Researchers at Arizona State University and colleagues have described a novel technique for probing the microbiome in deeper detail. The method provides greater simplicity and ease of use compared with existing approaches, according to the scientists, who add that the new approach demonstrates an improved ability to pinpoint biologically relevant characteristics, including a subject’s age and sex based on microbiome samples.

Qiyun Zhu, PhD, is a researcher in the Biodesign Center for Fundamental and Applied Microbiology and ASU’s School of Life Sciences. [The Biodesign Institute at Arizona State University]
The team believes its research holds the promise of rapidly advancing investigations into the mysteries of the microbiome. With such knowledge, researchers hope to better understand how these microbes collectively act to safeguard human health and how their dysfunction can lead to a broad range of diseases. In time, drugs and other therapies may even be tailor-made based on a patient’s microbiomic profile.

Two powerful technologies have been used to help researchers unlock the diversity and complexity of the microbiome, by sequencing the microbial DNA present in a sample. These are known as 16S and metagenomic sequencing. The technique described in the current study draws on the strengths of both methods to create a new way of processing data from the microbiome.

“We borrow some of the wisdom that developed from 16S RNA sequencing and apply it to metagenomics,” says Qiyun Zhu, PhD, a researcher in the Biodesign Center for Fundamental and Applied Microbiology and the School of Life Sciences at ASU. The research team includes collaborators from the University of California, San Diego, including co-corresponding author Rob Knight, PhD, Zhu’s former mentor. Their paper (“Phylogeny-aware analysis of metagenome community ecology based on matched reference genomes while bypassing taxonomy”) appears in mSystems.

Unlike other sequencing methods, including 16S, metagenomics allows researchers to sequence all the DNA information present in a microbiome sample. But the new study shows that the metagenomic approach has room for improvement. “The way people currently analyze metagenomic data is limited, because whole genome data has to first be translated into taxonomy,” adds Zhu.

Operational Genomics Units

The new technique, known as Operational Genomic Units (OGU), does away with the laborious and sometimes misleading practice of assigning taxonomic categories like genus and species to the multitude of microbes present in a sample, he explains. Instead, the method uses individual genomes as the basic units for statistical analysis and attempts to align sequences present in a sample to sequences found in existing genomic databases.

By doing this, researchers can get much more fine-grained resolution, which is particularly useful when microbes are present that are closely related in DNA sequence, according to Zhu. This is true because most taxonomic classifications are based on sequence similarity. If two sequences differ by less than a certain threshold, they fall into the same taxonomic category.

However the OGU approach can help scientists tell them apart, notes the research team, who point out that the method overcomes errors in taxonomy that persist as relics from the pre-sequencing epoch, when different species were defined by their morphology rather than from DNA sequence data.

In addition to improvements in resolution and simplicity, OGU can help researchers analyze data using phylogenetic trees. As the name implies, these are branching structures that can describe the degree of relatedness between organisms, based on their sequence similarity. Just as two distantly related species like worms and antelope will appear on more distant branches of a phylogenetic tree, so will more distantly related bacteria and other constituents of the microbiome.

The OGU technique reportedly streamlines metagenomic sequencing, while providing greater resolution. The approach classifies microbes in a sample strictly according to their alignment with a reference database—no taxonomic assignment required. The approach enables researchers to evaluate the degree of species diversity present in a sample.

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