Männle Daniel, McKinnie Shaun M K, Mantri Shrikant S, Steinke Katharina, Lu Zeyin, Moore Bradley S, Ziemert Nadine, Kaysser Leonard
Pharmaceutical Biology, Eberhard Karls University Tübingen, Tübingen, Germany.
German Centre for Infection Research (DZIF), Tübingen, Germany.
mSystems. 2020 Jun 2;5(3):e00125-20. doi: 10.1128/mSystems.00125-20.
Using automated genome analysis tools, it is often unclear to what degree genetic variability in homologous biosynthetic pathways relates to structural variation. This hampers strain prioritization and compound identification and can lead to overinterpretation of chemical diversity. Here, we assessed the metabolic potential of , an underinvestigated actinobacterial genus that is known to comprise opportunistic human pathogens. Our analysis revealed a plethora of putative biosynthetic gene clusters of various classes, including polyketide, nonribosomal peptide, and terpenoid pathways. Furthermore, we used the highly conserved biosynthetic pathway for nocobactin-like siderophores to investigate how gene cluster differences correlate to structural differences in the produced compounds. Sequence similarity networks generated by BiG-SCAPE (Biosynthetic Gene Similarity Clustering and Prospecting Engine) showed the presence of several distinct gene cluster families. Metabolic profiling of selected strains using liquid chromatography-mass spectrometry (LC-MS) metabolomics data, nuclear magnetic resonance (NMR) spectroscopy, and GNPS (Global Natural Product Social molecular networking) revealed that nocobactin-like biosynthetic gene cluster (BGC) families above a BiG-SCAPE threshold of 70% can be assigned to distinct structural types of nocobactin-like siderophores. Our work emphasizes that represent a prolific source for natural products rivaling better-characterized genera such as or Furthermore, we showed that large-scale analysis of biosynthetic gene clusters using similarity networks with high stringency allows the distinction and prediction of natural product structural variations. This will facilitate future genomics-driven drug discovery campaigns.
使用自动化基因组分析工具时，同源生物合成途径中的遗传变异性与结构变异的关联程度往往并不明确。这阻碍了菌株优先级排序和化合物鉴定，并可能导致对化学多样性的过度解读。在这里，我们评估了 （一种研究较少的放线菌属，已知其中包含机会性人类病原体）的代谢潜力。我们的分析揭示了大量各类推定的生物合成基因簇，包括聚酮化合物、非核糖体肽和萜类途径。此外，我们利用诺卡菌素样铁载体的高度保守生物合成途径来研究基因簇差异如何与所产生化合物的结构差异相关联。由BiG-SCAPE（生物合成基因相似性聚类与勘探引擎）生成的序列相似性网络显示存在几个不同的基因簇家族。使用液相色谱 - 质谱（LC-MS）代谢组学数据、核磁共振（NMR）光谱和GNPS（全球天然产物社会分子网络）对选定的 菌株进行代谢谱分析，结果表明，高于BiG-SCAPE阈值70%的诺卡菌素样生物合成基因簇（BGC）家族可被归为不同结构类型的诺卡菌素样铁载体。我们的工作强调， 是天然产物的丰富来源，可与 或 等特征更明确的属相媲美。此外，我们表明，使用具有高严格度的相似性网络对生物合成基因簇进行大规模分析能够区分和预测天然产物的结构变异。这将促进未来基于基因组学的药物发现活动。
