npj Parkinson's Disease

We address an unmet challenge in Parkinsons disease: the lack of biomarkers to identify the right patients for the right therapy, which is a main reason clinical trials for disease modifying treatments have all failed. The gut microbiome is a new target for treatment of Parkinsons disease, with potential to halt disease progression. Our aim was to develop microbiome-based biomarkers to guide patient selection for microbiome-based clinical trials. We used microbial taxa that have been robustly associated with Parkinsons disease across studies and at high significance as dysbiotic features of Parkinsons disease. Using individual-level taxonomic relative abundance data, we classified patients according to their dysbiotic features, effectively defining microbiome-based subtypes of PD. We show that not all persons with Parkinsons disease have a dysbiotic microbiome, and not all dysbiotic Parkinsons disease microbiomes have the same features. Grounded in robust and reproducible data from differential abundance studies, we propose an intuitive and easily modifiable method to identify the optimal candidates for microbiome-based clinical trials, and subsequently, for treatments that are personalized for each individuals dysbiotic features. We demonstrate the method for Parkinsons disease. The concept, and the method, is generalizable for any disease with a microbiome component.

Parkinsons disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms. DNA repair dysfunction and integrated stress response (ISR) dysregulation have been suggested to be relevant in PD pathophysiology, but their role during the prodromal phase, before motor symptoms manifest, remains unclear. In this study, we analyzed longitudinal blood transcriptomic data from the Parkinsons Progression Markers Initiative (PPMI) to investigate how DNA repair and ISR gene expression differ across healthy individuals, prodromal PD patients, and individuals with established PD. Using logistic regression classifiers, a type of supervised machine learning model, we found that DNA repair and ISR gene expression effectively distinguished prodromal PD from healthy individuals, with classification accuracy increasing over time and peaking at later prodromal stages. In contrast, these pathways failed to differentiate established PD from healthy controls, suggesting that DNA repair and ISR dysregulation play a more distinct role early in disease progression. Gene expression variability was high in prodromal PD at baseline but reduced over time, indicating a convergence in gene expression patterns as the disease advances. Notably, 50% of DNA repair genes and 74% of ISR genes exhibited nonlinear expression patterns, with an initial increase followed by a decline. This suggests a transient adaptive response that fades out as PD progresses. Feature importance analysis identified key genes, including ERCC6, PRIMPOL, NEIL2, and NTHL1, as important predictors of prodromal PD. These findings suggest that DNA repair and ISR dysregulation contribute to early-stage PD pathology and may serve as biomarkers for early disease detection and possible treatment avenues during the prodromal phase. This study highlights the role of DNA repair and ISR pathways in the prodromal phase of PD, demonstrating their potential as early molecular indicators of disease onset. Future research should validate these findings in larger cohorts and explore their relevance for early diagnostics and intervention strategies.

BackgroundParkinsons disease (PD) is a neurodegenerative disorder for which there is currently no cure or reliable biomarker for early detection or for evaluating the effectiveness of potential treatments. PD pathology is driven by misfolding and subsequent accumulation of alpha-synuclein (Syn) protein into pathological aggregates within neurons and glial cells. Seed amplification assay (SAA) is a highly sensitive and specific diagnostic tool developed to detect pathological Syn species in the cerebrospinal fluid (CSF) of PD patients. However, Syn aggregates are present in multiple tissues and biosamples, including stools. In this study, we aimed to investigate the potential diagnostic value of SAA using stool samples from PD patients and healthy controls (HC). MethodsStool samples from PD patients (n=45) and healthy controls (n=35) were analysed for the presence of Syn species using slot blot assays with a panel of six Syn antibodies, and ELISA assays. Samples were subjected to SAA, and the end-point products (SAA EP) were characterised using transmission electron microscopy (TEM). Extracellular vesicles (EVs) were isolated from the subset of samples (n=5 per group) using size exclusion chromatography and characterized by TEM. The seeding activity of isolated EVs was evaluated using SAA, followed by TEM analysis of SAA EP. ResultsProtein extracts from both PD and HC stool samples revealed pathological Syn species in the slot blot assay using the phosphorylated Syn antibody, pS129 and conformation-specific antibodies, MJFR-14 and 5G4. ELISA showed significantly elevated total Syn levels in PD samples compared to HC, although no differences in aggregated Syn levels were detected. In stool protein extracts, SAA demonstrated 55.6% sensitivity and 60% specificity. When applied to stool-derived EVs from PD patients and controls, sensitivity increased to 100%, while specificity remained at 60%. Notably, SAA applied to stool-derived EVs pre-incubated with recombinant monomeric Syn achieved 100% sensitivity and 100% specificity. ConclusionThese findings suggest that SAA applied to EVs isolated from stool samples, particularly after pre-incubation with recombinant monomeric Syn, may serve as a valuable, non-invasive screening tool for the diagnosis 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.

Human midbrain organoids (hMOs) show promise as a patient-derived model for the study of Parkinsons disease (PD). Yet, much remains unknown about how accurately hMOs recapitulate key features of PD in the human brain. In both PD patients and animal models, disease progression leads to characteristic changes in neural activity throughout the basal ganglia. Here we demonstrate that patient-derived induced pluripotent stem cell (iPSC) hMOs harboring a triplication in the SNCA gene, encoding -synuclein, a key protein in PD pathogenesis, can recapitulate PD-associated changes in neural activity. Namely, we observe hyperactive network activity in SNCA triplication hMOs, but not in isogenic, CRISPR-corrected iPSC hMOs. These changes are characterized by an increase in the number of bursts and network-wide bursts. Moreover, SNCA triplication hMOs exhibit an increase in network synchrony and burst/network burst strength similar to observations in animal and human PD brains. Subsequently, we show that the observed changes in neuronal activity are attributed to dopamine D2 receptor hypoactivity due to dopamine depletion, which could be reversed by the D2 receptor agonist quinpirole. Thus, hMOs faithfully model network wide electrophysiological changes associated with PD progression and serve as a promising tool for PD research and personalized medicine.

Mitochondrial and lysosomal dysfunction are central features of Parkinsons disease (PD) across major genetic forms including PRKN, SNCA, and LRRK2. We applied cell morphomics, a machine-learning-based framework combining high-content imaging with quantitative feature extraction, to analyse mitochondrial and lysosomal morphology at single-cell resolution in iPS cell-derived cortical neurons from PD patients and healthy controls (13 lines total). Supervised machine-learning models distinguished PD neurons from controls with high accuracy (AUC = 0.87) and reliably separated individual genotypes. Feature importance and attribution analysis revealed genotype-specific organelle biases, with mitochondrial features dominating classification in PRKN neurons, balanced mitochondrial and lysosomal contributions in SNCA neurons, and a greater lysosomal contribution in LRRK2 neurons. Multi-class models retained strong performance, and findings were reproduced across two independent laboratories using different dyes and imaging conditions. These results demonstrate that morphomics provides a robust and scalable framework to quantify genotype-specific organelle abnormalities in PD neurons and supports its application for cellular stratification and biomarker discovery.

Extracellular vesicles (EVs) hold promise as minimally invasive biomarkers for neurodegenerative proteinopathies, but disease- and stage-specific profiles remain unclear. For this study, we enrolled 378 participants across five centers and the MJFF-BioFIND cohort: 100 healthy controls [HC], 64 isolated REM sleep behavior disorder [iRBD], 41 DeNovo Parkinsons Disease [PD], 89 Late PD, 32 other Synucleinopathies, and 52 Tauopathies. All participants underwent clinical evaluation and blood collection. The 77 subjects from the BioFIND cohort also provided CSF samples. EV concentration and size were assessed by nanoparticle tracking analysis; flow cytometry quantified tetraspanins (CD9/CD63/CD81) and 37 surface markers. Multivariable logistic regression, receiver operating characteristic analyses (ROC), and repeated random forest (rRF) classifiers evaluated diagnostic utility. Late PD showed the highest EV concentrations compared to HC and other disease groups. Participants exhibited distinct EV surface immunophenotypes, with the iRBD group displaying the most extensive immune activation signature vs HC, followed by PD patients. Multivariate logistic regression analysis identified diagnostic marker panels: CD3/CD9/CD25/CD56 for iRBD, SSEA4 for Late PD, CD146/CD209 for Synucleinopathies, and CD8/CD45/CD62P for Tauopathies. ROC confirmed good discriminatory performance, with CD56 emerging as the strongest single predictor for iRBD vs HC, SSEA4 showing high sensitivity for Late PD, and marker combinations providing optimal balance for Synucleinopathy/Tauopathy classification vs HC. In the CSF BioFIND subset, Late PD EVs exhibited increased myeloid (CD1c), adhesion (CD29), activation (CD69), and epithelial (CD326) markers compared to HC. Among these, CD326 was independently associated with Late PD diagnosis. Machine learning classifiers using all 37 surface antigens achieved excellent training performance (91.7-94.3% accuracy for iRBD/Synucleinopathies vs HC) and maintained robust validation accuracy, particularly for iRBD (77.8%) and DeNovo PD (76.6%) vs HC. EV immuno-phenotyping reveals distinct signatures across the neurodegenerative proteinopathies spectrum, with the highest diagnostic utility for prodromal iRBD detection. Longitudinal validation and cell-of-origin refinement represent key next steps toward clinical translation.

There are no approved treatments that slow Parkinsons disease (PD) progression and therefore it is important to identify novel pathogenic mechanisms that can be targeted. Loss of the epigenetic marker, Tet2 appears to have some beneficial effects in PD models, but the underlying mechanism of action is not well understood. We performed an unbiased transcriptomic analysis of cortical neurons isolated from patients with PD to identify dysregulated pathways and determine their potential contributions to the disease process. We discovered that genes associated with primary cilia, non-synaptic sensory and signaling organelles, are upregulated in both early and late PD patients. Enhancing ciliogenesis in primary cortical neurons via sonic hedgehog signaling suppressed the accumulation of -synuclein pathology in vitro. Interestingly, deletion of Tet2 in mice also enhanced the expression of primary cilia and sonic hedgehog signaling genes and rescued the accumulation of -synuclein pathology and dopamine neuron degeneration in vivo. Our findings demonstrate the crucial role of Tet2 loss in regulating ciliogenesis and potentially affecting the progression of PD pathology.

Parkinsons disease (PD) is a multifactorial neurodegenerative disorder that has been modeled extensively in animals, primarily rodents, but also in non-human primates and non-mammalian organisms. However, no single animal model fully recapitulates the hallmarks of PD pathology. Here, we extend this work by modeling PD for the first time in the spiny mouse (Acomys dimidiatus), a mammal notable for its robust regeneration of multiple tissues. We show that the nigrostriatal pathway of A. dimidiatus is vulnerable to both acute 6-hydroxydopamine (6-OHDA) toxicity and chronic -synuclein (Syn) preformed fibril-induced aggregation. Mouse Syn PFFs produced widespread pS129-positive Syn inclusions across multiple brain regions, mirroring a key pathological hallmark of PD. Compared to C57BL/6J mice, A. dimidiatus exhibited more pronounced behavioral impairments, greater nigrostriatal degeneration, and higher pS129-Syn inclusion burden within substantia nigra pars compacta (SNpc) neurons. To probe the molecular underpinnings behind the vulnerability, we performed single-cell spatial proteomics, which revealed extensive proteomic alterations in dopaminergic neurons associated with Syn aggregation. Multiple proteins were dysregulated in A. dimidiatus, including those involved in proteasomal function, mitochondrial pathways, and oxidative stress regulation, which are processes commonly implicated in PD. Notably, proteomic analysis identified heightened astrocytic activation in the SNpc, which we validated histologically, suggesting a distinct glial response compared to mice. Together, these findings expand our understanding of PD-relevant pathophysiology across species and establish A. dimidiatus as a model for studying mechanisms of neurodegeneration.

The intracellular misfolding and accumulation of alpha-synuclein into structures collectively called Lewy pathology is a central phenomenon for the pathogenesis of Parkinsons disease (PD), Dementia with Lewy Bodies (DLB), and Multiple System Atrophy. Understanding the molecular architecture of Lewy pathology is crucial for understanding disease origins and progression. Here we developed a method to label, extract, and purify molecules from Lewy pathology of formalin fixed PD and DLB brain for blotting and mass spectrometry analysis. Using the biotinylation antibody recognition (BAR) technique, we labeled phosphoserine 129 alpha-synuclein positive pathology and associated molecules with biotin. Formalin crosslinks were then reversed, protein extracted, and pathology associated molecules isolated with streptavidin beads. Results showed superior immunohistochemical staining of Lewy pathology following the BAR protocol when compared to standard avidin biotin complex (ABC) based detection. The enhanced staining was particularly apparent for fibers of the medial forebrain bundle and punctate pathology within the striatum and cortex, which otherwise were weakly labeled or not detected. Subsequent immunoblotting BAR-labeled Lewy pathology extracts revealed the presence of high molecular weight alpha-synuclein, ubiquitin protein conjugates, and phosphoserine 129 alpha-synuclein. Mass spectrometry analysis of BAR-labeled Lewy pathology extracts from PD and DLB patients identified 815 proteins with significant enrichment for many pathways. Notably the most significant KEGG pathway was Parkinsons disease (FDR = 2.48 x 10-26) and GO Cellular compartment was extracellular exosomes (GO Cellular Compartment; FDR = 2.66x 10-34). We used enrichment data to create a functional map of Lewy Pathology from primary disease tissues, which implicated Vesicle Trafficking as the primary disease associated pathway in DLB and PD. In summary, this protocol can be used to enrich for Lewy pathology from formalin fixed human primary tissues, which allows the determination of molecular signatures of Lewy pathology. This technique has broad potential to help understand the phenomenon of Lewy pathology in primary human tissue and animal models.

BackgroundAlthough there is no cure, early diagnosis of Parkinsons disease allows effective management and symptomatic relief, potentially delaying the need for more potent medications. Blood-based biomarkers can facilitate early detection, symptom monitoring, and targeted therapy. MethodsGene expression and plasma proteomics data from two Parkinsons disease cohorts were integrated. Machine learning models were trained to classify disease status using protein, gene expression, and combined datasets. A severity score was derived by regressing protein levels against clinical motor ratings and tested longitudinally. Enrichment and network analyses identified biological context, and drug perturbation databases were queried for candidate therapies. ResultsProteomic models outperformed gene-based approaches and generalized well to external data. The severity score correlated with clinical burden and predicted future progression. Enriched pathways involved extracellular signaling, immune response, and post-translational regulation. Several compounds were identified as potential therapeutic candidates based on network targeting and reversal potential. ConclusionsPeripheral proteomic signatures offer classification, progression, and therapeutic insights in Parkinsons disease, supporting their biological relevance.

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.

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) usually has a late clinical onset. The lack of early biomarkers for the disease represents a major challenge for developing timely treatment interventions. Here, we use an -synuclein-overexpressing transgenic (Th-1-SNCA-A30P) mouse model of PD to identify appropriate candidate markers in the gut for early stages of PD before hallmark symptoms begin to manifest. A30P mice did not show alterations in gait parameters at 2 months of age, and these mice were therefore defined as pre-symptomatic A30P mice (psA30P). We discovered early functional motility changes in the gut and early molecular dysregulations in the myenteric plexus of psA30P mice by comparative protein and miRNA profiling and cell culture experiments. We found that the proteins neurofilament light chain, vesicle-associated membrane protein 2 and calbindin 2, together with the miRNAs that regulate them, are potential biomarkers of early PD that may facilitate timely treatment and/or prevention of PD in men.

Use of digital sensors to passively collect long-term, longitudinal data offers a step change in our ability to monitor Parkinsons disease (PD). However, to date the evaluation of long-term digital sensor data has been neglected in favour of evaluating short-term data collected in controlled settings. To address this, we combined longitudinal clinical and biological assessment data from the Parkinsons Progression Marker Initiative (PPMI) cohort with long-term (mean: 485 days) at-home digital monitoring data collected with the Verily Study Watch. We then derived digital timeseries components leveraging the long-term monitoring of the PPMI. We found three key findings: Firstly, that these digital timeseries components correlated with the rate of progression of motor (r = 0.23, p-value = 8.5x10-3, r = 0.26, p-value = 2.2x10-3) and autonomic symptoms (r = -0.23, p-value = 8.2x10-3), impairments in daily living (r = 0.26, p-value = 2.5x10-3), increase in medication requirements and complications (r = -0.25, p-value = 4.2x10-3), and rate of increase in cerebrospinal fluid (CSF) tau (ptau: r = 0.28, p-value = 2.6x10-3; ttau: r = 0.34, p-value = 1.2x10-4). Second, we derived digitally informed subtypes of PD and found higher similarity with CSF (0.35) and DaTscan (0.35) subtypes than has been found for previously published subtypes (CSF: 0.31{+/-}0.01, DaTscan: 0.31{+/-}0.02). Finally, we showed that long-term digital monitoring can inform PD risk and sensitively detect individuals with probable prodromal PD. Our findings highlight the wealth of application areas for digital sensors in PD research.

Parkinsons disease (PD) is characterized by the accumulation of -synuclein (-syn) aggregates, which are thought to drive neurodegeneration in vulnerable neuronal populations, particularly within the dopaminergic nigrostriatal pathway. Here, we aimed to investigate the spatiotemporal relationship between -syn aggregation and dopaminergic cell degeneration by developing an optimized analysis pipeline for efficient whole-brain mapping. We injected -syn preformed fibrils (PFFs) into the substantia nigra pars compacta (SNc) of wild-type mice, using monomeric -syn-injected mice as the control group. Aggregate propagation and dopaminergic degeneration were analyzed using antibodies against phosphorylated -syn (pS129) and tyrosine hydroxylase, followed by brain clearing combined with high-resolution (<4 {micro}m) light-sheet microscopy (LSM), coupled with automated pipeline data analysis. Whole-brain LSM mapping revealed dense somatic and neuritic phosphorylated -syn aggregates within dopaminergic neurons of the SNc 12 weeks post-unilateral PFF injection, accompanied by significant loss of tyrosine hydroxylase-positive neurons and prion-like propagation of -syn aggregates to anatomically connected brain regions, including the striatum. The distribution pattern of -syn pathology visualized by LSM was validated by whole-brain immunohistochemical analysis of PFF-injected mouse brains. The 3D LSM approach introduced here uniquely captures the spatial organization and propagation of -syn pathology and dopaminergic degeneration across interconnected brain networks.

Fecal gut microbiota changes are associated with Parkinsons disease (PD). However, disease related changes cannot readily be discerned from medication effects, as almost all participants in previous studies were using PD medication, and conclusive longitudinal data related to treatment initiation is lacking. Here, fecal gut microbiota composition was assessed in 62 de novo PD participants who were untreated at baseline and used PD medication at one-year follow-up, by means of 16S-sequencing. In addition, participants were stratified for the type of dopaminergic medication. Overall gut microbiota composition did not differ between baseline and one-year follow-up, but was associated with levodopa dose and levodopa equivalent daily dose (LEDD). Several differentially abundant taxa are in line with previously described changes in PD. These included reduced levels of amplicon sequence variants (ASVs) belonging to Faecalibacterium prausnitzii and Lachnospiraceae in all participants at follow-up, and increased levels of an ASV belonging to Bifidobacterium in dopamine agonist users. The family Bifidobacteriaceae was increased in dopamine agonist users who only used pramipexole. Levodopa dose was inversely related to the abundance of the families Ruminococcaceae and Lachnospiraceae, and the genus Lachnospiraceae ND3007 group. PD medications exert a measurable and dose-dependent effect on gut microbiota composition and accentuate several previously described gut microbiota changes in PD. Detailed knowledge of medication effects should be part of future trial designs of gut microbiome studies in PD and are necessary to interpret previously published data.

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.

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.

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.