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Immunostaining for DNA Modifications: Computational Analysis of Confocal Images
Published on: September 7, 2017
Metagenomic strain-resolved DNA modification patterns link extrachromosomal genetic elements to host strains
Abstract:
DNA modification is central to microbial defense against extrachromosomal genetic elements (ECEs), consequently ECEs tend to adopt their host's modification patterns. Shared ECE-host modification patterns enable linking ECEs to their hosts, but modification detection tools are designed for single genomes and are ineffective at metagenome scale. Here, we present MODIFI, software for detecting DNA modifications in metagenomes. MODIFI assumes that each k-mer in a metagenome is mostly unmodified and calculates background signal levels for that k-mer from PacBio HiFi reads, eliminating the need for matched control experiments. MODIFI ECE-host linkages were validated using >1,000 isolate and mock microbiome datasets. Illustrating the approach, we identified 315 strain-resolved, non-redundant ECE-host linkages in environmental and human metagenomes. In infant gut microbiomes, a chromosomal inversion in Enterococcus faecalis alters host and associated plasmid methylation motifs simultaneously. Overall, MODIFI solves a major bottleneck in DNA modification analysis and provides a foundational tool for understanding microbial epigenomics.
Insights
We developed MODIFI, a new software tool to detect DNA modifications in metagenomes. This enables linking microbial hosts to their extrachromosomal genetic elements (ECEs) at a large scale.
Area of Science:
- Microbial epigenomics
- Metagenomics
- Bioinformatics
Background:
- DNA modification is crucial for microbial defense against extrachromosomal genetic elements (ECEs).
- ECEs typically mimic their host's DNA modification patterns.
- Existing tools for DNA modification detection are ineffective for metagenomic analysis.
Purpose of the Study:
- To introduce MODIFI, a novel software for detecting DNA modifications within metagenomes.
- To enable the linkage of ECEs to their microbial hosts using metagenomic data.
- To overcome the limitations of current tools in analyzing microbial epigenomics at scale.
Main Methods:
- MODIFI utilizes PacBio HiFi reads to calculate background signal levels for k-mers, assuming most are unmodified.
- This approach eliminates the necessity for matched control experiments.
- The software was validated using over 1,000 isolate and mock microbiome datasets.
Main Results:
- MODIFI successfully identified 315 strain-resolved, non-redundant ECE-host linkages in diverse metagenomes.
- The study demonstrated simultaneous alterations in host and plasmid methylation motifs in Enterococcus faecalis within infant gut microbiomes.
- The software proves effective in linking ECEs to their hosts in complex microbial communities.
Conclusions:
- MODIFI addresses a significant challenge in DNA modification analysis, particularly in metagenomic contexts.
- This tool provides a foundational capability for advancing the field of microbial epigenomics.
- MODIFI facilitates a deeper understanding of host-ECE interactions and microbial community dynamics.
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