Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

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Nicholas D. Henkel
Marissa A. Smail
Xiaojun Wu
Heather A. Enright
Nicholas O. Fischer
Hunter M. Eby
Robert E. McCullumsmith
Rammohan Shukla
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[1] University of Toledo,Department of Neuroscience, College of Medicine and Life Sciences
[2] University of Cincinnati,Neuroscience Graduate Program
[3] Lawrence Livermore National Lab,Bioscience and Biotechnology Division
[4] ProMedica,undefined
[5] Neurosciences Institute,undefined
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Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.
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