Immune metabolic changes identify causal candidate genes and enable diagnostic frameworks in MAFLD

Area of Science:

• Immunology
• Metabolic Diseases
• Genetics

Background:

• Metabolic dysfunction-associated fatty liver disease (MAFLD) is a prevalent global health issue driven by complex immune-metabolic interactions.
• The precise causal links between immune cell gene expression changes and MAFLD progression are not fully understood.
• Existing research often lacks integrated approaches combining single-cell data, causal inference, and functional validation.

Purpose of the Study:

• To identify causal genes linking immune cell perturbations to MAFLD pathogenesis.
• To develop and validate a machine learning-based diagnostic model for MAFLD.
• To functionally validate candidate genes and their role in liver steatosis.

Main Methods:

• Integrated single-cell RNA sequencing (scRNA-seq) of PBMCs from MAFLD patients and controls.
• Two-sample Mendelian randomization (MR) analysis using large-scale GWAS data to identify causal genes.
• Machine learning algorithms for diagnostic model development and multi-cohort validation.
• In vivo and in vitro functional assays in mouse models (HFD, ob/ob) and hepatocytes.

Main Results:

• scRNA-seq revealed significant gene expression differences in CD4+ T cells and monocytes.
• MR identified 37 causal candidate genes, including protective PRF1 and risk gene EVI2B.
• A five-gene ML model (PRF1, EVI2B, CST7, GNG2, KLHL24) demonstrated high diagnostic accuracy.
• EVI2B overexpression was confirmed in hepatic tissue and exacerbated lipid accumulation in hepatocytes.

Conclusions:

• This study establishes a framework for dissecting immune-driven MAFLD using integrated omics and validation.
• EVI2B is identified as a pro-steatotic gene contributing to MAFLD pathogenesis.
• The developed diagnostic model offers a promising tool for MAFLD detection and highlights potential therapeutic targets.