NPJ Parkinson's Disease最新文献

In this review, we summarize preclinical and clinical trials investigating innovative neuromodulatory approaches for Parkinson disease (PD) motor symptom management. We highlight the following technologies: temporal interference, nanoparticles for drug delivery, blood-brain barrier opening, gene therapy, optogenetics, upconversion nanoparticles, magnetothermal nanoparticles, magnetoelectric nanoparticles, ultrasound-responsive nanoparticles, and designer receptors exclusively activated by designer drugs. These studies establish the basis for novel and promising neuromodulatory treatments for PD motor symptoms.

Cereblon (CRBN) is a substrate recruiter for CRL4^CRBN E3 ubiquitin ligase system playing a plethora of pivotal roles for biological systems. Here, we identified DNAJB1 (DJ1) as endogenous substrate of CRBN and report how CRBN influences the aggregation and toxicity of alpha-synuclein (α-SYN) via modulation of DJ1. CRBN interferes with molecular activities of DJ1 in vitro, in cells, and in vivo resulting in a reduced disaggregation of α-SYN fibrils, increased formation of preformed fibrils (PFFs) of α-SYN, and high susceptibility of mice to MPTP and PFF-induced neurotoxicity. Depletion of Crbn improves the behavioral and biochemical responses of mice towards neurotoxic insult. Finally, we designed a peptide inhibitor to inhibit the recruitment of DJ1 to CRBN for ubiquitination, resulting in an enhanced supply of DJ1 to counteract the toxicity of aggregated α-SYN. Our data has important implications for development of CRBN-targeting therapies that could prevent or delay progression of neurodegenerative synucleinopathy.

Mutations in GBA1 encoding the lysosomal enzyme β-glucocerebrosidase (GCase) are among the most prevalent genetic susceptibility factors for Parkinson’s disease (PD), with 10–30% of carriers developing the disease. To identify genetic modifiers contributing to the incomplete penetrance, we examined the effect of 1634 human transcription factors (TFs) on GCase activity in lysates of an engineered human glioblastoma line homozygous for the pathogenic GBA1 L444P variant. Using an arrayed CRISPR activation library, we uncovered 11 TFs as regulators of GCase activity. Among these, activation of MITF and TFEC increased lysosomal GCase activity in live cells, while activation of ONECUT2 and USF2 decreased it. While MITF, TFEC, and USF2 affected GBA1 transcription, ONECUT2 might control GCase trafficking. The effects of MITF, TFEC, and USF2 on lysosomal GCase activity were reproducible in iPSC-derived neurons from PD patients. Our study provides a systematic approach to identifying modulators of GCase activity and deepens our understanding of the mechanisms regulating GCase.

To examine the association and modifiable risk factors between grip strength (GS) and Parkinson’s disease (PD) incidence considering genetic factors, a total of 411,648 individuals without PD at baseline from the UK Biobank were included. GS was measured by a hydraulic dynamometer. The polygenic risk score assessed the genetic predisposition. Multivariable Cox regression models were performed. During a median follow-up of 12.3 years, 2409 individuals developed PD. Compared with those with high GS, low-GS individuals had a 58.5% increased risk of PD (42.7%–76.1%), and 16.3% of this excess risk could be explained by adjusted risk factors. Low GS and high genetic predisposition contribute to the highest PD risk in an additive interaction. We observed that low GS was associated with higher PD incidence, particularly among individuals with high genetic predisposition. In addition to enhancing GS, interventions targeting risk factors (e.g., unhealthy lifestyles) might also reduce the excess risk.

Although α-synuclein seed amplification assays (α-syn SAA) are promising, its sensitivity may be affected by heterogeneity among patients with Lewy body disease (LBD). We evaluated whether α-syn SAA sensitivity is affected by patient heterogeneity, using ¹²³I-meta-iodobenzylguanidine (MIBG) cardiac scintigraphy in early drug-naïve patients. Thirty-four patients with clinically established or probable Parkinson’s disease (PD) and seven with dementia with Lewy bodies (DLB) or prodromal DLB were included. While 85.2% of patients with abnormal cardiac MIBG were α-syn SAA positive, only 14.3% were positive among those with normal scans. Logistic regression analysis showed that MIBG positivity was the only significant variable associated with α-syn SAA positivity (odds ratio 74.2 [95% confidence interval 6.1–909]). Although α-syn SAA is sensitive for LBD in patients with abnormal MIBG, the sensitivity may be lower in those with normal MIBG. Further studies are necessary to evaluate the association between patient heterogeneity and α-syn SAA sensitivity.

Parkinson’s disease (PD) is a neurodegenerative disorder marked by the loss of dopaminergic neurons in the substantia nigra. Despite progress, the pathogenesis remains unclear. Human midbrain organoids (hMLOs) have emerged as a promising model for studying PD, drug screening, and potential treatments. This review discusses the development of hMLOs, their application in PD research, and current challenges in organoid construction, highlighting possible optimization strategies.

The Parkinson’s Families Project is a UK-wide study aimed at identifying genetic variation associated with familial and early-onset Parkinson’s disease (PD). We recruited individuals with a clinical diagnosis of PD and age at motor symptom onset ≤45 years and/or a family history of PD in up to third-degree relatives. Where possible, we also recruited affected and unaffected relatives. We analysed DNA samples with a combination of single nucleotide polymorphism (SNP) array genotyping, multiplex ligation-dependent probe amplification (MLPA), and whole-genome sequencing (WGS). We investigated the association between identified pathogenic mutations and demographic and clinical factors such as age at motor symptom onset, family history, motor symptoms (MDS-UPDRS) and cognitive performance (MoCA). We performed baseline genetic analysis in 718 families, of which 205 had sporadic early-onset PD (sEOPD), 113 had familial early-onset PD (fEOPD), and 400 had late-onset familial PD (fLOPD). 69 (9.6%) of these families carried pathogenic variants in known monogenic PD-related genes. The rate of a molecular diagnosis increased to 28.1% in PD with motor onset ≤35 years. We identified pathogenic variants in LRRK2 in 4.2% of families, and biallelic pathogenic variants in PRKN in 3.6% of families. We also identified two families with SNCA duplications and three families with a pathogenic repeat expansion in ATXN2, as well as single families with pathogenic variants in VCP, PINK1, PNPLA6, PLA2G6, SPG7, GCH1, and RAB32. An additional 73 (10.2%) families were carriers of at least one pathogenic or risk GBA1 variant. Most early-onset and familial PD cases do not have a known genetic cause, indicating that there are likely to be further monogenic causes for PD.

There is an urgent need to identify predictive biomarkers of Parkinson’s disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ability of these proteins in combination with neuroimaging. In the discover cohort (n = 100), we identified 25 protein features with best results in the machine learning model, including top-ranked PSAP and H3C15. The two-proteins were used for model construction, achieving an Area under the curve (AUC) of 0.951 in the train set and AUC of 0.981 in the test set. Similarly, the model gives a rank list of endogenous metabolite features, Glycocholic Acid and 6-Methylnicotinamide were two top features. Combining these two markers further got the AUC of 0.969 in train set and 0.867 in the test set. To validate the performance of the protein biomarkers, we performed targeted analysis of selected proteins (H3C15 and PSAP) and proteins likely associated with PDCI (NCAM2 and LAMB2) using parallel reaction monitoring in validation cohort (n = 116). The AUC of the classifier built with H3C15 and PSAP is 0.813. Moreover, when combining H3C15, PSAP, NCAM2, and LAMB2, the model achieved AUC of 0.983 in the train set, AUC of 0.981 in the test set, and AUC of 0.839 in the validation set. Furthermore, we verified that these protein markers we discovered can improve the predictive effect of neuroimaging on PDCI: the classifier built with neuroimaging features had AUC of 0.833, which improved to 0.905 when combined with H3C15. Taken together, our integrated proteomics and metabolomics analysis successfully identified potential biomarkers for PDCI. Additionally, H3C15 showed promise in enhancing the predictive performance of neuroimaging for cognitive impairment.

MicroRNAs (miRNAs) are small non-coding RNA molecules that regulate gene expression by binding to target messenger RNA (mRNA) molecules and promoting their degradation or blocking their translation. Parkinson’s disease (PD) is a neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra. There is increasing evidence to suggest that miRNAs play a role in the pathogenesis of PD. Studies have identified several miRNAs that are dysregulated in the brains of PD patients, and animal models of the disease. MiRNA expression dysregulation contributes to the onset and progression of PD by modulating neuroinflammation, oxidative stress, and protein aggregation genes. Moreover, miRNAs have emerged as potential therapeutic targets for PD. This review elucidates the changes in miRNA expression profiles associated with PD, emphasising their potential as diagnostic biomarkers and therapeutic targets, and detailing specific miRNAs implicated in PD and their downstream targets.