Differential Transcript Expression and Alternative RNA Splicing Patterns to Differentiate Focal vs. Generalized-Onset...

Area of Science:

• Neuroscience
• Genetics
• Molecular Biology

Background:

• Accurate seizure type diagnosis is crucial for epilepsy treatment.
• Previous research showed RNA signatures can distinguish epileptic seizures from non-epileptic events.
• Distinguishing between focal and generalized seizures is clinically important.

Purpose of the Study:

• To investigate the utility of alternative RNA splicing in distinguishing focal from generalized epileptic seizures.
• To analyze blood transcriptome changes following different seizure types.
• To identify potential blood-based biomarkers for seizure classification.

Main Methods:

• Collected blood samples from 27 epilepsy patients undergoing video-electroencephalogram (vEEG) monitoring.
• Analyzed RNA expression and transcript usage, focusing on alternative splicing.
• Developed machine learning models to classify seizure types based on transcriptomic data.

Main Results:

• Generalized seizures showed broader transcriptomic changes compared to focal seizures post-seizure.
• Identified significant isoform switching without changes in overall gene expression.
• Machine learning models achieved high accuracy (up to 100%) in classifying seizure types based on blood RNA profiles.

Conclusions:

• Blood-based transcriptome analysis, including isoform switching, can retrospectively distinguish between focal and generalized seizures.
• Identified potential novel RNA biomarkers for epilepsy management.
• Alternative RNA splicing provides valuable insights into seizure type classification.