Dataset Information

PICK1 uncoupling from mGluR7a causes absence-like seizures.

ABSTRACT: Absence epilepsy is a neurological disorder that causes a recurrent loss of consciousness and generalized spike-and-wave discharges on an electroencephalogram (EEG). The role of metabotropic glutamate receptors (mGluRs) and associated scaffolding proteins in absence epilepsy has been unclear to date. We investigated a possible role for these proteins in absence epilepsy, focusing on the mGluR7a receptor and its PDZ-interacting protein, protein interacting with C kinase 1 (PICK1), in rats and mice. Injection of a cell-permeant dominant-negative peptide or targeted mutation of the mGluR7a C terminus, both of which disrupt the interaction between the receptor and PDZ proteins, caused behavioral symptoms and EEG discharges that are characteristic of absence epilepsy. Inactivation of the Pick1 gene also facilitated pharmacological induction of the absence epilepsy phenotype. The cortex and thalamus, which are known to participate in absence epilepsy, were involved, but the hippocampus was not. Our results indicate that disruption of the mGluR7a-PICK1 complex is sufficient to induce absence epilepsy-like seizures in rats and mice, thus providing, to the best of our knowledge, the first animal model of metabotropic glutamate receptor-PDZ protein interaction in absence epilepsy.

SUBMITTER: Bertaso F

PROVIDER: S-EPMC2764992 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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PICK1 uncoupling from mGluR7a causes absence-like seizures.

Bertaso Federica F Zhang Chuansheng C Scheschonka Astrid A de Bock Frédéric F Fontanaud Pierre P Marin Philippe P Huganir Richard L RL Betz Heinrich H Bockaert Joël J Fagni Laurent L Lerner-Natoli Mireille M

Nature neuroscience 20080720 8

Absence epilepsy is a neurological disorder that causes a recurrent loss of consciousness and generalized spike-and-wave discharges on an electroencephalogram (EEG). The role of metabotropic glutamate receptors (mGluRs) and associated scaffolding proteins in absence epilepsy has been unclear to date. We investigated a possible role for these proteins in absence epilepsy, focusing on the mGluR7a receptor and its PDZ-interacting protein, protein interacting with C kinase 1 (PICK1), in rats and mic ...[more]

PMID: 18641645

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Project description:Absence seizures-generalized rhythmic spike-and-wave discharges (SWDs) are the defining property of childhood (CAE) and juvenile (JAE) absence epilepsies. Such seizures are the most compelling examples of pathological neuronal hypersynchrony. All the absence detection algorithms proposed so far have been derived from the properties of individual SWDs. In this work, we investigate EEG phase synchronization in patients with CAE/JAE and healthy subjects to explore the possibility of using the wavelet phase synchronization index to detect seizures and quantify their disorganization (fragmentation). The overlap of the ictal and interictal probability density functions was high enough to preclude effective seizure detection based solely on changes in EEG synchronization. We used a machine learning classifier with the phase synchronization index (calculated for 1 s data segments with 0.5 s overlap) and the normalized amplitude as features to detect generalized SWDs. Using 19 channels (10-20 setup), we identified 99.2% of absences. However, the overlap of the segments classified as ictal with seizures was only 83%. The analysis showed that seizures were disorganized in approximately half of the 65 subjects. On average, generalized SWDs lasted about 80% of the duration of abnormal EEG activity. The disruption of the ictal rhythm can manifest itself as the disappearance of epileptic spikes (with high-amplitude delta waves persisting), transient cessation of epileptic discharges, or loss of global synchronization. The detector can analyze a real-time data stream. Its performance is good for a six-channel setup (Fp1, Fp2, F7, F8, O1, O2), which can be implemented as an unobtrusive EEG headband. False detections are rare for controls and young adults (0.03% and 0.02%, respectively). In patients, they are more frequent (0.5%), but in approximately 82% cases, classification errors are caused by short epileptiform discharges. Most importantly, the proposed detector can be applied to parts of EEG with abnormal EEG activity to quantitatively determine seizure fragmentation. This property is important because a previous study reported that the probability of disorganized discharges is eight times higher in JAE than in CAE. Future research must establish whether seizure properties (frequency, length, fragmentation, etc.) and clinical characteristics can help distinguish CAE and JAE.

Dataset Information

PICK1 uncoupling from mGluR7a causes absence-like seizures.

Publications

PICK1 uncoupling from mGluR7a causes absence-like seizures.

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