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BMC Bioinformatics. 2019 Dec 2;20(Suppl 16):585. doi: 10.1186/s12859-019-3073-1.

Intestinal microbiota domination under extreme selective pressures characterized by metagenomic read cloud sequencing and assembly.

Author information

1
Harvard Medical School, Harvard University, Boston, MA, 02115, USA.
2
Department of Genetics, Stanford University, Stanford, CA, 94305, USA.
3
Department of Medicine, Division of Infectious Diseases and Geographic Medicine, Stanford University, Stanford, CA, 94305, USA.
4
Clinical Microbiology Laboratory, Stanford University Medical Center, Stanford, CA, 94305, USA.
5
Department of Pathology, Stanford University, Stanford, CA, 94305, USA.
6
Department of Medicine, Division of Infectious Diseases, University of North Carolina, Chapel Hill, NC, 27599, USA. tessaa@stanford.edu.
7
Department of Genetics, Stanford University, Stanford, CA, 94035, USA. asbhatt@stanford.edu.
8
Department of Medicine, Division of Hematology, Stanford University, Stanford, CA, 94305, USA. asbhatt@stanford.edu.

Abstract

BACKGROUND:

Low diversity of the gut microbiome, often progressing to the point of intestinal domination by a single species, has been linked to poor outcomes in patients undergoing hematopoietic cell transplantation (HCT). Our ability to understand how certain organisms attain intestinal domination over others has been restricted in part by current metagenomic sequencing technologies that are typically unable to reconstruct complete genomes for individual organisms present within a sequenced microbial community. We recently developed a metagenomic read cloud sequencing and assembly approach that generates improved draft genomes for individual organisms compared to conventional short-read sequencing and assembly methods. Herein, we applied metagenomic read cloud sequencing to four stool samples collected longitudinally from an HCT patient preceding treatment and over the course of heavy antibiotic exposure.

RESULTS:

Characterization of microbiome composition by taxonomic classification of reads reveals that that upon antibiotic exposure, the subject's gut microbiome experienced a marked decrease in diversity and became dominated by Escherichia coli. While diversity is restored at the final time point, this occurs without recovery of the original species and strain-level composition. Draft genomes for individual organisms within each sample were generated using both read cloud and conventional assembly. Read clouds were found to improve the completeness and contiguity of genome assemblies compared to conventional assembly. Moreover, read clouds enabled the placement of antibiotic resistance genes present in multiple copies both within a single draft genome and across multiple organisms. The occurrence of resistance genes associates with the timing of antibiotics administered to the patient, and comparative genomic analysis of the various intestinal E. coli strains across time points as well as the bloodstream isolate showed that the subject's E. coli bloodstream infection likely originated from the intestine. The E. coli genome from the initial pre-transplant stool sample harbors 46 known antimicrobial resistance genes, while all other species from the pre-transplant sample each contain at most 5 genes, consistent with a model of heavy antibiotic exposure resulting in selective outgrowth of the highly antibiotic-resistant E. coli.

CONCLUSION:

This study demonstrates the application and utility of metagenomic read cloud sequencing and assembly to study the underlying strain-level genomic factors influencing gut microbiome dynamics under extreme selective pressures in the clinical context of HCT.

KEYWORDS:

Antibiotic resistance; Hematopoietic cell transplantation; Metagenomics; Microbiome; Read cloud sequencing

PMID:
31787070
DOI:
10.1186/s12859-019-3073-1
Free PMC Article

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