<br>非靶向质谱代谢组学鉴定复杂天然产物混合物中生物活性成分的机会和局限性。,Journal of Natural Products

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非靶向质谱代谢组学鉴定复杂天然产物混合物中生物活性成分的机会和局限性。
Journal of Natural Products ( IF 3.3 ) Pub Date : 2019-03-07 , DOI: 10.1021/acs.jnatprod.9b00176
Lindsay K Caesar ₁ , Joshua J Kellogg ₁ , Olav M Kvalheim ₂ , Nadja B Cech ₁

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天然来源的化合物代表了当今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，能够选择性地合成针对生物大分子的多种次生代谢产物。尽管植物药的化学前景广阔，但由于标准实践成本高昂且耗时，以及化合物重新发现率很高，来自这些来源的药物发现计划已经减少。整合生物和化学数据集的非靶向代谢组学方法可能能够在分馏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生成模型的成功产生重大影响。使用掺有已知抗菌化合物的无活性植物混合物，将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模型。使用选择性比模型来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)，它被混合物中存在的拮抗剂所掩盖。这些研究发现，数据处理方法，特别是使用方差截止的数据转换和模型简化工具，对生成的模型具有重大影响，要么掩盖或增强检测样品中活性成分的能力。当前的研究强调了数据处理步骤对于从代谢组学模型中获取可靠信息的重要性，并证明了选择性比分析在全面评估复杂植物混合物方面的优势和局限性。

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Opportunities and Limitations for Untargeted Mass Spectrometry Metabolomics to Identify Biologically Active Constituents in Complex Natural Product Mixtures.

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.

更新日期：2019-03-07

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