Biology subjects

Grosset, D. G.

Publications and source records attributed to Grosset, D. G..

3 recordsLinked to original sources

bioRxiv2025

Longitudinal Proteomic Profiling Defines Robust Molecular Subtypes Underlying the Heterogeneity of Parkinson's Disease

Heterogeneity of Parkinsons disease (PD) pathology is a barrier to developing therapeutics and understanding progression and prognosis. High throughput proteomic measures can be used to better interpret PD pathophysiology and generate clusters to define disease subtypes. Identification of subtypes related to clinical phenotypes will help researchers understand PD progression. We analyzed longitudinal proteomic data from the Tracking Parkinsons Cohort, consisting of recent-onset PD patients across 72 UK sites. 794 patients were measured on the Somalogic platform (7596 proteins) for three time points. Weighted Gene Co-Expression Network Analysis (WGCNA) at each time point revealed consistent protein co-expression modules. Two modules were strongly preserved across all three time points and in validation in the Global Neurodegenerative Proteomics Consortia (GNPC) datasets. The brown module was enriched for metabolite pathways and the blue module with cellular signaling pathways and associated with quality of life scores. Conversely, the smaller red module had distinct cognitive function phenotypes and changed protein expression between visit time points. Using detailed characterization of proteomic clusters we have provided a comprehensive view of PD progression offering deeper insights into the conservation of proteomic expression, suggesting new module subsets and providing candidate target proteins for further study.

neuroscience↗

bioRxiv2019

Universal continuous severity traits underlying hundreds of Parkinson’s disease clinical features

The generation of deeply phenotyped patient cohorts offers an enormous potential to identify disease subtypes but are currently limited by the cohort size and the heterogeneity of the clinical assessments collected across different cohorts. Identifying the universal axes of clinal severity and progression is key to accelerating our understanding of how disease manifests and progresses. These universal axes would accelerate our understanding of how Parkinsons disease (PD) manifests and progresses through which patients may be appropriately compared appropriately stratified, and personalised therapeutic strategies and treatments can be developed and targeted. We developed a Bayesian multiple phenotype mixed model incorporating the genetic relationships between individuals which is able to reduce a wide-array of different clinical measurements into a smaller number of continuous underlying factors named phenotypic axis. We identify three principal axes of PD patient phenotypic variation which are reproducibly found across three independent, deeply and diversely phenotyped cohorts. Together they explain over 75% of the observed clinical variation and remain robustly captured with a fraction of the clinically-recorded features. The most influential axis was associated with the genetic risk of Alzheimers disease (AD) and involves genetic pathways associated with neuroinflammation. Our results suggest PD patients with a high genetic risk for AD are more likely to develop a more aggressive form of PD including, but not limited to, dementia.

neuroscience↗

bioRxiv2018

Parkinson disease age of onset GWAS: defining heritability, genetic loci and a-synuclein mechanisms

Increasing evidence supports an extensive and complex genetic contribution to Parkinsons disease (PD). Previous genome-wide association studies (GWAS) have shed light on the genetic basis of risk for this disease. However, the genetic determinants of PD age of onset are largely unknown. Here we performed an age of onset GWAS based on 28,568 PD cases. We estimated that the heritability of PD age of onset due to common genetic variation was ~0.11, lower than the overall heritability of risk for PD (~0.27) likely in part because of the subjective nature of this measure. We found two genome-wide significant association signals, one at SNCA and the other a protein-coding variant in TMEM175, both of which are known PD risk loci and a Bonferroni corrected significant effect at other known PD risk loci, INPP5F/BAG3, FAM47E/SCARB2, and MCCC1. In addition, we identified that GBA coding variant carriers had an earlier age of onset compared to non-carriers. Notably, SNCA, TMEM175, SCARB2, BAG3 and GBA have all been shown to either directly influence alpha-synuclein aggregation or are implicated in alpha-synuclein aggregation pathways. Remarkably, other well-established PD risk loci such as GCH1, MAPT and RAB7L1/NUCKS1 (PARK16) did not show a significant effect on age of onset of PD. While for some loci, this may be a measure of power, this is clearly not the case for the MAPT locus; thus genetic variability at this locus influences whether but not when an individual develops disease. We believe this is an important mechanistic and therapeutic distinction. Furthermore, these data support a model in which alpha-synuclein and lysosomal mechanisms impact not only PD risk but also age of disease onset and highlights that therapies that target alpha-synuclein aggregation are more likely to be disease-modifying than therapies targeting other pathways.

genetics↗