MASH-GA: a manually curated cross-species transcriptomic database for metabolic-associated steatohepatitis

Area of Science:

• Hepatology
• Metabolic Disease Research
• Systems Biology
• Genomics

Background:

• Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive liver disease linked to metabolic dysregulation.
• It is a primary driver of liver failure and transplantation globally.
• A lack of centralized, accessible transcriptomic data hinders MASH research.

Purpose of the Study:

• To develop the first comprehensive, cross-species transcriptomic database for MASH research.
• To provide a centralized platform for integrating and analyzing human and mouse MASH data.
• To facilitate reproducible research and discovery in MASH.

Main Methods:

• Systematic integration of 45 human transcriptomic datasets (2740 samples) and 126 mouse datasets (1950 samples).
• Development of MASH-GA (MASH-Genomic Atlas) database with features like WGCNA, cross-model comparison, and interactive visualizations.
• Separate, intuitive modules for human and mouse data exploration.

Main Results:

• MASH-GA is the first and only database integrating transcriptomic data across human cohorts and mouse models for MASH.
• The platform offers advanced analytical tools including WGCNA, cross-model classification, and multigene correlation analysis.
• Standardized, multicohort, cross-species data enables robust exploration and identification of regulatory modules.

Conclusions:

• MASH-GA serves as a valuable, unique resource for the fatty liver research community.
• It empowers researchers to conduct reproducible data exploration and informed model selection.
• The database aids in identifying key regulatory modules relevant to MASH pathogenesis and treatment.