Principal component analysis of ion channel splice variant expression patterns for all experimental groups

<p><b>Copyright information:</b></p><p>Taken from "Alternative ion channel splicing in mesial temporal lobe epilepsy and Alzheimer's disease"</p><p>http://genomebiology.com/2007/8/3/R32</p><p>Genome Biology 2007;8(3):R32-R32.</p><p>Published online 7 Mar 2007</p><p>PMCID:PMC1868939.</p><p></p> Colors separate groups based on statistical clustering (k-means clustering) of individuals with similar patterns of ion channel splicing. Colors separate groups based on disease state and brain structure: control TC (blue), mTLE NC (red), AD TC (green), control CB (black), AD CB (yellow). The first principal component explains 43% of the variation in log expression ratios, while the second principal component accounts for 13% of the variation. Separation of clusters along principal components 1 and 2 is, to a large extent, governed by brain structure and disease state

Representative mTLE-associated alternative splicing event identified using splice array technology

<p><b>Copyright information:</b></p><p>Taken from "Alternative ion channel splicing in mesial temporal lobe epilepsy and Alzheimer's disease"</p><p>http://genomebiology.com/2007/8/3/R32</p><p>Genome Biology 2007;8(3):R32-R32.</p><p>Published online 7 Mar 2007</p><p>PMCID:PMC1868939.</p><p></p> Schematic of alternative splicing event associated with mTLE. Exons are shown in orange and intronic regions are shown in gray. Data collected for the alternative splice event in control and mTLE brain tissue samples using the splice array technology (left) and quantitative rtPCR (qrtPCR, right). Data are presented as mean ± standard error of the mean; *< 0.05 when compared to control, Student's -test. rtPCR confirmation of the pattern of transcript expression in brain tissue collected from ten subjects from each group. Both reference and variant transcript forms were amplified using the following primer sequences (indicated in the figure by arrows above mRNA transcripts): CLCN7F-GGCAAATACGCCCTGATG, CLCN7R-CTCAGCACGTCCACAATGAC

Development and validation of a predictive model of drug-resistant genetic generalized epilepsy.

Objective: To develop and validate a clinical prediction model for antiepileptic drug (AED)-resistant genetic generalized epilepsy (GGE). Method: We performed a case-control study of patients with and without drug-resistant GGE, nested within ongoing longitudinal observational studies of AED response at 2 tertiary epilepsy centers. Using a validation dataset, we tested the predictive performance of 3 candidate models, developed from a training dataset. We then tested the candidate models' predictive ability on an external testing dataset. Results: Of 5,189 patients in the ongoing longitudinal study, 121 met criteria for AED-resistant GGE and 468 met criteria for AED-responsive GGE. There were 66 patients with GGE in the external dataset, of whom 17 were cases. Catamenial epilepsy, history of a psychiatric condition, and seizure types were strongly related with drug-resistant GGE case status. Compared to women without catamenial epilepsy, women with catamenial epilepsy had about a fourfold increased risk for AED resistance. The calibration of 3 models, assessing the agreement between observed outcomes and predictions, was adequate. Discriminative ability, as measured with area under the receiver operating characteristic curve (AUC), ranged from 0.58 to 0.65. Conclusion: Catamenial epilepsy, history of a psychiatric condition, and the seizure type combination of generalized tonic clonic, myoclonic, and absence seizures are negative prognostic factors of drug-resistant GGE. The AUC of 0.6 is not consistent with truly effective separation of the groups, suggesting other unmeasured variables may need to be considered in future studies to improve predictability.</p