SPP1 and CXCL9 Promote Non-alcoholic Steatohepatitis Progression Based on Bioinformatics Analysis and Experimental Studies

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitis

Non-alcoholic steatohepatitis (NASH) is a hepatocyte inflammation based on hepatocellular steatosis, yet there is no effective drug treatment. Atherosclerosis (AS) is caused by lipid deposition in the endothelium, which can lead to various cardiovascular diseases. NASH and AS share common risk factors, and NASH can also elevate the risk of AS, causing a higher morbidity and mortality rate for atherosclerotic heart disease. Therefore, timely detection and diagnosis of NASH and AS are particularly important. In this study, differential gene expression analysis and weighted gene co-expression network analysis were performed on the AS (GSE100927) and NASH (GSE89632) datasets to obtain common crosstalk genes, respectively. Then, candidate Hub genes were screened using four topological algorithms and externally validated in the GSE43292 and GSE63067 datasets to obtain Hub genes. Furthermore, immune infiltration analysis and gene set variation analysis were performed on the Hub genes to explore the underlying mechanisms. The DGIbd database was used to screen candidate drugs for AS and NASH. Finally, a NASH model was constructed using free fatty acid-induced human L02 cells, an AS model was constructed using lipopolysaccharide-induced HUVECs, and a co-morbidity model was constructed using L02 cells and HUVECs to verify Hub gene expression. The result showed that a total of 113 genes common to both AS and NASH were identified as crosstalk genes, and enrichment analysis indicated that these genes were mainly involved in the regulation of immune and metabolism-related pathways. 28 candidate Hub genes were screened according to four topological algorithms, and CXCL9, IL2RB, and SPP1 were identified as Hub genes after in vitro experiments and external dataset validation. The ROC curves and SVM modeling demonstrated the good diagnostic efficacy of these three Hub genes. In addition, the Hub genes are strongly associated with immune cell infiltration, especially macrophages and γ-δ T cell infiltration. Finally, five potential therapeutic drugs were identified. has-miR-185 and hsa-miR-335 were closely related to AS and NASH. This study demonstrates that CXCL9, IL2RB, and SPP1 may serve as potential biomarkers for the diagnosis of the co-morbidity patterns of AS and NASH and as potential targets for drug therapy.

Exploring shared molecular signatures and regulatory mechanisms in nonalcoholic steatohepatitis and inflammatory bowel disease using integrative bioinformatics analysis

The co-existence of inflammatory bowel disease (IBD) and non-alcoholic steatohepatitis (NASH) has raised interest in identifying shared molecular mechanisms and potential therapeutic targets. However, the relationship between these two diseases remains unclear and effective medical treatments are still lacking. Through the bioinformatics analysis in this study, 116 shared differentially expressed genes (SDEGs) were identified between IBD and NASH datasets. GO and KEGG pathway analyses revealed significant involvement of SDEGs in apoptotic processes, cell death, defense response, cytokine and chemokine activity, and signaling pathways. Furthermore, weighted gene co-expression network analysis (WGCNA) identified five shared signature genes associated specifically with IBD and NASH, they were CXCL9, GIMAP2, ADAMTS5, GRAP, and PRF1. These five genes represented potential diagnostic biomarkers for distinguishing patients with diseases from healthy individuals by using two classifier algorithms and were positively related to autophagy, ferroptosis, angiogenesis, and immune checkpoint factors in the two diseases. Additionally, single-cell analysis of IBD and NASH samples highlighted the expression of regulatory genes in various immune cell subtypes, emphasizing their significance in disease pathogenesis. Our work elucidated the shared signature genes and regulatory mechanisms of IBD and NASH, which could provide new potential therapies for patients with IBD and NASH.

Lipidomic signatures discriminate subtle hepatic changes in the progression of porcine nonalcoholic steatohepatitis

Recently, the development of nonalcoholic steatohepatitis (NASH) in common strains of pigs has been achieved using a diet high in saturated fat, fructose, cholesterol, and cholate and deficient in choline and methionine. The aim of the present work was to characterize the hepatic and plasma lipidomic changes that accompany the progression of NASH and its reversal by switching pigs back to a chow diet. One month of this extreme steatotic diet was sufficient to induce porcine NASH. The lipidomic platform using liquid chromatography-mass spectrometry analyzed 467 lipid species. Seven hepatic phospholipids [PC(30:0), PC(32:0), PC(33:0), PC(33:1), PC(34:0), PC(34:3) and PC(36:2)] significantly discriminated the time of dietary exposure, and PC(30:0), PC(33:0), PC(33:1) and PC(34:0) showed rapid adaptation in the reversion period. Three transcripts (CS, MAT1A, and SPP1) showed significant changes associated with hepatic triglycerides and PC(33:0). Plasma lipidomics revealed that these species [FA 16:0, FA 18:0, LPC(17:1), PA(40:5), PC(37:1), TG(45:0), TG(47:2) and TG(51:0)] were able to discriminate the time of dietary exposure. Among them, FA 16:0, FA 18:0, LPC(17:1) and PA(40:5) changed the trend in the reversion phase. Plasma LDL-cholesterol and IL12P40 were good parameters to study the progression of NASH, but their capacity was surpassed by hepatic [PC(33:0), PC(33:1), and PC(34:0)] or plasma lipid [FA 16:0, FA 18:0, and LPC(17:1)] species. Taken together, these lipid species can be used as biomarkers of metabolic changes in the progression and regression of NASH in this model. The lipid changes suggest that the development of NASH also affects peripheral lipid metabolism.NEW & NOTEWORTHY A NASH stage was obtained in crossbred pigs. Hepatic [PC(33:0), PC(33:1) and PC(34:0)] or plasma [FA 16:0, FA 18:0 and LPC(17:1)] species were sensitive parameters to detect subtle changes in development and regression of nonalcoholic steatohepatitis (NASH). These findings may delineate the liquid biopsy to detect subtle changes in progression or in treatments. Furthermore, phospholipid changes according to the insult-inducing NASH may play an important role in accepting or rejecting fatty livers in transplantation.

Identification and validation of hub genes expressed in ulcerative colitis with metabolic dysfunction-associated steatotic liver disease

Objective

Ulcerative colitis (UC) and metabolic dysfunction-associated steatotic liver disease (MASLD) are closely intertwined; however, the precise molecular mechanisms governing their coexistence remain unclear.

Methods

We obtained UC (GSE75214) and MASLD (GSE151158) datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were acquired by the 'edgeR' and 'limma' packages of R. We then performed functional enrichment analysis of common DEGs. Hub genes were selected using the cytoHubba plugin and validated using GSE87466 for UC and GSE33814 for MASLD. Immunohistochemistry was employed to validate the hub genes' expression in clinical samples. Immune infiltration and gene set enrichment analyses of the hub genes were performed. Finally, we estimated the Spearman's correlation coefficients for the clinical correlation of the core genes.

Results

Within a cohort of 26 differentially regulated genes in both UC and MASLD, pathways involving cytokine-mediated signaling, cell chemotaxis, and leukocyte migration were enriched. After further validation, CXCR4, THY1, CCL20, and CD2 were identified as the hub genes. Analysis of immune infiltration patterns highlighted an association between elevated pivotal gene expression and M1 macrophage activation. Immunohistochemical staining revealed widespread expression of pivotal genes in UC- and MASLD-affected tissues. Furthermore, significant correlations were observed between the increased expression of hub genes and biochemical markers, such as albumin and prothrombin time.

Conclusion

This bioinformatics analysis highlights CXCR4, THY1, CCL20, and CD2 as crucial genes involved in the co-occurrence of UC and MASLD, providing insights into the underlying mechanisms of these two conditions.

Machine learning-based integration identifies the ferroptosis hub genes in nonalcoholic steatohepatitis

BACKGROUND

Ferroptosis, is characterized by lipid peroxidation of fatty acids in the presence of iron ions, which leads to cell apoptosis. This leads to the disruption of metabolic pathways, ultimately resulting in liver dysfunction. Although ferroptosis is linked to nonalcoholic steatohepatitis (NASH), understanding the key ferroptosis-related genes (FRGs) involved in NASH remains incomplete. NASH may be targeted therapeutically by identifying the genes responsible for ferroptosis.

METHODS

To identify ferroptosis-related genes and develop a ferroptosis-related signature (FeRS), 113 machine-learning algorithm combinations were used.

RESULTS

The FeRS constructed using the Generalized Linear Model Boosting algorithm and Gradient Boosting Machine algorithms exhibited the best prediction performance for NASH. Eight FRGs, with ZFP36 identified by the algorithms as the most crucial, were incorporated into in FeRS. ZFP36 is significantly enriched in various immune cell types and exhibits significant positive correlations with most immune signatures.

CONCLUSION

ZFP36 is a key FRG involved in NASH pathogenesis.

A genetic basis of mitochondrial DNAJA3 in nonalcoholic steatohepatitis-related hepatocellular carcinoma

BACKGROUND AND AIMS

NAFLD is the most common form of liver disease worldwide, but only a subset of individuals with NAFLD may progress to NASH. While NASH is an important etiology of HCC, the underlying mechanisms responsible for the conversion of NAFLD to NASH and then to HCC are poorly understood. We aimed to identify genetic risk genes that drive NASH and NASH-related HCC.

APPROACH AND RESULTS

We searched genetic alleles among the 24 most significant alleles associated with body fat distribution from a genome-wide association study of 344,369 individuals and validated the top allele in 3 independent cohorts of American and European patients (N=1380) with NAFLD/NASH/HCC. We identified an rs3747579-TT variant significantly associated with NASH-related HCC and demonstrated that rs3747579 is expression quantitative trait loci of a mitochondrial DnaJ Heat Shock Protein Family (Hsp40) Member A3 ( DNAJA3 ). We also found that rs3747579-TT and a previously identified PNPLA3 as a functional variant of NAFLD to have significant additional interactions with NASH/HCC risk. Patients with HCC with rs3747579-TT had a reduced expression of DNAJA3 and had an unfavorable prognosis. Furthermore, mice with hepatocyte-specific Dnaja3 depletion developed NASH-dependent HCC either spontaneously under a normal diet or enhanced by diethylnitrosamine. Dnaja3 -deficient mice developed NASH/HCC characterized by significant mitochondrial dysfunction, which was accompanied by excessive lipid accumulation and inflammatory responses. The molecular features of NASH/HCC in the Dnaja3 -deficient mice were closely associated with human NASH/HCC.

CONCLUSIONS

We uncovered a genetic basis of DNAJA3 as a key player of NASH-related HCC.

Exploring Key Genes with Diagnostic Value for Nonalcoholic Steatohepatitis Based on Bioinformatics Analysis

We aimed to screen specific genes in liver tissue samples of patients with nonalcoholic steatohepatitis (NASH) with clinical diagnostic value based on bioinformatics analysis. The datasets of liver tissue samples from healthy individuals and NASH patients were retrieved for consistency cluster analysis to obtain the NASH sample typing, followed by verification of the diagnostic value of sample genotyping-specific genes. All samples were subjected to logistic regression analysis, followed by the establishment of the risk model, and then, the diagnostic value was determined by receiver operating characteristic curve analysis. NASH samples could be divided into cluster 1, cluster 2, and cluster 3, which could predict the nonalcoholic fatty liver disease activity score of patients. A total of 162 sample genotyping-specific genes were extracted from patient clinical parameters, and the top 20 core genes in the protein interaction network were obtained for logistic regression analysis. Five sample genotyping-specific genes (WD repeat and HMG-box DNA-binding protein 1 [WDHD1], GINS complex subunit 2 [GINS2], replication factor C subunit 3 (RFC3), secreted phosphoprotein 1 [SPP1], and spleen tyrosine kinase [SYK]) were extracted to construct the risk models with high diagnostic value in NASH. Compared with the low-risk group, the high-risk group of the model showed increased lipoproduction and decreased lipolysis and lipid β oxidation. The risk models based on WDHD1, GINS2, RFC3, SPP1, and SYK have high diagnostic value in NASH, and this risk model is closely related to lipid metabolism pathways.