α-syn and SNP rs356219 as a potential biomarker in blood for Parkinson's disease in Mexican Mestizos

Differential expression of PSMC4, SKP1, and HSPA8 in Parkinson's disease: insights from a Mexican mestizo population

Parkinson's disease (PD) is a complex neurodegenerative condition characterized by alpha-synuclein aggregation and dysfunctional protein degradation pathways. This study investigates the differential gene expression of pivotal components (UBE2K, PSMC4, SKP1, and HSPA8) within these pathways in a Mexican-Mestizo PD population compared to healthy controls. We enrolled 87 PD patients and 87 controls, assessing their gene expression levels via RT-qPCR. Our results reveal a significant downregulation of PSMC4, SKP1, and HSPA8 in the PD group (p = 0.033, p = 0.003, and p = 0.002, respectively). Logistic regression analyses establish a strong association between PD and reduced expression of PSMC4, SKP1, and HSPA8 (OR = 0.640, 95% CI = 0.415-0.987; OR = 0.000, 95% CI = 0.000-0.075; OR = 0.550, 95% CI = 0.368-0.823, respectively). Conversely, UBE2K exhibited no significant association or expression difference between the groups. Furthermore, we develop a gene expression model based on HSPA8, PSMC4, and SKP1, demonstrating robust discrimination between healthy controls and PD patients. Notably, the model's diagnostic efficacy is particularly pronounced in early-stage PD. In conclusion, our study provides compelling evidence linking decreased gene expression of PSMC4, SKP1, and HSPA8 to PD in the Mexican-Mestizo population. Additionally, our gene expression model exhibits promise as a diagnostic tool, particularly for early-stage PD diagnosis.

A genome-wide association study coupled with machine learning approaches to identify influential demographic and genomic factors underlying Parkinson's disease

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. We first applied correlation and GWAS analyses to find the top demographic and genomic factors associated with PD, respectively. We further developed and compared a variety of machine learning (ML) models for predicting PD. From the developed ML models, we performed feature importance analysis to reveal the predictability of each demographic or the genomic input feature for PD. Finally, we performed gene set enrichment analysis on our GWAS results to identify PD-associated pathways. Results: In our study, we identified both novel and well-known demographic and genetic factors (along with the enriched pathways) related to PD. In addition, we developed predictive models that performed robustly, with AUC = 0.89 for demographic data and AUC = 0.74 for genomic data. Our GWAS analysis identified several novel and significant variants and gene loci, including three intron variants in LMNA (p-values smaller than 4.0e-21) and one missense variant in SEMA4A (p-value = 1.11e-26). Our feature importance analysis from the PD-predictive ML models highlighted some significant and novel variants from our GWAS analysis (e.g., the intron variant rs1749409 in the RIT1 gene) and helped identify potentially causative variants that were missed by GWAS, such as rs11264300, a missense variant in the gene DCST1, and rs11584630, an intron variant in the gene KCNN3. Conclusion: In summary, by combining a GWAS with advanced machine learning models, we identified both known and novel demographic and genomic factors as well as built well-performing ML models for predicting Parkinson's disease.