Summary-data-based Mendelian randomization prioritizes potential druggable targets for multiple sclerosis

An Overview of Epigenetic Changes in the Parkinson's Disease Brain

Parkinson's disease is a progressive neurodegenerative disorder, predominantly of the motor system. Although some genetic components and cellular mechanisms of Parkinson's have been identified, much is still unknown. In recent years, emerging evidence has indicated that non-DNA-sequence variation (in particular epigenetic mechanisms) is likely to play a crucial role in the development and progression of the disease. Here, we present an up-to-date overview of epigenetic processes including DNA methylation, DNA hydroxymethylation, histone modifications and non-coding RNAs implicated in the brain of those with Parkinson's disease. We will also discuss the limitations of current epigenetic research in Parkinson's disease, the advantages of simultaneously studying genetics and epigenetics, and putative novel epigenetic therapies.

Antigen-specific immunotherapy via delivery of tolerogenic dendritic cells for multiple sclerosis

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of the central nervous system resulting from loss of immune tolerance. Many disease-modifying therapies for MS have broad immunosuppressive effects on peripheral immune cells, but this can increase risks of infection and attenuate vaccine-elicited immunity. A more targeted approach is to re-establish immune tolerance in an autoantigen-specific manner. This review discusses methods to achieve this, focusing on tolerogenic dendritic cells. Clinical trials in other autoimmune diseases also provide learnings with regards to clinical translation of this approach, including identification of autoantigen(s), selection of appropriate patients and administration route and frequency.

Endoplasmic reticulum stress related genome-wide Mendelian randomization identifies therapeutic genes for ulcerative colitis and Crohn's disease

Background: Endoplasmic reticulum stress (ERS) is an important pathophysiological mechanism in ulcerative colitis (UC) and Crohn's disease (CD). ERS-related genes may be influenced by genetic factors and intestinal inflammation. However, the role of ERS as a trigger or potential etiological factor for UC and CD is unclear, as the expression of ERS-related genes in UC and CD may be the cause or subsequent changes in intestinal inflammation. Here, we used a three-step summary data-based Mendelian randomization (SMR) approach integrating multi-omics data to identify putative causal effects of ERS-related genes in UC and CD. Methods: Genome-wide association study (GWAS) summary data for UC (6,968 cases and 20,464 controls) and CD (5,956 cases and 14,927 controls) were extracted as outcome, and DNA methylation quantitative trait loci (mQTL, 1,980 participants) data and expression QTL data (eQTL, 31,684 participants) from the blood were obtained as exposure. The ERS-related genes were extracted from the GeneCards database, and then the GWAS summary data were integrated with the mQTL and eQTL data associated with ERS genes by SMR. Sensitivity analysis included two-sample MR analysis, power calculations, Bayesian co-localization analysis, and phenotype scanning were performed to evaluate the robustness of the results. Results: A total of 1,193 ERS-related genes were obtained. The three-step SMR analysis showed that cg24011261 CpG site regulating GPX1 expression was associated with a low risk of UC, whereas GPX1 expression regulated by a combination of cg05055782, cg24011261, and cg05551922 CpG sites was associated with a low risk of CD. Sensitivity analysis further supports these findings. Conclusion: This multi-omics integration study identifies a causal relationship between the role of ERS in UC and CD and suggests potential new therapeutic targets for clinical practice.

Whole-genome sequencing analysis of suicide deaths integrating brain-regulatory eQTLs data to identify risk loci and genes

Recent large-scale genome-wide association studies (GWAS) have started to identify potential genetic risk loci associated with risk of suicide; however, a large portion of suicide-associated genetic factors affecting gene expression remain elusive. Dysregulated gene expression, not assessed by GWAS, may play a significant role in increasing the risk of suicide death. We performed the first comprehensive genomic association analysis prioritizing brain expression quantitative trait loci (eQTLs) within regulatory regions in suicide deaths from the Utah Suicide Genetic Risk Study (USGRS). 440,324 brain-regulatory eQTLs were obtained by integrating brain eQTLs, histone modification ChIP-seq, ATAC-seq, DNase-seq, and Hi-C results from publicly available data. Subsequent genomic analyses were conducted in whole-genome sequencing (WGS) data from 986 suicide deaths of non-Finnish European (NFE) ancestry and 415 ancestrally matched controls. Additional independent USGRS suicide deaths with genotyping array data (n = 4657) and controls from the Genome Aggregation Database were explored for WGS result replication. One significant eQTL locus, rs926308 (p = 3.24e-06), was identified. The rs926308-T is associated with lower expression of RFPL3S, a gene important for neocortex development and implicated in arousal. Gene-based analyses performed using Sherlock Bayesian statistical integrative analysis also detected 20 genes with expression changes that may contribute to suicide risk. From analyzing publicly available transcriptomic data, ten of these genes have previous evidence of differential expression in suicide death or in psychiatric disorders that may be associated with suicide, including schizophrenia and autism (ZNF501, ZNF502, CNN3, IGF1R, KLHL36, NBL1, PDCD6IP, SNX19, BCAP29, and ARSA). Electronic health records (EHR) data was further merged to evaluate if there were clinically relevant subsets of suicide deaths associated with genetic variants. In summary, our study identified one risk locus and ten genes associated with suicide risk via gene expression, providing new insight into possible genetic and molecular mechanisms leading to suicide.

Oxidative stress gene expression, DNA methylation, and gut microbiota interaction trigger Crohn's disease: a multi-omics Mendelian randomization study

BACKGROUND

Oxidative stress (OS) is a key pathophysiological mechanism in Crohn's disease (CD). OS-related genes can be affected by environmental factors, intestinal inflammation, gut microbiota, and epigenetic changes. However, the role of OS as a potential CD etiological factor or triggering factor is unknown, as differentially expressed OS genes in CD can be either a cause or a subsequent change of intestinal inflammation. Herein, we used a multi-omics summary data-based Mendelian randomization (SMR) approach to identify putative causal effects and underlying mechanisms of OS genes in CD.

METHODS

OS-related genes were extracted from the GeneCards database. Intestinal transcriptome datasets were collected from the Gene Expression Omnibus (GEO) database and meta-analyzed to identify differentially expressed genes (DEGs) related to OS in CD. Integration analyses of the largest CD genome-wide association study (GWAS) summaries with expression quantitative trait loci (eQTLs) and DNA methylation QTLs (mQTLs) from the blood were performed using SMR methods to prioritize putative blood OS genes and their regulatory elements associated with CD risk. Up-to-date intestinal eQTLs and fecal microbial QTLs (mbQTLs) were integrated to uncover potential interactions between host OS gene expression and gut microbiota through SMR and colocalization analysis. Two additional Mendelian randomization (MR) methods were used as sensitivity analyses. Putative results were validated in an independent multi-omics cohort from the First Affiliated Hospital of Sun Yat-sen University (FAH-SYS).

RESULTS

A meta-analysis from six datasets identified 438 OS-related DEGs enriched in intestinal enterocytes in CD from 817 OS-related genes. Five genes from blood tissue were prioritized as candidate CD-causal genes using three-step SMR methods: BAD, SHC1, STAT3, MUC1, and GPX3. Furthermore, SMR analysis also identified five putative intestinal genes, three of which were involved in gene-microbiota interactions through colocalization analysis: MUC1, CD40, and PRKAB1. Validation results showed that 88.79% of DEGs were replicated in the FAH-SYS cohort. Associations between pairs of MUC1-Bacillus aciditolerans and PRKAB1-Escherichia coli in the FAH-SYS cohort were consistent with eQTL-mbQTL colocalization.

CONCLUSIONS

This multi-omics integration study highlighted that OS genes causal to CD are regulated by DNA methylation and host-microbiota interactions. This provides evidence for future targeted functional research aimed at developing suitable therapeutic interventions and disease prevention.

Genome-Wide Gene-Set Analysis Identifies Molecular Mechanisms Associated with ALS

Amyotrophic lateral sclerosis (ALS) is a fatal late-onset motor neuron disease characterized by the loss of the upper and lower motor neurons. Our understanding of the molecular basis of ALS pathology remains elusive, complicating the development of efficient treatment. Gene-set analyses of genome-wide data have offered insight into the biological processes and pathways of complex diseases and can suggest new hypotheses regarding causal mechanisms. Our aim in this study was to identify and explore biological pathways and other gene sets having genomic association to ALS. Two cohorts of genomic data from the dbGaP repository were combined: (a) the largest available ALS individual-level genotype dataset (N = 12,319), and (b) a similarly sized control cohort (N = 13,210). Following comprehensive quality control pipelines, imputation and meta-analysis, we assembled a large European descent ALS-control cohort of 9244 ALS cases and 12,795 healthy controls represented by genetic variants of 19,242 genes. Multi-marker analysis of genomic annotation (MAGMA) gene-set analysis was applied to an extensive collection of 31,454 gene sets from the molecular signatures database (MSigDB). Statistically significant associations were observed for gene sets related to immune response, apoptosis, lipid metabolism, neuron differentiation, muscle cell function, synaptic plasticity and development. We also report novel interactions between gene sets, suggestive of mechanistic overlaps. A manual meta-categorization and enrichment mapping approach is used to explore the overlap of gene membership between significant gene sets, revealing a number of shared mechanisms.

Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

From the perspective of precision medicine, the challenge for the future is to improve the accuracy of diagnosis, prognosis, and prediction of therapeutic responses through the identification of biomarkers. In this framework, the omics sciences (genomics, transcriptomics, proteomics, and metabolomics) and their combined use represent innovative approaches for the exploration of the complexity and heterogeneity of multiple sclerosis (MS). This review examines the evidence currently available on the application of omics sciences to MS, analyses the methods, their limitations, the samples used, and their characteristics, with a particular focus on biomarkers associated with the disease state, exposure to disease-modifying treatments (DMTs), and drug efficacies and safety profiles.

Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease

Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD-specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co-expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short-chain acylcarnitines/amino acids and medium/long-chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co-expression network analysis of the AMP-AD brain RNA-seq data suggests the CPT1A- and ABCA1-centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short-chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large-scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.

Genetics and functional genomics of multiple sclerosis

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease with genetic predisposition. Over the last decade, genome-wide association studies with increasing sample size led to the discovery of robustly associated genetic variants at an exponential rate. More than 200 genetic loci have been associated with MS susceptibility and almost half of its heritability can be accounted for. However, many challenges and unknowns remain. Definitive studies of disease progression and endophenotypes are yet to be performed, whereas the majority of the identified MS variants are not yet functionally characterized. Despite these shortcomings, the unraveling of MS genetics has opened up a new chapter on our understanding MS causal mechanisms.

Finding genetically-supported drug targets for Parkinson's disease using Mendelian randomization of the druggable genome

Parkinson's disease is a neurodegenerative movement disorder that currently has no disease-modifying treatment, partly owing to inefficiencies in drug target identification and validation. We use Mendelian randomization to investigate over 3,000 genes that encode druggable proteins and predict their efficacy as drug targets for Parkinson's disease. We use expression and protein quantitative trait loci to mimic exposure to medications, and we examine the causal effect on Parkinson's disease risk (in two large cohorts), age at onset and progression. We propose 23 drug-targeting mechanisms for Parkinson's disease, including four possible drug repurposing opportunities and two drugs which may increase Parkinson's disease risk. Of these, we put forward six drug targets with the strongest Mendelian randomization evidence. There is remarkably little overlap between our drug targets to reduce Parkinson's disease risk versus progression, suggesting different molecular mechanisms. Drugs with genetic support are considerably more likely to succeed in clinical trials, and we provide compelling genetic evidence and an analysis pipeline to prioritise Parkinson's disease drug development.

Investigating the shared genetic architecture between multiple sclerosis and inflammatory bowel diseases

An epidemiological association between multiple sclerosis (MS) and inflammatory bowel disease (IBD) is well established, but whether this reflects a shared genetic aetiology, and whether consistent genetic relationships exist between MS and the two predominant IBD subtypes, ulcerative colitis (UC) and Crohn’s disease (CD), remains unclear. Here, we use large-scale genome-wide association study summary data to investigate the shared genetic architecture between MS and IBD overall and UC and CD independently. We find a significantly greater genetic correlation between MS and UC than between MS and CD, and identify three SNPs shared between MS and IBD (rs13428812), UC (rs116555563) and CD (rs13428812, rs9977672) in cross-trait meta-analyses. We find suggestive evidence for a causal effect of MS on UC and IBD using Mendelian randomization, but no or weak and inconsistent evidence for a causal effect of IBD or UC on MS. We observe largely consistent patterns of tissue-specific heritability enrichment for MS and IBDs in lung, spleen, whole blood and small intestine, and identify cell-type-specific enrichment for MS and IBDs in CD4⁺ T cells in lung and CD8⁺ cytotoxic T cells in lung and spleen. Our study sheds light on the biological basis of comorbidity between MS and IBD.

An epidemiological association between multiple sclerosis (MS) and inflammatory bowel disease (IBD) is well-established, but a genetic link is unclear. Here, the authors investigate the shared genetic architecture between MS and IBD to shed light on the biological basis of comorbidity.

Novel DNA methylation loci and genes showing pleiotropic association with Alzheimer's dementia: a network Mendelian randomization analysis

Previous genome-wide association studies (GWAS) have identified potential genetic variants involved in the risk of Alzheimer's dementia, but their underlying biological interpretation remains largely unclear. In addition, the effects of DNA methylation and gene expression on Alzheimer's dementia are not well understood. A network summary data-based Mendelian randomization (SMR) analysis was performed integrating cis- DNA methylation quantitative trait loci (mQTL) /cis- gene expression QTL (eQTL) data in the brain and blood, as well as GWAS summarized data for Alzheimer's dementia to evaluate the pleiotropic associations of DNA methylation and gene expression with Alzheimer's dementia and to explore the complex mechanisms underpinning Alzheimer's dementia. After correction for multiple testing (false discovery rate [FDR] P < 0.05) and filtering using the heterogeneity in dependent instruments (HEIDI) test (P_HEIDI>0.01), we identified dozens of DNA methylation sites and genes showing pleiotropic associations with Alzheimer's dementia. We found 22 and 16 potentially causal pathways of Alzheimer's dementia (i.e., SNP→DNA methylation→Gene expression→Alzheimer's dementia) in the brain and blood, respectively. Approximately two-thirds of the identified DNA methylation sites had an influence on gene expression and the expression of almost all the identified genes was regulated by DNA methylation. Our network SMR analysis provided evidence supporting the pleiotropic association of some novel DNA methylation sites and genes with Alzheimer's dementia and revealed possible causal pathways underlying the pathogenesis of Alzheimer's dementia. Our findings shed light on the role of DNA methylation in gene expression and in the development of Alzheimer's dementia.