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

摘要/摘要 每年约有1.5亿人经历尿路感染(UTI)，这是 即尿路致病性大肠杆菌(UPEC)。肠道是已知的UPEC储存库，通常驻留在 低丰度，但可超越尿道区侵犯膀胱。而体内的大肠杆菌数量 肠道可以是多样化的，有人认为某些菌株有更大的迁移和引起 感染。这可能是一个驱动因素，可以解释为什么一半的急性感染患者复发。 即使在服用抗生素后，第一次尿路感染也会被清除。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UPEC的内脏运输。其中一种临床应用是及早发现并进行干预。 感染的发作。不幸的是，当前的元基因组学算法不能执行应变跟踪 对于临床相关性足够准确，特别是对低丰度物种，如大肠杆菌。一个主要因素是 这种准确性的缺乏是目前所有最先进的元基因组学工具完全忽略了时间 样本之间的相关性。即使已知多个样本来自同一患者，目前的工具 分析这些样本，就好像它们是独立的一样。此外，许多元基因组学工具忽略了该序列 在每次阅读中为每个核苷酸数据库提供的高质量信息。我们建议开发一种更精确的 应变跟踪算法，确实考虑了这些附加信息，使工具具有主机时间质量 意识到了。最后，我们将在一个临床相关的灵芝定植模型上试验和验证我们的算法。 具体地说，人源化的无菌小鼠将经历两轮大肠杆菌的挑战，具有治疗性 来自抗生素或甘露糖苷的扰动，一种精确的节省抗生素的小分子治疗。我们 提出以下具体目标：(1)开发第一个专门用于跟踪的计算方法 微生物组中的细菌菌株随时间的变化，(2)接受UPEC挑战的诺生菌小鼠模型和 治疗上的干扰。这些目标将推动微生物组领域向前发展，从而为未来 治疗学和临床诊断学的发展。

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.