive to [K+]o than sigh activity. A, Voltage traces of in silico experiments where VK, which is directly correlated to [K+]o was progressively increased (from top to bottom). B, Bar plots representing the mean frequency for eupnea (left, unfilled bars) and sigh bursts (right, shaded bars) for different VK. C, Extracellular recordings of spontaneous preBötC activity from slice preparation bathed in aCSF containing 4, 6, 8, and 10 mM of [K+]o (respectively, from top to bottom). D, Histograms of mean (+SEM) eupneic (unfilled bars) and sigh (shaded bars) burst frequencies for different [K+]o. Sigh bursts were generated at a relatively constant frequency whereas the eupnea burst frequency increased with increasing [K+]o both in silico and in vitro. *p < 0.05. Orange stars indicate sigh events.

Mentions: We hypothesized that sighs are generated mainly through ultra-slow (order of minutes) [Ca2+]i oscillations. In our model, such metabolic oscillations rely on periodic Ca2+ release from ER store. Although depolarization-activated Ca2+ influx through voltage-gated Ca2+channels might participate to the generation of these oscillations, the release from ER is only weekly voltage-dependent and thus depolarization should not significantly affect the frequency of such metabolic oscillations. In contrast, oscillations in the eupnea compartment reflect depolarization-dependent kinetics of INaP and its frequency should directly reflect the changes in depolarization. Therefore, we expected that oscillations in the eupnea compartment of our model would be more sensitive to external K+ concentration ([K+]o) than oscillations in the sigh compartment. We tested the effect of changing [K+]o on the sigh and eupnea rhythm frequencies in silico by progressively increasing the reversal potential for the K+ dominated leak current (VK). Figure 5A shows average voltage profiles of the eupnea−sigh model for four different values of VK. For VK = −64 mV, the eupnea compartment was below oscillatory threshold but the sigh compartment was still able to generate slow Ca2+ oscillations (0.4 burst/min). Thus, the model produced only the ultra-slow sigh oscillations and no eupneic activity (Fig. 5A, top panel). Note that this feature might be specific of the developmental stage examined here, since this is not observed at postnatal ages (Ruangkittisakul et al., 2011). As VK increased (VK = −62 mV), the eupnea compartment reached its threshold and the two-compartment model produced both sigh and eupnea oscillations (Fig. 5A, second panel). The eupnea frequency was 8.1 burst/min and the sigh frequency increased slightly to 0.6 burst/min. A further increase in VK to its control value (VK = −60 mV) doubled the eupnea frequency (14.8 burst/min), but the frequency of sighs increased only slightly to 0.8 burst/min (Fig. 5A, third panel). For a relatively depolarized value of VK = −58 mV, we observed a much greater increase in the eupnea frequency compared to sighs, with the former increasing geometrically to 25 burst/min, and the latter increasing linearly to 1.1 burst/min (Fig. 5A, bottom panel). These data clearly demonstrate that for different levels of VK, the range of frequency increase in the eupnea compartment is significantly greater than that of the sigh compartment (Fig. 5B).


Sigh and Eupnea Rhythmogenesis Involve Distinct Interconnected Subpopulations: A Combined Computational and Experimental Study(1,2,3).

Toporikova N, Chevalier M, Thoby-Brisson M - eNeuro (2015)

Eupneic activity is more sensitive to [K+]o than sigh activity. A, Voltage traces of in silico experiments where VK, which is directly correlated to [K+]o was progressively increased (from top to bottom). B, Bar plots representing the mean frequency for eupnea (left, unfilled bars) and sigh bursts (right, shaded bars) for different VK. C, Extracellular recordings of spontaneous preBötC activity from slice preparation bathed in aCSF containing 4, 6, 8, and 10 mM of [K+]o (respectively, from top to bottom). D, Histograms of mean (+SEM) eupneic (unfilled bars) and sigh (shaded bars) burst frequencies for different [K+]o. Sigh bursts were generated at a relatively constant frequency whereas the eupnea burst frequency increased with increasing [K+]o both in silico and in vitro. *p < 0.05. Orange stars indicate sigh events.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4596094&req=5

Figure 5: Eupneic activity is more sensitive to [K+]o than sigh activity. A, Voltage traces of in silico experiments where VK, which is directly correlated to [K+]o was progressively increased (from top to bottom). B, Bar plots representing the mean frequency for eupnea (left, unfilled bars) and sigh bursts (right, shaded bars) for different VK. C, Extracellular recordings of spontaneous preBötC activity from slice preparation bathed in aCSF containing 4, 6, 8, and 10 mM of [K+]o (respectively, from top to bottom). D, Histograms of mean (+SEM) eupneic (unfilled bars) and sigh (shaded bars) burst frequencies for different [K+]o. Sigh bursts were generated at a relatively constant frequency whereas the eupnea burst frequency increased with increasing [K+]o both in silico and in vitro. *p < 0.05. Orange stars indicate sigh events.
Mentions: We hypothesized that sighs are generated mainly through ultra-slow (order of minutes) [Ca2+]i oscillations. In our model, such metabolic oscillations rely on periodic Ca2+ release from ER store. Although depolarization-activated Ca2+ influx through voltage-gated Ca2+channels might participate to the generation of these oscillations, the release from ER is only weekly voltage-dependent and thus depolarization should not significantly affect the frequency of such metabolic oscillations. In contrast, oscillations in the eupnea compartment reflect depolarization-dependent kinetics of INaP and its frequency should directly reflect the changes in depolarization. Therefore, we expected that oscillations in the eupnea compartment of our model would be more sensitive to external K+ concentration ([K+]o) than oscillations in the sigh compartment. We tested the effect of changing [K+]o on the sigh and eupnea rhythm frequencies in silico by progressively increasing the reversal potential for the K+ dominated leak current (VK). Figure 5A shows average voltage profiles of the eupnea−sigh model for four different values of VK. For VK = −64 mV, the eupnea compartment was below oscillatory threshold but the sigh compartment was still able to generate slow Ca2+ oscillations (0.4 burst/min). Thus, the model produced only the ultra-slow sigh oscillations and no eupneic activity (Fig. 5A, top panel). Note that this feature might be specific of the developmental stage examined here, since this is not observed at postnatal ages (Ruangkittisakul et al., 2011). As VK increased (VK = −62 mV), the eupnea compartment reached its threshold and the two-compartment model produced both sigh and eupnea oscillations (Fig. 5A, second panel). The eupnea frequency was 8.1 burst/min and the sigh frequency increased slightly to 0.6 burst/min. A further increase in VK to its control value (VK = −60 mV) doubled the eupnea frequency (14.8 burst/min), but the frequency of sighs increased only slightly to 0.8 burst/min (Fig. 5A, third panel). For a relatively depolarized value of VK = −58 mV, we observed a much greater increase in the eupnea frequency compared to sighs, with the former increasing geometrically to 25 burst/min, and the latter increasing linearly to 1.1 burst/min (Fig. 5A, bottom panel). These data clearly demonstrate that for different levels of VK, the range of frequency increase in the eupnea compartment is significantly greater than that of the sigh compartment (Fig. 5B).

Bottom Line: Based on recent in vitro data obtained in the mouse embryo, we have built a computational model consisting of two compartments, interconnected through appropriate synapses.The model reproduces basic features of simultaneous sigh and eupnea generation (two types of bursts differing in terms of shape, amplitude, and frequency of occurrence) and mimics the effect of blocking glycinergic synapses.Furthermore, we used this model to make predictions that were subsequently tested on the isolated preBötC in mouse brainstem slice preparations.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biology, Washington and Lee University , Lexington, Virginia 24450.

ABSTRACT
Neural networks control complex motor outputs by generating several rhythmic neuronal activities, often with different time scales. One example of such a network is the pre-Bötzinger complex respiratory network (preBötC) that can simultaneously generate fast, small-amplitude, monophasic eupneic breaths together with slow, high-amplitude, biphasic augmented breaths (sighs). However, the underlying rhythmogenic mechanisms for this bimodal discharge pattern remain unclear, leaving two possible explanations: the existence of either reconfiguring processes within the same network or two distinct subnetworks. Based on recent in vitro data obtained in the mouse embryo, we have built a computational model consisting of two compartments, interconnected through appropriate synapses. One compartment generates sighs and the other produces eupneic bursts. The model reproduces basic features of simultaneous sigh and eupnea generation (two types of bursts differing in terms of shape, amplitude, and frequency of occurrence) and mimics the effect of blocking glycinergic synapses. Furthermore, we used this model to make predictions that were subsequently tested on the isolated preBötC in mouse brainstem slice preparations. Through a combination of in vitro and in silico approaches we find that (1) sigh events are less sensitive to network excitability than eupneic activity, (2) calcium-dependent mechanisms and the Ih current play a prominent role in sigh generation, and (3) specific parameters of Ih activation set the low sensitivity to excitability in the sigh neuronal subset. Altogether, our results strongly support the hypothesis that distinct subpopulations within the preBötC network are responsible for sigh and eupnea rhythmogenesis.

No MeSH data available.


Related in: MedlinePlus