|1||Pharmacogenomics 2011 Feb 12: 171-84|
|Title||Genome-wide expression profiling of human lymphoblastoid cell lines identifies CHL1 as a putative SSRI antidepressant response biomarker.|
|Abstract||Selective serotonin reuptake inhibitors (SSRIs) are the most commonly used class of antidepressants for treating major depression. However, approximately 30% of patients do not respond sufficiently to first-line antidepressant drug treatment and require alternative therapeutics. Genome-wide studies searching for SSRI response DNA biomarkers or studies of candidate serotonin-related genes so far have given inconclusive or contradictory results. Here, we present an alternative transcriptome-based genome-wide approach for searching antidepressant drug-response biomarkers by using drug-effect phenotypes in human lymphoblastoid cell lines (LCLs).|
We screened 80 LCLs from healthy adult female individuals for growth inhibition by paroxetine. A total of 14 LCLs with reproducible high and low sensitivities to paroxetine (seven from each phenotypic group) were chosen for genome-wide expression profiling with commercial microarrays.
The most notable genome-wide transcriptome difference between LCLs displaying high versus low paroxetine sensitivities was a 6.3-fold lower (p = 0.0000256) basal expression of CHL1, a gene coding for a neuronal cell adhesion protein implicated in correct thalamocortical circuitry, schizophrenia and autism. The microarray findings were confirmed by real-time PCR (36-fold lower CHL1 expression levels in the high paroxetine sensitivity group). Several additional genes implicated in synaptogenesis or in psychiatric disorders, including ARRB1, CCL5, DDX60, DDX60L, ENDOD1, ENPP2, FLT1, GABRA4, GAP43, MCTP2 and SPRY2, also differed by more than 1.5-fold and a p-value of less than 0.005 between the two paroxetine sensitivity groups, as confirmed by real-time PCR experiments.
Genome-wide transcriptional profiling of in vitro phenotyped LCLs identified CHL1 and additional genes implicated in synaptogenesis and brain circuitry as putative SSRI response biomarkers. This method might be used as a preliminary tool for searching for potential depression treatment biomarkers.