Dataset Information

Revealing the in vivo growth and division patterns of mouse gut bacteria.

ABSTRACT: Current techniques for studying gut microbiota are unable to answer some important microbiology questions, like how different bacteria grow and divide in the gut. We propose a method that integrates the use of sequential d-amino acid-based in vivo metabolic labeling with fluorescence in situ hybridization (FISH), for characterizing the growth and division patterns of gut bacteria. After sequentially administering two d-amino acid-based probes containing different fluorophores to mice by gavage, the resulting dual-labeled peptidoglycans provide temporal information on cell wall synthesis of gut bacteria. Following taxonomic identification with FISH probes, the growth and division patterns of the corresponding bacterial taxa, including species that cannot be cultured separately in vitro, are revealed. Our method offers a facile yet powerful tool for investigating the in vivo growth dynamics of the bacterial gut microbiota, which will advance our understanding of bacterial cytology and facilitate elucidation of the basic microbiology of this gut "dark matter."

SUBMITTER: Lin L

PROVIDER: S-EPMC7473744 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Revealing the in vivo growth and division patterns of mouse gut bacteria.

Lin Liyuan L Wu Qiuyue Q Song Jia J Du Yahui Y Gao Juan J Song Yanling Y Wang Wei W Yang Chaoyong C

Science advances 20200904 36

Current techniques for studying gut microbiota are unable to answer some important microbiology questions, like how different bacteria grow and divide in the gut. We propose a method that integrates the use of sequential d-amino acid-based in vivo metabolic labeling with fluorescence in situ hybridization (FISH), for characterizing the growth and division patterns of gut bacteria. After sequentially administering two d-amino acid-based probes containing different fluorophores to mice by gavage, ...[more]

PMID: 32917613

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Project description:Obtaining complete and accurate bacterial genomes is vital for studying the characteristics of uncultured bacteria. Single-cell genomics is a promising approach for the culture-independent recovery of bacterial genomes from individual cells. However, single-amplified genomes (SAGs) often have fragmented and incomplete sequences due to chimeric and biased sequences introduced during the genome amplification process. To address this, we developed a single-cell amplified genome long-read assembly (scALA) workflow to construct complete circular SAGs (cSAGs) from long-read single-cell sequencing data of uncultured bacteria. We used the SAG-gel platform, which is both cost-effective and high-throughput, to obtain hundreds of short-read and long-read sequencing data for specific bacterial strains. The scALA workflow generated cSAGs by repeated in silico processing for sequence bias reduction and contig assembly. From 12 human fecal samples, including two cohabitant groups, scALA generated 16 cSAGs of three specifically targeted bacterial species: Anaerostipes hadrus, Agathobacter rectalis, and Ruminococcus gnavus. We discovered strain-specific structural variations shared among cohabiting hosts, while all cSAGs of the same species showed high homology in aligned genomic regions. A. hadrus cSAGs exhibited 10 kbp-long phage insertions, various saccharide metabolic capabilities, and different CRISPR-Cas systems in each strain. The sequence similarity of A. hadrus genomes did not necessarily correspond with orthologous functional genes, while host geographical regionality seemed to be highly related to gene possession. scALA allowed us to obtain closed circular genomes of specifically targeted bacteria from human microbiota samples, leading to an understanding of within-species diversities, including structural variations and linking mobile genetic elements, such as phages, to hosts. These analyses provide insight into microbial evolution, the adaptation of the community to environmental changes, and interactions with hosts. cSAGs constructed using this method can expand bacterial genome databases and our understanding of within-species diversities in uncultured bacteria.

Dataset Information

Revealing the in vivo growth and division patterns of mouse gut bacteria.

Publications

Revealing the in vivo growth and division patterns of mouse gut bacteria.

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