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

ABSTRACT: 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.

SUBMITTER: Zlitni S

PROVIDER: S-EPMC7260799 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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

Zlitni Soumaya S Bishara Alex A Moss Eli L EL Tkachenko Ekaterina E Kang Joyce B JB Culver Rebecca N RN Andermann Tessa M TM Weng Ziming Z Wood Christina C Handy Christine C Ji Hanlee P HP Batzoglou Serafim S Bhatt Ami S AS

Genome medicine 20200529 1

<h4>Background</h4>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 strai ...[more]

PMID: 32471482

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Project description:In cystic fibrosis, dynamic and complex communities of microbial pathogens and commensals can colonize the lung. Cultured isolates from lung sputum reveal high inter- and intraindividual variability in pathogen strains, sequence variants, and phenotypes; disease progression likely depends on the precise combination of infecting lineages. Routine clinical protocols, however, provide a limited overview of the colonizer populations. Therefore, a more comprehensive and precise identification and characterization of infecting lineages could assist in making corresponding decisions on treatment. Here, we describe longitudinal tracking for four cystic fibrosis patients who exhibited extreme clinical phenotypes and, thus, were selected from a pilot cohort of 11 patients with repeated sampling for more than a year. Following metagenomics sequencing of lung sputum, we find that the taxonomic identity of individual colonizer lineages can be easily established. Crucially, even superficially clonal pathogens can be subdivided into multiple sublineages at the sequence level. By tracking individual allelic differences over time, an assembly-free clustering approach allows us to reconstruct multiple lineage-specific genomes with clear structural differences. Our study showcases a culture-independent shotgun metagenomics approach for longitudinal tracking of sublineage pathogen dynamics, opening up the possibility of using such methods to assist in monitoring disease progression through providing high-resolution routine characterization of the cystic fibrosis lung microbiome.IMPORTANCE Cystic fibrosis patients frequently suffer from recurring respiratory infections caused by colonizing pathogenic and commensal bacteria. Although modern therapies can sometimes alleviate respiratory symptoms by ameliorating residual function of the protein responsible for the disorder, management of chronic respiratory infections remains an issue. Here, we propose a minimally invasive and culture-independent method to monitor microbial lung content in patients with cystic fibrosis at minimal additional effort on the patient's part. Through repeated sampling and metagenomics sequencing of our selected cystic fibrosis patients, we successfully classify infecting bacterial lineages and deconvolute multiple lineage variants of the same species within a given patient. This study explores the application of modern computational methods for deconvoluting lineages in the cystic fibrosis lung microbiome, an environment known to be inhabited by a heterogeneous pathogen population that complicates management of the disorder.

Dataset Information

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

Publications

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

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