Bayesian Network and Mechanistic Hierarchical Structure Modeling of Increased likelihood of Developing Intractable Childhood Epilepsy from the Combined Effect of mtDNA Variants, Oxidative Damage, and Copy Number

Abstract

Despite that mutations in mitochondrial DNA (mtDNA) have been associated with major epilepsy syndromes, the role of mtDNA instability and mitochondrial dysfunction in epileptogenesis has not been comprehensively examined. In the present study, we investigated the role of mtDNA copy number, oxidative damage, and mtDNA variants as independent or combined risk factors for the development of intractable childhood epilepsy. We analyzed mtDNA copy number and oxidative damage by quantitative polymerase chain reaction (PCR), and mtDNA variants by dot blot in brain tissue specimens collected from 21 pediatric intractable epilepsy patients and 11 non-epileptic patients. Bayesian network and mechanistic hierarchical structure Markov chain Monte Carlo (MCMC) modeling were used to analyze the relationship between these variables. The combined effects of oxidative mtDNA damages and mtDNA copy number produced more significant correlation with epilepsy than that of mtDNA copy number alone with epilepsy. Epilepsy patients showed significant correlations with mtDNA single nucleotide polymorphisms (SNPs)--A1555G, G3196A, T3197C, G9952A, A10006G, A10398G, cortical dysplasia status, oxidative mtDNA damage and relative mtDNA copy number. Comparison of 12 mechanistic structure models suggested that female children who have the wild type allele 10398A and variant allele 9952A, and high mtDNA copy number and oxidative stress have increased probability of developing intractable epilepsy. Estimation of nuclear genes controlling mitochondrial biogenesis, cortical dysplasia, and the effect of the environment using MCMC method showed that these latent variables had a very significant contribution in the development of intractable epilepsy. These data suggest that mitochondrial genetics play a significant role in the pathogenesis of epilepsy in children, and findings of this study may guide the prospects for targeting mitochondria for therapeutic treatment of childhood intractable epilepsy.

Anticonvulsant effect of piperine ameliorates memory impairment, inflammation and oxidative stress in a rat model of pilocarpine-induced epilepsy

“…Therefore, antioxidants that reduce oxidative stress have recently attracted attention in the treatment of epilepsy (26). However, oxidative stress damage has been shown to occur in all models of epilepsy seizures (27). In the present investigation, the results demonstrated an increase in TNF-α, IL-1β and MDA expression levels, and a reduction in SOD, CAT and GSH expression levels in the rats with pilocarpine-induced epilepsy.…”

Environmental DNA and RNA as Records of Human Exposome, Including Biotic/Abiotic Exposures and Its Implications in the Assessment of the Role of Environment in Chronic Diseases

“…The above two machine learning biostatistical methods are more well suited to accurately analyzing environmental insults recorded in eDNA/eRNA, characterizing a range of past and current exposure, and reconstructing functional relationships in human exposome. The feasibility of these bioinformatic approaches has been shown in the reconstruction of temporal environmental exposure to children resulting in genomic and proteomic changes that may cause cortical dysplasia and benign brain lesions [ 79 , 80 ]. Statistical machine learning graphical methods have previously been used to reconstruct temporal change in the genome of aDNA [ 4 , 5 ].…”

Dynamic causal modelling of seizure activity in a rat model

“…These include stochastic models to help predict occurrence of seizures (Esmaeili et al 2014;Wong et al 2007); phenomenological models of EEG dynamics that can be used to identify contributing factors leading to seizure onset (Benjamin et al 2012;O'Sullivan-Greene et al 2009;Geier et al 2015); and biophysically informed dynamic models attempting to identify abnormal synaptic parameters leading to epileptic activity in simplified models of population dynamics (neural mass models) (Goodfellow et al 2012;Breakspear et al 2006;Aram et al 2015). This work has gained particular traction since the development of model inversion techniques that allow model parameters to be estimated directly from empirical data, such as the variations of the Kalman filter (Ullah & Schiff 2010;Haykin & Arasaratnam 2009), dynamic causal modelling (Papadopoulou et al 2015;Cooray et al 2015;Moran et al 2011), and related machine learning techniques such as Markov chain Monte Carlo (MCMC) (Luna et al 2014;Wulsin et al 2014). …”