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 亿人经历尿路感染 (UTI)，这是尿路感染的最常见原因 这是尿路致病性大肠杆菌（UPEC）。肠道是 UPEC 的已知储存库，通常位于 丰度较低，但可以超越尿道周围区域侵入膀胱。而内的大肠杆菌种群 肠道可能是多种多样的，有人认为某些菌株具有更大的迁移倾向并导致 感染。这可能是解释为什么一半急性感染者会复发的驱动因素之一 即使在服用抗生​​素清除泌尿道的首次感染后也是如此。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有直接的临床应用 由于肠道 UPEC 运输。其中一种临床应用是在疾病发生之前进行早期检测和干预。 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 足够准确地满足临床相关性，特别是对于低丰度物种，例如大肠杆菌。一个主要因素 这种准确性的缺乏是因为所有当前最先进的宏基因组工具完全忽略了时间 样本之间的依赖性。即使已知多个样本来自同一患者，当前的工具 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 每次读取中为每个核碱基提供的质量信息。我们建议开发一个更精确的 应变跟踪算法确实考虑了这些附加信息，使该工具具有主机时间质量 意识到的。最后，我们将在临床相关的限菌定植模型上试验和验证我们的算法。 具体来说，人源化无菌小鼠将接受两轮大肠杆菌的治疗挑战 来自抗生素或甘露糖苷的干扰，甘露糖苷是一种小分子精密抗生素节约疗法。我们 提出以下具体目标：（1）开发第一个专用的跟踪计算方法 随着时间的推移，微生物组中的细菌菌株，(2) 经历 UPEC 挑战的知生小鼠模型和 治疗性扰动。这些目标将推动微生物组领域向前发展，为未来做好准备 治疗学和临床诊断学的发展。