Experimental and Computational Methods for Allelic Imbalance Analysis from Single-Nucleus RNA-seq Data.

Single-cell RNA-seq (scRNA-seq) is emerging as a powerful tool for understanding gene function across diverse cells. Recently, this has included the use of allele-specific expression (ASE) analysis to better understand how variation in the human genome affects RNA expression at the single-cell level. We reasoned that because intronic reads are more prevalent in single-nucleus RNA-Seq (snRNA-Seq), and introns are under lower purifying selection and thus enriched for genetic variants, that snRNA-seq should facilitate single-cell analysis of ASE. Here we demonstrate how experimental and computational choices can improve the results of allelic imbalance analysis. We explore how experimental choices, such as RNA source, read length, sequencing depth, genotyping, etc., impact the power of ASE-based methods. We developed a new suite of computational tools to process and analyze scRNA-seq and snRNA-seq for ASE. As hypothesized, we extracted more ASE information from reads in intronic regions than those in exonic regions and show how read length can be set to increase power. Additionally, hybrid selection improved our power to detect allelic imbalance in genes of interest. We also explored methods to recover allele-specific isoform expression levels from both long- and short-read snRNA-seq. To further investigate ASE in the context of human disease, we applied our methods to a Parkinson's disease cohort of 94 individuals and show that ASE analysis had more power than eQTL analysis to identify significant SNP/gene pairs in our direct comparison of the two methods. Overall, we provide an end-to-end experimental and computational approach for future studies.

Transcriptional pathobiology and multi-omics predictors for Parkinson's disease.

Early diagnosis and biomarker discovery to bolster the therapeutic pipeline for Parkinson's disease (PD) are urgently needed. In this study, we leverage the large-scale whole-blood total RNA-seq dataset from the Accelerating Medicine Partnership in Parkinson's Disease (AMP PD) program to identify PD-associated RNAs, including both known genes and novel circular RNAs (circRNA) and enhancer RNAs (eRNAs). There were 1,111 significant marker RNAs, including 491 genes, 599 eRNAs, and 21 circRNAs, that were first discovered in the PPMI cohort (FDR < 0.05) and confirmed in the PDBP/BioFIND cohorts (nominal p < 0.05). Functional enrichment analysis showed that the PD-associated genes are involved in neutrophil activation and degranulation, as well as the TNF-alpha signaling pathway. We further compare the PD-associated genes in blood with those in post-mortem brain dopamine neurons in our BRAINcode cohort. 44 genes show significant changes with the same direction in both PD brain neurons and PD blood, including neuroinflammation-associated genes IKBIP, CXCR2, and NFKBIB. Finally, we built a novel multi-omics machine learning model to predict PD diagnosis with high performance (AUC = 0.89), which was superior to previous studies and might aid the decision-making for PD diagnosis in clinical practice. In summary, this study delineates a wide spectrum of the known and novel RNAs linked to PD and are detectable in circulating blood cells in a harmonized, large-scale dataset. It provides a generally useful computational framework for further biomarker development and early disease prediction.

Genetic screening and metabolomics identify glial adenosine metabolism as a therapeutic target in Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disorder and lacks disease-modifying therapies. We developed a Drosophila model for identifying novel glial-based therapeutic targets for PD. Human alpha-synuclein is expressed in neurons and individual genes are independently knocked down in glia. We performed a forward genetic screen, knocking down the entire Drosophila kinome in glia in alpha-synuclein expressing flies. Among the top hits were five genes (Ak1, Ak6, Adk1, Adk2, and awd) involved in adenosine metabolism. Knockdown of each gene improved locomotor dysfunction, rescued neurodegeneration, and increased brain adenosine levels. We determined that the mechanism of neuroprotection involves adenosine itself, as opposed to a downstream metabolite. We dove deeper into the mechanism for one gene, Ak1, finding rescue of dopaminergic neuron loss, alpha-synuclein aggregation, and bioenergetic dysfunction after glial Ak1 knockdown. We performed metabolomics in Drosophila and in human PD patients, allowing us to comprehensively characterize changes in purine metabolism and identify potential biomarkers of dysfunctional adenosine metabolism in people. These experiments support glial adenosine as a novel therapeutic target in PD.

An integrative systems-biology approach defines mechanisms of Alzheimer's disease neurodegeneration.

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer's disease, the most common neurodegenerative disorder, remain incompletely understood. To define relevant pathways, we integrated the results of an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila with human and Drosophila Alzheimer's disease-associated multi-omics. We measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease and identified Alzheimer's disease human genetic variants that modify expression in disease-vulnerable neurons. We used a network optimization approach to integrate these data with previously published Alzheimer's disease multi-omic data. We computationally predicted and experimentally demonstrated how HNRNPA2B1 and MEPCE enhance tau-mediated neurotoxicity. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human iPSC-derived neural progenitor cells. Our work identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer's disease.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data, and advances in natural language processing, particularly large language models, allow for a more granular comparison of populations than previously possible. This study includes two research populations and two real-world data-derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using a manually validated natural language processing with a large language model to extract measurements of PD progression. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p < 0.001; mini-mental state exam median decline 0.28 vs. 0.11, p < 0.001; and clinically recognized cognitive decline, p = 0.001). In real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p < 0.001). After diagnosis, in real-world cohorts, treatment with PD medications has initiated an average of 2.3 years later (95% CI: [2.1-2.4]; p < 0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real-world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Deep sequencing of proteotoxicity modifier genes uncovers a Presenilin-2/beta-amyloid-actin genetic risk module shared among alpha-synucleinopathies.

Whether neurodegenerative diseases linked to misfolding of the same protein share genetic risk drivers or whether different protein-aggregation pathologies in neurodegeneration are mechanistically related remains uncertain. Conventional genetic analyses are underpowered to address these questions. Through careful selection of patients based on protein aggregation phenotype (rather than clinical diagnosis) we can increase statistical power to detect associated variants in a targeted set of genes that modify proteotoxicities. Genetic modifiers of alpha-synuclein (ɑS) and beta-amyloid (Aß) cytotoxicity in yeast are enriched in risk factors for Parkinson's disease (PD) and Alzheimer's disease (AD), respectively. Here, along with known AD/PD risk genes, we deeply sequenced exomes of 430 ɑS/Aß modifier genes in patients across alpha-synucleinopathies (PD, Lewy body dementia and multiple system atrophy). Beyond known PD genes GBA1 and LRRK2, rare variants AD genes (CD33, CR1 and PSEN2) and Aß toxicity modifiers involved in RhoA/actin cytoskeleton regulation (ARGHEF1, ARHGEF28, MICAL3, PASK, PKN2, PSEN2) were shared risk factors across synucleinopathies. Actin pathology occurred in iPSC synucleinopathy models and RhoA downregulation exacerbated ɑS pathology. Even in sporadic PD, the expression of these genes was altered across CNS cell types. Genome-wide CRISPR screens revealed the essentiality of PSEN2 in both human cortical and dopaminergic neurons, and PSEN2 mutation carriers exhibited diffuse brainstem and cortical synucleinopathy independent of AD pathology. PSEN2 contributes to a common-risk signal in PD GWAS and regulates ɑS expression in neurons. Our results identify convergent mechanisms across synucleinopathies, some shared with AD.

Disease progression strikingly differs in research and real-world Parkinson's populations.

Characterization of Parkinson's disease (PD) progression using real-world evidence could guide clinical trial design and identify subpopulations. Efforts to curate research populations, the increasing availability of real-world data and recent advances in natural language processing, particularly large language models, allow for a more granular comparison of populations and the methods of data collection describing these populations than previously possible. This study includes two research populations and two real-world data derived (RWD) populations. The research populations are the Harvard Biomarkers Study (HBS, N = 935), a longitudinal biomarkers cohort study with in-person structured study visits; and Fox Insights (N = 36,660), an online self-survey-based research study of the Michael J. Fox Foundation. Real-world cohorts are the Optum Integrated Claims-electronic health records (N = 157,475), representing wide-scale linked medical and claims data and de-identified data from Mass General Brigham (MGB, N = 22,949), an academic hospital system. Structured, de-identified electronic health records data at MGB are supplemented using natural language processing with a large language model to extract measurements of PD progression. This extraction process is manually validated for accuracy. Motor and cognitive progression scores change more rapidly in MGB than HBS (median survival until H&Y 3: 5.6 years vs. >10, p<0.001; mini-mental state exam median decline 0.28 vs. 0.11, p<0.001; and clinically recognized cognitive decline, p=0.001). In the real-world populations, patients are diagnosed more than eleven years later (RWD mean of 72.2 vs. research mean of 60.4, p<0.001). After diagnosis, in real-world cohorts, treatment with PD medications is initiated 2.3 years later on average (95% CI: [2.1-2.4]; p<0.001). This study provides a detailed characterization of Parkinson's progression in diverse populations. It delineates systemic divergences in the patient populations enrolled in research settings vs. patients in the real world. These divergences are likely due to a combination of selection bias and real population differences, but exact attribution of the causes is challenging using existing data. This study emphasizes a need to utilize multiple data sources and to diligently consider potential biases when planning, choosing data sources, and performing downstream tasks and analyses.

Association Between Use of Any of the Drugs Prescribed in Norway and the Subsequent Risk of Parkinson Disease: A Drug-wide Association Study.

BACKGROUND AND OBJECTIVES: The incidence rate of Parkinson disease (PD) has been increasing rapidly during the past years. Yet, no treatments exist to prevent or slow the progression of the disease. Moreover, we are unable to detect early disease stages during which intervention with disease-modifying therapies is most likely to succeed. The objective of this study was to perform an agnostic drug-wide association study estimating the association between the use of any of the drugs prescribed in Norway and the subsequent risk of PD. METHODS: This registry-based cohort study use data from the entire Norwegian population between 2004 and 2019 linked to the Norwegian Prescription Registry, with more than 600 million individual prescriptions. Drug classes were screened according to Anatomical Therapeutic Chemical codes at level 2, corresponding to therapeutic subgroups. We used Cox regression models to estimate hazard ratios (HRs) and 95% CIs for the associations between drug classes and PD risk. All p values were corrected for multiple testing using the false discovery rate. In addition, we conducted sensitivity analyses of exposure definition as well as time-lag and dose-response analyses. RESULTS: The study population comprised 3,223,672 individuals, 15,849 of whom developed PD during the follow-up. We identified 31 drug classes that were statistically significantly associated with PD risk in Norway during the follow-up. Drugs acting on the renin-angiotensin system (HR 0.92, 95% CI 0.89-0.95), corticosteroids for systemic use (0.88, 95% CI 0.84-0.93), and vaccines (0.89, 95% CI 0.82-0.96) were associated with a decreased risk of PD even up to 10 years before PD onset. Drug classes used to treat symptoms related to prodromal signs of PD, such as constipation, urological issues, and depression, were associated with an increased risk of subsequent diagnosis of PD with HRs of 1.6 (95% CI 1.49-1.73), 1.48 (1.42-1.53), and 1.94 (1.87-2.01), respectively. DISCUSSION: This drug-wide study identified 31 drug classes that were associated with the PD risk change. It reveals the links of renin-angiotensin system medications, vaccines, and corticosteroids with PD risk and suggests that monitoring drug usage using pharmacoepidemiology may allow identifying individuals with prodromal PD.

Circular RNAs in the human brain are tailored to neuron identity and neuropsychiatric disease.

Little is known about circular RNAs (circRNAs) in specific brain cells and human neuropsychiatric disease. Here, we systematically identify over 11,039 circRNAs expressed in vulnerable dopamine and pyramidal neurons laser-captured from 190 human brains and non-neuronal cells using ultra-deep, total RNA sequencing. 1526 and 3308 circRNAs are custom-tailored to the cell identity of dopamine and pyramidal neurons and enriched in synapse pathways. 29% of Parkinson's and 12% of Alzheimer's disease-associated genes produced validated circRNAs. circDNAJC6, which is transcribed from a juvenile-onset Parkinson's gene, is already dysregulated during prodromal, onset stages of common Parkinson's disease neuropathology. Globally, addiction-associated genes preferentially produce circRNAs in dopamine neurons, autism-associated genes in pyramidal neurons, and cancers in non-neuronal cells. This study shows that circular RNAs in the human brain are tailored to neuron identity and implicate circRNA-regulated synaptic specialization in neuropsychiatric diseases.

Safety and efficacy of venglustat in GBA1-associated Parkinson's disease: an international, multicentre, double-blind, randomised, placebo-controlled, phase 2 trial.

BACKGROUND: Variants in the GBA1 gene, which encodes lysosomal acid glucocerebrosidase, are among the most common genetic risk factors for Parkinson's disease and are associated with faster disease progression. The mechanisms involved are unresolved but might include accumulation of glucosylceramide. Venglustat is a brain-penetrant glucosylceramide synthase inhibitor that, in previous studies, reduced amounts of the glycosphingolipid. We aimed to assess the safety, efficacy, and target engagement of venglustat in people with early-stage Parkinson's disease carrying pathogenic GBA1 variants. METHODS: MOVES-PD part 2 was a randomised, double-blinded, placebo-controlled phase 2 study done at 52 centres (academic sites, specialty clinics, and general neurology centres) in 16 countries. Eligible adults aged 18-80 years with Parkinson's disease (Hoehn and Yahr stage ≤2) and one or more GBA1 variants were randomly assigned using an interactive voice-response system (1:1) to 52 weeks of treatment with oral venglustat (15 mg/day) or matching placebo. Investigators, site personnel, participants, and their caregivers were masked to treatment allocation. The primary outcome measure was the change from baseline to 52 weeks in the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) parts II and III combined score (a higher score indicates greater impairment), and it was analysed in a modified intention-to-treat population (ie, all randomly assigned participants with a baseline and at least one post-baseline measurement during the treatment period). This study was registered with ClinicalTrials.gov (NCT02906020) and is closed to recruitment. FINDINGS: Between Dec 15, 2016, and May 27, 2021, 221 participants were randomly assigned to venglustat (n=110) or placebo (n=111). The least squares mean change in MDS-UPDRS parts II and III combined score was 7·29 (SE 1·36) for venglustat (n=96) and 4·71 (SE 1·27) for placebo (n=105); the absolute difference between groups was 2·58 (95% CI -1·10 to 6·27; p=0·17). The most common treatment-emergent adverse events (TEAEs) were constipation and nausea (both were reported by 23 [21%] of 110 participants in the venglustat group and eight [7%] of 111 participants in the placebo group). Serious TEAEs were reported for 12 (11%) participants in each group. There was one death in the venglustat group owing to an unrelated cardiopulmonary arrest and there were no deaths in the placebo group. INTERPRETATION: In people with GBA1-associated Parkinson's disease in our study, venglustat had a satisfactory safety profile but showed no beneficial treatment effect compared with placebo. These findings indicate that glucosylceramide synthase inhibition with venglustat might not be a viable therapeutic approach for GBA1-associated Parkinson's disease. FUNDING: Sanofi.

GBA1 Variants and Parkinson's Disease: Paving the Way for Targeted Therapy.

Glucosylceramidase beta 1 (GBA1) variants have attracted enormous attention as the most promising and important genetic candidates for precision medicine in Parkinson's disease (PD). A substantial correlation between GBA1 genotypes and PD phenotypes could inform the prediction of disease progression and promote the development of a preventive intervention for individuals at a higher risk of a worse disease prognosis. Moreover, the GBA1-regulated pathway provides new perspectives on the pathogenesis of PD, such as dysregulated sphingolipid metabolism, impaired protein quality control, and disrupted endoplasmic reticulum-Golgi trafficking. These perspectives have led to the development of novel disease-modifying therapies for PD targeting the GBA1-regulated pathway by repositioning treatment strategies for Gaucher's disease. This review summarizes the current hypotheses on a mechanistic link between GBA1 variants and PD and possible therapeutic options for modulating GBA1-regulated pathways in PD patients.

Circular RNAs in the human brain are tailored to neuron identity and neuropsychiatric disease.

Little is known about circular RNAs (circRNAs) in specific brain cells and human neuropsychiatric disease. Here, we systematically identified over 11,039 circRNAs expressed in vulnerable dopamine and pyramidal neurons laser-captured from 190 human brains and non-neuronal cells using ultra-deep, total RNA sequencing. 1,526 and 3,308 circRNAs were custom-tailored to the cell identity of dopamine and pyramidal neurons and enriched in synapse pathways. 88% of Parkinson's and 80% of Alzheimer's disease-associated genes produced circRNAs. circDNAJC6, produced from a juvenile-onset Parkinson's gene, was already dysregulated during prodromal, onset stages of common Parkinson's disease neuropathology. Globally, addiction-associated genes preferentially produced circRNAs in dopamine neurons, autism-associated genes in pyramidal neurons, and cancers in non-neuronal cells. This study shows that circular RNAs in the human brain are tailored to neuron identity and implicate circRNA- regulated synaptic specialization in neuropsychiatric diseases.

Beta2-adrenoreceptor agonists and long-term risk of Parkinson's disease.

INTRODUCTION: There is limited information on how the association between Parkinson's disease and the use of beta2-adrenoreceptor (ß2AR) agonists varies among groups of short-, long-, and ultra-long-acting ß2AR agonists (SABA, LABA and ultraLABA). METHODS: In this prospective study of the Norwegian population, we estimated the incidence of Parkinson's disease according to exposure to ß2AR agonists as a time-dependent variable by means of Cox regression. We adjusted for educational level, comorbidity and performed a sensitivity analysis excluding individuals with chronic obstructive pulmonary disease (COPD), all factors associated with smoking. Anticholinergics and corticosteroids as drugs with the same indication were analyzed for comparison. RESULTS: In the follow-up period from 2005 to 2019, 15,807 incident Parkinson's cases were identified. After adjustments for sex, education and age as the timescale, SABA (Hazard ratio (HR) = 0.84; 95%CI: 0.79, 0.89; p < 0.001), LABA (HR = 0.85; 95%CI: 0.81, 0.90; p < 0.001) and ultraLABA (HR = 0.6; 95%CI: 0.49, 0.73; p < 0.001) were all associated with a lower risk of Parkinson's disease. After exclusion of COPD patients, corticosteroids and anticholinergics were no longer inversely associated, whereas ß2AR agonists remained associated. CONCLUSION: Of drugs with the same indication of use, only ß2AR agonists remained inversely associated with PD risk after all adjustments, with ultraLABA displaying the overall strongest association. Although the precision of the estimate is limited by the modest number of exposed PD cases without COPD, the association is intriguing and suggest that longer-acting, more lipophilic, and thus likely more brain-penetrant ß2AR agonists could be prioritized for further studies.

Plasma biomarkers for diagnosis of Alzheimer's disease and prediction of cognitive decline in individuals with mild cognitive impairment.

Background: The last few years have seen major advances in blood biomarkers for Alzheimer's Disease (AD) with the development of ultrasensitive immunoassays, promising to transform how we diagnose, prognose, and track progression of neurodegenerative dementias. Methods: We evaluated a panel of four novel ultrasensitive electrochemiluminescence (ECL) immunoassays against presumed CNS derived proteins of interest in AD in plasma [phosphorylated-Tau181 (pTau181), total Tau (tTau), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP)]. Two sets of banked plasma samples from the Massachusetts Alzheimer's Disease Research Center's longitudinal cohort study were examined: A longitudinal prognostic sample (n = 85) consisting of individuals with mild cognitive impairment (MCI) and 4 years of follow-up and a cross-sectional sample (n = 238) consisting of individuals with AD, other neurodegenerative diseases (OND), and normal cognition (CN). Results: Participants with MCI who progressed to dementia due to probable AD during follow-up had higher baseline plasma concentrations of pTau181, NfL, and GFAP compared to non-progressors. The best prognostic discrimination was observed with pTau181 (AUC = 0.83, 1.7-fold increase) and GFAP (AUC = 0.83, 1.6-fold increase). Participants with autopsy- and/or biomarker verified AD had higher plasma levels of pTau181, tTau and GFAP compared to CN and OND, while NfL was elevated in AD and further increased in OND. The best diagnostic discrimination was observed with pTau181 (AD vs CN: AUC = 0.90, 2-fold increase; AD vs. OND: AUC = 0.84, 1.5-fold increase) but tTau, NfL, and GFAP also showed good discrimination between AD and CN (AUC = 0.81-0.85; 1.5-2.2 fold increase). Conclusions: These new ultrasensitive ECL plasma assays for pTau181, tTau, NfL, and GFAP demonstrated diagnostic utility for detection of AD. Moreover, the absolute baseline plasma levels of pTau181 and GFAP reflect cognitive decline over the next 4 years, providing prognostic information that may have utility in both clinical practice and clinical trial populations.

Mitochondrial haplogroups and cognitive progression in Parkinson's disease.

Mitochondria are a culprit in the onset of Parkinson's disease, but their role during disease progression is unclear. Here we used Cox proportional hazards models to exam the effect of variation in the mitochondrial genome on longitudinal cognitive and motor progression over time in 4064 patients with Parkinson's disease. Mitochondrial macro-haplogroup was associated with reduced risk of cognitive disease progression in the discovery and replication population. In the combined analysis, patients with the super macro-haplogroup J, T, U# had a 41% lower risk of cognitive progression with P = 2.42 × 10-6 compared to those with macro-haplogroup H. Exploratory analysis indicated that the common mitochondrial DNA variant, m.2706A>G, was associated with slower cognitive decline with a hazard ratio of 0.68 (95% confidence interval 0.56-0.81) and P = 2.46 × 10-5. Mitochondrial haplogroups were not appreciably linked to motor progression. This initial genetic survival study of the mitochondrial genome suggests that mitochondrial haplogroups may be associated with the pace of cognitive progression in Parkinson's disease over time.

Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping.

Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.

Parkinson-causing mutations in LRRK2 impair the physiological tetramerization of endogenous α-synuclein in human neurons.

α-Synuclein (αSyn) aggregation in Lewy bodies and neurites defines both familial and 'sporadic' Parkinson's disease. We previously identified α-helically folded αSyn tetramers, in addition to the long-known unfolded monomers, in normal cells. PD-causing αSyn mutations decrease the tetramer:monomer (T:M) ratio, associated with αSyn hyperphosphorylation and cytotoxicity in neurons and a motor syndrome of tremor and gait deficits in transgenic mice that responds in part to L-DOPA. Here, we asked whether LRRK2 mutations, the most common genetic cause of cases previously considered sporadic PD, also alter tetramer homeostasis. Patient neurons carrying G2019S, the most prevalent LRRK2 mutation, or R1441C each had decreased T:M ratios and pSer129 hyperphosphorylation of their endogenous αSyn along with increased phosphorylation of Rab10, a widely reported substrate of LRRK2 kinase activity. Two LRRK2 kinase inhibitors normalized the T:M ratio and the hyperphosphorylation in the G2019S and R1441C patient neurons. An inhibitor of stearoyl-CoA desaturase, the rate-limiting enzyme for monounsaturated fatty acid synthesis, also restored the αSyn T:M ratio and reversed pSer129 hyperphosphorylation in both mutants. Coupled with the recent discovery that PD-causing mutations of glucocerebrosidase in Gaucher's neurons also decrease T:M ratios, our findings indicate that three dominant genetic forms of PD involve life-long destabilization of αSyn physiological tetramers as a common pathogenic mechanism that can occur upstream of progressive neuronal synucleinopathy. Based on αSyn's finely-tuned interaction with certain vesicles, we hypothesize that the fatty acid composition and fluidity of membranes regulate αSyn's correct binding to highly curved membranes and subsequent assembly into metastable tetramers.

Plasma biomarkers for prognosis of cognitive decline in patients with mild cognitive impairment.

Plasma-based biomarkers present a promising approach in the research and clinical practice of Alzheimer's disease as they are inexpensive, accessible and minimally invasive. In particular, prognostic biomarkers of cognitive decline may aid in planning and management of clinical care. Although recent studies have demonstrated the prognostic utility of plasma biomarkers of Alzheimer pathology or neurodegeneration, such as pTau-181 and NF-L, whether other plasma biomarkers can further improve prediction of cognitive decline is undetermined. We conducted an observational cohort study to determine the prognostic utility of plasma biomarkers in predicting progression to dementia for individuals presenting with mild cognitive impairment due to probable Alzheimer's disease. We used the Olink™ Proximity Extension Assay technology to measure the level of 460 circulating proteins in banked plasma samples of all participants. We used a discovery data set comprised 60 individuals with mild cognitive impairment (30 progressors and 30 stable) and a validation data set consisting of 21 stable and 21 progressors. We developed a machine learning model to distinguish progressors from stable and used 44 proteins with significantly different plasma levels in progressors versus stable along with age, sex, education and baseline cognition as candidate features. A model with age, education, APOE genotype, baseline cognition, plasma pTau-181 and 12 plasma Olink protein biomarker levels was able to distinguish progressors from stable with 86.7% accuracy (mean area under the curve = 0.88). In the validation data set, the model accuracy was 78.6%. The Olink proteins selected by the model included those associated with vascular injury and neuroinflammation (e.g. IL-8, IL-17A, TIMP-4, MMP7). In addition, to compare these prognostic biomarkers to those that are altered in Alzheimer's disease or other types of dementia relative to controls, we analyzed samples from 20 individuals with Alzheimer, 30 with non-Alzheimer dementias and 34 with normal cognition. The proteins NF-L and PTP-1B were significantly higher in both Alzheimer and non-Alzheimer dementias compared with cognitively normal individuals. Interestingly, the prognostic markers of decline at the mild cognitive impairment stage did not overlap with those that differed between dementia and control cases. In summary, our findings suggest that plasma biomarkers of inflammation and vascular injury are associated with cognitive decline. Developing a plasma biomarker profile could aid in prognostic deliberations and identify individuals at higher risk of dementia in clinical practice.