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

鉴定共生放线菌生物活性天然产物的生物信息学和化学生物学方法

• 批准号: 9540546
• 负责人: Allison Sara Walker
• 金额: $ 5.83万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9540546
• 关键词: Actinobacteria class; Age; Agriculture; Algorithms; Anti-Bacterial Agents; Antibiotics; Antifungal Agents; Ants; Bacteria; Bacterial Antibiotic Resistance; Bioinformatics; Biological Assay; Biology; Breathing; Chemical Structure; Chemicals; Collaborations; Computational Biology; Computing Methodologies; Data Set; Development; Ecosystem; Gene Cluster; Genome; Genomics; Growth; Health; Human; Infection; Insecta; Learning; Life; Literature; Machine Learning; Mediating; Medicine; Methods; Mining; Modernization; Molecular Structure; Mycoses; Natural Products; Organism; Pathogenicity; Pharmaceutical Preparations; Phenotype; Play; Public Health; Resistance; Role; Soil; Source; System; Techniques; Therapeutic; Therapeutic Agents; Time; Training; Treatment Efficacy; Validation; Work; base; bioactive natural products; drug discovery; fungus; genetic information; genome sequencing; human disease; mortality; novel; pathogen; post-doctoral training; prediction algorithm; small molecule; symbiont

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.

项目概要/摘要 真菌和细菌病原体是对人类健康的主要威胁，目前几乎没有治疗真菌的疗法。 感染，而细菌对人类一直在使用的现有疗法越来越有抵抗力。 数千年来，天然产物一直用于治疗感染，早在感染的病原体被发现之前 在现代医学中，天然产品继续被用作治疗药物。 在传统天然产品来源（例如土壤）中重新发现已知天然产品的次数有所增加 最近，来自昆虫农业系统的共生放线菌被认为是一种细菌。 这些细菌通常会产生天然的生物活性化合物，尤其是抗真菌剂。 保护昆虫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物质。 生物学方法，例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他生物体产生的生物活性天然产物 在昆虫农业系统中。 该提案的第一部分重点是利用现有技术来识别新的生物活性天然产品。 表型相互作用筛选可以通过确定共生细菌是否存在来识别生物活性天然产物 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来识别和表征生物合成基因簇 （BGC）在表型相互作用筛选中不表达。 拟议工作的第二部分涉及使用新的生物信息学技术来识别有趣的 现有的生物信息学技术可识别 BGC 并预测最可能的化学物质。 然而，他们没有得出任何有关功能的结论。 这里开发的技术将使用机器学习来预测天然产物的功能。 该算法将促进天然产物在生物体的生态环境中的实现。 鉴定具有治疗相关功能的生物活性天然产物。