Academic Background

Metabolic-associated Steatotic Liver Disease (MASLD) is a major health issue affecting over 30% of the global population and is closely related to various chronic diseases such as cancer, cardiovascular disease, and renal dysfunction. The pathological spectrum of MASLD ranges from simple steatosis to steatohepatitis and progressive fibrosis. Although steatosis is an early manifestation of MASLD, its heterogeneous progression to more severe stages and the limited efficacy of existing treatments have prompted researchers to seek early markers for preventive interventions in the population. However, traditional liver biopsy, as the gold standard for diagnosis, is neither practical nor ethical in large-scale population studies. Therefore, research combining imaging, blood genomics, and non-genomic biomarkers has become a current hotspot.

Although studies have revealed potential targets such as PNPLA3 through large population cohorts, relying solely on genetic variation cannot capture the dynamic behavioral and environmental factors (e.g., diet, obesity, and diabetes) influencing steatosis. Thus, integrating circulating proteomics and tissue transcriptomics has become key to uncovering the early mechanisms of steatosis.

Source of the Paper

This paper was co-authored by Andrew S. Perry, Niran Hadad, Emeli Chatterjee, and others, published on December 17, 2024, in the journal Cell Reports Medicine. The authors are from several renowned research institutions, including Vanderbilt University School of Medicine, Translational Genomics Research Institute, and Massachusetts General Hospital. The paper identifies dynamic functional biomarkers of steatosis by integrating clinical phenotypes, circulating proteomics, and liver tissue transcriptomics, and explores the association of these biomarkers with metabolically relevant clinical outcomes.

Research Process and Results

Research Process

The study is divided into six main steps:

1. Identification and Validation of the “Steatosis” Proteome: The study identified proteins associated with steatosis through multi-modality imaging and extensive proteomic analysis in three prospective observational studies (CARDIA, UK Biobank, and Cameron County Hispanic Cohort).

2. Association of the Proteome with Clinical Outcomes: The study explored the association of the steatosis proteome with metabolically relevant clinical outcomes (e.g., cardiovascular disease, diabetes) in 26,421 participants from the UK Biobank.

3. Characterization of Tissue Origin and Molecular Pathways: The study analyzed the tissue origin and molecular pathways involved in these proteins using transcriptomic and proteomic data.

4. Gene Expression Analysis: The study analyzed the expression differences of genes encoding the steatosis proteome across different stages of MASLD using RNA sequencing data from human liver.

5. Single-Cell and Spatial Transcriptomic Analysis: The study explored the spatial expression patterns of these genes in steatotic regions using single-cell RNA sequencing and spatial transcriptomics.

6. Liver-on-a-Chip Model Validation: The study validated the transcriptional and protein expression changes of these genes during steatosis induction using a humanized liver-on-a-chip model.

Main Results

1. Identification of the Steatosis Proteome: The study identified 237 proteins associated with steatosis in the CARDIA cohort. These proteins were significantly enriched in the liver at the transcriptional and protein levels and involved key pathways such as metabolism, inflammation, and fibrosis.

2. Generation and Validation of the Proteomic Score: The study generated a steatosis proteomic score using LASSO regression and validated its correlation with steatosis in the UK Biobank and Cameron County Hispanic Cohort.

3. Association of the Proteomic Score with Clinical Outcomes: The study found that the steatosis proteomic score was significantly associated with fatty liver disease, diabetes, and other metabolically relevant outcomes, significantly improving the discriminative ability of the models in predicting these outcomes.

4. Spatial and Cellular Expression Patterns: Through single-cell and spatial transcriptomic analysis, the study revealed significant differences in the spatial expression patterns of these genes in steatotic regions, with predominant enrichment in hepatocytes.

5. Liver-on-a-Chip Model Validation: The study validated the transcriptional and protein expression changes of these genes during steatosis induction using a liver-on-a-chip model, further supporting the biological significance of these biomarkers.

Conclusions and Significance

By integrating clinical phenotypes, circulating proteomics, and liver tissue transcriptomics, the study identified dynamic functional biomarkers of steatosis and validated their association with metabolically relevant clinical outcomes. The study not only provides potential biomarkers for the early diagnosis of steatosis but also offers important resources for subsequent mechanistic research and therapeutic development.

Research Highlights

1. Multi-Level Integrated Research: The study provides a multi-level research framework from population to cells by integrating clinical phenotypes, circulating proteomics, liver tissue transcriptomics, and liver-on-a-chip models.

2. Identification of Dynamic Functional Biomarkers: The study identified dynamic functional biomarkers of steatosis and validated their expression changes during steatosis induction.

3. Revelation of Spatial and Cellular Expression Patterns: Through single-cell and spatial transcriptomics, the study revealed the spatial expression patterns of these genes in steatotic regions, further supporting the biological significance of these biomarkers.

Other Valuable Information

The study also explored the performance of the steatosis proteomic score in populations with different metabolic risks, finding that the score had stronger correlations in high metabolic risk populations. Additionally, the study validated the cell-specific expression of these biomarkers using a liver-on-a-chip model, providing an important experimental basis for subsequent mechanistic research.

Summary

Through a multi-level integrated approach, the study identified dynamic functional biomarkers of steatosis and validated their association with metabolically relevant clinical outcomes. The study not only provides potential biomarkers for the early diagnosis of steatosis but also offers important resources for subsequent mechanistic research and therapeutic development.