Systematic druggable genome-wide Mendelian randomization identifies therapeutic targets for sarcopenia

Abstract

Background: There are no effective pharmacological treatments for sarcopenia. We aim to identify potential therapeutic targets for sarcopenia by integrating various publicly available datasets.

Methods: We 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.

Results: MR 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.

Conclusions: Our 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.

Keywords: Colocalization; Druggable genes; Mendelian randomization; Sarcopenia.

Figure 1

Flow diagram of the study. Initially, we obtained 2532 known druggable genes from the DGIdb database and the research of Finan et al. subsequently, we utilized eQTL/pQTL data from human blood and skeletal muscle tissues to construct an eQTL/pQTL tool for druggable genes, filtering out independent genetic variants significantly associated with druggable gene expression (serving as IVs), which are located within a 1 Mb range upstream and downstream of the coding sequence (cis). In the MR analysis, we preliminarily identified potential causal genetic variants for sarcopenia using these IVs. We also employed Bayesian colocalization methods to detect shared causal genetic variants. Finally, we assessed the potential side effects or additional indications of the identified priority druggable genes through a Phe‐MR in UK biobank GWASs, and researched their druggability and clinical development capabilities in relevant databases or websites. eQTL, expression quantitative trait loci; eQTLGen consortium, expression quantitative trait loci generation consortium; GTEx, genotype‐tissue expression; pQTL, protein quantitative trait lociDGIdb, drug‐gene interaction database; Phe‐MR, phenome‐wide Mendelian randomization analysis.

Figure 2

Summary of causal druggable genes for sarcopenia identified through MR and Bayesian colocalization analysis (at least in two outcomes or datasets). To ensure effective IVs and mitigate pleiotropy, we primarily used single SNPs as our tools for exposure. The figure denotes a Steiger P‐value < 0.05 as ‘true’, supporting our hypothesis that our IVs cause the outcome variables. Panel (A) shows that three druggable genes passed both Mendelian randomization and Bayesian colocalization analysis and are simultaneously present in two outcomes. Panel (B) shows that three druggable genes passed both Mendelian randomization and Bayesian colocalization analysis and are simultaneously present in two datasets. If the posterior probability of PPH4 is >90%, it is considered that the QTL dataset and sarcopenia share the same variant. ‘ ^# ’ shows that the outcome (low hand grip strength) is binary traits, and the association strength between exposure and outcome is represented by the odds ratio (OR). OR value definition: If OR > 1, exposure may promote the outcome; if OR < 1, exposure may inhibit the outcome. Beta value definition: positive values suggest exposure may promote the outcome, negative values suggest it may inhibit it. ALM, appendicular lean mass.