The intersection of virus infection and liver disease: A comprehensive review of pathogenesis, diagnosis, and treatment.

Liver disease represents a significant global burden, placing individuals at a heightened risk of developing cirrhosis and liver cancer. Viral infections act as a primary cause of liver diseases on a worldwide scale. Infections involving hepatitis viruses, notably hepatitis B, C, and E viruses, stand out as the most prevalent contributors to acute and chronic intrahepatic adverse outcome, although the hepatitis C virus (HCV) can be effectively cured with antiviral drugs, but no preventative vaccination developed. Hepatitis B virus (HBV) and HCV can lead to both acute and chronic liver diseases, including liver cirrhosis and hepatocellular carcinoma (HCC), which are principal causes of worldwide morbidity and mortality. Other viruses, such as Epstein-Barr virus (EBV) and cytomegalovirus (CMV), are capable of causing liver damage. Therefore, it is essential to recognize that virus infections and liver diseases are intricate and interconnected processes. A profound understanding of the underlying relationship between virus infections and liver diseases proves pivotal in the effective prevention, diagnosis, and treatment of these conditions. In this review, we delve into the mechanisms by which virus infections induce liver diseases, as well as explore the pathogenesis, diagnosis, and treatment of liver diseases. This article is categorized under: Infectious Diseases > Biomedical Engineering.

Oral-Gut Microbiome Analysis in Patients With Metabolic-Associated Fatty Liver Disease Having Different Tongue Image Feature.

Objective: The objective of this study was to identify the biological correlation between the tongue coating color and oral and gut micro-characteristics in metabolic-associated fatty liver disease (MAFLD) patients. Method: The characteristics of the tongue coating were examined using an automatic tongue diagnosis system. Tongue coating and stool samples were collected from 38 MAFLD patients, and 16S rDNA full-length assembly sequencing technology (16S-FAST) was used for bioinformatic analysis. Results: Twenty-two and 16 subjects were included in two distinct clusters according to the white/yellow color of the tongue coating, which was assessed by the L*a*b* values of the image. Upon analyzing the microorganisms in the tongue coating, 66 and 62 pathognomonic bacterial genera were found in the White and Yellow Coating Groups, respectively. The abundance of Stomatobaculumis positively correlated with the a* values of the tongue coating in the White Coating Group, while Fusobacterium, Leptotrichia, and Tannerella abundance was significantly correlated with the b* values in the Yellow Coating Group. Function prediction mainly showed the involvement of protein families related to BRITE hierarchies and metabolism. The MHR (MONO%/high-density lipoprotein cholesterol) of the Yellow Coating Group was higher than that of the White Coating Group. Conclusion: In MAFLD patients, lower a* values and higher b* values are indicators of a yellow tongue coating. There were also significant differences in the flora of different tongue coatings, with corresponding changes in the intestinal flora, indicating a correlation between carbohydrate metabolism disorders and inflammation in the oral microbiome.

Diversity and molecular network patterns of symptom phenotypes.

Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein-protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.

Farnesoid X receptor via Notch1 directs asymmetric cell division of Sox9+ cells to prevent the development of liver cancer in a mouse model.

BACKGROUND: Asymmetrical cell division (ACD) maintains the proper number of stem cells to ensure self-renewal. The rate of symmetric division increases as more cancer stem cells (CSCs) become malignant; however, the signaling pathway network involved in CSC division remains elusive. FXR (Farnesoid X receptor), a ligand-activated transcription factor, has several anti-tumor effects and has been shown to target CSCs. Here, we aimed at evaluating the role of FXR in the regulation of the cell division of CSCs. METHODS: The FXR target gene and downstream molecular mechanisms were confirmed by qRT-PCR, Western blot, luciferase reporter assay, EMAS, Chip, and IF analyses. Pulse-chase BrdU labeling and paired-cell experiments were used to detect the cell division of liver CSCs. Gain- and loss-of-function experiments in Huh7 cells and mouse models were performed to support findings and elucidate the function and underlying mechanisms of FXR-Notch1 in liver CSC division. RESULTS: We demonstrated that activation of Notch1 was significantly elevated in the livers of hepatocellular carcinoma (HCC) in Farnesoid X receptor-knockout (FXR-KO) mice and that FXR expression negatively correlated with Notch1 level during chronic liver injury. Activation of FXR induced the asymmetric divisions of Sox9+ liver CSCs and ameliorated liver injury. Mechanistically, FXR directs Sox9+ liver CSCs from symmetry to asymmetry via inhibition of Notch1 expression and activity. Deletion of FXR signaling or over-expression of Notch1 greatly increased Notch1 expression and activity along with ACD reduction. FXR inhibited Notch1 expression by directly binding to its promoter FXRE. FXR also positively regulated Numb expression, contributing to a feedback circuit, which decreased Notch1 activity and directed ACD. CONCLUSION: Our findings suggest that FXR represses Notch1 expression and directs ACD of Sox9+ cells to prevent the development of liver cancer.