Transcriptome sequencing of neuronal cell lines from patients with schizophrenia Project summary The primary objective of this project is to identify genes, and related pathways, which are involved in the etiology of schizophrenia, using transcriptome sequencing (RNA-Seq). We will recruit 145 patients with schizophrenia (SZ) and 145 controls (no psychiatric disorders) matched ancestrally and by ethnicity, sex and age, collect nasal biopsy samples, establish neuronal progenitor cell lines and purify RNA from these cells. The cells are grown in environmentally controlled conditions, resulting in greatly reduced variation in expression profile, allowing smaller number of samples, and providing more reliable detection of differently expressed genes, as compared to post-mortem brain or blood samples. We will analyze data in order to identify genes which are different in expression level, frequency of particular splicing variants and allele frequencies of SNPs / mutations. We will validate differential expression of 96 genes/isoforms using BioMark Real Time PCR System (Fluidigm) on 96.96 Dynamic Array Chips. We expect detection of up to several hundred differently expressed genes, or those with different frequency of splicing variants or missense, nonsense or otherwise important alleles. We will analyze these data jointly with genotyping data for 1 million SNP, available for the same persons from the NIH-funded GPC study, in order to find a relationship between genes with mutations or altered expression in schizophrenia with those associated with the disease. We will analyze these data using IPA pathway analysis to identify pathways, or networks of genes which are differentially expressed between schizophrenia and control groups and hence are likely to contribute to the development of schizophrenia. It will allow identification of new drug targets in order to normalize expression of affected pathways or networks. In the future, neuronal cell lines from patients can be used for translational research for development of diagnostic tools and personalized medicine.
|Chen, Emily A; Souaiaia, Tade; Herstein, Jennifer S et al. (2014) Effect of RNA integrity on uniquely mapped reads in RNA-Seq. BMC Res Notes 7:753|