Differential modulation of short-term synaptic dynamics by long-term potentiation at mouse hippocampal mossy fibre synapses

Abstract

Synapses continuously experience short- and long-lasting activity-dependent changes in synaptic strength. Long-term plasticity refers to persistent alterations in synaptic efficacy, whereas short-term plasticity (STP) reflects the instantaneous and reversible modulation of synaptic strength in response to varying presynaptic stimuli. The hippocampal mossy fibre synapse onto CA3 pyramidal cells is known to exhibit both a presynaptic, NMDA receptor-independent form of long-term potentiation (LTP) and a pronounced form of STP. A detailed description of their exact interdependence is, however, lacking. Here, using electrophysiological and computational techniques, we have developed a descriptive model of transmission dynamics to quantify plasticity at the mossy fibre synapse. STP at this synapse is best described by two facilitatory processes acting on time-scales of a few hundred milliseconds and about 10 s. We find that these distinct types of facilitation are differentially influenced by LTP such that the impact of the fast process is weakened as compared to that of the slow process. This attenuation is reflected by a selective decrease of not only the amplitude but also the time constant of the fast facilitation. We henceforth argue that LTP, involving a modulation of parameters determining both amplitude and time course of STP, serves as a mechanism to adapt the mossy fibre synapse to its temporal input.

Figure 1. Modulation of synaptic efficacy by irregular stimulus trains

A, presynaptic mossy fibres were extracellularly stimulated with an irregular stimulus train. B, in a whole-cell recording of a CA3 pyramidal cell, highly dynamic postsynaptic response amplitudes were elicited by the stimulus train. EPSCs exhibited failures of transmission pointing at minimal stimulation strength. Inset depicts example traces from time windows marked by grey area. C, gains of synaptic responses during stimulus trains (i.e. instantaneous amplitude/mean basal response to 0.05 Hz) ranged from 0 to ∼13. Left panel shows gains of the single experiment from B; right panel histogram is derived from data of n = 6 cells. D, two consecutive repetitions of the same stimulus train in a single whole-cell recording reveal large variability. Variability in EPSC amplitudes is mainly due to variance in synaptic transmission. E, in a field potential recording, postsynaptic mossy fibre responses are similarly modulated by the same irregular stimulus train. The fibre volley (*) is clearly separated from the postsynaptic component (°). F, gains of fEPSP responses of the experiment in E (left panel) and n = 13 fEPSP recordings (right panel). G, field EPSP responses to a given stimulus train were highly reproducible as seen in n = 7 consecutive presentations of identical stimulus train in one slice.

Figure 2. Model of mossy fibre STP

A, to identify the most reliable model of mossy fibre STP, we tested several different additive and multiplicative interactions of slow and fast facilitatory processes for goodness of fit. The combination ‘s⁴+f’ yielded the lowest χ² values and was therefore chosen for all further analysis (highlighted in grey; n = 12 experiments). B, in our chosen model, a random stimulus train (top) is translated into two dynamical variables, x_slow and x_fast, via first-order kinetics. To account for saturation of facilitatory amplitudes, x_slow is inserted into a nonlinearity x_slow→y_slow=G(x_slow). Powers of y_slow and x_fast are then scaled by amplitudes a_slow and a_fast, respectively. Finally, the two components are added. Incorporating the baseline amplitude A₀, we obtained predictions for the fEPSP amplitudes A.

Figure 3. Characteristics and parameters of the ‘s⁴+f’ model

A, fitted model amplitudes were highly correlated to measured fEPSP amplitudes, as shown for one example with corresponding experimental and model data. B, correlations between experimental and model amplitudes were generally high as seen in the histogram for correlation coefficients of n = 19 fits under control condition. C, predictions for response amplitudes, with parameters obtained from a fit of the model to a different data set, show similarly good correlations (n = 28 predictions). D and E, the time constant τ_slow of the slow facilitatory process ranged between 5 and 17 s, while the time constant τ_fast of the fast process was between 150 and 330 ms (n = 19).

Figure 4. STP before and after induction of LTP

After initial constant stimulation at 0.05 Hz for establishing a basal response amplitude (solid lines), an irregular stimulus train was applied at least twice. Induction of LTP (arrows) led to increased responses in both basal response amplitude and mean response during complete stimulus train (dashed lines). Insets show averages of 10 sweeps each for 0.05 Hz stimulation before (1) and after LTP induction (3) and averages of all responses during the stimulus train (2 and 4).

Figure 5. Interaction of LTP and STP

A, distributions of amplitude gains in a single experiment (upper panels) and pooled data (lower panels; n = 8 experiments with 3 different stimulus trains) differ remarkably before (left) and after the induction of LTP (right). B, gains of response amplitudes during the stimulus train are decreased after induction of LTP. The dynamic range of responses is significantly reduced, both in a single experiment and the summary of several analogue experiments (corresponding data to A). C, the amount of short-term potentiation after LTP induction is related to the corresponding response amplitude before LTP. Initially small responses are increased, whereas initially large ones are less potentiated or even slightly depressed as seen in single experiment (upper panel) and the summary of n = 7 analogue experiments (lower panel; mean ± s.d.). Amplitudes are normalized to the mean response amplitude during stimulus train in control condition.

Figure 6. Goodness of model fit before and after induction of LTP

A, after the induction of LTP, the model ‘s⁴+f’ still yielded the lowest χ² value (n = 12). For comparison, control data from Fig. 2A are replotted. B, correlations between experimental and model amplitudes were high before and after the induction of LTP using the ‘s⁴+f’ model.

Figure 7. Differential impact of LTP on STP

A, change in model parameters of mossy fibre STP after the induction of LTP. Asterisks point to significant changes in mean parameter values (paired Student's t test; *P < 0.001, ns: P > 0.14). B, both amplitude and time constant of the fast facilitation were significantly decreased in contrast to no significant changes in the corresponding parameters of the slow process. Changes are normalized to mean parameter values before LTP. Bars show mean ± s.e.m.; n = 12 experiments. Relative changes of parameters, for example , were calculated as Δa = a_LTP − a_control and . C, the decrease of the fast time constant τ_fast after LTP is significantly correlated to the relative change in A₀, while the amplitude of the fast process a_fast is not.

Figure 8. Effect of change in Ca²⁺/Mg²⁺ ratio on model parameters

Model parameters of mossy fibre STP are modulated through increasing the Ca²⁺/Mg²⁺ ratio. Mean parameter values of basal response amplitude A₀, saturation g and facilitatory amplitude a_fast were significantly changed (paired Student's t test; *P < 0.05, ns: P > 0.13; n = 9).