Full-length transcriptome profiling of KCNQ2-Encephalopthy during development using iPSC-derived neurons.

Heterozygous pathogenic variants in KCNQ2 with either a Dominant-negative (DN) or more rarely Gain of Function effect, are the most common cause of neonatal developmental and epileptic encephalopathy, named KCNQ2-Encephalopthy (KCNQ2-E). KCNQ2-E is characterized by difficult to treat neonatal seizures and severe developmental delay. The KCNQ2 encoded Kv7.2 subunit is part of a potassium channel that plays a key role in regulating the resting membrane potential of neurons, to control neuronal excitability. Although a role for Kv7.2 in neurodevelopment is increasingly accepted in the field, there are still many knowledge gaps to be addressed. Very recently, we highlighted eevidence for KCNQ2 expression in Human Induced Pluripotent Stem Cells (hiPSCs) and neural progenitor cells (NPCs), suggesting a role for Kv7.2 in earlier stages of neurodevelopment than anticipated. Based on publicly available short-read RNA sequencing datasets from iPSC-derived neuronal cultures, we furthermore hypothesize that different KCNQ2 transcripts are expressed during the course of neuronal development, which regulate Kv7.2 channel current densities. Although these available RNA sequencing datasets are of high value, it is very challenging to identify the exact transcripts that are expressed, due to the complex nature of the transcriptome, consisting of variable lengths and alternatively spliced transcripts for most genes, including KCNQ2. To overcome these limitations, in this project we will perform long-read RNA sequencing of hiPSC-derived neuronal cultures during development, using in-house Oxford Nanopore technology. By generating the full-length transcriptome profile of hiPSCs, including two control lines and two lines with recurrent DN KCNQ2-E variants, we will be able to unravel the differential expression pattern of KCNQ2 transcripts during the course of neurodevelopment, as well as the effect of DN KCNQ2-E variants on gene and transcript expression levels. Based on this information, we will identify key pathways involved in the neurodevelopmental aspect of KCNQ2-E, opening possibilities for future projects. Finally, by profiling the full transcriptome rather than targeted RNA sequencing, the generated full-length transcriptome dataset will be of use to study expression and splicing profiles of many other genes of interest by multiple groups within the department and broader scientific community.

Keywords:BRAIN DISORDERS, TRANSCRIPTOMICS, HUMAN IPSC, EPILEPSY

Disciplines:Analysis of next-generation sequence data, Computational transcriptomics and epigenomics, Cell growth and development, Developmental biology, Genetics, Developmental neuroscience