Pregnenolone sulfate enables synapse-specific metaplastic gating of heterosynaptic GABAergic plasticity.

Abstract

Background and purpose: Neurosteroids are well recognized for their modulation of GABAergic signalling, yet their effects on specific inhibitory synapses and on long-term plasticity remain poorly understood. Herein, we investigated how neurosteroids influence synaptic transmission, as well as short- and long-term plasticity at identified excitatory and inhibitory synapses onto CA1 pyramidal neurons.

Experimental approach: We combined whole-cell patch-clamp recordings with optogenetic stimulation of parvalbumin-positive and somatostatin-positive interneurons to characterize synaptic responses before and after the application of allopregnanolone or pregnenolone sulfate in slices from male mice.

Key results: The two neurosteroids differentially affected inhibitory inputs depending on the presynaptic interneuron type. Specifically, allopregnanolone modulated synaptic responses in a way that is consistent with differences in neurotransmitter transients across studied synapses. In contrast, pregnenolone sulfate selectively altered the balance between excitatory and distinct inhibitory inputs to the same postsynaptic pyramidal neuron. Furthermore, we identified a novel physiological mechanism whereby high-frequency stimulation of excitatory synapses induces heterosynaptic plasticity at inhibitory inputs. This plasticity was expressed in a synapse-specific manner, selectively enhancing inhibition from somatostatin interneurons. Notably, exposure to pregnenolone sulfate revealed a previously hidden capacity for plasticity at parvalbumin synapses.

Conclusion and implications: Our findings demonstrate that neurosteroids can selectively shape synaptic integration across distinct inhibitory circuits and metaplastically regulate excitatory-inhibitory co-plasticity in an input-specific manner. By uncovering synapse-specific and intersynaptic mechanisms of neurosteroid action, our study provides new insights into how inhibitory circuit dynamics are fine-tuned to support network stability and learning.