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Western Diet-Induced Nonalcoholic Fatty Liver Disease Mice Mimic the Key Transcriptomic Signatures Observed in
T Ishigure1, T Sasase, M Tohma
1Biological/Pharmacological Research Laboratories, Takatsuki Research Center, Central Pharmaceutical Research Institute, Japan Tobacco Inc, Osaka, Japan. tomohiko.sasase@jt.com.
Physiological Research
|September 12, 2024
Summary
Nonalcoholic fatty liver disease (NAFLD) mouse models fed a Western diet show gene expression patterns similar to human NAFLD. This validates their use in preclinical research for understanding inflammatory responses in NAFLD.
Area of Science:
- Hepatology
- Translational Medicine
- Bioinformatics
Background:
- Nonalcoholic fatty liver disease (NAFLD) is a growing global health concern.
- Animal models are crucial for NAFLD research but their human relevance needs validation.
- Gene expression analysis can assess the translational utility of animal models.
Purpose of the Study:
- To evaluate the relevance of Western diet-induced NAFLD mouse models to human NAFLD.
- To identify conserved gene co-expression networks between mouse and human NAFLD.
Main Methods:
- Weighted gene co-expression network analysis (WGCNA) was applied to liver tissues from NAFLD mice.
- Module preservation analysis was used to compare mouse and human gene expression datasets.
- Functional enrichment analysis identified key biological processes.
Main Results:
- Nineteen modules associated with NAFLD progression were identified in mice.
- A "brown" module strongly correlated with disease severity and showed enrichment for inflammatory responses.
- This "brown" module's gene co-expression network was highly preserved in human NAFLD liver samples.
Conclusions:
- Western diet-induced NAFLD mouse models exhibit conserved gene co-expression networks with human NAFLD, particularly for inflammatory pathways.
- These findings support the utility of this animal model for preclinical NAFLD research.
- The study highlights the importance of gene co-expression network analysis for validating translational models.

