Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures
Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures
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DOI：
10.1021/acs.jnatprod.9b00176
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发表时间：
2019-03-01
影响因子：
5.1
通讯作者：
Cech, Nadja B.
中科院分区：
文献类型：
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作者：
Caesar, Lindsay K.;Kellogg, Joshua J.;Cech, Nadja B.
来自天然来源的化合物代表了今天使用的大多数小分子药物。植物由于其复杂的生物合成途径，可以合成多种选择性靶向生物大分子的次生代谢物。尽管植物药的化学领域广阔，但由于标准实践的成本高、耗时长以及化合物重新发现的高比率，这些来源的药物发现项目已经减少。整合生物和化学数据集的非靶向代谢组学方法有可能在分离过程的早期预测活性成分。然而，数据采集和数据处理参数可能对模型的成功产生重大影响。使用无活性的植物混合物加入已知的抗菌化合物，基于非靶向质谱的代谢组学数据与生物活性数据相结合，产生受各种数据采集和数据处理参数影响的选择性比率模型。选择性比率模型用于识别有意添加到混合物中的活性成分，以及额外的抗菌化合物randainal(5)，该化合物被混合物中拮抗剂的存在所掩盖。这些研究发现，数据处理方法，特别是使用方差截断的数据转换和模型简化工具，对生成的模型有显著影响，要么掩盖，要么增强检测样本中活性成分的能力。本研究强调了数据处理步骤对于从代谢组学模型中获得可靠信息的重要性，并展示了选择性比分析在综合评估复杂植物混合物方面的优势和局限性。
Compounds derived from natural sources represent the majority of small-molecule drugs utilized today. Plants, owing to their complex biosynthetic pathways, are poised to synthesize diverse secondary metabolites that selectively target biological macromolecules. Despite the vast chemical landscape of botanicals, drug discovery programs from these sources have diminished due to the costly and time-consuming nature of standard practices and high rates of compound rediscovery. Untargeted metabolomics approaches that integrate biological and chemical data sets potentially enable the prediction of active constituents early in the fractionation process. However, data acquisition and data processing parameters may have major impacts on the success of models produced. Using an inactive botanical mixture spiked with known antimicrobial compounds, untargeted mass spectrometry-based metabolomics data were combined with bioactivity data to produce selectivity ratio models subjected to a variety of data acquisition and data processing parameters. Selectivity ratio models were used to identify active constituents that were intentionally added to the mixture, along with an additional antimicrobial compound, randainal (5), which was masked by the presence of antagonists in the mixture. These studies found that data-processing approaches, particularly data transformation and model simplification tools using a variance cutoff, had significant impacts on the models produced, either masking or enhancing the ability to detect active constituents in samples. The current study highlights the importance of the data processing step for obtaining reliable information from metabolomics models and demonstrates the strengths and limitations of selectivity ratio analysis to comprehensively assess complex botanical mixtures.