Strain-resolved microbiome sequencing reveals mobile elements that drive bacterial competition on a clinical timescale

Autor: Hanlee P. Ji, Rebecca N. Culver, Ziming Weng, Alex Bishara, Soumaya Zlitni, Tessa M. Andermann, Christine Handy, Christina Wood, Ekaterina Tkachenko, Eli L. Moss, Serafim Batzoglou, Ami S. Bhatt, Joyce B. Kang
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Male
Antibiotic resistance
Sequence assembly
lcsh:Medicine
Azithromycin
Genome
Feces
Anti-Infective Agents
Ciprofloxacin
Sequencing
RNA-Seq
Genetics (clinical)
2. Zero hunger
0303 health sciences
Linked reads
Strain (biology)
Hematopoietic Stem Cell Transplantation
Middle Aged
Azacitidine
Molecular Medicine
Immunosuppressive Agents
DNA
Bacterial
lcsh:QH426-470
Strain diversity
Computational biology
Biology
Structural variation
03 medical and health sciences
Genetics
Humans
Microbiome
Molecular Biology
030304 developmental biology
Whole genome sequencing
Gut microbiome
Hematopoietic cell
Bacteria
030306 microbiology
Research
lcsh:R
Read cloud assembly
Sequence Analysis
DNA
DNA
BACTEROIDES CACCAE
Diet
Gastrointestinal Microbiome
Transplantation
lcsh:Genetics
Metagenomics
Primary Myelofibrosis
Myelodysplastic Syndromes
Metagenome
Mobile genetic elements
Genome
Bacterial
Zdroj: Genome Medicine, Vol 12, Iss 1, Pp 1-17 (2020)
Genome Medicine
DOI: 10.1186/s13073-020-00747-0
Popis: Background Populations of closely related microbial strains can be simultaneously present in bacterial communities such as the human gut microbiome. We recently developed a de novo genome assembly approach that uses read cloud sequencing to provide more complete microbial genome drafts, enabling precise differentiation and tracking of strain-level dynamics across metagenomic samples. In this case study, we present a proof-of-concept using read cloud sequencing to describe bacterial strain diversity in the gut microbiome of one hematopoietic cell transplantation patient over a 2-month time course and highlight temporal strain variation of gut microbes during therapy. The treatment was accompanied by diet changes and administration of multiple immunosuppressants and antimicrobials. Methods We conducted short-read and read cloud metagenomic sequencing of DNA extracted from four longitudinal stool samples collected during the course of treatment of one hematopoietic cell transplantation (HCT) patient. After applying read cloud metagenomic assembly to discover strain-level sequence variants in these complex microbiome samples, we performed metatranscriptomic analysis to investigate differential expression of antibiotic resistance genes. Finally, we validated predictions from the genomic and metatranscriptomic findings through in vitro antibiotic susceptibility testing and whole genome sequencing of isolates derived from the patient stool samples. Results During the 56-day longitudinal time course that was studied, the patient’s microbiome was profoundly disrupted and eventually dominated by Bacteroides caccae. Comparative analysis of B. caccae genomes obtained using read cloud sequencing together with metagenomic RNA sequencing allowed us to identify differences in substrain populations over time. Based on this, we predicted that particular mobile element integrations likely resulted in increased antibiotic resistance, which we further supported using in vitro antibiotic susceptibility testing. Conclusions We find read cloud assembly to be useful in identifying key structural genomic strain variants within a metagenomic sample. These strains have fluctuating relative abundance over relatively short time periods in human microbiomes. We also find specific structural genomic variations that are associated with increased antibiotic resistance over the course of clinical treatment.
Databáze: OpenAIRE
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