RESUMEN
A series of novel phenazine derivatives (1~27) containing the Michael acceptor scaffolds were designed and synthesized in this study. Some compounds exhibited selective cytotoxicity against Bel-7402 cancer cell line in vitro, in which compound 26 were found to have the best antiproliferative activity. Meanwhile, compound 26 showed no obvious cell toxicity against human normal liver epithelial L02 cells, which means this compound possessed a better safety potential. In the following research, compound 26 was verified to inhibit TrxR1 enzyme activity, ultimately resulting in cellular molecular mechanism events of apoptosis including growth of intracellular ROS level, depletion of reduced Trx1, liberation of ASK1 and up-regulation of p38, respectively. Together, all these evidences implicated that compound 26 acted as the TrxR1 inhibitor against Bel-7402 cells, and could activate apoptosis through the ROS-Trx-ASK1-p38 pathway.
Asunto(s)
Antineoplásicos/farmacología , Diseño de Fármacos , Inhibidores Enzimáticos/farmacología , Fenazinas/farmacología , Tiorredoxina Reductasa 1/antagonistas & inhibidores , Antineoplásicos/síntesis química , Antineoplásicos/química , Apoptosis/efectos de los fármacos , Proliferación Celular/efectos de los fármacos , Relación Dosis-Respuesta a Droga , Ensayos de Selección de Medicamentos Antitumorales , Inhibidores Enzimáticos/síntesis química , Inhibidores Enzimáticos/química , Humanos , Estructura Molecular , Fenazinas/síntesis química , Fenazinas/química , Relación Estructura-Actividad , Tiorredoxina Reductasa 1/metabolismo , Células Tumorales CultivadasRESUMEN
Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a "meta-pathway" structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: https://github.com/BIGchix/GINv2.0 .