npj Parkinson's Disease

Parkinsons disease (PD) is characterized by a long prodromal phase with a multitude of markers indicating an increased PD risk prior to clinical diagnosis based on motor symptoms. Current PD prediction models do not consider interdependencies of single predictors, lack differentiation by subtypes of prodromal PD, and may be limited and potentially biased by confounding factors, unspecific assessment methods and restricted access to comprehensive marker data of prospective cohorts. We used prospective data of 20 established risk and prodromal markers of PD in 1178 healthy, PD-free individuals and 24 incident PD cases collected longitudinally in the Tubingen evaluation of Risk factors for Early detection of NeuroDegeneration (TREND) study at 4 visits over up to 10 years. We employed artificial intelligence (AI) to learn and quantify PD marker interdependencies via a Bayesian network (BN) with uncertainty estimation using bootstrapping. The BN was employed to generate a synthetic cohort and individual marker profiles. Robust interdependencies were observed for BN edges from age to subthreshold parkinsonism and urinary dysfunction, sex to substantia nigra hyperechogenicity, depression, non-smoking and to constipation; depression to symptomatic hypotension and excessive daytime somnolence; solvent exposure to cognitive deficits and to physical inactivity; and non-smoking to physical inactivity. Conversion to PD was interdependent with prior subthreshold parkinsonism, sex and substantia nigra hyperechogenicity. Several additional interdependencies with higher statistical uncertainty were identified. Synthetic subjects generated via the BN based representation of the TREND study were realistic as assessed through multiple comparison approaches of real and synthetic data. Altogether our work demonstrates the potential of modern AI approaches (specifically BNs) in two ways: First, to model and understand interdependencies between PD risk and prodromal markers, which are so far not accounted for in PD prediction models. Second, the generative nature of BNs opens the door for facilitating data sharing in a legally compliant and privacy preserving manner.

BackgroundHigh throughput proteomics has enabled hypothesis free biomarker discovery. However, differences in sample sizes, biological fluid, and quantification technologies have limited replication and validation of the results, and studies on the cross-platform variability are lacking. Here, we present the first orthogonal validation across three platforms in Parkinsons disease (PD) to understand the technical and biological challenges of proteomic studies. MethodsWe have leveraged publicly available proteomic data from cerebrospinal fluid (CSF), plasma, and urine within the Parkinsons Progression Markers Initiative (PPMI) cohort, generated using SomaScan5K (CSF), mass spectrometry (MS; CSF, plasma, and urine), and Olink Explore (CSF and plasma). Across platforms, we compared 375 proteins that were consistently quantified. We performed differential abundance analysis comparing PD versus healthy controls followed by sensitivity analyses (mutation carriers, at-risk participants, longitudinal analyses) to further understand the findings. ResultsIn CSF, we found significant correlations between effect sizes from the 375 proteins quantified by SomaScan5K and MS ({rho}=0.42, p=2.60x10 {square} {square}), as well as SomaScan5K and Olink Explore ({rho}=0.15, p=3.15x10{square}3) while MS and Olink Explore showed no significant correlations in CSF or plasma. Orthogonal validation identified two proteins (DLK1, GSTA3) replicated between SomaScan5K and Olink Explore and seven proteins (ALCAM, CHL1, CNDP1, NCAM2, PEBP1, PTPRS, SCG2) replicated between MS and SomaScan5K. No proteins replicated between MS and Olink Explore in CSF or plasma. DDC showed consistent dysregulation across analyses. In CSF (Olink Explore), it was dysregulated in PD participants (beta=0.79, p=8.49x10-16), and in at-risk individuals (beta=0.64, p=1.41x10-7) including those with hyposmia (beta=0.70, p=2.13x10-5) and REM Sleep Behavior Disorder (beta=0.52, p=1.00x10-3). In urine, DDC was higher in at-risk individuals (beta=0.43, p=7.28x10-5), driven by LRRK2+ at-risk participants (beta=0.59, p=1.74x10-6), as well as in symptomatic mutation carriers, LRRK2+ (beta=0.68, p=9.08x10-8), and GBA+ (beta=0.28, p=0.04). ConclusionsBiologically, these findings add further evidence that DDC has strong potential as a biomarker. Methodologically, our findings emphasize that platform selection can introduce more variance than that originating from disease status, which limits the reproducibility across technologies. This highlights the challenges and importance of cross-platform validation in proteomic biomarker research, and the translation of those discoveries to the clinic.

Combining multiple Parkinsons disease (PD) relevant cellular phenotypes might increase the accuracy of midbrain dopaminergic (mDA) in vitro models. We differentiated patient-derived induced pluripotent stem cells (iPSCs) with a LRRK2 G2019S mutation, isogenic control and genetically unrelated iPSCs into mDA neurons. Using automated fluorescence microscopy in 384-well plate format, we identified elevated levels of -synuclein and Serine 129 phosphorylation (pS129), reduced dendritic complexity, and mitochondrial dysfunction. Next, we measured additional image-based phenotypes and used machine learning (ML) to accurately classify mDA neurons according to their genotype. Additionally, we show that chemical compound treatments, targeting LRRK2 kinase activity or -synuclein levels, are detectable when using ML classification based on multiple image-based phenotypes. We validated our approach using a second isogenic patient derived SNCA gene triplication mDA neuronal model. This phenotyping and classification strategy improves the exploitability of mDA neurons for disease modelling and the identification of novel PD drug targets.

As the number of Parkinsons patients is expected to increase with the growth of the aging population there is a growing need to identify new diagnostic markers that can be used cheaply and routinely to monitor the population, stratify patients towards treatment paths and provide new therapeutic leads. Genetic predisposition and familial forms account for only around 10% of PD cases [1] leaving a large fraction of the population with minimal effective markers for identifying high risk individuals. The establishment of population-wide omics and longitudinal health monitoring studies provides an opportunity to apply machine learning approaches on these unbiased cohorts to identify novel PD markers. Here we present the application of three machine learning models to identify protein plasma biomarkers of PD using plasma proteomics measurements from 43,408 UK Biobank subjects as the training and test set and an additional 103 samples from Parkinsons Progression Markers Initiative (PPMI) as external validation. We identified a group of highly predictive plasma protein markers including known markers such as DDC and CALB2 as well as new markers involved in the JAK-STAT, PI3K-AKT pathways and hormonal signaling. We further demonstrate that these features are well correlated with UPDRS severity scores and stratify these to protective and adversarial features that potentially contribute to the pathogenesis of PD.

Parkinsons disease (PD) is increasingly recognized as a multi-system disorder with immune dysregulation extending beyond the central nervous system. Although numerous studies have examined peripheral immune alterations in people with PD, findings remain heterogenous and difficult to reconcile. To clarify the current landscape, we conducted a comprehensive scoping review of human studies profiling peripheral blood immune cells in PD. Following PRISMA-ScR guidelines, we systematically screened the literature and curated studies reporting in vivo and ex vivo immune characterizations from PD patients. Eligible studies based on pre-defined criteria were assessed for patient demographics and clinical variables, experimental and analytical approaches, and reported immune outcomes. Our synthesis reveals a steady expansion and diversification of peripheral immune cell research in PD especially over the last decade. Deep immunophenotyping identifies convergent signatures across in vivo studies of both innate and adaptive compartments, including expanded pro-inflammatory T-cell subsets, altered monocyte subset distributions, increased cytotoxic natural killer cells and neutrophil-to-lymphocyte ratio, and dysregulated pathways related to immune activation, chemotaxis, mitochondrial function, and autophagy-lysosomal processes. Stimulation-based ex vivo assays further demonstrate recurrent T-cell hyper-responsiveness in PD, whereas myeloid cell responses are more variable and context dependent. Critically, this review highlights substantial variability and under-reporting in study design, which impeded our ability to make strong conclusions relating to many aspects of PD peripheral immunity.

Parkinsons disease (PD) is a neurodegenerative disorder that results in the loss of dopaminergic neurons in the substantia nigra pars compacta. Despite advances in understanding PD, there is a critical need for novel therapeutics that can slow or halt its progression. Induced pluripotent stem cell (iPSC)-derived dopaminergic neurons have been used to model PD but measuring differences between PD and control cells in a robust, reproducible, and scalable manner remains a challenge. In this study, we developed a binary classifier convolutional neural network (CNN) to accurately classify microscopy images of PD models and matched control cells. We acquired images of iPSC-derived neural precursor cells (NPCs) and dopaminergic (DANs) and trained multiple CNN models comparing control cells to genetic and chemical models of PD. Our CNN accurately predicted whether control NPC cells were treated with the PD-inducing pesticide rotenone with 97.60% accuracy. We also compared control to a genetic model of PD (deletion of the Parkin gene) and found a predictive accuracy of 86.77% and 95.47% for NPC and DAN CNNs, respectively. Our cells were stained for nuclei, mitochondria, and plasma membrane, and we compared the contribution of each to the CNNs accuracy. Using all three features together produced the best accuracy, but nuclear staining alone produced a highly predictive CNN. Our study demonstrates the power of deep learning and computer vision for analyzing complex PD-related phenotypes in DANs and suggests that these tools hold promise for identifying new targets for therapy and improving our understanding of PD.

Parkinsons disease (PD) is a neurodegenerative disorder marked by the development of cardinal motor deficits preceded by a protracted prodromal period of non-motor symptoms often involving the gastrointestinal (GI) tract. There is an emerging consensus that both the peripheral immune system and local neuroinflammation play key roles in the etiology of PD. We previously demonstrated a critical function for the Parkinsons related proteins PINK1 and Parkin as repressors of the innate to adaptive immune response in cultured cells and mouse models of infection. However, it remained unclear whether these processes were conserved in patient-derived models, and precisely how immune signaling may ultimately drive the death of dopaminergic neurons. Here we show that GI infection of PINK1 knockout (KO) mice triggered acute neurodegeneration which was evident early in the enteric nervous system. Treating wild type enteric or dopaminergic neurons with conditioned medium from immune-stimulated PINK1 KO macrophages was sufficient to promote neuronal disruption in both mouse and human neurons in vitro. Within immune-activated macrophages, we reveal that loss of PINK1 led to an enhanced release of mitochondrial DNA (mtDNA) within mitochondrial derived vesicles, leading to the activation of cGAS/STING pathways. These changes were seen in both mouse/human in vitro models and in PD patient-derived primary macrophages. Notably, pharmacological modulation using a PINK1 activator with high therapeutic potential attenuated pro-inflammatory profiles elicited by the mtDNA-dependent STING/NF-{kappa}B pathway in idiopathic patient-derived macrophages. Ultimately, our study lays the foundation for understanding PINK1-related peripheral macrophage mechanisms in idiopathic PD and provides a target for further development to treat the disease at early stages. Graphical abstractPINK1-related immune mechanisms of Parkinsons disease and associations with early neurodegenerative events. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=120 SRC="FIGDIR/small/694713v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@6f6488org.highwire.dtl.DTLVardef@13139aaorg.highwire.dtl.DTLVardef@c0d4d1org.highwire.dtl.DTLVardef@1d41e25_HPS_FORMAT_FIGEXP M_FIG C_FIG

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.

BackgroundParkinsons disease (PD) is associated with multiple clinical manifestations including motor and non-motor symptoms, and understanding of its etiologies has been informed by a growing number of genetic mutations, and various fluid-based and brain imaging biomarkers. However, the precise mechanisms by which these phenotypic features interact remain elusive. Therefore, we aimed to generate the phenotypic association graph of multiple heterogeneous features within PD to reveal pathological pathways of the complex disease. MethodsA data-driven approach was introduced to generate the phenotypic association graphs using data from the Parkinsons Progression Markers Initiative (PPMI) and Fox Investigation for New Discovery of Biomarkers (BioFIND) studies. We grouped features based on the structure of the learned graphs in both cohorts, and investigated their dynamic patterns in the longitudinal PPMI cohort. Findings424 patients with PD from the PPMI study and 126 patients with PD from the BioFIND study were available for analysis. For PPMI, the phenotypic association graphs were generated at different time points of the disease, including baseline (without any PD treatments), and 1-, 2-, 3-, 4-, and 5-year follow-up time points. Based on topological structure of the learned graph, clinical features were classified into homogeneous groups, that were densely intra-connected while sparsely inter-connected. Importantly, we observed both stable and longitudinally changing relations in the graphs generated, likely reflecting the dynamic pathologies of PD. By cross-cohort comparison, we observed very similar structure for graphs constructed from BioFIND (in which patients have a much longer duration of PD at enrollment than PPMI) and later-period (4- and 5-year follow-up) data from PPMI. This consistency demonstrates the effectiveness of our method. InterpretationWe analyzed the heterogeneous features of PD by generating the phenotypic association graphs. By analyzing the structural relationships among the features over time, our findings could improve the understanding of the pathologies of PD. FundingMichael J Fox Foundation for Parkinsons Research.

Parkinsons disease (PD) is a progressive neurodegenerative disorder characterized by alpha-()-Synuclein neuronal aggregation and loss of dopaminergic (DA) neurons. Developing animal models that replicate PDs neuropathological phenotypes is critical for understanding its pathophysiology and evaluating potential therapeutic targets. In this study, we show that direct unilateral injection of human -Synuclein PFFs into the Substantia Nigra (SN) of mutant A53T -synuclein overexpressing mice induce bilateral phosphorylated -Synuclein (pS129) pathology in the SN. This pathology spreads to the striatum, cerebral cortex, and midbrain within 60 days and is accompanied by neuroinflammation in the midbrain and cerebral cortex. Additionally, we observed synuclein-dependent neurodegeneration, with a 50% reduction in Tyrosine Hydroxylase (TH) intensity in the SN and a 40% reduction in Striatum, both bilaterally. The model also revealed a compromised blood-brain barrier (BBB) and T-cell infiltration in the PFF injected animals, correlating with pS129 pathology and neuroinflammation. Taken together, we developed a mouse model that recapitulates multiple PD phenotypes, providing a valuable platform for testing therapeutic strategies targeting human -Synuclein pathology and for exploring CNS-peripheral immune interactions in PD. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/690780v2_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@19c6234org.highwire.dtl.DTLVardef@1f36bbaorg.highwire.dtl.DTLVardef@25ac6borg.highwire.dtl.DTLVardef@15ad61d_HPS_FORMAT_FIGEXP M_FIG C_FIG

In vitro modelling of highly vulnerable nigral dopaminergic (DA) neuronal subtypes in Parkinsons disease (PD), is necessary for studying disease mechanisms. Here, we optimized a new approach by expressing the pioneer neurogenic transcription factor, Achaete-scute-like 1 (Ascl1), implicated in determining dopaminergic fate. Sequential small-molecule patterning of iPSCs into early floor plate mesencephalic progenitors, followed by inducible Ascl1 expression, rapidly differentiates midbrain DA neurons. Immunocytochemistry and transcriptomic analysis of these patterned Ascl1-driven DA neurons (PA-DANs) confirmed midbrain-lineage specificity. Importantly, we found an enrichment of DA subpopulations that corresponded to the adult human ventral SOX6-positive A9 DA subtypes vulnerable in PD. Furthermore, we combined these ventral A9-like PA-DANs with human iPSC-derived midbrain astrocytes and microglia in defined ratios to generate mature 3D A9-like assembled organoids that display characteristic spontaneous neuronal activity and electrical propagation along the axon. Our method efficiently generates a mature and functional A9-like DA neuronal platform to study PD. HighlightsO_LISequential midbrain patterning and Ascl1 expression accelerates DA differentiation C_LIO_LIPA-DANs resemble human adult ventral A9-like DA subtypes vulnerable in PD C_LIO_LI3D assembled organoids show mature identity of PA-DANs, iAstrocytes and iMicroglia C_LIO_LIPA-DANs matured in 3D organoids show neuronal network activity within weeks C_LI eTOC blurbIn this study, Ullian and colleagues have developed a rapid method to differentiate dopaminergic neurons, using small molecules to generate early floor plate mesencephalic progenitors from human iPSCs and sequentially expressing a pioneer transcription factor, Ascl1, that accelerates uniform dopaminergic neurogenesis. Patterned Ascl1-driven dopaminergic neurons (PA-DANs) in 2D and 3D assembled organoids serve as a platform to study Parkinsons disease O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/685897v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1d83eb3org.highwire.dtl.DTLVardef@1fc7fa5org.highwire.dtl.DTLVardef@2033e3org.highwire.dtl.DTLVardef@2e8fa3_HPS_FORMAT_FIGEXP M_FIG C_FIG

Parkinsons disease (PD) is a progressive age-related neurodegenerative disorder characterized by both motor and non-motor symptoms. The poorly understood prodromal period, decades-long progression, and disease-phenotype heterogeneity continue to impede the development of preventive and curative therapies. A growing appreciation of immune system changes during the progression of PD suggests that evaluating peripheral immune cells may help identify signatures relevant to disease etiology. We employed single-cell RNA sequencing to profile the transcriptomes of peripheral blood mononuclear cells (PBMCs) from a cohort of 12 patients with PD and 12 healthy controls, equally distributed by sex. Analysis identified gene expression signatures specific to immune cell lineages in PD when compared with healthy controls. Analysis of the dataset indicated that aspects of the PD-related changes were associated with sex, including metabolic and inflammatory changes. Further analysis of myeloid and T cell subsets identified additional pathways and gene expression profiles associated with PD. Trajectory analysis of the myeloid and T cell datasets indicated significant changes in the distribution of cells across states of gene expression in PD compared with controls. This work provides new evidence of peripheral immune cell changes in PD utilizing high-resolution transcriptomics in a cohort powered to analyze sex as a variable. HighlightsTranscriptomic dataset in a cohort powered to analyze immune phenotype in Parkinsons disease Parkinsons disease-specific gene expression signatures in peripheral immune cell lineages Identification of sex differences in the immune cell transcriptome in Parkinsons disease Trajectory analysis identifies changes in immune cell phenotypic distribution in Parkinsons monocytes

Parkinsons disease (PD) is a common, devastating, and incurable neurodegenerative disorder. Several molecular mechanisms have been proposed to drive PD, with genetic and pathological evidence pointing towards aberrant protein homeostasis and mitochondrial dysfunction. PD is clinically highly heterogeneous, it is likely that different mechanisms underlie the pathology in different individuals, each requiring a specific targeted treatment. Recent advances in stem cell technology and fluorescent live-cell imaging have enabled the generation of patient-derived neurons with different mechanistic subtypes of PD. Here, we performed multi-dimensional fluorescent labelling of organelles in iPSC-derived neurons, in healthy control cells, and in four different disease subclasses. We generated a machine learning-based model that can simultaneously predict the presence of disease, and its primary mechanistic subtype. We independently trained a series of classifiers using both quantitative single-cell fluorescence variables and images to build deep neural networks. Quantitative cellular profile-based classifiers achieve an accuracy of 82%, whilst image based deep neural networks predict control, and four distinct disease subtypes with an accuracy of 95%. The classifiers achieve their accuracy across all subtypes primarily utilizing the organellar features of the mitochondria, with additional contribution of the lysosomes, confirming their biological importance in PD. Taken together, we show that machine learning approaches applied to patient-derived cells are able to predict disease subtypes, demonstrating that this approach may be used to guide personalized treatment approaches in the future.

Midbrain organoids are advanced in vitro cellular models for disease modelling. They have been used successfully over the past decade for Parkinsons disease (PD) research and drug development. The three-dimensional structure and multicellular composition allow disease research under more physiological conditions than is possible with conventional 2D cellular models. However, there are concerns in the field regarding the organoid batch-to-batch variability and thus the reproducibility of the results. In this manuscript, we generate multiple independent midbrain organoid batches derived from healthy individuals or GBA-N370S mutation-carrying PD patients to evaluate the reproducibility of the GBA-N370S mutation-associated PD transcriptomic and metabolic signature as well as selected protein abundance. Our analysis shows that GBA-PD-associated phenotypes are reproducible across organoid generation batches and time points. This proves that midbrain organoids are not only suitable for PD in vitro modelling, but also represent robust and highly reproducible cellular models.

Parkinsons disease is a progressive neurodegenerative disorder and idiopathic REM-sleep behaviour disorder (iRBD) has been identified as its single most specific early symptom. To facilitate the screening of individuals at high risk to develop Parkinsons disease, we developed a multiplexed panel of urine proteomics using machine learning and targeted mass spectrometry to detect iRBD. Random urine samples from clinically and genetically well characterized patients with iRBD, idiopathic and hereditary forms of Parkinsons disease, and matching controls, collected in two academic centres, were analysed in a standardized way. First, a biomarker discovery and exploratory comparison of samples from randomly selected idiopathic Parkinsons patients and age/sex-matched healthy controls were proteomically profiled and quantified (> 2500 proteins). The most differentially expressed biomarkers were combined into a high-throughput, multiplexed assay using targeted proteomics designed for use of tandem mass spectrometers for potential translation into clinical practise. This was then validated on independent patient and control samples (n=184). After detecting a major influence of sex on the proteome, we focused subsequent analyses on the larger group of available male samples (n = 114) and report results based on iRBD (n = 14), idiopathic (n = 35) and young-onset Parkinsons disease (n = 15), carriers of LRRK2 (n = 10), PARKIN-gene mutations (n = 5), and healthy control subjects (n = 35). After establishing excellent compatibility between the two study sites, orthogonal partial least squares discriminant analysis (OPLS-DA) excluded a relevant effect of aging, but detected significant differences between iRBD and healthy controls (ANOVA-CV P = 0.002), as well as the combination of iPD/iRBD and healthy controls (ANOVA-CV P = 0.01). Uni- and multivariate analyses detected a shared expression pattern for the protein biomarkers UBC, NCAM1, MIEN1, SPP2, REG1B, ITIH2, BCHE and C3 between iRBD and idiopathic Parkinsons disease. Utilizing split train/test-datasets in a multiple-regression classifier model resulted in a mean accuracy of 78% to detect iRBD, matching iRBDs conversion rate to Parkinsons disease. Hierarchical clustering revealed greater similarities in urine proteomic changes between iRBD and idiopathic than monogenic Parkinsons disease. Several proteins identified correlated either with clinical severity (e.g. VCAM1, MSN, HPX), or risk for future conversion to Parkinsons disease (VCAM1, MSN, MYO10, HSPAIL). This demonstrates the power of machine learning and urine biomarkers to identify iRBD patients. As we develop new therapies and interventions, the ability to detect individuals at-risk of neurodegeneration in very early disease stage will be invaluable for treatment success.

We aimed to identify plasma cell-free transcripts (cfRNA) associated with Parkinsons disease (PD) that also have a high predictive value to differentiate PD from healthy controls. Leveraging two independent populations from two different movement disorder centers we identified 2,188 differentially expressed cfRNAs after meta-analysis. The identified transcripts were enriched in PD relevant pathways, such as PD (p=9.26x10-4), ubiquitin-mediated proteolysis (p=7.41x10-5) and endocytosis (p=4.21x10-6). Utilizing in-house and publicly available brain, whole blood, and acellular plasma transcriptomic and proteomic PD datasets, we found significant overlap across dysregulated biological species in the different tissues and the different biological layers. We developed three predictive models containing increasing number of transcripts that can distinguish PD from healthy control with an area under the ROC Curve (AUC) [≥]0.85. Finally, we showed that several of the predictive transcripts significantly correlate with symptom severity measured by UPDRS-III. Overall, we have demonstrated that cfRNA contains pathological signatures and has the potential to be utilized as biomarker to aid in PD diagnostics and monitoring.

BackgroundThe discovery and development of therapeutics for Parkinsons disease (PD) requires preclinical models and an understanding of the disease mechanisms reflected in each model is crucial to success. ObjectiveTo illuminate disease mechanisms and translational value of two commonly utilized rat models of synucleinopathy - AAV-delivered human mutant hA53T alpha synuclein (-Syn) and -Syn preformed fibril (PFF) injection - using a top-down, unbiased, large-scale approach. MethodsTandem mass tag mass spectrometry (TMT-MS), RNA sequencing, and bioinformatic analyses were used to assess proteins, genes, and pathways disrupted in rat striatum and substantia nigra. Comparative analyses were performed with PD drug candidate targets and an existing human PD and dementia with Lewy body (DLB) proteomics dataset. ResultsUnbiased proteomics identified 388 proteins significantly altered by hA53T--Syn and 1550 by PFF--Syn compared to sham controls. Pathway and correlation analyses of these revealed common and distinct pathophysiological processes altered in each model: dopaminergic signaling/metabolism, mitochondria and energy metabolism, and motor processes were disrupted in AAV-hA53T--Syn, while immune response, intracellular/secretory vesicles, synaptic vesicles, and autophagy were more impacted by PFF--Syn. Synapses, neural growth and remodeling, and protein localization were prominently represented in both models. Analyses revealed potential biomarkers of disease processes and proteins and pathways also altered in patients, elucidating drug targets/ disease mechanisms the models best reflect. ConclusionsAlignment of unbiased multi-omics analyses of AAV-hA53T and PFF--Syn models of synucleinopathy with PD and DLB patient data and PD drug development pipeline candidates identifies optimal models for testing novel therapeutics based on biological mechanisms.

Parkinsons disease (PD) is debilitating neurodegenerative condition that results in the loss of mobility and muscle control. A neuropathological hallmark of PD is the presence aberrant inclusions, known as Lewy pathology, of which -synuclein (-Syn) is a major component. The accumulation of -Syn is a likely consequence of an age-related impairment of the proteostasis system regulating -Syn. To investigate this phenomenon, we map the proteostasis network (PN) of -Syn in the Substania nigra at the proteomic and transcriptomic levels. We then define a -Syn proteostasis activity score (PAS) that quantifies the activity of the PN in regulating -Syn. We thus obtain a PAS signature indicative of the disease state, as well as the age-of-death in PD patients, and the brain regional vulnerability to -Syn aggregation. We then outline a digital twin of the -Syn PN in the Substantia nigra cells by training a model on single-cell data. This digital twin is applied towards target identification for PD. In addition, we further describe the application of the PN to facilitate drug repurposing. Overall, our study highlights the implication of the -Syn PN in PD and how simulations and measurements of its activity can help efforts in translational research for PD.

Parkinsons disease (PD) is characterized by the loss of A9 midbrain dopaminergic neurons and the accumulation of alpha-synuclein aggregates in remaining neurons. Many studies of the molecular and cellular basis of neurodegeneration in PD have made use of iPSC-derived neurons from patients with familial PD mutations. However, approximately half of the cells in the brain are glia, and their role facilitating neurodegeneration is unclear. We developed a novel serum-free protocol to generate midbrain astrocytes from patient-derived iPSCs harbouring the pathogenic p.A30P, p.A53T mutations in SNCA, as well as duplication and triplication of the SNCA locus. In our cellular model, aggregates of alpha-synuclein occurred only within the GFAP+ astrocytes carrying the pathogenic SNCA mutations. Assessment of spontaneous cytosolic calcium (Ca2+) release using Fluo4 revealed that SNCA mutant astrocytes released excess Ca2+ compared to controls. Unbiased evaluation of 3D mitochondrial morphometric parameters showed that these SNCA mutant astrocytes had increased mitochondrial fragmentation and decreased mitochondrial connectivity compared to controls, and reduced mitochondrial bioenergetic function. This comprehensive assessment of different pathogenic SNCA mutations derived from PD patients using the same cellular model enabled assessment of the mutation effect, showing that p.A53T and triplication astrocytes were the most severely affected. Together, our results indicate that astrocytes harbouring the familial PD mutations in SNCA are dysfunctional, suggesting a contributory role for dysfunctional astrocytes in the disease mechanism and pathogenesis of PD. Table of Contents Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/053470v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@974d72org.highwire.dtl.DTLVardef@15d9da0org.highwire.dtl.DTLVardef@1177d4eorg.highwire.dtl.DTLVardef@123c0c2_HPS_FORMAT_FIGEXP M_FIG C_FIG Main PointsO_LIWe used a novel serum-free protocol to generate midbrain-specific functional astrocytes from Parkinsons disease patients carrying pathological mutations in SNCA C_LIO_LIPatient-derived astrocytes show morphological and functional impairments C_LI

-Synuclein (-syn) strains can serve as discriminators between Parkinsons disease (PD) and related -synucleinopathies. The relationship between -syn strain dynamics and clinical performance as patients transition from normal cognition (NC) to cognitive impairment (CI) is not known. Here, we show that the biophysical properties and neurotoxicity of -syn strains change as PD cognitive status transitions from NC to mild cognitive impairment (PD-MCI) and dementia (PD-D). Both cross-sectional and longitudinal analyses reveal distinct -syn strains in PD patients correlating to their level of cognitive impairment. Machine learning (ML) was employed to achieve high classification accuracy. The combination of thioflavin T (ThT) maximal fluorescence intensity (mfi), max slope of rise curve (forming rate), lag time (tlag), 20% time (t20), and half-time (t50), dynamic light scattering (DLS) (peak number, [1/2] peak size, [1/2] peak intensity) and neurotoxicity together with demographic variables for model training yielded superior performance (89[~]99% accuracy in the 4- and 2- classification schema) compared to individual features alone in classifying cognitive status. For the longitudinal study, DLS peak number emerged as the strongest predictor of cognitive transition (HR = 0.12, P = 0.002), with the optimal predictive model combining DLS peak number, sex, education, DLS peak 1 size, and DLS peak 2 polydispersity achieving high accuracy (C-index of [~]93%). This study presents evidence that individuals with PD have different -syn strains correlating to their cognitive status and highlights the potential of -syn strain dynamics to guide future diagnosis, management, and stratification of PD patients. One Sentence SummaryDistinct features of -syn strains change with cognitive decline in Parkinsons disease and AI-based analysis incorporating these combined characteristics serves as a powerful tool for PD clinical stratification.