Project description:This experiment set is used for the manuscript entitled: Pharmacogenomics of Interferon-b therapy in multiple sclerosis: Baseline IFN signature determines pharmacological differences between patients. In this study we generated and analyzed pre- and post- IFNb treatment gene expression patterns of RRMS patients with the aim of identifying pre-existing and/or drug-induced signatures that will allow us to make predictions on the expected pharmacological effects of IFNb treatment. We show that the expression level of IFN response genes prior to treatment, determines the pharmacological differences between patients with MS at the molecular level. A group of 16 Dutch patients (10 females and 6 males) with clinically definite relapsing-remitting MS was recruited from the outpatient clinic of the MS Centre Amsterdam. Mean age at start of IFNb therapy is 40.6 +/- 7.7, mean EDSS is 2.3 +/- 1.3 (range 1-6). Blood samples were obtained just before treatment and 1 month after start of the therapy. Patients received Avonex, Betaferon, Rebif 22 or Rebif 44. A compound treatment design type is where the response to administration of a compound or chemical (including biological compounds such as hormones) is assayed. Compound Based Treatment: Before and 1 month after IFNb therapy Keywords: compound_treatment_design
Project description:This experiment set is used for the manuscript entitled: Pharmacogenomics of Interferon-b therapy in multiple sclerosis: Baseline IFN signature determines pharmacological differences between patients. In this study we generated and analyzed pre- and post- IFNb treatment gene expression patterns of RRMS patients with the aim of identifying pre-existing and/or drug-induced signatures that will allow us to make predictions on the expected pharmacological effects of IFNb treatment. We show that the expression level of IFN response genes prior to treatment, determines the pharmacological differences between patients with MS at the molecular level. A group of 16 Dutch patients (10 females and 6 males) with clinically definite relapsing-remitting MS was recruited from the outpatient clinic of the MS Centre Amsterdam. Mean age at start of IFNb therapy is 40.6 +/- 7.7, mean EDSS is 2.3 +/- 1.3 (range 1-6). Blood samples were obtained just before treatment and 1 month after start of the therapy. Patients received Avonex, Betaferon, Rebif 22 or Rebif 44. A compound treatment design type is where the response to administration of a compound or chemical (including biological compounds such as hormones) is assayed. Compound Based Treatment: Before and 1 month after IFNb therapy Keywords: compound_treatment_design Complex
Project description:Statins reduce cardiovascular disease risk by lowering plasma low density lipoprotein (LDL)-cholesterol. To identify novel pathways that modulate statin response, we assessed the influence of simvastatin exposure on expression quantitative trait locus (eQTL) associations across the genome in 480 lymphoblastoid cell lines (LCLs). Cell lines were derived blood samples collected ant entry visit from participants in the Cholesterol and Pharmacogenomics (CAP) trial, who underwent a 6 week 40mg/day simvastatin trial. We identified 4590 cis-eQTLS that were independent of treatment status (FDR=1%) and six cis-eQTLS for which there was evidence of an interaction with treatment (FDR=20%). Genotypes and Phenotypes derived from these indivudals are available through dbGaP (Accession Number). eQTL results are available at: http://eqtl.uchicago.edu/cgi=bin/gbrowse/eqtl/
Project description:Patients infected with Leishmania braziliensis develop chronic lesions that often fail to respond to treatment. To determine whether genes whose expression is highly variable in lesions might influence disease outcome, we obtained biopsies of lesions from patients prior to drug treatment, performed transcriptomic profiling, and identified highly variable genes whose expression correlated with treatment outcome. Amongst the most variable genes were components of the cytolytic pathway, the expression of which appeared to be driven by parasite load in the skin. We demonstrated that treatment failure can be directly linked to the cytolytic pathway activated during infection. Using this host-pathogen biomarker profile, we show that treatment outcome can be predicted before the start of treatment. These findings not only raise the possibility of point-of-care diagnostic screening to identify patients at high risk of treatment failure and provide a rationale for a precision medicine approach to drug selection in cutaneous leishmaniasis, but more broadly also demonstrate the value of identifying genes of high variability in other diseases to better understand and predict diverse clinical outcomes.