Blood-based biomarkers for Parkinson's disease

The rise of Parkinson's disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and "eat, move, sleep" lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

Lipoprotein Particles in Cerebrospinal Fluid

The brain is the most lipid-rich organ in the body, and the intricate interplay between lipid metabolism and pathologies associated with neurodegenerative disorders is being increasingly recognized. The brain is bathed in cerebrospinal fluid (CSF), which, like plasma, contains lipid-protein complexes called lipoproteins that are responsible for extracellular lipid transport. Multiple CSF lipoprotein populations exist, some of which are produced de novo in the central nervous system and others that appear to be generated from protein constituents that are produced in the periphery. These CSF lipoproteins are thought to play key roles in maintaining lipid homeostasis in the central nervous system, while little else is known due to their limited accessibility and their low abundance in CSF. Recent work has provided new insights into the compositional complexity of CSF lipoprotein families and their metabolism in cerebral circulation. The purpose of this review is to summarize our current state of knowledge on the composition, origin, and metabolism of CSF lipoproteins.

Prodromal Parkinson disease signs are predicted by a whole-blood inflammatory transcriptional signature in young Pink1-/- rats

BACKGROUND

Parkinson disease (PD) is the fastest growing neurodegenerative disease. The molecular pathology of PD in the prodromal phase is poorly understood; as such, there are no specific prognostic or diagnostic tests. A validated Pink1 genetic knockout rat was used to model early-onset and progressive PD. Male Pink1-/- rats exhibit progressive declines in ultrasonic vocalizations as well as hindlimb and forelimb motor deficits by mid-to-late adulthood. Previous RNA-sequencing work identified upregulation of genes involved in disease pathways and inflammation within the brainstem and vocal fold muscle. The purpose of this study was to identify gene pathways within the whole blood of young Pink1-/- rats (3 months of age) and to link gene expression to early acoustical changes. To accomplish this, limb motor testing (open field and cylinder tests) and ultrasonic vocalization data were collected, immediately followed by the collection of whole blood and RNA extraction. Illumina® Total RNA-Seq TruSeq platform was used to profile differential expression of genes. Statistically significant genes were identified and Weighted Gene Co-expression Network Analysis was used to construct co-expression networks and modules from the whole blood gene expression dataset as well as the open field, cylinder, and USV acoustical dataset. ENRICHR was used to identify the top up-regulated biological pathways.

RESULTS

The data suggest that inflammation and interferon signaling upregulation in the whole blood is present during early PD. We also identified genes involved in the dysregulation of ribosomal protein and RNA processing gene expression as well as prion protein gene expression.

CONCLUSIONS

These data identified several potential blood biomarkers and pathways that may be linked to anxiety and vocalization acoustic parameters and are key candidates for future drug-repurposing work and comparison to human datasets.

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.

Transcriptomics and Proteomics Approach for the Identification of Altered Blood microRNAs and Plasma Proteins in Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disorder caused by the selective destruction of dopaminergic neurons (DA-nergic). Clinically, PD is diagnosed based on developing signs and symptoms. A neurological and physical examination and sometimes medical and family history also help in the diagnosis of PD. However, most of these features are visible when more than 80% of the dopaminergic neurons have degenerated. An understanding of the selective degeneration process at the cellular and molecular level and the development of new biomarkers are required for effective PD management. Several studies have been carried out using a selected set of miRNAs/ mRNAs and proteins to develop biomarkers of PD; however, an unbiased and combined miRNA-protein profiling study was required to identify the markers of progressive and selected degeneration of dopaminergic neurons in PD patients. In the present study, we have carried out global protein profiling through LC-MS/MS and miRNA profiling by using a "brain-specific" miRNA array panel of 112 miRNAs in PD patients and healthy controls to find the unprejudiced group of proteins and miRNAs that are deregulating in PD. In the whole blood samples of PD patients compared to healthy controls, the expression of 23 miRNAs and 289 proteins was significantly increased, whereas the expression of 4 miRNAs and 132 proteins was considerably downregulated. Network analysis, functional enrichment, annotation, and analysis of miRNA-protein interactions were also performed as part of the bioinformatics investigation of the discovered miRNAs and proteins revealing several pathways that lead to PD development and pathogenesis. Based on the analysis of miRNA and protein profiling, we have identified four miRNAs (hsa-miR-186-5p, miR-29b, miR-139 & has-miR-150-5p) and four proteins (YWHAZ, PSMA4, HYOU1, & SERPINA1), which can be targeted for the development of new biomarkers of PD. In vitro studies have identified the role of miR-186-5p in regulating the levels of the YWHAZ/YWHAB & CALM2 gene, which has shown maximum downregulation in PD patients and is known for its role in neuroprotection from apoptotic cell death & calcium regulation. In conclusion, our research has identified a group of miRNA-proteins that can be developed as PD biomarkers; however, future studies on the release of these miRNAs and proteins in extracellular vesicles circulating in the blood of PD patients can further validate these as specific biomarkers of PD.

Prodromal Parkinson disease signs are predicted by a whole-blood inflammatory transcriptional signature in young Pink1-/- rats

Background

Parkinson disease (PD) is the fastest growing neurodegenerative disease. The molecular pathology of PD in the prodromal phase is poorly understood; as such, there are no specific prognostic or diagnostic tests. A validated Pink1 genetic knockout rat was used to model early-onset and progressive PD. Male Pink1-/- rats exhibit progressive declines in ultrasonic vocalizations as well as hindlimb and forelimb motor deficits by mid-to-late adulthood. Previous RNA-sequencing work identified upregulation of genes involved in disease pathways and inflammation within the brainstem and vocal fold muscle. The purpose of this study was to identify gene pathways within the whole blood of young Pink1-/- rats (3 months of age) and to link gene expression to early acoustical changes. To accomplish this, limb motor testing (open field and cylinder tests) and ultrasonic vocalization data were collected, immediately followed by the collection of whole blood and RNA extraction. Illumina® Total RNA-Seq TruSeq platform was used to profile differential expression of genes. Statistically significant genes were identified and Weighted Gene Co-expression Network Analysis was used to construct co-expression networks and modules from the whole blood gene expression dataset as well as the open field, cylinder, and USV acoustical dataset. ENRICHR was used to identify the top up-regulated biological pathways.

Results

The data suggest that inflammation and interferon signaling upregulation in the whole blood is present during early PD. We also identified genes involved in the dysregulation of ribosomal protein and RNA processing gene expression as well as prion protein gene expression.

Conclusions

These data identified several potential blood biomarkers and pathways that may be linked to anxiety and vocalization acoustic parameters and are key candidates for future drug-repurposing work and comparison to human datasets.

Shared Molecular Signatures Across Zika Virus Infection and Multiple Sclerosis Highlight AP-1 Transcription Factor as a Potential Player in Post-ZIKV MS-Like Phenotypes

Zika virus (ZIKV) is an arbovirus of the Flaviviridae genus that has rapidly disseminated from across the Pacific to the Americas. Robust evidence has indicated a crucial role of ZIKV in congenital virus syndrome, including neonatal microcephaly. Moreover, emerging evidence suggests an association between ZIKV infection and the development of an extensive spectrum of central nervous system inflammatory demyelinating diseases (CNS IDD), such as multiple sclerosis-like clinical phenotypes. However, the underlying mechanisms of host-pathogen neuro-immune interactions remain to be elucidated. This study aimed to identify common transcriptional signatures between multiple sclerosis (MS) and ZIKV infection to generate molecular interaction networks, thereby leading to the identification of deregulated processes and pathways, which could give an insight of these underlying molecular mechanisms. Our investigation included publicly available transcriptomic data from MS patients in either relapse or remission (RR-MS) and datasets of subjects acutely infected by ZIKV for both immune peripheral cells and central nervous system cells. The protein-protein interaction (PPI) analysis showed upregulated AP-1 transcription factors (JUN and FOS) among the top hub and bottleneck genes in RR-MS and ZIKV data. Gene enrichment analysis retrieved a remarkable presence of ontologies and pathways linked to oxidative stress responses, immune cell function, inflammation, interleukin signaling, cell division, and transcriptional regulation commonly enriched in both scenarios. Considering the recent findings concerning AP-1 function in immunological tolerance breakdown, regulation of inflammation, and its function as an oxidative stress sensor, we postulate that the ZIKV trigger may contribute as a boost for the activation of such AP-1-regulated mechanisms that could favor the development of MS-like phenotypes following ZIKV infection in a genetically susceptible individual.

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

BACKGROUND

Diagnosis of Parkinson's disease (PD) is associated with a vast number of challenges. This study aimed to assess the overlap of PD patients' transcriptomes in the substantia nigra (SN) with peripheral blood mononuclear cells (PBMCs) to discover potential biomarkers for diagnosis.

METHODS

GEO data were used to select genes with significant changes in expression level in the SN region and eligible studies. Also, transcriptome data related to blood of PD patients with other neurodegenerative diseases (ND) was considered. Differential expression genes between PD and control were evaluated in the SN and blood, and RT-qPCR was applied to validate the findings.

RESULTS

At the expression level, no significant similarity in long non-coding RNA was found between the patients' SN and blood. While in silico results revealed 16 common mRNAs in SN and blood with significant expression levels. Among all overexpressed mRNAs, HSPA1A/B expression level had the highest expression difference between control and PD samples. Moreover, DGKH had the highest score of down-regulated genes in both blood and SN. The NOTCH pathway had the highest score pathway among up-regulated pathways, and the expression levels of NOTCH2, H4C8, and H2BC21 associated with this pathway had the most ability to separate the control and PD populations. Furthermore, RT-qPCR results revealed that HSPA1A/B, NOTCH2, and H4C8 were overexpressed in PD PBMCs, while DGKH expression levels were lower compared to controls.

CONCLUSION

Our findings indicate that expression levels of HSPA1A/B, DGKH, and NOTCH2 could be applied as candidate biomarkers to diagnose PD patients in the SN region and PBMCs.

The shared genetic landscape of blood cell traits and risk of neurological and psychiatric disorders

Phenotypic associations have been reported between blood cell traits (BCTs) and a range of neurological and psychiatric disorders (NPDs), but in most cases, it remains unclear whether these associations have a genetic basis and, if so, to what extent genetic correlations reflect causality. Here, we report genetic correlations and Mendelian randomization analyses between 11 NPDs and 29 BCTs, using genome-wide association study summary statistics. We found significant genetic correlations for four BCT-NPD pairs, all of which have prior evidence for a phenotypic correlation. We identified a previously unreported causal effect of increased platelet distribution width on susceptibility to Parkinson's disease. We identified multiple functional genes and regulatory elements for specific BCT-NPD pairs, some of which are targets of known drugs. These results enrich our understanding of the shared genetic landscape underlying BCTs and NPDs and provide a robust foundation for future work to improve prognosis and treatment of common NPDs.

Employing nanoparticle tracking analysis of salivary neuronal exosomes for early detection of neurodegenerative diseases

Neurodegenerative diseases are a set of progressive and currently incurable diseases that are primarily caused by neuron degeneration. Neurodegenerative diseases often lead to cognitive impairment and dyskinesias. It is now well recognized that molecular events precede the onset of clinical symptoms by years. Over the past decade, intensive research attempts have been aimed at the early diagnosis of these diseases. Recently, exosomes have been shown to play a pivotal role in the occurrence and progression of many diseases including cancer and neurodegenerative diseases. Additionally, because exosomes can cross the blood-brain barrier, they may serve as a diagnostic tool for neural dysfunction. In this review, we detail the mechanisms and current challenges of these diseases, briefly review the role of exosomes in the progression of neurodegenerative diseases, and propose a novel strategy based on salivary neuronal exosomes and nanoparticle tracking analysis that could be employed for screening the early onset of neurodegenerative diseases.

A computational approach based on weighted gene co-expression network analysis for biomarkers analysis of Parkinson's disease and construction of diagnostic model

Background

Parkinson's disease (PD) is a common age-related chronic neurodegenerative disease. There is currently no affordable, effective, and less invasive test for PD diagnosis. Metabolite profiling in blood and blood-based gene transcripts is thought to be an ideal method for diagnosing PD.

Aim

In this study, the objective is to identify the potential diagnostic biomarkers of PD by analyzing microarray gene expression data of samples from PD patients.

Methods

A computational approach, namely, Weighted Gene Co-expression Network Analysis (WGCNA) was used to construct co-expression gene networks and identify the key modules that were highly correlated with PD from the GSE99039 dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to identify the hub genes in the key modules with strong association with PD. The selected hub genes were then used to construct a diagnostic model based on logistic regression analysis, and the Receiver Operating Characteristic (ROC) curves were used to evaluate the efficacy of the model using the GSE99039 dataset. Finally, Reverse Transcription-Polymerase Chain Reaction (RT-PCR) was used to validate the hub genes.

Results

WGCNA identified two key modules associated with inflammation and immune response. Seven hub genes, LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3 were identified from the two modules and used to construct diagnostic models. ROC analysis showed that the diagnostic model had a good diagnostic performance for PD in the training and testing datasets. Results of the RT-PCR experiments showed that there were significant differences in the mRNA expression of LILRB1, LSP1, and MBOAT7 among the seven hub genes.

Conclusion

The 7-gene panel (LILRB1, LSP1, SIPA1, SLC15A3, MBOAT7, RNF24, and TLE3) will serve as a potential diagnostic signature for PD.

Integrative analysis of DNA methylation and gene expression data for the diagnosis and underlying mechanism of Parkinson’s disease

Background

Parkinson’s disease (PD) is the second most common progressive neurodegenerative disorder and the leading cause of disability in the daily activities. In the management of PD, accurate and specific biomarkers in blood for the early diagnosis of PD are urgently needed. DNA methylation is one of the main epigenetic mechanisms and associated with the gene expression and disease initiation of PD. We aimed to construct a methylation signature for the diagnosis of PD patients, and explore the potential value of DNA methylation in therapeutic options.

Materials and methods

Whole blood DNA methylation and gene expression data of PD patients as well as healthy controls were extracted from Gene Expression Omnibus database. Next, differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between PD patients and healthy controls were identified. Least absolute shrinkage and selection operator cox regression analysis was carried out to construct a diagnostic signature based on the overlapped genes. And, the receiver operating characteristic (ROC) curves were drawn and the area under the curve (AUC) was used to assess the diagnostic performance of the signature in both the training and testing datasets. Finally, gene ontology and gene set enrichment analysis were subsequently carried out to explore the underlying mechanisms.

Results

We obtained a total of 9,596 DMGs, 1,058 DEGs, and 237 overlapped genes in the whole blood between PD patients and healthy controls. Eight methylation-driven genes (HIST1H4L, CDC42EP3, KIT, GNLY, SLC22A1, GCM1, INO80B, and ARHGAP26) were identified to construct the gene expression signature. The AUCs in predicting PD patients were 0.84 and 0.76 in training dataset and testing dataset, respectively. Additionally, eight methylation-altered CpGs were also identified to construct the CpGs signature which showed a similarly robust diagnostic capability, with AUCs of 0.8 and 0.73 in training dataset and testing dataset, respectively.

Conclusion

We conducted an integrated analysis of the gene expression and DNA methylation data, and constructed a methylation-driven genes signature and a methylation-altered CpGs signature to distinguish the patients with PD from healthy controls. Both of them had a robust prediction power and provide a new insight into personalized diagnostic and therapeutic strategies for PD.

Quantitative susceptibility mapping and blood neurofilament light chain differentiate between parkinsonian disorders

Objectives

We employed quantitative susceptibility mapping (QSM) to assess iron deposition in parkinsonian disorders and explored whether combining QSM values and neurofilament light (NfL) chain levels can improve the accuracy of distinguishing Parkinson’s disease (PD) from multiple system atrophy (MSA) and progressive supranuclear palsy (PSP).

Materials and methods

Forty-seven patients with PD, 28 patients with MSA, 18 patients with PSP, and 28 healthy controls (HC) were enrolled, and QSM data were reconstructed. Susceptibility values in the bilateral globus pallidus (GP), putamen (PUT), caudate nucleus (CN), red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN) were obtained. Plasma NfL levels of 47 PD, 18 MSA, and 14 PSP patients and 22 HC were measured by ultrasensitive Simoa technology.

Results

The highest diagnostic accuracy distinguishing MSA from PD patients was observed with increased susceptibility values in CN (AUC: 0.740). The susceptibility values in RN yielded the highest diagnostic performance for distinguishing PSP from PD patients (AUC: 0.829). Plasma NfL levels were significantly higher in the MSA and PSP groups than in PD and HC groups. Combining the susceptibility values in the RN and plasma NfL levels improved the diagnostic performance for PSP vs. PD (AUC: 0.904), whereas plasma NfL levels had higher diagnostic accuracy for MSA vs. PD (AUC: 0.877).

Conclusion

The exploratory study indicates different patterns of iron accumulation in deep gray matter nuclei in Parkinsonian disorders. Combining QSM values with NfL levels may be a promising biomarker for distinguishing PSP from PD, whereas plasma NfL may be a reliable biomarker for differentiating MSA from PD. QSM and NfL measures appeared to have low accuracy for separating PD from controls.

Diagnosis and Drug Prediction of Parkinson's Disease Based on Immune-Related Genes

Parkinson's disease (PD) is the second most common neurodegenerative disorder. Immune mechanisms play an important role in the development of PD. The purpose of this study was to identify potential differentially expressed immune-related genes (IRGs), signaling pathways, and drugs in PD, which may provide new diagnostic markers and therapeutic targets for PD. Differentially expressed genes (DEGs) and IRGs were respectively obtained from the Gene Expression Omnibus (GEO) dataset and the ImmPort database. Weighted gene co-expression network analysis (WGCNA) was utilized to further identify hub IRGs. Core IRGs were obtained by intersection of DEGs and hub genes in the module of WGCNA, followed by construction of diagnostic models and regulation network establishment of long non-coding RNAs (lncRNAs)-miRNAs-diagnostic IRGs. Analysis of functional enrichment and protein-protein interaction (PPI) network and identification of related drugs of DEGs was performed. LILRB3 and CSF3R were identified as potential diagnostic markers for PD. Two regulatory pairs were identified based on LILRB3 and CSF3R, including XIST-hsa-miR-214-3p/hsa-miR-761-LILRB3 and XIST-hsa-miR-485-5p/hsa-miR-654-5p-CSF3R. LEP and IL1A were drug targets of Olanzapine. MMP9 and HSP90AB1 were drug targets of Bevacizumab. In addition, LEP and MMP9 were respectively drug targets of Lovastatin and Celecoxib. Herpes simplex infection (involved TNFRSF1A) and cytokine-cytokine receptor interaction (involved CSF3R, LEP, and IL1A) were the most remarkably enriched signaling pathways of DEGs. Identified IRGs and related signaling pathways may play critical roles in the development of PD. Additionally, LILRB3 and CSF3R can be considered as potential immune-related diagnostic markers for PD. LEP, IL1A, MMP9, and HSP90AB1 may be regarded as immune-related therapeutic targets for PD.

Potential Protein Blood-Based Biomarkers in Different Types of Dementia: A Therapeutic Overview

Abstract:

Biomarkers capable of identifying and distinguishing types of dementia such as Alzheimer's disease (AD), Parkinson's disease dementia (PDD), Lewy body dementia (LBD), and frontotemporal dementia (FTD) have been become increasingly relentless. Studies of possible biomarker proteins in the blood that can help formulate new diagnostic proposals and therapeutic visions of different types of dementia are needed. However, due to several limitations of these biomarkers, especially in discerning dementia, their clinical applications are still undetermined. Thus, the updating of biomarker blood proteins that can help in the diagnosis and discrimination of these main dementia conditions is essential to enable new pharmacological and clinical management strategies, with specificities for each type of dementia. To review the literature concerning protein blood-based AD and non-AD biomarkers as new pharmacological targets and/or therapeutic strategies. Recent findings for protein-based AD, PDD, LBD, and FTD biomarkers are focused on in this review. Protein biomarkers were classified according to the pathophysiology of the dementia types. The diagnosis and distinction of dementia through protein biomarkers is still a challenge. The lack of exclusive biomarkers for each type of dementia highlights the need for further studies in this field. Only after this, blood biomarkers may have a valid use in clinical practice as they are promising to help in diagnosis and in the differentiation of diseases.

Factors Contributing to Malnutrition in Parkinson's Disease Patients With Freezing of Gait

Background and Purpose

Little is known about the nutritional status and clinical characteristics of patients with Parkinson's disease with freezing of gait (PDFOG). The purpose of this study was to describe the relationship between nutritional status and characteristics of patients with PDFOG.

Methods

In this cross-sectional study, 178 PDFOG patients were recruited and classified as nutritionally normal or at risk of malnutrition/already malnourished based on their Mini Nutritional Assessment (MNA) scores. Each participant underwent a structured questionnaire, physical examination and routine serum biochemical tests.

Results

We found that 44.4 and 37.1% of PDFOG patients were malnourished [mini nutritional assessment (MNA) score <17] and at risk of malnutrition (17 ≤ MNA score ≤ 23.5), respectively. Compared to patients with normal nutrition, PDFOG patients with malnutrition and at risk of malnutrition had longer duration of Parkinson's disease (PD) and freezing of gait (FOG), more levodopa equivalent daily doses (LEDD), lower body mass index (BMI), more motor symptoms according to the Unified PD Rating Scale-III (UPDRS-III) and non-motor symptoms according to the PD Non-motor Symptoms Questionnaire (PD-NMS) (P < 0.05). Uric acid, albumin, prealbumin, and total cholesterol (TC) differed between the two groups (P < 0.05). High Hoehn and Yahr (H-Y) stage, high Freezing of Gait Questionnaire (FOGQ) scores, low TC and low uric acid were risk factors for malnutrition in patients with PDFOG.

Conclusion

Our results showed disease severity, motor symptoms, TC levels and uric acid levels were all associated with nutritional status in patients with PDFOG. This study suggest early discovery of the nutritional status of PDFOG patients is important.

HDL Accessory Proteins in Parkinson’s Disease—Focusing on Clusterin (Apolipoprotein J) in Regard to Its Involvement in Pathology and Diagnostics—A Review

Parkinson’s disease (PD)—a neurodegenerative disorder (NDD) characterized by progressive destruction of dopaminergic neurons within the substantia nigra of the brain—is associated with the formation of Lewy bodies containing mainly α-synuclein. HDL-related proteins such as paraoxonase 1 and apolipoproteins A1, E, D, and J are implicated in NDDs, including PD. Apolipoprotein J (ApoJ, clusterin) is a ubiquitous, multifunctional protein; besides its engagement in lipid transport, it modulates a variety of other processes such as immune system functionality and cellular death signaling. Furthermore, being an extracellular chaperone, ApoJ interacts with proteins associated with NDD pathogenesis (amyloid β, tau, and α-synuclein), thus modulating their properties. In this review, the association of clusterin with PD is delineated, with respect to its putative involvement in the pathological mechanism and its application in PD prognosis/diagnosis.

Blood SSR1: A Possible Biomarker for Early Prediction of Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease associated with age. Early diagnosis of PD is key to preventing the loss of dopamine neurons. Peripheral-blood biomarkers have shown their value in recent years because of their easy access and long-term monitoring advantages. However, few peripheral-blood biomarkers have proven useful. This study aims to explore potential peripheral-blood biomarkers for the early diagnosis of PD. Three substantia nigra (SN) transcriptome datasets from the Gene Expression Omnibus (GEO) database were divided into a training cohort and a test cohort. We constructed a protein–protein interaction (PPI) network and a weighted gene co-expression network analysis (WGCNA) network, found their overlapping differentially expressed genes and studied them as the key genes. Analysis of the peripheral-blood transcriptome datasets of PD patients from GEO showed that three key genes were upregulated in PD over healthy participants. Analysis of the relationship between their expression and survival and analysis of their brain expression suggested that these key genes could become biomarkers. Then, animal models were studied to validate the expression of the key genes, and only SSR1 (the signal sequence receptor subunit1) was significantly upregulated in both animal models in peripheral blood. Correlation analysis and logistic regression analysis were used to analyze the correlation between brain dopaminergic neurons and SSR1 expression, and it was found that SSR1 expression was negatively correlated with dopaminergic neuron survival. The upregulation of SSR1 expression in peripheral blood was also found to precede the abnormal behavior of animals. In addition, the application of artificial intelligence technology further showed the value of SSR1 in clinical PD prediction. The three classifiers all showed that SSR1 had high predictability for PD. The classifier with the best prediction accuracy was selected through AUC and MCC to construct a prediction model. In short, this research not only provides potential biomarkers for the early diagnosis of PD but also establishes a possible artificial intelligence model for predicting PD.

Cytokines, miRNAs, and Antioxidants as Combined Non-invasive Biomarkers for Parkinson's Disease

Parkinson's disease (PD) is one of the most common long-term degenerative disorders of the CNS that primarily affects the human locomotor system. Owing to the heterogeneity of PD etiology and the lack of appropriate diagnostic tests, blood-based biomarkers became the most promising method for diagnosing PD. Even though various biomarkers for PD have been found, their specificity and sensitivity are not optimum when used alone. Therefore, the aim of this study was directed to evaluate changes in a group of sensitive blood-based biomarkers in the same PD patients compared to healthy individuals. Serum samples were collected from 20 PD patients and 15 age-matched healthy controls. We analyzed serum levels of cytokines (IL10, IL12, and TNF-α), α-synuclein proteins, miRNAs (miR-214, miR-221, and miR-141), and antioxidants (UA, PON1, ARE). Our results showed an increase in sera levels of cytokines in PD patients as well as a positive correlation among them. Also, we found a significant increase in sera levels of α-synuclein protein associated with a decrease in miR-214 which regulates its gene expression. Lastly, we observed a decrease in sera levels of miR-221, miR-141, UA, PON1, and ARE, which have a prominent role against oxidative stress. Because of the many etiologies of PD, a single measure is unlikely to become a useful biomarker. Therefore, to correctly predict disease state and progression, a mix of noninvasive biomarkers is required. Although considerable work has to be done, this study sheds light on the role of certain biomarkers in the diagnosis of PD.

Biosensor approaches on the diagnosis of neurodegenerative diseases: Sensing the past to the future

Early diagnosis of neurodegeneration-oriented diseases that develop with the aging world is essential for improving the patient's living conditions as well as the treatment of the disease. Alzheimer's and Parkinson's diseases are prominent examples of neurodegeneration characterized by dementia leading to the death of nerve cells. The clinical diagnosis of these diseases only after the symptoms appear, delays the treatment process. Detection of biomarkers, which are distinctive molecules in biological fluids, involved in neurodegeneration processes, has the potential to allow early diagnosis of neurodegenerative diseases. Studies on biosensors, whose main responsibility is to detect the target analyte with high specificity, has gained momentum in recent years with the aim of high detection of potential biomarkers of neurodegeneration process. This study aims to provide an overview of neuro-biosensors developed on the basis of biomarkers identified in biological fluids for the diagnosis of neurodegenerative diseases such as Alzheimer's disease (AD), and Parkinson's disease (PD), and to provide an overview of the urgent needs in this field, emphasizing the importance of early diagnosis in the general lines of the neurodegeneration pathway. In this review, biosensor systems developed for the detection of biomarkers of neurodegenerative diseases, especially in the last 5 years, are discussed.

Identification of blood-based biomarkers for diagnosis and prognosis of Parkinson's disease: A systematic review of proteomics studies

Parkinson's Disease (PD), a neurodegenerative disorder, is characterised by the loss of motor function and dopamine neurons. Therapeutic avenues remain a challenge due to lack of accuracy in early diagnosis, monitoring of disease progression and limited therapeutic options. Proteomic platforms have been utilised to discover biomarkers for numerous diseases, a tool that may benefit the diagnosis and monitoring of disease progression in PD patients. Therefore, this systematic review focuses on analysing blood-based candidate biomarkers (CB) identified via proteomics platforms for PD. This study systematically reviewed articles across six databases (EMBASE, Cochrane, Ovid Medline, Scopus, Science Direct and PubMed) published between 2010 and 2020. Of the 504 articles identified, 12 controlled-PD studies were selected for further analysis. A total of 115 candidate biomarkers (CB) were identified across selected 12-controlled studies, of which 23 CB were found to be replicable in more than two cohorts. Using the PANTHER Go-Slim classification system and STRING network, the gene function and protein interactions between biomarkers were analysed. Our analysis highlights Apolipoprotein A-I (ApoA-I), which is essential in lipid metabolism, oxidative stress, and neuroprotection demonstrates high replicability across five cohorts with consistent downregulation across four cohorts. Since ApoA-I was highly replicable across blood fractions, proteomic platforms and continents, its relationship with cholesterol, statin and oxidative stress as PD biomarker, its role in the pathogenesis of PD is discussed in this paper. The present study identified ApoA-I as a potential biomarker via proteomics analysis of PD for the early diagnosis and prediction of disease progression.

Metabolomics in Parkinson's disease

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder in which environmental (lifestyle, dietary, infectious disease) factors as well as genetic make-up play a role. Metabolomics, an evolving research field combining biomarker discovery and pathogenetics, is particularly useful in studying complex pathophysiology in general and Parkinson's disease (PD) specifically. PD, the second most frequent neurodegenerative disorder, is characterized by the loss of dopaminergic neurons in the substantia nigra and the presence of intraneural inclusions of α-synuclein aggregates. Although considered a predominantly movement disorder, PD is also associated with number of non-motor features. Metabolomics has provided useful information regarding this neurodegenerative process with the aim of identifying a disease-specific fingerprint. Unfortunately, many disease variables such as clinical presentation, motor system involvement, disease stage and duration substantially affect biomarker relevance. As such, metabolomics provides a unique approach to studying this multifactorial neurodegenerative disorder.

Cell Death-Osis of Dopaminergic Neurons and the Role of Iron in Parkinson's Disease

Significance: There is still no cure for neurodegenerative diseases, such as Parkinson's disease (PD). Current treatments are based on the attempt to reduce dopaminergic neuronal loss, and multidisciplinary approaches have been used to provide only a temporary symptoms' relief. In addition to the difficulties of drugs developed against PD to access the brain, the specificity of those inhibitory compounds could be a concern. This because neurons might degenerate by activating distinct signaling pathways, which are often initiated by the same stimulus. Recent Advances: Apoptosis, necroptosis, and ferroptosis were shown to significantly contribute to PD progression and, so far, are the main death programs described as capable to alter brain homeostasis. Their activation is characterized by different biochemical and morphological features, some of which might even share the same molecular players. Critical Issues: If there is a pathological need to engage, in PD, multiple death programs, sequentially or simultaneously, is not clear yet. Possibly the activation of apoptosis, necroptosis, and/or ferroptosis correlates to different PD stages and symptom severities. This would imply that the efficacy of therapeutic approaches against neuronal death might depend on the death program they target and the relevance of this death pathway on a specific PD phase. Future Directions: In this review, we describe the molecular mechanisms underlying the activation of apoptosis, necroptosis, and ferroptosis in PD. Understanding the interrelationship between different death pathways' activation in PD is of utmost importance for the development of therapeutic approaches against disease progression. Antioxid. Redox Signal. 35, 453-473.

Pursuing Multiple Biomarkers for Early Idiopathic Parkinson's Disease Diagnosis

Parkinson's disease (PD) ranks first in the world as a neurodegenerative movement disorder and occurs most commonly in an idiopathic form. PD patients may have motor symptoms, non-motor symptoms, including cognitive and behavioral changes, and symptoms related to autonomic nervous system (ANS) failures, such as gastrointestinal, urinary, and cardiovascular symptoms. Unfortunately, the diagnostic accuracy of PD by general neurologists is relatively low. Currently, there is no objective molecular or biochemical test for PD; its diagnosis is based on clinical criteria, mainly by cardinal motor symptoms, which manifest when patients have lost about 60-80% of dopaminergic neurons. Therefore, it is urgent to establish a panel of biomarkers for the early and accurate diagnosis of PD. Once the disease is accurately diagnosed, it may be easier to unravel idiopathic PD's pathogenesis, and ultimately, finding a cure. This review discusses several biomarkers' potential to set a panel for early idiopathic PD diagnosis and future directions.

Validation of serum apolipoprotein A1 in rabies virus-infected mice as a biomarker for the preclinical diagnosis of rabies

Rabies is a type of acute fetal encephalitis caused by rabies virus (RABV). While it becomes incurable after symptom onset, it can be prevented by post‐exposure prophylaxis (PEP) during the long incubation period. While preclinical diagnosis aids the appropriate PEP administration, it is mostly nonfeasible owing to the absence of viremia or a specific antibody response during the incubation period. Here, an attempt was made to identify a serum biomarker for the preclinical diagnosis of rabies. Using the serum from a mouse inoculated intramuscularly (i.m.) with 5 × 10⁵ focus‐forming units (FFU) of recombinant RABV expressing red firefly luciferase (1088/RFLuc) immediately before symptom onset, two‐dimensional differential gel electrophoresis was conducted, followed by mass spectrometry, and it was confirmed that apolipoprotein A1 (ApoA1) was up‐regulated. ELISA showed that the serum ApoA1 and specific antibody levels increased during the incubation period and on the day of symptom onset. Since a lower infectious dose can be used to induce the unstable and long incubation period generally observed in natural infection, the ApoA1 level in mice inoculated i.m. with 10³ FFU of 1088/RFLuc was examined by monitoring viral dynamics using in vivo imaging. The serum ApoA1 and specific antibody levels were up‐regulated in 50% and 58.3% of mice exhibiting robust RABV replication, respectively, but not in mice exhibiting weak RABV replication. In addition, it was reported that ApoA1 was found to be a biomarker for neuronal damage. Additional biomarker candidates will be needed for the effective preclinical diagnosis of rabies.

The Footprint of Kynurenine Pathway in Neurodegeneration: Janus-Faced Role in Parkinson's Disorder and Therapeutic Implications

Progressive degeneration of neurons and aggravation of dopaminergic neurons in the substantia nigra pars compacta results in the loss of dopamine in the brain of Parkinson's disease (PD) patients. Numerous therapies, exhibiting transient efficacy have been developed; however, they are mostly accompanied by side effects and limited reliability, therefore instigating the need to develop novel optimistic treatment targets. Significant therapeutic targets have been identified, namely: chaperones, protein Abelson, glucocerebrosidase-1, calcium, neuromelanin, ubiquitin-proteasome system, neuroinflammation, mitochondrial dysfunction, and the kynurenine pathway (KP). The role of KP and its metabolites and enzymes in PD, namely quinolinic acid (QUIN), kynurenic acid (KYNA), 3-hydroxykynurenine (3-HK), 3-hydroxyanthranillic acid (3-HAA), kunurenine-3-monooxygenase (KMO), etc. has been reported. The neurotoxic QUIN, N-methyl-D-aspartate (NMDA) receptor agonist, and neuroprotective KYNA-which antagonizes QUIN actions-primarily justify the Janus-faced role of KP in PD. Moreover, KP has been reported to play a biomarker role in PD detection. Therefore, the authors detail the neurotoxic, neuroprotective, and immunomodulatory neuroactive components, alongside the upstream and downstream metabolic pathways of KP, forming a basis for a therapeutic paradigm of the disease while recognizing KP as a potential biomarker in PD, thus facilitating the development of a suitable target in PD management.

Frequency Shift Surface-Enhanced Raman Spectroscopy Sensing: An Ultrasensitive Multiplex Assay for Biomarkers in Human Health

The sensitive and selective detection of biomarkers for human health remains one of the grand challenges of the analytical sciences. Compared to established methods (colorimetric, (chemi) luminescent), surface-enhanced Raman spectroscopy (SERS) is an emerging alternative with enormous potential for ultrasensitive biological detection. Indeed even attomolar (10^-18 M) detection limits are possible for SERS due to an orders-of-magnitude boosting of Raman signals at the surface of metallic nanostructures by surface plasmons. However, challenges remain for SERS assays of large biomolecules, as the largest enhancements require the biomarker to enter a "hot spot" nanogap between metal nanostructures. The frequency-shift SERS method has gained popularity in recent years as an alternative assay that overcomes this drawback. It measures frequency shifts in intense SERS peaks of a Raman reporter during binding events on biomolecules (protein coupling, DNA hybridization, etc.) driven by mechanical transduction, charge transfer, or local electric field effects. As such, it retains the excellent multiplexing capability of SERS, with multiple analytes being identifiable by a spectral fingerprint in a single read-out. Meanwhile, like refractive index surface plasmon resonance methods, frequency-shift SERS measures the shift of an intense signal rather than resolving a peak above noise, easing spectroscopic resolution requirements. SERS frequency-shift assays have proved particularly suitable for sensing large, highly charged biomolecules that alter hydrogen-bonding networks upon specific binding. Herein we discuss the frequency-shift SERS method and promising applications in (multiplex) biomarker sensing as well as extensions to ion and gas sensing and much more.

A Pilot Study of Changes in the Level of Catecholamines and the Activity of α-2-Macroglobulin in the Tear Fluid of Patients with Parkinson's Disease and Parkinsonian Mice

Development of differential and early (preclinical) diagnostics of Parkinson’s disease (PD) is among the priorities in neuroscience. We searched for changes in the level of catecholamines and α-2-macroglobulin activity in the tear fluid (TF) in PD patients at an early clinical stage. It was shown that TF in patients is characterized by an increased level of noradrenaline mainly on the ipsilateral side of pronounced motor symptoms (72%, p = 0.049), a decreased level of adrenaline on both sides (ipsilateral—53%, p = 0.004; contralateral—42%, p = 0.02), and an increased α-2-macroglobulin activity on both sides (ipsilateral—53%, p = 0.03; contralateral—56%, p = 0.037) compared to controls. These changes are considered as potential biomarkers for differential diagnosis. Similar changes in the TF were found in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated mice when modeling clinical and preclinical stages of PD. These data show the adequacy of models to the pathogenesis of PD along the selected metabolic pathways, and also suggest that the found TF changes can be considered as potential biomarkers for preclinical diagnosis of PD. In Parkinsonian mice, the level of catecholamines also changes in the lacrimal glands, which makes it possible to consider them as one of the sources of catecholamines in the TF.

DailyCog: A Real-World Functional Cognitive Mobile Application for Evaluating Mild Cognitive Impairment (MCI) in Parkinson's Disease

Parkinson's disease (PD) is the second most common progressive neurodegenerative disorder affecting patient functioning and quality of life. Aside from the motor symptoms of PD, cognitive impairment may occur at early stages of PD and has a substantial impact on patient emotional and physical health. Detecting these early signs through actual daily functioning while the patient is still functionally independent is challenging. We developed DailyCog-a smartphone application for the detection of mild cognitive impairment. DailyCog includes an environment that simulates daily tasks, such as making a drink and shopping, as well as a self-report questionnaire related to daily events performed at home requiring executive functions and visual-spatial abilities, and psychomotor speed. We present the detailed design of DailyCog and discuss various considerations that influenced the design. We tested DailyCog on patients with mild cognitive impairment in PD. Our case study demonstrates how the markers we used coincide with the cognitive levels of the users. We present the outcome of our usability study that found that most users were able to use our app with ease, and provide details on how various features were used, along with some of the difficulties that were identified.

Simultaneously Detecting Monoamine Oxidase A and B in Disease Cell/Tissue Samples Using Paper-Based Devices

As enzymes in the outer membrane of the mitochondrion, monoamine oxidases (MAOs) can catalyze the oxidative deamination of monoamines in the human body. According to different substrates, MAOs can be divided into MAO-A and MAO-B. The imbalance of the MAO-A is associated with neurological degeneration, while excess MAO-B activity is closely connected with Parkinson's disease (PD) and Alzheimer's disease (AD); therefore, detection of MAOs is of great significance for the diagnosis and treatment of these diseases. This work reports the multiplexed detection of MAO-A and MAO-B using paper-based devices based on chemiluminescence (CL). The detection limits were 5.01 pg/mL for MAO-A and 8.50 pg/mL for MAO-B in human serum. In addition, we used paper-based devices to detect MAOs in human cells and tissue samples and found that the results of paper-based detection and Western blotting (WB) showed the same trend. While only one antibody can be incubated on the same membrane by WB, multiple antibodies incubated on the same paper enabled simultaneous detection of MAO-A and MAO-B by paper-based devices. The paper-based assay could be used for preliminary early screening of clinical samples for MAOs and can be extended as an alternative to WB for multiplexed detection of various proteins in disease cell or tissue samples.

Unraveling Molecular Pathways Altered in MeCP2-Related Syndromes, in the Search for New Potential Avenues for Therapy

Methyl-CpG-binding protein 2 (MeCP2) is an X-linked epigenetic modulator whose dosage is critical for neural development and function. Loss-of-function mutations in MECP2 cause Rett Syndrome (RTT, OMIM #312750) while duplications in the Xq28 locus containing MECP2 and Interleukin-1 receptor-associated kinase 1 (IRAK1) cause MECP2 duplication syndrome (MDS, OMIM #300260). Both are rare neurodevelopmental disorders that share clinical symptoms, including intellectual disability, loss of speech, hand stereotypies, vasomotor deficits and seizures. The main objective of this exploratory study is to identify novel signaling pathways and potential quantitative biomarkers that could aid early diagnosis and/or the monitoring of disease progression in clinical trials. We analyzed by RT-PCR gene expression in whole blood and microRNA (miRNA) expression in plasma, in a cohort of 20 females with Rett syndrome, 2 males with MECP2 duplication syndrome and 28 healthy controls, and correlated RNA expression with disease and clinical parameters. We have identified a set of potential biomarker panels for RTT diagnostic and disease stratification of patients with microcephaly and vasomotor deficits. Our study sets the basis for larger studies leading to the identification of specific miRNA signatures for early RTT detection, stratification, disease progression and segregation from other neurodevelopmental disorders. Nevertheless, these data will require verification and validation in further studies with larger sample size including a whole range of ages.

Kynurenine pathway in Parkinson's disease-An update

Parkinson's disease (PD) is a complex multi-factorial neurodegenerative disorder where various altered metabolic pathways contribute to the progression of the disease. Tryptophan (TRP) is a major precursor in kynurenine pathway (KP) and it has been discussed in various in vitro studies that the metabolites quinolinic acid (QUIN) causes neurotoxicity and kynurenic acid (KYNA) acts as neuroprotectant respectively. More studies are also focused on the effects of other KP metabolites and its enzymes as it has an association with ageing and PD pathogenesis. Until now, very few studies have targeted the role of genetic mutations in abnormal KP metabolism in adverse conditions of PD. Therefore, the present review gives an updated research studies on KP in connection with PD. Moreover, the review emphasizes on the urge for the development of biomarkers and also this would be an initiative in generating an alternative therapeutic approach for PD.

Identification of BAG2 and Cathepsin D as Plasma Biomarkers for Parkinson's Disease

The current diagnosis of Parkinson’s disease (PD) mostly relies on clinical rating scales related to motor dysfunction. Given that clinical symptoms of PD appear after significant neuronal cell death in the brain, it is required to identify accessible, objective, and quantifiable biomarkers for early diagnosis of PD. In this study, a total of 20 patients with idiopathic PD and 20 age‐matched patients with essential tremor according to the UK Brain Bank Criteria were consecutively enrolled to identify peripheral blood biomarkers for PD. Clinical data were obtained by clinical survey and assessment. Using albumin‐depleted and immunoglobulin G‐depleted plasma samples, we performed immunoblot analysis of seven autophagy‐related proteins and compared the levels of proteins to those of the control group. We also analyzed the correlation between the levels of candidate proteins and clinical characteristics. Finally, we validated our biomarker models using receiver operating characteristic curve analysis. We found that the levels of BCL2‐associated athanogene 2 (BAG2) and cathepsin D were significantly decreased in plasma of patients with PD (P = 0.009 and P = 0.0077, respectively). The level of BAG2 in patients with PD was significantly correlated with Cross‐Culture Smell Identification Test score, which indicates olfactory dysfunction. We found that our biomarker model distinguishes PD with 87.5% diagnostic accuracy (area under the curve (AUC) = 0.875, P < 0.0001). Our result suggests BAG2 and cathepsin D as candidates for early‐diagnosis plasma biomarkers for PD. We provide the possibility of plasma biomarkers related to the autophagy pathway, by which decreased levels of BAG2 and cathepsin D might lead to dysfunction of autophagy.

Facile Impedimetric Analysis of Neuronal Exosome Markers in Parkinson's Disease Diagnostics

The egress of α-synuclein in neuronally derived exosomes predates the clinical presentation of Parkinson’s disease (PD), offering a means of developing a predictive or prognostic test. Here, we report the reagentless impedimetric assay of two internal exosome markers (α-synuclein and syntenin-1) from neuronal exosomes. Exosomes were efficiently extracted from patient sera using anti-L1CAM conjugated zwitterionic polymer-modified magnetic beads prior to lysis and analyzed by electrochemical impedance spectroscopy. The quantification of α-synuclein level across 40 clinical samples resolved statistically significant differences between PD patients and healthy controls (HC).

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.

Small GTPases of the Ras and Rho Families Switch on/off Signaling Pathways in Neurodegenerative Diseases

Small guanosine triphosphatases (GTPases) of the Ras superfamily are key regulators of many key cellular events such as proliferation, differentiation, cell cycle regulation, migration, or apoptosis. To control these biological responses, GTPases activity is regulated by guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and in some small GTPases also guanine nucleotide dissociation inhibitors (GDIs). Moreover, small GTPases transduce signals by their downstream effector molecules. Many studies demonstrate that small GTPases of the Ras family are involved in neurodegeneration processes. Here, in this review, we focus on the signaling pathways controlled by these small protein superfamilies that culminate in neurodegenerative pathologies, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Specifically, we concentrate on the two most studied families of the Ras superfamily: the Ras and Rho families. We summarize the latest findings of small GTPases of the Ras and Rho families in neurodegeneration in order to highlight these small proteins as potential therapeutic targets capable of slowing down different neurodegenerative diseases.

Small GTPases of the Ras and Rho Families Switch on/off Signaling Pathways in Neurodegenerative Diseases

Small guanosine triphosphatases (GTPases) of the Ras superfamily are key regulators of many key cellular events such as proliferation, differentiation, cell cycle regulation, migration, or apoptosis. To control these biological responses, GTPases activity is regulated by guanine nucleotide exchange factors (GEFs), GTPase activating proteins (GAPs), and in some small GTPases also guanine nucleotide dissociation inhibitors (GDIs). Moreover, small GTPases transduce signals by their downstream effector molecules. Many studies demonstrate that small GTPases of the Ras family are involved in neurodegeneration processes. Here, in this review, we focus on the signaling pathways controlled by these small protein superfamilies that culminate in neurodegenerative pathologies, such as Alzheimer's disease (AD) and Parkinson's disease (PD). Specifically, we concentrate on the two most studied families of the Ras superfamily: the Ras and Rho families. We summarize the latest findings of small GTPases of the Ras and Rho families in neurodegeneration in order to highlight these small proteins as potential therapeutic targets capable of slowing down different neurodegenerative diseases.

Early Biomarkers of Neurodegenerative and Neurovascular Disorders in Diabetes

Diabetes mellitus (DM) is a common disease worldwide. There is a strong association between DM and neurovascular and neurodegenerative disorders. The first group mainly consists of diabetic retinopathy, diabetic neuropathy and stroke, whereas, the second group includes Alzheimer's disease, Parkinson's disease, mild cognitive impairment and dementia. The aforementioned diseases have a common pathophysiological background including insulin resistance, oxidative stress, atherosclerosis and vascular injury. The increasing prevalence of neurovascular and neurodegenerative disorders among diabetic patients has resulted in an urgent need to develop biomarkers for their prediction and/or early detection. The aim of this review is to present the potential application of the most promising biomarkers of diabetes-related neurodegenerative and neurovascular disorders, including amylin, β-amyloid, C-reactive protein (CRP), dopamine, gamma-glutamyl transferase (GGT), glycogen synthase kinase 3β, homocysteine, microRNAs (mi-RNAs), paraoxonase 1, phosphoinositide 3-kinases, tau protein and various growth factors. The most clinically promising biomarkers of neurovascular and neurodegenerative complications in DM are hsCRP, GGT, homocysteine and miRNAs. However, all biomarkers discussed in this review could become a part of the potential multi-biomarker screening panel for diabetic patients at risk of neurovascular and neurodegenerative complications.

Robust Dopaminergic Differentiation and Enhanced LPS-Induced Neuroinflammatory Response in Serum-Deprived Human SH-SY5Y Cells: Implication for Parkinson's Disease

Parkinson's disease (PD) is a chronic neurodegenerative condition characterized by motor symptoms such as bradykinesia, resting tremor, and rigidity. PD diagnosis is based on medical history, review of signs, symptoms, neurological and physical examinations. Unfortunately, by the time the disease is diagnosed, dopamine (DA) neuronal loss is often extended, thereby resulting in ineffective therapies. Recent evidence suggests that neuroinflammation may be pivotal during PD onset and progression. However, suitable cellular models and biomarkers to detect early signs of neuroinflammation are still missing. In this study, we developed a well-differentiated DAergic neuronal cell line where we triggered a neuroinflammatory response to assess the temporal expression of the tissue- and urokinase plasminogen activators (tPA and uPA) and their endogenous inhibitor (PAI-1) along with that of pro-inflammatory mediators and the neuronal marker nNOS. Human neuroblastoma cells SH-SY5Y were differentiated into DAergic neuronal-like cells using a combination of 12-O-tetradecanoylphorbol-13-acetate (TPA) and serum depletion. Terminally-differentiated neurons were then exposed to lipopolysaccharide (LPS) for short (up to 24 h) or long term (up to 10 days) to mimic acute or chronic inflammation. Results demonstrated that uPA protein expression was stably upregulated during chronic inflammation, whereas the expression of nNOS protein better reflected the cellular response to acute inflammation. Additional studies revealed that the temporal induction of uPA was associated with increased AKT phosphorylation, but did not seem to involve cAMP-responsive element-binding protein (CREB) activation, nor the mitogen-activated protein kinase (MAPK) pathway. In conclusion, our in vitro data suggests that nNOS and uPA may serve as viable candidate biomarkers of acute and chronic neuroinflammation.

Systematic metabolic analysis of potential target, therapeutic drug, diagnostic method and animal model applicability in three neurodegenerative diseases

Considerable evidence suggests that metabolic abnormalities are associated with neurodegenerative diseases. This study aimed to conduct a systematic metabolic analysis of Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD). Human and mouse model microarray datasets were downloaded from the Gene Expression Omnibus database. The metabolic genes and pathways were collected from the Recon 3D human metabolic model. Drug and target information was obtained from the DrugBank database. This study identified ATP1A1, ATP6V1G2, GOT1, HPRT1, MAP2K1, PCMT1 and PLK2 as key metabolic genes that were downregulated in AD, PD and HD. We screened 57 drugs that target these genes, such as digoxin, ouabain and diazoxide. This study constructed multigene diagnostic models for AD, PD and HD by using metabolic gene expression profiles in blood, all models showed high accuracy (AUC > 0.8) both in the experimental and validation sets. Furthermore, analysis of animal models showed that there was almost no consistency among the metabolic changes between mouse models and human diseases. This study systematically revealed the metabolic damage among AD, PD, and HD and uncovered the differences between animal models and human diseases. This information may be helpful for understanding the metabolic mechanisms and drug development for neurodegenerative diseases.

Altered Plasma Acylcarnitines and Amino Acids Profile in Spinocerebellar Ataxia Type 7

Spinocerebellar ataxia type 7 (SCA7), a neurodegenerative disease characterized by cerebellar ataxia and retinal degeneration, is caused by an abnormal CAG repeat expansion in the ATXN7 gene coding region. The onset and severity of SCA7 are highly variable between patients, thus identification of sensitive biomarkers that accurately diagnose the disease and monitoring its progression are needed. With the aim of identified SCA7-specific metabolites with clinical relevance, we report for the first time, to the best of our knowledge, a metabolomics profiling of circulating acylcarnitines and amino acids in SCA7 patients. We identified 21 metabolites with altered levels in SCA7 patients and determined two different sets of metabolites with diagnostic power. The first signature of metabolites (Valine, Leucine, and Tyrosine) has the ability to discriminate between SCA7 patients and healthy controls, while the second one (Methionine, 3-hydroxytetradecanoyl-carnitine, and 3-hydroxyoctadecanoyl-carnitine) possess the capability to differentiate between early-onset and adult-onset patients, as shown by the multivariate model and ROC analyses. Furthermore, enrichment analyses of metabolic pathways suggest alterations in mitochondrial function, energy metabolism, and fatty acid beta-oxidation in SCA7 patients. In summary, circulating SCA7-specific metabolites identified in this study could serve as effective predictors of SCA7 progression in the clinics, as they are sampled in accessible biofluid and assessed by a relatively simple biochemical assay.

A Topological Data Analysis Approach on Predicting Phenotypes from Gene Expression Data

The goal of this study was to investigate if gene expression measured from RNA sequencing contains enough signal to separate healthy and afflicted individuals in the context of phenotype prediction. We observed that standard machine learning methods alone performed somewhat poorly on the disease phenotype prediction task; therefore we devised an approach augmenting machine learning with topological data analysis.

We describe a framework for predicting phenotype values by utilizing gene expression data transformed into sample-specific topological signatures by employing feature subsampling and persistent homology. The topological data analysis approach developed in this work yielded improved results on Parkinson’s disease phenotype prediction when measured against standard machine learning methods.

This study confirms that gene expression can be a useful indicator of the presence or absence of a condition, and the subtle signal contained in this high dimensional data reveals itself when considering the intricate topological connections between expressed genes.

Blood biomarkers with Parkinson's disease clusters and prognosis: The oxford discovery cohort

Background

Predicting prognosis in Parkinson's disease (PD) has important implications for individual prognostication and clinical trials design and targeting novel treatments. Blood biomarkers could help in this endeavor.

Methods

We identified 4 blood biomarkers that might predict prognosis: apolipoprotein A1, C‐reactive protein, uric acid and vitamin D. These biomarkers were measured in baseline serum from 624 Parkinson's disease subjects (median disease duration, 1.0 years; interquartile range, 0.5–2.0) from the Oxford Discovery prospective cohort. We compared these biomarkers against PD subtypes derived from clinical features in the baseline cohort using data‐driven approaches. We used multilevel models with MDS‐UPDRS parts I, II, and III and Montreal Cognitive Assessment as outcomes to test whether the biomarkers predicted subsequent progression in motor and nonmotor domains. We compared the biomarkers against age of PD onset and age at diagnosis. The q value, a false‐discovery rate alternative to P values, was calculated as an adjustment for multiple comparisons.

Results

Apolipoprotein A1 and C‐reactive protein levels differed across our PD subtypes, with severe motor disease phenotype, poor psychological well‐being, and poor sleep subtype having reduced apolipoprotein A1 and higher C‐reactive protein levels. Reduced apolipoprotein A1, higher C‐reactive protein, and reduced vitamin D were associated with worse baseline activities of daily living (MDS‐UPDRS II).

Conclusion

Baseline clinical subtyping identified a pro‐inflammatory biomarker profile significantly associated with a severe motor/nonmotor disease phenotype, lending biological validity to subtyping approaches. No blood biomarker predicted motor or nonmotor prognosis. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Serum Bile Acids in Patients With Primary Open-angle Glaucoma

PURPOSE

To measure the serum bile acids (SBA) in patients with primary open-angle glaucoma (POAG) and to compare with nonglaucomatous controls.

PATIENTS AND METHODS

A hospital-based case control study of 90 patients above the age of 40 years was conducted. Patients with POAG formed the study group and individuals without glaucoma in the same age group formed the control group. SBA estimation was done using ELISA kit enzymatic assay technique and the median value was compared between the 2 groups.

RESULTS

The median of SBA level was found to be more in POAG patients when compared with the control group. The difference was statistically significant (P<0.001). Median SBA level was not affected by the age or sex of the participants in the study.

CONCLUSIONS

"Bile acids" may have a role in the oxidative stress and apoptosis involved in the pathophysiology of POAG.

Shared Molecular Signatures Across Neurodegenerative Diseases and Herpes Virus Infections Highlights Potential Mechanisms for Maladaptive Innate Immune Responses

Growing evidence suggests that peripheral factors to the brain driving neuro-inflammation could affect Alzheimer’s Disease (AD) and Parkinson’s Disease (PD) severity. Herpes simplex virus type 1 (HSV1) infection has been associated with AD while other related viruses, including cytomegalovirus (CMV), Epstein-Bar virus and human herpesvirus 6 (HHV6), are known to infect neurons. Here we compare gene expression profiles between AD or PD patients to those afflicted with herpes viral infections as to discover novel potential neuro-inflammation pathways. We found multiple significant differentially expressed genes (DEGs) shared between AD/PD and viral infections including SESN3 which has a genetic association for increased AD risk. Pathway enrichment analysis revealed viruses shared Oxidative Stress Defense System and LRRK2 pathways with AD and PD, respectively. We further processed our data to identify novel target and drug-repurposing opportunities including anti-inflammatory therapy, immune-modulators and cholinesterase inhibitors which could lead to new therapeutics paradigms for these neurodegenerative diseases.

Elevated Serum SIRT 2 May Differentiate Parkinson's Disease From Atypical Parkinsonian Syndromes

Atypical Parkinson syndromes (APSs) often have symptoms that overlap with those of Parkinson's disease (PD), especially early in the disease, making these disorders difficult to diagnose. Previous studies have demonstrated an association of oligomeric α-synuclein (α-Syn), a key element in the pathogenesis of PD, with Sirtuin (SIRT)2 proteins for modulating PD. We aimed to evaluate SIRT protein expression in serum of PD patients and compare it with APSs and normal elderly control (GC) and to correlate this with α-Syn. SIRT protein expression was evaluated in sera of 68 PD; 34 APS and 68 GC without any neuro-psychiatric illness as controls by surface plasmon resonance (SPR). SIRT2 expression was correlated with α-Syn in PD and GC. Significant (p < 0.0001) differences were observed between serum SIRT2 concentration in PD and APS and GC as well as between APS and GC. Receiver operating characteristic (ROC) analysis revealed the strong cut-off value to differentiate PD from APS and GC and also APS from GC. Significant correlation was observed among SIRT2 levels in early PD patients with Unified Parkinson's Disease Rating Scale (UPDRS), Hoehn & Yahr (H & Y) and increased duration of disease. In addition, a strong positive correlation of SIRT2 with α-Syn (p < 0.0001) was observed. However, no such difference was detected for serum SIRT1 in cases of PD and APS or for GC. The present study is the first to report elevated serum SIRT2 in PD. The study also provided a simple test to distinguish PD from APS and may have translational utility for diagnosis.