Tracking the microbiome: purpose-built machine learning tools for tracking microbial strains over time

跟踪微生物组：专用机器学习工具，用于随时间跟踪微生物菌株

• 批准号: 10401922
• 负责人: Travis Eli Gibson
• 金额: $ 22.38万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-06 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10401922
• 关键词: Address; Algorithms; Antibiotics; Area; Automobile Driving; Awareness; Bacterial Infections; Bayesian Modeling; Bayesian learning; Bladder; Communicable Diseases; Complex; Computer Models; Computer software; Computing Methodologies; Consensus; Custom; Data; Dependence; Development; Dropout; Early Diagnosis; Early Intervention; Enterobacteriaceae; Escherichia coli; Future; Germ-Free; Gnotobiotic; Goals; Infection; Invaded; Lead; Machine Learning; Mannosides; Metagenomics; Methods; Modeling; Mus; Pathogenesis; Patients; Persons; Population; Process; Publishing; Recurrence; Research; Resolution; Sampling; Series; Shotgun Sequencing; Shotguns; Source; Statistical Models; Techniques; Therapeutic; Time; Transcend; Urinary tract infection; Uropathogenic E. coli; Validation; Variant; acute infection; base; clinical application; clinical diagnostics; clinically relevant; de novo mutation; experience; experimental study; flexibility; human microbiota; improved; in vivo; machine learning method; microbial; microbiome; microbiome therapeutics; microbiota; mouse model; novel therapeutics; nucleobase; pathogen; recurrent infection; small molecule; stem; targeted treatment; therapeutic development; time use; tool

Summary/Abstract Approximately 150 million people annually experience urinary tract infections (UTI), the most common cause of which is uropathogenic Escherichia coli (UPEC). The gut is a known reservoir of UPEC, which typically reside at low abundance, but can transcend the periurethral area to invade the bladder. While the E. coli population within the gut can be diverse, it has been suggested that certain strains have a greater propensity to migrate and cause infection. This may be one driving factor to explain why half of those with an acute infection have a recurrence even after taking antibiotics that clear the first infection from the urinary tract. Being able to detect and track E. coli strains over time would have direct clinical applications for those patients who have frequent recurrences due to gut UPEC carriage. One such clinical application would be early detection and intervention before the onset of infection. Unfortunately, current metagenomic algorithms are not capable of performing strain tracking accurately enough for clinical relevance, especially for low abundance species such as E. coli. A major factor for this lack of accuracy is that all current state-of-the-art metagenomic tools completely ignore temporal dependence between samples. Even if it is known that multiple samples are from the same patient, current tools analyze those samples as if they were independent. Furthermore, many metagenomic tools ignore the sequence quality information that is provided for every nucleobase in every read. We propose to develop a more precise strain tracking algorithm that does take this additional information into account, making the tool host-time-quality aware. Finally, we will pilot and validate our algorithm on a clinically relevant gnotobiotic colonization model. Specifically, humanized germ-free mice will be undergoing two rounds of E. coli challenges with therapeutic perturbations from antibiotics or mannosides, a small molecule precision antibiotic-sparing therapeutic. We propose the following specific aims: (1) Develop the first purpose-built computational method for tracking bacterial strains in the microbiome over time, (2) Gnotobiotic mouse model undergoing UPEC challenges and a therapeutic perturbation. These aims would advance the microbiome field forward allowing for the future development of therapeutics and clinical diagnostics.

摘要/摘要 每年约有1.5亿人会经历尿路感染（UTI），这是最常见的原因 这是肝病大肠杆菌（UPEC）。肠道是已知的UPEC储层，通常位于 低丰度，但可以超越尿道周围区域以侵入膀胱。而大肠杆菌种群 肠道可能是多种多样的，有人建议某些菌株具有更大的迁移和引起的倾向 感染。这可能是一个驱动因素来解释为什么一半的急性感染患者复发 即使服用了抗生素清除了尿路首次感染。能够检测和跟踪E。 随着时间的推移，大肠杆菌菌株将对那些经常复发的患者进行直接临床应用 由于肠运输。一种这样的临床应用将是早期检测和干预措施 感染发作。不幸的是，当前的宏基因组算法无法执行应变跟踪 对于临床相关性，尤其是对于大肠杆菌等低丰度物种而言，足够准确。一个主要因素 缺乏准确性的是，所有当前最新的宏基因组工具完全忽略了时间 样品之间的依赖性。即使知道多个样本来自同一患者，当前工具 分析这些样本，就好像它们是独立的一样。此外，许多宏基因组工具忽略了序列 每个读取中为每个核碱剂提供的质量信息。我们建议开发更精确的 应考虑这些其他信息的应变跟踪算法，使工具寄托时间质量 意识到的。最后，我们将在临床相关的gnotobiotic殖民化模型上进行试验和验证我们的算法。 具体而言，无人源化的无菌小鼠将在治疗方面承受两轮大肠杆菌的挑战 抗生素或甘露糖苷的扰动，一种小分子精密抗生素的治疗。我们 提出以下特定目的：（1）开发跟踪的第一个专用计算方法 随着时间的推移，微生物组中的细菌菌株，（2）经历UPEC挑战的Gnotobiotic小鼠模型和A 治疗性扰动。这些目标将推进微生物组的前进，从而允许未来 治疗和临床诊断的开发。