The electrophysiological impact of oligomeric alpha-Synuclein on thick-tufted layer V pyramidal neurons in the neocortex of mice
Alpha-Synuclein (αSyn), a presynaptic protein found abundantly throughout the brain, was one of the first proteins to be pathologically associated with Parkinson’s disease (PD). This association came initially from identifying αSyn as the main component of neuritic plaques found in disease patients. Indeed, the detection of αSyn-containing plaques has become a clinical hallmark across numerous neurodegenerative disorders collectively termed Synucleinopathies. Point mutations in αSyn as well as gene multiplications both result in early onset PD through changes in the molecular and biophysical properties of αSyn aggregation.
Increasing evidence suggests that it is soluble oligomeric intermediates that are the main species responsible for neurotoxicity, disease propagation and cell death rather than the large insoluble aggregates. Both in vitro and in vivo studies have generated numerous theories on the mechanism of toxicity including: membrane permeabilisation, Ca2+ influx, synaptic alterations and mitochondrial dysfunction. Evidence for these pathologies are supported by recent investigations that have shown changes in hippocampal LTP and effects on AMPA-receptor-mediated synaptic transmission in the presence of extracellularly applied αSyn oligomers. However, the in-depth electrophysiological analysis needed to link these proposed mechanisms to observed neuronal changes is still lacking.
My work combined the fields of structural biology and neuroscience to investigate exactly how disease-causing protein constructs disrupt normal neuron electrophysiology.
My aims were to:
Generate and structurally characterise populations of αSyn aggregates: from monomer to oligomer to fibrils.
Intracellularly apply these different αSyn aggregates into thick-tufted layer 5 (TTL5) pyramidal neurons in the mouse neocortex by Whole-Cell Patch Clamping technique.
Quantify specific changes in the electrophysiological response properties over time using the dynamic I-V curve method (Badel. et al, 2008), with the accuracy of the derived parameters tested using an exponential-integrate and fire model.