A novel experimental mouse model of diabetic nonalcoholic steatohepatitis: A critical role for acid-sensitive Ion Channel 1a.

BACKGROUND: A two-way relationship exists between type 2 diabetes (T2DM) and human nonalcoholic steatohepatitis (NASH). Several diabetic NASH models have the disadvantages of long cycles or inconsistent with the actual incidence of human disease, which would be costly and time-consuming to investigate disease pathogenesis and develop drugs. Therefore, there is an urgent need to establish a diabetic NASH mouse model. METHODS: The combination between Fructose-palmitate-cholesterol diet (FPC) and Streptozotocin (STZ) (FPC+STZ) was used to construct diabetic NASH mouse model. The in vivo effects of silencing acid-sensitive Ion Channel 1a (ASIC1a) were examined with an adeno-associated virus 9 (AAV9) carrying ASIC1a short hairpin RNA (shRNA) in FPC+STZ model. RESULTS: The mice fed with FPC for 12 weeks had insulin resistance, hyperinsulinemia, lipid accumulation, and increased hepatic levels of inflammatory factors. However, it still did not develop remarkable liver fibrosis. Most interestingly, noticeable fibrotic scars were observed in the liver of mice from FPC+STZ group. Furthermore, insulin therapy significantly ameliorated FPC+STZ-induced NASH-related liver fibrosis, indicating that hyperglycemia is of great significance in NASH development and progression. Importantly, ASIC1a was found to be involved in the pathogenesis of diabetic NASH as demonstrated that silencing ASIC1a in HSCs significantly ameliorated FPC+STZ-induced NASH fibrosis. Mechanistically, ASIC1a interacted with Poly Adp-adenosine ribose polymerase (PARP1) to promote HSC activation by inducing autophagy. CONCLUSION: A FPC diet combined with an injection of STZ induces a diabetic NASH mouse model in a shorter period. Targeting ASIC1a may provide a novel therapeutic target for the treatment of diabetic NASH.

New advances in drug development for metabolic dysfunction-associated diseases and alcohol-associated liver disease.

Metabolic disorders are currently threatening public health worldwide. Discovering new targets and developing promising drugs will reduce the global metabolic-related disease burden. Metabolic disorders primarily consist of lipid and glucose metabolic disorders. Specifically, metabolic dysfunction-associated steatosis liver disease (MASLD) and alcohol-associated liver disease (ALD) are two representative lipid metabolism disorders, while diabetes mellitus is a typical glucose metabolism disorder. In this review, we aimed to summarize the new drug candidates with promising efficacy identified in clinical trials for these diseases. These drug candidates may provide alternatives for patients with metabolic disorders and advance the progress of drug discovery for the large disease burden.

Inflammation in Steatotic Liver Diseases: Pathogenesis and Therapeutic Targets.

Alcohol-related liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD), two main types of steatotic liver disease (SLDs), are characterized by a wide spectrum of several different liver disorders, including simple steatosis, steatohepatitis, cirrhosis, and hepatocellular carcinoma. Multiple immune cell-mediated inflammatory responses not only orchestrate the killing and removal of infected/damaged cells but also exacerbate the development of SLDs when excessive or persistent inflammation occurs. In recent years, single-cell and spatial transcriptome analyses have revealed the heterogeneity of liver-infiltrated immune cells in ALD and MASLD, revealing a new immunopathological picture of SLDs. In this review, we will emphasize the roles of several key immune cells in the pathogenesis of ALD and MASLD and discuss inflammation-based approaches for effective SLD intervention. In conclusion, the study of immunological mechanisms, especially highly specific immune cell population functions, may provide novel therapeutic opportunities for this life-threatening disease.

In silico production of relative correction factor for the quantitative analysis of multi-components by single marker method.

INTRODUCTION: Traditional methods to derive experimentally-generated relative correction factors (RCFs) for the quantitative analysis of herbal multi-components by single marker (QAMS) method require reference standards and multiple validations with different instruments and columns, which hampers high throughput implementation. OBJECTIVES: To effectively reduce the application amounts of raw material and provide higher and more stable accuracy, this study aimed to develop a method to computationally generate RCFs of herbal components. MATERIALS AND METHODS: This strategy included the published data collection, calibration curves screening, computer algorithm-based RCFs generation and accuracy validation. RESULTS: Using the in silico approach, we have successfully produced 133 RCFs for the multi-component quantitative analysis of 63 widely used herbs. CONCLUSION: Compared with conventional RCFs, this in silico method would be a low cost and highly efficient way to produce practical RCFs for the QAMS method.