Sequencing Approach Helps Sort Through Vast Microbiome Data

Researchers in Arizona have described a new method for effectively probing the 39 trillion non-human microbes that dwell on and within the human body with unprecedented detail and greater ease than existing approaches. This vast assemblage microbes, also known as the microbiome, includes protists, archaea, fungi, viruses and vast numbers of bacteria living in symbiotic ecosystems, making it difficult to extract biologically relevant information.

Two microbial DNA sequencing technologies have been used to help researchers manage the diversity and complexity of the microbiome in a given sample: 16S and metagenomic sequencing. Although 16S is an inexpensive and well-developed method, it can only give a general idea of the kinds of bacteria present with limited resolution and is only accurate to the genus level of identification. Metagenomics provides accurate, species-level resolution, but is more expensive and time-consuming and produces data that is far more computationally challenging to analyze than 16s data.

The new technique described by researchers at Arizona State University (ASU) draws on the strengths of both methods to create a more efficient way of processing microbiome data. "We borrow some of the wisdom that developed from 16S RNA sequencing and apply it to metagenomics,” Qiyun Zhu, lead author of the study and researcher in the Biodesign Center for Fundamental and Applied Microbiology at ASU.

Dubbed Operational Genomic Units (OGU), the technique 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. Instead, the method uses individual genomes as the basic units for statistical analysis and simply 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. 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 researchers tell them apart.

To demonstrate OGU’s superiority in ferreting out biologically relevant information compared to metagenomics and 16s, the researchers used the classic Human Microbiome Project dataset of 210 metagenomes sampled from seven body sites of male and female human subjects. They found OGU analysis provided better correlation between body site and host sex than both of the older methods.

Next, 6,430 stool samples, collected through a random sampling of the Finnish population called FINRISK, were analyzed using both 16S and metagenomic sequencing. The aim was to predict the age of sampled individuals based on gut microbial composition. Again, the OGU method outperformed 16S and conventional metagenomic analysis, providing more accurate predictions.

New research drawing on still larger datasets will further enhance the resolution of the new technique and expand the descriptive power of taxonomy-independent analysis. And, with better knowledge of the microbiome, 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.

The findings were published recently in mSystems.