Spatial single-cell transcriptomics of MASLD-affected liver biopsy samples.
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ABSTRACT:
PROVIDER: PRJNA1237793 | ENA |
REPOSITORIES: ENA
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Project description:Hepatic fibrosis is the strongest contributor to hepatocarcinogenesis in metabolic dysfunction-associated steatotic liver disease (MASLD); however, the underlying mechanisms have yet to be fully elucidated. In 94 human MASLD biopsy samples, artificial intelligence-based morphological phenotyping of hepatic fiber and multi-omics analyses revealed that insulin growth factor-binding protein 7 (IGFBP-7) secreted from senescent periportal endothelial cells might transform stellate cells into a hepatocarcinogenesis-promoting phenotype. To test the effect of IGFBP-7 on HSC, a hepatic stellate cell line, LX-2, was cultured with recombinant IGFBP-7 (100ng/mL), resulting in their transformation to a more activated form than the control.
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Project description:These data were used in the spatial transcriptomics analysis of the article titled \\"Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging\\".
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Project description:Background: N6-methyladenosine (m6A) RNA modification plays a crucial role in various biological events and is implicated in various metabolic-related diseases. However, its role in MASLD remains unclear. This study aims to investigate the impact of Mettl3 on MASLD through multi-omics analysis, with a focus on exploring its potential mechanisms of action. Methods: MASLD mouse models were established by feeding a high-fat diet for 12 weeks, and Mettl3 stable overexpression AML12 cell models were constructed via lentiviral transfection. Subsequent transcriptomic and proteomic analyses, as well as integrated analysis between different omics datasets, were conducted. Results: Mettl3 expression significantly increased in MASLD mouse models. In the transcriptomic and proteomic analyses, we identified 848 genes with significant inconsistencies between transcriptomic and proteomic datasets. GO/KEGG enrichment terms may involve post-transcriptional modifications, particularly Mettl3-mediated m6A modification. Subsequently, through integrated proteomic analysis of Mettl3-overexpressed AML12 cell models and MASLD mouse models, we selected the top 20 co-upregulated and co-downregulated GO/KEGG terms as the main biological processes influenced by Mettl3 in MASLD. By intersecting with pathways obtained from previous integrated analyses, we identified GO/KEGG terms affected by Mettl3-induced m6A modification. Protein-protein interaction analysis of proteins involved in these pathways highlighted GAPDH, ENO1, and TPI1 as three key hub genes. Conclusion: In MASLD, Mettl3 regulates the glycolytic pathway through m6A modification, influencing the occurrence and development of the disease via the key hub genes GAPDH, ENO1, and TPI1. These findings expand our understanding of MASLD and provide strong evidence for potential therapeutic targets and drug development.
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