Leveraging evolutionary analyses and machine learning to discover multiscale molecular features associated with antibiotic resistance

利用进化分析和机器学习发现与抗生素耐药性相关的多尺度分子特征

• 批准号: 10658686
• 负责人: Janani Ravi
• 金额: $ 45.15万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-14 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10658686
• 关键词: Amino Acid Motifs; Amino Acid Sequence; Antibiotic Resistance; Antibiotics; Award; Bacteria; Bacteriophages; Bioconductor; Bioinformatics; Case Study; Cell surface; Cessation of life; Clinical; Collaborations; Communities; Computer software; Consumption; Data; Data Science; Data Set; Databases; Development; Diagnostic; Documentation; Drug Screening; ESKAPE pathogens; Ecosystem; Educational workshop; Ensure; Evolution; FAIR principles; Family; Funding; Genes; Genetic Screening; Genome; Genome Scan; Genomic Islands; Genomics; Healthcare; Individual; Infection; Libraries; Machine Learning; Metabolic; Methods; Microbe; Mission; Modernization; Molecular; Molecular Evolution; Mutation; National Institute of Allergy and Infectious Disease; Neighborhoods; Pathway interactions; Pharmaceutical Preparations; Phase; Phenotype; Plasmids; Point Mutation; Predisposition; Proteins; Public Health; Research; Research Personnel; Resistance; Resistance development; Scanning; Shock; Structure; Sulfur; System; Technology; Tertiary Protein Structure; Testing; Time; Treatment outcome; Virulence Factors; Work; arms race; base; care burden; community building; comparative; computational platform; computer framework; data integration; data repository; data reuse; diverse data; emerging pathogen; experimental study; genomic variation; helicase; heterogenous data; improved; insight; interoperability; knowledge graph; knowledgebase; machine learning prediction; microbial; molecular scale; multiple data types; novel; open data; pathogen; pathogen genome; resistance allele; resistance gene; resistance mechanism; resistance mutation; software development; tool; usability; user-friendly; web platform; webinar

Summary Antibiotic resistance (AR) is a high-priority urgent threat. AR pathogens such as the ESKAPE group cause millions of infections and hundreds of thousands of deaths. While current strategies such as genetic and drug screens have helped identify genes and mutations critical for AR in specific pathogens, there is a broad lack of methods to help understand AR’s origin and continuous adaptation. AR can arise in a pathogen via a variety of molecular changes, including acquiring protein domains, individual genes, or metabolic capabilities. Hence, predicting and overcoming AR in emerging pathogens or discovering new AR mechanisms requires a holistic understanding of AR evolution across multiple molecular scales. However, leveraging these diverse datasets is challenging because original databases are siloed from each other. Further, the different data types are hard to integrate in a biologically-meaningful way across scales. In this project, we describe a computational discovery framework combining evolutionary analyses and machine learning to integrate AR data across multiple scales to gain mechanistic insights into AR molecular features in ESKAPE pathogens and predict AR in new (re)emerging genomes. We will implement our approach as open FAIR data repositories, open software, and web platforms for the computational, experimental, and clinical AR communities. We will work closely with AR collaborators, end-users, and the open software community during and following the project duration to ensure the release of accessible, user-friendly, interactive platforms. Finally, in the post-award expansion phase, we will work with NIAID-funded bioinformatics consortia for downstream integration of data and methods and long-term sustainability. The framework will develop in this project will be broadly applicable to advance understanding of AR in understudied and emerging pathogens (beyond ESKAPE) towards ending the arms race between microbes and drugs by creating better treatment outcomes.

摘要 抗生素耐药性(AR)是一个高度优先的紧迫威胁。AR病原体，如ESKAPE组引起 数百万人感染，数十万人死亡。虽然目前的策略，如遗传和药物 筛查已经帮助识别了特定病原体中对AR至关重要的基因和突变，但普遍缺乏 帮助了解AR的起源和持续适应的方法。AR可通过多种途径在病原体中出现 分子变化，包括获得蛋白质结构域、单个基因或代谢能力。因此， 预测和克服新出现的病原体的AR或发现新的AR机制需要整体的 了解AR在多个分子尺度上的进化。然而，利用这些不同的数据集 具有挑战性，因为原始数据库彼此孤立。此外，不同的数据类型很难 以一种对生物有意义的方式跨规模整合。在这个项目中，我们描述了一个计算发现 结合进化分析和机器学习的框架，以跨多个尺度集成AR数据 从机理上深入了解ESKAPE病原体的AR分子特征并预测新的AR (重新)新兴基因组。我们将把我们的方法作为开放公平的数据存储库、开放软件和 面向计算、实验和临床AR社区的Web平台。我们将与AR密切合作 项目期间和之后的协作者、最终用户和开放软件社区，以确保 发布可访问的、用户友好的互动平台。最后，在获奖后扩大阶段，我们 将与NIAID资助的生物信息学财团合作，进行数据和方法的下游整合，并 长期可持续性。该框架将在本项目中开发，将广泛适用于推进 对未被研究的和新出现的病原体(超越ESKAPE)对AR的理解 通过创造更好的治疗结果在微生物和药物之间竞赛。