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

ABSTRACT: Multiple sclerosis is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study findings into therapeutics and effective preventive strategies has been limited to date. We used summary-data-based Mendelian randomization to synthesize findings from public expression quantitative trait locus, methylation quantitative trait locus and Multiple Sclerosis Genome-Wide Association Study datasets. By correlating the effects of methylation on multiple sclerosis, methylation on expression and expression on multiple sclerosis susceptibility, we prioritize genetic loci with evidence of influencing multiple sclerosis susceptibility. We overlay these findings onto a list of 'druggable' genes, i.e. genes which are currently, or could theoretically, be targeted by therapeutic compounds. We use GeNets and search tool for the retrieval of interacting genes/proteins to identify protein-protein interactions and druggable pathways enriched in our results. We extend these findings to a model of Epstein-Barr virus-infected B cells, lymphoblastoid cell lines. We conducted a systematic review of prioritized genes using the Open Targets platform to identify completed and planned trials targeting prioritized genes in multiple sclerosis and related disease areas. Expression of 45 genes in peripheral blood was strongly associated with multiple sclerosis susceptibility (False discovery rate 0.05). Of these 45 genes, 20 encode a protein which is currently targeted by an existing therapeutic compound. These genes were enriched for Gene Ontology terms pertaining to immune system function and leucocyte signalling. We refined this prioritized gene list by restricting to loci where CpG site methylation was associated with multiple sclerosis susceptibility, with gene expression and where expression was associated with multiple sclerosis susceptibility. This approach yielded a list of 15 prioritized druggable target genes for which there was evidence of a pathway linking methylation, expression and multiple sclerosis. Five of these 15 genes are targeted by existing drugs and three were replicated in a smaller expression Quantitative Trait Loci dataset (CD40, MERTK and PARP1). In lymphoblastoid cell lines, this approach prioritized 7 druggable gene targets, of which only one was prioritized by the multi-omic approach in peripheral blood (FCRL3). Systematic review of Open Targets revealed multiple early-phase trials targeting 13/20 prioritized genes in disorders related to multiple sclerosis. We use public datasets and summary-data-based Mendelian randomization to identify a list of prioritized druggable genetic targets in multiple sclerosis. We hope our findings could be translated into a platform for developing targeted preventive therapies.

SUBMITTER: Jacobs BM

PROVIDER: S-EPMC7519728 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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

Jacobs Benjamin M BM Taylor Thomas T Awad Amine A Baker David D Giovanonni Gavin G Noyce Alastair J AJ Dobson Ruth R

Brain communications 20200814 2

Multiple sclerosis is a complex autoimmune disease caused by a combination of genetic and environmental factors. Translation of Genome-Wide Association Study findings into therapeutics and effective preventive strategies has been limited to date. We used summary-data-based Mendelian randomization to synthesize findings from public expression quantitative trait locus, methylation quantitative trait locus and Multiple Sclerosis Genome-Wide Association Study datasets. By correlating the effects of ...[more]

PMID: 33005893

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Project description:Multiple sclerosis is a complex autoimmune disease, and several therapies for multiple sclerosis have been developed and widely used. However, existing medications for multiple sclerosis were far from satisfactory due to their failure to suppress relapses and alleviate disease progression. Novel drug targets for multiple sclerosis prevention are still needed. We performed Mendelian randomization to explore potential drug targets for multiple sclerosis using summary statistics from the International Multiple Sclerosis Genetics Consortium (nCase = 47 429, nControl = 68 374) and further replicated in UK Biobank (nCase = 1356, nControl = 395 209) and FinnGen cohorts (nCase = 1326, nControl = 359 815). Genetic instruments for 734 plasma and 154 CSF proteins were obtained from recently published genome-wide association studies. The reverse causality detection using bidirectional Mendelian randomization analysis and Steiger filtering, Bayesian co-localization, and phenotype scanning that searched previously reported genetic variant-trait associations were implemented to consolidate the Mendelian randomization findings further. In addition, the protein-protein interaction network was performed to reveal potential associations among proteins and/or present multiple sclerosis medications. At Bonferroni significance (P < 5.63 × 10-5), Mendelian randomization analysis revealed six protein-multiple sclerosis pairs. In plasma, per standard deviation increase in FCRL3, TYMP and AHSG had a protective effect. Odds ratios for the proteins above were 0.83 (95% CI, 0.79-0.89), 0.59 (95% CI, 0.48-0.71) and 0.88 (95% CI, 0.83-0.94), respectively. In CSF, per 10-fold increase in MMEL1 (OR, 5.03; 95% CI, 3.42-7.41) increased the risk of multiple sclerosis, while SLAMF7 (OR, 0.42; 95% CI, 0.29-0.60) and CD5L (OR, 0.30; 95%CI, 0.18-0.52) decreased the risk. None of the six proteins had reverse causality. Bayesian co-localization suggested that FCRL3 [coloc.abf-posterior probability of hypothesis 4 (PPH4) = 0.889], TYMP (coloc.susie-PPH4 = 0.896), AHSG (coloc.abf-PPH4 = 0.957, coloc.susie-PPH4 = 0.973), MMEL1 (coloc.abf-PPH4 = 0.930) and SLAMF7 (coloc.abf-PPH4 = 0.947) shared the same variant with multiple sclerosis. FCRL3, TYMP and SLAMF7 interacted with target proteins of current multiple sclerosis medications. MMEL1 was replicated in both UK Biobank and FinnGen cohorts. Our integrative analysis suggested that genetically determined levels of circulating FCRL3, TYMP, AHSG, CSF MMEL1 and SLAMF7 had causal effects on multiple sclerosis risk. These findings suggested those five proteins might be promising drug targets for multiple sclerosis and warrant further clinical investigation, especially FCRL3 and SLAMF7.

Project description:BackgroundThere are no effective pharmacological treatments for sarcopenia. We aim to identify potential therapeutic targets for sarcopenia by integrating various publicly available datasets.MethodsWe integrated druggable genome data, cis-eQTL/cis-pQTL from human blood and skeletal muscle tissue, and GWAS summary data of sarcopenia-related traits to analyse the potential causal relationships between drug target genes and sarcopenia using the Mendelian Randomization (MR) method. Sensitivity analyses and Bayesian colocalization were employed to validate the causal relationships. We also assessed the side effects or additional indications of the identified drug targets using a phenome-wide MR (Phe-MR) approach and investigated actionable drugs for target genes using available databases.ResultsMR analysis identified 17 druggable genes with potential causation to sarcopenia in human blood or skeletal muscle tissue. Six of them (HP, HLA-DRA, MAP 3K3, MFGE8, COL15A1, and AURKA) were further confirmed by Bayesian colocalization (PPH4 > 90%). The up-regulation of HP [higher ALM (beta: 0.012, 95% CI: 0.007-0.018, P = 1.2*10-5) and higher grip strength (OR: 0.96, 95% CI: 0.94-0.98, P = 4.2*10-5)], MAP 3K3 [higher ALM (beta: 0.24, 95% CI: 0.21-0.26, P = 1.8*10-94), higher grip strength (OR: 0.82, 95% CI: 0.75-0.90, P = 2.1*10-5), and faster walking pace (beta: 0.03, 95% CI: 0.02-0.05, P = 8.5*10-6)], and MFGE8 [higher ALM (muscle eQTL, beta: 0.09, 95% CI: 0.06-0.11, P = 6.1*10-13; blood pQTL, beta: 0.05, 95% CI: 0.03-0.07, P = 3.8*10-09)], as well as the down-regulation of HLA-DRA [lower ALM (beta: -0.09, 95% CI: -0.11 to -0.08, P = 5.4*10-36) and lower grip strength (OR: 1.13, 95% CI: 1.07-1.20, P = 1.8*10-5)] and COL15A1 [higher ALM (muscle eQTL, beta: -0.07, 95% CI: -0.10 to -0.04, P = 3.4*10-07; blood pQTL, beta: -0.05, 95% CI: -0.06 to -0.03, P = 1.6*10-07)], decreased the risk of sarcopenia. AURKA in blood (beta: -0.16, 95% CI: -0.22 to -0.09, P = 2.1*10-06) and skeletal muscle (beta: 0.03, 95% CI: 0.02 to 0.05, P = 5.3*10-05) tissues showed an inverse relationship with sarcopenia risk. The Phe-MR indicated that the six potential therapeutic targets for sarcopenia had no significant adverse effects. Drug repurposing analysis supported zinc supplementation and collagenase clostridium histolyticum might be potential therapeutics for sarcopenia by activating HP and inhibiting COL15A1, respectively.ConclusionsOur research indicated MAP 3K3, MFGE8, COL15A1, HP, and HLA-DRA may serve as promising targets for sarcopenia, while the effectiveness of zinc supplementation and collagenase clostridium histolyticum for sarcopenia requires further validation.

Project description:BackgroundObservational studies have reported an association between obesity, as measured by elevated body mass index (BMI), in early adulthood and risk of multiple sclerosis (MS). However, bias potentially introduced by confounding and reverse causation may have influenced these findings. Therefore, we elected to perform Mendelian randomization (MR) analyses to evaluate whether genetically increased BMI is associated with an increased risk of MS.Methods and findingsEmploying a two-sample MR approach, we used summary statistics from the Genetic Investigation of Anthropometric Traits (GIANT) consortium and the International MS Genetics Consortium (IMSGC), the largest genome-wide association studies for BMI and MS, respectively (GIANT: n = 322,105; IMSGC: n = 14,498 cases and 24,091 controls). Seventy single nucleotide polymorphisms (SNPs) were genome-wide significant (p < 5 x 10-8) for BMI in GIANT (n = 322,105) and were investigated for their association with MS risk in the IMSGC. The effect of each SNP on MS was weighted by its effect on BMI, and estimates were pooled to provide a summary measure for the effect of increased BMI upon risk of MS. Our results suggest that increased BMI influences MS susceptibility, where a 1 standard deviation increase in genetically determined BMI (kg/m2) increased odds of MS by 41% (odds ratio [OR]: 1.41, 95% CI 1.20-1.66, p = 2.7 x 10-5, I2 = 0%, 95% CI 0-29). Sensitivity analyses, including MR-Egger regression, and the weighted median approach provided no evidence of pleiotropic effects. The main study limitations are that, while these sensitivity analyses reduce the possibility that pleiotropy influenced our results, residual pleiotropy is difficult to exclude entirely.ConclusionGenetically elevated BMI is associated with risk of MS, providing evidence for a causal role for obesity in MS etiology. While obesity has been associated with many late-life outcomes, these findings suggest an important consequence of childhood and/or early adulthood obesity.

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

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

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