Verma Rashi, Bullinger Katie L, Pearson Andrea, Dhakar Monica, Guven Emine, Amini Elham, Simon Roger P, Meller Robert
Neuroscience Institute, Morehouse School of Medicine, 720 Westview Drive SW, Atlanta, GA, USA.
Department of Neurology, Emory University, Atlanta, GA, USA.
Mol Neurobiol. 2025 Jun 11. doi: 10.1007/s12035-025-05110-1.
Retrospective diagnosis of a seizure type is pivotal for effective management and treatment of epilepsy. Previously, we demonstrated that RNA signatures could discriminate between non-epileptic spells and epileptic seizures. Here, we investigate the utility of alternative RNA splicing to distinguish generalized versus focal epileptic seizures. Blood samples were collected at baseline, 4-6 h post-seizure, and at discharge from 27 patients undergoing video-electroencephalogram (vEEG) monitoring at the Emory University Hospital. Epileptologists determined seizure classification through vEEG data review. RNA was extracted, sequenced, and analyzed for RNA expression and transcript usage. Classification models were generated to distinguish between patients who had a focal or generalized seizure. The study shows transcriptomic profile changes following EEG-verified focal and generalized seizures. Compared to baseline, focal seizure exhibits limited changes in transcriptomic expression 4-6 h post-seizure and discharge samples. In contrast, generalized seizures demonstrated a broader transcript response, with 74 differentially expressed transcripts at 4-6 h and 70 at discharge. The changes were also evident across different time points between focal and generalized seizure. The study for the first time described the landscape of isoform switching in seizure type. Notably, significant isoform switching without differences in gene expression was observed. We identified 2689 isoform switches linked to 1249 genes among which 742 genes were sensitive to nonsense-mediated mRNA decay (NMD). Significant switches were observed in genes such as CORO1C, ZBTB44, SNHG1, and RPS17. Notably, we also observed novel isoforms, including CD300 (MSTRG.26116.1), RNF216 (MSTRG.52862.7), and RN7SL1 (MSTRG.17010.3) which exhibited significant switching, revealing potential new regulators of gene expression. Differentially expressed transcripts were utilized as classifiers for machine learning (ML) modeling using random forest (rf) and radial support vector machine (rSVM) algorithms, achieving ~ 83% accuracy in classifying generalized seizures, and multivariate adaptive regression splines (mars) algorithm achieving 100% accuracy in identifying focal seizure events. Our findings of blood transcript expression changes, including isoform switch analysis, underscore the potential of blood-based transcriptome analysis for retrospectively distinguishing seizure types and identifying biomarkers for epilepsy management.
癫痫发作类型的回顾性诊断对于癫痫的有效管理和治疗至关重要。此前，我们证明RNA特征可区分非癫痫性发作和癫痫性发作。在此，我们研究可变RNA剪接在区分全身性癫痫发作和局灶性癫痫发作方面的效用。在埃默里大学医院对27例接受视频脑电图（vEEG）监测的患者，于基线、发作后4 - 6小时和出院时采集血样。癫痫专家通过vEEG数据回顾确定癫痫发作分类。提取RNA、进行测序并分析RNA表达和转录本使用情况。生成分类模型以区分发生局灶性或全身性癫痫发作的患者。该研究显示了经脑电图证实的局灶性和全身性癫痫发作后的转录组谱变化。与基线相比，局灶性癫痫发作在发作后4 - 6小时和出院样本中的转录组表达变化有限。相比之下，全身性癫痫发作表现出更广泛的转录反应，在发作后4 - 6小时有74个差异表达转录本，出院时有70个。这些变化在局灶性和全身性癫痫发作的不同时间点也很明显。该研究首次描述了癫痫发作类型中异构体转换的情况。值得注意的是，观察到了基因表达无差异的显著异构体转换。我们鉴定出2689个与1249个基因相关的异构体转换，其中742个基因对无义介导的mRNA降解（NMD）敏感。在CORO1C、ZBTB44、SNHG1和RPS17等基因中观察到显著转换。值得注意的是我们还观察到新的异构体，包括表现出显著转换的CD300（MSTRG.26116.1）、RNF216（MSTRG.52862.7）和RN7SL1（MSTRG.17010.3），揭示了潜在的新基因表达调节因子。差异表达转录本被用作使用随机森林（rf）和径向支持向量机（rSVM）算法进行机器学习（ML）建模的分类器，在全身性癫痫发作分类中准确率约为83%，多变量自适应回归样条（mars）算法在识别局灶性癫痫发作事件中准确率达到100%。我们关于血液转录本表达变化的发现，包括异构体转换分析，强调了基于血液的转录组分析在回顾性区分癫痫发作类型和识别癫痫管理生物标志物方面的潜力。
