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The objective of this study was to evaluate the pathology time course of the LRRK2 knockout rat model of Parkinson's disease at 1-, 2-, 4-, 8-, 12-, and 16-months of age. The evaluation consisted of histopathology and ultrastructure examination of selected organs, including the kidneys, lungs, spleen, heart, and liver, as well as hematology, serum, and urine analysis. The LRRK2 knockout rat, starting at 2-months of age, displayed abnormal kidney staining patterns and/or morphologic changes that were associated with higher serum phosphorous, creatinine, cholesterol, and sorbitol dehydrogenase, and lower serum sodium and chloride compared to the LRRK2 wild-type rat. Urinalysis indicated pronounced changes in LRRK2 knockout rats in urine specific gravity, total volume, urine potassium, creatinine, sodium, and chloride that started as early as 1- to 2-months of age. Electron microscopy of 16-month old LRRK2 knockout rats displayed an abnormal kidney, lung, and liver phenotype. In contrast, there were equivocal or no differences in the heart and spleen of LRRK2 wild-type and knockout rats. These findings partially replicate data from a recent study in 4-month old LRRK2 knockout rats and expand the analysis to demonstrate that the renal and possibly lung and liver abnormalities progress with age. The characterization of LRRK2 knockout rats may prove to be extremely valuable in understanding potential safety liabilities of LRRK2 kinase inhibitor therapeutics for treating Parkinson's disease.

Alpha-synuclein (aSyn) is linked to Parkinson’s disease (PD) through SNCA genetic mutations, phosphorylated aSyn in Lewy bodies and Lewy neurites, and most recently through evidence of aSyn aggregation in patient spinal fluid using the aSyn seed amplification assay. Therefore, understanding the biology of this protein and developing therapeutic interventions targeting pathological processing of aSyn are a key area of focus for novel treatments to slow or stop PD. Reliable preclinical models are imperative for these efforts. To this end, we developed a novel model using CRISPR/Cas9 to humanize the regions surrounding the naturally occurring threonine 53 amino acid in the Sprague Dawley rat to generate a humanized A53T aSyn rat model (aSyn A53T KI). We also generated an Snca knockout (aSyn KO) line to pair with the humanized A53T aSyn rat line to confirm that phenotypes were not due to loss of endogenous rat aSyn protein. A systematic phenotyping study was performed on these lines, assessing PD-related pathology and phenotypes at multiple timepoints. The aSyn KO rat line was profiled at 6 and 12 months of age, revealing successful aSyn protein knockout. The aSyn A53T KI model was profiled at 4, 8, 12, and 18 months of age for motor and non-motor phenotypes, nigrostriatal degeneration, and brain pathology. We confirmed the aSyn A53T KI rat expresses human aSyn while lacking endogenous rat aSyn. Motor function and non-motor function remain largely unaffected in this model, and no overt nigrostriatal degeneration or brain pathology are observed up to 18 months of age. Although the aSyn A53T KI rat lacks the ability to model PD pathology and phenotypes at baseline, it is an ideal model for investigating the impact of exogenous synuclein aggregates or environmental triggers on human aSyn in an in vivo model system.

Now that wearable sensors have become more commonplace, it is possible to monitor individual healthcare-related activity outside the clinic, unleashing potential for early detection of events in diseases such as Parkinson’s disease (PD). However, the unsupervised and “open world” nature of this type of data collection make such applications difficult to develop. In this proof-of-concept study, we used inertial sensor data from Verily Study Watches worn by individuals for up to 23 h per day over several months to distinguish between seven subjects with PD and four without. Since motor-related PD symptoms such as bradykinesia and gait abnormalities typically present when a PD subject is walking, we initially used human activity recognition (HAR) techniques to identify walk-like activity in the unconstrained, unlabeled data. We then used these “walk-like” events to train one-dimensional convolutional neural networks (1D-CNNs) to determine the presence of PD. We report classification accuracies near 90% on single 5-s walk-like events and 100% accuracy when taking the majority vote over single-event classifications that span a duration of one day. Though based on a small cohort, this study shows the feasibility of leveraging unconstrained wearable sensor data to accurately detect the presence or absence of PD.

Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900–0·946) with high sensitivity (0·834, 95% CI 0·711–0·883) and specificity (0·903, 95% CI 0·824–0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867–0·921) in the PDBP cohort, 0·998 (0·992–1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896–0·962) in LABS-PD, and 0·939 (0·891–0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003). Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Heterozygous mutations in the GBA1 gene – encoding lysosomal glucocerebrosidase (GCase) – are the most common genetic risk factors for Parkinson's disease (PD). Experimental evidence suggests a correlation between decreased GCase activity and accumulation of alpha-synuclein (aSyn). To enable a better understanding of the relationship between aSyn and GCase activity, we developed and characterized two mouse models that investigate aSyn pathology in the context of reduced GCase activity. The first model used constitutive overexpression of wild-type human aSyn in the context of the homozygous GCase activity-reducing D409V mutant form of GBA1. Although increased aSyn pathology and grip strength reductions were observed in this model, the nigrostriatal system remained largely intact. The second model involved injection of aSyn preformed fibrils (PFFs) into the striatum of the homozygous GBA1 D409V knock-in mouse model. The GBA1 D409V mutation did not exacerbate the pathology induced by aSyn PFF injection. This study sheds light on the relationship between aSyn and GCase in mouse models, highlighting the impact of model design on the ability to model a relationship between these proteins in PD-related pathology.

Background Efforts to identify fluid biomarkers of Parkinson’s disease (PD) have intensified in the last decade. As the role of inflammation in PD pathophysiology becomes increasingly recognized, investigators aim to define inflammatory signatures to help elucidate underlying mechanisms of disease pathogenesis and aid in identification of patients with inflammatory endophenotypes that could benefit from immunomodulatory interventions. However, discordant results in the literature and a lack of information regarding the stability of inflammatory factors over a 24-h period have hampered progress. Methods Here, we measured inflammatory proteins in serum and CSF of a small cohort of PD ( n  = 12) and age-matched healthy control (HC) subjects ( n  = 6) at 11 time points across 24 h to (1) identify potential diurnal variation, (2) reveal differences in PD vs HC, and (3) to correlate with CSF levels of amyloid β (Aβ) and α-synuclein in an effort to generate data-driven hypotheses regarding candidate biomarkers of PD. Results Despite significant variability in other factors, a repeated measures two-way analysis of variance by time and disease state for each analyte revealed that serum IFNγ, TNF, and neutrophil gelatinase-associated lipocalin (NGAL) were stable across 24 h and different between HC and PD. Regression analysis revealed that C-reactive protein (CRP) was the only factor with a strong linear relationship between CSF and serum. PD and HC subjects showed significantly different relationships between CSF Aβ proteins and α-synuclein and specific inflammatory factors, and CSF IFNγ and serum IL-8 positively correlated with clinical measures of PD. Finally, linear discriminant analysis revealed that serum TNF and CSF α-synuclein discriminated between PD and HC with a minimum of 82% sensitivity and 83% specificity. Conclusions Our findings identify a panel of inflammatory factors in serum and CSF that can be reliably measured, distinguish between PD and HC, and monitor inflammation as disease progresses or in response to interventional therapies. This panel may aid in generating hypotheses and feasible experimental designs towards identifying biomarkers of neurodegenerative disease by focusing on analytes that remain stable regardless of time of sample collection.

The pathogenesis and clinical heterogeneity of Parkinson’s disease (PD) have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of cerebrospinal fluid (CSF) opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in PD with 569 patients (350 idiopathic patients, 65 GBA  + mutation carriers and 154 LRRK2  + mutation carriers), 534 controls, and 4135 proteins analyzed. Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest PD genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB, was previously reported to be upregulated in the substantia nigra of PD patients. We also compared the CSF proteomes of patients and controls. Proteome differences between GBA  + patients and unaffected GBA  + controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. In the LRRK2  + subcohort we found dysregulated lysosomal degradation, altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction/oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances. Finally, we used proteomic data to stratify idiopathic patients into “endotypes”. The identified endotypes show differences in cognitive and motor disease progression based on previously reported protein-based risk scores.Our findings not only contribute to the identification of new therapeutic targets but also to shape personalized medicine in CNS neurodegeneration.

The Neuronal alpha-Synuclein Disease (NSD) biological definition and Integrated Staging System (NSD-ISS) provide a research framework to identify individuals with Lewy body pathology and stage them based on underlying biology and increasing degree of functional impairment. Utilizing data from the PPMI, PASADENA, and SPARK studies, we developed and applied biologic and clinical data-informed definitions for the NSD-ISS across the disease continuum. Individuals enrolled as Parkinson’s disease, Prodromal, or Healthy Controls were defined and staged based on biological, clinical, and functional anchors at baseline. Across the three studies 1741 participants had SAA data and of these 1030 (59%) were S+ consistent with NSD. Among sporadic PD, 683/736 (93%) were NSD, and the distribution for Stages 2B, 3, and 4 was 25%, 63%, and 9%, respectively. Median (95% CI) time to developing a clinically meaningful outcome was 8.3 (6.2, 10.1), 5.9 (4.1, 6.0), and 2.4 (1.0, 4.0) years for baseline stage 2B, 3, and 4, respectively. We propose pilot biologic and clinical anchors for NSD-ISS. Our results highlight the baseline heterogeneity of individuals currently defined as early PD. Baseline stage predicts time to progression to clinically meaningful milestones. Further research on validation of the anchors in longitudinal cohorts is necessary.

Over the past decade, biomedical researchers have generally become increasingly open to sharing resources, yet this shift in mindset has barely begun for Parkinson's disease. Parkinson's researchers and funders have tended to plan for the short term, focusing on hypothesis-driven studies. The foresight and funding for long-term infrastructure that would enable data sharing have been missing.

Biomarkers will be essential if research on Parkinson's is to progress, but their discovery depends on scientists sharing data, says Mark Frasier.