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Proliferation rates and gene expression profiles in human lymphoblastoid cell lines from patients with depression characterized in response to antidepressant drug therapy.

ABSTRACT: The current therapy success of depressive disorders remains in need of improvement due to low response rates and a delay in symptomatic improvement. Reliable functional biomarkers would be necessary to predict the individual treatment outcome. On the basis of the neurotrophic hypothesis of antidepressant's action, effects of antidepressant drugs on proliferation may serve as tentative individual markers for treatment efficacy. We studied individual differences in antidepressant drug effects on cell proliferation and gene expression in lymphoblastoid cell lines (LCLs) derived from patients treated for depression with documented clinical treatment outcome. Cell proliferation was characterized by EdU (5-ethynyl-2'-deoxyuridine) incorporation assays following a 3-week incubation with therapeutic concentrations of fluoxetine. Genome-wide expression profiling was conducted by microarrays, and candidate genes such as betacellulin-a gene involved in neuronal stem cell regeneration-were validated by quantitative real-time PCR. Ex vivo assessment of proliferation revealed large differences in fluoxetine-induced proliferation inhibition between donor LCLs, but no association with clinical response was observed. Genome-wide expression analyses followed by pathway and gene ontology analyses identified genes with different expression before vs after 21-day incubation with fluoxetine. Significant correlations between proliferation and gene expression of WNT2B, FZD7, TCF7L2, SULT4A1 and ABCB1 (all involved in neurogenesis or brain protection) were also found. Basal gene expression of SULT4A1 (P=0.029), and gene expression fold changes of WNT2B by ex vivo fluoxetine (P=0.025) correlated with clinical response and clinical remission, respectively. Thus, we identified potential gene expression biomarkers eventually being useful as baseline predictors or as longitudinal targets in antidepressant therapy.

SUBMITTER: Breitfeld J

PROVIDER: S-EPMC5314111 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Proliferation rates and gene expression profiles in human lymphoblastoid cell lines from patients with depression characterized in response to antidepressant drug therapy.

Breitfeld J J Scholl C C Steffens M M Brandenburg K K Probst-Schendzielorz K K Efimkina O O Gurwitz D D Ising M M Holsboer F F Lucae S S Stingl J C JC

Translational psychiatry 20161115 11

The current therapy success of depressive disorders remains in need of improvement due to low response rates and a delay in symptomatic improvement. Reliable functional biomarkers would be necessary to predict the individual treatment outcome. On the basis of the neurotrophic hypothesis of antidepressant's action, effects of antidepressant drugs on proliferation may serve as tentative individual markers for treatment efficacy. We studied individual differences in antidepressant drug effects on c ...[more]

PMID: 27845776

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Project description:BACKGROUND:Heritable factors are evidently involved in prostate cancer (PrCa) carcinogenesis, but currently, genetic markers are not routinely used in screening or diagnostics of the disease. More precise information is needed for making treatment decisions to distinguish aggressive cases from indolent disease, for which heritable factors could be a useful tool. The genetic makeup of PrCa has only recently begun to be unravelled through large-scale genome-wide association studies (GWAS). The thus far identified Single Nucleotide Polymorphisms (SNPs) explain, however, only a fraction of familial clustering. Moreover, the known risk SNPs are not associated with the clinical outcome of the disease, such as aggressive or metastasised disease, and therefore cannot be used to predict the prognosis. Annotating the SNPs with deep clinical data together with miRNA expression profiles can improve the understanding of the underlying mechanisms of different phenotypes of prostate cancer. RESULTS:In this study microRNA (miRNA) profiles were studied as potential biomarkers to predict the disease outcome. The study subjects were from Finnish high risk prostate cancer families. To identify potential biomarkers we combined a novel non-parametrical test with an importance measure provided from a Random Forest classifier. This combination delivered a set of nine miRNAs that was able to separate cases from controls. The detected miRNA expression profiles could predict the development of the disease years before the actual PrCa diagnosis or detect the existence of other cancers in the studied individuals. Furthermore, using an expression Quantitative Trait Loci (eQTL) analysis, regulatory SNPs for miRNA miR-483-3p that were also directly associated with PrCa were found. CONCLUSION:Based on our findings, we suggest that blood-based miRNA expression profiling can be used in the diagnosis and maybe even prognosis of the disease. In the future, miRNA profiling could possibly be used in targeted screening, together with Prostate Specific Antigene (PSA) testing, to identify men with an elevated PrCa risk.

Project description:Antidepressant therapy is still associated with delays in symptomatic improvement and low response rates. Incomplete understanding of molecular mechanisms underlying antidepressant effects hampered the identification of objective biomarkers for antidepressant response. In this work, we studied transcriptome-wide expression followed by pathway analysis in lymphoblastoid cell lines (LCLs) derived from 17 patients documented for response to SSRI antidepressants from the Munich Antidepressant Response Signatures (MARS) study upon short-term incubation (24 and 48 h) with citalopram. Candidate transcripts were further validated with qPCR in MARS LCLs from responders (n = 33) vs. non-responders (n = 36) and afterward in an independent cohort of treatment-resistant patients (n = 20) vs. first-line responders (n = 24) from the STAR*D study. In MARS cohort we observed significant associations of GAD1 (glutamate decarboxylase 1; p = 0.045), TBC1D9 (TBC1 Domain Family Member 9; p = 0.014-0.021) and NFIB (nuclear factor I B; p = 0.015-0.025) expression with response status, remission status and improvement in depression scale, respectively. Pathway analysis of citalopram-altered gene expression indicated response-status-dependent transcriptional reactions. Whereas in clinical responders neural function pathways were primarily up- or downregulated after incubation with citalopram, deregulated pathways in non-responders LCLs mainly involved cell adhesion and immune response. Results from the STAR*D study showed a marginal association of treatment-resistant depression with NFIB (p = 0.068) but not with GAD1 (p = 0.23) and TBC1D9 (p = 0.27). Our results propose the existence of distinct pathway regulation mechanisms in responders vs. non-responders and suggest GAD1, TBC1D9, and NFIB as tentative predictors for clinical response, full remission, and improvement in depression scale, respectively, with only a weak overlap in predictors of different therapy outcome phenotypes.

Dataset Information

Proliferation rates and gene expression profiles in human lymphoblastoid cell lines from patients with depression characterized in response to antidepressant drug therapy.

Publications

Proliferation rates and gene expression profiles in human lymphoblastoid cell lines from patients with depression characterized in response to antidepressant drug therapy.

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