Dopamine maintains network synchrony via direct modulation of gap junctions in the crustacean cardiac ganglion

Abstract

Dopamine maintains network1 synchrony via direct modulation of 2 gap junctions in the crustacean 3 cardiac ganglion Abstract The Large Cell (LC) motor neurons of the crab (C. borealis) cardiac ganglion have 9variable membrane conductance magnitudes even within the same individual, yet produce 10 identical synchronized activity in the intact network. In our previous study (Lane et al., 2016) we 11 blocked a subset of K + conductances across LCs, resulting in loss of synchronous activity. In this 12 study, we hypothesized that this same variability of conductances could make LCs vulnerable to 13 desynchronization during neuromodulation. We exposed the LCs to serotonin (5HT) and dopamine 14 (DA) while recording simultaneously from multiple LCs. Both amines had distinct excitatory effects 15 on LC output, but only 5HT caused desynchronized output. We further determined that DA rapidly 16 increased gap junctional conductance. Co-application of both amines induced 5HT-like output, but 17 waveforms remained synchronized. Furthermore, DA prevented desynchronization induced by the 18 K+ channel blocker tetraethylammonium (TEA), suggesting that dopaminergic modulation of 19 electrical coupling plays a protective role in maintaining network synchrony. 20 21 22Neural networks must be capable of producing output that is robust and reliable, yet also flexible 23 enough to meet changing environmental demands. One mechanism of providing flexibility to 24 network activity is neuromodulation, which reconfigures network output by altering a subset 25 of cellular and synaptic conductances (Harris-Warrick, 2011; Bargmann, 2012; Daur et al., 2016). 26 However, many networks achieve stable output by a variety of solutions; intrinsic membrane 27 conductances and synaptic strengths can be highly variable yet still produce nearly identical 28 physiological activity (Ball et al., 2010; Calabrese et al., 2011; Marder, 2011; Ransdell et al., 2013b). 29 This raises a fundamental question about neuromodulation, highlighted in a recent review by 30 Marder et al. (2014), as to whether modulation of networks with variable underlying parameters 31 can produce predictable and reliable results. These authors demonstrate computationally that 32 modulation of neurons with similar outputs arising from variable underlying conductances can 33 cause anywhere from relatively small to fairly substantial differences in output (Marder et al., 2014). 34 Therefore, the response of any neural network to modulation is likely state-dependent (Goldman 35 et al., 2001; Nadim et al., 2008; Gutierrez and Marder, 2014; Williams et al., 2013; Marder et al., 36 2014), and potentially unpredictable as a result of these varying underlying conductances (Marder 37 et al., 2014). In some cases neuromodulation can expand the parameter space in which a given 38 activity feature is maintained (Grashow et al., 2009), potentially leading to protective effects of 39 1 of 18 216 Both 5HT and DA have been extensively studied in crustacean motor neurons, particularly those of 2...