The Gut Microbiome and Autoimmune Hepatitis: Implications for Early Diagnostic Biomarkers and Novel Therapies

The development of probiotics and prebiotics therapy to ulcerative colitis: a therapy that has gained considerable momentum

Ulcerative colitis (UC) is increasingly common, and it is gradually become a kind of global epidemic. UC is a type of inflammatory bowel disease (IBD), and it is a lifetime recurrent disease. UC as a common disease has become a financial burden for many people and has the potential to develop into cancer if not prevented or treated. There are multiple factors such as genetic factors, host immune system disorders, and environmental factors to cause UC. A growing body of research have suggested that intestinal microbiota as an environmental factor play an important role in the occurrence and development of UC. Meanwhile, evidence to date suggests that manipulating the gut microbiome may represent effective treatment for the prevention or management of UC. In addition, the main clinical drugs to treat UC are amino salicylate and corticosteroid. These clinical drugs always have some side effects and low success rate when treating patients with UC. Therefore, there is an urgent need for safe and efficient methods to treat UC. Based on this, probiotics and prebiotics may be a valuable treatment for UC. In order to promote the wide clinical application of probiotics and prebiotics in the treatment of UC. This review aims to summarize the recent literature as an aid to better understanding how the probiotics and prebiotics contributes to UC while evaluating and prospecting the therapeutic effect of the probiotics and prebiotics in the treatment of UC based on previous publications.

The significance of gut microbiota in the etiology of autoimmune hepatitis: a narrative review

Autoimmune hepatitis (AIH) is a chronic inflammatory disease of the liver that is mediated by autoimmunity and has complex pathogenesis. Its prevalence has increased globally. Since the liver is the first organ to be exposed to harmful substances, such as gut-derived intestinal microbiota and its metabolites, gut health is closely related to liver health, and the "liver-gut axis" allows abnormalities in the gut microbiota to influence the development of liver-related diseases such as AIH. Changes in the composition of the intestinal microbiota and its resultant disruption of the intestinal barrier and microbial transport are involved in multiple ways in the disruption of immune homeostasis and inflammation, thereby influencing the development of AIH. In terms of the mechanisms involved in immune, the gut microbiota or its metabolites, which is decreased in secondary bile acids, short-chain fatty acids (SCFAs), and polyamines, and increased in lipopolysaccharide (LPS), branched-chain amino acids (BCAA), tryptophan metabolite, amino acid, and bile acid, can disrupt immune homeostasis by activating various immune cells and immune-related signaling pathways, resulting in aberrant activation of the immune system. Clarifying this mechanism has significant clinical implications for the treatment of AIH with drugs that target intestinal microbiota and related signaling pathways. Therefore, this narrative review summarizes the progress in exploring the involvement of gut microbiota in the pathogenesis of AIH, with the aim of helping to improve the precise targeting of therapeutic treatments against AIH for the benefit of clinical AIH treatment.

WSGMB: weight signed graph neural network for microbial biomarker identification

The stability of the gut microenvironment is inextricably linked to human health, with the onset of many diseases accompanied by dysbiosis of the gut microbiota. It has been reported that there are differences in the microbial community composition between patients and healthy individuals, and many microbes are considered potential biomarkers. Accurately identifying these biomarkers can lead to more precise and reliable clinical decision-making. To improve the accuracy of microbial biomarker identification, this study introduces WSGMB, a computational framework that uses the relative abundance of microbial taxa and health status as inputs. This method has two main contributions: (1) viewing the microbial co-occurrence network as a weighted signed graph and applying graph convolutional neural network techniques for graph classification; (2) designing a new architecture to compute the role transitions of each microbial taxon between health and disease networks, thereby identifying disease-related microbial biomarkers. The weighted signed graph neural network enhances the quality of graph embeddings; quantifying the importance of microbes in different co-occurrence networks better identifies those microbes critical to health. Microbes are ranked according to their importance change scores, and when this score exceeds a set threshold, the microbe is considered a biomarker. This framework's identification performance is validated by comparing the biomarkers identified by WSGMB with actual microbial biomarkers associated with specific diseases from public literature databases. The study tests the proposed computational framework using actual microbial community data from colorectal cancer and Crohn's disease samples. It compares it with the most advanced microbial biomarker identification methods. The results show that the WSGMB method outperforms similar approaches in the accuracy of microbial biomarker identification.