Discovery and functional prioritization of Parkinson's disease candidate genes from large-scale whole exome sequencing.

BACKGROUND: Whole-exome sequencing (WES) has been successful in identifying genes that cause familial Parkinson's disease (PD). However, until now this approach has not been deployed to study large cohorts of unrelated participants. To discover rare PD susceptibility variants, we performed WES in 1148 unrelated cases and 503 control participants. Candidate genes were subsequently validated for functions relevant to PD based on parallel RNA-interference (RNAi) screens in human cell culture and Drosophila and C. elegans models. RESULTS: Assuming autosomal recessive inheritance, we identify 27 genes that have homozygous or compound heterozygous loss-of-function variants in PD cases. Definitive replication and confirmation of these findings were hindered by potential heterogeneity and by the rarity of the implicated alleles. We therefore looked for potential genetic interactions with established PD mechanisms. Following RNAi-mediated knockdown, 15 of the genes modulated mitochondrial dynamics in human neuronal cultures and four candidates enhanced α-synuclein-induced neurodegeneration in Drosophila. Based on complementary analyses in independent human datasets, five functionally validated genes-GPATCH2L, UHRF1BP1L, PTPRH, ARSB, and VPS13C-also showed evidence consistent with genetic replication. CONCLUSIONS: By integrating human genetic and functional evidence, we identify several PD susceptibility gene candidates for further investigation. Our approach highlights a powerful experimental strategy with broad applicability for future studies of disorders with complex genetic etiologies

Characterization of Pine Pellet and Peanut Hull Pyrolysis Bio-oils by Negative-Ion Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Pyrolysis of solid biomass, in this case pine pellets and peanut hulls, generates a hydrocarbon-rich liquid product (bio-oil) consisting of oily and aqueous phases. Here, each phase is characterized by negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS) to yield unique elemental compositions for thousands of compounds. Bio-oils are dominated by O_<i>x</i> species: few oxygens per molecule for the oily phase and many more oxygens per molecules for the aqueous phase. Thus, the increased oxygen content per molecule accounts for its water solubility. Peanut hull bio-oil is much more compositionally complex and contains more nitrogen-containing compounds than pine pellet bio-oil. Bulk C, H, N, O, and S measurements confirm the increased levels of nitrogen-containing species identified in the peanut hull pyrolysis oil by FT-ICR MS. The ability of FT-ICR MS to identify and assign unique elemental compositions to compositionally complex bio-oils based on ultrahigh mass resolution and mass accuracy is demonstrated

Characterization of Pine Pellet and Peanut Hull Pyrolysis Bio-oils by Negative-Ion Electrospray Ionization Fourier Transform Ion Cyclotron Resonance Mass Spectrometry

Pyrolysis of solid biomass, in this case pine pellets and peanut hulls, generates a hydrocarbon-rich liquid product (bio-oil) consisting of oily and aqueous phases. Here, each phase is characterized by negative-ion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS) to yield unique elemental compositions for thousands of compounds. Bio-oils are dominated by O_<i>x</i> species: few oxygens per molecule for the oily phase and many more oxygens per molecules for the aqueous phase. Thus, the increased oxygen content per molecule accounts for its water solubility. Peanut hull bio-oil is much more compositionally complex and contains more nitrogen-containing compounds than pine pellet bio-oil. Bulk C, H, N, O, and S measurements confirm the increased levels of nitrogen-containing species identified in the peanut hull pyrolysis oil by FT-ICR MS. The ability of FT-ICR MS to identify and assign unique elemental compositions to compositionally complex bio-oils based on ultrahigh mass resolution and mass accuracy is demonstrated

Prediction of cognition in Parkinson's disease with a clinical–genetic score: a longitudinal analysis of nine cohorts

International audienceSummary Background Cognitive decline is a debilitating manifestation of disease progression in Parkinson’s disease. We aimed to develop a clinical-genetic score to predict global cognitive impairment in patients with the disease. Methods A prediction algorithm for global cognitive impairment (defined as Mini Mental State Exam (MMSE) ≤25) was built using data from 1,350 patients with 5,165 longitudinal visits over 12.8 (median, 2.8) years. Age at onset, MMSE, education, motor exam score, gender, depression and GBA mutations, machine selected through stepwise Cox’ hazards analysis and Akaike’s information criterion, were used to compute the multivariable predictor. Independent validation was achieved in another 1,132 patients with 19,127 visits over 8.6 (median, 6.5) years. Findings The cognitive risk score accurately predicted cognitive impairment within ten years of disease onset with an area under the curve (AUC) of >0.85 in both the discovery (95% CI, 0.821–0.902) and validation populations (95% CI, 0.779 – 0.913). 72.6% of patients scoring in the highest quartile were cognitively impaired by ten years vs. 3.7% in the lowest quartile (hazard ratio, 18.4, 95% CI, 9.4 – 36.1). Dementia or disabling cognitive impairment was predicted with an AUC of 0.877 (95% CI 0.788–0.943) and high negative predictive value (0.920, 95% 0.877–0.954) at the predefined cutoff (0.196). Performance was stable in 10,000 randomly resampled subsets. Interpretation Our predictive algorithm provides a potential test for future cognitive health or impairment in patients with Parkinson’s. It could improve trials of cognitive interventions and inform on prognosis