5:2 intermittent fasting tapers food intake in the refeeding state and ameliorates metabolic disturbances in mice exposed to olanzapine

PCSK9 dysregulates cholesterol homeostasis and triglyceride metabolism in olanzapine-induced hepatic steatosis via both receptor-dependent and receptor-independent pathways

Schizophrenia, affecting approximately 1% of the global population, is often treated with olanzapine. Despite its efficacy, olanzapine's prolonged use has been associated with an increased risk of cardiovascular diseases and nonalcoholic fatty liver disease (NAFLD); however, the underlying mechanism remains unclear. Proprotein convertase subtilisin kexin type 9 (PCSK9) plays a crucial role in lipid metabolism and is involved in NAFLD pathogenesis via an unknown mechanism. This study aims to investigate the role of PCSK9 in olanzapine-induced NAFLD. C57BL/6J mice and HepG2 and AML12 cell lines were treated with varying concentrations of olanzapine to examine the effects of olanzapine on PCSK9 and lipid metabolism. PCSK9 levels were manipulated using recombinant proteins, plasmids, and small interfering RNAs in vitro, and the effects on hepatic lipid accumulation and gene expression related to lipid metabolism were assessed. Olanzapine treatment significantly increased PCSK9 levels in both animal and cell line models, correlating with elevated lipid accumulation. PCSK9 manipulation demonstrated its central role in mediating hepatic steatosis through both receptor-dependent pathways (impacting NPC1L1) and receptor-independent pathways (affecting lipid synthesis, uptake, and cholesterol biosynthesis). Interestingly, upregulation of SREBP-1c, rather than SREBP-2, was identified as a key driver of PCSK9 increase in olanzapine-induced NAFLD. Our findings establish PCSK9 as a pivotal factor in olanzapine-induced NAFLD, influencing both receptor-related and metabolic pathways. This highlights PCSK9 inhibitors as potential therapeutic agents for managing NAFLD in schizophrenia patients treated with olanzapine.

HER2 low expression breast cancer subtyping and their correlation with prognosis and immune landscape based on the histone modification related genes

Human epidermal growth factor receptor 2 (HER2) plays an important role in diagnosis and treatment of breast cancer (BRCA). The histone modification has been found to be related to the progression of cancer. This study aimed to probe the low HER2 expression BRCA heterogeneity by histone modification genes. The BRCA data and cell lines were collected from The Cancer Genome Atlas database. Weighted gene co-expression network analysis and non-negative matrix factorization clustering were jointly applied to obtain BRCA clusters. The expression of hub histone modification gene was detected using western blot assay. The gene ontology term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to reveal functional information. The overall survival analysis was performed using survival and survminer packages, and the immune landscape was mainly analyzed using CIBERSORT software. Totally 43 histone modification genes correlated with survival of BRCA patients with HER2 low expression were screened. Based on these 43 histone modification genes, the BRCA samples were classified into cluster1, cluster2 and cluster3. Histone modification gene NFKBIZ exhibited high expression, while RAD51 demonstrated low expression in low HER2 expression BRCA cell. Cluster1 exhibited the best prognosis, while cluster3 had the worse outcomes. Tumor mutational burden (TMB) was remarkably increased in cluster3 group compared to cluster1 and cluster2. Moreover, the relative proportion of 16 immune cell infiltration and 8 immune checkpoint expression were remarkably differential among cluster1, cluster2 and cluster3, and the drug sensitivity exhibited difference among cluster1, cluster2 and cluster3 in BRCA patients with low HER2 expression. This study identified three HER2 low expression BRCA clusters with different characteristics based on histone modification genes. The TMB, immune cell infiltration, immune checkpoints and drug sensitivity were different among the three clusters.