A clinical experience-based Chinese herbal formula improves ethanol-induced drunken behavior and hepatic steatohepatitis in mice models.

BACKGROUND: Bao-Gan-Xing-Jiu-Wan (BGXJW) is a clinical experience-based Chinese herbal formula. Its efficacy, pharmacological safety, targeted function, process quality, and other aspects have met the evaluation standards and the latest requirements of preparations. It could prevent and alleviate the symptoms of drunkenness and alcoholic liver injury clinically. The present work aims to elucidate whether BGXJW could protect against drunkenness and alcoholic liver disease in mice and explore the associated mechanism. MATERIAL AND METHODS: We used acute-on-chronic (NIAAA) mice model to induce alcoholic steatosis, and alcohol binge-drinking model to reappear the drunk condition. BGXJW at indicated doses were administered by oral gavage respectively to analyze its effects on alcoholic liver injury and the associated molecular mechanisms. RESULTS: BGXJW had no cardiac, hepatic, renal, or intestinal toxicity in mice. Alcoholic liver injury and steatosis in the NIAAA mode were effectively prevented by BGXJW treatment. BGXJW increased the expression of alcohol metabolizing enzymes ADH, CYP2E1, and ALDH2 to enhance alcohol metabolism, inhibited steatosis through regulating lipid metabolism, counteracted alcohol-induced upregulation of lipid synthesis related proteins SREBP1, FASN, and SCD1, meanwhile it enhanced fatty acids ß-oxidation related proteins PPAR-α and CPT1A. Alcohol taken enhanced pro-inflammatory TNF-α, IL-6 and down-regulated the anti-inflammatory IL-10 expression in the liver, which were also reversed by BGXJW administration. Moreover, BGXJW significantly decreased the blood ethanol concentration and alleviated drunkenness in the alcohol binge-drinking mice model. CONCLUSIONS: BGXJW could effectively relieve drunkenness and prevent alcoholic liver disease by regulating lipid metabolism, inflammatory response, and alcohol metabolism.

Metabolomic profiling for drug-induced liver injury with autoantibodies.

BACKGROUNDS: Drug induced liver injury (DILI) is sometimes similar to autoimmune hepatitis (AIH) in serology and histology. Clinicians empirically screened DILI with significant autoimmune characteristics to implement clinical intervention. We tried to characterize DILI with autoantibodies by metabolomics. METHODS: Untargeted metabolomics coupled with pattern recognition approaches were performed on sera samples including AIH (n = 59), DILI with autoantibodies (DILIAb+, n = 68), and DILI without autoantibodies (DILIAb-, n = 75). The differential metabolites and fingerprint metabolites between AIH and DILIAb- were screened by orthogonal partial least squares-discriminant analysis and hierarchical clustering respectively. RESULTS: Of the 388 annotated differential metabolites between AIH and DILIAb-, 74 fingerprint metabolites were screened. The eigenmetabolite compressed from the fingerprint possessed high discrimination efficacy (AUC:0.891; 95 %CI, 0.838-0.944). In the fingerprint-based PCA model, AIH and DILIAb- were separated into three regions: the "pure region" of AIH (Region 1), the "pure region" of DILIAb- (Region 3), the mixture region of AIH and DILIAb- (Region 2). After incorporated into the PCA model, DILIAb+ samples were distributed into the three regions, indicating that DILIAb+ samples had different etiological tendencies. Moreover, the fingerprint-based radar model verified the results of PCA model characterizing DILIAb+. Notably, the antibody titers of DILIAb+ in the three regions did not differ significantly, while the response rates for glucocorticoids were obviously different. The metabolic difference among DILIAb+ in different regions mainly lies in energy metabolism. CONCLUSIONS: In terms of metabolic signature, DILIAb+ may not be a community of same pathogenesis, including AIH-inclined parts. Which deserves further study.

Metabolomic Profiling for Histologically Fibrotic Stage in Chronic Drug-Induced Liver Injury.

Background and aims: Chronic drug-induced liver injury (DILI) is a rare but under-researched adverse drug reaction-related disease, which is highly likely to progress into liver fibrosis and even cirrhosis. In this study, metabolomics was used to screen out characteristic metabolites related to the histological progression of fibrosis in chronic DILI and analyze the metabolic changes during the development of fibrosis to explain the underlying mechanism. Methods: Chronic DILI patients who underwent liver biopsy were divided into different fibrosis grades. Serum was analyzed by untargeted metabolomics to find serological characteristic metabolite fingerprints. The screened fingerprints were validated by the validation group patients, and the identification ability of fingerprints was compared using FibroScan. Results: A total of 31 metabolites associated with fibrosis and 11 metabolites associated with advanced fibrosis were identified. The validation group confirmed the accuracy of the two metabolite fingerprints [area under the curve (AUC) value 0.753 and 0.944]. In addition, the fingerprints showed the ability to distinguish the grades of fibrosis by comparing using FibroScan. The metabolite fingerprint pathway showed that bile acid synthesis is disturbed while lipid metabolism is extremely active, resulting in an overload of lipid metabolites in the occurrence and development of chronic DILI-associated fibrosis. Conclusions: Our metabolomic analysis reveals the unique metabolomic fingerprints associated with chronic DILI fibrosis, which have potential clinical diagnostic and prognostic significances. The metabolomic fingerprints suggest the disturbance of the lipid metabolites as the most important factor in the development of DILI fibrosis.

Metabolomic Analysis Uncovers Lipid and Amino Acid Metabolism Disturbance During the Development of Ascites in Alcoholic Liver Disease.

Ascites is one of the most common complications of cirrhosis, and there is a dearth of knowledge about ascites-related pathologic metabolism. In this study, 122 alcoholic liver disease (ALD) patients, including 49 cases without ascites, 18 cases with mild-ascites, and 55 cases with large-ascites (1) were established according to the International Ascites Club (2), and untargeted metabolomics coupled with pattern recognition approaches were performed to profile and extract metabolite signatures. A total of 553 metabolites were uniquely discovered in patients with ascites, of which 136 metabolites had been annotated in the human metabolome database. Principal component analysis (PCA) analysis was used to further identify 21 ascites-related fingerprints. The eigenmetabolite calculated by reducing the dimensions of the 21 metabolites could be used to effectively identify those ALD patients with or without ascites. The eigenmetabolite showed a decreasing trend during ascites production and accumulation and was negatively related to the disease progress. These metabolic fingerprints mainly belong to the metabolites in lipid metabolism and the amino acid pathway. The results imply that lipid and amino acid metabolism disturbance may play a critical role in the development of ascites in ALD patients and could be a potent prognosis marker.

Integrated Analyses of Mouse Stem Cell Transcriptomes Provide Clues for Stem Cell Maintenance and Transdifferentiation.

In vivo cell fate reprogramming has emerged as a new method for understanding cell plasticity and as potential treatment for tissue regeneration. Highly efficient and precise reprogramming requires fully understanding of the transcriptomes which function within different cell types. Here, we adopt weighted gene co-expression network analysis (WGCNA) to explore the molecular mechanisms of self-renewal in several well-known stem cell types, including embryonic stem cells (ESC), primordial germ cells (PGC), spermatogonia stem cells (SSC), neural stem cells (NSC), mesenchymal stem cells (MSC), and hematopoietic stem cells (HSC). We identified 37 core genes that were up-regulated in all of the stem cell types examined, as well as stem cell correlated gene co-expression networks. The validation of the co-expression genes revealed a continued protein-protein interaction network that included 823 nodes and 3113 edges. Based on the topology, we identified six densely connected regions within the continued protein-protein interaction network. The SSC specific genes Itgam, Cxcr6, and Agtr2 bridged four densely connected regions that consisted primarily of HSC-, NSC-, and MSC-correlated genes. The expression levels of identified stem cell related transcription factors were confirmed consistent with bioinformatics prediction in ESCs and NSCs by qPCR. Exploring the mechanisms underlying adult stem cell self-renewal will aid in the understanding of stem cell pool maintenance and will promote more accurate and efficient strategies for tissue regeneration and repair.

Source Apportionment of Soil Samples by the Combination of Two Neural Networks Based on Computer-Controlled Scanning Electron Microscopy.

The apportionment of ambient aerosol mass to different sources of airborne soil is a difficult problem because of the similarity of the chemical composition of crustal sources. However, additional information can be obtained using individual particle analysis. A novel approach based on the combination of two neural networks, the adaptive resonance theory-based neural network (ART-2a) and the back-propagation (BP) neural network with electron microscopy data, has been developed to apportion the mass contributions of the crustal sources to ambient particle samples. The crustal source samples were analyzed using computer-controlled scanning electron microscopy (CCSEM). CCSEM provides elemental compositions and size parameters for individual particles as well as estimates of the shape and density from which the volume and mass of each particle can be estimated. The ART-2a neural network was first used to partition particles into homogeneous classes based on the elemental composition data. After the different particle type classes were produced by ART-2a, their mass fractions were calculated. In this way, the source profiles for the crustal dust sources can be obtained in terms of the mass fractions for different particle types. Then the BP neural network was applied to build the model between the mass fractions of different particle types and the mass contributions. Using the three physical source samples prepared for this study, artificial ambient samples were generated by randomly mixing particles from the three source samples. These samples were then used to examine the proposed method. Satisfactory predictions for the mass contributions of the three sources to the ambient samples have been obtained, indicating the proposed method is a promising tool for the source apportionment of chemically similar soil samples.