Parkinson's Disease Samples Research Articles

Cholinergic Basal Forebrain Integrity and Cognition in Parkinson's Disease: A Reappraisal of Magnetic Resonance Imaging Evidence.

Cognitive impairment is a well-recognized and debilitating symptom of Parkinson's disease (PD). Degradation in the cortical cholinergic system is thought to be a key contributor. Both postmortem and in vivo cholinergic positron emission tomography (PET) studies have provided valuable evidence of cholinergic system changes in PD, which are pronounced in PD dementia (PDD). A growing body of literature has employed magnetic resonance imaging (MRI), a noninvasive, more cost-effective alternative to PET, to examine cholinergic system structural changes in PD. This review provides a comprehensive discussion of the methodologies and findings of studies that have focused on the relationship between cholinergic basal forebrain (cBF) integrity, based on T1- and diffusion-weighted MRI, and cognitive function in PD. Nucleus basalis of Meynert (Ch4) volume has been consistently reduced in cognitively impaired PD samples and has shown potential utility as a prognostic indicator for future cognitive decline. However, the extent of structural changes in Ch4, especially in early stages of cognitive decline in PD, remains unclear. In addition, evidence for structural change in anterior cBF regions in PD has not been well established. This review underscores the importance of continued cross-sectional and longitudinal research to elucidate the role of cholinergic dysfunction in the cognitive manifestations of PD. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Primary cilia in Parkinson's disease: summative roles in signaling pathways, genes, defective mitochondrial function, and substantia nigra dopaminergic neurons.

Primary cilia (PC) are microtubules-based, independent antennal-like sensory organelles, that are seen in most vertebrate cells of different types, including astrocytes and neurons. They send signals to cells to control many physiological and cellular processes by detecting changes in the extracellular environment. Parkinson's disease (PD), a neurodegenerative disease that progresses over time, is primarily caused by a gradual degradation of the dopaminergic pathway in the striatum nigra, which results in a large loss of neurons in the substantia nigra compact (SNpc) and a depletion of dopamine (DA). PD samples have abnormalities in the structure and function of PC. The alterations contribute to the cause, development, and recovery of PD via influencing signaling pathways (SHH, Wnt, Notch-1, α-syn, and TGFβ), genes (MYH10 and LRRK2), defective mitochondrial function, and substantia nigra dopaminergic neurons. Thus, restoring the normal structure and physiological function of PC and neurons in the brain are effective treatment for PD. This review summarizes the function of PC in neurodegenerative diseases and explores the pathological mechanisms caused by PC alterations in PD, in order to provide references and ideas for future research.

Genetics of Parkinson's Disease: From Causes to Treatment.

The genetic architecture of Parkinson's disease (PD) comprises five autosomal dominantly inherited forms with a clinical picture overall resembling idiopathic disease (PARK-SNCA, PARK-LRRK2, PARK-VPS35, PARK-CHCHD2, and PARK-RAB32) and three recessive types (PARK-PRKN, PARK-PINK1, and PARK-PARK7), several monogenic forms causing atypical parkinsonism, as well as a plethora of known genetic risk factors, most notably SNCA and GBA1 including a recently discovered risk variant unique to individuals of African descent, as well as polygenic scores. The Movement Disorder Society Genetic mutation database (MDSGene) (www.mdsgene.org) provides PD genotype-phenotype relationships, whereas global PD genetics networks, such as the Global Parkinson's Genetics Program (www.gp2.org) elucidate PD genetic factors at an unprecedented scale. Two large studies in relatively unselected, multicenter PD samples estimate the frequency of genetic forms, including PARK-GBA1, at ∼15%. PD genetics are becoming increasingly actionable, with the first gene-targeted clinical trials underway. Furthermore, PD genetics has recently been incorporated into a new biological classification of PD.

Anxiety Symptoms and Disease Severity in Parkinson Disease.

BACKGROUND: Anxiety is prevalent in Parkinson disease (PD), negatively affecting quality of life for patients and their families. Targeted anxiety interventions are needed that account for variables such as disease severity; however, there is insufficient evidence regarding the trajectory of anxiety along the disease course. OBJECTIVE: The aim of this study was to investigate the association between disease severity and anxiety symptoms in a PD sample. METHODS: This descriptive study used secondary data analyses of data from a web-based survey study of individuals with PD in the United States. Participants were 21 years or older, had access to the Internet, and were given a diagnosis of PD (N = 72). We performed multiple regression analyses to assess the relationship between patient-reported disease severity and anxiety symptoms. RESULTS: There was a statistically significant association between disease severity and anxiety symptoms in people with PD, after controlling for all 5 explanatory variables (sex, age, wearing off, disease severity, and disease duration) ( P < .001). Age and wearing off PD medication also had statistically significant, although smaller, effects on anxiety symptoms ( P < .05). CONCLUSION: Disease severity was related to increased anxiety symptoms among individuals with PD. Anxiety intervention research is needed, and future studies should account for variations in disease severity and medication effects in both intervention and study design.

Association of Misfolded α-Synuclein Derived from Neuronal Exosomes in Blood with Parkinson's Disease Diagnosis and Duration.

Misfolded α-synuclein can be detected in blood samples of Parkinson's disease (PD) patients by a seed amplification assay (SAA), but the association with disease duration is not clear, yet. In the present study we aimed to elucidate whether seeding activity of misfolded α-synuclein derived from neuronal exosomes in blood is associated with PD diagnosis and disease duration. Cross-sectional samples of PD patients were analyzed and compared to samples of age- and gender-matched healthy controls using a blood-based SAA. Presence of α-synuclein seeding activity and differences in seeding parameters, including fluorescence response (in arbitrary units) at the end of the amplification assay (F60) were analyzed. Additionally, available PD samples collected longitudinally over 5-9 years were included. In the cross-sectional dataset, 79 of 80 PD patients (mean age 69 years, SD = 8; 56% male) and none of the healthy controls (n = 20, mean age 70 years, SD = 10; 55% male) showed seeding activity (sensitivity 98.8%). When comparing subgroups divided by disease duration, longer disease duration was associated with lower α-synuclein seeding activity (F60: p < 0.001). In the longitudinal analysis 10/11 patients showed a gradual decrease of α-synuclein seeding activity over time. This study confirms the high sensitivity of the blood-based α-synuclein SAA applied here. The negative association of α-synuclein seeding activity in blood with disease duration makes this parameter potentially interesting as biomarker for future studies on the pathophysiology of disease progression in PD, and for biologically oriented trials in this field.

Anticholinergic Medication Burden and Cognitive Subtypes in Parkinson's Disease without Dementia.

Cognitive changes are heterogeneous in Parkinson's disease (PD). This study compared whether anticholinergic burden drives differences in cognitive domain performance and empirically-derived PD-cognitive phenotypes. A retrospective chart review contained participants (n = 493) who had idiopathic PD without dementia. Participants' medications were scored (0-3) and summed based on the anticholinergic cognitive burden scale (ACBS). We examined the ACBS' relationship to five cognitive domain composites (normative z-scores) and three (K-means clustering based) cognitive phenotypes: cognitively intact, low executive function (EF), and predominately impaired EF/memory. Analyses included Spearman correlations, analysis of covariance, and Pearson chi-squared test. Overall, phenotypes did not differ in anticholinergic burden, and (after false-discovery-rate corrections) no cognitive domains related. When comparing those above and below the clinically relevant ACBS cutoff (i.e., score ≥3), no significant phenotype or domain differences were found. Anticholinergic medication usage did not drive cognitive performance in a large clinical sample of idiopathic PD without dementia.

Single-Cell Nuclear Sequencing Atlas Revealed the Induction of Parkinson's Disease by RELN+ Neuron 3 and the Gene Regulatory Network of MSRA.

To determine the cell types that promoted the progression of Parkinson's disease (PD) using the substantia nigra in the brain tissues derived from patients with PD and normal controls. PD is an incurable neurodegenerative disease that threatens the physical activity of the aging population, and the complex molecular mechanisms remain be comprehensively elucidated. To describe potential disease-promoting cell types in PD and to provide a theoretical basis. Single-cell nuclear sequencing data of nine PD samples and control samples from Gene Expression Omnibus (GEO) were included, and heterogeneous cell subpopulations in the substantia nigra were identified by annotation analysis. Potential pathogenic cell subpopulations of PD were determined based on the expression data of marker genes. Cell differentiation trajectories and communication networks were generated by Pseudotime trajectory analysis and cell communication analysis. Furthermore, single-- cell regulatory network inference and clustering (SCENIC) analysis was conducted to determine the regulatory network of transcription factor-target genes in PD. Among the nine cell subpopulations classified, RELN+neuron 3 showed reduced abundance and dopamine secretion capacity in PD and was therefore considered as a promoter of PD pathogenesis and progression. The regulatory network of MSRA action was involved in the developmental process of cells in the central nervous system, indicating that MSRA and its targets might serve as potential therapeutic targets for PD. RELN+neuron 3 had two directions of differentiation, specifically, branch 1 exhibited a high apoptotic profile and branch 2 exhibited a high cell death profile. In addition, the intensity of EPHA and EPHB signaling was attenuated between RELN+neuron 3 and other cell subpopulations. To conclude, this study identified a subpopulation of RELN+neuron 3 cells with markedly reduced abundance in the brain substantia nigra in PD. The MSRA-involved gene regulatory networks was considered as a novel therapeutic network for PD.

Comprehensive Analysis and Experimental Validation of the Parkinson's Disease Lysosomal Gene ACP2 and Pan-cancer.

The pivotal role of lysosomal function in preserving neuronal homeostasis is recognized, with its dysfunction being implicated in neurodegenerative processes, notably in Parkinson's disease (PD). Yet, the molecular underpinnings of lysosome-related genes (LRGs) in the context of PD remain partially elucidated. We collected RNA-seq data from the brain substantia nigra of 30 PD patients and 20 normal subjects from the GEO database. We obtained molecular classification clusters from the screened lysosomal expression patterns. The lysosome-related diagnostic model of Parkinson's disease was constructed by XGBoost and Random Forest. And we validated the expression patterns of signature LRGs in the diagnostic model by constructing a PD rat model. Finally, the linkage between PD and cancer through signature genes was explored. The expression patterns of the 33 LRGs screened can be divided into two groups of PD samples, enabling exploration of the variance in biological processes and immune elements. Cluster A had a higher disease severity. Subsequently, critical genes were sieved through the application of machine learning methodologies culminating in the identification of two intersecting feature genes (ACP2 and LRP2). A PD risk prediction model was constructed grounded on these signature genes. The model's validity was assessed through nomogram evaluation, which demonstrated robust confidence validity. Then we analyzed the correlation analysis, immune in-filtration, biological function, and rat expression validation of the two genes with common pathogenic genes in Parkinson's disease, indicating that these two genes play an important role in the pathogenesis of PD. We then selected ACP2, which had a significant immune infiltration correlation, as the entry gene for the pan-cancer analysis. The pan-cancer analysis revealed that ACP2 has profound associations with prognostic indicators, immune infiltration, and tumor-related regulatory processes across various neoplasms, suggesting its potential as a therapeutic target in a range of human diseases, including PD and cancers. Our study comprehensively analyzed the molecular grouping of LRGs expression patterns in Parkinson's disease, and the disease progression was more severe in cluster A. And the PD diagnosis model related to LRGs is constructed. Finally, ACP2 is a potential target for the relationship between Parkinson's disease and tumor.

Single-Cell Analyses Reveal Necroptosis's Potential Role in Neuron Degeneration and Show Enhanced Neuron-Immune Cell Interaction in Parkinson's Disease Progression.

Parkinson's disease (PD) is a common neuron degenerative disease among the old, characterized by uncontrollable movements and an impaired posture. Although widely investigated on its pathology and treatment, the disease remains incompletely understood. Single-cell RNA sequencing (scRNA-seq) has been applied to the area of PD, providing valuable data for related research. However, few works have taken deeper insights into the causes of neuron death and cell-cell interaction between the cell types in the brain. Our bioinformatics analyses revealed necroptosis-related genes (NRGs) enrichment in neuron degeneration and selecting the cells by NRGs levels showed two subtypes within the main degenerative cell types in the midbrain. NRG-low subtype was largely replaced by NRG-high subtype in the patients, indicating the striking change of cell state related to necroptosis in PD progression. Moreover, we carried out cell-cell interaction analyses between cell types and found that microglia (MG)'s interaction strength with glutamatergic neuron (GLU), GABAergic neuron (GABA), and dopaminergic neuron (DA) was significantly upregulated in PD. Also, MG show much stronger interaction with NRG-high subtypes and a stronger cell killing function in PD samples. Additionally, we identified CLDN11 as a novel interaction pattern specific to necroptosis neurons and MG. We also found LEF1 and TCF4 as key transcriptional regulators in neuron degeneration. These findings suggest that MG were significantly overactivated in PD patients to clear abnormal neurons, especially the NRG-high cells, explaining the neuron inflammation in PD. Our analyses provide insights into the causes of neuron death and inflammation in PD from single-cell resolution, which could be seriously considered in clinical trials.

Diagnosis of Disease Using Whole Blood from a Fingerstick Spotted on Filter Paper: Gene Expression Signature Test for Alzheimer's and Parkinson's Diseases

Background: There is an unmet need for a minimally invasive clinical test using samples collected by medical staff or by self-collection to screen persons for Alzheimer's disease (AD), Parkinson's Disease (PD) and other dementias and diseases. AD is underdiagnosed today, particularly in underserved populations where it is high incidence (non-Hispanic black and Hispanic) and in rural populations. Even in populations with access to health care, diagnosis is often delayed by years, even though the most recently approved therapies are most efficacious when given early in the progression of disease. While dementias are diseases of the brain, many publications link the underpinning inflammatory aspects to circulating neutrophils, macrophages, and lymphocytes. Thus, there is an opportunity to exploit the hematologic aspects of AD and PD in its diagnosis and potentially, therapeutically. AD gene expression signatures using RNA extracted from venipuncture whole blood have been reported. FDA-cleared tests based on circulating biomarkers are in use today but are not definitive tests for AD. We developed a direct, extraction-free TempO-Seq® assay of gene expression using blood from a finger prick spotted on filter paper, providing a simple and minimally invasive method of obtaining a blood sample and performing a test that can potentially be used to identify a host of diseases and disease risk. We then implemented a test that classified patients as having AD or PD. Method: The commercial targeted sequencing whole transcriptome TempO-Seq® gene expression assay (Yeakley, PLosOne doi.org/10.1371/journal.pone.0178302) was adapted to utilize blood from a simple fingerstick spotted on filter paper and dried (not a biohazard, simplifying transport and testing). Three independent patient cohorts of samples (~50:50 male:female) were obtained from normal volunteers and patients clinically diagnosed with AD or PD based on cognitive testing. AD, PD, and used for training/retraining and testing/retesting AD and PD classification signatures. To account for the presence of subtypes in our dataset, we contrasted AD and control samples by testing hundreds of random subsets and performing differential gene expression analyses (DESeq2). We used machine learning approaches (k-nearest neighbors, random forest, support vector machine with either linear, polynomial or rgb kernel and extreme gradient boosting) to build algorithms differentiating control, AD and PD. Randomly sampled subsets accounting for 80% of each cohort were used to train these classifiers. The accuracy of calling AD and PD samples was determined using the remaining 20% of samples as a test set. For each classifier an area under ROC curve (AUC-ROC) was calculated. Gene set enrichment was performed using the Gene Ontology database for biological processes (p value threshold 0.05) to identify expressed pathways. Result: A protocol that eliminated interference by components of whole blood with enzymes used in molecular biology assays (including PCR) was successfully implemented. This permitted a TempO-Seq assay to be carried out without extraction of RNA from the filter paper blood spots. Highly expressed genes were attenuated to increase the sensitivity with which AD biomarker genes could be measured. Within-spot reproducibility (r&amp;gt;0.96) and day-to-day reproducibility of blood collected from the same donor (r&amp;gt;0.94) was excellent. The AUC-ROC for the AD classifier was 0.86, and for the PD classifier was 0.87. Predominate pathways were immune response pathways, with the three most significantly differentially expressed pathways related to the involvement of neutrophils in the immune and inflammatory response. Conclusion: Patients with AD and PD, and potentially other dementias and diseases, can be classifiedusing a TempO-Seq gene expression signature obtained from fingerstick blood spotted on filter paper. Such samples are easily collected by medical staff or by self-collection, potentially transforming the diagnosis of AD and PD, reducing health disparities, and by speeding up diagnosis, enabling therapy for many to begin earlier. The data support that neutrophils and immune cells either impact disease and/or are impacted by disease, consequently resulting in expression of disease signatures capable of classifying patients.

Pathogenic mechanisms and potential therapeutic targets for Parkinson disease revealed by bioinformatic analysis of necroptosis and immune cell infiltration.

Parkinson disease (PD) is an age-dependent neurodegenerative disease with very high prevalence by age 80 years. Necroptosis is a newly identified form of programmed cell death implicated in neurodegenerative diseases, but has not yet been conclusively associated with PD. This study examined the contributions of necroptosis to PD using bioinformatics analysis. Datasets GSE26927, GSE49036, and GSE54536 from the gene expression omnibus database were analyzed for differentially expressed genes (DEGs). These DEGs were then subjected to gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis to identify associated functions and signaling mechanisms. Necroptosis-related differentially expressed genes (NRDEGs) were then identified by the overlap of DEGs and the necroptosis gene set hsa04217. The STRING database and Cytoscape software were then used to build and visualize a protein-protein interaction network and identify hubs and key functional modules among NRDEGs. In addition, immune cell type abundance was analyzed based on DEGs using ImmuCellAI. The identified DEGs, KEGG pathway enrichment terms, and protein-protein interaction network structures of NRDEGs were validated using an independent dataset (GSE54536). The necroptosis pathway was significantly enriched and activated in PD samples. Thirteen NRDEGs were identified in the GSE26927 and GSE49036 datasets, including receptor interacting serine/threonine kinase 1, CASP8 and FADD like apoptosis regulator, TNFRSF1A associated via death domain, and interleukin 1 beta, of which 6 were validated in the GSE54536 dataset. According to gene ontology and KEGG analyses, these NRDEGs are involved in necroptosis-related processes, apoptosis, B cell receptor signaling pathways, and NOD-like receptor signaling pathways. Analysis of DEGs also revealed significant increases in CD8 + T cell and Tex cell infiltration and significant decreases in B cell and T gamma delta cell infiltration within the PD brain. Necroptosis pathways are active in PD and associated with immune cell infiltration. The factors controlling necroptotic signaling and immune infiltration identified in this study may be valuable diagnostic markers and therapeutic targets for PD.

α-Synuclein seed amplification assay as a diagnostic tool for parkinsonian disorders

IntroductionSynucleinopathies such as Parkinson's disease (PD) and multiple system atrophy (MSA) can be challenging to diagnose due to the symptom overlap with, for example, atypical parkinsonisms like progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). Seed amplification assays (SAA), developed for the detection of α-synuclein (αSyn) aggregates in CSF, have been successful when used as a biomarker evaluation for synucleinopathies. In this study, we investigated the potential of this assay to not only detect αSyn seeds in CSF, but also discriminate between movement disorders. MethodsThe αSyn-SAA was tested in a Scandinavian cohort composed of 129 CSF samples from patients with PD (n = 55), MSA (n = 27), CBD (n = 7), and PSP (n = 16), as well as healthy controls (HC, n = 24). ResultsThe αSyn seed amplification assay (αSyn-SAA) was able to correctly identify all PD samples as positive (sensitivity of 100%) while also discriminating the PD group from HC (70.8% specificity, p < 0.0001) and tauopathies [CBD (71% specificity) and PSP (75% specificity), p < 0.0001)]. The αSyn-SAA was also able to identify almost all MSA samples as positive for αSyn aggregation (sensitivity of 92.6%). In general, this assay is able to discriminate between the synucleinopathies and tauopathies analyzed herein (p < 0.0001) despite the overlapping symptoms in these diseases. ConclusionThese findings suggest the αSyn-SAA is a useful diagnostic tool for differentiating between different parkinsonian disorders, although further optimization may be needed.

Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis.

The present study was designed to identify immune-related biomarker and candidate drugs for Parkinson disease (PD) by weighted gene co-expression network analysis. Differentially expressed genes were identified in PD and healthy samples in the Gene Expression Omnibus (GEO) database. Besides, immune-related genes were obtained from the immunology database. Then, a co-expression network was constructed by the weighted gene co-expression network analysis package. Diagnostic model for PD was constructed by Lasso and multivariate Cox regression. Furthermore, differentially expressed genes (DEGs) were used to establish PPI and competing endogenous RNA (ceRNA) networks. Functional enrichment and pathway analysis were performed. Drug-hub gene interaction analysis was performed via DGIdb database. PD samples and normal samples were found to have 220 upregulated genes and 216 downregulated genes in the GSE6613 dataset. The differentially expressed genes contained 50 immune-related genes, with 40 upregulated genes and 10 downregulated genes. We obtained 7 hub genes by intersecting the DEGs and candidate hub genes. As potential diagnostic markers, 2 immune-related DEGs were identified among the 7 hub genes. According to functional enrichment analysis, these DEGs were mainly enriched in immune response, inflammatory response, and cytokine-cytokine receptor interactions. Totally, we obtained 182 drug-gene interaction pairs in Drug-Gene Interaction database (DGIdb). Our results revealed crucial genes and candidate drugs for PD patients and deepen our understanding of the molecular mechanisms involved in PD.

A novel hybrid method for feature selection based on gender analysis for early Parkinson's disease diagnosis using speech analysis

Parkinson's disease (PD) is one of the neurologic disorders for older people that, because of similar symptoms to other neurologic diseases, is difficult to diagnose, mainly in the early stages. Recently, neurologists are trying non-medical methods based on speech processing for early diagnosis of PD. Since some speech characteristics are different between women and men, we analyze them separately in this research and propose a hybrid method to classify the PD and healthy samples. The proposed method's main structure relies on the features' scores, which are based on a projection on a two-dimensional plane. In the first step, several features inherently different between women and men (healthy and PD) are removed from the features. Then, statistical-Based Feature Score (SBFS) and Classification-Based Feature Score (CBFS) are introduced in the proposed method and used as the dimensions of a two-dimensional hyperplane to rank the features. To increase the stability of the model in speech feature selection, resampling of the dataset is considered in the methodology. Different classifiers (linear and nonlinear support vector machine (LSVM and NSVM respectively), K-nearest neighborhood (KNN), naïve Bayesian (NB), and random forest (RF)) are applied for sample classification. Finally, two sets of features are separately introduced for both groups of men and women. We have achieved 86% and 84% accuracy rates for men and women groups with 14 and 12 features, respectively. The results show that the number of selected features is less than the previous work that introduced and used this dataset. The results also present that although the chosen features are different for men and women, most of the selected features are in similar categories. Unlike the previous similar study where the validation method was based on Leave-one-out, in this study, the results are validated based on an independent set of the used dataset to analyze the stability of the selected features.

Handicap as a Measure of Perceived-Health Status in Parkinson's Disease.

Handicap is a patient-centered measure of health status that encompasses the impact of social and physical environment on daily living, having been assessed in advanced and late-stage Parkinson's Disease (PD). To characterize the handicap of a broader sample of patients. A cross-sectional study of 405 PD patients during the MDS-UPDRS Portuguese validation study, using the MDS-UPDRS, Unified Dyskinesias Rating Scale, Nonmotor symptoms questionnaire, PDQ-8 and EQ-5D-3L. Handicap was measured using the London Handicap Scale (LHS). Mean age was 64.42 (±10.3) years, mean disease duration 11.30 (±6.5) years and median HY 2 (IQR, 2-3). Mean LHS was 0.652 (±0.204); "Mobility," "Occupation" and "Physical Independence" were the most affected domains. LHS was significantly worse in patients with longer disease duration, older age and increased disability. In contrast, PDQ-8 did not differentiate age groups. Handicap was significantly correlated with disease duration (r = -0.35), nonmotor experiences of daily living (EDL) (MDS-UPDRS-I) (r = -0.51), motor EDL (MDS-UPDRS-II) (r = -0.69), motor disability (MDS-UPDRS-III) (r = -0.49), axial signs of MDS-UPDRS-III (r = -0.55), HY (r = -0.44), presence of nonmotor symptoms (r = -0.51) and PDQ-8 index (r = -0.64) (all P < 0.05). Motor EDL, MDS-UPDRS-III and PDQ-8 independently predicted Handicap (adjusted R 2 = 0.582; P = 0.007). The LHS was easily completed by patients and caregivers. Patients were mild-moderately handicapped, which was strongly determined by motor disability and its impact on EDL, and poor QoL. Despite correlated, handicap and QoL seem to differ in what they measure, and handicap may have an added value to QoL. Handicap seems to be a good measure of perceived-health status in a broad sample of PD.

Neuronally enriched microvesicle RNAs are differentially expressed in the serums of Parkinson's patients.

Circulating small RNAs (smRNAs) originate from diverse tissues and organs. Previous studies investigating smRNAs as potential biomarkers for Parkinson's disease (PD) have yielded inconsistent results. We investigated whether smRNA profiles from neuronally-enriched serum exosomes and microvesicles are altered in PD patients and discriminate PD subjects from controls. Demographic, clinical, and serum samples were obtained from 60 PD subjects and 40 age- and sex-matched controls. Exosomes and microvesicles were extracted and isolated using a validated neuronal membrane marker (CD171). Sequencing and bioinformatics analyses were used to identify differentially expressed smRNAs in PD and control samples. SmRNAs also were tested for association with clinical metrics. Logistic regression and random forest classification models evaluated the discriminative value of the smRNAs. In serum CD171 enriched exosomes and microvesicles, a panel of 29 smRNAs was expressed differentially between PD and controls (false discovery rate (FDR) < 0.05). Among the smRNAs, 23 were upregulated and 6 were downregulated in PD patients. Pathway analysis revealed links to cellular proliferation regulation and signaling. Least absolute shrinkage and selection operator adjusted for the multicollinearity of these smRNAs and association tests to clinical parameters via linear regression did not yield significant results. Univariate logistic regression models showed that four smRNAs achieved an AUC ≥ 0.74 to discriminate PD subjects from controls. The random forest model had an AUC of 0.942 for the 29 smRNA panel. CD171-enriched exosomes and microvesicles contain the differential expression of smRNAs between PD and controls. Future studies are warranted to follow up on the findings and understand the scientific and clinical relevance.

Identification of Hub Genes and Potential Molecular Pathogenesis in Substantia Nigra in Parkinson's Disease via Bioinformatics Analysis.

Parkinson's disease (PD) is the second most common neurodegenerative disease, with significant socioeconomic burdens. One of the crucial pathological features of PD is the loss of dopaminergic neurons in the substantia nigra (SN). However, the exact pathogenesis remains unknown. Moreover, therapies to prevent neurodegenerative progress are still being explored. We performed bioinformatics analysis to identify candidate genes and molecular pathogenesis in the SN of patients with PD. We analyzed the expression profiles, GSE49036 and GSE7621, which included 31 SN tissues in PD samples and 17 SN tissues in healthy control samples, and identified 86 common differentially expressed genes (DEGs). Then, GO and KEGG pathway analyses of the identified DEGs were performed to understand the biological processes and significant pathways of PD. Subsequently, a protein-protein interaction network was established, with 15 hub genes and four key modules which were screened in this network. The expression profiles, GSE8397 and GSE42966, were used to verify these hub genes. We demonstrated a decrease in the expression levels of 14 hub genes in the SN tissues of PD samples. Our results indicated that, among the 14 hub genes, DRD2, SLC18A2, and SLC6A3 may participate in the pathogenesis of PD by influencing the function of the dopaminergic synapse. CACNA1E, KCNJ6, and KCNB1 may affect the function of the dopaminergic synapse by regulating ion transmembrane transport. Moreover, we identified eight microRNAs (miRNAs) that can regulate the hub genes and 339 transcription factors (TFs) targeting these hub genes and miRNAs. Subsequently, we established an mTF-miRNA-gene-gTF regulatory network. Together, the identification of DEGs, hub genes, miRNAs, and TFs could provide better insights into the pathogenesis of PD and contribute to the diagnosis and therapies.

Coordinated modification in expression levels of HSPA1A/B, DGKH, and NOTCH2 in Parkinson's patients' blood and substantia nigra as a diagnostic sign: the transcriptomes' relationship.

Diagnosis of Parkinson's disease (PD) is associated with a vast number of challenges. This study aimed to assess the overlap of PD patients' transcriptomes in the substantia nigra (SN) with peripheral blood mononuclear cells (PBMCs) to discover potential biomarkers for diagnosis. GEO data were used to select genes with significant changes in expression level in the SN region and eligible studies. Also, transcriptome data related to blood of PD patients with other neurodegenerative diseases (ND) was considered. Differential expression genes between PD and control were evaluated in the SN and blood, and RT-qPCR was applied to validate the findings. At the expression level, no significant similarity in long non-coding RNA was found between the patients' SN and blood. While in silico results revealed 16 common mRNAs in SN and blood with significant expression levels. Among all overexpressed mRNAs, HSPA1A/B expression level had the highest expression difference between control and PD samples. Moreover, DGKH had the highest score of down-regulated genes in both blood and SN. The NOTCH pathway had the highest score pathway among up-regulated pathways, and the expression levels of NOTCH2, H4C8, and H2BC21 associated with this pathway had the most ability to separate the control and PD populations. Furthermore, RT-qPCR results revealed that HSPA1A/B, NOTCH2, and H4C8 were overexpressed in PD PBMCs, while DGKH expression levels were lower compared to controls. Our findings indicate that expression levels of HSPA1A/B, DGKH, and NOTCH2 could be applied as candidate biomarkers to diagnose PD patients in the SN region and PBMCs.

Accurate Detection of α-Synuclein Seeds in Cerebrospinal Fluid from Isolated Rapid Eye Movement Sleep Behavior Disorder and Patients with Parkinson's Disease in the DeNovo Parkinson (DeNoPa) Cohort.

Misfolded α-synuclein (αSyn) aggregates (αSyn-seeds) in cerebrospinal fluid (CSF) are biomarkers for synucleinopathies such as Parkinson's disease (PD). αSyn-seeds have been detected in prodromal cases with isolated rapid eye movement sleep behavior disorder (iRBD). The objective of this study was to determine the accuracy of the αSyn-seed amplification assay (αS-SAA) in a comprehensively characterized cohort with a high proportion of PD and iRBD CSF samples collected at baseline. We used a high-throughput αS-SAA to analyze 233 blinded CSF samples from 206 participants of the DeNovo Parkinson Cohort (DeNoPa) (113 de novo PD, 64 healthy controls, 29 iRBD confirmed by video polysomnography). Results were compared with the final diagnosis, which was determined after up to 10 years of longitudinal clinical evaluations, including dopamine-transporter-single-photon emission computed tomography (DAT-SPECT) at baseline, CSF proteins, Movement Disorder Society-Unified Parkinson's Disease Rating Scale, and various cognitive and nonmotor scales. αS-SAA detected αSyn-seeds in baseline PD-CSF with 98% accuracy. αSyn-seeds were detected in 93% of the iRBD cases. αS-SAA results showed higher agreement with the final than the initial diagnosis, as 14 patients were rediagnosed as non-αSyn aggregation disorder. For synucleinopathies, αS-SAA showed higher concordance with the final diagnosis than DAT-SPECT. Statistically significant correlations were found between assay parameters and disease progression. Our results confirm αS-SAA accuracy at the first clinical evaluation when a definite diagnosis is most consequential. αS-SAA conditions reported here are highly sensitive, enabling the detection of αSyn-seeds in CSF from iRBD just months after the first symptoms, suggesting that αSyn-seeds are present in the very early prodromal phase of synucleinopathies. Therefore, αSyn-seeds are clear risk markers for synuclein-related disorders, but not for time of phenoconversion. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Cross-sectional study and bioinformatics analysis to reveal the correlations of osteoporosis in patients with Parkinson's disease

ObjectivesOsteoporosis and Parkinson's disease (PD) are both aging-related diseases. PD patients with comorbid osteoporosis are vulnerable to the risk of fracture, which leads to a serious public health burden to the whole society. Therefore, this study sought to reveal the clinical and genetic correlations between PD and osteoporosis based on a cross-sectional study and bioinformatics analysis. MethodsA cross-sectional study of 95 PD patients and 99 healthy controls was conducted. Ordinal logistic regression analysis was utilized to investigate the clinical correlations between PD and osteoporosis. Two microarray datasets (GSE20292, GSE35958) including PD, osteoporosis and normal control samples were retrieved from the GEO database for GO analysis, KEGG pathway analysis and PPI network. ResultsPD patients had lower 25(OH)VitD, FN BMD, BMD and T-score of the LS and TH, as well as poorer bone mass diagnosis, yet higher PINP compared to healthy controls. Both age and UPDRS II score of PD patients were adversely correlated with BMD of LS and TH. PD diagnosis acted as an independent risk factor of osteoporosis, and PD patients had approximately double risk for osteoporosis. Bioinformatics analysis further revealed that SNAP25, AQP4, SV2B, KCND3, and ABCA2 had important diagnostic value and risk prediction value for both PD and osteoporosis. ConclusionsPD diagnosis can be used as an independent risk factor for osteoporosis. Moreover, SNAP25, AQP4, SV2B, KCND3 and ABCA2 as the top 5 hub genes have important diagnostic and risk predictive value for both PD and osteoporosis.