Blood transcriptomics of drug-naïve sporadic Parkinson's disease patients

Substantia nigra and blood gene signatures and biomarkers for Parkinson's disease from integrated multicenter microarray-based transcriptomic analyses

Background

Parkinson's disease (PD) is a complex, common neurodegenerative disorder with unclear etiology. The pathogenic hallmark is the death of dopaminergic neurons in the substantia nigra. PD diagnosis depends on clinical manifestation of symptoms but is lack of effective biomarker.

Methods

Available human microarray-based transcriptomic datasets of the substantia nigra and blood were acquired for PD cases and controls. Robust rank aggregation and Weighted Gene Co-expression Network analysis were performed to identify gene signatures in substantia nigra and blood of PD. An overlapping analysis and validation in an independent cohort were followed to identify PD blood biomarkers.

Results

Eight datasets of substantia nigra and 3 datasets of blood were retrieved, which comprised 150 substantia nigra and 571 blood samples. Integrated differentially expressed genes (DEG) and module analyses showed that the substantia nigra gene signature in PD comprised 170 key genes, mainly involved in dopaminergic synapse, neuroactive ligand-receptor interaction, calcium signaling pathway, and Parkinson disease. The blood gene signature had only 65 DEGs, but with no robust co-expression module identified. Two genes, LRRN3 and TUBB2A, were both downregulated in the substantia nigra and blood of PD. But only TUBB2A was validated in the blood of independent cohort and showed a capacity of PD prediction.

Conclusion

The present study identified PD-associated gene signatures of the substantia nigra and blood, and demonstrated that the reduced expression of TUBB2A in the blood is promising to predict PD. Our findings provide novel insight into the mechanisms underlying PD pathophysiology and the development of PD biomarkers.

The Exon-Based Transcriptomic Analysis of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disease with a complicated pathophysiology and diagnostics. Blood-based whole transcriptome analysis of the longitudinal PPMI cohort was performed with a focus on the change in the expression of exons to find potential RNA-based biomarkers. At the moment of diagnosis, the expression of exons was very similar in both control and PD patients. The exon-based analysis identified 27 differentially expressed exons in PD patients three years after the diagnosis compared to the health controls. Moreover, thirteen exons were differentially expressed during the three-year progression of the PD. At the same time, control subjects had only minimal changes that can mostly be attributed to being related to aging. Differentially regulated exons we identified in the PD cohort were mostly related to different aspects of the pathophysiology of PD, such as an innate immune response or lysosomal activity. We also observed a decline in the expression of the OPN1MW3 gene that is related to colour vision, which suggests that colour vision analysis could be a practical biomarker to monitor the progression of PD.

Histone post-translational modification and heterochromatin alterations in neurodegeneration: revealing novel disease pathways and potential therapeutics

Alzheimer's disease (AD), Parkinson's disease (PD), Frontotemporal Dementia (FTD), and Amyotrophic lateral sclerosis (ALS) are complex and fatal neurodegenerative diseases. While current treatments for these diseases do alleviate some symptoms, there is an imperative need for novel treatments able to stop their progression. For all of these ailments, most cases occur sporadically and have no known genetic cause. Only a small percentage of patients bear known mutations which occur in a multitude of genes. Hence, it is clear that genetic factors alone do not explain disease occurrence. Chromatin, a DNA-histone complex whose basic unit is the nucleosome, is divided into euchromatin, an open form accessible to the transcriptional machinery, and heterochromatin, which is closed and transcriptionally inactive. Protruding out of the nucleosome, histone tails undergo post-translational modifications (PTMs) including methylation, acetylation, and phosphorylation which occur at specific residues and are connected to different chromatin structural states and regulate access to transcriptional machinery. Epigenetic mechanisms, including histone PTMs and changes in chromatin structure, could help explain neurodegenerative disease processes and illuminate novel treatment targets. Recent research has revealed that changes in histone PTMs and heterochromatin loss or gain are connected to neurodegeneration. Here, we review evidence for epigenetic changes occurring in AD, PD, and FTD/ALS. We focus specifically on alterations in the histone PTMs landscape, changes in the expression of histone modifying enzymes and chromatin remodelers as well as the consequences of these changes in heterochromatin structure. We also highlight the potential for epigenetic therapies in neurodegenerative disease treatment. Given their reversibility and pharmacological accessibility, epigenetic mechanisms provide a promising avenue for novel treatments. Altogether, these findings underscore the need for thorough characterization of epigenetic mechanisms and chromatin structure in neurodegeneration.

Analysis of LRRN3, MEF2C, SLC22A, and P2RY12 Gene Expression in the Peripheral Blood of Patients in the Early Stages of Parkinson's Disease

Parkinson's disease (PD) is one of the most common human neurodegenerative diseases. Belated diagnoses of PD and late treatment are caused by its elongated prodromal phase. Thus, searching for new candidate genes participating in the development of the pathological process in the early stages of the disease in patients who have not yet received therapy is relevant. Changes in mRNA and protein levels have been described both in the peripheral blood and in the brain of patients with PD. Thus, analysis of changes in the mRNA expression in peripheral blood is of great importance in studying the early stages of PD. This work aimed to analyze the changes in MEF2C, SLC22A4, P2RY12, and LRRN3 gene expression in the peripheral blood of patients in the early stages of PD. We found a statistically relevant and PD-specific change in the expression of the LRRN3 gene, indicating a disruption in the processes of neuronal regeneration and the functioning of synapses. The data obtained during the study indicate that this gene can be considered a potential biomarker of the early stages of PD.

A Systematic Review of Extracellular Matrix-Related Alterations in Parkinson's Disease

The role of the extracellular matrix (ECM) in Parkinson's disease (PD) is not well understood, even though it is critical for neuronal structure and signaling. This systematic review identified the top deregulated ECM-related pathways in studies that used gene set enrichment analyses (GSEA) to document transcriptomic, proteomic, or genomic alterations in PD. PubMed and Google scholar were searched for transcriptomics, proteomics, or genomics studies that employed GSEA on data from PD tissues or cells and reported ECM-related pathways among the top-10 most enriched versus controls. Twenty-seven studies were included, two of which used multiple omics analyses. Transcriptomics and proteomics studies were conducted on a variety of tissue and cell types. Of the 17 transcriptomics studies (16 data sets), 13 identified one or more adhesion pathways in the top-10 deregulated gene sets or pathways, primarily related to cell adhesion and focal adhesion. Among the 8 proteomics studies, 5 identified altered overarching ECM gene sets or pathways among the top 10. Among the 4 genomics studies, 3 identified focal adhesion pathways among the top 10. The findings summarized here suggest that ECM organization/structure and cell adhesion (particularly focal adhesion) are altered in PD and should be the focus of future studies.

Analysis of ADORA2A, MTA1, PTGDS, PTGS2, NSF, and HNMT Gene Expression Levels in Peripheral Blood of Patients with Early Stages of Parkinson's Disease

Parkinson's disease (PD) is a common chronic, age-related neurodegenerative disease. This disease is characterized by a long prodromal period. In this context, it is important to search for the genes and mechanisms that are involved in the development of the pathological process in the earliest stages of the disease. Published data suggest that blood cells, particularly lymphocytes, may be a model for studying the processes that occur in the brain in PD. Thus, in the present work, we performed an analysis of changes in the expression of the genes ADORA2A, MTA1, PTGDS, PTGS2, NSF, and HNMT in the peripheral blood of patients with early stages of PD (stages 1 and 2 of the Hoehn-Yahr scale). We found significant and PD-specific expression changes of four genes, i.e., MTA1, PTGS2, NSF, and HNMT, in the peripheral blood of patients with early stages of PD. These genes may be associated with PD pathogenesis in the early clinical stages and can be considered as potential candidate genes for this disease. Altered expression of the ADORA2A gene in treated PD patients may indicate that this gene is involved in processes affected by antiparkinsonian therapy.

Unraveling the transcriptomic signatures of Parkinson's disease and major depression using single-cell and bulk data

Background

Motor symptoms are well-characterized in Parkinson's disease (PD). However, non-motor symptoms, such as depression, are commonly observed and can appear up to 10 years before motor features, resulting in one-third of individuals being misdiagnosed with a neuropsychiatric disorder. Thus, identifying diagnostic biomarkers is crucial for accurate PD diagnosis during its prodromal or early stages.

Methods

We employed an integrative approach, combining single nucleus RNA and bulk mRNA transcriptomics to perform comparative molecular signatures analysis between PD and major depressive disorder (MDD). We examined 39,834 nuclei from PD (GSE202210) and 32,707 nuclei from MDD (GSE144136) in the dorsolateral prefrontal cortex (dlPFC) of Brodmann area 9. Additionally, we analyzed bulk mRNA peripheral blood samples from PD compared to controls (GSE49126, GSE72267), as well as MDD compared to controls (GSE39653).

Results

Our findings show a higher proportion of astrocytes, and oligodendrocyte cells in the dlPFC of individuals with PD vs. MDD. The excitatory to inhibitory neurons (E/I) ratio analysis indicates that MDD has a ratio close to normal 80/20, while PD has a ratio of 62/38, indicating increased inhibition in the dlPFC. Microglia displayed the most pronounced differences in gene expression profiles between the two conditions. In PD, microglia display a pro-inflammatory phenotype, while in MDD, they regulate synaptic transmission through oligodendrocyte-microglia crosstalk. Analysis of bulk mRNA blood samples revealed that the COL5A, MID1, ZNF148, and CD22 genes were highly expressed in PD, whereas the DENR and RNU1G2 genes were highly expressed in MDD. CD22 is involved in B-cell activation and the negative regulation of B-cell receptor signaling. Additionally, CD86, which provides co-stimulatory signals for T-cell activation and survival, was found to be a commonly differentially expressed gene in both conditions. Pathway analysis revealed several immune-related pathways common in both conditions, including the complement and coagulation cascade, and B-cell receptor signaling.

Discussion

This study demonstrates that bulk peripheral immune cells play a role in both conditions, but neuroinflammation in the dlPFC specifically manifests in PD as evidenced by the analysis of single nucleus dlPFC datasets. Integrating these two omics levels offers a better understanding of the shared and distinct molecular pathophysiology of PD and MDD in both the periphery and the brain. These findings could lead to potential diagnostic biomarkers, improving accuracy and guiding pharmacological treatments.

Ferroptosis-Related Immune Genes in Hematological Diagnosis of Parkinson's Diseases

Emerging evidence suggested that ferroptosis and immune activation, as well as their interactions, played a crucial role in the occurrence and progression of Parkinson's disease (PD). However, whether this interaction could serve as the basis for a hematological diagnosis of PD remained poorly understood. This study aimed to construct a novel hematological model for PD diagnosis based on the ferroptosis-related immune genes. The brain imaging of PD patients was obtained from the Affiliated Hospital of Nantong University. We used least absolute shrinkage and selection operator (LASSO) to identify the optimal signature ferroptosis-related immune genes based on six gene expression profile datasets of substantia nigra (SN) and peripheral blood of PD patients. Then we used the support vector machine (SVM) classifier to construct the hematological diagnostic model named Ferr.Sig for PD. Gene set enrichment analysis was utilized to execute gene functional annotation. The brain imaging and functional annotation analysis revealed prominent iron deposition and immune activation in the SN region of PD patients. We identified a total of 17 signature ferroptosis-related immune genes using LASSO method and imported them to SVM classifier. The Ferr.Sig model exhibited a high diagnostic accuracy, and its area under the curve (AUC) for distinguishing PD patients from healthy controls in the training and internal validation cohort reached 0.856 and 0.704, respectively. We also used the Ferr.Sig into other external validation cohorts, and a comparable AUC with the internal cohort was obtained, with the AUC of 0.727 in Scherzer's cohort, 0.745 in Roncagli's cohort, and 0.778 in Meiklejohn's cohort. Furthermore, the diagnostic performance of Ferr.Sig was not interfered by the other neurodegenerative diseases. This study revealed the value of ferroptosis-related immune genes in PD diagnosis, which may provide a novel direction and strategy for the development of novel biomarkers with less invasiveness, low cost, and high accuracy for PD screening and diagnosis.

Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease

Alzheimer's disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

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.

Transcriptome Analysis Reveals a Two-Gene Signature Links to Motor Progression and Alterations of Immune Cells in Parkinson's Disease

BACKGROUND

The motor impairment in Parkinson's disease (PD) can be managed but effective treatments for stopping or slowing the disease process are lacking. The advent of transcriptomics studies in PD shed light on the development of promising measures to predict disease progression and discover novel therapeutic strategies.

OBJECTIVE

To reveal the potential role of transcripts in the motor impairment progression of patients with PD via transcriptome analysis.

METHODS

We separately analyzed the differentially expressed genes (DEGs) between PD cases and healthy controls in two cohorts using whole blood bulk transcriptome data. Based on the intersection of DEGs, we established a prognostic signature by regularized regression and Cox proportional hazards analysis. We further performed immune cell analysis and single-cell RNA sequencing analysis to study the biological features of this signature.

RESULTS

We identified a two-gene-based prognostic signature that links to PD motor progression and the two-gene signature-derived risk score was associated with several types of immune cells in blood. Notably, the fraction of neutrophils increased 5% and CD4+ T cells decreased 7% in patients with high-risk scores compared to that in patients with low-risk scores, suggesting these two types of immune cells might play key roles in the prognosis of PD. We also observed the downregulated genes in PD patients with high-risk scores that enriched in PD-associated pathways from iPSC-derived dopaminergic neurons single-cell RNA sequencing analysis.

CONCLUSION

We identified a two-gene signature linked to the motor progression in PD, which provides new insights into the motor prognosis of PD.

Transcriptome Profiling Reveals Differential Expression of Circadian Behavior Genes in Peripheral Blood of Monozygotic Twins Discordant for Parkinson's Disease

Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. Investigating individuals with the most identical genetic background is optimal for minimizing the genetic contribution to gene expression. These individuals include monozygotic twins discordant for PD. Monozygotic twins have the same genetic background, age, sex, and often similar environmental conditions. The aim of this study was to carry out a transcriptome analysis of the peripheral blood of three pairs of monozygotic twins discordant for PD. We identified the metabolic process “circadian behavior” as a priority process for further study. Different expression of genes included in the term “circadian behavior” confirms that this process is involved in PD pathogenesis. We found increased expression of three genes associated with circadian behavior, i.e., PTGDS, ADORA2A, and MTA1, in twins with PD. These genes can be considered as potential candidate genes for this disease.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

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.

A Machine Learning Approach to Parkinson’s Disease Blood Transcriptomics

The increased incidence and the significant health burden associated with Parkinson’s disease (PD) have stimulated substantial research efforts towards the identification of effective treatments and diagnostic procedures. Despite technological advancements, a cure is still not available and PD is often diagnosed a long time after onset when irreversible damage has already occurred. Blood transcriptomics represents a potentially disruptive technology for the early diagnosis of PD. We used transcriptome data from the PPMI study, a large cohort study with early PD subjects and age matched controls (HC), to perform the classification of PD vs. HC in around 550 samples. Using a nested feature selection procedure based on Random Forests and XGBoost we reached an AUC of 72% and found 493 candidate genes. We further discussed the importance of the selected genes through a functional analysis based on GOs and KEGG pathways.

Mechanisms of motor symptom improvement by long-term Tai Chi training in Parkinson’s disease patients

Background

Tai Chi has been shown to improve motor symptoms in Parkinson’s disease (PD), but its long-term effects and the related mechanisms remain to be elucidated. In this study, we investigated the effects of long-term Tai Chi training on motor symptoms in PD and the underlying mechanisms.

Methods

Ninety-five early-stage PD patients were enrolled and randomly divided into Tai Chi (n = 32), brisk walking (n = 31) and no-exercise (n = 32) groups. At baseline, 6 months and 12 months during one-year intervention, all participants underwent motor symptom evaluation by Berg balance scale (BBS), Unified PD rating-scale (UPDRS), Timed Up and Go test (TUG) and 3D gait analysis, functional magnetic resonance imaging (fMRI), plasma cytokine and metabolomics analysis, and blood Huntingtin interaction protein 2 (HIP2) mRNA level analysis. Longitudinal self-changes were calculated using repeated measures ANOVA. GEE (generalized estimating equations) was used to assess factors associated with the longitudinal data of rating scales. Switch rates were used for fMRI analysis. False discovery rate correction was used for multiple correction.

Results

Participants in the Tai Chi group had better performance in BBS, UPDRS, TUG and step width. Besides, Tai Chi was advantageous over brisk walking in improving BBS and step width. The improved BBS was correlated with enhanced visual network function and downregulation of interleukin-1β. The improvements in UPDRS were associated with enhanced default mode network function, decreased L-malic acid and 3-phosphoglyceric acid, and increased adenosine and HIP2 mRNA levels. In addition, arginine biosynthesis, urea cycle, tricarboxylic acid cycle and beta oxidation of very-long-chain fatty acids were also improved by Tai Chi training.

Conclusions

Long-term Tai Chi training improves motor function, especially gait and balance, in PD. The underlying mechanisms may include enhanced brain network function, reduced inflammation, improved amino acid metabolism, energy metabolism and neurotransmitter metabolism, and decreased vulnerability to dopaminergic degeneration.

Trial registration This study has been registered at Chinese Clinical Trial Registry (Registration number: ChiCTR2000036036; Registration date: August 22, 2020).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40035-022-00280-7.

A Comprehensive Evaluation of Cross-Omics Blood-Based Biomarkers for Neuropsychiatric Disorders

The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer's Disease (AD). We applied summary-based and two-sample Mendelian Randomization to estimate the associations between blood molecules and brain disorders. We identified 524 RNA, 807 methylation sites, 29 proteins, seven cytokines, and 22 metabolites having a significant association with at least one of 14 brain disorders. Simulation analyses indicated that a cross-omics combination of biomarkers had better performance for most disorders, and different disorders could associate with different omics. We identified an 11-methylation-site model for SCZ diagnosis (Area Under Curve, AUC = 0.74) by analyzing selected candidate markers in published datasets (total N = 6098). Moreover, we constructed an 18-methylation-sites model that could predict the prognosis of elders with mild cognitive impairment (hazard ratio = 2.32). We provided an association landscape between blood cross-omic biomarkers and 14 brain disorders as well as a suggestion guide for future clinical discovery and application.

Comprehensive methylation profile of CSF cfDNA revealed pathogenesis and diagnostic markers for early-onset Parkinson's disease

Background: Early-onset Parkinson's disease (EOPD) is one uncommon Parkinson's disease subtype with characteristic clinicopathological features. The full epigenomic profile of EOPD is largely unknown. Methods: We performed the first study to investigate the EOPD full methylation profile of cerebrospinal fluid (CSF) cell-free DNA (cfDNA) from 26 EOPD patients and 10 control patients. Results: 2220 differentially methylated genes were identified in EOPD. Hypermethylation far outweighed hypomethylation in gene numbers. Clustering and enrichment analyses identified aberrant neuronal function and immune response. Weighted correlation network analysis demonstrated significant correlation between methylation signatures and clock drawing test (CDT), mini-mental state examination (MMSE), education, working status, alcohol drinking history and Hamilton anxiety scale (HAMA). Several key networking genes in EOPD aberrant methylation were also identified. Conclusions: The methylation profile and signatures of CSF cfDNA were revealed for the first time in EOPD. Aberrant methylation signatures were correlated with education, working status, alcohol drinking history, CDT, MMSE and HAMA.

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.

Comprehensive Profiling of Blood Coagulation and Fibrinolysis Marker Reveals Elevated Plasmin-Antiplasmin Complexes in Parkinson's Disease

Parkinson's disease (PD) is the second most common age-related neurodegenerative disease. Accumulating evidence demonstrates that alpha-synuclein (α-Syn), an apparently predominant neuronal protein, is a major contributor to PD pathology. As α-Syn is also highly abundant in blood, particularly in red blood cells (RBCs) and platelets, this in turn raises the question on the function of presumably dysfunctional α-Syn in "peripheral" cells and its putative effect on the other enclosed constituents. Herein, we detected the internal variance in erythrocytes of PD patients by Raman spectroscopy, but no measurable amount of erythrocytic behavioural change (eryptosis) or any haemoglobin variation was noticed. An elevated level of plasmin-antiplasmin complexes (PAP) was observed in the plasma of PD patients, indicating activation of the fibrinolytic system, but platelet activation after thrombin stimulation was not altered. Sex-specific patterns were noticed for blood coagulation factor XIII and factor XII activity in PD patients. Additionally, the alterations in homocysteine levels which have often been observed in PD patients were found to be independent from L-DOPA usage and PAP levels. Furthermore, a selective gene expression analysis identified subsets of genes related to different blood-associated compartments (RBCs, platelets, coagulation-fibrinolysis) also involved in PD-related pathways.

Human Monocytes Plasticity in Neurodegeneration

Monocytes play a crucial role in immunity and tissue homeostasis. They constitute the first line of defense during the inflammatory process, playing a role in the pathogenesis and progression of diseases, making them an attractive therapeutic target. They are heterogeneous in morphology and surface marker expression, which suggest different molecular and physiological properties. Recent evidences have demonstrated their ability to enter the brain, and, as a consequence, their hypothetical role in different neurodegenerative diseases. In this review, we will discuss the current knowledge about the correlation between monocyte dysregulation in the brain and/or in the periphery and neurological diseases in humans. Here we will focus on the most common neurodegenerative disorders, such as Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis and multiple sclerosis.

Altered Transcriptional Profile of Mitochondrial DNA-Encoded OXPHOS Subunits, Mitochondria Quality Control Genes, and Intracellular ATP Levels in Blood Samples of Patients with Parkinson's Disease

Mitochondrial dysfunctions are significant contributors to neurodegeneration. One result or a cause of mitochondrial dysfunction might be the disruption of mtDNA transcription. Limited data indicated an altered expression of mtDNA encoded transcripts in Alzheimer's disease (AD) or Parkinson's disease (PD). The number of mitochondria is high in cells with a high energy demand, such as muscle or nerve cells. AD or PD involves increased risk of cardiomyopathy, suggesting that mitochondrial dysfunction might be systemic. If it is systemic, we should observe it in different cell types. Given that, we wanted to investigate any disruption in the regulation of mtDNA encoded gene expression in addition to PINK1, PARKIN, and ATP levels in peripheral blood samples of PD cases who are affected by a neurodegenerative disorder that is very well known by its mitochondrial aspects. Our results showed for the first time that: 1) age of onset > 50 PD sporadic (PDS) cases: mtDNA transcription and quality control genes were affected; 2) age of onset <50 PDS cases: only mtDNA transcription was affected; and 3) PD cases with familial background: only quality control genes were affected. mtDNA copy number was not a confounder. Intracellular ATP levels of PD case subgroups were significantly higher than those of healthy subjects. We suggest that a systemic dysregulation of transcription of mtDNA or mitochondrial quality control genes might result in the development of a sporadic form of the disease. Additionally, ATP elevation might be an independent compensatory and response mechanism. Hyperactive cells in AD and PD require further investigation.

Deep sequencing of sncRNAs reveals hallmarks and regulatory modules of the transcriptome during Parkinson’s disease progression

Noncoding RNAs have diagnostic and prognostic importance in Parkinson's disease (PD). We studied circulating small noncoding RNAs (sncRNAs) in two large-scale longitudinal PD cohorts (Parkinson's Progression Markers Initiative (PPMI) and Luxembourg Parkinson's Study (NCER-PD)) and modeled their impact on the transcriptome. Sequencing of sncRNAs in 5,450 blood samples of 1,614 individuals in PPMI yielded 323 billion reads, most of which mapped to microRNAs but covered also other RNA classes such as piwi-interacting RNAs, ribosomal RNAs and small nucleolar RNAs. Dysregulated microRNAs associated with disease and disease progression occur in two distinct waves in the third and seventh decade of life. Originating predominantly from immune cells, they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. Profiling 1,553 samples from 1,024 individuals in the NCER-PD cohort validated biomarkers and main findings by an independent technology. Finally, network analysis of sncRNA and transcriptome sequencing from PPMI identified regulatory modules emerging in patients with progressing PD.

Inhibition of histone deacetylase promotes a neuroprotective mechanism in an experimental model of Parkinson's disease

Introduction

Therapies targeting histone deacetylase (HDAC) have gained wider attention in the treatment of various clinical conditions. However, the use of HDAC inhibitors in pre-clinical trials in the case of Parkinson's disease (PD) is very limited. In the present study, the HDAC inhibitor, entinostat, was tested in animals induced with Parkinson's disease experimentally.

Material and methods

Wistar male rats (150 ±10 g) were administered with rotenone (2 mg/kg/day, s.c.) for 21 days to induce PD, while entinostat (20 mg/kg) was given intraperitoneally. Then, the neurological functions, PD markers, and HDACs were analysed in the control and experimental animals.

Results

The results demonstrated that rats that received entinostat displayed progressive motor, behavioural, and neurological function with attenuated α-synuclein and improved tyrosine-hydroxylase compared to control cells. Moreover, the induction of PD in rats demonstrated reduced levels of H2S, dopamine, 3, and 4-dihydroxyphenylacetic acid (DOPAC), and increased monoamine oxidase activity in PD rats. However, the rats that received entinostat demonstrated progressive levels of dopa and DOPAC, with attenuated levels of HDAC-2, -4, and -6 mRNA in the PD rats compared to controls. On the other hand, elevated (p < 0.01) levels of PD marker genes such as GDF3 and NMDA2b were reduced, with a significant increase in neuroprotective genes such as VDAC3 and CBX5 in entinostat-supplemented rats.

Conclusions

The study results suggest that inhibition of HDAC systematically improves the neurological functions, and hence treatments, emphasizing that HDACI, as the speculated mechanism, will be a promising mode of treatment in PD.

Transcriptomic profiles in Parkinson's disease

Transcriptomics in Parkinson's disease offers insights into the pathogenesis of Parkinson's disease but obtaining brain tissue has limitations. In order to bypass this issue, we profile and compare differentially expressed genes and enriched pathways (KEGG) in two peripheral tissues (blood and skin) of 12 Parkinson's disease patients and 12 healthy controls using RNA-sequencing technique and validation with RT-qPCR. Furthermore, we compare our results to previous Parkinson's disease post mortem brain tissue and blood results using the robust rank aggregation method. The results show no overlapping differentially expressed genes or enriched pathways in blood vs. skin in our sample sets (25 vs. 1068 differentially expressed genes with an FDR ≤ 0.05; 1 vs. 9 pathways in blood and skin, respectively). A meta-analysis from previous transcriptomic sample sets using either microarrays or RNA-Seq yields a robust rank aggregation list of cortical gene expression changes with 43 differentially expressed genes; a list of substantia nigra changes with 2 differentially expressed genes and a list of blood changes with 1 differentially expressed gene being statistically significant at FDR ≤ 0.05. In cortex 1, KEGG pathway was enriched, four in substantia nigra and two in blood. None of the differentially expressed genes or pathways overlap between these tissues. When comparing our previously published skin transcription analysis, two differentially expressed genes between the cortex robust rank aggregation and skin overlap. In this study, for the first time a meta-analysis is applied on transcriptomic sample sets in Parkinson's disease. Simultaneously, it explores the notion that Parkinson's disease is not just a neuronal tissue disease by exploring peripheral tissues. The comparison of different Parkinson's disease tissues yields surprisingly few significant differentially expressed genes and pathways, suggesting that divergent gene expression profiles in distinct cell lineages, metabolic and possibly iatrogenic effects create too much transcriptomic noise for detecting significant signal. On the other hand, there are signs that point towards Parkinson's disease-specific changes in non-neuronal peripheral tissues in Parkinson's disease, indicating that Parkinson's disease might be a multisystem disorder.

Inflammation and regulatory T cell genes are differentially expressed in peripheral blood mononuclear cells of Parkinson's disease patients

Our aim was to identify the differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMC) of Parkinson’s disease (PD) patients and healthy controls by microarray technology and analysis of related molecular pathways by functional annotation. Thirty PD patients and 30 controls were enrolled. Agilent Human 8X60 K Oligo Microarray was used for gene level expression identification. Gene ontology and pathway enrichment analyses were used for functional annotation of DEGs. Protein–protein interaction analyses were performed with STRING. Expression levels of randomly selected DEGs were quantified by real time quantitative polymerase chain reaction (RT-PCR) for validation. Flow cytometry was done to determine frequency of regulatory T cells (Tregs) in PBMC. A total of 361 DEGs (143 upregulated and 218 downregulated) were identified after GeneSpring analysis. DEGs were involved in 28 biological processes, 12 cellular components and 26 molecular functions. Pathway analyses demonstrated that upregulated genes mainly enriched in p53 (CASP3, TSC2, ATR, MDM4, CCNG1) and PI3K/Akt (IL2RA, IL4R, TSC2, VEGFA, PKN2, PIK3CA, ITGA4, BCL2L11) signaling pathways. TP53 and PIK3CA were identified as most significant hub proteins. Expression profiles obtained by RT-PCR were consistent with microarray findings. PD patients showed increased proportions of CD49d⁺ Tregs, which correlated with disability scores. Survival pathway genes were upregulated putatively to compensate neuronal degeneration. Bioinformatics analysis showed an association between survival and inflammation genes. Increased CD49d⁺ Treg ratios might signify the effort of the immune system to suppress ongoing neuroinflammation.

Common Factors in Neurodegeneration: A Meta-Study Revealing Shared Patterns on a Multi-Omics Scale

Neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS) are heterogeneous, progressive diseases with frequently overlapping symptoms characterized by a loss of neurons. Studies have suggested relations between neurodegenerative diseases for many years (e.g., regarding the aggregation of toxic proteins or triggering endogenous cell death pathways). We gathered publicly available genomic, transcriptomic, and proteomic data from 177 studies and more than one million patients to detect shared genetic patterns between the neurodegenerative diseases on three analyzed omics-layers. The results show a remarkably high number of shared differentially expressed genes between the transcriptomic and proteomic levels for all conditions, while showing a significant relation between genomic and proteomic data between AD and PD and AD and ALS. We identified a set of 139 genes being differentially expressed in several transcriptomic experiments of all four diseases. These 139 genes showed overrepresented gene ontology (GO) Terms involved in the development of neurodegeneration, such as response to heat and hypoxia, positive regulation of cytokines and angiogenesis, and RNA catabolic process. Furthermore, the four analyzed neurodegenerative diseases (NDDs) were clustered by their mean direction of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources.

Identifying potential gene biomarkers for Parkinson's disease through an information entropy based approach

Parkinson's disease (PD) is a chronic, progressive neurodegenerative disease and represents the most common disease of this type, after Alzheimer's dementia. It is characterized by motor and nonmotor features and by a long prodromal stage that lasts many years. Genetic research has shown that PD is a complex and multisystem disorder. To capture the molecular complexity of this disease we used a complex network approach. We maximized the information entropy of the gene co-expression matrix betweenness to obtain a gene adjacency matrix; then we used a fast greedy algorithm to detect communities. Finally we applied principal component analysis on the detected gene communities, with the ultimate purpose of discriminating between PD patients and healthy controls by means of a random forests classifier. We used a publicly available substantia nigra microarray dataset, GSE20163, from NCBI GEO database, containing gene expression profiles for 10 PD patients and 18 normal controls. With this methodology we identified two gene communities that discriminated between the two groups with mean accuracy of 0.88 ± 0.03 and 0.84 ± 0.03, respectively, and validated our results on an independent microarray experiment. The two gene communities presented a considerable reduction in size, over 100 times, compared to the initial network and were stable within a range of tested parameters. Further research focusing on the restricted number of genes belonging to the selected communities may reveal essential mechanisms responsible for PD at a network level and could contribute to the discovery of new biomarkers for PD.

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.

DNA methylation of the 5'-UTR DAT 1 gene in Parkinson's disease patients

OBJECTIVES

The involvement of epigenetics mechanisms in the transcriptional regulation of key genes has been investigated in the initiation and progression of neurodegenerative disorders, including Parkinson's disease (PD). Among others, we, here, focused the attention on the dopamine transporter (DAT) gene playing a critical role in maintaining the integrity of dopaminergic neurons.

MATERIALS AND METHODS

We performed bisulfite pyrosequencing to examine DNA methylation levels of six CpG sites in the 5'-UTR of DAT1 gene in human peripheral blood mononuclear cells (PBMCs) obtained from 101 sporadic PD patients and 59 healthy controls.

RESULTS

We selectively report for CpG5 an increase in DNA methylation levels in PD subjects respect to controls, that almost reaches statistical significance (30.06 ± 12.4 vs 26.58 ± 7.6, P = .052). Of interest, a significantly higher methylation at specific CpG sites (ANOVA: P = .029) was observed in PD subjects with advanced stage of illness. Namely, a multivariate regression analysis showed that a higher methylation level at specific CpG sites in the group of PD patients was associated with increased methylation at CpG2, CpG3, and with H&Y stage but not with age and gender. This regression model explains the 38% of the variance of methylation at CpG5.

CONCLUSION

Our results do seem to suggest that the methylation level of CpG5 is different between PD patients and controls. Moreover, this methylation level for CpG5 may be associated also with the stage of disease.

Discovery of new epigenomics-based biomarkers and the early diagnosis of neurodegenerative diseases

Treatment options for many neurodegenerative diseases are limited due to the lack of early diagnostic procedures that allow timely delivery of therapeutic agents to affected neurons prior to cell death. While notable advances have been made in neurodegenerative disease biomarkers, whether or not the biomarkers discovered to date are useful for early diagnosis remains an open question. Additionally, the reliability of these biomarkers has been disappointing, due in part to the large dissimilarities between the tissues traditionally used to source biomarkers and primarily diseased neurons. In this article, we review the potential viability of atypical epigenetic and/or consequent transcriptional alterations (ETAs) as biomarkers of early-stage neurodegenerative disease, and present our perspectives on the discovery and practical use of such biomarkers in patient-derived neural samples using single-cell level analyses, thereby greatly enhancing the reliability of biomarker application.

A human minisatellite hosts an alternative transcription start site for NPRL3 driving its expression in a repeat number-dependent manner

Minisatellites, also called variable number of tandem repeats (VNTRs), are a class of repetitive elements that may affect gene expression at multiple levels and have been correlated to disease. Their identification and role as expression quantitative trait loci (eQTL) have been limited by their absence in comparative genomic hybridization and single nucleotide polymorphisms arrays. By taking advantage of cap analysis of gene expression (CAGE), we describe a new example of a minisatellite hosting a transcription start site (TSS) which expression is dependent on the repeat number. It is located in the third intron of the gene nitrogen permease regulator like protein 3 (NPRL3). NPRL3 is a component of the GAP activity toward rags 1 protein complex that inhibits mammalian target of rapamycin complex 1 (mTORC1) activity and it is found mutated in familial focal cortical dysplasia and familial focal epilepsy. CAGE tags represent an alternative TSS identifying TAGNPRL3 messenger RNAs (mRNAs). TAGNPRL3 is expressed in red blood cells both at mRNA and protein levels, it interacts with its protein partner NPRL2 and its overexpression inhibits cell proliferation. This study provides an example of a minisatellite that is both a TSS and an eQTL as well as identifies a new VNTR that may modify mTORC1 activity.

Working Towards a Blood-Derived Gene Expression Biomarker Specific for Alzheimer's Disease

BACKGROUND

The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific.

OBJECTIVE

Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature.

METHODS

Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls.

RESULTS

The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4).

CONCLUSIONS

This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.

Expression Profiling of Blood microRNAs 885, 361, and 17 in the Patients with the Parkinson's disease: Integrating Interaction Data to Uncover the Possible Triggering Age-Related Mechanisms

MicroRNAs (miRNAs) have been reported to contribute to the pathophysiology of the Parkinson’s disease (PD), an age related-neurodegenerative disorder. The aim of present study was to compare the expression profiles of a new set of candidate miRNAs related to aging and cellular senescence in peripheral blood mononuclear cells (PBMCs) obtained from the PD patients with healthy controls and then in the early and advanced stages of the PD patients with their controls to clarify whether their expression was correlated with the disease severity. We have also proposed a consensus-based strategy to interpret the miRNAs expression data to gain a better insight into the molecular regulatory alterations during the incidence of PD. We evaluated the miRNA expression levels in the PBMCs obtained from 36 patients with PD and 16 healthy controls by the reverse transcription-quantitative real-time PCR and their performance to discriminate the PD patients from the healthy subjects assessed using the receiver operating characteristic curve analysis. Also, we applied our consensus and integration approach to construct a deregulated miRNA-based network in PD with the respective targets and transcription factors, and the enriched gene ontology and pathways using the enrichment analysis approach were obtained. There was a significant overexpression of miR-885 and miR-17 and the downregulation of miR-361 in the PD patients compared to the controls. The blood expression of miR-885 and miR-17 tended to increase along with the disease severity. On the other hand, the lower levels of miR-361 in the early stages of the PD patients, as compared to controls, and its higher levels in the advanced stages of PD patients, as compared to the early stages of the PD patients, were observed. Combination of all three miRNAs showed an appropriate value of AUC (0.985) to discriminate the PD patients from the healthy subjects. Also, the deregulated miRNAs were linked to the known PD pathways and the candidate related target genes were presented. We revealed 3 candidate biomarkers related to aging and cellular senescence for the first time in the patients with PD. Our in-silico analysis identified candidate target genes and TFs, including those related to neurodegeneration and PD. Overall, our findings provided novel insights into the probable age-regulatory mechanisms underlying PD and a rationale to further clarify the role of the identified miRNAs in the PD pathogenesis.

Identification of potential diagnostic biomarkers for Parkinson's disease

The identification of biomarkers for early diagnosis of Parkinson's disease (PD) prior to the onset of symptoms may improve the effectiveness of therapy. To identify potential biomarkers, we downloaded microarray datasets of PD from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between PD and normal control (NC) groups were obtained, and the feature selection procedure and classification model were used to identify optimal diagnostic gene biomarkers for PD. A total of 1229 genes (640 up‐regulated and 589 down‐regulated) were obtained for PD, and nine DEGs (PTGDS,GPX3,SLC25A20,CACNA1D,LRRN3,POLR1D,ARHGAP26,TNFSF14 and VPS11) were selected as optimal PD biomarkers with great diagnostic value. These nine DEGs were significantly enriched in regulation of circadian sleep/wake cycle, sleep and gonadotropin‐releasing hormone signaling pathway. Finally, we examined the expression of GPX3,SLC25A20,LRRN3 and POLR1D in blood samples of patients with PD by qRT‐PCR. GPX3,LRRN3 and POLR1D exhibited the same expression pattern as in our analysis. In conclusion, this study identified nine DEGs that may serve as potential biomarkers of PD.

Antisense Transcription in Loci Associated to Hereditary Neurodegenerative Diseases

Natural antisense transcripts are common features of mammalian genes providing additional regulatory layers of gene expression. A comprehensive description of antisense transcription in loci associated to familial neurodegenerative diseases may identify key players in gene regulation and provide tools for manipulating gene expression. We take advantage of the FANTOM5 sequencing datasets that represent the largest collection to date of genome-wide promoter usage in almost 2000 human samples. Transcription start sites (TSSs) are mapped at high resolution by the use of a modified protocol of cap analysis of gene expression (CAGE) for high-throughput single molecule next-generation sequencing with Helicos (hCAGE). Here we present the analysis of antisense transcription at 17 loci associated to hereditary Alzheimer’s disease, Frontotemporal Dementia, Parkinson’s disease, Amyotrophic Lateral Sclerosis, and Huntington’s disease. We focused our analysis on libraries derived from brain tissues and primary cells. We also screened libraries from total blood and blood cell populations in the quest for peripheral biomarkers of neurodegenerative diseases. We identified 63 robust promoters in antisense orientation to genes associated to familial neurodegeneration. When applying a less stringent cutoff, this number increases to over 400. A subset of these promoters represents alternative TSSs for 24 FANTOM5 annotated long noncoding RNA (lncRNA) genes, in antisense orientation to 13 of the loci analyzed here, while the remaining contribute to the expression of additional transcript variants. Intersection with GWAS studies, sample ontology, and dynamic expression reveals association to specific genetic traits as well as cell and tissue types, not limited to neurodegenerative diseases. Antisense transcription was validated for a subset of genes, including those encoding for Microtubule-Associated Protein Tau, α-synuclein, Parkinsonism-associated deglycase DJ-1, and Leucin-Rich Repeat Kinase 2. This work provides evidence for the existence of additional regulatory mechanisms of the expression of neurodegenerative disease-causing genes by previously not-annotated and/or not-validated antisense long noncoding RNAs.

Aging, rather than Parkinson's disease, affects the responsiveness of PBMCs to the immunosuppression of bone marrow mesenchymal stem cells

Whether aging or Parkinson's disease (PD) affects the responses of peripheral blood mononuclear cells (PBMCs) to immunosuppression by bone marrow‑derived mesenchymal stem cell (BM‑MSCs) and which cytokines are more effective in inducing BM‑MSCs to be immunosuppressive remains to be elucidated. PBMCs were isolated from healthy young (age 26‑35), healthy middle‑aged (age 56‑60) and middle‑aged PD‑affected individuals. All the recruits were male. The mitogen‑stimulated PBMCs and proinflammatory cytokine‑pretreated BM‑MSCs were co‑cultured. The PBMC proliferation was measured using Cell Counting Kit‑8, while the cytokine secretion was assayed by cytometric bead array technology. The immunosuppressive ability of BM‑MSCs was confirmed in young healthy, middle‑aged healthy and middle‑aged PD‑affected individuals. Among the three groups, the PBMC proliferation and cytokine secretion of the young healthy group were suppressed more significantly compared with those of the middle‑aged healthy and middle‑aged PD‑affected group. No significant differences were identified in the PBMC proliferation and cytokine secretion between the patients with PD and the middle‑aged healthy subjects. Interferon (IFN)‑γ synergized with tumor necrosis factor (TNF)‑α, interleukin (IL)‑1α or IL‑1β was more effective than either one alone, and the combinations of IFN‑γ + IL‑1α and IFN‑γ + IL‑1β were more effective than IFN‑γ + TNF‑α in inducing BM‑MSCs to inhibit PBMC proliferation. The results of the present study suggested that aging, rather than PD, affects the response of PBMCs toward the suppression of BM‑MSC, at least in middle‑aged males. Patients with PD aged 56‑60 remain eligible for anti‑inflammatory BM‑MSC‑based therapy. Treatment of BM‑MSCs with IFN‑γ + IL‑1α or IFN‑γ + IL‑1β prior to transplantation may result in improved immunosuppressive effects.

Reduction of HIP2 expression causes motor function impairment and increased vulnerability to dopaminergic degeneration in Parkinson's disease models

Huntingtin interaction protein 2 (HIP2) is an E2 ubiquitin-conjugating enzyme associated with neurodegenerative diseases, and HIP2 mRNA has been implicated as a potential blood biomarker for Parkinson's disease (PD). However, it is unclear whether the alteration of HIP2 expression may contribute to the development of PD, and whether the change of HIP2 in blood could reflect its expression in the brain or motor functions in PD patients. In this study, we established a mouse line with HIP2 haploinsufficiency. The reduction of the HIP2 expression led to spontaneous motor function impairment and dopaminergic neuronal loss. Furthermore, HIP2 haploinsufficiency increased the susceptibility of mice to 6-hydroxydopamine (6-OHDA) and caused severe loss of dopaminergic neurons. Interestingly, in a 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) mouse model for PD, we observed concurrent, highly correlated decrease of HIP2 expression in the brain and in the blood. Using blood samples from more than 300 patients, we validated the decreased HIP2 mRNA in PD patients, including de novo patients. Finally, in a 1-year, 20-patient study, we observed reversed blood HIP2 mRNA levels accompanying improved motor and overall daily functions in 75% of the PD patients with instructed Tai Chi training. Therefore, our in vivo studies have indicated HIP2 insufficiency as a contributing factor for PD, and functionally validated blood HIP2 as a useful and reversible biomarker for PD.

A monocyte gene expression signature in the early clinical course of Parkinson's disease

Microglia are the main immune cells of the brain and express a large genetic pattern of genes linked to Parkinson's disease risk alleles. Monocytes like microglia are myeloid-lineage cells, raising the questions of the extent to which they share gene expression with microglia and whether they are already altered early in the clinical course of the disease. To decipher a monocytic gene expression signature in Parkinson's disease, we performed RNA-seq and applied the two-sample Kolmogorov-Smirnov test to identify differentially expressed genes between controls and patients with Parkinson's disease and changes in gene expression variability and dysregulation. The gene expression profiles of normal human monocytes and microglia showed a plethora of differentially expressed genes. Additionally, we identified a distinct gene expression pattern of monocytes isolated from Parkinson's disease patients at an early disease stage compared to controls using the Kolmogorov-Smirnov test. Differentially expressed genes included genes involved in immune activation such as HLA-DQB1, MYD88, REL, and TNF-α. Our data suggest that future studies of distinct leukocyte subsets are warranted to identify possible surrogate biomarkers and may lead to the identification of novel interventions early in the disease course.

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.

A meta-analysis of public microarray data identifies biological regulatory networks in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is a long-term degenerative disease that is caused by environmental and genetic factors. The networks of genes and their regulators that control the progression and development of PD require further elucidation.

METHODS

We examine common differentially expressed genes (DEGs) from several PD blood and substantia nigra (SN) microarray datasets by meta-analysis. Further we screen the PD-specific genes from common DEGs using GCBI. Next, we used a series of bioinformatics software to analyze the miRNAs, lncRNAs and SNPs associated with the common PD-specific genes, and then identify the mTF-miRNA-gene-gTF network.

RESULT

Our results identified 36 common DEGs in PD blood studies and 17 common DEGs in PD SN studies, and five of the genes were previously known to be associated with PD. Further study of the regulatory miRNAs associated with the common PD-specific genes revealed 14 PD-specific miRNAs in our study. Analysis of the mTF-miRNA-gene-gTF network about PD-specific genes revealed two feed-forward loops: one involving the SPRK2 gene, hsa-miR-19a-3p and SPI1, and the second involving the SPRK2 gene, hsa-miR-17-3p and SPI. The long non-coding RNA (lncRNA)-mediated regulatory network identified lncRNAs associated with PD-specific genes and PD-specific miRNAs. Moreover, single nucleotide polymorphism (SNP) analysis of the PD-specific genes identified two significant SNPs, and SNP analysis of the neurodegenerative disease-specific genes identified seven significant SNPs. Most of these SNPs are present in the 3'-untranslated region of genes and are controlled by several miRNAs.

CONCLUSION

Our study identified a total of 53 common DEGs in PD patients compared with healthy controls in blood and brain datasets and five of these genes were previously linked with PD. Regulatory network analysis identified PD-specific miRNAs, associated long non-coding RNA and feed-forward loops, which contribute to our understanding of the mechanisms underlying PD. The SNPs identified in our study can determine whether a genetic variant is associated with PD. Overall, these findings will help guide our study of the complex molecular mechanism of PD.

Microarray Analysis of the Molecular Mechanism Involved in Parkinson's Disease

Purpose

This study aimed to investigate the underlying molecular mechanisms of Parkinson's disease (PD) by bioinformatics.

Methods

Using the microarray dataset GSE72267 from the Gene Expression Omnibus database, which included 40 blood samples from PD patients and 19 matched controls, differentially expressed genes (DEGs) were identified after data preprocessing, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network, microRNA- (miRNA-) target regulatory network, and transcription factor- (TF-) target regulatory networks were constructed.

Results

Of 819 DEGs obtained, 359 were upregulated and 460 were downregulated. Two GO terms, “rRNA processing” and “cytoplasm,” and two KEGG pathways, “metabolic pathways” and “TNF signaling pathway,” played roles in PD development. Intercellular adhesion molecule 1 (ICAM1) was the hub node in the PPI network; hsa-miR-7-5p, hsa-miR-433-3p, and hsa-miR-133b participated in PD pathogenesis. Six TFs, including zinc finger and BTB domain-containing 7A, ovo-like transcriptional repressor 1, GATA-binding protein 3, transcription factor dp-1, SMAD family member 1, and quiescin sulfhydryl oxidase 1, were related to PD.

Conclusions

“rRNA processing,” “cytoplasm,” “metabolic pathways,” and “TNF signaling pathway” were key pathways involved in PD. ICAM1, hsa-miR-7-5p, hsa-miR-433-3p, hsa-miR-133b, and the abovementioned six TFs might play important roles in PD development.

Exploring autophagy with Gene Ontology

Autophagy is a fundamental cellular process that is well conserved among eukaryotes. It is one of the strategies that cells use to catabolize substances in a controlled way. Autophagy is used for recycling cellular components, responding to cellular stresses and ridding cells of foreign material. Perturbations in autophagy have been implicated in a number of pathological conditions such as neurodegeneration, cardiac disease and cancer. The growing knowledge about autophagic mechanisms needs to be collected in a computable and shareable format to allow its use in data representation and interpretation. The Gene Ontology (GO) is a freely available resource that describes how and where gene products function in biological systems. It consists of 3 interrelated structured vocabularies that outline what gene products do at the biochemical level, where they act in a cell and the overall biological objectives to which their actions contribute. It also consists of ‘annotations’ that associate gene products with the terms. Here we describe how we represent autophagy in GO, how we create and define terms relevant to autophagy researchers and how we interrelate those terms to generate a coherent view of the process, therefore allowing an interoperable description of its biological aspects. We also describe how annotation of gene products with GO terms improves data analysis and interpretation, hence bringing a significant benefit to this field of study.

From Genomics to Omics Landscapes of Parkinson's Disease: Revealing the Molecular Mechanisms

Molecular mechanisms of Parkinson's disease (PD) have already been investigated in various different omics landscapes. We reviewed the literature about different omics approaches between November 2005 and November 2017 to depict the main pathological pathways for PD development. In total, 107 articles exploring different layers of omics data associated with PD were retrieved. The studies were grouped into 13 omics layers: genomics-DNA level, transcriptomics, epigenomics, proteomics, ncRNomics, interactomics, metabolomics, glycomics, lipidomics, phenomics, environmental omics, pharmacogenomics, and integromics. We discussed characteristics of studies from different landscapes, such as main findings, number of participants, sample type, methodology, and outcome. We also performed curation and preliminary synthesis of multiple omics data, and identified overlapping results, which could lead toward selection of biomarkers for further validation of PD risk loci. Biomarkers could support the development of targeted prognostic/diagnostic panels as a tool for early diagnosis and prediction of progression rate and prognosis. This review presents an example of a comprehensive approach to revealing the underlying processes and risk factors of a complex disease. It urges scientists to structure the already known data and integrate it into a meaningful context.