Modeling Parkinson’s Disease Heterogeneity to Accelerate Precision Medicine

The lysosomal β-glucocerebrosidase strikes mitochondria: implications for Parkinson's therapeutics

Parkinson's disease is a neurodegenerative disorder primarily known for typical motor features that arise due to the loss of dopaminergic neurons in the substantia nigra. However, the precise molecular aetiology of the disease is still unclear. Several cellular pathways have been linked to Parkinson's disease, including the autophagy-lysosome pathway, α-synuclein aggregation and mitochondrial function. Interestingly, the mechanistic link between GBA1, the gene that encodes for lysosomal β-glucocerebrosidase (GCase), and Parkinson's disease lies in the interplay between GCase functions in the lysosome and mitochondria. GCase mutations alter mitochondria-lysosome contact sites. In the lysosome, reduced GCase activity leads to glycosphingolipid build-up, disrupting lysosomal function and autophagy, thereby triggering α-synuclein accumulation. Additionally, α-synuclein aggregates reduce GCase activity, creating a self-perpetuating cycle of lysosomal dysfunction and α-synuclein accumulation. GCase can also be imported into the mitochondria, where it promotes the integrity and function of mitochondrial complex I. Thus, GCase mutations that impair its normal function increase oxidative stress in mitochondria, the compartment where dopamine is oxidized. In turn, the accumulation of oxidized dopamine adducts further impairs GCase activity, creating a second cycle of GCase dysfunction. The oxidative state triggered by GCase dysfunction can also induce mitochondrial DNA damage which, in turn, can cause dopaminergic cell death. In this review, we highlight the pivotal role of GCase in Parkinson's disease pathogenesis and discuss promising examples of GCase-based therapeutics, such as gene and enzyme replacement therapies, small molecule chaperones and substrate reduction therapies, among others, as potential therapeutic interventions.

Patient-specific game-based transfer method for Parkinson's disease severity prediction

Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is used to evaluate the feasibility and effectiveness. The mean values of mean absolute error, root mean square error, and volatility obtained by predicting motor-UPDRS and total-UPDRS for target patients are 1.59, 1.95, 1.56 and 1.98, 2.54, 1.94, respectively. Experiment results show that the PSGT has better performance in both prediction error and stability over compared methods.

Genetic Background Matters: Population-Based Studies in Model Organisms for Translational Research

We are all similar but a bit different. These differences are partially due to variations in our genomes and are related to the heterogeneity of symptoms and responses to treatments that patients exhibit. Most animal studies are performed in one single strain with one manipulation. However, due to the lack of variability, therapies are not always reproducible when treatments are translated to humans. Panels of already sequenced organisms are valuable tools for mimicking human phenotypic heterogeneities and gene mapping. This review summarizes the current knowledge of mouse, fly, and yeast panels with insightful applications for translational research.

Cell-free amplification of prions: Where do we stand?

Neurodegenerative diseases (NDs) such as Alzheimer's disease (AD), Parkinson's disease (PD), atypical parkinsonisms, frontotemporal dementia (FTLD) and prion diseases are characterized by the accumulation of misfolded proteins in the central nervous system (CNS). Although the cause for the initiation of protein aggregation is not well understood, these aggregates are disease-specific. For instance, AD is characterized by the intraneuronal accumulation of tau and extracellular deposition of amyloid-β (Aβ), PD is marked by the intraneuronal accumulation of α-synuclein, many FTLD are associated with the accumulation of TDP-43 while prion diseases show aggregates of misfolded prion protein. Hence, misfolded proteins are considered disease-specific biomarkers and their identification and localization in the CNS, collected postmortem, is required for a definitive diagnosis. With the development of two innovative cell-free amplification techniques named Protein Misfolding Cyclic Amplification (PMCA) and Real-Time Quaking-Induced Conversion (RT-QuIC), traces of disease-specific biomarkers were found in CSF and other peripheral tissues (e.g., urine, blood, and olfactory mucosa) of patients with different NDs. These techniques exploit an important feature shared by many misfolded proteins, that is their ability to interact with their normally folded counterparts and force them to undergo similar structural rearrangements. Essentially, RT-QuIC and PMCA mimic in vitro the same pathological processes of protein misfolding which occur in vivo in a very rapid manner. For this reason, they have been employed for studying different aspects of protein misfolding but, overall, they seem to be very promising for the premortem diagnosis of NDs.

The role of pharmacogenomics in the personalization of Parkinson's disease treatment

Parkinson's disease (PD)-related phenotypes can vary among patients substantially, including response to dopaminergic treatment in terms of efficacy and occurrence of adverse events. Many pharmacogenetic studies have already been conducted to find genetic markers of response to dopaminergic treatment. Integration of genetic and clinical data has already resulted in construction of clinical pharmacogenetic models for prediction of adverse events. However, the results of pharmacogenetic studies are inconsistent. More comprehensive genome-wide approaches are needed to find genetic biomarkers of PD-related phenotypes to better explain the variability in response to treatment. These genetic markers should be integrated with clinical, environmental, imaging, and other omics data to build clinically useful algorithms for personalization of PD management.