Potential Biomarker For Parkinson's Disease Research Articles

Evaluation of a Liquid Chromatography-Tandem Mass Spectrometry Method for the Analysis of Glucosylceramide and Galactosylceramide Isoforms in Cerebrospinal Fluid of Parkinson's Disease Patients.

Mutations in GBA1, encoding glucocerebrosidase beta 1 (GCase), are the most common genetic risk factor for Parkinson's disease (PD). GCase dysfunction leads to an accumulation of glucosylceramide (GluCer) substrates in different organs and fluids. Despite the challenges in quantifying GluCer isoforms in biological samples, their potential clinical interest as PD biomarkers justifies the development of robust assays. An extensively evaluated high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method for quantifying 14 GluCer and galactosylceramide (GalCer) isoforms in human cerebrospinal fluid (CSF) samples is presented. Sample pretreatment, HPLC, and MS/MS parameters were optimized. Evaluation was performed according to the recommendations of the Clinical and Laboratory Standards Institute and European Medicines Agency guidelines. Four 7-point calibration curves were generated, with a linearity interval from 2.5 to 200 nM (R2 ≥ 0.995). The limit of quantification was set at 5 nM. Between-run precision and accuracy were up to 12.5 and 9%, respectively. After method validation, we measured the levels of GluCer and GalCer isoforms in CSF human samples, including 6 healthy controls (HC), 22 idiopathic GBA1 wild-type PD (iPD) patients, and 5 GBA1-associated PD (PD-GBA) patients. GluCer/GalCer median ratios were found to be higher in the CSF of PD-GBA patients, particularly in severe GBA1 mutations, than those in iPD and HC. The observed trends in GluCer/GalCer ratios among groups provide novel information for the comprehensive analysis of sphingolipids as potential biomarkers of PD.

Diagnosing Parkinson’s disease and monitoring its progression: Biomarkers from combined GC-TOF MS and LC-MS/MS untargeted metabolomics

Parkinson's disease (PD) is a prevalent neurodegenerative disorder with a poorly understood etiology. An accurate diagnosis of idiopathic PD remains challenging as misdiagnosis is common in routine clinical practice. Moreover, current therapeutics focus on symptomatic management rather than curing or slowing down disease progression. Therefore, identification of potential PD biomarkers and providing a better understanding of the underlying disease pathophysiology are urgent. Herein, hydrophilic interaction liquid chromatography–mass spectrometry (LC-MS/MS) and gas chromatography-mass spectrometry (GC-TOF MS) based metabolomics approaches were used to profile the serum metabolome of 50 patients with different stages of idiopathic PD (early, mid and advanced) and 45 age-matched controls. Levels of 57 metabolites including cysteine-S-sulfate and N-acetyl tryptophan were significantly higher in patients with PD compared to controls, with lower amounts of additional 51 metabolites including vanillic acid, and N-acetylaspartic acid. Xanthines, including caffeine and its downstream metabolites, were lowered in patients with PD relative to controls indicating a potential role caffeine and its metabolites against neuronal damage. Seven metabolites, namely cysteine-S-sulfate, 1-methylxanthine, vanillic acid, N-acetylaspartic acid, 3-N-acetyl tryptophan, 5-methoxytryptophol, and 13-HODE yielded a ROC curve with a high classification accuracy (AUC 0.977). Comparison between different PD stages showed that cysteine-S-sulfate levels were significantly increasing with the advancement of PD stages while LPI 20:4 was significantly decreasing with disease progression. Our findings provide new biomarker candidates to assist in the diagnosis of PD and monitor its progression. Unusual metabolites like cysteine-S-sulfate might point to therapeutic targets that could enhance the development of novel PD treatments, such as NMDA antagonists.

Lymphocyte antigen 96: A new potential biomarker and immune target in Parkinson's disease

BackgroundLymphocyte antigen 96 (LY96) plays an important role in innate immunity and has been reported to be associated with various neurological diseases. However, its role in Parkinson's disease (PD) remains unclear. MethodsTranscriptome data from a total of 49 patients with PD and 34 healthy controls were downloaded from the Gene Expression Omnibus (GEO) database to analyse the expression pattern of LY96 and its relationship with gene function and immune-related markers. In addition, peripheral blood samples were collected from clinical patients to validate LY96 mRNA expression levels. Finally, an in vitro cell model of PD based on highly differentiated SH-SY5Y cells was constructed, with small interfering RNA-silenced LY96 expression, and LY96 mRNA level, cell viability, flow cytometry, and mitochondrial membrane potential assays were performed. ResultsThe results of the analyses of the GEO database and clinical samples revealed significantly abnormally high LY96 expression in patients with PD compared with healthy controls. The results of cell experiments showed that inhibiting LY96 expression alleviated adverse cellular effects by increasing cell viability, reducing apoptosis, and reducing oxidative stress. Gene set enrichment analysis showed that LY96 was positively correlated with T1 helper cells, T2 helper cells, neutrophils, natural killer T cells, myeloid-derived suppressor cells, macrophages, and activated CD4 cells, and may participate in PD through natural killer cell-mediated cytotoxicity pathways and extracellular matrix receptor interaction pathways. ConclusionThese findings suggested that LY96 might be a novel potential biomarker for PD, and offer insights into its immunoregulatory role.

Integrative analysis of gene expression profiles of substantia nigra identifies potential diagnosis biomarkers in Parkinson's disease

Parkinson's disease (PD) is a progressive neurodegenerative disease whose etiology is attributed to development of Lewy bodies and degeneration of dopaminergic neurons in the substantia nigra (SN). Currently, there are no definitive diagnostic indicators for PD. In this study, we aimed to identify potential diagnostic biomarkers for PD and analyzed the impact of immune cell infiltrations on disease pathogenesis. The PD expression profile data for human SN tissue, GSE7621, GSE20141, GSE20159, GSE20163 and GSE20164 were downloaded from the Gene Expression Omnibus (GEO) database for use in the training model. After normalization and merging, we identified differentially expressed genes (DEGs) using the Robust rank aggregation (RRA) analysis. Simultaneously, DEGs after batch correction were identified. Gene interactions were determined through venn Diagram analysis. Functional analyses and protein–protein interaction (PPI) networks were used to the identify hub genes, which were visualized through Cytoscape. A Lasso Cox regression model was employed to identify the potential diagnostic genes. The GSE20292 dataset was used for validation. The proportion of infiltrating immune cells in the samples were determined via the CIBERSORT method. Sixty-two DEGs were screened in this study. They were found to be enriched in nerve conduction, dopamine (DA) metabolism, and DA biosynthesis Gene Ontology (GO) terms. The PPI network and Lasso Cox regression analysis revealed seven potential diagnostic genes, namely SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1, were subsequently validated in peripheral blood samples obtained from healthy control (HC) and PD patients, as well as in the GSE20292 dataset. The results revealed the exceptional sensitivity and specificity of these genes in PD diagnosis and monitoring. Moreover, PD patients exhibited a higher number of plasma cells, compared to HC individuals. The SLC18A2, TAC1, PCDH8, KIAA0319, PDE6H, AXIN1, and AGTR1 are potential diagnostic biomarkers for PD. Our findings also reveal the essential roles of immune cell infiltration in both disease onset and trajectory.

Transcriptome-based biomarker prediction for Parkinson’s disease using genome-scale metabolic modeling

Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On–Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.

Epigenetic associations of GPNMB rs199347 variant with alcohol consumption in Parkinson's disease.

Alcohol consumption can induce a neuroinflammatory response and contribute to the progression of neurodegeneration. However, its association with Parkinson's disease (PD), the second most common neurodegenerative disorder, remains undetermined. Recent studies suggest that the glycoprotein non-metastatic melanoma protein B (GPNMB) is a potential biomarker for PD. We evaluated the association of rs199347, a variant of the GPNMB gene, with alcohol consumption and methylation upstream of GPNMB. We retrieved genetic and DNA methylation data obtained from participants enrolled in the Taiwan Biobank (TWB) between 2008 and 2016. After excluding individuals with incomplete or missing information about potential PD risk factors, we included 1,357 participants in our final analyses. We used multiple linear regression to assess the association of GPNMB rs199347 and chronic alcohol consumption (and other potential risk factors) with GPNMB cg17274742 methylation. There was no difference between the distribution of GPNMB rs199347 genotypes between chronic alcohol consumers and the other study participants. A significant interaction was observed between the GPNMB rs199347 variant and alcohol consumption (p = 0.0102) concerning cg17274742 methylation. Compared to non-chronic alcohol consumers with the AA genotype, alcohol drinkers with the rs199347 GG genotype had significantly lower levels (hypomethylation) of cg17274742 (p = 0.0187). Alcohol consumption among individuals with the rs199347 GG genotype was associated with lower levels of cg17274742 methylation, which could increase expression of the GPNMB gene, an important neuroinflammatory-related risk gene for PD.

Evaluation of Parkinson's disease early diagnosis using single-channel EEG features and auditory cognitive assessment.

Parkinson's disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients. These features, extracted using a single-channel EEG during an auditory cognitive assessment, include EEG feature A0 associated with cognitive load in healthy subjects, and EEG feature L1 associated with cognitive task differentiation. Participants in this study are comprised of cognitively healthy patients who had undergone an F-DOPA PET scan as a part of their standard care (n = 32), and cognitively healthy controls (n = 20). EEG data collected using the Neurosteer system during an auditory cognitive task, was decomposed using wavelet-packet analysis and machine learning methods for feature extraction. These features were used in a connectivity analysis that was applied in a similar manner to fMRI connectivity. A preliminary model that relies on the features and their connectivity was used to predict initially unrevealed F-DOPA test results. Then, generalized linear mixed models (LMM) were used to discern between PD and non-PD subjects based on EEG variables. The prediction model correctly classified patients with unrevealed scores as positive F-DOPA. EEG feature A0 and the Delta band revealed distinct activity patterns separating between study groups, with controls displaying higher activity than PD patients. In controls, EEG feature L1 showed variations between resting state and high-cognitive load, an effect lacking in PD patients. Our findings exhibit the potential of single-channel EEG technology in combination with an auditory cognitive assessment to distinguish positive from negative F-DOPA PET scores. This approach shows promise for early PD diagnosis. Additional studies are needed to further verify the utility of this tool as a potential biomarker for PD.

Metabolomic analysis of serum short-chain fatty acid concentrations in a mouse of MPTP-induced Parkinson's disease after dietary supplementation with branched-chain amino acids.

The gut microbiota and microbial metabolites influence the enteric nervous system and the central nervous system via the microbial-gut-brain axis. Increasing body of evidence suggests that disturbances in the metabolism of peripheral branched-chain amino acids (BCAAs) can contribute to the development of neurodegenerative diseases through neuroinflammatory signaling. Preliminary research has shown that longitudinal changes in serum amino acid levels in mouse models of Parkinson's disease (PD) are negatively correlated with disease progression. Therefore, the aim of the present study was to determine the changes in serum levels of short-chain fatty acids (SCFAs) in a mouse model of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD after dietary BCAA supplementation. In our research, gas chromatography-mass spectrometry was used to detect serum SCFA concentrations. The data were then analyzed with principal component analysis and orthogonal partial least squares discriminant analysis. Finally, the correlations of serum SCFA levels with gut and motor function in MPTP-induced PD mice were explored. Propionic acid, acetic acid, butyric acid, and isobutyric acid concentrations were elevated in MPTP + H-BCAA mice compared with MPTP mice. Propionic acid concentration was increased the most, while the isovaleric acid concentration was decreased. Propionic acid concentration was positively correlated with fecal weight and water content and negatively correlated with the pole-climbing duration. In conclusion, these results not only suggest that propionic acid may be a potential biomarker for PD, but also indicate the possibility that PD may be treated by altering circulating levels of SCFA.

Profiling Analysis of Tryptophan Metabolites in the Urine of Patients with Parkinson's Disease Using LC-MS/MS.

Although Parkinson's disease (PD) is a representative neurodegenerative disorder and shows characteristic motor impediments, the pathophysiological mechanisms and treatment targets for PD have not yet been clearly identified. Since several tryptophan metabolites produced by gut microbiota could pass the blood-brain barrier and, furthermore, might influence the central nervous system, tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways might be the most potent targets for PD development. Furthermore, most metabolites are circulated via the blood, play roles in and/or are metabolized via the host organs, and finally are excreted into the urine. Therefore, profiling the overall tryptophan metabolic pathways in urine samples of patients with PD is important to understanding the pathological mechanisms, finding biomarkers, and discovering therapeutic targets for PD. However, the development of profiling analysis based on tryptophan metabolism pathways in human urine samples is still challenging due to the wide physiological ranges, the varied signal response, and the structural diversity of tryptophan metabolites in complicated urine matrices. In this study, an LC-MS/MS method was developed to profile 21 tryptophan metabolites within the indole, kynurenine, and serotonin metabolic pathways in human urine samples using ion-pairing chromatography and multiple reaction monitoring determination. The developed method was successfully applied to urine samples of PD patients (n = 41) and controls (n = 20). Further, we investigated aberrant metabolites to find biomarkers for PD development and therapeutic targets based on the quantitative results. Unfortunately, most tryptophan metabolites in the urine samples did not present significant differences between control and PD patients, except for indole-3-acetic acid. Nonetheless, indole-3-acetic acid was reported for the first time for its aberrant urinary levels in PD patients and tentatively selected as a potential biomarker for PD. This study provides accurate quantitative results for 21 tryptophan metabolites in biological samples and will be helpful in revealing the pathological mechanisms of PD development, discovering biomarkers for PD, and further providing therapeutic targets for various PD symptoms. In the near future, to further investigate the relationship between gut microbial metabolites and PD, we will employ studies on microbial metabolites using plasma and stool samples from control and PD patients.

LncRNA MEG8 ameliorates Parkinson's disease neuro-inflammation through miR-485-3p/FBXO45 axis.

Studies suggest that LncRNA maternally expressed 8, small nucleolar RNA host gene (MEG8) contributes to inflammatory regulation, while the function and potential mechanisms of MEG8 in Parkinson's disease (PD) are unknown. This study aimed to assess the clinical value and biological function of MEG8 in PD. One hundred and two PD patients, eighty-six AD patients, and eighty healthy controls were enrolled in this study. Lipopolysaccharide (LPS)-induced microglia BV2 constructs an in vitro cell model. RT-qPCR was conducted to quantify the levels of MEG8, miR-485-3p, and FBXO45 in serum and cells. ROC curve was employed to examine the diagnostic value of MEG8 in PD. Serum and cellular pro-inflammatory factor secretion were quantified by ELISA. Dual-luciferase reporter and RIP assay to validate the targeting relationship between miR-485-3p and FBXO45. MEG8 and FBXO45 were significantly decreased in the serum of PD patients and LPS-induced bv2, while miR-485-3p was increased (P < 0.05). ROC curve confirmed that serum MEG8 has high sensitivity and specificity to identify PD patients from healthy controls and AD patients, respectively. Elevated MEG8 alleviated LPS-induced inflammatory factor overproduction compared with LPS-induced BV2 (P < 0.05), but this alleviating effect was eliminated by miR-485-3p (P < 0.05). The LPS-induced inflammatory response was suppressed by the low expression of miR-485-3p but significantly reversed by silencing of FBXO45. MEG8 was a sponge for miR-485-3p and inhibited its levels and promoted FBXO45 expression (P < 0.05). Elevated MEG8 is a potential diagnostic biomarker for PD and may mitigate inflammatory damage in PD via the miR-485-3p/FBXO45 axis.

Evaluation of ferritin and TfR level in plasma neural-derived exosomes as potential markers of Parkinson's disease.

Early diagnosis of Parkinson's disease (PD) remains challenging. It has been suggested that abnormal brain iron metabolism leads to excessive iron accumulation in PD, although the mechanism of iron deposition is not yet fully understood. Ferritin and transferrin receptor (TfR) are involved in iron metabolism, and the exosome pathway is one mechanism by which ferritin is transported and regulated. While the blood of healthy animals contains a plentiful supply of TfR-positive exosomes, no studies have examined ferritin and TfR in plasma neural-derived exosomes. Plasma exosomes were obtained from 43 patients with PD and 34 healthy controls. Neural-derived exosomes were isolated with anti-human L1CAM antibody immunoabsorption. Transmission electron microscopy and western blotting were used to identify the exosomes. ELISAs were used to quantify ferritin and TfR levels in plasma neural-derived exosomes of patients with PD and controls. Receivers operating characteristic (ROC) curves were applied to map the diagnostic accuracy of ferritin and TfR. Independent predictors of the disease were identified using logistic regression models. Neural-derived exosomes exhibited the typical exosomal morphology and expressed the specific exosome marker CD63. Ferritin and TfR levels in plasma neural-derived exosomes were significantly higher in patients with PD than controls (406.46 ± 241.86 vs. 245.62 ± 165.47 ng/μg, P = 0.001 and 1728.94 ± 766.71 vs. 1153.92 ± 539.30 ng/μg, P < 0.001, respectively). There were significant positive correlations between ferritin and TfR levels in plasma neural-derived exosomes in control group, PD group and all the individuals (rs = 0.744, 0.700, and 0.752, respectively). The level of TfR was independently associated with the disease (adjusted odds ratio 1.002; 95% CI 1.000-1.003). ROC performances of ferritin, TfR, and their combination were moderate (0.730, 0.812, and 0.808, respectively). However, no relationship was found between the biomarkers and disease progression. It is hypothesized that ferritin and TfR in plasma neural-derived exosomes may be potential biomarkers for PD, and that they may participate in the mechanism of excessive iron deposition in PD.

A lysosome-targeting ratiometric fluorescent probe used to detect Nitroxyl (HNO) in a Parkinson's disease model

Parkinson's disease (PD) is a neurodegenerative disease common in middle-aged and older individuals. At present, the exact cause is unknown, and the diagnosis is cumbersome, which seriously hinders the early diagnosis of patients. During the same time, during PD, there are conditions for the formation of nitroxyl (HNO). In addition, it has been shown that HNO has toxic effects on dopaminergic neurons and can lead to depletion of Glutathione (GSH), which is associated with features of PD. Therefore, HNO could be used as a potential biomarker for PD. Unfortunately, studies on changes in HNO content during PD are rarely reported. Here, we synthesized the first near-infrared ratiometric fluorescent probe HX-HNO for detecting HNO content in a PD model. The probe has excellent selectivity and sensitivity, and can detect HNO produced in various biochemical processes. Most importantly, in a PD model generated by rotenone, HX-HNO detected elevated levels of HNO. The visualization of HNO by HX-HNO provides a powerful tool for the early diagnosis of PD.

Peripheral Sphingolipids as Potential Biomarkers of Parkinson disease Including Sex-Related Differences (P3-11.007)

<h3>Objective:</h3> Assessment of glucocerebrosidase pathways to better understand the pathophysiology of sphingolipids in Parkinson Disease (PD) associated with a GBA variant (GBAPD), including sex-specific features. <h3>Background:</h3> Sex-specific differences in both idiopathic PD and GBAPD have been reported. Determining blood-based measures in the glucocerebrosidase pathway, which is perturbed in GBAPD might be useful in understanding these differences. <h3>Design/Methods:</h3> Tandem mass spectrometry was utilized to assess levels of targeted sphingolipid substrates and products in 109 GBAPD (62 men, 47 women) and 118 controls (50 men, 68 women) evaluated from four major studies (Mount Sinai (Beth Israel), Parkinson Disease Biomarker Program, Harvard Biomarker Study, BioFIND). <h3>Results:</h3> GBAPD had elevated hexosylceramides when compared to controls (mean [SD] 652.6 [188.3] pmol/100 μl plasma vs 586.2 [172.8]; p=0.006), while ceramide was reduced compared to controls (498.5 [118.7] vs 530.6 [110.9]; p=0.036). Very long chain ceramides, in particular, were decreased in GBAPD compared to controls (420.16 [101.58] vs 448.53 [98.68]; p=0.034). Levels of hexosylsphingosine were increased in GBAPD compared controls (0.08 [0.04] vs. 0.07 [0.04]; p=0.001). Sex-specific differences in measured levels were found. While Hexosylceramides were increased in GBAPD compared to controls, this increase was driven by women, but not men. Alternatively, while ceramides and were decreased in GBAPD compared to controls, this decrease was driven by men and not by women. Finally, while levels of hexosylsphingosine were increased in GBAPD compared to controls, this difference was driven by GBAPD men compared controls, but not by GBAPD women compared to controls. <h3>Conclusion:</h3> While there are differences between GBAPD and controls that are detected in plasma, these depend on sex and suggest that men are preferentially shunting from hexosylceramide to hexosylsphingosine whereas women to ceramides. This supports sex-related pathophysiologic differences, and randomization schema for GBA related studies may consider accounting for sex. <b>Disclosure:</b> Roberto Ortega has nothing to disclose. Prof. Burbulla has nothing to disclose. Izolda Mileva has nothing to disclose. Mr. Taylor has nothing to disclose. Dr. Cuperlovic-Culf has nothing to disclose. Ms. Glickman has nothing to disclose. The institution of Dr. Pullman has received research support from Empire Clinical Research Investigator Program. Dr. Scherzer has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Sanofi. The institution of Dr. Scherzer has received research support from NIH. The institution of Dr. Scherzer has received research support from Michael J. Fox Foundation. The institution of Dr. Scherzer has received research support from American Parkinson’s Disease Association. Dr. Scherzer has received intellectual property interests from a discovery or technology relating to health care. The institution of Ms. Raymond has received research support from NIH. Gabriel Miltenyi-Miltenberger has nothing to disclose. Dr. Yoo has received research support from Edmond J Safra Foundation. William Nichols has nothing to disclose. Dr. Krainc has nothing to disclose. The institution of Dr. Bressman has received research support from Michael J Fox Foundation . The institution of Dr. Bressman has received research support from NIH . Lina Obeid has nothing to disclose. Yusuf Hannun, 9207 has nothing to disclose. The institution of Dr. Bennett has received research support from Prevail. The institution of Dr. Saunders-Pullman has received research support from NIH, Bigglesworth Family Foundation, Empire Clinical Research Investigatory Program.

Role of saffron and chamomile in Parkinson diseases.

Madam, Parkinson's disease (PD) is a distinctive neurodegenerative disorder with a multifactorial range of etiology and symptoms. The pathophysiology behind the condition is a complex array of abnormal alpha-synuclein aggregation, mitochondrial dysfunction, oxidative stress, and neuroinflammation that leads to reduced dopaminergic expression, and neuronal cell death (1, 2). In multitudinous preclinical studies, Saffron and Chamomile are effective in treating PD. Saffron can mitigate the neurodegenerative progression of the disease by curtailing dopaminergic and neuronal loss and inhibiting alpha-synuclein aggregation. It also possesses antioxidant and anti-inflammatory activities (3). In-silico studies also demonstrated the action of active compounds of Saffron, including crocin, crocetin, and safranal, against potential targets of PD (1). Chamomile and its active compound Apigenin are beneficial in movement disorders in Parkinson's disease (4). It possesses neuroprotective, anti-inflammatory and antioxidant activities. It also has demonstrated activity against potential biomarkers of PD, including: Interleukin-6, Tumor Necrotic factor-alpha, Malonaldehyde, alpha-synuclein, Glial fibrillary acidic protein and also on Tyrosine Hydroxylase and dopamine receptors expression (2). Prolonged use of pharmacotherapy is not very beneficial in managing PD. Currently, dopamine-related drugs, including Levodopa, are considered first-line therapy. Despite temporarily alleviating the symptoms they cannot alter the degenerative process of PD (1). It warrants a crucial need for novel therapeutic agents working with diverse mechanisms. Pre-clinically, Saffron and chamomile are efficacious in altering potential disease biomarkers. Both herbs have been easily accessible and utilized since ancient times with numerous benefits and better tolerability. Clinical studies specifically for PD are yet to be analyzed; in different neurological disorders, daily oral consumption of 30mg of Saffron and 1gm to 1.5gm of Chamomile is recommended (3,5). Considering the high cost, a blended intake of saffron and Chamomile could be cost-effective and beneficial in treating the disease. Therefore, though clinical trials are needed for further elaboration, Parkinson's disease and related neurological disorders can be managed by synergising both drugs.

Changes in the intestinal microbiota ofpatients with Parkinson's disease and their clinical significance.

To investigate the differences and their clinical significance in the intestinal microbiota in patients with Parkinson's disease (PD) in comparison to those in healthy controls. 20 patients with PD who received treatment in the First Affiliated Hospital of Bengbu Medical College between January 2019 and December 2019 were selected as the research subjects to form the PD group, while 20 age- and gender-matched healthy volunteers were selected as the control group. Fecal samples from the two groups were collected, and the V4 region of 16S-ribosomal ribonucleic acid was selected for high-throughput sequencing analysis to explore any differences, as well as their significance, in the intestinal microbiota abundance at the class, family, and genus levels between the two study groups. The operational taxonomic unit cluster analysis revealed a high degree of overlap between the patients with PD and the controls. Compared with the controls, the relative abundance of Coriobacteriia and Coriobacteriaceae was increased in the PD group (p<0.01), while the relative abundance of Lachnospiraceae was significantly lower (p<0.01). The relative abundance of Collinsella, Escherichia, and Fusobacterium in the PD group was significantly higher than in the control group (p<0.05). Compared with the healthy subjects, the abundance of specific microflora was significantly different in the PD patients at the class, family, and genus level. Intestinal flora may act as a potential biomarker for PD and provide a theoretical basis for microflora transplantation therapy.

Upregulation of Formaldehyde in Parkinson's Disease Found by a Near-Infrared Lysosome-Targeted Fluorescent Probe.

Parkinson's disease (PD) is one of the major neurodegenerative diseases caused by complex pathological processes. As a signal molecule, formaldehyde is closely linked to nervous systems, but the relationship between PD and formaldehyde levels remains largely unclear. We speculated that formaldehyde might be a potential biomarker for PD. To prove it, we constructed the first near-infrared (NIR) lysosome-targeted formaldehyde fluorescent probe (named NIR-Lyso-FA) to explore the relationship between formaldehyde and PD. The novel fluorescent probe achieves formaldehyde detection in vitro and in vivo, thanks to its excellent properties such as NIR emission, large Stokes shift, and fast response to formaldehyde. Crucially, utilizing the novel probe NIR-Lyso-FA, formaldehyde overexpression was discovered for the first time in cellular, zebrafish, and mouse PD models, supporting our guess that formaldehyde can function as a possible biomarker for PD. We anticipate that this finding will offer insightful information for PD pathophysiology, diagnosis, medication development, and treatment.

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

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

Potential Biomarkers for Parkinson Disease from Functional Enrichment and Bioinformatic Analysis of Global Gene Expression Patterns of Blood and Substantia Nigra Tissues.

The Parkinson disease (PD) is the second most common neurodegenerative disorder affecting the central nervous system and motor functions. The biological complexity of PD is yet to reveal potential targets for intervention or to slow the disease severity. Therefore, this study aimed to compare the fidelity of blood to substantia nigra (SN) tissue gene expression from PD patients to provide a systematic approach to predict role of the key genes of PD pathobiology. Differentially expressed genes (DEGs) from multiple microarray data sets of PD blood and SN tissue from GEO database are identified. Using the theoretical network approach and variety of bioinformatic tools, we prioritized the key genes from DEGs. A total of 540 and 1024 DEGs were identified in blood and SN tissue samples, respectively. Functional pathways closely related to PD such as ERK1 and ERK2 cascades, mitogen-activated protein kinase (MAPK) signaling, Wnt, nuclear factor-κB (NF-κB), and PI3K-Akt signaling were observed by enrichment analysis. Expression patterns of 13 DEGs were similar in both blood and SN tissues. Comprehensive network topological analysis and gene regulatory networks identified additional 10 DEGs functionally connected with molecular mechanisms of PD through the mammalian target of rapamycin (mTOR), autophagy, and AMP-activated protein kinase (AMPK) signaling pathways. Potential drug molecules were identified by chemical-protein network and drug prediction analysis. These potential candidates can be further validated in vitro/in vivo to be used as biomarkers and/or novel drug targets for the PD pathology and/or to arrest or delay the neurodegeneration over the years, respectively.

Quantitative Determination of Endogenous Tetrahydroisoquinolines, Potential Parkinson's Disease Biomarkers, in Mammals.

Current diagnostic options for Parkinson's disease are very limited and primarily based on characteristic clinical symptoms. Thus, there are urgent needs for reliable biomarkers that enable us to diagnose the disease in the early stages, differentiate it from other atypical Parkinsonian syndromes, monitor its progression, increase knowledge of its pathogenesis, and improve the development of potent therapies. A promising group of potential biomarkers are endogenous tetrahydroisoquinoline metabolites, which are thought to contribute to the multifactorial etiology of Parkinson's disease. The aim of this critical review is to highlight trends and limitations of available traditional and modern analytical techniques for sample pretreatment (extraction and derivatization procedures) and quantitative determination of tetrahydroisoquinoline derivatives in various types of mammalian fluids and tissues (urine, plasma, cerebrospinal fluid, brain tissue, liver tissue). Particular attention is paid to the most sensitive and specific analytical techniques, involving immunochemistry and gas or liquid chromatography coupled with mass spectrometric, fluorescence, or electrochemical detection. The review also includes a discussion of other relevant agents proposed and tested in Parkinson's disease.

Different patterns of exosomal α-synuclein between Parkinson's disease and probable rapid eye movement sleep behavior disorder.

The insidious onset of Parkinson's disease (PD) makes early diagnosis difficult. Notably, idiopathic rapid eye movement sleep behavior disorder (iRBD) was reported as a prodrome of PD, which may represent a breakthrough for the early diagnosis of PD. However, currently there is no reliable biomarker for PD diagnosis. Considering that α-synuclein (α-Syn) and neuroinflammation are known to develop prior to the onset of clinical symptoms in PD, it was hypothesized that plasma total exosomal α-Syn (t-exo α-Syn), neural-derived exosomal α-Syn (n-exo α-Syn) and exosomal apoptosis-associated speck-like protein containing a caspase activation and recruitment domain (ASC) may be potential biomarkers of PD. In this study, 78 PD patients, 153 probable iRBD patients (pRBD) and 63 healthy controls (HCs) were recruited. α-Syn concentrations were measured using a one-step paramagnetic particle-based chemiluminescence immunoassay, and ASC levels were measured using the Ella system. It was found that t-exo α-Syn was significantly increased in the PD group compared to the pRBD and HC groups (p &lt; 0.0001), whilst n-exo α-Syn levels were significantly increased in both the PD and pRBD groups compared to HCs (p &lt; 0.0001). Furthermore, although no difference was found in ASC levels between the PD and pRBD groups, there was a positive correlation between ASC and α-Syn in exosomes. Our results suggest that both t-exo α-Syn and n-exo α-Syn were elevated in the PD group, whilst only n-exo α-Syn was elevated in the pRBD group. Additionally, the adaptor protein of inflammasome ASC is correlated with α-Syn and may facilitate synucleinopathy.