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December 12, 2017

Epigenetic Barcodes Scanned to Inventory Microbiomes More Exactly

  • Taking stock of microbiomes has implications for clinical care, but to date, inventory methods have struggled to identify individual microbial species and strains. Existing methods often classify microbial species as part of broader genetic families, missing details that could help clinicians predict the virulence, antibiotic resistance, and other traits of individual species and strains.

    Now, however, a new inventory method promises more accuracy. This method, which incorporates bacterial DNA methylation signatures, not only tracks the goods on the metagenomic shelves more closely, it also detects the genetic elements that the goods pass amongst themselves.

    The new method was developed by scientists from the Icahn School of Medicine at Mount Sinai and Sema4, as well as by scientists from two collaborating institutions, New York University and the University of Florida. These scientists report that they used single-molecule, real-time sequencing technology and novel computational tools to classify microbes for the first time by analyzing both their genetic codes and their methylation patterns.

    The scientific team published its results December 11 in the journal Nature Biotechnology, in an article entitled “Metagenomic Binning and Association of Plasmids with Bacterial Host Genomes Using DNA Methylation.” The article notes that existing methods characterize microbial communities by resorting to computational binning, a means of clustering DNA sequence information into genome “bins.”

    Although conventional approaches to computational binning can exploit sequence composition, species abundance, or chromosome organization, they may fail to segregate sequences from very similar genomes. Better segregation, the authors of the Nature Biotechnology article suggest, may be possible if computational binning also exploits epigenetic information.

    “We present a binning method that incorporates bacterial DNA methylation signatures, which are detected using single-molecule real-time sequencing,” wrote the article’s authors. “Our method takes advantage of these endogenous epigenetic barcodes to resolve individual reads and assembled contigs into species- and strain-level bins.”

    The new binning approach uses long-read sequencing to provide more resolution than is typically obtained with industry-standard protocols, such as 16S sequencing or short-read sequencing. For example, the new binning approach can correct errors and provide more comprehensive information about individual microbial species. Importantly, the new method provides a new way to link mobile genetic elements to their bacterial hosts.

    "The biomedical community has long needed a microbiome analysis method capable of resolving individual species and strains with high resolution," said Gang Fang, Ph.D., assistant professor of genetics and genomic sciences at Mount Sinai and senior author of the current paper. "We found that DNA methylation patterns can be exploited as highly informative natural barcodes to help discriminate microbial species from each other, help associate mobile genetic elements to their host genomes, and achieve more precise microbiome analysis."

    The study emphasized that methylation motifs can link mobile genetic elements to their host genomes in microbial samples. These elements are small (typically 1–200 kb), circular, and mobile DNA elements that can transfer between host bacteria by conjugation or natural transformation, making them important mediators of horizontal gene transfer.

    “In addition to genome binning,” asserted the article’s authors, “we show that our method links plasmids and other mobile genetic elements to their host species in a real microbiome sample.”

    The scientists validated their method in pilot projects using both synthetic and real-world microbiome samples, and they distinguished between even closely related species and strains of bacteria. They used methylation patterns to link related DNA sequence data, providing more holistic information about individual organisms.

    The team validated the method in low- to medium-complexity microbial communities, and is currently developing more advanced technologies to effectively resolve high-complexity communities, such as environmental microbiomes.

    "This project demonstrates the sophistication and power of analyzing many types of data together to yield insights that are not possible with more simplistic approaches," explained Eric Schadt, Ph.D., Sema4 CEO, dean for precision medicine at Mount Sinai, and a co-author of the paper. "Biology is complex, and our analyses must accurately represent that complexity if we hope to eventually deploy this information for clinical use."

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