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Differential triggering of spontaneous glutamate release by P/Q-, N- and R-type Ca2+ channels

Nature Neuroscience volume 16, pages 1754–1763 (2013)Cite this article

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Abstract

The role of voltage-gated Ca2+ channels (VGCCs) in spontaneous miniature neurotransmitter release is incompletely understood. We found that stochastic opening of P/Q-, N- and R-type VGCCs accounts for ∼50% of all spontaneous glutamate release at rat cultured hippocampal synapses, and that R-type channels have a far greater role in spontaneous than in action potential–evoked exocytosis. VGCC-dependent miniature neurotransmitter release (minis) showed similar sensitivity to presynaptic Ca2+ chelation as evoked release, arguing for direct triggering of spontaneous release by transient spatially localized Ca2+ domains. Experimentally constrained three-dimensional diffusion modeling of Ca2+ influx–exocytosis coupling was consistent with clustered distribution of VGCCs in the active zone of small hippocampal synapses and revealed that spontaneous VGCCs openings can account for the experimentally observed VGCC-dependent minis, although single channel openings triggered release with low probability. Uncorrelated stochastic VGCC opening is therefore a major trigger for spontaneous glutamate release, with differential roles for distinct channel subtypes.

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Figure 1: Differential triggering of spontaneous excitatory neurotransmission by P/Q-, N- and R-type Ca2+ channels.
Figure 2: Relative contributions of P/Q-, N- and R-type VGCCs to presynaptic Ca2+ dynamics assessed with fast Ca2+ fluorescence imaging.
Figure 3: Differential effects of fast (BAPTA) and slow (EGTA) exogenous Ca2+ buffers on VGCC-dependent minis.
Figure 4: FM dye imaging of action potential–evoked exocytosis reveals similar sensitivity of evoked and VGCC-dependent miniature release to presynaptic Ca2+ chelation.
Figure 5: Estimation of the numbers of P/Q-, N- and R-type VGCCs in an average presynaptic bouton.
Figure 6: Modeling action potential–evoked release in small hippocampal synapses.
Figure 7: Modeling VGCC-dependent glutamate miniature release.

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Acknowledgements

We are grateful to L. Savtchenko for help with stochastic VGCC modeling, to P. Volynsky for help with Monte-Carlo simulations of VGCC distributions in the active zone, to M. Cano for help with neuronal cultures, to V. Uebele (Merck) for the gift of TTA-P2, and to Y. Ushkaryov, D. Rusakov, C. Henneberger and M. Walker for critical reading of the manuscript. The study was supported by the Medical Research Council, the Wellcome Trust, the Biotechnology and Biological Sciences Research Council, the German Research Foundation, the Brain Research Trust, the European Research Council, the Special Trustees of the University College London Hospitals National Health Service Foundation Trust, Epilepsy Research UK, and The Worshipful Company of Pewterers.

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Author notes

  1. Rainer Surges

    Present address: Present address: Department of Epileptology, University Clinics Bonn, Bonn, Germany.,

  2. Yaroslav S Ermolyuk, Felicity G Alder and Rainer Surges: These authors contributed equally to this work.

Authors and Affiliations

  1. University College London Institute of Neurology, University College London, London, UK

    Yaroslav S Ermolyuk, Felicity G Alder, Rainer Surges, Ivan Y Pavlov, Dimitri M Kullmann & Kirill E Volynski

  2. Department of Computer Science, University of Warwick, Coventry, UK

    Yulia Timofeeva

  3. Centre for Complexity Science, University of Warwick, Coventry, UK

    Yulia Timofeeva

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  1. Yaroslav S Ermolyuk
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Contributions

Y.S.E., F.G.A., R.S. and I.Y.P. performed the experiments. Y.S.E., F.G.A., R.S., I.Y.P. and K.E.V. analyzed the data. Y.T. and K.E.V. performed the computational modeling. D.M.K. and K.E.V. conceived and designed the experiments. D.M.K., Y.T. and K.E.V. wrote the paper.

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Correspondence to Kirill E Volynski.

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Integrated supplementary information

Supplementary Figure 1 Membrane depolarization with elevated extracellular leads to an increase in VGCC-dependent miniature release.

(a, b) Time course of mEPSC frequency changes after application of 20 mM followed by simultaneous blockade of P/Q-, N-, and R-type VGCCs with ω-Aga, ω-Ctx and SNX. (a) mEPSC traces from a representative experiment and (b) average time course in N=7 cells (mean ± s.e.m). The integration periods used to determine the effects of 20mM and VGCC blockers on average mEPSC frequency are indicated by black circles. * P < 0.05, Wilcoxon signed rank test for paired data.

Supplementary Figure 2 Action potential-evoked currents mediated by single P/Q-, N-, and R-type VGCCs simulated using the six-state VGCC gating model.

Results of 20 representative simulations for each channel subtype (blue, P/Q-; green, N-; and brown, R-type channels). Action potential waveform is shown on the top left. Average current time courses including failures are shown at bottom (bold traces). The probability that an individual channel opens during an action potential (estimated from 500 simulations) was: Popen_P/Q = 0.50, Popen_N = 0.40, and Popen_R = 0.32.

Supplementary Figure 3 Clustered model, additional analysis: VGCC cooperativity in triggering action potential-evoked release varies with docked vesicle-VGCC cluster distance.

(a) Detailed schematic representation of the active zone for the Clustered model illustrated in Fig. 6c. VGCC positions, subtypes, and their open or closed status during the simulated action potential are specified as indicated in the insert on the right. In this model implementation 12 out of 32 VGCCs opened during the action potential (5 channels opened in the cluster located to the left side of the active zone and 7 channels opened in the cluster located to the right side of the active zone). (b) Spatiotemporal profile during the simulated action potential within a 5 nm thick plane immediately above the active zone. Top, action potential waveform; bottom, color-coded map at different time points as indicated. Note that at the late action potential repolarization stage when Ca2+ currents through individual VGCCs are maximal (i.e. at 0.45 ms and 0.6 ms) Ca2+ influx in each channel cluster is mediated only by 1 or 2 VGCCs. For example, the red circle in (a) and (b) highlights two VGCCs (of N-type) that contribute most of the action potential-evoked Ca2+ current in the left channel cluster. Scale bar 50 nm. (c, d) To determine the relative contributions of the two channels highlighted in (a) and (b) to triggering release of vesicles V1 - V4 we either selectively switched them off (c, top) or left them active and switched off all other channels (c, bottom). (d) Average concentration transients at vesicular release sensors and corresponding vesicle fusion probabilities Pv (shown above) for each vesicle in the active zone. Black traces, original control simulation; red traces, the two highlighted channels 'switched off', blue traces, all but the two highlighted channels 'switched off'. The results of the above simulations show that: (1) The two highlighted channels contributed most of the action potential-evoked concentration transients at release sensors of vesicle V1, which was located in the immediate vicinity of these two VGCCs (30 and 40 nm). Switching these two channels off led to a ∼22-fold reduction of the vesicle fusion probability Pv, (from 0.22 to 0.01). In contrast, switching off all other VGCCs in the active zone led to only a 2.4-fold reduction in Pv of Vesicle V1. Thus, in this model realization fusion of vesicle V1 is mainly controlled by the two highlighted channels. (2) The two highlighted channels had a minimal effect on concentration transients at the release sensors of vesicle V4, and as a consequence on Pv for this vesicle, which was located close (~30 nm) to the other VGCC cluster. This illustrates that action potential-evoked fusion of vesicles located in the immediate vicinity of VGCC clusters is mainly controlled by channels from the nearest cluster. (3) Finally, for vesicles that were further away from the VGCC clusters, and therefore had lower Pv (vesicles V2 and V4), action potential-evoked fusion was jointly controlled by VGCCs from both clusters.

Supplementary Figure 4 Dependency of stochastic VGCC openings on Vrest.

Representative traces of Ca2+ currents simulated using the six-state VGCC gating model at different Vrest in a typical active zone containing 15 P/Q-, 16 N-, and 2 R-type VGCCs. These simulations (in total 200 s for each Vrest value) showed that within the physiological Vrest range (from –55 to –80 mV) the probability of coincident opening of more than one channel in the active zone is lower than 0.002.

Supplementary Figure 5 Modeling of miniature glutamate release triggered by Ca2+ release from intracellular stores. Comparison of 0.5 mM BAPTA and 5 mM EGTA effects.

(a, b) To model the small (∼1 μM) elevations of presynaptic from = 50 nM, as might occur during Ca2+ release from intracellular stores (e.g. refs. 42,43), we used a single-compartment model of presynaptic Ca2+ dynamics (see Online Methods for details). (a) Ca2+ influx into the cytosol from the stores was approximated by a Gaussian function with a maximal rate = 1 μM ms-1 (~100 fold slower than that during an action potential) and with a characteristic duration σ = 2 s. (b) Resulting global presynaptic concentration transients predicted by the non-stationary model in 'Control' conditions (black trace) and in the presence of 0.5 mM BAPTA (red trace) and 5 mM EGTA (blue trace). Note that, in contrast to 5 mM EGTA, 0.5 mM BAPTA had only minor effect on the presynaptic transient. (c) Vesicular release rates and (d) fusion probabilities Pv, corresponding to transients shown in (b). These were calculated using the same allosteric model as used to model VGCC-dependent glutamate release (Fig. 6a). Consistent with the effect of Ca2+ chelators on the global presynaptic transients, 5 mM EGTA was much more efficient in inhibiting of store-mediated miniature release (by ~88%) than 0.5 mM BAPTA (by ~13%).

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Ermolyuk, Y., Alder, F., Surges, R. et al. Differential triggering of spontaneous glutamate release by P/Q-, N- and R-type Ca2+ channels. Nat Neurosci 16, 1754–1763 (2013). https://doi.org/10.1038/nn.3563

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