Transcriptional and Epigenetic Alterations in the Progression of Non-Alcoholic Fatty Liver Disease and Biomarkers Helping to Diagnose Non-Alcoholic Steatohepatitis

Top-Down Proteomic Profiling of Protein Corona by High-Throughput Capillary Isoelectric Focusing-Mass Spectrometry

In the rapidly evolving field of nanomedicine, understanding the interactions between nanoparticles (NPs) and biological systems is crucial. A pivotal aspect of these interactions is the formation of a protein corona when NPs are exposed to biological fluids (e.g., human plasma), which significantly influences their behavior and functionality. This study introduces an advanced capillary isoelectric focusing tandem mass spectrometry (cIEF-MS/MS) platform designed to enable high-throughput and reproducible top-down proteomic analysis of protein corona. Our cIEF-MS/MS technique completed each analysis within 30 min. It produced reproducible proteoform measurements of protein corona for at least 50 runs regarding the proteoforms' migration time [relative standard deviations (RSDs) <4%], the proteoforms' intensity (Pearson's correlation coefficients between any two runs >0.90), the number of proteoform identifications (71 ± 10), and the number of proteoform-spectrum matches (PrSMs) (196 ± 30). Of the 53 identified genes, 33 are potential biomarkers of various diseases (e.g., cancer, cardiovascular disease, and Alzheimer's disease). We identified 1-102 proteoforms per potential protein biomarker, containing various sequence variations or post-translational modifications. Delineating proteoforms in protein corona by our cIEF-MS/MS in a reproducible and high-throughput fashion will benefit our understanding of nanobiointeractions and advance both diagnostic and therapeutic nanomedicine technologies.

Afamin and Apolipoprotein F Associated With Liver Steatosis From People Living With HIV: A Discovery Study

INTRODUCTION

Liver steatosis (LS) is a condition that is characterised by hepatic fat accumulation unrelated to significant alcohol consumption. This study explored the serum proteomic profile associated with LS in people living with HIV (PLWH).

METHODS

The study cohort comprised 266 PLWH, 21.1% and 78.9% of whom had LS and no LS, respectively. Serum samples were analysed using liquid chromatography coupled with mass spectrometry (LC-MS).

RESULTS

Among the 220 proteins detected, afamin (AFM) and apolipoprotein F (APOF) were identified as proteins associated with LS. Differential expression of AFM and APOF was observed in under- and normoweight patients, emphasising their potential as biomarkers in patients without overweight or obesity.

CONCLUSIONS

These findings suggest that the identified proteins could serve as promising biomarkers of LS in PLWH, paving the way for further investigations into the roles of these proteins in LS development in this unique population.

Machine Learning-Based Assessment of Survival and Risk Factors in Non-Alcoholic Fatty Liver Disease-Related Hepatocellular Carcinoma for Optimized Patient Management

Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide, with an incidence that is exponentially increasing. Hepatocellular carcinoma (HCC) is the most frequent primary tumor. There is an increasing relationship between these entities due to the potential risk of developing NAFLD-related HCC and the prevalence of NAFLD. There is limited evidence regarding prognostic factors at the diagnosis of HCC. This study compares the prognosis of HCC in patients with NAFLD against other etiologies. It also evaluates the prognostic factors at the diagnosis of these patients. For this purpose, a multicenter retrospective study was conducted involving a total of 191 patients. Out of the total, 29 presented NAFLD-related HCC. The extreme gradient boosting (XGB) method was employed to develop the reference predictive model. Patients with NAFLD-related HCC showed a worse prognosis compared to other potential etiologies of HCC. Among the variables with the worst prognosis, alcohol consumption in NAFLD patients had the greatest weight within the developed predictive model. In comparison with other studied methods, XGB obtained the highest values for the analyzed metrics. In conclusion, patients with NAFLD-related HCC and alcohol consumption, obesity, cirrhosis, and clinically significant portal hypertension (CSPH) exhibited a worse prognosis than other patients. XGB developed a highly efficient predictive model for the assessment of these patients.

Identification of key genes and functional enrichment analysis of liver fibrosis in nonalcoholic fatty liver disease through weighted gene co-expression network analysis

Nonalcoholic fatty liver disease (NAFLD) is a common type of chronic liver disease, with severity levels ranging from nonalcoholic fatty liver to nonalcoholic steatohepatitis (NASH). The extent of liver fibrosis indicates the severity of NASH and the risk of liver cancer. However, the mechanism underlying NASH development, which is important for early screening and intervention, remains unclear. Weighted gene co-expression network analysis (WGCNA) is a useful method for identifying hub genes and screening specific targets for diseases. In this study, we utilized an mRNA dataset of the liver tissues of patients with NASH and conducted WGCNA for various stages of liver fibrosis. Subsequently, we employed two additional mRNA datasets for validation purposes. Gene set enrichment analysis (GSEA) was conducted to analyze gene function enrichment. Through WGCNA and subsequent analyses, complemented by validation using two additional datasets, we identified five genes (BICC1, C7, EFEMP1, LUM, and STMN2) as hub genes. GSEA analysis indicated that gene sets associated with liver metabolism and cholesterol homeostasis were uniformly downregulated. BICC1, C7, EFEMP1, LUM, and STMN2 were identified as hub genes of NASH, and were all related to liver metabolism, NAFLD, NASH, and related diseases. These hub genes might serve as potential targets for the early screening and treatment of NASH.

Comprehensive analysis of epigenetic and epitranscriptomic genes' expression in human NAFLD

Non-alcoholic fatty liver disease (NAFLD) is a multifactorial condition with a complex etiology. Its incidence is increasing globally in parallel with the obesity epidemic, and it is now considered the most common liver disease in Western countries. The precise mechanisms underlying the development and progression of NAFLD are complex and still poorly understood. The dysregulation of epigenetic and epitranscriptomic mechanisms is increasingly recognized to play pathogenic roles in multiple conditions, including chronic liver diseases. Here, we have performed a comprehensive analysis of the expression of epigenetic and epitranscriptomic genes in a total of 903 liver tissue samples corresponding to patients with normal liver, obese patients, and patients with non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH), advancing stages in NAFLD progression. We integrated ten transcriptomic datasets in an unbiased manner, enabling their robust analysis and comparison. We describe the complete landscape of epigenetic and epitranscriptomic genes' expression along the course of the disease. We identify signatures of genes significantly dysregulated in association with disease progression, particularly with liver fibrosis development. Most of these epigenetic and epitranscriptomic effectors have not been previously described in human NAFLD, and their altered expression may have pathogenic implications. We also performed a comprehensive analysis of the expression of enzymes involved in the metabolism of the substrates and cofactors of epigenetic and epitranscriptomic effectors. This study provides novel information on NAFLD pathogenesis and may also guide the identification of drug targets to treat this condition and its progression towards hepatocellular carcinoma.

Whole-genome DNA methylation and gene expression profiling in the livers of mice with nonalcoholic steatohepatitis

AIMS

Non-alcoholic fatty liver disease (NAFLD) has emerged as one of the major causes of liver-related morbidity and mortality. It ranges simple steatosis to non-alcoholic steatohepatitis (NASH). Previous studies have shown that epigenetic factors, such as DNA methylation, can contribute to the development and progression of simple steatosis. However, the profiling of whole-genome DNA methylation remains poorly characterized in NASH.

MAIN METHODS

In this study, we established a mouse model of diet-induced NASH, by maintaining male mice on a high-fructose-high-cholesterol diet (HFHC), to generate hepatic steatosis, inflammation and injury. We profiled hepatic gene expression by RNA-Sequencing and locus-specific 5-methylcytosine level, using Whole Genome Bisulfite Sequencing (WGBS).

KEY FINDINGS

We identified >1000 differentially methylated regions in NASH versus control group, indicating that NASH diet could modulate the liver methylome. Furthermore, integrated analysis of methylome and transcriptome identified certain key methylated genes and pathways, which may be involved in steroid metabolism and inflammation response. The liver methylation levels of key genes especially Tgfb, Msn, Iqgap1, Cyba, Fcgr1 decreased, and their consequent increased expression may lead to NASH development.

SIGNIFICANCE

We found that HFHC diet-induced NASH could induces genome-wide differential DNA methylation changes. Thus, we proposed that DNA methylation profiles of genomes may be a useful signature of gene transcription and may play an important role in the development of NASH. We also screened and validated the changes of key genes, which may provide new perspectives for the mechanistic study of NASH in future.