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Postsynaptic induction and presynaptic expression of long-term potentiation at excitatory synapses on layer 2/3 VIP interneurons in the somatosensory cortex

Bogaj, K.; Urban-Ciecko, J.

2025-08-01 neuroscience
10.1101/2025.07.31.667840 bioRxiv
Show abstract

Synaptic transmission between specific connection motifs undergoes plastic changes during learning process, however, exact mechanisms underlying synaptic plasticity are still under intense investigation. Long-term potentiation (LTP) of synaptic transmission is a widely used cellular model of synaptic plasticity occurring during learning. Here, we focused on studying LTP at excitatory synapses on layer (L) 2/3 vasoactive intestine polypeptide-expressing interneurons (VIP-INs) in the mouse somatosensory (barrel) cortex. LTP was induced by a pairing protocol of postsynaptic depolarization with extracellular stimulation in acute brain slices of young mice (P21-28). The pairing protocol evoked LTP in L2/3 VIP-INs in control condition, however, pharmacological blocking GABAaR inhibition enhanced LTP. Next, we found that LTP in L2/3 VIP-INs is dependent on metabotropic glutamate receptor type 1 (mGluR-1) and L-type voltage-gated calcium channels (L-type VGCC) but not on NMDARs nor mGluR-5. Here, mGluR-1 acts through G protein-coupled signaling, Src-family pathway, independently of transient receptor potential channels (TRPC). Analyses of paired-pulse ratio (PPR) and coefficient of variation (CV) indicated a presynaptic locus of LTP expression. Presynaptic expression of LTP in VIP-INs relies on retrograde signaling through endocannabinoids (eCBs) but not on brain-derived neurotrophic factor (BDNF). In conclusion, we dissected mechanisms of LTP induction and expression at excitatory inputs to L2/3 VIP-INs in the mouse barrel cortex. LTP at excitatory synapses on VIP-INs might serve as a positive feedback for enhanced VIP-IN-mediated inhibition of SST-INs, leading to disinhibition of excitatory neurons from SST-IN inhibition during learning process.

Published in European Journal of Neuroscience (predicted rank #10) · training set

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