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Updated: May 20, 2025
Optimized Analysis of In Vivo and In Vitro Hepatic Steatosis
Published on: March 11, 2017
Druggable genome-wide Mendelian randomization identifies therapeutic targets for metabolic dysfunction-associated
Xiaohui Ma1,2, Li Ding1, Shuo Li1
1Department of Endocrinology and Metabolism, Tianjin Medical University General Hospital, 154 Anshan Road, Heping District, Tianjin, 300052, China.
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.
Insights
This study identifies CD33 inhibition and MFGE8 activation as potential therapeutic strategies for metabolic dysfunction-associated steatotic liver disease (MASLD). These findings offer new avenues for treating this widespread liver condition.
Area of Science:
- Genetics
- Hepatology
- Pharmacology
Background:
- Metabolic dysfunction-associated steatotic liver disease (MASLD) affects over 25% of the global population.
- MASLD can lead to severe hepatic and extrahepatic complications, including metabolic dysfunction-associated steatohepatitis.
- Understanding MASLD pathophysiology is crucial for identifying therapeutic targets and optimizing treatment.
Purpose of the Study:
- To identify potential drug targets for MASLD using Mendelian randomization (MR).
- To validate the role of identified targets in MASLD pathogenesis.
- To explore therapeutic strategies for MASLD based on genetic findings.
Main Methods:
- Mendelian randomization (MR) analysis using large genome-wide association study datasets.
- Colocalization analyses to assess shared causal variants between MASLD and gene expression quantitative trait loci (eQTLs).
- Validation of gene expression using RNA-sequencing, qPCR, and immunohistochemistry on liver biopsies.
Main Results:
- MR analysis identified significant associations between MASLD and two drug targets: MFGE8 (milk fat globule-EGF factor 8) and CD33 (cluster of differentiation 33).
- Both MFGE8 and CD33 showed strong colocalization with MASLD.
- Genetic manipulation indicated that MFGE8 activation and CD33 inhibition did not increase the risk for other metabolic disorders.
Conclusions:
- CD33 inhibition presents a potential therapeutic strategy to reduce MASLD risk.
- MFGE8 activation may offer therapeutic benefits for MASLD treatment.
- These findings highlight MFGE8 and CD33 as promising targets for MASLD interventions.
