一种分子网络策略：利用高通量筛选和化学分析巴西塞拉多植物提取物对癌细胞的作用。
A Molecular Networking Strategy: High-Throughput Screening and Chemical Analysis of Brazilian Cerrado Plant Extracts against Cancer Cells.
机构信息
Laboratório de Farmacognosia, Universidade de Brasília, Campus Universitário Darcy Ribeiro, Brasília 70910-900, Brazil.
Molecular Targets Program, National Cancer Institute, Frederick, MD 21702, USA.
出版信息
Cells. 2021 Mar 20;10(3):691. doi: 10.3390/cells10030691.
Plants have historically been a rich source of successful anticancer drugs and chemotherapeutic agents, with research indicating that this trend will continue. In this contribution, we performed high-throughput cytotoxicity screening of 702 extracts from 95 plant species, representing 40 families of the Brazilian Cerrado biome. Activity was investigated against the following cancer cell lines: colon (Colo205 and Km12), renal (A498 and U031), liver (HEP3B and SKHEP), and osteosarcoma (MG63 and MG63.3). Dose-response tests were conducted with 44 of the most active extracts, with 22 demonstrating IC values ranging from <1.3 to 20 µg/mL. A molecular networking strategy was formulated using the Global Natural Product Social Molecular Networking (GNPS) platform to visualize, analyze, and annotate the compounds present in 17 extracts active against NCI-60 cell lines. Significant cytotoxic activity was found for , , , , , var. , , and . Molecular networking resulted in the annotation of 27 compounds. This strategy provided an initial overview of a complex and diverse natural product data set, yielded a large amount of chemical information, identified patterns and known compounds, and assisted in defining priorities for further studies.
植物一直是成功的抗癌药物和化疗药物的丰富来源，研究表明这种趋势将继续下去。在本研究中，我们对来自巴西塞拉多生物群落 40 个科的 95 种植物的 702 种提取物进行了高通量细胞毒性筛选。针对以下癌细胞系进行了活性研究：结肠（Colo205 和 Km12）、肾（A498 和 U031）、肝（HEP3B 和 SKHEP）和骨肉瘤（MG63 和 MG63.3）。对 44 种最活跃的提取物进行了剂量反应测试，其中 22 种提取物的 IC 值范围从<1.3 至 20µg/mL。利用全球天然产物社会分子网络（GNPS）平台制定了一种分子网络策略，用于可视化、分析和注释对 NCI-60 细胞系具有活性的 17 种提取物中的化合物。发现了 、 、 、 、 var. 、 、 和 具有显著的细胞毒性活性。分子网络对 27 种化合物进行了注释。该策略提供了对复杂多样的天然产物数据集的初步概述，提供了大量的化学信息，确定了模式和已知化合物，并有助于确定进一步研究的优先事项。
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