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Antidepressant Response Biomarkers: Lessons from Lymphoblastoid Cell Lines Ayelet Morag1, Varda Oron-Karni2, Metsada Pasmanik-Chor2, Moshe Rehavi1, Julia Stingl3 and David Gurwitz1,4 1Sackler Faculty of Medicine & 2Faculty of Life-Sciences, Tel Aviv University, Israel 3Institute of Clinical Pharmacology, University Ulm, Germany 4Funding: National Institute for Psychobiology in Israel The need of biomarkers for treating depression 2 • Major depression: lifetime prevalence of 10%-20% (for men & women, respectively) • WHO estimate: by 2020 depression will become the leading cause of healthcare morbidity in developed countries • Most common first-line antidepressants: Selective serotonin reuptake inhibitors (SSRIs): e.g. fluoxetine (Prozac), paroxetine (Seroxat); citalopram (Cipralex) • Other classes of antidepressant drugs: - SNRIs: serotonin-norepinephrine reuptake inhibitors e.g. venlafaxine (Effexor) - Tricyclic antidepressants (“1st generation antidepressants”) - Serotonin ligands, e.g. mirtazapine (Remeron) The need of biomarkers for treating depression 3 • SSRIs are the most-common first-line treatment for depression • >30% of patients do not respond to SSRIs • It takes >4 weeks to know if a patient responds to treatment • Drugs and dosages are replaced by ‘trial-and-error’ • Biomarkers are needed for individualizing treatment! Candidate Gene Studies for SSRI Response: The serotonin transporter promoter length polymorphism First report: 1998 12 years later: 742 PubMed papers on “SERT promoter polymorphism” Meta-analysis verdict: no clear association with SSRI response Genome-Wide Association Studies for SSRI Response Genome-Wide Association Studies for SSRI Response: No reliable DNA biomarkers found (2) 6 BIOL PSYCHIATRY 2010;67:133–138 Genome-Wide Association Studies for SSRI Response: No reliable DNA biomarkers found (3) 7 None of these 3 studies reported results with genome-wide significance Rare genotypes and epigenetics may have a role in antidepressant response AOP November 2010 Genome-Wide Expression Profiling for SSRI Response Biomarkers 8 The approach: • Comparing the sensitivity of human lymphoblastoid cell lines from individual healthy donors to the growth-inhibition effects of SSRIs • Comparing the expression profiles of ‘edge’ cell lines: LCLs with high vs. low sensitivity to growth-inhibition by SSRI drugs • Studying the genes (including deep sequencing, promoter methylation, relevant miRNAs) exhibiting differential expression • Later: clinical samples (patients’ lymphocytes) Lymphoblastoid cell lines (LCLs) 9 • HapMap and HGDP use LCLs for studying human genome diversity • LCLs retain most of the phenotypic properties of B lymphocytes - and can be grown for few months (vs. few days for PBL) • LCLs faithfully represent the donor’s DNA sequence (EBV does not incorporate into the host genome) • The serotonin transporter is functionally expressed by PBL and LCLs (serotonin is an immune modulator) PBL and LCLs express a functional serotonin transporter 10 SERT pharmacology is similar for brain and PBL Gene expression in LCLs correlated with PBL 11 - A fine correlation is observed for gene expression in LCLs and PBL from the same individuals (with very few exceptions) - Over 4,100 brain transcripts are also expressed in PBL Am J Med Genet Part B 153B:919–936 (2010) LCLs faithfully reflect the donor’s methylome ~92% of genes show similar methylation profiles in PBL and LCLs from the same donors 13 Source for LCLs: The National Laboratory for the Genetics of Israeli Populations (NLGIP) http://nlgip.tau.ac.il SSRI biomarkers: Phenotyping for in vitro paroxetine response 14 - Cells are plated in 96-well plates (20,000 cells per well) for 3 days, with different drugs added immediately after plating - On the third day, XTT reagent is added (last 6 h) and the absorption is measured with an ELISA spectrophotometer - The XTT assay reflects the activity of mitochondrial enzymes and thereby the number of live cells - The XTT color generation is not affected by tested drugs (last 6 h) The XTT assay can be easily scaled-up 15 Large spectrum of LCLs growth-inhibition by paroxetine 16 LCLs were tested for growth-inhibition (72 h) by 10 µM paroxetine Each bar represents 3-5 experiments (each in triplicates) Error bars represent SD for repeated experiments Similar growth inhibition observed by direct counting vs. XTT assay 17 Growth inhibition by paroxetine determined by direct counting vs. XTT (ELISA) LCLs were exposed to 10 µM paroxetine for 72 h (in triplicate) Paroxetine sensitivity phenotypes are stable (new batches were thawed and tested) 18 Paroxetine sensitivities are independent of cell growth rates (A) or densities (B) 19 Growth-inhibition of human LCLs by 10 µM paroxetine as function of: (A) Basal cell replication rates (B) Cell concentration immediately prior to experiments Linear regression analysis of pooled data; R2 = goodness-of-fit values Correlation of cell growth inhibition by different SSRIs 20 • Growth-inhibition phenotypes are related to the SSRI cellular pathway! Paroxetine vs. Citalopram Paroxetine vs. Fluvoxamine Growth inhibition of human LCLs by paired drugs; each dot is a different cell line R2 = goodness-of-fit values Poor correlations for cell growth inhibition by different drug classes 21 • A novel drug discovery tool – see Morag et al 2010 Paroxetine vs. Beclomethasone Dexamethasone vs. Beclomethasone Growth inhibition of human LCLs by paired drugs; each dot is a different cell line R2 = goodness-of-fit values Depression treatment biomarkers: Next phase – searching biomarkers 22 • 80 LCLs were screened for paroxetine sensitivity (healthy female donors) • Most LCLs had average sensitivities (IC50 of 10 uM paroxetine) • 14 lines selected for whole-genome profiling (7 from each phenotypic ‘edge’) • Expression profiles of the ‘edge’ LCLs were compared with the Affymetrix GeneChip Human Gene 1.0 ST array kit (28K genes) Affymetrix Human Genome 1.0 ST arrays: 23 • 61 genes show >1.5-fold basal expression differences between “edge paroxetine sensitivity lines” with p<0.001 • Our top candidate biomarker gene: CHL1 Cell adhesion molecule with homology to L1CAM 6.3-fold lower expression in paroxetine-sensitive cells; p=2.56E-05 - Verified by real-time PCR: 36-fold difference (compression by expression microarrays is common) - Expressed in brain - Implicated in cortical neurons differentiation - Reported among schizophrenia ‘risk genes’ - Implicated in novelty-seeking behavior CHL1 Cell adhesion molecule with homology to L1CAM Microarray expression levels correlated with paroxetine-mediated growth inhibition phenotypes GUSB: reference gene for LCLs CHL1 Cell adhesion molecule with homology to L1CAM Wild type mice: L1 and CHL1 are co-expressed in the dorsal thalamus and along the thalamo-cortical projections in the cortex Tissue culture: L1 and CHL1 are co-localized on growth cones and neurites of cortical and thalamic neurons L1 & CHL1(-/-)/ double mutant mice: Posterior shift of axons from motor thalamic nuclei to the visual cortex L1 and CHL1 guide thalamic axons to distinct neocortical areas Neuronal progenitor cells from CHL1−/− mice show increased proliferation and self-renewal - Neuronal tissue cultures from CHL1 −/− mice: Enhanced proliferation of neuronal progenitor cells - Neuronal progenitor cells may participate in recovery from depression! Affymetrix Human Genome 1.0 ST arrays: 2nd ‘top candidate biomarker gene’: GAP43 27 GAP43: Growth-cone associated protein 43 p= 1.78E-04 Fold-difference = -1.79 (high vs. low sensitivity) Real-time PCR = 30-fold difference Dev Neurobiol. 2010 Nov;70(13):897-911 Affymetrix Human Genome 1.0 ST arrays 3rd ‘top candidate biomarker gene’: ENPP2 28 • ENPP2: Autotaxin; Ectonucleotide pyrophosphatase/phosphodiesterase 2 • Implicated in myelin formation p= 1.55E-04 Fold-difference = -2.45 (high vs. low sensitivity) Real-time PCR = 6-fold difference Some genes are expressed at higher levels in the paroxetine-sensitive LCLs 29 • SEPT10 shows +3.51 higher expression in the paroxetine-sensitive LCLs • Septins are cytoskeletal proteins with GTPase activity • Chromosomal localization at 2q13 is associated with depression! Depression treatment biomarkers : Next phases – TAU 30 - Western blotting for proteins coded by ‘top candidate’ genes - Effects of SSRI treatment on gene expression - DNA: common SNPs, sequencing, promoter methylation - MicroRNAs with conserved 3’UTR binding sites on ‘top genes’ Depression treatment biomarkers : Next phases – Clinical samples 31 PADRE ERA-NET project (2011-2014; 0.5M Euro): - Clinical samples from Germany, Italy, Poland - Examine gene expression and protein levels in lymphocytes from depressed patients with known SSRI clinical response phenotype - Real-Time PCR for candidate genes (treatment onset) - Search genes with best correlations for SSRI clinical response - Build a “predictor tool” for SSRI response (with several genes) Blood cells expression profiling biomarkers: Eight-gene predictor for Infliximab response in RA 32 LCLs from healthy donors can facilitate early-phase research (prior to studies with patients’ samples) 33 - Stable phenotypes and close similarity to lymphocytes - Unlimited resource for DNA, mRNA, microRNAs and proteins - Cell growth may serve as surrogate drug response phenotype (other potential phenotypes: intracellular ATP or Ca2+ ) - Essential for study design: The drug target must be expressed in LCLs (human lymphocytes express ~half the human genome) This work was possible thanks to… 34 Microarray experiments and data analysis performed by: Dr. Varda Oron-Karni and Dr. Metsada Pasmanik-Chor TAU Bioinformatics Unit Everything else: Ayelet Morag M.D. / M.Sc. Honors Student Tel Aviv University, Faculty of Medicine http://NLGIP.TAU.AC.IL