Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depression. However, the link between inhibition of serotonin reuptake and remission from depression remains controversial: in spite of the rapid onset of serotonin reuptake inhibition, remission from depression takes several weeks, presumably reflecting synaptogenesis/neurogenesis and neuronal rewiring. We compared genome-wide expression profiles of human lymphoblastoid cell lines from unrelated individuals following treatment with 1 μM paroxetine for 21 days with untreated control cells and examined which genes and microRNAs (miRNAs) showed the most profound and consistent expression changes. ITGB3, coding for integrin beta-3, showed the most consistent altered expression (1.92-fold increase, P=7.5 × 10−8) following chronic paroxetine exposure. Using genome-wide miRNA arrays, we observed a corresponding decrease in the expression of two miRNAs, miR-221 and miR-222, both predicted to target ITGB3. ITGB3 is crucial for the activity of the serotonin transporter (SERT), the drug target of SSRIs. Moreover, it is presumably required for the neuronal guidance activity of CHL1, whose expression was formerly identified as a tentative SSRI response biomarker. Further genes whose expression was significantly modulated by chronic paroxetine are also implicated in neurogenesis. Surprisingly, the expression of SERT or serotonin receptors was not modified. Our findings implicate ITGB3 in the mode of action of SSRI antidepressants and provide a novel link between CHL1 and the SERT. Our observations suggest that SSRIs may relieve depression primarily by promoting neuronal synaptogenesis/neurogenesis rather than by modulating serotonin neurotransmission per se.

The potential value of these miRNAs as tentative SSRI response biomarkers awaits validation with lymphocyte samples of major depression patients.

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.

Comparing the growth-inhibition profiles of drugs (or compounds) of interest with the profiles of drugs with known pathways may assist in drug pathway classification. The method is useful for in vitro assessment of in silico-generated drug pathway predictions and for distinguishing shared versus distinct pathways for compounds of interest. Comparative transcriptomics analysis of human lymphoblastoid cell lines exhibiting 'edge' sensitivities can subsequently be utilized in the search for drug response biomarkers for personalized pharmacotherapy. The limitations and advantages of the method are discussed.

No abstract