Expression mRNA data from mouse blood samples measured using the NanoString nCounter platform
Ontology highlight
ABSTRACT: Background: The diagnosis of Parkinson’s disease (PD) is usually not established until advanced neurodegeneration leads to clinically detectable symptoms. Previous blood PD transcriptome studies show low concordance, possibly due to the use of microarray technology, which has high measurement variation. The Leucine-rich repeat kinase 2 (LRRK2) G2019S mutation predisposes to PD. Using preclinical and clinical studies, we sought to develop a novel statistically motivated transcriptomic-based approach to identify a molecular signature in the blood of Ashkenazi Jewish PD patients including LRRK2 mutation carriers. Methods: Using a digital gene expression platform to quantify 175 mRNA markers with low coefficients of variation (CV), we first compared whole blood transcript levels in mouse models 1) over-expressing wild-type (WT) LRRK2, 2) overexpressing G2019S LRRK2, 3) lacking LRRK2 (knockout), 4) and in WT controls. We then studied an Ashkenazi Jewish cohort of 34 symptomatic PD patients (both WT LRRK2 and G2019S LRRK2) and 32 asymptomatic controls. Results: The expression profiles distinguished the 4 mouse groups with different genetic background. In patients, we detected significant differences in blood transcript levels both between individuals differing in LRRK2 genotype and between PD patients and controls. Discriminatory PD markers included genes associated with innate and adaptive immunity and inflammatory disease. Notably, gene expression patterns in L-DOPA-treated PD patients were significantly closer to those of healthy controls in a dose-dependent manner. Conclusions: We identify whole blood low CV mRNA signatures correlating with LRRK2 genotype and with PD disease state. This approach may provide insight into pathogenesis and a route to early disease detection.
ORGANISM(S): Mus musculus Homo sapiens
PROVIDER: GSE62470 | GEO | 2015/10/17
SECONDARY ACCESSION(S): PRJNA264195
REPOSITORIES: GEO
ACCESS DATA