Synaptic proteostasis in Parkinson's disease

Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study

OBJECTIVE

To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD).

METHODS

This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model.

RESULTS

The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups.

CONCLUSIONS

The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.

Visual impairment cell non-autonomously dysregulates brain-wide proteostasis

Loss of hearing or vision has been identified as a significant risk factor for dementia but underlying molecular mechanisms are unknown. In different Drosophila models of blindness, we observe non-autonomous induction of stress granules in the brain and their reversal upon restoration of vision. Stress granules include cytosolic condensates of p62, ATF4 and XRP1. This cytosolic restraint of the ATF4 and XRP1 transcription factors dampens expression of their downstream targets during cellular stress. Cytosolic condensates of p62 and ATF4 were also evident in the thalamus and hippocampus of mouse models of congenital or degenerative blindness. These data indicate conservation of the link between loss of sensory input and dysregulation of stress responses critical for protein quality control in the brain.

The application of weighted gene co-expression network analysis and support vector machine learning in the screening of Parkinson's disease biomarkers and construction of diagnostic models

Background

This study aims to utilize Weighted Gene Co-expression Network Analysis (WGCNA) and Support Vector Machine (SVM) algorithm for screening biomarkers and constructing a diagnostic model for Parkinson's disease.

Methods

Firstly, we conducted WGCNA analysis on gene expression data from Parkinson's disease patients and control group using three GEO datasets (GSE8397, GSE20163, and GSE20164) to identify gene modules associated with Parkinson's disease. Then, key genes with significantly differential expression from these gene modules were selected as candidate biomarkers and validated using the GSE7621 dataset. Further functional analysis revealed the important roles of these genes in processes such as immune regulation, inflammatory response, and cell apoptosis. Based on these findings, we constructed a diagnostic model by using the expression data of FLT1, ATP6V0E1, ATP6V0E2, and H2BC12 as inputs and training and validating the model using SVM algorithm.

Results

The prediction model demonstrated an AUC greater than 0.8 in the training, test, and validation sets, thereby validating its performance through SMOTE analysis. These findings provide strong support for early diagnosis of Parkinson's disease and offer new opportunities for personalized treatment and disease management.

Conclusion

In conclusion, the combination of WGCNA and SVM holds potential in biomarker screening and diagnostic model construction for Parkinson's disease.

Proteomics analysis of the p.G849D variant in neurexin 2 alpha may reveal insight into Parkinson’s disease pathobiology

Parkinson’s disease (PD), the fastest-growing neurological disorder globally, has a complex etiology. A previous study by our group identified the p.G849D variant in neurexin 2 (NRXN2), encoding the synaptic protein, NRXN2α, as a possible causal variant of PD. Therefore, we aimed to perform functional studies using proteomics in an attempt to understand the biological pathways affected by the variant. We hypothesized that this may reveal insight into the pathobiology of PD. Wild-type and mutant NRXN2α plasmids were transfected into SH-SY5Y cells. Thereafter, total protein was extracted and prepared for mass spectrometry using a Thermo Scientific Fusion mass spectrometer equipped with a Nanospray Flex ionization source. The data were then interrogated against the UniProt H. sapiens database and afterward, pathway and enrichment analyses were performed using in silico tools. Overexpression of the wild-type protein led to the enrichment of proteins involved in neurodegenerative diseases, while overexpression of the mutant protein led to the decline of proteins involved in ribosomal functioning. Thus, we concluded that the wild-type NRXN2α may be involved in pathways related to the development of neurodegenerative disorders, and that biological processes related to the ribosome, transcription, and tRNA, specifically at the synapse, could be an important mechanism in PD. Future studies targeting translation at the synapse in PD could therefore provide further information on the pathobiology of the disease.

Identification of hub genes of Parkinson's disease through bioinformatics analysis

Parkinson's disease (PD) is a common neurodegenerative disease, and there is still a lack of effective diagnostic and treatment methods. This study aimed to search for hub genes that might serve as diagnostic or therapeutic targets for PD. All the analysis was performed in R software. The expression profile data of PD (number: GSE7621) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) associated with PD were screened by the “Limma” package of the R software. Key genes associated with PD were screened by the “WGCNA” package of the R software. Target genes were screened by merging the results of “Limma” and “WGCNA.” Enrichment analysis of target genes was performed by Gene Ontology (GO), Disease Ontology (DO), and Kyoto Enrichment of Genes and Genomes (KEGG). Machine learning algorithms were employed to screen for hub genes. Nomogram was constructed using the “rms” package. And the receiver operating characteristic curve (ROC) was plotted to detect and validate our prediction model sensitivity and specificity. Additional expression profile data of PD (number: GSE20141) was acquired from the GEO database to validate the nomogram. GSEA was used to determine the biological functions of the hub genes. Finally, RPL3L, PLEK2, PYCRL, CD99P1, LOC100133130, MELK, LINC01101, and DLG3-AS1 were identified as hub genes of PD. These findings can provide a new direction for the diagnosis and treatment of PD.

Misfolding at the synapse: A role in amyotrophic lateral sclerosis pathogenesis?

A growing wave of evidence has placed the concept of protein homeostasis at the center of the pathogenesis of amyotrophic lateral sclerosis (ALS). This is due primarily to the presence of pathological transactive response DNA-binding protein (TDP-43), fused in sarcoma (FUS) or superoxide dismutase-1 (SOD1) inclusions within motor neurons of ALS postmortem tissue. However, the earliest pathological alterations associated with ALS occur to the structure and function of the synapse, prior to motor neuron loss. Recent evidence demonstrates the pathological accumulation of ALS-associated proteins (TDP-43, FUS, C9orf72-associated di-peptide repeats and SOD1) within the axo-synaptic compartment of motor neurons. In this review, we discuss this recent evidence and how axo-synaptic proteome dyshomeostasis may contribute to synaptic dysfunction in ALS.

α-Synuclein in the Synaptic Vesicle Liquid Phase: Active Player or Passive Bystander?

The protein α-synuclein, which is well-known for its links to Parkinson’s Disease, is associated with synaptic vesicles (SVs) in nerve terminals. Despite intensive studies, its precise physiological function remains elusive. Accumulating evidence indicates that liquid-liquid phase separation takes part in the assembly and/or maintenance of different synaptic compartments. The current review discusses recent data suggesting α-synuclein as a component of the SV liquid phase. We also consider possible implications of these data for disease.

Modeling Parkinson's disease in LRRK2 mice: focus on synaptic dysfunction and the autophagy-lysosomal pathway

Mutations in the leucine-rich repeat kinase 2 (LRRK2) gene are associated with familial and sporadic forms of Parkinson's disease (PD), for which the LRRK2 locus itself represents a risk factor. Idiopathic and LRRK2-related PD share the main clinical and neuropathological features, thus animals harboring the most common LRRK2 mutations, i.e. G2019S and R1441C/G, have been generated to replicate the parkinsonian phenotype and investigate the underlying pathological mechanisms. Most LRRK2 rodent models, however, fail to show the main neuropathological hallmarks of the disease i.e. the degeneration of dopaminergic neurons in the substantia nigra pars compacta and presence of Lewy bodies or Lewy body-like aggregates of α-synuclein, lacking face validity. Rather, they manifest dysregulation in cellular pathways and functions that confer susceptibility to a variety of parkinsonian toxins/triggers and model the presymptomatic/premotor stages of the disease. Among such susceptibility factors, dysregulation of synaptic activity and proteostasis are evident in LRRK2 mutants. These abnormalities are also manifest in the PD brain and represent key events in the development and progression of the pathology. The present minireview covers recent articles (2018-2021) investigating the role of LRRK2 and LRRK2 mutants in the regulation of synaptic activity and autophagy-lysosomal pathway. These articles confirm a perturbation of synaptic vesicle endocytosis and glutamate release in LRRK2 mutants. Likewise, LRRK2 mutants show a marked impairment of selective forms of autophagy (i.e. mitophagy and chaperone-mediated autophagy) and lysosomal function, with minimal perturbations of nonselective autophagy. Thus, LRRK2 rodents might help understand the contribution of these pathways to PD.