Daily low-dose aspirin and incident type 2 diabetes in community-dwelling healthy older adults: a post-hoc analysis of efficacy and safety in the ASPREE randomised placebo-controlled trial

Progress based on a multi-omics research strategy in the biosynthesis and modernization of active ingredients of Herpetospermum pedunculosum seeds

Herpetospermum pedunculosum seeds also known as Herpetospermum caudigerum Wall. is the mature seed of the Herpetospermum pedunculosum(Ser.) C. B. Clarke,Cucurbitaceae. Modern pharmacological studies have shown that H. pedunculosum has hepatoprotective, anti-inflammatory, anti-gout and antibacterial pharmacological activities. The biologically active chemical components include lignin compounds such as Herpetin, Herpetetrone, Herpetoriol and so on. The natural product displays considerable skeletal diversity and structural complexity, offering significant opportunities for novel drug discovery. Based on the multi-omics research strategy and the 'gene-protein-metabolite' research framework, the biosynthetic pathway of terpenoids and lignans in H. pedunculosum has has been elucidated at multiple levels. These approaches provide comprehensive genetic information for cloning and identification of pertinent enzyme genes. Furthermore, the application of multi-omics integrative approaches provides a scientific means to elucidate entire secondary metabolic pathways. We investigated the biosynthetic pathways of lignin and terpene components in H. pedunculosum and conducted bioinformatics analysis of the crucial enzyme genes involved in the biosynthetic process using genomic and transcriptomic data. We identified candidate genes for six key enzymes in the biosynthetic pathway. This review reports on the current literature on pharmacological investigations of H. pedunculosum, proposing its potential as an antidiabetic agent. Moreover, we conclude, for the first time, the identification of key enzyme genes potentially involved in the biosynthesis of active compounds in H. pedunculosum. This review provides a scientific foundation for the discovery of novel therapeutic agents from natural sources.

Identification of ibuprofen targeting CXCR family members to alleviate metabolic disturbance in lipodystrophy based on bioinformatics and in vivo experimental verification

Background

Lipodystrophy is a rare disease that is poorly diagnosed due to its low prevalence and frequent phenotypic heterogeneity. The main therapeutic measures for patients with clinical lipodystrophy are aimed at improving general metabolic complications such as diabetes mellitus, insulin resistance, and hypertriglyceridemia. Therefore, there is an urgent need to find new biomarkers to aid in the diagnosis and targeted treatment of patients with congenital generalized lipodystrophy (CGL).

Methods

Dataset GSE159337 was obtained via the Gene Expression Omnibus database. First, differentially expressed genes (DEGs) between CGL and control samples were yielded via differential expression analysis and were analyzed for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment to explore the functional pathways. Next, protein-protein interaction analysis and the MCC algorithm were implemented to yield candidate genes, which were then subjected to receiver operating characteristic (ROC) analysis to identify biomarkers with an area under the curve value exceeding 0.8. Moreover, random forest (RF), logistic regression, and support vector machine (SVM) analyses were carried out to assess the diagnostic ability of biomarkers for CGL. Finally, the small-molecule drugs targeting biomarkers were predicted, and ibuprofen was further validated in lipodystrophy mice.

Results

A total of 71 DEGs in GSE159337 were sifted out and were involved in immune receptor activity, immune response-regulating signaling pathway, and secretory granule membrane. Moreover, CXCR2, TNFSF10, NLRC4, CCR2, CEACAM3, TLR10, TNFAIP3, and JUN were considered as biomarkers by performing ROC analysis on 10 candidate genes. Meanwhile, RF, logistic regression, and SVM analyses further described that those biomarkers had an excellent diagnosis capability for CGL. Eventually, the drug-gene network included ibuprofen-CXCR1, ibuprofen-CXCR1, cenicriviroc-CCR2, fenofibrate-JUN, and other relationship pairs. Ibuprofen treatment was also validated to downregulate CXCR1 and CXCR2 in peripheral blood mononuclear cells (PBMCs) and improve glucose tolerance, hypertriglyceridemia, hepatic steatosis, and liver inflammation in lipodystrophy mice.

Conclusion

Eight biomarkers, namely, CXCR2, TNFSF10, NLRC4, CCR2, CEACAM3, TLR10, TNFAIP3, and JUN, were identified through bioinformatic analyses, and ibuprofen targeting CXCR1 and CXCR2 in PBMCs was shown to improve metabolic disturbance in lipodystrophy, contributing to studies related to the diagnosis and treatment of lipodystrophy.