Parkinson’s disease (PD) is characterized by α-synuclein aggregation in dopaminergic (DA) neurons, which are sensitive to oxidative stress. Mitochondria aconitase 2 (ACO2) is an essential enzyme in the tricarboxylic acid cycle that orchestrates mitochondrial and autophagic functions to energy metabolism. Though widely linked to diseases, its relation to PD has not been fully clarified. Here we revealed that the peripheral ACO2 activity was significantly decreased in PD patients and associated with their onset age and disease durations. The knock-in mouse and Drosophila models with the A252T variant displayed aggravated motor deficits and DA neuron degeneration after 6-OHDA and rotenone-induction, and the ACO2 knockdown or blockade cells showed features of mitochondrial and autophagic dysfunction. Moreover, the transcription of autophagy-related genes LC3 and Atg5 was significantly downregulated via inhibited histone acetylation at the H3K9 and H4K5 sites. These data provided multi-dimensional evidences supporting the essential roles of ACO2, and as a potential early biomarker to be used in clinical trials for assessing the effects of antioxidants in PD. Moreover, ameliorating energy metabolism by targeting ACO2 could be considered as a potential therapeutic strategy for PD and other neurodegenerative disorders.

The importance of late-onset cobalamin C (cblC) disorder is underestimated in adults. Improved awareness on its clinical and neuroimaging features helps timely diagnosis and appropriate treatment. Totally 16 late-onset cblC cases were diagnosed based on clinical, biochemical findings and MMAHC gene mutation analysis. Clinical presentations, neuroimaging features and mutational spectrum were reviewed. The case series included 10 males and 6 females, with average age of 22 (range 13–40) years. All the 16 patients displayed bilateral pyramidal tract signs, and most of the cases (13) had cognitive impairment. Other symptoms included psychiatric symptoms (6), epilepsy (6), peripheral nerve damage (5), ocular symptoms (4) and lower-limb thrombosis (1). The neuroimaging findings were dominated by cerebral atrophy (11/16), followed by white matter lesions (4), cerebellar lesions/atrophy (2) and spinal cord lesions (1). There were also 2 patients with normal imaging. All the MMACHC mutations were compound heterozygous, of which the most and second frequent was c.482G > A (p.R161Q; 15/16 case; allele frequency: 46.88%) and c.609G > A(p.W203X; 6/16 case; allele frequency: 18.75%). In addition, patients carrying frameshift mutations (deletion/duplication) presented more frequently with psychiatric symptoms (57.1%) and optic nerve damages (42.9%) than those carrying point mutations (22.2 and 11.1%, respectively). In contrast, peripheral nerve (44.4%) and white matter lesions (33.3%) were more frequently identified in point mutation- carriers. However, the differences did not achieve statistical significance (all p > 0.05). Compared to the early-onset form, late-onset cblC displayed some clinical, neuroimaging and mutational profiles, which warrants particular attention in adult neurologic practice. These findings not only broaden our insights into the genotypes and phenotypes of the disease, but highlight the importance of early diagnosis and initiation of appropriate treatments.

Diagnosis of multiple system atrophy (MSA) remains a challenge, due to the complexity and overlapping of its symptoms with other Parkinsonian disorders. The critical role of alpha-synuclein (α-syn) in the pathogenesis of MSA makes it an ideal biomarker for the diagnosis of MSA. Although α-syn alterations in cerebrospinal fluid (CSF) and blood plasma have been extensively assessed for the utility in diagnosing MSA, inconsistent results have been obtained, presumably due to the contamination by hemolysis and other confounding factors. In this study, levels of serine 129-phosphorylated α-syn (pS-α-syn), a major pathologic form of α-syn, in red blood cells (RBCs), were measured using ELISA in a Chinese cohort consisting of 107 MSA patients and 220 healthy controls. A significant increase in the levels of pS-α-syn in RBCs (pS-α-syn-RBC) was observed in MSA patients than in healthy controls (14.02 ± 4.02 ng/mg versus 11.89 ± 3.57 ng/mg; p<0.0001). Receiver operating characteristic curve (ROC) indicated that pS-α-syn-RBC discriminated the patients well from the controls with a sensitivity of 80.37% (95% confidence interval (CI): 71.58%–87.42%), a specificity of 88.64% (95% CI: 83.68%–92.51%), and an area under the curve (AUC) of 0.91 (95% CI: 0.87–0.94). The levels of pS-α-syn-RBC were negatively correlated with RBD-HK scores and differed between MSA-P and MSA-C subtypes (13.27 ± 1.91 versus 12.19 ± 3.04; p=0.025). The difference between subtypes was seen at Hoehn and Yahr stages 3 and 4, and the age at onset (AAO) between 60 and 69 years (p=0.016). The results suggest that pS-α-syn-RBC is increased in MSA patients and can be used as a potential diagnostic biomarker for MSA.

Background: Accurate diagnosis of Parkinson’s disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. Objective: To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. Methods: A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator–logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. Results: SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908–0.947; accuracy: 0.844, 95% CI: 0.814–0.871; sensitivity: 0.826, 95% CI: 0.786–0.866; specificity: 0.861, 95% CI: 0.820–0.898; precision: 0.849, 95% CI: 0.807–0.891; F1 score: 0.837, 95% CI: 0.803–0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. Conclusion: We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients.

Background/Purpose: Sporadic early-onset Alzheimer disease (sEOAD) and its visual variant, posterior cortical atrophy (PCA), have a disease onset at less than 65 years of age with no familial aggregation. The etiology and genetic basis of these diseases remain poorly understood. Our study aimed to identify additional mutations or variants associated with sEOAD and PCA and to further examine their genetic and phenotypic spectrums. Methods: We performed whole-exome sequencing and analyzed the clinical and neuroimaging features of mutation carriers with 29 patients having sEOAD and 25 having PCA. Results: Nine rare damaging variants were identified in 4 patients with sEOAD and 3 with PCA. A novel mutation (p.A136V) in PSEN1 was identified in a patient with sEOAD and a likely pathogenic variant (p.M239T) was identified for PSEN2 in a patient with PCA. In addition, 7 rare damaging variants were detected in other genes related to neurodegenerative diseases. The patient carrying the PSEN1 p.A136V mutation presented with typical clinical and imaging features of sEOAD, and the PCA patient with the PSEN2 p.M239T mutation presented with visuospatial impairment as the initial symptom. Conclusion: Our study expands the PSEN1 mutation spectrum of sEOAD and highlights the importance of screening PSEN1 and/or PSEN2 mutations in PCA patients.