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SummaryBackgroundRecent research demonstrates that α-synuclein seed amplification assays (αSyn-SAA) accurately differentiate Parkinson’s disease (PD) patients from healthy controls (HC). We used the well-characterized, multicenter Parkinson’s Progression Markers Initiative (PPMI) cohort to further assess the diagnostic performance of αSyn-SAA and to examine whether the assay identifies heterogeneity among patients and enables early identification in at-risk groups.MethodsαSyn-SAA analysis of cerebrospinal fluid (CSF) was performed using previously described methods. We assessed sensitivity and specificity in PD and HC, including subgroups based on genetic and clinical features. We determined the frequency of positive αSyn-SAA results in prodromal participants (REM sleep behavior disorder and hyposmia) and non-manifesting carriers (NMCs) of genetic variants associated with PD and compared αSyn-SAA to clinical measures and other biomarkers.Findings1,123 participants were included: 545 PD, 163 HCs, 54 participants with scans without evidence of dopaminergic deficit (SWEDDs), 51 prodromal participants, and 310 NMCs. Sensitivity and specificity for PD versus HC were 88% and 96%, respectively. Sensitivity in sporadic PD with the typical olfactory deficit was 99%. The proportion of positive αSyn-SAA was lower in subgroups including LRRK2 PD (68%) and sporadic PD patients without olfactory deficit (78%). Participants with LRRK2 variant and normal olfaction had an even lower αSyn-SAA positivity rate (35%). Among prodromal and at-risk groups, 86% of RBD and hyposmic cases had positive αSyn-SAA. 8% of NMC (either LRRK2 or GBA) were positive.InterpretationThis study represents the largest analysis of αSyn-SAA for biochemical diagnosis of PD. Our results demonstrate that the assay classifies PD patients with high sensitivity and specificity, provides information about molecular heterogeneity, and detects prodromal individuals prior to diagnosis. These findings suggest a crucial role for αSyn-SAA in therapeutic development, both to identify pathologically defined subgroups of PD patients and to establish biomarker-defined at-risk cohorts.FundingPPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including: PPMI is supported by a consortium of private and philanthropic parnters: Abbvie, AcureX, Aligning Science Across Parkinson’s, Amathus Therapeutics, Avid Radiopharmaceuticals, Bial Biotech, Biohaven, Biogen, BioLegend, Bristol-Myers Squibb, Calico Labs, Celgene, Cerevel, Coave, DaCapo Brainscience, 4D Pharma, Denali, Edmond J. Safra Foundation, Eli Lilly, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Insitro, Janssen Neuroscience, Lundbeck, Merck, Meso Scale Discovery, Neurocrine Biosciences, Prevail Therapeutics, Roche, Sanofi Genzyme, Servier, Takeda, Teva, UCB, VanquaBio, Verily, Voyager Therapeutics, and Yumanity.Research in ContextEvidence before the studyWe searched PubMed with the terms Parkinson’s disease (PD), prodromal, Non-manifest carriers, GBA, LRRK2 and real-me quaking-induced conversion (RT-QuIC), protein misfolding cyclic amplification (PMCA), and seed amplification assay (SAA) for articles published in English on or before Oct 25, 2022, in any field. This is a large and rapidly growing literature, and a number of studies were identified, including case-series of PD patients with and without genetic variants, individuals with isolated REM sleep behavior disorder (RBD), and a small number of studies of non-manifesting carriers of genetic variants (4) associated with PD.Added value of the studyTo our knowledge, this is the largest report of comparative data from a cohort of PD patients, healthy controls, individuals with clinical syndromes prodromal to PD (hyposmia and RBD), and non-manifest carriers ofLRRK2 G2019SandGBA N370Smutations. The strengths of our data include a large sample size, robust clinical data set, a high percentage of DAT scans completed, and the ability to compare non-manifest carriers to similar aged healthy controls, which allows for inter-group comparisons and sub-group analysis. The key novel findings in this study include: 1) marked variability in rates of positive αSyn-SAA results particularly among LRRK2 variant carriers depending on olfactory performance and sex; 2) αSyn-SAA positivity in prodromal and NMCs without dopaminergic imaging abnormalities in a substantial number of cases, while the converse is less common, indicating that αSyn-SAA may be a very early indicator of synucleinopathy. We also confirmed the high diagnostic accuracy of αSyn-SAA for sporadic PD vs. HC and that αSyn-SAA is negative in the vast majority of NMC, suggesting that the presence of synuclein aggregates in CSF is not a life-long trait but rather acquired at some point relatively close to disease onset.Implications of all available evidenceOur results demonstrate that the assay classifies PD patients with high sensitivity and specificity, provides information about molecular heterogeneity, and detects prodromal individuals prior to diagnosis. These findings suggest a crucial role for αSyn-SAA in therapeutic development, both to identify pathologically defined subgroups of PD patients and to establish biomarker-defined at-risk cohorts.

Summary Background Parkinson's disease is heterogeneous in symptom presentation and progression. Increased understanding of both aspects can enable better patient management and improve clinical trial design. Previous approaches to modelling Parkinson's disease progression assumed static progression trajectories within subgroups and have not adequately accounted for complex medication effects. Our objective was to develop a statistical progression model of Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects. Methods In this longitudinal data study, data were collected for up to 7-years on 423 patients with early Parkinson's disease and 196 healthy controls from the Parkinson's Progression Markers Initiative (PPMI) longitudinal observational study. A contrastive latent variable model was applied followed by a novel personalised input-output hidden Markov model to define disease states. Clinical significance of the states was assessed using statistical tests on seven key motor or cognitive outcomes (mild cognitive impairment, dementia, dyskinesia, presence of motor fluctuations, functional impairment from motor fluctuations, Hoehn and Yahr score, and death) not used in the learning phase. The results were validated in an independent sample of 610 patients with Parkinson's disease from the National Institute of Neurological Disorders and Stroke Parkinson's Disease Biomarker Program (PDBP). Findings PPMI data were download July 25, 2018, medication information was downloaded on Sept 24, 2018, and PDBP data were downloaded between June 15 and June 24, 2020. The model discovered eight disease states, which are primarily differentiated by functional impairment, tremor, bradykinesia, and neuropsychiatric measures. State 8, the terminal state, had the highest prevalence of key clinical outcomes including 18 (95%) of 19 recorded instances of dementia. At study outset 4 (1%) of 333 patients were in state 8 and 138 (41%) of 333 patients reached stage 8 by year 5. However, the ranking of the starting state did not match the ranking of reaching state 8 within 5 years. Overall, patients starting in state 5 had the shortest time to terminal state (median 2·75 [95% CI 1·75–4·25] years). Interpretation We developed a statistical progression model of early Parkinson's disease that accounts for intra-individual and inter-individual variability and medication effects. Our predictive model discovered non-sequential, overlapping disease progression trajectories, supporting the use of non-deterministic disease progression models, and suggesting static subtype assignment might be ineffective at capturing the full spectrum of Parkinson's disease progression. Funding Michael J Fox Foundation.

Changes in the blood-based RNA transcriptome have the potential to inform biomarkers of Parkinson’s disease (PD) progression. Here we sequenced a discovery set of whole-blood RNA species in 4,871 longitudinally collected samples from 1,570 clinically phenotyped individuals from the Parkinson’s Progression Marker Initiative (PPMI) cohort. Samples were sequenced to an average of 100 million read pairs to create a high-quality transcriptome. Participants with PD in the PPMI had significantly altered RNA expression (>2,000 differentially expressed genes), including an early and persistent increase in neutrophil gene expression, with a concomitant decrease in lymphocyte cell counts. This was validated in a cohort from the Parkinson’s Disease Biomarkers Program (PDBP) consisting of 1,599 participants and by alterations in immune cell subtypes. This publicly available transcriptomic dataset, coupled with available detailed clinical data, provides new insights into PD biological processes impacting whole blood and new paths for developing diagnostic and prognostic PD biomarkers. The authors report whole-blood RNA-seq for 4,871 samples from 1,570 participants in the Parkinson Progression Marker Initiative. This Resource documents blood-based transcriptomic changes associated with PD, including early increases in neutrophil gene expression with a decrease in lymphocytes.

The pathogenesis and clinical heterogeneity of Parkinson’s disease have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of CSF has opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in Parkinson’s disease (1103 patients, 4135 proteins). Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest Parkinson’s disease 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 Parkinson’s disease patients.We also compared the CSF proteomes of patients and controls. The Parkinson’s disease cohort comprised not only LRRK2+ and GBA+ mutation carriers but also idiopathic patients. Proteome differences between GBA+ patients and unaffected GBA+ controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. The proteins discriminating LRRK2+ patients from unaffected LRRK2+ controls, revealed dysregulated lysosomal degradation, as well as 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 the use of previously reported protein-based risk scores.In summary, we: i) identified causal proteins for Parkinson’s disease, ii) assessed CSF proteome differences in Parkinson’s disease patients of genetic and idiopathic etiology, and. iii) stratified idiopathic patients into robust clinically relevant subtypes. Our findings not only contribute to the identification of new therapeutic targets but also to shaping personalized medicine in CNS neurodegeneration.

Author(s): Mollenhauer, Brit; Dakna, Mohammed; Kruse, Niels; Galasko, Douglas; Foroud, Tatiana; Zetterberg, Henrik; Schade, Sebastian; Gera, Roland G; Wang, Wenting; Gao, Feng; Frasier, Mark; Chahine, Lana M; Coffey, Christopher S; Singleton, Andrew B; Simuni, Tanya; Weintraub, Daniel; Seibyl, John; Toga, Arthur W; Tanner, Caroline M; Kieburtz, Karl; Marek, Kenneth; Siderowf, Andrew; Cedarbaum, Jesse M; Hutten, Samantha J; Trenkwalder, Claudia; Graham, Danielle | Abstract: BackgroundThe objective of this study was to assess neurofilament light chain as a Parkinson's disease biomarker.MethodsWe quantified neurofilament light chain in 2 independent cohorts: (1) longitudinal cerebrospinal fluid samples from the longitudinal de novo Parkinson's disease cohort and (2) a large longitudinal cohort with serum samples from Parkinson's disease, other cognate/neurodegenerative disorders, healthy controls, prodromal conditions, and mutation carriers.ResultsIn the Parkinson's Progression Marker Initiative cohort, mean baseline serum neurofilament light chain was higher in Parkinson's disease patients (13 ± 7.2 pg/mL) than in controls (12 ± 6.7 pg/mL), P = 0.0336. Serum neurofilament light chain increased longitudinally in Parkinson's disease patients versus controls (P l 0.01). Motor scores were positively associated with neurofilament light chain, whereas some cognitive scores showed a negative association.ConclusionsNeurofilament light chain in serum samples is increased in Parkinson's disease patients versus healthy controls, increases over time and with age, and correlates with clinical measures of Parkinson's disease severity. Although the specificity of neurofilament light chain for Parkinson's disease is low, it is the first blood-based biomarker candidate that could support disease stratification of Parkinson's disease versus other cognate/neurodegenerative disorders, track clinical progression, and possibly assess responsiveness to neuroprotective treatments. However, use of neurofilament light chain as a biomarker of response to neuroprotective interventions remains to be assessed. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Contains fulltext : 229477.pdf (Publisher’s version ) (Open Access) Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson’s Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.

BackgroundWe previously created the PREDIGT Score as an algorithm to predict the incidence of Parkinson disease. The model rests on a hypothesis-driven formula, [PR=(E+D+I)xGxT], that uses numerical values for five categories known to modulate Parkinson’s risk (PR): environmental exposure (E); DNA variants (D); evidence of gene-environment interactions (I); gender (G); and time (T). Notably, the formula does not rely on motor examination results.MethodsTo evaluate the PREDIGT Score, we tested it in two established case-control cohorts: ‘De Novo Parkinson Study’ (DeNoPa) and ‘Parkinson’s Progression Marker Initiative’ (PPMI). Using baseline data from 589 patients and 309 controls enrolled in the DeNoPa and PPMI cohorts, we evaluated the PREDIGT Score’s discriminative performance in distinguishing Parkinson’s patients from healthy controls by area-under-the-curve (AUC) analyses.FindingsWhen examining cohorts separately and using all available variables in each cohort to calculate the PREDIGT Score, AUCs were 0.83 (95% CI 0.77-0.89) for DeNoPa and 0.87 (95% CI 0.84-0.9) for PPMI, respectively, in distinguishing Parkinson disease patients from healthy individuals. When combining DeNoPa and PPMI data sets by using eleven variables that had been collected in both cohorts, the PREDIGT Score discriminated patients from controls with an AUC of 0.84 (95% CI 0.81-0.87). The mean score of Parkinson disease patients was significantly higher than that of control individuals at 108.48 (+52.08) and 47.33 (+34.1), respectively (p < 0.0001).InterpretationOur results demonstrate a robust performance of the original PREDIGT Score in distinguishing patients diagnosed with Parkinson disease from neurologically healthy subjects without reliance on motor examination data. In future efforts, the predictive performance of the algorithm will be studied in longitudinal cohorts of at-risk persons.FundingParkinson Canada, Michael J. Fox Foundation, Department of Medicine (The Ottawa Hospital), Uttra & Subash Bhargava Family, Paracelsus-Elena-Klinik Kassel, Parkinson Fond Deutschland, and Deutsche Parkinson Vereinigung.

The International Society for Central Nervous System Clinical Trials and Methodology (ISCTM) partnered with the Michael J. Fox Foundation for Parkinson’s Research to hold a joint session on innovation in Parkinson’s disease research at the ISCTM 14th Annual Scientific Meeting held February 20 to 22, 2018 in Washington, D.C. The session focused on (1) biomarkers and outcomes measures in Parkinson’s disease clinical trials; 2) clinical trial designs, outcomes, and statistical approaches; and 3) the path forward. This paper aims to summarize key takeaways from the session presenters, panelists, and audience members.

Author(s): Simuni, Tanya; Siderowf, Andrew; Lasch, Shirley; Coffey, Chris S; Caspell-Garcia, Chelsea; Jennings, Danna; Tanner, Caroline M; Trojanowski, John Q; Shaw, Leslie M; Seibyl, John; Schuff, Norbert; Singleton, Andrew; Kieburtz, Karl; Toga, Arthur W; Mollenhauer, Brit; Galasko, Doug; Chahine, Lana M; Weintraub, Daniel; Foroud, Tatiana; Tosun, Duygu; Poston, Kathleen; Arnedo, Vanessa; Frasier, Mark; Sherer, Todd; Chowdhury, Sohini; Marek, Kenneth; Parkinson's Progression Marker Initiative* | Abstract: OBJECTIVE:The objective of this study was to assess longitudinal change in clinical and dopamine transporter imaging outcomes in early, untreated PD. METHODS:We describe 5-year longitudinal change of the MDS-UPDRS and other clinical measures using results from the Parkinson's Progression Markers Initiative, a longitudinal cohort study of early Parkinson's disease (PD) participants untreated at baseline. We also provide data on the longitudinal change in dopamine transporter 123-I Ioflupane striatal binding and correlation between the 2 measures. RESULTS:A total of 423 PD participants were recruited, and 358 remain in the study at year 5. Baseline MDS-UPDRS total score was 32.4 (standard deviation 13.1), and the average annual change (assessed medications OFF for the treated participants) was 7.45 (11.6), 3.11 (11.7), 4(11.9), 4.7 (11.1), and 1.74(11.9) for years 1, 2, 3, 4, and 5, respectively (P l .0001 for the change over time), with a steeper change in year 1. Dopaminergic therapy had a significant effect on the change of MDS-UPDRS. There was a significant longitudinal change in dopamine transporter binding in all striatal regions (P l .001). There was a significant but weak correlation between MDS-UPDRS and dopamine transporter binding at baseline and years 1, 2, and 4, but no correlation between the rate of change of the 2 variables. CONCLUSIONS:We present 5-year longitudinal data on the change of the MDS-UPDRS and other clinical and dopamine transporter imaging outcome measures in early PD. These data can be used for sample size estimates for interventional studies in the de novo PD population. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Parkinson's disease biomarkers are needed to increase diagnostic accuracy, to objectively monitor disease progression and to assess therapeutic efficacy as well as target engagement when evaluating novel drug and therapeutic strategies. This article summarizes perianalytical considerations for biomarker studies (based on immunoassays) in Parkinson's disease, with emphasis on quantifying total α-synuclein protein in biological fluids. Current knowledge and pitfalls are discussed, and selected perianalytical variables are presented systematically, including different temperature of sample collection and types of collection tubes, gradient sampling, the addition of detergent, aliquot volume, the freezing time, and the different thawing methods. We also discuss analytical confounders. We identify gaps in the knowledge and delineate specific areas that require further investigation, such as the need to identify posttranslational modifications of α-synuclein and antibody-independent reference methods for quantification, as well as the analysis of potential confounders, such as comorbidities, medication, and phenotypes of Parkinson's disease in larger cohorts. This review could be used as a guideline for future Parkinson's disease biomarker studies and will require regular updating as more information arises in this growing field, including new technical developments as they become available. In addition to reviewing best practices, we also identify the current technical limitations and gaps in the knowledge that should be addressed to enable accurate and quantitative assessment of α-synuclein levels in the clinical setting. © 2017 International Parkinson and Movement Disorder Society.

Objective:To analyze longitudinal levels of CSF biomarkers in drug-naive patients with Parkinson disease (PD) and healthy controls (HC), examine the extent to which these biomarker changes relate to clinical measures of PD, and identify what may influence them.Methods:CSF α-synuclein (α-syn), total and phosphorylated tau (t- and p-tau), and β-amyloid 1–42 (Aβ42) were measured at baseline and 6 and 12 months in 173 patients with PD and 112 matched HC in the international multicenter Parkinson's Progression Marker Initiative. Baseline clinical and demographic variables, PD medications, neuroimaging, and genetic variables were evaluated as potential predictors of CSF biomarker changes.Results:CSF biomarkers were stable over 6 and 12 months, and there was a small but significant increase in CSF Aβ42 in both patients with patients with PD and HC from baseline to 12 months. The t-tau remained stable. The p-tau increased marginally more in patients with PD than in HC. α-syn remained relatively stable in patients with PD and HC. Ratios of p-tau/t-tau increased, while t-tau/Aβ42 decreased over 12 months in patients with PD. CSF biomarker changes did not correlate with changes in Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale motor scores or dopamine imaging. CSF α-syn levels at 12 months were lower in patients with PD treated with dopamine replacement therapy, especially dopamine agonists.Conclusions:These core CSF biomarkers remained stable over 6 and 12 months in patients with early PD and HC. PD medication use may influence CSF α-syn. Novel biomarkers are needed to better profile progressive neurodegeneration in PD.

ObjectiveTo assess neurofilament light chain (NfL), as a biomarker for Parkinson’s disease (PD).MethodsWe quantified NfL in (1) longitudinal CSF samples from PD, other cognate/neurodegenerative disorders (OND), and healthy controls (HC); (2) a cross-sectional cohort with paired CSF and serum samples from participants with PD, OND, and HC, and (3) a large longitudinal validation cohort with serum samples from PD, OND, HC, prodromal conditions, and mutation carriers.ResultsIn the longitudinal discovery cohort (1) NfL in CSF was highest in OND and higher in PD vs. HC across all visits (p; pConclusionsNfL levels in serum samples are increased in PD vs. HC, increase significantly over time, and correlate with clinical measures of PD severity. Although the specificity of NfL in PD is low and more specific biomarkers are needed, serum NfL is the first blood-based biomarker candidate that could support disease stratification (PD vs. OND), track clinical progression, and possibly assess responsiveness to neuroprotective treatments. NfL as a biomarker of response to neuroprotective interventions remains to be determined.Funding sources for studyPPMI is sponsored by the Michael J. Fox Foundation for Parkinson’s Research (MJFF) and is co-funded by MJFF, Abbvie, Avid Radiopharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Covance, Eli Lilly & Co., F. Hoffman-La Roche, Ltd., GE Healthcare, Genentech, GlaxoSmithKline, Lundbeck, Merck, MesoScale, Piramal, Pfizer and UCB. The funders had no role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, in the preparation, review, or approval of the manuscript or in the decision to submit the manuscript for publication.Financial Disclosure/Conflict of Interest concerning the research related to the manuscriptBrit Mollenhauer, Douglas Galasko, Tatiana Foroud, Lana M. Chahine, Christopher S. Coffey, Andrew B. Singleton, Tanya Simuni, Daniel Weintraub, John Seibyl, Arthur W. Toga, and Caroline M. Tanner received funding from The Michael J. Fox Foundation for Parkinson’s Research.Mohammed Dakna, Tzu-Ying Liu, Henrik Zetterberg, Sebastian Schade, Roland G. Gera, Wenting Wang, Feng Gao, Niels Kruse, Mark Frasier, Jesse M. Cedarbaum, Samantha J. Hutten, Claudia Trenkwalder, and Danielle Graham report no disclosures.

Background Identifying PD‐specific biomarkers in biofluids will greatly aid in diagnosis, monitoring progression, and therapeutic interventions. PD biomarkers have been limited by poor discriminatory power, partly driven by heterogeneity of the disease, variability of collection protocols, and focus on de novo, unmedicated patients. Thus, a platform for biomarker discovery and validation in well‐characterized, clinically typical, moderate to advanced PD cohorts is critically needed. Methods BioFIND (Fox Investigation for New Discovery of Biomarkers in Parkinson's Disease) is a cross‐sectional, multicenter biomarker study that established a repository of clinical data, blood, DNA, RNA, CSF, saliva, and urine samples from 118 moderate to advanced PD and 88 healthy control subjects. Inclusion criteria were designed to maximize diagnostic specificity by selecting participants with clinically typical PD symptoms, and clinical data and biospecimen collection utilized standardized procedures to minimize variability across sites. Results We present the study methodology and data on the cohort's clinical characteristics. Motor scores and biospecimen samples including plasma are available for practically defined off and on states and thus enable testing the effects of PD medications on biomarkers. Other biospecimens are available from off state PD assessments and from controls. Conclusion Our cohort provides a valuable resource for biomarker discovery and validation in PD. Clinical data and biospecimens, available through The Michael J. Fox Foundation for Parkinson's Research and the National Institute of Neurological Disorders and Stroke, can serve as a platform for discovering biomarkers in clinically typical PD and comparisons across PD's broad and heterogeneous spectrum. © 2016 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society

This interview was conducted by Lauren Pulling, Editor of Neurology Central. The full interview can be accessed on Neurology Central as part of the NCTalks podcast series which is available at: www.neurology-central.com/2016/11/28/nctalks-mark-frasier-precision-medicine-parkinsons-disease Mark Frasier co-manages a team of research professionals who stay closely linked to the Parkinson's research community in order to develop an aggressive and innovative agenda for accelerating research and drug development for Parkinson's disease. He also supports the foundation's priority interest in developing biomarkers for Parkinson's disease that will accelerate clinical trials of new drugs. He earned an undergraduate degree in biochemistry from the University of Dayton and a PhD in pharmacology from Loyola University Chicago. He completed his postdoctoral work in the Neuroscience Discovery Research Group at Eli Lilly, Inc., in Indianapolis, Indiana, where he worked on drug discovery research in Parkinson's and Alzheimer's disease. He joined the Michael J. Fox Foundation in 2006 and is now Senior Vice President of Research Programs.

Author(s): Marek, Kenneth; Chowdhury, Sohini; Siderowf, Andrew; Lasch, Shirley; Coffey, Christopher S; Caspell-Garcia, Chelsea; Simuni, Tanya; Jennings, Danna; Tanner, Caroline M; Trojanowski, John Q; Shaw, Leslie M; Seibyl, John; Schuff, Norbert; Singleton, Andrew; Kieburtz, Karl; Toga, Arthur W; Mollenhauer, Brit; Galasko, Doug; Chahine, Lana M; Weintraub, Daniel; Foroud, Tatiana; Tosun-Turgut, Duygu; Poston, Kathleen; Arnedo, Vanessa; Frasier, Mark; Sherer, Todd; Parkinson's Progression Markers Initiative | Abstract: ObjectiveThe Parkinson's Progression Markers Initiative (PPMI) is an observational, international study designed to establish biomarker-defined cohorts and identify clinical, imaging, genetic, and biospecimen Parkinson's disease (PD) progression markers to accelerate disease-modifying therapeutic trials.MethodsA total of 423 untreated PD, 196 Healthy Control (HC) and 64 SWEDD (scans without evidence of dopaminergic deficit) subjects were enrolled at 24 sites. To enroll PD subjects as early as possible following diagnosis, subjects were eligible with only asymmetric bradykinesia or tremor plus a dopamine transporter (DAT) binding deficit on SPECT imaging. Acquisition of data was standardized as detailed at www.ppmi-info.org.ResultsApproximately 9% of enrolled subjects had a single PD sign at baseline. DAT imaging excluded 16% of potential PD subjects with SWEDD. The total MDS-UPDRS for PD was 32.4 compared to 4.6 for HC and 28.2 for SWEDD. On average, PD subjects demonstrated 45% and 68% reduction in mean striatal and contralateral putamen Specific Binding Ratios (SBR), respectively. Cerebrospinal fluid (CSF) was acquired from g97% of all subjects. CSF (PD/HC/SWEDD pg/mL) α-synuclein (1845/2204/2141) was reduced in PD vs HC or SWEDD (Pnln0.03). Similarly, t-tau (45/53) and p-tau (16/18) were reduced in PD versus HC (Pnln0.01).InterpretationPPMI has detailed the biomarker signature for an early PD cohort defined by clinical features and imaging biomarkers. This strategy provides the framework to establish biomarker cohorts and to define longitudinal progression biomarkers to support future PD treatment trials.

© 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society. Objective: The objective of this study was to assess longitudinal change in clinical and dopamine transporter imaging outcomes in early, untreated PD. Methods: We describe 5-year longitudinal change of the MDS-UPDRS and other clinical measures using results from the Parkinson's Progression Markers Initiative, a longitudinal cohort study of early Parkinson's disease (PD) participants untreated at baseline. We also provide data on the longitudinal change in dopamine transporter 123-I Ioflupane striatal binding and correlation between the 2 measures. Results: A total of 423 PD participants were recruited, and 358 remain in the study at year 5. Baseline MDS-UPDRS total score was 32.4 (standard deviation 13.1), and the average annual change (assessed medications OFF for the treated participants) was 7.45 (11.6), 3.11 (11.7), 4(11.9), 4.7 (11.1), and 1.74(11.9) for years 1, 2, 3, 4, and 5, respectively (P

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.

Therapeutic development in Parkinson’s disease is hampered by the paucity of well-validated biomarkers that can assist with diagnosis and/or tracking the progression of the disease. Since its inception, the Michael J Fox Foundation for Parkinson’s Research has invested heavily in biomarker research and continues to prioritize discovery and development efforts. This article summarizes the history and evolution of the Michael J Fox Foundation’s role in supporting biomarker research and lays out the current challenges in successfully developing markers that can be used to test therapies, while also providing a vision of future funding efforts in Parkinson’s disease biomarkers.

The development of biomarkers to predict the progression of Parkinson’s disease (PD) from its earliest stage through its heterogeneous course is critical for research and therapeutic development. The Parkinson’s Progression Markers Initiative (PPMI) study is an ongoing international multicenter, prospective study to validate biomarkers in drug-naive PD patients and matched healthy controls (HC). We quantified cerebrospinal fluid (CSF) alpha-synuclein (α-syn), amyloid-beta1-42 (Aβ1-42), total tau (t-tau), and tau phosphorylated at Thr181 (p-tau) in 660 PPMI subjects at baseline, and correlated these data with measures of the clinical features of these subjects. We found that CSF α-syn, t-tau and p-tau levels, but not Aβ1-42, were significantly lower in PD compared with HC, while the diagnostic value of the individual CSF biomarkers for PD diagnosis was limited due to large overlap. The level of α-syn, but not other biomarkers, was significantly lower in PD patients with non-tremor-dominant phenotype compared with tremor-dominant phenotype. In addition, in PD patients the lowest Aβ1-42, or highest t-tau/Aβ1-42 and t-tau/α-syn quintile in PD patients were associated with more severe non-motor dysfunction compared with the highest or lowest quintiles, respectively. In a multivariate regression model, lower α-syn was significantly associated with worse cognitive test performance. APOE e4 genotype was associated with lower levels of Aβ1-42, but neither with PD diagnosis nor cognition. Our data suggest that the measurement of CSF biomarkers in early-stage PD patients may relate to disease heterogeneity seen in PD. Longitudinal observations in PPMI subjects are needed to define their prognostic performance.

Parkinson's disease (PD) is the most common neurodegenerative movement disorder and is characterized by the loss of dopaminergic neurons in the substantia nigra compacta. alpha-Synuclein is strongly implicated in the pathophysiology of PD because aggregated alpha-synuclein accumulates in the brains of subjects with PD, mutations in alpha-synuclein cause familial PD, and overexpressing mutant human alpha-synuclein (A30P or A53T) causes degenerative disease in mice or drosophila. The pathophysiology of PD is poorly understood, but increasing evidence implicates mitochondrial dysfunction and oxidative stress. To understand how mutations in alpha-synuclein contribute to the pathophysiology of PD, we undertook a proteomic analysis of transgenic mice overexpressing A30P alpha-synuclein to investigate which proteins are oxidized. We observed more than twofold selective increases in specific carbonyl levels of three metabolic proteins in brains of symptomatic A30P alpha-synuclein mice: carbonic anhydrase 2 (Car2), alpha-enolase (Eno1), and lactate dehydrogenase 2 (Ldh2). Analysis of the activities of these proteins demonstrates decreased functions of these oxidatively modified proteins in brains from the A30P compared to control mice. Our findings suggest that proteins associated with impaired energy metabolism and mitochondria are particularly prone to oxidative stress associated with A30P-mutant alpha-synuclein.

Multiple studies implicate metals in the pathophysiology of neurodegenerative diseases. Disturbances in brain iron metabolism are linked with synucleinopathies. For example, in Parkinson's disease, iron levels are increased and magnesium levels are reduced in the brains of patients. To understand how changes in iron and magnesium might affect the pathophysiology of Parkinson's disease, we investigated binding of iron to alpha-synuclein, which accumulates in Lewy bodies. Using fluorescence of the four tyrosines in alpha-synuclein as indicators of metal-related conformational changes in alpha-synuclein, we show that iron and magnesium both interact with alpha-synuclein. alpha-Synuclein exhibits fluorescence peaks at 310 and 375 nm. Iron lowers both fluorescence peaks, while magnesium increases the fluorescence peak only at 375 nm, which suggests that magnesium affects the conformation of alpha-synuclein differently than iron. Consistent with this hypothesis, we also observe that magnesium inhibits alpha-synuclein aggregation, measured by immunoblot, cellulose acetate filtration, or thioflavine-T fluorescence. In each of these studies, iron increases alpha-synuclein aggregation, while magnesium at concentrations >0.75 mm inhibits the aggregation of alpha-synuclein induced either spontaneously or by incubation with iron. These data suggest that the conformation of alpha-synuclein can be modulated by metals, with iron promoting aggregation and magnesium inhibiting aggregation.

Parkinson's disease (PD) is currently diagnosed using clinical features. While experienced neurologists can typically diagnose PD with 70–90% accuracy, there are many situations early in the course of the disease when clinical diagnosis is less precise. There is a paucity of objective measures that could be employed to improve the diagnosis, stratify patients by subtypes, and track underlying disease progression. Development of innovative new therapies that slow or stop the progression of the disease would be accelerated by such objective biomarkers. Various imaging modalities including specific dopaminergic markers and morphometric measures are available, but have not been firmly established as markers for these purposes. Markers that can be detected in easily obtainable biofluids would be ideal for these purposes and would increase the power to detect the effect of therapeutic agents in a shorter time with reduced cost. Unbiased exploratory examination of panels of RNAs, both mRNA and noncoding RNAs, exosomes, proteins, antibodies, and metabolites in small individual cohorts have yielded promising candidate markers that yearn for replication in independent cohorts (Kroksveen et al., 2011). More targeted approaches to identify biomarkers based on PD pathophysiology have shown disease-related proteins as strong candidates. Alpha-synuclein is decreased in the cerebrospinal fluid of PD patients (Hong et al., 2010), although significant overlap with controls makes it less useful in assisting individual diagnosis. Other potential protein candidates include DJ-1 and inflammatory cytokines. Post-translationally modified forms of these proteins may improve the biomarker specificity. Most of these studies have utilized CSF (Parnetti et al., 2013), although increased oxidized DJ-1 in PD patients was detected in blood (Saito et al., 2009) and epidermal growth factor levels in plasma has been shown to predict cognitive decline in PD (Chen-Plotkin et al., 2011). Ultimately one could predict that a panel of multiple biochemical markers could be combined to increase the accuracy of diagnosis and disease progression. Heterogeneity of patient populations and lack of standardization of collection methods may contribute to the inconsistent and variable observations in the literature. A major need in the field is the availability of biospecimens from well-characterized cohorts for discovery of new biomarkers and validation. In response to these needs, three major programs have been launched. The Parkinson's Progression Marker Initiative (PPMI) is a prospective study of 400 newly diagnosed PD patients and 200 controls that will be followed over 5 years and collect extensive clinical (motor and non-motor information), imaging, and biosample (blood, cerebrospinal fluid, DNA/RNA from blood, urine) information from all 600 subjects at sites around the world (Marek, 2011). PPMI expanded to include a prodromal cohort of 100 individuals at risk for developing PD to develop biomarkers that are present prior to the onset of clinical motor symptoms. The Fox Investigation for New Discovery of Biomarkers (BioFIND) is a study supported in collaboration with National Institute of Neurological Diseases and Stroke (NINDS) focused on novel biomarker discovery by enrolling 120 rigorously defined clinically typical PD in mid-stage and 120 age- and gender-matched controls at one timepoint in US sites. BioFIND was launched to serve as a platform to test new biomarkers in somewhat narrow spectrum of clinically typical PD in moderate stages to maximize the chance of discovering differences in a less heterogeneous population. The Parkinson's Disease Biomarker Program (PDBP) is a program designed to support new and existing biomarker cohorts that collect biospecimens using standardized protocols. Biospecimens and data from all above studies are available to the research community. Through standardization and coordination of data and sample collection, we are optimistic that new markers will emerge which will assist in clinical trials and ultimately result in improved management of the disease.

The development of an α-synuclein imaging agent could be transformative for Parkinson's disease research and drug development. The ability to image α-synuclein in the brain would enable tracking of the degree and location of pathology over time and monitoring of therapies aimed at reducing α-synuclein levels. The Michael J. Fox Foundation has assembled a consortium of researchers to develop an α-synuclein radiotracer for use in positron emission tomography (PET) imaging studies. While this poses a number of challenges they should not be insurmountable and lessons learned from the development of tau radiotracers should provide valuable insights.

The Air Force has recently built a vacuum chamber for simulating LEO and GEO space environments for testing small satellites or satellite subsystems. Four natural environmental sources are provided: broadband solar flux, and narrowband electron, proton, and atomic oxygen fluxes; together with spacecraft charging; outgassing and attitude control Xe ion simulators. Although the chamber has diagnostic sensors to measure the fluxes from the sources at one plane, uncertainties remain about the details of the source emissions, their interactions, and impacts on the chamber. To help address these uncertainties models of the sources are being developed. We have modeled the Atomic Oxygen source and present results of our recent computer simulations. We motivate our choice of the dynamical DSMC code used, and present results such as the AO fluence time history and its spatial distribution over the test article; chamber pressure time history; flow velocity contours; the importance of particle-particle collisions; and attempts to model the motion of residual source-produced O + ions in the geomagnetic field. We compare with existing measurements.

Importance We observed a significant correlation between cerebrospinal fluid (CSF) levels of tau proteins and α-synuclein, but not β-amyloid 1-42 (Aβ1-42), and lower concentration of CSF biomarkers, as compared with healthy controls, in a cohort of entirely untreated patients with Parkinson disease (PD) at the earliest stage of the disease studied so far. Objective To evaluate the baseline characteristics and relationship to clinical features of CSF biomarkers (Aβ1-42, total tau [T-tau], tau phosphorylated at threonine 181 [P-tau 181 ], and α-synuclein) in drug-naive patients with early PD and demographically matched healthy controls enrolled in the Parkinson’s Progression Markers Initiative (PPMI) study. Design, Setting, and Participants Cross-sectional study of the initial 102 research volunteers (63 patients with PD and 39 healthy controls) of the PPMI cohort. Main Outcomes and Measures The CSF biomarkers were measured by INNO-BIA AlzBio3 immunoassay (Aβ1-42, T-tau, and P-tau 181 ; Innogenetics Inc) or by enzyme-linked immunosorbent assay (α-synuclein). Clinical features including diagnosis, demographic characteristics, motor, neuropsychiatric, and cognitive assessments, and DaTscan were systematically assessed according to the PPMI study protocol. Results Slightly, but significantly, lower levels of Aβ1-42, T-tau, P-tau 181 , α-synuclein, and T-tau/Aβ1-42 were seen in subjects with PD compared with healthy controls but with a marked overlap between groups. Using multivariate regression analysis, we found that lower Aβ1-42 and P-tau 181 levels were associated with PD diagnosis and that decreased CSF T-tau and α-synuclein were associated with increased motor severity. Notably, when we classified patients with PD by their motor phenotypes, lower CSF Aβ1-42 and P-tau 181 concentrations were associated with the postural instability–gait disturbance–dominant phenotype but not with the tremor-dominant or intermediate phenotype. Finally, we found a significant correlation of the levels of α-synuclein with the levels of T-tau and P-tau 181 . Conclusions and Relevance In this first report of CSF biomarkers in PPMI study subjects, we found that measures of CSF Aβ1-42, T-tau, P-tau 181 , and α-synuclein have prognostic and diagnostic potential in early-stage PD. Further investigations using the entire PPMI cohort will test the predictive performance of CSF biomarkers for PD progression.

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 [1] 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.

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

The Parkinson Progression Marker Initiative (PPMI) is a comprehensive observational, international, multi-center study designed to identify PD progression biomarkers both to improve understanding of disease etiology and course and to provide crucial tools to enhance the likelihood of success of PD modifying therapeutic trials. The PPMI cohort will comprise 400 recently diagnosed PD and 200 healthy subjects followed longitudinally for clinical, imaging and biospecimen biomarker assessment using standardized data acquisition protocols at twenty-one clinical sites. All study data will be integrated in the PPMI study database and will be rapidly and publically available through the PPMI web site- www.ppmi-info.org. Biological samples including longitudinal collection of blood, cerebrospinal fluid (CSF) and urine will be available to scientists by application to an independent PPMI biospecimen review committee also through the PPMI web site. PPMI will rely on a partnership of government, PD foundations, industry and academics working cooperatively. This approach is crucial to enhance the potential for success of this ambitious strategy to develop PD progression biomarkers that will accelerate research in disease modifying therapeutics.

International audience; The loss of dopaminergic neurons in the substantia nigra pars compacta leads to the characteristic motor symptoms of Parkinson's disease: bradykinesia, rigidity and resting tremors. Although these symptoms can be improved using currently available dopamine replacement strategies, there is still a need to improve current strategies of treating these symptoms, together with a need to alleviate non-motor symptoms of the disease. Moreover, treatments that provide neuroprotection and/or disease-modifying effects remain an urgent unmet clinical need. This Review describes the most promising biological targets and therapeutic agents that are currently being assessed to address these treatment goals. Progress will rely on understanding genetic mutations or susceptibility factors that lead to Parkinson's disease, better translation between preclinical animal models and clinical research, and improving the design of future clinical trials.

Funding for biomedical and neuroscience research has increased over the last decade but without a concomitant increase in new therapies. This study's objectives were to determine the level and principal sources of recent funding for Parkinson disease (PD) research and to determine the current state of PD drug development. We determined the level and principal sources of recent funding for PD research from the following sources: US federal agencies, large PD foundations based in the United States, and global industry. We assessed the status of PD drug development through the use of a proprietary drug pipeline database. Funding for PD research from the sources examined was approximately $1.1 billion in 2003 and $1.2 billion in 2005. Industry accounted for 77% of support from 2003 to 2005. The number of drugs in development for PD increased from 67 in 2003 to 97 in 2007. Of the companies with at least one compound in development for PD in 2007, most were small (62% had annual revenue of less than $100 million), and most (53%) were based outside the United States. These companies will likely require partnerships to drive successful development of new PD therapies.

Various childhood mood disorders are being treated with serotonin selective reuptake inhibitors (SSRIs) such as fluoxetine (Prozac®), yet limited data are available on their effects on serotonergic systems prior to maturation. This study investigated the effects of chronic fluoxetine treatment on 5-HT2A serotonin receptor-mediated neuroendocrine responses in young male rats. Prepubescent male rats were treated with saline or fluoxetine (10 mg/kg/day, i.p.) for 14 days, a treatment regimen producing maximal changes in postsynaptic 5-HT2A function in adults. Eighteen hours post-treatment, the rats received saline or increasing doses (0.5, 2.0, or 5.0 mg/kg, i.p.) of the 5-HT2 receptor agonist (±)-1-(2,5-dimethoxy-4-iodophenyl)-2-aminopropane HCl ((±)-DOI). Trunk blood was obtained to determine changes in oxytocin, ACTH, corticosterone, and renin responses. Fluoxetine produced a small (∼6%) but significant reduction in body weight gain, but no changes were observed in basal hormone levels. In both saline- and fluoxetine-treated rats, (±)-DOI increased plasma oxytocin levels in a dose-dependent manner. However, the magnitude of the oxytocin responses to all doses of (±)-DOI were markedly attenuated (∼50%) in the fluoxetine-treated rats, indicating a functional reduction in the Emax of 5-HT2A receptor-mediated oxytocin responses. In contrast, fluoxetine did not alter the (±)-DOI-induced increases in plasma ACTH, corticosterone, or renin. These data provide the first demonstration of selective neuroadaptive responses in 5-HT2A serotonin receptor function due to prepubescent treatment with fluoxetine. These data may be clinically relevant with respect to the use of selective serotonin reuptake inhibitors in children and adolescents. Synapse 58:102–109, 2005. © 2005 Wiley-Liss, Inc.

Mice overexpressing mutant alpha-synuclein develop a progressive loss of motor function associated with the accumulation of aggregated alpha-synuclein in neurons of the brainstem. Recent reports suggest that tau pathology might also be associated with Parkinson disease (PD) and aggregation of alpha-synuclein. We now report that mice overexpressing A30P alpha-synuclein develop abnormally phosphorylated tau in parallel with the accumulation of aggregated alpha-synuclein. Enhanced phosphorylation of tau occurs only in symptomatic mice that also harbor abundant aggregated alpha-synuclein. The increased phosphorylation of tau occurs at S396/404 and S202 as shown by immunoblotting and immunocytochemical studies with the antibodies PHF-1 and AT8. Neurons that accumulated alpha-synuclein occurred in the dorsal brainstem and did not show strong colocalization with neurons that showed abnormal tau phosphorylation, which largely occurred in the ventral brainstem. Aggregation of alpha-synuclein and phosphorylation of tau are associated with increased levels of phosphorylated c-jun kinase (JNK), which is a stress kinase known to phosphorylate tau protein. These results suggest that alpha-synuclein pathology can stimulate early pathological changes in tau.