Association between α-synuclein blood transcripts and early, neuroimaging-supported Parkinson's disease

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.

Significance statement

Early and accurate diagnosis of Parkinson's disease (PD) is urgently needed. However, biomarkers for early detection of PD are still lacking. Also, the limit of sample size remains one of the main pitfalls of current PD biomarker studies. We employed an analysis of large-scale whole-blood RNA-seq data. By identifying 1,111 significant marker RNAs, we establish a robust foundation for early PD detection, which implicated in neutrophil activation, degranulation, and TNF-alpha signaling, offer unprecedented insights into PD pathogenesis. Our multi-omics machine learning model, boasting an AUC of 0.89, outperforms previous studies, promising a transformative tool for precise PD diagnosis in clinical settings. This study marks a pivotal step toward enhanced biomarker development and early disease prediction.

Malat1 fine-tunes bone homeostasis by orchestrating cellular crosstalk and the β-catenin-OPG/Jagged1 pathway

The IncRNA Malat1 was initially believed to be dispensable for physiology due to the lack of observable phenotypes in Malat1 knockout (KO) mice. However, our study challenges this conclusion. We found that both Malat1 KO and conditional KO mice in the osteoblast lineage exhibit significant osteoporosis. Mechanistically, Malat1 acts as an intrinsic regulator in osteoblasts to promote osteogenesis. Interestingly, Malat1 does not directly affect osteoclastogenesis but inhibits osteoclastogenesis in a non-autonomous manner in vivo via integrating crosstalk between multiple cell types, including osteoblasts, osteoclasts and chondrocytes. Our findings substantiate the existence of a novel remodeling network in which Malat1 serves as a central regulator by binding to β-catenin and functioning through the β-catenin-OPG/Jagged1 pathway in osteoblasts and chondrocytes. In pathological conditions, Malat1 significantly promotes bone regeneration in fracture healing. Bone homeostasis and regeneration are crucial to well-being. Our discoveries establish a previous unrecognized paradigm model of Malat1 function in the skeletal system, providing novel mechanistic insights into how a lncRNA integrates cellular crosstalk and molecular networks to fine tune tissue homeostasis, remodeling and repair.

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.

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.

Transcriptomic analysis reveals associations of blood-based A-to-I editing with Parkinson's disease

BACKGROUND

Adenosine-to-inosine (A-to-I) editing is the most common type of RNA editing in humans and the role of A-to-I RNA editing remains unclear in Parkinson's disease (PD).

OBJECTIVE

We aimed to explore the potential causal association between A-to-I editing and PD, and to assess whether changes in A-to-I editing were associated with cognitive progression in PD.

METHODS

The RNA-seq data from 380 PD patients and 178 healthy controls in the Parkinson's Progression Marker Initiative cohort was used to quantify A-to-I editing sites. We performed cis-RNA editing quantitative trait loci analysis and a two-sample Mendelian Randomization (MR) study by integrating genome-wide association studies to infer the potential causality between A-to-I editing and PD pathogenesis. The potential causal A-to-I editing sites were further confirmed by Summary-data-based MR analysis. Spearman's correlation analysis was performed to characterize the association between longitudinal A-to-I editing and cognitive progression in patients with PD.

RESULTS

We identified 17 potential causal A-to-I editing sites for PD and indicated that genetic risk variants may contribute to the risk of PD through A-to-I editing. These A-to-I editing sites were located in genes NCOR1, KANSL1 and BST1. Moreover, we observed 57 sites whose longitudinal A-to-I editing levels correlated with cognitive progression in PD.

CONCLUSIONS

We found potential causal A-to-I editing sites for PD onset and longitudinal changes of A-to-I editing were associated with cognitive progression in PD. We anticipate this study will provide new biological insights and drive the discovery of the epitranscriptomic role underlying Parkinson's disease.

RNA binding protein AUF1/HNRNPD regulates nuclear export, stability and translation of SNCA transcripts

Alpha-synuclein (SNCA) accumulation plays a central role in the pathogenesis of Parkinson's disease. Determining and interfering with the mechanisms that control SNCA expression is one approach to limiting disease progression. Currently, most of our understanding of SNCA regulation is protein-based. Post-transcriptional mechanisms directly regulating SNCA mRNA expression via its 3' untranslated region (3'UTR) were investigated here. Mass spectrometry of proteins pulled down from murine brain lysates using a biotinylated SNCA 3'UTR revealed multiple RNA-binding proteins, of which HNRNPD/AUF1 was chosen for further analysis. AUF1 bound both proximal and distal regions of the SNCA 3'UTR, but not the 5'UTR or CDS. In the nucleus, AUF1 attenuated SNCA pre-mRNA maturation and was indispensable for the export of SNCA transcripts. AUF1 destabilized SNCA transcripts in the cytosol, primarily those with shorter 3'UTRs, independently of microRNAs by recruiting the CNOT1-CNOT7 deadenylase complex to trim the polyA tail. Furthermore, AUF1 inhibited SNCA mRNA binding to ribosomes. These data identify AUF1 as a multi-tasking protein regulating maturation, nucleocytoplasmic shuttling, stability and translation of SNCA transcripts.

Peripheral α-synuclein isoforms are potential biomarkers for diagnosis and prognosis of isolated REM sleep behavior disorder

INTRODUCTION

Isolated REM sleep behavior disorder (IRBD) represents an early manifestation of the synucleinopathies Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Aggregation of abnormal α-synuclein and its increased expression in the brain is crucial in the development of the synucleinopathies. Whereas α-synuclein gene (SNCA) transcripts are overexpressed in brain, a concomitant reduction occurs in blood of DLB patients. We assessed whether this decrease is also detectable in IRBD.

METHODS

108 IRBD patients and 149 controls were included of which 29 IRBD and 32 control cases were available for expression studies. Expression of SNCAtv1, SNCAtv2, SNCAtv3 and SNCA126 isoforms, and GBA were determined by real-time PCR. Genotype distribution of SNCA SNPs, rs356219 and rs2736990, and correlation with SNCA expression was analyzed.

RESULTS

Expression of all SNCA transcripts was reduced in IRBD blood whereas GBA expression did not change. SNCAtv3 expression correlated inversely with IRBD duration, being lower in patients with longer follow-up. Rs356219-AA genotype frequency was increased in IRBD patients who later developed PD and DLB. Rs2736990-CC frequency was increased among IRBD cases who remained disease-free. No correlation was observed between rs356219 and rs2736990 genotypes and SNCA transcript levels.

CONCLUSION

SNCA transcript expression is decreased in blood in IRBD, and levels decrease with IRBD duration. Our findings indicate that changes in SNCA expression occur in the earliest stages of the synucleinopathies before motor and cognitive symptoms become apparent.

Integrative gene expression analysis for the diagnosis of Parkinson's disease using machine learning and explainable AI

Parkinson's disease (PD) is a progressive neurodegenerative disorder. Various symptoms and diagnostic tests are used in combination for the diagnosis of PD; however, accurate diagnosis at early stages is difficult. Blood-based markers can support physicians in the early diagnosis and treatment of PD. In this study, we used Machine Learning (ML) based methods for the diagnosis of PD by integrating gene expression data from different sources and applying explainable artificial intelligence (XAI) techniques to find the significant set of gene features contributing to diagnosis. We utilized the Least Absolute Shrinkage and Selection Operator (LASSO), and Ridge regression for the feature selection process. We utilized state-of-the-art ML techniques for the classification of PD cases and healthy controls. Logistic regression and Support Vector Machine showed the highest diagnostic accuracy. SHapley Additive exPlanations (SHAP) based global interpretable model-agnostic XAI method was utilized for the interpretation of the Support Vector Machine model. A set of significant biomarkers that contributed to the diagnosis of PD were identified. Some of these genes are associated with other neurodegenerative diseases. Our results suggest that the utilization of XAI can be useful in making early therapeutic decisions for the treatment of PD. The integration of datasets from different sources made this model robust. We believe that this research article will be of interest to clinicians as well as computational biologists in translational research.

Blood transcriptomic signatures associated with molecular changes in the brain and clinical outcomes in Parkinson's disease

The ability to use blood to predict the outcomes of Parkinson's disease, including disease progression and cognitive and motor complications, would be of significant clinical value. We undertook bulk RNA sequencing from the caudate and putamen of postmortem Parkinson's disease (n = 35) and control (n = 40) striatum, and compared molecular profiles with clinical features and bulk RNA sequencing data obtained from antemortem peripheral blood. Cognitive and motor complications of Parkinson's disease were associated with molecular changes in the caudate (stress response) and putamen (endothelial pathways) respectively. Later and earlier-onset Parkinson's disease were molecularly distinct, and disease duration was associated with changes in caudate (oligodendrocyte development) and putamen (cellular senescence), respectively. Transcriptome patterns in the postmortem Parkinson's disease brain were also evident in antemortem peripheral blood, and correlated with clinical features of the disease. Together, these findings identify molecular signatures in Parkinson's disease patients' brain and blood of potential pathophysiologic and prognostic importance.

Local genetic correlations exist among neurodegenerative and neuropsychiatric diseases

Genetic correlation ([Formula: see text]) between traits can offer valuable insight into underlying shared biological mechanisms. Neurodegenerative diseases overlap neuropathologically and often manifest comorbid neuropsychiatric symptoms. However, global [Formula: see text] analyses show minimal [Formula: see text] among neurodegenerative and neuropsychiatric diseases. Importantly, local [Formula: see text] s can exist in the absence of global relationships. To investigate this possibility, we applied LAVA, a tool for local [Formula: see text] analysis, to genome-wide association studies of 3 neurodegenerative diseases (Alzheimer's disease, Lewy body dementia and Parkinson's disease) and 3 neuropsychiatric disorders (bipolar disorder, major depressive disorder and schizophrenia). We identified several local [Formula: see text] s missed in global analyses, including between (i) all 3 neurodegenerative diseases and schizophrenia and (ii) Alzheimer's and Parkinson's disease. For those local [Formula: see text] s identified in genomic regions containing disease-implicated genes, such as SNCA, CLU and APOE, incorporation of expression quantitative trait loci identified genes that may drive genetic overlaps between diseases. Collectively, we demonstrate that complex genetic relationships exist among neurodegenerative and neuropsychiatric diseases, highlighting putative pleiotropic genomic regions and genes. These findings imply sharing of pathogenic processes and the potential existence of common therapeutic targets.

The Potential of ANK1 to Predict Parkinson's Disease

The main cause of Parkinson's disease (PD) remains unknown and the pathologic changes in the brain limit rapid diagnosis. Herein, differentially expressed genes (DEGs) in the Gene Expression Omnibus (GEO) database (GSE8397 and GSE22491) were assessed using linear models for microarray analysis (limma). Ankyrin 1 (ANK1) was the only common gene differentially down-regulated in lateral substantia nigra (LSN), medial substantia nigra (MSN) and blood. Additionally, DEGs between high ANK1 and low ANK1 in GSE99039 were picked out and then uploaded to the Database for Annotation, Visualization and Integrated Discovery (DAVID) for gene ontology (GO) functional annotation analysis. GO analysis displayed that these DEGs were mainly enriched in oxygen transport, myeloid cell development and gas transport (biological process (BP)); hemoglobin complex, haptoglobin-hemoglobin complex and cortical cytoskeleton (cellular component (CC)); and oxygen transporter activity, haptoglobin binding and oxygen binding (molecular function (MF)). Receiver operating characteristic (ROC) curve analysis showed ANK1 had good diagnostic accuracy and increased the area under the curve (AUC) value when combined with other biomarkers. Consistently, intraperitoneal injection of 1-methyl-4-phenyl-1,2,3,6-tetrahydropy-ridi-ne (MPTP) in C57BL/6J mice reduced ANK1 mRNA expression in both substantia nigra and blood compared to the control group. Thus, ANK1 may serve as a candidate biomarker for PD diagnosis.

Joint Modeling Study Identifies Blood-Based Transcripts Link to Cognitive Decline in Parkinson's Disease

BACKGROUND

Cognitive decline in Parkinson's disease (PD) is prevalent, insidious, and burdensome during the progression of the disease.

OBJECTIVES

We aimed to find transcriptome-wide biomarkers in blood to predict cognitive decline and identify patients at high risk with cognitive impairment in PD.

METHODS

We carried out joint modeling analysis to characterize transcriptome-wide longitudinal gene expression and its association with the progression of mild cognitive impairment (MCI) in PD patients. The average time-dependent area under the curves (AUCs) were used for evaluating the accuracy of the significant joint models. A cognitive survival score (CogSs) derived from joint model was leveraged to predict the occurrence of MCI. All predicting models were built in a discovery cohort with 272 patients and replicated in an independent cohort with 177 patients.

RESULTS

We identified five longitudinal varied expression of transcripts that were significantly associated with MCI progression in patients with PD. The most significant transcript IGLC1 joint model accurately predicted the progression of MCI in PD patients in the discovery and replication cohorts (average time-dependent AUCs >0.82). The CogSs derived from the optimal IGLC1 joint model had a high accuracy at early study stage in both cohorts (AUC ≥0.91).

CONCLUSIONS

Our transcriptome-wide joint modeling analysis uncovered five blood-based transcripts related to cognitive decline in PD. The joint models will serve as a useful resource for clinicians and researchers to screen PD patients with high risk of development of cognitive impairment and pave the path for Parkinson's personalized medicine. © 2022 International Parkinson and Movement Disorder Society.

Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer's Disease

The clinical diagnosis of neurodegenerative diseases is notoriously inaccurate and current methods are often expensive, time-consuming, or invasive. Simple inexpensive and noninvasive methods of diagnosis could provide valuable support for clinicians when combined with cognitive assessment scores. Biological processes leading to neuropathology progress silently for years and are reflected in both the central nervous system and vascular peripheral system. A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer's disease, Parkinson's disease, Huntington's disease, amyotrophic lateral sclerosis, Friedreich's ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

Comparative Analysis of Total Alpha-Synuclein (αSYN) Immunoassays Reveals That They Do Not Capture the Diversity of Modified αSYN Proteoforms

Background:

The development of therapeutics for Parkinson’s disease (PD) requires the establishment of biomarker assays to enable stratifying patients, monitoring disease progression, and assessing target engagement. Attempts to develop diagnostic assays based on detecting levels of the α-synuclein (αSYN) protein, a central player in the pathogenesis of PD, have yielded inconsistent results.

Objective:

To determine whether the three commercial kits that have been extensively used for total αSYN quantification in human biological fluids (from Euroimmun, MSD, and Biolegend) are capable of capturing the diversity and complexity of relevant αSYN proteoforms.

Methods:

We investigated and compared the ability of the different assays to detect the diversity of αSYN proteoforms using a library of αSYN proteins that comprise the majority of disease-relevant αSYN variants and post-translational modifications (PTMs).

Results:

Our findings showed that none of the three tested immunoassays accurately capture the totality of relevant αSYN species, and that these assays are unable to recognize most disease-associated C-terminally truncated variants of αSYN. Moreover, several N-terminal truncations and phosphorylation/nitration PTMs differentially modify the level of αSYN detection and recovery by different immunoassays, and a CSF matrix effect was observed for most of the αSYN proteoforms analyzed by the three immunoassays.

Conclusion:

Our results show that the tested immunoassays do not capture the totality of the relevant αSYN species and therefore may not be appropriate tools to provide an accurate measure of total αSYN levels in samples containing modified forms of the protein. This highlights the need for next generation αSYN immunoassays that capture the diversity of αSYN proteoforms.

The Parkinson's disease protein alpha-synuclein is a modulator of processing bodies and mRNA stability

Alpha-synuclein (αS) is a conformationally plastic protein that reversibly binds to cellular membranes. It aggregates and is genetically linked to Parkinson's disease (PD). Here, we show that αS directly modulates processing bodies (P-bodies), membraneless organelles that function in mRNA turnover and storage. The N terminus of αS, but not other synucleins, dictates mutually exclusive binding either to cellular membranes or to P-bodies in the cytosol. αS associates with multiple decapping proteins in close proximity on the Edc4 scaffold. As αS pathologically accumulates, aberrant interaction with Edc4 occurs at the expense of physiologic decapping-module interactions. mRNA decay kinetics within PD-relevant pathways are correspondingly disrupted in PD patient neurons and brain. Genetic modulation of P-body components alters αS toxicity, and human genetic analysis lends support to the disease-relevance of these interactions. Beyond revealing an unexpected aspect of αS function and pathology, our data highlight the versatility of conformationally plastic proteins with high intrinsic disorder.

Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson's disease using two-layer hybrid feature selection

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.

Multiple Criteria Optimization (MCO): A gene selection deterministic tool in RStudio

Identifying genes with the largest expression changes (gene selection) to characterize a given condition is a popular first step to drive exploration into molecular mechanisms and is, therefore, paramount for therapeutic development. Reproducibility in the sciences makes it necessary to emphasize objectivity and systematic repeatability in biological and informatics analyses, including gene selection. With these two characteristics in mind, in previous works our research team has proposed using multiple criteria optimization (MCO) in gene selection to analyze microarray datasets. The result of this effort is the MCO algorithm, which selects genes with the largest expression changes without user manipulation of neither informatics nor statistical parameters. Furthermore, the user is not required to choose either a preference structure among multiple measures or a predetermined quantity of genes to be deemed significant a priori. This implies that using the same datasets and performance measures (PMs), the method will converge to the same set of selected differentially expressed genes (repeatability) despite who carries out the analysis (objectivity). The present work describes the development of an open-source tool in RStudio to enable both: (1) individual analysis of single datasets with two or three PMs and (2) meta-analysis with up to five microarray datasets, using one PM from each dataset. The capabilities afforded by the code include license-free portability and the possibility to carry out analyses via modest computer hardware, such as personal laptops. The code provides affordable, repeatable, and objective detection of differentially expressed genes from microarrays. It can be used to analyze other experiments with similar experimental comparative layouts, such as microRNA arrays and protein arrays, among others. As a demonstration of the capabilities of the code, the analysis of four publicly-available microarray datasets related to Parkinson´s Disease (PD) is presented here, treating each dataset individually or as a four-way meta-analysis. These MCO-supported analyses made it possible to identify MMP9 and TUBB2A as potential PD genetic biomarkers based on their persistent appearance across each of the case studies. A literature search confirmed the importance of these genes in PD and indeed as PD biomarkers, which evidences the code´s potential.

Parkinson's Disease Subtyping Using Clinical Features and Biomarkers: Literature Review and Preliminary Study of Subtype Clustering

The second most common progressive neurodegenerative disorder, Parkinson’s disease (PD), is characterized by a broad spectrum of symptoms that are associated with its progression. Several studies have attempted to classify PD according to its clinical manifestations and establish objective biomarkers for early diagnosis and for predicting the prognosis of the disease. Recent comprehensive research on the classification of PD using clinical phenotypes has included factors such as dominance, severity, and prognosis of motor and non-motor symptoms and biomarkers. Additionally, neuroimaging studies have attempted to reveal the pathological substrate for motor symptoms. Genetic and transcriptomic studies have contributed to our understanding of the underlying molecular pathogenic mechanisms and provided a basis for classifying PD. Moreover, an understanding of the heterogeneity of clinical manifestations in PD is required for a personalized medicine approach. Herein, we discuss the possible subtypes of PD based on clinical features, neuroimaging, and biomarkers for developing personalized medicine for PD. In addition, we conduct a preliminary clustering using gait features for subtyping PD. We believe that subtyping may facilitate the development of therapeutic strategies for PD.

SNCA 3' UTR Genetic Variants in Patients with Parkinson's Disease

The SNCA (Synuclein Alpha) gene represents a major risk gene for Parkinson's disease (PD) and SNCA polymorphisms have been associated with the common sporadic form of PD. Numerous Genome-Wide Association Studies showed strong signals located in the SNCA 3' UTR (untranslated region) region indicating that variants in 3' UTRs of PD-associated genes could contribute to neurodegeneration and may regulate the risk for PD. Genetic variants in 3' UTR can affect miRNA activity and consequently change the translation process. The aim of this study was to access the differences in 3' UTR variants of SNCA genes in a cohort of PD patients and control subjects from Croatia. The cohort consisted of 52 PD patients and 23 healthy control subjects. Differences between 3' UTR allele and genotype frequencies were accessed through next generation sequencing approach from whole blood samples. In our study, we identified four previously reported single nucleotide polymorphisms (SNPs) and one insertion in the 3' UTR region of SNCA gene, namely rs1045722, rs3857053, rs577490090, rs356165, and rs777296100, and five variants not reported in the literature, namely rs35270750, rs529553259, rs377356638, rs571454522, and rs750347645. Our results indicate a significantly higher occurrence of the rs571454522 variant in the PD population. To the best of our knowledge, this variant has not been reported until now in the literature. We analyzed our results in the context of previous research, creating a brief overview of the importance of 3' UTR variants of the SNCA gene. Further studies will be needed to gain a more profound insight regarding their role in PD development, which will help to assess the role and impact of post-transcriptional regulation on disease pathology.

A Computational Analysis in a Cohort of Parkinson's Disease Patients and Clock-Modified Colorectal Cancer Cells Reveals Common Expression Alterations in Clock-Regulated Genes

Simple Summary

Cancer and neurodegenerative diseases are two aging-related pathologies with differential developmental characteristics, but they share altered cellular pathways. Interestingly, dysregulations in the biological clock are reported in both diseases, though the extent and potential consequences of such disruption have not been fully elucidated. In this study, we aimed at characterizing global changes on common cellular pathways associated with Parkinson’s disease (PD) and colorectal cancer (CRC). We used gene expression data retrieved from an idiopathic PD (IPD) patient cohort and from CRC cells with unmodified versus genetically altered clocks. Our results highlight common differentially expressed genes between IPD patients and cells with disrupted clocks, suggesting a role for the circadian clock in the regulation of pathways altered in both pathologies. Interestingly, several of these genes are related to cancer hallmarks and may have an impact on the overall survival of colon cancer patients, as suggested by our analysis.

Abstract

Increasing evidence suggests a role for circadian dysregulation in prompting disease-related phenotypes in mammals. Cancer and neurodegenerative disorders are two aging related diseases reported to be associated with circadian disruption. In this study, we investigated a possible effect of circadian disruption in Parkinson’s disease (PD) and colorectal cancer (CRC). We used high-throughput data sets retrieved from whole blood of idiopathic PD (IPD) patients and time course data sets derived from an in vitro model of CRC including the wildtype and three core-clock knockout (KO) cell lines. Several gene expression alterations in IPD patients resembled the expression profiles in the core-clock KO cells. These include expression changes in DBP, GBA, TEF, SNCA, SERPINA1 and TGFB1. Notably, our results pointed to alterations in the core-clock network in IPD patients when compared to healthy controls and revealed variations in the expression profile of PD-associated genes (e.g., HRAS and GBA) upon disruption of the core-clock genes. Our study characterizes changes at the transcriptomic level following circadian clock disruption on common cellular pathways associated with cancer and neurodegeneration (e.g., immune system, energy metabolism and RNA processing), and it points to a significant influence on the overall survival of colon cancer patients for several genes resulting from our analysis (e.g., TUBB6, PAK6, SLC11A1).

Functions of Intracellular Alpha-Synuclein in Microglia: Implications for Parkinson's Disease Risk

Alpha-synuclein accumulation in dopaminergic neurons is one of the primary features of Parkinson’s disease (PD). Despite its toxic properties during PD, alpha-synuclein has some important physiological functions. Although the activity of the protein has been extensively studied in neurons, the protein is also expressed in other cell types including immune cells and glia. Genetic studies show that mutations in synuclein alpha (SNCA), the gene that encodes alpha-synuclein, and alterations in its expression levels are a significant risk factor for PD, which likely impact the functions of a broad range of cell types. The consequences of altered SNCA expression in other cell types is beginning to be explored. Microglia, the primary macrophage population in the Central Nervous System (CNS), for example, are affected by variations in alpha-synuclein levels and functions. Studies suggest that deviations of alpha-synuclein’s normal activity influence hematopoiesis, the process that gives rise to microglia, and microglia’s immune functions. Alpha-synuclein levels also dictate the efficiency of SNARE-mediated vesicle formation, which could influence autophagy and cytokine release in microglia. Starting from the time of conception, these effects could impact one’s risk for developing PD. Further studies are needed to determine the physiological role of alpha-synuclein and how the protein is affected during PD in non-neuronal cells such as microglia. In this review we will discuss the known roles of alpha-synuclein in differentiation, immune responses, and vesicle formation, with insights into how abnormal alpha-synuclein expression and activity are linked to altered functions of microglia during PD.

The Impact of SNCA Variations and Its Product Alpha-Synuclein on Non-Motor Features of Parkinson's Disease

Parkinson’s disease (PD) is a common and progressive neurodegenerative disease, caused by the loss of dopaminergic neurons in the substantia nigra pars compacta in the midbrain, which is clinically characterized by a constellation of motor and non-motor manifestations. The latter include hyposmia, constipation, depression, pain and, in later stages, cognitive decline and dysautonomia. The main pathological features of PD are neuronal loss and consequent accumulation of Lewy bodies (LB) in the surviving neurons. Alpha-synuclein (α-syn) is the main component of LB, and α-syn aggregation and accumulation perpetuate neuronal degeneration. Mutations in the α-syn gene (SNCA) were the first genetic cause of PD to be identified. Generally, patients carrying SNCA mutations present early-onset parkinsonism with severe and early non-motor symptoms, including cognitive decline. Several SNCA polymorphisms were also identified, and some of them showed association with non-motor manifestations. The functional role of these polymorphisms is only partially understood. In this review we explore the contribution of SNCA and its product, α-syn, in predisposing to the non-motor manifestations of PD.

RNA sequencing of whole blood reveals early alterations in immune cells and gene expression in Parkinson's disease

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

Associations of Lower Caffeine Intake and Plasma Urate Levels with Idiopathic Parkinson's Disease in the Harvard Biomarkers Study

Two purines, caffeine and urate, have been associated with a reduced risk of idiopathic Parkinson's disease (PD) in multiple cohorts and populations. The Harvard Biomarkers Study (HBS) is a longitudinal study designed to accelerate the discovery and validation of molecular diagnostic and progression markers of early-stage PD. To investigate whether these 'reduced risk' factors are associated with PD within this cohort, we conducted a cross-sectional, case-control study in 566 subjects consisting of idiopathic PD patients and healthy controls. Caffeine intake as assessed by a validated questionnaire was significantly lower in idiopathic PD patients compared to healthy controls in males (mean difference -125 mg/day, p < 0.001) but not in females (mean difference -30 mg/day, p = 0.29). A strong inverse association was also observed with plasma urate levels both in males (mean difference -0.46 mg/dL, p = 0.017) and females (mean difference -0.45 mg/dL, p = 0.001). Both analyses stratified for sex and adjusted for age, body mass index, and either urate level or caffeine consumption, respectively. These results highlight the robustness of caffeine intake and urate as factors inversely associated with idiopathic PD.

Genome-wide survival study identifies a novel synaptic locus and polygenic score for cognitive progression in Parkinson's disease

A key driver of patients' well-being and clinical trials for Parkinson's disease (PD) is the course that the disease takes over time (progression and prognosis). To assess how genetic variation influences the progression of PD over time to dementia, a major determinant for quality of life, we performed a longitudinal genome-wide survival study of 11.2 million variants in 3,821 patients with PD over 31,053 visits. We discover RIMS2 as a progression locus and confirm this in a replicate population (hazard ratio (HR) = 4.77, P = 2.78 × 10-11), identify suggestive evidence for TMEM108 (HR = 2.86, P = 2.09 × 10-8) and WWOX (HR = 2.12, P = 2.37 × 10-8) as progression loci, and confirm associations for GBA (HR = 1.93, P = 0.0002) and APOE (HR = 1.48, P = 0.001). Polygenic progression scores exhibit a substantial aggregate association with dementia risk, while polygenic susceptibility scores are not predictive. This study identifies a novel synaptic locus and polygenic score for cognitive disease progression in PD and proposes diverging genetic architectures of progression and susceptibility.

α-syn and SNP rs356219 as a potential biomarker in blood for Parkinson's disease in Mexican Mestizos

Clinical criteria diagnose Parkinson's disease (PD), therefore, it is crucial to find biological elements that could support diagnosis or even act as prognostic tools of PD. The SNCA gene codifies a protein called α - synuclein; several studies associate genetic and biochemical factors of SNCA with PD, including transcript and plasmatic protein levels, however, contradictory evidence indicates inconclusive results. We aim to compare SNCA mRNA expression, plasmatic α-syn protein and rs356219 SNP between PD cases and a control group, and to identify a potential biomarker in Mexican mestizos', focusing on these three components determined in blood. We included 88 PD patients and 88 age-matched controls. We observed higher α-syn protein and decreased SNCA mRNA levels in PD subjects, compared to control group (p = 0.044 and p < 0.001, respectively). A statistically significant difference was found in allelic and genotypic frequencies of SNP rs356219 between PD patients and normal subjects (p = 0.006 and p = 0.023, respectively). Logistic regression analysis determined as optimal predictors of PD the GG genotype of SNP rs356219 (OR 2.49; p = 0.006) in a recessive model and α-syn protein (OR 1.057; p = 0.033). Furthermore, the G allele of SNP rs356219 was associated with higher plasmatic α-syn and mRNA levels in PD subjects. The receiver operating curves (ROC) distinguished PD from healthy controls with good sensitivity and specificity considering the plasmatic α-syn protein (AUC = 0.693, Sensitivity = 66.7 %, Specificity = 63.9 %) or a predictive probability of plasmatic α-syn protein and SNP rs356219 in a single model (AUC = 0.692, Sensitivity = 62.3 %, Specificity = 62.5 %). The performance of this classifier model in PD at early stage (n = 31) increase the discriminant power in both, plasmatic α-syn protein (AUC = 0.779, Sensitivity = 72.7 %, Specificity = 73.9 %) and predictive probability (AUC = 0.707, Sensitivity = 63.6 %, Specificity = 62.5 %). We propose that α-syn protein and SNP rs356219 together may work as a good signature of PD, and they can be suggested as a non-invasive biomarker of PD risk.

Vesicle trafficking and lipid metabolism in synucleinopathy

The neuronal protein α-synuclein (αS) is central to the pathogenesis of Parkinson's disease and other progressive brain diseases such as Lewy body dementia and multiple system atrophy. These diseases, collectively referred to as 'synucleinopathies', have long been considered purely proteinopathies: diseases characterized by the misfolding of a protein into small and large aggregates mainly consisting of that protein (in this case: α-synuclein). However, recent morphological insights into Lewy bodies, the hallmark neuropathology of human synucleinopathies, suggests these lesions are also rich in vesicles and other membranous organelles. Moreover, αS physiology and pathology are both strongly associated with various aspects of intracellular vesicle trafficking and lipid biology. αS physiologically binds to synaptic and other small vesicles, and several functions of αS in regulating vesicle biology have been proposed. Familial PD-linked αS excess and missense mutations have been shown to impair vesicle trafficking and alter lipid homeostasis. On the other hand, vesicle trafficking and lipid-related genes have emerged as Parkinson's risk factors, suggesting a bidirectional relationship. The answer to the question "Does abnormal αS accumulation cause impaired vesicle trafficking and lipid dyshomeostasis or is αS aggregation the consequence of such impairments?" may be "Both". Here, we review current knowledge of the αS-lipid and αS-vesicle trafficking interplay, with a special focus on Parkinson's disease and Lewy body dementia.

Biomarkers and the Role of α-Synuclein in Parkinson's Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by the presence of α-synuclein (α-Syn)-rich Lewy bodies (LBs) and the preferential loss of dopaminergic (DA) neurons in the substantia nigra (SN) pars compacta (SNpc). However, the widespread involvement of other central nervous systems (CNS) structures and peripheral tissues is now widely documented. The onset of the molecular and cellular neuropathology of PD likely occurs decades before the onset of the motor symptoms characteristic of PD, so early diagnosis of PD and adequate tracking of disease progression could significantly improve outcomes for patients. Because the clinical diagnosis of PD is challenging, misdiagnosis is common, which highlights the need for disease-specific and early-stage biomarkers. This review article aims to summarize useful biomarkers for the diagnosis of PD, as well as the biomarkers used to monitor disease progression. This review article describes the role of α-Syn in PD and how it could potentially be used as a biomarker for PD. Also, preclinical and clinical investigations encompassing genetics, immunology, fluid and tissue, imaging, as well as neurophysiology biomarkers are discussed. Knowledge of the novel biomarkers for preclinical detection and clinical evaluation will contribute to a deeper understanding of the disease mechanism, which should more effectively guide clinical applications.

Differential blood DNA methylation across Lewy body dementias

INTRODUCTION

Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are characterized by cognitive alterations, visual hallucinations, and motor impairment. Diagnosis is based on type and timing of clinical manifestations; however, determination of clinical subtypes is challenging. The utility of blood DNA methylation as a biomarker for Lewy body disorders (LBD) is mostly unexplored.

METHODS

We performed a cross-sectional analysis of blood methylation in 42 DLB and 50 PDD cases applying linear models to compare groups and logistic least absolute shrinkage and selection operator regression to explore the discriminant power of methylation signals.

RESULTS

DLB blood shows differential methylation compared to PDD. Some methylation changes associate with core features of LBD. Sets of probes show high predictive value to discriminate between variants.

DISCUSSION

Our study is the first to explore LBD blood methylation. Despite overlapping clinical presentation, we detected differential epigenetic signatures that, if confirmed in independent cohorts, could be developed into useful biomarkers.

Expression Levels of an Alpha-Synuclein Transcript in Blood May Distinguish between Early Dementia with Lewy Bodies and Parkinson's Disease

Lewy body diseases (LBD) including dementia with Lewy bodies (DLB) and Parkinson disease (PD) are characterized by alpha-synuclein pathology. DLB is difficult to diagnose and peripheral biomarkers are urgently needed. Therefore, we analyzed the expression of five alpha-synuclein gene (SNCA) transcripts, SNCAtv1, SNCAtv2, SNCAtv3, SNCA126, and SNCA112, in 45 LBD and control temporal cortex samples and in the blood of 72 DLB, 59 PD, and 54 control subjects. The results revealed overexpression of SNCAtv1 and SNCA112 in DLB, and SNCAtv2 in PD temporal cortices. In DLB blood, diminution of all SNCA transcripts was observed. SNCAtv1 and SNCAtv2 were diminished in PD with disease onset before 70 years. SNCAtv3, driven by its own promoter, showed opposite expression in early DLB and PD, suggesting that its amount may be an early, DLB specific biomarker. Correlation between blood transcript levels and disease duration was positive in DLB and negative in PD, possibly reflecting differences in brain alpha-synuclein aggregation rates associated with differences in disease courses. In conclusion, SNCA transcripts showed a disease-specific increase in the brain and were diminished in blood of LBD patients. SNCAtv3 expression was decreased in early DLB and increased in early PD and could be a biomarker for early DLB diagnosis.

Identification of Molecular Signatures in Neural Differentiation and Neurological Diseases Using Digital Color-Coded Molecular Barcoding

Human pluripotent stem cells (PSCs), including embryonic stem cells and induced pluripotent stem cells, represent powerful tools for disease modeling and for therapeutic applications. PSCs are particularly useful for the study of development and diseases of the nervous system. However, generating in vitro models that recapitulate the architecture and the full variety of subtypes of cells that make the complexity of our brain remains a challenge. In order to fully exploit the potential of PSCs, advanced methods that facilitate the identification of molecular signatures in neural differentiation and neurological diseases are highly demanded. Here, we review the literature on the development and application of digital color-coded molecular barcoding as a potential tool for standardizing PSC research and applications in neuroscience. We will also describe relevant examples of the use of this technique for the characterization of the heterogeneous composition of the brain tumor glioblastoma multiforme.

Mining genetic and transcriptomic data using machine learning approaches in Parkinson's disease

High-throughput techniques have generated abundant genetic and transcriptomic data of Parkinson’s disease (PD) patients but data analysis approaches such as traditional statistical methods have not provided much in the way of insightful integrated analysis or interpretation of the data. As an advanced computational approach, machine learning, which enables people to identify complex patterns and insight from data, has consequently been harnessed to analyze and interpret large, highly complex genetic and transcriptomic data toward a better understanding of PD. In particular, machine learning models have been developed to integrate patient genotype data alone or combined with demographic, clinical, neuroimaging, and other information, for PD outcome study. They have also been used to identify biomarkers of PD based on transcriptomic data, e.g., gene expression profiles from microarrays. This study overviews the relevant literature on using machine learning models for genetic and transcriptomic data analysis in PD, points out remaining challenges, and suggests future directions accordingly. Undoubtedly, the use of machine learning is amplifying PD genetic and transcriptomic achievements for accelerating the study of PD. Existing studies have demonstrated the great potential of machine learning in discovering hidden patterns within genetic or transcriptomic information and thus revealing clues underpinning pathology and pathogenesis. Moving forward, by addressing the remaining challenges, machine learning may advance our ability to precisely diagnose, prognose, and treat PD.

Classification algorithms applied to blood-based transcriptome meta-analysis to predict idiopathic Parkinson's disease

Diagnosis of Parkinson's disease (PD) remains a challenge in clinical practice, mostly due to lack of peripheral blood markers. Transcriptomic analysis of blood samples has emerged as a potential means to identify biomarkers and gene signatures of PD. In this context, classification algorithms can assist in detecting data patterns such as phenotypes and transcriptional signatures with potential diagnostic application. In this study, we performed gene expression meta-analysis of blood transcriptome from PD and control patients in order to identify a gene-set capable of predicting PD using classification algorithms. We examined microarray data from public repositories and, after systematic review, 4 independent cohorts (GSE6613, GSE57475, GSE72267 and GSE99039) comprising 711 samples (388 idiopathic PD and 323 healthy individuals) were selected. Initially, analysis of differentially expressed genes resulted in minimal overlap among datasets. To circumvent this, we carried out meta-analysis of 17,712 genes across datasets, and calculated weighted mean Hedges' g effect sizes. From the top-100- positive and negative gene effect sizes, algorithms of collinearity recognition and recursive feature elimination were used to generate a 59-gene signature of idiopathic PD. This signature was evaluated by 9 classification algorithms and 4 sample size-adjusted training groups to create 36 models. Of these, 33 showed accuracy higher than the non-information rate, and 2 models built on Support Vector Machine Regression bestowed best accuracy to predict PD and healthy control samples. In summary, the gene meta-analysis followed by machine learning methodology employed herein identified a gene-set capable of accurately predicting idiopathic PD in blood samples.

Iron-responsive-like elements and neurodegenerative ferroptosis

A set of common-acting iron-responsive 5′untranslated region (5′UTR) motifs can fold into RNA stem loops that appear significant to the biology of cognitive declines of Parkinson's disease dementia (PDD), Lewy body dementia (LDD), and Alzheimer's disease (AD). Neurodegenerative diseases exhibit perturbations of iron homeostasis in defined brain subregions over characteristic time intervals of progression. While misfolding of Aβ from the amyloid-precursor-protein (APP), alpha-synuclein, prion protein (PrP) each cause neuropathic protein inclusions in the brain subregions, iron-responsive-like element (IRE-like) RNA stem–loops reside in their transcripts. APP and αsyn have a role in iron transport while gene duplications elevate the expression of their products to cause rare familial cases of AD and PDD. Of note, IRE-like sequences are responsive to excesses of brain iron in a potential feedback loop to accelerate neuronal ferroptosis and cognitive declines as well as amyloidosis. This pathogenic feedback is consistent with the translational control of the iron storage protein ferritin. We discuss how the IRE-like RNA motifs in the 5′UTRs of APP, alpha-synuclein and PrP mRNAs represent uniquely folded drug targets for therapies to prevent perturbed iron homeostasis that accelerates AD, PD, PD dementia (PDD) and Lewy body dementia, thus preventing cognitive deficits. Inhibition of alpha-synuclein translation is an option to block manganese toxicity associated with early childhood cognitive problems and manganism while Pb toxicity is epigenetically associated with attention deficit and later-stage AD. Pathologies of heavy metal toxicity centered on an embargo of iron export may be treated with activators of APP and ferritin and inhibitors of alpha-synuclein translation.

β-Glucocerebrosidase activity in GBA-linked Parkinson disease: The type of mutation matters

OBJECTIVE

To test the relationship between clinically relevant types of GBA mutations (none, risk variants, mild mutations, severe mutations) and β-glucocerebrosidase activity in patients with Parkinson disease (PD) in cross-sectional and longitudinal case-control studies.

METHODS

A total of 481 participants from the Harvard Biomarkers Study (HBS) and the NIH Parkinson's Disease Biomarkers Program (PDBP) were analyzed, including 47 patients with PD carrying GBA variants (GBA-PD), 247 without a GBA variant (idiopathic PD), and 187 healthy controls. Longitudinal analysis comprised 195 participants with 548 longitudinal measurements over a median follow-up period of 2.0 years (interquartile range, 1-2 years).

RESULTS

β-Glucocerebrosidase activity was low in blood of patients with GBA-PD compared to healthy controls and patients with idiopathic PD, respectively, in HBS (p < 0.001) and PDBP (p < 0.05) in multivariate analyses adjusting for age, sex, blood storage time, and batch. Enzyme activity in patients with idiopathic PD was unchanged. Innovative enzymatic quantitative trait locus (xQTL) analysis revealed a negative linear association between residual β-glucocerebrosidase activity and mutation type with p < 0.0001. For each increment in the severity of mutation type, a reduction of mean β-glucocerebrosidase activity by 0.85 μmol/L/h (95% confidence interval, -1.17, -0.54) was predicted. In a first longitudinal analysis, increasing mutation severity types were prospectively associated with steeper declines in β-glucocerebrosidase activity during a median 2 years of follow-up (p = 0.02).

CONCLUSIONS

Residual activity of the β-glucocerebrosidase enzyme measured in blood inversely correlates with clinical severity types of GBA mutations in PD. β-Glucocerebrosidase activity is a quantitative endophenotype that can be monitored noninvasively and targeted therapeutically.

α-Synuclein in blood cells differentiates Parkinson's disease from healthy controls

OBJECTIVE

To determine whether blood cells expressed α-Syn can differentiate Parkinson's disease (PD) from healthy controls (HC).

METHODS

The concentrations of α-Syn were determined in samples of blood cell pellets using a quantitative Lipid-ELISA assay. In addition, the levels of total protein, hemoglobin, iron and H-ferritin were determined. The study includes samples from the Biofind cohort (n = 46 PD and 45 HC) and results were validated with an additional cohort (n = 35 PD and 28 HC).

RESULTS

A composite biomarker consisting of the concentrations of total α-Syn, proteinase-K resistant (PKres ) α-Syn and phospho-Serine 129 α-Syn (PSer 129), is designed based on the analysis of the discovery BioFIND cohort. This composite biomarker differentiates a PD subgroup, presenting motor symptoms without dementia from a HC group, with a convincing accuracy, represented by an AUC = 0.81 (95% CI, 0.71 to 0.92). Closely similar results were obtained for the validation cohort, that is, AUC = 0.81, (95% CI, 0.70 to 0.94).

INTERPRETATION

Our results demonstrate the potential usefulness of blood cells expressed α-Syn as a biomarker for PD.

Pathological changes are associated with shifts in the employment of synonymous codons at the transcriptome level

Background

The usage of different synonymous codons reflects the genome organization and has been connected to parameters such as mRNA abundance and protein folding. It is also been established that mutations targeting specific synonymous codons can trigger disease.

Results

We performed a systematic meta-analysis of transcriptome results from 75 datasets representing 40 pathologies. We found that a subset of codons was preferentially employed in abundant transcripts, while other codons were preferentially found in low-abundance transcripts. By comparing control and pathological transcriptomes, we observed a shift in the employment of synonymous codons for every analyzed disease. For example, cancerous tissue employed preferentially A- or U-ending codons, shifting from G- or C-ending codons, which were preferred by control tissues. This analysis was able to discriminate patients and controls with high specificity and sensitivity.

Conclusions

Here we show that the employment of specific synonymous codons, quantified at the whole transcriptome level, changes profoundly in many diseases. We propose that the changes in codon employment offer a novel perspective for disease studies, and could be used to design new diagnostic tools.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5921-9) contains supplementary material, which is available to authorized users.

Earliest Mechanisms of Dopaminergic Neurons Sufferance in a Novel Slow Progressing Ex Vivo Model of Parkinson Disease in Rat Organotypic Cultures of Substantia Nigra

The current treatments of Parkinson disease (PD) are ineffective mainly due to the poor understanding of the early events causing the decline of dopaminergic neurons (DOPAn). To overcome this problem, slow progressively degenerating models of PD allowing the study of the pre-clinical phase are crucial. We recreated in a short ex vivo time scale (96 h) all the features of human PD (needing dozens of years) by challenging organotypic culture of rat substantia nigra with low doses of rotenone. Thus, taking advantage of the existent knowledge, the model was used to perform a time-dependent comparative study of the principal possible causative molecular mechanisms undergoing DOPAn demise. Alteration in the redox state and inflammation started at 3 h, preceding the reduction in DOPAn number (pre-diagnosis phase). The number of DOPAn declined to levels compatible with diagnosis only at 12 h. The decline was accompanied by a persistent inflammation and redox imbalance. Significant microglia activation, apoptosis, a reduction in dopamine vesicle transporters, and the ubiquitination of misfolded protein clearance pathways were late (96 h, consequential) events. The work suggests inflammation and redox imbalance as simultaneous early mechanisms undergoing DOPAn sufferance, to be targeted for a causative treatment aimed to stop/delay PD.

Potential two-step proteomic signature for Parkinson's disease: Pilot analysis in the Harvard Biomarkers Study

Introduction

We sought to determine if our previously validated proteomic profile for detecting Alzheimer's disease would detect Parkinson's disease (PD) and distinguish PD from other neurodegenerative diseases.

Methods

Plasma samples were assayed from 150 patients of the Harvard Biomarkers Study (PD, n = 50; other neurodegenerative diseases, n = 50; healthy controls, n = 50) using electrochemiluminescence and Simoa platforms.

Results

The first step proteomic profile distinguished neurodegenerative diseases from controls with a diagnostic accuracy of 0.94. The second step profile distinguished PD cases from other neurodegenerative diseases with a diagnostic accuracy of 0.98. The proteomic profile differed in step 1 versus step 2, suggesting that a multistep proteomic profile algorithm to detecting and distinguishing between neurodegenerative diseases may be optimal.

Discussion

These data provide evidence of the potential use of a multitiered blood-based proteomic screening method for detecting individuals with neurodegenerative disease and then distinguishing PD from other neurodegenerative diseases.

DNA methylation changes associated with Parkinson's disease progression: outcomes from the first longitudinal genome-wide methylation analysis in blood

Parkinson's Disease (PD) is a common neurodegenerative disorder currently diagnosed based on the presentation of characteristic movement symptoms. Unfortunately, patients exhibiting these symptoms have already undergone significant dopaminergic neuronal loss. Earlier diagnosis, aided by molecular biomarkers specific to PD, would improve overall patient care. Epigenetic mechanisms, which are modified by both environment and disease pathophysiology, are emerging as important components of neurodegeneration. Alterations to the PD methylome have been reported in epigenome-wide association studies. However, the extent to which methylation changes correlate with disease progression has not yet been reported; nor the degree to which methylation is affected by PD medication. We performed a longitudinal genome-wide methylation study surveying ~850,000 CpG sites in whole blood from 189 well-characterized PD patients and 191 control individuals obtained at baseline and at a follow-up visit ~2 y later. We identified distinct patterns of methylation in PD cases versus controls. Importantly, we identified genomic sites where methylation changes longitudinally as the disease progresses. Moreover, we identified methylation changes associated with PD pathology through the analysis of PD cases that were not exposed to anti-parkinsonian therapy. In addition, we identified methylation sites modulated by exposure to dopamine replacement drugs. These results indicate that DNA methylation is dynamic in PD and changes over time during disease progression. To the best of our knowledge, this is the first longitudinal epigenome-wide methylation analysis for Parkinson's disease and reveals changes associated with disease progression and in response to dopaminergic medications in the blood methylome.

Effect of rs3910105 in the Synuclein Gene on Dopamine Transporter Availability in Healthy Subjects

PURPOSE

The present study investigated associations between dopamine transporter (DAT) availability and α-synuclein levels in cerebrospinal fluid, as well as synuclein gene (SNCA) transcripts, and the effect of single nucleotide polymorphism of SNCA on DAT availability in healthy subjects.

MATERIALS AND METHODS

The study population comprised healthy controls who underwent ¹²³I-FP-CIT single-photon emission computed tomography screening. Five SNCA probes were used to target the boundaries of exon 3 and exon 4 (SNCA-E3E4), transcripts with a long 3'UTR region (SNCA-3UTR-1, SNCA-3UTR-2), transcripts that skip exon 5 (SNCA-E4E6), and the rare short transcript isoforms that comprise exons 1-4 (SNCA-007).

RESULTS

In total, 123 healthy subjects (male 75, female 48) were included in this study. DAT availability in the caudate nucleus (p=0.0661) and putamen (p=0.0739) tended to differ according to rs3910105 genotype. In post-hoc analysis, DAT availability in the putamen was lower in subjects of TT genotype than those of CC/CT (p=0.0317). DAT availability in the caudate nucleus also showed a trend similar to that in the putamen (p=0.0597). Subjects of CT genotype with rs3910105 showed negative correlations with DAT availability in the putamen with SNCA-E3E4 (p=0.037, rho=-0.277), and SNCA-E4E6 (p=0.042, rho=-0.270), but not those of CC/TT genotypes.

CONCLUSION

This is the first study to investigate the association of rs3910105 in SNCA with DAT availability. rs3910105 had an effect on DAT availability, and the correlation between DAT availability and SNCA transcripts were significant in CT genotypes of rs3910105.

Evaluation of RNA Blood Biomarkers in the Parkinson's Disease Biomarkers Program

There is a high misdiagnosis rate between Parkinson’s disease (PD) and atypical parkinsonian disorders (APD), such as progressive supranuclear palsy (PSP), the second most common parkinsonian syndrome. In our earlier studies, we identified and replicated RNA blood biomarkers in several independent cohorts, however, replication in a cohort that includes PSP patients has not yet been performed. To this end, we evaluated the diagnostic potential of nine previously identified RNA biomarkers using quantitative PCR assays in 138 blood samples at baseline from PD, PSP and healthy controls (HCs) nested in the PD Biomarkers Program. Linear discriminant analysis showed that COPZ1 and PTPN1 distinguished PD from PSP patients with 62.5% accuracy. Five biomarkers, PTPN1, COPZ1, FAXDC2, SLC14A1s and NAMPT were useful for distinguishing PSP from controls with 69% accuracy. Several biomarkers correlated with clinical features in PD patients. SLC14A1-s correlated with Unified Parkinson’s Disease Rating Scale total and part III scores. In addition, COPZ1, PTPN1 and MLST8, correlated with Montreal Cognitive Assessment (MoCA). Interestingly, COPZ1, EFTUD2 and PTPN1 were downregulated in cognitively impaired (CI) compared to normal subjects. Linear discriminant analysis showed that age, PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 distinguished CI from normal subjects with 65.9% accuracy. These results suggest that COPZ1 and PTPN1 are useful for distinguishing PD from PSP patients. In addition, the combination of PTPN1, COPZ1, FAXDC2, EFTUD2 and MLST8 is a useful signature for cognitive impairment. Evaluation of these biomarkers in a larger study will be a key to advancing these biomarkers into the clinic.

Glucocerebrosidase mRNA is Diminished in Brain of Lewy Body Diseases and Changes with Disease Progression in Blood

Parkinson disease (PD) and dementia with Lewy bodies (DLB) are Lewy body diseases characterized by abnormal alpha-synuclein deposits and overlapping pathological features in the brain. Several studies have shown that glucocerebrosidase (GBA) deficiency is involved in the development of LB diseases. Here, we aimed to find out if this deficiency starts at the transcriptional level, also involves alternative splicing, and if GBA expression changes in brain are also detectable in blood of patients with LB diseases. The expression of three GBA transcript variants (GBAtv1, GBAtv2 and GBAtv5) was analyzed in samples from 20 DLB, 25 PD and 17 control brains and in blood of 20 DLB, 26 PD patients and 17 unaffected individuals. Relative mRNA expression was determined by real-time PCR. Expression changes were evaluated by the ΔΔCt method. In brain, specific expression profiles were identified in the temporal cortex of DLB and in the caudate nucleus of PD. In blood, significant GBA mRNA diminution was found in both DLB and PD patients. Early PD and early-onset DLB patients showed lowest GBA levels which were normal in PD patients with advanced disease and DLB patients who developed disease after 70 years of age. In conclusion, disease group specific GBA expression profiles were found in mostly affected areas of LBD. In blood, GBA expression was diminished in LB diseases, especially in patients with early onset DLB and in patients with early PD. Age of disease onset exerts an opposite effect on GBA expression in DLB and PD.

Alternative Splicing of Alpha- and Beta-Synuclein Genes Plays Differential Roles in Synucleinopathies

The synuclein family is composed of three members, two of which, α- and β-synuclein, play a major role in the development of synucleinopathies, including Parkinson’s disease (PD) as most important movement disorder, dementia with Lewy bodies (DLB) as the second most frequent cause of dementia after Alzheimer’s disease and multiple system atrophy. Whereas abnormal oligomerization and fibrillation of α-synuclein are now well recognized as initial steps in the development of synucleinopathies, β-synuclein is thought to be a natural α-synuclein anti-aggregant. α-synuclein is encoded by the SNCA gene, and β-synuclein by SNCB. Both genes are homologous and undergo complex splicing events. On one hand, in-frame splicing of coding exons gives rise to at least three shorter transcripts, and the functional properties of the corresponding protein isoforms are different. Another type of alternative splicing is the alternative inclusion of at least four initial exons in the case of SNCA, and two in the case of SNCB. Finally, different lengths of 3’ untranslated regions have been also reported for both genes. SNCB only expresses in the brain, but some of the numerous SNCA transcripts are also brain-specific. With the present article, we aim to provide a systematic review of disease related changes in the differential expression of the various SNCA and SNCB transcript variants in brain, blood, and non-neuronal tissue of synucleinopathies, but especially PD and DLB as major neurodegenerative disorders.

Discovering the 3' UTR-mediated regulation of alpha-synuclein

Recent evidence indicates a link between Parkinson's Disease (PD) and the expression of a-synuclein (SNCA) isoforms with different 3′ untranslated regions (3′UTRs). Yet, the post-transcriptional mechanisms regulating SNCA expression are unknown. Using a large-scale in vitro /in silico screening we identified RNA-binding proteins (RBPs) that interact with SNCA 3′ UTRs. We identified two RBPs, ELAVL1 and TIAR, that bind with high affinity to the most abundant and translationally active 3′ UTR isoform (575 nt). Knockdown and overexpression experiments indicate that both ELAVL1 and TIAR positively regulate endogenous SNCA in vivo. The mechanism of regulation implies mRNA stabilization as well as enhancement of translation in the case of TIAR. We observed significant alteration of both TIAR and ELAVL1 expression in motor cortex of post-mortem brain donors and primary cultured fibroblast from patients affected by PD and Multiple System Atrophy (MSA). Moreover, trans expression quantitative trait loci (trans-eQTLs) analysis revealed that a group of single nucleotide polymorphisms (SNPs) in TIAR genomic locus influences SNCA expression in two different brain areas, nucleus accumbens and hippocampus. Our study sheds light on the 3′ UTR-mediated regulation of SNCA and its link with PD pathogenesis, thus opening up new avenues for investigation of post-transcriptional mechanisms in neurodegeneration.

Can Biomarkers Help the Early Diagnosis of Parkinson's Disease?

Parkinson's disease (PD) is a complex neurodegenerative disease with progressive loss of dopamine neurons. PD patients usually manifest a series of motor and non-motor symptoms. In order to provide better early diagnosis and subsequent disease-modifying therapies for PD patients, there is an urgent need to identify sensitive and specific biomarkers. Biomarkers can be divided into four categories: clinical, imaging, biochemical, and genetic. Ideal biomarkers not only improve our understanding of PD pathogenesis and progression, but also provide benefits for early risk evaluation and clinical diagnosis of PD. Although many efforts have been made and several biomarkers have been extensively investigated, few if any have been found useful for early diagnosis. Here, we summarize recent developments in the discovered biomarkers of PD and discuss their merits and limitations for the early diagnosis of PD.

Candidate inflammatory biomarkers display unique relationships with alpha-synuclein and correlate with measures of disease severity in subjects with Parkinson's disease

Background

Efforts to identify fluid biomarkers of Parkinson’s disease (PD) have intensified in the last decade. As the role of inflammation in PD pathophysiology becomes increasingly recognized, investigators aim to define inflammatory signatures to help elucidate underlying mechanisms of disease pathogenesis and aid in identification of patients with inflammatory endophenotypes that could benefit from immunomodulatory interventions. However, discordant results in the literature and a lack of information regarding the stability of inflammatory factors over a 24-h period have hampered progress.

Methods

Here, we measured inflammatory proteins in serum and CSF of a small cohort of PD (n = 12) and age-matched healthy control (HC) subjects (n = 6) at 11 time points across 24 h to (1) identify potential diurnal variation, (2) reveal differences in PD vs HC, and (3) to correlate with CSF levels of amyloid β (Aβ) and α-synuclein in an effort to generate data-driven hypotheses regarding candidate biomarkers of PD.

Results

Despite significant variability in other factors, a repeated measures two-way analysis of variance by time and disease state for each analyte revealed that serum IFNγ, TNF, and neutrophil gelatinase-associated lipocalin (NGAL) were stable across 24 h and different between HC and PD. Regression analysis revealed that C-reactive protein (CRP) was the only factor with a strong linear relationship between CSF and serum. PD and HC subjects showed significantly different relationships between CSF Aβ proteins and α-synuclein and specific inflammatory factors, and CSF IFNγ and serum IL-8 positively correlated with clinical measures of PD. Finally, linear discriminant analysis revealed that serum TNF and CSF α-synuclein discriminated between PD and HC with a minimum of 82% sensitivity and 83% specificity.

Conclusions

Our findings identify a panel of inflammatory factors in serum and CSF that can be reliably measured, distinguish between PD and HC, and monitor inflammation as disease progresses or in response to interventional therapies. This panel may aid in generating hypotheses and feasible experimental designs towards identifying biomarkers of neurodegenerative disease by focusing on analytes that remain stable regardless of time of sample collection.

Electronic supplementary material

The online version of this article (doi:10.1186/s12974-017-0935-1) contains supplementary material, which is available to authorized users.