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Biology subjects

Ulrich, C.

Publications and source records attributed to Ulrich, C..

2 recordsLinked to original sources

Recapitulating Parkinson's pathology in human iPSC dopaminergic neurons reveals new mechanistic insights into Lewy body formation and heterogeneity.

The accumulation of alpha-synuclein (aSyn) into intraneuronal inclusions of heterogeneous morphology, known as Lewy bodies (LB), is one of the defining diagnostic features of Parkinsons disease (PD); yet, our understanding of the mechanisms underpinning their formation and heterogeneity remains incomplete. Here, we present a human isogenic iPSC-derived dopaminergic neuron (iDA) model that faithfully recapitulates the diverse biochemical, morphological, and ultrastructural features of LB neuropathology in PD. Unlike other iDA seeding models, our model does not rely on aSyn overexpression, mutations, or genetic engineering, making it a more physiologically relevant system for studying PD. We demonstrate that the iDA model accurately reproduces the temporal relationships between neuritic and cell-body aSyn pathology, recapitulating the full biochemical spectrum, post-translational modifications (PTM), and morphological diversity of aSyn aggregates found in human PD tissue. Moreover, our work provides critical insight into how different pathways to aSyn fibrillization and the complex interaction between aSyn fibrils and membranous organelles influence the morphological diversity of LB-like inclusions. This model represents a versatile platform for investigating the mechanisms of pathology formation, maturation, and neuronal dysfunction, as well as supporting the development of diagnostics that capture the diversity of aSyn pathology in PD and related synucleinopathies.

neuroscience↗

Pathway Polygenic Risk Scores (pPRS) for the Analysis of Gene-environment Interaction

A polygenic risk score (PRS) is used to quantify the combined disease risk of many genetic variants. For complex human traits there is interest in determining whether the PRS modifies, i.e. interacts with, important environmental (E) risk factors. Detection of a PRS by environment (PRS x E) interaction may provide clues to underlying biology and can be useful in developing targeted prevention strategies for modifiable risk factors. The standard PRS may include a subset of variants that interact with E but a much larger subset of variants that affect disease without regard to E. This latter subset will water down the underlying signal in former subset, leading to reduced power to detect PRS x E interaction. We explore the use of pathway-defined PRS (pPRS) scores, using state of the art tools to annotate subsets of variants to genomic pathways. We demonstrate via simulation that testing targeted pPRS x E interaction can yield substantially greater power than testing overall PRS x E interaction. We also analyze a large study (N=78,253) of colorectal cancer (CRC) where E = non-steroidal anti-inflammatory drugs (NSAIDs), a well-established protective exposure. While no evidence of overall PRS x NSAIDs interaction (p=0.41) is observed, a significant pPRS x NSAIDs interaction (p=0.0003) is identified based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. NSAIDS is protective (OR=0.84) for those at the 5th percentile of the TGF-{beta}/GRHR pPRS (low genetic risk, OR), but significantly more protective (OR=0.70) for those at the 95th percentile (high genetic risk). From a biological perspective, this suggests that NSAIDs may act to reduce CRC risk specifically through genes in these pathways. From a population health perspective, our result suggests that focusing on genes within these pathways may be effective at identifying those for whom NSAIDs-based CRC-prevention efforts may be most effective. Author SummaryThe identification of polygenic risk score (PRS) by environment (PRSxE) interactions may provide clues to underlying biology and facilitate targeted disease prevention strategies. The standard approach to computing a PRS likely includes many variants that affect disease without regard to E, reducing power to detect PRS x E interactions. We utilize gene annotation tools to develop pathway-based PRS (pPRS) scores and show by simulation studies that testing pPRS x E interaction can yield substantially greater power than testing PRS x E, while also integrating biological knowledge into the analysis. We apply our method to a large study of colorectal cancer to identify a significant pPRS x NSAIDs interaction (p=0.0003) based on SNPs within the TGF-{beta} / gonadotropin releasing hormone receptor (GRHR) pathway. Our findings suggest that focusing on genetic susceptibility within biologically informed pathways may be more sensitive for identifying exposures that can be considered as part of a precision prevention approach.

genetics↗