Kang Joyce, Siranosian Benjamin, Moss Eli, Andermann Tessa, Bhatt Ami
Harvard Medical School, Harvard University, Boston, USA.
Dept. of Genetics, Stanford University, Stanford, USA.
Proceedings (IEEE Int Conf Bioinformatics Biomed). 2018 Dec;2018:234-241. doi: 10.1109/bibm.2018.8621297. Epub 2019 Jan 24.
Low intestinal microbial diversity, often leading to domination of the intestine by a single organism, is associated with poor outcomes following hematopoietic cell transplantation (HCT). Understanding how certain organisms achieve domination in the intestine is limited by current metagenomic sequencing technologies, which are typically unable to reconstruct complete genome drafts without bacterial isolation and culture. Recently, we developed a metagenomic read cloud sequencing approach that provides significantly improved genome drafts for individual organisms compared to conventional short-read sequencing methods. Here, we apply read cloud sequencing to four longitudinal stool samples collected from an HCT patient before and after heavy antibiotic exposure. During this time period, the patient experienced gut domination and an bloodstream infection. We find that read clouds enable the placement of multiple copies of antibiotic resistance genes both within and across genomes, and the presence of resistance genes correlates with the timing of antibiotics administered to the patient. Comparative genomic analysis reveals that the bloodstream infection likely originated from the gut. The pre-transplant genome harbors 46 known resistance genes, whereas all other organisms from the pre-transplant time point contain 5 or fewer resistance genes, supporting a model in which the outgrowth was a result of selection by heavy antibiotic exposure. This case study highlights the application of metagenomic read cloud sequencing in a clinical context to elucidate the genomic underpinnings of microbiome dynamics under extreme selective pressures.
肠道微生物多样性较低通常会导致单一微生物在肠道中占据主导地位,这与造血细胞移植(HCT)后的不良预后相关。目前的宏基因组测序技术限制了我们对某些微生物如何在肠道中占据主导地位的理解,因为在没有细菌分离和培养的情况下,这些技术通常无法重建完整的基因组草图。最近,我们开发了一种宏基因组读云测序方法,与传统的短读长测序方法相比,该方法能显著改进个体微生物的基因组草图。在此,我们将读云测序应用于一名HCT患者在大量使用抗生素前后采集的4份纵向粪便样本。在此期间,该患者经历了肠道微生物主导和一次血流感染。我们发现读云能够将多个拷贝的抗生素抗性基因定位在基因组内和基因组间,并且抗性基因的存在与给予患者抗生素的时间相关。比较基因组分析表明,血流感染可能起源于肠道。移植前的基因组含有46个已知的抗性基因,而移植前时间点的所有其他微生物含有5个或更少的抗性基因,这支持了一种模型,即优势菌的生长是大量使用抗生素进行选择的结果。本案例研究突出了宏基因组读云测序在临床背景下的应用,以阐明在极端选择压力下微生物组动态变化的基因组基础。