A new approach for bacteria analysis

Researchers from Mount Sinai and collaborators develop new tactic for more specific identification of microbial strains

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NEW YORK—A multi-center team consisting of researchers from the Icahn School of Medicine at Mount Sinai, Sema4 (a Mount Sinai Health System venture) and collaborators New York University and the University of Florida have engineered a new approach to identifying microbes that offers greater accuracy.
At present, existing techniques for microbial identification don't offer enough resolution to identifier bacteria any more specifically than by genetic family, rather than species. These techniques also fail to accurate identify mobile genetic elements, which can move between different species of bacteria.
“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,” said Dr. Eric Schadt, Sema4 CEO, dean for Precision Medicine at Mount Sinai and a co-author of the paper covering this research. “Biology is complex, and our analyses must accurately represent that complexity if we hope to eventually deploy this information for clinical use.”
The scientists began with the application of Single Molecule, Real-Time Sequencing technology, as well as computational tools, to classify molecules by analyzing their genetic code and methylation patterns, the DNA code that regulates gene activity. With this long-read sequencing approach, they were able to produce more precise results than are possible from standard approaches such as 16S or short-read sequencing, and the new approach also corrected errors and incomplete results in identification resulting from those two methods. The team was also able to link mobile genetic elements to their bacterial hosts, which can also enable researchers to predict the virulence and antibiotic resistance of different bacterial species and strains.
“The biomedical community has long needed a microbiome analysis method capable of resolving individual species and strains with high resolution,” Dr. Gang Fang, Assistant Professor of Genetics and Genomic Sciences at Mount Sinai and senior author of the paper, said in a press release. “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 paper in question, “Metagenomic binning and association of plasmids with bacterial host genomes using DNA methylation,” was published in Nature Biotechnology.
As noted in the text, “Shotgun metagenomics methods enable characterization of microbial communities in human microbiome and environmental samples. Assembly of metagenome sequences does not output whole genomes, so computational binning methods have been developed to cluster sequences into genome 'bins'. These methods exploit sequence composition, species abundance or chromosome organization but cannot fully distinguish closely related species and strains.”
By comparison, the team's new method “incorporates bacterial DNA methylation signatures, which are detected using single-molecule real-time sequencing. Our method takes advantage of these endogenous epigenetic barcodes to resolve individual reads and assembled contigs into species- and strain-level bins. We validate our method using synthetic and real microbiome sequences. In addition to genome binning, we show that our method links plasmids and other mobile genetic elements to their host species in a real microbiome sample. Incorporation of DNA methylation information into shotgun metagenomics analyses will complement existing methods to enable more accurate sequence binning.”
Pilot projects using synthetic and real-world microbiome samples showed that with this method, scientists could differentiate between closely related species/strains of bacteria, and, using methylation patterns, could link related DNA sequence data to provide “more holistic information about individual organisms,” as noted in a Icahn press release. Their approach was also validated in low- to medium-complexity microbial communities. Moving forward, the team is working to develop more advanced technologies to resolve complex communities like environmental microbiomes.
SOURCE: Icahn School of Medicine press release

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