High-fat diet induced alterations in plasma membrane cholesterol content impairs insulin receptor binding and signalling in mouse liver but is ameliorated by atorvastatin

Basal MET phosphorylation is an indicator of hepatocyte dysregulation in liver disease

Chronic liver diseases are worldwide on the rise. Due to the rapidly increasing incidence, in particular in Western countries, metabolic dysfunction-associated steatotic liver disease (MASLD) is gaining importance as the disease can develop into hepatocellular carcinoma. Lipid accumulation in hepatocytes has been identified as the characteristic structural change in MASLD development, but molecular mechanisms responsible for disease progression remained unresolved. Here, we uncover in primary hepatocytes from a preclinical model fed with a Western diet (WD) an increased basal MET phosphorylation and a strong downregulation of the PI3K-AKT pathway. Dynamic pathway modeling of hepatocyte growth factor (HGF) signal transduction combined with global proteomics identifies that an elevated basal MET phosphorylation rate is the main driver of altered signaling leading to increased proliferation of WD-hepatocytes. Model-adaptation to patient-derived hepatocytes reveal patient-specific variability in basal MET phosphorylation, which correlates with patient outcome after liver surgery. Thus, dysregulated basal MET phosphorylation could be an indicator for the health status of the liver and thereby inform on the risk of a patient to suffer from liver failure after surgery.

Variation in responses to incretin therapy: Modifiable and non-modifiable factors

Type 2 diabetes (T2D) and obesity have reached epidemic proportions. Incretin therapy is the second line of treatment for T2D, improving both blood glucose regulation and weight loss. Glucagon-like peptide-1 (GLP-1) and glucose-stimulated insulinotropic polypeptide (GIP) are the incretin hormones that provide the foundations for these drugs. While these therapies have been highly effective for some, the results are variable. Incretin therapies target the class B G protein-coupled receptors GLP-1R and GIPR, expressed mainly in the pancreas and the hypothalamus, while some therapeutical approaches include additional targeting of the related glucagon receptor (GCGR) in the liver. The proper functioning of these receptors is crucial for incretin therapy success and here we review several mechanisms at the cellular and molecular level that influence an individual's response to incretin therapy.

Drug vector representation and potential efficacy prediction based on graph representation learning and transcriptome data: Acacetin from traditional Chinese Medicine model

ETHNOPHARMACOLOGICAL RELEVANCE

Acacetin is widely distributed in traditional Chinese medicine and traditional herbs, with strong biological activity. Perhaps there are many potential effects that have not been explored. In the field of drug discovery, Mainstream methods focus on chemical structure. Traditional medicine cannot adapt to the mainstream prediction methods due to its complex composition.

AIM OF THE STUDY

Our aim is that provide a prediction method more suitable for traditional medicine by graph representation learning and transcriptome data. And use this method to predict acacetin.

MATERIALS AND METHODS

Our method mainly consists of two parts. The first part is to use the method of graph representation learning to vectorize drugs as a database. The original data of this part comes from transcriptome data on Gene Expression Omnibus. The method of graph representation learning is an unsupervised learning. If there is no prior knowledge as the label data, the training effect cannot be analyzed. Therefore, we define a standard score to evaluate our results through the idea of Jaccard index. The second part is to put the target drug into our database. The potential similarity between drugs was evaluated by the Euclidean distance between vectors, and the potential efficacy of the target drug is predicted by combining the chemical-disease relationship data in the Comparative Toxicogenomics Database. The target drug in this paper uses acacetin. We compared the predicted results with existing reports, and we also experimentally verified the efficacy of improving insulin resistance in the predicted results.

RESULTS

The prediction results are relatively consistent with the existing reports, which demonstrated that our method has a certain degree of predictive performance. And for the efficacy of improving insulin resistance in the predicted result, we verified it through experiments.

CONCLUSIONS

We propose a method to predict the potential efficacy of drugs based on transcriptome data, using Graph representation learning, which is very suitable for traditional medicine. Through this method, we predicted the efficacy of acacetin, and the results are relatively consistent with the current reports. This provides a new idea for unsupervised learning to apply medical information.

Gut microbiota dysbiosis in polycystic ovary syndrome: Mechanisms of progression and clinical applications

Polycystic ovary syndrome (PCOS) is the most common endocrine diseases in women of childbearing age that leads to menstrual disorders and infertility. The pathogenesis of PCOS is complex and has not yet been fully clarified. Gut microbiota is associated with disorders of lipid, glucose, and steroid hormone metabolish. A large body of studies demonstrated that gut microbiota could regulate the synthesis and secretion of insulin, and affect androgen metabolism and follicle development, providing us a novel idea for unravelling the pathogenesis of PCOS. The relationship between gut microbiota and the pathogenesis of PCOS is particularly important. This study reviewed recent research advances in the roles of gut microbiota in the occurrence and development of PCOS. It is expected to provide a new direction for the treatment of PCOS based on gut microbiota.