| Autor: |
Kang J; Harvard Medical School, Harvard University, Boston, USA., Siranosian B; Dept. of Genetics, Stanford University, Stanford, USA., Moss E; Dept. of Genetics, Stanford University, Stanford, USA., Andermann T; Dept. of Medicine, Division of Infectious Diseases, Stanford University, Stanford, USA., Bhatt A; Depts. of Medicine and Genetics, Stanford University, Stanford, USA. |
| Jazyk: |
angličtina |
| Zdroj: |
Proceedings. IEEE International Conference on Bioinformatics and Biomedicine [Proceedings (IEEE Int Conf Bioinformatics Biomed)] 2018 Dec; Vol. 2018, pp. 234-241. Date of Electronic Publication: 2019 Jan 24. |
| DOI: |
10.1109/bibm.2018.8621297 |
| Abstrakt: |
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 Escherichia coli gut domination and an E. coli 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 E. coli bloodstream infection likely originated from the gut. The pre-transplant E. coli 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 E. coli 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. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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