POISEN: A Bioinformatics Pipeline to Identify Poison Exons in Neurodevelopment

PROJECT SUMMARY Alternative poison exon (PE) splicing is a critical regulatory mechanism that tightly controls protein expression across time and cell type. When included in an mRNA transcript, a PE introduces a premature termination codon that triggers nonsense-mediated mRNA decay (NMD) to degrade the transcript. Alternative PE splicing is critical for mediating proper neurodevelopment. For example, the voltage-gated sodium channel (VGSC) genes SCN1A (NaV1.1) and SCN8A (NaV1.6) splice PEs into mRNA transcripts in neural progenitor cells, but not in mature neurons, resulting in productive mRNA that can then be translated into functional VGSCs to support neuronal electrophysiology. Pathogenic variants near PE splice sites can cause aberrant PE splicing patterns that result in neurodevelopmental disorders (NDDs) like Dravet syndrome, caused by abnormal PE splicing in SCN1A and SCN8A. Although important in regulating the dynamics of neurodevelopment, PEs have been largely understudied due to the technical challenges in identifying these exons. PEs are inherently difficult to detect using short-read RNA sequencing (SRS) because NMD quickly degrades PE-containing transcripts, resulting in low transcript abundance. Moreover, it is challenging to computationally resolve the exact location of a PE in an mRNA isoform using SRS because the reads rarely span entire splice junctions. To overcome these biological and technical obstacles, I will develop POISEN (Poison exOn dIScovery for long-rEad traNscriptomes), a bioinformatics pipeline to identify PEs in long-read RNA sequencing (LRS) data. I hypothesize that the computational identification of PEs using LRS data will enable the discovery of novel PEs and define the cell type and temporal specificity of PEs across neurodevelopment. In Aim 1, I will perform bulk LRS on induced pluripotent stem cell-derived cerebral organoids (COs) grown at three distinct time points to recapitulate different stages of human neurodevelopment. Using these CO long-read transcriptomes, I will program POISEN to systematically identify PEs. To validate the PEs identified in CO transcriptomes, I will use cycloheximide (CHX) treatment to upregulate the abundance of PE-containing transcripts. I will then compare CHX-treated and control COs using bulk SRS, applying a differential exon usage analysis to evaluate the increase in PE expression in CHX-treated COs versus control COs. I will also perform an enrichment analysis on NDD- and epilepsy-related genes that express PEs to gain insight into the biological processes influenced by alternative PE splicing. In Aim 2, I will perform single-cell LRS on COs grown for the same time points as in Aim 1 and evaluate PE splicing patterns according to cell type and time point. I will create an online browser to house the data and results of this study as a useful resource for the scientific community to leverage in probing the understudied phenomenon of alternative PE splicing in neurodevelopment and NDDs. This study will result in the first bioinformatics pipeline for detecting PEs in LRS data, facilitating the discovery of PEs relevant to neurodevelopment and novel therapeutic targets for genetic epilepsy disorders and other NDDs.