AIMS: 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). MATERIALS & METHODS: 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. RESULTS: 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. CONCLUSION: 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.
AIMS: 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). MATERIALS & METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Authors: C Fabbri; C Crisafulli; D Gurwitz; J Stingl; R Calati; D Albani; G Forloni; M Calabrò; R Martines; S Kasper; J Zohar; A Juven-Wetzler; D Souery; S Montgomery; J Mendlewicz; G D Girolamo; A Serretti Journal: Pharmacogenomics J Date: 2015-04-07 Impact factor: 3.550
Authors: Elena Milanesi; Adva Hadar; Elisabetta Maffioletti; Haim Werner; Noam Shomron; Massimo Gennarelli; Thomas G Schulze; Marta Costa; Maria Del Zompo; Alessio Squassina; David Gurwitz Journal: J Mol Neurosci Date: 2015-03-05 Impact factor: 3.444
Authors: Allison E Ashley-Koch; Melanie E Garrett; Jason Gibson; Yutao Liu; Michelle F Dennis; Nathan A Kimbrel; Jean C Beckham; Michael A Hauser Journal: J Affect Disord Date: 2015-06-12 Impact factor: 4.839
Authors: Oliver Grünvogel; Katharina Esser-Nobis; Anna Reustle; Philipp Schult; Birthe Müller; Philippe Metz; Martin Trippler; Marc P Windisch; Michael Frese; Marco Binder; Oliver Fackler; Ralf Bartenschlager; Alessia Ruggieri; Volker Lohmann Journal: J Virol Date: 2015-08-12 Impact factor: 5.103