Bioinformatics and Chemical Biology Approaches for Identifying Bioactive Natural Products of Symbiotic Actinobacteria

项目总结/摘要 真菌和细菌病原体是人类健康的主要威胁。治疗真菌的疗法很少 感染，而细菌对现有疗法的抗性越来越强。人类一直在使用 数千年来，天然产品治疗感染，早在感染的病原体被发现之前， 明白天然产物在现代医学中继续被用作治疗剂。率 在土壤等传统天然产品来源中，重新发现已知天然产品的可能性有所增加 细菌最近，来自昆虫农业系统的共生放线菌已被认为是一种 生物活性化合物的有前途的来源，特别是抗真菌剂。这些细菌通常会产生天然的 保护昆虫的真菌作物免受病原真菌侵害的产品。这里提出的工作将使用化学 生物学方法，如表型相互作用筛选，基因组学和新的生物信息学方法， 系统地寻找放线菌共生体和其它生物产生生物活性天然产物 在昆虫农业系统中。 该提案的第一部分侧重于利用现有技术鉴定新的生物活性天然产物。 表型相互作用筛选可以通过确定共生细菌是否 产生抑制真菌病原体生长的天然产物，反之亦然。然后我们将使用基因组 测序、生物信息学和异源表达，以鉴定和表征生物合成基因簇 在表型相互作用筛选中不表达的BGC。 这项工作的第二部分涉及使用一种新的生物信息学技术来识别感兴趣的 生物活性天然产物。现有的生物信息学技术识别BGC并预测最可能的化学物质 相应的天然产物的结构。然而，他们没有得出任何关于功能的结论。 天然产物所起的作用。这里开发的技术将使用机器学习来预测函数 在生物体的生态环境中自然产物所能实现的。该算法将有助于 鉴定具有治疗相关功能的生物活性天然产物。

Project Summary/Abstract Fungal and bacterial pathogens are a major threat to human health. Few therapeutics exist to treat fungal infections while bacteria are becoming increasingly resistant to existing therapeutics. Humans have been using natural products to treat infections for thousands of years, long before the causal agents of infection were understood. Natural products have continued to be used as therapeutics in the modern age of medicine. Rates of rediscovery of known natural products have increased in traditional sources of natural products, such as soil bacteria. Recently, symbiotic Actinobacteria from insect agricultural systems have been recognized as a promising source of bioactive compounds, especially antifungal agents. These bacteria often produce natural products that defend an insect’s fungal crop from pathogenic fungus. The work proposed here will use chemical biology approaches such as phenotypic interaction screens, genomics, and a new bioinformatics approach to systematically search for bioactive natural products produced by Actinobacteria symbionts and other organisms in insect agricultural systems. The first part of this proposal focuses on using existing techniques to identify new bioactive natural products. Phenotypic interaction screens can identify bioactive natural products by determining if a symbiotic bacteria produces a natural product that inhibits the growth of a fungal pathogen and vice-versa. We will then use genomic sequencing, bioinformatics, and heterologous expression to identify and characterize biosynthetic gene clusters (BGCs) that are not expressed in the phenotypic interaction screens. The second part of the proposed work involves the use of a new bioinformatics technique to identify interesting bioactive natural products. Existing bioinformatics techniques identify BGCs and predict the most likely chemical structure of the corresponding natural product. However, they do not conclude anything concerning the functional role that the natural product plays. The technique developed here will use machine learning to predict the function that the natural product fulfills in the ecological context of the organism. This algorithm will facilitate the identification of bioactive natural products with therapeutically relevant functions.