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

总结/摘要 每年约有1.5亿人经历尿路感染（UTI），这是泌尿系统感染的最常见原因。 其为尿路致病性大肠杆菌（UPEC）。肠道是已知的UPEC储库，其通常驻留在 丰度低，但可越过尿道周围区域侵入膀胱。而E.内的大肠杆菌种群 肠道可以是多种多样的，已经表明某些菌株具有更大的迁移倾向， 感染这可能是一个驱动因素，以解释为什么有一半的急性感染复发 即使是在服用抗生素清除了第一次尿路感染之后。能够检测和跟踪E. 随着时间的推移，大肠杆菌菌株将直接应用于那些经常复发的患者 由于肠道UPEC运输。一种这样的临床应用是在患者出现症状之前进行早期检测和干预。 感染的开始。不幸的是，目前的宏基因组算法不能进行应变跟踪 对于临床相关性来说足够准确，特别是对于低丰度物种如E.杆菌的一个主要因素 这种准确性的缺乏是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， 分析这些样本，就像它们是独立的一样。此外，许多宏基因组学工具忽略了序列 为每个读段中的每个核碱基提供的质量信息。我们建议制定一个更精确的 应变跟踪算法，考虑到这一额外的信息，使工具主机的时间质量 知道。最后，我们将试点和验证我们的算法在临床相关的gnotobiotic殖民模式。 具体来说，人源化无菌小鼠将经历两轮E.大肠杆菌挑战与治疗 抗生素或甘露糖甙的干扰，一种小分子精确的抗生素保留治疗。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 （2）经历UPEC挑战的Gnotobiotic小鼠模型和 治疗扰动这些目标将推动微生物组领域向前发展， 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.