Effect of spatial complexity and heterogeneities on the efficiency of dopamine reuptake Cihan Kaya1, Mary H. Cheng1 , Ethan R. Block2, Alexander Sorkin2 , James R Faeder1and Ivet Bahar1
Short Abstract — Efficient clearance of neurotransmitters from the synapse by dopamine transporters (DATs) is critical to regulating dopamine (DA) signaling in the central nervous system. Despite significant advances in the field, we still lack a complete mechanistic understanding of DA transport events. We adopted a multiscale methodology to examine the effects of spatial complexity and firing patterns on DA reuptake by DATs: We used a kinetic scheme from one of our previous studies1 and we used these as input in MCell simulations2, together with fluorescence images of dopaminergic neurons. DAT spatial distribution and structural heterogeneities were observed to alter the efficiency of DA reuptake, suggesting that realistic spatial descriptions are required accurate assessment of the mechanism of DAT function. Using this model we also explored the effect of psychostimulant drugs and neurodegeneration on DA reuptake. Keywords — Dopamine, dopamine transporter, spatiotemporal models and simulations, neurotransmission.
I. INTRODUCTION
Mfunctions of the striatum including its motor and
IDBRAIN dopaminergic neurons strongly influence the
cognitive functions. Defficiencies in DA signaling can lead to diseases such as Parkinson’s disease and attention deficit hyperactivity disorder. DATs are usually located in the extrasynaptic surface of DA neurons and regulate DA signaling by clearing excess DA. The reuptake role of DATs is evident from the increase in EC DA concentration in the presence of DAT inhibitor drugs such as cocaine. The level of DA in the EC region depends on a subtle balance between the vesicular release rate and reuptake rate of DA, which in turn depends on the local DAT concentration and spatial distribution. Here, we present a model of synaptic signaling in DA neurons using realistic geometries that are reconstructed from fluorescence images of DA neurons3 (Fig. 1A-B). II. RESULTS AND DISCUSSION We performed our simulations under three different conditions, referred to as ‘well-mixed’, ‘complex uniform’ and ‘complex nonuniform’, and with two different action potential firing patterns, tonic and phasic. The complex uniform and complex nonuniform cases use the same complex geometry, but differ in the distribution of DAT molecules on the neuronal membranes. We find that for both firing patterns the average EC DA concentration is lower with complex geometry (Fig. 1C). Nonuniform DAT distribution further reduces average DA levels, suggesting that closer positioning of DAT molecules near the release site increases
Acknowledgements: This work was funded by NIH grant 5P41GM103712. 1 Department of Computational and Systems Biology,
Fig 1. (A) Fluorescence images from transgenic mice with HA-tagged DAT to view the spatial distribution of DAT. (B) A snapshot from the simulation in MCell. (C) The distribution of EC DA concentration from 100 stochastic simulations for 6 different simulation settings.
the effectiveness of clearance. Varying the firing pattern, while maintaining the same average frequency does not affect the average EC DA concentration, but greatly increases the range of variation such that phasic but not tonic firing can activate low affinity receptors4. Interestingly, variance in phasic signaling is greatly reduced when some of the axons in the modeled synapse are rendered inactive, such as may occur in degenerative diseases such as Parkinson’s disease. III. CONCLUSION Overall, our model provides a framework to investigate the effect of variations in different neuronal properties to gain a better understanding of the modulation of DA signaling in the central nervous system. Our results highlight the significance of considering the realistic geometry as well as the spatial heterogeneities from experiments as opposed to adopting well-mixed assumptions that overlook the heterogeneities. REFERENCES [1] Cheng, M.H. and Bahar, I. Structure 23.11 (2015): 2171-2181. [2] Kerr, R. A., et al. SIAM J on Sci. Computing, 30(6),(2008):3126-3149. [3] Block, E. R., et al. The J of Neuroscience 35.37 (2015): 12845-12858. [4] Dreyer, J. K., et al The J of Neuroscience 30.42 (2010): 14273-142283. 2
Department of Cell Biology, University of Pittsburgh.