A Bioinformatic Analysis of Correlations between Polymeric Immunoglobulin Receptor (PIGR) and Liver Fibrosis Progression

Enhancing liver fibrosis detection: a novel PIGR-utilizing approach in chronic hepatitis B injury assessment

BACKGROUND

Chronic Hepatitis B (CHB) is a leading cause of liver fibrosis and cirrhosis worldwide. The early detection of liver fibrosis remains challenging due to the lack of specific symptoms and noninvasive biomarkers with high sensitivity. The polymeric immunoglobulin receptor (PIGR) has recently emerged as a potential biomarker for liver fibrosis. This study aims to evaluate the utility of PIGR in CHB patients as a biomarker for liver fibrosis.

METHODS

This retrospective study analyzed 150 CHB patients from 2018 to 2023. Based on liver biopsy results, 34 patients were classified as having liver fibrosis, while 116 were categorized as non-fibrosis. Clinical data were compared to assess the relationship between PIGR expression levels and serum fibrosis indices. Logistic regression was performed to identify factors influencing liver fibrosis, and the predictive value of PIGR was evaluated using a receiver operating characteristic (ROC) curve.

RESULTS

Significant differences were observed in collagen type IV (CIV), procollagen type III N-terminal peptide (PCIIINP), and hyaluronic acid (HA) levels between the fibrosis and non-fibrosis groups (P < 0.05). PIGR levels were significantly higher in the fibrosis group (P < 0.05) and positively correlated with HA, laminin (LN), PCIII, and CIV levels (P < 0.05). Logistic regression identified HA, LN, PCIIINP, and CIV as risk factors, with PIGR being an independent predictor (P < 0.05). At a cutoff value of 0.35, PIGR showed an area under the curve (AUC) of 0.839, with 81.90% sensitivity, 79.41% specificity, and a Youden's index of 0.613. PIGR also provided a higher net benefit than APRI.

CONCLUSION

PIGR levels are significantly elevated in CHB-related liver fibrosis and correlate closely with established fibrosis markers. As an independent predictor, PIGR demonstrates high diagnostic accuracy and holds promise as a non-invasive biomarker for detecting liver fibrosis in CHB patients, with significant potential for clinical application.

Identification of Host Proteins Involved in Hepatitis B Virus Genome Packaging

A critical part of the hepatitis B virus (HBV) life cycle is the packaging of the pregenomic RNA (pgRNA) into nucleocapsids. While this process is known to involve several viral elements, much less is known about the identities and roles of host proteins in this process. To better understand the role of host proteins, we isolated pgRNA and characterized its protein interactome in cells expressing either packaging-competent or packaging-incompetent HBV genomes. We identified over 250 host proteins preferentially associated with pgRNA from the packaging-competent version of the virus. These included proteins already known to support capsid formation, enhance viral gene expression, catalyze nucleocapsid dephosphorylation, and bind to the viral genome, demonstrating the ability of the approach to effectively reveal functionally significant host-virus interactors. Three of these host proteins, AURKA, YTHDF2, and ATR, were selected for follow-up analysis. RNA immunoprecipitation qPCR (RIP-qPCR) confirmed pgRNA-protein association in cells, and siRNA knockdown of the proteins showed decreased encapsidation efficiency. This study provides a template for the use of comparative RNA-protein interactome analysis in conjunction with virus engineering to reveal functionally significant host-virus interactions.

EMP1 as a Potential Biomarker in Liver Fibrosis: A Bioinformatics Analysis

Liver fibrosis is a wound-healing response to chronic injury, which may result in cirrhosis and liver failure. Studies have been carried on the mechanisms and pathogenesis of liver fibrosis. However, the potential cell-specific expressed marker genes involved in fibrotic processes remain unknown. In this study, we combined a publicly accessible single-cell transcriptome of human liver with microarray datasets to evaluate the cell-specific expression patterns of differentially expressed genes in the liver. We noticed that EMP1 (epithelial membrane protein 1) is significantly active not only in CCl₄ (carbon tetrachloride)-treated mouse liver fibrosis but also in BDL (bile duct ligation)-induced liver fibrosis and even in human fibrotic liver tissues such as alcoholic hepatitis, NASH (nonalcoholic steatohepatitis), and advanced stage liver fibrosis. Furthermore, we demonstrated that EMP1 is a specific fibrotic gene expressed in HSCs (hepatic stellate cells) and endothelial cells using the Protein Atlas single-cell transcriptome RNA-sequencing clustering. Its expression was significantly elevated in fibrotic HSCs or CCl₄ and NASH-induced fibroblasts. Previous research revealed that EMP1 plays a role in proliferation, migration, metastasis, and tumorigeneses in different cancers via a variety of mechanisms. Because HSC activation and proliferation are two important steps following liver injury, it would be interesting to investigate the role of EMP1 in these processes. All of this information suggested that EMP1 could be used as a novel fibrotic liver marker and a possible target in the future.

Discovering Biomarkers for Non-Alcoholic Steatohepatitis Patients with and without Hepatocellular Carcinoma Using Fecal Metaproteomics

High-calorie diets lead to hepatic steatosis and to the development of non-alcoholic fatty liver disease (NAFLD), which can evolve over many years into the inflammatory form of non-alcoholic steatohepatitis (NASH), posing a risk for the development of hepatocellular carcinoma (HCC). Due to diet and liver alteration, the axis between liver and gut is disturbed, resulting in gut microbiome alterations. Consequently, detecting these gut microbiome alterations represents a promising strategy for early NASH and HCC detection. We analyzed medical parameters and the fecal metaproteome of 19 healthy controls, 32 NASH patients, and 29 HCC patients, targeting the discovery of diagnostic biomarkers. Here, NASH and HCC resulted in increased inflammation status and shifts within the composition of the gut microbiome. An increased abundance of kielin/chordin, E3 ubiquitin ligase, and nucleophosmin 1 represented valuable fecal biomarkers, indicating disease-related changes in the liver. Although a single biomarker failed to separate NASH and HCC, machine learning-based classification algorithms provided an 86% accuracy in distinguishing between controls, NASH, and HCC. Fecal metaproteomics enables early detection of NASH and HCC by providing single biomarkers and machine learning-based metaprotein panels.