Metabolic characterization of the early stage of hepatic fibrosis in rat using GC-TOF/MS and multivariate data analyses

Serum infrared spectral profile is predictive of the degree of hepatic fibrosis in chronic hepatitis C patients

Assessment of liver fibrosis is crucial to guide the therapeutic strategy in patients with chronic liver disease. We investigated the potential of serum Fourier transform infrared (FTIR) spectroscopy for assessing the degree of hepatic fibrosis in patients with chronic hepatitis C (CHC). The study was conducted on dried serum samples from 94 CHC patients at different histological stages of hepatic fibrosis: METAVIR F0 (n = 20), F1 (n = 17), F2 (n = 20), F3 (n = 20) and F4 (n = 17). Transmission FTIR spectra were acquired in the 4000-400 cm-1 range. Wavenumbers were selected by genetic algorithm (GA) according to their diagnostic performance as assessed by a partial least squares discriminant analysis (PLS-DA) model using a training and a validation set to differentiate severe stages of fibrosis from mild or moderate ones. The GA procedure was applied 50 times on randomly selected sets. Furthermore, the best set of wavenumbers was re-tested in 1000 randomly selected validation sets. Wavenumbers selected by GA corresponded to functional groups present in lipids, proteins, and carbohydrates. This model allowed to identify patients with cirrhosis (METAVIR F4), patients with advanced fibrosis (METAVIR F3 and F4), and patients with significant fibrosis (METAVIR F2, F3 and F4), with AUROC (Area Under the Receiver Operating Characteristic) of 0.88, 0.85 and 0.85, respectively. Thus, serum FTIR spectroscopy appears to have a strong potential as a new diagnostic tool for assessing the degree of fibrosis in patients with chronic liver disease.

Liver transcriptomic and proteomic analyses provide new insight into the pathogenesis of liver fibrosis in mice

BACKGROUND

Liver fibrosis (LF) is a kind of progressive liver injury reaction. The goal of this study was to achieve a more detailed understanding of the molecular changes in response to CCl4-induced LF through the identification of a differentially expressed liver transcriptomic and proteomic.

RESULTS

A total of 1224 differentially expressed genes (DEGs) and 302 differentially expressed proteins (DEPs) were significantly identified at the transcriptomic and proteomic level, respectively, and 69 genes (hereafter "cor-DEGs-DEPs" genes) were detected at both levels. Pathway enrichment analysis showed that these cor-DEGs-DEPs genes were significantly enriched in 133 pathways. Importantly, among the cor-DEGs-DEPs genes, Gstm1, Gstm3, Ephx1 and Gstp1 were shown to be associated with metabolic pathways, and confirmed by RT-qPCR and parallel reaction monitoring (PRM) verification.

CONCLUSIONS

Through the combined analysis of transcriptomic and proteomic data, this study provides valuable insights into the potential mechanism of the pathogenesis of LF, and lays a theoretical foundation for the further development of targeted therapy for LF.

Aggravated hepatic fibrosis induced by phenylalanine and tyrosine was ameliorated by chitooligosaccharides supplementation

Hepatic fibrosis is a classic pathological manifestation of metabolic chronic hepatopathy. The pathological process might either gradually deteriorate into cirrhosis and ultimately liver cancer with inappropriate nutrition supply, or be slowed down by several multifunctional nutrients, alternatively. Herein, we found diet with excessive phenylalanine (Phe) and tyrosine (Tyr) exacerbated hepatic fibrosis symptoms of liver dysfunction and gut microflora dysbiosis in mice. Chitooligosaccharides (COS) could ameliorate hepatic fibrosis with the regulation of amino acid metabolism by downregulating the mTORC1 pathway, especially that of Phe and Tyr, and also with the alleviation of the dysbiosis of gut microbiota, simultaneously. Conclusively, this work presents new insight into the role of Phe and Tyr in the pathologic process of hepatic fibrosis, while revealing the effectiveness and molecular mechanism of COS in improving hepatic fibrosis from the perspective of metabolites.

Rodent models and metabolomics in non-alcoholic fatty liver disease: What can we learn?

Non-alcoholic fatty liver disease (NAFLD) prevalence has increased drastically in recent decades, affecting up to 25% of the world’s population. NAFLD is a spectrum of different diseases that starts with asymptomatic steatosis and continues with development of an inflammatory response called steatohepatitis, which can progress to fibrosis. Several molecular and metabolic changes are required for the hepatocyte to finally vary its function; hence a “multiple hit” hypothesis seems a more accurate proposal. Previous studies and current knowledge suggest that in most cases, NAFLD initiates and progresses through most of nine hallmarks of the disease, although the triggers and mechanisms for these can vary widely. The use of animal models remains crucial for understanding the disease and for developing tools based on biological knowledge. Among certain requirements to be met, a good model must imitate certain aspects of the human NAFLD disorder, be reliable and reproducible, have low mortality, and be compatible with a simple and feasible method. Metabolism studies in these models provides a direct reflection of the workings of the cell and may be a useful approach to better understand the initiation and progression of the disease. Metabolomics seems a valid tool for studying metabolic pathways and crosstalk between organs affected in animal models of NAFLD and for the discovery and validation of relevant biomarkers with biological understanding. In this review, we provide a brief introduction to NAFLD hallmarks, the five groups of animal models available for studying NAFLD and the potential role of metabolomics in the study of experimental NAFLD.

Proteomic landscape of TGF-β1-induced fibrogenesis in renal fibroblasts

Transforming growth factor-β1 (TGF-β1) plays a premier role in fibrosis. To understand the molecular events underpinning TGF-β1-induced fibrogenesis, we examined the proteomic profiling of a TGF-β1-induced in vitro model of fibrosis in NRK-49F normal rat kidney fibroblasts. Mass spectrometric analysis indicated that 628 cell-lysate proteins enriched in 44 cellular component clusters, 24 biological processes and 27 molecular functions were regulated by TGF-β1. Cell-lysate proteins regulated by TGF-β1 were characterised by increased ribosomal proteins and dysregulated proteins involved in multiple metabolic pathways, including reduced Aldh3a1 and induced Enpp1 and Impdh2, which were validated by enzyme-linked immunosorbent assays (ELISA). In conditioned media, 62 proteins enriched in 20 cellular component clusters, 40 biological processes and 7 molecular functions were regulated by TGF-β1. Secretomic analysis and ELISA uncovered dysregulated collagen degradation regulators (induced PAI-1 and reduced Mmp3), collagen crosslinker (induced Plod2), signalling molecules (induced Ccn1, Ccn2 and Tsku, and reduced Ccn3) and chemokines (induced Ccl2 and Ccl7) in the TGF-β1 group. We conclude that TGF-β1-induced fibrogenesis in renal fibroblasts is an intracellular metabolic disorder and is inherently coupled with inflammation mediated by chemokines. Proteomic profiling established in this project may guide development of novel anti-fibrotic therapies in a network pharmacology approach.

Screening and evaluation of key genes in contributing to pathogenesis of hepatic fibrosis based on microarray data

BACKGROUND

Hepatic fibrosis (HF), which is characterized by the excessive accumulation of extracellular matrix (ECM) in the liver, usually progresses to liver cirrhosis and then death. To screen differentially expressed (DE) long non-coding RNAs (lncRNAs) and mRNAs, explore their potential functions to elucidate the underlying mechanisms of HF.

METHODS

The microarray of GSE80601 was downloaded from the Gene Expression Omnibus database, which is based on the GPL1355 platform. Screening for the differentially expressed LncRNAs and mRNAs was conducted between the control and model groups. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the biological functions and pathways of the DE mRNAs. Additionally, the protein-protein interaction (PPI) network was delineated. In addition, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) package and Cytoscape software, we constructed lncRNA-mRNA weighted co-expression networks.

RESULTS

A total of 254 significantly differentially expressed lncRNAs and 472 mRNAs were identified. GO and KEGG analyses revealed that DE mRNAs regulated HF by participating in the GO terms of metabolic process, inflammatory response, response to wounding and oxidation-reduction. DE mRNAs were also significantly enriched in the pathways of ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion (FA), retinol metabolism and metabolic pathways. Moreover, 24 lncRNAs associated with 40 differentially expressed genes were observed in the modules of lncRNA-mRNA weighted co-expression network.

CONCLUSIONS

This study revealed crucial information on the molecular mechanisms of HF and laid a foundation for subsequent genes validation and functional studies, which could contribute to the development of novel diagnostic markers and provide new therapeutic targets for the clinical treatment of HF.

Investigation of the therapeutic effect of Shaoyao Gancao decoction on CCL4 -induced liver injury in rats by metabolomic analysis

Shaoyao Gancao decoction (SGD) is a famous Chinese traditional prescription for treating liver injury. In this research, we investigated the therapeutic effects of SGD on liver injury and its metabolic mechanisms using ¹ H NMR and UPLC-MS. Serum biochemical indicators and histopathological methods were used to determine the mechanism of action of SGD in treating liver injury. An orthogonal partial least squares discriminant analysis method was used to screen potential metabolic markers, and the MetaboAnalyst and KEGG PATHWAY databases were used to find relevant metabolic pathways. A total of 26 significant metabolites were identified with significant changes in their abundance levels, and these metabolites are involved in many metabolic pathways such as amino acid and lipid metabolism. The changes in biomarker levels reveal the therapeutic effect of SGD on liver injury, which is of great significance to speculate on possible metabolic mechanisms.

A Network Pharmacology Approach to Explore the Mechanisms of Shugan Jianpi Formula in Liver Fibrosis

Purpose

We explored the mechanism of Shugan Jianpi Formula (SGJPF) and its effective components for the treatment of liver fibrosis (LF).

Materials and Methods

We collected the active ingredients in SGJPF through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and screened the effective components by absorption, distribution, metabolism, and excretion. Herb-associated target proteins were predicted and screened based on the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine and Search Tool for Interactions of Chemicals databases. LF-associated target proteins were predicted and screened based on the Online Mendelian Inheritance in Man® Database and Comparative Toxicogenomics Database. Common genes with LF and herbs were selected, and Cytoscape 3.5.1 software was used to construct an herb pathway and component-LF common target network. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to build a protein-protein interaction, and quantitative PCR was used to verify the related target genes. Finally, clusterProfiler was applied for the analysis of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways.

Results

The pharmacological network contained 252 active compounds (e.g., Astragaloside A, saikosaponin, linoleic acid, and Poria acid A), 84 common target genes, and 94 significant signaling pathways. Among them, interleukin 6 (IL-6), tumor protein 53 p53 (TP53), prostaglandin-endoperoxide synthase 2 (PTGS2), AKT1, IL-1β, and the nucleotide-binding and oligomerization domain-like receptor and Janus kinase-signal transducer and activator of transcription signaling pathways were selected as the critical target gene and critical signal pathway, respectively.

Conclusion

The mechanisms of SGJPF in protecting against LF include the regulation of multiple targets such as IL-6, TP53, PTGS2, and AKT1. These target proteins affect LF through various signal transduction pathways.

Metabolic Signature of Hepatic Fibrosis: From Individual Pathways to Systems Biology

Hepatic fibrosis is a major cause of morbidity and mortality worldwide, as it ultimately leads to cirrhosis, which is estimated to affect up to 2% of the global population. Hepatic fibrosis is confirmed by liver biopsy, and the erroneous nature of this technique necessitates the search for noninvasive alternatives. However, current biomarker algorithms for hepatic fibrosis have many limitations. Given that the liver is the largest organ and a major metabolic hub in the body, probing the metabolic signature of hepatic fibrosis holds promise for the discovery of new markers and therapeutic targets. Regarding individual metabolic pathways, accumulating evidence shows that hepatic fibrosis leads to alterations in carbohydrate metabolism, as aerobic glycolysis is aggravated in activated hepatic stellate cells (HSCs) and the whole fibrotic liver; in amino acid metabolism, as Fischer’s ratio (branched-chain amino acids/aromatic amino acids) decreases in patients with hepatic fibrosis; and in lipid metabolism, as HSCs lose vitamin A-containing lipid droplets during transdifferentiation, and cirrhotic patients have decreased serum lipids. The current review also summarizes recent findings of metabolic alterations relevant to hepatic fibrosis based on systems biology approaches, including transcriptomics, proteomics, and metabolomics in vitro, in animal models and in humans.

Identification of key metabolic changes during liver fibrosis progression in rats using a urine and serum metabolomics approach

Reversibility of hepatic fibrosis is an intrinsic response to chronic injury, and with on-going damage, fibrosis can progress to its end-stage consequence, cirrhosis. Non-invasive and reliable biomarkers for early detection of liver fibrosis are needed. Based on the CCl4-induced liver fibrosis rat model, urinary and serum metabolic profiling performed by LC-QTOF-MS associated with histological progression were utilized to identify liver fibrosis-specific potential biomarkers for early prediction and to reveal significant fibrotic pathways and their dynamic changes in different stages of liver fibrosis. Finally, nine differential metabolites in urine and ten in serum were selected and identified involving the most relevant metabolic pathways. Perturbations of tryptophan, valine, leucine, isoleucine, and citrate (TCA) cycle metabolites, along with sphingolipid and glycerophospholipid metabolites, occurred from the onset of liver fibrosis. Furthermore, dysregulation of valine and bile acid biosynthesis metabolites occurred in the intermediate and advanced stages. More importantly, among these metabolites, urinary kynurenic acid, 5-hydroxyindoleacetyl glycine, 4-(2-amino-3-hydroxyphenyl)-2,4-dioxobutanoic acid and serum sphinganine, sphingomyelin, L-leucine, L-tryptophan, and LysoPC(17:0) changed at all time points and may serve as potential early biomarkers for the diagnosis of hepatic fibrosis and as therapeutic targets. Overall, this work evaluates the potential of these metabolites for the early detection of liver fibrosis.

Technological advances in current metabolomics and its application in tradition Chinese medicine

During the last few years, many metabolomics technologies have been established in biomedical research for analyzing the changes of metabolite levels.