Bioinformatics analysis of differentially expressed proteins in alcoholic fatty liver disease treated with recombinant human cytoglobin

Transcriptomics of a cytoglobin knockout mouse: Insights from hepatic stellate cells and brain

The vertebrate respiratory protein cytoglobin (Cygb) is thought to exert multiple cellular functions. Here we studied the phenotypic effects of a Cygb knockout (KO) in mouse on the transcriptome level. RNA sequencing (RNA-Seq) was performed for the first time on sites of major endogenous Cygb expression, i.e. quiescent and activated hepatic stellate cells (HSCs) and two brain regions, hippocampus and hypothalamus. The data recapitulated the up-regulation of Cygb during HSC activation and its expression in the brain. Differential gene expression analyses suggested a role of Cygb in the response to inflammation in HSCs and its involvement in retinoid metabolism, retinoid X receptor (RXR) activation-induced xenobiotics metabolism, and RXR activation-induced lipid metabolism and signaling in activated cells. Unexpectedly, only minor effects of the Cygb KO were detected in the transcriptional profiles in hippocampus and hypothalamus, precluding any enrichment analyses. Furthermore, the transcriptome data pointed at a previously undescribed potential of the Cygb- knockout allele to produce cis-acting effects, necessitating future verification studies.

Identification of differentially expressed miRNAs and key genes involved in the progression of alcoholic fatty liver disease using rat models

BACKGROUND

Alcoholic fatty liver disease (AFLD) is a liver disease caused by prolonged heavy drinking and has a poor prognosis in the clinic. This study aimed to explore the differential miRNAs expression profiles in the AFLD rat model.

METHODS

The rat model of AFLD was established by ethanol intragastric administration and was used to explore the differential miRNAs expression profiles. We further analyzed the potential target mRNAs using the bioinformatics technique. GO and KEGG pathway enrichment analyses were carried out to better understand the biological function of differential expression genes (DEGs). We used the human Gene Expression Omnibus (GEO) dataset GSE28619 to further screen the key differentially expressed genes. The integration between the differentially expressed genes from the AFLD model and GEO was conducted and the key genes were identified.

RESULTS

The serum ALT, AST, TG, and TC levels in the AFLD model group were significantly higher than those in the normal control group. There are 45 miRNAs with significant changes including 26 upregulated and 19 down-regulated miRNAs. GO and KEGG enrichment showed various metabolic processes and signaling pathways were enriched in the progression of AFLD. After integrating the results of GSE28619 and DEGs, we observed that there are 12 genes with significant changes in two data sets, including PSAT1, TKFC, PTTG1, LCN2, CXCL1, NR4A1, RGS1, VCAN, FOS, CXCL10, ATF3, and CYP1A1.

CONCLUSION

AFLD showed differentially expressed miRNAs, which may be involved in the occurrence and progression of AFLD. Meanwhile, some signal metabolic pathways may be related to the pathogenesis of AFLD.