Lipids in health and disease · 2025
Druggable genome-wide Mendelian randomization identifies therapeutic targets for metabolic dysfunction-associated steatotic liver disease.
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Ma Xiaohui, Ding Li, Li Shuo, Fan Yu, Wang Xin, Han Yitong, Yuan Hengjie, Sun Longhao, He Qing, Liu Ming
Аннотация
BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) affects > 25% of the global population, potentially leading to severe hepatic and extrahepatic complications, including metabolic dysfunction-associated steatohepatitis. Given that the pathophysiology of MASLD is incompletely understood, identifying therapeutic targets and optimizing treatment strategies are crucial for addressing this severe condition.
METHODS: Mendelian randomization (MR) analysis was conducted using two genome-wide association study datasets: a European meta-analysis (8,434 cases; 770,180 controls) and an additional study (3,954 cases; 355,942 controls), identifying therapeutic targets for MASLD. Of 4302 drug-target genes, 2,664 genetic instrument variables were derived from cis-expression quantitative trait loci (cis-eQTLs). Colocalization analyses assessed shared causal variants between MASLD-associated single nucleotide polymorphisms and eQTLs. Using the drug target gene cis-eQTL of liver tissue from the genotype-tissue expression project, we performed MR and summary MR to validate the significance of the gene results of the blood eQTL MR. RNA-sequencing data from liver biopsies were validated using immunohistochemistry and quantitative polymerase chain reaction (qPCR) tests to confirm gene expression findings.
RESULT: MR analysis across both datasets identified significant MR associations between MASLD and two drug targets-milk fat globule-EGF factor 8 (MFGE8) (odds ratio [OR] 0.89, 95% confidence interval [CI] 0.85-0.94; P = 2.15 × 10-6) and cluster of differentiation 33 (CD33) (OR 1.17, 95% CI 1.10-1.25; P = 1.39 × 10-6). Both targets exhibited strong colocalization with MASLD. Genetic manipulation indicating MFGE8 activation and CD33 inhibition did not increase the risk for other metabolic disorders. RNA-sequencing, qPCR, and immunohistochemistry validation demonstrated consistent differential expressions of MFGE8 and CD33 in MASLD.
CONCLUSION: CD33 inhibition can reduce MASLD risk, while MFGE8 activation may offer therapeutic benefits for MASLD treatment.
METHODS: Mendelian randomization (MR) analysis was conducted using two genome-wide association study datasets: a European meta-analysis (8,434 cases; 770,180 controls) and an additional study (3,954 cases; 355,942 controls), identifying therapeutic targets for MASLD. Of 4302 drug-target genes, 2,664 genetic instrument variables were derived from cis-expression quantitative trait loci (cis-eQTLs). Colocalization analyses assessed shared causal variants between MASLD-associated single nucleotide polymorphisms and eQTLs. Using the drug target gene cis-eQTL of liver tissue from the genotype-tissue expression project, we performed MR and summary MR to validate the significance of the gene results of the blood eQTL MR. RNA-sequencing data from liver biopsies were validated using immunohistochemistry and quantitative polymerase chain reaction (qPCR) tests to confirm gene expression findings.
RESULT: MR analysis across both datasets identified significant MR associations between MASLD and two drug targets-milk fat globule-EGF factor 8 (MFGE8) (odds ratio [OR] 0.89, 95% confidence interval [CI] 0.85-0.94; P = 2.15 × 10-6) and cluster of differentiation 33 (CD33) (OR 1.17, 95% CI 1.10-1.25; P = 1.39 × 10-6). Both targets exhibited strong colocalization with MASLD. Genetic manipulation indicating MFGE8 activation and CD33 inhibition did not increase the risk for other metabolic disorders. RNA-sequencing, qPCR, and immunohistochemistry validation demonstrated consistent differential expressions of MFGE8 and CD33 in MASLD.
CONCLUSION: CD33 inhibition can reduce MASLD risk, while MFGE8 activation may offer therapeutic benefits for MASLD treatment.