| 1. |
- Mardinoglu, Adil, et al.
(författare)
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The Potential Use of Metabolic Cofactors in Treatment of NAFLD
- 2019
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Ingår i: Nutrients. - : MDPI. - 2072-6643. ; 11:7
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Forskningsöversikt (refereegranskat)abstract
- Non-alcoholic fatty liver disease (NAFLD) is caused by the imbalance between lipid deposition and lipid removal from the liver, and its global prevalence continues to increase dramatically. NAFLD encompasses a spectrum of pathological conditions including simple steatosis and non-alcoholic steatohepatitis (NASH), which can progress to cirrhosis and liver cancer. Even though there is a multi-disciplinary effort for development of a treatment strategy for NAFLD, there is not an approved effective medication available. Single or combined metabolic cofactors can be supplemented to boost the metabolic processes altered in NAFLD. Here, we review the dosage and usage of metabolic cofactors including l-carnitine, Nicotinamide riboside (NR), l-serine, and N-acetyl-l-cysteine (NAC) in human clinical studies to improve the altered biological functions associated with different human diseases. We also discuss the potential use of these substances in treatment of NAFLD and other metabolic diseases including neurodegenerative and cardiovascular diseases of which pathogenesis is linked to mitochondrial dysfunction.
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| 2. |
- Yang, Hong, et al.
(författare)
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Integrative proteo-transcriptomic characterization of advanced fibrosis in chronic liver disease across etiologies
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Various causes of chronic hepatic injury and inflammation can lead to fibrosis and cirrhosis, potentially predisposing individuals to hepatocellular carcinoma. Despite extensive research, the molecular mechanisms underlying liver fibrosis and its associated progression to cancer remain incompletely understood. In this study, we employed an integrated proteotranscriptomics approach to characterize the molecular pathophysiology of liver fibrosis in both liver and plasma samples from 330 individuals. This cohort included 40 healthy subjects and 290 patients with histologically characterized fibrosis due to chronic viral infection, alcohol consumption, or metabolic-dysfunction associated steatotic liver disease. We demonstrated that pathways related to extracellular matrix alterations, immune response, inflammation, and metabolism are dysregulated in advanced hepatic fibrosis, regardless of the underlying cause. Additionally, our analysis of peritumoral hepatic tissues revealed transcription signatures linked to cell proliferation, survival, and inflammation in hepatocellular carcinoma. Furthermore, we observed extensive remodeling of the plasma proteome linked with severe fibrosis and identified 132 circulating proteomic signatures associated with advanced fibrosis by integrative analysis of plasma proteomics with hepatic transcriptomics. We finally developed predictive models using machine learning to facilitate the non-invasive detection of advanced fibrosis and cirrhosis.
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| 3. |
- Zeybel, Mujdat, et al.
(författare)
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Multi-omics analysis reveals the influence of the oral and gut microbiome on host metabolism in non-alcoholic fatty liver disease
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Non-alcoholic fatty liver disease (NAFLD) is a complex disease involving alterations in multiple biological processes regulated by the interactions between obesity, genetic background and environmental factors including the microbiome. To decipher hepatic steatosis (HS) pathogenesis by excluding critical confounding factors including genetic variants, obesity and diabetes, we characterized 56 heterogeneous NAFLD patients by generating multi-omics data including oral and gut metagenomics as well as plasma metabolomics and inflammatory proteomics data. We explored the dysbiosis in the oral and gut microbiome and revealed host-microbiome interactions based on global metabolic and inflammatory processes. We integrated this multi-omics data using the biological network and identified HS's key features using multi-omics data. We finally predicted HS using these key features and validated our findings in a validation dataset, where we characterized 22 subjects with varying degree of HS
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