<b>Figure. 4. Harnessing Advanced Analytical Technologies for Efficient Natural Product Discovery and Biosynthetic Characterization</b>:
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<b>Figure. 4. Harnessing Advanced Analytical Technologies for Efficient Natural Product Discovery and Biosynthetic Characterization</b>: The application of advanced analytical technologies, such as liquid chromatography–high-resolution mass spectrometry (LCQ-TOF–MS), in modern natural product (NP)-based drug discovery. In one example, 234 bacterial extracts were subjected to image-based phenotypic bioactivity screening and LC-HRMS metabolomics, with data clustering enabling the prioritization of extracts, leading to the discovery of quinocinnolinomycins (a-d). Another approach involved heterologous expression of fungal artificial chromosomes (FACs) containing uncharacterized biosynthetic gene clusters (BGCs) from diverse fungal species in <i>Aspergillus nidulans</i>, followed by untargeted LC-HRMS analysis. Using the FAC-Score algorithm to analyze metabolomics data, researchers simultaneously discovered 15 new natural products and characterized their corresponding BGCs. These examples highlight the integration of advanced metabolomics, phenotypic screening, and heterologous gene expression for efficient discovery of novel NPs and their biosynthetic pathways (Foito and Stewart, 2018).<br>
<b>图4. 利用先进分析技术助力天然产物高效发现与生物合成表征</b>: 先进分析技术（如液相色谱-高分辨质谱（liquid chromatography–high-resolution mass spectrometry，LCQ-TOF–MS））在基于现代天然产物（natural product，NP）的药物发现中的应用。一则研究示例中，研究人员对234份细菌提取物开展基于图像的表型生物活性筛选与LC-HRMS代谢组学分析，通过数据聚类实现提取物的优先级排序，最终发现了喹诺新菌素（a-d）。另一项研究策略则是将来自多种真菌的未表征生物合成基因簇（biosynthetic gene clusters，BGCs）构建至真菌人工染色体（fungal artificial chromosomes，FACs），并在<i>Aspergillus nidulans</i>（构巢曲霉）中进行异源表达，随后开展非靶向LC-HRMS分析。借助FAC-Score算法分析代谢组学数据，研究人员一次性发现了15种新型天然产物，并完成了其对应生物合成基因簇的表征。上述案例彰显了先进代谢组学、表型筛选与异源基因表达的整合策略，可高效实现新型天然产物及其生物合成途径的发现（Foito与Stewart，2018）。
