Phylogenomic Analysis of Natural Products Biosynthetic Gene Clusters Allows Discovery of Arseno-Organic Metabolites in Model Streptomycetes.
Phylogenomic Analysis of Natural Products Biosynthetic Gene Clusters Allows Discovery of Arseno-Organic Metabolites in Model Streptomycetes.
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DOI：
10.1093/gbe/evw125
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发表时间：
2016-07-02
影响因子：
3.3
通讯作者：
Barona-Gómez F
中科院分区：
文献类型：
--
作者：
Cruz-Morales P;Kopp JF;Martínez-Guerrero C;Yáñez-Guerra LA;Selem-Mojica N;Ramos-Aboites H;Feldmann J;Barona-Gómez F
数千年来，微生物的天然产物为人类提供了有益的抗生素。然而，随着抗生素耐药性的传播，抗生素发现速度的下降对人类健康造成了压力，这一挑战可能通过揭示微生物产生的化学多样性来更好地应对。目前的微生物基因组挖掘方法已经振兴了抗生素的研究，但这些方法的经验性质限制了探索的化学空间。在这里，我们解决的问题，发现新的途径，将进化的原则到基因组挖掘。我们概括了23个酶家族的进化历史，以前未调查的背景下，天然产物的生物合成放线菌，最熟练的生产者的天然产物。我们的基因组进化分析基于这样的假设，即来自中心代谢的扩展-再利用的酶家族经常发生，因此有可能在天然产物生物合成的背景下催化新的转化。我们的分析导致了编码隐藏的化学多样性的生物合成基因簇的发现，通过将我们的预测与最先进的基因组挖掘工具的预测进行比较来验证;以及通过实验证明天蓝色链霉菌和变铅青链霉菌中砷有机代谢物的生物合成途径的存在，使用基因敲除和代谢物谱组合策略。由于我们的方法并不仅仅依赖于先前确定的生物合成酶的序列相似性搜索，这些结果为开发一种称为EvoMining的进化驱动的基因组挖掘工具奠定了基础，该工具补充了当前的平台。我们预计，通过这样做，真实的“化学暗物质”将被揭开。
Natural products from microbes have provided humans with beneficial antibiotics for millennia. However, a decline in the pace of antibiotic discovery exerts pressure on human health as antibiotic resistance spreads, a challenge that may better faced by unveiling chemical diversity produced by microbes. Current microbial genome mining approaches have revitalized research into antibiotics, but the empirical nature of these methods limits the chemical space that is explored. Here, we address the problem of finding novel pathways by incorporating evolutionary principles into genome mining. We recapitulated the evolutionary history of twenty-three enzyme families previously uninvestigated in the context of natural product biosynthesis in Actinobacteria, the most proficient producers of natural products. Our genome evolutionary analyses where based on the assumption that expanded—repurposed enzyme families—from central metabolism, occur frequently and thus have the potential to catalyze new conversions in the context of natural products biosynthesis. Our analyses led to the discovery of biosynthetic gene clusters coding for hidden chemical diversity, as validated by comparing our predictions with those from state-of-the-art genome mining tools; as well as experimentally demonstrating the existence of a biosynthetic pathway for arseno-organic metabolites in Streptomyces coelicolor and Streptomyces lividans, Using a gene knockout and metabolite profile combined strategy. As our approach does not rely solely on sequence similarity searches of previously identified biosynthetic enzymes, these results establish the basis for the development of an evolutionary-driven genome mining tool termed EvoMining that complements current platforms. We anticipate that by doing so real ‘chemical dark matter’ will be unveiled.