Description
Background: Non-alcoholic fatty liver disease (NAFLD) develops from fatty liver to steatohepatitis during which multiple cell types may play different roles. Aiming to understand the tissue composition of cell types, their gene expression and global gene regulation in the development of NAFLD, we performed single-nucleus and bulk ATAC-seq on the liver of rats fed with a high-fat diet (HFD). Methods: Male Spontaneously Hypertensive Rats were fed a normal diet or a HFD. Rats fed HFD for 4 weeks developed fatty liver, and those fed HFD for 8 weeks further developed steatohepatitis. Under the washout condition, where 4 weeks of HFD is followed by 4 weeks of a normal diet, fatty liver was partially ameliorated. For each dietary condition, we performed single-nucleus ATAC-seq on one animal and bulk ATAC-seq on four animals. Results: In accordance with the pathological progression from fatty liver to steatohepatitis, the proportion of inflammatory macrophages dramatically increased. By machine learning, we divided the global gene expression into modules, such that transcription factors in a module regulate the genes in the same module. Consequently, many of the modules rediscovered known regulatory relationship between the transcription factors and biological processes. For the discovered biological processes, we searched core genes, which were defined as genes central regarding co-expression and protein-protein interaction. A large part of the core genes overlapped with previously implicated NAFLD genes. Conclusions: Single-nucleus ATAC-seq combined with data-driven statistical analysis help elucidate the global gene regulation in vivo as a combination of modules and discover core genes of the relevant biological processes.
Citations (1)
Cited on 25 July 2023
Weight: 1.23
Mentions (1)
- https://github.com/fumi-github/rat_singlecell_liver_ArchRSoftware Heritage
Mentioned on 23 April 2025
Weight: 1.46
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Publication Details
Subfield
Cancer Research
Field
Biochemistry, Genetics and Molecular Biology
Domain
Life Sciences
Confidence Score
92%
Source
Open Alex