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

基本信息

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
项目类别：
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

项目摘要

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.

概括抗生素耐药性（AR）是高优先级的紧急威胁。 AR病原体（例如Eskape组）原因数百万感染和数十万人死亡。而遗传和药物等当前策略筛选有助于确定特定病原体中AR至关重要的基因和突变，存在广泛的缺乏帮助了解AR的起源和持续适应的方法。 AR可以通过多种病原体分子变化，包括蛋白质结构域，单个基因或代谢能力。因此，在新兴病原体或发现新的AR机制中预测和克服AR需要整体了解跨多个分子尺度的AR进化。但是，利用这些不同的数据集是具有挑战性，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在生物学上的范围内整合。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以整合多个尺度的AR数据的框架为了获得对Eskape病原体中AR分子特征的机械洞察力，并预测新的AR （重新）新兴基因组。我们将作为开放公平数据存储库，开放软件和计算，实验和临床AR社区的网络平台。我们将与AR紧密合作在项目持续时间内和之后的合作者，最终用户和开放软件社区，以确保发布可访问，用户友好的交互式平台的发布。最后，在敏捷后的扩展阶段，我们将与NIAID资助的生物信息学共同体一起工作，以便于数据和方法的下游集成长期可持续性。该项目将开发该框架将广泛适用于促进了解AR在理解和新兴病原体（超越Eskape）中的理解通过产生更好的治疗结果，在微生物和药物之间比赛。