Authors :
Presenting Author: Ruth Fulton, MS – University of Colorado Anschutz Medical Campus
Anna Figueroa, BS – University of Colorado Anschutz Medical Campus; Kelly Knupp, MD – Children's Hospital Colorado; Manisha Patel, PhD – University of Colorado Anschutz Medical Campus
Rationale:
Dravet syndrome (DS) is a devastating and life-long epilepsy syndrome which emerges shortly after birth. Patients present with focal and generalized convulsive seizures and a host of developmental delays. This condition often precludes the use of mainstay anticonvulsant medications, thus a more comprehensive understanding of the mechanistic underpinnings of the disease are needed in order to develop novel approaches to treatment regimens. Toward this purpose, we utilized RNA-seq technology to perform a transcriptome-wide comparison of gene expression in patient vs. control lymphoblast cells lines (LCLs).
Methods:
Blood samples from six DS patients were obtained and peripheral blood mononuclear cells were transformed via Epstein-Barr virus (Coriell Institute). Age and sex matched LCLs derived from healthy individuals were included as controls. RNA was collected and purified from individual patient and control LCLs using RNeasy spin columns (Qiagen). rRNA-depleted cDNA libraries were constructed and quantified using Universal RNA-Seq Library Preparation with NuQuant (Tecan) followed by sequencing on a NovaSeq 6000 at 2X150bp configuration at 40M read pairs/80M total reads per sample. The estimated count information from RSEM for each sample was rounded to the nearest integer and used for differential expression analysis using DESeq2 (Love, Huber, and Anders 2014).
Results:
Differential analysis was conducted via clusterProfle both with and without a design strategy allowing for age (under vs over seven years of age) and sex correction. A PCA analysis of the top 100 genes ranked by adjusted p-value shows clustering within control versus patient samples with the transcription profiles of control samples highly correlated with one another whereas the patient samples displayed greater variability. This intra variability was somewhat less evident when corrections for age and gender were applied. However the samples still separated into distinct and separate populations based on disease status. Pathway analyses (GO, KEGG, and reactome) consistently revealed changes in mRNA splicing and the spliceosome complex with several genes encoding splicesosome-associated proteins such as CCDC12, ACIN1, SNRPD1 and RBM17 upregulated in patient cell lines. Additionally, GO-CC uncovered a downregulation of genes associated with the recycling endosome and the recycling endosome membrane.
Conclusions:
Using RNA-seq technology, we have identified DS associated differential expression of splicesosome associated genes in LCLs. Spliceosomopathies have been implicated in neurodegenerative diseases including epilepsy (Ballif et al. 2012; Bramswig et al. 2017; Depienne et al. 2017). Furthermore, a DS mouse model identified altered expression of seven genes directed to the spliceosome (Shi et al. 2019). Our study suggests a systemic alteration in spliceosome associated genes. A growing body of evidence implicates alterations in spliceosome gene expression in genetic epilepsies and warrants further exploration in this field.
Funding:
Dravet Syndrome Foundation