North China Pharmaceutical Group Corp.

Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive liver disease characterized by steatosis, inflammatory responses, and fibrosis. Peroxisome proliferator-activated receptors (PPARs), master regulators of glucolipid homeostasis and inflammatory pathways, have emerged as promising therapeutic targets for MASH. PPAR agonists have demonstrated therapeutic potential in MASH by ameliorating hepatic lipid deposition, normalizing dyslipidemia, enhancing insulin sensitivity, and suppressing proinflammatory signaling. In this study, we reported that NCPC-626, a natural fungal metabolite, was discovered as a novel potent pan-PPAR agonist through high-throughput screening. In an in vitro model of MASH, NCPC-626 inhibited lipid accumulation, fibrosis, and inflammation. Moreover, NCPC-626 treatment reduced body weight, liver triglyceride levels, and improved glucose tolerance in db/db mice by regulating glucolipid metabolism. Additionally, NCPC-626 exhibited preventive and therapeutic effects against fibrosis in a CCl₄-induced fibrosis model and ameliorated MASH progression in a diet-induced model. This study highlights NCPC-626 as a pan-PPAR agonist with a novel chemical scaffold for MASH treatment. SIGNIFICANCE STATEMENT: This study identifies a novel structural compound, NCPC-626, demonstrating specific and balanced activation of peroxisome proliferator-activated receptors. NCPC-626 demonstrates therapeutic efficacy against metabolic dysfunction-associated steatohepatitis in experimental models.

Predicting interactions between microbes and hosts plays critical roles in microbiome population genetics and microbial ecology and evolution. How to systematically characterize the sophisticated mechanisms and signal interplay between microbes and hosts is a significant challenge for global health risks. Identifying microbe-host interactions (MHIs) can not only provide helpful insights into their fundamental regulatory mechanisms, but also facilitate the development of targeted therapies for microbial infections. In recent years, computational methods have become an appealing alternative due to the high risk and cost of wet-lab experiments. Therefore, in this study, we utilized rich microbial metagenomic information to construct a novel heterogeneous microbial network (HMN)-based model named KGVHI to predict candidate microbes for target hosts. Specifically, KGVHI first built a HMN by integrating human proteins, viruses and pathogenic bacteria with their biological attributes. Then KGVHI adopted a knowledge graph embedding strategy to capture the global topological structure information of the whole network. A natural language processing algorithm is used to extract the local biological attribute information from the nodes in HMN. Finally, we combined the local and global information and fed it into a blended deep neural network (DNN) for training and prediction. Compared to state-of-the-art methods, the comprehensive experimental results show that our model can obtain excellent results on the corresponding three MHI datasets. Furthermore, we also conducted two pathogenic bacteria case studies to further indicate that KGVHI has excellent predictive capabilities for potential MHI pairs.

Two glycosylated naphthacemycins (naphthacemycins D₁ and D₂) were identified in Streptomyces sp. N12W1565. These two compounds not only showed antimicrobial potential against bacteria but also exhibited more aqueous solubility than naphthacemycins. Furthermore, the whole genome of Streptomyces sp. N12W1565 has been sequenced, the natY gene, located outside the biosynthetic gene cluster encoding a D-glucose glycosyltransferase, was identified to mediate glycosylation in the phenolic hydroxyl of the naphthacemycin core scaffold. Glycosyltransferase was elucidated in vitro by using a homologous enzyme, which showed potential as a biocatalyst.