Integrative analysis of non-targeted lipidomic data and brain structural imaging identifies phosphatidylethanolamine associated with epileptogenesis.

Abstract

Epilepsy is a chronic disease, while epileptogenesis is a latent period where brain will be transformed into an epileptic one. Mechanisms of epileptogenesis remain unclear. We aim to provide information of hippocampal lipidomic changes related with epileptogenesis in two kindling models. Combining hippocampal structural imaging indices, our study also attempts to assess biochemical alterations as a function of epileptogenesis in a non-invasive way. We constructed two kinds of chemical kindling models, which have long been used as models of epileptogenesis. Two kindling and one control groups were all subjected to structural imaging acquisition after successfully kindled. Voxel-based morphometry, a postprocessing method for brain imaging data, was used to segment and extract hippocampal gray matter volume for subsequent integrative analysis. LC-MS based lipidomic analysis was applied to identify distinct hippocampal lipidomic profiles between kindling and control groups. Further, we regress hippocampal structural indices on lipids to identify those associated with both epileptogenesis and brain structural changes. We report distinct lipidomic profiles between kindling groups and control. A total of 638 lipids were detected in all three groups. Among them were 98 individual lipids, showing significant alterations, in particular lipid class of phosphatidylethanolamine (PE), glucosylceramide and phosphatidylcholine. Hippocampal gray matter volumes were found significant different between groups (P = 0.0223). After combining brain imaging data, we demonstrate several individual PE, namely PE(O-18:1_22:3), PE(O-18:1_22:6) and PE(18:1_18:1), are associated with both epileptogenesis and hippocampal gray matter volume. This study suggests metabolic pathway of PE might involve in epileptogenesis. Also, for the first time, we link level of PE with structural brain imaging indices, in an attempt to potentiate the futuristic application of noninvasive brain imaging techniques to identify epileptogenesis in its latent period.