Multidimensional Genetic Analysis of Repeated Seizures in the Hybrid Mouse Diversity Panel Reveals a Novel Epileptogenesis Susceptibility Locus

Genome-wide DNA methylation profiles of autism spectrum disorder

OBJECTIVES

We aimed to identify differentially methylated genes and related signaling pathways in autism spectrum disorder (ASD).

METHODS

First, the DNA methylation profile in the brain samples (GSE131706 and GSE80017) and peripheral blood samples (GSE109905) was downloaded from the Gene Expression Omnibus database (GEO) dataset, followed by identification of differentially methylated genes and functional analysis. Second, the GSE109905 data set was used to further validate the methylation state and test the ability to diagnose disease of identified differentially methylated genes. Third, expression measurement of selected differentially methylated genes was performed in whole blood from an independent sample. Finally, protein-protein interaction (PPI) network of core differentially methylated genes was constructed.

RESULTS

Totally, 74 differentially methylated genes were identified in ASD, including 38 hypermethylated genes and 36 hypomethylated genes. 15 differentially methylated genes were further identified after validation in the GSE109905 data set. Among these, major histocompatibility complex (HLA)-DQA1 was involved in the molecular function of myosin heavy chain class II receptor activity; HLA-DRB5 was involved in the signaling pathways of cell adhesion molecules, Epstein-Barr virus infection and antigen processing and presentation. In the PPI analysis, the interaction pairs of HLA-DQA1 and HLA-DRB5, FMN2 and ACTR3, and CALCOCO2 and BAZ2B were identified. Interestingly, FMN2, BAZ2B, HLA-DRB5, CALCOCO2 and DUSP22 had a potential diagnostic value for patients with ASD. The expression result of four differentially methylated genes (HLA-DRB5, NTM, IL16 and COL5A3) in the independent sample was consistent with the integrated analysis.

CONCLUSIONS

Identified differentially methylated genes and enriched signaling pathway could be associated with ASD.

Identification of genetic modifiers of murine hepatic β-glucocerebrosidase activity

The acid β-glucocerebrosidase (GCase) enzyme cleaves glucosylceramide into glucose and ceramide. Loss of function variants in the gene encoding for GCase can lead to Gaucher disease and Parkinson's disease. Therapeutic strategies aimed at increasing GCase activity by targeting a modulating factor are attractive and poorly explored. To identify genetic modifiers, we measured hepatic GCase activity in 27 inbred mouse strains. A genome-wide association study (GWAS) using GCase activity as a trait identified several candidate modifier genes, including Dmrtc2 and Arhgef1 (p=2.1x10⁻⁷), and Grik5 (p=2.1x10⁻⁷). Bayesian integration of the gene mapping with transcriptomics was used to build integrative networks. The analysis uncovered additional candidate GCase regulators, highlighting modules of the acute phase response (p=1.01x10⁻⁸), acute inflammatory response (p=1.01x10⁻⁸), fatty acid beta-oxidation (p=7.43x10⁻⁵), among others. Our study revealed previously unknown candidate modulators of GCase activity, which may facilitate the design of therapies for diseases with GCase dysfunction.

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Hepatic GCase activity significantly differs among mouse strains.

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Genome-wide association study revealed putative modifier genes of GCase activity.

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Bayesian integration of multi-omics identified a regulatory network of GCase activity.

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This study may facilitate the design of therapies for diseases with GCase dysfunction.

Association of KCNJ10 variants and the susceptibility to clinical epilepsy

We first enrolled the available case-control studies to investigate the genetic association between three polymorphisms (rs1130183, rs1890532, and rs2486253) of KCNJ10 (the potassium voltage-gated channel subfamily J member 10) gene and the susceptibility towards clinical epilepsy. We utilized the meta-analysis, FPRP (false-positive report probability) test, and the TSA (trial sequential analysis) for the data pooling and the evaluation of statistical power. Totally, eight eligible articles were finally included. For KCNJ10 rs1130183, compared with population-based controls, a reduced epilepsy risk in cases was observed in models of allelic T vs. C, heterozygotic CT vs. CC, dominant CT + TT vs. CC, carrier T vs. C [all OR (odds ratio) <1, P < 0.05, Benjamini & Hochberg-adjusted P < 0.05, bonferroni-adjusted P < 0.05]. There were similar results in the subgroup analysis of "Caucasian". The positive conclusion was also statistically supported by the result of the FPRP test and TSA. Nevertheless, no statistically significant differences between epilepsy cases and negative controls were detected in any comparison of KCNJ101890532 and rs2486253. In summary, it is possible that the CT genotype of KCNJ10 rs1130183 is related to a reduced clinical epilepsy susceptibility, especially in Caucasians. However, more sample sizes are still required for a more robust conclusion in different populations, and more adjusted factors should be considered.

Collaborative Cross mice reveal extreme epilepsy phenotypes and genetic loci for seizure susceptibility

OBJECTIVE

Animal studies remain essential for understanding mechanisms of epilepsy and identifying new therapeutic targets. However, existing animal models of epilepsy do not reflect the high level of genetic diversity found in the human population. The Collaborative Cross (CC) population is a genetically diverse recombinant inbred panel of mice. The CC offers large genotypic and phenotypic diversity, inbred strains with stable genomes that allow for repeated phenotypic measurements, and genomic tools including whole genome sequence to identify candidate genes and candidate variants.

METHODS

We evaluated multiple complex epileptic traits in a sampling of 35 CC inbred strains using the flurothyl-induced seizure and kindling paradigm. We created an F2 population of 297 mice with extreme seizure susceptibility and performed quantitative trait loci (QTL) mapping to identify genomic regions associated with seizure sensitivity. We used quantitative RNA sequencing from CC hippocampal tissue to identify candidate genes and whole genome sequence to identify genetic variants likely affecting gene expression.

RESULTS

We identified new mouse models with extreme seizure susceptibility, seizure propagation, epileptogenesis, and SUDEP (sudden unexpected death in epilepsy). We performed QTL mapping and identified one known and seven novel loci associated with seizure sensitivity. We combined whole genome sequencing and hippocampal gene expression to pinpoint biologically plausible candidate genes (eg, Gabra2) and variants associated with seizure sensitivity.

SIGNIFICANCE

New mouse models of epilepsy are needed to better understand the complex genetic architecture of seizures and to identify therapeutics. We performed a phenotypic screen utilizing a novel genetic reference population of CC mice. The data we provide enable the identification of protective/risk genes and novel molecular mechanisms linked to complex seizure traits that are currently challenging to study and treat.

Using BXD mouse strains in vision research: A systems genetics approach

We illustrate the growing power of the BXD family of mice (recombinant inbred strains from a cross of C57BL/6J and DBA/2J mice) and companion bioinformatic tools to study complex genome-phenome relations related to glaucoma. Over the past 16 years, our group has integrated powerful murine resources and web-accessible tools to identify networks modulating visual system traits-from photoreceptors to the visual cortex. Recent studies focused on retinal ganglion cells and glaucoma risk factors, including intraocular pressure (IOP), central corneal thickness (CCT), and susceptibility of cellular stress. The BXD family was exploited to define key gene variants and then establish linkage to glaucoma in human cohorts. The power of this experimental approach to precision medicine is highlighted by recent studies that defined cadherin 11 (Cdh11) and a calcium channel (Cacna2d1) as genes modulating IOP, Pou6f2 as a genetic link between CCT and retinal ganglion cell (RGC) death, and Aldh7a1 as a gene that modulates the susceptibility of RGCs to death after elevated IOP. The role of three of these gene variants in glaucoma is discussed, along with the pathways activated in the disease process.

Regression analysis for detecting epileptic seizure with different feature extracting strategies

Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

The relevance of inter- and intrastrain differences in mice and rats and their implications for models of seizures and epilepsy

It is becoming increasingly clear that the genetic background of mice and rats, even in inbred strains, can have a profound influence on measures of seizure susceptibility and epilepsy. These differences can be capitalized upon through genetic mapping studies to reveal genes important for seizures and epilepsy. However, strain background and particularly mixed genetic backgrounds of transgenic animals need careful consideration in both the selection of strains and in the interpretation of results and conclusions. For instance, mice with targeted deletions of genes involved in epilepsy can have profoundly disparate phenotypes depending on the background strain. In this review, we discuss findings related to how this genetic heterogeneity has and can be utilized in the epilepsy field to reveal novel insights into seizures and epilepsy. Moreover, we discuss how caution is needed in regards to rodent strain or even animal vendor choice, and how this can significantly influence seizure and epilepsy parameters in unexpected ways. This is particularly critical in decisions regarding the strain of choice used in generating mice with targeted deletions of genes. Finally, we discuss the role of environment (at vendor and/or laboratory) and epigenetic factors for inter- and intrastrain differences and how such differences can affect the expression of seizures and the animals' performance in behavioral tests that often accompany acute and chronic seizure testing.