Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy

Bioinformatics-based identification of key candidate genes and signaling pathways in patients with Parkinson's disease and obstructive sleep apnea

OBJECTIVES

Existing evidence exhibits that obstructive sleep apnea (OSA) is a potential consequence of Parkinson's disease (PD) or a contributor to PD progression. This investigation aimed to detect potential critical genes and molecular mechanisms underlying interactions between PD and OSA through bioinformatics analyses.

METHODS

The Gene Expression Omnibus (GEO) database was employed to obtain the expression profiles GSE20163 and GSE135917. The identification of common genes connected to PD and OSA was performed utilizing weighted gene co-expression network analysis and the R 4.0.4 program. The Cytoscape program was utilized to generate a network of protein-protein interactions (PPI), and the CytoHubba plugin was utilized to detect hub genes. Subsequently, functional enrichment analyses of the hub genes were conducted. Markers with increased diagnostic values for PD and OSA were confirmed using the GEO datasets GSE8397 and GSE38792.

RESULTS

Typically, 57 genes that are common were identified in PD and OSA. Among these common genes, the top 10 hub genes in the PPI network were chosen. The verified datasets confirmed the presence of three important genes: CADPS, CHGA, and SCG3. Functional enrichment analysis revealed that these hub genes mostly participate in GABAergic synapses.

CONCLUSION

Our findings suggest that CADPS, CHGA, and SCG3 are key genes involved in molecular mechanisms underlying interactions between OSA and PD. Functional enrichment of hub genes indicated a link between GABAergic synapses and the shared pathogenesis of PD and OSA. These candidate genes and corresponding pathways offer novel insights regarding biological targets that underlie the transcriptional connection between OSA and 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.

Perspective Strategies for Interventions in Parkinsonism: Remedying the Neglected Role of TPPP

Neurological disorders such as Parkinsonism cause serious socio-economic problems as there are, at present, only therapies that treat their symptoms. The well-established hallmark alpha-synuclein (SYN) is enriched in the inclusion bodies characteristic of Parkinsonism. We discovered a prominent partner of SYN, termed Tubulin Polymerization Promoting Protein (TPPP), which has important physiological and pathological activities such as the regulation of the microtubule network and the promotion of SYN aggregation. The role of TPPP in Parkinsonism is often neglected in research, which we here attempt to remedy. In the normal brain, SYN and TPPP are expressed endogenously in neurons and oligodendrocytes, respectively, whilst, at an early stage of Parkinsonism, soluble hetero-associations of these proteins are found in both cell types. The cell-to-cell transmission of these proteins, which is central to disease progression, provides a unique situation for specific drug targeting. Different strategies for intervention and for the discovery of biomarkers include (i) interface targeting of the SYN-TPPP hetero-complex; (ii) proteolytic degradation of SYN and/or TPPP using the PROTAC technology; and (iii) depletion of the proteins by miRNA technology. We also discuss the potential roles of SYN and TPPP in the phenotype stabilization of neurons and oligodendrocytes.

Cerebrospinal fluid metabolomic and proteomic characterization of neurologic post-acute sequelae of SARS-CoV-2 infection

The mechanism by which SARS-CoV-2 causes neurological post-acute sequelae of SARS-CoV-2 (neuro-PASC) remains unclear. Herein, we conducted proteomic and metabolomic analyses of cerebrospinal fluid (CSF) samples from 21 neuro-PASC patients, 45 healthy volunteers, and 26 inflammatory neurological diseases patients. Our data showed 69 differentially expressed metabolites and six differentially expressed proteins between neuro-PASC patients and healthy individuals. Elevated sphinganine and ST1A1, sphingolipid metabolism disorder, and attenuated inflammatory responses may contribute to the occurrence of neuro-PASC, whereas decreased levels of 7,8-dihydropterin and activation of steroid hormone biosynthesis may play a role in the repair process. Additionally, a biomarker cohort consisting of sphinganine, 7,8-dihydroneopterin, and ST1A1 was preliminarily demonstrated to have high value in diagnosing neuro-PASC. In summary, our study represents the first attempt to integrate the diagnostic benefits of CSF with the methodological advantages of multi-omics, thereby offering valuable insights into the pathogenesis of neuro-PASC and facilitating the work of neuroscientists in disclosing different neurological dimensions associated with COVID-19.

The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis

Background

In recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD.

Methods

We systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson's disease and various atypical parkinsonism syndromes (APS).

Results

Twenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833-0.891), 0.91 (95% CI: 0.86-0.94), and 0.93 (95% CI: 0.87-0.96) in the training set, and 0.871 (95% CI: 0.853-0.890), 0.86 (95% CI: 0.81-0.89), and 0.87 (95% CI: 0.83-0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843-0.889), 0.86 (95% CI: 0.84-0.88), and 0.80 (95% CI: 0.75-0.84) in the training set, and 0.879 (95% CI: 0.854-0.903), 0.87 (95% CI: 0.85-0.89), and 0.82 (95% CI: 0.77-0.86) in the validation set, respectively.

Conclusion

Radiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson's disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson's disease and related fields.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197.

Quantitative proteomics and phosphoproteomics of urinary extracellular vesicles define putative diagnostic biosignatures for Parkinson's disease

BACKGROUND

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene have been recognized as genetic risk factors for Parkinson's disease (PD). However, compared to cancer, fewer genetic mutations contribute to the cause of PD, propelling the search for protein biomarkers for early detection of the disease.

METHODS

Utilizing 138 urine samples from four groups, healthy individuals (control), healthy individuals with G2019S mutation in the LRRK2 gene (non-manifesting carrier/NMC), PD individuals without G2019S mutation (idiopathic PD/iPD), and PD individuals with G2019S mutation (LRRK2 PD), we applied a proteomics strategy to determine potential diagnostic biomarkers for PD from urinary extracellular vesicles (EVs).

RESULTS

After efficient isolation of urinary EVs through chemical affinity followed by mass spectrometric analyses of EV peptides and enriched phosphopeptides, we identify and quantify 4476 unique proteins and 2680 unique phosphoproteins. We detect multiple proteins and phosphoproteins elevated in PD EVs that are known to be involved in important PD pathways, in particular the autophagy pathway, as well as neuronal cell death, neuroinflammation, and formation of amyloid fibrils. We establish a panel of proteins and phosphoproteins as novel candidates for disease biomarkers and substantiate the biomarkers using machine learning, ROC, clinical correlation, and in-depth network analysis. Several putative disease biomarkers are further partially validated in patients with PD using parallel reaction monitoring (PRM) and immunoassay for targeted quantitation.

CONCLUSIONS

These findings demonstrate a general strategy of utilizing biofluid EV proteome/phosphoproteome as an outstanding and non-invasive source for a wide range of disease exploration.

Ganglioside analysis in body fluids by liquid-phase separation techniques hyphenated to mass spectrometry

The expression of gangliosides in central nervous system is a few times higher than in the extraneural tissue, a characteristic highlighting their major role at this level. Although in very low amounts, gangliosides are ubiquitously distributed in body fluids too, where, depending on many factors, including pathological states, their composition fluctuates, thus having diagnostic value. Ganglioside investigation in biological fluids, which, except for cerebrospinal fluid (CSF), may be sampled noninvasively, was for years impeded by the limited sensitivity of the analytical instrumentation available in glycomics. However, because the last decade has witnessed significant developments in biological mass spectrometry (MS) and the hyphenated separation techniques, marked by a major increase in sensitivity, reproducibility, and data reliability, ganglioside research started to be focused on biofluid analysis by separation techniques coupled to MS. In this context, our review presents the achievements in this emerging field of gangliosidomics, with a particular emphasis on modern liquid chromatography (LC), thin-layer chromatography, hydrophilic interaction LC, and ion mobility separation coupled to high-performance MS, as well as the results generated by these systems and allied experimental procedures in profiling and structural analysis of gangliosides in healthy or diseased body fluids, such as CSF, plasma/serum, and milk.

Diagnostic metabolomic profiling of Parkinson's disease biospecimens

BACKGROUND

Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson's disease (PD) diagnosis.

OBJECTIVE

To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD.

METHODS

Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression.

RESULTS

Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative.

CONCLUSIONS

UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD.