Shared Genetics and Comorbid Genes of Amyotrophic Lateral Sclerosis and Parkinson's Disease

Mechanism and therapeutic targets of the involvement of a novel lysosomal proton channel TMEM175 in Parkinson's disease

Parkinson's disease (PD), recognized as the second most prevalent neurodegenerative disease in the aging population, presents a significant challenge due to the current lack of effective treatment methods to mitigate its progression. Many pathogenesis of PD are related to lysosomal dysfunction. Moreover, extensive genetic studies have shown a significant correlation between the lysosomal membrane protein TMEM175 and the risk of developing PD. Building on this discovery, TMEM175 has been identified as a novel potassium ion channel. Intriguingly, further investigations have found that potassium ion channels gradually close and transform into hydrion "excretion" channels in the microenvironment of lysosomes. This finding was further substantiated by studies on TMEM175 knockout mice, which exhibited pronounced motor dysfunction in pole climbing and suspension tests, alongside a notable reduction in dopamine neurons within the substantia nigra compacta. Despite these advancements, the current research landscape is not without its controversies. In light of this, the present review endeavors to methodically examine and consolidate a vast array of recent literature on TMEM175. This comprehensive analysis spans from the foundational research on the structure and function of TMEM175 to expansive population genetics studies and mechanism research utilizing cellular and animal models.A thorough understanding of the structure and function of TMEM175, coupled with insights into the intricate mechanisms underpinning lysosomal dysfunction in PD dopaminergic neurons, is imperative. Such knowledge is crucial for pinpointing precise intervention targets, thereby paving the way for novel therapeutic strategies that could potentially alter the neurodegenerative trajectory of PD.

Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers

The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

Joint-tissue integrative analysis identifies high-risk genes for Parkinson's disease

The loss of dopaminergic neurons in the substantia nigra and the abnormal accumulation of synuclein proteins and neurotransmitters in Lewy bodies constitute the primary symptoms of Parkinson's disease (PD). Besides environmental factors, scholars are in the early stages of comprehending the genetic factors involved in the pathogenic mechanism of PD. Although genome-wide association studies (GWAS) have unveiled numerous genetic variants associated with PD, precisely pinpointing the causal variants remains challenging due to strong linkage disequilibrium (LD) among them. Addressing this issue, expression quantitative trait locus (eQTL) cohorts were employed in a transcriptome-wide association study (TWAS) to infer the genetic correlation between gene expression and a particular trait. Utilizing the TWAS theory alongside the enhanced Joint-Tissue Imputation (JTI) technique and Mendelian Randomization (MR) framework (MR-JTI), we identified a total of 159 PD-associated genes by amalgamating LD score, GTEx eQTL data, and GWAS summary statistic data from a substantial cohort. Subsequently, Fisher's exact test was conducted on these PD-associated genes using 5,152 differentially expressed genes sourced from 12 PD-related datasets. Ultimately, 29 highly credible PD-associated genes, including CTX1B, SCNA, and ARSA, were uncovered. Furthermore, GO and KEGG enrichment analyses indicated that these genes primarily function in tissue synthesis, regulation of neuron projection development, vesicle organization and transportation, and lysosomal impact. The potential PD-associated genes identified in this study not only offer fresh insights into the disease's pathophysiology but also suggest potential biomarkers for early disease detection.

Investigating the shared genetic architecture between frailty and insomnia

Background

The epidemiological association between frailty and insomnia is well established, yet the presence of a common genetic etiology is still uncertain. Further exploration is needed to ascertain the causal relationship between frailty and insomnia.

Methods

Utilizing data obtained from genome-wide association studies (GWAS) summaries, we utilized the linkage disequilibrium score regression (LDSC) to determine the genetic correlation existing between frailty and insomnia. The determination of causality was achieved through the application of two-sample Mendelian randomization. We investigated the enrichment of single nucleotide polymorphism (SNP) at various tissue types utilizing stratified LD score regression (S-LDSC) and multimarker analysis of genome annotation (MAGMA). Common risk SNPs were identified using Multi-Trait Analysis of GWAS (MTAG) and Cross-Phenotype Association (CPASSOC). We further investigated the expression profiles of risk genes in tissues using Summary-data-based Mendelian randomization(SMR) based on pooled data, to explore potential functional genes.

Results

Our findings indicated a significant genetic correlation between frailty and insomnia, highlighting SNPs sharing risk (rs34290943, rs10865954), with a pronounced correlation in the localized genomic region 3p21.31. Partitioned genetic analysis revealed 24 functional elements significantly associated with both frailty and insomnia. Furthermore, mendelian randomization revealed a causal connection between frailty and insomnia. The genetic correlation between frailty and insomnia showed enrichment in 11 brain regions (S-LDSC) and 9 brain regions (MAGMA), where four functional genes (RMB6, MST1R, RF123, and FAM212A) were identified.

Conclusion

This study suggests the existence of a genetic correlation and common risk genes between frailty and insomnia, contributing to a deeper comprehension of their pathogenesis and assists in identifying potential therapeutic targets.

Association Between Neurodegenerative Diseases and an Increased Risk of Epilepsy Based on Single Nucleotide Polymorphisms: A Mendelian Randomization Study

Epilepsy is a common neurological disorder characterized by transient brain dysfunction, attributed to a multitude of factors. The purpose of this study is to explore whether neurodegenerative diseases, specifically Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and multiple sclerosis (MS), have a causal effect on epilepsy. Mendelian randomization (MR) methods were used to analyze the causal association between neurodegenerative diseases (AD, PD, ALS, and MS) and epilepsy based on single nucleotide polymorphisms from genome-wide association studies, including inverse-variance weighted, weighted median, MR-Egger, and weighted mode methods. The reliability and stability of the MR analysis results were assessed by the MR-Egger intercept, MR-PRESSO, and heterogeneity tests. Forty-three SNPs were selected for the MR analysis of MS and epilepsy. The inverse-variance weighted method showed a significant causal association between MS and increased risk of epilepsy (odds ratio 1.046; 95% confidence interval 1.001-1.093; P = 0.043). However, AD (P = 0.986), PD (P = 0.894), and ALS (P = 0.533) were not causally associated with epilepsy. Sensitivity analysis showed that the results were robust. The MR study confirmed the causal relationship between genetically predicted MS and epilepsy but did not support the causal relationship between genetically predicted AD, PD, and ALS on epilepsy.