Background Parkinson’s disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use. Methods We presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts. Results A list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD. Conclusions This study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.
Parkinson's disease (PD) is a complex neurodegenerative disorder with no cure in sight. Clinical challenges of the disease include the inability to make a definitive diagnosis at the early stages and difficulties in predicting the disease progression. The unmet demand to identify reliable biomarkers for early diagnosis and management of the disease course of PD has attracted a lot of attention. However, only a few reported candidate biomarkers have been tried in clinical practice at the present time. Studies on PD biomarkers have often overemphasized the discovery of novel identity, whereas efforts to further evaluate such candidates are rare. Therefore, we update the new development of biomarker discovery in PD and discuss the standard process in the evaluation and assessment of the diagnostic or prognostic value of the identified potential PD biomarkers in this review article. Recent developments in combined biomarkers and the current status of clinical trials of biomarkers as outcome measures are also discussed. We believe that the combination of different biomarkers might enhance the specificity and sensitivity over a single measure that might not be sufficient for such a multiplex disease.
BackgroundPro-inflammatory stimuli, including cytokines like Interleukin-1β, Interleukin-6 and Interferon-γ, in the brain have been proposed to exacerbate existing Alzheimer’s disease (AD) neuropathology by increasing amyloidogenic processing of APP and promoting further Aβ accumulation in AD. On the other hand, anti-inflammatory cytokines have been suggested to be neuroprotective by reducing neuroinflammation and clearing Aβ. To test this hypothesis, we used adeno-associated virus serotype 1 (AAV2/1) to express an anti-inflammatory cytokine, murine Interleukin-4 (mIL-4), in the hippocampus of APP transgenic TgCRND8 mice with pre-existing plaques.ResultsmIL-4 expression resulted in establishment of an “M2-like” phenotype in the brain and was accompanied by exacerbated Aβ deposition in TgCRND8 mice brains. No change in holo APP or APP C terminal fragment or phosphorylated tau levels were detected in mIL-4 expressing CRND8 cohorts. Biochemical analysis shows increases in both SDS soluble and insoluble Aβ. mIL-4 treatment attenuates soluble Aβ40 uptake by microglia but does not affect aggregated Aβ42 internalization by microglia or soluble Aβ40 internalization by astrocytes.ConclusionsShort term focal mIL-4 expression in the hippocampus leads to exacerbation of amyloid deposition in vivo, possibly mediated by acute suppression of glial clearance mechanisms. Given that recent preclinical data from independent groups indicate engagement of the innate immune system early on during disease pathogenesis may be beneficial, our present study strongly argues for a cautious re-examination of unwarranted side–effects of anti-inflammatory therapies for neurodegenerative diseases, including AD.
The expending of elderly population worldwide has resulted in a dramatic rise in the incidence of chronic diseases such as Alzheimer's disease (AD). Inadequate understanding of the mechanisms underlying AD has hampered the development of efficient tools for definitive diagnosis and curative interventions. Previous studies have attempted to discover reliable biomarkers of AD, but these biomarkers can only be measured through invasive (neuropathological markers in cerebrospinal fluid) or expensive (positron emission tomography scanning or magnetic resonance imaging) techniques. Metabolomics is a high-throughput technology that can detect and catalog large numbers of small metabolites and may be a useful tool for characterization of AD and identification of biomarkers. In this study, we used ultra-performance liquid chromatography-mass spectrometry based untargeted metabolomics to measure the concentrations of plasma metabolites in a cohort of subjects with AD (n=44) and cognitively normal controls (Ctrl, n=94). The AD group showed marked reductions in levels of polyunsaturated fatty acids, acyl-carnitines, degradation products of tryptophan, and elevated levels of bile acids compared to the Ctrl group. We then validated the results using an independent cohort that included subjects with AD (n=30), mild cognitive impairment (MCI, n=13), healthy controls (n=43), and non-AD neurological disease controls (NDC, n=31). We identified five metabolites comprising cholic acid, chenodeoxycholic acid, allocholic acid, indolelactic acid, and tryptophan that were able to distinguish patients with AD from both Ctrl and NDC with satisfactory sensitivity and specificity. The concentrations of these metabolites were significantly correlated with disease severity. Our results also suggested that altered bile acid profiles in AD and MCI might indicate early risk for the development of AD. These findings may allow for development of new approaches for diagnosis of AD and may provide novel insights into AD pathogenesis.
MicroRNAs (miRNAs) are small and evolutionary conserved noncoding RNAs that are involved in posttranscriptional gene regulation. Differential expression levels of miRNAs can be used as potential biomarkers of disease. Previous animal studies have indicated that the expression level of miR-132 is negatively correlated with its downstream molecule nuclear receptor related 1 protein (Nurr1), which is one of the key factors for the maintenance of dopaminergic function and is particularly vulnerable in Parkinson's disease (PD). However, this correlation has not been confirmed in human patients with PD. Moreover, the possible involvement of miR-132 during the pathogenesis and progression of PD is not fully investigated. Therefore, in the present study, we determined the peripheral circulation levels of miR-132 and Nurr1 in patients with PD, neurological disease controls (NDC) and healthy controls (HC) by reverse transcription real-time quantitative PCR (RT-qPCR). Our data clearly demonstrated that the plasma miR-132 level in PD was significantly higher than those in HC (178%, p < 0.05) and NDC (188%, p < 0.001). When adjusted for gender and age, higher level of miR-132 expression was associated with the significantly increased risk for PD in males and was closely related with the disease stages and disease severity. Furthermore, peripheral Nurr1 was significantly decreased in PD compared with HC (56%, p < 0.001) and NDC (58%, p < 0.001). Much more interestingly, further analysis revealed a negative correlation between the decreased Nurr1 level and the elevated miR-132 level in PD. All these findings indicated that the combination of a high miR-132 level with the low level of its downstream Nurr1 might be a potential biomarker aiding in the diagnosis of PD and monitoring disease progression.
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