Identification of potential diagnostic biomarkers for Parkinson's disease

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.

Insights into the Role of Glutathione Peroxidase 3 in Non-Neoplastic Diseases

Reactive oxygen species (ROSs) are byproducts of normal cellular metabolism and play pivotal roles in various physiological processes. Disruptions in the balance between ROS levels and the body's antioxidant defenses can lead to the development of numerous diseases. Glutathione peroxidase 3 (GPX3), a key component of the body's antioxidant system, is an oxidoreductase enzyme. GPX3 mitigates oxidative damage by catalyzing the conversion of hydrogen peroxide into water. Beyond its antioxidant function, GPX3 is vital in regulating metabolism, modulating cell growth, inducing apoptosis and facilitating signal transduction. It also serves as a significant tumor suppressor in various cancers. Recent studies have revealed aberrant expression of GPX3 in several non-neoplastic diseases, associating it with multiple pathological processes. This review synthesizes the current understanding of GPX3 expression and regulation, highlighting its extensive roles in noncancerous diseases. Additionally, this paper evaluates the potential of GPX3 as a diagnostic biomarker and explores emerging therapeutic strategies targeting this enzyme, offering potential avenues for future clinical treatment of non-neoplastic conditions.

Non-HLA angiotensin-type-1 receptor autoantibodies mediate the long-term loss of grafted neurons in Parkinson's disease models

BACKGROUND

Clinical trials have provided evidence that transplants of dopaminergic precursors, which may be replaced by new in vitro stem cell sources, can integrate into the host tissue, and alleviate motor symptoms in Parkinson´s disease (PD). In some patients, deterioration of graft function occurred several months after observing a graft-derived functional improvement. Rejection of peripheral organs was initially related to HLA-specific antibodies. However, the role of non-HLA antibodies is now considered also relevant for rejection. Angiotensin-II type-1 receptor autoantibodies (AT1-AA) act as agonists of the AT1 receptors. AT1-AA are the non-HLA antibodies most widely associated with graft dysfunction or rejection after transplantation of different solid organs and hematopoietic stem cells. However, it is not known about the presence and possible functional effects of AT1-AA in dopaminergic grafts, and the effects of treatment with AT1 receptor blockers (ARBs) such as candesartan on graft survival.

METHODS

In a 6-hydroxydopamine PD rat model, we studied the short-term (10 days)- and long-term (3 months) effects of chronic treatment with the ARB candesartan on survival of grafted dopaminergic neurons and microglial graft infiltration, as well as the effects of dopaminergic denervation and grafting on serum and CSF AT1-AA levels. The expression of AT1 receptors in grafted neurons was determined by laser capture microdissection.

RESULTS

At the early period post-grafting, the number of grafted dopaminergic neurons that survived was not significantly different between treated and untreated hosts (i.e., control rats and rats treated with candesartan), probably because, just after grafting, other deleterious factors are predominant for dopaminergic cell death, such as mechanical trauma, lack of growth factors/nutrients and ischemia. However, several months post-grafting, we observed a significantly higher number of surviving dopaminergic neurons and a higher density of striatal dopaminergic terminals in the candesartan-treated group. For several months, grafted rats showed blood and cerebrospinal fluid levels of AT1-AA higher than normal controls, and also higher AT1-AA levels than non-grafted parkinsonian rats.

CONCLUSIONS

The results suggest the use of ARBs such as candesartan in PD patients, particularly before and after dopaminergic grafts, and the need to monitor AT1-AA levels in PD patients, particularly in those candidates for dopaminergic grafting.

Early-stage idiopathic Parkinson's disease is associated with reduced circular RNA expression

Neurodegeneration in Parkinson's disease (PD) precedes diagnosis by years. Early neurodegeneration may be reflected in RNA levels and measurable as a biomarker. Here, we present the largest quantification of whole blood linear and circular RNAs (circRNA) in early-stage idiopathic PD, using RNA sequencing data from two cohorts (PPMI = 259 PD, 161 Controls; ICICLE-PD = 48 PD, 48 Controls). We identified a replicable increase in TMEM252 and LMNB1 gene expression in PD. We identified novel differences in the expression of circRNAs from ESYT2, BMS1P1 and CCDC9, and replicated trends of previously reported circRNAs. Overall, using circRNA as a diagnostic biomarker in PD did not show any clear improvement over linear RNA, minimising its potential clinical utility. More interestingly, we observed a general reduction in circRNA expression in both PD cohorts, accompanied by an increase in RNASEL expression. This imbalance implicates the activation of an innate antiviral immune response and suggests a previously unknown aspect of circRNA regulation in PD.

Blood biomarker-based classification study for neurodegenerative diseases

As the population ages, neurodegenerative diseases are becoming more prevalent, making it crucial to comprehend the underlying disease mechanisms and identify biomarkers to allow for early diagnosis and effective screening for clinical trials. Thanks to advancements in gene expression profiling, it is now possible to search for disease biomarkers on an unprecedented scale.Here we applied a selection of five machine learning (ML) approaches to identify blood-based biomarkers for Alzheimer's (AD) and Parkinson's disease (PD) with the application of multiple feature selection methods. Based on ROC AUC performance, one optimal random forest (RF) model was discovered for AD with 159 gene markers (ROC-AUC = 0.886), while one optimal RF model was discovered for PD (ROC-AUC = 0.743). Additionally, in comparison to traditional ML approaches, deep learning approaches were applied to evaluate their potential applications in future works. We demonstrated that convolutional neural networks perform consistently well across both the Alzheimer's (ROC AUC = 0.810) and Parkinson's (ROC AUC = 0.715) datasets, suggesting its potential in gene expression biomarker detection with increased tuning of their architecture.

Multidimensional biomarkers for multiple system atrophy: an update and future directions

Multiple system atrophy (MSA) is a fatal progressive neurodegenerative disease. Biomarkers are urgently required for MSA to improve the diagnostic and prognostic accuracy in clinic and facilitate the development and monitoring of disease-modifying therapies. In recent years, significant research efforts have been made in exploring multidimensional biomarkers for MSA. However, currently few biomarkers are available in clinic. In this review, we systematically summarize the latest advances in multidimensional biomarkers for MSA, including biomarkers in fluids, tissues and gut microbiota as well as imaging biomarkers. Future directions for exploration of novel biomarkers and promotion of implementation in clinic are also discussed.

Novel variants in genes related to vesicle-mediated-transport modify Parkinson's disease risk

OBJECTIVES

VPS35 and VPS13 have been associated with Parkinson's disease (PD), and their shared phenotype in yeast when reduced in function is abnormal vacuolar transport. We aim to test if additional potentially deleterious variants in other genes that share this phenotype can modify the risk for PD.

METHODS

77 VPS and VPS-related genes were analyzed using whole-genome-sequencing data from 202 PD patients of Ashkenazi Jewish (AJ) ancestry. Filtering was done based on quality and functionality scores. Ten variants in nine genes were further genotyped in 1200 consecutively recruited unrelated AJ-PD patients, and allele frequencies and odds ratio calculated compared to gnomAD-AJ-non-neuro database, in un-stratified (n = 1200) and stratified manner (LRRK2-G2019S-PD patients (n = 145), GBA-PD patients (n = 235), and non-carriers of these mutations (NC, n = 787)).

RESULTS

Five variants in PIK3C3, VPS11, AP1G2, HGS and VPS13D were significantly associated with PD-risk. PIK3C3-R768W showed a significant association in an un-stratified (all PDs) analysis, as well as in stratified (LRRK2, GBA, and NC) analyses (Odds ratios = 2.71, 5.32, 3.26. and 2.19 with p = 0.0015, 0.002, 0.0287, and 0.0447, respectively). AP1G2-R563W was significantly associated in LRRK2-carriers (OR = 3.69, p = 0.006) while VPS13D-D2932N was significantly associated in GBA-carriers (OR = 5.45, p = 0.0027). VPS11-C846G and HGS-S243Y were significantly associated in NC (OR = 2.48 and 2.06, with p = 0.022 and 0.0163, respectively).

CONCLUSIONS

Variants in genes involved in vesicle-mediated protein transport and recycling pathways, including autophagy and mitophagy, may differentially modify PD-risk in LRRK2-carriers, GBA carriers, or NC. Specifically, PIK3C3-R768W is a PD-risk allele, with the highest effect size in LRRK2-G2019S carriers. These results suggest oligogenic effect that may depends on the genetic background of the patient. An unbiased burden of mutations approach in these genes should be evaluated in additional PD and control groups. The mechanisms by which these novel variants interact and increase PD-risk should be researched in depth for better tailoring therapeutic intervention for PD prevention or slowing disease progression.

Identification of PLOD3 and LRRN3 as potential biomarkers for Parkinson's disease based on integrative analysis

Parkinson's disease (PD) is one of the most prevalent movement disorders and its diagnosis relies heavily on the typical clinical manifestations in the late stages. This study aims to screen and identify biomarkers of PD for earlier intervention. We performed a differential analysis of postmortem brain transcriptome studies. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify biomarkers related to Braak stage. We found 58 genes with significantly different expression in both PD brain tissue and blood samples. PD gene signature and risk score model consisting of nine genes were constructed using least absolute shrinkage and selection operator regression (LASSO) and logistic regression. PLOD3 and LRRN3 in gene signature were identified to serve as key genes as well as potential risk factors in PD. Gene function enrichment analysis and evaluation of immune cell infiltration revealed that PLOD3 was implicated in suppression of cellular metabolic function and inflammatory cell infiltration, whereas LRRN3 exhibited an inverse trend. The cellular subpopulation expression of the PLOD3 and LRRN3 has significant distributional variability. The expression of PLOD3 was more enriched in inflammatory cell subpopulations, such as microglia, whereas LRRN3 was more enriched in neurons and oligodendrocyte progenitor cells clusters (OPC). Additionally, the expression of PLOD3 and LRRN3 in Qilu cohort was verified to be consistent with previous results. Collectively, we screened and identified the functions of PLOD3 and LRRN3 based the integrated study. The combined detection of PLOD3 and LRRN3 expression in blood samples can improve the early detection of PD.

Exploring the Role of ACE2 as a Connecting Link between COVID-19 and Parkinson's Disease

Coronavirus disease 2019 (COVID-19) is frequently accompanied by neurological manifestations such as headache, delirium, and epileptic seizures, whereas ageusia and anosmia may appear before respiratory symptoms. Among the various neurological COVID-19-related comorbidities, Parkinson's disease (PD) has gained increasing attention. Some cases of PD disease have been linked to COVID-19, and both motor and non-motor symptoms in Parkinson's disease patients frequently worsen following SARS-CoV-2 infection. Although it is still unclear whether PD increases the susceptibility to SARS-CoV-2 infection or whether COVID-19 increases the risk of or unmasks future cases of PD, emerging evidence sheds more light on the molecular mechanisms underlying the relationship between these two diseases. Among them, angiotensin-converting enzyme 2 (ACE2), a significant component of the renin-angiotensin system (RAS), seems to play a pivotal role. ACE2 is required for the entry of SARS-CoV-2 to the human host cells, and ACE2 dysregulation is implicated in the severity of COVID-19-related acute respiratory distress syndrome (ARDS). ACE2 imbalance is implicated in core shared pathophysiological mechanisms between PD and COVID-19, including aberrant inflammatory responses, oxidative stress, mitochondrial dysfunction, and immune dysregulation. ACE2 may also be implicated in alpha-synuclein-induced dopaminergic degeneration, gut-brain axis dysregulation, blood-brain axis disruption, autonomic dysfunction, depression, anxiety, and hyposmia, which are key features of PD.

The Potential of ANK1 to Predict Parkinson's Disease

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

Role and Dysregulation of miRNA in Patients with Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative synucleinopathy that has a not yet fully understood molecular pathomechanism behind it. The role of risk genes regulated by small non-coding RNAs, or microRNAs (miRNAs), has also been highlighted in PD, where they may influence disease progression and comorbidities. In this case-control study, we analyzed miRNAs on peripheral blood mononuclear cells by means of RNA-seq in 30 participants, with the aim of identifying miRNAs differentially expressed in PD compared to age-matched healthy controls. Additionally, we investigated the pathways influenced by differentially expressed miRNAs and assessed whether a specific pathway could potentially be associated with PD susceptibility (enrichment analyses performed using the Ingenuity Pathway Analysis tools). Overall, considering that the upregulation of miRNAs might be related with the downregulation of their messenger RNA targets, and vice versa, we found several putative targets of dysregulated miRNAs (i.e., upregulated: hsa-miR-1275, hsa-miR-23a-5p, hsa-miR-432-5p, hsa-miR-4433b-3p, and hsa-miR-4443; downregulated: hsa-miR-142-5p, hsa-miR-143-3p, hsa-miR-374a-3p, hsa-miR-542-3p, and hsa-miR-99a-5p). An inverse connection between cancer and neurodegeneration, called "inverse comorbidity", has also been noted, showing that some genes or miRNAs may be expressed oppositely in neurodegenerative disorders and in some cancers. Therefore, it may be reasonable to consider these miRNAs as potential diagnostic markers and outcome measures.

Light-Seq: light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing

We present Light-Seq, an approach for multiplexed spatial indexing of intact biological samples using light-directed DNA barcoding in fixed cells and tissues followed by ex situ sequencing. Light-Seq combines spatially targeted, rapid photocrosslinking of DNA barcodes onto complementary DNAs in situ with a one-step DNA stitching reaction to create pooled, spatially indexed sequencing libraries. This light-directed barcoding enables in situ selection of multiple cell populations in intact fixed tissue samples for full-transcriptome sequencing based on location, morphology or protein stains, without cellular dissociation. Applying Light-Seq to mouse retinal sections, we recovered thousands of differentially enriched transcripts from three cellular layers and discovered biomarkers for a very rare neuronal subtype, dopaminergic amacrine cells, from only four to eight individual cells per section. Light-Seq provides an accessible workflow to combine in situ imaging and protein staining with next generation sequencing of the same cells, leaving the sample intact for further analysis post-sequencing.

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.

Angiotensin type-1 receptor and ACE2 autoantibodies in Parkinson´s disease

The role of autoimmunity in neurodegeneration has been increasingly suggested. The renin-angiotensin system (RAS) autoantibodies play a major role in several peripheral inflammatory processes. Dysregulation of brain RAS has been involved in neuroinflammation and neurodegeneration. We aimed to know whether angiotensin type-1 receptor (AT1) autoantibodies (AT1 agonists) and angiotensin-converting enzyme 2 (ACE2) autoantibodies (ACE2 antagonists) may be involved in Parkinson's disease (PD) progression and constitute a new therapeutical target. Both AT1 and ACE2 serum autoantibodies were higher in a group of 117 PD patients than in a group of 106 controls. Serum AT1 autoantibodies correlated with several cytokines, particularly Tumor Necrosis Factor Ligand Superfamily Member 14 (TNFSF14, LIGHT), and 27-hydroxycholesterol levels. Serum ACE2 autoantibodies correlated with AT1 autoantibodies. Both autoantibodies were found in cerebrospinal fluid (CSF) of four PD patients with CSF samples. Consistent with the observations in patients, experimental dopaminergic degeneration, induced by 6-hydroxydopamine, increased levels of autoantibodies in serum and CSF in rats, as well as LIGHT levels and transglutaminase activity in rat substantia nigra. In cultures, administration of AT1 autoantibodies enhanced dopaminergic neuron degeneration and increased levels of neuroinflammation markers, which was inhibited by the AT1 antagonist candesartan. The results suggest dysregulation of RAS autoantibodies as a new mechanism that can contribute to PD progression. Therapeutical strategies blocking the production, or the effects of these autoantibodies may be useful for PD treatment, and the results further support repurposing AT1 blockers (ARBs) as treatment against PD progression.

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.

Lipidomics in Understanding Pathophysiology and Pharmacologic Effects in Inflammatory Diseases: Considerations for Drug Development

The lipidome has a broad range of biological and signaling functions, including serving as a structural scaffold for membranes and initiating and resolving inflammation. To investigate the biological activity of phospholipids and their bioactive metabolites, precise analytical techniques are necessary to identify specific lipids and quantify their levels. Simultaneous quantification of a set of lipids can be achieved using high sensitivity mass spectrometry (MS) techniques, whose technological advancements have significantly improved over the last decade. This has unlocked the power of metabolomics/lipidomics allowing the dynamic characterization of metabolic systems. Lipidomics is a subset of metabolomics for multianalyte identification and quantification of endogenous lipids and their metabolites. Lipidomics-based technology has the potential to drive novel biomarker discovery and therapeutic development programs; however, appropriate standards have not been established for the field. Standardization would improve lipidomic analyses and accelerate the development of innovative therapies. This review aims to summarize considerations for lipidomic study designs including instrumentation, sample stabilization, data validation, and data analysis. In addition, this review highlights how lipidomics can be applied to biomarker discovery and drug mechanism dissection in various inflammatory diseases including cardiovascular disease, neurodegeneration, lung disease, and autoimmune disease.

Bioinformatics Approach to Identify Significant Biomarkers, Drug Targets Shared Between Parkinson's Disease and Bipolar Disorder: A Pilot Study

Parkinson’s disease (PD) is a neurodegenerative disorder responsible for shaking, rigidity, and trouble in walking and patients’ coordination ability and physical stability deteriorate day by day. Bipolar disorder (BD) is a psychiatric disorder which is the reason behind extreme shiftiness in mood, and frequent mood inversion may reach too high called mania. People with BD have a greater chance of developing PD during the follow-up period. A lot of work has been done to understand the key factors for developing these 2 diseases. But the molecular functionalities that trigger the development of PD in people with BD are not clear yet. In our study, we are intended to identify the molecular biomarkers and pathways shared between BD and PD. We have investigated the RNA-Seq gene expression data sets of PD and BD. A total of 45 common unique genes (32 up-regulated and 13 down-regulated) abnormally expressed in both PD and BD were identified by applying statistical methods on the GEO data sets. Gene ontology (GO) and BioCarta, KEGG, and Reactome pathways analysis of these 45 common dysregulated genes identified numerous altered molecular pathways such as mineral absorption, Epstein-Barr virus infection, HTLV-I infection, antigen processing, and presentation. Analysis of protein-protein interactions revealed 9 significant hub-proteins, namely RPL21, RPL34, CKS2, B2M, TNFRSF10A, DTX2, HLA-B, ATP2A3, and TAPBP. Significant transcription factors (IRF8, SPI1, RUNX1, and FOXA1) and posttranscriptional regulator microRNAs (hsa-miR-491-3p and hsa-miR-1246) are also found by analyzing gene-transcription factors and gene-miRNAs interactions, respectively. Protein-drug interaction analysis revealed hub-protein B2M’s interaction with molecular drug candidates like N-formylmethionine, 3-indolebutyric acid, and doxycycline. Finally, a link between pathological processes of PD and BD is identified at transcriptional level. This study may help us to predict the development of PD among the people suffering from BD and gives some clue to understand significant pathological mechanisms.

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

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

Associations of ARHGAP26 Polymorphisms with Alzheimer's Disease and Cardiovascular Disease

The Rho GTPase activating protein 26 (ARHGAP26) gene has been reported to be associated with neuropsychiatric diseases and neurodegenerative diseases including Parkinson's disease. We examined whether the ARHGAP26 gene is associated with Alzheimer's disease (AD) and/or cardiovascular disease (CVD). Multivariable logistic regression model was used to examine the associations of 154 single nucleotide polymorphisms (SNPs) within the ARHGAP26 gene with AD and CVD using the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) cohort. Fourteen SNPs were associated with AD (top SNP rs3776362 with p = 3.43 × 10^-3), while 37 SNPs revealed associations with CVD (top SNP rs415235 with p = 2.06 × 10^-4). Interestingly, 13 SNPs were associated with both AD and CVD. SNP rs3776362 was associated with CVD, Functional Activities Questionnaire (FAQ), and Clinical Dementia Rating Sum of Boxes (CDR-SB). A replication study using a Caribbean Hispanics sample showed that 17 SNPs revealed associations with AD, and 12 SNPs were associated with CVD. The third sample using a family-based study design showed that 9 SNPs were associated with AD, and 3 SNPs were associated with CVD. SNP rs6836509 within the ARHGAP10 gene (an important paralogon of ARHGAP26) was associated with AD and cerebrospinal fluid total tau (t-tau) level in the ADNI sample. Several SNPs were functionally important using the RegulomeDB, while a number of SNPs were associated with significant expression quantitative trait loci (eQTLs) using Genotype-Tissue Expression (GTEx) databases. In conclusion, genetic variants within ARHGAP26 were associated with AD and CVD. These findings add important new insights into the potentially shared pathogenesis of AD and CVD.

Lipids and brain inflammation in APOE4-associated dementia

PURPOSE OF REVIEW

To highlight recent developments in studying mechanisms by which the apolipoprotein E4 (APOE4) allele affects the metabolism of brain lipids and predisposes the brain to inflammation and Alzheimer's disease (AD) dementia.

RECENT FINDINGS

APOE4 activates Ca2+ dependent phospholipase A2 (cPLA2) leading to changes in arachidonic acid (AA), eicosapentaenoic acid and docosahexaenoic acid signaling cascades in the brain. Among these changes, the increased conversion of AA to eicosanoids associates with sustained and unresolved chronic brain inflammation. The effects of APOE4 on the brain differ by age, disease stage, nutritional status and can be uncovered by brain imaging studies of brain fatty acid uptake. Reducing cPLA2 expression in the dementia brain presents a viable strategy that awaits to be tested.

SUMMARY

Fatty acid brain imaging techniques can clarify how changes to brain polyunsaturated fatty acid metabolism during the various phases of AD and guide the development of small molecules to mitigate brain inflammation.

Are ion channels potential therapeutic targets for Parkinson's disease?

Parkinson's disease (PD) is primarily associated with the progressive neurodegeneration of the dopaminergic neurons in the substantia nigra region of the brain. The resulting motor symptoms are managed with the help of dopamine replacement therapies. However, these therapeutics do not prevent the neurodegeneration underlying the disease and therefore lose their effectiveness in managing disease symptoms over time. Thus, there is an urgent need to develop newer therapeutics for the benefit of patients. The release of dopamine and the firing activity of substantia nigra neurons is regulated by several ion channels that act in concert. Dysregulations of these channels cause the aberrant movement of various ions in the intracellular milieu. This eventually leads to disruption of intracellular signalling cascades, alterations in cellular homeostasis, and bioenergetic deficits. Therefore, ion channels play a central role in driving the high vulnerability of dopaminergic neurons to degenerate during PD. Targeting ion channels offers an attractive mechanistic strategy to combat the process of neurodegeneration. In this review, we highlight the evidence pointing to the role of various ion channels in driving the PD processes. In addition, we also discuss the various drugs or compounds that target the ion channels and have shown neuroprotective potential in the in-vitro and in-vivo models of PD. We also discuss the current clinical status of various drugs targeting the ion channels in the context of PD.

Spatial RNA Sequencing Identifies Robust Markers of Vulnerable and Resistant Human Midbrain Dopamine Neurons and Their Expression in Parkinson's Disease

Defining transcriptional profiles of substantia nigra pars compacta (SNc) and ventral tegmental area (VTA) dopamine neurons is critical to understanding their differential vulnerability in Parkinson’s Disease (PD). Here, we determine transcriptomes of human SNc and VTA dopamine neurons using LCM-seq on a large sample cohort. We apply a bootstrapping strategy as sample input to DESeq2 and identify 33 stably differentially expressed genes (DEGs) between these two subpopulations. We also compute a minimal sample size for identification of stable DEGs, which highlights why previous reported profiles from small sample sizes display extensive variability. Network analysis reveal gene interactions unique to each subpopulation and highlight differences in regulation of mitochondrial stability, apoptosis, neuronal survival, cytoskeleton regulation, extracellular matrix modulation as well as synapse integrity, which could explain the relative resilience of VTA dopamine neurons. Analysis of PD tissues showed that while identified stable DEGs can distinguish the subpopulations also in disease, the SNc markers SLIT1 and ATP2A3 were down-regulated and thus appears to be biomarkers of disease. In summary, our study identifies human SNc and VTA marker profiles, which will be instrumental for studies aiming to modulate dopamine neuron resilience and to validate cell identity of stem cell-derived dopamine neurons.

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.

Silencing of UCA1 Protects Against MPP+-Induced Cytotoxicity in SK-N-SH Cells via Modulating KCTD20 Expression by Sponging miR-423-5p

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder. Long noncoding RNA (lncRNA) urothelial carcinoma-associated 1 (UCA1) has been implicated in PD development. Nevertheless, little insight has been gained on the mechanisms of UCA1 in PD pathogenesis. The levels of UCA1, miR-423-5p and potassium channel tetramerization domain containing 20 (KCTD20) were assessed by qRT-PCR and western blot. Cell viability was gauged by the CCK-8 assay, and cell apoptosis was detected by flow cytometry. Targeted relationships among UCA1, miR-423-5p and KCTD20 were verified by dual-luciferase reporter and RNA immunoprecipitation assays. Our data showed that MPP⁺ induced UCA1 expression in SK-N-SH cells. UCA1 silencing protected against MPP⁺-evoked cytotoxicity in SK-N-SH cells. UCA1 functioned as a miR-423-5p sponge, and the protective impact of UCA1 silencing on MPP⁺-evoked cytotoxicity was mediated by miR-423-5p. KCTD20 was a direct target of miR-423-5p, and miR-423-5p overexpression mitigated MPP⁺-triggered cell injury by down-regulating KCTD20. Furthermore, UCA1 regulated KCTD20 expression by acting as a sponge of miR-423-5p in SK-N-SH cells. Our study first identified that the silencing of UCA1 protected SK-N-SH cells from MPP⁺-evoked cytotoxicity at least in part by targeting the miR-423-5p/KCTD20 axis.

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.

Deficiency of NEAT1 prevented MPP+-induced inflammatory response, oxidative stress and apoptosis in dopaminergic SK-N-SH neuroblastoma cells via miR-1277-5p/ARHGAP26 axis

Noncoding RNAs including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been documented to play prominent role in neurodegenerative diseases including Parkinson's disease (PD). This study intended to investigate the role of lncRNA nuclear enriched assembly transcript 1 (NEAT1) in MPP⁺-induced PD model in dopaminergic neuronblastoma SK-N-SH cells, as well as its mechanism through sponging miRNA (miR)-1277-5p. Real-time PCR and western blotting revealed that NEAT1 and ARHGAP26 were upregulated, and miR-1277-5p was downregulated in MPP⁺-treated SK-N-SH cells in a certain of concentration- and time- dependent manner. MPP⁺ induced apoptosis in SK-N-SH cells, as evidenced by decreased cell viability and Bcl-2 expression, and elevated apoptosis rate and levels of Bax and cleaved caspase-3, which were examined by MTT assay, flow cytometry and western blotting. Moreover, commercial assay kits indicated that inflammatory response and oxidative stress were provoked in response to MPP⁺, due to promoted contents of interleukin (IL)-6, IL-1β, tumor necrosis factor-α, malondialdehyde, and lactate dehydrogenase, accompanied with suppressed superoxide dismutase and glutathione peroxidase levels. Notably, MPP⁺-induced apoptosis, inflammatory response and oxidative stress in SK-N-SH cells were mitigated by NEAT1 knockdown and/or miR-1277-5p overexpression. Moreover, silencing of miR-1277-5p could abrogate the suppression of NEAT1 deficiency on MPP⁺-induced cell injury. Similarly, upregulating miR-1277-5p-elicited neuroprotection in MPP⁺-induced SK-N-SH cells was reversed by ARHGAP26 restoration. Dual-luciferase reporter assay demonstrated a direct interaction between miR-1277-5p and NEAT1 or ARHGAP26. Collectively, NEAT1 upregulation might contribute to MPP⁺-induced neuron injury via NEAT1-miR-1277-5p-ARHGAP26 competing endogenous RNAs (ceRNAs) pathway.

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.

A supervised machine learning-based methodology for analyzing dysregulation in splicing machinery: An application in cancer diagnosis

Deregulated splicing machinery components have shown to be associated with the development of several types of cancer and, therefore, the determination of such alterations can help the development of tumor-specific molecular targets for early prognosis and therapy. Determining such splicing components, however, is not a straightforward task mainly due to the heterogeneity of tumors, the variability across samples, and the fat-short characteristic of genomic datasets. In this work, a supervised machine learning-based methodology is proposed, allowing the determination of subsets of relevant splicing components that best discriminate samples. The methodology comprises three main phases: first, a ranking of features is determined by means of applying feature weighting algorithms that compute the importance of each splicing component; second, the best subset of features that allows the induction of an accurate classifier is determined by means of conducting an effective heuristic search; then the confidence over the induced classifier is assessed by means of explaining the individual predictions and its global behavior. At the end, an extensive experimental study was conducted on a large collection of transcript-based datasets, illustrating the utility and benefit of the proposed methodology for analyzing dysregulation in splicing machinery.

Clinical Features of Parkinson's Disease: The Evolution of Critical Symptoms

Parkinson’s disease (PD) is a multi-attribute neurodegenerative disorder combining motor and nonmotor symptoms without well-defined diagnostic clinical markers. The presence of primary motor features (bradykinesia, rest tremor, rigidity and loss of postural reflexes) are the most characteristic signs of PD that are also utilized to identify patients in current clinical practice. The successful implementation of levodopa treatment revealed that nonmotor features are the main contributors of patient disability in PD, and their occurrence might be earlier than motor symptoms during disease progression. Targeted detection of prodromal PD symptoms can open up new possibilities in the identification of PD patients and provide potential patient populations for developing novel neuroprotective therapies. In this review, the evolution of critical features in PD diagnosis is described with special attention to nonmotor symptoms and their possible detection.

Identification of biomarkers associated with Parkinson's disease by gene expression profiling studies and bioinformatics analysis

Parkinson's disease (PD) is associated with a selective loss of the neurons in the midbrain area called the substantia nigra pars compacta and the loss of projecting nerve fibers in the striatum. Predominant pathological hallmarks of PD are the degeneration of discrete neuronal populations and progressive accumulation of α-synuclein-containing intracytoplasmic inclusions called Lewy bodies and dystrophic Lewy neuritis. There is currently no therapy to terminate or delay the neurodegenerative process as the exact mechanisms underlying the pathogenesis of PD require further investigation. The identification and validation of novel biomarkers for the diagnosis of PD is a great challenge using contemporary approaches and optimizing sampling handling as well as interpretation using bioinformatics analysis. In this review, recent evidences associated with multi-omic data-sets and molecular mechanisms underlying PD are examined. A combined mapping of several transcriptional evidences could establish a patient-specific signature for early diagnose of PD though eligible systems biology tools, which can also help develop effective drug-based therapeutic approaches.

Dysregulated Long Non-coding RNAs in Parkinson's Disease Contribute to the Apoptosis of Human Neuroblastoma Cells

The molecular mechanism underlying Parkinson's disease (PD), an increasingly common neurodegenerative disease, remains unclear. Long non-coding RNA (lncRNA) plays essential roles in gene expression and human diseases. We hypothesize that lncRNAs are involved in neuronal degeneration of PD. Using microarray, we identified 122 differentially expressed (DE) lncRNAs and 48 DE mRNAs between the circulating leukocytes from PD patients and healthy controls. There were 714 significant correlations (r ≥ 0.8 or ≤-0.8, p < 0.05) among the DE lncRNAs and mRNAs. Gene function and pathway analysis of the 48 DE mRNAs revealed biological pathways related to PD pathogenesis, including immune response, inflammatory response, MAPK, and Jak-STAT pathway. In a cohort of 72 PD patients and 22 healthy controls, the upregulation of four lncRNAs (AC131056.3-001, HOTAIRM1, lnc-MOK-6:1, and RF01976.1-201) in circulating leukocytes of PD patients were further confirmed. These lncRNAs were also upregulated in THP-1 cells, a human monocytic cell line, after inflammatory stimulation. Interestingly, the conditioned culture medium of THP-1 cells or 6-OHDA significantly increased the expression of these lncRNAs in SH-SY5Y cells, a human neuroblastoma cell line expressing dopaminergic markers. Importantly, overexpression of AC131056.3-001 or HOTAIRM1 increased baseline and 6-OHDA-induced apoptosis of SH-SY5Y cells. Taken together, we identified distinct expression profiles of lncRNA and mRNA in circulating leukocytes between PD patients and healthy controls. Dysregulated lncRNAs such as HOTAIRM1 and AC131056.3-001 may contribute to PD pathogenesis by promoting the apoptosis of dopaminergic neuron.

A systems biology approach towards the identification of candidate therapeutic genes and potential biomarkers for Parkinson's disease

Parkinson's disease (PD) is an irreversible and incurable multigenic neurodegenerative disorder. It involves progressive loss of mid brain dopaminergic neurons in the substantia nigra pars compacta (SN). We compared brain gene expression profiles with those from the peripheral blood cells of a separate sample of PD patients to identify disease-associated genes. Here, we demonstrate the use of gene expression profiling of brain and blood for detecting valid targets and identifying early PD biomarkers. Implementing this systematic approach, we discovered putative PD risk genes in brain, delineated biological processes and molecular functions that may be particularly disrupted in PD and also identified several putative PD biomarkers in blood. 20 of the differentially expressed genes in SN were also found to be differentially expressed in the blood. Further application of this methodology to other brain regions and neurological disorders should facilitate the discovery of highly reliable and reproducible candidate risk genes and biomarkers for PD. The identification of valid peripheral biomarkers for PD may ultimately facilitate early identification, intervention, and prevention efforts as well.