Association analysis and polygenic risk score evaluation of 38 GWAS-identified Loci in a Chinese population with Parkinson's disease

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.

Dyrk1a Phosphorylation of α-Synuclein Mediating Apoptosis of Dopaminergic Neurons in Parkinson's Disease

Objective

To investigate the role of aberrant Dyrk1a expression in phosphorylation modification at the α-synuclein serine 129 (Ser129) site to analyze its molecular mechanism in mediating apoptosis of PD.

Methods

The protein level of P-α-synuclein (Ser129), α-synuclein, Bcl-2, Bax, active caspase 3, GSK3β, PI3K, AKT, and cyclinD1 were detected. The mRNA transcript levels of Dyrk1a and DAT and protein levels of IL-1β, IL-6, COX-2, and TNF-α were detected.

Results

P-α-synuclein (Ser129), α-synuclein, Bax, active caspase 3, GSK3β, and cyclinD1 expressions were decreased in Dyrk1a-AAV-ShRNA (P < 0.05), and Bcl-2, AKT, and PI3K expressions were increased (P < 0.05). Increased TH protein expression was shown in Dyrk1a-AAV-ShRNA (P < 0.05). Dyrk1a mRNA was decreased in the Dyrk1a-AAV-ShRNA group (P < 0.05), and DAT mRNA was increased (P < 0.05). IL-1β, IL-6, COX-2, and TNF-α protein levels were decreased in Dyrk1al-AAV-Sh-RNA (P < 0.05). Transcriptome sequencing showed that Fam220a, which was expected to activate STAT family protein binding activity and participate in the negative regulation of transcription through RNA polymerase II and protein dephosphorylation showed differentially upregulated expression. The untargeted metabolome showed that the major compounds in the Dyrk1a-AAV-ShRNA group were hormones and transmission mediators and the most metabolism-related pathways. Fam220a showed differentially upregulated expression, and differentially expressed genes were enriched for the neuroactive ligand-receptor interaction, vascular smooth muscle contraction, and melanogenesis-related pathways.

Conclusion

Abnormal Dyrk1a expression can affect α-synuclein phosphorylation modifications, and dyrk1a knockdown activates the PI3K/AKT pathway and reduces dopaminergic neuron apoptosis. It provides a theoretical basis for the group to further investigate the molecular mechanism.

Autophagy and apoptosis cascade: which is more prominent in neuronal death?

Autophagy and apoptosis are two crucial self-destructive processes that maintain cellular homeostasis, which are characterized by their morphology and regulated through signal transduction mechanisms. These pathways determine the fate of cellular organelle and protein involved in human health and disease such as neurodegeneration, cancer, and cardiovascular disease. Cell death pathways share common molecular mechanisms, such as mitochondrial dysfunction, oxidative stress, calcium ion concentration, reactive oxygen species, and endoplasmic reticulum stress. Some key signaling molecules such as p53 and VEGF mediated angiogenic pathway exhibit cellular and molecular responses resulting in the triggering of apoptotic and autophagic pathways. Herein, based on previous studies, we describe the intricate relation between cell death pathways through their common genes and the role of various stress-causing agents. Further, extensive research on autophagy and apoptotic machinery excavates the implementation of selective biomarkers, for instance, mTOR, Bcl-2, BH3 family members, caspases, AMPK, PI3K/Akt/GSK3β, and p38/JNK/MAPK, in the pathogenesis and progression of neurodegenerative diseases. This molecular phenomenon will lead to the discovery of possible therapeutic biomolecules as a pharmacological intervention that are involved in the modulation of apoptosis and autophagy pathways. Moreover, we describe the potential role of micro-RNAs, long non-coding RNAs, and biomolecules as therapeutic agents that regulate cell death machinery to treat neurodegenerative diseases. Mounting evidence demonstrated that under stress conditions, such as calcium efflux, endoplasmic reticulum stress, the ubiquitin-proteasome system, and oxidative stress intermediate molecules, namely p53 and VEGF, activate and cause cell death. Further, activation of p53 and VEGF cause alteration in gene expression and dysregulated signaling pathways through the involvement of signaling molecules, namely mTOR, Bcl-2, BH3, AMPK, MAPK, JNK, and PI3K/Akt, and caspases. Alteration in gene expression and signaling cascades cause neurotoxicity and misfolded protein aggregates, which are characteristics features of neurodegenerative diseases. Excessive neurotoxicity and misfolded protein aggregates lead to neuronal cell death by activating death pathways like autophagy and apoptosis. However, autophagy has a dual role in the apoptosis pathways, i.e., activation and inhibition of the apoptosis signaling. Further, micro-RNAs and LncRNAs act as pharmacological regulators of autophagy and apoptosis cascade, whereas, natural compounds and chemical compounds act as pharmacological inhibitors that rescue neuronal cell death through inhibition of apoptosis and autophagic cell death.